sklearn
- 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 then_init
parameter: the clustering algorithm used to be runn_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.- Fixed 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
andsklearn.preprocessing.Scaler
work on sparse matrices by Olivier Grisel- Feature importances using decision trees and/or forest of trees, by Gilles Louppe.
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 withcopy_
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; useload_svmlight_files
instead. Also, the (unused)buffer_mb
parameter is gone.- Sparse estimators in the
sgd
module use dense parameter vectorcoef_
instead ofsparse_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 themetrics.cluster.supervised
, along withmetrics.cluster.unsupervised
which contains the Silhouette Coefficient.- The
permutation_test_score
function now behaves the same way ascross_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
andsparse_encode_parallel
have been combined intosklearn.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 bySparseCodingMixin
.
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.
- New
manifold
module by Jake Vanderplas and Fabian Pedregosa.- New
Dirichlet Process <dirichlet_process>
Gaussian Mixture Model by Alexandre Passosneighbors
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
andbernoulli_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 Louppemulticlass
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) andlinear_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
andmetrics.pairwise.pairwise_kernels
by Robert LaytonMini-Batch K-Means <cluster.MiniBatchKMeans>
by Nelle Varoquaux and Peter Prettenhofer.mldata
utilities by Pietro Berkes.olivetti_faces
by David Warde-Farley.
Here are the code migration instructions when updgrading from scikit-learn version 0.8:
The
scikits.learn
package was renamedsklearn
. There is still ascikits.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 publicset_params
method inhereted frombase.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 theX
data matrix.The
cross_val
package has been renamed tocross_validation
although there is also across_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 thesklearn.cross_validation.cross_val_score
function is now expected to accepty_test
andy_predicted
as only arguments for classification and regression tasks orX_test
for unsupervised estimators.gamma
parameter for support vector machine algorithms is set to1 / n_features
by default, instead of1 / 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
andNeighborsRegressor
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, andKNeighborsRegressor
&RadiusNeighborsRegressor
for supervised regression problems.sklearn.ball_tree.BallTree
has been moved tosklearn.neighbors.BallTree
. Using the former will generate a warning.sklearn.linear_model.LARS()
and related classes (LassoLARS, LassoLARSCV, etc.) have been renamed tosklearn.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 calledmanhattan_distance
, and by default returns the pairwise distance. For the component wise distance, set the parametersum_over_features
toFalse
.
Backward compatibilty package aliases and other deprecated classes and functions will be removed in version 0.11.
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
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.
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 Blondellabeled_faces_in_the_wild
by Olivier Grisel.- New
pls
module by Edouard Duchesnay.NMF
module Vlad Niculae- Implementation of the
oracle_approximating_shrinkage
algorithm by Virgile Fritsch in thecovariance
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
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 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
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.
- 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
andneighbors.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
andlinear_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
orlinear_model.LogisticRegression
[Fabian Pedregosa].- Performance and API improvements to
metrics.euclidean_distances
and topca.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 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
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.
- 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. Seeexample_gaussian_process_plot_gp_regression.py
orexample_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 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
- Support for sparse matrices in some classifiers of modules
svm
andlinear_model
(seesvm.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
andlinear_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
- 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.
- 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.
- 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.
- Joblib is now a dependencie of this package, although it is shipped with (sklearn.externals.joblib).
- Module ann (Artificial Neural Networks) has been removed from the distribution. Users wanting this sort of algorithms should take a look into pybrain.
- New sphinx theme for the web page.
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
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 ...
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