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0.15

Changelog

  • The Working With Text Data tutorial has now been worked in to the main documentation’s tutorial section. Includes exercises and skeletons for tutorial presentation. Original tutorial created by several authors including Olivier Grisel, Lars Buitinck and many others. Tutorial integration into the scikit-learn documentation by Jaques Grobler
  • Use of covariance.EllipticEnvelop has now been removed after deprecation. Please use covariance.EllipticEnvelope instead.
  • Added ensemble.BaggingClassifier and ensemble.BaggingRegressor meta-estimators for ensembling any kind of base estimator. See the Bagging section of the user guide for details and examples. By Gilles Louppe.
  • Speed improvement of the sklearn.ensemble.gradient_boosting module. By Gilles Louppe and Peter Prettenhofer.
  • Added metrics.pairwise_distances_argmin_min, by Philippe Gervais.
  • Added predict method to cluster.AffinityPropagation and cluster.MeanShift, by Mathieu Blondel.
  • Vector and matrix multiplications have been optimised throughout the library by Denis Engemann, and Alexandre Gramfort. In particular, they should take less memory with older NumPy versions (prior to 1.7.2).
  • New unsupervised feature selection algorithm feature_selection.VarianceThreshold, by Lars Buitinck.
  • Precision-recall and ROC examples now use train_test_split, and have more explanation of why these metrics are useful. By Kyle Kastner
  • The training algorithm for decomposition.NMF is faster for sparse matrices and has much lower memory complexity, meaning it will scale up gracefully to large datasets. By Lars Buitinck.
  • Added svd_method option with default value to “randomized” to decomposition.factor_analysis.FactorAnalysis to save memory and significantly speedup computation by Denis Engemann, and Alexandre Gramfort.
  • Memory improvements of extra trees and random forest by Arnaud Joly.
  • Changed cross_validation.StratifiedKFold to try and preserve as much of the original ordering of samples as possible so as not to hide overfitting on datasets with a non-negligible level of samples dependency. By Daniel Nouri and Olivier Grisel.
  • Add multi-output support to gaussian_process.GaussianProcess by John Novak.
  • Norm computations optimized for NumPy 1.6 and later versions by Lars Buitinck. In particular, the k-means algorithm no longer needs a temporary data structure the size of its input.
  • Added linear_model.RANSACRegressor meta-estimator for the robust fitting of regression models. By Johannes Schönberger.
  • Added Computational Performance documentation. Discussion and examples of prediction latency / throughput and different factors that have influence over speed. Additional tips for building faster models and choosing a relevant compromise between speed and predictive power. By Eustache Diemert.
  • Fixed bug in gradient_boosting.GradientBoostingRegressor with loss='huber': gamma might have not been initialized.
  • dummy.DummyClassifier can now be used to predict a constant output value. By Manoj Kumar.
  • Fixed bug in decomposition.MiniBatchDictionaryLearning : partial_fit was not working properly.
  • Multi-label classification output in multilabel indicator format is now supported by metrics.roc_auc_score and metrics.average_precision_score by Arnaud Joly.
  • Fixed bug in linear_model.stochastic_gradient : l1_ratio was used as (1.0 - l1_ratio) .
  • Fixed bug in multiclass.OneVsOneClassifier with string labels
  • Shorthand constructors pipeline.make_pipeline and pipeline.make_union were added by Lars Buitinck.
  • Reduce memory usage and overhead when fitting and predicting with forests of randomized trees in parallel with n_jobs != 1 by leveraging new threading backend of joblib 0.8 and releasing the GIL in the tree fitting Cython code. By Olivier Grisel and Gilles Louppe

API changes summary

  • Add score method to PCA following the model of probabilistic PCA and deprecate ProbabilisticPCA model whose score implementation is not correct. The computation now also exploits the matrix inversion lemma for faster computation. By Alexandre Gramfort.
  • The score method of FactorAnalysis now returns the average log-likelihood of the samples. Use score_samples to get log-likelihood of each sample. By Alexandre Gramfort.
  • Generating boolean masks (the setting indices=False) from cross-validation generators is deprecated. Support for masks will be removed in 0.17. The generators have produced arrays of indices by default since 0.10. By Joel Nothman.
  • 1-d arrays containing strings with dtype=object (as used in Pandas) are now considered valid classification targets. This fixes a regression from version 0.13 in some classifiers. By Joel Nothman.
  • Fix wrong explained_variance_ratio_ attribute in RandomizedPCA. By Alexandre Gramfort.

0.14

Changelog

API changes summary

People

List of contributors for release 0.14 by number of commits.

  • 277 Gilles Louppe
  • 245 Lars Buitinck
  • 187 Andreas Mueller
  • 124 Arnaud Joly
  • 112 Jaques Grobler
  • 109 Gael Varoquaux
  • 107 Olivier Grisel
  • 102 Noel Dawe
  • 99 Kemal Eren
  • 79 Joel Nothman
  • 75 Jake VanderPlas
  • 73 Nelle Varoquaux
  • 71 Vlad Niculae
  • 65 Peter Prettenhofer
  • 64 Alexandre Gramfort
  • 54 Mathieu Blondel
  • 38 Nicolas Trésegnie
  • 35 eustache
  • 27 Denis Engemann
  • 25 Yann N. Dauphin
  • 19 Justin Vincent
  • 17 Robert Layton
  • 15 Doug Coleman
  • 14 Michael Eickenberg
  • 13 Robert Marchman
  • 11 Fabian Pedregosa
  • 11 Philippe Gervais
  • 10 Jim Holmström
  • 10 Tadej Janež
  • 10 syhw
  • 9 Mikhail Korobov
  • 9 Steven De Gryze
  • 8 sergeyf
  • 7 Ben Root
  • 7 Hrishikesh Huilgolkar
  • 6 Kyle Kastner
  • 6 Martin Luessi
  • 6 Rob Speer
  • 5 Federico Vaggi
  • 5 Raul Garreta
  • 5 Rob Zinkov
  • 4 Ken Geis
  • 3 A. Flaxman
  • 3 Denton Cockburn
  • 3 Dougal Sutherland
  • 3 Ian Ozsvald
  • 3 Johannes Schönberger
  • 3 Robert McGibbon
  • 3 Roman Sinayev
  • 3 Szabo Roland
  • 2 Diego Molla
  • 2 Imran Haque
  • 2 Jochen Wersdörfer
  • 2 Sergey Karayev
  • 2 Yannick Schwartz
  • 2 jamestwebber
  • 1 Abhijeet Kolhe
  • 1 Alexander Fabisch
  • 1 Bastiaan van den Berg
  • 1 Benjamin Peterson
  • 1 Daniel Velkov
  • 1 Fazlul Shahriar
  • 1 Felix Brockherde
  • 1 Félix-Antoine Fortin
  • 1 Harikrishnan S
  • 1 Jack Hale
  • 1 JakeMick
  • 1 James McDermott
  • 1 John Benediktsson
  • 1 John Zwinck
  • 1 Joshua Vredevoogd
  • 1 Justin Pati
  • 1 Kevin Hughes
  • 1 Kyle Kelley
  • 1 Matthias Ekman
  • 1 Miroslav Shubernetskiy
  • 1 Naoki Orii
  • 1 Norbert Crombach
  • 1 Rafael Cunha de Almeida
  • 1 Rolando Espinoza La fuente
  • 1 Seamus Abshere
  • 1 Sergey Feldman
  • 1 Sergio Medina
  • 1 Stefano Lattarini
  • 1 Steve Koch
  • 1 Sturla Molden
  • 1 Thomas Jarosch
  • 1 Yaroslav Halchenko

0.13.1

The 0.13.1 release only fixes some bugs and does not add any new functionality.

Changelog

People

List of contributors for release 0.13.1 by number of commits.

0.13

New Estimator Classes

Changelog

API changes summary

People

List of contributors for release 0.13 by number of commits.

0.12.1

The 0.12.1 release is a bug-fix release with no additional features, but is instead a set of bug fixes

Changelog

0.12

Changelog

API changes summary

People

0.11

Changelog

Highlights

Other changes

API changes summary

  • covariance.EllipticEnvelop is now deprecated - Please use covariance.EllipticEnvelope instead.

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

  • Sparse classes in the Stochastic Gradient Descent module are now deprecated.

  • In mixture.GMM, mixture.DPGMM and mixture.VBGMM, parameters must be passed to an object when initialising it and not through fit. Now fit will only accept the data as an input parameter.

  • methods rvs and decode in GMM module are now deprecated. sample and score or predict should be used instead.

  • attribute _scores and _pvalues in univariate feature selection objects are now deprecated. scores_ or pvalues_ should be used instead.

  • In LogisticRegression, LinearSVC, SVC and NuSVC, the class_weight parameter is now an initialization parameter, not a parameter to fit. This makes grid searches over this parameter possible.

  • LFW data is now always shape (n_samples, n_features) to be consistent with the Olivetti faces dataset. Use images and pairs attribute to access the natural images shapes instead.

  • In svm.LinearSVC, the meaning of the multi_class parameter changed. Options now are ‘ovr’ and ‘crammer_singer’, with ‘ovr’ being the default. This does not change the default behavior but hopefully is less confusing.

  • Classs feature_selection.text.Vectorizer is deprecated and replaced by feature_selection.text.TfidfVectorizer.

  • The preprocessor / analyzer nested structure for text feature extraction has been removed. All those features are now directly passed as flat constructor arguments to feature_selection.text.TfidfVectorizer and feature_selection.text.CountVectorizer, in particular the following parameters are now used:

    • analyzer can be ‘word’ or ‘char’ to switch the default analysis scheme, or use a specific python callable (as previously).
    • tokenizer and preprocessor have been introduced to make it still possible to customize those steps with the new API.
    • input explicitly control how to interpret the sequence passed to fit and predict: filenames, file objects or direct (byte or unicode) strings.
    • charset decoding is explicit and strict by default.
    • the vocabulary, fitted or not is now stored in the vocabulary_ attribute to be consistent with the project conventions.
  • Class feature_selection.text.TfidfVectorizer now derives directly from feature_selection.text.CountVectorizer to make grid search trivial.

  • methods rvs in _BaseHMM module are now deprecated. sample should be used instead.

  • Beam pruning option in _BaseHMM module is removed since it is difficult to be Cythonized. If you are interested, you can look in the history codes by git.

  • The SVMlight format loader now supports files with both zero-based and one-based column indices, since both occur “in the wild”.

  • Arguments in class ShuffleSplit are now consistent with StratifiedShuffleSplit. Arguments test_fraction and train_fraction are deprecated and renamed to test_size and train_size and can accept both float and int.

  • Arguments in class Bootstrap are now consistent with StratifiedShuffleSplit. Arguments n_test and n_train are deprecated and renamed to test_size and train_size and can accept both float and int.

  • Argument p added to classes in Nearest Neighbors to specify an arbitrary Minkowski metric for nearest neighbors searches.

People

0.10

Changelog

API changes summary

Here are the code migration instructions when upgrading 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:

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.

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Changelog

API changes summary

Here are the code migration instructions when upgrading 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 inherited 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 compatibility 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, Cross decomposition, Non-negative matrix factorization (NMF or NNMF), initial support for Python 3 and by important enhancements and bug fixes.

Changelog

Several new modules where introduced during this release:

Some other modules benefited from significant improvements or cleanups.

People

People that made this release possible preceded 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

People

People that made this release possible preceded 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

People

People that made this release possible preceded by number of commits:

0.5

Changelog

New classes

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

External dependencies

  • Joblib is now a dependency 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, preceded 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

Earlier versions

Earlier versions included contributions by Fred Mailhot, David Cooke, David Huard, Dave Morrill, Ed Schofield, Travis Oliphant, Pearu Peterson.

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