---
- branch: MAIN
  date: Wed Sep 27 10:57:33 UTC 2023
  files:
  - new: '1.22'
    old: '1.21'
    path: pkgsrc/math/py-scikit-learn/Makefile
    pathrev: pkgsrc/math/py-scikit-learn/Makefile@1.22
    type: modified
  - new: '1.12'
    old: '1.11'
    path: pkgsrc/math/py-scikit-learn/distinfo
    pathrev: pkgsrc/math/py-scikit-learn/distinfo@1.12
    type: modified
  id: 20230927T105733Z.968514730dec9927f6c2bc5a60e3fe975145c8e5
  log: |
    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.
  module: pkgsrc
  subject: 'CVS commit: pkgsrc/math/py-scikit-learn'
  unixtime: '1695812253'
  user: adam