.. currentmodule:: sklearn
July 30, 2019
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 :func:`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)).
- |Fix| Fixed a bug in :class:`cluster.KMeans` where computation with init='random' was single threaded for n_jobs > 1 or n_jobs = -1. :pr:`12955` by :user:`Prabakaran Kumaresshan <nixphix>`.
- |Fix| Fixed a bug in :class:`cluster.OPTICS` where users were unable to pass float min_samples and min_cluster_size. :pr:`14496` by :user:`Fabian Klopfer <someusername1>` and :user:`Hanmin Qin <qinhanmin2014>`.
- |Fix| Fixed a bug in :class:`cluster.KMeans` where KMeans++ initialisation could rarely result in an IndexError. :issue:`11756` by `Joel Nothman`_.
- |Fix| Fixed an issue in :class:`compose.ColumnTransformer` where using
DataFrames whose column order differs between :func:
fitand :func:transformcould lead to silently passing incorrect columns to theremaindertransformer. :pr:`14237` by Andreas Schuderer <schuderer>.
- |Fix| :func:`datasets.fetch_california_housing`,
:func:`datasets.fetch_covtype`,
:func:`datasets.fetch_kddcup99`, :func:`datasets.fetch_olivetti_faces`,
:func:`datasets.fetch_rcv1`, and :func:`datasets.fetch_species_distributions`
try to persist the previously cache using the new
joblibif the cached data was persisted using the deprecatedsklearn.externals.joblib. This behavior is set to be deprecated and removed in v0.23. :pr:`14197` by `Adrin Jalali`_.
- |Fix| Fix zero division error in :func:`HistGradientBoostingClassifier` and :func:`HistGradientBoostingRegressor`. :pr:`14024` by Nicolas Hug <NicolasHug>.
- |Fix| Fixed a bug in :class:`impute.SimpleImputer` and :class:`impute.IterativeImputer` so that no errors are thrown when there are missing values in training data. :pr:`13974` by Frank Hoang <fhoang7>.
- |Fix| Fixed a bug in :func:`inspection.plot_partial_dependence` where
targetparameter was not being taken into account for multiclass problems. :pr:`14393` by :user:`Guillem G. Subies <guillemgsubies>`.
- |Fix| Fixed a bug in :class:`linear_model.LogisticRegressionCV` where
refit=Falsewould fail depending on the'multiclass'and'penalty'parameters (regression introduced in 0.21). :pr:`14087` by `Nicolas Hug`_. - |Fix| Compatibility fix for :class:`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. :pr:`14067` by :user:`Tim Staley <timstaley>`.
- |Fix| Fixed a bug in :class:`neighbors.NeighborhoodComponentsAnalysis` where
the validation of initial parameters
n_components,max_iterandtolrequired too strict types. :pr:`14092` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Fix| Fixed bug in :func:`tree.export_text` when the tree has one feature and a single feature name is passed in. :pr:`14053` by Thomas Fan.
- |Fix| Fixed an issue with :func:`plot_tree` where it displayed entropy calculations even for gini criterion in DecisionTreeClassifiers. :pr:`13947` by :user:`Frank Hoang <fhoang7>`.
24 May 2019
- |Fix| Fixed a bug in :class:`cross_decomposition.CCA` improving numerical stability when Y is close to zero. :pr:`13903` by `Thomas Fan`_.
- |Fix| Fixed a bug in :func:`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). :pr:`13910` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Fix| Fixed a bug in :class:`preprocessing.OneHotEncoder` where the new drop parameter was not reflected in get_feature_names. :pr:`13894` by :user:`James Myatt <jamesmyatt>`.
- |Fix| Fixed a bug where :func:`min_max_axis` would fail on 32-bit systems for certain large inputs. This affects :class:`preprocessing.MaxAbsScaler`, :func:`preprocessing.normalize` and :class:`preprocessing.LabelBinarizer`. :pr:`13741` by :user:`Roddy MacSween <rlms>`.
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.
- |Fix| Fixed a bug in :func:`inspection.partial_dependence` to only check classifier and not regressor for the multiclass-multioutput case. :pr:`14309` by :user:`Guillaume Lemaitre <glemaitre>`.
- |Fix| Fixed a bug in :class:`metrics.pairwise_distances` where it would raise
AttributeErrorfor boolean metrics whenXhad a boolean dtype andY == None. :issue:`13864` by :user:`Paresh Mathur <rick2047>`. - |Fix| Fixed two bugs in :class:`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 whenYisX. :issue:`13877` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Fix| Fixed a bug in :class:`neighbors.KernelDensity` which could not be
restored from a pickle if
sample_weighthad been used. :issue:`13772` by :user:`Aditya Vyas <aditya1702>`.
May 2019
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.
- :class:`discriminant_analysis.LinearDiscriminantAnalysis` for multiclass classification. |Fix|
- :class:`discriminant_analysis.LinearDiscriminantAnalysis` with 'eigen' solver. |Fix|
- :class:`linear_model.BayesianRidge` |Fix|
- Decision trees and derived ensembles when both max_depth and max_leaf_nodes are set. |Fix|
- :class:`linear_model.LogisticRegression` and :class:`linear_model.LogisticRegressionCV` with 'saga' solver. |Fix|
- :class:`ensemble.GradientBoostingClassifier` |Fix|
- :class:`sklearn.feature_extraction.text.HashingVectorizer`, :class:`sklearn.feature_extraction.text.TfidfVectorizer`, and :class:`sklearn.feature_extraction.text.CountVectorizer` |Fix|
- :class:`neural_network.MLPClassifier` |Fix|
- :func:`svm.SVC.decision_function` and :func:`multiclass.OneVsOneClassifier.decision_function`. |Fix|
- :class:`linear_model.SGDClassifier` and any derived classifiers. |Fix|
- Any model using the :func:`linear_model._sag.sag_solver` function with a 0 seed, including :class:`linear_model.LogisticRegression`, :class:`linear_model.LogisticRegressionCV`, :class:`linear_model.Ridge`, and :class:`linear_model.RidgeCV` with 'sag' solver. |Fix|
- :class:`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.)
- The default max_iter for :class:`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 :pr:`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 :pr:`13317`).
Support for Python 3.4 and below has been officially dropped.
- |API| The R2 score used when calling
scoreon a regressor will usemultioutput='uniform_average'from version 0.23 to keep consistent with :func:`metrics.r2_score`. This will influence thescoremethod of all the multioutput regressors (except for :class:`multioutput.MultiOutputRegressor`). :pr:`13157` by :user:`Hanmin Qin <qinhanmin2014>`.
- |Enhancement| Added support to bin the data passed into :class:`calibration.calibration_curve` by quantiles instead of uniformly between 0 and 1. :pr:`13086` by :user:`Scott Cole <srcole>`.
- |Enhancement| Allow n-dimensional arrays as input for calibration.CalibratedClassifierCV. :pr:`13485` by :user:`William de Vazelhes <wdevazelhes>`.
- |MajorFeature| A new clustering algorithm: :class:`cluster.OPTICS`: an algorithm related to :class:`cluster.DBSCAN`, that has hyperparameters easier to set and that scales better, by :user:`Shane <espg>`, `Adrin Jalali`_, :user:`Erich Schubert <kno10>`, `Hanmin Qin`_, and :user:`Assia Benbihi <assiaben>`.
- |Fix| Fixed a bug where :class:`cluster.Birch` could occasionally raise an AttributeError. :pr:`13651` by `Joel Nothman`_.
- |Fix| Fixed a bug in :class:`cluster.KMeans` where empty clusters weren't correctly relocated when using sample weights. :pr:`13486` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |API| The
n_components_attribute in :class:`cluster.AgglomerativeClustering` and :class:`cluster.FeatureAgglomeration` has been renamed ton_connected_components_. :pr:`13427` by :user:`Stephane Couvreur <scouvreur>`. - |Enhancement| :class:`cluster.AgglomerativeClustering` and
:class:`cluster.FeatureAgglomeration` now accept a
distance_thresholdparameter which can be used to find the clusters instead ofn_clusters. :issue:`9069` by :user:`Vathsala Achar <VathsalaAchar>` and `Adrin Jalali`_.
- |API| :class:`compose.ColumnTransformer` is no longer an experimental feature. :pr:`13835` by :user:`Hanmin Qin <qinhanmin2014>`.
- |Fix| Added support for 64-bit group IDs and pointers in SVMLight files. :pr:`10727` by :user:`Bryan K Woods <bryan-woods>`.
- |Fix| :func:`datasets.load_sample_images` returns images with a deterministic order. :pr:`13250` by :user:`Thomas Fan <thomasjpfan>`.
- |Enhancement| :class:`decomposition.KernelPCA` now has deterministic output (resolved sign ambiguity in eigenvalue decomposition of the kernel matrix). :pr:`13241` by :user:`Aurélien Bellet <bellet>`.
- |Fix| Fixed a bug in :class:`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. :pr:`12143` by :user:`Sylvain Marié <smarie>`
- |Fix| Fixed a bug in :class:`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). :pr:`11650` by :user:`Hossein Pourbozorg <hossein-pourbozorg>` and :user:`Zijie (ZJ) Poh <zjpoh>`.
- |API| The default value of the
initargument in :func:`decomposition.non_negative_factorization` will change fromrandomtoNonein version 0.23 to make it consistent with :class:`decomposition.NMF`. A FutureWarning is raised when the default value is used. :pr:`12988` by :user:`Zijie (ZJ) Poh <zjpoh>`.
- |Enhancement| :class:`discriminant_analysis.LinearDiscriminantAnalysis` now
preserves
float32andfloat64dtypes. :pr:`8769` and :pr:`11000` by :user:`Thibault Sejourne <thibsej>` - |Fix| A
ChangedBehaviourWarningis now raised when :class:`discriminant_analysis.LinearDiscriminantAnalysis` is given as parametern_components > min(n_features, n_classes - 1), andn_componentsis changed tomin(n_features, n_classes - 1)if so. Previously the change was made, but silently. :pr:`11526` by :user:`William de Vazelhes<wdevazelhes>`. - |Fix| Fixed a bug in :class:`discriminant_analysis.LinearDiscriminantAnalysis` where the predicted probabilities would be incorrectly computed in the multiclass case. :pr:`6848`, by :user:`Agamemnon Krasoulis <agamemnonc>` and Guillaume Lemaitre <glemaitre>.
- |Fix| Fixed a bug in :class:`discriminant_analysis.LinearDiscriminantAnalysis`
where the predicted probabilities would be incorrectly computed with
eigensolver. :pr:`11727`, by :user:`Agamemnon Krasoulis <agamemnonc>`.
- |Fix| Fixed a bug in :class:`dummy.DummyClassifier` where the
predict_probamethod was returning int32 array instead of float64 for thestratifiedstrategy. :pr:`13266` by :user:`Christos Aridas<chkoar>`. - |Fix| Fixed a bug in :class:`dummy.DummyClassifier` where it was throwing a
dimension mismatch error in prediction time if a column vector
ywithshape=(n, 1)was given atfittime. :pr:`13545` by :user:`Nick Sorros <nsorros>` and `Adrin Jalali`_.
|MajorFeature| Add two new implementations of gradient boosting trees: :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor`. The implementation of these estimators is inspired by LightGBM and can be orders of magnitude faster than :class:`ensemble.GradientBoostingRegressor` and :class:`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 :class:`ensemble.GradientBoostingClassifier` and :class:`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
|Feature| Add :class:`ensemble.VotingRegressor` which provides an equivalent of :class:`ensemble.VotingClassifier` for regression problems. :pr:`12513` by :user:`Ramil Nugmanov <stsouko>` and :user:`Mohamed Ali Jamaoui <mohamed-ali>`.
|Efficiency| Make :class:`ensemble.IsolationForest` prefer threads over processes when running with
n_jobs > 1as the underlying decision tree fit calls do release the GIL. This changes reduces memory usage and communication overhead. :pr:`12543` by :user:`Isaac Storch <istorch>` and `Olivier Grisel`_.|Efficiency| Make :class:`ensemble.IsolationForest` more memory efficient by avoiding keeping in memory each tree prediction. :pr:`13260` by `Nicolas Goix`_.
|Efficiency| :class:`ensemble.IsolationForest` now uses chunks of data at prediction step, thus capping the memory usage. :pr:`13283` by `Nicolas Goix`_.
|Efficiency| :class:`sklearn.ensemble.GradientBoostingClassifier` and :class:`sklearn.ensemble.GradientBoostingRegressor` now keep the input
yasfloat64to avoid it being copied internally by trees. :pr:`13524` by `Adrin Jalali`_.|Enhancement| Minimized the validation of X in :class:`ensemble.AdaBoostClassifier` and :class:`ensemble.AdaBoostRegressor` :pr:`13174` by :user:`Christos Aridas <chkoar>`.
|Enhancement| :class:`ensemble.IsolationForest` now exposes
warm_startparameter, allowing iterative addition of trees to an isolation forest. :pr:`13496` by :user:`Peter Marko <petibear>`.|Fix| The values of
feature_importances_in all random forest based models (i.e. :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.ExtraTreesClassifier`, :class:`ensemble.ExtraTreesRegressor`, :class:`ensemble.RandomTreesEmbedding`, :class:`ensemble.GradientBoostingClassifier`, and :class:`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.
:pr:`13636` and :pr:`13620` by `Adrin Jalali`_.
- sum up to
|Fix| Fixed a bug in :class:`ensemble.GradientBoostingClassifier` and :class:`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. :pr:`12436` by :user:`Jérémie du Boisberranger <jeremiedbb>` and :pr:`12983` by :user:`Nicolas Hug<NicolasHug>`.
|Fix| Fixed the output of the average path length computed in :class:`ensemble.IsolationForest` when the input is either 0, 1 or 2. :pr:`13251` by :user:`Albert Thomas <albertcthomas>` and :user:`joshuakennethjones <joshuakennethjones>`.
|Fix| Fixed a bug in :class:`ensemble.GradientBoostingClassifier` where the gradients would be incorrectly computed in multiclass classification problems. :pr:`12715` by :user:`Nicolas Hug<NicolasHug>`.
|Fix| Fixed a bug in :class:`ensemble.GradientBoostingClassifier` where validation sets for early stopping were not sampled with stratification. :pr:`13164` by :user:`Nicolas Hug<NicolasHug>`.
|Fix| Fixed a bug in :class:`ensemble.GradientBoostingClassifier` where the default initial prediction of a multiclass classifier would predict the classes priors instead of the log of the priors. :pr:`12983` by :user:`Nicolas Hug<NicolasHug>`.
|Fix| Fixed a bug in :class:`ensemble.RandomForestClassifier` where the
predictmethod would error for multiclass multioutput forests models if any targets were strings. :pr:`12834` by :user:`Elizabeth Sander <elsander>`.|Fix| Fixed a bug in :class:`ensemble.gradient_boosting.LossFunction` and :class:`ensemble.gradient_boosting.LeastSquaresError` where the default value of
learning_rateinupdate_terminal_regionsis not consistent with the document and the caller functions. Note however that directly using these loss functions is deprecated. :pr:`6463` by :user:`movelikeriver <movelikeriver>`.|Fix| :func:`ensemble.partial_dependence` (and consequently the new version :func:`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. :pr:`13193` by :user:`Samuel O. Ronsin <samronsin>`.
|API| :func:`ensemble.partial_dependence` and :func:`ensemble.plot_partial_dependence` are now deprecated in favor of :func:`inspection.partial_dependence<sklearn.inspection.partial_dependence>` and :func:`inspection.plot_partial_dependence<sklearn.inspection.plot_partial_dependence>`. :pr:`12599` by :user:`Trevor Stephens<trevorstephens>` and :user:`Nicolas Hug<NicolasHug>`.
|Fix| :class:`ensemble.VotingClassifier` and :class:`ensemble.VotingRegressor` were failing during
fitin one of the estimators was set toNoneandsample_weightwas notNone. :pr:`13779` by :user:`Guillaume Lemaitre <glemaitre>`.|API| :class:`ensemble.VotingClassifier` and :class:`ensemble.VotingRegressor` accept
'drop'to disable an estimator in addition toNoneto be consistent with other estimators (i.e., :class:`pipeline.FeatureUnion` and :class:`compose.ColumnTransformer`). :pr:`13780` by :user:`Guillaume Lemaitre <glemaitre>`.
- |API| Deprecated :mod:`externals.six` since we have dropped support for Python 2.7. :pr:`12916` by :user:`Hanmin Qin <qinhanmin2014>`.
- |Fix| If
input='file'orinput='filename', and a callable is given as theanalyzer, :class:`sklearn.feature_extraction.text.HashingVectorizer`, :class:`sklearn.feature_extraction.text.TfidfVectorizer`, and :class:`sklearn.feature_extraction.text.CountVectorizer` now read the data from the file(s) and then pass it to the givenanalyzer, instead of passing the file name(s) or the file object(s) to the analyzer. :pr:`13641` by `Adrin Jalali`_.
|MajorFeature| Added :class:`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. :pr:`8478` and :pr:`12177` by :user:`Sergey Feldman <sergeyf>` and :user:`Ben Lawson <benlawson>`.
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 :class:`impute.SimpleImputer` and :class:`impute.IterativeImputer` have a new parameter
'add_indicator', which simply stacks a :class:`impute.MissingIndicator` transform into the output of the imputer's transform. That allows a predictive estimator to account for missingness. :pr:`12583`, :pr:`13601` by :user:`Danylo Baibak <DanilBaibak>`.|Fix| In :class:`impute.MissingIndicator` avoid implicit densification by raising an exception if input is sparse add missing_values property is set to 0. :pr:`13240` by :user:`Bartosz Telenczuk <btel>`.
|Fix| Fixed two bugs in :class:`impute.MissingIndicator`. First, when
Xis sparse, all the non-zero non missing values used to become explicit False in the transformed data. Then, whenfeatures='missing-only', all features used to be kept if there were no missing values at all. :pr:`13562` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
(new subpackage)
- |Feature| Partial dependence plots (:func:`inspection.plot_partial_dependence`) are now supported for any regressor or classifier (provided that they have a predict_proba method). :pr:`12599` by :user:`Trevor Stephens <trevorstephens>` and :user:`Nicolas Hug <NicolasHug>`.
- |Feature| Allow different dtypes (such as float32) in :class:`isotonic.IsotonicRegression`. :pr:`8769` by :user:`Vlad Niculae <vene>`
- |Enhancement| :class:`linear_model.Ridge` now preserves
float32andfloat64dtypes. :issue:`8769` and :issue:`11000` by :user:`Guillaume Lemaitre <glemaitre>`, and :user:`Joan Massich <massich>` - |Feature| :class:`linear_model.LogisticRegression` and :class:`linear_model.LogisticRegressionCV` now support Elastic-Net penalty, with the 'saga' solver. :pr:`11646` by :user:`Nicolas Hug <NicolasHug>`.
- |Feature| Added :class:`linear_model.lars_path_gram`, which is
:class:`linear_model.lars_path` in the sufficient stats mode, allowing
users to compute :class:`linear_model.lars_path` without providing
Xandy. :pr:`11699` by :user:`Kuai Yu <yukuairoy>`. - |Efficiency| :func:`linear_model.make_dataset` now preserves
float32andfloat64dtypes, reducing memory consumption in stochastic gradient, SAG and SAGA solvers. :pr:`8769` and :pr:`11000` by :user:`Nelle Varoquaux <NelleV>`, :user:`Arthur Imbert <Henley13>`, :user:`Guillaume Lemaitre <glemaitre>`, and :user:`Joan Massich <massich>` - |Enhancement| :class:`linear_model.LogisticRegression` now supports an
unregularized objective when
penalty='none'is passed. This is equivalent to settingC=np.infwith l2 regularization. Not supported by the liblinear solver. :pr:`12860` by :user:`Nicolas Hug <NicolasHug>`. - |Enhancement| sparse_cg solver in :class:`linear_model.Ridge`
now supports fitting the intercept (i.e.
fit_intercept=True) when inputs are sparse. :pr:`13336` by :user:`Bartosz Telenczuk <btel>`. - |Enhancement| The coordinate descent solver used in Lasso, ElasticNet, etc. now issues a ConvergenceWarning when it completes without meeting the desired toleranbce. :pr:`11754` and :pr:`13397` by :user:`Brent Fagan <brentfagan>` and :user:`Adrin Jalali <adrinjalali>`.
- |Fix| Fixed a bug in :class:`linear_model.LogisticRegression` and :class:`linear_model.LogisticRegressionCV` with 'saga' solver, where the weights would not be correctly updated in some cases. :pr:`11646` by `Tom Dupre la Tour`_.
- |Fix| Fixed the posterior mean, posterior covariance and returned regularization parameters in :class:`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. :pr:`12174` by :user:`Albert Thomas <albertcthomas>`.
- |Fix| Fixed a bug in :class:`linear_model.LassoLarsIC`, where user input
copy_X=Falseat instance creation would be overridden by default parameter valuecopy_X=Trueinfit. :pr:`12972` by :user:`Lucio Fernandez-Arjona <luk-f-a>` - |Fix| Fixed a bug in :class:`linear_model.LinearRegression` that
was not returning the same coeffecients and intercepts with
fit_intercept=Truein sparse and dense case. :pr:`13279` by `Alexandre Gramfort`_ - |Fix| Fixed a bug in :class:`linear_model.HuberRegressor` that was
broken when
Xwas of dtype bool. :pr:`13328` by `Alexandre Gramfort`_. - |Fix| Fixed a performance issue of
sagaandsagsolvers when called in a :class:`joblib.Parallel` setting withn_jobs > 1andbackend="threading", causing them to perform worse than in the sequential case. :pr:`13389` by :user:`Pierre Glaser <pierreglaser>`. - |Fix| Fixed a bug in :class:`linear_model.stochastic_gradient.BaseSGDClassifier` that was not deterministic when trained in a multi-class setting on several threads. :pr:`13422` by :user:`Clément Doumouro <ClemDoum>`.
- |Fix| Fixed bug in :func:`linear_model.ridge_regression`,
:class:`linear_model.Ridge` and
:class:`linear_model.RidgeClassifier` that
caused unhandled exception for arguments
return_intercept=Trueandsolver=auto(default) or any other solver different fromsag. :pr:`13363` by :user:`Bartosz Telenczuk <btel>` - |Fix| :func:`linear_model.ridge_regression` will now raise an exception
if
return_intercept=Trueand solver is different fromsag. Previously, only warning was issued. :pr:`13363` by :user:`Bartosz Telenczuk <btel>` - |Fix| :func:`linear_model.ridge_regression` will choose
sparse_cgsolver for sparse inputs whensolver=autoandsample_weightis provided (previously cholesky solver was selected). :pr:`13363` by :user:`Bartosz Telenczuk <btel>` - |API| The use of :class:`linear_model.lars_path` with
X=Nonewhile passingGramis deprecated in version 0.21 and will be removed in version 0.23. Use :class:`linear_model.lars_path_gram` instead. :pr:`11699` by :user:`Kuai Yu <yukuairoy>`. - |API| :func:`linear_model.logistic_regression_path` is deprecated in version 0.21 and will be removed in version 0.23. :pr:`12821` by :user:`Nicolas Hug <NicolasHug>`.
- |Fix| :class:`linear_model.RidgeCV` with generalized cross-validation
now correctly fits an intercept when
fit_intercept=Trueand the design matrix is sparse. :issue:`13350` by :user:`Jérôme Dockès <jeromedockes>`
- |Efficiency| Make :func:`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. :pr:`9907` by :user:`William de Vazelhes <wdevazelhes>`.
- |Feature| Added the :func:`metrics.max_error` metric and a corresponding
'max_error'scorer for single output regression. :pr:`12232` by :user:`Krishna Sangeeth <whiletruelearn>`. - |Feature| Add :func:`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. :pr:`11179` by :user:`Shangwu Yao <ShangwuYao>` and `Joel Nothman`_.
- |Feature| :func:`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 :func:`metrics.f1_score`. :pr:`13151` by :user:`Gaurav Dhingra <gxyd>` and `Joel Nothman`_.
- |Feature| Added :func:`metrics.pairwise.haversine_distances` which can be accessed with metric='pairwise' through :func:`metrics.pairwise_distances` and estimators. (Haversine distance was previously available for nearest neighbors calculation.) :pr:`12568` by :user:`Wei Xue <xuewei4d>`, :user:`Emmanuel Arias <eamanu>` and `Joel Nothman`_.
- |Efficiency| Faster :func:`metrics.pairwise_distances` with n_jobs > 1 by using a thread-based backend, instead of process-based backends. :pr:`8216` by :user:`Pierre Glaser <pierreglaser>` and :user:`Romuald Menuet <zanospi>`
- |Efficiency| The pairwise manhattan distances with sparse input now uses the BLAS shipped with scipy instead of the bundled BLAS. :pr:`12732` by :user:`Jérémie du Boisberranger <jeremiedbb>`
- |Enhancement| Use label accuracy instead of micro-average on :func:`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. :pr:`12334` by :user:`Emmanuel Arias <[email protected]>`, `Joel Nothman`_ and `Andreas Müller`_
- |Enhancement| Added beta parameter to :func:`metrics.homogeneity_completeness_v_measure` and :func:`metrics.v_measure_score` to configure the tradeoff between homogeneity and completeness. :pr:`13607` by :user:`Stephane Couvreur <scouvreur>` and and :user:`Ivan Sanchez <ivsanro1>`.
- |Fix| The metric :func:`metrics.r2_score` is degenerate with a single sample and now it returns NaN and raises :class:`exceptions.UndefinedMetricWarning`. :pr:`12855` by :user:`Pawel Sendyk <psendyk>`.
- |Fix| Fixed a bug where :func:`metrics.brier_score_loss` will sometimes
return incorrect result when there's only one class in
y_true. :pr:`13628` by :user:`Hanmin Qin <qinhanmin2014>`. - |Fix| Fixed a bug in :func:`metrics.label_ranking_average_precision_score` where sample_weight wasn't taken into account for samples with degenerate labels. :pr:`13447` by :user:`Dan Ellis <dpwe>`.
- |API| The parameter
labelsin :func:`metrics.hamming_loss` is deprecated in version 0.21 and will be removed in version 0.23. :pr:`10580` by :user:`Reshama Shaikh <reshamas>` and :user:`Sandra Mitrovic <SandraMNE>`. - |Fix| The function :func:`metrics.pairwise.euclidean_distances`, and
therefore several estimators with
metric='euclidean', suffered from numerical precision issues withfloat32features. Precision has been increased at the cost of a small drop of performance. :pr:`13554` by :user:`Celelibi` and :user:`Jérémie du Boisberranger <jeremiedbb>`. - |API| :func:`metrics.jaccard_similarity_score` is deprecated in favour of the more consistent :func:`metrics.jaccard_score`. The former behavior for binary and multiclass targets is broken. :pr:`13151` by `Joel Nothman`_.
- |Fix| Fixed a bug in :class:`mixture.BaseMixture` and therefore on estimators
based on it, i.e. :class:`mixture.GaussianMixture` and
:class:`mixture.BayesianGaussianMixture`, where
fit_predictandfit.predictwere not equivalent. :pr:`13142` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
- |Feature| Classes :class:`~model_selection.GridSearchCV` and :class:`~model_selection.RandomizedSearchCV` now allow for refit=callable to add flexibility in identifying the best estimator. See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py`. :pr:`11354` by :user:`Wenhao Zhang <[email protected]>`, `Joel Nothman`_ and :user:`Adrin Jalali <adrinjalali>`.
- |Enhancement| Classes :class:`~model_selection.GridSearchCV`, :class:`~model_selection.RandomizedSearchCV`, and methods :func:`~model_selection.cross_val_score`, :func:`~model_selection.cross_val_predict`, :func:`~model_selection.cross_validate`, now print train scores when return_train_scores is True and verbose > 2. For :func:`~model_selection.learning_curve`, and :func:`~model_selection.validation_curve` only the latter is required. :pr:`12613` and :pr:`12669` by :user:`Marc Torrellas <marctorrellas>`.
- |Enhancement| Some :term:`CV splitter` classes and
model_selection.train_test_split now raise
ValueErrorwhen the resulting training set is empty. :pr:`12861` by :user:`Nicolas Hug <NicolasHug>`. - |Fix| Fixed a bug where :class:`model_selection.StratifiedKFold`
shuffles each class's samples with the same
random_state, makingshuffle=Trueineffective. :pr:`13124` by :user:`Hanmin Qin <qinhanmin2014>`. - |Fix| Added ability for :func:`model_selection.cross_val_predict` to handle
multi-label (and multioutput-multiclass) targets with
predict_proba-type methods. :pr:`8773` by :user:`Stephen Hoover <stephen-hoover>`. - |Fix| Fixed an issue in :func:`~model_selection.cross_val_predict` where method="predict_proba" returned always 0.0 when one of the classes was excluded in a cross-validation fold. :pr:`13366` by :user:`Guillaume Fournier <gfournier>`
- |Fix| Fixed an issue in :func:`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. :pr:`10440` by :user:`Jonathan Ohayon <Johayon>`.
- |Fix| Fixed a bug in :class:`multioutput.MultiOutputClassifier` where the predict_proba method incorrectly checked for predict_proba attribute in the estimator object. :pr:`12222` by :user:`Rebekah Kim <rebekahkim>`
- |MajorFeature| Added :class:`neighbors.NeighborhoodComponentsAnalysis` for metric learning, which implements the Neighborhood Components Analysis algorithm. :pr:`10058` by :user:`William de Vazelhes <wdevazelhes>` and :user:`John Chiotellis <johny-c>`.
- |API| Methods in :class:`neighbors.NearestNeighbors` :
:func:`~neighbors.NearestNeighbors.kneighbors`,
:func:`~neighbors.NearestNeighbors.radius_neighbors`,
:func:`~neighbors.NearestNeighbors.kneighbors_graph`,
:func:`~neighbors.NearestNeighbors.radius_neighbors_graph`
now raise
NotFittedError, rather thanAttributeError, when called beforefit:pr:`12279` by :user:`Krishna Sangeeth <whiletruelearn>`.
- |Fix| Fixed a bug in :class:`neural_network.MLPClassifier` and
:class:`neural_network.MLPRegressor` where the option
shuffle=Falsewas being ignored. :pr:`12582` by :user:`Sam Waterbury <samwaterbury>`. - |Fix| Fixed a bug in :class:`neural_network.MLPClassifier` where validation sets for early stopping were not sampled with stratification. In the multilabel case however, splits are still not stratified. :pr:`13164` by :user:`Nicolas Hug<NicolasHug>`.
- |Feature| :class:`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 accessingnamed_steps. :pr:`2568` by `Joel Nothman`_. - |Feature| Added optional parameter
verbosein :class:`pipeline.Pipeline`, :class:`compose.ColumnTransformer` and :class:`pipeline.FeatureUnion` and correspondingmake_helpers for showing progress and timing of each step. :pr:`11364` by :user:`Baze Petrushev <petrushev>`, :user:`Karan Desai <karandesai-96>`, `Joel Nothman`_, and :user:`Thomas Fan <thomasjpfan>`. - |Enhancement| :class:`pipeline.Pipeline` now supports using
'passthrough'as a transformer, with the same effect asNone. :pr:`11144` by :user:`Thomas Fan <thomasjpfan>`. - |Enhancement| :class:`pipeline.Pipeline` implements
__len__and thereforelen(pipeline)returns the number of steps in the pipeline. :pr:`13439` by :user:`Lakshya KD <LakshKD>`.
- |Feature| :class:`preprocessing.OneHotEncoder` now supports dropping one feature per category with a new drop parameter. :pr:`12908` by :user:`Drew Johnston <drewmjohnston>`.
- |Efficiency| :class:`preprocessing.OneHotEncoder` and :class:`preprocessing.OrdinalEncoder` now handle pandas DataFrames more efficiently. :pr:`13253` by :user:`maikia`.
- |Efficiency| Make :class:`preprocessing.MultiLabelBinarizer` cache class mappings instead of calculating it every time on the fly. :pr:`12116` by :user:`Ekaterina Krivich <kiote>` and `Joel Nothman`_.
- |Efficiency| :class:`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. :pr:`12197` by :user:`Andrew Nystrom <awnystrom>`.
- |Efficiency| Speed improvement in :class:`preprocessing.PolynomialFeatures`,
in the dense case. Also added a new parameter
orderwhich controls output order for further speed performances. :pr:`12251` by `Tom Dupre la Tour`_. - |Fix| Fixed the calculation overflow when using a float16 dtype with :class:`preprocessing.StandardScaler`. :pr:`13007` by :user:`Raffaello Baluyot <baluyotraf>`
- |Fix| Fixed a bug in :class:`preprocessing.QuantileTransformer` and :func:`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. :pr:`13333` by :user:`Albert Thomas <albertcthomas>`.
- |API| The default value of copy in :func:`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 :mod:`preprocessing` and prevent unexpected side effects by modifying the value of X inplace. :pr:`13459` by :user:`Hunter McGushion <HunterMcGushion>`.
- |Fix| Fixed an issue in :func:`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. :pr:`10440` by :user:`Jonathan Ohayon <Johayon>`.
- |Feature| Decision Trees can now be plotted with matplotlib using
:func:`tree.plot_tree` without relying on the
dotlibrary, removing a hard-to-install dependency. :pr:`8508` by `Andreas Müller`_. - |Feature| Decision Trees can now be exported in a human readable textual format using :func:`tree.export_text`. :pr:`6261` by Giuseppe Vettigli <JustGlowing>.
- |Feature|
get_n_leaves()andget_depth()have been added to :class:`tree.BaseDecisionTree` and consequently all estimators based on it, including :class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor`, :class:`tree.ExtraTreeClassifier`, and :class:`tree.ExtraTreeRegressor`. :pr:`12300` by :user:`Adrin Jalali <adrinjalali>`. - |Fix| Trees and forests did not previously predict multi-output classification targets with string labels, despite accepting them in fit. :pr:`11458` by :user:`Mitar Milutinovic <mitar>`.
- |Fix| Fixed an issue with :class:`tree.BaseDecisionTree`
and consequently all estimators based
on it, including :class:`tree.DecisionTreeClassifier`,
:class:`tree.DecisionTreeRegressor`, :class:`tree.ExtraTreeClassifier`,
and :class:`tree.ExtraTreeRegressor`, where they used to exceed the given
max_depthby 1 while expanding the tree ifmax_leaf_nodesandmax_depthwere both specified by the user. Please note that this also affects all ensemble methods using decision trees. :pr:`12344` by :user:`Adrin Jalali <adrinjalali>`.
- |Feature| :func:`utils.resample` now accepts a
stratifyparameter for sampling according to class distributions. :pr:`13549` by :user:`Nicolas Hug <NicolasHug>`. - |API| Deprecated
warn_on_dtypeparameter from :func:`utils.check_array` and :func:`utils.check_X_y`. Added explicit warning for dtype conversion in :func:`check_pairwise_arrays` if themetricbeing passed is a pairwise boolean metric. :pr:`13382` by :user:`Prathmesh Savale <praths007>`.
- |MajorFeature| 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_onlyoption in :func:`sklearn.set_config`. :pr:`11705` by :user:`Nicolas Hug <NicolasHug>`. - |MajorFeature| 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 :ref:`User Guide <estimator_tags>`. :pr:`8022` by :user:`Andreas Müller <amueller>`.
- |Efficiency| Memory copies are avoided when casting arrays to a different dtype in multiple estimators. :pr:`11973` by :user:`Roman Yurchak <rth>`.
- |Fix| Fixed a bug in the implementation of the :func:`our_rand_r` helper function that was not behaving consistently across platforms. :pr:`13422` by :user:`Madhura Parikh <jdnc>` and :user:`Clément Doumouro <ClemDoum>`.
- |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. :pr:`13531` by :user:`Roman Yurchak <rth>`.
These changes mostly affect library developers.
- Add
check_fit_idempotentto :func:`~utils.estimator_checks.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. :pr:`12328` by :user:`Nicolas Hug <NicolasHug>` - Many checks can now be disabled or configured with :ref:`estimator_tags`. :pr:`8022` by :user:`Andreas Müller <amueller>`.
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, ^__^