0.11

API changes summary

• NeighborsClassifier and NeighborsRegressor are gone in the module Nearest Neighbors. Use the classes KNeighborsClassifier, RadiusNeighborsClassifier, KNeighborsRegressor and/or RadiusNeighborsRegressor instead.

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 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 Support Vector Machines module by Brian Holt (issue #367).
• Faster tests by Fabian Pedregosa and others.
• 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 Stochastic Gradient Descent module; consolidated dense and sparse predict methods; Enhanced test time performance by converting model paramters to fortran-style arrays after fitting (only multi-class).
• 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 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.
• Novelty and Outlier Detection: outlier and novelty detection, by Virgile Fritsch.
• 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 Orthogonal Matching Pursuit (OMP) by Vlad Niculae.
• Sparse coding with a precomputed dictionary by Vlad Niculae.
• Mini Batch K-Means 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 and multilabel algorithms 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
• 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 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 Stochastic Gradient Descent module use dense parameter vector coef_ instead of sparse_coef_. This significantly improves test time performance.

• The Covariance estimation 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.

• sklearn.utils.extmath.fast_svd has been renamed 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 Virgile Fritsch
• 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

0.9

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

Changelog

• New Manifold learning module by Jake Vanderplas and Fabian Pedregosa.
• New Dirichlet Process Gaussian Mixture Model by Alexandre Passos
• Nearest 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.
• Sparse Principal Components Analysis (SparsePCA and MiniBatchSparsePCA) 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 by Mathieu Blondel and Lars Buitinck
• Documentation improvements: thumbnails in example gallery by Fabian Pedregosa.
• Important bugfixes in Support Vector Machines 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 and multilabel algorithms 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 Bootstrapping cross-validation, Random permutations cross-validation a.k.a. Shuffle & Split 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 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 by Nelle Varoquaux and Peter Prettenhofer.
• Downloading datasets from the mldata.org repository utilities by Pietro Berkes.
• The Olivetti faces dataset 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.

• 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 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

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, Partial Least Squares, Non-negative matrix factorization (NMF or NNMF), initial support for Python 3 and by important enhacements and bug fixes.

Changelog

Several new modules where introduced during this release:

• New Hierarchical clustering module by Vincent Michel, Bertrand Thirion, Alexandre Gramfort and Gael Varoquaux.
• Kernel PCA implementation by Mathieu Blondel
• The Labeled Faces in the Wild face recognition dataset by Olivier Grisel.
• New Partial Least Squares module by Edouard Duchesnay.
• Non-negative matrix factorization (NMF or NNMF) module Vlad Niculae
• Implementation of the Oracle Approximating Shrinkage algorithm by Virgile Fritsch in the Covariance estimation 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.
• decomposition.PCA is now usable from the Pipeline object by Olivier Grisel.
• Guide How to optimize for speed 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 Support Vector Machines 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:

• 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

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:

• 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

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 SVM: Weighted samples for an example).
• New Gaussian Processes module by Vincent Dubourg. This module also has great documentation and some very neat examples. See Gaussian Processes regression: basic introductory example or Gaussian Processes classification example: exploiting the probabilistic output 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: Faces recognition example using eigenfaces and SVMs, Species distribution modeling, Libsvm GUI, Wikipedia princial eigenvector 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:

• 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

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)
• 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: Classification of text documents: using a MLComp dataset, Classification of text documents using sparse features
• 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