.. currentmodule:: sklearn .. _changes_0_9: 0.9 === 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 `_ program. .. |banner1| image:: ./auto_examples/manifold/images/thumb/plot_compare_methods.png :target: auto_examples/manifold/plot_compare_methods.html .. |banner2| image:: ./auto_examples/linear_model/images/thumb/plot_omp.png :target: auto_examples/linear_model/plot_omp.html .. |banner3| image:: ./auto_examples/decomposition/images/thumb/plot_kernel_pca.png :target: auto_examples/decomposition/plot_kernel_pca.html .. |center-div| raw:: html
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|center-div| |banner2| |banner1| |banner3| |end-div| Changelog --------- - New :ref:`manifold` module by `Jake Vanderplas`_ and `Fabian Pedregosa`_. - New :ref:`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 Jean Kossaifi. - :ref:`Loader for libsvm/svmlight format ` by `Mathieu Blondel`_ and `Lars Buitinck`_ - Documentation improvements: thumbnails in :ref:`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.univariate_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 :ref:`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 ` by `Vlad Niculae`_ - Added 2D-patch extractor utilites 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.pairwise_distances` and :func:`metrics.pairwise.pairwise_kernels` by Robert Layton - :class:`Mini-Batch K-Means ` by Nelle Varoquaux and Peter Prettenhofer. - :ref:`mldata` utilities by Pietro Berkes. - :ref:`olivetti_faces` by `David Warde-Farley`_. API changes summary ------------------- Here are the code migration instructions when updgrading 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 inhereted 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 compatibilty 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 Jean Kossaifi - 30 Conrad Lee - 22 Pietro Berkes - 18 andy - 17 David Warde-Farley - 12 Brian Holt - 11 Robert - 8 Amit Aides - 8 `Virgile Fritsch`_ - 7 `Yaroslav Halchenko`_ - 6 Salvatore Masecchia - 5 Paolo Losi - 4 Vincent Schut - 3 Alexis Metaireau - 3 Bryan Silverthorn - 3 Andreas Mueller - 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: 0.8 === scikit-learn 0.8 was released on May 2011, one month after the first "international" `scikit-learn coding sprint `_ and is marked by the inclusion of important modules: :ref:`hierarchical_clustering`, :ref:`pls`, :ref:`NMF`, initial support for Python 3 and by important enhacements 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` by `Olivier Grisel`_. - New :ref:`pls` module by `Edouard Duchesnay`_. - :ref:`NMF` module `Vlad Niculae`_ - Implementation of the :ref:`oracle_approximating_shrinkage` algorithm by `Virgile Fritsch`_ 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 coverged to Gaussian Mixture Models by Vincent Schut. - Implement `transform`, `predict_log_proba` in :class:`lda.LDA` 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 preceeded 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 `Virgile Fritsch`_ - 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: 0.7 === 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 :class:`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 weigths. Also added some developer documentation for this module, see `notes_neighbors `_ for more information [`Fabian Pedregosa`_]. - Documentation improvements: Added :class:`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.euclidean_distances` and to :class:`pca.RandomizedPCA` [`James Bergstra`_]. - Fix compilation issues under NetBSD [Kamel Ibn Hassen Derouiche] - Allow input sequences of different lengths in :class:`hmm.GaussianHMM` [`Ron Weiss`_]. - Fix bug in affinity propagation caused by incorrect indexing [Xinfan Meng] People ------ People that made this release possible preceeded 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: 0.6 === 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 `_ 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 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:`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`_ * 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: 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 `) - 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 `_ and the complete `class reference `_. 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 `_ the full list of examples. External dependencies ~~~~~~~~~~~~~~~~~~~~~ - Joblib is now a dependencie 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, 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 .. _Olivier Grisel: http://twitter.com/ogrisel .. _Gael Varoquaux: http://gael-varoquaux.info .. _Alexandre Gramfort: http://www-sop.inria.fr/members/Alexandre.Gramfort/ .. _Fabian Pedregosa: http://fseoane.net/blog/ .. _Mathieu Blondel: http://www.mblondel.org/journal/ .. _James Bergstra: http://www-etud.iro.umontreal.ca/~bergstrj/ .. _liblinear: http://www.csie.ntu.edu.tw/~cjlin/liblinear/ .. _Yaroslav Halchenko: http://www.onerussian.com/ .. _Vlad Niculae: http://vene.ro .. _Edouard Duchesnay: http://www.lnao.fr/spip.php?rubrique30 .. _Peter Prettenhofer: http://sites.google.com/site/peterprettenhofer/ .. _Alexandre Passos: .. _Nicolas Pinto: http://pinto.scripts.mit.edu/ .. _Virgile Fritsch: http://parietal.saclay.inria.fr/Members/virgile-fritsch .. _Bertrand Thirion: http://parietal.saclay.inria.fr/Members/bertrand-thirion .. _Andreas Müller: http://www.ais.uni-bonn.de/~amueller/ .. _Matthieu Perrot: http://www.lnao.fr/spip.php?rubrique19 .. _Jake Vanderplas: http://www.astro.washington.edu/users/vanderplas/ .. _Gilles Louppe: http://www.montefiore.ulg.ac.be/~glouppe/ .. _INRIA: http://inria.fr .. _Parietal Team: http://parietal.saclay.inria.fr/ .. _Lars Buitinck: https://github.com/larsmans .. _David Warde-Farley: http://www-etud.iro.umontreal.ca/~wardefar/