scikit-learn sklearn contributors rst Older Versions changes012 1

Answer:
.. include:: _contributors.rst

.. currentmodule:: sklearn

==============
Older Versions
==============

.. _changes_0_12.1:

Version 0.12.1
===============

**October 8, 2012**

The 0.12.1 release is a bug-fix release with no additional features, but is
instead a set of bug fixes

Changelog
----------

- Improved numerical stability in spectral embedding by `Gael
  Varoquaux`_

- Doctest under windows 64bit by `Gael Varoquaux`_

- Documentation fixes for elastic net by `Andreas Müller`_ and
  `Alexandre Gramfort`_

- Proper behavior with fortran-ordered NumPy arrays by `Gael Varoquaux`_

- Make GridSearchCV work with non-CSR sparse matrix by `Lars Buitinck`_

- Fix parallel computing in MDS by `Gael Varoquaux`_

- Fix Unicode support in count vectorizer by `Andreas Müller`_

- Fix MinCovDet breaking with X.shape = (3, 1) by :user:`Virgile Fritsch <VirgileFritsch>`

- Fix clone of SGD objects by `Peter Prettenhofer`_

- Stabilize GMM by :user:`Virgile Fritsch <VirgileFritsch>`

People
------

*  14  `Peter Prettenhofer`_
*  12  `Gael Varoquaux`_
*  10  `Andreas Müller`_
*   5  `Lars Buitinck`_
*   3  :user:`Virgile Fritsch <VirgileFritsch>`
*   1  `Alexandre Gramfort`_
*   1  `Gilles Louppe`_
*   1  `Mathieu Blondel`_

.. _changes_0_12:

Version 0.12
============

**September 4, 2012**

Changelog
---------

- Various speed improvements of the :ref:`decision trees <tree>` module, by
  `Gilles Louppe`_.

- :class:`~ensemble.GradientBoostingRegressor` and
  :class:`~ensemble.GradientBoostingClassifier` now support feature subsampling
  via the ``max_features`` argument, by `Peter Prettenhofer`_.

- Added Huber and Quantile loss functions to
  :class:`~ensemble.GradientBoostingRegressor`, by `Peter Prettenhofer`_.

- :ref:`Decision trees <tree>` and :ref:`forests of randomized trees <forest>`
  now support multi-output classification and regression problems, by
  `Gilles Louppe`_.

- Added :class:`~preprocessing.LabelEncoder`, a simple utility class to
  normalize labels or transform non-numerical labels, by `Mathieu Blondel`_.

- Added the epsilon-insensitive loss and the ability to make probabilistic
  predictions with the modified huber loss in :ref:`sgd`, by
  `Mathieu Blondel`_.

- Added :ref:`multidimensional_scaling`, by Nelle Varoquaux.

- SVMlight file format loader now detects compressed (gzip/bzip2) files and
  decompresses them on the fly, by `Lars Buitinck`_.

- SVMlight file format serializer now preserves double precision floating
  point values, by `Olivier Grisel`_.

- A common testing framework for all estimators was added, by `Andreas Müller`_.

- Understandable error messages for estimators that do not accept
  sparse input by `Gael Varoquaux`_

- Speedups in hierarchical clustering by `Gael Varoquaux`_. In
  particular building the tree now supports early stopping. This is
  useful when the number of clusters is not small compared to the
  number of samples.

- Add MultiTaskLasso and MultiTaskElasticNet for joint feature selection,
  by `Alexandre Gramfort`_.

- Added `metrics.auc_score` and
  :func:`metrics.average_precision_score` convenience functions by `Andreas
  Müller`_.

- Improved sparse matrix support in the :ref:`feature_selection`
  module by `Andreas Müller`_.

- New word boundaries-aware character n-gram analyzer for the
  :ref:`text_feature_extraction` module by :user:`@kernc <kernc>`.

- Fixed bug in spectral clustering that led to single point clusters
  by `Andreas Müller`_.

- In :class:`~feature_extraction.text.CountVectorizer`, added an option to
  ignore infrequent words, ``min_df`` by  `Andreas Müller`_.

- Add support for multiple targets in some linear models (ElasticNet, Lasso
  and OrthogonalMatchingPursuit) by `Vlad Niculae`_ and
  `Alexandre Gramfort`_.

- Fixes in `decomposition.ProbabilisticPCA` score function by Wei Li.

- Fixed feature importance computation in
  :ref:`gradient_boosting`.

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

- The old ``scikits.learn`` package has disappeared; all code should import
  from ``sklearn`` instead, which was introduced in 0.9.

- In :func:`metrics.roc_curve`, the ``thresholds`` array is now returned
  with it's order reversed, in order to keep it consistent with the order
  of the returned ``fpr`` and ``tpr``.

- In `hmm` objects, like `hmm.GaussianHMM`,
  `hmm.MultinomialHMM`, etc., all parameters must be passed to the
  object when initialising it and not through ``fit``. Now ``fit`` will
  only accept the data as an input parameter.

- For all SVM classes, a faulty behavior of ``gamma`` was fixed. Previously,
  the default gamma value was only computed the first time ``fit`` was called
  and then stored. It is now recalculated on every call to ``fit``.

- All ``Base`` classes are now abstract meta classes so that they can not be
  instantiated.

- :func:`cluster.ward_tree` now also returns the parent array. This is
  necessary for early-stopping in which case the tree is not
  completely built.

- In :class:`~feature_extraction.text.CountVectorizer` the parameters
  ``min_n`` and ``max_n`` were joined to the parameter ``n_gram_range`` to
  enable grid-searching both at once.

- In :class:`~feature_extraction.text.CountVectorizer`, words that appear
  only in one document are now ignored by default. To reproduce
  the previous behavior, set ``min_df=1``.

- Fixed API inconsistency: :meth:`linear_model.SGDClassifier.predict_proba` now
  returns 2d array when fit on two classes.

- Fixed API inconsistency: :meth:`discriminant_analysis.QuadraticDiscriminantAnalysis.decision_function`
  and :meth:`discriminant_analysis.LinearDiscriminantAnalysis.decision_function` now return 1d arrays
  when fit on two classes.

- Grid of alphas used for fitting :class:`~linear_model.LassoCV` and
  :class:`~linear_model.ElasticNetCV` is now stored
  in the attribute ``alphas_`` rather than overriding the init parameter
  ``alphas``.

- Linear models when alpha is estimated by cross-validation store
  the estimated value in the ``alpha_`` attribute rather than just
  ``alpha`` or ``best_alpha``.

- :class:`~ensemble.GradientBoostingClassifier` now supports
  :meth:`~ensemble.GradientBoostingClassifier.staged_predict_proba`, and
  :meth:`~ensemble.GradientBoostingClassifier.staged_predict`.

- `svm.sparse.SVC` and other sparse SVM classes are now deprecated.
  The all classes in the :ref:`svm` module now automatically select the
  sparse or dense representation base on the input.

- All clustering algorithms now interpret the array ``X`` given to ``fit`` as
  input data, in particular :class:`~cluster.SpectralClustering` and
  :class:`~cluster.AffinityPropagation` which previously expected affinity matrices.

- For clustering algorithms that take the desired number of clusters as a parameter,
  this parameter is now called ``n_clusters``.

People
------
* 267  `Andreas Müller`_
*  94  `Gilles Louppe`_
*  89  `Gael Varoquaux`_
*  79  `Peter Prettenhofer`_
*  60  `Mathieu Blondel`_
*  57  `Alexandre Gramfort`_
*  52  `Vlad Niculae`_
*  45  `Lars Buitinck`_
*  44  Nelle Varoquaux
*  37  `Jaques Grobler`_
*  30  Alexis Mignon
*  30  Immanuel Bayer
*  27  `Olivier Grisel`_
*  16  Subhodeep Moitra
*  13  Yannick Schwartz
*  12  :user:`@kernc <kernc>`
*  11  :user:`Virgile Fritsch <VirgileFritsch>`
*   9  Daniel Duckworth
*   9  `Fabian Pedregosa`_
*   9  `Robert Layton`_
*   8  John Benediktsson
*   7  Marko Burjek
*   5  `Nicolas Pinto`_
*   4  Alexandre Abraham
*   4  `Jake Vanderplas`_
*   3  `Brian Holt`_
*   3  `Edouard Duchesnay`_
*   3  Florian Hoenig
*   3  flyingimmidev
*   2  Francois Savard
*   2  Hannes Schulz
*   2  Peter Welinder
*   2  `Yaroslav Halchenko`_
*   2  Wei Li
*   1  Alex Companioni
*   1  Brandyn A. White
*   1  Bussonnier Matthias
*   1  Charles-Pierre Astolfi
*   1  Dan O'Huiginn
*   1  David Cournapeau
*   1  Keith Goodman
*   1  Ludwig Schwardt
*   1  Olivier Hervieu
*   1  Sergio Medina
*   1  Shiqiao Du
*   1  Tim Sheerman-Chase
*   1  buguen

.. _changes_0_11:

Version 0.11
============

**May 7, 2012**

Changelog
---------

Highlights
.............

- Gradient boosted regression trees (:ref:`gradient_boosting`)
  for classification and regression by `Peter Prettenhofer`_
  and `Scott White`_ .

- Simple dict-based feature loader with support for categorical variables
  (:class:`~feature_extraction.DictVectorizer`) by `Lars Buitinck`_.

- Added Matthews correlation coefficient (:func:`metrics.matthews_corrcoef`)
  and added macro and micro average options to
  :func:`~metrics.precision_score`, :func:`metrics.recall_score` and
  :func:`~metrics.f1_score` by `Satrajit Ghosh`_.

- :ref:`out_of_bag` of generalization error for :ref:`ensemble`
  by `Andreas Müller`_.

- Randomized sparse linear models for feature
  selection, by `Alexandre Gramfort`_ and `Gael Varoquaux`_

- :ref:`label_propagation` for semi-supervised learning, by Clay
  Woolam. **Note** the semi-supervised API is still work in progress,
  and may change.

- Added BIC/AIC model selection to classical :ref:`gmm` and unified
  the API with the remainder of scikit-learn, by `Bertrand Thirion`_

- Added `sklearn.cross_validation.StratifiedShuffleSplit`, which is
  a `sklearn.cross_validation.ShuffleSplit` with balanced splits,
  by Yannick Schwartz.

- :class:`~sklearn.neighbors.NearestCentroid` classifier added, along with a
  ``shrink_threshold`` parameter, which implements **shrunken centroid
  classification**, by `Robert Layton`_.

Other changes
..............

- Merged dense and sparse implementations of :ref:`sgd` module and
  exposed utility extension types for sequential
  datasets ``seq_dataset`` and weight vectors ``weight_vector``
  by `Peter Prettenhofer`_.

- Added ``partial_fit`` (support for online/minibatch learning) and
  warm_start to the :ref:`sgd` module by `Mathieu Blondel`_.

- Dense and sparse implementations of :ref:`svm` classes and
  :class:`~linear_model.LogisticRegression` merged by `Lars Buitinck`_.

- Regressors can now be used as base estimator in the :ref:`multiclass`
  module by `Mathieu Blondel`_.

- Added n_jobs option to :func:`metrics.pairwise_distances`
  and :func:`metrics.pairwise.pairwise_kernels` for parallel computation,
  by `Mathieu Blondel`_.

- :ref:`k_means` can now be run in parallel, using the ``n_jobs`` argument
  to either :ref:`k_means` or :class:`cluster.KMeans`, by `Robert Layton`_.

- Improved :ref:`cross_validation` and :ref:`grid_search` documentation
  and introduced the new `cross_validation.train_test_split`
  helper function by `Olivier Grisel`_

- :class:`~svm.SVC` members ``coef_`` and ``intercept_`` changed sign for
  consistency with ``decision_function``; for ``kernel==linear``,
  ``coef_`` was fixed in the one-vs-one case, by `Andreas Müller`_.

- Performance improvements to efficient leave-one-out cross-validated
  Ridge regression, esp. for the ``n_samples > n_features`` case, in
  :class:`~linear_model.RidgeCV`, by Reuben Fletcher-Costin.

- Refactoring and simplification of the :ref:`text_feature_extraction`
  API and fixed a bug that caused possible negative IDF,
  by `Olivier Grisel`_.

- Beam pruning option in `_BaseHMM` module has been removed since it
  is difficult to Cythonize. If you are interested in contributing a Cython
  version, you can use the python version in the git history as a reference.

- Classes in :ref:`neighbors` now support arbitrary Minkowski metric for
  nearest neighbors searches. The metric can be specified by argument ``p``.

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

- `covariance.EllipticEnvelop` is now deprecated.
  Please use :class:`~covariance.EllipticEnvelope` instead.

- ``NeighborsClassifier`` and ``NeighborsRegressor`` are gone in the module
  :ref:`neighbors`. Use the classes :class:`~neighbors.KNeighborsClassifier`,
  :class:`~neighbors.RadiusNeighborsClassifier`, :class:`~neighbors.KNeighborsRegressor`
  and/or :class:`~neighbors.RadiusNeighborsRegressor` instead.

- Sparse classes in the :ref:`sgd` module are now deprecated.

- In `mixture.GMM`, `mixture.DPGMM` and `mixture.VBGMM`,
  parameters must be passed to an object when initialising it and not through
  ``fit``. Now ``fit`` will only accept the data as an input parameter.

- methods ``rvs`` and ``decode`` in `GMM` module are now deprecated.
  ``sample`` and ``score`` or ``predict`` should be used instead.

- attribute ``_scores`` and ``_pvalues`` in univariate feature selection
  objects are now deprecated.
  ``scores_`` or ``pvalues_`` should be used instead.

- In :class:`~linear_model.LogisticRegression`, :class:`~svm.LinearSVC`,
  :class:`~svm.SVC` and :class:`~svm.NuSVC`, the ``class_weight`` parameter is
  now an initialization parameter, not a parameter to fit. This makes grid
  searches over this parameter possible.

- LFW ``data`` is now always shape ``(n_samples, n_features)`` to be
  consistent with the Olivetti faces dataset. Use ``images`` and
  ``pairs`` attribute to access the natural images shapes instead.

- In :class:`~svm.LinearSVC`, the meaning of the ``multi_class`` parameter
  changed.  Options now are ``'ovr'`` and ``'crammer_singer'``, with
  ``'ovr'`` being the default.  This does not change the default behavior
  but hopefully is less confusing.

- Class `feature_selection.text.Vectorizer` is deprecated and
  replaced by `feature_selection.text.TfidfVectorizer`.

- The preprocessor / analyzer nested structure for text feature
  extraction has been removed. All those features are
  now directly passed as flat constructor arguments
  to `feature_selection.text.TfidfVectorizer` and
  `feature_selection.text.CountVectorizer`, in particular the
  following parameters are now used:

- ``analyzer`` can be ``'word'`` or ``'char'`` to switch the default
  analysis scheme, or use a specific python callable (as previously).

- ``tokenizer`` and ``preprocessor`` have been introduced to make it
  still possible to customize those steps with the new API.

- ``input`` explicitly control how to interpret the sequence passed to
  ``fit`` and ``predict``: filenames, file objects or direct (byte or
  Unicode) strings.

- charset decoding is explicit and strict by default.

- the ``vocabulary``, fitted or not is now stored in the
  ``vocabulary_`` attribute to be consistent with the project
  conventions.

- Class `feature_selection.text.TfidfVectorizer` now derives directly
  from `feature_selection.text.CountVectorizer` to make grid
  search trivial.

- methods ``rvs`` in `_BaseHMM` module are now deprecated.
  ``sample`` should be used instead.

- Beam pruning option in `_BaseHMM` module is removed since it is
  difficult to be Cythonized. If you are interested, you can look in the
  history codes by git.

- The SVMlight format loader now supports files with both zero-based and
  one-based column indices, since both occur "in the wild".

- Arguments in class :class:`~model_selection.ShuffleSplit` are now consistent with
  :class:`~model_selection.StratifiedShuffleSplit`. Arguments ``test_fraction`` and
  ``train_fraction`` are deprecated and renamed to ``test_size`` and
  ``train_size`` and can accept both ``float`` and ``int``.

- Arguments in class `Bootstrap` are now consistent with
  :class:`~model_selection.StratifiedShuffleSplit`. Arguments ``n_test`` and
  ``n_train`` are deprecated and renamed to ``test_size`` and
  ``train_size`` and can accept both ``float`` and ``int``.

- Argument ``p`` added to classes in :ref:`neighbors` to specify an
  arbitrary Minkowski metric for nearest neighbors searches.

People
------

* 282  `Andreas Müller`_
* 239  `Peter Prettenhofer`_
* 198  `Gael Varoquaux`_
* 129  `Olivier Grisel`_
* 114  `Mathieu Blondel`_
* 103  Clay Woolam
*  96  `Lars Buitinck`_
*  88  `Jaques Grobler`_
*  82  `Alexandre Gramfort`_
*  50  `Bertrand Thirion`_
*  42  `Robert Layton`_
*  28  flyingimmidev
*  26  `Jake Vanderplas`_
*  26  Shiqiao Du
*  21  `Satrajit Ghosh`_
*  17  `David Marek`_
*  17  `Gilles Louppe`_
*  14  `Vlad Niculae`_
*  11  Yannick Schwartz
*  10  `Fabian Pedregosa`_
*   9  fcostin
*   7  Nick Wilson
*   5  Adrien Gaidon
*   5  `Nicolas Pinto`_
*   4  `David Warde-Farley`_
*   5  Nelle Varoquaux
*   5  Emmanuelle Gouillart
*   3  Joonas Sillanpää
*   3  Paolo Losi
*   2  Charles McCarthy
*   2  Roy Hyunjin Han
*   2  Scott White
*   2  ibayer
*   1  Brandyn White
*   1  Carlos Scheidegger
*   1  Claire Revillet
*   1  Conrad Lee
*   1  `Edouard Duchesnay`_
*   1  Jan Hendrik Metzen
*   1  Meng Xinfan
*   1  `Rob Zinkov`_
*   1  Shiqiao
*   1  Udi Weinsberg
*   1  Virgile Fritsch
*   1  Xinfan Meng
*   1  Yaroslav Halchenko
*   1  jansoe
*   1  Leon Palafox

.. _changes_0_10:

Version 0.10
============

**January 11, 2012**

Changelog
---------

- Python 2.5 compatibility was dropped; the minimum Python version needed
  to use scikit-learn is now 2.6.

- :ref:`sparse_inverse_covariance` estimation using the graph Lasso, with
  associated cross-validated estimator, by `Gael Varoquaux`_

- New :ref:`Tree <tree>` module by `Brian Holt`_, `Peter Prettenhofer`_,
  `Satrajit Ghosh`_ and `Gilles Louppe`_. The module comes with complete
  documentation and examples.

- Fixed a bug in the RFE module by `Gilles Louppe`_ (issue #378).

- Fixed a memory leak in :ref:`svm` module by `Brian Holt`_ (issue #367).

- Faster tests by `Fabian Pedregosa`_ and others.

- Silhouette Coefficient cluster analysis evaluation metric added as
  :func:`~sklearn.metrics.silhouette_score` by Robert Layton.

- Fixed a bug in :ref:`k_means` in the handling of the ``n_init`` parameter:
  the clustering algorithm used to be run ``n_init`` times but the last
  solution was retained instead of the best solution by `Olivier Grisel`_.

- Minor refactoring in :ref:`sgd` module; consolidated dense and sparse
  predict methods; Enhanced test time performance by converting model
  parameters to fortran-style arrays after fitting (only multi-class).

- Adjusted Mutual Information metric added as
  :func:`~sklearn.metrics.adjusted_mutual_info_score` by Robert Layton.

- Models like SVC/SVR/LinearSVC/LogisticRegression from libsvm/liblinear
  now support scaling of C regularization parameter by the number of
  samples by `Alexandre Gramfort`_.

- New :ref:`Ensemble Methods <ensemble>` module by `Gilles Louppe`_ and
  `Brian Holt`_. The module comes with the random forest algorithm and the
  extra-trees method, along with documentation and examples.

- :ref:`outlier_detection`: outlier and novelty detection, by
  :user:`Virgile Fritsch <VirgileFritsch>`.

- :ref:`kernel_approximation`: a transform implementing kernel
  approximation for fast SGD on non-linear kernels by
  `Andreas Müller`_.

- Fixed a bug due to atom swapping in :ref:`OMP` by `Vlad Niculae`_.

- :ref:`SparseCoder` by `Vlad Niculae`_.

- :ref:`mini_batch_kmeans` performance improvements by `Olivier Grisel`_.

- :ref:`k_means` support for sparse matrices by `Mathieu Blondel`_.

- Improved documentation for developers and for the :mod:`sklearn.utils`
  module, by `Jake Vanderplas`_.

- Vectorized 20newsgroups dataset loader
  (:func:`~sklearn.datasets.fetch_20newsgroups_vectorized`) by
  `Mathieu Blondel`_.

- :ref:`multiclass` by `Lars Buitinck`_.

- Utilities for fast computation of mean and variance for sparse matrices
  by `Mathieu Blondel`_.

- Make :func:`~sklearn.preprocessing.scale` and
  `sklearn.preprocessing.Scaler` work on sparse matrices by
  `Olivier Grisel`_

- Feature importances using decision trees and/or forest of trees,
  by `Gilles Louppe`_.

- Parallel implementation of forests of randomized trees by
  `Gilles Louppe`_.

- `sklearn.cross_validation.ShuffleSplit` can subsample the train
  sets as well as the test sets by `Olivier Grisel`_.

- Errors in the build of the documentation fixed by `Andreas Müller`_.

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

Here are the code migration instructions when upgrading from scikit-learn
version 0.9:

- Some estimators that may overwrite their inputs to save memory previously
  had ``overwrite_`` parameters; these have been replaced with ``copy_``
  parameters with exactly the opposite meaning.

  This particularly affects some of the estimators in :mod:`~sklearn.linear_model`.
  The default behavior is still to copy everything passed in.

- The SVMlight dataset loader :func:`~sklearn.datasets.load_svmlight_file` no
  longer supports loading two files at once; use ``load_svmlight_files``
  instead. Also, the (unused) ``buffer_mb`` parameter is gone.

- Sparse estimators in the :ref:`sgd` module use dense parameter vector
  ``coef_`` instead of ``sparse_coef_``. This significantly improves
  test time performance.

- The :ref:`covariance` module now has a robust estimator of
  covariance, the Minimum Covariance Determinant estimator.

- Cluster evaluation metrics in :mod:`~sklearn.metrics.cluster` have been refactored
  but the changes are backwards compatible. They have been moved to the
  `metrics.cluster.supervised`, along with
  `metrics.cluster.unsupervised` which contains the Silhouette
  Coefficient.

- The ``permutation_test_score`` function now behaves the same way as
  ``cross_val_score`` (i.e. uses the mean score across the folds.)

- Cross Validation generators now use integer indices (``indices=True``)
  by default instead of boolean masks. This make it more intuitive to
  use with sparse matrix data.

- The functions used for sparse coding, ``sparse_encode`` and
  ``sparse_encode_parallel`` have been combined into
  :func:`~sklearn.decomposition.sparse_encode`, and the shapes of the arrays
  have been transposed for consistency with the matrix factorization setting,
  as opposed to the regression setting.

- Fixed an off-by-one error in the SVMlight/LibSVM file format handling;
  files generated using :func:`~sklearn.datasets.dump_svmlight_file` should be
  re-generated. (They should continue to work, but accidentally had one
  extra column of zeros prepended.)

- ``BaseDictionaryLearning`` class replaced by ``SparseCodingMixin``.

- `sklearn.utils.extmath.fast_svd` has been renamed
  :func:`~sklearn.utils.extmath.randomized_svd` and the default
  oversampling is now fixed to 10 additional random vectors instead
  of doubling the number of components to extract. The new behavior
  follows the reference paper.

People
------

The following people contributed to scikit-learn since last release:

* 246  `Andreas Müller`_
* 242  `Olivier Grisel`_
* 220  `Gilles Louppe`_
* 183  `Brian Holt`_
* 166  `Gael Varoquaux`_
* 144  `Lars Buitinck`_
*  73  `Vlad Niculae`_
*  65  `Peter Prettenhofer`_
*  64  `Fabian Pedregosa`_
*  60  Robert Layton
*  55  `Mathieu Blondel`_
*  52  `Jake Vanderplas`_
*  44  Noel Dawe
*  38  `Alexandre Gramfort`_
*  24  :user:`Virgile Fritsch <VirgileFritsch>`
*  23  `Satrajit Ghosh`_
*   3  Jan Hendrik Metzen
*   3  Kenneth C. Arnold
*   3  Shiqiao Du
*   3  Tim Sheerman-Chase
*   3  `Yaroslav Halchenko`_
*   2  Bala Subrahmanyam Varanasi
*   2  DraXus
*   2  Michael Eickenberg
*   1  Bogdan Trach
*   1  Félix-Antoine Fortin
*   1  Juan Manuel Caicedo Carvajal
*   1  Nelle Varoquaux
*   1  `Nicolas Pinto`_
*   1  Tiziano Zito
*   1  Xinfan Meng

.. _changes_0_9:

Version 0.9
===========

**September 21, 2011**

scikit-learn 0.9 was released on September 2011, three months after the 0.8
release and includes the new modules :ref:`manifold`, :ref:`dirichlet_process`
as well as several new algorithms and documentation improvements.

This release also includes the dictionary-learning work developed by
`Vlad Niculae`_ as part of the `Google Summer of Code
<https://developers.google.com/open-source/gsoc>`_ program.

.. |banner1| image:: ../auto_examples/manifold/images/thumb/sphx_glr_plot_compare_methods_thumb.png
   :target: ../auto_examples/manifold/plot_compare_methods.html

.. |banner2| image:: ../auto_examples/linear_model/images/thumb/sphx_glr_plot_omp_thumb.png
   :target: ../auto_examples/linear_model/plot_omp.html

.. |banner3| image:: ../auto_examples/decomposition/images/thumb/sphx_glr_plot_kernel_pca_thumb.png
   :target: ../auto_examples/decomposition/plot_kernel_pca.html

.. |center-div| raw:: html

    <div style="text-align: center; margin: 0px 0 -5px 0;">

.. |end-div| raw:: html

    </div>

|center-div| |banner2| |banner1| |banner3| |end-div|

Changelog
---------

- New :ref:`manifold` module by `Jake Vanderplas`_ and
  `Fabian Pedregosa`_.

- New :ref:`Dirichlet Process <dirichlet_process>` Gaussian Mixture
  Model by `Alexandre Passos`_

- :ref:`neighbors` module refactoring by `Jake Vanderplas`_ :
  general refactoring, support for sparse matrices in input, speed and
  documentation improvements. See the next section for a full list of API
  changes.

- Improvements on the :ref:`feature_selection` module by
  `Gilles Louppe`_ : refactoring of the RFE classes, documentation
  rewrite, increased efficiency and minor API changes.

- :ref:`SparsePCA` by `Vlad Niculae`_, `Gael Varoquaux`_ and
  `Alexandre Gramfort`_

- Printing an estimator now behaves independently of architectures
  and Python version thanks to :user:`Jean Kossaifi <JeanKossaifi>`.

- :ref:`Loader for libsvm/svmlight format <libsvm_loader>` by
  `Mathieu Blondel`_ and `Lars Buitinck`_

- Documentation improvements: thumbnails in
  example gallery by `Fabian Pedregosa`_.

- Important bugfixes in :ref:`svm` module (segfaults, bad
  performance) by `Fabian Pedregosa`_.

- Added :ref:`multinomial_naive_bayes` and :ref:`bernoulli_naive_bayes`
  by `Lars Buitinck`_

- Text feature extraction optimizations by Lars Buitinck

- Chi-Square feature selection
  (:func:`feature_selection.chi2`) by `Lars Buitinck`_.

- :ref:`sample_generators` module refactoring by `Gilles Louppe`_

- :ref:`multiclass` by `Mathieu Blondel`_

- Ball tree rewrite by `Jake Vanderplas`_

- Implementation of :ref:`dbscan` algorithm by Robert Layton

- Kmeans predict and transform by Robert Layton

- Preprocessing module refactoring by `Olivier Grisel`_

- Faster mean shift by Conrad Lee

- New ``Bootstrap``, :ref:`ShuffleSplit` and various other
  improvements in cross validation schemes by `Olivier Grisel`_ and
  `Gael Varoquaux`_

- Adjusted Rand index and V-Measure clustering evaluation metrics by `Olivier Grisel`_

- Added :class:`Orthogonal Matching Pursuit <linear_model.OrthogonalMatchingPursuit>` by `Vlad Niculae`_

- Added 2D-patch extractor utilities in the :ref:`feature_extraction` module by `Vlad Niculae`_

- Implementation of :class:`~linear_model.LassoLarsCV`
  (cross-validated Lasso solver using the Lars algorithm) and
  :class:`~linear_model.LassoLarsIC` (BIC/AIC model
  selection in Lars) by `Gael Varoquaux`_
  and `Alexandre Gramfort`_

- Scalability improvements to :func:`metrics.roc_curve` by Olivier Hervieu

- Distance helper functions :func:`metrics.pairwise_distances`
  and :func:`metrics.pairwise.pairwise_kernels` by Robert Layton

- :class:`Mini-Batch K-Means <cluster.MiniBatchKMeans>` by Nelle Varoquaux and Peter Prettenhofer.

- mldata utilities by Pietro Berkes.

- :ref:`olivetti_faces_dataset` by `David Warde-Farley`_.

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

Here are the code migration instructions when upgrading from scikit-learn
version 0.8:

- The ``scikits.learn`` package was renamed ``sklearn``. There is
  still a ``scikits.learn`` package alias for backward compatibility.

  Third-party projects with a dependency on scikit-learn 0.9+ should
  upgrade their codebase. For instance, under Linux / MacOSX just run
  (make a backup first!)::

      find -name "*.py" | xargs sed -i 's/\bscikits.learn\b/sklearn/g'

- Estimators no longer accept model parameters as ``fit`` arguments:
  instead all parameters must be only be passed as constructor
  arguments or using the now public ``set_params`` method inherited
  from :class:`~base.BaseEstimator`.

  Some estimators can still accept keyword arguments on the ``fit``
  but this is restricted to data-dependent values (e.g. a Gram matrix
  or an affinity matrix that are precomputed from the ``X`` data matrix.

- The ``cross_val`` package has been renamed to ``cross_validation``
  although there is also a ``cross_val`` package alias in place for
  backward compatibility.

  Third-party projects with a dependency on scikit-learn 0.9+ should
  upgrade their codebase. For instance, under Linux / MacOSX just run
  (make a backup first!)::

      find -name "*.py" | xargs sed -i 's/\bcross_val\b/cross_validation/g'

- The ``score_func`` argument of the
  ``sklearn.cross_validation.cross_val_score`` function is now expected
  to accept ``y_test`` and ``y_predicted`` as only arguments for
  classification and regression tasks or ``X_test`` for unsupervised
  estimators.

- ``gamma`` parameter for support vector machine algorithms is set
  to ``1 / n_features`` by default, instead of ``1 / n_samples``.

- The ``sklearn.hmm`` has been marked as orphaned: it will be removed
  from scikit-learn in version 0.11 unless someone steps up to
  contribute documentation, examples and fix lurking numerical
  stability issues.

- ``sklearn.neighbors`` has been made into a submodule.  The two previously
  available estimators, ``NeighborsClassifier`` and ``NeighborsRegressor``
  have been marked as deprecated.  Their functionality has been divided
  among five new classes: ``NearestNeighbors`` for unsupervised neighbors
  searches, ``KNeighborsClassifier`` & ``RadiusNeighborsClassifier``
  for supervised classification problems, and ``KNeighborsRegressor``
  & ``RadiusNeighborsRegressor`` for supervised regression problems.

- ``sklearn.ball_tree.BallTree`` has been moved to
  ``sklearn.neighbors.BallTree``.  Using the former will generate a warning.

- ``sklearn.linear_model.LARS()`` and related classes (LassoLARS,
  LassoLARSCV, etc.) have been renamed to
  ``sklearn.linear_model.Lars()``.

- All distance metrics and kernels in ``sklearn.metrics.pairwise`` now have a Y
  parameter, which by default is None. If not given, the result is the distance
  (or kernel similarity) between each sample in Y. If given, the result is the
  pairwise distance (or kernel similarity) between samples in X to Y.

- ``sklearn.metrics.pairwise.l1_distance`` is now called ``manhattan_distance``,
  and by default returns the pairwise distance. For the component wise distance,
  set the parameter ``sum_over_features`` to ``False``.

Backward compatibility package aliases and other deprecated classes and
functions will be removed in version 0.11.

People
------

38 people contributed to this release.

- 387  `Vlad Niculae`_
- 320  `Olivier Grisel`_
- 192  `Lars Buitinck`_
- 179  `Gael Varoquaux`_
- 168  `Fabian Pedregosa`_ (`INRIA`_, `Parietal Team`_)
- 127  `Jake Vanderplas`_
- 120  `Mathieu Blondel`_
- 85  `Alexandre Passos`_
- 67  `Alexandre Gramfort`_
- 57  `Peter Prettenhofer`_
- 56  `Gilles Louppe`_
- 42  Robert Layton
- 38  Nelle Varoquaux
- 32  :user:`Jean Kossaifi <JeanKossaifi>`
- 30  Conrad Lee
- 22  Pietro Berkes
- 18  andy
- 17  David Warde-Farley
- 12  Brian Holt
- 11  Robert
- 8  Amit Aides
- 8  :user:`Virgile Fritsch <VirgileFritsch>`
- 7  `Yaroslav Halchenko`_
- 6  Salvatore Masecchia
- 5  Paolo Losi
- 4  Vincent Schut
- 3  Alexis Metaireau
- 3  Bryan Silverthorn
- 3  `Andreas Müller`_
- 2  Minwoo Jake Lee
- 1  Emmanuelle Gouillart
- 1  Keith Goodman
- 1  Lucas Wiman
- 1  `Nicolas Pinto`_
- 1  Thouis (Ray) Jones
- 1  Tim Sheerman-Chase

.. _changes_0_8:

Version 0.8
===========

**May 11, 2011**

scikit-learn 0.8 was released on May 2011, one month after the first
"international" `scikit-learn coding sprint
<https://github.com/scikit-learn/scikit-learn/wiki/Upcoming-events>`_ and is
marked by the inclusion of important modules: :ref:`hierarchical_clustering`,
:ref:`cross_decomposition`, :ref:`NMF`, initial support for Python 3 and by important
enhancements and bug fixes.

Changelog
---------

Several new modules where introduced during this release:

- New :ref:`hierarchical_clustering` module by Vincent Michel,
  `Bertrand Thirion`_, `Alexandre Gramfort`_ and `Gael Varoquaux`_.

- :ref:`kernel_pca` implementation by `Mathieu Blondel`_

- :ref:`labeled_faces_in_the_wild_dataset` by `Olivier Grisel`_.

- New :ref:`cross_decomposition` module by `Edouard Duchesnay`_.

- :ref:`NMF` module `Vlad Niculae`_

- Implementation of the :ref:`oracle_approximating_shrinkage` algorithm by
  :user:`Virgile Fritsch <VirgileFritsch>` in the :ref:`covariance` module.

Some other modules benefited from significant improvements or cleanups.

- Initial support for Python 3: builds and imports cleanly,
  some modules are usable while others have failing tests by `Fabian Pedregosa`_.

- :class:`~decomposition.PCA` is now usable from the Pipeline object by `Olivier Grisel`_.

- Guide :ref:`performance-howto` by `Olivier Grisel`_.

- Fixes for memory leaks in libsvm bindings, 64-bit safer BallTree by Lars Buitinck.

- bug and style fixing in :ref:`k_means` algorithm by Jan Schlüter.

- Add attribute converged to Gaussian Mixture Models by Vincent Schut.

- Implemented ``transform``, ``predict_log_proba`` in
  :class:`~discriminant_analysis.LinearDiscriminantAnalysis` By `Mathieu Blondel`_.

- Refactoring in the :ref:`svm` module and bug fixes by `Fabian Pedregosa`_,
  `Gael Varoquaux`_ and Amit Aides.

- Refactored SGD module (removed code duplication, better variable naming),
  added interface for sample weight by `Peter Prettenhofer`_.

- Wrapped BallTree with Cython by Thouis (Ray) Jones.

- Added function :func:`svm.l1_min_c` by Paolo Losi.

- Typos, doc style, etc. by `Yaroslav Halchenko`_, `Gael Varoquaux`_,
  `Olivier Grisel`_, Yann Malet, `Nicolas Pinto`_, Lars Buitinck and
  `Fabian Pedregosa`_.

People
-------

People that made this release possible preceded by number of commits:

- 159  `Olivier Grisel`_
- 96  `Gael Varoquaux`_
- 96  `Vlad Niculae`_
- 94  `Fabian Pedregosa`_
- 36  `Alexandre Gramfort`_
- 32  Paolo Losi
- 31  `Edouard Duchesnay`_
- 30  `Mathieu Blondel`_
- 25  `Peter Prettenhofer`_
- 22  `Nicolas Pinto`_
- 11  :user:`Virgile Fritsch <VirgileFritsch>`
-  7  Lars Buitinck
-  6  Vincent Michel
-  5  `Bertrand Thirion`_
-  4  Thouis (Ray) Jones
-  4  Vincent Schut
-  3  Jan Schlüter
-  2  Julien Miotte
-  2  `Matthieu Perrot`_
-  2  Yann Malet
-  2  `Yaroslav Halchenko`_
-  1  Amit Aides
-  1  `Andreas Müller`_
-  1  Feth Arezki
-  1  Meng Xinfan

.. _changes_0_7:

Version 0.7
===========

**March 2, 2011**

scikit-learn 0.7 was released in March 2011, roughly three months
after the 0.6 release. This release is marked by the speed
improvements in existing algorithms like k-Nearest Neighbors and
K-Means algorithm and by the inclusion of an efficient algorithm for
computing the Ridge Generalized Cross Validation solution. Unlike the
preceding release, no new modules where added to this release.

Changelog
---------

- Performance improvements for Gaussian Mixture Model sampling [Jan
  Schlüter].

- Implementation of efficient leave-one-out cross-validated Ridge in
  :class:`~linear_model.RidgeCV` [`Mathieu Blondel`_]

- Better handling of collinearity and early stopping in
  :func:`linear_model.lars_path` [`Alexandre Gramfort`_ and `Fabian
  Pedregosa`_].

- Fixes for liblinear ordering of labels and sign of coefficients
  [Dan Yamins, Paolo Losi, `Mathieu Blondel`_ and `Fabian Pedregosa`_].

- Performance improvements for Nearest Neighbors algorithm in
  high-dimensional spaces [`Fabian Pedregosa`_].

- Performance improvements for :class:`~cluster.KMeans` [`Gael
  Varoquaux`_ and `James Bergstra`_].

- Sanity checks for SVM-based classes [`Mathieu Blondel`_].

- Refactoring of `neighbors.NeighborsClassifier` and
  :func:`neighbors.kneighbors_graph`: added different algorithms for
  the k-Nearest Neighbor Search and implemented a more stable
  algorithm for finding barycenter weights. Also added some
  developer documentation for this module, see
  `notes_neighbors
  <https://github.com/scikit-learn/scikit-learn/wiki/Neighbors-working-notes>`_ for more information [`Fabian Pedregosa`_].

- Documentation improvements: Added `pca.RandomizedPCA` and
  :class:`~linear_model.LogisticRegression` to the class
  reference. Also added references of matrices used for clustering
  and other fixes [`Gael Varoquaux`_, `Fabian Pedregosa`_, `Mathieu
  Blondel`_, `Olivier Grisel`_, Virgile Fritsch , Emmanuelle
  Gouillart]

- Binded decision_function in classes that make use of liblinear_,
  dense and sparse variants, like :class:`~svm.LinearSVC` or
  :class:`~linear_model.LogisticRegression` [`Fabian Pedregosa`_].

- Performance and API improvements to
  :func:`metrics.pairwise.euclidean_distances` and to
  `pca.RandomizedPCA` [`James Bergstra`_].

- Fix compilation issues under NetBSD [Kamel Ibn Hassen Derouiche]

- Allow input sequences of different lengths in `hmm.GaussianHMM`
  [`Ron Weiss`_].

- Fix bug in affinity propagation caused by incorrect indexing [Xinfan Meng]

People
------

People that made this release possible preceded by number of commits:

- 85  `Fabian Pedregosa`_
- 67  `Mathieu Blondel`_
- 20  `Alexandre Gramfort`_
- 19  `James Bergstra`_
- 14  Dan Yamins
- 13  `Olivier Grisel`_
- 12  `Gael Varoquaux`_
- 4  `Edouard Duchesnay`_
- 4  `Ron Weiss`_
- 2  Satrajit Ghosh
- 2  Vincent Dubourg
- 1  Emmanuelle Gouillart
- 1  Kamel Ibn Hassen Derouiche
- 1  Paolo Losi
- 1  VirgileFritsch
- 1  `Yaroslav Halchenko`_
- 1  Xinfan Meng

.. _changes_0_6:

Version 0.6
===========

**December 21, 2010**

scikit-learn 0.6 was released on December 2010. It is marked by the
inclusion of several new modules and a general renaming of old
ones. It is also marked by the inclusion of new example, including
applications to real-world datasets.

Changelog
---------

- New `stochastic gradient
  <https://scikit-learn.org/stable/modules/sgd.html>`_ descent
  module by Peter Prettenhofer. The module comes with complete
  documentation and examples.

- Improved svm module: memory consumption has been reduced by 50%,
  heuristic to automatically set class weights, possibility to
  assign weights to samples (see
  :ref:`sphx_glr_auto_examples_svm_plot_weighted_samples.py` for an example).

- New :ref:`gaussian_process` module by Vincent Dubourg. This module
  also has great documentation and some very neat examples. See
  example_gaussian_process_plot_gp_regression.py or
  example_gaussian_process_plot_gp_probabilistic_classification_after_regression.py
  for a taste of what can be done.

- It is now possible to use liblinear's Multi-class SVC (option
  multi_class in :class:`~svm.LinearSVC`)

- New features and performance improvements of text feature
  extraction.

- Improved sparse matrix support, both in main classes
  (:class:`~model_selection.GridSearchCV`) as in modules
  sklearn.svm.sparse and sklearn.linear_model.sparse.

- Lots of cool new examples and a new section that uses real-world
  datasets was created. These include:
  :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py`,
  :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py`,
  :ref:`sphx_glr_auto_examples_applications_wikipedia_principal_eigenvector.py` and
  others.

- Faster :ref:`least_angle_regression` algorithm. It is now 2x
  faster than the R version on worst case and up to 10x times faster
  on some cases.

- Faster coordinate descent algorithm. In particular, the full path
  version of lasso (:func:`linear_model.lasso_path`) is more than
  200x times faster than before.

- It is now possible to get probability estimates from a
  :class:`~linear_model.LogisticRegression` model.

- module renaming: the glm module has been renamed to linear_model,
  the gmm module has been included into the more general mixture
  model and the sgd module has been included in linear_model.

- Lots of bug fixes and documentation improvements.

People
------

People that made this release possible preceded by number of commits:

* 207  `Olivier Grisel`_

* 167 `Fabian Pedregosa`_

* 97 `Peter Prettenhofer`_

* 68 `Alexandre Gramfort`_

* 59  `Mathieu Blondel`_

* 55  `Gael Varoquaux`_

* 33  Vincent Dubourg

* 21  `Ron Weiss`_

* 9  Bertrand Thirion

* 3  `Alexandre Passos`_

* 3  Anne-Laure Fouque

* 2  Ronan Amicel

* 1 `Christian Osendorfer`_

.. _changes_0_5:

Version 0.5
===========

**October 11, 2010**

Changelog
---------

New classes
-----------

- Support for sparse matrices in some classifiers of modules
  ``svm`` and ``linear_model`` (see `svm.sparse.SVC`,
  `svm.sparse.SVR`, `svm.sparse.LinearSVC`,
  `linear_model.sparse.Lasso`, `linear_model.sparse.ElasticNet`)

- New :class:`~pipeline.Pipeline` object to compose different estimators.

- Recursive Feature Elimination routines in module
  :ref:`feature_selection`.

- Addition of various classes capable of cross validation in the
  linear_model module (:class:`~linear_model.LassoCV`, :class:`~linear_model.ElasticNetCV`,
  etc.).

- New, more efficient LARS algorithm implementation. The Lasso
  variant of the algorithm is also implemented. See
  :class:`~linear_model.lars_path`, :class:`~linear_model.Lars` and
  :class:`~linear_model.LassoLars`.

- New Hidden Markov Models module (see classes
  `hmm.GaussianHMM`, `hmm.MultinomialHMM`, `hmm.GMMHMM`)

- New module feature_extraction (see :ref:`class reference
  <feature_extraction_ref>`)

- New FastICA algorithm in module sklearn.fastica

Documentation
-------------

- Improved documentation for many modules, now separating
  narrative documentation from the class reference. As an example,
  see `documentation for the SVM module
  <https://scikit-learn.org/stable/modules/svm.html>`_ and the
  complete `class reference
  <https://scikit-learn.org/stable/modules/classes.html>`_.

Fixes
-----

- API changes: adhere variable names to PEP-8, give more
  meaningful names.

- Fixes for svm module to run on a shared memory context
  (multiprocessing).

- It is again possible to generate latex (and thus PDF) from the
  sphinx docs.

Examples
--------

- new examples using some of the mlcomp datasets:
  ``sphx_glr_auto_examples_mlcomp_sparse_document_classification.py`` (since removed) and
  :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`

- Many more examples. `See here
  <https://scikit-learn.org/stable/auto_examples/index.html>`_
  the full list of examples.

External dependencies
---------------------

- Joblib is now a dependency of this package, although it is
  shipped with (sklearn.externals.joblib).

Removed modules
---------------

- Module ann (Artificial Neural Networks) has been removed from
  the distribution. Users wanting this sort of algorithms should
  take a look into pybrain.

Misc
----

- New sphinx theme for the web page.

Authors
-------

The following is a list of authors for this release, preceded by
number of commits:

* 262  Fabian Pedregosa
* 240  Gael Varoquaux
* 149  Alexandre Gramfort
* 116  Olivier Grisel
*  40  Vincent Michel
*  38  Ron Weiss
*  23  Matthieu Perrot
*  10  Bertrand Thirion
*   7  Yaroslav Halchenko
*   9  VirgileFritsch
*   6  Edouard Duchesnay
*   4  Mathieu Blondel
*   1  Ariel Rokem
*   1  Matthieu Brucher

Version 0.4
===========

**August 26, 2010**

Changelog
---------

Major changes in this release include:

- Coordinate Descent algorithm (Lasso, ElasticNet) refactoring &
  speed improvements (roughly 100x times faster).

- Coordinate Descent Refactoring (and bug fixing) for consistency
  with R's package GLMNET.

- New metrics module.

- New GMM module contributed by Ron Weiss.

- Implementation of the LARS algorithm (without Lasso variant for now).

- feature_selection module redesign.

- Migration to GIT as version control system.

- Removal of obsolete attrselect module.

- Rename of private compiled extensions (added underscore).

- Removal of legacy unmaintained code.

- Documentation improvements (both docstring and rst).

- Improvement of the build system to (optionally) link with MKL.
  Also, provide a lite BLAS implementation in case no system-wide BLAS is
  found.

- Lots of new examples.

- Many, many bug fixes ...

Authors
-------

The committer list for this release is the following (preceded by number
of commits):

* 143  Fabian Pedregosa
* 35  Alexandre Gramfort
* 34  Olivier Grisel
* 11  Gael Varoquaux
*  5  Yaroslav Halchenko
*  2  Vincent Michel
*  1  Chris Filo Gorgolewski

Earlier versions
================

Earlier versions included contributions by Fred Mailhot, David Cooke,
David Huard, Dave Morrill, Ed Schofield, Travis Oliphant, Pearu Peterson.

include    contributors rst     currentmodule   sklearn                 Older Versions                     changes 0 12 1   Version 0 12 1                    October 8  2012    The 0 12 1 release is a bug fix release with no additional features  but is instead a set of bug fixes  Changelog               Improved numerical stability in spectral embedding by  Gael   Varoquaux      Doctest under windows 64bit by  Gael Varoquaux      Documentation fixes for elastic net by  Andreas M ller   and    Alexandre Gramfort      Proper behavior with fortran ordered NumPy arrays by  Gael Varoquaux      Make GridSearchCV work with non CSR sparse matrix by  Lars Buitinck      Fix parallel computing in MDS by  Gael Varoquaux      Fix Unicode support in count vectorizer by  Andreas M ller      Fix MinCovDet breaking with X shape    3  1  by  user  Virgile Fritsch  VirgileFritsch      Fix clone of SGD objects by  Peter Prettenhofer      Stabilize GMM by  user  Virgile Fritsch  VirgileFritsch    People            14   Peter Prettenhofer      12   Gael Varoquaux      10   Andreas M ller       5   Lars Buitinck       3   user  Virgile Fritsch  VirgileFritsch       1   Alexandre Gramfort       1   Gilles Louppe       1   Mathieu Blondel        changes 0 12   Version 0 12                 September 4  2012    Changelog              Various speed improvements of the  ref  decision trees  tree   module  by    Gilles Louppe        class   ensemble GradientBoostingRegressor  and    class   ensemble GradientBoostingClassifier  now support feature subsampling   via the   max features   argument  by  Peter Prettenhofer       Added Huber and Quantile loss functions to    class   ensemble GradientBoostingRegressor   by  Peter Prettenhofer        ref  Decision trees  tree   and  ref  forests of randomized trees  forest     now support multi output classification and regression problems  by    Gilles Louppe       Added  class   preprocessing LabelEncoder   a simple utility class to   normalize labels or transform non numerical labels  by  Mathieu Blondel       Added the epsilon insensitive loss and the ability to make probabilistic   predictions with the modified huber loss in  ref  sgd   by    Mathieu Blondel       Added  ref  multidimensional scaling   by Nelle Varoquaux     SVMlight file format loader now detects compressed  gzip bzip2  files and   decompresses them on the fly  by  Lars Buitinck       SVMlight file format serializer now preserves double precision floating   point values  by  Olivier Grisel       A common testing framework for all estimators was added  by  Andreas M ller       Understandable error messages for estimators that do not accept   sparse input by  Gael Varoquaux      Speedups in hierarchical clustering by  Gael Varoquaux    In   particular building the tree now supports early stopping  This is   useful when the number of clusters is not small compared to the   number of samples     Add MultiTaskLasso and MultiTaskElasticNet for joint feature selection    by  Alexandre Gramfort       Added  metrics auc score  and    func  metrics average precision score  convenience functions by  Andreas   M ller       Improved sparse matrix support in the  ref  feature selection    module by  Andreas M ller       New word boundaries aware character n gram analyzer for the    ref  text feature extraction  module by  user   kernc  kernc       Fixed bug in spectral clustering that led to single point clusters   by  Andreas M ller       In  class   feature extraction text CountVectorizer   added an option to   ignore infrequent words    min df   by   Andreas M ller       Add support for multiple targets in some linear models  ElasticNet  Lasso   and OrthogonalMatchingPursuit  by  Vlad Niculae   and    Alexandre Gramfort       Fixes in  decomposition ProbabilisticPCA  score function by Wei Li     Fixed feature importance computation in    ref  gradient boosting    API changes summary                        The old   scikits learn   package has disappeared  all code should import   from   sklearn   instead  which was introduced in 0 9     In  func  metrics roc curve   the   thresholds   array is now returned   with it s order reversed  in order to keep it consistent with the order   of the returned   fpr   and   tpr       In  hmm  objects  like  hmm GaussianHMM      hmm MultinomialHMM   etc   all parameters must be passed to the   object when initialising it and not through   fit    Now   fit   will   only accept the data as an input parameter     For all SVM classes  a faulty behavior of   gamma   was fixed  Previously    the default gamma value was only computed the first time   fit   was called   and then stored  It is now recalculated on every call to   fit       All   Base   classes are now abstract meta classes so that they can not be   instantiated      func  cluster ward tree  now also returns the parent array  This is   necessary for early stopping in which case the tree is not   completely built     In  class   feature extraction text CountVectorizer  the parameters     min n   and   max n   were joined to the parameter   n gram range   to   enable grid searching both at once     In  class   feature extraction text CountVectorizer   words that appear   only in one document are now ignored by default  To reproduce   the previous behavior  set   min df 1       Fixed API inconsistency   meth  linear model SGDClassifier predict proba  now   returns 2d array when fit on two classes     Fixed API inconsistency   meth  discriminant analysis QuadraticDiscriminantAnalysis decision function    and  meth  discriminant analysis LinearDiscriminantAnalysis decision function  now return 1d arrays   when fit on two classes     Grid of alphas used for fitting  class   linear model LassoCV  and    class   linear model ElasticNetCV  is now stored   in the attribute   alphas    rather than overriding the init parameter     alphas       Linear models when alpha is estimated by cross validation store   the estimated value in the   alpha    attribute rather than just     alpha   or   best alpha        class   ensemble GradientBoostingClassifier  now supports    meth   ensemble GradientBoostingClassifier staged predict proba   and    meth   ensemble GradientBoostingClassifier staged predict       svm sparse SVC  and other sparse SVM classes are now deprecated    The all classes in the  ref  svm  module now automatically select the   sparse or dense representation base on the input     All clustering algorithms now interpret the array   X   given to   fit   as   input data  in particular  class   cluster SpectralClustering  and    class   cluster AffinityPropagation  which previously expected affinity matrices     For clustering algorithms that take the desired number of clusters as a parameter    this parameter is now called   n clusters      People          267   Andreas M ller      94   Gilles Louppe      89   Gael Varoquaux      79   Peter Prettenhofer      60   Mathieu Blondel      57   Alexandre Gramfort      52   Vlad Niculae      45   Lars Buitinck      44  Nelle Varoquaux    37   Jaques Grobler      30  Alexis Mignon    30  Immanuel Bayer    27   Olivier Grisel      16  Subhodeep Moitra    13  Yannick Schwartz    12   user   kernc  kernc      11   user  Virgile Fritsch  VirgileFritsch       9  Daniel Duckworth     9   Fabian Pedregosa       9   Robert Layton       8  John Benediktsson     7  Marko Burjek     5   Nicolas Pinto       4  Alexandre Abraham     4   Jake Vanderplas       3   Brian Holt       3   Edouard Duchesnay       3  Florian Hoenig     3  flyingimmidev     2  Francois Savard     2  Hannes Schulz     2  Peter Welinder     2   Yaroslav Halchenko       2  Wei Li     1  Alex Companioni     1  Brandyn A  White     1  Bussonnier Matthias     1  Charles Pierre Astolfi     1  Dan O Huiginn     1  David Cournapeau     1  Keith Goodman     1  Ludwig Schwardt     1  Olivier Hervieu     1  Sergio Medina     1  Shiqiao Du     1  Tim Sheerman Chase     1  buguen        changes 0 11   Version 0 11                 May 7  2012    Changelog            Highlights                  Gradient boosted regression trees   ref  gradient boosting     for classification and regression by  Peter Prettenhofer     and  Scott White        Simple dict based feature loader with support for categorical variables     class   feature extraction DictVectorizer   by  Lars Buitinck       Added Matthews correlation coefficient   func  metrics matthews corrcoef     and added macro and micro average options to    func   metrics precision score    func  metrics recall score  and    func   metrics f1 score  by  Satrajit Ghosh        ref  out of bag  of generalization error for  ref  ensemble    by  Andreas M ller       Randomized sparse linear models for feature   selection  by  Alexandre Gramfort   and  Gael Varoquaux       ref  label propagation  for semi supervised learning  by Clay   Woolam    Note   the semi supervised API is still work in progress    and may change     Added BIC AIC model selection to classical  ref  gmm  and unified   the API with the remainder of scikit learn  by  Bertrand Thirion      Added  sklearn cross validation StratifiedShuffleSplit   which is   a  sklearn cross validation ShuffleSplit  with balanced splits    by Yannick Schwartz      class   sklearn neighbors NearestCentroid  classifier added  along with a     shrink threshold   parameter  which implements   shrunken centroid   classification    by  Robert Layton     Other changes                   Merged dense and sparse implementations of  ref  sgd  module and   exposed utility extension types for sequential   datasets   seq dataset   and weight vectors   weight vector     by  Peter Prettenhofer       Added   partial fit    support for online minibatch learning  and   warm start to the  ref  sgd  module by  Mathieu Blondel       Dense and sparse implementations of  ref  svm  classes and    class   linear model LogisticRegression  merged by  Lars Buitinck       Regressors can now be used as base estimator in the  ref  multiclass    module by  Mathieu Blondel       Added n jobs option to  func  metrics pairwise distances    and  func  metrics pairwise pairwise kernels  for parallel computation    by  Mathieu Blondel        ref  k means  can now be run in parallel  using the   n jobs   argument   to either  ref  k means  or  class  cluster KMeans   by  Robert Layton       Improved  ref  cross validation  and  ref  grid search  documentation   and introduced the new  cross validation train test split    helper function by  Olivier Grisel       class   svm SVC  members   coef    and   intercept    changed sign for   consistency with   decision function    for   kernel  linear        coef    was fixed in the one vs one case  by  Andreas M ller       Performance improvements to efficient leave one out cross validated   Ridge regression  esp  for the   n samples   n features   case  in    class   linear model RidgeCV   by Reuben Fletcher Costin     Refactoring and simplification of the  ref  text feature extraction    API and fixed a bug that caused possible negative IDF    by  Olivier Grisel       Beam pruning option in   BaseHMM  module has been removed since it   is difficult to Cythonize  If you are interested in contributing a Cython   version  you can use the python version in the git history as a reference     Classes in  ref  neighbors  now support arbitrary Minkowski metric for   nearest neighbors searches  The metric can be specified by argument   p     API changes summary                         covariance EllipticEnvelop  is now deprecated    Please use  class   covariance EllipticEnvelope  instead       NeighborsClassifier   and   NeighborsRegressor   are gone in the module    ref  neighbors   Use the classes  class   neighbors KNeighborsClassifier      class   neighbors RadiusNeighborsClassifier    class   neighbors KNeighborsRegressor    and or  class   neighbors RadiusNeighborsRegressor  instead     Sparse classes in the  ref  sgd  module are now deprecated     In  mixture GMM    mixture DPGMM  and  mixture VBGMM     parameters must be passed to an object when initialising it and not through     fit    Now   fit   will only accept the data as an input parameter     methods   rvs   and   decode   in  GMM  module are now deprecated      sample   and   score   or   predict   should be used instead     attribute    scores   and    pvalues   in univariate feature selection   objects are now deprecated      scores    or   pvalues    should be used instead     In  class   linear model LogisticRegression    class   svm LinearSVC      class   svm SVC  and  class   svm NuSVC   the   class weight   parameter is   now an initialization parameter  not a parameter to fit  This makes grid   searches over this parameter possible     LFW   data   is now always shape    n samples  n features    to be   consistent with the Olivetti faces dataset  Use   images   and     pairs   attribute to access the natural images shapes instead     In  class   svm LinearSVC   the meaning of the   multi class   parameter   changed   Options now are    ovr    and    crammer singer     with      ovr    being the default   This does not change the default behavior   but hopefully is less confusing     Class  feature selection text Vectorizer  is deprecated and   replaced by  feature selection text TfidfVectorizer      The preprocessor   analyzer nested structure for text feature   extraction has been removed  All those features are   now directly passed as flat constructor arguments   to  feature selection text TfidfVectorizer  and    feature selection text CountVectorizer   in particular the   following parameters are now used       analyzer   can be    word    or    char    to switch the default   analysis scheme  or use a specific python callable  as previously        tokenizer   and   preprocessor   have been introduced to make it   still possible to customize those steps with the new API       input   explicitly control how to interpret the sequence passed to     fit   and   predict    filenames  file objects or direct  byte or   Unicode  strings     charset decoding is explicit and strict by default     the   vocabulary    fitted or not is now stored in the     vocabulary    attribute to be consistent with the project   conventions     Class  feature selection text TfidfVectorizer  now derives directly   from  feature selection text CountVectorizer  to make grid   search trivial     methods   rvs   in   BaseHMM  module are now deprecated      sample   should be used instead     Beam pruning option in   BaseHMM  module is removed since it is   difficult to be Cythonized  If you are interested  you can look in the   history codes by git     The SVMlight format loader now supports files with both zero based and   one based column indices  since both occur  in the wild      Arguments in class  class   model selection ShuffleSplit  are now consistent with    class   model selection StratifiedShuffleSplit   Arguments   test fraction   and     train fraction   are deprecated and renamed to   test size   and     train size   and can accept both   float   and   int       Arguments in class  Bootstrap  are now consistent with    class   model selection StratifiedShuffleSplit   Arguments   n test   and     n train   are deprecated and renamed to   test size   and     train size   and can accept both   float   and   int       Argument   p   added to classes in  ref  neighbors  to specify an   arbitrary Minkowski metric for nearest neighbors searches    People           282   Andreas M ller     239   Peter Prettenhofer     198   Gael Varoquaux     129   Olivier Grisel     114   Mathieu Blondel     103  Clay Woolam    96   Lars Buitinck      88   Jaques Grobler      82   Alexandre Gramfort      50   Bertrand Thirion      42   Robert Layton      28  flyingimmidev    26   Jake Vanderplas      26  Shiqiao Du    21   Satrajit Ghosh      17   David Marek      17   Gilles Louppe      14   Vlad Niculae      11  Yannick Schwartz    10   Fabian Pedregosa       9  fcostin     7  Nick Wilson     5  Adrien Gaidon     5   Nicolas Pinto       4   David Warde Farley       5  Nelle Varoquaux     5  Emmanuelle Gouillart     3  Joonas Sillanp       3  Paolo Losi     2  Charles McCarthy     2  Roy Hyunjin Han     2  Scott White     2  ibayer     1  Brandyn White     1  Carlos Scheidegger     1  Claire Revillet     1  Conrad Lee     1   Edouard Duchesnay       1  Jan Hendrik Metzen     1  Meng Xinfan     1   Rob Zinkov       1  Shiqiao     1  Udi Weinsberg     1  Virgile Fritsch     1  Xinfan Meng     1  Yaroslav Halchenko     1  jansoe     1  Leon Palafox       changes 0 10   Version 0 10                 January 11  2012    Changelog              Python 2 5 compatibility was dropped  the minimum Python version needed   to use scikit learn is now 2 6      ref  sparse inverse covariance  estimation using the graph Lasso  with   associated cross validated estimator  by  Gael Varoquaux      New  ref  Tree  tree   module by  Brian Holt     Peter Prettenhofer       Satrajit Ghosh   and  Gilles Louppe    The module comes with complete   documentation and examples     Fixed a bug in the RFE module by  Gilles Louppe    issue  378      Fixed a memory leak in  ref  svm  module by  Brian Holt    issue  367      Faster tests by  Fabian Pedregosa   and others     Silhouette Coefficient cluster analysis evaluation metric added as    func   sklearn metrics silhouette score  by Robert Layton     Fixed a bug in  ref  k means  in the handling of the   n init   parameter    the clustering algorithm used to be run   n init   times but the last   solution was retained instead of the best solution by  Olivier Grisel       Minor refactoring in  ref  sgd  module  consolidated dense and sparse   predict methods  Enhanced test time performance by converting model   parameters to fortran style arrays after fitting  only multi class      Adjusted Mutual Information metric added as    func   sklearn metrics adjusted mutual info score  by Robert Layton     Models like SVC SVR LinearSVC LogisticRegression from libsvm liblinear   now support scaling of C regularization parameter by the number of   samples by  Alexandre Gramfort       New  ref  Ensemble Methods  ensemble   module by  Gilles Louppe   and    Brian Holt    The module comes with the random forest algorithm and the   extra trees method  along with documentation and examples      ref  outlier detection   outlier and novelty detection  by    user  Virgile Fritsch  VirgileFritsch        ref  kernel approximation   a transform implementing kernel   approximation for fast SGD on non linear kernels by    Andreas M ller       Fixed a bug due to atom swapping in  ref  OMP  by  Vlad Niculae        ref  SparseCoder  by  Vlad Niculae        ref  mini batch kmeans  performance improvements by  Olivier Grisel        ref  k means  support for sparse matrices by  Mathieu Blondel       Improved documentation for developers and for the  mod  sklearn utils    module  by  Jake Vanderplas       Vectorized 20newsgroups dataset loader     func   sklearn datasets fetch 20newsgroups vectorized   by    Mathieu Blondel        ref  multiclass  by  Lars Buitinck       Utilities for fast computation of mean and variance for sparse matrices   by  Mathieu Blondel       Make  func   sklearn preprocessing scale  and    sklearn preprocessing Scaler  work on sparse matrices by    Olivier Grisel      Feature importances using decision trees and or forest of trees    by  Gilles Louppe       Parallel implementation of forests of randomized trees by    Gilles Louppe        sklearn cross validation ShuffleSplit  can subsample the train   sets as well as the test sets by  Olivier Grisel       Errors in the build of the documentation fixed by  Andreas M ller      API changes summary                      Here are the code migration instructions when upgrading from scikit learn version 0 9     Some estimators that may overwrite their inputs to save memory previously   had   overwrite    parameters  these have been replaced with   copy      parameters with exactly the opposite meaning     This particularly affects some of the estimators in  mod   sklearn linear model     The default behavior is still to copy everything passed in     The SVMlight dataset loader  func   sklearn datasets load svmlight file  no   longer supports loading two files at once  use   load svmlight files     instead  Also  the  unused    buffer mb   parameter is gone     Sparse estimators in the  ref  sgd  module use dense parameter vector     coef    instead of   sparse coef     This significantly improves   test time performance     The  ref  covariance  module now has a robust estimator of   covariance  the Minimum Covariance Determinant estimator     Cluster evaluation metrics in  mod   sklearn metrics cluster  have been refactored   but the changes are backwards compatible  They have been moved to the    metrics cluster supervised   along with    metrics cluster unsupervised  which contains the Silhouette   Coefficient     The   permutation test score   function now behaves the same way as     cross val score    i e  uses the mean score across the folds      Cross Validation generators now use integer indices    indices True      by default instead of boolean masks  This make it more intuitive to   use with sparse matrix data     The functions used for sparse coding    sparse encode   and     sparse encode parallel   have been combined into    func   sklearn decomposition sparse encode   and the shapes of the arrays   have been transposed for consistency with the matrix factorization setting    as opposed to the regression setting     Fixed an off by one error in the SVMlight LibSVM file format handling    files generated using  func   sklearn datasets dump svmlight file  should be   re generated   They should continue to work  but accidentally had one   extra column of zeros prepended        BaseDictionaryLearning   class replaced by   SparseCodingMixin        sklearn utils extmath fast svd  has been renamed    func   sklearn utils extmath randomized svd  and the default   oversampling is now fixed to 10 additional random vectors instead   of doubling the number of components to extract  The new behavior   follows the reference paper    People         The following people contributed to scikit learn since last release     246   Andreas M ller     242   Olivier Grisel     220   Gilles Louppe     183   Brian Holt     166   Gael Varoquaux     144   Lars Buitinck      73   Vlad Niculae      65   Peter Prettenhofer      64   Fabian Pedregosa      60  Robert Layton    55   Mathieu Blondel      52   Jake Vanderplas      44  Noel Dawe    38   Alexandre Gramfort      24   user  Virgile Fritsch  VirgileFritsch      23   Satrajit Ghosh       3  Jan Hendrik Metzen     3  Kenneth C  Arnold     3  Shiqiao Du     3  Tim Sheerman Chase     3   Yaroslav Halchenko       2  Bala Subrahmanyam Varanasi     2  DraXus     2  Michael Eickenberg     1  Bogdan Trach     1  F lix Antoine Fortin     1  Juan Manuel Caicedo Carvajal     1  Nelle Varoquaux     1   Nicolas Pinto       1  Tiziano Zito     1  Xinfan Meng        changes 0 9   Version 0 9                September 21  2011    scikit learn 0 9 was released on September 2011  three months after the 0 8 release and includes the new modules  ref  manifold    ref  dirichlet process  as well as several new algorithms and documentation improvements   This release also includes the dictionary learning work developed by  Vlad Niculae   as part of the  Google Summer of Code  https   developers google com open source gsoc    program         banner1  image      auto examples manifold images thumb sphx glr plot compare methods thumb png     target     auto examples manifold plot compare methods html      banner2  image      auto examples linear model images thumb sphx glr plot omp thumb png     target     auto examples linear model plot omp html      banner3  image      auto examples decomposition images thumb sphx glr plot kernel pca thumb png     target     auto examples decomposition plot kernel pca html      center div  raw   html       div style  text align  center  margin  0px 0  5px 0         end div  raw   html        div     center div   banner2   banner1   banner3   end div   Changelog              New  ref  manifold  module by  Jake Vanderplas   and    Fabian Pedregosa       New  ref  Dirichlet Process  dirichlet process   Gaussian Mixture   Model by  Alexandre Passos       ref  neighbors  module refactoring by  Jake Vanderplas       general refactoring  support for sparse matrices in input  speed and   documentation improvements  See the next section for a full list of API   changes     Improvements on the  ref  feature selection  module by    Gilles Louppe     refactoring of the RFE classes  documentation   rewrite  increased efficiency and minor API changes      ref  SparsePCA  by  Vlad Niculae     Gael Varoquaux   and    Alexandre Gramfort      Printing an estimator now behaves independently of architectures   and Python version thanks to  user  Jean Kossaifi  JeanKossaifi        ref  Loader for libsvm svmlight format  libsvm loader   by    Mathieu Blondel   and  Lars Buitinck      Documentation improvements  thumbnails in   example gallery by  Fabian Pedregosa       Important bugfixes in  ref  svm  module  segfaults  bad   performance  by  Fabian Pedregosa       Added  ref  multinomial naive bayes  and  ref  bernoulli naive bayes    by  Lars Buitinck      Text feature extraction optimizations by Lars Buitinck    Chi Square feature selection     func  feature selection chi2   by  Lars Buitinck        ref  sample generators  module refactoring by  Gilles Louppe       ref  multiclass  by  Mathieu Blondel      Ball tree rewrite by  Jake Vanderplas      Implementation of  ref  dbscan  algorithm by Robert Layton    Kmeans predict and transform by Robert Layton    Preprocessing module refactoring by  Olivier Grisel      Faster mean shift by Conrad Lee    New   Bootstrap     ref  ShuffleSplit  and various other   improvements in cross validation schemes by  Olivier Grisel   and    Gael Varoquaux      Adjusted Rand index and V Measure clustering evaluation metrics by  Olivier Grisel      Added  class  Orthogonal Matching Pursuit  linear model OrthogonalMatchingPursuit   by  Vlad Niculae      Added 2D patch extractor utilities in the  ref  feature extraction  module by  Vlad Niculae      Implementation of  class   linear model LassoLarsCV     cross validated Lasso solver using the Lars algorithm  and    class   linear model LassoLarsIC   BIC AIC model   selection in Lars  by  Gael Varoquaux     and  Alexandre Gramfort      Scalability improvements to  func  metrics roc curve  by Olivier Hervieu    Distance helper functions  func  metrics pairwise distances    and  func  metrics pairwise pairwise kernels  by Robert Layton     class  Mini Batch K Means  cluster MiniBatchKMeans   by Nelle Varoquaux and Peter Prettenhofer     mldata utilities by Pietro Berkes      ref  olivetti faces dataset  by  David Warde Farley      API changes summary                      Here are the code migration instructions when upgrading from scikit learn version 0 8     The   scikits learn   package was renamed   sklearn    There is   still a   scikits learn   package alias for backward compatibility     Third party projects with a dependency on scikit learn 0 9  should   upgrade their codebase  For instance  under Linux   MacOSX just run    make a backup first            find  name    py    xargs sed  i  s  bscikits learn b sklearn g     Estimators no longer accept model parameters as   fit   arguments    instead all parameters must be only be passed as constructor   arguments or using the now public   set params   method inherited   from  class   base BaseEstimator      Some estimators can still accept keyword arguments on the   fit     but this is restricted to data dependent values  e g  a Gram matrix   or an affinity matrix that are precomputed from the   X   data matrix     The   cross val   package has been renamed to   cross validation     although there is also a   cross val   package alias in place for   backward compatibility     Third party projects with a dependency on scikit learn 0 9  should   upgrade their codebase  For instance  under Linux   MacOSX just run    make a backup first            find  name    py    xargs sed  i  s  bcross val b cross validation g     The   score func   argument of the     sklearn cross validation cross val score   function is now expected   to accept   y test   and   y predicted   as only arguments for   classification and regression tasks or   X test   for unsupervised   estimators       gamma   parameter for support vector machine algorithms is set   to   1   n features   by default  instead of   1   n samples       The   sklearn hmm   has been marked as orphaned  it will be removed   from scikit learn in version 0 11 unless someone steps up to   contribute documentation  examples and fix lurking numerical   stability issues       sklearn neighbors   has been made into a submodule   The two previously   available estimators    NeighborsClassifier   and   NeighborsRegressor     have been marked as deprecated   Their functionality has been divided   among five new classes    NearestNeighbors   for unsupervised neighbors   searches    KNeighborsClassifier       RadiusNeighborsClassifier     for supervised classification problems  and   KNeighborsRegressor         RadiusNeighborsRegressor   for supervised regression problems       sklearn ball tree BallTree   has been moved to     sklearn neighbors BallTree     Using the former will generate a warning       sklearn linear model LARS     and related classes  LassoLARS    LassoLARSCV  etc   have been renamed to     sklearn linear model Lars         All distance metrics and kernels in   sklearn metrics pairwise   now have a Y   parameter  which by default is None  If not given  the result is the distance    or kernel similarity  between each sample in Y  If given  the result is the   pairwise distance  or kernel similarity  between samples in X to Y       sklearn metrics pairwise l1 distance   is now called   manhattan distance      and by default returns the pairwise distance  For the component wise distance    set the parameter   sum over features   to   False     Backward compatibility package aliases and other deprecated classes and functions will be removed in version 0 11    People         38 people contributed to this release     387   Vlad Niculae     320   Olivier Grisel     192   Lars Buitinck     179   Gael Varoquaux     168   Fabian Pedregosa     INRIA     Parietal Team      127   Jake Vanderplas     120   Mathieu Blondel     85   Alexandre Passos     67   Alexandre Gramfort     57   Peter Prettenhofer     56   Gilles Louppe     42  Robert Layton   38  Nelle Varoquaux   32   user  Jean Kossaifi  JeanKossaifi     30  Conrad Lee   22  Pietro Berkes   18  andy   17  David Warde Farley   12  Brian Holt   11  Robert   8  Amit Aides   8   user  Virgile Fritsch  VirgileFritsch     7   Yaroslav Halchenko     6  Salvatore Masecchia   5  Paolo Losi   4  Vincent Schut   3  Alexis Metaireau   3  Bryan Silverthorn   3   Andreas M ller     2  Minwoo Jake Lee   1  Emmanuelle Gouillart   1  Keith Goodman   1  Lucas Wiman   1   Nicolas Pinto     1  Thouis  Ray  Jones   1  Tim Sheerman Chase       changes 0 8   Version 0 8                May 11  2011    scikit learn 0 8 was released on May 2011  one month after the first  international   scikit learn coding sprint  https   github com scikit learn scikit learn wiki Upcoming events    and is marked by the inclusion of important modules   ref  hierarchical clustering    ref  cross decomposition    ref  NMF   initial support for Python 3 and by important enhancements and bug fixes    Changelog            Several new modules where introduced during this release     New  ref  hierarchical clustering  module by Vincent Michel     Bertrand Thirion     Alexandre Gramfort   and  Gael Varoquaux        ref  kernel pca  implementation by  Mathieu Blondel       ref  labeled faces in the wild dataset  by  Olivier Grisel       New  ref  cross decomposition  module by  Edouard Duchesnay        ref  NMF  module  Vlad Niculae      Implementation of the  ref  oracle approximating shrinkage  algorithm by    user  Virgile Fritsch  VirgileFritsch   in the  ref  covariance  module    Some other modules benefited from significant improvements or cleanups      Initial support for Python 3  builds and imports cleanly    some modules are usable while others have failing tests by  Fabian Pedregosa        class   decomposition PCA  is now usable from the Pipeline object by  Olivier Grisel       Guide  ref  performance howto  by  Olivier Grisel       Fixes for memory leaks in libsvm bindings  64 bit safer BallTree by Lars Buitinck     bug and style fixing in  ref  k means  algorithm by Jan Schl ter     Add attribute converged to Gaussian Mixture Models by Vincent Schut     Implemented   transform      predict log proba   in    class   discriminant analysis LinearDiscriminantAnalysis  By  Mathieu Blondel       Refactoring in the  ref  svm  module and bug fixes by  Fabian Pedregosa       Gael Varoquaux   and Amit Aides     Refactored SGD module  removed code duplication  better variable naming     added interface for sample weight by  Peter Prettenhofer       Wrapped BallTree with Cython by Thouis  Ray  Jones     Added function  func  svm l1 min c  by Paolo Losi     Typos  doc style  etc  by  Yaroslav Halchenko     Gael Varoquaux       Olivier Grisel    Yann Malet   Nicolas Pinto    Lars Buitinck and    Fabian Pedregosa      People          People that made this release possible preceded by number of commits      159   Olivier Grisel     96   Gael Varoquaux     96   Vlad Niculae     94   Fabian Pedregosa     36   Alexandre Gramfort     32  Paolo Losi   31   Edouard Duchesnay     30   Mathieu Blondel     25   Peter Prettenhofer     22   Nicolas Pinto     11   user  Virgile Fritsch  VirgileFritsch      7  Lars Buitinck    6  Vincent Michel    5   Bertrand Thirion      4  Thouis  Ray  Jones    4  Vincent Schut    3  Jan Schl ter    2  Julien Miotte    2   Matthieu Perrot      2  Yann Malet    2   Yaroslav Halchenko      1  Amit Aides    1   Andreas M ller      1  Feth Arezki    1  Meng Xinfan       changes 0 7   Version 0 7                March 2  2011    scikit learn 0 7 was released in March 2011  roughly three months after the 0 6 release  This release is marked by the speed improvements in existing algorithms like k Nearest Neighbors and K Means algorithm and by the inclusion of an efficient algorithm for computing the Ridge Generalized Cross Validation solution  Unlike the preceding release  no new modules where added to this release   Changelog              Performance improvements for Gaussian Mixture Model sampling  Jan   Schl ter      Implementation of efficient leave one out cross validated Ridge in    class   linear model RidgeCV    Mathieu Blondel       Better handling of collinearity and early stopping in    func  linear model lars path    Alexandre Gramfort   and  Fabian   Pedregosa        Fixes for liblinear ordering of labels and sign of coefficients    Dan Yamins  Paolo Losi   Mathieu Blondel   and  Fabian Pedregosa        Performance improvements for Nearest Neighbors algorithm in   high dimensional spaces   Fabian Pedregosa        Performance improvements for  class   cluster KMeans    Gael   Varoquaux   and  James Bergstra        Sanity checks for SVM based classes   Mathieu Blondel        Refactoring of  neighbors NeighborsClassifier  and    func  neighbors kneighbors graph   added different algorithms for   the k Nearest Neighbor Search and implemented a more stable   algorithm for finding barycenter weights  Also added some   developer documentation for this module  see    notes neighbors    https   github com scikit learn scikit learn wiki Neighbors working notes    for more information   Fabian Pedregosa        Documentation improvements  Added  pca RandomizedPCA  and    class   linear model LogisticRegression  to the class   reference  Also added references of matrices used for clustering   and other fixes   Gael Varoquaux     Fabian Pedregosa     Mathieu   Blondel     Olivier Grisel    Virgile Fritsch   Emmanuelle   Gouillart     Binded decision function in classes that make use of liblinear     dense and sparse variants  like  class   svm LinearSVC  or    class   linear model LogisticRegression    Fabian Pedregosa        Performance and API improvements to    func  metrics pairwise euclidean distances  and to    pca RandomizedPCA    James Bergstra        Fix compilation issues under NetBSD  Kamel Ibn Hassen Derouiche     Allow input sequences of different lengths in  hmm GaussianHMM      Ron Weiss        Fix bug in affinity propagation caused by incorrect indexing  Xinfan Meng    People         People that made this release possible preceded by number of commits     85   Fabian Pedregosa     67   Mathieu Blondel     20   Alexandre Gramfort     19   James Bergstra     14  Dan Yamins   13   Olivier Grisel     12   Gael Varoquaux     4   Edouard Duchesnay     4   Ron Weiss     2  Satrajit Ghosh   2  Vincent Dubourg   1  Emmanuelle Gouillart   1  Kamel Ibn Hassen Derouiche   1  Paolo Losi   1  VirgileFritsch   1   Yaroslav Halchenko     1  Xinfan Meng       changes 0 6   Version 0 6                December 21  2010    scikit learn 0 6 was released on December 2010  It is marked by the inclusion of several new modules and a general renaming of old ones  It is also marked by the inclusion of new example  including applications to real world datasets    Changelog              New  stochastic gradient    https   scikit learn org stable modules sgd html    descent   module by Peter Prettenhofer  The module comes with complete   documentation and examples     Improved svm module  memory consumption has been reduced by 50     heuristic to automatically set class weights  possibility to   assign weights to samples  see    ref  sphx glr auto examples svm plot weighted samples py  for an example      New  ref  gaussian process  module by Vincent Dubourg  This module   also has great documentation and some very neat examples  See   example gaussian process plot gp regression py or   example gaussian process plot gp probabilistic classification after regression py   for a taste of what can be done     It is now possible to use liblinear s Multi class SVC  option   multi class in  class   svm LinearSVC      New features and performance improvements of text feature   extraction     Improved sparse matrix support  both in main classes     class   model selection GridSearchCV   as in modules   sklearn svm sparse and sklearn linear model sparse     Lots of cool new examples and a new section that uses real world   datasets was created  These include     ref  sphx glr auto examples applications plot face recognition py      ref  sphx glr auto examples applications plot species distribution modeling py      ref  sphx glr auto examples applications wikipedia principal eigenvector py  and   others     Faster  ref  least angle regression  algorithm  It is now 2x   faster than the R version on worst case and up to 10x times faster   on some cases     Faster coordinate descent algorithm  In particular  the full path   version of lasso   func  linear model lasso path   is more than   200x times faster than before     It is now possible to get probability estimates from a    class   linear model LogisticRegression  model     module renaming  the glm module has been renamed to linear model    the gmm module has been included into the more general mixture   model and the sgd module has been included in linear model     Lots of bug fixes and documentation improvements    People         People that made this release possible preceded by number of commits     207   Olivier Grisel      167  Fabian Pedregosa      97  Peter Prettenhofer      68  Alexandre Gramfort      59   Mathieu Blondel      55   Gael Varoquaux      33  Vincent Dubourg    21   Ron Weiss      9  Bertrand Thirion    3   Alexandre Passos      3  Anne Laure Fouque    2  Ronan Amicel    1  Christian Osendorfer          changes 0 5    Version 0 5                October 11  2010    Changelog            New classes                Support for sparse matrices in some classifiers of modules     svm   and   linear model    see  svm sparse SVC      svm sparse SVR    svm sparse LinearSVC      linear model sparse Lasso    linear model sparse ElasticNet      New  class   pipeline Pipeline  object to compose different estimators     Recursive Feature Elimination routines in module    ref  feature selection      Addition of various classes capable of cross validation in the   linear model module   class   linear model LassoCV    class   linear model ElasticNetCV     etc       New  more efficient LARS algorithm implementation  The Lasso   variant of the algorithm is also implemented  See    class   linear model lars path    class   linear model Lars  and    class   linear model LassoLars      New Hidden Markov Models module  see classes    hmm GaussianHMM    hmm MultinomialHMM    hmm GMMHMM      New module feature extraction  see  ref  class reference    feature extraction ref       New FastICA algorithm in module sklearn fastica   Documentation                  Improved documentation for many modules  now separating   narrative documentation from the class reference  As an example    see  documentation for the SVM module    https   scikit learn org stable modules svm html    and the   complete  class reference    https   scikit learn org stable modules classes html      Fixes          API changes  adhere variable names to PEP 8  give more   meaningful names     Fixes for svm module to run on a shared memory context    multiprocessing      It is again possible to generate latex  and thus PDF  from the   sphinx docs   Examples             new examples using some of the mlcomp datasets      sphx glr auto examples mlcomp sparse document classification py    since removed  and    ref  sphx glr auto examples text plot document classification 20newsgroups py     Many more examples   See here    https   scikit learn org stable auto examples index html      the full list of examples    External dependencies                          Joblib is now a dependency of this package  although it is   shipped with  sklearn externals joblib    Removed modules                    Module ann  Artificial Neural Networks  has been removed from   the distribution  Users wanting this sort of algorithms should   take a look into pybrain   Misc         New sphinx theme for the web page    Authors          The following is a list of authors for this release  preceded by number of commits     262  Fabian Pedregosa   240  Gael Varoquaux   149  Alexandre Gramfort   116  Olivier Grisel    40  Vincent Michel    38  Ron Weiss    23  Matthieu Perrot    10  Bertrand Thirion     7  Yaroslav Halchenko     9  VirgileFritsch     6  Edouard Duchesnay     4  Mathieu Blondel     1  Ariel Rokem     1  Matthieu Brucher  Version 0 4                August 26  2010    Changelog            Major changes in this release include     Coordinate Descent algorithm  Lasso  ElasticNet  refactoring     speed improvements  roughly 100x times faster      Coordinate Descent Refactoring  and bug fixing  for consistency   with R s package GLMNET     New metrics module     New GMM module contributed by Ron Weiss     Implementation of the LARS algorithm  without Lasso variant for now      feature selection module redesign     Migration to GIT as version control system     Removal of obsolete attrselect module     Rename of private compiled extensions  added underscore      Removal of legacy unmaintained code     Documentation improvements  both docstring and rst      Improvement of the build system to  optionally  link with MKL    Also  provide a lite BLAS implementation in case no system wide BLAS is   found     Lots of new examples     Many  many bug fixes       Authors          The committer list for this release is the following  preceded by number of commits      143  Fabian Pedregosa   35  Alexandre Gramfort   34  Olivier Grisel   11  Gael Varoquaux    5  Yaroslav Halchenko    2  Vincent Michel    1  Chris Filo Gorgolewski   Earlier versions                   Earlier versions included contributions by Fred Mailhot  David Cooke  David Huard  Dave Morrill  Ed Schofield  Travis Oliphant  Pearu Peterson