[ { "path": ".binder/postBuild", "content": "#!/bin/bash\n\nset -e\n\n# This script is called in a binder context. When this script is called, we are\n# inside a git checkout of the scikit-learn/scikit-learn repo. This script is\n# generating notebooks from the scikit-learn python examples.\n\nif [[ ! -f /.dockerenv ]]; then\n echo \"This script was written for repo2docker and is supposed to run inside a docker container.\"\n echo \"Exiting because this script can delete data if run outside of a docker container.\"\n exit 1\nfi\n\n# Back up content we need from the scikit-learn repo\nTMP_CONTENT_DIR=/tmp/scikit-learn\nmkdir -p $TMP_CONTENT_DIR\ncp -r examples .binder $TMP_CONTENT_DIR\n# delete everything in current directory including dot files and dot folders\nfind . -delete\n\n# Generate notebooks and remove other files from examples folder\nGENERATED_NOTEBOOKS_DIR=.generated-notebooks\ncp -r $TMP_CONTENT_DIR/examples $GENERATED_NOTEBOOKS_DIR\n\nfind $GENERATED_NOTEBOOKS_DIR -name '*.py' -exec sphx_glr_python_to_jupyter.py '{}' +\nNON_NOTEBOOKS=$(find $GENERATED_NOTEBOOKS_DIR -type f | grep -v '\\.ipynb')\nrm -f $NON_NOTEBOOKS\n\n# Put the .binder folder back (may be useful for debugging purposes)\nmv $TMP_CONTENT_DIR/.binder .\n# Final clean up\nrm -rf $TMP_CONTENT_DIR\n\n# This is for compatibility with binder sphinx-gallery integration: this makes\n# sure that the binder links generated by sphinx-gallery are correct even tough\n# the repo we use for binder (scikit-learn/scikit-learn) is not the repo of the\n# generated doc (scikit-learn/scikit-learn.github.io)\nmkdir notebooks\nln -s ../$GENERATED_NOTEBOOKS_DIR notebooks/auto_examples\n" }, { "path": ".binder/requirements.txt", "content": "--extra-index https://pypi.anaconda.org/scipy-wheels-nightly/simple scikit-learn\n--pre\nmatplotlib\nscikit-image\npandas\nsphinx-gallery\nscikit-learn\n\n" }, { "path": ".circleci/artifact_path", "content": "0/doc/_changed.html\n" }, { "path": ".circleci/config.yml", "content": "version: 2.1\n\njobs:\n doc-min-dependencies:\n docker:\n - image: circleci/python:3.7.7-buster\n environment:\n - OMP_NUM_THREADS: 2\n - MKL_NUM_THREADS: 2\n - CONDA_ENV_NAME: testenv\n - PYTHON_VERSION: 3.7\n - NUMPY_VERSION: 'min'\n - SCIPY_VERSION: 'min'\n - MATPLOTLIB_VERSION: 'min'\n - CYTHON_VERSION: 'min'\n - SCIKIT_IMAGE_VERSION: 'min'\n - SPHINX_VERSION: 'min'\n - PANDAS_VERSION: 'min'\n - SPHINX_GALLERY_VERSION: 'min'\n - NUMPYDOC_VERSION: 'min'\n - SPHINX_PROMPT_VERSION: 'min'\n - SPHINXEXT_OPENGRAPH_VERSION: 'min'\n steps:\n - checkout\n - run: ./build_tools/circle/checkout_merge_commit.sh\n - restore_cache:\n key: v1-datasets-{{ .Branch }}\n - restore_cache:\n keys:\n - doc-min-deps-ccache-{{ .Branch }}\n - doc-min-deps-ccache\n - run: ./build_tools/circle/build_doc.sh\n - save_cache:\n key: doc-min-deps-ccache-{{ .Branch }}-{{ .BuildNum }}\n paths:\n - ~/.ccache\n - ~/.cache/pip\n - save_cache:\n key: v1-datasets-{{ .Branch }}\n paths:\n - ~/scikit_learn_data\n - store_artifacts:\n path: doc/_build/html/stable\n destination: doc\n - store_artifacts:\n path: ~/log.txt\n destination: log.txt\n\n doc:\n docker:\n - image: circleci/python:3.7.7-buster\n environment:\n - OMP_NUM_THREADS: 2\n - MKL_NUM_THREADS: 2\n - CONDA_ENV_NAME: testenv\n - PYTHON_VERSION: 3\n - NUMPY_VERSION: 'latest'\n - SCIPY_VERSION: 'latest'\n - MATPLOTLIB_VERSION: 'latest'\n - CYTHON_VERSION: 'latest'\n - SCIKIT_IMAGE_VERSION: 'latest'\n # Bump the sphinx version from time to time. Avoid latest sphinx version\n # that tends to break things slightly too often\n - SPHINX_VERSION: 4.2.0\n - PANDAS_VERSION: 'latest'\n - SPHINX_GALLERY_VERSION: 'latest'\n - NUMPYDOC_VERSION: 'latest'\n - SPHINX_PROMPT_VERSION: 'latest'\n - SPHINXEXT_OPENGRAPH_VERSION: 'latest'\n steps:\n - checkout\n - run: ./build_tools/circle/checkout_merge_commit.sh\n - restore_cache:\n key: v1-datasets-{{ .Branch }}\n - restore_cache:\n keys:\n - doc-ccache-{{ .Branch }}\n - doc-ccache\n - run: ./build_tools/circle/build_doc.sh\n - save_cache:\n key: doc-ccache-{{ .Branch }}-{{ .BuildNum }}\n paths:\n - ~/.ccache\n - ~/.cache/pip\n - save_cache:\n key: v1-datasets-{{ .Branch }}\n paths:\n - ~/scikit_learn_data\n - store_artifacts:\n path: doc/_build/html/stable\n destination: doc\n - store_artifacts:\n path: ~/log.txt\n destination: log.txt\n # Persists generated documentation so that it can be attached and deployed\n # in the 'deploy' step.\n - persist_to_workspace:\n root: doc/_build/html\n paths: .\n\n lint:\n docker:\n - image: circleci/python:3.7\n steps:\n - checkout\n - run: ./build_tools/circle/checkout_merge_commit.sh\n - run:\n name: dependencies\n command: sudo pip install flake8\n - run:\n name: linting\n command: ./build_tools/circle/linting.sh\n\n linux-arm64:\n machine:\n image: ubuntu-2004:202101-01\n resource_class: arm.medium\n environment:\n # Use the latest supported version of python\n - PYTHON_VERSION: '3.9'\n - OMP_NUM_THREADS: 2\n - OPENBLAS_NUM_THREADS: 2\n - NUMPY_VERSION: 'latest'\n - SCIPY_VERSION: 'latest'\n - CYTHON_VERSION: 'latest'\n - JOBLIB_VERSION: 'latest'\n - THREADPOOLCTL_VERSION: 'latest'\n - PYTEST_VERSION: 'latest'\n - PYTEST_XDIST_VERSION: 'latest'\n - TEST_DOCSTRINGS: 'true'\n steps:\n - checkout\n - run: ./build_tools/circle/checkout_merge_commit.sh\n - restore_cache:\n key: linux-arm64-{{ .Branch }}\n - run: ./build_tools/circle/build_test_arm.sh\n - save_cache:\n key: linux-arm64-{{ .Branch }}\n paths:\n - ~/.cache/ccache\n - ~/.cache/pip\n - ~/scikit_learn_data\n # The source build folder.\n - ~/project/build\n deploy:\n docker:\n - image: circleci/python:3.7\n steps:\n - checkout\n - run: ./build_tools/circle/checkout_merge_commit.sh\n # Attach documentation generated in the 'doc' step so that it can be\n # deployed.\n - attach_workspace:\n at: doc/_build/html\n - run: ls -ltrh doc/_build/html/stable\n - deploy:\n command: |\n if [[ \"${CIRCLE_BRANCH}\" =~ ^main$|^[0-9]+\\.[0-9]+\\.X$ ]]; then\n bash build_tools/circle/push_doc.sh doc/_build/html/stable\n fi\n\nworkflows:\n version: 2\n build-doc-and-deploy:\n jobs:\n - lint\n - doc:\n requires:\n - lint\n - doc-min-dependencies:\n requires:\n - lint\n - deploy:\n requires:\n - doc\n linux-arm64:\n jobs:\n - linux-arm64\n" }, { "path": ".codecov.yml", "content": "comment: false\n\ncoverage:\n status:\n project:\n default:\n # Commits pushed to main should not make the overall\n # project coverage decrease by more than 1%:\n target: auto\n threshold: 1%\n patch:\n default:\n # Be tolerant on slight code coverage diff on PRs to limit\n # noisy red coverage status on github PRs.\n # Note: The coverage stats are still uploaded\n # to codecov so that PR reviewers can see uncovered lines\n target: auto\n threshold: 1%\n\ncodecov:\n notify:\n # Prevent coverage status to upload multiple times for parallel and long\n # running CI pipelines. This configuration is particularly useful on PRs\n # to avoid confusion. Note that this value is set to the number of Azure\n # Pipeline jobs uploading coverage reports.\n after_n_builds: 6\n\nignore:\n- \"sklearn/externals\"\n- \"sklearn/_build_utils\"\n- \"**/setup.py\"\n" }, { "path": ".coveragerc", "content": "[run]\nbranch = True\nsource = sklearn\nparallel = True\nomit =\n */sklearn/externals/*\n */sklearn/_build_utils/*\n */benchmarks/*\n **/setup.py\n" }, { "path": ".git-blame-ignore-revs", "content": "# Since git version 2.23, git-blame has a feature to ignore\n# certain commits.\n#\n# This file contains a list of commits that are not likely what\n# you are looking for in `git blame`. You can set this file as\n# a default ignore file for blame by running the following\n# command.\n#\n# $ git config blame.ignoreRevsFile .git-blame-ignore-revs\n\n# PR 18948: Migrate code style to Black\n82df48934eba1df9a1ed3be98aaace8eada59e6e\n\n# PR 20294: Use target_version >= 3.7 in Black\n351ace7935a4ea685171cc6d174890f08facd561\n\n# PR 20412: Use experimental_string_processing=true in Black\n3ae7c7615343bbd36acece57825d8b0d70fd9da4\n\n# PR 20502: Runs Black on examples\n70a185ae59b4362633d18b0d0083abb1b6f7370c\n" }, { "path": ".gitattributes", "content": "/doc/whats_new/v*.rst merge=union\n" }, { "path": ".github/FUNDING.yml", "content": "# These are supported funding model platforms\n\ngithub: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]\npatreon: # Replace with a single Patreon username\nopen_collective: # Replace with a single Open Collective username\nko_fi: # Replace with a single Ko-fi username\ntidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel\ncommunity_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry\nliberapay: # Replace with a single Liberapay username\nissuehunt: # Replace with a single IssueHunt username\notechie: # Replace with a single Otechie username\ncustom: ['https://numfocus.org/donate-to-scikit-learn']\n" }, { "path": ".github/ISSUE_TEMPLATE/bug_report.yml", "content": "name: Bug Report\ndescription: Create a report to help us reproduce and correct the bug\nlabels: ['Bug: triage']\n\nbody:\n- type: markdown\n attributes:\n value: >\n #### Before submitting a bug, please make sure the issue hasn't been already\n addressed by searching through [the past issues](https://github.com/scikit-learn/scikit-learn/issues).\n- type: textarea\n attributes:\n label: Describe the bug\n description: >\n A clear and concise description of what the bug is.\n validations:\n required: true\n- type: textarea\n attributes:\n label: Steps/Code to Reproduce\n description: |\n Please add a minimal example that we can reproduce the error by running the code. Be as succinct as possible, do not depend on external data. In short, we are going to copy-paste your code and we expect to get the same result as you. Example:\n\n ```python\n from sklearn.feature_extraction.text import CountVectorizer\n from sklearn.decomposition import LatentDirichletAllocation\n docs = [\"Help I have a bug\" for i in range(1000)]\n vectorizer = CountVectorizer(input=docs, analyzer='word')\n lda_features = vectorizer.fit_transform(docs)\n lda_model = LatentDirichletAllocation(\n n_topics=10,\n learning_method='online',\n evaluate_every=10,\n n_jobs=4,\n )\n model = lda_model.fit(lda_features)\n ```\n\n If the code is too long, feel free to put it in a public gist and link it in the issue: https://gist.github.com.\n placeholder: |\n ```\n Sample code to reproduce the problem\n ```\n validations:\n required: true\n- type: textarea\n attributes:\n label: Expected Results\n description: >\n Please paste or describe the expected results.\n placeholder: >\n Example: No error is thrown.\n validations:\n required: true\n- type: textarea\n attributes:\n label: Actual Results\n description: >\n Please paste or describe the results you observe instead of the expected results. If you observe an error, please paste the error message including the **full** traceback of the exception.\n placeholder: >\n Please paste or specifically describe the actual output or traceback.\n validations:\n required: true\n- type: textarea\n attributes:\n label: Versions\n description: |\n Please run the following and paste the output below.\n ```python\n import sklearn; sklearn.show_versions()\n ```\n validations:\n required: true\n- type: markdown\n attributes:\n value: >\n Thanks for contributing 🎉!\n" }, { "path": ".github/ISSUE_TEMPLATE/config.yml", "content": "blank_issues_enabled: true\ncontact_links:\n - name: Discussions\n url: https://github.com/scikit-learn/scikit-learn/discussions/new\n about: Ask questions and discuss with other scikit-learn community members\n - name: Stack Overflow\n url: https://stackoverflow.com/questions/tagged/scikit-learn\n about: Please ask and answer usage questions on Stack Overflow\n - name: Mailing list\n url: https://mail.python.org/mailman/listinfo/scikit-learn\n about: General discussions and announcements on the mailing list\n - name: Gitter\n url: https://gitter.im/scikit-learn/scikit-learn\n about: Users and developers can sometimes be found on the gitter channel\n - name: Blank issue\n url: https://github.com/scikit-learn/scikit-learn/issues/new\n about: Please note that Github Discussions should be used in most cases instead\n" }, { "path": ".github/ISSUE_TEMPLATE/doc_improvement.yml", "content": "name: Documentation improvement\ndescription: Create a report to help us improve the documentation. Alternatively you can just open a pull request with the suggested change.\nlabels: [Documentation]\n\nbody:\n- type: textarea\n attributes:\n label: Describe the issue linked to the documentation\n description: >\n Tell us about the confusion introduced in the documentation.\n validations:\n required: true\n- type: textarea\n attributes:\n label: Suggest a potential alternative/fix\n description: >\n Tell us how we could improve the documentation in this regard.\n" }, { "path": ".github/ISSUE_TEMPLATE/feature_request.yml", "content": "name: Feature request\ndescription: Suggest a new algorithm, enhancement to an existing algorithm, etc.\nlabels: ['New Feature']\n\nbody:\n- type: markdown\n attributes:\n value: >\n #### If you want to propose a new algorithm, please refer first to the [scikit-learn inclusion criterion](https://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms).\n- type: textarea\n attributes:\n label: Describe the workflow you want to enable\n validations:\n required: true\n- type: textarea\n attributes:\n label: Describe your proposed solution\n validations:\n required: true\n- type: textarea\n attributes:\n label: Describe alternatives you've considered, if relevant\n- type: textarea\n attributes:\n label: Additional context\n" }, { "path": ".github/PULL_REQUEST_TEMPLATE.md", "content": "\n\n#### Reference Issues/PRs\n\n\n\n#### What does this implement/fix? Explain your changes.\n\n\n#### Any other comments?\n\n\n\n" }, { "path": ".github/labeler-file-extensions.yml", "content": "cython:\n- sklearn/**/*.pyx\n- sklearn/**/*.pxd\n- sklearn/**/*.pxi\n# Tempita templates\n- sklearn/**/*.pyx.tp\n- sklearn/**/*.pxd.tp\n- sklearn/**/*.pxi.tp\n" }, { "path": ".github/labeler-module.yml", "content": "module:cluster:\n- sklearn/cluster/**/*\n\nmodule:common:\n- sklearn/common/**/*\n\nmodule:compose:\n- sklearn/compose/**/*\n\nmodule:covariance:\n- sklearn/covariance/**/*\n\nmodule:cross_decomposition:\n- sklearn/cross_decomposition/**/*\n\nmodule:datasets:\n- sklearn/datasets/**/*\n\nmodule:decomposition:\n- sklearn/decomposition/**/*\n\nmodule:ensemble:\n- sklearn/ensemble/**/*\n\nmodule:feature_extraction:\n- sklearn/feature_extraction/**/*\n\nmodule:feature_selection:\n- sklearn/feature_selection/**/*\n\nmodule:gaussian_process:\n- sklearn/gaussian_process/**/*\n\nmodule:impute:\n- sklearn/impute/**/*\n\nmodule:inspection:\n- sklearn/inspection/**/*\n\nmodule:linear_model:\n- sklearn/linear_model/**/*\n\nmodule:manifold:\n- sklearn/manifold/**/*\n\nmodule:metrics:\n- sklearn/metrics/**/*\n\nmodule:mixture:\n- sklearn/mixture/**/*\n\nmodule:model_selection:\n- sklearn/model_selection/**/*\n\nmodule:naive_bayes:\n- sklearn/naive_bayes.py\n\nmodule:neighbors:\n- sklearn/neighbors/**/*\n\nmodule:neural_network:\n- sklearn/neural_network/**/*\n\nmodule:pipeline:\n- sklearn/pipeline.py\n\nmodule:preprocessing:\n- sklearn/preprocessing/**/*\n\nmodule:semi_supervised:\n- sklearn/semi_supervised/**/*\n\nmodule:svm:\n- sklearn/svm/**/*\n\nmodule:tree:\n- sklearn/tree/**/*\n\nmodule:utils:\n- sklearn/utils/**/*\n" }, { "path": ".github/scripts/label_title_regex.py", "content": "\"\"\"Labels PRs based on title. Must be run in a github action with the\npull_request_target event.\"\"\"\nfrom github import Github\nimport os\nimport json\nimport re\n\ncontext_dict = json.loads(os.getenv(\"CONTEXT_GITHUB\"))\n\nrepo = context_dict[\"repository\"]\ng = Github(context_dict[\"token\"])\nrepo = g.get_repo(repo)\npr_number = context_dict[\"event\"][\"number\"]\nissue = repo.get_issue(number=pr_number)\ntitle = issue.title\n\n\nregex_to_labels = [(r\"\\bDOC\\b\", \"Documentation\"), (r\"\\bCI\\b\", \"Build / CI\")]\n\nlabels_to_add = [label for regex, label in regex_to_labels if re.search(regex, title)]\n\nif labels_to_add:\n issue.add_to_labels(*labels_to_add)\n" }, { "path": ".github/workflows/assign.yml", "content": "\nname: Assign\non:\n issue_comment:\n types: created\n\njobs:\n one:\n runs-on: ubuntu-latest\n if: >-\n (github.event.comment.body == 'take' ||\n github.event.comment.body == 'Take')\n && !github.event.issue.assignee\n steps:\n - run: |\n echo \"Assigning issue ${{ github.event.issue.number }} to ${{ github.event.comment.user.login }}\"\n curl -H \"Authorization: token ${{ secrets.GITHUB_TOKEN }}\" -d '{\"assignees\": [\"${{ github.event.comment.user.login }}\"]}' https://api.github.com/repos/${{ github.repository }}/issues/${{ github.event.issue.number }}/assignees\n curl -H \"Authorization: token ${{ secrets.GITHUB_TOKEN }}\" -X \"DELETE\" https://api.github.com/repos/${{ github.repository }}/issues/${{ github.event.issue.number }}/labels/help%20wanted\n" }, { "path": ".github/workflows/check-changelog.yml", "content": "name: Check Changelog\n# This check makes sure that the changelog is properly updated\n# when a PR introduces a change in a test file.\n# To bypass this check, label the PR with \"No Changelog Needed\".\non:\n pull_request:\n types: [opened, edited, labeled, unlabeled, synchronize]\n\njobs:\n check:\n runs-on: ubuntu-latest\n if: ${{ contains(github.event.pull_request.labels.*.name, 'No Changelog Needed') == 0 }}\n steps:\n - name: Get PR number and milestone\n run: |\n echo \"PR_NUMBER=${{ github.event.pull_request.number }}\" >> $GITHUB_ENV\n echo \"TAGGED_MILESTONE=${{ github.event.pull_request.milestone.title }}\" >> $GITHUB_ENV\n - uses: actions/checkout@v2\n with:\n fetch-depth: '0'\n - name: Check the changelog\n run: |\n set -xe\n changed_files=$(git diff --name-only origin/main)\n # Changelog should be updated only if tests have been modified\n if [[ ! \"$changed_files\" =~ tests ]]\n then\n exit 0\n fi\n all_changelogs=$(cat ./doc/whats_new/v*.rst)\n if [[ \"$all_changelogs\" =~ :pr:\\`$PR_NUMBER\\` ]]\n then\n echo \"Changelog has been updated.\"\n # If the pull request is milestoned check the correspondent changelog\n if exist -f ./doc/whats_new/v${TAGGED_MILESTONE:0:4}.rst\n then\n expected_changelog=$(cat ./doc/whats_new/v${TAGGED_MILESTONE:0:4}.rst)\n if [[ \"$expected_changelog\" =~ :pr:\\`$PR_NUMBER\\` ]]\n then\n echo \"Changelog and milestone correspond.\"\n else\n echo \"Changelog and milestone do not correspond.\"\n echo \"If you see this error make sure that the tagged milestone for the PR\"\n echo \"and the edited changelog filename properly match.\"\n exit 1\n fi\n fi\n else\n echo \"A Changelog entry is missing.\"\n echo \"\"\n echo \"Please add an entry to the changelog at 'doc/whats_new/v*.rst'\"\n echo \"to document your change assuming that the PR will be merged\"\n echo \"in time for the next release of scikit-learn.\"\n echo \"\"\n echo \"Look at other entries in that file for inspiration and please\"\n echo \"reference this pull request using the ':pr:' directive and\"\n echo \"credit yourself (and other contributors if applicable) with\"\n echo \"the ':user:' directive.\"\n echo \"\"\n echo \"If you see this error and there is already a changelog entry,\"\n echo \"check that the PR number is correct.\"\n echo \"\"\n echo\" If you believe that this PR does no warrant a changelog\"\n echo \"entry, say so in a comment so that a maintainer will label \"\n echo \"the PR with 'No Changelog Needed' to bypass this check.\"\n exit 1\n fi\n" }, { "path": ".github/workflows/check-manifest.yml", "content": "name: \"Check Manifest\"\n\non:\n schedule:\n - cron: '0 0 * * *'\n\njobs:\n check:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v2\n - uses: actions/setup-python@v2\n with:\n python-version: '3.9'\n - name: Install dependencies\n # scipy and cython are required to build sdist\n run: |\n python -m pip install --upgrade pip\n pip install check-manifest scipy cython\n - run: |\n check-manifest -v\n" }, { "path": ".github/workflows/labeler-module.yml", "content": "name: \"Pull Request Labeler\"\non: pull_request_target\n\njobs:\n triage:\n runs-on: ubuntu-latest\n steps:\n - uses: thomasjpfan/labeler@v2.5.0\n continue-on-error: true\n if: github.repository == 'scikit-learn/scikit-learn'\n with:\n repo-token: \"${{ secrets.GITHUB_TOKEN }}\"\n max-labels: \"3\"\n configuration-path: \".github/labeler-module.yml\"\n\n triage_file_extensions:\n runs-on: ubuntu-latest\n steps:\n - uses: thomasjpfan/labeler@v2.5.0\n continue-on-error: true\n if: github.repository == 'scikit-learn/scikit-learn'\n with:\n repo-token: \"${{ secrets.GITHUB_TOKEN }}\"\n configuration-path: \".github/labeler-file-extensions.yml\"" }, { "path": ".github/workflows/labeler-title-regex.yml", "content": "name: Pull Request Regex Title Labeler\non:\n pull_request_target:\n types: [opened, edited]\n\npermissions:\n contents: read\n pull-requests: write\n\njobs:\n\n labeler:\n runs-on: ubuntu-20.04\n steps:\n - uses: actions/checkout@v2\n - uses: actions/setup-python@v2\n with:\n python-version: '3.9'\n - name: Install PyGithub\n run: pip install -Uq PyGithub\n - name: Label pull request\n run: python .github/scripts/label_title_regex.py\n env:\n CONTEXT_GITHUB: ${{ toJson(github) }}\n" }, { "path": ".github/workflows/publish_pypi.yml", "content": "name: Publish to Pypi\non:\n workflow_dispatch:\n inputs:\n version:\n description: 'Version upload to pypi'\n required: true\n pypi_repo:\n description: 'Repo to upload to (testpypi or pypi)'\n default: 'testpypi'\n required: true\n\njobs:\n publish:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v2\n - uses: actions/setup-python@v2\n with:\n python-version: '3.8'\n - name: Install dependencies\n run: |\n pip install -U wheelhouse_uploader pyyaml\n - name: Downloading wheels and sdist from staging\n env:\n SKLEARN_VERSION: ${{ github.event.inputs.version }}\n run: |\n echo \"Download $SKLEARN_VERSION wheels and sdist\"\n python -m wheelhouse_uploader fetch \\\n --version $SKLEARN_VERSION \\\n --local-folder dist/ \\\n scikit-learn \\\n https://pypi.anaconda.org/scikit-learn-wheels-staging/simple/scikit-learn/\n - name: Check dist has the correct number of artifacts\n run: |\n python build_tools/github/check_wheels.py\n - name: Publish package to TestPyPI\n uses: pypa/gh-action-pypi-publish@v1.4.1\n with:\n user: __token__\n password: ${{ secrets.TEST_PYPI_TOKEN }}\n repository_url: https://test.pypi.org/legacy/\n if: ${{ github.event.inputs.pypi_repo == 'testpypi' }}\n - name: Publish package to PyPI\n uses: pypa/gh-action-pypi-publish@v1.4.1\n with:\n user: __token__\n password: ${{ secrets.PYPI_TOKEN }}\n if: ${{ github.event.inputs.pypi_repo == 'pypi' }}\n" }, { "path": ".github/workflows/twitter.yml", "content": "# Tweet the URL of a commit on @sklearn_commits whenever a push event\n# happens on the main branch\nname: Twitter Push Notification\n\n\non:\n push:\n branches:\n - main\n\n\njobs:\n tweet:\n name: Twitter Notification\n runs-on: ubuntu-latest\n steps:\n - name: Tweet URL of last commit as @sklearn_commits\n if: github.repository == 'scikit-learn/scikit-learn'\n uses: docker://thomasjpfan/twitter-action:0.3\n with:\n args: \"-message \\\"https://github.com/scikit-learn/scikit-learn/commit/${{ github.sha }}\\\"\"\n env:\n TWITTER_CONSUMER_KEY: ${{ secrets.TWITTER_CONSUMER_KEY }}\n TWITTER_CONSUMER_SECRET: ${{ secrets.TWITTER_CONSUMER_SECRET }}\n TWITTER_ACCESS_TOKEN: ${{ secrets.TWITTER_ACCESS_TOKEN }}\n TWITTER_ACCESS_SECRET: ${{ secrets.TWITTER_ACCESS_SECRET }}\n" }, { "path": ".github/workflows/unassign.yml", "content": "name: Unassign\n#Runs when a contributor has unassigned themselves from the issue and adds 'help wanted'\non:\n issues:\n types: unassigned\n\njobs:\n one:\n runs-on: ubuntu-latest\n steps:\n - name:\n if: github.event.issue.state == 'open'\n run: |\n echo \"Marking issue ${{ github.event.issue.number }} as help wanted\"\n curl -H \"Authorization: token ${{ secrets.GITHUB_TOKEN }}\" -d '{\"labels\": [\"help wanted\"]}' https://api.github.com/repos/${{ github.repository }}/issues/${{ github.event.issue.number }}/labels\n" }, { "path": ".github/workflows/wheels.yml", "content": "# Workflow to build and test wheels\nname: Wheel builder\n\non:\n schedule:\n # Nightly build at 3:42 A.M.\n - cron: \"42 3 */1 * *\"\n push:\n branches:\n - main\n # Release branches\n - \"[0-9]+.[0-9]+.X\"\n pull_request:\n branches:\n - main\n - \"[0-9]+.[0-9]+.X\"\n # Manual run\n workflow_dispatch:\n\njobs:\n # Check whether to build the wheels and the source tarball\n check_build_trigger:\n name: Check build trigger\n runs-on: ubuntu-latest\n if: github.repository == 'scikit-learn/scikit-learn'\n outputs:\n build: ${{ steps.check_build_trigger.outputs.build }}\n\n steps:\n - name: Checkout scikit-learn\n uses: actions/checkout@v2\n with:\n ref: ${{ github.event.pull_request.head.sha }}\n\n - id: check_build_trigger\n name: Check build trigger\n run: bash build_tools/github/check_build_trigger.sh\n\n # Build the wheels for Linux, Windows and macOS for Python 3.7 and newer\n build_wheels:\n name: Build wheel for cp${{ matrix.python }}-${{ matrix.platform_id }}-${{ matrix.manylinux_image }}\n runs-on: ${{ matrix.os }}\n needs: check_build_trigger\n if: needs.check_build_trigger.outputs.build\n\n strategy:\n # Ensure that a wheel builder finishes even if another fails\n fail-fast: false\n matrix:\n os: [windows-latest, ubuntu-latest, macos-latest]\n python: [37, 38, 39]\n bitness: [32, 64]\n manylinux_image: [manylinux1, manylinux2010]\n include:\n # Run 32 and 64 bit version in parallel for Linux and Windows\n - os: windows-latest\n bitness: 64\n platform_id: win_amd64\n - os: windows-latest\n bitness: 32\n platform_id: win32\n - os: ubuntu-latest\n bitness: 64\n platform_id: manylinux_x86_64\n - os: ubuntu-latest\n bitness: 32\n platform_id: manylinux_i686\n - os: macos-latest\n bitness: 64\n platform_id: macosx_x86_64\n exclude:\n - os: macos-latest\n bitness: 32\n # Remove manylinux1 from the windows and osx build matrix since\n # manylinux_image is not used for these platforms\n - os: windows-latest\n manylinux_image: manylinux1\n - os: macos-latest\n manylinux_image: manylinux1\n\n steps:\n - name: Checkout scikit-learn\n uses: actions/checkout@v1\n\n - name: Setup Python\n uses: actions/setup-python@v2\n with:\n python-version: '3.9' # update once build dependencies are available\n\n - name: Build and test wheels\n env:\n CONFTEST_PATH: ${{ github.workspace }}/conftest.py\n CONFTEST_NAME: conftest.py\n CIBW_ENVIRONMENT: OMP_NUM_THREADS=2\n OPENBLAS_NUM_THREADS=2\n SKLEARN_SKIP_NETWORK_TESTS=1\n SKLEARN_BUILD_PARALLEL=3\n MACOSX_DEPLOYMENT_TARGET=10.13\n CIBW_BUILD: cp${{ matrix.python }}-${{ matrix.platform_id }}\n CIBW_MANYLINUX_X86_64_IMAGE: ${{ matrix.manylinux_image }}\n CIBW_MANYLINUX_I686_IMAGE: ${{ matrix.manylinux_image }}\n CIBW_REPAIR_WHEEL_COMMAND_WINDOWS: bash build_tools/github/repair_windows_wheels.sh {wheel} {dest_dir} ${{ matrix.bitness }}\n CIBW_BEFORE_TEST_WINDOWS: bash build_tools/github/build_minimal_windows_image.sh ${{ matrix.python }} ${{ matrix.bitness }}\n CIBW_TEST_REQUIRES: pytest pandas threadpoolctl\n CIBW_TEST_COMMAND: bash {project}/build_tools/github/test_wheels.sh\n CIBW_TEST_COMMAND_WINDOWS: bash {project}/build_tools/github/test_windows_wheels.sh ${{ matrix.python }} ${{ matrix.bitness }}\n CIBW_BUILD_VERBOSITY: 1\n\n run: bash build_tools/github/build_wheels.sh\n\n - name: Store artifacts\n uses: actions/upload-artifact@v2\n with:\n path: wheelhouse/*.whl\n\n # Build the source distribution under Linux\n build_sdist:\n name: Source distribution\n runs-on: ubuntu-latest\n needs: check_build_trigger\n if: needs.check_build_trigger.outputs.build\n\n steps:\n - name: Checkout scikit-learn\n uses: actions/checkout@v1\n\n - name: Setup Python\n uses: actions/setup-python@v2\n with:\n python-version: '3.9' # update once build dependencies are available\n\n - name: Build source distribution\n run: bash build_tools/github/build_source.sh\n env:\n SKLEARN_BUILD_PARALLEL: 3\n\n - name: Test source distribution\n run: bash build_tools/github/test_source.sh\n env:\n OMP_NUM_THREADS: 2\n OPENBLAS_NUM_THREADS: 2\n SKLEARN_SKIP_NETWORK_TESTS: 1\n\n - name: Store artifacts\n uses: actions/upload-artifact@v2\n with:\n path: dist/*.tar.gz\n\n # Upload the wheels and the source distribution\n upload_anaconda:\n name: Upload to Anaconda\n runs-on: ubuntu-latest\n needs: [build_wheels, build_sdist]\n # The artifacts cannot be uploaded on PRs\n if: github.event_name != 'pull_request'\n\n steps:\n - name: Checkout scikit-learn\n uses: actions/checkout@v1\n\n - name: Download artifacts\n uses: actions/download-artifact@v2\n with:\n path: dist\n\n - name: Setup Python\n uses: actions/setup-python@v2\n\n - name: Upload artifacts\n env:\n # Secret variables need to be mapped to environment variables explicitly\n SCIKIT_LEARN_NIGHTLY_UPLOAD_TOKEN: ${{ secrets.SCIKIT_LEARN_NIGHTLY_UPLOAD_TOKEN }}\n SCIKIT_LEARN_STAGING_UPLOAD_TOKEN: ${{ secrets.SCIKIT_LEARN_STAGING_UPLOAD_TOKEN }}\n # Force a replacement if the remote file already exists\n run: bash build_tools/github/upload_anaconda.sh\n" }, { "path": ".gitignore", "content": "*.pyc\n*.so\n*.pyd\n*~\n.#*\n*.lprof\n*.swp\n*.swo\n.DS_Store\nbuild\nsklearn/datasets/__config__.py\nsklearn/**/*.html\n\ndist/\nMANIFEST\ndoc/_build/\ndoc/auto_examples/\ndoc/modules/generated/\ndoc/datasets/generated/\ndoc/min_dependency_table.rst\ndoc/min_dependency_substitutions.rst\n*.pdf\npip-log.txt\nscikit_learn.egg-info/\n.coverage\ncoverage\n*.py,cover\n.tags*\ntags\ncovtype.data.gz\n20news-18828/\n20news-18828.tar.gz\ncoverages.zip\nsamples.zip\ndoc/coverages.zip\ndoc/samples.zip\ncoverages\nsamples\ndoc/coverages\ndoc/samples\n*.prof\n.tox/\n.coverage\npip-wheel-metadata\n\nlfw_preprocessed/\nnips2010_pdf/\n\n*.nt.bz2\n*.tar.gz\n*.tgz\n\nexamples/cluster/joblib\nreuters/\nbenchmarks/bench_covertype_data/\n\n*.prefs\n.pydevproject\n.idea\n.vscode\n\n*.c\n*.cpp\n\n!/**/src/**/*.c\n!/**/src/**/*.cpp\n*.sln\n*.pyproj\n\n# Used by py.test\n.cache\n.pytest_cache/\n_configtest.o.d\n\n# Used by mypy\n.mypy_cache/\n\n# files generated from a template\nsklearn/utils/_seq_dataset.pyx\nsklearn/utils/_seq_dataset.pxd\nsklearn/utils/_weight_vector.pyx\nsklearn/utils/_weight_vector.pxd\nsklearn/linear_model/_sag_fast.pyx\n" }, { "path": ".mailmap", "content": "Alexandre Gramfort \nAlexandre Gramfort \nAlexandre Gramfort \nAlexandre Saint \nAndreas Mueller \nAndreas Mueller \nAndreas Mueller \nAndreas Mueller \nAndreas Mueller \nAndreas Mueller \nArnaud Joly \nArnaud Joly \nArnaud Joly \nAnne-Laure Fouque \nAriel Rokem arokem \nBala Subrahmanyam Varanasi \nBertrand Thirion \nBrandyn A. White \nBrian Cheung \nBrian Cheung \nBrian Cheung \nBrian Holt \nChristian Osendorfer \nClay Woolam \nDanny Sullivan \nDenis Engemann \nDenis Engemann \nDenis Engemann \nDenis Engemann dengemann \nDiego Molla \nDraXus draxus \nEdouard DUCHESNAY \nEdouard DUCHESNAY \nEdouard DUCHESNAY \nEmmanuelle Gouillart \nEmmanuelle Gouillart \nEustache Diemert \nFabian Pedregosa \nFabian Pedregosa \nFabian Pedregosa \nFederico Vaggi \nFederico Vaggi \nGael Varoquaux \nGael Varoquaux \nGael Varoquaux \nGiorgio Patrini \nGiorgio Patrini \nGilles Louppe \nHamzeh Alsalhi <93hamsal@gmail.com>\nHarikrishnan S \nHendrik Heuer \nHenry Lin \nHrishikesh Huilgolkar \nHugo Bowne-Anderson \nImaculate \nImmanuel Bayer \nJacob Schreiber \nJacob Schreiber \nJake VanderPlas \nJake VanderPlas \nJake VanderPlas \nJames Bergstra \nJaques Grobler \nJan Schlüter \nJean Kossaifi \nJean Kossaifi \nJean Kossaifi \nJoel Nothman \nKyle Kastner \nLars Buitinck \nLars Buitinck \nLars Buitinck \nLars Buitinck \nLars Buitinck \nLoic Esteve \nManoj Kumar \nMatthieu Perrot \nMaheshakya Wijewardena \nMichael Bommarito \nMichael Eickenberg \nMichael Eickenberg \nSamuel Charron \nSergio Medina \nNelle Varoquaux \nNelle Varoquaux \nNelle Varoquaux \nNicolas Goix \nNicolas Pinto \nNoel Dawe \nNoel Dawe \nOlivier Grisel \nOlivier Grisel \nOlivier Hervieu \nPaul Butler \nPeter Prettenhofer \nRaghav RV \nRaghav RV \nRobert Layton \nRoman Sinayev \nRoman Sinayev \nRonald Phlypo \nSatrajit Ghosh \nSebastian Raschka \nSebastian Raschka \nShiqiao Du \nShiqiao Du \nThomas Unterthiner \nTim Sheerman-Chase \nVincent Dubourg \nVincent Dubourg \nVincent Michel \nVincent Michel \nVincent Michel \nVincent Michel \nVincent Michel \nVincent Schut \nVirgile Fritsch \nVirgile Fritsch \nVlad Niculae \nWei Li \nWei Li \nX006 \nXinfan Meng \nYannick Schwartz \nYannick Schwartz \nYannick Schwartz \n" }, { "path": ".pre-commit-config.yaml", "content": "repos:\n- repo: https://github.com/pre-commit/pre-commit-hooks\n rev: v2.3.0\n hooks:\n - id: check-yaml\n - id: end-of-file-fixer\n - id: trailing-whitespace\n- repo: https://github.com/psf/black\n rev: 21.6b0\n hooks:\n - id: black\n- repo: https://gitlab.com/pycqa/flake8\n rev: 3.9.2\n hooks:\n - id: flake8\n types: [file, python]\n- repo: https://github.com/pre-commit/mirrors-mypy\n rev: v0.782\n hooks:\n - id: mypy\n files: sklearn/\n additional_dependencies: [pytest==6.2.4]\n" }, { "path": ".travis.yml", "content": "# Make it explicit that we favor the\n# new container-based Travis workers\nlanguage: python\ndist: xenial\n\ncache:\n apt: true\n directories:\n - $HOME/.cache/pip\n - $HOME/.ccache\n\nenv:\n global:\n - CPU_COUNT=3\n - TEST_DIR=/tmp/sklearn # Test directory for continuous integration jobs\n - PYTEST_VERSION=latest\n - OMP_NUM_THREADS=2\n - OPENBLAS_NUM_THREADS=2\n - SKLEARN_BUILD_PARALLEL=3\n - SKLEARN_SKIP_NETWORK_TESTS=1\n - PYTHONUNBUFFERED=1\n # Custom environment variables for the ARM wheel builder\n - CIBW_BUILD_VERBOSITY=1\n - CIBW_TEST_COMMAND=\"bash {project}/build_tools/travis/test_wheels.sh\"\n - CIBW_ENVIRONMENT=\"CPU_COUNT=2\n OMP_NUM_THREADS=2\n OPENBLAS_NUM_THREADS=2\n SKLEARN_BUILD_PARALLEL=10\n SKLEARN_SKIP_NETWORK_TESTS=1\n PYTHONUNBUFFERED=1\"\n\njobs:\n include:\n # Linux environments to build the scikit-learn wheels for the ARM64\n # architecture and Python 3.7 and newer. This is used both at release time\n # with the manual trigger in the commit message in the release branch and as\n # a scheduled task to build the weekly dev build on the main branch. The\n # weekly frequency is meant to avoid depleting the Travis CI credits too\n # fast.\n - python: 3.7\n os: linux\n arch: arm64-graviton2\n dist: focal\n virt: lxd\n group: edge\n if: type = cron or commit_message =~ /\\[cd build\\]/\n env:\n - BUILD_WHEEL=true\n - CIBW_BUILD=cp37-manylinux_aarch64\n\n - python: 3.8\n os: linux\n arch: arm64-graviton2\n dist: focal\n virt: lxd\n group: edge\n if: type = cron or commit_message =~ /\\[cd build\\]/\n env:\n - BUILD_WHEEL=true\n - CIBW_BUILD=cp38-manylinux_aarch64\n\n - python: 3.9\n os: linux\n arch: arm64-graviton2\n dist: focal\n virt: lxd\n group: edge\n if: type = cron or commit_message =~ /\\[cd build\\]/\n env:\n - BUILD_WHEEL=true\n - CIBW_BUILD=cp39-manylinux_aarch64\n\ninstall: source build_tools/travis/install.sh || travis_terminate 1\nscript: source build_tools/travis/script.sh || travis_terminate 1\nafter_success: source build_tools/travis/after_success.sh || travis_terminate 1\n\nnotifications:\n webhooks:\n urls:\n - https://webhooks.gitter.im/e/4ffabb4df010b70cd624\n on_success: change\n on_failure: always\n on_start: never\n" }, { "path": "CODE_OF_CONDUCT.md", "content": "# Code of Conduct\n\nWe are a community based on openness, as well as friendly and didactic discussions.\n\nWe aspire to treat everybody equally, and value their contributions.\n\nDecisions are made based on technical merit and consensus.\n\nCode is not the only way to help the project. Reviewing pull requests,\nanswering questions to help others on mailing lists or issues, organizing and\nteaching tutorials, working on the website, improving the documentation, are\nall priceless contributions.\n\nWe abide by the principles of openness, respect, and consideration of others of\nthe Python Software Foundation: https://www.python.org/psf/codeofconduct/\n\n" }, { "path": "CONTRIBUTING.md", "content": "\nContributing to scikit-learn\n============================\n\nThe latest contributing guide is available in the repository at\n`doc/developers/contributing.rst`, or online at:\n\nhttps://scikit-learn.org/dev/developers/contributing.html\n\nThere are many ways to contribute to scikit-learn, with the most common ones\nbeing contribution of code or documentation to the project. Improving the\ndocumentation is no less important than improving the library itself. If you\nfind a typo in the documentation, or have made improvements, do not hesitate to\nsend an email to the mailing list or preferably submit a GitHub pull request.\nDocumentation can be found under the\n[doc/](https://github.com/scikit-learn/scikit-learn/tree/main/doc) directory.\n\nBut there are many other ways to help. In particular answering queries on the\n[issue tracker](https://github.com/scikit-learn/scikit-learn/issues),\ninvestigating bugs, and [reviewing other developers' pull\nrequests](http://scikit-learn.org/dev/developers/contributing.html#code-review-guidelines)\nare very valuable contributions that decrease the burden on the project\nmaintainers.\n\nAnother way to contribute is to report issues you're facing, and give a \"thumbs\nup\" on issues that others reported and that are relevant to you. It also helps\nus if you spread the word: reference the project from your blog and articles,\nlink to it from your website, or simply star it in GitHub to say \"I use it\".\n\nQuick links\n-----------\n\n* [Submitting a bug report or feature request](http://scikit-learn.org/dev/developers/contributing.html#submitting-a-bug-report-or-a-feature-request)\n* [Contributing code](http://scikit-learn.org/dev/developers/contributing.html#contributing-code)\n* [Coding guidelines](https://scikit-learn.org/dev/developers/develop.html#coding-guidelines)\n* [Tips to read current code](https://scikit-learn.org/dev/developers/contributing.html#reading-the-existing-code-base)\n\nCode of Conduct\n---------------\n\nWe abide by the principles of openness, respect, and consideration of others\nof the Python Software Foundation: https://www.python.org/psf/codeofconduct/.\n" }, { "path": "COPYING", "content": "BSD 3-Clause License\n\nCopyright (c) 2007-2021 The scikit-learn developers.\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n\n* Neither the name of the copyright holder nor the names of its\n contributors may be used to endorse or promote products derived from\n this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n" }, { "path": "MANIFEST.in", "content": "include *.rst\nrecursive-include doc *\nrecursive-include examples *\nrecursive-include sklearn *.c *.h *.pyx *.pxd *.pxi *.tp\nrecursive-include sklearn/datasets *.csv *.csv.gz *.rst *.jpg *.txt *.arff.gz *.json.gz\ninclude COPYING\ninclude README.rst\ninclude pyproject.toml\ninclude sklearn/externals/README\ninclude sklearn/svm/src/liblinear/COPYRIGHT\ninclude sklearn/svm/src/libsvm/LIBSVM_CHANGES\ninclude conftest.py\ninclude Makefile\ninclude MANIFEST.in\ninclude .coveragerc\n\n# exclude from sdist\nrecursive-exclude asv_benchmarks *\nrecursive-exclude benchmarks *\nrecursive-exclude build_tools *\nrecursive-exclude maint_tools *\nrecursive-exclude benchmarks *\nrecursive-exclude .binder *\nrecursive-exclude .circleci *\nexclude .codecov.yml\nexclude .git-blame-ignore-revs\nexclude .mailmap\nexclude .pre-commit-config.yaml\nexclude azure-pipelines.yml\nexclude lgtm.yml\nexclude CODE_OF_CONDUCT.md\nexclude CONTRIBUTING.md\nexclude PULL_REQUEST_TEMPLATE.md\n" }, { "path": "Makefile", "content": "# simple makefile to simplify repetitive build env management tasks under posix\n\n# caution: testing won't work on windows, see README\n\nPYTHON ?= python\nCYTHON ?= cython\nPYTEST ?= pytest\nCTAGS ?= ctags\n\n# skip doctests on 32bit python\nBITS := $(shell python -c 'import struct; print(8 * struct.calcsize(\"P\"))')\n\nall: clean inplace test\n\nclean-ctags:\n\trm -f tags\n\nclean: clean-ctags\n\t$(PYTHON) setup.py clean\n\trm -rf dist\n\nin: inplace # just a shortcut\ninplace:\n\t$(PYTHON) setup.py build_ext -i\n\ntest-code: in\n\t$(PYTEST) --showlocals -v sklearn --durations=20\ntest-sphinxext:\n\t$(PYTEST) --showlocals -v doc/sphinxext/\ntest-doc:\nifeq ($(BITS),64)\n\t$(PYTEST) $(shell find doc -name '*.rst' | sort)\nendif\ntest-code-parallel: in\n\t$(PYTEST) -n auto --showlocals -v sklearn --durations=20\n\ntest-coverage:\n\trm -rf coverage .coverage\n\t$(PYTEST) sklearn --showlocals -v --cov=sklearn --cov-report=html:coverage\ntest-coverage-parallel:\n\trm -rf coverage .coverage .coverage.*\n\t$(PYTEST) sklearn -n auto --showlocals -v --cov=sklearn --cov-report=html:coverage\n\ntest: test-code test-sphinxext test-doc\n\ntrailing-spaces:\n\tfind sklearn -name \"*.py\" -exec perl -pi -e 's/[ \\t]*$$//' {} \\;\n\ncython:\n\tpython setup.py build_src\n\nctags:\n\t# make tags for symbol based navigation in emacs and vim\n\t# Install with: sudo apt-get install exuberant-ctags\n\t$(CTAGS) --python-kinds=-i -R sklearn\n\ndoc: inplace\n\t$(MAKE) -C doc html\n\ndoc-noplot: inplace\n\t$(MAKE) -C doc html-noplot\n\ncode-analysis:\n\tflake8 sklearn | grep -v __init__ | grep -v external\n\tpylint -E -i y sklearn/ -d E1103,E0611,E1101\n\nflake8-diff:\n\tgit diff upstream/main -u -- \"*.py\" | flake8 --diff\n" }, { "path": "README.rst", "content": ".. -*- mode: rst -*-\n\n|Azure|_ |Travis|_ |Codecov|_ |CircleCI|_ |Nightly wheels|_ |Black|_ |PythonVersion|_ |PyPi|_ |DOI|_\n\n.. |Azure| image:: https://dev.azure.com/scikit-learn/scikit-learn/_apis/build/status/scikit-learn.scikit-learn?branchName=main\n.. _Azure: https://dev.azure.com/scikit-learn/scikit-learn/_build/latest?definitionId=1&branchName=main\n\n.. |CircleCI| image:: https://circleci.com/gh/scikit-learn/scikit-learn/tree/main.svg?style=shield&circle-token=:circle-token\n.. _CircleCI: https://circleci.com/gh/scikit-learn/scikit-learn\n\n.. |Travis| image:: https://api.travis-ci.com/scikit-learn/scikit-learn.svg?branch=main\n.. _Travis: https://app.travis-ci.com/github/scikit-learn/scikit-learn\n\n.. |Codecov| image:: https://codecov.io/gh/scikit-learn/scikit-learn/branch/main/graph/badge.svg?token=Pk8G9gg3y9\n.. _Codecov: https://codecov.io/gh/scikit-learn/scikit-learn\n\n.. |Nightly wheels| image:: https://github.com/scikit-learn/scikit-learn/workflows/Wheel%20builder/badge.svg?event=schedule\n.. _`Nightly wheels`: https://github.com/scikit-learn/scikit-learn/actions?query=workflow%3A%22Wheel+builder%22+event%3Aschedule\n\n.. |PythonVersion| image:: https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue\n.. _PythonVersion: https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue\n\n.. |PyPi| image:: https://img.shields.io/pypi/v/scikit-learn\n.. _PyPi: https://pypi.org/project/scikit-learn\n\n.. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg\n.. _Black: https://github.com/psf/black\n\n.. |DOI| image:: https://zenodo.org/badge/21369/scikit-learn/scikit-learn.svg\n.. _DOI: https://zenodo.org/badge/latestdoi/21369/scikit-learn/scikit-learn\n\n\n.. |PythonMinVersion| replace:: 3.7\n.. |NumPyMinVersion| replace:: 1.14.6\n.. |SciPyMinVersion| replace:: 1.1.0\n.. |JoblibMinVersion| replace:: 0.11\n.. |ThreadpoolctlMinVersion| replace:: 2.0.0\n.. |MatplotlibMinVersion| replace:: 2.2.3\n.. |Scikit-ImageMinVersion| replace:: 0.14.5\n.. |PandasMinVersion| replace:: 0.25.0\n.. |SeabornMinVersion| replace:: 0.9.0\n.. |PytestMinVersion| replace:: 5.0.1\n\n.. image:: https://raw.githubusercontent.com/scikit-learn/scikit-learn/main/doc/logos/scikit-learn-logo.png\n :target: https://scikit-learn.org/\n\n**scikit-learn** is a Python module for machine learning built on top of\nSciPy and is distributed under the 3-Clause BSD license.\n\nThe project was started in 2007 by David Cournapeau as a Google Summer\nof Code project, and since then many volunteers have contributed. See\nthe `About us `__ page\nfor a list of core contributors.\n\nIt is currently maintained by a team of volunteers.\n\nWebsite: https://scikit-learn.org\n\nInstallation\n------------\n\nDependencies\n~~~~~~~~~~~~\n\nscikit-learn requires:\n\n- Python (>= |PythonMinVersion|)\n- NumPy (>= |NumPyMinVersion|)\n- SciPy (>= |SciPyMinVersion|)\n- joblib (>= |JoblibMinVersion|)\n- threadpoolctl (>= |ThreadpoolctlMinVersion|)\n\n=======\n\n**Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.**\nscikit-learn 0.23 and later require Python 3.6 or newer.\nscikit-learn 1.0 and later require Python 3.7 or newer.\n\nScikit-learn plotting capabilities (i.e., functions start with ``plot_`` and\nclasses end with \"Display\") require Matplotlib (>= |MatplotlibMinVersion|).\nFor running the examples Matplotlib >= |MatplotlibMinVersion| is required.\nA few examples require scikit-image >= |Scikit-ImageMinVersion|, a few examples\nrequire pandas >= |PandasMinVersion|, some examples require seaborn >=\n|SeabornMinVersion|.\n\nUser installation\n~~~~~~~~~~~~~~~~~\n\nIf you already have a working installation of numpy and scipy,\nthe easiest way to install scikit-learn is using ``pip`` ::\n\n pip install -U scikit-learn\n\nor ``conda``::\n\n conda install -c conda-forge scikit-learn\n\nThe documentation includes more detailed `installation instructions `_.\n\n\nChangelog\n---------\n\nSee the `changelog `__\nfor a history of notable changes to scikit-learn.\n\nDevelopment\n-----------\n\nWe welcome new contributors of all experience levels. The scikit-learn\ncommunity goals are to be helpful, welcoming, and effective. The\n`Development Guide `_\nhas detailed information about contributing code, documentation, tests, and\nmore. We've included some basic information in this README.\n\nImportant links\n~~~~~~~~~~~~~~~\n\n- Official source code repo: https://github.com/scikit-learn/scikit-learn\n- Download releases: https://pypi.org/project/scikit-learn/\n- Issue tracker: https://github.com/scikit-learn/scikit-learn/issues\n\nSource code\n~~~~~~~~~~~\n\nYou can check the latest sources with the command::\n\n git clone https://github.com/scikit-learn/scikit-learn.git\n\nContributing\n~~~~~~~~~~~~\n\nTo learn more about making a contribution to scikit-learn, please see our\n`Contributing guide\n`_.\n\nTesting\n~~~~~~~\n\nAfter installation, you can launch the test suite from outside the source\ndirectory (you will need to have ``pytest`` >= |PyTestMinVersion| installed)::\n\n pytest sklearn\n\nSee the web page https://scikit-learn.org/dev/developers/advanced_installation.html#testing\nfor more information.\n\n Random number generation can be controlled during testing by setting\n the ``SKLEARN_SEED`` environment variable.\n\nSubmitting a Pull Request\n~~~~~~~~~~~~~~~~~~~~~~~~~\n\nBefore opening a Pull Request, have a look at the\nfull Contributing page to make sure your code complies\nwith our guidelines: https://scikit-learn.org/stable/developers/index.html\n\nProject History\n---------------\n\nThe project was started in 2007 by David Cournapeau as a Google Summer\nof Code project, and since then many volunteers have contributed. See\nthe `About us `__ page\nfor a list of core contributors.\n\nThe project is currently maintained by a team of volunteers.\n\n**Note**: `scikit-learn` was previously referred to as `scikits.learn`.\n\nHelp and Support\n----------------\n\nDocumentation\n~~~~~~~~~~~~~\n\n- HTML documentation (stable release): https://scikit-learn.org\n- HTML documentation (development version): https://scikit-learn.org/dev/\n- FAQ: https://scikit-learn.org/stable/faq.html\n\nCommunication\n~~~~~~~~~~~~~\n\n- Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn\n- Gitter: https://gitter.im/scikit-learn/scikit-learn\n- Twitter: https://twitter.com/scikit_learn\n- Stack Overflow: https://stackoverflow.com/questions/tagged/scikit-learn\n- Github Discussions: https://github.com/scikit-learn/scikit-learn/discussions\n- Website: https://scikit-learn.org\n- LinkedIn: https://www.linkedin.com/company/scikit-learn\n\nCitation\n~~~~~~~~\n\nIf you use scikit-learn in a scientific publication, we would appreciate citations: https://scikit-learn.org/stable/about.html#citing-scikit-learn\n" }, { "path": "SECURITY.md", "content": "# Security Policy\n\n## Supported Versions\n\n| Version | Supported |\n| --------- | ------------------ |\n| 1.0.1 | :white_check_mark: |\n| < 1.0.1 | :x: |\n\n## Reporting a Vulnerability\n\nPlease report security vulnerabilities by email to `security@scikit-learn.org`.\nThis email is an alias to a subset of the scikit-learn maintainers' team.\n\nIf the security vulnerability is accepted, a patch will be crafted privately\nin order to prepare a dedicated bugfix release as timely as possible (depending\non the complexity of the fix).\n" }, { "path": "asv_benchmarks/.gitignore", "content": "*__pycache__*\nenv/\nhtml/\nresults/\nscikit-learn/\nbenchmarks/cache/\n" }, { "path": "asv_benchmarks/asv.conf.json", "content": "{\n // The version of the config file format. Do not change, unless\n // you know what you are doing.\n \"version\": 1,\n\n // The name of the project being benchmarked\n \"project\": \"scikit-learn\",\n\n // The project's homepage\n \"project_url\": \"scikit-learn.org/\",\n\n // The URL or local path of the source code repository for the\n // project being benchmarked\n \"repo\": \"..\",\n\n // The Python project's subdirectory in your repo. If missing or\n // the empty string, the project is assumed to be located at the root\n // of the repository.\n // \"repo_subdir\": \"\",\n\n // Customizable commands for building, installing, and\n // uninstalling the project. See asv.conf.json documentation.\n //\n // \"install_command\": [\"python -mpip install {wheel_file}\"],\n // \"uninstall_command\": [\"return-code=any python -mpip uninstall -y {project}\"],\n // \"build_command\": [\n // \"python setup.py build\",\n // \"PIP_NO_BUILD_ISOLATION=false python -mpip wheel --no-deps --no-index -w {build_cache_dir} {build_dir}\"\n // ],\n\n // List of branches to benchmark. If not provided, defaults to \"master\n // (for git) or \"default\" (for mercurial).\n \"branches\": [\"main\"],\n // \"branches\": [\"default\"], // for mercurial\n\n // The DVCS being used. If not set, it will be automatically\n // determined from \"repo\" by looking at the protocol in the URL\n // (if remote), or by looking for special directories, such as\n // \".git\" (if local).\n // \"dvcs\": \"git\",\n\n // The tool to use to create environments. May be \"conda\",\n // \"virtualenv\" or other value depending on the plugins in use.\n // If missing or the empty string, the tool will be automatically\n // determined by looking for tools on the PATH environment\n // variable.\n \"environment_type\": \"conda\",\n\n // timeout in seconds for installing any dependencies in environment\n // defaults to 10 min\n //\"install_timeout\": 600,\n\n // the base URL to show a commit for the project.\n \"show_commit_url\": \"https://github.com/scikit-learn/scikit-learn/commit/\",\n\n // The Pythons you'd like to test against. If not provided, defaults\n // to the current version of Python used to run `asv`.\n // \"pythons\": [\"3.6\"],\n\n // The list of conda channel names to be searched for benchmark\n // dependency packages in the specified order\n // \"conda_channels\": [\"conda-forge\", \"defaults\"]\n\n // The matrix of dependencies to test. Each key is the name of a\n // package (in PyPI) and the values are version numbers. An empty\n // list or empty string indicates to just test against the default\n // (latest) version. null indicates that the package is to not be\n // installed. If the package to be tested is only available from\n // PyPi, and the 'environment_type' is conda, then you can preface\n // the package name by 'pip+', and the package will be installed via\n // pip (with all the conda available packages installed first,\n // followed by the pip installed packages).\n //\n \"matrix\": {\n \"numpy\": [],\n \"scipy\": [],\n \"cython\": [],\n \"joblib\": [],\n \"threadpoolctl\": []\n },\n\n // Combinations of libraries/python versions can be excluded/included\n // from the set to test. Each entry is a dictionary containing additional\n // key-value pairs to include/exclude.\n //\n // An exclude entry excludes entries where all values match. The\n // values are regexps that should match the whole string.\n //\n // An include entry adds an environment. Only the packages listed\n // are installed. The 'python' key is required. The exclude rules\n // do not apply to includes.\n //\n // In addition to package names, the following keys are available:\n //\n // - python\n // Python version, as in the *pythons* variable above.\n // - environment_type\n // Environment type, as above.\n // - sys_platform\n // Platform, as in sys.platform. Possible values for the common\n // cases: 'linux2', 'win32', 'cygwin', 'darwin'.\n //\n // \"exclude\": [\n // {\"python\": \"3.2\", \"sys_platform\": \"win32\"}, // skip py3.2 on windows\n // {\"environment_type\": \"conda\", \"six\": null}, // don't run without six on conda\n // ],\n //\n // \"include\": [\n // // additional env for python2.7\n // {\"python\": \"2.7\", \"numpy\": \"1.8\"},\n // // additional env if run on windows+conda\n // {\"platform\": \"win32\", \"environment_type\": \"conda\", \"python\": \"2.7\", \"libpython\": \"\"},\n // ],\n\n // The directory (relative to the current directory) that benchmarks are\n // stored in. If not provided, defaults to \"benchmarks\"\n // \"benchmark_dir\": \"benchmarks\",\n\n // The directory (relative to the current directory) to cache the Python\n // environments in. If not provided, defaults to \"env\"\n // \"env_dir\": \"env\",\n\n // The directory (relative to the current directory) that raw benchmark\n // results are stored in. If not provided, defaults to \"results\".\n // \"results_dir\": \"results\",\n\n // The directory (relative to the current directory) that the html tree\n // should be written to. If not provided, defaults to \"html\".\n // \"html_dir\": \"html\",\n\n // The number of characters to retain in the commit hashes.\n // \"hash_length\": 8,\n\n // `asv` will cache results of the recent builds in each\n // environment, making them faster to install next time. This is\n // the number of builds to keep, per environment.\n // \"build_cache_size\": 2,\n\n // The commits after which the regression search in `asv publish`\n // should start looking for regressions. Dictionary whose keys are\n // regexps matching to benchmark names, and values corresponding to\n // the commit (exclusive) after which to start looking for\n // regressions. The default is to start from the first commit\n // with results. If the commit is `null`, regression detection is\n // skipped for the matching benchmark.\n //\n // \"regressions_first_commits\": {\n // \"some_benchmark\": \"352cdf\", // Consider regressions only after this commit\n // \"another_benchmark\": null, // Skip regression detection altogether\n // },\n\n // The thresholds for relative change in results, after which `asv\n // publish` starts reporting regressions. Dictionary of the same\n // form as in ``regressions_first_commits``, with values\n // indicating the thresholds. If multiple entries match, the\n // maximum is taken. If no entry matches, the default is 5%.\n //\n // \"regressions_thresholds\": {\n // \"some_benchmark\": 0.01, // Threshold of 1%\n // \"another_benchmark\": 0.5, // Threshold of 50%\n // },\n}\n" }, { "path": "asv_benchmarks/benchmarks/__init__.py", "content": "\"\"\"Benchmark suite for scikit-learn using ASV\"\"\"\n" }, { "path": "asv_benchmarks/benchmarks/cluster.py", "content": "from sklearn.cluster import KMeans, MiniBatchKMeans\n\nfrom .common import Benchmark, Estimator, Predictor, Transformer\nfrom .datasets import _blobs_dataset, _20newsgroups_highdim_dataset\nfrom .utils import neg_mean_inertia\n\n\nclass KMeansBenchmark(Predictor, Transformer, Estimator, Benchmark):\n \"\"\"\n Benchmarks for KMeans.\n \"\"\"\n\n param_names = [\"representation\", \"algorithm\", \"init\"]\n params = ([\"dense\", \"sparse\"], [\"full\", \"elkan\"], [\"random\", \"k-means++\"])\n\n def setup_cache(self):\n super().setup_cache()\n\n def make_data(self, params):\n representation, algorithm, init = params\n\n if representation == \"sparse\":\n data = _20newsgroups_highdim_dataset(n_samples=8000)\n else:\n data = _blobs_dataset(n_clusters=20)\n\n return data\n\n def make_estimator(self, params):\n representation, algorithm, init = params\n\n max_iter = 30 if representation == \"sparse\" else 100\n\n estimator = KMeans(\n n_clusters=20,\n algorithm=algorithm,\n init=init,\n n_init=1,\n max_iter=max_iter,\n tol=-1,\n random_state=0,\n )\n\n return estimator\n\n def make_scorers(self):\n self.train_scorer = lambda _, __: neg_mean_inertia(\n self.X, self.estimator.predict(self.X), self.estimator.cluster_centers_\n )\n self.test_scorer = lambda _, __: neg_mean_inertia(\n self.X_val,\n self.estimator.predict(self.X_val),\n self.estimator.cluster_centers_,\n )\n\n\nclass MiniBatchKMeansBenchmark(Predictor, Transformer, Estimator, Benchmark):\n \"\"\"\n Benchmarks for MiniBatchKMeans.\n \"\"\"\n\n param_names = [\"representation\", \"init\"]\n params = ([\"dense\", \"sparse\"], [\"random\", \"k-means++\"])\n\n def setup_cache(self):\n super().setup_cache()\n\n def make_data(self, params):\n representation, init = params\n\n if representation == \"sparse\":\n data = _20newsgroups_highdim_dataset()\n else:\n data = _blobs_dataset(n_clusters=20)\n\n return data\n\n def make_estimator(self, params):\n representation, init = params\n\n max_iter = 5 if representation == \"sparse\" else 2\n\n estimator = MiniBatchKMeans(\n n_clusters=20,\n init=init,\n n_init=1,\n max_iter=max_iter,\n batch_size=1000,\n max_no_improvement=None,\n compute_labels=False,\n random_state=0,\n )\n\n return estimator\n\n def make_scorers(self):\n self.train_scorer = lambda _, __: neg_mean_inertia(\n self.X, self.estimator.predict(self.X), self.estimator.cluster_centers_\n )\n self.test_scorer = lambda _, __: neg_mean_inertia(\n self.X_val,\n self.estimator.predict(self.X_val),\n self.estimator.cluster_centers_,\n )\n" }, { "path": "asv_benchmarks/benchmarks/common.py", "content": "import os\nimport json\nimport timeit\nimport pickle\nimport itertools\nfrom abc import ABC, abstractmethod\nfrom pathlib import Path\nfrom multiprocessing import cpu_count\n\nimport numpy as np\n\n\ndef get_from_config():\n \"\"\"Get benchmarks configuration from the config.json file\"\"\"\n current_path = Path(__file__).resolve().parent\n\n config_path = current_path / \"config.json\"\n with open(config_path, \"r\") as config_file:\n config_file = \"\".join(line for line in config_file if line and \"//\" not in line)\n config = json.loads(config_file)\n\n profile = os.getenv(\"SKLBENCH_PROFILE\", config[\"profile\"])\n\n n_jobs_vals_env = os.getenv(\"SKLBENCH_NJOBS\")\n if n_jobs_vals_env:\n n_jobs_vals = eval(n_jobs_vals_env)\n else:\n n_jobs_vals = config[\"n_jobs_vals\"]\n if not n_jobs_vals:\n n_jobs_vals = list(range(1, 1 + cpu_count()))\n\n cache_path = current_path / \"cache\"\n cache_path.mkdir(exist_ok=True)\n (cache_path / \"estimators\").mkdir(exist_ok=True)\n (cache_path / \"tmp\").mkdir(exist_ok=True)\n\n save_estimators = os.getenv(\"SKLBENCH_SAVE_ESTIMATORS\", config[\"save_estimators\"])\n save_dir = os.getenv(\"ASV_COMMIT\", \"new\")[:8]\n\n if save_estimators:\n (cache_path / \"estimators\" / save_dir).mkdir(exist_ok=True)\n\n base_commit = os.getenv(\"SKLBENCH_BASE_COMMIT\", config[\"base_commit\"])\n\n bench_predict = os.getenv(\"SKLBENCH_PREDICT\", config[\"bench_predict\"])\n bench_transform = os.getenv(\"SKLBENCH_TRANSFORM\", config[\"bench_transform\"])\n\n return (\n profile,\n n_jobs_vals,\n save_estimators,\n save_dir,\n base_commit,\n bench_predict,\n bench_transform,\n )\n\n\ndef get_estimator_path(benchmark, directory, params, save=False):\n \"\"\"Get path of pickled fitted estimator\"\"\"\n path = Path(__file__).resolve().parent / \"cache\"\n path = (path / \"estimators\" / directory) if save else (path / \"tmp\")\n\n filename = (\n benchmark.__class__.__name__\n + \"_estimator_\"\n + \"_\".join(list(map(str, params)))\n + \".pkl\"\n )\n\n return path / filename\n\n\ndef clear_tmp():\n \"\"\"Clean the tmp directory\"\"\"\n path = Path(__file__).resolve().parent / \"cache\" / \"tmp\"\n for child in path.iterdir():\n child.unlink()\n\n\nclass Benchmark(ABC):\n \"\"\"Abstract base class for all the benchmarks\"\"\"\n\n timer = timeit.default_timer # wall time\n processes = 1\n timeout = 500\n\n (\n profile,\n n_jobs_vals,\n save_estimators,\n save_dir,\n base_commit,\n bench_predict,\n bench_transform,\n ) = get_from_config()\n\n if profile == \"fast\":\n warmup_time = 0\n repeat = 1\n number = 1\n min_run_count = 1\n data_size = \"small\"\n elif profile == \"regular\":\n warmup_time = 1\n repeat = (3, 100, 30)\n data_size = \"small\"\n elif profile == \"large_scale\":\n warmup_time = 1\n repeat = 3\n number = 1\n data_size = \"large\"\n\n @property\n @abstractmethod\n def params(self):\n pass\n\n\nclass Estimator(ABC):\n \"\"\"Abstract base class for all benchmarks of estimators\"\"\"\n\n @abstractmethod\n def make_data(self, params):\n \"\"\"Return the dataset for a combination of parameters\"\"\"\n # The datasets are cached using joblib.Memory so it's fast and can be\n # called for each repeat\n pass\n\n @abstractmethod\n def make_estimator(self, params):\n \"\"\"Return an instance of the estimator for a combination of parameters\"\"\"\n pass\n\n def skip(self, params):\n \"\"\"Return True if the benchmark should be skipped for these params\"\"\"\n return False\n\n def setup_cache(self):\n \"\"\"Pickle a fitted estimator for all combinations of parameters\"\"\"\n # This is run once per benchmark class.\n\n clear_tmp()\n\n param_grid = list(itertools.product(*self.params))\n\n for params in param_grid:\n if self.skip(params):\n continue\n\n estimator = self.make_estimator(params)\n X, _, y, _ = self.make_data(params)\n\n estimator.fit(X, y)\n\n est_path = get_estimator_path(\n self, Benchmark.save_dir, params, Benchmark.save_estimators\n )\n with est_path.open(mode=\"wb\") as f:\n pickle.dump(estimator, f)\n\n def setup(self, *params):\n \"\"\"Generate dataset and load the fitted estimator\"\"\"\n # This is run once per combination of parameters and per repeat so we\n # need to avoid doing expensive operations there.\n\n if self.skip(params):\n raise NotImplementedError\n\n self.X, self.X_val, self.y, self.y_val = self.make_data(params)\n\n est_path = get_estimator_path(\n self, Benchmark.save_dir, params, Benchmark.save_estimators\n )\n with est_path.open(mode=\"rb\") as f:\n self.estimator = pickle.load(f)\n\n self.make_scorers()\n\n def time_fit(self, *args):\n self.estimator.fit(self.X, self.y)\n\n def peakmem_fit(self, *args):\n self.estimator.fit(self.X, self.y)\n\n def track_train_score(self, *args):\n if hasattr(self.estimator, \"predict\"):\n y_pred = self.estimator.predict(self.X)\n else:\n y_pred = None\n return float(self.train_scorer(self.y, y_pred))\n\n def track_test_score(self, *args):\n if hasattr(self.estimator, \"predict\"):\n y_val_pred = self.estimator.predict(self.X_val)\n else:\n y_val_pred = None\n return float(self.test_scorer(self.y_val, y_val_pred))\n\n\nclass Predictor(ABC):\n \"\"\"Abstract base class for benchmarks of estimators implementing predict\"\"\"\n\n if Benchmark.bench_predict:\n\n def time_predict(self, *args):\n self.estimator.predict(self.X)\n\n def peakmem_predict(self, *args):\n self.estimator.predict(self.X)\n\n if Benchmark.base_commit is not None:\n\n def track_same_prediction(self, *args):\n est_path = get_estimator_path(self, Benchmark.base_commit, args, True)\n with est_path.open(mode=\"rb\") as f:\n estimator_base = pickle.load(f)\n\n y_val_pred_base = estimator_base.predict(self.X_val)\n y_val_pred = self.estimator.predict(self.X_val)\n\n return np.allclose(y_val_pred_base, y_val_pred)\n\n @property\n @abstractmethod\n def params(self):\n pass\n\n\nclass Transformer(ABC):\n \"\"\"Abstract base class for benchmarks of estimators implementing transform\"\"\"\n\n if Benchmark.bench_transform:\n\n def time_transform(self, *args):\n self.estimator.transform(self.X)\n\n def peakmem_transform(self, *args):\n self.estimator.transform(self.X)\n\n if Benchmark.base_commit is not None:\n\n def track_same_transform(self, *args):\n est_path = get_estimator_path(self, Benchmark.base_commit, args, True)\n with est_path.open(mode=\"rb\") as f:\n estimator_base = pickle.load(f)\n\n X_val_t_base = estimator_base.transform(self.X_val)\n X_val_t = self.estimator.transform(self.X_val)\n\n return np.allclose(X_val_t_base, X_val_t)\n\n @property\n @abstractmethod\n def params(self):\n pass\n" }, { "path": "asv_benchmarks/benchmarks/config.json", "content": "{\n // \"regular\": Bencharks are run on small to medium datasets. Each benchmark\n // is run multiple times and averaged.\n // \"fast\": Benchmarks are run on small to medium datasets. Each benchmark\n // is run only once. May provide unstable benchmarks.\n // \"large_scale\": Benchmarks are run on large datasets. Each benchmark is\n // run multiple times and averaged. This profile is meant to\n // benchmark scalability and will take hours on single core.\n // Can be overridden by environment variable SKLBENCH_PROFILE.\n \"profile\": \"regular\",\n\n // List of values of n_jobs to use for estimators which accept this \n // parameter (-1 means all cores). An empty list means all values from 1 to\n // the maximum number of available cores.\n // Can be overridden by environment variable SKLBENCH_NJOBS.\n \"n_jobs_vals\": [1],\n\n // If true, fitted estimators are saved in ./cache/estimators/\n // Can be overridden by environment variable SKLBENCH_SAVE_ESTIMATORS.\n \"save_estimators\": false,\n\n // Commit hash to compare estimator predictions with.\n // If null, predictions are not compared.\n // Can be overridden by environment variable SKLBENCH_BASE_COMMIT.\n \"base_commit\": null,\n\n // If false, the predict (resp. transform) method of the estimators won't\n // be benchmarked.\n // Can be overridden by environment variables SKLBENCH_PREDICT and\n // SKLBENCH_TRANSFORM.\n \"bench_predict\": true,\n \"bench_transform\": true\n}\n" }, { "path": "asv_benchmarks/benchmarks/datasets.py", "content": "import numpy as np\nimport scipy.sparse as sp\nfrom joblib import Memory\nfrom pathlib import Path\n\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.datasets import (\n make_blobs,\n fetch_20newsgroups,\n fetch_openml,\n load_digits,\n make_regression,\n make_classification,\n fetch_olivetti_faces,\n)\nfrom sklearn.preprocessing import MaxAbsScaler, StandardScaler\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.model_selection import train_test_split\n\n# memory location for caching datasets\nM = Memory(location=str(Path(__file__).resolve().parent / \"cache\"))\n\n\n@M.cache\ndef _blobs_dataset(n_samples=500000, n_features=3, n_clusters=100, dtype=np.float32):\n X, _ = make_blobs(\n n_samples=n_samples, n_features=n_features, centers=n_clusters, random_state=0\n )\n X = X.astype(dtype, copy=False)\n\n X, X_val = train_test_split(X, test_size=0.1, random_state=0)\n return X, X_val, None, None\n\n\n@M.cache\ndef _20newsgroups_highdim_dataset(n_samples=None, ngrams=(1, 1), dtype=np.float32):\n newsgroups = fetch_20newsgroups(random_state=0)\n vectorizer = TfidfVectorizer(ngram_range=ngrams, dtype=dtype)\n X = vectorizer.fit_transform(newsgroups.data[:n_samples])\n y = newsgroups.target[:n_samples]\n\n X, X_val, y, y_val = train_test_split(X, y, test_size=0.1, random_state=0)\n return X, X_val, y, y_val\n\n\n@M.cache\ndef _20newsgroups_lowdim_dataset(n_components=100, ngrams=(1, 1), dtype=np.float32):\n newsgroups = fetch_20newsgroups()\n vectorizer = TfidfVectorizer(ngram_range=ngrams)\n X = vectorizer.fit_transform(newsgroups.data)\n X = X.astype(dtype, copy=False)\n svd = TruncatedSVD(n_components=n_components)\n X = svd.fit_transform(X)\n y = newsgroups.target\n\n X, X_val, y, y_val = train_test_split(X, y, test_size=0.1, random_state=0)\n return X, X_val, y, y_val\n\n\n@M.cache\ndef _mnist_dataset(dtype=np.float32):\n X, y = fetch_openml(\"mnist_784\", version=1, return_X_y=True, as_frame=False)\n X = X.astype(dtype, copy=False)\n X = MaxAbsScaler().fit_transform(X)\n\n X, X_val, y, y_val = train_test_split(X, y, test_size=0.1, random_state=0)\n return X, X_val, y, y_val\n\n\n@M.cache\ndef _digits_dataset(n_samples=None, dtype=np.float32):\n X, y = load_digits(return_X_y=True)\n X = X.astype(dtype, copy=False)\n X = MaxAbsScaler().fit_transform(X)\n X = X[:n_samples]\n y = y[:n_samples]\n\n X, X_val, y, y_val = train_test_split(X, y, test_size=0.1, random_state=0)\n return X, X_val, y, y_val\n\n\n@M.cache\ndef _synth_regression_dataset(n_samples=100000, n_features=100, dtype=np.float32):\n X, y = make_regression(\n n_samples=n_samples,\n n_features=n_features,\n n_informative=n_features // 10,\n noise=50,\n random_state=0,\n )\n X = X.astype(dtype, copy=False)\n X = StandardScaler().fit_transform(X)\n\n X, X_val, y, y_val = train_test_split(X, y, test_size=0.1, random_state=0)\n return X, X_val, y, y_val\n\n\n@M.cache\ndef _synth_regression_sparse_dataset(\n n_samples=10000, n_features=10000, density=0.01, dtype=np.float32\n):\n X = sp.random(\n m=n_samples, n=n_features, density=density, format=\"csr\", random_state=0\n )\n X.data = np.random.RandomState(0).randn(X.getnnz())\n X = X.astype(dtype, copy=False)\n coefs = sp.random(m=n_features, n=1, density=0.5, random_state=0)\n coefs.data = np.random.RandomState(0).randn(coefs.getnnz())\n y = X.dot(coefs.toarray()).reshape(-1)\n y += 0.2 * y.std() * np.random.randn(n_samples)\n\n X, X_val, y, y_val = train_test_split(X, y, test_size=0.1, random_state=0)\n return X, X_val, y, y_val\n\n\n@M.cache\ndef _synth_classification_dataset(\n n_samples=1000, n_features=10000, n_classes=2, dtype=np.float32\n):\n X, y = make_classification(\n n_samples=n_samples,\n n_features=n_features,\n n_classes=n_classes,\n random_state=0,\n n_informative=n_features,\n n_redundant=0,\n )\n X = X.astype(dtype, copy=False)\n X = StandardScaler().fit_transform(X)\n\n X, X_val, y, y_val = train_test_split(X, y, test_size=0.1, random_state=0)\n return X, X_val, y, y_val\n\n\n@M.cache\ndef _olivetti_faces_dataset():\n dataset = fetch_olivetti_faces(shuffle=True, random_state=42)\n faces = dataset.data\n n_samples, n_features = faces.shape\n faces_centered = faces - faces.mean(axis=0)\n # local centering\n faces_centered -= faces_centered.mean(axis=1).reshape(n_samples, -1)\n X = faces_centered\n\n X, X_val = train_test_split(X, test_size=0.1, random_state=0)\n return X, X_val, None, None\n\n\n@M.cache\ndef _random_dataset(\n n_samples=1000, n_features=1000, representation=\"dense\", dtype=np.float32\n):\n if representation == \"dense\":\n X = np.random.RandomState(0).random_sample((n_samples, n_features))\n X = X.astype(dtype, copy=False)\n else:\n X = sp.random(\n n_samples,\n n_features,\n density=0.05,\n format=\"csr\",\n dtype=dtype,\n random_state=0,\n )\n\n X, X_val = train_test_split(X, test_size=0.1, random_state=0)\n return X, X_val, None, None\n" }, { "path": "asv_benchmarks/benchmarks/decomposition.py", "content": "from sklearn.decomposition import PCA, DictionaryLearning, MiniBatchDictionaryLearning\n\nfrom .common import Benchmark, Estimator, Transformer\nfrom .datasets import _olivetti_faces_dataset, _mnist_dataset\nfrom .utils import make_pca_scorers, make_dict_learning_scorers\n\n\nclass PCABenchmark(Transformer, Estimator, Benchmark):\n \"\"\"\n Benchmarks for PCA.\n \"\"\"\n\n param_names = [\"svd_solver\"]\n params = ([\"full\", \"arpack\", \"randomized\"],)\n\n def setup_cache(self):\n super().setup_cache()\n\n def make_data(self, params):\n return _mnist_dataset()\n\n def make_estimator(self, params):\n (svd_solver,) = params\n\n estimator = PCA(n_components=32, svd_solver=svd_solver, random_state=0)\n\n return estimator\n\n def make_scorers(self):\n make_pca_scorers(self)\n\n\nclass DictionaryLearningBenchmark(Transformer, Estimator, Benchmark):\n \"\"\"\n Benchmarks for DictionaryLearning.\n \"\"\"\n\n param_names = [\"fit_algorithm\", \"n_jobs\"]\n params = ([\"lars\", \"cd\"], Benchmark.n_jobs_vals)\n\n def setup_cache(self):\n super().setup_cache()\n\n def make_data(self, params):\n return _olivetti_faces_dataset()\n\n def make_estimator(self, params):\n fit_algorithm, n_jobs = params\n\n estimator = DictionaryLearning(\n n_components=15,\n fit_algorithm=fit_algorithm,\n alpha=0.1,\n max_iter=20,\n tol=1e-16,\n random_state=0,\n n_jobs=n_jobs,\n )\n\n return estimator\n\n def make_scorers(self):\n make_dict_learning_scorers(self)\n\n\nclass MiniBatchDictionaryLearningBenchmark(Transformer, Estimator, Benchmark):\n \"\"\"\n Benchmarks for MiniBatchDictionaryLearning\n \"\"\"\n\n param_names = [\"fit_algorithm\", \"n_jobs\"]\n params = ([\"lars\", \"cd\"], Benchmark.n_jobs_vals)\n\n def setup_cache(self):\n super().setup_cache()\n\n def make_data(self, params):\n return _olivetti_faces_dataset()\n\n def make_estimator(self, params):\n fit_algorithm, n_jobs = params\n\n estimator = MiniBatchDictionaryLearning(\n n_components=15,\n fit_algorithm=fit_algorithm,\n alpha=0.1,\n batch_size=3,\n random_state=0,\n n_jobs=n_jobs,\n )\n\n return estimator\n\n def make_scorers(self):\n make_dict_learning_scorers(self)\n" }, { "path": "asv_benchmarks/benchmarks/ensemble.py", "content": "from sklearn.ensemble import (\n RandomForestClassifier,\n GradientBoostingClassifier,\n HistGradientBoostingClassifier,\n)\n\nfrom .common import Benchmark, Estimator, Predictor\nfrom .datasets import (\n _20newsgroups_highdim_dataset,\n _20newsgroups_lowdim_dataset,\n _synth_classification_dataset,\n)\nfrom .utils import make_gen_classif_scorers\n\n\nclass RandomForestClassifierBenchmark(Predictor, Estimator, Benchmark):\n \"\"\"\n Benchmarks for RandomForestClassifier.\n \"\"\"\n\n param_names = [\"representation\", \"n_jobs\"]\n params = ([\"dense\", \"sparse\"], Benchmark.n_jobs_vals)\n\n def setup_cache(self):\n super().setup_cache()\n\n def make_data(self, params):\n representation, n_jobs = params\n\n if representation == \"sparse\":\n data = _20newsgroups_highdim_dataset()\n else:\n data = _20newsgroups_lowdim_dataset()\n\n return data\n\n def make_estimator(self, params):\n representation, n_jobs = params\n\n n_estimators = 500 if Benchmark.data_size == \"large\" else 100\n\n estimator = RandomForestClassifier(\n n_estimators=n_estimators,\n min_samples_split=10,\n max_features=\"log2\",\n n_jobs=n_jobs,\n random_state=0,\n )\n\n return estimator\n\n def make_scorers(self):\n make_gen_classif_scorers(self)\n\n\nclass GradientBoostingClassifierBenchmark(Predictor, Estimator, Benchmark):\n \"\"\"\n Benchmarks for GradientBoostingClassifier.\n \"\"\"\n\n param_names = [\"representation\"]\n params = ([\"dense\", \"sparse\"],)\n\n def setup_cache(self):\n super().setup_cache()\n\n def make_data(self, params):\n (representation,) = params\n\n if representation == \"sparse\":\n data = _20newsgroups_highdim_dataset()\n else:\n data = _20newsgroups_lowdim_dataset()\n\n return data\n\n def make_estimator(self, params):\n (representation,) = params\n\n n_estimators = 100 if Benchmark.data_size == \"large\" else 10\n\n estimator = GradientBoostingClassifier(\n n_estimators=n_estimators,\n max_features=\"log2\",\n subsample=0.5,\n random_state=0,\n )\n\n return estimator\n\n def make_scorers(self):\n make_gen_classif_scorers(self)\n\n\nclass HistGradientBoostingClassifierBenchmark(Predictor, Estimator, Benchmark):\n \"\"\"\n Benchmarks for HistGradientBoostingClassifier.\n \"\"\"\n\n param_names = []\n params = ()\n\n def setup_cache(self):\n super().setup_cache()\n\n def make_data(self, params):\n data = _synth_classification_dataset(\n n_samples=10000, n_features=100, n_classes=5\n )\n\n return data\n\n def make_estimator(self, params):\n estimator = HistGradientBoostingClassifier(\n max_iter=100, max_leaf_nodes=15, early_stopping=False, random_state=0\n )\n\n return estimator\n\n def make_scorers(self):\n make_gen_classif_scorers(self)\n" }, { "path": "asv_benchmarks/benchmarks/linear_model.py", "content": "from sklearn.linear_model import (\n LogisticRegression,\n Ridge,\n ElasticNet,\n Lasso,\n LinearRegression,\n SGDRegressor,\n)\n\nfrom .common import Benchmark, Estimator, Predictor\nfrom .datasets import (\n _20newsgroups_highdim_dataset,\n _20newsgroups_lowdim_dataset,\n _synth_regression_dataset,\n _synth_regression_sparse_dataset,\n)\nfrom .utils import make_gen_classif_scorers, make_gen_reg_scorers\n\n\nclass LogisticRegressionBenchmark(Predictor, Estimator, Benchmark):\n \"\"\"\n Benchmarks for LogisticRegression.\n \"\"\"\n\n param_names = [\"representation\", \"solver\", \"n_jobs\"]\n params = ([\"dense\", \"sparse\"], [\"lbfgs\", \"saga\"], Benchmark.n_jobs_vals)\n\n def setup_cache(self):\n super().setup_cache()\n\n def make_data(self, params):\n representation, solver, n_jobs = params\n\n if Benchmark.data_size == \"large\":\n if representation == \"sparse\":\n data = _20newsgroups_highdim_dataset(n_samples=10000)\n else:\n data = _20newsgroups_lowdim_dataset(n_components=1e3)\n else:\n if representation == \"sparse\":\n data = _20newsgroups_highdim_dataset(n_samples=2500)\n else:\n data = _20newsgroups_lowdim_dataset()\n\n return data\n\n def make_estimator(self, params):\n representation, solver, n_jobs = params\n\n penalty = \"l2\" if solver == \"lbfgs\" else \"l1\"\n\n estimator = LogisticRegression(\n solver=solver,\n penalty=penalty,\n multi_class=\"multinomial\",\n tol=0.01,\n n_jobs=n_jobs,\n random_state=0,\n )\n\n return estimator\n\n def make_scorers(self):\n make_gen_classif_scorers(self)\n\n\nclass RidgeBenchmark(Predictor, Estimator, Benchmark):\n \"\"\"\n Benchmarks for Ridge.\n \"\"\"\n\n param_names = [\"representation\", \"solver\"]\n params = (\n [\"dense\", \"sparse\"],\n [\"auto\", \"svd\", \"cholesky\", \"lsqr\", \"sparse_cg\", \"sag\", \"saga\"],\n )\n\n def setup_cache(self):\n super().setup_cache()\n\n def make_data(self, params):\n representation, solver = params\n\n if representation == \"dense\":\n data = _synth_regression_dataset(n_samples=500000, n_features=100)\n else:\n data = _synth_regression_sparse_dataset(\n n_samples=100000, n_features=10000, density=0.005\n )\n\n return data\n\n def make_estimator(self, params):\n representation, solver = params\n\n estimator = Ridge(solver=solver, fit_intercept=False, random_state=0)\n\n return estimator\n\n def make_scorers(self):\n make_gen_reg_scorers(self)\n\n def skip(self, params):\n representation, solver = params\n\n if representation == \"sparse\" and solver == \"svd\":\n return True\n return False\n\n\nclass LinearRegressionBenchmark(Predictor, Estimator, Benchmark):\n \"\"\"\n Benchmarks for Linear Reagression.\n \"\"\"\n\n param_names = [\"representation\"]\n params = ([\"dense\", \"sparse\"],)\n\n def setup_cache(self):\n super().setup_cache()\n\n def make_data(self, params):\n (representation,) = params\n\n if representation == \"dense\":\n data = _synth_regression_dataset(n_samples=1000000, n_features=100)\n else:\n data = _synth_regression_sparse_dataset(\n n_samples=10000, n_features=100000, density=0.01\n )\n\n return data\n\n def make_estimator(self, params):\n estimator = LinearRegression()\n\n return estimator\n\n def make_scorers(self):\n make_gen_reg_scorers(self)\n\n\nclass SGDRegressorBenchmark(Predictor, Estimator, Benchmark):\n \"\"\"\n Benchmark for SGD\n \"\"\"\n\n param_names = [\"representation\"]\n params = ([\"dense\", \"sparse\"],)\n\n def setup_cache(self):\n super().setup_cache()\n\n def make_data(self, params):\n (representation,) = params\n\n if representation == \"dense\":\n data = _synth_regression_dataset(n_samples=100000, n_features=200)\n else:\n data = _synth_regression_sparse_dataset(\n n_samples=100000, n_features=1000, density=0.01\n )\n\n return data\n\n def make_estimator(self, params):\n estimator = SGDRegressor(max_iter=1000, tol=1e-16, random_state=0)\n\n return estimator\n\n def make_scorers(self):\n make_gen_reg_scorers(self)\n\n\nclass ElasticNetBenchmark(Predictor, Estimator, Benchmark):\n \"\"\"\n Benchmarks for ElasticNet.\n \"\"\"\n\n param_names = [\"representation\", \"precompute\"]\n params = ([\"dense\", \"sparse\"], [True, False])\n\n def setup_cache(self):\n super().setup_cache()\n\n def make_data(self, params):\n representation, precompute = params\n\n if representation == \"dense\":\n data = _synth_regression_dataset(n_samples=1000000, n_features=100)\n else:\n data = _synth_regression_sparse_dataset(\n n_samples=50000, n_features=5000, density=0.01\n )\n\n return data\n\n def make_estimator(self, params):\n representation, precompute = params\n\n estimator = ElasticNet(precompute=precompute, alpha=0.001, random_state=0)\n\n return estimator\n\n def make_scorers(self):\n make_gen_reg_scorers(self)\n\n def skip(self, params):\n representation, precompute = params\n\n if representation == \"sparse\" and precompute is False:\n return True\n return False\n\n\nclass LassoBenchmark(Predictor, Estimator, Benchmark):\n \"\"\"\n Benchmarks for Lasso.\n \"\"\"\n\n param_names = [\"representation\", \"precompute\"]\n params = ([\"dense\", \"sparse\"], [True, False])\n\n def setup_cache(self):\n super().setup_cache()\n\n def make_data(self, params):\n representation, precompute = params\n\n if representation == \"dense\":\n data = _synth_regression_dataset(n_samples=1000000, n_features=100)\n else:\n data = _synth_regression_sparse_dataset(\n n_samples=50000, n_features=5000, density=0.01\n )\n\n return data\n\n def make_estimator(self, params):\n representation, precompute = params\n\n estimator = Lasso(precompute=precompute, alpha=0.001, random_state=0)\n\n return estimator\n\n def make_scorers(self):\n make_gen_reg_scorers(self)\n\n def skip(self, params):\n representation, precompute = params\n\n if representation == \"sparse\" and precompute is False:\n return True\n return False\n" }, { "path": "asv_benchmarks/benchmarks/manifold.py", "content": "from sklearn.manifold import TSNE\n\nfrom .common import Benchmark, Estimator\nfrom .datasets import _digits_dataset\n\n\nclass TSNEBenchmark(Estimator, Benchmark):\n \"\"\"\n Benchmarks for t-SNE.\n \"\"\"\n\n param_names = [\"method\"]\n params = ([\"exact\", \"barnes_hut\"],)\n\n def setup_cache(self):\n super().setup_cache()\n\n def make_data(self, params):\n (method,) = params\n\n n_samples = 500 if method == \"exact\" else None\n\n return _digits_dataset(n_samples=n_samples)\n\n def make_estimator(self, params):\n (method,) = params\n\n estimator = TSNE(random_state=0, method=method)\n\n return estimator\n\n def make_scorers(self):\n self.train_scorer = lambda _, __: self.estimator.kl_divergence_\n self.test_scorer = lambda _, __: self.estimator.kl_divergence_\n" }, { "path": "asv_benchmarks/benchmarks/metrics.py", "content": "from sklearn.metrics.pairwise import pairwise_distances\n\nfrom .common import Benchmark\nfrom .datasets import _random_dataset\n\n\nclass PairwiseDistancesBenchmark(Benchmark):\n \"\"\"\n Benchmarks for pairwise distances.\n \"\"\"\n\n param_names = [\"representation\", \"metric\", \"n_jobs\"]\n params = (\n [\"dense\", \"sparse\"],\n [\"cosine\", \"euclidean\", \"manhattan\", \"correlation\"],\n Benchmark.n_jobs_vals,\n )\n\n def setup(self, *params):\n representation, metric, n_jobs = params\n\n if representation == \"sparse\" and metric == \"correlation\":\n raise NotImplementedError\n\n if Benchmark.data_size == \"large\":\n if metric in (\"manhattan\", \"correlation\"):\n n_samples = 8000\n else:\n n_samples = 24000\n else:\n if metric in (\"manhattan\", \"correlation\"):\n n_samples = 4000\n else:\n n_samples = 12000\n\n data = _random_dataset(n_samples=n_samples, representation=representation)\n self.X, self.X_val, self.y, self.y_val = data\n\n self.pdist_params = {\"metric\": metric, \"n_jobs\": n_jobs}\n\n def time_pairwise_distances(self, *args):\n pairwise_distances(self.X, **self.pdist_params)\n\n def peakmem_pairwise_distances(self, *args):\n pairwise_distances(self.X, **self.pdist_params)\n" }, { "path": "asv_benchmarks/benchmarks/model_selection.py", "content": "from sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import GridSearchCV, cross_val_score\n\nfrom .common import Benchmark, Estimator, Predictor\nfrom .datasets import _synth_classification_dataset\nfrom .utils import make_gen_classif_scorers\n\n\nclass CrossValidationBenchmark(Benchmark):\n \"\"\"\n Benchmarks for Cross Validation.\n \"\"\"\n\n timeout = 20000\n\n param_names = [\"n_jobs\"]\n params = (Benchmark.n_jobs_vals,)\n\n def setup(self, *params):\n (n_jobs,) = params\n\n data = _synth_classification_dataset(n_samples=50000, n_features=100)\n self.X, self.X_val, self.y, self.y_val = data\n\n self.clf = RandomForestClassifier(n_estimators=50, max_depth=10, random_state=0)\n\n cv = 16 if Benchmark.data_size == \"large\" else 4\n\n self.cv_params = {\"n_jobs\": n_jobs, \"cv\": cv}\n\n def time_crossval(self, *args):\n cross_val_score(self.clf, self.X, self.y, **self.cv_params)\n\n def peakmem_crossval(self, *args):\n cross_val_score(self.clf, self.X, self.y, **self.cv_params)\n\n def track_crossval(self, *args):\n return float(cross_val_score(self.clf, self.X, self.y, **self.cv_params).mean())\n\n\nclass GridSearchBenchmark(Predictor, Estimator, Benchmark):\n \"\"\"\n Benchmarks for GridSearch.\n \"\"\"\n\n timeout = 20000\n\n param_names = [\"n_jobs\"]\n params = (Benchmark.n_jobs_vals,)\n\n def setup_cache(self):\n super().setup_cache()\n\n def make_data(self, params):\n data = _synth_classification_dataset(n_samples=10000, n_features=100)\n\n return data\n\n def make_estimator(self, params):\n (n_jobs,) = params\n\n clf = RandomForestClassifier(random_state=0)\n\n if Benchmark.data_size == \"large\":\n n_estimators_list = [10, 25, 50, 100, 500]\n max_depth_list = [5, 10, None]\n max_features_list = [0.1, 0.4, 0.8, 1.0]\n else:\n n_estimators_list = [10, 25, 50]\n max_depth_list = [5, 10]\n max_features_list = [0.1, 0.4, 0.8]\n\n param_grid = {\n \"n_estimators\": n_estimators_list,\n \"max_depth\": max_depth_list,\n \"max_features\": max_features_list,\n }\n\n estimator = GridSearchCV(clf, param_grid, n_jobs=n_jobs, cv=4)\n\n return estimator\n\n def make_scorers(self):\n make_gen_classif_scorers(self)\n" }, { "path": "asv_benchmarks/benchmarks/neighbors.py", "content": "from sklearn.neighbors import KNeighborsClassifier\n\nfrom .common import Benchmark, Estimator, Predictor\nfrom .datasets import _20newsgroups_lowdim_dataset\nfrom .utils import make_gen_classif_scorers\n\n\nclass KNeighborsClassifierBenchmark(Predictor, Estimator, Benchmark):\n \"\"\"\n Benchmarks for KNeighborsClassifier.\n \"\"\"\n\n param_names = [\"algorithm\", \"dimension\", \"n_jobs\"]\n params = ([\"brute\", \"kd_tree\", \"ball_tree\"], [\"low\", \"high\"], Benchmark.n_jobs_vals)\n\n def setup_cache(self):\n super().setup_cache()\n\n def make_data(self, params):\n algorithm, dimension, n_jobs = params\n\n if Benchmark.data_size == \"large\":\n n_components = 40 if dimension == \"low\" else 200\n else:\n n_components = 10 if dimension == \"low\" else 50\n\n data = _20newsgroups_lowdim_dataset(n_components=n_components)\n\n return data\n\n def make_estimator(self, params):\n algorithm, dimension, n_jobs = params\n\n estimator = KNeighborsClassifier(algorithm=algorithm, n_jobs=n_jobs)\n\n return estimator\n\n def make_scorers(self):\n make_gen_classif_scorers(self)\n" }, { "path": "asv_benchmarks/benchmarks/svm.py", "content": "from sklearn.svm import SVC\n\nfrom .common import Benchmark, Estimator, Predictor\nfrom .datasets import _synth_classification_dataset\nfrom .utils import make_gen_classif_scorers\n\n\nclass SVCBenchmark(Predictor, Estimator, Benchmark):\n \"\"\"Benchmarks for SVC.\"\"\"\n\n param_names = [\"kernel\"]\n params = ([\"linear\", \"poly\", \"rbf\", \"sigmoid\"],)\n\n def setup_cache(self):\n super().setup_cache()\n\n def make_data(self, params):\n return _synth_classification_dataset()\n\n def make_estimator(self, params):\n (kernel,) = params\n\n estimator = SVC(\n max_iter=100, tol=1e-16, kernel=kernel, random_state=0, gamma=\"scale\"\n )\n\n return estimator\n\n def make_scorers(self):\n make_gen_classif_scorers(self)\n" }, { "path": "asv_benchmarks/benchmarks/utils.py", "content": "import numpy as np\n\nfrom sklearn.metrics import balanced_accuracy_score, r2_score\n\n\ndef neg_mean_inertia(X, labels, centers):\n return -(np.asarray(X - centers[labels]) ** 2).sum(axis=1).mean()\n\n\ndef make_gen_classif_scorers(caller):\n caller.train_scorer = balanced_accuracy_score\n caller.test_scorer = balanced_accuracy_score\n\n\ndef make_gen_reg_scorers(caller):\n caller.test_scorer = r2_score\n caller.train_scorer = r2_score\n\n\ndef neg_mean_data_error(X, U, V):\n return -np.sqrt(((X - U.dot(V)) ** 2).mean())\n\n\ndef make_dict_learning_scorers(caller):\n caller.train_scorer = lambda _, __: (\n neg_mean_data_error(\n caller.X, caller.estimator.transform(caller.X), caller.estimator.components_\n )\n )\n caller.test_scorer = lambda _, __: (\n neg_mean_data_error(\n caller.X_val,\n caller.estimator.transform(caller.X_val),\n caller.estimator.components_,\n )\n )\n\n\ndef explained_variance_ratio(Xt, X):\n return np.var(Xt, axis=0).sum() / np.var(X, axis=0).sum()\n\n\ndef make_pca_scorers(caller):\n caller.train_scorer = lambda _, __: caller.estimator.explained_variance_ratio_.sum()\n caller.test_scorer = lambda _, __: (\n explained_variance_ratio(caller.estimator.transform(caller.X_val), caller.X_val)\n )\n" }, { "path": "azure-pipelines.yml", "content": "# Adapted from https://github.com/pandas-dev/pandas/blob/master/azure-pipelines.yml\nschedules:\n- cron: \"30 2 * * *\"\n displayName: Run nightly build\n branches:\n include:\n - main\n always: true\n\njobs:\n- job: git_commit\n displayName: Get Git Commit\n pool:\n vmImage: ubuntu-20.04\n steps:\n - bash: |\n set -ex\n if [[ $BUILD_REASON == \"PullRequest\" ]]; then\n # By default pull requests use refs/pull/PULL_ID/merge as the source branch\n # which has a \"Merge ID into ID\" as a commit message. The latest commit\n # message is the second to last commit\n COMMIT_ID=$(echo $BUILD_SOURCEVERSIONMESSAGE | awk '{print $2}')\n message=$(git log $COMMIT_ID -1 --pretty=%B)\n else\n message=$BUILD_SOURCEVERSIONMESSAGE\n fi\n echo \"##vso[task.setvariable variable=message;isOutput=true]$message\"\n name: commit\n displayName: Get source version message\n\n- job: linting\n dependsOn: [git_commit]\n condition: |\n and(\n succeeded(),\n not(contains(dependencies['git_commit']['outputs']['commit.message'], '[lint skip]')),\n not(contains(dependencies['git_commit']['outputs']['commit.message'], '[ci skip]'))\n )\n displayName: Linting\n pool:\n vmImage: ubuntu-20.04\n steps:\n - task: UsePythonVersion@0\n inputs:\n versionSpec: '3.9'\n - bash: |\n # Include pytest compatibility with mypy\n pip install pytest flake8 mypy==0.782 black==21.6b0\n displayName: Install linters\n - bash: |\n black --check --diff .\n displayName: Run black\n - bash: |\n ./build_tools/circle/linting.sh\n displayName: Run linting\n - bash: |\n mypy sklearn/\n displayName: Run mypy\n\n- template: build_tools/azure/posix.yml\n parameters:\n name: Linux_Nightly\n vmImage: ubuntu-20.04\n dependsOn: [git_commit, linting]\n condition: |\n and(\n succeeded(),\n not(contains(dependencies['git_commit']['outputs']['commit.message'], '[ci skip]')),\n or(eq(variables['Build.Reason'], 'Schedule'),\n contains(dependencies['git_commit']['outputs']['commit.message'], '[scipy-dev]'\n )\n )\n )\n matrix:\n pylatest_pip_scipy_dev:\n DISTRIB: 'conda-pip-scipy-dev'\n PYTHON_VERSION: '*'\n CHECK_WARNINGS: 'true'\n CHECK_PYTEST_SOFT_DEPENDENCY: 'true'\n TEST_DOCSTRINGS: 'true'\n # Tests that require large downloads over the networks are skipped in CI.\n # Here we make sure, that they are still run on a regular basis.\n SKLEARN_SKIP_NETWORK_TESTS: '0'\n CREATE_ISSUE_ON_TRACKER: 'true'\n\n# Check compilation with intel C++ compiler (ICC)\n- template: build_tools/azure/posix.yml\n parameters:\n name: Linux_Nightly_ICC\n vmImage: ubuntu-20.04\n dependsOn: [git_commit, linting]\n condition: |\n and(\n succeeded(),\n not(contains(dependencies['git_commit']['outputs']['commit.message'], '[ci skip]')),\n or(eq(variables['Build.Reason'], 'Schedule'),\n contains(dependencies['git_commit']['outputs']['commit.message'], '[icc-build]')\n )\n )\n matrix:\n pylatest_conda_forge_mkl:\n DISTRIB: 'conda'\n CONDA_CHANNEL: 'conda-forge'\n PYTHON_VERSION: '*'\n BLAS: 'mkl'\n COVERAGE: 'false'\n BUILD_WITH_ICC: 'true'\n\n- template: build_tools/azure/posix-docker.yml\n parameters:\n name: Linux_Nightly_PyPy\n vmImage: ubuntu-20.04\n dependsOn: [linting, git_commit]\n condition: |\n and(\n succeeded(),\n not(contains(dependencies['git_commit']['outputs']['commit.message'], '[ci skip]')),\n or(\n eq(variables['Build.Reason'], 'Schedule'),\n contains(dependencies['git_commit']['outputs']['commit.message'], '[pypy]')\n )\n )\n matrix:\n pypy3:\n DISTRIB: 'conda-mamba-pypy3'\n DOCKER_CONTAINER: 'condaforge/mambaforge-pypy3:4.10.3-5'\n PILLOW_VERSION: 'none'\n PANDAS_VERSION: 'none'\n CREATE_ISSUE_ON_TRACKER: 'true'\n\n# Will run all the time regardless of linting outcome.\n- template: build_tools/azure/posix.yml\n parameters:\n name: Linux_Runs\n vmImage: ubuntu-20.04\n dependsOn: [git_commit]\n condition: |\n and(\n succeeded(),\n not(contains(dependencies['git_commit']['outputs']['commit.message'], '[ci skip]'))\n )\n matrix:\n pylatest_conda_forge_mkl:\n DISTRIB: 'conda'\n CONDA_CHANNEL: 'conda-forge'\n PYTHON_VERSION: '*'\n BLAS: 'mkl'\n COVERAGE: 'true'\n SHOW_SHORT_SUMMARY: 'true'\n\n# Check compilation with Ubuntu bionic 18.04 LTS and scipy from conda-forge\n- template: build_tools/azure/posix.yml\n parameters:\n name: Ubuntu_Bionic\n vmImage: ubuntu-18.04\n dependsOn: [git_commit, linting]\n condition: |\n and(\n succeeded(),\n not(contains(dependencies['git_commit']['outputs']['commit.message'], '[ci skip]')),\n ne(variables['Build.Reason'], 'Schedule')\n )\n matrix:\n py37_conda_forge_openblas_ubuntu_1804:\n DISTRIB: 'conda'\n CONDA_CHANNEL: 'conda-forge'\n PYTHON_VERSION: '3.7'\n BLAS: 'openblas'\n COVERAGE: 'false'\n BUILD_WITH_ICC: 'false'\n\n- template: build_tools/azure/posix.yml\n parameters:\n name: Linux\n vmImage: ubuntu-20.04\n dependsOn: [linting, git_commit]\n condition: |\n and(\n succeeded(),\n not(contains(dependencies['git_commit']['outputs']['commit.message'], '[ci skip]')),\n ne(variables['Build.Reason'], 'Schedule')\n )\n matrix:\n # Linux environment to test that scikit-learn can be built against\n # versions of numpy, scipy with ATLAS that comes with Ubuntu Focal 20.04\n # i.e. numpy 1.17.4 and scipy 1.3.3\n ubuntu_atlas:\n DISTRIB: 'ubuntu'\n JOBLIB_VERSION: 'min'\n PANDAS_VERSION: 'none'\n THREADPOOLCTL_VERSION: 'min'\n COVERAGE: 'false'\n # Linux + Python 3.7 build with OpenBLAS and without SITE_JOBLIB\n py37_conda_defaults_openblas:\n DISTRIB: 'conda'\n CONDA_CHANNEL: 'defaults' # Anaconda main channel\n PYTHON_VERSION: '3.7'\n BLAS: 'openblas'\n NUMPY_VERSION: 'min'\n SCIPY_VERSION: 'min'\n MATPLOTLIB_VERSION: 'min'\n THREADPOOLCTL_VERSION: '2.2.0'\n # Linux environment to test the latest available dependencies and MKL.\n # It runs tests requiring lightgbm, pandas and PyAMG.\n pylatest_pip_openblas_pandas:\n DISTRIB: 'conda-pip-latest'\n PYTHON_VERSION: '3.9'\n PANDAS_VERSION: 'none'\n CHECK_PYTEST_SOFT_DEPENDENCY: 'true'\n TEST_DOCSTRINGS: 'true'\n CHECK_WARNINGS: 'true'\n\n- template: build_tools/azure/posix-docker.yml\n parameters:\n name: Linux_Docker\n vmImage: ubuntu-20.04\n dependsOn: [linting, git_commit]\n condition: |\n and(\n succeeded(),\n not(contains(dependencies['git_commit']['outputs']['commit.message'], '[ci skip]')),\n ne(variables['Build.Reason'], 'Schedule')\n )\n matrix:\n debian_atlas_32bit:\n DISTRIB: 'debian-32'\n DOCKER_CONTAINER: 'i386/debian:10.9'\n JOBLIB_VERSION: 'min'\n # disable pytest xdist due to unknown bug with 32-bit container\n PYTEST_XDIST_VERSION: 'none'\n PYTEST_VERSION: 'min'\n THREADPOOLCTL_VERSION: '2.2.0'\n\n- template: build_tools/azure/posix.yml\n parameters:\n name: macOS\n vmImage: macOS-10.14\n dependsOn: [linting, git_commit]\n condition: |\n and(\n succeeded(),\n not(contains(dependencies['git_commit']['outputs']['commit.message'], '[ci skip]')),\n ne(variables['Build.Reason'], 'Schedule')\n )\n matrix:\n pylatest_conda_forge_mkl:\n DISTRIB: 'conda'\n BLAS: 'mkl'\n CONDA_CHANNEL: 'conda-forge'\n pylatest_conda_mkl_no_openmp:\n DISTRIB: 'conda'\n BLAS: 'mkl'\n SKLEARN_TEST_NO_OPENMP: 'true'\n SKLEARN_SKIP_OPENMP_TEST: 'true'\n\n- template: build_tools/azure/windows.yml\n parameters:\n name: Windows\n vmImage: windows-latest\n dependsOn: [linting, git_commit]\n condition: |\n and(\n succeeded(),\n not(contains(dependencies['git_commit']['outputs']['commit.message'], '[ci skip]')),\n ne(variables['Build.Reason'], 'Schedule')\n )\n matrix:\n py37_conda_forge_mkl:\n DISTRIB: 'conda'\n CONDA_CHANNEL: 'conda-forge'\n PYTHON_VERSION: '3.7'\n CHECK_WARNINGS: 'true'\n PYTHON_ARCH: '64'\n PYTEST_VERSION: '*'\n COVERAGE: 'true'\n py37_pip_openblas_32bit:\n PYTHON_VERSION: '3.7'\n PYTHON_ARCH: '32'\n" }, { "path": "benchmarks/.gitignore", "content": "/bhtsne\n*.npy\n*.json\n/mnist_tsne_output/\n" }, { "path": "benchmarks/bench_20newsgroups.py", "content": "from time import time\nimport argparse\nimport numpy as np\n\nfrom sklearn.dummy import DummyClassifier\n\nfrom sklearn.datasets import fetch_20newsgroups_vectorized\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.utils.validation import check_array\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import ExtraTreesClassifier\nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.naive_bayes import MultinomialNB\n\nESTIMATORS = {\n \"dummy\": DummyClassifier(),\n \"random_forest\": RandomForestClassifier(max_features=\"sqrt\", min_samples_split=10),\n \"extra_trees\": ExtraTreesClassifier(max_features=\"sqrt\", min_samples_split=10),\n \"logistic_regression\": LogisticRegression(),\n \"naive_bayes\": MultinomialNB(),\n \"adaboost\": AdaBoostClassifier(n_estimators=10),\n}\n\n\n###############################################################################\n# Data\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"-e\", \"--estimators\", nargs=\"+\", required=True, choices=ESTIMATORS\n )\n args = vars(parser.parse_args())\n\n data_train = fetch_20newsgroups_vectorized(subset=\"train\")\n data_test = fetch_20newsgroups_vectorized(subset=\"test\")\n X_train = check_array(data_train.data, dtype=np.float32, accept_sparse=\"csc\")\n X_test = check_array(data_test.data, dtype=np.float32, accept_sparse=\"csr\")\n y_train = data_train.target\n y_test = data_test.target\n\n print(\"20 newsgroups\")\n print(\"=============\")\n print(f\"X_train.shape = {X_train.shape}\")\n print(f\"X_train.format = {X_train.format}\")\n print(f\"X_train.dtype = {X_train.dtype}\")\n print(f\"X_train density = {X_train.nnz / np.product(X_train.shape)}\")\n print(f\"y_train {y_train.shape}\")\n print(f\"X_test {X_test.shape}\")\n print(f\"X_test.format = {X_test.format}\")\n print(f\"X_test.dtype = {X_test.dtype}\")\n print(f\"y_test {y_test.shape}\")\n print()\n print(\"Classifier Training\")\n print(\"===================\")\n accuracy, train_time, test_time = {}, {}, {}\n for name in sorted(args[\"estimators\"]):\n clf = ESTIMATORS[name]\n try:\n clf.set_params(random_state=0)\n except (TypeError, ValueError):\n pass\n\n print(\"Training %s ... \" % name, end=\"\")\n t0 = time()\n clf.fit(X_train, y_train)\n train_time[name] = time() - t0\n t0 = time()\n y_pred = clf.predict(X_test)\n test_time[name] = time() - t0\n accuracy[name] = accuracy_score(y_test, y_pred)\n print(\"done\")\n\n print()\n print(\"Classification performance:\")\n print(\"===========================\")\n print()\n print(\"%s %s %s %s\" % (\"Classifier \", \"train-time\", \"test-time\", \"Accuracy\"))\n print(\"-\" * 44)\n for name in sorted(accuracy, key=accuracy.get):\n print(\n \"%s %s %s %s\"\n % (\n name.ljust(16),\n (\"%.4fs\" % train_time[name]).center(10),\n (\"%.4fs\" % test_time[name]).center(10),\n (\"%.4f\" % accuracy[name]).center(10),\n )\n )\n\n print()\n" }, { "path": "benchmarks/bench_covertype.py", "content": "\"\"\"\n===========================\nCovertype dataset benchmark\n===========================\n\nBenchmark stochastic gradient descent (SGD), Liblinear, and Naive Bayes, CART\n(decision tree), RandomForest and Extra-Trees on the forest covertype dataset\nof Blackard, Jock, and Dean [1]. The dataset comprises 581,012 samples. It is\nlow dimensional with 54 features and a sparsity of approx. 23%. Here, we\nconsider the task of predicting class 1 (spruce/fir). The classification\nperformance of SGD is competitive with Liblinear while being two orders of\nmagnitude faster to train::\n\n [..]\n Classification performance:\n ===========================\n Classifier train-time test-time error-rate\n --------------------------------------------\n liblinear 15.9744s 0.0705s 0.2305\n GaussianNB 3.0666s 0.3884s 0.4841\n SGD 1.0558s 0.1152s 0.2300\n CART 79.4296s 0.0523s 0.0469\n RandomForest 1190.1620s 0.5881s 0.0243\n ExtraTrees 640.3194s 0.6495s 0.0198\n\nThe same task has been used in a number of papers including:\n\n * `\"SVM Optimization: Inverse Dependence on Training Set Size\"\n `_\n S. Shalev-Shwartz, N. Srebro - In Proceedings of ICML '08.\n\n * `\"Pegasos: Primal estimated sub-gradient solver for svm\"\n `_\n S. Shalev-Shwartz, Y. Singer, N. Srebro - In Proceedings of ICML '07.\n\n * `\"Training Linear SVMs in Linear Time\"\n `_\n T. Joachims - In SIGKDD '06\n\n[1] https://archive.ics.uci.edu/ml/datasets/Covertype\n\n\"\"\"\n\n# Author: Peter Prettenhofer \n# Arnaud Joly \n# License: BSD 3 clause\n\nimport os\nfrom time import time\nimport argparse\nimport numpy as np\nfrom joblib import Memory\n\nfrom sklearn.datasets import fetch_covtype, get_data_home\nfrom sklearn.svm import LinearSVC\nfrom sklearn.linear_model import SGDClassifier, LogisticRegression\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom sklearn.metrics import zero_one_loss\nfrom sklearn.utils import check_array\n\n# Memoize the data extraction and memory map the resulting\n# train / test splits in readonly mode\nmemory = Memory(\n os.path.join(get_data_home(), \"covertype_benchmark_data\"), mmap_mode=\"r\"\n)\n\n\n@memory.cache\ndef load_data(dtype=np.float32, order=\"C\", random_state=13):\n \"\"\"Load the data, then cache and memmap the train/test split\"\"\"\n ######################################################################\n # Load dataset\n print(\"Loading dataset...\")\n data = fetch_covtype(\n download_if_missing=True, shuffle=True, random_state=random_state\n )\n X = check_array(data[\"data\"], dtype=dtype, order=order)\n y = (data[\"target\"] != 1).astype(int)\n\n # Create train-test split (as [Joachims, 2006])\n print(\"Creating train-test split...\")\n n_train = 522911\n X_train = X[:n_train]\n y_train = y[:n_train]\n X_test = X[n_train:]\n y_test = y[n_train:]\n\n # Standardize first 10 features (the numerical ones)\n mean = X_train.mean(axis=0)\n std = X_train.std(axis=0)\n mean[10:] = 0.0\n std[10:] = 1.0\n X_train = (X_train - mean) / std\n X_test = (X_test - mean) / std\n return X_train, X_test, y_train, y_test\n\n\nESTIMATORS = {\n \"GBRT\": GradientBoostingClassifier(n_estimators=250),\n \"ExtraTrees\": ExtraTreesClassifier(n_estimators=20),\n \"RandomForest\": RandomForestClassifier(n_estimators=20),\n \"CART\": DecisionTreeClassifier(min_samples_split=5),\n \"SGD\": SGDClassifier(alpha=0.001),\n \"GaussianNB\": GaussianNB(),\n \"liblinear\": LinearSVC(loss=\"l2\", penalty=\"l2\", C=1000, dual=False, tol=1e-3),\n \"SAG\": LogisticRegression(solver=\"sag\", max_iter=2, C=1000),\n}\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--classifiers\",\n nargs=\"+\",\n choices=ESTIMATORS,\n type=str,\n default=[\"liblinear\", \"GaussianNB\", \"SGD\", \"CART\"],\n help=\"list of classifiers to benchmark.\",\n )\n parser.add_argument(\n \"--n-jobs\",\n nargs=\"?\",\n default=1,\n type=int,\n help=(\n \"Number of concurrently running workers for \"\n \"models that support parallelism.\"\n ),\n )\n parser.add_argument(\n \"--order\",\n nargs=\"?\",\n default=\"C\",\n type=str,\n choices=[\"F\", \"C\"],\n help=\"Allow to choose between fortran and C ordered data\",\n )\n parser.add_argument(\n \"--random-seed\",\n nargs=\"?\",\n default=13,\n type=int,\n help=\"Common seed used by random number generator.\",\n )\n args = vars(parser.parse_args())\n\n print(__doc__)\n\n X_train, X_test, y_train, y_test = load_data(\n order=args[\"order\"], random_state=args[\"random_seed\"]\n )\n\n print(\"\")\n print(\"Dataset statistics:\")\n print(\"===================\")\n print(\"%s %d\" % (\"number of features:\".ljust(25), X_train.shape[1]))\n print(\"%s %d\" % (\"number of classes:\".ljust(25), np.unique(y_train).size))\n print(\"%s %s\" % (\"data type:\".ljust(25), X_train.dtype))\n print(\n \"%s %d (pos=%d, neg=%d, size=%dMB)\"\n % (\n \"number of train samples:\".ljust(25),\n X_train.shape[0],\n np.sum(y_train == 1),\n np.sum(y_train == 0),\n int(X_train.nbytes / 1e6),\n )\n )\n print(\n \"%s %d (pos=%d, neg=%d, size=%dMB)\"\n % (\n \"number of test samples:\".ljust(25),\n X_test.shape[0],\n np.sum(y_test == 1),\n np.sum(y_test == 0),\n int(X_test.nbytes / 1e6),\n )\n )\n\n print()\n print(\"Training Classifiers\")\n print(\"====================\")\n error, train_time, test_time = {}, {}, {}\n for name in sorted(args[\"classifiers\"]):\n print(\"Training %s ... \" % name, end=\"\")\n estimator = ESTIMATORS[name]\n estimator_params = estimator.get_params()\n\n estimator.set_params(\n **{\n p: args[\"random_seed\"]\n for p in estimator_params\n if p.endswith(\"random_state\")\n }\n )\n\n if \"n_jobs\" in estimator_params:\n estimator.set_params(n_jobs=args[\"n_jobs\"])\n\n time_start = time()\n estimator.fit(X_train, y_train)\n train_time[name] = time() - time_start\n\n time_start = time()\n y_pred = estimator.predict(X_test)\n test_time[name] = time() - time_start\n\n error[name] = zero_one_loss(y_test, y_pred)\n\n print(\"done\")\n\n print()\n print(\"Classification performance:\")\n print(\"===========================\")\n print(\"%s %s %s %s\" % (\"Classifier \", \"train-time\", \"test-time\", \"error-rate\"))\n print(\"-\" * 44)\n for name in sorted(args[\"classifiers\"], key=error.get):\n print(\n \"%s %s %s %s\"\n % (\n name.ljust(12),\n (\"%.4fs\" % train_time[name]).center(10),\n (\"%.4fs\" % test_time[name]).center(10),\n (\"%.4f\" % error[name]).center(10),\n )\n )\n\n print()\n" }, { "path": "benchmarks/bench_feature_expansions.py", "content": "import matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.sparse as sparse\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom time import time\n\ndegree = 2\ntrials = 3\nnum_rows = 1000\ndimensionalities = np.array([1, 2, 8, 16, 32, 64])\ndensities = np.array([0.01, 0.1, 1.0])\ncsr_times = {d: np.zeros(len(dimensionalities)) for d in densities}\ndense_times = {d: np.zeros(len(dimensionalities)) for d in densities}\ntransform = PolynomialFeatures(\n degree=degree, include_bias=False, interaction_only=False\n)\n\nfor trial in range(trials):\n for density in densities:\n for dim_index, dim in enumerate(dimensionalities):\n print(trial, density, dim)\n X_csr = sparse.random(num_rows, dim, density).tocsr()\n X_dense = X_csr.toarray()\n # CSR\n t0 = time()\n transform.fit_transform(X_csr)\n csr_times[density][dim_index] += time() - t0\n # Dense\n t0 = time()\n transform.fit_transform(X_dense)\n dense_times[density][dim_index] += time() - t0\n\ncsr_linestyle = (0, (3, 1, 1, 1, 1, 1)) # densely dashdotdotted\ndense_linestyle = (0, ()) # solid\n\nfig, axes = plt.subplots(nrows=len(densities), ncols=1, figsize=(8, 10))\nfor density, ax in zip(densities, axes):\n\n ax.plot(\n dimensionalities,\n csr_times[density] / trials,\n label=\"csr\",\n linestyle=csr_linestyle,\n )\n ax.plot(\n dimensionalities,\n dense_times[density] / trials,\n label=\"dense\",\n linestyle=dense_linestyle,\n )\n ax.set_title(\"density %0.2f, degree=%d, n_samples=%d\" % (density, degree, num_rows))\n ax.legend()\n ax.set_xlabel(\"Dimensionality\")\n ax.set_ylabel(\"Time (seconds)\")\n\nplt.tight_layout()\nplt.show()\n" }, { "path": "benchmarks/bench_glm.py", "content": "\"\"\"\nA comparison of different methods in GLM\n\nData comes from a random square matrix.\n\n\"\"\"\nfrom datetime import datetime\nimport numpy as np\nfrom sklearn import linear_model\n\n\nif __name__ == \"__main__\":\n\n import matplotlib.pyplot as plt\n\n n_iter = 40\n\n time_ridge = np.empty(n_iter)\n time_ols = np.empty(n_iter)\n time_lasso = np.empty(n_iter)\n\n dimensions = 500 * np.arange(1, n_iter + 1)\n\n for i in range(n_iter):\n\n print(\"Iteration %s of %s\" % (i, n_iter))\n\n n_samples, n_features = 10 * i + 3, 10 * i + 3\n\n X = np.random.randn(n_samples, n_features)\n Y = np.random.randn(n_samples)\n\n start = datetime.now()\n ridge = linear_model.Ridge(alpha=1.0)\n ridge.fit(X, Y)\n time_ridge[i] = (datetime.now() - start).total_seconds()\n\n start = datetime.now()\n ols = linear_model.LinearRegression()\n ols.fit(X, Y)\n time_ols[i] = (datetime.now() - start).total_seconds()\n\n start = datetime.now()\n lasso = linear_model.LassoLars()\n lasso.fit(X, Y)\n time_lasso[i] = (datetime.now() - start).total_seconds()\n\n plt.figure(\"scikit-learn GLM benchmark results\")\n plt.xlabel(\"Dimensions\")\n plt.ylabel(\"Time (s)\")\n plt.plot(dimensions, time_ridge, color=\"r\")\n plt.plot(dimensions, time_ols, color=\"g\")\n plt.plot(dimensions, time_lasso, color=\"b\")\n\n plt.legend([\"Ridge\", \"OLS\", \"LassoLars\"], loc=\"upper left\")\n plt.axis(\"tight\")\n plt.show()\n" }, { "path": "benchmarks/bench_glmnet.py", "content": "\"\"\"\nTo run this, you'll need to have installed.\n\n * glmnet-python\n * scikit-learn (of course)\n\nDoes two benchmarks\n\nFirst, we fix a training set and increase the number of\nsamples. Then we plot the computation time as function of\nthe number of samples.\n\nIn the second benchmark, we increase the number of dimensions of the\ntraining set. Then we plot the computation time as function of\nthe number of dimensions.\n\nIn both cases, only 10% of the features are informative.\n\"\"\"\nimport numpy as np\nimport gc\nfrom time import time\nfrom sklearn.datasets import make_regression\n\nalpha = 0.1\n# alpha = 0.01\n\n\ndef rmse(a, b):\n return np.sqrt(np.mean((a - b) ** 2))\n\n\ndef bench(factory, X, Y, X_test, Y_test, ref_coef):\n gc.collect()\n\n # start time\n tstart = time()\n clf = factory(alpha=alpha).fit(X, Y)\n delta = time() - tstart\n # stop time\n\n print(\"duration: %0.3fs\" % delta)\n print(\"rmse: %f\" % rmse(Y_test, clf.predict(X_test)))\n print(\"mean coef abs diff: %f\" % abs(ref_coef - clf.coef_.ravel()).mean())\n return delta\n\n\nif __name__ == \"__main__\":\n from glmnet.elastic_net import Lasso as GlmnetLasso\n from sklearn.linear_model import Lasso as ScikitLasso\n\n # Delayed import of matplotlib.pyplot\n import matplotlib.pyplot as plt\n\n scikit_results = []\n glmnet_results = []\n n = 20\n step = 500\n n_features = 1000\n n_informative = n_features / 10\n n_test_samples = 1000\n for i in range(1, n + 1):\n print(\"==================\")\n print(\"Iteration %s of %s\" % (i, n))\n print(\"==================\")\n\n X, Y, coef_ = make_regression(\n n_samples=(i * step) + n_test_samples,\n n_features=n_features,\n noise=0.1,\n n_informative=n_informative,\n coef=True,\n )\n\n X_test = X[-n_test_samples:]\n Y_test = Y[-n_test_samples:]\n X = X[: (i * step)]\n Y = Y[: (i * step)]\n\n print(\"benchmarking scikit-learn: \")\n scikit_results.append(bench(ScikitLasso, X, Y, X_test, Y_test, coef_))\n print(\"benchmarking glmnet: \")\n glmnet_results.append(bench(GlmnetLasso, X, Y, X_test, Y_test, coef_))\n\n plt.clf()\n xx = range(0, n * step, step)\n plt.title(\"Lasso regression on sample dataset (%d features)\" % n_features)\n plt.plot(xx, scikit_results, \"b-\", label=\"scikit-learn\")\n plt.plot(xx, glmnet_results, \"r-\", label=\"glmnet\")\n plt.legend()\n plt.xlabel(\"number of samples to classify\")\n plt.ylabel(\"Time (s)\")\n plt.show()\n\n # now do a benchmark where the number of points is fixed\n # and the variable is the number of features\n\n scikit_results = []\n glmnet_results = []\n n = 20\n step = 100\n n_samples = 500\n\n for i in range(1, n + 1):\n print(\"==================\")\n print(\"Iteration %02d of %02d\" % (i, n))\n print(\"==================\")\n n_features = i * step\n n_informative = n_features / 10\n\n X, Y, coef_ = make_regression(\n n_samples=(i * step) + n_test_samples,\n n_features=n_features,\n noise=0.1,\n n_informative=n_informative,\n coef=True,\n )\n\n X_test = X[-n_test_samples:]\n Y_test = Y[-n_test_samples:]\n X = X[:n_samples]\n Y = Y[:n_samples]\n\n print(\"benchmarking scikit-learn: \")\n scikit_results.append(bench(ScikitLasso, X, Y, X_test, Y_test, coef_))\n print(\"benchmarking glmnet: \")\n glmnet_results.append(bench(GlmnetLasso, X, Y, X_test, Y_test, coef_))\n\n xx = np.arange(100, 100 + n * step, step)\n plt.figure(\"scikit-learn vs. glmnet benchmark results\")\n plt.title(\"Regression in high dimensional spaces (%d samples)\" % n_samples)\n plt.plot(xx, scikit_results, \"b-\", label=\"scikit-learn\")\n plt.plot(xx, glmnet_results, \"r-\", label=\"glmnet\")\n plt.legend()\n plt.xlabel(\"number of features\")\n plt.ylabel(\"Time (s)\")\n plt.axis(\"tight\")\n plt.show()\n" }, { "path": "benchmarks/bench_hist_gradient_boosting.py", "content": "from time import time\nimport argparse\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nfrom sklearn.ensemble import HistGradientBoostingClassifier\nfrom sklearn.datasets import make_classification\nfrom sklearn.datasets import make_regression\nfrom sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--n-leaf-nodes\", type=int, default=31)\nparser.add_argument(\"--n-trees\", type=int, default=10)\nparser.add_argument(\n \"--lightgbm\", action=\"store_true\", default=False, help=\"also plot lightgbm\"\n)\nparser.add_argument(\n \"--xgboost\", action=\"store_true\", default=False, help=\"also plot xgboost\"\n)\nparser.add_argument(\n \"--catboost\", action=\"store_true\", default=False, help=\"also plot catboost\"\n)\nparser.add_argument(\"--learning-rate\", type=float, default=0.1)\nparser.add_argument(\n \"--problem\",\n type=str,\n default=\"classification\",\n choices=[\"classification\", \"regression\"],\n)\nparser.add_argument(\"--loss\", type=str, default=\"default\")\nparser.add_argument(\"--missing-fraction\", type=float, default=0)\nparser.add_argument(\"--n-classes\", type=int, default=2)\nparser.add_argument(\"--n-samples-max\", type=int, default=int(1e6))\nparser.add_argument(\"--n-features\", type=int, default=20)\nparser.add_argument(\"--max-bins\", type=int, default=255)\nparser.add_argument(\n \"--random-sample-weights\",\n action=\"store_true\",\n default=False,\n help=\"generate and use random sample weights\",\n)\nargs = parser.parse_args()\n\nn_leaf_nodes = args.n_leaf_nodes\nn_trees = args.n_trees\nlr = args.learning_rate\nmax_bins = args.max_bins\n\n\ndef get_estimator_and_data():\n if args.problem == \"classification\":\n X, y = make_classification(\n args.n_samples_max * 2,\n n_features=args.n_features,\n n_classes=args.n_classes,\n n_clusters_per_class=1,\n n_informative=args.n_classes,\n random_state=0,\n )\n return X, y, HistGradientBoostingClassifier\n elif args.problem == \"regression\":\n X, y = make_regression(\n args.n_samples_max * 2, n_features=args.n_features, random_state=0\n )\n return X, y, HistGradientBoostingRegressor\n\n\nX, y, Estimator = get_estimator_and_data()\nif args.missing_fraction:\n mask = np.random.binomial(1, args.missing_fraction, size=X.shape).astype(bool)\n X[mask] = np.nan\n\nif args.random_sample_weights:\n sample_weight = np.random.rand(len(X)) * 10\nelse:\n sample_weight = None\n\nif sample_weight is not None:\n (X_train_, X_test_, y_train_, y_test_, sample_weight_train_, _) = train_test_split(\n X, y, sample_weight, test_size=0.5, random_state=0\n )\nelse:\n X_train_, X_test_, y_train_, y_test_ = train_test_split(\n X, y, test_size=0.5, random_state=0\n )\n sample_weight_train_ = None\n\n\ndef one_run(n_samples):\n X_train = X_train_[:n_samples]\n X_test = X_test_[:n_samples]\n y_train = y_train_[:n_samples]\n y_test = y_test_[:n_samples]\n if sample_weight is not None:\n sample_weight_train = sample_weight_train_[:n_samples]\n else:\n sample_weight_train = None\n assert X_train.shape[0] == n_samples\n assert X_test.shape[0] == n_samples\n print(\"Data size: %d samples train, %d samples test.\" % (n_samples, n_samples))\n print(\"Fitting a sklearn model...\")\n tic = time()\n est = Estimator(\n learning_rate=lr,\n max_iter=n_trees,\n max_bins=max_bins,\n max_leaf_nodes=n_leaf_nodes,\n early_stopping=False,\n random_state=0,\n verbose=0,\n )\n loss = args.loss\n if args.problem == \"classification\":\n if loss == \"default\":\n # loss='auto' does not work with get_equivalent_estimator()\n loss = (\n \"binary_crossentropy\"\n if args.n_classes == 2\n else \"categorical_crossentropy\"\n )\n else:\n # regression\n if loss == \"default\":\n loss = \"squared_error\"\n est.set_params(loss=loss)\n est.fit(X_train, y_train, sample_weight=sample_weight_train)\n sklearn_fit_duration = time() - tic\n tic = time()\n sklearn_score = est.score(X_test, y_test)\n sklearn_score_duration = time() - tic\n print(\"score: {:.4f}\".format(sklearn_score))\n print(\"fit duration: {:.3f}s,\".format(sklearn_fit_duration))\n print(\"score duration: {:.3f}s,\".format(sklearn_score_duration))\n\n lightgbm_score = None\n lightgbm_fit_duration = None\n lightgbm_score_duration = None\n if args.lightgbm:\n print(\"Fitting a LightGBM model...\")\n lightgbm_est = get_equivalent_estimator(\n est, lib=\"lightgbm\", n_classes=args.n_classes\n )\n\n tic = time()\n lightgbm_est.fit(X_train, y_train, sample_weight=sample_weight_train)\n lightgbm_fit_duration = time() - tic\n tic = time()\n lightgbm_score = lightgbm_est.score(X_test, y_test)\n lightgbm_score_duration = time() - tic\n print(\"score: {:.4f}\".format(lightgbm_score))\n print(\"fit duration: {:.3f}s,\".format(lightgbm_fit_duration))\n print(\"score duration: {:.3f}s,\".format(lightgbm_score_duration))\n\n xgb_score = None\n xgb_fit_duration = None\n xgb_score_duration = None\n if args.xgboost:\n print(\"Fitting an XGBoost model...\")\n xgb_est = get_equivalent_estimator(est, lib=\"xgboost\")\n\n tic = time()\n xgb_est.fit(X_train, y_train, sample_weight=sample_weight_train)\n xgb_fit_duration = time() - tic\n tic = time()\n xgb_score = xgb_est.score(X_test, y_test)\n xgb_score_duration = time() - tic\n print(\"score: {:.4f}\".format(xgb_score))\n print(\"fit duration: {:.3f}s,\".format(xgb_fit_duration))\n print(\"score duration: {:.3f}s,\".format(xgb_score_duration))\n\n cat_score = None\n cat_fit_duration = None\n cat_score_duration = None\n if args.catboost:\n print(\"Fitting a CatBoost model...\")\n cat_est = get_equivalent_estimator(est, lib=\"catboost\")\n\n tic = time()\n cat_est.fit(X_train, y_train, sample_weight=sample_weight_train)\n cat_fit_duration = time() - tic\n tic = time()\n cat_score = cat_est.score(X_test, y_test)\n cat_score_duration = time() - tic\n print(\"score: {:.4f}\".format(cat_score))\n print(\"fit duration: {:.3f}s,\".format(cat_fit_duration))\n print(\"score duration: {:.3f}s,\".format(cat_score_duration))\n\n return (\n sklearn_score,\n sklearn_fit_duration,\n sklearn_score_duration,\n lightgbm_score,\n lightgbm_fit_duration,\n lightgbm_score_duration,\n xgb_score,\n xgb_fit_duration,\n xgb_score_duration,\n cat_score,\n cat_fit_duration,\n cat_score_duration,\n )\n\n\nn_samples_list = [1000, 10000, 100000, 500000, 1000000, 5000000, 10000000]\nn_samples_list = [\n n_samples for n_samples in n_samples_list if n_samples <= args.n_samples_max\n]\n\nsklearn_scores = []\nsklearn_fit_durations = []\nsklearn_score_durations = []\nlightgbm_scores = []\nlightgbm_fit_durations = []\nlightgbm_score_durations = []\nxgb_scores = []\nxgb_fit_durations = []\nxgb_score_durations = []\ncat_scores = []\ncat_fit_durations = []\ncat_score_durations = []\n\nfor n_samples in n_samples_list:\n (\n sklearn_score,\n sklearn_fit_duration,\n sklearn_score_duration,\n lightgbm_score,\n lightgbm_fit_duration,\n lightgbm_score_duration,\n xgb_score,\n xgb_fit_duration,\n xgb_score_duration,\n cat_score,\n cat_fit_duration,\n cat_score_duration,\n ) = one_run(n_samples)\n\n for scores, score in (\n (sklearn_scores, sklearn_score),\n (sklearn_fit_durations, sklearn_fit_duration),\n (sklearn_score_durations, sklearn_score_duration),\n (lightgbm_scores, lightgbm_score),\n (lightgbm_fit_durations, lightgbm_fit_duration),\n (lightgbm_score_durations, lightgbm_score_duration),\n (xgb_scores, xgb_score),\n (xgb_fit_durations, xgb_fit_duration),\n (xgb_score_durations, xgb_score_duration),\n (cat_scores, cat_score),\n (cat_fit_durations, cat_fit_duration),\n (cat_score_durations, cat_score_duration),\n ):\n scores.append(score)\n\nfig, axs = plt.subplots(3, sharex=True)\n\naxs[0].plot(n_samples_list, sklearn_scores, label=\"sklearn\")\naxs[1].plot(n_samples_list, sklearn_fit_durations, label=\"sklearn\")\naxs[2].plot(n_samples_list, sklearn_score_durations, label=\"sklearn\")\n\nif args.lightgbm:\n axs[0].plot(n_samples_list, lightgbm_scores, label=\"lightgbm\")\n axs[1].plot(n_samples_list, lightgbm_fit_durations, label=\"lightgbm\")\n axs[2].plot(n_samples_list, lightgbm_score_durations, label=\"lightgbm\")\n\nif args.xgboost:\n axs[0].plot(n_samples_list, xgb_scores, label=\"XGBoost\")\n axs[1].plot(n_samples_list, xgb_fit_durations, label=\"XGBoost\")\n axs[2].plot(n_samples_list, xgb_score_durations, label=\"XGBoost\")\n\nif args.catboost:\n axs[0].plot(n_samples_list, cat_scores, label=\"CatBoost\")\n axs[1].plot(n_samples_list, cat_fit_durations, label=\"CatBoost\")\n axs[2].plot(n_samples_list, cat_score_durations, label=\"CatBoost\")\n\nfor ax in axs:\n ax.set_xscale(\"log\")\n ax.legend(loc=\"best\")\n ax.set_xlabel(\"n_samples\")\n\naxs[0].set_title(\"scores\")\naxs[1].set_title(\"fit duration (s)\")\naxs[2].set_title(\"score duration (s)\")\n\ntitle = args.problem\nif args.problem == \"classification\":\n title += \" n_classes = {}\".format(args.n_classes)\nfig.suptitle(title)\n\n\nplt.tight_layout()\nplt.show()\n" }, { "path": "benchmarks/bench_hist_gradient_boosting_adult.py", "content": "import argparse\nfrom time import time\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.metrics import accuracy_score, roc_auc_score\nfrom sklearn.ensemble import HistGradientBoostingClassifier\nfrom sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--n-leaf-nodes\", type=int, default=31)\nparser.add_argument(\"--n-trees\", type=int, default=100)\nparser.add_argument(\"--lightgbm\", action=\"store_true\", default=False)\nparser.add_argument(\"--learning-rate\", type=float, default=0.1)\nparser.add_argument(\"--max-bins\", type=int, default=255)\nparser.add_argument(\"--no-predict\", action=\"store_true\", default=False)\nparser.add_argument(\"--verbose\", action=\"store_true\", default=False)\nargs = parser.parse_args()\n\nn_leaf_nodes = args.n_leaf_nodes\nn_trees = args.n_trees\nlr = args.learning_rate\nmax_bins = args.max_bins\nverbose = args.verbose\n\n\ndef fit(est, data_train, target_train, libname, **fit_params):\n print(f\"Fitting a {libname} model...\")\n tic = time()\n est.fit(data_train, target_train, **fit_params)\n toc = time()\n print(f\"fitted in {toc - tic:.3f}s\")\n\n\ndef predict(est, data_test, target_test):\n if args.no_predict:\n return\n tic = time()\n predicted_test = est.predict(data_test)\n predicted_proba_test = est.predict_proba(data_test)\n toc = time()\n roc_auc = roc_auc_score(target_test, predicted_proba_test[:, 1])\n acc = accuracy_score(target_test, predicted_test)\n print(f\"predicted in {toc - tic:.3f}s, ROC AUC: {roc_auc:.4f}, ACC: {acc :.4f}\")\n\n\ndata = fetch_openml(data_id=179, as_frame=False) # adult dataset\nX, y = data.data, data.target\n\nn_features = X.shape[1]\nn_categorical_features = len(data.categories)\nn_numerical_features = n_features - n_categorical_features\nprint(f\"Number of features: {n_features}\")\nprint(f\"Number of categorical features: {n_categorical_features}\")\nprint(f\"Number of numerical features: {n_numerical_features}\")\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n\n# Note: no need to use an OrdinalEncoder because categorical features are\n# already clean\nis_categorical = [name in data.categories for name in data.feature_names]\nest = HistGradientBoostingClassifier(\n loss=\"binary_crossentropy\",\n learning_rate=lr,\n max_iter=n_trees,\n max_bins=max_bins,\n max_leaf_nodes=n_leaf_nodes,\n categorical_features=is_categorical,\n early_stopping=False,\n random_state=0,\n verbose=verbose,\n)\n\nfit(est, X_train, y_train, \"sklearn\")\npredict(est, X_test, y_test)\n\nif args.lightgbm:\n est = get_equivalent_estimator(est, lib=\"lightgbm\")\n est.set_params(max_cat_to_onehot=1) # dont use OHE\n categorical_features = [\n f_idx for (f_idx, is_cat) in enumerate(is_categorical) if is_cat\n ]\n fit(est, X_train, y_train, \"lightgbm\", categorical_feature=categorical_features)\n predict(est, X_test, y_test)\n" }, { "path": "benchmarks/bench_hist_gradient_boosting_categorical_only.py", "content": "import argparse\nfrom time import time\n\nfrom sklearn.preprocessing import KBinsDiscretizer\nfrom sklearn.datasets import make_classification\nfrom sklearn.ensemble import HistGradientBoostingClassifier\nfrom sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--n-leaf-nodes\", type=int, default=31)\nparser.add_argument(\"--n-trees\", type=int, default=100)\nparser.add_argument(\"--n-features\", type=int, default=20)\nparser.add_argument(\"--n-cats\", type=int, default=20)\nparser.add_argument(\"--n-samples\", type=int, default=10_000)\nparser.add_argument(\"--lightgbm\", action=\"store_true\", default=False)\nparser.add_argument(\"--learning-rate\", type=float, default=0.1)\nparser.add_argument(\"--max-bins\", type=int, default=255)\nparser.add_argument(\"--no-predict\", action=\"store_true\", default=False)\nparser.add_argument(\"--verbose\", action=\"store_true\", default=False)\nargs = parser.parse_args()\n\nn_leaf_nodes = args.n_leaf_nodes\nn_features = args.n_features\nn_categories = args.n_cats\nn_samples = args.n_samples\nn_trees = args.n_trees\nlr = args.learning_rate\nmax_bins = args.max_bins\nverbose = args.verbose\n\n\ndef fit(est, data_train, target_train, libname, **fit_params):\n print(f\"Fitting a {libname} model...\")\n tic = time()\n est.fit(data_train, target_train, **fit_params)\n toc = time()\n print(f\"fitted in {toc - tic:.3f}s\")\n\n\ndef predict(est, data_test):\n # We don't report accuracy or ROC because the dataset doesn't really make\n # sense: we treat ordered features as un-ordered categories.\n if args.no_predict:\n return\n tic = time()\n est.predict(data_test)\n toc = time()\n print(f\"predicted in {toc - tic:.3f}s\")\n\n\nX, y = make_classification(n_samples=n_samples, n_features=n_features, random_state=0)\n\nX = KBinsDiscretizer(n_bins=n_categories, encode=\"ordinal\").fit_transform(X)\n\nprint(f\"Number of features: {n_features}\")\nprint(f\"Number of samples: {n_samples}\")\n\nis_categorical = [True] * n_features\nest = HistGradientBoostingClassifier(\n loss=\"binary_crossentropy\",\n learning_rate=lr,\n max_iter=n_trees,\n max_bins=max_bins,\n max_leaf_nodes=n_leaf_nodes,\n categorical_features=is_categorical,\n early_stopping=False,\n random_state=0,\n verbose=verbose,\n)\n\nfit(est, X, y, \"sklearn\")\npredict(est, X)\n\nif args.lightgbm:\n est = get_equivalent_estimator(est, lib=\"lightgbm\")\n est.set_params(max_cat_to_onehot=1) # dont use OHE\n categorical_features = list(range(n_features))\n fit(est, X, y, \"lightgbm\", categorical_feature=categorical_features)\n predict(est, X)\n" }, { "path": "benchmarks/bench_hist_gradient_boosting_higgsboson.py", "content": "from urllib.request import urlretrieve\nimport os\nfrom gzip import GzipFile\nfrom time import time\nimport argparse\n\nimport numpy as np\nimport pandas as pd\nfrom joblib import Memory\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score, roc_auc_score\nfrom sklearn.ensemble import HistGradientBoostingClassifier\nfrom sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--n-leaf-nodes\", type=int, default=31)\nparser.add_argument(\"--n-trees\", type=int, default=10)\nparser.add_argument(\"--lightgbm\", action=\"store_true\", default=False)\nparser.add_argument(\"--xgboost\", action=\"store_true\", default=False)\nparser.add_argument(\"--catboost\", action=\"store_true\", default=False)\nparser.add_argument(\"--learning-rate\", type=float, default=1.0)\nparser.add_argument(\"--subsample\", type=int, default=None)\nparser.add_argument(\"--max-bins\", type=int, default=255)\nparser.add_argument(\"--no-predict\", action=\"store_true\", default=False)\nparser.add_argument(\"--cache-loc\", type=str, default=\"/tmp\")\nargs = parser.parse_args()\n\nHERE = os.path.dirname(__file__)\nURL = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz\"\nm = Memory(location=args.cache_loc, mmap_mode=\"r\")\n\nn_leaf_nodes = args.n_leaf_nodes\nn_trees = args.n_trees\nsubsample = args.subsample\nlr = args.learning_rate\nmax_bins = args.max_bins\n\n\n@m.cache\ndef load_data():\n filename = os.path.join(HERE, URL.rsplit(\"/\", 1)[-1])\n if not os.path.exists(filename):\n print(f\"Downloading {URL} to {filename} (2.6 GB)...\")\n urlretrieve(URL, filename)\n print(\"done.\")\n\n print(f\"Parsing {filename}...\")\n tic = time()\n with GzipFile(filename) as f:\n df = pd.read_csv(f, header=None, dtype=np.float32)\n toc = time()\n print(f\"Loaded {df.values.nbytes / 1e9:0.3f} GB in {toc - tic:0.3f}s\")\n return df\n\n\ndef fit(est, data_train, target_train, libname):\n print(f\"Fitting a {libname} model...\")\n tic = time()\n est.fit(data_train, target_train)\n toc = time()\n print(f\"fitted in {toc - tic:.3f}s\")\n\n\ndef predict(est, data_test, target_test):\n if args.no_predict:\n return\n tic = time()\n predicted_test = est.predict(data_test)\n predicted_proba_test = est.predict_proba(data_test)\n toc = time()\n roc_auc = roc_auc_score(target_test, predicted_proba_test[:, 1])\n acc = accuracy_score(target_test, predicted_test)\n print(f\"predicted in {toc - tic:.3f}s, ROC AUC: {roc_auc:.4f}, ACC: {acc :.4f}\")\n\n\ndf = load_data()\ntarget = df.values[:, 0]\ndata = np.ascontiguousarray(df.values[:, 1:])\ndata_train, data_test, target_train, target_test = train_test_split(\n data, target, test_size=0.2, random_state=0\n)\n\nif subsample is not None:\n data_train, target_train = data_train[:subsample], target_train[:subsample]\n\nn_samples, n_features = data_train.shape\nprint(f\"Training set with {n_samples} records with {n_features} features.\")\n\nest = HistGradientBoostingClassifier(\n loss=\"binary_crossentropy\",\n learning_rate=lr,\n max_iter=n_trees,\n max_bins=max_bins,\n max_leaf_nodes=n_leaf_nodes,\n early_stopping=False,\n random_state=0,\n verbose=1,\n)\nfit(est, data_train, target_train, \"sklearn\")\npredict(est, data_test, target_test)\n\nif args.lightgbm:\n est = get_equivalent_estimator(est, lib=\"lightgbm\")\n fit(est, data_train, target_train, \"lightgbm\")\n predict(est, data_test, target_test)\n\nif args.xgboost:\n est = get_equivalent_estimator(est, lib=\"xgboost\")\n fit(est, data_train, target_train, \"xgboost\")\n predict(est, data_test, target_test)\n\nif args.catboost:\n est = get_equivalent_estimator(est, lib=\"catboost\")\n fit(est, data_train, target_train, \"catboost\")\n predict(est, data_test, target_test)\n" }, { "path": "benchmarks/bench_hist_gradient_boosting_threading.py", "content": "from time import time\nimport argparse\nimport os\nfrom pprint import pprint\n\nimport numpy as np\nfrom threadpoolctl import threadpool_limits\nimport sklearn\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nfrom sklearn.ensemble import HistGradientBoostingClassifier\nfrom sklearn.datasets import make_classification\nfrom sklearn.datasets import make_regression\nfrom sklearn.ensemble._hist_gradient_boosting.utils import get_equivalent_estimator\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--n-leaf-nodes\", type=int, default=31)\nparser.add_argument(\"--n-trees\", type=int, default=10)\nparser.add_argument(\n \"--lightgbm\", action=\"store_true\", default=False, help=\"also benchmark lightgbm\"\n)\nparser.add_argument(\n \"--xgboost\", action=\"store_true\", default=False, help=\"also benchmark xgboost\"\n)\nparser.add_argument(\n \"--catboost\", action=\"store_true\", default=False, help=\"also benchmark catboost\"\n)\nparser.add_argument(\"--learning-rate\", type=float, default=0.1)\nparser.add_argument(\n \"--problem\",\n type=str,\n default=\"classification\",\n choices=[\"classification\", \"regression\"],\n)\nparser.add_argument(\"--loss\", type=str, default=\"default\")\nparser.add_argument(\"--missing-fraction\", type=float, default=0)\nparser.add_argument(\"--n-classes\", type=int, default=2)\nparser.add_argument(\"--n-samples\", type=int, default=int(1e6))\nparser.add_argument(\"--n-features\", type=int, default=100)\nparser.add_argument(\"--max-bins\", type=int, default=255)\n\nparser.add_argument(\"--print-params\", action=\"store_true\", default=False)\nparser.add_argument(\n \"--random-sample-weights\",\n action=\"store_true\",\n default=False,\n help=\"generate and use random sample weights\",\n)\nparser.add_argument(\n \"--plot\", action=\"store_true\", default=False, help=\"show a plot results\"\n)\nparser.add_argument(\n \"--plot-filename\", default=None, help=\"filename to save the figure to disk\"\n)\nargs = parser.parse_args()\n\nn_samples = args.n_samples\nn_leaf_nodes = args.n_leaf_nodes\nn_trees = args.n_trees\nlr = args.learning_rate\nmax_bins = args.max_bins\n\n\nprint(\"Data size: %d samples train, %d samples test.\" % (n_samples, n_samples))\nprint(f\"n_features: {args.n_features}\")\n\n\ndef get_estimator_and_data():\n if args.problem == \"classification\":\n X, y = make_classification(\n args.n_samples * 2,\n n_features=args.n_features,\n n_classes=args.n_classes,\n n_clusters_per_class=1,\n n_informative=args.n_features // 2,\n random_state=0,\n )\n return X, y, HistGradientBoostingClassifier\n elif args.problem == \"regression\":\n X, y = make_regression(\n args.n_samples_max * 2, n_features=args.n_features, random_state=0\n )\n return X, y, HistGradientBoostingRegressor\n\n\nX, y, Estimator = get_estimator_and_data()\nif args.missing_fraction:\n mask = np.random.binomial(1, args.missing_fraction, size=X.shape).astype(bool)\n X[mask] = np.nan\n\nif args.random_sample_weights:\n sample_weight = np.random.rand(len(X)) * 10\nelse:\n sample_weight = None\n\nif sample_weight is not None:\n (X_train_, X_test_, y_train_, y_test_, sample_weight_train_, _) = train_test_split(\n X, y, sample_weight, test_size=0.5, random_state=0\n )\nelse:\n X_train_, X_test_, y_train_, y_test_ = train_test_split(\n X, y, test_size=0.5, random_state=0\n )\n sample_weight_train_ = None\n\n\nsklearn_est = Estimator(\n learning_rate=lr,\n max_iter=n_trees,\n max_bins=max_bins,\n max_leaf_nodes=n_leaf_nodes,\n early_stopping=False,\n random_state=0,\n verbose=0,\n)\nloss = args.loss\nif args.problem == \"classification\":\n if loss == \"default\":\n # loss='auto' does not work with get_equivalent_estimator()\n loss = (\n \"binary_crossentropy\" if args.n_classes == 2 else \"categorical_crossentropy\"\n )\nelse:\n # regression\n if loss == \"default\":\n loss = \"squared_error\"\nsklearn_est.set_params(loss=loss)\n\n\nif args.print_params:\n print(\"scikit-learn\")\n pprint(sklearn_est.get_params())\n\n for libname in [\"lightgbm\", \"xgboost\", \"catboost\"]:\n if getattr(args, libname):\n print(libname)\n est = get_equivalent_estimator(\n sklearn_est, lib=libname, n_classes=args.n_classes\n )\n pprint(est.get_params())\n\n\ndef one_run(n_threads, n_samples):\n X_train = X_train_[:n_samples]\n X_test = X_test_[:n_samples]\n y_train = y_train_[:n_samples]\n y_test = y_test_[:n_samples]\n if sample_weight is not None:\n sample_weight_train = sample_weight_train_[:n_samples]\n else:\n sample_weight_train = None\n assert X_train.shape[0] == n_samples\n assert X_test.shape[0] == n_samples\n print(\"Fitting a sklearn model...\")\n tic = time()\n est = sklearn.base.clone(sklearn_est)\n\n with threadpool_limits(n_threads, user_api=\"openmp\"):\n est.fit(X_train, y_train, sample_weight=sample_weight_train)\n sklearn_fit_duration = time() - tic\n tic = time()\n sklearn_score = est.score(X_test, y_test)\n sklearn_score_duration = time() - tic\n print(\"score: {:.4f}\".format(sklearn_score))\n print(\"fit duration: {:.3f}s,\".format(sklearn_fit_duration))\n print(\"score duration: {:.3f}s,\".format(sklearn_score_duration))\n\n lightgbm_score = None\n lightgbm_fit_duration = None\n lightgbm_score_duration = None\n if args.lightgbm:\n print(\"Fitting a LightGBM model...\")\n lightgbm_est = get_equivalent_estimator(\n est, lib=\"lightgbm\", n_classes=args.n_classes\n )\n lightgbm_est.set_params(num_threads=n_threads)\n\n tic = time()\n lightgbm_est.fit(X_train, y_train, sample_weight=sample_weight_train)\n lightgbm_fit_duration = time() - tic\n tic = time()\n lightgbm_score = lightgbm_est.score(X_test, y_test)\n lightgbm_score_duration = time() - tic\n print(\"score: {:.4f}\".format(lightgbm_score))\n print(\"fit duration: {:.3f}s,\".format(lightgbm_fit_duration))\n print(\"score duration: {:.3f}s,\".format(lightgbm_score_duration))\n\n xgb_score = None\n xgb_fit_duration = None\n xgb_score_duration = None\n if args.xgboost:\n print(\"Fitting an XGBoost model...\")\n xgb_est = get_equivalent_estimator(est, lib=\"xgboost\")\n xgb_est.set_params(nthread=n_threads)\n\n tic = time()\n xgb_est.fit(X_train, y_train, sample_weight=sample_weight_train)\n xgb_fit_duration = time() - tic\n tic = time()\n xgb_score = xgb_est.score(X_test, y_test)\n xgb_score_duration = time() - tic\n print(\"score: {:.4f}\".format(xgb_score))\n print(\"fit duration: {:.3f}s,\".format(xgb_fit_duration))\n print(\"score duration: {:.3f}s,\".format(xgb_score_duration))\n\n cat_score = None\n cat_fit_duration = None\n cat_score_duration = None\n if args.catboost:\n print(\"Fitting a CatBoost model...\")\n cat_est = get_equivalent_estimator(est, lib=\"catboost\")\n cat_est.set_params(thread_count=n_threads)\n\n tic = time()\n cat_est.fit(X_train, y_train, sample_weight=sample_weight_train)\n cat_fit_duration = time() - tic\n tic = time()\n cat_score = cat_est.score(X_test, y_test)\n cat_score_duration = time() - tic\n print(\"score: {:.4f}\".format(cat_score))\n print(\"fit duration: {:.3f}s,\".format(cat_fit_duration))\n print(\"score duration: {:.3f}s,\".format(cat_score_duration))\n\n return (\n sklearn_score,\n sklearn_fit_duration,\n sklearn_score_duration,\n lightgbm_score,\n lightgbm_fit_duration,\n lightgbm_score_duration,\n xgb_score,\n xgb_fit_duration,\n xgb_score_duration,\n cat_score,\n cat_fit_duration,\n cat_score_duration,\n )\n\n\nmax_threads = os.cpu_count()\nn_threads_list = [2 ** i for i in range(8) if (2 ** i) < max_threads]\nn_threads_list.append(max_threads)\n\nsklearn_scores = []\nsklearn_fit_durations = []\nsklearn_score_durations = []\nlightgbm_scores = []\nlightgbm_fit_durations = []\nlightgbm_score_durations = []\nxgb_scores = []\nxgb_fit_durations = []\nxgb_score_durations = []\ncat_scores = []\ncat_fit_durations = []\ncat_score_durations = []\n\nfor n_threads in n_threads_list:\n print(f\"n_threads: {n_threads}\")\n (\n sklearn_score,\n sklearn_fit_duration,\n sklearn_score_duration,\n lightgbm_score,\n lightgbm_fit_duration,\n lightgbm_score_duration,\n xgb_score,\n xgb_fit_duration,\n xgb_score_duration,\n cat_score,\n cat_fit_duration,\n cat_score_duration,\n ) = one_run(n_threads, n_samples)\n\n for scores, score in (\n (sklearn_scores, sklearn_score),\n (sklearn_fit_durations, sklearn_fit_duration),\n (sklearn_score_durations, sklearn_score_duration),\n (lightgbm_scores, lightgbm_score),\n (lightgbm_fit_durations, lightgbm_fit_duration),\n (lightgbm_score_durations, lightgbm_score_duration),\n (xgb_scores, xgb_score),\n (xgb_fit_durations, xgb_fit_duration),\n (xgb_score_durations, xgb_score_duration),\n (cat_scores, cat_score),\n (cat_fit_durations, cat_fit_duration),\n (cat_score_durations, cat_score_duration),\n ):\n scores.append(score)\n\n\nif args.plot or args.plot_filename:\n import matplotlib.pyplot as plt\n import matplotlib\n\n fig, axs = plt.subplots(2, figsize=(12, 12))\n\n label = f\"sklearn {sklearn.__version__}\"\n axs[0].plot(n_threads_list, sklearn_fit_durations, label=label)\n axs[1].plot(n_threads_list, sklearn_score_durations, label=label)\n\n if args.lightgbm:\n import lightgbm\n\n label = f\"LightGBM {lightgbm.__version__}\"\n axs[0].plot(n_threads_list, lightgbm_fit_durations, label=label)\n axs[1].plot(n_threads_list, lightgbm_score_durations, label=label)\n\n if args.xgboost:\n import xgboost\n\n label = f\"XGBoost {xgboost.__version__}\"\n axs[0].plot(n_threads_list, xgb_fit_durations, label=label)\n axs[1].plot(n_threads_list, xgb_score_durations, label=label)\n\n if args.catboost:\n import catboost\n\n label = f\"CatBoost {catboost.__version__}\"\n axs[0].plot(n_threads_list, cat_fit_durations, label=label)\n axs[1].plot(n_threads_list, cat_score_durations, label=label)\n\n for ax in axs:\n ax.set_xscale(\"log\")\n ax.set_xlabel(\"n_threads\")\n ax.set_ylabel(\"duration (s)\")\n ax.set_ylim(0, None)\n ax.set_xticks(n_threads_list)\n ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n ax.legend(loc=\"best\")\n\n axs[0].set_title(\"fit duration (s)\")\n axs[1].set_title(\"score duration (s)\")\n\n title = args.problem\n if args.problem == \"classification\":\n title += \" n_classes = {}\".format(args.n_classes)\n fig.suptitle(title)\n\n plt.tight_layout()\n\n if args.plot_filename:\n plt.savefig(args.plot_filename)\n\n if args.plot:\n plt.show()\n" }, { "path": "benchmarks/bench_isolation_forest.py", "content": "\"\"\"\n==========================================\nIsolationForest benchmark\n==========================================\nA test of IsolationForest on classical anomaly detection datasets.\n\nThe benchmark is run as follows:\n1. The dataset is randomly split into a training set and a test set, both\nassumed to contain outliers.\n2. Isolation Forest is trained on the training set.\n3. The ROC curve is computed on the test set using the knowledge of the labels.\n\nNote that the smtp dataset contains a very small proportion of outliers.\nTherefore, depending on the seed of the random number generator, randomly\nsplitting the data set might lead to a test set containing no outliers. In this\ncase a warning is raised when computing the ROC curve.\n\"\"\"\n\nfrom time import time\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.ensemble import IsolationForest\nfrom sklearn.metrics import roc_curve, auc\nfrom sklearn.datasets import fetch_kddcup99, fetch_covtype, fetch_openml\nfrom sklearn.preprocessing import LabelBinarizer\nfrom sklearn.utils import shuffle as sh\n\nprint(__doc__)\n\n\ndef print_outlier_ratio(y):\n \"\"\"\n Helper function to show the distinct value count of element in the target.\n Useful indicator for the datasets used in bench_isolation_forest.py.\n \"\"\"\n uniq, cnt = np.unique(y, return_counts=True)\n print(\"----- Target count values: \")\n for u, c in zip(uniq, cnt):\n print(\"------ %s -> %d occurrences\" % (str(u), c))\n print(\"----- Outlier ratio: %.5f\" % (np.min(cnt) / len(y)))\n\n\nrandom_state = 1\nfig_roc, ax_roc = plt.subplots(1, 1, figsize=(8, 5))\n\n# Set this to true for plotting score histograms for each dataset:\nwith_decision_function_histograms = False\n\n# datasets available = ['http', 'smtp', 'SA', 'SF', 'shuttle', 'forestcover']\ndatasets = [\"http\", \"smtp\", \"SA\", \"SF\", \"shuttle\", \"forestcover\"]\n\n# Loop over all datasets for fitting and scoring the estimator:\nfor dat in datasets:\n\n # Loading and vectorizing the data:\n print(\"====== %s ======\" % dat)\n print(\"--- Fetching data...\")\n if dat in [\"http\", \"smtp\", \"SF\", \"SA\"]:\n dataset = fetch_kddcup99(\n subset=dat, shuffle=True, percent10=True, random_state=random_state\n )\n X = dataset.data\n y = dataset.target\n\n if dat == \"shuttle\":\n dataset = fetch_openml(\"shuttle\")\n X = dataset.data\n y = dataset.target\n X, y = sh(X, y, random_state=random_state)\n # we remove data with label 4\n # normal data are then those of class 1\n s = y != 4\n X = X[s, :]\n y = y[s]\n y = (y != 1).astype(int)\n print(\"----- \")\n\n if dat == \"forestcover\":\n dataset = fetch_covtype(shuffle=True, random_state=random_state)\n X = dataset.data\n y = dataset.target\n # normal data are those with attribute 2\n # abnormal those with attribute 4\n s = (y == 2) + (y == 4)\n X = X[s, :]\n y = y[s]\n y = (y != 2).astype(int)\n print_outlier_ratio(y)\n\n print(\"--- Vectorizing data...\")\n\n if dat == \"SF\":\n lb = LabelBinarizer()\n x1 = lb.fit_transform(X[:, 1].astype(str))\n X = np.c_[X[:, :1], x1, X[:, 2:]]\n y = (y != b\"normal.\").astype(int)\n print_outlier_ratio(y)\n\n if dat == \"SA\":\n lb = LabelBinarizer()\n x1 = lb.fit_transform(X[:, 1].astype(str))\n x2 = lb.fit_transform(X[:, 2].astype(str))\n x3 = lb.fit_transform(X[:, 3].astype(str))\n X = np.c_[X[:, :1], x1, x2, x3, X[:, 4:]]\n y = (y != b\"normal.\").astype(int)\n print_outlier_ratio(y)\n\n if dat in (\"http\", \"smtp\"):\n y = (y != b\"normal.\").astype(int)\n print_outlier_ratio(y)\n\n n_samples, n_features = X.shape\n n_samples_train = n_samples // 2\n\n X = X.astype(float)\n X_train = X[:n_samples_train, :]\n X_test = X[n_samples_train:, :]\n y_train = y[:n_samples_train]\n y_test = y[n_samples_train:]\n\n print(\"--- Fitting the IsolationForest estimator...\")\n model = IsolationForest(n_jobs=-1, random_state=random_state)\n tstart = time()\n model.fit(X_train)\n fit_time = time() - tstart\n tstart = time()\n\n scoring = -model.decision_function(X_test) # the lower, the more abnormal\n\n print(\"--- Preparing the plot elements...\")\n if with_decision_function_histograms:\n fig, ax = plt.subplots(3, sharex=True, sharey=True)\n bins = np.linspace(-0.5, 0.5, 200)\n ax[0].hist(scoring, bins, color=\"black\")\n ax[0].set_title(\"Decision function for %s dataset\" % dat)\n ax[1].hist(scoring[y_test == 0], bins, color=\"b\", label=\"normal data\")\n ax[1].legend(loc=\"lower right\")\n ax[2].hist(scoring[y_test == 1], bins, color=\"r\", label=\"outliers\")\n ax[2].legend(loc=\"lower right\")\n\n # Show ROC Curves\n predict_time = time() - tstart\n fpr, tpr, thresholds = roc_curve(y_test, scoring)\n auc_score = auc(fpr, tpr)\n label = \"%s (AUC: %0.3f, train_time= %0.2fs, test_time= %0.2fs)\" % (\n dat,\n auc_score,\n fit_time,\n predict_time,\n )\n # Print AUC score and train/test time:\n print(label)\n ax_roc.plot(fpr, tpr, lw=1, label=label)\n\n\nax_roc.set_xlim([-0.05, 1.05])\nax_roc.set_ylim([-0.05, 1.05])\nax_roc.set_xlabel(\"False Positive Rate\")\nax_roc.set_ylabel(\"True Positive Rate\")\nax_roc.set_title(\"Receiver operating characteristic (ROC) curves\")\nax_roc.legend(loc=\"lower right\")\nfig_roc.tight_layout()\nplt.show()\n" }, { "path": "benchmarks/bench_isotonic.py", "content": "\"\"\"\nBenchmarks of isotonic regression performance.\n\nWe generate a synthetic dataset of size 10^n, for n in [min, max], and\nexamine the time taken to run isotonic regression over the dataset.\n\nThe timings are then output to stdout, or visualized on a log-log scale\nwith matplotlib.\n\nThis allows the scaling of the algorithm with the problem size to be\nvisualized and understood.\n\"\"\"\nimport numpy as np\nimport gc\nfrom datetime import datetime\nfrom sklearn.isotonic import isotonic_regression\nfrom scipy.special import expit\nimport matplotlib.pyplot as plt\nimport argparse\n\n\ndef generate_perturbed_logarithm_dataset(size):\n return np.random.randint(-50, 50, size=size) + 50.0 * np.log(1 + np.arange(size))\n\n\ndef generate_logistic_dataset(size):\n X = np.sort(np.random.normal(size=size))\n return np.random.random(size=size) < expit(X)\n\n\ndef generate_pathological_dataset(size):\n # Triggers O(n^2) complexity on the original implementation.\n return np.r_[\n np.arange(size), np.arange(-(size - 1), size), np.arange(-(size - 1), 1)\n ]\n\n\nDATASET_GENERATORS = {\n \"perturbed_logarithm\": generate_perturbed_logarithm_dataset,\n \"logistic\": generate_logistic_dataset,\n \"pathological\": generate_pathological_dataset,\n}\n\n\ndef bench_isotonic_regression(Y):\n \"\"\"\n Runs a single iteration of isotonic regression on the input data,\n and reports the total time taken (in seconds).\n \"\"\"\n gc.collect()\n\n tstart = datetime.now()\n isotonic_regression(Y)\n return (datetime.now() - tstart).total_seconds()\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"Isotonic Regression benchmark tool\")\n parser.add_argument(\"--seed\", type=int, help=\"RNG seed\")\n parser.add_argument(\n \"--iterations\",\n type=int,\n required=True,\n help=\"Number of iterations to average timings over for each problem size\",\n )\n parser.add_argument(\n \"--log_min_problem_size\",\n type=int,\n required=True,\n help=\"Base 10 logarithm of the minimum problem size\",\n )\n parser.add_argument(\n \"--log_max_problem_size\",\n type=int,\n required=True,\n help=\"Base 10 logarithm of the maximum problem size\",\n )\n parser.add_argument(\n \"--show_plot\", action=\"store_true\", help=\"Plot timing output with matplotlib\"\n )\n parser.add_argument(\"--dataset\", choices=DATASET_GENERATORS.keys(), required=True)\n\n args = parser.parse_args()\n\n np.random.seed(args.seed)\n\n timings = []\n for exponent in range(args.log_min_problem_size, args.log_max_problem_size):\n n = 10 ** exponent\n Y = DATASET_GENERATORS[args.dataset](n)\n time_per_iteration = [\n bench_isotonic_regression(Y) for i in range(args.iterations)\n ]\n timing = (n, np.mean(time_per_iteration))\n timings.append(timing)\n\n # If we're not plotting, dump the timing to stdout\n if not args.show_plot:\n print(n, np.mean(time_per_iteration))\n\n if args.show_plot:\n plt.plot(*zip(*timings))\n plt.title(\"Average time taken running isotonic regression\")\n plt.xlabel(\"Number of observations\")\n plt.ylabel(\"Time (s)\")\n plt.axis(\"tight\")\n plt.loglog()\n plt.show()\n" }, { "path": "benchmarks/bench_kernel_pca_solvers_time_vs_n_components.py", "content": "\"\"\"\n=============================================================\nKernel PCA Solvers comparison benchmark: time vs n_components\n=============================================================\n\nThis benchmark shows that the approximate solvers provided in Kernel PCA can\nhelp significantly improve its execution speed when an approximate solution\n(small `n_components`) is acceptable. In many real-world datasets a few\nhundreds of principal components are indeed sufficient enough to capture the\nunderlying distribution.\n\nDescription:\n------------\nA fixed number of training (default: 2000) and test (default: 1000) samples\nwith 2 features is generated using the `make_circles` helper method.\n\nKernelPCA models are trained on the training set with an increasing number of\nprincipal components, between 1 and `max_n_compo` (default: 1999), with\n`n_compo_grid_size` positions (default: 10). For each value of `n_components`\nto try, KernelPCA models are trained for the various possible `eigen_solver`\nvalues. The execution times are displayed in a plot at the end of the\nexperiment.\n\nWhat you can observe:\n---------------------\nWhen the number of requested principal components is small, the dense solver\ntakes more time to complete, while the randomized method returns similar\nresults with shorter execution times.\n\nGoing further:\n--------------\nYou can adjust `max_n_compo` and `n_compo_grid_size` if you wish to explore a\ndifferent range of values for `n_components`.\n\nYou can also set `arpack_all=True` to activate arpack solver for large number\nof components (this takes more time).\n\"\"\"\n# Authors: Sylvain MARIE, Schneider Electric\n\nimport time\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom numpy.testing import assert_array_almost_equal\nfrom sklearn.decomposition import KernelPCA\nfrom sklearn.datasets import make_circles\n\n\nprint(__doc__)\n\n\n# 1- Design the Experiment\n# ------------------------\nn_train, n_test = 2000, 1000 # the sample sizes to use\nmax_n_compo = 1999 # max n_components to try\nn_compo_grid_size = 10 # nb of positions in the grid to try\n# generate the grid\nn_compo_range = [\n np.round(np.exp((x / (n_compo_grid_size - 1)) * np.log(max_n_compo)))\n for x in range(0, n_compo_grid_size)\n]\n\nn_iter = 3 # the number of times each experiment will be repeated\narpack_all = False # set to True if you wish to run arpack for all n_compo\n\n\n# 2- Generate random data\n# -----------------------\nn_features = 2\nX, y = make_circles(\n n_samples=(n_train + n_test), factor=0.3, noise=0.05, random_state=0\n)\nX_train, X_test = X[:n_train, :], X[n_train:, :]\n\n\n# 3- Benchmark\n# ------------\n# init\nref_time = np.empty((len(n_compo_range), n_iter)) * np.nan\na_time = np.empty((len(n_compo_range), n_iter)) * np.nan\nr_time = np.empty((len(n_compo_range), n_iter)) * np.nan\n# loop\nfor j, n_components in enumerate(n_compo_range):\n\n n_components = int(n_components)\n print(\"Performing kPCA with n_components = %i\" % n_components)\n\n # A- reference (dense)\n print(\" - dense solver\")\n for i in range(n_iter):\n start_time = time.perf_counter()\n ref_pred = (\n KernelPCA(n_components, eigen_solver=\"dense\").fit(X_train).transform(X_test)\n )\n ref_time[j, i] = time.perf_counter() - start_time\n\n # B- arpack (for small number of components only, too slow otherwise)\n if arpack_all or n_components < 100:\n print(\" - arpack solver\")\n for i in range(n_iter):\n start_time = time.perf_counter()\n a_pred = (\n KernelPCA(n_components, eigen_solver=\"arpack\")\n .fit(X_train)\n .transform(X_test)\n )\n a_time[j, i] = time.perf_counter() - start_time\n # check that the result is still correct despite the approx\n assert_array_almost_equal(np.abs(a_pred), np.abs(ref_pred))\n\n # C- randomized\n print(\" - randomized solver\")\n for i in range(n_iter):\n start_time = time.perf_counter()\n r_pred = (\n KernelPCA(n_components, eigen_solver=\"randomized\")\n .fit(X_train)\n .transform(X_test)\n )\n r_time[j, i] = time.perf_counter() - start_time\n # check that the result is still correct despite the approximation\n assert_array_almost_equal(np.abs(r_pred), np.abs(ref_pred))\n\n# Compute statistics for the 3 methods\navg_ref_time = ref_time.mean(axis=1)\nstd_ref_time = ref_time.std(axis=1)\navg_a_time = a_time.mean(axis=1)\nstd_a_time = a_time.std(axis=1)\navg_r_time = r_time.mean(axis=1)\nstd_r_time = r_time.std(axis=1)\n\n\n# 4- Plots\n# --------\nfig, ax = plt.subplots(figsize=(12, 8))\n\n# Display 1 plot with error bars per method\nax.errorbar(\n n_compo_range,\n avg_ref_time,\n yerr=std_ref_time,\n marker=\"x\",\n linestyle=\"\",\n color=\"r\",\n label=\"full\",\n)\nax.errorbar(\n n_compo_range,\n avg_a_time,\n yerr=std_a_time,\n marker=\"x\",\n linestyle=\"\",\n color=\"g\",\n label=\"arpack\",\n)\nax.errorbar(\n n_compo_range,\n avg_r_time,\n yerr=std_r_time,\n marker=\"x\",\n linestyle=\"\",\n color=\"b\",\n label=\"randomized\",\n)\nax.legend(loc=\"upper left\")\n\n# customize axes\nax.set_xscale(\"log\")\nax.set_xlim(1, max(n_compo_range) * 1.1)\nax.set_ylabel(\"Execution time (s)\")\nax.set_xlabel(\"n_components\")\n\nax.set_title(\n \"kPCA Execution time comparison on %i samples with %i \"\n \"features, according to the choice of `eigen_solver`\"\n \"\" % (n_train, n_features)\n)\n\nplt.show()\n" }, { "path": "benchmarks/bench_kernel_pca_solvers_time_vs_n_samples.py", "content": "\"\"\"\n==========================================================\nKernel PCA Solvers comparison benchmark: time vs n_samples\n==========================================================\n\nThis benchmark shows that the approximate solvers provided in Kernel PCA can\nhelp significantly improve its execution speed when an approximate solution\n(small `n_components`) is acceptable. In many real-world datasets the number of\nsamples is very large, but a few hundreds of principal components are\nsufficient enough to capture the underlying distribution.\n\nDescription:\n------------\nAn increasing number of examples is used to train a KernelPCA, between\n`min_n_samples` (default: 101) and `max_n_samples` (default: 4000) with\n`n_samples_grid_size` positions (default: 4). Samples have 2 features, and are\ngenerated using `make_circles`. For each training sample size, KernelPCA models\nare trained for the various possible `eigen_solver` values. All of them are\ntrained to obtain `n_components` principal components (default: 100). The\nexecution times are displayed in a plot at the end of the experiment.\n\nWhat you can observe:\n---------------------\nWhen the number of samples provided gets large, the dense solver takes a lot\nof time to complete, while the randomized method returns similar results in\nmuch shorter execution times.\n\nGoing further:\n--------------\nYou can increase `max_n_samples` and `nb_n_samples_to_try` if you wish to\nexplore a wider range of values for `n_samples`.\n\nYou can also set `include_arpack=True` to add this other solver in the\nexperiments (much slower).\n\nFinally you can have a look at the second example of this series, \"Kernel PCA\nSolvers comparison benchmark: time vs n_components\", where this time the number\nof examples is fixed, and the desired number of components varies.\n\"\"\"\n# Author: Sylvain MARIE, Schneider Electric\n\nimport time\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom numpy.testing import assert_array_almost_equal\nfrom sklearn.decomposition import KernelPCA\nfrom sklearn.datasets import make_circles\n\n\nprint(__doc__)\n\n\n# 1- Design the Experiment\n# ------------------------\nmin_n_samples, max_n_samples = 101, 4000 # min and max n_samples to try\nn_samples_grid_size = 4 # nb of positions in the grid to try\n# generate the grid\nn_samples_range = [\n min_n_samples\n + np.floor((x / (n_samples_grid_size - 1)) * (max_n_samples - min_n_samples))\n for x in range(0, n_samples_grid_size)\n]\n\nn_components = 100 # the number of principal components we want to use\nn_iter = 3 # the number of times each experiment will be repeated\ninclude_arpack = False # set this to True to include arpack solver (slower)\n\n\n# 2- Generate random data\n# -----------------------\nn_features = 2\nX, y = make_circles(n_samples=max_n_samples, factor=0.3, noise=0.05, random_state=0)\n\n\n# 3- Benchmark\n# ------------\n# init\nref_time = np.empty((len(n_samples_range), n_iter)) * np.nan\na_time = np.empty((len(n_samples_range), n_iter)) * np.nan\nr_time = np.empty((len(n_samples_range), n_iter)) * np.nan\n\n# loop\nfor j, n_samples in enumerate(n_samples_range):\n\n n_samples = int(n_samples)\n print(\"Performing kPCA with n_samples = %i\" % n_samples)\n\n X_train = X[:n_samples, :]\n X_test = X_train\n\n # A- reference (dense)\n print(\" - dense\")\n for i in range(n_iter):\n start_time = time.perf_counter()\n ref_pred = (\n KernelPCA(n_components, eigen_solver=\"dense\").fit(X_train).transform(X_test)\n )\n ref_time[j, i] = time.perf_counter() - start_time\n\n # B- arpack\n if include_arpack:\n print(\" - arpack\")\n for i in range(n_iter):\n start_time = time.perf_counter()\n a_pred = (\n KernelPCA(n_components, eigen_solver=\"arpack\")\n .fit(X_train)\n .transform(X_test)\n )\n a_time[j, i] = time.perf_counter() - start_time\n # check that the result is still correct despite the approx\n assert_array_almost_equal(np.abs(a_pred), np.abs(ref_pred))\n\n # C- randomized\n print(\" - randomized\")\n for i in range(n_iter):\n start_time = time.perf_counter()\n r_pred = (\n KernelPCA(n_components, eigen_solver=\"randomized\")\n .fit(X_train)\n .transform(X_test)\n )\n r_time[j, i] = time.perf_counter() - start_time\n # check that the result is still correct despite the approximation\n assert_array_almost_equal(np.abs(r_pred), np.abs(ref_pred))\n\n# Compute statistics for the 3 methods\navg_ref_time = ref_time.mean(axis=1)\nstd_ref_time = ref_time.std(axis=1)\navg_a_time = a_time.mean(axis=1)\nstd_a_time = a_time.std(axis=1)\navg_r_time = r_time.mean(axis=1)\nstd_r_time = r_time.std(axis=1)\n\n\n# 4- Plots\n# --------\nfig, ax = plt.subplots(figsize=(12, 8))\n\n# Display 1 plot with error bars per method\nax.errorbar(\n n_samples_range,\n avg_ref_time,\n yerr=std_ref_time,\n marker=\"x\",\n linestyle=\"\",\n color=\"r\",\n label=\"full\",\n)\nif include_arpack:\n ax.errorbar(\n n_samples_range,\n avg_a_time,\n yerr=std_a_time,\n marker=\"x\",\n linestyle=\"\",\n color=\"g\",\n label=\"arpack\",\n )\nax.errorbar(\n n_samples_range,\n avg_r_time,\n yerr=std_r_time,\n marker=\"x\",\n linestyle=\"\",\n color=\"b\",\n label=\"randomized\",\n)\nax.legend(loc=\"upper left\")\n\n# customize axes\nax.set_xlim(min(n_samples_range) * 0.9, max(n_samples_range) * 1.1)\nax.set_ylabel(\"Execution time (s)\")\nax.set_xlabel(\"n_samples\")\n\nax.set_title(\n \"Execution time comparison of kPCA with %i components on samples \"\n \"with %i features, according to the choice of `eigen_solver`\"\n \"\" % (n_components, n_features)\n)\n\nplt.show()\n" }, { "path": "benchmarks/bench_lasso.py", "content": "\"\"\"\nBenchmarks of Lasso vs LassoLars\n\nFirst, we fix a training set and increase the number of\nsamples. Then we plot the computation time as function of\nthe number of samples.\n\nIn the second benchmark, we increase the number of dimensions of the\ntraining set. Then we plot the computation time as function of\nthe number of dimensions.\n\nIn both cases, only 10% of the features are informative.\n\"\"\"\nimport gc\nfrom time import time\nimport numpy as np\n\nfrom sklearn.datasets import make_regression\n\n\ndef compute_bench(alpha, n_samples, n_features, precompute):\n lasso_results = []\n lars_lasso_results = []\n\n it = 0\n\n for ns in n_samples:\n for nf in n_features:\n it += 1\n print(\"==================\")\n print(\"Iteration %s of %s\" % (it, max(len(n_samples), len(n_features))))\n print(\"==================\")\n n_informative = nf // 10\n X, Y, coef_ = make_regression(\n n_samples=ns,\n n_features=nf,\n n_informative=n_informative,\n noise=0.1,\n coef=True,\n )\n\n X /= np.sqrt(np.sum(X ** 2, axis=0)) # Normalize data\n\n gc.collect()\n print(\"- benchmarking Lasso\")\n clf = Lasso(alpha=alpha, fit_intercept=False, precompute=precompute)\n tstart = time()\n clf.fit(X, Y)\n lasso_results.append(time() - tstart)\n\n gc.collect()\n print(\"- benchmarking LassoLars\")\n clf = LassoLars(\n alpha=alpha, fit_intercept=False, normalize=False, precompute=precompute\n )\n tstart = time()\n clf.fit(X, Y)\n lars_lasso_results.append(time() - tstart)\n\n return lasso_results, lars_lasso_results\n\n\nif __name__ == \"__main__\":\n from sklearn.linear_model import Lasso, LassoLars\n import matplotlib.pyplot as plt\n\n alpha = 0.01 # regularization parameter\n\n n_features = 10\n list_n_samples = np.linspace(100, 1000000, 5).astype(int)\n lasso_results, lars_lasso_results = compute_bench(\n alpha, list_n_samples, [n_features], precompute=True\n )\n\n plt.figure(\"scikit-learn LASSO benchmark results\")\n plt.subplot(211)\n plt.plot(list_n_samples, lasso_results, \"b-\", label=\"Lasso\")\n plt.plot(list_n_samples, lars_lasso_results, \"r-\", label=\"LassoLars\")\n plt.title(\"precomputed Gram matrix, %d features, alpha=%s\" % (n_features, alpha))\n plt.legend(loc=\"upper left\")\n plt.xlabel(\"number of samples\")\n plt.ylabel(\"Time (s)\")\n plt.axis(\"tight\")\n\n n_samples = 2000\n list_n_features = np.linspace(500, 3000, 5).astype(int)\n lasso_results, lars_lasso_results = compute_bench(\n alpha, [n_samples], list_n_features, precompute=False\n )\n plt.subplot(212)\n plt.plot(list_n_features, lasso_results, \"b-\", label=\"Lasso\")\n plt.plot(list_n_features, lars_lasso_results, \"r-\", label=\"LassoLars\")\n plt.title(\"%d samples, alpha=%s\" % (n_samples, alpha))\n plt.legend(loc=\"upper left\")\n plt.xlabel(\"number of features\")\n plt.ylabel(\"Time (s)\")\n plt.axis(\"tight\")\n plt.show()\n" }, { "path": "benchmarks/bench_lof.py", "content": "\"\"\"\n============================\nLocalOutlierFactor benchmark\n============================\n\nA test of LocalOutlierFactor on classical anomaly detection datasets.\n\nNote that LocalOutlierFactor is not meant to predict on a test set and its\nperformance is assessed in an outlier detection context:\n1. The model is trained on the whole dataset which is assumed to contain\noutliers.\n2. The ROC curve is computed on the same dataset using the knowledge of the\nlabels.\nIn this context there is no need to shuffle the dataset because the model\nis trained and tested on the whole dataset. The randomness of this benchmark\nis only caused by the random selection of anomalies in the SA dataset.\n\n\"\"\"\n\nfrom time import time\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.neighbors import LocalOutlierFactor\nfrom sklearn.metrics import roc_curve, auc\nfrom sklearn.datasets import fetch_kddcup99, fetch_covtype, fetch_openml\nfrom sklearn.preprocessing import LabelBinarizer\n\nprint(__doc__)\n\nrandom_state = 2 # to control the random selection of anomalies in SA\n\n# datasets available: ['http', 'smtp', 'SA', 'SF', 'shuttle', 'forestcover']\ndatasets = [\"http\", \"smtp\", \"SA\", \"SF\", \"shuttle\", \"forestcover\"]\n\nplt.figure()\nfor dataset_name in datasets:\n # loading and vectorization\n print(\"loading data\")\n if dataset_name in [\"http\", \"smtp\", \"SA\", \"SF\"]:\n dataset = fetch_kddcup99(\n subset=dataset_name, percent10=True, random_state=random_state\n )\n X = dataset.data\n y = dataset.target\n\n if dataset_name == \"shuttle\":\n dataset = fetch_openml(\"shuttle\")\n X = dataset.data\n y = dataset.target\n # we remove data with label 4\n # normal data are then those of class 1\n s = y != 4\n X = X[s, :]\n y = y[s]\n y = (y != 1).astype(int)\n\n if dataset_name == \"forestcover\":\n dataset = fetch_covtype()\n X = dataset.data\n y = dataset.target\n # normal data are those with attribute 2\n # abnormal those with attribute 4\n s = (y == 2) + (y == 4)\n X = X[s, :]\n y = y[s]\n y = (y != 2).astype(int)\n\n print(\"vectorizing data\")\n\n if dataset_name == \"SF\":\n lb = LabelBinarizer()\n x1 = lb.fit_transform(X[:, 1].astype(str))\n X = np.c_[X[:, :1], x1, X[:, 2:]]\n y = (y != b\"normal.\").astype(int)\n\n if dataset_name == \"SA\":\n lb = LabelBinarizer()\n x1 = lb.fit_transform(X[:, 1].astype(str))\n x2 = lb.fit_transform(X[:, 2].astype(str))\n x3 = lb.fit_transform(X[:, 3].astype(str))\n X = np.c_[X[:, :1], x1, x2, x3, X[:, 4:]]\n y = (y != b\"normal.\").astype(int)\n\n if dataset_name == \"http\" or dataset_name == \"smtp\":\n y = (y != b\"normal.\").astype(int)\n\n X = X.astype(float)\n\n print(\"LocalOutlierFactor processing...\")\n model = LocalOutlierFactor(n_neighbors=20)\n tstart = time()\n model.fit(X)\n fit_time = time() - tstart\n scoring = -model.negative_outlier_factor_ # the lower, the more normal\n fpr, tpr, thresholds = roc_curve(y, scoring)\n AUC = auc(fpr, tpr)\n plt.plot(\n fpr,\n tpr,\n lw=1,\n label=\"ROC for %s (area = %0.3f, train-time: %0.2fs)\"\n % (dataset_name, AUC, fit_time),\n )\n\nplt.xlim([-0.05, 1.05])\nplt.ylim([-0.05, 1.05])\nplt.xlabel(\"False Positive Rate\")\nplt.ylabel(\"True Positive Rate\")\nplt.title(\"Receiver operating characteristic\")\nplt.legend(loc=\"lower right\")\nplt.show()\n" }, { "path": "benchmarks/bench_mnist.py", "content": "\"\"\"\n=======================\nMNIST dataset benchmark\n=======================\n\nBenchmark on the MNIST dataset. The dataset comprises 70,000 samples\nand 784 features. Here, we consider the task of predicting\n10 classes - digits from 0 to 9 from their raw images. By contrast to the\ncovertype dataset, the feature space is homogeneous.\n\nExample of output :\n [..]\n\n Classification performance:\n ===========================\n Classifier train-time test-time error-rate\n ------------------------------------------------------------\n MLP_adam 53.46s 0.11s 0.0224\n Nystroem-SVM 112.97s 0.92s 0.0228\n MultilayerPerceptron 24.33s 0.14s 0.0287\n ExtraTrees 42.99s 0.57s 0.0294\n RandomForest 42.70s 0.49s 0.0318\n SampledRBF-SVM 135.81s 0.56s 0.0486\n LinearRegression-SAG 16.67s 0.06s 0.0824\n CART 20.69s 0.02s 0.1219\n dummy 0.00s 0.01s 0.8973\n\"\"\"\n\n# Author: Issam H. Laradji\n# Arnaud Joly \n# License: BSD 3 clause\n\nimport os\nfrom time import time\nimport argparse\nimport numpy as np\nfrom joblib import Memory\n\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.datasets import get_data_home\nfrom sklearn.ensemble import ExtraTreesClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.dummy import DummyClassifier\nfrom sklearn.kernel_approximation import Nystroem\nfrom sklearn.kernel_approximation import RBFSampler\nfrom sklearn.metrics import zero_one_loss\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.svm import LinearSVC\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.utils import check_array\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.neural_network import MLPClassifier\n\n# Memoize the data extraction and memory map the resulting\n# train / test splits in readonly mode\nmemory = Memory(os.path.join(get_data_home(), \"mnist_benchmark_data\"), mmap_mode=\"r\")\n\n\n@memory.cache\ndef load_data(dtype=np.float32, order=\"F\"):\n \"\"\"Load the data, then cache and memmap the train/test split\"\"\"\n ######################################################################\n # Load dataset\n print(\"Loading dataset...\")\n data = fetch_openml(\"mnist_784\")\n X = check_array(data[\"data\"], dtype=dtype, order=order)\n y = data[\"target\"]\n\n # Normalize features\n X = X / 255\n\n # Create train-test split (as [Joachims, 2006])\n print(\"Creating train-test split...\")\n n_train = 60000\n X_train = X[:n_train]\n y_train = y[:n_train]\n X_test = X[n_train:]\n y_test = y[n_train:]\n\n return X_train, X_test, y_train, y_test\n\n\nESTIMATORS = {\n \"dummy\": DummyClassifier(),\n \"CART\": DecisionTreeClassifier(),\n \"ExtraTrees\": ExtraTreesClassifier(),\n \"RandomForest\": RandomForestClassifier(),\n \"Nystroem-SVM\": make_pipeline(\n Nystroem(gamma=0.015, n_components=1000), LinearSVC(C=100)\n ),\n \"SampledRBF-SVM\": make_pipeline(\n RBFSampler(gamma=0.015, n_components=1000), LinearSVC(C=100)\n ),\n \"LogisticRegression-SAG\": LogisticRegression(solver=\"sag\", tol=1e-1, C=1e4),\n \"LogisticRegression-SAGA\": LogisticRegression(solver=\"saga\", tol=1e-1, C=1e4),\n \"MultilayerPerceptron\": MLPClassifier(\n hidden_layer_sizes=(100, 100),\n max_iter=400,\n alpha=1e-4,\n solver=\"sgd\",\n learning_rate_init=0.2,\n momentum=0.9,\n verbose=1,\n tol=1e-4,\n random_state=1,\n ),\n \"MLP-adam\": MLPClassifier(\n hidden_layer_sizes=(100, 100),\n max_iter=400,\n alpha=1e-4,\n solver=\"adam\",\n learning_rate_init=0.001,\n verbose=1,\n tol=1e-4,\n random_state=1,\n ),\n}\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--classifiers\",\n nargs=\"+\",\n choices=ESTIMATORS,\n type=str,\n default=[\"ExtraTrees\", \"Nystroem-SVM\"],\n help=\"list of classifiers to benchmark.\",\n )\n parser.add_argument(\n \"--n-jobs\",\n nargs=\"?\",\n default=1,\n type=int,\n help=(\n \"Number of concurrently running workers for \"\n \"models that support parallelism.\"\n ),\n )\n parser.add_argument(\n \"--order\",\n nargs=\"?\",\n default=\"C\",\n type=str,\n choices=[\"F\", \"C\"],\n help=\"Allow to choose between fortran and C ordered data\",\n )\n parser.add_argument(\n \"--random-seed\",\n nargs=\"?\",\n default=0,\n type=int,\n help=\"Common seed used by random number generator.\",\n )\n args = vars(parser.parse_args())\n\n print(__doc__)\n\n X_train, X_test, y_train, y_test = load_data(order=args[\"order\"])\n\n print(\"\")\n print(\"Dataset statistics:\")\n print(\"===================\")\n print(\"%s %d\" % (\"number of features:\".ljust(25), X_train.shape[1]))\n print(\"%s %d\" % (\"number of classes:\".ljust(25), np.unique(y_train).size))\n print(\"%s %s\" % (\"data type:\".ljust(25), X_train.dtype))\n print(\n \"%s %d (size=%dMB)\"\n % (\n \"number of train samples:\".ljust(25),\n X_train.shape[0],\n int(X_train.nbytes / 1e6),\n )\n )\n print(\n \"%s %d (size=%dMB)\"\n % (\n \"number of test samples:\".ljust(25),\n X_test.shape[0],\n int(X_test.nbytes / 1e6),\n )\n )\n\n print()\n print(\"Training Classifiers\")\n print(\"====================\")\n error, train_time, test_time = {}, {}, {}\n for name in sorted(args[\"classifiers\"]):\n print(\"Training %s ... \" % name, end=\"\")\n estimator = ESTIMATORS[name]\n estimator_params = estimator.get_params()\n\n estimator.set_params(\n **{\n p: args[\"random_seed\"]\n for p in estimator_params\n if p.endswith(\"random_state\")\n }\n )\n\n if \"n_jobs\" in estimator_params:\n estimator.set_params(n_jobs=args[\"n_jobs\"])\n\n time_start = time()\n estimator.fit(X_train, y_train)\n train_time[name] = time() - time_start\n\n time_start = time()\n y_pred = estimator.predict(X_test)\n test_time[name] = time() - time_start\n\n error[name] = zero_one_loss(y_test, y_pred)\n\n print(\"done\")\n\n print()\n print(\"Classification performance:\")\n print(\"===========================\")\n print(\n \"{0: <24} {1: >10} {2: >11} {3: >12}\".format(\n \"Classifier \", \"train-time\", \"test-time\", \"error-rate\"\n )\n )\n print(\"-\" * 60)\n for name in sorted(args[\"classifiers\"], key=error.get):\n\n print(\n \"{0: <23} {1: >10.2f}s {2: >10.2f}s {3: >12.4f}\".format(\n name, train_time[name], test_time[name], error[name]\n )\n )\n\n print()\n" }, { "path": "benchmarks/bench_multilabel_metrics.py", "content": "#!/usr/bin/env python\n\"\"\"\nA comparison of multilabel target formats and metrics over them\n\"\"\"\n\nfrom timeit import timeit\nfrom functools import partial\nimport itertools\nimport argparse\nimport sys\n\nimport matplotlib.pyplot as plt\nimport scipy.sparse as sp\nimport numpy as np\n\nfrom sklearn.datasets import make_multilabel_classification\nfrom sklearn.metrics import (\n f1_score,\n accuracy_score,\n hamming_loss,\n jaccard_similarity_score,\n)\nfrom sklearn.utils._testing import ignore_warnings\n\n\nMETRICS = {\n \"f1\": partial(f1_score, average=\"micro\"),\n \"f1-by-sample\": partial(f1_score, average=\"samples\"),\n \"accuracy\": accuracy_score,\n \"hamming\": hamming_loss,\n \"jaccard\": jaccard_similarity_score,\n}\n\nFORMATS = {\n \"sequences\": lambda y: [list(np.flatnonzero(s)) for s in y],\n \"dense\": lambda y: y,\n \"csr\": lambda y: sp.csr_matrix(y),\n \"csc\": lambda y: sp.csc_matrix(y),\n}\n\n\n@ignore_warnings\ndef benchmark(\n metrics=tuple(v for k, v in sorted(METRICS.items())),\n formats=tuple(v for k, v in sorted(FORMATS.items())),\n samples=1000,\n classes=4,\n density=0.2,\n n_times=5,\n):\n \"\"\"Times metric calculations for a number of inputs\n\n Parameters\n ----------\n metrics : array-like of callables (1d or 0d)\n The metric functions to time.\n\n formats : array-like of callables (1d or 0d)\n These may transform a dense indicator matrix into multilabel\n representation.\n\n samples : array-like of ints (1d or 0d)\n The number of samples to generate as input.\n\n classes : array-like of ints (1d or 0d)\n The number of classes in the input.\n\n density : array-like of ints (1d or 0d)\n The density of positive labels in the input.\n\n n_times : int\n Time calling the metric n_times times.\n\n Returns\n -------\n array of floats shaped like (metrics, formats, samples, classes, density)\n Time in seconds.\n \"\"\"\n metrics = np.atleast_1d(metrics)\n samples = np.atleast_1d(samples)\n classes = np.atleast_1d(classes)\n density = np.atleast_1d(density)\n formats = np.atleast_1d(formats)\n out = np.zeros(\n (len(metrics), len(formats), len(samples), len(classes), len(density)),\n dtype=float,\n )\n it = itertools.product(samples, classes, density)\n for i, (s, c, d) in enumerate(it):\n _, y_true = make_multilabel_classification(\n n_samples=s, n_features=1, n_classes=c, n_labels=d * c, random_state=42\n )\n _, y_pred = make_multilabel_classification(\n n_samples=s, n_features=1, n_classes=c, n_labels=d * c, random_state=84\n )\n for j, f in enumerate(formats):\n f_true = f(y_true)\n f_pred = f(y_pred)\n for k, metric in enumerate(metrics):\n t = timeit(partial(metric, f_true, f_pred), number=n_times)\n\n out[k, j].flat[i] = t\n return out\n\n\ndef _tabulate(results, metrics, formats):\n \"\"\"Prints results by metric and format\n\n Uses the last ([-1]) value of other fields\n \"\"\"\n column_width = max(max(len(k) for k in formats) + 1, 8)\n first_width = max(len(k) for k in metrics)\n head_fmt = \"{:<{fw}s}\" + \"{:>{cw}s}\" * len(formats)\n row_fmt = \"{:<{fw}s}\" + \"{:>{cw}.3f}\" * len(formats)\n print(head_fmt.format(\"Metric\", *formats, cw=column_width, fw=first_width))\n for metric, row in zip(metrics, results[:, :, -1, -1, -1]):\n print(row_fmt.format(metric, *row, cw=column_width, fw=first_width))\n\n\ndef _plot(\n results,\n metrics,\n formats,\n title,\n x_ticks,\n x_label,\n format_markers=(\"x\", \"|\", \"o\", \"+\"),\n metric_colors=(\"c\", \"m\", \"y\", \"k\", \"g\", \"r\", \"b\"),\n):\n \"\"\"\n Plot the results by metric, format and some other variable given by\n x_label\n \"\"\"\n fig = plt.figure(\"scikit-learn multilabel metrics benchmarks\")\n plt.title(title)\n ax = fig.add_subplot(111)\n for i, metric in enumerate(metrics):\n for j, format in enumerate(formats):\n ax.plot(\n x_ticks,\n results[i, j].flat,\n label=\"{}, {}\".format(metric, format),\n marker=format_markers[j],\n color=metric_colors[i % len(metric_colors)],\n )\n ax.set_xlabel(x_label)\n ax.set_ylabel(\"Time (s)\")\n ax.legend()\n plt.show()\n\n\nif __name__ == \"__main__\":\n ap = argparse.ArgumentParser()\n ap.add_argument(\n \"metrics\",\n nargs=\"*\",\n default=sorted(METRICS),\n help=\"Specifies metrics to benchmark, defaults to all. Choices are: {}\".format(\n sorted(METRICS)\n ),\n )\n ap.add_argument(\n \"--formats\",\n nargs=\"+\",\n choices=sorted(FORMATS),\n help=\"Specifies multilabel formats to benchmark (defaults to all).\",\n )\n ap.add_argument(\n \"--samples\", type=int, default=1000, help=\"The number of samples to generate\"\n )\n ap.add_argument(\"--classes\", type=int, default=10, help=\"The number of classes\")\n ap.add_argument(\n \"--density\",\n type=float,\n default=0.2,\n help=\"The average density of labels per sample\",\n )\n ap.add_argument(\n \"--plot\",\n choices=[\"classes\", \"density\", \"samples\"],\n default=None,\n help=(\n \"Plot time with respect to this parameter varying up to the specified value\"\n ),\n )\n ap.add_argument(\n \"--n-steps\", default=10, type=int, help=\"Plot this many points for each metric\"\n )\n ap.add_argument(\n \"--n-times\", default=5, type=int, help=\"Time performance over n_times trials\"\n )\n args = ap.parse_args()\n\n if args.plot is not None:\n max_val = getattr(args, args.plot)\n if args.plot in (\"classes\", \"samples\"):\n min_val = 2\n else:\n min_val = 0\n steps = np.linspace(min_val, max_val, num=args.n_steps + 1)[1:]\n if args.plot in (\"classes\", \"samples\"):\n steps = np.unique(np.round(steps).astype(int))\n setattr(args, args.plot, steps)\n\n if args.metrics is None:\n args.metrics = sorted(METRICS)\n if args.formats is None:\n args.formats = sorted(FORMATS)\n\n results = benchmark(\n [METRICS[k] for k in args.metrics],\n [FORMATS[k] for k in args.formats],\n args.samples,\n args.classes,\n args.density,\n args.n_times,\n )\n\n _tabulate(results, args.metrics, args.formats)\n\n if args.plot is not None:\n print(\"Displaying plot\", file=sys.stderr)\n title = \"Multilabel metrics with %s\" % \", \".join(\n \"{0}={1}\".format(field, getattr(args, field))\n for field in [\"samples\", \"classes\", \"density\"]\n if args.plot != field\n )\n _plot(results, args.metrics, args.formats, title, steps, args.plot)\n" }, { "path": "benchmarks/bench_online_ocsvm.py", "content": "\"\"\"\n=====================================\nSGDOneClassSVM benchmark\n=====================================\nThis benchmark compares the :class:`SGDOneClassSVM` with :class:`OneClassSVM`.\nThe former is an online One-Class SVM implemented with a Stochastic Gradient\nDescent (SGD). The latter is based on the LibSVM implementation. The\ncomplexity of :class:`SGDOneClassSVM` is linear in the number of samples\nwhereas the one of :class:`OneClassSVM` is at best quadratic in the number of\nsamples. We here compare the performance in terms of AUC and training time on\nclassical anomaly detection datasets.\n\nThe :class:`OneClassSVM` is applied with a Gaussian kernel and we therefore\nuse a kernel approximation prior to the application of :class:`SGDOneClassSVM`.\n\"\"\"\n\nfrom time import time\nimport numpy as np\n\nfrom scipy.interpolate import interp1d\n\nfrom sklearn.metrics import roc_curve, auc\nfrom sklearn.datasets import fetch_kddcup99, fetch_covtype\nfrom sklearn.preprocessing import LabelBinarizer, StandardScaler\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.utils import shuffle\nfrom sklearn.kernel_approximation import Nystroem\nfrom sklearn.svm import OneClassSVM\nfrom sklearn.linear_model import SGDOneClassSVM\n\nimport matplotlib.pyplot as plt\nimport matplotlib\n\nfont = {\"weight\": \"normal\", \"size\": 15}\n\nmatplotlib.rc(\"font\", **font)\n\nprint(__doc__)\n\n\ndef print_outlier_ratio(y):\n \"\"\"\n Helper function to show the distinct value count of element in the target.\n Useful indicator for the datasets used in bench_isolation_forest.py.\n \"\"\"\n uniq, cnt = np.unique(y, return_counts=True)\n print(\"----- Target count values: \")\n for u, c in zip(uniq, cnt):\n print(\"------ %s -> %d occurrences\" % (str(u), c))\n print(\"----- Outlier ratio: %.5f\" % (np.min(cnt) / len(y)))\n\n\n# for roc curve computation\nn_axis = 1000\nx_axis = np.linspace(0, 1, n_axis)\n\ndatasets = [\"http\", \"smtp\", \"SA\", \"SF\", \"forestcover\"]\n\nnovelty_detection = False # if False, training set polluted by outliers\n\nrandom_states = [42]\nnu = 0.05\n\nresults_libsvm = np.empty((len(datasets), n_axis + 5))\nresults_online = np.empty((len(datasets), n_axis + 5))\n\nfor dat, dataset_name in enumerate(datasets):\n\n print(dataset_name)\n\n # Loading datasets\n if dataset_name in [\"http\", \"smtp\", \"SA\", \"SF\"]:\n dataset = fetch_kddcup99(\n subset=dataset_name, shuffle=False, percent10=False, random_state=88\n )\n X = dataset.data\n y = dataset.target\n\n if dataset_name == \"forestcover\":\n dataset = fetch_covtype(shuffle=False)\n X = dataset.data\n y = dataset.target\n # normal data are those with attribute 2\n # abnormal those with attribute 4\n s = (y == 2) + (y == 4)\n X = X[s, :]\n y = y[s]\n y = (y != 2).astype(int)\n\n # Vectorizing data\n if dataset_name == \"SF\":\n # Casting type of X (object) as string is needed for string categorical\n # features to apply LabelBinarizer\n lb = LabelBinarizer()\n x1 = lb.fit_transform(X[:, 1].astype(str))\n X = np.c_[X[:, :1], x1, X[:, 2:]]\n y = (y != b\"normal.\").astype(int)\n\n if dataset_name == \"SA\":\n lb = LabelBinarizer()\n # Casting type of X (object) as string is needed for string categorical\n # features to apply LabelBinarizer\n x1 = lb.fit_transform(X[:, 1].astype(str))\n x2 = lb.fit_transform(X[:, 2].astype(str))\n x3 = lb.fit_transform(X[:, 3].astype(str))\n X = np.c_[X[:, :1], x1, x2, x3, X[:, 4:]]\n y = (y != b\"normal.\").astype(int)\n\n if dataset_name in [\"http\", \"smtp\"]:\n y = (y != b\"normal.\").astype(int)\n\n print_outlier_ratio(y)\n\n n_samples, n_features = np.shape(X)\n if dataset_name == \"SA\": # LibSVM too long with n_samples // 2\n n_samples_train = n_samples // 20\n else:\n n_samples_train = n_samples // 2\n\n n_samples_test = n_samples - n_samples_train\n print(\"n_train: \", n_samples_train)\n print(\"n_features: \", n_features)\n\n tpr_libsvm = np.zeros(n_axis)\n tpr_online = np.zeros(n_axis)\n fit_time_libsvm = 0\n fit_time_online = 0\n predict_time_libsvm = 0\n predict_time_online = 0\n\n X = X.astype(float)\n\n gamma = 1 / n_features # OCSVM default parameter\n\n for random_state in random_states:\n\n print(\"random state: %s\" % random_state)\n\n X, y = shuffle(X, y, random_state=random_state)\n X_train = X[:n_samples_train]\n X_test = X[n_samples_train:]\n y_train = y[:n_samples_train]\n y_test = y[n_samples_train:]\n\n if novelty_detection:\n X_train = X_train[y_train == 0]\n y_train = y_train[y_train == 0]\n\n std = StandardScaler()\n\n print(\"----------- LibSVM OCSVM ------------\")\n ocsvm = OneClassSVM(kernel=\"rbf\", gamma=gamma, nu=nu)\n pipe_libsvm = make_pipeline(std, ocsvm)\n\n tstart = time()\n pipe_libsvm.fit(X_train)\n fit_time_libsvm += time() - tstart\n\n tstart = time()\n # scoring such that the lower, the more normal\n scoring = -pipe_libsvm.decision_function(X_test)\n predict_time_libsvm += time() - tstart\n fpr_libsvm_, tpr_libsvm_, _ = roc_curve(y_test, scoring)\n\n f_libsvm = interp1d(fpr_libsvm_, tpr_libsvm_)\n tpr_libsvm += f_libsvm(x_axis)\n\n print(\"----------- Online OCSVM ------------\")\n nystroem = Nystroem(gamma=gamma, random_state=random_state)\n online_ocsvm = SGDOneClassSVM(nu=nu, random_state=random_state)\n pipe_online = make_pipeline(std, nystroem, online_ocsvm)\n\n tstart = time()\n pipe_online.fit(X_train)\n fit_time_online += time() - tstart\n\n tstart = time()\n # scoring such that the lower, the more normal\n scoring = -pipe_online.decision_function(X_test)\n predict_time_online += time() - tstart\n fpr_online_, tpr_online_, _ = roc_curve(y_test, scoring)\n\n f_online = interp1d(fpr_online_, tpr_online_)\n tpr_online += f_online(x_axis)\n\n tpr_libsvm /= len(random_states)\n tpr_libsvm[0] = 0.0\n fit_time_libsvm /= len(random_states)\n predict_time_libsvm /= len(random_states)\n auc_libsvm = auc(x_axis, tpr_libsvm)\n\n results_libsvm[dat] = [\n fit_time_libsvm,\n predict_time_libsvm,\n auc_libsvm,\n n_samples_train,\n n_features,\n ] + list(tpr_libsvm)\n\n tpr_online /= len(random_states)\n tpr_online[0] = 0.0\n fit_time_online /= len(random_states)\n predict_time_online /= len(random_states)\n auc_online = auc(x_axis, tpr_online)\n\n results_online[dat] = [\n fit_time_online,\n predict_time_online,\n auc_online,\n n_samples_train,\n n_features,\n ] + list(tpr_libsvm)\n\n\n# -------- Plotting bar charts -------------\nfit_time_libsvm_all = results_libsvm[:, 0]\npredict_time_libsvm_all = results_libsvm[:, 1]\nauc_libsvm_all = results_libsvm[:, 2]\nn_train_all = results_libsvm[:, 3]\nn_features_all = results_libsvm[:, 4]\n\nfit_time_online_all = results_online[:, 0]\npredict_time_online_all = results_online[:, 1]\nauc_online_all = results_online[:, 2]\n\n\nwidth = 0.7\nind = 2 * np.arange(len(datasets))\nx_tickslabels = [\n (name + \"\\n\" + r\"$n={:,d}$\" + \"\\n\" + r\"$d={:d}$\").format(int(n), int(d))\n for name, n, d in zip(datasets, n_train_all, n_features_all)\n]\n\n\ndef autolabel_auc(rects, ax):\n \"\"\"Attach a text label above each bar displaying its height.\"\"\"\n for rect in rects:\n height = rect.get_height()\n ax.text(\n rect.get_x() + rect.get_width() / 2.0,\n 1.05 * height,\n \"%.3f\" % height,\n ha=\"center\",\n va=\"bottom\",\n )\n\n\ndef autolabel_time(rects, ax):\n \"\"\"Attach a text label above each bar displaying its height.\"\"\"\n for rect in rects:\n height = rect.get_height()\n ax.text(\n rect.get_x() + rect.get_width() / 2.0,\n 1.05 * height,\n \"%.1f\" % height,\n ha=\"center\",\n va=\"bottom\",\n )\n\n\nfig, ax = plt.subplots(figsize=(15, 8))\nax.set_ylabel(\"AUC\")\nax.set_ylim((0, 1.3))\nrect_libsvm = ax.bar(ind, auc_libsvm_all, width=width, color=\"r\")\nrect_online = ax.bar(ind + width, auc_online_all, width=width, color=\"y\")\nax.legend((rect_libsvm[0], rect_online[0]), (\"LibSVM\", \"Online SVM\"))\nax.set_xticks(ind + width / 2)\nax.set_xticklabels(x_tickslabels)\nautolabel_auc(rect_libsvm, ax)\nautolabel_auc(rect_online, ax)\nplt.show()\n\n\nfig, ax = plt.subplots(figsize=(15, 8))\nax.set_ylabel(\"Training time (sec) - Log scale\")\nax.set_yscale(\"log\")\nrect_libsvm = ax.bar(ind, fit_time_libsvm_all, color=\"r\", width=width)\nrect_online = ax.bar(ind + width, fit_time_online_all, color=\"y\", width=width)\nax.legend((rect_libsvm[0], rect_online[0]), (\"LibSVM\", \"Online SVM\"))\nax.set_xticks(ind + width / 2)\nax.set_xticklabels(x_tickslabels)\nautolabel_time(rect_libsvm, ax)\nautolabel_time(rect_online, ax)\nplt.show()\n\n\nfig, ax = plt.subplots(figsize=(15, 8))\nax.set_ylabel(\"Testing time (sec) - Log scale\")\nax.set_yscale(\"log\")\nrect_libsvm = ax.bar(ind, predict_time_libsvm_all, color=\"r\", width=width)\nrect_online = ax.bar(ind + width, predict_time_online_all, color=\"y\", width=width)\nax.legend((rect_libsvm[0], rect_online[0]), (\"LibSVM\", \"Online SVM\"))\nax.set_xticks(ind + width / 2)\nax.set_xticklabels(x_tickslabels)\nautolabel_time(rect_libsvm, ax)\nautolabel_time(rect_online, ax)\nplt.show()\n" }, { "path": "benchmarks/bench_plot_fastkmeans.py", "content": "from collections import defaultdict\nfrom time import time\n\nimport numpy as np\nfrom numpy import random as nr\n\nfrom sklearn.cluster import KMeans, MiniBatchKMeans\n\n\ndef compute_bench(samples_range, features_range):\n\n it = 0\n results = defaultdict(lambda: [])\n chunk = 100\n\n max_it = len(samples_range) * len(features_range)\n for n_samples in samples_range:\n for n_features in features_range:\n it += 1\n print(\"==============================\")\n print(\"Iteration %03d of %03d\" % (it, max_it))\n print(\"==============================\")\n print()\n data = nr.randint(-50, 51, (n_samples, n_features))\n\n print(\"K-Means\")\n tstart = time()\n kmeans = KMeans(init=\"k-means++\", n_clusters=10).fit(data)\n\n delta = time() - tstart\n print(\"Speed: %0.3fs\" % delta)\n print(\"Inertia: %0.5f\" % kmeans.inertia_)\n print()\n\n results[\"kmeans_speed\"].append(delta)\n results[\"kmeans_quality\"].append(kmeans.inertia_)\n\n print(\"Fast K-Means\")\n # let's prepare the data in small chunks\n mbkmeans = MiniBatchKMeans(\n init=\"k-means++\", n_clusters=10, batch_size=chunk\n )\n tstart = time()\n mbkmeans.fit(data)\n delta = time() - tstart\n print(\"Speed: %0.3fs\" % delta)\n print(\"Inertia: %f\" % mbkmeans.inertia_)\n print()\n print()\n\n results[\"MiniBatchKMeans Speed\"].append(delta)\n results[\"MiniBatchKMeans Quality\"].append(mbkmeans.inertia_)\n\n return results\n\n\ndef compute_bench_2(chunks):\n results = defaultdict(lambda: [])\n n_features = 50000\n means = np.array(\n [\n [1, 1],\n [-1, -1],\n [1, -1],\n [-1, 1],\n [0.5, 0.5],\n [0.75, -0.5],\n [-1, 0.75],\n [1, 0],\n ]\n )\n X = np.empty((0, 2))\n for i in range(8):\n X = np.r_[X, means[i] + 0.8 * np.random.randn(n_features, 2)]\n max_it = len(chunks)\n it = 0\n for chunk in chunks:\n it += 1\n print(\"==============================\")\n print(\"Iteration %03d of %03d\" % (it, max_it))\n print(\"==============================\")\n print()\n\n print(\"Fast K-Means\")\n tstart = time()\n mbkmeans = MiniBatchKMeans(init=\"k-means++\", n_clusters=8, batch_size=chunk)\n\n mbkmeans.fit(X)\n delta = time() - tstart\n print(\"Speed: %0.3fs\" % delta)\n print(\"Inertia: %0.3fs\" % mbkmeans.inertia_)\n print()\n\n results[\"MiniBatchKMeans Speed\"].append(delta)\n results[\"MiniBatchKMeans Quality\"].append(mbkmeans.inertia_)\n\n return results\n\n\nif __name__ == \"__main__\":\n from mpl_toolkits.mplot3d import axes3d # noqa register the 3d projection\n import matplotlib.pyplot as plt\n\n samples_range = np.linspace(50, 150, 5).astype(int)\n features_range = np.linspace(150, 50000, 5).astype(int)\n chunks = np.linspace(500, 10000, 15).astype(int)\n\n results = compute_bench(samples_range, features_range)\n results_2 = compute_bench_2(chunks)\n\n max_time = max(\n [max(i) for i in [t for (label, t) in results.items() if \"speed\" in label]]\n )\n max_inertia = max(\n [max(i) for i in [t for (label, t) in results.items() if \"speed\" not in label]]\n )\n\n fig = plt.figure(\"scikit-learn K-Means benchmark results\")\n for c, (label, timings) in zip(\"brcy\", sorted(results.items())):\n if \"speed\" in label:\n ax = fig.add_subplot(2, 2, 1, projection=\"3d\")\n ax.set_zlim3d(0.0, max_time * 1.1)\n else:\n ax = fig.add_subplot(2, 2, 2, projection=\"3d\")\n ax.set_zlim3d(0.0, max_inertia * 1.1)\n\n X, Y = np.meshgrid(samples_range, features_range)\n Z = np.asarray(timings).reshape(samples_range.shape[0], features_range.shape[0])\n ax.plot_surface(X, Y, Z.T, cstride=1, rstride=1, color=c, alpha=0.5)\n ax.set_xlabel(\"n_samples\")\n ax.set_ylabel(\"n_features\")\n\n i = 0\n for c, (label, timings) in zip(\"br\", sorted(results_2.items())):\n i += 1\n ax = fig.add_subplot(2, 2, i + 2)\n y = np.asarray(timings)\n ax.plot(chunks, y, color=c, alpha=0.8)\n ax.set_xlabel(\"Chunks\")\n ax.set_ylabel(label)\n\n plt.show()\n" }, { "path": "benchmarks/bench_plot_hierarchical.py", "content": "from collections import defaultdict\nfrom time import time\n\nimport numpy as np\nfrom numpy import random as nr\n\nfrom sklearn.cluster import AgglomerativeClustering\n\n\ndef compute_bench(samples_range, features_range):\n\n it = 0\n results = defaultdict(lambda: [])\n\n max_it = len(samples_range) * len(features_range)\n for n_samples in samples_range:\n for n_features in features_range:\n it += 1\n print(\"==============================\")\n print(\"Iteration %03d of %03d\" % (it, max_it))\n print(\"n_samples %05d; n_features %02d\" % (n_samples, n_features))\n print(\"==============================\")\n print()\n data = nr.randint(-50, 51, (n_samples, n_features))\n\n for linkage in (\"single\", \"average\", \"complete\", \"ward\"):\n print(linkage.capitalize())\n tstart = time()\n AgglomerativeClustering(linkage=linkage, n_clusters=10).fit(data)\n\n delta = time() - tstart\n print(\"Speed: %0.3fs\" % delta)\n print()\n\n results[linkage].append(delta)\n\n return results\n\n\nif __name__ == \"__main__\":\n import matplotlib.pyplot as plt\n\n samples_range = np.linspace(1000, 15000, 8).astype(int)\n features_range = np.array([2, 10, 20, 50])\n\n results = compute_bench(samples_range, features_range)\n\n max_time = max([max(i) for i in [t for (label, t) in results.items()]])\n\n colors = plt.get_cmap(\"tab10\")(np.linspace(0, 1, 10))[:4]\n lines = {linkage: None for linkage in results.keys()}\n fig, axs = plt.subplots(2, 2, sharex=True, sharey=True)\n fig.suptitle(\"Scikit-learn agglomerative clustering benchmark results\", fontsize=16)\n for c, (label, timings) in zip(colors, sorted(results.items())):\n timing_by_samples = np.asarray(timings).reshape(\n samples_range.shape[0], features_range.shape[0]\n )\n\n for n in range(timing_by_samples.shape[1]):\n ax = axs.flatten()[n]\n (lines[label],) = ax.plot(\n samples_range, timing_by_samples[:, n], color=c, label=label\n )\n ax.set_title(\"n_features = %d\" % features_range[n])\n if n >= 2:\n ax.set_xlabel(\"n_samples\")\n if n % 2 == 0:\n ax.set_ylabel(\"time (s)\")\n\n fig.subplots_adjust(right=0.8)\n fig.legend(\n [lines[link] for link in sorted(results.keys())],\n sorted(results.keys()),\n loc=\"center right\",\n fontsize=8,\n )\n\n plt.show()\n" }, { "path": "benchmarks/bench_plot_incremental_pca.py", "content": "\"\"\"\n========================\nIncrementalPCA benchmark\n========================\n\nBenchmarks for IncrementalPCA\n\n\"\"\"\n\nimport numpy as np\nimport gc\nfrom time import time\nfrom collections import defaultdict\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import fetch_lfw_people\nfrom sklearn.decomposition import IncrementalPCA, PCA\n\n\ndef plot_results(X, y, label):\n plt.plot(X, y, label=label, marker=\"o\")\n\n\ndef benchmark(estimator, data):\n gc.collect()\n print(\"Benching %s\" % estimator)\n t0 = time()\n estimator.fit(data)\n training_time = time() - t0\n data_t = estimator.transform(data)\n data_r = estimator.inverse_transform(data_t)\n reconstruction_error = np.mean(np.abs(data - data_r))\n return {\"time\": training_time, \"error\": reconstruction_error}\n\n\ndef plot_feature_times(all_times, batch_size, all_components, data):\n plt.figure()\n plot_results(all_components, all_times[\"pca\"], label=\"PCA\")\n plot_results(\n all_components, all_times[\"ipca\"], label=\"IncrementalPCA, bsize=%i\" % batch_size\n )\n plt.legend(loc=\"upper left\")\n plt.suptitle(\n \"Algorithm runtime vs. n_components\\n LFW, size %i x %i\"\n % data.shape\n )\n plt.xlabel(\"Number of components (out of max %i)\" % data.shape[1])\n plt.ylabel(\"Time (seconds)\")\n\n\ndef plot_feature_errors(all_errors, batch_size, all_components, data):\n plt.figure()\n plot_results(all_components, all_errors[\"pca\"], label=\"PCA\")\n plot_results(\n all_components,\n all_errors[\"ipca\"],\n label=\"IncrementalPCA, bsize=%i\" % batch_size,\n )\n plt.legend(loc=\"lower left\")\n plt.suptitle(\"Algorithm error vs. n_components\\nLFW, size %i x %i\" % data.shape)\n plt.xlabel(\"Number of components (out of max %i)\" % data.shape[1])\n plt.ylabel(\"Mean absolute error\")\n\n\ndef plot_batch_times(all_times, n_features, all_batch_sizes, data):\n plt.figure()\n plot_results(all_batch_sizes, all_times[\"pca\"], label=\"PCA\")\n plot_results(all_batch_sizes, all_times[\"ipca\"], label=\"IncrementalPCA\")\n plt.legend(loc=\"lower left\")\n plt.suptitle(\n \"Algorithm runtime vs. batch_size for n_components %i\\n LFW,\"\n \" size %i x %i\" % (n_features, data.shape[0], data.shape[1])\n )\n plt.xlabel(\"Batch size\")\n plt.ylabel(\"Time (seconds)\")\n\n\ndef plot_batch_errors(all_errors, n_features, all_batch_sizes, data):\n plt.figure()\n plot_results(all_batch_sizes, all_errors[\"pca\"], label=\"PCA\")\n plot_results(all_batch_sizes, all_errors[\"ipca\"], label=\"IncrementalPCA\")\n plt.legend(loc=\"lower left\")\n plt.suptitle(\n \"Algorithm error vs. batch_size for n_components %i\\n LFW,\"\n \" size %i x %i\" % (n_features, data.shape[0], data.shape[1])\n )\n plt.xlabel(\"Batch size\")\n plt.ylabel(\"Mean absolute error\")\n\n\ndef fixed_batch_size_comparison(data):\n all_features = [\n i.astype(int) for i in np.linspace(data.shape[1] // 10, data.shape[1], num=5)\n ]\n batch_size = 1000\n # Compare runtimes and error for fixed batch size\n all_times = defaultdict(list)\n all_errors = defaultdict(list)\n for n_components in all_features:\n pca = PCA(n_components=n_components)\n ipca = IncrementalPCA(n_components=n_components, batch_size=batch_size)\n results_dict = {\n k: benchmark(est, data) for k, est in [(\"pca\", pca), (\"ipca\", ipca)]\n }\n\n for k in sorted(results_dict.keys()):\n all_times[k].append(results_dict[k][\"time\"])\n all_errors[k].append(results_dict[k][\"error\"])\n\n plot_feature_times(all_times, batch_size, all_features, data)\n plot_feature_errors(all_errors, batch_size, all_features, data)\n\n\ndef variable_batch_size_comparison(data):\n batch_sizes = [\n i.astype(int) for i in np.linspace(data.shape[0] // 10, data.shape[0], num=10)\n ]\n\n for n_components in [\n i.astype(int) for i in np.linspace(data.shape[1] // 10, data.shape[1], num=4)\n ]:\n all_times = defaultdict(list)\n all_errors = defaultdict(list)\n pca = PCA(n_components=n_components)\n rpca = PCA(\n n_components=n_components, svd_solver=\"randomized\", random_state=1999\n )\n results_dict = {\n k: benchmark(est, data) for k, est in [(\"pca\", pca), (\"rpca\", rpca)]\n }\n\n # Create flat baselines to compare the variation over batch size\n all_times[\"pca\"].extend([results_dict[\"pca\"][\"time\"]] * len(batch_sizes))\n all_errors[\"pca\"].extend([results_dict[\"pca\"][\"error\"]] * len(batch_sizes))\n all_times[\"rpca\"].extend([results_dict[\"rpca\"][\"time\"]] * len(batch_sizes))\n all_errors[\"rpca\"].extend([results_dict[\"rpca\"][\"error\"]] * len(batch_sizes))\n for batch_size in batch_sizes:\n ipca = IncrementalPCA(n_components=n_components, batch_size=batch_size)\n results_dict = {k: benchmark(est, data) for k, est in [(\"ipca\", ipca)]}\n all_times[\"ipca\"].append(results_dict[\"ipca\"][\"time\"])\n all_errors[\"ipca\"].append(results_dict[\"ipca\"][\"error\"])\n\n plot_batch_times(all_times, n_components, batch_sizes, data)\n plot_batch_errors(all_errors, n_components, batch_sizes, data)\n\n\nfaces = fetch_lfw_people(resize=0.2, min_faces_per_person=5)\n# limit dataset to 5000 people (don't care who they are!)\nX = faces.data[:5000]\nn_samples, h, w = faces.images.shape\nn_features = X.shape[1]\n\nX -= X.mean(axis=0)\nX /= X.std(axis=0)\n\nfixed_batch_size_comparison(X)\nvariable_batch_size_comparison(X)\nplt.show()\n" }, { "path": "benchmarks/bench_plot_lasso_path.py", "content": "\"\"\"Benchmarks of Lasso regularization path computation using Lars and CD\n\nThe input data is mostly low rank but is a fat infinite tail.\n\"\"\"\nfrom collections import defaultdict\nimport gc\nimport sys\nfrom time import time\n\nimport numpy as np\n\nfrom sklearn.linear_model import lars_path, lars_path_gram\nfrom sklearn.linear_model import lasso_path\nfrom sklearn.datasets import make_regression\n\n\ndef compute_bench(samples_range, features_range):\n\n it = 0\n\n results = defaultdict(lambda: [])\n\n max_it = len(samples_range) * len(features_range)\n for n_samples in samples_range:\n for n_features in features_range:\n it += 1\n print(\"====================\")\n print(\"Iteration %03d of %03d\" % (it, max_it))\n print(\"====================\")\n dataset_kwargs = {\n \"n_samples\": n_samples,\n \"n_features\": n_features,\n \"n_informative\": n_features // 10,\n \"effective_rank\": min(n_samples, n_features) / 10,\n # 'effective_rank': None,\n \"bias\": 0.0,\n }\n print(\"n_samples: %d\" % n_samples)\n print(\"n_features: %d\" % n_features)\n X, y = make_regression(**dataset_kwargs)\n\n gc.collect()\n print(\"benchmarking lars_path (with Gram):\", end=\"\")\n sys.stdout.flush()\n tstart = time()\n G = np.dot(X.T, X) # precomputed Gram matrix\n Xy = np.dot(X.T, y)\n lars_path_gram(Xy=Xy, Gram=G, n_samples=y.size, method=\"lasso\")\n delta = time() - tstart\n print(\"%0.3fs\" % delta)\n results[\"lars_path (with Gram)\"].append(delta)\n\n gc.collect()\n print(\"benchmarking lars_path (without Gram):\", end=\"\")\n sys.stdout.flush()\n tstart = time()\n lars_path(X, y, method=\"lasso\")\n delta = time() - tstart\n print(\"%0.3fs\" % delta)\n results[\"lars_path (without Gram)\"].append(delta)\n\n gc.collect()\n print(\"benchmarking lasso_path (with Gram):\", end=\"\")\n sys.stdout.flush()\n tstart = time()\n lasso_path(X, y, precompute=True)\n delta = time() - tstart\n print(\"%0.3fs\" % delta)\n results[\"lasso_path (with Gram)\"].append(delta)\n\n gc.collect()\n print(\"benchmarking lasso_path (without Gram):\", end=\"\")\n sys.stdout.flush()\n tstart = time()\n lasso_path(X, y, precompute=False)\n delta = time() - tstart\n print(\"%0.3fs\" % delta)\n results[\"lasso_path (without Gram)\"].append(delta)\n\n return results\n\n\nif __name__ == \"__main__\":\n from mpl_toolkits.mplot3d import axes3d # noqa register the 3d projection\n import matplotlib.pyplot as plt\n\n samples_range = np.linspace(10, 2000, 5).astype(int)\n features_range = np.linspace(10, 2000, 5).astype(int)\n results = compute_bench(samples_range, features_range)\n\n max_time = max(max(t) for t in results.values())\n\n fig = plt.figure(\"scikit-learn Lasso path benchmark results\")\n i = 1\n for c, (label, timings) in zip(\"bcry\", sorted(results.items())):\n ax = fig.add_subplot(2, 2, i, projection=\"3d\")\n X, Y = np.meshgrid(samples_range, features_range)\n Z = np.asarray(timings).reshape(samples_range.shape[0], features_range.shape[0])\n\n # plot the actual surface\n ax.plot_surface(X, Y, Z.T, cstride=1, rstride=1, color=c, alpha=0.8)\n\n # dummy point plot to stick the legend to since surface plot do not\n # support legends (yet?)\n # ax.plot([1], [1], [1], color=c, label=label)\n\n ax.set_xlabel(\"n_samples\")\n ax.set_ylabel(\"n_features\")\n ax.set_zlabel(\"Time (s)\")\n ax.set_zlim3d(0.0, max_time * 1.1)\n ax.set_title(label)\n # ax.legend()\n i += 1\n plt.show()\n" }, { "path": "benchmarks/bench_plot_neighbors.py", "content": "\"\"\"\nPlot the scaling of the nearest neighbors algorithms with k, D, and N\n\"\"\"\nfrom time import time\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import ticker\n\nfrom sklearn import neighbors, datasets\n\n\ndef get_data(N, D, dataset=\"dense\"):\n if dataset == \"dense\":\n np.random.seed(0)\n return np.random.random((N, D))\n elif dataset == \"digits\":\n X, _ = datasets.load_digits(return_X_y=True)\n i = np.argsort(X[0])[::-1]\n X = X[:, i]\n return X[:N, :D]\n else:\n raise ValueError(\"invalid dataset: %s\" % dataset)\n\n\ndef barplot_neighbors(\n Nrange=2 ** np.arange(1, 11),\n Drange=2 ** np.arange(7),\n krange=2 ** np.arange(10),\n N=1000,\n D=64,\n k=5,\n leaf_size=30,\n dataset=\"digits\",\n):\n algorithms = (\"kd_tree\", \"brute\", \"ball_tree\")\n fiducial_values = {\"N\": N, \"D\": D, \"k\": k}\n\n # ------------------------------------------------------------\n # varying N\n N_results_build = {alg: np.zeros(len(Nrange)) for alg in algorithms}\n N_results_query = {alg: np.zeros(len(Nrange)) for alg in algorithms}\n\n for i, NN in enumerate(Nrange):\n print(\"N = %i (%i out of %i)\" % (NN, i + 1, len(Nrange)))\n X = get_data(NN, D, dataset)\n for algorithm in algorithms:\n nbrs = neighbors.NearestNeighbors(\n n_neighbors=min(NN, k), algorithm=algorithm, leaf_size=leaf_size\n )\n t0 = time()\n nbrs.fit(X)\n t1 = time()\n nbrs.kneighbors(X)\n t2 = time()\n\n N_results_build[algorithm][i] = t1 - t0\n N_results_query[algorithm][i] = t2 - t1\n\n # ------------------------------------------------------------\n # varying D\n D_results_build = {alg: np.zeros(len(Drange)) for alg in algorithms}\n D_results_query = {alg: np.zeros(len(Drange)) for alg in algorithms}\n\n for i, DD in enumerate(Drange):\n print(\"D = %i (%i out of %i)\" % (DD, i + 1, len(Drange)))\n X = get_data(N, DD, dataset)\n for algorithm in algorithms:\n nbrs = neighbors.NearestNeighbors(\n n_neighbors=k, algorithm=algorithm, leaf_size=leaf_size\n )\n t0 = time()\n nbrs.fit(X)\n t1 = time()\n nbrs.kneighbors(X)\n t2 = time()\n\n D_results_build[algorithm][i] = t1 - t0\n D_results_query[algorithm][i] = t2 - t1\n\n # ------------------------------------------------------------\n # varying k\n k_results_build = {alg: np.zeros(len(krange)) for alg in algorithms}\n k_results_query = {alg: np.zeros(len(krange)) for alg in algorithms}\n\n X = get_data(N, DD, dataset)\n\n for i, kk in enumerate(krange):\n print(\"k = %i (%i out of %i)\" % (kk, i + 1, len(krange)))\n for algorithm in algorithms:\n nbrs = neighbors.NearestNeighbors(\n n_neighbors=kk, algorithm=algorithm, leaf_size=leaf_size\n )\n t0 = time()\n nbrs.fit(X)\n t1 = time()\n nbrs.kneighbors(X)\n t2 = time()\n\n k_results_build[algorithm][i] = t1 - t0\n k_results_query[algorithm][i] = t2 - t1\n\n plt.figure(figsize=(8, 11))\n\n for (sbplt, vals, quantity, build_time, query_time) in [\n (311, Nrange, \"N\", N_results_build, N_results_query),\n (312, Drange, \"D\", D_results_build, D_results_query),\n (313, krange, \"k\", k_results_build, k_results_query),\n ]:\n ax = plt.subplot(sbplt, yscale=\"log\")\n plt.grid(True)\n\n tick_vals = []\n tick_labels = []\n\n bottom = 10 ** np.min(\n [min(np.floor(np.log10(build_time[alg]))) for alg in algorithms]\n )\n\n for i, alg in enumerate(algorithms):\n xvals = 0.1 + i * (1 + len(vals)) + np.arange(len(vals))\n width = 0.8\n\n c_bar = plt.bar(xvals, build_time[alg] - bottom, width, bottom, color=\"r\")\n q_bar = plt.bar(xvals, query_time[alg], width, build_time[alg], color=\"b\")\n\n tick_vals += list(xvals + 0.5 * width)\n tick_labels += [\"%i\" % val for val in vals]\n\n plt.text(\n (i + 0.02) / len(algorithms),\n 0.98,\n alg,\n transform=ax.transAxes,\n ha=\"left\",\n va=\"top\",\n bbox=dict(facecolor=\"w\", edgecolor=\"w\", alpha=0.5),\n )\n\n plt.ylabel(\"Time (s)\")\n\n ax.xaxis.set_major_locator(ticker.FixedLocator(tick_vals))\n ax.xaxis.set_major_formatter(ticker.FixedFormatter(tick_labels))\n\n for label in ax.get_xticklabels():\n label.set_rotation(-90)\n label.set_fontsize(10)\n\n title_string = \"Varying %s\" % quantity\n\n descr_string = \"\"\n\n for s in \"NDk\":\n if s == quantity:\n pass\n else:\n descr_string += \"%s = %i, \" % (s, fiducial_values[s])\n\n descr_string = descr_string[:-2]\n\n plt.text(\n 1.01,\n 0.5,\n title_string,\n transform=ax.transAxes,\n rotation=-90,\n ha=\"left\",\n va=\"center\",\n fontsize=20,\n )\n\n plt.text(\n 0.99,\n 0.5,\n descr_string,\n transform=ax.transAxes,\n rotation=-90,\n ha=\"right\",\n va=\"center\",\n )\n\n plt.gcf().suptitle(\"%s data set\" % dataset.capitalize(), fontsize=16)\n\n plt.figlegend((c_bar, q_bar), (\"construction\", \"N-point query\"), \"upper right\")\n\n\nif __name__ == \"__main__\":\n barplot_neighbors(dataset=\"digits\")\n barplot_neighbors(dataset=\"dense\")\n plt.show()\n" }, { "path": "benchmarks/bench_plot_nmf.py", "content": "\"\"\"\nBenchmarks of Non-Negative Matrix Factorization\n\"\"\"\n# Authors: Tom Dupre la Tour (benchmark)\n# Chih-Jen Linn (original projected gradient NMF implementation)\n# Anthony Di Franco (projected gradient, Python and NumPy port)\n# License: BSD 3 clause\n\nfrom time import time\nimport sys\nimport warnings\nimport numbers\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom joblib import Memory\nimport pandas\n\nfrom sklearn.utils._testing import ignore_warnings\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.decomposition import NMF\nfrom sklearn.decomposition._nmf import _initialize_nmf\nfrom sklearn.decomposition._nmf import _beta_divergence\nfrom sklearn.decomposition._nmf import _check_init\nfrom sklearn.exceptions import ConvergenceWarning\nfrom sklearn.utils.extmath import safe_sparse_dot, squared_norm\nfrom sklearn.utils import check_array\nfrom sklearn.utils.validation import check_is_fitted, check_non_negative\n\n\nmem = Memory(cachedir=\".\", verbose=0)\n\n###################\n# Start of _PGNMF #\n###################\n# This class implements a projected gradient solver for the NMF.\n# The projected gradient solver was removed from scikit-learn in version 0.19,\n# and a simplified copy is used here for comparison purpose only.\n# It is not tested, and it may change or disappear without notice.\n\n\ndef _norm(x):\n \"\"\"Dot product-based Euclidean norm implementation\n See: http://fseoane.net/blog/2011/computing-the-vector-norm/\n \"\"\"\n return np.sqrt(squared_norm(x))\n\n\ndef _nls_subproblem(\n X, W, H, tol, max_iter, alpha=0.0, l1_ratio=0.0, sigma=0.01, beta=0.1\n):\n \"\"\"Non-negative least square solver\n Solves a non-negative least squares subproblem using the projected\n gradient descent algorithm.\n Parameters\n ----------\n X : array-like, shape (n_samples, n_features)\n Constant matrix.\n W : array-like, shape (n_samples, n_components)\n Constant matrix.\n H : array-like, shape (n_components, n_features)\n Initial guess for the solution.\n tol : float\n Tolerance of the stopping condition.\n max_iter : int\n Maximum number of iterations before timing out.\n alpha : double, default: 0.\n Constant that multiplies the regularization terms. Set it to zero to\n have no regularization.\n l1_ratio : double, default: 0.\n The regularization mixing parameter, with 0 <= l1_ratio <= 1.\n For l1_ratio = 0 the penalty is an L2 penalty.\n For l1_ratio = 1 it is an L1 penalty.\n For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.\n sigma : float\n Constant used in the sufficient decrease condition checked by the line\n search. Smaller values lead to a looser sufficient decrease condition,\n thus reducing the time taken by the line search, but potentially\n increasing the number of iterations of the projected gradient\n procedure. 0.01 is a commonly used value in the optimization\n literature.\n beta : float\n Factor by which the step size is decreased (resp. increased) until\n (resp. as long as) the sufficient decrease condition is satisfied.\n Larger values allow to find a better step size but lead to longer line\n search. 0.1 is a commonly used value in the optimization literature.\n Returns\n -------\n H : array-like, shape (n_components, n_features)\n Solution to the non-negative least squares problem.\n grad : array-like, shape (n_components, n_features)\n The gradient.\n n_iter : int\n The number of iterations done by the algorithm.\n References\n ----------\n C.-J. Lin. Projected gradient methods for non-negative matrix\n factorization. Neural Computation, 19(2007), 2756-2779.\n https://www.csie.ntu.edu.tw/~cjlin/nmf/\n \"\"\"\n WtX = safe_sparse_dot(W.T, X)\n WtW = np.dot(W.T, W)\n\n # values justified in the paper (alpha is renamed gamma)\n gamma = 1\n for n_iter in range(1, max_iter + 1):\n grad = np.dot(WtW, H) - WtX\n if alpha > 0 and l1_ratio == 1.0:\n grad += alpha\n elif alpha > 0:\n grad += alpha * (l1_ratio + (1 - l1_ratio) * H)\n\n # The following multiplication with a boolean array is more than twice\n # as fast as indexing into grad.\n if _norm(grad * np.logical_or(grad < 0, H > 0)) < tol:\n break\n\n Hp = H\n\n for inner_iter in range(20):\n # Gradient step.\n Hn = H - gamma * grad\n # Projection step.\n Hn *= Hn > 0\n d = Hn - H\n gradd = np.dot(grad.ravel(), d.ravel())\n dQd = np.dot(np.dot(WtW, d).ravel(), d.ravel())\n suff_decr = (1 - sigma) * gradd + 0.5 * dQd < 0\n if inner_iter == 0:\n decr_gamma = not suff_decr\n\n if decr_gamma:\n if suff_decr:\n H = Hn\n break\n else:\n gamma *= beta\n elif not suff_decr or (Hp == Hn).all():\n H = Hp\n break\n else:\n gamma /= beta\n Hp = Hn\n\n if n_iter == max_iter:\n warnings.warn(\"Iteration limit reached in nls subproblem.\", ConvergenceWarning)\n\n return H, grad, n_iter\n\n\ndef _fit_projected_gradient(X, W, H, tol, max_iter, nls_max_iter, alpha, l1_ratio):\n gradW = np.dot(W, np.dot(H, H.T)) - safe_sparse_dot(X, H.T, dense_output=True)\n gradH = np.dot(np.dot(W.T, W), H) - safe_sparse_dot(W.T, X, dense_output=True)\n\n init_grad = squared_norm(gradW) + squared_norm(gradH.T)\n # max(0.001, tol) to force alternating minimizations of W and H\n tolW = max(0.001, tol) * np.sqrt(init_grad)\n tolH = tolW\n\n for n_iter in range(1, max_iter + 1):\n # stopping condition as discussed in paper\n proj_grad_W = squared_norm(gradW * np.logical_or(gradW < 0, W > 0))\n proj_grad_H = squared_norm(gradH * np.logical_or(gradH < 0, H > 0))\n\n if (proj_grad_W + proj_grad_H) / init_grad < tol ** 2:\n break\n\n # update W\n Wt, gradWt, iterW = _nls_subproblem(\n X.T, H.T, W.T, tolW, nls_max_iter, alpha=alpha, l1_ratio=l1_ratio\n )\n W, gradW = Wt.T, gradWt.T\n\n if iterW == 1:\n tolW = 0.1 * tolW\n\n # update H\n H, gradH, iterH = _nls_subproblem(\n X, W, H, tolH, nls_max_iter, alpha=alpha, l1_ratio=l1_ratio\n )\n if iterH == 1:\n tolH = 0.1 * tolH\n\n H[H == 0] = 0 # fix up negative zeros\n\n if n_iter == max_iter:\n Wt, _, _ = _nls_subproblem(\n X.T, H.T, W.T, tolW, nls_max_iter, alpha=alpha, l1_ratio=l1_ratio\n )\n W = Wt.T\n\n return W, H, n_iter\n\n\nclass _PGNMF(NMF):\n \"\"\"Non-Negative Matrix Factorization (NMF) with projected gradient solver.\n\n This class is private and for comparison purpose only.\n It may change or disappear without notice.\n\n \"\"\"\n\n def __init__(\n self,\n n_components=None,\n solver=\"pg\",\n init=None,\n tol=1e-4,\n max_iter=200,\n random_state=None,\n alpha=0.0,\n l1_ratio=0.0,\n nls_max_iter=10,\n ):\n super().__init__(\n n_components=n_components,\n init=init,\n solver=solver,\n tol=tol,\n max_iter=max_iter,\n random_state=random_state,\n alpha=alpha,\n l1_ratio=l1_ratio,\n )\n self.nls_max_iter = nls_max_iter\n\n def fit(self, X, y=None, **params):\n self.fit_transform(X, **params)\n return self\n\n def transform(self, X):\n check_is_fitted(self)\n H = self.components_\n W, _, self.n_iter_ = self._fit_transform(X, H=H, update_H=False)\n return W\n\n def inverse_transform(self, W):\n check_is_fitted(self)\n return np.dot(W, self.components_)\n\n def fit_transform(self, X, y=None, W=None, H=None):\n W, H, self.n_iter = self._fit_transform(X, W=W, H=H, update_H=True)\n self.components_ = H\n return W\n\n def _fit_transform(self, X, y=None, W=None, H=None, update_H=True):\n X = check_array(X, accept_sparse=(\"csr\", \"csc\"))\n check_non_negative(X, \"NMF (input X)\")\n\n n_samples, n_features = X.shape\n n_components = self.n_components\n if n_components is None:\n n_components = n_features\n\n if not isinstance(n_components, numbers.Integral) or n_components <= 0:\n raise ValueError(\n \"Number of components must be a positive integer; got (n_components=%r)\"\n % n_components\n )\n if not isinstance(self.max_iter, numbers.Integral) or self.max_iter < 0:\n raise ValueError(\n \"Maximum number of iterations must be a positive \"\n \"integer; got (max_iter=%r)\"\n % self.max_iter\n )\n if not isinstance(self.tol, numbers.Number) or self.tol < 0:\n raise ValueError(\n \"Tolerance for stopping criteria must be positive; got (tol=%r)\"\n % self.tol\n )\n\n # check W and H, or initialize them\n if self.init == \"custom\" and update_H:\n _check_init(H, (n_components, n_features), \"NMF (input H)\")\n _check_init(W, (n_samples, n_components), \"NMF (input W)\")\n elif not update_H:\n _check_init(H, (n_components, n_features), \"NMF (input H)\")\n W = np.zeros((n_samples, n_components))\n else:\n W, H = _initialize_nmf(\n X, n_components, init=self.init, random_state=self.random_state\n )\n\n if update_H: # fit_transform\n W, H, n_iter = _fit_projected_gradient(\n X,\n W,\n H,\n self.tol,\n self.max_iter,\n self.nls_max_iter,\n self.alpha,\n self.l1_ratio,\n )\n else: # transform\n Wt, _, n_iter = _nls_subproblem(\n X.T,\n H.T,\n W.T,\n self.tol,\n self.nls_max_iter,\n alpha=self.alpha,\n l1_ratio=self.l1_ratio,\n )\n W = Wt.T\n\n if n_iter == self.max_iter and self.tol > 0:\n warnings.warn(\n \"Maximum number of iteration %d reached. Increase it\"\n \" to improve convergence.\"\n % self.max_iter,\n ConvergenceWarning,\n )\n\n return W, H, n_iter\n\n\n#################\n# End of _PGNMF #\n#################\n\n\ndef plot_results(results_df, plot_name):\n if results_df is None:\n return None\n\n plt.figure(figsize=(16, 6))\n colors = \"bgr\"\n markers = \"ovs\"\n ax = plt.subplot(1, 3, 1)\n for i, init in enumerate(np.unique(results_df[\"init\"])):\n plt.subplot(1, 3, i + 1, sharex=ax, sharey=ax)\n for j, method in enumerate(np.unique(results_df[\"method\"])):\n mask = np.logical_and(\n results_df[\"init\"] == init, results_df[\"method\"] == method\n )\n selected_items = results_df[mask]\n\n plt.plot(\n selected_items[\"time\"],\n selected_items[\"loss\"],\n color=colors[j % len(colors)],\n ls=\"-\",\n marker=markers[j % len(markers)],\n label=method,\n )\n\n plt.legend(loc=0, fontsize=\"x-small\")\n plt.xlabel(\"Time (s)\")\n plt.ylabel(\"loss\")\n plt.title(\"%s\" % init)\n plt.suptitle(plot_name, fontsize=16)\n\n\n@ignore_warnings(category=ConvergenceWarning)\n# use joblib to cache the results.\n# X_shape is specified in arguments for avoiding hashing X\n@mem.cache(ignore=[\"X\", \"W0\", \"H0\"])\ndef bench_one(\n name, X, W0, H0, X_shape, clf_type, clf_params, init, n_components, random_state\n):\n W = W0.copy()\n H = H0.copy()\n\n clf = clf_type(**clf_params)\n st = time()\n W = clf.fit_transform(X, W=W, H=H)\n end = time()\n H = clf.components_\n\n this_loss = _beta_divergence(X, W, H, 2.0, True)\n duration = end - st\n return this_loss, duration\n\n\ndef run_bench(X, clfs, plot_name, n_components, tol, alpha, l1_ratio):\n start = time()\n results = []\n for name, clf_type, iter_range, clf_params in clfs:\n print(\"Training %s:\" % name)\n for rs, init in enumerate((\"nndsvd\", \"nndsvdar\", \"random\")):\n print(\" %s %s: \" % (init, \" \" * (8 - len(init))), end=\"\")\n W, H = _initialize_nmf(X, n_components, init, 1e-6, rs)\n\n for max_iter in iter_range:\n clf_params[\"alpha\"] = alpha\n clf_params[\"l1_ratio\"] = l1_ratio\n clf_params[\"max_iter\"] = max_iter\n clf_params[\"tol\"] = tol\n clf_params[\"random_state\"] = rs\n clf_params[\"init\"] = \"custom\"\n clf_params[\"n_components\"] = n_components\n\n this_loss, duration = bench_one(\n name, X, W, H, X.shape, clf_type, clf_params, init, n_components, rs\n )\n\n init_name = \"init='%s'\" % init\n results.append((name, this_loss, duration, init_name))\n # print(\"loss: %.6f, time: %.3f sec\" % (this_loss, duration))\n print(\".\", end=\"\")\n sys.stdout.flush()\n print(\" \")\n\n # Use a panda dataframe to organize the results\n results_df = pandas.DataFrame(results, columns=\"method loss time init\".split())\n print(\"Total time = %0.3f sec\\n\" % (time() - start))\n\n # plot the results\n plot_results(results_df, plot_name)\n return results_df\n\n\ndef load_20news():\n print(\"Loading 20 newsgroups dataset\")\n print(\"-----------------------------\")\n from sklearn.datasets import fetch_20newsgroups\n\n dataset = fetch_20newsgroups(\n shuffle=True, random_state=1, remove=(\"headers\", \"footers\", \"quotes\")\n )\n vectorizer = TfidfVectorizer(max_df=0.95, min_df=2, stop_words=\"english\")\n tfidf = vectorizer.fit_transform(dataset.data)\n return tfidf\n\n\ndef load_faces():\n print(\"Loading Olivetti face dataset\")\n print(\"-----------------------------\")\n from sklearn.datasets import fetch_olivetti_faces\n\n faces = fetch_olivetti_faces(shuffle=True)\n return faces.data\n\n\ndef build_clfs(cd_iters, pg_iters, mu_iters):\n clfs = [\n (\"Coordinate Descent\", NMF, cd_iters, {\"solver\": \"cd\"}),\n (\"Projected Gradient\", _PGNMF, pg_iters, {\"solver\": \"pg\"}),\n (\"Multiplicative Update\", NMF, mu_iters, {\"solver\": \"mu\"}),\n ]\n return clfs\n\n\nif __name__ == \"__main__\":\n alpha = 0.0\n l1_ratio = 0.5\n n_components = 10\n tol = 1e-15\n\n # first benchmark on 20 newsgroup dataset: sparse, shape(11314, 39116)\n plot_name = \"20 Newsgroups sparse dataset\"\n cd_iters = np.arange(1, 30)\n pg_iters = np.arange(1, 6)\n mu_iters = np.arange(1, 30)\n clfs = build_clfs(cd_iters, pg_iters, mu_iters)\n X_20news = load_20news()\n run_bench(X_20news, clfs, plot_name, n_components, tol, alpha, l1_ratio)\n\n # second benchmark on Olivetti faces dataset: dense, shape(400, 4096)\n plot_name = \"Olivetti Faces dense dataset\"\n cd_iters = np.arange(1, 30)\n pg_iters = np.arange(1, 12)\n mu_iters = np.arange(1, 30)\n clfs = build_clfs(cd_iters, pg_iters, mu_iters)\n X_faces = load_faces()\n run_bench(\n X_faces,\n clfs,\n plot_name,\n n_components,\n tol,\n alpha,\n l1_ratio,\n )\n\n plt.show()\n" }, { "path": "benchmarks/bench_plot_omp_lars.py", "content": "\"\"\"Benchmarks of orthogonal matching pursuit (:ref:`OMP`) versus least angle\nregression (:ref:`least_angle_regression`)\n\nThe input data is mostly low rank but is a fat infinite tail.\n\"\"\"\nimport gc\nimport sys\nfrom time import time\n\nimport numpy as np\n\nfrom sklearn.linear_model import lars_path, lars_path_gram, orthogonal_mp\nfrom sklearn.datasets import make_sparse_coded_signal\n\n\ndef compute_bench(samples_range, features_range):\n\n it = 0\n\n results = dict()\n lars = np.empty((len(features_range), len(samples_range)))\n lars_gram = lars.copy()\n omp = lars.copy()\n omp_gram = lars.copy()\n\n max_it = len(samples_range) * len(features_range)\n for i_s, n_samples in enumerate(samples_range):\n for i_f, n_features in enumerate(features_range):\n it += 1\n n_informative = n_features / 10\n print(\"====================\")\n print(\"Iteration %03d of %03d\" % (it, max_it))\n print(\"====================\")\n # dataset_kwargs = {\n # 'n_train_samples': n_samples,\n # 'n_test_samples': 2,\n # 'n_features': n_features,\n # 'n_informative': n_informative,\n # 'effective_rank': min(n_samples, n_features) / 10,\n # #'effective_rank': None,\n # 'bias': 0.0,\n # }\n dataset_kwargs = {\n \"n_samples\": 1,\n \"n_components\": n_features,\n \"n_features\": n_samples,\n \"n_nonzero_coefs\": n_informative,\n \"random_state\": 0,\n }\n print(\"n_samples: %d\" % n_samples)\n print(\"n_features: %d\" % n_features)\n y, X, _ = make_sparse_coded_signal(**dataset_kwargs)\n X = np.asfortranarray(X)\n\n gc.collect()\n print(\"benchmarking lars_path (with Gram):\", end=\"\")\n sys.stdout.flush()\n tstart = time()\n G = np.dot(X.T, X) # precomputed Gram matrix\n Xy = np.dot(X.T, y)\n lars_path_gram(Xy=Xy, Gram=G, n_samples=y.size, max_iter=n_informative)\n delta = time() - tstart\n print(\"%0.3fs\" % delta)\n lars_gram[i_f, i_s] = delta\n\n gc.collect()\n print(\"benchmarking lars_path (without Gram):\", end=\"\")\n sys.stdout.flush()\n tstart = time()\n lars_path(X, y, Gram=None, max_iter=n_informative)\n delta = time() - tstart\n print(\"%0.3fs\" % delta)\n lars[i_f, i_s] = delta\n\n gc.collect()\n print(\"benchmarking orthogonal_mp (with Gram):\", end=\"\")\n sys.stdout.flush()\n tstart = time()\n orthogonal_mp(X, y, precompute=True, n_nonzero_coefs=n_informative)\n delta = time() - tstart\n print(\"%0.3fs\" % delta)\n omp_gram[i_f, i_s] = delta\n\n gc.collect()\n print(\"benchmarking orthogonal_mp (without Gram):\", end=\"\")\n sys.stdout.flush()\n tstart = time()\n orthogonal_mp(X, y, precompute=False, n_nonzero_coefs=n_informative)\n delta = time() - tstart\n print(\"%0.3fs\" % delta)\n omp[i_f, i_s] = delta\n\n results[\"time(LARS) / time(OMP)\\n (w/ Gram)\"] = lars_gram / omp_gram\n results[\"time(LARS) / time(OMP)\\n (w/o Gram)\"] = lars / omp\n return results\n\n\nif __name__ == \"__main__\":\n samples_range = np.linspace(1000, 5000, 5).astype(int)\n features_range = np.linspace(1000, 5000, 5).astype(int)\n results = compute_bench(samples_range, features_range)\n max_time = max(np.max(t) for t in results.values())\n\n import matplotlib.pyplot as plt\n\n fig = plt.figure(\"scikit-learn OMP vs. LARS benchmark results\")\n for i, (label, timings) in enumerate(sorted(results.items())):\n ax = fig.add_subplot(1, 2, i + 1)\n vmax = max(1 - timings.min(), -1 + timings.max())\n plt.matshow(timings, fignum=False, vmin=1 - vmax, vmax=1 + vmax)\n ax.set_xticklabels([\"\"] + [str(each) for each in samples_range])\n ax.set_yticklabels([\"\"] + [str(each) for each in features_range])\n plt.xlabel(\"n_samples\")\n plt.ylabel(\"n_features\")\n plt.title(label)\n\n plt.subplots_adjust(0.1, 0.08, 0.96, 0.98, 0.4, 0.63)\n ax = plt.axes([0.1, 0.08, 0.8, 0.06])\n plt.colorbar(cax=ax, orientation=\"horizontal\")\n plt.show()\n" }, { "path": "benchmarks/bench_plot_parallel_pairwise.py", "content": "# Author: Mathieu Blondel \n# License: BSD 3 clause\nimport time\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import check_random_state\nfrom sklearn.metrics.pairwise import pairwise_distances\nfrom sklearn.metrics.pairwise import pairwise_kernels\n\n\ndef plot(func):\n random_state = check_random_state(0)\n one_core = []\n multi_core = []\n sample_sizes = range(1000, 6000, 1000)\n\n for n_samples in sample_sizes:\n X = random_state.rand(n_samples, 300)\n\n start = time.time()\n func(X, n_jobs=1)\n one_core.append(time.time() - start)\n\n start = time.time()\n func(X, n_jobs=-1)\n multi_core.append(time.time() - start)\n\n plt.figure(\"scikit-learn parallel %s benchmark results\" % func.__name__)\n plt.plot(sample_sizes, one_core, label=\"one core\")\n plt.plot(sample_sizes, multi_core, label=\"multi core\")\n plt.xlabel(\"n_samples\")\n plt.ylabel(\"Time (s)\")\n plt.title(\"Parallel %s\" % func.__name__)\n plt.legend()\n\n\ndef euclidean_distances(X, n_jobs):\n return pairwise_distances(X, metric=\"euclidean\", n_jobs=n_jobs)\n\n\ndef rbf_kernels(X, n_jobs):\n return pairwise_kernels(X, metric=\"rbf\", n_jobs=n_jobs, gamma=0.1)\n\n\nplot(euclidean_distances)\nplot(rbf_kernels)\nplt.show()\n" }, { "path": "benchmarks/bench_plot_polynomial_kernel_approximation.py", "content": "\"\"\"\n========================================================================\nBenchmark for explicit feature map approximation of polynomial kernels\n========================================================================\n\nAn example illustrating the approximation of the feature map\nof an Homogeneous Polynomial kernel.\n\n.. currentmodule:: sklearn.kernel_approximation\n\nIt shows how to use :class:`PolynomialCountSketch` and :class:`Nystroem` to\napproximate the feature map of a polynomial kernel for\nclassification with an SVM on the digits dataset. Results using a linear\nSVM in the original space, a linear SVM using the approximate mappings\nand a kernelized SVM are compared.\n\nThe first plot shows the classification accuracy of Nystroem [2] and\nPolynomialCountSketch [1] as the output dimension (n_components) grows.\nIt also shows the accuracy of a linear SVM and a polynomial kernel SVM\non the same data.\n\nThe second plot explores the scalability of PolynomialCountSketch\nand Nystroem. For a sufficiently large output dimension,\nPolynomialCountSketch should be faster as it is O(n(d+klog k))\nwhile Nystroem is O(n(dk+k^2)). In addition, Nystroem requires\na time-consuming training phase, while training is almost immediate\nfor PolynomialCountSketch, whose training phase boils down to\ninitializing some random variables (because is data-independent).\n\n[1] Pham, N., & Pagh, R. (2013, August). Fast and scalable polynomial\nkernels via explicit feature maps. In Proceedings of the 19th ACM SIGKDD\ninternational conference on Knowledge discovery and data mining (pp. 239-247)\n(http://chbrown.github.io/kdd-2013-usb/kdd/p239.pdf)\n\n[2] Charikar, M., Chen, K., & Farach-Colton, M. (2002, July). Finding frequent\nitems in data streams. In International Colloquium on Automata, Languages, and\nProgramming (pp. 693-703). Springer, Berlin, Heidelberg.\n(http://www.vldb.org/pvldb/1/1454225.pdf)\n\n\"\"\"\n# Author: Daniel Lopez-Sanchez \n# License: BSD 3 clause\n\n# Load data manipulation functions\nfrom sklearn.datasets import load_digits\nfrom sklearn.model_selection import train_test_split\n\n# Some common libraries\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Will use this for timing results\nfrom time import time\n\n# Import SVM classifiers and feature map approximation algorithms\nfrom sklearn.svm import LinearSVC, SVC\nfrom sklearn.kernel_approximation import Nystroem, PolynomialCountSketch\nfrom sklearn.pipeline import Pipeline\n\n# Split data in train and test sets\nX, y = load_digits()[\"data\"], load_digits()[\"target\"]\nX_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7)\n\n# Set the range of n_components for our experiments\nout_dims = range(20, 400, 20)\n\n# Evaluate Linear SVM\nlsvm = LinearSVC().fit(X_train, y_train)\nlsvm_score = 100 * lsvm.score(X_test, y_test)\n\n# Evaluate kernelized SVM\nksvm = SVC(kernel=\"poly\", degree=2, gamma=1.0).fit(X_train, y_train)\nksvm_score = 100 * ksvm.score(X_test, y_test)\n\n# Evaluate PolynomialCountSketch + LinearSVM\nps_svm_scores = []\nn_runs = 5\n\n# To compensate for the stochasticity of the method, we make n_tets runs\nfor k in out_dims:\n score_avg = 0\n for _ in range(n_runs):\n ps_svm = Pipeline(\n [\n (\"PS\", PolynomialCountSketch(degree=2, n_components=k)),\n (\"SVM\", LinearSVC()),\n ]\n )\n score_avg += ps_svm.fit(X_train, y_train).score(X_test, y_test)\n ps_svm_scores.append(100 * score_avg / n_runs)\n\n# Evaluate Nystroem + LinearSVM\nny_svm_scores = []\nn_runs = 5\n\nfor k in out_dims:\n score_avg = 0\n for _ in range(n_runs):\n ny_svm = Pipeline(\n [\n (\n \"NY\",\n Nystroem(\n kernel=\"poly\", gamma=1.0, degree=2, coef0=0, n_components=k\n ),\n ),\n (\"SVM\", LinearSVC()),\n ]\n )\n score_avg += ny_svm.fit(X_train, y_train).score(X_test, y_test)\n ny_svm_scores.append(100 * score_avg / n_runs)\n\n# Show results\nfig, ax = plt.subplots(figsize=(6, 4))\nax.set_title(\"Accuracy results\")\nax.plot(out_dims, ps_svm_scores, label=\"PolynomialCountSketch + linear SVM\", c=\"orange\")\nax.plot(out_dims, ny_svm_scores, label=\"Nystroem + linear SVM\", c=\"blue\")\nax.plot(\n [out_dims[0], out_dims[-1]],\n [lsvm_score, lsvm_score],\n label=\"Linear SVM\",\n c=\"black\",\n dashes=[2, 2],\n)\nax.plot(\n [out_dims[0], out_dims[-1]],\n [ksvm_score, ksvm_score],\n label=\"Poly-kernel SVM\",\n c=\"red\",\n dashes=[2, 2],\n)\nax.legend()\nax.set_xlabel(\"N_components for PolynomialCountSketch and Nystroem\")\nax.set_ylabel(\"Accuracy (%)\")\nax.set_xlim([out_dims[0], out_dims[-1]])\nfig.tight_layout()\n\n# Now lets evaluate the scalability of PolynomialCountSketch vs Nystroem\n# First we generate some fake data with a lot of samples\n\nfakeData = np.random.randn(10000, 100)\nfakeDataY = np.random.randint(0, high=10, size=(10000))\n\nout_dims = range(500, 6000, 500)\n\n# Evaluate scalability of PolynomialCountSketch as n_components grows\nps_svm_times = []\nfor k in out_dims:\n ps = PolynomialCountSketch(degree=2, n_components=k)\n\n start = time()\n ps.fit_transform(fakeData, None)\n ps_svm_times.append(time() - start)\n\n# Evaluate scalability of Nystroem as n_components grows\n# This can take a while due to the inefficient training phase\nny_svm_times = []\nfor k in out_dims:\n ny = Nystroem(kernel=\"poly\", gamma=1.0, degree=2, coef0=0, n_components=k)\n\n start = time()\n ny.fit_transform(fakeData, None)\n ny_svm_times.append(time() - start)\n\n# Show results\nfig, ax = plt.subplots(figsize=(6, 4))\nax.set_title(\"Scalability results\")\nax.plot(out_dims, ps_svm_times, label=\"PolynomialCountSketch\", c=\"orange\")\nax.plot(out_dims, ny_svm_times, label=\"Nystroem\", c=\"blue\")\nax.legend()\nax.set_xlabel(\"N_components for PolynomialCountSketch and Nystroem\")\nax.set_ylabel(\"fit_transform time \\n(s/10.000 samples)\")\nax.set_xlim([out_dims[0], out_dims[-1]])\nfig.tight_layout()\nplt.show()\n" }, { "path": "benchmarks/bench_plot_randomized_svd.py", "content": "\"\"\"\nBenchmarks on the power iterations phase in randomized SVD.\n\nWe test on various synthetic and real datasets the effect of increasing\nthe number of power iterations in terms of quality of approximation\nand running time. A number greater than 0 should help with noisy matrices,\nwhich are characterized by a slow spectral decay.\n\nWe test several policy for normalizing the power iterations. Normalization\nis crucial to avoid numerical issues.\n\nThe quality of the approximation is measured by the spectral norm discrepancy\nbetween the original input matrix and the reconstructed one (by multiplying\nthe randomized_svd's outputs). The spectral norm is always equivalent to the\nlargest singular value of a matrix. (3) justifies this choice. However, one can\nnotice in these experiments that Frobenius and spectral norms behave\nvery similarly in a qualitative sense. Therefore, we suggest to run these\nbenchmarks with `enable_spectral_norm = False`, as Frobenius' is MUCH faster to\ncompute.\n\nThe benchmarks follow.\n\n(a) plot: time vs norm, varying number of power iterations\n data: many datasets\n goal: compare normalization policies and study how the number of power\n iterations affect time and norm\n\n(b) plot: n_iter vs norm, varying rank of data and number of components for\n randomized_SVD\n data: low-rank matrices on which we control the rank\n goal: study whether the rank of the matrix and the number of components\n extracted by randomized SVD affect \"the optimal\" number of power iterations\n\n(c) plot: time vs norm, varying datasets\n data: many datasets\n goal: compare default configurations\n\nWe compare the following algorithms:\n- randomized_svd(..., power_iteration_normalizer='none')\n- randomized_svd(..., power_iteration_normalizer='LU')\n- randomized_svd(..., power_iteration_normalizer='QR')\n- randomized_svd(..., power_iteration_normalizer='auto')\n- fbpca.pca() from https://github.com/facebook/fbpca (if installed)\n\nConclusion\n----------\n- n_iter=2 appears to be a good default value\n- power_iteration_normalizer='none' is OK if n_iter is small, otherwise LU\n gives similar errors to QR but is cheaper. That's what 'auto' implements.\n\nReferences\n----------\n(1) Finding structure with randomness: Stochastic algorithms for constructing\n approximate matrix decompositions\n Halko, et al., 2009 https://arxiv.org/abs/0909.4061\n\n(2) A randomized algorithm for the decomposition of matrices\n Per-Gunnar Martinsson, Vladimir Rokhlin and Mark Tygert\n\n(3) An implementation of a randomized algorithm for principal component\n analysis\n A. Szlam et al. 2014\n\"\"\"\n\n# Author: Giorgio Patrini\n\nimport numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as plt\n\nimport gc\nimport pickle\nfrom time import time\nfrom collections import defaultdict\nimport os.path\n\nfrom sklearn.utils._arpack import _init_arpack_v0\nfrom sklearn.utils import gen_batches\nfrom sklearn.utils.validation import check_random_state\nfrom sklearn.utils.extmath import randomized_svd\nfrom sklearn.datasets import make_low_rank_matrix, make_sparse_uncorrelated\nfrom sklearn.datasets import (\n fetch_lfw_people,\n fetch_openml,\n fetch_20newsgroups_vectorized,\n fetch_olivetti_faces,\n fetch_rcv1,\n)\n\ntry:\n import fbpca\n\n fbpca_available = True\nexcept ImportError:\n fbpca_available = False\n\n# If this is enabled, tests are much slower and will crash with the large data\nenable_spectral_norm = False\n\n# TODO: compute approximate spectral norms with the power method as in\n# Estimating the largest eigenvalues by the power and Lanczos methods with\n# a random start, Jacek Kuczynski and Henryk Wozniakowski, SIAM Journal on\n# Matrix Analysis and Applications, 13 (4): 1094-1122, 1992.\n# This approximation is a very fast estimate of the spectral norm, but depends\n# on starting random vectors.\n\n# Determine when to switch to batch computation for matrix norms,\n# in case the reconstructed (dense) matrix is too large\nMAX_MEMORY = int(2e9)\n\n# The following datasets can be downloaded manually from:\n# CIFAR 10: https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n# SVHN: http://ufldl.stanford.edu/housenumbers/train_32x32.mat\nCIFAR_FOLDER = \"./cifar-10-batches-py/\"\nSVHN_FOLDER = \"./SVHN/\"\n\ndatasets = [\n \"low rank matrix\",\n \"lfw_people\",\n \"olivetti_faces\",\n \"20newsgroups\",\n \"mnist_784\",\n \"CIFAR\",\n \"a3a\",\n \"SVHN\",\n \"uncorrelated matrix\",\n]\n\nbig_sparse_datasets = [\"big sparse matrix\", \"rcv1\"]\n\n\ndef unpickle(file_name):\n with open(file_name, \"rb\") as fo:\n return pickle.load(fo, encoding=\"latin1\")[\"data\"]\n\n\ndef handle_missing_dataset(file_folder):\n if not os.path.isdir(file_folder):\n print(\"%s file folder not found. Test skipped.\" % file_folder)\n return 0\n\n\ndef get_data(dataset_name):\n print(\"Getting dataset: %s\" % dataset_name)\n\n if dataset_name == \"lfw_people\":\n X = fetch_lfw_people().data\n elif dataset_name == \"20newsgroups\":\n X = fetch_20newsgroups_vectorized().data[:, :100000]\n elif dataset_name == \"olivetti_faces\":\n X = fetch_olivetti_faces().data\n elif dataset_name == \"rcv1\":\n X = fetch_rcv1().data\n elif dataset_name == \"CIFAR\":\n if handle_missing_dataset(CIFAR_FOLDER) == \"skip\":\n return\n X1 = [unpickle(\"%sdata_batch_%d\" % (CIFAR_FOLDER, i + 1)) for i in range(5)]\n X = np.vstack(X1)\n del X1\n elif dataset_name == \"SVHN\":\n if handle_missing_dataset(SVHN_FOLDER) == 0:\n return\n X1 = sp.io.loadmat(\"%strain_32x32.mat\" % SVHN_FOLDER)[\"X\"]\n X2 = [X1[:, :, :, i].reshape(32 * 32 * 3) for i in range(X1.shape[3])]\n X = np.vstack(X2)\n del X1\n del X2\n elif dataset_name == \"low rank matrix\":\n X = make_low_rank_matrix(\n n_samples=500,\n n_features=int(1e4),\n effective_rank=100,\n tail_strength=0.5,\n random_state=random_state,\n )\n elif dataset_name == \"uncorrelated matrix\":\n X, _ = make_sparse_uncorrelated(\n n_samples=500, n_features=10000, random_state=random_state\n )\n elif dataset_name == \"big sparse matrix\":\n sparsity = int(1e6)\n size = int(1e6)\n small_size = int(1e4)\n data = np.random.normal(0, 1, int(sparsity / 10))\n data = np.repeat(data, 10)\n row = np.random.uniform(0, small_size, sparsity)\n col = np.random.uniform(0, small_size, sparsity)\n X = sp.sparse.csr_matrix((data, (row, col)), shape=(size, small_size))\n del data\n del row\n del col\n else:\n X = fetch_openml(dataset_name).data\n return X\n\n\ndef plot_time_vs_s(time, norm, point_labels, title):\n plt.figure()\n colors = [\"g\", \"b\", \"y\"]\n for i, l in enumerate(sorted(norm.keys())):\n if l != \"fbpca\":\n plt.plot(time[l], norm[l], label=l, marker=\"o\", c=colors.pop())\n else:\n plt.plot(time[l], norm[l], label=l, marker=\"^\", c=\"red\")\n\n for label, x, y in zip(point_labels, list(time[l]), list(norm[l])):\n plt.annotate(\n label,\n xy=(x, y),\n xytext=(0, -20),\n textcoords=\"offset points\",\n ha=\"right\",\n va=\"bottom\",\n )\n plt.legend(loc=\"upper right\")\n plt.suptitle(title)\n plt.ylabel(\"norm discrepancy\")\n plt.xlabel(\"running time [s]\")\n\n\ndef scatter_time_vs_s(time, norm, point_labels, title):\n plt.figure()\n size = 100\n for i, l in enumerate(sorted(norm.keys())):\n if l != \"fbpca\":\n plt.scatter(time[l], norm[l], label=l, marker=\"o\", c=\"b\", s=size)\n for label, x, y in zip(point_labels, list(time[l]), list(norm[l])):\n plt.annotate(\n label,\n xy=(x, y),\n xytext=(0, -80),\n textcoords=\"offset points\",\n ha=\"right\",\n arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\"),\n va=\"bottom\",\n size=11,\n rotation=90,\n )\n else:\n plt.scatter(time[l], norm[l], label=l, marker=\"^\", c=\"red\", s=size)\n for label, x, y in zip(point_labels, list(time[l]), list(norm[l])):\n plt.annotate(\n label,\n xy=(x, y),\n xytext=(0, 30),\n textcoords=\"offset points\",\n ha=\"right\",\n arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\"),\n va=\"bottom\",\n size=11,\n rotation=90,\n )\n\n plt.legend(loc=\"best\")\n plt.suptitle(title)\n plt.ylabel(\"norm discrepancy\")\n plt.xlabel(\"running time [s]\")\n\n\ndef plot_power_iter_vs_s(power_iter, s, title):\n plt.figure()\n for l in sorted(s.keys()):\n plt.plot(power_iter, s[l], label=l, marker=\"o\")\n plt.legend(loc=\"lower right\", prop={\"size\": 10})\n plt.suptitle(title)\n plt.ylabel(\"norm discrepancy\")\n plt.xlabel(\"n_iter\")\n\n\ndef svd_timing(\n X, n_comps, n_iter, n_oversamples, power_iteration_normalizer=\"auto\", method=None\n):\n \"\"\"\n Measure time for decomposition\n \"\"\"\n print(\"... running SVD ...\")\n if method != \"fbpca\":\n gc.collect()\n t0 = time()\n U, mu, V = randomized_svd(\n X,\n n_comps,\n n_oversamples,\n n_iter,\n power_iteration_normalizer,\n random_state=random_state,\n transpose=False,\n )\n call_time = time() - t0\n else:\n gc.collect()\n t0 = time()\n # There is a different convention for l here\n U, mu, V = fbpca.pca(\n X, n_comps, raw=True, n_iter=n_iter, l=n_oversamples + n_comps\n )\n call_time = time() - t0\n\n return U, mu, V, call_time\n\n\ndef norm_diff(A, norm=2, msg=True, random_state=None):\n \"\"\"\n Compute the norm diff with the original matrix, when randomized\n SVD is called with *params.\n\n norm: 2 => spectral; 'fro' => Frobenius\n \"\"\"\n\n if msg:\n print(\"... computing %s norm ...\" % norm)\n if norm == 2:\n # s = sp.linalg.norm(A, ord=2) # slow\n v0 = _init_arpack_v0(min(A.shape), random_state)\n value = sp.sparse.linalg.svds(A, k=1, return_singular_vectors=False, v0=v0)\n else:\n if sp.sparse.issparse(A):\n value = sp.sparse.linalg.norm(A, ord=norm)\n else:\n value = sp.linalg.norm(A, ord=norm)\n return value\n\n\ndef scalable_frobenius_norm_discrepancy(X, U, s, V):\n # if the input is not too big, just call scipy\n if X.shape[0] * X.shape[1] < MAX_MEMORY:\n A = X - U.dot(np.diag(s).dot(V))\n return norm_diff(A, norm=\"fro\")\n\n print(\"... computing fro norm by batches...\")\n batch_size = 1000\n Vhat = np.diag(s).dot(V)\n cum_norm = 0.0\n for batch in gen_batches(X.shape[0], batch_size):\n M = X[batch, :] - U[batch, :].dot(Vhat)\n cum_norm += norm_diff(M, norm=\"fro\", msg=False)\n return np.sqrt(cum_norm)\n\n\ndef bench_a(X, dataset_name, power_iter, n_oversamples, n_comps):\n\n all_time = defaultdict(list)\n if enable_spectral_norm:\n all_spectral = defaultdict(list)\n X_spectral_norm = norm_diff(X, norm=2, msg=False, random_state=0)\n all_frobenius = defaultdict(list)\n X_fro_norm = norm_diff(X, norm=\"fro\", msg=False)\n\n for pi in power_iter:\n for pm in [\"none\", \"LU\", \"QR\"]:\n print(\"n_iter = %d on sklearn - %s\" % (pi, pm))\n U, s, V, time = svd_timing(\n X,\n n_comps,\n n_iter=pi,\n power_iteration_normalizer=pm,\n n_oversamples=n_oversamples,\n )\n label = \"sklearn - %s\" % pm\n all_time[label].append(time)\n if enable_spectral_norm:\n A = U.dot(np.diag(s).dot(V))\n all_spectral[label].append(\n norm_diff(X - A, norm=2, random_state=0) / X_spectral_norm\n )\n f = scalable_frobenius_norm_discrepancy(X, U, s, V)\n all_frobenius[label].append(f / X_fro_norm)\n\n if fbpca_available:\n print(\"n_iter = %d on fbca\" % (pi))\n U, s, V, time = svd_timing(\n X,\n n_comps,\n n_iter=pi,\n power_iteration_normalizer=pm,\n n_oversamples=n_oversamples,\n method=\"fbpca\",\n )\n label = \"fbpca\"\n all_time[label].append(time)\n if enable_spectral_norm:\n A = U.dot(np.diag(s).dot(V))\n all_spectral[label].append(\n norm_diff(X - A, norm=2, random_state=0) / X_spectral_norm\n )\n f = scalable_frobenius_norm_discrepancy(X, U, s, V)\n all_frobenius[label].append(f / X_fro_norm)\n\n if enable_spectral_norm:\n title = \"%s: spectral norm diff vs running time\" % (dataset_name)\n plot_time_vs_s(all_time, all_spectral, power_iter, title)\n title = \"%s: Frobenius norm diff vs running time\" % (dataset_name)\n plot_time_vs_s(all_time, all_frobenius, power_iter, title)\n\n\ndef bench_b(power_list):\n\n n_samples, n_features = 1000, 10000\n data_params = {\n \"n_samples\": n_samples,\n \"n_features\": n_features,\n \"tail_strength\": 0.7,\n \"random_state\": random_state,\n }\n dataset_name = \"low rank matrix %d x %d\" % (n_samples, n_features)\n ranks = [10, 50, 100]\n\n if enable_spectral_norm:\n all_spectral = defaultdict(list)\n all_frobenius = defaultdict(list)\n for rank in ranks:\n X = make_low_rank_matrix(effective_rank=rank, **data_params)\n if enable_spectral_norm:\n X_spectral_norm = norm_diff(X, norm=2, msg=False, random_state=0)\n X_fro_norm = norm_diff(X, norm=\"fro\", msg=False)\n\n for n_comp in [int(rank / 2), rank, rank * 2]:\n label = \"rank=%d, n_comp=%d\" % (rank, n_comp)\n print(label)\n for pi in power_list:\n U, s, V, _ = svd_timing(\n X,\n n_comp,\n n_iter=pi,\n n_oversamples=2,\n power_iteration_normalizer=\"LU\",\n )\n if enable_spectral_norm:\n A = U.dot(np.diag(s).dot(V))\n all_spectral[label].append(\n norm_diff(X - A, norm=2, random_state=0) / X_spectral_norm\n )\n f = scalable_frobenius_norm_discrepancy(X, U, s, V)\n all_frobenius[label].append(f / X_fro_norm)\n\n if enable_spectral_norm:\n title = \"%s: spectral norm diff vs n power iteration\" % (dataset_name)\n plot_power_iter_vs_s(power_iter, all_spectral, title)\n title = \"%s: Frobenius norm diff vs n power iteration\" % (dataset_name)\n plot_power_iter_vs_s(power_iter, all_frobenius, title)\n\n\ndef bench_c(datasets, n_comps):\n all_time = defaultdict(list)\n if enable_spectral_norm:\n all_spectral = defaultdict(list)\n all_frobenius = defaultdict(list)\n\n for dataset_name in datasets:\n X = get_data(dataset_name)\n if X is None:\n continue\n\n if enable_spectral_norm:\n X_spectral_norm = norm_diff(X, norm=2, msg=False, random_state=0)\n X_fro_norm = norm_diff(X, norm=\"fro\", msg=False)\n n_comps = np.minimum(n_comps, np.min(X.shape))\n\n label = \"sklearn\"\n print(\"%s %d x %d - %s\" % (dataset_name, X.shape[0], X.shape[1], label))\n U, s, V, time = svd_timing(X, n_comps, n_iter=2, n_oversamples=10, method=label)\n\n all_time[label].append(time)\n if enable_spectral_norm:\n A = U.dot(np.diag(s).dot(V))\n all_spectral[label].append(\n norm_diff(X - A, norm=2, random_state=0) / X_spectral_norm\n )\n f = scalable_frobenius_norm_discrepancy(X, U, s, V)\n all_frobenius[label].append(f / X_fro_norm)\n\n if fbpca_available:\n label = \"fbpca\"\n print(\"%s %d x %d - %s\" % (dataset_name, X.shape[0], X.shape[1], label))\n U, s, V, time = svd_timing(\n X, n_comps, n_iter=2, n_oversamples=2, method=label\n )\n all_time[label].append(time)\n if enable_spectral_norm:\n A = U.dot(np.diag(s).dot(V))\n all_spectral[label].append(\n norm_diff(X - A, norm=2, random_state=0) / X_spectral_norm\n )\n f = scalable_frobenius_norm_discrepancy(X, U, s, V)\n all_frobenius[label].append(f / X_fro_norm)\n\n if len(all_time) == 0:\n raise ValueError(\"No tests ran. Aborting.\")\n\n if enable_spectral_norm:\n title = \"normalized spectral norm diff vs running time\"\n scatter_time_vs_s(all_time, all_spectral, datasets, title)\n title = \"normalized Frobenius norm diff vs running time\"\n scatter_time_vs_s(all_time, all_frobenius, datasets, title)\n\n\nif __name__ == \"__main__\":\n random_state = check_random_state(1234)\n\n power_iter = np.linspace(0, 6, 7, dtype=int)\n n_comps = 50\n\n for dataset_name in datasets:\n X = get_data(dataset_name)\n if X is None:\n continue\n print(\n \" >>>>>> Benching sklearn and fbpca on %s %d x %d\"\n % (dataset_name, X.shape[0], X.shape[1])\n )\n bench_a(\n X,\n dataset_name,\n power_iter,\n n_oversamples=2,\n n_comps=np.minimum(n_comps, np.min(X.shape)),\n )\n\n print(\" >>>>>> Benching on simulated low rank matrix with variable rank\")\n bench_b(power_iter)\n\n print(\" >>>>>> Benching sklearn and fbpca default configurations\")\n bench_c(datasets + big_sparse_datasets, n_comps)\n\n plt.show()\n" }, { "path": "benchmarks/bench_plot_svd.py", "content": "\"\"\"Benchmarks of Singular Value Decomposition (Exact and Approximate)\n\nThe data is mostly low rank but is a fat infinite tail.\n\"\"\"\nimport gc\nfrom time import time\nimport numpy as np\nfrom collections import defaultdict\n\nfrom scipy.linalg import svd\nfrom sklearn.utils.extmath import randomized_svd\nfrom sklearn.datasets import make_low_rank_matrix\n\n\ndef compute_bench(samples_range, features_range, n_iter=3, rank=50):\n\n it = 0\n\n results = defaultdict(lambda: [])\n\n max_it = len(samples_range) * len(features_range)\n for n_samples in samples_range:\n for n_features in features_range:\n it += 1\n print(\"====================\")\n print(\"Iteration %03d of %03d\" % (it, max_it))\n print(\"====================\")\n X = make_low_rank_matrix(\n n_samples, n_features, effective_rank=rank, tail_strength=0.2\n )\n\n gc.collect()\n print(\"benchmarking scipy svd: \")\n tstart = time()\n svd(X, full_matrices=False)\n results[\"scipy svd\"].append(time() - tstart)\n\n gc.collect()\n print(\"benchmarking scikit-learn randomized_svd: n_iter=0\")\n tstart = time()\n randomized_svd(X, rank, n_iter=0)\n results[\"scikit-learn randomized_svd (n_iter=0)\"].append(time() - tstart)\n\n gc.collect()\n print(\"benchmarking scikit-learn randomized_svd: n_iter=%d \" % n_iter)\n tstart = time()\n randomized_svd(X, rank, n_iter=n_iter)\n results[\"scikit-learn randomized_svd (n_iter=%d)\" % n_iter].append(\n time() - tstart\n )\n\n return results\n\n\nif __name__ == \"__main__\":\n from mpl_toolkits.mplot3d import axes3d # noqa register the 3d projection\n import matplotlib.pyplot as plt\n\n samples_range = np.linspace(2, 1000, 4).astype(int)\n features_range = np.linspace(2, 1000, 4).astype(int)\n results = compute_bench(samples_range, features_range)\n\n label = \"scikit-learn singular value decomposition benchmark results\"\n fig = plt.figure(label)\n ax = fig.gca(projection=\"3d\")\n for c, (label, timings) in zip(\"rbg\", sorted(results.items())):\n X, Y = np.meshgrid(samples_range, features_range)\n Z = np.asarray(timings).reshape(samples_range.shape[0], features_range.shape[0])\n # plot the actual surface\n ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3, color=c)\n # dummy point plot to stick the legend to since surface plot do not\n # support legends (yet?)\n ax.plot([1], [1], [1], color=c, label=label)\n\n ax.set_xlabel(\"n_samples\")\n ax.set_ylabel(\"n_features\")\n ax.set_zlabel(\"Time (s)\")\n ax.legend()\n plt.show()\n" }, { "path": "benchmarks/bench_plot_ward.py", "content": "\"\"\"\nBenchmark scikit-learn's Ward implement compared to SciPy's\n\"\"\"\n\nimport time\n\nimport numpy as np\nfrom scipy.cluster import hierarchy\nimport matplotlib.pyplot as plt\n\nfrom sklearn.cluster import AgglomerativeClustering\n\nward = AgglomerativeClustering(n_clusters=3, linkage=\"ward\")\n\nn_samples = np.logspace(0.5, 3, 9)\nn_features = np.logspace(1, 3.5, 7)\nN_samples, N_features = np.meshgrid(n_samples, n_features)\nscikits_time = np.zeros(N_samples.shape)\nscipy_time = np.zeros(N_samples.shape)\n\nfor i, n in enumerate(n_samples):\n for j, p in enumerate(n_features):\n X = np.random.normal(size=(n, p))\n t0 = time.time()\n ward.fit(X)\n scikits_time[j, i] = time.time() - t0\n t0 = time.time()\n hierarchy.ward(X)\n scipy_time[j, i] = time.time() - t0\n\nratio = scikits_time / scipy_time\n\nplt.figure(\"scikit-learn Ward's method benchmark results\")\nplt.imshow(np.log(ratio), aspect=\"auto\", origin=\"lower\")\nplt.colorbar()\nplt.contour(\n ratio,\n levels=[\n 1,\n ],\n colors=\"k\",\n)\nplt.yticks(range(len(n_features)), n_features.astype(int))\nplt.ylabel(\"N features\")\nplt.xticks(range(len(n_samples)), n_samples.astype(int))\nplt.xlabel(\"N samples\")\nplt.title(\"Scikit's time, in units of scipy time (log)\")\nplt.show()\n" }, { "path": "benchmarks/bench_random_projections.py", "content": "\"\"\"\n===========================\nRandom projection benchmark\n===========================\n\nBenchmarks for random projections.\n\n\"\"\"\nimport gc\nimport sys\nimport optparse\nfrom datetime import datetime\nimport collections\n\nimport numpy as np\nimport scipy.sparse as sp\n\nfrom sklearn import clone\nfrom sklearn.random_projection import (\n SparseRandomProjection,\n GaussianRandomProjection,\n johnson_lindenstrauss_min_dim,\n)\n\n\ndef type_auto_or_float(val):\n if val == \"auto\":\n return \"auto\"\n else:\n return float(val)\n\n\ndef type_auto_or_int(val):\n if val == \"auto\":\n return \"auto\"\n else:\n return int(val)\n\n\ndef compute_time(t_start, delta):\n mu_second = 0.0 + 10 ** 6 # number of microseconds in a second\n\n return delta.seconds + delta.microseconds / mu_second\n\n\ndef bench_scikit_transformer(X, transformer):\n gc.collect()\n\n clf = clone(transformer)\n\n # start time\n t_start = datetime.now()\n clf.fit(X)\n delta = datetime.now() - t_start\n # stop time\n time_to_fit = compute_time(t_start, delta)\n\n # start time\n t_start = datetime.now()\n clf.transform(X)\n delta = datetime.now() - t_start\n # stop time\n time_to_transform = compute_time(t_start, delta)\n\n return time_to_fit, time_to_transform\n\n\n# Make some random data with uniformly located non zero entries with\n# Gaussian distributed values\ndef make_sparse_random_data(n_samples, n_features, n_nonzeros, random_state=None):\n rng = np.random.RandomState(random_state)\n data_coo = sp.coo_matrix(\n (\n rng.randn(n_nonzeros),\n (\n rng.randint(n_samples, size=n_nonzeros),\n rng.randint(n_features, size=n_nonzeros),\n ),\n ),\n shape=(n_samples, n_features),\n )\n return data_coo.toarray(), data_coo.tocsr()\n\n\ndef print_row(clf_type, time_fit, time_transform):\n print(\n \"%s | %s | %s\"\n % (\n clf_type.ljust(30),\n (\"%.4fs\" % time_fit).center(12),\n (\"%.4fs\" % time_transform).center(12),\n )\n )\n\n\nif __name__ == \"__main__\":\n ###########################################################################\n # Option parser\n ###########################################################################\n op = optparse.OptionParser()\n op.add_option(\n \"--n-times\",\n dest=\"n_times\",\n default=5,\n type=int,\n help=\"Benchmark results are average over n_times experiments\",\n )\n\n op.add_option(\n \"--n-features\",\n dest=\"n_features\",\n default=10 ** 4,\n type=int,\n help=\"Number of features in the benchmarks\",\n )\n\n op.add_option(\n \"--n-components\",\n dest=\"n_components\",\n default=\"auto\",\n help=\"Size of the random subspace. ('auto' or int > 0)\",\n )\n\n op.add_option(\n \"--ratio-nonzeros\",\n dest=\"ratio_nonzeros\",\n default=10 ** -3,\n type=float,\n help=\"Number of features in the benchmarks\",\n )\n\n op.add_option(\n \"--n-samples\",\n dest=\"n_samples\",\n default=500,\n type=int,\n help=\"Number of samples in the benchmarks\",\n )\n\n op.add_option(\n \"--random-seed\",\n dest=\"random_seed\",\n default=13,\n type=int,\n help=\"Seed used by the random number generators.\",\n )\n\n op.add_option(\n \"--density\",\n dest=\"density\",\n default=1 / 3,\n help=(\n \"Density used by the sparse random projection. ('auto' or float (0.0, 1.0]\"\n ),\n )\n\n op.add_option(\n \"--eps\",\n dest=\"eps\",\n default=0.5,\n type=float,\n help=\"See the documentation of the underlying transformers.\",\n )\n\n op.add_option(\n \"--transformers\",\n dest=\"selected_transformers\",\n default=\"GaussianRandomProjection,SparseRandomProjection\",\n type=str,\n help=(\n \"Comma-separated list of transformer to benchmark. \"\n \"Default: %default. Available: \"\n \"GaussianRandomProjection,SparseRandomProjection\"\n ),\n )\n\n op.add_option(\n \"--dense\",\n dest=\"dense\",\n default=False,\n action=\"store_true\",\n help=\"Set input space as a dense matrix.\",\n )\n\n (opts, args) = op.parse_args()\n if len(args) > 0:\n op.error(\"this script takes no arguments.\")\n sys.exit(1)\n opts.n_components = type_auto_or_int(opts.n_components)\n opts.density = type_auto_or_float(opts.density)\n selected_transformers = opts.selected_transformers.split(\",\")\n\n ###########################################################################\n # Generate dataset\n ###########################################################################\n n_nonzeros = int(opts.ratio_nonzeros * opts.n_features)\n\n print(\"Dataset statistics\")\n print(\"===========================\")\n print(\"n_samples \\t= %s\" % opts.n_samples)\n print(\"n_features \\t= %s\" % opts.n_features)\n if opts.n_components == \"auto\":\n print(\n \"n_components \\t= %s (auto)\"\n % johnson_lindenstrauss_min_dim(n_samples=opts.n_samples, eps=opts.eps)\n )\n else:\n print(\"n_components \\t= %s\" % opts.n_components)\n print(\"n_elements \\t= %s\" % (opts.n_features * opts.n_samples))\n print(\"n_nonzeros \\t= %s per feature\" % n_nonzeros)\n print(\"ratio_nonzeros \\t= %s\" % opts.ratio_nonzeros)\n print(\"\")\n\n ###########################################################################\n # Set transformer input\n ###########################################################################\n transformers = {}\n\n ###########################################################################\n # Set GaussianRandomProjection input\n gaussian_matrix_params = {\n \"n_components\": opts.n_components,\n \"random_state\": opts.random_seed,\n }\n transformers[\"GaussianRandomProjection\"] = GaussianRandomProjection(\n **gaussian_matrix_params\n )\n\n ###########################################################################\n # Set SparseRandomProjection input\n sparse_matrix_params = {\n \"n_components\": opts.n_components,\n \"random_state\": opts.random_seed,\n \"density\": opts.density,\n \"eps\": opts.eps,\n }\n\n transformers[\"SparseRandomProjection\"] = SparseRandomProjection(\n **sparse_matrix_params\n )\n\n ###########################################################################\n # Perform benchmark\n ###########################################################################\n time_fit = collections.defaultdict(list)\n time_transform = collections.defaultdict(list)\n\n print(\"Benchmarks\")\n print(\"===========================\")\n print(\"Generate dataset benchmarks... \", end=\"\")\n X_dense, X_sparse = make_sparse_random_data(\n opts.n_samples, opts.n_features, n_nonzeros, random_state=opts.random_seed\n )\n X = X_dense if opts.dense else X_sparse\n print(\"done\")\n\n for name in selected_transformers:\n print(\"Perform benchmarks for %s...\" % name)\n\n for iteration in range(opts.n_times):\n print(\"\\titer %s...\" % iteration, end=\"\")\n time_to_fit, time_to_transform = bench_scikit_transformer(\n X_dense, transformers[name]\n )\n time_fit[name].append(time_to_fit)\n time_transform[name].append(time_to_transform)\n print(\"done\")\n\n print(\"\")\n\n ###########################################################################\n # Print results\n ###########################################################################\n print(\"Script arguments\")\n print(\"===========================\")\n arguments = vars(opts)\n print(\n \"%s \\t | %s \"\n % (\n \"Arguments\".ljust(16),\n \"Value\".center(12),\n )\n )\n print(25 * \"-\" + (\"|\" + \"-\" * 14) * 1)\n for key, value in arguments.items():\n print(\"%s \\t | %s \" % (str(key).ljust(16), str(value).strip().center(12)))\n print(\"\")\n\n print(\"Transformer performance:\")\n print(\"===========================\")\n print(\"Results are averaged over %s repetition(s).\" % opts.n_times)\n print(\"\")\n print(\n \"%s | %s | %s\"\n % (\"Transformer\".ljust(30), \"fit\".center(12), \"transform\".center(12))\n )\n print(31 * \"-\" + (\"|\" + \"-\" * 14) * 2)\n\n for name in sorted(selected_transformers):\n print_row(name, np.mean(time_fit[name]), np.mean(time_transform[name]))\n\n print(\"\")\n print(\"\")\n" }, { "path": "benchmarks/bench_rcv1_logreg_convergence.py", "content": "# Authors: Tom Dupre la Tour \n# Olivier Grisel \n#\n# License: BSD 3 clause\n\nimport matplotlib.pyplot as plt\nfrom joblib import Memory\nimport numpy as np\nimport gc\nimport time\n\nfrom sklearn.linear_model import LogisticRegression, SGDClassifier\nfrom sklearn.datasets import fetch_rcv1\nfrom sklearn.linear_model._sag import get_auto_step_size\n\ntry:\n import lightning.classification as lightning_clf\nexcept ImportError:\n lightning_clf = None\n\nm = Memory(cachedir=\".\", verbose=0)\n\n\n# compute logistic loss\ndef get_loss(w, intercept, myX, myy, C):\n n_samples = myX.shape[0]\n w = w.ravel()\n p = np.mean(np.log(1.0 + np.exp(-myy * (myX.dot(w) + intercept))))\n print(\"%f + %f\" % (p, w.dot(w) / 2.0 / C / n_samples))\n p += w.dot(w) / 2.0 / C / n_samples\n return p\n\n\n# We use joblib to cache individual fits. Note that we do not pass the dataset\n# as argument as the hashing would be too slow, so we assume that the dataset\n# never changes.\n@m.cache()\ndef bench_one(name, clf_type, clf_params, n_iter):\n clf = clf_type(**clf_params)\n try:\n clf.set_params(max_iter=n_iter, random_state=42)\n except Exception:\n clf.set_params(n_iter=n_iter, random_state=42)\n\n st = time.time()\n clf.fit(X, y)\n end = time.time()\n\n try:\n C = 1.0 / clf.alpha / n_samples\n except Exception:\n C = clf.C\n\n try:\n intercept = clf.intercept_\n except Exception:\n intercept = 0.0\n\n train_loss = get_loss(clf.coef_, intercept, X, y, C)\n train_score = clf.score(X, y)\n test_score = clf.score(X_test, y_test)\n duration = end - st\n\n return train_loss, train_score, test_score, duration\n\n\ndef bench(clfs):\n for (\n name,\n clf,\n iter_range,\n train_losses,\n train_scores,\n test_scores,\n durations,\n ) in clfs:\n print(\"training %s\" % name)\n clf_type = type(clf)\n clf_params = clf.get_params()\n\n for n_iter in iter_range:\n gc.collect()\n\n train_loss, train_score, test_score, duration = bench_one(\n name, clf_type, clf_params, n_iter\n )\n\n train_losses.append(train_loss)\n train_scores.append(train_score)\n test_scores.append(test_score)\n durations.append(duration)\n print(\"classifier: %s\" % name)\n print(\"train_loss: %.8f\" % train_loss)\n print(\"train_score: %.8f\" % train_score)\n print(\"test_score: %.8f\" % test_score)\n print(\"time for fit: %.8f seconds\" % duration)\n print(\"\")\n\n print(\"\")\n return clfs\n\n\ndef plot_train_losses(clfs):\n plt.figure()\n for (name, _, _, train_losses, _, _, durations) in clfs:\n plt.plot(durations, train_losses, \"-o\", label=name)\n plt.legend(loc=0)\n plt.xlabel(\"seconds\")\n plt.ylabel(\"train loss\")\n\n\ndef plot_train_scores(clfs):\n plt.figure()\n for (name, _, _, _, train_scores, _, durations) in clfs:\n plt.plot(durations, train_scores, \"-o\", label=name)\n plt.legend(loc=0)\n plt.xlabel(\"seconds\")\n plt.ylabel(\"train score\")\n plt.ylim((0.92, 0.96))\n\n\ndef plot_test_scores(clfs):\n plt.figure()\n for (name, _, _, _, _, test_scores, durations) in clfs:\n plt.plot(durations, test_scores, \"-o\", label=name)\n plt.legend(loc=0)\n plt.xlabel(\"seconds\")\n plt.ylabel(\"test score\")\n plt.ylim((0.92, 0.96))\n\n\ndef plot_dloss(clfs):\n plt.figure()\n pobj_final = []\n for (name, _, _, train_losses, _, _, durations) in clfs:\n pobj_final.append(train_losses[-1])\n\n indices = np.argsort(pobj_final)\n pobj_best = pobj_final[indices[0]]\n\n for (name, _, _, train_losses, _, _, durations) in clfs:\n log_pobj = np.log(abs(np.array(train_losses) - pobj_best)) / np.log(10)\n\n plt.plot(durations, log_pobj, \"-o\", label=name)\n plt.legend(loc=0)\n plt.xlabel(\"seconds\")\n plt.ylabel(\"log(best - train_loss)\")\n\n\ndef get_max_squared_sum(X):\n \"\"\"Get the maximum row-wise sum of squares\"\"\"\n return np.sum(X ** 2, axis=1).max()\n\n\nrcv1 = fetch_rcv1()\nX = rcv1.data\nn_samples, n_features = X.shape\n\n# consider the binary classification problem 'CCAT' vs the rest\nccat_idx = rcv1.target_names.tolist().index(\"CCAT\")\ny = rcv1.target.tocsc()[:, ccat_idx].toarray().ravel().astype(np.float64)\ny[y == 0] = -1\n\n# parameters\nC = 1.0\nfit_intercept = True\ntol = 1.0e-14\n\n# max_iter range\nsgd_iter_range = list(range(1, 121, 10))\nnewton_iter_range = list(range(1, 25, 3))\nlbfgs_iter_range = list(range(1, 242, 12))\nliblinear_iter_range = list(range(1, 37, 3))\nliblinear_dual_iter_range = list(range(1, 85, 6))\nsag_iter_range = list(range(1, 37, 3))\n\nclfs = [\n (\n \"LR-liblinear\",\n LogisticRegression(\n C=C,\n tol=tol,\n solver=\"liblinear\",\n fit_intercept=fit_intercept,\n intercept_scaling=1,\n ),\n liblinear_iter_range,\n [],\n [],\n [],\n [],\n ),\n (\n \"LR-liblinear-dual\",\n LogisticRegression(\n C=C,\n tol=tol,\n dual=True,\n solver=\"liblinear\",\n fit_intercept=fit_intercept,\n intercept_scaling=1,\n ),\n liblinear_dual_iter_range,\n [],\n [],\n [],\n [],\n ),\n (\n \"LR-SAG\",\n LogisticRegression(C=C, tol=tol, solver=\"sag\", fit_intercept=fit_intercept),\n sag_iter_range,\n [],\n [],\n [],\n [],\n ),\n (\n \"LR-newton-cg\",\n LogisticRegression(\n C=C, tol=tol, solver=\"newton-cg\", fit_intercept=fit_intercept\n ),\n newton_iter_range,\n [],\n [],\n [],\n [],\n ),\n (\n \"LR-lbfgs\",\n LogisticRegression(C=C, tol=tol, solver=\"lbfgs\", fit_intercept=fit_intercept),\n lbfgs_iter_range,\n [],\n [],\n [],\n [],\n ),\n (\n \"SGD\",\n SGDClassifier(\n alpha=1.0 / C / n_samples,\n penalty=\"l2\",\n loss=\"log\",\n fit_intercept=fit_intercept,\n verbose=0,\n ),\n sgd_iter_range,\n [],\n [],\n [],\n [],\n ),\n]\n\n\nif lightning_clf is not None and not fit_intercept:\n alpha = 1.0 / C / n_samples\n # compute the same step_size than in LR-sag\n max_squared_sum = get_max_squared_sum(X)\n step_size = get_auto_step_size(max_squared_sum, alpha, \"log\", fit_intercept)\n\n clfs.append(\n (\n \"Lightning-SVRG\",\n lightning_clf.SVRGClassifier(\n alpha=alpha, eta=step_size, tol=tol, loss=\"log\"\n ),\n sag_iter_range,\n [],\n [],\n [],\n [],\n )\n )\n clfs.append(\n (\n \"Lightning-SAG\",\n lightning_clf.SAGClassifier(\n alpha=alpha, eta=step_size, tol=tol, loss=\"log\"\n ),\n sag_iter_range,\n [],\n [],\n [],\n [],\n )\n )\n\n # We keep only 200 features, to have a dense dataset,\n # and compare to lightning SAG, which seems incorrect in the sparse case.\n X_csc = X.tocsc()\n nnz_in_each_features = X_csc.indptr[1:] - X_csc.indptr[:-1]\n X = X_csc[:, np.argsort(nnz_in_each_features)[-200:]]\n X = X.toarray()\n print(\"dataset: %.3f MB\" % (X.nbytes / 1e6))\n\n\n# Split training and testing. Switch train and test subset compared to\n# LYRL2004 split, to have a larger training dataset.\nn = 23149\nX_test = X[:n, :]\ny_test = y[:n]\nX = X[n:, :]\ny = y[n:]\n\nclfs = bench(clfs)\n\nplot_train_scores(clfs)\nplot_test_scores(clfs)\nplot_train_losses(clfs)\nplot_dloss(clfs)\nplt.show()\n" }, { "path": "benchmarks/bench_saga.py", "content": "\"\"\"Author: Arthur Mensch, Nelle Varoquaux\n\nBenchmarks of sklearn SAGA vs lightning SAGA vs Liblinear. Shows the gain\nin using multinomial logistic regression in term of learning time.\n\"\"\"\nimport json\nimport time\nimport os\n\nfrom joblib import Parallel\nfrom sklearn.utils.fixes import delayed\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import (\n fetch_rcv1,\n load_iris,\n load_digits,\n fetch_20newsgroups_vectorized,\n)\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import log_loss\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelBinarizer, LabelEncoder\nfrom sklearn.utils.extmath import safe_sparse_dot, softmax\n\n\ndef fit_single(\n solver,\n X,\n y,\n penalty=\"l2\",\n single_target=True,\n C=1,\n max_iter=10,\n skip_slow=False,\n dtype=np.float64,\n):\n if skip_slow and solver == \"lightning\" and penalty == \"l1\":\n print(\"skip_slowping l1 logistic regression with solver lightning.\")\n return\n\n print(\n \"Solving %s logistic regression with penalty %s, solver %s.\"\n % (\"binary\" if single_target else \"multinomial\", penalty, solver)\n )\n\n if solver == \"lightning\":\n from lightning.classification import SAGAClassifier\n\n if single_target or solver not in [\"sag\", \"saga\"]:\n multi_class = \"ovr\"\n else:\n multi_class = \"multinomial\"\n X = X.astype(dtype)\n y = y.astype(dtype)\n X_train, X_test, y_train, y_test = train_test_split(\n X, y, random_state=42, stratify=y\n )\n n_samples = X_train.shape[0]\n n_classes = np.unique(y_train).shape[0]\n test_scores = [1]\n train_scores = [1]\n accuracies = [1 / n_classes]\n times = [0]\n\n if penalty == \"l2\":\n alpha = 1.0 / (C * n_samples)\n beta = 0\n lightning_penalty = None\n else:\n alpha = 0.0\n beta = 1.0 / (C * n_samples)\n lightning_penalty = \"l1\"\n\n for this_max_iter in range(1, max_iter + 1, 2):\n print(\n \"[%s, %s, %s] Max iter: %s\"\n % (\n \"binary\" if single_target else \"multinomial\",\n penalty,\n solver,\n this_max_iter,\n )\n )\n if solver == \"lightning\":\n lr = SAGAClassifier(\n loss=\"log\",\n alpha=alpha,\n beta=beta,\n penalty=lightning_penalty,\n tol=-1,\n max_iter=this_max_iter,\n )\n else:\n lr = LogisticRegression(\n solver=solver,\n multi_class=multi_class,\n C=C,\n penalty=penalty,\n fit_intercept=False,\n tol=0,\n max_iter=this_max_iter,\n random_state=42,\n )\n\n # Makes cpu cache even for all fit calls\n X_train.max()\n t0 = time.clock()\n\n lr.fit(X_train, y_train)\n train_time = time.clock() - t0\n\n scores = []\n for (X, y) in [(X_train, y_train), (X_test, y_test)]:\n try:\n y_pred = lr.predict_proba(X)\n except NotImplementedError:\n # Lightning predict_proba is not implemented for n_classes > 2\n y_pred = _predict_proba(lr, X)\n score = log_loss(y, y_pred, normalize=False) / n_samples\n score += 0.5 * alpha * np.sum(lr.coef_ ** 2) + beta * np.sum(\n np.abs(lr.coef_)\n )\n scores.append(score)\n train_score, test_score = tuple(scores)\n\n y_pred = lr.predict(X_test)\n accuracy = np.sum(y_pred == y_test) / y_test.shape[0]\n test_scores.append(test_score)\n train_scores.append(train_score)\n accuracies.append(accuracy)\n times.append(train_time)\n return lr, times, train_scores, test_scores, accuracies\n\n\ndef _predict_proba(lr, X):\n pred = safe_sparse_dot(X, lr.coef_.T)\n if hasattr(lr, \"intercept_\"):\n pred += lr.intercept_\n return softmax(pred)\n\n\ndef exp(\n solvers,\n penalty,\n single_target,\n n_samples=30000,\n max_iter=20,\n dataset=\"rcv1\",\n n_jobs=1,\n skip_slow=False,\n):\n dtypes_mapping = {\n \"float64\": np.float64,\n \"float32\": np.float32,\n }\n\n if dataset == \"rcv1\":\n rcv1 = fetch_rcv1()\n\n lbin = LabelBinarizer()\n lbin.fit(rcv1.target_names)\n\n X = rcv1.data\n y = rcv1.target\n y = lbin.inverse_transform(y)\n le = LabelEncoder()\n y = le.fit_transform(y)\n if single_target:\n y_n = y.copy()\n y_n[y > 16] = 1\n y_n[y <= 16] = 0\n y = y_n\n\n elif dataset == \"digits\":\n X, y = load_digits(return_X_y=True)\n if single_target:\n y_n = y.copy()\n y_n[y < 5] = 1\n y_n[y >= 5] = 0\n y = y_n\n elif dataset == \"iris\":\n iris = load_iris()\n X, y = iris.data, iris.target\n elif dataset == \"20newspaper\":\n ng = fetch_20newsgroups_vectorized()\n X = ng.data\n y = ng.target\n if single_target:\n y_n = y.copy()\n y_n[y > 4] = 1\n y_n[y <= 16] = 0\n y = y_n\n\n X = X[:n_samples]\n y = y[:n_samples]\n\n out = Parallel(n_jobs=n_jobs, mmap_mode=None)(\n delayed(fit_single)(\n solver,\n X,\n y,\n penalty=penalty,\n single_target=single_target,\n dtype=dtype,\n C=1,\n max_iter=max_iter,\n skip_slow=skip_slow,\n )\n for solver in solvers\n for dtype in dtypes_mapping.values()\n )\n\n res = []\n idx = 0\n for dtype_name in dtypes_mapping.keys():\n for solver in solvers:\n if not (skip_slow and solver == \"lightning\" and penalty == \"l1\"):\n lr, times, train_scores, test_scores, accuracies = out[idx]\n this_res = dict(\n solver=solver,\n penalty=penalty,\n dtype=dtype_name,\n single_target=single_target,\n times=times,\n train_scores=train_scores,\n test_scores=test_scores,\n accuracies=accuracies,\n )\n res.append(this_res)\n idx += 1\n\n with open(\"bench_saga.json\", \"w+\") as f:\n json.dump(res, f)\n\n\ndef plot(outname=None):\n import pandas as pd\n\n with open(\"bench_saga.json\", \"r\") as f:\n f = json.load(f)\n res = pd.DataFrame(f)\n res.set_index([\"single_target\"], inplace=True)\n\n grouped = res.groupby(level=[\"single_target\"])\n\n colors = {\"saga\": \"C0\", \"liblinear\": \"C1\", \"lightning\": \"C2\"}\n linestyles = {\"float32\": \"--\", \"float64\": \"-\"}\n alpha = {\"float64\": 0.5, \"float32\": 1}\n\n for idx, group in grouped:\n single_target = idx\n fig, axes = plt.subplots(figsize=(12, 4), ncols=4)\n ax = axes[0]\n\n for scores, times, solver, dtype in zip(\n group[\"train_scores\"], group[\"times\"], group[\"solver\"], group[\"dtype\"]\n ):\n ax.plot(\n times,\n scores,\n label=\"%s - %s\" % (solver, dtype),\n color=colors[solver],\n alpha=alpha[dtype],\n marker=\".\",\n linestyle=linestyles[dtype],\n )\n ax.axvline(\n times[-1],\n color=colors[solver],\n alpha=alpha[dtype],\n linestyle=linestyles[dtype],\n )\n ax.set_xlabel(\"Time (s)\")\n ax.set_ylabel(\"Training objective (relative to min)\")\n ax.set_yscale(\"log\")\n\n ax = axes[1]\n\n for scores, times, solver, dtype in zip(\n group[\"test_scores\"], group[\"times\"], group[\"solver\"], group[\"dtype\"]\n ):\n ax.plot(\n times,\n scores,\n label=solver,\n color=colors[solver],\n linestyle=linestyles[dtype],\n marker=\".\",\n alpha=alpha[dtype],\n )\n ax.axvline(\n times[-1],\n color=colors[solver],\n alpha=alpha[dtype],\n linestyle=linestyles[dtype],\n )\n\n ax.set_xlabel(\"Time (s)\")\n ax.set_ylabel(\"Test objective (relative to min)\")\n ax.set_yscale(\"log\")\n\n ax = axes[2]\n for accuracy, times, solver, dtype in zip(\n group[\"accuracies\"], group[\"times\"], group[\"solver\"], group[\"dtype\"]\n ):\n ax.plot(\n times,\n accuracy,\n label=\"%s - %s\" % (solver, dtype),\n alpha=alpha[dtype],\n marker=\".\",\n color=colors[solver],\n linestyle=linestyles[dtype],\n )\n ax.axvline(\n times[-1],\n color=colors[solver],\n alpha=alpha[dtype],\n linestyle=linestyles[dtype],\n )\n\n ax.set_xlabel(\"Time (s)\")\n ax.set_ylabel(\"Test accuracy\")\n ax.legend()\n name = \"single_target\" if single_target else \"multi_target\"\n name += \"_%s\" % penalty\n plt.suptitle(name)\n if outname is None:\n outname = name + \".png\"\n fig.tight_layout()\n fig.subplots_adjust(top=0.9)\n\n ax = axes[3]\n for scores, times, solver, dtype in zip(\n group[\"train_scores\"], group[\"times\"], group[\"solver\"], group[\"dtype\"]\n ):\n ax.plot(\n np.arange(len(scores)),\n scores,\n label=\"%s - %s\" % (solver, dtype),\n marker=\".\",\n alpha=alpha[dtype],\n color=colors[solver],\n linestyle=linestyles[dtype],\n )\n\n ax.set_yscale(\"log\")\n ax.set_xlabel(\"# iterations\")\n ax.set_ylabel(\"Objective function\")\n ax.legend()\n\n plt.savefig(outname)\n\n\nif __name__ == \"__main__\":\n solvers = [\"saga\", \"liblinear\", \"lightning\"]\n penalties = [\"l1\", \"l2\"]\n n_samples = [100000, 300000, 500000, 800000, None]\n single_target = True\n for penalty in penalties:\n for n_sample in n_samples:\n exp(\n solvers,\n penalty,\n single_target,\n n_samples=n_sample,\n n_jobs=1,\n dataset=\"rcv1\",\n max_iter=10,\n )\n if n_sample is not None:\n outname = \"figures/saga_%s_%d.png\" % (penalty, n_sample)\n else:\n outname = \"figures/saga_%s_all.png\" % (penalty,)\n try:\n os.makedirs(\"figures\")\n except OSError:\n pass\n plot(outname)\n" }, { "path": "benchmarks/bench_sample_without_replacement.py", "content": "\"\"\"\nBenchmarks for sampling without replacement of integer.\n\n\"\"\"\nimport gc\nimport sys\nimport optparse\nfrom datetime import datetime\nimport operator\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport random\n\nfrom sklearn.utils.random import sample_without_replacement\n\n\ndef compute_time(t_start, delta):\n mu_second = 0.0 + 10 ** 6 # number of microseconds in a second\n\n return delta.seconds + delta.microseconds / mu_second\n\n\ndef bench_sample(sampling, n_population, n_samples):\n gc.collect()\n # start time\n t_start = datetime.now()\n sampling(n_population, n_samples)\n delta = datetime.now() - t_start\n # stop time\n time = compute_time(t_start, delta)\n return time\n\n\nif __name__ == \"__main__\":\n ###########################################################################\n # Option parser\n ###########################################################################\n op = optparse.OptionParser()\n op.add_option(\n \"--n-times\",\n dest=\"n_times\",\n default=5,\n type=int,\n help=\"Benchmark results are average over n_times experiments\",\n )\n\n op.add_option(\n \"--n-population\",\n dest=\"n_population\",\n default=100000,\n type=int,\n help=\"Size of the population to sample from.\",\n )\n\n op.add_option(\n \"--n-step\",\n dest=\"n_steps\",\n default=5,\n type=int,\n help=\"Number of step interval between 0 and n_population.\",\n )\n\n default_algorithms = (\n \"custom-tracking-selection,custom-auto,\"\n \"custom-reservoir-sampling,custom-pool,\"\n \"python-core-sample,numpy-permutation\"\n )\n\n op.add_option(\n \"--algorithm\",\n dest=\"selected_algorithm\",\n default=default_algorithms,\n type=str,\n help=(\n \"Comma-separated list of transformer to benchmark. \"\n \"Default: %default. \\nAvailable: %default\"\n ),\n )\n\n # op.add_option(\"--random-seed\",\n # dest=\"random_seed\", default=13, type=int,\n # help=\"Seed used by the random number generators.\")\n\n (opts, args) = op.parse_args()\n if len(args) > 0:\n op.error(\"this script takes no arguments.\")\n sys.exit(1)\n\n selected_algorithm = opts.selected_algorithm.split(\",\")\n for key in selected_algorithm:\n if key not in default_algorithms.split(\",\"):\n raise ValueError(\n 'Unknown sampling algorithm \"%s\" not in (%s).'\n % (key, default_algorithms)\n )\n\n ###########################################################################\n # List sampling algorithm\n ###########################################################################\n # We assume that sampling algorithm has the following signature:\n # sample(n_population, n_sample)\n #\n sampling_algorithm = {}\n\n ###########################################################################\n # Set Python core input\n sampling_algorithm[\n \"python-core-sample\"\n ] = lambda n_population, n_sample: random.sample(range(n_population), n_sample)\n\n ###########################################################################\n # Set custom automatic method selection\n sampling_algorithm[\n \"custom-auto\"\n ] = lambda n_population, n_samples, random_state=None: sample_without_replacement(\n n_population, n_samples, method=\"auto\", random_state=random_state\n )\n\n ###########################################################################\n # Set custom tracking based method\n sampling_algorithm[\n \"custom-tracking-selection\"\n ] = lambda n_population, n_samples, random_state=None: sample_without_replacement(\n n_population, n_samples, method=\"tracking_selection\", random_state=random_state\n )\n\n ###########################################################################\n # Set custom reservoir based method\n sampling_algorithm[\n \"custom-reservoir-sampling\"\n ] = lambda n_population, n_samples, random_state=None: sample_without_replacement(\n n_population, n_samples, method=\"reservoir_sampling\", random_state=random_state\n )\n\n ###########################################################################\n # Set custom reservoir based method\n sampling_algorithm[\n \"custom-pool\"\n ] = lambda n_population, n_samples, random_state=None: sample_without_replacement(\n n_population, n_samples, method=\"pool\", random_state=random_state\n )\n\n ###########################################################################\n # Numpy permutation based\n sampling_algorithm[\n \"numpy-permutation\"\n ] = lambda n_population, n_sample: np.random.permutation(n_population)[:n_sample]\n\n ###########################################################################\n # Remove unspecified algorithm\n sampling_algorithm = {\n key: value\n for key, value in sampling_algorithm.items()\n if key in selected_algorithm\n }\n\n ###########################################################################\n # Perform benchmark\n ###########################################################################\n time = {}\n n_samples = np.linspace(start=0, stop=opts.n_population, num=opts.n_steps).astype(\n int\n )\n\n ratio = n_samples / opts.n_population\n\n print(\"Benchmarks\")\n print(\"===========================\")\n\n for name in sorted(sampling_algorithm):\n print(\"Perform benchmarks for %s...\" % name, end=\"\")\n time[name] = np.zeros(shape=(opts.n_steps, opts.n_times))\n\n for step in range(opts.n_steps):\n for it in range(opts.n_times):\n time[name][step, it] = bench_sample(\n sampling_algorithm[name], opts.n_population, n_samples[step]\n )\n\n print(\"done\")\n\n print(\"Averaging results...\", end=\"\")\n for name in sampling_algorithm:\n time[name] = np.mean(time[name], axis=1)\n print(\"done\\n\")\n\n # Print results\n ###########################################################################\n print(\"Script arguments\")\n print(\"===========================\")\n arguments = vars(opts)\n print(\n \"%s \\t | %s \"\n % (\n \"Arguments\".ljust(16),\n \"Value\".center(12),\n )\n )\n print(25 * \"-\" + (\"|\" + \"-\" * 14) * 1)\n for key, value in arguments.items():\n print(\"%s \\t | %s \" % (str(key).ljust(16), str(value).strip().center(12)))\n print(\"\")\n\n print(\"Sampling algorithm performance:\")\n print(\"===============================\")\n print(\"Results are averaged over %s repetition(s).\" % opts.n_times)\n print(\"\")\n\n fig = plt.figure(\"scikit-learn sample w/o replacement benchmark results\")\n plt.title(\"n_population = %s, n_times = %s\" % (opts.n_population, opts.n_times))\n ax = fig.add_subplot(111)\n for name in sampling_algorithm:\n ax.plot(ratio, time[name], label=name)\n\n ax.set_xlabel(\"ratio of n_sample / n_population\")\n ax.set_ylabel(\"Time (s)\")\n ax.legend()\n\n # Sort legend labels\n handles, labels = ax.get_legend_handles_labels()\n hl = sorted(zip(handles, labels), key=operator.itemgetter(1))\n handles2, labels2 = zip(*hl)\n ax.legend(handles2, labels2, loc=0)\n\n plt.show()\n" }, { "path": "benchmarks/bench_sgd_regression.py", "content": "# Author: Peter Prettenhofer \n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport gc\n\nfrom time import time\n\nfrom sklearn.linear_model import Ridge, SGDRegressor, ElasticNet\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.datasets import make_regression\n\n\"\"\"\nBenchmark for SGD regression\n\nCompares SGD regression against coordinate descent and Ridge\non synthetic data.\n\"\"\"\n\nprint(__doc__)\n\nif __name__ == \"__main__\":\n list_n_samples = np.linspace(100, 10000, 5).astype(int)\n list_n_features = [10, 100, 1000]\n n_test = 1000\n max_iter = 1000\n noise = 0.1\n alpha = 0.01\n sgd_results = np.zeros((len(list_n_samples), len(list_n_features), 2))\n elnet_results = np.zeros((len(list_n_samples), len(list_n_features), 2))\n ridge_results = np.zeros((len(list_n_samples), len(list_n_features), 2))\n asgd_results = np.zeros((len(list_n_samples), len(list_n_features), 2))\n for i, n_train in enumerate(list_n_samples):\n for j, n_features in enumerate(list_n_features):\n X, y, coef = make_regression(\n n_samples=n_train + n_test,\n n_features=n_features,\n noise=noise,\n coef=True,\n )\n\n X_train = X[:n_train]\n y_train = y[:n_train]\n X_test = X[n_train:]\n y_test = y[n_train:]\n\n print(\"=======================\")\n print(\"Round %d %d\" % (i, j))\n print(\"n_features:\", n_features)\n print(\"n_samples:\", n_train)\n\n # Shuffle data\n idx = np.arange(n_train)\n np.random.seed(13)\n np.random.shuffle(idx)\n X_train = X_train[idx]\n y_train = y_train[idx]\n\n std = X_train.std(axis=0)\n mean = X_train.mean(axis=0)\n X_train = (X_train - mean) / std\n X_test = (X_test - mean) / std\n\n std = y_train.std(axis=0)\n mean = y_train.mean(axis=0)\n y_train = (y_train - mean) / std\n y_test = (y_test - mean) / std\n\n gc.collect()\n print(\"- benchmarking ElasticNet\")\n clf = ElasticNet(alpha=alpha, l1_ratio=0.5, fit_intercept=False)\n tstart = time()\n clf.fit(X_train, y_train)\n elnet_results[i, j, 0] = mean_squared_error(clf.predict(X_test), y_test)\n elnet_results[i, j, 1] = time() - tstart\n\n gc.collect()\n print(\"- benchmarking SGD\")\n clf = SGDRegressor(\n alpha=alpha / n_train,\n fit_intercept=False,\n max_iter=max_iter,\n learning_rate=\"invscaling\",\n eta0=0.01,\n power_t=0.25,\n tol=1e-3,\n )\n\n tstart = time()\n clf.fit(X_train, y_train)\n sgd_results[i, j, 0] = mean_squared_error(clf.predict(X_test), y_test)\n sgd_results[i, j, 1] = time() - tstart\n\n gc.collect()\n print(\"max_iter\", max_iter)\n print(\"- benchmarking A-SGD\")\n clf = SGDRegressor(\n alpha=alpha / n_train,\n fit_intercept=False,\n max_iter=max_iter,\n learning_rate=\"invscaling\",\n eta0=0.002,\n power_t=0.05,\n tol=1e-3,\n average=(max_iter * n_train // 2),\n )\n\n tstart = time()\n clf.fit(X_train, y_train)\n asgd_results[i, j, 0] = mean_squared_error(clf.predict(X_test), y_test)\n asgd_results[i, j, 1] = time() - tstart\n\n gc.collect()\n print(\"- benchmarking RidgeRegression\")\n clf = Ridge(alpha=alpha, fit_intercept=False)\n tstart = time()\n clf.fit(X_train, y_train)\n ridge_results[i, j, 0] = mean_squared_error(clf.predict(X_test), y_test)\n ridge_results[i, j, 1] = time() - tstart\n\n # Plot results\n i = 0\n m = len(list_n_features)\n plt.figure(\"scikit-learn SGD regression benchmark results\", figsize=(5 * 2, 4 * m))\n for j in range(m):\n plt.subplot(m, 2, i + 1)\n plt.plot(list_n_samples, np.sqrt(elnet_results[:, j, 0]), label=\"ElasticNet\")\n plt.plot(list_n_samples, np.sqrt(sgd_results[:, j, 0]), label=\"SGDRegressor\")\n plt.plot(list_n_samples, np.sqrt(asgd_results[:, j, 0]), label=\"A-SGDRegressor\")\n plt.plot(list_n_samples, np.sqrt(ridge_results[:, j, 0]), label=\"Ridge\")\n plt.legend(prop={\"size\": 10})\n plt.xlabel(\"n_train\")\n plt.ylabel(\"RMSE\")\n plt.title(\"Test error - %d features\" % list_n_features[j])\n i += 1\n\n plt.subplot(m, 2, i + 1)\n plt.plot(list_n_samples, np.sqrt(elnet_results[:, j, 1]), label=\"ElasticNet\")\n plt.plot(list_n_samples, np.sqrt(sgd_results[:, j, 1]), label=\"SGDRegressor\")\n plt.plot(list_n_samples, np.sqrt(asgd_results[:, j, 1]), label=\"A-SGDRegressor\")\n plt.plot(list_n_samples, np.sqrt(ridge_results[:, j, 1]), label=\"Ridge\")\n plt.legend(prop={\"size\": 10})\n plt.xlabel(\"n_train\")\n plt.ylabel(\"Time [sec]\")\n plt.title(\"Training time - %d features\" % list_n_features[j])\n i += 1\n\n plt.subplots_adjust(hspace=0.30)\n\n plt.show()\n" }, { "path": "benchmarks/bench_sparsify.py", "content": "\"\"\"\nBenchmark SGD prediction time with dense/sparse coefficients.\n\nInvoke with\n-----------\n\n$ kernprof.py -l sparsity_benchmark.py\n$ python -m line_profiler sparsity_benchmark.py.lprof\n\nTypical output\n--------------\n\ninput data sparsity: 0.050000\ntrue coef sparsity: 0.000100\ntest data sparsity: 0.027400\nmodel sparsity: 0.000024\nr^2 on test data (dense model) : 0.233651\nr^2 on test data (sparse model) : 0.233651\nWrote profile results to sparsity_benchmark.py.lprof\nTimer unit: 1e-06 s\n\nFile: sparsity_benchmark.py\nFunction: benchmark_dense_predict at line 51\nTotal time: 0.532979 s\n\nLine # Hits Time Per Hit % Time Line Contents\n==============================================================\n 51 @profile\n 52 def benchmark_dense_predict():\n 53 301 640 2.1 0.1 for _ in range(300):\n 54 300 532339 1774.5 99.9 clf.predict(X_test)\n\nFile: sparsity_benchmark.py\nFunction: benchmark_sparse_predict at line 56\nTotal time: 0.39274 s\n\nLine # Hits Time Per Hit % Time Line Contents\n==============================================================\n 56 @profile\n 57 def benchmark_sparse_predict():\n 58 1 10854 10854.0 2.8 X_test_sparse = csr_matrix(X_test)\n 59 301 477 1.6 0.1 for _ in range(300):\n 60 300 381409 1271.4 97.1 clf.predict(X_test_sparse)\n\"\"\"\n\nfrom scipy.sparse.csr import csr_matrix\nimport numpy as np\nfrom sklearn.linear_model import SGDRegressor\nfrom sklearn.metrics import r2_score\n\nnp.random.seed(42)\n\n\ndef sparsity_ratio(X):\n return np.count_nonzero(X) / float(n_samples * n_features)\n\n\nn_samples, n_features = 5000, 300\nX = np.random.randn(n_samples, n_features)\ninds = np.arange(n_samples)\nnp.random.shuffle(inds)\nX[inds[int(n_features / 1.2) :]] = 0 # sparsify input\nprint(\"input data sparsity: %f\" % sparsity_ratio(X))\ncoef = 3 * np.random.randn(n_features)\ninds = np.arange(n_features)\nnp.random.shuffle(inds)\ncoef[inds[n_features // 2 :]] = 0 # sparsify coef\nprint(\"true coef sparsity: %f\" % sparsity_ratio(coef))\ny = np.dot(X, coef)\n\n# add noise\ny += 0.01 * np.random.normal((n_samples,))\n\n# Split data in train set and test set\nn_samples = X.shape[0]\nX_train, y_train = X[: n_samples // 2], y[: n_samples // 2]\nX_test, y_test = X[n_samples // 2 :], y[n_samples // 2 :]\nprint(\"test data sparsity: %f\" % sparsity_ratio(X_test))\n\n###############################################################################\nclf = SGDRegressor(penalty=\"l1\", alpha=0.2, max_iter=2000, tol=None)\nclf.fit(X_train, y_train)\nprint(\"model sparsity: %f\" % sparsity_ratio(clf.coef_))\n\n\ndef benchmark_dense_predict():\n for _ in range(300):\n clf.predict(X_test)\n\n\ndef benchmark_sparse_predict():\n X_test_sparse = csr_matrix(X_test)\n for _ in range(300):\n clf.predict(X_test_sparse)\n\n\ndef score(y_test, y_pred, case):\n r2 = r2_score(y_test, y_pred)\n print(\"r^2 on test data (%s) : %f\" % (case, r2))\n\n\nscore(y_test, clf.predict(X_test), \"dense model\")\nbenchmark_dense_predict()\nclf.sparsify()\nscore(y_test, clf.predict(X_test), \"sparse model\")\nbenchmark_sparse_predict()\n" }, { "path": "benchmarks/bench_text_vectorizers.py", "content": "\"\"\"\n\nTo run this benchmark, you will need,\n\n * scikit-learn\n * pandas\n * memory_profiler\n * psutil (optional, but recommended)\n\n\"\"\"\nimport timeit\nimport itertools\n\nimport numpy as np\nimport pandas as pd\nfrom memory_profiler import memory_usage\n\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.feature_extraction.text import (\n CountVectorizer,\n TfidfVectorizer,\n HashingVectorizer,\n)\n\nn_repeat = 3\n\n\ndef run_vectorizer(Vectorizer, X, **params):\n def f():\n vect = Vectorizer(**params)\n vect.fit_transform(X)\n\n return f\n\n\ntext = fetch_20newsgroups(subset=\"train\").data[:1000]\n\nprint(\"=\" * 80 + \"\\n#\" + \" Text vectorizers benchmark\" + \"\\n\" + \"=\" * 80 + \"\\n\")\nprint(\"Using a subset of the 20 newsgroups dataset ({} documents).\".format(len(text)))\nprint(\"This benchmarks runs in ~1 min ...\")\n\nres = []\n\nfor Vectorizer, (analyzer, ngram_range) in itertools.product(\n [CountVectorizer, TfidfVectorizer, HashingVectorizer],\n [(\"word\", (1, 1)), (\"word\", (1, 2)), (\"char\", (4, 4)), (\"char_wb\", (4, 4))],\n):\n\n bench = {\"vectorizer\": Vectorizer.__name__}\n params = {\"analyzer\": analyzer, \"ngram_range\": ngram_range}\n bench.update(params)\n dt = timeit.repeat(\n run_vectorizer(Vectorizer, text, **params), number=1, repeat=n_repeat\n )\n bench[\"time\"] = \"{:.3f} (+-{:.3f})\".format(np.mean(dt), np.std(dt))\n\n mem_usage = memory_usage(run_vectorizer(Vectorizer, text, **params))\n\n bench[\"memory\"] = \"{:.1f}\".format(np.max(mem_usage))\n\n res.append(bench)\n\n\ndf = pd.DataFrame(res).set_index([\"analyzer\", \"ngram_range\", \"vectorizer\"])\n\nprint(\"\\n========== Run time performance (sec) ===========\\n\")\nprint(\n \"Computing the mean and the standard deviation \"\n \"of the run time over {} runs...\\n\".format(n_repeat)\n)\nprint(df[\"time\"].unstack(level=-1))\n\nprint(\"\\n=============== Memory usage (MB) ===============\\n\")\nprint(df[\"memory\"].unstack(level=-1))\n" }, { "path": "benchmarks/bench_tree.py", "content": "\"\"\"\nTo run this, you'll need to have installed.\n\n * scikit-learn\n\nDoes two benchmarks\n\nFirst, we fix a training set, increase the number of\nsamples to classify and plot number of classified samples as a\nfunction of time.\n\nIn the second benchmark, we increase the number of dimensions of the\ntraining set, classify a sample and plot the time taken as a function\nof the number of dimensions.\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport gc\nfrom datetime import datetime\n\n# to store the results\nscikit_classifier_results = []\nscikit_regressor_results = []\n\nmu_second = 0.0 + 10 ** 6 # number of microseconds in a second\n\n\ndef bench_scikit_tree_classifier(X, Y):\n \"\"\"Benchmark with scikit-learn decision tree classifier\"\"\"\n\n from sklearn.tree import DecisionTreeClassifier\n\n gc.collect()\n\n # start time\n tstart = datetime.now()\n clf = DecisionTreeClassifier()\n clf.fit(X, Y).predict(X)\n delta = datetime.now() - tstart\n # stop time\n\n scikit_classifier_results.append(delta.seconds + delta.microseconds / mu_second)\n\n\ndef bench_scikit_tree_regressor(X, Y):\n \"\"\"Benchmark with scikit-learn decision tree regressor\"\"\"\n\n from sklearn.tree import DecisionTreeRegressor\n\n gc.collect()\n\n # start time\n tstart = datetime.now()\n clf = DecisionTreeRegressor()\n clf.fit(X, Y).predict(X)\n delta = datetime.now() - tstart\n # stop time\n\n scikit_regressor_results.append(delta.seconds + delta.microseconds / mu_second)\n\n\nif __name__ == \"__main__\":\n\n print(\"============================================\")\n print(\"Warning: this is going to take a looong time\")\n print(\"============================================\")\n\n n = 10\n step = 10000\n n_samples = 10000\n dim = 10\n n_classes = 10\n for i in range(n):\n print(\"============================================\")\n print(\"Entering iteration %s of %s\" % (i, n))\n print(\"============================================\")\n n_samples += step\n X = np.random.randn(n_samples, dim)\n Y = np.random.randint(0, n_classes, (n_samples,))\n bench_scikit_tree_classifier(X, Y)\n Y = np.random.randn(n_samples)\n bench_scikit_tree_regressor(X, Y)\n\n xx = range(0, n * step, step)\n plt.figure(\"scikit-learn tree benchmark results\")\n plt.subplot(211)\n plt.title(\"Learning with varying number of samples\")\n plt.plot(xx, scikit_classifier_results, \"g-\", label=\"classification\")\n plt.plot(xx, scikit_regressor_results, \"r-\", label=\"regression\")\n plt.legend(loc=\"upper left\")\n plt.xlabel(\"number of samples\")\n plt.ylabel(\"Time (s)\")\n\n scikit_classifier_results = []\n scikit_regressor_results = []\n n = 10\n step = 500\n start_dim = 500\n n_classes = 10\n\n dim = start_dim\n for i in range(0, n):\n print(\"============================================\")\n print(\"Entering iteration %s of %s\" % (i, n))\n print(\"============================================\")\n dim += step\n X = np.random.randn(100, dim)\n Y = np.random.randint(0, n_classes, (100,))\n bench_scikit_tree_classifier(X, Y)\n Y = np.random.randn(100)\n bench_scikit_tree_regressor(X, Y)\n\n xx = np.arange(start_dim, start_dim + n * step, step)\n plt.subplot(212)\n plt.title(\"Learning in high dimensional spaces\")\n plt.plot(xx, scikit_classifier_results, \"g-\", label=\"classification\")\n plt.plot(xx, scikit_regressor_results, \"r-\", label=\"regression\")\n plt.legend(loc=\"upper left\")\n plt.xlabel(\"number of dimensions\")\n plt.ylabel(\"Time (s)\")\n plt.axis(\"tight\")\n plt.show()\n" }, { "path": "benchmarks/bench_tsne_mnist.py", "content": "\"\"\"\n=============================\nMNIST dataset T-SNE benchmark\n=============================\n\n\"\"\"\n\n# License: BSD 3 clause\n\nimport os\nimport os.path as op\nfrom time import time\nimport numpy as np\nimport json\nimport argparse\nfrom joblib import Memory\n\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.manifold import TSNE\nfrom sklearn.neighbors import NearestNeighbors\nfrom sklearn.decomposition import PCA\nfrom sklearn.utils import check_array\nfrom sklearn.utils import shuffle as _shuffle\nfrom sklearn.utils._openmp_helpers import _openmp_effective_n_threads\n\nLOG_DIR = \"mnist_tsne_output\"\nif not os.path.exists(LOG_DIR):\n os.mkdir(LOG_DIR)\n\n\nmemory = Memory(os.path.join(LOG_DIR, \"mnist_tsne_benchmark_data\"), mmap_mode=\"r\")\n\n\n@memory.cache\ndef load_data(dtype=np.float32, order=\"C\", shuffle=True, seed=0):\n \"\"\"Load the data, then cache and memmap the train/test split\"\"\"\n print(\"Loading dataset...\")\n data = fetch_openml(\"mnist_784\")\n\n X = check_array(data[\"data\"], dtype=dtype, order=order)\n y = data[\"target\"]\n\n if shuffle:\n X, y = _shuffle(X, y, random_state=seed)\n\n # Normalize features\n X /= 255\n return X, y\n\n\ndef nn_accuracy(X, X_embedded, k=1):\n \"\"\"Accuracy of the first nearest neighbor\"\"\"\n knn = NearestNeighbors(n_neighbors=1, n_jobs=-1)\n _, neighbors_X = knn.fit(X).kneighbors()\n _, neighbors_X_embedded = knn.fit(X_embedded).kneighbors()\n return np.mean(neighbors_X == neighbors_X_embedded)\n\n\ndef tsne_fit_transform(model, data):\n transformed = model.fit_transform(data)\n return transformed, model.n_iter_\n\n\ndef sanitize(filename):\n return filename.replace(\"/\", \"-\").replace(\" \", \"_\")\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\"Benchmark for t-SNE\")\n parser.add_argument(\n \"--order\", type=str, default=\"C\", help=\"Order of the input data\"\n )\n parser.add_argument(\"--perplexity\", type=float, default=30)\n parser.add_argument(\n \"--bhtsne\",\n action=\"store_true\",\n help=(\n \"if set and the reference bhtsne code is \"\n \"correctly installed, run it in the benchmark.\"\n ),\n )\n parser.add_argument(\n \"--all\",\n action=\"store_true\",\n help=(\n \"if set, run the benchmark with the whole MNIST.\"\n \"dataset. Note that it will take up to 1 hour.\"\n ),\n )\n parser.add_argument(\n \"--profile\",\n action=\"store_true\",\n help=\"if set, run the benchmark with a memory profiler.\",\n )\n parser.add_argument(\"--verbose\", type=int, default=0)\n parser.add_argument(\n \"--pca-components\",\n type=int,\n default=50,\n help=\"Number of principal components for preprocessing.\",\n )\n args = parser.parse_args()\n\n print(\"Used number of threads: {}\".format(_openmp_effective_n_threads()))\n X, y = load_data(order=args.order)\n\n if args.pca_components > 0:\n t0 = time()\n X = PCA(n_components=args.pca_components).fit_transform(X)\n print(\n \"PCA preprocessing down to {} dimensions took {:0.3f}s\".format(\n args.pca_components, time() - t0\n )\n )\n\n methods = []\n\n # Put TSNE in methods\n tsne = TSNE(\n n_components=2,\n init=\"pca\",\n perplexity=args.perplexity,\n verbose=args.verbose,\n n_iter=1000,\n )\n methods.append((\"sklearn TSNE\", lambda data: tsne_fit_transform(tsne, data)))\n\n if args.bhtsne:\n try:\n from bhtsne.bhtsne import run_bh_tsne\n except ImportError as e:\n raise ImportError(\n \"\"\"\\\nIf you want comparison with the reference implementation, build the\nbinary from source (https://github.com/lvdmaaten/bhtsne) in the folder\nbenchmarks/bhtsne and add an empty `__init__.py` file in the folder:\n\n$ git clone git@github.com:lvdmaaten/bhtsne.git\n$ cd bhtsne\n$ g++ sptree.cpp tsne.cpp tsne_main.cpp -o bh_tsne -O2\n$ touch __init__.py\n$ cd ..\n\"\"\"\n ) from e\n\n def bhtsne(X):\n \"\"\"Wrapper for the reference lvdmaaten/bhtsne implementation.\"\"\"\n # PCA preprocessing is done elsewhere in the benchmark script\n n_iter = -1 # TODO find a way to report the number of iterations\n return (\n run_bh_tsne(\n X,\n use_pca=False,\n perplexity=args.perplexity,\n verbose=args.verbose > 0,\n ),\n n_iter,\n )\n\n methods.append((\"lvdmaaten/bhtsne\", bhtsne))\n\n if args.profile:\n\n try:\n from memory_profiler import profile\n except ImportError as e:\n raise ImportError(\n \"To run the benchmark with `--profile`, you \"\n \"need to install `memory_profiler`. Please \"\n \"run `pip install memory_profiler`.\"\n ) from e\n methods = [(n, profile(m)) for n, m in methods]\n\n data_size = [100, 500, 1000, 5000, 10000]\n if args.all:\n data_size.append(70000)\n\n results = []\n basename = os.path.basename(os.path.splitext(__file__)[0])\n log_filename = os.path.join(LOG_DIR, basename + \".json\")\n for n in data_size:\n X_train = X[:n]\n y_train = y[:n]\n n = X_train.shape[0]\n for name, method in methods:\n print(\"Fitting {} on {} samples...\".format(name, n))\n t0 = time()\n np.save(\n os.path.join(LOG_DIR, \"mnist_{}_{}.npy\".format(\"original\", n)), X_train\n )\n np.save(\n os.path.join(LOG_DIR, \"mnist_{}_{}.npy\".format(\"original_labels\", n)),\n y_train,\n )\n X_embedded, n_iter = method(X_train)\n duration = time() - t0\n precision_5 = nn_accuracy(X_train, X_embedded)\n print(\n \"Fitting {} on {} samples took {:.3f}s in {:d} iterations, \"\n \"nn accuracy: {:0.3f}\".format(name, n, duration, n_iter, precision_5)\n )\n results.append(dict(method=name, duration=duration, n_samples=n))\n with open(log_filename, \"w\", encoding=\"utf-8\") as f:\n json.dump(results, f)\n method_name = sanitize(name)\n np.save(\n op.join(LOG_DIR, \"mnist_{}_{}.npy\".format(method_name, n)), X_embedded\n )\n" }, { "path": "benchmarks/plot_tsne_mnist.py", "content": "import matplotlib.pyplot as plt\nimport numpy as np\nimport os.path as op\n\nimport argparse\n\n\nLOG_DIR = \"mnist_tsne_output\"\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\"Plot benchmark results for t-SNE\")\n parser.add_argument(\n \"--labels\",\n type=str,\n default=op.join(LOG_DIR, \"mnist_original_labels_10000.npy\"),\n help=\"1D integer numpy array for labels\",\n )\n parser.add_argument(\n \"--embedding\",\n type=str,\n default=op.join(LOG_DIR, \"mnist_sklearn_TSNE_10000.npy\"),\n help=\"2D float numpy array for embedded data\",\n )\n args = parser.parse_args()\n\n X = np.load(args.embedding)\n y = np.load(args.labels)\n\n for i in np.unique(y):\n mask = y == i\n plt.scatter(X[mask, 0], X[mask, 1], alpha=0.2, label=int(i))\n plt.legend(loc=\"best\")\n plt.show()\n" }, { "path": "build_tools/Makefile", "content": "# Makefile for maintenance tools\n\nauthors:\n\tpython generate_authors_table.py\n" }, { "path": "build_tools/azure/install.sh", "content": "#!/bin/bash\n\nset -e\nset -x\n\nUNAMESTR=`uname`\n\nif [[ \"$DISTRIB\" == \"conda-mamba-pypy3\" ]]; then\n # condaforge/mambaforge-pypy3 needs compilers\n apt-get -yq update\n apt-get -yq install build-essential\nfi\n\nmake_conda() {\n TO_INSTALL=\"$@\"\n if [[ \"$DISTRIB\" == *\"mamba\"* ]]; then\n mamba create -n $VIRTUALENV --yes $TO_INSTALL\n else\n conda config --show\n conda create -n $VIRTUALENV --yes $TO_INSTALL\n fi\n source activate $VIRTUALENV\n}\n\nsetup_ccache() {\n echo \"Setting up ccache\"\n mkdir /tmp/ccache/\n which ccache\n for name in gcc g++ cc c++ x86_64-linux-gnu-gcc x86_64-linux-gnu-c++; do\n ln -s $(which ccache) \"/tmp/ccache/${name}\"\n done\n export PATH=\"/tmp/ccache/:${PATH}\"\n ccache -M 256M\n}\n\n# imports get_dep\nsource build_tools/shared.sh\n\nif [[ \"$DISTRIB\" == \"conda\" || \"$DISTRIB\" == *\"mamba\"* ]]; then\n\n if [[ \"$CONDA_CHANNEL\" != \"\" ]]; then\n TO_INSTALL=\"--override-channels -c $CONDA_CHANNEL\"\n else\n TO_INSTALL=\"\"\n fi\n\n if [[ \"$DISTRIB\" == *\"pypy\"* ]]; then\n TO_INSTALL=\"$TO_INSTALL pypy\"\n else\n TO_INSTALL=\"$TO_INSTALL python=$PYTHON_VERSION\"\n fi\n\n TO_INSTALL=\"$TO_INSTALL ccache pip blas[build=$BLAS]\"\n\n TO_INSTALL=\"$TO_INSTALL $(get_dep numpy $NUMPY_VERSION)\"\n TO_INSTALL=\"$TO_INSTALL $(get_dep scipy $SCIPY_VERSION)\"\n TO_INSTALL=\"$TO_INSTALL $(get_dep cython $CYTHON_VERSION)\"\n TO_INSTALL=\"$TO_INSTALL $(get_dep joblib $JOBLIB_VERSION)\"\n TO_INSTALL=\"$TO_INSTALL $(get_dep pandas $PANDAS_VERSION)\"\n TO_INSTALL=\"$TO_INSTALL $(get_dep pyamg $PYAMG_VERSION)\"\n TO_INSTALL=\"$TO_INSTALL $(get_dep Pillow $PILLOW_VERSION)\"\n TO_INSTALL=\"$TO_INSTALL $(get_dep matplotlib $MATPLOTLIB_VERSION)\"\n\n if [[ \"$UNAMESTR\" == \"Darwin\" ]]; then\n if [[ \"$SKLEARN_TEST_NO_OPENMP\" != \"true\" ]]; then\n # on macOS, install an OpenMP-enabled clang/llvm from conda-forge.\n # TODO: Remove !=1.1.0 when the following is fixed:\n # sklearn/svm/_libsvm.cpython-38-darwin.so,\n # 2): Symbol not found: _svm_check_parameter error\n TO_INSTALL=\"$TO_INSTALL compilers>=1.0.4,!=1.1.0 llvm-openmp\"\n else\n # Without openmp, we use the system clang. Here we use /usr/bin/ar\n # instead because llvm-ar errors\n export AR=/usr/bin/ar\n fi\n else\n # FIXME: temporary fix to link against system libraries on linux\n export LDFLAGS=\"$LDFLAGS -Wl,--sysroot=/\"\n fi\n\tmake_conda $TO_INSTALL\n setup_ccache\n\nelif [[ \"$DISTRIB\" == \"ubuntu\" ]]; then\n sudo add-apt-repository --remove ppa:ubuntu-toolchain-r/test\n sudo apt-get update\n sudo apt-get install python3-scipy python3-matplotlib libatlas3-base libatlas-base-dev python3-virtualenv ccache\n python3 -m virtualenv --system-site-packages --python=python3 $VIRTUALENV\n source $VIRTUALENV/bin/activate\n setup_ccache\n python -m pip install $(get_dep cython $CYTHON_VERSION) \\\n $(get_dep joblib $JOBLIB_VERSION)\n\nelif [[ \"$DISTRIB\" == \"debian-32\" ]]; then\n apt-get update\n apt-get install -y python3-dev python3-numpy python3-scipy python3-matplotlib libatlas3-base libatlas-base-dev python3-virtualenv python3-pandas ccache\n\n python3 -m virtualenv --system-site-packages --python=python3 $VIRTUALENV\n source $VIRTUALENV/bin/activate\n setup_ccache\n python -m pip install $(get_dep cython $CYTHON_VERSION) \\\n $(get_dep joblib $JOBLIB_VERSION)\n\nelif [[ \"$DISTRIB\" == \"conda-pip-latest\" ]]; then\n # FIXME: temporary fix to link against system libraries on linux\n export LDFLAGS=\"$LDFLAGS -Wl,--sysroot=/\"\n # Since conda main channel usually lacks behind on the latest releases,\n # we use pypi to test against the latest releases of the dependencies.\n # conda is still used as a convenient way to install Python and pip.\n make_conda \"ccache python=$PYTHON_VERSION\"\n setup_ccache\n python -m pip install -U pip\n\n # Do not build scikit-image from source because it is an optional dependency\n python -m pip install --only-binary :all: scikit-image || true\n\n python -m pip install pandas matplotlib pyamg\n # do not install dependencies for lightgbm since it requires scikit-learn.\n python -m pip install \"lightgbm>=3.0.0\" --no-deps\nelif [[ \"$DISTRIB\" == \"conda-pip-scipy-dev\" ]]; then\n # FIXME: temporary fix to link against system libraries on linux\n export LDFLAGS=\"$LDFLAGS -Wl,--sysroot=/\"\n make_conda \"ccache python=$PYTHON_VERSION\"\n python -m pip install -U pip\n echo \"Installing numpy and scipy master wheels\"\n dev_anaconda_url=https://pypi.anaconda.org/scipy-wheels-nightly/simple\n pip install --pre --upgrade --timeout=60 --extra-index $dev_anaconda_url numpy pandas scipy\n pip install --pre cython\n setup_ccache\n echo \"Installing joblib master\"\n pip install https://github.com/joblib/joblib/archive/master.zip\n echo \"Installing pillow master\"\n pip install https://github.com/python-pillow/Pillow/archive/main.zip\nfi\n\npython -m pip install $(get_dep threadpoolctl $THREADPOOLCTL_VERSION) \\\n $(get_dep pytest $PYTEST_VERSION) \\\n $(get_dep pytest-xdist $PYTEST_XDIST_VERSION)\n\nif [[ \"$COVERAGE\" == \"true\" ]]; then\n python -m pip install codecov pytest-cov\nfi\n\nif [[ \"$PYTEST_XDIST_VERSION\" != \"none\" ]]; then\n python -m pip install pytest-xdist\nfi\n\nif [[ \"$TEST_DOCSTRINGS\" == \"true\" ]]; then\n # numpydoc requires sphinx\n python -m pip install sphinx\n python -m pip install numpydoc\nfi\n\npython --version\npython -c \"import numpy; print('numpy %s' % numpy.__version__)\"\npython -c \"import scipy; print('scipy %s' % scipy.__version__)\"\npython -c \"\\\ntry:\n import pandas\n print('pandas %s' % pandas.__version__)\nexcept ImportError:\n print('pandas not installed')\n\"\n# Set parallelism to 3 to overlap IO bound tasks with CPU bound tasks on CI\n# workers with 2 cores when building the compiled extensions of scikit-learn.\nexport SKLEARN_BUILD_PARALLEL=3\n\npython -m pip list\nif [[ \"$DISTRIB\" == \"conda-pip-latest\" ]]; then\n # Check that pip can automatically build scikit-learn with the build\n # dependencies specified in pyproject.toml using an isolated build\n # environment:\n pip install --verbose --editable .\nelse\n if [[ \"$BUILD_WITH_ICC\" == \"true\" ]]; then\n wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB\n sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB\n rm GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB\n sudo add-apt-repository \"deb https://apt.repos.intel.com/oneapi all main\"\n sudo apt-get update\n sudo apt-get install intel-oneapi-compiler-dpcpp-cpp-and-cpp-classic\n source /opt/intel/oneapi/setvars.sh\n\n # The \"build_clib\" command is implicitly used to build \"libsvm-skl\".\n # To compile with a different compiler, we also need to specify the\n # compiler for this command\n python setup.py build_ext --compiler=intelem -i build_clib --compiler=intelem\n fi\n # Use the pre-installed build dependencies and build directly in the\n # current environment.\n python setup.py develop\nfi\nccache -s\n" }, { "path": "build_tools/azure/install_win.sh", "content": "#!/bin/bash\n\nset -e\nset -x\n\nif [[ \"$PYTHON_ARCH\" == \"64\" ]]; then\n conda create -n $VIRTUALENV -q -y python=$PYTHON_VERSION numpy scipy cython matplotlib wheel pillow joblib\n\n source activate $VIRTUALENV\n\n pip install threadpoolctl\n\n if [[ \"$PYTEST_VERSION\" == \"*\" ]]; then\n pip install pytest\n else\n pip install pytest==$PYTEST_VERSION\n fi\nelse\n pip install numpy scipy cython pytest wheel pillow joblib threadpoolctl\nfi\n\nif [[ \"$PYTEST_XDIST_VERSION\" != \"none\" ]]; then\n pip install pytest-xdist\nfi\n\nif [[ \"$COVERAGE\" == \"true\" ]]; then\n pip install coverage codecov pytest-cov\nfi\n\npython --version\npip --version\n\n# Build scikit-learn\npython setup.py bdist_wheel\n\n# Install the generated wheel package to test it\npip install --pre --no-index --find-links dist scikit-learn\n" }, { "path": "build_tools/azure/posix-docker.yml", "content": "parameters:\n name: ''\n vmImage: ''\n matrix: []\n dependsOn: []\n condition: ne(variables['Build.Reason'], 'Schedule')\n\njobs:\n- job: ${{ parameters.name }}\n dependsOn: ${{ parameters.dependsOn }}\n condition: ${{ parameters.condition }}\n pool:\n vmImage: ${{ parameters.vmImage }}\n variables:\n TEST_DIR: '$(Agent.WorkFolder)/tmp_folder'\n JUNITXML: 'test-data.xml'\n OMP_NUM_THREADS: '2'\n OPENBLAS_NUM_THREADS: '2'\n SKLEARN_SKIP_NETWORK_TESTS: '1'\n NUMPY_VERSION: 'latest'\n SCIPY_VERSION: 'latest'\n CYTHON_VERSION: 'latest'\n JOBLIB_VERSION: 'latest'\n PANDAS_VERSION: 'latest'\n PYAMG_VERSION: 'latest'\n PILLOW_VERSION: 'latest'\n MATPLOTLIB_VERSION: 'latest'\n PYTEST_VERSION: 'latest'\n PYTEST_XDIST_VERSION: 'latest'\n THREADPOOLCTL_VERSION: 'latest'\n COVERAGE: 'false'\n TEST_DOCSTRINGS: 'false'\n BLAS: 'openblas'\n # Set in azure-pipelines.yml\n DISTRIB: ''\n DOCKER_CONTAINER: ''\n SHOW_SHORT_SUMMARY: 'false'\n strategy:\n matrix:\n ${{ insert }}: ${{ parameters.matrix }}\n\n steps:\n # Container is detached and sleeping, allowing steps to run commands\n # in the container. The TEST_DIR is mapped allowing the host to access\n # the JUNITXML file\n - script: >\n docker container run --rm\n --volume $TEST_DIR:/temp_dir\n --volume $PWD:/io\n -w /io\n --detach\n --name skcontainer\n -e DISTRIB=$DISTRIB\n -e TEST_DIR=/temp_dir\n -e JUNITXML=$JUNITXML\n -e VIRTUALENV=testvenv\n -e NUMPY_VERSION=$NUMPY_VERSION\n -e SCIPY_VERSION=$SCIPY_VERSION\n -e CYTHON_VERSION=$CYTHON_VERSION\n -e JOBLIB_VERSION=$JOBLIB_VERSION\n -e PANDAS_VERSION=$PANDAS_VERSION\n -e PYAMG_VERSION=$PYAMG_VERSION\n -e PILLOW_VERSION=$PILLOW_VERSION\n -e MATPLOTLIB_VERSION=$MATPLOTLIB_VERSION\n -e PYTEST_VERSION=$PYTEST_VERSION\n -e PYTEST_XDIST_VERSION=$PYTEST_XDIST_VERSION\n -e THREADPOOLCTL_VERSION=$THREADPOOLCTL_VERSION\n -e OMP_NUM_THREADS=$OMP_NUM_THREADS\n -e OPENBLAS_NUM_THREADS=$OPENBLAS_NUM_THREADS\n -e SKLEARN_SKIP_NETWORK_TESTS=$SKLEARN_SKIP_NETWORK_TESTS\n -e BLAS=$BLAS\n $DOCKER_CONTAINER\n sleep 1000000\n displayName: 'Start container'\n - script: >\n docker exec skcontainer ./build_tools/azure/install.sh\n displayName: 'Install'\n - script: >\n docker exec skcontainer ./build_tools/azure/test_script.sh\n displayName: 'Test Library'\n - task: PublishTestResults@2\n inputs:\n testResultsFiles: '$(TEST_DIR)/$(JUNITXML)'\n testRunTitle: ${{ format('{0}-$(Agent.JobName)', parameters.name) }}\n displayName: 'Publish Test Results'\n condition: succeededOrFailed()\n - script: >\n docker container stop skcontainer\n displayName: 'Stop container'\n condition: always()\n" }, { "path": "build_tools/azure/posix.yml", "content": "parameters:\n name: ''\n vmImage: ''\n matrix: []\n dependsOn: []\n condition: ''\n\njobs:\n- job: ${{ parameters.name }}\n dependsOn: ${{ parameters.dependsOn }}\n condition: ${{ parameters.condition }}\n pool:\n vmImage: ${{ parameters.vmImage }}\n variables:\n TEST_DIR: '$(Agent.WorkFolder)/tmp_folder'\n VIRTUALENV: 'testvenv'\n JUNITXML: 'test-data.xml'\n OMP_NUM_THREADS: '2'\n OPENBLAS_NUM_THREADS: '2'\n SKLEARN_SKIP_NETWORK_TESTS: '1'\n CCACHE_DIR: $(Pipeline.Workspace)/ccache\n CCACHE_COMPRESS: '1'\n NUMPY_VERSION: 'latest'\n SCIPY_VERSION: 'latest'\n CYTHON_VERSION: 'latest'\n JOBLIB_VERSION: 'latest'\n PANDAS_VERSION: 'latest'\n PYAMG_VERSION: 'latest'\n PILLOW_VERSION: 'latest'\n MATPLOTLIB_VERSION: 'latest'\n PYTEST_VERSION: 'latest'\n PYTEST_XDIST_VERSION: 'latest'\n THREADPOOLCTL_VERSION: 'latest'\n COVERAGE: 'true'\n TEST_DOCSTRINGS: 'false'\n CREATE_ISSUE_ON_TRACKER: 'false'\n SHOW_SHORT_SUMMARY: 'false'\n strategy:\n matrix:\n ${{ insert }}: ${{ parameters.matrix }}\n\n steps:\n - bash: echo \"##vso[task.prependpath]$CONDA/bin\"\n displayName: Add conda to PATH\n condition: startsWith(variables['DISTRIB'], 'conda')\n - bash: sudo chown -R $USER $CONDA\n displayName: Take ownership of conda installation\n condition: startsWith(variables['DISTRIB'], 'conda')\n - task: Cache@2\n inputs:\n key: '\"$(Agent.JobName)\"'\n path: $(CCACHE_DIR)\n displayName: ccache\n continueOnError: true\n - script: |\n build_tools/azure/install.sh\n displayName: 'Install'\n - script: |\n build_tools/azure/test_script.sh\n displayName: 'Test Library'\n - script: |\n build_tools/azure/test_docs.sh\n displayName: 'Test Docs'\n - script: |\n build_tools/azure/test_docstring.sh\n displayName: \"Numpydoc validation\"\n condition: eq(variables['TEST_DOCSTRINGS'], 'true')\n - script: |\n build_tools/azure/test_pytest_soft_dependency.sh\n displayName: 'Test Soft Dependency'\n condition: eq(variables['CHECK_PYTEST_SOFT_DEPENDENCY'], 'true')\n - task: PublishTestResults@2\n inputs:\n testResultsFiles: '$(TEST_DIR)/$(JUNITXML)'\n testRunTitle: ${{ format('{0}-$(Agent.JobName)', parameters.name) }}\n displayName: 'Publish Test Results'\n condition: succeededOrFailed()\n - task: UsePythonVersion@0\n inputs:\n versionSpec: '3.9'\n displayName: Place Python into path to update issue tracker\n condition: and(succeededOrFailed(), eq(variables['CREATE_ISSUE_ON_TRACKER'], 'true'),\n eq(variables['Build.Reason'], 'Schedule'))\n - bash: |\n set -ex\n if [[ $(BOT_GITHUB_TOKEN) == \"\" ]]; then\n echo \"GitHub Token is not set. Issue tracker will not be updated.\"\n exit\n fi\n\n LINK_TO_RUN=\"https://dev.azure.com/$BUILD_REPOSITORY_NAME/_build/results?buildId=$BUILD_BUILDID&view=logs&j=$SYSTEM_JOBID\"\n CI_NAME=\"$SYSTEM_JOBIDENTIFIER\"\n ISSUE_REPO=\"$BUILD_REPOSITORY_NAME\"\n\n pip install defusedxml PyGithub\n python maint_tools/create_issue_from_juint.py $(BOT_GITHUB_TOKEN) $CI_NAME $ISSUE_REPO $LINK_TO_RUN $JUNIT_FILE\n displayName: 'Update issue tracker'\n env:\n JUNIT_FILE: $(TEST_DIR)/$(JUNITXML)\n condition: and(succeededOrFailed(), eq(variables['CREATE_ISSUE_ON_TRACKER'], 'true'),\n eq(variables['Build.Reason'], 'Schedule'))\n - script: |\n build_tools/azure/upload_codecov.sh\n condition: and(succeeded(), eq(variables['COVERAGE'], 'true'))\n displayName: 'Upload To Codecov'\n env:\n CODECOV_TOKEN: $(CODECOV_TOKEN)\n" }, { "path": "build_tools/azure/test_docs.sh", "content": "#!/bin/bash\n\nset -e\n\nif [[ \"$DISTRIB\" =~ ^conda.* ]]; then\n source activate $VIRTUALENV\nelif [[ \"$DISTRIB\" == \"ubuntu\" ]]; then\n source $VIRTUALENV/bin/activate\nfi\n\nif [[ \"$BUILD_WITH_ICC\" == \"true\" ]]; then\n source /opt/intel/oneapi/setvars.sh\nfi\n\nmake test-doc\n" }, { "path": "build_tools/azure/test_docstring.sh", "content": "#!/bin/bash\n\nset -e\n\nif [[ \"$DISTRIB\" =~ ^conda.* ]]; then\n source activate $VIRTUALENV\nelif [[ \"$DISTRIB\" == \"ubuntu\" ]]; then\n source $VIRTUALENV/bin/activate\nfi\n\nif [[ \"$BUILD_WITH_ICC\" == \"true\" ]]; then\n source /opt/intel/oneapi/setvars.sh\nfi\n\npytest maint_tools/test_docstrings.py\n" }, { "path": "build_tools/azure/test_pytest_soft_dependency.sh", "content": "#!/bin/bash\n\nset -e\n\n# called when DISTRIB==\"conda\"\nsource activate $VIRTUALENV\nconda remove -y py pytest || pip uninstall -y py pytest\n\nif [[ \"$COVERAGE\" == \"true\" ]]; then\n # conda may remove coverage when uninstall pytest and py\n pip install coverage\n # Need to append the coverage to the existing .coverage generated by\n # running the tests. Make sure to reuse the same coverage\n # configuration as the one used by the main pytest run to be\n # able to combine the results.\n CMD=\"coverage run --rcfile=$BUILD_SOURCESDIRECTORY/.coveragerc\"\nelse\n CMD=\"python\"\nfi\n\n# .coverage from running the tests is in TEST_DIR\npushd $TEST_DIR\n$CMD -m sklearn.utils.tests.test_estimator_checks\npopd\n" }, { "path": "build_tools/azure/test_script.sh", "content": "#!/bin/bash\n\nset -e\n\nif [[ \"$DISTRIB\" =~ ^conda.* ]]; then\n source activate $VIRTUALENV\nelif [[ \"$DISTRIB\" == \"ubuntu\" ]] || [[ \"$DISTRIB\" == \"debian-32\" ]]; then\n source $VIRTUALENV/bin/activate\nfi\n\nif [[ \"$BUILD_WITH_ICC\" == \"true\" ]]; then\n source /opt/intel/oneapi/setvars.sh\nfi\n\nmkdir -p $TEST_DIR\ncp setup.cfg $TEST_DIR\ncd $TEST_DIR\n\npython -c \"import sklearn; sklearn.show_versions()\"\n\nif ! command -v conda &> /dev/null\nthen\n pip list\nelse\n # conda list provides more info than pip list (when available)\n conda list\nfi\n\nTEST_CMD=\"python -m pytest --showlocals --durations=20 --junitxml=$JUNITXML\"\n\nif [[ \"$COVERAGE\" == \"true\" ]]; then\n # Note: --cov-report= is used to disable to long text output report in the\n # CI logs. The coverage data is consolidated by codecov to get an online\n # web report across all the platforms so there is no need for this text\n # report that otherwise hides the test failures and forces long scrolls in\n # the CI logs.\n export COVERAGE_PROCESS_START=\"$BUILD_SOURCESDIRECTORY/.coveragerc\"\n TEST_CMD=\"$TEST_CMD --cov-config='$COVERAGE_PROCESS_START' --cov sklearn --cov-report=\"\nfi\n\nif [[ -n \"$CHECK_WARNINGS\" ]]; then\n # numpy's 1.19.0's tostring() deprecation is ignored until scipy and joblib removes its usage\n TEST_CMD=\"$TEST_CMD -Werror::DeprecationWarning -Werror::FutureWarning -Wignore:tostring:DeprecationWarning\"\n\n # Python 3.10 deprecates disutils and is imported by numpy interally during import time\n TEST_CMD=\"$TEST_CMD -Wignore:The\\ distutils:DeprecationWarning\"\n\n # Workaround for https://github.com/pypa/setuptools/issues/2885\n TEST_CMD=\"$TEST_CMD -Wignore:Creating\\ a\\ LegacyVersion:DeprecationWarning\"\nfi\n\nif [[ \"$PYTEST_XDIST_VERSION\" != \"none\" ]]; then\n TEST_CMD=\"$TEST_CMD -n2\"\nfi\n\nif [[ \"$SHOW_SHORT_SUMMARY\" == \"true\" ]]; then\n TEST_CMD=\"$TEST_CMD -ra\"\nfi\n\nset -x\neval \"$TEST_CMD --pyargs sklearn\"\nset +x\n" }, { "path": "build_tools/azure/upload_codecov.sh", "content": "#!/bin/bash\n\nset -e\n\n# called when COVERAGE==\"true\" and DISTRIB==\"conda\"\nexport PATH=$HOME/miniconda3/bin:$PATH\nsource activate $VIRTUALENV\n\n# Need to run codecov from a git checkout, so we copy .coverage\n# from TEST_DIR where pytest has been run\npushd $TEST_DIR\ncoverage combine --append\npopd\ncp $TEST_DIR/.coverage $BUILD_REPOSITORY_LOCALPATH\n\ncodecov --root $BUILD_REPOSITORY_LOCALPATH -t $CODECOV_TOKEN || echo \"codecov upload failed\"\n" }, { "path": "build_tools/azure/windows.yml", "content": "\nparameters:\n name: ''\n vmImage: ''\n matrix: []\n dependsOn: []\n condition: ne(variables['Build.Reason'], 'Schedule')\n\njobs:\n- job: ${{ parameters.name }}\n dependsOn: ${{ parameters.dependsOn }}\n condition: ${{ parameters.condition }}\n pool:\n vmImage: ${{ parameters.vmImage }}\n variables:\n VIRTUALENV: 'testvenv'\n JUNITXML: 'test-data.xml'\n SKLEARN_SKIP_NETWORK_TESTS: '1'\n PYTEST_VERSION: '5.2.1'\n PYTEST_XDIST: 'true'\n PYTEST_XDIST_VERSION: 'latest'\n TEST_DIR: '$(Agent.WorkFolder)/tmp_folder'\n SHOW_SHORT_SUMMARY: 'false'\n strategy:\n matrix:\n ${{ insert }}: ${{ parameters.matrix }}\n\n steps:\n - bash: echo \"##vso[task.prependpath]$CONDA/Scripts\"\n displayName: Add conda to PATH for 64 bit Python\n condition: eq(variables['PYTHON_ARCH'], '64')\n - task: UsePythonVersion@0\n inputs:\n versionSpec: '$(PYTHON_VERSION)'\n addToPath: true\n architecture: 'x86'\n displayName: Use 32 bit System Python\n condition: eq(variables['PYTHON_ARCH'], '32')\n - bash: ./build_tools/azure/install_win.sh\n displayName: 'Install'\n - bash: ./build_tools/azure/test_script.sh\n displayName: 'Test Library'\n - bash: ./build_tools/azure/upload_codecov.sh\n condition: and(succeeded(), eq(variables['COVERAGE'], 'true'))\n displayName: 'Upload To Codecov'\n env:\n CODECOV_TOKEN: $(CODECOV_TOKEN)\n - task: PublishTestResults@2\n inputs:\n testResultsFiles: '$(TEST_DIR)/$(JUNITXML)'\n testRunTitle: ${{ format('{0}-$(Agent.JobName)', parameters.name) }}\n displayName: 'Publish Test Results'\n condition: succeededOrFailed()\n" }, { "path": "build_tools/circle/build_doc.sh", "content": "#!/usr/bin/env bash\nset -x\nset -e\n\n# Decide what kind of documentation build to run, and run it.\n#\n# If the last commit message has a \"[doc skip]\" marker, do not build\n# the doc. On the contrary if a \"[doc build]\" marker is found, build the doc\n# instead of relying on the subsequent rules.\n#\n# We always build the documentation for jobs that are not related to a specific\n# PR (e.g. a merge to main or a maintenance branch).\n#\n# If this is a PR, do a full build if there are some files in this PR that are\n# under the \"doc/\" or \"examples/\" folders, otherwise perform a quick build.\n#\n# If the inspection of the current commit fails for any reason, the default\n# behavior is to quick build the documentation.\n\nget_build_type() {\n if [ -z \"$CIRCLE_SHA1\" ]\n then\n echo SKIP: undefined CIRCLE_SHA1\n return\n fi\n commit_msg=$(git log --format=%B -n 1 $CIRCLE_SHA1)\n if [ -z \"$commit_msg\" ]\n then\n echo QUICK BUILD: failed to inspect commit $CIRCLE_SHA1\n return\n fi\n if [[ \"$commit_msg\" =~ \\[doc\\ skip\\] ]]\n then\n echo SKIP: [doc skip] marker found\n return\n fi\n if [[ \"$commit_msg\" =~ \\[doc\\ quick\\] ]]\n then\n echo QUICK: [doc quick] marker found\n return\n fi\n if [[ \"$commit_msg\" =~ \\[doc\\ build\\] ]]\n then\n echo BUILD: [doc build] marker found\n return\n fi\n if [ -z \"$CI_PULL_REQUEST\" ]\n then\n echo BUILD: not a pull request\n return\n fi\n git_range=\"origin/main...$CIRCLE_SHA1\"\n git fetch origin main >&2 || (echo QUICK BUILD: failed to get changed filenames for $git_range; return)\n filenames=$(git diff --name-only $git_range)\n if [ -z \"$filenames\" ]\n then\n echo QUICK BUILD: no changed filenames for $git_range\n return\n fi\n changed_examples=$(echo \"$filenames\" | grep -E \"^examples/(.*/)*plot_\")\n\n # The following is used to extract the list of filenames of example python\n # files that sphinx-gallery needs to run to generate png files used as\n # figures or images in the .rst files from the documentation.\n # If the contributor changes a .rst file in a PR we need to run all\n # the examples mentioned in that file to get sphinx build the\n # documentation without generating spurious warnings related to missing\n # png files.\n\n if [[ -n \"$filenames\" ]]\n then\n # get rst files\n rst_files=\"$(echo \"$filenames\" | grep -E \"rst$\")\"\n\n # get lines with figure or images\n img_fig_lines=\"$(echo \"$rst_files\" | xargs grep -shE \"(figure|image)::\")\"\n\n # get only auto_examples\n auto_example_files=\"$(echo \"$img_fig_lines\" | grep auto_examples | awk -F \"/\" '{print $NF}')\"\n\n # remove \"sphx_glr_\" from path and accept replace _(\\d\\d\\d|thumb).png with .py\n scripts_names=\"$(echo \"$auto_example_files\" | sed 's/sphx_glr_//' | sed -E 's/_([[:digit:]][[:digit:]][[:digit:]]|thumb).png/.py/')\"\n\n # get unique values\n examples_in_rst=\"$(echo \"$scripts_names\" | uniq )\"\n fi\n\n # executed only if there are examples in the modified rst files\n if [[ -n \"$examples_in_rst\" ]]\n then\n if [[ -n \"$changed_examples\" ]]\n then\n changed_examples=\"$changed_examples|$examples_in_rst\"\n else\n changed_examples=\"$examples_in_rst\"\n fi\n fi\n\n if [[ -n \"$changed_examples\" ]]\n then\n echo BUILD: detected examples/ filename modified in $git_range: $changed_examples\n pattern=$(echo \"$changed_examples\" | paste -sd '|')\n # pattern for examples to run is the last line of output\n echo \"$pattern\"\n return\n fi\n echo QUICK BUILD: no examples/ filename modified in $git_range:\n echo \"$filenames\"\n}\n\nbuild_type=$(get_build_type)\nif [[ \"$build_type\" =~ ^SKIP ]]\nthen\n exit 0\nfi\n\nif [[ \"$CIRCLE_BRANCH\" =~ ^main$|^[0-9]+\\.[0-9]+\\.X$ && -z \"$CI_PULL_REQUEST\" ]]\nthen\n # ZIP linked into HTML\n make_args=dist\nelif [[ \"$build_type\" =~ ^QUICK ]]\nthen\n make_args=html-noplot\nelif [[ \"$build_type\" =~ ^'BUILD: detected examples' ]]\nthen\n # pattern for examples to run is the last line of output\n pattern=$(echo \"$build_type\" | tail -n 1)\n make_args=\"html EXAMPLES_PATTERN=$pattern\"\nelse\n make_args=html\nfi\n\nmake_args=\"SPHINXOPTS=-T $make_args\" # show full traceback on exception\n\n# Installing required system packages to support the rendering of math\n# notation in the HTML documentation and to optimize the image files\nsudo -E apt-get -yq update --allow-releaseinfo-change\nsudo -E apt-get -yq --no-install-suggests --no-install-recommends \\\n install dvipng gsfonts ccache zip optipng\n\n# deactivate circleci virtualenv and setup a miniconda env instead\nif [[ `type -t deactivate` ]]; then\n deactivate\nfi\n\nMINICONDA_PATH=$HOME/miniconda\n# Install dependencies with miniconda\nwget https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh \\\n -O miniconda.sh\nchmod +x miniconda.sh && ./miniconda.sh -b -p $MINICONDA_PATH\nexport PATH=\"/usr/lib/ccache:$MINICONDA_PATH/bin:$PATH\"\n\nccache -M 512M\nexport CCACHE_COMPRESS=1\n\n# Old packages coming from the 'free' conda channel have been removed but we\n# are using them for our min-dependencies doc generation. See\n# https://www.anaconda.com/why-we-removed-the-free-channel-in-conda-4-7/ for\n# more details.\nif [[ \"$CIRCLE_JOB\" == \"doc-min-dependencies\" ]]; then\n conda config --set restore_free_channel true\nfi\n\n# imports get_dep\nsource build_tools/shared.sh\n\n# packaging won't be needed once setuptools starts shipping packaging>=17.0\nmamba create -n $CONDA_ENV_NAME --yes --quiet \\\n python=\"${PYTHON_VERSION:-*}\" \\\n \"$(get_dep numpy $NUMPY_VERSION)\" \\\n \"$(get_dep scipy $SCIPY_VERSION)\" \\\n \"$(get_dep cython $CYTHON_VERSION)\" \\\n \"$(get_dep matplotlib $MATPLOTLIB_VERSION)\" \\\n \"$(get_dep sphinx $SPHINX_VERSION)\" \\\n \"$(get_dep pandas $PANDAS_VERSION)\" \\\n joblib memory_profiler packaging seaborn pillow pytest coverage\n\nsource activate testenv\n# Pin PyWavelet to 1.1.1 that is the latest version that support our minumum\n# NumPy version required. If PyWavelets 1.2+ is installed, it would require\n# NumPy 1.17+ that trigger a bug with Pandas 0.25:\n# https://github.com/numpy/numpy/issues/18355#issuecomment-774610226\npip install PyWavelets==1.1.1\npip install \"$(get_dep scikit-image $SCIKIT_IMAGE_VERSION)\"\npip install \"$(get_dep sphinx-gallery $SPHINX_GALLERY_VERSION)\"\npip install \"$(get_dep numpydoc $NUMPYDOC_VERSION)\"\npip install \"$(get_dep sphinx-prompt $SPHINX_PROMPT_VERSION)\"\npip install \"$(get_dep sphinxext-opengraph $SPHINXEXT_OPENGRAPH_VERSION)\"\n\n# Set parallelism to 3 to overlap IO bound tasks with CPU bound tasks on CI\n# workers with 2 cores when building the compiled extensions of scikit-learn.\nexport SKLEARN_BUILD_PARALLEL=3\npython setup.py develop\n\nexport OMP_NUM_THREADS=1\n\nif [[ \"$CIRCLE_BRANCH\" =~ ^main$ && -z \"$CI_PULL_REQUEST\" ]]\nthen\n # List available documentation versions if on main\n python build_tools/circle/list_versions.py > doc/versions.rst\nfi\n\n# The pipefail is requested to propagate exit code\nset -o pipefail && cd doc && make $make_args 2>&1 | tee ~/log.txt\n\n# Insert the version warning for deployment\nfind _build/html/stable -name \"*.html\" | xargs sed -i '/<\\/body>/ i \\\n\\ '\n\ncd -\nset +o pipefail\n\naffected_doc_paths() {\n files=$(git diff --name-only origin/main...$CIRCLE_SHA1)\n echo \"$files\" | grep ^doc/.*\\.rst | sed 's/^doc\\/\\(.*\\)\\.rst$/\\1.html/'\n echo \"$files\" | grep ^examples/.*.py | sed 's/^\\(.*\\)\\.py$/auto_\\1.html/'\n sklearn_files=$(echo \"$files\" | grep '^sklearn/')\n if [ -n \"$sklearn_files\" ]\n then\n grep -hlR -f<(echo \"$sklearn_files\" | sed 's/^/scikit-learn\\/blob\\/[a-z0-9]*\\//') doc/_build/html/stable/modules/generated | cut -d/ -f5-\n fi\n}\n\naffected_doc_warnings() {\n files=$(git diff --name-only origin/main...$CIRCLE_SHA1)\n # Look for sphinx warnings only in files affected by the PR\n if [ -n \"$files\" ]\n then\n for af in ${files[@]}\n do\n warn+=`grep WARNING ~/log.txt | grep $af`\n done\n fi\n echo \"$warn\"\n}\n\nif [ -n \"$CI_PULL_REQUEST\" ]\nthen\n echo \"The following documentation warnings may have been generated by PR #$CI_PULL_REQUEST:\"\n warnings=$(affected_doc_warnings)\n if [ -z \"$warnings\" ]\n then\n warnings=\"/home/circleci/project/ no warnings\"\n fi\n echo \"$warnings\"\n\n echo \"The following documentation files may have been changed by PR #$CI_PULL_REQUEST:\"\n affected=$(affected_doc_paths)\n echo \"$affected\"\n (\n echo '
    '\n echo \"$affected\" | sed 's|.*|
  • & [dev, stable]
    • |'\n echo '

General: Home | API Reference | Examples

'\n echo 'Sphinx Warnings in affected files
    '\n echo \"$warnings\" | sed 's/\\/home\\/circleci\\/project\\//
  • /g'\n echo '
'\n ) > 'doc/_build/html/stable/_changed.html'\n\n if [ \"$warnings\" != \"/home/circleci/project/ no warnings\" ]\n then\n echo \"Sphinx generated warnings when building the documentation related to files modified in this PR.\"\n echo \"Please check doc/_build/html/stable/_changed.html\"\n exit 1\n fi\nfi\n" }, { "path": "build_tools/circle/build_test_arm.sh", "content": "#!/bin/bash\n\nset -e\nset -x\n\nUNAMESTR=`uname`\nN_CORES=`nproc --all`\n\n\nsetup_ccache() {\n echo \"Setting up ccache\"\n mkdir /tmp/ccache/\n which ccache\n for name in gcc g++ cc c++ x86_64-linux-gnu-gcc x86_64-linux-gnu-c++; do\n ln -s $(which ccache) \"/tmp/ccache/${name}\"\n done\n export PATH=\"/tmp/ccache:${PATH}\"\n # Unset ccache limits\n ccache -F 0\n ccache -M 0\n}\n\n# imports get_dep\nsource build_tools/shared.sh\n\nsudo add-apt-repository --remove ppa:ubuntu-toolchain-r/test\nsudo apt-get update\n\n# Setup conda environment\nMINICONDA_URL=\"https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-aarch64.sh\"\n\n# Install Mambaforge\nwget $MINICONDA_URL -O mambaforge.sh\nMINICONDA_PATH=$HOME/miniconda\nchmod +x mambaforge.sh && ./mambaforge.sh -b -p $MINICONDA_PATH\nexport PATH=$MINICONDA_PATH/bin:$PATH\nmamba init --all --verbose\nmamba update --yes conda\n\n# Create environment and install dependencies\nmamba create -n testenv --yes $(get_dep python $PYTHON_VERSION)\nsource activate testenv\n\n# Use the latest by default\nmamba install --verbose -y ccache \\\n pip \\\n $(get_dep numpy $NUMPY_VERSION) \\\n $(get_dep scipy $SCIPY_VERSION) \\\n $(get_dep cython $CYTHON_VERSION) \\\n $(get_dep joblib $JOBLIB_VERSION) \\\n $(get_dep threadpoolctl $THREADPOOLCTL_VERSION) \\\n $(get_dep pytest $PYTEST_VERSION) \\\n $(get_dep pytest-xdist $PYTEST_XDIST_VERSION)\nsetup_ccache\n\nif [[ \"$COVERAGE\" == \"true\" ]]; then\n mamba install --verbose -y codecov pytest-cov\nfi\n\nif [[ \"$TEST_DOCSTRINGS\" == \"true\" ]]; then\n # numpydoc requires sphinx\n mamba install --verbose -y sphinx\n mamba install --verbose -y numpydoc\nfi\n\npython --version\n\n# Set parallelism to $N_CORES + 1 to overlap IO bound tasks with CPU bound tasks on CI\n# workers with $N_CORES cores when building the compiled extensions of scikit-learn.\nexport SKLEARN_BUILD_PARALLEL=$(($N_CORES + 1))\n\n# Disable the build isolation and build in the tree so that the same folder can be\n# cached between CI runs.\n# TODO: remove the '--use-feature' flag when made obsolete in pip 21.3.\npip install --verbose --no-build-isolation --use-feature=in-tree-build .\n\n# Report cache usage\nccache -s --verbose\n\nmamba list\n\n# Changing directory not to have module resolution use scikit-learn source\n# directory but to the installed package.\ncd /tmp\npython -c \"import sklearn; sklearn.show_versions()\"\npython -m threadpoolctl --import sklearn\n# Test using as many workers as available cores\npytest --pyargs -n $N_CORES sklearn\n" }, { "path": "build_tools/circle/build_test_pypy.sh", "content": "#!/usr/bin/env bash\nset -x\nset -e\n\n# System build tools\napt-get -yq update\napt-get -yq install wget bzip2 build-essential ccache\n\n# Install pypy and all the scikit-learn dependencies from conda-forge. In\n# particular, we want to install pypy compatible binary packages for numpy and\n# scipy as it would be to costly to build those from source.\nconda install -y mamba\nmamba create -n pypy -y \\\n pypy numpy scipy cython \\\n joblib threadpoolctl pillow pytest \\\n sphinx numpydoc docutils\n\neval \"$(conda shell.bash hook)\"\nconda activate pypy\n\n# Check that we are running PyPy instead of CPython in this environment.\npython --version\nwhich python\npython -c \"import platform; assert platform.python_implementation() == 'PyPy'\"\n\n# Build and install scikit-learn in dev mode\nccache -M 512M\nexport CCACHE_COMPRESS=1\nexport PATH=/usr/lib/ccache:$PATH\nexport LOKY_MAX_CPU_COUNT=\"2\"\nexport OMP_NUM_THREADS=\"1\"\n# Set parallelism to 3 to overlap IO bound tasks with CPU bound tasks on CI\n# workers with 2 cores when building the compiled extensions of scikit-learn.\nexport SKLEARN_BUILD_PARALLEL=3\npip install --no-build-isolation -e .\n\npython -m pytest sklearn\n" }, { "path": "build_tools/circle/checkout_merge_commit.sh", "content": "#!/bin/bash\n\n\n# Add `main` branch to the update list.\n# Otherwise CircleCI will give us a cached one.\nFETCH_REFS=\"+main:main\"\n\n# Update PR refs for testing.\nif [[ -n \"${CIRCLE_PR_NUMBER}\" ]]\nthen\n FETCH_REFS=\"${FETCH_REFS} +refs/pull/${CIRCLE_PR_NUMBER}/head:pr/${CIRCLE_PR_NUMBER}/head\"\n FETCH_REFS=\"${FETCH_REFS} +refs/pull/${CIRCLE_PR_NUMBER}/merge:pr/${CIRCLE_PR_NUMBER}/merge\"\nfi\n\n# Retrieve the refs.\ngit fetch -u origin ${FETCH_REFS}\n\n# Checkout the PR merge ref.\nif [[ -n \"${CIRCLE_PR_NUMBER}\" ]]\nthen\n git checkout -qf \"pr/${CIRCLE_PR_NUMBER}/merge\" || (\n echo Could not fetch merge commit. >&2\n echo There may be conflicts in merging PR \\#${CIRCLE_PR_NUMBER} with main. >&2;\n exit 1)\nfi\n\n# Check for merge conflicts.\nif [[ -n \"${CIRCLE_PR_NUMBER}\" ]]\nthen\n git branch --merged | grep main > /dev/null\n git branch --merged | grep \"pr/${CIRCLE_PR_NUMBER}/head\" > /dev/null\nfi\n" }, { "path": "build_tools/circle/linting.sh", "content": "#!/bin/bash\n\n# This script is used in CircleCI to check that PRs do not add obvious\n# flake8 violations. It relies on two things:\n# - find common ancestor between branch and\n# scikit-learn/scikit-learn remote\n# - run flake8 --diff on the diff between the branch and the common\n# ancestor\n#\n# Additional features:\n# - the line numbers in Travis match the local branch on the PR\n# author machine.\n# - ./build_tools/circle/flake8_diff.sh can be run locally for quick\n# turn-around\n\nset -e\n# pipefail is necessary to propagate exit codes\nset -o pipefail\n\nPROJECT=scikit-learn/scikit-learn\nPROJECT_URL=https://github.com/$PROJECT.git\n\n# Find the remote with the project name (upstream in most cases)\nREMOTE=$(git remote -v | grep $PROJECT | cut -f1 | head -1 || echo '')\n\n# Add a temporary remote if needed. For example this is necessary when\n# Travis is configured to run in a fork. In this case 'origin' is the\n# fork and not the reference repo we want to diff against.\nif [[ -z \"$REMOTE\" ]]; then\n TMP_REMOTE=tmp_reference_upstream\n REMOTE=$TMP_REMOTE\n git remote add $REMOTE $PROJECT_URL\nfi\n\necho \"Remotes:\"\necho '--------------------------------------------------------------------------------'\ngit remote --verbose\n\n# Travis does the git clone with a limited depth (50 at the time of\n# writing). This may not be enough to find the common ancestor with\n# $REMOTE/main so we unshallow the git checkout\nif [[ -a .git/shallow ]]; then\n echo -e '\\nTrying to unshallow the repo:'\n echo '--------------------------------------------------------------------------------'\n git fetch --unshallow\nfi\n\nif [[ \"$TRAVIS\" == \"true\" ]]; then\n if [[ \"$TRAVIS_PULL_REQUEST\" == \"false\" ]]\n then\n # In main repo, using TRAVIS_COMMIT_RANGE to test the commits\n # that were pushed into a branch\n if [[ \"$PROJECT\" == \"$TRAVIS_REPO_SLUG\" ]]; then\n if [[ -z \"$TRAVIS_COMMIT_RANGE\" ]]; then\n echo \"New branch, no commit range from Travis so passing this test by convention\"\n exit 0\n fi\n COMMIT_RANGE=$TRAVIS_COMMIT_RANGE\n fi\n else\n # We want to fetch the code as it is in the PR branch and not\n # the result of the merge into main. This way line numbers\n # reported by Travis will match with the local code.\n LOCAL_BRANCH_REF=travis_pr_$TRAVIS_PULL_REQUEST\n # In Travis the PR target is always origin\n git fetch origin pull/$TRAVIS_PULL_REQUEST/head:refs/$LOCAL_BRANCH_REF\n fi\nfi\n\n# If not using the commit range from Travis we need to find the common\n# ancestor between $LOCAL_BRANCH_REF and $REMOTE/main\nif [[ -z \"$COMMIT_RANGE\" ]]; then\n if [[ -z \"$LOCAL_BRANCH_REF\" ]]; then\n LOCAL_BRANCH_REF=$(git rev-parse --abbrev-ref HEAD)\n fi\n echo -e \"\\nLast 2 commits in $LOCAL_BRANCH_REF:\"\n echo '--------------------------------------------------------------------------------'\n git --no-pager log -2 $LOCAL_BRANCH_REF\n\n REMOTE_MAIN_REF=\"$REMOTE/main\"\n # Make sure that $REMOTE_MAIN_REF is a valid reference\n echo -e \"\\nFetching $REMOTE_MAIN_REF\"\n echo '--------------------------------------------------------------------------------'\n git fetch $REMOTE main:refs/remotes/$REMOTE_MAIN_REF\n LOCAL_BRANCH_SHORT_HASH=$(git rev-parse --short $LOCAL_BRANCH_REF)\n REMOTE_MAIN_SHORT_HASH=$(git rev-parse --short $REMOTE_MAIN_REF)\n\n COMMIT=$(git merge-base $LOCAL_BRANCH_REF $REMOTE_MAIN_REF) || \\\n echo \"No common ancestor found for $(git show $LOCAL_BRANCH_REF -q) and $(git show $REMOTE_MAIN_REF -q)\"\n\n if [ -z \"$COMMIT\" ]; then\n exit 1\n fi\n\n COMMIT_SHORT_HASH=$(git rev-parse --short $COMMIT)\n\n echo -e \"\\nCommon ancestor between $LOCAL_BRANCH_REF ($LOCAL_BRANCH_SHORT_HASH)\"\\\n \"and $REMOTE_MAIN_REF ($REMOTE_MAIN_SHORT_HASH) is $COMMIT_SHORT_HASH:\"\n echo '--------------------------------------------------------------------------------'\n git --no-pager show --no-patch $COMMIT_SHORT_HASH\n\n COMMIT_RANGE=\"$COMMIT_SHORT_HASH..$LOCAL_BRANCH_SHORT_HASH\"\n\n if [[ -n \"$TMP_REMOTE\" ]]; then\n git remote remove $TMP_REMOTE\n fi\n\nelse\n echo \"Got the commit range from Travis: $COMMIT_RANGE\"\nfi\n\necho -e '\\nRunning flake8 on the diff in the range' \"$COMMIT_RANGE\" \\\n \"($(git rev-list $COMMIT_RANGE | wc -l) commit(s)):\"\necho '--------------------------------------------------------------------------------'\n\n# We ignore files from sklearn/externals. Unfortunately there is no\n# way to do it with flake8 directly (the --exclude does not seem to\n# work with --diff). We could use the exclude magic in the git pathspec\n# ':!sklearn/externals' but it is only available on git 1.9 and Travis\n# uses git 1.8.\n# We need the following command to exit with 0 hence the echo in case\n# there is no match\nMODIFIED_FILES=\"$(git diff --name-only $COMMIT_RANGE | grep -v 'sklearn/externals' | \\\n grep -v 'doc/sphinxext' || echo \"no_match\")\"\n\ncheck_files() {\n files=\"$1\"\n shift\n options=\"$*\"\n if [ -n \"$files\" ]; then\n # Conservative approach: diff without context (--unified=0) so that code\n # that was not changed does not create failures\n git diff --unified=0 $COMMIT_RANGE -- $files | flake8 --diff --show-source $options\n fi\n}\n\nif [[ \"$MODIFIED_FILES\" == \"no_match\" ]]; then\n echo \"No file outside sklearn/externals and doc/sphinxext has been modified\"\nelse\n check_files \"$MODIFIED_FILES\"\n # check code for unused imports\n flake8 --exclude=sklearn/externals/ --select=F401 sklearn/ examples/\nfi\necho -e \"No problem detected by flake8\\n\"\n\n# For docstrings and warnings of deprecated attributes to be rendered\n# properly, the property decorator must come before the deprecated decorator\n# (else they are treated as functions)\n\n# do not error when grep -B1 \"@property\" finds nothing\nset +e\nbad_deprecation_property_order=`git grep -A 10 \"@property\" -- \"*.py\" | awk '/@property/,/def /' | grep -B1 \"@deprecated\"`\n\nif [ ! -z \"$bad_deprecation_property_order\" ]\nthen\n echo \"property decorator should come before deprecated decorator\"\n echo \"found the following occurrencies:\"\n echo $bad_deprecation_property_order\n exit 1\nfi\n\n# Check for default doctest directives ELLIPSIS and NORMALIZE_WHITESPACE\n\ndoctest_directive=\"$(git grep -nw -E \"# doctest\\: \\+(ELLIPSIS|NORMALIZE_WHITESPACE)\")\"\n\nif [ ! -z \"$doctest_directive\" ]\nthen\n echo \"ELLIPSIS and NORMALIZE_WHITESPACE doctest directives are enabled by default, but were found in:\"\n echo \"$doctest_directive\"\n exit 1\nfi\n\njoblib_import=\"$(git grep -l -A 10 -E \"joblib import.+delayed\" -- \"*.py\" \":!sklearn/utils/_joblib.py\" \":!sklearn/utils/fixes.py\")\"\n\nif [ ! -z \"$joblib_import\" ]; then\n echo \"Use from sklearn.utils.fixes import delayed instead of joblib delayed. The following files contains imports to joblib.delayed:\"\n echo \"$joblib_import\"\n exit 1\nfi\n" }, { "path": "build_tools/circle/list_versions.py", "content": "#!/usr/bin/env python3\n\n# List all available versions of the documentation\nimport json\nimport re\nimport sys\n\nfrom distutils.version import LooseVersion\nfrom urllib.request import urlopen\n\n\ndef json_urlread(url):\n try:\n return json.loads(urlopen(url).read().decode(\"utf8\"))\n except Exception:\n print(\"Error reading\", url, file=sys.stderr)\n raise\n\n\ndef human_readable_data_quantity(quantity, multiple=1024):\n # https://stackoverflow.com/questions/1094841/reusable-library-to-get-human-readable-version-of-file-size\n if quantity == 0:\n quantity = +0\n SUFFIXES = [\"B\"] + [i + {1000: \"B\", 1024: \"iB\"}[multiple] for i in \"KMGTPEZY\"]\n for suffix in SUFFIXES:\n if quantity < multiple or suffix == SUFFIXES[-1]:\n if suffix == SUFFIXES[0]:\n return \"%d %s\" % (quantity, suffix)\n else:\n return \"%.1f %s\" % (quantity, suffix)\n else:\n quantity /= multiple\n\n\ndef get_file_extension(version):\n if \"dev\" in version:\n # The 'dev' branch should be explicitly handled\n return \"zip\"\n\n current_version = LooseVersion(version)\n min_zip_version = LooseVersion(\"0.24\")\n\n return \"zip\" if current_version >= min_zip_version else \"pdf\"\n\n\ndef get_file_size(version):\n api_url = ROOT_URL + \"%s/_downloads\" % version\n for path_details in json_urlread(api_url):\n file_extension = get_file_extension(version)\n file_path = f\"scikit-learn-docs.{file_extension}\"\n if path_details[\"name\"] == file_path:\n return human_readable_data_quantity(path_details[\"size\"], 1000)\n\n\nprint(\":orphan:\")\nprint()\nheading = \"Available documentation for Scikit-learn\"\nprint(heading)\nprint(\"=\" * len(heading))\nprint()\nprint(\"Web-based documentation is available for versions listed below:\")\nprint()\n\nROOT_URL = (\n \"https://api.github.com/repos/scikit-learn/scikit-learn.github.io/contents/\" # noqa\n)\nRAW_FMT = \"https://raw.githubusercontent.com/scikit-learn/scikit-learn.github.io/master/%s/index.html\" # noqa\nVERSION_RE = re.compile(r\"scikit-learn ([\\w\\.\\-]+) documentation\")\nNAMED_DIRS = [\"dev\", \"stable\"]\n\n# Gather data for each version directory, including symlinks\ndirs = {}\nsymlinks = {}\nroot_listing = json_urlread(ROOT_URL)\nfor path_details in root_listing:\n name = path_details[\"name\"]\n if not (name[:1].isdigit() or name in NAMED_DIRS):\n continue\n if path_details[\"type\"] == \"dir\":\n html = urlopen(RAW_FMT % name).read().decode(\"utf8\")\n version_num = VERSION_RE.search(html).group(1)\n file_size = get_file_size(name)\n dirs[name] = (version_num, file_size)\n\n if path_details[\"type\"] == \"symlink\":\n symlinks[name] = json_urlread(path_details[\"_links\"][\"self\"])[\"target\"]\n\n\n# Symlinks should have same data as target\nfor src, dst in symlinks.items():\n if dst in dirs:\n dirs[src] = dirs[dst]\n\n# Output in order: dev, stable, decreasing other version\nseen = set()\nfor name in NAMED_DIRS + sorted(\n (k for k in dirs if k[:1].isdigit()), key=LooseVersion, reverse=True\n):\n version_num, file_size = dirs[name]\n if version_num in seen:\n # symlink came first\n continue\n else:\n seen.add(version_num)\n name_display = \"\" if name[:1].isdigit() else \" (%s)\" % name\n path = \"https://scikit-learn.org/%s/\" % name\n out = \"* `Scikit-learn %s%s documentation <%s>`_\" % (\n version_num,\n name_display,\n path,\n )\n if file_size is not None:\n file_extension = get_file_extension(version_num)\n out += (\n f\" (`{file_extension.upper()} {file_size} <{path}/\"\n f\"_downloads/scikit-learn-docs.{file_extension}>`_)\"\n )\n print(out)\n" }, { "path": "build_tools/circle/push_doc.sh", "content": "#!/bin/bash\n# This script is meant to be called in the \"deploy\" step defined in\n# circle.yml. See https://circleci.com/docs/ for more details.\n# The behavior of the script is controlled by environment variable defined\n# in the circle.yml in the top level folder of the project.\n\nset -ex\n\nif [ -z $CIRCLE_PROJECT_USERNAME ];\nthen USERNAME=\"sklearn-ci\";\nelse USERNAME=$CIRCLE_PROJECT_USERNAME;\nfi\n\nDOC_REPO=\"scikit-learn.github.io\"\nGENERATED_DOC_DIR=$1\n\nif [[ -z \"$GENERATED_DOC_DIR\" ]]; then\n echo \"Need to pass directory of the generated doc as argument\"\n echo \"Usage: $0 \"\n exit 1\nfi\n\n# Absolute path needed because we use cd further down in this script\nGENERATED_DOC_DIR=$(readlink -f $GENERATED_DOC_DIR)\n\nif [ \"$CIRCLE_BRANCH\" = \"main\" ]\nthen\n dir=dev\nelse\n # Strip off .X\n dir=\"${CIRCLE_BRANCH::-2}\"\nfi\n\nMSG=\"Pushing the docs to $dir/ for branch: $CIRCLE_BRANCH, commit $CIRCLE_SHA1\"\n\ncd $HOME\nif [ ! -d $DOC_REPO ];\nthen git clone --depth 1 --no-checkout \"git@github.com:scikit-learn/\"$DOC_REPO\".git\";\nfi\ncd $DOC_REPO\n\n# check if it's a new branch\n\necho $dir > .git/info/sparse-checkout\nif ! git show HEAD:$dir >/dev/null\nthen\n\t# directory does not exist. Need to make it so sparse checkout works\n\tmkdir $dir\n\ttouch $dir/index.html\n\tgit add $dir\nfi\ngit checkout main\ngit reset --hard origin/main\nif [ -d $dir ]\nthen\n\tgit rm -rf $dir/ && rm -rf $dir/\nfi\ncp -R $GENERATED_DOC_DIR $dir\ngit config user.email \"olivier.grisel+sklearn-ci@gmail.com\"\ngit config user.name $USERNAME\ngit config push.default matching\ngit add -f $dir/\ngit commit -m \"$MSG\" $dir\ngit push\necho $MSG\n" }, { "path": "build_tools/codespell_ignore_words.txt", "content": "aggresive\naline\nba\nbasf\nboun\nbre\ncach\ncomplies\ncoo\ncopys\ndeine\ndidi\nfeld\nfo\nfpr\nfro\nfwe\ngool\nhart\nhist\nines\ninout\nist\njaques\nlinke\nlod\nmape\nmor\nnd\nnmae\nocur\npullrequest\nro\nsoler\nsuh\nsuprised\nte\ntechnic\nteh\nthi\nusal\nvie\nwan\nwinn\nyau\n" }, { "path": "build_tools/generate_authors_table.py", "content": "\"\"\"\nThis script generates an html table of contributors, with names and avatars.\nThe list is generated from scikit-learn's teams on GitHub, plus a small number\nof hard-coded contributors.\n\nThe table should be updated for each new inclusion in the teams.\nGenerating the table requires admin rights.\n\"\"\"\nimport sys\nimport requests\nimport getpass\nimport time\nfrom pathlib import Path\nfrom os import path\n\nprint(\"user:\", file=sys.stderr)\nuser = input()\ntoken = getpass.getpass(\"access token:\\n\")\nauth = (user, token)\n\nLOGO_URL = \"https://avatars2.githubusercontent.com/u/365630?v=4\"\nREPO_FOLDER = Path(path.abspath(__file__)).parent.parent\n\n\ndef get(url):\n for sleep_time in [10, 30, 0]:\n reply = requests.get(url, auth=auth)\n api_limit = (\n \"message\" in reply.json()\n and \"API rate limit exceeded\" in reply.json()[\"message\"]\n )\n if not api_limit:\n break\n print(\"API rate limit exceeded, waiting..\")\n time.sleep(sleep_time)\n\n reply.raise_for_status()\n return reply\n\n\ndef get_contributors():\n \"\"\"Get the list of contributor profiles. Require admin rights.\"\"\"\n # get core devs and triage team\n core_devs = []\n triage_team = []\n comm_team = []\n core_devs_id = 11523\n triage_team_id = 3593183\n comm_team_id = 5368696\n for team_id, lst in zip(\n (core_devs_id, triage_team_id, comm_team_id),\n (core_devs, triage_team, comm_team),\n ):\n for page in [1, 2]: # 30 per page\n reply = get(f\"https://api.github.com/teams/{team_id}/members?page={page}\")\n lst.extend(reply.json())\n\n # get members of scikit-learn on GitHub\n members = []\n for page in [1, 2]: # 30 per page\n reply = get(\n \"https://api.github.com/orgs/scikit-learn/members?page=%d\" % (page,)\n )\n members.extend(reply.json())\n\n # keep only the logins\n core_devs = set(c[\"login\"] for c in core_devs)\n triage_team = set(c[\"login\"] for c in triage_team)\n comm_team = set(c[\"login\"] for c in comm_team)\n members = set(c[\"login\"] for c in members)\n\n # add missing contributors with GitHub accounts\n members |= {\"dubourg\", \"mbrucher\", \"thouis\", \"jarrodmillman\"}\n # add missing contributors without GitHub accounts\n members |= {\"Angel Soler Gollonet\"}\n # remove CI bots\n members -= {\"sklearn-ci\", \"sklearn-lgtm\", \"sklearn-wheels\"}\n triage_team -= core_devs # remove ogrisel from triage_team\n\n emeritus = members - core_devs - triage_team\n\n # get profiles from GitHub\n core_devs = [get_profile(login) for login in core_devs]\n emeritus = [get_profile(login) for login in emeritus]\n triage_team = [get_profile(login) for login in triage_team]\n comm_team = [get_profile(login) for login in comm_team]\n\n # sort by last name\n core_devs = sorted(core_devs, key=key)\n emeritus = sorted(emeritus, key=key)\n triage_team = sorted(triage_team, key=key)\n comm_team = sorted(comm_team, key=key)\n\n return core_devs, emeritus, triage_team, comm_team\n\n\ndef get_profile(login):\n \"\"\"Get the GitHub profile from login\"\"\"\n print(\"get profile for %s\" % (login,))\n try:\n profile = get(\"https://api.github.com/users/%s\" % login).json()\n except requests.exceptions.HTTPError:\n return dict(name=login, avatar_url=LOGO_URL, html_url=\"\")\n\n if profile[\"name\"] is None:\n profile[\"name\"] = profile[\"login\"]\n\n # fix missing names\n missing_names = {\n \"bthirion\": \"Bertrand Thirion\",\n \"dubourg\": \"Vincent Dubourg\",\n \"Duchesnay\": \"Edouard Duchesnay\",\n \"Lars\": \"Lars Buitinck\",\n \"MechCoder\": \"Manoj Kumar\",\n }\n if profile[\"name\"] in missing_names:\n profile[\"name\"] = missing_names[profile[\"name\"]]\n\n return profile\n\n\ndef key(profile):\n \"\"\"Get a sorting key based on the lower case last name, then firstname\"\"\"\n components = profile[\"name\"].lower().split(\" \")\n return \" \".join([components[-1]] + components[:-1])\n\n\ndef generate_table(contributors):\n lines = [\n \".. raw :: html\\n\",\n \" \",\n '
',\n \" \",\n ]\n for contributor in contributors:\n lines.append(\"
\")\n lines.append(\n \"
\"\n % (contributor[\"html_url\"], contributor[\"avatar_url\"])\n )\n lines.append(\"

%s

\" % (contributor[\"name\"],))\n lines.append(\"
\")\n lines.append(\"
\")\n return \"\\n\".join(lines)\n\n\ndef generate_list(contributors):\n lines = []\n for contributor in contributors:\n lines.append(\"- %s\" % (contributor[\"name\"],))\n return \"\\n\".join(lines)\n\n\nif __name__ == \"__main__\":\n\n core_devs, emeritus, triage_team, comm_team = get_contributors()\n\n with open(REPO_FOLDER / \"doc\" / \"authors.rst\", \"w+\") as rst_file:\n rst_file.write(generate_table(core_devs))\n\n with open(REPO_FOLDER / \"doc\" / \"authors_emeritus.rst\", \"w+\") as rst_file:\n rst_file.write(generate_list(emeritus))\n\n with open(REPO_FOLDER / \"doc\" / \"triage_team.rst\", \"w+\") as rst_file:\n rst_file.write(generate_table(triage_team))\n\n with open(REPO_FOLDER / \"doc\" / \"communication_team.rst\", \"w+\") as rst_file:\n rst_file.write(generate_table(comm_team))\n" }, { "path": "build_tools/github/Windows", "content": "# Get the Python version of the base image from a build argument\nARG PYTHON_VERSION\nFROM winamd64/python:$PYTHON_VERSION-windowsservercore\n\nARG WHEEL_NAME\nARG CONFTEST_NAME\nARG CIBW_TEST_REQUIRES\n\n# Copy and install the Windows wheel\nCOPY $WHEEL_NAME $WHEEL_NAME\nCOPY $CONFTEST_NAME $CONFTEST_NAME\nRUN pip install $env:WHEEL_NAME\n\n# Install the testing dependencies\nRUN pip install $env:CIBW_TEST_REQUIRES.split(\" \")\n" }, { "path": "build_tools/github/build_minimal_windows_image.sh", "content": "#!/bin/bash\n\nset -e\nset -x\n\nPYTHON_VERSION=$1\nBITNESS=$2\n\nif [[ \"$BITNESS\" == \"32\" ]]; then\n # 32-bit architectures are not supported\n # by the official Docker images: Tests will just be run\n # on the host (instead of the minimal Docker container).\n exit 0\nfi\n\nTEMP_FOLDER=\"$HOME/AppData/Local/Temp\"\nWHEEL_PATH=$(ls -d $TEMP_FOLDER/*/repaired_wheel/*)\nWHEEL_NAME=$(basename $WHEEL_PATH)\n\ncp $WHEEL_PATH $WHEEL_NAME\n\n# Dot the Python version for identyfing the base Docker image\nPYTHON_VERSION=$(echo ${PYTHON_VERSION:0:1}.${PYTHON_VERSION:1:2})\n\n# Build a minimal Windows Docker image for testing the wheels\ndocker build --build-arg PYTHON_VERSION=$PYTHON_VERSION \\\n --build-arg WHEEL_NAME=$WHEEL_NAME \\\n --build-arg CONFTEST_NAME=$CONFTEST_NAME \\\n --build-arg CIBW_TEST_REQUIRES=\"$CIBW_TEST_REQUIRES\" \\\n -f build_tools/github/Windows \\\n -t scikit-learn/minimal-windows .\n" }, { "path": "build_tools/github/build_source.sh", "content": "#!/bin/bash\n\nset -e\nset -x\n\n# Move up two levels to create the virtual\n# environment outside of the source folder\ncd ../../\n\npython -m venv build_env\nsource build_env/bin/activate\n\npython -m pip install numpy scipy cython\npython -m pip install twine\n\ncd scikit-learn/scikit-learn\npython setup.py sdist\n\n# Check whether the source distribution will render correctly\ntwine check dist/*.tar.gz\n" }, { "path": "build_tools/github/build_wheels.sh", "content": "#!/bin/bash\n\nset -e\nset -x\n\n# OpenMP is not present on macOS by default\nif [[ \"$RUNNER_OS\" == \"macOS\" ]]; then\n # Make sure to use a libomp version binary compatible with the oldest\n # supported version of the macos SDK as libomp will be vendored into the\n # scikit-learn wheels for macos. The list of binaries are in\n # https://packages.macports.org/libomp/. Currently, the oldest\n # supported macos version is: High Sierra / 10.13. When upgrading this, be\n # sure to update the MACOSX_DEPLOYMENT_TARGET environment variable in\n # wheels.yml accordingly. Note that Darwin_17 == High Sierra / 10.13.\n wget https://packages.macports.org/libomp/libomp-11.0.1_0+universal.darwin_17.i386-x86_64.tbz2 -O libomp.tbz2\n sudo tar -C / -xvjf libomp.tbz2 opt\n\n export CC=/usr/bin/clang\n export CXX=/usr/bin/clang++\n export CPPFLAGS=\"$CPPFLAGS -Xpreprocessor -fopenmp\"\n export CFLAGS=\"$CFLAGS -I/opt/local/include/libomp\"\n export CXXFLAGS=\"$CXXFLAGS -I/opt/local/include/libomp\"\n export LDFLAGS=\"$LDFLAGS -Wl,-rpath,/opt/local/lib/libomp -L/opt/local/lib/libomp -lomp\"\nfi\n\n# The version of the built dependencies are specified\n# in the pyproject.toml file, while the tests are run\n# against the most recent version of the dependencies\n\npython -m pip install cibuildwheel\npython -m cibuildwheel --output-dir wheelhouse\n" }, { "path": "build_tools/github/check_build_trigger.sh", "content": "#!/bin/bash\n\nset -e\nset -x\n\nCOMMIT_MSG=$(git log --no-merges -1 --oneline)\n\n# The commit marker \"[cd build]\" will trigger the build when required\nif [[ \"$GITHUB_EVENT_NAME\" == schedule ||\n \"$COMMIT_MSG\" =~ \\[cd\\ build\\] ]]; then\n echo \"::set-output name=build::true\"\nfi\n" }, { "path": "build_tools/github/check_wheels.py", "content": "\"\"\"Checks that dist/* contains the number of wheels built from the\n.github/workflows/wheels.yml config.\"\"\"\nimport yaml\nfrom pathlib import Path\nimport sys\n\ngh_wheel_path = Path.cwd() / \".github\" / \"workflows\" / \"wheels.yml\"\nwith gh_wheel_path.open(\"r\") as f:\n wheel_config = yaml.safe_load(f)\n\nbuild_matrix = wheel_config[\"jobs\"][\"build_wheels\"][\"strategy\"][\"matrix\"]\nn_python_versions = len(build_matrix[\"python\"])\n\n# For each python version we have: 7 wheels\n# 1 osx wheel (x86_64)\n# 4 linux wheel (i686 + x86_64) * (manylinux1 + manylinux2010)\n# 2 windows wheel (win32 + wind_amd64)\nn_wheels = 7 * n_python_versions\n\n# plus one more for the sdist\nn_wheels += 1\n\n# aarch64 builds from travis\ntravis_config_path = Path.cwd() / \".travis.yml\"\nwith travis_config_path.open(\"r\") as f:\n travis_config = yaml.safe_load(f)\n\njobs = travis_config[\"jobs\"][\"include\"]\ntravis_builds = [j for j in jobs if any(\"CIBW_BUILD\" in env for env in j[\"env\"])]\nn_wheels += len(travis_builds)\n\ndist_files = list(Path(\"dist\").glob(\"**/*\"))\nn_dist_files = len(dist_files)\n\nif n_dist_files != n_wheels:\n print(\n f\"Expected {n_wheels} wheels in dist/* but \"\n f\"got {n_dist_files} artifacts instead.\"\n )\n sys.exit(1)\n\nprint(f\"dist/* has the expected {n_wheels} wheels:\")\nprint(\"\\n\".join(file.name for file in dist_files))\n" }, { "path": "build_tools/github/repair_windows_wheels.sh", "content": "#!/bin/bash\n\nset -e\nset -x\n\nWHEEL=$1\nDEST_DIR=$2\nBITNESS=$3\n\n# By default, the Windows wheels are not repaired.\n# In this case, we need to vendor VCRUNTIME140.dll\nwheel unpack \"$WHEEL\"\nWHEEL_DIRNAME=$(ls -d scikit_learn-*)\npython build_tools/github/vendor.py \"$WHEEL_DIRNAME\" \"$BITNESS\"\nwheel pack \"$WHEEL_DIRNAME\" -d \"$DEST_DIR\"\nrm -rf \"$WHEEL_DIRNAME\"\n" }, { "path": "build_tools/github/test_source.sh", "content": "#!/bin/bash\n\nset -e\nset -x\n\ncd ../../\n\npython -m venv test_env\nsource test_env/bin/activate\n\npython -m pip install scikit-learn/scikit-learn/dist/*.tar.gz\npython -m pip install pytest pandas\n\n# Run the tests on the installed source distribution\nmkdir tmp_for_test\ncp scikit-learn/scikit-learn/conftest.py tmp_for_test\ncd tmp_for_test\n\npytest --pyargs sklearn\n" }, { "path": "build_tools/github/test_wheels.sh", "content": "#!/bin/bash\n\nset -e\nset -x\n\nif [[ \"$OSTYPE\" != \"linux-gnu\" ]]; then\n # The Linux test environment is run in a Docker container and\n # it is not possible to copy the test configuration file (yet)\n cp $CONFTEST_PATH $CONFTEST_NAME\nfi\n\n# Test that there are no links to system libraries in the\n# threadpoolctl output section of the show_versions output:\npython -c \"import sklearn; sklearn.show_versions()\"\npytest --pyargs sklearn\n" }, { "path": "build_tools/github/test_windows_wheels.sh", "content": "#!/bin/bash\n\nset -e\nset -x\n\nPYTHON_VERSION=$1\nBITNESS=$2\n\nif [[ \"$BITNESS\" == \"32\" ]]; then\n # 32-bit architectures use the regular\n # test command (outside of the minimal Docker container)\n cp $CONFTEST_PATH $CONFTEST_NAME\n python -c \"import sklearn; sklearn.show_versions()\"\n pytest --pyargs sklearn\nelse\n docker container run \\\n --rm scikit-learn/minimal-windows \\\n powershell -Command \"python -c 'import sklearn; sklearn.show_versions()'\"\n\n docker container run \\\n -e SKLEARN_SKIP_NETWORK_TESTS=1 \\\n -e OMP_NUM_THREADS=2 \\\n -e OPENBLAS_NUM_THREADS=2 \\\n --rm scikit-learn/minimal-windows \\\n powershell -Command \"pytest --pyargs sklearn\"\nfi\n" }, { "path": "build_tools/github/upload_anaconda.sh", "content": "#!/bin/bash\n\nset -e\nset -x\n\nif [ \"$GITHUB_EVENT_NAME\" == \"schedule\" ]; then\n ANACONDA_ORG=\"scipy-wheels-nightly\"\n ANACONDA_TOKEN=\"$SCIKIT_LEARN_NIGHTLY_UPLOAD_TOKEN\"\nelse\n ANACONDA_ORG=\"scikit-learn-wheels-staging\"\n ANACONDA_TOKEN=\"$SCIKIT_LEARN_STAGING_UPLOAD_TOKEN\"\nfi\n\n# Install Python 3.8 because of a bug with Python 3.9\nexport PATH=$CONDA/bin:$PATH\nconda create -n upload -y python=3.8\nsource activate upload\nconda install -y anaconda-client\n\n# Force a replacement if the remote file already exists\nanaconda -t $ANACONDA_TOKEN upload --force -u $ANACONDA_ORG dist/artifact/*\necho \"Index: https://pypi.anaconda.org/$ANACONDA_ORG/simple\"\n" }, { "path": "build_tools/github/vendor.py", "content": "\"\"\"Embed vcomp140.dll, vcruntime140.dll and vcruntime140_1.dll.\n\nNote that vcruntime140_1.dll is only required (and available)\nfor 64-bit architectures.\n\"\"\"\n\n\nimport os\nimport os.path as op\nimport shutil\nimport sys\nimport textwrap\n\n\nTARGET_FOLDER = op.join(\"sklearn\", \".libs\")\nDISTRIBUTOR_INIT = op.join(\"sklearn\", \"_distributor_init.py\")\nVCOMP140_SRC_PATH = \"C:\\\\Windows\\\\System32\\\\vcomp140.dll\"\nVCRUNTIME140_SRC_PATH = \"C:\\\\Windows\\\\System32\\\\vcruntime140.dll\"\nVCRUNTIME140_1_SRC_PATH = \"C:\\\\Windows\\\\System32\\\\vcruntime140_1.dll\"\n\n\ndef make_distributor_init_32_bits(\n distributor_init, vcomp140_dll_filename, vcruntime140_dll_filename\n):\n \"\"\"Create a _distributor_init.py file for 32-bit architectures.\n\n This file is imported first when importing the sklearn package\n so as to pre-load the vendored vcomp140.dll and vcruntime140.dll.\n \"\"\"\n with open(distributor_init, \"wt\") as f:\n f.write(\n textwrap.dedent(\n \"\"\"\n '''Helper to preload vcomp140.dll and vcruntime140.dll to\n prevent \"not found\" errors.\n\n Once vcomp140.dll and vcruntime140.dll are preloaded, the\n namespace is made available to any subsequent vcomp140.dll\n and vcruntime140.dll. This is created as part of the scripts\n that build the wheel.\n '''\n\n\n import os\n import os.path as op\n from ctypes import WinDLL\n\n\n if os.name == \"nt\":\n # Load vcomp140.dll and vcruntime140.dll\n libs_path = op.join(op.dirname(__file__), \".libs\")\n vcomp140_dll_filename = op.join(libs_path, \"{0}\")\n vcruntime140_dll_filename = op.join(libs_path, \"{1}\")\n WinDLL(op.abspath(vcomp140_dll_filename))\n WinDLL(op.abspath(vcruntime140_dll_filename))\n \"\"\".format(\n vcomp140_dll_filename, vcruntime140_dll_filename\n )\n )\n )\n\n\ndef make_distributor_init_64_bits(\n distributor_init,\n vcomp140_dll_filename,\n vcruntime140_dll_filename,\n vcruntime140_1_dll_filename,\n):\n \"\"\"Create a _distributor_init.py file for 64-bit architectures.\n\n This file is imported first when importing the sklearn package\n so as to pre-load the vendored vcomp140.dll, vcruntime140.dll\n and vcruntime140_1.dll.\n \"\"\"\n with open(distributor_init, \"wt\") as f:\n f.write(\n textwrap.dedent(\n \"\"\"\n '''Helper to preload vcomp140.dll, vcruntime140.dll and\n vcruntime140_1.dll to prevent \"not found\" errors.\n\n Once vcomp140.dll, vcruntime140.dll and vcruntime140_1.dll are\n preloaded, the namespace is made available to any subsequent\n vcomp140.dll, vcruntime140.dll and vcruntime140_1.dll. This is\n created as part of the scripts that build the wheel.\n '''\n\n\n import os\n import os.path as op\n from ctypes import WinDLL\n\n\n if os.name == \"nt\":\n # Load vcomp140.dll, vcruntime140.dll and vcruntime140_1.dll\n libs_path = op.join(op.dirname(__file__), \".libs\")\n vcomp140_dll_filename = op.join(libs_path, \"{0}\")\n vcruntime140_dll_filename = op.join(libs_path, \"{1}\")\n vcruntime140_1_dll_filename = op.join(libs_path, \"{2}\")\n WinDLL(op.abspath(vcomp140_dll_filename))\n WinDLL(op.abspath(vcruntime140_dll_filename))\n WinDLL(op.abspath(vcruntime140_1_dll_filename))\n \"\"\".format(\n vcomp140_dll_filename,\n vcruntime140_dll_filename,\n vcruntime140_1_dll_filename,\n )\n )\n )\n\n\ndef main(wheel_dirname, bitness):\n \"\"\"Embed vcomp140.dll, vcruntime140.dll and vcruntime140_1.dll.\"\"\"\n if not op.exists(VCOMP140_SRC_PATH):\n raise ValueError(f\"Could not find {VCOMP140_SRC_PATH}.\")\n\n if not op.exists(VCRUNTIME140_SRC_PATH):\n raise ValueError(f\"Could not find {VCRUNTIME140_SRC_PATH}.\")\n\n if not op.exists(VCRUNTIME140_1_SRC_PATH) and bitness == \"64\":\n raise ValueError(f\"Could not find {VCRUNTIME140_1_SRC_PATH}.\")\n\n if not op.isdir(wheel_dirname):\n raise RuntimeError(f\"Could not find {wheel_dirname} file.\")\n\n vcomp140_dll_filename = op.basename(VCOMP140_SRC_PATH)\n vcruntime140_dll_filename = op.basename(VCRUNTIME140_SRC_PATH)\n vcruntime140_1_dll_filename = op.basename(VCRUNTIME140_1_SRC_PATH)\n\n target_folder = op.join(wheel_dirname, TARGET_FOLDER)\n distributor_init = op.join(wheel_dirname, DISTRIBUTOR_INIT)\n\n # Create the \"sklearn/.libs\" subfolder\n if not op.exists(target_folder):\n os.mkdir(target_folder)\n\n print(f\"Copying {VCOMP140_SRC_PATH} to {target_folder}.\")\n shutil.copy2(VCOMP140_SRC_PATH, target_folder)\n\n print(f\"Copying {VCRUNTIME140_SRC_PATH} to {target_folder}.\")\n shutil.copy2(VCRUNTIME140_SRC_PATH, target_folder)\n\n if bitness == \"64\":\n print(f\"Copying {VCRUNTIME140_1_SRC_PATH} to {target_folder}.\")\n shutil.copy2(VCRUNTIME140_1_SRC_PATH, target_folder)\n\n # Generate the _distributor_init file in the source tree\n print(\"Generating the '_distributor_init.py' file.\")\n if bitness == \"32\":\n make_distributor_init_32_bits(\n distributor_init, vcomp140_dll_filename, vcruntime140_dll_filename\n )\n else:\n make_distributor_init_64_bits(\n distributor_init,\n vcomp140_dll_filename,\n vcruntime140_dll_filename,\n vcruntime140_1_dll_filename,\n )\n\n\nif __name__ == \"__main__\":\n _, wheel_file, bitness = sys.argv\n main(wheel_file, bitness)\n" }, { "path": "build_tools/shared.sh", "content": "get_dep() {\n package=\"$1\"\n version=\"$2\"\n if [[ \"$version\" == \"none\" ]]; then\n # do not install with none\n echo\n elif [[ \"${version%%[^0-9.]*}\" ]]; then\n # version number is explicitly passed\n echo \"$package==$version\"\n elif [[ \"$version\" == \"latest\" ]]; then\n # use latest\n echo \"$package\"\n elif [[ \"$version\" == \"min\" ]]; then\n echo \"$package==$(python sklearn/_min_dependencies.py $package)\"\n fi\n}\n" }, { "path": "build_tools/travis/after_success.sh", "content": "#!/bin/bash\n\n# This script is meant to be called by the \"after_success\" step\n# defined in \".travis.yml\". In particular, we upload the wheels\n# of the ARM64 architecture for the continuous deployment jobs.\n\nset -e\n\n# The wheels cannot be uploaded on PRs\nif [[ $BUILD_WHEEL == true && $TRAVIS_EVENT_TYPE != pull_request ]]; then\n # Nightly upload token and staging upload token are set in\n # Travis settings (originally generated at Anaconda cloud)\n if [[ $TRAVIS_EVENT_TYPE == cron ]]; then\n ANACONDA_ORG=\"scipy-wheels-nightly\"\n ANACONDA_TOKEN=\"$SCIKIT_LEARN_NIGHTLY_UPLOAD_TOKEN\"\n else\n ANACONDA_ORG=\"scikit-learn-wheels-staging\"\n ANACONDA_TOKEN=\"$SCIKIT_LEARN_STAGING_UPLOAD_TOKEN\"\n fi\n\n MINICONDA_URL=\"https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-aarch64.sh\"\n wget $MINICONDA_URL -O miniconda.sh\n MINICONDA_PATH=$HOME/miniconda\n chmod +x miniconda.sh && ./miniconda.sh -b -p $MINICONDA_PATH\n\n # Install Python 3.8 because of a bug with Python 3.9\n export PATH=$MINICONDA_PATH/bin:$PATH\n conda create -n upload -y python=3.8\n source activate upload\n conda install -y anaconda-client\n\n # Force a replacement if the remote file already exists\n anaconda -t $ANACONDA_TOKEN upload --force -u $ANACONDA_ORG wheelhouse/*.whl\n echo \"Index: https://pypi.anaconda.org/$ANACONDA_ORG/simple\"\nfi\n" }, { "path": "build_tools/travis/install.sh", "content": "#!/bin/bash\n\n# This script is meant to be called by the \"install\" step\n# defined in the \".travis.yml\" file. In particular, it is\n# important that we call to the right installation script.\n\nif [[ $BUILD_WHEEL == true ]]; then\n source build_tools/travis/install_wheels.sh || travis_terminate 1\nelse\n source build_tools/travis/install_main.sh || travis_terminate 1\nfi\n" }, { "path": "build_tools/travis/install_main.sh", "content": "#!/bin/bash\n\n# Travis clone \"scikit-learn/scikit-learn\" repository into\n# a local repository. We use a cached directory with three\n# scikit-learn repositories (one for each matrix entry for\n# non continuous deployment jobs) from which we pull local\n# Travis repository. This allows us to keep build artifact\n# for GCC + Cython, and gain time.\n\nset -e\n\necho \"CPU Arch: $TRAVIS_CPU_ARCH.\"\n\n# Import \"get_dep\"\nsource build_tools/shared.sh\n\necho \"List files from cached directories.\"\necho \"pip:\"\nls $HOME/.cache/pip\n\nexport CC=/usr/lib/ccache/gcc\nexport CXX=/usr/lib/ccache/g++\n\n# Useful for debugging how ccache is used\n# export CCACHE_LOGFILE=/tmp/ccache.log\n\n# 60MB are (more or less) used by .ccache, when\n# compiling from scratch at the time of writing\nccache --max-size 100M --show-stats\n\n# Deactivate the default virtual environment\n# to setup a conda-based environment instead\ndeactivate\n\nMINICONDA_URL=\"https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-aarch64.sh\"\n\n# Install Miniconda\nwget $MINICONDA_URL -O miniconda.sh\nMINICONDA_PATH=$HOME/miniconda\nchmod +x miniconda.sh && ./miniconda.sh -b -p $MINICONDA_PATH\nexport PATH=$MINICONDA_PATH/bin:$PATH\nconda update --yes conda\n\n# Create environment and install dependencies\nconda create -n testenv --yes python=3.7\n\nsource activate testenv\nconda install -y scipy numpy pandas cython\npip install joblib threadpoolctl\n\npip install $(get_dep pytest $PYTEST_VERSION) pytest-xdist\n\n# Build scikit-learn in this script to collapse the\n# verbose build output in the Travis output when it\n# succeeds\npython --version\npython -c \"import numpy; print(f'numpy {numpy.__version__}')\"\npython -c \"import scipy; print(f'scipy {scipy.__version__}')\"\n\npip install -e .\npython setup.py develop\n\nccache --show-stats\n\n# Useful for debugging how ccache is used\n# cat $CCACHE_LOGFILE\n" }, { "path": "build_tools/travis/install_wheels.sh", "content": "#!/bin/bash\n\npython -m pip install cibuildwheel || travis_terminate $?\npython -m cibuildwheel --output-dir wheelhouse || travis_terminate $?\n" }, { "path": "build_tools/travis/script.sh", "content": "#!/bin/bash\n\n# This script is meant to be called by the \"script\" step defined\n# in the \".travis.yml\" file. While this step is forbidden by the\n# continuous deployment jobs, we have to execute the scripts for\n# testing the continuous integration jobs.\n\nif [[ $BUILD_WHEEL != true ]]; then\n # This trick will make Travis terminate the continuation of the pipeline\n bash build_tools/travis/test_script.sh || travis_terminate 1\n bash build_tools/travis/test_docs.sh || travis_terminate 1\nfi\n" }, { "path": "build_tools/travis/test_docs.sh", "content": "#!/bin/bash\n\nset -e\n\nif [[ $TRAVIS_CPU_ARCH != arm64 ]]; then\n # Faster run of the documentation tests\n PYTEST=\"pytest -n $CPU_COUNT\" make test-doc\nfi\n" }, { "path": "build_tools/travis/test_script.sh", "content": "#!/bin/bash\n\nset -e\n\npython --version\npython -c \"import numpy; print(f'numpy {numpy.__version__}')\"\npython -c \"import scipy; print(f'scipy {scipy.__version__}')\"\npython -c \"\\\ntry:\n import pandas\n print(f'pandas {pandas.__version__}')\nexcept ImportError:\n pass\n\"\npython -c \"import joblib; print(f'{joblib.cpu_count()} CPUs')\"\npython -c \"import platform; print(f'{platform.machine()}')\"\n\nTEST_CMD=\"pytest --showlocals --durations=20 --pyargs\"\n\n# Run the tests on the installed version\nmkdir -p $TEST_DIR\n\n# Copy \"setup.cfg\" for the test settings\ncp setup.cfg $TEST_DIR\ncd $TEST_DIR\n\nif [[ $TRAVIS_CPU_ARCH == arm64 ]]; then\n # Faster run of the source code tests\n TEST_CMD=\"$TEST_CMD -n $CPU_COUNT\"\n\n # Remove the option to test the docstring\n sed -i -e 's/--doctest-modules//g' setup.cfg\nfi\n\nif [[ -n $CHECK_WARNINGS ]]; then\n TEST_CMD=\"$TEST_CMD -Werror::DeprecationWarning -Werror::FutureWarning\"\nfi\n\n$TEST_CMD sklearn\n" }, { "path": "build_tools/travis/test_wheels.sh", "content": "#!/bin/bash\n\npip install --upgrade pip || travis_terminate $?\npip install pytest pytest-xdist || travis_terminate $?\n\n# Test that there are no links to system libraries in the threadpoolctl\n# section of the show_versions output.\npython -c \"import sklearn; sklearn.show_versions()\" || travis_terminate $?\npython -m pytest -n $CPU_COUNT --pyargs sklearn || travis_terminate $?\n" }, { "path": "conftest.py", "content": "# Even if empty this file is useful so that when running from the root folder\n# ./sklearn is added to sys.path by pytest. See\n# https://docs.pytest.org/en/latest/explanation/pythonpath.html for more\n# details. For example, this allows to build extensions in place and run pytest\n# doc/modules/clustering.rst and use sklearn from the local folder rather than\n# the one from site-packages.\n" }, { "path": "doc/Makefile", "content": "# Makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line.\nSPHINXOPTS = -j auto\nSPHINXBUILD ?= sphinx-build\nPAPER =\nBUILDDIR = _build\nifneq ($(EXAMPLES_PATTERN),)\n EXAMPLES_PATTERN_OPTS := -D sphinx_gallery_conf.filename_pattern=\"$(EXAMPLES_PATTERN)\"\nendif\n\n# Internal variables.\nPAPEROPT_a4 = -D latex_paper_size=a4\nPAPEROPT_letter = -D latex_paper_size=letter\nALLSPHINXOPTS = -T -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS)\\\n $(EXAMPLES_PATTERN_OPTS) .\n\n\n.PHONY: help clean html dirhtml ziphtml pickle json latex latexpdf changes linkcheck doctest optipng\n\nall: html-noplot\n\nhelp:\n\t@echo \"Please use \\`make ' where is one of\"\n\t@echo \" html to make standalone HTML files\"\n\t@echo \" dirhtml to make HTML files named index.html in directories\"\n\t@echo \" ziphtml to make a ZIP of the HTML\"\n\t@echo \" pickle to make pickle files\"\n\t@echo \" json to make JSON files\"\n\t@echo \" latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter\"\n\t@echo \" latexpdf to make LaTeX files and run them through pdflatex\"\n\t@echo \" changes to make an overview of all changed/added/deprecated items\"\n\t@echo \" linkcheck to check all external links for integrity\"\n\t@echo \" doctest to run all doctests embedded in the documentation (if enabled)\"\n\nclean:\n\t-rm -rf $(BUILDDIR)/*\n\t-rm -rf auto_examples/\n\t-rm -rf generated/*\n\t-rm -rf modules/generated/\n\nhtml:\n\t# These two lines make the build a bit more lengthy, and the\n\t# the embedding of images more robust\n\trm -rf $(BUILDDIR)/html/_images\n\t#rm -rf _build/doctrees/\n\t$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html/stable\n\t@echo\n\t@echo \"Build finished. The HTML pages are in $(BUILDDIR)/html/stable\"\n\nhtml-noplot:\n\t$(SPHINXBUILD) -D plot_gallery=0 -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html/stable\n\t@echo\n\t@echo \"Build finished. The HTML pages are in $(BUILDDIR)/html/stable.\"\n\ndirhtml:\n\t$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml\n\t@echo\n\t@echo \"Build finished. The HTML pages are in $(BUILDDIR)/dirhtml.\"\n\nziphtml:\n\t@if [ ! -d \"$(BUILDDIR)/html/stable/\" ]; then \\\n\t\tmake html; \\\n\tfi\n\t# Optimize the images to reduce the size of the ZIP\n\toptipng $(BUILDDIR)/html/stable/_images/*.png\n\t# Exclude the output directory to avoid infinity recursion\n\tcd $(BUILDDIR)/html/stable; \\\n\tzip -q -x _downloads \\\n\t -r _downloads/scikit-learn-docs.zip .\n\t@echo\n\t@echo \"Build finished. The ZIP of the HTML is in $(BUILDDIR)/html/stable/_downloads.\"\n\npickle:\n\t$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle\n\t@echo\n\t@echo \"Build finished; now you can process the pickle files.\"\n\njson:\n\t$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json\n\t@echo\n\t@echo \"Build finished; now you can process the JSON files.\"\n\nlatex:\n\t$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex\n\t@echo\n\t@echo \"Build finished; the LaTeX files are in $(BUILDDIR)/latex.\"\n\t@echo \"Run \\`make' in that directory to run these through (pdf)latex\" \\\n\t \"(use \\`make latexpdf' here to do that automatically).\"\n\nlatexpdf:\n\t$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex\n\t@echo \"Running LaTeX files through pdflatex...\"\n\tmake -C $(BUILDDIR)/latex all-pdf\n\t@echo \"pdflatex finished; the PDF files are in $(BUILDDIR)/latex.\"\n\nchanges:\n\t$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes\n\t@echo\n\t@echo \"The overview file is in $(BUILDDIR)/changes.\"\n\nlinkcheck:\n\t$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck\n\t@echo\n\t@echo \"Link check complete; look for any errors in the above output \" \\\n\t \"or in $(BUILDDIR)/linkcheck/output.txt.\"\n\ndoctest:\n\t$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest\n\t@echo \"Testing of doctests in the sources finished, look at the \" \\\n\t \"results in $(BUILDDIR)/doctest/output.txt.\"\n\ndownload-data:\n\tpython -c \"from sklearn.datasets._lfw import _check_fetch_lfw; _check_fetch_lfw()\"\n\n# Optimize PNG files. Needs OptiPNG. Change the -P argument to the number of\n# cores you have available, so -P 64 if you have a real computer ;)\noptipng:\n\tfind _build auto_examples */generated -name '*.png' -print0 \\\n\t | xargs -0 -n 1 -P 4 optipng -o10\n\ndist: html ziphtml\n" }, { "path": "doc/README.md", "content": "# Documentation for scikit-learn\n\nThis directory contains the full manual and website as displayed at\nhttp://scikit-learn.org. See\nhttp://scikit-learn.org/dev/developers/contributing.html#documentation for\ndetailed information about the documentation. \n" }, { "path": "doc/about.rst", "content": ".. _about:\n\nAbout us\n========\n\nHistory\n-------\n\nThis project was started in 2007 as a Google Summer of Code project by\nDavid Cournapeau. Later that year, Matthieu Brucher started work on\nthis project as part of his thesis.\n\nIn 2010 Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort and Vincent\nMichel of INRIA took leadership of the project and made the first public\nrelease, February the 1st 2010. Since then, several releases have appeared\nfollowing a ~ 3-month cycle, and a thriving international community has\nbeen leading the development.\n\nGovernance\n----------\n\nThe decision making process and governance structure of scikit-learn is laid\nout in the :ref:`governance document `.\n\nAuthors\n-------\n\nThe following people are currently core contributors to scikit-learn's development\nand maintenance:\n\n.. include:: authors.rst\n\nPlease do not email the authors directly to ask for assistance or report issues.\nInstead, please see `What's the best way to ask questions about scikit-learn\n`_\nin the FAQ.\n\n.. seealso::\n\n :ref:`How you can contribute to the project `\n\nTriage Team\n-----------\n\nThe following people are active contributors who also help with\n:ref:`triaging issues `, PRs, and general\nmaintenance:\n\n.. include:: triage_team.rst\n\nCommunication Team\n------------------\n\nThe following people help with :ref:`communication around scikit-learn\n`.\n\n.. include:: communication_team.rst\n\n\nEmeritus Core Developers\n------------------------\n\nThe following people have been active contributors in the past, but are no\nlonger active in the project:\n\n.. include:: authors_emeritus.rst\n\n\n.. _citing-scikit-learn:\n\nCiting scikit-learn\n-------------------\n\nIf you use scikit-learn in a scientific publication, we would appreciate\ncitations to the following paper:\n\n `Scikit-learn: Machine Learning in Python\n `_, Pedregosa\n *et al.*, JMLR 12, pp. 2825-2830, 2011.\n\n Bibtex entry::\n\n @article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n }\n\nIf you want to cite scikit-learn for its API or design, you may also want to consider the\nfollowing paper:\n\n :arxiv:`API design for machine learning software: experiences from the scikit-learn\n project <1309.0238>`, Buitinck *et al.*, 2013.\n\n Bibtex entry::\n\n @inproceedings{sklearn_api,\n author = {Lars Buitinck and Gilles Louppe and Mathieu Blondel and\n Fabian Pedregosa and Andreas Mueller and Olivier Grisel and\n Vlad Niculae and Peter Prettenhofer and Alexandre Gramfort\n and Jaques Grobler and Robert Layton and Jake VanderPlas and\n Arnaud Joly and Brian Holt and Ga{\\\"{e}}l Varoquaux},\n title = {{API} design for machine learning software: experiences from the scikit-learn\n project},\n booktitle = {ECML PKDD Workshop: Languages for Data Mining and Machine Learning},\n year = {2013},\n pages = {108--122},\n }\n\nArtwork\n-------\n\nHigh quality PNG and SVG logos are available in the `doc/logos/\n`_\nsource directory.\n\n.. image:: images/scikit-learn-logo-notext.png\n :align: center\n\nFunding\n-------\nScikit-Learn is a community driven project, however institutional and private\ngrants help to assure its sustainability.\n\nThe project would like to thank the following funders.\n\n...................................\n\n.. raw:: html\n\n
\n
\n\nThe `Members `_ of\nthe `Scikit-Learn Consortium at Inria Foundation\n`_ fund Olivier\nGrisel, Guillaume Lemaitre, Jérémie du Boisberranger and Chiara Marmo.\n\n.. raw:: html\n\n
\n\n.. |msn| image:: images/microsoft.png\n :width: 100pt\n :target: https://www.microsoft.com/\n\n.. |bcg| image:: images/bcg.png\n :width: 100pt\n :target: https://www.bcg.com/beyond-consulting/bcg-gamma/default.aspx\n\n.. |axa| image:: images/axa.png\n :width: 50pt\n :target: https://www.axa.fr/\n\n.. |bnp| image:: images/bnp.png\n :width: 150pt\n :target: https://www.bnpparibascardif.com/\n\n.. |fujitsu| image:: images/fujitsu.png\n :width: 100pt\n :target: https://www.fujitsu.com/global/\n\n.. |dataiku| image:: images/dataiku.png\n :width: 70pt\n :target: https://www.dataiku.com/\n\n.. |aphp| image:: images/logo_APHP_text.png\n :width: 150pt\n :target: https://aphp.fr/\n\n.. |inria| image:: images/inria-logo.jpg\n :width: 100pt\n :target: https://www.inria.fr\n\n\n.. raw:: html\n\n
\n\n.. table::\n :class: sk-sponsor-table align-default\n\n +---------+----------+\n | |bcg| |\n +---------+----------+\n | |\n +---------+----------+\n | |axa| | |bnp| |\n +---------+----------+\n ||fujitsu|| |msn| |\n +---------+----------+\n | |\n +---------+----------+\n | |dataiku| |\n +---------+----------+\n | |aphp| |\n +---------+----------+\n | |\n +---------+----------+\n | |inria| |\n +---------+----------+\n\n.. raw:: html\n\n
\n
\n\n........\n\n.. raw:: html\n\n
\n
\n\n`The University of Sydney `_ funds Joel Nothman since\nJuly 2017.\n\n.. raw:: html\n\n
\n\n
\n\n.. image:: images/sydney-primary.jpeg\n :width: 100pt\n :align: center\n :target: https://sydney.edu.au/\n\n.. raw:: html\n\n
\n
\n\n..........\n\n.. raw:: html\n\n
\n
\n\n`Zalando SE `_ funds Adrin Jalali since\nAugust 2020.\n\n.. raw:: html\n\n
\n\n
\n\n.. image:: images/zalando_logo.png\n :width: 100pt\n :align: center\n :target: https://corporate.zalando.com/en\n\n.. raw:: html\n\n
\n
\n\n...........\n\n.. raw:: html\n\n
\n
\n\n`Microsoft `_ funds Andreas Müller since 2020.\n\n.. raw:: html\n\n
\n\n
\n\n.. image:: images/microsoft.png\n :width: 100pt\n :align: center\n :target: https://www.microsoft.com/\n\n.. raw:: html\n\n
\n
\n\n...........\n\n.. raw:: html\n\n
\n
\n\n`Quansight Labs `_ funds Thomas J. Fan since 2021.\n\n.. raw:: html\n\n
\n\n
\n\n.. image:: images/quansight-labs.png\n :width: 100pt\n :align: center\n :target: https://labs.quansight.org\n\n.. raw:: html\n\n
\n
\n\nPast Sponsors\n.............\n\n.. raw:: html\n\n
\n
\n\n`Columbia University `_ funded Andreas Müller\n(2016-2020).\n\n.. raw:: html\n\n
\n\n
\n\n.. image:: images/columbia.png\n :width: 50pt\n :align: center\n :target: https://www.columbia.edu/\n\n.. raw:: html\n\n
\n
\n\n...........\n\n.. raw:: html\n\n
\n
\n\nAndreas Müller received a grant to improve scikit-learn from the\n`Alfred P. Sloan Foundation `_ .\nThis grant supported the position of Nicolas Hug and Thomas J. Fan.\n\n.. raw:: html\n\n
\n\n
\n\n.. image:: images/sloan_banner.png\n :width: 100pt\n :align: center\n :target: https://sloan.org/\n\n.. raw:: html\n\n
\n
\n\n.............\n\n.. raw:: html\n\n
\n
\n\n`INRIA `_ actively supports this project. It has\nprovided funding for Fabian Pedregosa (2010-2012), Jaques Grobler\n(2012-2013) and Olivier Grisel (2013-2017) to work on this project\nfull-time. It also hosts coding sprints and other events.\n\n.. raw:: html\n\n
\n\n
\n\n.. image:: images/inria-logo.jpg\n :width: 100pt\n :align: center\n :target: https://www.inria.fr\n\n.. raw:: html\n\n
\n
\n\n.....................\n\n.. raw:: html\n\n
\n
\n\n`Paris-Saclay Center for Data Science\n`_\nfunded one year for a developer to work on the project full-time\n(2014-2015), 50% of the time of Guillaume Lemaitre (2016-2017) and 50% of the\ntime of Joris van den Bossche (2017-2018).\n\n.. raw:: html\n\n
\n
\n\n.. image:: images/cds-logo.png\n :width: 100pt\n :align: center\n :target: https://www.datascience-paris-saclay.fr/\n\n.. raw:: html\n\n
\n
\n\n............\n\n.. raw:: html\n\n
\n
\n\n`Anaconda, Inc `_ funded Adrin Jalali in 2019.\n\n.. raw:: html\n\n
\n\n
\n\n.. image:: images/anaconda.png\n :width: 100pt\n :align: center\n :target: https://www.anaconda.com/\n\n.. raw:: html\n\n
\n
\n\n..........................\n\n.. raw:: html\n\n
\n
\n\n`NYU Moore-Sloan Data Science Environment `_\nfunded Andreas Mueller (2014-2016) to work on this project. The Moore-Sloan\nData Science Environment also funds several students to work on the project\npart-time.\n\n.. raw:: html\n\n
\n
\n\n.. image:: images/nyu_short_color.png\n :width: 100pt\n :align: center\n :target: https://cds.nyu.edu/mooresloan/\n\n.. raw:: html\n\n
\n
\n\n........................\n\n.. raw:: html\n\n
\n
\n\n`Télécom Paristech `_ funded Manoj Kumar\n(2014), Tom Dupré la Tour (2015), Raghav RV (2015-2017), Thierry Guillemot\n(2016-2017) and Albert Thomas (2017) to work on scikit-learn.\n\n.. raw:: html\n\n
\n
\n\n.. image:: images/telecom.png\n :width: 50pt\n :align: center\n :target: https://www.telecom-paristech.fr/\n\n.. raw:: html\n\n
\n
\n\n.....................\n\n.. raw:: html\n\n
\n
\n\n`The Labex DigiCosme `_ funded Nicolas Goix\n(2015-2016), Tom Dupré la Tour (2015-2016 and 2017-2018), Mathurin Massias\n(2018-2019) to work part time on scikit-learn during their PhDs. It also\nfunded a scikit-learn coding sprint in 2015.\n\n.. raw:: html\n\n
\n
\n\n.. image:: images/digicosme.png\n :width: 100pt\n :align: center\n :target: https://digicosme.lri.fr\n\n.. raw:: html\n\n
\n
\n\n.....................\n\n.. raw:: html\n\n
\n
\n\n`The Chan-Zuckerberg Initiative `_ funded Nicolas\nHug to work full-time on scikit-learn in 2020.\n\n.. raw:: html\n\n
\n
\n\n.. image:: images/czi_logo.svg\n :width: 100pt\n :align: center\n :target: https://chanzuckerberg.com\n\n.. raw:: html\n\n
\n
\n\n......................\n\nThe following students were sponsored by `Google\n`_ to work on scikit-learn through\nthe `Google Summer of Code `_\nprogram.\n\n- 2007 - David Cournapeau\n- 2011 - `Vlad Niculae`_\n- 2012 - `Vlad Niculae`_, Immanuel Bayer.\n- 2013 - Kemal Eren, Nicolas Trésegnie\n- 2014 - Hamzeh Alsalhi, Issam Laradji, Maheshakya Wijewardena, Manoj Kumar.\n- 2015 - `Raghav RV `_, Wei Xue\n- 2016 - `Nelson Liu `_, `YenChen Lin `_\n\n.. _Vlad Niculae: https://vene.ro/\n\n...................\n\nThe `NeuroDebian `_ project providing `Debian\n`_ packaging and contributions is supported by\n`Dr. James V. Haxby `_ (`Dartmouth\nCollege `_).\n\nSprints\n-------\n\nThe International 2019 Paris sprint was kindly hosted by `AXA `_.\nAlso some participants could attend thanks to the support of the `Alfred P.\nSloan Foundation `_, the `Python Software\nFoundation `_ (PSF) and the `DATAIA Institute\n`_.\n\n.....................\n\nThe 2013 International Paris Sprint was made possible thanks to the support of\n`Télécom Paristech `_, `tinyclues\n`_, the `French Python Association\n`_ and the `Fonds de la Recherche Scientifique\n`_.\n\n..............\n\nThe 2011 International Granada sprint was made possible thanks to the support\nof the `PSF `_ and `tinyclues\n`_.\n\nDonating to the project\n.......................\n\nIf you are interested in donating to the project or to one of our code-sprints,\nyou can use the *Paypal* button below or the `NumFOCUS Donations Page\n`_ (if you use the latter,\nplease indicate that you are donating for the scikit-learn project).\n\nAll donations will be handled by `NumFOCUS\n`_, a non-profit-organization which is\nmanaged by a board of `Scipy community members\n`_. NumFOCUS's mission is to foster\nscientific computing software, in particular in Python. As a fiscal home\nof scikit-learn, it ensures that money is available when needed to keep\nthe project funded and available while in compliance with tax regulations.\n\nThe received donations for the scikit-learn project mostly will go towards\ncovering travel-expenses for code sprints, as well as towards the organization\nbudget of the project [#f1]_.\n\n.. raw :: html\n\n

\n \n
\n\n.. rubric:: Notes\n\n.. [#f1] Regarding the organization budget, in particular, we might use some of\n the donated funds to pay for other project expenses such as DNS,\n hosting or continuous integration services.\n\nInfrastructure support\n----------------------\n\n- We would also like to thank `Microsoft Azure\n `_, `Travis Cl `_,\n `CircleCl `_ for free CPU time on their Continuous\n Integration servers, and `Anaconda Inc. `_ for the\n storage they provide for our staging and nightly builds.\n" }, { "path": "doc/authors.rst", "content": ".. raw :: html\n\n \n
\n \n
\n
\n

Jérémie du Boisberranger

\n
\n
\n
\n

Joris Van den Bossche

\n
\n
\n
\n

Loïc Estève

\n
\n
\n
\n

Thomas J. Fan

\n
\n
\n
\n

Alexandre Gramfort

\n
\n
\n
\n

Olivier Grisel

\n
\n
\n
\n

Yaroslav Halchenko

\n
\n
\n
\n

Nicolas Hug

\n
\n
\n
\n

Adrin Jalali

\n
\n
\n
\n

Julien Jerphanion

\n
\n
\n
\n

Guillaume Lemaitre

\n
\n
\n
\n

Christian Lorentzen

\n
\n
\n
\n

Jan Hendrik Metzen

\n
\n
\n
\n

Andreas Mueller

\n
\n
\n
\n

Vlad Niculae

\n
\n
\n
\n

Joel Nothman

\n
\n
\n
\n

Hanmin Qin

\n
\n
\n
\n

Bertrand Thirion

\n
\n
\n
\n

Tom Dupré la Tour

\n
\n
\n
\n

Gael Varoquaux

\n
\n
\n
\n

Nelle Varoquaux

\n
\n
\n
\n

Roman Yurchak

\n
\n
\n" }, { "path": "doc/authors_emeritus.rst", "content": "- Mathieu Blondel\n- Matthieu Brucher\n- Lars Buitinck\n- David Cournapeau\n- Noel Dawe\n- Vincent Dubourg\n- Edouard Duchesnay\n- Alexander Fabisch\n- Virgile Fritsch\n- Satrajit Ghosh\n- Angel Soler Gollonet\n- Chris Gorgolewski\n- Jaques Grobler\n- Brian Holt\n- Arnaud Joly\n- Thouis (Ray) Jones\n- Kyle Kastner\n- manoj kumar\n- Robert Layton\n- Wei Li\n- Paolo Losi\n- Gilles Louppe\n- Vincent Michel\n- Jarrod Millman\n- Alexandre Passos\n- Fabian Pedregosa\n- Peter Prettenhofer\n- (Venkat) Raghav, Rajagopalan\n- Jacob Schreiber\n- Du Shiqiao\n- Jake Vanderplas\n- David Warde-Farley\n- Ron Weiss\n" }, { "path": "doc/binder/requirements.txt", "content": "# A binder requirement file is required by sphinx-gallery.\n# We don't really need one since our binder requirement file lives in the\n# .binder directory.\n# This file can be removed if 'dependencies' is made an optional key for\n# binder in sphinx-gallery.\n" }, { "path": "doc/common_pitfalls.rst", "content": ".. Places parent toc into the sidebar\n\n:parenttoc: True\n\n.. include:: includes/big_toc_css.rst\n\n.. _common_pitfalls:\n\n=========================================\nCommon pitfalls and recommended practices\n=========================================\n\nThe purpose of this chapter is to illustrate some common pitfalls and\nanti-patterns that occur when using scikit-learn. It provides\nexamples of what **not** to do, along with a corresponding correct\nexample.\n\nInconsistent preprocessing\n==========================\n\nscikit-learn provides a library of :ref:`data-transforms`, which\nmay clean (see :ref:`preprocessing`), reduce\n(see :ref:`data_reduction`), expand (see :ref:`kernel_approximation`)\nor generate (see :ref:`feature_extraction`) feature representations.\nIf these data transforms are used when training a model, they also\nmust be used on subsequent datasets, whether it's test data or\ndata in a production system. Otherwise, the feature space will change,\nand the model will not be able to perform effectively.\n\nFor the following example, let's create a synthetic dataset with a\nsingle feature::\n\n >>> from sklearn.datasets import make_regression\n >>> from sklearn.model_selection import train_test_split\n\n >>> random_state = 42\n >>> X, y = make_regression(random_state=random_state, n_features=1, noise=1)\n >>> X_train, X_test, y_train, y_test = train_test_split(\n ... X, y, test_size=0.4, random_state=random_state)\n\n**Wrong**\n\nThe train dataset is scaled, but not the test dataset, so model\nperformance on the test dataset is worse than expected::\n\n >>> from sklearn.metrics import mean_squared_error\n >>> from sklearn.linear_model import LinearRegression\n >>> from sklearn.preprocessing import StandardScaler\n\n >>> scaler = StandardScaler()\n >>> X_train_transformed = scaler.fit_transform(X_train)\n >>> model = LinearRegression().fit(X_train_transformed, y_train)\n >>> mean_squared_error(y_test, model.predict(X_test))\n 62.80...\n\n**Right**\n\nInstead of passing the non-transformed `X_test` to `predict`, we should\ntransform the test data, the same way we transformed the training data::\n\n >>> X_test_transformed = scaler.transform(X_test)\n >>> mean_squared_error(y_test, model.predict(X_test_transformed))\n 0.90...\n\nAlternatively, we recommend using a :class:`Pipeline\n`, which makes it easier to chain transformations\nwith estimators, and reduces the possibility of forgetting a transformation::\n\n >>> from sklearn.pipeline import make_pipeline\n\n >>> model = make_pipeline(StandardScaler(), LinearRegression())\n >>> model.fit(X_train, y_train)\n Pipeline(steps=[('standardscaler', StandardScaler()),\n ('linearregression', LinearRegression())])\n >>> mean_squared_error(y_test, model.predict(X_test))\n 0.90...\n\nPipelines also help avoiding another common pitfall: leaking the test data\ninto the training data.\n\n.. _data_leakage:\n\nData leakage\n============\n\nData leakage occurs when information that would not be available at prediction\ntime is used when building the model. This results in overly optimistic\nperformance estimates, for example from :ref:`cross-validation\n`, and thus poorer performance when the model is used\non actually novel data, for example during production.\n\nA common cause is not keeping the test and train data subsets separate.\nTest data should never be used to make choices about the model.\n**The general rule is to never call** `fit` **on the test data**. While this\nmay sound obvious, this is easy to miss in some cases, for example when\napplying certain pre-processing steps.\n\nAlthough both train and test data subsets should receive the same\npreprocessing transformation (as described in the previous section), it is\nimportant that these transformations are only learnt from the training data.\nFor example, if you have a\nnormalization step where you divide by the average value, the average should\nbe the average of the train subset, **not** the average of all the data. If the\ntest subset is included in the average calculation, information from the test\nsubset is influencing the model.\n\nAn example of data leakage during preprocessing is detailed below.\n\nData leakage during pre-processing\n----------------------------------\n\n.. note::\n We here choose to illustrate data leakage with a feature selection step.\n This risk of leakage is however relevant with almost all transformations\n in scikit-learn, including (but not limited to)\n :class:`~sklearn.preprocessing.StandardScaler`,\n :class:`~sklearn.impute.SimpleImputer`, and\n :class:`~sklearn.decomposition.PCA`.\n\nA number of :ref:`feature_selection` functions are available in scikit-learn.\nThey can help remove irrelevant, redundant and noisy features as well as\nimprove your model build time and performance. As with any other type of\npreprocessing, feature selection should **only** use the training data.\nIncluding the test data in feature selection will optimistically bias your\nmodel.\n\nTo demonstrate we will create this binary classification problem with\n10,000 randomly generated features::\n\n >>> import numpy as np\n >>> n_samples, n_features, n_classes = 200, 10000, 2\n >>> rng = np.random.RandomState(42)\n >>> X = rng.standard_normal((n_samples, n_features))\n >>> y = rng.choice(n_classes, n_samples)\n\n**Wrong**\n\nUsing all the data to perform feature selection results in an accuracy score\nmuch higher than chance, even though our targets are completely random.\nThis randomness means that our `X` and `y` are independent and we thus expect\nthe accuracy to be around 0.5. However, since the feature selection step\n'sees' the test data, the model has an unfair advantage. In the incorrect\nexample below we first use all the data for feature selection and then split\nthe data into training and test subsets for model fitting. The result is a\nmuch higher than expected accuracy score::\n\n >>> from sklearn.model_selection import train_test_split\n >>> from sklearn.feature_selection import SelectKBest\n >>> from sklearn.ensemble import GradientBoostingClassifier\n >>> from sklearn.metrics import accuracy_score\n\n >>> # Incorrect preprocessing: the entire data is transformed\n >>> X_selected = SelectKBest(k=25).fit_transform(X, y)\n\n >>> X_train, X_test, y_train, y_test = train_test_split(\n ... X_selected, y, random_state=42)\n >>> gbc = GradientBoostingClassifier(random_state=1)\n >>> gbc.fit(X_train, y_train)\n GradientBoostingClassifier(random_state=1)\n\n >>> y_pred = gbc.predict(X_test)\n >>> accuracy_score(y_test, y_pred)\n 0.76\n\n**Right**\n\nTo prevent data leakage, it is good practice to split your data into train\nand test subsets **first**. Feature selection can then be formed using just\nthe train dataset. Notice that whenever we use `fit` or `fit_transform`, we\nonly use the train dataset. The score is now what we would expect for the\ndata, close to chance::\n\n >>> X_train, X_test, y_train, y_test = train_test_split(\n ... X, y, random_state=42)\n >>> select = SelectKBest(k=25)\n >>> X_train_selected = select.fit_transform(X_train, y_train)\n\n >>> gbc = GradientBoostingClassifier(random_state=1)\n >>> gbc.fit(X_train_selected, y_train)\n GradientBoostingClassifier(random_state=1)\n\n >>> X_test_selected = select.transform(X_test)\n >>> y_pred = gbc.predict(X_test_selected)\n >>> accuracy_score(y_test, y_pred)\n 0.46\n\nHere again, we recommend using a :class:`~sklearn.pipeline.Pipeline` to chain\ntogether the feature selection and model estimators. The pipeline ensures\nthat only the training data is used when performing `fit` and the test data\nis used only for calculating the accuracy score::\n\n >>> from sklearn.pipeline import make_pipeline\n >>> X_train, X_test, y_train, y_test = train_test_split(\n ... X, y, random_state=42)\n >>> pipeline = make_pipeline(SelectKBest(k=25),\n ... GradientBoostingClassifier(random_state=1))\n >>> pipeline.fit(X_train, y_train)\n Pipeline(steps=[('selectkbest', SelectKBest(k=25)),\n ('gradientboostingclassifier',\n GradientBoostingClassifier(random_state=1))])\n\n >>> y_pred = pipeline.predict(X_test)\n >>> accuracy_score(y_test, y_pred)\n 0.46\n\nThe pipeline can also be fed into a cross-validation\nfunction such as :func:`~sklearn.model_selection.cross_val_score`.\nAgain, the pipeline ensures that the correct data subset and estimator\nmethod is used during fitting and predicting::\n\n >>> from sklearn.model_selection import cross_val_score\n >>> scores = cross_val_score(pipeline, X, y)\n >>> print(f\"Mean accuracy: {scores.mean():.2f}+/-{scores.std():.2f}\")\n Mean accuracy: 0.45+/-0.07\n\nHow to avoid data leakage\n-------------------------\n\nBelow are some tips on avoiding data leakage:\n\n* Always split the data into train and test subsets first, particularly\n before any preprocessing steps.\n* Never include test data when using the `fit` and `fit_transform`\n methods. Using all the data, e.g., `fit(X)`, can result in overly optimistic\n scores.\n\n Conversely, the `transform` method should be used on both train and test\n subsets as the same preprocessing should be applied to all the data.\n This can be achieved by using `fit_transform` on the train subset and\n `transform` on the test subset.\n* The scikit-learn :ref:`pipeline ` is a great way to prevent data\n leakage as it ensures that the appropriate method is performed on the\n correct data subset. The pipeline is ideal for use in cross-validation\n and hyper-parameter tuning functions.\n\n.. _randomness:\n\nControlling randomness\n======================\n\nSome scikit-learn objects are inherently random. These are usually estimators\n(e.g. :class:`~sklearn.ensemble.RandomForestClassifier`) and cross-validation\nsplitters (e.g. :class:`~sklearn.model_selection.KFold`). The randomness of\nthese objects is controlled via their `random_state` parameter, as described\nin the :term:`Glossary `. This section expands on the glossary\nentry, and describes good practices and common pitfalls w.r.t. to this\nsubtle parameter.\n\n.. note:: Recommendation summary\n\n For an optimal robustness of cross-validation (CV) results, pass\n `RandomState` instances when creating estimators, or leave `random_state`\n to `None`. Passing integers to CV splitters is usually the safest option\n and is preferable; passing `RandomState` instances to splitters may\n sometimes be useful to achieve very specific use-cases.\n For both estimators and splitters, passing an integer vs passing an\n instance (or `None`) leads to subtle but significant differences,\n especially for CV procedures. These differences are important to\n understand when reporting results.\n\n For reproducible results across executions, remove any use of\n `random_state=None`.\n\nUsing `None` or `RandomState` instances, and repeated calls to `fit` and `split`\n--------------------------------------------------------------------------------\n\nThe `random_state` parameter determines whether multiple calls to :term:`fit`\n(for estimators) or to :term:`split` (for CV splitters) will produce the same\nresults, according to these rules:\n\n- If an integer is passed, calling `fit` or `split` multiple times always\n yields the same results.\n- If `None` or a `RandomState` instance is passed: `fit` and `split` will\n yield different results each time they are called, and the succession of\n calls explores all sources of entropy. `None` is the default value for all\n `random_state` parameters.\n\nWe here illustrate these rules for both estimators and CV splitters.\n\n.. note::\n Since passing `random_state=None` is equivalent to passing the global\n `RandomState` instance from `numpy`\n (`random_state=np.random.mtrand._rand`), we will not explicitly mention\n `None` here. Everything that applies to instances also applies to using\n `None`.\n\nEstimators\n..........\n\nPassing instances means that calling `fit` multiple times will not yield the\nsame results, even if the estimator is fitted on the same data and with the\nsame hyper-parameters::\n\n >>> from sklearn.linear_model import SGDClassifier\n >>> from sklearn.datasets import make_classification\n >>> import numpy as np\n\n >>> rng = np.random.RandomState(0)\n >>> X, y = make_classification(n_features=5, random_state=rng)\n >>> sgd = SGDClassifier(random_state=rng)\n\n >>> sgd.fit(X, y).coef_\n array([[ 8.85418642, 4.79084103, -3.13077794, 8.11915045, -0.56479934]])\n\n >>> sgd.fit(X, y).coef_\n array([[ 6.70814003, 5.25291366, -7.55212743, 5.18197458, 1.37845099]])\n\nWe can see from the snippet above that repeatedly calling `sgd.fit` has\nproduced different models, even if the data was the same. This is because the\nRandom Number Generator (RNG) of the estimator is consumed (i.e. mutated)\nwhen `fit` is called, and this mutated RNG will be used in the subsequent\ncalls to `fit`. In addition, the `rng` object is shared across all objects\nthat use it, and as a consequence, these objects become somewhat\ninter-dependent. For example, two estimators that share the same\n`RandomState` instance will influence each other, as we will see later when\nwe discuss cloning. This point is important to keep in mind when debugging.\n\nIf we had passed an integer to the `random_state` parameter of the\n:class:`~sklearn.ensemble.RandomForestClassifier`, we would have obtained the\nsame models, and thus the same scores each time. When we pass an integer, the\nsame RNG is used across all calls to `fit`. What internally happens is that\neven though the RNG is consumed when `fit` is called, it is always reset to\nits original state at the beginning of `fit`.\n\nCV splitters\n............\n\nRandomized CV splitters have a similar behavior when a `RandomState`\ninstance is passed; calling `split` multiple times yields different data\nsplits::\n\n >>> from sklearn.model_selection import KFold\n >>> import numpy as np\n\n >>> X = y = np.arange(10)\n >>> rng = np.random.RandomState(0)\n >>> cv = KFold(n_splits=2, shuffle=True, random_state=rng)\n\n >>> for train, test in cv.split(X, y):\n ... print(train, test)\n [0 3 5 6 7] [1 2 4 8 9]\n [1 2 4 8 9] [0 3 5 6 7]\n\n >>> for train, test in cv.split(X, y):\n ... print(train, test)\n [0 4 6 7 8] [1 2 3 5 9]\n [1 2 3 5 9] [0 4 6 7 8]\n\nWe can see that the splits are different from the second time `split` is\ncalled. This may lead to unexpected results if you compare the performance of\nmultiple estimators by calling `split` many times, as we will see in the next\nsection.\n\nCommon pitfalls and subtleties\n------------------------------\n\nWhile the rules that govern the `random_state` parameter are seemingly simple,\nthey do however have some subtle implications. In some cases, this can even\nlead to wrong conclusions.\n\nEstimators\n..........\n\n**Different `random_state` types lead to different cross-validation\nprocedures**\n\nDepending on the type of the `random_state` parameter, estimators will behave\ndifferently, especially in cross-validation procedures. Consider the\nfollowing snippet::\n\n >>> from sklearn.ensemble import RandomForestClassifier\n >>> from sklearn.datasets import make_classification\n >>> from sklearn.model_selection import cross_val_score\n >>> import numpy as np\n\n >>> X, y = make_classification(random_state=0)\n\n >>> rf_123 = RandomForestClassifier(random_state=123)\n >>> cross_val_score(rf_123, X, y)\n array([0.85, 0.95, 0.95, 0.9 , 0.9 ])\n\n >>> rf_inst = RandomForestClassifier(random_state=np.random.RandomState(0))\n >>> cross_val_score(rf_inst, X, y)\n array([0.9 , 0.95, 0.95, 0.9 , 0.9 ])\n\nWe see that the cross-validated scores of `rf_123` and `rf_inst` are\ndifferent, as should be expected since we didn't pass the same `random_state`\nparameter. However, the difference between these scores is more subtle than\nit looks, and **the cross-validation procedures that were performed by**\n:func:`~sklearn.model_selection.cross_val_score` **significantly differ in\neach case**:\n\n- Since `rf_123` was passed an integer, every call to `fit` uses the same RNG:\n this means that all random characteristics of the random forest estimator\n will be the same for each of the 5 folds of the CV procedure. In\n particular, the (randomly chosen) subset of features of the estimator will\n be the same across all folds.\n- Since `rf_inst` was passed a `RandomState` instance, each call to `fit`\n starts from a different RNG. As a result, the random subset of features\n will be different for each folds.\n\nWhile having a constant estimator RNG across folds isn't inherently wrong, we\nusually want CV results that are robust w.r.t. the estimator's randomness. As\na result, passing an instance instead of an integer may be preferable, since\nit will allow the estimator RNG to vary for each fold.\n\n.. note::\n Here, :func:`~sklearn.model_selection.cross_val_score` will use a\n non-randomized CV splitter (as is the default), so both estimators will\n be evaluated on the same splits. This section is not about variability in\n the splits. Also, whether we pass an integer or an instance to\n :func:`~sklearn.datasets.make_classification` isn't relevant for our\n illustration purpose: what matters is what we pass to the\n :class:`~sklearn.ensemble.RandomForestClassifier` estimator.\n\n**Cloning**\n\nAnother subtle side effect of passing `RandomState` instances is how\n:func:`~sklearn.clone` will work::\n\n >>> from sklearn import clone\n >>> from sklearn.ensemble import RandomForestClassifier\n >>> import numpy as np\n\n >>> rng = np.random.RandomState(0)\n >>> a = RandomForestClassifier(random_state=rng)\n >>> b = clone(a)\n\nSince a `RandomState` instance was passed to `a`, `a` and `b` are not clones\nin the strict sense, but rather clones in the statistical sense: `a` and `b`\nwill still be different models, even when calling `fit(X, y)` on the same\ndata. Moreover, `a` and `b` will influence each-other since they share the\nsame internal RNG: calling `a.fit` will consume `b`'s RNG, and calling\n`b.fit` will consume `a`'s RNG, since they are the same. This bit is true for\nany estimators that share a `random_state` parameter; it is not specific to\nclones.\n\nIf an integer were passed, `a` and `b` would be exact clones and they would not\ninfluence each other.\n\n.. warning::\n Even though :func:`~sklearn.clone` is rarely used in user code, it is\n called pervasively throughout scikit-learn codebase: in particular, most\n meta-estimators that accept non-fitted estimators call\n :func:`~sklearn.clone` internally\n (:class:`~sklearn.model_selection.GridSearchCV`,\n :class:`~sklearn.ensemble.StackingClassifier`,\n :class:`~sklearn.calibration.CalibratedClassifierCV`, etc.).\n\nCV splitters\n............\n\nWhen passed a `RandomState` instance, CV splitters yield different splits\neach time `split` is called. When comparing different estimators, this can\nlead to overestimating the variance of the difference in performance between\nthe estimators::\n\n >>> from sklearn.naive_bayes import GaussianNB\n >>> from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n >>> from sklearn.datasets import make_classification\n >>> from sklearn.model_selection import KFold\n >>> from sklearn.model_selection import cross_val_score\n >>> import numpy as np\n\n >>> rng = np.random.RandomState(0)\n >>> X, y = make_classification(random_state=rng)\n >>> cv = KFold(shuffle=True, random_state=rng)\n >>> lda = LinearDiscriminantAnalysis()\n >>> nb = GaussianNB()\n\n >>> for est in (lda, nb):\n ... print(cross_val_score(est, X, y, cv=cv))\n [0.8 0.75 0.75 0.7 0.85]\n [0.85 0.95 0.95 0.85 0.95]\n\n\nDirectly comparing the performance of the\n:class:`~sklearn.discriminant_analysis.LinearDiscriminantAnalysis` estimator\nvs the :class:`~sklearn.naive_bayes.GaussianNB` estimator **on each fold** would\nbe a mistake: **the splits on which the estimators are evaluated are\ndifferent**. Indeed, :func:`~sklearn.model_selection.cross_val_score` will\ninternally call `cv.split` on the same\n:class:`~sklearn.model_selection.KFold` instance, but the splits will be\ndifferent each time. This is also true for any tool that performs model\nselection via cross-validation, e.g.\n:class:`~sklearn.model_selection.GridSearchCV` and\n:class:`~sklearn.model_selection.RandomizedSearchCV`: scores are not\ncomparable fold-to-fold across different calls to `search.fit`, since\n`cv.split` would have been called multiple times. Within a single call to\n`search.fit`, however, fold-to-fold comparison is possible since the search\nestimator only calls `cv.split` once.\n\nFor comparable fold-to-fold results in all scenarios, one should pass an\ninteger to the CV splitter: `cv = KFold(shuffle=True, random_state=0)`.\n\n.. note::\n While fold-to-fold comparison is not advisable with `RandomState`\n instances, one can however expect that average scores allow to conclude\n whether one estimator is better than another, as long as enough folds and\n data are used.\n\n.. note::\n What matters in this example is what was passed to\n :class:`~sklearn.model_selection.KFold`. Whether we pass a `RandomState`\n instance or an integer to :func:`~sklearn.datasets.make_classification`\n is not relevant for our illustration purpose. Also, neither\n :class:`~sklearn.discriminant_analysis.LinearDiscriminantAnalysis` nor\n :class:`~sklearn.naive_bayes.GaussianNB` are randomized estimators.\n\nGeneral recommendations\n-----------------------\n\nGetting reproducible results across multiple executions\n.......................................................\n\nIn order to obtain reproducible (i.e. constant) results across multiple\n*program executions*, we need to remove all uses of `random_state=None`, which\nis the default. The recommended way is to declare a `rng` variable at the top\nof the program, and pass it down to any object that accepts a `random_state`\nparameter::\n\n >>> from sklearn.ensemble import RandomForestClassifier\n >>> from sklearn.datasets import make_classification\n >>> from sklearn.model_selection import train_test_split\n >>> import numpy as np\n\n >>> rng = np.random.RandomState(0)\n >>> X, y = make_classification(random_state=rng)\n >>> rf = RandomForestClassifier(random_state=rng)\n >>> X_train, X_test, y_train, y_test = train_test_split(X, y,\n ... random_state=rng)\n >>> rf.fit(X_train, y_train).score(X_test, y_test)\n 0.84\n\nWe are now guaranteed that the result of this script will always be 0.84, no\nmatter how many times we run it. Changing the global `rng` variable to a\ndifferent value should affect the results, as expected.\n\nIt is also possible to declare the `rng` variable as an integer. This may\nhowever lead to less robust cross-validation results, as we will see in the\nnext section.\n\n.. note::\n We do not recommend setting the global `numpy` seed by calling\n `np.random.seed(0)`. See `here\n `_\n for a discussion.\n\nRobustness of cross-validation results\n......................................\n\nWhen we evaluate a randomized estimator performance by cross-validation, we\nwant to make sure that the estimator can yield accurate predictions for new\ndata, but we also want to make sure that the estimator is robust w.r.t. its\nrandom initialization. For example, we would like the random weights\ninitialization of a :class:`~sklearn.linear_model.SGDCLassifier` to be\nconsistently good across all folds: otherwise, when we train that estimator\non new data, we might get unlucky and the random initialization may lead to\nbad performance. Similarly, we want a random forest to be robust w.r.t the\nset of randomly selected features that each tree will be using.\n\nFor these reasons, it is preferable to evaluate the cross-validation\nperformance by letting the estimator use a different RNG on each fold. This\nis done by passing a `RandomState` instance (or `None`) to the estimator\ninitialization.\n\nWhen we pass an integer, the estimator will use the same RNG on each fold:\nif the estimator performs well (or bad), as evaluated by CV, it might just be\nbecause we got lucky (or unlucky) with that specific seed. Passing instances\nleads to more robust CV results, and makes the comparison between various\nalgorithms fairer. It also helps limiting the temptation to treat the\nestimator's RNG as a hyper-parameter that can be tuned.\n\nWhether we pass `RandomState` instances or integers to CV splitters has no\nimpact on robustness, as long as `split` is only called once. When `split`\nis called multiple times, fold-to-fold comparison isn't possible anymore. As\na result, passing integer to CV splitters is usually safer and covers most\nuse-cases.\n" }, { "path": "doc/communication_team.rst", "content": ".. raw :: html\n\n \n
\n \n
\n
\n

Reshama Shaikh

\n
\n
\n
\n

Lauren Burke

\n
\n
\n" }, { "path": "doc/computing/computational_performance.rst", "content": ".. Places parent toc into the sidebar\n\n:parenttoc: True\n\n.. _computational_performance:\n\n.. currentmodule:: sklearn\n\nComputational Performance\n=========================\n\nFor some applications the performance (mainly latency and throughput at\nprediction time) of estimators is crucial. It may also be of interest to\nconsider the training throughput but this is often less important in a\nproduction setup (where it often takes place offline).\n\nWe will review here the orders of magnitude you can expect from a number of\nscikit-learn estimators in different contexts and provide some tips and\ntricks for overcoming performance bottlenecks.\n\nPrediction latency is measured as the elapsed time necessary to make a\nprediction (e.g. in micro-seconds). Latency is often viewed as a distribution\nand operations engineers often focus on the latency at a given percentile of\nthis distribution (e.g. the 90 percentile).\n\nPrediction throughput is defined as the number of predictions the software can\ndeliver in a given amount of time (e.g. in predictions per second).\n\nAn important aspect of performance optimization is also that it can hurt\nprediction accuracy. Indeed, simpler models (e.g. linear instead of\nnon-linear, or with fewer parameters) often run faster but are not always able\nto take into account the same exact properties of the data as more complex ones.\n\nPrediction Latency\n------------------\n\nOne of the most straight-forward concerns one may have when using/choosing a\nmachine learning toolkit is the latency at which predictions can be made in a\nproduction environment.\n\nThe main factors that influence the prediction latency are\n 1. Number of features\n 2. Input data representation and sparsity\n 3. Model complexity\n 4. Feature extraction\n\nA last major parameter is also the possibility to do predictions in bulk or\none-at-a-time mode.\n\nBulk versus Atomic mode\n........................\n\nIn general doing predictions in bulk (many instances at the same time) is\nmore efficient for a number of reasons (branching predictability, CPU cache,\nlinear algebra libraries optimizations etc.). Here we see on a setting\nwith few features that independently of estimator choice the bulk mode is\nalways faster, and for some of them by 1 to 2 orders of magnitude:\n\n.. |atomic_prediction_latency| image:: ../auto_examples/applications/images/sphx_glr_plot_prediction_latency_001.png\n :target: ../auto_examples/applications/plot_prediction_latency.html\n :scale: 80\n\n.. centered:: |atomic_prediction_latency|\n\n.. |bulk_prediction_latency| image:: ../auto_examples/applications/images/sphx_glr_plot_prediction_latency_002.png\n :target: ../auto_examples/applications/plot_prediction_latency.html\n :scale: 80\n\n.. centered:: |bulk_prediction_latency|\n\nTo benchmark different estimators for your case you can simply change the\n``n_features`` parameter in this example:\n:ref:`sphx_glr_auto_examples_applications_plot_prediction_latency.py`. This should give\nyou an estimate of the order of magnitude of the prediction latency.\n\nConfiguring Scikit-learn for reduced validation overhead\n.........................................................\n\nScikit-learn does some validation on data that increases the overhead per\ncall to ``predict`` and similar functions. In particular, checking that\nfeatures are finite (not NaN or infinite) involves a full pass over the\ndata. If you ensure that your data is acceptable, you may suppress\nchecking for finiteness by setting the environment variable\n``SKLEARN_ASSUME_FINITE`` to a non-empty string before importing\nscikit-learn, or configure it in Python with :func:`set_config`.\nFor more control than these global settings, a :func:`config_context`\nallows you to set this configuration within a specified context::\n\n >>> import sklearn\n >>> with sklearn.config_context(assume_finite=True):\n ... pass # do learning/prediction here with reduced validation\n\nNote that this will affect all uses of\n:func:`~utils.assert_all_finite` within the context.\n\nInfluence of the Number of Features\n....................................\n\nObviously when the number of features increases so does the memory\nconsumption of each example. Indeed, for a matrix of :math:`M` instances\nwith :math:`N` features, the space complexity is in :math:`O(NM)`.\nFrom a computing perspective it also means that the number of basic operations\n(e.g., multiplications for vector-matrix products in linear models) increases\ntoo. Here is a graph of the evolution of the prediction latency with the\nnumber of features:\n\n.. |influence_of_n_features_on_latency| image:: ../auto_examples/applications/images/sphx_glr_plot_prediction_latency_003.png\n :target: ../auto_examples/applications/plot_prediction_latency.html\n :scale: 80\n\n.. centered:: |influence_of_n_features_on_latency|\n\nOverall you can expect the prediction time to increase at least linearly with\nthe number of features (non-linear cases can happen depending on the global\nmemory footprint and estimator).\n\nInfluence of the Input Data Representation\n...........................................\n\nScipy provides sparse matrix data structures which are optimized for storing\nsparse data. The main feature of sparse formats is that you don't store zeros\nso if your data is sparse then you use much less memory. A non-zero value in\na sparse (`CSR or CSC `_)\nrepresentation will only take on average one 32bit integer position + the 64\nbit floating point value + an additional 32bit per row or column in the matrix.\nUsing sparse input on a dense (or sparse) linear model can speedup prediction\nby quite a bit as only the non zero valued features impact the dot product\nand thus the model predictions. Hence if you have 100 non zeros in 1e6\ndimensional space, you only need 100 multiply and add operation instead of 1e6.\n\nCalculation over a dense representation, however, may leverage highly optimised\nvector operations and multithreading in BLAS, and tends to result in fewer CPU\ncache misses. So the sparsity should typically be quite high (10% non-zeros\nmax, to be checked depending on the hardware) for the sparse input\nrepresentation to be faster than the dense input representation on a machine\nwith many CPUs and an optimized BLAS implementation.\n\nHere is sample code to test the sparsity of your input::\n\n def sparsity_ratio(X):\n return 1.0 - np.count_nonzero(X) / float(X.shape[0] * X.shape[1])\n print(\"input sparsity ratio:\", sparsity_ratio(X))\n\nAs a rule of thumb you can consider that if the sparsity ratio is greater\nthan 90% you can probably benefit from sparse formats. Check Scipy's sparse\nmatrix formats `documentation `_\nfor more information on how to build (or convert your data to) sparse matrix\nformats. Most of the time the ``CSR`` and ``CSC`` formats work best.\n\nInfluence of the Model Complexity\n..................................\n\nGenerally speaking, when model complexity increases, predictive power and\nlatency are supposed to increase. Increasing predictive power is usually\ninteresting, but for many applications we would better not increase\nprediction latency too much. We will now review this idea for different\nfamilies of supervised models.\n\nFor :mod:`sklearn.linear_model` (e.g. Lasso, ElasticNet,\nSGDClassifier/Regressor, Ridge & RidgeClassifier,\nPassiveAggressiveClassifier/Regressor, LinearSVC, LogisticRegression...) the\ndecision function that is applied at prediction time is the same (a dot product)\n, so latency should be equivalent.\n\nHere is an example using\n:class:`~linear_model.SGDClassifier` with the\n``elasticnet`` penalty. The regularization strength is globally controlled by\nthe ``alpha`` parameter. With a sufficiently high ``alpha``,\none can then increase the ``l1_ratio`` parameter of ``elasticnet`` to\nenforce various levels of sparsity in the model coefficients. Higher sparsity\nhere is interpreted as less model complexity as we need fewer coefficients to\ndescribe it fully. Of course sparsity influences in turn the prediction time\nas the sparse dot-product takes time roughly proportional to the number of\nnon-zero coefficients.\n\n.. |en_model_complexity| image:: ../auto_examples/applications/images/sphx_glr_plot_model_complexity_influence_001.png\n :target: ../auto_examples/applications/plot_model_complexity_influence.html\n :scale: 80\n\n.. centered:: |en_model_complexity|\n\nFor the :mod:`sklearn.svm` family of algorithms with a non-linear kernel,\nthe latency is tied to the number of support vectors (the fewer the faster).\nLatency and throughput should (asymptotically) grow linearly with the number\nof support vectors in a SVC or SVR model. The kernel will also influence the\nlatency as it is used to compute the projection of the input vector once per\nsupport vector. In the following graph the ``nu`` parameter of\n:class:`~svm.NuSVR` was used to influence the number of\nsupport vectors.\n\n.. |nusvr_model_complexity| image:: ../auto_examples/applications/images/sphx_glr_plot_model_complexity_influence_002.png\n :target: ../auto_examples/applications/plot_model_complexity_influence.html\n :scale: 80\n\n.. centered:: |nusvr_model_complexity|\n\nFor :mod:`sklearn.ensemble` of trees (e.g. RandomForest, GBT,\nExtraTrees etc) the number of trees and their depth play the most\nimportant role. Latency and throughput should scale linearly with the number\nof trees. In this case we used directly the ``n_estimators`` parameter of\n:class:`~ensemble.GradientBoostingRegressor`.\n\n.. |gbt_model_complexity| image:: ../auto_examples/applications/images/sphx_glr_plot_model_complexity_influence_003.png\n :target: ../auto_examples/applications/plot_model_complexity_influence.html\n :scale: 80\n\n.. centered:: |gbt_model_complexity|\n\nIn any case be warned that decreasing model complexity can hurt accuracy as\nmentioned above. For instance a non-linearly separable problem can be handled\nwith a speedy linear model but prediction power will very likely suffer in\nthe process.\n\nFeature Extraction Latency\n..........................\n\nMost scikit-learn models are usually pretty fast as they are implemented\neither with compiled Cython extensions or optimized computing libraries.\nOn the other hand, in many real world applications the feature extraction\nprocess (i.e. turning raw data like database rows or network packets into\nnumpy arrays) governs the overall prediction time. For example on the Reuters\ntext classification task the whole preparation (reading and parsing SGML\nfiles, tokenizing the text and hashing it into a common vector space) is\ntaking 100 to 500 times more time than the actual prediction code, depending on\nthe chosen model.\n\n .. |prediction_time| image:: ../auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_004.png\n :target: ../auto_examples/applications/plot_out_of_core_classification.html\n :scale: 80\n\n.. centered:: |prediction_time|\n\nIn many cases it is thus recommended to carefully time and profile your\nfeature extraction code as it may be a good place to start optimizing when\nyour overall latency is too slow for your application.\n\nPrediction Throughput\n----------------------\n\nAnother important metric to care about when sizing production systems is the\nthroughput i.e. the number of predictions you can make in a given amount of\ntime. Here is a benchmark from the\n:ref:`sphx_glr_auto_examples_applications_plot_prediction_latency.py` example that measures\nthis quantity for a number of estimators on synthetic data:\n\n.. |throughput_benchmark| image:: ../auto_examples/applications/images/sphx_glr_plot_prediction_latency_004.png\n :target: ../auto_examples/applications/plot_prediction_latency.html\n :scale: 80\n\n.. centered:: |throughput_benchmark|\n\nThese throughputs are achieved on a single process. An obvious way to\nincrease the throughput of your application is to spawn additional instances\n(usually processes in Python because of the\n`GIL `_) that share the\nsame model. One might also add machines to spread the load. A detailed\nexplanation on how to achieve this is beyond the scope of this documentation\nthough.\n\nTips and Tricks\n----------------\n\nLinear algebra libraries\n.........................\n\nAs scikit-learn relies heavily on Numpy/Scipy and linear algebra in general it\nmakes sense to take explicit care of the versions of these libraries.\nBasically, you ought to make sure that Numpy is built using an optimized `BLAS\n`_ /\n`LAPACK `_ library.\n\nNot all models benefit from optimized BLAS and Lapack implementations. For\ninstance models based on (randomized) decision trees typically do not rely on\nBLAS calls in their inner loops, nor do kernel SVMs (``SVC``, ``SVR``,\n``NuSVC``, ``NuSVR``). On the other hand a linear model implemented with a\nBLAS DGEMM call (via ``numpy.dot``) will typically benefit hugely from a tuned\nBLAS implementation and lead to orders of magnitude speedup over a\nnon-optimized BLAS.\n\nYou can display the BLAS / LAPACK implementation used by your NumPy / SciPy /\nscikit-learn install with the following commands::\n\n from numpy.distutils.system_info import get_info\n print(get_info('blas_opt'))\n print(get_info('lapack_opt'))\n\nOptimized BLAS / LAPACK implementations include:\n - Atlas (need hardware specific tuning by rebuilding on the target machine)\n - OpenBLAS\n - MKL\n - Apple Accelerate and vecLib frameworks (OSX only)\n\nMore information can be found on the `Scipy install page `_\nand in this\n`blog post `_\nfrom Daniel Nouri which has some nice step by step install instructions for\nDebian / Ubuntu.\n\n.. _working_memory:\n\nLimiting Working Memory\n........................\n\nSome calculations when implemented using standard numpy vectorized operations\ninvolve using a large amount of temporary memory. This may potentially exhaust\nsystem memory. Where computations can be performed in fixed-memory chunks, we\nattempt to do so, and allow the user to hint at the maximum size of this\nworking memory (defaulting to 1GB) using :func:`set_config` or\n:func:`config_context`. The following suggests to limit temporary working\nmemory to 128 MiB::\n\n >>> import sklearn\n >>> with sklearn.config_context(working_memory=128):\n ... pass # do chunked work here\n\nAn example of a chunked operation adhering to this setting is\n:func:`~metrics.pairwise_distances_chunked`, which facilitates computing\nrow-wise reductions of a pairwise distance matrix.\n\nModel Compression\n..................\n\nModel compression in scikit-learn only concerns linear models for the moment.\nIn this context it means that we want to control the model sparsity (i.e. the\nnumber of non-zero coordinates in the model vectors). It is generally a good\nidea to combine model sparsity with sparse input data representation.\n\nHere is sample code that illustrates the use of the ``sparsify()`` method::\n\n clf = SGDRegressor(penalty='elasticnet', l1_ratio=0.25)\n clf.fit(X_train, y_train).sparsify()\n clf.predict(X_test)\n\nIn this example we prefer the ``elasticnet`` penalty as it is often a good\ncompromise between model compactness and prediction power. One can also\nfurther tune the ``l1_ratio`` parameter (in combination with the\nregularization strength ``alpha``) to control this tradeoff.\n\nA typical `benchmark `_\non synthetic data yields a >30% decrease in latency when both the model and\ninput are sparse (with 0.000024 and 0.027400 non-zero coefficients ratio\nrespectively). Your mileage may vary depending on the sparsity and size of\nyour data and model.\nFurthermore, sparsifying can be very useful to reduce the memory usage of\npredictive models deployed on production servers.\n\nModel Reshaping\n................\n\nModel reshaping consists in selecting only a portion of the available features\nto fit a model. In other words, if a model discards features during the\nlearning phase we can then strip those from the input. This has several\nbenefits. Firstly it reduces memory (and therefore time) overhead of the\nmodel itself. It also allows to discard explicit\nfeature selection components in a pipeline once we know which features to\nkeep from a previous run. Finally, it can help reduce processing time and I/O\nusage upstream in the data access and feature extraction layers by not\ncollecting and building features that are discarded by the model. For instance\nif the raw data come from a database, it can make it possible to write simpler\nand faster queries or reduce I/O usage by making the queries return lighter\nrecords.\nAt the moment, reshaping needs to be performed manually in scikit-learn.\nIn the case of sparse input (particularly in ``CSR`` format), it is generally\nsufficient to not generate the relevant features, leaving their columns empty.\n\nLinks\n......\n\n - :ref:`scikit-learn developer performance documentation `\n - `Scipy sparse matrix formats documentation `_\n" }, { "path": "doc/computing/parallelism.rst", "content": ".. Places parent toc into the sidebar\n\n:parenttoc: True\n\nParallelism, resource management, and configuration\n===================================================\n\n.. _parallelism:\n\nParallelism\n-----------\n\nSome scikit-learn estimators and utilities can parallelize costly operations\nusing multiple CPU cores, thanks to the following components:\n\n- via the `joblib `_ library. In\n this case the number of threads or processes can be controlled with the\n ``n_jobs`` parameter.\n- via OpenMP, used in C or Cython code.\n\nIn addition, some of the numpy routines that are used internally by\nscikit-learn may also be parallelized if numpy is installed with specific\nnumerical libraries such as MKL, OpenBLAS, or BLIS.\n\nWe describe these 3 scenarios in the following subsections.\n\nJoblib-based parallelism\n........................\n\nWhen the underlying implementation uses joblib, the number of workers\n(threads or processes) that are spawned in parallel can be controlled via the\n``n_jobs`` parameter.\n\n.. note::\n\n Where (and how) parallelization happens in the estimators is currently\n poorly documented. Please help us by improving our docs and tackle `issue\n 14228 `_!\n\nJoblib is able to support both multi-processing and multi-threading. Whether\njoblib chooses to spawn a thread or a process depends on the **backend**\nthat it's using.\n\nScikit-learn generally relies on the ``loky`` backend, which is joblib's\ndefault backend. Loky is a multi-processing backend. When doing\nmulti-processing, in order to avoid duplicating the memory in each process\n(which isn't reasonable with big datasets), joblib will create a `memmap\n`_\nthat all processes can share, when the data is bigger than 1MB.\n\nIn some specific cases (when the code that is run in parallel releases the\nGIL), scikit-learn will indicate to ``joblib`` that a multi-threading\nbackend is preferable.\n\nAs a user, you may control the backend that joblib will use (regardless of\nwhat scikit-learn recommends) by using a context manager::\n\n from joblib import parallel_backend\n\n with parallel_backend('threading', n_jobs=2):\n # Your scikit-learn code here\n\nPlease refer to the `joblib's docs\n`_\nfor more details.\n\nIn practice, whether parallelism is helpful at improving runtime depends on\nmany factors. It is usually a good idea to experiment rather than assuming\nthat increasing the number of workers is always a good thing. In some cases\nit can be highly detrimental to performance to run multiple copies of some\nestimators or functions in parallel (see oversubscription below).\n\nOpenMP-based parallelism\n........................\n\nOpenMP is used to parallelize code written in Cython or C, relying on\nmulti-threading exclusively. By default (and unless joblib is trying to\navoid oversubscription), the implementation will use as many threads as\npossible.\n\nYou can control the exact number of threads that are used via the\n``OMP_NUM_THREADS`` environment variable:\n\n.. prompt:: bash $\n\n OMP_NUM_THREADS=4 python my_script.py\n\nParallel Numpy routines from numerical libraries\n................................................\n\nScikit-learn relies heavily on NumPy and SciPy, which internally call\nmulti-threaded linear algebra routines implemented in libraries such as MKL,\nOpenBLAS or BLIS.\n\nThe number of threads used by the OpenBLAS, MKL or BLIS libraries can be set\nvia the ``MKL_NUM_THREADS``, ``OPENBLAS_NUM_THREADS``, and\n``BLIS_NUM_THREADS`` environment variables.\n\nPlease note that scikit-learn has no direct control over these\nimplementations. Scikit-learn solely relies on Numpy and Scipy.\n\n.. note::\n At the time of writing (2019), NumPy and SciPy packages distributed on\n pypi.org (used by ``pip``) and on the conda-forge channel are linked\n with OpenBLAS, while conda packages shipped on the \"defaults\" channel\n from anaconda.org are linked by default with MKL.\n\n\nOversubscription: spawning too many threads\n...........................................\n\nIt is generally recommended to avoid using significantly more processes or\nthreads than the number of CPUs on a machine. Over-subscription happens when\na program is running too many threads at the same time.\n\nSuppose you have a machine with 8 CPUs. Consider a case where you're running\na :class:`~sklearn.model_selection.GridSearchCV` (parallelized with joblib)\nwith ``n_jobs=8`` over a\n:class:`~sklearn.ensemble.HistGradientBoostingClassifier` (parallelized with\nOpenMP). Each instance of\n:class:`~sklearn.ensemble.HistGradientBoostingClassifier` will spawn 8 threads\n(since you have 8 CPUs). That's a total of ``8 * 8 = 64`` threads, which\nleads to oversubscription of physical CPU resources and to scheduling\noverhead.\n\nOversubscription can arise in the exact same fashion with parallelized\nroutines from MKL, OpenBLAS or BLIS that are nested in joblib calls.\n\nStarting from ``joblib >= 0.14``, when the ``loky`` backend is used (which\nis the default), joblib will tell its child **processes** to limit the\nnumber of threads they can use, so as to avoid oversubscription. In practice\nthe heuristic that joblib uses is to tell the processes to use ``max_threads\n= n_cpus // n_jobs``, via their corresponding environment variable. Back to\nour example from above, since the joblib backend of\n:class:`~sklearn.model_selection.GridSearchCV` is ``loky``, each process will\nonly be able to use 1 thread instead of 8, thus mitigating the\noversubscription issue.\n\nNote that:\n\n- Manually setting one of the environment variables (``OMP_NUM_THREADS``,\n ``MKL_NUM_THREADS``, ``OPENBLAS_NUM_THREADS``, or ``BLIS_NUM_THREADS``)\n will take precedence over what joblib tries to do. The total number of\n threads will be ``n_jobs * _NUM_THREADS``. Note that setting this\n limit will also impact your computations in the main process, which will\n only use ``_NUM_THREADS``. Joblib exposes a context manager for\n finer control over the number of threads in its workers (see joblib docs\n linked below).\n- Joblib is currently unable to avoid oversubscription in a\n multi-threading context. It can only do so with the ``loky`` backend\n (which spawns processes).\n\nYou will find additional details about joblib mitigation of oversubscription\nin `joblib documentation\n`_.\n\n\nConfiguration switches\n-----------------------\n\nPython runtime\n..............\n\n:func:`sklearn.set_config` controls the following behaviors:\n\n:assume_finite:\n\n used to skip validation, which enables faster computations but may\n lead to segmentation faults if the data contains NaNs.\n\n:working_memory:\n\n the optimal size of temporary arrays used by some algorithms.\n\n.. _environment_variable:\n\nEnvironment variables\n......................\n\nThese environment variables should be set before importing scikit-learn.\n\n:SKLEARN_SITE_JOBLIB:\n\n When this environment variable is set to a non zero value,\n scikit-learn uses the site joblib rather than its vendored version.\n Consequently, joblib must be installed for scikit-learn to run.\n Note that using the site joblib is at your own risks: the versions of\n scikit-learn and joblib need to be compatible. Currently, joblib 0.11+\n is supported. In addition, dumps from joblib.Memory might be incompatible,\n and you might loose some caches and have to redownload some datasets.\n\n .. deprecated:: 0.21\n\n As of version 0.21 this parameter has no effect, vendored joblib was\n removed and site joblib is always used.\n\n:SKLEARN_ASSUME_FINITE:\n\n Sets the default value for the `assume_finite` argument of\n :func:`sklearn.set_config`.\n\n:SKLEARN_WORKING_MEMORY:\n\n Sets the default value for the `working_memory` argument of\n :func:`sklearn.set_config`.\n\n:SKLEARN_SEED:\n\n Sets the seed of the global random generator when running the tests,\n for reproducibility.\n\n:SKLEARN_SKIP_NETWORK_TESTS:\n\n When this environment variable is set to a non zero value, the tests\n that need network access are skipped. When this environment variable is\n not set then network tests are skipped.\n" }, { "path": "doc/computing/scaling_strategies.rst", "content": ".. Places parent toc into the sidebar\n\n:parenttoc: True\n\n.. _scaling_strategies:\n\nStrategies to scale computationally: bigger data\n=================================================\n\nFor some applications the amount of examples, features (or both) and/or the\nspeed at which they need to be processed are challenging for traditional\napproaches. In these cases scikit-learn has a number of options you can\nconsider to make your system scale.\n\nScaling with instances using out-of-core learning\n--------------------------------------------------\n\nOut-of-core (or \"external memory\") learning is a technique used to learn from\ndata that cannot fit in a computer's main memory (RAM).\n\nHere is a sketch of a system designed to achieve this goal:\n\n 1. a way to stream instances\n 2. a way to extract features from instances\n 3. an incremental algorithm\n\nStreaming instances\n....................\n\nBasically, 1. may be a reader that yields instances from files on a\nhard drive, a database, from a network stream etc. However,\ndetails on how to achieve this are beyond the scope of this documentation.\n\nExtracting features\n...................\n\n\\2. could be any relevant way to extract features among the\ndifferent :ref:`feature extraction ` methods supported by\nscikit-learn. However, when working with data that needs vectorization and\nwhere the set of features or values is not known in advance one should take\nexplicit care. A good example is text classification where unknown terms are\nlikely to be found during training. It is possible to use a stateful\nvectorizer if making multiple passes over the data is reasonable from an\napplication point of view. Otherwise, one can turn up the difficulty by using\na stateless feature extractor. Currently the preferred way to do this is to\nuse the so-called :ref:`hashing trick` as implemented by\n:class:`sklearn.feature_extraction.FeatureHasher` for datasets with categorical\nvariables represented as list of Python dicts or\n:class:`sklearn.feature_extraction.text.HashingVectorizer` for text documents.\n\nIncremental learning\n.....................\n\nFinally, for 3. we have a number of options inside scikit-learn. Although not\nall algorithms can learn incrementally (i.e. without seeing all the instances\nat once), all estimators implementing the ``partial_fit`` API are candidates.\nActually, the ability to learn incrementally from a mini-batch of instances\n(sometimes called \"online learning\") is key to out-of-core learning as it\nguarantees that at any given time there will be only a small amount of\ninstances in the main memory. Choosing a good size for the mini-batch that\nbalances relevancy and memory footprint could involve some tuning [1]_.\n\nHere is a list of incremental estimators for different tasks:\n\n - Classification\n + :class:`sklearn.naive_bayes.MultinomialNB`\n + :class:`sklearn.naive_bayes.BernoulliNB`\n + :class:`sklearn.linear_model.Perceptron`\n + :class:`sklearn.linear_model.SGDClassifier`\n + :class:`sklearn.linear_model.PassiveAggressiveClassifier`\n + :class:`sklearn.neural_network.MLPClassifier`\n - Regression\n + :class:`sklearn.linear_model.SGDRegressor`\n + :class:`sklearn.linear_model.PassiveAggressiveRegressor`\n + :class:`sklearn.neural_network.MLPRegressor`\n - Clustering\n + :class:`sklearn.cluster.MiniBatchKMeans`\n + :class:`sklearn.cluster.Birch`\n - Decomposition / feature Extraction\n + :class:`sklearn.decomposition.MiniBatchDictionaryLearning`\n + :class:`sklearn.decomposition.IncrementalPCA`\n + :class:`sklearn.decomposition.LatentDirichletAllocation`\n - Preprocessing\n + :class:`sklearn.preprocessing.StandardScaler`\n + :class:`sklearn.preprocessing.MinMaxScaler`\n + :class:`sklearn.preprocessing.MaxAbsScaler`\n\nFor classification, a somewhat important thing to note is that although a\nstateless feature extraction routine may be able to cope with new/unseen\nattributes, the incremental learner itself may be unable to cope with\nnew/unseen targets classes. In this case you have to pass all the possible\nclasses to the first ``partial_fit`` call using the ``classes=`` parameter.\n\nAnother aspect to consider when choosing a proper algorithm is that not all of\nthem put the same importance on each example over time. Namely, the\n``Perceptron`` is still sensitive to badly labeled examples even after many\nexamples whereas the ``SGD*`` and ``PassiveAggressive*`` families are more\nrobust to this kind of artifacts. Conversely, the latter also tend to give less\nimportance to remarkably different, yet properly labeled examples when they\ncome late in the stream as their learning rate decreases over time.\n\nExamples\n..........\n\nFinally, we have a full-fledged example of\n:ref:`sphx_glr_auto_examples_applications_plot_out_of_core_classification.py`. It is aimed at\nproviding a starting point for people wanting to build out-of-core learning\nsystems and demonstrates most of the notions discussed above.\n\nFurthermore, it also shows the evolution of the performance of different\nalgorithms with the number of processed examples.\n\n.. |accuracy_over_time| image:: ../auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_001.png\n :target: ../auto_examples/applications/plot_out_of_core_classification.html\n :scale: 80\n\n.. centered:: |accuracy_over_time|\n\nNow looking at the computation time of the different parts, we see that the\nvectorization is much more expensive than learning itself. From the different\nalgorithms, ``MultinomialNB`` is the most expensive, but its overhead can be\nmitigated by increasing the size of the mini-batches (exercise: change\n``minibatch_size`` to 100 and 10000 in the program and compare).\n\n.. |computation_time| image:: ../auto_examples/applications/images/sphx_glr_plot_out_of_core_classification_003.png\n :target: ../auto_examples/applications/plot_out_of_core_classification.html\n :scale: 80\n\n.. centered:: |computation_time|\n\n\nNotes\n......\n\n.. [1] Depending on the algorithm the mini-batch size can influence results or\n not. SGD*, PassiveAggressive*, and discrete NaiveBayes are truly online\n and are not affected by batch size. Conversely, MiniBatchKMeans\n convergence rate is affected by the batch size. Also, its memory\n footprint can vary dramatically with batch size.\n" }, { "path": "doc/computing.rst", "content": ".. Places parent toc into the sidebar\n\n:parenttoc: True\n\n============================\nComputing with scikit-learn\n============================\n\n.. include:: includes/big_toc_css.rst\n\n.. toctree::\n :maxdepth: 2\n\n computing/scaling_strategies\n computing/computational_performance\n computing/parallelism\n" }, { "path": "doc/conf.py", "content": "# -*- coding: utf-8 -*-\n#\n# scikit-learn documentation build configuration file, created by\n# sphinx-quickstart on Fri Jan 8 09:13:42 2010.\n#\n# This file is execfile()d with the current directory set to its containing\n# dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\n\nimport sys\nimport os\nimport warnings\nimport re\nfrom datetime import datetime\nfrom packaging.version import parse\nfrom pathlib import Path\nfrom io import StringIO\n\n# If extensions (or modules to document with autodoc) are in another\n# directory, add these directories to sys.path here. If the directory\n# is relative to the documentation root, use os.path.abspath to make it\n# absolute, like shown here.\nsys.path.insert(0, os.path.abspath(\"sphinxext\"))\n\nfrom github_link import make_linkcode_resolve\nimport sphinx_gallery\nimport matplotlib as mpl\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones.\nextensions = [\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.autosummary\",\n \"numpydoc\",\n \"sphinx.ext.linkcode\",\n \"sphinx.ext.doctest\",\n \"sphinx.ext.intersphinx\",\n \"sphinx.ext.imgconverter\",\n \"sphinx_gallery.gen_gallery\",\n \"sphinx_issues\",\n \"add_toctree_functions\",\n \"sphinx-prompt\",\n \"sphinxext.opengraph\",\n \"doi_role\",\n]\n\n# Support for `plot::` directives in sphinx 3.2 requires matplotlib 3.1.0 or newer\nif parse(mpl.__version__) >= parse(\"3.1.0\"):\n extensions.append(\"matplotlib.sphinxext.plot_directive\")\n\n # Produce `plot::` directives for examples that contain `import matplotlib` or\n # `from matplotlib import`.\n numpydoc_use_plots = True\n\n # Options for the `::plot` directive:\n # https://matplotlib.org/stable/api/sphinxext_plot_directive_api.html\n plot_formats = [\"png\"]\n plot_include_source = True\n plot_html_show_formats = False\n plot_html_show_source_link = False\n\n# this is needed for some reason...\n# see https://github.com/numpy/numpydoc/issues/69\nnumpydoc_class_members_toctree = False\n\n\n# For maths, use mathjax by default and svg if NO_MATHJAX env variable is set\n# (useful for viewing the doc offline)\nif os.environ.get(\"NO_MATHJAX\"):\n extensions.append(\"sphinx.ext.imgmath\")\n imgmath_image_format = \"svg\"\n mathjax_path = \"\"\nelse:\n extensions.append(\"sphinx.ext.mathjax\")\n mathjax_path = \"https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js\"\n\nautodoc_default_options = {\"members\": True, \"inherited-members\": True}\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = [\"templates\"]\n\n# generate autosummary even if no references\nautosummary_generate = True\n\n# The suffix of source filenames.\nsource_suffix = \".rst\"\n\n# The encoding of source files.\n# source_encoding = 'utf-8'\n\n# The main toctree document.\nmain_doc = \"contents\"\n\n# General information about the project.\nproject = \"scikit-learn\"\ncopyright = f\"2007 - {datetime.now().year}, scikit-learn developers (BSD License)\"\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The short X.Y version.\nimport sklearn\n\nparsed_version = parse(sklearn.__version__)\nversion = \".\".join(parsed_version.base_version.split(\".\")[:2])\n# The full version, including alpha/beta/rc tags.\n# Removes post from release name\nif parsed_version.is_postrelease:\n release = parsed_version.base_version\nelse:\n release = sklearn.__version__\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n# language = None\n\n# There are two options for replacing |today|: either, you set today to some\n# non-false value, then it is used:\n# today = ''\n# Else, today_fmt is used as the format for a strftime call.\n# today_fmt = '%B %d, %Y'\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\nexclude_patterns = [\"_build\", \"templates\", \"includes\", \"themes\"]\n\n# The reST default role (used for this markup: `text`) to use for all\n# documents.\ndefault_role = \"literal\"\n\n# If true, '()' will be appended to :func: etc. cross-reference text.\nadd_function_parentheses = False\n\n# If true, the current module name will be prepended to all description\n# unit titles (such as .. function::).\n# add_module_names = True\n\n# If true, sectionauthor and moduleauthor directives will be shown in the\n# output. They are ignored by default.\n# show_authors = False\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = \"sphinx\"\n\n# A list of ignored prefixes for module index sorting.\n# modindex_common_prefix = []\n\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. Major themes that come with\n# Sphinx are currently 'default' and 'sphinxdoc'.\nhtml_theme = \"scikit-learn-modern\"\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\nhtml_theme_options = {\"google_analytics\": True, \"mathjax_path\": mathjax_path}\n\n# Add any paths that contain custom themes here, relative to this directory.\nhtml_theme_path = [\"themes\"]\n\n\n# The name for this set of Sphinx documents. If None, it defaults to\n# \" v documentation\".\n# html_title = None\n\n# A shorter title for the navigation bar. Default is the same as html_title.\nhtml_short_title = \"scikit-learn\"\n\n# The name of an image file (relative to this directory) to place at the top\n# of the sidebar.\nhtml_logo = \"logos/scikit-learn-logo-small.png\"\n\n# The name of an image file (within the static path) to use as favicon of the\n# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32\n# pixels large.\nhtml_favicon = \"logos/favicon.ico\"\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = [\"images\"]\n\n# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,\n# using the given strftime format.\n# html_last_updated_fmt = '%b %d, %Y'\n\n# Custom sidebar templates, maps document names to template names.\n# html_sidebars = {}\n\n# Additional templates that should be rendered to pages, maps page names to\n# template names.\nhtml_additional_pages = {\"index\": \"index.html\"}\n\n# If false, no module index is generated.\nhtml_domain_indices = False\n\n# If false, no index is generated.\nhtml_use_index = False\n\n# If true, the index is split into individual pages for each letter.\n# html_split_index = False\n\n# If true, links to the reST sources are added to the pages.\n# html_show_sourcelink = True\n\n# If true, an OpenSearch description file will be output, and all pages will\n# contain a tag referring to it. The value of this option must be the\n# base URL from which the finished HTML is served.\n# html_use_opensearch = ''\n\n# If nonempty, this is the file name suffix for HTML files (e.g. \".xhtml\").\n# html_file_suffix = ''\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = \"scikit-learndoc\"\n\n# If true, the reST sources are included in the HTML build as _sources/name.\nhtml_copy_source = True\n\n# Adds variables into templates\nhtml_context = {}\n# finds latest release highlights and places it into HTML context for\n# index.html\nrelease_highlights_dir = Path(\"..\") / \"examples\" / \"release_highlights\"\n# Finds the highlight with the latest version number\nlatest_highlights = sorted(release_highlights_dir.glob(\"plot_release_highlights_*.py\"))[\n -1\n]\nlatest_highlights = latest_highlights.with_suffix(\"\").name\nhtml_context[\n \"release_highlights\"\n] = f\"auto_examples/release_highlights/{latest_highlights}\"\n\n# get version from highlight name assuming highlights have the form\n# plot_release_highlights_0_22_0\nhighlight_version = \".\".join(latest_highlights.split(\"_\")[-3:-1])\nhtml_context[\"release_highlights_version\"] = highlight_version\n\n\n# redirects dictionary maps from old links to new links\nredirects = {\n \"documentation\": \"index\",\n \"auto_examples/feature_selection/plot_permutation_test_for_classification\": (\n \"auto_examples/model_selection/plot_permutation_tests_for_classification\"\n ),\n}\nhtml_context[\"redirects\"] = redirects\nfor old_link in redirects:\n html_additional_pages[old_link] = \"redirects.html\"\n\n\n# -- Options for LaTeX output ------------------------------------------------\nlatex_elements = {\n # The paper size ('letterpaper' or 'a4paper').\n # 'papersize': 'letterpaper',\n # The font size ('10pt', '11pt' or '12pt').\n # 'pointsize': '10pt',\n # Additional stuff for the LaTeX preamble.\n \"preamble\": r\"\"\"\n \\usepackage{amsmath}\\usepackage{amsfonts}\\usepackage{bm}\n \\usepackage{morefloats}\\usepackage{enumitem} \\setlistdepth{10}\n \\let\\oldhref\\href\n \\renewcommand{\\href}[2]{\\oldhref{#1}{\\hbox{#2}}}\n \"\"\"\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title, author, documentclass\n# [howto/manual]).\nlatex_documents = [\n (\n \"contents\",\n \"user_guide.tex\",\n \"scikit-learn user guide\",\n \"scikit-learn developers\",\n \"manual\",\n ),\n]\n\n# The name of an image file (relative to this directory) to place at the top of\n# the title page.\nlatex_logo = \"logos/scikit-learn-logo.png\"\n\n# Documents to append as an appendix to all manuals.\n# latex_appendices = []\n\n# If false, no module index is generated.\nlatex_domain_indices = False\n\ntrim_doctests_flags = True\n\n# intersphinx configuration\nintersphinx_mapping = {\n \"python\": (\"https://docs.python.org/{.major}\".format(sys.version_info), None),\n \"numpy\": (\"https://numpy.org/doc/stable\", None),\n \"scipy\": (\"https://docs.scipy.org/doc/scipy/reference\", None),\n \"matplotlib\": (\"https://matplotlib.org/\", None),\n \"pandas\": (\"https://pandas.pydata.org/pandas-docs/stable/\", None),\n \"joblib\": (\"https://joblib.readthedocs.io/en/latest/\", None),\n \"seaborn\": (\"https://seaborn.pydata.org/\", None),\n}\n\nv = parse(release)\nif v.release is None:\n raise ValueError(\n \"Ill-formed version: {!r}. Version should follow PEP440\".format(version)\n )\n\nif v.is_devrelease:\n binder_branch = \"main\"\nelse:\n major, minor = v.release[:2]\n binder_branch = \"{}.{}.X\".format(major, minor)\n\n\nclass SubSectionTitleOrder:\n \"\"\"Sort example gallery by title of subsection.\n\n Assumes README.txt exists for all subsections and uses the subsection with\n dashes, '---', as the adornment.\n \"\"\"\n\n def __init__(self, src_dir):\n self.src_dir = src_dir\n self.regex = re.compile(r\"^([\\w ]+)\\n-\", re.MULTILINE)\n\n def __repr__(self):\n return \"<%s>\" % (self.__class__.__name__,)\n\n def __call__(self, directory):\n src_path = os.path.normpath(os.path.join(self.src_dir, directory))\n\n # Forces Release Highlights to the top\n if os.path.basename(src_path) == \"release_highlights\":\n return \"0\"\n\n readme = os.path.join(src_path, \"README.txt\")\n\n try:\n with open(readme, \"r\") as f:\n content = f.read()\n except FileNotFoundError:\n return directory\n\n title_match = self.regex.search(content)\n if title_match is not None:\n return title_match.group(1)\n return directory\n\n\nsphinx_gallery_conf = {\n \"doc_module\": \"sklearn\",\n \"backreferences_dir\": os.path.join(\"modules\", \"generated\"),\n \"show_memory\": False,\n \"reference_url\": {\"sklearn\": None},\n \"examples_dirs\": [\"../examples\"],\n \"gallery_dirs\": [\"auto_examples\"],\n \"subsection_order\": SubSectionTitleOrder(\"../examples\"),\n \"binder\": {\n \"org\": \"scikit-learn\",\n \"repo\": \"scikit-learn\",\n \"binderhub_url\": \"https://mybinder.org\",\n \"branch\": binder_branch,\n \"dependencies\": \"./binder/requirements.txt\",\n \"use_jupyter_lab\": True,\n },\n # avoid generating too many cross links\n \"inspect_global_variables\": False,\n \"remove_config_comments\": True,\n}\n\n\n# The following dictionary contains the information used to create the\n# thumbnails for the front page of the scikit-learn home page.\n# key: first image in set\n# values: (number of plot in set, height of thumbnail)\ncarousel_thumbs = {\"sphx_glr_plot_classifier_comparison_001.png\": 600}\n\n\n# enable experimental module so that experimental estimators can be\n# discovered properly by sphinx\nfrom sklearn.experimental import enable_iterative_imputer # noqa\nfrom sklearn.experimental import enable_halving_search_cv # noqa\n\n\ndef make_carousel_thumbs(app, exception):\n \"\"\"produces the final resized carousel images\"\"\"\n if exception is not None:\n return\n print(\"Preparing carousel images\")\n\n image_dir = os.path.join(app.builder.outdir, \"_images\")\n for glr_plot, max_width in carousel_thumbs.items():\n image = os.path.join(image_dir, glr_plot)\n if os.path.exists(image):\n c_thumb = os.path.join(image_dir, glr_plot[:-4] + \"_carousel.png\")\n sphinx_gallery.gen_rst.scale_image(image, c_thumb, max_width, 190)\n\n\ndef filter_search_index(app, exception):\n if exception is not None:\n return\n\n # searchindex only exist when generating html\n if app.builder.name != \"html\":\n return\n\n print(\"Removing methods from search index\")\n\n searchindex_path = os.path.join(app.builder.outdir, \"searchindex.js\")\n with open(searchindex_path, \"r\") as f:\n searchindex_text = f.read()\n\n searchindex_text = re.sub(r\"{__init__.+?}\", \"{}\", searchindex_text)\n searchindex_text = re.sub(r\"{__call__.+?}\", \"{}\", searchindex_text)\n\n with open(searchindex_path, \"w\") as f:\n f.write(searchindex_text)\n\n\ndef generate_min_dependency_table(app):\n \"\"\"Generate min dependency table for docs.\"\"\"\n from sklearn._min_dependencies import dependent_packages\n\n # get length of header\n package_header_len = max(len(package) for package in dependent_packages) + 4\n version_header_len = len(\"Minimum Version\") + 4\n tags_header_len = max(len(tags) for _, tags in dependent_packages.values()) + 4\n\n output = StringIO()\n output.write(\n \" \".join(\n [\"=\" * package_header_len, \"=\" * version_header_len, \"=\" * tags_header_len]\n )\n )\n output.write(\"\\n\")\n dependency_title = \"Dependency\"\n version_title = \"Minimum Version\"\n tags_title = \"Purpose\"\n\n output.write(\n f\"{dependency_title:<{package_header_len}} \"\n f\"{version_title:<{version_header_len}} \"\n f\"{tags_title}\\n\"\n )\n\n output.write(\n \" \".join(\n [\"=\" * package_header_len, \"=\" * version_header_len, \"=\" * tags_header_len]\n )\n )\n output.write(\"\\n\")\n\n for package, (version, tags) in dependent_packages.items():\n output.write(\n f\"{package:<{package_header_len}} {version:<{version_header_len}} {tags}\\n\"\n )\n\n output.write(\n \" \".join(\n [\"=\" * package_header_len, \"=\" * version_header_len, \"=\" * tags_header_len]\n )\n )\n output.write(\"\\n\")\n output = output.getvalue()\n\n with (Path(\".\") / \"min_dependency_table.rst\").open(\"w\") as f:\n f.write(output)\n\n\ndef generate_min_dependency_substitutions(app):\n \"\"\"Generate min dependency substitutions for docs.\"\"\"\n from sklearn._min_dependencies import dependent_packages\n\n output = StringIO()\n\n for package, (version, _) in dependent_packages.items():\n package = package.capitalize()\n output.write(f\".. |{package}MinVersion| replace:: {version}\")\n output.write(\"\\n\")\n\n output = output.getvalue()\n\n with (Path(\".\") / \"min_dependency_substitutions.rst\").open(\"w\") as f:\n f.write(output)\n\n\n# Config for sphinx_issues\n\n# we use the issues path for PRs since the issues URL will forward\nissues_github_path = \"scikit-learn/scikit-learn\"\n\n\ndef setup(app):\n app.connect(\"builder-inited\", generate_min_dependency_table)\n app.connect(\"builder-inited\", generate_min_dependency_substitutions)\n # to hide/show the prompt in code examples:\n app.connect(\"build-finished\", make_carousel_thumbs)\n app.connect(\"build-finished\", filter_search_index)\n\n\n# The following is used by sphinx.ext.linkcode to provide links to github\nlinkcode_resolve = make_linkcode_resolve(\n \"sklearn\",\n \"https://github.com/scikit-learn/\"\n \"scikit-learn/blob/{revision}/\"\n \"{package}/{path}#L{lineno}\",\n)\n\nwarnings.filterwarnings(\n \"ignore\",\n category=UserWarning,\n message=(\n \"Matplotlib is currently using agg, which is a\"\n \" non-GUI backend, so cannot show the figure.\"\n ),\n)\n\n\n# maps functions with a class name that is indistinguishable when case is\n# ignore to another filename\nautosummary_filename_map = {\n \"sklearn.cluster.dbscan\": \"dbscan-function\",\n \"sklearn.covariance.oas\": \"oas-function\",\n \"sklearn.decomposition.fastica\": \"fastica-function\",\n}\n\n\n# Config for sphinxext.opengraph\n\nogp_site_url = \"https://scikit-learn/stable/\"\nogp_image = \"https://scikit-learn.org/stable/_static/scikit-learn-logo-small.png\"\nogp_use_first_image = True\nogp_site_name = \"scikit-learn\"\n" }, { "path": "doc/conftest.py", "content": "import os\nfrom os.path import exists\nfrom os.path import join\nfrom os import environ\nimport warnings\n\nfrom sklearn.utils import IS_PYPY\nfrom sklearn.utils._testing import SkipTest\nfrom sklearn.utils._testing import check_skip_network\nfrom sklearn.utils.fixes import parse_version\nfrom sklearn.datasets import get_data_home\nfrom sklearn.datasets._base import _pkl_filepath\nfrom sklearn.datasets._twenty_newsgroups import CACHE_NAME\n\n\ndef setup_labeled_faces():\n data_home = get_data_home()\n if not exists(join(data_home, \"lfw_home\")):\n raise SkipTest(\"Skipping dataset loading doctests\")\n\n\ndef setup_rcv1():\n check_skip_network()\n # skip the test in rcv1.rst if the dataset is not already loaded\n rcv1_dir = join(get_data_home(), \"RCV1\")\n if not exists(rcv1_dir):\n raise SkipTest(\"Download RCV1 dataset to run this test.\")\n\n\ndef setup_twenty_newsgroups():\n cache_path = _pkl_filepath(get_data_home(), CACHE_NAME)\n if not exists(cache_path):\n raise SkipTest(\"Skipping dataset loading doctests\")\n\n\ndef setup_working_with_text_data():\n if IS_PYPY and os.environ.get(\"CI\", None):\n raise SkipTest(\"Skipping too slow test with PyPy on CI\")\n check_skip_network()\n cache_path = _pkl_filepath(get_data_home(), CACHE_NAME)\n if not exists(cache_path):\n raise SkipTest(\"Skipping dataset loading doctests\")\n\n\ndef setup_loading_other_datasets():\n try:\n import pandas # noqa\n except ImportError:\n raise SkipTest(\"Skipping loading_other_datasets.rst, pandas not installed\")\n\n # checks SKLEARN_SKIP_NETWORK_TESTS to see if test should run\n run_network_tests = environ.get(\"SKLEARN_SKIP_NETWORK_TESTS\", \"1\") == \"0\"\n if not run_network_tests:\n raise SkipTest(\n \"Skipping loading_other_datasets.rst, tests can be \"\n \"enabled by setting SKLEARN_SKIP_NETWORK_TESTS=0\"\n )\n\n\ndef setup_compose():\n try:\n import pandas # noqa\n except ImportError:\n raise SkipTest(\"Skipping compose.rst, pandas not installed\")\n\n\ndef setup_impute():\n try:\n import pandas # noqa\n except ImportError:\n raise SkipTest(\"Skipping impute.rst, pandas not installed\")\n\n\ndef setup_grid_search():\n try:\n import pandas # noqa\n except ImportError:\n raise SkipTest(\"Skipping grid_search.rst, pandas not installed\")\n\n\ndef setup_preprocessing():\n try:\n import pandas # noqa\n\n if parse_version(pandas.__version__) < parse_version(\"1.1.0\"):\n raise SkipTest(\"Skipping preprocessing.rst, pandas version < 1.1.0\")\n except ImportError:\n raise SkipTest(\"Skipping preprocessing.rst, pandas not installed\")\n\n\ndef setup_unsupervised_learning():\n try:\n import skimage # noqa\n except ImportError:\n raise SkipTest(\"Skipping unsupervised_learning.rst, scikit-image not installed\")\n # ignore deprecation warnings from scipy.misc.face\n warnings.filterwarnings(\n \"ignore\", \"The binary mode of fromstring\", DeprecationWarning\n )\n\n\ndef skip_if_matplotlib_not_installed(fname):\n try:\n import matplotlib # noqa\n except ImportError:\n basename = os.path.basename(fname)\n raise SkipTest(f\"Skipping doctests for {basename}, matplotlib not installed\")\n\n\ndef pytest_runtest_setup(item):\n fname = item.fspath.strpath\n # normalise filename to use forward slashes on Windows for easier handling\n # later\n fname = fname.replace(os.sep, \"/\")\n\n is_index = fname.endswith(\"datasets/index.rst\")\n if fname.endswith(\"datasets/labeled_faces.rst\") or is_index:\n setup_labeled_faces()\n elif fname.endswith(\"datasets/rcv1.rst\") or is_index:\n setup_rcv1()\n elif fname.endswith(\"datasets/twenty_newsgroups.rst\") or is_index:\n setup_twenty_newsgroups()\n elif (\n fname.endswith(\"tutorial/text_analytics/working_with_text_data.rst\") or is_index\n ):\n setup_working_with_text_data()\n elif fname.endswith(\"modules/compose.rst\") or is_index:\n setup_compose()\n elif IS_PYPY and fname.endswith(\"modules/feature_extraction.rst\"):\n raise SkipTest(\"FeatureHasher is not compatible with PyPy\")\n elif fname.endswith(\"datasets/loading_other_datasets.rst\"):\n setup_loading_other_datasets()\n elif fname.endswith(\"modules/impute.rst\"):\n setup_impute()\n elif fname.endswith(\"modules/grid_search.rst\"):\n setup_grid_search()\n elif fname.endswith(\"modules/preprocessing.rst\"):\n setup_preprocessing()\n elif fname.endswith(\"statistical_inference/unsupervised_learning.rst\"):\n setup_unsupervised_learning()\n\n rst_files_requiring_matplotlib = [\n \"modules/partial_dependence.rst\",\n \"modules/tree.rst\",\n \"tutorial/statistical_inference/settings.rst\",\n \"tutorial/statistical_inference/supervised_learning.rst\",\n ]\n for each in rst_files_requiring_matplotlib:\n if fname.endswith(each):\n skip_if_matplotlib_not_installed(fname)\n\n\ndef pytest_configure(config):\n # Use matplotlib agg backend during the tests including doctests\n try:\n import matplotlib\n\n matplotlib.use(\"agg\")\n except ImportError:\n pass\n" }, { "path": "doc/contents.rst", "content": ".. include:: includes/big_toc_css.rst\n.. include:: tune_toc.rst\n\n.. Places global toc into the sidebar\n\n:globalsidebartoc: True\n\n=================\nTable Of Contents\n=================\n\n.. Define an order for the Table of Contents:\n\n.. toctree::\n :maxdepth: 2\n\n preface\n tutorial/index\n getting_started\n user_guide\n glossary\n auto_examples/index\n modules/classes\n developers/index\n" }, { "path": "doc/data_transforms.rst", "content": ".. Places parent toc into the sidebar\n\n:parenttoc: True\n\n.. include:: includes/big_toc_css.rst\n\n.. _data-transforms:\n\nDataset transformations\n-----------------------\n\nscikit-learn provides a library of transformers, which may clean (see\n:ref:`preprocessing`), reduce (see :ref:`data_reduction`), expand (see\n:ref:`kernel_approximation`) or generate (see :ref:`feature_extraction`)\nfeature representations.\n\nLike other estimators, these are represented by classes with a ``fit`` method,\nwhich learns model parameters (e.g. mean and standard deviation for\nnormalization) from a training set, and a ``transform`` method which applies\nthis transformation model to unseen data. ``fit_transform`` may be more\nconvenient and efficient for modelling and transforming the training data\nsimultaneously.\n\nCombining such transformers, either in parallel or series is covered in\n:ref:`combining_estimators`. :ref:`metrics` covers transforming feature\nspaces into affinity matrices, while :ref:`preprocessing_targets` considers\ntransformations of the target space (e.g. categorical labels) for use in\nscikit-learn.\n\n.. toctree::\n :maxdepth: 2\n\n modules/compose\n modules/feature_extraction\n modules/preprocessing\n modules/impute\n modules/unsupervised_reduction\n modules/random_projection\n modules/kernel_approximation\n modules/metrics\n modules/preprocessing_targets\n" }, { "path": "doc/datasets/loading_other_datasets.rst", "content": ".. Places parent toc into the sidebar\n\n:parenttoc: True\n\n.. _loading_other_datasets:\n\nLoading other datasets\n======================\n\n.. currentmodule:: sklearn.datasets\n\n.. _sample_images:\n\nSample images\n-------------\n\nScikit-learn also embeds a couple of sample JPEG images published under Creative\nCommons license by their authors. Those images can be useful to test algorithms\nand pipelines on 2D data.\n\n.. autosummary::\n\n load_sample_images\n load_sample_image\n\n.. image:: ../auto_examples/cluster/images/sphx_glr_plot_color_quantization_001.png\n :target: ../auto_examples/cluster/plot_color_quantization.html\n :scale: 30\n :align: right\n\n\n.. warning::\n\n The default coding of images is based on the ``uint8`` dtype to\n spare memory. Often machine learning algorithms work best if the\n input is converted to a floating point representation first. Also,\n if you plan to use ``matplotlib.pyplpt.imshow``, don't forget to scale to the range\n 0 - 1 as done in the following example.\n\n.. topic:: Examples:\n\n * :ref:`sphx_glr_auto_examples_cluster_plot_color_quantization.py`\n\n.. _libsvm_loader:\n\nDatasets in svmlight / libsvm format\n------------------------------------\n\nscikit-learn includes utility functions for loading\ndatasets in the svmlight / libsvm format. In this format, each line\ntakes the form ``