Release History

Release notes for current and recent releases are detailed on this page, with previous releases linked below.

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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.

Version 0.21.3

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.

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.

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.

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.

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 generalized 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 algoritm 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.
• 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
  

  #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 generalized 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.
• 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 ouput 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, ^__^

Version 0.20.4

July 30, 2019

This is a bug-fix release with some bug fixes applied to version 0.20.3.

Changelog

The bundled version of joblib was upgraded from 0.13.0 to 0.13.2.

sklearn.cluster

• 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.

sklearn.model_selection

• 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.

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 0.20.3

March 1, 2019

This is a bug-fix release with some minor documentation improvements and enhancements to features released in 0.20.0.

Changelog

sklearn.cluster

• Fix Fixed a bug in cluster.KMeans where computation was single threaded when n_jobs > 1 or n_jobs = -1. #12949 by Prabakaran Kumaresshan.

sklearn.compose

• Fix Fixed a bug in compose.ColumnTransformer to handle negative indexes in the columns list of the transformers. #12946 by Pierre Tallotte.

sklearn.covariance

• Fix Fixed a regression in covariance.graphical_lasso so that the case n_features=2 is handled correctly. #13276 by Aurélien Bellet.

sklearn.decomposition

• Fix Fixed a bug in decomposition.sparse_encode where computation was single threaded when n_jobs > 1 or n_jobs = -1. #13005 by Prabakaran Kumaresshan.

sklearn.datasets

• Efficiency sklearn.datasets.fetch_openml now loads data by streaming, avoiding high memory usage. #13312 by Joris Van den Bossche.

sklearn.feature_extraction

• Fix Fixed a bug in feature_extraction.text.CountVectorizer which would result in the sparse feature matrix having conflicting indptr and indices precisions under very large vocabularies. #11295 by Gabriel Vacaliuc.

sklearn.impute

• Fix add support for non-numeric data in sklearn.impute.MissingIndicator which was not supported while sklearn.impute.SimpleImputer was supporting this for some imputation strategies. #13046 by Guillaume Lemaitre.

sklearn.linear_model

• Fix Fixed a bug in linear_model.MultiTaskElasticNet and linear_model.MultiTaskLasso which were breaking when warm_start = True. #12360 by Aakanksha Joshi.

sklearn.preprocessing

• Fix Fixed a bug in preprocessing.KBinsDiscretizer where strategy='kmeans' fails with an error during transformation due to unsorted bin edges. #13134 by Sandro Casagrande.
• Fix Fixed a bug in preprocessing.OneHotEncoder where the deprecation of categorical_features was handled incorrectly in combination with handle_unknown='ignore'. #12881 by Joris Van den Bossche.
• Fix Bins whose width are too small (i.e., <= 1e-8) are removed with a warning in preprocessing.KBinsDiscretizer. #13165 by Hanmin Qin.

sklearn.svm

• Fix Fixed a bug in svm.SVC, svm.NuSVC, svm.SVR, svm.NuSVR and svm.OneClassSVM where the scale option of parameter gamma is erroneously defined as 1 / (n_features * X.std()). It’s now defined as 1 / (n_features * X.var()). #13221 by Hanmin Qin.

Code and Documentation Contributors

With thanks to:

Adrin Jalali, Agamemnon Krasoulis, Albert Thomas, Andreas Mueller, Aurélien Bellet, bertrandhaut, Bharat Raghunathan, Dowon, Emmanuel Arias, Fibinse Xavier, Finn O’Shea, Gabriel Vacaliuc, Gael Varoquaux, Guillaume Lemaitre, Hanmin Qin, joaak, Joel Nothman, Joris Van den Bossche, Jérémie Méhault, kms15, Kossori Aruku, Lakshya KD, maikia, Manuel López-Ibáñez, Marco Gorelli, MarcoGorelli, mferrari3, Mickaël Schoentgen, Nicolas Hug, pavlos kallis, Pierre Glaser, pierretallotte, Prabakaran Kumaresshan, Reshama Shaikh, Rohit Kapoor, Roman Yurchak, SandroCasagrande, Tashay Green, Thomas Fan, Vishaal Kapoor, Zhuyi Xue, Zijie (ZJ) Poh

Version 0.20.2

December 20, 2018

This is a bug-fix release with some minor documentation improvements and enhancements to features released in 0.20.0.

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.

• sklearn.neighbors when metric=='jaccard' (bug fix)
• use of 'seuclidean' or 'mahalanobis' metrics in some cases (bug fix)

Changelog

sklearn.compose

• Fix Fixed an issue in compose.make_column_transformer which raises unexpected error when columns is pandas Index or pandas Series. #12704 by Hanmin Qin.

sklearn.metrics

• Fix Fixed a bug in metrics.pairwise_distances and metrics.pairwise_distances_chunked where parameters V of "seuclidean" and VI of "mahalanobis" metrics were computed after the data was split into chunks instead of being pre-computed on whole data. #12701 by Jeremie du Boisberranger.

sklearn.neighbors

• Fix Fixed sklearn.neighbors.DistanceMetric jaccard distance function to return 0 when two all-zero vectors are compared. #12685 by Thomas Fan.

sklearn.utils

• Fix Calling utils.check_array on pandas.Series with categorical data, which raised an error in 0.20.0, now returns the expected output again. #12699 by Joris Van den Bossche.

Code and Documentation Contributors

With thanks to:

adanhawth, Adrin Jalali, Albert Thomas, Andreas Mueller, Dan Stine, Feda Curic, Hanmin Qin, Jan S, jeremiedbb, Joel Nothman, Joris Van den Bossche, josephsalmon, Katrin Leinweber, Loic Esteve, Muhammad Hassaan Rafique, Nicolas Hug, Olivier Grisel, Paul Paczuski, Reshama Shaikh, Sam Waterbury, Shivam Kotwalia, Thomas Fan

Version 0.20.1

November 21, 2018

This is a bug-fix release with some minor documentation improvements and enhancements to features released in 0.20.0. Note that we also include some API changes in this release, so you might get some extra warnings after updating from 0.20.0 to 0.20.1.

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.

• decomposition.IncrementalPCA (bug fix)

Changelog

sklearn.cluster

• Efficiency make cluster.MeanShift no longer try to do nested parallelism as the overhead would hurt performance significantly when n_jobs > 1. #12159 by Olivier Grisel.
• Fix Fixed a bug in cluster.DBSCAN with precomputed sparse neighbors graph, which would add explicitly zeros on the diagonal even when already present. #12105 by Tom Dupre la Tour.

sklearn.compose

• Fix Fixed an issue in compose.ColumnTransformer when stacking columns with types not convertible to a numeric. #11912 by Adrin Jalali.
• API Change compose.ColumnTransformer now applies the sparse_threshold even if all transformation results are sparse. #12304 by Andreas Müller.
• API Change compose.make_column_transformer now expects (transformer, columns) instead of (columns, transformer) to keep consistent with compose.ColumnTransformer. #12339 by Adrin Jalali.

sklearn.datasets

• Fix datasets.fetch_openml to correctly use the local cache. #12246 by Jan N. van Rijn.
• Fix datasets.fetch_openml to correctly handle ignore attributes and row id attributes. #12330 by Jan N. van Rijn.
• Fix Fixed integer overflow in datasets.make_classification for values of n_informative parameter larger than 64. #10811 by Roman Feldbauer.
• Fix Fixed olivetti faces dataset DESCR attribute to point to the right location in datasets.fetch_olivetti_faces. #12441 by Jérémie du Boisberranger
• Fix datasets.fetch_openml to retry downloading when reading from local cache fails. #12517 by Thomas Fan.

sklearn.decomposition

• Fix Fixed a regression in decomposition.IncrementalPCA where 0.20.0 raised an error if the number of samples in the final batch for fitting IncrementalPCA was smaller than n_components. #12234 by Ming Li.

sklearn.ensemble

• Fix Fixed a bug mostly affecting ensemble.RandomForestClassifier where class_weight='balanced_subsample' failed with more than 32 classes. #12165 by Joel Nothman.
• Fix Fixed a bug affecting ensemble.BaggingClassifier, ensemble.BaggingRegressor and ensemble.IsolationForest, where max_features was sometimes rounded down to zero. #12388 by Connor Tann.

sklearn.feature_extraction

• Fix Fixed a regression in v0.20.0 where feature_extraction.text.CountVectorizer and other text vectorizers could error during stop words validation with custom preprocessors or tokenizers. #12393 by Roman Yurchak.

sklearn.linear_model

• Fix linear_model.SGDClassifier and variants with early_stopping=True would not use a consistent validation split in the multiclass case and this would cause a crash when using those estimators as part of parallel parameter search or cross-validation. #12122 by Olivier Grisel.
• Fix Fixed a bug affecting SGDClassifier in the multiclass case. Each one-versus-all step is run in a joblib.Parallel call and mutating a common parameter, causing a segmentation fault if called within a backend using processes and not threads. We now use require=sharedmem at the joblib.Parallel instance creation. #12518 by Pierre Glaser and Olivier Grisel.

sklearn.metrics

• Fix Fixed a bug in metrics.pairwise.pairwise_distances_argmin_min which returned the square root of the distance when the metric parameter was set to “euclidean”. #12481 by Jérémie du Boisberranger.
• Fix Fixed a bug in metrics.pairwise.pairwise_distances_chunked which didn’t ensure the diagonal is zero for euclidean distances. #12612 by Andreas Müller.
• API Change The metrics.calinski_harabaz_score has been renamed to metrics.calinski_harabasz_score and will be removed in version 0.23. #12211 by Lisa Thomas, Mark Hannel and Melissa Ferrari.

sklearn.mixture

• Fix Ensure that the fit_predict method of mixture.GaussianMixture and mixture.BayesianGaussianMixture always yield assignments consistent with fit followed by predict even if the convergence criterion is too loose or not met. #12451 by Olivier Grisel.

sklearn.neighbors

• Fix force the parallelism backend to threading for neighbors.KDTree and neighbors.BallTree in Python 2.7 to avoid pickling errors caused by the serialization of their methods. #12171 by Thomas Moreau.

sklearn.preprocessing

• Fix Fixed bug in preprocessing.OrdinalEncoder when passing manually specified categories. #12365 by Joris Van den Bossche.
• Fix Fixed bug in preprocessing.KBinsDiscretizer where the transform method mutates the _encoder attribute. The transform method is now thread safe. #12514 by Hanmin Qin.
• Fix Fixed a bug in preprocessing.PowerTransformer where the Yeo-Johnson transform was incorrect for lambda parameters outside of [0, 2] #12522 by Nicolas Hug.
• Fix Fixed a bug in preprocessing.OneHotEncoder where transform failed when set to ignore unknown numpy strings of different lengths #12471 by Gabriel Marzinotto.
• API Change The default value of the method argument in preprocessing.power_transform will be changed from box-cox to yeo-johnson to match preprocessing.PowerTransformer in version 0.23. A FutureWarning is raised when the default value is used. #12317 by Eric Chang.

sklearn.utils

• Fix Use float64 for mean accumulator to avoid floating point precision issues in preprocessing.StandardScaler and decomposition.IncrementalPCA when using float32 datasets. #12338 by bauks.
• Fix Calling utils.check_array on pandas.Series, which raised an error in 0.20.0, now returns the expected output again. #12625 by Andreas Müller

Miscellaneous

• Fix When using site joblib by setting the environment variable SKLEARN_SITE_JOBLIB, added compatibility with joblib 0.11 in addition to 0.12+. #12350 by Joel Nothman and Roman Yurchak.
• Fix Make sure to avoid raising FutureWarning when calling np.vstack with numpy 1.16 and later (use list comprehensions instead of generator expressions in many locations of the scikit-learn code base). #12467 by Olivier Grisel.
• API Change Removed all mentions of sklearn.externals.joblib, and deprecated joblib methods exposed in sklearn.utils, except for utils.parallel_backend and utils.register_parallel_backend, which allow users to configure parallel computation in scikit-learn. Other functionalities are part of joblib. package and should be used directly, by installing it. The goal of this change is to prepare for unvendoring joblib in future version of scikit-learn. #12345 by Thomas Moreau

Code and Documentation Contributors

With thanks to:

^__^, Adrin Jalali, Andrea Navarrete, Andreas Mueller, bauks, BenjaStudio, Cheuk Ting Ho, Connossor, Corey Levinson, Dan Stine, daten-kieker, Denis Kataev, Dillon Gardner, Dmitry Vukolov, Dougal J. Sutherland, Edward J Brown, Eric Chang, Federico Caselli, Gabriel Marzinotto, Gael Varoquaux, GauravAhlawat, Gustavo De Mari Pereira, Hanmin Qin, haroldfox, JackLangerman, Jacopo Notarstefano, janvanrijn, jdethurens, jeremiedbb, Joel Nothman, Joris Van den Bossche, Koen, Kushal Chauhan, Lee Yi Jie Joel, Lily Xiong, mail-liam, Mark Hannel, melsyt, Ming Li, Nicholas Smith, Nicolas Hug, Nikolay Shebanov, Oleksandr Pavlyk, Olivier Grisel, Peter Hausamann, Pierre Glaser, Pulkit Maloo, Quentin Batista, Radostin Stoyanov, Ramil Nugmanov, Rebekah Kim, Reshama Shaikh, Rohan Singh, Roman Feldbauer, Roman Yurchak, Roopam Sharma, Sam Waterbury, Scott Lowe, Sebastian Raschka, Stephen Tierney, SylvainLan, TakingItCasual, Thomas Fan, Thomas Moreau, Tom Dupré la Tour, Tulio Casagrande, Utkarsh Upadhyay, Xing Han Lu, Yaroslav Halchenko, Zach Miller

Version 0.20.0

September 25, 2018

This release packs in a mountain of bug fixes, features and enhancements for the Scikit-learn library, and improvements to the documentation and examples. Thanks to our contributors!

This release is dedicated to the memory of Raghav Rajagopalan.

Warning

Version 0.20 is the last version of scikit-learn to support Python 2.7 and Python 3.4. Scikit-learn 0.21 will require Python 3.5 or higher.

Highlights

We have tried to improve our support for common data-science use-cases including missing values, categorical variables, heterogeneous data, and features/targets with unusual distributions. Missing values in features, represented by NaNs, are now accepted in column-wise preprocessing such as scalers. Each feature is fitted disregarding NaNs, and data containing NaNs can be transformed. The new impute module provides estimators for learning despite missing data.

ColumnTransformer handles the case where different features or columns of a pandas.DataFrame need different preprocessing. String or pandas Categorical columns can now be encoded with OneHotEncoder or OrdinalEncoder.

TransformedTargetRegressor helps when the regression target needs to be transformed to be modeled. PowerTransformer and KBinsDiscretizer join QuantileTransformer as non-linear transformations.

Beyond this, we have added sample_weight support to several estimators (including KMeans, BayesianRidge and KernelDensity) and improved stopping criteria in others (including MLPRegressor, GradientBoostingRegressor and SGDRegressor).

This release is also the first to be accompanied by a Glossary of Common Terms and API Elements developed by Joel Nothman. The glossary is a reference resource to help users and contributors become familiar with the terminology and conventions used in Scikit-learn.

Sorry if your contribution didn’t make it into the highlights. There’s a lot here…

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.

• cluster.MeanShift (bug fix)
• decomposition.IncrementalPCA in Python 2 (bug fix)
• decomposition.SparsePCA (bug fix)
• ensemble.GradientBoostingClassifier (bug fix affecting feature importances)
• isotonic.IsotonicRegression (bug fix)
• linear_model.ARDRegression (bug fix)
• linear_model.LogisticRegressionCV (bug fix)
• linear_model.OrthogonalMatchingPursuit (bug fix)
• linear_model.PassiveAggressiveClassifier (bug fix)
• linear_model.PassiveAggressiveRegressor (bug fix)
• linear_model.Perceptron (bug fix)
• linear_model.SGDClassifier (bug fix)
• linear_model.SGDRegressor (bug fix)
• metrics.roc_auc_score (bug fix)
• metrics.roc_curve (bug fix)
• neural_network.BaseMultilayerPerceptron (bug fix)
• neural_network.MLPClassifier (bug fix)
• neural_network.MLPRegressor (bug fix)
• The v0.19.0 release notes failed to mention a backwards incompatibility with model_selection.StratifiedKFold when shuffle=True due to #7823.

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

• #11924: linear_model.LogisticRegressionCV with solver='lbfgs' and multi_class='multinomial' may be non-deterministic or otherwise broken on macOS. This appears to be the case on Travis CI servers, but has not been confirmed on personal MacBooks! This issue has been present in previous releases.
• #9354: metrics.pairwise.euclidean_distances (which is used several times throughout the library) gives results with poor precision, which particularly affects its use with 32-bit float inputs. This became more problematic in versions 0.18 and 0.19 when some algorithms were changed to avoid casting 32-bit data into 64-bit.

Changelog

Support for Python 3.3 has been officially dropped.

sklearn.cluster

• Major Feature cluster.AgglomerativeClustering now supports Single Linkage clustering via linkage='single'. #9372 by Leland McInnes and Steve Astels.
• Feature cluster.KMeans and cluster.MiniBatchKMeans now support sample weights via new parameter sample_weight in fit function. #10933 by Johannes Hansen.
• Efficiency cluster.KMeans, cluster.MiniBatchKMeans and cluster.k_means passed with algorithm='full' now enforces row-major ordering, improving runtime. #10471 by Gaurav Dhingra.
• Efficiency cluster.DBSCAN now is parallelized according to n_jobs regardless of algorithm. #8003 by Joël Billaud.
• Enhancement cluster.KMeans now gives a warning if the number of distinct clusters found is smaller than n_clusters. This may occur when the number of distinct points in the data set is actually smaller than the number of cluster one is looking for. #10059 by Christian Braune.
• Fix Fixed a bug where the fit method of cluster.AffinityPropagation stored cluster centers as 3d array instead of 2d array in case of non-convergence. For the same class, fixed undefined and arbitrary behavior in case of training data where all samples had equal similarity. #9612. By Jonatan Samoocha.
• Fix Fixed a bug in cluster.spectral_clustering where the normalization of the spectrum was using a division instead of a multiplication. #8129 by Jan Margeta, Guillaume Lemaitre, and Devansh D..
• Fix Fixed a bug in cluster.k_means_elkan where the returned iteration was 1 less than the correct value. Also added the missing n_iter_ attribute in the docstring of cluster.KMeans. #11353 by Jeremie du Boisberranger.
• Fix Fixed a bug in cluster.mean_shift where the assigned labels were not deterministic if there were multiple clusters with the same intensities. #11901 by Adrin Jalali.
• API Change Deprecate pooling_func unused parameter in cluster.AgglomerativeClustering. #9875 by Kumar Ashutosh.

sklearn.compose

• New module.
• Major Feature Added compose.ColumnTransformer, which allows to apply different transformers to different columns of arrays or pandas DataFrames. #9012 by Andreas Müller and Joris Van den Bossche, and #11315 by Thomas Fan.
• Major Feature Added the compose.TransformedTargetRegressor which transforms the target y before fitting a regression model. The predictions are mapped back to the original space via an inverse transform. #9041 by Andreas Müller and Guillaume Lemaitre.

sklearn.covariance

• Efficiency Runtime improvements to covariance.GraphicalLasso. #9858 by Steven Brown.
• API Change The covariance.graph_lasso, covariance.GraphLasso and covariance.GraphLassoCV have been renamed to covariance.graphical_lasso, covariance.GraphicalLasso and covariance.GraphicalLassoCV respectively and will be removed in version 0.22. #9993 by Artiem Krinitsyn

sklearn.datasets

• Major Feature Added datasets.fetch_openml to fetch datasets from OpenML. OpenML is a free, open data sharing platform and will be used instead of mldata as it provides better service availability. #9908 by Andreas Müller and Jan N. van Rijn.
• Feature In datasets.make_blobs, one can now pass a list to the n_samples parameter to indicate the number of samples to generate per cluster. #8617 by Maskani Filali Mohamed and Konstantinos Katrioplas.
• Feature Add filename attribute to datasets that have a CSV file. #9101 by alex-33 and Maskani Filali Mohamed.
• Feature return_X_y parameter has been added to several dataset loaders. #10774 by Chris Catalfo.
• Fix Fixed a bug in datasets.load_boston which had a wrong data point. #10795 by Takeshi Yoshizawa.
• Fix Fixed a bug in datasets.load_iris which had two wrong data points. #11082 by Sadhana Srinivasan and Hanmin Qin.
• Fix Fixed a bug in datasets.fetch_kddcup99, where data were not properly shuffled. #9731 by Nicolas Goix.
• Fix Fixed a bug in datasets.make_circles, where no odd number of data points could be generated. #10045 by Christian Braune.
• API Change Deprecated sklearn.datasets.fetch_mldata to be removed in version 0.22. mldata.org is no longer operational. Until removal it will remain possible to load cached datasets. #11466 by Joel Nothman.

sklearn.decomposition

• Feature decomposition.dict_learning functions and models now support positivity constraints. This applies to the dictionary and sparse code. #6374 by John Kirkham.
• Feature Fix decomposition.SparsePCA now exposes normalize_components. When set to True, the train and test data are centered with the train mean repsectively during the fit phase and the transform phase. This fixes the behavior of SparsePCA. When set to False, which is the default, the previous abnormal behaviour still holds. The False value is for backward compatibility and should not be used. #11585 by Ivan Panico.
• Efficiency Efficiency improvements in decomposition.dict_learning. #11420 and others by John Kirkham.
• Fix Fix for uninformative error in decomposition.IncrementalPCA: now an error is raised if the number of components is larger than the chosen batch size. The n_components=None case was adapted accordingly. #6452. By Wally Gauze.
• Fix Fixed a bug where the partial_fit method of decomposition.IncrementalPCA used integer division instead of float division on Python 2. #9492 by James Bourbeau.
• Fix In decomposition.PCA selecting a n_components parameter greater than the number of samples now raises an error. Similarly, the n_components=None case now selects the minimum of n_samples and n_features. #8484 by Wally Gauze.
• Fix Fixed a bug in decomposition.PCA where users will get unexpected error with large datasets when n_components='mle' on Python 3 versions. #9886 by Hanmin Qin.
• Fix Fixed an underflow in calculating KL-divergence for decomposition.NMF #10142 by Tom Dupre la Tour.
• Fix Fixed a bug in decomposition.SparseCoder when running OMP sparse coding in parallel using read-only memory mapped datastructures. #5956 by Vighnesh Birodkar and Olivier Grisel.

sklearn.discriminant_analysis

• Efficiency Memory usage improvement for _class_means and _class_cov in discriminant_analysis. #10898 by Nanxin Chen.

sklearn.dummy

• Feature dummy.DummyRegressor now has a return_std option in its predict method. The returned standard deviations will be zeros.
• Feature dummy.DummyClassifier and dummy.DummyRegressor now only require X to be an object with finite length or shape. #9832 by Vrishank Bhardwaj.
• Feature dummy.DummyClassifier and dummy.DummyRegressor can now be scored without supplying test samples. #11951 by Rüdiger Busche.

sklearn.ensemble

• Feature ensemble.BaggingRegressor and ensemble.BaggingClassifier can now be fit with missing/non-finite values in X and/or multi-output Y to support wrapping pipelines that perform their own imputation. #9707 by Jimmy Wan.
• Feature ensemble.GradientBoostingClassifier and ensemble.GradientBoostingRegressor now support early stopping via n_iter_no_change, validation_fraction and tol. #7071 by Raghav RV
• Feature Added named_estimators_ parameter in ensemble.VotingClassifier to access fitted estimators. #9157 by Herilalaina Rakotoarison.
• Fix Fixed a bug when fitting ensemble.GradientBoostingClassifier or ensemble.GradientBoostingRegressor with warm_start=True which previously raised a segmentation fault due to a non-conversion of CSC matrix into CSR format expected by decision_function. Similarly, Fortran-ordered arrays are converted to C-ordered arrays in the dense case. #9991 by Guillaume Lemaitre.
• Fix Fixed a bug in ensemble.GradientBoostingRegressor and ensemble.GradientBoostingClassifier to have feature importances summed and then normalized, rather than normalizing on a per-tree basis. The previous behavior over-weighted the Gini importance of features that appear in later stages. This issue only affected feature importances. #11176 by Gil Forsyth.
• API Change The default value of the n_estimators parameter of ensemble.RandomForestClassifier, ensemble.RandomForestRegressor, ensemble.ExtraTreesClassifier, ensemble.ExtraTreesRegressor, and ensemble.RandomTreesEmbedding will change from 10 in version 0.20 to 100 in 0.22. A FutureWarning is raised when the default value is used. #11542 by Anna Ayzenshtat.
• API Change Classes derived from ensemble.BaseBagging. The attribute estimators_samples_ will return a list of arrays containing the indices selected for each bootstrap instead of a list of arrays containing the mask of the samples selected for each bootstrap. Indices allows to repeat samples while mask does not allow this functionality. #9524 by Guillaume Lemaitre.
• Fix ensemble.BaseBagging where one could not deterministically reproduce fit result using the object attributes when random_state is set. #9723 by Guillaume Lemaitre.

sklearn.feature_extraction

• Feature Enable the call to get_feature_names in unfitted feature_extraction.text.CountVectorizer initialized with a vocabulary. #10908 by Mohamed Maskani.
• Enhancement idf_ can now be set on a feature_extraction.text.TfidfTransformer. #10899 by Sergey Melderis.
• Fix Fixed a bug in feature_extraction.image.extract_patches_2d which would throw an exception if max_patches was greater than or equal to the number of all possible patches rather than simply returning the number of possible patches. #10101 by Varun Agrawal
• Fix Fixed a bug in feature_extraction.text.CountVectorizer, feature_extraction.text.TfidfVectorizer, feature_extraction.text.HashingVectorizer to support 64 bit sparse array indexing necessary to process large datasets with more than 2·10⁹ tokens (words or n-grams). #9147 by Claes-Fredrik Mannby and Roman Yurchak.
• Fix Fixed bug in feature_extraction.text.TfidfVectorizer which was ignoring the parameter dtype. In addition, feature_extraction.text.TfidfTransformer will preserve dtype for floating and raise a warning if dtype requested is integer. #10441 by Mayur Kulkarni and Guillaume Lemaitre.

sklearn.feature_selection

• Feature Added select K best features functionality to feature_selection.SelectFromModel. #6689 by Nihar Sheth and Quazi Rahman.
• Feature Added min_features_to_select parameter to feature_selection.RFECV to bound evaluated features counts. #11293 by Brent Yi.
• Feature feature_selection.RFECV’s fit method now supports groups. #9656 by Adam Greenhall.
• Fix Fixed computation of n_features_to_compute for edge case with tied CV scores in feature_selection.RFECV. #9222 by Nick Hoh.

sklearn.gaussian_process

• Efficiency In gaussian_process.GaussianProcessRegressor, method predict is faster when using return_std=True in particular more when called several times in a row. #9234 by andrewww and Minghui Liu.

sklearn.impute

• New module, adopting preprocessing.Imputer as impute.SimpleImputer with minor changes (see under preprocessing below).
• Major Feature Added impute.MissingIndicator which generates a binary indicator for missing values. #8075 by Maniteja Nandana and Guillaume Lemaitre.
• Feature The impute.SimpleImputer has a new strategy, 'constant', to complete missing values with a fixed one, given by the fill_value parameter. This strategy supports numeric and non-numeric data, and so does the 'most_frequent' strategy now. #11211 by Jeremie du Boisberranger.

sklearn.isotonic

• Fix Fixed a bug in isotonic.IsotonicRegression which incorrectly combined weights when fitting a model to data involving points with identical X values. #9484 by Dallas Card

sklearn.linear_model

• Feature linear_model.SGDClassifier, linear_model.SGDRegressor, linear_model.PassiveAggressiveClassifier, linear_model.PassiveAggressiveRegressor and linear_model.Perceptron now expose early_stopping, validation_fraction and n_iter_no_change parameters, to stop optimization monitoring the score on a validation set. A new learning rate "adaptive" strategy divides the learning rate by 5 each time n_iter_no_change consecutive epochs fail to improve the model. #9043 by Tom Dupre la Tour.
• Feature Add sample_weight parameter to the fit method of linear_model.BayesianRidge for weighted linear regression. #10112 by Peter St. John.
• Fix Fixed a bug in logistic.logistic_regression_path to ensure that the returned coefficients are correct when multiclass='multinomial'. Previously, some of the coefficients would override each other, leading to incorrect results in linear_model.LogisticRegressionCV. #11724 by Nicolas Hug.
• Fix Fixed a bug in linear_model.LogisticRegression where when using the parameter multi_class='multinomial', the predict_proba method was returning incorrect probabilities in the case of binary outcomes. #9939 by Roger Westover.
• Fix Fixed a bug in linear_model.LogisticRegressionCV where the score method always computes accuracy, not the metric given by the scoring parameter. #10998 by Thomas Fan.
• Fix Fixed a bug in linear_model.LogisticRegressionCV where the ‘ovr’ strategy was always used to compute cross-validation scores in the multiclass setting, even if 'multinomial' was set. #8720 by William de Vazelhes.
• Fix Fixed a bug in linear_model.OrthogonalMatchingPursuit that was broken when setting normalize=False. #10071 by Alexandre Gramfort.
• Fix Fixed a bug in linear_model.ARDRegression which caused incorrectly updated estimates for the standard deviation and the coefficients. #10153 by Jörg Döpfert.
• Fix Fixed a bug in linear_model.ARDRegression and linear_model.BayesianRidge which caused NaN predictions when fitted with a constant target. #10095 by Jörg Döpfert.
• Fix Fixed a bug in linear_model.RidgeClassifierCV where the parameter store_cv_values was not implemented though it was documented in cv_values as a way to set up the storage of cross-validation values for different alphas. #10297 by Mabel Villalba-Jiménez.
• Fix Fixed a bug in linear_model.ElasticNet which caused the input to be overridden when using parameter copy_X=True and check_input=False. #10581 by Yacine Mazari.
• Fix Fixed a bug in sklearn.linear_model.Lasso where the coefficient had wrong shape when fit_intercept=False. #10687 by Martin Hahn.
• Fix Fixed a bug in sklearn.linear_model.LogisticRegression where the multi_class='multinomial' with binary output with warm_start=True #10836 by Aishwarya Srinivasan.
• Fix Fixed a bug in linear_model.RidgeCV where using integer alphas raised an error. #10397 by Mabel Villalba-Jiménez.
• Fix Fixed condition triggering gap computation in linear_model.Lasso and linear_model.ElasticNet when working with sparse matrices. #10992 by Alexandre Gramfort.
• Fix Fixed a bug in linear_model.SGDClassifier, linear_model.SGDRegressor, linear_model.PassiveAggressiveClassifier, linear_model.PassiveAggressiveRegressor and linear_model.Perceptron, where the stopping criterion was stopping the algorithm before convergence. A parameter n_iter_no_change was added and set by default to 5. Previous behavior is equivalent to setting the parameter to 1. #9043 by Tom Dupre la Tour.
• Fix Fixed a bug where liblinear and libsvm-based estimators would segfault if passed a scipy.sparse matrix with 64-bit indices. They now raise a ValueError. #11327 by Karan Dhingra and Joel Nothman.
• API Change The default values of the solver and multi_class parameters of linear_model.LogisticRegression will change respectively from 'liblinear' and 'ovr' in version 0.20 to 'lbfgs' and 'auto' in version 0.22. A FutureWarning is raised when the default values are used. #11905 by Tom Dupre la Tour and Joel Nothman.
• API Change Deprecate positive=True option in linear_model.Lars as the underlying implementation is broken. Use linear_model.Lasso instead. #9837 by Alexandre Gramfort.
• API Change n_iter_ may vary from previous releases in linear_model.LogisticRegression with solver='lbfgs' and linear_model.HuberRegressor. For Scipy <= 1.0.0, the optimizer could perform more than the requested maximum number of iterations. Now both estimators will report at most max_iter iterations even if more were performed. #10723 by Joel Nothman.

sklearn.manifold

• Efficiency Speed improvements for both ‘exact’ and ‘barnes_hut’ methods in manifold.TSNE. #10593 and #10610 by Tom Dupre la Tour.
• Feature Support sparse input in manifold.Isomap.fit. #8554 by Leland McInnes.
• Feature manifold.t_sne.trustworthiness accepts metrics other than Euclidean. #9775 by William de Vazelhes.
• Fix Fixed a bug in manifold.spectral_embedding where the normalization of the spectrum was using a division instead of a multiplication. #8129 by Jan Margeta, Guillaume Lemaitre, and Devansh D..
• API Change Feature Deprecate precomputed parameter in function manifold.t_sne.trustworthiness. Instead, the new parameter metric should be used with any compatible metric including ‘precomputed’, in which case the input matrix X should be a matrix of pairwise distances or squared distances. #9775 by William de Vazelhes.
• API Change Deprecate precomputed parameter in function manifold.t_sne.trustworthiness. Instead, the new parameter metric should be used with any compatible metric including ‘precomputed’, in which case the input matrix X should be a matrix of pairwise distances or squared distances. #9775 by William de Vazelhes.

sklearn.metrics

• Major Feature Added the metrics.davies_bouldin_score metric for evaluation of clustering models without a ground truth. #10827 by Luis Osa.
• Major Feature Added the metrics.balanced_accuracy_score metric and a corresponding 'balanced_accuracy' scorer for binary and multiclass classification. #8066 by @xyguo and Aman Dalmia, and #10587 by Joel Nothman.
• Feature Partial AUC is available via max_fpr parameter in metrics.roc_auc_score. #3840 by Alexander Niederbühl.
• Feature A scorer based on metrics.brier_score_loss is also available. #9521 by Hanmin Qin.
• Feature Added control over the normalization in metrics.normalized_mutual_info_score and metrics.adjusted_mutual_info_score via the average_method parameter. In version 0.22, the default normalizer for each will become the arithmetic mean of the entropies of each clustering. #11124 by Arya McCarthy.
• Feature Added output_dict parameter in metrics.classification_report to return classification statistics as dictionary. #11160 by Dan Barkhorn.
• Feature metrics.classification_report now reports all applicable averages on the given data, including micro, macro and weighted average as well as samples average for multilabel data. #11679 by Alexander Pacha.
• Feature metrics.average_precision_score now supports binary y_true other than {0, 1} or {-1, 1} through pos_label parameter. #9980 by Hanmin Qin.
• Feature metrics.label_ranking_average_precision_score now supports sample_weight. #10845 by Jose Perez-Parras Toledano.
• Feature Add dense_output parameter to metrics.pairwise.linear_kernel. When False and both inputs are sparse, will return a sparse matrix. #10999 by Taylor G Smith.
• Efficiency metrics.silhouette_score and metrics.silhouette_samples are more memory efficient and run faster. This avoids some reported freezes and MemoryErrors. #11135 by Joel Nothman.
• Fix Fixed a bug in metrics.precision_recall_fscore_support when truncated range(n_labels) is passed as value for labels. #10377 by Gaurav Dhingra.
• Fix Fixed a bug due to floating point error in metrics.roc_auc_score with non-integer sample weights. #9786 by Hanmin Qin.
• Fix Fixed a bug where metrics.roc_curve sometimes starts on y-axis instead of (0, 0), which is inconsistent with the document and other implementations. Note that this will not influence the result from metrics.roc_auc_score #10093 by alexryndin and Hanmin Qin.
• Fix Fixed a bug to avoid integer overflow. Casted product to 64 bits integer in metrics.mutual_info_score. #9772 by Kumar Ashutosh.
• Fix Fixed a bug where metrics.average_precision_score will sometimes return nan when sample_weight contains 0. #9980 by Hanmin Qin.
• Fix Fixed a bug in metrics.fowlkes_mallows_score to avoid integer overflow. Casted return value of Contingency Matrix to int64 and computed product of square roots rather than square root of product. #9515 by Alan Liddell and Manh Dao.
• API Change Deprecate reorder parameter in metrics.auc as it’s no longer required for metrics.roc_auc_score. Moreover using reorder=True can hide bugs due to floating point error in the input. #9851 by Hanmin Qin.
• API Change In metrics.normalized_mutual_info_score and metrics.adjusted_mutual_info_score, warn that average_method will have a new default value. In version 0.22, the default normalizer for each will become the arithmetic mean of the entropies of each clustering. Currently, metrics.normalized_mutual_info_score uses the default of average_method='geometric', and metrics.adjusted_mutual_info_score uses the default of average_method='max' to match their behaviors in version 0.19. #11124 by Arya McCarthy.
• API Change The batch_size parameter to metrics.pairwise_distances_argmin_min and metrics.pairwise_distances_argmin is deprecated to be removed in v0.22. It no longer has any effect, as batch size is determined by global working_memory config. See Limiting Working Memory. #10280 by Joel Nothman and Aman Dalmia.

sklearn.mixture

• Feature Added function fit_predict to mixture.GaussianMixture and mixture.GaussianMixture, which is essentially equivalent to calling fit and predict. #10336 by Shu Haoran and Andrew Peng.
• Fix Fixed a bug in mixture.BaseMixture where the reported n_iter_ was missing an iteration. It affected mixture.GaussianMixture and mixture.BayesianGaussianMixture. #10740 by Erich Schubert and Guillaume Lemaitre.
• Fix Fixed a bug in mixture.BaseMixture and its subclasses mixture.GaussianMixture and mixture.BayesianGaussianMixture where the lower_bound_ was not the max lower bound across all initializations (when n_init > 1), but just the lower bound of the last initialization. #10869 by Aurélien Géron.

sklearn.model_selection

• Feature Add return_estimator parameter in model_selection.cross_validate to return estimators fitted on each split. #9686 by Aurélien Bellet.
• Feature New refit_time_ attribute will be stored in model_selection.GridSearchCV and model_selection.RandomizedSearchCV if refit is set to True. This will allow measuring the complete time it takes to perform hyperparameter optimization and refitting the best model on the whole dataset. #11310 by Matthias Feurer.
• Feature Expose error_score parameter in model_selection.cross_validate, model_selection.cross_val_score, model_selection.learning_curve and model_selection.validation_curve to control the behavior triggered when an error occurs in model_selection._fit_and_score. #11576 by Samuel O. Ronsin.
• Feature BaseSearchCV now has an experimental, private interface to support customized parameter search strategies, through its _run_search method. See the implementations in model_selection.GridSearchCV and model_selection.RandomizedSearchCV and please provide feedback if you use this. Note that we do not assure the stability of this API beyond version 0.20. #9599 by Joel Nothman
• Enhancement Add improved error message in model_selection.cross_val_score when multiple metrics are passed in scoring keyword. #11006 by Ming Li.
• API Change The default number of cross-validation folds cv and the default number of splits n_splits in the model_selection.KFold-like splitters will change from 3 to 5 in 0.22 as 3-fold has a lot of variance. #11557 by Alexandre Boucaud.
• API Change The default of iid parameter of model_selection.GridSearchCV and model_selection.RandomizedSearchCV will change from True to False in version 0.22 to correspond to the standard definition of cross-validation, and the parameter will be removed in version 0.24 altogether. This parameter is of greatest practical significance where the sizes of different test sets in cross-validation were very unequal, i.e. in group-based CV strategies. #9085 by Laurent Direr and Andreas Müller.
• API Change The default value of the error_score parameter in model_selection.GridSearchCV and model_selection.RandomizedSearchCV will change to np.NaN in version 0.22. #10677 by Kirill Zhdanovich.
• API Change Changed ValueError exception raised in model_selection.ParameterSampler to a UserWarning for case where the class is instantiated with a greater value of n_iter than the total space of parameters in the parameter grid. n_iter now acts as an upper bound on iterations. #10982 by Juliet Lawton
• API Change Invalid input for model_selection.ParameterGrid now raises TypeError. #10928 by Solutus Immensus

sklearn.multioutput

• Major Feature Added multioutput.RegressorChain for multi-target regression. #9257 by Kumar Ashutosh.

sklearn.naive_bayes

• Major Feature Added naive_bayes.ComplementNB, which implements the Complement Naive Bayes classifier described in Rennie et al. (2003). #8190 by Michael A. Alcorn.
• Feature Add var_smoothing parameter in naive_bayes.GaussianNB to give a precise control over variances calculation. #9681 by Dmitry Mottl.
• Fix Fixed a bug in naive_bayes.GaussianNB which incorrectly raised error for prior list which summed to 1. #10005 by Gaurav Dhingra.
• Fix Fixed a bug in naive_bayes.MultinomialNB which did not accept vector valued pseudocounts (alpha). #10346 by Tobias Madsen

sklearn.neighbors

• Efficiency neighbors.RadiusNeighborsRegressor and neighbors.RadiusNeighborsClassifier are now parallelized according to n_jobs regardless of algorithm. #10887 by Joël Billaud.
• Efficiency Nearest neighbors query methods are now more memory efficient when algorithm='brute'. #11136 by Joel Nothman and Aman Dalmia.
• Feature Add sample_weight parameter to the fit method of neighbors.KernelDensity to enable weighting in kernel density estimation. #4394 by Samuel O. Ronsin.
• Feature Novelty detection with neighbors.LocalOutlierFactor: Add a novelty parameter to neighbors.LocalOutlierFactor. When novelty is set to True, neighbors.LocalOutlierFactor can then be used for novelty detection, i.e. predict on new unseen data. Available prediction methods are predict, decision_function and score_samples. By default, novelty is set to False, and only the fit_predict method is avaiable. By Albert Thomas.
• Fix Fixed a bug in neighbors.NearestNeighbors where fitting a NearestNeighbors model fails when a) the distance metric used is a callable and b) the input to the NearestNeighbors model is sparse. #9579 by Thomas Kober.
• Fix Fixed a bug so predict in neighbors.RadiusNeighborsRegressor can handle empty neighbor set when using non uniform weights. Also raises a new warning when no neighbors are found for samples. #9655 by Andreas Bjerre-Nielsen.
• Fix Efficiency Fixed a bug in KDTree construction that results in faster construction and querying times. #11556 by Jake VanderPlas
• Fix Fixed a bug in neighbors.KDTree and neighbors.BallTree where pickled tree objects would change their type to the super class BinaryTree. #11774 by Nicolas Hug.

sklearn.neural_network

• Feature Add n_iter_no_change parameter in neural_network.BaseMultilayerPerceptron, neural_network.MLPRegressor, and neural_network.MLPClassifier to give control over maximum number of epochs to not meet tol improvement. #9456 by Nicholas Nadeau.
• Fix Fixed a bug in neural_network.BaseMultilayerPerceptron, neural_network.MLPRegressor, and neural_network.MLPClassifier with new n_iter_no_change parameter now at 10 from previously hardcoded 2. #9456 by Nicholas Nadeau.
• Fix Fixed a bug in neural_network.MLPRegressor where fitting quit unexpectedly early due to local minima or fluctuations. #9456 by Nicholas Nadeau

sklearn.pipeline

• Feature The predict method of pipeline.Pipeline now passes keyword arguments on to the pipeline’s last estimator, enabling the use of parameters such as return_std in a pipeline with caution. #9304 by Breno Freitas.
• API Change pipeline.FeatureUnion now supports 'drop' as a transformer to drop features. #11144 by Thomas Fan.

sklearn.preprocessing

• Major Feature Expanded preprocessing.OneHotEncoder to allow to encode categorical string features as a numeric array using a one-hot (or dummy) encoding scheme, and added preprocessing.OrdinalEncoder to convert to ordinal integers. Those two classes now handle encoding of all feature types (also handles string-valued features) and derives the categories based on the unique values in the features instead of the maximum value in the features. #9151 and #10521 by Vighnesh Birodkar and Joris Van den Bossche.
• Major Feature Added preprocessing.KBinsDiscretizer for turning continuous features into categorical or one-hot encoded features. #7668, #9647, #10195, #10192, #11272, #11467 and #11505. by Henry Lin, Hanmin Qin, Tom Dupre la Tour and Giovanni Giuseppe Costa.
• Major Feature Added preprocessing.PowerTransformer, which implements the Yeo-Johnson and Box-Cox power transformations. Power transformations try to find a set of feature-wise parametric transformations to approximately map data to a Gaussian distribution centered at zero and with unit variance. This is useful as a variance-stabilizing transformation in situations where normality and homoscedasticity are desirable. #10210 by Eric Chang and Maniteja Nandana, and #11520 by Nicolas Hug.
• Major Feature NaN values are ignored and handled in the following preprocessing methods: preprocessing.MaxAbsScaler, preprocessing.MinMaxScaler, preprocessing.RobustScaler, preprocessing.StandardScaler, preprocessing.PowerTransformer, preprocessing.QuantileTransformer classes and preprocessing.maxabs_scale, preprocessing.minmax_scale, preprocessing.robust_scale, preprocessing.scale, preprocessing.power_transform, preprocessing.quantile_transform functions respectively addressed in issues #11011, #11005, #11308, #11206, #11306, and #10437. By Lucija Gregov and Guillaume Lemaitre.
• Feature preprocessing.PolynomialFeatures now supports sparse input. #10452 by Aman Dalmia and Joel Nothman.
• Feature preprocessing.RobustScaler and preprocessing.robust_scale can be fitted using sparse matrices. #11308 by Guillaume Lemaitre.
• Feature preprocessing.OneHotEncoder now supports the get_feature_names method to obtain the transformed feature names. #10181 by Nirvan Anjirbag and Joris Van den Bossche.
• Feature A parameter check_inverse was added to preprocessing.FunctionTransformer to ensure that func and inverse_func are the inverse of each other. #9399 by Guillaume Lemaitre.
• Feature The transform method of sklearn.preprocessing.MultiLabelBinarizer now ignores any unknown classes. A warning is raised stating the unknown classes classes found which are ignored. #10913 by Rodrigo Agundez.
• Fix Fixed bugs in preprocessing.LabelEncoder which would sometimes throw errors when transform or inverse_transform was called with empty arrays. #10458 by Mayur Kulkarni.
• Fix Fix ValueError in preprocessing.LabelEncoder when using inverse_transform on unseen labels. #9816 by Charlie Newey.
• Fix Fix bug in preprocessing.OneHotEncoder which discarded the dtype when returning a sparse matrix output. #11042 by Daniel Morales.
• Fix Fix fit and partial_fit in preprocessing.StandardScaler in the rare case when with_mean=False and with_std=False which was crashing by calling fit more than once and giving inconsistent results for mean_ whether the input was a sparse or a dense matrix. mean_ will be set to None with both sparse and dense inputs. n_samples_seen_ will be also reported for both input types. #11235 by Guillaume Lemaitre.
• API Change Deprecate n_values and categorical_features parameters and active_features_, feature_indices_ and n_values_ attributes of preprocessing.OneHotEncoder. The n_values parameter can be replaced with the new categories parameter, and the attributes with the new categories_ attribute. Selecting the categorical features with the categorical_features parameter is now better supported using the compose.ColumnTransformer. #10521 by Joris Van den Bossche.
• API Change Deprecate preprocessing.Imputer and move the corresponding module to impute.SimpleImputer. #9726 by Kumar Ashutosh.
• API Change The axis parameter that was in preprocessing.Imputer is no longer present in impute.SimpleImputer. The behavior is equivalent to axis=0 (impute along columns). Row-wise imputation can be performed with FunctionTransformer (e.g., FunctionTransformer(lambda X: SimpleImputer().fit_transform(X.T).T)). #10829 by Guillaume Lemaitre and Gilberto Olimpio.
• API Change The NaN marker for the missing values has been changed between the preprocessing.Imputer and the impute.SimpleImputer. missing_values='NaN' should now be missing_values=np.nan. #11211 by Jeremie du Boisberranger.
• API Change In preprocessing.FunctionTransformer, the default of validate will be from True to False in 0.22. #10655 by Guillaume Lemaitre.

sklearn.svm

• Fix Fixed a bug in svm.SVC where when the argument kernel is unicode in Python2, the predict_proba method was raising an unexpected TypeError given dense inputs. #10412 by Jiongyan Zhang.
• API Change Deprecate random_state parameter in svm.OneClassSVM as the underlying implementation is not random. #9497 by Albert Thomas.
• API Change The default value of gamma parameter of svm.SVC, NuSVC, SVR, NuSVR, OneClassSVM will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. #8361 by Gaurav Dhingra and Ting Neo.

sklearn.tree

• Enhancement Although private (and hence not assured API stability), tree._criterion.ClassificationCriterion and tree._criterion.RegressionCriterion may now be cimported and extended. #10325 by Camil Staps.
• Fix Fixed a bug in tree.BaseDecisionTree with splitter="best" where split threshold could become infinite when values in X were near infinite. #10536 by Jonathan Ohayon.
• Fix Fixed a bug in tree.MAE to ensure sample weights are being used during the calculation of tree MAE impurity. Previous behaviour could cause suboptimal splits to be chosen since the impurity calculation considered all samples to be of equal weight importance. #11464 by John Stott.

sklearn.utils

• Feature utils.check_array and utils.check_X_y now have accept_large_sparse to control whether scipy.sparse matrices with 64-bit indices should be rejected. #11327 by Karan Dhingra and Joel Nothman.
• Efficiency Fix Avoid copying the data in utils.check_array when the input data is a memmap (and copy=False). #10663 by Arthur Mensch and Loïc Estève.
• API Change utils.check_array yield a FutureWarning indicating that arrays of bytes/strings will be interpreted as decimal numbers beginning in version 0.22. #10229 by Ryan Lee

Multiple modules

• Feature API Change More consistent outlier detection API: Add a score_samples method in svm.OneClassSVM, ensemble.IsolationForest, neighbors.LocalOutlierFactor, covariance.EllipticEnvelope. It allows to access raw score functions from original papers. A new offset_ parameter allows to link score_samples and decision_function methods. The contamination parameter of ensemble.IsolationForest and neighbors.LocalOutlierFactor decision_function methods is used to define this offset_ such that outliers (resp. inliers) have negative (resp. positive) decision_function values. By default, contamination is kept unchanged to 0.1 for a deprecation period. In 0.22, it will be set to “auto”, thus using method-specific score offsets. In covariance.EllipticEnvelope decision_function method, the raw_values parameter is deprecated as the shifted Mahalanobis distance will be always returned in 0.22. #9015 by Nicolas Goix.
• Feature API Change A behaviour parameter has been introduced in ensemble.IsolationForest to ensure backward compatibility. In the old behaviour, the decision_function is independent of the contamination parameter. A threshold attribute depending on the contamination parameter is thus used. In the new behaviour the decision_function is dependent on the contamination parameter, in such a way that 0 becomes its natural threshold to detect outliers. Setting behaviour to “old” is deprecated and will not be possible in version 0.22. Beside, the behaviour parameter will be removed in 0.24. #11553 by Nicolas Goix.
• API Change Added convergence warning to svm.LinearSVC and linear_model.LogisticRegression when verbose is set to 0. #10881 by Alexandre Sevin.
• API Change Changed warning type from UserWarning to exceptions.ConvergenceWarning for failing convergence in linear_model.logistic_regression_path, linear_model.RANSACRegressor, linear_model.ridge_regression, gaussian_process.GaussianProcessRegressor, gaussian_process.GaussianProcessClassifier, decomposition.fastica, cross_decomposition.PLSCanonical, cluster.AffinityPropagation, and cluster.Birch. #10306 by Jonathan Siebert.

Miscellaneous

• Major Feature A new configuration parameter, working_memory was added to control memory consumption limits in chunked operations, such as the new metrics.pairwise_distances_chunked. See Limiting Working Memory. #10280 by Joel Nothman and Aman Dalmia.
• Feature The version of joblib bundled with Scikit-learn is now 0.12. This uses a new default multiprocessing implementation, named loky. While this may incur some memory and communication overhead, it should provide greater cross-platform stability than relying on Python standard library multiprocessing. #11741 by the Joblib developers, especially Thomas Moreau and Olivier Grisel.
• Feature An environment variable to use the site joblib instead of the vendored one was added (Environment variables). The main API of joblib is now exposed in sklearn.utils. #11166 by Gael Varoquaux.
• Feature Add almost complete PyPy 3 support. Known unsupported functionalities are datasets.load_svmlight_file, feature_extraction.FeatureHasher and feature_extraction.text.HashingVectorizer. For running on PyPy, PyPy3-v5.10+, Numpy 1.14.0+, and scipy 1.1.0+ are required. #11010 by Ronan Lamy and Roman Yurchak.
• Feature A utility method sklearn.show_versions was added to print out information relevant for debugging. It includes the user system, the Python executable, the version of the main libraries and BLAS binding information. #11596 by Alexandre Boucaud
• Fix Fixed a bug when setting parameters on meta-estimator, involving both a wrapped estimator and its parameter. #9999 by Marcus Voss and Joel Nothman.
• Fix Fixed a bug where calling sklearn.base.clone was not thread safe and could result in a “pop from empty list” error. #9569 by Andreas Müller.
• API Change The default value of n_jobs is changed from 1 to None in all related functions and classes. n_jobs=None means unset. It will generally be interpreted as n_jobs=1, unless the current joblib.Parallel backend context specifies otherwise (See Glossary for additional information). Note that this change happens immediately (i.e., without a deprecation cycle). #11741 by Olivier Grisel.
• Fix Fixed a bug in validation helpers where passing a Dask DataFrame results in an error. #12462 by Zachariah Miller

Changes to estimator checks

These changes mostly affect library developers.

• Checks for transformers now apply if the estimator implements transform, regardless of whether it inherits from sklearn.base.TransformerMixin. #10474 by Joel Nothman.
• Classifiers are now checked for consistency between decision_function and categorical predictions. #10500 by Narine Kokhlikyan.
• Allow tests in utils.estimator_checks.check_estimator to test functions that accept pairwise data. #9701 by Kyle Johnson
• Allow utils.estimator_checks.check_estimator to check that there is no private settings apart from parameters during estimator initialization. #9378 by Herilalaina Rakotoarison
• The set of checks in utils.estimator_checks.check_estimator now includes a check_set_params test which checks that set_params is equivalent to passing parameters in __init__ and warns if it encounters parameter validation. #7738 by Alvin Chiang
• Add invariance tests for clustering metrics. #8102 by Ankita Sinha and Guillaume Lemaitre.
• Add check_methods_subset_invariance to check_estimator, which checks that estimator methods are invariant if applied to a data subset. #10428 by Jonathan Ohayon
• Add tests in utils.estimator_checks.check_estimator to check that an estimator can handle read-only memmap input data. #10663 by Arthur Mensch and Loïc Estève.
• check_sample_weights_pandas_series now uses 8 rather than 6 samples to accommodate for the default number of clusters in cluster.KMeans. #10933 by Johannes Hansen.
• Estimators are now checked for whether sample_weight=None equates to sample_weight=np.ones(...). #11558 by Sergul Aydore.

Code and Documentation Contributors

Thanks to everyone who has contributed to the maintenance and improvement of the project since version 0.19, including:

211217613, Aarshay Jain, absolutelyNoWarranty, Adam Greenhall, Adam Kleczewski, Adam Richie-Halford, adelr, AdityaDaflapurkar, Adrin Jalali, Aidan Fitzgerald, aishgrt1, Akash Shivram, Alan Liddell, Alan Yee, Albert Thomas, Alexander Lenail, Alexander-N, Alexandre Boucaud, Alexandre Gramfort, Alexandre Sevin, Alex Egg, Alvaro Perez-Diaz, Amanda, Aman Dalmia, Andreas Bjerre-Nielsen, Andreas Mueller, Andrew Peng, Angus Williams, Aniruddha Dave, annaayzenshtat, Anthony Gitter, Antonio Quinonez, Anubhav Marwaha, Arik Pamnani, Arthur Ozga, Artiem K, Arunava, Arya McCarthy, Attractadore, Aurélien Bellet, Aurélien Geron, Ayush Gupta, Balakumaran Manoharan, Bangda Sun, Barry Hart, Bastian Venthur, Ben Lawson, Benn Roth, Breno Freitas, Brent Yi, brett koonce, Caio Oliveira, Camil Staps, cclauss, Chady Kamar, Charlie Brummitt, Charlie Newey, chris, Chris, Chris Catalfo, Chris Foster, Chris Holdgraf, Christian Braune, Christian Hirsch, Christian Hogan, Christopher Jenness, Clement Joudet, cnx, cwitte, Dallas Card, Dan Barkhorn, Daniel, Daniel Ferreira, Daniel Gomez, Daniel Klevebring, Danielle Shwed, Daniel Mohns, Danil Baibak, Darius Morawiec, David Beach, David Burns, David Kirkby, David Nicholson, David Pickup, Derek, Didi Bar-Zev, diegodlh, Dillon Gardner, Dillon Niederhut, dilutedsauce, dlovell, Dmitry Mottl, Dmitry Petrov, Dor Cohen, Douglas Duhaime, Ekaterina Tuzova, Eric Chang, Eric Dean Sanchez, Erich Schubert, Eunji, Fang-Chieh Chou, FarahSaeed, felix, Félix Raimundo, fenx, filipj8, FrankHui, Franz Wompner, Freija Descamps, frsi, Gabriele Calvo, Gael Varoquaux, Gaurav Dhingra, Georgi Peev, Gil Forsyth, Giovanni Giuseppe Costa, gkevinyen5418, goncalo-rodrigues, Gryllos Prokopis, Guillaume Lemaitre, Guillaume “Vermeille” Sanchez, Gustavo De Mari Pereira, hakaa1, Hanmin Qin, Henry Lin, Hong, Honghe, Hossein Pourbozorg, Hristo, Hunan Rostomyan, iampat, Ivan PANICO, Jaewon Chung, Jake VanderPlas, jakirkham, James Bourbeau, James Malcolm, Jamie Cox, Jan Koch, Jan Margeta, Jan Schlüter, janvanrijn, Jason Wolosonovich, JC Liu, Jeb Bearer, jeremiedbb, Jimmy Wan, Jinkun Wang, Jiongyan Zhang, jjabl, jkleint, Joan Massich, Joël Billaud, Joel Nothman, Johannes Hansen, JohnStott, Jonatan Samoocha, Jonathan Ohayon, Jörg Döpfert, Joris Van den Bossche, Jose Perez-Parras Toledano, josephsalmon, jotasi, jschendel, Julian Kuhlmann, Julien Chaumond, julietcl, Justin Shenk, Karl F, Kasper Primdal Lauritzen, Katrin Leinweber, Kirill, ksemb, Kuai Yu, Kumar Ashutosh, Kyeongpil Kang, Kye Taylor, kyledrogo, Leland McInnes, Léo DS, Liam Geron, Liutong Zhou, Lizao Li, lkjcalc, Loic Esteve, louib, Luciano Viola, Lucija Gregov, Luis Osa, Luis Pedro Coelho, Luke M Craig, Luke Persola, Mabel, Mabel Villalba, Maniteja Nandana, MarkIwanchyshyn, Mark Roth, Markus Müller, MarsGuy, Martin Gubri, martin-hahn, martin-kokos, mathurinm, Matthias Feurer, Max Copeland, Mayur Kulkarni, Meghann Agarwal, Melanie Goetz, Michael A. Alcorn, Minghui Liu, Ming Li, Minh Le, Mohamed Ali Jamaoui, Mohamed Maskani, Mohammad Shahebaz, Muayyad Alsadi, Nabarun Pal, Nagarjuna Kumar, Naoya Kanai, Narendran Santhanam, NarineK, Nathaniel Saul, Nathan Suh, Nicholas Nadeau, P.Eng., AVS, Nick Hoh, Nicolas Goix, Nicolas Hug, Nicolau Werneck, nielsenmarkus11, Nihar Sheth, Nikita Titov, Nilesh Kevlani, Nirvan Anjirbag, notmatthancock, nzw, Oleksandr Pavlyk, oliblum90, Oliver Rausch, Olivier Grisel, Oren Milman, Osaid Rehman Nasir, pasbi, Patrick Fernandes, Patrick Olden, Paul Paczuski, Pedro Morales, Peter, Peter St. John, pierreablin, pietruh, Pinaki Nath Chowdhury, Piotr Szymański, Pradeep Reddy Raamana, Pravar D Mahajan, pravarmahajan, QingYing Chen, Raghav RV, Rajendra arora, RAKOTOARISON Herilalaina, Rameshwar Bhaskaran, RankyLau, Rasul Kerimov, Reiichiro Nakano, Rob, Roman Kosobrodov, Roman Yurchak, Ronan Lamy, rragundez, Rüdiger Busche, Ryan, Sachin Kelkar, Sagnik Bhattacharya, Sailesh Choyal, Sam Radhakrishnan, Sam Steingold, Samuel Bell, Samuel O. Ronsin, Saqib Nizam Shamsi, SATISH J, Saurabh Gupta, Scott Gigante, Sebastian Flennerhag, Sebastian Raschka, Sebastien Dubois, Sébastien Lerique, Sebastin Santy, Sergey Feldman, Sergey Melderis, Sergul Aydore, Shahebaz, Shalil Awaley, Shangwu Yao, Sharad Vijalapuram, Sharan Yalburgi, shenhanc78, Shivam Rastogi, Shu Haoran, siftikha, Sinclert Pérez, SolutusImmensus, Somya Anand, srajan paliwal, Sriharsha Hatwar, Sri Krishna, Stefan van der Walt, Stephen McDowell, Steven Brown, syonekura, Taehoon Lee, Takanori Hayashi, tarcusx, Taylor G Smith, theriley106, Thomas, Thomas Fan, Thomas Heavey, Tobias Madsen, tobycheese, Tom Augspurger, Tom Dupré la Tour, Tommy, Trevor Stephens, Trishnendu Ghorai, Tulio Casagrande, twosigmajab, Umar Farouk Umar, Urvang Patel, Utkarsh Upadhyay, Vadim Markovtsev, Varun Agrawal, Vathsala Achar, Vilhelm von Ehrenheim, Vinayak Mehta, Vinit, Vinod Kumar L, Viraj Mavani, Viraj Navkal, Vivek Kumar, Vlad Niculae, vqean3, Vrishank Bhardwaj, vufg, wallygauze, Warut Vijitbenjaronk, wdevazelhes, Wenhao Zhang, Wes Barnett, Will, William de Vazelhes, Will Rosenfeld, Xin Xiong, Yiming (Paul) Li, ymazari, Yufeng, Zach Griffith, Zé Vinícius, Zhenqing Hu, Zhiqing Xiao, Zijie (ZJ) Poh

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