8000 Merge branch 'pairwise-distances-argkmin' into pairwise-distances-arg… · scikit-learn/scikit-learn@1e462a4 · GitHub
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Merge branch 'pairwise-distances-argkmin' into pairwise-distances-argkmin-plug
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‎.github/ISSUE_TEMPLATE/bug_report.yml

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- type: textarea
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attributes:
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label: Versions
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render: shell
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description: |
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Please run the following and paste the output below.
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```python

‎.github/workflows/check-changelog.yml

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echo "If you see this error and there is already a changelog entry,"
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echo "check that the PR number is correct."
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echo ""
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echo" If you believe that this PR does no warrant a changelog"
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echo "If you believe that this PR does no warrant a changelog"
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echo "entry, say so in a comment so that a maintainer will label "
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echo "the PR with 'No Changelog Needed' to bypass this check."
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exit 1

‎README.rst

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.. |Nightly wheels| image:: https://github.com/scikit-learn/scikit-learn/workflows/Wheel%20builder/badge.svg?event=schedule
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.. _`Nightly wheels`: https://github.com/scikit-learn/scikit-learn/actions?query=workflow%3A%22Wheel+builder%22+event%3Aschedule
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.. |PythonVersion| image:: https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue
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.. _PythonVersion: https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue
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.. |PythonVersion| image:: https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-blue
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.. _PythonVersion: https://pypi.org/project/scikit-learn/
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.. |PyPi| image:: https://img.shields.io/pypi/v/scikit-learn
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.. _PyPi: https://pypi.org/project/scikit-learn

‎build_tools/azure/install.sh

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}
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setup_ccache() {
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echo "Setting up ccache"
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echo "Setting up ccache with CCACHE_DIR=${CCACHE_DIR}"
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mkdir /tmp/ccache/
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which ccache
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for name in gcc g++ cc c++ x86_64-linux-gnu-gcc x86_64-linux-gnu-c++; do
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for name in gcc g++ cc c++ clang clang++ i686-linux-gnu-gcc i686-linux-gnu-c++ x86_64-linux-gnu-gcc x86_64-linux-gnu-c++ x86_64-apple-darwin13.4.0-clang x86_64-apple-darwin13.4.0-clang++; do
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ln -s $(which ccache) "/tmp/ccache/${name}"
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done
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export PATH="/tmp/ccache/:${PATH}"

‎build_tools/azure/posix-docker.yml

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DISTRIB: ''
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DOCKER_CONTAINER: ''
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SHOW_SHORT_SUMMARY: 'false'
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CCACHE_DIR: $(Pipeline.Workspace)/ccache
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CCACHE_COMPRESS: '1'
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strategy:
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matrix:
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${{ insert }}: ${{ parameters.matrix }}
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steps:
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- task: Cache@2
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inputs:
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key: '"ccache-v1" | "$(Agent.JobName)" | "$(Build.BuildNumber)"'
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restoreKeys: |
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"ccache-v1" | "$(Agent.JobName)"
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path: $(CCACHE_DIR)
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displayName: ccache
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continueOnError: true
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- script: >
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mkdir -p $CCACHE_DIR
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# Container is detached and sleeping, allowing steps to run commands
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# in the container. The TEST_DIR is mapped allowing the host to access
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# the JUNITXML file
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- script: >
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docker container run --rm
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--volume $TEST_DIR:/temp_dir
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--volume $PWD:/io
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--volume $CCACHE_DIR:/ccache
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-w /io
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--detach
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--name skcontainer
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-e SKLEARN_SKIP_NETWORK_TESTS=$SKLEARN_SKIP_NETWORK_TESTS
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-e BLAS=$BLAS
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-e CPU_COUNT=$CPU_COUNT
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-e CCACHE_DIR=/ccache
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-e CCACHE_COMPRESS=$CCACHE_COMPRESS
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$DOCKER_CONTAINER
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sleep 1000000
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displayName: 'Start container'

‎build_tools/azure/posix.yml

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condition: startsWith(variables['DISTRIB'], 'conda')
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- task: Cache@2
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inputs:
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key: '"$(Agent.JobName)"'
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key: '"ccache-v1" | "$(Agent.JobName)" | "$(Build.BuildNumber)"'
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restoreKeys: |
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"ccache-v1" | "$(Agent.JobName)"
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path: $(CCACHE_DIR)
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displayName: ccache
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continueOnError: true

‎doc/glossary.rst

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number of different distinct random seeds. Popular integer
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random seeds are 0 and `42
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<https://en.wikipedia.org/wiki/Answer_to_the_Ultimate_Question_of_Life%2C_the_Universe%2C_and_Everything>`_.
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Integer values must be in the range `[0, 2**32 - 1]`.
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A :class:`numpy.random.RandomState` instance
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Use the provided random state, only affecting other users

‎doc/modules/model_evaluation.rst

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>>> jaccard_score(y_true[0], y_pred[0])
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0.6666...
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In the 2D comparison case (e.g. image similarity):
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>>> jaccard_score(y_true, y_pred, average="micro")
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0.6
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In the multilabel case with binary label indicators::
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>>> jaccard_score(y_true, y_pred, average='samples')

‎doc/whats_new/v1.1.rst

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`'auto'` in 1.3. `None` and `'warn'` will be removed in 1.3. :pr:`20145` by
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:user:`murata-yu`.
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:mod:`sklearn.feature_selection`
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................................
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- |Efficiency| Improve runtime performance of :func:`feature_selection.chi2`
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with boolean arrays. :pr:`22235` by `Thomas Fan`_.
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:mod:`sklearn.datasets`
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.......................
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:class:`ensemble.ExtraTreesClassifier`.
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:pr:`20803` by :user:`Brian Sun <bsun94>`.
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:mod:`sklearn.feature_selection`
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................................
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- |Efficiency| Reduced memory usage of :func:`feature_selection.chi2`.
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:pr:`21837` by :user:`Louis Wagner <lrwagner>`
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- |Efficiency| Fitting a :class:`ensemble.RandomForestClassifier`,
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:class:`ensemble.RandomForestRegressor`, :class:`ensemble.ExtraTreesClassifier`,
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:class:`ensemble.ExtraTreesRegressor`, and :class:`ensemble.RandomTreesEmbedding`
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F-statistic).
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:pr:`17819` by :user:`Juan Carlos Alfaro Jiménez <alfaro96>`.
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:mod:`sklearn.gaussian_process`
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...............................
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- |Fix| :class:`gaussian_process.GaussianProcessClassifier` raises
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a more informative error if `CompoundKernel` is passed via `kernel`.
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:pr:`22223` by :user:`MarcoM <marcozzxx810>`.
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:mod:`sklearn.impute`
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.....................
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- |Enhancement| :class:`linear_model.QuantileRegressor` support sparse input
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for the highs based solvers.
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:pr:`21086` by :user:`Venkatachalam Natchiappan <venkyyuvy>`.
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In addition, those solvers now use the CSC matrix right from the
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beginning which speeds up fitting.
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:pr:`22206` by :user:`Christian Lorentzen <lorentzenchr>`.
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- |Enhancement| Rename parameter `base_estimator` to `estimator` in
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:class:`linear_model.RANSACRegressor` to improve readability and consistency.
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:pr:`21481` by :user:`Guillaume Lemaitre <glemaitre>` and
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:user:`Andrés Babino <ababino>`.
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- |Enhancement| :func:`linear_model.ElasticNet` and
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and other linear model classes using coordinate descent show error
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messages when non-finite parameter weights are produced. :pr:`22148`
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by :user:`Christian Ritter <chritter>` and :user:`Norbert Preining <norbusan>`.
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- |Fix| :class:`linear_model.ElasticNetCV` now produces correct
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warning when `l1_ratio=0`.
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:pr:`21724` by :user:`Yar Khine Phyo <yarkhinephyo>`.
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- |Enhancement| :class:`linear_model.ElasticNet` and :class:`linear_model.Lasso`
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now raise consistent error messages when passed invalid values for `l1_ratio`,
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`alpha`, `max_iter` and `tol`.
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:pr:`22240` by :user:`Arturo Amor <ArturoAmorQ>`.
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:mod:`sklearn.metrics`
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......................
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A deprecation cycle was introduced.
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:pr:`21576` by :user:`Paul-Emile Dugnat <pedugnat>`.
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- |API| The `"wminkowski"` metric of :class:`sklearn.metrics.DistanceMetric` is deprecated
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and will be removed in version 1.3. Instead the existing `"minkowski"` metric now takes
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in an optional `w` parameter for weights. This deprecation aims at remaining consistent
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with SciPy 1.8 convention. :pr:`21873` by :user:`Yar Khine Phyo <yarkhinephyo>`
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- |Fix| :func:`metrics.silhouette_score` now supports integer input for precomputed
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distances. :pr:`22108` by `Thomas Fan`_.
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splits failed. Similarly raise an error during grid-search when the fits for
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all the models and all the splits failed. :pr:`21026` by :user:`Loïc Estève <lesteve>`.
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- |Enhancement| it is now possible to pass `scoring="matthews_corrcoef"` to all
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model selection tools with a `scoring` argument to use the Matthews
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correlation coefficient (MCC). :pr:`22203` by :user:`Olivier Grisel
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<ogrisel>`.
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- |Fix| :class:`model_selection.GridSearchCV`,
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:class:`model_selection.HalvingGridSearchCV`
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now validate input parameters in `fit` instead of `__init__`.
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ndarray with `np.nan` when passed a `Float32` or `Float64` pandas extension
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array with `pd.NA`. :pr:`21278` by `Thomas Fan`_.
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- |Enhancement| Adds :term:`get_feature_names_out` to
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:class:`neighbors.RadiusNeighborsTransformer`, :class:`neighbors.KNeighborsTransformer`
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and :class:`neighbors.NeighborhoodComponentsAnalysis`. :pr:`22212` by
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:user : `Meekail Zain <micky774>`.
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- |Fix| :class:`neighbors.KernelDensity` now validates input parameters in `fit`
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instead of `__init__`. :pr:`21430` by :user:`Desislava Vasileva <DessyVV>` and
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:user:`Lucy Jimenez <LucyJimenez>`.
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:mod:`sklearn.neural_network`
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.............................
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- |Enhancement| :func:`neural_network.MLPClassifier` and
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:func:`neural_network.MLPRegressor` show error
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messages when optimizers produce non-finite parameter weights. :pr:`22150`
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by :user:`Christian Ritter <chritter>` and :user:`Norbert Preining <norbusan>`.
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:mod:`sklearn.pipeline`
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.......................
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parameters in `fit` instead of `__init__`.
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- |Enhancement| :func:`svm.SVR`, :func:`svm.SVC`, :func:`svm.NuSVR`,
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:func:`svm.OneClassSVM`, :func:`svm.NuSVC` now raise an error
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when the dual-gap estimation produce non-finite parameter weights.
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:pr:`22149` by :user:`Christian Ritter <chritter>` and
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:user:`Norbert Preining <norbusan>`.
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:mod:`sklearn.utils`
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‎examples/applications/plot_model_complexity_influence.py

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"fit_intercept": True,
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"tol": 1e-3,
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"tol": 1e-1,
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"n_iter_no_change": 2,
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},
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"changing_param": "l1_ratio",
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"changing_param_values": [0.25, 0.5, 0.75, 0.9],

‎setup.py

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"Programming Language :: Python :: 3.7",
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"Programming Language :: Python :: 3.8",
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"Programming Language :: Python :: 3.9",
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"Programming Language :: Python :: 3.10",
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"Programming Language :: Python :: Implementation :: CPython",
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"Programming Language :: Python :: Implementation :: PyPy",
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],

‎sklearn/calibration.py

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.. versionadded:: 1.0
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Parameters
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----------
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The proportion of samples whose class is the positive class (fraction
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‎sklearn/cluster/_optics.py

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"precomputed", X is assumed to be a distance matrix and must be square.
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"precomputed", `X` is assumed to be a distance matrix and must be
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Algorithm used to compute the nearest neighbors:
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- 'ball_tree' will use :class:`BallTree`.
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- 'kd_tree' will use :class:`KDTree`.
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- 'brute' will use a brute-force search.
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- 'auto' will attempt to decide the most appropriate algorithm
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based on the values passed to :meth:`fit` method. (default)
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- 'auto' (default) will attempt to decide the most appropriate
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algorithm based on the values passed to :meth:`fit` method.
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(n_samples, n_samples) if metric='precomputed'
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A feature array, or array of distances between samples if
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Algorithm used to compute the nearest neighbors:
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- 'ball_tree' will use :class:`BallTree`
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- 'kd_tree' will use :class:`KDTree`
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- 'ball_tree' will use :class:`BallTree`.
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- 'kd_tree' will use :class:`KDTree`.
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- 'auto' will attempt to decide the most appropriate algorithm
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Reachability distances calculated by OPTICS (`reachability_`).
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predecessor : ndarray of shape (n_samples,)
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OPTICS ordered point indices (`ordering_`).
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‎sklearn/cluster/tests/test_hierarchical.py

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# TODO: Remove filterwarnings in 1.3 when wminkowski is removed
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@pytest.mark.filterwarnings("ignore:WMinkowskiDistance:FutureWarning:sklearn")
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def test_mst_linkage_core_memory_mapped(metric_param_grid):
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"""The MST-LINKAGE-CORE algorithm must work on mem-mapped dataset.

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