Version 0.14

August 7, 2013

Changelog

• Missing values with sparse and dense matrices can be imputed with the transformer preprocessing.Imputer by Nicolas Trésegnie.
• The core implementation of decisions trees has been rewritten from scratch, allowing for faster tree induction and lower memory consumption in all tree-based estimators. By Gilles Louppe.
• Added ensemble.AdaBoostClassifier and ensemble.AdaBoostRegressor, by Noel Dawe and Gilles Louppe. See the AdaBoost section of the user guide for details and examples.
• Added grid_search.RandomizedSearchCV and grid_search.ParameterSampler for randomized hyperparameter optimization. By Andreas Müller.
• Added biclustering algorithms (sklearn.cluster.bicluster.SpectralCoclustering and sklearn.cluster.bicluster.SpectralBiclustering), data generation methods (sklearn.datasets.make_biclusters and sklearn.datasets.make_checkerboard), and scoring metrics (sklearn.metrics.consensus_score). By Kemal Eren.
• Added Restricted Boltzmann Machines (neural_network.BernoulliRBM). By Yann Dauphin.
• Python 3 support by Justin Vincent, Lars Buitinck, Subhodeep Moitra and Olivier Grisel. All tests now pass under Python 3.3.
• Ability to pass one penalty (alpha value) per target in linear_model.Ridge, by @eickenberg and Mathieu Blondel.
• Fixed sklearn.linear_model.stochastic_gradient.py L2 regularization issue (minor practical significance). By Norbert Crombach and Mathieu Blondel .
• Added an interactive version of Andreas Müller’s Machine Learning Cheat Sheet (for scikit-learn) to the documentation. See Choosing the right estimator. By Jaques Grobler.
• grid_search.GridSearchCV and cross_validation.cross_val_score now support the use of advanced scoring function such as area under the ROC curve and f-beta scores. See The scoring parameter: defining model evaluation rules for details. By Andreas Müller and Lars Buitinck. Passing a function from sklearn.metrics as score_func is deprecated.
• Multi-label classification output is now supported by metrics.accuracy_score, metrics.zero_one_loss, metrics.f1_score, metrics.fbeta_score, metrics.classification_report, metrics.precision_score and metrics.recall_score by Arnaud Joly.
• Two new metrics metrics.hamming_loss and metrics.jaccard_similarity_score are added with multi-label support by Arnaud Joly.
• Speed and memory usage improvements in feature_extraction.text.CountVectorizer and feature_extraction.text.TfidfVectorizer, by Jochen Wersdörfer and Roman Sinayev.
• The min_df parameter in feature_extraction.text.CountVectorizer and feature_extraction.text.TfidfVectorizer, which used to be 2, has been reset to 1 to avoid unpleasant surprises (empty vocabularies) for novice users who try it out on tiny document collections. A value of at least 2 is still recommended for practical use.
• svm.LinearSVC, linear_model.SGDClassifier and linear_model.SGDRegressor now have a sparsify method that converts their coef_ into a sparse matrix, meaning stored models trained using these estimators can be made much more compact.
• linear_model.SGDClassifier now produces multiclass probability estimates when trained under log loss or modified Huber loss.
• Hyperlinks to documentation in example code on the website by Martin Luessi.
• Fixed bug in preprocessing.MinMaxScaler causing incorrect scaling of the features for non-default feature_range settings. By Andreas Müller.
• max_features in tree.DecisionTreeClassifier, tree.DecisionTreeRegressor and all derived ensemble estimators now supports percentage values. By Gilles Louppe.
• Performance improvements in isotonic.IsotonicRegression by Nelle Varoquaux.
• metrics.accuracy_score has an option normalize to return the fraction or the number of correctly classified sample by Arnaud Joly.
• Added metrics.log_loss that computes log loss, aka cross-entropy loss. By Jochen Wersdörfer and Lars Buitinck.
• A bug that caused ensemble.AdaBoostClassifier’s to output incorrect probabilities has been fixed.
• Feature selectors now share a mixin providing consistent transform, inverse_transform and get_support methods. By Joel Nothman.
• A fitted grid_search.GridSearchCV or grid_search.RandomizedSearchCV can now generally be pickled. By Joel Nothman.
• Refactored and vectorized implementation of metrics.roc_curve and metrics.precision_recall_curve. By Joel Nothman.
• The new estimator sklearn.decomposition.TruncatedSVD performs dimensionality reduction using SVD on sparse matrices, and can be used for latent semantic analysis (LSA). By Lars Buitinck.
• Added self-contained example of out-of-core learning on text data Out-of-core classification of text documents. By Eustache Diemert.
• The default number of components for sklearn.decomposition.RandomizedPCA is now correctly documented to be n_features. This was the default behavior, so programs using it will continue to work as they did.
• sklearn.cluster.KMeans now fits several orders of magnitude faster on sparse data (the speedup depends on the sparsity). By Lars Buitinck.
• Reduce memory footprint of FastICA by Denis Engemann and Alexandre Gramfort.
• Verbose output in sklearn.ensemble.gradient_boosting now uses a column format and prints progress in decreasing frequency. It also shows the remaining time. By Peter Prettenhofer.
• sklearn.ensemble.gradient_boosting provides out-of-bag improvement oob_improvement_ rather than the OOB score for model selection. An example that shows how to use OOB estimates to select the number of trees was added. By Peter Prettenhofer.
• Most metrics now support string labels for multiclass classification by Arnaud Joly and Lars Buitinck.
• New OrthogonalMatchingPursuitCV class by Alexandre Gramfort and Vlad Niculae.
• Fixed a bug in sklearn.covariance.GraphLassoCV: the ‘alphas’ parameter now works as expected when given a list of values. By Philippe Gervais.
• Fixed an important bug in sklearn.covariance.GraphLassoCV that prevented all folds provided by a CV object to be used (only the first 3 were used). When providing a CV object, execution time may thus increase significantly compared to the previous version (bug results are correct now). By Philippe Gervais.
• cross_validation.cross_val_score and the grid_search module is now tested with multi-output data by Arnaud Joly.
• datasets.make_multilabel_classification can now return the output in label indicator multilabel format by Arnaud Joly.
• K-nearest neighbors, neighbors.KNeighborsRegressor and neighbors.RadiusNeighborsRegressor, and radius neighbors, neighbors.RadiusNeighborsRegressor and neighbors.RadiusNeighborsClassifier support multioutput data by Arnaud Joly.
• Random state in LibSVM-based estimators (svm.SVC, NuSVC, OneClassSVM, svm.SVR, svm.NuSVR) can now be controlled. This is useful to ensure consistency in the probability estimates for the classifiers trained with probability=True. By Vlad Niculae.
• Out-of-core learning support for discrete naive Bayes classifiers sklearn.naive_bayes.MultinomialNB and sklearn.naive_bayes.BernoulliNB by adding the partial_fit method by Olivier Grisel.
• New website design and navigation by Gilles Louppe, Nelle Varoquaux, Vincent Michel and Andreas Müller.
• Improved documentation on multi-class, multi-label and multi-output classification by Yannick Schwartz and Arnaud Joly.
• Better input and error handling in the metrics module by Arnaud Joly and Joel Nothman.
• Speed optimization of the hmm module by Mikhail Korobov
• Significant speed improvements for sklearn.cluster.DBSCAN by cleverless

API changes summary

• The auc_score was renamed roc_auc_score.
• Testing scikit-learn with sklearn.test() is deprecated. Use nosetests sklearn from the command line.
• Feature importances in tree.DecisionTreeClassifier, tree.DecisionTreeRegressor and all derived ensemble estimators are now computed on the fly when accessing the feature_importances_ attribute. Setting compute_importances=True is no longer required. By Gilles Louppe.
• linear_model.lasso_path and linear_model.enet_path can return its results in the same format as that of linear_model.lars_path. This is done by setting the return_models parameter to False. By Jaques Grobler and Alexandre Gramfort
• grid_search.IterGrid was renamed to grid_search.ParameterGrid.
• Fixed bug in KFold causing imperfect class balance in some cases. By Alexandre Gramfort and Tadej Janež.
• sklearn.neighbors.BallTree has been refactored, and a sklearn.neighbors.KDTree has been added which shares the same interface. The Ball Tree now works with a wide variety of distance metrics. Both classes have many new methods, including single-tree and dual-tree queries, breadth-first and depth-first searching, and more advanced queries such as kernel density estimation and 2-point correlation functions. By Jake Vanderplas
• Support for scipy.spatial.cKDTree within neighbors queries has been removed, and the functionality replaced with the new KDTree class.
• sklearn.neighbors.KernelDensity has been added, which performs efficient kernel density estimation with a variety of kernels.
• sklearn.decomposition.KernelPCA now always returns output with n_components components, unless the new parameter remove_zero_eig is set to True. This new behavior is consistent with the way kernel PCA was always documented; previously, the removal of components with zero eigenvalues was tacitly performed on all data.
• gcv_mode="auto" no longer tries to perform SVD on a densified sparse matrix in sklearn.linear_model.RidgeCV.
• Sparse matrix support in sklearn.decomposition.RandomizedPCA is now deprecated in favor of the new TruncatedSVD.
• cross_validation.KFold and cross_validation.StratifiedKFold now enforce n_folds >= 2 otherwise a ValueError is raised. By Olivier Grisel.
• datasets.load_files’s charset and charset_errors parameters were renamed encoding and decode_errors.
• Attribute oob_score_ in sklearn.ensemble.GradientBoostingRegressor and sklearn.ensemble.GradientBoostingClassifier is deprecated and has been replaced by oob_improvement_ .
• Attributes in OrthogonalMatchingPursuit have been deprecated (copy_X, Gram, …) and precompute_gram renamed precompute for consistency. See #2224.
• sklearn.preprocessing.StandardScaler now converts integer input to float, and raises a warning. Previously it rounded for dense integer input.
• sklearn.multiclass.OneVsRestClassifier now has a decision_function method. This will return the distance of each sample from the decision boundary for each class, as long as the underlying estimators implement the decision_function method. By Kyle Kastner.
• Better input validation, warning on unexpected shapes for y.

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