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- Unlock numpy 2 (but don't force it for build)

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baserev update by copy to link target

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- update to 1.4.2:
  * This release only includes support for numpy 2.

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- Update to 1.4.1.post1
  ## Metadata Routing
  * Fix routing issue with ColumnTransformer when used inside
    another meta-estimator. #28188 by Adrin Jalali.
  * No error is raised when no metadata is passed to a
    metaestimator that includes a sub-estimator which doesn’t
    support metadata routing. #28256 by Adrin Jalali.
  * Fix multioutput.MultiOutputRegressor and
    multioutput.MultiOutputClassifier to work with estimators that
    don’t consume any metadata when metadata routing is enabled.
    #28240 by Adrin Jalali.
  ## DataFrame Support
  * Enhancement Fix Pandas and Polars dataframe are validated
    directly without ducktyping checks. #28195 by Thomas Fan.
  ## Changes impacting many modules
  * Efficiency Fix Partial revert of #28191 to avoid a performance
    regression for estimators relying on euclidean pairwise
    computation with sparse matrices. The impacted estimators are:
    - sklearn.metrics.pairwise_distances_argmin
    - sklearn.metrics.pairwise_distances_argmin_min
    - sklearn.cluster.AffinityPropagation
    - sklearn.cluster.Birch
    - sklearn.cluster.SpectralClustering
    - sklearn.neighbors.KNeighborsClassifier
    - sklearn.neighbors.KNeighborsRegressor
    - sklearn.neighbors.RadiusNeighborsClassifier
    - sklearn.neighbors.RadiusNeighborsRegressor
    - sklearn.neighbors.LocalOutlierFactor
    - sklearn.neighbors.NearestNeighbors
    - sklearn.manifold.Isomap
    - sklearn.manifold.TSNE
    - sklearn.manifold.trustworthiness
    - #28235 by Julien Jerphanion.
  * Fixes a bug for all scikit-learn transformers when using
    set_output with transform set to pandas or polars. The bug
    could lead to wrong naming of the columns of the returned
    dataframe. #28262 by Guillaume Lemaitre.
  * When users try to use a method in StackingClassifier,
    StackingClassifier, StackingClassifier, SelectFromModel, RFE,
    SelfTrainingClassifier, OneVsOneClassifier,
    OutputCodeClassifier or OneVsRestClassifier that their
    sub-estimators don’t implement, the AttributeError now reraises
    in the traceback. #28167 by Stefanie Senger.
- Release 1.4.0
  * HistGradientBoosting Natively Supports Categorical DTypes in
    DataFrames
  * Polars output in set_output
  * Missing value support for Random Forest
  * Add support for monotonic constraints in tree-based models
  * Enriched estimator displays
  * Metadata Routing Support
  * Improved memory and runtime efficiency for PCA on sparse data
  * Highlights and detailed changelog:
    * https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_4_0.html
    * https://scikit-learn.org/stable/whats_new/v1.4.html#release-notes-1-4
- Enable python312 test flavor, avoid testing it with the other
  flavors
- Prepare for python39 flavor drop

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- update to 1.3.2:
  * All dataset fetchers now accept `data_home` as any object that
    implements the :class:`os.PathLike` interface, for instance,
    :class:`pathlib.Path`.
  * 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.
  * 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.
  * Do not leak data via non-initialized memory in decision tree
    pickle files and make the generation of those files
    deterministic.
  * Ridge models with `solver='sparse_cg'` may have slightly
    different results with scipy>=1.12, because of an underlying
    change in the scipy solver
  * The `set_output` API correctly works with list input.
  * :class:`calibration.CalibratedClassifierCV` can now handle
    models that produce large prediction scores.
- Skip another recalcitrant test on 32 bit.
  * We are in the process of introducing a new way to route metadata
    such as sample_weight throughout the codebase, which would
    affect how meta-estimators such as pipeline.Pipeline and
  * Originally hosted in the scikit-learn-contrib repository,
  * A new category encoding strategy preprocessing.TargetEncoder
    encodes the categories based on a shrunk estimate of the average
  * The classes tree.DecisionTreeClassifier and tree.DecisionTreeRegressor
  * model_selection.ValidationCurveDisplay is now available to plot

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baserev update by copy to link target

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- Skip another recalcitrant test on 32 bit.

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baserev update by copy to link target

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- Python flavors shifted again, drop test-py38, add test-py311

Sadly, it still fails in the numpy staging

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baserev update by copy to link target

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- Update to 1.3.0
  * We are in the process of introducing a new way to route metadata 
    such as sample_weight throughout the codebase, which would 
    affect how meta-estimators such as pipeline.Pipeline and 
    model_selection.GridSearchCV route metadata.
  * Originally hosted in the scikit-learn-contrib repository, 
    cluster.HDBSCAN has been adopted into scikit-learn.
  * A new category encoding strategy preprocessing.TargetEncoder 
    encodes the categories based on a shrunk estimate of the average 
    target values for observations belonging to that category.
  * The classes tree.DecisionTreeClassifier and tree.DecisionTreeRegressor 
    now support missing values.
  * model_selection.ValidationCurveDisplay is now available to plot 
    results from model_selection.validation_curve
  * The class ensemble.HistGradientBoostingRegressor supports the 
    Gamma deviance loss function via loss="gamma".
  * Similarly to preprocessing.OneHotEncoder, the class preprocessing.OrdinalEncoder 
    now supports aggregating infrequent categories into a single 
    output for each feature.
  * More changes, see https://scikit-learn.org/stable/whats_new/v1.3.html

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- Add %{?sle15_python_module_pythons}

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update to latest version

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- Update to version 1.2.0
  * Pandas output with set_output API
  * Interaction constraints in Histogram-based Gradient Boosting
    Trees
  * New and enhanced displays
  * Faster parser in fetch_openml
  * Experimental Array API support in LinearDiscriminantAnalysis
  * Improved efficiency of many estimators
- Drop sklearn-pr24283-gradient-segfault.patch
- PEP517 build

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