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sklearn

0.10

Changelog

  • Python 2.5 compatibility was dropped; the minimum Python version needed to use scikit-learn is now 2.6.
  • sparse_inverse_covariance estimation using the graph Lasso, with associated cross-validated estimator, by Gael Varoquaux
  • New 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 in svm module by Brian Holt (issue #367).
  • Faster tests by Fabian Pedregosa.
  • Silhouette Coefficient cluster analysis evaluation metric added as sklearn.metrics.silhouette_score by Robert Layton.
  • Fixed a bug in 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.
  • Minor refactoring in sgd module; consolidated dense and sparse predict methods.
  • Adjusted Mutual Information metric added as 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 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.
  • outlier_detection: outlier and novelty detection, by Virgile Fritsch.
  • kernel_approximation: a transform implement kernel approximation for fast SGD on non-linear kernels by Andreas Müller.
  • Fix a bug due to atom swapping in OMP by Vlad Niculae.
  • SparseCoder by Vlad Niculae.
  • mini_batch_kmeans performance improvements by Olivier Grisel.
  • k_means support for sparse matrices by Mathieu Blondel.
  • Improved documentation for developers and for the sklearn.utils module, by Jake VanderPlas.
  • Vectorized 20newsgroups dataset loader (sklearn.datasets.fetch_20newsgroups_vectorized) by Mathieu Blondel.
  • multiclass by Lars Buitinck.
  • Utilities for fast computation of mean and variance for sparse matrices by Mathieu Blondel.
  • Make sklearn.preprocessing.scale and sklearn.preprocessing.Scaler work on sparse matrices by Olivier Grisel

API changes summary

Here are the code migration instructions when updgrading 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 linear_model. The default behavior is still to copy everything passed in.

  • The SVMlight dataset loader 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 sgd module use dense parameter vector coef_ instead of sparse_coef_. This significantly improves test time performance.
  • The covariance module now has a robust estimator of covariance, the Minimum Covariance Determinant estimator.
  • Cluster evaluation metrics in 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 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 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.

0.9

scikit-learn 0.9 was released on September 2011, three months after the 0.8 release and includes the new modules manifold, 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.

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Changelog

  • New manifold module by Jake Vanderplas and Fabian Pedregosa.
  • New Dirichlet Process <dirichlet_process> Gaussian Mixture Model by Alexandre Passos
  • 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 feature_selection module by Gilles Louppe : refactoring of the RFE classes, documentation rewrite, increased efficiency and minor API changes.
  • SparsePCA by Vlad Niculae, Gael Varoquaux and Alexandre Gramfort
  • Printing an estimator now behaves independently of architectures and Python version thanks to Jean Kossaifi.
  • Loader for libsvm/svmlight format <libsvm_loader> by Mathieu Blondel and Lars Buitinck
  • Documentation improvements: thumbnails in example gallery <examples-index> by Fabian Pedregosa.
  • Important bugfixes in svm module (segfaults, bad performance) by Fabian Pedregosa.
  • Added multinomial_naive_bayes and bernoulli_naive_bayes by Lars Buitinck
  • Text feature extraction optimizations by Lars Buitinck
  • Chi-Square feature selection (feature_selection.univariate_selection.chi2) by Lars Buitinck.
  • sample_generators module refactoring by Gilles Louppe
  • multiclass by Mathieu Blondel
  • Ball tree rewrite by Jake Vanderplas
  • Implementation of 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, 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 Orthogonal Matching Pursuit <linear_model.OrthogonalMatchingPursuit> by Vlad Niculae
  • Added 2D-patch extractor utilites in the feature_extraction module by Vlad Niculae
  • Implementation of linear_model.LassoLarsCV (cross-validated Lasso solver using the Lars algorithm) and linear_model.LassoLarsIC (BIC/AIC model selection in Lars) by Gael Varoquaux and Alexandre Gramfort
  • Scalability improvements to metrics.roc_curve by Olivier Hervieu
  • Distance helper functions metrics.pairwise.pairwise_distances and metrics.pairwise.pairwise_kernels by Robert Layton
  • Mini-Batch K-Means <cluster.MiniBatchKMeans> by Nelle Varoquaux and Peter Prettenhofer.
  • mldata utilities by Pietro Berkes.
  • 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 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.

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: hierarchical_clustering, pls, NMF, initial support for Python 3 and by important enhacements and bug fixes.

Changelog

Several new modules where introduced during this release:

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.
  • decomposition.PCA is now usable from the Pipeline object by Olivier Grisel.
  • Guide performance-howto by Olivier Grisel.
  • Fixes for memory leaks in libsvm bindings, 64-bit safer BallTree by Lars Buitinck.
  • bug and style fixing in k_means algorithm by Jan Schlüter.
  • Add attribute coverged to Gaussian Mixture Models by Vincent Schut.
  • Implement transform, predict_log_proba in lda.LDA by Mathieu Blondel.
  • Refactoring in the 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 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:

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 linear_model.RidgeCV [Mathieu Blondel]
  • Better handling of collinearity and early stopping in 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 cluster.KMeans [Gael Varoquaux and James Bergstra].
  • Sanity checks for SVM-based classes [Mathieu Blondel].
  • Refactoring of neighbors.NeighborsClassifier and 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 pca.RandomizedPCA and 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 svm.LinearSVC or linear_model.LogisticRegression [Fabian Pedregosa].
  • Performance and API improvements to metrics.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 preceeded by number of commits:

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 example_svm_plot_weighted_samples.py for an example).
  • New 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 svm.LinearSVC)
  • New features and performance improvements of text feature extraction.
  • Improved sparse matrix support, both in main classes (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: example_applications_face_recognition.py, example_applications_plot_species_distribution_modeling.py, example_applications_svm_gui.py, example_applications_wikipedia_principal_eigenvector.py and others.
  • Faster 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 (linear_model.lasso_path) is more than 200x times faster than before.
  • It is now possible to get probability estimates from a 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:

0.5

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 pipeline.Pipeline object to compose different estimators.
  • Recursive Feature Elimination routines in module feature_selection.
  • Addition of various classes capable of cross validation in the linear_model module (linear_model.LassoCV, linear_model.ElasticNetCV, etc.).
  • New, more efficient LARS algorithm implementation. The Lasso variant of the algorithm is also implemented. See linear_model.lars_path, linear_model.Lars and linear_model.LassoLars.
  • New Hidden Markov Models module (see classes hmm.GaussianHMM, hmm.MultinomialHMM, hmm.GMMHMM)
  • New module feature_extraction (see class reference <feature_extraction_ref>)
  • New FastICA algorithm in module sklearn.fastica

Documentation

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: example_mlcomp_sparse_document_classification.py, example_document_classification_20newsgroups.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