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comment	@# @;


1.13
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desc
@@


1.13
log
@py-scikit-learn: updated to 1.8.0

Version 1.8.0

Changes impacting many modules

- |Efficiency| Improved CPU and memory usage in estimators and metric functions that rely on
  weighted percentiles and better match NumPy and Scipy (un-weighted) implementations
  of percentiles.

Support for Array API

Additional estimators and functions have been updated to include support for all
`Array API <https://data-apis.org/array-api/latest/>`_ compliant inputs.
@
text
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@


1.12
log
@py-scikit-learn: updated to 1.6.1

1.6
https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_6_0.html
@
text
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@


1.11
log
@py-scikit-learn: updated to 1.5.2

Version 1.5.2

Changes impacting many modules

- |Fix| Fixed performance regression in a few Cython modules in
  `sklearn._loss`, `sklearn.manifold`, `sklearn.metrics` and `sklearn.utils`,
  which were built without OpenMP support.

Changelog

:mod:`sklearn.calibration`
- |Fix| Raise error when :class:`~sklearn.model_selection.LeaveOneOut` used in
  `cv`, matching what would happen if `KFold(n_splits=n_samples)` was used.

:mod:`sklearn.compose`
- |Fix| Fixed :class:`compose.TransformedTargetRegressor` not to raise `UserWarning` if
  transform output is set to `pandas` or `polars`, since it isn't a transformer.

:mod:`sklearn.decomposition`
- |Fix| Increase rank defficiency threshold in the whitening step of
  :class:`decomposition.FastICA` with `whiten_solver="eigh"` to improve the
  platform-agnosticity of the estimator.

:mod:`sklearn.metrics`
- |Fix| Fix a regression in :func:`metrics.accuracy_score` and in
  :func:`metrics.zero_one_loss` causing an error for Array API dispatch with multilabel
  inputs.

:mod:`sklearn.svm`
- |Fix| Fixed a regression in :class:`svm.SVC` and :class:`svm.SVR` such that we accept
@
text
@d1 1
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@


1.10
log
@py-scikit-learn: updated to 1.5.1

Version 1.5.1
=============

**July 2024**

Changes impacting many modules
------------------------------

- |Fix| Fixed a regression in the validation of the input data of all estimators where
  an unexpected error was raised when passing a DataFrame backed by a read-only buffer.
  :pr:`29018` by :user:`Jérémie du Boisberranger <jeremiedbb>`.

- |Fix| Fixed a regression causing a dead-lock at import time in some settings.
  :pr:`29235` by :user:`Jérémie du Boisberranger <jeremiedbb>`.

Changelog
---------

:mod:`sklearn.compose`
......................

- |Efficiency| Fix a performance regression in :class:`compose.ColumnTransformer`
  where the full input data was copied for each transformer when `n_jobs > 1`.
  :pr:`29330` by :user:`Jérémie du Boisberranger <jeremiedbb>`.

:mod:`sklearn.metrics`
......................

- |Fix| Fix a regression in :func:`metrics.r2_score`. Passing torch CPU tensors
  with array API dispatched disabled would complain about non-CPU devices
  instead of implicitly converting those inputs as regular NumPy arrays.
  :pr:`29119` by :user:`Olivier Grisel`.

- |Fix| Fix a regression in :func:`metrics.accuracy_score` and in
  :func:`metrics.zero_one_loss` causing an error for Array API dispatch with multilabel
  inputs.
  :pr:`29269` by :user:`Yaroslav Korobko <Tialo>` and
  :pr:`29336` by :user:`Edoardo Abati <EdAbati>`.

:mod:`sklearn.model_selection`
..............................

- |Fix| Fix a regression in :class:`model_selection.GridSearchCV` for parameter
  grids that have heterogeneous parameter values.
  :pr:`29078` by :user:`Loïc Estève <lesteve>`.

- |Fix| Fix a regression in :class:`model_selection.GridSearchCV` for parameter
  grids that have estimators as parameter values.
  :pr:`29179` by :user:`Marco Gorelli<MarcoGorelli>`.

- |Fix| Fix a regression in :class:`model_selection.GridSearchCV` for parameter
  grids that have arrays of different sizes as parameter values.
  :pr:`29314` by :user:`Marco Gorelli<MarcoGorelli>`.

:mod:`sklearn.tree`
...................

- |Fix| Fix an issue in :func:`tree.export_graphviz` and :func:`tree.plot_tree`
  that could potentially result in exception or wrong results on 32bit OSes.
  :pr:`29327` by :user:`Loïc Estève<lesteve>`.

:mod:`sklearn.utils`
....................

- |API| :func:`utils.validation.check_array` has a new parameter, `force_writeable`, to
  control the writeability of the output array. If set to `True`, the output array will
  be guaranteed to be writeable and a copy will be made if the input array is read-only.
  If set to `False`, no guarantee is made about the writeability of the output array.
  :pr:`29018` by :user:`Jérémie du Boisberranger <jeremiedbb>`.

.. _changes_1_5:

Version 1.5.0
=============

**May 2024**

Security
--------

- |Fix| :class:`feature_extraction.text.CountVectorizer` and
  :class:`feature_extraction.text.TfidfVectorizer` no longer store discarded
  tokens from the training set in their `stop_words_` attribute. This attribute
  would hold too frequent (above `max_df`) but also too rare tokens (below
  `min_df`). This fixes a potential security issue (data leak) if the discarded
  rare tokens hold sensitive information from the training set without the
  model developer's knowledge.

  Note: users of those classes are encouraged to either retrain their pipelines
  with the new scikit-learn version or to manually clear the `stop_words_`
  attribute from previously trained instances of those transformers. This
  attribute was designed only for model inspection purposes and has no impact
  on the behavior of the transformers.
  :pr:`28823` by :user:`Olivier Grisel <ogrisel>`.

Changed models
--------------

- |Efficiency| The subsampling in :class:`preprocessing.QuantileTransformer` is now
  more efficient for dense arrays but the fitted quantiles and the results of
  `transform` may be slightly different than before (keeping the same statistical
  properties).
  :pr:`27344` by :user:`Xuefeng Xu <xuefeng-xu>`.

- |Enhancement| :class:`decomposition.PCA`, :class:`decomposition.SparsePCA`
  and :class:`decomposition.TruncatedSVD` now set the sign of the `components_`
  attribute based on the component values instead of using the transformed data
  as reference. This change is needed to be able to offer consistent component
  signs across all `PCA` solvers, including the new
  `svd_solver="covariance_eigh"` option introduced in this release.

Changes impacting many modules
------------------------------

- |Fix| Raise `ValueError` with an informative error message when passing 1D
  sparse arrays to methods that expect 2D sparse inputs.
  :pr:`28988` by :user:`Olivier Grisel <ogrisel>`.

- |API| The name of the input of the `inverse_transform` method of estimators has been
  standardized to `X`. As a consequence, `Xt` is deprecated and will be removed in
  version 1.7 in the following estimators: :class:`cluster.FeatureAgglomeration`,
  :class:`decomposition.MiniBatchNMF`, :class:`decomposition.NMF`,
  :class:`model_selection.GridSearchCV`, :class:`model_selection.RandomizedSearchCV`,
  :class:`pipeline.Pipeline` and :class:`preprocessing.KBinsDiscretizer`.
  :pr:`28756` by :user:`Will Dean <wd60622>`.

Support for Array API
---------------------

Additional estimators and functions have been updated to include support for all
`Array API <https://data-apis.org/array-api/latest/>`_ compliant inputs.

See :ref:`array_api` for more details.

**Functions:**

- :func:`sklearn.metrics.r2_score` now supports Array API compliant inputs.
  :pr:`27904` by :user:`Eric Lindgren <elindgren>`, :user:`Franck Charras <fcharras>`,
  :user:`Olivier Grisel <ogrisel>` and :user:`Tim Head <betatim>`.

**Classes:**

- :class:`linear_model.Ridge` now supports the Array API for the `svd` solver.
  See :ref:`array_api` for more details.
  :pr:`27800` by :user:`Franck Charras <fcharras>`, :user:`Olivier Grisel <ogrisel>`
  and :user:`Tim Head <betatim>`.

Support for building with Meson
-------------------------------

From scikit-learn 1.5 onwards, Meson is the main supported way to build
scikit-learn, see :ref:`Building from source <install_bleeding_edge>` for more
details.

Unless we discover a major blocker, setuptools support will be dropped in
scikit-learn 1.6. The 1.5.x releases will support building scikit-learn with
setuptools.

Meson support for building scikit-learn was added in :pr:`28040` by
:user:`Loïc Estève <lesteve>`

Metadata Routing
----------------

The following models now support metadata routing in one or more or their
methods. Refer to the :ref:`Metadata Routing User Guide <metadata_routing>` for
more details.

- |Feature| :class:`impute.IterativeImputer` now supports metadata routing in
  its `fit` method. :pr:`28187` by :user:`Stefanie Senger <StefanieSenger>`.

- |Feature| :class:`ensemble.BaggingClassifier` and :class:`ensemble.BaggingRegressor`
  now support metadata routing. The fit methods now
  accept ``**fit_params`` which are passed to the underlying estimators
  via their `fit` methods.
  :pr:`28432` by :user:`Adam Li <adam2392>` and
  :user:`Benjamin Bossan <BenjaminBossan>`.

- |Feature| :class:`linear_model.RidgeCV` and
  :class:`linear_model.RidgeClassifierCV` now support metadata routing in
  their `fit` method and route metadata to the underlying
  :class:`model_selection.GridSearchCV` object or the underlying scorer.
  :pr:`27560` by :user:`Omar Salman <OmarManzoor>`.

- |Feature| :class:`GraphicalLassoCV` now supports metadata routing in it's
  `fit` method and routes metadata to the CV splitter.
  :pr:`27566` by :user:`Omar Salman <OmarManzoor>`.

- |Feature| :class:`linear_model.RANSACRegressor` now supports metadata routing
  in its ``fit``, ``score`` and ``predict`` methods and route metadata to its
  underlying estimator's' ``fit``, ``score`` and ``predict`` methods.
  :pr:`28261` by :user:`Stefanie Senger <StefanieSenger>`.

- |Feature| :class:`ensemble.VotingClassifier` and
  :class:`ensemble.VotingRegressor` now support metadata routing and pass
  ``**fit_params`` to the underlying estimators via their `fit` methods.
  :pr:`27584` by :user:`Stefanie Senger <StefanieSenger>`.

- |Feature| :class:`pipeline.FeatureUnion` now supports metadata routing in its
  ``fit`` and ``fit_transform`` methods and route metadata to the underlying
  transformers' ``fit`` and ``fit_transform``.
  :pr:`28205` by :user:`Stefanie Senger <StefanieSenger>`.

- |Fix| Fix an issue when resolving default routing requests set via class
  attributes.
  :pr:`28435` by `Adrin Jalali`_.

- |Fix| Fix an issue when `set_{method}_request` methods are used as unbound
  methods, which can happen if one tries to decorate them.
  :pr:`28651` by `Adrin Jalali`_.

- |FIX| Prevent a `RecursionError` when estimators with the default `scoring`
  param (`None`) route metadata.
  :pr:`28712` by :user:`Stefanie Senger <StefanieSenger>`.
@
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@


1.9
log
@py-scikit-learn: updated to 1.3.2

Version 1.3.2
=============

**October 2023**

Changelog
---------

:mod:`sklearn.datasets`
.......................

- |Fix| All dataset fetchers now accept `data_home` as any object that implements
  the :class:`os.PathLike` interface, for instance, :class:`pathlib.Path`.
  :pr:`27468` by :user:`Yao Xiao <Charlie-XIAO>`.

:mod:`sklearn.decomposition`
............................

- |Fix| Fixes a bug in :class:`decomposition.KernelPCA` by forcing the output of
  the internal :class:`preprocessing.KernelCenterer` to be a default array. When the
  arpack solver is used, it expects an array with a `dtype` attribute.
  :pr:`27583` by :user:`Guillaume Lemaitre <glemaitre>`.

:mod:`sklearn.metrics`
......................

- |Fix| Fixes a bug for metrics using `zero_division=np.nan`
  (e.g. :func:`~metrics.precision_score`) within a paralell loop
  (e.g. :func:`~model_selection.cross_val_score`) where the singleton for `np.nan`
  will be different in the sub-processes.
  :pr:`27573` by :user:`Guillaume Lemaitre <glemaitre>`.

:mod:`sklearn.tree`
...................

- |Fix| Do not leak data via non-initialized memory in decision tree pickle files and make
  the generation of those files deterministic. :pr:`27580` by :user:`Loïc Estève <lesteve>`.
@
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@


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log
@py-scikit-learn: updated to 1.3.0

1.3.0

Metadata Routing
HDBSCAN: hierarchical density-based clustering
TargetEncoder: a new category encoding strategy
Missing values support in decision trees
New display model_selection.ValidationCurveDisplay
Gamma loss for gradient boosting
Grouping infrequent categories in preprocessing.OrdinalEncoder
@
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${PYSITELIB}/${EGG_INFODIR}/top_level.txt
@


1.7
log
@py-scikit-learn: updated to 1.2.2

Version 1.2.2

Changelog

sklearn.base
Fix When set_output(transform="pandas"), base.TransformerMixin maintains the index if the transform output is already a DataFrame.
sklearn.calibration
Fix A deprecation warning is raised when using the base_estimator__ prefix to set parameters of the estimator used in calibration.CalibratedClassifierCV.
sklearn.cluster
Fix Fixed a bug in cluster.BisectingKMeans, preventing fit to randomly fail due to a permutation of the labels when running multiple inits.
sklearn.compose
Fix Fixes a bug in compose.ColumnTransformer which now supports empty selection of columns when set_output(transform="pandas").
sklearn.ensemble
Fix A deprecation warning is raised when using the base_estimator__ prefix to set parameters of the estimator used in ensemble.AdaBoostClassifier, ensemble.AdaBoostRegressor, ensemble.BaggingClassifier, and ensemble.BaggingRegressor.
sklearn.feature_selection
Fix Fixed a regression where a negative tol would not be accepted any more by feature_selection.SequentialFeatureSelector.
sklearn.inspection
Fix Raise a more informative error message in inspection.partial_dependence when dealing with mixed data type categories that cannot be sorted by numpy.unique. This problem usually happen when categories are str and missing values are present using np.nan.
sklearn.isotonic
Fix Fixes a bug in isotonic.IsotonicRegression where isotonic.IsotonicRegression.predict would return a pandas DataFrame when the global configuration sets transform_output="pandas".
sklearn.preprocessing
Fix preprocessing.OneHotEncoder.drop_idx_ now properly references the dropped category in the categories_ attribute when there are infrequent categories.
Fix preprocessing.OrdinalEncoder now correctly supports encoded_missing_value or unknown_value set to a categories’ cardinality when there is missing values in the training data.
sklearn.tree
Fix Fixed a regression in tree.DecisionTreeClassifier, tree.DecisionTreeRegressor, tree.ExtraTreeClassifier and tree.ExtraTreeRegressor where an error was no longer raised in version 1.2 when min_sample_split=1.
sklearn.utils
Fix Fixes a bug in utils.check_array which now correctly performs non-finite validation with the Array API specification.
Fix utils.multiclass.type_of_target can identify pandas nullable data types as classification targets.
@
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@py-scikit-learn: fix PLIST
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@


1.5
log
@math/py-scikit-learn: Update to 0.22.1

Highlights:

- New plotting API
- Stacking Classifier and Regressor
- Permutation-based feature importance
- Native support for missing values for gradient boosting
- Precomputed sparse nearest neighbors graph
- KNN Based Imputation
- Tree pruning
- Retrieve dataframes from OpenML
- Checking scikit-learn compatibility of an estimator
- ROC AUC now supports multiclass classification
@
text
@d1 1
a1 1
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a4 1
${PYSITELIB}/${EGG_INFODIR}/not-zip-safe
@


1.4
log
@py-scikit-learn: updated to 0.21.2

Version 0.21.2
Changelog
sklearn.decomposition
Fix Fixed a bug in cross_decomposition.CCA improving numerical stability when Y is close to zero.

sklearn.metrics
Fix Fixed a bug in metrics.pairwise.euclidean_distances where a part of the distance matrix was left un-instanciated for suffiently large float32 datasets (regression introduced in 0.21).

sklearn.preprocessing
Fix Fixed a bug in preprocessing.OneHotEncoder where the new drop parameter was not reflected in get_feature_names.

sklearn.utils.sparsefuncs
Fix Fixed a bug where min_max_axis would fail on 32-bit systems for certain large inputs. This affects preprocessing.MaxAbsScaler, preprocessing.normalize and preprocessing.LabelBinarizer.


Version 0.21.1
This is a bug-fix release to primarily resolve some packaging issues in version 0.21.0. It also includes minor documentation improvements and some bug fixes.

Changelog
sklearn.metrics
Fix Fixed a bug in metrics.pairwise_distances where it would raise AttributeError for boolean metrics when X had a boolean dtype and Y == None.
Fix Fixed two bugs in metrics.pairwise_distances when n_jobs > 1. First it used to return a distance matrix with same dtype as input, even for integer dtype. Then the diagonal was not zeros for euclidean metric when Y is X.

sklearn.neighbors
Fix Fixed a bug in neighbors.KernelDensity which could not be restored from a pickle if sample_weight had been used.

Version 0.21.0
Changed models
The following estimators and functions, when fit with the same data and parameters, may produce different models from the previous version. This often occurs due to changes in the modelling logic (bug fixes or enhancements), or in random sampling procedures.
discriminant_analysis.LinearDiscriminantAnalysis for multiclass classification. Fix
discriminant_analysis.LinearDiscriminantAnalysis with ‘eigen’ solver. Fix
linear_model.BayesianRidge Fix
Decision trees and derived ensembles when both max_depth and max_leaf_nodes are set. Fix
linear_model.LogisticRegression and linear_model.LogisticRegressionCV with ‘saga’ solver. Fix
ensemble.GradientBoostingClassifier Fix
sklearn.feature_extraction.text.HashingVectorizer, sklearn.feature_extraction.text.TfidfVectorizer, and sklearn.feature_extraction.text.CountVectorizer Fix
neural_network.MLPClassifier Fix
svm.SVC.decision_function and multiclass.OneVsOneClassifier.decision_function. Fix
linear_model.SGDClassifier and any derived classifiers. Fix
Any model using the linear_model.sag.sag_solver function with a 0 seed, including linear_model.LogisticRegression, linear_model.LogisticRegressionCV, linear_model.Ridge, and linear_model.RidgeCV with ‘sag’ solver. Fix
linear_model.RidgeCV when using generalized cross-validation with sparse inputs
@
text
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@


1.3
log
@math/py-scikit-learn: Update to 0.20.0

Highlights:

Missing values in features, represented by NaNs, are now accepted in
column-wise preprocessing such as scalers. Each feature is fitted
disregarding NaNs, and data containing NaNs can be transformed. The
new impute module provides estimators for learning despite missing
data.

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

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

Added sample_weight support to several estimators (including KMeans,
BayesianRidge and KernelDensity) and improved stopping criteria in
others (including MLPRegressor, GradientBoostingRegressor and
SGDRegressor).

This release is also the first to be accompanied by a Glossary of
Common Terms and API Elements.
@
text
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@


1.2
log
@math/py-scikit-learn: Update to 0.19.1

Notable new features since 0.18.2:
- `neighbors.LocalOutlierFactor` for anomaly detection
- `preprocessing.QuantileTransformer` for robust feature transformation
- `multioutput.ClassifierChain` meta-estimator to simply account
  for dependencies between classes in multilabel problem
- multiplicative update in `decomposition.NMF`
- multinomial `linear_model.LogisticRegression` with L1 loss
@
text
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@


1.1
log
@Import py-scikit-learn-0.18.1 from pkgsrc as math/py-scikit-learn

Packaged by Filip Hajny and updated by Kamel Derouiche and me.

scikit-learn is a Python module integrating classic machine learning
algorithms in the tightly-knit scientific Python world (numpy, scipy,
matplotlib). It aims to provide simple and efficient solutions to
learning problems, accessible to everybody and reusable in various
contexts: machine-learning as a versatile tool for science and
engineering.
@
text
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@