Log message:
py-scikit-learn: fix build on NetBSD

Log message:
py-scikit-learn: revert previous

Committed by accident.

Log message:
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>`.

Log message:
py-scikit-learn: updated to 1.3.1

Version 1.3.1
=============

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

The following estimators and functions, when fit with the same data and
parameters, may produce different models from the previous version. This often
occurs due to changes in the modelling logic (bug fixes or enhancements), or in
random sampling procedures.

- |Fix| Ridge models with `solver='sparse_cg'` may have slightly different
  results with scipy>=1.12, because of an underlying change in the scipy solver

Changes impacting all modules
-----------------------------

- |Fix| The `set_output` API correctly works with list input.

Changelog
---------

:mod:`sklearn.calibration`
..........................

- |Fix| :class:`calibration.CalibratedClassifierCV` can now handle models that
  produce large prediction scores. Before it was numerically unstable.

:mod:`sklearn.cluster`
......................

- |Fix| :class:`cluster.BisectingKMeans` could crash when predicting on data
  with a different scale than the data used to fit the model.

- |Fix| :class:`cluster.BisectingKMeans` now works with data that has a single \ 
feature.

:mod:`sklearn.cross_decomposition`
..................................

- |Fix| :class:`cross_decomposition.PLSRegression` now automatically ravels the \ 
output
  of `predict` if fitted with one dimensional `y`.

:mod:`sklearn.ensemble`
.......................

- |Fix| Fix a bug in :class:`ensemble.AdaBoostClassifier` with \ 
`algorithm="SAMME"`
  where the decision function of each weak learner should be symmetric (i.e.
  the sum of the scores should sum to zero for a sample).

:mod:`sklearn.feature_selection`
................................

- |Fix| :func:`feature_selection.mutual_info_regression` now correctly computes the
  result when `X` is of integer dtype.

:mod:`sklearn.impute`
.....................

- |Fix| :class:`impute.KNNImputer` now correctly adds a missing indicator column in
  ``transform`` when ``add_indicator`` is set to ``True`` and missing values are \ 
observed
  during ``fit``.

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

- |Fix| Scorers used with :func:`metrics.get_scorer` handle properly
  multilabel-indicator matrix.

:mod:`sklearn.mixture`
......................

- |Fix| The initialization of :class:`mixture.GaussianMixture` from user-provided
  `precisions_init` for `covariance_type` of `full` or `tied` was not correct,
  and has been fixed.

:mod:`sklearn.neighbors`
........................

- |Fix| :meth:`neighbors.KNeighborsClassifier.predict` no longer raises an
  exception for `pandas.DataFrames` input.

- |Fix| Reintroduce :attr:`sklearn.neighbors.BallTree.valid_metrics` and
  :attr:`sklearn.neighbors.KDTree.valid_metrics` as public class attributes.

- |Fix| :class:`sklearn.model_selection.HalvingRandomSearchCV` no longer raises
  when the input to the `param_distributions` parameter is a list of dicts.

- |Fix| Neighbors based estimators now correctly work when \ 
`metric="minkowski"` and the
  metric parameter `p` is in the range `0 < p < 1`, regardless of the \ 
`dtype` of `X`.

:mod:`sklearn.preprocessing`
............................

- |Fix| :class:`preprocessing.LabelEncoder` correctly accepts `y` as a keyword
  argument.

- |Fix| :class:`preprocessing.OneHotEncoder` shows a more informative error message
  when `sparse_output=True` and the output is configured to be pandas.

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

- |Fix| :func:`tree.plot_tree` now accepts `class_names=True` as documented.

- |Fix| The `feature_names` parameter of :func:`tree.plot_tree` now accepts any \ 
kind of
  array-like instead of just a list.

Log message:
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

Log message:
*: restrict py-numpy users to 3.9+ in preparation for update

Log message:
py-scikit-learn: remove unused patch

Log message:
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.