scikit-learn sklearn contributors rst Version 0 13 changes0131

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.. include:: _contributors.rst

.. currentmodule:: sklearn

============
Version 0.13
============

.. _changes_0_13_1:

Version 0.13.1
==============

**February 23, 2013**

The 0.13.1 release only fixes some bugs and does not add any new functionality.

Changelog
---------

- Fixed a testing error caused by the function `cross_validation.train_test_split` being
  interpreted as a test by `Yaroslav Halchenko`_.

- Fixed a bug in the reassignment of small clusters in the :class:`cluster.MiniBatchKMeans`
  by `Gael Varoquaux`_.

- Fixed default value of ``gamma`` in :class:`decomposition.KernelPCA` by `Lars Buitinck`_.

- Updated joblib to ``0.7.0d`` by `Gael Varoquaux`_.

- Fixed scaling of the deviance in :class:`ensemble.GradientBoostingClassifier` by `Peter Prettenhofer`_.

- Better tie-breaking in :class:`multiclass.OneVsOneClassifier` by `Andreas Müller`_.

- Other small improvements to tests and documentation.

People
------
List of contributors for release 0.13.1 by number of commits.

* 16  `Lars Buitinck`_
* 12  `Andreas Müller`_
*  8  `Gael Varoquaux`_
*  5  Robert Marchman
*  3  `Peter Prettenhofer`_
*  2  Hrishikesh Huilgolkar
*  1  Bastiaan van den Berg
*  1  Diego Molla
*  1  `Gilles Louppe`_
*  1  `Mathieu Blondel`_
*  1  `Nelle Varoquaux`_
*  1  Rafael Cunha de Almeida
*  1  Rolando Espinoza La fuente
*  1  `Vlad Niculae`_
*  1  `Yaroslav Halchenko`_

.. _changes_0_13:

Version 0.13
============

**January 21, 2013**

New Estimator Classes
---------------------

- :class:`dummy.DummyClassifier` and :class:`dummy.DummyRegressor`, two
  data-independent predictors by `Mathieu Blondel`_. Useful to sanity-check
  your estimators. See :ref:`dummy_estimators` in the user guide.
  Multioutput support added by `Arnaud Joly`_.

- :class:`decomposition.FactorAnalysis`, a transformer implementing the
  classical factor analysis, by `Christian Osendorfer`_ and `Alexandre
  Gramfort`_. See :ref:`FA` in the user guide.

- :class:`feature_extraction.FeatureHasher`, a transformer implementing the
  "hashing trick" for fast, low-memory feature extraction from string fields
  by `Lars Buitinck`_ and :class:`feature_extraction.text.HashingVectorizer`
  for text documents by `Olivier Grisel`_  See :ref:`feature_hashing` and
  :ref:`hashing_vectorizer` for the documentation and sample usage.

- :class:`pipeline.FeatureUnion`, a transformer that concatenates
  results of several other transformers by `Andreas Müller`_. See
  :ref:`feature_union` in the user guide.

- :class:`random_projection.GaussianRandomProjection`,
  :class:`random_projection.SparseRandomProjection` and the function
  :func:`random_projection.johnson_lindenstrauss_min_dim`. The first two are
  transformers implementing Gaussian and sparse random projection matrix
  by `Olivier Grisel`_ and `Arnaud Joly`_.
  See :ref:`random_projection` in the user guide.

- :class:`kernel_approximation.Nystroem`, a transformer for approximating
  arbitrary kernels by `Andreas Müller`_. See
  :ref:`nystroem_kernel_approx` in the user guide.

- :class:`preprocessing.OneHotEncoder`, a transformer that computes binary
  encodings of categorical features by `Andreas Müller`_. See
  :ref:`preprocessing_categorical_features` in the user guide.

- :class:`linear_model.PassiveAggressiveClassifier` and
  :class:`linear_model.PassiveAggressiveRegressor`, predictors implementing
  an efficient stochastic optimization for linear models by `Rob Zinkov`_ and
  `Mathieu Blondel`_. See :ref:`passive_aggressive` in the user
  guide.

- :class:`ensemble.RandomTreesEmbedding`, a transformer for creating high-dimensional
  sparse representations using ensembles of totally random trees by  `Andreas Müller`_.
  See :ref:`random_trees_embedding` in the user guide.

- :class:`manifold.SpectralEmbedding` and function
  :func:`manifold.spectral_embedding`, implementing the "laplacian
  eigenmaps" transformation for non-linear dimensionality reduction by Wei
  Li. See :ref:`spectral_embedding` in the user guide.

- :class:`isotonic.IsotonicRegression` by `Fabian Pedregosa`_, `Alexandre Gramfort`_
  and `Nelle Varoquaux`_,

Changelog
---------

- :func:`metrics.zero_one_loss` (formerly ``metrics.zero_one``) now has
  option for normalized output that reports the fraction of
  misclassifications, rather than the raw number of misclassifications. By
  Kyle Beauchamp.

- :class:`tree.DecisionTreeClassifier` and all derived ensemble models now
  support sample weighting, by `Noel Dawe`_  and `Gilles Louppe`_.

- Speedup improvement when using bootstrap samples in forests of randomized
  trees, by `Peter Prettenhofer`_  and `Gilles Louppe`_.

- Partial dependence plots for :ref:`gradient_boosting` in
  `ensemble.partial_dependence.partial_dependence` by `Peter
  Prettenhofer`_. See :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py` for an
  example.

- The table of contents on the website has now been made expandable by
  `Jaques Grobler`_.

- :class:`feature_selection.SelectPercentile` now breaks ties
  deterministically instead of returning all equally ranked features.

- :class:`feature_selection.SelectKBest` and
  :class:`feature_selection.SelectPercentile` are more numerically stable
  since they use scores, rather than p-values, to rank results. This means
  that they might sometimes select different features than they did
  previously.

- Ridge regression and ridge classification fitting with ``sparse_cg`` solver
  no longer has quadratic memory complexity, by `Lars Buitinck`_ and
  `Fabian Pedregosa`_.

- Ridge regression and ridge classification now support a new fast solver
  called ``lsqr``, by `Mathieu Blondel`_.

- Speed up of :func:`metrics.precision_recall_curve` by Conrad Lee.

- Added support for reading/writing svmlight files with pairwise
  preference attribute (qid in svmlight file format) in
  :func:`datasets.dump_svmlight_file` and
  :func:`datasets.load_svmlight_file` by `Fabian Pedregosa`_.

- Faster and more robust :func:`metrics.confusion_matrix` and
  :ref:`clustering_evaluation` by Wei Li.

- `cross_validation.cross_val_score` now works with precomputed kernels
  and affinity matrices, by `Andreas Müller`_.

- LARS algorithm made more numerically stable with heuristics to drop
  regressors too correlated as well as to stop the path when
  numerical noise becomes predominant, by `Gael Varoquaux`_.

- Faster implementation of :func:`metrics.precision_recall_curve` by
  Conrad Lee.

- New kernel `metrics.chi2_kernel` by `Andreas Müller`_, often used
  in computer vision applications.

- Fix of longstanding bug in :class:`naive_bayes.BernoulliNB` fixed by
  Shaun Jackman.

- Implemented ``predict_proba`` in :class:`multiclass.OneVsRestClassifier`,
  by Andrew Winterman.

- Improve consistency in gradient boosting: estimators
  :class:`ensemble.GradientBoostingRegressor` and
  :class:`ensemble.GradientBoostingClassifier` use the estimator
  :class:`tree.DecisionTreeRegressor` instead of the
  `tree._tree.Tree` data structure by `Arnaud Joly`_.

- Fixed a floating point exception in the :ref:`decision trees <tree>`
  module, by Seberg.

- Fix :func:`metrics.roc_curve` fails when y_true has only one class
  by Wei Li.

- Add the :func:`metrics.mean_absolute_error` function which computes the
  mean absolute error. The :func:`metrics.mean_squared_error`,
  :func:`metrics.mean_absolute_error` and
  :func:`metrics.r2_score` metrics support multioutput by `Arnaud Joly`_.

- Fixed ``class_weight`` support in :class:`svm.LinearSVC` and
  :class:`linear_model.LogisticRegression` by `Andreas Müller`_. The meaning
  of ``class_weight`` was reversed as erroneously higher weight meant less
  positives of a given class in earlier releases.

- Improve narrative documentation and consistency in
  :mod:`sklearn.metrics` for regression and classification metrics
  by `Arnaud Joly`_.

- Fixed a bug in :class:`sklearn.svm.SVC` when using csr-matrices with
  unsorted indices by Xinfan Meng and `Andreas Müller`_.

- :class:`cluster.MiniBatchKMeans`: Add random reassignment of cluster centers
  with little observations attached to them, by `Gael Varoquaux`_.

API changes summary
-------------------
- Renamed all occurrences of ``n_atoms`` to ``n_components`` for consistency.
  This applies to :class:`decomposition.DictionaryLearning`,
  :class:`decomposition.MiniBatchDictionaryLearning`,
  :func:`decomposition.dict_learning`, :func:`decomposition.dict_learning_online`.

- Renamed all occurrences of ``max_iters`` to ``max_iter`` for consistency.
  This applies to `semi_supervised.LabelPropagation` and
  `semi_supervised.label_propagation.LabelSpreading`.

- Renamed all occurrences of ``learn_rate`` to ``learning_rate`` for
  consistency in `ensemble.BaseGradientBoosting` and
  :class:`ensemble.GradientBoostingRegressor`.

- The module ``sklearn.linear_model.sparse`` is gone. Sparse matrix support
  was already integrated into the "regular" linear models.

- `sklearn.metrics.mean_square_error`, which incorrectly returned the
  accumulated error, was removed. Use :func:`metrics.mean_squared_error` instead.

- Passing ``class_weight`` parameters to ``fit`` methods is no longer
  supported. Pass them to estimator constructors instead.

- GMMs no longer have ``decode`` and ``rvs`` methods. Use the ``score``,
  ``predict`` or ``sample`` methods instead.

- The ``solver`` fit option in Ridge regression and classification is now
  deprecated and will be removed in v0.14. Use the constructor option
  instead.

- `feature_extraction.text.DictVectorizer` now returns sparse
  matrices in the CSR format, instead of COO.

- Renamed ``k`` in `cross_validation.KFold` and
  `cross_validation.StratifiedKFold` to ``n_folds``, renamed
  ``n_bootstraps`` to ``n_iter`` in ``cross_validation.Bootstrap``.

- Renamed all occurrences of ``n_iterations`` to ``n_iter`` for consistency.
  This applies to `cross_validation.ShuffleSplit`,
  `cross_validation.StratifiedShuffleSplit`,
  :func:`utils.extmath.randomized_range_finder` and
  :func:`utils.extmath.randomized_svd`.

- Replaced ``rho`` in :class:`linear_model.ElasticNet` and
  :class:`linear_model.SGDClassifier` by ``l1_ratio``. The ``rho`` parameter
  had different meanings; ``l1_ratio`` was introduced to avoid confusion.
  It has the same meaning as previously ``rho`` in
  :class:`linear_model.ElasticNet` and ``(1-rho)`` in
  :class:`linear_model.SGDClassifier`.

- :class:`linear_model.LassoLars` and :class:`linear_model.Lars` now
  store a list of paths in the case of multiple targets, rather than
  an array of paths.

- The attribute ``gmm`` of `hmm.GMMHMM` was renamed to ``gmm_``
  to adhere more strictly with the API.

- `cluster.spectral_embedding` was moved to
  :func:`manifold.spectral_embedding`.

- Renamed ``eig_tol`` in :func:`manifold.spectral_embedding`,
  :class:`cluster.SpectralClustering` to ``eigen_tol``, renamed ``mode``
  to ``eigen_solver``.

- Renamed ``mode`` in :func:`manifold.spectral_embedding` and
  :class:`cluster.SpectralClustering` to ``eigen_solver``.

- ``classes_`` and ``n_classes_`` attributes of
  :class:`tree.DecisionTreeClassifier` and all derived ensemble models are
  now flat in case of single output problems and nested in case of
  multi-output problems.

- The ``estimators_`` attribute of
  :class:`ensemble.GradientBoostingRegressor` and
  :class:`ensemble.GradientBoostingClassifier` is now an
  array of :class:`tree.DecisionTreeRegressor`.

- Renamed ``chunk_size`` to ``batch_size`` in
  :class:`decomposition.MiniBatchDictionaryLearning` and
  :class:`decomposition.MiniBatchSparsePCA` for consistency.

- :class:`svm.SVC` and :class:`svm.NuSVC` now provide a ``classes_``
  attribute and support arbitrary dtypes for labels ``y``.
  Also, the dtype returned by ``predict`` now reflects the dtype of
  ``y`` during ``fit`` (used to be ``np.float``).

- Changed default test_size in `cross_validation.train_test_split`
  to None, added possibility to infer ``test_size`` from ``train_size`` in
  `cross_validation.ShuffleSplit` and
  `cross_validation.StratifiedShuffleSplit`.

- Renamed function `sklearn.metrics.zero_one` to
  `sklearn.metrics.zero_one_loss`. Be aware that the default behavior
  in `sklearn.metrics.zero_one_loss` is different from
  `sklearn.metrics.zero_one`: ``normalize=False`` is changed to
  ``normalize=True``.

- Renamed function `metrics.zero_one_score` to
  :func:`metrics.accuracy_score`.

- :func:`datasets.make_circles` now has the same number of inner and outer points.

- In the Naive Bayes classifiers, the ``class_prior`` parameter was moved
  from ``fit`` to ``__init__``.

People
------
List of contributors for release 0.13 by number of commits.

* 364  `Andreas Müller`_
* 143  `Arnaud Joly`_
* 137  `Peter Prettenhofer`_
* 131  `Gael Varoquaux`_
* 117  `Mathieu Blondel`_
* 108  `Lars Buitinck`_
* 106  Wei Li
* 101  `Olivier Grisel`_
*  65  `Vlad Niculae`_
*  54  `Gilles Louppe`_
*  40  `Jaques Grobler`_
*  38  `Alexandre Gramfort`_
*  30  `Rob Zinkov`_
*  19  Aymeric Masurelle
*  18  Andrew Winterman
*  17  `Fabian Pedregosa`_
*  17  Nelle Varoquaux
*  16  `Christian Osendorfer`_
*  14  `Daniel Nouri`_
*  13  :user:`Virgile Fritsch <VirgileFritsch>`
*  13  syhw
*  12  `Satrajit Ghosh`_
*  10  Corey Lynch
*  10  Kyle Beauchamp
*   9  Brian Cheung
*   9  Immanuel Bayer
*   9  mr.Shu
*   8  Conrad Lee
*   8  `James Bergstra`_
*   7  Tadej Janež
*   6  Brian Cajes
*   6  `Jake Vanderplas`_
*   6  Michael
*   6  Noel Dawe
*   6  Tiago Nunes
*   6  cow
*   5  Anze
*   5  Shiqiao Du
*   4  Christian Jauvin
*   4  Jacques Kvam
*   4  Richard T. Guy
*   4  `Robert Layton`_
*   3  Alexandre Abraham
*   3  Doug Coleman
*   3  Scott Dickerson
*   2  ApproximateIdentity
*   2  John Benediktsson
*   2  Mark Veronda
*   2  Matti Lyra
*   2  Mikhail Korobov
*   2  Xinfan Meng
*   1  Alejandro Weinstein
*   1  `Alexandre Passos`_
*   1  Christoph Deil
*   1  Eugene Nizhibitsky
*   1  Kenneth C. Arnold
*   1  Luis Pedro Coelho
*   1  Miroslav Batchkarov
*   1  Pavel
*   1  Sebastian Berg
*   1  Shaun Jackman
*   1  Subhodeep Moitra
*   1  bob
*   1  dengemann
*   1  emanuele
*   1  x006

include    contributors rst     currentmodule   sklearn               Version 0 13                   changes 0 13 1   Version 0 13 1                   February 23  2013    The 0 13 1 release only fixes some bugs and does not add any new functionality   Changelog              Fixed a testing error caused by the function  cross validation train test split  being   interpreted as a test by  Yaroslav Halchenko       Fixed a bug in the reassignment of small clusters in the  class  cluster MiniBatchKMeans    by  Gael Varoquaux       Fixed default value of   gamma   in  class  decomposition KernelPCA  by  Lars Buitinck       Updated joblib to   0 7 0d   by  Gael Varoquaux       Fixed scaling of the deviance in  class  ensemble GradientBoostingClassifier  by  Peter Prettenhofer       Better tie breaking in  class  multiclass OneVsOneClassifier  by  Andreas M ller       Other small improvements to tests and documentation   People        List of contributors for release 0 13 1 by number of commits     16   Lars Buitinck     12   Andreas M ller      8   Gael Varoquaux      5  Robert Marchman    3   Peter Prettenhofer      2  Hrishikesh Huilgolkar    1  Bastiaan van den Berg    1  Diego Molla    1   Gilles Louppe      1   Mathieu Blondel      1   Nelle Varoquaux      1  Rafael Cunha de Almeida    1  Rolando Espinoza La fuente    1   Vlad Niculae      1   Yaroslav Halchenko         changes 0 13   Version 0 13                 January 21  2013    New Estimator Classes                           class  dummy DummyClassifier  and  class  dummy DummyRegressor   two   data independent predictors by  Mathieu Blondel    Useful to sanity check   your estimators  See  ref  dummy estimators  in the user guide    Multioutput support added by  Arnaud Joly        class  decomposition FactorAnalysis   a transformer implementing the   classical factor analysis  by  Christian Osendorfer   and  Alexandre   Gramfort    See  ref  FA  in the user guide      class  feature extraction FeatureHasher   a transformer implementing the    hashing trick  for fast  low memory feature extraction from string fields   by  Lars Buitinck   and  class  feature extraction text HashingVectorizer    for text documents by  Olivier Grisel    See  ref  feature hashing  and    ref  hashing vectorizer  for the documentation and sample usage      class  pipeline FeatureUnion   a transformer that concatenates   results of several other transformers by  Andreas M ller    See    ref  feature union  in the user guide      class  random projection GaussianRandomProjection      class  random projection SparseRandomProjection  and the function    func  random projection johnson lindenstrauss min dim   The first two are   transformers implementing Gaussian and sparse random projection matrix   by  Olivier Grisel   and  Arnaud Joly      See  ref  random projection  in the user guide      class  kernel approximation Nystroem   a transformer for approximating   arbitrary kernels by  Andreas M ller    See    ref  nystroem kernel approx  in the user guide      class  preprocessing OneHotEncoder   a transformer that computes binary   encodings of categorical features by  Andreas M ller    See    ref  preprocessing categorical features  in the user guide      class  linear model PassiveAggressiveClassifier  and    class  linear model PassiveAggressiveRegressor   predictors implementing   an efficient stochastic optimization for linear models by  Rob Zinkov   and    Mathieu Blondel    See  ref  passive aggressive  in the user   guide      class  ensemble RandomTreesEmbedding   a transformer for creating high dimensional   sparse representations using ensembles of totally random trees by   Andreas M ller      See  ref  random trees embedding  in the user guide      class  manifold SpectralEmbedding  and function    func  manifold spectral embedding   implementing the  laplacian   eigenmaps  transformation for non linear dimensionality reduction by Wei   Li  See  ref  spectral embedding  in the user guide      class  isotonic IsotonicRegression  by  Fabian Pedregosa     Alexandre Gramfort     and  Nelle Varoquaux      Changelog               func  metrics zero one loss   formerly   metrics zero one    now has   option for normalized output that reports the fraction of   misclassifications  rather than the raw number of misclassifications  By   Kyle Beauchamp      class  tree DecisionTreeClassifier  and all derived ensemble models now   support sample weighting  by  Noel Dawe    and  Gilles Louppe       Speedup improvement when using bootstrap samples in forests of randomized   trees  by  Peter Prettenhofer    and  Gilles Louppe       Partial dependence plots for  ref  gradient boosting  in    ensemble partial dependence partial dependence  by  Peter   Prettenhofer    See  ref  sphx glr auto examples inspection plot partial dependence py  for an   example     The table of contents on the website has now been made expandable by    Jaques Grobler        class  feature selection SelectPercentile  now breaks ties   deterministically instead of returning all equally ranked features      class  feature selection SelectKBest  and    class  feature selection SelectPercentile  are more numerically stable   since they use scores  rather than p values  to rank results  This means   that they might sometimes select different features than they did   previously     Ridge regression and ridge classification fitting with   sparse cg   solver   no longer has quadratic memory complexity  by  Lars Buitinck   and    Fabian Pedregosa       Ridge regression and ridge classification now support a new fast solver   called   lsqr    by  Mathieu Blondel       Speed up of  func  metrics precision recall curve  by Conrad Lee     Added support for reading writing svmlight files with pairwise   preference attribute  qid in svmlight file format  in    func  datasets dump svmlight file  and    func  datasets load svmlight file  by  Fabian Pedregosa       Faster and more robust  func  metrics confusion matrix  and    ref  clustering evaluation  by Wei Li      cross validation cross val score  now works with precomputed kernels   and affinity matrices  by  Andreas M ller       LARS algorithm made more numerically stable with heuristics to drop   regressors too correlated as well as to stop the path when   numerical noise becomes predominant  by  Gael Varoquaux       Faster implementation of  func  metrics precision recall curve  by   Conrad Lee     New kernel  metrics chi2 kernel  by  Andreas M ller    often used   in computer vision applications     Fix of longstanding bug in  class  naive bayes BernoulliNB  fixed by   Shaun Jackman     Implemented   predict proba   in  class  multiclass OneVsRestClassifier     by Andrew Winterman     Improve consistency in gradient boosting  estimators    class  ensemble GradientBoostingRegressor  and    class  ensemble GradientBoostingClassifier  use the estimator    class  tree DecisionTreeRegressor  instead of the    tree  tree Tree  data structure by  Arnaud Joly       Fixed a floating point exception in the  ref  decision trees  tree     module  by Seberg     Fix  func  metrics roc curve  fails when y true has only one class   by Wei Li     Add the  func  metrics mean absolute error  function which computes the   mean absolute error  The  func  metrics mean squared error      func  metrics mean absolute error  and    func  metrics r2 score  metrics support multioutput by  Arnaud Joly       Fixed   class weight   support in  class  svm LinearSVC  and    class  linear model LogisticRegression  by  Andreas M ller    The meaning   of   class weight   was reversed as erroneously higher weight meant less   positives of a given class in earlier releases     Improve narrative documentation and consistency in    mod  sklearn metrics  for regression and classification metrics   by  Arnaud Joly       Fixed a bug in  class  sklearn svm SVC  when using csr matrices with   unsorted indices by Xinfan Meng and  Andreas M ller        class  cluster MiniBatchKMeans   Add random reassignment of cluster centers   with little observations attached to them  by  Gael Varoquaux      API changes summary                       Renamed all occurrences of   n atoms   to   n components   for consistency    This applies to  class  decomposition DictionaryLearning      class  decomposition MiniBatchDictionaryLearning      func  decomposition dict learning    func  decomposition dict learning online      Renamed all occurrences of   max iters   to   max iter   for consistency    This applies to  semi supervised LabelPropagation  and    semi supervised label propagation LabelSpreading      Renamed all occurrences of   learn rate   to   learning rate   for   consistency in  ensemble BaseGradientBoosting  and    class  ensemble GradientBoostingRegressor      The module   sklearn linear model sparse   is gone  Sparse matrix support   was already integrated into the  regular  linear models      sklearn metrics mean square error   which incorrectly returned the   accumulated error  was removed  Use  func  metrics mean squared error  instead     Passing   class weight   parameters to   fit   methods is no longer   supported  Pass them to estimator constructors instead     GMMs no longer have   decode   and   rvs   methods  Use the   score        predict   or   sample   methods instead     The   solver   fit option in Ridge regression and classification is now   deprecated and will be removed in v0 14  Use the constructor option   instead      feature extraction text DictVectorizer  now returns sparse   matrices in the CSR format  instead of COO     Renamed   k   in  cross validation KFold  and    cross validation StratifiedKFold  to   n folds    renamed     n bootstraps   to   n iter   in   cross validation Bootstrap       Renamed all occurrences of   n iterations   to   n iter   for consistency    This applies to  cross validation ShuffleSplit      cross validation StratifiedShuffleSplit      func  utils extmath randomized range finder  and    func  utils extmath randomized svd      Replaced   rho   in  class  linear model ElasticNet  and    class  linear model SGDClassifier  by   l1 ratio    The   rho   parameter   had different meanings    l1 ratio   was introduced to avoid confusion    It has the same meaning as previously   rho   in    class  linear model ElasticNet  and    1 rho    in    class  linear model SGDClassifier       class  linear model LassoLars  and  class  linear model Lars  now   store a list of paths in the case of multiple targets  rather than   an array of paths     The attribute   gmm   of  hmm GMMHMM  was renamed to   gmm      to adhere more strictly with the API      cluster spectral embedding  was moved to    func  manifold spectral embedding      Renamed   eig tol   in  func  manifold spectral embedding      class  cluster SpectralClustering  to   eigen tol    renamed   mode     to   eigen solver       Renamed   mode   in  func  manifold spectral embedding  and    class  cluster SpectralClustering  to   eigen solver         classes    and   n classes    attributes of    class  tree DecisionTreeClassifier  and all derived ensemble models are   now flat in case of single output problems and nested in case of   multi output problems     The   estimators    attribute of    class  ensemble GradientBoostingRegressor  and    class  ensemble GradientBoostingClassifier  is now an   array of  class  tree DecisionTreeRegressor      Renamed   chunk size   to   batch size   in    class  decomposition MiniBatchDictionaryLearning  and    class  decomposition MiniBatchSparsePCA  for consistency      class  svm SVC  and  class  svm NuSVC  now provide a   classes      attribute and support arbitrary dtypes for labels   y      Also  the dtype returned by   predict   now reflects the dtype of     y   during   fit    used to be   np float        Changed default test size in  cross validation train test split    to None  added possibility to infer   test size   from   train size   in    cross validation ShuffleSplit  and    cross validation StratifiedShuffleSplit      Renamed function  sklearn metrics zero one  to    sklearn metrics zero one loss   Be aware that the default behavior   in  sklearn metrics zero one loss  is different from    sklearn metrics zero one     normalize False   is changed to     normalize True       Renamed function  metrics zero one score  to    func  metrics accuracy score       func  datasets make circles  now has the same number of inner and outer points     In the Naive Bayes classifiers  the   class prior   parameter was moved   from   fit   to     init       People        List of contributors for release 0 13 by number of commits     364   Andreas M ller     143   Arnaud Joly     137   Peter Prettenhofer     131   Gael Varoquaux     117   Mathieu Blondel     108   Lars Buitinck     106  Wei Li   101   Olivier Grisel      65   Vlad Niculae      54   Gilles Louppe      40   Jaques Grobler      38   Alexandre Gramfort      30   Rob Zinkov      19  Aymeric Masurelle    18  Andrew Winterman    17   Fabian Pedregosa      17  Nelle Varoquaux    16   Christian Osendorfer      14   Daniel Nouri      13   user  Virgile Fritsch  VirgileFritsch      13  syhw    12   Satrajit Ghosh      10  Corey Lynch    10  Kyle Beauchamp     9  Brian Cheung     9  Immanuel Bayer     9  mr Shu     8  Conrad Lee     8   James Bergstra       7  Tadej Jane      6  Brian Cajes     6   Jake Vanderplas       6  Michael     6  Noel Dawe     6  Tiago Nunes     6  cow     5  Anze     5  Shiqiao Du     4  Christian Jauvin     4  Jacques Kvam     4  Richard T  Guy     4   Robert Layton       3  Alexandre Abraham     3  Doug Coleman     3  Scott Dickerson     2  ApproximateIdentity     2  John Benediktsson     2  Mark Veronda     2  Matti Lyra     2  Mikhail Korobov     2  Xinfan Meng     1  Alejandro Weinstein     1   Alexandre Passos       1  Christoph Deil     1  Eugene Nizhibitsky     1  Kenneth C  Arnold     1  Luis Pedro Coelho     1  Miroslav Batchkarov     1  Pavel     1  Sebastian Berg     1  Shaun Jackman     1  Subhodeep Moitra     1  bob     1  dengemann     1  emanuele     1  x006


scikit-learn sklearn contributors rst changes0152 Version 0 15

Answer:
.. include:: _contributors.rst

.. currentmodule:: sklearn

============
Version 0.15
============

.. _changes_0_15_2:

Version 0.15.2
==============

**September 4, 2014**

Bug fixes
---------

- Fixed handling of the ``p`` parameter of the Minkowski distance that was
  previously ignored in nearest neighbors models. By :user:`Nikolay
  Mayorov <nmayorov>`.

- Fixed duplicated alphas in :class:`linear_model.LassoLars` with early
  stopping on 32 bit Python. By `Olivier Grisel`_ and `Fabian Pedregosa`_.

- Fixed the build under Windows when scikit-learn is built with MSVC while
  NumPy is built with MinGW. By `Olivier Grisel`_ and :user:`Federico
  Vaggi <FedericoV>`.

- Fixed an array index overflow bug in the coordinate descent solver. By
  `Gael Varoquaux`_.

- Better handling of numpy 1.9 deprecation warnings. By `Gael Varoquaux`_.

- Removed unnecessary data copy in :class:`cluster.KMeans`.
  By `Gael Varoquaux`_.

- Explicitly close open files to avoid ``ResourceWarnings`` under Python 3.
  By Calvin Giles.

- The ``transform`` of :class:`discriminant_analysis.LinearDiscriminantAnalysis`
  now projects the input on the most discriminant directions. By Martin Billinger.

- Fixed potential overflow in ``_tree.safe_realloc`` by `Lars Buitinck`_.

- Performance optimization in :class:`isotonic.IsotonicRegression`.
  By Robert Bradshaw.

- ``nose`` is non-longer a runtime dependency to import ``sklearn``, only for
  running the tests. By `Joel Nothman`_.

- Many documentation and website fixes by `Joel Nothman`_, `Lars Buitinck`_
  :user:`Matt Pico <MattpSoftware>`, and others.

.. _changes_0_15_1:

Version 0.15.1
==============

**August 1, 2014**

Bug fixes
---------

- Made `cross_validation.cross_val_score` use
  `cross_validation.KFold` instead of
  `cross_validation.StratifiedKFold` on multi-output classification
  problems. By :user:`Nikolay Mayorov <nmayorov>`.

- Support unseen labels :class:`preprocessing.LabelBinarizer` to restore
  the default behavior of 0.14.1 for backward compatibility. By
  :user:`Hamzeh Alsalhi <hamsal>`.

- Fixed the :class:`cluster.KMeans` stopping criterion that prevented early
  convergence detection. By Edward Raff and `Gael Varoquaux`_.

- Fixed the behavior of :class:`multiclass.OneVsOneClassifier`.
  in case of ties at the per-class vote level by computing the correct
  per-class sum of prediction scores. By `Andreas Müller`_.

- Made `cross_validation.cross_val_score` and
  `grid_search.GridSearchCV` accept Python lists as input data.
  This is especially useful for cross-validation and model selection of
  text processing pipelines. By `Andreas Müller`_.

- Fixed data input checks of most estimators to accept input data that
  implements the NumPy ``__array__`` protocol. This is the case for
  for ``pandas.Series`` and ``pandas.DataFrame`` in recent versions of
  pandas. By `Gael Varoquaux`_.

- Fixed a regression for :class:`linear_model.SGDClassifier` with
  ``class_weight="auto"`` on data with non-contiguous labels. By
  `Olivier Grisel`_.

.. _changes_0_15:

Version 0.15
============

**July 15, 2014**

Highlights
-----------

- Many speed and memory improvements all across the code

- Huge speed and memory improvements to random forests (and extra
  trees) that also benefit better from parallel computing.

- Incremental fit to :class:`BernoulliRBM <neural_network.BernoulliRBM>`

- Added :class:`cluster.AgglomerativeClustering` for hierarchical
  agglomerative clustering with average linkage, complete linkage and
  ward strategies.

- Added :class:`linear_model.RANSACRegressor` for robust regression
  models.

- Added dimensionality reduction with :class:`manifold.TSNE` which can be
  used to visualize high-dimensional data.

Changelog
---------

New features
............

- Added :class:`ensemble.BaggingClassifier` and
  :class:`ensemble.BaggingRegressor` meta-estimators for ensembling
  any kind of base estimator. See the :ref:`Bagging <bagging>` section of
  the user guide for details and examples. By `Gilles Louppe`_.

- New unsupervised feature selection algorithm
  :class:`feature_selection.VarianceThreshold`, by `Lars Buitinck`_.

- Added :class:`linear_model.RANSACRegressor` meta-estimator for the robust
  fitting of regression models. By :user:`Johannes Schönberger <ahojnnes>`.

- Added :class:`cluster.AgglomerativeClustering` for hierarchical
  agglomerative clustering with average linkage, complete linkage and
  ward strategies, by  `Nelle Varoquaux`_ and `Gael Varoquaux`_.

- Shorthand constructors :func:`pipeline.make_pipeline` and
  :func:`pipeline.make_union` were added by `Lars Buitinck`_.

- Shuffle option for `cross_validation.StratifiedKFold`.
  By :user:`Jeffrey Blackburne <jblackburne>`.

- Incremental learning (``partial_fit``) for Gaussian Naive Bayes by
  Imran Haque.

- Added ``partial_fit`` to :class:`BernoulliRBM
  <neural_network.BernoulliRBM>`
  By :user:`Danny Sullivan <dsullivan7>`.

- Added `learning_curve` utility to
  chart performance with respect to training size. See
  :ref:`sphx_glr_auto_examples_model_selection_plot_learning_curve.py`. By Alexander Fabisch.

- Add positive option in :class:`LassoCV <linear_model.LassoCV>` and
  :class:`ElasticNetCV <linear_model.ElasticNetCV>`.
  By Brian Wignall and `Alexandre Gramfort`_.

- Added :class:`linear_model.MultiTaskElasticNetCV` and
  :class:`linear_model.MultiTaskLassoCV`. By `Manoj Kumar`_.

- Added :class:`manifold.TSNE`. By Alexander Fabisch.

Enhancements
............

- Add sparse input support to :class:`ensemble.AdaBoostClassifier` and
  :class:`ensemble.AdaBoostRegressor` meta-estimators.
  By :user:`Hamzeh Alsalhi <hamsal>`.

- Memory improvements of decision trees, by `Arnaud Joly`_.

- Decision trees can now be built in best-first manner by using ``max_leaf_nodes``
  as the stopping criteria. Refactored the tree code to use either a
  stack or a priority queue for tree building.
  By `Peter Prettenhofer`_ and `Gilles Louppe`_.

- Decision trees can now be fitted on fortran- and c-style arrays, and
  non-continuous arrays without the need to make a copy.
  If the input array has a different dtype than ``np.float32``, a fortran-
  style copy will be made since fortran-style memory layout has speed
  advantages. By `Peter Prettenhofer`_ and `Gilles Louppe`_.

- Speed improvement of regression trees by optimizing the
  the computation of the mean square error criterion. This lead
  to speed improvement of the tree, forest and gradient boosting tree
  modules. By `Arnaud Joly`_

- The ``img_to_graph`` and ``grid_tograph`` functions in
  :mod:`sklearn.feature_extraction.image` now return ``np.ndarray``
  instead of ``np.matrix`` when ``return_as=np.ndarray``.  See the
  Notes section for more information on compatibility.

- Changed the internal storage of decision trees to use a struct array.
  This fixed some small bugs, while improving code and providing a small
  speed gain. By `Joel Nothman`_.

- Reduce memory usage and overhead when fitting and predicting with forests
  of randomized trees in parallel with ``n_jobs != 1`` by leveraging new
  threading backend of joblib 0.8 and releasing the GIL in the tree fitting
  Cython code.  By `Olivier Grisel`_ and `Gilles Louppe`_.

- Speed improvement of the `sklearn.ensemble.gradient_boosting` module.
  By `Gilles Louppe`_ and `Peter Prettenhofer`_.

- Various enhancements to the `sklearn.ensemble.gradient_boosting`
  module: a ``warm_start`` argument to fit additional trees,
  a ``max_leaf_nodes`` argument to fit GBM style trees,
  a ``monitor`` fit argument to inspect the estimator during training, and
  refactoring of the verbose code. By `Peter Prettenhofer`_.

- Faster `sklearn.ensemble.ExtraTrees` by caching feature values.
  By `Arnaud Joly`_.

- Faster depth-based tree building algorithm such as decision tree,
  random forest, extra trees or gradient tree boosting (with depth based
  growing strategy) by avoiding trying to split on found constant features
  in the sample subset. By `Arnaud Joly`_.

- Add ``min_weight_fraction_leaf`` pre-pruning parameter to tree-based
  methods: the minimum weighted fraction of the input samples required to be
  at a leaf node. By `Noel Dawe`_.

- Added :func:`metrics.pairwise_distances_argmin_min`, by Philippe Gervais.

- Added predict method to :class:`cluster.AffinityPropagation` and
  :class:`cluster.MeanShift`, by `Mathieu Blondel`_.

- Vector and matrix multiplications have been optimised throughout the
  library by `Denis Engemann`_, and `Alexandre Gramfort`_.
  In particular, they should take less memory with older NumPy versions
  (prior to 1.7.2).

- Precision-recall and ROC examples now use train_test_split, and have more
  explanation of why these metrics are useful. By `Kyle Kastner`_

- The training algorithm for :class:`decomposition.NMF` is faster for
  sparse matrices and has much lower memory complexity, meaning it will
  scale up gracefully to large datasets. By `Lars Buitinck`_.

- Added svd_method option with default value to "randomized" to
  :class:`decomposition.FactorAnalysis` to save memory and
  significantly speedup computation by `Denis Engemann`_, and
  `Alexandre Gramfort`_.

- Changed `cross_validation.StratifiedKFold` to try and
  preserve as much of the original ordering of samples as possible so as
  not to hide overfitting on datasets with a non-negligible level of
  samples dependency.
  By `Daniel Nouri`_ and `Olivier Grisel`_.

- Add multi-output support to :class:`gaussian_process.GaussianProcessRegressor`
  by John Novak.

- Support for precomputed distance matrices in nearest neighbor estimators
  by `Robert Layton`_ and `Joel Nothman`_.

- Norm computations optimized for NumPy 1.6 and later versions by
  `Lars Buitinck`_. In particular, the k-means algorithm no longer
  needs a temporary data structure the size of its input.

- :class:`dummy.DummyClassifier` can now be used to predict a constant
  output value. By `Manoj Kumar`_.

- :class:`dummy.DummyRegressor` has now a strategy parameter which allows
  to predict the mean, the median of the training set or a constant
  output value. By :user:`Maheshakya Wijewardena <maheshakya>`.

- Multi-label classification output in multilabel indicator format
  is now supported by :func:`metrics.roc_auc_score` and
  :func:`metrics.average_precision_score` by `Arnaud Joly`_.

- Significant performance improvements (more than 100x speedup for
  large problems) in :class:`isotonic.IsotonicRegression` by
  `Andrew Tulloch`_.

- Speed and memory usage improvements to the SGD algorithm for linear
  models: it now uses threads, not separate processes, when ``n_jobs>1``.
  By `Lars Buitinck`_.

- Grid search and cross validation allow NaNs in the input arrays so that
  preprocessors such as `preprocessing.Imputer` can be trained within the cross
  validation loop, avoiding potentially skewed results.

- Ridge regression can now deal with sample weights in feature space
  (only sample space until then). By :user:`Michael Eickenberg <eickenberg>`.
  Both solutions are provided by the Cholesky solver.

- Several classification and regression metrics now support weighted
  samples with the new ``sample_weight`` argument:
  :func:`metrics.accuracy_score`,
  :func:`metrics.zero_one_loss`,
  :func:`metrics.precision_score`,
  :func:`metrics.average_precision_score`,
  :func:`metrics.f1_score`,
  :func:`metrics.fbeta_score`,
  :func:`metrics.recall_score`,
  :func:`metrics.roc_auc_score`,
  :func:`metrics.explained_variance_score`,
  :func:`metrics.mean_squared_error`,
  :func:`metrics.mean_absolute_error`,
  :func:`metrics.r2_score`.
  By `Noel Dawe`_.

- Speed up of the sample generator
  :func:`datasets.make_multilabel_classification`. By `Joel Nothman`_.

Documentation improvements
...........................

- The Working With Text Data tutorial
  has now been worked in to the main documentation's tutorial section.
  Includes exercises and skeletons for tutorial presentation.
  Original tutorial created by several authors including
  `Olivier Grisel`_, Lars Buitinck and many others.
  Tutorial integration into the scikit-learn documentation
  by `Jaques Grobler`_

- Added :ref:`Computational Performance <computational_performance>`
  documentation. Discussion and examples of prediction latency / throughput
  and different factors that have influence over speed. Additional tips for
  building faster models and choosing a relevant compromise between speed
  and predictive power.
  By :user:`Eustache Diemert <oddskool>`.

Bug fixes
.........

- Fixed bug in :class:`decomposition.MiniBatchDictionaryLearning` :
  ``partial_fit`` was not working properly.

- Fixed bug in `linear_model.stochastic_gradient` :
  ``l1_ratio`` was used as ``(1.0 - l1_ratio)`` .

- Fixed bug in :class:`multiclass.OneVsOneClassifier` with string
  labels

- Fixed a bug in :class:`LassoCV <linear_model.LassoCV>` and
  :class:`ElasticNetCV <linear_model.ElasticNetCV>`: they would not
  pre-compute the Gram matrix with ``precompute=True`` or
  ``precompute="auto"`` and ``n_samples > n_features``. By `Manoj Kumar`_.

- Fixed incorrect estimation of the degrees of freedom in
  :func:`feature_selection.f_regression` when variates are not centered.
  By :user:`Virgile Fritsch <VirgileFritsch>`.

- Fixed a race condition in parallel processing with
  ``pre_dispatch != "all"`` (for instance, in ``cross_val_score``).
  By `Olivier Grisel`_.

- Raise error in :class:`cluster.FeatureAgglomeration` and
  `cluster.WardAgglomeration` when no samples are given,
  rather than returning meaningless clustering.

- Fixed bug in `gradient_boosting.GradientBoostingRegressor` with
  ``loss='huber'``: ``gamma`` might have not been initialized.

- Fixed feature importances as computed with a forest of randomized trees
  when fit with ``sample_weight != None`` and/or with ``bootstrap=True``.
  By `Gilles Louppe`_.

API changes summary
-------------------

- `sklearn.hmm` is deprecated. Its removal is planned
  for the 0.17 release.

- Use of `covariance.EllipticEnvelop` has now been removed after
  deprecation.
  Please use :class:`covariance.EllipticEnvelope` instead.

- `cluster.Ward` is deprecated. Use
  :class:`cluster.AgglomerativeClustering` instead.

- `cluster.WardClustering` is deprecated. Use
- :class:`cluster.AgglomerativeClustering` instead.

- `cross_validation.Bootstrap` is deprecated.
  `cross_validation.KFold` or
  `cross_validation.ShuffleSplit` are recommended instead.

- Direct support for the sequence of sequences (or list of lists) multilabel
  format is deprecated. To convert to and from the supported binary
  indicator matrix format, use
  :class:`preprocessing.MultiLabelBinarizer`.
  By `Joel Nothman`_.

- Add score method to :class:`decomposition.PCA` following the model of
  probabilistic PCA and deprecate
  `ProbabilisticPCA` model whose
  score implementation is not correct. The computation now also exploits the
  matrix inversion lemma for faster computation. By `Alexandre Gramfort`_.

- The score method of :class:`decomposition.FactorAnalysis`
  now returns the average log-likelihood of the samples. Use score_samples
  to get log-likelihood of each sample. By `Alexandre Gramfort`_.

- Generating boolean masks (the setting ``indices=False``)
  from cross-validation generators is deprecated.
  Support for masks will be removed in 0.17.
  The generators have produced arrays of indices by default since 0.10.
  By `Joel Nothman`_.

- 1-d arrays containing strings with ``dtype=object`` (as used in Pandas)
  are now considered valid classification targets. This fixes a regression
  from version 0.13 in some classifiers. By `Joel Nothman`_.

- Fix wrong ``explained_variance_ratio_`` attribute in
  `RandomizedPCA`.
  By `Alexandre Gramfort`_.

- Fit alphas for each ``l1_ratio`` instead of ``mean_l1_ratio`` in
  :class:`linear_model.ElasticNetCV` and :class:`linear_model.LassoCV`.
  This changes the shape of ``alphas_`` from ``(n_alphas,)`` to
  ``(n_l1_ratio, n_alphas)`` if the ``l1_ratio`` provided is a 1-D array like
  object of length greater than one.
  By `Manoj Kumar`_.

- Fix :class:`linear_model.ElasticNetCV` and :class:`linear_model.LassoCV`
  when fitting intercept and input data is sparse. The automatic grid
  of alphas was not computed correctly and the scaling with normalize
  was wrong. By `Manoj Kumar`_.

- Fix wrong maximal number of features drawn (``max_features``) at each split
  for decision trees, random forests and gradient tree boosting.
  Previously, the count for the number of drawn features started only after
  one non constant features in the split. This bug fix will affect
  computational and generalization performance of those algorithms in the
  presence of constant features. To get back previous generalization
  performance, you should modify the value of ``max_features``.
  By `Arnaud Joly`_.

- Fix wrong maximal number of features drawn (``max_features``) at each split
  for :class:`ensemble.ExtraTreesClassifier` and
  :class:`ensemble.ExtraTreesRegressor`. Previously, only non constant
  features in the split was counted as drawn. Now constant features are
  counted as drawn. Furthermore at least one feature must be non constant
  in order to make a valid split. This bug fix will affect
  computational and generalization performance of extra trees in the
  presence of constant features. To get back previous generalization
  performance, you should modify the value of ``max_features``.
  By `Arnaud Joly`_.

- Fix :func:`utils.class_weight.compute_class_weight` when ``class_weight=="auto"``.
  Previously it was broken for input of non-integer ``dtype`` and the
  weighted array that was returned was wrong. By `Manoj Kumar`_.

- Fix `cross_validation.Bootstrap` to return ``ValueError``
  when ``n_train + n_test > n``. By :user:`Ronald Phlypo <rphlypo>`.

People
------

List of contributors for release 0.15 by number of commits.

* 312	Olivier Grisel
* 275	Lars Buitinck
* 221	Gael Varoquaux
* 148	Arnaud Joly
* 134	Johannes Schönberger
* 119	Gilles Louppe
* 113	Joel Nothman
* 111	Alexandre Gramfort
*  95	Jaques Grobler
*  89	Denis Engemann
*  83	Peter Prettenhofer
*  83	Alexander Fabisch
*  62	Mathieu Blondel
*  60	Eustache Diemert
*  60	Nelle Varoquaux
*  49	Michael Bommarito
*  45	Manoj-Kumar-S
*  28	Kyle Kastner
*  26	Andreas Mueller
*  22	Noel Dawe
*  21	Maheshakya Wijewardena
*  21	Brooke Osborn
*  21	Hamzeh Alsalhi
*  21	Jake VanderPlas
*  21	Philippe Gervais
*  19	Bala Subrahmanyam Varanasi
*  12	Ronald Phlypo
*  10	Mikhail Korobov
*   8	Thomas Unterthiner
*   8	Jeffrey Blackburne
*   8	eltermann
*   8	bwignall
*   7	Ankit Agrawal
*   7	CJ Carey
*   6	Daniel Nouri
*   6	Chen Liu
*   6	Michael Eickenberg
*   6	ugurthemaster
*   5	Aaron Schumacher
*   5	Baptiste Lagarde
*   5	Rajat Khanduja
*   5	Robert McGibbon
*   5	Sergio Pascual
*   4	Alexis Metaireau
*   4	Ignacio Rossi
*   4	Virgile Fritsch
*   4	Sebastian Säger
*   4	Ilambharathi Kanniah
*   4	sdenton4
*   4	Robert Layton
*   4	Alyssa
*   4	Amos Waterland
*   3	Andrew Tulloch
*   3	murad
*   3	Steven Maude
*   3	Karol Pysniak
*   3	Jacques Kvam
*   3	cgohlke
*   3	cjlin
*   3	Michael Becker
*   3	hamzeh
*   3	Eric Jacobsen
*   3	john collins
*   3	kaushik94
*   3	Erwin Marsi
*   2	csytracy
*   2	LK
*   2	Vlad Niculae
*   2	Laurent Direr
*   2	Erik Shilts
*   2	Raul Garreta
*   2	Yoshiki Vázquez Baeza
*   2	Yung Siang Liau
*   2	abhishek thakur
*   2	James Yu
*   2	Rohit Sivaprasad
*   2	Roland Szabo
*   2	amormachine
*   2	Alexis Mignon
*   2	Oscar Carlsson
*   2	Nantas Nardelli
*   2	jess010
*   2	kowalski87
*   2	Andrew Clegg
*   2	Federico Vaggi
*   2	Simon Frid
*   2	Félix-Antoine Fortin
*   1	Ralf Gommers
*   1	t-aft
*   1	Ronan Amicel
*   1	Rupesh Kumar Srivastava
*   1	Ryan Wang
*   1	Samuel Charron
*   1	Samuel St-Jean
*   1	Fabian Pedregosa
*   1	Skipper Seabold
*   1	Stefan Walk
*   1	Stefan van der Walt
*   1	Stephan Hoyer
*   1	Allen Riddell
*   1	Valentin Haenel
*   1	Vijay Ramesh
*   1	Will Myers
*   1	Yaroslav Halchenko
*   1	Yoni Ben-Meshulam
*   1	Yury V. Zaytsev
*   1	adrinjalali
*   1	ai8rahim
*   1	alemagnani
*   1	alex
*   1	benjamin wilson
*   1	chalmerlowe
*   1	dzikie drożdże
*   1	jamestwebber
*   1	matrixorz
*   1	popo
*   1	samuela
*   1	François Boulogne
*   1	Alexander Measure
*   1	Ethan White
*   1	Guilherme Trein
*   1	Hendrik Heuer
*   1	IvicaJovic
*   1	Jan Hendrik Metzen
*   1	Jean Michel Rouly
*   1	Eduardo Ariño de la Rubia
*   1	Jelle Zijlstra
*   1	Eddy L O Jansson
*   1	Denis
*   1	John
*   1	John Schmidt
*   1	Jorge Cañardo Alastuey
*   1	Joseph Perla
*   1	Joshua Vredevoogd
*   1	José Ricardo
*   1	Julien Miotte
*   1	Kemal Eren
*   1	Kenta Sato
*   1	David Cournapeau
*   1	Kyle Kelley
*   1	Daniele Medri
*   1	Laurent Luce
*   1	Laurent Pierron
*   1	Luis Pedro Coelho
*   1	DanielWeitzenfeld
*   1	Craig Thompson
*   1	Chyi-Kwei Yau
*   1	Matthew Brett
*   1	Matthias Feurer
*   1	Max Linke
*   1	Chris Filo Gorgolewski
*   1	Charles Earl
*   1	Michael Hanke
*   1	Michele Orrù
*   1	Bryan Lunt
*   1	Brian Kearns
*   1	Paul Butler
*   1	Paweł Mandera
*   1	Peter
*   1	Andrew Ash
*   1	Pietro Zambelli
*   1	staubda

include    contributors rst     currentmodule   sklearn               Version 0 15                   changes 0 15 2   Version 0 15 2                   September 4  2014    Bug fixes              Fixed handling of the   p   parameter of the Minkowski distance that was   previously ignored in nearest neighbors models  By  user  Nikolay   Mayorov  nmayorov       Fixed duplicated alphas in  class  linear model LassoLars  with early   stopping on 32 bit Python  By  Olivier Grisel   and  Fabian Pedregosa       Fixed the build under Windows when scikit learn is built with MSVC while   NumPy is built with MinGW  By  Olivier Grisel   and  user  Federico   Vaggi  FedericoV       Fixed an array index overflow bug in the coordinate descent solver  By    Gael Varoquaux       Better handling of numpy 1 9 deprecation warnings  By  Gael Varoquaux       Removed unnecessary data copy in  class  cluster KMeans     By  Gael Varoquaux       Explicitly close open files to avoid   ResourceWarnings   under Python 3    By Calvin Giles     The   transform   of  class  discriminant analysis LinearDiscriminantAnalysis    now projects the input on the most discriminant directions  By Martin Billinger     Fixed potential overflow in    tree safe realloc   by  Lars Buitinck       Performance optimization in  class  isotonic IsotonicRegression     By Robert Bradshaw       nose   is non longer a runtime dependency to import   sklearn    only for   running the tests  By  Joel Nothman       Many documentation and website fixes by  Joel Nothman     Lars Buitinck      user  Matt Pico  MattpSoftware    and others       changes 0 15 1   Version 0 15 1                   August 1  2014    Bug fixes              Made  cross validation cross val score  use    cross validation KFold  instead of    cross validation StratifiedKFold  on multi output classification   problems  By  user  Nikolay Mayorov  nmayorov       Support unseen labels  class  preprocessing LabelBinarizer  to restore   the default behavior of 0 14 1 for backward compatibility  By    user  Hamzeh Alsalhi  hamsal       Fixed the  class  cluster KMeans  stopping criterion that prevented early   convergence detection  By Edward Raff and  Gael Varoquaux       Fixed the behavior of  class  multiclass OneVsOneClassifier     in case of ties at the per class vote level by computing the correct   per class sum of prediction scores  By  Andreas M ller       Made  cross validation cross val score  and    grid search GridSearchCV  accept Python lists as input data    This is especially useful for cross validation and model selection of   text processing pipelines  By  Andreas M ller       Fixed data input checks of most estimators to accept input data that   implements the NumPy     array     protocol  This is the case for   for   pandas Series   and   pandas DataFrame   in recent versions of   pandas  By  Gael Varoquaux       Fixed a regression for  class  linear model SGDClassifier  with     class weight  auto    on data with non contiguous labels  By    Olivier Grisel          changes 0 15   Version 0 15                 July 15  2014    Highlights                Many speed and memory improvements all across the code    Huge speed and memory improvements to random forests  and extra   trees  that also benefit better from parallel computing     Incremental fit to  class  BernoulliRBM  neural network BernoulliRBM      Added  class  cluster AgglomerativeClustering  for hierarchical   agglomerative clustering with average linkage  complete linkage and   ward strategies     Added  class  linear model RANSACRegressor  for robust regression   models     Added dimensionality reduction with  class  manifold TSNE  which can be   used to visualize high dimensional data    Changelog            New features                 Added  class  ensemble BaggingClassifier  and    class  ensemble BaggingRegressor  meta estimators for ensembling   any kind of base estimator  See the  ref  Bagging  bagging   section of   the user guide for details and examples  By  Gilles Louppe       New unsupervised feature selection algorithm    class  feature selection VarianceThreshold   by  Lars Buitinck       Added  class  linear model RANSACRegressor  meta estimator for the robust   fitting of regression models  By  user  Johannes Sch nberger  ahojnnes       Added  class  cluster AgglomerativeClustering  for hierarchical   agglomerative clustering with average linkage  complete linkage and   ward strategies  by   Nelle Varoquaux   and  Gael Varoquaux       Shorthand constructors  func  pipeline make pipeline  and    func  pipeline make union  were added by  Lars Buitinck       Shuffle option for  cross validation StratifiedKFold     By  user  Jeffrey Blackburne  jblackburne       Incremental learning    partial fit    for Gaussian Naive Bayes by   Imran Haque     Added   partial fit   to  class  BernoulliRBM    neural network BernoulliRBM     By  user  Danny Sullivan  dsullivan7       Added  learning curve  utility to   chart performance with respect to training size  See    ref  sphx glr auto examples model selection plot learning curve py   By Alexander Fabisch     Add positive option in  class  LassoCV  linear model LassoCV   and    class  ElasticNetCV  linear model ElasticNetCV      By Brian Wignall and  Alexandre Gramfort       Added  class  linear model MultiTaskElasticNetCV  and    class  linear model MultiTaskLassoCV   By  Manoj Kumar       Added  class  manifold TSNE   By Alexander Fabisch   Enhancements                 Add sparse input support to  class  ensemble AdaBoostClassifier  and    class  ensemble AdaBoostRegressor  meta estimators    By  user  Hamzeh Alsalhi  hamsal       Memory improvements of decision trees  by  Arnaud Joly       Decision trees can now be built in best first manner by using   max leaf nodes     as the stopping criteria  Refactored the tree code to use either a   stack or a priority queue for tree building    By  Peter Prettenhofer   and  Gilles Louppe       Decision trees can now be fitted on fortran  and c style arrays  and   non continuous arrays without the need to make a copy    If the input array has a different dtype than   np float32    a fortran    style copy will be made since fortran style memory layout has speed   advantages  By  Peter Prettenhofer   and  Gilles Louppe       Speed improvement of regression trees by optimizing the   the computation of the mean square error criterion  This lead   to speed improvement of the tree  forest and gradient boosting tree   modules  By  Arnaud Joly      The   img to graph   and   grid tograph   functions in    mod  sklearn feature extraction image  now return   np ndarray     instead of   np matrix   when   return as np ndarray     See the   Notes section for more information on compatibility     Changed the internal storage of decision trees to use a struct array    This fixed some small bugs  while improving code and providing a small   speed gain  By  Joel Nothman       Reduce memory usage and overhead when fitting and predicting with forests   of randomized trees in parallel with   n jobs    1   by leveraging new   threading backend of joblib 0 8 and releasing the GIL in the tree fitting   Cython code   By  Olivier Grisel   and  Gilles Louppe       Speed improvement of the  sklearn ensemble gradient boosting  module    By  Gilles Louppe   and  Peter Prettenhofer       Various enhancements to the  sklearn ensemble gradient boosting    module  a   warm start   argument to fit additional trees    a   max leaf nodes   argument to fit GBM style trees    a   monitor   fit argument to inspect the estimator during training  and   refactoring of the verbose code  By  Peter Prettenhofer       Faster  sklearn ensemble ExtraTrees  by caching feature values    By  Arnaud Joly       Faster depth based tree building algorithm such as decision tree    random forest  extra trees or gradient tree boosting  with depth based   growing strategy  by avoiding trying to split on found constant features   in the sample subset  By  Arnaud Joly       Add   min weight fraction leaf   pre pruning parameter to tree based   methods  the minimum weighted fraction of the input samples required to be   at a leaf node  By  Noel Dawe       Added  func  metrics pairwise distances argmin min   by Philippe Gervais     Added predict method to  class  cluster AffinityPropagation  and    class  cluster MeanShift   by  Mathieu Blondel       Vector and matrix multiplications have been optimised throughout the   library by  Denis Engemann    and  Alexandre Gramfort      In particular  they should take less memory with older NumPy versions    prior to 1 7 2      Precision recall and ROC examples now use train test split  and have more   explanation of why these metrics are useful  By  Kyle Kastner      The training algorithm for  class  decomposition NMF  is faster for   sparse matrices and has much lower memory complexity  meaning it will   scale up gracefully to large datasets  By  Lars Buitinck       Added svd method option with default value to  randomized  to    class  decomposition FactorAnalysis  to save memory and   significantly speedup computation by  Denis Engemann    and    Alexandre Gramfort       Changed  cross validation StratifiedKFold  to try and   preserve as much of the original ordering of samples as possible so as   not to hide overfitting on datasets with a non negligible level of   samples dependency    By  Daniel Nouri   and  Olivier Grisel       Add multi output support to  class  gaussian process GaussianProcessRegressor    by John Novak     Support for precomputed distance matrices in nearest neighbor estimators   by  Robert Layton   and  Joel Nothman       Norm computations optimized for NumPy 1 6 and later versions by    Lars Buitinck    In particular  the k means algorithm no longer   needs a temporary data structure the size of its input      class  dummy DummyClassifier  can now be used to predict a constant   output value  By  Manoj Kumar        class  dummy DummyRegressor  has now a strategy parameter which allows   to predict the mean  the median of the training set or a constant   output value  By  user  Maheshakya Wijewardena  maheshakya       Multi label classification output in multilabel indicator format   is now supported by  func  metrics roc auc score  and    func  metrics average precision score  by  Arnaud Joly       Significant performance improvements  more than 100x speedup for   large problems  in  class  isotonic IsotonicRegression  by    Andrew Tulloch       Speed and memory usage improvements to the SGD algorithm for linear   models  it now uses threads  not separate processes  when   n jobs 1      By  Lars Buitinck       Grid search and cross validation allow NaNs in the input arrays so that   preprocessors such as  preprocessing Imputer  can be trained within the cross   validation loop  avoiding potentially skewed results     Ridge regression can now deal with sample weights in feature space    only sample space until then   By  user  Michael Eickenberg  eickenberg      Both solutions are provided by the Cholesky solver     Several classification and regression metrics now support weighted   samples with the new   sample weight   argument     func  metrics accuracy score      func  metrics zero one loss      func  metrics precision score      func  metrics average precision score      func  metrics f1 score      func  metrics fbeta score      func  metrics recall score      func  metrics roc auc score      func  metrics explained variance score      func  metrics mean squared error      func  metrics mean absolute error      func  metrics r2 score     By  Noel Dawe       Speed up of the sample generator    func  datasets make multilabel classification   By  Joel Nothman     Documentation improvements                                The Working With Text Data tutorial   has now been worked in to the main documentation s tutorial section    Includes exercises and skeletons for tutorial presentation    Original tutorial created by several authors including    Olivier Grisel    Lars Buitinck and many others    Tutorial integration into the scikit learn documentation   by  Jaques Grobler      Added  ref  Computational Performance  computational performance     documentation  Discussion and examples of prediction latency   throughput   and different factors that have influence over speed  Additional tips for   building faster models and choosing a relevant compromise between speed   and predictive power    By  user  Eustache Diemert  oddskool     Bug fixes              Fixed bug in  class  decomposition MiniBatchDictionaryLearning        partial fit   was not working properly     Fixed bug in  linear model stochastic gradient        l1 ratio   was used as    1 0   l1 ratio         Fixed bug in  class  multiclass OneVsOneClassifier  with string   labels    Fixed a bug in  class  LassoCV  linear model LassoCV   and    class  ElasticNetCV  linear model ElasticNetCV    they would not   pre compute the Gram matrix with   precompute True   or     precompute  auto    and   n samples   n features    By  Manoj Kumar       Fixed incorrect estimation of the degrees of freedom in    func  feature selection f regression  when variates are not centered    By  user  Virgile Fritsch  VirgileFritsch       Fixed a race condition in parallel processing with     pre dispatch     all     for instance  in   cross val score       By  Olivier Grisel       Raise error in  class  cluster FeatureAgglomeration  and    cluster WardAgglomeration  when no samples are given    rather than returning meaningless clustering     Fixed bug in  gradient boosting GradientBoostingRegressor  with     loss  huber       gamma   might have not been initialized     Fixed feature importances as computed with a forest of randomized trees   when fit with   sample weight    None   and or with   bootstrap True      By  Gilles Louppe     API changes summary                         sklearn hmm  is deprecated  Its removal is planned   for the 0 17 release     Use of  covariance EllipticEnvelop  has now been removed after   deprecation    Please use  class  covariance EllipticEnvelope  instead      cluster Ward  is deprecated  Use    class  cluster AgglomerativeClustering  instead      cluster WardClustering  is deprecated  Use    class  cluster AgglomerativeClustering  instead      cross validation Bootstrap  is deprecated     cross validation KFold  or    cross validation ShuffleSplit  are recommended instead     Direct support for the sequence of sequences  or list of lists  multilabel   format is deprecated  To convert to and from the supported binary   indicator matrix format  use    class  preprocessing MultiLabelBinarizer     By  Joel Nothman       Add score method to  class  decomposition PCA  following the model of   probabilistic PCA and deprecate    ProbabilisticPCA  model whose   score implementation is not correct  The computation now also exploits the   matrix inversion lemma for faster computation  By  Alexandre Gramfort       The score method of  class  decomposition FactorAnalysis    now returns the average log likelihood of the samples  Use score samples   to get log likelihood of each sample  By  Alexandre Gramfort       Generating boolean masks  the setting   indices False      from cross validation generators is deprecated    Support for masks will be removed in 0 17    The generators have produced arrays of indices by default since 0 10    By  Joel Nothman       1 d arrays containing strings with   dtype object    as used in Pandas    are now considered valid classification targets  This fixes a regression   from version 0 13 in some classifiers  By  Joel Nothman       Fix wrong   explained variance ratio    attribute in    RandomizedPCA     By  Alexandre Gramfort       Fit alphas for each   l1 ratio   instead of   mean l1 ratio   in    class  linear model ElasticNetCV  and  class  linear model LassoCV     This changes the shape of   alphas    from    n alphas     to      n l1 ratio  n alphas    if the   l1 ratio   provided is a 1 D array like   object of length greater than one    By  Manoj Kumar       Fix  class  linear model ElasticNetCV  and  class  linear model LassoCV    when fitting intercept and input data is sparse  The automatic grid   of alphas was not computed correctly and the scaling with normalize   was wrong  By  Manoj Kumar       Fix wrong maximal number of features drawn    max features    at each split   for decision trees  random forests and gradient tree boosting    Previously  the count for the number of drawn features started only after   one non constant features in the split  This bug fix will affect   computational and generalization performance of those algorithms in the   presence of constant features  To get back previous generalization   performance  you should modify the value of   max features      By  Arnaud Joly       Fix wrong maximal number of features drawn    max features    at each split   for  class  ensemble ExtraTreesClassifier  and    class  ensemble ExtraTreesRegressor   Previously  only non constant   features in the split was counted as drawn  Now constant features are   counted as drawn  Furthermore at least one feature must be non constant   in order to make a valid split  This bug fix will affect   computational and generalization performance of extra trees in the   presence of constant features  To get back previous generalization   performance  you should modify the value of   max features      By  Arnaud Joly       Fix  func  utils class weight compute class weight  when   class weight   auto       Previously it was broken for input of non integer   dtype   and the   weighted array that was returned was wrong  By  Manoj Kumar       Fix  cross validation Bootstrap  to return   ValueError     when   n train   n test   n    By  user  Ronald Phlypo  rphlypo      People         List of contributors for release 0 15 by number of commits     312 Olivier Grisel   275 Lars Buitinck   221 Gael Varoquaux   148 Arnaud Joly   134 Johannes Sch nberger   119 Gilles Louppe   113 Joel Nothman   111 Alexandre Gramfort    95 Jaques Grobler    89 Denis Engemann    83 Peter Prettenhofer    83 Alexander Fabisch    62 Mathieu Blondel    60 Eustache Diemert    60 Nelle Varoquaux    49 Michael Bommarito    45 Manoj Kumar S    28 Kyle Kastner    26 Andreas Mueller    22 Noel Dawe    21 Maheshakya Wijewardena    21 Brooke Osborn    21 Hamzeh Alsalhi    21 Jake VanderPlas    21 Philippe Gervais    19 Bala Subrahmanyam Varanasi    12 Ronald Phlypo    10 Mikhail Korobov     8 Thomas Unterthiner     8 Jeffrey Blackburne     8 eltermann     8 bwignall     7 Ankit Agrawal     7 CJ Carey     6 Daniel Nouri     6 Chen Liu     6 Michael Eickenberg     6 ugurthemaster     5 Aaron Schumacher     5 Baptiste Lagarde     5 Rajat Khanduja     5 Robert McGibbon     5 Sergio Pascual     4 Alexis Metaireau     4 Ignacio Rossi     4 Virgile Fritsch     4 Sebastian S ger     4 Ilambharathi Kanniah     4 sdenton4     4 Robert Layton     4 Alyssa     4 Amos Waterland     3 Andrew Tulloch     3 murad     3 Steven Maude     3 Karol Pysniak     3 Jacques Kvam     3 cgohlke     3 cjlin     3 Michael Becker     3 hamzeh     3 Eric Jacobsen     3 john collins     3 kaushik94     3 Erwin Marsi     2 csytracy     2 LK     2 Vlad Niculae     2 Laurent Direr     2 Erik Shilts     2 Raul Garreta     2 Yoshiki V zquez Baeza     2 Yung Siang Liau     2 abhishek thakur     2 James Yu     2 Rohit Sivaprasad     2 Roland Szabo     2 amormachine     2 Alexis Mignon     2 Oscar Carlsson     2 Nantas Nardelli     2 jess010     2 kowalski87     2 Andrew Clegg     2 Federico Vaggi     2 Simon Frid     2 F lix Antoine Fortin     1 Ralf Gommers     1 t aft     1 Ronan Amicel     1 Rupesh Kumar Srivastava     1 Ryan Wang     1 Samuel Charron     1 Samuel St Jean     1 Fabian Pedregosa     1 Skipper Seabold     1 Stefan Walk     1 Stefan van der Walt     1 Stephan Hoyer     1 Allen Riddell     1 Valentin Haenel     1 Vijay Ramesh     1 Will Myers     1 Yaroslav Halchenko     1 Yoni Ben Meshulam     1 Yury V  Zaytsev     1 adrinjalali     1 ai8rahim     1 alemagnani     1 alex     1 benjamin wilson     1 chalmerlowe     1 dzikie dro d e     1 jamestwebber     1 matrixorz     1 popo     1 samuela     1 Fran ois Boulogne     1 Alexander Measure     1 Ethan White     1 Guilherme Trein     1 Hendrik Heuer     1 IvicaJovic     1 Jan Hendrik Metzen     1 Jean Michel Rouly     1 Eduardo Ari o de la Rubia     1 Jelle Zijlstra     1 Eddy L O Jansson     1 Denis     1 John     1 John Schmidt     1 Jorge Ca ardo Alastuey     1 Joseph Perla     1 Joshua Vredevoogd     1 Jos  Ricardo     1 Julien Miotte     1 Kemal Eren     1 Kenta Sato     1 David Cournapeau     1 Kyle Kelley     1 Daniele Medri     1 Laurent Luce     1 Laurent Pierron     1 Luis Pedro Coelho     1 DanielWeitzenfeld     1 Craig Thompson     1 Chyi Kwei Yau     1 Matthew Brett     1 Matthias Feurer     1 Max Linke     1 Chris Filo Gorgolewski     1 Charles Earl     1 Michael Hanke     1 Michele Orr      1 Bryan Lunt     1 Brian Kearns     1 Paul Butler     1 Pawe  Mandera     1 Peter     1 Andrew Ash     1 Pietro Zambelli     1 staubda


scikit-learn sklearn contributors rst Version 0 14 changes014

Answer:
.. include:: _contributors.rst

.. currentmodule:: sklearn

============
Version 0.14
============

.. _changes_0_14:

Version 0.14
===============

**August 7, 2013**

Changelog
---------

- Missing values with sparse and dense matrices can be imputed with the
  transformer `preprocessing.Imputer` by `Nicolas Trésegnie`_.

- The core implementation of decisions trees has been rewritten from
  scratch, allowing for faster tree induction and lower memory
  consumption in all tree-based estimators. By `Gilles Louppe`_.

- Added :class:`ensemble.AdaBoostClassifier` and
  :class:`ensemble.AdaBoostRegressor`, by `Noel Dawe`_  and
  `Gilles Louppe`_. See the :ref:`AdaBoost <adaboost>` section of the user
  guide for details and examples.

- Added `grid_search.RandomizedSearchCV` and
  `grid_search.ParameterSampler` for randomized hyperparameter
  optimization. By `Andreas Müller`_.

- Added :ref:`biclustering <biclustering>` algorithms
  (`sklearn.cluster.bicluster.SpectralCoclustering` and
  `sklearn.cluster.bicluster.SpectralBiclustering`), data
  generation methods (:func:`sklearn.datasets.make_biclusters` and
  :func:`sklearn.datasets.make_checkerboard`), and scoring metrics
  (:func:`sklearn.metrics.consensus_score`). By `Kemal Eren`_.

- Added :ref:`Restricted Boltzmann Machines<rbm>`
  (:class:`neural_network.BernoulliRBM`). By `Yann Dauphin`_.

- Python 3 support by :user:`Justin Vincent <justinvf>`, `Lars Buitinck`_,
  :user:`Subhodeep Moitra <smoitra87>` and `Olivier Grisel`_. All tests now pass under
  Python 3.3.

- Ability to pass one penalty (alpha value) per target in
  :class:`linear_model.Ridge`, by @eickenberg and `Mathieu Blondel`_.

- Fixed `sklearn.linear_model.stochastic_gradient.py` L2 regularization
  issue (minor practical significance).
  By :user:`Norbert Crombach <norbert>` and `Mathieu Blondel`_ .

- Added an interactive version of `Andreas Müller`_'s
  `Machine Learning Cheat Sheet (for scikit-learn)
  <https://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html>`_
  to the documentation. See :ref:`Choosing the right estimator <ml_map>`.
  By `Jaques Grobler`_.

- `grid_search.GridSearchCV` and
  `cross_validation.cross_val_score` now support the use of advanced
  scoring function such as area under the ROC curve and f-beta scores.
  See :ref:`scoring_parameter` for details. By `Andreas Müller`_
  and `Lars Buitinck`_.
  Passing a function from :mod:`sklearn.metrics` as ``score_func`` is
  deprecated.

- Multi-label classification output is now supported by
  :func:`metrics.accuracy_score`, :func:`metrics.zero_one_loss`,
  :func:`metrics.f1_score`, :func:`metrics.fbeta_score`,
  :func:`metrics.classification_report`,
  :func:`metrics.precision_score` and :func:`metrics.recall_score`
  by `Arnaud Joly`_.

- Two new metrics :func:`metrics.hamming_loss` and
  `metrics.jaccard_similarity_score`
  are added with multi-label support by `Arnaud Joly`_.

- Speed and memory usage improvements in
  :class:`feature_extraction.text.CountVectorizer` and
  :class:`feature_extraction.text.TfidfVectorizer`,
  by Jochen Wersdörfer and Roman Sinayev.

- The ``min_df`` parameter in
  :class:`feature_extraction.text.CountVectorizer` and
  :class:`feature_extraction.text.TfidfVectorizer`, which used to be 2,
  has been reset to 1 to avoid unpleasant surprises (empty vocabularies)
  for novice users who try it out on tiny document collections.
  A value of at least 2 is still recommended for practical use.

- :class:`svm.LinearSVC`, :class:`linear_model.SGDClassifier` and
  :class:`linear_model.SGDRegressor` now have a ``sparsify`` method that
  converts their ``coef_`` into a sparse matrix, meaning stored models
  trained using these estimators can be made much more compact.

- :class:`linear_model.SGDClassifier` now produces multiclass probability
  estimates when trained under log loss or modified Huber loss.

- Hyperlinks to documentation in example code on the website by
  :user:`Martin Luessi <mluessi>`.

- Fixed bug in :class:`preprocessing.MinMaxScaler` causing incorrect scaling
  of the features for non-default ``feature_range`` settings. By `Andreas
  Müller`_.

- ``max_features`` in :class:`tree.DecisionTreeClassifier`,
  :class:`tree.DecisionTreeRegressor` and all derived ensemble estimators
  now supports percentage values. By `Gilles Louppe`_.

- Performance improvements in :class:`isotonic.IsotonicRegression` by
  `Nelle Varoquaux`_.

- :func:`metrics.accuracy_score` has an option normalize to return
  the fraction or the number of correctly classified sample
  by `Arnaud Joly`_.

- Added :func:`metrics.log_loss` that computes log loss, aka cross-entropy
  loss. By Jochen Wersdörfer and `Lars Buitinck`_.

- A bug that caused :class:`ensemble.AdaBoostClassifier`'s to output
  incorrect probabilities has been fixed.

- Feature selectors now share a mixin providing consistent ``transform``,
  ``inverse_transform`` and ``get_support`` methods. By `Joel Nothman`_.

- A fitted `grid_search.GridSearchCV` or
  `grid_search.RandomizedSearchCV` can now generally be pickled.
  By `Joel Nothman`_.

- Refactored and vectorized implementation of :func:`metrics.roc_curve`
  and :func:`metrics.precision_recall_curve`. By `Joel Nothman`_.

- The new estimator :class:`sklearn.decomposition.TruncatedSVD`
  performs dimensionality reduction using SVD on sparse matrices,
  and can be used for latent semantic analysis (LSA).
  By `Lars Buitinck`_.

- Added self-contained example of out-of-core learning on text data
  :ref:`sphx_glr_auto_examples_applications_plot_out_of_core_classification.py`.
  By :user:`Eustache Diemert <oddskool>`.

- The default number of components for
  `sklearn.decomposition.RandomizedPCA` is now correctly documented
  to be ``n_features``. This was the default behavior, so programs using it
  will continue to work as they did.

- :class:`sklearn.cluster.KMeans` now fits several orders of magnitude
  faster on sparse data (the speedup depends on the sparsity). By
  `Lars Buitinck`_.

- Reduce memory footprint of FastICA by `Denis Engemann`_ and
  `Alexandre Gramfort`_.

- Verbose output in `sklearn.ensemble.gradient_boosting` now uses
  a column format and prints progress in decreasing frequency.
  It also shows the remaining time. By `Peter Prettenhofer`_.

- `sklearn.ensemble.gradient_boosting` provides out-of-bag improvement
  `oob_improvement_`
  rather than the OOB score for model selection. An example that shows
  how to use OOB estimates to select the number of trees was added.
  By `Peter Prettenhofer`_.

- Most metrics now support string labels for multiclass classification
  by `Arnaud Joly`_ and `Lars Buitinck`_.

- New OrthogonalMatchingPursuitCV class by `Alexandre Gramfort`_
  and `Vlad Niculae`_.

- Fixed a bug in `sklearn.covariance.GraphLassoCV`: the
  'alphas' parameter now works as expected when given a list of
  values. By Philippe Gervais.

- Fixed an important bug in `sklearn.covariance.GraphLassoCV`
  that prevented all folds provided by a CV object to be used (only
  the first 3 were used). When providing a CV object, execution
  time may thus increase significantly compared to the previous
  version (bug results are correct now). By Philippe Gervais.

- `cross_validation.cross_val_score` and the `grid_search`
  module is now tested with multi-output data by `Arnaud Joly`_.

- :func:`datasets.make_multilabel_classification` can now return
  the output in label indicator multilabel format  by `Arnaud Joly`_.

- K-nearest neighbors, :class:`neighbors.KNeighborsRegressor`
  and :class:`neighbors.RadiusNeighborsRegressor`,
  and radius neighbors, :class:`neighbors.RadiusNeighborsRegressor` and
  :class:`neighbors.RadiusNeighborsClassifier` support multioutput data
  by `Arnaud Joly`_.

- Random state in LibSVM-based estimators (:class:`svm.SVC`, :class:`svm.NuSVC`,
  :class:`svm.OneClassSVM`, :class:`svm.SVR`, :class:`svm.NuSVR`) can now be
  controlled.  This is useful to ensure consistency in the probability
  estimates for the classifiers trained with ``probability=True``. By
  `Vlad Niculae`_.

- Out-of-core learning support for discrete naive Bayes classifiers
  :class:`sklearn.naive_bayes.MultinomialNB` and
  :class:`sklearn.naive_bayes.BernoulliNB` by adding the ``partial_fit``
  method by `Olivier Grisel`_.

- New website design and navigation by `Gilles Louppe`_, `Nelle Varoquaux`_,
  Vincent Michel and `Andreas Müller`_.

- Improved documentation on :ref:`multi-class, multi-label and multi-output
  classification <multiclass>` by `Yannick Schwartz`_ and `Arnaud Joly`_.

- Better input and error handling in the :mod:`sklearn.metrics` module by
  `Arnaud Joly`_ and `Joel Nothman`_.

- Speed optimization of the `hmm` module by :user:`Mikhail Korobov <kmike>`

- Significant speed improvements for :class:`sklearn.cluster.DBSCAN`
  by `cleverless <https://github.com/cleverless>`_

API changes summary
-------------------

- The `auc_score` was renamed :func:`metrics.roc_auc_score`.

- Testing scikit-learn with ``sklearn.test()`` is deprecated. Use
  ``nosetests sklearn`` from the command line.

- Feature importances in :class:`tree.DecisionTreeClassifier`,
  :class:`tree.DecisionTreeRegressor` and all derived ensemble estimators
  are now computed on the fly when accessing  the ``feature_importances_``
  attribute. Setting ``compute_importances=True`` is no longer required.
  By `Gilles Louppe`_.

- :class:`linear_model.lasso_path` and
  :class:`linear_model.enet_path` can return its results in the same
  format as that of :class:`linear_model.lars_path`. This is done by
  setting the ``return_models`` parameter to ``False``. By
  `Jaques Grobler`_ and `Alexandre Gramfort`_

- `grid_search.IterGrid` was renamed to `grid_search.ParameterGrid`.

- Fixed bug in `KFold` causing imperfect class balance in some
  cases. By `Alexandre Gramfort`_ and Tadej Janež.

- :class:`sklearn.neighbors.BallTree` has been refactored, and a
  :class:`sklearn.neighbors.KDTree` has been
  added which shares the same interface.  The Ball Tree now works with
  a wide variety of distance metrics.  Both classes have many new
  methods, including single-tree and dual-tree queries, breadth-first
  and depth-first searching, and more advanced queries such as
  kernel density estimation and 2-point correlation functions.
  By `Jake Vanderplas`_

- Support for scipy.spatial.cKDTree within neighbors queries has been
  removed, and the functionality replaced with the new
  :class:`sklearn.neighbors.KDTree` class.

- :class:`sklearn.neighbors.KernelDensity` has been added, which performs
  efficient kernel density estimation with a variety of kernels.

- :class:`sklearn.decomposition.KernelPCA` now always returns output with
  ``n_components`` components, unless the new parameter ``remove_zero_eig``
  is set to ``True``. This new behavior is consistent with the way
  kernel PCA was always documented; previously, the removal of components
  with zero eigenvalues was tacitly performed on all data.

- ``gcv_mode="auto"`` no longer tries to perform SVD on a densified
  sparse matrix in :class:`sklearn.linear_model.RidgeCV`.

- Sparse matrix support in `sklearn.decomposition.RandomizedPCA`
  is now deprecated in favor of the new ``TruncatedSVD``.

- `cross_validation.KFold` and
  `cross_validation.StratifiedKFold` now enforce `n_folds >= 2`
  otherwise a ``ValueError`` is raised. By `Olivier Grisel`_.

- :func:`datasets.load_files`'s ``charset`` and ``charset_errors``
  parameters were renamed ``encoding`` and ``decode_errors``.

- Attribute ``oob_score_`` in :class:`sklearn.ensemble.GradientBoostingRegressor`
  and :class:`sklearn.ensemble.GradientBoostingClassifier`
  is deprecated and has been replaced by ``oob_improvement_`` .

- Attributes in OrthogonalMatchingPursuit have been deprecated
  (copy_X, Gram, ...) and precompute_gram renamed precompute
  for consistency. See #2224.

- :class:`sklearn.preprocessing.StandardScaler` now converts integer input
  to float, and raises a warning. Previously it rounded for dense integer
  input.

- :class:`sklearn.multiclass.OneVsRestClassifier` now has a
  ``decision_function`` method. This will return the distance of each
  sample from the decision boundary for each class, as long as the
  underlying estimators implement the ``decision_function`` method.
  By `Kyle Kastner`_.

- Better input validation, warning on unexpected shapes for y.

People
------
List of contributors for release 0.14 by number of commits.

* 277  Gilles Louppe
* 245  Lars Buitinck
* 187  Andreas Mueller
* 124  Arnaud Joly
* 112  Jaques Grobler
* 109  Gael Varoquaux
* 107  Olivier Grisel
* 102  Noel Dawe
*  99  Kemal Eren
*  79  Joel Nothman
*  75  Jake VanderPlas
*  73  Nelle Varoquaux
*  71  Vlad Niculae
*  65  Peter Prettenhofer
*  64  Alexandre Gramfort
*  54  Mathieu Blondel
*  38  Nicolas Trésegnie
*  35  eustache
*  27  Denis Engemann
*  25  Yann N. Dauphin
*  19  Justin Vincent
*  17  Robert Layton
*  15  Doug Coleman
*  14  Michael Eickenberg
*  13  Robert Marchman
*  11  Fabian Pedregosa
*  11  Philippe Gervais
*  10  Jim Holmström
*  10  Tadej Janež
*  10  syhw
*   9  Mikhail Korobov
*   9  Steven De Gryze
*   8  sergeyf
*   7  Ben Root
*   7  Hrishikesh Huilgolkar
*   6  Kyle Kastner
*   6  Martin Luessi
*   6  Rob Speer
*   5  Federico Vaggi
*   5  Raul Garreta
*   5  Rob Zinkov
*   4  Ken Geis
*   3  A. Flaxman
*   3  Denton Cockburn
*   3  Dougal Sutherland
*   3  Ian Ozsvald
*   3  Johannes Schönberger
*   3  Robert McGibbon
*   3  Roman Sinayev
*   3  Szabo Roland
*   2  Diego Molla
*   2  Imran Haque
*   2  Jochen Wersdörfer
*   2  Sergey Karayev
*   2  Yannick Schwartz
*   2  jamestwebber
*   1  Abhijeet Kolhe
*   1  Alexander Fabisch
*   1  Bastiaan van den Berg
*   1  Benjamin Peterson
*   1  Daniel Velkov
*   1  Fazlul Shahriar
*   1  Felix Brockherde
*   1  Félix-Antoine Fortin
*   1  Harikrishnan S
*   1  Jack Hale
*   1  JakeMick
*   1  James McDermott
*   1  John Benediktsson
*   1  John Zwinck
*   1  Joshua Vredevoogd
*   1  Justin Pati
*   1  Kevin Hughes
*   1  Kyle Kelley
*   1  Matthias Ekman
*   1  Miroslav Shubernetskiy
*   1  Naoki Orii
*   1  Norbert Crombach
*   1  Rafael Cunha de Almeida
*   1  Rolando Espinoza La fuente
*   1  Seamus Abshere
*   1  Sergey Feldman
*   1  Sergio Medina
*   1  Stefano Lattarini
*   1  Steve Koch
*   1  Sturla Molden
*   1  Thomas Jarosch
*   1  Yaroslav Halchenko

include    contributors rst     currentmodule   sklearn               Version 0 14                   changes 0 14   Version 0 14                    August 7  2013    Changelog              Missing values with sparse and dense matrices can be imputed with the   transformer  preprocessing Imputer  by  Nicolas Tr segnie       The core implementation of decisions trees has been rewritten from   scratch  allowing for faster tree induction and lower memory   consumption in all tree based estimators  By  Gilles Louppe       Added  class  ensemble AdaBoostClassifier  and    class  ensemble AdaBoostRegressor   by  Noel Dawe    and    Gilles Louppe    See the  ref  AdaBoost  adaboost   section of the user   guide for details and examples     Added  grid search RandomizedSearchCV  and    grid search ParameterSampler  for randomized hyperparameter   optimization  By  Andreas M ller       Added  ref  biclustering  biclustering   algorithms     sklearn cluster bicluster SpectralCoclustering  and    sklearn cluster bicluster SpectralBiclustering    data   generation methods   func  sklearn datasets make biclusters  and    func  sklearn datasets make checkerboard    and scoring metrics     func  sklearn metrics consensus score    By  Kemal Eren       Added  ref  Restricted Boltzmann Machines rbm       class  neural network BernoulliRBM    By  Yann Dauphin       Python 3 support by  user  Justin Vincent  justinvf     Lars Buitinck       user  Subhodeep Moitra  smoitra87   and  Olivier Grisel    All tests now pass under   Python 3 3     Ability to pass one penalty  alpha value  per target in    class  linear model Ridge   by  eickenberg and  Mathieu Blondel       Fixed  sklearn linear model stochastic gradient py  L2 regularization   issue  minor practical significance     By  user  Norbert Crombach  norbert   and  Mathieu Blondel        Added an interactive version of  Andreas M ller   s    Machine Learning Cheat Sheet  for scikit learn     https   peekaboo vision blogspot de 2013 01 machine learning cheat sheet for scikit html      to the documentation  See  ref  Choosing the right estimator  ml map      By  Jaques Grobler        grid search GridSearchCV  and    cross validation cross val score  now support the use of advanced   scoring function such as area under the ROC curve and f beta scores    See  ref  scoring parameter  for details  By  Andreas M ller     and  Lars Buitinck      Passing a function from  mod  sklearn metrics  as   score func   is   deprecated     Multi label classification output is now supported by    func  metrics accuracy score    func  metrics zero one loss      func  metrics f1 score    func  metrics fbeta score      func  metrics classification report      func  metrics precision score  and  func  metrics recall score    by  Arnaud Joly       Two new metrics  func  metrics hamming loss  and    metrics jaccard similarity score    are added with multi label support by  Arnaud Joly       Speed and memory usage improvements in    class  feature extraction text CountVectorizer  and    class  feature extraction text TfidfVectorizer     by Jochen Wersd rfer and Roman Sinayev     The   min df   parameter in    class  feature extraction text CountVectorizer  and    class  feature extraction text TfidfVectorizer   which used to be 2    has been reset to 1 to avoid unpleasant surprises  empty vocabularies    for novice users who try it out on tiny document collections    A value of at least 2 is still recommended for practical use      class  svm LinearSVC    class  linear model SGDClassifier  and    class  linear model SGDRegressor  now have a   sparsify   method that   converts their   coef    into a sparse matrix  meaning stored models   trained using these estimators can be made much more compact      class  linear model SGDClassifier  now produces multiclass probability   estimates when trained under log loss or modified Huber loss     Hyperlinks to documentation in example code on the website by    user  Martin Luessi  mluessi       Fixed bug in  class  preprocessing MinMaxScaler  causing incorrect scaling   of the features for non default   feature range   settings  By  Andreas   M ller         max features   in  class  tree DecisionTreeClassifier      class  tree DecisionTreeRegressor  and all derived ensemble estimators   now supports percentage values  By  Gilles Louppe       Performance improvements in  class  isotonic IsotonicRegression  by    Nelle Varoquaux        func  metrics accuracy score  has an option normalize to return   the fraction or the number of correctly classified sample   by  Arnaud Joly       Added  func  metrics log loss  that computes log loss  aka cross entropy   loss  By Jochen Wersd rfer and  Lars Buitinck       A bug that caused  class  ensemble AdaBoostClassifier  s to output   incorrect probabilities has been fixed     Feature selectors now share a mixin providing consistent   transform        inverse transform   and   get support   methods  By  Joel Nothman       A fitted  grid search GridSearchCV  or    grid search RandomizedSearchCV  can now generally be pickled    By  Joel Nothman       Refactored and vectorized implementation of  func  metrics roc curve    and  func  metrics precision recall curve   By  Joel Nothman       The new estimator  class  sklearn decomposition TruncatedSVD    performs dimensionality reduction using SVD on sparse matrices    and can be used for latent semantic analysis  LSA     By  Lars Buitinck       Added self contained example of out of core learning on text data    ref  sphx glr auto examples applications plot out of core classification py     By  user  Eustache Diemert  oddskool       The default number of components for    sklearn decomposition RandomizedPCA  is now correctly documented   to be   n features    This was the default behavior  so programs using it   will continue to work as they did      class  sklearn cluster KMeans  now fits several orders of magnitude   faster on sparse data  the speedup depends on the sparsity   By    Lars Buitinck       Reduce memory footprint of FastICA by  Denis Engemann   and    Alexandre Gramfort       Verbose output in  sklearn ensemble gradient boosting  now uses   a column format and prints progress in decreasing frequency    It also shows the remaining time  By  Peter Prettenhofer        sklearn ensemble gradient boosting  provides out of bag improvement    oob improvement     rather than the OOB score for model selection  An example that shows   how to use OOB estimates to select the number of trees was added    By  Peter Prettenhofer       Most metrics now support string labels for multiclass classification   by  Arnaud Joly   and  Lars Buitinck       New OrthogonalMatchingPursuitCV class by  Alexandre Gramfort     and  Vlad Niculae       Fixed a bug in  sklearn covariance GraphLassoCV   the    alphas  parameter now works as expected when given a list of   values  By Philippe Gervais     Fixed an important bug in  sklearn covariance GraphLassoCV    that prevented all folds provided by a CV object to be used  only   the first 3 were used   When providing a CV object  execution   time may thus increase significantly compared to the previous   version  bug results are correct now   By Philippe Gervais      cross validation cross val score  and the  grid search    module is now tested with multi output data by  Arnaud Joly        func  datasets make multilabel classification  can now return   the output in label indicator multilabel format  by  Arnaud Joly       K nearest neighbors   class  neighbors KNeighborsRegressor    and  class  neighbors RadiusNeighborsRegressor     and radius neighbors   class  neighbors RadiusNeighborsRegressor  and    class  neighbors RadiusNeighborsClassifier  support multioutput data   by  Arnaud Joly       Random state in LibSVM based estimators   class  svm SVC    class  svm NuSVC      class  svm OneClassSVM    class  svm SVR    class  svm NuSVR   can now be   controlled   This is useful to ensure consistency in the probability   estimates for the classifiers trained with   probability True    By    Vlad Niculae       Out of core learning support for discrete naive Bayes classifiers    class  sklearn naive bayes MultinomialNB  and    class  sklearn naive bayes BernoulliNB  by adding the   partial fit     method by  Olivier Grisel       New website design and navigation by  Gilles Louppe     Nelle Varoquaux      Vincent Michel and  Andreas M ller       Improved documentation on  ref  multi class  multi label and multi output   classification  multiclass   by  Yannick Schwartz   and  Arnaud Joly       Better input and error handling in the  mod  sklearn metrics  module by    Arnaud Joly   and  Joel Nothman       Speed optimization of the  hmm  module by  user  Mikhail Korobov  kmike      Significant speed improvements for  class  sklearn cluster DBSCAN    by  cleverless  https   github com cleverless      API changes summary                        The  auc score  was renamed  func  metrics roc auc score      Testing scikit learn with   sklearn test     is deprecated  Use     nosetests sklearn   from the command line     Feature importances in  class  tree DecisionTreeClassifier      class  tree DecisionTreeRegressor  and all derived ensemble estimators   are now computed on the fly when accessing  the   feature importances      attribute  Setting   compute importances True   is no longer required    By  Gilles Louppe        class  linear model lasso path  and    class  linear model enet path  can return its results in the same   format as that of  class  linear model lars path   This is done by   setting the   return models   parameter to   False    By    Jaques Grobler   and  Alexandre Gramfort       grid search IterGrid  was renamed to  grid search ParameterGrid      Fixed bug in  KFold  causing imperfect class balance in some   cases  By  Alexandre Gramfort   and Tadej Jane       class  sklearn neighbors BallTree  has been refactored  and a    class  sklearn neighbors KDTree  has been   added which shares the same interface   The Ball Tree now works with   a wide variety of distance metrics   Both classes have many new   methods  including single tree and dual tree queries  breadth first   and depth first searching  and more advanced queries such as   kernel density estimation and 2 point correlation functions    By  Jake Vanderplas      Support for scipy spatial cKDTree within neighbors queries has been   removed  and the functionality replaced with the new    class  sklearn neighbors KDTree  class      class  sklearn neighbors KernelDensity  has been added  which performs   efficient kernel density estimation with a variety of kernels      class  sklearn decomposition KernelPCA  now always returns output with     n components   components  unless the new parameter   remove zero eig     is set to   True    This new behavior is consistent with the way   kernel PCA was always documented  previously  the removal of components   with zero eigenvalues was tacitly performed on all data       gcv mode  auto    no longer tries to perform SVD on a densified   sparse matrix in  class  sklearn linear model RidgeCV      Sparse matrix support in  sklearn decomposition RandomizedPCA    is now deprecated in favor of the new   TruncatedSVD        cross validation KFold  and    cross validation StratifiedKFold  now enforce  n folds    2    otherwise a   ValueError   is raised  By  Olivier Grisel        func  datasets load files  s   charset   and   charset errors     parameters were renamed   encoding   and   decode errors       Attribute   oob score    in  class  sklearn ensemble GradientBoostingRegressor    and  class  sklearn ensemble GradientBoostingClassifier    is deprecated and has been replaced by   oob improvement         Attributes in OrthogonalMatchingPursuit have been deprecated    copy X  Gram       and precompute gram renamed precompute   for consistency  See  2224      class  sklearn preprocessing StandardScaler  now converts integer input   to float  and raises a warning  Previously it rounded for dense integer   input      class  sklearn multiclass OneVsRestClassifier  now has a     decision function   method  This will return the distance of each   sample from the decision boundary for each class  as long as the   underlying estimators implement the   decision function   method    By  Kyle Kastner       Better input validation  warning on unexpected shapes for y   People        List of contributors for release 0 14 by number of commits     277  Gilles Louppe   245  Lars Buitinck   187  Andreas Mueller   124  Arnaud Joly   112  Jaques Grobler   109  Gael Varoquaux   107  Olivier Grisel   102  Noel Dawe    99  Kemal Eren    79  Joel Nothman    75  Jake VanderPlas    73  Nelle Varoquaux    71  Vlad Niculae    65  Peter Prettenhofer    64  Alexandre Gramfort    54  Mathieu Blondel    38  Nicolas Tr segnie    35  eustache    27  Denis Engemann    25  Yann N  Dauphin    19  Justin Vincent    17  Robert Layton    15  Doug Coleman    14  Michael Eickenberg    13  Robert Marchman    11  Fabian Pedregosa    11  Philippe Gervais    10  Jim Holmstr m    10  Tadej Jane     10  syhw     9  Mikhail Korobov     9  Steven De Gryze     8  sergeyf     7  Ben Root     7  Hrishikesh Huilgolkar     6  Kyle Kastner     6  Martin Luessi     6  Rob Speer     5  Federico Vaggi     5  Raul Garreta     5  Rob Zinkov     4  Ken Geis     3  A  Flaxman     3  Denton Cockburn     3  Dougal Sutherland     3  Ian Ozsvald     3  Johannes Sch nberger     3  Robert McGibbon     3  Roman Sinayev     3  Szabo Roland     2  Diego Molla     2  Imran Haque     2  Jochen Wersd rfer     2  Sergey Karayev     2  Yannick Schwartz     2  jamestwebber     1  Abhijeet Kolhe     1  Alexander Fabisch     1  Bastiaan van den Berg     1  Benjamin Peterson     1  Daniel Velkov     1  Fazlul Shahriar     1  Felix Brockherde     1  F lix Antoine Fortin     1  Harikrishnan S     1  Jack Hale     1  JakeMick     1  James McDermott     1  John Benediktsson     1  John Zwinck     1  Joshua Vredevoogd     1  Justin Pati     1  Kevin Hughes     1  Kyle Kelley     1  Matthias Ekman     1  Miroslav Shubernetskiy     1  Naoki Orii     1  Norbert Crombach     1  Rafael Cunha de Almeida     1  Rolando Espinoza La fuente     1  Seamus Abshere     1  Sergey Feldman     1  Sergio Medina     1  Stefano Lattarini     1  Steve Koch     1  Sturla Molden     1  Thomas Jarosch     1  Yaroslav Halchenko