Log message:
*: clean-up after python38 removal

Log message:
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

Log message:
py-scikit-learn: meson checks for gcc>=8, GCC_REQD it

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