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‎.github/workflows/wheels.yml

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python: 311
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platform_id: macosx_x86_64
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# MacOS arm64
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# The latest Python version is built and tested on CirrusCI
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- os: macos-latest
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python: 38
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platform_id: macosx_arm64
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- os: macos-latest
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python: 39
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platform_id: macosx_arm64
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- os: macos-latest
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python: 310
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platform_id: macosx_arm64
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steps:
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- name: Checkout scikit-learn
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uses: actions/checkout@v3

‎.gitignore

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sklearn/metrics/_pairwise_distances_reduction/_middle_term_computer.pyx
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sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pxd
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sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx
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sklearn/neighbors/_ball_tree.pyx
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sklearn/neighbors/_binary_tree.pxi
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sklearn/neighbors/_kd_tree.pyx
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# Default JupyterLite content
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jupyterlite_contents

‎build_tools/cirrus/arm_wheel.yml

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@@ -16,12 +16,8 @@ macos_arm64_wheel_task:
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# See `maint_tools/update_tracking_issue.py` for details on the permissions the token requires.
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BOT_GITHUB_TOKEN: ENCRYPTED[9b50205e2693f9e4ce9a3f0fcb897a259289062fda2f5a3b8aaa6c56d839e0854a15872f894a70fca337dd4787274e0f]
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matrix:
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- env:
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CIBW_BUILD: cp38-macosx_arm64
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- env:
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CIBW_BUILD: cp39-macosx_arm64
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- env:
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CIBW_BUILD: cp310-macosx_arm64
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# Only the latest Python version is built and tested on CirrusCI, the other
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# macos arm64 builds are on GitHub Actions
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- env:
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CIBW_BUILD: cp311-macosx_arm64
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@@ -60,12 +56,16 @@ linux_arm64_wheel_task:
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# See `maint_tools/update_tracking_issue.py` for details on the permissions the token requires.
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BOT_GITHUB_TOKEN: ENCRYPTED[9b50205e2693f9e4ce9a3f0fcb897a259289062fda2f5a3b8aaa6c56d839e0854a15872f894a70fca337dd4787274e0f]
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matrix:
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# Only the latest Python version is tested
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- env:
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CIBW_BUILD: cp38-manylinux_aarch64
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CIBW_TEST_SKIP: "*_aarch64"
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- env:
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CIBW_BUILD: cp39-manylinux_aarch64
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CIBW_TEST_SKIP: "*_aarch64"
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- env:
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CIBW_BUILD: cp310-manylinux_aarch64
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CIBW_TEST_SKIP: "*_aarch64"
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- env:
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CIBW_BUILD: cp311-manylinux_aarch64
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‎build_tools/cirrus/build_test_arm.sh

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@@ -25,7 +25,7 @@ setup_ccache() {
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MAMBAFORGE_URL="https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-aarch64.sh"
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# Install Mambaforge
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wget $MAMBAFORGE_URL -O mambaforge.sh
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curl -L $MAMBAFORGE_URL -o mambaforge.sh
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MAMBAFORGE_PATH=$HOME/mambaforge
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bash ./mambaforge.sh -b -p $MAMBAFORGE_PATH
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export PATH=$MAMBAFORGE_PATH/bin:$PATH

‎doc/related_projects.rst

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@@ -136,6 +136,14 @@ enhance the functionality of scikit-learn's estimators.
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Compiles tree-based ensemble models into C code for minimizing prediction
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latency.
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- `micromlgen <https://github.com/eloquentarduino/micromlgen>`_
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MicroML brings Machine Learning algorithms to microcontrollers.
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Supports several scikit-learn classifiers by transpiling them to C code.
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- `emlearn <https://emlearn.org>`_
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Implements scikit-learn estimators in C99 for embedded devices and microcontrollers.
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Supports several classifier, regression and outlier detection models.
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**Model throughput**
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- `Intel(R) Extension for scikit-learn <https://github.com/intel/scikit-learn-intelex>`_

‎doc/themes/scikit-learn-modern/static/css/theme.css

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@@ -661,13 +661,19 @@ div.sk-sidebar-global-toc ul ul {
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div.sk-page-content h1 {
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background-color: #cde8ef;
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padding: 0.5rem;
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margin-top: calc(max(2.5rem, 1vh));
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margin-top: calc(max(1rem, 1vh));
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border-radius: 0 1rem;
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text-align: center;
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font-size: 2rem;
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word-wrap: break-word;
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}
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/* General sibling selector: does not apply to first h1, to avoid gap in
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* top of page */
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div.sk-page-content ~ h1 {
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margin-top: calc(max(2.5rem, 1vh));
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}
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div.sk-page-content h2 {
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padding: 0.5rem;
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background-color: #BED4EB;

‎doc/tutorial/machine_learning_map/pyparsing.py

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# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
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# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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#
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# flake8: noqa
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# ruff: noqa
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__doc__ = \
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"""

‎doc/whats_new/v1.4.rst

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and all metadata are passed as keyword arguments. :pr:`26909` by `Adrin
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Jalali`_.
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:mod:`sklearn.cluster`
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............................
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- |API| : `kdtree` and `balltree` values are now deprecated and are renamed as
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`kd_tree` and `ball_tree` respectively for the `algorithm` parameter of
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:class:`cluster.HDBSCAN` ensuring consistency in naming convention.
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`kdtree` and `balltree` values will be removed in 1.6.
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:pr:`26744` by :user:`Shreesha Kumar Bhat <Shreesha3112>`.
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:mod:`sklearn.cross_decomposition`
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..................................
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:pr:`13649` by :user:`Samuel Ronsin <samronsin>`,
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initiated by :user:`Patrick O'Reilly <pat-oreilly>`.
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- |Efficiency| Improves runtime and memory usage for
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:class:`ensemble.GradientBoostingClassifier` and
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:class:`ensemble.GradientBoostingRegressor` when trained on sparse data.
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:pr:`26957` by `Thomas Fan`_.
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:mod:`sklearn.feature_selection`
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................................
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:pr:`13649` by :user:`Samuel Ronsin <samronsin>`, initiated by
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:user:`Patrick O'Reilly <pat-oreilly>`.
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:mod:`sklearn.neighbors`
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........................
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- |API| :class:`neighbors.KNeighborsRegressor` now accepts
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:class:`metric.DistanceMetric` objects directly via the `metric` keyword
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argument allowing for the use of accelerated third-party
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:class:`metric.DistanceMetric` objects.
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:pr:`26267` by :user:`Meekail Zain <micky774>`
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:mod:`sklearn.metrics`
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......................
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- |Performance| Computing pairwise distances via :class:`metrics.DistanceMetric`
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- |Efficiency| Computing pairwise distances via :class:`metrics.DistanceMetric`
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for CSR × CSR, Dense × CSR, and CSR × Dense datasets is now 1.5x faster.
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:pr:`26765` by :user:`Meekail Zain <micky774>`
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‎examples/classification/plot_classifier_comparison.py

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classifiers = [
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KNeighborsClassifier(3),
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SVC(kernel="linear", C=0.025),
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SVC(gamma=2, C=1),
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GaussianProcessClassifier(1.0 * RBF(1.0)),
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DecisionTreeClassifier(max_depth=5),
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RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
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MLPClassifier(alpha=1, max_iter=1000),
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AdaBoostClassifier(),
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SVC(kernel="linear", C=0.025, random_state=42),
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SVC(gamma=2, C=1, random_state=42),
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GaussianProcessClassifier(1.0 * RBF(1.0), random_state=42),
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DecisionTreeClassifier(max_depth=5, random_state=42),
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RandomForestClassifier(
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max_depth=5, n_estimators=10, max_features=1, random_state=42
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),
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MLPClassifier(alpha=1, max_iter=1000, random_state=42),
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AdaBoostClassifier(random_state=42),
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GaussianNB(),
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QuadraticDiscriminantAnalysis(),
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]

‎examples/release_highlights/plot_release_highlights_0_23_0.py

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# flake8: noqa
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# ruff: noqa
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"""
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========================================
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Release Highlights for scikit-learn 0.23

‎examples/release_highlights/plot_release_highlights_0_24_0.py

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# flake8: noqa
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# ruff: noqa
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"""
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========================================
44
Release Highlights for scikit-learn 0.24

‎examples/release_highlights/plot_release_highlights_1_0_0.py

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# flake8: noqa
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# ruff: noqa
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"""
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=======================================
44
Release Highlights for scikit-learn 1.0

‎examples/release_highlights/plot_release_highlights_1_1_0.py

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# flake8: noqa
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# ruff: noqa
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"""
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=======================================
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Release Highlights for scikit-learn 1.1

‎examples/release_highlights/plot_release_highlights_1_2_0.py

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# flake8: noqa
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# ruff: noqa
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"""
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=======================================
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Release Highlights for scikit-learn 1.2

‎examples/release_highlights/plot_release_highlights_1_3_0.py

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# flake8: noqa
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# ruff: noqa
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"""
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=======================================
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Release Highlights for scikit-learn 1.3

‎pyproject.toml

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# + E501 (line too long) because keeping it < 88 in cython
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# often makes code less readable.
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ignore = [
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# check ignored by default in flake8. Meaning unclear.
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# multiple spaces/tab after comma
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'E24',
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# space before : (needed for how black formats slicing)
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'E203',

‎setup.cfg

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sklearn/metrics/_pairwise_distances_reduction/_middle_term_computer.pyx
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sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pxd
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sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx
56+
sklearn/neighbors/_ball_tree.pyx
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sklearn/neighbors/_binary_tree.pxi
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sklearn/neighbors/_kd_tree.pyx
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[codespell]

‎setup.py

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},
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],
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"neighbors": [
309-
{"sources": ["_ball_tree.pyx"], "include_np": True},
310-
{"sources": ["_kd_tree.pyx"], "include_np": True},
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{"sources": ["_binary_tree.pxi.tp"], "include_np": True},
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{"sources": ["_ball_tree.pyx.tp"], "include_np": True},
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{"sources": ["_kd_tree.pyx.tp"], "include_np": True},
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{"sources": ["_partition_nodes.pyx"], "language": "c++", "include_np": True},
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{"sources": ["_quad_tree.pyx"], "include_np": True},
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],
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499500
# `source` is a Tempita file
500501
tempita_sources.append(source)
501502

502-
# Do not include pxd files that were generated by tempita
503-
if os.path.splitext(new_source_path)[-1] == ".pxd":
504-
continue
505-
sources.append(new_source_path)
503+
# Only include source files that are pyx files
504+
if os.path.splitext(new_source_path)[-1] == ".pyx":
505+
sources.append(new_source_path)
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507507
gen_from_templates(tempita_sources)
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509+
# Do not progress if we only have a tempita file which we don't
510+
# want to include like the .pxi.tp extension. In such a case
511+
# sources would be empty.
512+
if not sources:
513+
continue
514+
509515
# By convention, our extensions always use the name of the first source
510516
source_name = os.path.splitext(os.path.basename(sources[0]))[0]
511517
if submodule:

‎sklearn/cluster/_hdbscan/hdbscan.py

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@@ -462,19 +462,27 @@ class HDBSCAN(ClusterMixin, BaseEstimator):
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A distance scaling parameter as used in robust single linkage.
463463
See [3]_ for more information.
464464
465-
algorithm : {"auto", "brute", "kdtree", "balltree"}, default="auto"
465+
algorithm : {"auto", "brute", "kd_tree", "ball_tree"}, default="auto"
466466
Exactly which algorithm to use for computing core distances; By default
467467
this is set to `"auto"` which attempts to use a
468468
:class:`~sklearn.neighbors.KDTree` tree if possible, otherwise it uses
469-
a :class:`~sklearn.neighbors.BallTree` tree. Both `"KDTree"` and
470-
`"BallTree"` algorithms use the
469+
a :class:`~sklearn.neighbors.BallTree` tree. Both `"kd_tree"` and
470+
`"ball_tree"` algorithms use the
471471
:class:`~sklearn.neighbors.NearestNeighbors` estimator.
472472
473473
If the `X` passed during `fit` is sparse or `metric` is invalid for
474474
both :class:`~sklearn.neighbors.KDTree` and
475475
:class:`~sklearn.neighbors.BallTree`, then it resolves to use the
476476
`"brute"` algorithm.
477477
478+
.. deprecated:: 1.4
479+
The `'kdtree'` option was deprecated in version 1.4,
480+
and will be renamed to `'kd_tree'` in 1.6.
481+
482+
.. deprecated:: 1.4
483+
The `'balltree'` option was deprecated in version 1.4,
484+
and will be renamed to `'ball_tree'` in 1.6.
485+
478486
leaf_size : int, default=40
479487
Leaf size for trees responsible for fast nearest neighbour queries when
480488
a KDTree or a BallTree are used as core-distance algorithms. A large
@@ -625,15 +633,12 @@ class HDBSCAN(ClusterMixin, BaseEstimator):
625633
"metric": [StrOptions(FAST_METRICS | {"precomputed"}), callable],
626634
"metric_params": [dict, None],
627635
"alpha": [Interval(Real, left=0, right=None, closed="neither")],
636+
# TODO(1.6): Remove "kdtree" and "balltree" option
628637< 10000 code class="diff-text syntax-highlighted-line">
"algorithm": [
629638
StrOptions(
630-
{
631-
"auto",
632-
"brute",
633-
"kdtree",
634-
"balltree",
635-
}
636-
)
639+
{"auto", "brute", "kd_tree", "ball_tree", "kdtree", "balltree"},
640+
deprecated={"kdtree", "balltree"},
641+
),
637642
],
638643
"leaf_size": [Interval(Integral, left=1, right=None, closed="left")],
639644
"n_jobs": [Integral, None],
@@ -759,6 +764,31 @@ def fit(self, X, y=None):
759764
f"min_samples ({self._min_samples}) must be at most the number of"
760765
f" samples in X ({X.shape[0]})"
761766
)
767+
768+
# TODO(1.6): Remove
769+
if self.algorithm == "kdtree":
770+
warn(
771+
(
772+
"`algorithm='kdtree'`has been deprecated in 1.4 and will be renamed"
773+
" to'kd_tree'`in 1.6. To keep the past behaviour, set"
774+
" `algorithm='kd_tree'`."
775+
),
776+
FutureWarning,
777+
)
778+
self.algorithm = "kd_tree"
779+
780+
# TODO(1.6): Remove
781+
if self.algorithm == "balltree":
782+
warn(
783+
(
784+
"`algorithm='balltree'`has been deprecated in 1.4 and will be"
785+
" renamed to'ball_tree'`in 1.6. To keep the past behaviour, set"
786+
" `algorithm='ball_tree'`."
787+
),
788+
FutureWarning,
789+
)
790+
self.algorithm = "ball_tree"
791+
762792
mst_func = None
763793
kwargs = dict(
764794
X=X,
@@ -768,12 +798,14 @@ def fit(self, X, y=None):
768798
n_jobs=self.n_jobs,
769799
**self._metric_params,
770800
)
771-
if self.algorithm == "kdtree" and self.metric not in KDTree.valid_metrics:
801+
if self.algorithm == "kd_tree" and self.metric not in KDTree.valid_metrics:
772802
raise ValueError(
773803
f"{self.metric} is not a valid metric for a KDTree-based algorithm."
774804
" Please select a different metric."
775805
)
776-
elif self.algorithm == "balltree" and self.metric not in BallTree.valid_metrics:
806+
elif (
807+
self.algorithm == "ball_tree" and self.metric not in BallTree.valid_metrics
808+
):
777809
raise ValueError(
778810
f"{self.metric} is not a valid metric for a BallTree-based algorithm."
779811
" Please select a different metric."
@@ -790,11 +822,11 @@ def fit(self, X, y=None):
790822
if self.algorithm == "brute":
791823
mst_func = _hdbscan_brute
792824
kwargs["copy"] = self.copy
793-
elif self.algorithm == "kdtree":
825+
elif self.algorithm == "kd_tree":
794826
mst_func = _hdbscan_prims
795827
kwargs["algo"] = "kd_tree"
796828
kwargs["leaf_size"] = self.leaf_size
797-
elif self.algorithm == "balltree":
829+
else:
798830
mst_func = _hdbscan_prims
799831
kwargs["algo"] = "ball_tree"
800832
kwargs["leaf_size"] = self.leaf_size

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