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Commit aeb79ca

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jjerphanjjerphan
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Merge branch 'main' into pairwise-distances-radius-neighborhood
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‎.git-blame-ignore-revs

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2020
# PR 20502: Runs Black on examples
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70a185ae59b4362633d18b0d0083abb1b6f7370c
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# PR 22474: Update to Black 22.1.0
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1fc86b6aacd89da44a3b4e8abf7c3e2ba4336ffe

‎.github/workflows/assign.yml

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jobs:
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one:
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runs-on: ubuntu-latest
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# Note that string comparisons is not case sensitive.
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if: >-
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(github.event.comment.body == 'take' ||
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github.event.comment.body == 'Take')
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&& !github.event.issue.assignee
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startsWith(github.event.comment.body, '/take')
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&& !github.event.issue.assignee
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steps:
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- run: |
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echo "Assigning issue ${{ github.event.issue.number }} to ${{ github.event.comment.user.login }}"

‎.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 "entry, say so in a comment so that a maintainer will label "
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echo "If you believe that this PR does not 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
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fi

‎.github/workflows/check-manifest.yml

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jobs:
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check:
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# Don't run on forks
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if: github.repository == 'scikit-learn/scikit-learn'
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v2

‎.github/workflows/wheels.yml

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matrix:
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include:
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# Window 64 bit
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- os: windows-latest
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# Note: windows-2019 is needed for older Python versions:
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# https://github.com/scikit-learn/scikit-learn/issues/22530
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- os: windows-2019
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python: 37
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bitness: 64
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platform_id: win_amd64
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- os: windows-latest
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- os: windows-2019
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python: 38
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bitness: 64
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platform_id: win_amd64

‎.gitignore

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# Used by mypy
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.mypy_cache/
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# virtualenv from advanced installation guide
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sklearn-env/
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# files generated from a template
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sklearn/_loss/_loss.pyx
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sklearn/utils/_seq_dataset.pyx

‎.pre-commit-config.yaml

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- id: end-of-file-fixer
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- id: trailing-whitespace
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- repo: https://github.com/psf/black
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rev: 21.6b0
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rev: 22.1.0
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hooks:
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- id: black
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- repo: https://gitlab.com/pycqa/flake8

‎README.rst

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.. |PythonMinVersion| replace:: 3.7
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.. |NumPyMinVersion| replace:: 1.14.6
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.. |SciPyMinVersion| replace:: 1.1.0
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.. |JoblibMinVersion| replace:: 0.11
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.. |JoblibMinVersion| replace:: 1.0.0
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.. |ThreadpoolctlMinVersion| replace:: 2.0.0
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.. |MatplotlibMinVersion| replace:: 2.2.3
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.. |Scikit-ImageMinVersion| replace:: 0.14.5
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~~~~~~~~~~~~~~~~~
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If you already have a working installation of numpy and scipy,
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the easiest way to install scikit-learn is using ``pip`` ::
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the easiest way to install scikit-learn is using ``pip``::
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pip install -U scikit-learn
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- Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn
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- Gitter: https://gitter.im/scikit-learn/scikit-learn
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- Blog: https://blog.scikit-learn.org
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- Calendar: https://blog.scikit-learn.org/calendar/
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- Twitter: https://twitter.com/scikit_learn
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- Twitter (commits): https://twitter.com/sklearn_commits
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- Stack Overflow: https://stackoverflow.com/questions/tagged/scikit-learn
@@ -193,6 +195,7 @@ Communication
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- YouTube: https://www.youtube.com/channel/UCJosFjYm0ZYVUARxuOZqnnw/playlists
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- Facebook: https://www.facebook.com/scikitlearnofficial/
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- Instagram: https://www.instagram.com/scikitlearnofficial/
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- TikTok: https://www.tiktok.com/@scikit.learn
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Citation
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~~~~~~~~

‎azure-pipelines.yml

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versionSpec: '3.9'
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# Include pytest compatibility with mypy
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pip install pytest flake8 mypy==0.782 black==21.6b0
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pip install pytest flake8 mypy==0.782 black==22.1.0
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black --check --diff .
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PANDAS_VERSION: 'none'
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COVERAGE: 'false'
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# Linux + Python 3.7 build with OpenBLAS and without SITE_JOBLIB
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# Linux + Python 3.7 build with OpenBLAS
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py37_conda_defaults_openblas:
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DISTRIB: 'conda'
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CONDA_CHANNEL: 'defaults' # Anaconda main channel
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pylatest_pip_openblas_pandas:
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DISTRIB: 'conda-pip-latest'
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PYTHON_VERSION: '3.9'
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PYTEST_VERSION: '6.2.5'
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CHECK_PYTEST_SOFT_DEPENDENCY: 'true'
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CHECK_WARNINGS: 'true'
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PYTHON_ARCH: '64'
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PYTEST_VERSION: '*'
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# Unpin when pytest stalling issue is fixed
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PYTEST_VERSION: '6.2.5'
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# Temporary fix for setuptools to use disutils from standard lib
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# https://github.com/numpy/numpy/issues/17216

‎benchmarks/bench_hist_gradient_boosting_threading.py

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max_threads = os.cpu_count()
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n_threads_list = [2 ** i for i in range(8) if (2 ** i) < max_threads]
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n_threads_list = [2**i for i in range(8) if (2**i) < max_threads]
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sklearn_scores = []

‎benchmarks/bench_isotonic.py

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89-
n = 10 ** exponent
89+
n = 10**exponent
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9191
time_per_iteration = [
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bench_isotonic_regression(Y) for i in range(args.iterations)

‎benchmarks/bench_lasso.py

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)
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X /= np.sqrt(np.sum(X ** 2, axis=0)) # Normalize data
42+
X /= np.sqrt(np.sum(X**2, axis=0)) # Normalize data
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4444
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4545
print("- benchmarking Lasso")

‎benchmarks/bench_plot_nmf.py

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@@ -162,7 +162,7 @@ def _fit_projected_gradient(X, W, H, tol, max_iter, nls_max_iter, alpha, l1_rati
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proj_grad_W = squared_norm(gradW * np.logical_or(gradW < 0, W > 0))
163163
proj_grad_H = squared_norm(gradH * np.logical_or(gradH < 0, H > 0))
164164

165-
if (proj_grad_W + proj_grad_H) / init_grad < tol ** 2:
165+
if (proj_grad_W + proj_grad_H) / init_grad < tol**2:
166166
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168168
# update W

‎benchmarks/bench_plot_randomized_svd.py

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References
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----------
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(1) Finding structure with randomness: Stochastic algorithms for constructing
54-
approximate matrix decompositions
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Halko, et al., 2009 https://arxiv.org/abs/0909.4061
53+
(1) :arxiv:`"Finding structure with randomness:
54+
Stochastic algorithms for constructing approximate matrix decompositions."
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<0909.4061>`
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Halko, et al., (2009)
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(2) A randomized algorithm for the decomposition of matrices
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Per-Gunnar Martinsson, Vladimir Rokhlin and Mark Tygert

‎benchmarks/bench_random_projections.py

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41+
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return delta.seconds + delta.microseconds / mu_second
4444

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109109
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112-
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default=10**4,
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114114
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115115
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124124
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125125
"--ratio-nonzeros",
126126
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127-
default=10 ** -3,
127+
default=10**-3,
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type=float,
129129
help="Number of features in the benchmarks",
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)

‎benchmarks/bench_rcv1_logreg_convergence.py

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152-
return np.sum(X ** 2, axis=1).max()
152+
return np.sum(X**2, axis=1).max()
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rcv1 = fetch_rcv1()

‎benchmarks/bench_saga.py

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# Lightning predict_proba is not implemented for n_classes > 2
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y_pred = _predict_proba(lr, X)
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score = log_loss(y, y_pred, normalize=False) / n_samples
122-
score += 0.5 * alpha * np.sum(lr.coef_ ** 2) + beta * np.sum(
122+
score += 0.5 * alpha * np.sum(lr.coef_**2) + beta * np.sum(
123123
np.abs(lr.coef_)
124124
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scores.append(score)

‎benchmarks/bench_sample_without_replacement.py

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mu_second = 0.0 + 10**6 # number of microseconds in a second
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‎benchmarks/bench_tree.py

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scikit_classifier_results = []
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‎build_tools/azure/install.sh

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# TODO: Remove !=1.1.0 when the following is fixed:
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# 2): Symbol not found: _svm_check_parameter error
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TO_INSTALL="$TO_INSTALL compilers>=1.0.4,!=1.1.0 llvm-openmp"
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if [[ "$PYTEST_XDIST_VERSION" != "none" ]]; then
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# numpydoc versions
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155145
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python --version

‎build_tools/azure/posix.yml

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PYTEST_VERSION: '6.2.5'
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PYTEST_XDIST_VERSION: 'latest'
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THREADPOOLCTL_VERSION: 'latest'

‎build_tools/circle/build_test_arm.sh

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mamba install --verbose -y numpydoc
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‎doc/about.rst

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<div class="sk-sponsor-div">
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<div class="sk-sponsor-div-box">
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`Zalando SE <https://corporate.zalando.com/en>`_ funds Adrin Jalali since
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August 2020.
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`Hugging Face <https://huggingface.co/>`_ funds Adrin Jalali since 2022.
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:width: 100pt
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:width: 55pt
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:target: https://corporate.zalando.com/en
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:target: https://huggingface.co/
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</div>
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</div>
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............
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`Anaconda, Inc <https://www.anaconda.com/>`_ funded Adrin Jalali in 2019.
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:width: 100pt
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:align: center
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</div>
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</div>
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.. raw:: html

‎doc/computing/parallelism.rst

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These environment variables should be set before importing scikit-learn.
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:SKLEARN_SITE_JOBLIB:
183-
184-
When this environment variable is set to a non zero value,
185-
scikit-learn uses the site joblib rather than its vendored version.
186-
Consequently, joblib must be installed for scikit-learn to run.
187-
Note that using the site joblib is at your own risks: the versions of
188-
scikit-learn and joblib need to be compatible. Currently, joblib 0.11+
189-
is supported. In addition, dumps from joblib.Memory might be incompatible,
190-
and you might loose some caches and have to redownload some datasets.
191-
192-
.. deprecated:: 0.21
193-
194-
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195-
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196-
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199184
Sets the default value for the `assume_finite` argument of

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