0.6

scikits.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 scikits.learn.svm.sparse and scikits.learn.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 scikits.learn.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, 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 (scikits.learn.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