.. currentmodule:: scikits.learn

.. _changes_0_6:

0.6
===

scikits.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.

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Changelog
---------

  - New `stochastic gradient
    <http://scikit-learn.sourceforge.net/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:`example_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
    :ref:`example_gaussian_process_plot_gp_regression.py` or
    :ref:`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:`grid_search.GridSearchCV`) as in modules
    scikits.learn.svm.sparse and scikits.learn.linear_model.sparse.

  - Lots of cool new examples and a new section that uses real-world
    datasets was created. These include:
    :ref:`example_applications_plot_face_recognition.py`,
    :ref:`example_applications_plot_species_distribution_modeling.py`,
    :ref:`example_applications_svm_gui.py`,
    :ref:`example_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 preceeded by number of commits:

   * 207  `Olivier Grisel <http://twitter.com/ogrisel>`_

   * 167 `Fabian Pedregosa <http://fseoane.net/blog/>`_

   * 97 `Peter Prettenhofer <http://sites.google.com/site/peterprettenhofer/>`_

   * 68 `Alexandre Gramfort
     <http://www-sop.inria.fr/members/Alexandre.Gramfort/index.fr.html>`_

   * 59  `Mathieu Blondel <http://www.mblondel.org/journal/>`_

   * 55  `Gael Varoquaux <http://gael-varoquaux.info/blog/>`_

   * 33  Vincent Dubourg

   * 21  `Ron Weiss <http://www.ee.columbia.edu/~ronw/>`_

   * 9  Bertrand Thirion

   * 3  `Alexandre Passos <http://atpassos.posterous.com>`_

   * 3  Anne-Laure Fouque

   * 2  Ronan Amicel

   * 1 `Christian Osendorfer <http://osdf.github.com/>`_



.. _changes_0_5:


0.5
===

Changelog
---------

New classes
~~~~~~~~~~~~

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

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

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

    - 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
      :class:`hmm.GaussianHMM`, :class:`hmm.MultinomialHMM`,
      :class:`hmm.GMMHMM`)

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

    - New FastICA algorithm in module scikits.learn.fastica


Documentation
~~~~~~~~~~~~~

    - Improved documentation for many modules, now separating
      narrative documentation from the class reference. As an example,
      see `documentation for the SVM module
      <http://scikit-learn.sourceforge.net/modules/svm.html>`_ and the
      complete `class reference
      <http://scikit-learn.sourceforge.net/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:
      :ref:`example_mlcomp_sparse_document_classification.py`,
      :ref:`example_mlcomp_document_classification.py`

    - Many more examaples. `See here
      <http://scikit-learn.sourceforge.net/auto_examples/index.html>`_
      the full list of examples.


External dependencies
~~~~~~~~~~~~~~~~~~~~~

    - Joblib is now a dependencie of this package, although it is
      shipped with (scikits.learn.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, preceeded 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

0.4
===

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 content management system.

    - Removal of obsolete attrselect module.

    - Rename of private compiled extensions (aded 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