.. 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. .. |banner1| image:: auto_examples/applications/images/plot_face_recognition.png :height: 150 :target: auto_examples/applications/plot_face_recognition.html .. |banner2| image:: auto_examples/applications/images/plot_species_distribution_modeling.png :height: 150 :target: auto_examples/linear_model/plot_species_distribution.html .. |banner3| image:: auto_examples/gaussian_process/images/plot_gp_regression.png :height: 150 :target: auto_examples/gaussian_process/plot_gp_regression.html .. |banner4| image:: auto_examples/linear_model/images/plot_sgd_iris.png :height: 150 :target: auto_examples/linear_model/plot_lasso_lars.html .. |center-div| raw:: html <div style="text-align: center; margin: 0px 0 -5px 0;"> .. |end-div| raw:: html </div> |center-div| |banner1| |banner2| |banner3| |banner4| |end-div| 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 liblinearâs 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