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sklearn

Version 0.20.3

??, 2019

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

Changelog

sklearn.cluster

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

sklearn.compose

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

sklearn.covariance ......................

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

sklearn.decomposition

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

sklearn.feature_extraction

  • 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 <gvacaliuc>.

sklearn.impute

  • 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 <glemaitre>.

sklearn.linear_model

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

sklearn.preprocessing

  • Fixed a bug in preprocessing.KBinsDiscretizer where strategy='kmeans' fails with an error during transformation due to unsorted bin edges. 13134 by Sandro Casagrande <SandroCasagrande>.
  • 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.
  • Bins whose width are too small (i.e., <= 1e-8) are removed with a warning in preprocessing.KBinsDiscretizer. 13165 by Hanmin Qin <qinhanmin2014>.

sklearn.svm

  • 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 <qinhanmin2014>.

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

  • Fixed an issue in compose.make_column_transformer which raises unexpected error when columns is pandas Index or pandas Series. 12704 by Hanmin Qin <qinhanmin2014>.

sklearn.metrics

  • 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 <jeremiedbb>.

sklearn.neighbors

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

sklearn.utils

  • 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

  • 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 <ogrisel>.
  • 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

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

sklearn.datasets

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

sklearn.decomposition

  • 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 <minggli>.

sklearn.ensemble

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

sklearn.feature_extraction

  • 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

  • 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 <ogrisel>.
  • 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 <pierreglaser> and Olivier Grisel <ogrisel>.

sklearn.metrics

  • 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 <jeremiedbb>.
  • Fixed a bug in metrics.pairwise.pairwise_distances_chunked which didn't ensure the diagonal is zero for euclidean distances. 12612 by Andreas Müller <amueller>.
  • 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 <LisaThomas9>, Mark Hannel <markhannel> and Melissa Ferrari <mferrari3>.

sklearn.mixture

  • 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 <ogrisel>.

sklearn.neighbors

  • 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 <tomMoral>.

sklearn.preprocessing

  • Fixed bug in preprocessing.OrdinalEncoder when passing manually specified categories. 12365 by Joris Van den Bossche.
  • Fixed bug in preprocessing.KBinsDiscretizer where the transform method mutates the _encoder attribute. The transform method is now thread safe. 12514 by Hanmin Qin <qinhanmin2014>.
  • Fixed a bug in preprocessing.PowerTransformer where the Yeo-Johnson transform was incorrect for lambda parameters outside of [0, 2] 12522 by Nicolas Hug<NicolasHug>.
  • Fixed a bug in preprocessing.OneHotEncoder where transform failed when set to ignore unknown numpy strings of different lengths 12471 by Gabriel Marzinotto<GMarzinotto>.
  • 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 <chang>.

sklearn.utils

  • Use float64 for mean accumulator to avoid floating point precision issues in preprocessing.StandardScaler and decomposition.IncrementalPCA when using float32 datasets. 12338 by bauks <bauks>.
  • 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

  • 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.
  • 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 <ogrisel>.
  • 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 <tomMoral>

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.

~compose.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 ~preprocessing.OneHotEncoder or ~preprocessing.OrdinalEncoder.

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

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

This release is also the first to be accompanied by a glossary 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

  • cluster.AgglomerativeClustering now supports Single Linkage clustering via linkage='single'. 9372 by Leland McInnes <lmcinnes> and Steve Astels <sastels>.
  • cluster.KMeans and cluster.MiniBatchKMeans now support sample weights via new parameter sample_weight in fit function. 10933 by Johannes Hansen <jnhansen>.
  • cluster.KMeans, cluster.MiniBatchKMeans and cluster.k_means passed with algorithm='full' now enforces row-major ordering, improving runtime. 10471 by Gaurav Dhingra <gxyd>.
  • cluster.DBSCAN now is parallelized according to n_jobs regardless of algorithm. 8003 by Joël Billaud <recamshak>.
  • 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 <christianbraune79>.
  • 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 <jsamoocha>.
  • 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 <jmargeta>, Guillaume Lemaitre <glemaitre>, and Devansh D. <devanshdalal>.
  • 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 <jeremiedbb>.
  • 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 <adrinjalali>.
  • Deprecate pooling_func unused parameter in cluster.AgglomerativeClustering. 9875 by Kumar Ashutosh <thechargedneutron>.

sklearn.compose

  • New module.
  • 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 <thomasjpfan>.
  • 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 <glemaitre>.

sklearn.covariance

  • Runtime improvements to covariance.GraphicalLasso. 9858 by Steven Brown <stevendbrown>.
  • 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 <artiemq>

sklearn.datasets

  • 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 <janvanrijn>.
  • 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 <maskani-moh> and Konstantinos Katrioplas <kkatrio>.
  • Add filename attribute to datasets that have a CSV file. 9101 by alex-33 <alex-33> and Maskani Filali Mohamed <maskani-moh>.
  • return_X_y parameter has been added to several dataset loaders. 10774 by Chris Catalfo <ccatalfo>.
  • Fixed a bug in datasets.load_boston which had a wrong data point. 10795 by Takeshi Yoshizawa <tarcusx>.
  • Fixed a bug in datasets.load_iris which had two wrong data points. 11082 by Sadhana Srinivasan <rotuna> and Hanmin Qin <qinhanmin2014>.
  • Fixed a bug in datasets.fetch_kddcup99, where data were not properly shuffled. 9731 by Nicolas Goix.
  • Fixed a bug in datasets.make_circles, where no odd number of data points could be generated. 10045 by Christian Braune <christianbraune79>.
  • 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

  • decomposition.dict_learning functions and models now support positivity constraints. This applies to the dictionary and sparse code. 6374 by John Kirkham <jakirkham>.
  • 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 <FollowKenny>.
  • Efficiency improvements in decomposition.dict_learning. 11420 and others by John Kirkham <jakirkham>.
  • 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 <wallygauze>.
  • 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 <jrbourbeau>.
  • 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 <wallygauze>.
  • 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 <qinhanmin2014>.
  • Fixed an underflow in calculating KL-divergence for decomposition.NMF 10142 by Tom Dupre la Tour.
  • Fixed a bug in decomposition.SparseCoder when running OMP sparse coding in parallel using read-only memory mapped datastructures. 5956 by Vighnesh Birodkar <vighneshbirodkar> and Olivier Grisel <ogrisel>.

sklearn.discriminant_analysis

  • Memory usage improvement for _class_means and _class_cov in discriminant_analysis. 10898 by Nanxin Chen <bobchennan>.

sklearn.dummy

  • dummy.DummyRegressor now has a return_std option in its predict method. The returned standard deviations will be zeros.
  • dummy.DummyClassifier and dummy.DummyRegressor now only require X to be an object with finite length or shape. 9832 by Vrishank Bhardwaj <vrishank97>.
  • dummy.DummyClassifier and dummy.DummyRegressor can now be scored without supplying test samples. 11951 by Rüdiger Busche <JarnoRFB>.

sklearn.ensemble

  • 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 <jimmywan>.
  • ensemble.GradientBoostingClassifier and ensemble.GradientBoostingRegressor now support early stopping via n_iter_no_change, validation_fraction and tol. 7071 by Raghav RV
  • Added named_estimators_ parameter in ensemble.VotingClassifier to access fitted estimators. 9157 by Herilalaina Rakotoarison <herilalaina>.
  • 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 <glemaitre>.
  • 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 <gforsyth>.
  • 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 <annaayzenshtat>.
  • 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 <glemaitre>.
  • ensemble.BaseBagging where one could not deterministically reproduce fit result using the object attributes when random_state is set. 9723 by Guillaume Lemaitre <glemaitre>.

sklearn.feature_extraction

  • Enable the call to get_feature_names in unfitted feature_extraction.text.CountVectorizer initialized with a vocabulary. 10908 by Mohamed Maskani <maskani-moh>.
  • idf_ can now be set on a feature_extraction.text.TfidfTransformer. 10899 by Sergey Melderis <serega>.
  • 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 <varunagrawal>
  • 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 <mannby> and Roman Yurchak.
  • 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 <maykulkarni> and Guillaume Lemaitre <glemaitre>.

sklearn.feature_selection

  • Added select K best features functionality to feature_selection.SelectFromModel. 6689 by Nihar Sheth <nsheth12> and Quazi Rahman <qmaruf>.
  • Added min_features_to_select parameter to feature_selection.RFECV to bound evaluated features counts. 11293 by Brent Yi <brentyi>.
  • feature_selection.RFECV's fit method now supports groups. 9656 by Adam Greenhall <adamgreenhall>.
  • Fixed computation of n_features_to_compute for edge case with tied CV scores in feature_selection.RFECV. 9222 by Nick Hoh <nickypie>.

sklearn.gaussian_process

  • 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 <andrewww> and Minghui Liu <minghui-liu>.

sklearn.impute

  • New module, adopting preprocessing.Imputer as impute.SimpleImputer with minor changes (see under preprocessing below).
  • Added impute.MissingIndicator which generates a binary indicator for missing values. 8075 by Maniteja Nandana <maniteja123> and Guillaume Lemaitre <glemaitre>.
  • 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 <jeremiedbb>.

sklearn.isotonic

  • 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 <dallascard>

sklearn.linear_model

  • 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.
  • Add sample_weight parameter to the fit method of linear_model.BayesianRidge for weighted linear regression. 10112 by Peter St. John <pstjohn>.
  • 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 <NicolasHug>.
  • 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 <rwolst>.
  • 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 <thomasjpfan>.
  • 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 <wdevazelhes>.
  • Fixed a bug in linear_model.OrthogonalMatchingPursuit that was broken when setting normalize=False. 10071 by Alexandre Gramfort.
  • Fixed a bug in linear_model.ARDRegression which caused incorrectly updated estimates for the standard deviation and the coefficients. 10153 by Jörg Döpfert <jdoepfert>.
  • 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 <jdoepfert>.
  • 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 <mabelvj>.
  • 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 <ymazari>.
  • Fixed a bug in sklearn.linear_model.Lasso where the coefficient had wrong shape when fit_intercept=False. 10687 by Martin Hahn <martin-hahn>.
  • Fixed a bug in sklearn.linear_model.LogisticRegression where the multi_class='multinomial' with binary output with warm_start=True 10836 by Aishwarya Srinivasan <aishgrt1>.
  • Fixed a bug in linear_model.RidgeCV where using integer alphas raised an error. 10397 by Mabel Villalba-Jiménez <mabelvj>.
  • Fixed condition triggering gap computation in linear_model.Lasso and linear_model.ElasticNet when working with sparse matrices. 10992 by Alexandre Gramfort.
  • 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.
  • 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 <kdhingra307> and Joel Nothman.
  • 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.
  • Deprecate positive=True option in linear_model.Lars as the underlying implementation is broken. Use linear_model.Lasso instead. 9837 by Alexandre Gramfort.
  • 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

  • Speed improvements for both 'exact' and 'barnes_hut' methods in manifold.TSNE. 10593 and 10610 by Tom Dupre la Tour.
  • Support sparse input in manifold.Isomap.fit. 8554 by Leland McInnes <lmcinnes>.
  • manifold.t_sne.trustworthiness accepts metrics other than Euclidean. 9775 by William de Vazelhes <wdevazelhes>.
  • 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 <jmargeta>, Guillaume Lemaitre <glemaitre>, and Devansh D. <devanshdalal>.
  • 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 <wdevazelhes>.
  • 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 <wdevazelhes>.

sklearn.metrics

  • Added the metrics.davies_bouldin_score metric for evaluation of clustering models without a ground truth. 10827 by Luis Osa <logc>.
  • Added the metrics.balanced_accuracy_score metric and a corresponding 'balanced_accuracy' scorer for binary and multiclass classification. 8066 by xyguo and Aman Dalmia <dalmia>, and 10587 by Joel Nothman.
  • Partial AUC is available via max_fpr parameter in metrics.roc_auc_score. 3840 by Alexander Niederbühl <Alexander-N>.
  • A scorer based on metrics.brier_score_loss is also available. 9521 by Hanmin Qin <qinhanmin2014>.
  • 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 <aryamccarthy>.
  • Added output_dict parameter in metrics.classification_report to return classification statistics as dictionary. 11160 by Dan Barkhorn <danielbarkhorn>.
  • 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 <apacha>.
  • metrics.average_precision_score now supports binary y_true other than {0, 1} or {-1, 1} through pos_label parameter. 9980 by Hanmin Qin <qinhanmin2014>.
  • metrics.label_ranking_average_precision_score now supports sample_weight. 10845 by Jose Perez-Parras Toledano <jopepato>.
  • 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 <tgsmith61591>.
  • 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.
  • Fixed a bug in metrics.precision_recall_fscore_support when truncated range(n_labels) is passed as value for labels. 10377 by Gaurav Dhingra <gxyd>.
  • Fixed a bug due to floating point error in metrics.roc_auc_score with non-integer sample weights. 9786 by Hanmin Qin <qinhanmin2014>.
  • 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 <alexryndin> and Hanmin Qin <qinhanmin2014>.
  • Fixed a bug to avoid integer overflow. Casted product to 64 bits integer in metrics.mutual_info_score. 9772 by Kumar Ashutosh <thechargedneutron>.
  • Fixed a bug where metrics.average_precision_score will sometimes return nan when sample_weight contains 0. 9980 by Hanmin Qin <qinhanmin2014>.
  • 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 <aliddell> and Manh Dao <manhdao>.
  • 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 <qinhanmin2014>.
  • 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 <aryamccarthy>.
  • 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 working_memory. 10280 by Joel Nothman and Aman Dalmia <dalmia>.

sklearn.mixture

  • Added function fit_predict to mixture.GaussianMixture and mixture.GaussianMixture, which is essentially equivalent to calling fit and predict. 10336 by Shu Haoran <haoranShu> and Andrew Peng <Andrew-peng>.
  • 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 <kno10> and Guillaume Lemaitre <glemaitre>.
  • 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 <ageron>.

sklearn.model_selection

  • Add return_estimator parameter in model_selection.cross_validate to return estimators fitted on each split. 9686 by Aurélien Bellet <bellet>.
  • 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 <mfeurer>.
  • 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 <samronsin>.
  • 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
  • Add improved error message in model_selection.cross_val_score when multiple metrics are passed in scoring keyword. 11006 by Ming Li <minggli>.
  • 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 <aboucaud>.
  • 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 <ldirer> and Andreas Müller.
  • 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 <Zhdanovich>.
  • 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 <julietcl>
  • Invalid input for model_selection.ParameterGrid now raises TypeError. 10928 by Solutus Immensus <solutusimmensus>

sklearn.multioutput

  • Added multioutput.RegressorChain for multi-target regression. 9257 by Kumar Ashutosh <thechargedneutron>.

sklearn.naive_bayes

  • Added naive_bayes.ComplementNB, which implements the Complement Naive Bayes classifier described in Rennie et al. (2003). 8190 by Michael A. Alcorn <airalcorn2>.
  • Add var_smoothing parameter in naive_bayes.GaussianNB to give a precise control over variances calculation. 9681 by Dmitry Mottl <Mottl>.
  • Fixed a bug in naive_bayes.GaussianNB which incorrectly raised error for prior list which summed to 1. 10005 by Gaurav Dhingra <gxyd>.
  • Fixed a bug in naive_bayes.MultinomialNB which did not accept vector valued pseudocounts (alpha). 10346 by Tobias Madsen <TobiasMadsen>

sklearn.neighbors

  • neighbors.RadiusNeighborsRegressor and neighbors.RadiusNeighborsClassifier are now parallelized according to n_jobs regardless of algorithm. 10887 by Joël Billaud <recamshak>.
  • Nearest neighbors <neighbors> query methods are now more memory efficient when algorithm='brute'. 11136 by Joel Nothman and Aman Dalmia <dalmia>.
  • Add sample_weight parameter to the fit method of neighbors.KernelDensity to enable weighting in kernel density estimation. 4394 by Samuel O. Ronsin <samronsin>.
  • 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 <albertcthomas>.
  • 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 <tttthomasssss>.
  • 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 <abjer>.
  • Fixed a bug in KDTree construction that results in faster construction and querying times. 11556 by Jake VanderPlas <jakevdp>
  • 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 <NicolasHug>.

sklearn.neural_network

  • 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 <nnadeau>.
  • 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 <nnadeau>.
  • Fixed a bug in neural_network.MLPRegressor where fitting quit unexpectedly early due to local minima or fluctuations. 9456 by Nicholas Nadeau <nnadeau>

sklearn.pipeline

  • 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 <brenolf>.
  • pipeline.FeatureUnion now supports 'drop' as a transformer to drop features. 11144 by Thomas Fan <thomasjpfan>.

sklearn.preprocessing

  • 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 <vighneshbirodkar> and Joris Van den Bossche.
  • 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 <hlin117>, Hanmin Qin, Tom Dupre la Tour and Giovanni Giuseppe Costa <ggc87>.
  • 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 <chang> and Maniteja Nandana <maniteja123>, and 11520 by Nicolas Hug <nicolashug>.
  • 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 <LucijaGregov> and Guillaume Lemaitre <glemaitre>.
  • preprocessing.PolynomialFeatures now supports sparse input. 10452 by Aman Dalmia <dalmia> and Joel Nothman.
  • preprocessing.RobustScaler and preprocessing.robust_scale can be fitted using sparse matrices. 11308 by Guillaume Lemaitre <glemaitre>.
  • preprocessing.OneHotEncoder now supports the get_feature_names method to obtain the transformed feature names. 10181 by Nirvan Anjirbag <Nirvan101> and Joris Van den Bossche.
  • 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 <glemaitre>.
  • 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 <rragundez>.
  • Fixed bugs in preprocessing.LabelEncoder which would sometimes throw errors when transform or inverse_transform was called with empty arrays. 10458 by Mayur Kulkarni <maykulkarni>.
  • Fix ValueError in preprocessing.LabelEncoder when using inverse_transform on unseen labels. 9816 by Charlie Newey <newey01c>.
  • Fix bug in preprocessing.OneHotEncoder which discarded the dtype when returning a sparse matrix output. 11042 by Daniel Morales <DanielMorales9>.
  • 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 <glemaitre>.
  • 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.
  • Deprecate preprocessing.Imputer and move the corresponding module to impute.SimpleImputer. 9726 by Kumar Ashutosh <thechargedneutron>.
  • 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 <glemaitre> and Gilberto Olimpio <gilbertoolimpio>.
  • 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 <jeremiedbb>.
  • In preprocessing.FunctionTransformer, the default of validate will be from True to False in 0.22. 10655 by Guillaume Lemaitre <glemaitre>.

sklearn.svm

  • 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 <qmick>.
  • Deprecate random_state parameter in svm.OneClassSVM as the underlying implementation is not random. 9497 by Albert Thomas <albertcthomas>.
  • The default value of gamma parameter of svm.SVC, ~svm.NuSVC, ~svm.SVR, ~svm.NuSVR, ~svm.OneClassSVM will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. 8361 by Gaurav Dhingra <gxyd> and Ting Neo <neokt>.

sklearn.tree

  • 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 <camilstaps>.
  • 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 <Johayon>.
  • 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 <JohnStott>.

sklearn.utils

  • 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 <kdhingra307> and Joel Nothman.
  • Avoid copying the data in utils.check_array when the input data is a memmap (and copy=False). 10663 by Arthur Mensch <arthurmensch> and Loïc Estève <lesteve>.
  • 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 <rtlee9>

Multiple modules

  • 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.
  • 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.
  • Added convergence warning to svm.LinearSVC and linear_model.LogisticRegression when verbose is set to 0. 10881 by Alexandre Sevin <AlexandreSev>.
  • 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 <jotasi>.

Miscellaneous

  • 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 working_memory. 10280 by Joel Nothman and Aman Dalmia <dalmia>.
  • 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 <tomMoral> and Olivier Grisel.
  • An environment variable to use the site joblib instead of the vendored one was added (environment_variable). The main API of joblib is now exposed in sklearn.utils. 11166 by Gael Varoquaux.
  • 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 <rlamy> and Roman Yurchak.
  • 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 <aboucaud>
  • Fixed a bug when setting parameters on meta-estimator, involving both a wrapped estimator and its parameter. 9999 by Marcus Voss <marcus-voss> and Joel Nothman.
  • 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.
  • 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 <n_jobs> for additional information). Note that this change happens immediately (i.e., without a deprecation cycle). 11741 by Olivier Grisel.
  • Fixed a bug in validation helpers where passing a Dask DataFrame results in an error. 12462 by Zachariah Miller <zwmiller>

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 <NarineK>.
  • Allow tests in utils.estimator_checks.check_estimator to test functions that accept pairwise data. 9701 by Kyle Johnson <gkjohns>
  • Allow utils.estimator_checks.check_estimator to check that there is no private settings apart from parameters during estimator initialization. 9378 by Herilalaina Rakotoarison <herilalaina>
  • 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 <absolutelyNoWarranty>
  • Add invariance tests for clustering metrics. 8102 by Ankita Sinha <anki08> and Guillaume Lemaitre <glemaitre>.
  • Add check_methods_subset_invariance to ~utils.estimator_checks.check_estimator, which checks that estimator methods are invariant if applied to a data subset. 10428 by Jonathan Ohayon <Johayon>
  • Add tests in utils.estimator_checks.check_estimator to check that an estimator can handle read-only memmap input data. 10663 by Arthur Mensch <arthurmensch> and Loïc Estève <lesteve>.
  • 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 <jnhansen>.
  • Estimators are now checked for whether sample_weight=None equates to sample_weight=np.ones(...). 11558 by Sergul Aydore <sergulaydore>.

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