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0.22.1 release (scikit-learn#15998)
* DOC fixed default values in dbscan (scikit-learn#15753) * DOC fix incorrect branch reference in contributing doc (scikit-learn#15779) * DOC relabel Feature -> Efficiency in change log (scikit-learn#15770) * DOC fixed Birch default value (scikit-learn#15780) * STY Minior change on code padding in website theme (scikit-learn#15768) * DOC Fix yticklabels order in permutation importances example (scikit-learn#15799) * Fix yticklabels order in permutation importances example * STY Update wrapper width (scikit-learn#15793) * DOC Long sentence was hard to parse and ambiguous in _classification.py (scikit-learn#15769) * DOC Removed duplicate 'classes_' attribute in Naive Bayes classifiers (scikit-learn#15811) * BUG Fixes pandas dataframe bug with boolean dtypes (scikit-learn#15797) * BUG Returns only public estimators in all_estimators (scikit-learn#15380) * DOC improve doc for multiclass and types_of_target (scikit-learn#15333) * TST Increases tol for check_pca_float_dtype_preservation assertion (scikit-learn#15775) * update _alpha_grid class in _coordinate_descent.py (scikit-learn#15835) * FIX Explicit conversion of ndarray to object dtype. (scikit-learn#15832) * BLD Parallelize sphinx builds on circle ci (scikit-learn#15745) * DOC correct url for preprocessing (scikit-learn#15853) * MNT avoid generating too many cross links in examples (scikit-learn#15844) * DOC Correct wrong doc in precision_recall_fscore_support (scikit-learn#15833) * DOC add comment in check_pca_float_dtype_preservation (scikit-learn#15819) Documenting the changes in scikit-learn#15775 * DOC correct indents in docstring _split.py (scikit-learn#15843) * DOC fix docstring of KMeans based on sklearn guideline (scikit-learn#15754) * DOC fix docstring of AgglomerativeClustering based on sklearn guideline (scikit-learn#15764) * DOC fix docstring of AffinityPropagation based on sklearn guideline (scikit-learn#15777) * DOC fixed SpectralCoclustering and SpectralBiclustering docstrings following sklearn guideline (scikit-learn#15778) * DOC fix FeatureAgglomeration and MiniBatchKMeans docstring following sklearn guideline (scikit-learn#15809) * TST Specify random_state in test_cv_iterable_wrapper (scikit-learn#15829) * DOC Include LinearSV{C, R} in models that support sample_weights (scikit-learn#15871) * DOC correct some indents (scikit-learn#15875) * DOC Fix documentation of default values in tree classes (scikit-learn#15870) * DOC fix typo in docstring (scikit-learn#15887) * DOC FIX default value for xticks_rotation in plot_confusion_matrix (scikit-learn#15890) * Fix imports in pip3 ubuntu by suffixing affected files (scikit-learn#15891) * MNT Raise erorr when normalize is invalid in confusion_matrix (scikit-learn#15888) * [MRG] DOC Increases search results for API object results (scikit-learn#15574) * MNT Ignores warning in pyamg for deprecated scipy.random (scikit-learn#15914) * DOC Instructions to troubleshoot Windows path length limit (scikit-learn#15916) * DOC add versionadded directive to some estimators (scikit-learn#15849) * DOC clarify doc-string of roc_auc_score and add references (scikit-learn#15293) * MNT Adds skip lint to azure pipeline CI (scikit-learn#15904) * BLD Fixes bug when building with NO_MATHJAX=1 (scikit-learn#15892) * [MRG] BUG Checks to number of axes in passed in ax more generically (scikit-learn#15760) * EXA Minor fixes in plot_sparse_logistic_regression_20newsgroups.py (scikit-learn#15925) * BUG Do not shadow public functions with deprecated modules (scikit-learn#15846) * Import sklearn._distributor_init first (scikit-learn#15929) * DOC Fix typos, via a Levenshtein-style corrector (scikit-learn#15923) * DOC in canned comment, mention that PR title becomes commit me… (scikit-learn#15935) * DOC/EXA Correct spelling of "Classification" (scikit-learn#15938) * BUG fix pip3 ubuntu update by suffixing file (scikit-learn#15928) * [MRG] Ways to compute center_shift_total were different in "full" and "elkan" algorithms. (scikit-learn#15930) * TST Fixes integer test for train and test indices (scikit-learn#15941) * BUG ensure that parallel/sequential give the same permutation importances (scikit-learn#15933) * Formatting fixes in changelog (scikit-learn#15944) * MRG FIX: order of values of self.quantiles_ in QuantileTransformer (scikit-learn#15751) * [MRG] BUG Fixes constrast in plot_confusion_matrix (scikit-learn#15936) * BUG use zero_division argument in classification_report (scikit-learn#15879) * DOC change logreg solver in plot_logistic_path (scikit-learn#15927) * DOC fix whats new ordering (scikit-learn#15961) * COSMIT use np.iinfo to define the max int32 (scikit-learn#15960) * DOC Apply numpydoc validation to VotingRegressor methods (scikit-learn#15969) Co-authored-by: Tiffany R. Williams <Tiffany8@users.noreply.github.com> * DOC improve naive_bayes.py documentation (scikit-learn#15943) Co-authored-by: Jigna Panchal <40188288+jigna-panchal@users.noreply.github.com> * DOC Fix default values in Perceptron documentation (scikit-learn#15965) * DOC Improve default values in logistic documentation (scikit-learn#15966) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * DOC Improve documentation of default values for imputers (scikit-learn#15964) * EXA/MAINT Simplify code in manifold learning example (scikit-learn#15949) * DOC Improve default values in SGD documentation (scikit-learn#15967) * DOC Improve defaults in neural network documentation (scikit-learn#15968) * FIX use safe_sparse_dot for callable kernel in LabelSpreading (scikit-learn#15868) * BUG Adds attributes back to check_is_fitted (scikit-learn#15947) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> * DOC update check_is_fitted what's new * DOC change python-devel to python3-devel for yum. (scikit-learn#15986) * DOC Correct the default value of values_format in plot_confusion_matrix (scikit-learn#15981) * [MRG] MNT Updates pypy to use 7.2.0 (scikit-learn#15954) * FIX Add missing 'values_format' param to disp.plot() in plot_confusion_matrix (scikit-learn#15937) * FIX support scalar values in fit_params in SearchCV (scikit-learn#15863) * support a scalar fit param * pep8 * TST add test for desired behavior * FIX introduce _check_fit_params to validate parameters * DOC update whats new * TST tests both grid-search and randomize-search * PEP8 * DOC revert unecessary change * TST add test for _check_fit_params * olivier comments * TST fixes * DOC whats new * DOC whats new * TST revert type of error * add olivier suggestions * address olivier comments * address thomas comments * PEP8 * comments olivier * TST fix test by passing X * avoid to call twice tocsr * add case column/row sparse in check_fit_param * provide optional indices * TST check content when indexing params * PEP8 * TST update tests to check identity * stupid fix * use a distribution in RandomizedSearchCV * MNT add lightgbm to one of the CI build * move to another build * do not install dependencies lightgbm * MNT comments on the CI setup * address some comments * Test fit_params compat without dependency on lightgbm Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> * Remove abstractmethod that silently brake downstream packages (scikit-learn#15996) * FIX restore BaseNB._check_X without abstractmethod decoration (scikit-learn#15997) * Update v0.22 changelog for 0.22.1 (scikit-learn#16002) - set the date - move entry for quantile transformer to the 0.22.1 section - fix alphabetical ordering of modules * STY Removes hidden scroll bar (scikit-learn#15999) * Flake8 fixes * Fix: remove left-over lines that should have been deleted during conflict resolution when rebasing * Fix missing imports * Update version * Fix test_check_is_fitted * Make test_sag_regressor_computed_correctly deterministic (scikit-learn#16003) Fix scikit-learn#15818. Co-authored-by: cgsavard <claire.savard@colorado.edu> Co-authored-by: Joel Nothman <joel.nothman@gmail.com> Co-authored-by: Thomas J Fan <thomasjpfan@gmail.com> Co-authored-by: Matt Hall <matt@agilegeoscience.com> Co-authored-by: Kathryn Poole <kathryn.poole2@gmail.com> Co-authored-by: lucyleeow <jliu176@gmail.com> Co-authored-by: JJmistry <jayminm22@gmail.com> Co-authored-by: Juan Carlos Alfaro Jiménez <JuanCarlos.Alfaro@uclm.es> Co-authored-by: SylvainLan <sylvain.s.lannuzel@gmail.com> Co-authored-by: Nicolas Hug <contact@nicolas-hug.com> Co-authored-by: Hanmin Qin <qinhanmin2005@sina.com> Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com> Co-authored-by: Vachan D A <vachanda@users.noreply.github.com> Co-authored-by: Sambhav Kothari <sambhavs.email@gmail.com> Co-authored-by: wenliwyan <12013376+wenliwyan@users.noreply.github.com> Co-authored-by: shivamgargsya <shivam.gargshya@gmail.com> Co-authored-by: Reshama Shaikh <rs2715@stern.nyu.edu> Co-authored-by: Oliver Urs Lenz <oulenz@users.noreply.github.com> Co-authored-by: Loïc Estève <loic.esteve@ymail.com> Co-authored-by: Brian Wignall <BrianWignall@gmail.com> Co-authored-by: Ritchie Ng <ritchieng@u.nus.edu> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: inderjeet <43402782+inder128@users.noreply.github.com> Co-authored-by: scibol <scibol@users.noreply.github.com> Co-authored-by: Tirth Patel <tirthasheshpatel@gmail.com> Co-authored-by: Bibhash Chandra Mitra <bibhashm220896@gmail.com> Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org> Co-authored-by: Tiffany R. Williams <Tiffany8@users.noreply.github.com> Co-authored-by: Jigna Panchal <40188288+jigna-panchal@users.noreply.github.com> Co-authored-by: @nkish <19225359+ankishb@users.noreply.github.com> Co-authored-by: Pulkit Mehta <pulkit_mehta_work@yahoo.com> Co-authored-by: David Breuer <DavidBreuer@users.noreply.github.com> Co-authored-by: Niklas <niklas.sm+github@gmail.com> Co-authored-by: Windber <guolipengyeah@126.com> Co-authored-by: Stephen Blystone <29995339+blynotes@users.noreply.github.com> Co-authored-by: Brigitta Sipőcz <b.sipocz@gmail.com>
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‎.circleci/config.yml

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pypy3:
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docker:
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- image: pypy:3.6-7.1.1
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- image: pypy:3.6-7.2.0
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steps:
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- restore_cache:
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keys:

‎azure-pipelines.yml

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- bash: conda create --name flake8_env --yes flake8
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displayName: Install flake8
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- bash: |
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source activate flake8_env
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./build_tools/circle/linting.sh
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if [[ $BUILD_SOURCEVERSIONMESSAGE =~ \[lint\ skip\] ]]; then
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# skip linting
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echo "Skipping linting"
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exit 0
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else
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source activate flake8_env
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./build_tools/circle/linting.sh
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fi
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displayName: Run linting
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JOBLIB_VERSION: '0.12.3'
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COVERAGE: 'true'
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# Linux environment to test the latest available dependencies and MKL.
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# It runs tests requiring pandas and PyAMG.
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# It runs tests requiring lightgbm, pandas and PyAMG.
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pylatest_pip_openblas_pandas:
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DISTRIB: 'conda-pip-latest'
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# FIXME: pinned until SciPy wheels are available for Pyhon 3.8
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PYTHON_VERSION: '3.8'
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PYTEST_VERSION: '4.6.2'
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COVERAGE: 'true'

‎benchmarks/bench_plot_randomized_svd.py

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# in case the reconstructed (dense) matrix is too large
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MAX_MEMORY = np.int(2e9)
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# The following datasets can be dowloaded manually from:
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# The following datasets can be downloaded manually from:
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# CIFAR 10: https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
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# SVHN: http://ufldl.stanford.edu/housenumbers/train_32x32.mat
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CIFAR_FOLDER = "./cifar-10-batches-py/"

‎benchmarks/bench_text_vectorizers.py

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print("Using a subset of the 20 newsrgoups dataset ({} documents)."
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print("Using a subset of the 20 newsgroups dataset ({} documents)."
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.format(len(text)))
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‎build_tools/azure/install.sh

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version_ge() {
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# The two version numbers are separated with a new line is piped to sort
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# -rV. The -V activates for version number sorting and -r sorts in
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# decending order. If the first argument is the top element of the sort, it
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# descending order. If the first argument is the top element of the sort, it
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python -m pip install pandas matplotlib pyamg
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# do not install dependencies for lightgbm since it requires scikit-learn
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python -m pip install lightgbm --no-deps
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‎build_tools/circle/build_test_pypy.sh

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# compatibility is resolved for numpy v1.6.x. For instance,
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# when PyPy3 >6.0 is released (see numpy/numpy#12740)
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pip install --extra-index https://antocuni.github.io/pypy-wheels/ubuntu numpy Cython pytest
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pip install -U pip
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# pins versions to install wheel from https://antocuni.github.io/pypy-wheels/manylinux2010
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pip install --extra-index-url https://antocuni.github.io/pypy-wheels/manylinux2010 numpy==1.18.0 scipy==1.3.2
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# Install Cython directly
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pip install https://antocuni.github.io/pypy-wheels/ubuntu/Cython/Cython-0.29.14-py3-none-any.whl
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‎conftest.py

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‎doc/Makefile

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# You can set these variables from the command line.
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‎doc/conf.py

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mathjax_path = ''
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},
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'inspect_global_variables': False,
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‎doc/developers/advanced_installation.rst

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#. Install a compiler with OpenMP_ support for your platform. See intructions
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#. Install a compiler with OpenMP_ support for your platform. See instructions
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

‎doc/developers/contributing.rst

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the user guide, with small code snippets. If relevant, please also add
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---------------------- -------------------
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[ci skip] CI is skipped completely
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[lint skip] Azure pipeline skips linting
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‎doc/developers/maintainer.rst

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‎doc/developers/tips.rst

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Please use links to comments, which make it a lot easier to see what you are referring to, rather than just linking to the issue. See [this](https://stackoverflow.com/questions/25163598/how-do-i-reference-a-specific-issue-comment-on-github) for more details.
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PR-NEW: Better description and title
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Thanks for the pull request! Please make the title of the PR descriptive so that we can easily recall the issue it is resolving. You should state what issue (or PR) it fixes/resolves in the description (see [here](http://scikit-learn.org/dev/developers/contributing.html#contributing-pull-requests)).
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Thanks for the pull request! Please make the title of the PR more descriptive. The title will become the commit message when this is merged. You should state what issue (or PR) it fixes/resolves in the description using the syntax described [here](http://scikit-learn.org/dev/developers/contributing.html#contributing-pull-requests).
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‎doc/glossary.rst

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highlights the use of the different formats for multilabel.
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‎doc/install.rst

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a Raspberry Pi).
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The `WinPython <https://winpython.github.io/>`_ project distributes
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Troubleshooting
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===============
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.. _windows_longpath:
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Error caused by file path length limit on Windows
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-------------------------------------------------
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It can happen that pip fails to install packages when reaching the default path
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size limit of Windows if Python is installed in a nested location such as the
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`AppData` folder structure under the user home directory, for instance::
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Collecting scikit-learn
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...
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Installing collected packages: scikit-learn
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ERROR: Could not install packages due to an EnvironmentError: [Errno 2] No such file or directory: 'C:\\Users\\username\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python37\\site-packages\\sklearn\\datasets\\tests\\data\\openml\\292\\api-v1-json-data-list-data_name-australian-limit-2-data_version-1-status-deactivated.json.gz'
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In this case it is possible to lift that limit in the Windows registry by
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#. Go to the
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key.
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it to 1.
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#. Reinstall scikit-learn (ignoring the previous broken installation)::
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pip install --exists-action=i scikit-learn

‎doc/modules/clustering.rst

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@@ -775,7 +775,7 @@ core sample, and is at least ``eps`` in distance from any core sample, is
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considered an outlier by the algorithm.
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While the parameter ``min_samples`` primarily controls how tolerant the
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algorithm is towards noise (on noisy and large data sets it may be desiable
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algorithm is towards noise (on noisy and large data sets it may be desirable
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to increase this parameter), the parameter ``eps`` is *crucial to choose
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appropriately* for the data set and distance function and usually cannot be
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left at the default value. It controls the local neighborhood of the points.

‎doc/modules/model_evaluation.rst

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@@ -1348,8 +1348,8 @@ the one-vs-rest algorithm computes the average of the ROC AUC scores for each
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class against all other classes. In both cases, the predicted labels are
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provided in an array with values from 0 to ``n_classes``, and the scores
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correspond to the probability estimates that a sample belongs to a particular
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class. The OvO and OvR algorithms supports weighting uniformly
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(``average='macro'``) and weighting by the prevalence (``average='weighted'``).
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class. The OvO and OvR algorithms support weighting uniformly
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(``average='macro'``) and by prevalence (``average='weighted'``).
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**One-vs-one Algorithm**: Computes the average AUC of all possible pairwise
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combinations of classes. [HT2001]_ defines a multiclass AUC metric weighted
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``'weighted'``. The ``'weighted'`` option returns a prevalence-weighted average
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as described in [FC2009]_.
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**One-vs-rest Algorithm**: Computes the AUC of each class against the rest.
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The algorithm is functionally the same as the multilabel case. To enable this
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algorithm set the keyword argument ``multiclass`` to ``'ovr'``. Similar to
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OvO, OvR supports two types of averaging: ``'macro'`` [F2006]_ and
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**One-vs-rest Algorithm**: Computes the AUC of each class against the rest
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[PD2000]_. The algorithm is functionally the same as the multilabel case. To
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enable this algorithm set the keyword argument ``multiclass`` to ``'ovr'``.
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Like OvO, OvR supports two types of averaging: ``'macro'`` [F2006]_ and
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``'weighted'`` [F2001]_.
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In applications where a high false positive rate is not tolerable the parameter
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<https://www.math.ucdavis.edu/~saito/data/roc/ferri-class-perf-metrics.pdf>`_
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Pattern Recognition Letters. 30. 27-38.
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.. [PD2000] Provost, F., Domingos, P. (2000). Well-trained PETs: Improving
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probability estimation trees (Section 6.2), CeDER Working Paper #IS-00-04,
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Stern School of Business, New York University.
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.. [F2006] Fawcett, T., 2006. `An introduction to ROC analysis.
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<http://www.sciencedirect.com/science/article/pii/S016786550500303X>`_
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Pattern Recognition Letters, 27(8), pp. 861-874.

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