Version 0.17.1

February 18, 2016

Changelog

Bug fixes

• Upgrade vendored joblib to version 0.9.4 that fixes an important bug in joblib.Parallel that can silently yield to wrong results when working on datasets larger than 1MB: https://github.com/joblib/joblib/blob/0.9.4/CHANGES.rst
• Fixed reading of Bunch pickles generated with scikit-learn version <= 0.16. This can affect users who have already downloaded a dataset with scikit-learn 0.16 and are loading it with scikit-learn 0.17. See #6196 for how this affected datasets.fetch_20newsgroups. By Loic Esteve.
• Fixed a bug that prevented using ROC AUC score to perform grid search on several CPU / cores on large arrays. See #6147 By Olivier Grisel.
• Fixed a bug that prevented to properly set the presort parameter in ensemble.GradientBoostingRegressor. See #5857 By Andrew McCulloh.
• Fixed a joblib error when evaluating the perplexity of a decomposition.LatentDirichletAllocation model. See #6258 By Chyi-Kwei Yau.

Version 0.17

November 5, 2015

Changelog

New features

• All the Scaler classes but preprocessing.RobustScaler can be fitted online by calling partial_fit. By Giorgio Patrini.
• The new class ensemble.VotingClassifier implements a “majority rule” / “soft voting” ensemble classifier to combine estimators for classification. By Sebastian Raschka.
• The new class preprocessing.RobustScaler provides an alternative to preprocessing.StandardScaler for feature-wise centering and range normalization that is robust to outliers. By Thomas Unterthiner.
• The new class preprocessing.MaxAbsScaler provides an alternative to preprocessing.MinMaxScaler for feature-wise range normalization when the data is already centered or sparse. By Thomas Unterthiner.
• The new class preprocessing.FunctionTransformer turns a Python function into a Pipeline-compatible transformer object. By Joe Jevnik.
• The new classes cross_validation.LabelKFold and cross_validation.LabelShuffleSplit generate train-test folds, respectively similar to cross_validation.KFold and cross_validation.ShuffleSplit, except that the folds are conditioned on a label array. By Brian McFee, Jean Kossaifi and Gilles Louppe.
• decomposition.LatentDirichletAllocation implements the Latent Dirichlet Allocation topic model with online variational inference. By Chyi-Kwei Yau, with code based on an implementation by Matt Hoffman. (#3659)
• The new solver sag implements a Stochastic Average Gradient descent and is available in both linear_model.LogisticRegression and linear_model.Ridge. This solver is very efficient for large datasets. By Danny Sullivan and Tom Dupre la Tour. (#4738)
• The new solver cd implements a Coordinate Descent in decomposition.NMF. Previous solver based on Projected Gradient is still available setting new parameter solver to pg, but is deprecated and will be removed in 0.19, along with decomposition.ProjectedGradientNMF and parameters sparseness, eta, beta and nls_max_iter. New parameters alpha and l1_ratio control L1 and L2 regularization, and shuffle adds a shuffling step in the cd solver. By Tom Dupre la Tour and Mathieu Blondel.

Enhancements

• manifold.TSNE now supports approximate optimization via the Barnes-Hut method, leading to much faster fitting. By Christopher Erick Moody. (#4025)
• cluster.mean_shift_.MeanShift now supports parallel execution, as implemented in the mean_shift function. By Martino Sorbaro.
• naive_bayes.GaussianNB now supports fitting with sample_weight. By Jan Hendrik Metzen.
• dummy.DummyClassifier now supports a prior fitting strategy. By Arnaud Joly.
• Added a fit_predict method for mixture.GMM and subclasses. By Cory Lorenz.
• Added the metrics.label_ranking_loss metric. By Arnaud Joly.
• Added the metrics.cohen_kappa_score metric.
• Added a warm_start constructor parameter to the bagging ensemble models to increase the size of the ensemble. By Tim Head.
• Added option to use multi-output regression metrics without averaging. By Konstantin Shmelkov and Michael Eickenberg.
• Added stratify option to cross_validation.train_test_split for stratified splitting. By Miroslav Batchkarov.
• The tree.export_graphviz function now supports aesthetic improvements for tree.DecisionTreeClassifier and tree.DecisionTreeRegressor, including options for coloring nodes by their majority class or impurity, showing variable names, and using node proportions instead of raw sample counts. By Trevor Stephens.
• Improved speed of newton-cg solver in linear_model.LogisticRegression, by avoiding loss computation. By Mathieu Blondel and Tom Dupre la Tour.
• The class_weight="auto" heuristic in classifiers supporting class_weight was deprecated and replaced by the class_weight="balanced" option, which has a simpler formula and interpretation. By Hanna Wallach and Andreas Müller.
• Add class_weight parameter to automatically weight samples by class frequency for linear_model.PassiveAggressiveClassifier. By Trevor Stephens.
• Added backlinks from the API reference pages to the user guide. By Andreas Müller.
• The labels parameter to sklearn.metrics.f1_score, sklearn.metrics.fbeta_score, sklearn.metrics.recall_score and sklearn.metrics.precision_score has been extended. It is now possible to ignore one or more labels, such as where a multiclass problem has a majority class to ignore. By Joel Nothman.
• Add sample_weight support to linear_model.RidgeClassifier. By Trevor Stephens.
• Provide an option for sparse output from sklearn.metrics.pairwise.cosine_similarity. By Jaidev Deshpande.
• Add minmax_scale to provide a function interface for MinMaxScaler. By Thomas Unterthiner.
• dump_svmlight_file now handles multi-label datasets. By Chih-Wei Chang.
• RCV1 dataset loader (sklearn.datasets.fetch_rcv1). By Tom Dupre la Tour.
• The “Wisconsin Breast Cancer” classical two-class classification dataset is now included in scikit-learn, available with sklearn.dataset.load_breast_cancer.
• Upgraded to joblib 0.9.3 to benefit from the new automatic batching of short tasks. This makes it possible for scikit-learn to benefit from parallelism when many very short tasks are executed in parallel, for instance by the grid_search.GridSearchCV meta-estimator with n_jobs > 1 used with a large grid of parameters on a small dataset. By Vlad Niculae, Olivier Grisel and Loic Esteve.
• For more details about changes in joblib 0.9.3 see the release notes: https://github.com/joblib/joblib/blob/master/CHANGES.rst#release-093
• Improved speed (3 times per iteration) of decomposition.DictLearning with coordinate descent method from linear_model.Lasso. By Arthur Mensch.
• Parallel processing (threaded) for queries of nearest neighbors (using the ball-tree) by Nikolay Mayorov.
• Allow datasets.make_multilabel_classification to output a sparse y. By Kashif Rasul.
• cluster.DBSCAN now accepts a sparse matrix of precomputed distances, allowing memory-efficient distance precomputation. By Joel Nothman.
• tree.DecisionTreeClassifier now exposes an apply method for retrieving the leaf indices samples are predicted as. By Daniel Galvez and Gilles Louppe.
• Speed up decision tree regressors, random forest regressors, extra trees regressors and gradient boosting estimators by computing a proxy of the impurity improvement during the tree growth. The proxy quantity is such that the split that maximizes this value also maximizes the impurity improvement. By Arnaud Joly, Jacob Schreiber and Gilles Louppe.
• Speed up tree based methods by reducing the number of computations needed when computing the impurity measure taking into account linear relationship of the computed statistics. The effect is particularly visible with extra trees and on datasets with categorical or sparse features. By Arnaud Joly.
• ensemble.GradientBoostingRegressor and ensemble.GradientBoostingClassifier now expose an apply method for retrieving the leaf indices each sample ends up in under each try. By Jacob Schreiber.
• Add sample_weight support to linear_model.LinearRegression. By Sonny Hu. (##4881)
• Add n_iter_without_progress to manifold.TSNE to control the stopping criterion. By Santi Villalba. (#5186)
• Added optional parameter random_state in linear_model.Ridge , to set the seed of the pseudo random generator used in sag solver. By Tom Dupre la Tour.
• Added optional parameter warm_start in linear_model.LogisticRegression. If set to True, the solvers lbfgs, newton-cg and sag will be initialized with the coefficients computed in the previous fit. By Tom Dupre la Tour.
• Added sample_weight support to linear_model.LogisticRegression for the lbfgs, newton-cg, and sag solvers. By Valentin Stolbunov. Support added to the liblinear solver. By Manoj Kumar.
• Added optional parameter presort to ensemble.GradientBoostingRegressor and ensemble.GradientBoostingClassifier, keeping default behavior the same. This allows gradient boosters to turn off presorting when building deep trees or using sparse data. By Jacob Schreiber.
• Altered metrics.roc_curve to drop unnecessary thresholds by default. By Graham Clenaghan.
• Added feature_selection.SelectFromModel meta-transformer which can be used along with estimators that have coef_ or feature_importances_ attribute to select important features of the input data. By Maheshakya Wijewardena, Joel Nothman and Manoj Kumar.
• Added metrics.pairwise.laplacian_kernel. By Clyde Fare.
• covariance.GraphLasso allows separate control of the convergence criterion for the Elastic-Net subproblem via the enet_tol parameter.
• Improved verbosity in decomposition.DictionaryLearning.
• ensemble.RandomForestClassifier and ensemble.RandomForestRegressor no longer explicitly store the samples used in bagging, resulting in a much reduced memory footprint for storing random forest models.
• Added positive option to linear_model.Lars and linear_model.lars_path to force coefficients to be positive. (#5131)
• Added the X_norm_squared parameter to metrics.pairwise.euclidean_distances to provide precomputed squared norms for X.
• Added the fit_predict method to pipeline.Pipeline.
• Added the preprocessing.min_max_scale function.

Bug fixes

• Fixed non-determinism in dummy.DummyClassifier with sparse multi-label output. By Andreas Müller.
• Fixed the output shape of linear_model.RANSACRegressor to (n_samples, ). By Andreas Müller.
• Fixed bug in decomposition.DictLearning when n_jobs < 0. By Andreas Müller.
• Fixed bug where grid_search.RandomizedSearchCV could consume a lot of memory for large discrete grids. By Joel Nothman.
• Fixed bug in linear_model.LogisticRegressionCV where penalty was ignored in the final fit. By Manoj Kumar.
• Fixed bug in ensemble.forest.ForestClassifier while computing oob_score and X is a sparse.csc_matrix. By Ankur Ankan.
• All regressors now consistently handle and warn when given y that is of shape (n_samples, 1). By Andreas Müller and Henry Lin. (#5431)
• Fix in cluster.KMeans cluster reassignment for sparse input by Lars Buitinck.
• Fixed a bug in lda.LDA that could cause asymmetric covariance matrices when using shrinkage. By Martin Billinger.
• Fixed cross_validation.cross_val_predict for estimators with sparse predictions. By Buddha Prakash.
• Fixed the predict_proba method of linear_model.LogisticRegression to use soft-max instead of one-vs-rest normalization. By Manoj Kumar. (#5182)
• Fixed the partial_fit method of linear_model.SGDClassifier when called with average=True. By Andrew Lamb. (#5282)
• Dataset fetchers use different filenames under Python 2 and Python 3 to avoid pickling compatibility issues. By Olivier Grisel. (#5355)
• Fixed a bug in naive_bayes.GaussianNB which caused classification results to depend on scale. By Jake Vanderplas.
• Fixed temporarily linear_model.Ridge, which was incorrect when fitting the intercept in the case of sparse data. The fix automatically changes the solver to ‘sag’ in this case. #5360 by Tom Dupre la Tour.
• Fixed a performance bug in decomposition.RandomizedPCA on data with a large number of features and fewer samples. (#4478) By Andreas Müller, Loic Esteve and Giorgio Patrini.
• Fixed bug in cross_decomposition.PLS that yielded unstable and platform dependent output, and failed on fit_transform. By Arthur Mensch.
• Fixes to the Bunch class used to store datasets.
• Fixed ensemble.plot_partial_dependence ignoring the percentiles parameter.
• Providing a set as vocabulary in CountVectorizer no longer leads to inconsistent results when pickling.
• Fixed the conditions on when a precomputed Gram matrix needs to be recomputed in linear_model.LinearRegression, linear_model.OrthogonalMatchingPursuit, linear_model.Lasso and linear_model.ElasticNet.
• Fixed inconsistent memory layout in the coordinate descent solver that affected linear_model.DictionaryLearning and covariance.GraphLasso. (#5337) By Olivier Grisel.
• manifold.LocallyLinearEmbedding no longer ignores the reg parameter.
• Nearest Neighbor estimators with custom distance metrics can now be pickled. (#4362)
• Fixed a bug in pipeline.FeatureUnion where transformer_weights were not properly handled when performing grid-searches.
• Fixed a bug in linear_model.LogisticRegression and linear_model.LogisticRegressionCV when using class_weight='balanced'```or ``class_weight='auto'. By Tom Dupre la Tour.
• Fixed bug #5495 when doing OVR(SVC(decision_function_shape=”ovr”)). Fixed by Elvis Dohmatob.

API changes summary

• Attribute data_min, data_max and data_range in preprocessing.MinMaxScaler are deprecated and won’t be available from 0.19. Instead, the class now exposes data_min_, data_max_ and data_range_. By Giorgio Patrini.
• All Scaler classes now have an scale_ attribute, the feature-wise rescaling applied by their transform methods. The old attribute std_ in preprocessing.StandardScaler is deprecated and superseded by scale_; it won’t be available in 0.19. By Giorgio Patrini.
• svm.SVC` and svm.NuSVC now have an decision_function_shape parameter to make their decision function of shape (n_samples, n_classes) by setting decision_function_shape='ovr'. This will be the default behavior starting in 0.19. By Andreas Müller.
• Passing 1D data arrays as input to estimators is now deprecated as it caused confusion in how the array elements should be interpreted as features or as samples. All data arrays are now expected to be explicitly shaped (n_samples, n_features). By Vighnesh Birodkar.
• lda.LDA and qda.QDA have been moved to discriminant_analysis.LinearDiscriminantAnalysis and discriminant_analysis.QuadraticDiscriminantAnalysis.
• The store_covariance and tol parameters have been moved from the fit method to the constructor in discriminant_analysis.LinearDiscriminantAnalysis and the store_covariances and tol parameters have been moved from the fit method to the constructor in discriminant_analysis.QuadraticDiscriminantAnalysis.
• Models inheriting from _LearntSelectorMixin will no longer support the transform methods. (i.e, RandomForests, GradientBoosting, LogisticRegression, DecisionTrees, SVMs and SGD related models). Wrap these models around the metatransfomer feature_selection.SelectFromModel to remove features (according to coefs_ or feature_importances_) which are below a certain threshold value instead.
• cluster.KMeans re-runs cluster-assignments in case of non-convergence, to ensure consistency of predict(X) and labels_. By Vighnesh Birodkar.
• Classifier and Regressor models are now tagged as such using the _estimator_type attribute.
• Cross-validation iterators always provide indices into training and test set, not boolean masks.
• The decision_function on all regressors was deprecated and will be removed in 0.19. Use predict instead.
• datasets.load_lfw_pairs is deprecated and will be removed in 0.19. Use datasets.fetch_lfw_pairs instead.
• The deprecated hmm module was removed.
• The deprecated Bootstrap cross-validation iterator was removed.
• The deprecated Ward and WardAgglomerative classes have been removed. Use clustering.AgglomerativeClustering instead.
• cross_validation.check_cv is now a public function.
• The property residues_ of linear_model.LinearRegression is deprecated and will be removed in 0.19.
• The deprecated n_jobs parameter of linear_model.LinearRegression has been moved to the constructor.
• Removed deprecated class_weight parameter from linear_model.SGDClassifier’s fit method. Use the construction parameter instead.
• The deprecated support for the sequence of sequences (or list of lists) multilabel format was removed. To convert to and from the supported binary indicator matrix format, use MultiLabelBinarizer.
• The behavior of calling the inverse_transform method of Pipeline.pipeline will change in 0.19. It will no longer reshape one-dimensional input to two-dimensional input.
• The deprecated attributes indicator_matrix_, multilabel_ and classes_ of preprocessing.LabelBinarizer were removed.
• Using gamma=0 in svm.SVC and svm.SVR to automatically set the gamma to 1. / n_features is deprecated and will be removed in 0.19. Use gamma="auto" instead.

Code Contributors

Aaron Schumacher, Adithya Ganesh, akitty, Alexandre Gramfort, Alexey Grigorev, Ali Baharev, Allen Riddell, Ando Saabas, Andreas Mueller, Andrew Lamb, Anish Shah, Ankur Ankan, Anthony Erlinger, Ari Rouvinen, Arnaud Joly, Arnaud Rachez, Arthur Mensch, banilo, Barmaley.exe, benjaminirving, Boyuan Deng, Brett Naul, Brian McFee, Buddha Prakash, Chi Zhang, Chih-Wei Chang, Christof Angermueller, Christoph Gohlke, Christophe Bourguignat, Christopher Erick Moody, Chyi-Kwei Yau, Cindy Sridharan, CJ Carey, Clyde-fare, Cory Lorenz, Dan Blanchard, Daniel Galvez, Daniel Kronovet, Danny Sullivan, Data1010, David, David D Lowe, David Dotson, djipey, Dmitry Spikhalskiy, Donne Martin, Dougal J. Sutherland, Dougal Sutherland, edson duarte, Eduardo Caro, Eric Larson, Eric Martin, Erich Schubert, Fernando Carrillo, Frank C. Eckert, Frank Zalkow, Gael Varoquaux, Ganiev Ibraim, Gilles Louppe, Giorgio Patrini, giorgiop, Graham Clenaghan, Gryllos Prokopis, gwulfs, Henry Lin, Hsuan-Tien Lin, Immanuel Bayer, Ishank Gulati, Jack Martin, Jacob Schreiber, Jaidev Deshpande, Jake Vanderplas, Jan Hendrik Metzen, Jean Kossaifi, Jeffrey04, Jeremy, jfraj, Jiali Mei, Joe Jevnik, Joel Nothman, John Kirkham, John Wittenauer, Joseph, Joshua Loyal, Jungkook Park, KamalakerDadi, Kashif Rasul, Keith Goodman, Kian Ho, Konstantin Shmelkov, Kyler Brown, Lars Buitinck, Lilian Besson, Loic Esteve, Louis Tiao, maheshakya, Maheshakya Wijewardena, Manoj Kumar, MarkTab marktab.net, Martin Ku, Martin Spacek, MartinBpr, martinosorb, MaryanMorel, Masafumi Oyamada, Mathieu Blondel, Matt Krump, Matti Lyra, Maxim Kolganov, mbillinger, mhg, Michael Heilman, Michael Patterson, Miroslav Batchkarov, Nelle Varoquaux, Nicolas, Nikolay Mayorov, Olivier Grisel, Omer Katz, Óscar Nájera, Pauli Virtanen, Peter Fischer, Peter Prettenhofer, Phil Roth, pianomania, Preston Parry, Raghav RV, Rob Zinkov, Robert Layton, Rohan Ramanath, Saket Choudhary, Sam Zhang, santi, saurabh.bansod, scls19fr, Sebastian Raschka, Sebastian Saeger, Shivan Sornarajah, SimonPL, sinhrks, Skipper Seabold, Sonny Hu, sseg, Stephen Hoover, Steven De Gryze, Steven Seguin, Theodore Vasiloudis, Thomas Unterthiner, Tiago Freitas Pereira, Tian Wang, Tim Head, Timothy Hopper, tokoroten, Tom Dupré la Tour, Trevor Stephens, Valentin Stolbunov, Vighnesh Birodkar, Vinayak Mehta, Vincent, Vincent Michel, vstolbunov, wangz10, Wei Xue, Yucheng Low, Yury Zhauniarovich, Zac Stewart, zhai_pro, Zichen Wang