Version 0.13.1

February 23, 2013

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

Changelog

• Fixed a testing error caused by the function cross_validation.train_test_split being interpreted as a test by Yaroslav Halchenko.
• Fixed a bug in the reassignment of small clusters in the cluster.MiniBatchKMeans by Gael Varoquaux.
• Fixed default value of gamma in decomposition.KernelPCA by Lars Buitinck.
• Updated joblib to 0.7.0d by Gael Varoquaux.
• Fixed scaling of the deviance in ensemble.GradientBoostingClassifier by Peter Prettenhofer.
• Better tie-breaking in multiclass.OneVsOneClassifier by Andreas Müller.
• Other small improvements to tests and documentation.

People

List of contributors for release 0.13.1 by number of commits.

• 16 Lars Buitinck
• 12 Andreas Müller
• 8 Gael Varoquaux
• 5 Robert Marchman
• 3 Peter Prettenhofer
• 2 Hrishikesh Huilgolkar
• 1 Bastiaan van den Berg
• 1 Diego Molla
• 1 Gilles Louppe
• 1 Mathieu Blondel
• 1 Nelle Varoquaux
• 1 Rafael Cunha de Almeida
• 1 Rolando Espinoza La fuente
• 1 Vlad Niculae
• 1 Yaroslav Halchenko

Version 0.13

January 21, 2013

New Estimator Classes

• dummy.DummyClassifier and dummy.DummyRegressor, two data-independent predictors by Mathieu Blondel. Useful to sanity-check your estimators. See Dummy estimators in the user guide. Multioutput support added by Arnaud Joly.
• decomposition.FactorAnalysis, a transformer implementing the classical factor analysis, by Christian Osendorfer and Alexandre Gramfort. See Factor Analysis in the user guide.
• feature_extraction.FeatureHasher, a transformer implementing the “hashing trick” for fast, low-memory feature extraction from string fields by Lars Buitinck and feature_extraction.text.HashingVectorizer for text documents by Olivier Grisel See Feature hashing and Vectorizing a large text corpus with the hashing trick for the documentation and sample usage.
• pipeline.FeatureUnion, a transformer that concatenates results of several other transformers by Andreas Müller. See FeatureUnion: composite feature spaces in the user guide.
• random_projection.GaussianRandomProjection, random_projection.SparseRandomProjection and the function random_projection.johnson_lindenstrauss_min_dim. The first two are transformers implementing Gaussian and sparse random projection matrix by Olivier Grisel and Arnaud Joly. See Random Projection in the user guide.
• kernel_approximation.Nystroem, a transformer for approximating arbitrary kernels by Andreas Müller. See Nystroem Method for Kernel Approximation in the user guide.
• preprocessing.OneHotEncoder, a transformer that computes binary encodings of categorical features by Andreas Müller. See Encoding categorical features in the user guide.
• linear_model.PassiveAggressiveClassifier and linear_model.PassiveAggressiveRegressor, predictors implementing an efficient stochastic optimization for linear models by Rob Zinkov and Mathieu Blondel. See Passive Aggressive Algorithms in the user guide.
• ensemble.RandomTreesEmbedding, a transformer for creating high-dimensional sparse representations using ensembles of totally random trees by Andreas Müller. See Totally Random Trees Embedding in the user guide.
• manifold.SpectralEmbedding and function manifold.spectral_embedding, implementing the “laplacian eigenmaps” transformation for non-linear dimensionality reduction by Wei Li. See Spectral Embedding in the user guide.
• isotonic.IsotonicRegression by Fabian Pedregosa, Alexandre Gramfort and Nelle Varoquaux,

Changelog

• metrics.zero_one_loss (formerly metrics.zero_one) now has option for normalized output that reports the fraction of misclassifications, rather than the raw number of misclassifications. By Kyle Beauchamp.
• tree.DecisionTreeClassifier and all derived ensemble models now support sample weighting, by Noel Dawe and Gilles Louppe.
• Speedup improvement when using bootstrap samples in forests of randomized trees, by Peter Prettenhofer and Gilles Louppe.
• Partial dependence plots for Gradient Tree Boosting in ensemble.partial_dependence.partial_dependence by Peter Prettenhofer. See sphx_glr_auto_examples_ensemble_plot_partial_dependence.py for an example.
• The table of contents on the website has now been made expandable by Jaques Grobler.
• feature_selection.SelectPercentile now breaks ties deterministically instead of returning all equally ranked features.
• feature_selection.SelectKBest and feature_selection.SelectPercentile are more numerically stable since they use scores, rather than p-values, to rank results. This means that they might sometimes select different features than they did previously.
• Ridge regression and ridge classification fitting with sparse_cg solver no longer has quadratic memory complexity, by Lars Buitinck and Fabian Pedregosa.
• Ridge regression and ridge classification now support a new fast solver called lsqr, by Mathieu Blondel.
• Speed up of metrics.precision_recall_curve by Conrad Lee.
• Added support for reading/writing svmlight files with pairwise preference attribute (qid in svmlight file format) in datasets.dump_svmlight_file and datasets.load_svmlight_file by Fabian Pedregosa.
• Faster and more robust metrics.confusion_matrix and Clustering performance evaluation by Wei Li.
• cross_validation.cross_val_score now works with precomputed kernels and affinity matrices, by Andreas Müller.
• LARS algorithm made more numerically stable with heuristics to drop regressors too correlated as well as to stop the path when numerical noise becomes predominant, by Gael Varoquaux.
• Faster implementation of metrics.precision_recall_curve by Conrad Lee.
• New kernel metrics.chi2_kernel by Andreas Müller, often used in computer vision applications.
• Fix of longstanding bug in naive_bayes.BernoulliNB fixed by Shaun Jackman.
• Implemented predict_proba in multiclass.OneVsRestClassifier, by Andrew Winterman.
• Improve consistency in gradient boosting: estimators ensemble.GradientBoostingRegressor and ensemble.GradientBoostingClassifier use the estimator tree.DecisionTreeRegressor instead of the tree._tree.Tree data structure by Arnaud Joly.
• Fixed a floating point exception in the decision trees module, by Seberg.
• Fix metrics.roc_curve fails when y_true has only one class by Wei Li.
• Add the metrics.mean_absolute_error function which computes the mean absolute error. The metrics.mean_squared_error, metrics.mean_absolute_error and metrics.r2_score metrics support multioutput by Arnaud Joly.
• Fixed class_weight support in svm.LinearSVC and linear_model.LogisticRegression by Andreas Müller. The meaning of class_weight was reversed as erroneously higher weight meant less positives of a given class in earlier releases.
• Improve narrative documentation and consistency in sklearn.metrics for regression and classification metrics by Arnaud Joly.
• Fixed a bug in sklearn.svm.SVC when using csr-matrices with unsorted indices by Xinfan Meng and Andreas Müller.
• MiniBatchKMeans: Add random reassignment of cluster centers with little observations attached to them, by Gael Varoquaux.

API changes summary

• Renamed all occurrences of n_atoms to n_components for consistency. This applies to decomposition.DictionaryLearning, decomposition.MiniBatchDictionaryLearning, decomposition.dict_learning, decomposition.dict_learning_online.
• Renamed all occurrences of max_iters to max_iter for consistency. This applies to semi_supervised.LabelPropagation and semi_supervised.label_propagation.LabelSpreading.
• Renamed all occurrences of learn_rate to learning_rate for consistency in ensemble.BaseGradientBoosting and ensemble.GradientBoostingRegressor.
• The module sklearn.linear_model.sparse is gone. Sparse matrix support was already integrated into the “regular” linear models.
• sklearn.metrics.mean_square_error, which incorrectly returned the accumulated error, was removed. Use mean_squared_error instead.
• Passing class_weight parameters to fit methods is no longer supported. Pass them to estimator constructors instead.
• GMMs no longer have decode and rvs methods. Use the score, predict or sample methods instead.
• The solver fit option in Ridge regression and classification is now deprecated and will be removed in v0.14. Use the constructor option instead.
• feature_extraction.text.DictVectorizer now returns sparse matrices in the CSR format, instead of COO.
• Renamed k in cross_validation.KFold and cross_validation.StratifiedKFold to n_folds, renamed n_bootstraps to n_iter in cross_validation.Bootstrap.
• Renamed all occurrences of n_iterations to n_iter for consistency. This applies to cross_validation.ShuffleSplit, cross_validation.StratifiedShuffleSplit, utils.randomized_range_finder and utils.randomized_svd.
• Replaced rho in linear_model.ElasticNet and linear_model.SGDClassifier by l1_ratio. The rho parameter had different meanings; l1_ratio was introduced to avoid confusion. It has the same meaning as previously rho in linear_model.ElasticNet and (1-rho) in linear_model.SGDClassifier.
• linear_model.LassoLars and linear_model.Lars now store a list of paths in the case of multiple targets, rather than an array of paths.
• The attribute gmm of hmm.GMMHMM was renamed to gmm_ to adhere more strictly with the API.
• cluster.spectral_embedding was moved to manifold.spectral_embedding.
• Renamed eig_tol in manifold.spectral_embedding, cluster.SpectralClustering to eigen_tol, renamed mode to eigen_solver.
• Renamed mode in manifold.spectral_embedding and cluster.SpectralClustering to eigen_solver.
• classes_ and n_classes_ attributes of tree.DecisionTreeClassifier and all derived ensemble models are now flat in case of single output problems and nested in case of multi-output problems.
• The estimators_ attribute of ensemble.gradient_boosting.GradientBoostingRegressor and ensemble.gradient_boosting.GradientBoostingClassifier is now an array of :class:’tree.DecisionTreeRegressor’.
• Renamed chunk_size to batch_size in decomposition.MiniBatchDictionaryLearning and decomposition.MiniBatchSparsePCA for consistency.
• svm.SVC and svm.NuSVC now provide a classes_ attribute and support arbitrary dtypes for labels y. Also, the dtype returned by predict now reflects the dtype of y during fit (used to be np.float).
• Changed default test_size in cross_validation.train_test_split to None, added possibility to infer test_size from train_size in cross_validation.ShuffleSplit and cross_validation.StratifiedShuffleSplit.
• Renamed function sklearn.metrics.zero_one to sklearn.metrics.zero_one_loss. Be aware that the default behavior in sklearn.metrics.zero_one_loss is different from sklearn.metrics.zero_one: normalize=False is changed to normalize=True.
• Renamed function metrics.zero_one_score to metrics.accuracy_score.
• datasets.make_circles now has the same number of inner and outer points.
• In the Naive Bayes classifiers, the class_prior parameter was moved from fit to __init__.

People

List of contributors for release 0.13 by number of commits.

• 364 Andreas Müller
• 143 Arnaud Joly
• 137 Peter Prettenhofer
• 131 Gael Varoquaux
• 117 Mathieu Blondel
• 108 Lars Buitinck
• 106 Wei Li
• 101 Olivier Grisel
• 65 Vlad Niculae
• 54 Gilles Louppe
• 40 Jaques Grobler
• 38 Alexandre Gramfort
• 30 Rob Zinkov
• 19 Aymeric Masurelle
• 18 Andrew Winterman
• 17 Fabian Pedregosa
• 17 Nelle Varoquaux
• 16 Christian Osendorfer
• 14 Daniel Nouri
• 13 Virgile Fritsch
• 13 syhw
• 12 Satrajit Ghosh
• 10 Corey Lynch
• 10 Kyle Beauchamp
• 9 Brian Cheung
• 9 Immanuel Bayer
• 9 mr.Shu
• 8 Conrad Lee
• 8 James Bergstra
• 7 Tadej Janež
• 6 Brian Cajes
• 6 Jake Vanderplas
• 6 Michael
• 6 Noel Dawe
• 6 Tiago Nunes
• 6 cow
• 5 Anze
• 5 Shiqiao Du
• 4 Christian Jauvin
• 4 Jacques Kvam
• 4 Richard T. Guy
• 4 Robert Layton
• 3 Alexandre Abraham
• 3 Doug Coleman
• 3 Scott Dickerson
• 2 ApproximateIdentity
• 2 John Benediktsson
• 2 Mark Veronda
• 2 Matti Lyra
• 2 Mikhail Korobov
• 2 Xinfan Meng
• 1 Alejandro Weinstein
• 1 Alexandre Passos
• 1 Christoph Deil
• 1 Eugene Nizhibitsky
• 1 Kenneth C. Arnold
• 1 Luis Pedro Coelho
• 1 Miroslav Batchkarov
• 1 Pavel
• 1 Sebastian Berg
• 1 Shaun Jackman
• 1 Subhodeep Moitra
• 1 bob
• 1 dengemann
• 1 emanuele
• 1 x006