version 0.16

version 0.16.1

April 14, 2015

Changelog

Bug fixes

• Allow input data larger than block_size in covariance.LedoitWolf by Andreas Müller.

• Fix a bug in isotonic.IsotonicRegression deduplication that caused unstable result in calibration.CalibratedClassifierCv by Jan Hendrik Metzen.

• Fix sorting of labels in preprocessing.label_binarize by Michael Heilman.

• Fix several stability and convergence issues in cross_decomposition.CCA and cross_decomposition.PLSCanonical by Andreas Müller

• Fix a bug in cluster.KMeans when precompute_distances=False on fortran-ordered data.

• Fix a speed regression in ensemble.RandomForestClassifier’s predict and predict_proba by Andreas Müller.

• Fix a regression where utils.shuffle converted lists and dataframes to arrays, by Olivier Grisel

version 0.16

March 26, 2015

Highlights

• Speed improvements (notably in cluster.DBSCAN), reduced memory requirements, bug-fixes and better default settings.

• Multinomial Logistic regression and a path algorithm in linear_model.LogisticRegressionCv.

• Out-of core learning of PCA via decomposition.IncrementalPCA.

• Probability calibration of classifiers using calibration.CalibratedClassifierCv.

• cluster.Birch clustering method for large-scale datasets.

• Scalable approximate nearest neighbors search with Locality-sensitive hashing forests in neighbors.LSHForest.

• Improved error messages and better validation when using malformed input data.

• More robust integration with pandas dataframes.

Changelog

New features

• The new neighbors.LSHForest implements locality-sensitive hashing for approximate nearest neighbors search. By Maheshakya Wijewardena.

• Added svm.LinearSvR. This class uses the liblinear implementation of Support vector Regression which is much faster for large sample sizes than svm.SvR with linear kernel. By Fabian Pedregosa and Qiang Luo.

• Incremental fit for GaussianNB.

• Added sample_weight support to dummy.DummyClassifier and dummy.DummyRegressor. By Arnaud Joly.

• Added the metrics.label_ranking_average_precision_score metrics. By Arnaud Joly.

• Add the metrics.coverage_error metrics. By Arnaud Joly.

• Added linear_model.LogisticRegressionCv. By Manoj Kumar, Fabian Pedregosa, Gael varoquaux and Alexandre Gramfort.

• Added warm_start constructor parameter to make it possible for any trained forest model to grow additional trees incrementally. By Laurent Direr.

• Added sample_weight support to ensemble.GradientBoostingClassifier and ensemble.GradientBoostingRegressor. By Peter Prettenhofer.

• Added decomposition.IncrementalPCA, an implementation of the PCA algorithm that supports out-of-core learning with a partial_fit method. By Kyle Kastner.

• Averaged SGD for SGDClassifier and SGDRegressor By Danny Sullivan.

• Added cross_val_predict function which computes cross-validated estimates. By Luis Pedro Coelho

• Added linear_model.TheilSenRegressor, a robust generalized-median-based estimator. By Florian Wilhelm.

• Added metrics.median_absolute_error, a robust metric. By Gael varoquaux and Florian Wilhelm.

• Add cluster.Birch, an online clustering algorithm. By Manoj Kumar, Alexandre Gramfort and Joel Nothman.

• Added shrinkage support to discriminant_analysis.LinearDiscriminantAnalysis using two new solvers. By Clemens Brunner and Martin Billinger.

• Added kernel_ridge.KernelRidge, an implementation of kernelized ridge regression. By Mathieu Blondel and Jan Hendrik Metzen.

• All solvers in linear_model.Ridge now support sample_weight. By Mathieu Blondel.

• Added cross_validation.PredefinedSplit cross-validation for fixed user-provided cross-validation folds. By Thomas Unterthiner.

• Added calibration.CalibratedClassifierCv, an approach for calibrating the predicted probabilities of a classifier. By Alexandre Gramfort, Jan Hendrik Metzen, Mathieu Blondel and Balazs Kegl.

Enhancements

• Add option return_distance in hierarchical.ward_tree to return distances between nodes for both structured and unstructured versions of the algorithm. By Matteo visconti di Oleggio Castello. The same option was added in hierarchical.linkage_tree. By Manoj Kumar

• Add support for sample weights in scorer objects. Metrics with sample weight support will automatically benefit from it. By Noel Dawe and vlad Niculae.

• Added newton-cg and lbfgs solver support in linear_model.LogisticRegression. By Manoj Kumar.

• Add selection="random" parameter to implement stochastic coordinate descent for linear_model.Lasso, linear_model.ElasticNet and related. By Manoj Kumar.

• Add sample_weight parameter to metrics.jaccard_similarity_score and metrics.log_loss. By Jatin Shah.

• Support sparse multilabel indicator representation in preprocessing.LabelBinarizer and multiclass.OnevsRestClassifier (by Hamzeh Alsalhi with thanks to Rohit Sivaprasad), as well as evaluation metrics (by Joel Nothman).

• Add sample_weight parameter to metrics.jaccard_similarity_score. By Jatin Shah.

• Add support for multiclass in metrics.hinge_loss. Added labels=None as optional parameter. By Saurabh Jha.

• Add sample_weight parameter to metrics.hinge_loss. By Saurabh Jha.

• Add multi_class="multinomial" option in linear_model.LogisticRegression to implement a Logistic Regression solver that minimizes the cross-entropy or multinomial loss instead of the default One-vs-Rest setting. Supports lbfgs and newton-cg solvers. By Lars Buitinck and Manoj Kumar. Solver option newton-cg by Simon Wu.

• Dictvectorizer can now perform fit_transform on an iterable in a single pass, when giving the option sort=False. By Dan Blanchard.

• model_selection.GridSearchCv and model_selection.RandomizedSearchCv can now be configured to work with estimators that may fail and raise errors on individual folds. This option is controlled by the error_score parameter. This does not affect errors raised on re-fit. By Michal Romaniuk.

• Add digits parameter to metrics.classification_report to allow report to show different precision of floating point numbers. By Ian Gilmore.

• Add a quantile prediction strategy to the dummy.DummyRegressor. By Aaron Staple.

• Add handle_unknown option to preprocessing.OneHotEncoder to handle unknown categorical features more gracefully during transform. By Manoj Kumar.

• Added support for sparse input data to decision trees and their ensembles. By Fares Hedyati and Arnaud Joly.

• Optimized cluster.AffinityPropagation by reducing the number of memory allocations of large temporary data-structures. By Antony Lee.

• Parallelization of the computation of feature importances in random forest. By Olivier Grisel and Arnaud Joly.

• Add n_iter_ attribute to estimators that accept a max_iter attribute in their constructor. By Manoj Kumar.

• Added decision function for multiclass.OnevsOneClassifier By Raghav Rv and Kyle Beauchamp.

• neighbors.kneighbors_graph and radius_neighbors_graph support non-Euclidean metrics. By Manoj Kumar

• Parameter connectivity in cluster.AgglomerativeClustering and family now accept callables that return a connectivity matrix. By Manoj Kumar.

• Sparse support for metrics.pairwise.paired_distances. By Joel Nothman.

• cluster.DBSCAN now supports sparse input and sample weights and has been optimized: the inner loop has been rewritten in Cython and radius neighbors queries are now computed in batch. By Joel Nothman and Lars Buitinck.

• Add class_weight parameter to automatically weight samples by class frequency for ensemble.RandomForestClassifier, tree.DecisionTreeClassifier, ensemble.ExtraTreesClassifier and tree.ExtraTreeClassifier. By Trevor Stephens.

• grid_search.RandomizedSearchCv now does sampling without replacement if all parameters are given as lists. By Andreas Müller.

• Parallelized calculation of metrics.pairwise_distances is now supported for scipy metrics and custom callables. By Joel Nothman.

• Allow the fitting and scoring of all clustering algorithms in pipeline.Pipeline. By Andreas Müller.

• More robust seeding and improved error messages in cluster.MeanShift by Andreas Müller.

• Make the stopping criterion for mixture.GMM, mixture.DPGMM and mixture.vBGMM less dependent on the number of samples by thresholding the average log-likelihood change instead of its sum over all samples. By Hervé Bredin.

• The outcome of manifold.spectral_embedding was made deterministic by flipping the sign of eigenvectors. By Hasil Sharma.

• Significant performance and memory usage improvements in preprocessing.PolynomialFeatures. By Eric Martin.

• Numerical stability improvements for preprocessing.StandardScaler and preprocessing.scale. By Nicolas Goix

• svm.SvC fitted on sparse input now implements decision_function. By Rob Zinkov and Andreas Müller.

• cross_validation.train_test_split now preserves the input type, instead of converting to numpy arrays.

Documentation improvements

• Added example of using pipeline.FeatureUnion for heterogeneous input. By Matt Terry

• Documentation on scorers was improved, to highlight the handling of loss functions. By Matt Pico.

• A discrepancy between liblinear output and scikit-learn’s wrappers is now noted. By Manoj Kumar.

• Improved documentation generation: examples referring to a class or function are now shown in a gallery on the class/function’s API reference page. By Joel Nothman.

• More explicit documentation of sample generators and of data transformation. By Joel Nothman.

• sklearn.neighbors.BallTree and sklearn.neighbors.KDTree used to point to empty pages stating that they are aliases of BinaryTree. This has been fixed to show the correct class docs. By Manoj Kumar.

• Added silhouette plots for analysis of KMeans clustering using metrics.silhouette_samples and metrics.silhouette_score. See Selecting the number of clusters with silhouette analysis on KMeans clustering

Bug fixes

• Metaestimators now support ducktyping for the presence of decision_function, predict_proba and other methods. This fixes behavior of grid_search.GridSearchCv, grid_search.RandomizedSearchCv, pipeline.Pipeline, feature_selection.RFE, feature_selection.RFECv when nested. By Joel Nothman

• The scoring attribute of grid-search and cross-validation methods is no longer ignored when a grid_search.GridSearchCv is given as a base estimator or the base estimator doesn’t have predict.

• The function hierarchical.ward_tree now returns the children in the same order for both the structured and unstructured versions. By Matteo visconti di Oleggio Castello.

• feature_selection.RFECv now correctly handles cases when step is not equal to 1. By Nikolay Mayorov

• The decomposition.PCA now undoes whitening in its inverse_transform. Also, its components_ now always have unit length. By Michael Eickenberg.

• Fix incomplete download of the dataset when datasets.download_20newsgroups is called. By Manoj Kumar.

• various fixes to the Gaussian processes subpackage by vincent Dubourg and Jan Hendrik Metzen.

• Calling partial_fit with class_weight=='auto' throws an appropriate error message and suggests a workaround. By Danny Sullivan.

• RBFSampler with gamma=g formerly approximated rbf_kernel with gamma=g/2.; the definition of gamma is now consistent, which may substantially change your results if you use a fixed value. (If you cross-validated over gamma, it probably doesn’t matter too much.) By Dougal Sutherland.

• Pipeline object delegates the classes_ attribute to the underlying estimator. It allows, for instance, to make bagging of a pipeline object. By Arnaud Joly

• neighbors.NearestCentroid now uses the median as the centroid when metric is set to manhattan. It was using the mean before. By Manoj Kumar

• Fix numerical stability issues in linear_model.SGDClassifier and linear_model.SGDRegressor by clipping large gradients and ensuring that weight decay rescaling is always positive (for large l2 regularization and large learning rate values). By Olivier Grisel

• When compute_full_tree is set to “auto”, the full tree is built when n_clusters is high and is early stopped when n_clusters is low, while the behavior should be vice versa in cluster.AgglomerativeClustering (and friends). This has been fixed By Manoj Kumar

• Fix lazy centering of data in linear_model.enet_path and linear_model.lasso_path. It was centered around one. It has been changed to be centered around the origin. By Manoj Kumar

• Fix handling of precomputed affinity matrices in cluster.AgglomerativeClustering when using connectivity constraints. By Cathy Deng

• Correct partial_fit handling of class_prior for sklearn.naive_bayes.MultinomialNB and sklearn.naive_bayes.BernoulliNB. By Trevor Stephens.

• Fixed a crash in metrics.precision_recall_fscore_support when using unsorted labels in the multi-label setting. By Andreas Müller.

• Avoid skipping the first nearest neighbor in the methods radius_neighbors, kneighbors, kneighbors_graph and radius_neighbors_graph in sklearn.neighbors.NearestNeighbors and family, when the query data is not the same as fit data. By Manoj Kumar.

• Fix log-density calculation in the mixture.GMM with tied covariance. By Will Dawson

• Fixed a scaling error in feature_selection.SelectFdr where a factor n_features was missing. By Andrew Tulloch

• Fix zero division in neighbors.KNeighborsRegressor and related classes when using distance weighting and having identical data points. By Garret-R.

• Fixed round off errors with non positive-definite covariance matrices in GMM. By Alexis Mignon.

• Fixed an error in the computation of conditional probabilities in naive_bayes.BernoulliNB. By Hanna Wallach.

• Make the method radius_neighbors of neighbors.NearestNeighbors return the samples lying on the boundary for algorithm='brute'. By Yan Yi.

• Flip sign of dual_coef_ of svm.SvC to make it consistent with the documentation and decision_function. By Artem Sobolev.

• Fixed handling of ties in isotonic.IsotonicRegression. We now use the weighted average of targets (secondary method). By Andreas Müller and Michael Bommarito.

API changes summary

• GridSearchCv and cross_val_score and other meta-estimators don’t convert pandas DataFrames into arrays any more, allowing DataFrame specific operations in custom estimators.

• multiclass.fit_ovr, multiclass.predict_ovr, predict_proba_ovr, multiclass.fit_ovo, multiclass.predict_ovo, multiclass.fit_ecoc and multiclass.predict_ecoc are deprecated. Use the underlying estimators instead.

• Nearest neighbors estimators used to take arbitrary keyword arguments and pass these to their distance metric. This will no longer be supported in scikit-learn 0.18; use the metric_params argument instead.

• n_jobs parameter of the fit method shifted to the constructor of the

  LinearRegression class.

• The predict_proba method of multiclass.OnevsRestClassifier now returns two probabilities per sample in the multiclass case; this is consistent with other estimators and with the method’s documentation, but previous versions accidentally returned only the positive probability. Fixed by Will Lamond and Lars Buitinck.

• Change default value of precompute in linear_model.ElasticNet and linear_model.Lasso to False. Setting precompute to “auto” was found to be slower when n_samples > n_features since the computation of the Gram matrix is computationally expensive and outweighs the benefit of fitting the Gram for just one alpha. precompute="auto" is now deprecated and will be removed in 0.18 By Manoj Kumar.

• Expose positive option in linear_model.enet_path and linear_model.enet_path which constrains coefficients to be positive. By Manoj Kumar.

• Users should now supply an explicit average parameter to sklearn.metrics.f1_score, sklearn.metrics.fbeta_score, sklearn.metrics.recall_score and sklearn.metrics.precision_score when performing multiclass or multilabel (i.e. not binary) classification. By Joel Nothman.

• scoring parameter for cross validation now accepts 'f1_micro', 'f1_macro' or 'f1_weighted'. 'f1' is now for binary classification only. Similar changes apply to 'precision' and 'recall'. By Joel Nothman.

• The fit_intercept, normalize and return_models parameters in linear_model.enet_path and linear_model.lasso_path have been removed. They were deprecated since 0.14

• From now onwards, all estimators will uniformly raise NotFittedError when any of the predict like methods are called before the model is fit. By Raghav Rv.

• Input data validation was refactored for more consistent input validation. The check_arrays function was replaced by check_array and check_X_y. By Andreas Müller.

• Allow X=None in the methods radius_neighbors, kneighbors, kneighbors_graph and radius_neighbors_graph in sklearn.neighbors.NearestNeighbors and family. If set to None, then for every sample this avoids setting the sample itself as the first nearest neighbor. By Manoj Kumar.

• Add parameter include_self in neighbors.kneighbors_graph and neighbors.radius_neighbors_graph which has to be explicitly set by the user. If set to True, then the sample itself is considered as the first nearest neighbor.

• thresh parameter is deprecated in favor of new tol parameter in GMM, DPGMM and vBGMM. See Enhancements section for details. By Hervé Bredin.

• Estimators will treat input with dtype object as numeric when possible. By Andreas Müller

• Estimators now raise valueError consistently when fitted on empty data (less than 1 sample or less than 1 feature for 2D input). By Olivier Grisel.

• The shuffle option of linear_model.SGDClassifier, linear_model.SGDRegressor, linear_model.Perceptron, linear_model.PassiveAggressiveClassifier and linear_model.PassiveAggressiveRegressor now defaults to True.

• cluster.DBSCAN now uses a deterministic initialization. The random_state parameter is deprecated. By Erich Schubert.

Code Contributors

A. Flaxman, Aaron Schumacher, Aaron Staple, abhishek thakur, Akshay, akshayah3, Aldrian Obaja, Alexander Fabisch, Alexandre Gramfort, Alexis Mignon, Anders Aagaard, Andreas Mueller, Andreas van Cranenburgh, Andrew Tulloch, Andrew Walker, Antony Lee, Arnaud Joly, banilo, Barmaley.exe, Ben Davies, Benedikt Koehler, bhsu, Boris Feld, Borja Ayerdi, Boyuan Deng, Brent Pedersen, Brian Wignall, Brooke Osborn, Calvin Giles, Cathy Deng, Celeo, cgohlke, chebee7i, Christian Stade-Schuldt, Christof Angermueller, Chyi-Kwei Yau, CJ Carey, Clemens Brunner, Daiki Aminaka, Dan Blanchard, danfrankj, Danny Sullivan, David Fletcher, Dmitrijs Milajevs, Dougal J. Sutherland, Erich Schubert, Fabian Pedregosa, Florian Wilhelm, floydsoft, Félix-Antoine Fortin, Gael varoquaux, Garrett-R, Gilles Louppe, gpassino, gwulfs, Hampus Bengtsson, Hamzeh Alsalhi, Hanna Wallach, Harry Mavroforakis, Hasil Sharma, Helder, Herve Bredin, Hsiang-Fu Yu, Hugues SALAMIN, Ian Gilmore, Ilambharathi Kanniah, Imran Haque, isms, Jake vanderPlas, Jan Dlabal, Jan Hendrik Metzen, Jatin Shah, Javier López Peña, jdcaballero, Jean Kossaifi, Jeff Hammerbacher, Joel Nothman, Jonathan Helmus, Joseph, Kaicheng Zhang, Kevin Markham, Kyle Beauchamp, Kyle Kastner, Lagacherie Matthieu, Lars Buitinck, Laurent Direr, leepei, Loic Esteve, Luis Pedro Coelho, Lukas Michelbacher, maheshakya, Manoj Kumar, Manuel, Mario Michael Krell, Martin, Martin Billinger, Martin Ku, Mateusz Susik, Mathieu Blondel, Matt Pico, Matt Terry, Matteo visconti dOC, Matti Lyra, Max Linke, Mehdi Cherti, Michael Bommarito, Michael Eickenberg, Michal Romaniuk, MLG, mr.Shu, Nelle varoquaux, Nicola Montecchio, Nicolas, Nikolay Mayorov, Noel Dawe, Okal Billy, Olivier Grisel, Óscar Nájera, Paolo Puggioni, Peter Prettenhofer, Pratap vardhan, pvnguyen, queqichao, Rafael Carrascosa, Raghav R v, Rahiel Kasim, Randall Mason, Rob Zinkov, Robert Bradshaw, Saket Choudhary, Sam Nicholls, Samuel Charron, Saurabh Jha, sethdandridge, sinhrks, snuderl, Stefan Otte, Stefan van der Walt, Steve Tjoa, swu, Sylvain Zimmer, tejesh95, terrycojones, Thomas Delteil, Thomas Unterthiner, Tomas Kazmar, trevorstephens, tttthomasssss, Tzu-Ming Kuo, ugurcaliskan, ugurthemaster, vinayak Mehta, vincent Dubourg, vjacheslav Murashkin, vlad Niculae, wadawson, Wei Xue, Will Lamond, Wu Jiang, x0l, Xinfan Meng, Yan Yi, Yu-Chin