v0.21.rst

.. currentmodule:: sklearn

Version 0.21.0

In development

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.

• :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` for multiclass classification. |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.)

Changelog

Support for Python 3.4 and below has been officially dropped.

:mod:`sklearn.calibration`

• |Enhancement| Added support to bin the data passed into :class:`calibration.calibration_curve` by quantiles instead of uniformly between 0 and 1. :issue:`13086` by :user:`Scott Cole <srcole>`.

:mod:`sklearn.cluster`

• |MajorFeature| A new clustering algorithm: :class:`cluster.OPTICS`: an algoritm related to :class:`cluster.DBSCAN`, that has hyperparameters easier to set and that scales better, by :user:`Shane <espg>`, :user:`Adrin Jalali <adrinjalali>`, and :user:`Erich Schubert <kno10>`.

:mod:`sklearn.datasets`

• |Fix| Added support for 64-bit group IDs and pointers in SVMLight files :class:`datasets.svmlight_format` :issue:`10727` by :user:`Bryan K Woods <bryan-woods>`.
• |Fix| :func:`datasets.load_sample_images` returns images with a deterministic order. :issue:`13250` by :user:`Thomas Fan <thomasjpfan>`.

:mod:`sklearn.decomposition`

• |API| The default value of the init argument in :func:`decomposition.non_negative_factorization` will change from random to None in version 0.23 to make it consistent with :class:`decomposition.NMF`. A FutureWarning is raised when the default value is used. :issue:`12988` by :user:`Zijie (ZJ) Poh <zjpoh>`.
• |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). :issue:`11650` by :user:`Hossein Pourbozorg <hossein-pourbozorg>` and :user:`Zijie (ZJ) Poh <zjpoh>`.
• |Enhancement| :class:`decomposition.KernelPCA` now has deterministic output (resolved sign ambiguity in eigenvalue decomposition of the kernel matrix). :issue:`13241` by :user:`Aurélien Bellet <bellet>`.

:mod:`sklearn.discriminant_analysis`

• |Enhancement| :class:`discriminant_analysis.LinearDiscriminantAnalysis` now preserves float32 and float64 dtypes. :issues:`8769` and :issues:`11000` by :user:`Thibault Sejourne <thibsej>`
• |Fix| A ChangedBehaviourWarning is now raised when :class:`discriminant_analysis.LinearDiscriminantAnalysis` is given as parameter n_components > min(n_features, n_classes - 1), and n_components is changed to min(n_features, n_classes - 1) if so. Previously the change was made, but silently. :issue:`11526` by :user:`William de Vazelhes<wdevazelhes>`.

:mod:`sklearn.dummy`

• |Fix| Fixed a bug in :class:`dummy.DummyClassifier` where the predict_proba method was returning int32 array instead of float64 for the stratified strategy. :issue:`13266` by :user:`Christos Aridas<chkoar>`.

:mod:`sklearn.ensemble`

• |Efficiency| Make :class:`ensemble.IsolationForest` prefer threads over processes when running with n_jobs > 1 as the underlying decision tree fit calls do release the GIL. This changes reduces memory usage and communication overhead. :issue:`12543` by :user:`Isaac Storch <istorch>` and `Olivier Grisel`_.
• |Fix| Fixed the output of the average path length computed in :class:`ensemble.IsolationForest` when the input is either 0, 1 or 2. :issue:`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. :issue:`12715` by :user:`Nicolas Hug<NicolasHug>`.
• |Fix| Fixed a bug in :mod:`ensemble` where the predict method would error for multiclass multioutput forests models if any targets were strings. :issue:`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_rate in update_terminal_regions is not consistent with the document and the caller functions. :issue:`6463` by :user:`movelikeriver <movelikeriver>`.

:mod:`sklearn.externals`

• |API| Deprecated :mod:`externals.six` since we have dropped support for Python 2.7. :issue:`12916` by :user:`Hanmin Qin <qinhanmin2014>`.

:mod:`sklearn.impute`

• |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. :issue:`8478` and :issue:`12177` by :user:`Sergey Feldman <sergeyf>` :user:`Ben Lawson <benlawson>`.
• |Fix| In :class:`impute.MissingIndicator` avoid implicit densification by raising an exception if input is sparse add missing_values property is set to 0. :issue:`13240` by :user:`Bartosz Telenczuk <btel>`.

:mod:`sklearn.linear_model`

• |Enhancement| :class:`linear_model.make_dataset` now preserves float32 and float64 dtypes. :issues:`8769` and :issues:`11000` by :user:`Nelle Varoquaux`_, :user:`Arthur Imbert <Henley13>`, :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. :issue:`11646` by :user:`Nicolas Hug <NicolasHug>`.
• |Enhancement| :class:`linear_model.LogisticRegression` now supports an unregularized objective by setting penalty to 'none'. This is equivalent to setting C=np.inf with l2 regularization. Not supported by the liblinear solver. :issue:`12860` by :user:`Nicolas Hug <NicolasHug>`.
• |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. :issue:`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. :issue:`12174` by :user:`Albert Thomas <albertcthomas>`.
• |API| :func:`linear_model.logistic_regression_path` is deprecated in version 0.21 and will be removed in version 0.23. :issue:`12821` by :user:`Nicolas Hug <NicolasHug>`.

• |Fix| Fixed a bug in :class:`linear_model.LassoLarsIC`, where user input

  copy_X=False at instance creation would be overridden by default parameter value copy_X=True in fit. :issue:`12972` by :user:`Lucio Fernandez-Arjona <luk-f-a>`

:mod:`sklearn.manifold`

• |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. :issue:`9907` by :user:`William de Vazelhes <wdevazelhes>`.

:mod:`sklearn.metrics`

• |Feature| Added the :func:`metrics.max_error` metric and a corresponding 'max_error' scorer for single output regression. :issue:`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. :issue:`11179` by :user:`Shangwu Yao <ShangwuYao>` and `Joel Nothman`_.
• |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. :issue:`12334` by :user:`Emmanuel Arias <eamanu@eamanu.com>`, `Joel Nothman`_ and `Andreas Müller`_
• |API| The parameter labels in :func:`metrics.hamming_loss` is deprecated in version 0.21 and will be removed in version 0.23. :issue:`10580` by :user:`Reshama Shaikh <reshamas>` and Sandra Mitrovic <SandraMNE>.
• |Fix| The metric :func:`metrics.r2_score` is degenerate with a single sample and now it returns NaN and raises :class:`exceptions.UndefinedMetricWarning`. :issue:`12855` by :user:`Pawel Sendyk <psendyk>.`
• |Efficiency| The pairwise manhattan distances with sparse input now uses the BLAS shipped with scipy instead of the bundled BLAS. :issue:`12732` by :user:`Jérémie du Boisberranger <jeremiedbb>`

:mod:`sklearn.model_selection`

• |Feature| Classes :class:`~model_selection.GridSearchCV` and :class:`~model_selection.RandomizedSearchCV` now allow for refit=callable to add flexibility in identifying the best estimator. An example for this interface has been added. :issue:`11354` by :user:`Wenhao Zhang <wenhaoz@ucla.edu>`, `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. :issue:`12613` and :issue:`12669` by :user:`Marc Torrellas <marctorrellas>`.

:mod:`sklearn.neighbors`

• |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 than AttributeError, when called before fit :issue:`12279` by :user:`Krishna Sangeeth <whiletruelearn>`.

:mod:`sklearn.neural_network`

• |Fix| Fixed a bug in :class:`neural_network.MLPClassifier` and :class:`neural_network.MLPRegressor` where the option shuffle=False was being ignored. :issue:`12582` by :user:`Sam Waterbury <samwaterbury>`.

:mod:`sklearn.pipeline`

• |API| :class:`pipeline.Pipeline` now supports using 'passthrough' as a transformer. :issue:`11144` by :user:`Thomas Fan <thomasjpfan>`.

:mod:`sklearn.preprocessing`

• |Efficiency| Make :class:`preprocessing.MultiLabelBinarizer` to cache class mappings instead of calculating it every time on the fly. :issue:`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. :issue:`12197` by :user:`Andrew Nystrom <awnystrom>`.
• |Efficiency| |API| Speed improvement in :class:`preprocessing.PolynomialFeatures`, in the dense case. Also added a new parameter order which controls output order for further speed performances. :issue:`12251` by `Tom Dupre la Tour`_.
• |Fix| Fixed the calculation overflow when using a float16 dtype with :class:`preprocessing.StandardScaler`. :issue:`13007` by :user:`Raffaello Baluyot <baluyotraf>`
• |Feature| :class:`OneHotEncoder` now supports dropping one feature per category with a new drop parameter. :issue:`12908` by :user:`Drew Johnston <drewmjohnston>`.

:mod:`sklearn.tree`

• |Feature| Decision Trees can now be plotted with matplotlib using :func:`tree.plot_tree` without relying on the dot library, removing a hard-to-install dependency. :issue:`8508` by `Andreas Müller`_.
• |Feature| Decision Trees can now be exported in a human readable textual format using :func:`tree.export.export_text`. :issue:`6261` by Giuseppe Vettigli <JustGlowing>.
• |Feature| get_n_leaves() and get_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`. :issue:`12300` by :user:`Adrin Jalali <adrinjalali>`.
• |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_depth by 1 while expanding the tree if max_leaf_nodes and max_depth were both specified by the user. Please note that this also affects all ensemble methods using decision trees. :issue:`12344` by :user:`Adrin Jalali <adrinjalali>`.

:mod:`sklearn.mixture`

• |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_predict and fit.predict were not equivalent. :issue:`13142` by :user:`Jérémie du Boisberranger <jeremiedbb>`.

:mod:`sklearn.isotonic`

• |Feature| Allow different dtypes (such as float32) in :class:`isotonic.IsotonicRegression` :issue:`8769` by :user:`Vlad Niculae <vene>`

Multiple modules

• The __repr__() method of all estimators (used when calling print(estimator)) has been entirely re-written, building on Python's pretty printing standard library. All parameters are printed by default, but this can be altered with the print_changed_only option in :func:`sklearn.set_config`. :issue:`11705` by :user:`Nicolas Hug <NicolasHug>`.

Changes to estimator checks

These changes mostly affect library developers.

• Add check_fit_idempotent to :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. :issue:`12328` by :user:`Nicolas Hug <NicolasHug>`