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‎build_tools/azure/pypy3_linux-64_conda.lock

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https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda#47d31b792659ce70f470b5c82fdfb7a4
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https://conda.anaconda.org/conda-forge/noarch/pluggy-1.4.0-pyhd8ed1ab_0.conda#139e9feb65187e916162917bb2484976
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https://conda.anaconda.org/conda-forge/noarch/pypy-7.3.15-1_pypy39.conda#a418a6c16bd6f7ed56b92194214791a0
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https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.3.0-pyhc1e730c_0.conda#698d2d2b621640bddb9191f132967c9f
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https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-21_linux64_openblas.conda#77cefbfb4d47ba8cafef8e3f768a4538
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https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.0-py39ha90811c_0.conda#f3b2afc64bf0cbe901a9b00d44611c61
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https://conda.anaconda.org/conda-forge/noarch/pytest-8.0.2-pyhd8ed1ab_0.conda#40bd3ef942b9642a3eb20b0bbf92469b
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https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.2-pyhd8ed1ab_0.tar.bz2#dd999d1cc9f79e67dbb855c8924c7984
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https://conda.anaconda.org/conda-forge/linux-64/scipy-1.12.0-py39h6dedee3_2.conda#6c5d74bac41838f4377dfd45085e1fec
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https://conda.anaconda.org/conda-forge/linux-64/blas-2.121-openblas.conda#4a279792fd8861a15705516a52872eb6

‎build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock

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https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlienc-1.1.0-h31becfc_1.conda#ad3d3a826b5848d99936e4466ebbaa26
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https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran-ng-13.2.0-he9431aa_5.conda#fab7c6a8c84492e18cbe578820e97a56
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https://conda.anaconda.org/conda-forge/linux-aarch64/libpng-1.6.42-h194ca79_0.conda#b8ff00cc9a5184726baea61244f8bec3
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https://conda.anaconda.org/conda-forge/linux-aarch64/libpng-1.6.43-h194ca79_0.conda#1123e504d9254dd9494267ab9aba95f0
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https://conda.anaconda.org/conda-forge/linux-aarch64/libsqlite-3.45.1-h194ca79_0.conda#4190198deb1ed253eb938f6a6d92ff4f
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https://conda.anaconda.org/conda-forge/linux-aarch64/libxcb-1.15-h2a766a3_0.conda#eb3d8c8170e3d03f2564ed2024aa00c8
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https://conda.anaconda.org/conda-forge/linux-aarch64/readline-8.2-h8fc344f_1.conda#105eb1e16bf83bfb2eb380a48032b655
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https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.1-pyhd8ed1ab_0.conda#176f7d56f0cfe9008bdf1bccd7de02fb
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https://conda.anaconda.org/conda-forge/noarch/setuptools-69.1.0-pyhd8ed1ab_0.conda#6df2be294365eca602cabb4f04a6efe2
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https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2
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https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.3.0-pyhc1e730c_0.conda#698d2d2b621640bddb9191f132967c9f
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https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96
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https://conda.anaconda.org/conda-forge/linux-aarch64/tornado-6.3.3-py39h7cc1d5f_1.conda#c383c279123694d7a586ec47320d1cb1
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https://conda.anaconda.org/conda-forge/linux-aarch64/tornado-6.4-py39h7cc1d5f_0.conda#2c06a653ebfa389c18aea2d8f338df3b
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https://conda.anaconda.org/conda-forge/linux-aarch64/unicodedata2-15.1.0-py39h898b7ef_0.conda#8c072c9329aeea97a46005625267a851
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https://conda.anaconda.org/conda-forge/noarch/wheel-0.42.0-pyhd8ed1ab_0.conda#1cdea58981c5cbc17b51973bcaddcea7
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https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda#2e4d6bc0b14e10f895fc6791a7d9b26a
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https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.1.1-pyhd8ed1ab_0.conda#d04bd1b5bed9177dd7c3cef15e2b6710
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https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-21_linuxaarch64_openblas.conda#be00a60ef5d88de133a28cb1fb6e0b31

‎doc/metadata_routing.rst

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- :class:`sklearn.linear_model.LogisticRegressionCV`
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- :class:`sklearn.linear_model.MultiTaskElasticNetCV`
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- :class:`sklearn.linear_model.MultiTaskLassoCV`
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- :class:`sklearn.linear_model.RANSACRegressor`
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- :class:`sklearn.model_selection.GridSearchCV`
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- :class:`sklearn.model_selection.HalvingGridSearchCV`
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- :class:`sklearn.model_selection.HalvingRandomSearchCV`
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- :class:`sklearn.feature_selection.RFE`
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- :class:`sklearn.feature_selection.RFECV`
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- :class:`sklearn.feature_selection.SequentialFeatureSelector`
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- :class:`sklearn.impute.IterativeImputer`
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- :class:`sklearn.linear_model.RANSACRegressor`
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- :class:`sklearn.linear_model.RidgeClassifierCV`
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- :class:`sklearn.linear_model.RidgeCV`

‎doc/modules/linear_model.rst

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1. Select ``min_samples`` random samples from the original data and check
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whether the set of data is valid (see ``is_data_valid``).
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2. Fit a model to the random subset (``base_estimator.fit``) and check
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2. Fit a model to the random subset (``estimator.fit``) and check
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whether the estimated model is valid (see ``is_model_valid``).
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3. Classify all data as inliers or outliers by calculating the residuals
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to the estimated model (``base_estimator.predict(X) - y``) - all data
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to the estimated model (``estimator.predict(X) - y``) - all data
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samples with absolute residuals smaller than or equal to the
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``residual_threshold`` are considered as inliers.
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4. Save fitted model as best model if number of inlier samples is

‎doc/whats_new/v1.5.rst

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via their `fit` methods.
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:pr:`28432` by :user:`Adam Li <adam2392>` and :user:`Benjamin Bossan <BenjaminBossan>`.
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Metadata Routing
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----------------
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The following models now support metadata routing in one or more or their
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methods. Refer to the :ref:`Metadata Routing User Guide <metadata_routing>` for
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more details.
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- |Feature| :class:`linear_model.RANSACRegressor` now supports metadata routing
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in its ``fit``, ``score`` and ``predict`` methods and route metadata to its
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underlying estimator's' ``fit``, ``score`` and ``predict`` methods.
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:pr:`28261` by :user:`Stefanie Senger <StefanieSenger>`.
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- |Feature| :class:`ensemble.VotingClassifier` and
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:class:`ensemble.VotingRegressor` now support metadata routing and pass
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``**fit_params`` to the underlying estimators via their `fit` methods.
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:pr:`123456` by :user:`Joe Bloggs <joeongithub>`.
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where 123455 is the *pull request* number, not the issue number.
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:mod:`sklearn.cluster`
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......................
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- |FIX| Create copy of precomputed sparse matrix within the `fit` method of
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:class:`~cluster.OPTICS` to avoid in-place modification of the sparse matrix.
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:pr:`28491` by :user:`Thanh Lam Dang <lamdang2k>`.
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:mod:`sklearn.compose`
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......................
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only `inverse_func` is provided without `func` (that would default to identity) being
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:mod:`sklearn.datasets`
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.......................
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- |Enhancement| Adds optional arguments `n_retries` and `delay` to functions
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:func:`datasets.fetch_20newsgroups`,
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:func:`datasets.fetch_20newsgroups_vectorized`,
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:func:`datasets.fetch_california_housing`,
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:func:`datasets.fetch_covtype`,
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:func:`datasets.fetch_kddcup99`,
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:func:`datasets.fetch_lfw_pairs`,
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:func:`datasets.fetch_lfw_people`,
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:func:`datasets.fetch_olivetti_faces`,
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:func:`datasets.fetch_rcv1`,
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and :func:`datasets.fetch_species_distributions`.
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By default, the functions will retry up to 3 times in case of network failures.
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:pr:`28160` by :user:`Zhehao Liu <MaxwellLZH>` and :user:`Filip Karlo Došilović <fkdosilovic>`.
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....................
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‎sklearn/cluster/_optics.py

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X = self._validate_data(X, dtype=dtype, accept_sparse="csr")
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if self.metric == "precomputed" and issparse(X):
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X = X.copy() # copy to avoid in-place modification
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with warnings.catch_warnings():
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# Set each diagonal to an explicit value so each point is its

‎sklearn/cluster/tests/test_optics.py

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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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def test_optics_input_not_modified_precomputed_sparse_nodiag(csr_container):
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"""Check that we don't modify in-place the pre-computed sparse matrix.
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Non-regression test for:
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https://github.com/scikit-learn/scikit-learn/issues/27508
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"""
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X = np.random.RandomState(0).rand(6, 6)
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# Add zeros on the diagonal that will be implicit when creating
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# the sparse matrix. If `X` is modified in-place, the zeros from
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# the diagonal will be made explicit.
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np.fill_diagonal(X, 0)
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X = csr_container(X)
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assert all(row != col for row, col in zip(*X.nonzero()))
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X_copy = X.copy()
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OPTICS(metric="precomputed").fit(X)
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# Make sure that we did not modify `X` in-place even by creating
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# explicit 0s values.
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assert X.nnz == X_copy.nnz
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assert_array_equal(X.toarray(), X_copy.toarray())
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def test_optics_predecessor_correction_ordering():
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‎sklearn/datasets/_base.py

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import hashlib
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import shutil
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import time
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import warnings
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from collections import namedtuple
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from importlib import resources
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from numbers import Integral
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from os import environ, listdir, makedirs
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from os.path import expanduser, isdir, join, splitext
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from pathlib import Path
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from urllib.error import URLError
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def _fetch_remote(remote, dirname=None):
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def _fetch_remote(remote, dirname=None, n_retries=3, delay=1):
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"""Helper function to download a remote dataset into path
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Fetch a dataset pointed by remote's url, save into path using remote's
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dirname : str
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Directory to save the file to.
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n_retries : int, default=3
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Number of retries when HTTP errors are encountered.
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.. versionadded:: 1.5
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delay : int, default=1
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Number of seconds between retries.
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.. versionadded:: 1.5
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Returns
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-------
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file_path: str
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Full path of the created file.
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"""
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file_path = remote.filename if dirname is None else join(dirname, remote.filename)
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urlretrieve(remote.url, file_path)
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while True:
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try:
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urlretrieve(remote.url, file_path)
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break
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except (URLError, TimeoutError):
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if n_retries == 0:
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# If no more retries are left, re-raise the caught exception.
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raise
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warnings.warn(f"Retry downloading from url: {remote.url}")
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n_retries -= 1
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time.sleep(delay)
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‎sklearn/datasets/_california_housing.py

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import logging
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import tarfile
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from os.path import exists
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import joblib
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import numpy as np
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from ..utils import Bunch
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from ..utils._param_validation import Interval, validate_params
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from . import get_data_home< 10000 /div>
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from ._base import (
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RemoteFileMetadata,
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"download_if_missing": ["boolean"],
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"return_X_y": ["boolean"],
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"as_frame": ["boolean"],
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"n_retries": [Interval(Integral, 1, None, closed="left")],
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"delay": [Interval(Real, 0.0, None, closed="neither")],
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},
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)
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def fetch_california_housing(
64-
*, data_home=None, download_if_missing=True, return_X_y=False, as_frame=False
67+
*,
68+
data_home=None,
69+
download_if_missing=True,
70+
return_X_y=False,
71+
as_frame=False,
72+
n_retries=3,
73+
delay=1.0,
6574
):
6675
"""Load the California housing dataset (regression).
6776
@@ -97,6 +106,16 @@ def fetch_california_housing(
97106
98107
.. versionadded:: 0.23
99108
109+
n_retries : int, default=3
110+
Number of retries when HTTP errors are encountered.
111+
112+
.. versionadded:: 1.5
113+
114+
delay : float, default=1.0
115+
Number of seconds between retries.
116+
117+
.. versionadded:: 1.5
118+
100119
Returns
101120
-------
102121
dataset : :class:`~sklearn.utils.Bunch`
@@ -154,7 +173,12 @@ def fetch_california_housing(
154173
"Downloading Cal. housing from {} to {}".format(ARCHIVE.url, data_home)
155174
)
156175

157-
archive_path = _fetch_remote(ARCHIVE, dirname=data_home)
176+
archive_path = _fetch_remote(
177+
ARCHIVE,
178+
dirname=data_home,
179+
n_retries=n_retries,
180+
delay=delay,
181+
)
158182

159183
with tarfile.open(mode="r:gz", name=archive_path) as f:
160184
cal_housing = np.loadtxt(

‎sklearn/datasets/_covtype.py

Lines changed: 19 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -17,14 +17,15 @@
1717
import logging
1818
import os
1919
from gzip import GzipFile
20+
from numbers import Integral, Real
2021
from os.path import exists, join
2122
from tempfile import TemporaryDirectory
2223

2324
import joblib
2425
import numpy as np
2526

2627
from ..utils import Bunch, check_random_state
27-
from ..utils._param_validation import validate_params
28+
from ..utils._param_validation import Interval, validate_params
2829
from . import get_data_home
2930
from ._base import (
3031
RemoteFileMetadata,
@@ -71,6 +72,8 @@
7172
"shuffle": ["boolean"],
7273
"return_X_y": ["boolean"],
7374
"as_frame": ["boolean"],
75+
"n_retries": [Interval(Integral, 1, None, closed="left")],
76+
"delay": [Interval(Real, 0.0, None, closed="neither")],
7477
},
7578
prefer_skip_nested_validation=True,
7679
)
@@ -82,6 +85,8 @@ def fetch_covtype(
8285
shuffle=False,
8386
return_X_y=False,
8487
as_frame=False,
88+
n_retries=3,
89+
delay=1.0,
8590
):
8691
"""Load the covertype dataset (classification).
8792
@@ -129,6 +134,16 @@ def fetch_covtype(
129134
130135
.. versionadded:: 0.24
131136
137+
n_retries : int, default=3
138+
Number of retries when HTTP errors are encountered.
139+
140+
.. versionadded:: 1.5
141+
142+
delay : float, default=1.0
143+
Number of seconds between retries.
144+
145+
.. versionadded:: 1.5
146+
132147
Returns
133148
-------
134149
dataset : :class:`~sklearn.utils.Bunch`
@@ -183,7 +198,9 @@ def fetch_covtype(
183198
# os.rename to atomically move the data files to their target location.
184199
with TemporaryDirectory(dir=covtype_dir) as temp_dir:
185200
logger.info(f"Downloading {ARCHIVE.url}")
186-
archive_path = _fetch_remote(ARCHIVE, dirname=temp_dir)
201+
archive_path = _fetch_remote(
202+
ARCHIVE, dirname=temp_dir, _retries=n_retries, delay=delay
203+
)
187204
Xy = np.genfromtxt(GzipFile(filename=archive_path), delimiter=",")
188205

189206
X = Xy[:, :-1]

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