Version 0.21.3

Legend for changelogs

• Major Feature : something big that you couldn’t do before.

• Feature : something that you couldn’t do before.

• Efficiency : an existing feature now may not require as much computation or memory.

• Enhancement : a miscellaneous minor improvement.

• Fix : something that previously didn’t work as documentated – or according to reasonable expectations – should now work.

• API Change : you will need to change your code to have the same effect in the future; or a feature will be removed in the future.

July 30, 2019

Changed models

The following estimators and functions, when fit with the same data and parameters, may produce different models from the previous version. This often occurs due to changes in the modelling logic (bug fixes or enhancements), or in random sampling procedures.

• The v0.20.0 release notes failed to mention a backwards incompatibility in metrics.make_scorer when needs_proba=True and y_true is binary. Now, the scorer function is supposed to accept a 1D y_pred (i.e., probability of the positive class, shape (n_samples,)), instead of a 2D y_pred (i.e., shape (n_samples, 2)).

Changelog

sklearn.cluster

• Fix Fixed a bug in cluster.KMeans where computation with init='random' was single threaded for n_jobs > 1 or n_jobs = -1. #12955 by Prabakaran Kumaresshan.

• Fix Fixed a bug in cluster.OPTICS where users were unable to pass float min_samples and min_cluster_size. #14496 by Fabian Klopfer and Hanmin Qin.

• Fix Fixed a bug in cluster.KMeans where KMeans++ initialisation could rarely result in an IndexError. #11756 by Joel Nothman.

sklearn.compose

• Fix Fixed an issue in compose.ColumnTransformer where using DataFrames whose column order differs between :func:fit and :func:transform could lead to silently passing incorrect columns to the remainder transformer. #14237 by Andreas Schuderer <schuderer>.

sklearn.datasets

• Fix datasets.fetch_california_housing, datasets.fetch_covtype, datasets.fetch_kddcup99, datasets.fetch_olivetti_faces, datasets.fetch_rcv1, and datasets.fetch_species_distributions try to persist the previously cache using the new joblib if the cached data was persisted using the deprecated sklearn.externals.joblib. This behavior is set to be deprecated and removed in v0.23. #14197 by Adrin Jalali.

sklearn.ensemble

• Fix Fix zero division error in HistGradientBoostingClassifier and HistGradientBoostingRegressor. #14024 by Nicolas Hug <NicolasHug>.

sklearn.impute

• Fix Fixed a bug in impute.SimpleImputer and impute.IterativeImputer so that no errors are thrown when there are missing values in training data. #13974 by Frank Hoang <fhoang7>.

sklearn.inspection

• Fix Fixed a bug in inspection.plot_partial_dependence where target parameter was not being taken into account for multiclass problems. #14393 by Guillem G. Subies.

sklearn.linear_model

• Fix Fixed a bug in linear_model.LogisticRegressionCV where refit=False would fail depending on the 'multiclass' and 'penalty' parameters (regression introduced in 0.21). #14087 by Nicolas Hug.

• Fix Compatibility fix for linear_model.ARDRegression and Scipy>=1.3.0. Adapts to upstream changes to the default pinvh cutoff threshold which otherwise results in poor accuracy in some cases. #14067 by Tim Staley.

sklearn.neighbors

• Fix Fixed a bug in neighbors.NeighborhoodComponentsAnalysis where the validation of initial parameters n_components, max_iter and tol required too strict types. #14092 by Jérémie du Boisberranger.

sklearn.tree

• Fix Fixed bug in tree.export_text when the tree has one feature and a single feature name is passed in. #14053 by Thomas Fan.

• Fix Fixed an issue with plot_tree where it displayed entropy calculations even for gini criterion in DecisionTreeClassifiers. #13947 by Frank Hoang.

Version 0.21.2

24 May 2019

Changelog

sklearn.decomposition

• Fix Fixed a bug in cross_decomposition.CCA improving numerical stability when Y is close to zero. #13903 by Thomas Fan.

sklearn.metrics

• Fix Fixed a bug in metrics.pairwise.euclidean_distances where a part of the distance matrix was left un-instanciated for suffiently large float32 datasets (regression introduced in 0.21). #13910 by Jérémie du Boisberranger.

sklearn.preprocessing

• Fix Fixed a bug in preprocessing.OneHotEncoder where the new drop parameter was not reflected in get_feature_names. #13894 by James Myatt.

sklearn.utils.sparsefuncs

• Fix Fixed a bug where min_max_axis would fail on 32-bit systems for certain large inputs. This affects preprocessing.MaxAbsScaler, preprocessing.normalize and preprocessing.LabelBinarizer. #13741 by Roddy MacSween.

Version 0.21.1

17 May 2019

This is a bug-fix release to primarily resolve some packaging issues in version 0.21.0. It also includes minor documentation improvements and some bug fixes.

Changelog

sklearn.inspection

• Fix Fixed a bug in inspection.partial_dependence to only check classifier and not regressor for the multiclass-multioutput case. #14309 by Guillaume Lemaitre.

sklearn.metrics

• Fix Fixed a bug in metrics.pairwise_distances where it would raise AttributeError for boolean metrics when X had a boolean dtype and Y == None. #13864 by Paresh Mathur.

• Fix Fixed two bugs in metrics.pairwise_distances when n_jobs > 1. First it used to return a distance matrix with same dtype as input, even for integer dtype. Then the diagonal was not zeros for euclidean metric when Y is X. #13877 by Jérémie du Boisberranger.

sklearn.neighbors

• Fix Fixed a bug in neighbors.KernelDensity which could not be restored from a pickle if sample_weight had been used. #13772 by Aditya Vyas.

Version 0.21.0

May 2019

Changed models

The following estimators and functions, when fit with the same data and parameters, may produce different models from the previous version. This often occurs due to changes in the modelling logic (bug fixes or enhancements), or in random sampling procedures.

• discriminant_analysis.LinearDiscriminantAnalysis for multiclass classification. Fix

• discriminant_analysis.LinearDiscriminantAnalysis with ‘eigen’ solver. Fix

• linear_model.BayesianRidge Fix

• Decision trees and derived ensembles when both max_depth and max_leaf_nodes are set. Fix

• linear_model.LogisticRegression and linear_model.LogisticRegressionCV with ‘saga’ solver. Fix

• ensemble.GradientBoostingClassifier Fix

• sklearn.feature_extraction.text.HashingVectorizer, sklearn.feature_extraction.text.TfidfVectorizer, and sklearn.feature_extraction.text.CountVectorizer Fix

• neural_network.MLPClassifier Fix

• svm.SVC.decision_function and multiclass.OneVsOneClassifier.decision_function. Fix

• linear_model.SGDClassifier and any derived classifiers. Fix

• Any model using the linear_model._sag.sag_solver function with a 0 seed, including linear_model.LogisticRegression, linear_model.LogisticRegressionCV, linear_model.Ridge, and linear_model.RidgeCV with ‘sag’ solver. Fix

• linear_model.RidgeCV when using leave-one-out cross-validation with sparse inputs. Fix

Details are listed in the changelog below.

(While we are trying to better inform users by providing this information, we cannot assure that this list is complete.)

Known Major Bugs

• The default max_iter for linear_model.LogisticRegression is too small for many solvers given the default tol. In particular, we accidentally changed the default max_iter for the liblinear solver from 1000 to 100 iterations in #3591 released in version 0.16. In a future release we hope to choose better default max_iter and tol heuristically depending on the solver (see #13317).

Changelog

Support for Python 3.4 and below has been officially dropped.

sklearn.base

• API Change The R2 score used when calling score on a regressor will use multioutput='uniform_average' from version 0.23 to keep consistent with metrics.r2_score. This will influence the score method of all the multioutput regressors (except for multioutput.MultiOutputRegressor). #13157 by Hanmin Qin.

sklearn.calibration

• Enhancement Added support to bin the data passed into calibration.calibration_curve by quantiles instead of uniformly between 0 and 1. #13086 by Scott Cole.

• Enhancement Allow n-dimensional arrays as input for calibration.CalibratedClassifierCV. #13485 by William de Vazelhes.

sklearn.cluster

• Major Feature A new clustering algorithm: cluster.OPTICS: an algorithm related to cluster.DBSCAN, that has hyperparameters easier to set and that scales better, by Shane, Adrin Jalali, Erich Schubert, Hanmin Qin, and Assia Benbihi.

• Fix Fixed a bug where cluster.Birch could occasionally raise an AttributeError. #13651 by Joel Nothman.

• Fix Fixed a bug in cluster.KMeans where empty clusters weren’t correctly relocated when using sample weights. #13486 by Jérémie du Boisberranger.

• API Change The n_components_ attribute in cluster.AgglomerativeClustering and cluster.FeatureAgglomeration has been renamed to n_connected_components_. #13427 by Stephane Couvreur.

• Enhancement cluster.AgglomerativeClustering and cluster.FeatureAgglomeration now accept a distance_threshold parameter which can be used to find the clusters instead of n_clusters. #9069 by Vathsala Achar and Adrin Jalali.

sklearn.compose

• API Change compose.ColumnTransformer is no longer an experimental feature. #13835 by Hanmin Qin.

sklearn.datasets

• Fix Added support for 64-bit group IDs and pointers in SVMLight files. #10727 by Bryan K Woods.

• Fix datasets.load_sample_images returns images with a deterministic order. #13250 by Thomas Fan.

sklearn.decomposition

• Enhancement decomposition.KernelPCA now has deterministic output (resolved sign ambiguity in eigenvalue decomposition of the kernel matrix). #13241 by Aurélien Bellet.

• Fix Fixed a bug in decomposition.KernelPCA, fit().transform() now produces the correct output (the same as fit_transform()) in case of non-removed zero eigenvalues (remove_zero_eig=False). fit_inverse_transform was also accelerated by using the same trick as fit_transform to compute the transform of X. #12143 by Sylvain Marié

• Fix Fixed a bug in decomposition.NMF where init = 'nndsvd', init = 'nndsvda', and init = 'nndsvdar' are allowed when n_components < n_features instead of n_components <= min(n_samples, n_features). #11650 by Hossein Pourbozorg and Zijie (ZJ) Poh.

• API Change The default value of the init argument in decomposition.non_negative_factorization will change from random to None in version 0.23 to make it consistent with decomposition.NMF. A FutureWarning is raised when the default value is used. #12988 by Zijie (ZJ) Poh.

sklearn.discriminant_analysis

• Enhancement discriminant_analysis.LinearDiscriminantAnalysis now preserves float32 and float64 dtypes. #8769 and #11000 by Thibault Sejourne

• Fix A ChangedBehaviourWarning is now raised when discriminant_analysis.LinearDiscriminantAnalysis is given as parameter n_components > min(n_features, n_classes - 1), and n_components is changed to min(n_features, n_classes - 1) if so. Previously the change was made, but silently. #11526 by William de Vazelhes.

• Fix Fixed a bug in discriminant_analysis.LinearDiscriminantAnalysis where the predicted probabilities would be incorrectly computed in the multiclass case. #6848, by Agamemnon Krasoulis and Guillaume Lemaitre <glemaitre>.

• Fix Fixed a bug in discriminant_analysis.LinearDiscriminantAnalysis where the predicted probabilities would be incorrectly computed with eigen solver. #11727, by Agamemnon Krasoulis.

sklearn.dummy

• Fix Fixed a bug in dummy.DummyClassifier where the predict_proba method was returning int32 array instead of float64 for the stratified strategy. #13266 by Christos Aridas.

• Fix Fixed a bug in dummy.DummyClassifier where it was throwing a dimension mismatch error in prediction time if a column vector y with shape=(n, 1) was given at fit time. #13545 by Nick Sorros and Adrin Jalali.

sklearn.ensemble

• Major Feature Add two new implementations of gradient boosting trees: ensemble.HistGradientBoostingClassifier and ensemble.HistGradientBoostingRegressor. The implementation of these estimators is inspired by LightGBM and can be orders of magnitude faster than ensemble.GradientBoostingRegressor and ensemble.GradientBoostingClassifier when the number of samples is larger than tens of thousands of samples. The API of these new estimators is slightly different, and some of the features from ensemble.GradientBoostingClassifier and ensemble.GradientBoostingRegressor are not yet supported.

  These new estimators are experimental, which means that their results or their API might change without any deprecation cycle. To use them, you need to explicitly import enable_hist_gradient_boosting:

  >>> # explicitly require this experimental feature
  >>> from sklearn.experimental import enable_hist_gradient_boosting  # noqa
  >>> # now you can import normally from sklearn.ensemble
  >>> from sklearn.ensemble import HistGradientBoostingClassifier
  

  Note

  Update: since version 1.0, these estimators are not experimental anymore and you don’t need to use from sklearn.experimental import enable_hist_gradient_boosting.

  #12807 by Nicolas Hug.

• Feature Add ensemble.VotingRegressor which provides an equivalent of ensemble.VotingClassifier for regression problems. #12513 by Ramil Nugmanov and Mohamed Ali Jamaoui.

• Efficiency Make ensemble.IsolationForest prefer threads over processes when running with n_jobs > 1 as the underlying decision tree fit calls do release the GIL. This changes reduces memory usage and communication overhead. #12543 by Isaac Storch and Olivier Grisel.

• Efficiency Make ensemble.IsolationForest more memory efficient by avoiding keeping in memory each tree prediction. #13260 by Nicolas Goix.

• Efficiency ensemble.IsolationForest now uses chunks of data at prediction step, thus capping the memory usage. #13283 by Nicolas Goix.

• Efficiency sklearn.ensemble.GradientBoostingClassifier and sklearn.ensemble.GradientBoostingRegressor now keep the input y as float64 to avoid it being copied internally by trees. #13524 by Adrin Jalali.

• Enhancement Minimized the validation of X in ensemble.AdaBoostClassifier and ensemble.AdaBoostRegressor #13174 by Christos Aridas.

• Enhancement ensemble.IsolationForest now exposes warm_start parameter, allowing iterative addition of trees to an isolation forest. #13496 by Peter Marko.

• Fix The values of feature_importances_ in all random forest based models (i.e. ensemble.RandomForestClassifier, ensemble.RandomForestRegressor, ensemble.ExtraTreesClassifier, ensemble.ExtraTreesRegressor, ensemble.RandomTreesEmbedding, ensemble.GradientBoostingClassifier, and ensemble.GradientBoostingRegressor) now:

  • sum up to 1

  • all the single node trees in feature importance calculation are ignored

  • in case all trees have only one single node (i.e. a root node), feature importances will be an array of all zeros.

  #13636 and #13620 by Adrin Jalali.

• Fix Fixed a bug in ensemble.GradientBoostingClassifier and ensemble.GradientBoostingRegressor, which didn’t support scikit-learn estimators as the initial estimator. Also added support of initial estimator which does not support sample weights. #12436 by Jérémie du Boisberranger and #12983 by Nicolas Hug.

• Fix Fixed the output of the average path length computed in ensemble.IsolationForest when the input is either 0, 1 or 2. #13251 by Albert Thomas and joshuakennethjones.

• Fix Fixed a bug in ensemble.GradientBoostingClassifier where the gradients would be incorrectly computed in multiclass classification problems. #12715 by Nicolas Hug.

• Fix Fixed a bug in ensemble.GradientBoostingClassifier where validation sets for early stopping were not sampled with stratification. #13164 by Nicolas Hug.

• Fix Fixed a bug in ensemble.GradientBoostingClassifier where the default initial prediction of a multiclass classifier would predict the classes priors instead of the log of the priors. #12983 by Nicolas Hug.

• Fix Fixed a bug in ensemble.RandomForestClassifier where the predict method would error for multiclass multioutput forests models if any targets were strings. #12834 by Elizabeth Sander.

• Fix Fixed a bug in ensemble.gradient_boosting.LossFunction and ensemble.gradient_boosting.LeastSquaresError where the default value of learning_rate in update_terminal_regions is not consistent with the document and the caller functions. Note however that directly using these loss functions is deprecated. #6463 by movelikeriver.

• Fix ensemble.partial_dependence (and consequently the new version sklearn.inspection.partial_dependence) now takes sample weights into account for the partial dependence computation when the gradient boosting model has been trained with sample weights. #13193 by Samuel O. Ronsin.

• API Change ensemble.partial_dependence and ensemble.plot_partial_dependence are now deprecated in favor of inspection.partial_dependence and inspection.plot_partial_dependence. #12599 by Trevor Stephens and Nicolas Hug.

• Fix ensemble.VotingClassifier and ensemble.VotingRegressor were failing during fit in one of the estimators was set to None and sample_weight was not None. #13779 by Guillaume Lemaitre.

• API Change ensemble.VotingClassifier and ensemble.VotingRegressor accept 'drop' to disable an estimator in addition to None to be consistent with other estimators (i.e., pipeline.FeatureUnion and compose.ColumnTransformer). #13780 by Guillaume Lemaitre.

sklearn.externals

• API Change Deprecated externals.six since we have dropped support for Python 2.7. #12916 by Hanmin Qin.

sklearn.feature_extraction

• Fix If input='file' or input='filename', and a callable is given as the analyzer, sklearn.feature_extraction.text.HashingVectorizer, sklearn.feature_extraction.text.TfidfVectorizer, and sklearn.feature_extraction.text.CountVectorizer now read the data from the file(s) and then pass it to the given analyzer, instead of passing the file name(s) or the file object(s) to the analyzer. #13641 by Adrin Jalali.

sklearn.impute

• Major Feature Added impute.IterativeImputer, which is a strategy for imputing missing values by modeling each feature with missing values as a function of other features in a round-robin fashion. #8478 and #12177 by Sergey Feldman and Ben Lawson.

  The API of IterativeImputer is experimental and subject to change without any deprecation cycle. To use them, you need to explicitly import enable_iterative_imputer:

  >>> from sklearn.experimental import enable_iterative_imputer  # noqa
  >>> # now you can import normally from sklearn.impute
  >>> from sklearn.impute import IterativeImputer
  

• Feature The impute.SimpleImputer and impute.IterativeImputer have a new parameter 'add_indicator', which simply stacks a impute.MissingIndicator transform into the output of the imputer’s transform. That allows a predictive estimator to account for missingness. #12583, #13601 by Danylo Baibak.

• Fix In impute.MissingIndicator avoid implicit densification by raising an exception if input is sparse add missing_values property is set to 0. #13240 by Bartosz Telenczuk.

• Fix Fixed two bugs in impute.MissingIndicator. First, when X is sparse, all the non-zero non missing values used to become explicit False in the transformed data. Then, when features='missing-only', all features used to be kept if there were no missing values at all. #13562 by Jérémie du Boisberranger.

sklearn.inspection

(new subpackage)

• Feature Partial dependence plots (inspection.plot_partial_dependence) are now supported for any regressor or classifier (provided that they have a predict_proba method). #12599 by Trevor Stephens and Nicolas Hug.

sklearn.isotonic

• Feature Allow different dtypes (such as float32) in isotonic.IsotonicRegression. #8769 by Vlad Niculae

sklearn.linear_model

• Enhancement linear_model.Ridge now preserves float32 and float64 dtypes. #8769 and #11000 by Guillaume Lemaitre, and Joan Massich

• Feature linear_model.LogisticRegression and linear_model.LogisticRegressionCV now support Elastic-Net penalty, with the ‘saga’ solver. #11646 by Nicolas Hug.

• Feature Added linear_model.lars_path_gram, which is linear_model.lars_path in the sufficient stats mode, allowing users to compute linear_model.lars_path without providing X and y. #11699 by Kuai Yu.

• Efficiency linear_model.make_dataset now preserves float32 and float64 dtypes, reducing memory consumption in stochastic gradient, SAG and SAGA solvers. #8769 and #11000 by Nelle Varoquaux, Arthur Imbert, Guillaume Lemaitre, and Joan Massich

• Enhancement linear_model.LogisticRegression now supports an unregularized objective when penalty='none' is passed. This is equivalent to setting C=np.inf with l2 regularization. Not supported by the liblinear solver. #12860 by Nicolas Hug.

• Enhancement sparse_cg solver in linear_model.Ridge now supports fitting the intercept (i.e. fit_intercept=True) when inputs are sparse. #13336 by Bartosz Telenczuk.

• Enhancement The coordinate descent solver used in Lasso, ElasticNet, etc. now issues a ConvergenceWarning when it completes without meeting the desired toleranbce. #11754 and #13397 by Brent Fagan and Adrin Jalali.

• Fix Fixed a bug in linear_model.LogisticRegression and linear_model.LogisticRegressionCV with ‘saga’ solver, where the weights would not be correctly updated in some cases. #11646 by Tom Dupre la Tour.

• Fix Fixed the posterior mean, posterior covariance and returned regularization parameters in linear_model.BayesianRidge. The posterior mean and the posterior covariance were not the ones computed with the last update of the regularization parameters and the returned regularization parameters were not the final ones. Also fixed the formula of the log marginal likelihood used to compute the score when compute_score=True. #12174 by Albert Thomas.

• Fix Fixed a bug in linear_model.LassoLarsIC, where user input copy_X=False at instance creation would be overridden by default parameter value copy_X=True in fit. #12972 by Lucio Fernandez-Arjona

• Fix Fixed a bug in linear_model.LinearRegression that was not returning the same coeffecients and intercepts with fit_intercept=True in sparse and dense case. #13279 by Alexandre Gramfort

• Fix Fixed a bug in linear_model.HuberRegressor that was broken when X was of dtype bool. #13328 by Alexandre Gramfort.

• Fix Fixed a performance issue of saga and sag solvers when called in a joblib.Parallel setting with n_jobs > 1 and backend="threading", causing them to perform worse than in the sequential case. #13389 by Pierre Glaser.

• Fix Fixed a bug in linear_model.stochastic_gradient.BaseSGDClassifier that was not deterministic when trained in a multi-class setting on several threads. #13422 by Clément Doumouro.

• Fix Fixed bug in linear_model.ridge_regression, linear_model.Ridge and linear_model.RidgeClassifier that caused unhandled exception for arguments return_intercept=True and solver=auto (default) or any other solver different from sag. #13363 by Bartosz Telenczuk

• Fix linear_model.ridge_regression will now raise an exception if return_intercept=True and solver is different from sag. Previously, only warning was issued. #13363 by Bartosz Telenczuk

• Fix linear_model.ridge_regression will choose sparse_cg solver for sparse inputs when solver=auto and sample_weight is provided (previously cholesky solver was selected). #13363 by Bartosz Telenczuk

• API Change The use of linear_model.lars_path with X=None while passing Gram is deprecated in version 0.21 and will be removed in version 0.23. Use linear_model.lars_path_gram instead. #11699 by Kuai Yu.

• API Change linear_model.logistic_regression_path is deprecated in version 0.21 and will be removed in version 0.23. #12821 by Nicolas Hug.

• Fix linear_model.RidgeCV with leave-one-out cross-validation now correctly fits an intercept when fit_intercept=True and the design matrix is sparse. #13350 by Jérôme Dockès

sklearn.manifold

• Efficiency Make manifold.tsne.trustworthiness use an inverted index instead of an np.where lookup to find the rank of neighbors in the input space. This improves efficiency in particular when computed with lots of neighbors and/or small datasets. #9907 by William de Vazelhes.

sklearn.metrics

• Feature Added the metrics.max_error metric and a corresponding 'max_error' scorer for single output regression. #12232 by Krishna Sangeeth.

• Feature Add metrics.multilabel_confusion_matrix, which calculates a confusion matrix with true positive, false positive, false negative and true negative counts for each class. This facilitates the calculation of set-wise metrics such as recall, specificity, fall out and miss rate. #11179 by Shangwu Yao and Joel Nothman.

• Feature metrics.jaccard_score has been added to calculate the Jaccard coefficient as an evaluation metric for binary, multilabel and multiclass tasks, with an interface analogous to metrics.f1_score. #13151 by Gaurav Dhingra and Joel Nothman.

• Feature Added metrics.pairwise.haversine_distances which can be accessed with metric='pairwise' through metrics.pairwise_distances and estimators. (Haversine distance was previously available for nearest neighbors calculation.) #12568 by Wei Xue, Emmanuel Arias and Joel Nothman.

• Efficiency Faster metrics.pairwise_distances with n_jobs > 1 by using a thread-based backend, instead of process-based backends. #8216 by Pierre Glaser and Romuald Menuet

• Efficiency The pairwise manhattan distances with sparse input now uses the BLAS shipped with scipy instead of the bundled BLAS. #12732 by Jérémie du Boisberranger

• Enhancement Use label accuracy instead of micro-average on metrics.classification_report to avoid confusion. micro-average is only shown for multi-label or multi-class with a subset of classes because it is otherwise identical to accuracy. #12334 by Emmanuel Arias, Joel Nothman and Andreas Müller

• Enhancement Added beta parameter to metrics.homogeneity_completeness_v_measure and metrics.v_measure_score to configure the tradeoff between homogeneity and completeness. #13607 by Stephane Couvreur and and Ivan Sanchez.

• Fix The metric metrics.r2_score is degenerate with a single sample and now it returns NaN and raises exceptions.UndefinedMetricWarning. #12855 by Pawel Sendyk.

• Fix Fixed a bug where metrics.brier_score_loss will sometimes return incorrect result when there’s only one class in y_true. #13628 by Hanmin Qin.

• Fix Fixed a bug in metrics.label_ranking_average_precision_score where sample_weight wasn’t taken into account for samples with degenerate labels. #13447 by Dan Ellis.

• API Change The parameter labels in metrics.hamming_loss is deprecated in version 0.21 and will be removed in version 0.23. #10580 by Reshama Shaikh and Sandra Mitrovic.

• Fix The function metrics.pairwise.euclidean_distances, and therefore several estimators with metric='euclidean', suffered from numerical precision issues with float32 features. Precision has been increased at the cost of a small drop of performance. #13554 by @Celelibi and Jérémie du Boisberranger.

• API Change metrics.jaccard_similarity_score is deprecated in favour of the more consistent metrics.jaccard_score. The former behavior for binary and multiclass targets is broken. #13151 by Joel Nothman.

sklearn.mixture

• Fix Fixed a bug in mixture.BaseMixture and therefore on estimators based on it, i.e. mixture.GaussianMixture and mixture.BayesianGaussianMixture, where fit_predict and fit.predict were not equivalent. #13142 by Jérémie du Boisberranger.

sklearn.model_selection

• Feature Classes GridSearchCV and RandomizedSearchCV now allow for refit=callable to add flexibility in identifying the best estimator. See Balance model complexity and cross-validated score. #11354 by Wenhao Zhang, Joel Nothman and Adrin Jalali.

• Enhancement Classes GridSearchCV, RandomizedSearchCV, and methods cross_val_score, cross_val_predict, cross_validate, now print train scores when return_train_scores is True and verbose > 2. For learning_curve, and validation_curve only the latter is required. #12613 and #12669 by Marc Torrellas.

• Enhancement Some CV splitter classes and model_selection.train_test_split now raise ValueError when the resulting training set is empty. #12861 by Nicolas Hug.

• Fix Fixed a bug where model_selection.StratifiedKFold shuffles each class’s samples with the same random_state, making shuffle=True ineffective. #13124 by Hanmin Qin.

• Fix Added ability for model_selection.cross_val_predict to handle multi-label (and multioutput-multiclass) targets with predict_proba-type methods. #8773 by Stephen Hoover.

• Fix Fixed an issue in cross_val_predict where method="predict_proba" returned always 0.0 when one of the classes was excluded in a cross-validation fold. #13366 by Guillaume Fournier

sklearn.multiclass

• Fix Fixed an issue in multiclass.OneVsOneClassifier.decision_function where the decision_function value of a given sample was different depending on whether the decision_function was evaluated on the sample alone or on a batch containing this same sample due to the scaling used in decision_function. #10440 by Jonathan Ohayon.

sklearn.multioutput

• Fix Fixed a bug in multioutput.MultiOutputClassifier where the predict_proba method incorrectly checked for predict_proba attribute in the estimator object. #12222 by Rebekah Kim

sklearn.neighbors

• Major Feature Added neighbors.NeighborhoodComponentsAnalysis for metric learning, which implements the Neighborhood Components Analysis algorithm. #10058 by William de Vazelhes and John Chiotellis.

• API Change Methods in neighbors.NearestNeighbors : kneighbors, radius_neighbors, kneighbors_graph, radius_neighbors_graph now raise NotFittedError, rather than AttributeError, when called before fit #12279 by Krishna Sangeeth.

sklearn.neural_network

• Fix Fixed a bug in neural_network.MLPClassifier and neural_network.MLPRegressor where the option shuffle=False was being ignored. #12582 by Sam Waterbury.

• Fix Fixed a bug in neural_network.MLPClassifier where validation sets for early stopping were not sampled with stratification. In the multilabel case however, splits are still not stratified. #13164 by Nicolas Hug.

sklearn.pipeline

• Feature pipeline.Pipeline can now use indexing notation (e.g. my_pipeline[0:-1]) to extract a subsequence of steps as another Pipeline instance. A Pipeline can also be indexed directly to extract a particular step (e.g. my_pipeline['svc']), rather than accessing named_steps. #2568 by Joel Nothman.

• Feature Added optional parameter verbose in pipeline.Pipeline, compose.ColumnTransformer and pipeline.FeatureUnion and corresponding make_ helpers for showing progress and timing of each step. #11364 by Baze Petrushev, Karan Desai, Joel Nothman, and Thomas Fan.

• Enhancement pipeline.Pipeline now supports using 'passthrough' as a transformer, with the same effect as None. #11144 by Thomas Fan.

• Enhancement pipeline.Pipeline implements __len__ and therefore len(pipeline) returns the number of steps in the pipeline. #13439 by Lakshya KD.

sklearn.preprocessing

• Feature preprocessing.OneHotEncoder now supports dropping one feature per category with a new drop parameter. #12908 by Drew Johnston.

• Efficiency preprocessing.OneHotEncoder and preprocessing.OrdinalEncoder now handle pandas DataFrames more efficiently. #13253 by @maikia.

• Efficiency Make preprocessing.MultiLabelBinarizer cache class mappings instead of calculating it every time on the fly. #12116 by Ekaterina Krivich and Joel Nothman.

• Efficiency preprocessing.PolynomialFeatures now supports compressed sparse row (CSR) matrices as input for degrees 2 and 3. This is typically much faster than the dense case as it scales with matrix density and expansion degree (on the order of density^degree), and is much, much faster than the compressed sparse column (CSC) case. #12197 by Andrew Nystrom.

• Efficiency Speed improvement in preprocessing.PolynomialFeatures, in the dense case. Also added a new parameter order which controls output order for further speed performances. #12251 by Tom Dupre la Tour.

• Fix Fixed the calculation overflow when using a float16 dtype with preprocessing.StandardScaler. #13007 by Raffaello Baluyot

• Fix Fixed a bug in preprocessing.QuantileTransformer and preprocessing.quantile_transform to force n_quantiles to be at most equal to n_samples. Values of n_quantiles larger than n_samples were either useless or resulting in a wrong approximation of the cumulative distribution function estimator. #13333 by Albert Thomas.

• API Change The default value of copy in preprocessing.quantile_transform will change from False to True in 0.23 in order to make it more consistent with the default copy values of other functions in preprocessing and prevent unexpected side effects by modifying the value of X inplace. #13459 by Hunter McGushion.

sklearn.svm

• Fix Fixed an issue in svm.SVC.decision_function when decision_function_shape='ovr'. The decision_function value of a given sample was different depending on whether the decision_function was evaluated on the sample alone or on a batch containing this same sample due to the scaling used in decision_function. #10440 by Jonathan Ohayon.

sklearn.tree

• Feature Decision Trees can now be plotted with matplotlib using tree.plot_tree without relying on the dot library, removing a hard-to-install dependency. #8508 by Andreas Müller.

• Feature Decision Trees can now be exported in a human readable textual format using tree.export_text. #6261 by Giuseppe Vettigli <JustGlowing>.

• Feature get_n_leaves() and get_depth() have been added to tree.BaseDecisionTree and consequently all estimators based on it, including tree.DecisionTreeClassifier, tree.DecisionTreeRegressor, tree.ExtraTreeClassifier, and tree.ExtraTreeRegressor. #12300 by Adrin Jalali.

• Fix Trees and forests did not previously predict multi-output classification targets with string labels, despite accepting them in fit. #11458 by Mitar Milutinovic.

• Fix Fixed an issue with tree.BaseDecisionTree and consequently all estimators based on it, including tree.DecisionTreeClassifier, tree.DecisionTreeRegressor, tree.ExtraTreeClassifier, and tree.ExtraTreeRegressor, where they used to exceed the given max_depth by 1 while expanding the tree if max_leaf_nodes and max_depth were both specified by the user. Please note that this also affects all ensemble methods using decision trees. #12344 by Adrin Jalali.

sklearn.utils

• Feature utils.resample now accepts a stratify parameter for sampling according to class distributions. #13549 by Nicolas Hug.

• API Change Deprecated warn_on_dtype parameter from utils.check_array and utils.check_X_y. Added explicit warning for dtype conversion in check_pairwise_arrays if the metric being passed is a pairwise boolean metric. #13382 by Prathmesh Savale.

Multiple modules

• Major Feature The __repr__() method of all estimators (used when calling print(estimator)) has been entirely re-written, building on Python’s pretty printing standard library. All parameters are printed by default, but this can be altered with the print_changed_only option in sklearn.set_config. #11705 by Nicolas Hug.

• Major Feature Add estimators tags: these are annotations of estimators that allow programmatic inspection of their capabilities, such as sparse matrix support, supported output types and supported methods. Estimator tags also determine the tests that are run on an estimator when check_estimator is called. Read more in the User Guide. #8022 by Andreas Müller.

• Efficiency Memory copies are avoided when casting arrays to a different dtype in multiple estimators. #11973 by Roman Yurchak.

• Fix Fixed a bug in the implementation of the our_rand_r helper function that was not behaving consistently across platforms. #13422 by Madhura Parikh and Clément Doumouro.

Miscellaneous

• Enhancement Joblib is no longer vendored in scikit-learn, and becomes a dependency. Minimal supported version is joblib 0.11, however using version >= 0.13 is strongly recommended. #13531 by Roman Yurchak.

Changes to estimator checks

These changes mostly affect library developers.

• Add check_fit_idempotent to check_estimator, which checks that when fit is called twice with the same data, the output of predict, predict_proba, transform, and decision_function does not change. #12328 by Nicolas Hug

• Many checks can now be disabled or configured with Estimator Tags. #8022 by Andreas Müller.

Code and Documentation Contributors

Thanks to everyone who has contributed to the maintenance and improvement of the project since version 0.20, including:

adanhawth, Aditya Vyas, Adrin Jalali, Agamemnon Krasoulis, Albert Thomas, Alberto Torres, Alexandre Gramfort, amourav, Andrea Navarrete, Andreas Mueller, Andrew Nystrom, assiaben, Aurélien Bellet, Bartosz Michałowski, Bartosz Telenczuk, bauks, BenjaStudio, bertrandhaut, Bharat Raghunathan, brentfagan, Bryan Woods, Cat Chenal, Cheuk Ting Ho, Chris Choe, Christos Aridas, Clément Doumouro, Cole Smith, Connossor, Corey Levinson, Dan Ellis, Dan Stine, Danylo Baibak, daten-kieker, Denis Kataev, Didi Bar-Zev, Dillon Gardner, Dmitry Mottl, Dmitry Vukolov, Dougal J. Sutherland, Dowon, drewmjohnston, Dror Atariah, Edward J Brown, Ekaterina Krivich, Elizabeth Sander, Emmanuel Arias, Eric Chang, Eric Larson, Erich Schubert, esvhd, Falak, Feda Curic, Federico Caselli, Frank Hoang, Fibinse Xavier`, Finn O’Shea, Gabriel Marzinotto, Gabriel Vacaliuc, Gabriele Calvo, Gael Varoquaux, GauravAhlawat, Giuseppe Vettigli, Greg Gandenberger, Guillaume Fournier, Guillaume Lemaitre, Gustavo De Mari Pereira, Hanmin Qin, haroldfox, hhu-luqi, Hunter McGushion, Ian Sanders, JackLangerman, Jacopo Notarstefano, jakirkham, James Bourbeau, Jan Koch, Jan S, janvanrijn, Jarrod Millman, jdethurens, jeremiedbb, JF, joaak, Joan Massich, Joel Nothman, Jonathan Ohayon, Joris Van den Bossche, josephsalmon, Jérémie Méhault, Katrin Leinweber, ken, kms15, Koen, Kossori Aruku, Krishna Sangeeth, Kuai Yu, Kulbear, Kushal Chauhan, Kyle Jackson, Lakshya KD, Leandro Hermida, Lee Yi Jie Joel, Lily Xiong, Lisa Sarah Thomas, Loic Esteve, louib, luk-f-a, maikia, mail-liam, Manimaran, Manuel López-Ibáñez, Marc Torrellas, Marco Gaido, Marco Gorelli, MarcoGorelli, marineLM, Mark Hannel, Martin Gubri, Masstran, mathurinm, Matthew Roeschke, Max Copeland, melsyt, mferrari3, Mickaël Schoentgen, Ming Li, Mitar, Mohammad Aftab, Mohammed AbdelAal, Mohammed Ibraheem, Muhammad Hassaan Rafique, mwestt, Naoya Iijima, Nicholas Smith, Nicolas Goix, Nicolas Hug, Nikolay Shebanov, Oleksandr Pavlyk, Oliver Rausch, Olivier Grisel, Orestis, Osman, Owen Flanagan, Paul Paczuski, Pavel Soriano, pavlos kallis, Pawel Sendyk, peay, Peter, Peter Cock, Peter Hausamann, Peter Marko, Pierre Glaser, pierretallotte, Pim de Haan, Piotr Szymański, Prabakaran Kumaresshan, Pradeep Reddy Raamana, Prathmesh Savale, Pulkit Maloo, Quentin Batista, Radostin Stoyanov, Raf Baluyot, Rajdeep Dua, Ramil Nugmanov, Raúl García Calvo, Rebekah Kim, Reshama Shaikh, Rohan Lekhwani, Rohan Singh, Rohan Varma, Rohit Kapoor, Roman Feldbauer, Roman Yurchak, Romuald M, Roopam Sharma, Ryan, Rüdiger Busche, Sam Waterbury, Samuel O. Ronsin, SandroCasagrande, Scott Cole, Scott Lowe, Sebastian Raschka, Shangwu Yao, Shivam Kotwalia, Shiyu Duan, smarie, Sriharsha Hatwar, Stephen Hoover, Stephen Tierney, Stéphane Couvreur, surgan12, SylvainLan, TakingItCasual, Tashay Green, thibsej, Thomas Fan, Thomas J Fan, Thomas Moreau, Tom Dupré la Tour, Tommy, Tulio Casagrande, Umar Farouk Umar, Utkarsh Upadhyay, Vinayak Mehta, Vishaal Kapoor, Vivek Kumar, Vlad Niculae, vqean3, Wenhao Zhang, William de Vazelhes, xhan, Xing Han Lu, xinyuliu12, Yaroslav Halchenko, Zach Griffith, Zach Miller, Zayd Hammoudeh, Zhuyi Xue, Zijie (ZJ) Poh, ^__^