8000 Merge branch 'main' into pairwise-distances-argkmin-plug-contd · scikit-learn/scikit-learn@7b90dfa · GitHub
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Merge branch 'main' into pairwise-distances-argkmin-plug-contd
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‎.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-manifest.yml

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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

‎.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

‎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
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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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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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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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COVERAGE: 'true'
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# https://github.com/numpy/numpy/issues/17216

‎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
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approximate matrix decompositions
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Halko, et al., 2009 https://arxiv.org/abs/0909.4061
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(1) :arxiv:`"Finding structure with randomness:
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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

‎build_tools/azure/install.sh

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if [[ "$PYTEST_XDIST_VERSION" != "none" ]]; then
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python -m pip install pytest-xdist
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‎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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‎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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`Anaconda, Inc <https://www.anaconda.com/>`_ funded Adrin Jalali in 2019.
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‎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:
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When this environment variable is set to a non zero value,
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scikit-learn uses the site joblib rather than its vendored version.
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Consequently, joblib must be installed for scikit-learn to run.
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Note that using the site joblib is at your own risks: the versions of
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scikit-learn and joblib need to be compatible. Currently, joblib 0.11+
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is supported. In addition, dumps from joblib.Memory might be incompatible,
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and you might loose some caches and have to redownload some datasets.
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.. deprecated:: 0.21
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