1121 lines
49 KiB
ReStructuredText
1121 lines
49 KiB
ReStructuredText
.. include:: _contributors.rst
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.. currentmodule:: sklearn
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============
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Version 0.21
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============
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.. include:: changelog_legend.inc
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.. _changes_0_21_3:
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Version 0.21.3
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==============
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**July 30, 2019**
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Changed models
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--------------
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The following estimators and functions, when fit with the same data and
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parameters, may produce different models from the previous version. This often
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occurs due to changes in the modelling logic (bug fixes or enhancements), or in
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random sampling procedures.
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- The v0.20.0 release notes failed to mention a backwards incompatibility in
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:func:`metrics.make_scorer` when `needs_proba=True` and `y_true` is binary.
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Now, the scorer function is supposed to accept a 1D `y_pred` (i.e.,
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probability of the positive class, shape `(n_samples,)`), instead of a 2D
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`y_pred` (i.e., shape `(n_samples, 2)`).
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Changelog
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---------
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:mod:`sklearn.cluster`
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......................
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- |Fix| Fixed a bug in :class:`cluster.KMeans` where computation with
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`init='random'` was single threaded for `n_jobs > 1` or `n_jobs = -1`.
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:pr:`12955` by :user:`Prabakaran Kumaresshan <nixphix>`.
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- |Fix| Fixed a bug in :class:`cluster.OPTICS` where users were unable to pass
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float `min_samples` and `min_cluster_size`. :pr:`14496` by
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:user:`Fabian Klopfer <someusername1>`
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and :user:`Hanmin Qin <qinhanmin2014>`.
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- |Fix| Fixed a bug in :class:`cluster.KMeans` where KMeans++ initialisation
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could rarely result in an IndexError. :issue:`11756` by `Joel Nothman`_.
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:mod:`sklearn.compose`
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......................
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- |Fix| Fixed an issue in :class:`compose.ColumnTransformer` where using
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DataFrames whose column order differs between :func:``fit`` and
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:func:``transform`` could lead to silently passing incorrect columns to the
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``remainder`` transformer.
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:pr:`14237` by `Andreas Schuderer <schuderer>`.
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:mod:`sklearn.datasets`
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.......................
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- |Fix| :func:`datasets.fetch_california_housing`,
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:func:`datasets.fetch_covtype`,
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:func:`datasets.fetch_kddcup99`, :func:`datasets.fetch_olivetti_faces`,
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:func:`datasets.fetch_rcv1`, and :func:`datasets.fetch_species_distributions`
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try to persist the previously cache using the new ``joblib`` if the cached
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data was persisted using the deprecated ``sklearn.externals.joblib``. This
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behavior is set to be deprecated and removed in v0.23.
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:pr:`14197` by `Adrin Jalali`_.
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:mod:`sklearn.ensemble`
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.......................
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- |Fix| Fix zero division error in :class:`ensemble.HistGradientBoostingClassifier` and
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:class:`ensemble.HistGradientBoostingRegressor`.
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:pr:`14024` by `Nicolas Hug <NicolasHug>`.
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:mod:`sklearn.impute`
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.....................
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- |Fix| Fixed a bug in :class:`impute.SimpleImputer` and
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:class:`impute.IterativeImputer` so that no errors are thrown when there are
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missing values in training data. :pr:`13974` by `Frank Hoang <fhoang7>`.
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:mod:`sklearn.inspection`
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.........................
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- |Fix| Fixed a bug in `inspection.plot_partial_dependence` where
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``target`` parameter was not being taken into account for multiclass problems.
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:pr:`14393` by :user:`Guillem G. Subies <guillemgsubies>`.
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:mod:`sklearn.linear_model`
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...........................
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- |Fix| Fixed a bug in :class:`linear_model.LogisticRegressionCV` where
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``refit=False`` would fail depending on the ``'multiclass'`` and
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``'penalty'`` parameters (regression introduced in 0.21). :pr:`14087` by
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`Nicolas Hug`_.
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- |Fix| Compatibility fix for :class:`linear_model.ARDRegression` and
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Scipy>=1.3.0. Adapts to upstream changes to the default `pinvh` cutoff
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threshold which otherwise results in poor accuracy in some cases.
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:pr:`14067` by :user:`Tim Staley <timstaley>`.
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:mod:`sklearn.neighbors`
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........................
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- |Fix| Fixed a bug in :class:`neighbors.NeighborhoodComponentsAnalysis` where
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the validation of initial parameters ``n_components``, ``max_iter`` and
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``tol`` required too strict types. :pr:`14092` by
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:user:`Jérémie du Boisberranger <jeremiedbb>`.
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:mod:`sklearn.tree`
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...................
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- |Fix| Fixed bug in :func:`tree.export_text` when the tree has one feature and
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a single feature name is passed in. :pr:`14053` by `Thomas Fan`.
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- |Fix| Fixed an issue with :func:`tree.plot_tree` where it displayed
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entropy calculations even for `gini` criterion in DecisionTreeClassifiers.
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:pr:`13947` by :user:`Frank Hoang <fhoang7>`.
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.. _changes_0_21_2:
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Version 0.21.2
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==============
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**24 May 2019**
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Changelog
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---------
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:mod:`sklearn.decomposition`
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............................
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- |Fix| Fixed a bug in :class:`cross_decomposition.CCA` improving numerical
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stability when `Y` is close to zero. :pr:`13903` by `Thomas Fan`_.
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:mod:`sklearn.metrics`
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......................
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- |Fix| Fixed a bug in :func:`metrics.pairwise.euclidean_distances` where a
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part of the distance matrix was left un-instanciated for sufficiently large
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float32 datasets (regression introduced in 0.21). :pr:`13910` by
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:user:`Jérémie du Boisberranger <jeremiedbb>`.
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:mod:`sklearn.preprocessing`
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............................
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- |Fix| Fixed a bug in :class:`preprocessing.OneHotEncoder` where the new
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`drop` parameter was not reflected in `get_feature_names`. :pr:`13894`
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by :user:`James Myatt <jamesmyatt>`.
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`sklearn.utils.sparsefuncs`
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...........................
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- |Fix| Fixed a bug where `min_max_axis` would fail on 32-bit systems
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for certain large inputs. This affects :class:`preprocessing.MaxAbsScaler`,
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:func:`preprocessing.normalize` and :class:`preprocessing.LabelBinarizer`.
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:pr:`13741` by :user:`Roddy MacSween <rlms>`.
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.. _changes_0_21_1:
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Version 0.21.1
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==============
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**17 May 2019**
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This is a bug-fix release to primarily resolve some packaging issues in version
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0.21.0. It also includes minor documentation improvements and some bug fixes.
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Changelog
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---------
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:mod:`sklearn.inspection`
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.........................
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- |Fix| Fixed a bug in :func:`inspection.partial_dependence` to only check
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classifier and not regressor for the multiclass-multioutput case.
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:pr:`14309` by :user:`Guillaume Lemaitre <glemaitre>`.
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:mod:`sklearn.metrics`
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......................
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- |Fix| Fixed a bug in :class:`metrics.pairwise_distances` where it would raise
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``AttributeError`` for boolean metrics when ``X`` had a boolean dtype and
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``Y == None``.
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:issue:`13864` by :user:`Paresh Mathur <rick2047>`.
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- |Fix| Fixed two bugs in :class:`metrics.pairwise_distances` when
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``n_jobs > 1``. First it used to return a distance matrix with same dtype as
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input, even for integer dtype. Then the diagonal was not zeros for euclidean
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metric when ``Y`` is ``X``. :issue:`13877` by
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:user:`Jérémie du Boisberranger <jeremiedbb>`.
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:mod:`sklearn.neighbors`
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........................
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- |Fix| Fixed a bug in :class:`neighbors.KernelDensity` which could not be
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restored from a pickle if ``sample_weight`` had been used.
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:issue:`13772` by :user:`Aditya Vyas <aditya1702>`.
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.. _changes_0_21:
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Version 0.21.0
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==============
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**May 2019**
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Changed models
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--------------
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The following estimators and functions, when fit with the same data and
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parameters, may produce different models from the previous version. This often
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occurs due to changes in the modelling logic (bug fixes or enhancements), or in
|
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random sampling procedures.
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- :class:`discriminant_analysis.LinearDiscriminantAnalysis` for multiclass
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classification. |Fix|
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- :class:`discriminant_analysis.LinearDiscriminantAnalysis` with 'eigen'
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solver. |Fix|
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- :class:`linear_model.BayesianRidge` |Fix|
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- Decision trees and derived ensembles when both `max_depth` and
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`max_leaf_nodes` are set. |Fix|
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- :class:`linear_model.LogisticRegression` and
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:class:`linear_model.LogisticRegressionCV` with 'saga' solver. |Fix|
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- :class:`ensemble.GradientBoostingClassifier` |Fix|
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- :class:`sklearn.feature_extraction.text.HashingVectorizer`,
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:class:`sklearn.feature_extraction.text.TfidfVectorizer`, and
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:class:`sklearn.feature_extraction.text.CountVectorizer` |Fix|
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- :class:`neural_network.MLPClassifier` |Fix|
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- :func:`svm.SVC.decision_function` and
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:func:`multiclass.OneVsOneClassifier.decision_function`. |Fix|
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- :class:`linear_model.SGDClassifier` and any derived classifiers. |Fix|
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- Any model using the `linear_model._sag.sag_solver` function with a `0`
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seed, including :class:`linear_model.LogisticRegression`,
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:class:`linear_model.LogisticRegressionCV`, :class:`linear_model.Ridge`,
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and :class:`linear_model.RidgeCV` with 'sag' solver. |Fix|
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- :class:`linear_model.RidgeCV` when using leave-one-out cross-validation
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with sparse inputs. |Fix|
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Details are listed in the changelog below.
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(While we are trying to better inform users by providing this information, we
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cannot assure that this list is complete.)
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Known Major Bugs
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----------------
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* The default `max_iter` for :class:`linear_model.LogisticRegression` is too
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small for many solvers given the default `tol`. In particular, we
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accidentally changed the default `max_iter` for the liblinear solver from
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1000 to 100 iterations in :pr:`3591` released in version 0.16.
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In a future release we hope to choose better default `max_iter` and `tol`
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heuristically depending on the solver (see :pr:`13317`).
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Changelog
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---------
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Support for Python 3.4 and below has been officially dropped.
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..
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Entries should be grouped by module (in alphabetic order) and prefixed with
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one of the labels: |MajorFeature|, |Feature|, |Efficiency|, |Enhancement|,
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|Fix| or |API| (see whats_new.rst for descriptions).
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Entries should be ordered by those labels (e.g. |Fix| after |Efficiency|).
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Changes not specific to a module should be listed under *Multiple Modules*
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or *Miscellaneous*.
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Entries should end with:
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:pr:`123456` by :user:`Joe Bloggs <joeongithub>`.
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where 123456 is the *pull request* number, not the issue number.
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:mod:`sklearn.base`
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...................
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- |API| The R2 score used when calling ``score`` on a regressor will use
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``multioutput='uniform_average'`` from version 0.23 to keep consistent with
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:func:`metrics.r2_score`. This will influence the ``score`` method of all
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the multioutput regressors (except for
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:class:`multioutput.MultiOutputRegressor`).
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:pr:`13157` by :user:`Hanmin Qin <qinhanmin2014>`.
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:mod:`sklearn.calibration`
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..........................
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- |Enhancement| Added support to bin the data passed into
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:class:`calibration.calibration_curve` by quantiles instead of uniformly
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between 0 and 1.
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:pr:`13086` by :user:`Scott Cole <srcole>`.
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- |Enhancement| Allow n-dimensional arrays as input for
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`calibration.CalibratedClassifierCV`. :pr:`13485` by
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:user:`William de Vazelhes <wdevazelhes>`.
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:mod:`sklearn.cluster`
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......................
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- |MajorFeature| A new clustering algorithm: :class:`cluster.OPTICS`: an
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algorithm related to :class:`cluster.DBSCAN`, that has hyperparameters easier
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to set and that scales better, by :user:`Shane <espg>`,
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`Adrin Jalali`_, :user:`Erich Schubert <kno10>`, `Hanmin Qin`_, and
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:user:`Assia Benbihi <assiaben>`.
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- |Fix| Fixed a bug where :class:`cluster.Birch` could occasionally raise an
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AttributeError. :pr:`13651` by `Joel Nothman`_.
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- |Fix| Fixed a bug in :class:`cluster.KMeans` where empty clusters weren't
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correctly relocated when using sample weights. :pr:`13486` by
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:user:`Jérémie du Boisberranger <jeremiedbb>`.
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- |API| The ``n_components_`` attribute in :class:`cluster.AgglomerativeClustering`
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and :class:`cluster.FeatureAgglomeration` has been renamed to
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``n_connected_components_``.
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:pr:`13427` by :user:`Stephane Couvreur <scouvreur>`.
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- |Enhancement| :class:`cluster.AgglomerativeClustering` and
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:class:`cluster.FeatureAgglomeration` now accept a ``distance_threshold``
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parameter which can be used to find the clusters instead of ``n_clusters``.
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:issue:`9069` by :user:`Vathsala Achar <VathsalaAchar>` and `Adrin Jalali`_.
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:mod:`sklearn.compose`
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......................
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- |API| :class:`compose.ColumnTransformer` is no longer an experimental
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feature. :pr:`13835` by :user:`Hanmin Qin <qinhanmin2014>`.
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:mod:`sklearn.datasets`
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.......................
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- |Fix| Added support for 64-bit group IDs and pointers in SVMLight files.
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:pr:`10727` by :user:`Bryan K Woods <bryan-woods>`.
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- |Fix| :func:`datasets.load_sample_images` returns images with a deterministic
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order. :pr:`13250` by :user:`Thomas Fan <thomasjpfan>`.
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:mod:`sklearn.decomposition`
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............................
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- |Enhancement| :class:`decomposition.KernelPCA` now has deterministic output
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(resolved sign ambiguity in eigenvalue decomposition of the kernel matrix).
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:pr:`13241` by :user:`Aurélien Bellet <bellet>`.
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- |Fix| Fixed a bug in :class:`decomposition.KernelPCA`, `fit().transform()`
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now produces the correct output (the same as `fit_transform()`) in case
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of non-removed zero eigenvalues (`remove_zero_eig=False`).
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`fit_inverse_transform` was also accelerated by using the same trick as
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`fit_transform` to compute the transform of `X`.
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:pr:`12143` by :user:`Sylvain Marié <smarie>`
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- |Fix| Fixed a bug in :class:`decomposition.NMF` where `init = 'nndsvd'`,
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`init = 'nndsvda'`, and `init = 'nndsvdar'` are allowed when
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`n_components < n_features` instead of
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`n_components <= min(n_samples, n_features)`.
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:pr:`11650` by :user:`Hossein Pourbozorg <hossein-pourbozorg>` and
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:user:`Zijie (ZJ) Poh <zjpoh>`.
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- |API| The default value of the :code:`init` argument in
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:func:`decomposition.non_negative_factorization` will change from
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:code:`random` to :code:`None` in version 0.23 to make it consistent with
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:class:`decomposition.NMF`. A FutureWarning is raised when
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the default value is used.
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:pr:`12988` by :user:`Zijie (ZJ) Poh <zjpoh>`.
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:mod:`sklearn.discriminant_analysis`
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....................................
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- |Enhancement| :class:`discriminant_analysis.LinearDiscriminantAnalysis` now
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preserves ``float32`` and ``float64`` dtypes. :pr:`8769` and
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:pr:`11000` by :user:`Thibault Sejourne <thibsej>`
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- |Fix| A ``ChangedBehaviourWarning`` is now raised when
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:class:`discriminant_analysis.LinearDiscriminantAnalysis` is given as
|
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parameter ``n_components > min(n_features, n_classes - 1)``, and
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``n_components`` is changed to ``min(n_features, n_classes - 1)`` if so.
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Previously the change was made, but silently. :pr:`11526` by
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:user:`William de Vazelhes<wdevazelhes>`.
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- |Fix| Fixed a bug in :class:`discriminant_analysis.LinearDiscriminantAnalysis`
|
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where the predicted probabilities would be incorrectly computed in the
|
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multiclass case. :pr:`6848`, by :user:`Agamemnon Krasoulis
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<agamemnonc>` and `Guillaume Lemaitre <glemaitre>`.
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- |Fix| Fixed a bug in :class:`discriminant_analysis.LinearDiscriminantAnalysis`
|
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where the predicted probabilities would be incorrectly computed with ``eigen``
|
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solver. :pr:`11727`, by :user:`Agamemnon Krasoulis
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<agamemnonc>`.
|
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|
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:mod:`sklearn.dummy`
|
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....................
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- |Fix| Fixed a bug in :class:`dummy.DummyClassifier` where the
|
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``predict_proba`` method was returning int32 array instead of
|
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float64 for the ``stratified`` strategy. :pr:`13266` by
|
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:user:`Christos Aridas<chkoar>`.
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- |Fix| Fixed a bug in :class:`dummy.DummyClassifier` where it was throwing a
|
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dimension mismatch error in prediction time if a column vector ``y`` with
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``shape=(n, 1)`` was given at ``fit`` time. :pr:`13545` by :user:`Nick
|
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Sorros <nsorros>` and `Adrin Jalali`_.
|
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|
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:mod:`sklearn.ensemble`
|
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.......................
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- |MajorFeature| Add two new implementations of
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gradient boosting trees: :class:`ensemble.HistGradientBoostingClassifier`
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and :class:`ensemble.HistGradientBoostingRegressor`. The implementation of
|
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these estimators is inspired by
|
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`LightGBM <https://github.com/Microsoft/LightGBM>`_ and can be orders of
|
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magnitude faster than :class:`ensemble.GradientBoostingRegressor` and
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:class:`ensemble.GradientBoostingClassifier` when the number of samples is
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larger than tens of thousands of samples. The API of these new estimators
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is slightly different, and some of the features from
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:class:`ensemble.GradientBoostingClassifier` and
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:class:`ensemble.GradientBoostingRegressor` are not yet supported.
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These new estimators are experimental, which means that their results or
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their API might change without any deprecation cycle. To use them, you
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need to explicitly import ``enable_hist_gradient_boosting``::
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>>> # explicitly require this experimental feature
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>>> from sklearn.experimental import enable_hist_gradient_boosting # noqa
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>>> # now you can import normally from sklearn.ensemble
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>>> from sklearn.ensemble import HistGradientBoostingClassifier
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.. note::
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Update: since version 1.0, these estimators are not experimental
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anymore and you don't need to use `from sklearn.experimental import
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enable_hist_gradient_boosting`.
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:pr:`12807` by :user:`Nicolas Hug<NicolasHug>`.
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- |Feature| Add :class:`ensemble.VotingRegressor`
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which provides an equivalent of :class:`ensemble.VotingClassifier`
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for regression problems.
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:pr:`12513` by :user:`Ramil Nugmanov <stsouko>` and
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:user:`Mohamed Ali Jamaoui <mohamed-ali>`.
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- |Efficiency| Make :class:`ensemble.IsolationForest` prefer threads over
|
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processes when running with ``n_jobs > 1`` as the underlying decision tree
|
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fit calls do release the GIL. This changes reduces memory usage and
|
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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 ``y`` as ``float64`` to 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_start``
|
|
parameter, 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`_.
|
|
|
|
- |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
|
|
``predict`` method would error for multiclass multioutput forests models
|
|
if any targets were strings. :pr:`12834` by :user:`Elizabeth Sander
|
|
<elsander>`.
|
|
|
|
- |Fix| Fixed a bug in `ensemble.gradient_boosting.LossFunction` and
|
|
`ensemble.gradient_boosting.LeastSquaresError` where the default
|
|
value of ``learning_rate`` in ``update_terminal_regions`` is not consistent
|
|
with the document and the caller functions. Note however that directly using
|
|
these loss functions is deprecated.
|
|
:pr:`6463` by :user:`movelikeriver <movelikeriver>`.
|
|
|
|
- |Fix| `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| `ensemble.partial_dependence` and
|
|
`ensemble.plot_partial_dependence` are now deprecated in favor of
|
|
:func:`inspection.partial_dependence<sklearn.inspection.partial_dependence>`
|
|
and
|
|
`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 ``fit`` in one
|
|
of the estimators was set to ``None`` and ``sample_weight`` was not ``None``.
|
|
:pr:`13779` by :user:`Guillaume Lemaitre <glemaitre>`.
|
|
|
|
- |API| :class:`ensemble.VotingClassifier` and
|
|
:class:`ensemble.VotingRegressor` accept ``'drop'`` to disable an estimator
|
|
in addition to ``None`` to be consistent with other estimators (i.e.,
|
|
:class:`pipeline.FeatureUnion` and :class:`compose.ColumnTransformer`).
|
|
:pr:`13780` by :user:`Guillaume Lemaitre <glemaitre>`.
|
|
|
|
`sklearn.externals`
|
|
...................
|
|
|
|
- |API| Deprecated `externals.six` since we have dropped support for
|
|
Python 2.7. :pr:`12916` by :user:`Hanmin Qin <qinhanmin2014>`.
|
|
|
|
:mod:`sklearn.feature_extraction`
|
|
.................................
|
|
|
|
- |Fix| If ``input='file'`` or ``input='filename'``, and a callable is given as
|
|
the ``analyzer``, :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 given ``analyzer``, instead of
|
|
passing the file name(s) or the file object(s) to the analyzer.
|
|
:pr:`13641` by `Adrin Jalali`_.
|
|
|
|
: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. :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
|
|
``X`` is sparse, all the non-zero non missing values used to become
|
|
explicit False in the transformed data. Then, when
|
|
``features='missing-only'``, all features used to be kept if there were no
|
|
missing values at all. :pr:`13562` by :user:`Jérémie du Boisberranger
|
|
<jeremiedbb>`.
|
|
|
|
:mod:`sklearn.inspection`
|
|
.........................
|
|
|
|
(new subpackage)
|
|
|
|
- |Feature| Partial dependence plots
|
|
(`inspection.plot_partial_dependence`) are now supported for
|
|
any regressor or classifier (provided that they have a `predict_proba`
|
|
method). :pr:`12599` by :user:`Trevor Stephens <trevorstephens>` and
|
|
:user:`Nicolas Hug <NicolasHug>`.
|
|
|
|
:mod:`sklearn.isotonic`
|
|
.......................
|
|
|
|
- |Feature| Allow different dtypes (such as float32) in
|
|
:class:`isotonic.IsotonicRegression`.
|
|
:pr:`8769` by :user:`Vlad Niculae <vene>`
|
|
|
|
:mod:`sklearn.linear_model`
|
|
...........................
|
|
|
|
- |Enhancement| :class:`linear_model.Ridge` now preserves ``float32`` and
|
|
``float64`` dtypes. :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
|
|
``X`` and ``y``. :pr:`11699` by :user:`Kuai Yu <yukuairoy>`.
|
|
|
|
- |Efficiency| `linear_model.make_dataset` now preserves
|
|
``float32`` and ``float64`` dtypes, 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 setting ``C=np.inf`` with 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=False`` at instance creation would be overridden by default
|
|
parameter value ``copy_X=True`` in ``fit``.
|
|
: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=True`` in sparse and dense case.
|
|
:pr:`13279` by `Alexandre Gramfort`_
|
|
|
|
- |Fix| Fixed a bug in :class:`linear_model.HuberRegressor` that was
|
|
broken when ``X`` was of dtype bool. :pr:`13328` by `Alexandre Gramfort`_.
|
|
|
|
- |Fix| Fixed a performance issue of ``saga`` and ``sag`` solvers when called
|
|
in a :class:`joblib.Parallel` setting with ``n_jobs > 1`` and
|
|
``backend="threading"``, causing them to perform worse than in the sequential
|
|
case. :pr:`13389` by :user:`Pierre Glaser <pierreglaser>`.
|
|
|
|
- |Fix| Fixed a bug in
|
|
`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=True`` and
|
|
``solver=auto`` (default) or any other solver different from ``sag``.
|
|
:pr:`13363` by :user:`Bartosz Telenczuk <btel>`
|
|
|
|
- |Fix| :func:`linear_model.ridge_regression` will now raise an exception
|
|
if ``return_intercept=True`` and solver is different from ``sag``. Previously,
|
|
only warning was issued. :pr:`13363` by :user:`Bartosz Telenczuk <btel>`
|
|
|
|
- |Fix| :func:`linear_model.ridge_regression` will choose ``sparse_cg``
|
|
solver for sparse inputs when ``solver=auto`` and ``sample_weight``
|
|
is provided (previously `cholesky` solver was selected).
|
|
:pr:`13363` by :user:`Bartosz Telenczuk <btel>`
|
|
|
|
- |API| The use of :class:`linear_model.lars_path` with ``X=None``
|
|
while passing ``Gram`` is deprecated in version 0.21 and will be removed
|
|
in version 0.23. Use :class:`linear_model.lars_path_gram` instead.
|
|
:pr:`11699` by :user:`Kuai Yu <yukuairoy>`.
|
|
|
|
- |API| `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 leave-one-out cross-validation
|
|
now correctly fits an intercept when ``fit_intercept=True`` and the design
|
|
matrix is sparse. :issue:`13350` by :user:`Jérôme Dockès <jeromedockes>`
|
|
|
|
:mod:`sklearn.manifold`
|
|
.......................
|
|
|
|
- |Efficiency| Make :func:`manifold.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>`.
|
|
|
|
:mod:`sklearn.metrics`
|
|
......................
|
|
|
|
- |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 <eamanu@eamanu.com>`,
|
|
`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 ``labels`` in :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 with ``float32`` features. 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| `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`_.
|
|
|
|
:mod:`sklearn.mixture`
|
|
......................
|
|
|
|
- |Fix| Fixed a bug in `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. :pr:`13142` 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.
|
|
See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py`.
|
|
:pr:`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.
|
|
:pr:`12613` and :pr:`12669` by :user:`Marc Torrellas <marctorrellas>`.
|
|
|
|
- |Enhancement| Some :term:`CV splitter` classes and
|
|
`model_selection.train_test_split` now raise ``ValueError`` when 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``,
|
|
making ``shuffle=True`` ineffective.
|
|
: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>`
|
|
|
|
:mod:`sklearn.multiclass`
|
|
.........................
|
|
|
|
- |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>`.
|
|
|
|
:mod:`sklearn.multioutput`
|
|
..........................
|
|
|
|
- |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>`
|
|
|
|
:mod:`sklearn.neighbors`
|
|
........................
|
|
|
|
- |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 than ``AttributeError``,
|
|
when called before ``fit`` :pr:`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 :code:`shuffle=False`
|
|
was 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>`.
|
|
|
|
:mod:`sklearn.pipeline`
|
|
.......................
|
|
|
|
- |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 accessing ``named_steps``.
|
|
:pr:`2568` by `Joel Nothman`_.
|
|
|
|
- |Feature| Added optional parameter ``verbose`` in :class:`pipeline.Pipeline`,
|
|
:class:`compose.ColumnTransformer` and :class:`pipeline.FeatureUnion`
|
|
and corresponding ``make_`` 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 as ``None``.
|
|
:pr:`11144` by :user:`Thomas Fan <thomasjpfan>`.
|
|
|
|
- |Enhancement| :class:`pipeline.Pipeline` implements ``__len__`` and
|
|
therefore ``len(pipeline)`` returns the number of steps in the pipeline.
|
|
:pr:`13439` by :user:`Lakshya KD <LakshKD>`.
|
|
|
|
:mod:`sklearn.preprocessing`
|
|
............................
|
|
|
|
- |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 ``order`` which 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:`sklearn.preprocessing` and prevent unexpected side effects by modifying
|
|
the value of `X` inplace.
|
|
:pr:`13459` by :user:`Hunter McGushion <HunterMcGushion>`.
|
|
|
|
:mod:`sklearn.svm`
|
|
..................
|
|
|
|
- |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>`.
|
|
|
|
:mod:`sklearn.tree`
|
|
...................
|
|
|
|
- |Feature| Decision Trees can now be plotted with matplotlib using
|
|
`tree.plot_tree` without relying on the ``dot`` library,
|
|
removing a hard-to-install dependency. :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()`` and ``get_depth()`` have been added to
|
|
`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 `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.
|
|
:pr:`12344` by :user:`Adrin Jalali <adrinjalali>`.
|
|
|
|
:mod:`sklearn.utils`
|
|
....................
|
|
|
|
- |Feature| :func:`utils.resample` now accepts a ``stratify`` parameter for
|
|
sampling according to class distributions. :pr:`13549` by :user:`Nicolas
|
|
Hug <NicolasHug>`.
|
|
|
|
- |API| Deprecated ``warn_on_dtype`` parameter from :func:`utils.check_array`
|
|
and :func:`utils.check_X_y`. Added explicit warning for dtype conversion
|
|
in `check_pairwise_arrays` if the ``metric`` being passed is a
|
|
pairwise boolean metric.
|
|
:pr:`13382` by :user:`Prathmesh Savale <praths007>`.
|
|
|
|
Multiple modules
|
|
................
|
|
|
|
- |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_only`` option 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 `our_rand_r`
|
|
helper function that was not behaving consistently across platforms.
|
|
:pr:`13422` by :user:`Madhura Parikh <jdnc>` and
|
|
:user:`Clément Doumouro <ClemDoum>`.
|
|
|
|
|
|
Miscellaneous
|
|
.............
|
|
|
|
- |Enhancement| Joblib is no longer vendored in scikit-learn, and becomes a
|
|
dependency. Minimal supported version is joblib 0.11, however using
|
|
version >= 0.13 is strongly recommended.
|
|
:pr:`13531` by :user:`Roman Yurchak <rth>`.
|
|
|
|
|
|
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 output 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>`.
|
|
|
|
.. rubric:: Code and documentation contributors
|
|
|
|
Thanks to everyone who has contributed to the maintenance and improvement of the
|
|
project since version 0.20, including:
|
|
|
|
adanhawth, Aditya Vyas, Adrin Jalali, Agamemnon Krasoulis, Albert Thomas,
|
|
Alberto Torres, Alexandre Gramfort, amourav, Andrea Navarrete, Andreas Mueller,
|
|
Andrew Nystrom, assiaben, Aurélien Bellet, Bartosz Michałowski, Bartosz
|
|
Telenczuk, bauks, BenjaStudio, bertrandhaut, Bharat Raghunathan, brentfagan,
|
|
Bryan Woods, Cat Chenal, Cheuk Ting Ho, Chris Choe, Christos Aridas, Clément
|
|
Doumouro, Cole Smith, Connossor, Corey Levinson, Dan Ellis, Dan Stine, Danylo
|
|
Baibak, daten-kieker, Denis Kataev, Didi Bar-Zev, Dillon Gardner, Dmitry Mottl,
|
|
Dmitry Vukolov, Dougal J. Sutherland, Dowon, drewmjohnston, Dror Atariah,
|
|
Edward J Brown, Ekaterina Krivich, Elizabeth Sander, Emmanuel Arias, Eric
|
|
Chang, Eric Larson, Erich Schubert, esvhd, Falak, Feda Curic, Federico Caselli,
|
|
Frank Hoang, Fibinse Xavier`, Finn O'Shea, Gabriel Marzinotto, Gabriel Vacaliuc,
|
|
Gabriele Calvo, Gael Varoquaux, GauravAhlawat, Giuseppe Vettigli, Greg Gandenberger,
|
|
Guillaume Fournier, Guillaume Lemaitre, Gustavo De Mari Pereira, Hanmin Qin,
|
|
haroldfox, hhu-luqi, Hunter McGushion, Ian Sanders, JackLangerman, Jacopo
|
|
Notarstefano, jakirkham, James Bourbeau, Jan Koch, Jan S, janvanrijn, Jarrod
|
|
Millman, jdethurens, jeremiedbb, JF, joaak, Joan Massich, Joel Nothman,
|
|
Jonathan Ohayon, Joris Van den Bossche, josephsalmon, Jérémie Méhault, Katrin
|
|
Leinweber, ken, kms15, Koen, Kossori Aruku, Krishna Sangeeth, Kuai Yu, Kulbear,
|
|
Kushal Chauhan, Kyle Jackson, Lakshya KD, Leandro Hermida, Lee Yi Jie Joel,
|
|
Lily Xiong, Lisa Sarah Thomas, Loic Esteve, louib, luk-f-a, maikia, mail-liam,
|
|
Manimaran, Manuel López-Ibáñez, Marc Torrellas, Marco Gaido, Marco Gorelli,
|
|
MarcoGorelli, marineLM, Mark Hannel, Martin Gubri, Masstran, mathurinm, Matthew
|
|
Roeschke, Max Copeland, melsyt, mferrari3, Mickaël Schoentgen, Ming Li, Mitar,
|
|
Mohammad Aftab, Mohammed AbdelAal, Mohammed Ibraheem, Muhammad Hassaan Rafique,
|
|
mwestt, Naoya Iijima, Nicholas Smith, Nicolas Goix, Nicolas Hug, Nikolay
|
|
Shebanov, Oleksandr Pavlyk, Oliver Rausch, Olivier Grisel, Orestis, Osman, Owen
|
|
Flanagan, Paul Paczuski, Pavel Soriano, pavlos kallis, Pawel Sendyk, peay,
|
|
Peter, Peter Cock, Peter Hausamann, Peter Marko, Pierre Glaser, pierretallotte,
|
|
Pim de Haan, Piotr Szymański, Prabakaran Kumaresshan, Pradeep Reddy Raamana,
|
|
Prathmesh Savale, Pulkit Maloo, Quentin Batista, Radostin Stoyanov, Raf
|
|
Baluyot, Rajdeep Dua, Ramil Nugmanov, Raúl García Calvo, Rebekah Kim, Reshama
|
|
Shaikh, Rohan Lekhwani, Rohan Singh, Rohan Varma, Rohit Kapoor, Roman
|
|
Feldbauer, Roman Yurchak, Romuald M, Roopam Sharma, Ryan, Rüdiger Busche, Sam
|
|
Waterbury, Samuel O. Ronsin, SandroCasagrande, Scott Cole, Scott Lowe,
|
|
Sebastian Raschka, Shangwu Yao, Shivam Kotwalia, Shiyu Duan, smarie, Sriharsha
|
|
Hatwar, Stephen Hoover, Stephen Tierney, Stéphane Couvreur, surgan12,
|
|
SylvainLan, TakingItCasual, Tashay Green, thibsej, Thomas Fan, Thomas J Fan,
|
|
Thomas Moreau, Tom Dupré la Tour, Tommy, Tulio Casagrande, Umar Farouk Umar,
|
|
Utkarsh Upadhyay, Vinayak Mehta, Vishaal Kapoor, Vivek Kumar, Vlad Niculae,
|
|
vqean3, Wenhao Zhang, William de Vazelhes, xhan, Xing Han Lu, xinyuliu12,
|
|
Yaroslav Halchenko, Zach Griffith, Zach Miller, Zayd Hammoudeh, Zhuyi Xue,
|
|
Zijie (ZJ) Poh, ^__^
|