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- update to 1.5.1:
  * small number of regression fixes, see
    https://scikit-learn.org/stable/whats_new/v1.5.html#version-1-5-1

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- prepare for python 3.13 testin

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- Add patch support-pytest-8.3.patch:
  * Fix property wrapping, uncovered by Pytest 8.3 changes.

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- Skip failing test in i586

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- Fix leap version check in build

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- Fix leap version check

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- Make it build for leap

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- Update to 1.5.0 (bsc#1226185, CVE-2024-5206):
  ## Security
  * Fix feature_extraction.text.CountVectorizer and
    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.
  ## Changed models
  * Efficiency The subsampling in 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). #27344 by Xuefeng Xu.
  * Enhancement decomposition.PCA, decomposition.SparsePCA and
    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.
    #28988 by Olivier Grisel.
  * API Change 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: cluster.FeatureAgglomeration,
    decomposition.MiniBatchNMF, decomposition.NMF,

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

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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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