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‎doc/developers/maintainer.rst

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@@ -17,6 +17,11 @@ Before a release
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1. Update authors table:
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Create a `classic token on GitHub <https://github.com/settings/tokens/new>`_
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with the ``read:org`` following permission.
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Run the following script, entering the token in:
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.. prompt:: bash $
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cd build_tools; make authors; cd ..
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**Permissions**
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The release manager requires a set of permissions on top of the usual
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permissions given to maintainers, which includes:
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The release manager must be a *maintainer* of the ``scikit-learn/scikit-learn``
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repository to be able to publish on ``pypi.org`` and ``test.pypi.org``
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(via a manual trigger of a dedicated Github Actions workflow).
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The release manager does not need extra permissions on ``pypi.org`` to publish a
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release in particular.
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- *maintainer* role on ``scikit-learn`` projects on ``pypi.org`` and
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``test.pypi.org``, separately.
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- become a member of the *scikit-learn* team on conda-forge by editing the
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``recipe/meta.yaml`` file on
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``https://github.com/conda-forge/scikit-learn-feedstock``
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The release manager must be a *maintainer* of the ``conda-forge/scikit-learn-feedstock``
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repository. This can be changed by editing the ``recipe/meta.yaml`` file in the
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first release pull-request.
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.. _preparing_a_release_pr:
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contribute their changelog entries to this file in parallel to the release
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process.
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Minor version release
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~~~~~~~~~~~~~~~~~~~~~
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Minor version release (also known as bug-fix release)
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The minor releases should include bug fixes and some relevant documentation
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changes only. Any PR resulting in a behavior change which is not a bug fix
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should be excluded.
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should be excluded. As an example, instructions are given for the `1.2.2` release.
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- Create a branch, **on your own fork** (here referred to as `fork`) for the release
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from `upstream/main`.
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.. prompt:: bash $
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git fetch upstream/main
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git checkout -b release-1.2.2 upstream/main
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git push -u fork release-1.2.2:release-1.2.2
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- Create a **draft** PR to the `upstream/1.2.X` branch (not to `upstream/main`)
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with all the desired changes.
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- Do not push anything on that branch yet.
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First, create a branch, **on your own fork** (to release e.g. `0.99.3`):
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- Locally rebase `release-1.2.2` from the `upstream/1.2.X` branch using:
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.. prompt:: bash $
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.. prompt:: bash $
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# assuming main and upstream/main are the same
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git checkout -b release-0.99.3 main
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git rebase -i upstream/1.2.X
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Then, create a PR **to the** `scikit-learn/0.99.X` **branch** (not to
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main!) with all the desired changes:
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This will open an interactive rebase with the `git-rebase-todo` containing all
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the latest commit on `main`. At this stage, you have to perform
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this interactive rebase with at least someone else (being three people rebasing
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is better not to forget something and to avoid any doubt).
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.. prompt:: bash $
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- **Do not remove lines but drop commit by replace** ``pick`` **with** ``drop``
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- Commits to pick for bug-fix release *generally* are prefixed with: `FIX`, `CI`,
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`DOC`. They should at least include all the commits of the merged PRs
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that were milestoned for this release on GitHub and/or documented as such in
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the changelog. It's likely that some bugfixes were documented in the
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changelog of the main major release instead of the next bugfix release,
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in which case, the matching changelog entries will need to be moved,
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first in the `main` branch then backported in the release PR.
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- Commits to drop for bug-fix release *generally* are prefixed with: `FEAT`,
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`MAINT`, `ENH`, `API`. Reasons for not including them is to prevent change of
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behavior (which only must feature in breaking or major releases).
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- After having dropped or picked commit, **do no exit** but paste the content
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of the `git-rebase-todo` message in the PR.
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This file is located at `.git/rebase-merge/git-rebase-todo`.
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- Save and exit, starting the interactive rebase.
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- Resolve merge conflicts when they happen.
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- Force push the result of the rebase and the extra release commits to the release PR:
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.. prompt:: bash $
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git rebase -i upstream/0.99.2
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git push -f fork release-1.2.2:release-1.2.2
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Copy the :ref:`release_checklist` templates in the description of the Pull
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Request to track progress.
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- Copy the :ref:`release_checklist` template and paste it in the description of the
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Pull Request to track progress.
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Do not forget to add a commit updating ``sklearn.__version__``.
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- Review all the commits included in the release to make sure that they do not
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introduce any new feature. We should not blindly trust the commit message prefixes.
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It's nice to have a copy of the ``git rebase -i`` log in the PR to help others
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understand what's included.
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- Remove the draft status of the release PR and invite other maintainers to review the
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list of included commits.
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.. _making_a_release:
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‎doc/modules/neighbors.rst

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@@ -136,9 +136,13 @@ have the same interface; we'll show an example of using the KD Tree here:
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Refer to the :class:`KDTree` and :class:`BallTree` class documentation
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for more information on the options available for nearest neighbors searches,
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including specification of query strategies, distance metrics, etc. For a list
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of available metrics, see the documentation of the :class:`DistanceMetric` class
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and the metrics listed in `sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS`.
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Note that the "cosine" metric uses :func:`~sklearn.metrics.pairwise.cosine_distances`.
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of valid metrics use :meth:`KDTree.valid_metrics` and :meth:`BallTree.valid_metrics`:
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>>> from sklearn.neighbors import KDTree, BallTree
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>>> KDTree.valid_metrics()
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['euclidean', 'l2', 'minkowski', 'p', 'manhattan', 'cityblock', 'l1', 'chebyshev', 'infinity']
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>>> BallTree.valid_metrics()
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['euclidean', 'l2', 'minkowski', 'p', 'manhattan', 'cityblock', 'l1', 'chebyshev', 'infinity', 'seuclidean', 'mahalanobis', 'wminkowski', 'hamming', 'canberra', 'braycurtis', 'matching', 'jaccard', 'dice', 'rogerstanimoto', 'russellrao', 'sokalmichener', 'sokalsneath', 'haversine', 'pyfunc']
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.. _classification:
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``valid_metric`` attribute. For example, valid metrics for ``KDTree`` can be generated by:
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>>> from sklearn.neighbors import KDTree
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>>> print(sorted(KDTree.valid_metrics))
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>>> print(sorted(KDTree.valid_metrics()))
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['chebyshev', 'cityblock', 'euclidean', 'infinity', 'l1', 'l2', 'manhattan', 'minkowski', 'p']
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‎doc/themes/scikit-learn-modern/nav.html

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('Support', pathto('support'), ''),
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('Related packages', pathto('related_projects'), ''),
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('Roadmap', pathto('roadmap'), ''),
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('Governance', pathto('governance'), ''),
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('About us', pathto('about'), ''),
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('GitHub', 'https://github.com/scikit-learn/scikit-learn', ''),
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('Other Versions and Download', 'https://scikit-learn.org/dev/versions.html', '')]

‎doc/whats_new/v1.2.rst

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Changelog
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---------
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:mod:`sklearn.calibration`
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..........................
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- |Fix| A deprecation warning is raised when using the `base_estimator__` prefix
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to set parameters of the estimator used in :class:`calibration.CalibratedClassifierCV`.
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:pr:`25477` by :user:`Tim Head <betatim>`.
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:mod:`sklearn.cluster`
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......................
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- |Fix| Fixed a bug in :class:`cluster.BisectingKMeans`, preventing `fit` to randomly
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fail due to a permutation of the labels when running multiple inits.
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:pr:`25563` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
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:mod:`sklearn.compose`
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......................
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- |Fix| Fixes a bug in :class:`compose.ColumnTransformer` which now supports
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empty selection of columns when `set_output(transform="pandas")`.
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:pr:`25570` by `Thomas Fan`_.
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:mod:`sklearn.ensemble`
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.......................
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- |Fix| A deprecation warning is raised when using the `base_estimator__` prefix
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to set parameters of the estimator used in :class:`ensemble.AdaBoostClassifier`,
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:class:`ensemble.AdaBoostRegressor`, :class:`ensemble.BaggingClassifier`,
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and :class:`ensemble.BaggingRegressor`.
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:pr:`25477` by :user:`Tim Head <betatim>`.
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:mod:`sklearn.isotonic`
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.......................
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when the global configuration sets `transform_output="pandas"`.
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:pr:`25500` by :user:`Guillaume Lemaitre <glemaitre>`.
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:mod:`sklearn.utils`
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....................
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- |Fix| Fixes a bug in :func:`utils.check_array` which now correctly performs
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non-finite validation with the Array API specification. :pr:`25619` by
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`Thomas Fan`_.
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.. _changes_1_2_1:
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Version 1.2.1

‎doc/whats_new/v1.3.rst

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inconsistent with the sckit-learn verion the estimator was pickled with.
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:pr:`25297` by `Thomas Fan`_.
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:mod:`sklearn.feature_selection`
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................................
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- |Enhancement| All selectors in :mod:`sklearn.feature_selection` will preserve
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a DataFrame's dtype when transformed. :pr:`25102` by `Thomas Fan`_.
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:mod:`sklearn.impute`
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.....................
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dissimilarity is not a metric and cannot be supported by the BallTree.
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:pr:`25417` by :user:`Guillaume Lemaitre <glemaitre>`.
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:mod:`sklearn.neural_network`
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.............................
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- |Fix| :class:`neural_network.MLPRegressor` and :class:`neural_network.MLPClassifier`
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reports the right `n_iter_` when `warm_start=True`. It corresponds to the number
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of iterations performed on the current call to `fit` instead of the total number
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of iterations performed since the initialization of the estimator.
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:pr:`25443` by :user:`Marvin Krawutschke <Marvvxi>`.
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:mod:`sklearn.pipeline`
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.......................
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‎examples/cluster/plot_kmeans_digits.py

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#
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# We will compare three approaches:
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#
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# * an initialization using `kmeans++`. This method is stochastic and we will
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# * an initialization using `k-means++`. This method is stochastic and we will
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# run the initialization 4 times;
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# * a random initialization. This method is stochastic as well and we will run
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# the initialization 4 times;

‎examples/cluster/plot_kmeans_plusplus.py

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)
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# Calculate seeds from k-means++
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centers_init, indices = kmeans_plusplus(X, n_clusters=4, random_state=0)
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# Plot init seeds along side sample data

‎examples/cluster/plot_kmeans_stability_low_dim_dense.py

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The first plot shows the best inertia reached for each combination
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of the model (``KMeans`` or ``MiniBatchKMeans``), and the init method
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(``init="random"`` or ``init="kmeans++"``) for increasing values of the
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(``init="random"`` or ``init="k-means++"``) for increasing values of the
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``n_init`` parameter that controls the number of initializations.
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The second plot demonstrates one single run of the ``MiniBatchKMeans``

‎examples/linear_model/plot_logistic_l1_l2_sparsity.py

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# Set regularization parameter
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for i, (C, axes_row) in enumerate(zip((1, 0.1, 0.01), axes)):
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# Increase tolerance for short training time
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clf_l1_LR = LogisticRegression(C=C, penalty="l1", tol=0.01, solver="saga")
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clf_l2_LR = LogisticRegression(C=C, penalty="l2", tol=0.01, solver="saga")
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clf_en_LR = LogisticRegression(

‎setup.py

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"sklearn.svm._libsvm",
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"sklearn.svm._libsvm_sparse",
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"sklearn.svm._newrand",
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"sklearn.tree._criterion",
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"sklearn.tree._splitter",
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"sklearn.tree._tree",
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"sklearn.tree._utils",
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"sklearn.utils._logistic_sigmoid",
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"sklearn.utils._openmp_helpers",
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"sklearn.utils._random",
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"sklearn.utils._readonly_array_wrapper",
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"sklearn.utils._seq_dataset",
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"sklearn.utils._sorting",
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"sklearn.utils._typedefs",
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{"sources": ["_isotonic.pyx"], "include_np": True},
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"_loss": [
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{"sources": ["_loss.pyx.tp"]},
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"cluster": [
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{"sources": ["_dbscan_inner.pyx"], "language": "c++", "include_np": True},
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},
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{"sources": ["_random.pyx"], "include_np": True},
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{"sources": ["_logistic_sigmoid.pyx"], "include_np": True},
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{"sources": ["_readonly_array_wrapper.pyx"], "include_np": True},
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{"sources": ["_typedefs.pyx"], "include_np": True},
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{"sources": ["_heap.pyx"], "include_np": True},
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{"sources": ["_sorting.pyx"], "include_np": True},

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