From d49cdc04c9a574a9ad8cd96eee2cb7d8eba91d9e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?= Date: Mon, 6 May 2024 10:52:36 +0200 Subject: [PATCH 001/275] Release 1.5.0rc1 (#28949) --- pyproject.toml | 4 ++-- sklearn/__init__.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 69d9702716cb5..d9b95422e7ee5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "scikit-learn" -version = "1.5.dev0" +version = "1.5.0rc1" description = "A set of python modules for machine learning and data mining" readme = "README.rst" maintainers = [ @@ -95,7 +95,7 @@ build-backend = "mesonpy" requires = [ "meson-python>=0.15.0", "Cython>=3.0.10", - "numpy>=1.25", + "numpy>=2.0.0rc1", "scipy>=1.6.0", ] diff --git a/sklearn/__init__.py b/sklearn/__init__.py index 45a26334a25f5..63b08e022f23d 100644 --- a/sklearn/__init__.py +++ b/sklearn/__init__.py @@ -42,7 +42,7 @@ # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer. # 'X.Y.dev0' is the canonical version of 'X.Y.dev' # -__version__ = "1.5.dev0" +__version__ = "1.5.0rc1" # On OSX, we can get a runtime error due to multiple OpenMP libraries loaded From 4023f7f2f341e331547c69a9ecb690197a52890b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?= Date: Mon, 6 May 2024 10:54:15 +0200 Subject: [PATCH 002/275] trigger wheel builder [cd build] From 567b955a9798fe9a3820909e7192e5bcfe045924 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 6 May 2024 10:59:57 +0200 Subject: [PATCH 003/275] :lock: :robot: CI Update lock files for cirrus-arm CI build(s) :lock: :robot: (#28954) --- ...pymin_conda_forge_linux-aarch64_conda.lock | 20 +++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock index d9fa69b319d28..585a75c078d8c 100644 --- a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock +++ b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock @@ -4,17 +4,17 @@ @EXPLICIT https://conda.anaconda.org/conda-forge/linux-aarch64/ca-certificates-2024.2.2-hcefe29a_0.conda#57c226edb90c4e973b9b7503537dd339 https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.40-hba4e955_0.conda#b55c1cb33c63d23b542fa53f24541e56 -https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-ng-13.2.0-h9a76618_5.conda#1b79d37dce0fad96bdf3de03925f43b4 +https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-ng-13.2.0-h3f4de04_6.conda#dfe2ae16945dc08f163307a6bb3e70e0 https://conda.anaconda.org/conda-forge/linux-aarch64/python_abi-3.9-4_cp39.conda#c191905a08694e4a5cb1238e90233878 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 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-https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran5-13.2.0-h582850c_5.conda#547486aac825d236de3beecb927b389c +https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran5-13.2.0-h87d9d71_6.conda#a3fdb6378e561e73c735ec30207daa15 https://conda.anaconda.org/conda-forge/linux-aarch64/libjpeg-turbo-3.0.0-h31becfc_1.conda#ed24e702928be089d9ba3f05618515c6 https://conda.anaconda.org/conda-forge/linux-aarch64/libnsl-2.0.1-h31becfc_0.conda#c14f32510f694e3185704d89967ec422 https://conda.anaconda.org/conda-forge/linux-aarch64/libuuid-2.38.1-hb4cce97_0.conda#000e30b09db0b7c775b21695dff30969 @@ -23,26 +23,26 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/libxcrypt-4.4.36-h31becfc_1 https://conda.anaconda.org/conda-forge/linux-aarch64/libzlib-1.2.13-h31becfc_5.conda#b213aa87eea9491ef7b129179322e955 https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.4.20240210-h0425590_0.conda#c1a1612ddaee95c83abfa0b2ec858626 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https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlienc-1.1.0-h31becfc_1.conda#ad3d3a826b5848d99936e4466ebbaa26 -https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran-ng-13.2.0-he9431aa_5.conda#fab7c6a8c84492e18cbe578820e97a56 +https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran-ng-13.2.0-he9431aa_6.conda#c8ab19934c000ea8cc9cf1fc6c2aa83d https://conda.anaconda.org/conda-forge/linux-aarch64/libpng-1.6.43-h194ca79_0.conda#1123e504d9254dd9494267ab9aba95f0 https://conda.anaconda.org/conda-forge/linux-aarch64/libsqlite-3.45.3-h194ca79_0.conda#fb35b8afbe9e92467ac7b5608d60b775 https://conda.anaconda.org/conda-forge/linux-aarch64/libxcb-1.15-h2a766a3_0.conda#eb3d8c8170e3d03f2564ed2024aa00c8 https://conda.anaconda.org/conda-forge/linux-aarch64/readline-8.2-h8fc344f_1.conda#105eb1e16bf83bfb2eb380a48032b655 https://conda.anaconda.org/conda-forge/linux-aarch64/tk-8.6.13-h194ca79_0.conda#f75105e0585851f818e0009dd1dde4dc -https://conda.anaconda.org/conda-forge/linux-aarch64/zstd-1.5.5-h4c53e97_0.conda#b74eb9dbb5c3c15cb3cee7cbdf198c75 +https://conda.anaconda.org/conda-forge/linux-aarch64/zstd-1.5.6-h02f22dd_0.conda#be8d5f8cf21aed237b8b182ea86b3dd6 https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-bin-1.1.0-h31becfc_1.conda#9e4a13596ab651ea8d77aae023d0ce3f https://conda.anaconda.org/conda-forge/linux-aarch64/freetype-2.12.1-hf0a5ef3_2.conda#a5ab74c5bd158c3d5532b66d8d83d907 https://conda.anaconda.org/conda-forge/linux-aarch64/libhiredis-1.0.2-h05efe27_0.tar.bz2#a87f068744fd20334cd41489eb163bee https://conda.anaconda.org/conda-forge/linux-aarch64/libopenblas-0.3.27-pthreads_h5a5ec62_0.conda#ffecca8f4f31cd50b92c0e6e6bfe4416 https://conda.anaconda.org/conda-forge/linux-aarch64/libtiff-4.6.0-hf980d43_3.conda#b6f3abf5726ae33094bee238b4eb492f -https://conda.anaconda.org/conda-forge/linux-aarch64/llvm-openmp-18.1.3-h8b0cb96_0.conda#cd4d2b7580dd020814ea34ebbbca8c5e +https://conda.anaconda.org/conda-forge/linux-aarch64/llvm-openmp-18.1.4-h767c9be_0.conda#2572130272fb725d825c9b52e5ce096b https://conda.anaconda.org/conda-forge/linux-aarch64/python-3.9.19-h4ac3b42_0_cpython.conda#1501507cd9451472ec8900d587ce872f https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-1.1.0-h31becfc_1.conda#e41f5862ac746428407f3fd44d2ed01f https://conda.anaconda.org/conda-forge/linux-aarch64/ccache-4.9.1-h6552966_0.conda#758b202f61f6bbfd2c6adf0fde043276 @@ -64,7 +64,7 @@ https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda#d3 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.2-pyhd8ed1ab_0.conda#b9a4dacf97241704529131a0dfc0494f https://conda.anaconda.org/conda-forge/noarch/setuptools-69.5.1-pyhd8ed1ab_0.conda#7462280d81f639363e6e63c81276bd9e https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2 -https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.4.0-pyhc1e730c_0.conda#b296278eef667c673bf51de6535bad88 +https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96 https://conda.anaconda.org/conda-forge/linux-aarch64/tornado-6.4-py39h7cc1d5f_0.conda#2c06a653ebfa389c18aea2d8f338df3b https://conda.anaconda.org/conda-forge/linux-aarch64/unicodedata2-15.1.0-py39h898b7ef_0.conda#8c072c9329aeea97a46005625267a851 @@ -72,7 +72,7 @@ https://conda.anaconda.org/conda-forge/noarch/wheel-0.43.0-pyhd8ed1ab_1.conda#0b https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda#2e4d6bc0b14e10f895fc6791a7d9b26a https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.51.0-py39h898b7ef_0.conda#7b6a069c66a729454fb4c534ed145dcd https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1ab_0.conda#c5d3907ad8bd7bf557521a1833cf7e6d -https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.0-pyhd8ed1ab_0.conda#e0ed1bf13ce3a440e022157bf4764465 +https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-22_linuxaarch64_openblas.conda#fbe7fe553f2cc78a0311e009b26f180d https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.9.0-22_linuxaarch64_openblas.conda#8c709d281609792c39b1d5c0241f90f1 https://conda.anaconda.org/conda-forge/noarch/meson-1.4.0-pyhd8ed1ab_0.conda#52a0660cfa40b45bf254ecc3374cb2e0 @@ -88,7 +88,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-1.26.4-py39h91c28bb_0 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-22_linuxaarch64_openblas.conda#a5b77b6c6807661afd716f33e85814b3 https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.2.1-py39hd16970a_0.conda#66b9718539ecdd38876b0176c315bcad -https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.13.0-py39h91c28bb_0.conda#2b6f1ed053a61c2447304e4b810fc397 +https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.13.0-py39hb921187_1.conda#2717303c0d13a5646308b3763bf4daa4 https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.122-openblas.conda#65bc48b3bc85f8eeeab54311443a83aa https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.8.4-py39h8e43113_0.conda#f397ddfe5c551732de61a92106a14cf3 https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-3.8.4-py39ha65689a_0.conda#d501bb96ff505fdd431fd8fdac8efbf9 From 3b99b256919d8f8bb9267d1e95a9b5301b81c403 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 6 May 2024 11:01:04 +0200 Subject: [PATCH 004/275] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#28955) --- ...pylatest_pip_scipy_dev_linux-64_conda.lock | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index dd70d9af4d30a..8324d1edb36b7 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -10,27 +10,27 @@ https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda#b37 https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-11.2.0-h1234567_1.conda#57623d10a70e09e1d048c2b2b6f4e2dd https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d281e9c2192cb3fa425655a8defb85 https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464 -https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h5eee18b_5.conda#9c8dec113089c4aca7392c6a3864f505 +https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h5eee18b_6.conda#f21a3ff51c1b271977f53ce956a69297 https://repo.anaconda.com/pkgs/main/linux-64/expat-2.6.2-h6a678d5_0.conda#55049db2772dae035f6b8a95f72b5970 -https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_0.conda#06e288f9250abef59b9a367d151fc339 +https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_1.conda#70646cc713f0c43926cfdcfe9b695fe0 https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.41.5-h5eee18b_0.conda#4a6a2354414c9080327274aa514e5299 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c -https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.13-h7f8727e_0.conda#c73d46a4d666da0ae3dcd3fd8f805122 -https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_0.conda#81a9916f581d4da15a3839216a487c66 -https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_0.conda#333e31fbfbb5057c92fa845ad6adef93 +https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.13-h7f8727e_1.conda#d1d1fc47640fe0d9f7fa64c0a054bfd8 +https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_1.conda#1562802f843297ee776a50b9329597ed +https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be42180685cce6e6b0329201d9f48efb https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.12-h1ccaba5_0.conda#fa10ff4aa631fa4aa090a6234d7770b9 -https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.41.2-h5eee18b_0.conda#c7086c9ceb6cfe1c4c729a774a2d88a5 +https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda#acf93d6aceb74d6110e20b44cc45939e https://repo.anaconda.com/pkgs/main/linux-64/python-3.12.3-h996f2a0_0.conda#77af2bd351a8311d1e780bcfa7819bb8 https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.2.2-py312h06a4308_0.conda#83ba634cde4f30d9e0b88e4ac9716ca4 -https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.41.2-py312h06a4308_0.conda#b2c4f82880d58d679f3982370d80c0e2 +https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py312h06a4308_0.conda#18d5f3b68a175c72576876db4afc9e9e https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py312h06a4308_0.conda#e1d44bca4a257e84af33503233491107 # pip alabaster @ https://files.pythonhosted.org/packages/32/34/d4e1c02d3bee589efb5dfa17f88ea08bdb3e3eac12bc475462aec52ed223/alabaster-0.7.16-py3-none-any.whl#sha256=b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92 -# pip babel @ https://files.pythonhosted.org/packages/0d/35/4196b21041e29a42dc4f05866d0c94fa26c9da88ce12c38c2265e42c82fb/Babel-2.14.0-py3-none-any.whl#sha256=efb1a25b7118e67ce3a259bed20545c29cb68be8ad2c784c83689981b7a57287 +# pip babel @ https://files.pythonhosted.org/packages/27/45/377f7e32a5c93d94cd56542349b34efab5ca3f9e2fd5a68c5e93169aa32d/Babel-2.15.0-py3-none-any.whl#sha256=08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb # pip certifi @ https://files.pythonhosted.org/packages/ba/06/a07f096c664aeb9f01624f858c3add0a4e913d6c96257acb4fce61e7de14/certifi-2024.2.2-py3-none-any.whl#sha256=dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1 # pip charset-normalizer @ https://files.pythonhosted.org/packages/ee/fb/14d30eb4956408ee3ae09ad34299131fb383c47df355ddb428a7331cfa1e/charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b -# pip coverage @ https://files.pythonhosted.org/packages/fa/d9/ec4ba0913195d240d026670d41b91f3e5b9a8a143a385f93a09e97c90f5c/coverage-7.5.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=adf032b6c105881f9d77fa17d9eebe0ad1f9bfb2ad25777811f97c5362aa07f2 +# pip coverage @ https://files.pythonhosted.org/packages/3f/4f/fcad903698f02ac0d7501432449db12e15fbe5ecfbc01e363eb752c65cbd/coverage-7.5.1-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=8748731ad392d736cc9ccac03c9845b13bb07d020a33423fa5b3a36521ac6e4e # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc # pip idna @ https://files.pythonhosted.org/packages/e5/3e/741d8c82801c347547f8a2a06aa57dbb1992be9e948df2ea0eda2c8b79e8/idna-3.7-py3-none-any.whl#sha256=82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0 @@ -42,7 +42,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py312h06a4308_0.conda#e1 # pip packaging @ https://files.pythonhosted.org/packages/49/df/1fceb2f8900f8639e278b056416d49134fb8d84c5942ffaa01ad34782422/packaging-24.0-py3-none-any.whl#sha256=2ddfb553fdf02fb784c234c7ba6ccc288296ceabec964ad2eae3777778130bc5 # pip platformdirs @ https://files.pythonhosted.org/packages/b0/15/1691fa5aaddc0c4ea4901c26f6137c29d5f6673596fe960a0340e8c308e1/platformdirs-4.2.1-py3-none-any.whl#sha256=17d5a1161b3fd67b390023cb2d3b026bbd40abde6fdb052dfbd3a29c3ba22ee1 # pip pluggy @ https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl#sha256=44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669 -# pip pygments @ https://files.pythonhosted.org/packages/97/9c/372fef8377a6e340b1704768d20daaded98bf13282b5327beb2e2fe2c7ef/pygments-2.17.2-py3-none-any.whl#sha256=b27c2826c47d0f3219f29554824c30c5e8945175d888647acd804ddd04af846c +# pip pygments @ https://files.pythonhosted.org/packages/f7/3f/01c8b82017c199075f8f788d0d906b9ffbbc5a47dc9918a945e13d5a2bda/pygments-2.18.0-py3-none-any.whl#sha256=b8e6aca0523f3ab76fee51799c488e38782ac06eafcf95e7ba832985c8e7b13a # pip six @ https://files.pythonhosted.org/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl#sha256=8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254 # pip snowballstemmer @ https://files.pythonhosted.org/packages/ed/dc/c02e01294f7265e63a7315fe086dd1df7dacb9f840a804da846b96d01b96/snowballstemmer-2.2.0-py2.py3-none-any.whl#sha256=c8e1716e83cc398ae16824e5572ae04e0d9fc2c6b985fb0f900f5f0c96ecba1a # pip sphinxcontrib-applehelp @ https://files.pythonhosted.org/packages/56/89/fea3fbf6785b388e6cb8a1beaf62f96e80b37311bdeed6e133388a732426/sphinxcontrib_applehelp-1.0.8-py3-none-any.whl#sha256=cb61eb0ec1b61f349e5cc36b2028e9e7ca765be05e49641c97241274753067b4 @@ -52,9 +52,9 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py312h06a4308_0.conda#e1 # pip sphinxcontrib-qthelp @ https://files.pythonhosted.org/packages/80/b3/1beac14a88654d2e5120d0143b49be5ad450b86eb1963523d8dbdcc51eb2/sphinxcontrib_qthelp-1.0.7-py3-none-any.whl#sha256=e2ae3b5c492d58fcbd73281fbd27e34b8393ec34a073c792642cd8e529288182 # pip sphinxcontrib-serializinghtml @ https://files.pythonhosted.org/packages/38/24/228bb903ea87b9e08ab33470e6102402a644127108c7117ac9c00d849f82/sphinxcontrib_serializinghtml-1.1.10-py3-none-any.whl#sha256=326369b8df80a7d2d8d7f99aa5ac577f51ea51556ed974e7716cfd4fca3f6cb7 # pip tabulate @ https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl#sha256=024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f -# pip threadpoolctl @ https://files.pythonhosted.org/packages/1e/84/ccd9b08653022b7785b6e3ee070ffb2825841e0dc119be22f0840b2b35cb/threadpoolctl-3.4.0-py3-none-any.whl#sha256=8f4c689a65b23e5ed825c8436a92b818aac005e0f3715f6a1664d7c7ee29d262 +# pip threadpoolctl @ https://files.pythonhosted.org/packages/4b/2c/ffbf7a134b9ab11a67b0cf0726453cedd9c5043a4fe7a35d1cefa9a1bcfb/threadpoolctl-3.5.0-py3-none-any.whl#sha256=56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467 # pip urllib3 @ https://files.pythonhosted.org/packages/a2/73/a68704750a7679d0b6d3ad7aa8d4da8e14e151ae82e6fee774e6e0d05ec8/urllib3-2.2.1-py3-none-any.whl#sha256=450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d -# pip jinja2 @ https://files.pythonhosted.org/packages/30/6d/6de6be2d02603ab56e72997708809e8a5b0fbfee080735109b40a3564843/Jinja2-3.1.3-py3-none-any.whl#sha256=7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa +# pip jinja2 @ https://files.pythonhosted.org/packages/31/80/3a54838c3fb461f6fec263ebf3a3a41771bd05190238de3486aae8540c36/jinja2-3.1.4-py3-none-any.whl#sha256=bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d # pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526 # pip pytest @ https://files.pythonhosted.org/packages/51/ff/f6e8b8f39e08547faece4bd80f89d5a8de68a38b2d179cc1c4490ffa3286/pytest-7.4.4-py3-none-any.whl#sha256=b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 From 5487b58c626544852aa394cc342e798cd8f626a2 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 6 May 2024 11:03:03 +0200 Subject: [PATCH 005/275] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#28957) --- build_tools/azure/debian_atlas_32bit_lock.txt | 2 +- ...latest_conda_forge_mkl_linux-64_conda.lock | 30 ++++---- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 38 +++++----- ...test_conda_mkl_no_openmp_osx-64_conda.lock | 32 ++++----- ...st_pip_openblas_pandas_linux-64_conda.lock | 26 +++---- ...onda_defaults_openblas_linux-64_conda.lock | 24 +++---- .../pymin_conda_forge_mkl_win-64_conda.lock | 24 +++---- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 28 ++++---- build_tools/circle/doc_linux-64_conda.lock | 70 +++++++++---------- .../doc_min_dependencies_linux-64_conda.lock | 50 ++++++------- 10 files changed, 159 insertions(+), 165 deletions(-) diff --git a/build_tools/azure/debian_atlas_32bit_lock.txt b/build_tools/azure/debian_atlas_32bit_lock.txt index 61ad07e857cb8..7971e64b72560 100644 --- a/build_tools/azure/debian_atlas_32bit_lock.txt +++ b/build_tools/azure/debian_atlas_32bit_lock.txt @@ -6,7 +6,7 @@ # attrs==23.2.0 # via pytest -coverage==7.5.0 +coverage==7.5.1 # via pytest-cov cython==3.0.10 # via -r build_tools/azure/debian_atlas_32bit_requirements.txt diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index 3194bf106d6c2..932fc6ad670f7 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -7,15 +7,15 @@ https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.2.2-hbcca05 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb -https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_1.conda#6185f640c43843e5ad6fd1c5372c3f80 +https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h55db66e_0.conda#10569984e7db886e4f1abc2b47ad79a1 -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-h95c4c6d_6.conda#3cfab3e709f77e9f1b3d380eb622494a +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-hc0a3c3a_6.conda#2f18345bbc433c8a1ed887d7161e86a6 https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-4_cp311.conda#d786502c97404c94d7d58d258a445a65 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-hc881cc4_6.conda#df88796bd09a0d2ed292e59101478ad8 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h77fa898_6.conda#4398809ac84d0b8c28beebaaa83277f5 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.11-hd590300_1.conda#0bb492cca54017ea314b809b1ee3a176 https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2#d9c69a24ad678ffce24c6543a0176b00 https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.0-hd590300_0.conda#71b89db63b5b504e7afc8ad901172e1e @@ -41,7 +41,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-h43f5ff8_6.c https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda#d66573916ffcf376178462f1b61c941e https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda#ea25936bb4080d843790b586850f82b8 https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda#30fd6e37fe21f86f4bd26d6ee73eeec7 -https://conda.anaconda.org/conda-forge/linux-64/libnuma-2.0.18-hd590300_0.conda#8feeecae73aeef0a2985af46b5a2c1df +https://conda.anaconda.org/conda-forge/linux-64/libnuma-2.0.18-h4ab18f5_2.conda#a263760479dbc7bc1f3df12707bd90dc https://conda.anaconda.org/conda-forge/linux-64/libogg-1.3.4-h7f98852_1.tar.bz2#6e8cc2173440d77708196c5b93771680 https://conda.anaconda.org/conda-forge/linux-64/libopus-1.3.1-h7f98852_1.tar.bz2#15345e56d527b330e1cacbdf58676e8f https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.8.0-h166bdaf_0.tar.bz2#ede4266dc02e875fe1ea77b25dd43747 @@ -54,7 +54,7 @@ https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.4.20240210-h59595ed_0.conda#97da8860a0da5413c7c98a3b3838a645 https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.0-h00ab1b0_0.conda#b048701d52e7cbb5f59ddd4d3b17bbf5 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+https://repo.anaconda.com/pkgs/main/osx-64/brotli-1.0.9-h6c40b1e_8.conda#10f89677a3898d0113dc354adf643df3 https://repo.anaconda.com/pkgs/main/osx-64/libtiff-4.5.1-hcec6c5f_0.conda#e127a800ffd9d300ed7d5e1b026944ec https://repo.anaconda.com/pkgs/main/osx-64/python-3.12.3-hd58486a_0.conda#1a287cfa37c5a92972f5f527b6af7eed https://repo.anaconda.com/pkgs/main/osx-64/coverage-7.2.2-py312h6c40b1e_0.conda#b6e4b9fba325047c07f3c9211ae91d1c @@ -59,11 +59,11 @@ https://repo.anaconda.com/pkgs/main/noarch/six-1.16.0-pyhd3eb1b0_1.conda#3458682 https://repo.anaconda.com/pkgs/main/noarch/toml-0.10.2-pyhd3eb1b0_0.conda#cda05f5f6d8509529d1a2743288d197a https://repo.anaconda.com/pkgs/main/osx-64/tornado-6.3.3-py312h6c40b1e_0.conda#49173b5a36c9134865221f29d4a73fb6 https://repo.anaconda.com/pkgs/main/osx-64/unicodedata2-15.1.0-py312h6c40b1e_0.conda#65bd2cb787fc99662d9bb6e6520c5826 -https://repo.anaconda.com/pkgs/main/osx-64/wheel-0.41.2-py312hecd8cb5_0.conda#e7aea266d81142e2bb0bbc2280e64526 +https://repo.anaconda.com/pkgs/main/osx-64/wheel-0.43.0-py312hecd8cb5_0.conda#c0bdd5748b170523232e8ad1d667136c https://repo.anaconda.com/pkgs/main/osx-64/fonttools-4.51.0-py312h6c40b1e_0.conda#8f55fa86b73e8a7f4403503f9b7a9959 https://repo.anaconda.com/pkgs/main/osx-64/meson-1.3.1-py312hecd8cb5_0.conda#43963a2b38becce4caa95434b8c96837 https://repo.anaconda.com/pkgs/main/osx-64/numpy-base-1.26.4-py312h6f81483_0.conda#87f73efbf26ab2e2ea7c32481a71bd47 -https://repo.anaconda.com/pkgs/main/osx-64/pillow-10.2.0-py312h6c40b1e_0.conda#5a44bd28cf26fff2d6219e76a86db126 +https://repo.anaconda.com/pkgs/main/osx-64/pillow-10.3.0-py312h6c40b1e_0.conda#fe883fa4247d35fe6de49f713529ca02 https://repo.anaconda.com/pkgs/main/osx-64/pip-23.3.1-py312hecd8cb5_0.conda#efc3db40cac09f74bb480d28d3a0b260 https://repo.anaconda.com/pkgs/main/osx-64/pyproject-metadata-0.7.1-py312hecd8cb5_0.conda#e91ce37477d24dcdf7e0a8b93c5e72fd https://repo.anaconda.com/pkgs/main/osx-64/pytest-7.4.0-py312hecd8cb5_0.conda#b816a2439ba9b87524aec74d58e55b0a @@ -83,4 +83,4 @@ https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.11.4-py312h81688c2_0.conda#7d https://repo.anaconda.com/pkgs/main/osx-64/pandas-2.2.1-py312he282a81_0.conda#021b70a1e40efb75b89eb8ebdb347132 https://repo.anaconda.com/pkgs/main/osx-64/pyamg-4.2.3-py312h44cbcf4_0.conda#3bdc7be74087b3a5a83c520a74e1e8eb # pip cython @ https://files.pythonhosted.org/packages/d5/6d/06c08d75adb98cdf72af18801e193d22580cc86ca553610f430f18ea26b3/Cython-3.0.10-cp312-cp312-macosx_10_9_x86_64.whl#sha256=8f2864ab5fcd27a346f0b50f901ebeb8f60b25a60a575ccfd982e7f3e9674914 -# pip threadpoolctl @ https://files.pythonhosted.org/packages/1e/84/ccd9b08653022b7785b6e3ee070ffb2825841e0dc119be22f0840b2b35cb/threadpoolctl-3.4.0-py3-none-any.whl#sha256=8f4c689a65b23e5ed825c8436a92b818aac005e0f3715f6a1664d7c7ee29d262 +# pip threadpoolctl @ https://files.pythonhosted.org/packages/4b/2c/ffbf7a134b9ab11a67b0cf0726453cedd9c5043a4fe7a35d1cefa9a1bcfb/threadpoolctl-3.5.0-py3-none-any.whl#sha256=56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467 diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index 7534de9fbd5f6..c497709ca347e 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -10,21 +10,21 @@ https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda#b37 https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-11.2.0-h1234567_1.conda#57623d10a70e09e1d048c2b2b6f4e2dd https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d281e9c2192cb3fa425655a8defb85 https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464 -https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_0.conda#06e288f9250abef59b9a367d151fc339 +https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_1.conda#70646cc713f0c43926cfdcfe9b695fe0 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c -https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.13-h7f8727e_0.conda#c73d46a4d666da0ae3dcd3fd8f805122 -https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_0.conda#81a9916f581d4da15a3839216a487c66 -https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_0.conda#333e31fbfbb5057c92fa845ad6adef93 +https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.13-h7f8727e_1.conda#d1d1fc47640fe0d9f7fa64c0a054bfd8 +https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_1.conda#1562802f843297ee776a50b9329597ed +https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be42180685cce6e6b0329201d9f48efb https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.12-h1ccaba5_0.conda#fa10ff4aa631fa4aa090a6234d7770b9 -https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.41.2-h5eee18b_0.conda#c7086c9ceb6cfe1c4c729a774a2d88a5 +https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda#acf93d6aceb74d6110e20b44cc45939e https://repo.anaconda.com/pkgs/main/linux-64/python-3.9.19-h955ad1f_0.conda#33cb019c40e3409df392c99e3c34f352 https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.2.2-py39h06a4308_0.conda#5b42cae5548732ae5c167bb1066085de -https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.41.2-py39h06a4308_0.conda#ec1b8213c3585defaa6042ed2f95861d +https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py39h06a4308_0.conda#40bb60408c7433d767fd8c65b35bc4a0 https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py39h06a4308_0.conda#685007e3dae59d211620f19926577bd6 # pip alabaster @ https://files.pythonhosted.org/packages/32/34/d4e1c02d3bee589efb5dfa17f88ea08bdb3e3eac12bc475462aec52ed223/alabaster-0.7.16-py3-none-any.whl#sha256=b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92 -# pip babel @ https://files.pythonhosted.org/packages/0d/35/4196b21041e29a42dc4f05866d0c94fa26c9da88ce12c38c2265e42c82fb/Babel-2.14.0-py3-none-any.whl#sha256=efb1a25b7118e67ce3a259bed20545c29cb68be8ad2c784c83689981b7a57287 +# pip babel @ https://files.pythonhosted.org/packages/27/45/377f7e32a5c93d94cd56542349b34efab5ca3f9e2fd5a68c5e93169aa32d/Babel-2.15.0-py3-none-any.whl#sha256=08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb # pip certifi @ https://files.pythonhosted.org/packages/ba/06/a07f096c664aeb9f01624f858c3add0a4e913d6c96257acb4fce61e7de14/certifi-2024.2.2-py3-none-any.whl#sha256=dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1 # pip charset-normalizer @ https://files.pythonhosted.org/packages/98/69/5d8751b4b670d623aa7a47bef061d69c279e9f922f6705147983aa76c3ce/charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b261ccdec7821281dade748d088bb6e9b69e6d15b30652b74cbbac25e280b796 # pip cycler @ https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl#sha256=85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 @@ -36,7 +36,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py39h06a4308_0.conda#685 # pip idna @ https://files.pythonhosted.org/packages/e5/3e/741d8c82801c347547f8a2a06aa57dbb1992be9e948df2ea0eda2c8b79e8/idna-3.7-py3-none-any.whl#sha256=82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0 # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b # pip iniconfig @ https://files.pythonhosted.org/packages/ef/a6/62565a6e1cf69e10f5727360368e451d4b7f58beeac6173dc9db836a5b46/iniconfig-2.0.0-py3-none-any.whl#sha256=b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374 -# pip joblib @ https://files.pythonhosted.org/packages/ae/e2/4dea6313ef2b38442fccbbaf4017e50a6c3c8a50e8ee9b512783e5c90409/joblib-1.4.0-py3-none-any.whl#sha256=42942470d4062537be4d54c83511186da1fc14ba354961a2114da91efa9a4ed7 +# pip joblib @ https://files.pythonhosted.org/packages/91/29/df4b9b42f2be0b623cbd5e2140cafcaa2bef0759a00b7b70104dcfe2fb51/joblib-1.4.2-py3-none-any.whl#sha256=06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6 # pip kiwisolver @ https://files.pythonhosted.org/packages/c0/a8/841594f11d0b88d8aeb26991bc4dac38baa909dc58d0c4262a4f7893bcbf/kiwisolver-1.4.5-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=6c3bd3cde54cafb87d74d8db50b909705c62b17c2099b8f2e25b461882e544ff # pip markupsafe @ https://files.pythonhosted.org/packages/5f/5a/360da85076688755ea0cceb92472923086993e86b5613bbae9fbc14136b0/MarkupSafe-2.1.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=17b950fccb810b3293638215058e432159d2b71005c74371d784862b7e4683f3 # pip meson @ https://files.pythonhosted.org/packages/33/75/b1a37fa7b2dbca8c0dbb04d5cdd7e2720c8ef6febe41b4a74866350e041c/meson-1.4.0-py3-none-any.whl#sha256=476a458d51fcfa322a6bdc64da5138997c542d08e6b2e49b9fa68c46fd7c4475 @@ -46,7 +46,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py39h06a4308_0.conda#685 # pip packaging @ https://files.pythonhosted.org/packages/49/df/1fceb2f8900f8639e278b056416d49134fb8d84c5942ffaa01ad34782422/packaging-24.0-py3-none-any.whl#sha256=2ddfb553fdf02fb784c234c7ba6ccc288296ceabec964ad2eae3777778130bc5 # pip pillow @ https://files.pythonhosted.org/packages/f5/6d/52e82352670e850f468de9e6bccced4202a09f58e7ea5ecdbf08283d85cb/pillow-10.3.0-cp39-cp39-manylinux_2_28_x86_64.whl#sha256=1dfc94946bc60ea375cc39cff0b8da6c7e5f8fcdc1d946beb8da5c216156ddd8 # pip pluggy @ https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl#sha256=44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669 -# pip pygments @ https://files.pythonhosted.org/packages/97/9c/372fef8377a6e340b1704768d20daaded98bf13282b5327beb2e2fe2c7ef/pygments-2.17.2-py3-none-any.whl#sha256=b27c2826c47d0f3219f29554824c30c5e8945175d888647acd804ddd04af846c +# pip pygments @ https://files.pythonhosted.org/packages/f7/3f/01c8b82017c199075f8f788d0d906b9ffbbc5a47dc9918a945e13d5a2bda/pygments-2.18.0-py3-none-any.whl#sha256=b8e6aca0523f3ab76fee51799c488e38782ac06eafcf95e7ba832985c8e7b13a # pip pyparsing @ https://files.pythonhosted.org/packages/9d/ea/6d76df31432a0e6fdf81681a895f009a4bb47b3c39036db3e1b528191d52/pyparsing-3.1.2-py3-none-any.whl#sha256=f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742 # pip pytz @ https://files.pythonhosted.org/packages/9c/3d/a121f284241f08268b21359bd425f7d4825cffc5ac5cd0e1b3d82ffd2b10/pytz-2024.1-py2.py3-none-any.whl#sha256=328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319 # pip six @ 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-https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.15.0-pyhd8ed1ab_0.conda#1a49ca9515ef9a96edff2eea06143dc6 +https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.16.0-pyhd8ed1ab_0.conda#add28691ee89e875b190eda07929d5d4 https://conda.anaconda.org/conda-forge/noarch/sphinx-prompt-1.4.0-pyhd8ed1ab_0.tar.bz2#88ee91e8679603f2a5bd036d52919cc2 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-1.0.8-pyhd8ed1ab_0.conda#611a35a27914fac3aa37611a6fe40bb5 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-1.0.6-pyhd8ed1ab_0.conda#d7e4954df0d3aea2eacc7835ad12671d @@ -299,7 +299,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip cffi @ https://files.pythonhosted.org/packages/ea/ac/e9e77bc385729035143e54cc8c4785bd480eaca9df17565963556b0b7a93/cffi-1.16.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=8f8e709127c6c77446a8c0a8c8bf3c8ee706a06cd44b1e827c3e6a2ee6b8c098 # pip doit @ 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https://files.pythonhosted.org/packages/7b/44/4e421b96b67b2daff264473f7465db72fbdf36a07e05494f50300cc7b0c6/rfc3339_validator-0.1.4-py2.py3-none-any.whl#sha256=24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa # pip terminado @ https://files.pythonhosted.org/packages/6a/9e/2064975477fdc887e47ad42157e214526dcad8f317a948dee17e1659a62f/terminado-0.18.1-py3-none-any.whl#sha256=a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0 # pip tinycss2 @ https://files.pythonhosted.org/packages/2c/4d/0db5b8a613d2a59bbc29bc5bb44a2f8070eb9ceab11c50d477502a8a0092/tinycss2-1.3.0-py3-none-any.whl#sha256=54a8dbdffb334d536851be0226030e9505965bb2f30f21a4a82c55fb2a80fae7 @@ -308,15 +308,15 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip jsonschema-specifications @ 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https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda#d3483c8fc2dc2cc3f5cf43e26d60cabf https://conda.anaconda.org/conda-forge/noarch/ply-3.11-pyhd8ed1ab_2.conda#18c6deb6f9602e32446398203c8f0e91 https://conda.anaconda.org/conda-forge/linux-64/psutil-5.9.8-py39hd1e30aa_0.conda#ec86403fde8793ac1c36f8afa3d15902 -https://conda.anaconda.org/conda-forge/noarch/pygments-2.17.2-pyhd8ed1ab_0.conda#140a7f159396547e9799aa98f9f0742e +https://conda.anaconda.org/conda-forge/noarch/pygments-2.18.0-pyhd8ed1ab_0.conda#b7f5c092b8f9800150d998a71b76d5a1 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.2-pyhd8ed1ab_0.conda#b9a4dacf97241704529131a0dfc0494f https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2#2a7de29fb590ca14b5243c4c812c8025 https://conda.anaconda.org/conda-forge/noarch/pytz-2024.1-pyhd8ed1ab_0.conda#3eeeeb9e4827ace8c0c1419c85d590ad @@ -161,7 +161,7 @@ https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-2.2.0-pyhd8ed1ab_0 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_0.conda#da1d979339e2714c30a8e806a33ec087 https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.12.0-h00ab1b0_0.conda#f1b776cff1b426e7e7461a8502a3b731 https://conda.anaconda.org/conda-forge/noarch/tenacity-8.2.3-pyhd8ed1ab_0.conda#1482e77f87c6a702a7e05ef22c9b197b -https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.4.0-pyhc1e730c_0.conda#b296278eef667c673bf51de6535bad88 +https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_0.tar.bz2#f832c45a477c78bebd107098db465095 https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96 https://conda.anaconda.org/conda-forge/noarch/toolz-0.12.1-pyhd8ed1ab_0.conda#2fcb582444635e2c402e8569bb94e039 @@ -175,13 +175,13 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.11-hd590300_ https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda#2e4d6bc0b14e10f895fc6791a7d9b26a https://conda.anaconda.org/conda-forge/noarch/babel-2.14.0-pyhd8ed1ab_0.conda#9669586875baeced8fc30c0826c3270e https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-h3faef2a_0.conda#f907bb958910dc404647326ca80c263e -https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.7.0-h00ab1b0_0.conda#b4537c98cb59f8725b0e1e65816b4a28 +https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.7.0-h00ab1b0_1.conda#28de2e073db9ca9b72858bee9fb6f571 https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.12.3-py39hd1e30aa_0.conda#dc0fb8e157c7caba4c98f1e1f9d2e5f4 -https://conda.anaconda.org/conda-forge/linux-64/fortran-compiler-1.7.0-heb67821_0.conda#7ef7c0f111dad1c8006504a0f1ccd820 +https://conda.anaconda.org/conda-forge/linux-64/fortran-compiler-1.7.0-heb67821_1.conda#cf4b0e7c4c78bb0662aed9b27c414a3c https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.0-hf2295e7_6.conda#a1e026a82a562b443845db5614ca568a https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-7.1.0-pyha770c72_0.conda#0896606848b2dc5cebdf111b6543aa04 https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.3-pyhd8ed1ab_0.conda#e7d8df6509ba635247ff9aea31134262 -https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.0-pyhd8ed1ab_0.conda#e0ed1bf13ce3a440e022157bf4764465 +https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-1.10.3-hd590300_0.conda#32d16ad533c59bb0a3c5ffaf16110829 https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.conda#ef1910918dd895516a769ed36b5b3a4e https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.7.0-h662e7e4_0.conda#b32c0da42b1f24a98577bb3d7fc0b995 @@ -197,8 +197,8 @@ https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.1-pyhd8ed1ab_0.conda#08807a87fa7af10754d46f63b368e016 -https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_0.conda#81458b3aed8ab8711951ec3c0c04e097 -https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.1-h98fc4e7_1.conda#b04b5cdf3ba01430db27979250bc5a1d +https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_1.conda#d8d07866ac3b5b6937213c89a1874f08 +https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.3-haf2f30d_0.conda#f3df87cc9ef0b5113bff55aefcbcafd5 https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.4.0-h3d44ed6_0.conda#27f46291a6aaa3c2a4f798ebd35a7ddb https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-7.1.0-hd8ed1ab_0.conda#6ef2b72d291b39e479d7694efa2b2b98 https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-22_linux64_mkl.conda#eb6deb4ba6f92ea3f31c09cb8b764738 @@ -208,8 +208,8 @@ https://conda.anaconda.org/conda-forge/linux-64/mkl-devel-2024.1.0-ha770c72_692. https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5.conda#93aff412f3e49fdb43361c0215cbd72d https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b https://conda.anaconda.org/conda-forge/noarch/requests-2.31.0-pyhd8ed1ab_0.conda#a30144e4156cdbb236f99ebb49828f8b -https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.4.2-pyhd8ed1ab_0.conda#bb4e6c52855aa64a5443ca4eedaa6cfe -https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.1-hfa15dee_1.conda#a6dd2bbc684913e2bef0a54ce56fcbfb +https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.5.0-pyhd8ed1ab_0.conda#8472f598970b9af96ca8106fa243ab67 +https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.3-h9ad1361_0.conda#8fb0e954c616bb0f9389efac4b4ed44b https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-22_linux64_mkl.conda#d6f942423116553f068b2f2d93ffea2e https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-22_linux64_mkl.conda#4edf2e7ce63920e4f539d12e32fb478e https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.1-pyhd8ed1ab_0.conda#d15917f33140f8d2ac9ca44db7ec8a25 From c20f44f5750920076258517e61e0af8a16139908 Mon Sep 17 00:00:00 2001 From: Omar Salman Date: Mon, 6 May 2024 14:27:10 +0500 Subject: [PATCH 006/275] DOC move d2_log_loss_score in the classification metrics section (#28938) Co-authored-by: Omar Salman --- doc/modules/classes.rst | 1 + doc/modules/model_evaluation.rst | 112 +++++++++++++++---------- sklearn/tests/test_public_functions.py | 1 + 3 files changed, 69 insertions(+), 45 deletions(-) diff --git a/doc/modules/classes.rst b/doc/modules/classes.rst index 804546eababef..1da5b337ad7a4 100644 --- a/doc/modules/classes.rst +++ b/doc/modules/classes.rst @@ -982,6 +982,7 @@ details. metrics.classification_report metrics.cohen_kappa_score metrics.confusion_matrix + metrics.d2_log_loss_score metrics.dcg_score metrics.det_curve metrics.f1_score diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst index 7caacd697ea1c..d2e0203424c64 100644 --- a/doc/modules/model_evaluation.rst +++ b/doc/modules/model_evaluation.rst @@ -77,6 +77,7 @@ Scoring Function 'roc_auc_ovo' :func:`metrics.roc_auc_score` 'roc_auc_ovr_weighted' :func:`metrics.roc_auc_score` 'roc_auc_ovo_weighted' :func:`metrics.roc_auc_score` +'d2_log_loss_score' :func:`metrics.d2_log_loss_score` **Clustering** 'adjusted_mutual_info_score' :func:`metrics.adjusted_mutual_info_score` @@ -377,6 +378,7 @@ Some also work in the multilabel case: recall_score roc_auc_score zero_one_loss + d2_log_loss_score And some work with binary and multilabel (but not multiclass) problems: @@ -1986,6 +1988,71 @@ see the example below. |details-end| +.. _d2_score_classification: + +D² score for classification +--------------------------- + +The D² score computes the fraction of deviance explained. +It is a generalization of R², where the squared error is generalized and replaced +by a classification deviance of choice :math:`\text{dev}(y, \hat{y})` +(e.g., Log loss). D² is a form of a *skill score*. +It is calculated as + +.. math:: + + D^2(y, \hat{y}) = 1 - \frac{\text{dev}(y, \hat{y})}{\text{dev}(y, y_{\text{null}})} \,. + +Where :math:`y_{\text{null}}` is the optimal prediction of an intercept-only model +(e.g., the per-class proportion of `y_true` in the case of the Log loss). + +Like R², the best possible score is 1.0 and it can be negative (because the +model can be arbitrarily worse). A constant model that always predicts +:math:`y_{\text{null}}`, disregarding the input features, would get a D² score +of 0.0. + +|details-start| +**D2 log loss score** +|details-split| + +The :func:`d2_log_loss_score` function implements the special case +of D² with the log loss, see :ref:`log_loss`, i.e.: + +.. math:: + + \text{dev}(y, \hat{y}) = \text{log_loss}(y, \hat{y}). + +Here are some usage examples of the :func:`d2_log_loss_score` function:: + + >>> from sklearn.metrics import d2_log_loss_score + >>> y_true = [1, 1, 2, 3] + >>> y_pred = [ + ... [0.5, 0.25, 0.25], + ... [0.5, 0.25, 0.25], + ... [0.5, 0.25, 0.25], + ... [0.5, 0.25, 0.25], + ... ] + >>> d2_log_loss_score(y_true, y_pred) + 0.0 + >>> y_true = [1, 2, 3] + >>> y_pred = [ + ... [0.98, 0.01, 0.01], + ... [0.01, 0.98, 0.01], + ... [0.01, 0.01, 0.98], + ... ] + >>> d2_log_loss_score(y_true, y_pred) + 0.981... + >>> y_true = [1, 2, 3] + >>> y_pred = [ + ... [0.1, 0.6, 0.3], + ... [0.1, 0.6, 0.3], + ... [0.4, 0.5, 0.1], + ... ] + >>> d2_log_loss_score(y_true, y_pred) + -0.552... + +|details-end| + .. _multilabel_ranking_metrics: Multilabel ranking metrics @@ -2826,51 +2893,6 @@ Here are some usage examples of the :func:`d2_absolute_error_score` function:: |details-end| -|details-start| -**D² log loss score** -|details-split| - -The :func:`d2_log_loss_score` function implements the special case -of D² with the log loss, see :ref:`log_loss`, i.e.: - -.. math:: - - \text{dev}(y, \hat{y}) = \text{log_loss}(y, \hat{y}). - -The :math:`y_{\text{null}}` for the :func:`log_loss` is the per-class -proportion. - -Here are some usage examples of the :func:`d2_log_loss_score` function:: - - >>> from sklearn.metrics import d2_log_loss_score - >>> y_true = [1, 1, 2, 3] - >>> y_pred = [ - ... [0.5, 0.25, 0.25], - ... [0.5, 0.25, 0.25], - ... [0.5, 0.25, 0.25], - ... [0.5, 0.25, 0.25], - ... ] - >>> d2_log_loss_score(y_true, y_pred) - 0.0 - >>> y_true = [1, 2, 3] - >>> y_pred = [ - ... [0.98, 0.01, 0.01], - ... [0.01, 0.98, 0.01], - ... [0.01, 0.01, 0.98], - ... ] - >>> d2_log_loss_score(y_true, y_pred) - 0.981... - >>> y_true = [1, 2, 3] - >>> y_pred = [ - ... [0.1, 0.6, 0.3], - ... [0.1, 0.6, 0.3], - ... [0.4, 0.5, 0.1], - ... ] - >>> d2_log_loss_score(y_true, y_pred) - -0.552... - -|details-end| - .. _visualization_regression_evaluation: Visual evaluation of regression models diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py index 41629aa189941..707aa37737c1b 100644 --- a/sklearn/tests/test_public_functions.py +++ b/sklearn/tests/test_public_functions.py @@ -234,6 +234,7 @@ def _check_function_param_validation( "sklearn.metrics.consensus_score", "sklearn.metrics.coverage_error", "sklearn.metrics.d2_absolute_error_score", + "sklearn.metrics.d2_log_loss_score", "sklearn.metrics.d2_pinball_score", "sklearn.metrics.d2_tweedie_score", "sklearn.metrics.davies_bouldin_score", From 5c0f6d7fe67a415572194e8b6905923bae251a42 Mon Sep 17 00:00:00 2001 From: "Adam J. Stewart" Date: Mon, 6 May 2024 11:45:48 +0200 Subject: [PATCH 007/275] DOC update r2_score default in regression metrics tutorial (#28958) --- doc/modules/model_evaluation.rst | 3 --- 1 file changed, 3 deletions(-) diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst index d2e0203424c64..056bf9a56d42c 100644 --- a/doc/modules/model_evaluation.rst +++ b/doc/modules/model_evaluation.rst @@ -2297,9 +2297,6 @@ leads to a weighting of each individual score by the variance of the corresponding target variable. This setting quantifies the globally captured unscaled variance. If the target variables are of different scale, then this score puts more importance on explaining the higher variance variables. -``multioutput='variance_weighted'`` is the default value for :func:`r2_score` -for backward compatibility. This will be changed to ``uniform_average`` in the -future. .. _r2_score: From 4ea48e66b314e0537b5d9136a98d00760a350cff Mon Sep 17 00:00:00 2001 From: Tuhin Sharma Date: Mon, 6 May 2024 20:27:39 +0530 Subject: [PATCH 008/275] DOC add links to examples/linear_model/plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py (#28895) Co-authored-by: bivav --- doc/modules/linear_model.rst | 1 + sklearn/linear_model/_coordinate_descent.py | 3 +++ 2 files changed, 4 insertions(+) diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst index dd975c4d6e417..275ee01eb022f 100644 --- a/doc/modules/linear_model.rst +++ b/doc/modules/linear_model.rst @@ -530,6 +530,7 @@ The class :class:`ElasticNetCV` can be used to set the parameters * :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py` * :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_coordinate_descent_path.py` + * :ref:`sphx_glr_auto_examples_linear_model_plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py` |details-start| **References** diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py index 45cdb8bdf2ebb..6a62fa1e245e2 100644 --- a/sklearn/linear_model/_coordinate_descent.py +++ b/sklearn/linear_model/_coordinate_descent.py @@ -776,6 +776,9 @@ class ElasticNet(MultiOutputMixin, RegressorMixin, LinearModel): Whether to use a precomputed Gram matrix to speed up calculations. The Gram matrix can also be passed as argument. For sparse input this option is always ``False`` to preserve sparsity. + Check :ref:`an example on how to use a precomputed Gram Matrix in ElasticNet + ` + for details. max_iter : int, default=1000 The maximum number of iterations. From fc8e4434edd970abb77b8591b904a6f56a7cc274 Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Tue, 7 May 2024 11:19:03 +0200 Subject: [PATCH 009/275] TST Fix tolerance for seed-sensitive test `test_pca_solver_equivalence` (#28961) --- sklearn/decomposition/tests/test_pca.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/decomposition/tests/test_pca.py b/sklearn/decomposition/tests/test_pca.py index 59401fd8742da..bd7f60061abdc 100644 --- a/sklearn/decomposition/tests/test_pca.py +++ b/sklearn/decomposition/tests/test_pca.py @@ -309,7 +309,7 @@ def test_pca_solver_equivalence( X_train, X_test = X[:n_samples], X[n_samples:] if global_dtype == np.float32: - tols = dict(atol=1e-2, rtol=1e-5) + tols = dict(atol=3e-2, rtol=1e-5) variance_threshold = 1e-5 else: tols = dict(atol=1e-10, rtol=1e-12) From c8905fec3f7b8cd7e37fd4620fd31625c6172df5 Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Tue, 7 May 2024 13:39:52 +0200 Subject: [PATCH 010/275] DOC add Tidelift to sponsors (#28918) Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> --- doc/about.rst | 26 +++++++++++++++++++++ doc/images/Tidelift-logo-on-light.svg | 33 +++++++++++++++++++++++++++ 2 files changed, 59 insertions(+) create mode 100644 doc/images/Tidelift-logo-on-light.svg diff --git a/doc/about.rst b/doc/about.rst index e7083569fd128..035bddb0ea4dc 100644 --- a/doc/about.rst +++ b/doc/about.rst @@ -347,6 +347,32 @@ and is part of the scikit-learn consortium at Inria. +........... + +.. raw:: html + +
+
+ +`Tidelift `_ supports the project via their service +agreement. + +.. raw:: html + +
+ +
+ +.. image:: images/Tidelift-logo-on-light.svg + :width: 100pt + :align: center + :target: https://tidelift.com/ + +.. raw:: html + +
+
+ Past Sponsors ............. diff --git a/doc/images/Tidelift-logo-on-light.svg b/doc/images/Tidelift-logo-on-light.svg new file mode 100644 index 0000000000000..af12d68417235 --- /dev/null +++ b/doc/images/Tidelift-logo-on-light.svg @@ -0,0 +1,33 @@ + + + + + + + + + + + + + + + + + + + From 21bc79201e4b2f57752bc7f148fa585993321216 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?= Date: Tue, 7 May 2024 13:49:31 +0200 Subject: [PATCH 011/275] DOC Mention the renaming of check_estimator_sparse_data in 1.5 changelog (#28968) --- doc/whats_new/v1.5.rst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst index ede5d5dcbf1ec..e50309a330e39 100644 --- a/doc/whats_new/v1.5.rst +++ b/doc/whats_new/v1.5.rst @@ -527,6 +527,11 @@ Changelog `axis=0` and supports indexing polars Series. :pr:`28521` by :user:`Yao Xiao `. +- |API| :func:`utils.estimator_checks.check_estimator_sparse_data` was split into two + functions: :func:`utils.estimator_checks.check_estimator_sparse_matrix` and + :func:`utils.estimator_checks.check_estimator_sparse_array`. + :pr:`27576` by :user:`Stefanie Senger `. + .. rubric:: Code and documentation contributors Thanks to everyone who has contributed to the maintenance and improvement of From 92a315c837611ca22f1b8607d212420bf018262d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?= Date: Tue, 7 May 2024 13:50:46 +0200 Subject: [PATCH 012/275] DOC Update release docs (#28965) --- doc/developers/maintainer.rst | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/doc/developers/maintainer.rst b/doc/developers/maintainer.rst index e82a7993997b2..70d132d2af604 100644 --- a/doc/developers/maintainer.rst +++ b/doc/developers/maintainer.rst @@ -105,14 +105,13 @@ in the description of the Pull Request to track progress. This PR will be used to push commits related to the release as explained in :ref:`making_a_release`. -You can also create a second PR from main and targeting main to increment -the ``__version__`` variable in `sklearn/__init__.py` to increment the dev -version. This means while we're in the release candidate period, the latest -stable is two versions behind the main branch, instead of one. In this PR -targeting main you should also include a new file for the matching version -under the ``doc/whats_new/`` folder so PRs that target the next version can -contribute their changelog entries to this file in parallel to the release -process. +You can also create a second PR from main and targeting main to increment the +``__version__`` variable in `sklearn/__init__.py` and in `pyproject.toml` to increment +the dev version. This means while we're in the release candidate period, the latest +stable is two versions behind the main branch, instead of one. In this PR targeting +main you should also include a new file for the matching version under the +``doc/whats_new/`` folder so PRs that target the next version can contribute their +changelog entries to this file in parallel to the release process. Minor version release (also known as bug-fix release) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -212,8 +211,8 @@ Making a release the old entries (two years or three releases are typically good enough) and to update the on-going development entry. -2. On the branch for releasing, update the version number in - ``sklearn/__init__.py``, the ``__version__``. +2. On the branch for releasing, update the version number in ``sklearn/__init__.py``, + the ``__version__`` variable, and in `pyproject.toml`. For major releases, please add a 0 at the end: `0.99.0` instead of `0.99`. From 1f364173b8affa02080f3019cadfe71f629029e1 Mon Sep 17 00:00:00 2001 From: Omar Salman Date: Tue, 7 May 2024 19:16:11 +0500 Subject: [PATCH 013/275] DOC updates for d2_log_loss_score (#28969) --- sklearn/metrics/_classification.py | 6 +++--- sklearn/metrics/tests/test_classification.py | 3 ++- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py index 04894a4d7a7e7..b68f1593e317e 100644 --- a/sklearn/metrics/_classification.py +++ b/sklearn/metrics/_classification.py @@ -3277,10 +3277,10 @@ def d2_log_loss_score(y_true, y_pred, *, sample_weight=None, labels=None): :math:`D^2` score function, fraction of log loss explained. Best possible score is 1.0 and it can be negative (because the model can be - arbitrarily worse). A model that always uses the empirical mean of `y_true` as - constant prediction, disregarding the input features, gets a D^2 score of 0.0. + arbitrarily worse). A model that always predicts the per-class proportions + of `y_true`, disregarding the input features, gets a D^2 score of 0.0. - Read more in the :ref:`User Guide `. + Read more in the :ref:`User Guide `. .. versionadded:: 1.5 diff --git a/sklearn/metrics/tests/test_classification.py b/sklearn/metrics/tests/test_classification.py index 40b762bfa7308..b87e76ba2fb42 100644 --- a/sklearn/metrics/tests/test_classification.py +++ b/sklearn/metrics/tests/test_classification.py @@ -3048,7 +3048,8 @@ def test_d2_log_loss_score(): def test_d2_log_loss_score_raises(): - """Test that d2_log_loss raises error on invalid input.""" + """Test that d2_log_loss_score raises the appropriate errors on + invalid inputs.""" y_true = [0, 1, 2] y_pred = [[0.2, 0.8], [0.5, 0.5], [0.4, 0.6]] err = "contain different number of classes" From 21e8a2486e9d6accf2a44191052f53878f172194 Mon Sep 17 00:00:00 2001 From: Abdulaziz Aloqeely <52792999+Aloqeely@users.noreply.github.com> Date: Fri, 10 May 2024 01:11:16 +0300 Subject: [PATCH 014/275] Update supported python versions in docs (#28986) --- doc/install.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/install.rst b/doc/install.rst index c4a3548016021..89851171f4588 100644 --- a/doc/install.rst +++ b/doc/install.rst @@ -166,7 +166,8 @@ purpose. Scikit-learn 0.22 supported Python 3.5-3.8. Scikit-learn 0.23 - 0.24 require Python 3.6 or newer. Scikit-learn 1.0 supported Python 3.7-3.10. - Scikit-learn 1.1 and later requires Python 3.8 or newer. + Scikit-learn 1.1, 1.2 and 1.3 support Python 3.8-3.12 + Scikit-learn 1.4 requires Python 3.9 or newer. .. _install_by_distribution: From f53fd43f180d8ecc4d1711bc02a3a7934bcb30a3 Mon Sep 17 00:00:00 2001 From: Conrad Stevens Date: Sun, 12 May 2024 07:29:55 +1000 Subject: [PATCH 015/275] DOC fix gp predic doc typo (#28987) Co-authored-by: Conrad --- sklearn/gaussian_process/_gpr.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sklearn/gaussian_process/_gpr.py b/sklearn/gaussian_process/_gpr.py index 67bba2e29c857..829c1e2fad2d8 100644 --- a/sklearn/gaussian_process/_gpr.py +++ b/sklearn/gaussian_process/_gpr.py @@ -384,7 +384,7 @@ def predict(self, X, return_std=False, return_cov=False): Returns ------- y_mean : ndarray of shape (n_samples,) or (n_samples, n_targets) - Mean of predictive distribution a query points. + Mean of predictive distribution at query points. y_std : ndarray of shape (n_samples,) or (n_samples, n_targets), optional Standard deviation of predictive distribution at query points. @@ -392,7 +392,7 @@ def predict(self, X, return_std=False, return_cov=False): y_cov : ndarray of shape (n_samples, n_samples) or \ (n_samples, n_samples, n_targets), optional - Covariance of joint predictive distribution a query points. + Covariance of joint predictive distribution at query points. Only returned when `return_cov` is True. """ if return_std and return_cov: From a660d89038434b9fd10a775baf86865e2f9c7310 Mon Sep 17 00:00:00 2001 From: Nathan Goldbaum Date: Mon, 13 May 2024 01:53:27 -0600 Subject: [PATCH 016/275] MAINT: specify C17 as C standard in meson.build (#28980) --- meson.build | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/meson.build b/meson.build index 3835a5099abb0..52c7deb962277 100644 --- a/meson.build +++ b/meson.build @@ -6,7 +6,7 @@ project( meson_version: '>= 1.1.0', default_options: [ 'buildtype=debugoptimized', - 'c_std=c99', + 'c_std=c17', 'cpp_std=c++14', ], ) From 47476144a4063813442eaf25908747e1a9c2dcc7 Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Mon, 13 May 2024 09:58:57 +0200 Subject: [PATCH 017/275] MNT remove author and license in GLM files (#28799) --- .../plot_poisson_regression_non_normal_loss.py | 7 ++----- .../plot_tweedie_regression_insurance_claims.py | 7 ++----- sklearn/linear_model/_glm/__init__.py | 4 ++-- sklearn/linear_model/_glm/_newton_solver.py | 5 ++--- sklearn/linear_model/_glm/glm.py | 6 ++---- sklearn/linear_model/_glm/tests/__init__.py | 3 ++- sklearn/linear_model/_glm/tests/test_glm.py | 6 ++---- 7 files changed, 14 insertions(+), 24 deletions(-) diff --git a/examples/linear_model/plot_poisson_regression_non_normal_loss.py b/examples/linear_model/plot_poisson_regression_non_normal_loss.py index 2a80c3db0ff40..180ee3b70671c 100644 --- a/examples/linear_model/plot_poisson_regression_non_normal_loss.py +++ b/examples/linear_model/plot_poisson_regression_non_normal_loss.py @@ -1,3 +1,5 @@ +# Authors: The scikit-learn developers +# SPDX-License-Identifier: BSD-3-Clause """ ====================================== Poisson regression and non-normal loss @@ -36,11 +38,6 @@ """ -# Authors: Christian Lorentzen -# Roman Yurchak -# Olivier Grisel -# License: BSD 3 clause - import matplotlib.pyplot as plt import numpy as np import pandas as pd diff --git a/examples/linear_model/plot_tweedie_regression_insurance_claims.py b/examples/linear_model/plot_tweedie_regression_insurance_claims.py index 96e32ee031190..31a91fb37c766 100644 --- a/examples/linear_model/plot_tweedie_regression_insurance_claims.py +++ b/examples/linear_model/plot_tweedie_regression_insurance_claims.py @@ -1,3 +1,5 @@ +# Authors: The scikit-learn developers +# SPDX-License-Identifier: BSD-3-Clause """ ====================================== Tweedie regression on insurance claims @@ -37,11 +39,6 @@ `_ """ -# Authors: Christian Lorentzen -# Roman Yurchak -# Olivier Grisel -# License: BSD 3 clause - # %% from functools import partial diff --git a/sklearn/linear_model/_glm/__init__.py b/sklearn/linear_model/_glm/__init__.py index 1b82bbd77bcf9..199b938b023d0 100644 --- a/sklearn/linear_model/_glm/__init__.py +++ b/sklearn/linear_model/_glm/__init__.py @@ -1,5 +1,5 @@ -# License: BSD 3 clause - +# Authors: The scikit-learn developers +# SPDX-License-Identifier: BSD-3-Clause from .glm import ( GammaRegressor, PoissonRegressor, diff --git a/sklearn/linear_model/_glm/_newton_solver.py b/sklearn/linear_model/_glm/_newton_solver.py index 20df35e6b48c2..b2be604d931c5 100644 --- a/sklearn/linear_model/_glm/_newton_solver.py +++ b/sklearn/linear_model/_glm/_newton_solver.py @@ -1,10 +1,9 @@ +# Authors: The scikit-learn developers +# SPDX-License-Identifier: BSD-3-Clause """ Newton solver for Generalized Linear Models """ -# Author: Christian Lorentzen -# License: BSD 3 clause - import warnings from abc import ABC, abstractmethod diff --git a/sklearn/linear_model/_glm/glm.py b/sklearn/linear_model/_glm/glm.py index 4cac889a4da51..14caa4fd733c2 100644 --- a/sklearn/linear_model/_glm/glm.py +++ b/sklearn/linear_model/_glm/glm.py @@ -1,11 +1,9 @@ +# Authors: The scikit-learn developers +# SPDX-License-Identifier: BSD-3-Clause """ Generalized Linear Models with Exponential Dispersion Family """ -# Author: Christian Lorentzen -# some parts and tricks stolen from other sklearn files. -# License: BSD 3 clause - from numbers import Integral, Real import numpy as np diff --git a/sklearn/linear_model/_glm/tests/__init__.py b/sklearn/linear_model/_glm/tests/__init__.py index 588cf7e93eef0..67dd18fb94b59 100644 --- a/sklearn/linear_model/_glm/tests/__init__.py +++ b/sklearn/linear_model/_glm/tests/__init__.py @@ -1 +1,2 @@ -# License: BSD 3 clause +# Authors: The scikit-learn developers +# SPDX-License-Identifier: BSD-3-Clause diff --git a/sklearn/linear_model/_glm/tests/test_glm.py b/sklearn/linear_model/_glm/tests/test_glm.py index 26f6bdc08d254..7f6ec64c15ad4 100644 --- a/sklearn/linear_model/_glm/tests/test_glm.py +++ b/sklearn/linear_model/_glm/tests/test_glm.py @@ -1,7 +1,5 @@ -# Authors: Christian Lorentzen -# -# License: BSD 3 clause - +# Authors: The scikit-learn developers +# SPDX-License-Identifier: BSD-3-Clause import itertools import warnings from functools import partial From 431f158e4f147075d4ecfc5c4239953ed267d66d Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Mon, 13 May 2024 18:49:23 +1000 Subject: [PATCH 018/275] DOC Update warm start example in ensemble user guide (#28998) --- doc/modules/ensemble.rst | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst index 4237d023973f7..d18dd2f65009e 100644 --- a/doc/modules/ensemble.rst +++ b/doc/modules/ensemble.rst @@ -603,7 +603,22 @@ fitted model. :: - >>> _ = est.set_params(n_estimators=200, warm_start=True) # set warm_start and new nr of trees + >>> import numpy as np + >>> from sklearn.metrics import mean_squared_error + >>> from sklearn.datasets import make_friedman1 + >>> from sklearn.ensemble import GradientBoostingRegressor + + >>> X, y = make_friedman1(n_samples=1200, random_state=0, noise=1.0) + >>> X_train, X_test = X[:200], X[200:] + >>> y_train, y_test = y[:200], y[200:] + >>> est = GradientBoostingRegressor( + ... n_estimators=100, learning_rate=0.1, max_depth=1, random_state=0, + ... loss='squared_error' + ... ) + >>> est = est.fit(X_train, y_train) # fit with 100 trees + >>> mean_squared_error(y_test, est.predict(X_test)) + 5.00... + >>> _ = est.set_params(n_estimators=200, warm_start=True) # set warm_start and increase num of trees >>> _ = est.fit(X_train, y_train) # fit additional 100 trees to est >>> mean_squared_error(y_test, est.predict(X_test)) 3.84... From 6ef4ef9aaed7f8d52ea06612e2cb6a88066c0a4a Mon Sep 17 00:00:00 2001 From: Christian Veenhuis <124370897+ChVeen@users.noreply.github.com> Date: Mon, 13 May 2024 11:43:52 +0200 Subject: [PATCH 019/275] MAINT fix redirected link for `Matthews Correlation Coefficient` (#28991) --- sklearn/metrics/_classification.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py index b68f1593e317e..1fb4c1d694be0 100644 --- a/sklearn/metrics/_classification.py +++ b/sklearn/metrics/_classification.py @@ -967,8 +967,8 @@ def matthews_corrcoef(y_true, y_pred, *, sample_weight=None): accuracy of prediction algorithms for classification: an overview. <10.1093/bioinformatics/16.5.412>` - .. [2] `Wikipedia entry for the Matthews Correlation Coefficient - `_. + .. [2] `Wikipedia entry for the Matthews Correlation Coefficient (phi coefficient) + `_. .. [3] `Gorodkin, (2004). Comparing two K-category assignments by a K-category correlation coefficient From 0b380137919cbc572d8aac096dae9e8a79627dd0 Mon Sep 17 00:00:00 2001 From: Ivan Wiryadi <44887783+strivn@users.noreply.github.com> Date: Mon, 13 May 2024 17:01:45 +0700 Subject: [PATCH 020/275] DOC Add links to digit denoising examples in docs and the user guide (#28929) --- doc/modules/decomposition.rst | 2 ++ sklearn/decomposition/_kernel_pca.py | 8 ++++++-- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/doc/modules/decomposition.rst b/doc/modules/decomposition.rst index e8241a92cfc3b..e34818a322c7d 100644 --- a/doc/modules/decomposition.rst +++ b/doc/modules/decomposition.rst @@ -291,6 +291,8 @@ prediction (kernel dependency estimation). :class:`KernelPCA` supports both .. topic:: Examples: * :ref:`sphx_glr_auto_examples_decomposition_plot_kernel_pca.py` + * :ref:`sphx_glr_auto_examples_applications_plot_digits_denoising.py` + .. topic:: References: diff --git a/sklearn/decomposition/_kernel_pca.py b/sklearn/decomposition/_kernel_pca.py index edfd49c2e87a0..0f45bc7c9239c 100644 --- a/sklearn/decomposition/_kernel_pca.py +++ b/sklearn/decomposition/_kernel_pca.py @@ -30,7 +30,7 @@ class KernelPCA(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator): - """Kernel Principal component analysis (KPCA) [1]_. + """Kernel Principal Component Analysis (KPCA) [1]_. Non-linear dimensionality reduction through the use of kernels (see :ref:`metrics`). @@ -41,9 +41,13 @@ class KernelPCA(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator components to extract. It can also use a randomized truncated SVD by the method proposed in [3]_, see `eigen_solver`. - For a usage example, see + For a usage example and comparison between + Principal Components Analysis (PCA) and its kernelized version (KPCA), see :ref:`sphx_glr_auto_examples_decomposition_plot_kernel_pca.py`. + For a usage example in denoising images using KPCA, see + :ref:`sphx_glr_auto_examples_applications_plot_digits_denoising.py`. + Read more in the :ref:`User Guide `. Parameters From 39191c93d389acdddab651d1d1df096a2f58477d Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 13 May 2024 12:22:35 +0200 Subject: [PATCH 021/275] :lock: :robot: CI Update lock files for cirrus-arm CI build(s) :lock: :robot: (#29003) --- .../pymin_conda_forge_linux-aarch64_conda.lock | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock index 585a75c078d8c..660bc9de9ecda 100644 --- a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock +++ b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock @@ -4,25 +4,25 @@ @EXPLICIT https://conda.anaconda.org/conda-forge/linux-aarch64/ca-certificates-2024.2.2-hcefe29a_0.conda#57c226edb90c4e973b9b7503537dd339 https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.40-hba4e955_0.conda#b55c1cb33c63d23b542fa53f24541e56 -https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-ng-13.2.0-h3f4de04_6.conda#dfe2ae16945dc08f163307a6bb3e70e0 +https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-ng-13.2.0-h3f4de04_7.conda#2a54872c7fab2db99b0074212d8efe64 https://conda.anaconda.org/conda-forge/linux-aarch64/python_abi-3.9-4_cp39.conda#c191905a08694e4a5cb1238e90233878 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 https://conda.anaconda.org/conda-forge/linux-aarch64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#98a1185182fec3c434069fa74e6473d6 -https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-ng-13.2.0-he277a41_6.conda#5ca8651e635390d41004c847f03c2d3c +https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-ng-13.2.0-he277a41_7.conda#01c5b27ce46f50abab2dc8454842c792 https://conda.anaconda.org/conda-forge/linux-aarch64/bzip2-1.0.8-h31becfc_5.conda#a64e35f01e0b7a2a152eca87d33b9c87 https://conda.anaconda.org/conda-forge/linux-aarch64/lerc-4.0.0-h4de3ea5_0.tar.bz2#1a0ffc65e03ce81559dbcb0695ad1476 https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlicommon-1.1.0-h31becfc_1.conda#1b219fd801eddb7a94df5bd001053ad9 https://conda.anaconda.org/conda-forge/linux-aarch64/libdeflate-1.20-h31becfc_0.conda#018592a3d691662f451f89d0de474a20 https://conda.anaconda.org/conda-forge/linux-aarch64/libffi-3.4.2-h3557bc0_5.tar.bz2#dddd85f4d52121fab0a8b099c5e06501 -https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran5-13.2.0-h87d9d71_6.conda#a3fdb6378e561e73c735ec30207daa15 +https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran5-13.2.0-h87d9d71_7.conda#423eb7de085dd6b46928723edf5f8767 https://conda.anaconda.org/conda-forge/linux-aarch64/libjpeg-turbo-3.0.0-h31becfc_1.conda#ed24e702928be089d9ba3f05618515c6 https://conda.anaconda.org/conda-forge/linux-aarch64/libnsl-2.0.1-h31becfc_0.conda#c14f32510f694e3185704d89967ec422 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https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.3.0-h31becfc_0.conda#36ca60a3afaf2ea2c460daeebd67430e https://conda.anaconda.org/conda-forge/linux-aarch64/pthread-stubs-0.4-hb9de7d4_1001.tar.bz2#d0183ec6ce0b5aaa3486df25fa5f0ded https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxau-1.0.11-h31becfc_0.conda#13de34f69cb73165dbe08c1e9148bedb @@ -30,7 +30,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxdmcp-1.1.3-h3557bc https://conda.anaconda.org/conda-forge/linux-aarch64/xz-5.2.6-h9cdd2b7_0.tar.bz2#83baad393a31d59c20b63ba4da6592df https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlidec-1.1.0-h31becfc_1.conda#8db7cff89510bec0b863a0a8ee6a7bce https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlienc-1.1.0-h31becfc_1.conda#ad3d3a826b5848d99936e4466ebbaa26 -https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran-ng-13.2.0-he9431aa_6.conda#c8ab19934c000ea8cc9cf1fc6c2aa83d 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-https://conda.anaconda.org/conda-forge/linux-aarch64/llvm-openmp-18.1.4-h767c9be_0.conda#2572130272fb725d825c9b52e5ce096b +https://conda.anaconda.org/conda-forge/linux-aarch64/llvm-openmp-18.1.5-h767c9be_0.conda#a9c2771c36671707f1992e4d0c32aa54 https://conda.anaconda.org/conda-forge/linux-aarch64/python-3.9.19-h4ac3b42_0_cpython.conda#1501507cd9451472ec8900d587ce872f https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-1.1.0-h31becfc_1.conda#e41f5862ac746428407f3fd44d2ed01f https://conda.anaconda.org/conda-forge/linux-aarch64/ccache-4.9.1-h6552966_0.conda#758b202f61f6bbfd2c6adf0fde043276 From cad4b59e2b5cd02acbc2d58a2d2655b8cf265c23 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 13 May 2024 12:23:28 +0200 Subject: [PATCH 022/275] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29005) --- ...latest_conda_forge_mkl_linux-64_conda.lock | 26 ++++----- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 38 ++++++------ ...test_conda_mkl_no_openmp_osx-64_conda.lock | 10 ++-- ...st_pip_openblas_pandas_linux-64_conda.lock | 10 ++-- ...onda_defaults_openblas_linux-64_conda.lock | 14 ++--- .../pymin_conda_forge_mkl_win-64_conda.lock | 8 +-- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 26 ++++----- build_tools/circle/doc_linux-64_conda.lock | 58 +++++++++---------- .../doc_min_dependencies_linux-64_conda.lock | 50 ++++++++-------- 9 files changed, 120 insertions(+), 120 deletions(-) diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index 932fc6ad670f7..3d895fda71bc3 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -9,13 +9,13 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed3 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https://conda.anaconda.org/conda-forge/osx-64/pyamg-5.1.0-py312h3db3e91_0.conda#c6d6248b99fc11b15c9becea581a1462 -https://conda.anaconda.org/conda-forge/osx-64/clang_osx-64-16.0.6-hb91bd55_12.conda#4ef6f9a82654ad497e2334471832e774 +https://conda.anaconda.org/conda-forge/osx-64/clang_osx-64-16.0.6-hb91bd55_14.conda#3d0d9c725912bb0cb4cd301d2a5d31d7 https://conda.anaconda.org/conda-forge/osx-64/matplotlib-3.8.4-py312hb401068_0.conda#187ee42addd449b4899b55c304012436 https://conda.anaconda.org/conda-forge/osx-64/c-compiler-1.7.0-h282daa2_1.conda#d27411cb82bc1b76b9f487da6ae97f1d -https://conda.anaconda.org/conda-forge/osx-64/clangxx_impl_osx-64-16.0.6-h6d92fbe_12.conda#c1b8987b40123346ee3fe120c3b66b3d +https://conda.anaconda.org/conda-forge/osx-64/clangxx_impl_osx-64-16.0.6-h6d92fbe_14.conda#66b9f06d5f0d0ea47ffcb3a9ca65774a https://conda.anaconda.org/conda-forge/osx-64/gfortran_osx-64-12.3.0-h18f7dce_1.conda#436af2384c47aedb94af78a128e174f1 -https://conda.anaconda.org/conda-forge/osx-64/clangxx_osx-64-16.0.6-hb91bd55_12.conda#4e8cca2283e843a8df8b2e747d36226d +https://conda.anaconda.org/conda-forge/osx-64/clangxx_osx-64-16.0.6-hb91bd55_14.conda#a4504c1a7beab8875d6f765941e77248 https://conda.anaconda.org/conda-forge/osx-64/gfortran-12.3.0-h2c809b3_1.conda#c48adbaa8944234b80ef287c37e329b0 https://conda.anaconda.org/conda-forge/osx-64/cxx-compiler-1.7.0-h7728843_1.conda#e04cb15a20553b973dd068c2dc81d682 https://conda.anaconda.org/conda-forge/osx-64/fortran-compiler-1.7.0-h6c2ab21_1.conda#48319058089f492d5059e04494b81ed9 diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock index c687f8fb76fb1..ec92612048448 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock @@ -29,7 +29,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/ninja-base-1.10.2-haf03e11_5.conda#c8 https://repo.anaconda.com/pkgs/main/osx-64/openssl-3.0.13-hca72f7f_1.conda#e526d7e2e79132a11b4746cf305c45b5 https://repo.anaconda.com/pkgs/main/osx-64/readline-8.2-hca72f7f_0.conda#971667436260e523f6f7355fdfa238bf https://repo.anaconda.com/pkgs/main/osx-64/tbb-2021.8.0-ha357a0b_0.conda#fb48530a3eea681c11dafb95b3387c0f -https://repo.anaconda.com/pkgs/main/osx-64/tk-8.6.12-h5d9f67b_0.conda#047f0af5486d19163e37fd7f8ae3d29f +https://repo.anaconda.com/pkgs/main/osx-64/tk-8.6.14-h4d00af3_0.conda#a2c03940c2ae54614301ec82e6a98d75 https://repo.anaconda.com/pkgs/main/osx-64/brotli-bin-1.0.9-h6c40b1e_8.conda#11053f9c6b8d8a8348d0c33450c23ce9 https://repo.anaconda.com/pkgs/main/osx-64/freetype-2.12.1-hd8bbffd_0.conda#1f276af321375ee7fe8056843044fa76 https://repo.anaconda.com/pkgs/main/osx-64/libgfortran-5.0.0-11_3_0_hecd8cb5_28.conda#2eb13b680803f1064e53873ae0aaafb3 @@ -38,7 +38,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/sqlite-3.45.3-h6c40b1e_0.conda#2edf90 https://repo.anaconda.com/pkgs/main/osx-64/zstd-1.5.5-hc035e20_2.conda#c033bf68c12f8c71fd916f000f3dc118 https://repo.anaconda.com/pkgs/main/osx-64/brotli-1.0.9-h6c40b1e_8.conda#10f89677a3898d0113dc354adf643df3 https://repo.anaconda.com/pkgs/main/osx-64/libtiff-4.5.1-hcec6c5f_0.conda#e127a800ffd9d300ed7d5e1b026944ec -https://repo.anaconda.com/pkgs/main/osx-64/python-3.12.3-hd58486a_0.conda#1a287cfa37c5a92972f5f527b6af7eed +https://repo.anaconda.com/pkgs/main/osx-64/python-3.12.3-hd58486a_1.conda#cdc61e8f6c2d77b3b263e720048c4b54 https://repo.anaconda.com/pkgs/main/osx-64/coverage-7.2.2-py312h6c40b1e_0.conda#b6e4b9fba325047c07f3c9211ae91d1c https://repo.anaconda.com/pkgs/main/noarch/cycler-0.11.0-pyhd3eb1b0_0.conda#f5e365d2cdb66d547eb8c3ab93843aab https://repo.anaconda.com/pkgs/main/noarch/execnet-1.9.0-pyhd3eb1b0_0.conda#f895937671af67cebb8af617494b3513 @@ -54,7 +54,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/pluggy-1.0.0-py312hecd8cb5_1.conda#64 https://repo.anaconda.com/pkgs/main/osx-64/pyparsing-3.0.9-py312hecd8cb5_0.conda#d85cf2b81c6d9326a57a6418e14db258 https://repo.anaconda.com/pkgs/main/noarch/python-tzdata-2023.3-pyhd3eb1b0_0.conda#479c037de0186d114b9911158427624e https://repo.anaconda.com/pkgs/main/osx-64/pytz-2024.1-py312hecd8cb5_0.conda#2b28ec0e0d07f5c0c701f75200b1e8b6 -https://repo.anaconda.com/pkgs/main/osx-64/setuptools-68.2.2-py312hecd8cb5_0.conda#64235f0c451427d86808c70c1c31cb8b +https://repo.anaconda.com/pkgs/main/osx-64/setuptools-69.5.1-py312hecd8cb5_0.conda#5c7c7ef1e0762e3ca1f543d28310946f https://repo.anaconda.com/pkgs/main/noarch/six-1.16.0-pyhd3eb1b0_1.conda#34586824d411d36af2fa40e799c172d0 https://repo.anaconda.com/pkgs/main/noarch/toml-0.10.2-pyhd3eb1b0_0.conda#cda05f5f6d8509529d1a2743288d197a https://repo.anaconda.com/pkgs/main/osx-64/tornado-6.3.3-py312h6c40b1e_0.conda#49173b5a36c9134865221f29d4a73fb6 @@ -64,10 +64,10 @@ https://repo.anaconda.com/pkgs/main/osx-64/fonttools-4.51.0-py312h6c40b1e_0.cond https://repo.anaconda.com/pkgs/main/osx-64/meson-1.3.1-py312hecd8cb5_0.conda#43963a2b38becce4caa95434b8c96837 https://repo.anaconda.com/pkgs/main/osx-64/numpy-base-1.26.4-py312h6f81483_0.conda#87f73efbf26ab2e2ea7c32481a71bd47 https://repo.anaconda.com/pkgs/main/osx-64/pillow-10.3.0-py312h6c40b1e_0.conda#fe883fa4247d35fe6de49f713529ca02 -https://repo.anaconda.com/pkgs/main/osx-64/pip-23.3.1-py312hecd8cb5_0.conda#efc3db40cac09f74bb480d28d3a0b260 +https://repo.anaconda.com/pkgs/main/osx-64/pip-24.0-py312hecd8cb5_0.conda#7a8e0b1d3742ddf1c8aa97fbaa158039 https://repo.anaconda.com/pkgs/main/osx-64/pyproject-metadata-0.7.1-py312hecd8cb5_0.conda#e91ce37477d24dcdf7e0a8b93c5e72fd https://repo.anaconda.com/pkgs/main/osx-64/pytest-7.4.0-py312hecd8cb5_0.conda#b816a2439ba9b87524aec74d58e55b0a -https://repo.anaconda.com/pkgs/main/noarch/python-dateutil-2.8.2-pyhd3eb1b0_0.conda#211ee00320b08a1ac9fea6677649f6c9 +https://repo.anaconda.com/pkgs/main/osx-64/python-dateutil-2.9.0post0-py312hecd8cb5_0.conda#b3ed54eb118325785284dd18bfceca19 https://repo.anaconda.com/pkgs/main/osx-64/meson-python-0.15.0-py312h6c40b1e_0.conda#688ab56b9d8e5a2e3f018ca3ce34e061 https://repo.anaconda.com/pkgs/main/osx-64/pytest-cov-4.1.0-py312hecd8cb5_1.conda#a33a24eb20359f464938e75b2f57e23a https://repo.anaconda.com/pkgs/main/osx-64/pytest-xdist-3.5.0-py312hecd8cb5_0.conda#d1ecfb3691cceecb1f16bcfdf0b67bb5 diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index c497709ca347e..46fd0d308eaa2 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -17,12 +17,12 @@ https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_1.conda#1562802f8 https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be42180685cce6e6b0329201d9f48efb -https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.12-h1ccaba5_0.conda#fa10ff4aa631fa4aa090a6234d7770b9 +https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.14-h39e8969_0.conda#78dbc5e3c69143ebc037fc5d5b22e597 https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda#acf93d6aceb74d6110e20b44cc45939e -https://repo.anaconda.com/pkgs/main/linux-64/python-3.9.19-h955ad1f_0.conda#33cb019c40e3409df392c99e3c34f352 -https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.2.2-py39h06a4308_0.conda#5b42cae5548732ae5c167bb1066085de +https://repo.anaconda.com/pkgs/main/linux-64/python-3.9.19-h955ad1f_1.conda#4b453281859c293c9d577271f3b18a0d +https://repo.anaconda.com/pkgs/main/linux-64/setuptools-69.5.1-py39h06a4308_0.conda#3eb144d481b39c0fbbced789dd9b76b3 https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py39h06a4308_0.conda#40bb60408c7433d767fd8c65b35bc4a0 -https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py39h06a4308_0.conda#685007e3dae59d211620f19926577bd6 +https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py39h06a4308_0.conda#7f8ce3af15cfecd12e4dda8c5cef5fb7 # pip alabaster @ https://files.pythonhosted.org/packages/32/34/d4e1c02d3bee589efb5dfa17f88ea08bdb3e3eac12bc475462aec52ed223/alabaster-0.7.16-py3-none-any.whl#sha256=b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92 # pip babel @ https://files.pythonhosted.org/packages/27/45/377f7e32a5c93d94cd56542349b34efab5ca3f9e2fd5a68c5e93169aa32d/Babel-2.15.0-py3-none-any.whl#sha256=08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb # pip certifi @ https://files.pythonhosted.org/packages/ba/06/a07f096c664aeb9f01624f858c3add0a4e913d6c96257acb4fce61e7de14/certifi-2024.2.2-py3-none-any.whl#sha256=dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1 @@ -75,7 +75,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py39h06a4308_0.conda#685 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/70/8e/0e2d847013cb52cd35b38c009bb167a1a26b2ce6cd6965bf26b47bc0bf44/requests-2.31.0-py3-none-any.whl#sha256=58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f # pip scipy @ https://files.pythonhosted.org/packages/c6/ba/a778e6c0020d728c119b0379805a357135fe8c9bc87fdb7e0750ca11319f/scipy-1.13.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=28e286bf9ac422d6beb559bc61312c348ca9b0f0dae0d7c5afde7f722d6ea13d -# pip tifffile @ https://files.pythonhosted.org/packages/c1/cf/dd1cdf85db58c811816377afd6ba8a240f4611e16f4085201598fb2d5578/tifffile-2024.5.3-py3-none-any.whl#sha256=cac4d939156ff7f16d65fd689637808a7b5b3ad58f9c73327fc009b0aa32c7d5 +# pip tifffile @ https://files.pythonhosted.org/packages/c1/79/29d0fa40017f7b749ce344759dcc21e2ec9bbb81fc69ca2ce06e261f83f0/tifffile-2024.5.10-py3-none-any.whl#sha256=4154f091aa24d4e75bfad9ab2d5424a68c70e67b8220188066dc61946d4551bd # pip lightgbm @ https://files.pythonhosted.org/packages/ba/11/cb8b67f3cbdca05b59a032bb57963d4fe8c8d18c3870f30bed005b7f174d/lightgbm-4.3.0-py3-none-manylinux_2_28_x86_64.whl#sha256=104496a3404cb2452d3412cbddcfbfadbef9c372ea91e3a9b8794bcc5183bf07 # pip matplotlib @ https://files.pythonhosted.org/packages/5e/2c/513395a63a9e1124a5648addbf73be23cc603f955af026b04416da98dc96/matplotlib-3.8.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=606e3b90897554c989b1e38a258c626d46c873523de432b1462f295db13de6f9 # pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 diff --git a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock index ff7bcd028c7f6..6e46719df47c4 100644 --- a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock @@ -39,7 +39,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/libpng-1.6.39-h5eee18b_0.conda#f6ae 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https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96 @@ -198,10 +198,10 @@ https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-h3faef2a_0.conda#f9 https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.7.0-h00ab1b0_1.conda#28de2e073db9ca9b72858bee9fb6f571 https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.51.0-py39hd1e30aa_0.conda#79f5dd8778873faa54e8f7b2729fe8a6 https://conda.anaconda.org/conda-forge/linux-64/fortran-compiler-1.7.0-heb67821_1.conda#cf4b0e7c4c78bb0662aed9b27c414a3c -https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.0-hf2295e7_6.conda#a1e026a82a562b443845db5614ca568a +https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.2-hf974151_0.conda#d427988dc3dbd0a4c136f52db356cc6a https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-7.1.0-pyha770c72_0.conda#0896606848b2dc5cebdf111b6543aa04 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https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip python-json-logger @ https://files.pythonhosted.org/packages/35/a6/145655273568ee78a581e734cf35beb9e33a370b29c5d3c8fee3744de29f/python_json_logger-2.0.7-py3-none-any.whl#sha256=f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd # pip pyyaml @ https://files.pythonhosted.org/packages/7d/39/472f2554a0f1e825bd7c5afc11c817cd7a2f3657460f7159f691fbb37c51/PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c # pip rfc3986-validator @ https://files.pythonhosted.org/packages/9e/51/17023c0f8f1869d8806b979a2bffa3f861f26a3f1a66b094288323fba52f/rfc3986_validator-0.1.1-py2.py3-none-any.whl#sha256=2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9 -# pip rpds-py @ https://files.pythonhosted.org/packages/fd/ea/92231b62681961812e9fbd8ef9be7137856784406bf6a384976bb7b46472/rpds_py-0.18.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ddc2f4dfd396c7bfa18e6ce371cba60e4cf9d2e5cdb71376aa2da264605b60b9 +# pip rpds-py @ https://files.pythonhosted.org/packages/97/b1/12238bd8cdf3cef71e85188af133399bfde1bddf319007361cc869d6f6a7/rpds_py-0.18.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=e4c39ad2f512b4041343ea3c7894339e4ca7839ac38ca83d68a832fc8b3748ab # pip send2trash @ https://files.pythonhosted.org/packages/40/b0/4562db6223154aa4e22f939003cb92514c79f3d4dccca3444253fd17f902/Send2Trash-1.8.3-py3-none-any.whl#sha256=0c31227e0bd08961c7665474a3d1ef7193929fedda4233843689baa056be46c9 # pip sniffio @ https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl#sha256=2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2 # pip soupsieve @ https://files.pythonhosted.org/packages/4c/f3/038b302fdfbe3be7da016777069f26ceefe11a681055ea1f7817546508e3/soupsieve-2.5-py3-none-any.whl#sha256=eaa337ff55a1579b6549dc679565eac1e3d000563bcb1c8ab0d0fefbc0c2cdc7 diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index dd291f8882efb..e08a14c235079 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -10,21 +10,21 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77 https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-2.6.32-he073ed8_17.conda#d731b543793afc0433c4fd593e693fce https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h55db66e_0.conda#10569984e7db886e4f1abc2b47ad79a1 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https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 -https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.2.0-h77fa898_6.conda#e733e0573651a1f0639fa8ce066a286e +https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.2.0-h77fa898_7.conda#abf3fec87c2563697defa759dec3d639 https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.12-he073ed8_17.conda#595db67e32b276298ff3d94d07d47fbf https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.40-ha885e6a_0.conda#800a4c872b5bc06fa83888d112fe6c4f https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-64/binutils-2.40-h4852527_0.conda#a05c7712be80622934f7011e0a1d43fc https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.40-hdade7a5_3.conda#2d9a60578bc28469d9aeef9aea5520c3 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h77fa898_6.conda#4398809ac84d0b8c28beebaaa83277f5 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h77fa898_7.conda#72ec1b1b04c4d15d4204ece1ecea5978 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.11-hd590300_1.conda#0bb492cca54017ea314b809b1ee3a176 https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2#d9c69a24ad678ffce24c6543a0176b00 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hd590300_5.conda#69b8b6202a07720f448be700e300ccf4 @@ -39,21 +39,21 @@ https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.20-hd590300_0.conda https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.2-h59595ed_0.conda#e7ba12deb7020dd080c6c70e7b6f6a3d https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2#d645c6d2ac96843a2bfaccd2d62b3ac3 https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-0.22.5-h59595ed_2.conda#172bcc51059416e7ce99e7b528cede83 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-h43f5ff8_6.conda#e54a5ddc67e673f9105cf2a2e9c070b0 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-hca663fb_7.conda#c0bd771f09a326fdcd95a60b617795bf https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda#d66573916ffcf376178462f1b61c941e https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda#ea25936bb4080d843790b586850f82b8 https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda#30fd6e37fe21f86f4bd26d6ee73eeec7 https://conda.anaconda.org/conda-forge/linux-64/libogg-1.3.4-h7f98852_1.tar.bz2#6e8cc2173440d77708196c5b93771680 https://conda.anaconda.org/conda-forge/linux-64/libopus-1.3.1-h7f98852_1.tar.bz2#15345e56d527b330e1cacbdf58676e8f -https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-12.3.0-hb8811af_6.conda#a9a764e2e753ed038da59343560d8a66 +https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-12.3.0-hb8811af_7.conda#ee573415c47ce17f65101d0b3fba396d https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.conda#b26e8aa824079e1be0294e7152ca4559 https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.13-hd590300_5.conda#f36c115f1ee199da648e0597ec2047ad https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda#318b08df404f9c9be5712aaa5a6f0bb0 https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9160cdeb523a1b20cf8d2a0bf821f45d -https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.4.20240210-h59595ed_0.conda#97da8860a0da5413c7c98a3b3838a645 -https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.0-h00ab1b0_0.conda#b048701d52e7cbb5f59ddd4d3b17bbf5 +https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 +https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-hd590300_0.conda#c0f3abb4a16477208bbd43a39bd56f18 https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123 @@ -69,13 +69,13 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-xproto-7.0.31-h7f98852_1007 https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2#2161070d867d1b1204ea749c8eec4ef0 https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2#4cb3ad778ec2d5a7acbdf254eb1c42ae https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.2-h59595ed_0.conda#53fb86322bdb89496d7579fe3f02fd61 -https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-12.3.0-h58ffeeb_6.conda#53914a98926ce169b83726cb78366a6c +https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-12.3.0-h58ffeeb_7.conda#95f78565a09852783d3e90e0389cfa5f https://conda.anaconda.org/conda-forge/linux-64/libasprintf-devel-0.22.5-h661eb56_2.conda#02e41ab5834dcdcc8590cf29d9526f50 https://conda.anaconda.org/conda-forge/linux-64/libcap-2.69-h0f662aa_0.conda#25cb5999faa414e5ccb2c1388f62d3d5 https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20191231-he28a2e2_2.tar.bz2#4d331e44109e3f0e19b4cb8f9b82f3e1 https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda#a1cfcc585f0c42bf8d5546bb1dfb668d https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-devel-0.22.5-h59595ed_2.conda#b63d9b6da3653179a278077f0de20014 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-13.2.0-h69a702a_6.conda#3666a850342f8f3be88f9a93d948d027 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-13.2.0-h69a702a_7.conda#1b84f26d9f4f6026e179e7805d5a15cd https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.43-h2797004_0.conda#009981dd9cfcaa4dbfa25ffaed86bcae https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.45.3-h2797004_0.conda#b3316cbe90249da4f8e84cd66e1cc55b https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h9c3ff4c_0.tar.bz2#309dec04b70a3cc0f1e84a4013683bc0 @@ -89,20 +89,20 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.4-h7391055_0.cond https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.13-hd590300_5.conda#68c34ec6149623be41a1933ab996a209 https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda#4d056880988120e29d75bfff282e0f45 https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-h267a509_2.conda#9ae35c3d96db2c94ce0cef86efdfa2cb -https://conda.anaconda.org/conda-forge/linux-64/gcc-12.3.0-h915e2ae_6.conda#ec683e084ea08ef94528f15d30fa1e03 +https://conda.anaconda.org/conda-forge/linux-64/gcc-12.3.0-h915e2ae_7.conda#84b1c5cebd0a0443f3d7f90a4be93fc6 https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-12.3.0-h6477408_3.conda#7a53f84c45bdf4656ba27b9e9ed68b3d https://conda.anaconda.org/conda-forge/linux-64/gettext-0.22.5-h59595ed_2.conda#219ba82e95d7614cf7140d2a4afc0926 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https://conda.anaconda.org/conda-forge/linux-64/libllvm15-15.0.7-hb3ce162_4.conda#8a35df3cbc0c8b12cc8af9473ae75eef https://conda.anaconda.org/conda-forge/linux-64/libllvm18-18.1.5-hb77312f_0.conda#efd221d3668077ca067a206269418dec https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-h1dd3fc0_3.conda#66f03896ffbe1a110ffda05c7a856504 -https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.4-ha31de31_0.conda#48b9991e66abc186a7ad7975e97bd4d0 +https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.5-ha31de31_0.conda#b923cdb6e567ada84f991ffcc5848afb https://conda.anaconda.org/conda-forge/linux-64/mysql-libs-8.3.0-hca2cd23_4.conda#1b50eebe2a738a3146c154d2eceaa8b6 -https://conda.anaconda.org/conda-forge/linux-64/nss-3.98-h1d7d5a4_0.conda#54b56c2fdf973656b748e0378900ec13 +https://conda.anaconda.org/conda-forge/linux-64/nss-3.100-hca3bf56_0.conda#949c4a82290ee58b3c970cef4bcfd4ad https://conda.anaconda.org/conda-forge/linux-64/python-3.9.19-h0755675_0_cpython.conda#d9ee3647fbd9e8595b8df759b2bbefb8 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-0.4.0-hd590300_1.conda#9bfac7ccd94d54fd21a0501296d60424 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-keysyms-0.4.0-h8ee46fc_1.conda#632413adcd8bc16b515cab87a2932913 @@ -125,10 +125,10 @@ https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.0-pyhd8ed1ab_2. https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_0.conda#15dda3cdbf330abfe9f555d22f66db46 https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.14.2-h14ed4e7_0.conda#0f69b688f52ff6da70bccb7ff7001d1d https://conda.anaconda.org/conda-forge/noarch/fsspec-2024.3.1-pyhca7485f_0.conda#b7f0662ef2c9d4404f0af9eef5ed2fde -https://conda.anaconda.org/conda-forge/linux-64/gfortran-12.3.0-h915e2ae_6.conda#84b517f4f53e56256dbd65133aae04ac +https://conda.anaconda.org/conda-forge/linux-64/gfortran-12.3.0-h915e2ae_7.conda#8efa768f7f74085629f3e1090e7f0569 https://conda.anaconda.org/conda-forge/linux-64/gfortran_linux-64-12.3.0-h617cb40_3.conda#3a9e5b8a6f651ff14e74d896d8f04ab6 -https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.80.0-hde27a5a_6.conda#a9d23c02485c5cf055f9ac90eb9c9c63 -https://conda.anaconda.org/conda-forge/linux-64/gxx-12.3.0-h915e2ae_6.conda#0d977804df65082e17c860600ca2894b +https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.80.2-hb6ce0ca_0.conda#a965aeaf060289528a3fbe09326edae2 +https://conda.anaconda.org/conda-forge/linux-64/gxx-12.3.0-h915e2ae_7.conda#721c5433122a02bf3a081db10a2e68e2 https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-12.3.0-h4a1b8e8_3.conda#9ec22c7c544f4a4f6d660f0a3b0fd15c https://conda.anaconda.org/conda-forge/noarch/idna-3.7-pyhd8ed1ab_0.conda#c0cc1420498b17414d8617d0b9f506ca https://conda.anaconda.org/conda-forge/noarch/imagesize-1.4.1-pyhd8ed1ab_0.tar.bz2#7de5386c8fea29e76b303f37dde4c352 @@ -140,7 +140,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libclang13-18.1.5-default_h5d682 https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-h4637d8d_4.conda#d4529f4dff3057982a7617c7ac58fde3 https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda#ee48bf17cc83a00f59ca1494d5646869 https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.49-h4f305b6_0.conda#dfcfd72c7a430d3616763ecfbefe4ca9 -https://conda.anaconda.org/conda-forge/linux-64/libpq-16.2-h33b98f1_1.conda#9e49ec2a61d02623b379dc332eb6889d +https://conda.anaconda.org/conda-forge/linux-64/libpq-16.3-ha72fbe1_0.conda#bac737ae28b79cfbafd515258d97d29e https://conda.anaconda.org/conda-forge/noarch/locket-1.0.0-pyhd8ed1ab_0.tar.bz2#91e27ef3d05cc772ce627e51cff111c4 https://conda.anaconda.org/conda-forge/linux-64/markupsafe-2.1.5-py39hd1e30aa_0.conda#9a9a22eb1f83c44953319ee3b027769f https://conda.anaconda.org/conda-forge/noarch/networkx-3.2-pyhd8ed1ab_0.conda#cec8cc498664cc00a070676aa89e69a7 @@ -160,7 +160,7 @@ https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5 https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-2.2.0-pyhd8ed1ab_0.tar.bz2#4d22a9315e78c6827f806065957d566e https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_0.conda#da1d979339e2714c30a8e806a33ec087 https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.12.0-h00ab1b0_0.conda#f1b776cff1b426e7e7461a8502a3b731 -https://conda.anaconda.org/conda-forge/noarch/tenacity-8.2.3-pyhd8ed1ab_0.conda#1482e77f87c6a702a7e05ef22c9b197b +https://conda.anaconda.org/conda-forge/noarch/tenacity-8.3.0-pyhd8ed1ab_0.conda#216cfa8e32bcd1447646768351df6059 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_0.tar.bz2#f832c45a477c78bebd107098db465095 https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96 @@ -178,9 +178,9 @@ https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-h3faef2a_0.conda#f9 https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.7.0-h00ab1b0_1.conda#28de2e073db9ca9b72858bee9fb6f571 https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.12.3-py39hd1e30aa_0.conda#dc0fb8e157c7caba4c98f1e1f9d2e5f4 https://conda.anaconda.org/conda-forge/linux-64/fortran-compiler-1.7.0-heb67821_1.conda#cf4b0e7c4c78bb0662aed9b27c414a3c -https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.0-hf2295e7_6.conda#a1e026a82a562b443845db5614ca568a +https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.2-hf974151_0.conda#d427988dc3dbd0a4c136f52db356cc6a https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-7.1.0-pyha770c72_0.conda#0896606848b2dc5cebdf111b6543aa04 -https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.3-pyhd8ed1ab_0.conda#e7d8df6509ba635247ff9aea31134262 +https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda#7b86ecb7d3557821c649b3c31e3eb9f2 https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-1.10.3-hd590300_0.conda#32d16ad533c59bb0a3c5ffaf16110829 https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.conda#ef1910918dd895516a769ed36b5b3a4e @@ -188,7 +188,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.7.0-h662e7e4_0.co https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhd8ed1ab_0.tar.bz2#8b45f9f2b2f7a98b0ec179c8991a4a9b https://conda.anaconda.org/conda-forge/noarch/meson-1.4.0-pyhd8ed1ab_0.conda#52a0660cfa40b45bf254ecc3374cb2e0 https://conda.anaconda.org/conda-forge/linux-64/mkl-2024.1.0-ha957f24_692.conda#e7f5c5cda17c6f5047db27d44367c19d -https://conda.anaconda.org/conda-forge/noarch/partd-1.4.1-pyhd8ed1ab_0.conda#acf4b7c0bcd5fa3b0e05801c4d2accd6 +https://conda.anaconda.org/conda-forge/noarch/partd-1.4.2-pyhd8ed1ab_0.conda#0badf9c54e24cecfb0ad2f99d680c163 https://conda.anaconda.org/conda-forge/linux-64/pillow-10.3.0-py39h90c7501_0.conda#1e3b6af9592be71ce19f0a6aae05d97b https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/plotly-5.14.0-pyhd8ed1ab_0.conda#6a7bcc42ef58dd6cf3da9333ea102433 From e0579ee40c75aa1a0842de60a8b026f53d5ec616 Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Mon, 13 May 2024 13:28:17 +0200 Subject: [PATCH 023/275] FIX 1d sparse array validation (#28988) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Jérémie du Boisberranger Co-authored-by: Christian Lorentzen --- doc/whats_new/v1.5.rst | 4 ++++ sklearn/preprocessing/tests/test_data.py | 4 ++++ sklearn/utils/tests/test_validation.py | 8 ++++++++ sklearn/utils/validation.py | 7 +++++++ 4 files changed, 23 insertions(+) diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst index e50309a330e39..55a5546453f5f 100644 --- a/doc/whats_new/v1.5.rst +++ b/doc/whats_new/v1.5.rst @@ -67,6 +67,10 @@ Changes impacting many modules :class:`pipeline.Pipeline` and :class:`preprocessing.KBinsDiscretizer`. :pr:`28756` by :user:`Will Dean `. +- |Fix| Raise `ValueError` with an informative error message when passing 1D + sparse arrays to methods that expect 2D sparse inputs. + :pr:`28988` by :user:`Olivier Grisel `. + Support for Array API --------------------- diff --git a/sklearn/preprocessing/tests/test_data.py b/sklearn/preprocessing/tests/test_data.py index b7e8e4e40686e..3810e485ae301 100644 --- a/sklearn/preprocessing/tests/test_data.py +++ b/sklearn/preprocessing/tests/test_data.py @@ -595,6 +595,10 @@ def test_standard_scaler_partial_fit_numerical_stability(sparse_container): scaler_incr = StandardScaler(with_mean=False) for chunk in X: + if chunk.ndim == 1: + # Sparse arrays can be 1D (in scipy 1.14 and later) while old + # sparse matrix instances are always 2D. + chunk = chunk.reshape(1, -1) scaler_incr = scaler_incr.partial_fit(chunk) # Regardless of magnitude, they must not differ more than of 6 digits diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py index 4b4eed2522102..92fff950e875e 100644 --- a/sklearn/utils/tests/test_validation.py +++ b/sklearn/utils/tests/test_validation.py @@ -361,6 +361,14 @@ def test_check_array(): with pytest.raises(ValueError, match="Expected 2D array, got scalar array instead"): check_array(10, ensure_2d=True) + # ensure_2d=True with 1d sparse array + if hasattr(sp, "csr_array"): + sparse_row = next(iter(sp.csr_array(X))) + if sparse_row.ndim == 1: + # In scipy 1.14 and later, sparse row is 1D while it was 2D before. + with pytest.raises(ValueError, match="Expected 2D input, got"): + check_array(sparse_row, accept_sparse=True, ensure_2d=True) + # don't allow ndim > 3 X_ndim = np.arange(8).reshape(2, 2, 2) with pytest.raises(ValueError): diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py index 5fac2ae6ae6c2..cdda749ec70a2 100644 --- a/sklearn/utils/validation.py +++ b/sklearn/utils/validation.py @@ -973,6 +973,13 @@ def is_sparse(dtype): estimator_name=estimator_name, input_name=input_name, ) + if ensure_2d and array.ndim < 2: + raise ValueError( + f"Expected 2D input, got input with shape {array.shape}.\n" + "Reshape your data either using array.reshape(-1, 1) if " + "your data has a single feature or array.reshape(1, -1) " + "if it contains a single sample." + ) else: # If np.array(..) gives ComplexWarning, then we convert the warning # to an error. This is needed because specifying a non complex From c201a0f1891746d28d27598838dfa16185365dec Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 13 May 2024 15:19:29 +0200 Subject: [PATCH 024/275] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#29004) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Jérémie du Boisberranger --- .../azure/pylatest_pip_scipy_dev_linux-64_conda.lock | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index 8324d1edb36b7..c1a50c7c8c140 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -20,12 +20,12 @@ https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_1.conda#1562802f8 https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be42180685cce6e6b0329201d9f48efb -https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.12-h1ccaba5_0.conda#fa10ff4aa631fa4aa090a6234d7770b9 +https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.14-h39e8969_0.conda#78dbc5e3c69143ebc037fc5d5b22e597 https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda#acf93d6aceb74d6110e20b44cc45939e -https://repo.anaconda.com/pkgs/main/linux-64/python-3.12.3-h996f2a0_0.conda#77af2bd351a8311d1e780bcfa7819bb8 -https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.2.2-py312h06a4308_0.conda#83ba634cde4f30d9e0b88e4ac9716ca4 +https://repo.anaconda.com/pkgs/main/linux-64/python-3.12.3-h996f2a0_1.conda#0e22ed7e6df024e4f7467e75c8575301 +https://repo.anaconda.com/pkgs/main/linux-64/setuptools-69.5.1-py312h06a4308_0.conda#ce85d9a864a73e0b12d31a97733c9fca https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py312h06a4308_0.conda#18d5f3b68a175c72576876db4afc9e9e -https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py312h06a4308_0.conda#e1d44bca4a257e84af33503233491107 +https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py312h06a4308_0.conda#6d9697bb8b9f3212be10b3b8e01a12b9 # pip alabaster @ https://files.pythonhosted.org/packages/32/34/d4e1c02d3bee589efb5dfa17f88ea08bdb3e3eac12bc475462aec52ed223/alabaster-0.7.16-py3-none-any.whl#sha256=b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92 # pip babel @ https://files.pythonhosted.org/packages/27/45/377f7e32a5c93d94cd56542349b34efab5ca3f9e2fd5a68c5e93169aa32d/Babel-2.15.0-py3-none-any.whl#sha256=08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb # pip certifi @ https://files.pythonhosted.org/packages/ba/06/a07f096c664aeb9f01624f858c3add0a4e913d6c96257acb4fce61e7de14/certifi-2024.2.2-py3-none-any.whl#sha256=dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1 From e81412776957629b2e5f058fe0bf6cf14f8dc41b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?= Date: Mon, 13 May 2024 16:34:37 +0200 Subject: [PATCH 025/275] CI Fix wheel builder windows (#29006) --- .github/workflows/wheels.yml | 2 -- build_tools/github/repair_windows_wheels.sh | 1 + 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml index d30f85ff3d1e6..8bd7ffc17beca 100644 --- a/.github/workflows/wheels.yml +++ b/.github/workflows/wheels.yml @@ -53,8 +53,6 @@ jobs: matrix: include: # Window 64 bit - # Note: windows-2019 is needed for older Python versions: - # https://github.com/scikit-learn/scikit-learn/issues/22530 - os: windows-latest python: 39 platform_id: win_amd64 diff --git a/build_tools/github/repair_windows_wheels.sh b/build_tools/github/repair_windows_wheels.sh index cdd0c0c79d8c4..8f51a34d4039b 100755 --- a/build_tools/github/repair_windows_wheels.sh +++ b/build_tools/github/repair_windows_wheels.sh @@ -8,6 +8,7 @@ DEST_DIR=$2 # By default, the Windows wheels are not repaired. # In this case, we need to vendor VCRUNTIME140.dll +pip install wheel wheel unpack "$WHEEL" WHEEL_DIRNAME=$(ls -d scikit_learn-*) python build_tools/github/vendor.py "$WHEEL_DIRNAME" From c828bb0e5953eec61315bc64f9fe748201a6608d Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Mon, 13 May 2024 16:40:26 +0200 Subject: [PATCH 026/275] DOC persistence page revamp (#28889) --- doc/model_persistence.rst | 643 +++++++++++++++++++++----------------- 1 file changed, 349 insertions(+), 294 deletions(-) diff --git a/doc/model_persistence.rst b/doc/model_persistence.rst index afd492d805e58..0c11349a68e22 100644 --- a/doc/model_persistence.rst +++ b/doc/model_persistence.rst @@ -1,294 +1,349 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - -.. _model_persistence: - -================= -Model persistence -================= - -After training a scikit-learn model, it is desirable to have a way to persist -the model for future use without having to retrain. This can be accomplished -using `pickle `_, `joblib -`_, `skops -`_, `ONNX `_, -or `PMML `_. In most cases -`pickle` can be used to persist a trained scikit-learn model. Once all -transitive scikit-learn dependencies have been pinned, the trained model can -then be loaded and executed under conditions similar to those in which it was -originally pinned. The following sections will give you some hints on how to -persist a scikit-learn model and will provide details on what each alternative -can offer. - -Workflow Overview ------------------ - -In this section we present a general workflow on how to persist a -scikit-learn model. We will demonstrate this with a simple example using -Python's built-in persistence module, namely `pickle -`_. - -Storing the model in an artifact -................................ - -Once the model training process in completed, the trained model can be stored -as an artifact with the help of `pickle`. The model can be saved using the -process of serialization, where the Python object hierarchy is converted into -a byte stream. We can persist a trained model in the following manner:: - - >>> from sklearn import svm - >>> from sklearn import datasets - >>> import pickle - >>> clf = svm.SVC() - >>> X, y = datasets.load_iris(return_X_y=True) - >>> clf.fit(X, y) - SVC() - >>> s = pickle.dumps(clf) - -Replicating the training environment in production -.................................................. - -The versions of the dependencies used may differ from training to production. -This may result in unexpected behaviour and errors while using the trained -model. To prevent such situations it is recommended to use the same -dependencies and versions in both the training and production environment. -These transitive dependencies can be pinned with the help of `pip`, `conda`, -`poetry`, `conda-lock`, `pixi`, etc. - -.. note:: - - To execute a pickled scikit-learn model in a reproducible environment it is - advisable to pin all transitive scikit-learn dependencies. This prevents - any incompatibility issues that may arise while trying to load the pickled - model. You can read more about persisting models with `pickle` over - :ref:`here `. - -Loading the model artifact -.......................... - -The saved scikit-learn model can be loaded using `pickle` for future use -without having to re-train the entire model from scratch. The saved model -artifact can be unpickled by converting the byte stream into an object -hierarchy. This can be done with the help of `pickle` as follows:: - - >>> clf2 = pickle.loads(s) # doctest:+SKIP - >>> clf2.predict(X[0:1]) # doctest:+SKIP - array([0]) - >>> y[0] # doctest:+SKIP - 0 - -Serving the model artifact -.......................... - -The last step after training a scikit-learn model is serving the model. -Once the trained model is successfully loaded it can be served to manage -different prediction requests. This can involve deploying the model as a -web service using containerization, or other model deployment strategies, -according to the specifications. In the next sections, we will explore -different approaches to persist a trained scikit-learn model. - -.. _persisting_models_with_pickle: - -Persisting models with pickle ------------------------------ - -As demonstrated in the previous section, `pickle` uses serialization and -deserialization to persist scikit-learn models. Instead of using `dumps` and -`loads`, `dump` and `load` can also be used in the following way:: - - >>> from sklearn.tree import DecisionTreeClassifier - >>> from sklearn import datasets - >>> clf = DecisionTreeClassifier() - >>> X, y = datasets.load_iris(return_X_y=True) - >>> clf.fit(X, y) - DecisionTreeClassifier() - >>> from pickle import dump, load - >>> with open('filename.pkl', 'wb') as f: dump(clf, f) # doctest:+SKIP - >>> with open('filename.pkl', 'rb') as f: clf2 = load(f) # doctest:+SKIP - >>> clf2.predict(X[0:1]) # doctest:+SKIP - array([0]) - >>> y[0] - 0 - -For applications that involve writing and loading the serialized object to or -from a file, `dump` and `load` can be used instead of `dumps` and `loads`. When -file operations are not required the pickled representation of the object can -be returned as a bytes object with the help of the `dumps` function. The -reconstituted object hierarchy of the pickled data can then be returned using -the `loads` function. - -Persisting models with joblib ------------------------------ - -In the specific case of scikit-learn, it may be better to use joblib's -replacement of pickle (``dump`` & ``load``), which is more efficient on -objects that carry large numpy arrays internally as is often the case for -fitted scikit-learn estimators, but can only pickle to the disk and not to a -string:: - - >>> from joblib import dump, load - >>> dump(clf, 'filename.joblib') # doctest:+SKIP - -Later you can load back the pickled model (possibly in another Python process) -with:: - - >>> clf = load('filename.joblib') # doctest:+SKIP - -.. note:: - - ``dump`` and ``load`` functions also accept file-like object - instead of filenames. More information on data persistence with Joblib is - available `here - `_. - -|details-start| -**InconsistentVersionWarning** -|details-split| - -When an estimator is unpickled with a scikit-learn version that is inconsistent -with the version the estimator was pickled with, a -:class:`~sklearn.exceptions.InconsistentVersionWarning` is raised. This warning -can be caught to obtain the original version the estimator was pickled with:: - - from sklearn.exceptions import InconsistentVersionWarning - warnings.simplefilter("error", InconsistentVersionWarning) - - try: - est = pickle.loads("model_from_prevision_version.pickle") - except InconsistentVersionWarning as w: - print(w.original_sklearn_version) - -|details-end| - -.. _persistence_limitations: - -Security & maintainability limitations for pickle and joblib ------------------------------------------------------------- - -pickle (and joblib by extension), has some issues regarding maintainability -and security. Because of this, - -* Never unpickle untrusted data as it could lead to malicious code being - executed upon loading. -* While models saved using one version of scikit-learn might load in - other versions, this is entirely unsupported and inadvisable. It should - also be kept in mind that operations performed on such data could give - different and unexpected results. - -In order to rebuild a similar model with future versions of scikit-learn, -additional metadata should be saved along the pickled model: - -* The training data, e.g. a reference to an immutable snapshot -* The python source code used to generate the model -* The versions of scikit-learn and its dependencies -* The cross validation score obtained on the training data - -This should make it possible to check that the cross-validation score is in the -same range as before. - -Aside for a few exceptions, pickled models should be portable across -architectures assuming the same versions of dependencies and Python are used. -If you encounter an estimator that is not portable please open an issue on -GitHub. Pickled models are often deployed in production using containers, like -Docker, in order to freeze the environment and dependencies. - -If you want to know more about these issues and explore other possible -serialization methods, please refer to this -`talk by Alex Gaynor -`_. - -Persisting models with a more secure format using skops -------------------------------------------------------- - -`skops `__ provides a more secure -format via the :mod:`skops.io` module. It avoids using :mod:`pickle` and only -loads files which have types and references to functions which are trusted -either by default or by the user. - -|details-start| -**Using skops** -|details-split| - -The API is very similar to ``pickle``, and -you can persist your models as explain in the `docs -`__ using -:func:`skops.io.dump` and :func:`skops.io.dumps`:: - - import skops.io as sio - obj = sio.dumps(clf) - -And you can load them back using :func:`skops.io.load` and -:func:`skops.io.loads`. However, you need to specify the types which are -trusted by you. You can get existing unknown types in a dumped object / file -using :func:`skops.io.get_untrusted_types`, and after checking its contents, -pass it to the load function:: - - unknown_types = sio.get_untrusted_types(data=obj) - clf = sio.loads(obj, trusted=unknown_types) - -If you trust the source of the file / object, you can pass ``trusted=True``:: - - clf = sio.loads(obj, trusted=True) - -Please report issues and feature requests related to this format on the `skops -issue tracker `__. - -|details-end| - -Persisting models with interoperable formats --------------------------------------------- - -For reproducibility and quality control needs, when different architectures -and environments should be taken into account, exporting the model in -`Open Neural Network -Exchange `_ format or `Predictive Model Markup Language -(PMML) `_ format -might be a better approach than using `pickle` alone. -These are helpful where you may want to use your model for prediction in a -different environment from where the model was trained. - -ONNX is a binary serialization of the model. It has been developed to improve -the usability of the interoperable representation of data models. -It aims to facilitate the conversion of the data -models between different machine learning frameworks, and to improve their -portability on different computing architectures. More details are available -from the `ONNX tutorial `_. -To convert scikit-learn model to ONNX a specific tool `sklearn-onnx -`_ has been developed. - -PMML is an implementation of the `XML -`_ document standard -defined to represent data models together with the data used to generate them. -Being human and machine readable, -PMML is a good option for model validation on different platforms and -long term archiving. On the other hand, as XML in general, its verbosity does -not help in production when performance is critical. -To convert scikit-learn model to PMML you can use for example `sklearn2pmml -`_ distributed under the Affero GPLv3 -license. - -Summarizing the keypoints -------------------------- - -Based on the different approaches for model persistence, the keypoints for each -approach can be summarized as follows: - -* `pickle`: It is native to Python and any Python object can be serialized and - deserialized using `pickle`, including custom Python classes and objects. - While `pickle` can be used to easily save and load scikit-learn models, - unpickling of untrusted data might lead to security issues. -* `joblib`: Efficient storage and memory mapping techniques make it faster - when working with large machine learning models or large numpy arrays. However, - it may trigger the execution of malicious code while loading untrusted data. -* `skops`: Trained scikit-learn models can be easily shared and put into - production using `skops`. It is more secure compared to alternate approaches - as it allows users to load data from trusted sources. It however, does not - allow for persistence of arbitrary Python code. -* `ONNX`: It provides a uniform format for persisting any machine learning - or deep learning model (other than scikit-learn) and is useful - for model inference. It can however, result in compatibility issues with - different frameworks. -* `PMML`: Platform independent format that can be used to persist models - and reduce the risk of vendor lock-ins. The complexity and verbosity of - this format might make it harder to use for larger models. \ No newline at end of file +.. Places parent toc into the sidebar + +:parenttoc: True + +.. _model_persistence: + +================= +Model persistence +================= + +After training a scikit-learn model, it is desirable to have a way to persist +the model for future use without having to retrain. Based on your use-case, +there are a few different ways to persist a scikit-learn model, and here we +help you decide which one suits you best. In order to make a decision, you need +to answer the following questions: + +1. Do you need the Python object after persistence, or do you only need to + persist in order to serve the model and get predictions out of it? + +If you only need to serve the model and no further investigation on the Python +object itself is required, then :ref:`ONNX ` might be the +best fit for you. Note that not all models are supported by ONNX. + +In case ONNX is not suitable for your use-case, the next question is: + +2. Do you absolutely trust the source of the model, or are there any security + concerns regarding where the persisted model comes from? + +If you have security concerns, then you should consider using :ref:`skops.io +` which gives you back the Python object, but unlike +`pickle` based persistence solutions, loading the persisted model doesn't +automatically allow arbitrary code execution. Note that this requires manual +investigation of the persisted file, which :mod:`skops.io` allows you to do. + +The other solutions assume you absolutely trust the source of the file to be +loaded, as they are all susceptible to arbitrary code execution upon loading +the persisted file since they all use the pickle protocol under the hood. + +3. Do you care about the performance of loading the model, and sharing it + between processes where a memory mapped object on disk is beneficial? + +If yes, then you can consider using :ref:`joblib `. If this +is not a major concern for you, then you can use the built-in :mod:`pickle` +module. + +4. Did you try :mod:`pickle` or :mod:`joblib` and found that the model cannot + be persisted? It can happen for instance when you have user defined + functions in your model. + +If yes, then you can use `cloudpickle`_ which can serialize certain objects +which cannot be serialized by :mod:`pickle` or :mod:`joblib`. + + +Workflow Overview +----------------- + +In a typical workflow, the first step is to train the model using scikit-learn +and scikit-learn compatible libraries. Note that support for scikit-learn and +third party estimators varies across the different persistence methods. + +Train and Persist the Model +........................... + +Creating an appropriate model depends on your use-case. As an example, here we +train a :class:`sklearn.ensemble.HistGradientBoostingClassifier` on the iris +dataset:: + + >>> from sklearn import ensemble + >>> from sklearn import datasets + >>> clf = ensemble.HistGradientBoostingClassifier() + >>> X, y = datasets.load_iris(return_X_y=True) + >>> clf.fit(X, y) + HistGradientBoostingClassifier() + +Once the model is trained, you can persist it using your desired method, and +then you can load the model in a separate environment and get predictions from +it given input data. Here there are two major paths depending on how you +persist and plan to serve the model: + +- :ref:`ONNX `: You need an `ONNX` runtime and an environment + with appropriate dependencies installed to load the model and use the runtime + to get predictions. This environment can be minimal and does not necessarily + even require `python` to be installed. + +- :mod:`skops.io`, :mod:`pickle`, :mod:`joblib`, `cloudpickle`_: You need a + Python environment with the appropriate dependencies installed to load the + model and get predictions from it. This environment should have the same + **packages** and the same **versions** as the environment where the model was + trained. Note that none of these methods support loading a model trained with + a different version of scikit-learn, and possibly different versions of other + dependencies such as `numpy` and `scipy`. Another concern would be running + the persisted model on a different hardware, and in most cases you should be + able to load your persisted model on a different hardware. + + +.. _onnx_persistence: + +ONNX +---- + +`ONNX`, or `Open Neural Network Exchange `__ format is best +suitable in use-cases where one needs to persist the model and then use the +persisted artifact to get predictions without the need to load the Python +object itself. It is also useful in cases where the serving environment needs +to be lean and minimal, since the `ONNX` runtime does not require `python`. + +`ONNX` is a binary serialization of the model. It has been developed to improve +the usability of the interoperable representation of data models. It aims to +facilitate the conversion of the data models between different machine learning +frameworks, and to improve their portability on different computing +architectures. More details are available from the `ONNX tutorial +`__. To convert scikit-learn model to `ONNX` +`sklearn-onnx `__ has been developed. However, +not all scikit-learn models are supported, and it is limited to the core +scikit-learn and does not support most third party estimators. One can write a +custom converter for third party or custom estimators, but the documentation to +do that is sparse and it might be challenging to do so. + +|details-start| +**Using ONNX** +|details-split| + +To convert the model to `ONNX` format, you need to give the converter some +information about the input as well, about which you can read more `here +`__:: + + from skl2onnx import to_onnx + onx = to_onnx(clf, X[:1].astype(numpy.float32), target_opset=12) + with open("filename.onnx", "wb") as f: + f.write(onx.SerializeToString()) + +You can load the model in Python and use the `ONNX` runtime to get +predictions:: + + from onnxruntime import InferenceSession + with open("filename.onnx", "rb") as f: + onx = f.read() + sess = InferenceSession(onx, providers=["CPUExecutionProvider"]) + pred_ort = sess.run(None, {"X": X_test.astype(numpy.float32)})[0] + + +|details-end| + +.. _skops_persistence: + +`skops.io` +---------- + +:mod:`skops.io` avoids using :mod:`pickle` and only loads files which have types +and references to functions which are trusted either by default or by the user. +Therefore it provides a more secure format than :mod:`pickle`, :mod:`joblib`, +and `cloudpickle`_. + + +|details-start| +**Using skops** +|details-split| + +The API is very similar to :mod:`pickle`, and you can persist your models as +explained in the `documentation +`__ using +:func:`skops.io.dump` and :func:`skops.io.dumps`:: + + import skops.io as sio + obj = sio.dump(clf, "filename.skops") + +And you can load them back using :func:`skops.io.load` and +:func:`skops.io.loads`. However, you need to specify the types which are +trusted by you. You can get existing unknown types in a dumped object / file +using :func:`skops.io.get_untrusted_types`, and after checking its contents, +pass it to the load function:: + + unknown_types = sio.get_untrusted_types(file="filename.skops") + # investigate the contents of unknown_types, and only load if you trust + # everything you see. + clf = sio.load("filename.skops", trusted=unknown_types) + +Please report issues and feature requests related to this format on the `skops +issue tracker `__. + +|details-end| + +.. _pickle_persistence: + +`pickle`, `joblib`, and `cloudpickle` +------------------------------------- + +These three modules / packages, use the `pickle` protocol under the hood, but +come with slight variations: + +- :mod:`pickle` is a module from the Python Standard Library. It can serialize + and deserialize any Python object, including custom Python classes and + objects. +- :mod:`joblib` is more efficient than `pickle` when working with large machine + learning models or large numpy arrays. +- `cloudpickle`_ can serialize certain objects which cannot be serialized by + :mod:`pickle` or :mod:`joblib`, such as user defined functions and lambda + functions. This can happen for instance, when using a + :class:`~sklearn.preprocessing.FunctionTransformer` and using a custom + function to transform the data. + +|details-start| +**Using** ``pickle``, ``joblib``, **or** ``cloudpickle`` +|details-split| + +Depending on your use-case, you can choose one of these three methods to +persist and load your scikit-learn model, and they all follow the same API:: + + # Here you can replace pickle with joblib or cloudpickle + from pickle import dump + with open('filename.pkl', 'wb') as f: dump(clf, f) + +And later when needed, you can load the same object from the persisted file:: + + # Here you can replace pickle with joblib or cloudpickle + from pickle import load + with open('filename.pkl', 'rb') as f: clf = load(f) + +|details-end| + +.. _persistence_limitations: + +Security & Maintainability Limitations +-------------------------------------- + +:mod:`pickle` (and :mod:`joblib` and :mod:`clouldpickle` by extension), has +many documented security vulnerabilities and should only be used if the +artifact, i.e. the pickle-file, is coming from a trusted and verified source. + +Also note that arbitrary computations can be represented using the `ONNX` +format, and therefore a sandbox used to serve models using `ONNX` also needs to +safeguard against computational and memory exploits. + +Also note that there are no supported ways to load a model trained with a +different version of scikit-learn. While using :mod:`skops.io`, :mod:`joblib`, +:mod:`pickle`, or `cloudpickle`_, models saved using one version of +scikit-learn might load in other versions, however, this is entirely +unsupported and inadvisable. It should also be kept in mind that operations +performed on such data could give different and unexpected results, or even +crash your Python process. + +In order to rebuild a similar model with future versions of scikit-learn, +additional metadata should be saved along the pickled model: + +* The training data, e.g. a reference to an immutable snapshot +* The Python source code used to generate the model +* The versions of scikit-learn and its dependencies +* The cross validation score obtained on the training data + +This should make it possible to check that the cross-validation score is in the +same range as before. + +Aside for a few exceptions, persisted models should be portable across +operating systems and hardware architectures assuming the same versions of +dependencies and Python are used. If you encounter an estimator that is not +portable, please open an issue on GitHub. Persisted models are often deployed +in production using containers like Docker, in order to freeze the environment +and dependencies. + +If you want to know more about these issues, please refer to these talks: + +- `Adrin Jalali: Let's exploit pickle, and skops to the rescue! | PyData + Amsterdam 2023 `__. +- `Alex Gaynor: Pickles are for Delis, not Software - PyCon 2014 + `__. + + +.. _serving_environment: + +Replicating the training environment in production +.................................................. + +If the versions of the dependencies used may differ from training to +production, it may result in unexpected behaviour and errors while using the +trained model. To prevent such situations it is recommended to use the same +dependencies and versions in both the training and production environment. +These transitive dependencies can be pinned with the help of package management +tools like `pip`, `mamba`, `conda`, `poetry`, `conda-lock`, `pixi`, etc. + +It is not always possible to load an model trained with older versions of the +scikit-learn library and its dependencies in an updated software environment. +Instead, you might need to retrain the model with the new versions of the all +the libraries. So when training a model, it is important to record the training +recipe (e.g. a Python script) and training set information, and metadata about +all the dependencies to be able to automatically reconstruct the same training +environment for the updated software. + +|details-start| +**InconsistentVersionWarning** +|details-split| + +When an estimator is loaded with a scikit-learn version that is inconsistent +with the version the estimator was pickled with, a +:class:`~sklearn.exceptions.InconsistentVersionWarning` is raised. This warning +can be caught to obtain the original version the estimator was pickled with:: + + from sklearn.exceptions import InconsistentVersionWarning + warnings.simplefilter("error", InconsistentVersionWarning) + + try: + est = pickle.loads("model_from_prevision_version.pickle") + except InconsistentVersionWarning as w: + print(w.original_sklearn_version) + +|details-end| + + +Serving the model artifact +.......................... + +The last step after training a scikit-learn model is serving the model. +Once the trained model is successfully loaded, it can be served to manage +different prediction requests. This can involve deploying the model as a +web service using containerization, or other model deployment strategies, +according to the specifications. + + +Summarizing the key points +-------------------------- + +Based on the different approaches for model persistence, the key points for +each approach can be summarized as follows: + +* `ONNX`: It provides a uniform format for persisting any machine learning or + deep learning model (other than scikit-learn) and is useful for model + inference (predictions). It can however, result in compatibility issues with + different frameworks. +* :mod:`skops.io`: Trained scikit-learn models can be easily shared and put + into production using :mod:`skops.io`. It is more secure compared to + alternate approaches based on :mod:`pickle` because it does not load + arbitrary code unless explicitly asked for by the user. +* :mod:`joblib`: Efficient memory mapping techniques make it faster when using + the same persisted model in multiple Python processes. It also gives easy + shortcuts to compress and decompress the persisted object without the need + for extra code. However, it may trigger the execution of malicious code while + untrusted data as any other pickle-based persistence mechanism. +* :mod:`pickle`: It is native to Python and any Python object can be serialized + and deserialized using :mod:`pickle`, including custom Python classes and + objects. While :mod:`pickle` can be used to easily save and load scikit-learn + models, it may trigger the execution of malicious code while loading + untrusted data. +* `cloudpickle`_: It is slower than :mod:`pickle` and :mod:`joblib`, and is + more insecure than :mod:`pickle` and :mod:`joblib` since it can serialize + arbitrary code. However, in certain cases it might be a last resort to + persist certain models. Note that this is discouraged by `cloudpickle`_ + itself since there are no forward compatibility guarantees and you might need + the same version of `cloudpickle`_ to load the persisted model. + +.. _cloudpickle: https://github.com/cloudpipe/cloudpickle From 3dc8b30fcad7831717a66d05881cbd31b980f7b7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Mon, 13 May 2024 18:06:05 +0200 Subject: [PATCH 027/275] DOC Mention that Meson is the main supported way to build scikit-learn (#29008) Co-authored-by: Tim Head --- doc/whats_new/v1.5.rst | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst index 55a5546453f5f..5fdc0707ffbee 100644 --- a/doc/whats_new/v1.5.rst +++ b/doc/whats_new/v1.5.rst @@ -95,12 +95,16 @@ See :ref:`array_api` for more details. Support for building with Meson ------------------------------- -Meson is now supported as a build backend, see :ref:`Building from source -` for more details. +From scikit-learn 1.5 onwards, Meson is the main supported way to build +scikit-learn, see :ref:`Building from source ` for more +details. -:pr:`28040` by :user:`Loïc Estève ` +Unless we discover a major blocker, setuptools support will be dropped in +scikit-learn 1.6. The 1.5.x releases will support building scikit-learn with +setuptools. -TODO Fill more details before the 1.5 release, when the Meson story has settled down. +Meson support for building scikit-learn was added in :pr:`28040` by :user:`Loïc +Estève ` Metadata Routing ---------------- From 94bbcc614ab35a52ff84781b647ada5f2a15e13f Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Tue, 14 May 2024 12:17:05 +0200 Subject: [PATCH 028/275] DOC More improvements to the documentation on model persistence (#29011) Co-authored-by: Adrin Jalali --- doc/model_persistence.rst | 72 ++++++++++++++++++++++++++------------- 1 file changed, 48 insertions(+), 24 deletions(-) diff --git a/doc/model_persistence.rst b/doc/model_persistence.rst index 0c11349a68e22..0bc7384ec3d46 100644 --- a/doc/model_persistence.rst +++ b/doc/model_persistence.rst @@ -80,7 +80,9 @@ persist and plan to serve the model: - :ref:`ONNX `: You need an `ONNX` runtime and an environment with appropriate dependencies installed to load the model and use the runtime to get predictions. This environment can be minimal and does not necessarily - even require `python` to be installed. + even require Python to be installed to load the model and compute + predictions. Also note that `onnxruntime` typically requires much less RAM + than Python to to compute predictions from small models. - :mod:`skops.io`, :mod:`pickle`, :mod:`joblib`, `cloudpickle`_: You need a Python environment with the appropriate dependencies installed to load the @@ -208,13 +210,20 @@ persist and load your scikit-learn model, and they all follow the same API:: # Here you can replace pickle with joblib or cloudpickle from pickle import dump - with open('filename.pkl', 'wb') as f: dump(clf, f) + with open("filename.pkl", "wb") as f: + dump(clf, f, protocol=5) + +Using `protocol=5` is recommended to reduce memory usage and make it faster to +store and load any large NumPy array stored as a fitted attribute in the model. +You can alternatively pass `protocol=pickle.HIGHEST_PROTOCOL` which is +equivalent to `protocol=5` in Python 3.8 and later (at the time of writing). And later when needed, you can load the same object from the persisted file:: # Here you can replace pickle with joblib or cloudpickle from pickle import load - with open('filename.pkl', 'rb') as f: clf = load(f) + with open("filename.pkl", "rb") as f: + clf = load(f) |details-end| @@ -224,12 +233,14 @@ Security & Maintainability Limitations -------------------------------------- :mod:`pickle` (and :mod:`joblib` and :mod:`clouldpickle` by extension), has -many documented security vulnerabilities and should only be used if the -artifact, i.e. the pickle-file, is coming from a trusted and verified source. +many documented security vulnerabilities by design and should only be used if +the artifact, i.e. the pickle-file, is coming from a trusted and verified +source. You should never load a pickle file from an untrusted source, similarly +to how you should never execute code from an untrusted source. Also note that arbitrary computations can be represented using the `ONNX` -format, and therefore a sandbox used to serve models using `ONNX` also needs to -safeguard against computational and memory exploits. +format, and it is therefore recommended to serve models using `ONNX` in a +sandboxed environment to safeguard against computational and memory exploits. Also note that there are no supported ways to load a model trained with a different version of scikit-learn. While using :mod:`skops.io`, :mod:`joblib`, @@ -298,7 +309,8 @@ can be caught to obtain the original version the estimator was pickled with:: warnings.simplefilter("error", InconsistentVersionWarning) try: - est = pickle.loads("model_from_prevision_version.pickle") + with open("model_from_prevision_version.pickle", "rb") as f: + est = pickle.load(f) except InconsistentVersionWarning as w: print(w.original_sklearn_version) @@ -328,22 +340,34 @@ each approach can be summarized as follows: * :mod:`skops.io`: Trained scikit-learn models can be easily shared and put into production using :mod:`skops.io`. It is more secure compared to alternate approaches based on :mod:`pickle` because it does not load - arbitrary code unless explicitly asked for by the user. + arbitrary code unless explicitly asked for by the user. Such code needs to be + packaged and importable in the target Python environment. * :mod:`joblib`: Efficient memory mapping techniques make it faster when using - the same persisted model in multiple Python processes. It also gives easy - shortcuts to compress and decompress the persisted object without the need - for extra code. However, it may trigger the execution of malicious code while - untrusted data as any other pickle-based persistence mechanism. -* :mod:`pickle`: It is native to Python and any Python object can be serialized - and deserialized using :mod:`pickle`, including custom Python classes and - objects. While :mod:`pickle` can be used to easily save and load scikit-learn - models, it may trigger the execution of malicious code while loading - untrusted data. -* `cloudpickle`_: It is slower than :mod:`pickle` and :mod:`joblib`, and is - more insecure than :mod:`pickle` and :mod:`joblib` since it can serialize - arbitrary code. However, in certain cases it might be a last resort to - persist certain models. Note that this is discouraged by `cloudpickle`_ - itself since there are no forward compatibility guarantees and you might need - the same version of `cloudpickle`_ to load the persisted model. + the same persisted model in multiple Python processes when using + `mmap_mode="r"`. It also gives easy shortcuts to compress and decompress the + persisted object without the need for extra code. However, it may trigger the + execution of malicious code when loading a model from an untrusted source as + any other pickle-based persistence mechanism. +* :mod:`pickle`: It is native to Python and most Python objects can be + serialized and deserialized using :mod:`pickle`, including custom Python + classes and functions as long as they are defined in a package that can be + imported in the target environment. While :mod:`pickle` can be used to easily + save and load scikit-learn models, it may trigger the execution of malicious + code while loading a model from an untrusted source. :mod:`pickle` can also + be very efficient memorywise if the model was persisted with `protocol=5` but + it does not support memory mapping. +* `cloudpickle`_: It has comparable loading efficiency as :mod:`pickle` and + :mod:`joblib` (without memory mapping), but offers additional flexibility to + serialize custom Python code such as lambda expressions and interactively + defined functions and classes. It might be a last resort to persist pipelines + with custom Python components such as a + :class:`sklearn.preprocessing.FunctionTransformer` that wraps a function + defined in the training script itself or more generally outside of any + importable Python package. Note that `cloudpickle`_ offers no forward + compatibility guarantees and you might need the same version of + `cloudpickle`_ to load the persisted model along with the same version of all + the libraries used to define the model. As the other pickle-based persistence + mechanisms, it may trigger the execution of malicious code while loading + a model from an untrusted source. .. _cloudpickle: https://github.com/cloudpipe/cloudpickle From 8e7eceedbdce9b8a12cebde0551fedd468655ee0 Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Tue, 14 May 2024 22:30:17 +1000 Subject: [PATCH 029/275] DOC Add warm start section for tree ensembles (#29001) --- doc/modules/ensemble.rst | 37 +++++++++++++++++++++++++++++++++++++ sklearn/ensemble/_forest.py | 10 +++++----- 2 files changed, 42 insertions(+), 5 deletions(-) diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst index d18dd2f65009e..9120bd855fd01 100644 --- a/doc/modules/ensemble.rst +++ b/doc/modules/ensemble.rst @@ -1247,6 +1247,43 @@ estimation. representations of feature space, also these approaches focus also on dimensionality reduction. +.. _tree_ensemble_warm_start: + +Fitting additional trees +------------------------ + +RandomForest, Extra-Trees and :class:`RandomTreesEmbedding` estimators all support +``warm_start=True`` which allows you to add more trees to an already fitted model. + +:: + + >>> from sklearn.datasets import make_classification + >>> from sklearn.ensemble import RandomForestClassifier + + >>> X, y = make_classification(n_samples=100, random_state=1) + >>> clf = RandomForestClassifier(n_estimators=10) + >>> clf = clf.fit(X, y) # fit with 10 trees + >>> len(clf.estimators_) + 10 + >>> # set warm_start and increase num of estimators + >>> _ = clf.set_params(n_estimators=20, warm_start=True) + >>> _ = clf.fit(X, y) # fit additional 10 trees + >>> len(clf.estimators_) + 20 + +When ``random_state`` is also set, the internal random state is also preserved +between ``fit`` calls. This means that training a model once with ``n`` estimators is +the same as building the model iteratively via multiple ``fit`` calls, where the +final number of estimators is equal to ``n``. + +:: + + >>> clf = RandomForestClassifier(n_estimators=20) # set `n_estimators` to 10 + 10 + >>> _ = clf.fit(X, y) # fit `estimators_` will be the same as `clf` above + +Note that this differs from the usual behavior of :term:`random_state` in that it does +*not* result in the same result across different calls. + .. _bagging: Bagging meta-estimator diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py index 6b1b842f5367b..28c404c3e406b 100644 --- a/sklearn/ensemble/_forest.py +++ b/sklearn/ensemble/_forest.py @@ -1308,7 +1308,7 @@ class RandomForestClassifier(ForestClassifier): When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. See :term:`Glossary ` and - :ref:`gradient_boosting_warm_start` for details. + :ref:`tree_ensemble_warm_start` for details. class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, \ default=None @@ -1710,7 +1710,7 @@ class RandomForestRegressor(ForestRegressor): When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. See :term:`Glossary ` and - :ref:`gradient_boosting_warm_start` for details. + :ref:`tree_ensemble_warm_start` for details. ccp_alpha : non-negative float, default=0.0 Complexity parameter used for Minimal Cost-Complexity Pruning. The @@ -2049,7 +2049,7 @@ class ExtraTreesClassifier(ForestClassifier): When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. See :term:`Glossary ` and - :ref:`gradient_boosting_warm_start` for details. + :ref:`tree_ensemble_warm_start` for details. class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, \ default=None @@ -2434,7 +2434,7 @@ class ExtraTreesRegressor(ForestRegressor): When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. See :term:`Glossary ` and - :ref:`gradient_boosting_warm_start` for details. + :ref:`tree_ensemble_warm_start` for details. ccp_alpha : non-negative float, default=0.0 Complexity parameter used for Minimal Cost-Complexity Pruning. The @@ -2727,7 +2727,7 @@ class RandomTreesEmbedding(TransformerMixin, BaseForest): When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. See :term:`Glossary ` and - :ref:`gradient_boosting_warm_start` for details. + :ref:`tree_ensemble_warm_start` for details. Attributes ---------- From 1434bb14ca52af94dd49658b7352cbf62d951c93 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 14 May 2024 17:26:53 +0200 Subject: [PATCH 030/275] MNT Use c11 rather than c17 in meson.build to work-around Pyodide issue (#29015) --- meson.build | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/meson.build b/meson.build index 52c7deb962277..b6b3652a82268 100644 --- a/meson.build +++ b/meson.build @@ -6,7 +6,7 @@ project( meson_version: '>= 1.1.0', default_options: [ 'buildtype=debugoptimized', - 'c_std=c17', + 'c_std=c11', 'cpp_std=c++14', ], ) From f8be06c50b402b840d1b3fa2bd92a16a73ef1f9a Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Tue, 14 May 2024 22:02:20 +0200 Subject: [PATCH 031/275] DOC fix dollar sign to euro sign (#29020) Co-authored-by: Guillaume Lemaitre --- .../plot_cost_sensitive_learning.py | 22 +++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/examples/model_selection/plot_cost_sensitive_learning.py b/examples/model_selection/plot_cost_sensitive_learning.py index 7b64af48139f2..be0900d50e4ba 100644 --- a/examples/model_selection/plot_cost_sensitive_learning.py +++ b/examples/model_selection/plot_cost_sensitive_learning.py @@ -489,7 +489,7 @@ def plot_roc_pr_curves(vanilla_model, tuned_model, *, title): _, ax = plt.subplots() ax.hist(amount_fraud, bins=100) ax.set_title("Amount of fraud transaction") -_ = ax.set_xlabel("Amount ($)") +_ = ax.set_xlabel("Amount (€)") # %% # Addressing the problem with a business metric @@ -501,8 +501,8 @@ def plot_roc_pr_curves(vanilla_model, tuned_model, *, title): # transaction result in a loss of the amount of the transaction. As stated in [2]_, the # gain and loss related to refusals (of fraudulent and legitimate transactions) are not # trivial to define. Here, we define that a refusal of a legitimate transaction is -# estimated to a loss of $5 while the refusal of a fraudulent transaction is estimated -# to a gain of $50 dollars and the amount of the transaction. Therefore, we define the +# estimated to a loss of 5€ while the refusal of a fraudulent transaction is estimated +# to a gain of 50€ and the amount of the transaction. Therefore, we define the # following function to compute the total benefit of a given decision: @@ -557,22 +557,22 @@ def business_metric(y_true, y_pred, amount): benefit_cost = business_scorer( easy_going_classifier, data_test, target_test, amount=amount_test ) -print(f"Benefit/cost of our easy-going classifier: ${benefit_cost:,.2f}") +print(f"Benefit/cost of our easy-going classifier: {benefit_cost:,.2f}€") # %% # A classifier that predict all transactions as legitimate would create a profit of -# around $220,000. We make the same evaluation for a classifier that predicts all +# around 220,000.€ We make the same evaluation for a classifier that predicts all # transactions as fraudulent. intolerant_classifier = DummyClassifier(strategy="constant", constant=1) intolerant_classifier.fit(data_train, target_train) benefit_cost = business_scorer( intolerant_classifier, data_test, target_test, amount=amount_test ) -print(f"Benefit/cost of our intolerant classifier: ${benefit_cost:,.2f}") +print(f"Benefit/cost of our intolerant classifier: {benefit_cost:,.2f}€") # %% -# Such a classifier create a loss of around $670,000. A predictive model should allow -# us to make a profit larger than $220,000. It is interesting to compare this business +# Such a classifier create a loss of around 670,000.€ A predictive model should allow +# us to make a profit larger than 220,000.€ It is interesting to compare this business # metric with another "standard" statistical metric such as the balanced accuracy. from sklearn.metrics import get_scorer @@ -607,7 +607,7 @@ def business_metric(y_true, y_pred, amount): print( "Benefit/cost of our logistic regression: " - f"${business_scorer(model, data_test, target_test, amount=amount_test):,.2f}" + f"{business_scorer(model, data_test, target_test, amount=amount_test):,.2f}€" ) print( "Balanced accuracy of our logistic regression: " @@ -645,7 +645,7 @@ def business_metric(y_true, y_pred, amount): # %% print( "Benefit/cost of our logistic regression: " - f"${business_scorer(tuned_model, data_test, target_test, amount=amount_test):,.2f}" + f"{business_scorer(tuned_model, data_test, target_test, amount=amount_test):,.2f}€" ) print( "Balanced accuracy of our logistic regression: " @@ -691,7 +691,7 @@ def business_metric(y_true, y_pred, amount): business_score = business_scorer( model_fixed_threshold, data_test, target_test, amount=amount_test ) -print(f"Benefit/cost of our logistic regression: ${business_score:,.2f}") +print(f"Benefit/cost of our logistic regression: {business_score:,.2f}€") print( "Balanced accuracy of our logistic regression: " f"{balanced_accuracy_scorer(model_fixed_threshold, data_test, target_test):.3f}" From b9bdb973f505f574700f6e6bc59da26cb91bee88 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Wed, 15 May 2024 14:01:27 +0200 Subject: [PATCH 032/275] TST check compatibility with metadata routing for *ThresholdClassifier* (#29021) --- .../_classification_threshold.py | 19 +++++++----- sklearn/tests/metadata_routing_common.py | 29 ++++++++++++------- .../test_metaestimators_metadata_routing.py | 21 ++++++++++++++ 3 files changed, 51 insertions(+), 18 deletions(-) diff --git a/sklearn/model_selection/_classification_threshold.py b/sklearn/model_selection/_classification_threshold.py index d5a864da10653..1f891577b4680 100644 --- a/sklearn/model_selection/_classification_threshold.py +++ b/sklearn/model_selection/_classification_threshold.py @@ -106,6 +106,14 @@ def __init__(self, estimator, *, response_method="auto"): self.estimator = estimator self.response_method = response_method + def _get_response_method(self): + """Define the response method.""" + if self.response_method == "auto": + response_method = ["predict_proba", "decision_function"] + else: + response_method = self.response_method + return response_method + @_fit_context( # *ThresholdClassifier*.estimator is not validated yet prefer_skip_nested_validation=False @@ -140,11 +148,6 @@ def fit(self, X, y, **params): f"Only binary classification is supported. Unknown label type: {y_type}" ) - if self.response_method == "auto": - self._response_method = ["predict_proba", "decision_function"] - else: - self._response_method = self.response_method - self._fit(X, y, **params) if hasattr(self.estimator_, "n_features_in_"): @@ -374,7 +377,7 @@ def predict(self, X): y_score, _, response_method_used = _get_response_values_binary( self.estimator_, X, - self._response_method, + self._get_response_method(), pos_label=self.pos_label, return_response_method_used=True, ) @@ -954,7 +957,7 @@ def predict(self, X): y_score, _ = _get_response_values_binary( self.estimator_, X, - self._response_method, + self._get_response_method(), pos_label=pos_label, ) @@ -995,6 +998,6 @@ def _get_curve_scorer(self): """Get the curve scorer based on the objective metric used.""" scoring = check_scoring(self.estimator, scoring=self.scoring) curve_scorer = _CurveScorer.from_scorer( - scoring, self._response_method, self.thresholds + scoring, self._get_response_method(), self.thresholds ) return curve_scorer diff --git a/sklearn/tests/metadata_routing_common.py b/sklearn/tests/metadata_routing_common.py index 889524bc05ddb..6fba2f037fd15 100644 --- a/sklearn/tests/metadata_routing_common.py +++ b/sklearn/tests/metadata_routing_common.py @@ -194,7 +194,10 @@ def decision_function(self, X): return self.predict(X) def predict(self, X): - return np.ones(len(X)) + y_pred = np.empty(shape=(len(X),)) + y_pred[: len(X) // 2] = 0 + y_pred[len(X) // 2 :] = 1 + return y_pred class NonConsumingRegressor(RegressorMixin, BaseEstimator): @@ -257,16 +260,19 @@ def predict(self, X, sample_weight="default", metadata="default"): record_metadata_not_default( self, "predict", sample_weight=sample_weight, metadata=metadata ) - return np.zeros(shape=(len(X),)) + y_score = np.empty(shape=(len(X),), dtype="int8") + y_score[len(X) // 2 :] = 0 + y_score[: len(X) // 2] = 1 + return y_score def predict_proba(self, X, sample_weight="default", metadata="default"): - pass # pragma: no cover - - # uncomment when needed - # record_metadata_not_default( - # self, "predict_proba", sample_weight=sample_weight, metadata=metadata - # ) - # return np.asarray([[0.0, 1.0]] * len(X)) + record_metadata_not_default( + self, "predict_proba", sample_weight=sample_weight, metadata=metadata + ) + y_proba = np.empty(shape=(len(X), 2)) + y_proba[: len(X) // 2, :] = np.asarray([1.0, 0.0]) + y_proba[len(X) // 2 :, :] = np.asarray([0.0, 1.0]) + return y_proba def predict_log_proba(self, X, sample_weight="default", metadata="default"): pass # pragma: no cover @@ -281,7 +287,10 @@ def decision_function(self, X, sample_weight="default", metadata="default"): record_metadata_not_default( self, "predict_proba", sample_weight=sample_weight, metadata=metadata ) - return np.zeros(shape=(len(X),)) + y_score = np.empty(shape=(len(X),)) + y_score[len(X) // 2 :] = 0 + y_score[: len(X) // 2] = 1 + return y_score # uncomment when needed # def score(self, X, y, sample_weight="default", metadata="default"): diff --git a/sklearn/tests/test_metaestimators_metadata_routing.py b/sklearn/tests/test_metaestimators_metadata_routing.py index aa6af5bd09aac..d9a7d6c9e5952 100644 --- a/sklearn/tests/test_metaestimators_metadata_routing.py +++ b/sklearn/tests/test_metaestimators_metadata_routing.py @@ -43,10 +43,12 @@ RidgeCV, ) from sklearn.model_selection import ( + FixedThresholdClassifier, GridSearchCV, HalvingGridSearchCV, HalvingRandomSearchCV, RandomizedSearchCV, + TunedThresholdClassifierCV, ) from sklearn.multiclass import ( OneVsOneClassifier, @@ -77,6 +79,7 @@ N, M = 100, 4 X = rng.rand(N, M) y = rng.randint(0, 3, size=N) +y_binary = (y >= 1).astype(int) classes = np.unique(y) y_multi = rng.randint(0, 3, size=(N, 3)) classes_multi = [np.unique(y_multi[:, i]) for i in range(y_multi.shape[1])] @@ -200,6 +203,24 @@ def enable_slep006(): "cv_name": "cv", "cv_routing_methods": ["fit"], }, + { + "metaestimator": FixedThresholdClassifier, + "estimator_name": "estimator", + "estimator": "classifier", + "X": X, + "y": y_binary, + "estimator_routing_methods": ["fit"], + "preserves_metadata": "subset", + }, + { + "metaestimator": TunedThresholdClassifierCV, + "estimator_name": "estimator", + "estimator": "classifier", + "X": X, + "y": y_binary, + "estimator_routing_methods": ["fit"], + "preserves_metadata": "subset", + }, { "metaestimator": OneVsRestClassifier, "estimator_name": "estimator", From 12ea35979e552f5fc938ac56573accd5ba894b69 Mon Sep 17 00:00:00 2001 From: Aswathavicky Date: Thu, 16 May 2024 10:01:37 +0200 Subject: [PATCH 033/275] DOC add link to sklearn_example_ensemeble_plot_adboost_twoclass (#29023) --- sklearn/ensemble/_weight_boosting.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/sklearn/ensemble/_weight_boosting.py b/sklearn/ensemble/_weight_boosting.py index 0461a397983be..6bbac0613de71 100644 --- a/sklearn/ensemble/_weight_boosting.py +++ b/sklearn/ensemble/_weight_boosting.py @@ -482,6 +482,10 @@ class AdaBoostClassifier( For a detailed example of using AdaBoost to fit a sequence of DecisionTrees as weaklearners, please refer to :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_multiclass.py`. + + For a detailed example of using AdaBoost to fit a non-linearly seperable + classification dataset composed of two Gaussian quantiles clusters, please + refer to :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_twoclass.py`. """ # TODO(1.6): Modify _parameter_constraints for "algorithm" to only check From 43b02ec6479728080f6751f5d1a1a97f99df2bbf Mon Sep 17 00:00:00 2001 From: Tialo <65392801+Tialo@users.noreply.github.com> Date: Thu, 16 May 2024 11:11:54 +0300 Subject: [PATCH 034/275] DOC Fix default value of n in check_cv (#29024) --- sklearn/model_selection/_split.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/model_selection/_split.py b/sklearn/model_selection/_split.py index 53c11a665ccf4..1f9d78d3e4cbd 100644 --- a/sklearn/model_selection/_split.py +++ b/sklearn/model_selection/_split.py @@ -2596,7 +2596,7 @@ def check_cv(cv=5, y=None, *, classifier=False): Parameters ---------- - cv : int, cross-validation generator or an iterable, default=None + cv : int, cross-validation generator, iterable or None, default=5 Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, From 37f544db78503ed1a50da02cbb4f1a4e466fb0a7 Mon Sep 17 00:00:00 2001 From: raisadz <34237447+raisadz@users.noreply.github.com> Date: Fri, 17 May 2024 09:13:51 +0100 Subject: [PATCH 035/275] DOC replace pandas with Polars in examples/gaussian_process/plot_gpr_co2.py (#28804) Co-authored-by: raisa <> Co-authored-by: Adrin Jalali --- ...latest_conda_forge_mkl_linux-64_conda.lock | 6 +-- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 2 +- ...pylatest_pip_scipy_dev_linux-64_conda.lock | 2 +- .../pymin_conda_forge_mkl_win-64_conda.lock | 2 +- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 4 +- build_tools/azure/pypy3_linux-64_conda.lock | 34 +++++++-------- build_tools/circle/doc_linux-64_conda.lock | 8 ++-- .../doc_min_dependencies_environment.yml | 2 +- .../doc_min_dependencies_linux-64_conda.lock | 10 ++--- examples/gaussian_process/plot_gpr_co2.py | 41 ++++++++++--------- pyproject.toml | 4 +- sklearn/_min_dependencies.py | 2 +- sklearn/tests/test_base.py | 2 +- 13 files changed, 61 insertions(+), 58 deletions(-) diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index 3d895fda71bc3..bf5bcd3daff08 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -91,7 +91,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.45.3-h2797004_0.cond https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.0-h0841786_0.conda#1f5a58e686b13bcfde88b93f547d23fe https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h9c3ff4c_0.tar.bz2#309dec04b70a3cc0f1e84a4013683bc0 https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.15-h0b41bf4_0.conda#33277193f5b92bad9fdd230eb700929c -https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.6-h232c23b_2.conda#9a3a42df8a95f65334dfc7b80da1195d +https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.7-hc051c1a_0.conda#5d801a4906adc712d480afc362623b59 https://conda.anaconda.org/conda-forge/linux-64/mysql-common-8.3.0-hf1915f5_4.conda#784a4df6676c581ca624fbe460703a6d https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.43-hcad00b1_0.conda#8292dea9e022d9610a11fce5e0896ed8 https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda#47d31b792659ce70f470b5c82fdfb7a4 @@ -191,7 +191,7 @@ https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py311hb755f60_0.conda https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.3.14-hf3aad02_1.conda#a968ffa7e9fe0c257628033d393e512f https://conda.anaconda.org/conda-forge/linux-64/blas-1.0-mkl.tar.bz2#349aef876b1d8c9dccae01de20d5b385 https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.3-haf2f30d_0.conda#f3df87cc9ef0b5113bff55aefcbcafd5 -https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.4.0-h3d44ed6_0.conda#27f46291a6aaa3c2a4f798ebd35a7ddb +https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.5.0-hfac3d4d_0.conda#f5126317dd0ce0ba26945e411ecc6960 https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-16_linux64_mkl.tar.bz2#85f61af03fd291dae33150ffe89dc09a https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-255-h3516f8a_1.conda#3366af27f0b593544a6cd453c7932ac5 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 @@ -210,7 +210,7 @@ https://conda.anaconda.org/conda-forge/noarch/array-api-strict-1.1.1-pyhd8ed1ab_ https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py311h9547e67_0.conda#74ad0ae64f1ef565e27eda87fa749e84 https://conda.anaconda.org/conda-forge/linux-64/libarrow-12.0.1-hb87d912_8_cpu.conda#3f3b11398fe79b578e3c44dd00a44e4a https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.2-py311h320fe9a_0.conda#c79e96ece4110fdaf2657c9f8e16f749 -https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.25-py311h00856b1_0.conda#84ad7fa8742f6d34784a961337622c55 +https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.26-py311h00856b1_0.conda#d9002441c9b75b188f9cdc51bf4f22c7 https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.9-py311hf0fb5b6_5.conda#ec7e45bc76d9d0b69a74a2075932b8e8 https://conda.anaconda.org/conda-forge/linux-64/pytorch-1.13.1-cpu_py311h410fd25_1.conda#ddd2fadddf89e3dc3d541a2537fce010 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.13.0-py311h517d4fd_1.conda#a86b8bea39e292a23b2cf9a750f49ea1 diff --git a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock index ce2d5e2c383a3..c0e54faa37bc6 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock @@ -28,7 +28,7 @@ https://conda.anaconda.org/conda-forge/osx-64/libcxx-17.0.6-h88467a6_0.conda#0fe https://conda.anaconda.org/conda-forge/osx-64/libpng-1.6.43-h92b6c6a_0.conda#65dcddb15965c9de2c0365cb14910532 https://conda.anaconda.org/conda-forge/osx-64/libsqlite-3.45.3-h92b6c6a_0.conda#68e462226209f35182ef66eda0f794ff https://conda.anaconda.org/conda-forge/osx-64/libxcb-1.15-hb7f2c08_0.conda#5513f57e0238c87c12dffedbcc9c1a4a -https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.12.6-hc0ae0f7_2.conda#50b997370584f2c83ca0c38e9028eab9 +https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.12.7-h3e169fe_0.conda#4c04ba47fdd2ebecc1d3b6a77534d9ef https://conda.anaconda.org/conda-forge/osx-64/llvm-openmp-18.1.5-h39e0ece_0.conda#ee12a644568269838b91f901b2537425 https://conda.anaconda.org/conda-forge/osx-64/openssl-3.3.0-hd75f5a5_0.conda#eb8c33aa7929a7714eab8b90c1d88afe https://conda.anaconda.org/conda-forge/osx-64/readline-8.2-h9e318b2_1.conda#f17f77f2acf4d344734bda76829ce14e diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index c1a50c7c8c140..e4305c97b76bc 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -40,7 +40,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py312h06a4308_0.conda#6d96 # pip meson @ https://files.pythonhosted.org/packages/33/75/b1a37fa7b2dbca8c0dbb04d5cdd7e2720c8ef6febe41b4a74866350e041c/meson-1.4.0-py3-none-any.whl#sha256=476a458d51fcfa322a6bdc64da5138997c542d08e6b2e49b9fa68c46fd7c4475 # pip ninja @ https://files.pythonhosted.org/packages/6d/92/8d7aebd4430ab5ff65df2bfee6d5745f95c004284db2d8ca76dcbfd9de47/ninja-1.11.1.1-py2.py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl#sha256=84502ec98f02a037a169c4b0d5d86075eaf6afc55e1879003d6cab51ced2ea4b # pip packaging @ https://files.pythonhosted.org/packages/49/df/1fceb2f8900f8639e278b056416d49134fb8d84c5942ffaa01ad34782422/packaging-24.0-py3-none-any.whl#sha256=2ddfb553fdf02fb784c234c7ba6ccc288296ceabec964ad2eae3777778130bc5 -# pip platformdirs @ https://files.pythonhosted.org/packages/b0/15/1691fa5aaddc0c4ea4901c26f6137c29d5f6673596fe960a0340e8c308e1/platformdirs-4.2.1-py3-none-any.whl#sha256=17d5a1161b3fd67b390023cb2d3b026bbd40abde6fdb052dfbd3a29c3ba22ee1 +# pip platformdirs @ https://files.pythonhosted.org/packages/68/13/2aa1f0e1364feb2c9ef45302f387ac0bd81484e9c9a4c5688a322fbdfd08/platformdirs-4.2.2-py3-none-any.whl#sha256=2d7a1657e36a80ea911db832a8a6ece5ee53d8de21edd5cc5879af6530b1bfee # pip pluggy @ https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl#sha256=44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669 # pip pygments @ https://files.pythonhosted.org/packages/f7/3f/01c8b82017c199075f8f788d0d906b9ffbbc5a47dc9918a945e13d5a2bda/pygments-2.18.0-py3-none-any.whl#sha256=b8e6aca0523f3ab76fee51799c488e38782ac06eafcf95e7ba832985c8e7b13a # pip six @ https://files.pythonhosted.org/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl#sha256=8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254 diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock index d95e56378ae56..8f0a473c031ca 100644 --- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock @@ -39,7 +39,7 @@ https://conda.anaconda.org/conda-forge/win-64/libbrotlienc-1.1.0-hcfcfb64_1.cond https://conda.anaconda.org/conda-forge/win-64/libintl-0.22.5-h5728263_2.conda#aa622c938af057adc119f8b8eecada01 https://conda.anaconda.org/conda-forge/win-64/libpng-1.6.43-h19919ed_0.conda#77e398acc32617a0384553aea29e866b https://conda.anaconda.org/conda-forge/win-64/libvorbis-1.3.7-h0e60522_0.tar.bz2#e1a22282de0169c93e4ffe6ce6acc212 -https://conda.anaconda.org/conda-forge/win-64/libxml2-2.12.6-hc3477c8_2.conda#ac7af7a949db01dae61ddc48f4a93d79 +https://conda.anaconda.org/conda-forge/win-64/libxml2-2.12.7-h283a6d9_0.conda#1451be68a5549561979125c1827b79ed https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libs-5.3.0-7.tar.bz2#fe759119b8b3bfa720b8762c6fdc35de https://conda.anaconda.org/conda-forge/win-64/pcre2-10.43-h17e33f8_0.conda#d0485b8aa2cedb141a7bd27b4efa4c9c https://conda.anaconda.org/conda-forge/win-64/python-3.9.19-h4de0772_0_cpython.conda#b6999bc275e0e6beae7b1c8ea0be1e85 diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock index 231cd528ecd0e..1a4d0feae1773 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock @@ -71,7 +71,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.43-h2797004_0.conda#0 https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.45.3-h2797004_0.conda#b3316cbe90249da4f8e84cd66e1cc55b https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h9c3ff4c_0.tar.bz2#309dec04b70a3cc0f1e84a4013683bc0 https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.15-h0b41bf4_0.conda#33277193f5b92bad9fdd230eb700929c -https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.6-h232c23b_2.conda#9a3a42df8a95f65334dfc7b80da1195d +https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.7-hc051c1a_0.conda#5d801a4906adc712d480afc362623b59 https://conda.anaconda.org/conda-forge/linux-64/mysql-common-8.3.0-hf1915f5_4.conda#784a4df6676c581ca624fbe460703a6d https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.43-hcad00b1_0.conda#8292dea9e022d9610a11fce5e0896ed8 https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda#47d31b792659ce70f470b5c82fdfb7a4 @@ -175,7 +175,7 @@ https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0 https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.1-pyhd8ed1ab_0.conda#08807a87fa7af10754d46f63b368e016 https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.3-haf2f30d_0.conda#f3df87cc9ef0b5113bff55aefcbcafd5 -https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.4.0-h3d44ed6_0.conda#27f46291a6aaa3c2a4f798ebd35a7ddb +https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.5.0-hfac3d4d_0.conda#f5126317dd0ce0ba26945e411ecc6960 https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.0-pyhd8ed1ab_0.conda#dcbadab7a68738a028e195ab68ab2d2e https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-22_linux64_openblas.conda#1fd156abd41a4992835952f6f4d951d0 https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-255-h3516f8a_1.conda#3366af27f0b593544a6cd453c7932ac5 diff --git a/build_tools/azure/pypy3_linux-64_conda.lock b/build_tools/azure/pypy3_linux-64_conda.lock index 23710cfe35cb8..ab6a908edf340 100644 --- a/build_tools/azure/pypy3_linux-64_conda.lock +++ b/build_tools/azure/pypy3_linux-64_conda.lock @@ -4,24 +4,24 @@ @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.2.2-hbcca054_0.conda#2f4327a1cbe7f022401b236e915a5fef -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-h7e041cc_5.conda#f6f6600d18a4047b54f803cf708b868a +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-hc0a3c3a_7.conda#53ebd4c833fa01cb2c6353e99f905406 https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.9-4_pypy39_pp73.conda#c1b2f29111681a4036ed21eaa3f44620 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h807b86a_5.conda#d4ff227c46917d3b4565302a2bbb276b +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h77fa898_7.conda#72ec1b1b04c4d15d4204ece1ecea5978 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hd590300_5.conda#69b8b6202a07720f448be700e300ccf4 https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2#76bbff344f0134279f225174e9064c8f https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hd590300_1.conda#aec6c91c7371c26392a06708a73c70e5 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.20-hd590300_0.conda#8e88f9389f1165d7c0936fe40d9a9a79 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.2-h59595ed_0.conda#e7ba12deb7020dd080c6c70e7b6f6a3d https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2#d645c6d2ac96843a2bfaccd2d62b3ac3 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-ha4646dd_5.conda#7a6bd7a12a4bd359e2afe6c0fa1acace +https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-hca663fb_7.conda#c0bd771f09a326fdcd95a60b617795bf https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda#ea25936bb4080d843790b586850f82b8 -https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.3.2-hd590300_1.conda#049b7df8bae5e184d1de42cdf64855f8 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https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.11-hd590300_0.conda#2c80dc38fface310c9bd81b17037fee5 @@ -32,22 +32,22 @@ https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2#2161 https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.2-h59595ed_0.conda#53fb86322bdb89496d7579fe3f02fd61 https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hd590300_1.conda#f07002e225d7a60a694d42a7bf5ff53f https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.1.0-hd590300_1.conda#5fc11c6020d421960607d821310fcd4d -https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-13.2.0-h69a702a_5.conda#e73e9cfd1191783392131e6238bdb3e9 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-https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.5-hfc55251_0.conda#04b88013080254850d6c01ed54810589 +https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda#4d056880988120e29d75bfff282e0f45 https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hd590300_1.conda#39f910d205726805a958da408ca194ba https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-h267a509_2.conda#9ae35c3d96db2c94ce0cef86efdfa2cb https://conda.anaconda.org/conda-forge/linux-64/gdbm-1.18-h0a1914f_2.tar.bz2#b77bc399b07a19c00fe12fdc95ee0297 https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.27-pthreads_h413a1c8_0.conda#a356024784da6dfd4683dc5ecf45b155 https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-h1dd3fc0_3.conda#66f03896ffbe1a110ffda05c7a856504 -https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.3-h4dfa4b3_0.conda#d39965123dffcad4d750989be65bcb7c -https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.45.2-h2c6b66d_0.conda#1423efca06ed343c1da0fc429bae0779 +https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.5-ha31de31_0.conda#b923cdb6e567ada84f991ffcc5848afb +https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.45.3-h2c6b66d_0.conda#be7d70f2db41b674733667bdd69bd000 https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.9-h8ee46fc_0.conda#077b6e8ad6a3ddb741fce2496dd01bec https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hd590300_1.conda#f27a24d46e3ea7b70a1f98e50c62508f https://conda.anaconda.org/conda-forge/linux-64/ccache-4.9.1-h1fcd64f_0.conda#3620f564bcf28c3524951b6f64f5c5ac @@ -72,12 +72,12 @@ https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2 https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py39h6dedee3_0.conda#557d64563e84ff21b14f586c7f662b7f https://conda.anaconda.org/conda-forge/noarch/packaging-24.0-pyhd8ed1ab_0.conda#248f521b64ce055e7feae3105e7abeb8 https://conda.anaconda.org/conda-forge/linux-64/pillow-10.3.0-py39h90a76f3_0.conda#799e6519cfffe2784db27b1db2ef33f3 -https://conda.anaconda.org/conda-forge/noarch/pluggy-1.4.0-pyhd8ed1ab_0.conda#139e9feb65187e916162917bb2484976 +https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda#d3483c8fc2dc2cc3f5cf43e26d60cabf https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.2-pyhd8ed1ab_0.conda#b9a4dacf97241704529131a0dfc0494f https://conda.anaconda.org/conda-forge/noarch/pypy-7.3.15-1_pypy39.conda#a418a6c16bd6f7ed56b92194214791a0 https://conda.anaconda.org/conda-forge/noarch/setuptools-69.5.1-pyhd8ed1ab_0.conda#7462280d81f639363e6e63c81276bd9e https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2 -https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.4.0-pyhc1e730c_0.conda#b296278eef667c673bf51de6535bad88 +https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96 https://conda.anaconda.org/conda-forge/linux-64/tornado-6.4-py39hf860d4a_0.conda#e7fded713fb466e1e0670afce1761b47 https://conda.anaconda.org/conda-forge/linux-64/unicodedata2-15.1.0-py39hf860d4a_0.conda#f699157518d28d00c87542b4ec1273be @@ -87,16 +87,16 @@ https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-22_linux64_open https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py39ha90811c_0.conda#07ed14c8326da42356514bcbc0b04802 https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.51.0-py39hf860d4a_0.conda#63421b4dd7222fad555e34ec9af015a1 https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1ab_0.conda#c5d3907ad8bd7bf557521a1833cf7e6d -https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.0-pyhd8ed1ab_0.conda#e0ed1bf13ce3a440e022157bf4764465 +https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/noarch/meson-1.4.0-pyhd8ed1ab_0.conda#52a0660cfa40b45bf254ecc3374cb2e0 https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 -https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.7.1-pyhd8ed1ab_0.conda#dcb27826ffc94d5f04e241322239983b +https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/scipy-1.12.0-py39h6dedee3_2.conda#6c5d74bac41838f4377dfd45085e1fec https://conda.anaconda.org/conda-forge/linux-64/blas-2.122-openblas.conda#5065468105542a8b23ea47bd8b6fa55f https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.0-pyhd8ed1ab_0.conda#dcbadab7a68738a028e195ab68ab2d2e -https://conda.anaconda.org/conda-forge/noarch/meson-python-0.15.0-pyh0c530f3_0.conda#3bc64565ca78ce3bb80248d09926d8f9 +https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.1.0-py39h5fd064f_0.conda#04676d2a49da3cb608af77e04b796ce1 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.8.4-py39h4e7d633_0.conda#58272019e595dde98d0844ae3ebf0cfe diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index e2584c2d27333..34ec64ad5863b 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -95,7 +95,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.43-h2797004_0.conda#0 https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.45.3-h2797004_0.conda#b3316cbe90249da4f8e84cd66e1cc55b https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h9c3ff4c_0.tar.bz2#309dec04b70a3cc0f1e84a4013683bc0 https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.15-h0b41bf4_0.conda#33277193f5b92bad9fdd230eb700929c -https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.6-h232c23b_2.conda#9a3a42df8a95f65334dfc7b80da1195d +https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.7-hc051c1a_0.conda#5d801a4906adc712d480afc362623b59 https://conda.anaconda.org/conda-forge/linux-64/mysql-common-8.3.0-hf1915f5_4.conda#784a4df6676c581ca624fbe460703a6d https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.43-hcad00b1_0.conda#8292dea9e022d9610a11fce5e0896ed8 https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda#47d31b792659ce70f470b5c82fdfb7a4 @@ -165,7 +165,7 @@ https://conda.anaconda.org/conda-forge/noarch/networkx-3.2.1-pyhd8ed1ab_0.conda# https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.27-pthreads_h7a3da1a_0.conda#4b422ebe8fc6a5320d0c1c22e5a46032 https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.2-h488ebb8_0.conda#7f2e286780f072ed750df46dc2631138 https://conda.anaconda.org/conda-forge/noarch/packaging-24.0-pyhd8ed1ab_0.conda#248f521b64ce055e7feae3105e7abeb8 -https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.2.1-pyhd8ed1ab_0.conda#d478a8a3044cdff1aa6e62f9269cefe0 +https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.2.2-pyhd8ed1ab_0.conda#6f6cf28bf8e021933869bae3f84b8fc9 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda#d3483c8fc2dc2cc3f5cf43e26d60cabf https://conda.anaconda.org/conda-forge/noarch/ply-3.11-pyhd8ed1ab_2.conda#18c6deb6f9602e32446398203c8f0e91 https://conda.anaconda.org/conda-forge/linux-64/psutil-5.9.8-py39hd1e30aa_0.conda#ec86403fde8793ac1c36f8afa3d15902 @@ -220,7 +220,7 @@ https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda# https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.1-pyhd8ed1ab_0.conda#08807a87fa7af10754d46f63b368e016 https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_1.conda#d8d07866ac3b5b6937213c89a1874f08 https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.3-haf2f30d_0.conda#f3df87cc9ef0b5113bff55aefcbcafd5 -https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.4.0-h3d44ed6_0.conda#27f46291a6aaa3c2a4f798ebd35a7ddb +https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.5.0-hfac3d4d_0.conda#f5126317dd0ce0ba26945e411ecc6960 https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.0-pyhd8ed1ab_0.conda#dcbadab7a68738a028e195ab68ab2d2e https://conda.anaconda.org/conda-forge/noarch/lazy_loader-0.4-pyhd8ed1ab_0.conda#a284ff318fbdb0dd83928275b4b6087c https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-22_linux64_openblas.conda#1fd156abd41a4992835952f6f4d951d0 @@ -237,7 +237,7 @@ https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.1.1-py39ha98d97 https://conda.anaconda.org/conda-forge/noarch/imageio-2.34.1-pyh4b66e23_0.conda#bcf6a6f4c6889ca083e8d33afbafb8d5 https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.2-py39hddac248_0.conda#259c4e76e6bda8888aefc098ae1ba749 https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.6-pyhd8ed1ab_0.conda#a5b55d1cb110cdcedc748b5c3e16e687 -https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.25-py39ha963410_0.conda#d14227f0e141af743374d845fd4f5ccd +https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.26-py39ha963410_0.conda#d138679a254e4e0918cfc1114c928bb8 https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.1-pyhd8ed1ab_0.conda#d15917f33140f8d2ac9ca44db7ec8a25 https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-client-17.0-hb77b528_0.conda#07f45f1be1c25345faddb8db0de8039b https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.4.1-py39h44dd56e_1.conda#d037c20e3da2e85f03ebd20ad480c359 diff --git a/build_tools/circle/doc_min_dependencies_environment.yml b/build_tools/circle/doc_min_dependencies_environment.yml index 298a60e8ec4ff..14f4485295455 100644 --- a/build_tools/circle/doc_min_dependencies_environment.yml +++ b/build_tools/circle/doc_min_dependencies_environment.yml @@ -30,7 +30,7 @@ dependencies: - numpydoc=1.2.0 # min - sphinx-prompt=1.3.0 # min - plotly=5.14.0 # min - - polars=0.19.12 # min + - polars=0.20.23 # min - pooch - pip - pip: diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index e08a14c235079..043587152c63b 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 32601810330a8200864f7908d07d870a3a58931be4f833691b2b5c7937f2d330 +# input_hash: 08b61aae27c59a8d35d008fa2f947440f3cbcbc41622112e33e68f90d69b621c @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.2.2-hbcca054_0.conda#2f4327a1cbe7f022401b236e915a5fef @@ -80,7 +80,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.43-h2797004_0.conda#0 https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.45.3-h2797004_0.conda#b3316cbe90249da4f8e84cd66e1cc55b https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h9c3ff4c_0.tar.bz2#309dec04b70a3cc0f1e84a4013683bc0 https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.15-h0b41bf4_0.conda#33277193f5b92bad9fdd230eb700929c -https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.6-h232c23b_2.conda#9a3a42df8a95f65334dfc7b80da1195d +https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.7-hc051c1a_0.conda#5d801a4906adc712d480afc362623b59 https://conda.anaconda.org/conda-forge/linux-64/mysql-common-8.3.0-hf1915f5_4.conda#784a4df6676c581ca624fbe460703a6d https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.43-hcad00b1_0.conda#8292dea9e022d9610a11fce5e0896ed8 https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda#47d31b792659ce70f470b5c82fdfb7a4 @@ -146,7 +146,7 @@ https://conda.anaconda.org/conda-forge/linux-64/markupsafe-2.1.5-py39hd1e30aa_0. https://conda.anaconda.org/conda-forge/noarch/networkx-3.2-pyhd8ed1ab_0.conda#cec8cc498664cc00a070676aa89e69a7 https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.2-h488ebb8_0.conda#7f2e286780f072ed750df46dc2631138 https://conda.anaconda.org/conda-forge/noarch/packaging-24.0-pyhd8ed1ab_0.conda#248f521b64ce055e7feae3105e7abeb8 -https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.2.1-pyhd8ed1ab_0.conda#d478a8a3044cdff1aa6e62f9269cefe0 +https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.2.2-pyhd8ed1ab_0.conda#6f6cf28bf8e021933869bae3f84b8fc9 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda#d3483c8fc2dc2cc3f5cf43e26d60cabf https://conda.anaconda.org/conda-forge/noarch/ply-3.11-pyhd8ed1ab_2.conda#18c6deb6f9602e32446398203c8f0e91 https://conda.anaconda.org/conda-forge/linux-64/psutil-5.9.8-py39hd1e30aa_0.conda#ec86403fde8793ac1c36f8afa3d15902 @@ -199,7 +199,7 @@ https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda# https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.1-pyhd8ed1ab_0.conda#08807a87fa7af10754d46f63b368e016 https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_1.conda#d8d07866ac3b5b6937213c89a1874f08 https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.3-haf2f30d_0.conda#f3df87cc9ef0b5113bff55aefcbcafd5 -https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.4.0-h3d44ed6_0.conda#27f46291a6aaa3c2a4f798ebd35a7ddb +https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.5.0-hfac3d4d_0.conda#f5126317dd0ce0ba26945e411ecc6960 https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-7.1.0-hd8ed1ab_0.conda#6ef2b72d291b39e479d7694efa2b2b98 https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-22_linux64_mkl.conda#eb6deb4ba6f92ea3f31c09cb8b764738 https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-255-h3516f8a_1.conda#3366af27f0b593544a6cd453c7932ac5 @@ -223,7 +223,7 @@ https://conda.anaconda.org/conda-forge/noarch/imageio-2.34.1-pyh4b66e23_0.conda# https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.3.4-py39h2fa2bec_0.tar.bz2#9ec0b2186fab9121c54f4844f93ee5b7 https://conda.anaconda.org/conda-forge/linux-64/pandas-1.1.5-py39hde0f152_0.tar.bz2#79fc4b5b3a865b90dd3701cecf1ad33c https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.6-pyhd8ed1ab_0.conda#a5b55d1cb110cdcedc748b5c3e16e687 -https://conda.anaconda.org/conda-forge/linux-64/polars-0.19.12-py39h90d8ae4_0.conda#191828961c95f8d59fa2b86a590f9905 +https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.23-py39ha963410_0.conda#4871f09d653e979d598d2d4cd5fa868d https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.9-py39h52134e7_5.conda#e1f148e57d071b09187719df86f513c1 https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.3.0-py39hd257fcd_1.tar.bz2#c4b698994b2d8d2e659ae02202e6abe4 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.6.0-py39hee8e79c_0.tar.bz2#3afcb78281836e61351a2924f3230060 diff --git a/examples/gaussian_process/plot_gpr_co2.py b/examples/gaussian_process/plot_gpr_co2.py index 33b0ab7271549..b3da30daa0f6d 100644 --- a/examples/gaussian_process/plot_gpr_co2.py +++ b/examples/gaussian_process/plot_gpr_co2.py @@ -33,24 +33,25 @@ # We will derive a dataset from the Mauna Loa Observatory that collected air # samples. We are interested in estimating the concentration of CO2 and # extrapolate it for further year. First, we load the original dataset available -# in OpenML. +# in OpenML as a pandas dataframe. This will be replaced with Polars +# once `fetch_openml` adds a native support for it. from sklearn.datasets import fetch_openml co2 = fetch_openml(data_id=41187, as_frame=True) co2.frame.head() # %% -# First, we process the original dataframe to create a date index and select -# only the CO2 column. -import pandas as pd +# First, we process the original dataframe to create a date column and select +# it along with the CO2 column. +import polars as pl -co2_data = co2.frame -co2_data["date"] = pd.to_datetime(co2_data[["year", "month", "day"]]) -co2_data = co2_data[["date", "co2"]].set_index("date") +co2_data = pl.DataFrame(co2.frame[["year", "month", "day", "co2"]]).select( + pl.date("year", "month", "day"), "co2" +) co2_data.head() # %% -co2_data.index.min(), co2_data.index.max() +co2_data["date"].min(), co2_data["date"].max() # %% # We see that we get CO2 concentration for some days from March, 1958 to @@ -58,7 +59,8 @@ # understanding. import matplotlib.pyplot as plt -co2_data.plot() +plt.plot(co2_data["date"], co2_data["co2"]) +plt.xlabel("date") plt.ylabel("CO$_2$ concentration (ppm)") _ = plt.title("Raw air samples measurements from the Mauna Loa Observatory") @@ -67,15 +69,14 @@ # for which no measurements were collected. Such a processing will have an # smoothing effect on the data. -try: - co2_data_resampled_monthly = co2_data.resample("ME") -except ValueError: - # pandas < 2.2 uses M instead of ME - co2_data_resampled_monthly = co2_data.resample("M") - - -co2_data = co2_data_resampled_monthly.mean().dropna(axis="index", how="any") -co2_data.plot() +co2_data = ( + co2_data.sort(by="date") + .group_by_dynamic("date", every="1mo") + .agg(pl.col("co2").mean()) + .drop_nulls() +) +plt.plot(co2_data["date"], co2_data["co2"]) +plt.xlabel("date") plt.ylabel("Monthly average of CO$_2$ concentration (ppm)") _ = plt.title( "Monthly average of air samples measurements\nfrom the Mauna Loa Observatory" @@ -88,7 +89,9 @@ # # As a first step, we will divide the data and the target to estimate. The data # being a date, we will convert it into a numeric. -X = (co2_data.index.year + co2_data.index.month / 12).to_numpy().reshape(-1, 1) +X = co2_data.select( + pl.col("date").dt.year() + pl.col("date").dt.month() / 12 +).to_numpy() y = co2_data["co2"].to_numpy() # %% diff --git a/pyproject.toml b/pyproject.toml index d9b95422e7ee5..f4bed8f20fa4a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -62,7 +62,7 @@ docs = [ "sphinx-prompt>=1.3.0", "sphinxext-opengraph>=0.4.2", "plotly>=5.14.0", - "polars>=0.19.12" + "polars>=0.20.23" ] examples = [ "matplotlib>=3.3.4", @@ -82,7 +82,7 @@ tests = [ "black>=24.3.0", "mypy>=1.9", "pyamg>=4.0.0", - "polars>=0.19.12", + "polars>=0.20.23", "pyarrow>=12.0.0", "numpydoc>=1.2.0", "pooch>=1.6.0", diff --git a/sklearn/_min_dependencies.py b/sklearn/_min_dependencies.py index 00315f31d4c3f..0b1a96748a588 100644 --- a/sklearn/_min_dependencies.py +++ b/sklearn/_min_dependencies.py @@ -33,7 +33,7 @@ "black": ("24.3.0", "tests"), "mypy": ("1.9", "tests"), "pyamg": ("4.0.0", "tests"), - "polars": ("0.19.12", "docs, tests"), + "polars": ("0.20.23", "docs, tests"), "pyarrow": ("12.0.0", "tests"), "sphinx": ("6.0.0", "docs"), "sphinx-copybutton": ("0.5.2", "docs"), diff --git a/sklearn/tests/test_base.py b/sklearn/tests/test_base.py index 3bbc236e703df..a1cd3b8fc8c7b 100644 --- a/sklearn/tests/test_base.py +++ b/sklearn/tests/test_base.py @@ -834,7 +834,7 @@ class Estimator(BaseEstimator, WithSlots): [ ("dataframe", "1.5.0"), ("pyarrow", "12.0.0"), - ("polars", "0.19.12"), + ("polars", "0.20.23"), ], ) def test_dataframe_protocol(constructor_name, minversion): From b79420f1c2e82d814dec8026e96421751bfc9c96 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Fri, 17 May 2024 15:14:33 +0200 Subject: [PATCH 036/275] FIX add long long for int32/int64 windows compat in NumPy 2.0 (#29029) --- sklearn/utils/arrayfuncs.pyx | 1 + sklearn/utils/tests/test_arrayfuncs.py | 4 +++- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/sklearn/utils/arrayfuncs.pyx b/sklearn/utils/arrayfuncs.pyx index 346531d325ca5..1ad5804770358 100644 --- a/sklearn/utils/arrayfuncs.pyx +++ b/sklearn/utils/arrayfuncs.pyx @@ -16,6 +16,7 @@ ctypedef fused real_numeric: short int long + long long float double diff --git a/sklearn/utils/tests/test_arrayfuncs.py b/sklearn/utils/tests/test_arrayfuncs.py index 4a80a4c1edefd..a5c99427cbd00 100644 --- a/sklearn/utils/tests/test_arrayfuncs.py +++ b/sklearn/utils/tests/test_arrayfuncs.py @@ -26,7 +26,9 @@ def test_min_pos_no_positive(dtype): assert min_pos(X) == np.finfo(dtype).max -@pytest.mark.parametrize("dtype", [np.int16, np.int32, np.float32, np.float64]) +@pytest.mark.parametrize( + "dtype", [np.int16, np.int32, np.int64, np.float32, np.float64] +) @pytest.mark.parametrize("value", [0, 1.5, -1]) def test_all_with_any_reduction_axis_1(dtype, value): # Check that return value is False when there is no row equal to `value` From 9bd7047b4a6c673bcfd2911997f124e265f8ad57 Mon Sep 17 00:00:00 2001 From: Akihiro Kuno Date: Fri, 17 May 2024 23:57:47 +0900 Subject: [PATCH 037/275] FIX convergence criterion of MeanShift (#28951) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Olivier Grisel Co-authored-by: Jérémie du Boisberranger --- doc/whats_new/v1.5.rst | 3 +++ sklearn/cluster/_mean_shift.py | 2 +- sklearn/cluster/tests/test_mean_shift.py | 9 +++++++++ 3 files changed, 13 insertions(+), 1 deletion(-) diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst index 5fdc0707ffbee..6dc76ceefaf5f 100644 --- a/doc/whats_new/v1.5.rst +++ b/doc/whats_new/v1.5.rst @@ -183,6 +183,9 @@ Changelog :mod:`sklearn.cluster` ...................... +- |Fix| The :class:`cluster.MeanShift` class now properly converges for constant data. + :pr:`28951` by :user:`Akihiro Kuno `. + - |FIX| Create copy of precomputed sparse matrix within the `fit` method of :class:`~cluster.OPTICS` to avoid in-place modification of the sparse matrix. :pr:`28491` by :user:`Thanh Lam Dang `. diff --git a/sklearn/cluster/_mean_shift.py b/sklearn/cluster/_mean_shift.py index fae11cca7df23..a99a607f3cf0d 100644 --- a/sklearn/cluster/_mean_shift.py +++ b/sklearn/cluster/_mean_shift.py @@ -122,7 +122,7 @@ def _mean_shift_single_seed(my_mean, X, nbrs, max_iter): my_mean = np.mean(points_within, axis=0) # If converged or at max_iter, adds the cluster if ( - np.linalg.norm(my_mean - my_old_mean) < stop_thresh + np.linalg.norm(my_mean - my_old_mean) <= stop_thresh or completed_iterations == max_iter ): break diff --git a/sklearn/cluster/tests/test_mean_shift.py b/sklearn/cluster/tests/test_mean_shift.py index 265c72d0c4ce1..d2d73ba11a3ec 100644 --- a/sklearn/cluster/tests/test_mean_shift.py +++ b/sklearn/cluster/tests/test_mean_shift.py @@ -25,6 +25,15 @@ ) +def test_convergence_of_1d_constant_data(): + # Test convergence using 1D constant data + # Non-regression test for: + # https://github.com/scikit-learn/scikit-learn/issues/28926 + model = MeanShift() + n_iter = model.fit(np.ones(10).reshape(-1, 1)).n_iter_ + assert n_iter < model.max_iter + + def test_estimate_bandwidth(): # Test estimate_bandwidth bandwidth = estimate_bandwidth(X, n_samples=200) From 4647729e5ee8c46e4fedace2d3c50c37f0a6693d Mon Sep 17 00:00:00 2001 From: jpienaar-tuks <112702520+jpienaar-tuks@users.noreply.github.com> Date: Sat, 18 May 2024 14:46:56 +0200 Subject: [PATCH 038/275] DOC Fix time complexity of MLP (#28592) Co-authored-by: Johann --- doc/modules/neural_networks_supervised.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/modules/neural_networks_supervised.rst b/doc/modules/neural_networks_supervised.rst index 95d0a1be38238..7ee2387068c81 100644 --- a/doc/modules/neural_networks_supervised.rst +++ b/doc/modules/neural_networks_supervised.rst @@ -229,7 +229,7 @@ Complexity Suppose there are :math:`n` training samples, :math:`m` features, :math:`k` hidden layers, each containing :math:`h` neurons - for simplicity, and :math:`o` output neurons. The time complexity of backpropagation is -:math:`O(n\cdot m \cdot h^k \cdot o \cdot i)`, where :math:`i` is the number +:math:`O(i \cdot n \cdot (m \cdot h + (k - 1) \cdot h \cdot h + h \cdot o))`, where :math:`i` is the number of iterations. Since backpropagation has a high time complexity, it is advisable to start with smaller number of hidden neurons and few hidden layers for training. From ffbe4ab45bd9a113737231721fa2f55a70f3d0ab Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Sun, 19 May 2024 21:14:50 +0200 Subject: [PATCH 039/275] DOC remove obsolete SVM example (#27108) --- doc/conf.py | 1 + examples/svm/plot_svm_kernels.py | 59 +++++++++++++++++++++++------- examples/svm/plot_svm_nonlinear.py | 45 ----------------------- 3 files changed, 46 insertions(+), 59 deletions(-) delete mode 100644 examples/svm/plot_svm_nonlinear.py diff --git a/doc/conf.py b/doc/conf.py index 9d77fc68d0f71..0587e98130118 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -301,6 +301,7 @@ "auto_examples/decomposition/plot_beta_divergence": ( "auto_examples/applications/plot_topics_extraction_with_nmf_lda" ), + "auto_examples/svm/plot_svm_nonlinear": "auto_examples/svm/plot_svm_kernels", "auto_examples/ensemble/plot_adaboost_hastie_10_2": ( "auto_examples/ensemble/plot_adaboost_multiclass" ), diff --git a/examples/svm/plot_svm_kernels.py b/examples/svm/plot_svm_kernels.py index d801e2477e682..a63de6765f083 100644 --- a/examples/svm/plot_svm_kernels.py +++ b/examples/svm/plot_svm_kernels.py @@ -110,12 +110,15 @@ from sklearn.inspection import DecisionBoundaryDisplay -def plot_training_data_with_decision_boundary(kernel): +def plot_training_data_with_decision_boundary( + kernel, ax=None, long_title=True, support_vectors=True +): # Train the SVC clf = svm.SVC(kernel=kernel, gamma=2).fit(X, y) # Settings for plotting - _, ax = plt.subplots(figsize=(4, 3)) + if ax is None: + _, ax = plt.subplots(figsize=(4, 3)) x_min, x_max, y_min, y_max = -3, 3, -3, 3 ax.set(xlim=(x_min, x_max), ylim=(y_min, y_max)) @@ -136,20 +139,26 @@ def plot_training_data_with_decision_boundary(kernel): linestyles=["--", "-", "--"], ) - # Plot bigger circles around samples that serve as support vectors - ax.scatter( - clf.support_vectors_[:, 0], - clf.support_vectors_[:, 1], - s=250, - facecolors="none", - edgecolors="k", - ) + if support_vectors: + # Plot bigger circles around samples that serve as support vectors + ax.scatter( + clf.support_vectors_[:, 0], + clf.support_vectors_[:, 1], + s=150, + facecolors="none", + edgecolors="k", + ) + # Plot samples by color and add legend - ax.scatter(X[:, 0], X[:, 1], c=y, s=150, edgecolors="k") + ax.scatter(X[:, 0], X[:, 1], c=y, s=30, edgecolors="k") ax.legend(*scatter.legend_elements(), loc="upper right", title="Classes") - ax.set_title(f" Decision boundaries of {kernel} kernel in SVC") + if long_title: + ax.set_title(f" Decision boundaries of {kernel} kernel in SVC") + else: + ax.set_title(kernel) - _ = plt.show() + if ax is None: + plt.show() # %% @@ -237,7 +246,6 @@ def plot_training_data_with_decision_boundary(kernel): # using the hyperbolic tangent function (:math:`\tanh`). The kernel function # scales and possibly shifts the dot product of the two points # (:math:`\mathbf{x}_1` and :math:`\mathbf{x}_2`). - plot_training_data_with_decision_boundary("sigmoid") # %% @@ -271,3 +279,26 @@ def plot_training_data_with_decision_boundary(kernel): # parameters using techniques such as # :class:`~sklearn.model_selection.GridSearchCV` is recommended to capture the # underlying structures within the data. + +# %% +# XOR dataset +# ----------- +# A classical example of a dataset which is not linearly separable is the XOR +# pattern. HEre we demonstrate how different kernels work on such a dataset. + +xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500)) +np.random.seed(0) +X = np.random.randn(300, 2) +y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0) + +_, ax = plt.subplots(2, 2, figsize=(8, 8)) +args = dict(long_title=False, support_vectors=False) +plot_training_data_with_decision_boundary("linear", ax[0, 0], **args) +plot_training_data_with_decision_boundary("poly", ax[0, 1], **args) +plot_training_data_with_decision_boundary("rbf", ax[1, 0], **args) +plot_training_data_with_decision_boundary("sigmoid", ax[1, 1], **args) +plt.show() + +# %% +# As you can see from the plots above, only the `rbf` kernel can find a +# reasonable decision boundary for the above dataset. diff --git a/examples/svm/plot_svm_nonlinear.py b/examples/svm/plot_svm_nonlinear.py deleted file mode 100644 index 4990e509661a1..0000000000000 --- a/examples/svm/plot_svm_nonlinear.py +++ /dev/null @@ -1,45 +0,0 @@ -""" -============== -Non-linear SVM -============== - -Perform binary classification using non-linear SVC -with RBF kernel. The target to predict is a XOR of the -inputs. - -The color map illustrates the decision function learned by the SVC. - -""" - -import matplotlib.pyplot as plt -import numpy as np - -from sklearn import svm - -xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500)) -np.random.seed(0) -X = np.random.randn(300, 2) -Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0) - -# fit the model -clf = svm.NuSVC(gamma="auto") -clf.fit(X, Y) - -# plot the decision function for each datapoint on the grid -Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) -Z = Z.reshape(xx.shape) - -plt.imshow( - Z, - interpolation="nearest", - extent=(xx.min(), xx.max(), yy.min(), yy.max()), - aspect="auto", - origin="lower", - cmap=plt.cm.PuOr_r, -) -contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2, linestyles="dashed") -plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired, edgecolors="k") -plt.xticks(()) -plt.yticks(()) -plt.axis([-3, 3, -3, 3]) -plt.show() From 1e50434f18275bb8727c2a2e24cb953db143d8a5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?= Date: Mon, 20 May 2024 11:51:20 +0200 Subject: [PATCH 040/275] set version --- pyproject.toml | 2 +- sklearn/__init__.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index f4bed8f20fa4a..e365ef454b21b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "scikit-learn" -version = "1.5.0rc1" +version = "1.5.0" description = "A set of python modules for machine learning and data mining" readme = "README.rst" maintainers = [ diff --git a/sklearn/__init__.py b/sklearn/__init__.py index 63b08e022f23d..d794f2489b92b 100644 --- a/sklearn/__init__.py +++ b/sklearn/__init__.py @@ -42,7 +42,7 @@ # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer. # 'X.Y.dev0' is the canonical version of 'X.Y.dev' # -__version__ = "1.5.0rc1" +__version__ = "1.5.0" # On OSX, we can get a runtime error due to multiple OpenMP libraries loaded From 729b54d5af208432f788ae7945842f0cf597bd36 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?= Date: Mon, 20 May 2024 12:29:29 +0200 Subject: [PATCH 041/275] test py3.12 against numpy 2 [cd build] --- .github/workflows/wheels.yml | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml index 8bd7ffc17beca..8e0073e67426b 100644 --- a/.github/workflows/wheels.yml +++ b/.github/workflows/wheels.yml @@ -167,7 +167,7 @@ jobs: CIBW_CONFIG_SETTINGS_WINDOWS: "setup-args=--vsenv" CIBW_REPAIR_WHEEL_COMMAND_WINDOWS: bash build_tools/github/repair_windows_wheels.sh {wheel} {dest_dir} CIBW_BEFORE_TEST_WINDOWS: bash build_tools/github/build_minimal_windows_image.sh ${{ matrix.python }} - CIBW_TEST_REQUIRES: pytest pandas + CIBW_TEST_REQUIRES: pytest pandas ${{ matrix.python == 312 && 'numpy>=2.0.0rc2' || '' }} CIBW_TEST_COMMAND: bash {project}/build_tools/wheels/test_wheels.sh CIBW_TEST_COMMAND_WINDOWS: bash {project}/build_tools/github/test_windows_wheels.sh ${{ matrix.python }} CIBW_BUILD_VERBOSITY: 1 diff --git a/pyproject.toml b/pyproject.toml index e365ef454b21b..f244745f37d30 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -95,7 +95,7 @@ build-backend = "mesonpy" requires = [ "meson-python>=0.15.0", "Cython>=3.0.10", - "numpy>=2.0.0rc1", + "numpy>=2.0.0rc2", "scipy>=1.6.0", ] From 0ac28ade871ca71a89a71c834a7b47829b075829 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?= Date: Mon, 20 May 2024 14:31:55 +0200 Subject: [PATCH 042/275] DOC Release highlights 1.5 (#29007) Co-authored-by: Tim Head Co-authored-by: Guillaume Lemaitre Co-authored-by: Christian Lorentzen --- .../plot_release_highlights_1_5_0.py | 183 ++++++++++++++++++ 1 file changed, 183 insertions(+) create mode 100644 examples/release_highlights/plot_release_highlights_1_5_0.py diff --git a/examples/release_highlights/plot_release_highlights_1_5_0.py b/examples/release_highlights/plot_release_highlights_1_5_0.py new file mode 100644 index 0000000000000..0acc6fda6589d --- /dev/null +++ b/examples/release_highlights/plot_release_highlights_1_5_0.py @@ -0,0 +1,183 @@ +# ruff: noqa +""" +======================================= +Release Highlights for scikit-learn 1.5 +======================================= + +.. currentmodule:: sklearn + +We are pleased to announce the release of scikit-learn 1.5! Many bug fixes +and improvements were added, as well as some key new features. Below we +detail the highlights of this release. **For an exhaustive list of +all the changes**, please refer to the :ref:`release notes `. + +To install the latest version (with pip):: + + pip install --upgrade scikit-learn + +or with conda:: + + conda install -c conda-forge scikit-learn + +""" + +# %% +# FixedThresholdClassifier: Setting the decision threshold of a binary classifier +# ------------------------------------------------------------------------------- +# All binary classifiers of scikit-learn use a fixed decision threshold of 0.5 to +# convert probability estimates (i.e. output of `predict_proba`) into class +# predictions. However, 0.5 is almost never the desired threshold for a given problem. +# :class:`~model_selection.FixedThresholdClassifier` allows to wrap any binary +# classifier and set a custom decision threshold. +from sklearn.datasets import make_classification +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import confusion_matrix + +X, y = make_classification(n_samples=1_000, weights=[0.9, 0.1], random_state=0) +classifier = LogisticRegression(random_state=0).fit(X, y) + +print("confusion matrix:\n", confusion_matrix(y, classifier.predict(X))) + +# %% +# Lowering the threshold, i.e. allowing more samples to be classified as the positive +# class, increases the number of true positives at the cost of more false positives +# (as is well known from the concavity of the ROC curve). +from sklearn.model_selection import FixedThresholdClassifier + +wrapped_classifier = FixedThresholdClassifier(classifier, threshold=0.1).fit(X, y) + +print("confusion matrix:\n", confusion_matrix(y, wrapped_classifier.predict(X))) + +# %% +# TunedThresholdClassifierCV: Tuning the decision threshold of a binary classifier +# -------------------------------------------------------------------------------- +# The decision threshold of a binary classifier can be tuned to optimize a given +# metric, using :class:`~model_selection.TunedThresholdClassifierCV`. +from sklearn.metrics import balanced_accuracy_score + +# Due to the class imbalance, the balanced accuracy is not optimal for the default +# threshold. The classifier tends to over predict the majority class. +print(f"balanced accuracy: {balanced_accuracy_score(y, classifier.predict(X)):.2f}") + +# %% +# Tuning the threshold to optimize the balanced accuracy gives a smaller threshold +# that allows more samples to be classified as the positive class. +from sklearn.model_selection import TunedThresholdClassifierCV + +tuned_classifier = TunedThresholdClassifierCV( + classifier, cv=5, scoring="balanced_accuracy" +).fit(X, y) + +print(f"new threshold: {tuned_classifier.best_threshold_:.4f}") +print( + f"balanced accuracy: {balanced_accuracy_score(y, tuned_classifier.predict(X)):.2f}" +) + +# %% +# :class:`~model_selection.TunedThresholdClassifierCV` also benefits from the +# metadata routing support (:ref:`Metadata Routing User Guide`) +# allowing to optimze complex business metrics, detailed +# in :ref:`Post-tuning the decision threshold for cost-sensitive learning +# `. + +# %% +# Performance improvements in PCA +# ------------------------------- +# :class:`~decomposition.PCA` has a new solver, "covariance_eigh", which is faster +# and more memory efficient than the other solvers for datasets with a large number +# of samples and a small number of features. +from sklearn.datasets import make_low_rank_matrix +from sklearn.decomposition import PCA + +X = make_low_rank_matrix( + n_samples=10_000, n_features=100, tail_strength=0.1, random_state=0 +) + +pca = PCA(n_components=10).fit(X) + +print(f"explained variance: {pca.explained_variance_ratio_.sum():.2f}") + +# %% +# The "full" solver has also been improved to use less memory and allows to +# transform faster. The "auto" option for the solver takes advantage of the +# new solver and is now able to select an appropriate solver for sparse +# datasets. +from scipy.sparse import random + +X = random(10000, 100, format="csr", random_state=0) + +pca = PCA(n_components=10, svd_solver="auto").fit(X) + +# %% +# ColumnTransformer is subscriptable +# ---------------------------------- +# The transformers of a :class:`~compose.ColumnTransformer` can now be directly +# accessed using indexing by name. +import numpy as np +from sklearn.compose import ColumnTransformer +from sklearn.preprocessing import StandardScaler, OneHotEncoder + +X = np.array([[0, 1, 2], [3, 4, 5]]) +column_transformer = ColumnTransformer( + [("std_scaler", StandardScaler(), [0]), ("one_hot", OneHotEncoder(), [1, 2])] +) + +column_transformer.fit(X) + +print(column_transformer["std_scaler"]) +print(column_transformer["one_hot"]) + +# %% +# Custom imputation strategies for the SimpleImputer +# -------------------------------------------------- +# :class:`~impute.SimpleImputer` now supports custom strategies for imputation, +# using a callable that computes a scalar value from the non missing values of +# a column vector. +from sklearn.impute import SimpleImputer + +X = np.array( + [ + [-1.1, 1.1, 1.1], + [3.9, -1.2, np.nan], + [np.nan, 1.3, np.nan], + [-0.1, -1.4, -1.4], + [-4.9, 1.5, -1.5], + [np.nan, 1.6, 1.6], + ] +) + + +def smallest_abs(arr): + """Return the smallest absolute value of a 1D array.""" + return np.min(np.abs(arr)) + + +imputer = SimpleImputer(strategy=smallest_abs) + +imputer.fit_transform(X) + +# %% +# Pairwise distances with non-numeric arrays +# ------------------------------------------ +# :func:`~metrics.pairwise_distances` can now compute distances between +# non-numeric arrays using a callable metric. +from sklearn.metrics import pairwise_distances + +X = ["cat", "dog"] +Y = ["cat", "fox"] + + +def levenshtein_distance(x, y): + """Return the Levenshtein distance between two strings.""" + if x == "" or y == "": + return max(len(x), len(y)) + if x[0] == y[0]: + return levenshtein_distance(x[1:], y[1:]) + return 1 + min( + levenshtein_distance(x[1:], y), + levenshtein_distance(x, y[1:]), + levenshtein_distance(x[1:], y[1:]), + ) + + +pairwise_distances(X, Y, metric=levenshtein_distance) From 919ae9bf72554a180baa3d8f4537b49c730b7580 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?= Date: Mon, 20 May 2024 16:13:08 +0200 Subject: [PATCH 043/275] MAINT Reoder what's new for 1.5 (#29039) --- doc/templates/index.html | 10 +-- doc/whats_new/v1.5.rst | 143 +++++++++++++++++++++++---------------- 2 files changed, 88 insertions(+), 65 deletions(-) diff --git a/doc/templates/index.html b/doc/templates/index.html index 5b3a61a5b98bb..74816a4b473d3 100644 --- a/doc/templates/index.html +++ b/doc/templates/index.html @@ -167,7 +167,9 @@

Machine Learning in

News

  • On-going development: - scikit-learn 1.5 (Changelog) + scikit-learn 1.6 (Changelog) +
    • +
  • May 2024. scikit-learn 1.5.0 is available for download (Changelog).
  • April 2024. scikit-learn 1.4.2 is available for download (Changelog).
    • @@ -175,12 +177,6 @@

    News

  • January 2024. scikit-learn 1.4.0 is available for download (Changelog).
    • -
  • October 2023. scikit-learn 1.3.2 is available for download (Changelog). -
    • -
  • September 2023. scikit-learn 1.3.1 is available for download (Changelog). -
    • -
  • June 2023. scikit-learn 1.3.0 is available for download (Changelog). -
  • All releases: What's new (Changelog)
    • diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst index 6dc76ceefaf5f..c2c64e24ba9e0 100644 --- a/doc/whats_new/v1.5.rst +++ b/doc/whats_new/v1.5.rst @@ -8,10 +8,8 @@ Version 1.5 =========== -.. - -- UNCOMMENT WHEN 1.5.0 IS RELEASED -- - For a short description of the main highlights of the release, please refer to - :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_5_0.py`. +For a short description of the main highlights of the release, please refer to +:ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_5_0.py`. .. include:: changelog_legend.inc @@ -20,7 +18,7 @@ Version 1.5 Version 1.5.0 ============= -**In Development** +**May 2024** Security -------- @@ -59,6 +57,10 @@ Changed models Changes impacting many modules ------------------------------ +- |Fix| Raise `ValueError` with an informative error message when passing 1D + sparse arrays to methods that expect 2D sparse inputs. + :pr:`28988` by :user:`Olivier Grisel `. + - |API| The name of the input of the `inverse_transform` method of estimators has been standardized to `X`. As a consequence, `Xt` is deprecated and will be removed in version 1.7 in the following estimators: :class:`cluster.FeatureAgglomeration`, @@ -67,10 +69,6 @@ Changes impacting many modules :class:`pipeline.Pipeline` and :class:`preprocessing.KBinsDiscretizer`. :pr:`28756` by :user:`Will Dean `. -- |Fix| Raise `ValueError` with an informative error message when passing 1D - sparse arrays to methods that expect 2D sparse inputs. - :pr:`28988` by :user:`Olivier Grisel `. - Support for Array API --------------------- @@ -82,8 +80,8 @@ See :ref:`array_api` for more details. **Functions:** - :func:`sklearn.metrics.r2_score` now supports Array API compliant inputs. - :pr:`27904` by :user:`Eric Lindgren `, `Franck Charras `, - `Olivier Grisel ` and `Tim Head `. + :pr:`27904` by :user:`Eric Lindgren `, :user:`Franck Charras `, + :user:`Olivier Grisel ` and :user:`Tim Head `. **Classes:** @@ -103,8 +101,8 @@ Unless we discover a major blocker, setuptools support will be dropped in scikit-learn 1.6. The 1.5.x releases will support building scikit-learn with setuptools. -Meson support for building scikit-learn was added in :pr:`28040` by :user:`Loïc -Estève ` +Meson support for building scikit-learn was added in :pr:`28040` by +:user:`Loïc Estève ` Metadata Routing ---------------- @@ -120,7 +118,8 @@ more details. now support metadata routing. The fit methods now accept ``**fit_params`` which are passed to the underlying estimators via their `fit` methods. - :pr:`28432` by :user:`Adam Li ` and :user:`Benjamin Bossan `. + :pr:`28432` by :user:`Adam Li ` and + :user:`Benjamin Bossan `. - |Feature| :class:`linear_model.RidgeCV` and :class:`linear_model.RidgeClassifierCV` now support metadata routing in @@ -144,8 +143,8 @@ more details. - |Feature| :class:`pipeline.FeatureUnion` now supports metadata routing in its ``fit`` and ``fit_transform`` methods and route metadata to the underlying - transformers' ``fit`` and ``fit_transform``. :pr:`28205` by :user:`Stefanie - Senger `. + transformers' ``fit`` and ``fit_transform``. + :pr:`28205` by :user:`Stefanie Senger `. - |Fix| Fix an issue when resolving default routing requests set via class attributes. @@ -156,8 +155,8 @@ more details. :pr:`28651` by `Adrin Jalali`_. - |FIX| Prevent a `RecursionError` when estimators with the default `scoring` - param (`None`) route metadata. :pr:`28712` by :user:`Stefanie Senger - `. + param (`None`) route metadata. + :pr:`28712` by :user:`Stefanie Senger `. Changelog --------- @@ -217,7 +216,13 @@ Changelog :mod:`sklearn.cross_decomposition` .................................. -- |API| Deprecates `Y` in favor of `y` in the methods fit, transform and inverse_transform of: +- |Fix| The `coef_` fitted attribute of :class:`cross_decomposition.PLSRegression` + now takes into account both the scale of `X` and `Y` when `scale=True`. Note that + the previous predicted values were not affected by this bug. + :pr:`28612` by :user:`Guillaume Lemaitre `. + +- |API| Deprecates `Y` in favor of `y` in the methods fit, transform and + inverse_transform of: :class:`cross_decomposition.PLSRegression`. :class:`cross_decomposition.PLSCanonical`, :class:`cross_decomposition.CCA`, @@ -225,11 +230,6 @@ Changelog `Y` will be removed in version 1.7. :pr:`28604` by :user:`David Leon `. -- |Fix| The `coef_` fitted attribute of :class:`cross_decomposition.PLSRegression` - now takes into account both the scale of `X` and `Y` when `scale=True`. Note that - the previous predicted values were not affected by this bug. - :pr:`28612` by :user:`Guillaume Lemaitre `. - :mod:`sklearn.datasets` ....................... @@ -245,7 +245,8 @@ Changelog :func:`datasets.fetch_rcv1`, and :func:`datasets.fetch_species_distributions`. By default, the functions will retry up to 3 times in case of network failures. - :pr:`28160` by :user:`Zhehao Liu ` and :user:`Filip Karlo Došilović `. + :pr:`28160` by :user:`Zhehao Liu ` and + :user:`Filip Karlo Došilović `. :mod:`sklearn.decomposition` ............................ @@ -350,13 +351,8 @@ Changelog - |Fix| :class:`linear_model.ElasticNet`, :class:`linear_model.ElasticNetCV`, :class:`linear_model.Lasso` and :class:`linear_model.LassoCV` now explicitly don't - accept large sparse data formats. :pr:`27576` by :user:`Stefanie Senger - `. - -- |API| :class:`linear_model.RidgeCV` and :class:`linear_model.RidgeClassifierCV` - will now allow `alpha=0` when `cv != None`, which is consistent with - :class:`linear_model.Ridge` and :class:`linear_model.RidgeClassifier`. - :pr:`28425` by :user:`Lucy Liu `. + accept large sparse data formats. + :pr:`27576` by :user:`Stefanie Senger `. - |Fix| :class:`linear_model.RidgeCV` and :class:`RidgeClassifierCV` correctly pass `sample_weight` to the underlying scorer when `cv` is None. @@ -366,6 +362,11 @@ Changelog will now always be `None` when `tol` is set, as `n_nonzero_coefs` is ignored in this case. :pr:`28557` by :user:`Lucy Liu `. +- |API| :class:`linear_model.RidgeCV` and :class:`linear_model.RidgeClassifierCV` + will now allow `alpha=0` when `cv != None`, which is consistent with + :class:`linear_model.Ridge` and :class:`linear_model.RidgeClassifier`. + :pr:`28425` by :user:`Lucy Liu `. + - |API| Passing `average=0` to disable averaging is deprecated in :class:`linear_model.PassiveAggressiveClassifier`, :class:`linear_model.PassiveAggressiveRegressor`, @@ -382,7 +383,8 @@ Changelog :pr:`28703` by :user:`Christian Lorentzen `. - |API| `store_cv_values` and `cv_values_` are deprecated in favor of - `store_cv_results` and `cv_results_` in `RidgeCV` and `RidgeClassifierCV`. + `store_cv_results` and `cv_results_` in `~linear_model.RidgeCV` and + `~linear_model.RidgeClassifierCV`. :pr:`28915` by :user:`Lucy Liu `. :mod:`sklearn.manifold` @@ -401,8 +403,15 @@ Changelog :pr:`27456` by :user:`Venkatachalam N `, :user:`Kshitij Mathur ` and :user:`Julian Libiseller-Egger `. +- |Feature| :func:`sklearn.metrics.check_scoring` now returns a multi-metric scorer + when `scoring` as a `dict`, `set`, `tuple`, or `list`. :pr:`28360` by `Thomas Fan`_. + +- |Feature| :func:`metrics.d2_log_loss_score` has been added which + calculates the D^2 score for the log loss. + :pr:`28351` by :user:`Omar Salman `. + - |Efficiency| Improve efficiency of functions :func:`~metrics.brier_score_loss`, - :func:`~metrics.calibration_curve`, :func:`~metrics.det_curve`, + :func:`~calibration.calibration_curve`, :func:`~metrics.det_curve`, :func:`~metrics.precision_recall_curve`, :func:`~metrics.roc_curve` when `pos_label` argument is specified. Also improve efficiency of methods `from_estimator` @@ -411,9 +420,6 @@ Changelog :class:`~calibration.CalibrationDisplay`. :pr:`28051` by :user:`Pierre de Fréminville `. -- |Feature| :func:`sklearn.metrics.check_scoring` now returns a multi-metric scorer - when `scoring` as a `dict`, `set`, `tuple`, or `list`. :pr:`28360` by `Thomas Fan`_. - - |Fix|:class:`metrics.classification_report` now shows only accuracy and not micro-average when input is a subset of labels. :pr:`28399` by :user:`Vineet Joshi `. @@ -422,8 +428,8 @@ Changelog computation. This is likely to affect neighbor-based algorithms. :pr:`28692` by :user:`Loïc Estève `. -- |API| :func:`metrics.precision_recall_curve` deprecated the keyword argument `probas_pred` - in favor of `y_score`. `probas_pred` will be removed in version 1.7. +- |API| :func:`metrics.precision_recall_curve` deprecated the keyword argument + `probas_pred` in favor of `y_score`. `probas_pred` will be removed in version 1.7. :pr:`28092` by :user:`Adam Li `. - |API| :func:`metrics.brier_score_loss` deprecated the keyword argument `y_prob` @@ -434,10 +440,6 @@ Changelog is deprecated and will raise an error in v1.7. :pr:`18555` by :user:`Kaushik Amar Das `. -- |Feature| :func:`metrics.d2_log_loss_score` has been added which - calculates the D^2 score for the log loss. - :pr:`28351` by :user:`Omar Salman `. - :mod:`sklearn.mixture` ...................... @@ -460,22 +462,22 @@ Changelog raises a warning when groups are passed in to :term:`split`. :pr:`28210` by `Thomas Fan`_. +- |Enhancement| The HTML diagram representation of + :class:`~model_selection.GridSearchCV`, + :class:`~model_selection.RandomizedSearchCV`, + :class:`~model_selection.HalvingGridSearchCV`, and + :class:`~model_selection.HalvingRandomSearchCV` will show the best estimator when + `refit=True`. :pr:`28722` by :user:`Yao Xiao ` and `Thomas Fan`_. + - |Fix| the ``cv_results_`` attribute (of :class:`model_selection.GridSearchCV`) now returns masked arrays of the appropriate NumPy dtype, as opposed to always returning dtype ``object``. :pr:`28352` by :user:`Marco Gorelli`. -- |Fix| :func:`sklearn.model_selection.train_test_score` works with Array API inputs. +- |Fix| :func:`model_selection.train_test_split` works with Array API inputs. Previously indexing was not handled correctly leading to exceptions when using strict implementations of the Array API like CuPY. :pr:`28407` by :user:`Tim Head `. -- |Enhancement| The HTML diagram representation of - :class:`~model_selection.GridSearchCV`, - :class:`~model_selection.RandomizedSearchCV`, - :class:`~model_selection.HalvingGridSearchCV`, and - :class:`~model_selection.HalvingRandomSearchCV` will show the best estimator when - `refit=True`. :pr:`28722` by :user:`Yao Xiao ` and `Thomas Fan`_. - :mod:`sklearn.multioutput` .......................... @@ -518,6 +520,10 @@ Changelog :mod:`sklearn.utils` .................... +- |Fix| :func:`~utils._safe_indexing` now works correctly for polars DataFrame when + `axis=0` and supports indexing polars Series. + :pr:`28521` by :user:`Yao Xiao `. + - |API| :data:`utils.IS_PYPY` is deprecated and will be removed in version 1.7. :pr:`28768` by :user:`Jérémie du Boisberranger `. @@ -529,15 +535,11 @@ Changelog `joblib.register_parallel_backend` instead. :pr:`28847` by :user:`Jérémie du Boisberranger `. -- |API| Raise informative warning message in :func:`type_of_target` when - represented as bytes. For classifiers and classification metrics, labels encoded +- |API| Raise informative warning message in :func:`~utils.multiclass.type_of_target` + when represented as bytes. For classifiers and classification metrics, labels encoded as bytes is deprecated and will raise an error in v1.7. :pr:`18555` by :user:`Kaushik Amar Das `. -- |Fix| :func:`~utils._safe_indexing` now works correctly for polars DataFrame when - `axis=0` and supports indexing polars Series. - :pr:`28521` by :user:`Yao Xiao `. - - |API| :func:`utils.estimator_checks.check_estimator_sparse_data` was split into two functions: :func:`utils.estimator_checks.check_estimator_sparse_matrix` and :func:`utils.estimator_checks.check_estimator_sparse_array`. @@ -548,4 +550,29 @@ Changelog Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.4, including: -TODO: update at the time of the release. +101AlexMartin, Abdulaziz Aloqeely, Adam J. Stewart, Adam Li, Adarsh Wase, Adrin +Jalali, Advik Sinha, Akash Srivastava, Akihiro Kuno, Alan Guedes, Alexis +IMBERT, Ana Paula Gomes, Anderson Nelson, Andrei Dzis, Arnaud Capitaine, Arturo +Amor, Aswathavicky, Bharat Raghunathan, Brendan Lu, Bruno, Cemlyn, Christian +Lorentzen, Christian Veenhuis, Cindy Liang, Claudio Salvatore Arcidiacono, +Connor Boyle, Conrad Stevens, crispinlogan, davidleon123, DerWeh, Dipan Banik, +Duarte São José, DUONG, Eddie Bergman, Edoardo Abati, Egehan Gunduz, Emad +Izadifar, Erich Schubert, Filip Karlo Došilović, Franck Charras, Gael +Varoquaux, Gönül Aycı, Guillaume Lemaitre, Gyeongjae Choi, Harmanan Kohli, +Hong Xiang Yue, Ian Faust, itsaphel, Ivan Wiryadi, Jack Bowyer, Javier Marin +Tur, Jérémie du Boisberranger, Jérôme Dockès, Jiawei Zhang, Joel Nothman, +Johanna Bayer, John Cant, John Hopfensperger, jpcars, jpienaar-tuks, Julian +Libiseller-Egger, Julien Jerphanion, KanchiMoe, Kaushik Amar Das, keyber, +Koustav Ghosh, kraktus, Krsto Proroković, ldwy4, LeoGrin, lihaitao, Linus +Sommer, Loic Esteve, Lucy Liu, Lukas Geiger, manasimj, Manuel Labbé, Manuel +Morales, Marco Edward Gorelli, Maren Westermann, Marija Vlajic, Mark Elliot, +Mateusz Sokół, Mavs, Michael Higgins, Michael Mayer, miguelcsilva, Miki +Watanabe, Mohammed Hamdy, myenugula, Nathan Goldbaum, Naziya Mahimkar, Neto, +Olivier Grisel, Omar Salman, Patrick Wang, Pierre de Fréminville, Priyash +Shah, Puneeth K, Rahil Parikh, raisadz, Raj Pulapakura, Ralf Gommers, Ralph +Urlus, Randolf Scholz, Reshama Shaikh, Richard Barnes, Rodrigo Romero, Saad +Mahmood, Salim Dohri, Sandip Dutta, SarahRemus, scikit-learn-bot, Shaharyar +Choudhry, Shubham, sperret6, Stefanie Senger, Suha Siddiqui, Thanh Lam DANG, +thebabush, Thomas J. Fan, Thomas Lazarus, Thomas Li, Tialo, Tim Head, Tuhin +Sharma, VarunChaduvula, Vineet Joshi, virchan, Waël Boukhobza, Weyb, Will +Dean, Xavier Beltran, Xiao Yuan, Xuefeng Xu, Yao Xiao From b51d0c9648241d1fd53dc9151689f62a61633a3d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?= Date: Tue, 21 May 2024 15:59:29 +0200 Subject: [PATCH 044/275] trigger whell builder [cd build] From f83f9bf663815bd29b3e9cd67a5e95b460406f09 Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Mon, 20 May 2024 19:36:54 +0200 Subject: [PATCH 045/275] DOC use pydata-sphinx-theme for the website (#29038) Co-authored-by: Yao Xiao <108576690+Charlie-XIAO@users.noreply.github.com> Co-authored-by: Thomas J. Fan Co-authored-by: Guillaume Lemaitre --- .gitignore | 4 + .pre-commit-config.yaml | 7 + build_tools/azure/pypy3_linux-64_conda.lock | 6 +- build_tools/circle/build_doc.sh | 8 +- build_tools/circle/doc_environment.yml | 4 + build_tools/circle/doc_linux-64_conda.lock | 13 +- .../doc_min_dependencies_environment.yml | 14 +- .../doc_min_dependencies_linux-64_conda.lock | 25 +- build_tools/circle/list_versions.py | 64 +- .../update_environments_and_lock_files.py | 24 +- doc/Makefile | 8 + doc/about.rst | 656 +- doc/api/deprecated.rst.template | 24 + doc/api/index.rst.template | 77 + doc/api/module.rst.template | 46 + doc/api_reference.py | 1336 ++++ doc/common_pitfalls.rst | 71 +- doc/computing.rst | 6 - doc/computing/computational_performance.rst | 4 - doc/computing/parallelism.rst | 4 - doc/computing/scaling_strategies.rst | 4 - doc/conf.py | 401 +- doc/contents.rst | 24 - doc/css/.gitkeep | 0 doc/data_transforms.rst | 6 - doc/datasets.rst | 6 - doc/datasets/loading_other_datasets.rst | 23 +- doc/datasets/real_world.rst | 4 - doc/datasets/sample_generators.rst | 4 - doc/datasets/toy_dataset.rst | 4 - doc/developers/contributing.rst | 520 +- doc/developers/index.rst | 7 - doc/developers/maintainer.rst | 5 +- doc/dispatching.rst | 6 - doc/faq.rst | 46 +- doc/images/ml_map.png | Bin 761071 -> 0 bytes doc/images/ml_map.svg | 4 + doc/includes/big_toc_css.rst | 40 - doc/includes/bigger_toc_css.rst | 60 - doc/index.rst.template | 25 + doc/inspection.rst | 10 +- doc/install.rst | 301 +- doc/js/scripts/api-search.js | 12 + doc/js/scripts/dropdown.js | 61 + doc/js/scripts/vendor/svg-pan-zoom.min.js | 31 + doc/js/scripts/version-switcher.js | 40 + doc/make.bat | 27 +- doc/min_dependency_substitutions.rst.template | 3 + doc/min_dependency_table.rst.template | 13 + doc/model_persistence.rst | 142 +- doc/model_selection.rst | 6 - doc/modules/array_api.rst | 4 - doc/modules/biclustering.rst | 42 +- doc/modules/calibration.rst | 96 +- doc/modules/classes.rst | 1916 ------ doc/modules/clustering.rst | 1015 ++- doc/modules/compose.rst | 215 +- doc/modules/covariance.rst | 103 +- doc/modules/cross_decomposition.rst | 70 +- doc/modules/cross_validation.rst | 140 +- doc/modules/decomposition.rst | 426 +- doc/modules/density.rst | 47 +- doc/modules/ensemble.rst | 671 +- doc/modules/feature_extraction.rst | 481 +- doc/modules/feature_selection.rst | 145 +- doc/modules/gaussian_process.rst | 225 +- doc/modules/grid_search.rst | 83 +- doc/modules/impute.rst | 12 +- doc/modules/isotonic.rst | 4 +- doc/modules/kernel_approximation.rst | 70 +- doc/modules/kernel_ridge.rst | 10 +- doc/modules/lda_qda.rst | 34 +- doc/modules/learning_curve.rst | 8 +- doc/modules/linear_model.rst | 890 ++- doc/modules/manifold.rst | 586 +- doc/modules/metrics.rst | 20 +- doc/modules/mixture.rst | 278 +- doc/modules/model_evaluation.rst | 863 ++- doc/modules/multiclass.rst | 35 +- doc/modules/naive_bayes.rst | 114 +- doc/modules/neighbors.rst | 375 +- doc/modules/neural_networks_supervised.rst | 180 +- doc/modules/neural_networks_unsupervised.rst | 22 +- doc/modules/outlier_detection.rst | 110 +- doc/modules/partial_dependence.rst | 63 +- doc/modules/permutation_importance.rst | 92 +- doc/modules/preprocessing.rst | 387 +- doc/modules/random_projection.rst | 57 +- doc/modules/semi_supervised.rst | 40 +- doc/modules/sgd.rst | 169 +- doc/modules/svm.rst | 337 +- doc/modules/tree.rst | 278 +- doc/modules/unsupervised_reduction.rst | 14 +- doc/preface.rst | 32 - doc/scss/api-search.scss | 114 + doc/scss/api.scss | 52 + doc/scss/colors.scss | 51 + doc/scss/custom.scss | 192 + doc/scss/index.scss | 175 + doc/scss/install.scss | 33 + doc/sphinxext/add_toctree_functions.py | 160 - doc/sphinxext/autoshortsummary.py | 53 + doc/sphinxext/dropdown_anchors.py | 78 + doc/sphinxext/move_gallery_links.py | 193 + doc/sphinxext/override_pst_pagetoc.py | 84 + doc/supervised_learning.rst | 6 - doc/templates/base.rst | 36 + doc/templates/class.rst | 17 - doc/templates/class_with_call.rst | 21 - doc/templates/deprecated_class.rst | 28 - doc/templates/deprecated_class_with_call.rst | 29 - .../deprecated_class_without_init.rst | 24 - doc/templates/deprecated_function.rst | 24 - doc/templates/display_all_class_methods.rst | 19 - doc/templates/display_only_from_estimator.rst | 18 - doc/templates/function.rst | 17 - doc/templates/generate_deprecated.sh | 8 - doc/templates/index.html | 369 +- doc/testimonials/testimonials.rst | 1285 ++-- .../scikit-learn-modern/javascript.html | 56 - doc/themes/scikit-learn-modern/layout.html | 150 - doc/themes/scikit-learn-modern/nav.html | 102 - doc/themes/scikit-learn-modern/search.html | 8 - .../scikit-learn-modern/static/css/theme.css | 1412 ---- .../static/css/vendor/bootstrap.min.css | 6 - .../static/js/details-permalink.js | 47 - .../static/js/vendor/bootstrap.min.js | 6 - .../static/js/vendor/jquery-3.6.3.slim.min.js | 2 - doc/themes/scikit-learn-modern/theme.conf | 10 - doc/tune_toc.rst | 131 - doc/tutorial/index.rst | 12 - .../machine_learning_map/ML_MAPS_README.txt | 93 - doc/tutorial/machine_learning_map/README.md | 17 + doc/tutorial/machine_learning_map/index.rst | 102 +- .../machine_learning_map/parse_path.py | 192 - .../machine_learning_map/pyparsing.py | 5715 ----------------- .../machine_learning_map/svg2imagemap.py | 111 - doc/tutorial/statistical_inference/index.rst | 10 +- .../statistical_inference/model_selection.rst | 67 +- .../supervised_learning.rst | 11 +- doc/unsupervised_learning.rst | 6 - doc/user_guide.rst | 12 - doc/visualizations.rst | 16 +- doc/whats_new/_contributors.rst | 12 +- doc/whats_new/older_versions.rst | 1 - examples/README.txt | 5 + .../applications/plot_digits_denoising.py | 10 +- .../covariance/plot_mahalanobis_distances.py | 18 +- examples/ensemble/plot_adaboost_multiclass.py | 22 +- examples/ensemble/plot_hgbt_regression.py | 11 +- examples/gaussian_process/plot_gpr_co2.py | 11 +- .../inspection/plot_permutation_importance.py | 6 +- examples/linear_model/plot_lasso_lars_ic.py | 10 +- .../plot_cost_sensitive_learning.py | 24 +- .../model_selection/plot_grid_search_stats.py | 48 +- .../plot_nested_cross_validation_iris.py | 12 +- ...ot_permutation_tests_for_classification.py | 10 +- .../plot_release_highlights_1_1_0.py | 4 +- .../plot_release_highlights_1_3_0.py | 8 +- pyproject.toml | 16 +- setup.cfg | 2 +- sklearn/__init__.py | 5 +- sklearn/_min_dependencies.py | 12 +- sklearn/base.py | 2 +- sklearn/calibration.py | 2 +- sklearn/cluster/__init__.py | 5 +- sklearn/compose/__init__.py | 6 +- sklearn/covariance/__init__.py | 11 +- sklearn/covariance/_shrunk_covariance.py | 4 +- sklearn/cross_decomposition/__init__.py | 2 + sklearn/datasets/__init__.py | 6 +- sklearn/datasets/descr/breast_cancer.rst | 28 +- sklearn/datasets/descr/california_housing.rst | 6 +- sklearn/datasets/descr/digits.rst | 28 +- sklearn/datasets/descr/iris.rst | 36 +- sklearn/datasets/descr/kddcup99.rst | 18 +- sklearn/datasets/descr/lfw.rst | 136 +- sklearn/datasets/descr/linnerud.rst | 10 +- sklearn/datasets/descr/rcv1.rst | 8 +- .../datasets/descr/species_distributions.rst | 8 +- sklearn/datasets/descr/twenty_newsgroups.rst | 396 +- sklearn/datasets/descr/wine_data.rst | 42 +- sklearn/decomposition/__init__.py | 8 +- sklearn/decomposition/_kernel_pca.py | 6 +- sklearn/discriminant_analysis.py | 4 +- sklearn/dummy.py | 2 + sklearn/ensemble/__init__.py | 5 +- sklearn/exceptions.py | 5 +- sklearn/experimental/__init__.py | 10 +- sklearn/feature_extraction/__init__.py | 6 +- sklearn/feature_extraction/image.py | 5 +- sklearn/feature_extraction/text.py | 6 +- sklearn/feature_selection/__init__.py | 8 +- sklearn/gaussian_process/__init__.py | 7 +- sklearn/gaussian_process/kernels.py | 5 +- sklearn/impute/__init__.py | 2 +- sklearn/inspection/__init__.py | 2 +- sklearn/isotonic.py | 2 + sklearn/kernel_approximation.py | 6 +- sklearn/kernel_ridge.py | 2 +- sklearn/linear_model/__init__.py | 4 +- sklearn/linear_model/_least_angle.py | 8 +- sklearn/manifold/__init__.py | 4 +- sklearn/metrics/__init__.py | 5 +- sklearn/metrics/cluster/__init__.py | 9 +- sklearn/metrics/cluster/_supervised.py | 4 +- sklearn/metrics/pairwise.py | 2 + sklearn/mixture/__init__.py | 4 +- sklearn/model_selection/__init__.py | 2 + sklearn/multiclass.py | 11 +- sklearn/multioutput.py | 3 +- sklearn/naive_bayes.py | 6 +- sklearn/neighbors/__init__.py | 5 +- sklearn/neighbors/_binary_tree.pxi.tp | 5 +- sklearn/neural_network/__init__.py | 5 +- sklearn/pipeline.py | 5 +- sklearn/preprocessing/__init__.py | 5 +- sklearn/random_projection.py | 7 +- sklearn/semi_supervised/__init__.py | 9 +- sklearn/svm/__init__.py | 4 +- sklearn/tree/__init__.py | 5 +- sklearn/utils/__init__.py | 4 +- sklearn/utils/arrayfuncs.pyx | 5 +- sklearn/utils/class_weight.py | 5 +- sklearn/utils/discovery.py | 5 +- sklearn/utils/estimator_checks.py | 5 +- sklearn/utils/extmath.py | 5 +- sklearn/utils/graph.py | 4 +- sklearn/utils/metadata_routing.py | 5 +- sklearn/utils/metaestimators.py | 4 +- sklearn/utils/multiclass.py | 6 +- sklearn/utils/parallel.py | 4 +- sklearn/utils/random.py | 4 +- sklearn/utils/sparsefuncs.py | 5 +- sklearn/utils/sparsefuncs_fast.pyx | 5 +- sklearn/utils/validation.py | 5 +- 236 files changed, 9384 insertions(+), 18266 deletions(-) create mode 100644 doc/api/deprecated.rst.template create mode 100644 doc/api/index.rst.template create mode 100644 doc/api/module.rst.template create mode 100644 doc/api_reference.py delete mode 100644 doc/contents.rst create mode 100644 doc/css/.gitkeep delete mode 100644 doc/images/ml_map.png create mode 100644 doc/images/ml_map.svg delete mode 100644 doc/includes/big_toc_css.rst delete mode 100644 doc/includes/bigger_toc_css.rst create mode 100644 doc/index.rst.template create mode 100644 doc/js/scripts/api-search.js create mode 100644 doc/js/scripts/dropdown.js create mode 100644 doc/js/scripts/vendor/svg-pan-zoom.min.js create mode 100644 doc/js/scripts/version-switcher.js create mode 100644 doc/min_dependency_substitutions.rst.template create mode 100644 doc/min_dependency_table.rst.template delete mode 100644 doc/modules/classes.rst delete mode 100644 doc/preface.rst create mode 100644 doc/scss/api-search.scss create mode 100644 doc/scss/api.scss create mode 100644 doc/scss/colors.scss create mode 100644 doc/scss/custom.scss create mode 100644 doc/scss/index.scss create mode 100644 doc/scss/install.scss delete mode 100644 doc/sphinxext/add_toctree_functions.py create mode 100644 doc/sphinxext/autoshortsummary.py create mode 100644 doc/sphinxext/dropdown_anchors.py create mode 100644 doc/sphinxext/move_gallery_links.py create mode 100644 doc/sphinxext/override_pst_pagetoc.py create mode 100644 doc/templates/base.rst delete mode 100644 doc/templates/class.rst delete mode 100644 doc/templates/class_with_call.rst delete mode 100644 doc/templates/deprecated_class.rst delete mode 100644 doc/templates/deprecated_class_with_call.rst delete mode 100644 doc/templates/deprecated_class_without_init.rst delete mode 100644 doc/templates/deprecated_function.rst delete mode 100644 doc/templates/display_all_class_methods.rst delete mode 100644 doc/templates/display_only_from_estimator.rst delete mode 100644 doc/templates/function.rst delete mode 100755 doc/templates/generate_deprecated.sh delete mode 100644 doc/themes/scikit-learn-modern/javascript.html delete mode 100644 doc/themes/scikit-learn-modern/layout.html delete mode 100644 doc/themes/scikit-learn-modern/nav.html delete mode 100644 doc/themes/scikit-learn-modern/search.html delete mode 100644 doc/themes/scikit-learn-modern/static/css/theme.css delete mode 100644 doc/themes/scikit-learn-modern/static/css/vendor/bootstrap.min.css delete mode 100644 doc/themes/scikit-learn-modern/static/js/details-permalink.js delete mode 100644 doc/themes/scikit-learn-modern/static/js/vendor/bootstrap.min.js delete mode 100644 doc/themes/scikit-learn-modern/static/js/vendor/jquery-3.6.3.slim.min.js delete mode 100644 doc/themes/scikit-learn-modern/theme.conf delete mode 100644 doc/tune_toc.rst delete mode 100644 doc/tutorial/machine_learning_map/ML_MAPS_README.txt create mode 100644 doc/tutorial/machine_learning_map/README.md delete mode 100644 doc/tutorial/machine_learning_map/parse_path.py delete mode 100644 doc/tutorial/machine_learning_map/pyparsing.py delete mode 100644 doc/tutorial/machine_learning_map/svg2imagemap.py diff --git a/.gitignore b/.gitignore index 9f3b453bbfd74..61c89bcb96491 100644 --- a/.gitignore +++ b/.gitignore @@ -15,9 +15,13 @@ dist/ MANIFEST doc/sg_execution_times.rst doc/_build/ +doc/api/*.rst doc/auto_examples/ +doc/css/* +!doc/css/.gitkeep doc/modules/generated/ doc/datasets/generated/ +doc/index.rst doc/min_dependency_table.rst doc/min_dependency_substitutions.rst *.pdf diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 31af43b6bbab0..abe14acc7778c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -27,3 +27,10 @@ repos: # TODO: add the double-quote-cython-strings hook when it's usability has improved: # possibility to pass a directory and use it as a check instead of auto-formatter. - id: cython-lint +- repo: https://github.com/pre-commit/mirrors-prettier + rev: v2.7.1 + hooks: + - id: prettier + files: ^doc/scss/|^doc/js/scripts/ + exclude: ^doc/js/scripts/vendor/ + types_or: ["scss", "javascript"] diff --git a/build_tools/azure/pypy3_linux-64_conda.lock b/build_tools/azure/pypy3_linux-64_conda.lock index ab6a908edf340..520a4935c8af5 100644 --- a/build_tools/azure/pypy3_linux-64_conda.lock +++ b/build_tools/azure/pypy3_linux-64_conda.lock @@ -97,7 +97,7 @@ https://conda.anaconda.org/conda-forge/linux-64/scipy-1.12.0-py39h6dedee3_2.cond https://conda.anaconda.org/conda-forge/linux-64/blas-2.122-openblas.conda#5065468105542a8b23ea47bd8b6fa55f https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.0-pyhd8ed1ab_0.conda#dcbadab7a68738a028e195ab68ab2d2e https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 -https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.1.0-py39h5fd064f_0.conda#04676d2a49da3cb608af77e04b796ce1 +https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.1.0-py39h3c335be_1.conda#7278eb55a7e97a0ba2376a6c608e7c46 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b -https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.8.4-py39h4e7d633_0.conda#58272019e595dde98d0844ae3ebf0cfe -https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.8.4-py39h4162558_0.conda#b0f7702a174422ff1db58190495fd766 +https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.8.4-py39h6fb8a73_2.conda#3212f51613e10b3ee319f3f2bf8ee5a8 +https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.8.4-py39h4162558_2.conda#05babd7bae196648bfc6b7e3d9ea7630 diff --git a/build_tools/circle/build_doc.sh b/build_tools/circle/build_doc.sh index 35fee3ae50b65..c569f4913d4d8 100755 --- a/build_tools/circle/build_doc.sh +++ b/build_tools/circle/build_doc.sh @@ -190,17 +190,13 @@ export OMP_NUM_THREADS=1 if [[ "$CIRCLE_BRANCH" =~ ^main$ && -z "$CI_PULL_REQUEST" ]] then # List available documentation versions if on main - python build_tools/circle/list_versions.py > doc/versions.rst + python build_tools/circle/list_versions.py --json doc/js/versions.json --rst doc/versions.rst fi # The pipefail is requested to propagate exit code set -o pipefail && cd doc && make $make_args 2>&1 | tee ~/log.txt -# Insert the version warning for deployment -find _build/html/stable -name "*.html" | xargs sed -i '/<\/body>/ i \ -\ ' - cd - set +o pipefail @@ -244,7 +240,7 @@ then ( echo '
      ' echo "$affected" | sed 's|.*|
    • & [dev, stable]
      • |' - echo '

    General: Home | API Reference | Examples

    ' + echo '

General: Home | API Reference | Examples

' echo 'Sphinx Warnings in affected files
    ' echo "$warnings" | sed 's/\/home\/circleci\/project\//
  • /g' echo '
' diff --git a/build_tools/circle/doc_environment.yml b/build_tools/circle/doc_environment.yml index 4df22341635a3..bc4405983a1b6 100644 --- a/build_tools/circle/doc_environment.yml +++ b/build_tools/circle/doc_environment.yml @@ -33,7 +33,11 @@ dependencies: - polars - pooch - sphinxext-opengraph + - sphinx-remove-toctrees + - sphinx-design + - pydata-sphinx-theme - pip - pip: - jupyterlite-sphinx - jupyterlite-pyodide-kernel + - sphinxcontrib-sass diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index 34ec64ad5863b..1435bebedb865 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: b57888763997b08b2f240b5ff1ed6afcf88685f3d8c791ea8eba4d80483c43d0 +# input_hash: beab3d7262ec74c4ef8c9050098de8b9fe7910606e7bd4ff52687972bff35868 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.2.2-hbcca054_0.conda#2f4327a1cbe7f022401b236e915a5fef @@ -177,6 +177,7 @@ https://conda.anaconda.org/conda-forge/noarch/pytz-2024.1-pyhd8ed1ab_0.conda#3ee https://conda.anaconda.org/conda-forge/noarch/setuptools-69.5.1-pyhd8ed1ab_0.conda#7462280d81f639363e6e63c81276bd9e https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2 https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-2.2.0-pyhd8ed1ab_0.tar.bz2#4d22a9315e78c6827f806065957d566e +https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.5-pyhd8ed1ab_1.conda#3f144b2c34f8cb5a9abd9ed23a39c561 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_0.conda#da1d979339e2714c30a8e806a33ec087 https://conda.anaconda.org/conda-forge/noarch/tabulate-0.9.0-pyhd8ed1ab_1.tar.bz2#4759805cce2d914c38472f70bf4d8bcb https://conda.anaconda.org/conda-forge/noarch/tenacity-8.3.0-pyhd8ed1ab_0.conda#216cfa8e32bcd1447646768351df6059 @@ -192,7 +193,9 @@ https://conda.anaconda.org/conda-forge/linux-64/xkeyboard-config-2.41-hd590300_0 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.4-h0b41bf4_2.conda#82b6df12252e6f32402b96dacc656fec https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.11-hd590300_0.conda#ed67c36f215b310412b2af935bf3e530 https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda#2e4d6bc0b14e10f895fc6791a7d9b26a +https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.4-pyhd8ed1ab_0.conda#46a2e6e3dfa718ce3492018d5a110dd6 https://conda.anaconda.org/conda-forge/noarch/babel-2.14.0-pyhd8ed1ab_0.conda#9669586875baeced8fc30c0826c3270e +https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.12.3-pyha770c72_0.conda#332493000404d8411859539a5a630865 https://conda.anaconda.org/conda-forge/linux-64/brunsli-0.1-h9c3ff4c_0.tar.bz2#c1ac6229d0bfd14f8354ff9ad2a26cad https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-h3faef2a_0.conda#f907bb958910dc404647326ca80c263e https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.7.0-h00ab1b0_1.conda#28de2e073db9ca9b72858bee9fb6f571 @@ -217,6 +220,7 @@ https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1a https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb +https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.11.0-hd8ed1ab_0.conda#471e3988f8ca5e9eb3ce6be7eac3bcee https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.1-pyhd8ed1ab_0.conda#08807a87fa7af10754d46f63b368e016 https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_1.conda#d8d07866ac3b5b6937213c89a1874f08 https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.3-haf2f30d_0.conda#f3df87cc9ef0b5113bff55aefcbcafd5 @@ -254,9 +258,12 @@ https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_2.c https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.8.4-py39hf3d152e_0.conda#c66d2da2669fddc657b679bccab95775 https://conda.anaconda.org/conda-forge/noarch/seaborn-0.13.2-hd8ed1ab_2.conda#a79d8797f62715255308d92d3a91ef2e https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.7.0-pyhd8ed1ab_0.conda#1ad3afced398492586ca1bef70328be4 +https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.15.2-pyhd8ed1ab_0.conda#ce99859070b0e17ccc63234ca58f3ed8 https://conda.anaconda.org/conda-forge/noarch/sphinx-copybutton-0.5.2-pyhd8ed1ab_0.conda#ac832cc43adc79118cf6e23f1f9b8995 +https://conda.anaconda.org/conda-forge/noarch/sphinx-design-0.5.0-pyhd8ed1ab_0.conda#264b3c697fa9cdade87eb0abe4440d54 https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.16.0-pyhd8ed1ab_0.conda#add28691ee89e875b190eda07929d5d4 https://conda.anaconda.org/conda-forge/noarch/sphinx-prompt-1.4.0-pyhd8ed1ab_0.tar.bz2#88ee91e8679603f2a5bd036d52919cc2 +https://conda.anaconda.org/conda-forge/noarch/sphinx-remove-toctrees-1.0.0.post1-pyhd8ed1ab_0.conda#6dee8412218288a17f99f2cfffab334d https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-1.0.8-pyhd8ed1ab_0.conda#611a35a27914fac3aa37611a6fe40bb5 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-1.0.6-pyhd8ed1ab_0.conda#d7e4954df0d3aea2eacc7835ad12671d https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.0.5-pyhd8ed1ab_0.conda#7e1e7437273682ada2ed5e9e9714b140 @@ -272,6 +279,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip json5 @ https://files.pythonhosted.org/packages/8a/3c/4f8791ee53ab9eeb0b022205aa79387119a74cc9429582ce04098e6fc540/json5-0.9.25-py3-none-any.whl#sha256=34ed7d834b1341a86987ed52f3f76cd8ee184394906b6e22a1e0deb9ab294e8f # pip jsonpointer @ https://files.pythonhosted.org/packages/12/f6/0232cc0c617e195f06f810534d00b74d2f348fe71b2118009ad8ad31f878/jsonpointer-2.4-py2.py3-none-any.whl#sha256=15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a # pip jupyterlab-pygments @ https://files.pythonhosted.org/packages/b1/dd/ead9d8ea85bf202d90cc513b533f9c363121c7792674f78e0d8a854b63b4/jupyterlab_pygments-0.3.0-py3-none-any.whl#sha256=841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780 +# pip libsass @ https://files.pythonhosted.org/packages/fd/5a/eb5b62641df0459a3291fc206cf5bd669c0feed7814dded8edef4ade8512/libsass-0.23.0-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=4a218406d605f325d234e4678bd57126a66a88841cb95bee2caeafdc6f138306 # pip mistune @ https://files.pythonhosted.org/packages/f0/74/c95adcdf032956d9ef6c89a9b8a5152bf73915f8c633f3e3d88d06bd699c/mistune-3.0.2-py3-none-any.whl#sha256=71481854c30fdbc938963d3605b72501f5c10a9320ecd412c121c163a1c7d205 # pip overrides @ https://files.pythonhosted.org/packages/2c/ab/fc8290c6a4c722e5514d80f62b2dc4c4df1a68a41d1364e625c35990fcf3/overrides-7.7.0-py3-none-any.whl#sha256=c7ed9d062f78b8e4c1a7b70bd8796b35ead4d9f510227ef9c5dc7626c60d7e49 # pip pandocfilters @ https://files.pythonhosted.org/packages/ef/af/4fbc8cab944db5d21b7e2a5b8e9211a03a79852b1157e2c102fcc61ac440/pandocfilters-1.5.1-py2.py3-none-any.whl#sha256=93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc @@ -285,7 +293,6 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip rpds-py @ https://files.pythonhosted.org/packages/97/b1/12238bd8cdf3cef71e85188af133399bfde1bddf319007361cc869d6f6a7/rpds_py-0.18.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=e4c39ad2f512b4041343ea3c7894339e4ca7839ac38ca83d68a832fc8b3748ab # pip send2trash @ https://files.pythonhosted.org/packages/40/b0/4562db6223154aa4e22f939003cb92514c79f3d4dccca3444253fd17f902/Send2Trash-1.8.3-py3-none-any.whl#sha256=0c31227e0bd08961c7665474a3d1ef7193929fedda4233843689baa056be46c9 # pip sniffio @ https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl#sha256=2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2 -# pip soupsieve @ https://files.pythonhosted.org/packages/4c/f3/038b302fdfbe3be7da016777069f26ceefe11a681055ea1f7817546508e3/soupsieve-2.5-py3-none-any.whl#sha256=eaa337ff55a1579b6549dc679565eac1e3d000563bcb1c8ab0d0fefbc0c2cdc7 # pip traitlets @ https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl#sha256=b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f # pip types-python-dateutil @ https://files.pythonhosted.org/packages/c7/1b/af4f4c4f3f7339a4b7eb3c0ab13416db98f8ac09de3399129ee5fdfa282b/types_python_dateutil-2.9.0.20240316-py3-none-any.whl#sha256=6b8cb66d960771ce5ff974e9dd45e38facb81718cc1e208b10b1baccbfdbee3b # pip uri-template @ https://files.pythonhosted.org/packages/e7/00/3fca040d7cf8a32776d3d81a00c8ee7457e00f80c649f1e4a863c8321ae9/uri_template-1.3.0-py3-none-any.whl#sha256=a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363 @@ -294,13 +301,13 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip websocket-client @ https://files.pythonhosted.org/packages/5a/84/44687a29792a70e111c5c477230a72c4b957d88d16141199bf9acb7537a3/websocket_client-1.8.0-py3-none-any.whl#sha256=17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526 # pip anyio @ https://files.pythonhosted.org/packages/14/fd/2f20c40b45e4fb4324834aea24bd4afdf1143390242c0b33774da0e2e34f/anyio-4.3.0-py3-none-any.whl#sha256=048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8 # pip arrow @ https://files.pythonhosted.org/packages/f8/ed/e97229a566617f2ae958a6b13e7cc0f585470eac730a73e9e82c32a3cdd2/arrow-1.3.0-py3-none-any.whl#sha256=c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80 -# pip beautifulsoup4 @ https://files.pythonhosted.org/packages/b1/fe/e8c672695b37eecc5cbf43e1d0638d88d66ba3a44c4d321c796f4e59167f/beautifulsoup4-4.12.3-py3-none-any.whl#sha256=b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed # pip bleach @ https://files.pythonhosted.org/packages/ea/63/da7237f805089ecc28a3f36bca6a21c31fcbc2eb380f3b8f1be3312abd14/bleach-6.1.0-py3-none-any.whl#sha256=3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6 # pip cffi @ https://files.pythonhosted.org/packages/ea/ac/e9e77bc385729035143e54cc8c4785bd480eaca9df17565963556b0b7a93/cffi-1.16.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=8f8e709127c6c77446a8c0a8c8bf3c8ee706a06cd44b1e827c3e6a2ee6b8c098 # pip doit @ https://files.pythonhosted.org/packages/44/83/a2960d2c975836daa629a73995134fd86520c101412578c57da3d2aa71ee/doit-0.36.0-py3-none-any.whl#sha256=ebc285f6666871b5300091c26eafdff3de968a6bd60ea35dd1e3fc6f2e32479a # pip jupyter-core @ https://files.pythonhosted.org/packages/c9/fb/108ecd1fe961941959ad0ee4e12ee7b8b1477247f30b1fdfd83ceaf017f0/jupyter_core-5.7.2-py3-none-any.whl#sha256=4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409 # pip referencing @ https://files.pythonhosted.org/packages/b7/59/2056f61236782a2c86b33906c025d4f4a0b17be0161b63b70fd9e8775d36/referencing-0.35.1-py3-none-any.whl#sha256=eda6d3234d62814d1c64e305c1331c9a3a6132da475ab6382eaa997b21ee75de # pip rfc3339-validator @ https://files.pythonhosted.org/packages/7b/44/4e421b96b67b2daff264473f7465db72fbdf36a07e05494f50300cc7b0c6/rfc3339_validator-0.1.4-py2.py3-none-any.whl#sha256=24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa +# pip sphinxcontrib-sass @ https://files.pythonhosted.org/packages/2e/87/7c2eb08e3ca1d6baae32c0a5e005330fe1cec93a36aa085e714c3b3a3c7d/sphinxcontrib_sass-0.3.4-py2.py3-none-any.whl#sha256=a0c79a44ae8b8935c02dc340ebe40c9e002c839331201c899dc93708970c355a # pip terminado @ https://files.pythonhosted.org/packages/6a/9e/2064975477fdc887e47ad42157e214526dcad8f317a948dee17e1659a62f/terminado-0.18.1-py3-none-any.whl#sha256=a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0 # pip tinycss2 @ https://files.pythonhosted.org/packages/2c/4d/0db5b8a613d2a59bbc29bc5bb44a2f8070eb9ceab11c50d477502a8a0092/tinycss2-1.3.0-py3-none-any.whl#sha256=54a8dbdffb334d536851be0226030e9505965bb2f30f21a4a82c55fb2a80fae7 # pip argon2-cffi-bindings @ https://files.pythonhosted.org/packages/ec/f7/378254e6dd7ae6f31fe40c8649eea7d4832a42243acaf0f1fff9083b2bed/argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b746dba803a79238e925d9046a63aa26bf86ab2a2fe74ce6b009a1c3f5c8f2ae diff --git a/build_tools/circle/doc_min_dependencies_environment.yml b/build_tools/circle/doc_min_dependencies_environment.yml index 14f4485295455..8148ee330bb35 100644 --- a/build_tools/circle/doc_min_dependencies_environment.yml +++ b/build_tools/circle/doc_min_dependencies_environment.yml @@ -24,14 +24,18 @@ dependencies: - seaborn - memory_profiler - compilers - - sphinx=6.0.0 # min - - sphinx-gallery=0.15.0 # min + - sphinx=7.3.7 # min + - sphinx-gallery=0.16.0 # min - sphinx-copybutton=0.5.2 # min - numpydoc=1.2.0 # min - - sphinx-prompt=1.3.0 # min + - sphinx-prompt=1.4.0 # min - plotly=5.14.0 # min - polars=0.20.23 # min - - pooch + - pooch=1.6.0 # min + - sphinx-remove-toctrees=1.0.0.post1 # min + - sphinx-design=0.5.0 # min + - pydata-sphinx-theme=0.15.2 # min - pip - pip: - - sphinxext-opengraph==0.4.2 # min + - sphinxext-opengraph==0.9.1 # min + - sphinxcontrib-sass==0.3.4 # min diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index 043587152c63b..22023274e25ce 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 08b61aae27c59a8d35d008fa2f947440f3cbcbc41622112e33e68f90d69b621c +# input_hash: 6c9ff93ed18fe7c2e8387a4c3d7104701555959a32e797c1cb83593137afe155 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.2.2-hbcca054_0.conda#2f4327a1cbe7f022401b236e915a5fef @@ -110,6 +110,7 @@ https://conda.anaconda.org/conda-forge/linux-64/xcb-util-renderutil-0.3.9-hd5903 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-wm-0.4.1-h8ee46fc_1.conda#90108a432fb5c6150ccfee3f03388656 https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.9-h8ee46fc_0.conda#077b6e8ad6a3ddb741fce2496dd01bec https://conda.anaconda.org/conda-forge/noarch/alabaster-0.7.16-pyhd8ed1ab_0.conda#def531a3ac77b7fb8c21d17bb5d0badb +https://conda.anaconda.org/conda-forge/noarch/appdirs-1.4.4-pyh9f0ad1d_0.tar.bz2#5f095bc6454094e96f146491fd03633b https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py39h3d6467e_1.conda#c48418c8b35f1d59ae9ae1174812b40a https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.7.0-hd590300_1.conda#e9dffe1056994133616378309f932d77 https://conda.anaconda.org/conda-forge/noarch/certifi-2024.2.2-pyhd8ed1ab_0.conda#0876280e409658fc6f9e75d035960333 @@ -120,7 +121,7 @@ https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda#5cd86562580f274031ede6aa6aa24441 https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.10-py39h3d6467e_0.conda#76b5d215fb735a6dc43010ffbe78040e https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2#ecfff944ba3960ecb334b9a2663d708d -https://conda.anaconda.org/conda-forge/linux-64/docutils-0.19-py39hf3d152e_1.tar.bz2#adb733ec2ee669f6d010758d054da60f +https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_0.conda#e8cd5d629f65bdf0f3bb312cde14659e https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.0-pyhd8ed1ab_2.conda#8d652ea2ee8eaee02ed8dc820bc794aa https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_0.conda#15dda3cdbf330abfe9f555d22f66db46 https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.14.2-h14ed4e7_0.conda#0f69b688f52ff6da70bccb7ff7001d1d @@ -146,7 +147,6 @@ https://conda.anaconda.org/conda-forge/linux-64/markupsafe-2.1.5-py39hd1e30aa_0. https://conda.anaconda.org/conda-forge/noarch/networkx-3.2-pyhd8ed1ab_0.conda#cec8cc498664cc00a070676aa89e69a7 https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.2-h488ebb8_0.conda#7f2e286780f072ed750df46dc2631138 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https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-2.2.0-pyhd8ed1ab_0.tar.bz2#4d22a9315e78c6827f806065957d566e +https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.5-pyhd8ed1ab_1.conda#3f144b2c34f8cb5a9abd9ed23a39c561 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_0.conda#da1d979339e2714c30a8e806a33ec087 https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.12.0-h00ab1b0_0.conda#f1b776cff1b426e7e7461a8502a3b731 https://conda.anaconda.org/conda-forge/noarch/tenacity-8.3.0-pyhd8ed1ab_0.conda#216cfa8e32bcd1447646768351df6059 @@ -173,7 +174,9 @@ https://conda.anaconda.org/conda-forge/linux-64/xkeyboard-config-2.41-hd590300_0 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.4-h0b41bf4_2.conda#82b6df12252e6f32402b96dacc656fec https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.11-hd590300_0.conda#ed67c36f215b310412b2af935bf3e530 https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda#2e4d6bc0b14e10f895fc6791a7d9b26a +https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.4-pyhd8ed1ab_0.conda#46a2e6e3dfa718ce3492018d5a110dd6 https://conda.anaconda.org/conda-forge/noarch/babel-2.14.0-pyhd8ed1ab_0.conda#9669586875baeced8fc30c0826c3270e +https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.12.3-pyha770c72_0.conda#332493000404d8411859539a5a630865 https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-h3faef2a_0.conda#f907bb958910dc404647326ca80c263e https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.7.0-h00ab1b0_1.conda#28de2e073db9ca9b72858bee9fb6f571 https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.12.3-py39hd1e30aa_0.conda#dc0fb8e157c7caba4c98f1e1f9d2e5f4 @@ -196,6 +199,7 @@ https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1a https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb +https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.11.0-hd8ed1ab_0.conda#471e3988f8ca5e9eb3ce6be7eac3bcee https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.1-pyhd8ed1ab_0.conda#08807a87fa7af10754d46f63b368e016 https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_1.conda#d8d07866ac3b5b6937213c89a1874f08 https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.3-haf2f30d_0.conda#f3df87cc9ef0b5113bff55aefcbcafd5 @@ -212,7 +216,7 @@ https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.5.0-pyhd8ed1ab_0.co https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.3-h9ad1361_0.conda#8fb0e954c616bb0f9389efac4b4ed44b https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-22_linux64_mkl.conda#d6f942423116553f068b2f2d93ffea2e https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-22_linux64_mkl.conda#4edf2e7ce63920e4f539d12e32fb478e -https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.1-pyhd8ed1ab_0.conda#d15917f33140f8d2ac9ca44db7ec8a25 +https://conda.anaconda.org/conda-forge/noarch/pooch-1.6.0-pyhd8ed1ab_0.tar.bz2#6429e1d1091c51f626b5dcfdd38bf429 https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-client-17.0-hb77b528_0.conda#07f45f1be1c25345faddb8db0de8039b https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-22_linux64_mkl.conda#aa0a5a70e1c957d5911e76ac98e471e1 https://conda.anaconda.org/conda-forge/linux-64/numpy-1.19.5-py39hd249d9e_3.tar.bz2#0cf333996ebdeeba8d1c8c1c0ee9eff9 @@ -236,13 +240,18 @@ https://conda.anaconda.org/conda-forge/noarch/tifffile-2020.6.3-py_0.tar.bz2#1fb https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.17.2-py39hde0f152_4.tar.bz2#2a58a7e382317b03f023b2fddf40f8a1 https://conda.anaconda.org/conda-forge/noarch/seaborn-0.12.2-hd8ed1ab_0.conda#50847a47c07812f88581081c620f5160 https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.2-pyhd8ed1ab_0.tar.bz2#025ad7ca2c7f65007ab6b6f5d93a56eb +https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.15.2-pyhd8ed1ab_0.conda#ce99859070b0e17ccc63234ca58f3ed8 https://conda.anaconda.org/conda-forge/noarch/sphinx-copybutton-0.5.2-pyhd8ed1ab_0.conda#ac832cc43adc79118cf6e23f1f9b8995 -https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.15.0-pyhd8ed1ab_0.conda#1a49ca9515ef9a96edff2eea06143dc6 -https://conda.anaconda.org/conda-forge/noarch/sphinx-prompt-1.3.0-py_0.tar.bz2#9363002e2a134a287af4e32ff0f26cdc +https://conda.anaconda.org/conda-forge/noarch/sphinx-design-0.5.0-pyhd8ed1ab_0.conda#264b3c697fa9cdade87eb0abe4440d54 +https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.16.0-pyhd8ed1ab_0.conda#add28691ee89e875b190eda07929d5d4 +https://conda.anaconda.org/conda-forge/noarch/sphinx-prompt-1.4.0-pyhd8ed1ab_0.tar.bz2#88ee91e8679603f2a5bd036d52919cc2 +https://conda.anaconda.org/conda-forge/noarch/sphinx-remove-toctrees-1.0.0.post1-pyhd8ed1ab_0.conda#6dee8412218288a17f99f2cfffab334d https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-1.0.8-pyhd8ed1ab_0.conda#611a35a27914fac3aa37611a6fe40bb5 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-1.0.6-pyhd8ed1ab_0.conda#d7e4954df0d3aea2eacc7835ad12671d https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.0.5-pyhd8ed1ab_0.conda#7e1e7437273682ada2ed5e9e9714b140 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-1.0.7-pyhd8ed1ab_0.conda#26acae54b06f178681bfb551760f5dd1 -https://conda.anaconda.org/conda-forge/noarch/sphinx-6.0.0-pyhd8ed1ab_2.conda#ac1d3b55da1669ee3a56973054fd7efb +https://conda.anaconda.org/conda-forge/noarch/sphinx-7.3.7-pyhd8ed1ab_0.conda#7b1465205e28d75d2c0e1a868ee00a67 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda#e507335cb4ca9cff4c3d0fa9cdab255e -# pip sphinxext-opengraph @ https://files.pythonhosted.org/packages/50/ac/c105ed3e0a00b14b28c0aa630935af858fd8a32affeff19574b16e2c6ae8/sphinxext_opengraph-0.4.2-py3-none-any.whl#sha256=a51f2604f9a5b6c0d25d3a88e694d5c02e20812dc0e482adf96c8628f9109357 +# pip libsass @ https://files.pythonhosted.org/packages/fd/5a/eb5b62641df0459a3291fc206cf5bd669c0feed7814dded8edef4ade8512/libsass-0.23.0-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=4a218406d605f325d234e4678bd57126a66a88841cb95bee2caeafdc6f138306 +# pip sphinxcontrib-sass @ https://files.pythonhosted.org/packages/2e/87/7c2eb08e3ca1d6baae32c0a5e005330fe1cec93a36aa085e714c3b3a3c7d/sphinxcontrib_sass-0.3.4-py2.py3-none-any.whl#sha256=a0c79a44ae8b8935c02dc340ebe40c9e002c839331201c899dc93708970c355a +# pip sphinxext-opengraph @ https://files.pythonhosted.org/packages/92/0a/970b80b4fa1feeb6deb6f2e22d4cb14e388b27b315a1afdb9db930ff91a4/sphinxext_opengraph-0.9.1-py3-none-any.whl#sha256=b3b230cc6a5b5189139df937f0d9c7b23c7c204493b22646273687969dcb760e diff --git a/build_tools/circle/list_versions.py b/build_tools/circle/list_versions.py index 345e08b4bece4..e1f8d54b84ec5 100755 --- a/build_tools/circle/list_versions.py +++ b/build_tools/circle/list_versions.py @@ -1,6 +1,11 @@ #!/usr/bin/env python3 -# List all available versions of the documentation +# Write the available versions page (--rst) and the version switcher JSON (--json). +# Version switcher see: +# https://pydata-sphinx-theme.readthedocs.io/en/stable/user_guide/version-dropdown.html +# https://pydata-sphinx-theme.readthedocs.io/en/stable/user_guide/announcements.html#announcement-banners + +import argparse import json import re import sys @@ -52,14 +57,19 @@ def get_file_size(version): return human_readable_data_quantity(path_details["size"], 1000) -print(":orphan:") -print() -heading = "Available documentation for Scikit-learn" -print(heading) -print("=" * len(heading)) -print() -print("Web-based documentation is available for versions listed below:") -print() +parser = argparse.ArgumentParser() +parser.add_argument("--rst", type=str, required=True) +parser.add_argument("--json", type=str, required=True) +args = parser.parse_args() + +heading = "Available documentation for scikit-learn" +json_content = [] +rst_content = [ + ":orphan:\n", + heading, + "=" * len(heading) + "\n", + "Web-based documentation is available for versions listed below:\n", +] ROOT_URL = ( "https://api.github.com/repos/scikit-learn/scikit-learn.github.io/contents/" # noqa @@ -93,8 +103,9 @@ def get_file_size(version): # Output in order: dev, stable, decreasing other version seen = set() -for name in NAMED_DIRS + sorted( - (k for k in dirs if k[:1].isdigit()), key=parse_version, reverse=True +for i, name in enumerate( + NAMED_DIRS + + sorted((k for k in dirs if k[:1].isdigit()), key=parse_version, reverse=True) ): version_num, file_size = dirs[name] if version_num in seen: @@ -102,17 +113,32 @@ def get_file_size(version): continue else: seen.add(version_num) - name_display = "" if name[:1].isdigit() else " (%s)" % name - path = "https://scikit-learn.org/%s/" % name - out = "* `Scikit-learn %s%s documentation <%s>`_" % ( - version_num, - name_display, - path, - ) + + full_name = f"{version_num}" if name[:1].isdigit() else f"{version_num} ({name})" + path = f"https://scikit-learn.org/{name}/" + + # Update JSON for the version switcher; only keep the 8 latest versions to avoid + # overloading the version switcher dropdown + if i < 8: + info = {"name": full_name, "version": version_num, "url": path} + if name == "stable": + info["preferred"] = True + json_content.append(info) + + # Printout for the historical version page + out = f"* `scikit-learn {full_name} documentation <{path}>`_" if file_size is not None: file_extension = get_file_extension(version_num) out += ( f" (`{file_extension.upper()} {file_size} <{path}/" f"_downloads/scikit-learn-docs.{file_extension}>`_)" ) - print(out) + rst_content.append(out) + +with open(args.rst, "w", encoding="utf-8") as f: + f.write("\n".join(rst_content) + "\n") +print(f"Written {args.rst}") + +with open(args.json, "w", encoding="utf-8") as f: + json.dump(json_content, f, indent=2) +print(f"Written {args.json}") diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index 86da119ec4547..bf086e21716e3 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -307,8 +307,14 @@ def remove_from(alist, to_remove): "plotly", "polars", "pooch", + "sphinx-remove-toctrees", + "sphinx-design", + "pydata-sphinx-theme", + ], + "pip_dependencies": [ + "sphinxext-opengraph", + "sphinxcontrib-sass", ], - "pip_dependencies": ["sphinxext-opengraph"], "package_constraints": { "python": "3.9", "numpy": "min", @@ -325,6 +331,11 @@ def remove_from(alist, to_remove): "sphinxext-opengraph": "min", "plotly": "min", "polars": "min", + "pooch": "min", + "sphinx-design": "min", + "sphinxcontrib-sass": "min", + "sphinx-remove-toctrees": "min", + "pydata-sphinx-theme": "min", }, }, { @@ -349,8 +360,15 @@ def remove_from(alist, to_remove): "polars", "pooch", "sphinxext-opengraph", + "sphinx-remove-toctrees", + "sphinx-design", + "pydata-sphinx-theme", + ], + "pip_dependencies": [ + "jupyterlite-sphinx", + "jupyterlite-pyodide-kernel", + "sphinxcontrib-sass", ], - "pip_dependencies": ["jupyterlite-sphinx", "jupyterlite-pyodide-kernel"], "package_constraints": { "python": "3.9", }, @@ -426,7 +444,7 @@ def execute_command(command_list): ) out, err = proc.communicate() - out, err = out.decode(), err.decode() + out, err = out.decode(errors="replace"), err.decode(errors="replace") if proc.returncode != 0: command_str = " ".join(command_list) diff --git a/doc/Makefile b/doc/Makefile index 44f02585f6205..f84d3c78b8051 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -47,9 +47,17 @@ help: clean: -rm -rf $(BUILDDIR)/* + @echo "Removed $(BUILDDIR)/*" -rm -rf auto_examples/ + @echo "Removed auto_examples/" -rm -rf generated/* + @echo "Removed generated/" -rm -rf modules/generated/ + @echo "Removed modules/generated/" + -rm -rf css/styles/ + @echo "Removed css/styles/" + -rm -rf api/*.rst + @echo "Removed api/*.rst" # Default to SPHINX_NUMJOBS=1 for full documentation build. Using # SPHINX_NUMJOBS!=1 may actually slow down the build, or cause weird issues in diff --git a/doc/about.rst b/doc/about.rst index 035bddb0ea4dc..47d57e4737318 100644 --- a/doc/about.rst +++ b/doc/about.rst @@ -13,8 +13,8 @@ this project as part of his thesis. In 2010 Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort and Vincent Michel of INRIA took leadership of the project and made the first public release, February the 1st 2010. Since then, several releases have appeared -following a ~ 3-month cycle, and a thriving international community has -been leading the development. +following an approximately 3-month cycle, and a thriving international +community has been leading the development. Governance ---------- @@ -28,7 +28,7 @@ out in the :ref:`governance document `. .. _authors: The people behind scikit-learn -------------------------------- +------------------------------ Scikit-learn is a community project, developed by a large group of people, all across the world. A few teams, listed below, have central @@ -44,14 +44,16 @@ consolidating scikit-learn's development and maintenance: .. include:: maintainers.rst -Please do not email the authors directly to ask for assistance or report issues. -Instead, please see `What's the best way to ask questions about scikit-learn -`_ -in the FAQ. +.. note:: + + Please do not email the authors directly to ask for assistance or report issues. + Instead, please see `What's the best way to ask questions about scikit-learn + `_ + in the FAQ. .. seealso:: - :ref:`How you can contribute to the project ` + How you can :ref:`contribute to the project `. Documentation Team .................. @@ -77,9 +79,8 @@ The following people help with :ref:`communication around scikit-learn .. include:: communication_team.rst - Emeritus Core Developers ------------------------- +........................ The following people have been active contributors in the past, but are no longer active in the project: @@ -87,7 +88,7 @@ longer active in the project: .. include:: maintainers_emeritus.rst Emeritus Communication Team ---------------------------- +........................... The following people have been active in the communication team in the past, but no longer have communication responsibilities: @@ -95,7 +96,7 @@ past, but no longer have communication responsibilities: .. include:: communication_team_emeritus.rst Emeritus Contributor Experience Team ------------------------------------- +.................................... The following people have been active in the contributor experience team in the past: @@ -157,488 +158,305 @@ High quality PNG and SVG logos are available in the `doc/logos/ source directory. .. image:: images/scikit-learn-logo-notext.png - :align: center + :align: center Funding ------- -Scikit-Learn is a community driven project, however institutional and private + +Scikit-learn is a community driven project, however institutional and private grants help to assure its sustainability. The project would like to thank the following funders. ................................... +.. div:: sk-text-image-grid-small -.. raw:: html - -
-
- -`:probabl. `_ funds Adrin Jalali, Arturo Amor, -François Goupil, Guillaume Lemaitre, Jérémie du Boisberranger, Olivier Grisel, and -Stefanie Senger. + .. div:: text-box -.. raw:: html - -
- -
- -.. image:: images/probabl.png - :width: 75pt - :align: center - :target: https://probabl.ai + `:probabl. `_ funds Adrin Jalali, Arturo Amor, François Goupil, + Guillaume Lemaitre, Jérémie du Boisberranger, Olivier Grisel, and Stefanie Senger. -.. raw:: html + .. div:: image-box -
-
+ .. image:: images/probabl.png + :target: https://probabl.ai .......... -.. raw:: html - -
-
- -The `Members `_ of -the `Scikit-Learn Consortium at Inria Foundation -`_ help at maintaining and -improving the project through their financial support. - -.. raw:: html - -
- .. |chanel| image:: images/chanel.png - :width: 55pt - :target: https://www.chanel.com + :target: https://www.chanel.com .. |axa| image:: images/axa.png - :width: 40pt - :target: https://www.axa.fr/ + :target: https://www.axa.fr/ .. |bnp| image:: images/bnp.png - :width: 120pt - :target: https://www.bnpparibascardif.com/ + :target: https://www.bnpparibascardif.com/ .. |dataiku| image:: images/dataiku.png - :width: 55pt - :target: https://www.dataiku.com/ + :target: https://www.dataiku.com/ .. |hf| image:: images/huggingface_logo-noborder.png - :width: 55pt - :target: https://huggingface.co + :target: https://huggingface.co .. |nvidia| image:: images/nvidia.png - :width: 55pt - :target: https://www.nvidia.com + :target: https://www.nvidia.com .. |inria| image:: images/inria-logo.jpg - :width: 75pt - :target: https://www.inria.fr - - -.. raw:: html - -
- -.. table:: - :class: sk-sponsor-table - - +----------+-----------+ - | |chanel| | - +----------+-----------+ - | | - +----------+-----------+ - | |axa| | |bnp| | - +----------+-----------+ - | | - +----------+-----------+ - | |nvidia| | |hf| | - +----------+-----------+ - | | - +----------+-----------+ - | |dataiku| | - +----------+-----------+ - | | - +----------+-----------+ - | |inria| | - +----------+-----------+ + :target: https://www.inria.fr .. raw:: html -
-
+ -.. raw:: html +.. div:: sk-text-image-grid-small - + .. div:: text-box -
+ The `Members `_ of + the `Scikit-learn Consortium at Inria Foundation + `_ help at maintaining and + improving the project through their financial support. -.. image:: images/nvidia.png - :width: 55pt - :align: center - :target: https://nvidia.com + .. div:: image-box -.. raw:: html + .. table:: + :class: image-subtable -
- + +----------+-----------+ + | |chanel| | + +----------+-----------+ + | |axa| | |bnp| | + +----------+-----------+ + | |nvidia| | |hf| | + +----------+-----------+ + | |dataiku| | + +----------+-----------+ + | |inria| | + +----------+-----------+ .......... -.. raw:: html - -
-
+.. div:: sk-text-image-grid-small -`Microsoft `_ funds Andreas Müller since 2020. + .. div:: text-box -.. raw:: html - -
+ `NVidia `_ funds Tim Head since 2022 + and is part of the scikit-learn consortium at Inria. -
+ .. div:: image-box -.. image:: images/microsoft.png - :width: 100pt - :align: center - :target: https://www.microsoft.com/ + .. image:: images/nvidia.png + :target: https://nvidia.com -.. raw:: html +.......... -
-
+.. div:: sk-text-image-grid-small -........... + .. div:: text-box -.. raw:: html + `Microsoft `_ funds Andreas Müller since 2020. -
-
+ .. div:: image-box -`Quansight Labs `_ funds Lucy Liu since 2022. + .. image:: images/microsoft.png + :target: https://microsoft.com -.. raw:: html +........... -
+.. div:: sk-text-image-grid-small -
+ .. div:: text-box -.. image:: images/quansight-labs.png - :width: 100pt - :align: center - :target: https://labs.quansight.org + `Quansight Labs `_ funds Lucy Liu since 2022. -.. raw:: html + .. div:: image-box -
-
+ .. image:: images/quansight-labs.png + :target: https://labs.quansight.org ........... -.. raw:: html - -
-
- -`Tidelift `_ supports the project via their service -agreement. +.. div:: sk-text-image-grid-small -.. raw:: html + .. div:: text-box -
+ `Tidelift `_ supports the project via their service + agreement. -
+ .. div:: image-box -.. image:: images/Tidelift-logo-on-light.svg - :width: 100pt - :align: center - :target: https://tidelift.com/ + .. image:: images/Tidelift-logo-on-light.svg + :target: https://tidelift.com/ -.. raw:: html +........... -
-
Past Sponsors ............. -.. raw:: html - -
-
- -`Quansight Labs `_ funded Meekail Zain in 2022 and 2023 and, -funded Thomas J. Fan from 2021 to 2023. - -.. raw:: html - -
+.. div:: sk-text-image-grid-small -
+ .. div:: text-box -.. image:: images/quansight-labs.png - :width: 100pt - :align: center - :target: https://labs.quansight.org + `Quansight Labs `_ funded Meekail Zain in 2022 and 2023, + and funded Thomas J. Fan from 2021 to 2023. -.. raw:: html + .. div:: image-box -
-
+ .. image:: images/quansight-labs.png + :target: https://labs.quansight.org ........... -.. raw:: html - -
-
+.. div:: sk-text-image-grid-small -`Columbia University `_ funded Andreas Müller -(2016-2020). + .. div:: text-box -.. raw:: html - -
- -
- -.. image:: images/columbia.png - :width: 50pt - :align: center - :target: https://www.columbia.edu/ + `Columbia University `_ funded Andreas Müller + (2016-2020). -.. raw:: html + .. div:: image-box -
-
+ .. image:: images/columbia.png + :target: https://columbia.edu ........ -.. raw:: html - -
-
+.. div:: sk-text-image-grid-small -`The University of Sydney `_ funded Joel Nothman -(2017-2021). + .. div:: text-box -.. raw:: html + `The University of Sydney `_ funded Joel Nothman + (2017-2021). -
+ .. div:: image-box -
- -.. image:: images/sydney-primary.jpeg - :width: 100pt - :align: center - :target: https://sydney.edu.au/ - -.. raw:: html - -
-
+ .. image:: images/sydney-primary.jpeg + :target: https://sydney.edu.au/ ........... -.. raw:: html - -
-
- -Andreas Müller received a grant to improve scikit-learn from the -`Alfred P. Sloan Foundation `_ . -This grant supported the position of Nicolas Hug and Thomas J. Fan. - -.. raw:: html - -
+.. div:: sk-text-image-grid-small -
+ .. div:: text-box -.. image:: images/sloan_banner.png - :width: 100pt - :align: center - :target: https://sloan.org/ + Andreas Müller received a grant to improve scikit-learn from the + `Alfred P. Sloan Foundation `_ . + This grant supported the position of Nicolas Hug and Thomas J. Fan. -.. raw:: html + .. div:: image-box -
-
+ .. image:: images/sloan_banner.png + :target: https://sloan.org/ ............. -.. raw:: html - -
-
- -`INRIA `_ actively supports this project. It has -provided funding for Fabian Pedregosa (2010-2012), Jaques Grobler -(2012-2013) and Olivier Grisel (2013-2017) to work on this project -full-time. It also hosts coding sprints and other events. - -.. raw:: html - -
+.. div:: sk-text-image-grid-small -
+ .. div:: text-box -.. image:: images/inria-logo.jpg - :width: 100pt - :align: center - :target: https://www.inria.fr + `INRIA `_ actively supports this project. It has + provided funding for Fabian Pedregosa (2010-2012), Jaques Grobler + (2012-2013) and Olivier Grisel (2013-2017) to work on this project + full-time. It also hosts coding sprints and other events. -.. raw:: html + .. div:: image-box -
-
+ .. image:: images/inria-logo.jpg + :target: https://www.inria.fr ..................... -.. raw:: html - -
-
+.. div:: sk-text-image-grid-small -`Paris-Saclay Center for Data Science -`_ -funded one year for a developer to work on the project full-time -(2014-2015), 50% of the time of Guillaume Lemaitre (2016-2017) and 50% of the -time of Joris van den Bossche (2017-2018). + .. div:: text-box -.. raw:: html - -
-
+ `Paris-Saclay Center for Data Science `_ + funded one year for a developer to work on the project full-time (2014-2015), 50% + of the time of Guillaume Lemaitre (2016-2017) and 50% of the time of Joris van den + Bossche (2017-2018). -.. image:: images/cds-logo.png - :width: 100pt - :align: center - :target: http://www.datascience-paris-saclay.fr/ - -.. raw:: html + .. div:: image-box -
-
+ .. image:: images/cds-logo.png + :target: http://www.datascience-paris-saclay.fr/ .......................... -.. raw:: html - -
-
- -`NYU Moore-Sloan Data Science Environment `_ -funded Andreas Mueller (2014-2016) to work on this project. The Moore-Sloan -Data Science Environment also funds several students to work on the project -part-time. - -.. raw:: html +.. div:: sk-text-image-grid-small -
-
+ .. div:: text-box -.. image:: images/nyu_short_color.png - :width: 100pt - :align: center - :target: https://cds.nyu.edu/mooresloan/ + `NYU Moore-Sloan Data Science Environment `_ + funded Andreas Mueller (2014-2016) to work on this project. The Moore-Sloan + Data Science Environment also funds several students to work on the project + part-time. -.. raw:: html + .. div:: image-box -
-
+ .. image:: images/nyu_short_color.png + :target: https://cds.nyu.edu/mooresloan/ ........................ -.. raw:: html +.. div:: sk-text-image-grid-small -
-
+ .. div:: text-box -`Télécom Paristech `_ funded Manoj Kumar -(2014), Tom Dupré la Tour (2015), Raghav RV (2015-2017), Thierry Guillemot -(2016-2017) and Albert Thomas (2017) to work on scikit-learn. + `Télécom Paristech `_ funded Manoj Kumar + (2014), Tom Dupré la Tour (2015), Raghav RV (2015-2017), Thierry Guillemot + (2016-2017) and Albert Thomas (2017) to work on scikit-learn. -.. raw:: html + .. div:: image-box -
-
- -.. image:: images/telecom.png - :width: 50pt - :align: center - :target: https://www.telecom-paristech.fr/ - -.. raw:: html - -
-
+ .. image:: images/telecom.png + :target: https://www.telecom-paristech.fr/ ..................... -.. raw:: html - -
-
- -`The Labex DigiCosme `_ funded Nicolas Goix -(2015-2016), Tom Dupré la Tour (2015-2016 and 2017-2018), Mathurin Massias -(2018-2019) to work part time on scikit-learn during their PhDs. It also -funded a scikit-learn coding sprint in 2015. - -.. raw:: html +.. div:: sk-text-image-grid-small -
-
+ .. div:: text-box -.. image:: images/digicosme.png - :width: 100pt - :align: center - :target: https://digicosme.lri.fr + `The Labex DigiCosme `_ funded Nicolas Goix + (2015-2016), Tom Dupré la Tour (2015-2016 and 2017-2018), Mathurin Massias + (2018-2019) to work part time on scikit-learn during their PhDs. It also + funded a scikit-learn coding sprint in 2015. -.. raw:: html + .. div:: image-box -
-
+ .. image:: images/digicosme.png + :target: https://digicosme.lri.fr ..................... -.. raw:: html - -
-
+.. div:: sk-text-image-grid-small -`The Chan-Zuckerberg Initiative `_ funded Nicolas -Hug to work full-time on scikit-learn in 2020. + .. div:: text-box -.. raw:: html - -
-
+ `The Chan-Zuckerberg Initiative `_ funded Nicolas + Hug to work full-time on scikit-learn in 2020. -.. image:: images/czi_logo.svg - :width: 100pt - :align: center - :target: https://chanzuckerberg.com - -.. raw:: html + .. div:: image-box -
-
+ .. image:: images/czi_logo.svg + :target: https://chanzuckerberg.com ...................... @@ -649,9 +467,9 @@ program. - 2007 - David Cournapeau - 2011 - `Vlad Niculae`_ -- 2012 - `Vlad Niculae`_, Immanuel Bayer. +- 2012 - `Vlad Niculae`_, Immanuel Bayer - 2013 - Kemal Eren, Nicolas Trésegnie -- 2014 - Hamzeh Alsalhi, Issam Laradji, Maheshakya Wijewardena, Manoj Kumar. +- 2014 - Hamzeh Alsalhi, Issam Laradji, Maheshakya Wijewardena, Manoj Kumar - 2015 - `Raghav RV `_, Wei Xue - 2016 - `Nelson Liu `_, `YenChen Lin `_ @@ -670,86 +488,112 @@ The following organizations funded the scikit-learn consortium at Inria in the past: .. |msn| image:: images/microsoft.png - :width: 100pt - :target: https://www.microsoft.com/ + :target: https://www.microsoft.com/ .. |bcg| image:: images/bcg.png - :width: 100pt - :target: https://www.bcg.com/beyond-consulting/bcg-gamma/default.aspx + :target: https://www.bcg.com/beyond-consulting/bcg-gamma/default.aspx .. |fujitsu| image:: images/fujitsu.png - :width: 100pt - :target: https://www.fujitsu.com/global/ + :target: https://www.fujitsu.com/global/ .. |aphp| image:: images/logo_APHP_text.png - :width: 150pt - :target: https://aphp.fr/ + :target: https://aphp.fr/ +.. raw:: html + + + +.. grid:: 2 2 4 4 + :class-row: image-subgrid + :gutter: 1 + + .. grid-item:: + :class: sd-text-center + :child-align: center + + |msn| + + .. grid-item:: + :class: sd-text-center + :child-align: center + + |bcg| + + .. grid-item:: + :class: sd-text-center + :child-align: center -|bcg| |msn| |fujitsu| |aphp| + |fujitsu| + + .. grid-item:: + :class: sd-text-center + :child-align: center + + |aphp| Sprints ------- -The International 2019 Paris sprint was kindly hosted by `AXA `_. -Also some participants could attend thanks to the support of the `Alfred P. -Sloan Foundation `_, the `Python Software -Foundation `_ (PSF) and the `DATAIA Institute -`_. - -..................... +- The International 2019 Paris sprint was kindly hosted by `AXA `_. + Also some participants could attend thanks to the support of the `Alfred P. + Sloan Foundation `_, the `Python Software + Foundation `_ (PSF) and the `DATAIA Institute + `_. -The 2013 International Paris Sprint was made possible thanks to the support of -`Télécom Paristech `_, `tinyclues -`_, the `French Python Association -`_ and the `Fonds de la Recherche Scientifique -`_. +- The 2013 International Paris Sprint was made possible thanks to the support of + `Télécom Paristech `_, `tinyclues + `_, the `French Python Association + `_ and the `Fonds de la Recherche Scientifique + `_. -.............. +- The 2011 International Granada sprint was made possible thanks to the support + of the `PSF `_ and `tinyclues + `_. -The 2011 International Granada sprint was made possible thanks to the support -of the `PSF `_ and `tinyclues -`_. Donating to the project -....................... +----------------------- If you are interested in donating to the project or to one of our code-sprints, please donate via the `NumFOCUS Donations Page `_. -.. raw :: html - - -
+.. raw:: html -All donations will be handled by `NumFOCUS -`_, a non-profit-organization which is -managed by a board of `Scipy community members -`_. NumFOCUS's mission is to foster -scientific computing software, in particular in Python. As a fiscal home -of scikit-learn, it ensures that money is available when needed to keep -the project funded and available while in compliance with tax regulations. +

+ + Help us, donate! + +

-The received donations for the scikit-learn project mostly will go towards -covering travel-expenses for code sprints, as well as towards the organization -budget of the project [#f1]_. +All donations will be handled by `NumFOCUS `_, a non-profit +organization which is managed by a board of `Scipy community members +`_. NumFOCUS's mission is to foster scientific +computing software, in particular in Python. As a fiscal home of scikit-learn, it +ensures that money is available when needed to keep the project funded and available +while in compliance with tax regulations. +The received donations for the scikit-learn project mostly will go towards covering +travel-expenses for code sprints, as well as towards the organization budget of the +project [#f1]_. .. rubric:: Notes .. [#f1] Regarding the organization budget, in particular, we might use some of - the donated funds to pay for other project expenses such as DNS, - hosting or continuous integration services. + the donated funds to pay for other project expenses such as DNS, + hosting or continuous integration services. + Infrastructure support ---------------------- -- We would also like to thank `Microsoft Azure - `_, `Cirrus Cl `_, - `CircleCl `_ for free CPU time on their Continuous - Integration servers, and `Anaconda Inc. `_ for the - storage they provide for our staging and nightly builds. +We would also like to thank `Microsoft Azure `_, +`Cirrus Cl `_, `CircleCl `_ for free CPU +time on their Continuous Integration servers, and `Anaconda Inc. `_ +for the storage they provide for our staging and nightly builds. diff --git a/doc/api/deprecated.rst.template b/doc/api/deprecated.rst.template new file mode 100644 index 0000000000000..a48f0180f76ed --- /dev/null +++ b/doc/api/deprecated.rst.template @@ -0,0 +1,24 @@ +:html_theme.sidebar_secondary.remove: + +.. _api_depr_ref: + +Recently Deprecated +=================== + +.. currentmodule:: sklearn + +{% for ver, objs in DEPRECATED_API_REFERENCE %} +.. _api_depr_ref-{{ ver|replace(".", "-") }}: + +.. rubric:: To be removed in {{ ver }} + +.. autosummary:: + :nosignatures: + :toctree: ../modules/generated/ + :template: base.rst + +{% for obj in objs %} + {{ obj }} +{%- endfor %} + +{% endfor %} diff --git a/doc/api/index.rst.template b/doc/api/index.rst.template new file mode 100644 index 0000000000000..a9f3209d350de --- /dev/null +++ b/doc/api/index.rst.template @@ -0,0 +1,77 @@ +:html_theme.sidebar_secondary.remove: + +.. _api_ref: + +============= +API Reference +============= + +This is the class and function reference of scikit-learn. Please refer to the +:ref:`full user guide ` for further details, as the raw specifications of +classes and functions may not be enough to give full guidelines on their uses. For +reference on concepts repeated across the API, see :ref:`glossary`. + +.. toctree:: + :maxdepth: 2 + :hidden: + +{% for module, _ in API_REFERENCE %} + {{ module }} +{%- endfor %} +{%- if DEPRECATED_API_REFERENCE %} + deprecated +{%- endif %} + +.. list-table:: + :header-rows: 1 + :class: apisearch-table + + * - Object + - Description + +{% for module, module_info in API_REFERENCE %} +{% for section in module_info["sections"] %} +{% for obj in section["autosummary"] %} +{% set parts = obj.rsplit(".", 1) %} +{% if parts|length > 1 %} +{% set full_module = module + "." + parts[0] %} +{% else %} +{% set full_module = module %} +{% endif %} + * - :obj:`~{{ module }}.{{ obj }}` + + - .. div:: sk-apisearch-desc + + .. currentmodule:: {{ full_module }} + + .. autoshortsummary:: {{ module }}.{{ obj }} + + .. div:: caption + + :mod:`{{ full_module }}` +{% endfor %} +{% endfor %} +{% endfor %} + +{% for ver, objs in DEPRECATED_API_REFERENCE %} +{% for obj in objs %} +{% set parts = obj.rsplit(".", 1) %} +{% if parts|length > 1 %} +{% set full_module = "sklearn." + parts[0] %} +{% else %} +{% set full_module = "sklearn" %} +{% endif %} + * - :obj:`~sklearn.{{ obj }}` + + - .. div:: sk-apisearch-desc + + .. currentmodule:: {{ full_module }} + + .. autoshortsummary:: sklearn.{{ obj }} + + .. div:: caption + + :mod:`{{ full_module }}` + :bdg-ref-danger-line:`Deprecated in version {{ ver }} ` +{% endfor %} +{% endfor %} diff --git a/doc/api/module.rst.template b/doc/api/module.rst.template new file mode 100644 index 0000000000000..1980f27aad158 --- /dev/null +++ b/doc/api/module.rst.template @@ -0,0 +1,46 @@ +:html_theme.sidebar_secondary.remove: + +{% if module == "sklearn" -%} +{%- set module_hook = "sklearn" -%} +{%- elif module.startswith("sklearn.") -%} +{%- set module_hook = module[8:] -%} +{%- else -%} +{%- set module_hook = None -%} +{%- endif -%} + +{% if module_hook %} +.. _{{ module_hook }}_ref: +{% endif %} + +{{ module }} +{{ "=" * module|length }} + +.. automodule:: {{ module }} + +{% if module_info["description"] %} +{{ module_info["description"] }} +{% endif %} + +{% for section in module_info["sections"] %} +{% if section["title"] and module_hook %} +.. _{{ module_hook }}_ref-{{ section["title"]|lower|replace(" ", "-") }}: +{% endif %} + +{% if section["title"] %} +{{ section["title"] }} +{{ "-" * section["title"]|length }} +{% endif %} + +{% if section["description"] %} +{{ section["description"] }} +{% endif %} + +.. autosummary:: + :nosignatures: + :toctree: ../modules/generated/ + :template: base.rst + +{% for obj in section["autosummary"] %} + {{ obj }} +{%- endfor %} +{% endfor %} diff --git a/doc/api_reference.py b/doc/api_reference.py new file mode 100644 index 0000000000000..c8a22ebc2d5b3 --- /dev/null +++ b/doc/api_reference.py @@ -0,0 +1,1336 @@ +"""Configuration for the API reference documentation.""" + + +def _get_guide(*refs, is_developer=False): + """Get the rst to refer to user/developer guide. + + `refs` is several references that can be used in the :ref:`...` directive. + """ + if len(refs) == 1: + ref_desc = f":ref:`{refs[0]}` section" + elif len(refs) == 2: + ref_desc = f":ref:`{refs[0]}` and :ref:`{refs[1]}` sections" + else: + ref_desc = ", ".join(f":ref:`{ref}`" for ref in refs[:-1]) + ref_desc += f", and :ref:`{refs[-1]}` sections" + + guide_name = "Developer" if is_developer else "User" + return f"**{guide_name} guide.** See the {ref_desc} for further details." + + +def _get_submodule(module_name, submodule_name): + """Get the submodule docstring and automatically add the hook. + + `module_name` is e.g. `sklearn.feature_extraction`, and `submodule_name` is e.g. + `image`, so we get the docstring and hook for `sklearn.feature_extraction.image` + submodule. `module_name` is used to reset the current module because autosummary + automatically changes the current module. + """ + lines = [ + f".. automodule:: {module_name}.{submodule_name}", + f".. currentmodule:: {module_name}", + ] + return "\n\n".join(lines) + + +""" +CONFIGURING API_REFERENCE +========================= + +API_REFERENCE maps each module name to a dictionary that consists of the following +components: + +short_summary (required) + The text to be printed on the index page; it has nothing to do the API reference + page of each module. +description (required, `None` if not needed) + The additional description for the module to be placed under the module + docstring, before the sections start. +sections (required) + A list of sections, each of which consists of: + - title (required, `None` if not needed): the section title, commonly it should + not be `None` except for the first section of a module, + - description (optional): the optional additional description for the section, + - autosummary (required): an autosummary block, assuming current module is the + current module name. + +Essentially, the rendered page would look like the following: + +|---------------------------------------------------------------------------------| +| {{ module_name }} | +| ================= | +| {{ module_docstring }} | +| {{ description }} | +| | +| {{ section_title_1 }} <-------------- Optional if one wants the first | +| --------------------- section to directly follow | +| {{ section_description_1 }} without a second-level heading. | +| {{ section_autosummary_1 }} | +| | +| {{ section_title_2 }} | +| --------------------- | +| {{ section_description_2 }} | +| {{ section_autosummary_2 }} | +| | +| More sections... | +|---------------------------------------------------------------------------------| + +Hooks will be automatically generated for each module and each section. For a module, +e.g., `sklearn.feature_extraction`, the hook would be `feature_extraction_ref`; for a +section, e.g., "From text" under `sklearn.feature_extraction`, the hook would be +`feature_extraction_ref-from-text`. However, note that a better way is to refer using +the :mod: directive, e.g., :mod:`sklearn.feature_extraction` for the module and +:mod:`sklearn.feature_extraction.text` for the section. Only in case that a section +is not a particular submodule does the hook become useful, e.g., the "Loaders" section +under `sklearn.datasets`. +""" + +API_REFERENCE = { + "sklearn": { + "short_summary": "Settings and information tools.", + "description": None, + "sections": [ + { + "title": None, + "autosummary": [ + "config_context", + "get_config", + "set_config", + "show_versions", + ], + }, + ], + }, + "sklearn.base": { + "short_summary": "Base classes and utility functions.", + "description": None, + "sections": [ + { + "title": None, + "autosummary": [ + "BaseEstimator", + "BiclusterMixin", + "ClassNamePrefixFeaturesOutMixin", + "ClassifierMixin", + "ClusterMixin", + "DensityMixin", + "MetaEstimatorMixin", + "OneToOneFeatureMixin", + "OutlierMixin", + "RegressorMixin", + "TransformerMixin", + "clone", + "is_classifier", + "is_regressor", + ], + } + ], + }, + "sklearn.calibration": { + "short_summary": "Probability calibration.", + "description": _get_guide("calibration"), + "sections": [ + { + "title": None, + "autosummary": ["CalibratedClassifierCV", "calibration_curve"], + }, + { + "title": "Visualization", + "autosummary": ["CalibrationDisplay"], + }, + ], + }, + "sklearn.cluster": { + "short_summary": "Clustering.", + "description": _get_guide("clustering", "biclustering"), + "sections": [ + { + "title": None, + "autosummary": [ + "AffinityPropagation", + "AgglomerativeClustering", + "Birch", + "BisectingKMeans", + "DBSCAN", + "FeatureAgglomeration", + "HDBSCAN", + "KMeans", + "MeanShift", + "MiniBatchKMeans", + "OPTICS", + "SpectralBiclustering", + "SpectralClustering", + "SpectralCoclustering", + "affinity_propagation", + "cluster_optics_dbscan", + "cluster_optics_xi", + "compute_optics_graph", + "dbscan", + "estimate_bandwidth", + "k_means", + "kmeans_plusplus", + "mean_shift", + "spectral_clustering", + "ward_tree", + ], + }, + ], + }, + "sklearn.compose": { + "short_summary": "Composite estimators.", + "description": _get_guide("combining_estimators"), + "sections": [ + { + "title": None, + "autosummary": [ + "ColumnTransformer", + "TransformedTargetRegressor", + "make_column_selector", + "make_column_transformer", + ], + }, + ], + }, + "sklearn.covariance": { + "short_summary": "Covariance estimation.", + "description": _get_guide("covariance"), + "sections": [ + { + "title": None, + "autosummary": [ + "EllipticEnvelope", + "EmpiricalCovariance", + "GraphicalLasso", + "GraphicalLassoCV", + "LedoitWolf", + "MinCovDet", + "OAS", + "ShrunkCovariance", + "empirical_covariance", + "graphical_lasso", + "ledoit_wolf", + "ledoit_wolf_shrinkage", + "oas", + "shrunk_covariance", + ], + }, + ], + }, + "sklearn.cross_decomposition": { + "short_summary": "Cross decomposition.", + "description": _get_guide("cross_decomposition"), + "sections": [ + { + "title": None, + "autosummary": ["CCA", "PLSCanonical", "PLSRegression", "PLSSVD"], + }, + ], + }, + "sklearn.datasets": { + "short_summary": "Datasets.", + "description": _get_guide("datasets"), + "sections": [ + { + "title": "Loaders", + "autosummary": [ + "clear_data_home", + "dump_svmlight_file", + "fetch_20newsgroups", + "fetch_20newsgroups_vectorized", + "fetch_california_housing", + "fetch_covtype", + "fetch_kddcup99", + "fetch_lfw_pairs", + "fetch_lfw_people", + "fetch_olivetti_faces", + "fetch_openml", + "fetch_rcv1", + "fetch_species_distributions", + "get_data_home", + "load_breast_cancer", + "load_diabetes", + "load_digits", + "load_files", + "load_iris", + "load_linnerud", + "load_sample_image", + "load_sample_images", + "load_svmlight_file", + "load_svmlight_files", + "load_wine", + ], + }, + { + "title": "Sample generators", + "autosummary": [ + "make_biclusters", + "make_blobs", + "make_checkerboard", + "make_circles", + "make_classification", + "make_friedman1", + "make_friedman2", + "make_friedman3", + "make_gaussian_quantiles", + "make_hastie_10_2", + "make_low_rank_matrix", + "make_moons", + "make_multilabel_classification", + "make_regression", + "make_s_curve", + "make_sparse_coded_signal", + "make_sparse_spd_matrix", + "make_sparse_uncorrelated", + "make_spd_matrix", + "make_swiss_roll", + ], + }, + ], + }, + "sklearn.decomposition": { + "short_summary": "Matrix decomposition.", + "description": _get_guide("decompositions"), + "sections": [ + { + "title": None, + "autosummary": [ + "DictionaryLearning", + "FactorAnalysis", + "FastICA", + "IncrementalPCA", + "KernelPCA", + "LatentDirichletAllocation", + "MiniBatchDictionaryLearning", + "MiniBatchNMF", + "MiniBatchSparsePCA", + "NMF", + "PCA", + "SparseCoder", + "SparsePCA", + "TruncatedSVD", + "dict_learning", + "dict_learning_online", + "fastica", + "non_negative_factorization", + "sparse_encode", + ], + }, + ], + }, + "sklearn.discriminant_analysis": { + "short_summary": "Discriminant analysis.", + "description": _get_guide("lda_qda"), + "sections": [ + { + "title": None, + "autosummary": [ + "LinearDiscriminantAnalysis", + "QuadraticDiscriminantAnalysis", + ], + }, + ], + }, + "sklearn.dummy": { + "short_summary": "Dummy estimators.", + "description": _get_guide("model_evaluation"), + "sections": [ + { + "title": None, + "autosummary": ["DummyClassifier", "DummyRegressor"], + }, + ], + }, + "sklearn.ensemble": { + "short_summary": "Ensemble methods.", + "description": _get_guide("ensemble"), + "sections": [ + { + "title": None, + "autosummary": [ + "AdaBoostClassifier", + "AdaBoostRegressor", + "BaggingClassifier", + "BaggingRegressor", + "ExtraTreesClassifier", + "ExtraTreesRegressor", + "GradientBoostingClassifier", + "GradientBoostingRegressor", + "HistGradientBoostingClassifier", + "HistGradientBoostingRegressor", + "IsolationForest", + "RandomForestClassifier", + "RandomForestRegressor", + "RandomTreesEmbedding", + "StackingClassifier", + "StackingRegressor", + "VotingClassifier", + "VotingRegressor", + ], + }, + ], + }, + "sklearn.exceptions": { + "short_summary": "Exceptions and warnings.", + "description": None, + "sections": [ + { + "title": None, + "autosummary": [ + "ConvergenceWarning", + "DataConversionWarning", + "DataDimensionalityWarning", + "EfficiencyWarning", + "FitFailedWarning", + "InconsistentVersionWarning", + "NotFittedError", + "UndefinedMetricWarning", + ], + }, + ], + }, + "sklearn.experimental": { + "short_summary": "Experimental tools.", + "description": None, + "sections": [ + { + "title": None, + "autosummary": ["enable_halving_search_cv", "enable_iterative_imputer"], + }, + ], + }, + "sklearn.feature_extraction": { + "short_summary": "Feature extraction.", + "description": _get_guide("feature_extraction"), + "sections": [ + { + "title": None, + "autosummary": ["DictVectorizer", "FeatureHasher"], + }, + { + "title": "From images", + "description": _get_submodule("sklearn.feature_extraction", "image"), + "autosummary": [ + "image.PatchExtractor", + "image.extract_patches_2d", + "image.grid_to_graph", + "image.img_to_graph", + "image.reconstruct_from_patches_2d", + ], + }, + { + "title": "From text", + "description": _get_submodule("sklearn.feature_extraction", "text"), + "autosummary": [ + "text.CountVectorizer", + "text.HashingVectorizer", + "text.TfidfTransformer", + "text.TfidfVectorizer", + ], + }, + ], + }, + "sklearn.feature_selection": { + "short_summary": "Feature selection.", + "description": _get_guide("feature_selection"), + "sections": [ + { + "title": None, + "autosummary": [ + "GenericUnivariateSelect", + "RFE", + "RFECV", + "SelectFdr", + "SelectFpr", + "SelectFromModel", + "SelectFwe", + "SelectKBest", + "SelectPercentile", + "SelectorMixin", + "SequentialFeatureSelector", + "VarianceThreshold", + "chi2", + "f_classif", + "f_regression", + "mutual_info_classif", + "mutual_info_regression", + "r_regression", + ], + }, + ], + }, + "sklearn.gaussian_process": { + "short_summary": "Gaussian processes.", + "description": _get_guide("gaussian_process"), + "sections": [ + { + "title": None, + "autosummary": [ + "GaussianProcessClassifier", + "GaussianProcessRegressor", + ], + }, + { + "title": "Kernels", + "description": _get_submodule("sklearn.gaussian_process", "kernels"), + "autosummary": [ + "kernels.CompoundKernel", + "kernels.ConstantKernel", + "kernels.DotProduct", + "kernels.ExpSineSquared", + "kernels.Exponentiation", + "kernels.Hyperparameter", + "kernels.Kernel", + "kernels.Matern", + "kernels.PairwiseKernel", + "kernels.Product", + "kernels.RBF", + "kernels.RationalQuadratic", + "kernels.Sum", + "kernels.WhiteKernel", + ], + }, + ], + }, + "sklearn.impute": { + "short_summary": "Imputation.", + "description": _get_guide("impute"), + "sections": [ + { + "title": None, + "autosummary": [ + "IterativeImputer", + "KNNImputer", + "MissingIndicator", + "SimpleImputer", + ], + }, + ], + }, + "sklearn.inspection": { + "short_summary": "Inspection.", + "description": _get_guide("inspection"), + "sections": [ + { + "title": None, + "autosummary": ["partial_dependence", "permutation_importance"], + }, + { + "title": "Plotting", + "autosummary": ["DecisionBoundaryDisplay", "PartialDependenceDisplay"], + }, + ], + }, + "sklearn.isotonic": { + "short_summary": "Isotonic regression.", + "description": _get_guide("isotonic"), + "sections": [ + { + "title": None, + "autosummary": [ + "IsotonicRegression", + "check_increasing", + "isotonic_regression", + ], + }, + ], + }, + "sklearn.kernel_approximation": { + "short_summary": "Isotonic regression.", + "description": _get_guide("kernel_approximation"), + "sections": [ + { + "title": None, + "autosummary": [ + "AdditiveChi2Sampler", + "Nystroem", + "PolynomialCountSketch", + "RBFSampler", + "SkewedChi2Sampler", + ], + }, + ], + }, + "sklearn.kernel_ridge": { + "short_summary": "Kernel ridge regression.", + "description": _get_guide("kernel_ridge"), + "sections": [ + { + "title": None, + "autosummary": ["KernelRidge"], + }, + ], + }, + "sklearn.linear_model": { + "short_summary": "Generalized linear models.", + "description": ( + _get_guide("linear_model") + + "\n\nThe following subsections are only rough guidelines: the same " + "estimator can fall into multiple categories, depending on its parameters." + ), + "sections": [ + { + "title": "Linear classifiers", + "autosummary": [ + "LogisticRegression", + "LogisticRegressionCV", + "PassiveAggressiveClassifier", + "Perceptron", + "RidgeClassifier", + "RidgeClassifierCV", + "SGDClassifier", + "SGDOneClassSVM", + ], + }, + { + "title": "Classical linear regressors", + "autosummary": ["LinearRegression", "Ridge", "RidgeCV", "SGDRegressor"], + }, + { + "title": "Regressors with variable selection", + "description": ( + "The following estimators have built-in variable selection fitting " + "procedures, but any estimator using a L1 or elastic-net penalty " + "also performs variable selection: typically " + ":class:`~linear_model.SGDRegressor` or " + ":class:`~sklearn.linear_model.SGDClassifier` with an appropriate " + "penalty." + ), + "autosummary": [ + "ElasticNet", + "ElasticNetCV", + "Lars", + "LarsCV", + "Lasso", + "LassoCV", + "LassoLars", + "LassoLarsCV", + "LassoLarsIC", + "OrthogonalMatchingPursuit", + "OrthogonalMatchingPursuitCV", + ], + }, + { + "title": "Bayesian regressors", + "autosummary": ["ARDRegression", "BayesianRidge"], + }, + { + "title": "Multi-task linear regressors with variable selection", + "description": ( + "These estimators fit multiple regression problems (or tasks)" + " jointly, while inducing sparse coefficients. While the inferred" + " coefficients may differ between the tasks, they are constrained" + " to agree on the features that are selected (non-zero" + " coefficients)." + ), + "autosummary": [ + "MultiTaskElasticNet", + "MultiTaskElasticNetCV", + "MultiTaskLasso", + "MultiTaskLassoCV", + ], + }, + { + "title": "Outlier-robust regressors", + "description": ( + "Any estimator using the Huber loss would also be robust to " + "outliers, e.g., :class:`~linear_model.SGDRegressor` with " + "``loss='huber'``." + ), + "autosummary": [ + "HuberRegressor", + "QuantileRegressor", + "RANSACRegressor", + "TheilSenRegressor", + ], + }, + { + "title": "Generalized linear models (GLM) for regression", + "description": ( + "These models allow for response variables to have error " + "distributions other than a normal distribution." + ), + "autosummary": [ + "GammaRegressor", + "PoissonRegressor", + "TweedieRegressor", + ], + }, + { + "title": "Miscellaneous", + "autosummary": [ + "PassiveAggressiveRegressor", + "enet_path", + "lars_path", + "lars_path_gram", + "lasso_path", + "orthogonal_mp", + "orthogonal_mp_gram", + "ridge_regression", + ], + }, + ], + }, + "sklearn.manifold": { + "short_summary": "Manifold learning.", + "description": _get_guide("manifold"), + "sections": [ + { + "title": None, + "autosummary": [ + "Isomap", + "LocallyLinearEmbedding", + "MDS", + "SpectralEmbedding", + "TSNE", + "locally_linear_embedding", + "smacof", + "spectral_embedding", + "trustworthiness", + ], + }, + ], + }, + "sklearn.metrics": { + "short_summary": "Metrics.", + "description": _get_guide("model_evaluation", "metrics"), + "sections": [ + { + "title": "Model selection interface", + "description": _get_guide("scoring_parameter"), + "autosummary": [ + "check_scoring", + "get_scorer", + "get_scorer_names", + "make_scorer", + ], + }, + { + "title": "Classification metrics", + "description": _get_guide("classification_metrics"), + "autosummary": [ + "accuracy_score", + "auc", + "average_precision_score", + "balanced_accuracy_score", + "brier_score_loss", + "class_likelihood_ratios", + "classification_report", + "cohen_kappa_score", + "confusion_matrix", + "d2_log_loss_score", + "dcg_score", + "det_curve", + "f1_score", + "fbeta_score", + "hamming_loss", + "hinge_loss", + "jaccard_score", + "log_loss", + "matthews_corrcoef", + "multilabel_confusion_matrix", + "ndcg_score", + "precision_recall_curve", + "precision_recall_fscore_support", + "precision_score", + "recall_score", + "roc_auc_score", + "roc_curve", + "top_k_accuracy_score", + "zero_one_loss", + ], + }, + { + "title": "Regression metrics", + "description": _get_guide("regression_metrics"), + "autosummary": [ + "d2_absolute_error_score", + "d2_pinball_score", + "d2_tweedie_score", + "explained_variance_score", + "max_error", + "mean_absolute_error", + "mean_absolute_percentage_error", + "mean_gamma_deviance", + "mean_pinball_loss", + "mean_poisson_deviance", + "mean_squared_error", + "mean_squared_log_error", + "mean_tweedie_deviance", + "median_absolute_error", + "r2_score", + "root_mean_squared_error", + "root_mean_squared_log_error", + ], + }, + { + "title": "Multilabel ranking metrics", + "description": _get_guide("multilabel_ranking_metrics"), + "autosummary": [ + "coverage_error", + "label_ranking_average_precision_score", + "label_ranking_loss", + ], + }, + { + "title": "Clustering metrics", + "description": ( + _get_submodule("sklearn.metrics", "cluster") + + "\n\n" + + _get_guide("clustering_evaluation") + ), + "autosummary": [ + "adjusted_mutual_info_score", + "adjusted_rand_score", + "calinski_harabasz_score", + "cluster.contingency_matrix", + "cluster.pair_confusion_matrix", + "completeness_score", + "davies_bouldin_score", + "fowlkes_mallows_score", + "homogeneity_completeness_v_measure", + "homogeneity_score", + "mutual_info_score", + "normalized_mutual_info_score", + "rand_score", + "silhouette_samples", + "silhouette_score", + "v_measure_score", + ], + }, + { + "title": "Biclustering metrics", + "description": _get_guide("biclustering_evaluation"), + "autosummary": ["consensus_score"], + }, + { + "title": "Distance metrics", + "autosummary": ["DistanceMetric"], + }, + { + "title": "Pairwise metrics", + "description": ( + _get_submodule("sklearn.metrics", "pairwise") + + "\n\n" + + _get_guide("metrics") + ), + "autosummary": [ + "pairwise.additive_chi2_kernel", + "pairwise.chi2_kernel", + "pairwise.cosine_distances", + "pairwise.cosine_similarity", + "pairwise.distance_metrics", + "pairwise.euclidean_distances", + "pairwise.haversine_distances", + "pairwise.kernel_metrics", + "pairwise.laplacian_kernel", + "pairwise.linear_kernel", + "pairwise.manhattan_distances", + "pairwise.nan_euclidean_distances", + "pairwise.paired_cosine_distances", + "pairwise.paired_distances", + "pairwise.paired_euclidean_distances", + "pairwise.paired_manhattan_distances", + "pairwise.pairwise_kernels", + "pairwise.polynomial_kernel", + "pairwise.rbf_kernel", + "pairwise.sigmoid_kernel", + "pairwise_distances", + "pairwise_distances_argmin", + "pairwise_distances_argmin_min", + "pairwise_distances_chunked", + ], + }, + { + "title": "Plotting", + "description": _get_guide("visualizations"), + "autosummary": [ + "ConfusionMatrixDisplay", + "DetCurveDisplay", + "PrecisionRecallDisplay", + "PredictionErrorDisplay", + "RocCurveDisplay", + ], + }, + ], + }, + "sklearn.mixture": { + "short_summary": "Gaussian mixture models.", + "description": _get_guide("mixture"), + "sections": [ + { + "title": None, + "autosummary": ["BayesianGaussianMixture", "GaussianMixture"], + }, + ], + }, + "sklearn.model_selection": { + "short_summary": "Model selection.", + "description": _get_guide("cross_validation", "grid_search", "learning_curve"), + "sections": [ + { + "title": "Splitters", + "autosummary": [ + "GroupKFold", + "GroupShuffleSplit", + "KFold", + "LeaveOneGroupOut", + "LeaveOneOut", + "LeavePGroupsOut", + "LeavePOut", + "PredefinedSplit", + "RepeatedKFold", + "RepeatedStratifiedKFold", + "ShuffleSplit", + "StratifiedGroupKFold", + "StratifiedKFold", + "StratifiedShuffleSplit", + "TimeSeriesSplit", + "check_cv", + "train_test_split", + ], + }, + { + "title": "Hyper-parameter optimizers", + "autosummary": [ + "GridSearchCV", + "HalvingGridSearchCV", + "HalvingRandomSearchCV", + "ParameterGrid", + "ParameterSampler", + "RandomizedSearchCV", + ], + }, + { + "title": "Post-fit model tuning", + "autosummary": [ + "FixedThresholdClassifier", + "TunedThresholdClassifierCV", + ], + }, + { + "title": "Model validation", + "autosummary": [ + "cross_val_predict", + "cross_val_score", + "cross_validate", + "learning_curve", + "permutation_test_score", + "validation_curve", + ], + }, + { + "title": "Visualization", + "autosummary": ["LearningCurveDisplay", "ValidationCurveDisplay"], + }, + ], + }, + "sklearn.multiclass": { + "short_summary": "Multiclass classification.", + "description": _get_guide("multiclass_classification"), + "sections": [ + { + "title": None, + "autosummary": [ + "OneVsOneClassifier", + "OneVsRestClassifier", + "OutputCodeClassifier", + ], + }, + ], + }, + "sklearn.multioutput": { + "short_summary": "Multioutput regression and classification.", + "description": _get_guide( + "multilabel_classification", + "multiclass_multioutput_classification", + "multioutput_regression", + ), + "sections": [ + { + "title": None, + "autosummary": [ + "ClassifierChain", + "MultiOutputClassifier", + "MultiOutputRegressor", + "RegressorChain", + ], + }, + ], + }, + "sklearn.naive_bayes": { + "short_summary": "Naive Bayes.", + "description": _get_guide("naive_bayes"), + "sections": [ + { + "title": None, + "autosummary": [ + "BernoulliNB", + "CategoricalNB", + "ComplementNB", + "GaussianNB", + "MultinomialNB", + ], + }, + ], + }, + "sklearn.neighbors": { + "short_summary": "Nearest neighbors.", + "description": _get_guide("neighbors"), + "sections": [ + { + "title": None, + "autosummary": [ + "BallTree", + "KDTree", + "KNeighborsClassifier", + "KNeighborsRegressor", + "KNeighborsTransformer", + "KernelDensity", + "LocalOutlierFactor", + "NearestCentroid", + "NearestNeighbors", + "NeighborhoodComponentsAnalysis", + "RadiusNeighborsClassifier", + "RadiusNeighborsRegressor", + "RadiusNeighborsTransformer", + "kneighbors_graph", + "radius_neighbors_graph", + "sort_graph_by_row_values", + ], + }, + ], + }, + "sklearn.neural_network": { + "short_summary": "Neural network models.", + "description": _get_guide( + "neural_networks_supervised", "neural_networks_unsupervised" + ), + "sections": [ + { + "title": None, + "autosummary": ["BernoulliRBM", "MLPClassifier", "MLPRegressor"], + }, + ], + }, + "sklearn.pipeline": { + "short_summary": "Pipeline.", + "description": _get_guide("combining_estimators"), + "sections": [ + { + "title": None, + "autosummary": [ + "FeatureUnion", + "Pipeline", + "make_pipeline", + "make_union", + ], + }, + ], + }, + "sklearn.preprocessing": { + "short_summary": "Preprocessing and normalization.", + "description": _get_guide("preprocessing"), + "sections": [ + { + "title": None, + "autosummary": [ + "Binarizer", + "FunctionTransformer", + "KBinsDiscretizer", + "KernelCenterer", + "LabelBinarizer", + "LabelEncoder", + "MaxAbsScaler", + "MinMaxScaler", + "MultiLabelBinarizer", + "Normalizer", + "OneHotEncoder", + "OrdinalEncoder", + "PolynomialFeatures", + "PowerTransformer", + "QuantileTransformer", + "RobustScaler", + "SplineTransformer", + "StandardScaler", + "TargetEncoder", + "add_dummy_feature", + "binarize", + "label_binarize", + "maxabs_scale", + "minmax_scale", + "normalize", + "power_transform", + "quantile_transform", + "robust_scale", + "scale", + ], + }, + ], + }, + "sklearn.random_projection": { + "short_summary": "Random projection.", + "description": _get_guide("random_projection"), + "sections": [ + { + "title": None, + "autosummary": [ + "GaussianRandomProjection", + "SparseRandomProjection", + "johnson_lindenstrauss_min_dim", + ], + }, + ], + }, + "sklearn.semi_supervised": { + "short_summary": "Semi-supervised learning.", + "description": _get_guide("semi_supervised"), + "sections": [ + { + "title": None, + "autosummary": [ + "LabelPropagation", + "LabelSpreading", + "SelfTrainingClassifier", + ], + }, + ], + }, + "sklearn.svm": { + "short_summary": "Support vector machines.", + "description": _get_guide("svm"), + "sections": [ + { + "title": None, + "autosummary": [ + "LinearSVC", + "LinearSVR", + "NuSVC", + "NuSVR", + "OneClassSVM", + "SVC", + "SVR", + "l1_min_c", + ], + }, + ], + }, + "sklearn.tree": { + "short_summary": "Decision trees.", + "description": _get_guide("tree"), + "sections": [ + { + "title": None, + "autosummary": [ + "DecisionTreeClassifier", + "DecisionTreeRegressor", + "ExtraTreeClassifier", + "ExtraTreeRegressor", + ], + }, + { + "title": "Exporting", + "autosummary": ["export_graphviz", "export_text"], + }, + { + "title": "Plotting", + "autosummary": ["plot_tree"], + }, + ], + }, + "sklearn.utils": { + "short_summary": "Utilities.", + "description": _get_guide("developers-utils", is_developer=True), + "sections": [ + { + "title": None, + "autosummary": [ + "Bunch", + "_safe_indexing", + "as_float_array", + "assert_all_finite", + "deprecated", + "estimator_html_repr", + "gen_batches", + "gen_even_slices", + "indexable", + "murmurhash3_32", + "resample", + "safe_mask", + "safe_sqr", + "shuffle", + ], + }, + { + "title": "Input and parameter validation", + "description": _get_submodule("sklearn.utils", "validation"), + "autosummary": [ + "check_X_y", + "check_array", + "check_consistent_length", + "check_random_state", + "check_scalar", + "validation.check_is_fitted", + "validation.check_memory", + "validation.check_symmetric", + "validation.column_or_1d", + "validation.has_fit_parameter", + ], + }, + { + "title": "Meta-estimators", + "description": _get_submodule("sklearn.utils", "metaestimators"), + "autosummary": ["metaestimators.available_if"], + }, + { + "title": "Weight handling based on class labels", + "description": _get_submodule("sklearn.utils", "class_weight"), + "autosummary": [ + "class_weight.compute_class_weight", + "class_weight.compute_sample_weight", + ], + }, + { + "title": "Dealing with multiclass target in classifiers", + "description": _get_submodule("sklearn.utils", "multiclass"), + "autosummary": [ + "multiclass.is_multilabel", + "multiclass.type_of_target", + "multiclass.unique_labels", + ], + }, + { + "title": "Optimal mathematical operations", + "description": _get_submodule("sklearn.utils", "extmath"), + "autosummary": [ + "extmath.density", + "extmath.fast_logdet", + "extmath.randomized_range_finder", + "extmath.randomized_svd", + "extmath.safe_sparse_dot", + "extmath.weighted_mode", + ], + }, + { + "title": "Working with sparse matrices and arrays", + "description": _get_submodule("sklearn.utils", "sparsefuncs"), + "autosummary": [ + "sparsefuncs.incr_mean_variance_axis", + "sparsefuncs.inplace_column_scale", + "sparsefuncs.inplace_csr_column_scale", + "sparsefuncs.inplace_row_scale", + "sparsefuncs.inplace_swap_column", + "sparsefuncs.inplace_swap_row", + "sparsefuncs.mean_variance_axis", + ], + }, + { + "title": None, + "description": _get_submodule("sklearn.utils", "sparsefuncs_fast"), + "autosummary": [ + "sparsefuncs_fast.inplace_csr_row_normalize_l1", + "sparsefuncs_fast.inplace_csr_row_normalize_l2", + ], + }, + { + "title": "Working with graphs", + "description": _get_submodule("sklearn.utils", "graph"), + "autosummary": ["graph.single_source_shortest_path_length"], + }, + { + "title": "Random sampling", + "description": _get_submodule("sklearn.utils", "random"), + "autosummary": ["random.sample_without_replacement"], + }, + { + "title": "Auxiliary functions that operate on arrays", + "description": _get_submodule("sklearn.utils", "arrayfuncs"), + "autosummary": ["arrayfuncs.min_pos"], + }, + { + "title": "Metadata routing", + "description": ( + _get_submodule("sklearn.utils", "metadata_routing") + + "\n\n" + + _get_guide("metadata_routing") + ), + "autosummary": [ + "metadata_routing.MetadataRequest", + "metadata_routing.MetadataRouter", + "metadata_routing.MethodMapping", + "metadata_routing.get_routing_for_object", + "metadata_routing.process_routing", + ], + }, + { + "title": "Discovering scikit-learn objects", + "description": _get_submodule("sklearn.utils", "discovery"), + "autosummary": [ + "discovery.all_displays", + ], + }, + { + "title": "API compatibility checkers", + "description": _get_submodule("sklearn.utils", "estimator_checks"), + "autosummary": [ + "estimator_checks.check_estimator", + "estimator_checks.parametrize_with_checks", + ], + }, + { + "title": "Parallel computing", + "description": _get_submodule("sklearn.utils", "parallel"), + "autosummary": [ + "parallel.Parallel", + "parallel.delayed", + ], + }, + ], + }, +} + + +""" +CONFIGURING DEPRECATED_API_REFERENCE +==================================== + +DEPRECATED_API_REFERENCE maps each deprecation target version to a corresponding +autosummary block. It will be placed at the bottom of the API index page under the +"Recently deprecated" section. Essentially, the rendered section would look like the +following: + +|------------------------------------------| +| To be removed in {{ version_1 }} | +| -------------------------------- | +| {{ autosummary_1 }} | +| | +| To be removed in {{ version_2 }} | +| -------------------------------- | +| {{ autosummary_2 }} | +| | +| More versions... | +|------------------------------------------| + +Note that the autosummary here assumes that the current module is `sklearn`, i.e., if +`sklearn.utils.Memory` is deprecated, one should put `utils.Memory` in the "entries" +slot of the autosummary block. + +Example: + +DEPRECATED_API_REFERENCE = { + "0.24": [ + "model_selection.fit_grid_point", + "utils.safe_indexing", + ], +} +""" + +DEPRECATED_API_REFERENCE = { + "1.6": [ + "utils.parallel_backend", + "utils.register_parallel_backend", + ], + "1.7": [ + "utils.discovery.all_estimators", + "utils.discovery.all_functions", + ], +} # type: ignore diff --git a/doc/common_pitfalls.rst b/doc/common_pitfalls.rst index 41eb16665a612..c16385943f9ad 100644 --- a/doc/common_pitfalls.rst +++ b/doc/common_pitfalls.rst @@ -1,9 +1,3 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - -.. include:: includes/big_toc_css.rst - .. _common_pitfalls: ========================================= @@ -414,43 +408,40 @@ it will allow the estimator RNG to vary for each fold. illustration purpose: what matters is what we pass to the :class:`~sklearn.ensemble.RandomForestClassifier` estimator. -|details-start| -**Cloning** -|details-split| +.. dropdown:: Cloning -Another subtle side effect of passing `RandomState` instances is how -:func:`~sklearn.base.clone` will work:: + Another subtle side effect of passing `RandomState` instances is how + :func:`~sklearn.base.clone` will work:: - >>> from sklearn import clone - >>> from sklearn.ensemble import RandomForestClassifier - >>> import numpy as np + >>> from sklearn import clone + >>> from sklearn.ensemble import RandomForestClassifier + >>> import numpy as np + + >>> rng = np.random.RandomState(0) + >>> a = RandomForestClassifier(random_state=rng) + >>> b = clone(a) + + Since a `RandomState` instance was passed to `a`, `a` and `b` are not clones + in the strict sense, but rather clones in the statistical sense: `a` and `b` + will still be different models, even when calling `fit(X, y)` on the same + data. Moreover, `a` and `b` will influence each-other since they share the + same internal RNG: calling `a.fit` will consume `b`'s RNG, and calling + `b.fit` will consume `a`'s RNG, since they are the same. This bit is true for + any estimators that share a `random_state` parameter; it is not specific to + clones. + + If an integer were passed, `a` and `b` would be exact clones and they would not + influence each other. + + .. warning:: + Even though :func:`~sklearn.base.clone` is rarely used in user code, it is + called pervasively throughout scikit-learn codebase: in particular, most + meta-estimators that accept non-fitted estimators call + :func:`~sklearn.base.clone` internally + (:class:`~sklearn.model_selection.GridSearchCV`, + :class:`~sklearn.ensemble.StackingClassifier`, + :class:`~sklearn.calibration.CalibratedClassifierCV`, etc.). - >>> rng = np.random.RandomState(0) - >>> a = RandomForestClassifier(random_state=rng) - >>> b = clone(a) - -Since a `RandomState` instance was passed to `a`, `a` and `b` are not clones -in the strict sense, but rather clones in the statistical sense: `a` and `b` -will still be different models, even when calling `fit(X, y)` on the same -data. Moreover, `a` and `b` will influence each-other since they share the -same internal RNG: calling `a.fit` will consume `b`'s RNG, and calling -`b.fit` will consume `a`'s RNG, since they are the same. This bit is true for -any estimators that share a `random_state` parameter; it is not specific to -clones. - -If an integer were passed, `a` and `b` would be exact clones and they would not -influence each other. - -.. warning:: - Even though :func:`~sklearn.base.clone` is rarely used in user code, it is - called pervasively throughout scikit-learn codebase: in particular, most - meta-estimators that accept non-fitted estimators call - :func:`~sklearn.base.clone` internally - (:class:`~sklearn.model_selection.GridSearchCV`, - :class:`~sklearn.ensemble.StackingClassifier`, - :class:`~sklearn.calibration.CalibratedClassifierCV`, etc.). - -|details-end| CV splitters ............ diff --git a/doc/computing.rst b/doc/computing.rst index 6732b754918b0..9f166432006b2 100644 --- a/doc/computing.rst +++ b/doc/computing.rst @@ -1,13 +1,7 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - ============================ Computing with scikit-learn ============================ -.. include:: includes/big_toc_css.rst - .. toctree:: :maxdepth: 2 diff --git a/doc/computing/computational_performance.rst b/doc/computing/computational_performance.rst index d6864689502c2..a7b6d3a37001e 100644 --- a/doc/computing/computational_performance.rst +++ b/doc/computing/computational_performance.rst @@ -1,7 +1,3 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - .. _computational_performance: .. currentmodule:: sklearn diff --git a/doc/computing/parallelism.rst b/doc/computing/parallelism.rst index 53cef5603c5be..5c15cd9db440e 100644 --- a/doc/computing/parallelism.rst +++ b/doc/computing/parallelism.rst @@ -1,7 +1,3 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - Parallelism, resource management, and configuration =================================================== diff --git a/doc/computing/scaling_strategies.rst b/doc/computing/scaling_strategies.rst index 143643131b0e8..286a1e79d0a8c 100644 --- a/doc/computing/scaling_strategies.rst +++ b/doc/computing/scaling_strategies.rst @@ -1,7 +1,3 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - .. _scaling_strategies: Strategies to scale computationally: bigger data diff --git a/doc/conf.py b/doc/conf.py index 0587e98130118..f025c77dcce0c 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -15,7 +15,6 @@ import sys import warnings from datetime import datetime -from io import StringIO from pathlib import Path from sklearn.externals._packaging.version import parse @@ -25,8 +24,10 @@ # directory, add these directories to sys.path here. If the directory # is relative to the documentation root, use os.path.abspath to make it # absolute, like shown here. +sys.path.insert(0, os.path.abspath(".")) sys.path.insert(0, os.path.abspath("sphinxext")) +import jinja2 import sphinx_gallery from github_link import make_linkcode_resolve from sphinx_gallery.notebook import add_code_cell, add_markdown_cell @@ -56,14 +57,21 @@ "sphinx.ext.intersphinx", "sphinx.ext.imgconverter", "sphinx_gallery.gen_gallery", - "sphinx_issues", - "add_toctree_functions", "sphinx-prompt", "sphinx_copybutton", "sphinxext.opengraph", - "doi_role", - "allow_nan_estimators", "matplotlib.sphinxext.plot_directive", + "sphinxcontrib.sass", + "sphinx_remove_toctrees", + "sphinx_design", + # See sphinxext/ + "allow_nan_estimators", + "autoshortsummary", + "doi_role", + "dropdown_anchors", + "move_gallery_links", + "override_pst_pagetoc", + "sphinx_issues", ] # Specify how to identify the prompt when copying code snippets @@ -96,8 +104,12 @@ plot_html_show_formats = False plot_html_show_source_link = False -# this is needed for some reason... -# see https://github.com/numpy/numpydoc/issues/69 +# We do not need the table of class members because `sphinxext/override_pst_pagetoc.py` +# will show them in the secondary sidebar +numpydoc_show_class_members = False +numpydoc_show_inherited_class_members = False + +# We want in-page toc of class members instead of a separate page for each entry numpydoc_class_members_toctree = False @@ -111,8 +123,6 @@ extensions.append("sphinx.ext.mathjax") mathjax_path = "https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js" -autodoc_default_options = {"members": True, "inherited-members": True} - # Add any paths that contain templates here, relative to this directory. templates_path = ["templates"] @@ -123,10 +133,10 @@ source_suffix = ".rst" # The encoding of source files. -# source_encoding = 'utf-8' +source_encoding = "utf-8" # The main toctree document. -root_doc = "contents" +root_doc = "index" # General information about the project. project = "scikit-learn" @@ -160,7 +170,12 @@ # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. -exclude_patterns = ["_build", "templates", "includes", "themes"] +exclude_patterns = [ + "_build", + "templates", + "includes", + "**/sg_execution_times.rst", +] # The reST default role (used for this markup: `text`) to use for all # documents. @@ -177,9 +192,6 @@ # output. They are ignored by default. # show_authors = False -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = "sphinx" - # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] @@ -188,21 +200,89 @@ # The theme to use for HTML and HTML Help pages. Major themes that come with # Sphinx are currently 'default' and 'sphinxdoc'. -html_theme = "scikit-learn-modern" +html_theme = "pydata_sphinx_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = { - "legacy_google_analytics": True, - "analytics": True, - "mathjax_path": mathjax_path, - "link_to_live_contributing_page": not parsed_version.is_devrelease, + # -- General configuration ------------------------------------------------ + "sidebar_includehidden": True, + "use_edit_page_button": True, + "external_links": [], + "icon_links_label": "Icon Links", + "icon_links": [ + { + "name": "GitHub", + "url": "https://github.com/scikit-learn/scikit-learn", + "icon": "fa-brands fa-square-github", + "type": "fontawesome", + }, + ], + "analytics": { + "plausible_analytics_domain": "scikit-learn.org", + "plausible_analytics_url": "https://views.scientific-python.org/js/script.js", + }, + # If "prev-next" is included in article_footer_items, then setting show_prev_next + # to True would repeat prev and next links. See + # https://github.com/pydata/pydata-sphinx-theme/blob/b731dc230bc26a3d1d1bb039c56c977a9b3d25d8/src/pydata_sphinx_theme/theme/pydata_sphinx_theme/layout.html#L118-L129 + "show_prev_next": False, + "search_bar_text": "Search the docs ...", + "navigation_with_keys": False, + "collapse_navigation": False, + "navigation_depth": 2, + "show_nav_level": 1, + "show_toc_level": 1, + "navbar_align": "left", + "header_links_before_dropdown": 5, + "header_dropdown_text": "More", + # The switcher requires a JSON file with the list of documentation versions, which + # is generated by the script `build_tools/circle/list_versions.py` and placed under + # the `js/` static directory; it will then be copied to the `_static` directory in + # the built documentation + "switcher": { + "json_url": "https://scikit-learn.org/dev/_static/versions.json", + "version_match": release, + }, + # check_switcher may be set to False if docbuild pipeline fails. See + # https://pydata-sphinx-theme.readthedocs.io/en/stable/user_guide/version-dropdown.html#configure-switcher-json-url + "check_switcher": True, + "pygment_light_style": "tango", + "pygment_dark_style": "monokai", + "logo": { + "alt_text": "scikit-learn homepage", + "image_relative": "logos/scikit-learn-logo-small.png", + "image_light": "logos/scikit-learn-logo-small.png", + "image_dark": "logos/scikit-learn-logo-small.png", + }, + "surface_warnings": True, + # -- Template placement in theme layouts ---------------------------------- + "navbar_start": ["navbar-logo"], + # Note that the alignment of navbar_center is controlled by navbar_align + "navbar_center": ["navbar-nav"], + "navbar_end": ["theme-switcher", "navbar-icon-links", "version-switcher"], + # navbar_persistent is persistent right (even when on mobiles) + "navbar_persistent": ["search-button"], + "article_header_start": ["breadcrumbs"], + "article_header_end": [], + "article_footer_items": ["prev-next"], + "content_footer_items": [], + # Use html_sidebars that map page patterns to list of sidebar templates + "primary_sidebar_end": [], + "footer_start": ["copyright"], + "footer_center": [], + "footer_end": [], + # When specified as a dictionary, the keys should follow glob-style patterns, as in + # https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-exclude_patterns + # In particular, "**" specifies the default for all pages + # Use :html_theme.sidebar_secondary.remove: for file-wide removal + "secondary_sidebar_items": {"**": ["page-toc", "sourcelink"]}, + "show_version_warning_banner": True, + "announcement": None, } # Add any paths that contain custom themes here, relative to this directory. -html_theme_path = ["themes"] - +# html_theme_path = ["themes"] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". @@ -211,10 +291,6 @@ # A shorter title for the navigation bar. Default is the same as html_title. html_short_title = "scikit-learn" -# The name of an image file (relative to this directory) to place at the top -# of the sidebar. -html_logo = "logos/scikit-learn-logo-small.png" - # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. @@ -223,19 +299,77 @@ # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ["images"] +html_static_path = ["images", "css", "js"] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. # html_last_updated_fmt = '%b %d, %Y' # Custom sidebar templates, maps document names to template names. -# html_sidebars = {} +# Workaround for removing the left sidebar on pages without TOC +# A better solution would be to follow the merge of: +# https://github.com/pydata/pydata-sphinx-theme/pull/1682 +html_sidebars = { + "install": [], + "getting_started": [], + "glossary": [], + "faq": [], + "support": [], + "related_projects": [], + "roadmap": [], + "governance": [], + "about": [], +} # Additional templates that should be rendered to pages, maps page names to # template names. html_additional_pages = {"index": "index.html"} +# Additional files to copy +# html_extra_path = [] + +# Additional JS files +html_js_files = [ + "scripts/dropdown.js", + "scripts/version-switcher.js", +] + +# Compile scss files into css files using sphinxcontrib-sass +sass_src_dir, sass_out_dir = "scss", "css/styles" +sass_targets = { + f"{file.stem}.scss": f"{file.stem}.css" + for file in Path(sass_src_dir).glob("*.scss") +} + +# Additional CSS files, should be subset of the values of `sass_targets` +html_css_files = ["styles/colors.css", "styles/custom.css"] + + +def add_js_css_files(app, pagename, templatename, context, doctree): + """Load additional JS and CSS files only for certain pages. + + Note that `html_js_files` and `html_css_files` are included in all pages and + should be used for the ones that are used by multiple pages. All page-specific + JS and CSS files should be added here instead. + """ + if pagename == "api/index": + # External: jQuery and DataTables + app.add_js_file("https://code.jquery.com/jquery-3.7.0.js") + app.add_js_file("https://cdn.datatables.net/2.0.0/js/dataTables.min.js") + app.add_css_file( + "https://cdn.datatables.net/2.0.0/css/dataTables.dataTables.min.css" + ) + # Internal: API search intialization and styling + app.add_js_file("scripts/api-search.js") + app.add_css_file("styles/api-search.css") + elif pagename == "index": + app.add_css_file("styles/index.css") + elif pagename == "install": + app.add_css_file("styles/install.css") + elif pagename.startswith("modules/generated/"): + app.add_css_file("styles/api.css") + + # If false, no module index is generated. html_domain_indices = False @@ -285,6 +419,9 @@ # redirects dictionary maps from old links to new links redirects = { "documentation": "index", + "contents": "index", + "preface": "index", + "modules/classes": "api/index", "auto_examples/feature_selection/plot_permutation_test_for_classification": ( "auto_examples/model_selection/plot_permutation_tests_for_classification" ), @@ -316,32 +453,13 @@ for old_link in redirects: html_additional_pages[old_link] = "redirects.html" +# See https://github.com/scikit-learn/scikit-learn/pull/22550 +html_context["is_devrelease"] = parsed_version.is_devrelease + # Not showing the search summary makes the search page load faster. html_show_search_summary = True -# The "summary-anchor" IDs will be overwritten via JavaScript to be unique. -# See `doc/theme/scikit-learn-modern/static/js/details-permalink.js`. -rst_prolog = """ -.. |details-start| raw:: html - -
- - -.. |details-split| raw:: html - - Click for more details - - -
- -.. |details-end| raw:: html - -
-
- -""" - # -- Options for LaTeX output ------------------------------------------------ latex_elements = { # The paper size ('letterpaper' or 'a4paper'). @@ -528,14 +646,16 @@ def reset_sklearn_config(gallery_conf, fname): sklearn.set_config(**default_global_config) +sg_examples_dir = "../examples" +sg_gallery_dir = "auto_examples" sphinx_gallery_conf = { "doc_module": "sklearn", "backreferences_dir": os.path.join("modules", "generated"), "show_memory": False, "reference_url": {"sklearn": None}, - "examples_dirs": ["../examples"], - "gallery_dirs": ["auto_examples"], - "subsection_order": SubSectionTitleOrder("../examples"), + "examples_dirs": [sg_examples_dir], + "gallery_dirs": [sg_gallery_dir], + "subsection_order": SubSectionTitleOrder(sg_examples_dir), "within_subsection_order": SKExampleTitleSortKey, "binder": { "org": "scikit-learn", @@ -549,7 +669,7 @@ def reset_sklearn_config(gallery_conf, fname): "inspect_global_variables": False, "remove_config_comments": True, "plot_gallery": "True", - "recommender": {"enable": True, "n_examples": 5, "min_df": 12}, + "recommender": {"enable": True, "n_examples": 4, "min_df": 12}, "reset_modules": ("matplotlib", "seaborn", reset_sklearn_config), } if with_jupyterlite: @@ -557,6 +677,26 @@ def reset_sklearn_config(gallery_conf, fname): "notebook_modification_function": notebook_modification_function } +# Secondary sidebar configuration for pages generated by sphinx-gallery + +# For the index page of the gallery and each nested section, we hide the secondary +# sidebar by specifying an empty list (no components), because there is no meaningful +# in-page toc for these pages, and they are generated so "sourcelink" is not useful +# either. + +# For each example page we keep default ["page-toc", "sourcelink"] specified by the +# "**" key. "page-toc" is wanted for these pages. "sourcelink" is also necessary since +# otherwise the secondary sidebar will degenerate when "page-toc" is empty, and the +# script `sphinxext/move_gallery_links.py` will fail (it assumes the existence of the +# secondary sidebar). The script will remove "sourcelink" in the end. + +html_theme_options["secondary_sidebar_items"][f"{sg_gallery_dir}/index"] = [] +for sub_sg_dir in (Path(".") / sg_examples_dir).iterdir(): + if sub_sg_dir.is_dir(): + html_theme_options["secondary_sidebar_items"][ + f"{sg_gallery_dir}/{sub_sg_dir.name}/index" + ] = [] + # The following dictionary contains the information used to create the # thumbnails for the front page of the scikit-learn home page. @@ -606,73 +746,6 @@ def filter_search_index(app, exception): f.write(searchindex_text) -def generate_min_dependency_table(app): - """Generate min dependency table for docs.""" - from sklearn._min_dependencies import dependent_packages - - # get length of header - package_header_len = max(len(package) for package in dependent_packages) + 4 - version_header_len = len("Minimum Version") + 4 - tags_header_len = max(len(tags) for _, tags in dependent_packages.values()) + 4 - - output = StringIO() - output.write( - " ".join( - ["=" * package_header_len, "=" * version_header_len, "=" * tags_header_len] - ) - ) - output.write("\n") - dependency_title = "Dependency" - version_title = "Minimum Version" - tags_title = "Purpose" - - output.write( - f"{dependency_title:<{package_header_len}} " - f"{version_title:<{version_header_len}} " - f"{tags_title}\n" - ) - - output.write( - " ".join( - ["=" * package_header_len, "=" * version_header_len, "=" * tags_header_len] - ) - ) - output.write("\n") - - for package, (version, tags) in dependent_packages.items(): - output.write( - f"{package:<{package_header_len}} {version:<{version_header_len}} {tags}\n" - ) - - output.write( - " ".join( - ["=" * package_header_len, "=" * version_header_len, "=" * tags_header_len] - ) - ) - output.write("\n") - output = output.getvalue() - - with (Path(".") / "min_dependency_table.rst").open("w") as f: - f.write(output) - - -def generate_min_dependency_substitutions(app): - """Generate min dependency substitutions for docs.""" - from sklearn._min_dependencies import dependent_packages - - output = StringIO() - - for package, (version, _) in dependent_packages.items(): - package = package.capitalize() - output.write(f".. |{package}MinVersion| replace:: {version}") - output.write("\n") - - output = output.getvalue() - - with (Path(".") / "min_dependency_substitutions.rst").open("w") as f: - f.write(output) - - # Config for sphinx_issues # we use the issues path for PRs since the issues URL will forward @@ -688,10 +761,11 @@ def setup(app): # do not run the examples when using linkcheck by using a small priority # (default priority is 500 and sphinx-gallery using builder-inited event too) app.connect("builder-inited", disable_plot_gallery_for_linkcheck, priority=50) - app.connect("builder-inited", generate_min_dependency_table) - app.connect("builder-inited", generate_min_dependency_substitutions) - # to hide/show the prompt in code examples: + # triggered just before the HTML for an individual page is created + app.connect("html-page-context", add_js_css_files) + + # to hide/show the prompt in code examples app.connect("build-finished", make_carousel_thumbs) app.connect("build-finished", filter_search_index) @@ -796,6 +870,10 @@ def setup(app): "consistently-create-same-random-numpy-array/5837352#comment6712034_5837352", ] +# Config for sphinx-remove-toctrees + +remove_from_toctrees = ["metadata_routing.rst"] + # Use a browser-like user agent to avoid some "403 Client Error: Forbidden for # url" errors. This is taken from the variable navigator.userAgent inside a # browser console. @@ -813,3 +891,78 @@ def setup(app): linkcheck_request_headers = { "https://github.com/": {"Authorization": f"token {github_token}"}, } + + +# -- Convert .rst.template files to .rst --------------------------------------- + +from api_reference import API_REFERENCE, DEPRECATED_API_REFERENCE + +from sklearn._min_dependencies import dependent_packages + +# If development build, link to local page in the top navbar; otherwise link to the +# development version; see https://github.com/scikit-learn/scikit-learn/pull/22550 +if parsed_version.is_devrelease: + development_link = "developers/index" +else: + development_link = "https://scikit-learn.org/dev/developers/index.html" + +# Define the templates and target files for conversion +# Each entry is in the format (template name, file name, kwargs for rendering) +rst_templates = [ + ("index", "index", {"development_link": development_link}), + ( + "min_dependency_table", + "min_dependency_table", + {"dependent_packages": dependent_packages}, + ), + ( + "min_dependency_substitutions", + "min_dependency_substitutions", + {"dependent_packages": dependent_packages}, + ), + ( + "api/index", + "api/index", + { + "API_REFERENCE": sorted(API_REFERENCE.items(), key=lambda x: x[0]), + "DEPRECATED_API_REFERENCE": sorted( + DEPRECATED_API_REFERENCE.items(), key=lambda x: x[0], reverse=True + ), + }, + ), +] + +# Convert each module API reference page +for module in API_REFERENCE: + rst_templates.append( + ( + "api/module", + f"api/{module}", + {"module": module, "module_info": API_REFERENCE[module]}, + ) + ) + +# Convert the deprecated API reference page (if there exists any) +if DEPRECATED_API_REFERENCE: + rst_templates.append( + ( + "api/deprecated", + "api/deprecated", + { + "DEPRECATED_API_REFERENCE": sorted( + DEPRECATED_API_REFERENCE.items(), key=lambda x: x[0], reverse=True + ) + }, + ) + ) + +for rst_template_name, rst_target_name, kwargs in rst_templates: + # Read the corresponding template file into jinja2 + with (Path(".") / f"{rst_template_name}.rst.template").open( + "r", encoding="utf-8" + ) as f: + t = jinja2.Template(f.read()) + + # Render the template and write to the target + with (Path(".") / f"{rst_target_name}.rst").open("w", encoding="utf-8") as f: + f.write(t.render(**kwargs)) diff --git a/doc/contents.rst b/doc/contents.rst deleted file mode 100644 index a28634621d558..0000000000000 --- a/doc/contents.rst +++ /dev/null @@ -1,24 +0,0 @@ -.. include:: includes/big_toc_css.rst -.. include:: tune_toc.rst - -.. Places global toc into the sidebar - -:globalsidebartoc: True - -================= -Table Of Contents -================= - -.. Define an order for the Table of Contents: - -.. toctree:: - :maxdepth: 2 - - preface - tutorial/index - getting_started - user_guide - glossary - auto_examples/index - modules/classes - developers/index diff --git a/doc/css/.gitkeep b/doc/css/.gitkeep new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/doc/data_transforms.rst b/doc/data_transforms.rst index 084214cb094f5..536539ec97007 100644 --- a/doc/data_transforms.rst +++ b/doc/data_transforms.rst @@ -1,9 +1,3 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - -.. include:: includes/big_toc_css.rst - .. _data-transforms: Dataset transformations diff --git a/doc/datasets.rst b/doc/datasets.rst index b9484a02ce84c..ee767e5843256 100644 --- a/doc/datasets.rst +++ b/doc/datasets.rst @@ -1,9 +1,3 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - -.. include:: includes/big_toc_css.rst - .. _datasets: ========================= diff --git a/doc/datasets/loading_other_datasets.rst b/doc/datasets/loading_other_datasets.rst index fdd7fd1666cce..004aa66c001e5 100644 --- a/doc/datasets/loading_other_datasets.rst +++ b/doc/datasets/loading_other_datasets.rst @@ -1,7 +1,3 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - .. _loading_other_datasets: Loading other datasets @@ -37,9 +33,9 @@ and pipelines on 2D data. if you plan to use ``matplotlib.pyplpt.imshow``, don't forget to scale to the range 0 - 1 as done in the following example. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_cluster_plot_color_quantization.py` +* :ref:`sphx_glr_auto_examples_cluster_plot_color_quantization.py` .. _libsvm_loader: @@ -72,11 +68,10 @@ features:: ... "/path/to/test_dataset.txt", n_features=X_train.shape[1]) ... # doctest: +SKIP -.. topic:: Related links: - - _`Public datasets in svmlight / libsvm format`: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets +.. rubric:: Related links - _`Faster API-compatible implementation`: https://github.com/mblondel/svmlight-loader +- `Public datasets in svmlight / libsvm format`: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets +- `Faster API-compatible implementation`: https://github.com/mblondel/svmlight-loader .. For doctests: @@ -219,11 +214,11 @@ identifies the dataset:: '969' -.. topic:: References: +.. rubric:: References - * :arxiv:`Vanschoren, van Rijn, Bischl and Torgo. "OpenML: networked science in - machine learning" ACM SIGKDD Explorations Newsletter, 15(2), 49-60, 2014. - <1407.7722>` +* :arxiv:`Vanschoren, van Rijn, Bischl and Torgo. "OpenML: networked science in + machine learning" ACM SIGKDD Explorations Newsletter, 15(2), 49-60, 2014. + <1407.7722>` .. _openml_parser: diff --git a/doc/datasets/real_world.rst b/doc/datasets/real_world.rst index 78b09e6f722b0..f05d475b0db78 100644 --- a/doc/datasets/real_world.rst +++ b/doc/datasets/real_world.rst @@ -1,7 +1,3 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - .. _real_world_datasets: Real world datasets diff --git a/doc/datasets/sample_generators.rst b/doc/datasets/sample_generators.rst index 7dc123f08424c..5b8264c2a22b5 100644 --- a/doc/datasets/sample_generators.rst +++ b/doc/datasets/sample_generators.rst @@ -1,7 +1,3 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - .. _sample_generators: Generated datasets diff --git a/doc/datasets/toy_dataset.rst b/doc/datasets/toy_dataset.rst index 65fd20abd361d..d7edecddd3510 100644 --- a/doc/datasets/toy_dataset.rst +++ b/doc/datasets/toy_dataset.rst @@ -1,7 +1,3 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - .. _toy_datasets: Toy datasets diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst index 9f43d8ed52c38..402711dcd1bf3 100644 --- a/doc/developers/contributing.rst +++ b/doc/developers/contributing.rst @@ -70,10 +70,10 @@ link to it from your website, or simply star to say "I use it": .. raw:: html - Star - + Star + In case a contribution/issue involves changes to the API principles or changes to dependencies or supported versions, it must be backed by a @@ -82,31 +82,27 @@ or changes to dependencies or supported versions, it must be backed by a using the `SLEP template `_ and follows the decision-making process outlined in :ref:`governance`. -|details-start| -**Contributing to related projects** -|details-split| +.. dropdown:: Contributing to related projects - Scikit-learn thrives in an ecosystem of several related projects, which also - may have relevant issues to work on, including smaller projects such as: + Scikit-learn thrives in an ecosystem of several related projects, which also + may have relevant issues to work on, including smaller projects such as: - * `scikit-learn-contrib `__ - * `joblib `__ - * `sphinx-gallery `__ - * `numpydoc `__ - * `liac-arff `__ + * `scikit-learn-contrib `__ + * `joblib `__ + * `sphinx-gallery `__ + * `numpydoc `__ + * `liac-arff `__ - and larger projects: + and larger projects: - * `numpy `__ - * `scipy `__ - * `matplotlib `__ - * and so on. + * `numpy `__ + * `scipy `__ + * `matplotlib `__ + * and so on. - Look for issues marked "help wanted" or similar. - Helping these projects may help Scikit-learn too. - See also :ref:`related_projects`. - -|details-end| + Look for issues marked "help wanted" or similar. + Helping these projects may help Scikit-learn too. + See also :ref:`related_projects`. Submitting a bug report or a feature request ============================================ @@ -674,219 +670,200 @@ We are glad to accept any sort of documentation: useful information (e.g., the :ref:`contributing` guide) and live in `doc/ `_. -|details-start| -**Guidelines for writing docstrings** -|details-split| - -* When documenting the parameters and attributes, here is a list of some - well-formatted examples:: - - n_clusters : int, default=3 - The number of clusters detected by the algorithm. - - some_param : {'hello', 'goodbye'}, bool or int, default=True - The parameter description goes here, which can be either a string - literal (either `hello` or `goodbye`), a bool, or an int. The default - value is True. - array_parameter : {array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples,) - This parameter accepts data in either of the mentioned forms, with one - of the mentioned shapes. The default value is - `np.ones(shape=(n_samples,))`. +.. dropdown:: Guidelines for writing docstrings - list_param : list of int + * When documenting the parameters and attributes, here is a list of some + well-formatted examples:: - typed_ndarray : ndarray of shape (n_samples,), dtype=np.int32 + n_clusters : int, default=3 + The number of clusters detected by the algorithm. - sample_weight : array-like of shape (n_samples,), default=None + some_param : {'hello', 'goodbye'}, bool or int, default=True + The parameter description goes here, which can be either a string + literal (either `hello` or `goodbye`), a bool, or an int. The default + value is True. - multioutput_array : ndarray of shape (n_samples, n_classes) or list of such arrays + array_parameter : {array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples,) + This parameter accepts data in either of the mentioned forms, with one + of the mentioned shapes. The default value is + `np.ones(shape=(n_samples,))`. - In general have the following in mind: + list_param : list of int - * Use Python basic types. (``bool`` instead of ``boolean``) - * Use parenthesis for defining shapes: ``array-like of shape (n_samples,)`` - or ``array-like of shape (n_samples, n_features)`` - * For strings with multiple options, use brackets: ``input: {'log', - 'squared', 'multinomial'}`` - * 1D or 2D data can be a subset of ``{array-like, ndarray, sparse matrix, - dataframe}``. Note that ``array-like`` can also be a ``list``, while - ``ndarray`` is explicitly only a ``numpy.ndarray``. - * Specify ``dataframe`` when "frame-like" features are being used, such as - the column names. - * When specifying the data type of a list, use ``of`` as a delimiter: ``list - of int``. When the parameter supports arrays giving details about the - shape and/or data type and a list of such arrays, you can use one of - ``array-like of shape (n_samples,) or list of such arrays``. - * When specifying the dtype of an ndarray, use e.g. ``dtype=np.int32`` after - defining the shape: ``ndarray of shape (n_samples,), dtype=np.int32``. You - can specify multiple dtype as a set: ``array-like of shape (n_samples,), - dtype={np.float64, np.float32}``. If one wants to mention arbitrary - precision, use `integral` and `floating` rather than the Python dtype - `int` and `float`. When both `int` and `floating` are supported, there is - no need to specify the dtype. - * When the default is ``None``, ``None`` only needs to be specified at the - end with ``default=None``. Be sure to include in the docstring, what it - means for the parameter or attribute to be ``None``. + typed_ndarray : ndarray of shape (n_samples,), dtype=np.int32 -* Add "See Also" in docstrings for related classes/functions. + sample_weight : array-like of shape (n_samples,), default=None -* "See Also" in docstrings should be one line per reference, with a colon and an - explanation, for example:: + multioutput_array : ndarray of shape (n_samples, n_classes) or list of such arrays - See Also - -------- - SelectKBest : Select features based on the k highest scores. - SelectFpr : Select features based on a false positive rate test. + In general have the following in mind: -* Add one or two snippets of code in "Example" section to show how it can be used. + * Use Python basic types. (``bool`` instead of ``boolean``) + * Use parenthesis for defining shapes: ``array-like of shape (n_samples,)`` + or ``array-like of shape (n_samples, n_features)`` + * For strings with multiple options, use brackets: ``input: {'log', + 'squared', 'multinomial'}`` + * 1D or 2D data can be a subset of ``{array-like, ndarray, sparse matrix, + dataframe}``. Note that ``array-like`` can also be a ``list``, while + ``ndarray`` is explicitly only a ``numpy.ndarray``. + * Specify ``dataframe`` when "frame-like" features are being used, such as + the column names. + * When specifying the data type of a list, use ``of`` as a delimiter: ``list + of int``. When the parameter supports arrays giving details about the + shape and/or data type and a list of such arrays, you can use one of + ``array-like of shape (n_samples,) or list of such arrays``. + * When specifying the dtype of an ndarray, use e.g. ``dtype=np.int32`` after + defining the shape: ``ndarray of shape (n_samples,), dtype=np.int32``. You + can specify multiple dtype as a set: ``array-like of shape (n_samples,), + dtype={np.float64, np.float32}``. If one wants to mention arbitrary + precision, use `integral` and `floating` rather than the Python dtype + `int` and `float`. When both `int` and `floating` are supported, there is + no need to specify the dtype. + * When the default is ``None``, ``None`` only needs to be specified at the + end with ``default=None``. Be sure to include in the docstring, what it + means for the parameter or attribute to be ``None``. -|details-end| + * Add "See Also" in docstrings for related classes/functions. -|details-start| -**Guidelines for writing the user guide and other reStructuredText documents** -|details-split| + * "See Also" in docstrings should be one line per reference, with a colon and an + explanation, for example:: -It is important to keep a good compromise between mathematical and algorithmic -details, and give intuition to the reader on what the algorithm does. + See Also + -------- + SelectKBest : Select features based on the k highest scores. + SelectFpr : Select features based on a false positive rate test. -* Begin with a concise, hand-waving explanation of what the algorithm/code does on - the data. + * Add one or two snippets of code in "Example" section to show how it can be used. -* Highlight the usefulness of the feature and its recommended application. - Consider including the algorithm's complexity - (:math:`O\left(g\left(n\right)\right)`) if available, as "rules of thumb" can - be very machine-dependent. Only if those complexities are not available, then - rules of thumb may be provided instead. -* Incorporate a relevant figure (generated from an example) to provide intuitions. +.. dropdown:: Guidelines for writing the user guide and other reStructuredText documents -* Include one or two short code examples to demonstrate the feature's usage. + It is important to keep a good compromise between mathematical and algorithmic + details, and give intuition to the reader on what the algorithm does. -* Introduce any necessary mathematical equations, followed by references. By - deferring the mathematical aspects, the documentation becomes more accessible - to users primarily interested in understanding the feature's practical - implications rather than its underlying mechanics. + * Begin with a concise, hand-waving explanation of what the algorithm/code does on + the data. -* When editing reStructuredText (``.rst``) files, try to keep line length under - 88 characters when possible (exceptions include links and tables). + * Highlight the usefulness of the feature and its recommended application. + Consider including the algorithm's complexity + (:math:`O\left(g\left(n\right)\right)`) if available, as "rules of thumb" can + be very machine-dependent. Only if those complexities are not available, then + rules of thumb may be provided instead. -* In scikit-learn reStructuredText files both single and double backticks - surrounding text will render as inline literal (often used for code, e.g., - `list`). This is due to specific configurations we have set. Single - backticks should be used nowadays. + * Incorporate a relevant figure (generated from an example) to provide intuitions. -* Too much information makes it difficult for users to access the content they - are interested in. Use dropdowns to factorize it by using the following - syntax:: + * Include one or two short code examples to demonstrate the feature's usage. - |details-start| - **Dropdown title** - |details-split| + * Introduce any necessary mathematical equations, followed by references. By + deferring the mathematical aspects, the documentation becomes more accessible + to users primarily interested in understanding the feature's practical + implications rather than its underlying mechanics. - Dropdown content. + * When editing reStructuredText (``.rst``) files, try to keep line length under + 88 characters when possible (exceptions include links and tables). - |details-end| + * In scikit-learn reStructuredText files both single and double backticks + surrounding text will render as inline literal (often used for code, e.g., + `list`). This is due to specific configurations we have set. Single + backticks should be used nowadays. - The snippet above will result in the following dropdown: + * Too much information makes it difficult for users to access the content they + are interested in. Use dropdowns to factorize it by using the following syntax:: - |details-start| - **Dropdown title** - |details-split| + .. dropdown:: Dropdown title - Dropdown content. + Dropdown content. - |details-end| + The snippet above will result in the following dropdown: -* Information that can be hidden by default using dropdowns is: + .. dropdown:: Dropdown title - * low hierarchy sections such as `References`, `Properties`, etc. (see for - instance the subsections in :ref:`det_curve`); + Dropdown content. - * in-depth mathematical details; + * Information that can be hidden by default using dropdowns is: - * narrative that is use-case specific; + * low hierarchy sections such as `References`, `Properties`, etc. (see for + instance the subsections in :ref:`det_curve`); - * in general, narrative that may only interest users that want to go beyond - the pragmatics of a given tool. + * in-depth mathematical details; -* Do not use dropdowns for the low level section `Examples`, as it should stay - visible to all users. Make sure that the `Examples` section comes right after - the main discussion with the least possible folded section in-between. + * narrative that is use-case specific; -* Be aware that dropdowns break cross-references. If that makes sense, hide the - reference along with the text mentioning it. Else, do not use dropdown. + * in general, narrative that may only interest users that want to go beyond + the pragmatics of a given tool. -|details-end| + * Do not use dropdowns for the low level section `Examples`, as it should stay + visible to all users. Make sure that the `Examples` section comes right after + the main discussion with the least possible folded section in-between. + * Be aware that dropdowns break cross-references. If that makes sense, hide the + reference along with the text mentioning it. Else, do not use dropdown. -|details-start| -**Guidelines for writing references** -|details-split| -* When bibliographic references are available with `arxiv `_ - or `Digital Object Identifier `_ identification numbers, - use the sphinx directives `:arxiv:` or `:doi:`. For example, see references in - :ref:`Spectral Clustering Graphs `. +.. dropdown:: Guidelines for writing references -* For "References" in docstrings, see the Silhouette Coefficient - (:func:`sklearn.metrics.silhouette_score`). + * When bibliographic references are available with `arxiv `_ + or `Digital Object Identifier `_ identification numbers, + use the sphinx directives `:arxiv:` or `:doi:`. For example, see references in + :ref:`Spectral Clustering Graphs `. -* To cross-reference to other pages in the scikit-learn documentation use the - reStructuredText cross-referencing syntax: + * For "References" in docstrings, see the Silhouette Coefficient + (:func:`sklearn.metrics.silhouette_score`). - * Section - to link to an arbitrary section in the documentation, use - reference labels (see `Sphinx docs - `_). - For example: + * To cross-reference to other pages in the scikit-learn documentation use the + reStructuredText cross-referencing syntax: - .. code-block:: rst + * Section - to link to an arbitrary section in the documentation, use + reference labels (see `Sphinx docs + `_). + For example: - .. _my-section: + .. code-block:: rst - My section - ---------- + .. _my-section: - This is the text of the section. + My section + ---------- - To refer to itself use :ref:`my-section`. + This is the text of the section. - You should not modify existing sphinx reference labels as this would break - existing cross references and external links pointing to specific sections - in the scikit-learn documentation. + To refer to itself use :ref:`my-section`. - * Glossary - linking to a term in the :ref:`glossary`: + You should not modify existing sphinx reference labels as this would break + existing cross references and external links pointing to specific sections + in the scikit-learn documentation. - .. code-block:: rst + * Glossary - linking to a term in the :ref:`glossary`: - :term:`cross_validation` + .. code-block:: rst - * Function - to link to the documentation of a function, use the full import - path to the function: + :term:`cross_validation` - .. code-block:: rst + * Function - to link to the documentation of a function, use the full import + path to the function: - :func:`~sklearn.model_selection.cross_val_score` + .. code-block:: rst - However, if there is a `.. currentmodule::` directive above you in the document, - you will only need to use the path to the function succeeding the current - module specified. For example: + :func:`~sklearn.model_selection.cross_val_score` - .. code-block:: rst + However, if there is a `.. currentmodule::` directive above you in the document, + you will only need to use the path to the function succeeding the current + module specified. For example: - .. currentmodule:: sklearn.model_selection + .. code-block:: rst - :func:`cross_val_score` + .. currentmodule:: sklearn.model_selection - * Class - to link to documentation of a class, use the full import path to the - class, unless there is a 'currentmodule' directive in the document above - (see above): + :func:`cross_val_score` - .. code-block:: rst + * Class - to link to documentation of a class, use the full import path to the + class, unless there is a 'currentmodule' directive in the document above + (see above): - :class:`~sklearn.preprocessing.StandardScaler` + .. code-block:: rst -|details-end| + :class:`~sklearn.preprocessing.StandardScaler` You can edit the documentation using any text editor, and then generate the HTML output by following :ref:`building_documentation`. The resulting HTML files @@ -914,7 +891,9 @@ Building the documentation requires installing some additional packages: pip install sphinx sphinx-gallery numpydoc matplotlib Pillow pandas \ polars scikit-image packaging seaborn sphinx-prompt \ - sphinxext-opengraph sphinx-copybutton plotly pooch + sphinxext-opengraph sphinx-copybutton plotly pooch \ + pydata-sphinx-theme sphinxcontrib-sass sphinx-design \ + sphinx-remove-toctrees To build the documentation, you need to be in the ``doc`` folder: @@ -956,7 +935,8 @@ To build the PDF manual, run: make latexpdf -.. warning:: **Sphinx version** +.. admonition:: Sphinx version + :class: warning While we do our best to have the documentation build under as many versions of Sphinx as possible, the different versions tend to @@ -997,45 +977,37 @@ subpackages. For a more detailed `pytest` workflow, please refer to the We expect code coverage of new features to be at least around 90%. -|details-start| -**Writing matplotlib related tests** -|details-split| +.. dropdown:: Writing matplotlib related tests -Test fixtures ensure that a set of tests will be executing with the appropriate -initialization and cleanup. The scikit-learn test suite implements a fixture -which can be used with ``matplotlib``. + Test fixtures ensure that a set of tests will be executing with the appropriate + initialization and cleanup. The scikit-learn test suite implements a fixture + which can be used with ``matplotlib``. -``pyplot`` - The ``pyplot`` fixture should be used when a test function is dealing with - ``matplotlib``. ``matplotlib`` is a soft dependency and is not required. - This fixture is in charge of skipping the tests if ``matplotlib`` is not - installed. In addition, figures created during the tests will be - automatically closed once the test function has been executed. + ``pyplot`` + The ``pyplot`` fixture should be used when a test function is dealing with + ``matplotlib``. ``matplotlib`` is a soft dependency and is not required. + This fixture is in charge of skipping the tests if ``matplotlib`` is not + installed. In addition, figures created during the tests will be + automatically closed once the test function has been executed. -To use this fixture in a test function, one needs to pass it as an -argument:: + To use this fixture in a test function, one needs to pass it as an + argument:: - def test_requiring_mpl_fixture(pyplot): - # you can now safely use matplotlib + def test_requiring_mpl_fixture(pyplot): + # you can now safely use matplotlib -|details-end| +.. dropdown:: Workflow to improve test coverage -|details-start| -**Workflow to improve test coverage** -|details-split| + To test code coverage, you need to install the `coverage + `_ package in addition to pytest. -To test code coverage, you need to install the `coverage -`_ package in addition to pytest. + 1. Run 'make test-coverage'. The output lists for each file the line + numbers that are not tested. -1. Run 'make test-coverage'. The output lists for each file the line - numbers that are not tested. + 2. Find a low hanging fruit, looking at which lines are not tested, + write or adapt a test specifically for these lines. -2. Find a low hanging fruit, looking at which lines are not tested, - write or adapt a test specifically for these lines. - -3. Loop. - -|details-end| + 3. Loop. .. _monitoring_performances: @@ -1365,95 +1337,87 @@ up this process by providing your feedback. retraction. Regarding docs: typos, grammar issues and disambiguations are better addressed immediately. -|details-start| -**Important aspects to be covered in any code review** -|details-split| - -Here are a few important aspects that need to be covered in any code review, -from high-level questions to a more detailed check-list. +.. dropdown:: Important aspects to be covered in any code review -- Do we want this in the library? Is it likely to be used? Do you, as - a scikit-learn user, like the change and intend to use it? Is it in - the scope of scikit-learn? Will the cost of maintaining a new - feature be worth its benefits? + Here are a few important aspects that need to be covered in any code review, + from high-level questions to a more detailed check-list. -- Is the code consistent with the API of scikit-learn? Are public - functions/classes/parameters well named and intuitively designed? + - Do we want this in the library? Is it likely to be used? Do you, as + a scikit-learn user, like the change and intend to use it? Is it in + the scope of scikit-learn? Will the cost of maintaining a new + feature be worth its benefits? -- Are all public functions/classes and their parameters, return types, and - stored attributes named according to scikit-learn conventions and documented clearly? + - Is the code consistent with the API of scikit-learn? Are public + functions/classes/parameters well named and intuitively designed? -- Is any new functionality described in the user-guide and illustrated with examples? + - Are all public functions/classes and their parameters, return types, and + stored attributes named according to scikit-learn conventions and documented clearly? -- Is every public function/class tested? Are a reasonable set of - parameters, their values, value types, and combinations tested? Do - the tests validate that the code is correct, i.e. doing what the - documentation says it does? If the change is a bug-fix, is a - non-regression test included? Look at `this - `__ - to get started with testing in Python. + - Is any new functionality described in the user-guide and illustrated with examples? -- Do the tests pass in the continuous integration build? If - appropriate, help the contributor understand why tests failed. + - Is every public function/class tested? Are a reasonable set of + parameters, their values, value types, and combinations tested? Do + the tests validate that the code is correct, i.e. doing what the + documentation says it does? If the change is a bug-fix, is a + non-regression test included? Look at `this + `__ + to get started with testing in Python. -- Do the tests cover every line of code (see the coverage report in the build - log)? If not, are the lines missing coverage good exceptions? + - Do the tests pass in the continuous integration build? If + appropriate, help the contributor understand why tests failed. -- Is the code easy to read and low on redundancy? Should variable names be - improved for clarity or consistency? Should comments be added? Should comments - be removed as unhelpful or extraneous? + - Do the tests cover every line of code (see the coverage report in the build + log)? If not, are the lines missing coverage good exceptions? -- Could the code easily be rewritten to run much more efficiently for - relevant settings? + - Is the code easy to read and low on redundancy? Should variable names be + improved for clarity or consistency? Should comments be added? Should comments + be removed as unhelpful or extraneous? -- Is the code backwards compatible with previous versions? (or is a - deprecation cycle necessary?) + - Could the code easily be rewritten to run much more efficiently for + relevant settings? -- Will the new code add any dependencies on other libraries? (this is - unlikely to be accepted) + - Is the code backwards compatible with previous versions? (or is a + deprecation cycle necessary?) -- Does the documentation render properly (see the - :ref:`contribute_documentation` section for more details), and are the plots - instructive? + - Will the new code add any dependencies on other libraries? (this is + unlikely to be accepted) -:ref:`saved_replies` includes some frequent comments that reviewers may make. + - Does the documentation render properly (see the + :ref:`contribute_documentation` section for more details), and are the plots + instructive? -|details-end| + :ref:`saved_replies` includes some frequent comments that reviewers may make. .. _communication: -|details-start| -**Communication Guidelines** -|details-split| - -Reviewing open pull requests (PRs) helps move the project forward. It is a -great way to get familiar with the codebase and should motivate the -contributor to keep involved in the project. [1]_ - -- Every PR, good or bad, is an act of generosity. Opening with a positive - comment will help the author feel rewarded, and your subsequent remarks may - be heard more clearly. You may feel good also. -- Begin if possible with the large issues, so the author knows they've been - understood. Resist the temptation to immediately go line by line, or to open - with small pervasive issues. -- Do not let perfect be the enemy of the good. If you find yourself making - many small suggestions that don't fall into the :ref:`code_review`, consider - the following approaches: - - - refrain from submitting these; - - prefix them as "Nit" so that the contributor knows it's OK not to address; - - follow up in a subsequent PR, out of courtesy, you may want to let the - original contributor know. - -- Do not rush, take the time to make your comments clear and justify your - suggestions. -- You are the face of the project. Bad days occur to everyone, in that - occasion you deserve a break: try to take your time and stay offline. - -.. [1] Adapted from the numpy `communication guidelines - `_. - -|details-end| +.. dropdown:: Communication Guidelines + + Reviewing open pull requests (PRs) helps move the project forward. It is a + great way to get familiar with the codebase and should motivate the + contributor to keep involved in the project. [1]_ + + - Every PR, good or bad, is an act of generosity. Opening with a positive + comment will help the author feel rewarded, and your subsequent remarks may + be heard more clearly. You may feel good also. + - Begin if possible with the large issues, so the author knows they've been + understood. Resist the temptation to immediately go line by line, or to open + with small pervasive issues. + - Do not let perfect be the enemy of the good. If you find yourself making + many small suggestions that don't fall into the :ref:`code_review`, consider + the following approaches: + + - refrain from submitting these; + - prefix them as "Nit" so that the contributor knows it's OK not to address; + - follow up in a subsequent PR, out of courtesy, you may want to let the + original contributor know. + + - Do not rush, take the time to make your comments clear and justify your + suggestions. + - You are the face of the project. Bad days occur to everyone, in that + occasion you deserve a break: try to take your time and stay offline. + + .. [1] Adapted from the numpy `communication guidelines + `_. Reading the existing code base ============================== diff --git a/doc/developers/index.rst b/doc/developers/index.rst index c2cc35928cbf9..cca77b6a015c9 100644 --- a/doc/developers/index.rst +++ b/doc/developers/index.rst @@ -1,16 +1,9 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - .. _developers_guide: ================= Developer's Guide ================= -.. include:: ../includes/big_toc_css.rst -.. include:: ../tune_toc.rst - .. toctree:: contributing diff --git a/doc/developers/maintainer.rst b/doc/developers/maintainer.rst index 70d132d2af604..ffc9b73156fa8 100644 --- a/doc/developers/maintainer.rst +++ b/doc/developers/maintainer.rst @@ -1,6 +1,5 @@ -Maintainer / core-developer information -======================================== - +Maintainer/Core-Developer Information +====================================== Releasing --------- diff --git a/doc/dispatching.rst b/doc/dispatching.rst index d42fdcc86f9e8..101e493ee96b7 100644 --- a/doc/dispatching.rst +++ b/doc/dispatching.rst @@ -1,9 +1,3 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - -.. include:: includes/big_toc_css.rst - =========== Dispatching =========== diff --git a/doc/faq.rst b/doc/faq.rst index 8ddf0c4c238f6..81f03b49bc7c9 100644 --- a/doc/faq.rst +++ b/doc/faq.rst @@ -1,3 +1,32 @@ +.. raw:: html + + + .. _faq: ========================== @@ -9,8 +38,9 @@ Frequently Asked Questions Here we try to give some answers to questions that regularly pop up on the mailing list. .. contents:: Table of Contents - :local: - :depth: 2 + :local: + :depth: 2 + About the project ----------------- @@ -323,12 +353,14 @@ Using scikit-learn What's the best way to get help on scikit-learn usage? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -**For general machine learning questions**, please use -`Cross Validated `_ with the ``[machine-learning]`` tag. -**For scikit-learn usage questions**, please use `Stack Overflow `_ -with the ``[scikit-learn]`` and ``[python]`` tags. You can alternatively use the `mailing list -`_. +* General machine learning questions: use `Cross Validated + `_ with the ``[machine-learning]`` tag. + +* scikit-learn usage questions: use `Stack Overflow + `_ with the + ``[scikit-learn]`` and ``[python]`` tags. You can alternatively use the `mailing list + `_. 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    START
    START
    >50
    samples
    >50...
    get
    more
    data
    get...
    NO
    NO
    predicting a
    category
    predicting...
    YES
    YES
    do you have
    labeled
    data
    do you hav...
    YES
    YES
    predicting a
    quantity
    predicting...
    NO
    NO
    just
    looking
    just...
    NO
    NO
    predicting
    structure
    predicting...
    NO
    NO
    tough
    luck
    tough...
    <100K
    samples
    <100K...
    YES
    YES
    SGD
    Classifier
    SGD...
    NO
    NO
    Linear
    SVC
    Linear...
    YES
    YES
    text
    data
    text...
    😭
    😭
    Kernel
    Approximation
    Kernel...
    😭
    😭
    KNeighbors
    Classifier
    KNeighbors...
    NO
    NO
    SVC
    SVC
    Ensemble
    Classifiers
    Ensemble...
    😭
    😭
    Naive
    Bayes
    Naive...
    YES
    YES
    classification
    classification
    number of
    categories
    known
    number of...
    NO
    NO
    <10K
    samples
    <10K...
    <10K
    samples
    <10K...
    NO
    NO
    NO
    NO
    YES
    YES
    MeanShift
    MeanShift
    VBGMM
    VBGMM
    YES
    YES
    MiniBatch
    KMeans
    MiniBatch...
    NO
    NO
    clustering
    clustering
    KMeans
    KMeans
    YES
    YES
    Spectral
    Clustering
    Spectral...
    GMM
    GMM
    😭
    😭
    <100K
    samples
    <100K...
    YES
    YES
    few features
    should be
    important
    few features...
    YES
    YES
    SGD
    Regressor
    SGD...
    NO
    NO
    Lasso
    Lasso
    ElasticNet
    ElasticNet
    YES
    YES
    RidgeRegression
    RidgeRegression
    SVR(kernel="linear")
    SVR(kernel="linea...
    NO
    NO
    SVR(kernel="rbf")
    SVR(kernel="rbf...
    Ensemble
    Regressors
    Ensemble...
    😭
    😭
    regression
    regression
    Ramdomized
    PCA
    Ramdomized...
    YES
    YES
    <10K
    samples
    <10K...
    😭
    😭
    Kernel
    Approximation
    Kernel...
    NO
    NO
    IsoMap
    IsoMap
    Spectral
    Embedding
    Spectral...
    YES
    YES
    LLE
    LLE
    😭
    😭
    dimensionality
    reduction
    dimensionality...
    scikit-learn
    algorithm cheat sheet
    scikit-learn...    Text is not SVG - cannot display     diff --git a/doc/includes/big_toc_css.rst b/doc/includes/big_toc_css.rst deleted file mode 100644 index a8ba83e99c5b8..0000000000000 --- a/doc/includes/big_toc_css.rst +++ /dev/null @@ -1,40 +0,0 @@ -.. - File to ..include in a document with a big table of content, to give - it 'style' - -.. raw:: html - - - - - diff --git a/doc/includes/bigger_toc_css.rst b/doc/includes/bigger_toc_css.rst deleted file mode 100644 index d866bd145d883..0000000000000 --- a/doc/includes/bigger_toc_css.rst +++ /dev/null @@ -1,60 +0,0 @@ -.. - File to ..include in a document with a very big table of content, to - give it 'style' - -.. raw:: html - - - - - diff --git a/doc/index.rst.template b/doc/index.rst.template new file mode 100644 index 0000000000000..df058f5fb6185 --- /dev/null +++ b/doc/index.rst.template @@ -0,0 +1,25 @@ +.. title:: Index + +.. Define the overall structure, that affects the prev-next buttons and the order + of the sections in the top navbar. + +.. toctree:: + :hidden: + :maxdepth: 2 + + Install + user_guide + API + auto_examples/index + Community + getting_started + Tutorials + whats_new + Glossary + Development <{{ development_link }}> + FAQ + support + related_projects + roadmap + Governance + about diff --git a/doc/inspection.rst b/doc/inspection.rst index 57c1cfc3275e8..95d121ec10d7d 100644 --- a/doc/inspection.rst +++ b/doc/inspection.rst @@ -1,9 +1,3 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - -.. include:: includes/big_toc_css.rst - .. _inspection: Inspection @@ -21,9 +15,9 @@ predictions from a model and what affects them. This can be used to evaluate assumptions and biases of a model, design a better model, or to diagnose issues with model performance. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py` +* :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py` .. toctree:: diff --git a/doc/install.rst b/doc/install.rst index 89851171f4588..be924b012ce65 100644 --- a/doc/install.rst +++ b/doc/install.rst @@ -6,21 +6,21 @@ Installing scikit-learn There are different ways to install scikit-learn: - * :ref:`Install the latest official release `. This - is the best approach for most users. It will provide a stable version - and pre-built packages are available for most platforms. +* :ref:`Install the latest official release `. This + is the best approach for most users. It will provide a stable version + and pre-built packages are available for most platforms. - * Install the version of scikit-learn provided by your - :ref:`operating system or Python distribution `. - This is a quick option for those who have operating systems or Python - distributions that distribute scikit-learn. - It might not provide the latest release version. +* Install the version of scikit-learn provided by your + :ref:`operating system or Python distribution `. + This is a quick option for those who have operating systems or Python + distributions that distribute scikit-learn. + It might not provide the latest release version. - * :ref:`Building the package from source - `. This is best for users who want the - latest-and-greatest features and aren't afraid of running - brand-new code. This is also needed for users who wish to contribute to the - project. +* :ref:`Building the package from source + `. This is best for users who want the + latest-and-greatest features and aren't afraid of running + brand-new code. This is also needed for users who wish to contribute to the + project. .. _install_official_release: @@ -28,117 +28,132 @@ There are different ways to install scikit-learn: Installing the latest release ============================= -.. This quickstart installation is a hack of the awesome - https://spacy.io/usage/#quickstart page. - See the original javascript implementation - https://github.com/ines/quickstart - - -.. raw:: html - -
    - Operating System - - - - - -
    - Packager - - - -
    - - - - -.. raw:: html - -
    - Install the 64bit version of Python 3, for instance from https://www.python.org.Install Python 3 using homebrew (brew install python) or by manually installing the package from https://www.python.org.Install python3 and python3-pip using the package manager of the Linux Distribution.Install conda using the Anaconda or miniconda - installers or the miniforge installers - (no administrator permission required for any of those). -
    - -Then run: - -.. raw:: html - -
    - 
    pip3 install -U scikit-learn
    - - 
    pip install -U scikit-learn
    - - 
    pip install -U scikit-learn
    - - 
    python3 -m venv sklearn-venv
-  source sklearn-venv/bin/activate
-  pip3 install -U scikit-learn
    - - 
    python -m venv sklearn-venv
-  sklearn-venv\Scripts\activate
-  pip install -U scikit-learn
    - - 
    python -m venv sklearn-venv
-  source sklearn-venv/bin/activate
-  pip install -U scikit-learn
    - - 
    conda create -n sklearn-env -c conda-forge scikit-learn
-  conda activate sklearn-env
    -
    - -In order to check your installation you can use - -.. raw:: html - -
    - 
    python3 -m pip show scikit-learn  # to see which version and where scikit-learn is installed
-  python3 -m pip freeze  # to see all packages installed in the active virtualenv
-  python3 -c "import sklearn; sklearn.show_versions()"
    - - 
    python -m pip show scikit-learn  # to see which version and where scikit-learn is installed
-  python -m pip freeze  # to see all packages installed in the active virtualenv
-  python -c "import sklearn; sklearn.show_versions()"
    - - 
    python -m pip show scikit-learn  # to see which version and where scikit-learn is installed
-  python -m pip freeze  # to see all packages installed in the active virtualenv
-  python -c "import sklearn; sklearn.show_versions()"
    - - 
    python -m pip show scikit-learn  # to see which version and where scikit-learn is installed
-  python -m pip freeze  # to see all packages installed in the active virtualenv
-  python -c "import sklearn; sklearn.show_versions()"
    - - 
    conda list scikit-learn  # to see which scikit-learn version is installed
-  conda list  # to see all packages installed in the active conda environment
-  python -c "import sklearn; sklearn.show_versions()"
    -
    - -Note that in order to avoid potential conflicts with other packages it is -strongly recommended to use a `virtual environment (venv) -`_ or a `conda environment -`_. - -Using such an isolated environment makes it possible to install a specific -version of scikit-learn with pip or conda and its dependencies independently of -any previously installed Python packages. In particular under Linux is it -discouraged to install pip packages alongside the packages managed by the +.. `scss/install.scss` overrides some default sphinx-design styling for the tabs + +.. div:: install-instructions + + .. tab-set:: + + .. tab-item:: pip + :class-label: tab-6 + :sync: packager-pip + + .. tab-set:: + + .. tab-item:: Windows + :class-label: tab-4 + :sync: os-windows + + Install the 64-bit version of Python 3, for instance from the + `official website `__. + + Now create a `virtual environment (venv) + `_ and install scikit-learn. + Note that the virtual environment is optional but strongly recommended, in + order to avoid potential conflicts with other packages. + + .. prompt:: powershell + + python -m venv sklearn-env + sklearn-env\Scripts\activate # activate + pip install -U scikit-learn + + In order to check your installation, you can use: + + .. prompt:: powershell + + python -m pip show scikit-learn # show scikit-learn version and location + python -m pip freeze # show all installed packages in the environment + python -c "import sklearn; sklearn.show_versions()" + + .. tab-item:: macOS + :class-label: tab-4 + :sync: os-macos + + Install Python 3 using `homebrew `_ (`brew install python`) + or by manually installing the package from the `official website + `__. + + Now create a `virtual environment (venv) + `_ and install scikit-learn. + Note that the virtual environment is optional but strongly recommended, in + order to avoid potential conflicts with other packges. + + .. prompt:: bash + + python -m venv sklearn-env + source sklearn-env/bin/activate # activate + pip install -U scikit-learn + + In order to check your installation, you can use: + + .. prompt:: bash + + python -m pip show scikit-learn # show scikit-learn version and location + python -m pip freeze # show all installed packages in the environment + python -c "import sklearn; sklearn.show_versions()" + + .. tab-item:: Linux + :class-label: tab-4 + :sync: os-linux + + Python 3 is usually installed by default on most Linux distributions. To + check if you have it installed, try: + + .. prompt:: bash + + python3 --version + pip3 --version + + If you don't have Python 3 installed, please install `python3` and + `python3-pip` from your distribution's package manager. + + Now create a `virtual environment (venv) + `_ and install scikit-learn. + Note that the virtual environment is optional but strongly recommended, in + order to avoid potential conflicts with other packages. + + .. prompt:: bash + + python3 -m venv sklearn-env + source sklearn-env/bin/activate # activate + pip3 install -U scikit-learn + + In order to check your installation, you can use: + + .. prompt:: bash + + python3 -m pip show scikit-learn # show scikit-learn version and location + python3 -m pip freeze # show all installed packages in the environment + python3 -c "import sklearn; sklearn.show_versions()" + + .. tab-item:: conda + :class-label: tab-6 + :sync: packager-conda + + Install conda using the `Anaconda or miniconda installers + `__ + or the `miniforge installers + `__ (no administrator + permission required for any of those). Then run: + + .. prompt:: bash + + conda create -n sklearn-env -c conda-forge scikit-learn + conda activate sklearn-env + + In order to check your installation, you can use: + + .. prompt:: bash + + conda list scikit-learn # show scikit-learn version and location + conda list # show all installed packages in the environment + python -c "import sklearn; sklearn.show_versions()" + +Using an isolated environment such as pip venv or conda makes it possible to +install a specific version of scikit-learn with pip or conda and its dependencies +independently of any previously installed Python packages. In particular under Linux +it is discouraged to install pip packages alongside the packages managed by the package manager of the distribution (apt, dnf, pacman...). Note that you should always remember to activate the environment of your choice @@ -150,11 +165,10 @@ and NumPy and SciPy are not recompiled from source, which can happen when using particular configurations of operating system and hardware (such as Linux on a Raspberry Pi). - -Scikit-learn plotting capabilities (i.e., functions start with "plot\_" -and classes end with "Display") require Matplotlib. The examples require +Scikit-learn plotting capabilities (i.e., functions starting with `plot\_` +and classes ending with `Display`) require Matplotlib. The examples require Matplotlib and some examples require scikit-image, pandas, or seaborn. The -minimum version of Scikit-learn dependencies are listed below along with its +minimum version of scikit-learn dependencies are listed below along with its purpose. .. include:: min_dependency_table.rst @@ -164,12 +178,11 @@ purpose. Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4. Scikit-learn 0.21 supported Python 3.5-3.7. Scikit-learn 0.22 supported Python 3.5-3.8. - Scikit-learn 0.23 - 0.24 require Python 3.6 or newer. + Scikit-learn 0.23-0.24 required Python 3.6 or newer. Scikit-learn 1.0 supported Python 3.7-3.10. Scikit-learn 1.1, 1.2 and 1.3 support Python 3.8-3.12 Scikit-learn 1.4 requires Python 3.9 or newer. - .. _install_by_distribution: Third party distributions of scikit-learn @@ -193,7 +206,7 @@ Alpine Linux's package is provided through the `official repositories ``py3-scikit-learn`` for Python. It can be installed by typing the following command: -.. prompt:: bash $ +.. prompt:: bash sudo apk add py3-scikit-learn @@ -206,7 +219,7 @@ Arch Linux's package is provided through the `official repositories ``python-scikit-learn`` for Python. It can be installed by typing the following command: -.. prompt:: bash $ +.. prompt:: bash sudo pacman -S python-scikit-learn @@ -221,7 +234,7 @@ Note that scikit-learn requires Python 3, hence the need to use the `python3-` suffixed package names. Packages can be installed using ``apt-get``: -.. prompt:: bash $ +.. prompt:: bash sudo apt-get install python3-sklearn python3-sklearn-lib python3-sklearn-doc @@ -233,7 +246,7 @@ The Fedora package is called ``python3-scikit-learn`` for the python 3 version, the only one available in Fedora. It can be installed using ``dnf``: -.. prompt:: bash $ +.. prompt:: bash sudo dnf install python3-scikit-learn @@ -241,10 +254,8 @@ It can be installed using ``dnf``: NetBSD ------ -scikit-learn is available via `pkgsrc-wip -`_: - - https://pkgsrc.se/math/py-scikit-learn +scikit-learn is available via `pkgsrc-wip `_: +https://pkgsrc.se/math/py-scikit-learn MacPorts for Mac OSX @@ -255,7 +266,7 @@ where ``XY`` denotes the Python version. It can be installed by typing the following command: -.. prompt:: bash $ +.. prompt:: bash sudo port install py39-scikit-learn @@ -277,7 +288,7 @@ Intel Extension for Scikit-learn Intel maintains an optimized x86_64 package, available in PyPI (via `pip`), and in the `main`, `conda-forge` and `intel` conda channels: -.. prompt:: bash $ +.. prompt:: bash conda install scikit-learn-intelex @@ -303,7 +314,7 @@ with `scikit-learn-intelex`, please report the issue on their WinPython for Windows ------------------------ +--------------------- The `WinPython `_ project distributes scikit-learn as an additional plugin. @@ -312,6 +323,10 @@ scikit-learn as an additional plugin. Troubleshooting =============== +If you encounter unexpected failures when installing scikit-learn, you may submit +an issue to the `issue tracker `_. +Before that, please also make sure to check the following common issues. + .. _windows_longpath: Error caused by file path length limit on Windows @@ -341,6 +356,6 @@ using the ``regedit`` tool: #. Reinstall scikit-learn (ignoring the previous broken installation): -.. prompt:: bash $ + .. prompt:: powershell - pip install --exists-action=i scikit-learn + pip install --exists-action=i scikit-learn diff --git a/doc/js/scripts/api-search.js b/doc/js/scripts/api-search.js new file mode 100644 index 0000000000000..2148e0c429aaa --- /dev/null +++ b/doc/js/scripts/api-search.js @@ -0,0 +1,12 @@ +/** + * This script is for initializing the search table on the API index page. See + * DataTables documentation for more information: https://datatables.net/ + */ + +document.addEventListener("DOMContentLoaded", function () { + new DataTable("table.apisearch-table", { + order: [], // Keep original order + lengthMenu: [10, 25, 50, 100, { label: "All", value: -1 }], + pageLength: -1, // Show all entries by default + }); +}); diff --git a/doc/js/scripts/dropdown.js b/doc/js/scripts/dropdown.js new file mode 100644 index 0000000000000..ec2e6d9419a28 --- /dev/null +++ b/doc/js/scripts/dropdown.js @@ -0,0 +1,61 @@ +/** + * This script is used to add the functionality of collapsing/expanding all dropdowns + * on the page to the sphinx-design dropdowns. This is because some browsers cannot + * search into collapsed
    (such as Firefox). + * + * The reason why the buttons are added to the page with JS (dynamic) instead of with + * sphinx (static) is that the button will not work without JS activated, so we do not + * want them to show up in that case. + */ + +function addToggleAllButtons() { + // Get all sphinx-design dropdowns + const allDropdowns = document.querySelectorAll("details.sd-dropdown"); + + function collapseAll() { + // Function to collapse all dropdowns on the page + console.log("[SK] Collapsing all dropdowns..."); + allDropdowns.forEach((dropdown) => { + dropdown.removeAttribute("open"); + }); + } + + function expandAll() { + // Function to expand all dropdowns on the page + console.log("[SK] Expanding all dropdowns..."); + allDropdowns.forEach((dropdown) => { + dropdown.setAttribute("open", ""); + }); + } + + const buttonConfigs = new Map([ + ["up", { desc: "Collapse", action: collapseAll }], + ["down", { desc: "Expand", action: expandAll }], + ]); + + allDropdowns.forEach((dropdown) => { + // Get the summary element of the dropdown, where we will place the buttons + const summaryTitle = dropdown.querySelector("summary.sd-summary-title"); + for (const [direction, config] of buttonConfigs) { + // Button with icon inside + var newButton = document.createElement("button"); + var newIcon = document.createElement("i"); + newIcon.classList.add("fa-solid", `fa-angles-${direction}`); + newButton.appendChild(newIcon); + // Class for styling; `sd-summary-up/down` is implemented by sphinx-design; + // `sk-toggle-all` is implemented by us + newButton.classList.add(`sd-summary-${direction}`, `sk-toggle-all`); + // Bootstrap tooltip configurations + newButton.setAttribute("data-bs-toggle", "tooltip"); + newButton.setAttribute("data-bs-placement", "top"); + newButton.setAttribute("data-bs-offset", "0,10"); + newButton.setAttribute("data-bs-title", `${config.desc} all dropdowns`); + // Assign the collapse/expand action to the button + newButton.onclick = config.action; + // Append the button to the summary element + summaryTitle.appendChild(newButton); + } + }); +} + +document.addEventListener("DOMContentLoaded", addToggleAllButtons); diff --git a/doc/js/scripts/vendor/svg-pan-zoom.min.js b/doc/js/scripts/vendor/svg-pan-zoom.min.js new file mode 100644 index 0000000000000..bde44a689bfe1 --- /dev/null +++ b/doc/js/scripts/vendor/svg-pan-zoom.min.js @@ -0,0 +1,31 @@ +/** + * svg-pan-zoom v3.6.2 + * + * https://github.com/bumbu/svg-pan-zoom + * + * Copyright 2009-2010 Andrea Leofreddi + * All rights reserved. + * + * Redistribution and use in source and binary forms, with or without modification, + * are permitted provided that the following conditions are met: + * + * * Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * + * * Redistributions in binary form must reproduce the above copyright notice, this + * list of conditions and the following disclaimer in the documentation and/or + * other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND + * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR + * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON + * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + */ +!function s(r,a,l){function u(e,t){if(!a[e]){if(!r[e]){var o="function"==typeof require&&require;if(!t&&o)return o(e,!0);if(h)return h(e,!0);var n=new Error("Cannot find module '"+e+"'");throw n.code="MODULE_NOT_FOUND",n}var i=a[e]={exports:{}};r[e][0].call(i.exports,function(t){return u(r[e][1][t]||t)},i,i.exports,s,r,a,l)}return a[e].exports}for(var h="function"==typeof 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n=Array.prototype.slice.call(t.childNodes||t.children).filter(function(t){return"defs"!==t.nodeName&&"#text"!==t.nodeName});1===n.length&&"g"===n[0].nodeName&&null===n[0].getAttribute("transform")&&(o=n[0])}if(!o){var i="viewport-"+(new Date).toISOString().replace(/\D/g,"");(o=document.createElementNS(this.svgNS,"g")).setAttribute("id",i);var s=t.childNodes||t.children;if(s&&0`__:: +.. dropdown:: Using ONNX - from skl2onnx import to_onnx - onx = to_onnx(clf, X[:1].astype(numpy.float32), target_opset=12) - with open("filename.onnx", "wb") as f: - f.write(onx.SerializeToString()) + To convert the model to `ONNX` format, you need to give the converter some + information about the input as well, about which you can read more `here + `__:: -You can load the model in Python and use the `ONNX` runtime to get -predictions:: + from skl2onnx import to_onnx + onx = to_onnx(clf, X[:1].astype(numpy.float32), target_opset=12) + with open("filename.onnx", "wb") as f: + f.write(onx.SerializeToString()) - from onnxruntime import InferenceSession - with open("filename.onnx", "rb") as f: - onx = f.read() - sess = InferenceSession(onx, providers=["CPUExecutionProvider"]) - pred_ort = sess.run(None, {"X": X_test.astype(numpy.float32)})[0] + You can load the model in Python and use the `ONNX` runtime to get + predictions:: - -|details-end| + from onnxruntime import InferenceSession + with open("filename.onnx", "rb") as f: + onx = f.read() + sess = InferenceSession(onx, providers=["CPUExecutionProvider"]) + pred_ort = sess.run(None, {"X": X_test.astype(numpy.float32)})[0] .. _skops_persistence: @@ -154,33 +145,30 @@ Therefore it provides a more secure format than :mod:`pickle`, :mod:`joblib`, and `cloudpickle`_. -|details-start| -**Using skops** -|details-split| +.. dropdown:: Using skops -The API is very similar to :mod:`pickle`, and you can persist your models as -explained in the `documentation -`__ using -:func:`skops.io.dump` and :func:`skops.io.dumps`:: + The API is very similar to :mod:`pickle`, and you can persist your models as + explained in the `documentation + `__ using + :func:`skops.io.dump` and :func:`skops.io.dumps`:: - import skops.io as sio - obj = sio.dump(clf, "filename.skops") + import skops.io as sio + obj = sio.dump(clf, "filename.skops") -And you can load them back using :func:`skops.io.load` and -:func:`skops.io.loads`. However, you need to specify the types which are -trusted by you. You can get existing unknown types in a dumped object / file -using :func:`skops.io.get_untrusted_types`, and after checking its contents, -pass it to the load function:: + And you can load them back using :func:`skops.io.load` and + :func:`skops.io.loads`. However, you need to specify the types which are + trusted by you. You can get existing unknown types in a dumped object / file + using :func:`skops.io.get_untrusted_types`, and after checking its contents, + pass it to the load function:: - unknown_types = sio.get_untrusted_types(file="filename.skops") - # investigate the contents of unknown_types, and only load if you trust - # everything you see. - clf = sio.load("filename.skops", trusted=unknown_types) + unknown_types = sio.get_untrusted_types(file="filename.skops") + # investigate the contents of unknown_types, and only load if you trust + # everything you see. + clf = sio.load("filename.skops", trusted=unknown_types) -Please report issues and feature requests related to this format on the `skops -issue tracker `__. + Please report issues and feature requests related to this format on the `skops + issue tracker `__. -|details-end| .. _pickle_persistence: @@ -201,31 +189,27 @@ come with slight variations: :class:`~sklearn.preprocessing.FunctionTransformer` and using a custom function to transform the data. -|details-start| -**Using** ``pickle``, ``joblib``, **or** ``cloudpickle`` -|details-split| - -Depending on your use-case, you can choose one of these three methods to -persist and load your scikit-learn model, and they all follow the same API:: +.. dropdown:: Using `pickle`, `joblib`, or `cloudpickle` - # Here you can replace pickle with joblib or cloudpickle - from pickle import dump - with open("filename.pkl", "wb") as f: - dump(clf, f, protocol=5) + Depending on your use-case, you can choose one of these three methods to + persist and load your scikit-learn model, and they all follow the same API:: -Using `protocol=5` is recommended to reduce memory usage and make it faster to -store and load any large NumPy array stored as a fitted attribute in the model. -You can alternatively pass `protocol=pickle.HIGHEST_PROTOCOL` which is -equivalent to `protocol=5` in Python 3.8 and later (at the time of writing). + # Here you can replace pickle with joblib or cloudpickle + from pickle import dump + with open("filename.pkl", "wb") as f: + dump(clf, f, protocol=5) -And later when needed, you can load the same object from the persisted file:: + Using `protocol=5` is recommended to reduce memory usage and make it faster to + store and load any large NumPy array stored as a fitted attribute in the model. + You can alternatively pass `protocol=pickle.HIGHEST_PROTOCOL` which is + equivalent to `protocol=5` in Python 3.8 and later (at the time of writing). - # Here you can replace pickle with joblib or cloudpickle - from pickle import load - with open("filename.pkl", "rb") as f: - clf = load(f) + And later when needed, you can load the same object from the persisted file:: -|details-end| + # Here you can replace pickle with joblib or cloudpickle + from pickle import load + with open("filename.pkl", "rb") as f: + clf = load(f) .. _persistence_limitations: @@ -296,25 +280,21 @@ recipe (e.g. a Python script) and training set information, and metadata about all the dependencies to be able to automatically reconstruct the same training environment for the updated software. -|details-start| -**InconsistentVersionWarning** -|details-split| - -When an estimator is loaded with a scikit-learn version that is inconsistent -with the version the estimator was pickled with, a -:class:`~sklearn.exceptions.InconsistentVersionWarning` is raised. This warning -can be caught to obtain the original version the estimator was pickled with:: +.. dropdown:: InconsistentVersionWarning - from sklearn.exceptions import InconsistentVersionWarning - warnings.simplefilter("error", InconsistentVersionWarning) + When an estimator is loaded with a scikit-learn version that is inconsistent + with the version the estimator was pickled with, a + :class:`~sklearn.exceptions.InconsistentVersionWarning` is raised. This warning + can be caught to obtain the original version the estimator was pickled with:: - try: - with open("model_from_prevision_version.pickle", "rb") as f: - est = pickle.load(f) - except InconsistentVersionWarning as w: - print(w.original_sklearn_version) + from sklearn.exceptions import InconsistentVersionWarning + warnings.simplefilter("error", InconsistentVersionWarning) -|details-end| + try: + with open("model_from_prevision_version.pickle", "rb") as f: + est = pickle.load(f) + except InconsistentVersionWarning as w: + print(w.original_sklearn_version) Serving the model artifact diff --git a/doc/model_selection.rst b/doc/model_selection.rst index 522544aefc820..b78c9ff4c3aa8 100644 --- a/doc/model_selection.rst +++ b/doc/model_selection.rst @@ -1,9 +1,3 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - -.. include:: includes/big_toc_css.rst - .. _model_selection: Model selection and evaluation diff --git a/doc/modules/array_api.rst b/doc/modules/array_api.rst index 7a21274a7250f..22bdbd2da6b9c 100644 --- a/doc/modules/array_api.rst +++ b/doc/modules/array_api.rst @@ -1,7 +1,3 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - .. _array_api: ================================ diff --git a/doc/modules/biclustering.rst b/doc/modules/biclustering.rst index 2189e85e0f0ef..503a535c408f0 100644 --- a/doc/modules/biclustering.rst +++ b/doc/modules/biclustering.rst @@ -147,21 +147,21 @@ Then the rows of :math:`Z` are clustered using :ref:`k-means and the remaining ``n_columns`` labels provide the column partitioning. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_bicluster_plot_spectral_coclustering.py`: A simple example - showing how to generate a data matrix with biclusters and apply - this method to it. +* :ref:`sphx_glr_auto_examples_bicluster_plot_spectral_coclustering.py`: A simple example + showing how to generate a data matrix with biclusters and apply + this method to it. - * :ref:`sphx_glr_auto_examples_bicluster_plot_bicluster_newsgroups.py`: An example of finding - biclusters in the twenty newsgroup dataset. +* :ref:`sphx_glr_auto_examples_bicluster_plot_bicluster_newsgroups.py`: An example of finding + biclusters in the twenty newsgroup dataset. -.. topic:: References: +.. rubric:: References - * Dhillon, Inderjit S, 2001. :doi:`Co-clustering documents and words using - bipartite spectral graph partitioning - <10.1145/502512.502550>` +* Dhillon, Inderjit S, 2001. :doi:`Co-clustering documents and words using + bipartite spectral graph partitioning + <10.1145/502512.502550>` .. _spectral_biclustering: @@ -234,17 +234,17 @@ Similarly, projecting the columns to :math:`A^{\top} * U` and clustering this :math:`n \times q` matrix yields the column labels. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_bicluster_plot_spectral_biclustering.py`: a simple example - showing how to generate a checkerboard matrix and bicluster it. +* :ref:`sphx_glr_auto_examples_bicluster_plot_spectral_biclustering.py`: a simple example + showing how to generate a checkerboard matrix and bicluster it. -.. topic:: References: +.. rubric:: References - * Kluger, Yuval, et. al., 2003. :doi:`Spectral biclustering of microarray - data: coclustering genes and conditions - <10.1101/gr.648603>` +* Kluger, Yuval, et. al., 2003. :doi:`Spectral biclustering of microarray + data: coclustering genes and conditions + <10.1101/gr.648603>` .. _biclustering_evaluation: @@ -298,8 +298,8 @@ are totally dissimilar. The maximum score, 1, occurs when both sets are identical. -.. topic:: References: +.. rubric:: References - * Hochreiter, Bodenhofer, et. al., 2010. `FABIA: factor analysis - for bicluster acquisition - `__. +* Hochreiter, Bodenhofer, et. al., 2010. `FABIA: factor analysis + for bicluster acquisition + `__. diff --git a/doc/modules/calibration.rst b/doc/modules/calibration.rst index c0a6edb837b2f..a2bfa152d2b26 100644 --- a/doc/modules/calibration.rst +++ b/doc/modules/calibration.rst @@ -262,51 +262,51 @@ probabilities, the calibrated probabilities for each class are predicted separately. As those probabilities do not necessarily sum to one, a postprocessing is performed to normalize them. -.. topic:: Examples: - - * :ref:`sphx_glr_auto_examples_calibration_plot_calibration_curve.py` - * :ref:`sphx_glr_auto_examples_calibration_plot_calibration_multiclass.py` - * :ref:`sphx_glr_auto_examples_calibration_plot_calibration.py` - * :ref:`sphx_glr_auto_examples_calibration_plot_compare_calibration.py` - -.. topic:: References: - - .. [1] Allan H. Murphy (1973). - :doi:`"A New Vector Partition of the Probability Score" - <10.1175/1520-0450(1973)012%3C0595:ANVPOT%3E2.0.CO;2>` - Journal of Applied Meteorology and Climatology - - .. [2] `On the combination of forecast probabilities for - consecutive precipitation periods. - `_ - Wea. Forecasting, 5, 640–650., Wilks, D. S., 1990a - - .. [3] `Predicting Good Probabilities with Supervised Learning - `_, - A. Niculescu-Mizil & R. Caruana, ICML 2005 - - - .. [4] `Probabilistic Outputs for Support Vector Machines and Comparisons - to Regularized Likelihood Methods. - `_ - J. Platt, (1999) - - .. [5] `Transforming Classifier Scores into Accurate Multiclass - Probability Estimates. - `_ - B. Zadrozny & C. Elkan, (KDD 2002) - - .. [6] `Predicting accurate probabilities with a ranking loss. - `_ - Menon AK, Jiang XJ, Vembu S, Elkan C, Ohno-Machado L. - Proc Int Conf Mach Learn. 2012;2012:703-710 - - .. [7] `Beyond sigmoids: How to obtain well-calibrated probabilities from - binary classifiers with beta calibration - `_ - Kull, M., Silva Filho, T. M., & Flach, P. (2017). - - .. [8] Mario V. Wüthrich, Michael Merz (2023). - :doi:`"Statistical Foundations of Actuarial Learning and its Applications" - <10.1007/978-3-031-12409-9>` - Springer Actuarial +.. rubric:: Examples + +* :ref:`sphx_glr_auto_examples_calibration_plot_calibration_curve.py` +* :ref:`sphx_glr_auto_examples_calibration_plot_calibration_multiclass.py` +* :ref:`sphx_glr_auto_examples_calibration_plot_calibration.py` +* :ref:`sphx_glr_auto_examples_calibration_plot_compare_calibration.py` + +.. rubric:: References + +.. [1] Allan H. Murphy (1973). + :doi:`"A New Vector Partition of the Probability Score" + <10.1175/1520-0450(1973)012%3C0595:ANVPOT%3E2.0.CO;2>` + Journal of Applied Meteorology and Climatology + +.. [2] `On the combination of forecast probabilities for + consecutive precipitation periods. + `_ + Wea. Forecasting, 5, 640–650., Wilks, D. S., 1990a + +.. [3] `Predicting Good Probabilities with Supervised Learning + `_, + A. Niculescu-Mizil & R. Caruana, ICML 2005 + + +.. [4] `Probabilistic Outputs for Support Vector Machines and Comparisons + to Regularized Likelihood Methods. + `_ + J. Platt, (1999) + +.. [5] `Transforming Classifier Scores into Accurate Multiclass + Probability Estimates. + `_ + B. Zadrozny & C. Elkan, (KDD 2002) + +.. [6] `Predicting accurate probabilities with a ranking loss. + `_ + Menon AK, Jiang XJ, Vembu S, Elkan C, Ohno-Machado L. + Proc Int Conf Mach Learn. 2012;2012:703-710 + +.. [7] `Beyond sigmoids: How to obtain well-calibrated probabilities from + binary classifiers with beta calibration + `_ + Kull, M., Silva Filho, T. M., & Flach, P. (2017). + +.. [8] Mario V. Wüthrich, Michael Merz (2023). + :doi:`"Statistical Foundations of Actuarial Learning and its Applications" + <10.1007/978-3-031-12409-9>` + Springer Actuarial diff --git a/doc/modules/classes.rst b/doc/modules/classes.rst deleted file mode 100644 index 1da5b337ad7a4..0000000000000 --- a/doc/modules/classes.rst +++ /dev/null @@ -1,1916 +0,0 @@ -.. _api_ref: - -============= -API Reference -============= - -This is the class and function reference of scikit-learn. Please refer to -the :ref:`full user guide ` for further details, as the class and -function raw specifications may not be enough to give full guidelines on their -uses. -For reference on concepts repeated across the API, see :ref:`glossary`. - -:mod:`sklearn`: Settings and information tools -============================================== - -.. automodule:: sklearn - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - config_context - get_config - set_config - show_versions - -:mod:`sklearn.base`: Base classes and utility functions -======================================================= - -.. automodule:: sklearn.base - :no-members: - :no-inherited-members: - -Base classes ------------- -.. currentmodule:: sklearn - -.. autosummary:: - :nosignatures: - :toctree: generated/ - :template: class.rst - - base.BaseEstimator - base.BiclusterMixin - base.ClassifierMixin - base.ClusterMixin - base.DensityMixin - base.RegressorMixin - base.TransformerMixin - base.MetaEstimatorMixin - base.OneToOneFeatureMixin - base.OutlierMixin - base.ClassNamePrefixFeaturesOutMixin - feature_selection.SelectorMixin - -Functions ---------- -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - base.clone - base.is_classifier - base.is_regressor - -.. _calibration_ref: - -:mod:`sklearn.calibration`: Probability Calibration -=================================================== - -.. automodule:: sklearn.calibration - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`calibration` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - calibration.CalibratedClassifierCV - - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - calibration.calibration_curve - -.. _cluster_ref: - -:mod:`sklearn.cluster`: Clustering -================================== - -.. automodule:: sklearn.cluster - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`clustering` and :ref:`biclustering` sections for -further details. - -Classes -------- -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - cluster.AffinityPropagation - cluster.AgglomerativeClustering - cluster.Birch - cluster.DBSCAN - cluster.HDBSCAN - cluster.FeatureAgglomeration - cluster.KMeans - cluster.BisectingKMeans - cluster.MiniBatchKMeans - cluster.MeanShift - cluster.OPTICS - cluster.SpectralClustering - cluster.SpectralBiclustering - cluster.SpectralCoclustering - -Functions ---------- -.. autosummary:: - :toctree: generated/ - :template: function.rst - - cluster.affinity_propagation - cluster.cluster_optics_dbscan - cluster.cluster_optics_xi - cluster.compute_optics_graph - cluster.dbscan - cluster.estimate_bandwidth - cluster.k_means - cluster.kmeans_plusplus - cluster.mean_shift - cluster.spectral_clustering - cluster.ward_tree - -.. _compose_ref: - -:mod:`sklearn.compose`: Composite Estimators -============================================ - -.. automodule:: sklearn.compose - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`combining_estimators` section for further -details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - compose.ColumnTransformer - compose.TransformedTargetRegressor - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - compose.make_column_transformer - compose.make_column_selector - -.. _covariance_ref: - -:mod:`sklearn.covariance`: Covariance Estimators -================================================ - -.. automodule:: sklearn.covariance - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`covariance` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - covariance.EmpiricalCovariance - covariance.EllipticEnvelope - covariance.GraphicalLasso - covariance.GraphicalLassoCV - covariance.LedoitWolf - covariance.MinCovDet - covariance.OAS - covariance.ShrunkCovariance - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - covariance.empirical_covariance - covariance.graphical_lasso - covariance.ledoit_wolf - covariance.ledoit_wolf_shrinkage - covariance.oas - covariance.shrunk_covariance - -.. _cross_decomposition_ref: - -:mod:`sklearn.cross_decomposition`: Cross decomposition -======================================================= - -.. automodule:: sklearn.cross_decomposition - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`cross_decomposition` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - cross_decomposition.CCA - cross_decomposition.PLSCanonical - cross_decomposition.PLSRegression - cross_decomposition.PLSSVD - -.. _datasets_ref: - -:mod:`sklearn.datasets`: Datasets -================================= - -.. automodule:: sklearn.datasets - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`datasets` section for further details. - -Loaders -------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - datasets.clear_data_home - datasets.dump_svmlight_file - datasets.fetch_20newsgroups - datasets.fetch_20newsgroups_vectorized - datasets.fetch_california_housing - datasets.fetch_covtype - datasets.fetch_kddcup99 - datasets.fetch_lfw_pairs - datasets.fetch_lfw_people - datasets.fetch_olivetti_faces - datasets.fetch_openml - datasets.fetch_rcv1 - datasets.fetch_species_distributions - datasets.get_data_home - datasets.load_breast_cancer - datasets.load_diabetes - datasets.load_digits - datasets.load_files - datasets.load_iris - datasets.load_linnerud - datasets.load_sample_image - datasets.load_sample_images - datasets.load_svmlight_file - datasets.load_svmlight_files - datasets.load_wine - -Samples generator ------------------ - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - datasets.make_biclusters - datasets.make_blobs - datasets.make_checkerboard - datasets.make_circles - datasets.make_classification - datasets.make_friedman1 - datasets.make_friedman2 - datasets.make_friedman3 - datasets.make_gaussian_quantiles - datasets.make_hastie_10_2 - datasets.make_low_rank_matrix - datasets.make_moons - datasets.make_multilabel_classification - datasets.make_regression - datasets.make_s_curve - datasets.make_sparse_coded_signal - datasets.make_sparse_spd_matrix - datasets.make_sparse_uncorrelated - datasets.make_spd_matrix - datasets.make_swiss_roll - - -.. _decomposition_ref: - -:mod:`sklearn.decomposition`: Matrix Decomposition -================================================== - -.. automodule:: sklearn.decomposition - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`decompositions` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - decomposition.DictionaryLearning - decomposition.FactorAnalysis - decomposition.FastICA - decomposition.IncrementalPCA - decomposition.KernelPCA - decomposition.LatentDirichletAllocation - decomposition.MiniBatchDictionaryLearning - decomposition.MiniBatchSparsePCA - decomposition.NMF - decomposition.MiniBatchNMF - decomposition.PCA - decomposition.SparsePCA - decomposition.SparseCoder - decomposition.TruncatedSVD - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - decomposition.dict_learning - decomposition.dict_learning_online - decomposition.fastica - decomposition.non_negative_factorization - decomposition.sparse_encode - -.. _lda_ref: - -:mod:`sklearn.discriminant_analysis`: Discriminant Analysis -=========================================================== - -.. automodule:: sklearn.discriminant_analysis - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`lda_qda` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - discriminant_analysis.LinearDiscriminantAnalysis - discriminant_analysis.QuadraticDiscriminantAnalysis - -.. _dummy_ref: - -:mod:`sklearn.dummy`: Dummy estimators -====================================== - -.. automodule:: sklearn.dummy - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`model_evaluation` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - dummy.DummyClassifier - dummy.DummyRegressor - -.. autosummary:: - :toctree: generated/ - :template: function.rst - -.. _ensemble_ref: - -:mod:`sklearn.ensemble`: Ensemble Methods -========================================= - -.. automodule:: sklearn.ensemble - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`ensemble` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - ensemble.AdaBoostClassifier - ensemble.AdaBoostRegressor - ensemble.BaggingClassifier - ensemble.BaggingRegressor - ensemble.ExtraTreesClassifier - ensemble.ExtraTreesRegressor - ensemble.GradientBoostingClassifier - ensemble.GradientBoostingRegressor - ensemble.IsolationForest - ensemble.RandomForestClassifier - ensemble.RandomForestRegressor - ensemble.RandomTreesEmbedding - ensemble.StackingClassifier - ensemble.StackingRegressor - ensemble.VotingClassifier - ensemble.VotingRegressor - ensemble.HistGradientBoostingRegressor - ensemble.HistGradientBoostingClassifier - - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - -.. _exceptions_ref: - -:mod:`sklearn.exceptions`: Exceptions and warnings -================================================== - -.. automodule:: sklearn.exceptions - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - exceptions.ConvergenceWarning - exceptions.DataConversionWarning - exceptions.DataDimensionalityWarning - exceptions.EfficiencyWarning - exceptions.FitFailedWarning - exceptions.InconsistentVersionWarning - exceptions.NotFittedError - exceptions.UndefinedMetricWarning - - -:mod:`sklearn.experimental`: Experimental -========================================= - -.. automodule:: sklearn.experimental - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - - experimental.enable_iterative_imputer - experimental.enable_halving_search_cv - - -.. _feature_extraction_ref: - -:mod:`sklearn.feature_extraction`: Feature Extraction -===================================================== - -.. automodule:: sklearn.feature_extraction - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`feature_extraction` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - feature_extraction.DictVectorizer - feature_extraction.FeatureHasher - -From images ------------ - -.. automodule:: sklearn.feature_extraction.image - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - feature_extraction.image.extract_patches_2d - feature_extraction.image.grid_to_graph - feature_extraction.image.img_to_graph - feature_extraction.image.reconstruct_from_patches_2d - - :template: class.rst - - feature_extraction.image.PatchExtractor - -.. _text_feature_extraction_ref: - -From text ---------- - -.. automodule:: sklearn.feature_extraction.text - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - feature_extraction.text.CountVectorizer - feature_extraction.text.HashingVectorizer - feature_extraction.text.TfidfTransformer - feature_extraction.text.TfidfVectorizer - - -.. _feature_selection_ref: - -:mod:`sklearn.feature_selection`: Feature Selection -=================================================== - -.. automodule:: sklearn.feature_selection - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`feature_selection` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - feature_selection.GenericUnivariateSelect - feature_selection.SelectPercentile - feature_selection.SelectKBest - feature_selection.SelectFpr - feature_selection.SelectFdr - feature_selection.SelectFromModel - feature_selection.SelectFwe - feature_selection.SequentialFeatureSelector - feature_selection.RFE - feature_selection.RFECV - feature_selection.VarianceThreshold - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - feature_selection.chi2 - feature_selection.f_classif - feature_selection.f_regression - feature_selection.r_regression - feature_selection.mutual_info_classif - feature_selection.mutual_info_regression - - -.. _gaussian_process_ref: - -:mod:`sklearn.gaussian_process`: Gaussian Processes -=================================================== - -.. automodule:: sklearn.gaussian_process - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`gaussian_process` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - gaussian_process.GaussianProcessClassifier - gaussian_process.GaussianProcessRegressor - -Kernels -------- - -.. automodule:: sklearn.gaussian_process.kernels - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class_with_call.rst - - gaussian_process.kernels.CompoundKernel - gaussian_process.kernels.ConstantKernel - gaussian_process.kernels.DotProduct - gaussian_process.kernels.ExpSineSquared - gaussian_process.kernels.Exponentiation - gaussian_process.kernels.Hyperparameter - gaussian_process.kernels.Kernel - gaussian_process.kernels.Matern - gaussian_process.kernels.PairwiseKernel - gaussian_process.kernels.Product - gaussian_process.kernels.RBF - gaussian_process.kernels.RationalQuadratic - gaussian_process.kernels.Sum - gaussian_process.kernels.WhiteKernel - - -.. _impute_ref: - -:mod:`sklearn.impute`: Impute -============================= - -.. automodule:: sklearn.impute - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`Impute` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - impute.SimpleImputer - impute.IterativeImputer - impute.MissingIndicator - impute.KNNImputer - - -.. _inspection_ref: - -:mod:`sklearn.inspection`: Inspection -===================================== - -.. automodule:: sklearn.inspection - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - inspection.partial_dependence - inspection.permutation_importance - -Plotting --------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: display_only_from_estimator.rst - - inspection.DecisionBoundaryDisplay - inspection.PartialDependenceDisplay - -.. _isotonic_ref: - -:mod:`sklearn.isotonic`: Isotonic regression -============================================ - -.. automodule:: sklearn.isotonic - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`isotonic` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - isotonic.IsotonicRegression - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - isotonic.check_increasing - isotonic.isotonic_regression - - -.. _kernel_approximation_ref: - -:mod:`sklearn.kernel_approximation`: Kernel Approximation -========================================================= - -.. automodule:: sklearn.kernel_approximation - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`kernel_approximation` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - kernel_approximation.AdditiveChi2Sampler - kernel_approximation.Nystroem - kernel_approximation.PolynomialCountSketch - kernel_approximation.RBFSampler - kernel_approximation.SkewedChi2Sampler - -.. _kernel_ridge_ref: - -:mod:`sklearn.kernel_ridge`: Kernel Ridge Regression -==================================================== - -.. automodule:: sklearn.kernel_ridge - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`kernel_ridge` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - kernel_ridge.KernelRidge - -.. _linear_model_ref: - -:mod:`sklearn.linear_model`: Linear Models -========================================== - -.. automodule:: sklearn.linear_model - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`linear_model` section for further details. - -The following subsections are only rough guidelines: the same estimator can -fall into multiple categories, depending on its parameters. - -.. currentmodule:: sklearn - -Linear classifiers ------------------- -.. autosummary:: - :toctree: generated/ - :template: class.rst - - linear_model.LogisticRegression - linear_model.LogisticRegressionCV - linear_model.PassiveAggressiveClassifier - linear_model.Perceptron - linear_model.RidgeClassifier - linear_model.RidgeClassifierCV - linear_model.SGDClassifier - linear_model.SGDOneClassSVM - -Classical linear regressors ---------------------------- - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - linear_model.LinearRegression - linear_model.Ridge - linear_model.RidgeCV - linear_model.SGDRegressor - -Regressors with variable selection ----------------------------------- - -The following estimators have built-in variable selection fitting -procedures, but any estimator using a L1 or elastic-net penalty also -performs variable selection: typically :class:`~linear_model.SGDRegressor` -or :class:`~sklearn.linear_model.SGDClassifier` with an appropriate penalty. - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - linear_model.ElasticNet - linear_model.ElasticNetCV - linear_model.Lars - linear_model.LarsCV - linear_model.Lasso - linear_model.LassoCV - linear_model.LassoLars - linear_model.LassoLarsCV - linear_model.LassoLarsIC - linear_model.OrthogonalMatchingPursuit - linear_model.OrthogonalMatchingPursuitCV - -Bayesian regressors -------------------- - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - linear_model.ARDRegression - linear_model.BayesianRidge - -Multi-task linear regressors with variable selection ----------------------------------------------------- - -These estimators fit multiple regression problems (or tasks) jointly, while -inducing sparse coefficients. While the inferred coefficients may differ -between the tasks, they are constrained to agree on the features that are -selected (non-zero coefficients). - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - linear_model.MultiTaskElasticNet - linear_model.MultiTaskElasticNetCV - linear_model.MultiTaskLasso - linear_model.MultiTaskLassoCV - -Outlier-robust regressors -------------------------- - -Any estimator using the Huber loss would also be robust to outliers, e.g. -:class:`~linear_model.SGDRegressor` with ``loss='huber'``. - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - linear_model.HuberRegressor - linear_model.QuantileRegressor - linear_model.RANSACRegressor - linear_model.TheilSenRegressor - -Generalized linear models (GLM) for regression ----------------------------------------------- - -These models allow for response variables to have error distributions other -than a normal distribution: - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - linear_model.PoissonRegressor - linear_model.TweedieRegressor - linear_model.GammaRegressor - - -Miscellaneous -------------- - -.. autosummary:: - :toctree: generated/ - :template: classes.rst - - linear_model.PassiveAggressiveRegressor - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - linear_model.enet_path - linear_model.lars_path - linear_model.lars_path_gram - linear_model.lasso_path - linear_model.orthogonal_mp - linear_model.orthogonal_mp_gram - linear_model.ridge_regression - - -.. _manifold_ref: - -:mod:`sklearn.manifold`: Manifold Learning -========================================== - -.. automodule:: sklearn.manifold - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`manifold` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated - :template: class.rst - - manifold.Isomap - manifold.LocallyLinearEmbedding - manifold.MDS - manifold.SpectralEmbedding - manifold.TSNE - -.. autosummary:: - :toctree: generated - :template: function.rst - - manifold.locally_linear_embedding - manifold.smacof - manifold.spectral_embedding - manifold.trustworthiness - - -.. _metrics_ref: - -:mod:`sklearn.metrics`: Metrics -=============================== - -See the :ref:`model_evaluation` section and the :ref:`metrics` section of the -user guide for further details. - -.. automodule:: sklearn.metrics - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -Model Selection Interface -------------------------- -See the :ref:`scoring_parameter` section of the user guide for further -details. - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - metrics.check_scoring - metrics.get_scorer - metrics.get_scorer_names - metrics.make_scorer - -Classification metrics ----------------------- - -See the :ref:`classification_metrics` section of the user guide for further -details. - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - metrics.accuracy_score - metrics.auc - metrics.average_precision_score - metrics.balanced_accuracy_score - metrics.brier_score_loss - metrics.class_likelihood_ratios - metrics.classification_report - metrics.cohen_kappa_score - metrics.confusion_matrix - metrics.d2_log_loss_score - metrics.dcg_score - metrics.det_curve - metrics.f1_score - metrics.fbeta_score - metrics.hamming_loss - metrics.hinge_loss - metrics.jaccard_score - metrics.log_loss - metrics.matthews_corrcoef - metrics.multilabel_confusion_matrix - metrics.ndcg_score - metrics.precision_recall_curve - metrics.precision_recall_fscore_support - metrics.precision_score - metrics.recall_score - metrics.roc_auc_score - metrics.roc_curve - metrics.top_k_accuracy_score - metrics.zero_one_loss - -Regression metrics ------------------- - -See the :ref:`regression_metrics` section of the user guide for further -details. - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - metrics.explained_variance_score - metrics.max_error - metrics.mean_absolute_error - metrics.mean_squared_error - metrics.mean_squared_log_error - metrics.median_absolute_error - metrics.mean_absolute_percentage_error - metrics.r2_score - metrics.root_mean_squared_log_error - metrics.root_mean_squared_error - metrics.mean_poisson_deviance - metrics.mean_gamma_deviance - metrics.mean_tweedie_deviance - metrics.d2_tweedie_score - metrics.mean_pinball_loss - metrics.d2_pinball_score - metrics.d2_absolute_error_score - -Multilabel ranking metrics --------------------------- -See the :ref:`multilabel_ranking_metrics` section of the user guide for further -details. - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - metrics.coverage_error - metrics.label_ranking_average_precision_score - metrics.label_ranking_loss - - -Clustering metrics ------------------- - -See the :ref:`clustering_evaluation` section of the user guide for further -details. - -.. automodule:: sklearn.metrics.cluster - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - metrics.adjusted_mutual_info_score - metrics.adjusted_rand_score - metrics.calinski_harabasz_score - metrics.davies_bouldin_score - metrics.completeness_score - metrics.cluster.contingency_matrix - metrics.cluster.pair_confusion_matrix - metrics.fowlkes_mallows_score - metrics.homogeneity_completeness_v_measure - metrics.homogeneity_score - metrics.mutual_info_score - metrics.normalized_mutual_info_score - metrics.rand_score - metrics.silhouette_score - metrics.silhouette_samples - metrics.v_measure_score - -Biclustering metrics --------------------- - -See the :ref:`biclustering_evaluation` section of the user guide for -further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - metrics.consensus_score - -Distance metrics ----------------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - metrics.DistanceMetric - -Pairwise metrics ----------------- - -See the :ref:`metrics` section of the user guide for further details. - -.. automodule:: sklearn.metrics.pairwise - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - metrics.pairwise.additive_chi2_kernel - metrics.pairwise.chi2_kernel - metrics.pairwise.cosine_similarity - metrics.pairwise.cosine_distances - metrics.pairwise.distance_metrics - metrics.pairwise.euclidean_distances - metrics.pairwise.haversine_distances - metrics.pairwise.kernel_metrics - metrics.pairwise.laplacian_kernel - metrics.pairwise.linear_kernel - metrics.pairwise.manhattan_distances - metrics.pairwise.nan_euclidean_distances - metrics.pairwise.pairwise_kernels - metrics.pairwise.polynomial_kernel - metrics.pairwise.rbf_kernel - metrics.pairwise.sigmoid_kernel - metrics.pairwise.paired_euclidean_distances - metrics.pairwise.paired_manhattan_distances - metrics.pairwise.paired_cosine_distances - metrics.pairwise.paired_distances - metrics.pairwise_distances - metrics.pairwise_distances_argmin - metrics.pairwise_distances_argmin_min - metrics.pairwise_distances_chunked - - -Plotting --------- - -See the :ref:`visualizations` section of the user guide for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: display_all_class_methods.rst - - metrics.ConfusionMatrixDisplay - metrics.DetCurveDisplay - metrics.PrecisionRecallDisplay - metrics.PredictionErrorDisplay - metrics.RocCurveDisplay - calibration.CalibrationDisplay - -.. _mixture_ref: - -:mod:`sklearn.mixture`: Gaussian Mixture Models -=============================================== - -.. automodule:: sklearn.mixture - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`mixture` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - mixture.BayesianGaussianMixture - mixture.GaussianMixture - -.. _modelselection_ref: - -:mod:`sklearn.model_selection`: Model Selection -=============================================== - -.. automodule:: sklearn.model_selection - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`cross_validation`, :ref:`grid_search` and -:ref:`learning_curve` sections for further details. - -Splitter Classes ----------------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - model_selection.GroupKFold - model_selection.GroupShuffleSplit - model_selection.KFold - model_selection.LeaveOneGroupOut - model_selection.LeavePGroupsOut - model_selection.LeaveOneOut - model_selection.LeavePOut - model_selection.PredefinedSplit - model_selection.RepeatedKFold - model_selection.RepeatedStratifiedKFold - model_selection.ShuffleSplit - model_selection.StratifiedKFold - model_selection.StratifiedShuffleSplit - model_selection.StratifiedGroupKFold - model_selection.TimeSeriesSplit - -Splitter Functions ------------------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - model_selection.check_cv - model_selection.train_test_split - -.. _hyper_parameter_optimizers: - -Hyper-parameter optimizers --------------------------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - model_selection.GridSearchCV - model_selection.HalvingGridSearchCV - model_selection.ParameterGrid - model_selection.ParameterSampler - model_selection.RandomizedSearchCV - model_selection.HalvingRandomSearchCV - -Post-fit model tuning ---------------------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - model_selection.FixedThresholdClassifier - model_selection.TunedThresholdClassifierCV - -Model validation ----------------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - model_selection.cross_validate - model_selection.cross_val_predict - model_selection.cross_val_score - model_selection.learning_curve - model_selection.permutation_test_score - model_selection.validation_curve - -Visualization -------------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: display_only_from_estimator.rst - - model_selection.LearningCurveDisplay - model_selection.ValidationCurveDisplay - -.. _multiclass_ref: - -:mod:`sklearn.multiclass`: Multiclass classification -==================================================== - -.. automodule:: sklearn.multiclass - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`multiclass_classification` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - multiclass.OneVsRestClassifier - multiclass.OneVsOneClassifier - multiclass.OutputCodeClassifier - -.. _multioutput_ref: - -:mod:`sklearn.multioutput`: Multioutput regression and classification -===================================================================== - -.. automodule:: sklearn.multioutput - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`multilabel_classification`, -:ref:`multiclass_multioutput_classification`, and -:ref:`multioutput_regression` sections for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated - :template: class.rst - - multioutput.ClassifierChain - multioutput.MultiOutputRegressor - multioutput.MultiOutputClassifier - multioutput.RegressorChain - -.. _naive_bayes_ref: - -:mod:`sklearn.naive_bayes`: Naive Bayes -======================================= - -.. automodule:: sklearn.naive_bayes - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`naive_bayes` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - naive_bayes.BernoulliNB - naive_bayes.CategoricalNB - naive_bayes.ComplementNB - naive_bayes.GaussianNB - naive_bayes.MultinomialNB - - -.. _neighbors_ref: - -:mod:`sklearn.neighbors`: Nearest Neighbors -=========================================== - -.. automodule:: sklearn.neighbors - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`neighbors` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - neighbors.BallTree - neighbors.KDTree - neighbors.KernelDensity - neighbors.KNeighborsClassifier - neighbors.KNeighborsRegressor - neighbors.KNeighborsTransformer - neighbors.LocalOutlierFactor - neighbors.RadiusNeighborsClassifier - neighbors.RadiusNeighborsRegressor - neighbors.RadiusNeighborsTransformer - neighbors.NearestCentroid - neighbors.NearestNeighbors - neighbors.NeighborhoodComponentsAnalysis - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - neighbors.kneighbors_graph - neighbors.radius_neighbors_graph - neighbors.sort_graph_by_row_values - -.. _neural_network_ref: - -:mod:`sklearn.neural_network`: Neural network models -==================================================== - -.. automodule:: sklearn.neural_network - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`neural_networks_supervised` and :ref:`neural_networks_unsupervised` sections for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - neural_network.BernoulliRBM - neural_network.MLPClassifier - neural_network.MLPRegressor - -.. _pipeline_ref: - -:mod:`sklearn.pipeline`: Pipeline -================================= - -.. automodule:: sklearn.pipeline - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`combining_estimators` section for further -details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - pipeline.FeatureUnion - pipeline.Pipeline - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - pipeline.make_pipeline - pipeline.make_union - -.. _preprocessing_ref: - -:mod:`sklearn.preprocessing`: Preprocessing and Normalization -============================================================= - -.. automodule:: sklearn.preprocessing - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`preprocessing` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - preprocessing.Binarizer - preprocessing.FunctionTransformer - preprocessing.KBinsDiscretizer - preprocessing.KernelCenterer - preprocessing.LabelBinarizer - preprocessing.LabelEncoder - preprocessing.MultiLabelBinarizer - preprocessing.MaxAbsScaler - preprocessing.MinMaxScaler - preprocessing.Normalizer - preprocessing.OneHotEncoder - preprocessing.OrdinalEncoder - preprocessing.PolynomialFeatures - preprocessing.PowerTransformer - preprocessing.QuantileTransformer - preprocessing.RobustScaler - preprocessing.SplineTransformer - preprocessing.StandardScaler - preprocessing.TargetEncoder - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - preprocessing.add_dummy_feature - preprocessing.binarize - preprocessing.label_binarize - preprocessing.maxabs_scale - preprocessing.minmax_scale - preprocessing.normalize - preprocessing.quantile_transform - preprocessing.robust_scale - preprocessing.scale - preprocessing.power_transform - - -.. _random_projection_ref: - -:mod:`sklearn.random_projection`: Random projection -=================================================== - -.. automodule:: sklearn.random_projection - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`random_projection` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - random_projection.GaussianRandomProjection - random_projection.SparseRandomProjection - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - random_projection.johnson_lindenstrauss_min_dim - - -.. _semi_supervised_ref: - -:mod:`sklearn.semi_supervised`: Semi-Supervised Learning -======================================================== - -.. automodule:: sklearn.semi_supervised - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`semi_supervised` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - semi_supervised.LabelPropagation - semi_supervised.LabelSpreading - semi_supervised.SelfTrainingClassifier - - -.. _svm_ref: - -:mod:`sklearn.svm`: Support Vector Machines -=========================================== - -.. automodule:: sklearn.svm - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`svm` section for further details. - -Estimators ----------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - svm.LinearSVC - svm.LinearSVR - svm.NuSVC - svm.NuSVR - svm.OneClassSVM - svm.SVC - svm.SVR - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - svm.l1_min_c - -.. _tree_ref: - -:mod:`sklearn.tree`: Decision Trees -=================================== - -.. automodule:: sklearn.tree - :no-members: - :no-inherited-members: - -**User guide:** See the :ref:`tree` section for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - tree.DecisionTreeClassifier - tree.DecisionTreeRegressor - tree.ExtraTreeClassifier - tree.ExtraTreeRegressor - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - tree.export_graphviz - tree.export_text - -Plotting --------- - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - tree.plot_tree - -.. _utils_ref: - -:mod:`sklearn.utils`: Utilities -=============================== - -.. automodule:: sklearn.utils - :no-members: - :no-inherited-members: - -**Developer guide:** See the :ref:`developers-utils` page for further details. - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - utils.Bunch - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.as_float_array - utils.assert_all_finite - utils.deprecated - utils.estimator_html_repr - utils.gen_batches - utils.gen_even_slices - utils.indexable - utils.murmurhash3_32 - utils.resample - utils._safe_indexing - utils.safe_mask - utils.safe_sqr - utils.shuffle - -Input and parameter validation ------------------------------- - -.. automodule:: sklearn.utils.validation - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.check_X_y - utils.check_array - utils.check_scalar - utils.check_consistent_length - utils.check_random_state - utils.validation.check_is_fitted - utils.validation.check_memory - utils.validation.check_symmetric - utils.validation.column_or_1d - utils.validation.has_fit_parameter - -Utilities used in meta-estimators ---------------------------------- - -.. automodule:: sklearn.utils.metaestimators - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.metaestimators.available_if - -Utilities to handle weights based on class labels -------------------------------------------------- - -.. automodule:: sklearn.utils.class_weight - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.class_weight.compute_class_weight - utils.class_weight.compute_sample_weight - -Utilities to deal with multiclass target in classifiers -------------------------------------------------------- - -.. automodule:: sklearn.utils.multiclass - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.multiclass.type_of_target - utils.multiclass.is_multilabel - utils.multiclass.unique_labels - -Utilities for optimal mathematical operations ---------------------------------------------- - -.. automodule:: sklearn.utils.extmath - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.extmath.safe_sparse_dot - utils.extmath.randomized_range_finder - utils.extmath.randomized_svd - utils.extmath.fast_logdet - utils.extmath.density - utils.extmath.weighted_mode - -Utilities to work with sparse matrices and arrays -------------------------------------------------- - -.. automodule:: sklearn.utils.sparsefuncs - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.sparsefuncs.incr_mean_variance_axis - utils.sparsefuncs.inplace_column_scale - utils.sparsefuncs.inplace_row_scale - utils.sparsefuncs.inplace_swap_row - utils.sparsefuncs.inplace_swap_column - utils.sparsefuncs.mean_variance_axis - utils.sparsefuncs.inplace_csr_column_scale - -.. automodule:: sklearn.utils.sparsefuncs_fast - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.sparsefuncs_fast.inplace_csr_row_normalize_l1 - utils.sparsefuncs_fast.inplace_csr_row_normalize_l2 - -Utilities to work with graphs ------------------------------ - -.. automodule:: sklearn.utils.graph - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.graph.single_source_shortest_path_length - -Utilities for random sampling ------------------------------ - -.. automodule:: sklearn.utils.random - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.random.sample_without_replacement - - -Utilities to operate on arrays ------------------------------- - -.. automodule:: sklearn.utils.arrayfuncs - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.arrayfuncs.min_pos - -Metadata routing ----------------- - -.. automodule:: sklearn.utils.metadata_routing - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.metadata_routing.get_routing_for_object - utils.metadata_routing.process_routing - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - utils.metadata_routing.MetadataRouter - utils.metadata_routing.MetadataRequest - utils.metadata_routing.MethodMapping - -Scikit-learn object discovery ------------------------------ - -.. automodule:: sklearn.utils.discovery - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.discovery.all_estimators - utils.discovery.all_displays - utils.discovery.all_functions - -Scikit-learn compatibility checker ----------------------------------- - -.. automodule:: sklearn.utils.estimator_checks - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.estimator_checks.check_estimator - utils.estimator_checks.parametrize_with_checks - -Utilities for parallel computing --------------------------------- - -.. automodule:: sklearn.utils.parallel - :no-members: - :no-inherited-members: - -.. currentmodule:: sklearn - -.. autosummary:: - :toctree: generated/ - :template: function.rst - - utils.parallel.delayed - utils.parallel_backend - utils.register_parallel_backend - -.. autosummary:: - :toctree: generated/ - :template: class.rst - - utils.parallel.Parallel - - -Recently deprecated -=================== diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst index ed27b369171e5..2de39d0317bf5 100644 --- a/doc/modules/clustering.rst +++ b/doc/modules/clustering.rst @@ -241,13 +241,13 @@ K-means can be used for vector quantization. This is achieved using the performing vector quantization on an image refer to :ref:`sphx_glr_auto_examples_cluster_plot_color_quantization.py`. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_cluster_plot_cluster_iris.py`: Example usage of - :class:`KMeans` using the iris dataset +* :ref:`sphx_glr_auto_examples_cluster_plot_cluster_iris.py`: Example usage of + :class:`KMeans` using the iris dataset - * :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`: Document clustering - using :class:`KMeans` and :class:`MiniBatchKMeans` based on sparse data +* :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`: Document clustering + using :class:`KMeans` and :class:`MiniBatchKMeans` based on sparse data Low-level parallelism --------------------- @@ -257,24 +257,20 @@ chunks of data (256 samples) are processed in parallel, which in addition yields a low memory footprint. For more details on how to control the number of threads, please refer to our :ref:`parallelism` notes. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_assumptions.py`: Demonstrating - when k-means performs intuitively and when it does not - * :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py`: Clustering - handwritten digits +* :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_assumptions.py`: Demonstrating when + k-means performs intuitively and when it does not +* :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py`: Clustering handwritten digits +.. dropdown:: References -|details-start| -**References** -|details-split| + * `"k-means++: The advantages of careful seeding" + `_ + Arthur, David, and Sergei Vassilvitskii, + *Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete + algorithms*, Society for Industrial and Applied Mathematics (2007) -* `"k-means++: The advantages of careful seeding" - `_ Arthur, David, and - Sergei Vassilvitskii, *Proceedings of the eighteenth annual ACM-SIAM symposium - on Discrete algorithms*, Society for Industrial and Applied Mathematics (2007) - -|details-end| .. _mini_batch_kmeans: @@ -310,24 +306,22 @@ small, as shown in the example and cited reference. :scale: 100 -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_cluster_plot_mini_batch_kmeans.py`: Comparison of - :class:`KMeans` and :class:`MiniBatchKMeans` +* :ref:`sphx_glr_auto_examples_cluster_plot_mini_batch_kmeans.py`: Comparison of + :class:`KMeans` and :class:`MiniBatchKMeans` - * :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`: Document clustering - using :class:`KMeans` and :class:`MiniBatchKMeans` based on sparse data +* :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`: Document clustering + using :class:`KMeans` and :class:`MiniBatchKMeans` based on sparse data -|details-start| -**References** -|details-split| +* :ref:`sphx_glr_auto_examples_cluster_plot_dict_face_patches.py` -* `"Web Scale K-Means clustering" - `_ - D. Sculley, *Proceedings of the 19th international conference on World - wide web* (2010) +.. dropdown:: References -|details-end| + * `"Web Scale K-Means clustering" + `_ + D. Sculley, *Proceedings of the 19th international conference on World + wide web* (2010) .. _affinity_propagation: @@ -364,55 +358,50 @@ convergence. Further, the memory complexity is of the order sparse similarity matrix is used. This makes Affinity Propagation most appropriate for small to medium sized datasets. -|details-start| -**Algorithm description** -|details-split| - -The messages sent between points belong to one of two categories. The first is -the responsibility :math:`r(i, k)`, which is the accumulated evidence that -sample :math:`k` should be the exemplar for sample :math:`i`. The second is the -availability :math:`a(i, k)` which is the accumulated evidence that sample -:math:`i` should choose sample :math:`k` to be its exemplar, and considers the -values for all other samples that :math:`k` should be an exemplar. In this way, -exemplars are chosen by samples if they are (1) similar enough to many samples -and (2) chosen by many samples to be representative of themselves. +.. dropdown:: Algorithm description -More formally, the responsibility of a sample :math:`k` to be the exemplar of -sample :math:`i` is given by: + The messages sent between points belong to one of two categories. The first is + the responsibility :math:`r(i, k)`, which is the accumulated evidence that + sample :math:`k` should be the exemplar for sample :math:`i`. The second is the + availability :math:`a(i, k)` which is the accumulated evidence that sample + :math:`i` should choose sample :math:`k` to be its exemplar, and considers the + values for all other samples that :math:`k` should be an exemplar. In this way, + exemplars are chosen by samples if they are (1) similar enough to many samples + and (2) chosen by many samples to be representative of themselves. -.. math:: + More formally, the responsibility of a sample :math:`k` to be the exemplar of + sample :math:`i` is given by: - r(i, k) \leftarrow s(i, k) - max [ a(i, k') + s(i, k') \forall k' \neq k ] + .. math:: -Where :math:`s(i, k)` is the similarity between samples :math:`i` and :math:`k`. -The availability of sample :math:`k` to be the exemplar of sample :math:`i` is -given by: - -.. math:: + r(i, k) \leftarrow s(i, k) - max [ a(i, k') + s(i, k') \forall k' \neq k ] - a(i, k) \leftarrow min [0, r(k, k) + \sum_{i'~s.t.~i' \notin \{i, k\}}{r(i', - k)}] + Where :math:`s(i, k)` is the similarity between samples :math:`i` and :math:`k`. + The availability of sample :math:`k` to be the exemplar of sample :math:`i` is + given by: -To begin with, all values for :math:`r` and :math:`a` are set to zero, and the -calculation of each iterates until convergence. As discussed above, in order to -avoid numerical oscillations when updating the messages, the damping factor -:math:`\lambda` is introduced to iteration process: + .. math:: -.. math:: r_{t+1}(i, k) = \lambda\cdot r_{t}(i, k) + (1-\lambda)\cdot r_{t+1}(i, k) -.. math:: a_{t+1}(i, k) = \lambda\cdot a_{t}(i, k) + (1-\lambda)\cdot a_{t+1}(i, k) + a(i, k) \leftarrow min [0, r(k, k) + \sum_{i'~s.t.~i' \notin \{i, k\}}{r(i', + k)}] -where :math:`t` indicates the iteration times. + To begin with, all values for :math:`r` and :math:`a` are set to zero, and the + calculation of each iterates until convergence. As discussed above, in order to + avoid numerical oscillations when updating the messages, the damping factor + :math:`\lambda` is introduced to iteration process: -|details-end| + .. math:: r_{t+1}(i, k) = \lambda\cdot r_{t}(i, k) + (1-\lambda)\cdot r_{t+1}(i, k) + .. math:: a_{t+1}(i, k) = \lambda\cdot a_{t}(i, k) + (1-\lambda)\cdot a_{t+1}(i, k) + where :math:`t` indicates the iteration times. -.. topic:: Examples: - * :ref:`sphx_glr_auto_examples_cluster_plot_affinity_propagation.py`: Affinity - Propagation on a synthetic 2D datasets with 3 classes. +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_applications_plot_stock_market.py` Affinity - Propagation on Financial time series to find groups of companies +* :ref:`sphx_glr_auto_examples_cluster_plot_affinity_propagation.py`: Affinity + Propagation on a synthetic 2D datasets with 3 classes +* :ref:`sphx_glr_auto_examples_applications_plot_stock_market.py` Affinity Propagation + on financial time series to find groups of companies .. _mean_shift: @@ -425,43 +414,40 @@ for centroids to be the mean of the points within a given region. These candidates are then filtered in a post-processing stage to eliminate near-duplicates to form the final set of centroids. -|details-start| -**Mathematical details** -|details-split| +.. dropdown:: Mathematical details -The position of centroid candidates is iteratively adjusted using a technique -called hill climbing, which finds local maxima of the estimated probability -density. Given a candidate centroid :math:`x` for iteration :math:`t`, the -candidate is updated according to the following equation: + The position of centroid candidates is iteratively adjusted using a technique + called hill climbing, which finds local maxima of the estimated probability + density. Given a candidate centroid :math:`x` for iteration :math:`t`, the + candidate is updated according to the following equation: -.. math:: + .. math:: - x^{t+1} = x^t + m(x^t) + x^{t+1} = x^t + m(x^t) -Where :math:`m` is the *mean shift* vector that is computed for each centroid -that points towards a region of the maximum increase in the density of points. -To compute :math:`m` we define :math:`N(x)` as the neighborhood of samples -within a given distance around :math:`x`. Then :math:`m` is computed using the -following equation, effectively updating a centroid to be the mean of the -samples within its neighborhood: + Where :math:`m` is the *mean shift* vector that is computed for each centroid + that points towards a region of the maximum increase in the density of points. + To compute :math:`m` we define :math:`N(x)` as the neighborhood of samples + within a given distance around :math:`x`. Then :math:`m` is computed using the + following equation, effectively updating a centroid to be the mean of the + samples within its neighborhood: -.. math:: + .. math:: - m(x) = \frac{1}{|N(x)|} \sum_{x_j \in N(x)}x_j - x + m(x) = \frac{1}{|N(x)|} \sum_{x_j \in N(x)}x_j - x -In general, the equation for :math:`m` depends on a kernel used for density -estimation. The generic formula is: + In general, the equation for :math:`m` depends on a kernel used for density + estimation. The generic formula is: -.. math:: + .. math:: - m(x) = \frac{\sum_{x_j \in N(x)}K(x_j - x)x_j}{\sum_{x_j \in N(x)}K(x_j - - x)} - x + m(x) = \frac{\sum_{x_j \in N(x)}K(x_j - x)x_j}{\sum_{x_j \in N(x)}K(x_j - + x)} - x -In our implementation, :math:`K(x)` is equal to 1 if :math:`x` is small enough -and is equal to 0 otherwise. Effectively :math:`K(y - x)` indicates whether -:math:`y` is in the neighborhood of :math:`x`. + In our implementation, :math:`K(x)` is equal to 1 if :math:`x` is small enough + and is equal to 0 otherwise. Effectively :math:`K(y - x)` indicates whether + :math:`y` is in the neighborhood of :math:`x`. -|details-end| The algorithm automatically sets the number of clusters, instead of relying on a parameter ``bandwidth``, which dictates the size of the region to search through. @@ -483,21 +469,17 @@ given sample. :scale: 50 -.. topic:: Examples: - - * :ref:`sphx_glr_auto_examples_cluster_plot_mean_shift.py`: Mean Shift - clustering on a synthetic 2D datasets with 3 classes. +.. rubric:: Examples +* :ref:`sphx_glr_auto_examples_cluster_plot_mean_shift.py`: Mean Shift clustering + on a synthetic 2D datasets with 3 classes. -|details-start| -**References** -|details-split| +.. dropdown:: References -* :doi:`"Mean shift: A robust approach toward feature space analysis" - <10.1109/34.1000236>` D. Comaniciu and P. Meer, *IEEE Transactions on Pattern - Analysis and Machine Intelligence* (2002) + * :doi:`"Mean shift: A robust approach toward feature space analysis" + <10.1109/34.1000236>` D. Comaniciu and P. Meer, *IEEE Transactions on Pattern + Analysis and Machine Intelligence* (2002) -|details-end| .. _spectral_clustering: @@ -547,13 +529,13 @@ computed using a function of a gradient of the image. See the examples for such an application. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_cluster_plot_segmentation_toy.py`: Segmenting - objects from a noisy background using spectral clustering. +* :ref:`sphx_glr_auto_examples_cluster_plot_segmentation_toy.py`: Segmenting objects + from a noisy background using spectral clustering. +* :ref:`sphx_glr_auto_examples_cluster_plot_coin_segmentation.py`: Spectral clustering + to split the image of coins in regions. - * :ref:`sphx_glr_auto_examples_cluster_plot_coin_segmentation.py`: Spectral - clustering to split the image of coins in regions. .. |coin_kmeans| image:: ../auto_examples/cluster/images/sphx_glr_plot_coin_segmentation_001.png :target: ../auto_examples/cluster/plot_coin_segmentation.html @@ -588,18 +570,15 @@ below. |coin_kmeans| |coin_discretize| |coin_cluster_qr| ================================ ================================ ================================ -|details-start| -**References** -|details-split| +.. dropdown:: References -* `"Multiclass spectral clustering" - `_ - Stella X. Yu, Jianbo Shi, 2003 + * `"Multiclass spectral clustering" + `_ + Stella X. Yu, Jianbo Shi, 2003 -* :doi:`"Simple, direct, and efficient multi-way spectral clustering"<10.1093/imaiai/iay008>` - Anil Damle, Victor Minden, Lexing Ying, 2019 + * :doi:`"Simple, direct, and efficient multi-way spectral clustering"<10.1093/imaiai/iay008>` + Anil Damle, Victor Minden, Lexing Ying, 2019 -|details-end| .. _spectral_clustering_graph: @@ -615,28 +594,25 @@ graph, and SpectralClustering is initialized with `affinity='precomputed'`:: ... assign_labels='discretize') >>> sc.fit_predict(adjacency_matrix) # doctest: +SKIP -|details-start| -**References** -|details-split| +.. dropdown:: References -* :doi:`"A Tutorial on Spectral Clustering" <10.1007/s11222-007-9033-z>` Ulrike - von Luxburg, 2007 + * :doi:`"A Tutorial on Spectral Clustering" <10.1007/s11222-007-9033-z>` Ulrike + von Luxburg, 2007 -* :doi:`"Normalized cuts and image segmentation" <10.1109/34.868688>` Jianbo - Shi, Jitendra Malik, 2000 + * :doi:`"Normalized cuts and image segmentation" <10.1109/34.868688>` Jianbo + Shi, Jitendra Malik, 2000 -* `"A Random Walks View of Spectral Segmentation" - `_ - Marina Meila, Jianbo Shi, 2001 + * `"A Random Walks View of Spectral Segmentation" + `_ + Marina Meila, Jianbo Shi, 2001 -* `"On Spectral Clustering: Analysis and an algorithm" - `_ - Andrew Y. Ng, Michael I. Jordan, Yair Weiss, 2001 + * `"On Spectral Clustering: Analysis and an algorithm" + `_ + Andrew Y. Ng, Michael I. Jordan, Yair Weiss, 2001 -* :arxiv:`"Preconditioned Spectral Clustering for Stochastic Block Partition - Streaming Graph Challenge" <1708.07481>` David Zhuzhunashvili, Andrew Knyazev + * :arxiv:`"Preconditioned Spectral Clustering for Stochastic Block Partition + Streaming Graph Challenge" <1708.07481>` David Zhuzhunashvili, Andrew Knyazev -|details-end| .. _hierarchical_clustering: @@ -697,10 +673,10 @@ while not robust to noisy data, can be computed very efficiently and can therefore be useful to provide hierarchical clustering of larger datasets. Single linkage can also perform well on non-globular data. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_cluster_plot_digits_linkage.py`: exploration of - the different linkage strategies in a real dataset. +* :ref:`sphx_glr_auto_examples_cluster_plot_digits_linkage.py`: exploration of the + different linkage strategies in a real dataset. * :ref:`sphx_glr_auto_examples_cluster_plot_linkage_comparison.py`: exploration of the different linkage strategies in toy datasets. @@ -717,9 +693,9 @@ of the data, though more so in the case of small sample sizes. :target: ../auto_examples/cluster/plot_agglomerative_dendrogram.html :scale: 42 -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_dendrogram.py` +* :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_dendrogram.py` Adding connectivity constraints @@ -788,20 +764,20 @@ enable only merging of neighboring pixels on an image, as in the :target: ../auto_examples/cluster/plot_agglomerative_clustering.html :scale: 38 -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_cluster_plot_coin_ward_segmentation.py`: Ward - clustering to split the image of coins in regions. +* :ref:`sphx_glr_auto_examples_cluster_plot_coin_ward_segmentation.py`: Ward + clustering to split the image of coins in regions. - * :ref:`sphx_glr_auto_examples_cluster_plot_ward_structured_vs_unstructured.py`: Example - of Ward algorithm on a swiss-roll, comparison of structured approaches - versus unstructured approaches. +* :ref:`sphx_glr_auto_examples_cluster_plot_ward_structured_vs_unstructured.py`: Example + of Ward algorithm on a swiss-roll, comparison of structured approaches + versus unstructured approaches. - * :ref:`sphx_glr_auto_examples_cluster_plot_feature_agglomeration_vs_univariate_selection.py`: Example - of dimensionality reduction with feature agglomeration based on Ward - hierarchical clustering. +* :ref:`sphx_glr_auto_examples_cluster_plot_feature_agglomeration_vs_univariate_selection.py`: Example + of dimensionality reduction with feature agglomeration based on Ward + hierarchical clustering. - * :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_clustering.py` +* :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_clustering.py` Varying the metric @@ -835,9 +811,9 @@ each class. :target: ../auto_examples/cluster/plot_agglomerative_clustering_metrics.html :scale: 32 -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_clustering_metrics.py` +* :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_clustering_metrics.py` Bisecting K-Means @@ -881,26 +857,23 @@ Difference between Bisecting K-Means and regular K-Means can be seen on example While the regular K-Means algorithm tends to create non-related clusters, clusters from Bisecting K-Means are well ordered and create quite a visible hierarchy. -|details-start| -**References** -|details-split| - -* `"A Comparison of Document Clustering Techniques" - `_ Michael - Steinbach, George Karypis and Vipin Kumar, Department of Computer Science and - Egineering, University of Minnesota (June 2000) -* `"Performance Analysis of K-Means and Bisecting K-Means Algorithms in Weblog - Data" - `_ - K.Abirami and Dr.P.Mayilvahanan, International Journal of Emerging - Technologies in Engineering Research (IJETER) Volume 4, Issue 8, (August 2016) -* `"Bisecting K-means Algorithm Based on K-valued Self-determining and - Clustering Center Optimization" - `_ Jian Di, Xinyue Gou School - of Control and Computer Engineering,North China Electric Power University, - Baoding, Hebei, China (August 2017) - -|details-end| +.. dropdown:: References + + * `"A Comparison of Document Clustering Techniques" + `_ Michael + Steinbach, George Karypis and Vipin Kumar, Department of Computer Science and + Egineering, University of Minnesota (June 2000) + * `"Performance Analysis of K-Means and Bisecting K-Means Algorithms in Weblog + Data" + `_ + K.Abirami and Dr.P.Mayilvahanan, International Journal of Emerging + Technologies in Engineering Research (IJETER) Volume 4, Issue 8, (August 2016) + * `"Bisecting K-means Algorithm Based on K-valued Self-determining and + Clustering Center Optimization" + `_ Jian Di, Xinyue Gou School + of Control and Computer Engineering,North China Electric Power University, + Baoding, Hebei, China (August 2017) + .. _dbscan: @@ -954,79 +927,68 @@ samples that are still part of a cluster. Moreover, the outliers are indicated by black points below. .. |dbscan_results| image:: ../auto_examples/cluster/images/sphx_glr_plot_dbscan_002.png - :target: ../auto_examples/cluster/plot_dbscan.html - :scale: 50 + :target: ../auto_examples/cluster/plot_dbscan.html + :scale: 50 .. centered:: |dbscan_results| -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_cluster_plot_dbscan.py` +* :ref:`sphx_glr_auto_examples_cluster_plot_dbscan.py` -|details-start| -**Implementation** -|details-split| +.. dropdown:: Implementation -The DBSCAN algorithm is deterministic, always generating the same clusters when -given the same data in the same order. However, the results can differ when -data is provided in a different order. First, even though the core samples will -always be assigned to the same clusters, the labels of those clusters will -depend on the order in which those samples are encountered in the data. Second -and more importantly, the clusters to which non-core samples are assigned can -differ depending on the data order. This would happen when a non-core sample -has a distance lower than ``eps`` to two core samples in different clusters. By -the triangular inequality, those two core samples must be more distant than -``eps`` from each other, or they would be in the same cluster. The non-core -sample is assigned to whichever cluster is generated first in a pass through the -data, and so the results will depend on the data ordering. + The DBSCAN algorithm is deterministic, always generating the same clusters when + given the same data in the same order. However, the results can differ when + data is provided in a different order. First, even though the core samples will + always be assigned to the same clusters, the labels of those clusters will + depend on the order in which those samples are encountered in the data. Second + and more importantly, the clusters to which non-core samples are assigned can + differ depending on the data order. This would happen when a non-core sample + has a distance lower than ``eps`` to two core samples in different clusters. By + the triangular inequality, those two core samples must be more distant than + ``eps`` from each other, or they would be in the same cluster. The non-core + sample is assigned to whichever cluster is generated first in a pass through the + data, and so the results will depend on the data ordering. -The current implementation uses ball trees and kd-trees to determine the -neighborhood of points, which avoids calculating the full distance matrix (as -was done in scikit-learn versions before 0.14). The possibility to use custom -metrics is retained; for details, see :class:`NearestNeighbors`. + The current implementation uses ball trees and kd-trees to determine the + neighborhood of points, which avoids calculating the full distance matrix (as + was done in scikit-learn versions before 0.14). The possibility to use custom + metrics is retained; for details, see :class:`NearestNeighbors`. -|details-end| +.. dropdown:: Memory consumption for large sample sizes -|details-start| -**Memory consumption for large sample sizes** -|details-split| + This implementation is by default not memory efficient because it constructs a + full pairwise similarity matrix in the case where kd-trees or ball-trees cannot + be used (e.g., with sparse matrices). This matrix will consume :math:`n^2` + floats. A couple of mechanisms for getting around this are: -This implementation is by default not memory efficient because it constructs a -full pairwise similarity matrix in the case where kd-trees or ball-trees cannot -be used (e.g., with sparse matrices). This matrix will consume :math:`n^2` -floats. A couple of mechanisms for getting around this are: + - Use :ref:`OPTICS ` clustering in conjunction with the `extract_dbscan` + method. OPTICS clustering also calculates the full pairwise matrix, but only + keeps one row in memory at a time (memory complexity n). -- Use :ref:`OPTICS ` clustering in conjunction with the `extract_dbscan` - method. OPTICS clustering also calculates the full pairwise matrix, but only - keeps one row in memory at a time (memory complexity n). + - A sparse radius neighborhood graph (where missing entries are presumed to be + out of eps) can be precomputed in a memory-efficient way and dbscan can be run + over this with ``metric='precomputed'``. See + :meth:`sklearn.neighbors.NearestNeighbors.radius_neighbors_graph`. -- A sparse radius neighborhood graph (where missing entries are presumed to be - out of eps) can be precomputed in a memory-efficient way and dbscan can be run - over this with ``metric='precomputed'``. See - :meth:`sklearn.neighbors.NearestNeighbors.radius_neighbors_graph`. + - The dataset can be compressed, either by removing exact duplicates if these + occur in your data, or by using BIRCH. Then you only have a relatively small + number of representatives for a large number of points. You can then provide a + ``sample_weight`` when fitting DBSCAN. -- The dataset can be compressed, either by removing exact duplicates if these - occur in your data, or by using BIRCH. Then you only have a relatively small - number of representatives for a large number of points. You can then provide a - ``sample_weight`` when fitting DBSCAN. - -|details-end| - -|details-start| -**References** -|details-split| +.. dropdown:: References * `A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise `_ Ester, M., H. P. Kriegel, J. Sander, and X. Xu, In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, - AAAI Press, pp. 226–231. 1996 + AAAI Press, pp. 226-231. 1996 * :doi:`DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. <10.1145/3068335>` Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). In ACM Transactions on Database Systems (TODS), 42(3), 19. -|details-end| .. _hdbscan: @@ -1046,9 +1008,9 @@ scales by building an alternative representation of the clustering problem. This implementation is adapted from the original implementation of HDBSCAN, `scikit-learn-contrib/hdbscan `_ based on [LJ2017]_. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_cluster_plot_hdbscan.py` +* :ref:`sphx_glr_auto_examples_cluster_plot_hdbscan.py` Mutual Reachability Graph ------------------------- @@ -1109,11 +1071,11 @@ it relies solely on the choice of `min_samples`, which tends to be a more robust hyperparameter. .. |hdbscan_ground_truth| image:: ../auto_examples/cluster/images/sphx_glr_plot_hdbscan_005.png - :target: ../auto_examples/cluster/plot_hdbscan.html - :scale: 75 + :target: ../auto_examples/cluster/plot_hdbscan.html + :scale: 75 .. |hdbscan_results| image:: ../auto_examples/cluster/images/sphx_glr_plot_hdbscan_007.png - :target: ../auto_examples/cluster/plot_hdbscan.html - :scale: 75 + :target: ../auto_examples/cluster/plot_hdbscan.html + :scale: 75 .. centered:: |hdbscan_ground_truth| .. centered:: |hdbscan_results| @@ -1124,19 +1086,19 @@ than `minimum_cluster_size` many samples are considered noise. In practice, one can set `minimum_cluster_size = min_samples` to couple the parameters and simplify the hyperparameter space. -.. topic:: References: +.. rubric:: References - .. [CM2013] Campello, R.J.G.B., Moulavi, D., Sander, J. (2013). Density-Based - Clustering Based on Hierarchical Density Estimates. In: Pei, J., Tseng, V.S., - Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data - Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, - Berlin, Heidelberg. :doi:`Density-Based Clustering Based on Hierarchical - Density Estimates <10.1007/978-3-642-37456-2_14>` +.. [CM2013] Campello, R.J.G.B., Moulavi, D., Sander, J. (2013). Density-Based + Clustering Based on Hierarchical Density Estimates. In: Pei, J., Tseng, V.S., + Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data + Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, + Berlin, Heidelberg. :doi:`Density-Based Clustering Based on Hierarchical + Density Estimates <10.1007/978-3-642-37456-2_14>` - .. [LJ2017] L. McInnes and J. Healy, (2017). Accelerated Hierarchical Density - Based Clustering. In: IEEE International Conference on Data Mining Workshops - (ICDMW), 2017, pp. 33-42. :doi:`Accelerated Hierarchical Density Based - Clustering <10.1109/ICDMW.2017.12>` +.. [LJ2017] L. McInnes and J. Healy, (2017). Accelerated Hierarchical Density + Based Clustering. In: IEEE International Conference on Data Mining Workshops + (ICDMW), 2017, pp. 33-42. :doi:`Accelerated Hierarchical Density Based + Clustering <10.1109/ICDMW.2017.12>` .. _optics: @@ -1182,58 +1144,48 @@ the linear segment clusters of the reachability plot. Note that the blue and red clusters are adjacent in the reachability plot, and can be hierarchically represented as children of a larger parent cluster. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_cluster_plot_optics.py` +* :ref:`sphx_glr_auto_examples_cluster_plot_optics.py` -|details-start| -**Comparison with DBSCAN** -|details-split| +.. dropdown:: Comparison with DBSCAN -The results from OPTICS ``cluster_optics_dbscan`` method and DBSCAN are very -similar, but not always identical; specifically, labeling of periphery and noise -points. This is in part because the first samples of each dense area processed -by OPTICS have a large reachability value while being close to other points in -their area, and will thus sometimes be marked as noise rather than periphery. -This affects adjacent points when they are considered as candidates for being -marked as either periphery or noise. + The results from OPTICS ``cluster_optics_dbscan`` method and DBSCAN are very + similar, but not always identical; specifically, labeling of periphery and noise + points. This is in part because the first samples of each dense area processed + by OPTICS have a large reachability value while being close to other points in + their area, and will thus sometimes be marked as noise rather than periphery. + This affects adjacent points when they are considered as candidates for being + marked as either periphery or noise. -Note that for any single value of ``eps``, DBSCAN will tend to have a shorter -run time than OPTICS; however, for repeated runs at varying ``eps`` values, a -single run of OPTICS may require less cumulative runtime than DBSCAN. It is also -important to note that OPTICS' output is close to DBSCAN's only if ``eps`` and -``max_eps`` are close. + Note that for any single value of ``eps``, DBSCAN will tend to have a shorter + run time than OPTICS; however, for repeated runs at varying ``eps`` values, a + single run of OPTICS may require less cumulative runtime than DBSCAN. It is also + important to note that OPTICS' output is close to DBSCAN's only if ``eps`` and + ``max_eps`` are close. -|details-end| +.. dropdown:: Computational Complexity -|details-start| -**Computational Complexity** -|details-split| + Spatial indexing trees are used to avoid calculating the full distance matrix, + and allow for efficient memory usage on large sets of samples. Different + distance metrics can be supplied via the ``metric`` keyword. -Spatial indexing trees are used to avoid calculating the full distance matrix, -and allow for efficient memory usage on large sets of samples. Different -distance metrics can be supplied via the ``metric`` keyword. + For large datasets, similar (but not identical) results can be obtained via + :class:`HDBSCAN`. The HDBSCAN implementation is multithreaded, and has better + algorithmic runtime complexity than OPTICS, at the cost of worse memory scaling. + For extremely large datasets that exhaust system memory using HDBSCAN, OPTICS + will maintain :math:`n` (as opposed to :math:`n^2`) memory scaling; however, + tuning of the ``max_eps`` parameter will likely need to be used to give a + solution in a reasonable amount of wall time. -For large datasets, similar (but not identical) results can be obtained via -:class:`HDBSCAN`. The HDBSCAN implementation is multithreaded, and has better -algorithmic runtime complexity than OPTICS, at the cost of worse memory scaling. -For extremely large datasets that exhaust system memory using HDBSCAN, OPTICS -will maintain :math:`n` (as opposed to :math:`n^2`) memory scaling; however, -tuning of the ``max_eps`` parameter will likely need to be used to give a -solution in a reasonable amount of wall time. -|details-end| +.. dropdown:: References -|details-start| -**References** -|details-split| + * "OPTICS: ordering points to identify the clustering structure." Ankerst, + Mihael, Markus M. Breunig, Hans-Peter Kriegel, and Jörg Sander. In ACM Sigmod + Record, vol. 28, no. 2, pp. 49-60. ACM, 1999. -* "OPTICS: ordering points to identify the clustering structure." Ankerst, - Mihael, Markus M. Breunig, Hans-Peter Kriegel, and Jörg Sander. In ACM Sigmod - Record, vol. 28, no. 2, pp. 49-60. ACM, 1999. - -|details-end| .. _birch: @@ -1269,75 +1221,60 @@ If ``n_clusters`` is set to None, the subclusters from the leaves are directly read off, otherwise a global clustering step labels these subclusters into global clusters (labels) and the samples are mapped to the global label of the nearest subcluster. -|details-start| -**Algorithm description** -|details-split| - -- A new sample is inserted into the root of the CF Tree which is a CF Node. It - is then merged with the subcluster of the root, that has the smallest radius - after merging, constrained by the threshold and branching factor conditions. - If the subcluster has any child node, then this is done repeatedly till it - reaches a leaf. After finding the nearest subcluster in the leaf, the - properties of this subcluster and the parent subclusters are recursively - updated. - -- If the radius of the subcluster obtained by merging the new sample and the - nearest subcluster is greater than the square of the threshold and if the - number of subclusters is greater than the branching factor, then a space is - temporarily allocated to this new sample. The two farthest subclusters are - taken and the subclusters are divided into two groups on the basis of the - distance between these subclusters. - -- If this split node has a parent subcluster and there is room for a new - subcluster, then the parent is split into two. If there is no room, then this - node is again split into two and the process is continued recursively, till it - reaches the root. - -|details-end| - -|details-start| -**BIRCH or MiniBatchKMeans?** -|details-split| - -- BIRCH does not scale very well to high dimensional data. As a rule of thumb if - ``n_features`` is greater than twenty, it is generally better to use MiniBatchKMeans. -- If the number of instances of data needs to be reduced, or if one wants a - large number of subclusters either as a preprocessing step or otherwise, - BIRCH is more useful than MiniBatchKMeans. - -.. image:: ../auto_examples/cluster/images/sphx_glr_plot_birch_vs_minibatchkmeans_001.png +.. dropdown:: Algorithm description + + - A new sample is inserted into the root of the CF Tree which is a CF Node. It + is then merged with the subcluster of the root, that has the smallest radius + after merging, constrained by the threshold and branching factor conditions. + If the subcluster has any child node, then this is done repeatedly till it + reaches a leaf. After finding the nearest subcluster in the leaf, the + properties of this subcluster and the parent subclusters are recursively + updated. + + - If the radius of the subcluster obtained by merging the new sample and the + nearest subcluster is greater than the square of the threshold and if the + number of subclusters is greater than the branching factor, then a space is + temporarily allocated to this new sample. The two farthest subclusters are + taken and the subclusters are divided into two groups on the basis of the + distance between these subclusters. + + - If this split node has a parent subcluster and there is room for a new + subcluster, then the parent is split into two. If there is no room, then this + node is again split into two and the process is continued recursively, till it + reaches the root. + +.. dropdown:: BIRCH or MiniBatchKMeans? + + - BIRCH does not scale very well to high dimensional data. As a rule of thumb if + ``n_features`` is greater than twenty, it is generally better to use MiniBatchKMeans. + - If the number of instances of data needs to be reduced, or if one wants a + large number of subclusters either as a preprocessing step or otherwise, + BIRCH is more useful than MiniBatchKMeans. + + .. image:: ../auto_examples/cluster/images/sphx_glr_plot_birch_vs_minibatchkmeans_001.png :target: ../auto_examples/cluster/plot_birch_vs_minibatchkmeans.html -|details-end| - -|details-start| -**How to use partial_fit?** -|details-split| +.. dropdown:: How to use partial_fit? -To avoid the computation of global clustering, for every call of ``partial_fit`` -the user is advised + To avoid the computation of global clustering, for every call of ``partial_fit`` + the user is advised: -1. To set ``n_clusters=None`` initially -2. Train all data by multiple calls to partial_fit. -3. Set ``n_clusters`` to a required value using - ``brc.set_params(n_clusters=n_clusters)``. -4. Call ``partial_fit`` finally with no arguments, i.e. ``brc.partial_fit()`` - which performs the global clustering. + 1. To set ``n_clusters=None`` initially. + 2. Train all data by multiple calls to partial_fit. + 3. Set ``n_clusters`` to a required value using + ``brc.set_params(n_clusters=n_clusters)``. + 4. Call ``partial_fit`` finally with no arguments, i.e. ``brc.partial_fit()`` + which performs the global clustering. -|details-end| +.. dropdown:: References -|details-start| -**References** -|details-split| + * Tian Zhang, Raghu Ramakrishnan, Maron Livny BIRCH: An efficient data + clustering method for large databases. + https://www.cs.sfu.ca/CourseCentral/459/han/papers/zhang96.pdf -* Tian Zhang, Raghu Ramakrishnan, Maron Livny BIRCH: An efficient data - clustering method for large databases. - https://www.cs.sfu.ca/CourseCentral/459/han/papers/zhang96.pdf + * Roberto Perdisci JBirch - Java implementation of BIRCH clustering algorithm + https://code.google.com/archive/p/jbirch -* Roberto Perdisci JBirch - Java implementation of BIRCH clustering algorithm - https://code.google.com/archive/p/jbirch - -|details-end| .. _clustering_evaluation: @@ -1460,64 +1397,53 @@ will not necessarily be close to zero.:: ground truth clustering resulting in a high proportion of pair labels that agree, which leads subsequently to a high score. -.. topic:: Examples: - - * :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py`: - Analysis of the impact of the dataset size on the value of clustering measures - for random assignments. - - -|details-start| -**Mathematical formulation** -|details-split| +.. rubric:: Examples -If C is a ground truth class assignment and K the clustering, let us define -:math:`a` and :math:`b` as: +* :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py`: + Analysis of the impact of the dataset size on the value of + clustering measures for random assignments. -- :math:`a`, the number of pairs of elements that are in the same set in C and - in the same set in K +.. dropdown:: Mathematical formulation -- :math:`b`, the number of pairs of elements that are in different sets in C and - in different sets in K + If C is a ground truth class assignment and K the clustering, let us define + :math:`a` and :math:`b` as: -The unadjusted Rand index is then given by: + - :math:`a`, the number of pairs of elements that are in the same set in C and + in the same set in K -.. math:: \text{RI} = \frac{a + b}{C_2^{n_{samples}}} + - :math:`b`, the number of pairs of elements that are in different sets in C and + in different sets in K -where :math:`C_2^{n_{samples}}` is the total number of possible pairs in the -dataset. It does not matter if the calculation is performed on ordered pairs or -unordered pairs as long as the calculation is performed consistently. + The unadjusted Rand index is then given by: -However, the Rand index does not guarantee that random label assignments will -get a value close to zero (esp. if the number of clusters is in the same order -of magnitude as the number of samples). + .. math:: \text{RI} = \frac{a + b}{C_2^{n_{samples}}} -To counter this effect we can discount the expected RI :math:`E[\text{RI}]` of -random labelings by defining the adjusted Rand index as follows: + where :math:`C_2^{n_{samples}}` is the total number of possible pairs in the + dataset. It does not matter if the calculation is performed on ordered pairs or + unordered pairs as long as the calculation is performed consistently. -.. math:: \text{ARI} = \frac{\text{RI} - E[\text{RI}]}{\max(\text{RI}) - E[\text{RI}]} + However, the Rand index does not guarantee that random label assignments will + get a value close to zero (esp. if the number of clusters is in the same order + of magnitude as the number of samples). -|details-end| + To counter this effect we can discount the expected RI :math:`E[\text{RI}]` of + random labelings by defining the adjusted Rand index as follows: -|details-start| -**References** -|details-split| + .. math:: \text{ARI} = \frac{\text{RI} - E[\text{RI}]}{\max(\text{RI}) - E[\text{RI}]} -* `Comparing Partitions - `_ L. Hubert and P. - Arabie, Journal of Classification 1985 +.. dropdown:: References -* `Properties of the Hubert-Arabie adjusted Rand index - `_ D. Steinley, Psychological - Methods 2004 + * `Comparing Partitions + `_ L. Hubert and P. + Arabie, Journal of Classification 1985 -* `Wikipedia entry for the Rand index - `_ + * `Properties of the Hubert-Arabie adjusted Rand index + `_ D. Steinley, Psychological + Methods 2004 -* `Wikipedia entry for the adjusted Rand index - `_ + * `Wikipedia entry for the Rand index + `_ -|details-end| .. _mutual_info_score: @@ -1598,80 +1524,77 @@ Bad (e.g. independent labelings) have non-positive scores:: - NMI and MI are not adjusted against chance. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py`: Analysis - of the impact of the dataset size on the value of clustering measures for - random assignments. This example also includes the Adjusted Rand Index. +* :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py`: Analysis + of the impact of the dataset size on the value of clustering measures for random + assignments. This example also includes the Adjusted Rand Index. +.. dropdown:: Mathematical formulation -|details-start| -**Mathematical formulation** -|details-split| + Assume two label assignments (of the same N objects), :math:`U` and :math:`V`. + Their entropy is the amount of uncertainty for a partition set, defined by: -Assume two label assignments (of the same N objects), :math:`U` and :math:`V`. -Their entropy is the amount of uncertainty for a partition set, defined by: + .. math:: H(U) = - \sum_{i=1}^{|U|}P(i)\log(P(i)) -.. math:: H(U) = - \sum_{i=1}^{|U|}P(i)\log(P(i)) + where :math:`P(i) = |U_i| / N` is the probability that an object picked at + random from :math:`U` falls into class :math:`U_i`. Likewise for :math:`V`: -where :math:`P(i) = |U_i| / N` is the probability that an object picked at -random from :math:`U` falls into class :math:`U_i`. Likewise for :math:`V`: + .. math:: H(V) = - \sum_{j=1}^{|V|}P'(j)\log(P'(j)) -.. math:: H(V) = - \sum_{j=1}^{|V|}P'(j)\log(P'(j)) + With :math:`P'(j) = |V_j| / N`. The mutual information (MI) between :math:`U` + and :math:`V` is calculated by: -With :math:`P'(j) = |V_j| / N`. The mutual information (MI) between :math:`U` -and :math:`V` is calculated by: + .. math:: \text{MI}(U, V) = \sum_{i=1}^{|U|}\sum_{j=1}^{|V|}P(i, j)\log\left(\frac{P(i,j)}{P(i)P'(j)}\right) -.. math:: \text{MI}(U, V) = \sum_{i=1}^{|U|}\sum_{j=1}^{|V|}P(i, j)\log\left(\frac{P(i,j)}{P(i)P'(j)}\right) + where :math:`P(i, j) = |U_i \cap V_j| / N` is the probability that an object + picked at random falls into both classes :math:`U_i` and :math:`V_j`. -where :math:`P(i, j) = |U_i \cap V_j| / N` is the probability that an object -picked at random falls into both classes :math:`U_i` and :math:`V_j`. + It also can be expressed in set cardinality formulation: -It also can be expressed in set cardinality formulation: + .. math:: \text{MI}(U, V) = \sum_{i=1}^{|U|} \sum_{j=1}^{|V|} \frac{|U_i \cap V_j|}{N}\log\left(\frac{N|U_i \cap V_j|}{|U_i||V_j|}\right) -.. math:: \text{MI}(U, V) = \sum_{i=1}^{|U|} \sum_{j=1}^{|V|} \frac{|U_i \cap V_j|}{N}\log\left(\frac{N|U_i \cap V_j|}{|U_i||V_j|}\right) + The normalized mutual information is defined as -The normalized mutual information is defined as + .. math:: \text{NMI}(U, V) = \frac{\text{MI}(U, V)}{\text{mean}(H(U), H(V))} -.. math:: \text{NMI}(U, V) = \frac{\text{MI}(U, V)}{\text{mean}(H(U), H(V))} + This value of the mutual information and also the normalized variant is not + adjusted for chance and will tend to increase as the number of different labels + (clusters) increases, regardless of the actual amount of "mutual information" + between the label assignments. -This value of the mutual information and also the normalized variant is not -adjusted for chance and will tend to increase as the number of different labels -(clusters) increases, regardless of the actual amount of "mutual information" -between the label assignments. + The expected value for the mutual information can be calculated using the + following equation [VEB2009]_. In this equation, :math:`a_i = |U_i|` (the number + of elements in :math:`U_i`) and :math:`b_j = |V_j|` (the number of elements in + :math:`V_j`). -The expected value for the mutual information can be calculated using the -following equation [VEB2009]_. In this equation, :math:`a_i = |U_i|` (the number -of elements in :math:`U_i`) and :math:`b_j = |V_j|` (the number of elements in -:math:`V_j`). + .. math:: E[\text{MI}(U,V)]=\sum_{i=1}^{|U|} \sum_{j=1}^{|V|} \sum_{n_{ij}=(a_i+b_j-N)^+ + }^{\min(a_i, b_j)} \frac{n_{ij}}{N}\log \left( \frac{ N.n_{ij}}{a_i b_j}\right) + \frac{a_i!b_j!(N-a_i)!(N-b_j)!}{N!n_{ij}!(a_i-n_{ij})!(b_j-n_{ij})! + (N-a_i-b_j+n_{ij})!} -.. math:: E[\text{MI}(U,V)]=\sum_{i=1}^{|U|} \sum_{j=1}^{|V|} \sum_{n_{ij}=(a_i+b_j-N)^+ - }^{\min(a_i, b_j)} \frac{n_{ij}}{N}\log \left( \frac{ N.n_{ij}}{a_i b_j}\right) - \frac{a_i!b_j!(N-a_i)!(N-b_j)!}{N!n_{ij}!(a_i-n_{ij})!(b_j-n_{ij})! - (N-a_i-b_j+n_{ij})!} + Using the expected value, the adjusted mutual information can then be calculated + using a similar form to that of the adjusted Rand index: -Using the expected value, the adjusted mutual information can then be calculated -using a similar form to that of the adjusted Rand index: + .. math:: \text{AMI} = \frac{\text{MI} - E[\text{MI}]}{\text{mean}(H(U), H(V)) - E[\text{MI}]} -.. math:: \text{AMI} = \frac{\text{MI} - E[\text{MI}]}{\text{mean}(H(U), H(V)) - E[\text{MI}]} + For normalized mutual information and adjusted mutual information, the + normalizing value is typically some *generalized* mean of the entropies of each + clustering. Various generalized means exist, and no firm rules exist for + preferring one over the others. The decision is largely a field-by-field basis; + for instance, in community detection, the arithmetic mean is most common. Each + normalizing method provides "qualitatively similar behaviours" [YAT2016]_. In + our implementation, this is controlled by the ``average_method`` parameter. -For normalized mutual information and adjusted mutual information, the -normalizing value is typically some *generalized* mean of the entropies of each -clustering. Various generalized means exist, and no firm rules exist for -preferring one over the others. The decision is largely a field-by-field basis; -for instance, in community detection, the arithmetic mean is most common. Each -normalizing method provides "qualitatively similar behaviours" [YAT2016]_. In -our implementation, this is controlled by the ``average_method`` parameter. + Vinh et al. (2010) named variants of NMI and AMI by their averaging method + [VEB2010]_. Their 'sqrt' and 'sum' averages are the geometric and arithmetic + means; we use these more broadly common names. -Vinh et al. (2010) named variants of NMI and AMI by their averaging method -[VEB2010]_. Their 'sqrt' and 'sum' averages are the geometric and arithmetic -means; we use these more broadly common names. + .. rubric:: References -.. topic:: References: - - * Strehl, Alexander, and Joydeep Ghosh (2002). "Cluster ensembles – a + * Strehl, Alexander, and Joydeep Ghosh (2002). "Cluster ensembles - a knowledge reuse framework for combining multiple partitions". Journal of - Machine Learning Research 3: 583–617. `doi:10.1162/153244303321897735 + Machine Learning Research 3: 583-617. `doi:10.1162/153244303321897735 `_. * `Wikipedia entry for the (normalized) Mutual Information @@ -1696,7 +1619,6 @@ means; we use these more broadly common names. Reports 6: 30750. `doi:10.1038/srep30750 `_. -|details-end| .. _homogeneity_completeness: @@ -1814,57 +1736,53 @@ homogeneous but not complete:: almost never available in practice or requires manual assignment by human annotators (as in the supervised learning setting). -.. topic:: Examples: - - * :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py`: Analysis - of the impact of the dataset size on the value of clustering measures for - random assignments. +.. rubric:: Examples +* :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py`: Analysis + of the impact of the dataset size on the value of clustering measures for + random assignments. -|details-start| -**Mathematical formulation** -|details-split| +.. dropdown:: Mathematical formulation -Homogeneity and completeness scores are formally given by: + Homogeneity and completeness scores are formally given by: -.. math:: h = 1 - \frac{H(C|K)}{H(C)} + .. math:: h = 1 - \frac{H(C|K)}{H(C)} -.. math:: c = 1 - \frac{H(K|C)}{H(K)} + .. math:: c = 1 - \frac{H(K|C)}{H(K)} -where :math:`H(C|K)` is the **conditional entropy of the classes given the -cluster assignments** and is given by: + where :math:`H(C|K)` is the **conditional entropy of the classes given the + cluster assignments** and is given by: -.. math:: H(C|K) = - \sum_{c=1}^{|C|} \sum_{k=1}^{|K|} \frac{n_{c,k}}{n} - \cdot \log\left(\frac{n_{c,k}}{n_k}\right) + .. math:: H(C|K) = - \sum_{c=1}^{|C|} \sum_{k=1}^{|K|} \frac{n_{c,k}}{n} + \cdot \log\left(\frac{n_{c,k}}{n_k}\right) -and :math:`H(C)` is the **entropy of the classes** and is given by: + and :math:`H(C)` is the **entropy of the classes** and is given by: -.. math:: H(C) = - \sum_{c=1}^{|C|} \frac{n_c}{n} \cdot \log\left(\frac{n_c}{n}\right) + .. math:: H(C) = - \sum_{c=1}^{|C|} \frac{n_c}{n} \cdot \log\left(\frac{n_c}{n}\right) -with :math:`n` the total number of samples, :math:`n_c` and :math:`n_k` the -number of samples respectively belonging to class :math:`c` and cluster -:math:`k`, and finally :math:`n_{c,k}` the number of samples from class -:math:`c` assigned to cluster :math:`k`. + with :math:`n` the total number of samples, :math:`n_c` and :math:`n_k` the + number of samples respectively belonging to class :math:`c` and cluster + :math:`k`, and finally :math:`n_{c,k}` the number of samples from class + :math:`c` assigned to cluster :math:`k`. -The **conditional entropy of clusters given class** :math:`H(K|C)` and the -**entropy of clusters** :math:`H(K)` are defined in a symmetric manner. + The **conditional entropy of clusters given class** :math:`H(K|C)` and the + **entropy of clusters** :math:`H(K)` are defined in a symmetric manner. -Rosenberg and Hirschberg further define **V-measure** as the **harmonic mean of -homogeneity and completeness**: + Rosenberg and Hirschberg further define **V-measure** as the **harmonic mean of + homogeneity and completeness**: -.. math:: v = 2 \cdot \frac{h \cdot c}{h + c} + .. math:: v = 2 \cdot \frac{h \cdot c}{h + c} -|details-end| +.. rubric:: References -.. topic:: References: +* `V-Measure: A conditional entropy-based external cluster evaluation measure + `_ Andrew Rosenberg and Julia + Hirschberg, 2007 - * `V-Measure: A conditional entropy-based external cluster evaluation measure - `_ Andrew Rosenberg and Julia - Hirschberg, 2007 +.. [B2011] `Identification and Characterization of Events in Social Media + `_, Hila + Becker, PhD Thesis. - .. [B2011] `Identification and Characterization of Events in Social Media - `_, Hila - Becker, PhD Thesis. .. _fowlkes_mallows_scores: @@ -1941,19 +1859,15 @@ Bad (e.g. independent labelings) have zero scores:: manual assignment by human annotators (as in the supervised learning setting). -|details-start| -**References** -|details-split| +.. dropdown:: References -* E. B. Fowkles and C. L. Mallows, 1983. "A method for comparing two - hierarchical clusterings". Journal of the American Statistical - Association. - https://www.tandfonline.com/doi/abs/10.1080/01621459.1983.10478008 + * E. B. Fowkles and C. L. Mallows, 1983. "A method for comparing two + hierarchical clusterings". Journal of the American Statistical Association. + https://www.tandfonline.com/doi/abs/10.1080/01621459.1983.10478008 -* `Wikipedia entry for the Fowlkes-Mallows Index - `_ + * `Wikipedia entry for the Fowlkes-Mallows Index + `_ -|details-end| .. _silhouette_coefficient: @@ -1997,7 +1911,6 @@ cluster analysis. >>> metrics.silhouette_score(X, labels, metric='euclidean') 0.55... - .. topic:: Advantages: - The score is bounded between -1 for incorrect clustering and +1 for highly @@ -2012,23 +1925,18 @@ cluster analysis. other concepts of clusters, such as density based clusters like those obtained through DBSCAN. -.. topic:: Examples: - - * :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py` : In - this example the silhouette analysis is used to choose an optimal value for - n_clusters. +.. rubric:: Examples +* :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py` : In + this example the silhouette analysis is used to choose an optimal value for + n_clusters. -|details-start| -**References** -|details-split| +.. dropdown:: References -* Peter J. Rousseeuw (1987). :doi:`"Silhouettes: a Graphical Aid to the - Interpretation and Validation of Cluster - Analysis"<10.1016/0377-0427(87)90125-7>` . Computational and Applied - Mathematics 20: 53–65. + * Peter J. Rousseeuw (1987). :doi:`"Silhouettes: a Graphical Aid to the + Interpretation and Validation of Cluster Analysis"<10.1016/0377-0427(87)90125-7>`. + Computational and Applied Mathematics 20: 53-65. -|details-end| .. _calinski_harabasz_index: @@ -2074,42 +1982,35 @@ cluster analysis: other concepts of clusters, such as density based clusters like those obtained through DBSCAN. -|details-start| -**Mathematical formulation** -|details-split| +.. dropdown:: Mathematical formulation -For a set of data :math:`E` of size :math:`n_E` which has been clustered into -:math:`k` clusters, the Calinski-Harabasz score :math:`s` is defined as the -ratio of the between-clusters dispersion mean and the within-cluster -dispersion: + For a set of data :math:`E` of size :math:`n_E` which has been clustered into + :math:`k` clusters, the Calinski-Harabasz score :math:`s` is defined as the + ratio of the between-clusters dispersion mean and the within-cluster + dispersion: -.. math:: - s = \frac{\mathrm{tr}(B_k)}{\mathrm{tr}(W_k)} \times \frac{n_E - k}{k - 1} - -where :math:`\mathrm{tr}(B_k)` is trace of the between group dispersion matrix -and :math:`\mathrm{tr}(W_k)` is the trace of the within-cluster dispersion -matrix defined by: + .. math:: + s = \frac{\mathrm{tr}(B_k)}{\mathrm{tr}(W_k)} \times \frac{n_E - k}{k - 1} -.. math:: W_k = \sum_{q=1}^k \sum_{x \in C_q} (x - c_q) (x - c_q)^T + where :math:`\mathrm{tr}(B_k)` is trace of the between group dispersion matrix + and :math:`\mathrm{tr}(W_k)` is the trace of the within-cluster dispersion + matrix defined by: -.. math:: B_k = \sum_{q=1}^k n_q (c_q - c_E) (c_q - c_E)^T + .. math:: W_k = \sum_{q=1}^k \sum_{x \in C_q} (x - c_q) (x - c_q)^T -with :math:`C_q` the set of points in cluster :math:`q`, :math:`c_q` the -center of cluster :math:`q`, :math:`c_E` the center of :math:`E`, and -:math:`n_q` the number of points in cluster :math:`q`. + .. math:: B_k = \sum_{q=1}^k n_q (c_q - c_E) (c_q - c_E)^T -|details-end| + with :math:`C_q` the set of points in cluster :math:`q`, :math:`c_q` the + center of cluster :math:`q`, :math:`c_E` the center of :math:`E`, and + :math:`n_q` the number of points in cluster :math:`q`. -|details-start| -**References** -|details-split| +.. dropdown:: References -* Caliński, T., & Harabasz, J. (1974). `"A Dendrite Method for Cluster Analysis" - `_. - :doi:`Communications in Statistics-theory and Methods 3: 1-27 - <10.1080/03610927408827101>`. + * Caliński, T., & Harabasz, J. (1974). `"A Dendrite Method for Cluster Analysis" + `_. + :doi:`Communications in Statistics-theory and Methods 3: 1-27 + <10.1080/03610927408827101>`. -|details-end| .. _davies-bouldin_index: @@ -2156,49 +2057,41 @@ cluster analysis as follows: - The usage of centroid distance limits the distance metric to Euclidean space. +.. dropdown:: Mathematical formulation -|details-start| -**Mathematical formulation** -|details-split| - -The index is defined as the average similarity between each cluster :math:`C_i` -for :math:`i=1, ..., k` and its most similar one :math:`C_j`. In the context of -this index, similarity is defined as a measure :math:`R_{ij}` that trades off: - -- :math:`s_i`, the average distance between each point of cluster :math:`i` and - the centroid of that cluster -- also know as cluster diameter. -- :math:`d_{ij}`, the distance between cluster centroids :math:`i` and - :math:`j`. + The index is defined as the average similarity between each cluster :math:`C_i` + for :math:`i=1, ..., k` and its most similar one :math:`C_j`. In the context of + this index, similarity is defined as a measure :math:`R_{ij}` that trades off: -A simple choice to construct :math:`R_{ij}` so that it is nonnegative and -symmetric is: + - :math:`s_i`, the average distance between each point of cluster :math:`i` and + the centroid of that cluster -- also know as cluster diameter. + - :math:`d_{ij}`, the distance between cluster centroids :math:`i` and + :math:`j`. -.. math:: - R_{ij} = \frac{s_i + s_j}{d_{ij}} + A simple choice to construct :math:`R_{ij}` so that it is nonnegative and + symmetric is: -Then the Davies-Bouldin index is defined as: + .. math:: + R_{ij} = \frac{s_i + s_j}{d_{ij}} -.. math:: - DB = \frac{1}{k} \sum_{i=1}^k \max_{i \neq j} R_{ij} + Then the Davies-Bouldin index is defined as: -|details-end| + .. math:: + DB = \frac{1}{k} \sum_{i=1}^k \max_{i \neq j} R_{ij} -|details-start| -**References** -|details-split| +.. dropdown:: References -* Davies, David L.; Bouldin, Donald W. (1979). :doi:`"A Cluster Separation - Measure" <10.1109/TPAMI.1979.4766909>` IEEE Transactions on Pattern Analysis - and Machine Intelligence. PAMI-1 (2): 224-227. + * Davies, David L.; Bouldin, Donald W. (1979). :doi:`"A Cluster Separation + Measure" <10.1109/TPAMI.1979.4766909>` IEEE Transactions on Pattern Analysis + and Machine Intelligence. PAMI-1 (2): 224-227. -* Halkidi, Maria; Batistakis, Yannis; Vazirgiannis, Michalis (2001). :doi:`"On - Clustering Validation Techniques" <10.1023/A:1012801612483>` Journal of - Intelligent Information Systems, 17(2-3), 107-145. + * Halkidi, Maria; Batistakis, Yannis; Vazirgiannis, Michalis (2001). :doi:`"On + Clustering Validation Techniques" <10.1023/A:1012801612483>` Journal of + Intelligent Information Systems, 17(2-3), 107-145. -* `Wikipedia entry for Davies-Bouldin index - `_. + * `Wikipedia entry for Davies-Bouldin index + `_. -|details-end| .. _contingency_matrix: @@ -2248,15 +2141,11 @@ of classes. - It doesn't give a single metric to use as an objective for clustering optimisation. +.. dropdown:: References -|details-start| -**References** -|details-split| + * `Wikipedia entry for contingency matrix + `_ -* `Wikipedia entry for contingency matrix - `_ - -|details-end| .. _pair_confusion_matrix: @@ -2334,11 +2223,7 @@ diagonal entries:: array([[ 0, 0], [12, 0]]) -|details-start| -**References** -|details-split| - - * :doi:`"Comparing Partitions" <10.1007/BF01908075>` L. Hubert and P. Arabie, - Journal of Classification 1985 +.. dropdown:: References -|details-end| + * :doi:`"Comparing Partitions" <10.1007/BF01908075>` L. Hubert and P. Arabie, + Journal of Classification 1985 diff --git a/doc/modules/compose.rst b/doc/modules/compose.rst index 28931cf52f283..655ea551e0375 100644 --- a/doc/modules/compose.rst +++ b/doc/modules/compose.rst @@ -79,20 +79,16 @@ is an estimator object:: >>> pipe Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC())]) -|details-start| -**Shorthand version using :func:`make_pipeline`** -|details-split| +.. dropdown:: Shorthand version using :func:`make_pipeline` -The utility function :func:`make_pipeline` is a shorthand -for constructing pipelines; -it takes a variable number of estimators and returns a pipeline, -filling in the names automatically:: + The utility function :func:`make_pipeline` is a shorthand + for constructing pipelines; + it takes a variable number of estimators and returns a pipeline, + filling in the names automatically:: - >>> from sklearn.pipeline import make_pipeline - >>> make_pipeline(PCA(), SVC()) - Pipeline(steps=[('pca', PCA()), ('svc', SVC())]) - -|details-end| + >>> from sklearn.pipeline import make_pipeline + >>> make_pipeline(PCA(), SVC()) + Pipeline(steps=[('pca', PCA()), ('svc', SVC())]) Access pipeline steps ..................... @@ -108,27 +104,23 @@ permitted). This is convenient for performing only some of the transformations >>> pipe[-1:] Pipeline(steps=[('clf', SVC())]) -|details-start| -**Accessing a step by name or position** -|details-split| - -A specific step can also be accessed by index or name by indexing (with ``[idx]``) the -pipeline:: +.. dropdown:: Accessing a step by name or position - >>> pipe.steps[0] - ('reduce_dim', PCA()) - >>> pipe[0] - PCA() - >>> pipe['reduce_dim'] - PCA() + A specific step can also be accessed by index or name by indexing (with ``[idx]``) the + pipeline:: -`Pipeline`'s `named_steps` attribute allows accessing steps by name with tab -completion in interactive environments:: + >>> pipe.steps[0] + ('reduce_dim', PCA()) + >>> pipe[0] + PCA() + >>> pipe['reduce_dim'] + PCA() - >>> pipe.named_steps.reduce_dim is pipe['reduce_dim'] - True + `Pipeline`'s `named_steps` attribute allows accessing steps by name with tab + completion in interactive environments:: -|details-end| + >>> pipe.named_steps.reduce_dim is pipe['reduce_dim'] + True Tracking feature names in a pipeline .................................... @@ -149,17 +141,13 @@ pipeline slicing to get the feature names going into each step:: >>> pipe[:-1].get_feature_names_out() array(['x2', 'x3'], ...) -|details-start| -**Customize feature names** -|details-split| - -You can also provide custom feature names for the input data using -``get_feature_names_out``:: +.. dropdown:: Customize feature names - >>> pipe[:-1].get_feature_names_out(iris.feature_names) - array(['petal length (cm)', 'petal width (cm)'], ...) + You can also provide custom feature names for the input data using + ``get_feature_names_out``:: -|details-end| + >>> pipe[:-1].get_feature_names_out(iris.feature_names) + array(['petal length (cm)', 'petal width (cm)'], ...) .. _pipeline_nested_parameters: @@ -175,40 +163,37 @@ syntax:: >>> pipe.set_params(clf__C=10) Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC(C=10))]) -|details-start| -**When does it matter?** -|details-split| +.. dropdown:: When does it matter? -This is particularly important for doing grid searches:: + This is particularly important for doing grid searches:: - >>> from sklearn.model_selection import GridSearchCV - >>> param_grid = dict(reduce_dim__n_components=[2, 5, 10], - ... clf__C=[0.1, 10, 100]) - >>> grid_search = GridSearchCV(pipe, param_grid=param_grid) + >>> from sklearn.model_selection import GridSearchCV + >>> param_grid = dict(reduce_dim__n_components=[2, 5, 10], + ... clf__C=[0.1, 10, 100]) + >>> grid_search = GridSearchCV(pipe, param_grid=param_grid) -Individual steps may also be replaced as parameters, and non-final steps may be -ignored by setting them to ``'passthrough'``:: + Individual steps may also be replaced as parameters, and non-final steps may be + ignored by setting them to ``'passthrough'``:: - >>> param_grid = dict(reduce_dim=['passthrough', PCA(5), PCA(10)], - ... clf=[SVC(), LogisticRegression()], - ... clf__C=[0.1, 10, 100]) - >>> grid_search = GridSearchCV(pipe, param_grid=param_grid) + >>> param_grid = dict(reduce_dim=['passthrough', PCA(5), PCA(10)], + ... clf=[SVC(), LogisticRegression()], + ... clf__C=[0.1, 10, 100]) + >>> grid_search = GridSearchCV(pipe, param_grid=param_grid) -.. topic:: See Also: + .. seealso:: - * :ref:`composite_grid_search` + * :ref:`composite_grid_search` -|details-end| -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection_pipeline.py` - * :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` - * :ref:`sphx_glr_auto_examples_compose_plot_digits_pipe.py` - * :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py` - * :ref:`sphx_glr_auto_examples_svm_plot_svm_anova.py` - * :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py` - * :ref:`sphx_glr_auto_examples_miscellaneous_plot_pipeline_display.py` +* :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection_pipeline.py` +* :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` +* :ref:`sphx_glr_auto_examples_compose_plot_digits_pipe.py` +* :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py` +* :ref:`sphx_glr_auto_examples_svm_plot_svm_anova.py` +* :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py` +* :ref:`sphx_glr_auto_examples_miscellaneous_plot_pipeline_display.py` .. _pipeline_cache: @@ -245,53 +230,49 @@ object:: >>> # Clear the cache directory when you don't need it anymore >>> rmtree(cachedir) -|details-start| -**Warning: Side effect of caching transformers** -|details-split| - -Using a :class:`Pipeline` without cache enabled, it is possible to -inspect the original instance such as:: - - >>> from sklearn.datasets import load_digits - >>> X_digits, y_digits = load_digits(return_X_y=True) - >>> pca1 = PCA(n_components=10) - >>> svm1 = SVC() - >>> pipe = Pipeline([('reduce_dim', pca1), ('clf', svm1)]) - >>> pipe.fit(X_digits, y_digits) - Pipeline(steps=[('reduce_dim', PCA(n_components=10)), ('clf', SVC())]) - >>> # The pca instance can be inspected directly - >>> pca1.components_.shape - (10, 64) - - -Enabling caching triggers a clone of the transformers before fitting. -Therefore, the transformer instance given to the pipeline cannot be -inspected directly. -In following example, accessing the :class:`~sklearn.decomposition.PCA` -instance ``pca2`` will raise an ``AttributeError`` since ``pca2`` will be an -unfitted transformer. -Instead, use the attribute ``named_steps`` to inspect estimators within -the pipeline:: - - >>> cachedir = mkdtemp() - >>> pca2 = PCA(n_components=10) - >>> svm2 = SVC() - >>> cached_pipe = Pipeline([('reduce_dim', pca2), ('clf', svm2)], - ... memory=cachedir) - >>> cached_pipe.fit(X_digits, y_digits) - Pipeline(memory=..., - steps=[('reduce_dim', PCA(n_components=10)), ('clf', SVC())]) - >>> cached_pipe.named_steps['reduce_dim'].components_.shape - (10, 64) - >>> # Remove the cache directory - >>> rmtree(cachedir) - - -|details-end| - -.. topic:: Examples: - - * :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py` +.. dropdown:: Side effect of caching transformers + :color: warning + + Using a :class:`Pipeline` without cache enabled, it is possible to + inspect the original instance such as:: + + >>> from sklearn.datasets import load_digits + >>> X_digits, y_digits = load_digits(return_X_y=True) + >>> pca1 = PCA(n_components=10) + >>> svm1 = SVC() + >>> pipe = Pipeline([('reduce_dim', pca1), ('clf', svm1)]) + >>> pipe.fit(X_digits, y_digits) + Pipeline(steps=[('reduce_dim', PCA(n_components=10)), ('clf', SVC())]) + >>> # The pca instance can be inspected directly + >>> pca1.components_.shape + (10, 64) + + Enabling caching triggers a clone of the transformers before fitting. + Therefore, the transformer instance given to the pipeline cannot be + inspected directly. + In following example, accessing the :class:`~sklearn.decomposition.PCA` + instance ``pca2`` will raise an ``AttributeError`` since ``pca2`` will be an + unfitted transformer. + Instead, use the attribute ``named_steps`` to inspect estimators within + the pipeline:: + + >>> cachedir = mkdtemp() + >>> pca2 = PCA(n_components=10) + >>> svm2 = SVC() + >>> cached_pipe = Pipeline([('reduce_dim', pca2), ('clf', svm2)], + ... memory=cachedir) + >>> cached_pipe.fit(X_digits, y_digits) + Pipeline(memory=..., + steps=[('reduce_dim', PCA(n_components=10)), ('clf', SVC())]) + >>> cached_pipe.named_steps['reduce_dim'].components_.shape + (10, 64) + >>> # Remove the cache directory + >>> rmtree(cachedir) + + +.. rubric:: Examples + +* :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py` .. _transformed_target_regressor: @@ -364,9 +345,9 @@ each other. However, it is possible to bypass this checking by setting pair of functions ``func`` and ``inverse_func``. However, setting both options will raise an error. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_compose_plot_transformed_target.py` +* :ref:`sphx_glr_auto_examples_compose_plot_transformed_target.py` .. _feature_union: @@ -428,9 +409,9 @@ and ignored by setting to ``'drop'``:: FeatureUnion(transformer_list=[('linear_pca', PCA()), ('kernel_pca', 'drop')]) -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_compose_plot_feature_union.py` +* :ref:`sphx_glr_auto_examples_compose_plot_feature_union.py` .. _column_transformer: @@ -623,7 +604,7 @@ As an alternative, the HTML can be written to a file using >>> with open('my_estimator.html', 'w') as f: # doctest: +SKIP ... f.write(estimator_html_repr(clf)) -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_compose_plot_column_transformer.py` - * :ref:`sphx_glr_auto_examples_compose_plot_column_transformer_mixed_types.py` +* :ref:`sphx_glr_auto_examples_compose_plot_column_transformer.py` +* :ref:`sphx_glr_auto_examples_compose_plot_column_transformer_mixed_types.py` diff --git a/doc/modules/covariance.rst b/doc/modules/covariance.rst index 50927f9a677f6..847e489c87333 100644 --- a/doc/modules/covariance.rst +++ b/doc/modules/covariance.rst @@ -40,11 +40,10 @@ on whether the data are centered, so one may want to use the same mean vector as the training set. If not, both should be centered by the user, and ``assume_centered=True`` should be used. -.. topic:: Examples: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_covariance_plot_covariance_estimation.py` for - an example on how to fit an :class:`EmpiricalCovariance` object - to data. +* See :ref:`sphx_glr_auto_examples_covariance_plot_covariance_estimation.py` for + an example on how to fit an :class:`EmpiricalCovariance` object to data. .. _shrunk_covariance: @@ -84,11 +83,10 @@ Tr}\hat{\Sigma}}{p}\rm Id`. Choosing the amount of shrinkage, :math:`\alpha` amounts to setting a bias/variance trade-off, and is discussed below. -.. topic:: Examples: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_covariance_plot_covariance_estimation.py` for - an example on how to fit a :class:`ShrunkCovariance` object - to data. +* See :ref:`sphx_glr_auto_examples_covariance_plot_covariance_estimation.py` for + an example on how to fit a :class:`ShrunkCovariance` object to data. Ledoit-Wolf shrinkage @@ -121,18 +119,18 @@ fitting a :class:`LedoitWolf` object to the same sample. Since the population covariance is already a multiple of the identity matrix, the Ledoit-Wolf solution is indeed a reasonable estimate. -.. topic:: Examples: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_covariance_plot_covariance_estimation.py` for - an example on how to fit a :class:`LedoitWolf` object to data and - for visualizing the performances of the Ledoit-Wolf estimator in - terms of likelihood. +* See :ref:`sphx_glr_auto_examples_covariance_plot_covariance_estimation.py` for + an example on how to fit a :class:`LedoitWolf` object to data and + for visualizing the performances of the Ledoit-Wolf estimator in + terms of likelihood. -.. topic:: References: +.. rubric:: References - .. [1] O. Ledoit and M. Wolf, "A Well-Conditioned Estimator for Large-Dimensional - Covariance Matrices", Journal of Multivariate Analysis, Volume 88, Issue 2, - February 2004, pages 365-411. +.. [1] O. Ledoit and M. Wolf, "A Well-Conditioned Estimator for Large-Dimensional + Covariance Matrices", Journal of Multivariate Analysis, Volume 88, Issue 2, + February 2004, pages 365-411. .. _oracle_approximating_shrinkage: @@ -158,22 +156,21 @@ object to the same sample. Bias-variance trade-off when setting the shrinkage: comparing the choices of Ledoit-Wolf and OAS estimators -.. topic:: References: +.. rubric:: References - .. [2] :arxiv:`"Shrinkage algorithms for MMSE covariance estimation.", - Chen, Y., Wiesel, A., Eldar, Y. C., & Hero, A. O. - IEEE Transactions on Signal Processing, 58(10), 5016-5029, 2010. - <0907.4698>` +.. [2] :arxiv:`"Shrinkage algorithms for MMSE covariance estimation.", + Chen, Y., Wiesel, A., Eldar, Y. C., & Hero, A. O. + IEEE Transactions on Signal Processing, 58(10), 5016-5029, 2010. + <0907.4698>` -.. topic:: Examples: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_covariance_plot_covariance_estimation.py` for - an example on how to fit an :class:`OAS` object - to data. +* See :ref:`sphx_glr_auto_examples_covariance_plot_covariance_estimation.py` for + an example on how to fit an :class:`OAS` object to data. - * See :ref:`sphx_glr_auto_examples_covariance_plot_lw_vs_oas.py` to visualize the - Mean Squared Error difference between a :class:`LedoitWolf` and - an :class:`OAS` estimator of the covariance. +* See :ref:`sphx_glr_auto_examples_covariance_plot_lw_vs_oas.py` to visualize the + Mean Squared Error difference between a :class:`LedoitWolf` and + an :class:`OAS` estimator of the covariance. .. figure:: ../auto_examples/covariance/images/sphx_glr_plot_lw_vs_oas_001.png @@ -254,20 +251,20 @@ problem is the GLasso algorithm, from the Friedman 2008 Biostatistics paper. It is the same algorithm as in the R ``glasso`` package. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_covariance_plot_sparse_cov.py`: example on synthetic - data showing some recovery of a structure, and comparing to other - covariance estimators. +* :ref:`sphx_glr_auto_examples_covariance_plot_sparse_cov.py`: example on synthetic + data showing some recovery of a structure, and comparing to other + covariance estimators. - * :ref:`sphx_glr_auto_examples_applications_plot_stock_market.py`: example on real - stock market data, finding which symbols are most linked. +* :ref:`sphx_glr_auto_examples_applications_plot_stock_market.py`: example on real + stock market data, finding which symbols are most linked. -.. topic:: References: +.. rubric:: References - * Friedman et al, `"Sparse inverse covariance estimation with the - graphical lasso" `_, - Biostatistics 9, pp 432, 2008 +* Friedman et al, `"Sparse inverse covariance estimation with the + graphical lasso" `_, + Biostatistics 9, pp 432, 2008 .. _robust_covariance: @@ -313,24 +310,24 @@ the same time. Raw estimates can be accessed as ``raw_location_`` and ``raw_covariance_`` attributes of a :class:`MinCovDet` robust covariance estimator object. -.. topic:: References: +.. rubric:: References - .. [3] P. J. Rousseeuw. Least median of squares regression. - J. Am Stat Ass, 79:871, 1984. - .. [4] A Fast Algorithm for the Minimum Covariance Determinant Estimator, - 1999, American Statistical Association and the American Society - for Quality, TECHNOMETRICS. +.. [3] P. J. Rousseeuw. Least median of squares regression. + J. Am Stat Ass, 79:871, 1984. +.. [4] A Fast Algorithm for the Minimum Covariance Determinant Estimator, + 1999, American Statistical Association and the American Society + for Quality, TECHNOMETRICS. -.. topic:: Examples: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_covariance_plot_robust_vs_empirical_covariance.py` for - an example on how to fit a :class:`MinCovDet` object to data and see how - the estimate remains accurate despite the presence of outliers. +* See :ref:`sphx_glr_auto_examples_covariance_plot_robust_vs_empirical_covariance.py` for + an example on how to fit a :class:`MinCovDet` object to data and see how + the estimate remains accurate despite the presence of outliers. - * See :ref:`sphx_glr_auto_examples_covariance_plot_mahalanobis_distances.py` to - visualize the difference between :class:`EmpiricalCovariance` and - :class:`MinCovDet` covariance estimators in terms of Mahalanobis distance - (so we get a better estimate of the precision matrix too). +* See :ref:`sphx_glr_auto_examples_covariance_plot_mahalanobis_distances.py` to + visualize the difference between :class:`EmpiricalCovariance` and + :class:`MinCovDet` covariance estimators in terms of Mahalanobis distance + (so we get a better estimate of the precision matrix too). .. |robust_vs_emp| image:: ../auto_examples/covariance/images/sphx_glr_plot_robust_vs_empirical_covariance_001.png :target: ../auto_examples/covariance/plot_robust_vs_empirical_covariance.html diff --git a/doc/modules/cross_decomposition.rst b/doc/modules/cross_decomposition.rst index 8f8d217f87144..2d630de699c7a 100644 --- a/doc/modules/cross_decomposition.rst +++ b/doc/modules/cross_decomposition.rst @@ -92,42 +92,35 @@ Step *a)* may be performed in two ways: either by computing the whole SVD of values, or by directly computing the singular vectors using the power method (cf section 11.3 in [1]_), which corresponds to the `'nipals'` option of the `algorithm` parameter. -|details-start| -**Transforming data** -|details-split| +.. dropdown:: Transforming data -To transform :math:`X` into :math:`\bar{X}`, we need to find a projection -matrix :math:`P` such that :math:`\bar{X} = XP`. We know that for the -training data, :math:`\Xi = XP`, and :math:`X = \Xi \Gamma^T`. Setting -:math:`P = U(\Gamma^T U)^{-1}` where :math:`U` is the matrix with the -:math:`u_k` in the columns, we have :math:`XP = X U(\Gamma^T U)^{-1} = \Xi -(\Gamma^T U) (\Gamma^T U)^{-1} = \Xi` as desired. The rotation matrix -:math:`P` can be accessed from the `x_rotations_` attribute. + To transform :math:`X` into :math:`\bar{X}`, we need to find a projection + matrix :math:`P` such that :math:`\bar{X} = XP`. We know that for the + training data, :math:`\Xi = XP`, and :math:`X = \Xi \Gamma^T`. Setting + :math:`P = U(\Gamma^T U)^{-1}` where :math:`U` is the matrix with the + :math:`u_k` in the columns, we have :math:`XP = X U(\Gamma^T U)^{-1} = \Xi + (\Gamma^T U) (\Gamma^T U)^{-1} = \Xi` as desired. The rotation matrix + :math:`P` can be accessed from the `x_rotations_` attribute. -Similarly, :math:`Y` can be transformed using the rotation matrix -:math:`V(\Delta^T V)^{-1}`, accessed via the `y_rotations_` attribute. -|details-end| + Similarly, :math:`Y` can be transformed using the rotation matrix + :math:`V(\Delta^T V)^{-1}`, accessed via the `y_rotations_` attribute. -|details-start| -**Predicting the targets Y** -|details-split| +.. dropdown:: Predicting the targets `Y` -To predict the targets of some data :math:`X`, we are looking for a -coefficient matrix :math:`\beta \in R^{d \times t}` such that :math:`Y = -X\beta`. + To predict the targets of some data :math:`X`, we are looking for a + coefficient matrix :math:`\beta \in R^{d \times t}` such that :math:`Y = + X\beta`. -The idea is to try to predict the transformed targets :math:`\Omega` as a -function of the transformed samples :math:`\Xi`, by computing :math:`\alpha -\in \mathbb{R}` such that :math:`\Omega = \alpha \Xi`. + The idea is to try to predict the transformed targets :math:`\Omega` as a + function of the transformed samples :math:`\Xi`, by computing :math:`\alpha + \in \mathbb{R}` such that :math:`\Omega = \alpha \Xi`. -Then, we have :math:`Y = \Omega \Delta^T = \alpha \Xi \Delta^T`, and since -:math:`\Xi` is the transformed training data we have that :math:`Y = X \alpha -P \Delta^T`, and as a result the coefficient matrix :math:`\beta = \alpha P -\Delta^T`. + Then, we have :math:`Y = \Omega \Delta^T = \alpha \Xi \Delta^T`, and since + :math:`\Xi` is the transformed training data we have that :math:`Y = X \alpha + P \Delta^T`, and as a result the coefficient matrix :math:`\beta = \alpha P + \Delta^T`. -:math:`\beta` can be accessed through the `coef_` attribute. - -|details-end| + :math:`\beta` can be accessed through the `coef_` attribute. PLSSVD ------ @@ -184,18 +177,13 @@ Since :class:`CCA` involves the inversion of :math:`X_k^TX_k` and :math:`Y_k^TY_k`, this estimator can be unstable if the number of features or targets is greater than the number of samples. -|details-start| -**Reference** -|details-split| - - .. [1] `A survey of Partial Least Squares (PLS) methods, with emphasis on - the two-block case - `_ - JA Wegelin +.. rubric:: References -|details-end| +.. [1] `A survey of Partial Least Squares (PLS) methods, with emphasis on the two-block + case `_, + JA Wegelin -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py` - * :ref:`sphx_glr_auto_examples_cross_decomposition_plot_pcr_vs_pls.py` +* :ref:`sphx_glr_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py` +* :ref:`sphx_glr_auto_examples_cross_decomposition_plot_pcr_vs_pls.py` diff --git a/doc/modules/cross_validation.rst b/doc/modules/cross_validation.rst index 34f14fe6846a2..defcd91a6008a 100644 --- a/doc/modules/cross_validation.rst +++ b/doc/modules/cross_validation.rst @@ -170,36 +170,33 @@ indices, for example:: >>> cross_val_score(clf, X, y, cv=custom_cv) array([1. , 0.973...]) -|details-start| -**Data transformation with held out data** -|details-split| +.. dropdown:: Data transformation with held-out data - Just as it is important to test a predictor on data held-out from - training, preprocessing (such as standardization, feature selection, etc.) - and similar :ref:`data transformations ` similarly should - be learnt from a training set and applied to held-out data for prediction:: + Just as it is important to test a predictor on data held-out from + training, preprocessing (such as standardization, feature selection, etc.) + and similar :ref:`data transformations ` similarly should + be learnt from a training set and applied to held-out data for prediction:: - >>> from sklearn import preprocessing - >>> X_train, X_test, y_train, y_test = train_test_split( - ... X, y, test_size=0.4, random_state=0) - >>> scaler = preprocessing.StandardScaler().fit(X_train) - >>> X_train_transformed = scaler.transform(X_train) - >>> clf = svm.SVC(C=1).fit(X_train_transformed, y_train) - >>> X_test_transformed = scaler.transform(X_test) - >>> clf.score(X_test_transformed, y_test) - 0.9333... + >>> from sklearn import preprocessing + >>> X_train, X_test, y_train, y_test = train_test_split( + ... X, y, test_size=0.4, random_state=0) + >>> scaler = preprocessing.StandardScaler().fit(X_train) + >>> X_train_transformed = scaler.transform(X_train) + >>> clf = svm.SVC(C=1).fit(X_train_transformed, y_train) + >>> X_test_transformed = scaler.transform(X_test) + >>> clf.score(X_test_transformed, y_test) + 0.9333... - A :class:`Pipeline ` makes it easier to compose - estimators, providing this behavior under cross-validation:: + A :class:`Pipeline ` makes it easier to compose + estimators, providing this behavior under cross-validation:: - >>> from sklearn.pipeline import make_pipeline - >>> clf = make_pipeline(preprocessing.StandardScaler(), svm.SVC(C=1)) - >>> cross_val_score(clf, X, y, cv=cv) - array([0.977..., 0.933..., 0.955..., 0.933..., 0.977...]) + >>> from sklearn.pipeline import make_pipeline + >>> clf = make_pipeline(preprocessing.StandardScaler(), svm.SVC(C=1)) + >>> cross_val_score(clf, X, y, cv=cv) + array([0.977..., 0.933..., 0.955..., 0.933..., 0.977...]) - See :ref:`combining_estimators`. + See :ref:`combining_estimators`. -|details-end| .. _multimetric_cross_validation: @@ -294,14 +291,14 @@ The function :func:`cross_val_predict` is appropriate for: The available cross validation iterators are introduced in the following section. -.. topic:: Examples +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_model_selection_plot_roc_crossval.py`, - * :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py`, - * :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`, - * :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`, - * :ref:`sphx_glr_auto_examples_model_selection_plot_cv_predict.py`, - * :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py`. +* :ref:`sphx_glr_auto_examples_model_selection_plot_roc_crossval.py`, +* :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py`, +* :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`, +* :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`, +* :ref:`sphx_glr_auto_examples_model_selection_plot_cv_predict.py`, +* :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py`. Cross validation iterators ========================== @@ -442,23 +439,19 @@ then 5- or 10- fold cross validation can overestimate the generalization error. As a general rule, most authors, and empirical evidence, suggest that 5- or 10- fold cross validation should be preferred to LOO. -|details-start| -**References** -|details-split| +.. dropdown:: References - * ``_; - * T. Hastie, R. Tibshirani, J. Friedman, `The Elements of Statistical Learning - `_, Springer 2009 - * L. Breiman, P. Spector `Submodel selection and evaluation in regression: The X-random case - `_, International Statistical Review 1992; - * R. Kohavi, `A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection - `_, Intl. Jnt. Conf. AI - * R. Bharat Rao, G. Fung, R. Rosales, `On the Dangers of Cross-Validation. An Experimental Evaluation - `_, SIAM 2008; - * G. James, D. Witten, T. Hastie, R Tibshirani, `An Introduction to - Statistical Learning `_, Springer 2013. - -|details-end| + * ``_; + * T. Hastie, R. Tibshirani, J. Friedman, `The Elements of Statistical Learning + `_, Springer 2009 + * L. Breiman, P. Spector `Submodel selection and evaluation in regression: The X-random case + `_, International Statistical Review 1992; + * R. Kohavi, `A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection + `_, Intl. Jnt. Conf. AI + * R. Bharat Rao, G. Fung, R. Rosales, `On the Dangers of Cross-Validation. An Experimental Evaluation + `_, SIAM 2008; + * G. James, D. Witten, T. Hastie, R Tibshirani, `An Introduction to + Statistical Learning `_, Springer 2013. .. _leave_p_out: @@ -700,30 +693,27 @@ Example:: [ 0 1 4 5 6 7 8 9 11 12 13 14] [ 2 3 10 15 16 17] [ 1 2 3 8 9 10 12 13 14 15 16 17] [ 0 4 5 6 7 11] -|details-start| -**Implementation notes** -|details-split| +.. dropdown:: Implementation notes -- With the current implementation full shuffle is not possible in most - scenarios. When shuffle=True, the following happens: + - With the current implementation full shuffle is not possible in most + scenarios. When shuffle=True, the following happens: - 1. All groups are shuffled. - 2. Groups are sorted by standard deviation of classes using stable sort. - 3. Sorted groups are iterated over and assigned to folds. + 1. All groups are shuffled. + 2. Groups are sorted by standard deviation of classes using stable sort. + 3. Sorted groups are iterated over and assigned to folds. - That means that only groups with the same standard deviation of class - distribution will be shuffled, which might be useful when each group has only - a single class. -- The algorithm greedily assigns each group to one of n_splits test sets, - choosing the test set that minimises the variance in class distribution - across test sets. Group assignment proceeds from groups with highest to - lowest variance in class frequency, i.e. large groups peaked on one or few - classes are assigned first. -- This split is suboptimal in a sense that it might produce imbalanced splits - even if perfect stratification is possible. If you have relatively close - distribution of classes in each group, using :class:`GroupKFold` is better. + That means that only groups with the same standard deviation of class + distribution will be shuffled, which might be useful when each group has only + a single class. + - The algorithm greedily assigns each group to one of n_splits test sets, + choosing the test set that minimises the variance in class distribution + across test sets. Group assignment proceeds from groups with highest to + lowest variance in class frequency, i.e. large groups peaked on one or few + classes are assigned first. + - This split is suboptimal in a sense that it might produce imbalanced splits + even if perfect stratification is possible. If you have relatively close + distribution of classes in each group, using :class:`GroupKFold` is better. -|details-end| Here is a visualization of cross-validation behavior for uneven groups: @@ -999,16 +989,12 @@ using brute force and internally fits ``(n_permutations + 1) * n_cv`` models. It is therefore only tractable with small datasets for which fitting an individual model is very fast. -.. topic:: Examples - - * :ref:`sphx_glr_auto_examples_model_selection_plot_permutation_tests_for_classification.py` +.. rubric:: Examples -|details-start| -**References** -|details-split| +* :ref:`sphx_glr_auto_examples_model_selection_plot_permutation_tests_for_classification.py` - * Ojala and Garriga. `Permutation Tests for Studying Classifier Performance - `_. - J. Mach. Learn. Res. 2010. +.. dropdown:: References -|details-end| + * Ojala and Garriga. `Permutation Tests for Studying Classifier Performance + `_. + J. Mach. Learn. Res. 2010. diff --git a/doc/modules/decomposition.rst b/doc/modules/decomposition.rst index e34818a322c7d..926a4482f1428 100644 --- a/doc/modules/decomposition.rst +++ b/doc/modules/decomposition.rst @@ -51,11 +51,11 @@ data based on the amount of variance it explains. As such it implements a :scale: 75% -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_decomposition_plot_pca_iris.py` - * :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_lda.py` - * :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_fa_model_selection.py` +* :ref:`sphx_glr_auto_examples_decomposition_plot_pca_iris.py` +* :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_lda.py` +* :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_fa_model_selection.py` .. _IncrementalPCA: @@ -97,9 +97,9 @@ input data for each feature before applying the SVD. :scale: 75% -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_decomposition_plot_incremental_pca.py` +* :ref:`sphx_glr_auto_examples_decomposition_plot_incremental_pca.py` .. _RandomizedPCA: @@ -160,20 +160,20 @@ Note: the implementation of ``inverse_transform`` in :class:`PCA` with ``transform`` even when ``whiten=False`` (default). -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py` - * :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py` +* :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py` +* :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py` -.. topic:: References: +.. rubric:: References - * Algorithm 4.3 in - :arxiv:`"Finding structure with randomness: Stochastic algorithms for - constructing approximate matrix decompositions" <0909.4061>` - Halko, et al., 2009 +* Algorithm 4.3 in + :arxiv:`"Finding structure with randomness: Stochastic algorithms for + constructing approximate matrix decompositions" <0909.4061>` + Halko, et al., 2009 - * :arxiv:`"An implementation of a randomized algorithm for principal component - analysis" <1412.3510>` A. Szlam et al. 2014 +* :arxiv:`"An implementation of a randomized algorithm for principal component + analysis" <1412.3510>` A. Szlam et al. 2014 .. _SparsePCA: @@ -248,18 +248,18 @@ factorization, while larger values shrink many coefficients to zero. the algorithm is online along the features direction, not the samples direction. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py` +* :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py` -.. topic:: References: +.. rubric:: References - .. [Mrl09] `"Online Dictionary Learning for Sparse Coding" - `_ - J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009 - .. [Jen09] `"Structured Sparse Principal Component Analysis" - `_ - R. Jenatton, G. Obozinski, F. Bach, 2009 +.. [Mrl09] `"Online Dictionary Learning for Sparse Coding" + `_ + J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009 +.. [Jen09] `"Structured Sparse Principal Component Analysis" + `_ + R. Jenatton, G. Obozinski, F. Bach, 2009 .. _kernel_PCA: @@ -288,24 +288,23 @@ prediction (kernel dependency estimation). :class:`KernelPCA` supports both :meth:`KernelPCA.inverse_transform` is an approximation. See the example linked below for more details. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_decomposition_plot_kernel_pca.py` - * :ref:`sphx_glr_auto_examples_applications_plot_digits_denoising.py` +* :ref:`sphx_glr_auto_examples_decomposition_plot_kernel_pca.py` +* :ref:`sphx_glr_auto_examples_applications_plot_digits_denoising.py` +.. rubric:: References -.. topic:: References: +.. [Scholkopf1997] Schölkopf, Bernhard, Alexander Smola, and Klaus-Robert Müller. + `"Kernel principal component analysis." + `_ + International conference on artificial neural networks. + Springer, Berlin, Heidelberg, 1997. - .. [Scholkopf1997] Schölkopf, Bernhard, Alexander Smola, and Klaus-Robert Müller. - `"Kernel principal component analysis." - `_ - International conference on artificial neural networks. - Springer, Berlin, Heidelberg, 1997. - - .. [Bakir2003] Bakır, Gökhan H., Jason Weston, and Bernhard Schölkopf. - `"Learning to find pre-images." - `_ - Advances in neural information processing systems 16 (2003): 449-456. +.. [Bakir2003] Bakır, Gökhan H., Jason Weston, and Bernhard Schölkopf. + `"Learning to find pre-images." + `_ + Advances in neural information processing systems 16 (2003): 449-456. .. _kPCA_Solvers: @@ -323,36 +322,33 @@ is much smaller than its size. This is a situation where approximate eigensolvers can provide speedup with very low precision loss. -|details-start| -**Eigensolvers** -|details-split| - -The optional parameter ``eigen_solver='randomized'`` can be used to -*significantly* reduce the computation time when the number of requested -``n_components`` is small compared with the number of samples. It relies on -randomized decomposition methods to find an approximate solution in a shorter -time. +.. dropdown:: Eigensolvers -The time complexity of the randomized :class:`KernelPCA` is -:math:`O(n_{\mathrm{samples}}^2 \cdot n_{\mathrm{components}})` -instead of :math:`O(n_{\mathrm{samples}}^3)` for the exact method -implemented with ``eigen_solver='dense'``. + The optional parameter ``eigen_solver='randomized'`` can be used to + *significantly* reduce the computation time when the number of requested + ``n_components`` is small compared with the number of samples. It relies on + randomized decomposition methods to find an approximate solution in a shorter + time. -The memory footprint of randomized :class:`KernelPCA` is also proportional to -:math:`2 \cdot n_{\mathrm{samples}} \cdot n_{\mathrm{components}}` instead of -:math:`n_{\mathrm{samples}}^2` for the exact method. + The time complexity of the randomized :class:`KernelPCA` is + :math:`O(n_{\mathrm{samples}}^2 \cdot n_{\mathrm{components}})` + instead of :math:`O(n_{\mathrm{samples}}^3)` for the exact method + implemented with ``eigen_solver='dense'``. -Note: this technique is the same as in :ref:`RandomizedPCA`. + The memory footprint of randomized :class:`KernelPCA` is also proportional to + :math:`2 \cdot n_{\mathrm{samples}} \cdot n_{\mathrm{components}}` instead of + :math:`n_{\mathrm{samples}}^2` for the exact method. -In addition to the above two solvers, ``eigen_solver='arpack'`` can be used as -an alternate way to get an approximate decomposition. In practice, this method -only provides reasonable execution times when the number of components to find -is extremely small. It is enabled by default when the desired number of -components is less than 10 (strict) and the number of samples is more than 200 -(strict). See :class:`KernelPCA` for details. + Note: this technique is the same as in :ref:`RandomizedPCA`. + In addition to the above two solvers, ``eigen_solver='arpack'`` can be used as + an alternate way to get an approximate decomposition. In practice, this method + only provides reasonable execution times when the number of components to find + is extremely small. It is enabled by default when the desired number of + components is less than 10 (strict) and the number of samples is more than 200 + (strict). See :class:`KernelPCA` for details. -.. topic:: References: + .. rubric:: References * *dense* solver: `scipy.linalg.eigh documentation @@ -374,8 +370,6 @@ components is less than 10 (strict) and the number of samples is more than 200 `_ R. B. Lehoucq, D. C. Sorensen, and C. Yang, (1998) -|details-end| - .. _LSA: @@ -392,72 +386,67 @@ When the columnwise (per-feature) means of :math:`X` are subtracted from the feature values, truncated SVD on the resulting matrix is equivalent to PCA. -|details-start| -**About truncated SVD and latent semantic analysis (LSA)** -|details-split| - -When truncated SVD is applied to term-document matrices -(as returned by :class:`~sklearn.feature_extraction.text.CountVectorizer` or -:class:`~sklearn.feature_extraction.text.TfidfVectorizer`), -this transformation is known as -`latent semantic analysis `_ -(LSA), because it transforms such matrices -to a "semantic" space of low dimensionality. -In particular, LSA is known to combat the effects of synonymy and polysemy -(both of which roughly mean there are multiple meanings per word), -which cause term-document matrices to be overly sparse -and exhibit poor similarity under measures such as cosine similarity. +.. dropdown:: About truncated SVD and latent semantic analysis (LSA) -.. note:: - LSA is also known as latent semantic indexing, LSI, - though strictly that refers to its use in persistent indexes - for information retrieval purposes. + When truncated SVD is applied to term-document matrices + (as returned by :class:`~sklearn.feature_extraction.text.CountVectorizer` or + :class:`~sklearn.feature_extraction.text.TfidfVectorizer`), + this transformation is known as + `latent semantic analysis `_ + (LSA), because it transforms such matrices + to a "semantic" space of low dimensionality. + In particular, LSA is known to combat the effects of synonymy and polysemy + (both of which roughly mean there are multiple meanings per word), + which cause term-document matrices to be overly sparse + and exhibit poor similarity under measures such as cosine similarity. -Mathematically, truncated SVD applied to training samples :math:`X` -produces a low-rank approximation :math:`X`: + .. note:: + LSA is also known as latent semantic indexing, LSI, + though strictly that refers to its use in persistent indexes + for information retrieval purposes. -.. math:: - X \approx X_k = U_k \Sigma_k V_k^\top + Mathematically, truncated SVD applied to training samples :math:`X` + produces a low-rank approximation :math:`X`: -After this operation, :math:`U_k \Sigma_k` -is the transformed training set with :math:`k` features -(called ``n_components`` in the API). + .. math:: + X \approx X_k = U_k \Sigma_k V_k^\top -To also transform a test set :math:`X`, we multiply it with :math:`V_k`: + After this operation, :math:`U_k \Sigma_k` + is the transformed training set with :math:`k` features + (called ``n_components`` in the API). -.. math:: - X' = X V_k - -.. note:: - Most treatments of LSA in the natural language processing (NLP) - and information retrieval (IR) literature - swap the axes of the matrix :math:`X` so that it has shape - ``n_features`` × ``n_samples``. - We present LSA in a different way that matches the scikit-learn API better, - but the singular values found are the same. + To also transform a test set :math:`X`, we multiply it with :math:`V_k`: + .. math:: + X' = X V_k -While the :class:`TruncatedSVD` transformer -works with any feature matrix, -using it on tf–idf matrices is recommended over raw frequency counts -in an LSA/document processing setting. -In particular, sublinear scaling and inverse document frequency -should be turned on (``sublinear_tf=True, use_idf=True``) -to bring the feature values closer to a Gaussian distribution, -compensating for LSA's erroneous assumptions about textual data. + .. note:: + Most treatments of LSA in the natural language processing (NLP) + and information retrieval (IR) literature + swap the axes of the matrix :math:`X` so that it has shape + ``(n_features, n_samples)``. + We present LSA in a different way that matches the scikit-learn API better, + but the singular values found are the same. -|details-end| + While the :class:`TruncatedSVD` transformer + works with any feature matrix, + using it on tf-idf matrices is recommended over raw frequency counts + in an LSA/document processing setting. + In particular, sublinear scaling and inverse document frequency + should be turned on (``sublinear_tf=True, use_idf=True``) + to bring the feature values closer to a Gaussian distribution, + compensating for LSA's erroneous assumptions about textual data. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py` +* :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py` -.. topic:: References: +.. rubric:: References - * Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze (2008), - *Introduction to Information Retrieval*, Cambridge University Press, - chapter 18: `Matrix decompositions & latent semantic indexing - `_ +* Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze (2008), + *Introduction to Information Retrieval*, Cambridge University Press, + chapter 18: `Matrix decompositions & latent semantic indexing + `_ @@ -511,9 +500,9 @@ the split code is filled with the negative part of the code vector, only with a positive sign. Therefore, the split_code is non-negative. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_decomposition_plot_sparse_coding.py` +* :ref:`sphx_glr_auto_examples_decomposition_plot_sparse_coding.py` Generic dictionary learning @@ -593,16 +582,16 @@ extracted from part of the image of a raccoon face looks like. :scale: 50% -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_decomposition_plot_image_denoising.py` +* :ref:`sphx_glr_auto_examples_decomposition_plot_image_denoising.py` -.. topic:: References: +.. rubric:: References - * `"Online dictionary learning for sparse coding" - `_ - J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009 +* `"Online dictionary learning for sparse coding" + `_ + J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009 .. _MiniBatchDictionaryLearning: @@ -733,10 +722,10 @@ Varimax rotation maximizes the sum of the variances of the squared loadings, i.e., it tends to produce sparser factors, which are influenced by only a few features each (the "simple structure"). See e.g., the first example below. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_decomposition_plot_varimax_fa.py` - * :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_fa_model_selection.py` +* :ref:`sphx_glr_auto_examples_decomposition_plot_varimax_fa.py` +* :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_fa_model_selection.py` .. _ICA: @@ -775,11 +764,11 @@ components with some sparsity: .. centered:: |pca_img4| |ica_img4| -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_decomposition_plot_ica_blind_source_separation.py` - * :ref:`sphx_glr_auto_examples_decomposition_plot_ica_vs_pca.py` - * :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py` +* :ref:`sphx_glr_auto_examples_decomposition_plot_ica_blind_source_separation.py` +* :ref:`sphx_glr_auto_examples_decomposition_plot_ica_vs_pca.py` +* :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py` .. _NMF: @@ -902,24 +891,20 @@ Note that this definition is not valid if :math:`\beta \in (0; 1)`, yet it can be continuously extended to the definitions of :math:`d_{KL}` and :math:`d_{IS}` respectively. -|details-start| -**NMF implemented solvers** -|details-split| - -:class:`NMF` implements two solvers, using Coordinate Descent ('cd') [5]_, and -Multiplicative Update ('mu') [6]_. The 'mu' solver can optimize every -beta-divergence, including of course the Frobenius norm (:math:`\beta=2`), the -(generalized) Kullback-Leibler divergence (:math:`\beta=1`) and the -Itakura-Saito divergence (:math:`\beta=0`). Note that for -:math:`\beta \in (1; 2)`, the 'mu' solver is significantly faster than for other -values of :math:`\beta`. Note also that with a negative (or 0, i.e. -'itakura-saito') :math:`\beta`, the input matrix cannot contain zero values. +.. dropdown:: NMF implemented solvers -The 'cd' solver can only optimize the Frobenius norm. Due to the -underlying non-convexity of NMF, the different solvers may converge to -different minima, even when optimizing the same distance function. + :class:`NMF` implements two solvers, using Coordinate Descent ('cd') [5]_, and + Multiplicative Update ('mu') [6]_. The 'mu' solver can optimize every + beta-divergence, including of course the Frobenius norm (:math:`\beta=2`), the + (generalized) Kullback-Leibler divergence (:math:`\beta=1`) and the + Itakura-Saito divergence (:math:`\beta=0`). Note that for + :math:`\beta \in (1; 2)`, the 'mu' solver is significantly faster than for other + values of :math:`\beta`. Note also that with a negative (or 0, i.e. + 'itakura-saito') :math:`\beta`, the input matrix cannot contain zero values. -|details-end| + The 'cd' solver can only optimize the Frobenius norm. Due to the + underlying non-convexity of NMF, the different solvers may converge to + different minima, even when optimizing the same distance function. NMF is best used with the ``fit_transform`` method, which returns the matrix W. The matrix H is stored into the fitted model in the ``components_`` attribute; @@ -937,10 +922,10 @@ stored components:: -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py` - * :ref:`sphx_glr_auto_examples_applications_plot_topics_extraction_with_nmf_lda.py` +* :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py` +* :ref:`sphx_glr_auto_examples_applications_plot_topics_extraction_with_nmf_lda.py` .. _MiniBatchNMF: @@ -965,33 +950,33 @@ The estimator also implements ``partial_fit``, which updates ``H`` by iterating only once over a mini-batch. This can be used for online learning when the data is not readily available from the start, or when the data does not fit into memory. -.. topic:: References: +.. rubric:: References - .. [1] `"Learning the parts of objects by non-negative matrix factorization" - `_ - D. Lee, S. Seung, 1999 +.. [1] `"Learning the parts of objects by non-negative matrix factorization" + `_ + D. Lee, S. Seung, 1999 - .. [2] `"Non-negative Matrix Factorization with Sparseness Constraints" - `_ - P. Hoyer, 2004 +.. [2] `"Non-negative Matrix Factorization with Sparseness Constraints" + `_ + P. Hoyer, 2004 - .. [4] `"SVD based initialization: A head start for nonnegative - matrix factorization" - `_ - C. Boutsidis, E. Gallopoulos, 2008 +.. [4] `"SVD based initialization: A head start for nonnegative + matrix factorization" + `_ + C. Boutsidis, E. Gallopoulos, 2008 - .. [5] `"Fast local algorithms for large scale nonnegative matrix and tensor - factorizations." - `_ - A. Cichocki, A. Phan, 2009 +.. [5] `"Fast local algorithms for large scale nonnegative matrix and tensor + factorizations." + `_ + A. Cichocki, A. Phan, 2009 - .. [6] :arxiv:`"Algorithms for nonnegative matrix factorization with - the beta-divergence" <1010.1763>` - C. Fevotte, J. Idier, 2011 +.. [6] :arxiv:`"Algorithms for nonnegative matrix factorization with + the beta-divergence" <1010.1763>` + C. Fevotte, J. Idier, 2011 - .. [7] :arxiv:`"Online algorithms for nonnegative matrix factorization with the - Itakura-Saito divergence" <1106.4198>` - A. Lefevre, F. Bach, C. Fevotte, 2011 +.. [7] :arxiv:`"Online algorithms for nonnegative matrix factorization with the + Itakura-Saito divergence" <1106.4198>` + A. Lefevre, F. Bach, C. Fevotte, 2011 .. _LatentDirichletAllocation: @@ -1023,51 +1008,48 @@ of topics in the corpus and the distribution of words in the documents. The goal of LDA is to use the observed words to infer the hidden topic structure. -|details-start| -**Details on modeling text corpora** -|details-split| +.. dropdown:: Details on modeling text corpora -When modeling text corpora, the model assumes the following generative process -for a corpus with :math:`D` documents and :math:`K` topics, with :math:`K` -corresponding to `n_components` in the API: + When modeling text corpora, the model assumes the following generative process + for a corpus with :math:`D` documents and :math:`K` topics, with :math:`K` + corresponding to `n_components` in the API: -1. For each topic :math:`k \in K`, draw :math:`\beta_k \sim - \mathrm{Dirichlet}(\eta)`. This provides a distribution over the words, - i.e. the probability of a word appearing in topic :math:`k`. - :math:`\eta` corresponds to `topic_word_prior`. + 1. For each topic :math:`k \in K`, draw :math:`\beta_k \sim + \mathrm{Dirichlet}(\eta)`. This provides a distribution over the words, + i.e. the probability of a word appearing in topic :math:`k`. + :math:`\eta` corresponds to `topic_word_prior`. -2. For each document :math:`d \in D`, draw the topic proportions - :math:`\theta_d \sim \mathrm{Dirichlet}(\alpha)`. :math:`\alpha` - corresponds to `doc_topic_prior`. + 2. For each document :math:`d \in D`, draw the topic proportions + :math:`\theta_d \sim \mathrm{Dirichlet}(\alpha)`. :math:`\alpha` + corresponds to `doc_topic_prior`. -3. For each word :math:`i` in document :math:`d`: + 3. For each word :math:`i` in document :math:`d`: - a. Draw the topic assignment :math:`z_{di} \sim \mathrm{Multinomial} - (\theta_d)` - b. Draw the observed word :math:`w_{ij} \sim \mathrm{Multinomial} - (\beta_{z_{di}})` + a. Draw the topic assignment :math:`z_{di} \sim \mathrm{Multinomial} + (\theta_d)` + b. Draw the observed word :math:`w_{ij} \sim \mathrm{Multinomial} + (\beta_{z_{di}})` -For parameter estimation, the posterior distribution is: + For parameter estimation, the posterior distribution is: -.. math:: - p(z, \theta, \beta |w, \alpha, \eta) = - \frac{p(z, \theta, \beta|\alpha, \eta)}{p(w|\alpha, \eta)} + .. math:: + p(z, \theta, \beta |w, \alpha, \eta) = + \frac{p(z, \theta, \beta|\alpha, \eta)}{p(w|\alpha, \eta)} -Since the posterior is intractable, variational Bayesian method -uses a simpler distribution :math:`q(z,\theta,\beta | \lambda, \phi, \gamma)` -to approximate it, and those variational parameters :math:`\lambda`, -:math:`\phi`, :math:`\gamma` are optimized to maximize the Evidence -Lower Bound (ELBO): + Since the posterior is intractable, variational Bayesian method + uses a simpler distribution :math:`q(z,\theta,\beta | \lambda, \phi, \gamma)` + to approximate it, and those variational parameters :math:`\lambda`, + :math:`\phi`, :math:`\gamma` are optimized to maximize the Evidence + Lower Bound (ELBO): -.. math:: - \log\: P(w | \alpha, \eta) \geq L(w,\phi,\gamma,\lambda) \overset{\triangle}{=} - E_{q}[\log\:p(w,z,\theta,\beta|\alpha,\eta)] - E_{q}[\log\:q(z, \theta, \beta)] + .. math:: + \log\: P(w | \alpha, \eta) \geq L(w,\phi,\gamma,\lambda) \overset{\triangle}{=} + E_{q}[\log\:p(w,z,\theta,\beta|\alpha,\eta)] - E_{q}[\log\:q(z, \theta, \beta)] -Maximizing ELBO is equivalent to minimizing the Kullback-Leibler(KL) divergence -between :math:`q(z,\theta,\beta)` and the true posterior -:math:`p(z, \theta, \beta |w, \alpha, \eta)`. + Maximizing ELBO is equivalent to minimizing the Kullback-Leibler(KL) divergence + between :math:`q(z,\theta,\beta)` and the true posterior + :math:`p(z, \theta, \beta |w, \alpha, \eta)`. -|details-end| :class:`LatentDirichletAllocation` implements the online variational Bayes algorithm and supports both online and batch update methods. @@ -1089,27 +1071,27 @@ can be calculated from ``transform`` method. :class:`LatentDirichletAllocation` also implements ``partial_fit`` method. This is used when data can be fetched sequentially. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_applications_plot_topics_extraction_with_nmf_lda.py` +* :ref:`sphx_glr_auto_examples_applications_plot_topics_extraction_with_nmf_lda.py` -.. topic:: References: +.. rubric:: References - * `"Latent Dirichlet Allocation" - `_ - D. Blei, A. Ng, M. Jordan, 2003 +* `"Latent Dirichlet Allocation" + `_ + D. Blei, A. Ng, M. Jordan, 2003 - * `"Online Learning for Latent Dirichlet Allocation” - `_ - M. Hoffman, D. Blei, F. Bach, 2010 +* `"Online Learning for Latent Dirichlet Allocation” + `_ + M. Hoffman, D. Blei, F. Bach, 2010 - * `"Stochastic Variational Inference" - `_ - M. Hoffman, D. Blei, C. Wang, J. Paisley, 2013 +* `"Stochastic Variational Inference" + `_ + M. Hoffman, D. Blei, C. Wang, J. Paisley, 2013 - * `"The varimax criterion for analytic rotation in factor analysis" - `_ - H. F. Kaiser, 1958 +* `"The varimax criterion for analytic rotation in factor analysis" + `_ + H. F. Kaiser, 1958 See also :ref:`nca_dim_reduction` for dimensionality reduction with Neighborhood Components Analysis. diff --git a/doc/modules/density.rst b/doc/modules/density.rst index 5a9b456010aa3..39264f226185d 100644 --- a/doc/modules/density.rst +++ b/doc/modules/density.rst @@ -113,37 +113,34 @@ forms, which are shown in the following figure: .. centered:: |kde_kernels| -|details-start| -**kernels' mathematical expressions** -|details-split| +.. dropdown:: Kernels' mathematical expressions -The form of these kernels is as follows: + The form of these kernels is as follows: -* Gaussian kernel (``kernel = 'gaussian'``) + * Gaussian kernel (``kernel = 'gaussian'``) - :math:`K(x; h) \propto \exp(- \frac{x^2}{2h^2} )` + :math:`K(x; h) \propto \exp(- \frac{x^2}{2h^2} )` -* Tophat kernel (``kernel = 'tophat'``) + * Tophat kernel (``kernel = 'tophat'``) - :math:`K(x; h) \propto 1` if :math:`x < h` + :math:`K(x; h) \propto 1` if :math:`x < h` -* Epanechnikov kernel (``kernel = 'epanechnikov'``) + * Epanechnikov kernel (``kernel = 'epanechnikov'``) - :math:`K(x; h) \propto 1 - \frac{x^2}{h^2}` + :math:`K(x; h) \propto 1 - \frac{x^2}{h^2}` -* Exponential kernel (``kernel = 'exponential'``) + * Exponential kernel (``kernel = 'exponential'``) - :math:`K(x; h) \propto \exp(-x/h)` + :math:`K(x; h) \propto \exp(-x/h)` -* Linear kernel (``kernel = 'linear'``) + * Linear kernel (``kernel = 'linear'``) - :math:`K(x; h) \propto 1 - x/h` if :math:`x < h` + :math:`K(x; h) \propto 1 - x/h` if :math:`x < h` -* Cosine kernel (``kernel = 'cosine'``) + * Cosine kernel (``kernel = 'cosine'``) - :math:`K(x; h) \propto \cos(\frac{\pi x}{2h})` if :math:`x < h` + :math:`K(x; h) \propto \cos(\frac{\pi x}{2h})` if :math:`x < h` -|details-end| The kernel density estimator can be used with any of the valid distance metrics (see :class:`~sklearn.metrics.DistanceMetric` for a list of @@ -177,14 +174,14 @@ on a PCA projection of the data: The "new" data consists of linear combinations of the input data, with weights probabilistically drawn given the KDE model. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_neighbors_plot_kde_1d.py`: computation of simple kernel - density estimates in one dimension. +* :ref:`sphx_glr_auto_examples_neighbors_plot_kde_1d.py`: computation of simple kernel + density estimates in one dimension. - * :ref:`sphx_glr_auto_examples_neighbors_plot_digits_kde_sampling.py`: an example of using - Kernel Density estimation to learn a generative model of the hand-written - digits data, and drawing new samples from this model. +* :ref:`sphx_glr_auto_examples_neighbors_plot_digits_kde_sampling.py`: an example of using + Kernel Density estimation to learn a generative model of the hand-written + digits data, and drawing new samples from this model. - * :ref:`sphx_glr_auto_examples_neighbors_plot_species_kde.py`: an example of Kernel Density - estimation using the Haversine distance metric to visualize geospatial data +* :ref:`sphx_glr_auto_examples_neighbors_plot_species_kde.py`: an example of Kernel Density + estimation using the Haversine distance metric to visualize geospatial data diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst index 9120bd855fd01..58ac09583ea6c 100644 --- a/doc/modules/ensemble.rst +++ b/doc/modules/ensemble.rst @@ -18,10 +18,6 @@ trees, in averaging methods such as :ref:`Bagging methods `, :ref:`model stacking `, or :ref:`Voting `, or in boosting, as :ref:`AdaBoost `. -.. contents:: - :local: - :depth: 1 - .. _gradient_boosting: Gradient-boosted trees @@ -78,10 +74,10 @@ estimators is slightly different, and some of the features from :class:`GradientBoostingClassifier` and :class:`GradientBoostingRegressor` are not yet supported, for instance some loss functions. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py` - * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py` +* :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py` Usage ^^^^^ @@ -126,43 +122,40 @@ in [XGBoost]_): \mathcal{L}(\phi) = \sum_i l(\hat{y}_i, y_i) + \frac12 \sum_k \lambda ||w_k||^2 -|details-start| -**Details on l2 regularization**: -|details-split| - -It is important to notice that the loss term :math:`l(\hat{y}_i, y_i)` describes -only half of the actual loss function except for the pinball loss and absolute -error. - -The index :math:`k` refers to the k-th tree in the ensemble of trees. In the -case of regression and binary classification, gradient boosting models grow one -tree per iteration, then :math:`k` runs up to `max_iter`. In the case of -multiclass classification problems, the maximal value of the index :math:`k` is -`n_classes` :math:`\times` `max_iter`. - -If :math:`T_k` denotes the number of leaves in the k-th tree, then :math:`w_k` -is a vector of length :math:`T_k`, which contains the leaf values of the form `w -= -sum_gradient / (sum_hessian + l2_regularization)` (see equation (5) in -[XGBoost]_). - -The leaf values :math:`w_k` are derived by dividing the sum of the gradients of -the loss function by the combined sum of hessians. Adding the regularization to -the denominator penalizes the leaves with small hessians (flat regions), -resulting in smaller updates. Those :math:`w_k` values contribute then to the -model's prediction for a given input that ends up in the corresponding leaf. The -final prediction is the sum of the base prediction and the contributions from -each tree. The result of that sum is then transformed by the inverse link -function depending on the choice of the loss function (see -:ref:`gradient_boosting_formulation`). - -Notice that the original paper [XGBoost]_ introduces a term :math:`\gamma\sum_k -T_k` that penalizes the number of leaves (making it a smooth version of -`max_leaf_nodes`) not presented here as it is not implemented in scikit-learn; -whereas :math:`\lambda` penalizes the magnitude of the individual tree -predictions before being rescaled by the learning rate, see -:ref:`gradient_boosting_shrinkage`. - -|details-end| +.. dropdown:: Details on l2 regularization + + It is important to notice that the loss term :math:`l(\hat{y}_i, y_i)` describes + only half of the actual loss function except for the pinball loss and absolute + error. + + The index :math:`k` refers to the k-th tree in the ensemble of trees. In the + case of regression and binary classification, gradient boosting models grow one + tree per iteration, then :math:`k` runs up to `max_iter`. In the case of + multiclass classification problems, the maximal value of the index :math:`k` is + `n_classes` :math:`\times` `max_iter`. + + If :math:`T_k` denotes the number of leaves in the k-th tree, then :math:`w_k` + is a vector of length :math:`T_k`, which contains the leaf values of the form `w + = -sum_gradient / (sum_hessian + l2_regularization)` (see equation (5) in + [XGBoost]_). + + The leaf values :math:`w_k` are derived by dividing the sum of the gradients of + the loss function by the combined sum of hessians. Adding the regularization to + the denominator penalizes the leaves with small hessians (flat regions), + resulting in smaller updates. Those :math:`w_k` values contribute then to the + model's prediction for a given input that ends up in the corresponding leaf. The + final prediction is the sum of the base prediction and the contributions from + each tree. The result of that sum is then transformed by the inverse link + function depending on the choice of the loss function (see + :ref:`gradient_boosting_formulation`). + + Notice that the original paper [XGBoost]_ introduces a term :math:`\gamma\sum_k + T_k` that penalizes the number of leaves (making it a smooth version of + `max_leaf_nodes`) not presented here as it is not implemented in scikit-learn; + whereas :math:`\lambda` penalizes the magnitude of the individual tree + predictions before being rescaled by the learning rate, see + :ref:`gradient_boosting_shrinkage`. + Note that **early-stopping is enabled by default if the number of samples is larger than 10,000**. The early-stopping behaviour is controlled via the @@ -213,9 +206,9 @@ If no missing values were encountered for a given feature during training, then samples with missing values are mapped to whichever child has the most samples. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` .. _sw_hgbdt: @@ -302,30 +295,25 @@ the most samples (just like for continuous features). When predicting, categories that were not seen during fit time will be treated as missing values. -|details-start| -**Split finding with categorical features**: -|details-split| +.. dropdown:: Split finding with categorical features -The canonical way of considering -categorical splits in a tree is to consider -all of the :math:`2^{K - 1} - 1` partitions, where :math:`K` is the number of -categories. This can quickly become prohibitive when :math:`K` is large. -Fortunately, since gradient boosting trees are always regression trees (even -for classification problems), there exist a faster strategy that can yield -equivalent splits. First, the categories of a feature are sorted according to -the variance of the target, for each category `k`. Once the categories are -sorted, one can consider *continuous partitions*, i.e. treat the categories -as if they were ordered continuous values (see Fisher [Fisher1958]_ for a -formal proof). As a result, only :math:`K - 1` splits need to be considered -instead of :math:`2^{K - 1} - 1`. The initial sorting is a -:math:`\mathcal{O}(K \log(K))` operation, leading to a total complexity of -:math:`\mathcal{O}(K \log(K) + K)`, instead of :math:`\mathcal{O}(2^K)`. + The canonical way of considering categorical splits in a tree is to consider + all of the :math:`2^{K - 1} - 1` partitions, where :math:`K` is the number of + categories. This can quickly become prohibitive when :math:`K` is large. + Fortunately, since gradient boosting trees are always regression trees (even + for classification problems), there exist a faster strategy that can yield + equivalent splits. First, the categories of a feature are sorted according to + the variance of the target, for each category `k`. Once the categories are + sorted, one can consider *continuous partitions*, i.e. treat the categories + as if they were ordered continuous values (see Fisher [Fisher1958]_ for a + formal proof). As a result, only :math:`K - 1` splits need to be considered + instead of :math:`2^{K - 1} - 1`. The initial sorting is a + :math:`\mathcal{O}(K \log(K))` operation, leading to a total complexity of + :math:`\mathcal{O}(K \log(K) + K)`, instead of :math:`\mathcal{O}(2^K)`. -|details-end| +.. rubric:: Examples -.. topic:: Examples: - - * :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py` .. _monotonic_cst_gbdt: @@ -378,10 +366,10 @@ Also, monotonic constraints are not supported for multiclass classification. Since categories are unordered quantities, it is not possible to enforce monotonic constraints on categorical features. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_ensemble_plot_monotonic_constraints.py` - * :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_monotonic_constraints.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` .. _interaction_cst_hgbt: @@ -414,16 +402,16 @@ Note that features not listed in ``interaction_cst`` are automatically assigned an interaction group for themselves. With again 3 features, this means that ``[{0}]`` is equivalent to ``[{0}, {1, 2}]``. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py` +* :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py` -.. topic:: References +.. rubric:: References - .. [Mayer2022] M. Mayer, S.C. Bourassa, M. Hoesli, and D.F. Scognamiglio. - 2022. :doi:`Machine Learning Applications to Land and Structure Valuation - <10.3390/jrfm15050193>`. - Journal of Risk and Financial Management 15, no. 5: 193 +.. [Mayer2022] M. Mayer, S.C. Bourassa, M. Hoesli, and D.F. Scognamiglio. + 2022. :doi:`Machine Learning Applications to Land and Structure Valuation + <10.3390/jrfm15050193>`. + Journal of Risk and Financial Management 15, no. 5: 193 Low-level parallelism ^^^^^^^^^^^^^^^^^^^^^ @@ -479,18 +467,18 @@ Finally, many parts of the implementation of :class:`HistGradientBoostingClassifier` and :class:`HistGradientBoostingRegressor` are parallelized. -.. topic:: References +.. rubric:: References - .. [XGBoost] Tianqi Chen, Carlos Guestrin, :arxiv:`"XGBoost: A Scalable Tree - Boosting System" <1603.02754>` +.. [XGBoost] Tianqi Chen, Carlos Guestrin, :arxiv:`"XGBoost: A Scalable Tree + Boosting System" <1603.02754>` - .. [LightGBM] Ke et. al. `"LightGBM: A Highly Efficient Gradient - BoostingDecision Tree" `_ +.. [LightGBM] Ke et. al. `"LightGBM: A Highly Efficient Gradient + BoostingDecision Tree" `_ - .. [Fisher1958] Fisher, W.D. (1958). `"On Grouping for Maximum Homogeneity" - `_ - Journal of the American Statistical Association, 53, 789-798. +.. [Fisher1958] Fisher, W.D. (1958). `"On Grouping for Maximum Homogeneity" + `_ + Journal of the American Statistical Association, 53, 789-798. @@ -501,96 +489,88 @@ The usage and the parameters of :class:`GradientBoostingClassifier` and :class:`GradientBoostingRegressor` are described below. The 2 most important parameters of these estimators are `n_estimators` and `learning_rate`. -|details-start| -**Classification** -|details-split| - -:class:`GradientBoostingClassifier` supports both binary and multi-class -classification. -The following example shows how to fit a gradient boosting classifier -with 100 decision stumps as weak learners:: - - >>> from sklearn.datasets import make_hastie_10_2 - >>> from sklearn.ensemble import GradientBoostingClassifier - - >>> X, y = make_hastie_10_2(random_state=0) - >>> X_train, X_test = X[:2000], X[2000:] - >>> y_train, y_test = y[:2000], y[2000:] - - >>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, - ... max_depth=1, random_state=0).fit(X_train, y_train) - >>> clf.score(X_test, y_test) - 0.913... - -The number of weak learners (i.e. regression trees) is controlled by the -parameter ``n_estimators``; :ref:`The size of each tree -` can be controlled either by setting the tree -depth via ``max_depth`` or by setting the number of leaf nodes via -``max_leaf_nodes``. The ``learning_rate`` is a hyper-parameter in the range -(0.0, 1.0] that controls overfitting via :ref:`shrinkage -` . - -.. note:: - - Classification with more than 2 classes requires the induction - of ``n_classes`` regression trees at each iteration, - thus, the total number of induced trees equals - ``n_classes * n_estimators``. For datasets with a large number - of classes we strongly recommend to use - :class:`HistGradientBoostingClassifier` as an alternative to - :class:`GradientBoostingClassifier` . - -|details-end| - -|details-start| -**Regression** -|details-split| - -:class:`GradientBoostingRegressor` supports a number of -:ref:`different loss functions ` -for regression which can be specified via the argument -``loss``; the default loss function for regression is squared error -(``'squared_error'``). - -:: - - >>> import numpy as np - >>> from sklearn.metrics import mean_squared_error - >>> from sklearn.datasets import make_friedman1 - >>> from sklearn.ensemble import GradientBoostingRegressor - - >>> X, y = make_friedman1(n_samples=1200, random_state=0, noise=1.0) - >>> X_train, X_test = X[:200], X[200:] - >>> y_train, y_test = y[:200], y[200:] - >>> est = GradientBoostingRegressor( - ... n_estimators=100, learning_rate=0.1, max_depth=1, random_state=0, - ... loss='squared_error' - ... ).fit(X_train, y_train) - >>> mean_squared_error(y_test, est.predict(X_test)) - 5.00... - -The figure below shows the results of applying :class:`GradientBoostingRegressor` -with least squares loss and 500 base learners to the diabetes dataset -(:func:`sklearn.datasets.load_diabetes`). -The plot shows the train and test error at each iteration. -The train error at each iteration is stored in the -`train_score_` attribute of the gradient boosting model. -The test error at each iterations can be obtained -via the :meth:`~GradientBoostingRegressor.staged_predict` method which returns a -generator that yields the predictions at each stage. Plots like these can be used -to determine the optimal number of trees (i.e. ``n_estimators``) by early stopping. - -.. figure:: ../auto_examples/ensemble/images/sphx_glr_plot_gradient_boosting_regression_001.png - :target: ../auto_examples/ensemble/plot_gradient_boosting_regression.html - :align: center - :scale: 75 - -|details-end| +.. dropdown:: Classification + + :class:`GradientBoostingClassifier` supports both binary and multi-class + classification. + The following example shows how to fit a gradient boosting classifier + with 100 decision stumps as weak learners:: + + >>> from sklearn.datasets import make_hastie_10_2 + >>> from sklearn.ensemble import GradientBoostingClassifier + + >>> X, y = make_hastie_10_2(random_state=0) + >>> X_train, X_test = X[:2000], X[2000:] + >>> y_train, y_test = y[:2000], y[2000:] + + >>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, + ... max_depth=1, random_state=0).fit(X_train, y_train) + >>> clf.score(X_test, y_test) + 0.913... + + The number of weak learners (i.e. regression trees) is controlled by the + parameter ``n_estimators``; :ref:`The size of each tree + ` can be controlled either by setting the tree + depth via ``max_depth`` or by setting the number of leaf nodes via + ``max_leaf_nodes``. The ``learning_rate`` is a hyper-parameter in the range + (0.0, 1.0] that controls overfitting via :ref:`shrinkage + ` . + + .. note:: + + Classification with more than 2 classes requires the induction + of ``n_classes`` regression trees at each iteration, + thus, the total number of induced trees equals + ``n_classes * n_estimators``. For datasets with a large number + of classes we strongly recommend to use + :class:`HistGradientBoostingClassifier` as an alternative to + :class:`GradientBoostingClassifier` . + +.. dropdown:: Regression + + :class:`GradientBoostingRegressor` supports a number of + :ref:`different loss functions ` + for regression which can be specified via the argument + ``loss``; the default loss function for regression is squared error + (``'squared_error'``). + + :: + + >>> import numpy as np + >>> from sklearn.metrics import mean_squared_error + >>> from sklearn.datasets import make_friedman1 + >>> from sklearn.ensemble import GradientBoostingRegressor + + >>> X, y = make_friedman1(n_samples=1200, random_state=0, noise=1.0) + >>> X_train, X_test = X[:200], X[200:] + >>> y_train, y_test = y[:200], y[200:] + >>> est = GradientBoostingRegressor( + ... n_estimators=100, learning_rate=0.1, max_depth=1, random_state=0, + ... loss='squared_error' + ... ).fit(X_train, y_train) + >>> mean_squared_error(y_test, est.predict(X_test)) + 5.00... + + The figure below shows the results of applying :class:`GradientBoostingRegressor` + with least squares loss and 500 base learners to the diabetes dataset + (:func:`sklearn.datasets.load_diabetes`). + The plot shows the train and test error at each iteration. + The train error at each iteration is stored in the + `train_score_` attribute of the gradient boosting model. + The test error at each iterations can be obtained + via the :meth:`~GradientBoostingRegressor.staged_predict` method which returns a + generator that yields the predictions at each stage. Plots like these can be used + to determine the optimal number of trees (i.e. ``n_estimators``) by early stopping. + + .. figure:: ../auto_examples/ensemble/images/sphx_glr_plot_gradient_boosting_regression_001.png + :target: ../auto_examples/ensemble/plot_gradient_boosting_regression.html + :align: center + :scale: 75 -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py` - * :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_oob.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_oob.py` .. _gradient_boosting_warm_start: @@ -660,116 +640,108 @@ Mathematical formulation We first present GBRT for regression, and then detail the classification case. -|details-start| -**Regression** -|details-split| +.. dropdown:: Regression -GBRT regressors are additive models whose prediction :math:`\hat{y}_i` for a -given input :math:`x_i` is of the following form: + GBRT regressors are additive models whose prediction :math:`\hat{y}_i` for a + given input :math:`x_i` is of the following form: -.. math:: - - \hat{y}_i = F_M(x_i) = \sum_{m=1}^{M} h_m(x_i) - -where the :math:`h_m` are estimators called *weak learners* in the context -of boosting. Gradient Tree Boosting uses :ref:`decision tree regressors -` of fixed size as weak learners. The constant M corresponds to the -`n_estimators` parameter. + .. math:: -Similar to other boosting algorithms, a GBRT is built in a greedy fashion: + \hat{y}_i = F_M(x_i) = \sum_{m=1}^{M} h_m(x_i) -.. math:: + where the :math:`h_m` are estimators called *weak learners* in the context + of boosting. Gradient Tree Boosting uses :ref:`decision tree regressors + ` of fixed size as weak learners. The constant M corresponds to the + `n_estimators` parameter. - F_m(x) = F_{m-1}(x) + h_m(x), + Similar to other boosting algorithms, a GBRT is built in a greedy fashion: -where the newly added tree :math:`h_m` is fitted in order to minimize a sum -of losses :math:`L_m`, given the previous ensemble :math:`F_{m-1}`: + .. math:: -.. math:: + F_m(x) = F_{m-1}(x) + h_m(x), - h_m = \arg\min_{h} L_m = \arg\min_{h} \sum_{i=1}^{n} - l(y_i, F_{m-1}(x_i) + h(x_i)), + where the newly added tree :math:`h_m` is fitted in order to minimize a sum + of losses :math:`L_m`, given the previous ensemble :math:`F_{m-1}`: -where :math:`l(y_i, F(x_i))` is defined by the `loss` parameter, detailed -in the next section. + .. math:: -By default, the initial model :math:`F_{0}` is chosen as the constant that -minimizes the loss: for a least-squares loss, this is the empirical mean of -the target values. The initial model can also be specified via the ``init`` -argument. + h_m = \arg\min_{h} L_m = \arg\min_{h} \sum_{i=1}^{n} + l(y_i, F_{m-1}(x_i) + h(x_i)), -Using a first-order Taylor approximation, the value of :math:`l` can be -approximated as follows: + where :math:`l(y_i, F(x_i))` is defined by the `loss` parameter, detailed + in the next section. -.. math:: + By default, the initial model :math:`F_{0}` is chosen as the constant that + minimizes the loss: for a least-squares loss, this is the empirical mean of + the target values. The initial model can also be specified via the ``init`` + argument. - l(y_i, F_{m-1}(x_i) + h_m(x_i)) \approx - l(y_i, F_{m-1}(x_i)) - + h_m(x_i) - \left[ \frac{\partial l(y_i, F(x_i))}{\partial F(x_i)} \right]_{F=F_{m - 1}}. + Using a first-order Taylor approximation, the value of :math:`l` can be + approximated as follows: -.. note:: + .. math:: - Briefly, a first-order Taylor approximation says that - :math:`l(z) \approx l(a) + (z - a) \frac{\partial l}{\partial z}(a)`. - Here, :math:`z` corresponds to :math:`F_{m - 1}(x_i) + h_m(x_i)`, and - :math:`a` corresponds to :math:`F_{m-1}(x_i)` + l(y_i, F_{m-1}(x_i) + h_m(x_i)) \approx + l(y_i, F_{m-1}(x_i)) + + h_m(x_i) + \left[ \frac{\partial l(y_i, F(x_i))}{\partial F(x_i)} \right]_{F=F_{m - 1}}. -The quantity :math:`\left[ \frac{\partial l(y_i, F(x_i))}{\partial F(x_i)} -\right]_{F=F_{m - 1}}` is the derivative of the loss with respect to its -second parameter, evaluated at :math:`F_{m-1}(x)`. It is easy to compute for -any given :math:`F_{m - 1}(x_i)` in a closed form since the loss is -differentiable. We will denote it by :math:`g_i`. + .. note:: -Removing the constant terms, we have: + Briefly, a first-order Taylor approximation says that + :math:`l(z) \approx l(a) + (z - a) \frac{\partial l}{\partial z}(a)`. + Here, :math:`z` corresponds to :math:`F_{m - 1}(x_i) + h_m(x_i)`, and + :math:`a` corresponds to :math:`F_{m-1}(x_i)` -.. math:: + The quantity :math:`\left[ \frac{\partial l(y_i, F(x_i))}{\partial F(x_i)} + \right]_{F=F_{m - 1}}` is the derivative of the loss with respect to its + second parameter, evaluated at :math:`F_{m-1}(x)`. It is easy to compute for + any given :math:`F_{m - 1}(x_i)` in a closed form since the loss is + differentiable. We will denote it by :math:`g_i`. - h_m \approx \arg\min_{h} \sum_{i=1}^{n} h(x_i) g_i + Removing the constant terms, we have: -This is minimized if :math:`h(x_i)` is fitted to predict a value that is -proportional to the negative gradient :math:`-g_i`. Therefore, at each -iteration, **the estimator** :math:`h_m` **is fitted to predict the negative -gradients of the samples**. The gradients are updated at each iteration. -This can be considered as some kind of gradient descent in a functional -space. + .. math:: -.. note:: + h_m \approx \arg\min_{h} \sum_{i=1}^{n} h(x_i) g_i - For some losses, e.g. ``'absolute_error'`` where the gradients - are :math:`\pm 1`, the values predicted by a fitted :math:`h_m` are not - accurate enough: the tree can only output integer values. As a result, the - leaves values of the tree :math:`h_m` are modified once the tree is - fitted, such that the leaves values minimize the loss :math:`L_m`. The - update is loss-dependent: for the absolute error loss, the value of - a leaf is updated to the median of the samples in that leaf. + This is minimized if :math:`h(x_i)` is fitted to predict a value that is + proportional to the negative gradient :math:`-g_i`. Therefore, at each + iteration, **the estimator** :math:`h_m` **is fitted to predict the negative + gradients of the samples**. The gradients are updated at each iteration. + This can be considered as some kind of gradient descent in a functional + space. -|details-end| + .. note:: -|details-start| -**Classification** -|details-split| + For some losses, e.g. ``'absolute_error'`` where the gradients + are :math:`\pm 1`, the values predicted by a fitted :math:`h_m` are not + accurate enough: the tree can only output integer values. As a result, the + leaves values of the tree :math:`h_m` are modified once the tree is + fitted, such that the leaves values minimize the loss :math:`L_m`. The + update is loss-dependent: for the absolute error loss, the value of + a leaf is updated to the median of the samples in that leaf. -Gradient boosting for classification is very similar to the regression case. -However, the sum of the trees :math:`F_M(x_i) = \sum_m h_m(x_i)` is not -homogeneous to a prediction: it cannot be a class, since the trees predict -continuous values. +.. dropdown:: Classification -The mapping from the value :math:`F_M(x_i)` to a class or a probability is -loss-dependent. For the log-loss, the probability that -:math:`x_i` belongs to the positive class is modeled as :math:`p(y_i = 1 | -x_i) = \sigma(F_M(x_i))` where :math:`\sigma` is the sigmoid or expit function. + Gradient boosting for classification is very similar to the regression case. + However, the sum of the trees :math:`F_M(x_i) = \sum_m h_m(x_i)` is not + homogeneous to a prediction: it cannot be a class, since the trees predict + continuous values. -For multiclass classification, K trees (for K classes) are built at each of -the :math:`M` iterations. The probability that :math:`x_i` belongs to class -k is modeled as a softmax of the :math:`F_{M,k}(x_i)` values. + The mapping from the value :math:`F_M(x_i)` to a class or a probability is + loss-dependent. For the log-loss, the probability that + :math:`x_i` belongs to the positive class is modeled as :math:`p(y_i = 1 | + x_i) = \sigma(F_M(x_i))` where :math:`\sigma` is the sigmoid or expit function. -Note that even for a classification task, the :math:`h_m` sub-estimator is -still a regressor, not a classifier. This is because the sub-estimators are -trained to predict (negative) *gradients*, which are always continuous -quantities. + For multiclass classification, K trees (for K classes) are built at each of + the :math:`M` iterations. The probability that :math:`x_i` belongs to class + k is modeled as a softmax of the :math:`F_{M,k}(x_i)` values. -|details-end| + Note that even for a classification task, the :math:`h_m` sub-estimator is + still a regressor, not a classifier. This is because the sub-estimators are + trained to predict (negative) *gradients*, which are always continuous + quantities. .. _gradient_boosting_loss: @@ -779,9 +751,7 @@ Loss Functions The following loss functions are supported and can be specified using the parameter ``loss``: -|details-start| -**Regression** -|details-split| +.. dropdown:: Regression * Squared error (``'squared_error'``): The natural choice for regression due to its superior computational properties. The initial model is @@ -798,12 +768,7 @@ the parameter ``loss``: can be used to create prediction intervals (see :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_quantile.py`). -|details-end| - - -|details-start| -**Classification** -|details-split| +.. dropdown:: Classification * Binary log-loss (``'log-loss'``): The binomial negative log-likelihood loss function for binary classification. It provides @@ -821,8 +786,6 @@ the parameter ``loss``: examples than ``'log-loss'``; can only be used for binary classification. -|details-end| - .. _gradient_boosting_shrinkage: Shrinkage via learning rate @@ -889,11 +852,11 @@ the optimal number of iterations. OOB estimates are usually very pessimistic thu we recommend to use cross-validation instead and only use OOB if cross-validation is too time consuming. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py` - * :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_oob.py` - * :ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_oob.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py` Interpretation with feature importance ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -936,22 +899,22 @@ Note that this computation of feature importance is based on entropy, and it is distinct from :func:`sklearn.inspection.permutation_importance` which is based on permutation of the features. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py` -.. topic:: References +.. rubric:: References - .. [Friedman2001] Friedman, J.H. (2001). :doi:`Greedy function approximation: A gradient - boosting machine <10.1214/aos/1013203451>`. - Annals of Statistics, 29, 1189-1232. +.. [Friedman2001] Friedman, J.H. (2001). :doi:`Greedy function approximation: A gradient + boosting machine <10.1214/aos/1013203451>`. + Annals of Statistics, 29, 1189-1232. - .. [Friedman2002] Friedman, J.H. (2002). `Stochastic gradient boosting. - `_. - Computational Statistics & Data Analysis, 38, 367-378. +.. [Friedman2002] Friedman, J.H. (2002). `Stochastic gradient boosting. + `_. + Computational Statistics & Data Analysis, 38, 367-378. - .. [R2007] G. Ridgeway (2006). `Generalized Boosted Models: A guide to the gbm - package `_ +.. [R2007] G. Ridgeway (2006). `Generalized Boosted Models: A guide to the gbm + package `_ .. _forest: @@ -1035,9 +998,9 @@ characteristics of the dataset and the modeling task. It's a good idea to try both models and compare their performance and computational efficiency on your specific problem to determine which model is the best fit. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py` Extremely Randomized Trees -------------------------- @@ -1134,20 +1097,20 @@ fast). Significant speedup can still be achieved though when building a large number of trees, or when building a single tree requires a fair amount of time (e.g., on large datasets). -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_iris.py` - * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances_faces.py` - * :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_forest_iris.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances_faces.py` +* :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py` -.. topic:: References +.. rubric:: References - .. [B2001] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001. +.. [B2001] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001. - .. [B1998] L. Breiman, "Arcing Classifiers", Annals of Statistics 1998. +.. [B1998] L. Breiman, "Arcing Classifiers", Annals of Statistics 1998. - * P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized - trees", Machine Learning, 63(1), 3-42, 2006. +* P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized + trees", Machine Learning, 63(1), 3-42, 2006. .. _random_forest_feature_importance: @@ -1199,16 +1162,16 @@ In practice those estimates are stored as an attribute named the value, the more important is the contribution of the matching feature to the prediction function. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances_faces.py` - * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances_faces.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances.py` -.. topic:: References +.. rubric:: References - .. [L2014] G. Louppe, :arxiv:`"Understanding Random Forests: From Theory to - Practice" <1407.7502>`, - PhD Thesis, U. of Liege, 2014. +.. [L2014] G. Louppe, :arxiv:`"Understanding Random Forests: From Theory to + Practice" <1407.7502>`, + PhD Thesis, U. of Liege, 2014. .. _random_trees_embedding: @@ -1231,15 +1194,15 @@ As neighboring data points are more likely to lie within the same leaf of a tree, the transformation performs an implicit, non-parametric density estimation. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_ensemble_plot_random_forest_embedding.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_random_forest_embedding.py` - * :ref:`sphx_glr_auto_examples_manifold_plot_lle_digits.py` compares non-linear - dimensionality reduction techniques on handwritten digits. +* :ref:`sphx_glr_auto_examples_manifold_plot_lle_digits.py` compares non-linear + dimensionality reduction techniques on handwritten digits. - * :ref:`sphx_glr_auto_examples_ensemble_plot_feature_transformation.py` compares - supervised and unsupervised tree based feature transformations. +* :ref:`sphx_glr_auto_examples_ensemble_plot_feature_transformation.py` compares + supervised and unsupervised tree based feature transformations. .. seealso:: @@ -1335,24 +1298,23 @@ subsets of 50% of the samples and 50% of the features. >>> bagging = BaggingClassifier(KNeighborsClassifier(), ... max_samples=0.5, max_features=0.5) -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_ensemble_plot_bias_variance.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_bias_variance.py` -.. topic:: References +.. rubric:: References - .. [B1999] L. Breiman, "Pasting small votes for classification in large - databases and on-line", Machine Learning, 36(1), 85-103, 1999. +.. [B1999] L. Breiman, "Pasting small votes for classification in large + databases and on-line", Machine Learning, 36(1), 85-103, 1999. - .. [B1996] L. Breiman, "Bagging predictors", Machine Learning, 24(2), - 123-140, 1996. +.. [B1996] L. Breiman, "Bagging predictors", Machine Learning, 24(2), + 123-140, 1996. - .. [H1998] T. Ho, "The random subspace method for constructing decision - forests", Pattern Analysis and Machine Intelligence, 20(8), 832-844, - 1998. +.. [H1998] T. Ho, "The random subspace method for constructing decision + forests", Pattern Analysis and Machine Intelligence, 20(8), 832-844, 1998. - .. [LG2012] G. Louppe and P. Geurts, "Ensembles on Random Patches", - Machine Learning and Knowledge Discovery in Databases, 346-361, 2012. +.. [LG2012] G. Louppe and P. Geurts, "Ensembles on Random Patches", + Machine Learning and Knowledge Discovery in Databases, 346-361, 2012. @@ -1507,29 +1469,25 @@ Optionally, weights can be provided for the individual classifiers:: ... voting='soft', weights=[2,5,1] ... ) -|details-start| -**Using the `VotingClassifier` with `GridSearchCV`** -|details-split| - -The :class:`VotingClassifier` can also be used together with -:class:`~sklearn.model_selection.GridSearchCV` in order to tune the -hyperparameters of the individual estimators:: +.. dropdown:: Using the :class:`VotingClassifier` with :class:`~sklearn.model_selection.GridSearchCV` - >>> from sklearn.model_selection import GridSearchCV - >>> clf1 = LogisticRegression(random_state=1) - >>> clf2 = RandomForestClassifier(random_state=1) - >>> clf3 = GaussianNB() - >>> eclf = VotingClassifier( - ... estimators=[('lr', clf1), ('rf', clf2), ('gnb', clf3)], - ... voting='soft' - ... ) + The :class:`VotingClassifier` can also be used together with + :class:`~sklearn.model_selection.GridSearchCV` in order to tune the + hyperparameters of the individual estimators:: - >>> params = {'lr__C': [1.0, 100.0], 'rf__n_estimators': [20, 200]} + >>> from sklearn.model_selection import GridSearchCV + >>> clf1 = LogisticRegression(random_state=1) + >>> clf2 = RandomForestClassifier(random_state=1) + >>> clf3 = GaussianNB() + >>> eclf = VotingClassifier( + ... estimators=[('lr', clf1), ('rf', clf2), ('gnb', clf3)], + ... voting='soft' + ... ) - >>> grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5) - >>> grid = grid.fit(iris.data, iris.target) + >>> params = {'lr__C': [1.0, 100.0], 'rf__n_estimators': [20, 200]} -|details-end| + >>> grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5) + >>> grid = grid.fit(iris.data, iris.target) .. _voting_regressor: @@ -1567,9 +1525,9 @@ The following example shows how to fit the VotingRegressor:: :align: center :scale: 75% -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_ensemble_plot_voting_regressor.py` +* :ref:`sphx_glr_auto_examples_ensemble_plot_voting_regressor.py` .. _stacking: @@ -1688,10 +1646,10 @@ computationally expensive. ... .format(multi_layer_regressor.score(X_test, y_test))) R2 score: 0.53 -.. topic:: References +.. rubric:: References - .. [W1992] Wolpert, David H. "Stacked generalization." Neural networks 5.2 - (1992): 241-259. +.. [W1992] Wolpert, David H. "Stacked generalization." Neural networks 5.2 + (1992): 241-259. @@ -1757,27 +1715,26 @@ The main parameters to tune to obtain good results are ``n_estimators`` and the complexity of the base estimators (e.g., its depth ``max_depth`` or minimum required number of samples to consider a split ``min_samples_split``). -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_multiclass.py` shows the performance - of AdaBoost on a multi-class problem. +* :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_multiclass.py` shows the performance + of AdaBoost on a multi-class problem. - * :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_twoclass.py` shows the decision boundary - and decision function values for a non-linearly separable two-class problem - using AdaBoost-SAMME. +* :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_twoclass.py` shows the decision boundary + and decision function values for a non-linearly separable two-class problem + using AdaBoost-SAMME. - * :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_regression.py` demonstrates regression - with the AdaBoost.R2 algorithm. +* :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_regression.py` demonstrates regression + with the AdaBoost.R2 algorithm. -.. topic:: References +.. rubric:: References - .. [FS1995] Y. Freund, and R. Schapire, "A Decision-Theoretic Generalization of - On-Line Learning and an Application to Boosting", 1997. +.. [FS1995] Y. Freund, and R. Schapire, "A Decision-Theoretic Generalization of + On-Line Learning and an Application to Boosting", 1997. - .. [ZZRH2009] J. Zhu, H. Zou, S. Rosset, T. Hastie. "Multi-class AdaBoost", - 2009. +.. [ZZRH2009] J. Zhu, H. Zou, S. Rosset, T. Hastie. "Multi-class AdaBoost", 2009. - .. [D1997] H. Drucker. "Improving Regressors using Boosting Techniques", 1997. +.. [D1997] H. Drucker. "Improving Regressors using Boosting Techniques", 1997. - .. [HTF] T. Hastie, R. Tibshirani and J. Friedman, "Elements of - Statistical Learning Ed. 2", Springer, 2009. +.. [HTF] T. Hastie, R. Tibshirani and J. Friedman, "Elements of Statistical Learning + Ed. 2", Springer, 2009. diff --git a/doc/modules/feature_extraction.rst b/doc/modules/feature_extraction.rst index 7ac538a89849b..2181014644e15 100644 --- a/doc/modules/feature_extraction.rst +++ b/doc/modules/feature_extraction.rst @@ -206,35 +206,32 @@ Note the use of a generator comprehension, which introduces laziness into the feature extraction: tokens are only processed on demand from the hasher. -|details-start| -**Implementation details** -|details-split| +.. dropdown:: Implementation details -:class:`FeatureHasher` uses the signed 32-bit variant of MurmurHash3. -As a result (and because of limitations in ``scipy.sparse``), -the maximum number of features supported is currently :math:`2^{31} - 1`. + :class:`FeatureHasher` uses the signed 32-bit variant of MurmurHash3. + As a result (and because of limitations in ``scipy.sparse``), + the maximum number of features supported is currently :math:`2^{31} - 1`. -The original formulation of the hashing trick by Weinberger et al. -used two separate hash functions :math:`h` and :math:`\xi` -to determine the column index and sign of a feature, respectively. -The present implementation works under the assumption -that the sign bit of MurmurHash3 is independent of its other bits. + The original formulation of the hashing trick by Weinberger et al. + used two separate hash functions :math:`h` and :math:`\xi` + to determine the column index and sign of a feature, respectively. + The present implementation works under the assumption + that the sign bit of MurmurHash3 is independent of its other bits. -Since a simple modulo is used to transform the hash function to a column index, -it is advisable to use a power of two as the ``n_features`` parameter; -otherwise the features will not be mapped evenly to the columns. + Since a simple modulo is used to transform the hash function to a column index, + it is advisable to use a power of two as the ``n_features`` parameter; + otherwise the features will not be mapped evenly to the columns. -.. topic:: References: + .. rubric:: References * `MurmurHash3 `_. -|details-end| -.. topic:: References: +.. rubric:: References - * Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola and - Josh Attenberg (2009). `Feature hashing for large scale multitask learning - `_. Proc. ICML. +* Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola and + Josh Attenberg (2009). `Feature hashing for large scale multitask learning + `_. Proc. ICML. .. _text_feature_extraction: @@ -310,7 +307,7 @@ counting in a single class:: This model has many parameters, however the default values are quite reasonable (please see the :ref:`reference documentation -` for the details):: +` for the details):: >>> vectorizer = CountVectorizer() >>> vectorizer @@ -422,12 +419,12 @@ tokenizer, so if *we've* is in ``stop_words``, but *ve* is not, *ve* will be retained from *we've* in transformed text. Our vectorizers will try to identify and warn about some kinds of inconsistencies. -.. topic:: References +.. rubric:: References - .. [NQY18] J. Nothman, H. Qin and R. Yurchak (2018). - `"Stop Word Lists in Free Open-source Software Packages" - `__. - In *Proc. Workshop for NLP Open Source Software*. +.. [NQY18] J. Nothman, H. Qin and R. Yurchak (2018). + `"Stop Word Lists in Free Open-source Software Packages" + `__. + In *Proc. Workshop for NLP Open Source Software*. .. _tfidf: @@ -492,132 +489,126 @@ class:: TfidfTransformer(smooth_idf=False) Again please see the :ref:`reference documentation -` for the details on all the parameters. - -|details-start| -**Numeric example of a tf-idf matrix** -|details-split| - -Let's take an example with the following counts. The first term is present -100% of the time hence not very interesting. The two other features only -in less than 50% of the time hence probably more representative of the -content of the documents:: - - >>> counts = [[3, 0, 1], - ... [2, 0, 0], - ... [3, 0, 0], - ... [4, 0, 0], - ... [3, 2, 0], - ... [3, 0, 2]] - ... - >>> tfidf = transformer.fit_transform(counts) - >>> tfidf - <6x3 sparse matrix of type '<... 'numpy.float64'>' - with 9 stored elements in Compressed Sparse ... format> +` for the details on all the parameters. - >>> tfidf.toarray() - array([[0.81940995, 0. , 0.57320793], - [1. , 0. , 0. ], - [1. , 0. , 0. ], - [1. , 0. , 0. ], - [0.47330339, 0.88089948, 0. ], - [0.58149261, 0. , 0.81355169]]) +.. dropdown:: Numeric example of a tf-idf matrix -Each row is normalized to have unit Euclidean norm: + Let's take an example with the following counts. The first term is present + 100% of the time hence not very interesting. The two other features only + in less than 50% of the time hence probably more representative of the + content of the documents:: -:math:`v_{norm} = \frac{v}{||v||_2} = \frac{v}{\sqrt{v{_1}^2 + -v{_2}^2 + \dots + v{_n}^2}}` + >>> counts = [[3, 0, 1], + ... [2, 0, 0], + ... [3, 0, 0], + ... [4, 0, 0], + ... [3, 2, 0], + ... [3, 0, 2]] + ... + >>> tfidf = transformer.fit_transform(counts) + >>> tfidf + <6x3 sparse matrix of type '<... 'numpy.float64'>' + with 9 stored elements in Compressed Sparse ... format> -For example, we can compute the tf-idf of the first term in the first -document in the `counts` array as follows: + >>> tfidf.toarray() + array([[0.81940995, 0. , 0.57320793], + [1. , 0. , 0. ], + [1. , 0. , 0. ], + [1. , 0. , 0. ], + [0.47330339, 0.88089948, 0. ], + [0.58149261, 0. , 0.81355169]]) -:math:`n = 6` + Each row is normalized to have unit Euclidean norm: -:math:`\text{df}(t)_{\text{term1}} = 6` + :math:`v_{norm} = \frac{v}{||v||_2} = \frac{v}{\sqrt{v{_1}^2 + + v{_2}^2 + \dots + v{_n}^2}}` -:math:`\text{idf}(t)_{\text{term1}} = -\log \frac{n}{\text{df}(t)} + 1 = \log(1)+1 = 1` + For example, we can compute the tf-idf of the first term in the first + document in the `counts` array as follows: -:math:`\text{tf-idf}_{\text{term1}} = \text{tf} \times \text{idf} = 3 \times 1 = 3` + :math:`n = 6` -Now, if we repeat this computation for the remaining 2 terms in the document, -we get + :math:`\text{df}(t)_{\text{term1}} = 6` -:math:`\text{tf-idf}_{\text{term2}} = 0 \times (\log(6/1)+1) = 0` + :math:`\text{idf}(t)_{\text{term1}} = + \log \frac{n}{\text{df}(t)} + 1 = \log(1)+1 = 1` -:math:`\text{tf-idf}_{\text{term3}} = 1 \times (\log(6/2)+1) \approx 2.0986` + :math:`\text{tf-idf}_{\text{term1}} = \text{tf} \times \text{idf} = 3 \times 1 = 3` -and the vector of raw tf-idfs: + Now, if we repeat this computation for the remaining 2 terms in the document, + we get -:math:`\text{tf-idf}_{\text{raw}} = [3, 0, 2.0986].` + :math:`\text{tf-idf}_{\text{term2}} = 0 \times (\log(6/1)+1) = 0` + :math:`\text{tf-idf}_{\text{term3}} = 1 \times (\log(6/2)+1) \approx 2.0986` -Then, applying the Euclidean (L2) norm, we obtain the following tf-idfs -for document 1: + and the vector of raw tf-idfs: -:math:`\frac{[3, 0, 2.0986]}{\sqrt{\big(3^2 + 0^2 + 2.0986^2\big)}} -= [ 0.819, 0, 0.573].` + :math:`\text{tf-idf}_{\text{raw}} = [3, 0, 2.0986].` -Furthermore, the default parameter ``smooth_idf=True`` adds "1" to the numerator -and denominator as if an extra document was seen containing every term in the -collection exactly once, which prevents zero divisions: -:math:`\text{idf}(t) = \log{\frac{1 + n}{1+\text{df}(t)}} + 1` + Then, applying the Euclidean (L2) norm, we obtain the following tf-idfs + for document 1: -Using this modification, the tf-idf of the third term in document 1 changes to -1.8473: + :math:`\frac{[3, 0, 2.0986]}{\sqrt{\big(3^2 + 0^2 + 2.0986^2\big)}} + = [ 0.819, 0, 0.573].` -:math:`\text{tf-idf}_{\text{term3}} = 1 \times \log(7/3)+1 \approx 1.8473` + Furthermore, the default parameter ``smooth_idf=True`` adds "1" to the numerator + and denominator as if an extra document was seen containing every term in the + collection exactly once, which prevents zero divisions: -And the L2-normalized tf-idf changes to + :math:`\text{idf}(t) = \log{\frac{1 + n}{1+\text{df}(t)}} + 1` -:math:`\frac{[3, 0, 1.8473]}{\sqrt{\big(3^2 + 0^2 + 1.8473^2\big)}} -= [0.8515, 0, 0.5243]`:: + Using this modification, the tf-idf of the third term in document 1 changes to + 1.8473: - >>> transformer = TfidfTransformer() - >>> transformer.fit_transform(counts).toarray() - array([[0.85151335, 0. , 0.52433293], - [1. , 0. , 0. ], - [1. , 0. , 0. ], - [1. , 0. , 0. ], - [0.55422893, 0.83236428, 0. ], - [0.63035731, 0. , 0.77630514]]) + :math:`\text{tf-idf}_{\text{term3}} = 1 \times \log(7/3)+1 \approx 1.8473` -The weights of each -feature computed by the ``fit`` method call are stored in a model -attribute:: + And the L2-normalized tf-idf changes to - >>> transformer.idf_ - array([1. ..., 2.25..., 1.84...]) + :math:`\frac{[3, 0, 1.8473]}{\sqrt{\big(3^2 + 0^2 + 1.8473^2\big)}} + = [0.8515, 0, 0.5243]`:: + >>> transformer = TfidfTransformer() + >>> transformer.fit_transform(counts).toarray() + array([[0.85151335, 0. , 0.52433293], + [1. , 0. , 0. ], + [1. , 0. , 0. ], + [1. , 0. , 0. ], + [0.55422893, 0.83236428, 0. ], + [0.63035731, 0. , 0.77630514]]) + The weights of each + feature computed by the ``fit`` method call are stored in a model + attribute:: + >>> transformer.idf_ + array([1. ..., 2.25..., 1.84...]) -As tf–idf is very often used for text features, there is also another -class called :class:`TfidfVectorizer` that combines all the options of -:class:`CountVectorizer` and :class:`TfidfTransformer` in a single model:: + As tf-idf is very often used for text features, there is also another + class called :class:`TfidfVectorizer` that combines all the options of + :class:`CountVectorizer` and :class:`TfidfTransformer` in a single model:: - >>> from sklearn.feature_extraction.text import TfidfVectorizer - >>> vectorizer = TfidfVectorizer() - >>> vectorizer.fit_transform(corpus) - <4x9 sparse matrix of type '<... 'numpy.float64'>' - with 19 stored elements in Compressed Sparse ... format> + >>> from sklearn.feature_extraction.text import TfidfVectorizer + >>> vectorizer = TfidfVectorizer() + >>> vectorizer.fit_transform(corpus) + <4x9 sparse matrix of type '<... 'numpy.float64'>' + with 19 stored elements in Compressed Sparse ... format> -While the tf–idf normalization is often very useful, there might -be cases where the binary occurrence markers might offer better -features. This can be achieved by using the ``binary`` parameter -of :class:`CountVectorizer`. In particular, some estimators such as -:ref:`bernoulli_naive_bayes` explicitly model discrete boolean random -variables. Also, very short texts are likely to have noisy tf–idf values -while the binary occurrence info is more stable. + While the tf-idf normalization is often very useful, there might + be cases where the binary occurrence markers might offer better + features. This can be achieved by using the ``binary`` parameter + of :class:`CountVectorizer`. In particular, some estimators such as + :ref:`bernoulli_naive_bayes` explicitly model discrete boolean random + variables. Also, very short texts are likely to have noisy tf-idf values + while the binary occurrence info is more stable. -As usual the best way to adjust the feature extraction parameters -is to use a cross-validated grid search, for instance by pipelining the -feature extractor with a classifier: + As usual the best way to adjust the feature extraction parameters + is to use a cross-validated grid search, for instance by pipelining the + feature extractor with a classifier: -* :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` + * :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` -|details-end| Decoding text files ------------------- @@ -646,64 +637,60 @@ or ``"replace"``. See the documentation for the Python function ``bytes.decode`` for more details (type ``help(bytes.decode)`` at the Python prompt). -|details-start| -**Troubleshooting decoding text** -|details-split| - -If you are having trouble decoding text, here are some things to try: - -- Find out what the actual encoding of the text is. The file might come - with a header or README that tells you the encoding, or there might be some - standard encoding you can assume based on where the text comes from. - -- You may be able to find out what kind of encoding it is in general - using the UNIX command ``file``. The Python ``chardet`` module comes with - a script called ``chardetect.py`` that will guess the specific encoding, - though you cannot rely on its guess being correct. - -- You could try UTF-8 and disregard the errors. You can decode byte - strings with ``bytes.decode(errors='replace')`` to replace all - decoding errors with a meaningless character, or set - ``decode_error='replace'`` in the vectorizer. This may damage the - usefulness of your features. - -- Real text may come from a variety of sources that may have used different - encodings, or even be sloppily decoded in a different encoding than the - one it was encoded with. This is common in text retrieved from the Web. - The Python package `ftfy`_ can automatically sort out some classes of - decoding errors, so you could try decoding the unknown text as ``latin-1`` - and then using ``ftfy`` to fix errors. - -- If the text is in a mish-mash of encodings that is simply too hard to sort - out (which is the case for the 20 Newsgroups dataset), you can fall back on - a simple single-byte encoding such as ``latin-1``. Some text may display - incorrectly, but at least the same sequence of bytes will always represent - the same feature. - -For example, the following snippet uses ``chardet`` -(not shipped with scikit-learn, must be installed separately) -to figure out the encoding of three texts. -It then vectorizes the texts and prints the learned vocabulary. -The output is not shown here. - - >>> import chardet # doctest: +SKIP - >>> text1 = b"Sei mir gegr\xc3\xbc\xc3\x9ft mein Sauerkraut" - >>> text2 = b"holdselig sind deine Ger\xfcche" - >>> text3 = b"\xff\xfeA\x00u\x00f\x00 \x00F\x00l\x00\xfc\x00g\x00e\x00l\x00n\x00 \x00d\x00e\x00s\x00 \x00G\x00e\x00s\x00a\x00n\x00g\x00e\x00s\x00,\x00 \x00H\x00e\x00r\x00z\x00l\x00i\x00e\x00b\x00c\x00h\x00e\x00n\x00,\x00 \x00t\x00r\x00a\x00g\x00 \x00i\x00c\x00h\x00 \x00d\x00i\x00c\x00h\x00 \x00f\x00o\x00r\x00t\x00" - >>> decoded = [x.decode(chardet.detect(x)['encoding']) - ... for x in (text1, text2, text3)] # doctest: +SKIP - >>> v = CountVectorizer().fit(decoded).vocabulary_ # doctest: +SKIP - >>> for term in v: print(v) # doctest: +SKIP - -(Depending on the version of ``chardet``, it might get the first one wrong.) - -For an introduction to Unicode and character encodings in general, -see Joel Spolsky's `Absolute Minimum Every Software Developer Must Know -About Unicode `_. - -.. _`ftfy`: https://github.com/LuminosoInsight/python-ftfy - -|details-end| +.. dropdown:: Troubleshooting decoding text + + If you are having trouble decoding text, here are some things to try: + + - Find out what the actual encoding of the text is. The file might come + with a header or README that tells you the encoding, or there might be some + standard encoding you can assume based on where the text comes from. + + - You may be able to find out what kind of encoding it is in general + using the UNIX command ``file``. The Python ``chardet`` module comes with + a script called ``chardetect.py`` that will guess the specific encoding, + though you cannot rely on its guess being correct. + + - You could try UTF-8 and disregard the errors. You can decode byte + strings with ``bytes.decode(errors='replace')`` to replace all + decoding errors with a meaningless character, or set + ``decode_error='replace'`` in the vectorizer. This may damage the + usefulness of your features. + + - Real text may come from a variety of sources that may have used different + encodings, or even be sloppily decoded in a different encoding than the + one it was encoded with. This is common in text retrieved from the Web. + The Python package `ftfy `__ + can automatically sort out some classes of + decoding errors, so you could try decoding the unknown text as ``latin-1`` + and then using ``ftfy`` to fix errors. + + - If the text is in a mish-mash of encodings that is simply too hard to sort + out (which is the case for the 20 Newsgroups dataset), you can fall back on + a simple single-byte encoding such as ``latin-1``. Some text may display + incorrectly, but at least the same sequence of bytes will always represent + the same feature. + + For example, the following snippet uses ``chardet`` + (not shipped with scikit-learn, must be installed separately) + to figure out the encoding of three texts. + It then vectorizes the texts and prints the learned vocabulary. + The output is not shown here. + + >>> import chardet # doctest: +SKIP + >>> text1 = b"Sei mir gegr\xc3\xbc\xc3\x9ft mein Sauerkraut" + >>> text2 = b"holdselig sind deine Ger\xfcche" + >>> text3 = b"\xff\xfeA\x00u\x00f\x00 \x00F\x00l\x00\xfc\x00g\x00e\x00l\x00n\x00 \x00d\x00e\x00s\x00 \x00G\x00e\x00s\x00a\x00n\x00g\x00e\x00s\x00,\x00 \x00H\x00e\x00r\x00z\x00l\x00i\x00e\x00b\x00c\x00h\x00e\x00n\x00,\x00 \x00t\x00r\x00a\x00g\x00 \x00i\x00c\x00h\x00 \x00d\x00i\x00c\x00h\x00 \x00f\x00o\x00r\x00t\x00" + >>> decoded = [x.decode(chardet.detect(x)['encoding']) + ... for x in (text1, text2, text3)] # doctest: +SKIP + >>> v = CountVectorizer().fit(decoded).vocabulary_ # doctest: +SKIP + >>> for term in v: print(v) # doctest: +SKIP + + (Depending on the version of ``chardet``, it might get the first one wrong.) + + For an introduction to Unicode and character encodings in general, + see Joel Spolsky's `Absolute Minimum Every Software Developer Must Know + About Unicode `_. + Applications and examples ------------------------- @@ -884,28 +871,25 @@ The :class:`HashingVectorizer` also comes with the following limitations: model. A :class:`TfidfTransformer` can be appended to it in a pipeline if required. -|details-start| -**Performing out-of-core scaling with HashingVectorizer** -|details-split| +.. dropdown:: Performing out-of-core scaling with HashingVectorizer -An interesting development of using a :class:`HashingVectorizer` is the ability -to perform `out-of-core`_ scaling. This means that we can learn from data that -does not fit into the computer's main memory. + An interesting development of using a :class:`HashingVectorizer` is the ability + to perform `out-of-core`_ scaling. This means that we can learn from data that + does not fit into the computer's main memory. -.. _out-of-core: https://en.wikipedia.org/wiki/Out-of-core_algorithm + .. _out-of-core: https://en.wikipedia.org/wiki/Out-of-core_algorithm -A strategy to implement out-of-core scaling is to stream data to the estimator -in mini-batches. Each mini-batch is vectorized using :class:`HashingVectorizer` -so as to guarantee that the input space of the estimator has always the same -dimensionality. The amount of memory used at any time is thus bounded by the -size of a mini-batch. Although there is no limit to the amount of data that can -be ingested using such an approach, from a practical point of view the learning -time is often limited by the CPU time one wants to spend on the task. + A strategy to implement out-of-core scaling is to stream data to the estimator + in mini-batches. Each mini-batch is vectorized using :class:`HashingVectorizer` + so as to guarantee that the input space of the estimator has always the same + dimensionality. The amount of memory used at any time is thus bounded by the + size of a mini-batch. Although there is no limit to the amount of data that can + be ingested using such an approach, from a practical point of view the learning + time is often limited by the CPU time one wants to spend on the task. -For a full-fledged example of out-of-core scaling in a text classification -task see :ref:`sphx_glr_auto_examples_applications_plot_out_of_core_classification.py`. + For a full-fledged example of out-of-core scaling in a text classification + task see :ref:`sphx_glr_auto_examples_applications_plot_out_of_core_classification.py`. -|details-end| Customizing the vectorizer classes ---------------------------------- @@ -945,65 +929,58 @@ parameters it is possible to derive from the class and override the ``build_preprocessor``, ``build_tokenizer`` and ``build_analyzer`` factory methods instead of passing custom functions. -|details-start| -**Tips and tricks** -|details-split| - -Some tips and tricks: - -* If documents are pre-tokenized by an external package, then store them in - files (or strings) with the tokens separated by whitespace and pass - ``analyzer=str.split`` -* Fancy token-level analysis such as stemming, lemmatizing, compound - splitting, filtering based on part-of-speech, etc. are not included in the - scikit-learn codebase, but can be added by customizing either the - tokenizer or the analyzer. - Here's a ``CountVectorizer`` with a tokenizer and lemmatizer using - `NLTK `_:: - - >>> from nltk import word_tokenize # doctest: +SKIP - >>> from nltk.stem import WordNetLemmatizer # doctest: +SKIP - >>> class LemmaTokenizer: - ... def __init__(self): - ... self.wnl = WordNetLemmatizer() - ... def __call__(self, doc): - ... return [self.wnl.lemmatize(t) for t in word_tokenize(doc)] - ... - >>> vect = CountVectorizer(tokenizer=LemmaTokenizer()) # doctest: +SKIP - - (Note that this will not filter out punctuation.) - - - The following example will, for instance, transform some British spelling - to American spelling:: - - >>> import re - >>> def to_british(tokens): - ... for t in tokens: - ... t = re.sub(r"(...)our$", r"\1or", t) - ... t = re.sub(r"([bt])re$", r"\1er", t) - ... t = re.sub(r"([iy])s(e$|ing|ation)", r"\1z\2", t) - ... t = re.sub(r"ogue$", "og", t) - ... yield t - ... - >>> class CustomVectorizer(CountVectorizer): - ... def build_tokenizer(self): - ... tokenize = super().build_tokenizer() - ... return lambda doc: list(to_british(tokenize(doc))) - ... - >>> print(CustomVectorizer().build_analyzer()(u"color colour")) - [...'color', ...'color'] - - for other styles of preprocessing; examples include stemming, lemmatization, - or normalizing numerical tokens, with the latter illustrated in: - - * :ref:`sphx_glr_auto_examples_bicluster_plot_bicluster_newsgroups.py` - - -Customizing the vectorizer can also be useful when handling Asian languages -that do not use an explicit word separator such as whitespace. - -|details-end| +.. dropdown:: Tips and tricks + :color: success + + * If documents are pre-tokenized by an external package, then store them in + files (or strings) with the tokens separated by whitespace and pass + ``analyzer=str.split`` + * Fancy token-level analysis such as stemming, lemmatizing, compound + splitting, filtering based on part-of-speech, etc. are not included in the + scikit-learn codebase, but can be added by customizing either the + tokenizer or the analyzer. + Here's a ``CountVectorizer`` with a tokenizer and lemmatizer using + `NLTK `_:: + + >>> from nltk import word_tokenize # doctest: +SKIP + >>> from nltk.stem import WordNetLemmatizer # doctest: +SKIP + >>> class LemmaTokenizer: + ... def __init__(self): + ... self.wnl = WordNetLemmatizer() + ... def __call__(self, doc): + ... return [self.wnl.lemmatize(t) for t in word_tokenize(doc)] + ... + >>> vect = CountVectorizer(tokenizer=LemmaTokenizer()) # doctest: +SKIP + + (Note that this will not filter out punctuation.) + + The following example will, for instance, transform some British spelling + to American spelling:: + + >>> import re + >>> def to_british(tokens): + ... for t in tokens: + ... t = re.sub(r"(...)our$", r"\1or", t) + ... t = re.sub(r"([bt])re$", r"\1er", t) + ... t = re.sub(r"([iy])s(e$|ing|ation)", r"\1z\2", t) + ... t = re.sub(r"ogue$", "og", t) + ... yield t + ... + >>> class CustomVectorizer(CountVectorizer): + ... def build_tokenizer(self): + ... tokenize = super().build_tokenizer() + ... return lambda doc: list(to_british(tokenize(doc))) + ... + >>> print(CustomVectorizer().build_analyzer()(u"color colour")) + [...'color', ...'color'] + + for other styles of preprocessing; examples include stemming, lemmatization, + or normalizing numerical tokens, with the latter illustrated in: + + * :ref:`sphx_glr_auto_examples_bicluster_plot_bicluster_newsgroups.py` + + Customizing the vectorizer can also be useful when handling Asian languages + that do not use an explicit word separator such as whitespace. .. _image_feature_extraction: diff --git a/doc/modules/feature_selection.rst b/doc/modules/feature_selection.rst index 1b5ce57b0074f..6746f2f65da00 100644 --- a/doc/modules/feature_selection.rst +++ b/doc/modules/feature_selection.rst @@ -114,11 +114,11 @@ applied to non-negative features, such as frequencies. feature selection as well. One needs to provide a `score_func` where `y=None`. The `score_func` should use internally `X` to compute the scores. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection.py` +* :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection.py` - * :ref:`sphx_glr_auto_examples_feature_selection_plot_f_test_vs_mi.py` +* :ref:`sphx_glr_auto_examples_feature_selection_plot_f_test_vs_mi.py` .. _rfe: @@ -144,14 +144,14 @@ of selected features and aggregated together. Finally, the scores are averaged across folds and the number of features selected is set to the number of features that maximize the cross-validation score. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_digits.py`: A recursive feature elimination example - showing the relevance of pixels in a digit classification task. +* :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_digits.py`: A recursive feature elimination example + showing the relevance of pixels in a digit classification task. - * :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py`: A recursive feature - elimination example with automatic tuning of the number of features - selected with cross-validation. +* :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py`: A recursive feature + elimination example with automatic tuning of the number of features + selected with cross-validation. .. _select_from_model: @@ -171,9 +171,9 @@ Available heuristics are "mean", "median" and float multiples of these like For examples on how it is to be used refer to the sections below. -.. topic:: Examples +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py` +* :ref:`sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py` .. _l1_feature_selection: @@ -207,42 +207,39 @@ With SVMs and logistic-regression, the parameter C controls the sparsity: the smaller C the fewer features selected. With Lasso, the higher the alpha parameter, the fewer features selected. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_dense_vs_sparse_data.py`. +* :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_dense_vs_sparse_data.py`. .. _compressive_sensing: -|details-start| -**L1-recovery and compressive sensing** -|details-split| - -For a good choice of alpha, the :ref:`lasso` can fully recover the -exact set of non-zero variables using only few observations, provided -certain specific conditions are met. In particular, the number of -samples should be "sufficiently large", or L1 models will perform at -random, where "sufficiently large" depends on the number of non-zero -coefficients, the logarithm of the number of features, the amount of -noise, the smallest absolute value of non-zero coefficients, and the -structure of the design matrix X. In addition, the design matrix must -display certain specific properties, such as not being too correlated. - -There is no general rule to select an alpha parameter for recovery of -non-zero coefficients. It can by set by cross-validation -(:class:`~sklearn.linear_model.LassoCV` or -:class:`~sklearn.linear_model.LassoLarsCV`), though this may lead to -under-penalized models: including a small number of non-relevant variables -is not detrimental to prediction score. BIC -(:class:`~sklearn.linear_model.LassoLarsIC`) tends, on the opposite, to set -high values of alpha. - -.. topic:: Reference - - Richard G. Baraniuk "Compressive Sensing", IEEE Signal - Processing Magazine [120] July 2007 - http://users.isr.ist.utl.pt/~aguiar/CS_notes.pdf - -|details-end| +.. dropdown:: L1-recovery and compressive sensing + + For a good choice of alpha, the :ref:`lasso` can fully recover the + exact set of non-zero variables using only few observations, provided + certain specific conditions are met. In particular, the number of + samples should be "sufficiently large", or L1 models will perform at + random, where "sufficiently large" depends on the number of non-zero + coefficients, the logarithm of the number of features, the amount of + noise, the smallest absolute value of non-zero coefficients, and the + structure of the design matrix X. In addition, the design matrix must + display certain specific properties, such as not being too correlated. + + There is no general rule to select an alpha parameter for recovery of + non-zero coefficients. It can by set by cross-validation + (:class:`~sklearn.linear_model.LassoCV` or + :class:`~sklearn.linear_model.LassoLarsCV`), though this may lead to + under-penalized models: including a small number of non-relevant variables + is not detrimental to prediction score. BIC + (:class:`~sklearn.linear_model.LassoLarsIC`) tends, on the opposite, to set + high values of alpha. + + .. rubric:: References + + Richard G. Baraniuk "Compressive Sensing", IEEE Signal + Processing Magazine [120] July 2007 + http://users.isr.ist.utl.pt/~aguiar/CS_notes.pdf + Tree-based feature selection ---------------------------- @@ -268,14 +265,13 @@ meta-transformer):: >>> X_new.shape # doctest: +SKIP (150, 2) -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances.py`: example on - synthetic data showing the recovery of the actually meaningful - features. +* :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances.py`: example on + synthetic data showing the recovery of the actually meaningful features. - * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances_faces.py`: example - on face recognition data. +* :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances_faces.py`: example + on face recognition data. .. _sequential_feature_selection: @@ -299,38 +295,35 @@ instead of starting with no features and greedily adding features, we start with *all* the features and greedily *remove* features from the set. The `direction` parameter controls whether forward or backward SFS is used. -|details-start| -**Detail on Sequential Feature Selection** -|details-split| - -In general, forward and backward selection do not yield equivalent results. -Also, one may be much faster than the other depending on the requested number -of selected features: if we have 10 features and ask for 7 selected features, -forward selection would need to perform 7 iterations while backward selection -would only need to perform 3. - -SFS differs from :class:`~sklearn.feature_selection.RFE` and -:class:`~sklearn.feature_selection.SelectFromModel` in that it does not -require the underlying model to expose a `coef_` or `feature_importances_` -attribute. It may however be slower considering that more models need to be -evaluated, compared to the other approaches. For example in backward -selection, the iteration going from `m` features to `m - 1` features using k-fold -cross-validation requires fitting `m * k` models, while -:class:`~sklearn.feature_selection.RFE` would require only a single fit, and -:class:`~sklearn.feature_selection.SelectFromModel` always just does a single -fit and requires no iterations. - -.. topic:: Reference - - .. [sfs] Ferri et al, `Comparative study of techniques for +.. dropdown:: Details on Sequential Feature Selection + + In general, forward and backward selection do not yield equivalent results. + Also, one may be much faster than the other depending on the requested number + of selected features: if we have 10 features and ask for 7 selected features, + forward selection would need to perform 7 iterations while backward selection + would only need to perform 3. + + SFS differs from :class:`~sklearn.feature_selection.RFE` and + :class:`~sklearn.feature_selection.SelectFromModel` in that it does not + require the underlying model to expose a `coef_` or `feature_importances_` + attribute. It may however be slower considering that more models need to be + evaluated, compared to the other approaches. For example in backward + selection, the iteration going from `m` features to `m - 1` features using k-fold + cross-validation requires fitting `m * k` models, while + :class:`~sklearn.feature_selection.RFE` would require only a single fit, and + :class:`~sklearn.feature_selection.SelectFromModel` always just does a single + fit and requires no iterations. + + .. rubric:: References + + .. [sfs] Ferri et al, `Comparative study of techniques for large-scale feature selection `_. -|details-end| -.. topic:: Examples +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py` +* :ref:`sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py` Feature selection as part of a pipeline ======================================= diff --git a/doc/modules/gaussian_process.rst b/doc/modules/gaussian_process.rst index 58e56a557ed73..fb87120205f96 100644 --- a/doc/modules/gaussian_process.rst +++ b/doc/modules/gaussian_process.rst @@ -88,12 +88,12 @@ the API of standard scikit-learn estimators, :class:`GaussianProcessRegressor`: externally for other ways of selecting hyperparameters, e.g., via Markov chain Monte Carlo. -.. topic:: Examples +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_noisy_targets.py` - * :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_noisy.py` - * :ref:`sphx_glr_auto_examples_gaussian_process_plot_compare_gpr_krr.py` - * :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_co2.py` +* :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_noisy_targets.py` +* :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_noisy.py` +* :ref:`sphx_glr_auto_examples_gaussian_process_plot_compare_gpr_krr.py` +* :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_co2.py` .. _gpc: @@ -239,93 +239,88 @@ also invariant to rotations in the input space. For more details, we refer to Chapter 4 of [RW2006]_. For guidance on how to best combine different kernels, we refer to [Duv2014]_. -|details-start| -**Gaussian Process Kernel API** -|details-split| - -The main usage of a :class:`Kernel` is to compute the GP's covariance between -datapoints. For this, the method ``__call__`` of the kernel can be called. This -method can either be used to compute the "auto-covariance" of all pairs of -datapoints in a 2d array X, or the "cross-covariance" of all combinations -of datapoints of a 2d array X with datapoints in a 2d array Y. The following -identity holds true for all kernels k (except for the :class:`WhiteKernel`): -``k(X) == K(X, Y=X)`` - -If only the diagonal of the auto-covariance is being used, the method ``diag()`` -of a kernel can be called, which is more computationally efficient than the -equivalent call to ``__call__``: ``np.diag(k(X, X)) == k.diag(X)`` - -Kernels are parameterized by a vector :math:`\theta` of hyperparameters. These -hyperparameters can for instance control length-scales or periodicity of a -kernel (see below). All kernels support computing analytic gradients -of the kernel's auto-covariance with respect to :math:`log(\theta)` via setting -``eval_gradient=True`` in the ``__call__`` method. -That is, a ``(len(X), len(X), len(theta))`` array is returned where the entry -``[i, j, l]`` contains :math:`\frac{\partial k_\theta(x_i, x_j)}{\partial log(\theta_l)}`. -This gradient is used by the Gaussian process (both regressor and classifier) -in computing the gradient of the log-marginal-likelihood, which in turn is used -to determine the value of :math:`\theta`, which maximizes the log-marginal-likelihood, -via gradient ascent. For each hyperparameter, the initial value and the -bounds need to be specified when creating an instance of the kernel. The -current value of :math:`\theta` can be get and set via the property -``theta`` of the kernel object. Moreover, the bounds of the hyperparameters can be -accessed by the property ``bounds`` of the kernel. Note that both properties -(theta and bounds) return log-transformed values of the internally used values -since those are typically more amenable to gradient-based optimization. -The specification of each hyperparameter is stored in the form of an instance of -:class:`Hyperparameter` in the respective kernel. Note that a kernel using a -hyperparameter with name "x" must have the attributes self.x and self.x_bounds. - -The abstract base class for all kernels is :class:`Kernel`. Kernel implements a -similar interface as :class:`~sklearn.base.BaseEstimator`, providing the -methods ``get_params()``, ``set_params()``, and ``clone()``. This allows -setting kernel values also via meta-estimators such as -:class:`~sklearn.pipeline.Pipeline` or -:class:`~sklearn.model_selection.GridSearchCV`. Note that due to the nested -structure of kernels (by applying kernel operators, see below), the names of -kernel parameters might become relatively complicated. In general, for a binary -kernel operator, parameters of the left operand are prefixed with ``k1__`` and -parameters of the right operand with ``k2__``. An additional convenience method -is ``clone_with_theta(theta)``, which returns a cloned version of the kernel -but with the hyperparameters set to ``theta``. An illustrative example: - - >>> from sklearn.gaussian_process.kernels import ConstantKernel, RBF - >>> kernel = ConstantKernel(constant_value=1.0, constant_value_bounds=(0.0, 10.0)) * RBF(length_scale=0.5, length_scale_bounds=(0.0, 10.0)) + RBF(length_scale=2.0, length_scale_bounds=(0.0, 10.0)) - >>> for hyperparameter in kernel.hyperparameters: print(hyperparameter) - Hyperparameter(name='k1__k1__constant_value', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False) - Hyperparameter(name='k1__k2__length_scale', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False) - Hyperparameter(name='k2__length_scale', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False) - >>> params = kernel.get_params() - >>> for key in sorted(params): print("%s : %s" % (key, params[key])) - k1 : 1**2 * RBF(length_scale=0.5) - k1__k1 : 1**2 - k1__k1__constant_value : 1.0 - k1__k1__constant_value_bounds : (0.0, 10.0) - k1__k2 : RBF(length_scale=0.5) - k1__k2__length_scale : 0.5 - k1__k2__length_scale_bounds : (0.0, 10.0) - k2 : RBF(length_scale=2) - k2__length_scale : 2.0 - k2__length_scale_bounds : (0.0, 10.0) - >>> print(kernel.theta) # Note: log-transformed - [ 0. -0.69314718 0.69314718] - >>> print(kernel.bounds) # Note: log-transformed - [[ -inf 2.30258509] - [ -inf 2.30258509] - [ -inf 2.30258509]] - - -All Gaussian process kernels are interoperable with :mod:`sklearn.metrics.pairwise` -and vice versa: instances of subclasses of :class:`Kernel` can be passed as -``metric`` to ``pairwise_kernels`` from :mod:`sklearn.metrics.pairwise`. Moreover, -kernel functions from pairwise can be used as GP kernels by using the wrapper -class :class:`PairwiseKernel`. The only caveat is that the gradient of -the hyperparameters is not analytic but numeric and all those kernels support -only isotropic distances. The parameter ``gamma`` is considered to be a -hyperparameter and may be optimized. The other kernel parameters are set -directly at initialization and are kept fixed. - -|details-end| +.. dropdown:: Gaussian Process Kernel API + + The main usage of a :class:`Kernel` is to compute the GP's covariance between + datapoints. For this, the method ``__call__`` of the kernel can be called. This + method can either be used to compute the "auto-covariance" of all pairs of + datapoints in a 2d array X, or the "cross-covariance" of all combinations + of datapoints of a 2d array X with datapoints in a 2d array Y. The following + identity holds true for all kernels k (except for the :class:`WhiteKernel`): + ``k(X) == K(X, Y=X)`` + + If only the diagonal of the auto-covariance is being used, the method ``diag()`` + of a kernel can be called, which is more computationally efficient than the + equivalent call to ``__call__``: ``np.diag(k(X, X)) == k.diag(X)`` + + Kernels are parameterized by a vector :math:`\theta` of hyperparameters. These + hyperparameters can for instance control length-scales or periodicity of a + kernel (see below). All kernels support computing analytic gradients + of the kernel's auto-covariance with respect to :math:`log(\theta)` via setting + ``eval_gradient=True`` in the ``__call__`` method. + That is, a ``(len(X), len(X), len(theta))`` array is returned where the entry + ``[i, j, l]`` contains :math:`\frac{\partial k_\theta(x_i, x_j)}{\partial log(\theta_l)}`. + This gradient is used by the Gaussian process (both regressor and classifier) + in computing the gradient of the log-marginal-likelihood, which in turn is used + to determine the value of :math:`\theta`, which maximizes the log-marginal-likelihood, + via gradient ascent. For each hyperparameter, the initial value and the + bounds need to be specified when creating an instance of the kernel. The + current value of :math:`\theta` can be get and set via the property + ``theta`` of the kernel object. Moreover, the bounds of the hyperparameters can be + accessed by the property ``bounds`` of the kernel. Note that both properties + (theta and bounds) return log-transformed values of the internally used values + since those are typically more amenable to gradient-based optimization. + The specification of each hyperparameter is stored in the form of an instance of + :class:`Hyperparameter` in the respective kernel. Note that a kernel using a + hyperparameter with name "x" must have the attributes self.x and self.x_bounds. + + The abstract base class for all kernels is :class:`Kernel`. Kernel implements a + similar interface as :class:`~sklearn.base.BaseEstimator`, providing the + methods ``get_params()``, ``set_params()``, and ``clone()``. This allows + setting kernel values also via meta-estimators such as + :class:`~sklearn.pipeline.Pipeline` or + :class:`~sklearn.model_selection.GridSearchCV`. Note that due to the nested + structure of kernels (by applying kernel operators, see below), the names of + kernel parameters might become relatively complicated. In general, for a binary + kernel operator, parameters of the left operand are prefixed with ``k1__`` and + parameters of the right operand with ``k2__``. An additional convenience method + is ``clone_with_theta(theta)``, which returns a cloned version of the kernel + but with the hyperparameters set to ``theta``. An illustrative example: + + >>> from sklearn.gaussian_process.kernels import ConstantKernel, RBF + >>> kernel = ConstantKernel(constant_value=1.0, constant_value_bounds=(0.0, 10.0)) * RBF(length_scale=0.5, length_scale_bounds=(0.0, 10.0)) + RBF(length_scale=2.0, length_scale_bounds=(0.0, 10.0)) + >>> for hyperparameter in kernel.hyperparameters: print(hyperparameter) + Hyperparameter(name='k1__k1__constant_value', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False) + Hyperparameter(name='k1__k2__length_scale', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False) + Hyperparameter(name='k2__length_scale', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False) + >>> params = kernel.get_params() + >>> for key in sorted(params): print("%s : %s" % (key, params[key])) + k1 : 1**2 * RBF(length_scale=0.5) + k1__k1 : 1**2 + k1__k1__constant_value : 1.0 + k1__k1__constant_value_bounds : (0.0, 10.0) + k1__k2 : RBF(length_scale=0.5) + k1__k2__length_scale : 0.5 + k1__k2__length_scale_bounds : (0.0, 10.0) + k2 : RBF(length_scale=2) + k2__length_scale : 2.0 + k2__length_scale_bounds : (0.0, 10.0) + >>> print(kernel.theta) # Note: log-transformed + [ 0. -0.69314718 0.69314718] + >>> print(kernel.bounds) # Note: log-transformed + [[ -inf 2.30258509] + [ -inf 2.30258509] + [ -inf 2.30258509]] + + All Gaussian process kernels are interoperable with :mod:`sklearn.metrics.pairwise` + and vice versa: instances of subclasses of :class:`Kernel` can be passed as + ``metric`` to ``pairwise_kernels`` from :mod:`sklearn.metrics.pairwise`. Moreover, + kernel functions from pairwise can be used as GP kernels by using the wrapper + class :class:`PairwiseKernel`. The only caveat is that the gradient of + the hyperparameters is not analytic but numeric and all those kernels support + only isotropic distances. The parameter ``gamma`` is considered to be a + hyperparameter and may be optimized. The other kernel parameters are set + directly at initialization and are kept fixed. Basic kernels ------------- @@ -388,42 +383,38 @@ The :class:`Matern` kernel is a stationary kernel and a generalization of the :class:`RBF` kernel. It has an additional parameter :math:`\nu` which controls the smoothness of the resulting function. It is parameterized by a length-scale parameter :math:`l>0`, which can either be a scalar (isotropic variant of the kernel) or a vector with the same number of dimensions as the inputs :math:`x` (anisotropic variant of the kernel). -|details-start| -**Mathematical implementation of Matérn kernel** -|details-split| +.. dropdown:: Mathematical implementation of Matérn kernel -The kernel is given by: - -.. math:: + The kernel is given by: - k(x_i, x_j) = \frac{1}{\Gamma(\nu)2^{\nu-1}}\Bigg(\frac{\sqrt{2\nu}}{l} d(x_i , x_j )\Bigg)^\nu K_\nu\Bigg(\frac{\sqrt{2\nu}}{l} d(x_i , x_j )\Bigg), + .. math:: -where :math:`d(\cdot,\cdot)` is the Euclidean distance, :math:`K_\nu(\cdot)` is a modified Bessel function and :math:`\Gamma(\cdot)` is the gamma function. -As :math:`\nu\rightarrow\infty`, the Matérn kernel converges to the RBF kernel. -When :math:`\nu = 1/2`, the Matérn kernel becomes identical to the absolute -exponential kernel, i.e., + k(x_i, x_j) = \frac{1}{\Gamma(\nu)2^{\nu-1}}\Bigg(\frac{\sqrt{2\nu}}{l} d(x_i , x_j )\Bigg)^\nu K_\nu\Bigg(\frac{\sqrt{2\nu}}{l} d(x_i , x_j )\Bigg), -.. math:: - k(x_i, x_j) = \exp \Bigg(- \frac{1}{l} d(x_i , x_j ) \Bigg) \quad \quad \nu= \tfrac{1}{2} + where :math:`d(\cdot,\cdot)` is the Euclidean distance, :math:`K_\nu(\cdot)` is a modified Bessel function and :math:`\Gamma(\cdot)` is the gamma function. + As :math:`\nu\rightarrow\infty`, the Matérn kernel converges to the RBF kernel. + When :math:`\nu = 1/2`, the Matérn kernel becomes identical to the absolute + exponential kernel, i.e., -In particular, :math:`\nu = 3/2`: + .. math:: + k(x_i, x_j) = \exp \Bigg(- \frac{1}{l} d(x_i , x_j ) \Bigg) \quad \quad \nu= \tfrac{1}{2} -.. math:: - k(x_i, x_j) = \Bigg(1 + \frac{\sqrt{3}}{l} d(x_i , x_j )\Bigg) \exp \Bigg(-\frac{\sqrt{3}}{l} d(x_i , x_j ) \Bigg) \quad \quad \nu= \tfrac{3}{2} + In particular, :math:`\nu = 3/2`: -and :math:`\nu = 5/2`: + .. math:: + k(x_i, x_j) = \Bigg(1 + \frac{\sqrt{3}}{l} d(x_i , x_j )\Bigg) \exp \Bigg(-\frac{\sqrt{3}}{l} d(x_i , x_j ) \Bigg) \quad \quad \nu= \tfrac{3}{2} -.. math:: - k(x_i, x_j) = \Bigg(1 + \frac{\sqrt{5}}{l} d(x_i , x_j ) +\frac{5}{3l} d(x_i , x_j )^2 \Bigg) \exp \Bigg(-\frac{\sqrt{5}}{l} d(x_i , x_j ) \Bigg) \quad \quad \nu= \tfrac{5}{2} + and :math:`\nu = 5/2`: -are popular choices for learning functions that are not infinitely -differentiable (as assumed by the RBF kernel) but at least once (:math:`\nu = -3/2`) or twice differentiable (:math:`\nu = 5/2`). + .. math:: + k(x_i, x_j) = \Bigg(1 + \frac{\sqrt{5}}{l} d(x_i , x_j ) +\frac{5}{3l} d(x_i , x_j )^2 \Bigg) \exp \Bigg(-\frac{\sqrt{5}}{l} d(x_i , x_j ) \Bigg) \quad \quad \nu= \tfrac{5}{2} -The flexibility of controlling the smoothness of the learned function via :math:`\nu` -allows adapting to the properties of the true underlying functional relation. + are popular choices for learning functions that are not infinitely + differentiable (as assumed by the RBF kernel) but at least once (:math:`\nu = + 3/2`) or twice differentiable (:math:`\nu = 5/2`). -|details-end| + The flexibility of controlling the smoothness of the learned function via :math:`\nu` + allows adapting to the properties of the true underlying functional relation. The prior and posterior of a GP resulting from a Matérn kernel are shown in the following figure: diff --git a/doc/modules/grid_search.rst b/doc/modules/grid_search.rst index 01c5a5c72ee52..12ee76d8e4d39 100644 --- a/doc/modules/grid_search.rst +++ b/doc/modules/grid_search.rst @@ -72,35 +72,35 @@ evaluated and the best combination is retained. .. currentmodule:: sklearn.model_selection -.. topic:: Examples: +.. rubric:: Examples - - See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py` for an example of - Grid Search computation on the digits dataset. +- See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py` for an example of + Grid Search computation on the digits dataset. - - See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` for an example - of Grid Search coupling parameters from a text documents feature - extractor (n-gram count vectorizer and TF-IDF transformer) with a - classifier (here a linear SVM trained with SGD with either elastic - net or L2 penalty) using a :class:`~sklearn.pipeline.Pipeline` instance. +- See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` for an example + of Grid Search coupling parameters from a text documents feature + extractor (n-gram count vectorizer and TF-IDF transformer) with a + classifier (here a linear SVM trained with SGD with either elastic + net or L2 penalty) using a :class:`~sklearn.pipeline.Pipeline` instance. - - See :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py` - for an example of Grid Search within a cross validation loop on the iris - dataset. This is the best practice for evaluating the performance of a - model with grid search. +- See :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py` + for an example of Grid Search within a cross validation loop on the iris + dataset. This is the best practice for evaluating the performance of a + model with grid search. - - See :ref:`sphx_glr_auto_examples_model_selection_plot_multi_metric_evaluation.py` - for an example of :class:`GridSearchCV` being used to evaluate multiple - metrics simultaneously. +- See :ref:`sphx_glr_auto_examples_model_selection_plot_multi_metric_evaluation.py` + for an example of :class:`GridSearchCV` being used to evaluate multiple + metrics simultaneously. - - See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py` - for an example of using ``refit=callable`` interface in - :class:`GridSearchCV`. The example shows how this interface adds certain - amount of flexibility in identifying the "best" estimator. This interface - can also be used in multiple metrics evaluation. +- See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py` + for an example of using ``refit=callable`` interface in + :class:`GridSearchCV`. The example shows how this interface adds certain + amount of flexibility in identifying the "best" estimator. This interface + can also be used in multiple metrics evaluation. - - See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_stats.py` - for an example of how to do a statistical comparison on the outputs of - :class:`GridSearchCV`. +- See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_stats.py` + for an example of how to do a statistical comparison on the outputs of + :class:`GridSearchCV`. .. _randomized_parameter_search: @@ -161,16 +161,16 @@ variable that is log-uniformly distributed between ``1e0`` and ``1e3``:: 'kernel': ['rbf'], 'class_weight':['balanced', None]} -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_model_selection_plot_randomized_search.py` compares the usage and efficiency - of randomized search and grid search. +* :ref:`sphx_glr_auto_examples_model_selection_plot_randomized_search.py` compares the usage and efficiency + of randomized search and grid search. -.. topic:: References: +.. rubric:: References - * Bergstra, J. and Bengio, Y., - Random search for hyper-parameter optimization, - The Journal of Machine Learning Research (2012) +* Bergstra, J. and Bengio, Y., + Random search for hyper-parameter optimization, + The Journal of Machine Learning Research (2012) .. _successive_halving_user_guide: @@ -222,10 +222,10 @@ need to explicitly import ``enable_halving_search_cv``:: >>> from sklearn.model_selection import HalvingGridSearchCV >>> from sklearn.model_selection import HalvingRandomSearchCV -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_model_selection_plot_successive_halving_heatmap.py` - * :ref:`sphx_glr_auto_examples_model_selection_plot_successive_halving_iterations.py` +* :ref:`sphx_glr_auto_examples_model_selection_plot_successive_halving_heatmap.py` +* :ref:`sphx_glr_auto_examples_model_selection_plot_successive_halving_iterations.py` Choosing ``min_resources`` and the number of candidates ------------------------------------------------------- @@ -528,15 +528,16 @@ In the example above, the best parameter combination is ``{'criterion': since it has reached the last iteration (3) with the highest score: 0.96. -.. topic:: References: +.. rubric:: References - .. [1] K. Jamieson, A. Talwalkar, - `Non-stochastic Best Arm Identification and Hyperparameter - Optimization `_, in - proc. of Machine Learning Research, 2016. - .. [2] L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, A. Talwalkar, - :arxiv:`Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization - <1603.06560>`, in Machine Learning Research 18, 2018. +.. [1] K. Jamieson, A. Talwalkar, + `Non-stochastic Best Arm Identification and Hyperparameter + Optimization `_, in + proc. of Machine Learning Research, 2016. + +.. [2] L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, A. Talwalkar, + :arxiv:`Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization + <1603.06560>`, in Machine Learning Research 18, 2018. .. _grid_search_tips: diff --git a/doc/modules/impute.rst b/doc/modules/impute.rst index f5879cbffc0a5..1431f26132338 100644 --- a/doc/modules/impute.rst +++ b/doc/modules/impute.rst @@ -224,13 +224,13 @@ neighbors of samples with missing values:: For another example on usage, see :ref:`sphx_glr_auto_examples_impute_plot_missing_values.py`. -.. topic:: References +.. rubric:: References - .. [OL2001] `Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, - Trevor Hastie, Robert Tibshirani, David Botstein and Russ B. Altman, - Missing value estimation methods for DNA microarrays, BIOINFORMATICS - Vol. 17 no. 6, 2001 Pages 520-525. - `_ +.. [OL2001] `Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, + Trevor Hastie, Robert Tibshirani, David Botstein and Russ B. Altman, + Missing value estimation methods for DNA microarrays, BIOINFORMATICS + Vol. 17 no. 6, 2001 Pages 520-525. + `_ Keeping the number of features constant ======================================= diff --git a/doc/modules/isotonic.rst b/doc/modules/isotonic.rst index 6cfdc1669de5d..50fbdb24e72c7 100644 --- a/doc/modules/isotonic.rst +++ b/doc/modules/isotonic.rst @@ -32,6 +32,6 @@ thus form a function that is piecewise linear: :target: ../auto_examples/miscellaneous/plot_isotonic_regression.html :align: center -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_miscellaneous_plot_isotonic_regression.py` +* :ref:`sphx_glr_auto_examples_miscellaneous_plot_isotonic_regression.py` diff --git a/doc/modules/kernel_approximation.rst b/doc/modules/kernel_approximation.rst index 0c67c36178e3b..305c3cc6601fb 100644 --- a/doc/modules/kernel_approximation.rst +++ b/doc/modules/kernel_approximation.rst @@ -88,12 +88,12 @@ function or a precomputed kernel matrix. The number of samples used - which is also the dimensionality of the features computed - is given by the parameter ``n_components``. -.. topic:: Examples: +.. rubric:: Examples - * See the example entitled - :ref:`sphx_glr_auto_examples_applications_plot_cyclical_feature_engineering.py`, - that shows an efficient machine learning pipeline that uses a - :class:`Nystroem` kernel. +* See the example entitled + :ref:`sphx_glr_auto_examples_applications_plot_cyclical_feature_engineering.py`, + that shows an efficient machine learning pipeline that uses a + :class:`Nystroem` kernel. .. _rbf_kernel_approx: @@ -143,9 +143,9 @@ use of larger feature spaces more efficient. Comparing an exact RBF kernel (left) with the approximation (right) -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py` +* :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py` .. _additive_chi_kernel_approx: @@ -241,9 +241,9 @@ In addition, this method can transform samples in time, where :math:`n_{\text{components}}` is the desired output dimension, determined by ``n_components``. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_kernel_approximation_plot_scalable_poly_kernels.py` +* :ref:`sphx_glr_auto_examples_kernel_approximation_plot_scalable_poly_kernels.py` .. _tensor_sketch_kernel_approx: @@ -283,29 +283,29 @@ The classes in this submodule allow to approximate the embedding or store training examples. -.. topic:: References: - - .. [WS2001] `"Using the Nyström method to speed up kernel machines" - `_ - Williams, C.K.I.; Seeger, M. - 2001. - .. [RR2007] `"Random features for large-scale kernel machines" - `_ - Rahimi, A. and Recht, B. - Advances in neural information processing 2007, - .. [LS2010] `"Random Fourier approximations for skewed multiplicative histogram kernels" - `_ - Li, F., Ionescu, C., and Sminchisescu, C. - - Pattern Recognition, DAGM 2010, Lecture Notes in Computer Science. - .. [VZ2010] `"Efficient additive kernels via explicit feature maps" - `_ - Vedaldi, A. and Zisserman, A. - Computer Vision and Pattern Recognition 2010 - .. [VVZ2010] `"Generalized RBF feature maps for Efficient Detection" - `_ - Vempati, S. and Vedaldi, A. and Zisserman, A. and Jawahar, CV - 2010 - .. [PP2013] :doi:`"Fast and scalable polynomial kernels via explicit feature maps" - <10.1145/2487575.2487591>` - Pham, N., & Pagh, R. - 2013 - .. [CCF2002] `"Finding frequent items in data streams" - `_ - Charikar, M., Chen, K., & Farach-Colton - 2002 - .. [WIKICS] `"Wikipedia: Count sketch" - `_ +.. rubric:: References + +.. [WS2001] `"Using the Nyström method to speed up kernel machines" + `_ + Williams, C.K.I.; Seeger, M. - 2001. +.. [RR2007] `"Random features for large-scale kernel machines" + `_ + Rahimi, A. and Recht, B. - Advances in neural information processing 2007, +.. [LS2010] `"Random Fourier approximations for skewed multiplicative histogram kernels" + `_ + Li, F., Ionescu, C., and Sminchisescu, C. + - Pattern Recognition, DAGM 2010, Lecture Notes in Computer Science. +.. [VZ2010] `"Efficient additive kernels via explicit feature maps" + `_ + Vedaldi, A. and Zisserman, A. - Computer Vision and Pattern Recognition 2010 +.. [VVZ2010] `"Generalized RBF feature maps for Efficient Detection" + `_ + Vempati, S. and Vedaldi, A. and Zisserman, A. and Jawahar, CV - 2010 +.. [PP2013] :doi:`"Fast and scalable polynomial kernels via explicit feature maps" + <10.1145/2487575.2487591>` + Pham, N., & Pagh, R. - 2013 +.. [CCF2002] `"Finding frequent items in data streams" + `_ + Charikar, M., Chen, K., & Farach-Colton - 2002 +.. [WIKICS] `"Wikipedia: Count sketch" + `_ diff --git a/doc/modules/kernel_ridge.rst b/doc/modules/kernel_ridge.rst index 5d25ce71f5ea1..fcc19a49628c4 100644 --- a/doc/modules/kernel_ridge.rst +++ b/doc/modules/kernel_ridge.rst @@ -55,11 +55,11 @@ dense model. :target: ../auto_examples/miscellaneous/plot_kernel_ridge_regression.html :align: center -.. topic:: Examples +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_ridge_regression.py` +* :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_ridge_regression.py` -.. topic:: References: +.. rubric:: References - .. [M2012] "Machine Learning: A Probabilistic Perspective" - Murphy, K. P. - chapter 14.4.3, pp. 492-493, The MIT Press, 2012 +.. [M2012] "Machine Learning: A Probabilistic Perspective" + Murphy, K. P. - chapter 14.4.3, pp. 492-493, The MIT Press, 2012 diff --git a/doc/modules/lda_qda.rst b/doc/modules/lda_qda.rst index 850a848fe3f73..0d264ec662a9f 100644 --- a/doc/modules/lda_qda.rst +++ b/doc/modules/lda_qda.rst @@ -29,10 +29,10 @@ Discriminant Analysis can only learn linear boundaries, while Quadratic Discriminant Analysis can learn quadratic boundaries and is therefore more flexible. -.. topic:: Examples: +.. rubric:: Examples - :ref:`sphx_glr_auto_examples_classification_plot_lda_qda.py`: Comparison of LDA and QDA - on synthetic data. +* :ref:`sphx_glr_auto_examples_classification_plot_lda_qda.py`: Comparison of LDA and + QDA on synthetic data. Dimensionality reduction using Linear Discriminant Analysis =========================================================== @@ -49,10 +49,10 @@ This is implemented in the `transform` method. The desired dimensionality can be set using the ``n_components`` parameter. This parameter has no influence on the `fit` and `predict` methods. -.. topic:: Examples: +.. rubric:: Examples - :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_lda.py`: Comparison of LDA and PCA - for dimensionality reduction of the Iris dataset +* :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_lda.py`: Comparison of LDA and + PCA for dimensionality reduction of the Iris dataset .. _lda_qda_math: @@ -194,7 +194,7 @@ Oracle Approximating Shrinkage estimator :class:`sklearn.covariance.OAS` yields a smaller Mean Squared Error than the one given by Ledoit and Wolf's formula used with shrinkage="auto". In LDA, the data are assumed to be gaussian conditionally to the class. If these assumptions hold, using LDA with -the OAS estimator of covariance will yield a better classification +the OAS estimator of covariance will yield a better classification accuracy than if Ledoit and Wolf or the empirical covariance estimator is used. The covariance estimator can be chosen using with the ``covariance_estimator`` @@ -210,10 +210,10 @@ class. A covariance estimator should have a :term:`fit` method and a .. centered:: |shrinkage| -.. topic:: Examples: +.. rubric:: Examples - :ref:`sphx_glr_auto_examples_classification_plot_lda.py`: Comparison of LDA classifiers - with Empirical, Ledoit Wolf and OAS covariance estimator. +* :ref:`sphx_glr_auto_examples_classification_plot_lda.py`: Comparison of LDA classifiers + with Empirical, Ledoit Wolf and OAS covariance estimator. Estimation algorithms ===================== @@ -253,13 +253,13 @@ transform, and it supports shrinkage. However, the 'eigen' solver needs to compute the covariance matrix, so it might not be suitable for situations with a high number of features. -.. topic:: References: +.. rubric:: References - .. [1] "The Elements of Statistical Learning", Hastie T., Tibshirani R., - Friedman J., Section 4.3, p.106-119, 2008. +.. [1] "The Elements of Statistical Learning", Hastie T., Tibshirani R., + Friedman J., Section 4.3, p.106-119, 2008. - .. [2] Ledoit O, Wolf M. Honey, I Shrunk the Sample Covariance Matrix. - The Journal of Portfolio Management 30(4), 110-119, 2004. +.. [2] Ledoit O, Wolf M. Honey, I Shrunk the Sample Covariance Matrix. + The Journal of Portfolio Management 30(4), 110-119, 2004. - .. [3] R. O. Duda, P. E. Hart, D. G. Stork. Pattern Classification - (Second Edition), section 2.6.2. +.. [3] R. O. Duda, P. E. Hart, D. G. Stork. Pattern Classification + (Second Edition), section 2.6.2. diff --git a/doc/modules/learning_curve.rst b/doc/modules/learning_curve.rst index 3d458a1a67416..f5af5a748500a 100644 --- a/doc/modules/learning_curve.rst +++ b/doc/modules/learning_curve.rst @@ -39,11 +39,11 @@ easy to see whether the estimator suffers from bias or variance. However, in high-dimensional spaces, models can become very difficult to visualize. For this reason, it is often helpful to use the tools described below. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_model_selection_plot_underfitting_overfitting.py` - * :ref:`sphx_glr_auto_examples_model_selection_plot_validation_curve.py` - * :ref:`sphx_glr_auto_examples_model_selection_plot_learning_curve.py` +* :ref:`sphx_glr_auto_examples_model_selection_plot_underfitting_overfitting.py` +* :ref:`sphx_glr_auto_examples_model_selection_plot_validation_curve.py` +* :ref:`sphx_glr_auto_examples_model_selection_plot_learning_curve.py` .. _validation_curve: diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst index 275ee01eb022f..d06101adabdb5 100644 --- a/doc/modules/linear_model.rst +++ b/doc/modules/linear_model.rst @@ -57,9 +57,9 @@ to random errors in the observed target, producing a large variance. This situation of *multicollinearity* can arise, for example, when data are collected without an experimental design. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_linear_model_plot_ols.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_ols.py` Non-Negative Least Squares -------------------------- @@ -71,9 +71,9 @@ quantities (e.g., frequency counts or prices of goods). parameter: when set to `True` `Non-Negative Least Squares `_ are then applied. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py` Ordinary Least Squares Complexity --------------------------------- @@ -172,11 +172,11 @@ Machines `_ with a linear kernel. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_linear_model_plot_ridge_path.py` - * :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` - * :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_ridge_path.py` +* :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` +* :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py` Ridge Complexity ---------------- @@ -216,13 +216,11 @@ cross-validation with :class:`~sklearn.model_selection.GridSearchCV`, for example `cv=10` for 10-fold cross-validation, rather than Leave-One-Out Cross-Validation. -.. topic:: References: - +.. dropdown:: References .. [RL2007] "Notes on Regularized Least Squares", Rifkin & Lippert (`technical report `_, - `course slides - `_). + `course slides `_). .. _lasso: @@ -262,11 +260,11 @@ for another implementation:: The function :func:`lasso_path` is useful for lower-level tasks, as it computes the coefficients along the full path of possible values. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py` - * :ref:`sphx_glr_auto_examples_applications_plot_tomography_l1_reconstruction.py` - * :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py` +* :ref:`sphx_glr_auto_examples_applications_plot_tomography_l1_reconstruction.py` +* :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py` .. note:: **Feature selection with Lasso** @@ -275,23 +273,19 @@ computes the coefficients along the full path of possible values. thus be used to perform feature selection, as detailed in :ref:`l1_feature_selection`. -|details-start| -**References** -|details-split| - -The following two references explain the iterations -used in the coordinate descent solver of scikit-learn, as well as -the duality gap computation used for convergence control. +.. dropdown:: References -* "Regularization Path For Generalized linear Models by Coordinate Descent", - Friedman, Hastie & Tibshirani, J Stat Softw, 2010 (`Paper - `__). -* "An Interior-Point Method for Large-Scale L1-Regularized Least Squares," - S. J. Kim, K. Koh, M. Lustig, S. Boyd and D. Gorinevsky, - in IEEE Journal of Selected Topics in Signal Processing, 2007 - (`Paper `__) + The following two references explain the iterations + used in the coordinate descent solver of scikit-learn, as well as + the duality gap computation used for convergence control. -|details-end| + * "Regularization Path For Generalized linear Models by Coordinate Descent", + Friedman, Hastie & Tibshirani, J Stat Softw, 2010 (`Paper + `__). + * "An Interior-Point Method for Large-Scale L1-Regularized Least Squares," + S. J. Kim, K. Koh, M. Lustig, S. Boyd and D. Gorinevsky, + in IEEE Journal of Selected Topics in Signal Processing, 2007 + (`Paper `__) Setting regularization parameter -------------------------------- @@ -348,10 +342,10 @@ the problem is badly conditioned (e.g. more features than samples). :align: center :scale: 50% -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_model_selection.py` - * :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_lars_ic.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_model_selection.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_lars_ic.py` .. _aic_bic: @@ -362,59 +356,57 @@ The definition of AIC (and thus BIC) might differ in the literature. In this section, we give more information regarding the criterion computed in scikit-learn. -|details-start| -**Mathematical details** -|details-split| +.. dropdown:: Mathematical details -The AIC criterion is defined as: + The AIC criterion is defined as: -.. math:: - AIC = -2 \log(\hat{L}) + 2 d + .. math:: + AIC = -2 \log(\hat{L}) + 2 d -where :math:`\hat{L}` is the maximum likelihood of the model and -:math:`d` is the number of parameters (as well referred to as degrees of -freedom in the previous section). + where :math:`\hat{L}` is the maximum likelihood of the model and + :math:`d` is the number of parameters (as well referred to as degrees of + freedom in the previous section). -The definition of BIC replace the constant :math:`2` by :math:`\log(N)`: + The definition of BIC replace the constant :math:`2` by :math:`\log(N)`: -.. math:: - BIC = -2 \log(\hat{L}) + \log(N) d + .. math:: + BIC = -2 \log(\hat{L}) + \log(N) d -where :math:`N` is the number of samples. + where :math:`N` is the number of samples. -For a linear Gaussian model, the maximum log-likelihood is defined as: + For a linear Gaussian model, the maximum log-likelihood is defined as: -.. math:: - \log(\hat{L}) = - \frac{n}{2} \log(2 \pi) - \frac{n}{2} \ln(\sigma^2) - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{2\sigma^2} + .. math:: + \log(\hat{L}) = - \frac{n}{2} \log(2 \pi) - \frac{n}{2} \ln(\sigma^2) - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{2\sigma^2} -where :math:`\sigma^2` is an estimate of the noise variance, -:math:`y_i` and :math:`\hat{y}_i` are respectively the true and predicted -targets, and :math:`n` is the number of samples. + where :math:`\sigma^2` is an estimate of the noise variance, + :math:`y_i` and :math:`\hat{y}_i` are respectively the true and predicted + targets, and :math:`n` is the number of samples. -Plugging the maximum log-likelihood in the AIC formula yields: + Plugging the maximum log-likelihood in the AIC formula yields: -.. math:: - AIC = n \log(2 \pi \sigma^2) + \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{\sigma^2} + 2 d + .. math:: + AIC = n \log(2 \pi \sigma^2) + \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{\sigma^2} + 2 d -The first term of the above expression is sometimes discarded since it is a -constant when :math:`\sigma^2` is provided. In addition, -it is sometimes stated that the AIC is equivalent to the :math:`C_p` statistic -[12]_. In a strict sense, however, it is equivalent only up to some constant -and a multiplicative factor. + The first term of the above expression is sometimes discarded since it is a + constant when :math:`\sigma^2` is provided. In addition, + it is sometimes stated that the AIC is equivalent to the :math:`C_p` statistic + [12]_. In a strict sense, however, it is equivalent only up to some constant + and a multiplicative factor. -At last, we mentioned above that :math:`\sigma^2` is an estimate of the -noise variance. In :class:`LassoLarsIC` when the parameter `noise_variance` is -not provided (default), the noise variance is estimated via the unbiased -estimator [13]_ defined as: + At last, we mentioned above that :math:`\sigma^2` is an estimate of the + noise variance. In :class:`LassoLarsIC` when the parameter `noise_variance` is + not provided (default), the noise variance is estimated via the unbiased + estimator [13]_ defined as: -.. math:: - \sigma^2 = \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{n - p} + .. math:: + \sigma^2 = \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{n - p} -where :math:`p` is the number of features and :math:`\hat{y}_i` is the -predicted target using an ordinary least squares regression. Note, that this -formula is valid only when `n_samples > n_features`. + where :math:`p` is the number of features and :math:`\hat{y}_i` is the + predicted target using an ordinary least squares regression. Note, that this + formula is valid only when `n_samples > n_features`. -.. topic:: References: + .. rubric:: References .. [12] :arxiv:`Zou, Hui, Trevor Hastie, and Robert Tibshirani. "On the degrees of freedom of the lasso." @@ -426,8 +418,6 @@ formula is valid only when `n_samples > n_features`. Neural computation 15.7 (2003): 1691-1714. <10.1162/089976603321891864>` -|details-end| - Comparison with the regularization parameter of SVM ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -463,33 +453,29 @@ the MultiTaskLasso are full columns. .. centered:: Fitting a time-series model, imposing that any active feature be active at all times. -.. topic:: Examples: - - * :ref:`sphx_glr_auto_examples_linear_model_plot_multi_task_lasso_support.py` +.. rubric:: Examples +* :ref:`sphx_glr_auto_examples_linear_model_plot_multi_task_lasso_support.py` -|details-start| -**Mathematical details** -|details-split| -Mathematically, it consists of a linear model trained with a mixed -:math:`\ell_1` :math:`\ell_2`-norm for regularization. -The objective function to minimize is: +.. dropdown:: Mathematical details -.. math:: \min_{W} { \frac{1}{2n_{\text{samples}}} ||X W - Y||_{\text{Fro}} ^ 2 + \alpha ||W||_{21}} + Mathematically, it consists of a linear model trained with a mixed + :math:`\ell_1` :math:`\ell_2`-norm for regularization. + The objective function to minimize is: -where :math:`\text{Fro}` indicates the Frobenius norm + .. math:: \min_{W} { \frac{1}{2n_{\text{samples}}} ||X W - Y||_{\text{Fro}} ^ 2 + \alpha ||W||_{21}} -.. math:: ||A||_{\text{Fro}} = \sqrt{\sum_{ij} a_{ij}^2} + where :math:`\text{Fro}` indicates the Frobenius norm -and :math:`\ell_1` :math:`\ell_2` reads + .. math:: ||A||_{\text{Fro}} = \sqrt{\sum_{ij} a_{ij}^2} -.. math:: ||A||_{2 1} = \sum_i \sqrt{\sum_j a_{ij}^2}. + and :math:`\ell_1` :math:`\ell_2` reads -The implementation in the class :class:`MultiTaskLasso` uses -coordinate descent as the algorithm to fit the coefficients. + .. math:: ||A||_{2 1} = \sum_i \sqrt{\sum_j a_{ij}^2}. -|details-end| + The implementation in the class :class:`MultiTaskLasso` uses + coordinate descent as the algorithm to fit the coefficients. .. _elastic_net: @@ -526,29 +512,25 @@ The objective function to minimize is in this case The class :class:`ElasticNetCV` can be used to set the parameters ``alpha`` (:math:`\alpha`) and ``l1_ratio`` (:math:`\rho`) by cross-validation. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py` - * :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_coordinate_descent_path.py` - * :ref:`sphx_glr_auto_examples_linear_model_plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_coordinate_descent_path.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py` -|details-start| -**References** -|details-split| +.. dropdown:: References -The following two references explain the iterations -used in the coordinate descent solver of scikit-learn, as well as -the duality gap computation used for convergence control. + The following two references explain the iterations + used in the coordinate descent solver of scikit-learn, as well as + the duality gap computation used for convergence control. -* "Regularization Path For Generalized linear Models by Coordinate Descent", - Friedman, Hastie & Tibshirani, J Stat Softw, 2010 (`Paper - `__). -* "An Interior-Point Method for Large-Scale L1-Regularized Least Squares," - S. J. Kim, K. Koh, M. Lustig, S. Boyd and D. Gorinevsky, - in IEEE Journal of Selected Topics in Signal Processing, 2007 - (`Paper `__) - -|details-end| + * "Regularization Path For Generalized linear Models by Coordinate Descent", + Friedman, Hastie & Tibshirani, J Stat Softw, 2010 (`Paper + `__). + * "An Interior-Point Method for Large-Scale L1-Regularized Least Squares," + S. J. Kim, K. Koh, M. Lustig, S. Boyd and D. Gorinevsky, + in IEEE Journal of Selected Topics in Signal Processing, 2007 + (`Paper `__) .. _multi_task_elastic_net: @@ -641,37 +623,33 @@ function of the norm of its coefficients. >>> reg.coef_ array([0.6..., 0. ]) -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_lars.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_lars.py` The Lars algorithm provides the full path of the coefficients along the regularization parameter almost for free, thus a common operation is to retrieve the path with one of the functions :func:`lars_path` or :func:`lars_path_gram`. -|details-start| -**Mathematical formulation** -|details-split| - -The algorithm is similar to forward stepwise regression, but instead -of including features at each step, the estimated coefficients are -increased in a direction equiangular to each one's correlations with -the residual. +.. dropdown:: Mathematical formulation -Instead of giving a vector result, the LARS solution consists of a -curve denoting the solution for each value of the :math:`\ell_1` norm of the -parameter vector. The full coefficients path is stored in the array -``coef_path_`` of shape `(n_features, max_features + 1)`. The first -column is always zero. + The algorithm is similar to forward stepwise regression, but instead + of including features at each step, the estimated coefficients are + increased in a direction equiangular to each one's correlations with + the residual. -.. topic:: References: + Instead of giving a vector result, the LARS solution consists of a + curve denoting the solution for each value of the :math:`\ell_1` norm of the + parameter vector. The full coefficients path is stored in the array + ``coef_path_`` of shape `(n_features, max_features + 1)`. The first + column is always zero. - * Original Algorithm is detailed in the paper `Least Angle Regression - `_ - by Hastie et al. + .. rubric:: References -|details-end| + * Original Algorithm is detailed in the paper `Least Angle Regression + `_ + by Hastie et al. .. _omp: @@ -702,21 +680,17 @@ residual is recomputed using an orthogonal projection on the space of the previously chosen dictionary elements. -.. topic:: Examples: - - * :ref:`sphx_glr_auto_examples_linear_model_plot_omp.py` +.. rubric:: Examples -|details-start| -**References** -|details-split| +* :ref:`sphx_glr_auto_examples_linear_model_plot_omp.py` -* https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf +.. dropdown:: References -* `Matching pursuits with time-frequency dictionaries - `_, - S. G. Mallat, Z. Zhang, + * https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf -|details-end| + * `Matching pursuits with time-frequency dictionaries + `_, + S. G. Mallat, Z. Zhang, .. _bayesian_regression: @@ -755,17 +729,13 @@ The disadvantages of Bayesian regression include: - Inference of the model can be time consuming. -|details-start| -**References** -|details-split| +.. dropdown:: References -* A good introduction to Bayesian methods is given in C. Bishop: Pattern - Recognition and Machine learning + * A good introduction to Bayesian methods is given in C. Bishop: Pattern + Recognition and Machine learning -* Original Algorithm is detailed in the book `Bayesian learning for neural - networks` by Radford M. Neal - -|details-end| + * Original Algorithm is detailed in the book `Bayesian learning for neural + networks` by Radford M. Neal .. _bayesian_ridge_regression: @@ -822,21 +792,17 @@ Due to the Bayesian framework, the weights found are slightly different to the ones found by :ref:`ordinary_least_squares`. However, Bayesian Ridge Regression is more robust to ill-posed problems. -.. topic:: Examples: - - * :ref:`sphx_glr_auto_examples_linear_model_plot_bayesian_ridge_curvefit.py` +.. rubric:: Examples -|details-start| -**References** -|details-split| +* :ref:`sphx_glr_auto_examples_linear_model_plot_bayesian_ridge_curvefit.py` -* Section 3.3 in Christopher M. Bishop: Pattern Recognition and Machine Learning, 2006 +.. dropdown:: References -* David J. C. MacKay, `Bayesian Interpolation `_, 1992. + * Section 3.3 in Christopher M. Bishop: Pattern Recognition and Machine Learning, 2006 -* Michael E. Tipping, `Sparse Bayesian Learning and the Relevance Vector Machine `_, 2001. + * David J. C. MacKay, `Bayesian Interpolation `_, 1992. -|details-end| + * Michael E. Tipping, `Sparse Bayesian Learning and the Relevance Vector Machine `_, 2001. .. _automatic_relevance_determination: @@ -868,20 +834,20 @@ ARD is also known in the literature as *Sparse Bayesian Learning* and *Relevance Vector Machine* [3]_ [4]_. For a worked-out comparison between ARD and `Bayesian Ridge Regression`_, see the example below. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_linear_model_plot_ard.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_ard.py` -.. topic:: References: +.. rubric:: References - .. [1] Christopher M. Bishop: Pattern Recognition and Machine Learning, Chapter 7.2.1 +.. [1] Christopher M. Bishop: Pattern Recognition and Machine Learning, Chapter 7.2.1 - .. [2] David Wipf and Srikantan Nagarajan: `A New View of Automatic Relevance Determination `_ +.. [2] David Wipf and Srikantan Nagarajan: `A New View of Automatic Relevance Determination `_ - .. [3] Michael E. Tipping: `Sparse Bayesian Learning and the Relevance Vector Machine `_ +.. [3] Michael E. Tipping: `Sparse Bayesian Learning and the Relevance Vector Machine `_ - .. [4] Tristan Fletcher: `Relevance Vector Machines Explained `_ +.. [4] Tristan Fletcher: `Relevance Vector Machines Explained `_ .. _Logistic_regression: @@ -918,17 +884,13 @@ regularization. implemented in scikit-learn, so it expects a categorical target, making the Logistic Regression a classifier. -.. topic:: Examples - - * :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_l1_l2_sparsity.py` +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py` - - * :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_multinomial.py` - - * :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_20newsgroups.py` - - * :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_mnist.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_l1_l2_sparsity.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_multinomial.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_20newsgroups.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_mnist.py` Binary Case ----------- @@ -1000,47 +962,43 @@ logistic regression, see also `log-linear model especially important when using regularization. The choice of overparameterization can be detrimental for unpenalized models since then the solution may not be unique, as shown in [16]_. -|details-start| -**Mathematical details** -|details-split| +.. dropdown:: Mathematical details -Let :math:`y_i \in {1, \ldots, K}` be the label (ordinal) encoded target variable for observation :math:`i`. -Instead of a single coefficient vector, we now have -a matrix of coefficients :math:`W` where each row vector :math:`W_k` corresponds to class -:math:`k`. We aim at predicting the class probabilities :math:`P(y_i=k|X_i)` via -:meth:`~sklearn.linear_model.LogisticRegression.predict_proba` as: + Let :math:`y_i \in {1, \ldots, K}` be the label (ordinal) encoded target variable for observation :math:`i`. + Instead of a single coefficient vector, we now have + a matrix of coefficients :math:`W` where each row vector :math:`W_k` corresponds to class + :math:`k`. We aim at predicting the class probabilities :math:`P(y_i=k|X_i)` via + :meth:`~sklearn.linear_model.LogisticRegression.predict_proba` as: -.. math:: \hat{p}_k(X_i) = \frac{\exp(X_i W_k + W_{0, k})}{\sum_{l=0}^{K-1} \exp(X_i W_l + W_{0, l})}. + .. math:: \hat{p}_k(X_i) = \frac{\exp(X_i W_k + W_{0, k})}{\sum_{l=0}^{K-1} \exp(X_i W_l + W_{0, l})}. -The objective for the optimization becomes - -.. math:: - \min_W -\frac{1}{S}\sum_{i=1}^n \sum_{k=0}^{K-1} s_{ik} [y_i = k] \log(\hat{p}_k(X_i)) - + \frac{r(W)}{S C}\,. + The objective for the optimization becomes -Where :math:`[P]` represents the Iverson bracket which evaluates to :math:`0` -if :math:`P` is false, otherwise it evaluates to :math:`1`. + .. math:: + \min_W -\frac{1}{S}\sum_{i=1}^n \sum_{k=0}^{K-1} s_{ik} [y_i = k] \log(\hat{p}_k(X_i)) + + \frac{r(W)}{S C}\,, -Again, :math:`s_{ik}` are the weights assigned by the user (multiplication of sample -weights and class weights) with their sum :math:`S = \sum_{i=1}^n \sum_{k=0}^{K-1} s_{ik}`. + where :math:`[P]` represents the Iverson bracket which evaluates to :math:`0` + if :math:`P` is false, otherwise it evaluates to :math:`1`. -We currently provide four choices -for the regularization term :math:`r(W)` via the `penalty` argument, where :math:`m` -is the number of features: + Again, :math:`s_{ik}` are the weights assigned by the user (multiplication of sample + weights and class weights) with their sum :math:`S = \sum_{i=1}^n \sum_{k=0}^{K-1} s_{ik}`. -+----------------+----------------------------------------------------------------------------------+ -| penalty | :math:`r(W)` | -+================+==================================================================================+ -| `None` | :math:`0` | -+----------------+----------------------------------------------------------------------------------+ -| :math:`\ell_1` | :math:`\|W\|_{1,1} = \sum_{i=1}^m\sum_{j=1}^{K}|W_{i,j}|` | -+----------------+----------------------------------------------------------------------------------+ -| :math:`\ell_2` | :math:`\frac{1}{2}\|W\|_F^2 = \frac{1}{2}\sum_{i=1}^m\sum_{j=1}^{K} W_{i,j}^2` | -+----------------+----------------------------------------------------------------------------------+ -| `ElasticNet` | :math:`\frac{1 - \rho}{2}\|W\|_F^2 + \rho \|W\|_{1,1}` | -+----------------+----------------------------------------------------------------------------------+ + We currently provide four choices + for the regularization term :math:`r(W)` via the `penalty` argument, where :math:`m` + is the number of features: -|details-end| + +----------------+----------------------------------------------------------------------------------+ + | penalty | :math:`r(W)` | + +================+==================================================================================+ + | `None` | :math:`0` | + +----------------+----------------------------------------------------------------------------------+ + | :math:`\ell_1` | :math:`\|W\|_{1,1} = \sum_{i=1}^m\sum_{j=1}^{K}|W_{i,j}|` | + +----------------+----------------------------------------------------------------------------------+ + | :math:`\ell_2` | :math:`\frac{1}{2}\|W\|_F^2 = \frac{1}{2}\sum_{i=1}^m\sum_{j=1}^{K} W_{i,j}^2` | + +----------------+----------------------------------------------------------------------------------+ + | `ElasticNet` | :math:`\frac{1 - \rho}{2}\|W\|_F^2 + \rho \|W\|_{1,1}` | + +----------------+----------------------------------------------------------------------------------+ Solvers ------- @@ -1097,56 +1055,54 @@ with ``fit_intercept=False`` and having many samples with ``decision_function`` zero, is likely to be a underfit, bad model and you are advised to set ``fit_intercept=True`` and increase the ``intercept_scaling``. -|details-start| -**Solvers' details** -|details-split| - -* The solver "liblinear" uses a coordinate descent (CD) algorithm, and relies - on the excellent C++ `LIBLINEAR library - `_, which is shipped with - scikit-learn. However, the CD algorithm implemented in liblinear cannot learn - a true multinomial (multiclass) model; instead, the optimization problem is - decomposed in a "one-vs-rest" fashion so separate binary classifiers are - trained for all classes. This happens under the hood, so - :class:`LogisticRegression` instances using this solver behave as multiclass - classifiers. For :math:`\ell_1` regularization :func:`sklearn.svm.l1_min_c` allows to - calculate the lower bound for C in order to get a non "null" (all feature - weights to zero) model. - -* The "lbfgs", "newton-cg" and "sag" solvers only support :math:`\ell_2` - regularization or no regularization, and are found to converge faster for some - high-dimensional data. Setting `multi_class` to "multinomial" with these solvers - learns a true multinomial logistic regression model [5]_, which means that its - probability estimates should be better calibrated than the default "one-vs-rest" - setting. - -* The "sag" solver uses Stochastic Average Gradient descent [6]_. It is faster - than other solvers for large datasets, when both the number of samples and the - number of features are large. - -* The "saga" solver [7]_ is a variant of "sag" that also supports the - non-smooth `penalty="l1"`. This is therefore the solver of choice for sparse - multinomial logistic regression. It is also the only solver that supports - `penalty="elasticnet"`. - -* The "lbfgs" is an optimization algorithm that approximates the - Broyden–Fletcher–Goldfarb–Shanno algorithm [8]_, which belongs to - quasi-Newton methods. As such, it can deal with a wide range of different training - data and is therefore the default solver. Its performance, however, suffers on poorly - scaled datasets and on datasets with one-hot encoded categorical features with rare - categories. - -* The "newton-cholesky" solver is an exact Newton solver that calculates the hessian - matrix and solves the resulting linear system. It is a very good choice for - `n_samples` >> `n_features`, but has a few shortcomings: Only :math:`\ell_2` - regularization is supported. Furthermore, because the hessian matrix is explicitly - computed, the memory usage has a quadratic dependency on `n_features` as well as on - `n_classes`. As a consequence, only the one-vs-rest scheme is implemented for the - multiclass case. - -For a comparison of some of these solvers, see [9]_. - -.. topic:: References: +.. dropdown:: Solvers' details + + * The solver "liblinear" uses a coordinate descent (CD) algorithm, and relies + on the excellent C++ `LIBLINEAR library + `_, which is shipped with + scikit-learn. However, the CD algorithm implemented in liblinear cannot learn + a true multinomial (multiclass) model; instead, the optimization problem is + decomposed in a "one-vs-rest" fashion so separate binary classifiers are + trained for all classes. This happens under the hood, so + :class:`LogisticRegression` instances using this solver behave as multiclass + classifiers. For :math:`\ell_1` regularization :func:`sklearn.svm.l1_min_c` allows to + calculate the lower bound for C in order to get a non "null" (all feature + weights to zero) model. + + * The "lbfgs", "newton-cg" and "sag" solvers only support :math:`\ell_2` + regularization or no regularization, and are found to converge faster for some + high-dimensional data. Setting `multi_class` to "multinomial" with these solvers + learns a true multinomial logistic regression model [5]_, which means that its + probability estimates should be better calibrated than the default "one-vs-rest" + setting. + + * The "sag" solver uses Stochastic Average Gradient descent [6]_. It is faster + than other solvers for large datasets, when both the number of samples and the + number of features are large. + + * The "saga" solver [7]_ is a variant of "sag" that also supports the + non-smooth `penalty="l1"`. This is therefore the solver of choice for sparse + multinomial logistic regression. It is also the only solver that supports + `penalty="elasticnet"`. + + * The "lbfgs" is an optimization algorithm that approximates the + Broyden–Fletcher–Goldfarb–Shanno algorithm [8]_, which belongs to + quasi-Newton methods. As such, it can deal with a wide range of different training + data and is therefore the default solver. Its performance, however, suffers on poorly + scaled datasets and on datasets with one-hot encoded categorical features with rare + categories. + + * The "newton-cholesky" solver is an exact Newton solver that calculates the hessian + matrix and solves the resulting linear system. It is a very good choice for + `n_samples` >> `n_features`, but has a few shortcomings: Only :math:`\ell_2` + regularization is supported. Furthermore, because the hessian matrix is explicitly + computed, the memory usage has a quadratic dependency on `n_features` as well as on + `n_classes`. As a consequence, only the one-vs-rest scheme is implemented for the + multiclass case. + + For a comparison of some of these solvers, see [9]_. + + .. rubric:: References .. [5] Christopher M. Bishop: Pattern Recognition and Machine Learning, Chapter 4.3.4 @@ -1165,8 +1121,6 @@ For a comparison of some of these solvers, see [9]_. "A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression." <1311.6529>` -|details-end| - .. note:: **Feature selection with sparse logistic regression** @@ -1263,38 +1217,34 @@ The choice of the distribution depends on the problem at hand: used for multiclass classification. -|details-start| -**Examples of use cases** -|details-split| - -* Agriculture / weather modeling: number of rain events per year (Poisson), - amount of rainfall per event (Gamma), total rainfall per year (Tweedie / - Compound Poisson Gamma). -* Risk modeling / insurance policy pricing: number of claim events / - policyholder per year (Poisson), cost per event (Gamma), total cost per - policyholder per year (Tweedie / Compound Poisson Gamma). -* Credit Default: probability that a loan can't be paid back (Bernoulli). -* Fraud Detection: probability that a financial transaction like a cash transfer - is a fraudulent transaction (Bernoulli). -* Predictive maintenance: number of production interruption events per year - (Poisson), duration of interruption (Gamma), total interruption time per year - (Tweedie / Compound Poisson Gamma). -* Medical Drug Testing: probability of curing a patient in a set of trials or - probability that a patient will experience side effects (Bernoulli). -* News Classification: classification of news articles into three categories - namely Business News, Politics and Entertainment news (Categorical). +.. dropdown:: Examples of use cases -|details-end| + * Agriculture / weather modeling: number of rain events per year (Poisson), + amount of rainfall per event (Gamma), total rainfall per year (Tweedie / + Compound Poisson Gamma). + * Risk modeling / insurance policy pricing: number of claim events / + policyholder per year (Poisson), cost per event (Gamma), total cost per + policyholder per year (Tweedie / Compound Poisson Gamma). + * Credit Default: probability that a loan can't be paid back (Bernoulli). + * Fraud Detection: probability that a financial transaction like a cash transfer + is a fraudulent transaction (Bernoulli). + * Predictive maintenance: number of production interruption events per year + (Poisson), duration of interruption (Gamma), total interruption time per year + (Tweedie / Compound Poisson Gamma). + * Medical Drug Testing: probability of curing a patient in a set of trials or + probability that a patient will experience side effects (Bernoulli). + * News Classification: classification of news articles into three categories + namely Business News, Politics and Entertainment news (Categorical). -.. topic:: References: +.. rubric:: References - .. [10] McCullagh, Peter; Nelder, John (1989). Generalized Linear Models, - Second Edition. Boca Raton: Chapman and Hall/CRC. ISBN 0-412-31760-5. +.. [10] McCullagh, Peter; Nelder, John (1989). Generalized Linear Models, + Second Edition. Boca Raton: Chapman and Hall/CRC. ISBN 0-412-31760-5. - .. [11] Jørgensen, B. (1992). The theory of exponential dispersion models - and analysis of deviance. Monografias de matemática, no. 51. See also - `Exponential dispersion model. - `_ +.. [11] Jørgensen, B. (1992). The theory of exponential dispersion models + and analysis of deviance. Monografias de matemática, no. 51. See also + `Exponential dispersion model. + `_ Usage ----- @@ -1328,37 +1278,33 @@ Usage example:: -0.7638... -.. topic:: Examples - - * :ref:`sphx_glr_auto_examples_linear_model_plot_poisson_regression_non_normal_loss.py` - * :ref:`sphx_glr_auto_examples_linear_model_plot_tweedie_regression_insurance_claims.py` +.. rubric:: Examples -|details-start| -**Practical considerations** -|details-split| +* :ref:`sphx_glr_auto_examples_linear_model_plot_poisson_regression_non_normal_loss.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_tweedie_regression_insurance_claims.py` -The feature matrix `X` should be standardized before fitting. This ensures -that the penalty treats features equally. +.. dropdown:: Practical considerations -Since the linear predictor :math:`Xw` can be negative and Poisson, -Gamma and Inverse Gaussian distributions don't support negative values, it -is necessary to apply an inverse link function that guarantees the -non-negativeness. For example with `link='log'`, the inverse link function -becomes :math:`h(Xw)=\exp(Xw)`. + The feature matrix `X` should be standardized before fitting. This ensures + that the penalty treats features equally. -If you want to model a relative frequency, i.e. counts per exposure (time, -volume, ...) you can do so by using a Poisson distribution and passing -:math:`y=\frac{\mathrm{counts}}{\mathrm{exposure}}` as target values -together with :math:`\mathrm{exposure}` as sample weights. For a concrete -example see e.g. -:ref:`sphx_glr_auto_examples_linear_model_plot_tweedie_regression_insurance_claims.py`. + Since the linear predictor :math:`Xw` can be negative and Poisson, + Gamma and Inverse Gaussian distributions don't support negative values, it + is necessary to apply an inverse link function that guarantees the + non-negativeness. For example with `link='log'`, the inverse link function + becomes :math:`h(Xw)=\exp(Xw)`. -When performing cross-validation for the `power` parameter of -`TweedieRegressor`, it is advisable to specify an explicit `scoring` function, -because the default scorer :meth:`TweedieRegressor.score` is a function of -`power` itself. + If you want to model a relative frequency, i.e. counts per exposure (time, + volume, ...) you can do so by using a Poisson distribution and passing + :math:`y=\frac{\mathrm{counts}}{\mathrm{exposure}}` as target values + together with :math:`\mathrm{exposure}` as sample weights. For a concrete + example see e.g. + :ref:`sphx_glr_auto_examples_linear_model_plot_tweedie_regression_insurance_claims.py`. -|details-end| + When performing cross-validation for the `power` parameter of + `TweedieRegressor`, it is advisable to specify an explicit `scoring` function, + because the default scorer :meth:`TweedieRegressor.score` is a function of + `power` itself. Stochastic Gradient Descent - SGD ================================= @@ -1416,15 +1362,11 @@ For classification, :class:`PassiveAggressiveClassifier` can be used with ``loss='epsilon_insensitive'`` (PA-I) or ``loss='squared_epsilon_insensitive'`` (PA-II). -|details-start| -**References** -|details-split| +.. dropdown:: References -* `"Online Passive-Aggressive Algorithms" - `_ - K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR 7 (2006) - -|details-end| + * `"Online Passive-Aggressive Algorithms" + `_ + K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR 7 (2006) Robustness regression: outliers and modeling errors ===================================================== @@ -1534,56 +1476,48 @@ estimated only from the determined inliers. :align: center :scale: 50% -.. topic:: Examples - - * :ref:`sphx_glr_auto_examples_linear_model_plot_ransac.py` - * :ref:`sphx_glr_auto_examples_linear_model_plot_robust_fit.py` - -|details-start| -**Details of the algorithm** -|details-split| - -Each iteration performs the following steps: - -1. Select ``min_samples`` random samples from the original data and check - whether the set of data is valid (see ``is_data_valid``). -2. Fit a model to the random subset (``estimator.fit``) and check - whether the estimated model is valid (see ``is_model_valid``). -3. Classify all data as inliers or outliers by calculating the residuals - to the estimated model (``estimator.predict(X) - y``) - all data - samples with absolute residuals smaller than or equal to the - ``residual_threshold`` are considered as inliers. -4. Save fitted model as best model if number of inlier samples is - maximal. In case the current estimated model has the same number of - inliers, it is only considered as the best model if it has better score. - -These steps are performed either a maximum number of times (``max_trials``) or -until one of the special stop criteria are met (see ``stop_n_inliers`` and -``stop_score``). The final model is estimated using all inlier samples (consensus -set) of the previously determined best model. - -The ``is_data_valid`` and ``is_model_valid`` functions allow to identify and reject -degenerate combinations of random sub-samples. If the estimated model is not -needed for identifying degenerate cases, ``is_data_valid`` should be used as it -is called prior to fitting the model and thus leading to better computational -performance. - -|details-end| - -|details-start| -**References** -|details-split| - -* https://en.wikipedia.org/wiki/RANSAC -* `"Random Sample Consensus: A Paradigm for Model Fitting with Applications to - Image Analysis and Automated Cartography" - `_ - Martin A. Fischler and Robert C. Bolles - SRI International (1981) -* `"Performance Evaluation of RANSAC Family" - `_ - Sunglok Choi, Taemin Kim and Wonpil Yu - BMVC (2009) - -|details-end| +.. rubric:: Examples + +* :ref:`sphx_glr_auto_examples_linear_model_plot_ransac.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_robust_fit.py` + +.. dropdown:: Details of the algorithm + + Each iteration performs the following steps: + + 1. Select ``min_samples`` random samples from the original data and check + whether the set of data is valid (see ``is_data_valid``). + 2. Fit a model to the random subset (``estimator.fit``) and check + whether the estimated model is valid (see ``is_model_valid``). + 3. Classify all data as inliers or outliers by calculating the residuals + to the estimated model (``estimator.predict(X) - y``) - all data + samples with absolute residuals smaller than or equal to the + ``residual_threshold`` are considered as inliers. + 4. Save fitted model as best model if number of inlier samples is + maximal. In case the current estimated model has the same number of + inliers, it is only considered as the best model if it has better score. + + These steps are performed either a maximum number of times (``max_trials``) or + until one of the special stop criteria are met (see ``stop_n_inliers`` and + ``stop_score``). The final model is estimated using all inlier samples (consensus + set) of the previously determined best model. + + The ``is_data_valid`` and ``is_model_valid`` functions allow to identify and reject + degenerate combinations of random sub-samples. If the estimated model is not + needed for identifying degenerate cases, ``is_data_valid`` should be used as it + is called prior to fitting the model and thus leading to better computational + performance. + +.. dropdown:: References + + * https://en.wikipedia.org/wiki/RANSAC + * `"Random Sample Consensus: A Paradigm for Model Fitting with Applications to + Image Analysis and Automated Cartography" + `_ + Martin A. Fischler and Robert C. Bolles - SRI International (1981) + * `"Performance Evaluation of RANSAC Family" + `_ + Sunglok Choi, Taemin Kim and Wonpil Yu - BMVC (2009) .. _theil_sen_regression: @@ -1596,47 +1530,45 @@ that the robustness of the estimator decreases quickly with the dimensionality of the problem. It loses its robustness properties and becomes no better than an ordinary least squares in high dimension. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_linear_model_plot_theilsen.py` - * :ref:`sphx_glr_auto_examples_linear_model_plot_robust_fit.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_theilsen.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_robust_fit.py` -|details-start| -**Theoretical considerations** -|details-split| +.. dropdown:: Theoretical considerations -:class:`TheilSenRegressor` is comparable to the :ref:`Ordinary Least Squares -(OLS) ` in terms of asymptotic efficiency and as an -unbiased estimator. In contrast to OLS, Theil-Sen is a non-parametric -method which means it makes no assumption about the underlying -distribution of the data. Since Theil-Sen is a median-based estimator, it -is more robust against corrupted data aka outliers. In univariate -setting, Theil-Sen has a breakdown point of about 29.3% in case of a -simple linear regression which means that it can tolerate arbitrary -corrupted data of up to 29.3%. + :class:`TheilSenRegressor` is comparable to the :ref:`Ordinary Least Squares + (OLS) ` in terms of asymptotic efficiency and as an + unbiased estimator. In contrast to OLS, Theil-Sen is a non-parametric + method which means it makes no assumption about the underlying + distribution of the data. Since Theil-Sen is a median-based estimator, it + is more robust against corrupted data aka outliers. In univariate + setting, Theil-Sen has a breakdown point of about 29.3% in case of a + simple linear regression which means that it can tolerate arbitrary + corrupted data of up to 29.3%. -.. figure:: ../auto_examples/linear_model/images/sphx_glr_plot_theilsen_001.png - :target: ../auto_examples/linear_model/plot_theilsen.html - :align: center - :scale: 50% + .. figure:: ../auto_examples/linear_model/images/sphx_glr_plot_theilsen_001.png + :target: ../auto_examples/linear_model/plot_theilsen.html + :align: center + :scale: 50% -The implementation of :class:`TheilSenRegressor` in scikit-learn follows a -generalization to a multivariate linear regression model [#f1]_ using the -spatial median which is a generalization of the median to multiple -dimensions [#f2]_. + The implementation of :class:`TheilSenRegressor` in scikit-learn follows a + generalization to a multivariate linear regression model [#f1]_ using the + spatial median which is a generalization of the median to multiple + dimensions [#f2]_. -In terms of time and space complexity, Theil-Sen scales according to + In terms of time and space complexity, Theil-Sen scales according to -.. math:: - \binom{n_{\text{samples}}}{n_{\text{subsamples}}} + .. math:: + \binom{n_{\text{samples}}}{n_{\text{subsamples}}} -which makes it infeasible to be applied exhaustively to problems with a -large number of samples and features. Therefore, the magnitude of a -subpopulation can be chosen to limit the time and space complexity by -considering only a random subset of all possible combinations. + which makes it infeasible to be applied exhaustively to problems with a + large number of samples and features. Therefore, the magnitude of a + subpopulation can be chosen to limit the time and space complexity by + considering only a random subset of all possible combinations. -.. topic:: References: + .. rubric:: References .. [#f1] Xin Dang, Hanxiang Peng, Xueqin Wang and Heping Zhang: `Theil-Sen Estimators in a Multiple Linear Regression Model. `_ @@ -1644,8 +1576,6 @@ considering only a random subset of all possible combinations. Also see the `Wikipedia page `_ -|details-end| - .. _huber_regression: @@ -1664,39 +1594,35 @@ but gives a lesser weight to them. :align: center :scale: 50% -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_linear_model_plot_huber_vs_ridge.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_huber_vs_ridge.py` -|details-start| -**Mathematical details** -|details-split| +.. dropdown:: Mathematical details -The loss function that :class:`HuberRegressor` minimizes is given by + The loss function that :class:`HuberRegressor` minimizes is given by -.. math:: + .. math:: - \min_{w, \sigma} {\sum_{i=1}^n\left(\sigma + H_{\epsilon}\left(\frac{X_{i}w - y_{i}}{\sigma}\right)\sigma\right) + \alpha {||w||_2}^2} + \min_{w, \sigma} {\sum_{i=1}^n\left(\sigma + H_{\epsilon}\left(\frac{X_{i}w - y_{i}}{\sigma}\right)\sigma\right) + \alpha {||w||_2}^2} -where + where -.. math:: + .. math:: - H_{\epsilon}(z) = \begin{cases} - z^2, & \text {if } |z| < \epsilon, \\ - 2\epsilon|z| - \epsilon^2, & \text{otherwise} - \end{cases} + H_{\epsilon}(z) = \begin{cases} + z^2, & \text {if } |z| < \epsilon, \\ + 2\epsilon|z| - \epsilon^2, & \text{otherwise} + \end{cases} -It is advised to set the parameter ``epsilon`` to 1.35 to achieve 95% -statistical efficiency. + It is advised to set the parameter ``epsilon`` to 1.35 to achieve 95% + statistical efficiency. -.. topic:: References: + .. rubric:: References * Peter J. Huber, Elvezio M. Ronchetti: Robust Statistics, Concomitant scale estimates, pg 172 -|details-end| - The :class:`HuberRegressor` differs from using :class:`SGDRegressor` with loss set to `huber` in the following ways. @@ -1746,59 +1672,51 @@ Most implementations of quantile regression are based on linear programming problem. The current implementation is based on :func:`scipy.optimize.linprog`. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_linear_model_plot_quantile_regression.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_quantile_regression.py` -|details-start| -**Mathematical details** -|details-split| +.. dropdown:: Mathematical details -As a linear model, the :class:`QuantileRegressor` gives linear predictions -:math:`\hat{y}(w, X) = Xw` for the :math:`q`-th quantile, :math:`q \in (0, 1)`. -The weights or coefficients :math:`w` are then found by the following -minimization problem: + As a linear model, the :class:`QuantileRegressor` gives linear predictions + :math:`\hat{y}(w, X) = Xw` for the :math:`q`-th quantile, :math:`q \in (0, 1)`. + The weights or coefficients :math:`w` are then found by the following + minimization problem: -.. math:: - \min_{w} {\frac{1}{n_{\text{samples}}} - \sum_i PB_q(y_i - X_i w) + \alpha ||w||_1}. + .. math:: + \min_{w} {\frac{1}{n_{\text{samples}}} + \sum_i PB_q(y_i - X_i w) + \alpha ||w||_1}. -This consists of the pinball loss (also known as linear loss), -see also :class:`~sklearn.metrics.mean_pinball_loss`, + This consists of the pinball loss (also known as linear loss), + see also :class:`~sklearn.metrics.mean_pinball_loss`, -.. math:: - PB_q(t) = q \max(t, 0) + (1 - q) \max(-t, 0) = - \begin{cases} - q t, & t > 0, \\ - 0, & t = 0, \\ - (q-1) t, & t < 0 - \end{cases} - -and the L1 penalty controlled by parameter ``alpha``, similar to -:class:`Lasso`. + .. math:: + PB_q(t) = q \max(t, 0) + (1 - q) \max(-t, 0) = + \begin{cases} + q t, & t > 0, \\ + 0, & t = 0, \\ + (q-1) t, & t < 0 + \end{cases} -As the pinball loss is only linear in the residuals, quantile regression is -much more robust to outliers than squared error based estimation of the mean. -Somewhat in between is the :class:`HuberRegressor`. + and the L1 penalty controlled by parameter ``alpha``, similar to + :class:`Lasso`. -|details-end| + As the pinball loss is only linear in the residuals, quantile regression is + much more robust to outliers than squared error based estimation of the mean. + Somewhat in between is the :class:`HuberRegressor`. -|details-start| -**References** -|details-split| +.. dropdown:: References -* Koenker, R., & Bassett Jr, G. (1978). `Regression quantiles. - `_ - Econometrica: journal of the Econometric Society, 33-50. + * Koenker, R., & Bassett Jr, G. (1978). `Regression quantiles. + `_ + Econometrica: journal of the Econometric Society, 33-50. -* Portnoy, S., & Koenker, R. (1997). :doi:`The Gaussian hare and the Laplacian - tortoise: computability of squared-error versus absolute-error estimators. - Statistical Science, 12, 279-300 <10.1214/ss/1030037960>`. + * Portnoy, S., & Koenker, R. (1997). :doi:`The Gaussian hare and the Laplacian + tortoise: computability of squared-error versus absolute-error estimators. + Statistical Science, 12, 279-300 <10.1214/ss/1030037960>`. -* Koenker, R. (2005). :doi:`Quantile Regression <10.1017/CBO9780511754098>`. - Cambridge University Press. - -|details-end| + * Koenker, R. (2005). :doi:`Quantile Regression <10.1017/CBO9780511754098>`. + Cambridge University Press. .. _polynomial_regression: @@ -1813,38 +1731,34 @@ on nonlinear functions of the data. This approach maintains the generally fast performance of linear methods, while allowing them to fit a much wider range of data. -|details-start| -**Mathematical details** -|details-split| - -For example, a simple linear regression can be extended by constructing -**polynomial features** from the coefficients. In the standard linear -regression case, you might have a model that looks like this for -two-dimensional data: +.. dropdown:: Mathematical details -.. math:: \hat{y}(w, x) = w_0 + w_1 x_1 + w_2 x_2 + For example, a simple linear regression can be extended by constructing + **polynomial features** from the coefficients. In the standard linear + regression case, you might have a model that looks like this for + two-dimensional data: -If we want to fit a paraboloid to the data instead of a plane, we can combine -the features in second-order polynomials, so that the model looks like this: + .. math:: \hat{y}(w, x) = w_0 + w_1 x_1 + w_2 x_2 -.. math:: \hat{y}(w, x) = w_0 + w_1 x_1 + w_2 x_2 + w_3 x_1 x_2 + w_4 x_1^2 + w_5 x_2^2 + If we want to fit a paraboloid to the data instead of a plane, we can combine + the features in second-order polynomials, so that the model looks like this: -The (sometimes surprising) observation is that this is *still a linear model*: -to see this, imagine creating a new set of features + .. math:: \hat{y}(w, x) = w_0 + w_1 x_1 + w_2 x_2 + w_3 x_1 x_2 + w_4 x_1^2 + w_5 x_2^2 -.. math:: z = [x_1, x_2, x_1 x_2, x_1^2, x_2^2] + The (sometimes surprising) observation is that this is *still a linear model*: + to see this, imagine creating a new set of features -With this re-labeling of the data, our problem can be written + .. math:: z = [x_1, x_2, x_1 x_2, x_1^2, x_2^2] -.. math:: \hat{y}(w, z) = w_0 + w_1 z_1 + w_2 z_2 + w_3 z_3 + w_4 z_4 + w_5 z_5 + With this re-labeling of the data, our problem can be written -We see that the resulting *polynomial regression* is in the same class of -linear models we considered above (i.e. the model is linear in :math:`w`) -and can be solved by the same techniques. By considering linear fits within -a higher-dimensional space built with these basis functions, the model has the -flexibility to fit a much broader range of data. + .. math:: \hat{y}(w, z) = w_0 + w_1 z_1 + w_2 z_2 + w_3 z_3 + w_4 z_4 + w_5 z_5 -|details-end| + We see that the resulting *polynomial regression* is in the same class of + linear models we considered above (i.e. the model is linear in :math:`w`) + and can be solved by the same techniques. By considering linear fits within + a higher-dimensional space built with these basis functions, the model has the + flexibility to fit a much broader range of data. Here is an example of applying this idea to one-dimensional data, using polynomial features of varying degrees: diff --git a/doc/modules/manifold.rst b/doc/modules/manifold.rst index 7cc6776e37daa..785fba3097edf 100644 --- a/doc/modules/manifold.rst +++ b/doc/modules/manifold.rst @@ -102,13 +102,13 @@ unsupervised: it learns the high-dimensional structure of the data from the data itself, without the use of predetermined classifications. -.. topic:: Examples: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_manifold_plot_lle_digits.py` for an example of - dimensionality reduction on handwritten digits. +* See :ref:`sphx_glr_auto_examples_manifold_plot_lle_digits.py` for an example of + dimensionality reduction on handwritten digits. - * See :ref:`sphx_glr_auto_examples_manifold_plot_compare_methods.py` for an example of - dimensionality reduction on a toy "S-curve" dataset. +* See :ref:`sphx_glr_auto_examples_manifold_plot_compare_methods.py` for an example of + dimensionality reduction on a toy "S-curve" dataset. The manifold learning implementations available in scikit-learn are summarized below @@ -130,47 +130,43 @@ distances between all points. Isomap can be performed with the object :align: center :scale: 50 -|details-start| -**Complexity** -|details-split| +.. dropdown:: Complexity -The Isomap algorithm comprises three stages: + The Isomap algorithm comprises three stages: -1. **Nearest neighbor search.** Isomap uses - :class:`~sklearn.neighbors.BallTree` for efficient neighbor search. - The cost is approximately :math:`O[D \log(k) N \log(N)]`, for :math:`k` - nearest neighbors of :math:`N` points in :math:`D` dimensions. + 1. **Nearest neighbor search.** Isomap uses + :class:`~sklearn.neighbors.BallTree` for efficient neighbor search. + The cost is approximately :math:`O[D \log(k) N \log(N)]`, for :math:`k` + nearest neighbors of :math:`N` points in :math:`D` dimensions. -2. **Shortest-path graph search.** The most efficient known algorithms - for this are *Dijkstra's Algorithm*, which is approximately - :math:`O[N^2(k + \log(N))]`, or the *Floyd-Warshall algorithm*, which - is :math:`O[N^3]`. The algorithm can be selected by the user with - the ``path_method`` keyword of ``Isomap``. If unspecified, the code - attempts to choose the best algorithm for the input data. + 2. **Shortest-path graph search.** The most efficient known algorithms + for this are *Dijkstra's Algorithm*, which is approximately + :math:`O[N^2(k + \log(N))]`, or the *Floyd-Warshall algorithm*, which + is :math:`O[N^3]`. The algorithm can be selected by the user with + the ``path_method`` keyword of ``Isomap``. If unspecified, the code + attempts to choose the best algorithm for the input data. -3. **Partial eigenvalue decomposition.** The embedding is encoded in the - eigenvectors corresponding to the :math:`d` largest eigenvalues of the - :math:`N \times N` isomap kernel. For a dense solver, the cost is - approximately :math:`O[d N^2]`. This cost can often be improved using - the ``ARPACK`` solver. The eigensolver can be specified by the user - with the ``eigen_solver`` keyword of ``Isomap``. If unspecified, the - code attempts to choose the best algorithm for the input data. + 3. **Partial eigenvalue decomposition.** The embedding is encoded in the + eigenvectors corresponding to the :math:`d` largest eigenvalues of the + :math:`N \times N` isomap kernel. For a dense solver, the cost is + approximately :math:`O[d N^2]`. This cost can often be improved using + the ``ARPACK`` solver. The eigensolver can be specified by the user + with the ``eigen_solver`` keyword of ``Isomap``. If unspecified, the + code attempts to choose the best algorithm for the input data. -The overall complexity of Isomap is -:math:`O[D \log(k) N \log(N)] + O[N^2(k + \log(N))] + O[d N^2]`. + The overall complexity of Isomap is + :math:`O[D \log(k) N \log(N)] + O[N^2(k + \log(N))] + O[d N^2]`. -* :math:`N` : number of training data points -* :math:`D` : input dimension -* :math:`k` : number of nearest neighbors -* :math:`d` : output dimension + * :math:`N` : number of training data points + * :math:`D` : input dimension + * :math:`k` : number of nearest neighbors + * :math:`d` : output dimension -|details-end| +.. rubric:: References -.. topic:: References: - - * `"A global geometric framework for nonlinear dimensionality reduction" - `_ - Tenenbaum, J.B.; De Silva, V.; & Langford, J.C. Science 290 (5500) +* `"A global geometric framework for nonlinear dimensionality reduction" + `_ + Tenenbaum, J.B.; De Silva, V.; & Langford, J.C. Science 290 (5500) .. _locally_linear_embedding: @@ -191,36 +187,32 @@ Locally linear embedding can be performed with function :align: center :scale: 50 -|details-start| -**Complexity** -|details-split| - -The standard LLE algorithm comprises three stages: +.. dropdown:: Complexity -1. **Nearest Neighbors Search**. See discussion under Isomap above. + The standard LLE algorithm comprises three stages: -2. **Weight Matrix Construction**. :math:`O[D N k^3]`. - The construction of the LLE weight matrix involves the solution of a - :math:`k \times k` linear equation for each of the :math:`N` local - neighborhoods + 1. **Nearest Neighbors Search**. See discussion under Isomap above. -3. **Partial Eigenvalue Decomposition**. See discussion under Isomap above. + 2. **Weight Matrix Construction**. :math:`O[D N k^3]`. + The construction of the LLE weight matrix involves the solution of a + :math:`k \times k` linear equation for each of the :math:`N` local + neighborhoods. -The overall complexity of standard LLE is -:math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[d N^2]`. + 3. **Partial Eigenvalue Decomposition**. See discussion under Isomap above. -* :math:`N` : number of training data points -* :math:`D` : input dimension -* :math:`k` : number of nearest neighbors -* :math:`d` : output dimension + The overall complexity of standard LLE is + :math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[d N^2]`. -|details-end| + * :math:`N` : number of training data points + * :math:`D` : input dimension + * :math:`k` : number of nearest neighbors + * :math:`d` : output dimension -.. topic:: References: +.. rubric:: References - * `"Nonlinear dimensionality reduction by locally linear embedding" - `_ - Roweis, S. & Saul, L. Science 290:2323 (2000) +* `"Nonlinear dimensionality reduction by locally linear embedding" + `_ + Roweis, S. & Saul, L. Science 290:2323 (2000) Modified Locally Linear Embedding @@ -248,38 +240,34 @@ It requires ``n_neighbors > n_components``. :align: center :scale: 50 -|details-start| -**Complexity** -|details-split| - -The MLLE algorithm comprises three stages: +.. dropdown:: Complexity -1. **Nearest Neighbors Search**. Same as standard LLE + The MLLE algorithm comprises three stages: -2. **Weight Matrix Construction**. Approximately - :math:`O[D N k^3] + O[N (k-D) k^2]`. The first term is exactly equivalent - to that of standard LLE. The second term has to do with constructing the - weight matrix from multiple weights. In practice, the added cost of - constructing the MLLE weight matrix is relatively small compared to the - cost of stages 1 and 3. + 1. **Nearest Neighbors Search**. Same as standard LLE -3. **Partial Eigenvalue Decomposition**. Same as standard LLE + 2. **Weight Matrix Construction**. Approximately + :math:`O[D N k^3] + O[N (k-D) k^2]`. The first term is exactly equivalent + to that of standard LLE. The second term has to do with constructing the + weight matrix from multiple weights. In practice, the added cost of + constructing the MLLE weight matrix is relatively small compared to the + cost of stages 1 and 3. -The overall complexity of MLLE is -:math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[N (k-D) k^2] + O[d N^2]`. + 3. **Partial Eigenvalue Decomposition**. Same as standard LLE -* :math:`N` : number of training data points -* :math:`D` : input dimension -* :math:`k` : number of nearest neighbors -* :math:`d` : output dimension + The overall complexity of MLLE is + :math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[N (k-D) k^2] + O[d N^2]`. -|details-end| + * :math:`N` : number of training data points + * :math:`D` : input dimension + * :math:`k` : number of nearest neighbors + * :math:`d` : output dimension -.. topic:: References: +.. rubric:: References - * `"MLLE: Modified Locally Linear Embedding Using Multiple Weights" - `_ - Zhang, Z. & Wang, J. +* `"MLLE: Modified Locally Linear Embedding Using Multiple Weights" + `_ + Zhang, Z. & Wang, J. Hessian Eigenmapping @@ -301,36 +289,32 @@ It requires ``n_neighbors > n_components * (n_components + 3) / 2``. :align: center :scale: 50 -|details-start| -**Complexity** -|details-split| +.. dropdown:: Complexity The HLLE algorithm comprises three stages: -1. **Nearest Neighbors Search**. Same as standard LLE + 1. **Nearest Neighbors Search**. Same as standard LLE -2. **Weight Matrix Construction**. Approximately - :math:`O[D N k^3] + O[N d^6]`. The first term reflects a similar - cost to that of standard LLE. The second term comes from a QR - decomposition of the local hessian estimator. + 2. **Weight Matrix Construction**. Approximately + :math:`O[D N k^3] + O[N d^6]`. The first term reflects a similar + cost to that of standard LLE. The second term comes from a QR + decomposition of the local hessian estimator. -3. **Partial Eigenvalue Decomposition**. Same as standard LLE + 3. **Partial Eigenvalue Decomposition**. Same as standard LLE -The overall complexity of standard HLLE is -:math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[N d^6] + O[d N^2]`. + The overall complexity of standard HLLE is + :math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[N d^6] + O[d N^2]`. -* :math:`N` : number of training data points -* :math:`D` : input dimension -* :math:`k` : number of nearest neighbors -* :math:`d` : output dimension + * :math:`N` : number of training data points + * :math:`D` : input dimension + * :math:`k` : number of nearest neighbors + * :math:`d` : output dimension -|details-end| +.. rubric:: References -.. topic:: References: - - * `"Hessian Eigenmaps: Locally linear embedding techniques for - high-dimensional data" `_ - Donoho, D. & Grimes, C. Proc Natl Acad Sci USA. 100:5591 (2003) +* `"Hessian Eigenmaps: Locally linear embedding techniques for + high-dimensional data" `_ + Donoho, D. & Grimes, C. Proc Natl Acad Sci USA. 100:5591 (2003) .. _spectral_embedding: @@ -348,38 +332,34 @@ preserving local distances. Spectral embedding can be performed with the function :func:`spectral_embedding` or its object-oriented counterpart :class:`SpectralEmbedding`. -|details-start| -**Complexity** -|details-split| - -The Spectral Embedding (Laplacian Eigenmaps) algorithm comprises three stages: +.. dropdown:: Complexity -1. **Weighted Graph Construction**. Transform the raw input data into - graph representation using affinity (adjacency) matrix representation. + The Spectral Embedding (Laplacian Eigenmaps) algorithm comprises three stages: -2. **Graph Laplacian Construction**. unnormalized Graph Laplacian - is constructed as :math:`L = D - A` for and normalized one as - :math:`L = D^{-\frac{1}{2}} (D - A) D^{-\frac{1}{2}}`. + 1. **Weighted Graph Construction**. Transform the raw input data into + graph representation using affinity (adjacency) matrix representation. -3. **Partial Eigenvalue Decomposition**. Eigenvalue decomposition is - done on graph Laplacian + 2. **Graph Laplacian Construction**. unnormalized Graph Laplacian + is constructed as :math:`L = D - A` for and normalized one as + :math:`L = D^{-\frac{1}{2}} (D - A) D^{-\frac{1}{2}}`. -The overall complexity of spectral embedding is -:math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[d N^2]`. + 3. **Partial Eigenvalue Decomposition**. Eigenvalue decomposition is + done on graph Laplacian. -* :math:`N` : number of training data points -* :math:`D` : input dimension -* :math:`k` : number of nearest neighbors -* :math:`d` : output dimension + The overall complexity of spectral embedding is + :math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[d N^2]`. -|details-end| + * :math:`N` : number of training data points + * :math:`D` : input dimension + * :math:`k` : number of nearest neighbors + * :math:`d` : output dimension -.. topic:: References: +.. rubric:: References - * `"Laplacian Eigenmaps for Dimensionality Reduction - and Data Representation" - `_ - M. Belkin, P. Niyogi, Neural Computation, June 2003; 15 (6):1373-1396 +* `"Laplacian Eigenmaps for Dimensionality Reduction + and Data Representation" + `_ + M. Belkin, P. Niyogi, Neural Computation, June 2003; 15 (6):1373-1396 Local Tangent Space Alignment @@ -399,36 +379,32 @@ tangent spaces to learn the embedding. LTSA can be performed with function :align: center :scale: 50 -|details-start| -**Complexity** -|details-split| +.. dropdown:: Complexity -The LTSA algorithm comprises three stages: + The LTSA algorithm comprises three stages: -1. **Nearest Neighbors Search**. Same as standard LLE + 1. **Nearest Neighbors Search**. Same as standard LLE -2. **Weight Matrix Construction**. Approximately - :math:`O[D N k^3] + O[k^2 d]`. The first term reflects a similar - cost to that of standard LLE. + 2. **Weight Matrix Construction**. Approximately + :math:`O[D N k^3] + O[k^2 d]`. The first term reflects a similar + cost to that of standard LLE. -3. **Partial Eigenvalue Decomposition**. Same as standard LLE + 3. **Partial Eigenvalue Decomposition**. Same as standard LLE -The overall complexity of standard LTSA is -:math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[k^2 d] + O[d N^2]`. + The overall complexity of standard LTSA is + :math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[k^2 d] + O[d N^2]`. -* :math:`N` : number of training data points -* :math:`D` : input dimension -* :math:`k` : number of nearest neighbors -* :math:`d` : output dimension + * :math:`N` : number of training data points + * :math:`D` : input dimension + * :math:`k` : number of nearest neighbors + * :math:`d` : output dimension -|details-end| +.. rubric:: References -.. topic:: References: - - * :arxiv:`"Principal manifolds and nonlinear dimensionality reduction via - tangent space alignment" - ` - Zhang, Z. & Zha, H. Journal of Shanghai Univ. 8:406 (2004) +* :arxiv:`"Principal manifolds and nonlinear dimensionality reduction via + tangent space alignment" + ` + Zhang, Z. & Zha, H. Journal of Shanghai Univ. 8:406 (2004) .. _multidimensional_scaling: @@ -467,67 +443,59 @@ the similarities chosen in some optimal ways. The objective, called the stress, is then defined by :math:`\sum_{i < j} d_{ij}(X) - \hat{d}_{ij}(X)` -|details-start| -**Metric MDS** -|details-split| - -The simplest metric :class:`MDS` model, called *absolute MDS*, disparities are defined by -:math:`\hat{d}_{ij} = S_{ij}`. With absolute MDS, the value :math:`S_{ij}` -should then correspond exactly to the distance between point :math:`i` and -:math:`j` in the embedding point. +.. dropdown:: Metric MDS -Most commonly, disparities are set to :math:`\hat{d}_{ij} = b S_{ij}`. + The simplest metric :class:`MDS` model, called *absolute MDS*, disparities are defined by + :math:`\hat{d}_{ij} = S_{ij}`. With absolute MDS, the value :math:`S_{ij}` + should then correspond exactly to the distance between point :math:`i` and + :math:`j` in the embedding point. -|details-end| + Most commonly, disparities are set to :math:`\hat{d}_{ij} = b S_{ij}`. -|details-start| -**Nonmetric MDS** -|details-split| +.. dropdown:: Nonmetric MDS -Non metric :class:`MDS` focuses on the ordination of the data. If -:math:`S_{ij} > S_{jk}`, then the embedding should enforce :math:`d_{ij} < -d_{jk}`. For this reason, we discuss it in terms of dissimilarities -(:math:`\delta_{ij}`) instead of similarities (:math:`S_{ij}`). Note that -dissimilarities can easily be obtained from similarities through a simple -transform, e.g. :math:`\delta_{ij}=c_1-c_2 S_{ij}` for some real constants -:math:`c_1, c_2`. A simple algorithm to enforce proper ordination is to use a -monotonic regression of :math:`d_{ij}` on :math:`\delta_{ij}`, yielding -disparities :math:`\hat{d}_{ij}` in the same order as :math:`\delta_{ij}`. + Non metric :class:`MDS` focuses on the ordination of the data. If + :math:`S_{ij} > S_{jk}`, then the embedding should enforce :math:`d_{ij} < + d_{jk}`. For this reason, we discuss it in terms of dissimilarities + (:math:`\delta_{ij}`) instead of similarities (:math:`S_{ij}`). Note that + dissimilarities can easily be obtained from similarities through a simple + transform, e.g. :math:`\delta_{ij}=c_1-c_2 S_{ij}` for some real constants + :math:`c_1, c_2`. A simple algorithm to enforce proper ordination is to use a + monotonic regression of :math:`d_{ij}` on :math:`\delta_{ij}`, yielding + disparities :math:`\hat{d}_{ij}` in the same order as :math:`\delta_{ij}`. -A trivial solution to this problem is to set all the points on the origin. In -order to avoid that, the disparities :math:`\hat{d}_{ij}` are normalized. Note -that since we only care about relative ordering, our objective should be -invariant to simple translation and scaling, however the stress used in metric -MDS is sensitive to scaling. To address this, non-metric MDS may use a -normalized stress, known as Stress-1 defined as + A trivial solution to this problem is to set all the points on the origin. In + order to avoid that, the disparities :math:`\hat{d}_{ij}` are normalized. Note + that since we only care about relative ordering, our objective should be + invariant to simple translation and scaling, however the stress used in metric + MDS is sensitive to scaling. To address this, non-metric MDS may use a + normalized stress, known as Stress-1 defined as -.. math:: - \sqrt{\frac{\sum_{i < j} (d_{ij} - \hat{d}_{ij})^2}{\sum_{i < j} d_{ij}^2}}. + .. math:: + \sqrt{\frac{\sum_{i < j} (d_{ij} - \hat{d}_{ij})^2}{\sum_{i < j} d_{ij}^2}}. -The use of normalized Stress-1 can be enabled by setting `normalized_stress=True`, -however it is only compatible with the non-metric MDS problem and will be ignored -in the metric case. - -.. figure:: ../auto_examples/manifold/images/sphx_glr_plot_mds_001.png - :target: ../auto_examples/manifold/plot_mds.html - :align: center - :scale: 60 + The use of normalized Stress-1 can be enabled by setting `normalized_stress=True`, + however it is only compatible with the non-metric MDS problem and will be ignored + in the metric case. -|details-end| + .. figure:: ../auto_examples/manifold/images/sphx_glr_plot_mds_001.png + :target: ../auto_examples/manifold/plot_mds.html + :align: center + :scale: 60 -.. topic:: References: +.. rubric:: References - * `"Modern Multidimensional Scaling - Theory and Applications" - `_ - Borg, I.; Groenen P. Springer Series in Statistics (1997) +* `"Modern Multidimensional Scaling - Theory and Applications" + `_ + Borg, I.; Groenen P. Springer Series in Statistics (1997) - * `"Nonmetric multidimensional scaling: a numerical method" - `_ - Kruskal, J. Psychometrika, 29 (1964) +* `"Nonmetric multidimensional scaling: a numerical method" + `_ + Kruskal, J. Psychometrika, 29 (1964) - * `"Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis" - `_ - Kruskal, J. Psychometrika, 29, (1964) +* `"Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis" + `_ + Kruskal, J. Psychometrika, 29, (1964) .. _t_sne: @@ -575,120 +543,110 @@ The disadvantages to using t-SNE are roughly: :align: center :scale: 50 -|details-start| -**Optimizing t-SNE** -|details-split| - -The main purpose of t-SNE is visualization of high-dimensional data. Hence, -it works best when the data will be embedded on two or three dimensions. - -Optimizing the KL divergence can be a little bit tricky sometimes. There are -five parameters that control the optimization of t-SNE and therefore possibly -the quality of the resulting embedding: - -* perplexity -* early exaggeration factor -* learning rate -* maximum number of iterations -* angle (not used in the exact method) - -The perplexity is defined as :math:`k=2^{(S)}` where :math:`S` is the Shannon -entropy of the conditional probability distribution. The perplexity of a -:math:`k`-sided die is :math:`k`, so that :math:`k` is effectively the number of -nearest neighbors t-SNE considers when generating the conditional probabilities. -Larger perplexities lead to more nearest neighbors and less sensitive to small -structure. Conversely a lower perplexity considers a smaller number of -neighbors, and thus ignores more global information in favour of the -local neighborhood. As dataset sizes get larger more points will be -required to get a reasonable sample of the local neighborhood, and hence -larger perplexities may be required. Similarly noisier datasets will require -larger perplexity values to encompass enough local neighbors to see beyond -the background noise. - -The maximum number of iterations is usually high enough and does not need -any tuning. The optimization consists of two phases: the early exaggeration -phase and the final optimization. During early exaggeration the joint -probabilities in the original space will be artificially increased by -multiplication with a given factor. Larger factors result in larger gaps -between natural clusters in the data. If the factor is too high, the KL -divergence could increase during this phase. Usually it does not have to be -tuned. A critical parameter is the learning rate. If it is too low gradient -descent will get stuck in a bad local minimum. If it is too high the KL -divergence will increase during optimization. A heuristic suggested in -Belkina et al. (2019) is to set the learning rate to the sample size -divided by the early exaggeration factor. We implement this heuristic -as `learning_rate='auto'` argument. More tips can be found in -Laurens van der Maaten's FAQ (see references). The last parameter, angle, -is a tradeoff between performance and accuracy. Larger angles imply that we -can approximate larger regions by a single point, leading to better speed -but less accurate results. - -`"How to Use t-SNE Effectively" `_ -provides a good discussion of the effects of the various parameters, as well -as interactive plots to explore the effects of different parameters. - -|details-end| - -|details-start| -**Barnes-Hut t-SNE** -|details-split| - -The Barnes-Hut t-SNE that has been implemented here is usually much slower than -other manifold learning algorithms. The optimization is quite difficult -and the computation of the gradient is :math:`O[d N log(N)]`, where :math:`d` -is the number of output dimensions and :math:`N` is the number of samples. The -Barnes-Hut method improves on the exact method where t-SNE complexity is -:math:`O[d N^2]`, but has several other notable differences: - -* The Barnes-Hut implementation only works when the target dimensionality is 3 - or less. The 2D case is typical when building visualizations. -* Barnes-Hut only works with dense input data. Sparse data matrices can only be - embedded with the exact method or can be approximated by a dense low rank - projection for instance using :class:`~sklearn.decomposition.PCA` -* Barnes-Hut is an approximation of the exact method. The approximation is - parameterized with the angle parameter, therefore the angle parameter is - unused when method="exact" -* Barnes-Hut is significantly more scalable. Barnes-Hut can be used to embed - hundred of thousands of data points while the exact method can handle - thousands of samples before becoming computationally intractable - -For visualization purpose (which is the main use case of t-SNE), using the -Barnes-Hut method is strongly recommended. The exact t-SNE method is useful -for checking the theoretically properties of the embedding possibly in higher -dimensional space but limit to small datasets due to computational constraints. - -Also note that the digits labels roughly match the natural grouping found by -t-SNE while the linear 2D projection of the PCA model yields a representation -where label regions largely overlap. This is a strong clue that this data can -be well separated by non linear methods that focus on the local structure (e.g. -an SVM with a Gaussian RBF kernel). However, failing to visualize well -separated homogeneously labeled groups with t-SNE in 2D does not necessarily -imply that the data cannot be correctly classified by a supervised model. It -might be the case that 2 dimensions are not high enough to accurately represent -the internal structure of the data. - -|details-end| - -.. topic:: References: - - * `"Visualizing High-Dimensional Data Using t-SNE" - `_ - van der Maaten, L.J.P.; Hinton, G. Journal of Machine Learning Research - (2008) - - * `"t-Distributed Stochastic Neighbor Embedding" - `_ - van der Maaten, L.J.P. - - * `"Accelerating t-SNE using Tree-Based Algorithms" - `_ - van der Maaten, L.J.P.; Journal of Machine Learning Research 15(Oct):3221-3245, 2014. - - * `"Automated optimized parameters for T-distributed stochastic neighbor - embedding improve visualization and analysis of large datasets" - `_ - Belkina, A.C., Ciccolella, C.O., Anno, R., Halpert, R., Spidlen, J., - Snyder-Cappione, J.E., Nature Communications 10, 5415 (2019). +.. dropdown:: Optimizing t-SNE + + The main purpose of t-SNE is visualization of high-dimensional data. Hence, + it works best when the data will be embedded on two or three dimensions. + + Optimizing the KL divergence can be a little bit tricky sometimes. There are + five parameters that control the optimization of t-SNE and therefore possibly + the quality of the resulting embedding: + + * perplexity + * early exaggeration factor + * learning rate + * maximum number of iterations + * angle (not used in the exact method) + + The perplexity is defined as :math:`k=2^{(S)}` where :math:`S` is the Shannon + entropy of the conditional probability distribution. The perplexity of a + :math:`k`-sided die is :math:`k`, so that :math:`k` is effectively the number of + nearest neighbors t-SNE considers when generating the conditional probabilities. + Larger perplexities lead to more nearest neighbors and less sensitive to small + structure. Conversely a lower perplexity considers a smaller number of + neighbors, and thus ignores more global information in favour of the + local neighborhood. As dataset sizes get larger more points will be + required to get a reasonable sample of the local neighborhood, and hence + larger perplexities may be required. Similarly noisier datasets will require + larger perplexity values to encompass enough local neighbors to see beyond + the background noise. + + The maximum number of iterations is usually high enough and does not need + any tuning. The optimization consists of two phases: the early exaggeration + phase and the final optimization. During early exaggeration the joint + probabilities in the original space will be artificially increased by + multiplication with a given factor. Larger factors result in larger gaps + between natural clusters in the data. If the factor is too high, the KL + divergence could increase during this phase. Usually it does not have to be + tuned. A critical parameter is the learning rate. If it is too low gradient + descent will get stuck in a bad local minimum. If it is too high the KL + divergence will increase during optimization. A heuristic suggested in + Belkina et al. (2019) is to set the learning rate to the sample size + divided by the early exaggeration factor. We implement this heuristic + as `learning_rate='auto'` argument. More tips can be found in + Laurens van der Maaten's FAQ (see references). The last parameter, angle, + is a tradeoff between performance and accuracy. Larger angles imply that we + can approximate larger regions by a single point, leading to better speed + but less accurate results. + + `"How to Use t-SNE Effectively" `_ + provides a good discussion of the effects of the various parameters, as well + as interactive plots to explore the effects of different parameters. + +.. dropdown:: Barnes-Hut t-SNE + + The Barnes-Hut t-SNE that has been implemented here is usually much slower than + other manifold learning algorithms. The optimization is quite difficult + and the computation of the gradient is :math:`O[d N log(N)]`, where :math:`d` + is the number of output dimensions and :math:`N` is the number of samples. The + Barnes-Hut method improves on the exact method where t-SNE complexity is + :math:`O[d N^2]`, but has several other notable differences: + + * The Barnes-Hut implementation only works when the target dimensionality is 3 + or less. The 2D case is typical when building visualizations. + * Barnes-Hut only works with dense input data. Sparse data matrices can only be + embedded with the exact method or can be approximated by a dense low rank + projection for instance using :class:`~sklearn.decomposition.PCA` + * Barnes-Hut is an approximation of the exact method. The approximation is + parameterized with the angle parameter, therefore the angle parameter is + unused when method="exact" + * Barnes-Hut is significantly more scalable. Barnes-Hut can be used to embed + hundred of thousands of data points while the exact method can handle + thousands of samples before becoming computationally intractable + + For visualization purpose (which is the main use case of t-SNE), using the + Barnes-Hut method is strongly recommended. The exact t-SNE method is useful + for checking the theoretically properties of the embedding possibly in higher + dimensional space but limit to small datasets due to computational constraints. + + Also note that the digits labels roughly match the natural grouping found by + t-SNE while the linear 2D projection of the PCA model yields a representation + where label regions largely overlap. This is a strong clue that this data can + be well separated by non linear methods that focus on the local structure (e.g. + an SVM with a Gaussian RBF kernel). However, failing to visualize well + separated homogeneously labeled groups with t-SNE in 2D does not necessarily + imply that the data cannot be correctly classified by a supervised model. It + might be the case that 2 dimensions are not high enough to accurately represent + the internal structure of the data. + +.. rubric:: References + +* `"Visualizing High-Dimensional Data Using t-SNE" + `_ + van der Maaten, L.J.P.; Hinton, G. Journal of Machine Learning Research (2008) + +* `"t-Distributed Stochastic Neighbor Embedding" + `_ van der Maaten, L.J.P. + +* `"Accelerating t-SNE using Tree-Based Algorithms" + `_ + van der Maaten, L.J.P.; Journal of Machine Learning Research 15(Oct):3221-3245, 2014. + +* `"Automated optimized parameters for T-distributed stochastic neighbor + embedding improve visualization and analysis of large datasets" + `_ + Belkina, A.C., Ciccolella, C.O., Anno, R., Halpert, R., Spidlen, J., + Snyder-Cappione, J.E., Nature Communications 10, 5415 (2019). Tips on practical use ===================== diff --git a/doc/modules/metrics.rst b/doc/modules/metrics.rst index caea39319e869..63ea797223c22 100644 --- a/doc/modules/metrics.rst +++ b/doc/modules/metrics.rst @@ -87,11 +87,11 @@ represented as tf-idf vectors. can produce normalized vectors, in which case :func:`cosine_similarity` is equivalent to :func:`linear_kernel`, only slower.) -.. topic:: References: +.. rubric:: References - * C.D. Manning, P. Raghavan and H. Schütze (2008). Introduction to - Information Retrieval. Cambridge University Press. - https://nlp.stanford.edu/IR-book/html/htmledition/the-vector-space-model-for-scoring-1.html +* C.D. Manning, P. Raghavan and H. Schütze (2008). Introduction to + Information Retrieval. Cambridge University Press. + https://nlp.stanford.edu/IR-book/html/htmledition/the-vector-space-model-for-scoring-1.html .. _linear_kernel: @@ -222,10 +222,10 @@ which is a distance between discrete probability distributions. The chi squared kernel is most commonly used on histograms (bags) of visual words. -.. topic:: References: +.. rubric:: References - * Zhang, J. and Marszalek, M. and Lazebnik, S. and Schmid, C. - Local features and kernels for classification of texture and object - categories: A comprehensive study - International Journal of Computer Vision 2007 - https://hal.archives-ouvertes.fr/hal-00171412/document +* Zhang, J. and Marszalek, M. and Lazebnik, S. and Schmid, C. + Local features and kernels for classification of texture and object + categories: A comprehensive study + International Journal of Computer Vision 2007 + https://hal.archives-ouvertes.fr/hal-00171412/document diff --git a/doc/modules/mixture.rst b/doc/modules/mixture.rst index df5d8020a1369..1fd72c3158336 100644 --- a/doc/modules/mixture.rst +++ b/doc/modules/mixture.rst @@ -60,128 +60,111 @@ full covariance. :align: center :scale: 75% -.. topic:: Examples: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_covariances.py` for an example of - using the Gaussian mixture as clustering on the iris dataset. +* See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_covariances.py` for an example of + using the Gaussian mixture as clustering on the iris dataset. - * See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_pdf.py` for an example on plotting the - density estimation. +* See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_pdf.py` for an example on plotting the + density estimation. -|details-start| -**Pros and cons of class GaussianMixture** -|details-split| +.. dropdown:: Pros and cons of class GaussianMixture -.. topic:: Pros: + .. rubric:: Pros - :Speed: It is the fastest algorithm for learning mixture models + :Speed: It is the fastest algorithm for learning mixture models - :Agnostic: As this algorithm maximizes only the likelihood, it - will not bias the means towards zero, or bias the cluster sizes to - have specific structures that might or might not apply. + :Agnostic: As this algorithm maximizes only the likelihood, it + will not bias the means towards zero, or bias the cluster sizes to + have specific structures that might or might not apply. -.. topic:: Cons: + .. rubric:: Cons - :Singularities: When one has insufficiently many points per - mixture, estimating the covariance matrices becomes difficult, - and the algorithm is known to diverge and find solutions with - infinite likelihood unless one regularizes the covariances artificially. + :Singularities: When one has insufficiently many points per + mixture, estimating the covariance matrices becomes difficult, + and the algorithm is known to diverge and find solutions with + infinite likelihood unless one regularizes the covariances artificially. - :Number of components: This algorithm will always use all the - components it has access to, needing held-out data - or information theoretical criteria to decide how many components to use - in the absence of external cues. + :Number of components: This algorithm will always use all the + components it has access to, needing held-out data + or information theoretical criteria to decide how many components to use + in the absence of external cues. -|details-end| +.. dropdown:: Selecting the number of components in a classical Gaussian Mixture model + The BIC criterion can be used to select the number of components in a Gaussian + Mixture in an efficient way. In theory, it recovers the true number of + components only in the asymptotic regime (i.e. if much data is available and + assuming that the data was actually generated i.i.d. from a mixture of Gaussian + distribution). Note that using a :ref:`Variational Bayesian Gaussian mixture ` + avoids the specification of the number of components for a Gaussian mixture + model. -|details-start| -**Selecting the number of components in a classical Gaussian Mixture model** -|details-split| + .. figure:: ../auto_examples/mixture/images/sphx_glr_plot_gmm_selection_002.png + :target: ../auto_examples/mixture/plot_gmm_selection.html + :align: center + :scale: 50% -The BIC criterion can be used to select the number of components in a Gaussian -Mixture in an efficient way. In theory, it recovers the true number of -components only in the asymptotic regime (i.e. if much data is available and -assuming that the data was actually generated i.i.d. from a mixture of Gaussian -distribution). Note that using a :ref:`Variational Bayesian Gaussian mixture ` -avoids the specification of the number of components for a Gaussian mixture -model. + .. rubric:: Examples -.. figure:: ../auto_examples/mixture/images/sphx_glr_plot_gmm_selection_002.png - :target: ../auto_examples/mixture/plot_gmm_selection.html - :align: center - :scale: 50% - -.. topic:: Examples: - - * See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_selection.py` for an example - of model selection performed with classical Gaussian mixture. - -|details-end| + * See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_selection.py` for an example + of model selection performed with classical Gaussian mixture. .. _expectation_maximization: -|details-start| -**Estimation algorithm expectation-maximization** -|details-split| - -The main difficulty in learning Gaussian mixture models from unlabeled -data is that one usually doesn't know which points came from -which latent component (if one has access to this information it gets -very easy to fit a separate Gaussian distribution to each set of -points). `Expectation-maximization -`_ -is a well-founded statistical -algorithm to get around this problem by an iterative process. First -one assumes random components (randomly centered on data points, -learned from k-means, or even just normally distributed around the -origin) and computes for each point a probability of being generated by -each component of the model. Then, one tweaks the -parameters to maximize the likelihood of the data given those -assignments. Repeating this process is guaranteed to always converge -to a local optimum. - -|details-end| - -|details-start| -**Choice of the Initialization method** -|details-split| - -There is a choice of four initialization methods (as well as inputting user defined -initial means) to generate the initial centers for the model components: - -k-means (default) - This applies a traditional k-means clustering algorithm. - This can be computationally expensive compared to other initialization methods. - -k-means++ - This uses the initialization method of k-means clustering: k-means++. - This will pick the first center at random from the data. Subsequent centers will be - chosen from a weighted distribution of the data favouring points further away from - existing centers. k-means++ is the default initialization for k-means so will be - quicker than running a full k-means but can still take a significant amount of - time for large data sets with many components. - -random_from_data - This will pick random data points from the input data as the initial - centers. This is a very fast method of initialization but can produce non-convergent - results if the chosen points are too close to each other. - -random - Centers are chosen as a small perturbation away from the mean of all data. - This method is simple but can lead to the model taking longer to converge. - -.. figure:: ../auto_examples/mixture/images/sphx_glr_plot_gmm_init_001.png - :target: ../auto_examples/mixture/plot_gmm_init.html - :align: center - :scale: 50% - -.. topic:: Examples: - - * See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_init.py` for an example of - using different initializations in Gaussian Mixture. - -|details-end| +.. dropdown:: Estimation algorithm expectation-maximization + + The main difficulty in learning Gaussian mixture models from unlabeled + data is that one usually doesn't know which points came from + which latent component (if one has access to this information it gets + very easy to fit a separate Gaussian distribution to each set of + points). `Expectation-maximization + `_ + is a well-founded statistical + algorithm to get around this problem by an iterative process. First + one assumes random components (randomly centered on data points, + learned from k-means, or even just normally distributed around the + origin) and computes for each point a probability of being generated by + each component of the model. Then, one tweaks the + parameters to maximize the likelihood of the data given those + assignments. Repeating this process is guaranteed to always converge + to a local optimum. + +.. dropdown:: Choice of the Initialization method + + There is a choice of four initialization methods (as well as inputting user defined + initial means) to generate the initial centers for the model components: + + k-means (default) + This applies a traditional k-means clustering algorithm. + This can be computationally expensive compared to other initialization methods. + + k-means++ + This uses the initialization method of k-means clustering: k-means++. + This will pick the first center at random from the data. Subsequent centers will be + chosen from a weighted distribution of the data favouring points further away from + existing centers. k-means++ is the default initialization for k-means so will be + quicker than running a full k-means but can still take a significant amount of + time for large data sets with many components. + + random_from_data + This will pick random data points from the input data as the initial + centers. This is a very fast method of initialization but can produce non-convergent + results if the chosen points are too close to each other. + + random + Centers are chosen as a small perturbation away from the mean of all data. + This method is simple but can lead to the model taking longer to converge. + + .. figure:: ../auto_examples/mixture/images/sphx_glr_plot_gmm_init_001.png + :target: ../auto_examples/mixture/plot_gmm_init.html + :align: center + :scale: 50% + + .. rubric:: Examples + + * See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_init.py` for an example of + using different initializations in Gaussian Mixture. .. _bgmm: @@ -276,63 +259,58 @@ from the two resulting mixtures. -.. topic:: Examples: - - * See :ref:`sphx_glr_auto_examples_mixture_plot_gmm.py` for an example on - plotting the confidence ellipsoids for both :class:`GaussianMixture` - and :class:`BayesianGaussianMixture`. - - * :ref:`sphx_glr_auto_examples_mixture_plot_gmm_sin.py` shows using - :class:`GaussianMixture` and :class:`BayesianGaussianMixture` to fit a - sine wave. +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_mixture_plot_concentration_prior.py` - for an example plotting the confidence ellipsoids for the - :class:`BayesianGaussianMixture` with different - ``weight_concentration_prior_type`` for different values of the parameter - ``weight_concentration_prior``. +* See :ref:`sphx_glr_auto_examples_mixture_plot_gmm.py` for an example on + plotting the confidence ellipsoids for both :class:`GaussianMixture` + and :class:`BayesianGaussianMixture`. -|details-start| -**Pros and cons of variational inference with BayesianGaussianMixture** -|details-split| +* :ref:`sphx_glr_auto_examples_mixture_plot_gmm_sin.py` shows using + :class:`GaussianMixture` and :class:`BayesianGaussianMixture` to fit a + sine wave. -.. topic:: Pros: +* See :ref:`sphx_glr_auto_examples_mixture_plot_concentration_prior.py` + for an example plotting the confidence ellipsoids for the + :class:`BayesianGaussianMixture` with different + ``weight_concentration_prior_type`` for different values of the parameter + ``weight_concentration_prior``. - :Automatic selection: when ``weight_concentration_prior`` is small enough and - ``n_components`` is larger than what is found necessary by the model, the - Variational Bayesian mixture model has a natural tendency to set some mixture - weights values close to zero. This makes it possible to let the model choose - a suitable number of effective components automatically. Only an upper bound - of this number needs to be provided. Note however that the "ideal" number of - active components is very application specific and is typically ill-defined - in a data exploration setting. +.. dropdown:: Pros and cons of variational inference with BayesianGaussianMixture - :Less sensitivity to the number of parameters: unlike finite models, which will - almost always use all components as much as they can, and hence will produce - wildly different solutions for different numbers of components, the - variational inference with a Dirichlet process prior - (``weight_concentration_prior_type='dirichlet_process'``) won't change much - with changes to the parameters, leading to more stability and less tuning. + .. rubric:: Pros - :Regularization: due to the incorporation of prior information, - variational solutions have less pathological special cases than - expectation-maximization solutions. + :Automatic selection: when ``weight_concentration_prior`` is small enough and + ``n_components`` is larger than what is found necessary by the model, the + Variational Bayesian mixture model has a natural tendency to set some mixture + weights values close to zero. This makes it possible to let the model choose + a suitable number of effective components automatically. Only an upper bound + of this number needs to be provided. Note however that the "ideal" number of + active components is very application specific and is typically ill-defined + in a data exploration setting. + :Less sensitivity to the number of parameters: unlike finite models, which will + almost always use all components as much as they can, and hence will produce + wildly different solutions for different numbers of components, the + variational inference with a Dirichlet process prior + (``weight_concentration_prior_type='dirichlet_process'``) won't change much + with changes to the parameters, leading to more stability and less tuning. -.. topic:: Cons: + :Regularization: due to the incorporation of prior information, + variational solutions have less pathological special cases than + expectation-maximization solutions. - :Speed: the extra parametrization necessary for variational inference makes - inference slower, although not by much. + .. rubric:: Cons - :Hyperparameters: this algorithm needs an extra hyperparameter - that might need experimental tuning via cross-validation. + :Speed: the extra parametrization necessary for variational inference makes + inference slower, although not by much. - :Bias: there are many implicit biases in the inference algorithms (and also in - the Dirichlet process if used), and whenever there is a mismatch between - these biases and the data it might be possible to fit better models using a - finite mixture. + :Hyperparameters: this algorithm needs an extra hyperparameter + that might need experimental tuning via cross-validation. -|details-end| + :Bias: there are many implicit biases in the inference algorithms (and also in + the Dirichlet process if used), and whenever there is a mismatch between + these biases and the data it might be possible to fit better models using a + finite mixture. .. _dirichlet_process: diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst index 056bf9a56d42c..81615b4419bba 100644 --- a/doc/modules/model_evaluation.rst +++ b/doc/modules/model_evaluation.rst @@ -172,58 +172,53 @@ measuring a prediction error given ground truth and prediction: parameter description below). -|details-start| -**Custom scorer objects** -|details-split| - - -The second use case is to build a completely custom scorer object -from a simple python function using :func:`make_scorer`, which can -take several parameters: - -* the python function you want to use (``my_custom_loss_func`` - in the example below) - -* whether the python function returns a score (``greater_is_better=True``, - the default) or a loss (``greater_is_better=False``). If a loss, the output - of the python function is negated by the scorer object, conforming to - the cross validation convention that scorers return higher values for better models. - -* for classification metrics only: whether the python function you provided requires - continuous decision certainties. If the scoring function only accepts probability - estimates (e.g. :func:`metrics.log_loss`) then one needs to set the parameter - `response_method`, thus in this case `response_method="predict_proba"`. Some scoring - function do not necessarily require probability estimates but rather non-thresholded - decision values (e.g. :func:`metrics.roc_auc_score`). In this case, one provides a - list such as `response_method=["decision_function", "predict_proba"]`. In this case, - the scorer will use the first available method, in the order given in the list, - to compute the scores. - -* any additional parameters, such as ``beta`` or ``labels`` in :func:`f1_score`. - -Here is an example of building custom scorers, and of using the -``greater_is_better`` parameter:: - - >>> import numpy as np - >>> def my_custom_loss_func(y_true, y_pred): - ... diff = np.abs(y_true - y_pred).max() - ... return np.log1p(diff) - ... - >>> # score will negate the return value of my_custom_loss_func, - >>> # which will be np.log(2), 0.693, given the values for X - >>> # and y defined below. - >>> score = make_scorer(my_custom_loss_func, greater_is_better=False) - >>> X = [[1], [1]] - >>> y = [0, 1] - >>> from sklearn.dummy import DummyClassifier - >>> clf = DummyClassifier(strategy='most_frequent', random_state=0) - >>> clf = clf.fit(X, y) - >>> my_custom_loss_func(y, clf.predict(X)) - 0.69... - >>> score(clf, X, y) - -0.69... - -|details-end| +.. dropdown:: Custom scorer objects + + The second use case is to build a completely custom scorer object + from a simple python function using :func:`make_scorer`, which can + take several parameters: + + * the python function you want to use (``my_custom_loss_func`` + in the example below) + + * whether the python function returns a score (``greater_is_better=True``, + the default) or a loss (``greater_is_better=False``). If a loss, the output + of the python function is negated by the scorer object, conforming to + the cross validation convention that scorers return higher values for better models. + + * for classification metrics only: whether the python function you provided requires + continuous decision certainties. If the scoring function only accepts probability + estimates (e.g. :func:`metrics.log_loss`) then one needs to set the parameter + `response_method`, thus in this case `response_method="predict_proba"`. Some scoring + function do not necessarily require probability estimates but rather non-thresholded + decision values (e.g. :func:`metrics.roc_auc_score`). In this case, one provides a + list such as `response_method=["decision_function", "predict_proba"]`. In this case, + the scorer will use the first available method, in the order given in the list, + to compute the scores. + + * any additional parameters, such as ``beta`` or ``labels`` in :func:`f1_score`. + + Here is an example of building custom scorers, and of using the + ``greater_is_better`` parameter:: + + >>> import numpy as np + >>> def my_custom_loss_func(y_true, y_pred): + ... diff = np.abs(y_true - y_pred).max() + ... return np.log1p(diff) + ... + >>> # score will negate the return value of my_custom_loss_func, + >>> # which will be np.log(2), 0.693, given the values for X + >>> # and y defined below. + >>> score = make_scorer(my_custom_loss_func, greater_is_better=False) + >>> X = [[1], [1]] + >>> y = [0, 1] + >>> from sklearn.dummy import DummyClassifier + >>> clf = DummyClassifier(strategy='most_frequent', random_state=0) + >>> clf = clf.fit(X, y) + >>> my_custom_loss_func(y, clf.predict(X)) + 0.69... + >>> score(clf, X, y) + -0.69... .. _diy_scoring: @@ -234,51 +229,47 @@ You can generate even more flexible model scorers by constructing your own scoring object from scratch, without using the :func:`make_scorer` factory. -|details-start| -**How to build a scorer from scratch** -|details-split| +.. dropdown:: How to build a scorer from scratch -For a callable to be a scorer, it needs to meet the protocol specified by -the following two rules: + For a callable to be a scorer, it needs to meet the protocol specified by + the following two rules: -- It can be called with parameters ``(estimator, X, y)``, where ``estimator`` - is the model that should be evaluated, ``X`` is validation data, and ``y`` is - the ground truth target for ``X`` (in the supervised case) or ``None`` (in the - unsupervised case). + - It can be called with parameters ``(estimator, X, y)``, where ``estimator`` + is the model that should be evaluated, ``X`` is validation data, and ``y`` is + the ground truth target for ``X`` (in the supervised case) or ``None`` (in the + unsupervised case). -- It returns a floating point number that quantifies the - ``estimator`` prediction quality on ``X``, with reference to ``y``. - Again, by convention higher numbers are better, so if your scorer - returns loss, that value should be negated. + - It returns a floating point number that quantifies the + ``estimator`` prediction quality on ``X``, with reference to ``y``. + Again, by convention higher numbers are better, so if your scorer + returns loss, that value should be negated. -- Advanced: If it requires extra metadata to be passed to it, it should expose - a ``get_metadata_routing`` method returning the requested metadata. The user - should be able to set the requested metadata via a ``set_score_request`` - method. Please see :ref:`User Guide ` and :ref:`Developer - Guide ` for - more details. + - Advanced: If it requires extra metadata to be passed to it, it should expose + a ``get_metadata_routing`` method returning the requested metadata. The user + should be able to set the requested metadata via a ``set_score_request`` + method. Please see :ref:`User Guide ` and :ref:`Developer + Guide ` for + more details. -.. note:: **Using custom scorers in functions where n_jobs > 1** + .. note:: **Using custom scorers in functions where n_jobs > 1** - While defining the custom scoring function alongside the calling function - should work out of the box with the default joblib backend (loky), - importing it from another module will be a more robust approach and work - independently of the joblib backend. + While defining the custom scoring function alongside the calling function + should work out of the box with the default joblib backend (loky), + importing it from another module will be a more robust approach and work + independently of the joblib backend. - For example, to use ``n_jobs`` greater than 1 in the example below, - ``custom_scoring_function`` function is saved in a user-created module - (``custom_scorer_module.py``) and imported:: + For example, to use ``n_jobs`` greater than 1 in the example below, + ``custom_scoring_function`` function is saved in a user-created module + (``custom_scorer_module.py``) and imported:: - >>> from custom_scorer_module import custom_scoring_function # doctest: +SKIP - >>> cross_val_score(model, - ... X_train, - ... y_train, - ... scoring=make_scorer(custom_scoring_function, greater_is_better=False), - ... cv=5, - ... n_jobs=-1) # doctest: +SKIP - -|details-end| + >>> from custom_scorer_module import custom_scoring_function # doctest: +SKIP + >>> cross_val_score(model, + ... X_train, + ... y_train, + ... scoring=make_scorer(custom_scoring_function, greater_is_better=False), + ... cv=5, + ... n_jobs=-1) # doctest: +SKIP .. _multimetric_scoring: @@ -474,11 +465,11 @@ In the multilabel case with binary label indicators:: >>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2))) 0.5 -.. topic:: Example: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_model_selection_plot_permutation_tests_for_classification.py` - for an example of accuracy score usage using permutations of - the dataset. +* See :ref:`sphx_glr_auto_examples_model_selection_plot_permutation_tests_for_classification.py` + for an example of accuracy score usage using permutations of + the dataset. .. _top_k_accuracy_score: @@ -589,22 +580,20 @@ or *informedness*. * Balanced Accuracy as described in [Urbanowicz2015]_: the average of sensitivity and specificity is computed for each class and then averaged over total number of classes. -.. topic:: References: - - .. [Guyon2015] I. Guyon, K. Bennett, G. Cawley, H.J. Escalante, S. Escalera, T.K. Ho, N. Macià, - B. Ray, M. Saeed, A.R. Statnikov, E. Viegas, `Design of the 2015 ChaLearn AutoML Challenge - `_, - IJCNN 2015. - .. [Mosley2013] L. Mosley, `A balanced approach to the multi-class imbalance problem - `_, - IJCV 2010. - .. [Kelleher2015] John. D. Kelleher, Brian Mac Namee, Aoife D'Arcy, `Fundamentals of - Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, - and Case Studies `_, - 2015. - .. [Urbanowicz2015] Urbanowicz R.J., Moore, J.H. :doi:`ExSTraCS 2.0: description - and evaluation of a scalable learning classifier - system <10.1007/s12065-015-0128-8>`, Evol. Intel. (2015) 8: 89. +.. rubric:: References + +.. [Guyon2015] I. Guyon, K. Bennett, G. Cawley, H.J. Escalante, S. Escalera, T.K. Ho, N. Macià, + B. Ray, M. Saeed, A.R. Statnikov, E. Viegas, `Design of the 2015 ChaLearn AutoML Challenge + `_, IJCNN 2015. +.. [Mosley2013] L. Mosley, `A balanced approach to the multi-class imbalance problem + `_, IJCV 2010. +.. [Kelleher2015] John. D. Kelleher, Brian Mac Namee, Aoife D'Arcy, `Fundamentals of + Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, + and Case Studies `_, + 2015. +.. [Urbanowicz2015] Urbanowicz R.J., Moore, J.H. :doi:`ExSTraCS 2.0: description + and evaluation of a scalable learning classifier + system <10.1007/s12065-015-0128-8>`, Evol. Intel. (2015) 8: 89. .. _cohen_kappa: @@ -683,19 +672,19 @@ false negatives and true positives as follows:: >>> tn, fp, fn, tp (2, 1, 2, 3) -.. topic:: Example: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_model_selection_plot_confusion_matrix.py` - for an example of using a confusion matrix to evaluate classifier output - quality. +* See :ref:`sphx_glr_auto_examples_model_selection_plot_confusion_matrix.py` + for an example of using a confusion matrix to evaluate classifier output + quality. - * See :ref:`sphx_glr_auto_examples_classification_plot_digits_classification.py` - for an example of using a confusion matrix to classify - hand-written digits. +* See :ref:`sphx_glr_auto_examples_classification_plot_digits_classification.py` + for an example of using a confusion matrix to classify + hand-written digits. - * See :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` - for an example of using a confusion matrix to classify text - documents. +* See :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` + for an example of using a confusion matrix to classify text + documents. .. _classification_report: @@ -722,15 +711,15 @@ and inferred labels:: weighted avg 0.67 0.60 0.59 5 -.. topic:: Example: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_classification_plot_digits_classification.py` - for an example of classification report usage for - hand-written digits. +* See :ref:`sphx_glr_auto_examples_classification_plot_digits_classification.py` + for an example of classification report usage for + hand-written digits. - * See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py` - for an example of classification report usage for - grid search with nested cross-validation. +* See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py` + for an example of classification report usage for + grid search with nested cross-validation. .. _hamming_loss: @@ -848,31 +837,31 @@ precision-recall curve as follows. :scale: 75 :align: center -.. topic:: Examples: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py` - for an example of :func:`precision_score` and :func:`recall_score` usage - to estimate parameters using grid search with nested cross-validation. +* See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py` + for an example of :func:`precision_score` and :func:`recall_score` usage + to estimate parameters using grid search with nested cross-validation. - * See :ref:`sphx_glr_auto_examples_model_selection_plot_precision_recall.py` - for an example of :func:`precision_recall_curve` usage to evaluate - classifier output quality. +* See :ref:`sphx_glr_auto_examples_model_selection_plot_precision_recall.py` + for an example of :func:`precision_recall_curve` usage to evaluate + classifier output quality. -.. topic:: References: +.. rubric:: References - .. [Manning2008] C.D. Manning, P. Raghavan, H. Schütze, `Introduction to Information Retrieval - `_, - 2008. - .. [Everingham2010] M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn, A. Zisserman, - `The Pascal Visual Object Classes (VOC) Challenge - `_, - IJCV 2010. - .. [Davis2006] J. Davis, M. Goadrich, `The Relationship Between Precision-Recall and ROC Curves - `_, - ICML 2006. - .. [Flach2015] P.A. Flach, M. Kull, `Precision-Recall-Gain Curves: PR Analysis Done Right - `_, - NIPS 2015. +.. [Manning2008] C.D. Manning, P. Raghavan, H. Schütze, `Introduction to Information Retrieval + `_, + 2008. +.. [Everingham2010] M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn, A. Zisserman, + `The Pascal Visual Object Classes (VOC) Challenge + `_, + IJCV 2010. +.. [Davis2006] J. Davis, M. Goadrich, `The Relationship Between Precision-Recall and ROC Curves + `_, + ICML 2006. +.. [Flach2015] P.A. Flach, M. Kull, `Precision-Recall-Gain Curves: PR Analysis Done Right + `_, + NIPS 2015. Binary classification ^^^^^^^^^^^^^^^^^^^^^ @@ -1041,10 +1030,10 @@ Similarly, labels not present in the data sample may be accounted for in macro-a >>> metrics.precision_score(y_true, y_pred, labels=[0, 1, 2, 3], average='macro') 0.166... -.. topic:: References: +.. rubric:: References - .. [OB2019] :arxiv:`Opitz, J., & Burst, S. (2019). "Macro f1 and macro f1." - <1911.03347>` +.. [OB2019] :arxiv:`Opitz, J., & Burst, S. (2019). "Macro f1 and macro f1." + <1911.03347>` .. _jaccard_similarity_score: @@ -1496,65 +1485,57 @@ correspond to the probability estimates that a sample belongs to a particular class. The OvO and OvR algorithms support weighting uniformly (``average='macro'``) and by prevalence (``average='weighted'``). -|details-start| -**One-vs-one Algorithm** -|details-split| +.. dropdown:: One-vs-one Algorithm -Computes the average AUC of all possible pairwise -combinations of classes. [HT2001]_ defines a multiclass AUC metric weighted -uniformly: + Computes the average AUC of all possible pairwise + combinations of classes. [HT2001]_ defines a multiclass AUC metric weighted + uniformly: -.. math:: + .. math:: - \frac{1}{c(c-1)}\sum_{j=1}^{c}\sum_{k > j}^c (\text{AUC}(j | k) + - \text{AUC}(k | j)) + \frac{1}{c(c-1)}\sum_{j=1}^{c}\sum_{k > j}^c (\text{AUC}(j | k) + + \text{AUC}(k | j)) -where :math:`c` is the number of classes and :math:`\text{AUC}(j | k)` is the -AUC with class :math:`j` as the positive class and class :math:`k` as the -negative class. In general, -:math:`\text{AUC}(j | k) \neq \text{AUC}(k | j))` in the multiclass -case. This algorithm is used by setting the keyword argument ``multiclass`` -to ``'ovo'`` and ``average`` to ``'macro'``. + where :math:`c` is the number of classes and :math:`\text{AUC}(j | k)` is the + AUC with class :math:`j` as the positive class and class :math:`k` as the + negative class. In general, + :math:`\text{AUC}(j | k) \neq \text{AUC}(k | j))` in the multiclass + case. This algorithm is used by setting the keyword argument ``multiclass`` + to ``'ovo'`` and ``average`` to ``'macro'``. -The [HT2001]_ multiclass AUC metric can be extended to be weighted by the -prevalence: + The [HT2001]_ multiclass AUC metric can be extended to be weighted by the + prevalence: -.. math:: + .. math:: - \frac{1}{c(c-1)}\sum_{j=1}^{c}\sum_{k > j}^c p(j \cup k)( - \text{AUC}(j | k) + \text{AUC}(k | j)) + \frac{1}{c(c-1)}\sum_{j=1}^{c}\sum_{k > j}^c p(j \cup k)( + \text{AUC}(j | k) + \text{AUC}(k | j)) -where :math:`c` is the number of classes. This algorithm is used by setting -the keyword argument ``multiclass`` to ``'ovo'`` and ``average`` to -``'weighted'``. The ``'weighted'`` option returns a prevalence-weighted average -as described in [FC2009]_. + where :math:`c` is the number of classes. This algorithm is used by setting + the keyword argument ``multiclass`` to ``'ovo'`` and ``average`` to + ``'weighted'``. The ``'weighted'`` option returns a prevalence-weighted average + as described in [FC2009]_. -|details-end| +.. dropdown:: One-vs-rest Algorithm -|details-start| -**One-vs-rest Algorithm** -|details-split| + Computes the AUC of each class against the rest + [PD2000]_. The algorithm is functionally the same as the multilabel case. To + enable this algorithm set the keyword argument ``multiclass`` to ``'ovr'``. + Additionally to ``'macro'`` [F2006]_ and ``'weighted'`` [F2001]_ averaging, OvR + supports ``'micro'`` averaging. -Computes the AUC of each class against the rest -[PD2000]_. The algorithm is functionally the same as the multilabel case. To -enable this algorithm set the keyword argument ``multiclass`` to ``'ovr'``. -Additionally to ``'macro'`` [F2006]_ and ``'weighted'`` [F2001]_ averaging, OvR -supports ``'micro'`` averaging. + In applications where a high false positive rate is not tolerable the parameter + ``max_fpr`` of :func:`roc_auc_score` can be used to summarize the ROC curve up + to the given limit. -In applications where a high false positive rate is not tolerable the parameter -``max_fpr`` of :func:`roc_auc_score` can be used to summarize the ROC curve up -to the given limit. + The following figure shows the micro-averaged ROC curve and its corresponding + ROC-AUC score for a classifier aimed to distinguish the different species in + the :ref:`iris_dataset`: -The following figure shows the micro-averaged ROC curve and its corresponding -ROC-AUC score for a classifier aimed to distinguish the different species in -the :ref:`iris_dataset`: - -.. image:: ../auto_examples/model_selection/images/sphx_glr_plot_roc_002.png - :target: ../auto_examples/model_selection/plot_roc.html - :scale: 75 - :align: center - -|details-end| + .. image:: ../auto_examples/model_selection/images/sphx_glr_plot_roc_002.png + :target: ../auto_examples/model_selection/plot_roc.html + :scale: 75 + :align: center .. _roc_auc_multilabel: @@ -1584,46 +1565,43 @@ And the decision values do not require such processing. >>> roc_auc_score(y, y_score, average=None) array([0.81..., 0.84... , 0.93..., 0.87..., 0.94...]) -.. topic:: Examples: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_model_selection_plot_roc.py` - for an example of using ROC to - evaluate the quality of the output of a classifier. +* See :ref:`sphx_glr_auto_examples_model_selection_plot_roc.py` for an example of + using ROC to evaluate the quality of the output of a classifier. - * See :ref:`sphx_glr_auto_examples_model_selection_plot_roc_crossval.py` - for an example of using ROC to - evaluate classifier output quality, using cross-validation. +* See :ref:`sphx_glr_auto_examples_model_selection_plot_roc_crossval.py` for an + example of using ROC to evaluate classifier output quality, using cross-validation. - * See :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py` - for an example of using ROC to - model species distribution. +* See :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py` + for an example of using ROC to model species distribution. -.. topic:: References: +.. rubric:: References - .. [HT2001] Hand, D.J. and Till, R.J., (2001). `A simple generalisation - of the area under the ROC curve for multiple class classification problems. - `_ - Machine learning, 45(2), pp. 171-186. +.. [HT2001] Hand, D.J. and Till, R.J., (2001). `A simple generalisation + of the area under the ROC curve for multiple class classification problems. + `_ + Machine learning, 45(2), pp. 171-186. - .. [FC2009] Ferri, Cèsar & Hernandez-Orallo, Jose & Modroiu, R. (2009). - `An Experimental Comparison of Performance Measures for Classification. - `_ - Pattern Recognition Letters. 30. 27-38. +.. [FC2009] Ferri, Cèsar & Hernandez-Orallo, Jose & Modroiu, R. (2009). + `An Experimental Comparison of Performance Measures for Classification. + `_ + Pattern Recognition Letters. 30. 27-38. - .. [PD2000] Provost, F., Domingos, P. (2000). `Well-trained PETs: Improving - probability estimation trees - `_ - (Section 6.2), CeDER Working Paper #IS-00-04, Stern School of Business, - New York University. +.. [PD2000] Provost, F., Domingos, P. (2000). `Well-trained PETs: Improving + probability estimation trees + `_ + (Section 6.2), CeDER Working Paper #IS-00-04, Stern School of Business, + New York University. - .. [F2006] Fawcett, T., 2006. `An introduction to ROC analysis. - `_ - Pattern Recognition Letters, 27(8), pp. 861-874. +.. [F2006] Fawcett, T., 2006. `An introduction to ROC analysis. + `_ + Pattern Recognition Letters, 27(8), pp. 861-874. - .. [F2001] Fawcett, T., 2001. `Using rule sets to maximize - ROC performance `_ - In Data Mining, 2001. - Proceedings IEEE International Conference, pp. 131-138. +.. [F2001] Fawcett, T., 2001. `Using rule sets to maximize + ROC performance `_ + In Data Mining, 2001. + Proceedings IEEE International Conference, pp. 131-138. .. _det_curve: @@ -1659,67 +1637,57 @@ same classification task: :scale: 75 :align: center -.. topic:: Examples: - - * See :ref:`sphx_glr_auto_examples_model_selection_plot_det.py` - for an example comparison between receiver operating characteristic (ROC) - curves and Detection error tradeoff (DET) curves. +.. dropdown:: Properties -|details-start| -**Properties** -|details-split| + * DET curves form a linear curve in normal deviate scale if the detection + scores are normally (or close-to normally) distributed. + It was shown by [Navratil2007]_ that the reverse is not necessarily true and + even more general distributions are able to produce linear DET curves. -* DET curves form a linear curve in normal deviate scale if the detection - scores are normally (or close-to normally) distributed. - It was shown by [Navratil2007]_ that the reverse is not necessarily true and - even more general distributions are able to produce linear DET curves. + * The normal deviate scale transformation spreads out the points such that a + comparatively larger space of plot is occupied. + Therefore curves with similar classification performance might be easier to + distinguish on a DET plot. -* The normal deviate scale transformation spreads out the points such that a - comparatively larger space of plot is occupied. - Therefore curves with similar classification performance might be easier to - distinguish on a DET plot. + * With False Negative Rate being "inverse" to True Positive Rate the point + of perfection for DET curves is the origin (in contrast to the top left + corner for ROC curves). -* With False Negative Rate being "inverse" to True Positive Rate the point - of perfection for DET curves is the origin (in contrast to the top left - corner for ROC curves). +.. dropdown:: Applications and limitations -|details-end| + DET curves are intuitive to read and hence allow quick visual assessment of a + classifier's performance. + Additionally DET curves can be consulted for threshold analysis and operating + point selection. + This is particularly helpful if a comparison of error types is required. -|details-start| -**Applications and limitations** -|details-split| + On the other hand DET curves do not provide their metric as a single number. + Therefore for either automated evaluation or comparison to other + classification tasks metrics like the derived area under ROC curve might be + better suited. -DET curves are intuitive to read and hence allow quick visual assessment of a -classifier's performance. -Additionally DET curves can be consulted for threshold analysis and operating -point selection. -This is particularly helpful if a comparison of error types is required. +.. rubric:: Examples -On the other hand DET curves do not provide their metric as a single number. -Therefore for either automated evaluation or comparison to other -classification tasks metrics like the derived area under ROC curve might be -better suited. +* See :ref:`sphx_glr_auto_examples_model_selection_plot_det.py` + for an example comparison between receiver operating characteristic (ROC) + curves and Detection error tradeoff (DET) curves. -|details-end| +.. rubric:: References -.. topic:: References: +.. [WikipediaDET2017] Wikipedia contributors. Detection error tradeoff. + Wikipedia, The Free Encyclopedia. September 4, 2017, 23:33 UTC. + Available at: https://en.wikipedia.org/w/index.php?title=Detection_error_tradeoff&oldid=798982054. + Accessed February 19, 2018. - .. [WikipediaDET2017] Wikipedia contributors. Detection error tradeoff. - Wikipedia, The Free Encyclopedia. September 4, 2017, 23:33 UTC. - Available at: https://en.wikipedia.org/w/index.php?title=Detection_error_tradeoff&oldid=798982054. - Accessed February 19, 2018. +.. [Martin1997] A. Martin, G. Doddington, T. Kamm, M. Ordowski, and M. Przybocki, + `The DET Curve in Assessment of Detection Task Performance + `_, NIST 1997. - .. [Martin1997] A. Martin, G. Doddington, T. Kamm, M. Ordowski, and M. Przybocki, - `The DET Curve in Assessment of Detection Task Performance - `_, - NIST 1997. - - .. [Navratil2007] J. Navractil and D. Klusacek, - "`On Linear DETs, - `_" - 2007 IEEE International Conference on Acoustics, - Speech and Signal Processing - ICASSP '07, Honolulu, - HI, 2007, pp. IV-229-IV-232. +.. [Navratil2007] J. Navractil and D. Klusacek, + `"On Linear DETs" `_, + 2007 IEEE International Conference on Acoustics, + Speech and Signal Processing - ICASSP '07, Honolulu, + HI, 2007, pp. IV-229-IV-232. .. _zero_one_loss: @@ -1767,11 +1735,11 @@ set [0,1] has an error:: >>> zero_one_loss(np.array([[0, 1], [1, 1]]), np.ones((2, 2)), normalize=False) 1.0 -.. topic:: Example: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py` - for an example of zero one loss usage to perform recursive feature - elimination with cross-validation. +* See :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py` + for an example of zero one loss usage to perform recursive feature + elimination with cross-validation. .. _brier_score_loss: @@ -1827,28 +1795,27 @@ necessarily mean a better calibrated model. "Only when refinement loss remains the same does a lower Brier score loss always mean better calibration" [Bella2012]_, [Flach2008]_. -.. topic:: Example: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_calibration_plot_calibration.py` - for an example of Brier score loss usage to perform probability - calibration of classifiers. +* See :ref:`sphx_glr_auto_examples_calibration_plot_calibration.py` + for an example of Brier score loss usage to perform probability + calibration of classifiers. -.. topic:: References: +.. rubric:: References - .. [Brier1950] G. Brier, `Verification of forecasts expressed in terms of - probability - `_, - Monthly weather review 78.1 (1950) +.. [Brier1950] G. Brier, `Verification of forecasts expressed in terms of probability + `_, + Monthly weather review 78.1 (1950) - .. [Bella2012] Bella, Ferri, Hernández-Orallo, and Ramírez-Quintana - `"Calibration of Machine Learning Models" - `_ - in Khosrow-Pour, M. "Machine learning: concepts, methodologies, tools - and applications." Hershey, PA: Information Science Reference (2012). +.. [Bella2012] Bella, Ferri, Hernández-Orallo, and Ramírez-Quintana + `"Calibration of Machine Learning Models" + `_ + in Khosrow-Pour, M. "Machine learning: concepts, methodologies, tools + and applications." Hershey, PA: Information Science Reference (2012). - .. [Flach2008] Flach, Peter, and Edson Matsubara. `"On classification, ranking, - and probability estimation." `_ - Dagstuhl Seminar Proceedings. Schloss Dagstuhl-Leibniz-Zentrum fr Informatik (2008). +.. [Flach2008] Flach, Peter, and Edson Matsubara. `"On classification, ranking, + and probability estimation." `_ + Dagstuhl Seminar Proceedings. Schloss Dagstuhl-Leibniz-Zentrum fr Informatik (2008). .. _class_likelihood_ratios: @@ -1901,82 +1868,72 @@ counts ``tp`` (see `the wikipedia page `_ for the actual formulas). -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_model_selection_plot_likelihood_ratios.py` +* :ref:`sphx_glr_auto_examples_model_selection_plot_likelihood_ratios.py` -|details-start| -**Interpretation across varying prevalence** -|details-split| +.. dropdown:: Interpretation across varying prevalence -Both class likelihood ratios are interpretable in terms of an odds ratio -(pre-test and post-tests): + Both class likelihood ratios are interpretable in terms of an odds ratio + (pre-test and post-tests): -.. math:: + .. math:: - \text{post-test odds} = \text{Likelihood ratio} \times \text{pre-test odds}. + \text{post-test odds} = \text{Likelihood ratio} \times \text{pre-test odds}. -Odds are in general related to probabilities via + Odds are in general related to probabilities via -.. math:: + .. math:: - \text{odds} = \frac{\text{probability}}{1 - \text{probability}}, + \text{odds} = \frac{\text{probability}}{1 - \text{probability}}, -or equivalently + or equivalently -.. math:: + .. math:: - \text{probability} = \frac{\text{odds}}{1 + \text{odds}}. + \text{probability} = \frac{\text{odds}}{1 + \text{odds}}. -On a given population, the pre-test probability is given by the prevalence. By -converting odds to probabilities, the likelihood ratios can be translated into a -probability of truly belonging to either class before and after a classifier -prediction: + On a given population, the pre-test probability is given by the prevalence. By + converting odds to probabilities, the likelihood ratios can be translated into a + probability of truly belonging to either class before and after a classifier + prediction: -.. math:: + .. math:: - \text{post-test odds} = \text{Likelihood ratio} \times - \frac{\text{pre-test probability}}{1 - \text{pre-test probability}}, + \text{post-test odds} = \text{Likelihood ratio} \times + \frac{\text{pre-test probability}}{1 - \text{pre-test probability}}, -.. math:: - - \text{post-test probability} = \frac{\text{post-test odds}}{1 + \text{post-test odds}}. - -|details-end| + .. math:: -|details-start| -**Mathematical divergences** -|details-split| + \text{post-test probability} = \frac{\text{post-test odds}}{1 + \text{post-test odds}}. -The positive likelihood ratio is undefined when :math:`fp = 0`, which can be -interpreted as the classifier perfectly identifying positive cases. If :math:`fp -= 0` and additionally :math:`tp = 0`, this leads to a zero/zero division. This -happens, for instance, when using a `DummyClassifier` that always predicts the -negative class and therefore the interpretation as a perfect classifier is lost. +.. dropdown:: Mathematical divergences -The negative likelihood ratio is undefined when :math:`tn = 0`. Such divergence -is invalid, as :math:`LR_- > 1` would indicate an increase in the odds of a -sample belonging to the positive class after being classified as negative, as if -the act of classifying caused the positive condition. This includes the case of -a `DummyClassifier` that always predicts the positive class (i.e. when -:math:`tn=fn=0`). + The positive likelihood ratio is undefined when :math:`fp = 0`, which can be + interpreted as the classifier perfectly identifying positive cases. If :math:`fp + = 0` and additionally :math:`tp = 0`, this leads to a zero/zero division. This + happens, for instance, when using a `DummyClassifier` that always predicts the + negative class and therefore the interpretation as a perfect classifier is lost. -Both class likelihood ratios are undefined when :math:`tp=fn=0`, which means -that no samples of the positive class were present in the testing set. This can -also happen when cross-validating highly imbalanced data. + The negative likelihood ratio is undefined when :math:`tn = 0`. Such divergence + is invalid, as :math:`LR_- > 1` would indicate an increase in the odds of a + sample belonging to the positive class after being classified as negative, as if + the act of classifying caused the positive condition. This includes the case of + a `DummyClassifier` that always predicts the positive class (i.e. when + :math:`tn=fn=0`). -In all the previous cases the :func:`class_likelihood_ratios` function raises by -default an appropriate warning message and returns `nan` to avoid pollution when -averaging over cross-validation folds. + Both class likelihood ratios are undefined when :math:`tp=fn=0`, which means + that no samples of the positive class were present in the testing set. This can + also happen when cross-validating highly imbalanced data. -For a worked-out demonstration of the :func:`class_likelihood_ratios` function, -see the example below. + In all the previous cases the :func:`class_likelihood_ratios` function raises by + default an appropriate warning message and returns `nan` to avoid pollution when + averaging over cross-validation folds. -|details-end| + For a worked-out demonstration of the :func:`class_likelihood_ratios` function, + see the example below. -|details-start| -**References** -|details-split| +.. dropdown:: References * `Wikipedia entry for Likelihood ratios in diagnostic testing `_ @@ -1986,7 +1943,6 @@ see the example below. values with disease prevalence. Statistics in medicine, 16(9), 981-991. -|details-end| .. _d2_score_classification: @@ -2011,47 +1967,44 @@ model can be arbitrarily worse). A constant model that always predicts :math:`y_{\text{null}}`, disregarding the input features, would get a D² score of 0.0. -|details-start| -**D2 log loss score** -|details-split| +.. dropdown:: D2 log loss score -The :func:`d2_log_loss_score` function implements the special case -of D² with the log loss, see :ref:`log_loss`, i.e.: + The :func:`d2_log_loss_score` function implements the special case + of D² with the log loss, see :ref:`log_loss`, i.e.: -.. math:: + .. math:: - \text{dev}(y, \hat{y}) = \text{log_loss}(y, \hat{y}). + \text{dev}(y, \hat{y}) = \text{log_loss}(y, \hat{y}). -Here are some usage examples of the :func:`d2_log_loss_score` function:: + Here are some usage examples of the :func:`d2_log_loss_score` function:: - >>> from sklearn.metrics import d2_log_loss_score - >>> y_true = [1, 1, 2, 3] - >>> y_pred = [ - ... [0.5, 0.25, 0.25], - ... [0.5, 0.25, 0.25], - ... [0.5, 0.25, 0.25], - ... [0.5, 0.25, 0.25], - ... ] - >>> d2_log_loss_score(y_true, y_pred) - 0.0 - >>> y_true = [1, 2, 3] - >>> y_pred = [ - ... [0.98, 0.01, 0.01], - ... [0.01, 0.98, 0.01], - ... [0.01, 0.01, 0.98], - ... ] - >>> d2_log_loss_score(y_true, y_pred) - 0.981... - >>> y_true = [1, 2, 3] - >>> y_pred = [ - ... [0.1, 0.6, 0.3], - ... [0.1, 0.6, 0.3], - ... [0.4, 0.5, 0.1], - ... ] - >>> d2_log_loss_score(y_true, y_pred) - -0.552... + >>> from sklearn.metrics import d2_log_loss_score + >>> y_true = [1, 1, 2, 3] + >>> y_pred = [ + ... [0.5, 0.25, 0.25], + ... [0.5, 0.25, 0.25], + ... [0.5, 0.25, 0.25], + ... [0.5, 0.25, 0.25], + ... ] + >>> d2_log_loss_score(y_true, y_pred) + 0.0 + >>> y_true = [1, 2, 3] + >>> y_pred = [ + ... [0.98, 0.01, 0.01], + ... [0.01, 0.98, 0.01], + ... [0.01, 0.01, 0.98], + ... ] + >>> d2_log_loss_score(y_true, y_pred) + 0.981... + >>> y_true = [1, 2, 3] + >>> y_pred = [ + ... [0.1, 0.6, 0.3], + ... [0.1, 0.6, 0.3], + ... [0.4, 0.5, 0.1], + ... ] + >>> d2_log_loss_score(y_true, y_pred) + -0.552... -|details-end| .. _multilabel_ranking_metrics: @@ -2191,14 +2144,11 @@ Here is a small example of usage of this function:: 0.0 -|details-start| -**References** -|details-split| +.. dropdown:: References * Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010). Mining multi-label data. In Data mining and knowledge discovery handbook (pp. 667-685). Springer US. -|details-end| .. _ndcg: @@ -2244,9 +2194,7 @@ DCG score is and the NDCG score is the DCG score divided by the DCG score obtained for :math:`y`. -|details-start| -**References** -|details-split| +.. dropdown:: References * `Wikipedia entry for Discounted Cumulative Gain `_ @@ -2264,7 +2212,6 @@ and the NDCG score is the DCG score divided by the DCG score obtained for European conference on information retrieval (pp. 414-421). Springer, Berlin, Heidelberg. -|details-end| .. _regression_metrics: @@ -2374,11 +2321,11 @@ Here is a small example of usage of the :func:`r2_score` function:: >>> r2_score(y_true, y_pred, force_finite=False) -inf -.. topic:: Example: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py` - for an example of R² score usage to - evaluate Lasso and Elastic Net on sparse signals. +* See :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py` + for an example of R² score usage to + evaluate Lasso and Elastic Net on sparse signals. .. _mean_absolute_error: @@ -2445,11 +2392,10 @@ function:: >>> mean_squared_error(y_true, y_pred) 0.7083... -.. topic:: Examples: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py` - for an example of mean squared error usage to - evaluate gradient boosting regression. +* See :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py` + for an example of mean squared error usage to evaluate gradient boosting regression. Taking the square root of the MSE, called the root mean squared error (RMSE), is another common metric that provides a measure in the same units as the target variable. RSME is @@ -2787,12 +2733,12 @@ It is also possible to build scorer objects for hyper-parameter tuning. The sign of the loss must be switched to ensure that greater means better as explained in the example linked below. -.. topic:: Example: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_quantile.py` - for an example of using the pinball loss to evaluate and tune the - hyper-parameters of quantile regression models on data with non-symmetric - noise and outliers. +* See :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_quantile.py` + for an example of using the pinball loss to evaluate and tune the + hyper-parameters of quantile regression models on data with non-symmetric + noise and outliers. .. _d2_score: @@ -2818,77 +2764,66 @@ model can be arbitrarily worse). A constant model that always predicts :math:`y_{\text{null}}`, disregarding the input features, would get a D² score of 0.0. -|details-start| -**D² Tweedie score** -|details-split| - -The :func:`d2_tweedie_score` function implements the special case of D² -where :math:`\text{dev}(y, \hat{y})` is the Tweedie deviance, see :ref:`mean_tweedie_deviance`. -It is also known as D² Tweedie and is related to McFadden's likelihood ratio index. +.. dropdown:: D² Tweedie score -The argument ``power`` defines the Tweedie power as for -:func:`mean_tweedie_deviance`. Note that for `power=0`, -:func:`d2_tweedie_score` equals :func:`r2_score` (for single targets). + The :func:`d2_tweedie_score` function implements the special case of D² + where :math:`\text{dev}(y, \hat{y})` is the Tweedie deviance, see :ref:`mean_tweedie_deviance`. + It is also known as D² Tweedie and is related to McFadden's likelihood ratio index. -A scorer object with a specific choice of ``power`` can be built by:: + The argument ``power`` defines the Tweedie power as for + :func:`mean_tweedie_deviance`. Note that for `power=0`, + :func:`d2_tweedie_score` equals :func:`r2_score` (for single targets). - >>> from sklearn.metrics import d2_tweedie_score, make_scorer - >>> d2_tweedie_score_15 = make_scorer(d2_tweedie_score, power=1.5) + A scorer object with a specific choice of ``power`` can be built by:: -|details-end| + >>> from sklearn.metrics import d2_tweedie_score, make_scorer + >>> d2_tweedie_score_15 = make_scorer(d2_tweedie_score, power=1.5) -|details-start| -**D² pinball score** -|details-split| +.. dropdown:: D² pinball score -The :func:`d2_pinball_score` function implements the special case -of D² with the pinball loss, see :ref:`pinball_loss`, i.e.: - -.. math:: + The :func:`d2_pinball_score` function implements the special case + of D² with the pinball loss, see :ref:`pinball_loss`, i.e.: - \text{dev}(y, \hat{y}) = \text{pinball}(y, \hat{y}). + .. math:: -The argument ``alpha`` defines the slope of the pinball loss as for -:func:`mean_pinball_loss` (:ref:`pinball_loss`). It determines the -quantile level ``alpha`` for which the pinball loss and also D² -are optimal. Note that for `alpha=0.5` (the default) :func:`d2_pinball_score` -equals :func:`d2_absolute_error_score`. + \text{dev}(y, \hat{y}) = \text{pinball}(y, \hat{y}). -A scorer object with a specific choice of ``alpha`` can be built by:: + The argument ``alpha`` defines the slope of the pinball loss as for + :func:`mean_pinball_loss` (:ref:`pinball_loss`). It determines the + quantile level ``alpha`` for which the pinball loss and also D² + are optimal. Note that for `alpha=0.5` (the default) :func:`d2_pinball_score` + equals :func:`d2_absolute_error_score`. - >>> from sklearn.metrics import d2_pinball_score, make_scorer - >>> d2_pinball_score_08 = make_scorer(d2_pinball_score, alpha=0.8) + A scorer object with a specific choice of ``alpha`` can be built by:: -|details-end| + >>> from sklearn.metrics import d2_pinball_score, make_scorer + >>> d2_pinball_score_08 = make_scorer(d2_pinball_score, alpha=0.8) -|details-start| -**D² absolute error score** -|details-split| +.. dropdown:: D² absolute error score -The :func:`d2_absolute_error_score` function implements the special case of -the :ref:`mean_absolute_error`: + The :func:`d2_absolute_error_score` function implements the special case of + the :ref:`mean_absolute_error`: -.. math:: + .. math:: - \text{dev}(y, \hat{y}) = \text{MAE}(y, \hat{y}). + \text{dev}(y, \hat{y}) = \text{MAE}(y, \hat{y}). -Here are some usage examples of the :func:`d2_absolute_error_score` function:: + Here are some usage examples of the :func:`d2_absolute_error_score` function:: - >>> from sklearn.metrics import d2_absolute_error_score - >>> y_true = [3, -0.5, 2, 7] - >>> y_pred = [2.5, 0.0, 2, 8] - >>> d2_absolute_error_score(y_true, y_pred) - 0.764... - >>> y_true = [1, 2, 3] - >>> y_pred = [1, 2, 3] - >>> d2_absolute_error_score(y_true, y_pred) - 1.0 - >>> y_true = [1, 2, 3] - >>> y_pred = [2, 2, 2] - >>> d2_absolute_error_score(y_true, y_pred) - 0.0 + >>> from sklearn.metrics import d2_absolute_error_score + >>> y_true = [3, -0.5, 2, 7] + >>> y_pred = [2.5, 0.0, 2, 8] + >>> d2_absolute_error_score(y_true, y_pred) + 0.764... + >>> y_true = [1, 2, 3] + >>> y_pred = [1, 2, 3] + >>> d2_absolute_error_score(y_true, y_pred) + 1.0 + >>> y_true = [1, 2, 3] + >>> y_pred = [2, 2, 2] + >>> d2_absolute_error_score(y_true, y_pred) + 0.0 -|details-end| .. _visualization_regression_evaluation: @@ -2958,12 +2893,12 @@ model might be useful. Refer to the example below to see a model evaluation that makes use of this display. -.. topic:: Example: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_compose_plot_transformed_target.py` for - an example on how to use :class:`~sklearn.metrics.PredictionErrorDisplay` - to visualize the prediction quality improvement of a regression model - obtained by transforming the target before learning. +* See :ref:`sphx_glr_auto_examples_compose_plot_transformed_target.py` for + an example on how to use :class:`~sklearn.metrics.PredictionErrorDisplay` + to visualize the prediction quality improvement of a regression model + obtained by transforming the target before learning. .. _clustering_metrics: diff --git a/doc/modules/multiclass.rst b/doc/modules/multiclass.rst index 42762690ce8f7..b5f7611bdfd91 100644 --- a/doc/modules/multiclass.rst +++ b/doc/modules/multiclass.rst @@ -222,9 +222,9 @@ in which cell [i, j] indicates the presence of label j in sample i. :scale: 75% -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_miscellaneous_plot_multilabel.py` +* :ref:`sphx_glr_auto_examples_miscellaneous_plot_multilabel.py` .. _ovo_classification: @@ -263,10 +263,10 @@ Below is an example of multiclass learning using OvO:: 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) -.. topic:: References: +.. rubric:: References - * "Pattern Recognition and Machine Learning. Springer", - Christopher M. Bishop, page 183, (First Edition) +* "Pattern Recognition and Machine Learning. Springer", + Christopher M. Bishop, page 183, (First Edition) .. _ecoc: @@ -321,21 +321,16 @@ Below is an example of multiclass learning using Output-Codes:: 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) -.. topic:: References: +.. rubric:: References - * "Solving multiclass learning problems via error-correcting output codes", - Dietterich T., Bakiri G., - Journal of Artificial Intelligence Research 2, - 1995. +* "Solving multiclass learning problems via error-correcting output codes", + Dietterich T., Bakiri G., Journal of Artificial Intelligence Research 2, 1995. - .. [3] "The error coding method and PICTs", - James G., Hastie T., - Journal of Computational and Graphical statistics 7, - 1998. +.. [3] "The error coding method and PICTs", James G., Hastie T., + Journal of Computational and Graphical statistics 7, 1998. - * "The Elements of Statistical Learning", - Hastie T., Tibshirani R., Friedman J., page 606 (second-edition) - 2008. +* "The Elements of Statistical Learning", + Hastie T., Tibshirani R., Friedman J., page 606 (second-edition), 2008. .. _multilabel_classification: @@ -432,10 +427,10 @@ one does not know the optimal ordering of the models in the chain so typically many randomly ordered chains are fit and their predictions are averaged together. -.. topic:: References: +.. rubric:: References - Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank, - "Classifier Chains for Multi-label Classification", 2009. +* Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank, + "Classifier Chains for Multi-label Classification", 2009. .. _multiclass_multioutput_classification: diff --git a/doc/modules/naive_bayes.rst b/doc/modules/naive_bayes.rst index 05ca928dfae0b..6e80ec6145919 100644 --- a/doc/modules/naive_bayes.rst +++ b/doc/modules/naive_bayes.rst @@ -69,15 +69,11 @@ On the flip side, although naive Bayes is known as a decent classifier, it is known to be a bad estimator, so the probability outputs from ``predict_proba`` are not to be taken too seriously. -|details-start| -**References** -|details-split| +.. dropdown:: References -* H. Zhang (2004). `The optimality of Naive Bayes. - `_ - Proc. FLAIRS. - -|details-end| + * H. Zhang (2004). `The optimality of Naive Bayes. + `_ + Proc. FLAIRS. .. _gaussian_naive_bayes: @@ -153,47 +149,40 @@ The inventors of CNB show empirically that the parameter estimates for CNB are more stable than those for MNB. Further, CNB regularly outperforms MNB (often by a considerable margin) on text classification tasks. -|details-start| -**Weights calculation** -|details-split| - -The procedure for calculating the weights is as follows: +.. dropdown:: Weights calculation -.. math:: + The procedure for calculating the weights is as follows: - \hat{\theta}_{ci} = \frac{\alpha_i + \sum_{j:y_j \neq c} d_{ij}} - {\alpha + \sum_{j:y_j \neq c} \sum_{k} d_{kj}} + .. math:: - w_{ci} = \log \hat{\theta}_{ci} + \hat{\theta}_{ci} = \frac{\alpha_i + \sum_{j:y_j \neq c} d_{ij}} + {\alpha + \sum_{j:y_j \neq c} \sum_{k} d_{kj}} - w_{ci} = \frac{w_{ci}}{\sum_{j} |w_{cj}|} + w_{ci} = \log \hat{\theta}_{ci} -where the summations are over all documents :math:`j` not in class :math:`c`, -:math:`d_{ij}` is either the count or tf-idf value of term :math:`i` in document -:math:`j`, :math:`\alpha_i` is a smoothing hyperparameter like that found in -MNB, and :math:`\alpha = \sum_{i} \alpha_i`. The second normalization addresses -the tendency for longer documents to dominate parameter estimates in MNB. The -classification rule is: + w_{ci} = \frac{w_{ci}}{\sum_{j} |w_{cj}|} -.. math:: + where the summations are over all documents :math:`j` not in class :math:`c`, + :math:`d_{ij}` is either the count or tf-idf value of term :math:`i` in document + :math:`j`, :math:`\alpha_i` is a smoothing hyperparameter like that found in + MNB, and :math:`\alpha = \sum_{i} \alpha_i`. The second normalization addresses + the tendency for longer documents to dominate parameter estimates in MNB. The + classification rule is: - \hat{c} = \arg\min_c \sum_{i} t_i w_{ci} + .. math:: -i.e., a document is assigned to the class that is the *poorest* complement -match. + \hat{c} = \arg\min_c \sum_{i} t_i w_{ci} -|details-end| + i.e., a document is assigned to the class that is the *poorest* complement + match. -|details-start| -**References** -|details-split| +.. dropdown:: References -* Rennie, J. D., Shih, L., Teevan, J., & Karger, D. R. (2003). - `Tackling the poor assumptions of naive bayes text classifiers. - `_ - In ICML (Vol. 3, pp. 616-623). + * Rennie, J. D., Shih, L., Teevan, J., & Karger, D. R. (2003). + `Tackling the poor assumptions of naive bayes text classifiers. + `_ + In ICML (Vol. 3, pp. 616-623). -|details-end| .. _bernoulli_naive_bayes: @@ -224,24 +213,21 @@ count vectors) may be used to train and use this classifier. :class:`BernoulliNB might perform better on some datasets, especially those with shorter documents. It is advisable to evaluate both models, if time permits. -|details-start| -**References** -|details-split| +.. dropdown:: References -* C.D. Manning, P. Raghavan and H. Schütze (2008). Introduction to - Information Retrieval. Cambridge University Press, pp. 234-265. + * C.D. Manning, P. Raghavan and H. Schütze (2008). Introduction to + Information Retrieval. Cambridge University Press, pp. 234-265. -* A. McCallum and K. Nigam (1998). - `A comparison of event models for Naive Bayes text classification. - `_ - Proc. AAAI/ICML-98 Workshop on Learning for Text Categorization, pp. 41-48. + * A. McCallum and K. Nigam (1998). + `A comparison of event models for Naive Bayes text classification. + `_ + Proc. AAAI/ICML-98 Workshop on Learning for Text Categorization, pp. 41-48. -* V. Metsis, I. Androutsopoulos and G. Paliouras (2006). - `Spam filtering with Naive Bayes -- Which Naive Bayes? - `_ - 3rd Conf. on Email and Anti-Spam (CEAS). + * V. Metsis, I. Androutsopoulos and G. Paliouras (2006). + `Spam filtering with Naive Bayes -- Which Naive Bayes? + `_ + 3rd Conf. on Email and Anti-Spam (CEAS). -|details-end| .. _categorical_naive_bayes: @@ -258,25 +244,21 @@ For each feature :math:`i` in the training set :math:`X`, of X conditioned on the class y. The index set of the samples is defined as :math:`J = \{ 1, \dots, m \}`, with :math:`m` as the number of samples. -|details-start| -**Probability calculation** -|details-split| - -The probability of category :math:`t` in feature :math:`i` given class -:math:`c` is estimated as: +.. dropdown:: Probability calculation -.. math:: + The probability of category :math:`t` in feature :math:`i` given class + :math:`c` is estimated as: - P(x_i = t \mid y = c \: ;\, \alpha) = \frac{ N_{tic} + \alpha}{N_{c} + - \alpha n_i}, + .. math:: -where :math:`N_{tic} = |\{j \in J \mid x_{ij} = t, y_j = c\}|` is the number -of times category :math:`t` appears in the samples :math:`x_{i}`, which belong -to class :math:`c`, :math:`N_{c} = |\{ j \in J\mid y_j = c\}|` is the number -of samples with class c, :math:`\alpha` is a smoothing parameter and -:math:`n_i` is the number of available categories of feature :math:`i`. + P(x_i = t \mid y = c \: ;\, \alpha) = \frac{ N_{tic} + \alpha}{N_{c} + + \alpha n_i}, -|details-end| + where :math:`N_{tic} = |\{j \in J \mid x_{ij} = t, y_j = c\}|` is the number + of times category :math:`t` appears in the samples :math:`x_{i}`, which belong + to class :math:`c`, :math:`N_{c} = |\{ j \in J\mid y_j = c\}|` is the number + of samples with class c, :math:`\alpha` is a smoothing parameter and + :math:`n_i` is the number of available categories of feature :math:`i`. :class:`CategoricalNB` assumes that the sample matrix :math:`X` is encoded (for instance with the help of :class:`~sklearn.preprocessing.OrdinalEncoder`) such diff --git a/doc/modules/neighbors.rst b/doc/modules/neighbors.rst index b081b29572d8a..de0eff67018bc 100644 --- a/doc/modules/neighbors.rst +++ b/doc/modules/neighbors.rst @@ -192,10 +192,10 @@ distance can be supplied to compute the weights. .. centered:: |classification_1| -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`: an example of - classification using nearest neighbors. +* :ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`: an example of + classification using nearest neighbors. .. _regression: @@ -241,13 +241,13 @@ the lower half of those faces. :align: center -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_neighbors_plot_regression.py`: an example of regression - using nearest neighbors. +* :ref:`sphx_glr_auto_examples_neighbors_plot_regression.py`: an example of regression + using nearest neighbors. - * :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`: an example of - multi-output regression using nearest neighbors. +* :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`: + an example of multi-output regression using nearest neighbors. Nearest Neighbor Algorithms @@ -304,15 +304,13 @@ In scikit-learn, KD tree neighbors searches are specified using the keyword ``algorithm = 'kd_tree'``, and are computed using the class :class:`KDTree`. -|details-start| -**References** -|details-split| - * `"Multidimensional binary search trees used for associative searching" - `_, - Bentley, J.L., Communications of the ACM (1975) +.. dropdown:: References + + * `"Multidimensional binary search trees used for associative searching" + `_, + Bentley, J.L., Communications of the ACM (1975) -|details-end| .. _ball_tree: @@ -345,156 +343,142 @@ neighbors searches are specified using the keyword ``algorithm = 'ball_tree'``, and are computed using the class :class:`BallTree`. Alternatively, the user can work with the :class:`BallTree` class directly. -|details-start| -**References** -|details-split| - - * `"Five Balltree Construction Algorithms" - `_, - Omohundro, S.M., International Computer Science Institute - Technical Report (1989) - -|details-end| - -|details-start| -**Choice of Nearest Neighbors Algorithm** -|details-split| - -The optimal algorithm for a given dataset is a complicated choice, and -depends on a number of factors: - -* number of samples :math:`N` (i.e. ``n_samples``) and dimensionality - :math:`D` (i.e. ``n_features``). - - * *Brute force* query time grows as :math:`O[D N]` - * *Ball tree* query time grows as approximately :math:`O[D \log(N)]` - * *KD tree* query time changes with :math:`D` in a way that is difficult - to precisely characterise. For small :math:`D` (less than 20 or so) - the cost is approximately :math:`O[D\log(N)]`, and the KD tree - query can be very efficient. - For larger :math:`D`, the cost increases to nearly :math:`O[DN]`, and - the overhead due to the tree - structure can lead to queries which are slower than brute force. - - For small data sets (:math:`N` less than 30 or so), :math:`\log(N)` is - comparable to :math:`N`, and brute force algorithms can be more efficient - than a tree-based approach. Both :class:`KDTree` and :class:`BallTree` - address this through providing a *leaf size* parameter: this controls the - number of samples at which a query switches to brute-force. This allows both - algorithms to approach the efficiency of a brute-force computation for small - :math:`N`. - -* data structure: *intrinsic dimensionality* of the data and/or *sparsity* - of the data. Intrinsic dimensionality refers to the dimension - :math:`d \le D` of a manifold on which the data lies, which can be linearly - or non-linearly embedded in the parameter space. Sparsity refers to the - degree to which the data fills the parameter space (this is to be - distinguished from the concept as used in "sparse" matrices. The data - matrix may have no zero entries, but the **structure** can still be - "sparse" in this sense). - - * *Brute force* query time is unchanged by data structure. - * *Ball tree* and *KD tree* query times can be greatly influenced - by data structure. In general, sparser data with a smaller intrinsic - dimensionality leads to faster query times. Because the KD tree - internal representation is aligned with the parameter axes, it will not - generally show as much improvement as ball tree for arbitrarily - structured data. - - Datasets used in machine learning tend to be very structured, and are - very well-suited for tree-based queries. - -* number of neighbors :math:`k` requested for a query point. - - * *Brute force* query time is largely unaffected by the value of :math:`k` - * *Ball tree* and *KD tree* query time will become slower as :math:`k` - increases. This is due to two effects: first, a larger :math:`k` leads - to the necessity to search a larger portion of the parameter space. - Second, using :math:`k > 1` requires internal queueing of results - as the tree is traversed. - - As :math:`k` becomes large compared to :math:`N`, the ability to prune - branches in a tree-based query is reduced. In this situation, Brute force - queries can be more efficient. - -* number of query points. Both the ball tree and the KD Tree - require a construction phase. The cost of this construction becomes - negligible when amortized over many queries. If only a small number of - queries will be performed, however, the construction can make up - a significant fraction of the total cost. If very few query points - will be required, brute force is better than a tree-based method. - -Currently, ``algorithm = 'auto'`` selects ``'brute'`` if any of the following -conditions are verified: - -* input data is sparse -* ``metric = 'precomputed'`` -* :math:`D > 15` -* :math:`k >= N/2` -* ``effective_metric_`` isn't in the ``VALID_METRICS`` list for either - ``'kd_tree'`` or ``'ball_tree'`` - -Otherwise, it selects the first out of ``'kd_tree'`` and ``'ball_tree'`` that -has ``effective_metric_`` in its ``VALID_METRICS`` list. This heuristic is -based on the following assumptions: - -* the number of query points is at least the same order as the number of - training points -* ``leaf_size`` is close to its default value of ``30`` -* when :math:`D > 15`, the intrinsic dimensionality of the data is generally - too high for tree-based methods - -|details-end| - -|details-start| -**Effect of ``leaf_size``** -|details-split| - -As noted above, for small sample sizes a brute force search can be more -efficient than a tree-based query. This fact is accounted for in the ball -tree and KD tree by internally switching to brute force searches within -leaf nodes. The level of this switch can be specified with the parameter -``leaf_size``. This parameter choice has many effects: - -**construction time** - A larger ``leaf_size`` leads to a faster tree construction time, because - fewer nodes need to be created - -**query time** - Both a large or small ``leaf_size`` can lead to suboptimal query cost. - For ``leaf_size`` approaching 1, the overhead involved in traversing - nodes can significantly slow query times. For ``leaf_size`` approaching - the size of the training set, queries become essentially brute force. - A good compromise between these is ``leaf_size = 30``, the default value - of the parameter. - -**memory** - As ``leaf_size`` increases, the memory required to store a tree structure - decreases. This is especially important in the case of ball tree, which - stores a :math:`D`-dimensional centroid for each node. The required - storage space for :class:`BallTree` is approximately ``1 / leaf_size`` times - the size of the training set. - -``leaf_size`` is not referenced for brute force queries. -|details-end| - -|details-start| -**Valid Metrics for Nearest Neighbor Algorithms** -|details-split| - -For a list of available metrics, see the documentation of the -:class:`~sklearn.metrics.DistanceMetric` class and the metrics listed in -`sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS`. Note that the "cosine" -metric uses :func:`~sklearn.metrics.pairwise.cosine_distances`. - -A list of valid metrics for any of the above algorithms can be obtained by using their -``valid_metric`` attribute. For example, valid metrics for ``KDTree`` can be generated by: - - >>> from sklearn.neighbors import KDTree - >>> print(sorted(KDTree.valid_metrics)) - ['chebyshev', 'cityblock', 'euclidean', 'infinity', 'l1', 'l2', 'manhattan', 'minkowski', 'p'] -|details-end| +.. dropdown:: References + + * `"Five Balltree Construction Algorithms" + `_, + Omohundro, S.M., International Computer Science Institute + Technical Report (1989) + +.. dropdown:: Choice of Nearest Neighbors Algorithm + + The optimal algorithm for a given dataset is a complicated choice, and + depends on a number of factors: + + * number of samples :math:`N` (i.e. ``n_samples``) and dimensionality + :math:`D` (i.e. ``n_features``). + + * *Brute force* query time grows as :math:`O[D N]` + * *Ball tree* query time grows as approximately :math:`O[D \log(N)]` + * *KD tree* query time changes with :math:`D` in a way that is difficult + to precisely characterise. For small :math:`D` (less than 20 or so) + the cost is approximately :math:`O[D\log(N)]`, and the KD tree + query can be very efficient. + For larger :math:`D`, the cost increases to nearly :math:`O[DN]`, and + the overhead due to the tree + structure can lead to queries which are slower than brute force. + + For small data sets (:math:`N` less than 30 or so), :math:`\log(N)` is + comparable to :math:`N`, and brute force algorithms can be more efficient + than a tree-based approach. Both :class:`KDTree` and :class:`BallTree` + address this through providing a *leaf size* parameter: this controls the + number of samples at which a query switches to brute-force. This allows both + algorithms to approach the efficiency of a brute-force computation for small + :math:`N`. + + * data structure: *intrinsic dimensionality* of the data and/or *sparsity* + of the data. Intrinsic dimensionality refers to the dimension + :math:`d \le D` of a manifold on which the data lies, which can be linearly + or non-linearly embedded in the parameter space. Sparsity refers to the + degree to which the data fills the parameter space (this is to be + distinguished from the concept as used in "sparse" matrices. The data + matrix may have no zero entries, but the **structure** can still be + "sparse" in this sense). + + * *Brute force* query time is unchanged by data structure. + * *Ball tree* and *KD tree* query times can be greatly influenced + by data structure. In general, sparser data with a smaller intrinsic + dimensionality leads to faster query times. Because the KD tree + internal representation is aligned with the parameter axes, it will not + generally show as much improvement as ball tree for arbitrarily + structured data. + + Datasets used in machine learning tend to be very structured, and are + very well-suited for tree-based queries. + + * number of neighbors :math:`k` requested for a query point. + + * *Brute force* query time is largely unaffected by the value of :math:`k` + * *Ball tree* and *KD tree* query time will become slower as :math:`k` + increases. This is due to two effects: first, a larger :math:`k` leads + to the necessity to search a larger portion of the parameter space. + Second, using :math:`k > 1` requires internal queueing of results + as the tree is traversed. + + As :math:`k` becomes large compared to :math:`N`, the ability to prune + branches in a tree-based query is reduced. In this situation, Brute force + queries can be more efficient. + + * number of query points. Both the ball tree and the KD Tree + require a construction phase. The cost of this construction becomes + negligible when amortized over many queries. If only a small number of + queries will be performed, however, the construction can make up + a significant fraction of the total cost. If very few query points + will be required, brute force is better than a tree-based method. + + Currently, ``algorithm = 'auto'`` selects ``'brute'`` if any of the following + conditions are verified: + + * input data is sparse + * ``metric = 'precomputed'`` + * :math:`D > 15` + * :math:`k >= N/2` + * ``effective_metric_`` isn't in the ``VALID_METRICS`` list for either + ``'kd_tree'`` or ``'ball_tree'`` + + Otherwise, it selects the first out of ``'kd_tree'`` and ``'ball_tree'`` that + has ``effective_metric_`` in its ``VALID_METRICS`` list. This heuristic is + based on the following assumptions: + + * the number of query points is at least the same order as the number of + training points + * ``leaf_size`` is close to its default value of ``30`` + * when :math:`D > 15`, the intrinsic dimensionality of the data is generally + too high for tree-based methods + +.. dropdown:: Effect of ``leaf_size`` + + As noted above, for small sample sizes a brute force search can be more + efficient than a tree-based query. This fact is accounted for in the ball + tree and KD tree by internally switching to brute force searches within + leaf nodes. The level of this switch can be specified with the parameter + ``leaf_size``. This parameter choice has many effects: + + **construction time** + A larger ``leaf_size`` leads to a faster tree construction time, because + fewer nodes need to be created + + **query time** + Both a large or small ``leaf_size`` can lead to suboptimal query cost. + For ``leaf_size`` approaching 1, the overhead involved in traversing + nodes can significantly slow query times. For ``leaf_size`` approaching + the size of the training set, queries become essentially brute force. + A good compromise between these is ``leaf_size = 30``, the default value + of the parameter. + + **memory** + As ``leaf_size`` increases, the memory required to store a tree structure + decreases. This is especially important in the case of ball tree, which + stores a :math:`D`-dimensional centroid for each node. The required + storage space for :class:`BallTree` is approximately ``1 / leaf_size`` times + the size of the training set. + + ``leaf_size`` is not referenced for brute force queries. + +.. dropdown:: Valid Metrics for Nearest Neighbor Algorithms + + For a list of available metrics, see the documentation of the + :class:`~sklearn.metrics.DistanceMetric` class and the metrics listed in + `sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS`. Note that the "cosine" + metric uses :func:`~sklearn.metrics.pairwise.cosine_distances`. + + A list of valid metrics for any of the above algorithms can be obtained by using their + ``valid_metric`` attribute. For example, valid metrics for ``KDTree`` can be generated by: + + >>> from sklearn.neighbors import KDTree + >>> print(sorted(KDTree.valid_metrics)) + ['chebyshev', 'cityblock', 'euclidean', 'infinity', 'l1', 'l2', 'manhattan', 'minkowski', 'p'] .. _nearest_centroid_classifier: @@ -547,10 +531,10 @@ the model from 0.81 to 0.82. .. centered:: |nearest_centroid_1| |nearest_centroid_2| -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_neighbors_plot_nearest_centroid.py`: an example of - classification using nearest centroid with different shrink thresholds. +* :ref:`sphx_glr_auto_examples_neighbors_plot_nearest_centroid.py`: an example of + classification using nearest centroid with different shrink thresholds. .. _neighbors_transformer: @@ -635,17 +619,17 @@ implementation with special data types. The precomputed neighbors include one extra neighbor in a custom nearest neighbors estimator, since unnecessary neighbors will be filtered by following estimators. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_neighbors_approximate_nearest_neighbors.py`: - an example of pipelining :class:`KNeighborsTransformer` and - :class:`~sklearn.manifold.TSNE`. Also proposes two custom nearest neighbors - estimators based on external packages. +* :ref:`sphx_glr_auto_examples_neighbors_approximate_nearest_neighbors.py`: + an example of pipelining :class:`KNeighborsTransformer` and + :class:`~sklearn.manifold.TSNE`. Also proposes two custom nearest neighbors + estimators based on external packages. - * :ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`: - an example of pipelining :class:`KNeighborsTransformer` and - :class:`KNeighborsClassifier` to enable caching of the neighbors graph - during a hyper-parameter grid-search. +* :ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`: + an example of pipelining :class:`KNeighborsTransformer` and + :class:`KNeighborsClassifier` to enable caching of the neighbors graph + during a hyper-parameter grid-search. .. _nca: @@ -769,11 +753,11 @@ by each method. Each data sample belongs to one of 10 classes. .. centered:: |nca_dim_reduction_1| |nca_dim_reduction_2| |nca_dim_reduction_3| -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_neighbors_plot_nca_classification.py` - * :ref:`sphx_glr_auto_examples_neighbors_plot_nca_dim_reduction.py` - * :ref:`sphx_glr_auto_examples_manifold_plot_lle_digits.py` +* :ref:`sphx_glr_auto_examples_neighbors_plot_nca_classification.py` +* :ref:`sphx_glr_auto_examples_neighbors_plot_nca_dim_reduction.py` +* :ref:`sphx_glr_auto_examples_manifold_plot_lle_digits.py` .. _nca_mathematical_formulation: @@ -806,20 +790,17 @@ space: p_{i j} = \frac{\exp(-||L x_i - L x_j||^2)}{\sum\limits_{k \ne i} {\exp{-(||L x_i - L x_k||^2)}}} , \quad p_{i i} = 0 -|details-start| -**Mahalanobis distance** -|details-split| +.. dropdown:: Mahalanobis distance -NCA can be seen as learning a (squared) Mahalanobis distance metric: + NCA can be seen as learning a (squared) Mahalanobis distance metric: -.. math:: + .. math:: - || L(x_i - x_j)||^2 = (x_i - x_j)^TM(x_i - x_j), + || L(x_i - x_j)||^2 = (x_i - x_j)^TM(x_i - x_j), -where :math:`M = L^T L` is a symmetric positive semi-definite matrix of size -``(n_features, n_features)``. + where :math:`M = L^T L` is a symmetric positive semi-definite matrix of size + ``(n_features, n_features)``. -|details-end| Implementation -------------- @@ -851,14 +832,12 @@ complexity equals ``n_components * n_features * n_samples_test``. There is no added space complexity in the operation. -.. topic:: References: - - .. [1] `"Neighbourhood Components Analysis" - `_, - J. Goldberger, S. Roweis, G. Hinton, R. Salakhutdinov, Advances in - Neural Information Processing Systems, Vol. 17, May 2005, pp. 513-520. +.. rubric:: References - `Wikipedia entry on Neighborhood Components Analysis - `_ +.. [1] `"Neighbourhood Components Analysis" + `_, + J. Goldberger, S. Roweis, G. Hinton, R. Salakhutdinov, Advances in + Neural Information Processing Systems, Vol. 17, May 2005, pp. 513-520. -|details-end| +* `Wikipedia entry on Neighborhood Components Analysis + `_ diff --git a/doc/modules/neural_networks_supervised.rst b/doc/modules/neural_networks_supervised.rst index 7ee2387068c81..5c6baecb7e2ff 100644 --- a/doc/modules/neural_networks_supervised.rst +++ b/doc/modules/neural_networks_supervised.rst @@ -49,33 +49,30 @@ The module contains the public attributes ``coefs_`` and ``intercepts_``. :math:`i+1`. ``intercepts_`` is a list of bias vectors, where the vector at index :math:`i` represents the bias values added to layer :math:`i+1`. -|details-start| -**Advantages and disadvantages of Multi-layer Perceptron** -|details-split| +.. dropdown:: Advantages and disadvantages of Multi-layer Perceptron -The advantages of Multi-layer Perceptron are: + The advantages of Multi-layer Perceptron are: -+ Capability to learn non-linear models. + + Capability to learn non-linear models. -+ Capability to learn models in real-time (on-line learning) - using ``partial_fit``. + + Capability to learn models in real-time (on-line learning) + using ``partial_fit``. -The disadvantages of Multi-layer Perceptron (MLP) include: + The disadvantages of Multi-layer Perceptron (MLP) include: -+ MLP with hidden layers have a non-convex loss function where there exists - more than one local minimum. Therefore different random weight - initializations can lead to different validation accuracy. + + MLP with hidden layers have a non-convex loss function where there exists + more than one local minimum. Therefore different random weight + initializations can lead to different validation accuracy. -+ MLP requires tuning a number of hyperparameters such as the number of - hidden neurons, layers, and iterations. + + MLP requires tuning a number of hyperparameters such as the number of + hidden neurons, layers, and iterations. -+ MLP is sensitive to feature scaling. + + MLP is sensitive to feature scaling. -Please see :ref:`Tips on Practical Use ` section that addresses -some of these disadvantages. + Please see :ref:`Tips on Practical Use ` section that addresses + some of these disadvantages. -|details-end| Classification ============== @@ -148,11 +145,11 @@ indices where the value is `1` represents the assigned classes of that sample:: See the examples below and the docstring of :meth:`MLPClassifier.fit` for further information. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_training_curves.py` - * See :ref:`sphx_glr_auto_examples_neural_networks_plot_mnist_filters.py` for - visualized representation of trained weights. +* :ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_training_curves.py` +* See :ref:`sphx_glr_auto_examples_neural_networks_plot_mnist_filters.py` for + visualized representation of trained weights. Regression ========== @@ -181,9 +178,9 @@ decision function with value of alpha. See the examples below for further information. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_alpha.py` +* :ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_alpha.py` Algorithms ========== @@ -234,83 +231,78 @@ of iterations. Since backpropagation has a high time complexity, it is advisable to start with smaller number of hidden neurons and few hidden layers for training. -|details-start| -Mathematical formulation -|details-split| +.. dropdown:: Mathematical formulation -Given a set of training examples :math:`(x_1, y_1), (x_2, y_2), \ldots, (x_n, y_n)` -where :math:`x_i \in \mathbf{R}^n` and :math:`y_i \in \{0, 1\}`, a one hidden -layer one hidden neuron MLP learns the function :math:`f(x) = W_2 g(W_1^T x + b_1) + b_2` -where :math:`W_1 \in \mathbf{R}^m` and :math:`W_2, b_1, b_2 \in \mathbf{R}` are -model parameters. :math:`W_1, W_2` represent the weights of the input layer and -hidden layer, respectively; and :math:`b_1, b_2` represent the bias added to -the hidden layer and the output layer, respectively. -:math:`g(\cdot) : R \rightarrow R` is the activation function, set by default as -the hyperbolic tan. It is given as, + Given a set of training examples :math:`(x_1, y_1), (x_2, y_2), \ldots, (x_n, y_n)` + where :math:`x_i \in \mathbf{R}^n` and :math:`y_i \in \{0, 1\}`, a one hidden + layer one hidden neuron MLP learns the function :math:`f(x) = W_2 g(W_1^T x + b_1) + b_2` + where :math:`W_1 \in \mathbf{R}^m` and :math:`W_2, b_1, b_2 \in \mathbf{R}` are + model parameters. :math:`W_1, W_2` represent the weights of the input layer and + hidden layer, respectively; and :math:`b_1, b_2` represent the bias added to + the hidden layer and the output layer, respectively. + :math:`g(\cdot) : R \rightarrow R` is the activation function, set by default as + the hyperbolic tan. It is given as, -.. math:: - g(z)= \frac{e^z-e^{-z}}{e^z+e^{-z}} - -For binary classification, :math:`f(x)` passes through the logistic function -:math:`g(z)=1/(1+e^{-z})` to obtain output values between zero and one. A -threshold, set to 0.5, would assign samples of outputs larger or equal 0.5 -to the positive class, and the rest to the negative class. + .. math:: + g(z)= \frac{e^z-e^{-z}}{e^z+e^{-z}} -If there are more than two classes, :math:`f(x)` itself would be a vector of -size (n_classes,). Instead of passing through logistic function, it passes -through the softmax function, which is written as, - -.. math:: - \text{softmax}(z)_i = \frac{\exp(z_i)}{\sum_{l=1}^k\exp(z_l)} + For binary classification, :math:`f(x)` passes through the logistic function + :math:`g(z)=1/(1+e^{-z})` to obtain output values between zero and one. A + threshold, set to 0.5, would assign samples of outputs larger or equal 0.5 + to the positive class, and the rest to the negative class. -where :math:`z_i` represents the :math:`i` th element of the input to softmax, -which corresponds to class :math:`i`, and :math:`K` is the number of classes. -The result is a vector containing the probabilities that sample :math:`x` -belong to each class. The output is the class with the highest probability. + If there are more than two classes, :math:`f(x)` itself would be a vector of + size (n_classes,). Instead of passing through logistic function, it passes + through the softmax function, which is written as, -In regression, the output remains as :math:`f(x)`; therefore, output activation -function is just the identity function. + .. math:: + \text{softmax}(z)_i = \frac{\exp(z_i)}{\sum_{l=1}^k\exp(z_l)} -MLP uses different loss functions depending on the problem type. The loss -function for classification is Average Cross-Entropy, which in binary case is -given as, + where :math:`z_i` represents the :math:`i` th element of the input to softmax, + which corresponds to class :math:`i`, and :math:`K` is the number of classes. + The result is a vector containing the probabilities that sample :math:`x` + belong to each class. The output is the class with the highest probability. -.. math:: + In regression, the output remains as :math:`f(x)`; therefore, output activation + function is just the identity function. - Loss(\hat{y},y,W) = -\dfrac{1}{n}\sum_{i=0}^n(y_i \ln {\hat{y_i}} + (1-y_i) \ln{(1-\hat{y_i})}) + \dfrac{\alpha}{2n} ||W||_2^2 + MLP uses different loss functions depending on the problem type. The loss + function for classification is Average Cross-Entropy, which in binary case is + given as, -where :math:`\alpha ||W||_2^2` is an L2-regularization term (aka penalty) -that penalizes complex models; and :math:`\alpha > 0` is a non-negative -hyperparameter that controls the magnitude of the penalty. + .. math:: -For regression, MLP uses the Mean Square Error loss function; written as, + Loss(\hat{y},y,W) = -\dfrac{1}{n}\sum_{i=0}^n(y_i \ln {\hat{y_i}} + (1-y_i) \ln{(1-\hat{y_i})}) + \dfrac{\alpha}{2n} ||W||_2^2 -.. math:: + where :math:`\alpha ||W||_2^2` is an L2-regularization term (aka penalty) + that penalizes complex models; and :math:`\alpha > 0` is a non-negative + hyperparameter that controls the magnitude of the penalty. - Loss(\hat{y},y,W) = \frac{1}{2n}\sum_{i=0}^n||\hat{y}_i - y_i ||_2^2 + \frac{\alpha}{2n} ||W||_2^2 + For regression, MLP uses the Mean Square Error loss function; written as, + .. math:: -Starting from initial random weights, multi-layer perceptron (MLP) minimizes -the loss function by repeatedly updating these weights. After computing the -loss, a backward pass propagates it from the output layer to the previous -layers, providing each weight parameter with an update value meant to decrease -the loss. + Loss(\hat{y},y,W) = \frac{1}{2n}\sum_{i=0}^n||\hat{y}_i - y_i ||_2^2 + \frac{\alpha}{2n} ||W||_2^2 -In gradient descent, the gradient :math:`\nabla Loss_{W}` of the loss with respect -to the weights is computed and deducted from :math:`W`. -More formally, this is expressed as, + Starting from initial random weights, multi-layer perceptron (MLP) minimizes + the loss function by repeatedly updating these weights. After computing the + loss, a backward pass propagates it from the output layer to the previous + layers, providing each weight parameter with an update value meant to decrease + the loss. -.. math:: - W^{i+1} = W^i - \epsilon \nabla {Loss}_{W}^{i} + In gradient descent, the gradient :math:`\nabla Loss_{W}` of the loss with respect + to the weights is computed and deducted from :math:`W`. + More formally, this is expressed as, + .. math:: + W^{i+1} = W^i - \epsilon \nabla {Loss}_{W}^{i} -where :math:`i` is the iteration step, and :math:`\epsilon` is the learning rate -with a value larger than 0. + where :math:`i` is the iteration step, and :math:`\epsilon` is the learning rate + with a value larger than 0. -The algorithm stops when it reaches a preset maximum number of iterations; or -when the improvement in loss is below a certain, small number. + The algorithm stops when it reaches a preset maximum number of iterations; or + when the improvement in loss is below a certain, small number. -|details-end| .. _mlp_tips: @@ -361,25 +353,19 @@ or want to do additional monitoring, using ``warm_start=True`` and ... # additional monitoring / inspection MLPClassifier(... -|details-start| -**References** -|details-split| - - * `"Learning representations by back-propagating errors." - `_ - Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. +.. dropdown:: References - * `"Stochastic Gradient Descent" `_ L. Bottou - Website, 2010. + * `"Learning representations by back-propagating errors." + `_ + Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. - * `"Backpropagation" `_ - Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen - Website, 2011. + * `"Stochastic Gradient Descent" `_ L. Bottou - Website, 2010. - * `"Efficient BackProp" `_ - Y. LeCun, L. Bottou, G. Orr, K. Müller - In Neural Networks: Tricks - of the Trade 1998. + * `"Backpropagation" `_ + Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen - Website, 2011. - * :arxiv:`"Adam: A method for stochastic optimization." - <1412.6980>` - Kingma, Diederik, and Jimmy Ba (2014) + * `"Efficient BackProp" `_ + Y. LeCun, L. Bottou, G. Orr, K. Müller - In Neural Networks: Tricks of the Trade 1998. -|details-end| + * :arxiv:`"Adam: A method for stochastic optimization." <1412.6980>` + Kingma, Diederik, and Jimmy Ba (2014) diff --git a/doc/modules/neural_networks_unsupervised.rst b/doc/modules/neural_networks_unsupervised.rst index aca56ae8aaf2e..7f6c0016d183b 100644 --- a/doc/modules/neural_networks_unsupervised.rst +++ b/doc/modules/neural_networks_unsupervised.rst @@ -37,9 +37,9 @@ weights of independent RBMs. This method is known as unsupervised pre-training. :align: center :scale: 100% -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_neural_networks_plot_rbm_logistic_classification.py` +* :ref:`sphx_glr_auto_examples_neural_networks_plot_rbm_logistic_classification.py` Graphical model and parametrization @@ -57,7 +57,7 @@ visible and hidden unit, omitted from the image for simplicity. The energy function measures the quality of a joint assignment: -.. math:: +.. math:: E(\mathbf{v}, \mathbf{h}) = -\sum_i \sum_j w_{ij}v_ih_j - \sum_i b_iv_i - \sum_j c_jh_j @@ -149,13 +149,13 @@ step, in PCD we keep a number of chains (fantasy particles) that are updated :math:`k` Gibbs steps after each weight update. This allows the particles to explore the space more thoroughly. -.. topic:: References: +.. rubric:: References - * `"A fast learning algorithm for deep belief nets" - `_ - G. Hinton, S. Osindero, Y.-W. Teh, 2006 +* `"A fast learning algorithm for deep belief nets" + `_, + G. Hinton, S. Osindero, Y.-W. Teh, 2006 - * `"Training Restricted Boltzmann Machines using Approximations to - the Likelihood Gradient" - `_ - T. Tieleman, 2008 +* `"Training Restricted Boltzmann Machines using Approximations to + the Likelihood Gradient" + `_, + T. Tieleman, 2008 diff --git a/doc/modules/outlier_detection.rst b/doc/modules/outlier_detection.rst index d003b645eb19c..0c6891ed119bd 100644 --- a/doc/modules/outlier_detection.rst +++ b/doc/modules/outlier_detection.rst @@ -123,19 +123,19 @@ refer to the example :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py` and the sections hereunder. -.. topic:: Examples: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py` - for a comparison of the :class:`svm.OneClassSVM`, the - :class:`ensemble.IsolationForest`, the - :class:`neighbors.LocalOutlierFactor` and - :class:`covariance.EllipticEnvelope`. +* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py` + for a comparison of the :class:`svm.OneClassSVM`, the + :class:`ensemble.IsolationForest`, the + :class:`neighbors.LocalOutlierFactor` and + :class:`covariance.EllipticEnvelope`. - * See :ref:`sphx_glr_auto_examples_miscellaneous_plot_outlier_detection_bench.py` - for an example showing how to evaluate outlier detection estimators, - the :class:`neighbors.LocalOutlierFactor` and the - :class:`ensemble.IsolationForest`, using ROC curves from - :class:`metrics.RocCurveDisplay`. +* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_outlier_detection_bench.py` + for an example showing how to evaluate outlier detection estimators, + the :class:`neighbors.LocalOutlierFactor` and the + :class:`ensemble.IsolationForest`, using ROC curves from + :class:`metrics.RocCurveDisplay`. Novelty Detection ================= @@ -167,18 +167,18 @@ implementation. The `nu` parameter, also known as the margin of the One-Class SVM, corresponds to the probability of finding a new, but regular, observation outside the frontier. -.. topic:: References: +.. rubric:: References - * `Estimating the support of a high-dimensional distribution - `_ - Schölkopf, Bernhard, et al. Neural computation 13.7 (2001): 1443-1471. +* `Estimating the support of a high-dimensional distribution + `_ + Schölkopf, Bernhard, et al. Neural computation 13.7 (2001): 1443-1471. -.. topic:: Examples: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_svm_plot_oneclass.py` for visualizing the - frontier learned around some data by a - :class:`svm.OneClassSVM` object. - * :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py` +* See :ref:`sphx_glr_auto_examples_svm_plot_oneclass.py` for visualizing the + frontier learned around some data by a :class:`svm.OneClassSVM` object. + +* :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py` .. figure:: ../auto_examples/svm/images/sphx_glr_plot_oneclass_001.png :target: ../auto_examples/svm/plot_oneclass.html @@ -196,11 +196,11 @@ approximate the solution of a kernelized :class:`svm.OneClassSVM` whose complexity is at best quadratic in the number of samples. See section :ref:`sgd_online_one_class_svm` for more details. -.. topic:: Examples: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_linear_model_plot_sgdocsvm_vs_ocsvm.py` - for an illustration of the approximation of a kernelized One-Class SVM - with the `linear_model.SGDOneClassSVM` combined with kernel approximation. +* See :ref:`sphx_glr_auto_examples_linear_model_plot_sgdocsvm_vs_ocsvm.py` + for an illustration of the approximation of a kernelized One-Class SVM + with the `linear_model.SGDOneClassSVM` combined with kernel approximation. Outlier Detection @@ -238,18 +238,18 @@ This strategy is illustrated below. :align: center :scale: 75% -.. topic:: Examples: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_covariance_plot_mahalanobis_distances.py` for - an illustration of the difference between using a standard - (:class:`covariance.EmpiricalCovariance`) or a robust estimate - (:class:`covariance.MinCovDet`) of location and covariance to - assess the degree of outlyingness of an observation. +* See :ref:`sphx_glr_auto_examples_covariance_plot_mahalanobis_distances.py` for + an illustration of the difference between using a standard + (:class:`covariance.EmpiricalCovariance`) or a robust estimate + (:class:`covariance.MinCovDet`) of location and covariance to + assess the degree of outlyingness of an observation. -.. topic:: References: +.. rubric:: References - * Rousseeuw, P.J., Van Driessen, K. "A fast algorithm for the minimum - covariance determinant estimator" Technometrics 41(3), 212 (1999) +* Rousseeuw, P.J., Van Driessen, K. "A fast algorithm for the minimum + covariance determinant estimator" Technometrics 41(3), 212 (1999) .. _isolation_forest: @@ -299,22 +299,22 @@ allows you to add more trees to an already fitted model:: >>> clf.set_params(n_estimators=20) # add 10 more trees # doctest: +SKIP >>> clf.fit(X) # fit the added trees # doctest: +SKIP -.. topic:: Examples: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_ensemble_plot_isolation_forest.py` for - an illustration of the use of IsolationForest. +* See :ref:`sphx_glr_auto_examples_ensemble_plot_isolation_forest.py` for + an illustration of the use of IsolationForest. - * See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py` - for a comparison of :class:`ensemble.IsolationForest` with - :class:`neighbors.LocalOutlierFactor`, - :class:`svm.OneClassSVM` (tuned to perform like an outlier detection - method), :class:`linear_model.SGDOneClassSVM`, and a covariance-based - outlier detection with :class:`covariance.EllipticEnvelope`. +* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py` + for a comparison of :class:`ensemble.IsolationForest` with + :class:`neighbors.LocalOutlierFactor`, + :class:`svm.OneClassSVM` (tuned to perform like an outlier detection + method), :class:`linear_model.SGDOneClassSVM`, and a covariance-based + outlier detection with :class:`covariance.EllipticEnvelope`. -.. topic:: References: +.. rubric:: References - * Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation forest." - Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. +* Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation forest." + Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. .. _local_outlier_factor: @@ -370,20 +370,20 @@ This strategy is illustrated below. :align: center :scale: 75% -.. topic:: Examples: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_neighbors_plot_lof_outlier_detection.py` - for an illustration of the use of :class:`neighbors.LocalOutlierFactor`. +* See :ref:`sphx_glr_auto_examples_neighbors_plot_lof_outlier_detection.py` + for an illustration of the use of :class:`neighbors.LocalOutlierFactor`. - * See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py` - for a comparison with other anomaly detection methods. +* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py` + for a comparison with other anomaly detection methods. -.. topic:: References: +.. rubric:: References - * Breunig, Kriegel, Ng, and Sander (2000) - `LOF: identifying density-based local outliers. - `_ - Proc. ACM SIGMOD +* Breunig, Kriegel, Ng, and Sander (2000) + `LOF: identifying density-based local outliers. + `_ + Proc. ACM SIGMOD .. _novelty_with_lof: diff --git a/doc/modules/partial_dependence.rst b/doc/modules/partial_dependence.rst index 94f7206140b90..40f691a9e6dcc 100644 --- a/doc/modules/partial_dependence.rst +++ b/doc/modules/partial_dependence.rst @@ -79,25 +79,21 @@ parameter takes a list of indices, names of the categorical features or a boolea mask. The graphical representation of partial dependence for categorical features is a bar plot or a 2D heatmap. -|details-start| -**PDPs for multi-class classification** -|details-split| - -For multi-class classification, you need to set the class label for which -the PDPs should be created via the ``target`` argument:: - - >>> from sklearn.datasets import load_iris - >>> iris = load_iris() - >>> mc_clf = GradientBoostingClassifier(n_estimators=10, - ... max_depth=1).fit(iris.data, iris.target) - >>> features = [3, 2, (3, 2)] - >>> PartialDependenceDisplay.from_estimator(mc_clf, X, features, target=0) - <...> +.. dropdown:: PDPs for multi-class classification + + For multi-class classification, you need to set the class label for which + the PDPs should be created via the ``target`` argument:: -The same parameter ``target`` is used to specify the target in multi-output -regression settings. + >>> from sklearn.datasets import load_iris + >>> iris = load_iris() + >>> mc_clf = GradientBoostingClassifier(n_estimators=10, + ... max_depth=1).fit(iris.data, iris.target) + >>> features = [3, 2, (3, 2)] + >>> PartialDependenceDisplay.from_estimator(mc_clf, X, features, target=0) + <...> -|details-end| + The same parameter ``target`` is used to specify the target in multi-output + regression settings. If you need the raw values of the partial dependence function rather than the plots, you can use the @@ -266,9 +262,9 @@ estimators that support it, and 'brute' is used for the rest. interpreting PDPs is that the features should be independent. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py` +* :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py` .. rubric:: Footnotes @@ -276,21 +272,20 @@ estimators that support it, and 'brute' is used for the rest. class (the positive class for binary classification), or the decision function. -.. topic:: References +.. rubric:: References - .. [H2009] T. Hastie, R. Tibshirani and J. Friedman, - `The Elements of Statistical Learning - `_, - Second Edition, Section 10.13.2, Springer, 2009. +.. [H2009] T. Hastie, R. Tibshirani and J. Friedman, + `The Elements of Statistical Learning + `_, + Second Edition, Section 10.13.2, Springer, 2009. - .. [M2019] C. Molnar, - `Interpretable Machine Learning - `_, - Section 5.1, 2019. +.. [M2019] C. Molnar, + `Interpretable Machine Learning + `_, + Section 5.1, 2019. - .. [G2015] :arxiv:`A. Goldstein, A. Kapelner, J. Bleich, and E. Pitkin, - "Peeking Inside the Black Box: Visualizing Statistical - Learning With Plots of Individual Conditional Expectation" - Journal of Computational and Graphical Statistics, - 24(1): 44-65, Springer, 2015. - <1309.6392>` +.. [G2015] :arxiv:`A. Goldstein, A. Kapelner, J. Bleich, and E. Pitkin, + "Peeking Inside the Black Box: Visualizing Statistical + Learning With Plots of Individual Conditional Expectation" + Journal of Computational and Graphical Statistics, + 24(1): 44-65, Springer, 2015. <1309.6392>` diff --git a/doc/modules/permutation_importance.rst b/doc/modules/permutation_importance.rst index 368c6a6409aa0..12a20a8bcaa6c 100644 --- a/doc/modules/permutation_importance.rst +++ b/doc/modules/permutation_importance.rst @@ -110,48 +110,44 @@ which is more computationally efficient than sequentially calling :func:`permutation_importance` several times with a different scorer, as it reuses model predictions. -|details-start| -**Example of permutation feature importance using multiple scorers** -|details-split| - -In the example below we use a list of metrics, but more input formats are -possible, as documented in :ref:`multimetric_scoring`. - - >>> scoring = ['r2', 'neg_mean_absolute_percentage_error', 'neg_mean_squared_error'] - >>> r_multi = permutation_importance( - ... model, X_val, y_val, n_repeats=30, random_state=0, scoring=scoring) - ... - >>> for metric in r_multi: - ... print(f"{metric}") - ... r = r_multi[metric] - ... for i in r.importances_mean.argsort()[::-1]: - ... if r.importances_mean[i] - 2 * r.importances_std[i] > 0: - ... print(f" {diabetes.feature_names[i]:<8}" - ... f"{r.importances_mean[i]:.3f}" - ... f" +/- {r.importances_std[i]:.3f}") - ... - r2 - s5 0.204 +/- 0.050 - bmi 0.176 +/- 0.048 - bp 0.088 +/- 0.033 - sex 0.056 +/- 0.023 - neg_mean_absolute_percentage_error - s5 0.081 +/- 0.020 - bmi 0.064 +/- 0.015 - bp 0.029 +/- 0.010 - neg_mean_squared_error - s5 1013.866 +/- 246.445 - bmi 872.726 +/- 240.298 - bp 438.663 +/- 163.022 - sex 277.376 +/- 115.123 - -The ranking of the features is approximately the same for different metrics even -if the scales of the importance values are very different. However, this is not -guaranteed and different metrics might lead to significantly different feature -importances, in particular for models trained for imbalanced classification problems, -for which **the choice of the classification metric can be critical**. - -|details-end| +.. dropdown:: Example of permutation feature importance using multiple scorers + + In the example below we use a list of metrics, but more input formats are + possible, as documented in :ref:`multimetric_scoring`. + + >>> scoring = ['r2', 'neg_mean_absolute_percentage_error', 'neg_mean_squared_error'] + >>> r_multi = permutation_importance( + ... model, X_val, y_val, n_repeats=30, random_state=0, scoring=scoring) + ... + >>> for metric in r_multi: + ... print(f"{metric}") + ... r = r_multi[metric] + ... for i in r.importances_mean.argsort()[::-1]: + ... if r.importances_mean[i] - 2 * r.importances_std[i] > 0: + ... print(f" {diabetes.feature_names[i]:<8}" + ... f"{r.importances_mean[i]:.3f}" + ... f" +/- {r.importances_std[i]:.3f}") + ... + r2 + s5 0.204 +/- 0.050 + bmi 0.176 +/- 0.048 + bp 0.088 +/- 0.033 + sex 0.056 +/- 0.023 + neg_mean_absolute_percentage_error + s5 0.081 +/- 0.020 + bmi 0.064 +/- 0.015 + bp 0.029 +/- 0.010 + neg_mean_squared_error + s5 1013.866 +/- 246.445 + bmi 872.726 +/- 240.298 + bp 438.663 +/- 163.022 + sex 277.376 +/- 115.123 + + The ranking of the features is approximately the same for different metrics even + if the scales of the importance values are very different. However, this is not + guaranteed and different metrics might lead to significantly different feature + importances, in particular for models trained for imbalanced classification problems, + for which **the choice of the classification metric can be critical**. Outline of the permutation importance algorithm ----------------------------------------------- @@ -228,12 +224,12 @@ keep one feature from each cluster. For more details on such strategy, see the example :ref:`sphx_glr_auto_examples_inspection_plot_permutation_importance_multicollinear.py`. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_inspection_plot_permutation_importance.py` - * :ref:`sphx_glr_auto_examples_inspection_plot_permutation_importance_multicollinear.py` +* :ref:`sphx_glr_auto_examples_inspection_plot_permutation_importance.py` +* :ref:`sphx_glr_auto_examples_inspection_plot_permutation_importance_multicollinear.py` -.. topic:: References: +.. rubric:: References - .. [1] L. Breiman, :doi:`"Random Forests" <10.1023/A:1010933404324>`, - Machine Learning, 45(1), 5-32, 2001. +.. [1] L. Breiman, :doi:`"Random Forests" <10.1023/A:1010933404324>`, + Machine Learning, 45(1), 5-32, 2001. diff --git a/doc/modules/preprocessing.rst b/doc/modules/preprocessing.rst index 99678f2b3e45b..90889ad5af7e0 100644 --- a/doc/modules/preprocessing.rst +++ b/doc/modules/preprocessing.rst @@ -219,28 +219,22 @@ of the data is likely to not work very well. In these cases, you can use :class:`RobustScaler` as a drop-in replacement instead. It uses more robust estimates for the center and range of your data. -|details-start| -**References** -|details-split| -Further discussion on the importance of centering and scaling data is -available on this FAQ: `Should I normalize/standardize/rescale the data? -`_ +.. dropdown:: References -|details-end| + Further discussion on the importance of centering and scaling data is + available on this FAQ: `Should I normalize/standardize/rescale the data? + `_ -|details-start| -**Scaling vs Whitening** -|details-split| +.. dropdown:: Scaling vs Whitening -It is sometimes not enough to center and scale the features -independently, since a downstream model can further make some assumption -on the linear independence of the features. + It is sometimes not enough to center and scale the features + independently, since a downstream model can further make some assumption + on the linear independence of the features. -To address this issue you can use :class:`~sklearn.decomposition.PCA` with -``whiten=True`` to further remove the linear correlation across features. + To address this issue you can use :class:`~sklearn.decomposition.PCA` with + ``whiten=True`` to further remove the linear correlation across features. -|details-end| .. _kernel_centering: @@ -255,63 +249,59 @@ followed by the removal of the mean in that space. In other words, :class:`KernelCenterer` computes the centered Gram matrix associated to a positive semidefinite kernel :math:`K`. -|details-start| -**Mathematical formulation** -|details-split| +.. dropdown:: Mathematical formulation -We can have a look at the mathematical formulation now that we have the -intuition. Let :math:`K` be a kernel matrix of shape `(n_samples, n_samples)` -computed from :math:`X`, a data matrix of shape `(n_samples, n_features)`, -during the `fit` step. :math:`K` is defined by + We can have a look at the mathematical formulation now that we have the + intuition. Let :math:`K` be a kernel matrix of shape `(n_samples, n_samples)` + computed from :math:`X`, a data matrix of shape `(n_samples, n_features)`, + during the `fit` step. :math:`K` is defined by -.. math:: - K(X, X) = \phi(X) . \phi(X)^{T} + .. math:: + K(X, X) = \phi(X) . \phi(X)^{T} -:math:`\phi(X)` is a function mapping of :math:`X` to a Hilbert space. A -centered kernel :math:`\tilde{K}` is defined as: + :math:`\phi(X)` is a function mapping of :math:`X` to a Hilbert space. A + centered kernel :math:`\tilde{K}` is defined as: -.. math:: - \tilde{K}(X, X) = \tilde{\phi}(X) . \tilde{\phi}(X)^{T} + .. math:: + \tilde{K}(X, X) = \tilde{\phi}(X) . \tilde{\phi}(X)^{T} -where :math:`\tilde{\phi}(X)` results from centering :math:`\phi(X)` in the -Hilbert space. + where :math:`\tilde{\phi}(X)` results from centering :math:`\phi(X)` in the + Hilbert space. -Thus, one could compute :math:`\tilde{K}` by mapping :math:`X` using the -function :math:`\phi(\cdot)` and center the data in this new space. However, -kernels are often used because they allows some algebra calculations that -avoid computing explicitly this mapping using :math:`\phi(\cdot)`. Indeed, one -can implicitly center as shown in Appendix B in [Scholkopf1998]_: + Thus, one could compute :math:`\tilde{K}` by mapping :math:`X` using the + function :math:`\phi(\cdot)` and center the data in this new space. However, + kernels are often used because they allows some algebra calculations that + avoid computing explicitly this mapping using :math:`\phi(\cdot)`. Indeed, one + can implicitly center as shown in Appendix B in [Scholkopf1998]_: -.. math:: - \tilde{K} = K - 1_{\text{n}_{samples}} K - K 1_{\text{n}_{samples}} + 1_{\text{n}_{samples}} K 1_{\text{n}_{samples}} + .. math:: + \tilde{K} = K - 1_{\text{n}_{samples}} K - K 1_{\text{n}_{samples}} + 1_{\text{n}_{samples}} K 1_{\text{n}_{samples}} -:math:`1_{\text{n}_{samples}}` is a matrix of `(n_samples, n_samples)` where -all entries are equal to :math:`\frac{1}{\text{n}_{samples}}`. In the -`transform` step, the kernel becomes :math:`K_{test}(X, Y)` defined as: + :math:`1_{\text{n}_{samples}}` is a matrix of `(n_samples, n_samples)` where + all entries are equal to :math:`\frac{1}{\text{n}_{samples}}`. In the + `transform` step, the kernel becomes :math:`K_{test}(X, Y)` defined as: -.. math:: - K_{test}(X, Y) = \phi(Y) . \phi(X)^{T} + .. math:: + K_{test}(X, Y) = \phi(Y) . \phi(X)^{T} -:math:`Y` is the test dataset of shape `(n_samples_test, n_features)` and thus -:math:`K_{test}` is of shape `(n_samples_test, n_samples)`. In this case, -centering :math:`K_{test}` is done as: + :math:`Y` is the test dataset of shape `(n_samples_test, n_features)` and thus + :math:`K_{test}` is of shape `(n_samples_test, n_samples)`. In this case, + centering :math:`K_{test}` is done as: -.. math:: - \tilde{K}_{test}(X, Y) = K_{test} - 1'_{\text{n}_{samples}} K - K_{test} 1_{\text{n}_{samples}} + 1'_{\text{n}_{samples}} K 1_{\text{n}_{samples}} + .. math:: + \tilde{K}_{test}(X, Y) = K_{test} - 1'_{\text{n}_{samples}} K - K_{test} 1_{\text{n}_{samples}} + 1'_{\text{n}_{samples}} K 1_{\text{n}_{samples}} -:math:`1'_{\text{n}_{samples}}` is a matrix of shape -`(n_samples_test, n_samples)` where all entries are equal to -:math:`\frac{1}{\text{n}_{samples}}`. + :math:`1'_{\text{n}_{samples}}` is a matrix of shape + `(n_samples_test, n_samples)` where all entries are equal to + :math:`\frac{1}{\text{n}_{samples}}`. -.. topic:: References + .. rubric:: References .. [Scholkopf1998] B. Schölkopf, A. Smola, and K.R. Müller, `"Nonlinear component analysis as a kernel eigenvalue problem." `_ Neural computation 10.5 (1998): 1299-1319. -|details-end| - .. _preprocessing_transformer: Non-linear transformation @@ -383,54 +373,46 @@ possible in order to stabilize variance and minimize skewness. :class:`PowerTransformer` currently provides two such power transformations, the Yeo-Johnson transform and the Box-Cox transform. -|details-start| -**Yeo-Johnson transform** -|details-split| - -.. math:: - x_i^{(\lambda)} = - \begin{cases} - [(x_i + 1)^\lambda - 1] / \lambda & \text{if } \lambda \neq 0, x_i \geq 0, \\[8pt] - \ln{(x_i + 1)} & \text{if } \lambda = 0, x_i \geq 0 \\[8pt] - -[(-x_i + 1)^{2 - \lambda} - 1] / (2 - \lambda) & \text{if } \lambda \neq 2, x_i < 0, \\[8pt] - - \ln (- x_i + 1) & \text{if } \lambda = 2, x_i < 0 - \end{cases} - -|details-end| - -|details-start| -**Box-Cox transform** -|details-split| - -.. math:: - x_i^{(\lambda)} = - \begin{cases} - \dfrac{x_i^\lambda - 1}{\lambda} & \text{if } \lambda \neq 0, \\[8pt] - \ln{(x_i)} & \text{if } \lambda = 0, - \end{cases} - - -Box-Cox can only be applied to strictly positive data. In both methods, the -transformation is parameterized by :math:`\lambda`, which is determined through -maximum likelihood estimation. Here is an example of using Box-Cox to map -samples drawn from a lognormal distribution to a normal distribution:: - - >>> pt = preprocessing.PowerTransformer(method='box-cox', standardize=False) - >>> X_lognormal = np.random.RandomState(616).lognormal(size=(3, 3)) - >>> X_lognormal - array([[1.28..., 1.18..., 0.84...], - [0.94..., 1.60..., 0.38...], - [1.35..., 0.21..., 1.09...]]) - >>> pt.fit_transform(X_lognormal) - array([[ 0.49..., 0.17..., -0.15...], - [-0.05..., 0.58..., -0.57...], - [ 0.69..., -0.84..., 0.10...]]) - -While the above example sets the `standardize` option to `False`, -:class:`PowerTransformer` will apply zero-mean, unit-variance normalization -to the transformed output by default. - -|details-end| +.. dropdown:: Yeo-Johnson transform + + .. math:: + x_i^{(\lambda)} = + \begin{cases} + [(x_i + 1)^\lambda - 1] / \lambda & \text{if } \lambda \neq 0, x_i \geq 0, \\[8pt] + \ln{(x_i + 1)} & \text{if } \lambda = 0, x_i \geq 0 \\[8pt] + -[(-x_i + 1)^{2 - \lambda} - 1] / (2 - \lambda) & \text{if } \lambda \neq 2, x_i < 0, \\[8pt] + - \ln (- x_i + 1) & \text{if } \lambda = 2, x_i < 0 + \end{cases} + +.. dropdown:: Box-Cox transform + + .. math:: + x_i^{(\lambda)} = + \begin{cases} + \dfrac{x_i^\lambda - 1}{\lambda} & \text{if } \lambda \neq 0, \\[8pt] + \ln{(x_i)} & \text{if } \lambda = 0, + \end{cases} + + Box-Cox can only be applied to strictly positive data. In both methods, the + transformation is parameterized by :math:`\lambda`, which is determined through + maximum likelihood estimation. Here is an example of using Box-Cox to map + samples drawn from a lognormal distribution to a normal distribution:: + + >>> pt = preprocessing.PowerTransformer(method='box-cox', standardize=False) + >>> X_lognormal = np.random.RandomState(616).lognormal(size=(3, 3)) + >>> X_lognormal + array([[1.28..., 1.18..., 0.84...], + [0.94..., 1.60..., 0.38...], + [1.35..., 0.21..., 1.09...]]) + >>> pt.fit_transform(X_lognormal) + array([[ 0.49..., 0.17..., -0.15...], + [-0.05..., 0.58..., -0.57...], + [ 0.69..., -0.84..., 0.10...]]) + + While the above example sets the `standardize` option to `False`, + :class:`PowerTransformer` will apply zero-mean, unit-variance normalization + to the transformed output by default. + Below are examples of Box-Cox and Yeo-Johnson applied to various probability distributions. Note that when applied to certain distributions, the power @@ -518,9 +500,8 @@ The normalizer instance can then be used on sample vectors as any transformer:: Note: L2 normalization is also known as spatial sign preprocessing. -|details-start| -**Sparse input** -|details-split| +.. dropdown:: Sparse input + :func:`normalize` and :class:`Normalizer` accept **both dense array-like and sparse matrices from scipy.sparse as input**. @@ -529,12 +510,11 @@ Note: L2 normalization is also known as spatial sign preprocessing. efficient Cython routines. To avoid unnecessary memory copies, it is recommended to choose the CSR representation upstream. -|details-end| - .. _preprocessing_categorical_features: Encoding categorical features ============================= + Often features are not given as continuous values but categorical. For example a person could have features ``["male", "female"]``, ``["from Europe", "from US", "from Asia"]``, @@ -721,42 +701,39 @@ not dropped:: >>> drop_enc.inverse_transform(X_trans) array([['female', None, None]], dtype=object) -|details-start| -**Support of categorical features with missing values** -|details-split| +.. dropdown:: Support of categorical features with missing values -:class:`OneHotEncoder` supports categorical features with missing values by -considering the missing values as an additional category:: + :class:`OneHotEncoder` supports categorical features with missing values by + considering the missing values as an additional category:: - >>> X = [['male', 'Safari'], - ... ['female', None], - ... [np.nan, 'Firefox']] - >>> enc = preprocessing.OneHotEncoder(handle_unknown='error').fit(X) - >>> enc.categories_ - [array(['female', 'male', nan], dtype=object), - array(['Firefox', 'Safari', None], dtype=object)] - >>> enc.transform(X).toarray() - array([[0., 1., 0., 0., 1., 0.], - [1., 0., 0., 0., 0., 1.], - [0., 0., 1., 1., 0., 0.]]) - -If a feature contains both `np.nan` and `None`, they will be considered -separate categories:: - - >>> X = [['Safari'], [None], [np.nan], ['Firefox']] - >>> enc = preprocessing.OneHotEncoder(handle_unknown='error').fit(X) - >>> enc.categories_ - [array(['Firefox', 'Safari', None, nan], dtype=object)] - >>> enc.transform(X).toarray() - array([[0., 1., 0., 0.], - [0., 0., 1., 0.], - [0., 0., 0., 1.], - [1., 0., 0., 0.]]) + >>> X = [['male', 'Safari'], + ... ['female', None], + ... [np.nan, 'Firefox']] + >>> enc = preprocessing.OneHotEncoder(handle_unknown='error').fit(X) + >>> enc.categories_ + [array(['female', 'male', nan], dtype=object), + array(['Firefox', 'Safari', None], dtype=object)] + >>> enc.transform(X).toarray() + array([[0., 1., 0., 0., 1., 0.], + [1., 0., 0., 0., 0., 1.], + [0., 0., 1., 1., 0., 0.]]) + + If a feature contains both `np.nan` and `None`, they will be considered + separate categories:: + + >>> X = [['Safari'], [None], [np.nan], ['Firefox']] + >>> enc = preprocessing.OneHotEncoder(handle_unknown='error').fit(X) + >>> enc.categories_ + [array(['Firefox', 'Safari', None, nan], dtype=object)] + >>> enc.transform(X).toarray() + array([[0., 1., 0., 0.], + [0., 0., 1., 0.], + [0., 0., 0., 1.], + [1., 0., 0., 0.]]) -See :ref:`dict_feature_extraction` for categorical features that are -represented as a dict, not as scalars. + See :ref:`dict_feature_extraction` for categorical features that are + represented as a dict, not as scalars. -|details-end| .. _encoder_infrequent_categories: @@ -910,66 +887,55 @@ cardinality, where one-hot encoding would inflate the feature space making it more expensive for a downstream model to process. A classical example of high cardinality categories are location based such as zip code or region. -|details-start| -**Binary classification targets** -|details-split| - -For the binary classification target, the target encoding is given by: - -.. math:: - S_i = \lambda_i\frac{n_{iY}}{n_i} + (1 - \lambda_i)\frac{n_Y}{n} +.. dropdown:: Binary classification targets -where :math:`S_i` is the encoding for category :math:`i`, :math:`n_{iY}` is the -number of observations with :math:`Y=1` and category :math:`i`, :math:`n_i` is -the number of observations with category :math:`i`, :math:`n_Y` is the number of -observations with :math:`Y=1`, :math:`n` is the number of observations, and -:math:`\lambda_i` is a shrinkage factor for category :math:`i`. The shrinkage -factor is given by: + For the binary classification target, the target encoding is given by: -.. math:: - \lambda_i = \frac{n_i}{m + n_i} + .. math:: + S_i = \lambda_i\frac{n_{iY}}{n_i} + (1 - \lambda_i)\frac{n_Y}{n} -where :math:`m` is a smoothing factor, which is controlled with the `smooth` -parameter in :class:`TargetEncoder`. Large smoothing factors will put more -weight on the global mean. When `smooth="auto"`, the smoothing factor is -computed as an empirical Bayes estimate: :math:`m=\sigma_i^2/\tau^2`, where -:math:`\sigma_i^2` is the variance of `y` with category :math:`i` and -:math:`\tau^2` is the global variance of `y`. + where :math:`S_i` is the encoding for category :math:`i`, :math:`n_{iY}` is the + number of observations with :math:`Y=1` and category :math:`i`, :math:`n_i` is + the number of observations with category :math:`i`, :math:`n_Y` is the number of + observations with :math:`Y=1`, :math:`n` is the number of observations, and + :math:`\lambda_i` is a shrinkage factor for category :math:`i`. The shrinkage + factor is given by: -|details-end| + .. math:: + \lambda_i = \frac{n_i}{m + n_i} -|details-start| -**Multiclass classification targets** -|details-split| + where :math:`m` is a smoothing factor, which is controlled with the `smooth` + parameter in :class:`TargetEncoder`. Large smoothing factors will put more + weight on the global mean. When `smooth="auto"`, the smoothing factor is + computed as an empirical Bayes estimate: :math:`m=\sigma_i^2/\tau^2`, where + :math:`\sigma_i^2` is the variance of `y` with category :math:`i` and + :math:`\tau^2` is the global variance of `y`. -For multiclass classification targets, the formulation is similar to binary -classification: +.. dropdown:: Multiclass classification targets -.. math:: - S_{ij} = \lambda_i\frac{n_{iY_j}}{n_i} + (1 - \lambda_i)\frac{n_{Y_j}}{n} + For multiclass classification targets, the formulation is similar to binary + classification: -where :math:`S_{ij}` is the encoding for category :math:`i` and class :math:`j`, -:math:`n_{iY_j}` is the number of observations with :math:`Y=j` and category -:math:`i`, :math:`n_i` is the number of observations with category :math:`i`, -:math:`n_{Y_j}` is the number of observations with :math:`Y=j`, :math:`n` is the -number of observations, and :math:`\lambda_i` is a shrinkage factor for category -:math:`i`. + .. math:: + S_{ij} = \lambda_i\frac{n_{iY_j}}{n_i} + (1 - \lambda_i)\frac{n_{Y_j}}{n} -|details-end| + where :math:`S_{ij}` is the encoding for category :math:`i` and class :math:`j`, + :math:`n_{iY_j}` is the number of observations with :math:`Y=j` and category + :math:`i`, :math:`n_i` is the number of observations with category :math:`i`, + :math:`n_{Y_j}` is the number of observations with :math:`Y=j`, :math:`n` is the + number of observations, and :math:`\lambda_i` is a shrinkage factor for category + :math:`i`. -|details-start| -**Continuous targets** -|details-split| +.. dropdown:: Continuous targets -For continuous targets, the formulation is similar to binary classification: + For continuous targets, the formulation is similar to binary classification: -.. math:: - S_i = \lambda_i\frac{\sum_{k\in L_i}Y_k}{n_i} + (1 - \lambda_i)\frac{\sum_{k=1}^{n}Y_k}{n} + .. math:: + S_i = \lambda_i\frac{\sum_{k\in L_i}Y_k}{n_i} + (1 - \lambda_i)\frac{\sum_{k=1}^{n}Y_k}{n} -where :math:`L_i` is the set of observations with category :math:`i` and -:math:`n_i` is the number of observations with category :math:`i`. + where :math:`L_i` is the set of observations with category :math:`i` and + :math:`n_i` is the number of observations with category :math:`i`. -|details-end| :meth:`~TargetEncoder.fit_transform` internally relies on a :term:`cross fitting` scheme to prevent target information from leaking into the train-time @@ -1005,21 +971,21 @@ encoding learned in :meth:`~TargetEncoder.fit_transform`. that are not seen during `fit` are encoded with the target mean, i.e. `target_mean_`. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder.py` - * :ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder_cross_val.py` +* :ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder.py` +* :ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder_cross_val.py` -.. topic:: References +.. rubric:: References - .. [MIC] :doi:`Micci-Barreca, Daniele. "A preprocessing scheme for high-cardinality - categorical attributes in classification and prediction problems" - SIGKDD Explor. Newsl. 3, 1 (July 2001), 27–32. <10.1145/507533.507538>` +.. [MIC] :doi:`Micci-Barreca, Daniele. "A preprocessing scheme for high-cardinality + categorical attributes in classification and prediction problems" + SIGKDD Explor. Newsl. 3, 1 (July 2001), 27-32. <10.1145/507533.507538>` - .. [PAR] :doi:`Pargent, F., Pfisterer, F., Thomas, J. et al. "Regularized target - encoding outperforms traditional methods in supervised machine learning with - high cardinality features" Comput Stat 37, 2671–2692 (2022) - <10.1007/s00180-022-01207-6>` +.. [PAR] :doi:`Pargent, F., Pfisterer, F., Thomas, J. et al. "Regularized target + encoding outperforms traditional methods in supervised machine learning with + high cardinality features" Comput Stat 37, 2671-2692 (2022) + <10.1007/s00180-022-01207-6>` .. _preprocessing_discretization: @@ -1097,11 +1063,11 @@ For instance, we can use the Pandas function :func:`pandas.cut`:: ['infant', 'kid', 'teen', 'adult', 'senior citizen'] Categories (5, object): ['infant' < 'kid' < 'teen' < 'adult' < 'senior citizen'] -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization.py` - * :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_classification.py` - * :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_strategies.py` +* :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization.py` +* :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_classification.py` +* :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_strategies.py` .. _preprocessing_binarization: @@ -1294,23 +1260,20 @@ Interestingly, a :class:`SplineTransformer` of ``degree=0`` is the same as ``encode='onehot-dense'`` and ``n_bins = n_knots - 1`` if ``knots = strategy``. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_linear_model_plot_polynomial_interpolation.py` - * :ref:`sphx_glr_auto_examples_applications_plot_cyclical_feature_engineering.py` +* :ref:`sphx_glr_auto_examples_linear_model_plot_polynomial_interpolation.py` +* :ref:`sphx_glr_auto_examples_applications_plot_cyclical_feature_engineering.py` -|details-start| -**References** -|details-split| +.. dropdown:: References - * Eilers, P., & Marx, B. (1996). :doi:`Flexible Smoothing with B-splines and - Penalties <10.1214/ss/1038425655>`. Statist. Sci. 11 (1996), no. 2, 89--121. + * Eilers, P., & Marx, B. (1996). :doi:`Flexible Smoothing with B-splines and + Penalties <10.1214/ss/1038425655>`. Statist. Sci. 11 (1996), no. 2, 89--121. - * Perperoglou, A., Sauerbrei, W., Abrahamowicz, M. et al. :doi:`A review of - spline function procedures in R <10.1186/s12874-019-0666-3>`. - BMC Med Res Methodol 19, 46 (2019). + * Perperoglou, A., Sauerbrei, W., Abrahamowicz, M. et al. :doi:`A review of + spline function procedures in R <10.1186/s12874-019-0666-3>`. + BMC Med Res Methodol 19, 46 (2019). -|details-end| .. _function_transformer: diff --git a/doc/modules/random_projection.rst b/doc/modules/random_projection.rst index 6931feb34ad1d..173aee434576c 100644 --- a/doc/modules/random_projection.rst +++ b/doc/modules/random_projection.rst @@ -19,19 +19,19 @@ samples of the dataset. Thus random projection is a suitable approximation technique for distance based method. -.. topic:: References: +.. rubric:: References - * Sanjoy Dasgupta. 2000. - `Experiments with random projection. `_ - In Proceedings of the Sixteenth conference on Uncertainty in artificial - intelligence (UAI'00), Craig Boutilier and Moisés Goldszmidt (Eds.). Morgan - Kaufmann Publishers Inc., San Francisco, CA, USA, 143-151. +* Sanjoy Dasgupta. 2000. + `Experiments with random projection. `_ + In Proceedings of the Sixteenth conference on Uncertainty in artificial + intelligence (UAI'00), Craig Boutilier and Moisés Goldszmidt (Eds.). Morgan + Kaufmann Publishers Inc., San Francisco, CA, USA, 143-151. - * Ella Bingham and Heikki Mannila. 2001. - `Random projection in dimensionality reduction: applications to image and text data. `_ - In Proceedings of the seventh ACM SIGKDD international conference on - Knowledge discovery and data mining (KDD '01). ACM, New York, NY, USA, - 245-250. +* Ella Bingham and Heikki Mannila. 2001. + `Random projection in dimensionality reduction: applications to image and text data. `_ + In Proceedings of the seventh ACM SIGKDD international conference on + Knowledge discovery and data mining (KDD '01). ACM, New York, NY, USA, + 245-250. .. _johnson_lindenstrauss: @@ -74,17 +74,17 @@ bounded distortion introduced by the random projection:: :scale: 75 :align: center -.. topic:: Example: +.. rubric:: Examples - * See :ref:`sphx_glr_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py` - for a theoretical explication on the Johnson-Lindenstrauss lemma and an - empirical validation using sparse random matrices. +* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py` + for a theoretical explication on the Johnson-Lindenstrauss lemma and an + empirical validation using sparse random matrices. -.. topic:: References: +.. rubric:: References - * Sanjoy Dasgupta and Anupam Gupta, 1999. - `An elementary proof of the Johnson-Lindenstrauss Lemma. - `_ +* Sanjoy Dasgupta and Anupam Gupta, 1999. + `An elementary proof of the Johnson-Lindenstrauss Lemma. + `_ .. _gaussian_random_matrix: @@ -148,18 +148,17 @@ projection transformer:: (100, 3947) -.. topic:: References: +.. rubric:: References - * D. Achlioptas. 2003. - `Database-friendly random projections: Johnson-Lindenstrauss with binary - coins `_. - Journal of Computer and System Sciences 66 (2003) 671–687 +* D. Achlioptas. 2003. + `Database-friendly random projections: Johnson-Lindenstrauss with binary + coins `_. + Journal of Computer and System Sciences 66 (2003) 671-687. - * Ping Li, Trevor J. Hastie, and Kenneth W. Church. 2006. - `Very sparse random projections. `_ - In Proceedings of the 12th ACM SIGKDD international conference on - Knowledge discovery and data mining (KDD '06). ACM, New York, NY, USA, - 287-296. +* Ping Li, Trevor J. Hastie, and Kenneth W. Church. 2006. + `Very sparse random projections. `_ + In Proceedings of the 12th ACM SIGKDD international conference on + Knowledge discovery and data mining (KDD '06). ACM, New York, NY, USA, 287-296. .. _random_projection_inverse_transform: diff --git a/doc/modules/semi_supervised.rst b/doc/modules/semi_supervised.rst index f8cae0a9ddcdf..8ba33638c6eec 100644 --- a/doc/modules/semi_supervised.rst +++ b/doc/modules/semi_supervised.rst @@ -60,18 +60,18 @@ until all samples have labels or no new samples are selected in that iteration. When using the self-training classifier, the :ref:`calibration ` of the classifier is important. -.. topic:: Examples +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_semi_supervised_plot_self_training_varying_threshold.py` - * :ref:`sphx_glr_auto_examples_semi_supervised_plot_semi_supervised_versus_svm_iris.py` +* :ref:`sphx_glr_auto_examples_semi_supervised_plot_self_training_varying_threshold.py` +* :ref:`sphx_glr_auto_examples_semi_supervised_plot_semi_supervised_versus_svm_iris.py` -.. topic:: References +.. rubric:: References - .. [1] :doi:`"Unsupervised word sense disambiguation rivaling supervised methods" - <10.3115/981658.981684>` - David Yarowsky, Proceedings of the 33rd annual meeting on Association for - Computational Linguistics (ACL '95). Association for Computational Linguistics, - Stroudsburg, PA, USA, 189-196. +.. [1] :doi:`"Unsupervised word sense disambiguation rivaling supervised methods" + <10.3115/981658.981684>` + David Yarowsky, Proceedings of the 33rd annual meeting on Association for + Computational Linguistics (ACL '95). Association for Computational Linguistics, + Stroudsburg, PA, USA, 189-196. .. _label_propagation: @@ -134,18 +134,18 @@ algorithm can lead to prohibitively long running times. On the other hand, the KNN kernel will produce a much more memory-friendly sparse matrix which can drastically reduce running times. -.. topic:: Examples +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_semi_supervised_plot_semi_supervised_versus_svm_iris.py` - * :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_structure.py` - * :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits.py` - * :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits_active_learning.py` +* :ref:`sphx_glr_auto_examples_semi_supervised_plot_semi_supervised_versus_svm_iris.py` +* :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_structure.py` +* :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits.py` +* :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits_active_learning.py` -.. topic:: References +.. rubric:: References - [2] Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux. In Semi-Supervised - Learning (2006), pp. 193-216 +[2] Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux. In Semi-Supervised +Learning (2006), pp. 193-216 - [3] Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux. Efficient - Non-Parametric Function Induction in Semi-Supervised Learning. AISTAT 2005 - https://www.gatsby.ucl.ac.uk/aistats/fullpapers/204.pdf +[3] Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux. Efficient +Non-Parametric Function Induction in Semi-Supervised Learning. AISTAT 2005 +https://www.gatsby.ucl.ac.uk/aistats/fullpapers/204.pdf diff --git a/doc/modules/sgd.rst b/doc/modules/sgd.rst index a7981e9d4ec28..73df123b4ed19 100644 --- a/doc/modules/sgd.rst +++ b/doc/modules/sgd.rst @@ -189,14 +189,14 @@ For classification with a logistic loss, another variant of SGD with an averaging strategy is available with Stochastic Average Gradient (SAG) algorithm, available as a solver in :class:`LogisticRegression`. -.. topic:: Examples: +.. rubric:: Examples - - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_separating_hyperplane.py`, - - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_iris.py` - - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_weighted_samples.py` - - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_comparison.py` - - :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane_unbalanced.py` - (See the Note in the example) +- :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_separating_hyperplane.py` +- :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_iris.py` +- :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_weighted_samples.py` +- :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_comparison.py` +- :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane_unbalanced.py` + (See the Note in the example) Regression ========== @@ -249,44 +249,40 @@ quadratic in the number of samples. with a large number of training samples (> 10,000) for which the SGD variant can be several orders of magnitude faster. -|details-start| -**Mathematical details** -|details-split| +.. dropdown:: Mathematical details -Its implementation is based on the implementation of the stochastic -gradient descent. Indeed, the original optimization problem of the One-Class -SVM is given by + Its implementation is based on the implementation of the stochastic + gradient descent. Indeed, the original optimization problem of the One-Class + SVM is given by -.. math:: - - \begin{aligned} - \min_{w, \rho, \xi} & \quad \frac{1}{2}\Vert w \Vert^2 - \rho + \frac{1}{\nu n} \sum_{i=1}^n \xi_i \\ - \text{s.t.} & \quad \langle w, x_i \rangle \geq \rho - \xi_i \quad 1 \leq i \leq n \\ - & \quad \xi_i \geq 0 \quad 1 \leq i \leq n - \end{aligned} + .. math:: -where :math:`\nu \in (0, 1]` is the user-specified parameter controlling the -proportion of outliers and the proportion of support vectors. Getting rid of -the slack variables :math:`\xi_i` this problem is equivalent to + \begin{aligned} + \min_{w, \rho, \xi} & \quad \frac{1}{2}\Vert w \Vert^2 - \rho + \frac{1}{\nu n} \sum_{i=1}^n \xi_i \\ + \text{s.t.} & \quad \langle w, x_i \rangle \geq \rho - \xi_i \quad 1 \leq i \leq n \\ + & \quad \xi_i \geq 0 \quad 1 \leq i \leq n + \end{aligned} -.. math:: + where :math:`\nu \in (0, 1]` is the user-specified parameter controlling the + proportion of outliers and the proportion of support vectors. Getting rid of + the slack variables :math:`\xi_i` this problem is equivalent to - \min_{w, \rho} \frac{1}{2}\Vert w \Vert^2 - \rho + \frac{1}{\nu n} \sum_{i=1}^n \max(0, \rho - \langle w, x_i \rangle) \, . + .. math:: -Multiplying by the constant :math:`\nu` and introducing the intercept -:math:`b = 1 - \rho` we obtain the following equivalent optimization problem + \min_{w, \rho} \frac{1}{2}\Vert w \Vert^2 - \rho + \frac{1}{\nu n} \sum_{i=1}^n \max(0, \rho - \langle w, x_i \rangle) \, . -.. math:: + Multiplying by the constant :math:`\nu` and introducing the intercept + :math:`b = 1 - \rho` we obtain the following equivalent optimization problem - \min_{w, b} \frac{\nu}{2}\Vert w \Vert^2 + b\nu + \frac{1}{n} \sum_{i=1}^n \max(0, 1 - (\langle w, x_i \rangle + b)) \, . + .. math:: -This is similar to the optimization problems studied in section -:ref:`sgd_mathematical_formulation` with :math:`y_i = 1, 1 \leq i \leq n` and -:math:`\alpha = \nu/2`, :math:`L` being the hinge loss function and :math:`R` -being the L2 norm. We just need to add the term :math:`b\nu` in the -optimization loop. + \min_{w, b} \frac{\nu}{2}\Vert w \Vert^2 + b\nu + \frac{1}{n} \sum_{i=1}^n \max(0, 1 - (\langle w, x_i \rangle + b)) \, . -|details-end| + This is similar to the optimization problems studied in section + :ref:`sgd_mathematical_formulation` with :math:`y_i = 1, 1 \leq i \leq n` and + :math:`\alpha = \nu/2`, :math:`L` being the hinge loss function and :math:`R` + being the L2 norm. We just need to add the term :math:`b\nu` in the + optimization loop. As :class:`SGDClassifier` and :class:`SGDRegressor`, :class:`SGDOneClassSVM` supports averaged SGD. Averaging can be enabled by setting ``average=True``. @@ -305,9 +301,9 @@ efficiency, however, use the CSR matrix format as defined in `scipy.sparse.csr_matrix `_. -.. topic:: Examples: +.. rubric:: Examples - - :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` +- :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` Complexity ========== @@ -385,11 +381,11 @@ Tips on Practical Use * We found that Averaged SGD works best with a larger number of features and a higher eta0. -.. topic:: References: +.. rubric:: References - * `"Efficient BackProp" `_ - Y. LeCun, L. Bottou, G. Orr, K. Müller - In Neural Networks: Tricks - of the Trade 1998. +* `"Efficient BackProp" `_ + Y. LeCun, L. Bottou, G. Orr, K. Müller - In Neural Networks: Tricks + of the Trade 1998. .. _sgd_mathematical_formulation: @@ -416,32 +412,28 @@ where :math:`L` is a loss function that measures model (mis)fit and complexity; :math:`\alpha > 0` is a non-negative hyperparameter that controls the regularization strength. -|details-start| -**Loss functions details** -|details-split| - -Different choices for :math:`L` entail different classifiers or regressors: - -- Hinge (soft-margin): equivalent to Support Vector Classification. - :math:`L(y_i, f(x_i)) = \max(0, 1 - y_i f(x_i))`. -- Perceptron: - :math:`L(y_i, f(x_i)) = \max(0, - y_i f(x_i))`. -- Modified Huber: - :math:`L(y_i, f(x_i)) = \max(0, 1 - y_i f(x_i))^2` if :math:`y_i f(x_i) > - -1`, and :math:`L(y_i, f(x_i)) = -4 y_i f(x_i)` otherwise. -- Log Loss: equivalent to Logistic Regression. - :math:`L(y_i, f(x_i)) = \log(1 + \exp (-y_i f(x_i)))`. -- Squared Error: Linear regression (Ridge or Lasso depending on - :math:`R`). - :math:`L(y_i, f(x_i)) = \frac{1}{2}(y_i - f(x_i))^2`. -- Huber: less sensitive to outliers than least-squares. It is equivalent to - least squares when :math:`|y_i - f(x_i)| \leq \varepsilon`, and - :math:`L(y_i, f(x_i)) = \varepsilon |y_i - f(x_i)| - \frac{1}{2} - \varepsilon^2` otherwise. -- Epsilon-Insensitive: (soft-margin) equivalent to Support Vector Regression. - :math:`L(y_i, f(x_i)) = \max(0, |y_i - f(x_i)| - \varepsilon)`. - -|details-end| +.. dropdown:: Loss functions details + + Different choices for :math:`L` entail different classifiers or regressors: + + - Hinge (soft-margin): equivalent to Support Vector Classification. + :math:`L(y_i, f(x_i)) = \max(0, 1 - y_i f(x_i))`. + - Perceptron: + :math:`L(y_i, f(x_i)) = \max(0, - y_i f(x_i))`. + - Modified Huber: + :math:`L(y_i, f(x_i)) = \max(0, 1 - y_i f(x_i))^2` if :math:`y_i f(x_i) > + -1`, and :math:`L(y_i, f(x_i)) = -4 y_i f(x_i)` otherwise. + - Log Loss: equivalent to Logistic Regression. + :math:`L(y_i, f(x_i)) = \log(1 + \exp (-y_i f(x_i)))`. + - Squared Error: Linear regression (Ridge or Lasso depending on + :math:`R`). + :math:`L(y_i, f(x_i)) = \frac{1}{2}(y_i - f(x_i))^2`. + - Huber: less sensitive to outliers than least-squares. It is equivalent to + least squares when :math:`|y_i - f(x_i)| \leq \varepsilon`, and + :math:`L(y_i, f(x_i)) = \varepsilon |y_i - f(x_i)| - \frac{1}{2} + \varepsilon^2` otherwise. + - Epsilon-Insensitive: (soft-margin) equivalent to Support Vector Regression. + :math:`L(y_i, f(x_i)) = \max(0, |y_i - f(x_i)| - \varepsilon)`. All of the above loss functions can be regarded as an upper bound on the misclassification error (Zero-one loss) as shown in the Figure below. @@ -553,32 +545,29 @@ We use the truncated gradient algorithm proposed in [#3]_ for L1 regularization (and the Elastic Net). The code is written in Cython. -.. topic:: References: +.. rubric:: References - .. [#1] `"Stochastic Gradient Descent" - `_ L. Bottou - Website, 2010. +.. [#1] `"Stochastic Gradient Descent" + `_ L. Bottou - Website, 2010. - .. [#2] :doi:`"Pegasos: Primal estimated sub-gradient solver for svm" - <10.1145/1273496.1273598>` - S. Shalev-Shwartz, Y. Singer, N. Srebro - In Proceedings of ICML '07. +.. [#2] :doi:`"Pegasos: Primal estimated sub-gradient solver for svm" + <10.1145/1273496.1273598>` + S. Shalev-Shwartz, Y. Singer, N. Srebro - In Proceedings of ICML '07. - .. [#3] `"Stochastic gradient descent training for l1-regularized - log-linear models with cumulative penalty" - `_ - Y. Tsuruoka, J. Tsujii, S. Ananiadou - In Proceedings of the AFNLP/ACL - '09. +.. [#3] `"Stochastic gradient descent training for l1-regularized + log-linear models with cumulative penalty" + `_ + Y. Tsuruoka, J. Tsujii, S. Ananiadou - In Proceedings of the AFNLP/ACL'09. - .. [#4] :arxiv:`"Towards Optimal One Pass Large Scale Learning with - Averaged Stochastic Gradient Descent" - <1107.2490v2>` - Xu, Wei (2011) +.. [#4] :arxiv:`"Towards Optimal One Pass Large Scale Learning with + Averaged Stochastic Gradient Descent" + <1107.2490v2>`. Xu, Wei (2011) - .. [#5] :doi:`"Regularization and variable selection via the elastic net" - <10.1111/j.1467-9868.2005.00503.x>` - H. Zou, T. Hastie - Journal of the Royal Statistical Society Series B, - 67 (2), 301-320. +.. [#5] :doi:`"Regularization and variable selection via the elastic net" + <10.1111/j.1467-9868.2005.00503.x>` + H. Zou, T. Hastie - Journal of the Royal Statistical Society Series B, + 67 (2), 301-320. - .. [#6] :doi:`"Solving large scale linear prediction problems using stochastic - gradient descent algorithms" - <10.1145/1015330.1015332>` - T. Zhang - In Proceedings of ICML '04. +.. [#6] :doi:`"Solving large scale linear prediction problems using stochastic + gradient descent algorithms" <10.1145/1015330.1015332>` + T. Zhang - In Proceedings of ICML '04. diff --git a/doc/modules/svm.rst b/doc/modules/svm.rst index e3bc1395819e9..47115e43a89e0 100644 --- a/doc/modules/svm.rst +++ b/doc/modules/svm.rst @@ -108,11 +108,10 @@ properties of these support vectors can be found in attributes >>> clf.n_support_ array([1, 1]...) -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane.py`, - * :ref:`sphx_glr_auto_examples_svm_plot_svm_nonlinear.py` - * :ref:`sphx_glr_auto_examples_svm_plot_svm_anova.py`, +* :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane.py` +* :ref:`sphx_glr_auto_examples_svm_plot_svm_anova.py` .. _svm_multi_class: @@ -154,65 +153,61 @@ multi-class strategy, thus training `n_classes` models. See :ref:`svm_mathematical_formulation` for a complete description of the decision function. -|details-start| -**Details on multi-class strategies** -|details-split| - -Note that the :class:`LinearSVC` also implements an alternative multi-class -strategy, the so-called multi-class SVM formulated by Crammer and Singer -[#8]_, by using the option ``multi_class='crammer_singer'``. In practice, -one-vs-rest classification is usually preferred, since the results are mostly -similar, but the runtime is significantly less. - -For "one-vs-rest" :class:`LinearSVC` the attributes ``coef_`` and ``intercept_`` -have the shape ``(n_classes, n_features)`` and ``(n_classes,)`` respectively. -Each row of the coefficients corresponds to one of the ``n_classes`` -"one-vs-rest" classifiers and similar for the intercepts, in the -order of the "one" class. - -In the case of "one-vs-one" :class:`SVC` and :class:`NuSVC`, the layout of -the attributes is a little more involved. In the case of a linear -kernel, the attributes ``coef_`` and ``intercept_`` have the shape -``(n_classes * (n_classes - 1) / 2, n_features)`` and ``(n_classes * -(n_classes - 1) / 2)`` respectively. This is similar to the layout for -:class:`LinearSVC` described above, with each row now corresponding -to a binary classifier. The order for classes -0 to n is "0 vs 1", "0 vs 2" , ... "0 vs n", "1 vs 2", "1 vs 3", "1 vs n", . . -. "n-1 vs n". - -The shape of ``dual_coef_`` is ``(n_classes-1, n_SV)`` with -a somewhat hard to grasp layout. -The columns correspond to the support vectors involved in any -of the ``n_classes * (n_classes - 1) / 2`` "one-vs-one" classifiers. -Each support vector ``v`` has a dual coefficient in each of the -``n_classes - 1`` classifiers comparing the class of ``v`` against another class. -Note that some, but not all, of these dual coefficients, may be zero. -The ``n_classes - 1`` entries in each column are these dual coefficients, -ordered by the opposing class. - -This might be clearer with an example: consider a three class problem with -class 0 having three support vectors -:math:`v^{0}_0, v^{1}_0, v^{2}_0` and class 1 and 2 having two support vectors -:math:`v^{0}_1, v^{1}_1` and :math:`v^{0}_2, v^{1}_2` respectively. For each -support vector :math:`v^{j}_i`, there are two dual coefficients. Let's call -the coefficient of support vector :math:`v^{j}_i` in the classifier between -classes :math:`i` and :math:`k` :math:`\alpha^{j}_{i,k}`. -Then ``dual_coef_`` looks like this: - -+------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+ -|:math:`\alpha^{0}_{0,1}`|:math:`\alpha^{1}_{0,1}`|:math:`\alpha^{2}_{0,1}`|:math:`\alpha^{0}_{1,0}`|:math:`\alpha^{1}_{1,0}`|:math:`\alpha^{0}_{2,0}`|:math:`\alpha^{1}_{2,0}`| -+------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+ -|:math:`\alpha^{0}_{0,2}`|:math:`\alpha^{1}_{0,2}`|:math:`\alpha^{2}_{0,2}`|:math:`\alpha^{0}_{1,2}`|:math:`\alpha^{1}_{1,2}`|:math:`\alpha^{0}_{2,1}`|:math:`\alpha^{1}_{2,1}`| -+------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+ -|Coefficients |Coefficients |Coefficients | -|for SVs of class 0 |for SVs of class 1 |for SVs of class 2 | -+--------------------------------------------------------------------------+-------------------------------------------------+-------------------------------------------------+ - -|details-end| - -.. topic:: Examples: - - * :ref:`sphx_glr_auto_examples_svm_plot_iris_svc.py`, +.. dropdown:: Details on multi-class strategies + + Note that the :class:`LinearSVC` also implements an alternative multi-class + strategy, the so-called multi-class SVM formulated by Crammer and Singer + [#8]_, by using the option ``multi_class='crammer_singer'``. In practice, + one-vs-rest classification is usually preferred, since the results are mostly + similar, but the runtime is significantly less. + + For "one-vs-rest" :class:`LinearSVC` the attributes ``coef_`` and ``intercept_`` + have the shape ``(n_classes, n_features)`` and ``(n_classes,)`` respectively. + Each row of the coefficients corresponds to one of the ``n_classes`` + "one-vs-rest" classifiers and similar for the intercepts, in the + order of the "one" class. + + In the case of "one-vs-one" :class:`SVC` and :class:`NuSVC`, the layout of + the attributes is a little more involved. In the case of a linear + kernel, the attributes ``coef_`` and ``intercept_`` have the shape + ``(n_classes * (n_classes - 1) / 2, n_features)`` and ``(n_classes * + (n_classes - 1) / 2)`` respectively. This is similar to the layout for + :class:`LinearSVC` described above, with each row now corresponding + to a binary classifier. The order for classes + 0 to n is "0 vs 1", "0 vs 2" , ... "0 vs n", "1 vs 2", "1 vs 3", "1 vs n", . . + . "n-1 vs n". + + The shape of ``dual_coef_`` is ``(n_classes-1, n_SV)`` with + a somewhat hard to grasp layout. + The columns correspond to the support vectors involved in any + of the ``n_classes * (n_classes - 1) / 2`` "one-vs-one" classifiers. + Each support vector ``v`` has a dual coefficient in each of the + ``n_classes - 1`` classifiers comparing the class of ``v`` against another class. + Note that some, but not all, of these dual coefficients, may be zero. + The ``n_classes - 1`` entries in each column are these dual coefficients, + ordered by the opposing class. + + This might be clearer with an example: consider a three class problem with + class 0 having three support vectors + :math:`v^{0}_0, v^{1}_0, v^{2}_0` and class 1 and 2 having two support vectors + :math:`v^{0}_1, v^{1}_1` and :math:`v^{0}_2, v^{1}_2` respectively. For each + support vector :math:`v^{j}_i`, there are two dual coefficients. Let's call + the coefficient of support vector :math:`v^{j}_i` in the classifier between + classes :math:`i` and :math:`k` :math:`\alpha^{j}_{i,k}`. + Then ``dual_coef_`` looks like this: + + +------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+ + |:math:`\alpha^{0}_{0,1}`|:math:`\alpha^{1}_{0,1}`|:math:`\alpha^{2}_{0,1}`|:math:`\alpha^{0}_{1,0}`|:math:`\alpha^{1}_{1,0}`|:math:`\alpha^{0}_{2,0}`|:math:`\alpha^{1}_{2,0}`| + +------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+ + |:math:`\alpha^{0}_{0,2}`|:math:`\alpha^{1}_{0,2}`|:math:`\alpha^{2}_{0,2}`|:math:`\alpha^{0}_{1,2}`|:math:`\alpha^{1}_{1,2}`|:math:`\alpha^{0}_{2,1}`|:math:`\alpha^{1}_{2,1}`| + +------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+ + |Coefficients |Coefficients |Coefficients | + |for SVs of class 0 |for SVs of class 1 |for SVs of class 2 | + +--------------------------------------------------------------------------+-------------------------------------------------+-------------------------------------------------+ + +.. rubric:: Examples + +* :ref:`sphx_glr_auto_examples_svm_plot_iris_svc.py` .. _scores_probabilities: @@ -295,10 +290,10 @@ to the sample weights: :align: center :scale: 75 -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane_unbalanced.py` - * :ref:`sphx_glr_auto_examples_svm_plot_weighted_samples.py`, +* :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane_unbalanced.py` +* :ref:`sphx_glr_auto_examples_svm_plot_weighted_samples.py` .. _svm_regression: @@ -343,9 +338,9 @@ floating point values instead of integer values:: array([1.5]) -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_svm_plot_svm_regression.py` +* :ref:`sphx_glr_auto_examples_svm_plot_svm_regression.py` .. _svm_outlier_detection: @@ -516,11 +511,10 @@ Proper choice of ``C`` and ``gamma`` is critical to the SVM's performance. One is advised to use :class:`~sklearn.model_selection.GridSearchCV` with ``C`` and ``gamma`` spaced exponentially far apart to choose good values. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_svm_plot_rbf_parameters.py` - * :ref:`sphx_glr_auto_examples_svm_plot_svm_nonlinear.py` - * :ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py` +* :ref:`sphx_glr_auto_examples_svm_plot_rbf_parameters.py` +* :ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py` Custom Kernels -------------- @@ -539,60 +533,52 @@ classifiers, except that: use of ``fit()`` and ``predict()`` you will have unexpected results. -|details-start| -**Using Python functions as kernels** -|details-split| +.. dropdown:: Using Python functions as kernels -You can use your own defined kernels by passing a function to the -``kernel`` parameter. + You can use your own defined kernels by passing a function to the + ``kernel`` parameter. -Your kernel must take as arguments two matrices of shape -``(n_samples_1, n_features)``, ``(n_samples_2, n_features)`` -and return a kernel matrix of shape ``(n_samples_1, n_samples_2)``. + Your kernel must take as arguments two matrices of shape + ``(n_samples_1, n_features)``, ``(n_samples_2, n_features)`` + and return a kernel matrix of shape ``(n_samples_1, n_samples_2)``. -The following code defines a linear kernel and creates a classifier -instance that will use that kernel:: + The following code defines a linear kernel and creates a classifier + instance that will use that kernel:: - >>> import numpy as np - >>> from sklearn import svm - >>> def my_kernel(X, Y): - ... return np.dot(X, Y.T) - ... - >>> clf = svm.SVC(kernel=my_kernel) - -|details-end| + >>> import numpy as np + >>> from sklearn import svm + >>> def my_kernel(X, Y): + ... return np.dot(X, Y.T) + ... + >>> clf = svm.SVC(kernel=my_kernel) -|details-start| -**Using the Gram matrix** -|details-split| +.. dropdown:: Using the Gram matrix -You can pass pre-computed kernels by using the ``kernel='precomputed'`` -option. You should then pass Gram matrix instead of X to the `fit` and -`predict` methods. The kernel values between *all* training vectors and the -test vectors must be provided: + You can pass pre-computed kernels by using the ``kernel='precomputed'`` + option. You should then pass Gram matrix instead of X to the `fit` and + `predict` methods. The kernel values between *all* training vectors and the + test vectors must be provided: - >>> import numpy as np - >>> from sklearn.datasets import make_classification - >>> from sklearn.model_selection import train_test_split - >>> from sklearn import svm - >>> X, y = make_classification(n_samples=10, random_state=0) - >>> X_train , X_test , y_train, y_test = train_test_split(X, y, random_state=0) - >>> clf = svm.SVC(kernel='precomputed') - >>> # linear kernel computation - >>> gram_train = np.dot(X_train, X_train.T) - >>> clf.fit(gram_train, y_train) - SVC(kernel='precomputed') - >>> # predict on training examples - >>> gram_test = np.dot(X_test, X_train.T) - >>> clf.predict(gram_test) - array([0, 1, 0]) + >>> import numpy as np + >>> from sklearn.datasets import make_classification + >>> from sklearn.model_selection import train_test_split + >>> from sklearn import svm + >>> X, y = make_classification(n_samples=10, random_state=0) + >>> X_train , X_test , y_train, y_test = train_test_split(X, y, random_state=0) + >>> clf = svm.SVC(kernel='precomputed') + >>> # linear kernel computation + >>> gram_train = np.dot(X_train, X_train.T) + >>> clf.fit(gram_train, y_train) + SVC(kernel='precomputed') + >>> # predict on training examples + >>> gram_test = np.dot(X_test, X_train.T) + >>> clf.predict(gram_test) + array([0, 1, 0]) -|details-end| +.. rubric:: Examples -.. topic:: Examples: - - * :ref:`sphx_glr_auto_examples_svm_plot_custom_kernel.py`. +* :ref:`sphx_glr_auto_examples_svm_plot_custom_kernel.py` .. _svm_mathematical_formulation: @@ -689,43 +675,35 @@ term :math:`b` estimator used is :class:`~sklearn.linear_model.Ridge` regression, the relation between them is given as :math:`C = \frac{1}{alpha}`. -|details-start| -**LinearSVC** -|details-split| +.. dropdown:: LinearSVC -The primal problem can be equivalently formulated as + The primal problem can be equivalently formulated as -.. math:: + .. math:: - \min_ {w, b} \frac{1}{2} w^T w + C \sum_{i=1}^{n}\max(0, 1 - y_i (w^T \phi(x_i) + b)), + \min_ {w, b} \frac{1}{2} w^T w + C \sum_{i=1}^{n}\max(0, 1 - y_i (w^T \phi(x_i) + b)), -where we make use of the `hinge loss -`_. This is the form that is -directly optimized by :class:`LinearSVC`, but unlike the dual form, this one -does not involve inner products between samples, so the famous kernel trick -cannot be applied. This is why only the linear kernel is supported by -:class:`LinearSVC` (:math:`\phi` is the identity function). - -|details-end| + where we make use of the `hinge loss + `_. This is the form that is + directly optimized by :class:`LinearSVC`, but unlike the dual form, this one + does not involve inner products between samples, so the famous kernel trick + cannot be applied. This is why only the linear kernel is supported by + :class:`LinearSVC` (:math:`\phi` is the identity function). .. _nu_svc: -|details-start| -**NuSVC** -|details-split| - -The :math:`\nu`-SVC formulation [#7]_ is a reparameterization of the -:math:`C`-SVC and therefore mathematically equivalent. +.. dropdown:: NuSVC -We introduce a new parameter :math:`\nu` (instead of :math:`C`) which -controls the number of support vectors and *margin errors*: -:math:`\nu \in (0, 1]` is an upper bound on the fraction of margin errors and -a lower bound of the fraction of support vectors. A margin error corresponds -to a sample that lies on the wrong side of its margin boundary: it is either -misclassified, or it is correctly classified but does not lie beyond the -margin. + The :math:`\nu`-SVC formulation [#7]_ is a reparameterization of the + :math:`C`-SVC and therefore mathematically equivalent. -|details-end| + We introduce a new parameter :math:`\nu` (instead of :math:`C`) which + controls the number of support vectors and *margin errors*: + :math:`\nu \in (0, 1]` is an upper bound on the fraction of margin errors and + a lower bound of the fraction of support vectors. A margin error corresponds + to a sample that lies on the wrong side of its margin boundary: it is either + misclassified, or it is correctly classified but does not lie beyond the + margin. SVR --- @@ -774,21 +752,17 @@ which holds the difference :math:`\alpha_i - \alpha_i^*`, ``support_vectors_`` w holds the support vectors, and ``intercept_`` which holds the independent term :math:`b` -|details-start| -**LinearSVR** -|details-split| +.. dropdown:: LinearSVR -The primal problem can be equivalently formulated as - -.. math:: + The primal problem can be equivalently formulated as - \min_ {w, b} \frac{1}{2} w^T w + C \sum_{i=1}^{n}\max(0, |y_i - (w^T \phi(x_i) + b)| - \varepsilon), + .. math:: -where we make use of the epsilon-insensitive loss, i.e. errors of less than -:math:`\varepsilon` are ignored. This is the form that is directly optimized -by :class:`LinearSVR`. + \min_ {w, b} \frac{1}{2} w^T w + C \sum_{i=1}^{n}\max(0, |y_i - (w^T \phi(x_i) + b)| - \varepsilon), -|details-end| + where we make use of the epsilon-insensitive loss, i.e. errors of less than + :math:`\varepsilon` are ignored. This is the form that is directly optimized + by :class:`LinearSVR`. .. _svm_implementation_details: @@ -804,38 +778,37 @@ used, please refer to their respective papers. .. _`libsvm`: https://www.csie.ntu.edu.tw/~cjlin/libsvm/ .. _`liblinear`: https://www.csie.ntu.edu.tw/~cjlin/liblinear/ -.. topic:: References: +.. rubric:: References - .. [#1] Platt `"Probabilistic outputs for SVMs and comparisons to - regularized likelihood methods" - `_. +.. [#1] Platt `"Probabilistic outputs for SVMs and comparisons to + regularized likelihood methods" + `_. - .. [#2] Wu, Lin and Weng, `"Probability estimates for multi-class - classification by pairwise coupling" - `_, JMLR - 5:975-1005, 2004. +.. [#2] Wu, Lin and Weng, `"Probability estimates for multi-class + classification by pairwise coupling" + `_, + JMLR 5:975-1005, 2004. - .. [#3] Fan, Rong-En, et al., - `"LIBLINEAR: A library for large linear classification." - `_, - Journal of machine learning research 9.Aug (2008): 1871-1874. +.. [#3] Fan, Rong-En, et al., + `"LIBLINEAR: A library for large linear classification." + `_, + Journal of machine learning research 9.Aug (2008): 1871-1874. - .. [#4] Chang and Lin, `LIBSVM: A Library for Support Vector Machines - `_. +.. [#4] Chang and Lin, `LIBSVM: A Library for Support Vector Machines + `_. - .. [#5] Bishop, `Pattern recognition and machine learning - `_, - chapter 7 Sparse Kernel Machines +.. [#5] Bishop, `Pattern recognition and machine learning + `_, + chapter 7 Sparse Kernel Machines - .. [#6] :doi:`"A Tutorial on Support Vector Regression" - <10.1023/B:STCO.0000035301.49549.88>` - Alex J. Smola, Bernhard Schölkopf - Statistics and Computing archive - Volume 14 Issue 3, August 2004, p. 199-222. +.. [#6] :doi:`"A Tutorial on Support Vector Regression" + <10.1023/B:STCO.0000035301.49549.88>` + Alex J. Smola, Bernhard Schölkopf - Statistics and Computing archive + Volume 14 Issue 3, August 2004, p. 199-222. - .. [#7] Schölkopf et. al `New Support Vector Algorithms - `_ +.. [#7] Schölkopf et. al `New Support Vector Algorithms + `_ - .. [#8] Crammer and Singer `On the Algorithmic Implementation ofMulticlass - Kernel-based Vector Machines - `_, - JMLR 2001. +.. [#8] Crammer and Singer `On the Algorithmic Implementation ofMulticlass + Kernel-based Vector Machines + `_, JMLR 2001. diff --git a/doc/modules/tree.rst b/doc/modules/tree.rst index b54b913573a34..9b475d6c09f5f 100644 --- a/doc/modules/tree.rst +++ b/doc/modules/tree.rst @@ -146,82 +146,78 @@ Once trained, you can plot the tree with the :func:`plot_tree` function:: :scale: 75 :align: center -|details-start| -**Alternative ways to export trees** -|details-split| - -We can also export the tree in `Graphviz -`_ format using the :func:`export_graphviz` -exporter. If you use the `conda `_ package manager, the graphviz binaries -and the python package can be installed with `conda install python-graphviz`. - -Alternatively binaries for graphviz can be downloaded from the graphviz project homepage, -and the Python wrapper installed from pypi with `pip install graphviz`. - -Below is an example graphviz export of the above tree trained on the entire -iris dataset; the results are saved in an output file `iris.pdf`:: - - - >>> import graphviz # doctest: +SKIP - >>> dot_data = tree.export_graphviz(clf, out_file=None) # doctest: +SKIP - >>> graph = graphviz.Source(dot_data) # doctest: +SKIP - >>> graph.render("iris") # doctest: +SKIP - -The :func:`export_graphviz` exporter also supports a variety of aesthetic -options, including coloring nodes by their class (or value for regression) and -using explicit variable and class names if desired. Jupyter notebooks also -render these plots inline automatically:: - - >>> dot_data = tree.export_graphviz(clf, out_file=None, # doctest: +SKIP - ... feature_names=iris.feature_names, # doctest: +SKIP - ... class_names=iris.target_names, # doctest: +SKIP - ... filled=True, rounded=True, # doctest: +SKIP - ... special_characters=True) # doctest: +SKIP - >>> graph = graphviz.Source(dot_data) # doctest: +SKIP - >>> graph # doctest: +SKIP - -.. only:: html - - .. figure:: ../images/iris.svg - :align: center - -.. only:: latex - - .. figure:: ../images/iris.pdf - :align: center - -.. figure:: ../auto_examples/tree/images/sphx_glr_plot_iris_dtc_001.png - :target: ../auto_examples/tree/plot_iris_dtc.html - :align: center - :scale: 75 - -Alternatively, the tree can also be exported in textual format with the -function :func:`export_text`. This method doesn't require the installation -of external libraries and is more compact: - - >>> from sklearn.datasets import load_iris - >>> from sklearn.tree import DecisionTreeClassifier - >>> from sklearn.tree import export_text - >>> iris = load_iris() - >>> decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2) - >>> decision_tree = decision_tree.fit(iris.data, iris.target) - >>> r = export_text(decision_tree, feature_names=iris['feature_names']) - >>> print(r) - |--- petal width (cm) <= 0.80 - | |--- class: 0 - |--- petal width (cm) > 0.80 - | |--- petal width (cm) <= 1.75 - | | |--- class: 1 - | |--- petal width (cm) > 1.75 - | | |--- class: 2 - - -|details-end| - -.. topic:: Examples: - - * :ref:`sphx_glr_auto_examples_tree_plot_iris_dtc.py` - * :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py` +.. dropdown:: Alternative ways to export trees + + We can also export the tree in `Graphviz + `_ format using the :func:`export_graphviz` + exporter. If you use the `conda `_ package manager, the graphviz binaries + and the python package can be installed with `conda install python-graphviz`. + + Alternatively binaries for graphviz can be downloaded from the graphviz project homepage, + and the Python wrapper installed from pypi with `pip install graphviz`. + + Below is an example graphviz export of the above tree trained on the entire + iris dataset; the results are saved in an output file `iris.pdf`:: + + + >>> import graphviz # doctest: +SKIP + >>> dot_data = tree.export_graphviz(clf, out_file=None) # doctest: +SKIP + >>> graph = graphviz.Source(dot_data) # doctest: +SKIP + >>> graph.render("iris") # doctest: +SKIP + + The :func:`export_graphviz` exporter also supports a variety of aesthetic + options, including coloring nodes by their class (or value for regression) and + using explicit variable and class names if desired. Jupyter notebooks also + render these plots inline automatically:: + + >>> dot_data = tree.export_graphviz(clf, out_file=None, # doctest: +SKIP + ... feature_names=iris.feature_names, # doctest: +SKIP + ... class_names=iris.target_names, # doctest: +SKIP + ... filled=True, rounded=True, # doctest: +SKIP + ... special_characters=True) # doctest: +SKIP + >>> graph = graphviz.Source(dot_data) # doctest: +SKIP + >>> graph # doctest: +SKIP + + .. only:: html + + .. figure:: ../images/iris.svg + :align: center + + .. only:: latex + + .. figure:: ../images/iris.pdf + :align: center + + .. figure:: ../auto_examples/tree/images/sphx_glr_plot_iris_dtc_001.png + :target: ../auto_examples/tree/plot_iris_dtc.html + :align: center + :scale: 75 + + Alternatively, the tree can also be exported in textual format with the + function :func:`export_text`. This method doesn't require the installation + of external libraries and is more compact: + + >>> from sklearn.datasets import load_iris + >>> from sklearn.tree import DecisionTreeClassifier + >>> from sklearn.tree import export_text + >>> iris = load_iris() + >>> decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2) + >>> decision_tree = decision_tree.fit(iris.data, iris.target) + >>> r = export_text(decision_tree, feature_names=iris['feature_names']) + >>> print(r) + |--- petal width (cm) <= 0.80 + | |--- class: 0 + |--- petal width (cm) > 0.80 + | |--- petal width (cm) <= 1.75 + | | |--- class: 1 + | |--- petal width (cm) > 1.75 + | | |--- class: 2 + + +.. rubric:: Examples + +* :ref:`sphx_glr_auto_examples_tree_plot_iris_dtc.py` +* :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py` .. _tree_regression: @@ -248,9 +244,9 @@ instead of integer values:: >>> clf.predict([[1, 1]]) array([0.5]) -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_tree_plot_tree_regression.py` +* :ref:`sphx_glr_auto_examples_tree_plot_tree_regression.py` .. _tree_multioutput: @@ -306,21 +302,17 @@ the lower half of those faces. :scale: 75 :align: center -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_tree_plot_tree_regression_multioutput.py` - * :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py` +* :ref:`sphx_glr_auto_examples_tree_plot_tree_regression_multioutput.py` +* :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py` -|details-start| -**References** -|details-split| +.. rubric:: References * M. Dumont et al, `Fast multi-class image annotation with random subwindows and multiple output randomized trees - `_, International Conference on - Computer Vision Theory and Applications 2009 - -|details-end| + `_, + International Conference on Computer Vision Theory and Applications 2009 .. _tree_complexity: @@ -412,36 +404,32 @@ Tree algorithms: ID3, C4.5, C5.0 and CART What are all the various decision tree algorithms and how do they differ from each other? Which one is implemented in scikit-learn? -|details-start| -**Various decision tree algorithms** -|details-split| - -ID3_ (Iterative Dichotomiser 3) was developed in 1986 by Ross Quinlan. -The algorithm creates a multiway tree, finding for each node (i.e. in -a greedy manner) the categorical feature that will yield the largest -information gain for categorical targets. Trees are grown to their -maximum size and then a pruning step is usually applied to improve the -ability of the tree to generalize to unseen data. - -C4.5 is the successor to ID3 and removed the restriction that features -must be categorical by dynamically defining a discrete attribute (based -on numerical variables) that partitions the continuous attribute value -into a discrete set of intervals. C4.5 converts the trained trees -(i.e. the output of the ID3 algorithm) into sets of if-then rules. -The accuracy of each rule is then evaluated to determine the order -in which they should be applied. Pruning is done by removing a rule's -precondition if the accuracy of the rule improves without it. - -C5.0 is Quinlan's latest version release under a proprietary license. -It uses less memory and builds smaller rulesets than C4.5 while being -more accurate. - -CART (Classification and Regression Trees) is very similar to C4.5, but -it differs in that it supports numerical target variables (regression) and -does not compute rule sets. CART constructs binary trees using the feature -and threshold that yield the largest information gain at each node. - -|details-end| +.. dropdown:: Various decision tree algorithms + + ID3_ (Iterative Dichotomiser 3) was developed in 1986 by Ross Quinlan. + The algorithm creates a multiway tree, finding for each node (i.e. in + a greedy manner) the categorical feature that will yield the largest + information gain for categorical targets. Trees are grown to their + maximum size and then a pruning step is usually applied to improve the + ability of the tree to generalize to unseen data. + + C4.5 is the successor to ID3 and removed the restriction that features + must be categorical by dynamically defining a discrete attribute (based + on numerical variables) that partitions the continuous attribute value + into a discrete set of intervals. C4.5 converts the trained trees + (i.e. the output of the ID3 algorithm) into sets of if-then rules. + The accuracy of each rule is then evaluated to determine the order + in which they should be applied. Pruning is done by removing a rule's + precondition if the accuracy of the rule improves without it. + + C5.0 is Quinlan's latest version release under a proprietary license. + It uses less memory and builds smaller rulesets than C4.5 while being + more accurate. + + CART (Classification and Regression Trees) is very similar to C4.5, but + it differs in that it supports numerical target variables (regression) and + does not compute rule sets. CART constructs binary trees using the feature + and threshold that yield the largest information gain at each node. scikit-learn uses an optimized version of the CART algorithm; however, the scikit-learn implementation does not support categorical variables for now. @@ -515,39 +503,35 @@ Log Loss or Entropy: H(Q_m) = - \sum_k p_{mk} \log(p_{mk}) -|details-start| -**Shannon entropy** -|details-split| - -The entropy criterion computes the Shannon entropy of the possible classes. It -takes the class frequencies of the training data points that reached a given -leaf :math:`m` as their probability. Using the **Shannon entropy as tree node -splitting criterion is equivalent to minimizing the log loss** (also known as -cross-entropy and multinomial deviance) between the true labels :math:`y_i` -and the probabilistic predictions :math:`T_k(x_i)` of the tree model :math:`T` for class :math:`k`. +.. dropdown:: Shannon entropy -To see this, first recall that the log loss of a tree model :math:`T` -computed on a dataset :math:`D` is defined as follows: + The entropy criterion computes the Shannon entropy of the possible classes. It + takes the class frequencies of the training data points that reached a given + leaf :math:`m` as their probability. Using the **Shannon entropy as tree node + splitting criterion is equivalent to minimizing the log loss** (also known as + cross-entropy and multinomial deviance) between the true labels :math:`y_i` + and the probabilistic predictions :math:`T_k(x_i)` of the tree model :math:`T` for class :math:`k`. -.. math:: + To see this, first recall that the log loss of a tree model :math:`T` + computed on a dataset :math:`D` is defined as follows: - \mathrm{LL}(D, T) = -\frac{1}{n} \sum_{(x_i, y_i) \in D} \sum_k I(y_i = k) \log(T_k(x_i)) + .. math:: -where :math:`D` is a training dataset of :math:`n` pairs :math:`(x_i, y_i)`. + \mathrm{LL}(D, T) = -\frac{1}{n} \sum_{(x_i, y_i) \in D} \sum_k I(y_i = k) \log(T_k(x_i)) -In a classification tree, the predicted class probabilities within leaf nodes -are constant, that is: for all :math:`(x_i, y_i) \in Q_m`, one has: -:math:`T_k(x_i) = p_{mk}` for each class :math:`k`. + where :math:`D` is a training dataset of :math:`n` pairs :math:`(x_i, y_i)`. -This property makes it possible to rewrite :math:`\mathrm{LL}(D, T)` as the -sum of the Shannon entropies computed for each leaf of :math:`T` weighted by -the number of training data points that reached each leaf: + In a classification tree, the predicted class probabilities within leaf nodes + are constant, that is: for all :math:`(x_i, y_i) \in Q_m`, one has: + :math:`T_k(x_i) = p_{mk}` for each class :math:`k`. -.. math:: + This property makes it possible to rewrite :math:`\mathrm{LL}(D, T)` as the + sum of the Shannon entropies computed for each leaf of :math:`T` weighted by + the number of training data points that reached each leaf: - \mathrm{LL}(D, T) = \sum_{m \in T} \frac{n_m}{n} H(Q_m) + .. math:: -|details-end| + \mathrm{LL}(D, T) = \sum_{m \in T} \frac{n_m}{n} H(Q_m) Regression criteria ------------------- @@ -685,13 +669,11 @@ with the smallest value of :math:`\alpha_{eff}` is the weakest link and will be pruned. This process stops when the pruned tree's minimal :math:`\alpha_{eff}` is greater than the ``ccp_alpha`` parameter. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py` +* :ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py` -|details-start| -**References** -|details-split| +.. rubric:: References .. [BRE] L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, Belmont, CA, 1984. @@ -705,5 +687,3 @@ be pruned. This process stops when the pruned tree's minimal * T. Hastie, R. Tibshirani and J. Friedman. Elements of Statistical Learning, Springer, 2009. - -|details-end| diff --git a/doc/modules/unsupervised_reduction.rst b/doc/modules/unsupervised_reduction.rst index 90c80714c3131..f94d6ac301e47 100644 --- a/doc/modules/unsupervised_reduction.rst +++ b/doc/modules/unsupervised_reduction.rst @@ -24,9 +24,9 @@ PCA: principal component analysis :class:`decomposition.PCA` looks for a combination of features that capture well the variance of the original features. See :ref:`decompositions`. -.. topic:: **Examples** +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py` +* :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py` Random projections ------------------- @@ -35,9 +35,9 @@ The module: :mod:`~sklearn.random_projection` provides several tools for data reduction by random projections. See the relevant section of the documentation: :ref:`random_projection`. -.. topic:: **Examples** +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py` +* :ref:`sphx_glr_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py` Feature agglomeration ------------------------ @@ -46,10 +46,10 @@ Feature agglomeration :ref:`hierarchical_clustering` to group together features that behave similarly. -.. topic:: **Examples** +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_cluster_plot_feature_agglomeration_vs_univariate_selection.py` - * :ref:`sphx_glr_auto_examples_cluster_plot_digits_agglomeration.py` +* :ref:`sphx_glr_auto_examples_cluster_plot_feature_agglomeration_vs_univariate_selection.py` +* :ref:`sphx_glr_auto_examples_cluster_plot_digits_agglomeration.py` .. topic:: **Feature scaling** diff --git a/doc/preface.rst b/doc/preface.rst deleted file mode 100644 index 447083a3a8136..0000000000000 --- a/doc/preface.rst +++ /dev/null @@ -1,32 +0,0 @@ -.. This helps define the TOC ordering for "about us" sections. Particularly - useful for PDF output as this section is not linked from elsewhere. - -.. Places global toc into the sidebar - -:globalsidebartoc: True - -.. _preface_menu: - -.. include:: includes/big_toc_css.rst -.. include:: tune_toc.rst - -======================= -Welcome to scikit-learn -======================= - -| - -.. toctree:: - :maxdepth: 2 - - install - faq - support - related_projects - about - testimonials/testimonials - whats_new - roadmap - governance - -| diff --git a/doc/scss/api-search.scss b/doc/scss/api-search.scss new file mode 100644 index 0000000000000..5e9bbfdcf27ba --- /dev/null +++ b/doc/scss/api-search.scss @@ -0,0 +1,114 @@ +/** + * This is the styling for the API index page (`api/index`), in particular for the API + * search table. It involves overriding the style sheet of DataTables which does not + * fit well into the theme, especially in dark theme; see https://datatables.net/ + */ + +.dt-container { + margin-bottom: 2rem; + + // Fix the selection box for entries per page + select.dt-input { + padding: 0 !important; + margin-right: 0.4rem !important; + + > option { + color: var(--pst-color-text-base); + background-color: var(--pst-color-background); + } + } + + // Fix the search box + input.dt-input { + width: 50%; + line-height: normal; + padding: 0.1rem 0.3rem !important; + margin-left: 0.4rem !important; + } + + table.dataTable { + th { + // Fix border color of the header + border-color: inherit; + + // Disabled the bottom margin of

    in table cells to avoid making it too tall + p { + margin-bottom: 0; + } + + // Fix the ascending/descending order buttons in the header + span.dt-column-order { + &::before, + &::after { + color: var(--pst-color-text-base); + line-height: 0.7rem !important; + } + } + } + + td { + // Fix color of text warning no records found + &.dt-empty { + color: var(--pst-color-text-base) !important; + } + } + + // Fix border color of the last row + tr:last-child > * { + border-bottom-color: var(--bs-table-border-color) !important; + } + } + + div.dt-paging button.dt-paging-button { + padding: 0 0.5rem; + + &.disabled { + color: var(--pst-color-border) !important; + + // Overwrite the !important color assigned by DataTables because we must keep + // the color of disabled buttons consistent with and without hovering + &:hover { + color: var(--pst-color-border) !important; + } + } + + // Fix colors of paging buttons + &.current, + &:not(.disabled):not(.current):hover { + color: var(--pst-color-on-surface) !important; + border-color: var(--pst-color-surface) !important; + background: var(--pst-color-surface) !important; + } + + // Highlight the border of the current selected paging button + &.current { + border-color: var(--pst-color-text-base) !important; + } + } +} + +// Styling the object description cells in the table +div.sk-apisearch-desc { + p { + margin-bottom: 0; + } + + div.caption > p { + a, + code { + color: var(--pst-color-text-muted); + } + + code { + padding: 0; + font-size: 0.7rem; + font-weight: var(--pst-font-weight-caption); + background-color: transparent; + } + + .sd-badge { + font-size: 0.7rem; + margin-left: 0.3rem; + } + } +} diff --git a/doc/scss/api.scss b/doc/scss/api.scss new file mode 100644 index 0000000000000..d7110def4ac09 --- /dev/null +++ b/doc/scss/api.scss @@ -0,0 +1,52 @@ +/** + * This is the styling for API reference pages, currently under `modules/generated`. + * Note that it should be applied *ONLY* to API reference pages, as the selectors are + * designed based on how `autodoc` and `autosummary` generate the stuff. + */ + +// Make the admonitions more compact +div.versionadded, +div.versionchanged, +div.deprecated { + margin: 1rem auto; + + > p { + margin: 0.3rem auto; + } +} + +// Make docstrings more compact +dd { + p:not(table *) { + margin-bottom: 0.5rem !important; + } + + ul { + margin-bottom: 0.5rem !important; + padding-left: 2rem !important; + } +} + +// The first method is too close the the docstring above +dl.py.method:first-of-type { + margin-top: 2rem; +} + +// https://github.com/pydata/pydata-sphinx-theme/blob/8cf45f835bfdafc5f3821014a18f3b7e0fc2d44b/src/pydata_sphinx_theme/assets/styles/content/_api.scss +dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple) { + margin-bottom: 1.5rem; + + dd { + margin-left: 1.2rem; + } + + // "Parameters", "Returns", etc. in the docstring + dt.field-odd, + dt.field-even { + margin: 0.5rem 0; + + + dd > dl { + margin-bottom: 0.5rem; + } + } +} diff --git a/doc/scss/colors.scss b/doc/scss/colors.scss new file mode 100644 index 0000000000000..bbc6aa6c2a3d6 --- /dev/null +++ b/doc/scss/colors.scss @@ -0,0 +1,51 @@ +/** + * This is the style sheet for customized colors of scikit-learn. + * Tints and shades are generated by https://colorkit.co/color-shades-generator/ + * + * This file is compiled into styles/colors.css by sphinxcontrib.sass, see: + * https://sass-lang.com/guide/ + */ + +:root { + /* scikit-learn cyan */ + --sk-cyan-tint-9: #edf7fd; + --sk-cyan-tint-8: #daeffa; + --sk-cyan-tint-7: #c8e6f8; + --sk-cyan-tint-6: #b5def5; + --sk-cyan-tint-5: #a2d6f2; + --sk-cyan-tint-4: #8fcdef; + --sk-cyan-tint-3: #7ac5ec; + --sk-cyan-tint-2: #64bce9; + --sk-cyan-tint-1: #4bb4e5; + --sk-cyan: #29abe2; + --sk-cyan-shades-1: #2294c4; + --sk-cyan-shades-2: #1c7ea8; + --sk-cyan-shades-3: #15688c; + --sk-cyan-shades-4: #0f5471; + --sk-cyan-shades-5: #094057; + --sk-cyan-shades-6: #052d3e; + --sk-cyan-shades-7: #021b27; + --sk-cyan-shades-8: #010b12; + --sk-cyan-shades-9: #000103; + + /* scikit-learn orange */ + --sk-orange-tint-9: #fff5ec; + --sk-orange-tint-8: #ffead9; + --sk-orange-tint-7: #ffe0c5; + --sk-orange-tint-6: #ffd5b2; + --sk-orange-tint-5: #fecb9e; + --sk-orange-tint-4: #fdc08a; + --sk-orange-tint-3: #fcb575; + --sk-orange-tint-2: #fbaa5e; + --sk-orange-tint-1: #f99f44; + --sk-orange: #f7931e; + --sk-orange-shades-1: #d77f19; + --sk-orange-shades-2: #b76c13; + --sk-orange-shades-3: #99590e; + --sk-orange-shades-4: #7c4709; + --sk-orange-shades-5: #603605; + --sk-orange-shades-6: #452503; + --sk-orange-shades-7: #2c1601; + --sk-orange-shades-8: #150800; + --sk-orange-shades-9: #030100; +} diff --git a/doc/scss/custom.scss b/doc/scss/custom.scss new file mode 100644 index 0000000000000..ce4451fce4467 --- /dev/null +++ b/doc/scss/custom.scss @@ -0,0 +1,192 @@ +/** + * This is a general styling sheet. + * It should be used for customizations that affect multiple pages. + * + * This file is compiled into styles/custom.css by sphinxcontrib.sass, see: + * https://sass-lang.com/guide/ + */ + +/* Global */ + +code.literal { + border: 0; +} + +/* Version switcher */ + +.version-switcher__menu a.list-group-item.sk-avail-docs-link { + display: flex; + align-items: center; + + &:after { + content: var(--pst-icon-external-link); + font: var(--fa-font-solid); + font-size: 0.75rem; + margin-left: 0.5rem; + } +} + +/* Primary sidebar */ + +.bd-sidebar-primary { + width: 22.5%; + min-width: 16rem; + + // The version switcher button in the sidebar is ill-styled + button.version-switcher__button { + margin-bottom: unset; + margin-left: 0.3rem; + font-size: 1rem; + } + + // The section navigation part is to close to the right boundary (originally an even + // larger negative right margin was used) + nav.bd-links { + margin-right: -0.5rem; + } +} + +/* Article content */ + +.bd-article { + h1 { + font-weight: 500; + margin-bottom: 2rem; + } + + h2 { + font-weight: 500; + margin-bottom: 1.5rem; + } + + // Avoid changing the aspect ratio of images; add some padding so that at least + // there is some space between image and background in dark mode + img { + height: unset !important; + padding: 1%; + } + + // Resize table of contents to make the top few levels of headings more visible + li.toctree-l1 { + padding-bottom: 0.5em; + + > a { + font-size: 150%; + font-weight: bold; + } + } + + li.toctree-l2, + li.toctree-l3, + li.toctree-l4 { + margin-left: 15px; + } +} + +/* Dropdowns (sphinx-design) */ + +details.sd-dropdown { + &:hover > summary.sd-summary-title > a.headerlink { + visibility: visible; + } + + > summary.sd-summary-title { + > a.headerlink { + font-size: 1rem; + } + + // See `js/scripts/dropdown.js`: this is styling the "expand/collapse all" button + > button.sk-toggle-all { + color: var(--pst-sd-dropdown-color); + top: 0.9rem !important; + right: 3rem !important; + pointer-events: auto !important; + display: none; + border: none; + background: transparent; + } + } + + &[open] > summary.sd-summary-title:hover > .sd-summary-up.sk-toggle-all, + &:not([open]) + > summary.sd-summary-title:hover + > .sd-summary-down.sk-toggle-all { + display: block; + } +} + +/* scikit-learn buttons */ + +a.btn { + &.sk-btn-orange { + background-color: var(--sk-orange-tint-1); + color: black !important; + + &:hover { + background-color: var(--sk-orange-tint-3); + } + } + + &.sk-btn-cyan { + background-color: var(--sk-cyan-shades-2); + color: white !important; + + &:hover { + background-color: var(--sk-cyan-shades-1); + } + } +} + +/* scikit-learn avatar grid, see build_tools/generate_authors_table.py */ + +div.sk-authors-container { + display: flex; + flex-wrap: wrap; + justify-content: center; + + > div { + width: 6rem; + margin: 0.5rem; + font-size: 0.9rem; + } +} + +/* scikit-learn text-image grid, used in testimonials and sponsors pages */ + +@mixin sk-text-image-grid($img-max-height) { + display: flex; + align-items: center; + flex-wrap: wrap; + + div.text-box, + div.image-box { + width: 50%; + + @media screen and (max-width: 500px) { + width: 100%; + } + } + + div.text-box .annotation { + font-size: 0.9rem; + font-style: italic; + color: var(--pst-color-text-muted); + } + + div.image-box { + text-align: center; + + img { + max-height: $img-max-height; + max-width: 50%; + } + } +} + +div.sk-text-image-grid-small { + @include sk-text-image-grid(60px); +} + +div.sk-text-image-grid-large { + @include sk-text-image-grid(100px); +} diff --git a/doc/scss/index.scss b/doc/scss/index.scss new file mode 100644 index 0000000000000..4e3f371f236d4 --- /dev/null +++ b/doc/scss/index.scss @@ -0,0 +1,175 @@ +/** + * Styling sheet for the scikit-learn landing page. This should be loaded only for the + * landing page. + * + * This file is compiled into styles/index.css by sphinxcontrib.sass, see: + * https://sass-lang.com/guide/ + */ + +/* Theme-aware colors for the landing page */ + +html { + &[data-theme="light"] { + --sk-landing-bg-1: var(--sk-cyan-shades-3); + --sk-landing-bg-2: var(--sk-cyan); + --sk-landing-bg-3: var(--sk-orange-tint-8); + --sk-landing-bg-4: var(--sk-orange-tint-3); + } + + &[data-theme="dark"] { + --sk-landing-bg-1: var(--sk-cyan-shades-5); + --sk-landing-bg-2: var(--sk-cyan-shades-2); + --sk-landing-bg-3: var(--sk-orange-tint-4); + --sk-landing-bg-4: var(--sk-orange-tint-1); + } +} + +/* General */ + +div.sk-landing-container { + max-width: 1400px; +} + +/* Top bar */ + +div.sk-landing-top-bar { + background-image: linear-gradient( + 160deg, + var(--sk-landing-bg-1) 0%, + var(--sk-landing-bg-2) 17%, + var(--sk-landing-bg-3) 59%, + var(--sk-landing-bg-4) 100% + ); + + .sk-landing-header, + .sk-landing-subheader { + color: white; + text-shadow: 0px 0px 8px var(--sk-landing-bg-1); + } + + .sk-landing-header { + font-size: 3.2rem; + margin-bottom: 0.5rem; + } + + .sk-landing-subheader { + letter-spacing: 0.17rem; + margin-top: 0; + font-weight: 500; + } + + a.sk-btn-orange { + font-size: 1.1rem; + font-weight: 500; + } + + ul.sk-landing-header-body { + margin-top: auto; + margin-bottom: auto; + font-size: 1.2rem; + font-weight: 500; + color: black; + } +} + +/* Body */ + +div.sk-landing-body { + div.card { + background-color: var(--pst-color-background); + border-color: var(--pst-color-border); + } + + .sk-px-xl-4 { + @media screen and (min-width: 1200px) { + padding-left: 1.3rem !important; + padding-right: 1.3rem !important; + } + } + + .card-body { + p { + margin-bottom: 0.8rem; + } + + .sk-card-title { + font-weight: 700; + margin: 0 0 1rem 0; + } + } + + .sk-card-img-container { + display: flex; + justify-content: center; + align-items: end; + margin-bottom: 1rem; + + img { + max-width: unset; + height: 15rem; + } + } +} + +/* More info */ + +div.sk-landing-more-info { + font-size: 0.96rem; + background-color: var(--pst-color-surface); + + .sk-landing-call-header { + font-weight: 700; + margin-top: 0; + + html[data-theme="light"] & { + color: var(--sk-orange-shades-1); + } + + html[data-theme="dark"] & { + color: var(--sk-orange); + } + } + + ul.sk-landing-call-list > li { + margin-bottom: 0.25rem; + } + + .sk-who-uses-carousel { + min-height: 200px; + + .carousel-item img { + max-height: 100px; + max-width: 50%; + margin: 0.5rem; + } + } + + .sk-more-testimonials { + text-align: right !important; + } +} + +/* Footer */ + +div.sk-landing-footer { + a.sk-footer-funding-link { + text-decoration: none; + + p.sk-footer-funding-text { + color: var(--pst-color-link); + + &:hover { + color: var(--pst-color-secondary); + } + } + + div.sk-footer-funding-logos > img { + max-height: 40px; + max-width: 85px; + margin: 0 8px 8px 8px; + padding: 5px; + border-radius: 3px; + background-color: white; + } + } +} diff --git a/doc/scss/install.scss b/doc/scss/install.scss new file mode 100644 index 0000000000000..965b3d589e86d --- /dev/null +++ b/doc/scss/install.scss @@ -0,0 +1,33 @@ +/** + * Styling for the installation page, including overriding some default styling of + * sphinx-design. This style sheet should be included only for the install page. + * + * This file is compiled into styles/install.css by sphinxcontrib.sass, see: + * https://sass-lang.com/guide/ + */ + +.install-instructions .sd-tab-set { + .sd-tab-content { + box-shadow: 0 -0.0625rem var(--pst-color-border); + padding: 0.5rem 0 0 0; + + > p:first-child { + margin-top: 1.25rem !important; + } + } + + > label.sd-tab-label { + border-top: none !important; + padding: 0 0 0.1rem 0; + margin: 0; + text-align: center; + + &.tab-6 { + width: 50% !important; + } + + &.tab-4 { + width: calc(100% / 3) !important; + } + } +} diff --git a/doc/sphinxext/add_toctree_functions.py b/doc/sphinxext/add_toctree_functions.py deleted file mode 100644 index 4459ab971f4c4..0000000000000 --- a/doc/sphinxext/add_toctree_functions.py +++ /dev/null @@ -1,160 +0,0 @@ -"""Inspired by https://github.com/pandas-dev/pydata-sphinx-theme - -BSD 3-Clause License - -Copyright (c) 2018, pandas -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -* Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -* Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -* Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -""" - -import docutils - - -def add_toctree_functions(app, pagename, templatename, context, doctree): - """Add functions so Jinja templates can add toctree objects. - - This converts the docutils nodes into a nested dictionary that Jinja can - use in our templating. - """ - from sphinx.environment.adapters.toctree import TocTree - - def get_nav_object(maxdepth=None, collapse=True, numbered=False, **kwargs): - """Return a list of nav links that can be accessed from Jinja. - - Parameters - ---------- - maxdepth: int - How many layers of TocTree will be returned - collapse: bool - Whether to only include sub-pages of the currently-active page, - instead of sub-pages of all top-level pages of the site. - numbered: bool - Whether to add section number to title - kwargs: key/val pairs - Passed to the `TocTree.get_toctree_for` Sphinx method - """ - # The TocTree will contain the full site TocTree including sub-pages. - # "collapse=True" collapses sub-pages of non-active TOC pages. - # maxdepth controls how many TOC levels are returned - toctree = TocTree(app.env).get_toctree_for( - pagename, app.builder, collapse=collapse, maxdepth=maxdepth, **kwargs - ) - # If no toctree is defined (AKA a single-page site), skip this - if toctree is None: - return [] - - # toctree has this structure - # - # - # - # - # `list_item`s are the actual TOC links and are the only thing we want - toc_items = [ - item - for child in toctree.children - for item in child - if isinstance(item, docutils.nodes.list_item) - ] - - # Now convert our docutils nodes into dicts that Jinja can use - nav = [ - docutils_node_to_jinja(child, only_pages=True, numbered=numbered) - for child in toc_items - ] - - return nav - - context["get_nav_object"] = get_nav_object - - -def docutils_node_to_jinja(list_item, only_pages=False, numbered=False): - """Convert a docutils node to a structure that can be read by Jinja. - - Parameters - ---------- - list_item : docutils list_item node - A parent item, potentially with children, corresponding to the level - of a TocTree. - only_pages : bool - Only include items for full pages in the output dictionary. Exclude - anchor links (TOC items with a URL that starts with #) - numbered: bool - Whether to add section number to title - - Returns - ------- - nav : dict - The TocTree, converted into a dictionary with key/values that work - within Jinja. - """ - if not list_item.children: - return None - - # We assume this structure of a list item: - # - # - # <-- the thing we want - reference = list_item.children[0].children[0] - title = reference.astext() - url = reference.attributes["refuri"] - active = "current" in list_item.attributes["classes"] - - secnumber = reference.attributes.get("secnumber", None) - if numbered and secnumber is not None: - secnumber = ".".join(str(n) for n in secnumber) - title = f"{secnumber}. {title}" - - # If we've got an anchor link, skip it if we wish - if only_pages and "#" in url: - return None - - # Converting the docutils attributes into jinja-friendly objects - nav = {} - nav["title"] = title - nav["url"] = url - nav["active"] = active - - # Recursively convert children as well - # If there are sub-pages for this list_item, there should be two children: - # a paragraph, and a bullet_list. - nav["children"] = [] - if len(list_item.children) > 1: - # The `.children` of the bullet_list has the nodes of the sub-pages. - subpage_list = list_item.children[1].children - for sub_page in subpage_list: - child_nav = docutils_node_to_jinja( - sub_page, only_pages=only_pages, numbered=numbered - ) - if child_nav is not None: - nav["children"].append(child_nav) - return nav - - -def setup(app): - app.connect("html-page-context", add_toctree_functions) - - return {"parallel_read_safe": True, "parallel_write_safe": True} diff --git a/doc/sphinxext/autoshortsummary.py b/doc/sphinxext/autoshortsummary.py new file mode 100644 index 0000000000000..8451f3133d05b --- /dev/null +++ b/doc/sphinxext/autoshortsummary.py @@ -0,0 +1,53 @@ +from sphinx.ext.autodoc import ModuleLevelDocumenter + + +class ShortSummaryDocumenter(ModuleLevelDocumenter): + """An autodocumenter that only renders the short summary of the object.""" + + # Defines the usage: .. autoshortsummary:: {{ object }} + objtype = "shortsummary" + + # Disable content indentation + content_indent = "" + + # Avoid being selected as the default documenter for some objects, because we are + # returning `can_document_member` as True for all objects + priority = -99 + + @classmethod + def can_document_member(cls, member, membername, isattr, parent): + """Allow documenting any object.""" + return True + + def get_object_members(self, want_all): + """Document no members.""" + return (False, []) + + def add_directive_header(self, sig): + """Override default behavior to add no directive header or options.""" + pass + + def add_content(self, more_content): + """Override default behavior to add only the first line of the docstring. + + Modified based on the part of processing docstrings in the original + implementation of this method. + + https://github.com/sphinx-doc/sphinx/blob/faa33a53a389f6f8bc1f6ae97d6015fa92393c4a/sphinx/ext/autodoc/__init__.py#L609-L622 + """ + sourcename = self.get_sourcename() + docstrings = self.get_doc() + + if docstrings is not None: + if not docstrings: + docstrings.append([]) + # Get the first non-empty line of the processed docstring; this could lead + # to unexpected results if the object does not have a short summary line. + short_summary = next( + (s for s in self.process_doc(docstrings) if s), "" + ) + self.add_line(short_summary, sourcename, 0) + + +def setup(app): + app.add_autodocumenter(ShortSummaryDocumenter) diff --git a/doc/sphinxext/dropdown_anchors.py b/doc/sphinxext/dropdown_anchors.py new file mode 100644 index 0000000000000..eb0b414de6ae8 --- /dev/null +++ b/doc/sphinxext/dropdown_anchors.py @@ -0,0 +1,78 @@ +import re + +from docutils import nodes +from sphinx.transforms.post_transforms import SphinxPostTransform +from sphinx_design.dropdown import dropdown_main, dropdown_title + + +class DropdownAnchorAdder(SphinxPostTransform): + """Insert anchor links to the sphinx-design dropdowns. + + Some of the dropdowns were originally headers that had automatic anchors, so we + need to make sure that the old anchors still work. See the original implementation + (in JS): https://github.com/scikit-learn/scikit-learn/pull/27409 + + The structure of each sphinx-design dropdown node is expected to be: + + + + ...icon <-- This exists if the "icon" option of the sphinx-design + dropdown is set; we do not use it in our documentation + + ...title <-- This may contain multiple nodes, e.g. literal nodes if + there are inline codes; we use the concatenated text of + all these nodes to generate the anchor ID + + Here we insert the anchor link! + + <-- The "dropdown closed" marker + <-- The "dropdown open" marker + + + ...main contents + + + """ + + default_priority = 9999 # Apply later than everything else + formats = ["html"] + + def run(self): + """Run the post transformation.""" + # Counter to store the duplicated summary text to add it as a suffix in the + # anchor ID + anchor_id_counters = {} + + for sd_dropdown in self.document.findall(dropdown_main): + # Grab the dropdown title + sd_dropdown_title = sd_dropdown.next_node(dropdown_title) + + # Concatenate the text of relevant nodes as the title text + # Since we do not have the prefix icon, the relevant nodes are the very + # first child node until the third last node (last two are markers) + title_text = "".join( + node.astext() for node in sd_dropdown_title.children[:-2] + ) + + # The ID uses the first line, lowercased, with spaces replaced by dashes; + # suffix the anchor ID with a counter if it already exists + anchor_id = re.sub(r"\s+", "-", title_text.strip().split("\n")[0]).lower() + if anchor_id in anchor_id_counters: + anchor_id_counters[anchor_id] += 1 + anchor_id = f"{anchor_id}-{anchor_id_counters[anchor_id]}" + else: + anchor_id_counters[anchor_id] = 1 + sd_dropdown["ids"].append(anchor_id) + + # Create the anchor element and insert after the title text; we do this + # directly with raw HTML + anchor_html = ( + f'#' + ) + anchor_node = nodes.raw("", anchor_html, format="html") + sd_dropdown_title.insert(-2, anchor_node) # before the two markers + + +def setup(app): + app.add_post_transform(DropdownAnchorAdder) diff --git a/doc/sphinxext/move_gallery_links.py b/doc/sphinxext/move_gallery_links.py new file mode 100644 index 0000000000000..dff27f7358c7f --- /dev/null +++ b/doc/sphinxext/move_gallery_links.py @@ -0,0 +1,193 @@ +""" +This script intends to better integrate sphinx-gallery into pydata-sphinx-theme. In +particular, it moves the download links and badge links in the footer of each generated +example page into the secondary sidebar, then removes the footer and the top note +pointing to the footer. + +The download links are for Python source code and Jupyter notebook respectively, and +the badge links are for JupyterLite and Binder. + +Currently this is achieved via post-processing the HTML generated by sphinx-gallery. +This hack can be removed if the following upstream issue is resolved: +https://github.com/sphinx-gallery/sphinx-gallery/issues/1258 +""" + +from pathlib import Path + +from bs4 import BeautifulSoup +from sphinx.util.display import status_iterator +from sphinx.util.logging import getLogger + +logger = getLogger(__name__) + + +def move_gallery_links(app, exception): + if exception is not None: + return + + for gallery_dir in app.config.sphinx_gallery_conf["gallery_dirs"]: + html_gallery_dir = Path(app.builder.outdir, gallery_dir) + + # Get all gallery example files to be tweaked; tuples (file, docname) + flat = [] + for file in html_gallery_dir.rglob("*.html"): + if file.name in ("index.html", "sg_execution_times.html"): + # These are not gallery example pages, skip + continue + + # Extract the documentation name from the path + docname = file.relative_to(app.builder.outdir).with_suffix("").as_posix() + if docname in app.config.html_context["redirects"]: + # This is a redirected page, skip + continue + if docname not in app.project.docnames: + # This should not happen, warn + logger.warning(f"Document {docname} not found but {file} exists") + continue + flat.append((file, docname)) + + for html_file, _ in status_iterator( + flat, + length=len(flat), + summary="Tweaking gallery links... ", + verbosity=app.verbosity, + stringify_func=lambda x: x[1], # display docname + ): + with html_file.open("r", encoding="utf-8") as f: + html = f.read() + soup = BeautifulSoup(html, "html.parser") + + # Find the secondary sidebar; it should exist in all gallery example pages + secondary_sidebar = soup.find("div", class_="sidebar-secondary-items") + if secondary_sidebar is None: + logger.warning(f"Secondary sidebar not found in {html_file}") + continue + + def _create_secondary_sidebar_component(items): + """Create a new component in the secondary sidebar. + + `items` should be a list of dictionaries with "element" being the bs4 + tag of the component and "title" being the title (None if not needed). + """ + component = soup.new_tag("div", **{"class": "sidebar-secondary-item"}) + for item in items: + item_wrapper = soup.new_tag("div") + item_wrapper.append(item["element"]) + if item["title"]: + item_wrapper["title"] = item["title"] + component.append(item_wrapper) + secondary_sidebar.append(component) + + def _create_download_link(link, is_jupyter=False): + """Create a download link to be appended to a component. + + `link` should be the bs4 tag of the original download link, either for + the Python source code (is_jupyter=False) of for the Jupyter notebook + (is_jupyter=True). `link` will not be removed; instead the whole + footnote would be removed where `link` is located. + + This returns a dictionary with "element" being the bs4 tag of the new + download link and "title" being the name of the file to download. + """ + new_link = soup.new_tag("a", href=link["href"], download="") + + # Place a download icon at the beginning of the new link + download_icon = soup.new_tag("i", **{"class": "fa-solid fa-download"}) + new_link.append(download_icon) + + # Create the text of the new link; it is shortend to fit better into + # the secondary sidebar. The leading space before "Download ..." is + # intentional to create a small gap between the icon and the text, + # being consistent with the other pydata-sphinx-theme components + link_type = "Jupyter notebook" if is_jupyter else "source code" + new_text = soup.new_string(f" Download {link_type}") + new_link.append(new_text) + + # Get the file name to download and use it as the title of the new link + # which will show up when hovering over the link; the file name is + # expected to be in the last span of `link` + link_spans = link.find_all("span") + title = link_spans[-1].text if link_spans else None + + return {"element": new_link, "title": title} + + def _create_badge_link(link): + """Create a badge link to be appended to a component. + + `link` should be the bs4 tag of the original badge link, either for + binder or JupyterLite. `link` will not be removed; instead the whole + footnote would be removed where `link` is located. + + This returns a dictionary with "element" being the bs4 tag of the new + download link and "title" being `None` (no need). + """ + new_link = soup.new_tag("a", href=link["href"]) + + # The link would essentially be an anchor wrapper outside the image of + # the badge; we get the src and alt attributes by finding the original + # image and limit the height to 20px (fixed) so that the secondary + # sidebar will appear neater + badge_img = link.find("img") + new_img = soup.new_tag( + "img", src=badge_img["src"], alt=badge_img["alt"], height=20 + ) + new_link.append(new_img) + + return {"element": new_link, "title": None} + + try: + # `sg_note` is the "go to the end" note at the top of the page + # `sg_footer` is the footer with the download links and badge links + # These will be removed at the end if new links are successfully created + sg_note = soup.find("div", class_="sphx-glr-download-link-note") + sg_footer = soup.find("div", class_="sphx-glr-footer") + + # If any one of these two is not found, we directly give up tweaking + if sg_note is None or sg_footer is None: + continue + + # Move the download links into the secondary sidebar + py_link_div = sg_footer.find("div", class_="sphx-glr-download-python") + ipy_link_div = sg_footer.find("div", class_="sphx-glr-download-jupyter") + _create_secondary_sidebar_component( + [ + _create_download_link(py_link_div.a, is_jupyter=False), + _create_download_link(ipy_link_div.a, is_jupyter=True), + ] + ) + + # Move the badge links into the secondary sidebar + lite_link_div = sg_footer.find("div", class_="lite-badge") + binder_link_div = sg_footer.find("div", class_="binder-badge") + _create_secondary_sidebar_component( + [ + _create_badge_link(lite_link_div.a), + _create_badge_link(binder_link_div.a), + ] + ) + + # Remove the sourcelink component from the secondary sidebar; the reason + # we do not remove it by configuration is that we need the secondary + # sidebar to be present for this script to work, while in-page toc alone + # could have been empty + sourcelink = secondary_sidebar.find("div", class_="sourcelink") + if sourcelink is not None: + sourcelink.parent.extract() # because sourcelink has a wrapper div + + # Remove the the top note and the whole footer + sg_note.extract() + sg_footer.extract() + + except Exception: + # If any step fails we directly skip the file + continue + + # Write the modified file back + with html_file.open("w", encoding="utf-8") as f: + f.write(str(soup)) + + +def setup(app): + # Default priority is 500 which sphinx-gallery uses for its build-finished events; + # we need a larger priority to run after sphinx-gallery (larger is later) + app.connect("build-finished", move_gallery_links, priority=900) diff --git a/doc/sphinxext/override_pst_pagetoc.py b/doc/sphinxext/override_pst_pagetoc.py new file mode 100644 index 0000000000000..f5697de8ef155 --- /dev/null +++ b/doc/sphinxext/override_pst_pagetoc.py @@ -0,0 +1,84 @@ +from functools import cache + +from sphinx.util.logging import getLogger + +logger = getLogger(__name__) + + +def override_pst_pagetoc(app, pagename, templatename, context, doctree): + """Overrides the `generate_toc_html` function of pydata-sphinx-theme for API.""" + + @cache + def generate_api_toc_html(kind="html"): + """Generate the in-page toc for an API page. + + This relies on the `generate_toc_html` function added by pydata-sphinx-theme + into the context. We save the original function into `pst_generate_toc_html` + and override `generate_toc_html` with this function for generated API pages. + + The pagetoc of an API page would look like the following: + +

      <-- Unwrap +
    • <-- Unwrap + {{obj}} <-- Decompose + +
        +
      • + ...object +
          <-- Set visible if exists +
        • ...method 1
          • <-- Shorten +
        • ...method 2
          • <-- Shorten + ...more methods <-- Shorten +
        +
        • +
      • ...gallery examples
        • +
      + +
      • <-- Unwrapped +
    <-- Unwrapped + """ + soup = context["pst_generate_toc_html"](kind="soup") + + try: + # Unwrap the outermost level + soup.ul.unwrap() + soup.li.unwrap() + soup.a.decompose() + + # Get all toc-h2 level entries, where the first one should be the function + # or class, and the second one, if exists, should be the examples; there + # should be no more than two entries at this level for generated API pages + lis = soup.ul.select("li.toc-h2") + main_li = lis[0] + meth_list = main_li.ul + + if meth_list is not None: + # This is a class API page, we remove the class name from the method + # names to make them better fit into the secondary sidebar; also we + # make the toc-h3 level entries always visible to more easily navigate + # through the methods + meth_list["class"].append("visible") + for meth in meth_list.find_all("li", {"class": "toc-h3"}): + target = meth.a.code.span + target.string = target.string.split(".", 1)[1] + + # This corresponds to the behavior of `generate_toc_html` + return str(soup) if kind == "html" else soup + + except Exception as e: + # Upon any failure we return the original pagetoc + logger.warning( + f"Failed to generate API pagetoc for {pagename}: {e}; falling back" + ) + return context["pst_generate_toc_html"](kind=kind) + + # Override the pydata-sphinx-theme implementation for generate API pages + if pagename.startswith("modules/generated/"): + context["pst_generate_toc_html"] = context["generate_toc_html"] + context["generate_toc_html"] = generate_api_toc_html + + +def setup(app): + # Need to be triggered after `pydata_sphinx_theme.toctree.add_toctree_functions`, + # and since default priority is 500 we set 900 for safety + app.connect("html-page-context", override_pst_pagetoc, priority=900) diff --git a/doc/supervised_learning.rst b/doc/supervised_learning.rst index 71fb3007c2e3c..ba24e8ee23c6f 100644 --- a/doc/supervised_learning.rst +++ b/doc/supervised_learning.rst @@ -1,9 +1,3 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - -.. include:: includes/big_toc_css.rst - .. _supervised-learning: Supervised learning diff --git a/doc/templates/base.rst b/doc/templates/base.rst new file mode 100644 index 0000000000000..ee86bd8a18dbe --- /dev/null +++ b/doc/templates/base.rst @@ -0,0 +1,36 @@ +{{ objname | escape | underline(line="=") }} + +{% if objtype == "module" -%} + +.. automodule:: {{ fullname }} + +{%- elif objtype == "function" -%} + +.. currentmodule:: {{ module }} + +.. autofunction:: {{ objname }} + +.. minigallery:: {{ module }}.{{ objname }} + :add-heading: Gallery examples + :heading-level: - + +{%- elif objtype == "class" -%} + +.. currentmodule:: {{ module }} + +.. autoclass:: {{ objname }} + :members: + :inherited-members: + :special-members: __call__ + +.. minigallery:: {{ module }}.{{ objname }} {% for meth in methods %}{{ module }}.{{ objname }}.{{ meth }} {% endfor %} + :add-heading: Gallery examples + :heading-level: - + +{%- else -%} + +.. currentmodule:: {{ module }} + +.. auto{{ objtype }}:: {{ objname }} + +{%- endif -%} diff --git a/doc/templates/class.rst b/doc/templates/class.rst deleted file mode 100644 index 1e98be4099b73..0000000000000 --- a/doc/templates/class.rst +++ /dev/null @@ -1,17 +0,0 @@ -.. - The empty line below should not be removed. It is added such that the `rst_prolog` - is added before the :mod: directive. Otherwise, the rendering will show as a - paragraph instead of a header. - -:mod:`{{module}}`.{{objname}} -{{ underline }}============== - -.. currentmodule:: {{ module }} - -.. autoclass:: {{ objname }} - -.. include:: {{module}}.{{objname}}.examples - -.. raw:: html - -
    diff --git a/doc/templates/class_with_call.rst b/doc/templates/class_with_call.rst deleted file mode 100644 index bc1567709c9d3..0000000000000 --- a/doc/templates/class_with_call.rst +++ /dev/null @@ -1,21 +0,0 @@ -.. - The empty line below should not be removed. It is added such that the `rst_prolog` - is added before the :mod: directive. Otherwise, the rendering will show as a - paragraph instead of a header. - -:mod:`{{module}}`.{{objname}} -{{ underline }}=============== - -.. currentmodule:: {{ module }} - -.. autoclass:: {{ objname }} - - {% block methods %} - .. automethod:: __call__ - {% endblock %} - -.. include:: {{module}}.{{objname}}.examples - -.. raw:: html - -
    diff --git a/doc/templates/deprecated_class.rst b/doc/templates/deprecated_class.rst deleted file mode 100644 index 5c31936f6fc36..0000000000000 --- a/doc/templates/deprecated_class.rst +++ /dev/null @@ -1,28 +0,0 @@ -.. - The empty line below should not be removed. It is added such that the `rst_prolog` - is added before the :mod: directive. Otherwise, the rendering will show as a - paragraph instead of a header. - -:mod:`{{module}}`.{{objname}} -{{ underline }}============== - -.. meta:: - :robots: noindex - -.. warning:: - **DEPRECATED** - - -.. currentmodule:: {{ module }} - -.. autoclass:: {{ objname }} - - {% block methods %} - .. automethod:: __init__ - {% endblock %} - -.. include:: {{module}}.{{objname}}.examples - -.. raw:: html - -
    diff --git a/doc/templates/deprecated_class_with_call.rst b/doc/templates/deprecated_class_with_call.rst deleted file mode 100644 index 072a31112be50..0000000000000 --- a/doc/templates/deprecated_class_with_call.rst +++ /dev/null @@ -1,29 +0,0 @@ -.. - The empty line below should not be removed. It is added such that the `rst_prolog` - is added before the :mod: directive. Otherwise, the rendering will show as a - paragraph instead of a header. - -:mod:`{{module}}`.{{objname}} -{{ underline }}=============== - -.. meta:: - :robots: noindex - -.. warning:: - **DEPRECATED** - - -.. currentmodule:: {{ module }} - -.. autoclass:: {{ objname }} - - {% block methods %} - .. automethod:: __init__ - .. automethod:: __call__ - {% endblock %} - -.. include:: {{module}}.{{objname}}.examples - -.. raw:: html - -
    diff --git a/doc/templates/deprecated_class_without_init.rst b/doc/templates/deprecated_class_without_init.rst deleted file mode 100644 index a26afbead5451..0000000000000 --- a/doc/templates/deprecated_class_without_init.rst +++ /dev/null @@ -1,24 +0,0 @@ -.. - The empty line below should not be removed. It is added such that the `rst_prolog` - is added before the :mod: directive. Otherwise, the rendering will show as a - paragraph instead of a header. - -:mod:`{{module}}`.{{objname}} -{{ underline }}============== - -.. meta:: - :robots: noindex - -.. warning:: - **DEPRECATED** - - -.. currentmodule:: {{ module }} - -.. autoclass:: {{ objname }} - -.. include:: {{module}}.{{objname}}.examples - -.. raw:: html - -
    diff --git a/doc/templates/deprecated_function.rst b/doc/templates/deprecated_function.rst deleted file mode 100644 index ead5abec27076..0000000000000 --- a/doc/templates/deprecated_function.rst +++ /dev/null @@ -1,24 +0,0 @@ -.. - The empty line below should not be removed. It is added such that the `rst_prolog` - is added before the :mod: directive. Otherwise, the rendering will show as a - paragraph instead of a header. - -:mod:`{{module}}`.{{objname}} -{{ underline }}==================== - -.. meta:: - :robots: noindex - -.. warning:: - **DEPRECATED** - - -.. currentmodule:: {{ module }} - -.. autofunction:: {{ objname }} - -.. include:: {{module}}.{{objname}}.examples - -.. raw:: html - -
    diff --git a/doc/templates/display_all_class_methods.rst b/doc/templates/display_all_class_methods.rst deleted file mode 100644 index b179473cf841e..0000000000000 --- a/doc/templates/display_all_class_methods.rst +++ /dev/null @@ -1,19 +0,0 @@ -.. - The empty line below should not be removed. It is added such that the `rst_prolog` - is added before the :mod: directive. Otherwise, the rendering will show as a - paragraph instead of a header. - -:mod:`{{module}}`.{{objname}} -{{ underline }}============== - -.. currentmodule:: {{ module }} - -.. autoclass:: {{ objname }} - -.. include:: {{module}}.{{objname}}.examples -.. include:: {{module}}.{{objname}}.from_estimator.examples -.. include:: {{module}}.{{objname}}.from_predictions.examples - -.. raw:: html - -
    diff --git a/doc/templates/display_only_from_estimator.rst b/doc/templates/display_only_from_estimator.rst deleted file mode 100644 index 9981910dc8be7..0000000000000 --- a/doc/templates/display_only_from_estimator.rst +++ /dev/null @@ -1,18 +0,0 @@ -.. - The empty line below should not be removed. It is added such that the `rst_prolog` - is added before the :mod: directive. Otherwise, the rendering will show as a - paragraph instead of a header. - -:mod:`{{module}}`.{{objname}} -{{ underline }}============== - -.. currentmodule:: {{ module }} - -.. autoclass:: {{ objname }} - -.. include:: {{module}}.{{objname}}.examples -.. include:: {{module}}.{{objname}}.from_estimator.examples - -.. raw:: html - -
    diff --git a/doc/templates/function.rst b/doc/templates/function.rst deleted file mode 100644 index 93d368ecfe6d5..0000000000000 --- a/doc/templates/function.rst +++ /dev/null @@ -1,17 +0,0 @@ -.. - The empty line below should not be removed. It is added such that the `rst_prolog` - is added before the :mod: directive. Otherwise, the rendering will show as a - paragraph instead of a header. - -:mod:`{{module}}`.{{objname}} -{{ underline }}==================== - -.. currentmodule:: {{ module }} - -.. autofunction:: {{ objname }} - -.. include:: {{module}}.{{objname}}.examples - -.. raw:: html - -
    diff --git a/doc/templates/generate_deprecated.sh b/doc/templates/generate_deprecated.sh deleted file mode 100755 index a7301fb5dc419..0000000000000 --- a/doc/templates/generate_deprecated.sh +++ /dev/null @@ -1,8 +0,0 @@ -#!/bin/bash -for f in [^d]*; do (head -n2 < $f; echo ' -.. meta:: - :robots: noindex - -.. warning:: - **DEPRECATED** -'; tail -n+3 $f) > deprecated_$f; done diff --git a/doc/templates/index.html b/doc/templates/index.html index 74816a4b473d3..61457be2494ea 100644 --- a/doc/templates/index.html +++ b/doc/templates/index.html @@ -1,25 +1,27 @@ {% extends "layout.html" %} {% set title = 'scikit-learn: machine learning in Python' %} -{% if theme_link_to_live_contributing_page|tobool %} +{% if is_devrelease|tobool %} + {%- set contributing_link = pathto("developers/contributing") %} + {%- set contributing_attrs = "" %} +{%- else %} {%- set contributing_link = "https://scikit-learn.org/dev/developers/contributing.html" %} {%- set contributing_attrs = 'target="_blank" rel="noopener noreferrer"' %} -{%- else %} - {%- set contributing_link = pathto('developers/contributing') %} - {%- set contributing_attrs = '' %} {%- endif %} +{%- import "static/webpack-macros.html" as _webpack with context %} -{% block content %} -
    +{% block docs_navbar %} +{{ super() }} + +
    -

    scikit-learn

    -

    Machine Learning in Python

    - Getting Started - Release Highlights for {{ release_highlights_version }} - GitHub +

    scikit-learn

    +

    Machine Learning in Python

    + Getting Started + Release Highlights for {{ release_highlights_version }}
      @@ -33,236 +35,279 @@

      Machine Learning in

    -
    +{% endblock docs_navbar %} + +{% block docs_main %} + +
    +
    -
    +
    -

    Classification

     -

    Identifying which category an object belongs to.

    -

    Applications: Spam detection, image recognition.
    - Algorithms: - Gradient boosting, - nearest neighbors, - random forest, - logistic regression, - and more...

    +

    + Classification +

    +

    Identifying which category an object belongs to.

    +

    + Applications: Spam detection, image recognition.
    + Algorithms: + Gradient boosting, + nearest neighbors, + random forest, + logistic regression, + and more... +

    -
    - - Classifier comparison +
    + + Classifier comparison
    - Examples + Examples
    +
    -
    +
    -

    Regression

     -

    Predicting a continuous-valued attribute associated with an object.

    -

    Applications: Drug response, Stock prices.
    - Algorithms: - Gradient boosting, - nearest neighbors, - random forest, - ridge, - and more...

    +

    + Regression +

    +

    Predicting a continuous-valued attribute associated with an object.

    +

    + Applications: Drug response, stock prices.
    + Algorithms: + Gradient boosting, + nearest neighbors, + random forest, + ridge, + and more... +

    -
    - - Decision Tree Regression with HGBT +
    + + Decision Tree Regression with HGBT
    - Examples + Examples
    +
    -
    +
    -

    Clustering

     -

    Automatic grouping of similar objects into sets.

    -

    Applications: Customer segmentation, Grouping experiment outcomes
    - Algorithms: - k-Means, - HDBSCAN, - hierarchical - clustering, - and more...

    +

    + Clustering +

    +

    Automatic grouping of similar objects into sets.

    +

    + Applications: Customer segmentation, grouping experiment outcomes.
    + Algorithms: + k-Means, + HDBSCAN, + hierarchical clustering, + and more... +

    -
    - - A demo of K-Means clustering on the handwritten digits data +
    + + A demo of K-Means clustering on the handwritten digits data
    - Examples + Examples
    +
    -
    +
    -

    Dimensionality reduction

     -

    Reducing the number of random variables to consider.

    -

    Applications: Visualization, Increased efficiency
    - Algorithms: - PCA, - feature selection, - non-negative matrix factorization, - and more...

    +

    + Dimensionality reduction +

    +

    Reducing the number of random variables to consider.

    +

    + Applications: Visualization, increased efficiency.
    + Algorithms: + PCA, + feature selection, + non-negative matrix factorization, + and more... +

    -
    - - PCA example with Iris Data-set +
    + + PCA example with Iris Data-set
    - Examples + Examples
    +
    -
    +
    -

    Model selection

     -

    Comparing, validating and choosing parameters and models.

    -

    Applications: Improved accuracy via parameter tuning
    - Algorithms: - grid search, - cross validation, - metrics, - and more...

    +

    + Model selection +

    +

    Comparing, validating and choosing parameters and models.

    +

    + Applications: Improved accuracy via parameter tuning.
    + Algorithms: + Grid search, + cross validation, + metrics, + and more... +

    -
    - - Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV +
    + + Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV
    - Examples + Examples
    +
    -
    +
    -

    Preprocessing

     -

    Feature extraction and normalization.

    -

    Applications: Transforming input data such as text for use with machine learning algorithms.
    - Algorithms: - preprocessing, - feature extraction, - and more...

    +

    + Preprocessing +

    +

    Feature extraction and normalization.

    +

    + Applications: Transforming input data such as text for use with machine learning algorithms.
    + Algorithms: + Preprocessing, + feature extraction, + and more... +

    -
    - - Demonstrating the different strategies of KBinsDiscretizer +
    + + Demonstrating the different strategies of KBinsDiscretizer
    - Examples + Examples
    -
    -
    +{% endblock docs_main %} + +{% block footer %} + +
    +
    +

    News

      -
    • On-going development: - scikit-learn 1.6 (Changelog) -
      • -
    • May 2024. scikit-learn 1.5.0 is available for download (Changelog). -
      • -
    • April 2024. scikit-learn 1.4.2 is available for download (Changelog). -
      • -
    • February 2024. scikit-learn 1.4.1.post1 is available for download (Changelog). -
      • -
    • January 2024. scikit-learn 1.4.0 is available for download (Changelog). -
      • -
    • All releases: - What's new (Changelog) -
      • +
    • On-going development: scikit-learn 1.6 (Changelog).
      • +
    • May 2024. scikit-learn 1.5.0 is available for download (Changelog).
      • +
    • April 2024. scikit-learn 1.4.2 is available for download (Changelog).
      • +
    • February 2024. scikit-learn 1.4.1.post1 is available for download (Changelog).
      • +
    • January 2024. scikit-learn 1.4.0 is available for download (Changelog).
      • +
    • October 2023. scikit-learn 1.3.2 is available for download (Changelog).
      • +
    • September 2023. scikit-learn 1.3.1 is available for download (Changelog).
      • +
    • June 2023. scikit-learn 1.3.0 is available for download (Changelog).
      • +
    • All releases: What's new (Changelog).
    +

    Community

    - - Help us, donate! - Cite us! +

    + Help us, donate! + Cite us! +

    +

    Who uses scikit-learn?

    -
    -
    + +
    - -
    -

    - scikit-learn development and maintenance are financially supported by -

    - - - - - - - - - - +     +
    +

    + scikit-learn development and maintenance are financially supported by +

    +
    + + + + + + + + + +
    -     +
    +
    -{% endblock %} + +{% endblock footer %} + +{%- block scripts_end %} +{{ _webpack.body_post() }} +{%- endblock scripts_end %} diff --git a/doc/testimonials/testimonials.rst b/doc/testimonials/testimonials.rst index fbf53ae36ef2c..3c8c15b2e25ee 100644 --- a/doc/testimonials/testimonials.rst +++ b/doc/testimonials/testimonials.rst @@ -1,1151 +1,752 @@ -.. _testimonials: - -================================================================================ -Who is using scikit-learn? -================================================================================ +:orphan: -.. raw:: html - -
    +.. title:: Testimonials +.. _testimonials: -.. to add a testimonials, just XXX +========================== +Who is using scikit-learn? +========================== `J.P.Morgan `_ ------------------------------------------- - -.. raw:: html - -
    -
    - -Scikit-learn is an indispensable part of the Python machine learning -toolkit at JPMorgan. It is very widely used across all parts of the bank -for classification, predictive analytics, and very many other machine -learning tasks. Its straightforward API, its breadth of algorithms, and -the quality of its documentation combine to make scikit-learn -simultaneously very approachable and very powerful. +---------------------------------------- -.. raw:: html +.. div:: sk-text-image-grid-large - + .. div:: text-box -Stephen Simmons, VP, Athena Research, JPMorgan + Scikit-learn is an indispensable part of the Python machine learning + toolkit at JPMorgan. It is very widely used across all parts of the bank + for classification, predictive analytics, and very many other machine + learning tasks. Its straightforward API, its breadth of algorithms, and + the quality of its documentation combine to make scikit-learn + simultaneously very approachable and very powerful. -.. raw:: html + .. rst-class:: annotation - -
    -
    + Stephen Simmons, VP, Athena Research, JPMorgan -.. image:: images/jpmorgan.png - :width: 120pt - :align: center - :target: https://www.jpmorgan.com + .. div:: image-box -.. raw:: html + .. image:: images/jpmorgan.png + :target: https://www.jpmorgan.com -
    -
    `Spotify `_ ------------------------------------ -.. raw:: html - -
    -
    - -Scikit-learn provides a toolbox with solid implementations of a bunch of -state-of-the-art models and makes it easy to plug them into existing -applications. We've been using it quite a lot for music recommendations at -Spotify and I think it's the most well-designed ML package I've seen so -far. - -.. raw:: html +.. div:: sk-text-image-grid-large - + .. div:: text-box -Erik Bernhardsson, Engineering Manager Music Discovery & Machine Learning, Spotify + Scikit-learn provides a toolbox with solid implementations of a bunch of + state-of-the-art models and makes it easy to plug them into existing + applications. We've been using it quite a lot for music recommendations at + Spotify and I think it's the most well-designed ML package I've seen so far. -.. raw:: html + .. rst-class:: annotation - -
    -
    + Erik Bernhardsson, Engineering Manager Music Discovery & Machine Learning, Spotify -.. image:: images/spotify.png - :width: 120pt - :align: center - :target: https://www.spotify.com + .. div:: image-box -.. raw:: html + .. image:: images/spotify.png + :target: https://www.spotify.com -
    -
    `Inria `_ -------------------------------- -.. raw:: html - -
    -
    - -.. title Scikit-learn for efficient and easier machine learning research -.. Author: Gaël Varoquaux - - -At INRIA, we use scikit-learn to support leading-edge basic research in many -teams: `Parietal `_ for neuroimaging, `Lear -`_ for computer vision, `Visages -`_ for medical image analysis, `Privatics -`_ for security. The project is a fantastic -tool to address difficult applications of machine learning in an academic -environment as it is performant and versatile, but all easy-to-use and well -documented, which makes it well suited to grad students. +.. div:: sk-text-image-grid-large + .. div:: text-box -.. raw:: html + At INRIA, we use scikit-learn to support leading-edge basic research in many + teams: `Parietal `_ for neuroimaging, `Lear + `_ for computer vision, `Visages + `_ for medical image analysis, `Privatics + `_ for security. The project is a fantastic + tool to address difficult applications of machine learning in an academic + environment as it is performant and versatile, but all easy-to-use and well + documented, which makes it well suited to grad students. - + .. rst-class:: annotation -Gaël Varoquaux, research at Parietal + Gaël Varoquaux, research at Parietal -.. raw:: html + .. div:: image-box - -
    -
    - -.. image:: images/inria.png - :width: 120pt - :align: center - :target: https://www.inria.fr/ - -.. raw:: html - -
    -
    + .. image:: images/inria.png + :target: https://www.inria.fr/ `betaworks `_ ------------------------------------ -.. raw:: html - -
    -
    - -Betaworks is a NYC-based startup studio that builds new products, grows -companies, and invests in others. Over the past 8 years we've launched a -handful of social data analytics-driven services, such as Bitly, Chartbeat, -digg and Scale Model. Consistently the betaworks data science team uses -Scikit-learn for a variety of tasks. From exploratory analysis, to product -development, it is an essential part of our toolkit. Recent uses are included -in `digg's new video recommender system -`_, -and Poncho's `dynamic heuristic subspace clustering -`_. +.. div:: sk-text-image-grid-large -.. raw:: html + .. div:: text-box - + Betaworks is a NYC-based startup studio that builds new products, grows + companies, and invests in others. Over the past 8 years we've launched a + handful of social data analytics-driven services, such as Bitly, Chartbeat, + digg and Scale Model. Consistently the betaworks data science team uses + Scikit-learn for a variety of tasks. From exploratory analysis, to product + development, it is an essential part of our toolkit. Recent uses are included + in `digg's new video recommender system + `_, + and Poncho's `dynamic heuristic subspace clustering + `_. -Gilad Lotan, Chief Data Scientist + .. rst-class:: annotation -.. raw:: html + Gilad Lotan, Chief Data Scientist - -
    -
    + .. div:: image-box -.. image:: images/betaworks.png - :width: 120pt - :align: center - :target: https://betaworks.com - -.. raw:: html - -
    -
    + .. image:: images/betaworks.png + :target: https://betaworks.com `Hugging Face `_ ---------------------------------------- -.. raw:: html - -
    -
    - -At Hugging Face we're using NLP and probabilistic models to generate -conversational Artificial intelligences that are fun to chat with. Despite using -deep neural nets for `a few `_ -of our `NLP tasks `_, scikit-learn is still the bread-and-butter of -our daily machine learning routine. The ease of use and predictability of the -interface, as well as the straightforward mathematical explanations that are -here when you need them, is the killer feature. We use a variety of scikit-learn -models in production and they are also operationally very pleasant to work with. +.. div:: sk-text-image-grid-large -.. raw:: html + .. div:: text-box - + At Hugging Face we're using NLP and probabilistic models to generate + conversational Artificial intelligences that are fun to chat with. Despite using + deep neural nets for `a few `_ + of our `NLP tasks `_, scikit-learn is still the + bread-and-butter of our daily machine learning routine. The ease of use and + predictability of the interface, as well as the straightforward mathematical + explanations that are here when you need them, is the killer feature. We use a + variety of scikit-learn models in production and they are also operationally very + pleasant to work with. -Julien Chaumond, Chief Technology Officer + .. rst-class:: annotation -.. raw:: html + Julien Chaumond, Chief Technology Officer - -
    -
    + .. div:: image-box -.. image:: images/huggingface.png - :width: 120pt - :align: center - :target: https://huggingface.co - -.. raw:: html - -
    -
    + .. image:: images/huggingface.png + :target: https://huggingface.co `Evernote `_ ---------------------------------- -.. raw:: html - -
    -
    - -Building a classifier is typically an iterative process of exploring -the data, selecting the features (the attributes of the data believed -to be predictive in some way), training the models, and finally -evaluating them. For many of these tasks, we relied on the excellent -scikit-learn package for Python. +.. div:: sk-text-image-grid-large -`Read more `_ + .. div:: text-box -.. raw:: html + Building a classifier is typically an iterative process of exploring + the data, selecting the features (the attributes of the data believed + to be predictive in some way), training the models, and finally + evaluating them. For many of these tasks, we relied on the excellent + scikit-learn package for Python. - + `Read more `_ -Mark Ayzenshtat, VP, Augmented Intelligence + .. rst-class:: annotation -.. raw:: html + Mark Ayzenshtat, VP, Augmented Intelligence - -
    -
    + .. div:: image-box -.. image:: images/evernote.png - :width: 120pt - :align: center - :target: https://evernote.com + .. image:: images/evernote.png + :target: https://evernote.com -.. raw:: html - -
    -
    `Télécom ParisTech `_ -------------------------------------------------------- -.. raw:: html - -
    -
    - -At Telecom ParisTech, scikit-learn is used for hands-on sessions and home -assignments in introductory and advanced machine learning courses. The classes -are for undergrads and masters students. The great benefit of scikit-learn is -its fast learning curve that allows students to quickly start working on -interesting and motivating problems. - -.. raw:: html +.. div:: sk-text-image-grid-large - + .. div:: text-box -Alexandre Gramfort, Assistant Professor + At Telecom ParisTech, scikit-learn is used for hands-on sessions and home + assignments in introductory and advanced machine learning courses. The classes + are for undergrads and masters students. The great benefit of scikit-learn is + its fast learning curve that allows students to quickly start working on + interesting and motivating problems. -.. raw:: html + .. rst-class:: annotation - -
    -
    + Alexandre Gramfort, Assistant Professor -.. image:: images/telecomparistech.jpg - :width: 120pt - :align: center - :target: https://www.telecom-paristech.fr/ + .. div:: image-box -.. raw:: html - -
    -
    + .. image:: images/telecomparistech.jpg + :target: https://www.telecom-paristech.fr/ `Booking.com `_ ------------------------------------------ -.. raw:: html - -
    -
    - -At Booking.com, we use machine learning algorithms for many different -applications, such as recommending hotels and destinations to our customers, -detecting fraudulent reservations, or scheduling our customer service agents. -Scikit-learn is one of the tools we use when implementing standard algorithms -for prediction tasks. Its API and documentations are excellent and make it easy -to use. The scikit-learn developers do a great job of incorporating state of -the art implementations and new algorithms into the package. Thus, scikit-learn -provides convenient access to a wide spectrum of algorithms, and allows us to -readily find the right tool for the right job. - +---------------------------------------- -.. raw:: html +.. div:: sk-text-image-grid-large - + .. div:: text-box -Melanie Mueller, Data Scientist + At Booking.com, we use machine learning algorithms for many different + applications, such as recommending hotels and destinations to our customers, + detecting fraudulent reservations, or scheduling our customer service agents. + Scikit-learn is one of the tools we use when implementing standard algorithms + for prediction tasks. Its API and documentations are excellent and make it easy + to use. The scikit-learn developers do a great job of incorporating state of + the art implementations and new algorithms into the package. Thus, scikit-learn + provides convenient access to a wide spectrum of algorithms, and allows us to + readily find the right tool for the right job. -.. raw:: html + .. rst-class:: annotation - -
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    + Melanie Mueller, Data Scientist -.. image:: images/booking.png - :width: 120pt - :align: center - :target: https://www.booking.com + .. div:: image-box -.. raw:: html + .. image:: images/booking.png + :target: https://www.booking.com -
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    `AWeber `_ ------------------------------------------- - -.. raw:: html - -
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    - -The scikit-learn toolkit is indispensable for the Data Analysis and Management -team at AWeber. It allows us to do AWesome stuff we would not otherwise have -the time or resources to accomplish. The documentation is excellent, allowing -new engineers to quickly evaluate and apply many different algorithms to our -data. The text feature extraction utilities are useful when working with the -large volume of email content we have at AWeber. The RandomizedPCA -implementation, along with Pipelining and FeatureUnions, allows us to develop -complex machine learning algorithms efficiently and reliably. +----------------------------------- -Anyone interested in learning more about how AWeber deploys scikit-learn in a -production environment should check out talks from PyData Boston by AWeber's -Michael Becker available at https://github.com/mdbecker/pydata_2013 +.. div:: sk-text-image-grid-large -.. raw:: html + .. div:: text-box - + The scikit-learn toolkit is indispensable for the Data Analysis and Management + team at AWeber. It allows us to do AWesome stuff we would not otherwise have + the time or resources to accomplish. The documentation is excellent, allowing + new engineers to quickly evaluate and apply many different algorithms to our + data. The text feature extraction utilities are useful when working with the + large volume of email content we have at AWeber. The RandomizedPCA + implementation, along with Pipelining and FeatureUnions, allows us to develop + complex machine learning algorithms efficiently and reliably. -Michael Becker, Software Engineer, Data Analysis and Management Ninjas + Anyone interested in learning more about how AWeber deploys scikit-learn in a + production environment should check out talks from PyData Boston by AWeber's + Michael Becker available at https://github.com/mdbecker/pydata_2013. -.. raw:: html + .. rst-class:: annotation - -
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    + Michael Becker, Software Engineer, Data Analysis and Management Ninjas -.. image:: images/aweber.png - :width: 120pt - :align: center - :target: https://www.aweber.com/ + .. div:: image-box -.. raw:: html + .. image:: images/aweber.png + :target: https://www.aweber.com -
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    `Yhat `_ ------------------------------------------- - -.. raw:: html +------------------------------ -
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    +.. div:: sk-text-image-grid-large -The combination of consistent APIs, thorough documentation, and top notch -implementation make scikit-learn our favorite machine learning package in -Python. scikit-learn makes doing advanced analysis in Python accessible to -anyone. At Yhat, we make it easy to integrate these models into your production -applications. Thus eliminating the unnecessary dev time encountered -productionizing analytical work. + .. div:: text-box + The combination of consistent APIs, thorough documentation, and top notch + implementation make scikit-learn our favorite machine learning package in + Python. scikit-learn makes doing advanced analysis in Python accessible to + anyone. At Yhat, we make it easy to integrate these models into your production + applications. Thus eliminating the unnecessary dev time encountered + productionizing analytical work. -.. raw:: html + .. rst-class:: annotation - + Greg Lamp, Co-founder -Greg Lamp, Co-founder Yhat + .. div:: image-box -.. raw:: html + .. image:: images/yhat.png + :target: https://www.yhat.com - -
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    - -.. image:: images/yhat.png - :width: 120pt - :align: center - :target: https://www.yhat.com - -.. raw:: html - -
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    `Rangespan `_ ----------------------------------------- +--------------------------------------- -.. raw:: html +.. div:: sk-text-image-grid-large -
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    + .. div:: text-box -The Python scikit-learn toolkit is a core tool in the data science -group at Rangespan. Its large collection of well documented models and -algorithms allow our team of data scientists to prototype fast and -quickly iterate to find the right solution to our learning problems. -We find that scikit-learn is not only the right tool for prototyping, -but its careful and well tested implementation give us the confidence -to run scikit-learn models in production. + The Python scikit-learn toolkit is a core tool in the data science + group at Rangespan. Its large collection of well documented models and + algorithms allow our team of data scientists to prototype fast and + quickly iterate to find the right solution to our learning problems. + We find that scikit-learn is not only the right tool for prototyping, + but its careful and well tested implementation give us the confidence + to run scikit-learn models in production. -.. raw:: html + .. rst-class:: annotation - + Jurgen Van Gael, Data Science Director -Jurgen Van Gael, Data Science Director at Rangespan Ltd + .. div:: image-box -.. raw:: html + .. image:: images/rangespan.png + :target: http://www.rangespan.com - -
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    `Birchbox `_ ------------------------------------------- - -.. raw:: html - -
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    - -At Birchbox, we face a range of machine learning problems typical to -E-commerce: product recommendation, user clustering, inventory prediction, -trends detection, etc. Scikit-learn lets us experiment with many models, -especially in the exploration phase of a new project: the data can be passed -around in a consistent way; models are easy to save and reuse; updates keep us -informed of new developments from the pattern discovery research community. -Scikit-learn is an important tool for our team, built the right way in the -right language. - -.. raw:: html +-------------------------------------- - +.. div:: sk-text-image-grid-large -Thierry Bertin-Mahieux, Birchbox, Data Scientist + .. div:: text-box -.. raw:: html + At Birchbox, we face a range of machine learning problems typical to + E-commerce: product recommendation, user clustering, inventory prediction, + trends detection, etc. Scikit-learn lets us experiment with many models, + especially in the exploration phase of a new project: the data can be passed + around in a consistent way; models are easy to save and reuse; updates keep us + informed of new developments from the pattern discovery research community. + Scikit-learn is an important tool for our team, built the right way in the + right language. - -
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    + .. rst-class:: annotation -.. image:: images/birchbox.jpg - :width: 120pt - :align: center - :target: https://www.birchbox.com + Thierry Bertin-Mahieux, Data Scientist -.. raw:: html + .. div:: image-box -
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    + .. image:: images/birchbox.jpg + :target: https://www.birchbox.com `Bestofmedia Group `_ --------------------------------------------------- +------------------------------------------------- -.. raw:: html +.. div:: sk-text-image-grid-large -
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    + .. div:: text-box -Scikit-learn is our #1 toolkit for all things machine learning -at Bestofmedia. We use it for a variety of tasks (e.g. spam fighting, -ad click prediction, various ranking models) thanks to the varied, -state-of-the-art algorithm implementations packaged into it. -In the lab it accelerates prototyping of complex pipelines. In -production I can say it has proven to be robust and efficient enough -to be deployed for business critical components. + Scikit-learn is our #1 toolkit for all things machine learning + at Bestofmedia. We use it for a variety of tasks (e.g. spam fighting, + ad click prediction, various ranking models) thanks to the varied, + state-of-the-art algorithm implementations packaged into it. + In the lab it accelerates prototyping of complex pipelines. In + production I can say it has proven to be robust and efficient enough + to be deployed for business critical components. -.. raw:: html + .. rst-class:: annotation - + Eustache Diemert, Lead Scientist -Eustache Diemert, Lead Scientist Bestofmedia Group + .. div:: image-box -.. raw:: html + .. image:: images/bestofmedia-logo.png + :target: http://www.bestofmedia.com - -
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    `Change.org `_ -------------------------------------- -.. raw:: html - -
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    +.. div:: sk-text-image-grid-large -At change.org we automate the use of scikit-learn's RandomForestClassifier -in our production systems to drive email targeting that reaches millions -of users across the world each week. In the lab, scikit-learn's ease-of-use, -performance, and overall variety of algorithms implemented has proved invaluable -in giving us a single reliable source to turn to for our machine-learning needs. + .. div:: text-box -.. raw:: html + At change.org we automate the use of scikit-learn's RandomForestClassifier + in our production systems to drive email targeting that reaches millions + of users across the world each week. In the lab, scikit-learn's ease-of-use, + performance, and overall variety of algorithms implemented has proved invaluable + in giving us a single reliable source to turn to for our machine-learning needs. - + .. rst-class:: annotation -Vijay Ramesh, Software Engineer in Data/science at Change.org + Vijay Ramesh, Software Engineer in Data/science at Change.org -.. raw:: html + .. div:: image-box - -
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    + .. image:: images/change-logo.png + :target: https://www.change.org -.. image:: images/change-logo.png - :width: 120pt - :align: center - :target: https://www.change.org - -.. raw:: html - -
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    `PHIMECA Engineering `_ ----------------------------------------------------------- - -.. raw:: html - -
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    - -At PHIMECA Engineering, we use scikit-learn estimators as surrogates for -expensive-to-evaluate numerical models (mostly but not exclusively -finite-element mechanical models) for speeding up the intensive post-processing -operations involved in our simulation-based decision making framework. -Scikit-learn's fit/predict API together with its efficient cross-validation -tools considerably eases the task of selecting the best-fit estimator. We are -also using scikit-learn for illustrating concepts in our training sessions. -Trainees are always impressed by the ease-of-use of scikit-learn despite the -apparent theoretical complexity of machine learning. +--------------------------------------------------------- -.. raw:: html +.. div:: sk-text-image-grid-large - + .. div:: text-box -Vincent Dubourg, PHIMECA Engineering, PhD Engineer + At PHIMECA Engineering, we use scikit-learn estimators as surrogates for + expensive-to-evaluate numerical models (mostly but not exclusively + finite-element mechanical models) for speeding up the intensive post-processing + operations involved in our simulation-based decision making framework. + Scikit-learn's fit/predict API together with its efficient cross-validation + tools considerably eases the task of selecting the best-fit estimator. We are + also using scikit-learn for illustrating concepts in our training sessions. + Trainees are always impressed by the ease-of-use of scikit-learn despite the + apparent theoretical complexity of machine learning. -.. raw:: html + .. rst-class:: annotation - -
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    + Vincent Dubourg, PHIMECA Engineering, PhD Engineer -.. image:: images/phimeca.png - :width: 120pt - :align: center - :target: https://www.phimeca.com/?lang=en + .. div:: image-box -.. raw:: html + .. image:: images/phimeca.png + :target: https://www.phimeca.com/?lang=en -
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    `HowAboutWe `_ ----------------------------------------------------------- - -.. raw:: html - -
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    - -At HowAboutWe, scikit-learn lets us implement a wide array of machine learning -techniques in analysis and in production, despite having a small team. We use -scikit-learn's classification algorithms to predict user behavior, enabling us -to (for example) estimate the value of leads from a given traffic source early -in the lead's tenure on our site. Also, our users' profiles consist of -primarily unstructured data (answers to open-ended questions), so we use -scikit-learn's feature extraction and dimensionality reduction tools to -translate these unstructured data into inputs for our matchmaking system. - -.. raw:: html +------------------------------------------ - +.. div:: sk-text-image-grid-large -Daniel Weitzenfeld, Senior Data Scientist at HowAboutWe + .. div:: text-box -.. raw:: html + At HowAboutWe, scikit-learn lets us implement a wide array of machine learning + techniques in analysis and in production, despite having a small team. We use + scikit-learn's classification algorithms to predict user behavior, enabling us + to (for example) estimate the value of leads from a given traffic source early + in the lead's tenure on our site. Also, our users' profiles consist of + primarily unstructured data (answers to open-ended questions), so we use + scikit-learn's feature extraction and dimensionality reduction tools to + translate these unstructured data into inputs for our matchmaking system. - -
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    + .. rst-class:: annotation -.. image:: images/howaboutwe.png - :width: 120pt - :align: center - :target: http://www.howaboutwe.com/ + Daniel Weitzenfeld, Senior Data Scientist at HowAboutWe -.. raw:: html + .. div:: image-box -
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    + .. image:: images/howaboutwe.png + :target: http://www.howaboutwe.com/ `PeerIndex `_ ------------------------------------------------------------------ -.. raw:: html - -
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    - -At PeerIndex we use scientific methodology to build the Influence Graph - a -unique dataset that allows us to identify who's really influential and in which -context. To do this, we have to tackle a range of machine learning and -predictive modeling problems. Scikit-learn has emerged as our primary tool for -developing prototypes and making quick progress. From predicting missing data -and classifying tweets to clustering communities of social media users, scikit- -learn proved useful in a variety of applications. Its very intuitive interface -and excellent compatibility with other python tools makes it and indispensable -tool in our daily research efforts. +.. div:: sk-text-image-grid-large -.. raw:: html + .. div:: text-box - + At PeerIndex we use scientific methodology to build the Influence Graph - a + unique dataset that allows us to identify who's really influential and in which + context. To do this, we have to tackle a range of machine learning and + predictive modeling problems. Scikit-learn has emerged as our primary tool for + developing prototypes and making quick progress. From predicting missing data + and classifying tweets to clustering communities of social media users, scikit- + learn proved useful in a variety of applications. Its very intuitive interface + and excellent compatibility with other python tools makes it and indispensable + tool in our daily research efforts. -Ferenc Huszar - Senior Data Scientist at Peerindex + .. rst-class:: annotation -.. raw:: html + Ferenc Huszar, Senior Data Scientist at Peerindex - -
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    + .. div:: image-box -.. image:: images/peerindex.png - :width: 120pt - :align: center - :target: https://www.brandwatch.com/peerindex-and-brandwatch - -.. raw:: html - -
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    + .. image:: images/peerindex.png + :target: https://www.brandwatch.com/peerindex-and-brandwatch `DataRobot `_ ---------------------------------------- -.. raw:: html - -
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    - -DataRobot is building next generation predictive analytics software to make data scientists more productive, and scikit-learn is an integral part of our system. The variety of machine learning techniques in combination with the solid implementations that scikit-learn offers makes it a one-stop-shopping library for machine learning in Python. Moreover, its consistent API, well-tested code and permissive licensing allow us to use it in a production environment. Scikit-learn has literally saved us years of work we would have had to do ourselves to bring our product to market. - -.. raw:: html +.. div:: sk-text-image-grid-large - + .. div:: text-box -Jeremy Achin, CEO & Co-founder DataRobot Inc. + DataRobot is building next generation predictive analytics software to make data + scientists more productive, and scikit-learn is an integral part of our system. The + variety of machine learning techniques in combination with the solid implementations + that scikit-learn offers makes it a one-stop-shopping library for machine learning + in Python. Moreover, its consistent API, well-tested code and permissive licensing + allow us to use it in a production environment. Scikit-learn has literally saved us + years of work we would have had to do ourselves to bring our product to market. -.. raw:: html + .. rst-class:: annotation - -
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    + Jeremy Achin, CEO & Co-founder DataRobot Inc. -.. image:: images/datarobot.png - :width: 120pt - :align: center - :target: https://www.datarobot.com + .. div:: image-box -.. raw:: html - -
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    + .. image:: images/datarobot.png + :target: https://www.datarobot.com `OkCupid `_ --------------------------------------- - -.. raw:: html - -
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    +------------------------------------- -We're using scikit-learn at OkCupid to evaluate and improve our matchmaking -system. The range of features it has, especially preprocessing utilities, means -we can use it for a wide variety of projects, and it's performant enough to -handle the volume of data that we need to sort through. The documentation is -really thorough, as well, which makes the library quite easy to use. +.. div:: sk-text-image-grid-large -.. raw:: html + .. div:: text-box - + We're using scikit-learn at OkCupid to evaluate and improve our matchmaking + system. The range of features it has, especially preprocessing utilities, means + we can use it for a wide variety of projects, and it's performant enough to + handle the volume of data that we need to sort through. The documentation is + really thorough, as well, which makes the library quite easy to use. -David Koh - Senior Data Scientist at OkCupid + .. rst-class:: annotation -.. raw:: html + David Koh - Senior Data Scientist at OkCupid - -
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    + .. div:: image-box -.. image:: images/okcupid.png - :width: 120pt - :align: center - :target: https://www.okcupid.com - -.. raw:: html - -
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    + .. image:: images/okcupid.png + :target: https://www.okcupid.com `Lovely `_ ----------------------------------- -.. raw:: html - -
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    - -At Lovely, we strive to deliver the best apartment marketplace, with respect to -our users and our listings. From understanding user behavior, improving data -quality, and detecting fraud, scikit-learn is a regular tool for gathering -insights, predictive modeling and improving our product. The easy-to-read -documentation and intuitive architecture of the API makes machine learning both -explorable and accessible to a wide range of python developers. I'm constantly -recommending that more developers and scientists try scikit-learn. - -.. raw:: html +.. div:: sk-text-image-grid-large - + .. div:: text-box -Simon Frid - Data Scientist, Lead at Lovely + At Lovely, we strive to deliver the best apartment marketplace, with respect to + our users and our listings. From understanding user behavior, improving data + quality, and detecting fraud, scikit-learn is a regular tool for gathering + insights, predictive modeling and improving our product. The easy-to-read + documentation and intuitive architecture of the API makes machine learning both + explorable and accessible to a wide range of python developers. I'm constantly + recommending that more developers and scientists try scikit-learn. -.. raw:: html + .. rst-class:: annotation - -
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    + Simon Frid - Data Scientist, Lead at Lovely -.. image:: images/lovely.png - :width: 120pt - :align: center - :target: https://livelovely.com - -.. raw:: html - -
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    + .. div:: image-box + .. image:: images/lovely.png + :target: https://livelovely.com `Data Publica `_ ---------------------------------------------- -.. raw:: html - -
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    - -Data Publica builds a new predictive sales tool for commercial and marketing teams called C-Radar. -We extensively use scikit-learn to build segmentations of customers through clustering, and to predict future customers based on past partnerships success or failure. -We also categorize companies using their website communication thanks to scikit-learn and its machine learning algorithm implementations. -Eventually, machine learning makes it possible to detect weak signals that traditional tools cannot see. -All these complex tasks are performed in an easy and straightforward way thanks to the great quality of the scikit-learn framework. +.. div:: sk-text-image-grid-large -.. raw:: html + .. div:: text-box - + Data Publica builds a new predictive sales tool for commercial and marketing teams + called C-Radar. We extensively use scikit-learn to build segmentations of customers + through clustering, and to predict future customers based on past partnerships + success or failure. We also categorize companies using their website communication + thanks to scikit-learn and its machine learning algorithm implementations. + Eventually, machine learning makes it possible to detect weak signals that + traditional tools cannot see. All these complex tasks are performed in an easy and + straightforward way thanks to the great quality of the scikit-learn framework. -Guillaume Lebourgeois & Samuel Charron - Data Scientists at Data Publica + .. rst-class:: annotation -.. raw:: html + Guillaume Lebourgeois & Samuel Charron - Data Scientists at Data Publica - -
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    - -.. image:: images/datapublica.png - :width: 120pt - :align: center - :target: http://www.data-publica.com/ - -.. raw:: html - -
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    + .. div:: image-box + .. image:: images/datapublica.png + :target: http://www.data-publica.com/ `Machinalis `_ ------------------------------------------- -.. raw:: html - -
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    +.. div:: sk-text-image-grid-large -Scikit-learn is the cornerstone of all the machine learning projects carried at -Machinalis. It has a consistent API, a wide selection of algorithms and lots -of auxiliary tools to deal with the boilerplate. -We have used it in production environments on a variety of projects -including click-through rate prediction, `information extraction `_, -and even counting sheep! + .. div:: text-box -In fact, we use it so much that we've started to freeze our common use cases -into Python packages, some of them open-sourced, like -`FeatureForge `_ . -Scikit-learn in one word: Awesome. + Scikit-learn is the cornerstone of all the machine learning projects carried at + Machinalis. It has a consistent API, a wide selection of algorithms and lots of + auxiliary tools to deal with the boilerplate. We have used it in production + environments on a variety of projects including click-through rate prediction, + `information extraction `_, and even counting + sheep! -.. raw:: html + In fact, we use it so much that we've started to freeze our common use cases + into Python packages, some of them open-sourced, like `FeatureForge + `_. Scikit-learn in one word: Awesome. - + .. rst-class:: annotation -Rafael Carrascosa, Lead developer + Rafael Carrascosa, Lead developer -.. raw:: html + .. div:: image-box - -
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    - -.. image:: images/machinalis.png - :width: 120pt - :align: center - :target: https://www.machinalis.com/ - -.. raw:: html - -
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    + .. image:: images/machinalis.png + :target: https://www.machinalis.com/ `solido `_ ----------------------------------------- -.. raw:: html - -
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    +.. div:: sk-text-image-grid-large -Scikit-learn is helping to drive Moore's Law, via Solido. Solido creates -computer-aided design tools used by the majority of top-20 semiconductor -companies and fabs, to design the bleeding-edge chips inside smartphones, -automobiles, and more. Scikit-learn helps to power Solido's algorithms for -rare-event estimation, worst-case verification, optimization, and more. At -Solido, we are particularly fond of scikit-learn's libraries for Gaussian -Process models, large-scale regularized linear regression, and classification. -Scikit-learn has increased our productivity, because for many ML problems we no -longer need to “roll our own” code. `This PyData 2014 talk `_ has details. + .. div:: text-box + Scikit-learn is helping to drive Moore's Law, via Solido. Solido creates + computer-aided design tools used by the majority of top-20 semiconductor + companies and fabs, to design the bleeding-edge chips inside smartphones, + automobiles, and more. Scikit-learn helps to power Solido's algorithms for + rare-event estimation, worst-case verification, optimization, and more. At + Solido, we are particularly fond of scikit-learn's libraries for Gaussian + Process models, large-scale regularized linear regression, and classification. + Scikit-learn has increased our productivity, because for many ML problems we no + longer need to “roll our own” code. `This PyData 2014 talk + `_ has details. -.. raw:: html + .. rst-class:: annotation - + Trent McConaghy, founder, Solido Design Automation Inc. -Trent McConaghy, founder, Solido Design Automation Inc. - -.. raw:: html - - -
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    - -.. image:: images/solido_logo.png - :width: 120pt - :align: center - :target: https://www.solidodesign.com/ - -.. raw:: html - -
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    + .. div:: image-box + .. image:: images/solido_logo.png + :target: https://www.solidodesign.com/ `INFONEA `_ ------------------------------------------ - -.. raw:: html - -
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    - -We employ scikit-learn for rapid prototyping and custom-made Data Science -solutions within our in-memory based Business Intelligence Software -INFONEA®. As a well-documented and comprehensive collection of -state-of-the-art algorithms and pipelining methods, scikit-learn enables -us to provide flexible and scalable scientific analysis solutions. Thus, -scikit-learn is immensely valuable in realizing a powerful integration of -Data Science technology within self-service business analytics. +--------------------------------------- -.. raw:: html +.. div:: sk-text-image-grid-large - + .. div:: text-box -Thorsten Kranz, Data Scientist, Coma Soft AG. + We employ scikit-learn for rapid prototyping and custom-made Data Science + solutions within our in-memory based Business Intelligence Software + INFONEA®. As a well-documented and comprehensive collection of + state-of-the-art algorithms and pipelining methods, scikit-learn enables + us to provide flexible and scalable scientific analysis solutions. Thus, + scikit-learn is immensely valuable in realizing a powerful integration of + Data Science technology within self-service business analytics. -.. raw:: html + .. rst-class:: annotation - -
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    + Thorsten Kranz, Data Scientist, Coma Soft AG. -.. image:: images/infonea.jpg - :width: 120pt - :align: center - :target: http://www.infonea.com/en/ + .. div:: image-box -.. raw:: html - -
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    + .. image:: images/infonea.jpg + :target: http://www.infonea.com/en/ `Dataiku `_ ------------------------------------------ - -.. raw:: html - -
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    +------------------------------------- -Our software, Data Science Studio (DSS), enables users to create data services -that combine `ETL `_ with -Machine Learning. Our Machine Learning module integrates -many scikit-learn algorithms. The scikit-learn library is a perfect integration -with DSS because it offers algorithms for virtually all business cases. Our goal -is to offer a transparent and flexible tool that makes it easier to optimize -time consuming aspects of building a data service, preparing data, and training -machine learning algorithms on all types of data. +.. div:: sk-text-image-grid-large + .. div:: text-box -.. raw:: html + Our software, Data Science Studio (DSS), enables users to create data services + that combine `ETL `_ with + Machine Learning. Our Machine Learning module integrates + many scikit-learn algorithms. The scikit-learn library is a perfect integration + with DSS because it offers algorithms for virtually all business cases. Our goal + is to offer a transparent and flexible tool that makes it easier to optimize + time consuming aspects of building a data service, preparing data, and training + machine learning algorithms on all types of data. - + .. rst-class:: annotation -Florian Douetteau, CEO, Dataiku + Florian Douetteau, CEO, Dataiku -.. raw:: html + .. div:: image-box - -
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    + .. image:: images/dataiku_logo.png + :target: https://www.dataiku.com/ -.. image:: images/dataiku_logo.png - :width: 120pt - :align: center - :target: https://www.dataiku.com/ - -.. raw:: html - -
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    `Otto Group `_ ------------------------------------------ - -.. raw:: html - -
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    - -Here at Otto Group, one of global Big Five B2C online retailers, we are using -scikit-learn in all aspects of our daily work from data exploration to development -of machine learning application to the productive deployment of those services. -It helps us to tackle machine learning problems ranging from e-commerce to logistics. -It consistent APIs enabled us to build the `Palladium REST-API framework -`_ around it and continuously deliver -scikit-learn based services. - +-------------------------------------- -.. raw:: html +.. div:: sk-text-image-grid-large - + .. div:: text-box -Christian Rammig, Head of Data Science, Otto Group + Here at Otto Group, one of global Big Five B2C online retailers, we are using + scikit-learn in all aspects of our daily work from data exploration to development + of machine learning application to the productive deployment of those services. + It helps us to tackle machine learning problems ranging from e-commerce to logistics. + It consistent APIs enabled us to build the `Palladium REST-API framework + `_ around it and continuously deliver + scikit-learn based services. -.. raw:: html + .. rst-class:: annotation - -
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    + Christian Rammig, Head of Data Science, Otto Group -.. image:: images/ottogroup_logo.png - :width: 120pt - :align: center - :target: https://ottogroup.com + .. div:: image-box -.. raw:: html + .. image:: images/ottogroup_logo.png + :target: https://ottogroup.com -
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    `Zopa `_ ------------------------------------------ +--------------------------- -.. raw:: html +.. div:: sk-text-image-grid-large -
    -
    + .. div:: text-box -At Zopa, the first ever Peer-to-Peer lending platform, we extensively use scikit-learn -to run the business and optimize our users' experience. It powers our -Machine Learning models involved in credit risk, fraud risk, marketing, and pricing, -and has been used for originating at least 1 billion GBP worth of Zopa loans. -It is very well documented, powerful, and simple to use. We are grateful for the -capabilities it has provided, and for allowing us to deliver on our mission of making -money simple and fair. + At Zopa, the first ever Peer-to-Peer lending platform, we extensively use + scikit-learn to run the business and optimize our users' experience. It powers our + Machine Learning models involved in credit risk, fraud risk, marketing, and pricing, + and has been used for originating at least 1 billion GBP worth of Zopa loans. It is + very well documented, powerful, and simple to use. We are grateful for the + capabilities it has provided, and for allowing us to deliver on our mission of + making money simple and fair. -.. raw:: html + .. rst-class:: annotation - + Vlasios Vasileiou, Head of Data Science, Zopa -Vlasios Vasileiou, Head of Data Science, Zopa + .. div:: image-box -.. raw:: html + .. image:: images/zopa.png + :target: https://zopa.com - -
    -
    - -.. image:: images/zopa.png - :width: 120pt - :align: center - :target: https://zopa.com - -.. raw:: html - -
    -
    `MARS `_ --------------------------------------- +------------------------------------- -.. raw:: html +.. div:: sk-text-image-grid-large -
    -
    + .. div:: text-box -Scikit-Learn is integral to the Machine Learning Ecosystem at Mars. Whether -we're designing better recipes for petfood or closely analysing our cocoa -supply chain, Scikit-Learn is used as a tool for rapidly prototyping ideas -and taking them to production. This allows us to better understand and meet -the needs of our consumers worldwide. Scikit-Learn's feature-rich toolset is -easy to use and equips our associates with the capabilities they need to -solve the business challenges they face every day. + Scikit-Learn is integral to the Machine Learning Ecosystem at Mars. Whether + we're designing better recipes for petfood or closely analysing our cocoa + supply chain, Scikit-Learn is used as a tool for rapidly prototyping ideas + and taking them to production. This allows us to better understand and meet + the needs of our consumers worldwide. Scikit-Learn's feature-rich toolset is + easy to use and equips our associates with the capabilities they need to + solve the business challenges they face every day. -.. raw:: html + .. rst-class:: annotation - + Michael Fitzke, Next Generation Technologies Sr Leader, Mars Inc. -Michael Fitzke Next Generation Technologies Sr Leader, Mars Inc. + .. div:: image-box -.. raw:: html - - -
    -
    - -.. image:: images/mars.png - :width: 120pt - :align: center - :target: https://www.mars.com/global - -.. raw:: html - -
    -
    + .. image:: images/mars.png + :target: https://www.mars.com/global `BNP Paribas Cardif `_ --------------------------------------------------------- -.. raw:: html - -
    -
    - -BNP Paribas Cardif uses scikit-learn for several of its machine learning models -in production. Our internal community of developers and data scientists has -been using scikit-learn since 2015, for several reasons: the quality of the -developments, documentation and contribution governance, and the sheer size of -the contributing community. We even explicitly mention the use of -scikit-learn's pipelines in our internal model risk governance as one of our -good practices to decrease operational risks and overfitting risk. As a way to -support open source software development and in particular scikit-learn -project, we decided to participate to scikit-learn's consortium at La Fondation -Inria since its creation in 2018. - -.. raw:: html - - +.. div:: sk-text-image-grid-large -Sébastien Conort, Chief Data Scientist, BNP Paribas Cardif + .. div:: text-box -.. raw:: html + BNP Paribas Cardif uses scikit-learn for several of its machine learning models + in production. Our internal community of developers and data scientists has + been using scikit-learn since 2015, for several reasons: the quality of the + developments, documentation and contribution governance, and the sheer size of + the contributing community. We even explicitly mention the use of + scikit-learn's pipelines in our internal model risk governance as one of our + good practices to decrease operational risks and overfitting risk. As a way to + support open source software development and in particular scikit-learn + project, we decided to participate to scikit-learn's consortium at La Fondation + Inria since its creation in 2018. - -
    -
    + .. rst-class:: annotation -.. image:: images/bnp_paribas_cardif.png - :width: 120pt - :align: center - :target: https://www.bnpparibascardif.com/ + Sébastien Conort, Chief Data Scientist, BNP Paribas Cardif -.. raw:: html + .. div:: image-box -
    -
    + .. image:: images/bnp_paribas_cardif.png + :target: https://www.bnpparibascardif.com/ diff --git a/doc/themes/scikit-learn-modern/javascript.html b/doc/themes/scikit-learn-modern/javascript.html deleted file mode 100644 index be4cf26073441..0000000000000 --- a/doc/themes/scikit-learn-modern/javascript.html +++ /dev/null @@ -1,56 +0,0 @@ -{% if theme_legacy_google_analytics|tobool %} - - -{% endif %} - -{% if theme_analytics|tobool %} - -{% endif %} - - - - - -{%- if pagename != 'index' and pagename != 'documentation' %} - {% if theme_mathjax_path %} - - {% endif %} -{%- endif %} diff --git a/doc/themes/scikit-learn-modern/layout.html b/doc/themes/scikit-learn-modern/layout.html deleted file mode 100644 index c95184d42c671..0000000000000 --- a/doc/themes/scikit-learn-modern/layout.html +++ /dev/null @@ -1,150 +0,0 @@ -{# TEMPLATE VAR SETTINGS #} -{%- set url_root = pathto('', 1) %} -{%- if url_root == '#' %}{% set url_root = '' %}{% endif %} -{%- if not embedded and docstitle %} - {%- set titlesuffix = " — "|safe + docstitle|e %} -{%- else %} - {%- set titlesuffix = "" %} -{%- endif %} -{%- set lang_attr = 'en' %} - - - - - - - - {{ metatags }} - - - {% block htmltitle %} - {{ title|striptags|e }}{{ titlesuffix }} - {% endblock %} - - - {% if favicon_url %} - - {% endif %} - - - {%- for css in css_files %} - {%- if css|attr("rel") %} - - {%- else %} - - {%- endif %} - {%- endfor %} - - - - -{%- block extrahead %} {% endblock %} - - -{% include "nav.html" %} -{%- block content %} -
    - - -
    -
    -
    - {%- if prev %} - Prev - {%- else %} - Prev - {%- endif %} - {%- if parents -%} - Up - {%- else %} - Up - {%- endif %} - {%- if next %} - Next - {%- else %} - Next - {%- endif %} -
    - {%- if pagename != "install" %} - - {%- endif %} - - {%- if meta and meta['parenttoc']|tobool %} -
    - {% set nav = get_nav_object(maxdepth=3, collapse=True, numbered=True) %} -
      - {% for main_nav_item in nav %} - {% if main_nav_item.active %} -
    • - {{ main_nav_item.title }} -
      • -
        - {% for nav_item in main_nav_item.children %} -
      • - {{ nav_item.title }} - {% if nav_item.children %} - - {% endif %} -
        • - {% endfor %} -
      - {% endif %} - {% endfor %} -
    -
    - {%- elif meta and meta['globalsidebartoc']|tobool %} -
    - {{ toctree(maxdepth=2, titles_only=True) }} -
    - {%- else %} -
    - {{ toc }} -
    - {%- endif %} -
    -
    -
    -
    - {% block body %}{% endblock %} -
    -
    -
    - {%- if pagename != 'index' %} - {%- if show_copyright %} - {%- if hasdoc('copyright') %} - {% trans path=pathto('copyright'), copyright=copyright|e %}© {{ copyright }}.{% endtrans %} - {%- else %} - {% trans copyright=copyright|e %}© {{ copyright }}.{% endtrans %} - {%- endif %} - {%- endif %} - {%- if last_updated %} - {% trans last_updated=last_updated|e %}Last updated on {{ last_updated }}.{% endtrans %} - {%- endif %} - {%- if show_source and has_source and sourcename %} - {{ _('Show this page source') }} - {%- endif %} - {%- endif %} -
    -
    -
    -
    -{%- endblock %} - -{% include "javascript.html" %} - - diff --git a/doc/themes/scikit-learn-modern/nav.html b/doc/themes/scikit-learn-modern/nav.html deleted file mode 100644 index 14d82e2e46e95..0000000000000 --- a/doc/themes/scikit-learn-modern/nav.html +++ /dev/null @@ -1,102 +0,0 @@ -{%- if pagename != 'index' and pagename != 'documentation' %} - {%- set nav_bar_class = "sk-docs-navbar" %} - {%- set top_container_cls = "sk-docs-container" %} -{%- else %} - {%- set nav_bar_class = "sk-landing-navbar" %} - {%- set top_container_cls = "sk-landing-container" %} -{%- endif %} - -{% if theme_link_to_live_contributing_page|tobool %} -{# Link to development page for live builds #} - {%- set development_link = "https://scikit-learn.org/dev/developers/index.html" %} -{# Open on a new development page in new window/tab for live builds #} - {%- set development_attrs = 'target="_blank" rel="noopener noreferrer"' %} -{%- else %} - {%- set development_link = pathto('developers/index') %} - {%- set development_attrs = '' %} -{%- endif %} - -{# title, link, link_attrs #} -{%- set drop_down_navigation = [ - ('Getting Started', pathto('getting_started'), ''), - ('Tutorial', pathto('tutorial/index'), ''), - ("What's new", pathto('whats_new/v' + version), ''), - ('Glossary', pathto('glossary'), ''), - ('Development', development_link, development_attrs), - ('FAQ', pathto('faq'), ''), - ('Support', pathto('support'), ''), - ('Related packages', pathto('related_projects'), ''), - ('Roadmap', pathto('roadmap'), ''), - ('Governance', pathto('governance'), ''), - ('About us', pathto('about'), ''), - ('GitHub', 'https://github.com/scikit-learn/scikit-learn', ''), - ('Other Versions and Download', 'https://scikit-learn.org/dev/versions.html', '')] --%} - - diff --git a/doc/themes/scikit-learn-modern/search.html b/doc/themes/scikit-learn-modern/search.html deleted file mode 100644 index 81e000bf9e5c4..0000000000000 --- a/doc/themes/scikit-learn-modern/search.html +++ /dev/null @@ -1,8 +0,0 @@ -{%- extends "basic/search.html" %} -{% block extrahead %} - - - - - -{% endblock %} diff --git a/doc/themes/scikit-learn-modern/static/css/theme.css b/doc/themes/scikit-learn-modern/static/css/theme.css deleted file mode 100644 index bd447d88e0b3b..0000000000000 --- a/doc/themes/scikit-learn-modern/static/css/theme.css +++ /dev/null @@ -1,1412 +0,0 @@ -/* Elements */ -a { - color: #2878A2; - word-wrap: break-word; -} - -a:focus { - outline: none; -} - -/* Anchor links */ - -a.headerlink { - color: #c60f0f; - font-size: 0.8em; - padding: 0 4px 0 4px; - text-decoration: none; - visibility: hidden; -} - -a.headerlink:hover { - background-color: #c60f0f; - color: white; -} - -p { - word-break: break-word; - hyphens: auto; -} - -input:focus { - outline: none; -} - -code { - color: #222; - background-color: #ecf0f3; - border-radius: 0.2rem; - padding: 0.15rem; - word-break: normal; -} - -nav { - z-index: 3; -} - -h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { - background-color: transparent; -} - -h4 .section-number, h5 .section-number, h6 .section-number { - display: none; -} - -h1:hover a.headerlink, -h2:hover a.headerlink, -h3:hover a.headerlink, -h4:hover a.headerlink, -h5:hover a.headerlink, -h6:hover a.headerlink, -dt:hover a.headerlink { - visibility: visible; -} - -strong { - font-weight: bold; -} - -a code { - color: inherit; -} - -a code { - background-color: transparent; - font-weight: bold; - color: #2878A2; - border-radius: 0; - padding: 0; - white-space: nowrap; -} - -img { - max-width: 100%; -} - -span.highlighted { - background-color: #fbe54e; -} - -div.highlight { - border: 1px solid #ddd; - margin-bottom: 1rem; -} - -div.highlight pre { - padding: 0.2rem 0.5rem; - margin-bottom: 0; - line-height: 1.2rem; -} - -div.highlight a { - text-decoration: underline; -} - -.versionmodified { - font-style: italic; -} - -a.sk-landing-btn { - background-color: #ff9c34; - color: black; - cursor: pointer; - font-size: 1.1rem; - font-weight: 500; -} - -a.sk-landing-btn:hover { - background-color: #ffb05f; -} - -.sk-donate-btn { - cursor: pointer; -} - -.sk-page-content div.logo { - float: left; - width: 200px; -} - -@media screen and (min-width: 992px) { - .sk-page-content { - padding-left: 2rem!important; - padding-right: 2rem!important; - } -} - -@media screen and (min-width: 1200px) { - .sk-px-xl-4 { - padding-left: 1.3rem!important; - padding-right: 1.3rem!important; - } -} - -/* clearfix */ - -div.clearer { - clear: both; -} - -/* details / summary */ - -/* Enables section links to be visible when anchor-linked */ -div.sk-page-content details::before { - display: block; - height: 52px; - margin-top: -52px; - visibility: hidden; - content: ""; -} - -div.sk-page-content details { - margin: 4ex 0pt; -} - -div.sk-page-content summary.btn { - display: list-item; - padding: 6px 20px; - border: 1pt solid #999; -} - -div.sk-page-content details div.card { - padding: 0pt .5ex; - margin: 1ex 0pt; - border: 1px solid #e9ecef; - border-left-width: .25rem; - border-radius: .25rem; - background: rgb(250, 252, 253) -} - -div.sk-page-content summary { - position: relative; /* Needed for the tooltips */ -} - -div.sk-page-content summary .tooltiptext { - visibility: hidden; - width: 120px; - background-color: black; - color: #fff; - text-align: center; - border-radius: 6px; - padding: 5px 0; - position: absolute; - z-index: 1; - bottom: 150%; - left: 50%; - margin-left: -60px; -} - -div.sk-page-content summary .tooltiptext::after { - content: ""; - position: absolute; - top: 100%; - left: 50%; - margin-left: -5px; - border-width: 5px; - border-style: solid; - border-color: black transparent transparent transparent; -} - -div.sk-page-content summary:hover .tooltiptext { - visibility: visible; -} - -div.sk-page-content summary:hover .headerlink { - visibility: visible; -} - -/* Button */ - -.sk-btn-primary { - background-color: #30799C; - border-color: #30799C; - color: white; -} - -.sk-btn-primary:hover, -.sk-btn-primary:active { - background-color: #3499cd; - border-color: #3499cd; -} - -/* Quote */ - -.quote { - text-align: right; - line-height: 1.5em; - font-style: italic; - margin: 2em 3em 1em 3em; -} - -.line-block { - display: block; - margin-top: 1em; - margin-bottom: 1em; -} - -/* Search */ - -#search-results { - margin-top: 1rem; -} - -#searchbox { - padding-top: 0.1rem; -} - -.sk-search-text-input { - width: 12rem; -} - -.sk-search-text-btn { - padding-left: 0.2rem; - padding-right: 0.2rem; -} - -ul.search li div.context { - color: #888; - margin: 0.1rem 0 0 0; - text-align: left; -} - -@media screen and (min-width: 768px) { - ul.search li div.context { - margin-left: 1rem; - } - - .sk-search-text-input { - width: 5rem; - } -} - -@media screen and (min-width: 806px) { - .sk-search-text-input { - width: 7rem; - } -} - -@media screen and (min-width: 820px) { - .sk-search-text-input { - width: 8rem; - } -} - -@media screen and (min-width: 886px) { - .sk-search-text-input { - width: 12rem; - } -} - -ul.search li a { - font-weight: bold; -} -/* navbar */ - -img.sk-brand-img { - height: 48px; -} - -.navbar-light .navbar-nav a.nav-link, a.sk-dropdown-item { - color: rgba(77, 77, 77, 1); - font-weight: 500; -} - -.navbar-light .navbar-nav a.nav-link:hover, a.sk-dropdown-item:hover { - color: rgba(246, 126, 0, 1); -} - -a.sk-nav-dropdown-item:active { - color: white; - background-color: rgba(246, 126, 0, 1); -} - -.nav-more-item-mobile-items { - display: inherit; -} - -.nav-more-item-dropdown { - display: none; -} - -@media screen and (min-width: 768px) { - .nav-more-item-dropdown { - display: inherit; - } - - .nav-more-item-mobile-items { - display: none; - } -} -/* LANDING PAGE STYLE */ - -div.sk-landing-container { - max-width: 1400px; -} - -div.sk-landing-container .text-white { - text-shadow: 0px 0px 8px rgb(42, 98, 128); -} - -ul.sk-landing-header-body { - margin-top: auto; - margin-bottom: auto; - font-size: 1.2rem; - font-weight: 500; -} - -div.sk-landing-bg-more-info dd { - padding-left: 0; -} - -div.sk-landing-bg { - background-image: linear-gradient(160deg, rgba(42,98,128,1) 0%, rgba(52,153,205,1) 17%, rgba(255,243,211,1) 59%, rgba(255,178,96,1) 100%); -} - -div.sk-landing-bg-more-info { - background-color: #f8f8f8; - font-size: 0.96rem; -} - -.sk-card-title { - font-weight: 700; -} - -.sk-landing-header { - font-size: 3.2rem; -} - -.sk-landing-subheader { - letter-spacing: 0.17rem; -} - -.sk-landing-call-header { - color: #E07200; - font-weight: 700; -} - -img.sk-index-img { - max-height: 240px; - margin: auto; - margin-bottom: 1em; - width: auto; -} - -@media screen and (min-width: 768px) { - img.sk-index-img { - width: 100% - } -} - -img.sk-who-uses-carousel-img { - max-height: 100px; - max-width: 50%; -} - -div#carouselExampleSlidesOnly { - min-height: 200px; -} - -ul.sk-landing-call-list li { - margin-bottom: 0.25rem; -} - -img.sk-footer-funding-logo { - max-height: 36px; - max-width: 80px; - margin: 0 8px; - margin-bottom: 8px; -} - -a.sk-footer-funding-link:hover { - text-decoration: none; -} -/* DOCS STYLE */ - -.navbar > .sk-docs-container { - max-width: 1400px; - margin: 0 auto; -} - -#sk-sidebar-wrapper { - height: 100%; - overflow-y: hidden; - overflow-x: hidden; - position: fixed; - margin-left: -240px; - width: 240px; - -webkit-transition: margin 0.25s ease-out, opacity 0.25s ease-out; - -moz-transition: margin 0.25s ease-out, opacity 0.25s ease-out; - -o-transition: margin 0.25s ease-out, opacity 0.25s ease-out; - transition: margin 0.25s ease-out, opacity 0.25s ease-out; - background-color: white; - opacity: 0; - top: 0; - padding: 0 0.5rem 0.5rem 0.5rem; - z-index: 2; -} - -#sk-toggle-checkbox { - display: none; -} - -#sk-toggle-checkbox:checked ~ #sk-sidebar-wrapper { - margin-left: 0; - opacity: 1; -} - -#sk-doc-wrapper { - max-width: 1400px; - margin: 0 auto; -} - -#sk-page-content-wrapper { - width: 100%; -} - -/* Enables section links to be visible when anchor-linked */ -section[id]::before { - display: block; - height: 52px; - margin-top: -52px; - visibility: hidden; - content: ""; -} - -div.sk-page-content { - background-color: white; - position: relative; - margin-top: 0.5rem; -} - -div.sk-page-content { - table-layout: fixed; - max-width: 100%; -} - -div.section h2, -div.section h3, -div.section h4, -div.section h5, -div.section h6 { - margin-top: 1rem; -} - -.sk-btn-toggle-toc { - position: fixed; - bottom: 0; - margin: 0; - border-radius: 0; - border-top-right-radius: 0.5rem; - z-index: 3; - cursor: pointer; -} - -div.sk-page-content { - margin-top: 52px; -} - -@media screen and (min-width: 1400px) { - .sk-btn-toggle-toc { - border-top-left-radius: 0.5rem; - } -} - -.sk-btn-toggle-toc:hover { - color: white; - background-color: #297ca7; -} - -footer.sk-content-footer { - padding: 1rem 0; - color: #999; - text-align: right; -} - -nav.sk-docs-navbar { - width: 100%; - z-index: 3; - -webkit-transition: top .2s ease-in-out; - -moz-transition: top .2s ease-in-out .05s; - -o-transition: top .2s ease-in-out .05s; - transition: top .2s ease-in-out .05s; - position: fixed; - max-height: 100vh; - overflow-y: auto; - align-items: initial; -} - -div.sk-navbar-collapse { - padding-bottom: 4rem; -} - -@media screen and (min-width: 768px) { - - nav.sk-docs-navbar { - overflow-y: visible; - max-height: none; - } - - div.sk-navbar-collapse { - padding-bottom: 0; - } - - #sk-page-content-wrapper { - padding-left: 240px; - max-width: 1240px; - margin-left: auto; - margin-right: auto; - } - - #sk-sidebar-wrapper { - margin-left: 0; - opacity: 1; - } - - #sk-toggle-checkbox:checked ~ #sk-sidebar-wrapper { - margin-left: -240px; - opacity: 0; - } - - #sk-toggle-checkbox:checked ~ #sk-page-content-wrapper { - padding-left: 0; - margin-left: auto; - margin-right: auto; - } -} - -.centered { - text-align: center; -} - -dl.citation > dd > ol > li { - display: inline; -} - -dl.citation > dd > ol { - margin-bottom: 0; -} - -/* docs index */ - -div.sk-documentation-index-card { - border-left: 0.15rem solid #ff9c34; -} -div.sk-documentation-index-card:hover { - box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15); -} - -a.sk-documentation-index-anchor:hover { - text-decoration: none; - color: #2878A2; -} - -.sk-documentation-index-header { - background-color: #cde8ef; - padding: 0.5rem; - border-radius: 0 1rem; - text-align: center; - font-size: 2rem; - font-weight: 500; -} - -/* toc */ - -.sk-toc-active { - font-weight: bold; -} - -div.sk-sidebar-toc-wrapper { - font-size: 0.9rem; - width: 252px; - overflow-x: hidden; - overflow-y: scroll; - height: 100vh; - padding-right: 1.75rem; - padding-top: 52px; - - /* Hide scrollbar for IE and Edge */ - -ms-overflow-style: none; - - /* Hide scrollbar for Firefox */ - scrollbar-width: none; -} - -div.sk-sidebar-toc-wrapper::-webkit-scrollbar { - display: none; -} - -div.sk-sidebar-toc-wrapper::after { - display: block; - content: ""; - height: 3rem; - visibility: hidden; -} - -div.sk-sidebar-toc > ul > li > a{ - font-weight: bold; -} - -div.sk-sidebar-toc > ul, -div.sk-sidebar-toc ul ul { - list-style: none; - margin-left: 0; - padding-left: 0; -} - -div.sk-sidebar-toc ul ul ul { - margin-left: 1rem; -} - - -div.sk-sidebar-toc ul li ul li ul{ - display: none; -} - -div.sk-sidebar-toc span { - white-space: pre; -} - -div.sk-sidebar-global-toc ul ul { - padding-left: 0.75rem; -} -/* content styling element style */ - -div.sk-page-content h1 { - background-color: #cde8ef; - padding: 0.5rem; - margin-top: calc(max(1rem, 1vh)); - border-radius: 0 1rem; - text-align: center; - font-size: 2rem; - word-wrap: break-word; -} - -/* General sibling selector: does not apply to first h1, to avoid gap in - * top of page */ -div.sk-page-content ~ h1 { - margin-top: calc(max(2.5rem, 1vh)); -} - -div.sk-page-content h2 { - padding: 0.5rem; - background-color: #BED4EB; - border-radius: 0.3rem; - font-size: 1.5rem; - margin-top: calc(max(2rem, .7vh)); - margin-bottom: 1rem; - word-wrap: break-word; -} - -div.sk-page-content h3 { - padding: 0.3rem; - background-color: #eee; - border-radius: 0.3rem; - font-size: 1.2rem; - word-wrap: break-word; - margin-top: 1.5rem; -} - -div.sk-page-content h4 { - padding: 0.2rem; - background-color: #F4F4F4; - border-radius: 0.3rem; - font-size: 1.2rem; - word-wrap: break-word; -} - -div.sk-page-content h1 code, -div.sk-page-content h2 code, -div.sk-page-content h3 code, -div.sk-page-content h4 code { - white-space: normal; -} - -/* longtables */ - -table.longtable p { - -moz-hyphens: none; - -ms-hyphens: none; - -webkit-hyphens: none; - hyphens: none; - line-height: 1.1em; - margin-bottom: 0; -} - -table.longtable td, table.longtable th { - border-top: 1px solid #ddd; - border-bottom: 1px solid #ddd; - padding-right: 0.5rem; - white-space:nowrap; -} - -table.longtable tr.row-odd { - background-color: #F0F7FA; -} - -/* api docs */ - -.class > dt, .function > dt, .method > dt { - padding: 0.5rem; - background-color: #f8f8f8; 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Places global toc into the sidebar - -:globalsidebartoc: True - .. _tutorial_menu: - -.. include:: ../includes/big_toc_css.rst -.. include:: ../tune_toc.rst - ====================== scikit-learn Tutorials ====================== -| - .. toctree:: :maxdepth: 2 @@ -23,8 +13,6 @@ scikit-learn Tutorials machine_learning_map/index ../presentations -| - .. note:: **Doctest Mode** The code-examples in the above tutorials are written in a diff --git a/doc/tutorial/machine_learning_map/ML_MAPS_README.txt b/doc/tutorial/machine_learning_map/ML_MAPS_README.txt deleted file mode 100644 index 114ecb2d13f59..0000000000000 --- a/doc/tutorial/machine_learning_map/ML_MAPS_README.txt +++ /dev/null @@ -1,93 +0,0 @@ -Machine Learning Cheat Sheet (for scikit-learn) -=============================================== - -This document is intended to explain how to edit -the machine learning cheat sheet, originally created -by Andreas Mueller: - -(https://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html) - -The image is made interactive using an imagemap, and uses the jQuery Map Highlight plugin module -by David Lynch (https://davidlynch.org/projects/maphilight/docs/) to highlight -the different items on the image upon mouseover. - -Modifying the map on the docs is currently a little bit tedious, -so I'll try to make it as simple as possible. - -1. Editing the layout of the map and its paths. ------------------------------------------------- - -Use a Graphics editor like Inkscape Vector Graphics Editor -to open the ml_map.svg file, in this folder. From there -you can move objects around, etc. as you need. - -Save when done, and make sure to export a .PNG file -to replace the old-outdated ml_map.png, as that file -is used as a background image. - -2. Accessing the paths of the SVG file and exporting them. ----------------------------------------------------------- - -Use an image manipulation package like GIMP Image Editor to open -the ml_map.svg file, in this folder. With GIMP, make sure -to select 'Import paths'. - -Once the image has been opened, you can see all imported paths on the paths tab. -You can edit these or create new paths. In GIMP, right-clicking one of the -paths and choosing: Path Tool will allow you to see the paths on -the image. The paths will be exported later and will be used to -make the click able regions on our image map. - -3. Export paths as SVG files ----------------------------- - -After you've edited a path or created a new one, right click it on -the paths menu and choose 'Export Path..'. This way we extract just -that path on its own as 'new_area.svg' for example. - -4. Edit the SVG file ---------------------- -Using a script made by David Lynch, we will convert the svg files into -html maps. To do this, open the svg file in question in any text editor. -Make sure that the 'width' and 'height' are not in 'in' or 'px', i.e -"100" is OK, but "100px" or "1.25in" are not. - -Then wrap the tags in and tags. -Then the file is ready for the script. - -5. From SVG to HTML map ------------------------ - -Use the provided svg2imagemap.py script on your edited svg file: - -$ python svg2imagemap.py new_area.svg - -where new_area.svg is our file. - -6. Add the new map to the main html file ------------------------------------------- - -Copy the code from the newly created 'new_area.html' -file. Open the ml_map.html file. - -Add the that you copied -after the last tag in the ml_map.html file. - -Add the link address to 'href' and a tooltip to -'title' within your tag. - -If you wish to add the green and blue hover effect -to the area, add -data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}' - -to your area tag, as done in the other tags above. - -Save the file, and you're done. - ------------------------------------------------------ - -I'll take some time to make some scripts to automate this process -a bit more at some point, as it is not difficult to do, -but tedious. - --Jaques Grobler diff --git a/doc/tutorial/machine_learning_map/README.md b/doc/tutorial/machine_learning_map/README.md new file mode 100644 index 0000000000000..006b1e5e1a38c --- /dev/null +++ b/doc/tutorial/machine_learning_map/README.md @@ -0,0 +1,17 @@ +The scikit-learn machine learning cheat sheet was originally created by Andreas Mueller: +https://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html + +The current version of the chart is located at `doc/images/ml_map.svg` in SVG+XML +format, created using [draw.io](https://draw.io/). To edit the chart, open the file in +draw.io, make changes, and export as SVG with the same filename. Export configurations +are: + +- Zoom: 100% +- Border width: 15 +- Size: Diagram +- Transparent Background: False +- Appearance: Light + +Each node in the chart that contains an estimator should have a link, where the root +directory is at `../../`. Note that after exporting the SVG, the links may be prefixed +with e.g. `https://app.diagrams.net/`. Remember to check and remove them. diff --git a/doc/tutorial/machine_learning_map/index.rst b/doc/tutorial/machine_learning_map/index.rst index 708f8bc43bf73..5fd6879563489 100644 --- a/doc/tutorial/machine_learning_map/index.rst +++ b/doc/tutorial/machine_learning_map/index.rst @@ -1,55 +1,75 @@ +:html_theme.sidebar_secondary.remove: + .. _ml_map: +Choosing the right estimator +============================ + +Often the hardest part of solving a machine learning problem can be finding the right +estimator for the job. Different estimators are better suited for different types of +data and different problems. -.. include:: ../../includes/big_toc_css.rst +The flowchart below is designed to give users a bit of a rough guide on how to approach +problems with regard to which estimators to try on your data. Click on any estimator in +the chart below to see its documentation. Use scroll wheel to zoom in and out, and click +and drag to pan around. You can also download the chart: +:download:`ml_map.svg <../../images/ml_map.svg>`. -Choosing the right estimator -======================================================= +.. raw:: html + + -Different estimators are better suited for different types of data -and different problems. + + +
    + +.. raw:: html + :file: ../../images/ml_map.svg .. raw:: html - Move mouse over image - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +
    diff --git a/doc/tutorial/machine_learning_map/parse_path.py b/doc/tutorial/machine_learning_map/parse_path.py deleted file mode 100644 index b1c68cec7f76b..0000000000000 --- a/doc/tutorial/machine_learning_map/parse_path.py +++ /dev/null @@ -1,192 +0,0 @@ -#!/usr/local/bin/python - -""" -Based on: http://wxpsvg.googlecode.com/svn/trunk/svg/pathdata.py -According to that project, this file is licensed under the LGPL -""" - -try: - from pyparsing import (ParserElement, Literal, Word, CaselessLiteral, - Optional, Combine, Forward, ZeroOrMore, nums, oneOf, Group, ParseException, OneOrMore) -except ImportError: - import sys - sys.exit("pyparsing is required") - - -#ParserElement.enablePackrat() - -def Command(char): - """ Case insensitive but case preserving""" - return CaselessPreservingLiteral(char) - -def Arguments(token): - return Group(token) - - -class CaselessPreservingLiteral(CaselessLiteral): - """ Like CaselessLiteral, but returns the match as found - instead of as defined. - """ - def __init__( self, matchString ): - super().__init__(matchString.upper()) - self.name = "'%s'" % matchString - self.errmsg = "Expected " + self.name - self.myException.msg = self.errmsg - - def parseImpl( self, instring, loc, doActions=True ): - test = instring[ loc:loc+self.matchLen ] - if test.upper() == self.match: - return loc+self.matchLen, test - #~ raise ParseException( instring, loc, self.errmsg ) - exc = self.myException - exc.loc = loc - exc.pstr = instring - raise exc - -def Sequence(token): - """ A sequence of the token""" - return OneOrMore(token+maybeComma) - -digit_sequence = Word(nums) - -sign = oneOf("+ -") - -def convertToFloat(s, loc, toks): - try: - return float(toks[0]) - except BaseException as e: - raise ParseException(loc, "invalid float format %s" % toks[0]) from e - -exponent = CaselessLiteral("e")+Optional(sign)+Word(nums) - -#note that almost all these fields are optional, -#and this can match almost anything. We rely on Pythons built-in -#float() function to clear out invalid values - loosely matching like this -#speeds up parsing quite a lot -floatingPointConstant = Combine( - Optional(sign) + - Optional(Word(nums)) + - Optional(Literal(".") + Optional(Word(nums)))+ - Optional(exponent) -) - -floatingPointConstant.setParseAction(convertToFloat) - -number = floatingPointConstant - -#same as FP constant but don't allow a - sign -nonnegativeNumber = Combine( - Optional(Word(nums)) + - Optional(Literal(".") + Optional(Word(nums)))+ - Optional(exponent) -) -nonnegativeNumber.setParseAction(convertToFloat) - -coordinate = number - -#comma or whitespace can separate values all over the place in SVG -maybeComma = Optional(Literal(',')).suppress() - -coordinateSequence = Sequence(coordinate) - -coordinatePair = (coordinate + maybeComma + coordinate).setParseAction(tuple) -coordinatePairSequence = Sequence(coordinatePair) - -coordinatePairPair = coordinatePair + maybeComma + coordinatePair -coordinatePairPairSequence = Sequence(Group(coordinatePairPair)) - -coordinatePairTriple = coordinatePair + maybeComma + coordinatePair + maybeComma + coordinatePair -coordinatePairTripleSequence = Sequence(Group(coordinatePairTriple)) - -#commands -lineTo = Group(Command("L") + Arguments(coordinatePairSequence)) -curve = Group(Command("C") + Arguments(coordinatePairSequence)) - -moveTo = Group(Command("M") + Arguments(coordinatePairSequence)) - -closePath = Group(Command("Z")).setParseAction(lambda t: ('Z', (None,))) - -flag = oneOf("1 0").setParseAction(lambda t: bool(int((t[0])))) - -arcRadius = ( - nonnegativeNumber + maybeComma + #rx - nonnegativeNumber #ry -).setParseAction(tuple) - -arcFlags = (flag + maybeComma + flag).setParseAction(tuple) - -ellipticalArcArgument = Group( - arcRadius + maybeComma + #rx, ry - number + maybeComma +#rotation - arcFlags + #large-arc-flag, sweep-flag - coordinatePair #(x,y) -) - -ellipticalArc = Group(Command("A") + Arguments(Sequence(ellipticalArcArgument))) - -smoothQuadraticBezierCurveto = Group(Command("T") + Arguments(coordinatePairSequence)) - -quadraticBezierCurveto = Group(Command("Q") + Arguments(coordinatePairPairSequence)) - -smoothCurve = Group(Command("S") + Arguments(coordinatePairPairSequence)) - -#curve = Group(Command("C") + Arguments(coordinatePairTripleSequence)) - -horizontalLine = Group(Command("H") + Arguments(coordinateSequence)) -verticalLine = Group(Command("V") + Arguments(coordinateSequence)) - -drawToCommand = ( - lineTo | moveTo | closePath | ellipticalArc | smoothQuadraticBezierCurveto | - quadraticBezierCurveto | smoothCurve | curve | horizontalLine | verticalLine - ) - -#~ number.debug = True -moveToDrawToCommands = moveTo + ZeroOrMore(drawToCommand) - -path = ZeroOrMore(moveToDrawToCommands) -path.keepTabs = True - -def get_points(d): - commands = path.parseString(d) - points = [] - currentset = None - for command in commands: - if command[0] == 'M' or command[0] == 'm': - currentset = [] - points.append(currentset) - currentset.append(command[1][-1]) - elif command[0] == 'L' or command[0] == 'l': - currentset.extend(command[1]) - elif command[0] == 'C' or command[0] == 'c': - currentset.extend(command[1]) - return points - -if __name__ == "__main__": - s = ("M 242.96145,653.59282 L 244.83646,650.1553 L 247.02397,649.8428 " - "L 247.33647,650.62405 L 245.30521,653.59282 L 242.96145,653.59282 z " - "M 252.80525,649.99905 L 258.74278,652.49906 L 260.77404,652.18656 " - "L 262.33654,648.43654 L 261.71154,645.15528 L 257.64902,644.68653 " - "L 253.74275,646.40528 L 252.80525,649.99905 z M 282.49289,659.6866 " - "L 286.08665,664.99912 L 288.43041,664.68662 L 289.52417,664.21787 " - "L 290.93042,665.46787 L 294.52419,665.31162 L 295.4617,663.90537 " - "L 292.64918,662.18661 L 290.77417,658.59284 L 288.74291,655.15533 " - "L 283.11789,657.96784 L 282.49289,659.6866 z M 302.02423,668.28039 " - "L 303.27423,666.40538 L 307.8055,667.34288 L 308.43051,666.87413 " - "L 314.36803,667.49913 L 314.05553,668.74914 L 311.55552,670.15539 " - "L 307.33675,669.84289 L 302.02423,668.28039 z M 307.1805,673.28041 " - "L 309.05551,677.03043 L 312.02427,675.93667 L 312.33677,674.37416 " - "L 310.77427,672.3429 L 307.1805,672.0304 L 307.1805,673.28041 z " - "M 313.89928,672.18665 L 316.08679,669.37414 L 320.61806,671.7179 " - "L 324.83683,672.81166 L 329.0556,675.46792 L 329.0556,677.34293 " - "L 325.61809,679.06169 L 320.93056,679.99919 L 318.5868,678.59293 " - "L 313.89928,672.18665 z M 329.99311,687.18672 L 331.55561,685.93672 " - "L 334.83688,687.49923 L 342.18066,690.93674 L 345.46193,692.968 " - "L 347.02443,695.31176 L 348.89944,699.53053 L 352.80571,702.03054 " - "L 352.49321,703.28055 L 348.74319,706.40556 L 344.68067,707.81182 " - "L 343.27442,707.18682 L 340.30565,708.90557 L 337.96189,712.03059 " - "L 335.77438,714.8431 L 334.05562,714.68685 L 330.61811,712.18684 " - "L 330.30561,707.81182 L 330.93061,705.46806 L 329.3681,699.99928 " - "L 327.33684,698.28052 L 327.18059,695.78051 L 329.3681,694.84301 " - "L 331.39936,691.87425 L 331.86811,690.93674 L 330.30561,689.21798 " - "L 329.99311,687.18672 z ") - print(path.parseString(s)) diff --git a/doc/tutorial/machine_learning_map/pyparsing.py b/doc/tutorial/machine_learning_map/pyparsing.py deleted file mode 100644 index 88d00e138d02c..0000000000000 --- a/doc/tutorial/machine_learning_map/pyparsing.py +++ /dev/null @@ -1,5715 +0,0 @@ -# module pyparsing.py -# -# Copyright (c) 2003-2016 Paul T. McGuire -# -# Permission is hereby granted, free of charge, to any person obtaining -# a copy of this software and associated documentation files (the -# "Software"), to deal in the Software without restriction, including -# without limitation the rights to use, copy, modify, merge, publish, -# distribute, sublicense, and/or sell copies of the Software, and to -# permit persons to whom the Software is furnished to do so, subject to -# the following conditions: -# -# The above copyright notice and this permission notice shall be -# included in all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, -# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF -# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. -# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY -# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, -# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE -# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. -# -# ruff: noqa - -__doc__ = \ -""" -pyparsing module - Classes and methods to define and execute parsing grammars - -The pyparsing module is an alternative approach to creating and executing simple grammars, -vs. the traditional lex/yacc approach, or the use of regular expressions. With pyparsing, you -don't need to learn a new syntax for defining grammars or matching expressions - the parsing module -provides a library of classes that you use to construct the grammar directly in Python. - -Here is a program to parse "Hello, World!" (or any greeting of the form -C{", !"}), built up using L{Word}, L{Literal}, and L{And} elements -(L{'+'} operator gives L{And} expressions, strings are auto-converted to -L{Literal} expressions):: - - from pyparsing import Word, alphas - - # define grammar of a greeting - greet = Word(alphas) + "," + Word(alphas) + "!" - - hello = "Hello, World!" - print (hello, "->", greet.parseString(hello)) - -The program outputs the following:: - - Hello, World! -> ['Hello', ',', 'World', '!'] - -The Python representation of the grammar is quite readable, owing to the self-explanatory -class names, and the use of '+', '|' and '^' operators. - -The L{ParseResults} object returned from L{ParserElement.parseString} can be accessed as a nested list, a dictionary, or an -object with named attributes. - -The pyparsing module handles some of the problems that are typically vexing when writing text parsers: - - extra or missing whitespace (the above program will also handle "Hello,World!", "Hello , World !", etc.) - - quoted strings - - embedded comments -""" - -__version__ = "2.2.0" -__versionTime__ = "06 Mar 2017 02:06 UTC" -__author__ = "Paul McGuire " - -import string -from weakref import ref as wkref -import copy -import sys -import warnings -import re -import sre_constants -import collections -import pprint -import traceback -import types -from datetime import datetime - -try: - from _thread import RLock -except ImportError: - from threading import RLock - -try: - from collections import OrderedDict as _OrderedDict -except ImportError: - try: - from ordereddict import OrderedDict as _OrderedDict - except ImportError: - _OrderedDict = None - -#~ sys.stderr.write( "testing pyparsing module, version %s, %s\n" % (__version__,__versionTime__ ) ) - -__all__ = [ -'And', 'CaselessKeyword', 'CaselessLiteral', 'CharsNotIn', 'Combine', 'Dict', 'Each', 'Empty', -'FollowedBy', 'Forward', 'GoToColumn', 'Group', 'Keyword', 'LineEnd', 'LineStart', 'Literal', -'MatchFirst', 'NoMatch', 'NotAny', 'OneOrMore', 'OnlyOnce', 'Optional', 'Or', -'ParseBaseException', 'ParseElementEnhance', 'ParseException', 'ParseExpression', 'ParseFatalException', -'ParseResults', 'ParseSyntaxException', 'ParserElement', 'QuotedString', 'RecursiveGrammarException', -'Regex', 'SkipTo', 'StringEnd', 'StringStart', 'Suppress', 'Token', 'TokenConverter', -'White', 'Word', 'WordEnd', 'WordStart', 'ZeroOrMore', -'alphanums', 'alphas', 'alphas8bit', 'anyCloseTag', 'anyOpenTag', 'cStyleComment', 'col', -'commaSeparatedList', 'commonHTMLEntity', 'countedArray', 'cppStyleComment', 'dblQuotedString', -'dblSlashComment', 'delimitedList', 'dictOf', 'downcaseTokens', 'empty', 'hexnums', -'htmlComment', 'javaStyleComment', 'line', 'lineEnd', 'lineStart', 'lineno', -'makeHTMLTags', 'makeXMLTags', 'matchOnlyAtCol', 'matchPreviousExpr', 'matchPreviousLiteral', -'nestedExpr', 'nullDebugAction', 'nums', 'oneOf', 'opAssoc', 'operatorPrecedence', 'printables', -'punc8bit', 'pythonStyleComment', 'quotedString', 'removeQuotes', 'replaceHTMLEntity', -'replaceWith', 'restOfLine', 'sglQuotedString', 'srange', 'stringEnd', -'stringStart', 'traceParseAction', 'unicodeString', 'upcaseTokens', 'withAttribute', -'indentedBlock', 'originalTextFor', 'ungroup', 'infixNotation','locatedExpr', 'withClass', -'CloseMatch', 'tokenMap', 'pyparsing_common', -] - -system_version = tuple(sys.version_info)[:3] -PY_3 = system_version[0] == 3 -if PY_3: - _MAX_INT = sys.maxsize - basestring = str - unichr = chr - _ustr = str - - # build list of single arg builtins, that can be used as parse actions - singleArgBuiltins = [sum, len, sorted, reversed, list, tuple, set, any, all, min, max] - -else: - _MAX_INT = sys.maxint - range = xrange - - def _ustr(obj): - """Drop-in replacement for str(obj) that tries to be Unicode friendly. It first tries - str(obj). If that fails with a UnicodeEncodeError, then it tries unicode(obj). It - then < returns the unicode object | encodes it with the default encoding | ... >. - """ - if isinstance(obj,unicode): - return obj - - try: - # If this works, then _ustr(obj) has the same behaviour as str(obj), so - # it won't break any existing code. - return str(obj) - - except UnicodeEncodeError: - # Else encode it - ret = unicode(obj).encode(sys.getdefaultencoding(), 'xmlcharrefreplace') - xmlcharref = Regex(r'&#\d+;') - xmlcharref.setParseAction(lambda t: '\\u' + hex(int(t[0][2:-1]))[2:]) - return xmlcharref.transformString(ret) - - # build list of single arg builtins, tolerant of Python version, that can be used as parse actions - singleArgBuiltins = [] - import __builtin__ - for fname in "sum len sorted reversed list tuple set any all min max".split(): - try: - singleArgBuiltins.append(getattr(__builtin__,fname)) - except AttributeError: - continue - -_generatorType = type((y for y in range(1))) - -def _xml_escape(data): - """Escape &, <, >, ", ', etc. in a string of data.""" - - # ampersand must be replaced first - from_symbols = '&><"\'' - to_symbols = ('&'+s+';' for s in "amp gt lt quot apos".split()) - for from_,to_ in zip(from_symbols, to_symbols): - data = data.replace(from_, to_) - return data - -class _Constants(object): - pass - -alphas = string.ascii_uppercase + string.ascii_lowercase -nums = "0123456789" -hexnums = nums + "ABCDEFabcdef" -alphanums = alphas + nums -_bslash = chr(92) -printables = "".join(c for c in string.printable if c not in string.whitespace) - -class ParseBaseException(Exception): - """base exception class for all parsing runtime exceptions""" - # Performance tuning: we construct a *lot* of these, so keep this - # constructor as small and fast as possible - def __init__( self, pstr, loc=0, msg=None, elem=None ): - self.loc = loc - if msg is None: - self.msg = pstr - self.pstr = "" - else: - self.msg = msg - self.pstr = pstr - self.parserElement = elem - self.args = (pstr, loc, msg) - - @classmethod - def _from_exception(cls, pe): - """ - internal factory method to simplify creating one type of ParseException - from another - avoids having __init__ signature conflicts among subclasses - """ - return cls(pe.pstr, pe.loc, pe.msg, pe.parserElement) - - def __getattr__( self, aname ): - """supported attributes by name are: - - lineno - returns the line number of the exception text - - col - returns the column number of the exception text - - line - returns the line containing the exception text - """ - if( aname == "lineno" ): - return lineno( self.loc, self.pstr ) - elif( aname in ("col", "column") ): - return col( self.loc, self.pstr ) - elif( aname == "line" ): - return line( self.loc, self.pstr ) - else: - raise AttributeError(aname) - - def __str__( self ): - return "%s (at char %d), (line:%d, col:%d)" % \ - ( self.msg, self.loc, self.lineno, self.column ) - def __repr__( self ): - return _ustr(self) - def markInputline( self, markerString = ">!<" ): - """Extracts the exception line from the input string, and marks - the location of the exception with a special symbol. - """ - line_str = self.line - line_column = self.column - 1 - if markerString: - line_str = "".join((line_str[:line_column], - markerString, line_str[line_column:])) - return line_str.strip() - def __dir__(self): - return "lineno col line".split() + dir(type(self)) - -class ParseException(ParseBaseException): - """ - Exception thrown when parse expressions don't match class; - supported attributes by name are: - - lineno - returns the line number of the exception text - - col - returns the column number of the exception text - - line - returns the line containing the exception text - - Example:: - try: - Word(nums).setName("integer").parseString("ABC") - except ParseException as pe: - print(pe) - print("column: {}".format(pe.col)) - - prints:: - Expected integer (at char 0), (line:1, col:1) - column: 1 - """ - pass - -class ParseFatalException(ParseBaseException): - """user-throwable exception thrown when inconsistent parse content - is found; stops all parsing immediately""" - pass - -class ParseSyntaxException(ParseFatalException): - """just like L{ParseFatalException}, but thrown internally when an - L{ErrorStop} ('-' operator) indicates that parsing is to stop - immediately because an unbacktrackable syntax error has been found""" - pass - -#~ class ReparseException(ParseBaseException): - #~ """Experimental class - parse actions can raise this exception to cause - #~ pyparsing to reparse the input string: - #~ - with a modified input string, and/or - #~ - with a modified start location - #~ Set the values of the ReparseException in the constructor, and raise the - #~ exception in a parse action to cause pyparsing to use the new string/location. - #~ Setting the values as None causes no change to be made. - #~ """ - #~ def __init_( self, newstring, restartLoc ): - #~ self.newParseText = newstring - #~ self.reparseLoc = restartLoc - -class RecursiveGrammarException(Exception): - """exception thrown by L{ParserElement.validate} if the grammar could be improperly recursive""" - def __init__( self, parseElementList ): - self.parseElementTrace = parseElementList - - def __str__( self ): - return "RecursiveGrammarException: %s" % self.parseElementTrace - -class _ParseResultsWithOffset(object): - def __init__(self,p1,p2): - self.tup = (p1,p2) - def __getitem__(self,i): - return self.tup[i] - def __repr__(self): - return repr(self.tup[0]) - def setOffset(self,i): - self.tup = (self.tup[0],i) - -class ParseResults(object): - """ - Structured parse results, to provide multiple means of access to the parsed data: - - as a list (C{len(results)}) - - by list index (C{results[0], results[1]}, etc.) - - by attribute (C{results.} - see L{ParserElement.setResultsName}) - - Example:: - integer = Word(nums) - date_str = (integer.setResultsName("year") + '/' - + integer.setResultsName("month") + '/' - + integer.setResultsName("day")) - # equivalent form: - # date_str = integer("year") + '/' + integer("month") + '/' + integer("day") - - # parseString returns a ParseResults object - result = date_str.parseString("1999/12/31") - - def test(s, fn=repr): - print("%s -> %s" % (s, fn(eval(s)))) - test("list(result)") - test("result[0]") - test("result['month']") - test("result.day") - test("'month' in result") - test("'minutes' in result") - test("result.dump()", str) - prints:: - list(result) -> ['1999', '/', '12', '/', '31'] - result[0] -> '1999' - result['month'] -> '12' - result.day -> '31' - 'month' in result -> True - 'minutes' in result -> False - result.dump() -> ['1999', '/', '12', '/', '31'] - - day: 31 - - month: 12 - - year: 1999 - """ - def __new__(cls, toklist=None, name=None, asList=True, modal=True ): - if isinstance(toklist, cls): - return toklist - retobj = object.__new__(cls) - retobj.__doinit = True - return retobj - - # Performance tuning: we construct a *lot* of these, so keep this - # constructor as small and fast as possible - def __init__( self, toklist=None, name=None, asList=True, modal=True, isinstance=isinstance ): - if self.__doinit: - self.__doinit = False - self.__name = None - self.__parent = None - self.__accumNames = {} - self.__asList = asList - self.__modal = modal - if toklist is None: - toklist = [] - if isinstance(toklist, list): - self.__toklist = toklist[:] - elif isinstance(toklist, _generatorType): - self.__toklist = list(toklist) - else: - self.__toklist = [toklist] - self.__tokdict = dict() - - if name is not None and name: - if not modal: - self.__accumNames[name] = 0 - if isinstance(name,int): - name = _ustr(name) # will always return a str, but use _ustr for consistency - self.__name = name - if not (isinstance(toklist, (type(None), basestring, list)) and toklist in (None,'',[])): - if isinstance(toklist,basestring): - toklist = [ toklist ] - if asList: - if isinstance(toklist,ParseResults): - self[name] = _ParseResultsWithOffset(toklist.copy(),0) - else: - self[name] = _ParseResultsWithOffset(ParseResults(toklist[0]),0) - self[name].__name = name - else: - try: - self[name] = toklist[0] - except (KeyError,TypeError,IndexError): - self[name] = toklist - - def __getitem__( self, i ): - if isinstance( i, (int,slice) ): - return self.__toklist[i] - else: - if i not in self.__accumNames: - return self.__tokdict[i][-1][0] - else: - return ParseResults([ v[0] for v in self.__tokdict[i] ]) - - def __setitem__( self, k, v, isinstance=isinstance ): - if isinstance(v,_ParseResultsWithOffset): - self.__tokdict[k] = self.__tokdict.get(k,list()) + [v] - sub = v[0] - elif isinstance(k,(int,slice)): - self.__toklist[k] = v - sub = v - else: - self.__tokdict[k] = self.__tokdict.get(k,list()) + [_ParseResultsWithOffset(v,0)] - sub = v - if isinstance(sub,ParseResults): - sub.__parent = wkref(self) - - def __delitem__( self, i ): - if isinstance(i,(int,slice)): - mylen = len( self.__toklist ) - del self.__toklist[i] - - # convert int to slice - if isinstance(i, int): - if i < 0: - i += mylen - i = slice(i, i+1) - # get removed indices - removed = list(range(*i.indices(mylen))) - removed.reverse() - # fixup indices in token dictionary - for name,occurrences in self.__tokdict.items(): - for j in removed: - for k, (value, position) in enumerate(occurrences): - occurrences[k] = _ParseResultsWithOffset(value, position - (position > j)) - else: - del self.__tokdict[i] - - def __contains__( self, k ): - return k in self.__tokdict - - def __len__( self ): return len( self.__toklist ) - def __bool__(self): return ( not not self.__toklist ) - __nonzero__ = __bool__ - def __iter__( self ): return iter( self.__toklist ) - def __reversed__( self ): return iter( self.__toklist[::-1] ) - def _iterkeys( self ): - if hasattr(self.__tokdict, "iterkeys"): - return self.__tokdict.iterkeys() - else: - return iter(self.__tokdict) - - def _itervalues( self ): - return (self[k] for k in self._iterkeys()) - - def _iteritems( self ): - return ((k, self[k]) for k in self._iterkeys()) - - if PY_3: - keys = _iterkeys - """Returns an iterator of all named result keys (Python 3.x only).""" - - values = _itervalues - """Returns an iterator of all named result values (Python 3.x only).""" - - items = _iteritems - """Returns an iterator of all named result key-value tuples (Python 3.x only).""" - - else: - iterkeys = _iterkeys - """Returns an iterator of all named result keys (Python 2.x only).""" - - itervalues = _itervalues - """Returns an iterator of all named result values (Python 2.x only).""" - - iteritems = _iteritems - """Returns an iterator of all named result key-value tuples (Python 2.x only).""" - - def keys( self ): - """Returns all named result keys (as a list in Python 2.x, as an iterator in Python 3.x).""" - return list(self.iterkeys()) - - def values( self ): - """Returns all named result values (as a list in Python 2.x, as an iterator in Python 3.x).""" - return list(self.itervalues()) - - def items( self ): - """Returns all named result key-values (as a list of tuples in Python 2.x, as an iterator in Python 3.x).""" - return list(self.iteritems()) - - def haskeys( self ): - """Since keys() returns an iterator, this method is helpful in bypassing - code that looks for the existence of any defined results names.""" - return bool(self.__tokdict) - - def pop( self, *args, **kwargs): - """ - Removes and returns item at specified index (default=C{last}). - Supports both C{list} and C{dict} semantics for C{pop()}. If passed no - argument or an integer argument, it will use C{list} semantics - and pop tokens from the list of parsed tokens. If passed a - non-integer argument (most likely a string), it will use C{dict} - semantics and pop the corresponding value from any defined - results names. A second default return value argument is - supported, just as in C{dict.pop()}. - - Example:: - def remove_first(tokens): - tokens.pop(0) - print(OneOrMore(Word(nums)).parseString("0 123 321")) # -> ['0', '123', '321'] - print(OneOrMore(Word(nums)).addParseAction(remove_first).parseString("0 123 321")) # -> ['123', '321'] - - label = Word(alphas) - patt = label("LABEL") + OneOrMore(Word(nums)) - print(patt.parseString("AAB 123 321").dump()) - - # Use pop() in a parse action to remove named result (note that corresponding value is not - # removed from list form of results) - def remove_LABEL(tokens): - tokens.pop("LABEL") - return tokens - patt.addParseAction(remove_LABEL) - print(patt.parseString("AAB 123 321").dump()) - prints:: - ['AAB', '123', '321'] - - LABEL: AAB - - ['AAB', '123', '321'] - """ - if not args: - args = [-1] - for k,v in kwargs.items(): - if k == 'default': - args = (args[0], v) - else: - raise TypeError("pop() got an unexpected keyword argument '%s'" % k) - if (isinstance(args[0], int) or - len(args) == 1 or - args[0] in self): - index = args[0] - ret = self[index] - del self[index] - return ret - else: - defaultvalue = args[1] - return defaultvalue - - def get(self, key, defaultValue=None): - """ - Returns named result matching the given key, or if there is no - such name, then returns the given C{defaultValue} or C{None} if no - C{defaultValue} is specified. - - Similar to C{dict.get()}. - - Example:: - integer = Word(nums) - date_str = integer("year") + '/' + integer("month") + '/' + integer("day") - - result = date_str.parseString("1999/12/31") - print(result.get("year")) # -> '1999' - print(result.get("hour", "not specified")) # -> 'not specified' - print(result.get("hour")) # -> None - """ - if key in self: - return self[key] - else: - return defaultValue - - def insert( self, index, insStr ): - """ - Inserts new element at location index in the list of parsed tokens. - - Similar to C{list.insert()}. - - Example:: - print(OneOrMore(Word(nums)).parseString("0 123 321")) # -> ['0', '123', '321'] - - # use a parse action to insert the parse location in the front of the parsed results - def insert_locn(locn, tokens): - tokens.insert(0, locn) - print(OneOrMore(Word(nums)).addParseAction(insert_locn).parseString("0 123 321")) # -> [0, '0', '123', '321'] - """ - self.__toklist.insert(index, insStr) - # fixup indices in token dictionary - for name,occurrences in self.__tokdict.items(): - for k, (value, position) in enumerate(occurrences): - occurrences[k] = _ParseResultsWithOffset(value, position + (position > index)) - - def append( self, item ): - """ - Add single element to end of ParseResults list of elements. - - Example:: - print(OneOrMore(Word(nums)).parseString("0 123 321")) # -> ['0', '123', '321'] - - # use a parse action to compute the sum of the parsed integers, and add it to the end - def append_sum(tokens): - tokens.append(sum(map(int, tokens))) - print(OneOrMore(Word(nums)).addParseAction(append_sum).parseString("0 123 321")) # -> ['0', '123', '321', 444] - """ - self.__toklist.append(item) - - def extend( self, itemseq ): - """ - Add sequence of elements to end of ParseResults list of elements. - - Example:: - patt = OneOrMore(Word(alphas)) - - # use a parse action to append the reverse of the matched strings, to make a palindrome - def make_palindrome(tokens): - tokens.extend(reversed([t[::-1] for t in tokens])) - return ''.join(tokens) - print(patt.addParseAction(make_palindrome).parseString("lskdj sdlkjf lksd")) # -> 'lskdjsdlkjflksddsklfjkldsjdksl' - """ - if isinstance(itemseq, ParseResults): - self += itemseq - else: - self.__toklist.extend(itemseq) - - def clear( self ): - """ - Clear all elements and results names. - """ - del self.__toklist[:] - self.__tokdict.clear() - - def __getattr__( self, name ): - try: - return self[name] - except KeyError: - return "" - - if name in self.__tokdict: - if name not in self.__accumNames: - return self.__tokdict[name][-1][0] - else: - return ParseResults([ v[0] for v in self.__tokdict[name] ]) - else: - return "" - - def __add__( self, other ): - ret = self.copy() - ret += other - return ret - - def __iadd__( self, other ): - if other.__tokdict: - offset = len(self.__toklist) - addoffset = lambda a: offset if a<0 else a+offset - otheritems = other.__tokdict.items() - otherdictitems = [(k, _ParseResultsWithOffset(v[0],addoffset(v[1])) ) - for (k,vlist) in otheritems for v in vlist] - for k,v in otherdictitems: - self[k] = v - if isinstance(v[0],ParseResults): - v[0].__parent = wkref(self) - - self.__toklist += other.__toklist - self.__accumNames.update( other.__accumNames ) - return self - - def __radd__(self, other): - if isinstance(other,int) and other == 0: - # useful for merging many ParseResults using sum() builtin - return self.copy() - else: - # this may raise a TypeError - so be it - return other + self - - def __repr__( self ): - return "(%s, %s)" % ( repr( self.__toklist ), repr( self.__tokdict ) ) - - def __str__( self ): - return '[' + ', '.join(_ustr(i) if isinstance(i, ParseResults) else repr(i) for i in self.__toklist) + ']' - - def _asStringList( self, sep='' ): - out = [] - for item in self.__toklist: - if out and sep: - out.append(sep) - if isinstance( item, ParseResults ): - out += item._asStringList() - else: - out.append( _ustr(item) ) - return out - - def asList( self ): - """ - Returns the parse results as a nested list of matching tokens, all converted to strings. - - Example:: - patt = OneOrMore(Word(alphas)) - result = patt.parseString("sldkj lsdkj sldkj") - # even though the result prints in string-like form, it is actually a pyparsing ParseResults - print(type(result), result) # -> ['sldkj', 'lsdkj', 'sldkj'] - - # Use asList() to create an actual list - result_list = result.asList() - print(type(result_list), result_list) # -> ['sldkj', 'lsdkj', 'sldkj'] - """ - return [res.asList() if isinstance(res,ParseResults) else res for res in self.__toklist] - - def asDict( self ): - """ - Returns the named parse results as a nested dictionary. - - Example:: - integer = Word(nums) - date_str = integer("year") + '/' + integer("month") + '/' + integer("day") - - result = date_str.parseString('12/31/1999') - print(type(result), repr(result)) # -> (['12', '/', '31', '/', '1999'], {'day': [('1999', 4)], 'year': [('12', 0)], 'month': [('31', 2)]}) - - result_dict = result.asDict() - print(type(result_dict), repr(result_dict)) # -> {'day': '1999', 'year': '12', 'month': '31'} - - # even though a ParseResults supports dict-like access, sometime you just need to have a dict - import json - print(json.dumps(result)) # -> Exception: TypeError: ... is not JSON serializable - print(json.dumps(result.asDict())) # -> {"month": "31", "day": "1999", "year": "12"} - """ - if PY_3: - item_fn = self.items - else: - item_fn = self.iteritems - - def toItem(obj): - if isinstance(obj, ParseResults): - if obj.haskeys(): - return obj.asDict() - else: - return [toItem(v) for v in obj] - else: - return obj - - return dict((k,toItem(v)) for k,v in item_fn()) - - def copy( self ): - """ - Returns a new copy of a C{ParseResults} object. - """ - ret = ParseResults( self.__toklist ) - ret.__tokdict = self.__tokdict.copy() - ret.__parent = self.__parent - ret.__accumNames.update( self.__accumNames ) - ret.__name = self.__name - return ret - - def asXML( self, doctag=None, namedItemsOnly=False, indent="", formatted=True ): - """ - (Deprecated) Returns the parse results as XML. Tags are created for tokens and lists that have defined results names. - """ - nl = "\n" - out = [] - namedItems = dict((v[1],k) for (k,vlist) in self.__tokdict.items() - for v in vlist) - nextLevelIndent = indent + " " - - # collapse out indents if formatting is not desired - if not formatted: - indent = "" - nextLevelIndent = "" - nl = "" - - selfTag = None - if doctag is not None: - selfTag = doctag - else: - if self.__name: - selfTag = self.__name - - if not selfTag: - if namedItemsOnly: - return "" - else: - selfTag = "ITEM" - - out += [ nl, indent, "<", selfTag, ">" ] - - for i,res in enumerate(self.__toklist): - if isinstance(res,ParseResults): - if i in namedItems: - out += [ res.asXML(namedItems[i], - namedItemsOnly and doctag is None, - nextLevelIndent, - formatted)] - else: - out += [ res.asXML(None, - namedItemsOnly and doctag is None, - nextLevelIndent, - formatted)] - else: - # individual token, see if there is a name for it - resTag = None - if i in namedItems: - resTag = namedItems[i] - if not resTag: - if namedItemsOnly: - continue - else: - resTag = "ITEM" - xmlBodyText = _xml_escape(_ustr(res)) - out += [ nl, nextLevelIndent, "<", resTag, ">", - xmlBodyText, - "" ] - - out += [ nl, indent, "" ] - return "".join(out) - - def __lookup(self,sub): - for k,vlist in self.__tokdict.items(): - for v,loc in vlist: - if sub is v: - return k - return None - - def getName(self): - r""" - Returns the results name for this token expression. Useful when several - different expressions might match at a particular location. - - Example:: - integer = Word(nums) - ssn_expr = Regex(r"\d\d\d-\d\d-\d\d\d\d") - house_number_expr = Suppress('#') + Word(nums, alphanums) - user_data = (Group(house_number_expr)("house_number") - | Group(ssn_expr)("ssn") - | Group(integer)("age")) - user_info = OneOrMore(user_data) - - result = user_info.parseString("22 111-22-3333 #221B") - for item in result: - print(item.getName(), ':', item[0]) - prints:: - age : 22 - ssn : 111-22-3333 - house_number : 221B - """ - if self.__name: - return self.__name - elif self.__parent: - par = self.__parent() - if par: - return par.__lookup(self) - else: - return None - elif (len(self) == 1 and - len(self.__tokdict) == 1 and - next(iter(self.__tokdict.values()))[0][1] in (0,-1)): - return next(iter(self.__tokdict.keys())) - else: - return None - - def dump(self, indent='', depth=0, full=True): - """ - Diagnostic method for listing out the contents of a C{ParseResults}. - Accepts an optional C{indent} argument so that this string can be embedded - in a nested display of other data. - - Example:: - integer = Word(nums) - date_str = integer("year") + '/' + integer("month") + '/' + integer("day") - - result = date_str.parseString('12/31/1999') - print(result.dump()) - prints:: - ['12', '/', '31', '/', '1999'] - - day: 1999 - - month: 31 - - year: 12 - """ - out = [] - NL = '\n' - out.append( indent+_ustr(self.asList()) ) - if full: - if self.haskeys(): - items = sorted((str(k), v) for k,v in self.items()) - for k,v in items: - if out: - out.append(NL) - out.append( "%s%s- %s: " % (indent,(' '*depth), k) ) - if isinstance(v,ParseResults): - if v: - out.append( v.dump(indent,depth+1) ) - else: - out.append(_ustr(v)) - else: - out.append(repr(v)) - elif any(isinstance(vv,ParseResults) for vv in self): - v = self - for i,vv in enumerate(v): - if isinstance(vv,ParseResults): - out.append("\n%s%s[%d]:\n%s%s%s" % (indent,(' '*(depth)),i,indent,(' '*(depth+1)),vv.dump(indent,depth+1) )) - else: - out.append("\n%s%s[%d]:\n%s%s%s" % (indent,(' '*(depth)),i,indent,(' '*(depth+1)),_ustr(vv))) - - return "".join(out) - - def pprint(self, *args, **kwargs): - """ - Pretty-printer for parsed results as a list, using the C{pprint} module. - Accepts additional positional or keyword args as defined for the - C{pprint.pprint} method. (U{https://docs.python.org/3/library/pprint.html#pprint.pprint}) - - Example:: - ident = Word(alphas, alphanums) - num = Word(nums) - func = Forward() - term = ident | num | Group('(' + func + ')') - func <<= ident + Group(Optional(delimitedList(term))) - result = func.parseString("fna a,b,(fnb c,d,200),100") - result.pprint(width=40) - prints:: - ['fna', - ['a', - 'b', - ['(', 'fnb', ['c', 'd', '200'], ')'], - '100']] - """ - pprint.pprint(self.asList(), *args, **kwargs) - - # add support for pickle protocol - def __getstate__(self): - return ( self.__toklist, - ( self.__tokdict.copy(), - self.__parent is not None and self.__parent() or None, - self.__accumNames, - self.__name ) ) - - def __setstate__(self,state): - self.__toklist = state[0] - (self.__tokdict, - par, - inAccumNames, - self.__name) = state[1] - self.__accumNames = {} - self.__accumNames.update(inAccumNames) - if par is not None: - self.__parent = wkref(par) - else: - self.__parent = None - - def __getnewargs__(self): - return self.__toklist, self.__name, self.__asList, self.__modal - - def __dir__(self): - return (dir(type(self)) + list(self.keys())) - -collections.MutableMapping.register(ParseResults) - -def col (loc,strg): - """Returns current column within a string, counting newlines as line separators. - The first column is number 1. - - Note: the default parsing behavior is to expand tabs in the input string - before starting the parsing process. See L{I{ParserElement.parseString}} for more information - on parsing strings containing C{}s, and suggested methods to maintain a - consistent view of the parsed string, the parse location, and line and column - positions within the parsed string. - """ - s = strg - return 1 if 0} for more information - on parsing strings containing C{}s, and suggested methods to maintain a - consistent view of the parsed string, the parse location, and line and column - positions within the parsed string. - """ - return strg.count("\n",0,loc) + 1 - -def line( loc, strg ): - """Returns the line of text containing loc within a string, counting newlines as line separators. - """ - lastCR = strg.rfind("\n", 0, loc) - nextCR = strg.find("\n", loc) - if nextCR >= 0: - return strg[lastCR+1:nextCR] - else: - return strg[lastCR+1:] - -def _defaultStartDebugAction( instring, loc, expr ): - print (("Match " + _ustr(expr) + " at loc " + _ustr(loc) + "(%d,%d)" % ( lineno(loc,instring), col(loc,instring) ))) - -def _defaultSuccessDebugAction( instring, startloc, endloc, expr, toks ): - print ("Matched " + _ustr(expr) + " -> " + str(toks.asList())) - -def _defaultExceptionDebugAction( instring, loc, expr, exc ): - print ("Exception raised:" + _ustr(exc)) - -def nullDebugAction(*args): - """'Do-nothing' debug action, to suppress debugging output during parsing.""" - pass - -# Only works on Python 3.x - nonlocal is toxic to Python 2 installs -#~ 'decorator to trim function calls to match the arity of the target' -#~ def _trim_arity(func, maxargs=3): - #~ if func in singleArgBuiltins: - #~ return lambda s,l,t: func(t) - #~ limit = 0 - #~ foundArity = False - #~ def wrapper(*args): - #~ nonlocal limit,foundArity - #~ while 1: - #~ try: - #~ ret = func(*args[limit:]) - #~ foundArity = True - #~ return ret - #~ except TypeError: - #~ if limit == maxargs or foundArity: - #~ raise - #~ limit += 1 - #~ continue - #~ return wrapper - -# this version is Python 2.x-3.x cross-compatible -'decorator to trim function calls to match the arity of the target' -def _trim_arity(func, maxargs=2): - if func in singleArgBuiltins: - return lambda s,l,t: func(t) - limit = [0] - foundArity = [False] - - def extract_stack(limit=0): - offset = -2 - frame_summary = traceback.extract_stack(limit=-offset+limit-1)[offset] - return [(frame_summary.filename, frame_summary.lineno)] - def extract_tb(tb, limit=0): - frames = traceback.extract_tb(tb, limit=limit) - frame_summary = frames[-1] - return [(frame_summary.filename, frame_summary.lineno)] - - # synthesize what would be returned by traceback.extract_stack at the call to - # user's parse action 'func', so that we don't incur call penalty at parse time - - LINE_DIFF = 6 - # IF ANY CODE CHANGES, EVEN JUST COMMENTS OR BLANK LINES, BETWEEN THE NEXT LINE AND - # THE CALL TO FUNC INSIDE WRAPPER, LINE_DIFF MUST BE MODIFIED!!!! - this_line = extract_stack(limit=2)[-1] - pa_call_line_synth = (this_line[0], this_line[1]+LINE_DIFF) - - def wrapper(*args): - while 1: - try: - ret = func(*args[limit[0]:]) - foundArity[0] = True - return ret - except TypeError: - # re-raise TypeErrors if they did not come from our arity testing - if foundArity[0]: - raise - else: - try: - tb = sys.exc_info()[-1] - if not extract_tb(tb, limit=2)[-1][:2] == pa_call_line_synth: - raise - finally: - del tb - - if limit[0] <= maxargs: - limit[0] += 1 - continue - raise - - # copy func name to wrapper for sensible debug output - func_name = "" - try: - func_name = getattr(func, '__name__', - getattr(func, '__class__').__name__) - except Exception: - func_name = str(func) - wrapper.__name__ = func_name - - return wrapper - -class ParserElement(object): - """Abstract base level parser element class.""" - DEFAULT_WHITE_CHARS = " \n\t\r" - verbose_stacktrace = False - - @staticmethod - def setDefaultWhitespaceChars( chars ): - r""" - Overrides the default whitespace chars - - Example:: - # default whitespace chars are space, and newline - OneOrMore(Word(alphas)).parseString("abc def\nghi jkl") # -> ['abc', 'def', 'ghi', 'jkl'] - - # change to just treat newline as significant - ParserElement.setDefaultWhitespaceChars(" \t") - OneOrMore(Word(alphas)).parseString("abc def\nghi jkl") # -> ['abc', 'def'] - """ - ParserElement.DEFAULT_WHITE_CHARS = chars - - @staticmethod - def inlineLiteralsUsing(cls): - """ - Set class to be used for inclusion of string literals into a parser. - - Example:: - # default literal class used is Literal - integer = Word(nums) - date_str = integer("year") + '/' + integer("month") + '/' + integer("day") - - date_str.parseString("1999/12/31") # -> ['1999', '/', '12', '/', '31'] - - - # change to Suppress - ParserElement.inlineLiteralsUsing(Suppress) - date_str = integer("year") + '/' + integer("month") + '/' + integer("day") - - date_str.parseString("1999/12/31") # -> ['1999', '12', '31'] - """ - ParserElement._literalStringClass = cls - - def __init__( self, savelist=False ): - self.parseAction = list() - self.failAction = None - #~ self.name = "" # don't define self.name, let subclasses try/except upcall - self.strRepr = None - self.resultsName = None - self.saveAsList = savelist - self.skipWhitespace = True - self.whiteChars = ParserElement.DEFAULT_WHITE_CHARS - self.copyDefaultWhiteChars = True - self.mayReturnEmpty = False # used when checking for left-recursion - self.keepTabs = False - self.ignoreExprs = list() - self.debug = False - self.streamlined = False - self.mayIndexError = True # used to optimize exception handling for subclasses that don't advance parse index - self.errmsg = "" - self.modalResults = True # used to mark results names as modal (report only last) or cumulative (list all) - self.debugActions = ( None, None, None ) #custom debug actions - self.re = None - self.callPreparse = True # used to avoid redundant calls to preParse - self.callDuringTry = False - - def copy( self ): - """ - Make a copy of this C{ParserElement}. Useful for defining different parse actions - for the same parsing pattern, using copies of the original parse element. - - Example:: - integer = Word(nums).setParseAction(lambda toks: int(toks[0])) - integerK = integer.copy().addParseAction(lambda toks: toks[0]*1024) + Suppress("K") - integerM = integer.copy().addParseAction(lambda toks: toks[0]*1024*1024) + Suppress("M") - - print(OneOrMore(integerK | integerM | integer).parseString("5K 100 640K 256M")) - prints:: - [5120, 100, 655360, 268435456] - Equivalent form of C{expr.copy()} is just C{expr()}:: - integerM = integer().addParseAction(lambda toks: toks[0]*1024*1024) + Suppress("M") - """ - cpy = copy.copy( self ) - cpy.parseAction = self.parseAction[:] - cpy.ignoreExprs = self.ignoreExprs[:] - if self.copyDefaultWhiteChars: - cpy.whiteChars = ParserElement.DEFAULT_WHITE_CHARS - return cpy - - def setName( self, name ): - """ - Define name for this expression, makes debugging and exception messages clearer. - - Example:: - Word(nums).parseString("ABC") # -> Exception: Expected W:(0123...) (at char 0), (line:1, col:1) - Word(nums).setName("integer").parseString("ABC") # -> Exception: Expected integer (at char 0), (line:1, col:1) - """ - self.name = name - self.errmsg = "Expected " + self.name - if hasattr(self,"exception"): - self.exception.msg = self.errmsg - return self - - def setResultsName( self, name, listAllMatches=False ): - """ - Define name for referencing matching tokens as a nested attribute - of the returned parse results. - NOTE: this returns a *copy* of the original C{ParserElement} object; - this is so that the client can define a basic element, such as an - integer, and reference it in multiple places with different names. - - You can also set results names using the abbreviated syntax, - C{expr("name")} in place of C{expr.setResultsName("name")} - - see L{I{__call__}<__call__>}. - - Example:: - date_str = (integer.setResultsName("year") + '/' - + integer.setResultsName("month") + '/' - + integer.setResultsName("day")) - - # equivalent form: - date_str = integer("year") + '/' + integer("month") + '/' + integer("day") - """ - newself = self.copy() - if name.endswith("*"): - name = name[:-1] - listAllMatches=True - newself.resultsName = name - newself.modalResults = not listAllMatches - return newself - - def setBreak(self,breakFlag = True): - """Method to invoke the Python pdb debugger when this element is - about to be parsed. Set C{breakFlag} to True to enable, False to - disable. - """ - if breakFlag: - _parseMethod = self._parse - def breaker(instring, loc, doActions=True, callPreParse=True): - import pdb - pdb.set_trace() - return _parseMethod( instring, loc, doActions, callPreParse ) - breaker._originalParseMethod = _parseMethod - self._parse = breaker - else: - if hasattr(self._parse,"_originalParseMethod"): - self._parse = self._parse._originalParseMethod - return self - - def setParseAction( self, *fns, **kwargs ): - """ - Define one or more actions to perform when successfully matching parse element definition. - Parse action fn is a callable method with 0-3 arguments, called as C{fn(s,loc,toks)}, - C{fn(loc,toks)}, C{fn(toks)}, or just C{fn()}, where: - - s = the original string being parsed (see note below) - - loc = the location of the matching substring - - toks = a list of the matched tokens, packaged as a C{L{ParseResults}} object - If the functions in fns modify the tokens, they can return them as the return - value from fn, and the modified list of tokens will replace the original. - Otherwise, fn does not need to return any value. - - Optional keyword arguments: - - callDuringTry = (default=C{False}) indicate if parse action should be run during lookaheads and alternate testing - - Note: the default parsing behavior is to expand tabs in the input string - before starting the parsing process. See L{I{parseString}} for more information - on parsing strings containing C{}s, and suggested methods to maintain a - consistent view of the parsed string, the parse location, and line and column - positions within the parsed string. - - Example:: - integer = Word(nums) - date_str = integer + '/' + integer + '/' + integer - - date_str.parseString("1999/12/31") # -> ['1999', '/', '12', '/', '31'] - - # use parse action to convert to ints at parse time - integer = Word(nums).setParseAction(lambda toks: int(toks[0])) - date_str = integer + '/' + integer + '/' + integer - - # note that integer fields are now ints, not strings - date_str.parseString("1999/12/31") # -> [1999, '/', 12, '/', 31] - """ - self.parseAction = list(map(_trim_arity, list(fns))) - self.callDuringTry = kwargs.get("callDuringTry", False) - return self - - def addParseAction( self, *fns, **kwargs ): - """ - Add one or more parse actions to expression's list of parse actions. See L{I{setParseAction}}. - - See examples in L{I{copy}}. - """ - self.parseAction += list(map(_trim_arity, list(fns))) - self.callDuringTry = self.callDuringTry or kwargs.get("callDuringTry", False) - return self - - def addCondition(self, *fns, **kwargs): - """Add a boolean predicate function to expression's list of parse actions. See - L{I{setParseAction}} for function call signatures. Unlike C{setParseAction}, - functions passed to C{addCondition} need to return boolean success/fail of the condition. - - Optional keyword arguments: - - message = define a custom message to be used in the raised exception - - fatal = if True, will raise ParseFatalException to stop parsing immediately; otherwise will raise ParseException - - Example:: - integer = Word(nums).setParseAction(lambda toks: int(toks[0])) - year_int = integer.copy() - year_int.addCondition(lambda toks: toks[0] >= 2000, message="Only support years 2000 and later") - date_str = year_int + '/' + integer + '/' + integer - - result = date_str.parseString("1999/12/31") # -> Exception: Only support years 2000 and later (at char 0), (line:1, col:1) - """ - msg = kwargs.get("message", "failed user-defined condition") - exc_type = ParseFatalException if kwargs.get("fatal", False) else ParseException - for fn in fns: - def pa(s,l,t): - if not bool(_trim_arity(fn)(s,l,t)): - raise exc_type(s,l,msg) - self.parseAction.append(pa) - self.callDuringTry = self.callDuringTry or kwargs.get("callDuringTry", False) - return self - - def setFailAction( self, fn ): - """Define action to perform if parsing fails at this expression. - Fail action fn is a callable function that takes the arguments - C{fn(s,loc,expr,err)} where: - - s = string being parsed - - loc = location where expression match was attempted and failed - - expr = the parse expression that failed - - err = the exception thrown - The function returns no value. It may throw C{L{ParseFatalException}} - if it is desired to stop parsing immediately.""" - self.failAction = fn - return self - - def _skipIgnorables( self, instring, loc ): - exprsFound = True - while exprsFound: - exprsFound = False - for e in self.ignoreExprs: - try: - while 1: - loc,dummy = e._parse( instring, loc ) - exprsFound = True - except ParseException: - pass - return loc - - def preParse( self, instring, loc ): - if self.ignoreExprs: - loc = self._skipIgnorables( instring, loc ) - - if self.skipWhitespace: - wt = self.whiteChars - instrlen = len(instring) - while loc < instrlen and instring[loc] in wt: - loc += 1 - - return loc - - def parseImpl( self, instring, loc, doActions=True ): - return loc, [] - - def postParse( self, instring, loc, tokenlist ): - return tokenlist - - #~ @profile - def _parseNoCache( self, instring, loc, doActions=True, callPreParse=True ): - debugging = ( self.debug ) #and doActions ) - - if debugging or self.failAction: - #~ print ("Match",self,"at loc",loc,"(%d,%d)" % ( lineno(loc,instring), col(loc,instring) )) - if (self.debugActions[0] ): - self.debugActions[0]( instring, loc, self ) - if callPreParse and self.callPreparse: - preloc = self.preParse( instring, loc ) - else: - preloc = loc - tokensStart = preloc - try: - try: - loc,tokens = self.parseImpl( instring, preloc, doActions ) - except IndexError: - raise ParseException( instring, len(instring), self.errmsg, self ) - except ParseBaseException as err: - #~ print ("Exception raised:", err) - if self.debugActions[2]: - self.debugActions[2]( instring, tokensStart, self, err ) - if self.failAction: - self.failAction( instring, tokensStart, self, err ) - raise - else: - if callPreParse and self.callPreparse: - preloc = self.preParse( instring, loc ) - else: - preloc = loc - tokensStart = preloc - if self.mayIndexError or loc >= len(instring): - try: - loc,tokens = self.parseImpl( instring, preloc, doActions ) - except IndexError: - raise ParseException( instring, len(instring), self.errmsg, self ) - else: - loc,tokens = self.parseImpl( instring, preloc, doActions ) - - tokens = self.postParse( instring, loc, tokens ) - - retTokens = ParseResults( tokens, self.resultsName, asList=self.saveAsList, modal=self.modalResults ) - if self.parseAction and (doActions or self.callDuringTry): - if debugging: - try: - for fn in self.parseAction: - tokens = fn( instring, tokensStart, retTokens ) - if tokens is not None: - retTokens = ParseResults( tokens, - self.resultsName, - asList=self.saveAsList and isinstance(tokens,(ParseResults,list)), - modal=self.modalResults ) - except ParseBaseException as err: - #~ print "Exception raised in user parse action:", err - if (self.debugActions[2] ): - self.debugActions[2]( instring, tokensStart, self, err ) - raise - else: - for fn in self.parseAction: - tokens = fn( instring, tokensStart, retTokens ) - if tokens is not None: - retTokens = ParseResults( tokens, - self.resultsName, - asList=self.saveAsList and isinstance(tokens,(ParseResults,list)), - modal=self.modalResults ) - - if debugging: - #~ print ("Matched",self,"->",retTokens.asList()) - if (self.debugActions[1] ): - self.debugActions[1]( instring, tokensStart, loc, self, retTokens ) - - return loc, retTokens - - def tryParse( self, instring, loc ): - try: - return self._parse( instring, loc, doActions=False )[0] - except ParseFatalException: - raise ParseException( instring, loc, self.errmsg, self) - - def canParseNext(self, instring, loc): - try: - self.tryParse(instring, loc) - except (ParseException, IndexError): - return False - else: - return True - - class _UnboundedCache(object): - def __init__(self): - cache = {} - self.not_in_cache = not_in_cache = object() - - def get(self, key): - return cache.get(key, not_in_cache) - - def set(self, key, value): - cache[key] = value - - def clear(self): - cache.clear() - - def cache_len(self): - return len(cache) - - self.get = types.MethodType(get, self) - self.set = types.MethodType(set, self) - self.clear = types.MethodType(clear, self) - self.__len__ = types.MethodType(cache_len, self) - - if _OrderedDict is not None: - class _FifoCache(object): - def __init__(self, size): - self.not_in_cache = not_in_cache = object() - - cache = _OrderedDict() - - def get(self, key): - return cache.get(key, not_in_cache) - - def set(self, key, value): - cache[key] = value - while len(cache) > size: - try: - cache.popitem(False) - except KeyError: - pass - - def clear(self): - cache.clear() - - def cache_len(self): - return len(cache) - - self.get = types.MethodType(get, self) - self.set = types.MethodType(set, self) - self.clear = types.MethodType(clear, self) - self.__len__ = types.MethodType(cache_len, self) - - else: - class _FifoCache(object): - def __init__(self, size): - self.not_in_cache = not_in_cache = object() - - cache = {} - key_fifo = collections.deque([], size) - - def get(self, key): - return cache.get(key, not_in_cache) - - def set(self, key, value): - cache[key] = value - while len(key_fifo) > size: - cache.pop(key_fifo.popleft(), None) - key_fifo.append(key) - - def clear(self): - cache.clear() - key_fifo.clear() - - def cache_len(self): - return len(cache) - - self.get = types.MethodType(get, self) - self.set = types.MethodType(set, self) - self.clear = types.MethodType(clear, self) - self.__len__ = types.MethodType(cache_len, self) - - # argument cache for optimizing repeated calls when backtracking through recursive expressions - packrat_cache = {} # this is set later by enabledPackrat(); this is here so that resetCache() doesn't fail - packrat_cache_lock = RLock() - packrat_cache_stats = [0, 0] - - # this method gets repeatedly called during backtracking with the same arguments - - # we can cache these arguments and save ourselves the trouble of re-parsing the contained expression - def _parseCache( self, instring, loc, doActions=True, callPreParse=True ): - HIT, MISS = 0, 1 - lookup = (self, instring, loc, callPreParse, doActions) - with ParserElement.packrat_cache_lock: - cache = ParserElement.packrat_cache - value = cache.get(lookup) - if value is cache.not_in_cache: - ParserElement.packrat_cache_stats[MISS] += 1 - try: - value = self._parseNoCache(instring, loc, doActions, callPreParse) - except ParseBaseException as pe: - # cache a copy of the exception, without the traceback - cache.set(lookup, pe.__class__(*pe.args)) - raise - else: - cache.set(lookup, (value[0], value[1].copy())) - return value - else: - ParserElement.packrat_cache_stats[HIT] += 1 - if isinstance(value, Exception): - raise value - return (value[0], value[1].copy()) - - _parse = _parseNoCache - - @staticmethod - def resetCache(): - ParserElement.packrat_cache.clear() - ParserElement.packrat_cache_stats[:] = [0] * len(ParserElement.packrat_cache_stats) - - _packratEnabled = False - @staticmethod - def enablePackrat(cache_size_limit=128): - """Enables "packrat" parsing, which adds memoizing to the parsing logic. - Repeated parse attempts at the same string location (which happens - often in many complex grammars) can immediately return a cached value, - instead of re-executing parsing/validating code. Memoizing is done of - both valid results and parsing exceptions. - - Parameters: - - cache_size_limit - (default=C{128}) - if an integer value is provided - will limit the size of the packrat cache; if None is passed, then - the cache size will be unbounded; if 0 is passed, the cache will - be effectively disabled. - - This speedup may break existing programs that use parse actions that - have side-effects. For this reason, packrat parsing is disabled when - you first import pyparsing. To activate the packrat feature, your - program must call the class method C{ParserElement.enablePackrat()}. If - your program uses C{psyco} to "compile as you go", you must call - C{enablePackrat} before calling C{psyco.full()}. If you do not do this, - Python will crash. For best results, call C{enablePackrat()} immediately - after importing pyparsing. - - Example:: - import pyparsing - pyparsing.ParserElement.enablePackrat() - """ - if not ParserElement._packratEnabled: - ParserElement._packratEnabled = True - if cache_size_limit is None: - ParserElement.packrat_cache = ParserElement._UnboundedCache() - else: - ParserElement.packrat_cache = ParserElement._FifoCache(cache_size_limit) - ParserElement._parse = ParserElement._parseCache - - def parseString( self, instring, parseAll=False ): - """ - Execute the parse expression with the given string. - This is the main interface to the client code, once the complete - expression has been built. - - If you want the grammar to require that the entire input string be - successfully parsed, then set C{parseAll} to True (equivalent to ending - the grammar with C{L{StringEnd()}}). - - Note: C{parseString} implicitly calls C{expandtabs()} on the input string, - in order to report proper column numbers in parse actions. - If the input string contains tabs and - the grammar uses parse actions that use the C{loc} argument to index into the - string being parsed, you can ensure you have a consistent view of the input - string by: - - calling C{parseWithTabs} on your grammar before calling C{parseString} - (see L{I{parseWithTabs}}) - - define your parse action using the full C{(s,loc,toks)} signature, and - reference the input string using the parse action's C{s} argument - - explicitly expand the tabs in your input string before calling - C{parseString} - - Example:: - Word('a').parseString('aaaaabaaa') # -> ['aaaaa'] - Word('a').parseString('aaaaabaaa', parseAll=True) # -> Exception: Expected end of text - """ - ParserElement.resetCache() - if not self.streamlined: - self.streamline() - #~ self.saveAsList = True - for e in self.ignoreExprs: - e.streamline() - if not self.keepTabs: - instring = instring.expandtabs() - try: - loc, tokens = self._parse( instring, 0 ) - if parseAll: - loc = self.preParse( instring, loc ) - se = Empty() + StringEnd() - se._parse( instring, loc ) - except ParseBaseException as exc: - if ParserElement.verbose_stacktrace: - raise - else: - # catch and re-raise exception from here, clears out pyparsing internal stack trace - raise exc - else: - return tokens - - def scanString( self, instring, maxMatches=_MAX_INT, overlap=False ): - """ - Scan the input string for expression matches. Each match will return the - matching tokens, start location, and end location. May be called with optional - C{maxMatches} argument, to clip scanning after 'n' matches are found. If - C{overlap} is specified, then overlapping matches will be reported. - - Note that the start and end locations are reported relative to the string - being parsed. See L{I{parseString}} for more information on parsing - strings with embedded tabs. - - Example:: - source = "sldjf123lsdjjkf345sldkjf879lkjsfd987" - print(source) - for tokens,start,end in Word(alphas).scanString(source): - print(' '*start + '^'*(end-start)) - print(' '*start + tokens[0]) - - prints:: - - sldjf123lsdjjkf345sldkjf879lkjsfd987 - ^^^^^ - sldjf - ^^^^^^^ - lsdjjkf - ^^^^^^ - sldkjf - ^^^^^^ - lkjsfd - """ - if not self.streamlined: - self.streamline() - for e in self.ignoreExprs: - e.streamline() - - if not self.keepTabs: - instring = _ustr(instring).expandtabs() - instrlen = len(instring) - loc = 0 - preparseFn = self.preParse - parseFn = self._parse - ParserElement.resetCache() - matches = 0 - try: - while loc <= instrlen and matches < maxMatches: - try: - preloc = preparseFn( instring, loc ) - nextLoc,tokens = parseFn( instring, preloc, callPreParse=False ) - except ParseException: - loc = preloc+1 - else: - if nextLoc > loc: - matches += 1 - yield tokens, preloc, nextLoc - if overlap: - nextloc = preparseFn( instring, loc ) - if nextloc > loc: - loc = nextLoc - else: - loc += 1 - else: - loc = nextLoc - else: - loc = preloc+1 - except ParseBaseException as exc: - if ParserElement.verbose_stacktrace: - raise - else: - # catch and re-raise exception from here, clears out pyparsing internal stack trace - raise exc - - def transformString( self, instring ): - """ - Extension to C{L{scanString}}, to modify matching text with modified tokens that may - be returned from a parse action. To use C{transformString}, define a grammar and - attach a parse action to it that modifies the returned token list. - Invoking C{transformString()} on a target string will then scan for matches, - and replace the matched text patterns according to the logic in the parse - action. C{transformString()} returns the resulting transformed string. - - Example:: - wd = Word(alphas) - wd.setParseAction(lambda toks: toks[0].title()) - - print(wd.transformString("now is the winter of our discontent made glorious summer by this sun of york.")) - Prints:: - Now Is The Winter Of Our Discontent Made Glorious Summer By This Sun Of York. - """ - out = [] - lastE = 0 - # force preservation of s, to minimize unwanted transformation of string, and to - # keep string locs straight between transformString and scanString - self.keepTabs = True - try: - for t,s,e in self.scanString( instring ): - out.append( instring[lastE:s] ) - if t: - if isinstance(t,ParseResults): - out += t.asList() - elif isinstance(t,list): - out += t - else: - out.append(t) - lastE = e - out.append(instring[lastE:]) - out = [o for o in out if o] - return "".join(map(_ustr,_flatten(out))) - except ParseBaseException as exc: - if ParserElement.verbose_stacktrace: - raise - else: - # catch and re-raise exception from here, clears out pyparsing internal stack trace - raise exc - - def searchString( self, instring, maxMatches=_MAX_INT ): - """ - Another extension to C{L{scanString}}, simplifying the access to the tokens found - to match the given parse expression. May be called with optional - C{maxMatches} argument, to clip searching after 'n' matches are found. - - Example:: - # a capitalized word starts with an uppercase letter, followed by zero or more lowercase letters - cap_word = Word(alphas.upper(), alphas.lower()) - - print(cap_word.searchString("More than Iron, more than Lead, more than Gold I need Electricity")) - - # the sum() builtin can be used to merge results into a single ParseResults object - print(sum(cap_word.searchString("More than Iron, more than Lead, more than Gold I need Electricity"))) - prints:: - [['More'], ['Iron'], ['Lead'], ['Gold'], ['I'], ['Electricity']] - ['More', 'Iron', 'Lead', 'Gold', 'I', 'Electricity'] - """ - try: - return ParseResults([ t for t,s,e in self.scanString( instring, maxMatches ) ]) - except ParseBaseException as exc: - if ParserElement.verbose_stacktrace: - raise - else: - # catch and re-raise exception from here, clears out pyparsing internal stack trace - raise exc - - def split(self, instring, maxsplit=_MAX_INT, includeSeparators=False): - """ - Generator method to split a string using the given expression as a separator. - May be called with optional C{maxsplit} argument, to limit the number of splits; - and the optional C{includeSeparators} argument (default=C{False}), if the separating - matching text should be included in the split results. - - Example:: - punc = oneOf(list(".,;:/-!?")) - print(list(punc.split("This, this?, this sentence, is badly punctuated!"))) - prints:: - ['This', ' this', '', ' this sentence', ' is badly punctuated', ''] - """ - splits = 0 - last = 0 - for t,s,e in self.scanString(instring, maxMatches=maxsplit): - yield instring[last:s] - if includeSeparators: - yield t[0] - last = e - yield instring[last:] - - def __add__(self, other ): - """ - Implementation of + operator - returns C{L{And}}. Adding strings to a ParserElement - converts them to L{Literal}s by default. - - Example:: - greet = Word(alphas) + "," + Word(alphas) + "!" - hello = "Hello, World!" - print (hello, "->", greet.parseString(hello)) - Prints:: - Hello, World! -> ['Hello', ',', 'World', '!'] - """ - if isinstance( other, basestring ): - other = ParserElement._literalStringClass( other ) - if not isinstance( other, ParserElement ): - warnings.warn("Cannot combine element of type %s with ParserElement" % type(other), - SyntaxWarning, stacklevel=2) - return None - return And( [ self, other ] ) - - def __radd__(self, other ): - """ - Implementation of + operator when left operand is not a C{L{ParserElement}} - """ - if isinstance( other, basestring ): - other = ParserElement._literalStringClass( other ) - if not isinstance( other, ParserElement ): - warnings.warn("Cannot combine element of type %s with ParserElement" % type(other), - SyntaxWarning, stacklevel=2) - return None - return other + self - - def __sub__(self, other): - """ - Implementation of - operator, returns C{L{And}} with error stop - """ - if isinstance( other, basestring ): - other = ParserElement._literalStringClass( other ) - if not isinstance( other, ParserElement ): - warnings.warn("Cannot combine element of type %s with ParserElement" % type(other), - SyntaxWarning, stacklevel=2) - return None - return self + And._ErrorStop() + other - - def __rsub__(self, other ): - """ - Implementation of - operator when left operand is not a C{L{ParserElement}} - """ - if isinstance( other, basestring ): - other = ParserElement._literalStringClass( other ) - if not isinstance( other, ParserElement ): - warnings.warn("Cannot combine element of type %s with ParserElement" % type(other), - SyntaxWarning, stacklevel=2) - return None - return other - self - - def __mul__(self,other): - """ - Implementation of * operator, allows use of C{expr * 3} in place of - C{expr + expr + expr}. Expressions may also me multiplied by a 2-integer - tuple, similar to C{{min,max}} multipliers in regular expressions. Tuples - may also include C{None} as in: - - C{expr*(n,None)} or C{expr*(n,)} is equivalent - to C{expr*n + L{ZeroOrMore}(expr)} - (read as "at least n instances of C{expr}") - - C{expr*(None,n)} is equivalent to C{expr*(0,n)} - (read as "0 to n instances of C{expr}") - - C{expr*(None,None)} is equivalent to C{L{ZeroOrMore}(expr)} - - C{expr*(1,None)} is equivalent to C{L{OneOrMore}(expr)} - - Note that C{expr*(None,n)} does not raise an exception if - more than n exprs exist in the input stream; that is, - C{expr*(None,n)} does not enforce a maximum number of expr - occurrences. If this behavior is desired, then write - C{expr*(None,n) + ~expr} - """ - if isinstance(other,int): - minElements, optElements = other,0 - elif isinstance(other,tuple): - other = (other + (None, None))[:2] - if other[0] is None: - other = (0, other[1]) - if isinstance(other[0],int) and other[1] is None: - if other[0] == 0: - return ZeroOrMore(self) - if other[0] == 1: - return OneOrMore(self) - else: - return self*other[0] + ZeroOrMore(self) - elif isinstance(other[0],int) and isinstance(other[1],int): - minElements, optElements = other - optElements -= minElements - else: - raise TypeError("cannot multiply 'ParserElement' and ('%s','%s') objects", type(other[0]),type(other[1])) - else: - raise TypeError("cannot multiply 'ParserElement' and '%s' objects", type(other)) - - if minElements < 0: - raise ValueError("cannot multiply ParserElement by negative value") - if optElements < 0: - raise ValueError("second tuple value must be greater or equal to first tuple value") - if minElements == optElements == 0: - raise ValueError("cannot multiply ParserElement by 0 or (0,0)") - - if (optElements): - def makeOptionalList(n): - if n>1: - return Optional(self + makeOptionalList(n-1)) - else: - return Optional(self) - if minElements: - if minElements == 1: - ret = self + makeOptionalList(optElements) - else: - ret = And([self]*minElements) + makeOptionalList(optElements) - else: - ret = makeOptionalList(optElements) - else: - if minElements == 1: - ret = self - else: - ret = And([self]*minElements) - return ret - - def __rmul__(self, other): - return self.__mul__(other) - - def __or__(self, other ): - """ - Implementation of | operator - returns C{L{MatchFirst}} - """ - if isinstance( other, basestring ): - other = ParserElement._literalStringClass( other ) - if not isinstance( other, ParserElement ): - warnings.warn("Cannot combine element of type %s with ParserElement" % type(other), - SyntaxWarning, stacklevel=2) - return None - return MatchFirst( [ self, other ] ) - - def __ror__(self, other ): - """ - Implementation of | operator when left operand is not a C{L{ParserElement}} - """ - if isinstance( other, basestring ): - other = ParserElement._literalStringClass( other ) - if not isinstance( other, ParserElement ): - warnings.warn("Cannot combine element of type %s with ParserElement" % type(other), - SyntaxWarning, stacklevel=2) - return None - return other | self - - def __xor__(self, other ): - """ - Implementation of ^ operator - returns C{L{Or}} - """ - if isinstance( other, basestring ): - other = ParserElement._literalStringClass( other ) - if not isinstance( other, ParserElement ): - warnings.warn("Cannot combine element of type %s with ParserElement" % type(other), - SyntaxWarning, stacklevel=2) - return None - return Or( [ self, other ] ) - - def __rxor__(self, other ): - """ - Implementation of ^ operator when left operand is not a C{L{ParserElement}} - """ - if isinstance( other, basestring ): - other = ParserElement._literalStringClass( other ) - if not isinstance( other, ParserElement ): - warnings.warn("Cannot combine element of type %s with ParserElement" % type(other), - SyntaxWarning, stacklevel=2) - return None - return other ^ self - - def __and__(self, other ): - """ - Implementation of & operator - returns C{L{Each}} - """ - if isinstance( other, basestring ): - other = ParserElement._literalStringClass( other ) - if not isinstance( other, ParserElement ): - warnings.warn("Cannot combine element of type %s with ParserElement" % type(other), - SyntaxWarning, stacklevel=2) - return None - return Each( [ self, other ] ) - - def __rand__(self, other ): - """ - Implementation of & operator when left operand is not a C{L{ParserElement}} - """ - if isinstance( other, basestring ): - other = ParserElement._literalStringClass( other ) - if not isinstance( other, ParserElement ): - warnings.warn("Cannot combine element of type %s with ParserElement" % type(other), - SyntaxWarning, stacklevel=2) - return None - return other & self - - def __invert__( self ): - """ - Implementation of ~ operator - returns C{L{NotAny}} - """ - return NotAny( self ) - - def __call__(self, name=None): - """ - Shortcut for C{L{setResultsName}}, with C{listAllMatches=False}. - - If C{name} is given with a trailing C{'*'} character, then C{listAllMatches} will be - passed as C{True}. - - If C{name} is omitted, same as calling C{L{copy}}. - - Example:: - # these are equivalent - userdata = Word(alphas).setResultsName("name") + Word(nums+"-").setResultsName("socsecno") - userdata = Word(alphas)("name") + Word(nums+"-")("socsecno") - """ - if name is not None: - return self.setResultsName(name) - else: - return self.copy() - - def suppress( self ): - """ - Suppresses the output of this C{ParserElement}; useful to keep punctuation from - cluttering up returned output. - """ - return Suppress( self ) - - def leaveWhitespace( self ): - """ - Disables the skipping of whitespace before matching the characters in the - C{ParserElement}'s defined pattern. This is normally only used internally by - the pyparsing module, but may be needed in some whitespace-sensitive grammars. - """ - self.skipWhitespace = False - return self - - def setWhitespaceChars( self, chars ): - """ - Overrides the default whitespace chars - """ - self.skipWhitespace = True - self.whiteChars = chars - self.copyDefaultWhiteChars = False - return self - - def parseWithTabs( self ): - """ - Overrides default behavior to expand C{}s to spaces before parsing the input string. - Must be called before C{parseString} when the input grammar contains elements that - match C{} characters. - """ - self.keepTabs = True - return self - - def ignore( self, other ): - """ - Define expression to be ignored (e.g., comments) while doing pattern - matching; may be called repeatedly, to define multiple comment or other - ignorable patterns. - - Example:: - patt = OneOrMore(Word(alphas)) - patt.parseString('ablaj /* comment */ lskjd') # -> ['ablaj'] - - patt.ignore(cStyleComment) - patt.parseString('ablaj /* comment */ lskjd') # -> ['ablaj', 'lskjd'] - """ - if isinstance(other, basestring): - other = Suppress(other) - - if isinstance( other, Suppress ): - if other not in self.ignoreExprs: - self.ignoreExprs.append(other) - else: - self.ignoreExprs.append( Suppress( other.copy() ) ) - return self - - def setDebugActions( self, startAction, successAction, exceptionAction ): - """ - Enable display of debugging messages while doing pattern matching. - """ - self.debugActions = (startAction or _defaultStartDebugAction, - successAction or _defaultSuccessDebugAction, - exceptionAction or _defaultExceptionDebugAction) - self.debug = True - return self - - def setDebug( self, flag=True ): - """ - Enable display of debugging messages while doing pattern matching. - Set C{flag} to True to enable, False to disable. - - Example:: - wd = Word(alphas).setName("alphaword") - integer = Word(nums).setName("numword") - term = wd | integer - - # turn on debugging for wd - wd.setDebug() - - OneOrMore(term).parseString("abc 123 xyz 890") - - prints:: - Match alphaword at loc 0(1,1) - Matched alphaword -> ['abc'] - Match alphaword at loc 3(1,4) - Exception raised:Expected alphaword (at char 4), (line:1, col:5) - Match alphaword at loc 7(1,8) - Matched alphaword -> ['xyz'] - Match alphaword at loc 11(1,12) - Exception raised:Expected alphaword (at char 12), (line:1, col:13) - Match alphaword at loc 15(1,16) - Exception raised:Expected alphaword (at char 15), (line:1, col:16) - - The output shown is that produced by the default debug actions - custom debug actions can be - specified using L{setDebugActions}. Prior to attempting - to match the C{wd} expression, the debugging message C{"Match at loc (,)"} - is shown. Then if the parse succeeds, a C{"Matched"} message is shown, or an C{"Exception raised"} - message is shown. Also note the use of L{setName} to assign a human-readable name to the expression, - which makes debugging and exception messages easier to understand - for instance, the default - name created for the C{Word} expression without calling C{setName} is C{"W:(ABCD...)"}. - """ - if flag: - self.setDebugActions( _defaultStartDebugAction, _defaultSuccessDebugAction, _defaultExceptionDebugAction ) - else: - self.debug = False - return self - - def __str__( self ): - return self.name - - def __repr__( self ): - return _ustr(self) - - def streamline( self ): - self.streamlined = True - self.strRepr = None - return self - - def checkRecursion( self, parseElementList ): - pass - - def validate( self, validateTrace=[] ): - """ - Check defined expressions for valid structure, check for infinite recursive definitions. - """ - self.checkRecursion( [] ) - - def parseFile( self, file_or_filename, parseAll=False ): - """ - Execute the parse expression on the given file or filename. - If a filename is specified (instead of a file object), - the entire file is opened, read, and closed before parsing. - """ - try: - file_contents = file_or_filename.read() - except AttributeError: - with open(file_or_filename, "r") as f: - file_contents = f.read() - try: - return self.parseString(file_contents, parseAll) - except ParseBaseException as exc: - if ParserElement.verbose_stacktrace: - raise - else: - # catch and re-raise exception from here, clears out pyparsing internal stack trace - raise exc - - def __eq__(self,other): - if isinstance(other, ParserElement): - return self is other or vars(self) == vars(other) - elif isinstance(other, basestring): - return self.matches(other) - else: - return super(ParserElement,self)==other - - def __ne__(self,other): - return not (self == other) - - def __hash__(self): - return hash(id(self)) - - def __req__(self,other): - return self == other - - def __rne__(self,other): - return not (self == other) - - def matches(self, testString, parseAll=True): - """ - Method for quick testing of a parser against a test string. Good for simple - inline microtests of sub expressions while building up larger parser. - - Parameters: - - testString - to test against this expression for a match - - parseAll - (default=C{True}) - flag to pass to C{L{parseString}} when running tests - - Example:: - expr = Word(nums) - assert expr.matches("100") - """ - try: - self.parseString(_ustr(testString), parseAll=parseAll) - return True - except ParseBaseException: - return False - - def runTests(self, tests, parseAll=True, comment='#', fullDump=True, printResults=True, failureTests=False): - """ - Execute the parse expression on a series of test strings, showing each - test, the parsed results or where the parse failed. Quick and easy way to - run a parse expression against a list of sample strings. - - Parameters: - - tests - a list of separate test strings, or a multiline string of test strings - - parseAll - (default=C{True}) - flag to pass to C{L{parseString}} when running tests - - comment - (default=C{'#'}) - expression for indicating embedded comments in the test - string; pass None to disable comment filtering - - fullDump - (default=C{True}) - dump results as list followed by results names in nested outline; - if False, only dump nested list - - printResults - (default=C{True}) prints test output to stdout - - failureTests - (default=C{False}) indicates if these tests are expected to fail parsing - - Returns: a (success, results) tuple, where success indicates that all tests succeeded - (or failed if C{failureTests} is True), and the results contain a list of lines of each - test's output - - Example:: - number_expr = pyparsing_common.number.copy() - - result = number_expr.runTests(''' - # unsigned integer - 100 - # negative integer - -100 - # float with scientific notation - 6.02e23 - # integer with scientific notation - 1e-12 - ''') - print("Success" if result[0] else "Failed!") - - result = number_expr.runTests(''' - # stray character - 100Z - # missing leading digit before '.' - -.100 - # too many '.' - 3.14.159 - ''', failureTests=True) - print("Success" if result[0] else "Failed!") - prints:: - # unsigned integer - 100 - [100] - - # negative integer - -100 - [-100] - - # float with scientific notation - 6.02e23 - [6.02e+23] - - # integer with scientific notation - 1e-12 - [1e-12] - - Success - - # stray character - 100Z - ^ - FAIL: Expected end of text (at char 3), (line:1, col:4) - - # missing leading digit before '.' - -.100 - ^ - FAIL: Expected {real number with scientific notation | real number | signed integer} (at char 0), (line:1, col:1) - - # too many '.' - 3.14.159 - ^ - FAIL: Expected end of text (at char 4), (line:1, col:5) - - Success - - Each test string must be on a single line. If you want to test a string that spans multiple - lines, create a test like this:: - - expr.runTest(r"this is a test\\n of strings that spans \\n 3 lines") - - (Note that this is a raw string literal, you must include the leading 'r'.) - """ - if isinstance(tests, basestring): - tests = list(map(str.strip, tests.rstrip().splitlines())) - if isinstance(comment, basestring): - comment = Literal(comment) - allResults = [] - comments = [] - success = True - for t in tests: - if comment is not None and comment.matches(t, False) or comments and not t: - comments.append(t) - continue - if not t: - continue - out = ['\n'.join(comments), t] - comments = [] - try: - t = t.replace(r'\n','\n') - result = self.parseString(t, parseAll=parseAll) - out.append(result.dump(full=fullDump)) - success = success and not failureTests - except ParseBaseException as pe: - fatal = "(FATAL)" if isinstance(pe, ParseFatalException) else "" - if '\n' in t: - out.append(line(pe.loc, t)) - out.append(' '*(col(pe.loc,t)-1) + '^' + fatal) - else: - out.append(' '*pe.loc + '^' + fatal) - out.append("FAIL: " + str(pe)) - success = success and failureTests - result = pe - except Exception as exc: - out.append("FAIL-EXCEPTION: " + str(exc)) - success = success and failureTests - result = exc - - if printResults: - if fullDump: - out.append('') - print('\n'.join(out)) - - allResults.append((t, result)) - - return success, allResults - - -class Token(ParserElement): - """ - Abstract C{ParserElement} subclass, for defining atomic matching patterns. - """ - def __init__( self ): - super(Token,self).__init__( savelist=False ) - - -class Empty(Token): - """ - An empty token, will always match. - """ - def __init__( self ): - super(Empty,self).__init__() - self.name = "Empty" - self.mayReturnEmpty = True - self.mayIndexError = False - - -class NoMatch(Token): - """ - A token that will never match. - """ - def __init__( self ): - super(NoMatch,self).__init__() - self.name = "NoMatch" - self.mayReturnEmpty = True - self.mayIndexError = False - self.errmsg = "Unmatchable token" - - def parseImpl( self, instring, loc, doActions=True ): - raise ParseException(instring, loc, self.errmsg, self) - - -class Literal(Token): - """ - Token to exactly match a specified string. - - Example:: - Literal('blah').parseString('blah') # -> ['blah'] - Literal('blah').parseString('blahfooblah') # -> ['blah'] - Literal('blah').parseString('bla') # -> Exception: Expected "blah" - - For case-insensitive matching, use L{CaselessLiteral}. - - For keyword matching (force word break before and after the matched string), - use L{Keyword} or L{CaselessKeyword}. - """ - def __init__( self, matchString ): - super(Literal,self).__init__() - self.match = matchString - self.matchLen = len(matchString) - try: - self.firstMatchChar = matchString[0] - except IndexError: - warnings.warn("null string passed to Literal; use Empty() instead", - SyntaxWarning, stacklevel=2) - self.__class__ = Empty - self.name = '"%s"' % _ustr(self.match) - self.errmsg = "Expected " + self.name - self.mayReturnEmpty = False - self.mayIndexError = False - - # Performance tuning: this routine gets called a *lot* - # if this is a single character match string and the first character matches, - # short-circuit as quickly as possible, and avoid calling startswith - #~ @profile - def parseImpl( self, instring, loc, doActions=True ): - if (instring[loc] == self.firstMatchChar and - (self.matchLen==1 or instring.startswith(self.match,loc)) ): - return loc+self.matchLen, self.match - raise ParseException(instring, loc, self.errmsg, self) -_L = Literal -ParserElement._literalStringClass = Literal - -class Keyword(Token): - """ - Token to exactly match a specified string as a keyword, that is, it must be - immediately followed by a non-keyword character. Compare with C{L{Literal}}: - - C{Literal("if")} will match the leading C{'if'} in C{'ifAndOnlyIf'}. - - C{Keyword("if")} will not; it will only match the leading C{'if'} in C{'if x=1'}, or C{'if(y==2)'} - Accepts two optional constructor arguments in addition to the keyword string: - - C{identChars} is a string of characters that would be valid identifier characters, - defaulting to all alphanumerics + "_" and "$" - - C{caseless} allows case-insensitive matching, default is C{False}. - - Example:: - Keyword("start").parseString("start") # -> ['start'] - Keyword("start").parseString("starting") # -> Exception - - For case-insensitive matching, use L{CaselessKeyword}. - """ - DEFAULT_KEYWORD_CHARS = alphanums+"_$" - - def __init__( self, matchString, identChars=None, caseless=False ): - super(Keyword,self).__init__() - if identChars is None: - identChars = Keyword.DEFAULT_KEYWORD_CHARS - self.match = matchString - self.matchLen = len(matchString) - try: - self.firstMatchChar = matchString[0] - except IndexError: - warnings.warn("null string passed to Keyword; use Empty() instead", - SyntaxWarning, stacklevel=2) - self.name = '"%s"' % self.match - self.errmsg = "Expected " + self.name - self.mayReturnEmpty = False - self.mayIndexError = False - self.caseless = caseless - if caseless: - self.caselessmatch = matchString.upper() - identChars = identChars.upper() - self.identChars = set(identChars) - - def parseImpl( self, instring, loc, doActions=True ): - if self.caseless: - if ( (instring[ loc:loc+self.matchLen ].upper() == self.caselessmatch) and - (loc >= len(instring)-self.matchLen or instring[loc+self.matchLen].upper() not in self.identChars) and - (loc == 0 or instring[loc-1].upper() not in self.identChars) ): - return loc+self.matchLen, self.match - else: - if (instring[loc] == self.firstMatchChar and - (self.matchLen==1 or instring.startswith(self.match,loc)) and - (loc >= len(instring)-self.matchLen or instring[loc+self.matchLen] not in self.identChars) and - (loc == 0 or instring[loc-1] not in self.identChars) ): - return loc+self.matchLen, self.match - raise ParseException(instring, loc, self.errmsg, self) - - def copy(self): - c = super(Keyword,self).copy() - c.identChars = Keyword.DEFAULT_KEYWORD_CHARS - return c - - @staticmethod - def setDefaultKeywordChars( chars ): - """Overrides the default Keyword chars - """ - Keyword.DEFAULT_KEYWORD_CHARS = chars - -class CaselessLiteral(Literal): - """ - Token to match a specified string, ignoring case of letters. - Note: the matched results will always be in the case of the given - match string, NOT the case of the input text. - - Example:: - OneOrMore(CaselessLiteral("CMD")).parseString("cmd CMD Cmd10") # -> ['CMD', 'CMD', 'CMD'] - - (Contrast with example for L{CaselessKeyword}.) - """ - def __init__( self, matchString ): - super(CaselessLiteral,self).__init__( matchString.upper() ) - # Preserve the defining literal. - self.returnString = matchString - self.name = "'%s'" % self.returnString - self.errmsg = "Expected " + self.name - - def parseImpl( self, instring, loc, doActions=True ): - if instring[ loc:loc+self.matchLen ].upper() == self.match: - return loc+self.matchLen, self.returnString - raise ParseException(instring, loc, self.errmsg, self) - -class CaselessKeyword(Keyword): - """ - Caseless version of L{Keyword}. - - Example:: - OneOrMore(CaselessKeyword("CMD")).parseString("cmd CMD Cmd10") # -> ['CMD', 'CMD'] - - (Contrast with example for L{CaselessLiteral}.) - """ - def __init__( self, matchString, identChars=None ): - super(CaselessKeyword,self).__init__( matchString, identChars, caseless=True ) - - def parseImpl( self, instring, loc, doActions=True ): - if ( (instring[ loc:loc+self.matchLen ].upper() == self.caselessmatch) and - (loc >= len(instring)-self.matchLen or instring[loc+self.matchLen].upper() not in self.identChars) ): - return loc+self.matchLen, self.match - raise ParseException(instring, loc, self.errmsg, self) - -class CloseMatch(Token): - """ - A variation on L{Literal} which matches "close" matches, that is, - strings with at most 'n' mismatching characters. C{CloseMatch} takes parameters: - - C{match_string} - string to be matched - - C{maxMismatches} - (C{default=1}) maximum number of mismatches allowed to count as a match - - The results from a successful parse will contain the matched text from the input string and the following named results: - - C{mismatches} - a list of the positions within the match_string where mismatches were found - - C{original} - the original match_string used to compare against the input string - - If C{mismatches} is an empty list, then the match was an exact match. - - Example:: - patt = CloseMatch("ATCATCGAATGGA") - patt.parseString("ATCATCGAAXGGA") # -> (['ATCATCGAAXGGA'], {'mismatches': [[9]], 'original': ['ATCATCGAATGGA']}) - patt.parseString("ATCAXCGAAXGGA") # -> Exception: Expected 'ATCATCGAATGGA' (with up to 1 mismatches) (at char 0), (line:1, col:1) - - # exact match - patt.parseString("ATCATCGAATGGA") # -> (['ATCATCGAATGGA'], {'mismatches': [[]], 'original': ['ATCATCGAATGGA']}) - - # close match allowing up to 2 mismatches - patt = CloseMatch("ATCATCGAATGGA", maxMismatches=2) - patt.parseString("ATCAXCGAAXGGA") # -> (['ATCAXCGAAXGGA'], {'mismatches': [[4, 9]], 'original': ['ATCATCGAATGGA']}) - """ - def __init__(self, match_string, maxMismatches=1): - super(CloseMatch,self).__init__() - self.name = match_string - self.match_string = match_string - self.maxMismatches = maxMismatches - self.errmsg = "Expected %r (with up to %d mismatches)" % (self.match_string, self.maxMismatches) - self.mayIndexError = False - self.mayReturnEmpty = False - - def parseImpl( self, instring, loc, doActions=True ): - start = loc - instrlen = len(instring) - maxloc = start + len(self.match_string) - - if maxloc <= instrlen: - match_string = self.match_string - match_stringloc = 0 - mismatches = [] - maxMismatches = self.maxMismatches - - for match_stringloc,s_m in enumerate(zip(instring[loc:maxloc], self.match_string)): - src,mat = s_m - if src != mat: - mismatches.append(match_stringloc) - if len(mismatches) > maxMismatches: - break - else: - loc = match_stringloc + 1 - results = ParseResults([instring[start:loc]]) - results['original'] = self.match_string - results['mismatches'] = mismatches - return loc, results - - raise ParseException(instring, loc, self.errmsg, self) - - -class Word(Token): - """ - Token for matching words composed of allowed character sets. - Defined with string containing all allowed initial characters, - an optional string containing allowed body characters (if omitted, - defaults to the initial character set), and an optional minimum, - maximum, and/or exact length. The default value for C{min} is 1 (a - minimum value < 1 is not valid); the default values for C{max} and C{exact} - are 0, meaning no maximum or exact length restriction. An optional - C{excludeChars} parameter can list characters that might be found in - the input C{bodyChars} string; useful to define a word of all printables - except for one or two characters, for instance. - - L{srange} is useful for defining custom character set strings for defining - C{Word} expressions, using range notation from regular expression character sets. - - A common mistake is to use C{Word} to match a specific literal string, as in - C{Word("Address")}. Remember that C{Word} uses the string argument to define - I{sets} of matchable characters. This expression would match "Add", "AAA", - "dAred", or any other word made up of the characters 'A', 'd', 'r', 'e', and 's'. - To match an exact literal string, use L{Literal} or L{Keyword}. - - pyparsing includes helper strings for building Words: - - L{alphas} - - L{nums} - - L{alphanums} - - L{hexnums} - - L{alphas8bit} (alphabetic characters in ASCII range 128-255 - accented, tilded, umlauted, etc.) - - L{punc8bit} (non-alphabetic characters in ASCII range 128-255 - currency, symbols, superscripts, diacriticals, etc.) - - L{printables} (any non-whitespace character) - - Example:: - # a word composed of digits - integer = Word(nums) # equivalent to Word("0123456789") or Word(srange("0-9")) - - # a word with a leading capital, and zero or more lowercase - capital_word = Word(alphas.upper(), alphas.lower()) - - # hostnames are alphanumeric, with leading alpha, and '-' - hostname = Word(alphas, alphanums+'-') - - # roman numeral (not a strict parser, accepts invalid mix of characters) - roman = Word("IVXLCDM") - - # any string of non-whitespace characters, except for ',' - csv_value = Word(printables, excludeChars=",") - """ - def __init__( self, initChars, bodyChars=None, min=1, max=0, exact=0, asKeyword=False, excludeChars=None ): - super(Word,self).__init__() - if excludeChars: - initChars = ''.join(c for c in initChars if c not in excludeChars) - if bodyChars: - bodyChars = ''.join(c for c in bodyChars if c not in excludeChars) - self.initCharsOrig = initChars - self.initChars = set(initChars) - if bodyChars : - self.bodyCharsOrig = bodyChars - self.bodyChars = set(bodyChars) - else: - self.bodyCharsOrig = initChars - self.bodyChars = set(initChars) - - self.maxSpecified = max > 0 - - if min < 1: - raise ValueError("cannot specify a minimum length < 1; use Optional(Word()) if zero-length word is permitted") - - self.minLen = min - - if max > 0: - self.maxLen = max - else: - self.maxLen = _MAX_INT - - if exact > 0: - self.maxLen = exact - self.minLen = exact - - self.name = _ustr(self) - self.errmsg = "Expected " + self.name - self.mayIndexError = False - self.asKeyword = asKeyword - - if ' ' not in self.initCharsOrig+self.bodyCharsOrig and (min==1 and max==0 and exact==0): - if self.bodyCharsOrig == self.initCharsOrig: - self.reString = "[%s]+" % _escapeRegexRangeChars(self.initCharsOrig) - elif len(self.initCharsOrig) == 1: - self.reString = "%s[%s]*" % \ - (re.escape(self.initCharsOrig), - _escapeRegexRangeChars(self.bodyCharsOrig),) - else: - self.reString = "[%s][%s]*" % \ - (_escapeRegexRangeChars(self.initCharsOrig), - _escapeRegexRangeChars(self.bodyCharsOrig),) - if self.asKeyword: - self.reString = r"\b"+self.reString+r"\b" - try: - self.re = re.compile( self.reString ) - except Exception: - self.re = None - - def parseImpl( self, instring, loc, doActions=True ): - if self.re: - result = self.re.match(instring,loc) - if not result: - raise ParseException(instring, loc, self.errmsg, self) - - loc = result.end() - return loc, result.group() - - if not(instring[ loc ] in self.initChars): - raise ParseException(instring, loc, self.errmsg, self) - - start = loc - loc += 1 - instrlen = len(instring) - bodychars = self.bodyChars - maxloc = start + self.maxLen - maxloc = min( maxloc, instrlen ) - while loc < maxloc and instring[loc] in bodychars: - loc += 1 - - throwException = False - if loc - start < self.minLen: - throwException = True - if self.maxSpecified and loc < instrlen and instring[loc] in bodychars: - throwException = True - if self.asKeyword: - if (start>0 and instring[start-1] in bodychars) or (loc4: - return s[:4]+"..." - else: - return s - - if ( self.initCharsOrig != self.bodyCharsOrig ): - self.strRepr = "W:(%s,%s)" % ( charsAsStr(self.initCharsOrig), charsAsStr(self.bodyCharsOrig) ) - else: - self.strRepr = "W:(%s)" % charsAsStr(self.initCharsOrig) - - return self.strRepr - - -class Regex(Token): - r""" - Token for matching strings that match a given regular expression. - Defined with string specifying the regular expression in a form recognized by the inbuilt Python re module. - If the given regex contains named groups (defined using C{(?P...)}), these will be preserved as - named parse results. - - Example:: - realnum = Regex(r"[+-]?\d+\.\d*") - date = Regex(r'(?P\d{4})-(?P\d\d?)-(?P\d\d?)') - # ref: https://stackoverflow.com/questions/267399/how-do-you-match-only-valid-roman-numerals-with-a-regular-expression - roman = Regex(r"M{0,4}(CM|CD|D?C{0,3})(XC|XL|L?X{0,3})(IX|IV|V?I{0,3})") - """ - compiledREtype = type(re.compile("[A-Z]")) - def __init__( self, pattern, flags=0): - """The parameters C{pattern} and C{flags} are passed to the C{re.compile()} function as-is. See the Python C{re} module for an explanation of the acceptable patterns and flags.""" - super(Regex,self).__init__() - - if isinstance(pattern, basestring): - if not pattern: - warnings.warn("null string passed to Regex; use Empty() instead", - SyntaxWarning, stacklevel=2) - - self.pattern = pattern - self.flags = flags - - try: - self.re = re.compile(self.pattern, self.flags) - self.reString = self.pattern - except sre_constants.error: - warnings.warn("invalid pattern (%s) passed to Regex" % pattern, - SyntaxWarning, stacklevel=2) - raise - - elif isinstance(pattern, Regex.compiledREtype): - self.re = pattern - self.pattern = \ - self.reString = str(pattern) - self.flags = flags - - else: - raise ValueError("Regex may only be constructed with a string or a compiled RE object") - - self.name = _ustr(self) - self.errmsg = "Expected " + self.name - self.mayIndexError = False - self.mayReturnEmpty = True - - def parseImpl( self, instring, loc, doActions=True ): - result = self.re.match(instring,loc) - if not result: - raise ParseException(instring, loc, self.errmsg, self) - - loc = result.end() - d = result.groupdict() - ret = ParseResults(result.group()) - if d: - for k in d: - ret[k] = d[k] - return loc,ret - - def __str__( self ): - try: - return super(Regex,self).__str__() - except Exception: - pass - - if self.strRepr is None: - self.strRepr = "Re:(%s)" % repr(self.pattern) - - return self.strRepr - - -class QuotedString(Token): - r""" - Token for matching strings that are delimited by quoting characters. - - Defined with the following parameters: - - quoteChar - string of one or more characters defining the quote delimiting string - - escChar - character to escape quotes, typically backslash (default=C{None}) - - escQuote - special quote sequence to escape an embedded quote string (such as SQL's "" to escape an embedded ") (default=C{None}) - - multiline - boolean indicating whether quotes can span multiple lines (default=C{False}) - - unquoteResults - boolean indicating whether the matched text should be unquoted (default=C{True}) - - endQuoteChar - string of one or more characters defining the end of the quote delimited string (default=C{None} => same as quoteChar) - - convertWhitespaceEscapes - convert escaped whitespace (C{'\t'}, C{'\n'}, etc.) to actual whitespace (default=C{True}) - - Example:: - qs = QuotedString('"') - print(qs.searchString('lsjdf "This is the quote" sldjf')) - complex_qs = QuotedString('{{', endQuoteChar='}}') - print(complex_qs.searchString('lsjdf {{This is the "quote"}} sldjf')) - sql_qs = QuotedString('"', escQuote='""') - print(sql_qs.searchString('lsjdf "This is the quote with ""embedded"" quotes" sldjf')) - prints:: - [['This is the quote']] - [['This is the "quote"']] - [['This is the quote with "embedded" quotes']] - """ - def __init__( self, quoteChar, escChar=None, escQuote=None, multiline=False, unquoteResults=True, endQuoteChar=None, convertWhitespaceEscapes=True): - super(QuotedString,self).__init__() - - # remove white space from quote chars - won't work anyway - quoteChar = quoteChar.strip() - if not quoteChar: - warnings.warn("quoteChar cannot be the empty string",SyntaxWarning,stacklevel=2) - raise SyntaxError() - - if endQuoteChar is None: - endQuoteChar = quoteChar - else: - endQuoteChar = endQuoteChar.strip() - if not endQuoteChar: - warnings.warn("endQuoteChar cannot be the empty string",SyntaxWarning,stacklevel=2) - raise SyntaxError() - - self.quoteChar = quoteChar - self.quoteCharLen = len(quoteChar) - self.firstQuoteChar = quoteChar[0] - self.endQuoteChar = endQuoteChar - self.endQuoteCharLen = len(endQuoteChar) - self.escChar = escChar - self.escQuote = escQuote - self.unquoteResults = unquoteResults - self.convertWhitespaceEscapes = convertWhitespaceEscapes - - if multiline: - self.flags = re.MULTILINE | re.DOTALL - self.pattern = r'%s(?:[^%s%s]' % \ - ( re.escape(self.quoteChar), - _escapeRegexRangeChars(self.endQuoteChar[0]), - (escChar is not None and _escapeRegexRangeChars(escChar) or '') ) - else: - self.flags = 0 - self.pattern = r'%s(?:[^%s\n\r%s]' % \ - ( re.escape(self.quoteChar), - _escapeRegexRangeChars(self.endQuoteChar[0]), - (escChar is not None and _escapeRegexRangeChars(escChar) or '') ) - if len(self.endQuoteChar) > 1: - self.pattern += ( - '|(?:' + ')|(?:'.join("%s[^%s]" % (re.escape(self.endQuoteChar[:i]), - _escapeRegexRangeChars(self.endQuoteChar[i])) - for i in range(len(self.endQuoteChar)-1,0,-1)) + ')' - ) - if escQuote: - self.pattern += (r'|(?:%s)' % re.escape(escQuote)) - if escChar: - self.pattern += (r'|(?:%s.)' % re.escape(escChar)) - self.escCharReplacePattern = re.escape(self.escChar)+"(.)" - self.pattern += (r')*%s' % re.escape(self.endQuoteChar)) - - try: - self.re = re.compile(self.pattern, self.flags) - self.reString = self.pattern - except sre_constants.error: - warnings.warn("invalid pattern (%s) passed to Regex" % self.pattern, - SyntaxWarning, stacklevel=2) - raise - - self.name = _ustr(self) - self.errmsg = "Expected " + self.name - self.mayIndexError = False - self.mayReturnEmpty = True - - def parseImpl( self, instring, loc, doActions=True ): - result = instring[loc] == self.firstQuoteChar and self.re.match(instring,loc) or None - if not result: - raise ParseException(instring, loc, self.errmsg, self) - - loc = result.end() - ret = result.group() - - if self.unquoteResults: - - # strip off quotes - ret = ret[self.quoteCharLen:-self.endQuoteCharLen] - - if isinstance(ret,basestring): - # replace escaped whitespace - if '\\' in ret and self.convertWhitespaceEscapes: - ws_map = { - r'\t' : '\t', - r'\n' : '\n', - r'\f' : '\f', - r'\r' : '\r', - } - for wslit,wschar in ws_map.items(): - ret = ret.replace(wslit, wschar) - - # replace escaped characters - if self.escChar: - ret = re.sub(self.escCharReplacePattern, r"\g<1>", ret) - - # replace escaped quotes - if self.escQuote: - ret = ret.replace(self.escQuote, self.endQuoteChar) - - return loc, ret - - def __str__( self ): - try: - return super(QuotedString,self).__str__() - except Exception: - pass - - if self.strRepr is None: - self.strRepr = "quoted string, starting with %s ending with %s" % (self.quoteChar, self.endQuoteChar) - - return self.strRepr - - -class CharsNotIn(Token): - """ - Token for matching words composed of characters I{not} in a given set (will - include whitespace in matched characters if not listed in the provided exclusion set - see example). - Defined with string containing all disallowed characters, and an optional - minimum, maximum, and/or exact length. The default value for C{min} is 1 (a - minimum value < 1 is not valid); the default values for C{max} and C{exact} - are 0, meaning no maximum or exact length restriction. - - Example:: - # define a comma-separated-value as anything that is not a ',' - csv_value = CharsNotIn(',') - print(delimitedList(csv_value).parseString("dkls,lsdkjf,s12 34,@!#,213")) - prints:: - ['dkls', 'lsdkjf', 's12 34', '@!#', '213'] - """ - def __init__( self, notChars, min=1, max=0, exact=0 ): - super(CharsNotIn,self).__init__() - self.skipWhitespace = False - self.notChars = notChars - - if min < 1: - raise ValueError("cannot specify a minimum length < 1; use Optional(CharsNotIn()) if zero-length char group is permitted") - - self.minLen = min - - if max > 0: - self.maxLen = max - else: - self.maxLen = _MAX_INT - - if exact > 0: - self.maxLen = exact - self.minLen = exact - - self.name = _ustr(self) - self.errmsg = "Expected " + self.name - self.mayReturnEmpty = ( self.minLen == 0 ) - self.mayIndexError = False - - def parseImpl( self, instring, loc, doActions=True ): - if instring[loc] in self.notChars: - raise ParseException(instring, loc, self.errmsg, self) - - start = loc - loc += 1 - notchars = self.notChars - maxlen = min( start+self.maxLen, len(instring) ) - while loc < maxlen and \ - (instring[loc] not in notchars): - loc += 1 - - if loc - start < self.minLen: - raise ParseException(instring, loc, self.errmsg, self) - - return loc, instring[start:loc] - - def __str__( self ): - try: - return super(CharsNotIn, self).__str__() - except Exception: - pass - - if self.strRepr is None: - if len(self.notChars) > 4: - self.strRepr = "!W:(%s...)" % self.notChars[:4] - else: - self.strRepr = "!W:(%s)" % self.notChars - - return self.strRepr - -class White(Token): - """ - Special matching class for matching whitespace. Normally, whitespace is ignored - by pyparsing grammars. This class is included when some whitespace structures - are significant. Define with a string containing the whitespace characters to be - matched; default is C{" \\t\\r\\n"}. Also takes optional C{min}, C{max}, and C{exact} arguments, - as defined for the C{L{Word}} class. - """ - whiteStrs = { - " " : "", - "\t": "", - "\n": "", - "\r": "", - "\f": "", - } - def __init__(self, ws=" \t\r\n", min=1, max=0, exact=0): - super(White,self).__init__() - self.matchWhite = ws - self.setWhitespaceChars( "".join(c for c in self.whiteChars if c not in self.matchWhite) ) - #~ self.leaveWhitespace() - self.name = ("".join(White.whiteStrs[c] for c in self.matchWhite)) - self.mayReturnEmpty = True - self.errmsg = "Expected " + self.name - - self.minLen = min - - if max > 0: - self.maxLen = max - else: - self.maxLen = _MAX_INT - - if exact > 0: - self.maxLen = exact - self.minLen = exact - - def parseImpl( self, instring, loc, doActions=True ): - if not(instring[ loc ] in self.matchWhite): - raise ParseException(instring, loc, self.errmsg, self) - start = loc - loc += 1 - maxloc = start + self.maxLen - maxloc = min( maxloc, len(instring) ) - while loc < maxloc and instring[loc] in self.matchWhite: - loc += 1 - - if loc - start < self.minLen: - raise ParseException(instring, loc, self.errmsg, self) - - return loc, instring[start:loc] - - -class _PositionToken(Token): - def __init__( self ): - super(_PositionToken,self).__init__() - self.name=self.__class__.__name__ - self.mayReturnEmpty = True - self.mayIndexError = False - -class GoToColumn(_PositionToken): - """ - Token to advance to a specific column of input text; useful for tabular report scraping. - """ - def __init__( self, colno ): - super(GoToColumn,self).__init__() - self.col = colno - - def preParse( self, instring, loc ): - if col(loc,instring) != self.col: - instrlen = len(instring) - if self.ignoreExprs: - loc = self._skipIgnorables( instring, loc ) - while loc < instrlen and instring[loc].isspace() and col( loc, instring ) != self.col : - loc += 1 - return loc - - def parseImpl( self, instring, loc, doActions=True ): - thiscol = col( loc, instring ) - if thiscol > self.col: - raise ParseException( instring, loc, "Text not in expected column", self ) - newloc = loc + self.col - thiscol - ret = instring[ loc: newloc ] - return newloc, ret - - -class LineStart(_PositionToken): - """ - Matches if current position is at the beginning of a line within the parse string - - Example:: - - test = '''\ - AAA this line - AAA and this line - AAA but not this one - B AAA and definitely not this one - ''' - - for t in (LineStart() + 'AAA' + restOfLine).searchString(test): - print(t) - - Prints:: - ['AAA', ' this line'] - ['AAA', ' and this line'] - - """ - def __init__( self ): - super(LineStart,self).__init__() - self.errmsg = "Expected start of line" - - def parseImpl( self, instring, loc, doActions=True ): - if col(loc, instring) == 1: - return loc, [] - raise ParseException(instring, loc, self.errmsg, self) - -class LineEnd(_PositionToken): - """ - Matches if current position is at the end of a line within the parse string - """ - def __init__( self ): - super(LineEnd,self).__init__() - self.setWhitespaceChars( ParserElement.DEFAULT_WHITE_CHARS.replace("\n","") ) - self.errmsg = "Expected end of line" - - def parseImpl( self, instring, loc, doActions=True ): - if loc len(instring): - return loc, [] - else: - raise ParseException(instring, loc, self.errmsg, self) - -class WordStart(_PositionToken): - """ - Matches if the current position is at the beginning of a Word, and - is not preceded by any character in a given set of C{wordChars} - (default=C{printables}). To emulate the C{\b} behavior of regular expressions, - use C{WordStart(alphanums)}. C{WordStart} will also match at the beginning of - the string being parsed, or at the beginning of a line. - """ - def __init__(self, wordChars = printables): - super(WordStart,self).__init__() - self.wordChars = set(wordChars) - self.errmsg = "Not at the start of a word" - - def parseImpl(self, instring, loc, doActions=True ): - if loc != 0: - if (instring[loc-1] in self.wordChars or - instring[loc] not in self.wordChars): - raise ParseException(instring, loc, self.errmsg, self) - return loc, [] - -class WordEnd(_PositionToken): - """ - Matches if the current position is at the end of a Word, and - is not followed by any character in a given set of C{wordChars} - (default=C{printables}). To emulate the C{\b} behavior of regular expressions, - use C{WordEnd(alphanums)}. C{WordEnd} will also match at the end of - the string being parsed, or at the end of a line. - """ - def __init__(self, wordChars = printables): - super(WordEnd,self).__init__() - self.wordChars = set(wordChars) - self.skipWhitespace = False - self.errmsg = "Not at the end of a word" - - def parseImpl(self, instring, loc, doActions=True ): - instrlen = len(instring) - if instrlen>0 and loc maxExcLoc: - maxException = err - maxExcLoc = err.loc - except IndexError: - if len(instring) > maxExcLoc: - maxException = ParseException(instring,len(instring),e.errmsg,self) - maxExcLoc = len(instring) - else: - # save match among all matches, to retry longest to shortest - matches.append((loc2, e)) - - if matches: - matches.sort(key=lambda x: -x[0]) - for _,e in matches: - try: - return e._parse( instring, loc, doActions ) - except ParseException as err: - err.__traceback__ = None - if err.loc > maxExcLoc: - maxException = err - maxExcLoc = err.loc - - if maxException is not None: - maxException.msg = self.errmsg - raise maxException - else: - raise ParseException(instring, loc, "no defined alternatives to match", self) - - - def __ixor__(self, other ): - if isinstance( other, basestring ): - other = ParserElement._literalStringClass( other ) - return self.append( other ) #Or( [ self, other ] ) - - def __str__( self ): - if hasattr(self,"name"): - return self.name - - if self.strRepr is None: - self.strRepr = "{" + " ^ ".join(_ustr(e) for e in self.exprs) + "}" - - return self.strRepr - - def checkRecursion( self, parseElementList ): - subRecCheckList = parseElementList[:] + [ self ] - for e in self.exprs: - e.checkRecursion( subRecCheckList ) - - -class MatchFirst(ParseExpression): - """ - Requires that at least one C{ParseExpression} is found. - If two expressions match, the first one listed is the one that will match. - May be constructed using the C{'|'} operator. - - Example:: - # construct MatchFirst using '|' operator - - # watch the order of expressions to match - number = Word(nums) | Combine(Word(nums) + '.' + Word(nums)) - print(number.searchString("123 3.1416 789")) # Fail! -> [['123'], ['3'], ['1416'], ['789']] - - # put more selective expression first - number = Combine(Word(nums) + '.' + Word(nums)) | Word(nums) - print(number.searchString("123 3.1416 789")) # Better -> [['123'], ['3.1416'], ['789']] - """ - def __init__( self, exprs, savelist = False ): - super(MatchFirst,self).__init__(exprs, savelist) - if self.exprs: - self.mayReturnEmpty = any(e.mayReturnEmpty for e in self.exprs) - else: - self.mayReturnEmpty = True - - def parseImpl( self, instring, loc, doActions=True ): - maxExcLoc = -1 - maxException = None - for e in self.exprs: - try: - ret = e._parse( instring, loc, doActions ) - return ret - except ParseException as err: - if err.loc > maxExcLoc: - maxException = err - maxExcLoc = err.loc - except IndexError: - if len(instring) > maxExcLoc: - maxException = ParseException(instring,len(instring),e.errmsg,self) - maxExcLoc = len(instring) - - # only got here if no expression matched, raise exception for match that made it the furthest - else: - if maxException is not None: - maxException.msg = self.errmsg - raise maxException - else: - raise ParseException(instring, loc, "no defined alternatives to match", self) - - def __ior__(self, other ): - if isinstance( other, basestring ): - other = ParserElement._literalStringClass( other ) - return self.append( other ) #MatchFirst( [ self, other ] ) - - def __str__( self ): - if hasattr(self,"name"): - return self.name - - if self.strRepr is None: - self.strRepr = "{" + " | ".join(_ustr(e) for e in self.exprs) + "}" - - return self.strRepr - - def checkRecursion( self, parseElementList ): - subRecCheckList = parseElementList[:] + [ self ] - for e in self.exprs: - e.checkRecursion( subRecCheckList ) - - -class Each(ParseExpression): - """ - Requires all given C{ParseExpression}s to be found, but in any order. - Expressions may be separated by whitespace. - May be constructed using the C{'&'} operator. - - Example:: - color = oneOf("RED ORANGE YELLOW GREEN BLUE PURPLE BLACK WHITE BROWN") - shape_type = oneOf("SQUARE CIRCLE TRIANGLE STAR HEXAGON OCTAGON") - integer = Word(nums) - shape_attr = "shape:" + shape_type("shape") - posn_attr = "posn:" + Group(integer("x") + ',' + integer("y"))("posn") - color_attr = "color:" + color("color") - size_attr = "size:" + integer("size") - - # use Each (using operator '&') to accept attributes in any order - # (shape and posn are required, color and size are optional) - shape_spec = shape_attr & posn_attr & Optional(color_attr) & Optional(size_attr) - - shape_spec.runTests(''' - shape: SQUARE color: BLACK posn: 100, 120 - shape: CIRCLE size: 50 color: BLUE posn: 50,80 - color:GREEN size:20 shape:TRIANGLE posn:20,40 - ''' - ) - prints:: - shape: SQUARE color: BLACK posn: 100, 120 - ['shape:', 'SQUARE', 'color:', 'BLACK', 'posn:', ['100', ',', '120']] - - color: BLACK - - posn: ['100', ',', '120'] - - x: 100 - - y: 120 - - shape: SQUARE - - - shape: CIRCLE size: 50 color: BLUE posn: 50,80 - ['shape:', 'CIRCLE', 'size:', '50', 'color:', 'BLUE', 'posn:', ['50', ',', '80']] - - color: BLUE - - posn: ['50', ',', '80'] - - x: 50 - - y: 80 - - shape: CIRCLE - - size: 50 - - - color: GREEN size: 20 shape: TRIANGLE posn: 20,40 - ['color:', 'GREEN', 'size:', '20', 'shape:', 'TRIANGLE', 'posn:', ['20', ',', '40']] - - color: GREEN - - posn: ['20', ',', '40'] - - x: 20 - - y: 40 - - shape: TRIANGLE - - size: 20 - """ - def __init__( self, exprs, savelist = True ): - super(Each,self).__init__(exprs, savelist) - self.mayReturnEmpty = all(e.mayReturnEmpty for e in self.exprs) - self.skipWhitespace = True - self.initExprGroups = True - - def parseImpl( self, instring, loc, doActions=True ): - if self.initExprGroups: - self.opt1map = dict((id(e.expr),e) for e in self.exprs if isinstance(e,Optional)) - opt1 = [ e.expr for e in self.exprs if isinstance(e,Optional) ] - opt2 = [ e for e in self.exprs if e.mayReturnEmpty and not isinstance(e,Optional)] - self.optionals = opt1 + opt2 - self.multioptionals = [ e.expr for e in self.exprs if isinstance(e,ZeroOrMore) ] - self.multirequired = [ e.expr for e in self.exprs if isinstance(e,OneOrMore) ] - self.required = [ e for e in self.exprs if not isinstance(e,(Optional,ZeroOrMore,OneOrMore)) ] - self.required += self.multirequired - self.initExprGroups = False - tmpLoc = loc - tmpReqd = self.required[:] - tmpOpt = self.optionals[:] - matchOrder = [] - - keepMatching = True - while keepMatching: - tmpExprs = tmpReqd + tmpOpt + self.multioptionals + self.multirequired - failed = [] - for e in tmpExprs: - try: - tmpLoc = e.tryParse( instring, tmpLoc ) - except ParseException: - failed.append(e) - else: - matchOrder.append(self.opt1map.get(id(e),e)) - if e in tmpReqd: - tmpReqd.remove(e) - elif e in tmpOpt: - tmpOpt.remove(e) - if len(failed) == len(tmpExprs): - keepMatching = False - - if tmpReqd: - missing = ", ".join(_ustr(e) for e in tmpReqd) - raise ParseException(instring,loc,"Missing one or more required elements (%s)" % missing ) - - # add any unmatched Optionals, in case they have default values defined - matchOrder += [e for e in self.exprs if isinstance(e,Optional) and e.expr in tmpOpt] - - resultlist = [] - for e in matchOrder: - loc,results = e._parse(instring,loc,doActions) - resultlist.append(results) - - finalResults = sum(resultlist, ParseResults([])) - return loc, finalResults - - def __str__( self ): - if hasattr(self,"name"): - return self.name - - if self.strRepr is None: - self.strRepr = "{" + " & ".join(_ustr(e) for e in self.exprs) + "}" - - return self.strRepr - - def checkRecursion( self, parseElementList ): - subRecCheckList = parseElementList[:] + [ self ] - for e in self.exprs: - e.checkRecursion( subRecCheckList ) - - -class ParseElementEnhance(ParserElement): - """ - Abstract subclass of C{ParserElement}, for combining and post-processing parsed tokens. - """ - def __init__( self, expr, savelist=False ): - super(ParseElementEnhance,self).__init__(savelist) - if isinstance( expr, basestring ): - if issubclass(ParserElement._literalStringClass, Token): - expr = ParserElement._literalStringClass(expr) - else: - expr = ParserElement._literalStringClass(Literal(expr)) - self.expr = expr - self.strRepr = None - if expr is not None: - self.mayIndexError = expr.mayIndexError - self.mayReturnEmpty = expr.mayReturnEmpty - self.setWhitespaceChars( expr.whiteChars ) - self.skipWhitespace = expr.skipWhitespace - self.saveAsList = expr.saveAsList - self.callPreparse = expr.callPreparse - self.ignoreExprs.extend(expr.ignoreExprs) - - def parseImpl( self, instring, loc, doActions=True ): - if self.expr is not None: - return self.expr._parse( instring, loc, doActions, callPreParse=False ) - else: - raise ParseException("",loc,self.errmsg,self) - - def leaveWhitespace( self ): - self.skipWhitespace = False - self.expr = self.expr.copy() - if self.expr is not None: - self.expr.leaveWhitespace() - return self - - def ignore( self, other ): - if isinstance( other, Suppress ): - if other not in self.ignoreExprs: - super( ParseElementEnhance, self).ignore( other ) - if self.expr is not None: - self.expr.ignore( self.ignoreExprs[-1] ) - else: - super( ParseElementEnhance, self).ignore( other ) - if self.expr is not None: - self.expr.ignore( self.ignoreExprs[-1] ) - return self - - def streamline( self ): - super(ParseElementEnhance,self).streamline() - if self.expr is not None: - self.expr.streamline() - return self - - def checkRecursion( self, parseElementList ): - if self in parseElementList: - raise RecursiveGrammarException( parseElementList+[self] ) - subRecCheckList = parseElementList[:] + [ self ] - if self.expr is not None: - self.expr.checkRecursion( subRecCheckList ) - - def validate( self, validateTrace=[] ): - tmp = validateTrace[:]+[self] - if self.expr is not None: - self.expr.validate(tmp) - self.checkRecursion( [] ) - - def __str__( self ): - try: - return super(ParseElementEnhance,self).__str__() - except Exception: - pass - - if self.strRepr is None and self.expr is not None: - self.strRepr = "%s:(%s)" % ( self.__class__.__name__, _ustr(self.expr) ) - return self.strRepr - - -class FollowedBy(ParseElementEnhance): - """ - Lookahead matching of the given parse expression. C{FollowedBy} - does I{not} advance the parsing position within the input string, it only - verifies that the specified parse expression matches at the current - position. C{FollowedBy} always returns a null token list. - - Example:: - # use FollowedBy to match a label only if it is followed by a ':' - data_word = Word(alphas) - label = data_word + FollowedBy(':') - attr_expr = Group(label + Suppress(':') + OneOrMore(data_word, stopOn=label).setParseAction(' '.join)) - - OneOrMore(attr_expr).parseString("shape: SQUARE color: BLACK posn: upper left").pprint() - prints:: - [['shape', 'SQUARE'], ['color', 'BLACK'], ['posn', 'upper left']] - """ - def __init__( self, expr ): - super(FollowedBy,self).__init__(expr) - self.mayReturnEmpty = True - - def parseImpl( self, instring, loc, doActions=True ): - self.expr.tryParse( instring, loc ) - return loc, [] - - -class NotAny(ParseElementEnhance): - """ - Lookahead to disallow matching with the given parse expression. C{NotAny} - does I{not} advance the parsing position within the input string, it only - verifies that the specified parse expression does I{not} match at the current - position. Also, C{NotAny} does I{not} skip over leading whitespace. C{NotAny} - always returns a null token list. May be constructed using the '~' operator. - - Example:: - - """ - def __init__( self, expr ): - super(NotAny,self).__init__(expr) - #~ self.leaveWhitespace() - self.skipWhitespace = False # do NOT use self.leaveWhitespace(), don't want to propagate to exprs - self.mayReturnEmpty = True - self.errmsg = "Found unwanted token, "+_ustr(self.expr) - - def parseImpl( self, instring, loc, doActions=True ): - if self.expr.canParseNext(instring, loc): - raise ParseException(instring, loc, self.errmsg, self) - return loc, [] - - def __str__( self ): - if hasattr(self,"name"): - return self.name - - if self.strRepr is None: - self.strRepr = "~{" + _ustr(self.expr) + "}" - - return self.strRepr - -class _MultipleMatch(ParseElementEnhance): - def __init__( self, expr, stopOn=None): - super(_MultipleMatch, self).__init__(expr) - self.saveAsList = True - ender = stopOn - if isinstance(ender, basestring): - ender = ParserElement._literalStringClass(ender) - self.not_ender = ~ender if ender is not None else None - - def parseImpl( self, instring, loc, doActions=True ): - self_expr_parse = self.expr._parse - self_skip_ignorables = self._skipIgnorables - check_ender = self.not_ender is not None - if check_ender: - try_not_ender = self.not_ender.tryParse - - # must be at least one (but first see if we are the stopOn sentinel; - # if so, fail) - if check_ender: - try_not_ender(instring, loc) - loc, tokens = self_expr_parse( instring, loc, doActions, callPreParse=False ) - try: - hasIgnoreExprs = bool(self.ignoreExprs) - while 1: - if check_ender: - try_not_ender(instring, loc) - if hasIgnoreExprs: - preloc = self_skip_ignorables( instring, loc ) - else: - preloc = loc - loc, tmptokens = self_expr_parse( instring, preloc, doActions ) - if tmptokens or tmptokens.haskeys(): - tokens += tmptokens - except (ParseException,IndexError): - pass - - return loc, tokens - -class OneOrMore(_MultipleMatch): - """ - Repetition of one or more of the given expression. - - Parameters: - - expr - expression that must match one or more times - - stopOn - (default=C{None}) - expression for a terminating sentinel - (only required if the sentinel would ordinarily match the repetition - expression) - - Example:: - data_word = Word(alphas) - label = data_word + FollowedBy(':') - attr_expr = Group(label + Suppress(':') + OneOrMore(data_word).setParseAction(' '.join)) - - text = "shape: SQUARE posn: upper left color: BLACK" - OneOrMore(attr_expr).parseString(text).pprint() # Fail! read 'color' as data instead of next label -> [['shape', 'SQUARE color']] - - # use stopOn attribute for OneOrMore to avoid reading label string as part of the data - attr_expr = Group(label + Suppress(':') + OneOrMore(data_word, stopOn=label).setParseAction(' '.join)) - OneOrMore(attr_expr).parseString(text).pprint() # Better -> [['shape', 'SQUARE'], ['posn', 'upper left'], ['color', 'BLACK']] - - # could also be written as - (attr_expr * (1,)).parseString(text).pprint() - """ - - def __str__( self ): - if hasattr(self,"name"): - return self.name - - if self.strRepr is None: - self.strRepr = "{" + _ustr(self.expr) + "}..." - - return self.strRepr - -class ZeroOrMore(_MultipleMatch): - """ - Optional repetition of zero or more of the given expression. - - Parameters: - - expr - expression that must match zero or more times - - stopOn - (default=C{None}) - expression for a terminating sentinel - (only required if the sentinel would ordinarily match the repetition - expression) - - Example: similar to L{OneOrMore} - """ - def __init__( self, expr, stopOn=None): - super(ZeroOrMore,self).__init__(expr, stopOn=stopOn) - self.mayReturnEmpty = True - - def parseImpl( self, instring, loc, doActions=True ): - try: - return super(ZeroOrMore, self).parseImpl(instring, loc, doActions) - except (ParseException,IndexError): - return loc, [] - - def __str__( self ): - if hasattr(self,"name"): - return self.name - - if self.strRepr is None: - self.strRepr = "[" + _ustr(self.expr) + "]..." - - return self.strRepr - -class _NullToken(object): - def __bool__(self): - return False - __nonzero__ = __bool__ - def __str__(self): - return "" - -_optionalNotMatched = _NullToken() -class Optional(ParseElementEnhance): - """ - Optional matching of the given expression. - - Parameters: - - expr - expression that must match zero or more times - - default (optional) - value to be returned if the optional expression is not found. - - Example:: - # US postal code can be a 5-digit zip, plus optional 4-digit qualifier - zip = Combine(Word(nums, exact=5) + Optional('-' + Word(nums, exact=4))) - zip.runTests(''' - # traditional ZIP code - 12345 - - # ZIP+4 form - 12101-0001 - - # invalid ZIP - 98765- - ''') - prints:: - # traditional ZIP code - 12345 - ['12345'] - - # ZIP+4 form - 12101-0001 - ['12101-0001'] - - # invalid ZIP - 98765- - ^ - FAIL: Expected end of text (at char 5), (line:1, col:6) - """ - def __init__( self, expr, default=_optionalNotMatched ): - super(Optional,self).__init__( expr, savelist=False ) - self.saveAsList = self.expr.saveAsList - self.defaultValue = default - self.mayReturnEmpty = True - - def parseImpl( self, instring, loc, doActions=True ): - try: - loc, tokens = self.expr._parse( instring, loc, doActions, callPreParse=False ) - except (ParseException,IndexError): - if self.defaultValue is not _optionalNotMatched: - if self.expr.resultsName: - tokens = ParseResults([ self.defaultValue ]) - tokens[self.expr.resultsName] = self.defaultValue - else: - tokens = [ self.defaultValue ] - else: - tokens = [] - return loc, tokens - - def __str__( self ): - if hasattr(self,"name"): - return self.name - - if self.strRepr is None: - self.strRepr = "[" + _ustr(self.expr) + "]" - - return self.strRepr - -class SkipTo(ParseElementEnhance): - """ - Token for skipping over all undefined text until the matched expression is found. - - Parameters: - - expr - target expression marking the end of the data to be skipped - - include - (default=C{False}) if True, the target expression is also parsed - (the skipped text and target expression are returned as a 2-element list). - - ignore - (default=C{None}) used to define grammars (typically quoted strings and - comments) that might contain false matches to the target expression - - failOn - (default=C{None}) define expressions that are not allowed to be - included in the skipped test; if found before the target expression is found, - the SkipTo is not a match - - Example:: - report = ''' - Outstanding Issues Report - 1 Jan 2000 - - # | Severity | Description | Days Open - -----+----------+-------------------------------------------+----------- - 101 | Critical | Intermittent system crash | 6 - 94 | Cosmetic | Spelling error on Login ('log|n') | 14 - 79 | Minor | System slow when running too many reports | 47 - ''' - integer = Word(nums) - SEP = Suppress('|') - # use SkipTo to simply match everything up until the next SEP - # - ignore quoted strings, so that a '|' character inside a quoted string does not match - # - parse action will call token.strip() for each matched token, i.e., the description body - string_data = SkipTo(SEP, ignore=quotedString) - string_data.setParseAction(tokenMap(str.strip)) - ticket_expr = (integer("issue_num") + SEP - + string_data("sev") + SEP - + string_data("desc") + SEP - + integer("days_open")) - - for tkt in ticket_expr.searchString(report): - print tkt.dump() - prints:: - ['101', 'Critical', 'Intermittent system crash', '6'] - - days_open: 6 - - desc: Intermittent system crash - - issue_num: 101 - - sev: Critical - ['94', 'Cosmetic', "Spelling error on Login ('log|n')", '14'] - - days_open: 14 - - desc: Spelling error on Login ('log|n') - - issue_num: 94 - - sev: Cosmetic - ['79', 'Minor', 'System slow when running too many reports', '47'] - - days_open: 47 - - desc: System slow when running too many reports - - issue_num: 79 - - sev: Minor - """ - def __init__( self, other, include=False, ignore=None, failOn=None ): - super( SkipTo, self ).__init__( other ) - self.ignoreExpr = ignore - self.mayReturnEmpty = True - self.mayIndexError = False - self.includeMatch = include - self.asList = False - if isinstance(failOn, basestring): - self.failOn = ParserElement._literalStringClass(failOn) - else: - self.failOn = failOn - self.errmsg = "No match found for "+_ustr(self.expr) - - def parseImpl( self, instring, loc, doActions=True ): - startloc = loc - instrlen = len(instring) - expr = self.expr - expr_parse = self.expr._parse - self_failOn_canParseNext = self.failOn.canParseNext if self.failOn is not None else None - self_ignoreExpr_tryParse = self.ignoreExpr.tryParse if self.ignoreExpr is not None else None - - tmploc = loc - while tmploc <= instrlen: - if self_failOn_canParseNext is not None: - # break if failOn expression matches - if self_failOn_canParseNext(instring, tmploc): - break - - if self_ignoreExpr_tryParse is not None: - # advance past ignore expressions - while 1: - try: - tmploc = self_ignoreExpr_tryParse(instring, tmploc) - except ParseBaseException: - break - - try: - expr_parse(instring, tmploc, doActions=False, callPreParse=False) - except (ParseException, IndexError): - # no match, advance loc in string - tmploc += 1 - else: - # matched skipto expr, done - break - - else: - # ran off the end of the input string without matching skipto expr, fail - raise ParseException(instring, loc, self.errmsg, self) - - # build up return values - loc = tmploc - skiptext = instring[startloc:loc] - skipresult = ParseResults(skiptext) - - if self.includeMatch: - loc, mat = expr_parse(instring,loc,doActions,callPreParse=False) - skipresult += mat - - return loc, skipresult - -class Forward(ParseElementEnhance): - """ - Forward declaration of an expression to be defined later - - used for recursive grammars, such as algebraic infix notation. - When the expression is known, it is assigned to the C{Forward} variable using the '<<' operator. - - Note: take care when assigning to C{Forward} not to overlook precedence of operators. - Specifically, '|' has a lower precedence than '<<', so that:: - fwdExpr << a | b | c - will actually be evaluated as:: - (fwdExpr << a) | b | c - thereby leaving b and c out as parseable alternatives. It is recommended that you - explicitly group the values inserted into the C{Forward}:: - fwdExpr << (a | b | c) - Converting to use the '<<=' operator instead will avoid this problem. - - See L{ParseResults.pprint} for an example of a recursive parser created using - C{Forward}. - """ - def __init__( self, other=None ): - super(Forward,self).__init__( other, savelist=False ) - - def __lshift__( self, other ): - if isinstance( other, basestring ): - other = ParserElement._literalStringClass(other) - self.expr = other - self.strRepr = None - self.mayIndexError = self.expr.mayIndexError - self.mayReturnEmpty = self.expr.mayReturnEmpty - self.setWhitespaceChars( self.expr.whiteChars ) - self.skipWhitespace = self.expr.skipWhitespace - self.saveAsList = self.expr.saveAsList - self.ignoreExprs.extend(self.expr.ignoreExprs) - return self - - def __ilshift__(self, other): - return self << other - - def leaveWhitespace( self ): - self.skipWhitespace = False - return self - - def streamline( self ): - if not self.streamlined: - self.streamlined = True - if self.expr is not None: - self.expr.streamline() - return self - - def validate( self, validateTrace=[] ): - if self not in validateTrace: - tmp = validateTrace[:]+[self] - if self.expr is not None: - self.expr.validate(tmp) - self.checkRecursion([]) - - def __str__( self ): - if hasattr(self,"name"): - return self.name - return self.__class__.__name__ + ": ..." - - # stubbed out for now - creates awful memory and perf issues - self._revertClass = self.__class__ - self.__class__ = _ForwardNoRecurse - try: - if self.expr is not None: - retString = _ustr(self.expr) - else: - retString = "None" - finally: - self.__class__ = self._revertClass - return self.__class__.__name__ + ": " + retString - - def copy(self): - if self.expr is not None: - return super(Forward,self).copy() - else: - ret = Forward() - ret <<= self - return ret - -class _ForwardNoRecurse(Forward): - def __str__( self ): - return "..." - -class TokenConverter(ParseElementEnhance): - """ - Abstract subclass of C{ParseExpression}, for converting parsed results. - """ - def __init__( self, expr, savelist=False ): - super(TokenConverter,self).__init__( expr )#, savelist ) - self.saveAsList = False - -class Combine(TokenConverter): - """ - Converter to concatenate all matching tokens to a single string. - By default, the matching patterns must also be contiguous in the input string; - this can be disabled by specifying C{'adjacent=False'} in the constructor. - - Example:: - real = Word(nums) + '.' + Word(nums) - print(real.parseString('3.1416')) # -> ['3', '.', '1416'] - # will also erroneously match the following - print(real.parseString('3. 1416')) # -> ['3', '.', '1416'] - - real = Combine(Word(nums) + '.' + Word(nums)) - print(real.parseString('3.1416')) # -> ['3.1416'] - # no match when there are internal spaces - print(real.parseString('3. 1416')) # -> Exception: Expected W:(0123...) - """ - def __init__( self, expr, joinString="", adjacent=True ): - super(Combine,self).__init__( expr ) - # suppress whitespace-stripping in contained parse expressions, but re-enable it on the Combine itself - if adjacent: - self.leaveWhitespace() - self.adjacent = adjacent - self.skipWhitespace = True - self.joinString = joinString - self.callPreparse = True - - def ignore( self, other ): - if self.adjacent: - ParserElement.ignore(self, other) - else: - super( Combine, self).ignore( other ) - return self - - def postParse( self, instring, loc, tokenlist ): - retToks = tokenlist.copy() - del retToks[:] - retToks += ParseResults([ "".join(tokenlist._asStringList(self.joinString)) ], modal=self.modalResults) - - if self.resultsName and retToks.haskeys(): - return [ retToks ] - else: - return retToks - -class Group(TokenConverter): - """ - Converter to return the matched tokens as a list - useful for returning tokens of C{L{ZeroOrMore}} and C{L{OneOrMore}} expressions. - - Example:: - ident = Word(alphas) - num = Word(nums) - term = ident | num - func = ident + Optional(delimitedList(term)) - print(func.parseString("fn a,b,100")) # -> ['fn', 'a', 'b', '100'] - - func = ident + Group(Optional(delimitedList(term))) - print(func.parseString("fn a,b,100")) # -> ['fn', ['a', 'b', '100']] - """ - def __init__( self, expr ): - super(Group,self).__init__( expr ) - self.saveAsList = True - - def postParse( self, instring, loc, tokenlist ): - return [ tokenlist ] - -class Dict(TokenConverter): - """ - Converter to return a repetitive expression as a list, but also as a dictionary. - Each element can also be referenced using the first token in the expression as its key. - Useful for tabular report scraping when the first column can be used as a item key. - - Example:: - data_word = Word(alphas) - label = data_word + FollowedBy(':') - attr_expr = Group(label + Suppress(':') + OneOrMore(data_word).setParseAction(' '.join)) - - text = "shape: SQUARE posn: upper left color: light blue texture: burlap" - attr_expr = (label + Suppress(':') + OneOrMore(data_word, stopOn=label).setParseAction(' '.join)) - - # print attributes as plain groups - print(OneOrMore(attr_expr).parseString(text).dump()) - - # instead of OneOrMore(expr), parse using Dict(OneOrMore(Group(expr))) - Dict will auto-assign names - result = Dict(OneOrMore(Group(attr_expr))).parseString(text) - print(result.dump()) - - # access named fields as dict entries, or output as dict - print(result['shape']) - print(result.asDict()) - prints:: - ['shape', 'SQUARE', 'posn', 'upper left', 'color', 'light blue', 'texture', 'burlap'] - - [['shape', 'SQUARE'], ['posn', 'upper left'], ['color', 'light blue'], ['texture', 'burlap']] - - color: light blue - - posn: upper left - - shape: SQUARE - - texture: burlap - SQUARE - {'color': 'light blue', 'posn': 'upper left', 'texture': 'burlap', 'shape': 'SQUARE'} - See more examples at L{ParseResults} of accessing fields by results name. - """ - def __init__( self, expr ): - super(Dict,self).__init__( expr ) - self.saveAsList = True - - def postParse( self, instring, loc, tokenlist ): - for i,tok in enumerate(tokenlist): - if len(tok) == 0: - continue - ikey = tok[0] - if isinstance(ikey,int): - ikey = _ustr(tok[0]).strip() - if len(tok)==1: - tokenlist[ikey] = _ParseResultsWithOffset("",i) - elif len(tok)==2 and not isinstance(tok[1],ParseResults): - tokenlist[ikey] = _ParseResultsWithOffset(tok[1],i) - else: - dictvalue = tok.copy() #ParseResults(i) - del dictvalue[0] - if len(dictvalue)!= 1 or (isinstance(dictvalue,ParseResults) and dictvalue.haskeys()): - tokenlist[ikey] = _ParseResultsWithOffset(dictvalue,i) - else: - tokenlist[ikey] = _ParseResultsWithOffset(dictvalue[0],i) - - if self.resultsName: - return [ tokenlist ] - else: - return tokenlist - - -class Suppress(TokenConverter): - """ - Converter for ignoring the results of a parsed expression. - - Example:: - source = "a, b, c,d" - wd = Word(alphas) - wd_list1 = wd + ZeroOrMore(',' + wd) - print(wd_list1.parseString(source)) - - # often, delimiters that are useful during parsing are just in the - # way afterward - use Suppress to keep them out of the parsed output - wd_list2 = wd + ZeroOrMore(Suppress(',') + wd) - print(wd_list2.parseString(source)) - prints:: - ['a', ',', 'b', ',', 'c', ',', 'd'] - ['a', 'b', 'c', 'd'] - (See also L{delimitedList}.) - """ - def postParse( self, instring, loc, tokenlist ): - return [] - - def suppress( self ): - return self - - -class OnlyOnce(object): - """ - Wrapper for parse actions, to ensure they are only called once. - """ - def __init__(self, methodCall): - self.callable = _trim_arity(methodCall) - self.called = False - def __call__(self,s,l,t): - if not self.called: - results = self.callable(s,l,t) - self.called = True - return results - raise ParseException(s,l,"") - def reset(self): - self.called = False - -def traceParseAction(f): - """ - Decorator for debugging parse actions. - - When the parse action is called, this decorator will print C{">> entering I{method-name}(line:I{current_source_line}, I{parse_location}, I{matched_tokens})".} - When the parse action completes, the decorator will print C{"<<"} followed by the returned value, or any exception that the parse action raised. - - Example:: - wd = Word(alphas) - - @traceParseAction - def remove_duplicate_chars(tokens): - return ''.join(sorted(set(''.join(tokens))) - - wds = OneOrMore(wd).setParseAction(remove_duplicate_chars) - print(wds.parseString("slkdjs sld sldd sdlf sdljf")) - prints:: - >>entering remove_duplicate_chars(line: 'slkdjs sld sldd sdlf sdljf', 0, (['slkdjs', 'sld', 'sldd', 'sdlf', 'sdljf'], {})) - <3: - thisFunc = paArgs[0].__class__.__name__ + '.' + thisFunc - sys.stderr.write( ">>entering %s(line: '%s', %d, %r)\n" % (thisFunc,line(l,s),l,t) ) - try: - ret = f(*paArgs) - except Exception as exc: - sys.stderr.write( "< ['aa', 'bb', 'cc'] - delimitedList(Word(hexnums), delim=':', combine=True).parseString("AA:BB:CC:DD:EE") # -> ['AA:BB:CC:DD:EE'] - """ - dlName = _ustr(expr)+" ["+_ustr(delim)+" "+_ustr(expr)+"]..." - if combine: - return Combine( expr + ZeroOrMore( delim + expr ) ).setName(dlName) - else: - return ( expr + ZeroOrMore( Suppress( delim ) + expr ) ).setName(dlName) - -def countedArray( expr, intExpr=None ): - """ - Helper to define a counted list of expressions. - This helper defines a pattern of the form:: - integer expr expr expr... - where the leading integer tells how many expr expressions follow. - The matched tokens returns the array of expr tokens as a list - the leading count token is suppressed. - - If C{intExpr} is specified, it should be a pyparsing expression that produces an integer value. - - Example:: - countedArray(Word(alphas)).parseString('2 ab cd ef') # -> ['ab', 'cd'] - - # in this parser, the leading integer value is given in binary, - # '10' indicating that 2 values are in the array - binaryConstant = Word('01').setParseAction(lambda t: int(t[0], 2)) - countedArray(Word(alphas), intExpr=binaryConstant).parseString('10 ab cd ef') # -> ['ab', 'cd'] - """ - arrayExpr = Forward() - def countFieldParseAction(s,l,t): - n = t[0] - arrayExpr << (n and Group(And([expr]*n)) or Group(empty)) - return [] - if intExpr is None: - intExpr = Word(nums).setParseAction(lambda t:int(t[0])) - else: - intExpr = intExpr.copy() - intExpr.setName("arrayLen") - intExpr.addParseAction(countFieldParseAction, callDuringTry=True) - return ( intExpr + arrayExpr ).setName('(len) ' + _ustr(expr) + '...') - -def _flatten(L): - ret = [] - for i in L: - if isinstance(i,list): - ret.extend(_flatten(i)) - else: - ret.append(i) - return ret - -def matchPreviousLiteral(expr): - """ - Helper to define an expression that is indirectly defined from - the tokens matched in a previous expression, that is, it looks - for a 'repeat' of a previous expression. For example:: - first = Word(nums) - second = matchPreviousLiteral(first) - matchExpr = first + ":" + second - will match C{"1:1"}, but not C{"1:2"}. Because this matches a - previous literal, will also match the leading C{"1:1"} in C{"1:10"}. - If this is not desired, use C{matchPreviousExpr}. - Do I{not} use with packrat parsing enabled. - """ - rep = Forward() - def copyTokenToRepeater(s,l,t): - if t: - if len(t) == 1: - rep << t[0] - else: - # flatten t tokens - tflat = _flatten(t.asList()) - rep << And(Literal(tt) for tt in tflat) - else: - rep << Empty() - expr.addParseAction(copyTokenToRepeater, callDuringTry=True) - rep.setName('(prev) ' + _ustr(expr)) - return rep - -def matchPreviousExpr(expr): - """ - Helper to define an expression that is indirectly defined from - the tokens matched in a previous expression, that is, it looks - for a 'repeat' of a previous expression. For example:: - first = Word(nums) - second = matchPreviousExpr(first) - matchExpr = first + ":" + second - will match C{"1:1"}, but not C{"1:2"}. Because this matches by - expressions, will I{not} match the leading C{"1:1"} in C{"1:10"}; - the expressions are evaluated first, and then compared, so - C{"1"} is compared with C{"10"}. - Do I{not} use with packrat parsing enabled. - """ - rep = Forward() - e2 = expr.copy() - rep <<= e2 - def copyTokenToRepeater(s,l,t): - matchTokens = _flatten(t.asList()) - def mustMatchTheseTokens(s,l,t): - theseTokens = _flatten(t.asList()) - if theseTokens != matchTokens: - raise ParseException("",0,"") - rep.setParseAction( mustMatchTheseTokens, callDuringTry=True ) - expr.addParseAction(copyTokenToRepeater, callDuringTry=True) - rep.setName('(prev) ' + _ustr(expr)) - return rep - -def _escapeRegexRangeChars(s): - #~ escape these chars: ^-] - for c in r"\^-]": - s = s.replace(c,_bslash+c) - s = s.replace("\n",r"\n") - s = s.replace("\t",r"\t") - return _ustr(s) - -def oneOf( strs, caseless=False, useRegex=True ): - """ - Helper to quickly define a set of alternative Literals, and makes sure to do - longest-first testing when there is a conflict, regardless of the input order, - but returns a C{L{MatchFirst}} for best performance. - - Parameters: - - strs - a string of space-delimited literals, or a collection of string literals - - caseless - (default=C{False}) - treat all literals as caseless - - useRegex - (default=C{True}) - as an optimization, will generate a Regex - object; otherwise, will generate a C{MatchFirst} object (if C{caseless=True}, or - if creating a C{Regex} raises an exception) - - Example:: - comp_oper = oneOf("< = > <= >= !=") - var = Word(alphas) - number = Word(nums) - term = var | number - comparison_expr = term + comp_oper + term - print(comparison_expr.searchString("B = 12 AA=23 B<=AA AA>12")) - prints:: - [['B', '=', '12'], ['AA', '=', '23'], ['B', '<=', 'AA'], ['AA', '>', '12']] - """ - if caseless: - isequal = ( lambda a,b: a.upper() == b.upper() ) - masks = ( lambda a,b: b.upper().startswith(a.upper()) ) - parseElementClass = CaselessLiteral - else: - isequal = ( lambda a,b: a == b ) - masks = ( lambda a,b: b.startswith(a) ) - parseElementClass = Literal - - symbols = [] - if isinstance(strs,basestring): - symbols = strs.split() - elif isinstance(strs, collections.Iterable): - symbols = list(strs) - else: - warnings.warn("Invalid argument to oneOf, expected string or iterable", - SyntaxWarning, stacklevel=2) - if not symbols: - return NoMatch() - - i = 0 - while i < len(symbols)-1: - cur = symbols[i] - for j,other in enumerate(symbols[i+1:]): - if ( isequal(other, cur) ): - del symbols[i+j+1] - break - elif ( masks(cur, other) ): - del symbols[i+j+1] - symbols.insert(i,other) - cur = other - break - else: - i += 1 - - if not caseless and useRegex: - #~ print (strs,"->", "|".join( [ _escapeRegexChars(sym) for sym in symbols] )) - try: - if len(symbols)==len("".join(symbols)): - return Regex( "[%s]" % "".join(_escapeRegexRangeChars(sym) for sym in symbols) ).setName(' | '.join(symbols)) - else: - return Regex( "|".join(re.escape(sym) for sym in symbols) ).setName(' | '.join(symbols)) - except Exception: - warnings.warn("Exception creating Regex for oneOf, building MatchFirst", - SyntaxWarning, stacklevel=2) - - - # last resort, just use MatchFirst - return MatchFirst(parseElementClass(sym) for sym in symbols).setName(' | '.join(symbols)) - -def dictOf( key, value ): - """ - Helper to easily and clearly define a dictionary by specifying the respective patterns - for the key and value. Takes care of defining the C{L{Dict}}, C{L{ZeroOrMore}}, and C{L{Group}} tokens - in the proper order. The key pattern can include delimiting markers or punctuation, - as long as they are suppressed, thereby leaving the significant key text. The value - pattern can include named results, so that the C{Dict} results can include named token - fields. - - Example:: - text = "shape: SQUARE posn: upper left color: light blue texture: burlap" - attr_expr = (label + Suppress(':') + OneOrMore(data_word, stopOn=label).setParseAction(' '.join)) - print(OneOrMore(attr_expr).parseString(text).dump()) - - attr_label = label - attr_value = Suppress(':') + OneOrMore(data_word, stopOn=label).setParseAction(' '.join) - - # similar to Dict, but simpler call format - result = dictOf(attr_label, attr_value).parseString(text) - print(result.dump()) - print(result['shape']) - print(result.shape) # object attribute access works too - print(result.asDict()) - prints:: - [['shape', 'SQUARE'], ['posn', 'upper left'], ['color', 'light blue'], ['texture', 'burlap']] - - color: light blue - - posn: upper left - - shape: SQUARE - - texture: burlap - SQUARE - SQUARE - {'color': 'light blue', 'shape': 'SQUARE', 'posn': 'upper left', 'texture': 'burlap'} - """ - return Dict( ZeroOrMore( Group ( key + value ) ) ) - -def originalTextFor(expr, asString=True): - """ - Helper to return the original, untokenized text for a given expression. Useful to - restore the parsed fields of an HTML start tag into the raw tag text itself, or to - revert separate tokens with intervening whitespace back to the original matching - input text. By default, returns astring containing the original parsed text. - - If the optional C{asString} argument is passed as C{False}, then the return value is a - C{L{ParseResults}} containing any results names that were originally matched, and a - single token containing the original matched text from the input string. So if - the expression passed to C{L{originalTextFor}} contains expressions with defined - results names, you must set C{asString} to C{False} if you want to preserve those - results name values. - - Example:: - src = "this is test bold text normal text " - for tag in ("b","i"): - opener,closer = makeHTMLTags(tag) - patt = originalTextFor(opener + SkipTo(closer) + closer) - print(patt.searchString(src)[0]) - prints:: - [' bold text '] - ['text'] - """ - locMarker = Empty().setParseAction(lambda s,loc,t: loc) - endlocMarker = locMarker.copy() - endlocMarker.callPreparse = False - matchExpr = locMarker("_original_start") + expr + endlocMarker("_original_end") - if asString: - extractText = lambda s,l,t: s[t._original_start:t._original_end] - else: - def extractText(s,l,t): - t[:] = [s[t.pop('_original_start'):t.pop('_original_end')]] - matchExpr.setParseAction(extractText) - matchExpr.ignoreExprs = expr.ignoreExprs - return matchExpr - -def ungroup(expr): - """ - Helper to undo pyparsing's default grouping of And expressions, even - if all but one are non-empty. - """ - return TokenConverter(expr).setParseAction(lambda t:t[0]) - -def locatedExpr(expr): - """ - Helper to decorate a returned token with its starting and ending locations in the input string. - This helper adds the following results names: - - locn_start = location where matched expression begins - - locn_end = location where matched expression ends - - value = the actual parsed results - - Be careful if the input text contains C{} characters, you may want to call - C{L{ParserElement.parseWithTabs}} - - Example:: - wd = Word(alphas) - for match in locatedExpr(wd).searchString("ljsdf123lksdjjf123lkkjj1222"): - print(match) - prints:: - [[0, 'ljsdf', 5]] - [[8, 'lksdjjf', 15]] - [[18, 'lkkjj', 23]] - """ - locator = Empty().setParseAction(lambda s,l,t: l) - return Group(locator("locn_start") + expr("value") + locator.copy().leaveWhitespace()("locn_end")) - - -# convenience constants for positional expressions -empty = Empty().setName("empty") -lineStart = LineStart().setName("lineStart") -lineEnd = LineEnd().setName("lineEnd") -stringStart = StringStart().setName("stringStart") -stringEnd = StringEnd().setName("stringEnd") - -_escapedPunc = Word( _bslash, r"\[]-*.$+^?()~ ", exact=2 ).setParseAction(lambda s,l,t:t[0][1]) -_escapedHexChar = Regex(r"\\0?[xX][0-9a-fA-F]+").setParseAction(lambda s,l,t:unichr(int(t[0].lstrip(r'\0x'),16))) -_escapedOctChar = Regex(r"\\0[0-7]+").setParseAction(lambda s,l,t:unichr(int(t[0][1:],8))) -_singleChar = _escapedPunc | _escapedHexChar | _escapedOctChar | Word(printables, excludeChars=r'\]', exact=1) | Regex(r"\w", re.UNICODE) -_charRange = Group(_singleChar + Suppress("-") + _singleChar) -_reBracketExpr = Literal("[") + Optional("^").setResultsName("negate") + Group( OneOrMore( _charRange | _singleChar ) ).setResultsName("body") + "]" - -def srange(s): - r""" - Helper to easily define string ranges for use in Word construction. Borrows - syntax from regexp '[]' string range definitions:: - srange("[0-9]") -> "0123456789" - srange("[a-z]") -> "abcdefghijklmnopqrstuvwxyz" - srange("[a-z$_]") -> "abcdefghijklmnopqrstuvwxyz$_" - The input string must be enclosed in []'s, and the returned string is the expanded - character set joined into a single string. - The values enclosed in the []'s may be: - - a single character - - an escaped character with a leading backslash (such as C{\-} or C{\]}) - - an escaped hex character with a leading C{'\x'} (C{\x21}, which is a C{'!'} character) - (C{\0x##} is also supported for backwards compatibility) - - an escaped octal character with a leading C{'\0'} (C{\041}, which is a C{'!'} character) - - a range of any of the above, separated by a dash (C{'a-z'}, etc.) - - any combination of the above (C{'aeiouy'}, C{'a-zA-Z0-9_$'}, etc.) - """ - _expanded = lambda p: p if not isinstance(p,ParseResults) else ''.join(unichr(c) for c in range(ord(p[0]),ord(p[1])+1)) - try: - return "".join(_expanded(part) for part in _reBracketExpr.parseString(s).body) - except Exception: - return "" - -def matchOnlyAtCol(n): - """ - Helper method for defining parse actions that require matching at a specific - column in the input text. - """ - def verifyCol(strg,locn,toks): - if col(locn,strg) != n: - raise ParseException(strg,locn,"matched token not at column %d" % n) - return verifyCol - -def replaceWith(replStr): - """ - Helper method for common parse actions that simply return a literal value. Especially - useful when used with C{L{transformString}()}. - - Example:: - num = Word(nums).setParseAction(lambda toks: int(toks[0])) - na = oneOf("N/A NA").setParseAction(replaceWith(math.nan)) - term = na | num - - OneOrMore(term).parseString("324 234 N/A 234") # -> [324, 234, nan, 234] - """ - return lambda s,l,t: [replStr] - -def removeQuotes(s,l,t): - """ - Helper parse action for removing quotation marks from parsed quoted strings. - - Example:: - # by default, quotation marks are included in parsed results - quotedString.parseString("'Now is the Winter of our Discontent'") # -> ["'Now is the Winter of our Discontent'"] - - # use removeQuotes to strip quotation marks from parsed results - quotedString.setParseAction(removeQuotes) - quotedString.parseString("'Now is the Winter of our Discontent'") # -> ["Now is the Winter of our Discontent"] - """ - return t[0][1:-1] - -def tokenMap(func, *args): - """ - Helper to define a parse action by mapping a function to all elements of a ParseResults list.If any additional - args are passed, they are forwarded to the given function as additional arguments after - the token, as in C{hex_integer = Word(hexnums).setParseAction(tokenMap(int, 16))}, which will convert the - parsed data to an integer using base 16. - - Example (compare the last to example in L{ParserElement.transformString}:: - hex_ints = OneOrMore(Word(hexnums)).setParseAction(tokenMap(int, 16)) - hex_ints.runTests(''' - 00 11 22 aa FF 0a 0d 1a - ''') - - upperword = Word(alphas).setParseAction(tokenMap(str.upper)) - OneOrMore(upperword).runTests(''' - my kingdom for a horse - ''') - - wd = Word(alphas).setParseAction(tokenMap(str.title)) - OneOrMore(wd).setParseAction(' '.join).runTests(''' - now is the winter of our discontent made glorious summer by this sun of york - ''') - prints:: - 00 11 22 aa FF 0a 0d 1a - [0, 17, 34, 170, 255, 10, 13, 26] - - my kingdom for a horse - ['MY', 'KINGDOM', 'FOR', 'A', 'HORSE'] - - now is the winter of our discontent made glorious summer by this sun of york - ['Now Is The Winter Of Our Discontent Made Glorious Summer By This Sun Of York'] - """ - def pa(s,l,t): - return [func(tokn, *args) for tokn in t] - - try: - func_name = getattr(func, '__name__', - getattr(func, '__class__').__name__) - except Exception: - func_name = str(func) - pa.__name__ = func_name - - return pa - -upcaseTokens = tokenMap(lambda t: _ustr(t).upper()) -"""(Deprecated) Helper parse action to convert tokens to upper case. Deprecated in favor of L{pyparsing_common.upcaseTokens}""" - -downcaseTokens = tokenMap(lambda t: _ustr(t).lower()) -"""(Deprecated) Helper parse action to convert tokens to lower case. Deprecated in favor of L{pyparsing_common.downcaseTokens}""" - -def _makeTags(tagStr, xml): - """Internal helper to construct opening and closing tag expressions, given a tag name""" - if isinstance(tagStr,basestring): - resname = tagStr - tagStr = Keyword(tagStr, caseless=not xml) - else: - resname = tagStr.name - - tagAttrName = Word(alphas,alphanums+"_-:") - if (xml): - tagAttrValue = dblQuotedString.copy().setParseAction( removeQuotes ) - openTag = Suppress("<") + tagStr("tag") + \ - Dict(ZeroOrMore(Group( tagAttrName + Suppress("=") + tagAttrValue ))) + \ - Optional("/",default=[False]).setResultsName("empty").setParseAction(lambda s,l,t:t[0]=='/') + Suppress(">") - else: - printablesLessRAbrack = "".join(c for c in printables if c not in ">") - tagAttrValue = quotedString.copy().setParseAction( removeQuotes ) | Word(printablesLessRAbrack) - openTag = Suppress("<") + tagStr("tag") + \ - Dict(ZeroOrMore(Group( tagAttrName.setParseAction(downcaseTokens) + \ - Optional( Suppress("=") + tagAttrValue ) ))) + \ - Optional("/",default=[False]).setResultsName("empty").setParseAction(lambda s,l,t:t[0]=='/') + Suppress(">") - closeTag = Combine(_L("") - - openTag = openTag.setResultsName("start"+"".join(resname.replace(":"," ").title().split())).setName("<%s>" % resname) - closeTag = closeTag.setResultsName("end"+"".join(resname.replace(":"," ").title().split())).setName("" % resname) - openTag.tag = resname - closeTag.tag = resname - return openTag, closeTag - -def makeHTMLTags(tagStr): - """ - Helper to construct opening and closing tag expressions for HTML, given a tag name. Matches - tags in either upper or lower case, attributes with namespaces and with quoted or unquoted values. - - Example:: - text = 'More info at the pyparsing wiki page' - # makeHTMLTags returns pyparsing expressions for the opening and closing tags as a 2-tuple - a,a_end = makeHTMLTags("A") - link_expr = a + SkipTo(a_end)("link_text") + a_end - - for link in link_expr.searchString(text): - # attributes in the tag (like "href" shown here) are also accessible as named results - print(link.link_text, '->', link.href) - prints:: - pyparsing -> http://pyparsing.wikispaces.com - """ - return _makeTags( tagStr, False ) - -def makeXMLTags(tagStr): - """ - Helper to construct opening and closing tag expressions for XML, given a tag name. Matches - tags only in the given upper/lower case. - - Example: similar to L{makeHTMLTags} - """ - return _makeTags( tagStr, True ) - -def withAttribute(*args,**attrDict): - """ - Helper to create a validating parse action to be used with start tags created - with C{L{makeXMLTags}} or C{L{makeHTMLTags}}. Use C{withAttribute} to qualify a starting tag - with a required attribute value, to avoid false matches on common tags such as - C{} or C{
    }. - - Call C{withAttribute} with a series of attribute names and values. Specify the list - of filter attributes names and values as: - - keyword arguments, as in C{(align="right")}, or - - as an explicit dict with C{**} operator, when an attribute name is also a Python - reserved word, as in C{**{"class":"Customer", "align":"right"}} - - a list of name-value tuples, as in ( ("ns1:class", "Customer"), ("ns2:align","right") ) - For attribute names with a namespace prefix, you must use the second form. Attribute - names are matched insensitive to upper/lower case. - - If just testing for C{class} (with or without a namespace), use C{L{withClass}}. - - To verify that the attribute exists, but without specifying a value, pass - C{withAttribute.ANY_VALUE} as the value. - - Example:: - html = ''' -
    - Some text -
    1 4 0 1 0
    -
    1,3 2,3 1,1
    -
    this has no type
    -
    - - ''' - div,div_end = makeHTMLTags("div") - - # only match div tag having a type attribute with value "grid" - div_grid = div().setParseAction(withAttribute(type="grid")) - grid_expr = div_grid + SkipTo(div | div_end)("body") - for grid_header in grid_expr.searchString(html): - print(grid_header.body) - - # construct a match with any div tag having a type attribute, regardless of the value - div_any_type = div().setParseAction(withAttribute(type=withAttribute.ANY_VALUE)) - div_expr = div_any_type + SkipTo(div | div_end)("body") - for div_header in div_expr.searchString(html): - print(div_header.body) - prints:: - 1 4 0 1 0 - - 1 4 0 1 0 - 1,3 2,3 1,1 - """ - if args: - attrs = args[:] - else: - attrs = attrDict.items() - attrs = [(k,v) for k,v in attrs] - def pa(s,l,tokens): - for attrName,attrValue in attrs: - if attrName not in tokens: - raise ParseException(s,l,"no matching attribute " + attrName) - if attrValue != withAttribute.ANY_VALUE and tokens[attrName] != attrValue: - raise ParseException(s,l,"attribute '%s' has value '%s', must be '%s'" % - (attrName, tokens[attrName], attrValue)) - return pa -withAttribute.ANY_VALUE = object() - -def withClass(classname, namespace=''): - """ - Simplified version of C{L{withAttribute}} when matching on a div class - made - difficult because C{class} is a reserved word in Python. - - Example:: - html = ''' -
    - Some text -
    1 4 0 1 0
    -
    1,3 2,3 1,1
    -
    this <div> has no class
    -
    - - ''' - div,div_end = makeHTMLTags("div") - div_grid = div().setParseAction(withClass("grid")) - - grid_expr = div_grid + SkipTo(div | div_end)("body") - for grid_header in grid_expr.searchString(html): - print(grid_header.body) - - div_any_type = div().setParseAction(withClass(withAttribute.ANY_VALUE)) - div_expr = div_any_type + SkipTo(div | div_end)("body") - for div_header in div_expr.searchString(html): - print(div_header.body) - prints:: - 1 4 0 1 0 - - 1 4 0 1 0 - 1,3 2,3 1,1 - """ - classattr = "%s:class" % namespace if namespace else "class" - return withAttribute(**{classattr : classname}) - -opAssoc = _Constants() -opAssoc.LEFT = object() -opAssoc.RIGHT = object() - -def infixNotation( baseExpr, opList, lpar=Suppress('('), rpar=Suppress(')') ): - """ - Helper method for constructing grammars of expressions made up of - operators working in a precedence hierarchy. Operators may be unary or - binary, left- or right-associative. Parse actions can also be attached - to operator expressions. The generated parser will also recognize the use - of parentheses to override operator precedences (see example below). - - Note: if you define a deep operator list, you may see performance issues - when using infixNotation. See L{ParserElement.enablePackrat} for a - mechanism to potentially improve your parser performance. - - Parameters: - - baseExpr - expression representing the most basic element for the nested - - opList - list of tuples, one for each operator precedence level in the - expression grammar; each tuple is of the form - (opExpr, numTerms, rightLeftAssoc, parseAction), where: - - opExpr is the pyparsing expression for the operator; - may also be a string, which will be converted to a Literal; - if numTerms is 3, opExpr is a tuple of two expressions, for the - two operators separating the 3 terms - - numTerms is the number of terms for this operator (must - be 1, 2, or 3) - - rightLeftAssoc is the indicator whether the operator is - right or left associative, using the pyparsing-defined - constants C{opAssoc.RIGHT} and C{opAssoc.LEFT}. - - parseAction is the parse action to be associated with - expressions matching this operator expression (the - parse action tuple member may be omitted); if the parse action - is passed a tuple or list of functions, this is equivalent to - calling C{setParseAction(*fn)} (L{ParserElement.setParseAction}) - - lpar - expression for matching left-parentheses (default=C{Suppress('(')}) - - rpar - expression for matching right-parentheses (default=C{Suppress(')')}) - - Example:: - # simple example of four-function arithmetic with ints and variable names - integer = pyparsing_common.signed_integer - varname = pyparsing_common.identifier - - arith_expr = infixNotation(integer | varname, - [ - ('-', 1, opAssoc.RIGHT), - (oneOf('* /'), 2, opAssoc.LEFT), - (oneOf('+ -'), 2, opAssoc.LEFT), - ]) - - arith_expr.runTests(''' - 5+3*6 - (5+3)*6 - -2--11 - ''', fullDump=False) - prints:: - 5+3*6 - [[5, '+', [3, '*', 6]]] - - (5+3)*6 - [[[5, '+', 3], '*', 6]] - - -2--11 - [[['-', 2], '-', ['-', 11]]] - """ - ret = Forward() - lastExpr = baseExpr | ( lpar + ret + rpar ) - for i,operDef in enumerate(opList): - opExpr,arity,rightLeftAssoc,pa = (operDef + (None,))[:4] - termName = "%s term" % opExpr if arity < 3 else "%s%s term" % opExpr - if arity == 3: - if opExpr is None or len(opExpr) != 2: - raise ValueError("if numterms=3, opExpr must be a tuple or list of two expressions") - opExpr1, opExpr2 = opExpr - thisExpr = Forward().setName(termName) - if rightLeftAssoc == opAssoc.LEFT: - if arity == 1: - matchExpr = FollowedBy(lastExpr + opExpr) + Group( lastExpr + OneOrMore( opExpr ) ) - elif arity == 2: - if opExpr is not None: - matchExpr = FollowedBy(lastExpr + opExpr + lastExpr) + Group( lastExpr + OneOrMore( opExpr + lastExpr ) ) - else: - matchExpr = FollowedBy(lastExpr+lastExpr) + Group( lastExpr + OneOrMore(lastExpr) ) - elif arity == 3: - matchExpr = FollowedBy(lastExpr + opExpr1 + lastExpr + opExpr2 + lastExpr) + \ - Group( lastExpr + opExpr1 + lastExpr + opExpr2 + lastExpr ) - else: - raise ValueError("operator must be unary (1), binary (2), or ternary (3)") - elif rightLeftAssoc == opAssoc.RIGHT: - if arity == 1: - # try to avoid LR with this extra test - if not isinstance(opExpr, Optional): - opExpr = Optional(opExpr) - matchExpr = FollowedBy(opExpr.expr + thisExpr) + Group( opExpr + thisExpr ) - elif arity == 2: - if opExpr is not None: - matchExpr = FollowedBy(lastExpr + opExpr + thisExpr) + Group( lastExpr + OneOrMore( opExpr + thisExpr ) ) - else: - matchExpr = FollowedBy(lastExpr + thisExpr) + Group( lastExpr + OneOrMore( thisExpr ) ) - elif arity == 3: - matchExpr = FollowedBy(lastExpr + opExpr1 + thisExpr + opExpr2 + thisExpr) + \ - Group( lastExpr + opExpr1 + thisExpr + opExpr2 + thisExpr ) - else: - raise ValueError("operator must be unary (1), binary (2), or ternary (3)") - else: - raise ValueError("operator must indicate right or left associativity") - if pa: - if isinstance(pa, (tuple, list)): - matchExpr.setParseAction(*pa) - else: - matchExpr.setParseAction(pa) - thisExpr <<= ( matchExpr.setName(termName) | lastExpr ) - lastExpr = thisExpr - ret <<= lastExpr - return ret - -operatorPrecedence = infixNotation -"""(Deprecated) Former name of C{L{infixNotation}}, will be dropped in a future release.""" - -dblQuotedString = Combine(Regex(r'"(?:[^"\n\r\\]|(?:"")|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*')+'"').setName("string enclosed in double quotes") -sglQuotedString = Combine(Regex(r"'(?:[^'\n\r\\]|(?:'')|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*")+"'").setName("string enclosed in single quotes") -quotedString = Combine(Regex(r'"(?:[^"\n\r\\]|(?:"")|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*')+'"'| - Regex(r"'(?:[^'\n\r\\]|(?:'')|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*")+"'").setName("quotedString using single or double quotes") -unicodeString = Combine(_L('u') + quotedString.copy()).setName("unicode string literal") - -def nestedExpr(opener="(", closer=")", content=None, ignoreExpr=quotedString.copy()): - """ - Helper method for defining nested lists enclosed in opening and closing - delimiters ("(" and ")" are the default). - - Parameters: - - opener - opening character for a nested list (default=C{"("}); can also be a pyparsing expression - - closer - closing character for a nested list (default=C{")"}); can also be a pyparsing expression - - content - expression for items within the nested lists (default=C{None}) - - ignoreExpr - expression for ignoring opening and closing delimiters (default=C{quotedString}) - - If an expression is not provided for the content argument, the nested - expression will capture all whitespace-delimited content between delimiters - as a list of separate values. - - Use the C{ignoreExpr} argument to define expressions that may contain - opening or closing characters that should not be treated as opening - or closing characters for nesting, such as quotedString or a comment - expression. Specify multiple expressions using an C{L{Or}} or C{L{MatchFirst}}. - The default is L{quotedString}, but if no expressions are to be ignored, - then pass C{None} for this argument. - - Example:: - data_type = oneOf("void int short long char float double") - decl_data_type = Combine(data_type + Optional(Word('*'))) - ident = Word(alphas+'_', alphanums+'_') - number = pyparsing_common.number - arg = Group(decl_data_type + ident) - LPAR,RPAR = map(Suppress, "()") - - code_body = nestedExpr('{', '}', ignoreExpr=(quotedString | cStyleComment)) - - c_function = (decl_data_type("type") - + ident("name") - + LPAR + Optional(delimitedList(arg), [])("args") + RPAR - + code_body("body")) - c_function.ignore(cStyleComment) - - source_code = ''' - int is_odd(int x) { - return (x%2); - } - - int dec_to_hex(char hchar) { - if (hchar >= '0' && hchar <= '9') { - return (ord(hchar)-ord('0')); - } else { - return (10+ord(hchar)-ord('A')); - } - } - ''' - for func in c_function.searchString(source_code): - print("%(name)s (%(type)s) args: %(args)s" % func) - - prints:: - is_odd (int) args: [['int', 'x']] - dec_to_hex (int) args: [['char', 'hchar']] - """ - if opener == closer: - raise ValueError("opening and closing strings cannot be the same") - if content is None: - if isinstance(opener,basestring) and isinstance(closer,basestring): - if len(opener) == 1 and len(closer)==1: - if ignoreExpr is not None: - content = (Combine(OneOrMore(~ignoreExpr + - CharsNotIn(opener+closer+ParserElement.DEFAULT_WHITE_CHARS,exact=1)) - ).setParseAction(lambda t:t[0].strip())) - else: - content = (empty.copy()+CharsNotIn(opener+closer+ParserElement.DEFAULT_WHITE_CHARS - ).setParseAction(lambda t:t[0].strip())) - else: - if ignoreExpr is not None: - content = (Combine(OneOrMore(~ignoreExpr + - ~Literal(opener) + ~Literal(closer) + - CharsNotIn(ParserElement.DEFAULT_WHITE_CHARS,exact=1)) - ).setParseAction(lambda t:t[0].strip())) - else: - content = (Combine(OneOrMore(~Literal(opener) + ~Literal(closer) + - CharsNotIn(ParserElement.DEFAULT_WHITE_CHARS,exact=1)) - ).setParseAction(lambda t:t[0].strip())) - else: - raise ValueError("opening and closing arguments must be strings if no content expression is given") - ret = Forward() - if ignoreExpr is not None: - ret <<= Group( Suppress(opener) + ZeroOrMore( ignoreExpr | ret | content ) + Suppress(closer) ) - else: - ret <<= Group( Suppress(opener) + ZeroOrMore( ret | content ) + Suppress(closer) ) - ret.setName('nested %s%s expression' % (opener,closer)) - return ret - -def indentedBlock(blockStatementExpr, indentStack, indent=True): - """ - Helper method for defining space-delimited indentation blocks, such as - those used to define block statements in Python source code. - - Parameters: - - blockStatementExpr - expression defining syntax of statement that - is repeated within the indented block - - indentStack - list created by caller to manage indentation stack - (multiple statementWithIndentedBlock expressions within a single grammar - should share a common indentStack) - - indent - boolean indicating whether block must be indented beyond the - the current level; set to False for block of left-most statements - (default=C{True}) - - A valid block must contain at least one C{blockStatement}. - - Example:: - data = ''' - def A(z): - A1 - B = 100 - G = A2 - A2 - A3 - B - def BB(a,b,c): - BB1 - def BBA(): - bba1 - bba2 - bba3 - C - D - def spam(x,y): - def eggs(z): - pass - ''' - - - indentStack = [1] - stmt = Forward() - - identifier = Word(alphas, alphanums) - funcDecl = ("def" + identifier + Group( "(" + Optional( delimitedList(identifier) ) + ")" ) + ":") - func_body = indentedBlock(stmt, indentStack) - funcDef = Group( funcDecl + func_body ) - - rvalue = Forward() - funcCall = Group(identifier + "(" + Optional(delimitedList(rvalue)) + ")") - rvalue << (funcCall | identifier | Word(nums)) - assignment = Group(identifier + "=" + rvalue) - stmt << ( funcDef | assignment | identifier ) - - module_body = OneOrMore(stmt) - - parseTree = module_body.parseString(data) - parseTree.pprint() - prints:: - [['def', - 'A', - ['(', 'z', ')'], - ':', - [['A1'], [['B', '=', '100']], [['G', '=', 'A2']], ['A2'], ['A3']]], - 'B', - ['def', - 'BB', - ['(', 'a', 'b', 'c', ')'], - ':', - [['BB1'], [['def', 'BBA', ['(', ')'], ':', [['bba1'], ['bba2'], ['bba3']]]]]], - 'C', - 'D', - ['def', - 'spam', - ['(', 'x', 'y', ')'], - ':', - [[['def', 'eggs', ['(', 'z', ')'], ':', [['pass']]]]]]] - """ - def checkPeerIndent(s,l,t): - if l >= len(s): return - curCol = col(l,s) - if curCol != indentStack[-1]: - if curCol > indentStack[-1]: - raise ParseFatalException(s,l,"illegal nesting") - raise ParseException(s,l,"not a peer entry") - - def checkSubIndent(s,l,t): - curCol = col(l,s) - if curCol > indentStack[-1]: - indentStack.append( curCol ) - else: - raise ParseException(s,l,"not a subentry") - - def checkUnindent(s,l,t): - if l >= len(s): return - curCol = col(l,s) - if not(indentStack and curCol < indentStack[-1] and curCol <= indentStack[-2]): - raise ParseException(s,l,"not an unindent") - indentStack.pop() - - NL = OneOrMore(LineEnd().setWhitespaceChars("\t ").suppress()) - INDENT = (Empty() + Empty().setParseAction(checkSubIndent)).setName('INDENT') - PEER = Empty().setParseAction(checkPeerIndent).setName('') - UNDENT = Empty().setParseAction(checkUnindent).setName('UNINDENT') - if indent: - smExpr = Group( Optional(NL) + - #~ FollowedBy(blockStatementExpr) + - INDENT + (OneOrMore( PEER + Group(blockStatementExpr) + Optional(NL) )) + UNDENT) - else: - smExpr = Group( Optional(NL) + - (OneOrMore( PEER + Group(blockStatementExpr) + Optional(NL) )) ) - blockStatementExpr.ignore(_bslash + LineEnd()) - return smExpr.setName('indented block') - -alphas8bit = srange(r"[\0xc0-\0xd6\0xd8-\0xf6\0xf8-\0xff]") -punc8bit = srange(r"[\0xa1-\0xbf\0xd7\0xf7]") - -anyOpenTag,anyCloseTag = makeHTMLTags(Word(alphas,alphanums+"_:").setName('any tag')) -_htmlEntityMap = dict(zip("gt lt amp nbsp quot apos".split(),'><& "\'')) -commonHTMLEntity = Regex('&(?P' + '|'.join(_htmlEntityMap.keys()) +");").setName("common HTML entity") -def replaceHTMLEntity(t): - """Helper parser action to replace common HTML entities with their special characters""" - return _htmlEntityMap.get(t.entity) - -# it's easy to get these comment structures wrong - they're very common, so may as well make them available -cStyleComment = Combine(Regex(r"/\*(?:[^*]|\*(?!/))*") + '*/').setName("C style comment") -"Comment of the form C{/* ... */}" - -htmlComment = Regex(r"").setName("HTML comment") -"Comment of the form C{}" - -restOfLine = Regex(r".*").leaveWhitespace().setName("rest of line") -dblSlashComment = Regex(r"//(?:\\\n|[^\n])*").setName("// comment") -"Comment of the form C{// ... (to end of line)}" - -cppStyleComment = Combine(Regex(r"/\*(?:[^*]|\*(?!/))*") + '*/'| dblSlashComment).setName("C++ style comment") -"Comment of either form C{L{cStyleComment}} or C{L{dblSlashComment}}" - -javaStyleComment = cppStyleComment -"Same as C{L{cppStyleComment}}" - -pythonStyleComment = Regex(r"#.*").setName("Python style comment") -"Comment of the form C{# ... (to end of line)}" - -_commasepitem = Combine(OneOrMore(Word(printables, excludeChars=',') + - Optional( Word(" \t") + - ~Literal(",") + ~LineEnd() ) ) ).streamline().setName("commaItem") -commaSeparatedList = delimitedList( Optional( quotedString.copy() | _commasepitem, default="") ).setName("commaSeparatedList") -"""(Deprecated) Predefined expression of 1 or more printable words or quoted strings, separated by commas. - This expression is deprecated in favor of L{pyparsing_common.comma_separated_list}.""" - -# some other useful expressions - using lower-case class name since we are really using this as a namespace -class pyparsing_common: - """ - Here are some common low-level expressions that may be useful in jump-starting parser development: - - numeric forms (L{integers}, L{reals}, L{scientific notation}) - - common L{programming identifiers} - - network addresses (L{MAC}, L{IPv4}, L{IPv6}) - - ISO8601 L{dates} and L{datetime} - - L{UUID} - - L{comma-separated list} - Parse actions: - - C{L{convertToInteger}} - - C{L{convertToFloat}} - - C{L{convertToDate}} - - C{L{convertToDatetime}} - - C{L{stripHTMLTags}} - - C{L{upcaseTokens}} - - C{L{downcaseTokens}} - - Example:: - pyparsing_common.number.runTests(''' - # any int or real number, returned as the appropriate type - 100 - -100 - +100 - 3.14159 - 6.02e23 - 1e-12 - ''') - - pyparsing_common.fnumber.runTests(''' - # any int or real number, returned as float - 100 - -100 - +100 - 3.14159 - 6.02e23 - 1e-12 - ''') - - pyparsing_common.hex_integer.runTests(''' - # hex numbers - 100 - FF - ''') - - pyparsing_common.fraction.runTests(''' - # fractions - 1/2 - -3/4 - ''') - - pyparsing_common.mixed_integer.runTests(''' - # mixed fractions - 1 - 1/2 - -3/4 - 1-3/4 - ''') - - import uuid - pyparsing_common.uuid.setParseAction(tokenMap(uuid.UUID)) - pyparsing_common.uuid.runTests(''' - # uuid - 12345678-1234-5678-1234-567812345678 - ''') - prints:: - # any int or real number, returned as the appropriate type - 100 - [100] - - -100 - [-100] - - +100 - [100] - - 3.14159 - [3.14159] - - 6.02e23 - [6.02e+23] - - 1e-12 - [1e-12] - - # any int or real number, returned as float - 100 - [100.0] - - -100 - [-100.0] - - +100 - [100.0] - - 3.14159 - [3.14159] - - 6.02e23 - [6.02e+23] - - 1e-12 - [1e-12] - - # hex numbers - 100 - [256] - - FF - [255] - - # fractions - 1/2 - [0.5] - - -3/4 - [-0.75] - - # mixed fractions - 1 - [1] - - 1/2 - [0.5] - - -3/4 - [-0.75] - - 1-3/4 - [1.75] - - # uuid - 12345678-1234-5678-1234-567812345678 - [UUID('12345678-1234-5678-1234-567812345678')] - """ - - convertToInteger = tokenMap(int) - """ - Parse action for converting parsed integers to Python int - """ - - convertToFloat = tokenMap(float) - """ - Parse action for converting parsed numbers to Python float - """ - - integer = Word(nums).setName("integer").setParseAction(convertToInteger) - """expression that parses an unsigned integer, returns an int""" - - hex_integer = Word(hexnums).setName("hex integer").setParseAction(tokenMap(int,16)) - """expression that parses a hexadecimal integer, returns an int""" - - signed_integer = Regex(r'[+-]?\d+').setName("signed integer").setParseAction(convertToInteger) - """expression that parses an integer with optional leading sign, returns an int""" - - fraction = (signed_integer().setParseAction(convertToFloat) + '/' + signed_integer().setParseAction(convertToFloat)).setName("fraction") - """fractional expression of an integer divided by an integer, returns a float""" - fraction.addParseAction(lambda t: t[0]/t[-1]) - - mixed_integer = (fraction | signed_integer + Optional(Optional('-').suppress() + fraction)).setName("fraction or mixed integer-fraction") - """mixed integer of the form 'integer - fraction', with optional leading integer, returns float""" - mixed_integer.addParseAction(sum) - - real = Regex(r'[+-]?\d+\.\d*').setName("real number").setParseAction(convertToFloat) - """expression that parses a floating point number and returns a float""" - - sci_real = Regex(r'[+-]?\d+([eE][+-]?\d+|\.\d*([eE][+-]?\d+)?)').setName("real number with scientific notation").setParseAction(convertToFloat) - """expression that parses a floating point number with optional scientific notation and returns a float""" - - # streamlining this expression makes the docs nicer-looking - number = (sci_real | real | signed_integer).streamline() - """any numeric expression, returns the corresponding Python type""" - - fnumber = Regex(r'[+-]?\d+\.?\d*([eE][+-]?\d+)?').setName("fnumber").setParseAction(convertToFloat) - """any int or real number, returned as float""" - - identifier = Word(alphas+'_', alphanums+'_').setName("identifier") - """typical code identifier (leading alpha or '_', followed by 0 or more alphas, nums, or '_')""" - - ipv4_address = Regex(r'(25[0-5]|2[0-4][0-9]|1?[0-9]{1,2})(\.(25[0-5]|2[0-4][0-9]|1?[0-9]{1,2})){3}').setName("IPv4 address") - "IPv4 address (C{0.0.0.0 - 255.255.255.255})" - - _ipv6_part = Regex(r'[0-9a-fA-F]{1,4}').setName("hex_integer") - _full_ipv6_address = (_ipv6_part + (':' + _ipv6_part)*7).setName("full IPv6 address") - _short_ipv6_address = (Optional(_ipv6_part + (':' + _ipv6_part)*(0,6)) + "::" + Optional(_ipv6_part + (':' + _ipv6_part)*(0,6))).setName("short IPv6 address") - _short_ipv6_address.addCondition(lambda t: sum(1 for tt in t if pyparsing_common._ipv6_part.matches(tt)) < 8) - _mixed_ipv6_address = ("::ffff:" + ipv4_address).setName("mixed IPv6 address") - ipv6_address = Combine((_full_ipv6_address | _mixed_ipv6_address | _short_ipv6_address).setName("IPv6 address")).setName("IPv6 address") - "IPv6 address (long, short, or mixed form)" - - mac_address = Regex(r'[0-9a-fA-F]{2}([:.-])[0-9a-fA-F]{2}(?:\1[0-9a-fA-F]{2}){4}').setName("MAC address") - "MAC address xx:xx:xx:xx:xx (may also have '-' or '.' delimiters)" - - @staticmethod - def convertToDate(fmt="%Y-%m-%d"): - """ - Helper to create a parse action for converting parsed date string to Python datetime.date - - Params - - - fmt - format to be passed to datetime.strptime (default=C{"%Y-%m-%d"}) - - Example:: - date_expr = pyparsing_common.iso8601_date.copy() - date_expr.setParseAction(pyparsing_common.convertToDate()) - print(date_expr.parseString("1999-12-31")) - prints:: - [datetime.date(1999, 12, 31)] - """ - def cvt_fn(s,l,t): - try: - return datetime.strptime(t[0], fmt).date() - except ValueError as ve: - raise ParseException(s, l, str(ve)) - return cvt_fn - - @staticmethod - def convertToDatetime(fmt="%Y-%m-%dT%H:%M:%S.%f"): - """ - Helper to create a parse action for converting parsed datetime string to Python datetime.datetime - - Params - - - fmt - format to be passed to datetime.strptime (default=C{"%Y-%m-%dT%H:%M:%S.%f"}) - - Example:: - dt_expr = pyparsing_common.iso8601_datetime.copy() - dt_expr.setParseAction(pyparsing_common.convertToDatetime()) - print(dt_expr.parseString("1999-12-31T23:59:59.999")) - prints:: - [datetime.datetime(1999, 12, 31, 23, 59, 59, 999000)] - """ - def cvt_fn(s,l,t): - try: - return datetime.strptime(t[0], fmt) - except ValueError as ve: - raise ParseException(s, l, str(ve)) - return cvt_fn - - iso8601_date = Regex(r'(?P\d{4})(?:-(?P\d\d)(?:-(?P\d\d))?)?').setName("ISO8601 date") - "ISO8601 date (C{yyyy-mm-dd})" - - iso8601_datetime = Regex(r'(?P\d{4})-(?P\d\d)-(?P\d\d)[T ](?P\d\d):(?P\d\d)(:(?P\d\d(\.\d*)?)?)?(?PZ|[+-]\d\d:?\d\d)?').setName("ISO8601 datetime") - "ISO8601 datetime (C{yyyy-mm-ddThh:mm:ss.s(Z|+-00:00)}) - trailing seconds, milliseconds, and timezone optional; accepts separating C{'T'} or C{' '}" - - uuid = Regex(r'[0-9a-fA-F]{8}(-[0-9a-fA-F]{4}){3}-[0-9a-fA-F]{12}').setName("UUID") - "UUID (C{xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx})" - - _html_stripper = anyOpenTag.suppress() | anyCloseTag.suppress() - @staticmethod - def stripHTMLTags(s, l, tokens): - """ - Parse action to remove HTML tags from web page HTML source - - Example:: - # strip HTML links from normal text - text = 'More info at the     pyparsing wiki page' - td,td_end = makeHTMLTags("TD") - table_text = td + SkipTo(td_end).setParseAction(pyparsing_common.stripHTMLTags)("body") + td_end - - print(table_text.parseString(text).body) # -> 'More info at the pyparsing wiki page' - """ - return pyparsing_common._html_stripper.transformString(tokens[0]) - - _commasepitem = Combine(OneOrMore(~Literal(",") + ~LineEnd() + Word(printables, excludeChars=',') - + Optional( White(" \t") ) ) ).streamline().setName("commaItem") - comma_separated_list = delimitedList( Optional( quotedString.copy() | _commasepitem, default="") ).setName("comma separated list") - """Predefined expression of 1 or more printable words or quoted strings, separated by commas.""" - - upcaseTokens = staticmethod(tokenMap(lambda t: _ustr(t).upper())) - """Parse action to convert tokens to upper case.""" - - downcaseTokens = staticmethod(tokenMap(lambda t: _ustr(t).lower())) - """Parse action to convert tokens to lower case.""" - - -if __name__ == "__main__": - - selectToken = CaselessLiteral("select") - fromToken = CaselessLiteral("from") - - ident = Word(alphas, alphanums + "_$") - - columnName = delimitedList(ident, ".", combine=True).setParseAction(upcaseTokens) - columnNameList = Group(delimitedList(columnName)).setName("columns") - columnSpec = ('*' | columnNameList) - - tableName = delimitedList(ident, ".", combine=True).setParseAction(upcaseTokens) - tableNameList = Group(delimitedList(tableName)).setName("tables") - - simpleSQL = selectToken("command") + columnSpec("columns") + fromToken + tableNameList("tables") - - # demo runTests method, including embedded comments in test string - simpleSQL.runTests(""" - # '*' as column list and dotted table name - select * from SYS.XYZZY - - # caseless match on "SELECT", and casts back to "select" - SELECT * from XYZZY, ABC - - # list of column names, and mixed case SELECT keyword - Select AA,BB,CC from Sys.dual - - # multiple tables - Select A, B, C from Sys.dual, Table2 - - # invalid SELECT keyword - should fail - Xelect A, B, C from Sys.dual - - # incomplete command - should fail - Select - - # invalid column name - should fail - Select ^^^ frox Sys.dual - - """) - - pyparsing_common.number.runTests(""" - 100 - -100 - +100 - 3.14159 - 6.02e23 - 1e-12 - """) - - # any int or real number, returned as float - pyparsing_common.fnumber.runTests(""" - 100 - -100 - +100 - 3.14159 - 6.02e23 - 1e-12 - """) - - pyparsing_common.hex_integer.runTests(""" - 100 - FF - """) - - import uuid - pyparsing_common.uuid.setParseAction(tokenMap(uuid.UUID)) - pyparsing_common.uuid.runTests(""" - 12345678-1234-5678-1234-567812345678 - """) diff --git a/doc/tutorial/machine_learning_map/svg2imagemap.py b/doc/tutorial/machine_learning_map/svg2imagemap.py deleted file mode 100644 index 80f06c8fb9738..0000000000000 --- a/doc/tutorial/machine_learning_map/svg2imagemap.py +++ /dev/null @@ -1,111 +0,0 @@ -#!/usr/local/bin/python - -""" -This script converts a subset of SVG into an HTML imagemap - -Note *subset*. It only handles elements, for which it only pays -attention to the M and L commands. Further, it only notices the "translate" -transform. - -It was written to generate the examples in the documentation for maphilight, -and thus is very squarely aimed at handling several SVG maps from wikipedia. -It *assumes* that all the s it will need are inside a . Any -outside of a will be ignored. - -It takes several possible arguments, in the form: -$ svn2imagemap.py FILENAME [x y [group1 group2 ... groupN]] - -FILENAME must be the name of an SVG file. All other arguments are optional. - -x and y, if present, are the dimensions of the image you'll be creating from -the SVG. If not present, it assumes the values of the width and height -attributes in the SVG file. - -group1 through groupN are group ids. If only want particular groups used, -enter their ids here and all others will be ignored. -""" -import os -import re -import sys -import xml.dom.minidom - -import parse_path - -if len(sys.argv) == 1: - sys.exit("svn2imagemap.py FILENAME [x y [group1 group2 ... groupN]]") -if not os.path.exists(sys.argv[1]): - sys.exit("Input file does not exist") -x, y, groups = None, None, None -if len(sys.argv) >= 3: - x = float(sys.argv[2]) - y = float(sys.argv[3]) - if len(sys.argv) > 3: - groups = sys.argv[4:] - -svg_file = xml.dom.minidom.parse(sys.argv[1]) -svg = svg_file.getElementsByTagName("svg")[0] - -raw_width = float(svg.getAttribute("width")) -raw_height = float(svg.getAttribute("height")) -width_ratio = x and (x / raw_width) or 1 -height_ratio = y and (y / raw_height) or 1 - -if groups: - elements = [ - g - for g in svg.getElementsByTagName("g") - if (g.hasAttribute("id") and g.getAttribute("id") in groups) - ] - elements.extend( - [ - p - for p in svg.getElementsByTagName("path") - if (p.hasAttribute("id") and p.getAttribute("id") in groups) - ] - ) -else: - elements = svg.getElementsByTagName("g") - -parsed_groups = {} -for e in elements: - paths = [] - if e.nodeName == "g": - for path in e.getElementsByTagName("path"): - points = parse_path.get_points(path.getAttribute("d")) - for pointset in points: - paths.append([path.getAttribute("id"), pointset]) - else: - points = parse_path.get_points(e.getAttribute("d")) - for pointset in points: - paths.append([e.getAttribute("id"), pointset]) - if e.hasAttribute("transform"): - print(e.getAttribute("id"), e.getAttribute("transform")) - for transform in re.findall( - r"(\w+)\((-?\d+.?\d*),(-?\d+.?\d*)\)", e.getAttribute("transform") - ): - if transform[0] == "translate": - x_shift = float(transform[1]) - y_shift = float(transform[2]) - for path in paths: - path[1] = [(p[0] + x_shift, p[1] + y_shift) for p in path[1]] - - parsed_groups[e.getAttribute("id")] = paths - -out = [] -for g in parsed_groups: - for path in parsed_groups[g]: - out.append( - '' - % ( - path[0], - ", ".join( - [ - "%d,%d" % (p[0] * width_ratio, p[1] * height_ratio) - for p in path[1] - ] - ), - ) - ) - -with open(sys.argv[1].replace(".svg", ".html"), "w") as f: - f.write("\n".join(out)) diff --git a/doc/tutorial/statistical_inference/index.rst b/doc/tutorial/statistical_inference/index.rst index 1ea527054fc38..358bf16512254 100644 --- a/doc/tutorial/statistical_inference/index.rst +++ b/doc/tutorial/statistical_inference/index.rst @@ -4,17 +4,17 @@ A tutorial on statistical-learning for scientific data processing ========================================================================== -.. topic:: Statistical learning +.. topic:: Statistical learning `Machine learning `_ is a technique with a growing importance, as the size of the datasets experimental sciences are facing is rapidly growing. Problems it tackles range from building a prediction function linking different observations, to classifying observations, or - learning the structure in an unlabeled dataset. - + learning the structure in an unlabeled dataset. + This tutorial will explore *statistical learning*, the use of - machine learning techniques with the goal of `statistical inference + machine learning techniques with the goal of `statistical inference `_: drawing conclusions on the data at hand. @@ -24,8 +24,6 @@ A tutorial on statistical-learning for scientific data processing `_, `matplotlib `_). -.. include:: ../../includes/big_toc_css.rst - .. toctree:: :maxdepth: 2 diff --git a/doc/tutorial/statistical_inference/model_selection.rst b/doc/tutorial/statistical_inference/model_selection.rst index 87423ef1c3925..7d7d5f69f18c4 100644 --- a/doc/tutorial/statistical_inference/model_selection.rst +++ b/doc/tutorial/statistical_inference/model_selection.rst @@ -196,41 +196,38 @@ scoring method. >>> scores = list() >>> scores_std = list() - |details-start| - **Solution** - |details-split| - - .. plot:: - :context: close-figs - :align: center - - import numpy as np - from sklearn import datasets, svm - from sklearn.model_selection import cross_val_score - X, y = datasets.load_digits(return_X_y=True) - svc = svm.SVC(kernel="linear") - C_s = np.logspace(-10, 0, 10) - scores = list() - scores_std = list() - for C in C_s: - svc.C = C - this_scores = cross_val_score(svc, X, y, n_jobs=1) - scores.append(np.mean(this_scores)) - scores_std.append(np.std(this_scores)) - - import matplotlib.pyplot as plt - - plt.figure() - plt.semilogx(C_s, scores) - plt.semilogx(C_s, np.array(scores) + np.array(scores_std), "b--") - plt.semilogx(C_s, np.array(scores) - np.array(scores_std), "b--") - locs, labels = plt.yticks() - plt.yticks(locs, list(map(lambda x: "%g" % x, locs))) - plt.ylabel("CV score") - plt.xlabel("Parameter C") - plt.ylim(0, 1.1) - plt.show() - |details-end| + .. dropdown:: Solution + + .. plot:: + :context: close-figs + :align: center + + import numpy as np + from sklearn import datasets, svm + from sklearn.model_selection import cross_val_score + X, y = datasets.load_digits(return_X_y=True) + svc = svm.SVC(kernel="linear") + C_s = np.logspace(-10, 0, 10) + scores = list() + scores_std = list() + for C in C_s: + svc.C = C + this_scores = cross_val_score(svc, X, y, n_jobs=1) + scores.append(np.mean(this_scores)) + scores_std.append(np.std(this_scores)) + + import matplotlib.pyplot as plt + + plt.figure() + plt.semilogx(C_s, scores) + plt.semilogx(C_s, np.array(scores) + np.array(scores_std), "b--") + plt.semilogx(C_s, np.array(scores) - np.array(scores_std), "b--") + locs, labels = plt.yticks() + plt.yticks(locs, list(map(lambda x: "%g" % x, locs))) + plt.ylabel("CV score") + plt.xlabel("Parameter C") + plt.ylim(0, 1.1) + plt.show() Grid-search and cross-validated estimators ============================================ diff --git a/doc/tutorial/statistical_inference/supervised_learning.rst b/doc/tutorial/statistical_inference/supervised_learning.rst index 45fc4cf5b9bc0..41adf60c44fc7 100644 --- a/doc/tutorial/statistical_inference/supervised_learning.rst +++ b/doc/tutorial/statistical_inference/supervised_learning.rst @@ -426,9 +426,9 @@ the separating line (less regularization). **Regularized SVM (default)** -.. topic:: Example: +.. rubric:: Examples - - :ref:`sphx_glr_auto_examples_svm_plot_iris_svc.py` +- :ref:`sphx_glr_auto_examples_svm_plot_iris_svc.py` SVMs can be used in regression --:class:`SVR` (Support Vector Regression)--, or in @@ -503,13 +503,6 @@ Sigmoid kernel :target: ../../auto_examples/svm/plot_svm_kernels.html - -.. topic:: **Interactive example** - - See the :ref:`SVM GUI ` to download - ``svm_gui.py``; add data points of both classes with right and left button, - fit the model and change parameters and data. - .. topic:: **Exercise** :class: green diff --git a/doc/unsupervised_learning.rst b/doc/unsupervised_learning.rst index 9c1de0c134623..57e8b429fd67e 100644 --- a/doc/unsupervised_learning.rst +++ b/doc/unsupervised_learning.rst @@ -1,9 +1,3 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - -.. include:: includes/big_toc_css.rst - .. _unsupervised-learning: Unsupervised learning diff --git a/doc/user_guide.rst b/doc/user_guide.rst index cd2f331004aa9..a32c63db9de01 100644 --- a/doc/user_guide.rst +++ b/doc/user_guide.rst @@ -1,21 +1,9 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - -.. title:: User guide: contents - .. _user_guide: ========== User Guide ========== -.. include:: includes/big_toc_css.rst - -.. nice layout in the toc - -.. include:: tune_toc.rst - .. toctree:: :numbered: :maxdepth: 3 diff --git a/doc/visualizations.rst b/doc/visualizations.rst index 9a44f6feb1b48..7252a2900eb26 100644 --- a/doc/visualizations.rst +++ b/doc/visualizations.rst @@ -1,9 +1,3 @@ -.. Places parent toc into the sidebar - -:parenttoc: True - -.. include:: includes/big_toc_css.rst - .. _visualizations: ============== @@ -63,12 +57,12 @@ the previously computed roc curve again by using the `plot` method of the Notice that we pass `alpha=0.8` to the plot functions to adjust the alpha values of the curves. -.. topic:: Examples: +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py` - * :ref:`sphx_glr_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py` - * :ref:`sphx_glr_auto_examples_miscellaneous_plot_display_object_visualization.py` - * :ref:`sphx_glr_auto_examples_calibration_plot_compare_calibration.py` +* :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py` +* :ref:`sphx_glr_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py` +* :ref:`sphx_glr_auto_examples_miscellaneous_plot_display_object_visualization.py` +* :ref:`sphx_glr_auto_examples_calibration_plot_compare_calibration.py` Available Plotting Utilities ============================ diff --git a/doc/whats_new/_contributors.rst b/doc/whats_new/_contributors.rst index 21559e8112c0a..83f6ca5448b24 100644 --- a/doc/whats_new/_contributors.rst +++ b/doc/whats_new/_contributors.rst @@ -12,12 +12,12 @@ .. role:: raw-latex(raw) :format: latex -.. |MajorFeature| replace:: :raw-html:`Major Feature` :raw-latex:`{\small\sc [Major Feature]}` -.. |Feature| replace:: :raw-html:`Feature` :raw-latex:`{\small\sc [Feature]}` -.. |Efficiency| replace:: :raw-html:`Efficiency` :raw-latex:`{\small\sc [Efficiency]}` -.. |Enhancement| replace:: :raw-html:`Enhancement` :raw-latex:`{\small\sc [Enhancement]}` -.. |Fix| replace:: :raw-html:`Fix` :raw-latex:`{\small\sc [Fix]}` -.. |API| replace:: :raw-html:`API Change` :raw-latex:`{\small\sc [API Change]}` +.. |MajorFeature| replace:: :raw-html:`Major Feature` :raw-latex:`{\small\sc [Major Feature]}` +.. |Feature| replace:: :raw-html:`Feature` :raw-latex:`{\small\sc [Feature]}` +.. |Efficiency| replace:: :raw-html:`Efficiency` :raw-latex:`{\small\sc [Efficiency]}` +.. |Enhancement| replace:: :raw-html:`Enhancement` :raw-latex:`{\small\sc [Enhancement]}` +.. |Fix| replace:: :raw-html:`Fix` :raw-latex:`{\small\sc [Fix]}` +.. |API| replace:: :raw-html:`API Change` :raw-latex:`{\small\sc [API Change]}` .. _Olivier Grisel: https://twitter.com/ogrisel diff --git a/doc/whats_new/older_versions.rst b/doc/whats_new/older_versions.rst index f4e1d1c0cdf10..dc440fcc800fe 100644 --- a/doc/whats_new/older_versions.rst +++ b/doc/whats_new/older_versions.rst @@ -1153,7 +1153,6 @@ Changelog datasets was created. These include: :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py`, :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py`, - :ref:`sphx_glr_auto_examples_applications_svm_gui.py`, :ref:`sphx_glr_auto_examples_applications_wikipedia_principal_eigenvector.py` and others. diff --git a/examples/README.txt b/examples/README.txt index 958de667a5c69..57a4e7e60eb32 100644 --- a/examples/README.txt +++ b/examples/README.txt @@ -2,3 +2,8 @@ Examples ======== + +This is the gallery of examples that showcase how scikit-learn can be used. Some +examples demonstrate the use of the :ref:`API ` in general and some +demonstrate specific applications in tutorial form. Also check out our +:ref:`user guide ` for more detailed illustrations. diff --git a/examples/applications/plot_digits_denoising.py b/examples/applications/plot_digits_denoising.py index 10d94aa0212d6..9d4b0cae79d06 100644 --- a/examples/applications/plot_digits_denoising.py +++ b/examples/applications/plot_digits_denoising.py @@ -12,12 +12,12 @@ We will use USPS digits dataset to reproduce presented in Sect. 4 of [1]_. -.. topic:: References +.. rubric:: References - .. [1] `Bakır, Gökhan H., Jason Weston, and Bernhard Schölkopf. - "Learning to find pre-images." - Advances in neural information processing systems 16 (2004): 449-456. - `_ +.. [1] `Bakır, Gökhan H., Jason Weston, and Bernhard Schölkopf. + "Learning to find pre-images." + Advances in neural information processing systems 16 (2004): 449-456. + `_ """ diff --git a/examples/covariance/plot_mahalanobis_distances.py b/examples/covariance/plot_mahalanobis_distances.py index b82c861133de7..537d3fa0d9d8a 100644 --- a/examples/covariance/plot_mahalanobis_distances.py +++ b/examples/covariance/plot_mahalanobis_distances.py @@ -50,15 +50,15 @@ See also :ref:`sphx_glr_auto_examples_covariance_plot_robust_vs_empirical_covariance.py` -.. topic:: References: - - .. [1] P. J. Rousseeuw. `Least median of squares regression - `_. J. Am - Stat Ass, 79:871, 1984. - .. [2] Wilson, E. B., & Hilferty, M. M. (1931). `The distribution of chi-square. - `_ - Proceedings of the National Academy of Sciences of the United States - of America, 17, 684-688. +.. rubric:: References + +.. [1] P. J. Rousseeuw. `Least median of squares regression + `_. J. Am + Stat Ass, 79:871, 1984. +.. [2] Wilson, E. B., & Hilferty, M. M. (1931). `The distribution of chi-square. + `_ + Proceedings of the National Academy of Sciences of the United States + of America, 17, 684-688. """ # noqa: E501 diff --git a/examples/ensemble/plot_adaboost_multiclass.py b/examples/ensemble/plot_adaboost_multiclass.py index 35b0d1bb86470..8c14d7196999d 100644 --- a/examples/ensemble/plot_adaboost_multiclass.py +++ b/examples/ensemble/plot_adaboost_multiclass.py @@ -17,11 +17,11 @@ be selected. This ensures that subsequent iterations of the algorithm focus on the difficult-to-classify samples. -.. topic:: References: +.. rubric:: References - .. [1] :doi:`J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class adaboost." - Statistics and its Interface 2.3 (2009): 349-360. - <10.4310/SII.2009.v2.n3.a8>` +.. [1] :doi:`J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class adaboost." + Statistics and its Interface 2.3 (2009): 349-360. + <10.4310/SII.2009.v2.n3.a8>` """ @@ -231,16 +231,16 @@ def misclassification_error(y_true, y_pred): # decision. Indeed, this exactly is the formulation of updating the base # estimators' weights after each iteration in AdaBoost. # -# |details-start| Mathematical details |details-split| +# .. dropdown:: Mathematical details # -# The weight associated with a weak learner trained at the stage :math:`m` is -# inversely associated with its misclassification error such that: +# The weight associated with a weak learner trained at the stage :math:`m` is +# inversely associated with its misclassification error such that: # -# .. math:: \alpha^{(m)} = \log \frac{1 - err^{(m)}}{err^{(m)}} + \log (K - 1), +# .. math:: \alpha^{(m)} = \log \frac{1 - err^{(m)}}{err^{(m)}} + \log (K - 1), # -# where :math:`\alpha^{(m)}` and :math:`err^{(m)}` are the weight and the error -# of the :math:`m` th weak learner, respectively, and :math:`K` is the number of -# classes in our classification problem. |details-end| +# where :math:`\alpha^{(m)}` and :math:`err^{(m)}` are the weight and the error +# of the :math:`m` th weak learner, respectively, and :math:`K` is the number of +# classes in our classification problem. # # Another interesting observation boils down to the fact that the first weak # learners of the model make fewer errors than later weak learners of the diff --git a/examples/ensemble/plot_hgbt_regression.py b/examples/ensemble/plot_hgbt_regression.py index 55ca65ea4a3b8..a704fefbe9a31 100644 --- a/examples/ensemble/plot_hgbt_regression.py +++ b/examples/ensemble/plot_hgbt_regression.py @@ -42,11 +42,12 @@ # May 1996 to 5 December 1998. Each sample of the dataset refers to a period of # 30 minutes, i.e. there are 48 instances for each time period of one day. Each # sample on the dataset has 7 columns: -# - date: between 7 May 1996 to 5 December 1998. Normalized between 0 and 1; -# - day: day of week (1-7); -# - period: half hour intervals over 24 hours. Normalized between 0 and 1; -# - nswprice/nswdemand: electricity price/demand of New South Wales; -# - vicprice/vicdemand: electricity price/demand of Victoria. +# +# - date: between 7 May 1996 to 5 December 1998. Normalized between 0 and 1; +# - day: day of week (1-7); +# - period: half hour intervals over 24 hours. Normalized between 0 and 1; +# - nswprice/nswdemand: electricity price/demand of New South Wales; +# - vicprice/vicdemand: electricity price/demand of Victoria. # # Originally, it is a classification task, but here we use it for the regression # task to predict the scheduled electricity transfer between states. diff --git a/examples/gaussian_process/plot_gpr_co2.py b/examples/gaussian_process/plot_gpr_co2.py index b3da30daa0f6d..bf64092b0482e 100644 --- a/examples/gaussian_process/plot_gpr_co2.py +++ b/examples/gaussian_process/plot_gpr_co2.py @@ -4,7 +4,7 @@ ==================================================================================== This example is based on Section 5.4.3 of "Gaussian Processes for Machine -Learning" [RW2006]_. It illustrates an example of complex kernel engineering +Learning" [1]_. It illustrates an example of complex kernel engineering and hyperparameter optimization using gradient ascent on the log-marginal-likelihood. The data consists of the monthly average atmospheric CO2 concentrations (in parts per million by volume (ppm)) collected at the @@ -12,12 +12,11 @@ model the CO2 concentration as a function of the time :math:`t` and extrapolate for years after 2001. -.. topic: References +.. rubric:: References - .. [RW2006] `Rasmussen, Carl Edward. - "Gaussian processes in machine learning." - Summer school on machine learning. Springer, Berlin, Heidelberg, 2003 - `_. +.. [1] `Rasmussen, Carl Edward. "Gaussian processes in machine learning." + Summer school on machine learning. Springer, Berlin, Heidelberg, 2003 + `_. """ print(__doc__) diff --git a/examples/inspection/plot_permutation_importance.py b/examples/inspection/plot_permutation_importance.py index 8cf63dd80fd4d..66b40fcd25bc8 100644 --- a/examples/inspection/plot_permutation_importance.py +++ b/examples/inspection/plot_permutation_importance.py @@ -18,10 +18,10 @@ This example shows how to use Permutation Importances as an alternative that can mitigate those limitations. -.. topic:: References: +.. rubric:: References - * :doi:`L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, - 2001. <10.1023/A:1010933404324>` +* :doi:`L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, + 2001. <10.1023/A:1010933404324>` """ diff --git a/examples/linear_model/plot_lasso_lars_ic.py b/examples/linear_model/plot_lasso_lars_ic.py index 8f1e7034a108a..ab53a4f7612e3 100644 --- a/examples/linear_model/plot_lasso_lars_ic.py +++ b/examples/linear_model/plot_lasso_lars_ic.py @@ -16,12 +16,12 @@ of the two approaches, you can refer to the following example: :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_model_selection.py`. -.. topic:: References +.. rubric:: References - .. [ZHT2007] :arxiv:`Zou, Hui, Trevor Hastie, and Robert Tibshirani. - "On the degrees of freedom of the lasso." - The Annals of Statistics 35.5 (2007): 2173-2192. - <0712.0881>` +.. [ZHT2007] :arxiv:`Zou, Hui, Trevor Hastie, and Robert Tibshirani. + "On the degrees of freedom of the lasso." + The Annals of Statistics 35.5 (2007): 2173-2192. + <0712.0881>` """ # Author: Alexandre Gramfort diff --git a/examples/model_selection/plot_cost_sensitive_learning.py b/examples/model_selection/plot_cost_sensitive_learning.py index be0900d50e4ba..12d636d556c66 100644 --- a/examples/model_selection/plot_cost_sensitive_learning.py +++ b/examples/model_selection/plot_cost_sensitive_learning.py @@ -4,10 +4,10 @@ ============================================================== Once a classifier is trained, the output of the :term:`predict` method outputs class -label predictions corresponding to a thresholding of either the :term:`decision -function` or the :term:`predict_proba` output. For a binary classifier, the default -threshold is defined as a posterior probability estimate of 0.5 or a decision score of -0.0. +label predictions corresponding to a thresholding of either the +:term:`decision_function` or the :term:`predict_proba` output. For a binary classifier, +the default threshold is defined as a posterior probability estimate of 0.5 or a +decision score of 0.0. However, this default strategy is most likely not optimal for the task at hand. Here, we use the "Statlog" German credit dataset [1]_ to illustrate a use case. @@ -23,16 +23,16 @@ In the second part of the example, we further extend this approach by considering the problem of fraud detection in credit card transactions: in this case, the business metric depends on the amount of each individual transaction. -.. topic:: References - .. [1] "Statlog (German Credit Data) Data Set", UCI Machine Learning Repository, - `Link - `_. +.. rubric :: References - .. [2] `Charles Elkan, "The Foundations of Cost-Sensitive Learning", - International joint conference on artificial intelligence. - Vol. 17. No. 1. Lawrence Erlbaum Associates Ltd, 2001. - `_ +.. [1] "Statlog (German Credit Data) Data Set", UCI Machine Learning Repository, + `Link `_. + +.. [2] `Charles Elkan, "The Foundations of Cost-Sensitive Learning", + International joint conference on artificial intelligence. + Vol. 17. No. 1. Lawrence Erlbaum Associates Ltd, 2001. + `_ """ # %% diff --git a/examples/model_selection/plot_grid_search_stats.py b/examples/model_selection/plot_grid_search_stats.py index fbeb485d8db44..9335b12055515 100644 --- a/examples/model_selection/plot_grid_search_stats.py +++ b/examples/model_selection/plot_grid_search_stats.py @@ -540,27 +540,27 @@ def compute_corrected_ttest(differences, df, n_train, n_test): # correction is needed when using the frequentist approach. # %% -# .. topic:: References -# -# .. [1] Dietterich, T. G. (1998). `Approximate statistical tests for -# comparing supervised classification learning algorithms -# `_. -# Neural computation, 10(7). -# .. [2] Nadeau, C., & Bengio, Y. (2000). `Inference for the generalization -# error -# `_. -# In Advances in neural information processing systems. -# .. [3] Bouckaert, R. R., & Frank, E. (2004). `Evaluating the replicability -# of significance tests for comparing learning algorithms -# `_. -# In Pacific-Asia Conference on Knowledge Discovery and Data Mining. -# .. [4] Benavoli, A., Corani, G., Demšar, J., & Zaffalon, M. (2017). `Time -# for a change: a tutorial for comparing multiple classifiers through -# Bayesian analysis -# `_. -# The Journal of Machine Learning Research, 18(1). See the Python -# library that accompanies this paper `here -# `_. -# .. [5] Diebold, F.X. & Mariano R.S. (1995). `Comparing predictive accuracy -# `_ -# Journal of Business & economic statistics, 20(1), 134-144. +# .. rubric:: References +# +# .. [1] Dietterich, T. G. (1998). `Approximate statistical tests for +# comparing supervised classification learning algorithms +# `_. +# Neural computation, 10(7). +# .. [2] Nadeau, C., & Bengio, Y. (2000). `Inference for the generalization +# error +# `_. +# In Advances in neural information processing systems. +# .. [3] Bouckaert, R. R., & Frank, E. (2004). `Evaluating the replicability +# of significance tests for comparing learning algorithms +# `_. +# In Pacific-Asia Conference on Knowledge Discovery and Data Mining. +# .. [4] Benavoli, A., Corani, G., Demšar, J., & Zaffalon, M. (2017). `Time +# for a change: a tutorial for comparing multiple classifiers through +# Bayesian analysis +# `_. +# The Journal of Machine Learning Research, 18(1). See the Python +# library that accompanies this paper `here +# `_. +# .. [5] Diebold, F.X. & Mariano R.S. (1995). `Comparing predictive accuracy +# `_ +# Journal of Business & economic statistics, 20(1), 134-144. diff --git a/examples/model_selection/plot_nested_cross_validation_iris.py b/examples/model_selection/plot_nested_cross_validation_iris.py index 7513a078b68ce..38defdadf4165 100644 --- a/examples/model_selection/plot_nested_cross_validation_iris.py +++ b/examples/model_selection/plot_nested_cross_validation_iris.py @@ -30,17 +30,17 @@ performance of non-nested and nested CV strategies by taking the difference between their scores. -.. topic:: See Also: +.. seealso:: - :ref:`cross_validation` - :ref:`grid_search` -.. topic:: References: +.. rubric:: References - .. [1] `Cawley, G.C.; Talbot, N.L.C. On over-fitting in model selection and - subsequent selection bias in performance evaluation. - J. Mach. Learn. Res 2010,11, 2079-2107. - `_ +.. [1] `Cawley, G.C.; Talbot, N.L.C. On over-fitting in model selection and + subsequent selection bias in performance evaluation. + J. Mach. Learn. Res 2010,11, 2079-2107. + `_ """ diff --git a/examples/model_selection/plot_permutation_tests_for_classification.py b/examples/model_selection/plot_permutation_tests_for_classification.py index a02f6d188f006..102dfaf1fb466 100644 --- a/examples/model_selection/plot_permutation_tests_for_classification.py +++ b/examples/model_selection/plot_permutation_tests_for_classification.py @@ -128,10 +128,10 @@ # Finally, note that this test has been shown to produce low p-values even # if there is only weak structure in the data [1]_. # -# .. topic:: References: +# .. rubric:: References # -# .. [1] Ojala and Garriga. `Permutation Tests for Studying Classifier -# Performance -# `_. The -# Journal of Machine Learning Research (2010) vol. 11 +# .. [1] Ojala and Garriga. `Permutation Tests for Studying Classifier +# Performance +# `_. The +# Journal of Machine Learning Research (2010) vol. 11 # diff --git a/examples/release_highlights/plot_release_highlights_1_1_0.py b/examples/release_highlights/plot_release_highlights_1_1_0.py index 44f85a8bbdf8b..16b359a9b03e7 100644 --- a/examples/release_highlights/plot_release_highlights_1_1_0.py +++ b/examples/release_highlights/plot_release_highlights_1_1_0.py @@ -24,8 +24,8 @@ # %% # .. _quantile_support_hgbdt: # -# Quantile loss in :class:`ensemble.HistGradientBoostingRegressor` -# ---------------------------------------------------------------- +# Quantile loss in :class:`~ensemble.HistGradientBoostingRegressor` +# ----------------------------------------------------------------- # :class:`~ensemble.HistGradientBoostingRegressor` can model quantiles with # `loss="quantile"` and the new parameter `quantile`. from sklearn.ensemble import HistGradientBoostingRegressor diff --git a/examples/release_highlights/plot_release_highlights_1_3_0.py b/examples/release_highlights/plot_release_highlights_1_3_0.py index 8521ac3554c46..ebb109e524f1d 100644 --- a/examples/release_highlights/plot_release_highlights_1_3_0.py +++ b/examples/release_highlights/plot_release_highlights_1_3_0.py @@ -101,8 +101,8 @@ tree.predict(X) # %% -# New display `model_selection.ValidationCurveDisplay` -# ---------------------------------------------------- +# New display :class:`~model_selection.ValidationCurveDisplay` +# ------------------------------------------------------------ # :class:`model_selection.ValidationCurveDisplay` is now available to plot results # from :func:`model_selection.validation_curve`. from sklearn.datasets import make_classification @@ -141,8 +141,8 @@ cross_val_score(gbdt, X, y).mean() # %% -# Grouping infrequent categories in :class:`preprocessing.OrdinalEncoder` -# ----------------------------------------------------------------------- +# Grouping infrequent categories in :class:`~preprocessing.OrdinalEncoder` +# ------------------------------------------------------------------------ # Similarly to :class:`preprocessing.OneHotEncoder`, the class # :class:`preprocessing.OrdinalEncoder` now supports aggregating infrequent categories # into a single output for each feature. The parameters to enable the gathering of diff --git a/pyproject.toml b/pyproject.toml index f244745f37d30..f54cdb703b7e3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -53,16 +53,20 @@ docs = [ "pandas>=1.1.5", "seaborn>=0.9.0", "memory_profiler>=0.57.0", - "sphinx>=6.0.0", + "sphinx>=7.3.7", "sphinx-copybutton>=0.5.2", - "sphinx-gallery>=0.15.0", + "sphinx-gallery>=0.16.0", "numpydoc>=1.2.0", "Pillow>=7.1.2", "pooch>=1.6.0", - "sphinx-prompt>=1.3.0", - "sphinxext-opengraph>=0.4.2", + "sphinx-prompt>=1.4.0", + "sphinxext-opengraph>=0.9.1", "plotly>=5.14.0", - "polars>=0.20.23" + "polars>=0.20.23", + "sphinx-design>=0.5.0", + "sphinxcontrib-sass>=0.3.4", + "pydata-sphinx-theme>=0.15.2", + "sphinx-remove-toctrees>=1.0.0.post1", ] examples = [ "matplotlib>=3.3.4", @@ -101,7 +105,7 @@ requires = [ [tool.black] line-length = 88 -target_version = ['py39', 'py310', 'py311'] +target-version = ['py39', 'py310', 'py311'] preview = true exclude = ''' /( diff --git a/setup.cfg b/setup.cfg index f2052de285ed6..a11ec816abdc9 100644 --- a/setup.cfg +++ b/setup.cfg @@ -60,5 +60,5 @@ ignore = [codespell] -skip = ./.git,./.mypy_cache,./doc/themes/scikit-learn-modern/static/js,./sklearn/feature_extraction/_stop_words.py,./doc/_build,./doc/auto_examples,./doc/modules/generated +skip = ./.git,./.mypy_cache,./sklearn/feature_extraction/_stop_words.py,./doc/_build,./doc/auto_examples,./doc/modules/generated ignore-words = build_tools/codespell_ignore_words.txt diff --git a/sklearn/__init__.py b/sklearn/__init__.py index d794f2489b92b..6f97cc57a664f 100644 --- a/sklearn/__init__.py +++ b/sklearn/__init__.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn` module includes functions to configure global settings and -get information about the working environment. -""" +"""Configure global settings and get information about the working environment.""" # Machine learning module for Python # ================================== diff --git a/sklearn/_min_dependencies.py b/sklearn/_min_dependencies.py index 0b1a96748a588..b066c22145263 100644 --- a/sklearn/_min_dependencies.py +++ b/sklearn/_min_dependencies.py @@ -35,15 +35,19 @@ "pyamg": ("4.0.0", "tests"), "polars": ("0.20.23", "docs, tests"), "pyarrow": ("12.0.0", "tests"), - "sphinx": ("6.0.0", "docs"), + "sphinx": ("7.3.7", "docs"), "sphinx-copybutton": ("0.5.2", "docs"), - "sphinx-gallery": ("0.15.0", "docs"), + "sphinx-gallery": ("0.16.0", "docs"), "numpydoc": ("1.2.0", "docs, tests"), "Pillow": ("7.1.2", "docs"), "pooch": ("1.6.0", "docs, examples, tests"), - "sphinx-prompt": ("1.3.0", "docs"), - "sphinxext-opengraph": ("0.4.2", "docs"), + "sphinx-prompt": ("1.4.0", "docs"), + "sphinxext-opengraph": ("0.9.1", "docs"), "plotly": ("5.14.0", "docs, examples"), + "sphinxcontrib-sass": ("0.3.4", "docs"), + "sphinx-remove-toctrees": ("1.0.0.post1", "docs"), + "sphinx-design": ("0.5.0", "docs"), + "pydata-sphinx-theme": ("0.15.2", "docs"), # XXX: Pin conda-lock to the latest released version (needs manual update # from time to time) "conda-lock": ("2.5.6", "maintenance"), diff --git a/sklearn/base.py b/sklearn/base.py index d0f861bd2278f..0aa7af1041368 100644 --- a/sklearn/base.py +++ b/sklearn/base.py @@ -1,4 +1,4 @@ -"""Base classes for all estimators.""" +"""Base classes for all estimators and various utility functions.""" # Author: Gael Varoquaux # License: BSD 3 clause diff --git a/sklearn/calibration.py b/sklearn/calibration.py index 2e1a46e6889b8..4fb0c508dba71 100644 --- a/sklearn/calibration.py +++ b/sklearn/calibration.py @@ -1,4 +1,4 @@ -"""Calibration of predicted probabilities.""" +"""Methods for calibrating predicted probabilities.""" # Author: Alexandre Gramfort # Balazs Kegl diff --git a/sklearn/cluster/__init__.py b/sklearn/cluster/__init__.py index f5d3104d816bf..5d497d2976ad1 100644 --- a/sklearn/cluster/__init__.py +++ b/sklearn/cluster/__init__.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.cluster` module gathers popular unsupervised clustering -algorithms. -""" +"""Popular unsupervised clustering algorithms.""" from ._affinity_propagation import AffinityPropagation, affinity_propagation from ._agglomerative import ( diff --git a/sklearn/compose/__init__.py b/sklearn/compose/__init__.py index 7b137cdf9e07f..e6e8fc6fd2c1b 100644 --- a/sklearn/compose/__init__.py +++ b/sklearn/compose/__init__.py @@ -1,8 +1,8 @@ -"""Meta-estimators for building composite models with transformers +"""Meta-estimators for building composite models with transformers. In addition to its current contents, this module will eventually be home to -refurbished versions of Pipeline and FeatureUnion. - +refurbished versions of :class:`~sklearn.pipeline.Pipeline` and +:class:`~sklearn.pipeline.FeatureUnion`. """ from ._column_transformer import ( diff --git a/sklearn/covariance/__init__.py b/sklearn/covariance/__init__.py index 8fcf8c68444e5..19ee1d8a09759 100644 --- a/sklearn/covariance/__init__.py +++ b/sklearn/covariance/__init__.py @@ -1,9 +1,8 @@ -""" -The :mod:`sklearn.covariance` module includes methods and algorithms to -robustly estimate the covariance of features given a set of points. The -precision matrix defined as the inverse of the covariance is also estimated. -Covariance estimation is closely related to the theory of Gaussian Graphical -Models. +"""Methods and algorithms to robustly estimate covariance. + +They estimate the covariance of features at given sets of points, as well as the +precision matrix defined as the inverse of the covariance. Covariance estimation is +closely related to the theory of Gaussian graphical models. """ from ._elliptic_envelope import EllipticEnvelope diff --git a/sklearn/covariance/_shrunk_covariance.py b/sklearn/covariance/_shrunk_covariance.py index 2c8248d0f6502..caf23ffaf687e 100644 --- a/sklearn/covariance/_shrunk_covariance.py +++ b/sklearn/covariance/_shrunk_covariance.py @@ -617,7 +617,7 @@ def fit(self, X, y=None): prefer_skip_nested_validation=False, ) def oas(X, *, assume_centered=False): - """Estimate covariance with the Oracle Approximating Shrinkage as proposed in [1]_. + """Estimate covariance with the Oracle Approximating Shrinkage. Read more in the :ref:`User Guide `. @@ -684,7 +684,7 @@ def oas(X, *, assume_centered=False): class OAS(EmpiricalCovariance): - """Oracle Approximating Shrinkage Estimator as proposed in [1]_. + """Oracle Approximating Shrinkage Estimator. Read more in the :ref:`User Guide `. diff --git a/sklearn/cross_decomposition/__init__.py b/sklearn/cross_decomposition/__init__.py index 47b78783caf9c..15eb21467daee 100644 --- a/sklearn/cross_decomposition/__init__.py +++ b/sklearn/cross_decomposition/__init__.py @@ -1,3 +1,5 @@ +"""Algorithms for cross decomposition.""" + from ._pls import CCA, PLSSVD, PLSCanonical, PLSRegression __all__ = ["PLSCanonical", "PLSRegression", "PLSSVD", "CCA"] diff --git a/sklearn/datasets/__init__.py b/sklearn/datasets/__init__.py index 6f61e027dceaa..58cddb099faff 100644 --- a/sklearn/datasets/__init__.py +++ b/sklearn/datasets/__init__.py @@ -1,8 +1,4 @@ -""" -The :mod:`sklearn.datasets` module includes utilities to load datasets, -including methods to load and fetch popular reference datasets. It also -features some artificial data generators. -""" +"""Utilities to load popular datasets and artificial data generators.""" import textwrap diff --git a/sklearn/datasets/descr/breast_cancer.rst b/sklearn/datasets/descr/breast_cancer.rst index ceabd33e14ddc..cedfa9b4ff0f4 100644 --- a/sklearn/datasets/descr/breast_cancer.rst +++ b/sklearn/datasets/descr/breast_cancer.rst @@ -104,19 +104,15 @@ This database is also available through the UW CS ftp server: ftp ftp.cs.wisc.edu cd math-prog/cpo-dataset/machine-learn/WDBC/ -|details-start| -**References** -|details-split| - -- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction - for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on - Electronic Imaging: Science and Technology, volume 1905, pages 861-870, - San Jose, CA, 1993. -- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and - prognosis via linear programming. Operations Research, 43(4), pages 570-577, - July-August 1995. -- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques - to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) - 163-171. - -|details-end| +.. dropdown:: References + + - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction + for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on + Electronic Imaging: Science and Technology, volume 1905, pages 861-870, + San Jose, CA, 1993. + - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and + prognosis via linear programming. Operations Research, 43(4), pages 570-577, + July-August 1995. + - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques + to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) + 163-171. diff --git a/sklearn/datasets/descr/california_housing.rst b/sklearn/datasets/descr/california_housing.rst index 33ff111fef541..3173a057d1d5a 100644 --- a/sklearn/datasets/descr/california_housing.rst +++ b/sklearn/datasets/descr/california_housing.rst @@ -40,7 +40,7 @@ and many empty houses, such as vacation resorts. It can be downloaded/loaded using the :func:`sklearn.datasets.fetch_california_housing` function. -.. topic:: References +.. rubric:: References - - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions, - Statistics and Probability Letters, 33 (1997) 291-297 +- Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions, + Statistics and Probability Letters, 33 (1997) 291-297 diff --git a/sklearn/datasets/descr/digits.rst b/sklearn/datasets/descr/digits.rst index 3b07233721d69..7297584a1b4ac 100644 --- a/sklearn/datasets/descr/digits.rst +++ b/sklearn/datasets/descr/digits.rst @@ -32,19 +32,15 @@ T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C. L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469, 1994. -|details-start| -**References** -|details-split| - -- C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their - Applications to Handwritten Digit Recognition, MSc Thesis, Institute of - Graduate Studies in Science and Engineering, Bogazici University. -- E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika. -- Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. - Linear dimensionalityreduction using relevance weighted LDA. School of - Electrical and Electronic Engineering Nanyang Technological University. - 2005. -- Claudio Gentile. A New Approximate Maximal Margin Classification - Algorithm. NIPS. 2000. - -|details-end| +.. dropdown:: References + + - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their + Applications to Handwritten Digit Recognition, MSc Thesis, Institute of + Graduate Studies in Science and Engineering, Bogazici University. + - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika. + - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. + Linear dimensionalityreduction using relevance weighted LDA. School of + Electrical and Electronic Engineering Nanyang Technological University. + 2005. + - Claudio Gentile. A New Approximate Maximal Margin Classification + Algorithm. NIPS. 2000. diff --git a/sklearn/datasets/descr/iris.rst b/sklearn/datasets/descr/iris.rst index 771c92faa9899..98651543620e6 100644 --- a/sklearn/datasets/descr/iris.rst +++ b/sklearn/datasets/descr/iris.rst @@ -45,23 +45,19 @@ data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other. -|details-start| -**References** -|details-split| - -- Fisher, R.A. "The use of multiple measurements in taxonomic problems" - Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to - Mathematical Statistics" (John Wiley, NY, 1950). -- Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis. - (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218. -- Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System - Structure and Classification Rule for Recognition in Partially Exposed - Environments". IEEE Transactions on Pattern Analysis and Machine - Intelligence, Vol. PAMI-2, No. 1, 67-71. -- Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions - on Information Theory, May 1972, 431-433. -- See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II - conceptual clustering system finds 3 classes in the data. -- Many, many more ... - -|details-end| +.. dropdown:: References + + - Fisher, R.A. "The use of multiple measurements in taxonomic problems" + Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to + Mathematical Statistics" (John Wiley, NY, 1950). + - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis. + (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218. + - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System + Structure and Classification Rule for Recognition in Partially Exposed + Environments". IEEE Transactions on Pattern Analysis and Machine + Intelligence, Vol. PAMI-2, No. 1, 67-71. + - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions + on Information Theory, May 1972, 431-433. + - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II + conceptual clustering system finds 3 classes in the data. + - Many, many more ... diff --git a/sklearn/datasets/descr/kddcup99.rst b/sklearn/datasets/descr/kddcup99.rst index fe8a0c8f4168c..0eae813be27a9 100644 --- a/sklearn/datasets/descr/kddcup99.rst +++ b/sklearn/datasets/descr/kddcup99.rst @@ -81,14 +81,14 @@ and the target values in ``target``. The "as_frame" optional argument converts ``data`` into a pandas DataFrame and ``target`` into a pandas Series. The dataset will be downloaded from the web if necessary. -.. topic:: References +.. rubric:: References - .. [2] Analysis and Results of the 1999 DARPA Off-Line Intrusion - Detection Evaluation, Richard Lippmann, Joshua W. Haines, - David J. Fried, Jonathan Korba, Kumar Das. +.. [2] Analysis and Results of the 1999 DARPA Off-Line Intrusion + Detection Evaluation, Richard Lippmann, Joshua W. Haines, + David J. Fried, Jonathan Korba, Kumar Das. - .. [3] K. Yamanishi, J.-I. Takeuchi, G. Williams, and P. Milne. Online - unsupervised outlier detection using finite mixtures with - discounting learning algorithms. In Proceedings of the sixth - ACM SIGKDD international conference on Knowledge discovery - and data mining, pages 320-324. ACM Press, 2000. +.. [3] K. Yamanishi, J.-I. Takeuchi, G. Williams, and P. Milne. Online + unsupervised outlier detection using finite mixtures with + discounting learning algorithms. In Proceedings of the sixth + ACM SIGKDD international conference on Knowledge discovery + and data mining, pages 320-324. ACM Press, 2000. diff --git a/sklearn/datasets/descr/lfw.rst b/sklearn/datasets/descr/lfw.rst index f7d80558be373..fc23c9566bd64 100644 --- a/sklearn/datasets/descr/lfw.rst +++ b/sklearn/datasets/descr/lfw.rst @@ -32,97 +32,93 @@ Dimensionality 5828 Features real, between 0 and 255 ================= ======================= -|details-start| -**Usage** -|details-split| +.. dropdown:: Usage -``scikit-learn`` provides two loaders that will automatically download, -cache, parse the metadata files, decode the jpeg and convert the -interesting slices into memmapped numpy arrays. This dataset size is more -than 200 MB. The first load typically takes more than a couple of minutes -to fully decode the relevant part of the JPEG files into numpy arrays. If -the dataset has been loaded once, the following times the loading times -less than 200ms by using a memmapped version memoized on the disk in the -``~/scikit_learn_data/lfw_home/`` folder using ``joblib``. + ``scikit-learn`` provides two loaders that will automatically download, + cache, parse the metadata files, decode the jpeg and convert the + interesting slices into memmapped numpy arrays. This dataset size is more + than 200 MB. The first load typically takes more than a couple of minutes + to fully decode the relevant part of the JPEG files into numpy arrays. If + the dataset has been loaded once, the following times the loading times + less than 200ms by using a memmapped version memoized on the disk in the + ``~/scikit_learn_data/lfw_home/`` folder using ``joblib``. -The first loader is used for the Face Identification task: a multi-class -classification task (hence supervised learning):: + The first loader is used for the Face Identification task: a multi-class + classification task (hence supervised learning):: - >>> from sklearn.datasets import fetch_lfw_people - >>> lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4) + >>> from sklearn.datasets import fetch_lfw_people + >>> lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4) - >>> for name in lfw_people.target_names: - ... print(name) - ... - Ariel Sharon - Colin Powell - Donald Rumsfeld - George W Bush - Gerhard Schroeder - Hugo Chavez - Tony Blair + >>> for name in lfw_people.target_names: + ... print(name) + ... + Ariel Sharon + Colin Powell + Donald Rumsfeld + George W Bush + Gerhard Schroeder + Hugo Chavez + Tony Blair -The default slice is a rectangular shape around the face, removing -most of the background:: + The default slice is a rectangular shape around the face, removing + most of the background:: - >>> lfw_people.data.dtype - dtype('float32') + >>> lfw_people.data.dtype + dtype('float32') - >>> lfw_people.data.shape - (1288, 1850) + >>> lfw_people.data.shape + (1288, 1850) - >>> lfw_people.images.shape - (1288, 50, 37) + >>> lfw_people.images.shape + (1288, 50, 37) -Each of the ``1140`` faces is assigned to a single person id in the ``target`` -array:: + Each of the ``1140`` faces is assigned to a single person id in the ``target`` + array:: - >>> lfw_people.target.shape - (1288,) + >>> lfw_people.target.shape + (1288,) - >>> list(lfw_people.target[:10]) - [5, 6, 3, 1, 0, 1, 3, 4, 3, 0] + >>> list(lfw_people.target[:10]) + [5, 6, 3, 1, 0, 1, 3, 4, 3, 0] -The second loader is typically used for the face verification task: each sample -is a pair of two picture belonging or not to the same person:: + The second loader is typically used for the face verification task: each sample + is a pair of two picture belonging or not to the same person:: - >>> from sklearn.datasets import fetch_lfw_pairs - >>> lfw_pairs_train = fetch_lfw_pairs(subset='train') + >>> from sklearn.datasets import fetch_lfw_pairs + >>> lfw_pairs_train = fetch_lfw_pairs(subset='train') - >>> list(lfw_pairs_train.target_names) - ['Different persons', 'Same person'] + >>> list(lfw_pairs_train.target_names) + ['Different persons', 'Same person'] - >>> lfw_pairs_train.pairs.shape - (2200, 2, 62, 47) + >>> lfw_pairs_train.pairs.shape + (2200, 2, 62, 47) - >>> lfw_pairs_train.data.shape - (2200, 5828) + >>> lfw_pairs_train.data.shape + (2200, 5828) - >>> lfw_pairs_train.target.shape - (2200,) + >>> lfw_pairs_train.target.shape + (2200,) -Both for the :func:`sklearn.datasets.fetch_lfw_people` and -:func:`sklearn.datasets.fetch_lfw_pairs` function it is -possible to get an additional dimension with the RGB color channels by -passing ``color=True``, in that case the shape will be -``(2200, 2, 62, 47, 3)``. + Both for the :func:`sklearn.datasets.fetch_lfw_people` and + :func:`sklearn.datasets.fetch_lfw_pairs` function it is + possible to get an additional dimension with the RGB color channels by + passing ``color=True``, in that case the shape will be + ``(2200, 2, 62, 47, 3)``. -The :func:`sklearn.datasets.fetch_lfw_pairs` datasets is subdivided into -3 subsets: the development ``train`` set, the development ``test`` set and -an evaluation ``10_folds`` set meant to compute performance metrics using a -10-folds cross validation scheme. + The :func:`sklearn.datasets.fetch_lfw_pairs` datasets is subdivided into + 3 subsets: the development ``train`` set, the development ``test`` set and + an evaluation ``10_folds`` set meant to compute performance metrics using a + 10-folds cross validation scheme. -|details-end| +.. rubric:: References -.. topic:: References: +* `Labeled Faces in the Wild: A Database for Studying Face Recognition + in Unconstrained Environments. + `_ + Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. + University of Massachusetts, Amherst, Technical Report 07-49, October, 2007. - * `Labeled Faces in the Wild: A Database for Studying Face Recognition - in Unconstrained Environments. - `_ - Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. - University of Massachusetts, Amherst, Technical Report 07-49, October, 2007. +.. rubric:: Examples -.. topic:: Examples: - - * :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py` +* :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py` diff --git a/sklearn/datasets/descr/linnerud.rst b/sklearn/datasets/descr/linnerud.rst index 108611a4722ad..6deb231fe67a9 100644 --- a/sklearn/datasets/descr/linnerud.rst +++ b/sklearn/datasets/descr/linnerud.rst @@ -18,11 +18,7 @@ twenty middle-aged men in a fitness club: - *exercise* - CSV containing 20 observations on 3 exercise variables: Chins, Situps and Jumps. -|details-start| -**References** -|details-split| +.. dropdown:: References -* Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: - Editions Technic. - -|details-end| + * Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris: + Editions Technic. diff --git a/sklearn/datasets/descr/rcv1.rst b/sklearn/datasets/descr/rcv1.rst index 7cf3730a17554..3f14cf01934a0 100644 --- a/sklearn/datasets/descr/rcv1.rst +++ b/sklearn/datasets/descr/rcv1.rst @@ -65,8 +65,8 @@ The compressed size is about 656 MB. .. _rcv1 homepage: http://jmlr.csail.mit.edu/papers/volume5/lewis04a/ -.. topic:: References +.. rubric:: References - .. [1] Lewis, D. D., Yang, Y., Rose, T. G., & Li, F. (2004). - RCV1: A new benchmark collection for text categorization research. - The Journal of Machine Learning Research, 5, 361-397. +.. [1] Lewis, D. D., Yang, Y., Rose, T. G., & Li, F. (2004). + RCV1: A new benchmark collection for text categorization research. + The Journal of Machine Learning Research, 5, 361-397. diff --git a/sklearn/datasets/descr/species_distributions.rst b/sklearn/datasets/descr/species_distributions.rst index a2c2243de5567..c2c0014b875e9 100644 --- a/sklearn/datasets/descr/species_distributions.rst +++ b/sklearn/datasets/descr/species_distributions.rst @@ -29,8 +29,8 @@ of a species at a specific location. The dataset is provided by Phillips et. al. (2006). -.. topic:: References +.. rubric:: References - * `"Maximum entropy modeling of species geographic distributions" - `_ S. J. Phillips, - R. P. Anderson, R. E. Schapire - Ecological Modelling, 190:231-259, 2006. +* `"Maximum entropy modeling of species geographic distributions" + `_ S. J. Phillips, + R. P. Anderson, R. E. Schapire - Ecological Modelling, 190:231-259, 2006. diff --git a/sklearn/datasets/descr/twenty_newsgroups.rst b/sklearn/datasets/descr/twenty_newsgroups.rst index d1a049869dd7f..182272b888d45 100644 --- a/sklearn/datasets/descr/twenty_newsgroups.rst +++ b/sklearn/datasets/descr/twenty_newsgroups.rst @@ -27,204 +27,191 @@ Dimensionality 1 Features text ================= ========== -|details-start| -**Usage** -|details-split| - -The :func:`sklearn.datasets.fetch_20newsgroups` function is a data -fetching / caching functions that downloads the data archive from -the original `20 newsgroups website`_, extracts the archive contents -in the ``~/scikit_learn_data/20news_home`` folder and calls the -:func:`sklearn.datasets.load_files` on either the training or -testing set folder, or both of them:: - - >>> from sklearn.datasets import fetch_20newsgroups - >>> newsgroups_train = fetch_20newsgroups(subset='train') - - >>> from pprint import pprint - >>> pprint(list(newsgroups_train.target_names)) - ['alt.atheism', - 'comp.graphics', - 'comp.os.ms-windows.misc', - 'comp.sys.ibm.pc.hardware', - 'comp.sys.mac.hardware', - 'comp.windows.x', - 'misc.forsale', - 'rec.autos', - 'rec.motorcycles', - 'rec.sport.baseball', - 'rec.sport.hockey', - 'sci.crypt', - 'sci.electronics', - 'sci.med', - 'sci.space', - 'soc.religion.christian', - 'talk.politics.guns', - 'talk.politics.mideast', - 'talk.politics.misc', - 'talk.religion.misc'] - -The real data lies in the ``filenames`` and ``target`` attributes. The target -attribute is the integer index of the category:: - - >>> newsgroups_train.filenames.shape - (11314,) - >>> newsgroups_train.target.shape - (11314,) - >>> newsgroups_train.target[:10] - array([ 7, 4, 4, 1, 14, 16, 13, 3, 2, 4]) - -It is possible to load only a sub-selection of the categories by passing the -list of the categories to load to the -:func:`sklearn.datasets.fetch_20newsgroups` function:: - - >>> cats = ['alt.atheism', 'sci.space'] - >>> newsgroups_train = fetch_20newsgroups(subset='train', categories=cats) - - >>> list(newsgroups_train.target_names) - ['alt.atheism', 'sci.space'] - >>> newsgroups_train.filenames.shape - (1073,) - >>> newsgroups_train.target.shape - (1073,) - >>> newsgroups_train.target[:10] - array([0, 1, 1, 1, 0, 1, 1, 0, 0, 0]) - -|details-end| - -|details-start| -**Converting text to vectors** -|details-split| - -In order to feed predictive or clustering models with the text data, -one first need to turn the text into vectors of numerical values suitable -for statistical analysis. This can be achieved with the utilities of the -``sklearn.feature_extraction.text`` as demonstrated in the following -example that extract `TF-IDF`_ vectors of unigram tokens -from a subset of 20news:: - - >>> from sklearn.feature_extraction.text import TfidfVectorizer - >>> categories = ['alt.atheism', 'talk.religion.misc', - ... 'comp.graphics', 'sci.space'] - >>> newsgroups_train = fetch_20newsgroups(subset='train', - ... categories=categories) - >>> vectorizer = TfidfVectorizer() - >>> vectors = vectorizer.fit_transform(newsgroups_train.data) - >>> vectors.shape - (2034, 34118) - -The extracted TF-IDF vectors are very sparse, with an average of 159 non-zero -components by sample in a more than 30000-dimensional space -(less than .5% non-zero features):: - - >>> vectors.nnz / float(vectors.shape[0]) - 159.01327... - -:func:`sklearn.datasets.fetch_20newsgroups_vectorized` is a function which -returns ready-to-use token counts features instead of file names. - -.. _`20 newsgroups website`: http://people.csail.mit.edu/jrennie/20Newsgroups/ -.. _`TF-IDF`: https://en.wikipedia.org/wiki/Tf-idf - -|details-end| - -|details-start| -**Filtering text for more realistic training** -|details-split| - -It is easy for a classifier to overfit on particular things that appear in the -20 Newsgroups data, such as newsgroup headers. Many classifiers achieve very -high F-scores, but their results would not generalize to other documents that -aren't from this window of time. - -For example, let's look at the results of a multinomial Naive Bayes classifier, -which is fast to train and achieves a decent F-score:: - - >>> from sklearn.naive_bayes import MultinomialNB - >>> from sklearn import metrics - >>> newsgroups_test = fetch_20newsgroups(subset='test', - ... categories=categories) - >>> vectors_test = vectorizer.transform(newsgroups_test.data) - >>> clf = MultinomialNB(alpha=.01) - >>> clf.fit(vectors, newsgroups_train.target) - MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True) - - >>> pred = clf.predict(vectors_test) - >>> metrics.f1_score(newsgroups_test.target, pred, average='macro') - 0.88213... - -(The example :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` shuffles -the training and test data, instead of segmenting by time, and in that case -multinomial Naive Bayes gets a much higher F-score of 0.88. Are you suspicious -yet of what's going on inside this classifier?) - -Let's take a look at what the most informative features are: - - >>> import numpy as np - >>> def show_top10(classifier, vectorizer, categories): - ... feature_names = vectorizer.get_feature_names_out() - ... for i, category in enumerate(categories): - ... top10 = np.argsort(classifier.coef_[i])[-10:] - ... print("%s: %s" % (category, " ".join(feature_names[top10]))) - ... - >>> show_top10(clf, vectorizer, newsgroups_train.target_names) - alt.atheism: edu it and in you that is of to the - comp.graphics: edu in graphics it is for and of to the - sci.space: edu it that is in and space to of the - talk.religion.misc: not it you in is that and to of the - - -You can now see many things that these features have overfit to: - -- Almost every group is distinguished by whether headers such as - ``NNTP-Posting-Host:`` and ``Distribution:`` appear more or less often. -- Another significant feature involves whether the sender is affiliated with - a university, as indicated either by their headers or their signature. -- The word "article" is a significant feature, based on how often people quote - previous posts like this: "In article [article ID], [name] <[e-mail address]> - wrote:" -- Other features match the names and e-mail addresses of particular people who - were posting at the time. - -With such an abundance of clues that distinguish newsgroups, the classifiers -barely have to identify topics from text at all, and they all perform at the -same high level. - -For this reason, the functions that load 20 Newsgroups data provide a -parameter called **remove**, telling it what kinds of information to strip out -of each file. **remove** should be a tuple containing any subset of -``('headers', 'footers', 'quotes')``, telling it to remove headers, signature -blocks, and quotation blocks respectively. - - >>> newsgroups_test = fetch_20newsgroups(subset='test', - ... remove=('headers', 'footers', 'quotes'), - ... categories=categories) - >>> vectors_test = vectorizer.transform(newsgroups_test.data) - >>> pred = clf.predict(vectors_test) - >>> metrics.f1_score(pred, newsgroups_test.target, average='macro') - 0.77310... - -This classifier lost over a lot of its F-score, just because we removed -metadata that has little to do with topic classification. -It loses even more if we also strip this metadata from the training data: - - >>> newsgroups_train = fetch_20newsgroups(subset='train', - ... remove=('headers', 'footers', 'quotes'), - ... categories=categories) - >>> vectors = vectorizer.fit_transform(newsgroups_train.data) - >>> clf = MultinomialNB(alpha=.01) - >>> clf.fit(vectors, newsgroups_train.target) - MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True) - - >>> vectors_test = vectorizer.transform(newsgroups_test.data) - >>> pred = clf.predict(vectors_test) - >>> metrics.f1_score(newsgroups_test.target, pred, average='macro') - 0.76995... - -Some other classifiers cope better with this harder version of the task. Try the -:ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` -example with and without the `remove` option to compare the results. -|details-end| +.. dropdown:: Usage + + The :func:`sklearn.datasets.fetch_20newsgroups` function is a data + fetching / caching functions that downloads the data archive from + the original `20 newsgroups website `__, + extracts the archive contents + in the ``~/scikit_learn_data/20news_home`` folder and calls the + :func:`sklearn.datasets.load_files` on either the training or + testing set folder, or both of them:: + + >>> from sklearn.datasets import fetch_20newsgroups + >>> newsgroups_train = fetch_20newsgroups(subset='train') + + >>> from pprint import pprint + >>> pprint(list(newsgroups_train.target_names)) + ['alt.atheism', + 'comp.graphics', + 'comp.os.ms-windows.misc', + 'comp.sys.ibm.pc.hardware', + 'comp.sys.mac.hardware', + 'comp.windows.x', + 'misc.forsale', + 'rec.autos', + 'rec.motorcycles', + 'rec.sport.baseball', + 'rec.sport.hockey', + 'sci.crypt', + 'sci.electronics', + 'sci.med', + 'sci.space', + 'soc.religion.christian', + 'talk.politics.guns', + 'talk.politics.mideast', + 'talk.politics.misc', + 'talk.religion.misc'] + + The real data lies in the ``filenames`` and ``target`` attributes. The target + attribute is the integer index of the category:: + + >>> newsgroups_train.filenames.shape + (11314,) + >>> newsgroups_train.target.shape + (11314,) + >>> newsgroups_train.target[:10] + array([ 7, 4, 4, 1, 14, 16, 13, 3, 2, 4]) + + It is possible to load only a sub-selection of the categories by passing the + list of the categories to load to the + :func:`sklearn.datasets.fetch_20newsgroups` function:: + + >>> cats = ['alt.atheism', 'sci.space'] + >>> newsgroups_train = fetch_20newsgroups(subset='train', categories=cats) + + >>> list(newsgroups_train.target_names) + ['alt.atheism', 'sci.space'] + >>> newsgroups_train.filenames.shape + (1073,) + >>> newsgroups_train.target.shape + (1073,) + >>> newsgroups_train.target[:10] + array([0, 1, 1, 1, 0, 1, 1, 0, 0, 0]) + +.. dropdown:: Converting text to vectors + + In order to feed predictive or clustering models with the text data, + one first need to turn the text into vectors of numerical values suitable + for statistical analysis. This can be achieved with the utilities of the + ``sklearn.feature_extraction.text`` as demonstrated in the following + example that extract `TF-IDF `__ vectors + of unigram tokens from a subset of 20news:: + + >>> from sklearn.feature_extraction.text import TfidfVectorizer + >>> categories = ['alt.atheism', 'talk.religion.misc', + ... 'comp.graphics', 'sci.space'] + >>> newsgroups_train = fetch_20newsgroups(subset='train', + ... categories=categories) + >>> vectorizer = TfidfVectorizer() + >>> vectors = vectorizer.fit_transform(newsgroups_train.data) + >>> vectors.shape + (2034, 34118) + + The extracted TF-IDF vectors are very sparse, with an average of 159 non-zero + components by sample in a more than 30000-dimensional space + (less than .5% non-zero features):: + + >>> vectors.nnz / float(vectors.shape[0]) + 159.01327... + + :func:`sklearn.datasets.fetch_20newsgroups_vectorized` is a function which + returns ready-to-use token counts features instead of file names. + +.. dropdown:: Filtering text for more realistic training + + It is easy for a classifier to overfit on particular things that appear in the + 20 Newsgroups data, such as newsgroup headers. Many classifiers achieve very + high F-scores, but their results would not generalize to other documents that + aren't from this window of time. + + For example, let's look at the results of a multinomial Naive Bayes classifier, + which is fast to train and achieves a decent F-score:: + + >>> from sklearn.naive_bayes import MultinomialNB + >>> from sklearn import metrics + >>> newsgroups_test = fetch_20newsgroups(subset='test', + ... categories=categories) + >>> vectors_test = vectorizer.transform(newsgroups_test.data) + >>> clf = MultinomialNB(alpha=.01) + >>> clf.fit(vectors, newsgroups_train.target) + MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True) + + >>> pred = clf.predict(vectors_test) + >>> metrics.f1_score(newsgroups_test.target, pred, average='macro') + 0.88213... + + (The example :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` shuffles + the training and test data, instead of segmenting by time, and in that case + multinomial Naive Bayes gets a much higher F-score of 0.88. Are you suspicious + yet of what's going on inside this classifier?) + + Let's take a look at what the most informative features are: + + >>> import numpy as np + >>> def show_top10(classifier, vectorizer, categories): + ... feature_names = vectorizer.get_feature_names_out() + ... for i, category in enumerate(categories): + ... top10 = np.argsort(classifier.coef_[i])[-10:] + ... print("%s: %s" % (category, " ".join(feature_names[top10]))) + ... + >>> show_top10(clf, vectorizer, newsgroups_train.target_names) + alt.atheism: edu it and in you that is of to the + comp.graphics: edu in graphics it is for and of to the + sci.space: edu it that is in and space to of the + talk.religion.misc: not it you in is that and to of the + + + You can now see many things that these features have overfit to: + + - Almost every group is distinguished by whether headers such as + ``NNTP-Posting-Host:`` and ``Distribution:`` appear more or less often. + - Another significant feature involves whether the sender is affiliated with + a university, as indicated either by their headers or their signature. + - The word "article" is a significant feature, based on how often people quote + previous posts like this: "In article [article ID], [name] <[e-mail address]> + wrote:" + - Other features match the names and e-mail addresses of particular people who + were posting at the time. + + With such an abundance of clues that distinguish newsgroups, the classifiers + barely have to identify topics from text at all, and they all perform at the + same high level. + + For this reason, the functions that load 20 Newsgroups data provide a + parameter called **remove**, telling it what kinds of information to strip out + of each file. **remove** should be a tuple containing any subset of + ``('headers', 'footers', 'quotes')``, telling it to remove headers, signature + blocks, and quotation blocks respectively. + + >>> newsgroups_test = fetch_20newsgroups(subset='test', + ... remove=('headers', 'footers', 'quotes'), + ... categories=categories) + >>> vectors_test = vectorizer.transform(newsgroups_test.data) + >>> pred = clf.predict(vectors_test) + >>> metrics.f1_score(pred, newsgroups_test.target, average='macro') + 0.77310... + + This classifier lost over a lot of its F-score, just because we removed + metadata that has little to do with topic classification. + It loses even more if we also strip this metadata from the training data: + + >>> newsgroups_train = fetch_20newsgroups(subset='train', + ... remove=('headers', 'footers', 'quotes'), + ... categories=categories) + >>> vectors = vectorizer.fit_transform(newsgroups_train.data) + >>> clf = MultinomialNB(alpha=.01) + >>> clf.fit(vectors, newsgroups_train.target) + MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True) + + >>> vectors_test = vectorizer.transform(newsgroups_test.data) + >>> pred = clf.predict(vectors_test) + >>> metrics.f1_score(newsgroups_test.target, pred, average='macro') + 0.76995... + + Some other classifiers cope better with this harder version of the task. Try the + :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` + example with and without the `remove` option to compare the results. .. topic:: Data Considerations @@ -253,12 +240,9 @@ example with and without the `remove` option to compare the results. should be taken into consideration when using the dataset, reviewing the output, and the bias should be documented. -.. topic:: Examples +.. rubric:: Examples - * :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` - - * :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` - - * :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py` - - * :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py` +* :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` +* :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` +* :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py` +* :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py` diff --git a/sklearn/datasets/descr/wine_data.rst b/sklearn/datasets/descr/wine_data.rst index 0325af6233c17..64efe49900ebf 100644 --- a/sklearn/datasets/descr/wine_data.rst +++ b/sklearn/datasets/descr/wine_data.rst @@ -73,26 +73,22 @@ Lichman, M. (2013). UCI Machine Learning Repository [https://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. -|details-start| -**References** -|details-split| - -(1) S. Aeberhard, D. Coomans and O. de Vel, -Comparison of Classifiers in High Dimensional Settings, -Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of -Mathematics and Statistics, James Cook University of North Queensland. -(Also submitted to Technometrics). - -The data was used with many others for comparing various -classifiers. The classes are separable, though only RDA -has achieved 100% correct classification. -(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) -(All results using the leave-one-out technique) - -(2) S. Aeberhard, D. Coomans and O. de Vel, -"THE CLASSIFICATION PERFORMANCE OF RDA" -Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of -Mathematics and Statistics, James Cook University of North Queensland. -(Also submitted to Journal of Chemometrics). - -|details-end| +.. dropdown:: References + + (1) S. Aeberhard, D. Coomans and O. de Vel, + Comparison of Classifiers in High Dimensional Settings, + Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of + Mathematics and Statistics, James Cook University of North Queensland. + (Also submitted to Technometrics). + + The data was used with many others for comparing various + classifiers. The classes are separable, though only RDA + has achieved 100% correct classification. + (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) + (All results using the leave-one-out technique) + + (2) S. Aeberhard, D. Coomans and O. de Vel, + "THE CLASSIFICATION PERFORMANCE OF RDA" + Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of + Mathematics and Statistics, James Cook University of North Queensland. + (Also submitted to Journal of Chemometrics). diff --git a/sklearn/decomposition/__init__.py b/sklearn/decomposition/__init__.py index 3d33938a755a7..4730bda31ebbd 100644 --- a/sklearn/decomposition/__init__.py +++ b/sklearn/decomposition/__init__.py @@ -1,7 +1,7 @@ -""" -The :mod:`sklearn.decomposition` module includes matrix decomposition -algorithms, including among others PCA, NMF or ICA. Most of the algorithms of -this module can be regarded as dimensionality reduction techniques. +"""Matrix decomposition algorithms. + +These include PCA, NMF, ICA, and more. Most of the algorithms of this module can be +regarded as dimensionality reduction techniques. """ from ..utils.extmath import randomized_svd diff --git a/sklearn/decomposition/_kernel_pca.py b/sklearn/decomposition/_kernel_pca.py index 0f45bc7c9239c..88b1bb63a4fb0 100644 --- a/sklearn/decomposition/_kernel_pca.py +++ b/sklearn/decomposition/_kernel_pca.py @@ -30,10 +30,10 @@ class KernelPCA(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator): - """Kernel Principal Component Analysis (KPCA) [1]_. + """Kernel Principal component analysis (KPCA). - Non-linear dimensionality reduction through the use of kernels (see - :ref:`metrics`). + Non-linear dimensionality reduction through the use of kernels [1]_, see also + :ref:`metrics`. It uses the :func:`scipy.linalg.eigh` LAPACK implementation of the full SVD or the :func:`scipy.sparse.linalg.eigsh` ARPACK implementation of the diff --git a/sklearn/discriminant_analysis.py b/sklearn/discriminant_analysis.py index 01a1004012787..e5eaee2db11bb 100644 --- a/sklearn/discriminant_analysis.py +++ b/sklearn/discriminant_analysis.py @@ -1,6 +1,4 @@ -""" -Linear Discriminant Analysis and Quadratic Discriminant Analysis -""" +"""Linear and quadratic discriminant analysis.""" # Authors: Clemens Brunner # Martin Billinger diff --git a/sklearn/dummy.py b/sklearn/dummy.py index 17812fe1b3d05..9a30e6a84118a 100644 --- a/sklearn/dummy.py +++ b/sklearn/dummy.py @@ -1,3 +1,5 @@ +"""Dummy estimators that implement simple rules of thumb.""" + # Author: Mathieu Blondel # Arnaud Joly # Maheshakya Wijewardena diff --git a/sklearn/ensemble/__init__.py b/sklearn/ensemble/__init__.py index 8ddf05084f1be..e49d744ed6391 100644 --- a/sklearn/ensemble/__init__.py +++ b/sklearn/ensemble/__init__.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.ensemble` module includes ensemble-based methods for -classification, regression and anomaly detection. -""" +"""Ensemble-based methods for classification, regression and anomaly detection.""" from ._bagging import BaggingClassifier, BaggingRegressor from ._base import BaseEnsemble diff --git a/sklearn/exceptions.py b/sklearn/exceptions.py index 1466ce783ee00..7f6b26163cc87 100644 --- a/sklearn/exceptions.py +++ b/sklearn/exceptions.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.exceptions` module includes all custom warnings and error -classes used across scikit-learn. -""" +"""Custom warnings and errors used across scikit-learn.""" __all__ = [ "NotFittedError", diff --git a/sklearn/experimental/__init__.py b/sklearn/experimental/__init__.py index 0effaf5b05fa0..205a11a3d524b 100644 --- a/sklearn/experimental/__init__.py +++ b/sklearn/experimental/__init__.py @@ -1,7 +1,7 @@ -""" -The :mod:`sklearn.experimental` module provides importable modules that enable -the use of experimental features or estimators. +"""Importable modules that enable the use of experimental features or estimators. + +.. warning:: -The features and estimators that are experimental aren't subject to -deprecation cycles. Use them at your own risks! + The features and estimators that are experimental aren't subject to + deprecation cycles. Use them at your own risks! """ diff --git a/sklearn/feature_extraction/__init__.py b/sklearn/feature_extraction/__init__.py index f4db85303f4b6..dced9d46b4cc7 100644 --- a/sklearn/feature_extraction/__init__.py +++ b/sklearn/feature_extraction/__init__.py @@ -1,8 +1,4 @@ -""" -The :mod:`sklearn.feature_extraction` module deals with feature extraction -from raw data. It currently includes methods to extract features from text and -images. -""" +"""Feature extraction from raw data.""" from . import text from ._dict_vectorizer import DictVectorizer diff --git a/sklearn/feature_extraction/image.py b/sklearn/feature_extraction/image.py index 3f64ff11e246f..66dfc6f872b59 100644 --- a/sklearn/feature_extraction/image.py +++ b/sklearn/feature_extraction/image.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.feature_extraction.image` submodule gathers utilities to -extract features from images. -""" +"""Utilities to extract features from images.""" # Authors: Emmanuelle Gouillart # Gael Varoquaux diff --git a/sklearn/feature_extraction/text.py b/sklearn/feature_extraction/text.py index 826b3bc7a6706..8607d6dab4373 100644 --- a/sklearn/feature_extraction/text.py +++ b/sklearn/feature_extraction/text.py @@ -1,3 +1,5 @@ +"""Utilities to build feature vectors from text documents.""" + # Authors: Olivier Grisel # Mathieu Blondel # Lars Buitinck @@ -6,10 +8,6 @@ # Roman Sinayev # # License: BSD 3 clause -""" -The :mod:`sklearn.feature_extraction.text` submodule gathers utilities to -build feature vectors from text documents. -""" import array import re diff --git a/sklearn/feature_selection/__init__.py b/sklearn/feature_selection/__init__.py index 4fbc631155078..d431a5b016807 100644 --- a/sklearn/feature_selection/__init__.py +++ b/sklearn/feature_selection/__init__.py @@ -1,7 +1,7 @@ -""" -The :mod:`sklearn.feature_selection` module implements feature selection -algorithms. It currently includes univariate filter selection methods and the -recursive feature elimination algorithm. +"""Feature selection algorithms. + +These include univariate filter selection methods and the recursive feature elimination +algorithm. """ from ._base import SelectorMixin diff --git a/sklearn/gaussian_process/__init__.py b/sklearn/gaussian_process/__init__.py index bc0d902b45b18..b1488a8c3e663 100644 --- a/sklearn/gaussian_process/__init__.py +++ b/sklearn/gaussian_process/__init__.py @@ -1,13 +1,10 @@ +"""Gaussian process based regression and classification.""" + # Author: Jan Hendrik Metzen # Vincent Dubourg # (mostly translation, see implementation details) # License: BSD 3 clause -""" -The :mod:`sklearn.gaussian_process` module implements Gaussian Process -based regression and classification. -""" - from . import kernels from ._gpc import GaussianProcessClassifier from ._gpr import GaussianProcessRegressor diff --git a/sklearn/gaussian_process/kernels.py b/sklearn/gaussian_process/kernels.py index c31335696944c..9c26d4fc466ee 100644 --- a/sklearn/gaussian_process/kernels.py +++ b/sklearn/gaussian_process/kernels.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.gaussian_process.kernels` module implements a set of kernels that -can be combined by operators and used in Gaussian processes. -""" +"""A set of kernels that can be combined by operators and used in Gaussian processes.""" # Kernels for Gaussian process regression and classification. # diff --git a/sklearn/impute/__init__.py b/sklearn/impute/__init__.py index 380bcecaf65b5..a40c6b432d6d5 100644 --- a/sklearn/impute/__init__.py +++ b/sklearn/impute/__init__.py @@ -1,4 +1,4 @@ -"""Transformers for missing value imputation""" +"""Transformers for missing value imputation.""" import typing diff --git a/sklearn/inspection/__init__.py b/sklearn/inspection/__init__.py index f254967f96166..312a19d2c9bce 100644 --- a/sklearn/inspection/__init__.py +++ b/sklearn/inspection/__init__.py @@ -1,4 +1,4 @@ -"""The :mod:`sklearn.inspection` module includes tools for model inspection.""" +"""Tools for model inspection.""" from ._partial_dependence import partial_dependence from ._permutation_importance import permutation_importance diff --git a/sklearn/isotonic.py b/sklearn/isotonic.py index 04456b1763791..b77ffc04185c0 100644 --- a/sklearn/isotonic.py +++ b/sklearn/isotonic.py @@ -1,3 +1,5 @@ +"""Isotonic regression for obtaining monotonic fit to data.""" + # Authors: Fabian Pedregosa # Alexandre Gramfort # Nelle Varoquaux diff --git a/sklearn/kernel_approximation.py b/sklearn/kernel_approximation.py index 44bfb0b898913..118b37a9b03f2 100644 --- a/sklearn/kernel_approximation.py +++ b/sklearn/kernel_approximation.py @@ -1,11 +1,7 @@ -""" -The :mod:`sklearn.kernel_approximation` module implements several -approximate kernel feature maps based on Fourier transforms and Count Sketches. -""" +"""Approximate kernel feature maps based on Fourier transforms and count sketches.""" # Author: Andreas Mueller # Daniel Lopez-Sanchez (TensorSketch) - # License: BSD 3 clause import warnings diff --git a/sklearn/kernel_ridge.py b/sklearn/kernel_ridge.py index 23890f3a68cd7..bdd31d5105336 100644 --- a/sklearn/kernel_ridge.py +++ b/sklearn/kernel_ridge.py @@ -1,4 +1,4 @@ -"""Module :mod:`sklearn.kernel_ridge` implements kernel ridge regression.""" +"""Kernel ridge regression.""" # Authors: Mathieu Blondel # Jan Hendrik Metzen diff --git a/sklearn/linear_model/__init__.py b/sklearn/linear_model/__init__.py index 45c99d4d36df1..a2b0ffba0e728 100644 --- a/sklearn/linear_model/__init__.py +++ b/sklearn/linear_model/__init__.py @@ -1,6 +1,4 @@ -""" -The :mod:`sklearn.linear_model` module implements a variety of linear models. -""" +"""A variety of linear models.""" # See http://scikit-learn.sourceforge.net/modules/sgd.html and # http://scikit-learn.sourceforge.net/modules/linear_model.html for diff --git a/sklearn/linear_model/_least_angle.py b/sklearn/linear_model/_least_angle.py index 81e8abb8bc5d6..fae9f523847ad 100644 --- a/sklearn/linear_model/_least_angle.py +++ b/sklearn/linear_model/_least_angle.py @@ -79,14 +79,14 @@ def lars_path( return_n_iter=False, positive=False, ): - """Compute Least Angle Regression or Lasso path using the LARS algorithm [1]. + """Compute Least Angle Regression or Lasso path using the LARS algorithm. The optimization objective for the case method='lasso' is:: (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1 in the case of method='lar', the objective function is only known in - the form of an implicit equation (see discussion in [1]). + the form of an implicit equation (see discussion in [1]_). Read more in the :ref:`User Guide `. @@ -268,14 +268,14 @@ def lars_path_gram( return_n_iter=False, positive=False, ): - """The lars_path in the sufficient stats mode [1]. + """The lars_path in the sufficient stats mode. The optimization objective for the case method='lasso' is:: (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1 in the case of method='lar', the objective function is only known in - the form of an implicit equation (see discussion in [1]) + the form of an implicit equation (see discussion in [1]_). Read more in the :ref:`User Guide `. diff --git a/sklearn/manifold/__init__.py b/sklearn/manifold/__init__.py index 1e8d96c7cf94b..b3f53e84b04fb 100644 --- a/sklearn/manifold/__init__.py +++ b/sklearn/manifold/__init__.py @@ -1,6 +1,4 @@ -""" -The :mod:`sklearn.manifold` module implements data embedding techniques. -""" +"""Data embedding techniques.""" from ._isomap import Isomap from ._locally_linear import LocallyLinearEmbedding, locally_linear_embedding diff --git a/sklearn/metrics/__init__.py b/sklearn/metrics/__init__.py index af25a219c79f1..81a67d9793bb0 100644 --- a/sklearn/metrics/__init__.py +++ b/sklearn/metrics/__init__.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.metrics` module includes score functions, performance metrics -and pairwise metrics and distance computations. -""" +"""Score functions, performance metrics, pairwise metrics and distance computations.""" from . import cluster from ._classification import ( diff --git a/sklearn/metrics/cluster/__init__.py b/sklearn/metrics/cluster/__init__.py index 44da911061bc8..776b2f2dbd063 100644 --- a/sklearn/metrics/cluster/__init__.py +++ b/sklearn/metrics/cluster/__init__.py @@ -1,9 +1,8 @@ -""" -The :mod:`sklearn.metrics.cluster` submodule contains evaluation metrics for -cluster analysis results. There are two forms of evaluation: +"""Evaluation metrics for cluster analysis results. -- supervised, which uses a ground truth class values for each sample. -- unsupervised, which does not and measures the 'quality' of the model itself. +- Supervised evaluation uses a ground truth class values for each sample. +- Unsupervised evaluation does use ground truths and measures the "quality" of the + model itself. """ from ._bicluster import consensus_score diff --git a/sklearn/metrics/cluster/_supervised.py b/sklearn/metrics/cluster/_supervised.py index 992b460329302..1f72eae3725f6 100644 --- a/sklearn/metrics/cluster/_supervised.py +++ b/sklearn/metrics/cluster/_supervised.py @@ -188,12 +188,12 @@ def contingency_matrix( prefer_skip_nested_validation=True, ) def pair_confusion_matrix(labels_true, labels_pred): - """Pair confusion matrix arising from two clusterings [1]_. + """Pair confusion matrix arising from two clusterings. The pair confusion matrix :math:`C` computes a 2 by 2 similarity matrix between two clusterings by considering all pairs of samples and counting pairs that are assigned into the same or into different clusters under - the true and predicted clusterings. + the true and predicted clusterings [1]_. Considering a pair of samples that is clustered together a positive pair, then as in binary classification the count of true negatives is diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py index d30c1775823a5..289db23955a17 100644 --- a/sklearn/metrics/pairwise.py +++ b/sklearn/metrics/pairwise.py @@ -1,3 +1,5 @@ +"""Metrics for pairwise distances and affinity of sets of samples.""" + # Authors: Alexandre Gramfort # Mathieu Blondel # Robert Layton diff --git a/sklearn/mixture/__init__.py b/sklearn/mixture/__init__.py index f0018196ffc98..9eb449226a9cb 100644 --- a/sklearn/mixture/__init__.py +++ b/sklearn/mixture/__init__.py @@ -1,6 +1,4 @@ -""" -The :mod:`sklearn.mixture` module implements mixture modeling algorithms. -""" +"""Mixture modeling algorithms.""" from ._bayesian_mixture import BayesianGaussianMixture from ._gaussian_mixture import GaussianMixture diff --git a/sklearn/model_selection/__init__.py b/sklearn/model_selection/__init__.py index c97d48f4b20b7..8a50d032dc164 100644 --- a/sklearn/model_selection/__init__.py +++ b/sklearn/model_selection/__init__.py @@ -1,3 +1,5 @@ +"""Tools for model selection, such as cross validation and hyper-parameter tuning.""" + import typing from ._classification_threshold import ( diff --git a/sklearn/multiclass.py b/sklearn/multiclass.py index d8c7904b81cdf..5883f4a05f9a0 100644 --- a/sklearn/multiclass.py +++ b/sklearn/multiclass.py @@ -1,11 +1,8 @@ -""" -Multiclass classification strategies -==================================== +"""Multiclass learning algorithms. -This module implements multiclass learning algorithms: - - one-vs-the-rest / one-vs-all - - one-vs-one - - error correcting output codes +- one-vs-the-rest / one-vs-all +- one-vs-one +- error correcting output codes The estimators provided in this module are meta-estimators: they require a base estimator to be provided in their constructor. For example, it is possible to diff --git a/sklearn/multioutput.py b/sklearn/multioutput.py index d1f45f91d2db6..3851bc58aa151 100644 --- a/sklearn/multioutput.py +++ b/sklearn/multioutput.py @@ -1,5 +1,4 @@ -""" -This module implements multioutput regression and classification. +"""Multioutput regression and classification. The estimators provided in this module are meta-estimators: they require a base estimator to be provided in their constructor. The meta-estimator diff --git a/sklearn/naive_bayes.py b/sklearn/naive_bayes.py index c5a129779dd89..1c9b92ddd0fc9 100644 --- a/sklearn/naive_bayes.py +++ b/sklearn/naive_bayes.py @@ -1,6 +1,6 @@ -""" -The :mod:`sklearn.naive_bayes` module implements Naive Bayes algorithms. These -are supervised learning methods based on applying Bayes' theorem with strong +"""Naive Bayes algorithms. + +These are supervised learning methods based on applying Bayes' theorem with strong (naive) feature independence assumptions. """ diff --git a/sklearn/neighbors/__init__.py b/sklearn/neighbors/__init__.py index ce697656b4c2e..e2c1a50632564 100644 --- a/sklearn/neighbors/__init__.py +++ b/sklearn/neighbors/__init__.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.neighbors` module implements the k-nearest neighbors -algorithm. -""" +"""The k-nearest neighbors algorithms.""" from ._ball_tree import BallTree from ._base import VALID_METRICS, VALID_METRICS_SPARSE, sort_graph_by_row_values diff --git a/sklearn/neighbors/_binary_tree.pxi.tp b/sklearn/neighbors/_binary_tree.pxi.tp index 5cf7b0ad99990..c25740c0d6f6c 100644 --- a/sklearn/neighbors/_binary_tree.pxi.tp +++ b/sklearn/neighbors/_binary_tree.pxi.tp @@ -230,10 +230,7 @@ NodeData = np.asarray((&nd_tmp)).dtype ###################################################################### # Define doc strings, substituting the appropriate class name using # the DOC_DICT variable defined in the pyx files. -CLASS_DOC = """ -{BinaryTree}(X, leaf_size=40, metric='minkowski', **kwargs) - -{BinaryTree} for fast generalized N-point problems +CLASS_DOC = """{BinaryTree} for fast generalized N-point problems Read more in the :ref:`User Guide `. diff --git a/sklearn/neural_network/__init__.py b/sklearn/neural_network/__init__.py index 0b321b605de0b..0b349b05f9a0c 100644 --- a/sklearn/neural_network/__init__.py +++ b/sklearn/neural_network/__init__.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.neural_network` module includes models based on neural -networks. -""" +"""Models based on neural networks.""" # License: BSD 3 clause diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py index b200177b8606f..4d8e268163940 100644 --- a/sklearn/pipeline.py +++ b/sklearn/pipeline.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.pipeline` module implements utilities to build a composite -estimator, as a chain of transforms and estimators. -""" +"""Utilities to build a composite estimator as a chain of transforms and estimators.""" # Author: Edouard Duchesnay # Gael Varoquaux diff --git a/sklearn/preprocessing/__init__.py b/sklearn/preprocessing/__init__.py index c730a71260808..9e49379c9122a 100644 --- a/sklearn/preprocessing/__init__.py +++ b/sklearn/preprocessing/__init__.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.preprocessing` module includes scaling, centering, -normalization, binarization methods. -""" +"""Methods for scaling, centering, normalization, binarization, and more.""" from ._data import ( Binarizer, diff --git a/sklearn/random_projection.py b/sklearn/random_projection.py index 886a805960d52..c6f7057396d4a 100644 --- a/sklearn/random_projection.py +++ b/sklearn/random_projection.py @@ -1,11 +1,11 @@ -"""Random Projection transformers. +"""Random projection transformers. -Random Projections are a simple and computationally efficient way to +Random projections are a simple and computationally efficient way to reduce the dimensionality of the data by trading a controlled amount of accuracy (as additional variance) for faster processing times and smaller model sizes. -The dimensions and distribution of Random Projections matrices are +The dimensions and distribution of random projections matrices are controlled so as to preserve the pairwise distances between any two samples of the dataset. @@ -20,7 +20,6 @@ much lower dimension in such a way that distances between the points are nearly preserved. The map used for the embedding is at least Lipschitz, and can even be taken to be an orthogonal projection. - """ # Authors: Olivier Grisel , diff --git a/sklearn/semi_supervised/__init__.py b/sklearn/semi_supervised/__init__.py index 126906cdde1d7..055c3f431b2c1 100644 --- a/sklearn/semi_supervised/__init__.py +++ b/sklearn/semi_supervised/__init__.py @@ -1,8 +1,7 @@ -""" -The :mod:`sklearn.semi_supervised` module implements semi-supervised learning -algorithms. These algorithms utilize small amounts of labeled data and large -amounts of unlabeled data for classification tasks. This module includes Label -Propagation. +"""Semi-supervised learning algorithms. + +These algorithms utilize small amounts of labeled data and large amounts of unlabeled +data for classification tasks. """ from ._label_propagation import LabelPropagation, LabelSpreading diff --git a/sklearn/svm/__init__.py b/sklearn/svm/__init__.py index 0d64ce24cdd63..660427198991a 100644 --- a/sklearn/svm/__init__.py +++ b/sklearn/svm/__init__.py @@ -1,6 +1,4 @@ -""" -The :mod:`sklearn.svm` module includes Support Vector Machine algorithms. -""" +"""Support vector machine algorithms.""" # See http://scikit-learn.sourceforge.net/modules/svm.html for complete # documentation. diff --git a/sklearn/tree/__init__.py b/sklearn/tree/__init__.py index 8cfb42c73e118..23ab17aa0bbbe 100644 --- a/sklearn/tree/__init__.py +++ b/sklearn/tree/__init__.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.tree` module includes decision tree-based models for -classification and regression. -""" +"""Decision tree based models for classification and regression.""" from ._classes import ( BaseDecisionTree, diff --git a/sklearn/utils/__init__.py b/sklearn/utils/__init__.py index af02393966cc2..1e38b807973b2 100644 --- a/sklearn/utils/__init__.py +++ b/sklearn/utils/__init__.py @@ -1,6 +1,4 @@ -""" -The :mod:`sklearn.utils` module includes various utilities. -""" +"""Various utilities to help with development.""" import warnings from collections.abc import Sequence diff --git a/sklearn/utils/arrayfuncs.pyx b/sklearn/utils/arrayfuncs.pyx index 1ad5804770358..2cf98e0f5cc3e 100644 --- a/sklearn/utils/arrayfuncs.pyx +++ b/sklearn/utils/arrayfuncs.pyx @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.utils.arrayfuncs` module includes a small collection of auxiliary -functions that operate on arrays. -""" +"""A small collection of auxiliary functions that operate on arrays.""" from cython cimport floating from cython.parallel cimport prange diff --git a/sklearn/utils/class_weight.py b/sklearn/utils/class_weight.py index 55802f780ed41..b3c0aaaaf4411 100644 --- a/sklearn/utils/class_weight.py +++ b/sklearn/utils/class_weight.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.utils.class_weight` module includes utilities for handling -weights based on class labels. -""" +"""Utilities for handling weights based on class labels.""" # Authors: Andreas Mueller # Manoj Kumar diff --git a/sklearn/utils/discovery.py b/sklearn/utils/discovery.py index 1b31a843ffd8a..c7bebe5df0093 100644 --- a/sklearn/utils/discovery.py +++ b/sklearn/utils/discovery.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.utils.discovery` module includes utilities to discover -objects (i.e. estimators, displays, functions) from the `sklearn` package. -""" +"""Utilities to discover scikit-learn objects.""" import inspect import pkgutil diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py index 59d371bad57cd..6c5e78acb14cb 100644 --- a/sklearn/utils/estimator_checks.py +++ b/sklearn/utils/estimator_checks.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.utils.estimator_checks` module includes various utilities to -check the compatibility of estimators with the scikit-learn API. -""" +"""Various utilities to check the compatibility of estimators with scikit-learn API.""" import pickle import re diff --git a/sklearn/utils/extmath.py b/sklearn/utils/extmath.py index 44f70deaa3f18..638423e1ebc54 100644 --- a/sklearn/utils/extmath.py +++ b/sklearn/utils/extmath.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.utils.extmath` module includes utilities to perform -optimal mathematical operations in scikit-learn that are not available in SciPy. -""" +"""Utilities to perform optimal mathematical operations in scikit-learn.""" # Authors: Gael Varoquaux # Alexandre Gramfort diff --git a/sklearn/utils/graph.py b/sklearn/utils/graph.py index 06b2e152101a9..fd06f1df5f6c9 100644 --- a/sklearn/utils/graph.py +++ b/sklearn/utils/graph.py @@ -1,6 +1,4 @@ -""" -The :mod:`sklearn.utils.graph` module includes graph utilities and algorithms. -""" +"""Graph utilities and algorithms.""" # Authors: Aric Hagberg # Gael Varoquaux diff --git a/sklearn/utils/metadata_routing.py b/sklearn/utils/metadata_routing.py index bb98d2f08b93e..b50c54b86b7a1 100644 --- a/sklearn/utils/metadata_routing.py +++ b/sklearn/utils/metadata_routing.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.utils.metadata_routing` module includes utilities to route -metadata within scikit-learn estimators. -""" +"""Utilities to route metadata within scikit-learn estimators.""" # This module is not a separate sub-folder since that would result in a circular # import issue. diff --git a/sklearn/utils/metaestimators.py b/sklearn/utils/metaestimators.py index 639e000dd77a7..36edf4d166083 100644 --- a/sklearn/utils/metaestimators.py +++ b/sklearn/utils/metaestimators.py @@ -1,6 +1,4 @@ -""" -The :mod:`sklearn.utils.metaestimators` module includes utilities for meta-estimators. -""" +"""Utilities for meta-estimators.""" # Author: Joel Nothman # Andreas Mueller diff --git a/sklearn/utils/multiclass.py b/sklearn/utils/multiclass.py index 2d87bfb77839e..1abc7832f4407 100644 --- a/sklearn/utils/multiclass.py +++ b/sklearn/utils/multiclass.py @@ -1,11 +1,9 @@ -""" -The :mod:`sklearn.utils.multiclass` module includes utilities to handle -multiclass/multioutput target in classifiers. -""" +"""Utilities to handle multiclass/multioutput target in classifiers.""" # Author: Arnaud Joly, Joel Nothman, Hamzeh Alsalhi # # License: BSD 3 clause + import warnings from collections.abc import Sequence from itertools import chain diff --git a/sklearn/utils/parallel.py b/sklearn/utils/parallel.py index d0dc2ec2be030..f6b3d8c4c4a6e 100644 --- a/sklearn/utils/parallel.py +++ b/sklearn/utils/parallel.py @@ -1,6 +1,4 @@ -""" -The :mod:`sklearn.utils.parallel` customizes `joblib` tools for scikit-learn usage. -""" +"""Customizations of :mod:`joblib` tools for scikit-learn usage.""" import functools import warnings diff --git a/sklearn/utils/random.py b/sklearn/utils/random.py index 1dfe8d83a94b3..9121f8425b58b 100644 --- a/sklearn/utils/random.py +++ b/sklearn/utils/random.py @@ -1,6 +1,4 @@ -""" -The mod:`sklearn.utils.random` module includes utilities for random sampling. -""" +"""Utilities for random sampling.""" # Author: Hamzeh Alsalhi # diff --git a/sklearn/utils/sparsefuncs.py b/sklearn/utils/sparsefuncs.py index a46e9e4d9ed93..29fb8fab8b75f 100644 --- a/sklearn/utils/sparsefuncs.py +++ b/sklearn/utils/sparsefuncs.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.utils.sparsefuncs` module includes a collection of utilities to -work with sparse matrices and arrays. -""" +"""A collection of utilities to work with sparse matrices and arrays.""" # Authors: Manoj Kumar # Thomas Unterthiner diff --git a/sklearn/utils/sparsefuncs_fast.pyx b/sklearn/utils/sparsefuncs_fast.pyx index c3bd0370d8b96..a6fdcd8c94463 100644 --- a/sklearn/utils/sparsefuncs_fast.pyx +++ b/sklearn/utils/sparsefuncs_fast.pyx @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.utils.sparsefuncs_fast` module includes a collection of utilities to -work with sparse matrices and arrays written in Cython. -""" +"""Utilities to work with sparse matrices and arrays written in Cython.""" # Authors: Mathieu Blondel # Olivier Grisel diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py index cdda749ec70a2..1f25a5b360679 100644 --- a/sklearn/utils/validation.py +++ b/sklearn/utils/validation.py @@ -1,7 +1,4 @@ -""" -The :mod:`sklearn.utils.validation` module includes functions to validate -input and parameters within scikit-learn estimators. -""" +"""Functions to validate input and parameters within scikit-learn estimators.""" # Authors: Olivier Grisel # Gael Varoquaux From 5491dc695dbe2c9bec3452be5f3c409706ff7ee7 Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Tue, 21 May 2024 16:51:01 +0200 Subject: [PATCH 046/275] DOC Various improvements and more details in release highlights for 1.5 (#29056) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Jérémie du Boisberranger --- .../plot_release_highlights_1_5_0.py | 127 ++++++++++++------ 1 file changed, 87 insertions(+), 40 deletions(-) diff --git a/examples/release_highlights/plot_release_highlights_1_5_0.py b/examples/release_highlights/plot_release_highlights_1_5_0.py index 0acc6fda6589d..20ca85a8002da 100644 --- a/examples/release_highlights/plot_release_highlights_1_5_0.py +++ b/examples/release_highlights/plot_release_highlights_1_5_0.py @@ -24,19 +24,22 @@ # %% # FixedThresholdClassifier: Setting the decision threshold of a binary classifier # ------------------------------------------------------------------------------- -# All binary classifiers of scikit-learn use a fixed decision threshold of 0.5 to -# convert probability estimates (i.e. output of `predict_proba`) into class -# predictions. However, 0.5 is almost never the desired threshold for a given problem. -# :class:`~model_selection.FixedThresholdClassifier` allows to wrap any binary -# classifier and set a custom decision threshold. +# All binary classifiers of scikit-learn use a fixed decision threshold of 0.5 +# to convert probability estimates (i.e. output of `predict_proba`) into class +# predictions. However, 0.5 is almost never the desired threshold for a given +# problem. :class:`~model_selection.FixedThresholdClassifier` allows wrapping any +# binary classifier and setting a custom decision threshold. from sklearn.datasets import make_classification +from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression -from sklearn.metrics import confusion_matrix +from sklearn.metrics import ConfusionMatrixDisplay + -X, y = make_classification(n_samples=1_000, weights=[0.9, 0.1], random_state=0) -classifier = LogisticRegression(random_state=0).fit(X, y) +X, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0) +X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) -print("confusion matrix:\n", confusion_matrix(y, classifier.predict(X))) +classifier_05 = LogisticRegression(C=1e6, random_state=0).fit(X_train, y_train) +_ = ConfusionMatrixDisplay.from_estimator(classifier_05, X_test, y_test) # %% # Lowering the threshold, i.e. allowing more samples to be classified as the positive @@ -44,48 +47,84 @@ # (as is well known from the concavity of the ROC curve). from sklearn.model_selection import FixedThresholdClassifier -wrapped_classifier = FixedThresholdClassifier(classifier, threshold=0.1).fit(X, y) - -print("confusion matrix:\n", confusion_matrix(y, wrapped_classifier.predict(X))) +classifier_01 = FixedThresholdClassifier(classifier_05, threshold=0.1) +classifier_01.fit(X_train, y_train) +_ = ConfusionMatrixDisplay.from_estimator(classifier_01, X_test, y_test) # %% # TunedThresholdClassifierCV: Tuning the decision threshold of a binary classifier # -------------------------------------------------------------------------------- -# The decision threshold of a binary classifier can be tuned to optimize a given -# metric, using :class:`~model_selection.TunedThresholdClassifierCV`. -from sklearn.metrics import balanced_accuracy_score +# The decision threshold of a binary classifier can be tuned to optimize a +# given metric, using :class:`~model_selection.TunedThresholdClassifierCV`. +# +# It is particularly useful to find the best decision threshold when the model +# is meant to be deployed in a specific application context where we can assign +# different gains or costs for true positives, true negatives, false positives, +# and false negatives. +# +# Let's illustrate this by considering an arbitrary case where: +# +# - each true positive gains 1 unit of profit, e.g. euro, year of life in good +# health, etc.; +# - true negatives gain or cost nothing; +# - each false negative costs 2; +# - each false positive costs 0.1. +# +# Our metric quantifies the average profit per sample, which is defined by the +# following Python function: +from sklearn.metrics import confusion_matrix + + +def custom_score(y_observed, y_pred): + tn, fp, fn, tp = confusion_matrix(y_observed, y_pred, normalize="all").ravel() + return tp - 2 * fn - 0.1 * fp -# Due to the class imbalance, the balanced accuracy is not optimal for the default -# threshold. The classifier tends to over predict the majority class. -print(f"balanced accuracy: {balanced_accuracy_score(y, classifier.predict(X)):.2f}") + +print("Untuned decision threshold: 0.5") +print(f"Custom score: {custom_score(y_test, classifier_05.predict(X_test)):.2f}") # %% -# Tuning the threshold to optimize the balanced accuracy gives a smaller threshold -# that allows more samples to be classified as the positive class. +# It is interesting to observe that the average gain per prediction is negative +# which means that this decision system is making a loss on average. +# +# Tuning the threshold to optimize this custom metric gives a smaller threshold +# that allows more samples to be classified as the positive class. As a result, +# the average gain per prediction improves. from sklearn.model_selection import TunedThresholdClassifierCV +from sklearn.metrics import make_scorer +custom_scorer = make_scorer( + custom_score, response_method="predict", greater_is_better=True +) tuned_classifier = TunedThresholdClassifierCV( - classifier, cv=5, scoring="balanced_accuracy" + classifier_05, cv=5, scoring=custom_scorer ).fit(X, y) -print(f"new threshold: {tuned_classifier.best_threshold_:.4f}") -print( - f"balanced accuracy: {balanced_accuracy_score(y, tuned_classifier.predict(X)):.2f}" -) +print(f"Tuned decision threshold: {tuned_classifier.best_threshold_:.3f}") +print(f"Custom score: {custom_score(y_test, tuned_classifier.predict(X_test)):.2f}") # %% -# :class:`~model_selection.TunedThresholdClassifierCV` also benefits from the -# metadata routing support (:ref:`Metadata Routing User Guide`) -# allowing to optimze complex business metrics, detailed -# in :ref:`Post-tuning the decision threshold for cost-sensitive learning +# We observe that tuning the decision threshold can turn a machine +# learning-based system that makes a loss on average into a beneficial one. +# +# In practice, defining a meaningful application-specific metric might involve +# making those costs for bad predictions and gains for good predictions depend on +# auxiliary metadata specific to each individual data point such as the amount +# of a transaction in a fraud detection system. +# +# To achieve this, :class:`~model_selection.TunedThresholdClassifierCV` +# leverages metadata routing support (:ref:`Metadata Routing User +# Guide`) allowing to optimize complex business metrics as +# detailed in :ref:`Post-tuning the decision threshold for cost-sensitive +# learning # `. # %% # Performance improvements in PCA # ------------------------------- -# :class:`~decomposition.PCA` has a new solver, "covariance_eigh", which is faster -# and more memory efficient than the other solvers for datasets with a large number -# of samples and a small number of features. +# :class:`~decomposition.PCA` has a new solver, `"covariance_eigh"`, which is +# up to an order of magnitude faster and more memory efficient than the other +# solvers for datasets with many data points and few features. from sklearn.datasets import make_low_rank_matrix from sklearn.decomposition import PCA @@ -93,20 +132,28 @@ n_samples=10_000, n_features=100, tail_strength=0.1, random_state=0 ) -pca = PCA(n_components=10).fit(X) +pca = PCA(n_components=10, svd_solver="covariance_eigh").fit(X) +print(f"Explained variance: {pca.explained_variance_ratio_.sum():.2f}") -print(f"explained variance: {pca.explained_variance_ratio_.sum():.2f}") # %% -# The "full" solver has also been improved to use less memory and allows to -# transform faster. The "auto" option for the solver takes advantage of the -# new solver and is now able to select an appropriate solver for sparse -# datasets. +# The new solver also accepts sparse input data: from scipy.sparse import random -X = random(10000, 100, format="csr", random_state=0) +X = random(10_000, 100, format="csr", random_state=0) -pca = PCA(n_components=10, svd_solver="auto").fit(X) +pca = PCA(n_components=10, svd_solver="covariance_eigh").fit(X) +print(f"Explained variance: {pca.explained_variance_ratio_.sum():.2f}") + +# %% +# The `"full"` solver has also been improved to use less memory and allows +# faster transformation. The default `svd_solver="auto"`` option takes +# advantage of the new solver and is now able to select an appropriate solver +# for sparse datasets. +# +# Similarly to most other PCA solvers, the new `"covariance_eigh"` solver can leverage +# GPU computation if the input data is passed as a PyTorch or CuPy array by +# enabling the experimental support for :ref:`Array API `. # %% # ColumnTransformer is subscriptable From 2621573e60c295a435c62137c65ae787bf438e61 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?= Date: Tue, 11 Jun 2024 19:03:12 +0200 Subject: [PATCH 047/275] DOC Backport the machine learning map fix in 1.5.X (#29123) (#29236) Co-authored-by: Yao Xiao <108576690+Charlie-XIAO@users.noreply.github.com> --- doc/images/ml_map.svg | 2 +- doc/tutorial/machine_learning_map/README.md | 11 +++++++---- doc/tutorial/machine_learning_map/index.rst | 7 ++++--- 3 files changed, 12 insertions(+), 8 deletions(-) diff --git a/doc/images/ml_map.svg b/doc/images/ml_map.svg index 629b4c91172fb..7c587cef011b9 100644 --- a/doc/images/ml_map.svg +++ b/doc/images/ml_map.svg @@ -1,4 +1,4 @@ -
    START
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    do you have
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    do you hav...
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    just
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    YES
    text
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    text...
    😭
    😭
    Kernel
    Approximation
    Kernel...
    😭
    😭
    KNeighbors
    Classifier
    KNeighbors...
    NO
    NO
    SVC
    SVC
    Ensemble
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    Ensemble...
    😭
    😭
    Naive
    Bayes
    Naive...
    YES
    YES
    classification
    classification
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    known
    number of...
    NO
    NO
    <10K
    samples
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    <10K
    samples
    <10K...
    NO
    NO
    NO
    NO
    YES
    YES
    MeanShift
    MeanShift
    VBGMM
    VBGMM
    YES
    YES
    MiniBatch
    KMeans
    MiniBatch...
    NO
    NO
    clustering
    clustering
    KMeans
    KMeans
    YES
    YES
    Spectral
    Clustering
    Spectral...
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    😭
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    <100K
    samples
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    YES
    YES
    few features
    should be
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    few features...
    YES
    YES
    SGD
    Regressor
    SGD...
    NO
    NO
    Lasso
    Lasso
    ElasticNet
    ElasticNet
    YES
    YES
    RidgeRegression
    RidgeRegression
    SVR(kernel="linear")
    SVR(kernel="linea...
    NO
    NO
    SVR(kernel="rbf")
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    Ensemble
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    😭
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    Ramdomized...
    YES
    YES
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    NO
    IsoMap
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    Embedding
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    scikit-learn
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    scikit-learn...    Text is not SVG - cannot display     +
    START
    START
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    NO
    predicting a
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    predicting...
    YES
    YES
    do you have
    labeled
    data
    do you hav...
    YES
    YES
    predicting a
    quantity
    predicting...
    NO
    NO
    just
    looking
    just...
    NO
    NO
    predicting
    structure
    predicting...
    NO
    NO
    tough
    luck
    tough...
    <100K
    samples
    <100K...
    YES
    YES
    SGD
    Classifier
    SGD...
    NO
    NO
    Linear
    SVC
    Linear...
    YES
    YES
    text
    data
    text...
    😭
    😭
    Kernel
    Approximation
    Kernel...
    😭
    😭
    KNeighbors
    Classifier
    KNeighbors...
    NO
    NO
    SVC
    SVC
    Ensemble
    Classifiers
    Ensemble...
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    😭
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    YES
    classification
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    known
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    NO
    <10K
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    NO
    NO
    NO
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    YES
    MeanShift
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    VBGMM
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    YES
    MiniBatch
    KMeans
    MiniBatch...
    NO
    NO
    clustering
    clustering
    KMeans
    KMeans
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    YES
    Spectral
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    Spectral...
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    GMM
    😭
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    few features
    should be
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    few features...
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    YES
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    SGD...
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    NO
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    Lasso
    ElasticNet
    ElasticNet
    YES
    YES
    RidgeRegression
    RidgeRegression
    SVR(kernel="linear")
    SVR(kernel="linea...
    NO
    NO
    SVR(kernel="rbf")
    SVR(kernel="rbf...
    Ensemble
    Regressors
    Ensemble...
    😭
    😭
    regression
    regression
    Ramdomized
    PCA
    Ramdomized...
    YES
    YES
    <10K
    samples
    <10K...
    😭
    😭
    Kernel
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    Kernel...
    NO
    NO
    IsoMap
    IsoMap
    Spectral
    Embedding
    Spectral...
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    dimensionality
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    scikit-learn...    Text is not SVG - cannot display     diff --git a/doc/tutorial/machine_learning_map/README.md b/doc/tutorial/machine_learning_map/README.md index 006b1e5e1a38c..8d82c175dad58 100644 --- a/doc/tutorial/machine_learning_map/README.md +++ b/doc/tutorial/machine_learning_map/README.md @@ -3,8 +3,9 @@ https://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-sci The current version of the chart is located at `doc/images/ml_map.svg` in SVG+XML format, created using [draw.io](https://draw.io/). To edit the chart, open the file in -draw.io, make changes, and export as SVG with the same filename. Export configurations -are: +draw.io, make changes, and save. This should update the chart in-place. Another option +would be to re-export the chart as SVG and replace the existing file. The options used +for exporting the chart are: - Zoom: 100% - Border width: 15 @@ -13,5 +14,7 @@ are: - Appearance: Light Each node in the chart that contains an estimator should have a link, where the root -directory is at `../../`. Note that after exporting the SVG, the links may be prefixed -with e.g. `https://app.diagrams.net/`. Remember to check and remove them. +directory is at `../../`. Note that after updating or re-exporting the SVG, the links +may be prefixed with e.g. `https://app.diagrams.net/`. Remember to check and remove +them, for instance by replacing all occurrences of `https://app.diagrams.net/../../` +with `../../`. diff --git a/doc/tutorial/machine_learning_map/index.rst b/doc/tutorial/machine_learning_map/index.rst index 5fd6879563489..e3d6531d05bd5 100644 --- a/doc/tutorial/machine_learning_map/index.rst +++ b/doc/tutorial/machine_learning_map/index.rst @@ -11,9 +11,10 @@ data and different problems. The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data. Click on any estimator in -the chart below to see its documentation. Use scroll wheel to zoom in and out, and click -and drag to pan around. You can also download the chart: -:download:`ml_map.svg <../../images/ml_map.svg>`. +the chart below to see its documentation. The 😭 emoji is to be read as "if this +estimator does not achieve the desired outcome, then follow the arrow and try the next +one". Use scroll wheel to zoom in and out, and click and drag to pan around. You can +also download the chart: :download:`ml_map.svg <../../images/ml_map.svg>`. .. raw:: html From 7e0171eb28c7b7005cc74fb8ce929fdb69168284 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 20 May 2024 11:46:54 +0200 Subject: [PATCH 048/275] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29053) Co-authored-by: Lock file bot --- ...ylatest_conda_forge_mkl_linux-64_conda.lock | 14 +++++++------- .../pylatest_conda_forge_mkl_osx-64_conda.lock | 12 ++++++------ ...est_pip_openblas_pandas_linux-64_conda.lock | 4 ++-- .../pymin_conda_forge_mkl_win-64_conda.lock | 8 ++++---- ...ge_openblas_ubuntu_2204_linux-64_conda.lock | 8 ++++---- build_tools/circle/doc_linux-64_conda.lock | 18 +++++++++--------- .../doc_min_dependencies_linux-64_conda.lock | 10 +++++----- 7 files changed, 37 insertions(+), 37 deletions(-) diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index bf5bcd3daff08..752e32b1d6220 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -109,7 +109,7 @@ https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.2-h659d440_0.conda#cd9 https://conda.anaconda.org/conda-forge/linux-64/libglib-2.80.2-hf974151_0.conda#72724f6a78ecb15559396966226d5838 https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.54.3-hb20ce57_0.conda#7af7c59ab24db007dfd82e0a3a343f66 https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a -https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.10.0-default_h2fb2949_1000.conda#7e3726e647a619c6ce5939014dfde86d +https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.10.0-default_h5622ce7_1001.conda#fc2d5b79c2d3f8568fbab31db7ae02f3 https://conda.anaconda.org/conda-forge/linux-64/libllvm15-15.0.7-hb3ce162_4.conda#8a35df3cbc0c8b12cc8af9473ae75eef https://conda.anaconda.org/conda-forge/linux-64/libllvm18-18.1.5-hb77312f_0.conda#efd221d3668077ca067a206269418dec https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.18.1-h8fd135c_2.conda#bbf65f7688512872f063810623b755dc @@ -158,7 +158,7 @@ https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2024.1-pyhd8ed1ab_0. https://conda.anaconda.org/conda-forge/noarch/pytz-2024.1-pyhd8ed1ab_0.conda#3eeeeb9e4827ace8c0c1419c85d590ad https://conda.anaconda.org/conda-forge/noarch/setuptools-69.5.1-pyhd8ed1ab_0.conda#7462280d81f639363e6e63c81276bd9e https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2 -https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.12.0-h00ab1b0_0.conda#f1b776cff1b426e7e7461a8502a3b731 +https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.12.0-h297d8ca_1.conda#3ff978d8994f591818a506640c6a7071 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https://conda.anaconda.org/conda-forge/osx-64/scipy-1.13.0-py312h741d2f9_1.conda#c416453a8ea3b38d823fe8dcecdb6a12 https://conda.anaconda.org/conda-forge/osx-64/blas-2.120-mkl.conda#b041a7677a412f3d925d8208936cb1e2 https://conda.anaconda.org/conda-forge/osx-64/clang_impl_osx-64-16.0.6-h8787910_14.conda#fc1a7d3f1bf236f63c58bab6e36844cb -https://conda.anaconda.org/conda-forge/osx-64/matplotlib-base-3.8.4-py312h1fe5000_0.conda#3e3097734a5042cb6d2675e69bf1fc5a -https://conda.anaconda.org/conda-forge/osx-64/pyamg-5.1.0-py312h3db3e91_0.conda#c6d6248b99fc11b15c9becea581a1462 +https://conda.anaconda.org/conda-forge/osx-64/matplotlib-base-3.8.4-py312hb6d62fa_2.conda#6c5cf505d118f4b58961191fd5e0d030 +https://conda.anaconda.org/conda-forge/osx-64/pyamg-5.1.0-py312h44e70fa_1.conda#ffbfe3b3d5e9675541ee516badfb7729 https://conda.anaconda.org/conda-forge/osx-64/clang_osx-64-16.0.6-hb91bd55_14.conda#3d0d9c725912bb0cb4cd301d2a5d31d7 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https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py39h06a4308_0.conda#7f8ce # pip tomli @ https://files.pythonhosted.org/packages/97/75/10a9ebee3fd790d20926a90a2547f0bf78f371b2f13aa822c759680ca7b9/tomli-2.0.1-py3-none-any.whl#sha256=939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc # pip tzdata @ https://files.pythonhosted.org/packages/65/58/f9c9e6be752e9fcb8b6a0ee9fb87e6e7a1f6bcab2cdc73f02bb7ba91ada0/tzdata-2024.1-py2.py3-none-any.whl#sha256=9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252 # pip urllib3 @ https://files.pythonhosted.org/packages/a2/73/a68704750a7679d0b6d3ad7aa8d4da8e14e151ae82e6fee774e6e0d05ec8/urllib3-2.2.1-py3-none-any.whl#sha256=450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d -# pip zipp @ https://files.pythonhosted.org/packages/c2/0a/ba9d0ee9536d3ef73a3448e931776e658b36f128d344e175bc32b092a8bf/zipp-3.18.1-py3-none-any.whl#sha256=206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b +# pip zipp @ https://files.pythonhosted.org/packages/da/55/a03fd7240714916507e1fcf7ae355bd9d9ed2e6db492595f1a67f61681be/zipp-3.18.2-py3-none-any.whl#sha256=dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e # pip contourpy @ https://files.pythonhosted.org/packages/31/a2/2f12e3a6e45935ff694654b710961b03310b0e1ec997ee9f416d3c873f87/contourpy-1.2.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=e1d59258c3c67c865435d8fbeb35f8c59b8bef3d6f46c1f29f6123556af28445 # pip coverage @ https://files.pythonhosted.org/packages/c1/50/b7d6f236c20334b0378ed88078e830640a64ad8eb9f11f818b2af34d00c0/coverage-7.5.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=d21918e9ef11edf36764b93101e2ae8cc82aa5efdc7c5a4e9c6c35a48496d601 # pip imageio @ 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https://files.pythonhosted.org/packages/ba/11/cb8b67f3cbdca05b59a032bb57963d4fe8c8d18c3870f30bed005b7f174d/lightgbm-4.3.0-py3-none-manylinux_2_28_x86_64.whl#sha256=104496a3404cb2452d3412cbddcfbfadbef9c372ea91e3a9b8794bcc5183bf07 -# pip matplotlib @ https://files.pythonhosted.org/packages/5e/2c/513395a63a9e1124a5648addbf73be23cc603f955af026b04416da98dc96/matplotlib-3.8.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=606e3b90897554c989b1e38a258c626d46c873523de432b1462f295db13de6f9 +# pip matplotlib @ https://files.pythonhosted.org/packages/d3/6d/45837c5b3d0005a5a9b04729b218a16bf3aa195701c6b33b2cc39ae943b6/matplotlib-3.9.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=81c40af649d19c85f8073e25e5806926986806fa6d54be506fbf02aef47d5a89 # pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 # pip pandas @ https://files.pythonhosted.org/packages/bb/30/f6f1f1ac36250f50c421b1b6af08c35e5a8b5a84385ef928625336b93e6f/pandas-2.2.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=66b479b0bd07204e37583c191535505410daa8df638fd8e75ae1b383851fe921 # pip pyamg @ https://files.pythonhosted.org/packages/68/a9/aed9f557e7eb779d2cb4fa090663f8540979e0c04dadd16e9a0bdc9632c5/pyamg-5.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=5817d4567fb240dab4779bb1630bbb3035b3827731fcdaeb9ecc9c8814319995 diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock index 8f0a473c031ca..206a72f334f6e 100644 --- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock +++ 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https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-1.0.6-pyhd8ed1ab_0.conda#d7e4954df0d3aea2eacc7835ad12671d diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index 1435bebedb865..b959b3250c851 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -68,7 +68,7 @@ https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#7 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.6.6-he8a937b_2.conda#77d9955b4abddb811cb8ab1aa7d743e4 https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.0-hdb0a2a9_1.conda#843bbb8ace1d64ac50d64639ff38b014 -https://conda.anaconda.org/conda-forge/linux-64/svt-av1-2.0.0-h59595ed_0.conda#207e01ffa0eb2d2efb83fb6f46365a21 +https://conda.anaconda.org/conda-forge/linux-64/svt-av1-2.1.0-hac33072_0.conda#2a08edb7cd75e56623f2712292a97325 https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.1-hd590300_0.conda#b462a33c0be1421532f28bfe8f4a7514 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.11-hd590300_0.conda#2c80dc38fface310c9bd81b17037fee5 @@ -83,7 +83,7 @@ https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.0.7-h0b41bf4_0.conda#4 https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.2-h59595ed_0.conda#53fb86322bdb89496d7579fe3f02fd61 https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-12.3.0-h58ffeeb_7.conda#95f78565a09852783d3e90e0389cfa5f https://conda.anaconda.org/conda-forge/linux-64/libasprintf-devel-0.22.5-h661eb56_2.conda#02e41ab5834dcdcc8590cf29d9526f50 -https://conda.anaconda.org/conda-forge/linux-64/libavif16-1.0.4-hfa3d5b6_3.conda#3518d00de414c39b46d87dcc1ff65661 +https://conda.anaconda.org/conda-forge/linux-64/libavif16-1.0.4-hd2f8ffe_4.conda#cb911b3e0d863ca9caafd767525f7cac https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hd590300_1.conda#f07002e225d7a60a694d42a7bf5ff53f https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.1.0-hd590300_1.conda#5fc11c6020d421960607d821310fcd4d https://conda.anaconda.org/conda-forge/linux-64/libcap-2.69-h0f662aa_0.conda#25cb5999faa414e5ccb2c1388f62d3d5 @@ -239,23 +239,23 @@ https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py39h7633fee_0.c https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.3-h9ad1361_0.conda#8fb0e954c616bb0f9389efac4b4ed44b https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.1.1-py39ha98d97a_6.conda#9ada409e8a8202f848abfed8e4e3f6be https://conda.anaconda.org/conda-forge/noarch/imageio-2.34.1-pyh4b66e23_0.conda#bcf6a6f4c6889ca083e8d33afbafb8d5 -https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.2-py39hddac248_0.conda#259c4e76e6bda8888aefc098ae1ba749 +https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.2-py39hfc16268_1.conda#8b23d2b425035a7468d17e6fe1d54124 https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.6-pyhd8ed1ab_0.conda#a5b55d1cb110cdcedc748b5c3e16e687 -https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.26-py39ha963410_0.conda#d138679a254e4e0918cfc1114c928bb8 +https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.26-py39ha963410_1.conda#da14fdc7c8400f6b6505b710dd129636 https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.1-pyhd8ed1ab_0.conda#d15917f33140f8d2ac9ca44db7ec8a25 https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-client-17.0-hb77b528_0.conda#07f45f1be1c25345faddb8db0de8039b https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.4.1-py39h44dd56e_1.conda#d037c20e3da2e85f03ebd20ad480c359 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.13.0-py39haf93ffa_1.conda#57ce54e228e3fbc60e42fa368eff3251 https://conda.anaconda.org/conda-forge/linux-64/blas-2.122-openblas.conda#5065468105542a8b23ea47bd8b6fa55f -https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.8.4-py39he9076e7_0.conda#1919384a8420e7bb25f6c3a582e0857c -https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.1.0-py39hda80f44_0.conda#f225666c47726329201b604060f1436c +https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.8.4-py39h10d1fc8_2.conda#c9fb6571b93b1dd490ea627af7344f36 +https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.1.0-py39h85c637f_1.conda#b2b15112d019e27e62f9433e31607d08 https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.8-hc9dc06e_21.conda#b325046180590c868ce0dbf267b82eb8 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https://conda.anaconda.org/conda-forge/noarch/seaborn-0.13.2-hd8ed1ab_2.conda#a79d8797f62715255308d92d3a91ef2e https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.7.0-pyhd8ed1ab_0.conda#1ad3afced398492586ca1bef70328be4 https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.15.2-pyhd8ed1ab_0.conda#ce99859070b0e17ccc63234ca58f3ed8 @@ -326,4 +326,4 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip nbconvert @ https://files.pythonhosted.org/packages/b8/bb/bb5b6a515d1584aa2fd89965b11db6632e4bdc69495a52374bcc36e56cfa/nbconvert-7.16.4-py3-none-any.whl#sha256=05873c620fe520b6322bf8a5ad562692343fe3452abda5765c7a34b7d1aa3eb3 # pip jupyter-server @ https://files.pythonhosted.org/packages/07/46/6bb926b3bf878bf687b952fb6a4c09d014b4575a25960f2cd1a61793763f/jupyter_server-2.14.0-py3-none-any.whl#sha256=fb6be52c713e80e004fac34b35a0990d6d36ba06fd0a2b2ed82b899143a64210 # pip jupyterlab-server @ https://files.pythonhosted.org/packages/2f/b9/ed4ecad7cf1863a64920dc4c19b0376628b5d6bd28d2ec1e00cbac4ba2fb/jupyterlab_server-2.27.1-py3-none-any.whl#sha256=f5e26156e5258b24d532c84e7c74cc212e203bff93eb856f81c24c16daeecc75 -# pip jupyterlite-sphinx @ https://files.pythonhosted.org/packages/7c/c7/5c0f4dc5408122881a32b1809529d1d7adcc60cb176c7b50725910c328cc/jupyterlite_sphinx-0.14.0-py3-none-any.whl#sha256=144edf37e8a77f49b249dd57e3a22ce19ff87805ed79b460e831dc90bf38c269 +# pip jupyterlite-sphinx @ https://files.pythonhosted.org/packages/71/2c/bd797dc46a7281d43444c79ff312d4f8d27d41a0de05f48cad81c7939966/jupyterlite_sphinx-0.15.0-py3-none-any.whl#sha256=344d1f9ee5a20b141a4a4139874eae30a68216f0c995d03ea2e3b3e9d29c4cd5 diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index 22023274e25ce..f0d02542f4b98 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -96,7 +96,7 @@ https://conda.anaconda.org/conda-forge/linux-64/gfortran_impl_linux-64-12.3.0-h1 https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-12.3.0-h2a574ab_7.conda#265caa78b979f112fc241cecd0015c91 https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.2-h659d440_0.conda#cd95826dbd331ed1be26bdf401432844 https://conda.anaconda.org/conda-forge/linux-64/libglib-2.80.2-hf974151_0.conda#72724f6a78ecb15559396966226d5838 -https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.10.0-default_h2fb2949_1000.conda#7e3726e647a619c6ce5939014dfde86d +https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.10.0-default_h5622ce7_1001.conda#fc2d5b79c2d3f8568fbab31db7ae02f3 https://conda.anaconda.org/conda-forge/linux-64/libllvm15-15.0.7-hb3ce162_4.conda#8a35df3cbc0c8b12cc8af9473ae75eef https://conda.anaconda.org/conda-forge/linux-64/libllvm18-18.1.5-hb77312f_0.conda#efd221d3668077ca067a206269418dec https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-h1dd3fc0_3.conda#66f03896ffbe1a110ffda05c7a856504 @@ -125,7 +125,7 @@ https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_0.conda https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.0-pyhd8ed1ab_2.conda#8d652ea2ee8eaee02ed8dc820bc794aa https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_0.conda#15dda3cdbf330abfe9f555d22f66db46 https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.14.2-h14ed4e7_0.conda#0f69b688f52ff6da70bccb7ff7001d1d -https://conda.anaconda.org/conda-forge/noarch/fsspec-2024.3.1-pyhca7485f_0.conda#b7f0662ef2c9d4404f0af9eef5ed2fde +https://conda.anaconda.org/conda-forge/noarch/fsspec-2024.5.0-pyhff2d567_0.conda#d73e9932511ef7670b2cc0ebd9dfbd30 https://conda.anaconda.org/conda-forge/linux-64/gfortran-12.3.0-h915e2ae_7.conda#8efa768f7f74085629f3e1090e7f0569 https://conda.anaconda.org/conda-forge/linux-64/gfortran_linux-64-12.3.0-h617cb40_3.conda#3a9e5b8a6f651ff14e74d896d8f04ab6 https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.80.2-hb6ce0ca_0.conda#a965aeaf060289528a3fbe09326edae2 @@ -160,7 +160,7 @@ https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5 https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-2.2.0-pyhd8ed1ab_0.tar.bz2#4d22a9315e78c6827f806065957d566e https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.5-pyhd8ed1ab_1.conda#3f144b2c34f8cb5a9abd9ed23a39c561 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_0.conda#da1d979339e2714c30a8e806a33ec087 -https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.12.0-h00ab1b0_0.conda#f1b776cff1b426e7e7461a8502a3b731 +https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.12.0-h297d8ca_1.conda#3ff978d8994f591818a506640c6a7071 https://conda.anaconda.org/conda-forge/noarch/tenacity-8.3.0-pyhd8ed1ab_0.conda#216cfa8e32bcd1447646768351df6059 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_0.tar.bz2#f832c45a477c78bebd107098db465095 @@ -212,7 +212,7 @@ https://conda.anaconda.org/conda-forge/linux-64/mkl-devel-2024.1.0-ha770c72_692. https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5.conda#93aff412f3e49fdb43361c0215cbd72d https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b https://conda.anaconda.org/conda-forge/noarch/requests-2.31.0-pyhd8ed1ab_0.conda#a30144e4156cdbb236f99ebb49828f8b -https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.5.0-pyhd8ed1ab_0.conda#8472f598970b9af96ca8106fa243ab67 +https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.5.1-pyhd8ed1ab_0.conda#d4f60ccc5421472d2583efd9ce39d8b1 https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.3-h9ad1361_0.conda#8fb0e954c616bb0f9389efac4b4ed44b https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-22_linux64_mkl.conda#d6f942423116553f068b2f2d93ffea2e https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-22_linux64_mkl.conda#4edf2e7ce63920e4f539d12e32fb478e @@ -222,7 +222,7 @@ https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-22_linux64_mkl. https://conda.anaconda.org/conda-forge/linux-64/numpy-1.19.5-py39hd249d9e_3.tar.bz2#0cf333996ebdeeba8d1c8c1c0ee9eff9 https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.8-hc9dc06e_21.conda#b325046180590c868ce0dbf267b82eb8 https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-22_linux64_mkl.conda#3cb0e51433c88d2f4cdfb50c5c08a683 -https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-lite-2019.12.3-py39hd257fcd_5.tar.bz2#32dba66d6abc2b4b5b019c9e54307312 +https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-lite-2019.12.3-py39hd92a3bb_8.conda#5eb64443d4d973c31e179a498e1bb4a2 https://conda.anaconda.org/conda-forge/noarch/imageio-2.34.1-pyh4b66e23_0.conda#bcf6a6f4c6889ca083e8d33afbafb8d5 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.3.4-py39h2fa2bec_0.tar.bz2#9ec0b2186fab9121c54f4844f93ee5b7 https://conda.anaconda.org/conda-forge/linux-64/pandas-1.1.5-py39hde0f152_0.tar.bz2#79fc4b5b3a865b90dd3701cecf1ad33c From 6ee8a4bd3257d8373108c0bab0a4d2612d19b379 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 20 May 2024 11:47:21 +0200 Subject: [PATCH 049/275] :lock: :robot: CI Update lock files for cirrus-arm CI build(s) :lock: :robot: (#29052) Co-authored-by: Lock file bot --- build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock index 660bc9de9ecda..8db59cbb2c373 100644 --- a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock +++ b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock @@ -90,5 +90,5 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-22_linuxaa https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.2.1-py39hd16970a_0.conda#66b9718539ecdd38876b0176c315bcad https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.13.0-py39hb921187_1.conda#2717303c0d13a5646308b3763bf4daa4 https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.122-openblas.conda#65bc48b3bc85f8eeeab54311443a83aa -https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.8.4-py39h8e43113_0.conda#f397ddfe5c551732de61a92106a14cf3 -https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-3.8.4-py39ha65689a_0.conda#d501bb96ff505fdd431fd8fdac8efbf9 +https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.8.4-py39hf44f4b6_2.conda#fadf734d38ed608c9f0b5c91fe79cfb4 +https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-3.8.4-py39ha65689a_2.conda#c0472e3c4b3f007de6d643317c30963b From 539a0b24ea7865b512fb70905aed308c8c0af2c8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Morais?= <15629444+ojoaomorais@users.noreply.github.com> Date: Mon, 20 May 2024 11:05:26 -0300 Subject: [PATCH 050/275] DOC Add plot face recognition example to API docs (#29049) --- sklearn/datasets/_lfw.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/sklearn/datasets/_lfw.py b/sklearn/datasets/_lfw.py index cb62288646d23..be72baa981da7 100644 --- a/sklearn/datasets/_lfw.py +++ b/sklearn/datasets/_lfw.py @@ -281,6 +281,9 @@ def fetch_lfw_people( Features real, between 0 and 255 ================= ======================= + For a usage example of this dataset, see + :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py`. + Read more in the :ref:`User Guide `. Parameters From a07f9f683d4ed83f9959aa323feba4bd68a17f1c Mon Sep 17 00:00:00 2001 From: Yao Xiao <108576690+Charlie-XIAO@users.noreply.github.com> Date: Tue, 21 May 2024 15:00:14 +0800 Subject: [PATCH 051/275] DOC fix APIs marked incorrectly as deprecated (#29059) --- doc/api_reference.py | 15 +++++---------- 1 file changed, 5 insertions(+), 10 deletions(-) diff --git a/doc/api_reference.py b/doc/api_reference.py index c8a22ebc2d5b3..1aa6455fb7e44 100644 --- a/doc/api_reference.py +++ b/doc/api_reference.py @@ -1266,6 +1266,8 @@ def _get_submodule(module_name, submodule_name): "description": _get_submodule("sklearn.utils", "discovery"), "autosummary": [ "discovery.all_displays", + "discovery.all_estimators", + "discovery.all_functions", ], }, { @@ -1282,6 +1284,8 @@ def _get_submodule(module_name, submodule_name): "autosummary": [ "parallel.Parallel", "parallel.delayed", + "parallel_backend", + "register_parallel_backend", ], }, ], @@ -1324,13 +1328,4 @@ def _get_submodule(module_name, submodule_name): } """ -DEPRECATED_API_REFERENCE = { - "1.6": [ - "utils.parallel_backend", - "utils.register_parallel_backend", - ], - "1.7": [ - "utils.discovery.all_estimators", - "utils.discovery.all_functions", - ], -} # type: ignore +DEPRECATED_API_REFERENCE = {} # type: ignore From c50882bb100d401bcf8044da8ab0cc1929de3b31 Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Wed, 22 May 2024 10:01:25 +0200 Subject: [PATCH 052/275] DOC do not recommend a particular Python version (#29071) --- doc/developers/advanced_installation.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/doc/developers/advanced_installation.rst b/doc/developers/advanced_installation.rst index ed25d30601e45..88521c6c51867 100644 --- a/doc/developers/advanced_installation.rst +++ b/doc/developers/advanced_installation.rst @@ -58,9 +58,9 @@ feature, code or documentation improvement). If you plan on submitting a pull-request, you should clone from your fork instead. -#. Install a recent version of Python (3.9 is recommended at the time of writing) - for instance using Miniforge3_. Miniforge provides a conda-based distribution - of Python and the most popular scientific libraries. +#. Install a recent version of Python (3.9 or later at the time of writing) for + instance using Miniforge3_. Miniforge provides a conda-based distribution of + Python and the most popular scientific libraries. If you installed Python with conda, we recommend to create a dedicated `conda environment`_ with all the build dependencies of scikit-learn @@ -68,7 +68,7 @@ feature, code or documentation improvement). .. prompt:: bash $ - conda create -n sklearn-env -c conda-forge python=3.9 numpy scipy cython meson-python ninja + conda create -n sklearn-env -c conda-forge python numpy scipy cython meson-python ninja It is not always necessary but it is safer to open a new prompt before activating the newly created conda environment. From d1024c884d23c1e50524f90891717d7d5634a977 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Wed, 22 May 2024 11:35:49 +0200 Subject: [PATCH 053/275] BLD Fix another meson.build dependency (#29068) --- sklearn/neighbors/meson.build | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/neighbors/meson.build b/sklearn/neighbors/meson.build index b85188cab98be..7e361be25052e 100644 --- a/sklearn/neighbors/meson.build +++ b/sklearn/neighbors/meson.build @@ -24,7 +24,7 @@ foreach name: name_list ) py.extension_module( name, - [pyx, neighbors_cython_tree, utils_cython_tree], + [pyx, neighbors_cython_tree, utils_cython_tree, metrics_cython_tree], dependencies: [np_dep], cython_args: cython_args, subdir: 'sklearn/neighbors', From 38d8db138b7dd299dc3132654acc2aa480521110 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Morais?= <15629444+ojoaomorais@users.noreply.github.com> Date: Wed, 22 May 2024 09:39:56 -0300 Subject: [PATCH 054/275] DOC Removing warnings from plot CV examples (#29072) --- examples/model_selection/plot_cv_indices.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/model_selection/plot_cv_indices.py b/examples/model_selection/plot_cv_indices.py index e6c3580c787f0..d456546891069 100644 --- a/examples/model_selection/plot_cv_indices.py +++ b/examples/model_selection/plot_cv_indices.py @@ -99,9 +99,10 @@ def visualize_groups(classes, groups, name): def plot_cv_indices(cv, X, y, group, ax, n_splits, lw=10): """Create a sample plot for indices of a cross-validation object.""" - + use_groups = "Group" in type(cv).__name__ + groups = group if use_groups else None # Generate the training/testing visualizations for each CV split - for ii, (tr, tt) in enumerate(cv.split(X=X, y=y, groups=group)): + for ii, (tr, tt) in enumerate(cv.split(X=X, y=y, groups=groups)): # Fill in indices with the training/test groups indices = np.array([np.nan] * len(X)) indices[tt] = 1 From 1fa62f25751a544fcc3f0fa52435ffe882cf2b1a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Thu, 23 May 2024 16:47:12 +0200 Subject: [PATCH 055/275] FIX GridSearchCV regression in 1.5 with parameter grid with heterogeneous parameter values (#29078) --- doc/whats_new/v1.5.rst | 68 +++++++++++++------- sklearn/model_selection/_search.py | 2 +- sklearn/model_selection/tests/test_search.py | 45 +++++++++++++ 3 files changed, 89 insertions(+), 26 deletions(-) diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst index c2c64e24ba9e0..8286f4044941f 100644 --- a/doc/whats_new/v1.5.rst +++ b/doc/whats_new/v1.5.rst @@ -13,6 +13,24 @@ For a short description of the main highlights of the release, please refer to .. include:: changelog_legend.inc +.. _changes_1_5_1: + +Version 1.5.1 +============= + +**TODO** + +Changelog +--------- + +:mod:`sklearn.model_selection` +.............................. + +- |Fix| Fix a regression in :class:`model_selection.GridSearchCV` for parameter + grids that have heterogeneous parameter values. + :pr:`29078` by :user:`Loïc Estève ` + + .. _changes_1_5: Version 1.5.0 @@ -550,29 +568,29 @@ Changelog Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.4, including: -101AlexMartin, Abdulaziz Aloqeely, Adam J. Stewart, Adam Li, Adarsh Wase, Adrin -Jalali, Advik Sinha, Akash Srivastava, Akihiro Kuno, Alan Guedes, Alexis -IMBERT, Ana Paula Gomes, Anderson Nelson, Andrei Dzis, Arnaud Capitaine, Arturo -Amor, Aswathavicky, Bharat Raghunathan, Brendan Lu, Bruno, Cemlyn, Christian -Lorentzen, Christian Veenhuis, Cindy Liang, Claudio Salvatore Arcidiacono, -Connor Boyle, Conrad Stevens, crispinlogan, davidleon123, DerWeh, Dipan Banik, -Duarte São José, DUONG, Eddie Bergman, Edoardo Abati, Egehan Gunduz, Emad -Izadifar, Erich Schubert, Filip Karlo Došilović, Franck Charras, Gael -Varoquaux, Gönül Aycı, Guillaume Lemaitre, Gyeongjae Choi, Harmanan Kohli, -Hong Xiang Yue, Ian Faust, itsaphel, Ivan Wiryadi, Jack Bowyer, Javier Marin -Tur, Jérémie du Boisberranger, Jérôme Dockès, Jiawei Zhang, Joel Nothman, -Johanna Bayer, John Cant, John Hopfensperger, jpcars, jpienaar-tuks, Julian -Libiseller-Egger, Julien Jerphanion, KanchiMoe, Kaushik Amar Das, keyber, -Koustav Ghosh, kraktus, Krsto Proroković, ldwy4, LeoGrin, lihaitao, Linus -Sommer, Loic Esteve, Lucy Liu, Lukas Geiger, manasimj, Manuel Labbé, Manuel -Morales, Marco Edward Gorelli, Maren Westermann, Marija Vlajic, Mark Elliot, -Mateusz Sokół, Mavs, Michael Higgins, Michael Mayer, miguelcsilva, Miki -Watanabe, Mohammed Hamdy, myenugula, Nathan Goldbaum, Naziya Mahimkar, Neto, -Olivier Grisel, Omar Salman, Patrick Wang, Pierre de Fréminville, Priyash -Shah, Puneeth K, Rahil Parikh, raisadz, Raj Pulapakura, Ralf Gommers, Ralph -Urlus, Randolf Scholz, Reshama Shaikh, Richard Barnes, Rodrigo Romero, Saad -Mahmood, Salim Dohri, Sandip Dutta, SarahRemus, scikit-learn-bot, Shaharyar -Choudhry, Shubham, sperret6, Stefanie Senger, Suha Siddiqui, Thanh Lam DANG, -thebabush, Thomas J. Fan, Thomas Lazarus, Thomas Li, Tialo, Tim Head, Tuhin -Sharma, VarunChaduvula, Vineet Joshi, virchan, Waël Boukhobza, Weyb, Will +101AlexMartin, Abdulaziz Aloqeely, Adam J. Stewart, Adam Li, Adarsh Wase, Adrin +Jalali, Advik Sinha, Akash Srivastava, Akihiro Kuno, Alan Guedes, Alexis +IMBERT, Ana Paula Gomes, Anderson Nelson, Andrei Dzis, Arnaud Capitaine, Arturo +Amor, Aswathavicky, Bharat Raghunathan, Brendan Lu, Bruno, Cemlyn, Christian +Lorentzen, Christian Veenhuis, Cindy Liang, Claudio Salvatore Arcidiacono, +Connor Boyle, Conrad Stevens, crispinlogan, davidleon123, DerWeh, Dipan Banik, +Duarte São José, DUONG, Eddie Bergman, Edoardo Abati, Egehan Gunduz, Emad +Izadifar, Erich Schubert, Filip Karlo Došilović, Franck Charras, Gael +Varoquaux, Gönül Aycı, Guillaume Lemaitre, Gyeongjae Choi, Harmanan Kohli, +Hong Xiang Yue, Ian Faust, itsaphel, Ivan Wiryadi, Jack Bowyer, Javier Marin +Tur, Jérémie du Boisberranger, Jérôme Dockès, Jiawei Zhang, Joel Nothman, +Johanna Bayer, John Cant, John Hopfensperger, jpcars, jpienaar-tuks, Julian +Libiseller-Egger, Julien Jerphanion, KanchiMoe, Kaushik Amar Das, keyber, +Koustav Ghosh, kraktus, Krsto Proroković, ldwy4, LeoGrin, lihaitao, Linus +Sommer, Loic Esteve, Lucy Liu, Lukas Geiger, manasimj, Manuel Labbé, Manuel +Morales, Marco Edward Gorelli, Maren Westermann, Marija Vlajic, Mark Elliot, +Mateusz Sokół, Mavs, Michael Higgins, Michael Mayer, miguelcsilva, Miki +Watanabe, Mohammed Hamdy, myenugula, Nathan Goldbaum, Naziya Mahimkar, Neto, +Olivier Grisel, Omar Salman, Patrick Wang, Pierre de Fréminville, Priyash +Shah, Puneeth K, Rahil Parikh, raisadz, Raj Pulapakura, Ralf Gommers, Ralph +Urlus, Randolf Scholz, Reshama Shaikh, Richard Barnes, Rodrigo Romero, Saad +Mahmood, Salim Dohri, Sandip Dutta, SarahRemus, scikit-learn-bot, Shaharyar +Choudhry, Shubham, sperret6, Stefanie Senger, Suha Siddiqui, Thanh Lam DANG, +thebabush, Thomas J. Fan, Thomas Lazarus, Thomas Li, Tialo, Tim Head, Tuhin +Sharma, VarunChaduvula, Vineet Joshi, virchan, Waël Boukhobza, Weyb, Will Dean, Xavier Beltran, Xiao Yuan, Xuefeng Xu, Yao Xiao diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py index a26ec0786849d..856276229ba5c 100644 --- a/sklearn/model_selection/_search.py +++ b/sklearn/model_selection/_search.py @@ -1090,7 +1090,7 @@ def _store(key_name, array, weights=None, splits=False, rank=False): param_list = list(param_result.values()) try: arr_dtype = np.result_type(*param_list) - except TypeError: + except (TypeError, ValueError): arr_dtype = object if len(param_list) == n_candidates and arr_dtype != object: # Exclude `object` else the numpy constructor might infer a list of diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py index b59ed7168ff10..cb4af646aee39 100644 --- a/sklearn/model_selection/tests/test_search.py +++ b/sklearn/model_selection/tests/test_search.py @@ -2641,3 +2641,48 @@ def test_score_rejects_params_with_no_routing_enabled(SearchCV, param_search): # End of Metadata Routing Tests # ============================= + + +def test_cv_results_dtype_issue_29074(): + """Non-regression test for https://github.com/scikit-learn/scikit-learn/issues/29074""" + + class MetaEstimator(BaseEstimator, ClassifierMixin): + def __init__( + self, + base_clf, + parameter1=None, + parameter2=None, + parameter3=None, + parameter4=None, + ): + self.base_clf = base_clf + self.parameter1 = parameter1 + self.parameter2 = parameter2 + self.parameter3 = parameter3 + self.parameter4 = parameter4 + + def fit(self, X, y=None): + self.base_clf.fit(X, y) + return self + + def score(self, X, y): + return self.base_clf.score(X, y) + + # Values of param_grid are such that np.result_type gives slightly + # different errors, in particular ValueError and TypeError + param_grid = { + "parameter1": [None, {"option": "A"}, {"option": "B"}], + "parameter2": [None, [1, 2]], + "parameter3": [{"a": 1}], + "parameter4": ["str1", "str2"], + } + grid_search = GridSearchCV( + estimator=MetaEstimator(LogisticRegression()), + param_grid=param_grid, + cv=3, + ) + + X, y = make_blobs(random_state=0) + grid_search.fit(X, y) + for param in param_grid: + assert grid_search.cv_results_[f"param_{param}"].dtype == object From a5f399adb7ef623b24e6f795e7a3579998481006 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Thu, 23 May 2024 17:36:36 +0200 Subject: [PATCH 056/275] BLD Fix yet another meson.build dependency (#29087) --- sklearn/metrics/_pairwise_distances_reduction/meson.build | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/sklearn/metrics/_pairwise_distances_reduction/meson.build b/sklearn/metrics/_pairwise_distances_reduction/meson.build index e22cf70164f7f..ced94fd8b694e 100644 --- a/sklearn/metrics/_pairwise_distances_reduction/meson.build +++ b/sklearn/metrics/_pairwise_distances_reduction/meson.build @@ -11,6 +11,11 @@ # needed for the cimport to work _pairwise_distances_reduction_cython_tree = [ fs.copyfile('__init__.py'), + # We are in a sub-module of metrics, so we always need to have + # sklearn/metrics/__init__.py copied to the build directory to avoid the + # error: + # relative cimport beyond main package is not allowed + metrics_cython_tree ] _classmode_pxd = fs.copyfile('_classmode.pxd') @@ -30,7 +35,7 @@ _datasets_pair_pyx = custom_target( _datasets_pair = py.extension_module( '_datasets_pair', [_datasets_pair_pxd, _datasets_pair_pyx, - _pairwise_distances_reduction_cython_tree, utils_cython_tree], + _pairwise_distances_reduction_cython_tree, utils_cython_tree], dependencies: [np_dep, openmp_dep], override_options: ['cython_language=cpp'], cython_args: cython_args, From 090e88e2ce32775b3cb728d2c23fcbbab5f733bf Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Thu, 23 May 2024 22:01:15 +0200 Subject: [PATCH 057/275] FIX avoid warning in input validation with array_api_strict (#29086) --- sklearn/utils/validation.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py index 1f25a5b360679..dd30ae6a6da94 100644 --- a/sklearn/utils/validation.py +++ b/sklearn/utils/validation.py @@ -95,7 +95,7 @@ def _assert_all_finite( ): """Like assert_all_finite, but only for ndarray.""" - xp, _ = get_namespace(X) + xp, is_array_api = get_namespace(X) if _get_config()["assume_finite"]: return @@ -103,7 +103,7 @@ def _assert_all_finite( X = xp.asarray(X) # for object dtype data, we only check for NaNs (GH-13254) - if X.dtype == np.dtype("object") and not allow_nan: + if not is_array_api and X.dtype == np.dtype("object") and not allow_nan: if _object_dtype_isnan(X).any(): raise ValueError("Input contains NaN") From c0055874db5eaca101fbcef30259343ad6fbe8b8 Mon Sep 17 00:00:00 2001 From: Yao Xiao <108576690+Charlie-XIAO@users.noreply.github.com> Date: Fri, 24 May 2024 16:29:30 +0800 Subject: [PATCH 058/275] DOC temporary fix for search summary (#29090) --- doc/js/searchtools.js | 628 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 628 insertions(+) create mode 100644 doc/js/searchtools.js diff --git a/doc/js/searchtools.js b/doc/js/searchtools.js new file mode 100644 index 0000000000000..ca53342fb4788 --- /dev/null +++ b/doc/js/searchtools.js @@ -0,0 +1,628 @@ +/* + * searchtools.js + * ~~~~~~~~~~~~~~~~ + * + * This is vendored from Sphinx 7.3.7 (subject to formatting with `prettier`). The + * original file is available at: + * https://github.com/sphinx-doc/sphinx/blob/v7.3.7/sphinx/themes/basic/static/searchtools.js + * + * This file is used to override the default `searchtools.js`. Every occurrence of + * `[role="main"]` is replaced with `#main-content` to make searching summary work with + * `pydata-sphinx-theme==0.15.2`. After upgrading to `pydata-sphinx-theme>=0.15.3` this + * file should be no longer needed. + * + * Sphinx JavaScript utilities for the full-text search. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + "Search finished, found ${resultCount} page(s) matching the search query." + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlinks", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`#main-content ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '#main-content ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector("#main-content"); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '#main-content'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); From 9a3f00097fff63a5f7bc59e264a99eb4dc9eae57 Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Fri, 24 May 2024 11:20:42 +0200 Subject: [PATCH 059/275] =?UTF-8?q?MNT=20DOC=20deprecation=20note=20in=20d?= =?UTF-8?q?ocstring=20of=20PassiveAggressive=20Classifier=E2=80=A6=20(#290?= =?UTF-8?q?96)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- sklearn/linear_model/_passive_aggressive.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/sklearn/linear_model/_passive_aggressive.py b/sklearn/linear_model/_passive_aggressive.py index 2de019b6d986c..3a7ffbf9bfaea 100644 --- a/sklearn/linear_model/_passive_aggressive.py +++ b/sklearn/linear_model/_passive_aggressive.py @@ -145,6 +145,10 @@ class PassiveAggressiveClassifier(BaseSGDClassifier): loss_function_ : callable Loss function used by the algorithm. + .. deprecated:: 1.4 + Attribute `loss_function_` was deprecated in version 1.4 and will be + removed in 1.6. + See Also -------- SGDClassifier : Incrementally trained logistic regression. From 74678bda7bdce997fd74a6a0709e9b2b50f61185 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Fri, 24 May 2024 13:20:20 +0200 Subject: [PATCH 060/275] MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081) --- ...latest_conda_forge_mkl_linux-64_conda.lock | 10 +++++----- ...t_conda_forge_mkl_linux-64_environment.yml | 2 +- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 8 ++++---- ...est_conda_forge_mkl_osx-64_environment.yml | 2 +- ...latest_conda_mkl_no_openmp_environment.yml | 4 ++-- ...test_conda_mkl_no_openmp_osx-64_conda.lock | 10 +++++----- ...latest_pip_openblas_pandas_environment.yml | 2 +- ...st_pip_openblas_pandas_linux-64_conda.lock | 6 +++--- .../pylatest_pip_scipy_dev_environment.yml | 2 +- ...pylatest_pip_scipy_dev_linux-64_conda.lock | 6 +++--- ...in_conda_defaults_openblas_environment.yml | 2 +- ...onda_defaults_openblas_linux-64_conda.lock | 2 +- .../pymin_conda_forge_mkl_environment.yml | 2 +- .../pymin_conda_forge_mkl_win-64_conda.lock | 14 +++++++------- ...forge_openblas_ubuntu_2204_environment.yml | 2 +- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 10 +++++----- build_tools/azure/pypy3_environment.yml | 2 +- build_tools/azure/pypy3_linux-64_conda.lock | 8 ++++---- build_tools/azure/python_nogil_lock.txt | 19 ++++++++++--------- build_tools/azure/ubuntu_atlas_lock.txt | 2 +- .../azure/ubuntu_atlas_requirements.txt | 2 +- build_tools/circle/doc_environment.yml | 2 +- build_tools/circle/doc_linux-64_conda.lock | 10 +++++----- .../doc_min_dependencies_environment.yml | 2 +- .../doc_min_dependencies_linux-64_conda.lock | 8 ++++---- .../cirrus/pymin_conda_forge_environment.yml | 2 +- ...pymin_conda_forge_linux-aarch64_conda.lock | 8 ++++---- .../update_environments_and_lock_files.py | 15 +++++---------- pyproject.toml | 4 ++-- sklearn/_min_dependencies.py | 2 +- sklearn/linear_model/tests/test_logistic.py | 2 ++ sklearn/tests/test_common.py | 7 ------- 32 files changed, 85 insertions(+), 94 deletions(-) diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index 752e32b1d6220..e59404401fb68 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 2622dc7361d0af53cfb31534b939a13e48192a3260137ba4ec20083659c2e5fa +# input_hash: 29e3d76c436ee8cb86d5976aa41ef8822f991c469202b8d12fc27e43576ec805 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.2.2-hbcca054_0.conda#2f4327a1cbe7f022401b236e915a5fef @@ -54,7 +54,7 @@ https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-hd590300_0.conda#c0f3abb4a16477208bbd43a39bd56f18 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_2.conda#b8934d399b56d73e323403e183d009c5 https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/rdma-core-28.9-h59595ed_1.conda#aeffb7c06b5f65e55e6c637408dc4100 @@ -144,7 +144,7 @@ https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.16-hb7c19ff_0.conda#51bb https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp15-15.0.7-default_h127d8a8_5.conda#d0a9633b53cdc319b8a1a532ae7822b8 https://conda.anaconda.org/conda-forge/linux-64/libclang13-18.1.5-default_h5d6823c_0.conda#60c39a00b694c98da03f67a3ba1d7499 https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-h4637d8d_4.conda#d4529f4dff3057982a7617c7ac58fde3 -https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.7.1-hca28451_0.conda#755c7f876815003337d2c61ff5d047e5 +https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.8.0-hca28451_0.conda#f21c27f076a07907e70c49bb57bd0f20 https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda#ee48bf17cc83a00f59ca1494d5646869 https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.49-h4f305b6_0.conda#dfcfd72c7a430d3616763ecfbefe4ca9 https://conda.anaconda.org/conda-forge/linux-64/libpq-16.3-ha72fbe1_0.conda#bac737ae28b79cfbafd515258d97d29e @@ -156,7 +156,7 @@ https://conda.anaconda.org/conda-forge/noarch/ply-3.11-pyhd8ed1ab_2.conda#18c6de https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.2-pyhd8ed1ab_0.conda#b9a4dacf97241704529131a0dfc0494f https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2024.1-pyhd8ed1ab_0.conda#98206ea9954216ee7540f0c773f2104d https://conda.anaconda.org/conda-forge/noarch/pytz-2024.1-pyhd8ed1ab_0.conda#3eeeeb9e4827ace8c0c1419c85d590ad -https://conda.anaconda.org/conda-forge/noarch/setuptools-69.5.1-pyhd8ed1ab_0.conda#7462280d81f639363e6e63c81276bd9e +https://conda.anaconda.org/conda-forge/noarch/setuptools-70.0.0-pyhd8ed1ab_0.conda#c8ddb4f34a208df4dd42509a0f6a1c89 https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2 https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.12.0-h297d8ca_1.conda#3ff978d8994f591818a506640c6a7071 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd @@ -185,7 +185,7 @@ https://conda.anaconda.org/conda-forge/linux-64/mkl-2022.2.1-h84fe81f_16997.cond https://conda.anaconda.org/conda-forge/linux-64/pillow-10.3.0-py311h18e6fac_0.conda#6c520a9d36c9d7270988c7a6c360d6d4 https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 -https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4 +https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py311hb755f60_0.conda#02336abab4cb5dd794010ef53c54bd09 https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.3.14-hf3aad02_1.conda#a968ffa7e9fe0c257628033d393e512f diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_environment.yml b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_environment.yml index 30686a983ab35..30a2fe1d1812a 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_environment.yml +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_environment.yml @@ -14,7 +14,7 @@ dependencies: - matplotlib - pandas - pyamg - - pytest<8 + - pytest - pytest-xdist - pillow - pip diff --git a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock index 5e83abb9667a2..827eaabd3aaac 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: osx-64 -# input_hash: 05036df523e23d48cff7b6355ca081c5e5b41d8c5078cb9e1352f79e661d0549 +# input_hash: 134a56cb648e4588930416968ccdd27a7b83285f04712b032c6d07291b51b526 @EXPLICIT https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.8-h10d778d_5.conda#6097a6ca9ada32699b5fc4312dd6ef18 https://conda.anaconda.org/conda-forge/osx-64/ca-certificates-2024.2.2-h8857fd0_0.conda#f2eacee8c33c43692f1ccfd33d0f50b1 @@ -30,7 +30,7 @@ https://conda.anaconda.org/conda-forge/osx-64/libsqlite-3.45.3-h92b6c6a_0.conda# https://conda.anaconda.org/conda-forge/osx-64/libxcb-1.15-hb7f2c08_0.conda#5513f57e0238c87c12dffedbcc9c1a4a https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.12.7-h3e169fe_0.conda#4c04ba47fdd2ebecc1d3b6a77534d9ef https://conda.anaconda.org/conda-forge/osx-64/llvm-openmp-18.1.5-h39e0ece_0.conda#ee12a644568269838b91f901b2537425 -https://conda.anaconda.org/conda-forge/osx-64/openssl-3.3.0-hd75f5a5_0.conda#eb8c33aa7929a7714eab8b90c1d88afe +https://conda.anaconda.org/conda-forge/osx-64/openssl-3.3.0-h87427d6_2.conda#fa4859f58b1810d77089b0482c886da0 https://conda.anaconda.org/conda-forge/osx-64/readline-8.2-h9e318b2_1.conda#f17f77f2acf4d344734bda76829ce14e https://conda.anaconda.org/conda-forge/osx-64/tk-8.6.13-h1abcd95_1.conda#bf830ba5afc507c6232d4ef0fb1a882d https://conda.anaconda.org/conda-forge/osx-64/zlib-1.2.13-h8a1eda9_5.conda#75a8a98b1c4671c5d2897975731da42d @@ -68,7 +68,7 @@ https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda#d3 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.2-pyhd8ed1ab_0.conda#b9a4dacf97241704529131a0dfc0494f https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2024.1-pyhd8ed1ab_0.conda#98206ea9954216ee7540f0c773f2104d https://conda.anaconda.org/conda-forge/noarch/pytz-2024.1-pyhd8ed1ab_0.conda#3eeeeb9e4827ace8c0c1419c85d590ad -https://conda.anaconda.org/conda-forge/noarch/setuptools-69.5.1-pyhd8ed1ab_0.conda#7462280d81f639363e6e63c81276bd9e +https://conda.anaconda.org/conda-forge/noarch/setuptools-70.0.0-pyhd8ed1ab_0.conda#c8ddb4f34a208df4dd42509a0f6a1c89 https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2 https://conda.anaconda.org/conda-forge/osx-64/tbb-2021.12.0-h3c5361c_1.conda#e23dd312f13ffe470cc4fdeaddc7a32e https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd @@ -90,7 +90,7 @@ https://conda.anaconda.org/conda-forge/osx-64/mpc-1.3.1-h81bd1dd_0.conda#c752c0e https://conda.anaconda.org/conda-forge/osx-64/openjpeg-2.5.2-h7310d3a_0.conda#05a14cc9d725dd74995927968d6547e3 https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 -https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4 +https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/osx-64/ccache-4.9.1-h41adc32_0.conda#45aaf96b67840bd98a928de8679098fa https://conda.anaconda.org/conda-forge/osx-64/cctools-986-h40f6528_0.conda#b7a2ca0062a6ee8bc4e83ec887bef942 diff --git a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_environment.yml b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_environment.yml index cfa1b7689a4ad..ad177e4ed391b 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_environment.yml +++ b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_environment.yml @@ -14,7 +14,7 @@ dependencies: - matplotlib - pandas - pyamg - - pytest<8 + - pytest - pytest-xdist - pillow - pip diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml b/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml index 01bd378aa121a..7e85b28b3f6c4 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml @@ -12,12 +12,11 @@ dependencies: - matplotlib - pandas - pyamg - - pytest<8 + - pytest - pytest-xdist - pillow - pip - ninja - - meson-python - pytest-cov - coverage - ccache @@ -25,3 +24,4 @@ dependencies: - pip: - cython - threadpoolctl + - meson-python diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock index ec92612048448..418cf7a32a350 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: osx-64 -# input_hash: e0d2cf2593df1f2c6969d68cf849136bee785b51f6cfc50ea1bdca2143d4a051 +# input_hash: 197b213ba4980b3e95dcd438da09eb9b55e4bf5a4d8e6d3ddab29275aa22556a @EXPLICIT https://repo.anaconda.com/pkgs/main/osx-64/blas-1.0-mkl.conda#cb2c87e85ac8e0ceae776d26d4214c8a https://repo.anaconda.com/pkgs/main/osx-64/bzip2-1.0.8-h6c40b1e_6.conda#96224786021d0765ce05818fa3c59bdb @@ -26,7 +26,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/libgfortran5-11.3.0-h9dfd629_28.conda https://repo.anaconda.com/pkgs/main/osx-64/libpng-1.6.39-h6c40b1e_0.conda#a3c824835f53ad27aeb86d2b55e47804 https://repo.anaconda.com/pkgs/main/osx-64/lz4-c-1.9.4-hcec6c5f_1.conda#aee0efbb45220e1985533dbff48551f8 https://repo.anaconda.com/pkgs/main/osx-64/ninja-base-1.10.2-haf03e11_5.conda#c857c13129710a61395270656905c4a2 -https://repo.anaconda.com/pkgs/main/osx-64/openssl-3.0.13-hca72f7f_1.conda#e526d7e2e79132a11b4746cf305c45b5 +https://repo.anaconda.com/pkgs/main/osx-64/openssl-3.0.13-hca72f7f_2.conda#4f840ec6217dff98040ff6be19cf3afb https://repo.anaconda.com/pkgs/main/osx-64/readline-8.2-hca72f7f_0.conda#971667436260e523f6f7355fdfa238bf https://repo.anaconda.com/pkgs/main/osx-64/tbb-2021.8.0-ha357a0b_0.conda#fb48530a3eea681c11dafb95b3387c0f https://repo.anaconda.com/pkgs/main/osx-64/tk-8.6.14-h4d00af3_0.conda#a2c03940c2ae54614301ec82e6a98d75 @@ -61,14 +61,11 @@ https://repo.anaconda.com/pkgs/main/osx-64/tornado-6.3.3-py312h6c40b1e_0.conda#4 https://repo.anaconda.com/pkgs/main/osx-64/unicodedata2-15.1.0-py312h6c40b1e_0.conda#65bd2cb787fc99662d9bb6e6520c5826 https://repo.anaconda.com/pkgs/main/osx-64/wheel-0.43.0-py312hecd8cb5_0.conda#c0bdd5748b170523232e8ad1d667136c https://repo.anaconda.com/pkgs/main/osx-64/fonttools-4.51.0-py312h6c40b1e_0.conda#8f55fa86b73e8a7f4403503f9b7a9959 -https://repo.anaconda.com/pkgs/main/osx-64/meson-1.3.1-py312hecd8cb5_0.conda#43963a2b38becce4caa95434b8c96837 https://repo.anaconda.com/pkgs/main/osx-64/numpy-base-1.26.4-py312h6f81483_0.conda#87f73efbf26ab2e2ea7c32481a71bd47 https://repo.anaconda.com/pkgs/main/osx-64/pillow-10.3.0-py312h6c40b1e_0.conda#fe883fa4247d35fe6de49f713529ca02 https://repo.anaconda.com/pkgs/main/osx-64/pip-24.0-py312hecd8cb5_0.conda#7a8e0b1d3742ddf1c8aa97fbaa158039 -https://repo.anaconda.com/pkgs/main/osx-64/pyproject-metadata-0.7.1-py312hecd8cb5_0.conda#e91ce37477d24dcdf7e0a8b93c5e72fd https://repo.anaconda.com/pkgs/main/osx-64/pytest-7.4.0-py312hecd8cb5_0.conda#b816a2439ba9b87524aec74d58e55b0a https://repo.anaconda.com/pkgs/main/osx-64/python-dateutil-2.9.0post0-py312hecd8cb5_0.conda#b3ed54eb118325785284dd18bfceca19 -https://repo.anaconda.com/pkgs/main/osx-64/meson-python-0.15.0-py312h6c40b1e_0.conda#688ab56b9d8e5a2e3f018ca3ce34e061 https://repo.anaconda.com/pkgs/main/osx-64/pytest-cov-4.1.0-py312hecd8cb5_1.conda#a33a24eb20359f464938e75b2f57e23a https://repo.anaconda.com/pkgs/main/osx-64/pytest-xdist-3.5.0-py312hecd8cb5_0.conda#d1ecfb3691cceecb1f16bcfdf0b67bb5 https://repo.anaconda.com/pkgs/main/osx-64/bottleneck-1.3.7-py312h32608ca_0.conda#f96a01eba5ea542cf9c7cc8d77447627 @@ -83,4 +80,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.11.4-py312h81688c2_0.conda#7d https://repo.anaconda.com/pkgs/main/osx-64/pandas-2.2.1-py312he282a81_0.conda#021b70a1e40efb75b89eb8ebdb347132 https://repo.anaconda.com/pkgs/main/osx-64/pyamg-4.2.3-py312h44cbcf4_0.conda#3bdc7be74087b3a5a83c520a74e1e8eb # pip cython @ https://files.pythonhosted.org/packages/d5/6d/06c08d75adb98cdf72af18801e193d22580cc86ca553610f430f18ea26b3/Cython-3.0.10-cp312-cp312-macosx_10_9_x86_64.whl#sha256=8f2864ab5fcd27a346f0b50f901ebeb8f60b25a60a575ccfd982e7f3e9674914 +# pip meson @ https://files.pythonhosted.org/packages/33/75/b1a37fa7b2dbca8c0dbb04d5cdd7e2720c8ef6febe41b4a74866350e041c/meson-1.4.0-py3-none-any.whl#sha256=476a458d51fcfa322a6bdc64da5138997c542d08e6b2e49b9fa68c46fd7c4475 # pip threadpoolctl @ https://files.pythonhosted.org/packages/4b/2c/ffbf7a134b9ab11a67b0cf0726453cedd9c5043a4fe7a35d1cefa9a1bcfb/threadpoolctl-3.5.0-py3-none-any.whl#sha256=56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467 +# pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526 +# pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml b/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml index 0f82886f4acb2..adb7add7622e1 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml +++ b/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml @@ -16,7 +16,7 @@ dependencies: - matplotlib - pandas - pyamg - - pytest<8 + - pytest - pytest-xdist - pillow - ninja diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index afccc559e409a..c841fae4fe6b5 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: d4063b0b99f7a39e30c5f6e2d9c5dd293d9b206ce326841bf811534ea1be79f0 +# input_hash: af52e4ce613b7668e1e28daaea07461722275d345395a5eaced4e07a16998179 @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2024.3.11-h06a4308_0.conda#08529eb3504712baabcbda266a19feb7 @@ -71,9 +71,9 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py39h06a4308_0.conda#7f8ce # pip jinja2 @ https://files.pythonhosted.org/packages/31/80/3a54838c3fb461f6fec263ebf3a3a41771bd05190238de3486aae8540c36/jinja2-3.1.4-py3-none-any.whl#sha256=bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d # pip lazy-loader @ https://files.pythonhosted.org/packages/83/60/d497a310bde3f01cb805196ac61b7ad6dc5dcf8dce66634dc34364b20b4f/lazy_loader-0.4-py3-none-any.whl#sha256=342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc # pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526 -# pip pytest @ https://files.pythonhosted.org/packages/51/ff/f6e8b8f39e08547faece4bd80f89d5a8de68a38b2d179cc1c4490ffa3286/pytest-7.4.4-py3-none-any.whl#sha256=b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8 +# pip pytest @ https://files.pythonhosted.org/packages/b4/c1/27a1274b73712232328cb5115030057b7dec377f36a518c83f2e01d4f305/pytest-8.2.1-py3-none-any.whl#sha256=faccc5d332b8c3719f40283d0d44aa5cf101cec36f88cde9ed8f2bc0538612b1 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 -# pip requests @ https://files.pythonhosted.org/packages/70/8e/0e2d847013cb52cd35b38c009bb167a1a26b2ce6cd6965bf26b47bc0bf44/requests-2.31.0-py3-none-any.whl#sha256=58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f +# pip requests @ https://files.pythonhosted.org/packages/c3/20/748e38b466e0819491f0ce6e90ebe4184966ee304fe483e2c414b0f4ef07/requests-2.32.2-py3-none-any.whl#sha256=fc06670dd0ed212426dfeb94fc1b983d917c4f9847c863f313c9dfaaffb7c23c # pip scipy @ https://files.pythonhosted.org/packages/c6/ba/a778e6c0020d728c119b0379805a357135fe8c9bc87fdb7e0750ca11319f/scipy-1.13.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=28e286bf9ac422d6beb559bc61312c348ca9b0f0dae0d7c5afde7f722d6ea13d # pip tifffile @ https://files.pythonhosted.org/packages/c1/79/29d0fa40017f7b749ce344759dcc21e2ec9bbb81fc69ca2ce06e261f83f0/tifffile-2024.5.10-py3-none-any.whl#sha256=4154f091aa24d4e75bfad9ab2d5424a68c70e67b8220188066dc61946d4551bd # pip lightgbm @ https://files.pythonhosted.org/packages/ba/11/cb8b67f3cbdca05b59a032bb57963d4fe8c8d18c3870f30bed005b7f174d/lightgbm-4.3.0-py3-none-manylinux_2_28_x86_64.whl#sha256=104496a3404cb2452d3412cbddcfbfadbef9c372ea91e3a9b8794bcc5183bf07 diff --git a/build_tools/azure/pylatest_pip_scipy_dev_environment.yml b/build_tools/azure/pylatest_pip_scipy_dev_environment.yml index 7d8e7a66d987e..01709b79e3720 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_environment.yml +++ b/build_tools/azure/pylatest_pip_scipy_dev_environment.yml @@ -9,7 +9,7 @@ dependencies: - pip - pip: - threadpoolctl - - pytest<8 + - pytest - pytest-xdist - pip - ninja diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index e4305c97b76bc..f8cc75936e789 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 777413179f12c3f7972520657eb2c826ffd6ff4c15e5da73631696b7ef07c3f2 +# input_hash: 893691ececb72bbf81101535204fa5694d898312941037a946e81dcb5cc2c322 @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2024.3.11-h06a4308_0.conda#08529eb3504712baabcbda266a19feb7 @@ -56,9 +56,9 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py312h06a4308_0.conda#6d96 # pip urllib3 @ https://files.pythonhosted.org/packages/a2/73/a68704750a7679d0b6d3ad7aa8d4da8e14e151ae82e6fee774e6e0d05ec8/urllib3-2.2.1-py3-none-any.whl#sha256=450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d # pip jinja2 @ https://files.pythonhosted.org/packages/31/80/3a54838c3fb461f6fec263ebf3a3a41771bd05190238de3486aae8540c36/jinja2-3.1.4-py3-none-any.whl#sha256=bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d # pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526 -# pip pytest @ https://files.pythonhosted.org/packages/51/ff/f6e8b8f39e08547faece4bd80f89d5a8de68a38b2d179cc1c4490ffa3286/pytest-7.4.4-py3-none-any.whl#sha256=b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8 +# pip pytest @ https://files.pythonhosted.org/packages/b4/c1/27a1274b73712232328cb5115030057b7dec377f36a518c83f2e01d4f305/pytest-8.2.1-py3-none-any.whl#sha256=faccc5d332b8c3719f40283d0d44aa5cf101cec36f88cde9ed8f2bc0538612b1 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 -# pip requests @ https://files.pythonhosted.org/packages/70/8e/0e2d847013cb52cd35b38c009bb167a1a26b2ce6cd6965bf26b47bc0bf44/requests-2.31.0-py3-none-any.whl#sha256=58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f +# pip requests @ https://files.pythonhosted.org/packages/c3/20/748e38b466e0819491f0ce6e90ebe4184966ee304fe483e2c414b0f4ef07/requests-2.32.2-py3-none-any.whl#sha256=fc06670dd0ed212426dfeb94fc1b983d917c4f9847c863f313c9dfaaffb7c23c # pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 # pip pooch @ https://files.pythonhosted.org/packages/f4/72/8ae0f1ba4ce6a4f6d4d01a60a9fdf690fde188c45c1872b0b4ddb0607ace/pooch-1.8.1-py3-none-any.whl#sha256=6b56611ac320c239faece1ac51a60b25796792599ce5c0b1bb87bf01df55e0a9 # pip pytest-cov @ https://files.pythonhosted.org/packages/78/3a/af5b4fa5961d9a1e6237b530eb87dd04aea6eb83da09d2a4073d81b54ccf/pytest_cov-5.0.0-py3-none-any.whl#sha256=4f0764a1219df53214206bf1feea4633c3b558a2925c8b59f144f682861ce652 diff --git a/build_tools/azure/pymin_conda_defaults_openblas_environment.yml b/build_tools/azure/pymin_conda_defaults_openblas_environment.yml index 3a8379e28068e..a82ba18e27980 100644 --- a/build_tools/azure/pymin_conda_defaults_openblas_environment.yml +++ b/build_tools/azure/pymin_conda_defaults_openblas_environment.yml @@ -12,7 +12,7 @@ dependencies: - joblib=1.2.0 # min - matplotlib=3.3.4 # min - pyamg - - pytest<8 + - pytest - pytest-xdist - pillow - pytest-cov diff --git a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock index 6e46719df47c4..8cecfc60c4c9d 100644 --- a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 7d61cf4d650f87956531ca703b2ac2eabd6d427b07664416d5420eb73b39bdf1 +# input_hash: 244b6a064d3785ea62baaf9436848821d153846b455c2976f5e811182e848c83 @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-openblas.conda#9ddfcaef10d79366c90128f5dc444be8 diff --git a/build_tools/azure/pymin_conda_forge_mkl_environment.yml b/build_tools/azure/pymin_conda_forge_mkl_environment.yml index fbad1d5bd42a8..a219e4b3daa8f 100644 --- a/build_tools/azure/pymin_conda_forge_mkl_environment.yml +++ b/build_tools/azure/pymin_conda_forge_mkl_environment.yml @@ -12,7 +12,7 @@ dependencies: - joblib - threadpoolctl - matplotlib - - pytest<8 + - pytest - pytest-xdist - pillow - pip diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock index 206a72f334f6e..4b517f825d4fb 100644 --- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: win-64 -# input_hash: 4a2ac6360285edd6c1e8182dd51ef698c0041fa9843e4ad9d9bc9dec6a7c8d1d +# input_hash: 9b46e8490ff2c4c93104b20d3bffb2d04120288c8c8680bef8376ebd9d9b8eb3 @EXPLICIT https://conda.anaconda.org/conda-forge/win-64/ca-certificates-2024.2.2-h56e8100_0.conda#63da060240ab8087b60d1357051ea7d6 https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2024.1.0-h57928b3_965.conda#c66eb2fd33b999ccc258aef85689758e @@ -11,10 +11,10 @@ https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#1610 https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.22621.0-h57928b3_0.tar.bz2#72608f6cd3e5898229c3ea16deb1ac43 https://conda.anaconda.org/conda-forge/win-64/m2w64-gmp-6.1.0-2.tar.bz2#53a1c73e1e3d185516d7e3af177596d9 https://conda.anaconda.org/conda-forge/win-64/m2w64-libwinpthread-git-5.0.0.4634.697f757-2.tar.bz2#774130a326dee16f1ceb05cc687ee4f0 -https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.38.33130-h82b7239_18.conda#8be79fdd2725ddf7bbf8a27a4c1f79ba +https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.38.33135-h835141b_20.conda#e971b35a5765862fabc4ba6e5ddf9470 https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libs-core-5.3.0-7.tar.bz2#4289d80fb4d272f1f3b56cfe87ac90bd -https://conda.anaconda.org/conda-forge/win-64/vc-14.3-hcf57466_18.conda#20e1e652a4c740fa719002a8449994a2 -https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.38.33130-hcb4865c_18.conda#10d42885e3ed84e575b454db30f1aa93 +https://conda.anaconda.org/conda-forge/win-64/vc-14.3-ha32ba9b_20.conda#2abfb5cb1b9d41a50f765d60f0be563d +https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.38.33135-h22015db_20.conda#bb4f5ab332e46e1b022d8842e72905b1 https://conda.anaconda.org/conda-forge/win-64/bzip2-1.0.8-hcfcfb64_5.conda#26eb8ca6ea332b675e11704cce84a3be https://conda.anaconda.org/conda-forge/win-64/icu-73.2-h63175ca_0.conda#0f47d9e3192d9e09ae300da0d28e0f56 https://conda.anaconda.org/conda-forge/win-64/lerc-4.0.0-h63175ca_0.tar.bz2#1900cb3cab5055833cfddb0ba233b074 @@ -29,7 +29,7 @@ https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.4.0-hcfcfb64_0.cond https://conda.anaconda.org/conda-forge/win-64/libzlib-1.2.13-hcfcfb64_5.conda#5fdb9c6a113b6b6cb5e517fd972d5f41 https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libgfortran-5.3.0-6.tar.bz2#066552ac6b907ec6d72c0ddab29050dc https://conda.anaconda.org/conda-forge/win-64/ninja-1.12.1-hc790b64_0.conda#a557dde55343e03c68cd7e29e7f87279 -https://conda.anaconda.org/conda-forge/win-64/openssl-3.3.0-hcfcfb64_0.conda#a6c544c9f060740c625dbf6d92cf3495 +https://conda.anaconda.org/conda-forge/win-64/openssl-3.3.0-h2466b09_2.conda#67d5f8749ee4ae3c4060d7a13fc8872f https://conda.anaconda.org/conda-forge/win-64/pthreads-win32-2.9.1-hfa6e2cd_3.tar.bz2#e2da8758d7d51ff6aa78a14dfb9dbed4 https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h5226925_1.conda#fc048363eb8f03cd1737600a5d08aafe https://conda.anaconda.org/conda-forge/win-64/xz-5.2.6-h8d14728_0.tar.bz2#515d77642eaa3639413c6b1bc3f94219 @@ -65,7 +65,7 @@ https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda#d3 https://conda.anaconda.org/conda-forge/noarch/ply-3.11-pyhd8ed1ab_2.conda#18c6deb6f9602e32446398203c8f0e91 https://conda.anaconda.org/conda-forge/win-64/pthread-stubs-0.4-hcd874cb_1001.tar.bz2#a1f820480193ea83582b13249a7e7bd9 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.2-pyhd8ed1ab_0.conda#b9a4dacf97241704529131a0dfc0494f -https://conda.anaconda.org/conda-forge/noarch/setuptools-69.5.1-pyhd8ed1ab_0.conda#7462280d81f639363e6e63c81276bd9e +https://conda.anaconda.org/conda-forge/noarch/setuptools-70.0.0-pyhd8ed1ab_0.conda#c8ddb4f34a208df4dd42509a0f6a1c89 https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_0.tar.bz2#f832c45a477c78bebd107098db465095 @@ -87,7 +87,7 @@ https://conda.anaconda.org/conda-forge/noarch/meson-1.4.0-pyhd8ed1ab_0.conda#52a https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.2-h3d672ee_0.conda#7e7099ad94ac3b599808950cec30ad4e https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 -https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4 +https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/win-64/sip-6.7.12-py39h99910a6_0.conda#0cc5774390ada632ed7975203057c91c https://conda.anaconda.org/conda-forge/win-64/tbb-2021.12.0-hc790b64_1.conda#e98333643abc739ebea1bac97a479828 diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml index 855909a2c262a..38737e7c9c0b0 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml +++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml @@ -14,7 +14,7 @@ dependencies: - matplotlib - pandas - pyamg - - pytest<8 + - pytest - pytest-xdist - pillow - pip diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock index d0f35d5f79808..48dd5ab3c3021 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: a64ed7d3cc839a12cb1faa238a89d4aec55abc43d335791f0e8422f5722ff662 +# input_hash: be96a61e1a8c45c41b04e6e30fafbbf78e5b05202b348a702c726eced7c7df48 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.2.2-hbcca054_0.conda#2f4327a1cbe7f022401b236e915a5fef @@ -46,7 +46,7 @@ https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-hd590300_0.conda#c0f3abb4a16477208bbd43a39bd56f18 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_2.conda#b8934d399b56d73e323403e183d009c5 https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a @@ -137,7 +137,7 @@ https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.2-pyhd8ed1ab_0.conda https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2#2a7de29fb590ca14b5243c4c812c8025 https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2024.1-pyhd8ed1ab_0.conda#98206ea9954216ee7540f0c773f2104d https://conda.anaconda.org/conda-forge/noarch/pytz-2024.1-pyhd8ed1ab_0.conda#3eeeeb9e4827ace8c0c1419c85d590ad -https://conda.anaconda.org/conda-forge/noarch/setuptools-69.5.1-pyhd8ed1ab_0.conda#7462280d81f639363e6e63c81276bd9e +https://conda.anaconda.org/conda-forge/noarch/setuptools-70.0.0-pyhd8ed1ab_0.conda#c8ddb4f34a208df4dd42509a0f6a1c89 https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2 https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-2.2.0-pyhd8ed1ab_0.tar.bz2#4d22a9315e78c6827f806065957d566e https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_0.conda#da1d979339e2714c30a8e806a33ec087 @@ -170,7 +170,7 @@ https://conda.anaconda.org/conda-forge/noarch/meson-1.4.0-pyhd8ed1ab_0.conda#52a https://conda.anaconda.org/conda-forge/linux-64/pillow-10.3.0-py39h90c7501_0.conda#1e3b6af9592be71ce19f0a6aae05d97b https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 -https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4 +https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.1-pyhd8ed1ab_0.conda#08807a87fa7af10754d46f63b368e016 @@ -183,7 +183,7 @@ https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.c https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py39h474f0d3_0.conda#aa265f5697237aa13cc10f53fa8acc4f https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5.conda#93aff412f3e49fdb43361c0215cbd72d https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b -https://conda.anaconda.org/conda-forge/noarch/requests-2.31.0-pyhd8ed1ab_0.conda#a30144e4156cdbb236f99ebb49828f8b +https://conda.anaconda.org/conda-forge/noarch/requests-2.32.2-pyhd8ed1ab_0.conda#e1643b34b19df8c028a4f00bf5df58a6 https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-22_linux64_openblas.conda#63ddb593595c9cf5eb08d3de54d66df8 https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py39h7633fee_0.conda#bdc188e59857d6efab332714e0d01d93 https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.3-h9ad1361_0.conda#8fb0e954c616bb0f9389efac4b4ed44b diff --git a/build_tools/azure/pypy3_environment.yml b/build_tools/azure/pypy3_environment.yml index 285f1b0d51d17..84784d5a90b20 100644 --- a/build_tools/azure/pypy3_environment.yml +++ b/build_tools/azure/pypy3_environment.yml @@ -14,7 +14,7 @@ dependencies: - threadpoolctl - matplotlib - pyamg - - pytest<8 + - pytest - pytest-xdist - pip - ninja diff --git a/build_tools/azure/pypy3_linux-64_conda.lock b/build_tools/azure/pypy3_linux-64_conda.lock index 520a4935c8af5..a47c89e5a7aab 100644 --- a/build_tools/azure/pypy3_linux-64_conda.lock +++ b/build_tools/azure/pypy3_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: c4b15c5bfeffe4d558e4ece0c996e6cc04c00369326c72d19780ffc0209bd591 +# input_hash: cb8a71fc5a5762d803c62e60f01aaf1788c4357c1233fd623cecb1225076b9b5 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.2.2-hbcca054_0.conda#2f4327a1cbe7f022401b236e915a5fef @@ -21,7 +21,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.co https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.13-hd590300_5.conda#f36c115f1ee199da648e0597ec2047ad https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-hd590300_0.conda#c0f3abb4a16477208bbd43a39bd56f18 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_2.conda#b8934d399b56d73e323403e183d009c5 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.11-hd590300_0.conda#2c80dc38fface310c9bd81b17037fee5 @@ -75,7 +75,7 @@ https://conda.anaconda.org/conda-forge/linux-64/pillow-10.3.0-py39h90a76f3_0.con https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda#d3483c8fc2dc2cc3f5cf43e26d60cabf https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.2-pyhd8ed1ab_0.conda#b9a4dacf97241704529131a0dfc0494f https://conda.anaconda.org/conda-forge/noarch/pypy-7.3.15-1_pypy39.conda#a418a6c16bd6f7ed56b92194214791a0 -https://conda.anaconda.org/conda-forge/noarch/setuptools-69.5.1-pyhd8ed1ab_0.conda#7462280d81f639363e6e63c81276bd9e +https://conda.anaconda.org/conda-forge/noarch/setuptools-70.0.0-pyhd8ed1ab_0.conda#c8ddb4f34a208df4dd42509a0f6a1c89 https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96 @@ -91,7 +91,7 @@ https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25 https://conda.anaconda.org/conda-forge/noarch/meson-1.4.0-pyhd8ed1ab_0.conda#52a0660cfa40b45bf254ecc3374cb2e0 https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 -https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4 +https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/scipy-1.12.0-py39h6dedee3_2.conda#6c5d74bac41838f4377dfd45085e1fec https://conda.anaconda.org/conda-forge/linux-64/blas-2.122-openblas.conda#5065468105542a8b23ea47bd8b6fa55f diff --git a/build_tools/azure/python_nogil_lock.txt b/build_tools/azure/python_nogil_lock.txt index 03cd4f2e0c346..7f67a48842dea 100644 --- a/build_tools/azure/python_nogil_lock.txt +++ b/build_tools/azure/python_nogil_lock.txt @@ -13,15 +13,15 @@ cycler==0.12.1 # via matplotlib cython==3.0.10 # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt -exceptiongroup==1.2.0 +exceptiongroup==1.2.1 # via pytest -execnet==2.0.2 +execnet==2.1.1 # via pytest-xdist -fonttools==4.50.0 +fonttools==4.51.0 # via matplotlib iniconfig==2.0.0 # via pytest -joblib==1.3.2 +joblib==1.4.2 # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt kiwisolver==1.4.4 # via matplotlib @@ -29,7 +29,7 @@ matplotlib==3.6.2 # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt meson==1.4.0 # via meson-python -meson-python==0.15.0 +meson-python==0.16.0 # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt ninja==1.11.1.1 # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt @@ -42,21 +42,22 @@ numpy==1.24.0 packaging==24.0 # via # matplotlib + # meson-python # pyproject-metadata # pytest pillow==9.5.0 # via matplotlib -pluggy==1.4.0 +pluggy==1.5.0 # via pytest pyparsing==3.1.2 # via matplotlib -pyproject-metadata==0.7.1 +pyproject-metadata==0.8.0 # via meson-python pytest==7.4.4 # via # -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt # pytest-xdist -pytest-xdist==3.5.0 +pytest-xdist==3.6.1 # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt python-dateutil==2.9.0.post0 # via matplotlib @@ -64,7 +65,7 @@ scipy==1.9.3 # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt six==1.16.0 # via python-dateutil -threadpoolctl==3.4.0 +threadpoolctl==3.5.0 # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt tomli==2.0.1 # via diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt index d1674c678b254..e66f5ca8943fd 100644 --- a/build_tools/azure/ubuntu_atlas_lock.txt +++ b/build_tools/azure/ubuntu_atlas_lock.txt @@ -29,7 +29,7 @@ pluggy==1.5.0 # via pytest pyproject-metadata==0.8.0 # via meson-python -pytest==7.4.4 +pytest==8.2.1 # via # -r build_tools/azure/ubuntu_atlas_requirements.txt # pytest-xdist diff --git a/build_tools/azure/ubuntu_atlas_requirements.txt b/build_tools/azure/ubuntu_atlas_requirements.txt index 805d84d4d0aac..dfb0cfebc54d1 100644 --- a/build_tools/azure/ubuntu_atlas_requirements.txt +++ b/build_tools/azure/ubuntu_atlas_requirements.txt @@ -4,7 +4,7 @@ cython==3.0.10 # min joblib==1.2.0 # min threadpoolctl==3.1.0 # min -pytest<8 +pytest pytest-xdist ninja meson-python diff --git a/build_tools/circle/doc_environment.yml b/build_tools/circle/doc_environment.yml index bc4405983a1b6..ea930fadcb528 100644 --- a/build_tools/circle/doc_environment.yml +++ b/build_tools/circle/doc_environment.yml @@ -14,7 +14,7 @@ dependencies: - matplotlib - pandas - pyamg - - pytest<8 + - pytest - pytest-xdist - pillow - pip diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index b959b3250c851..4ef53ff72bc3f 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: beab3d7262ec74c4ef8c9050098de8b9fe7910606e7bd4ff52687972bff35868 +# input_hash: 46e3ce252a769181cca2ee8df6e2bfd3f6749900d7863cc5a3b41c300daa0380 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.2.2-hbcca054_0.conda#2f4327a1cbe7f022401b236e915a5fef @@ -63,7 +63,7 @@ https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-hd590300_0.conda#c0f3abb4a16477208bbd43a39bd56f18 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_2.conda#b8934d399b56d73e323403e183d009c5 https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.6.6-he8a937b_2.conda#77d9955b4abddb811cb8ab1aa7d743e4 @@ -174,7 +174,7 @@ https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.2-pyhd8ed1ab_0.conda https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2#2a7de29fb590ca14b5243c4c812c8025 https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2024.1-pyhd8ed1ab_0.conda#98206ea9954216ee7540f0c773f2104d https://conda.anaconda.org/conda-forge/noarch/pytz-2024.1-pyhd8ed1ab_0.conda#3eeeeb9e4827ace8c0c1419c85d590ad -https://conda.anaconda.org/conda-forge/noarch/setuptools-69.5.1-pyhd8ed1ab_0.conda#7462280d81f639363e6e63c81276bd9e +https://conda.anaconda.org/conda-forge/noarch/setuptools-70.0.0-pyhd8ed1ab_0.conda#c8ddb4f34a208df4dd42509a0f6a1c89 https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2 https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-2.2.0-pyhd8ed1ab_0.tar.bz2#4d22a9315e78c6827f806065957d566e https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.5-pyhd8ed1ab_1.conda#3f144b2c34f8cb5a9abd9ed23a39c561 @@ -217,7 +217,7 @@ https://conda.anaconda.org/conda-forge/linux-64/pillow-10.3.0-py39h90c7501_0.con https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/plotly-5.22.0-pyhd8ed1ab_0.conda#5b409a5f738e7d76c2b426eddb7e9956 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 -https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4 +https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.11.0-hd8ed1ab_0.conda#471e3988f8ca5e9eb3ce6be7eac3bcee @@ -233,7 +233,7 @@ https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.c https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py39h474f0d3_0.conda#aa265f5697237aa13cc10f53fa8acc4f https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5.conda#93aff412f3e49fdb43361c0215cbd72d https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b -https://conda.anaconda.org/conda-forge/noarch/requests-2.31.0-pyhd8ed1ab_0.conda#a30144e4156cdbb236f99ebb49828f8b +https://conda.anaconda.org/conda-forge/noarch/requests-2.32.2-pyhd8ed1ab_0.conda#e1643b34b19df8c028a4f00bf5df58a6 https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-22_linux64_openblas.conda#63ddb593595c9cf5eb08d3de54d66df8 https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py39h7633fee_0.conda#bdc188e59857d6efab332714e0d01d93 https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.3-h9ad1361_0.conda#8fb0e954c616bb0f9389efac4b4ed44b diff --git a/build_tools/circle/doc_min_dependencies_environment.yml b/build_tools/circle/doc_min_dependencies_environment.yml index 8148ee330bb35..e4824024381f4 100644 --- a/build_tools/circle/doc_min_dependencies_environment.yml +++ b/build_tools/circle/doc_min_dependencies_environment.yml @@ -14,7 +14,7 @@ dependencies: - matplotlib=3.3.4 # min - pandas=1.1.5 # min - pyamg - - pytest<8 + - pytest - pytest-xdist - pillow - pip diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index f0d02542f4b98..7a5a65ba03d41 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 6c9ff93ed18fe7c2e8387a4c3d7104701555959a32e797c1cb83593137afe155 +# input_hash: b285d0ba8ec3865f6ac4f6f990060713f430995f71bf0b3a4c94a6610defc5b9 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.2.2-hbcca054_0.conda#2f4327a1cbe7f022401b236e915a5fef @@ -55,7 +55,7 @@ https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-hd590300_0.conda#c0f3abb4a16477208bbd43a39bd56f18 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_2.conda#b8934d399b56d73e323403e183d009c5 https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a @@ -196,7 +196,7 @@ https://conda.anaconda.org/conda-forge/linux-64/pillow-10.3.0-py39h90c7501_0.con https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/plotly-5.14.0-pyhd8ed1ab_0.conda#6a7bcc42ef58dd6cf3da9333ea102433 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 -https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4 +https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.11.0-hd8ed1ab_0.conda#471e3988f8ca5e9eb3ce6be7eac3bcee @@ -211,7 +211,7 @@ https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.c https://conda.anaconda.org/conda-forge/linux-64/mkl-devel-2024.1.0-ha770c72_692.conda#56142862a71bcfdd6ef2ce95c8e90755 https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5.conda#93aff412f3e49fdb43361c0215cbd72d https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b -https://conda.anaconda.org/conda-forge/noarch/requests-2.31.0-pyhd8ed1ab_0.conda#a30144e4156cdbb236f99ebb49828f8b +https://conda.anaconda.org/conda-forge/noarch/requests-2.32.2-pyhd8ed1ab_0.conda#e1643b34b19df8c028a4f00bf5df58a6 https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.5.1-pyhd8ed1ab_0.conda#d4f60ccc5421472d2583efd9ce39d8b1 https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.3-h9ad1361_0.conda#8fb0e954c616bb0f9389efac4b4ed44b https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-22_linux64_mkl.conda#d6f942423116553f068b2f2d93ffea2e diff --git a/build_tools/cirrus/pymin_conda_forge_environment.yml b/build_tools/cirrus/pymin_conda_forge_environment.yml index 684c4636daad4..e41cc7f610ac0 100644 --- a/build_tools/cirrus/pymin_conda_forge_environment.yml +++ b/build_tools/cirrus/pymin_conda_forge_environment.yml @@ -12,7 +12,7 @@ dependencies: - joblib - threadpoolctl - matplotlib - - pytest<8 + - pytest - pytest-xdist - pillow - pip diff --git a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock index 8db59cbb2c373..0c88b051afb82 100644 --- a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock +++ b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-aarch64 -# input_hash: 80459c6003cbcd22780a22a62ed5cc116e951d5c2c14602af1281434263b9138 +# input_hash: bd97deca0a3c9c77631f0ee5b9933ef1a3dd81165c7ad29467c0f9504350507e @EXPLICIT https://conda.anaconda.org/conda-forge/linux-aarch64/ca-certificates-2024.2.2-hcefe29a_0.conda#57c226edb90c4e973b9b7503537dd339 https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.40-hba4e955_0.conda#b55c1cb33c63d23b542fa53f24541e56 @@ -23,7 +23,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/libxcrypt-4.4.36-h31becfc_1 https://conda.anaconda.org/conda-forge/linux-aarch64/libzlib-1.2.13-h31becfc_5.conda#b213aa87eea9491ef7b129179322e955 https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.5-h0425590_0.conda#38362af7bfac0efef69675acee564458 https://conda.anaconda.org/conda-forge/linux-aarch64/ninja-1.12.1-h70be974_0.conda#216635cea46498d8045c7cf0f03eaf72 -https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.3.0-h31becfc_0.conda#36ca60a3afaf2ea2c460daeebd67430e +https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.3.0-h68df207_2.conda#052eb8fb4acf496390ae7c5f2fd71d69 https://conda.anaconda.org/conda-forge/linux-aarch64/pthread-stubs-0.4-hb9de7d4_1001.tar.bz2#d0183ec6ce0b5aaa3486df25fa5f0ded https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxau-1.0.11-h31becfc_0.conda#13de34f69cb73165dbe08c1e9148bedb https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxdmcp-1.1.3-h3557bc0_0.tar.bz2#a6c9016ae1ca5c47a3603ed4cd65fedd @@ -62,7 +62,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/openjpeg-2.5.2-h0d9d63b_0.c https://conda.anaconda.org/conda-forge/noarch/packaging-24.0-pyhd8ed1ab_0.conda#248f521b64ce055e7feae3105e7abeb8 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda#d3483c8fc2dc2cc3f5cf43e26d60cabf https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.2-pyhd8ed1ab_0.conda#b9a4dacf97241704529131a0dfc0494f -https://conda.anaconda.org/conda-forge/noarch/setuptools-69.5.1-pyhd8ed1ab_0.conda#7462280d81f639363e6e63c81276bd9e +https://conda.anaconda.org/conda-forge/noarch/setuptools-70.0.0-pyhd8ed1ab_0.conda#c8ddb4f34a208df4dd42509a0f6a1c89 https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96 @@ -79,7 +79,7 @@ https://conda.anaconda.org/conda-forge/noarch/meson-1.4.0-pyhd8ed1ab_0.conda#52a https://conda.anaconda.org/conda-forge/linux-aarch64/pillow-10.3.0-py39h71661b1_0.conda#dae548b7b537d7ef796d1d4c38a55319 https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 -https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4 +https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.0-pyhd8ed1ab_0.conda#dcbadab7a68738a028e195ab68ab2d2e https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-22_linuxaarch64_openblas.conda#5acf669e0be669f30f4b813d2ecda7b8 diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index bf086e21716e3..ce457cabb1e53 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -82,12 +82,7 @@ docstring_test_dependencies = ["sphinx", "numpydoc"] -default_package_constraints = { - # TODO: somehow pytest 8 does not seem to work with meson editable - # install. Exit code is 5, i.e. no test collected - # This would be fixed by https://github.com/mesonbuild/meson-python/pull/569 - "pytest": "<8", -} +default_package_constraints = {} def remove_from(alist, to_remove): @@ -142,7 +137,7 @@ def remove_from(alist, to_remove): "platform": "osx-64", "channel": "defaults", "conda_dependencies": remove_from( - common_dependencies, ["cython", "threadpoolctl"] + common_dependencies, ["cython", "threadpoolctl", "meson-python"] ) + ["ccache"], "package_constraints": { @@ -152,9 +147,9 @@ def remove_from(alist, to_remove): # channel. "scipy": "<1.12", }, - # TODO: put cython and threadpoolctl back to conda dependencies when required - # version is available on the main channel - "pip_dependencies": ["cython", "threadpoolctl"], + # TODO: put cython, threadpoolctl and meson-python back to conda + # dependencies when required version is available on the main channel + "pip_dependencies": ["cython", "threadpoolctl", "meson-python"], }, { "name": "pymin_conda_defaults_openblas", diff --git a/pyproject.toml b/pyproject.toml index f54cdb703b7e3..224b4ac5bad20 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -44,7 +44,7 @@ tracker = "https://github.com/scikit-learn/scikit-learn/issues" "release notes" = "https://scikit-learn.org/stable/whats_new" [project.optional-dependencies] -build = ["numpy>=1.19.5", "scipy>=1.6.0", "cython>=3.0.10", "meson-python>=0.15.0"] +build = ["numpy>=1.19.5", "scipy>=1.6.0", "cython>=3.0.10", "meson-python>=0.16.0"] install = ["numpy>=1.19.5", "scipy>=1.6.0", "joblib>=1.2.0", "threadpoolctl>=3.1.0"] benchmark = ["matplotlib>=3.3.4", "pandas>=1.1.5", "memory_profiler>=0.57.0"] docs = [ @@ -97,7 +97,7 @@ maintenance = ["conda-lock==2.5.6"] build-backend = "mesonpy" # Minimum requirements for the build system to execute. requires = [ - "meson-python>=0.15.0", + "meson-python>=0.16.0", "Cython>=3.0.10", "numpy>=2.0.0rc2", "scipy>=1.6.0", diff --git a/sklearn/_min_dependencies.py b/sklearn/_min_dependencies.py index b066c22145263..05899cac96faf 100644 --- a/sklearn/_min_dependencies.py +++ b/sklearn/_min_dependencies.py @@ -21,7 +21,7 @@ "joblib": (JOBLIB_MIN_VERSION, "install"), "threadpoolctl": (THREADPOOLCTL_MIN_VERSION, "install"), "cython": (CYTHON_MIN_VERSION, "build"), - "meson-python": ("0.15.0", "build"), + "meson-python": ("0.16.0", "build"), "matplotlib": ("3.3.4", "benchmark, docs, examples, tests"), "scikit-image": ("0.17.2", "docs, examples, tests"), "pandas": ("1.1.5", "benchmark, docs, examples, tests"), diff --git a/sklearn/linear_model/tests/test_logistic.py b/sklearn/linear_model/tests/test_logistic.py index daa6f5114ebcc..f47b06a0cc4b1 100644 --- a/sklearn/linear_model/tests/test_logistic.py +++ b/sklearn/linear_model/tests/test_logistic.py @@ -1139,6 +1139,8 @@ def test_logreg_predict_proba_multinomial(): assert clf_wrong_loss > clf_multi_loss +# TODO(1.7): remove filterwarnings after the deprecation of multi_class +@pytest.mark.filterwarnings("ignore:.*'multi_class' was deprecated.*:FutureWarning") @pytest.mark.parametrize("max_iter", np.arange(1, 5)) @pytest.mark.parametrize("multi_class", ["ovr", "multinomial"]) @pytest.mark.parametrize( diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py index 9ff83953f4b0e..1b3b5b0128057 100644 --- a/sklearn/tests/test_common.py +++ b/sklearn/tests/test_common.py @@ -257,13 +257,6 @@ def test_root_import_all_completeness(): assert modname in sklearn.__all__ -@pytest.mark.skipif( - sklearn._BUILT_WITH_MESON, - reason=( - "This test fails with Meson editable installs see" - " https://github.com/mesonbuild/meson-python/issues/557 for more details" - ), -) def test_all_tests_are_importable(): # Ensure that for each contentful subpackage, there is a test directory # within it that is also a subpackage (i.e. a directory with __init__.py) From ac856c69955b7c3d3b44656c3bd77096d93d032a Mon Sep 17 00:00:00 2001 From: Priyank Shroff Date: Sat, 25 May 2024 07:27:34 -0400 Subject: [PATCH 061/275] DOC add link to gradient_boosting_regression plot in _gp.py (#29058) --- sklearn/ensemble/_gb.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/sklearn/ensemble/_gb.py b/sklearn/ensemble/_gb.py index 960e469a090cd..18d89b21f5512 100644 --- a/sklearn/ensemble/_gb.py +++ b/sklearn/ensemble/_gb.py @@ -2032,6 +2032,11 @@ class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting): array([-61...]) >>> reg.score(X_test, y_test) 0.4... + + For a detailed example of utilizing + :class:`~sklearn.ensemble.GradientBoostingRegressor` + to fit an ensemble of weak predictive models, please refer to + :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py`. """ _parameter_constraints: dict = { From f19a708dd456107a77c3fed0f5bc290ffcca18d7 Mon Sep 17 00:00:00 2001 From: Julian Chan <78489417+julian-8897@users.noreply.github.com> Date: Sat, 25 May 2024 12:35:43 +0100 Subject: [PATCH 062/275] DOC Add link to AgglomerativeClustering example in docs (#28922) Co-authored-by: Julian Chan --- sklearn/cluster/_agglomerative.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/sklearn/cluster/_agglomerative.py b/sklearn/cluster/_agglomerative.py index e5ba5f6efed61..d8cf1002dfcfe 100644 --- a/sklearn/cluster/_agglomerative.py +++ b/sklearn/cluster/_agglomerative.py @@ -818,6 +818,10 @@ class AgglomerativeClustering(ClusterMixin, BaseEstimator): `kneighbors_graph`. Default is ``None``, i.e, the hierarchical clustering algorithm is unstructured. + For an example of connectivity matrix using + :class:`~sklearn.neighbors.kneighbors_graph`, see + :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_clustering.py`. + compute_full_tree : 'auto' or bool, default='auto' Stop early the construction of the tree at ``n_clusters``. This is useful to decrease computation time if the number of clusters is not From 5814d12e96a971e615009492f3dca3b014d38614 Mon Sep 17 00:00:00 2001 From: Priyansh Gupta <49022779+priyansh16@users.noreply.github.com> Date: Sat, 25 May 2024 14:46:35 +0200 Subject: [PATCH 063/275] DOC Added link to plot_adaboost_regression.py example (#28997) Co-authored-by: Adrin Jalali --- sklearn/ensemble/_weight_boosting.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/sklearn/ensemble/_weight_boosting.py b/sklearn/ensemble/_weight_boosting.py index 6bbac0613de71..5af3c22bde52c 100644 --- a/sklearn/ensemble/_weight_boosting.py +++ b/sklearn/ensemble/_weight_boosting.py @@ -1080,6 +1080,10 @@ class AdaBoostRegressor(_RoutingNotSupportedMixin, RegressorMixin, BaseWeightBoo array([4.7972...]) >>> regr.score(X, y) 0.9771... + + For a detailed example of utilizing :class:`~sklearn.ensemble.AdaBoostRegressor` + to fit a sequence of decision trees as weak learners, please refer to + :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_regression.py`. """ _parameter_constraints: dict = { From 5ace424bac6f573dd6df579ff1521423d1fa5e4e Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 27 May 2024 10:06:25 +0200 Subject: [PATCH 064/275] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#29115) Co-authored-by: Lock file bot --- .../azure/pylatest_pip_scipy_dev_linux-64_conda.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index f8cc75936e789..370dd97916518 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 893691ececb72bbf81101535204fa5694d898312941037a946e81dcb5cc2c322 +# input_hash: 8a4a203136d97ff3b2c8657fce2dd2228215bfbf9c1cfbe271e401f934bdf1a7 @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2024.3.11-h06a4308_0.conda#08529eb3504712baabcbda266a19feb7 @@ -15,7 +15,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/expat-2.6.2-h6a678d5_0.conda#55049d https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_1.conda#70646cc713f0c43926cfdcfe9b695fe0 https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.41.5-h5eee18b_0.conda#4a6a2354414c9080327274aa514e5299 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c -https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.13-h7f8727e_1.conda#d1d1fc47640fe0d9f7fa64c0a054bfd8 +https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.13-h7f8727e_2.conda#0019453c25f5e72129f99236e60febaa https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_1.conda#1562802f843297ee776a50b9329597ed https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e @@ -30,7 +30,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py312h06a4308_0.conda#6d96 # pip babel @ https://files.pythonhosted.org/packages/27/45/377f7e32a5c93d94cd56542349b34efab5ca3f9e2fd5a68c5e93169aa32d/Babel-2.15.0-py3-none-any.whl#sha256=08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb # pip certifi @ https://files.pythonhosted.org/packages/ba/06/a07f096c664aeb9f01624f858c3add0a4e913d6c96257acb4fce61e7de14/certifi-2024.2.2-py3-none-any.whl#sha256=dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1 # pip charset-normalizer @ https://files.pythonhosted.org/packages/ee/fb/14d30eb4956408ee3ae09ad34299131fb383c47df355ddb428a7331cfa1e/charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b -# pip coverage @ https://files.pythonhosted.org/packages/3f/4f/fcad903698f02ac0d7501432449db12e15fbe5ecfbc01e363eb752c65cbd/coverage-7.5.1-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=8748731ad392d736cc9ccac03c9845b13bb07d020a33423fa5b3a36521ac6e4e +# pip coverage @ https://files.pythonhosted.org/packages/36/2e/f52bd5573fdf4b527ed0255ec8efc5fae77f5d0b086c41abae6eb175b426/coverage-7.5.2-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=3bb5b92a0ab3d22dfdbfe845e2fef92717b067bdf41a5b68c7e3e857c0cff1a4 # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc # pip idna @ https://files.pythonhosted.org/packages/e5/3e/741d8c82801c347547f8a2a06aa57dbb1992be9e948df2ea0eda2c8b79e8/idna-3.7-py3-none-any.whl#sha256=82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0 From 2485ecf09d87cfed7c99504ed9af1c0db93c3a04 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 27 May 2024 10:07:14 +0200 Subject: [PATCH 065/275] :lock: :robot: CI Update lock files for cirrus-arm CI build(s) :lock: :robot: (#29114) Co-authored-by: Lock file bot --- .../pymin_conda_forge_linux-aarch64_conda.lock | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock index 0c88b051afb82..5d0c005fbdb17 100644 --- a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock +++ b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock @@ -1,9 +1,9 @@ # Generated by conda-lock. # platform: linux-aarch64 -# input_hash: bd97deca0a3c9c77631f0ee5b9933ef1a3dd81165c7ad29467c0f9504350507e +# input_hash: 2d8c526ab7c0c2f0ca509bfec3f035e5bd33b8096f194f0747f167c8aff66383 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-aarch64/ca-certificates-2024.2.2-hcefe29a_0.conda#57c226edb90c4e973b9b7503537dd339 -https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.40-hba4e955_0.conda#b55c1cb33c63d23b542fa53f24541e56 +https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.40-h9fc2d93_1.conda#8a90b17a5d12ba55b7d90534f38579d0 https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-ng-13.2.0-h3f4de04_7.conda#2a54872c7fab2db99b0074212d8efe64 https://conda.anaconda.org/conda-forge/linux-aarch64/python_abi-3.9-4_cp39.conda#c191905a08694e4a5cb1238e90233878 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 @@ -23,7 +23,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/libxcrypt-4.4.36-h31becfc_1 https://conda.anaconda.org/conda-forge/linux-aarch64/libzlib-1.2.13-h31becfc_5.conda#b213aa87eea9491ef7b129179322e955 https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.5-h0425590_0.conda#38362af7bfac0efef69675acee564458 https://conda.anaconda.org/conda-forge/linux-aarch64/ninja-1.12.1-h70be974_0.conda#216635cea46498d8045c7cf0f03eaf72 -https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.3.0-h68df207_2.conda#052eb8fb4acf496390ae7c5f2fd71d69 +https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.3.0-h68df207_3.conda#f2dec4814ac9649cd3d3e474cd0b9a58 https://conda.anaconda.org/conda-forge/linux-aarch64/pthread-stubs-0.4-hb9de7d4_1001.tar.bz2#d0183ec6ce0b5aaa3486df25fa5f0ded https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxau-1.0.11-h31becfc_0.conda#13de34f69cb73165dbe08c1e9148bedb https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxdmcp-1.1.3-h3557bc0_0.tar.bz2#a6c9016ae1ca5c47a3603ed4cd65fedd @@ -42,7 +42,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/freetype-2.12.1-hf0a5ef3_2. https://conda.anaconda.org/conda-forge/linux-aarch64/libhiredis-1.0.2-h05efe27_0.tar.bz2#a87f068744fd20334cd41489eb163bee https://conda.anaconda.org/conda-forge/linux-aarch64/libopenblas-0.3.27-pthreads_h5a5ec62_0.conda#ffecca8f4f31cd50b92c0e6e6bfe4416 https://conda.anaconda.org/conda-forge/linux-aarch64/libtiff-4.6.0-hf980d43_3.conda#b6f3abf5726ae33094bee238b4eb492f -https://conda.anaconda.org/conda-forge/linux-aarch64/llvm-openmp-18.1.5-h767c9be_0.conda#a9c2771c36671707f1992e4d0c32aa54 +https://conda.anaconda.org/conda-forge/linux-aarch64/llvm-openmp-18.1.6-h767c9be_0.conda#67c8fa683c39feeae5d0e915d79fb061 https://conda.anaconda.org/conda-forge/linux-aarch64/python-3.9.19-h4ac3b42_0_cpython.conda#1501507cd9451472ec8900d587ce872f https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-1.1.0-h31becfc_1.conda#e41f5862ac746428407f3fd44d2ed01f https://conda.anaconda.org/conda-forge/linux-aarch64/ccache-4.9.1-h6552966_0.conda#758b202f61f6bbfd2c6adf0fde043276 @@ -70,7 +70,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/tornado-6.4-py39h7cc1d5f_0. https://conda.anaconda.org/conda-forge/linux-aarch64/unicodedata2-15.1.0-py39h898b7ef_0.conda#8c072c9329aeea97a46005625267a851 https://conda.anaconda.org/conda-forge/noarch/wheel-0.43.0-pyhd8ed1ab_1.conda#0b5293a157c2b5cd513dd1b03d8d3aae https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda#2e4d6bc0b14e10f895fc6791a7d9b26a -https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.51.0-py39h898b7ef_0.conda#7b6a069c66a729454fb4c534ed145dcd +https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.52.1-py39he257ee7_0.conda#06188260ef5320c7d6eb4f1467224bb0 https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1ab_0.conda#c5d3907ad8bd7bf557521a1833cf7e6d https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-22_linuxaarch64_openblas.conda#fbe7fe553f2cc78a0311e009b26f180d @@ -88,7 +88,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-1.26.4-py39h91c28bb_0 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-22_linuxaarch64_openblas.conda#a5b77b6c6807661afd716f33e85814b3 https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.2.1-py39hd16970a_0.conda#66b9718539ecdd38876b0176c315bcad -https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.13.0-py39hb921187_1.conda#2717303c0d13a5646308b3763bf4daa4 +https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.13.1-py39hb921187_0.conda#1aac9080de661e03d286f18fb71e5240 https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.122-openblas.conda#65bc48b3bc85f8eeeab54311443a83aa https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.8.4-py39hf44f4b6_2.conda#fadf734d38ed608c9f0b5c91fe79cfb4 https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-3.8.4-py39ha65689a_2.conda#c0472e3c4b3f007de6d643317c30963b From 3a92f9d57e0d9359835168e25c5cfd4157f5de58 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Morais?= <15629444+ojoaomorais@users.noreply.github.com> Date: Mon, 27 May 2024 05:31:12 -0300 Subject: [PATCH 066/275] DOC Removing warnings from plot_iris_dtc (#29109) --- examples/tree/plot_iris_dtc.py | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/tree/plot_iris_dtc.py b/examples/tree/plot_iris_dtc.py index 4c54a4119ced3..61c3bec3460ed 100644 --- a/examples/tree/plot_iris_dtc.py +++ b/examples/tree/plot_iris_dtc.py @@ -66,7 +66,6 @@ X[idx, 1], c=color, label=iris.target_names[i], - cmap=plt.cm.RdYlBu, edgecolor="black", s=15, ) From d5c94b38cd556626568008e1ce1ced5b0cabdcd8 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 27 May 2024 11:31:58 +0200 Subject: [PATCH 067/275] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29116) Co-authored-by: Lock file bot --- build_tools/azure/debian_atlas_32bit_lock.txt | 2 +- ...latest_conda_forge_mkl_linux-64_conda.lock | 22 +++++++------- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 20 ++++++------- ...test_conda_mkl_no_openmp_osx-64_conda.lock | 4 +-- ...st_pip_openblas_pandas_linux-64_conda.lock | 14 ++++----- ...onda_defaults_openblas_linux-64_conda.lock | 6 ++-- .../pymin_conda_forge_mkl_win-64_conda.lock | 14 ++++----- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 16 +++++----- build_tools/circle/doc_linux-64_conda.lock | 30 +++++++++---------- .../doc_min_dependencies_linux-64_conda.lock | 22 +++++++------- 10 files changed, 75 insertions(+), 75 deletions(-) diff --git a/build_tools/azure/debian_atlas_32bit_lock.txt b/build_tools/azure/debian_atlas_32bit_lock.txt index 7971e64b72560..92b4b864fb9f5 100644 --- a/build_tools/azure/debian_atlas_32bit_lock.txt +++ b/build_tools/azure/debian_atlas_32bit_lock.txt @@ -6,7 +6,7 @@ # attrs==23.2.0 # via pytest -coverage==7.5.1 +coverage==7.5.2 # via pytest-cov cython==3.0.10 # via -r build_tools/azure/debian_atlas_32bit_requirements.txt diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index e59404401fb68..bf4b0087c662b 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 29e3d76c436ee8cb86d5976aa41ef8822f991c469202b8d12fc27e43576ec805 +# input_hash: 50fed47bc507d9ee3dbf5ff7a2247cb88944928bd5797e534ebdf8ece2d858ec @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.2.2-hbcca054_0.conda#2f4327a1cbe7f022401b236e915a5fef @@ -8,7 +8,7 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f -https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h55db66e_0.conda#10569984e7db886e4f1abc2b47ad79a1 +https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_1.conda#33b7851c39c25da14f6a233a8ccbeeca https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-hc0a3c3a_7.conda#53ebd4c833fa01cb2c6353e99f905406 https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-4_cp311.conda#d786502c97404c94d7d58d258a445a65 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 @@ -54,7 +54,7 @@ https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_2.conda#b8934d399b56d73e323403e183d009c5 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_3.conda#12ea6d0d4ed54530eaed18e4835c1f7c https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/rdma-core-28.9-h59595ed_1.conda#aeffb7c06b5f65e55e6c637408dc4100 @@ -111,10 +111,10 @@ https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.54.3-hb20ce57_0.conda# https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.10.0-default_h5622ce7_1001.conda#fc2d5b79c2d3f8568fbab31db7ae02f3 https://conda.anaconda.org/conda-forge/linux-64/libllvm15-15.0.7-hb3ce162_4.conda#8a35df3cbc0c8b12cc8af9473ae75eef -https://conda.anaconda.org/conda-forge/linux-64/libllvm18-18.1.5-hb77312f_0.conda#efd221d3668077ca067a206269418dec +https://conda.anaconda.org/conda-forge/linux-64/libllvm18-18.1.6-hb77312f_0.conda#1246fc4b9f4db452e69cc297967d4b3e https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.18.1-h8fd135c_2.conda#bbf65f7688512872f063810623b755dc https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-h1dd3fc0_3.conda#66f03896ffbe1a110ffda05c7a856504 -https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.5-ha31de31_0.conda#b923cdb6e567ada84f991ffcc5848afb +https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.6-ha31de31_0.conda#8e9ad283cf953ebb4e6d1db9633b8344 https://conda.anaconda.org/conda-forge/linux-64/mysql-libs-8.3.0-hca2cd23_4.conda#1b50eebe2a738a3146c154d2eceaa8b6 https://conda.anaconda.org/conda-forge/linux-64/nss-3.100-hca3bf56_0.conda#949c4a82290ee58b3c970cef4bcfd4ad https://conda.anaconda.org/conda-forge/linux-64/orc-1.9.0-h2f23424_1.conda#9571eb3eb0f7fe8b59956a7786babbcd @@ -124,7 +124,7 @@ https://conda.anaconda.org/conda-forge/linux-64/xcb-util-keysyms-0.4.0-h8ee46fc_ https://conda.anaconda.org/conda-forge/linux-64/xcb-util-renderutil-0.3.9-hd590300_1.conda#e995b155d938b6779da6ace6c6b13816 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-wm-0.4.1-h8ee46fc_1.conda#90108a432fb5c6150ccfee3f03388656 https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.9-h8ee46fc_0.conda#077b6e8ad6a3ddb741fce2496dd01bec -https://conda.anaconda.org/conda-forge/noarch/array-api-compat-1.6-pyhd8ed1ab_0.conda#f04c36d7284243a7d982b4ef4982eb23 +https://conda.anaconda.org/conda-forge/noarch/array-api-compat-1.7-pyhd8ed1ab_0.conda#c359e5b3182a7147e29f9e29ebad7cdd https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.3.1-h2e3709c_4.conda#2cf21b1cbc1c096a28ffa2892257a2c1 https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.7.11-h00aa349_4.conda#cb932dff7328ff620ce8059c9968b095 https://conda.anaconda.org/conda-forge/linux-64/brotli-1.0.9-h166bdaf_9.conda#4601544b4982ba1861fa9b9c607b2c06 @@ -142,7 +142,7 @@ https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.5-py311h9547e67_1.conda#2c65bdf442b0d37aad080c8a4e0d452f https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.16-hb7c19ff_0.conda#51bb7010fc86f70eee639b4bb7a894f5 https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp15-15.0.7-default_h127d8a8_5.conda#d0a9633b53cdc319b8a1a532ae7822b8 -https://conda.anaconda.org/conda-forge/linux-64/libclang13-18.1.5-default_h5d6823c_0.conda#60c39a00b694c98da03f67a3ba1d7499 +https://conda.anaconda.org/conda-forge/linux-64/libclang13-18.1.6-default_h5d6823c_0.conda#fbe666f653068958eb27f549cb12f202 https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-h4637d8d_4.conda#d4529f4dff3057982a7617c7ac58fde3 https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.8.0-hca28451_0.conda#f21c27f076a07907e70c49bb57bd0f20 https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda#ee48bf17cc83a00f59ca1494d5646869 @@ -172,8 +172,8 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.11-hd590300_ https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.7.3-h28f7589_1.conda#97503d3e565004697f1651753aa95b9e https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.9.3-hb447be9_1.conda#c520669eb0be9269a5f0d8ef62531882 https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-h3faef2a_0.conda#f907bb958910dc404647326ca80c263e -https://conda.anaconda.org/conda-forge/linux-64/coverage-7.5.1-py311h331c9d8_0.conda#9f35e13e3b9e05e153b78f42662061f6 -https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.51.0-py311h459d7ec_0.conda#17e1997cc17c571d5ad27bd0159f616c +https://conda.anaconda.org/conda-forge/linux-64/coverage-7.5.2-py311h331c9d8_0.conda#6afe87fd0c278ed708393cc1cf085146 +https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.52.1-py311h331c9d8_0.conda#bb70cda5777de08084a402a2cdc13935 https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.2-hf974151_0.conda#d427988dc3dbd0a4c136f52db356cc6a https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-1.10.3-hd590300_0.conda#32d16ad533c59bb0a3c5ffaf16110829 @@ -210,10 +210,10 @@ https://conda.anaconda.org/conda-forge/noarch/array-api-strict-1.1.1-pyhd8ed1ab_ https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py311h9547e67_0.conda#74ad0ae64f1ef565e27eda87fa749e84 https://conda.anaconda.org/conda-forge/linux-64/libarrow-12.0.1-hb87d912_8_cpu.conda#3f3b11398fe79b578e3c44dd00a44e4a https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.2-py311h14de704_1.conda#84e2dd379d4edec4dd6382861486104d -https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.26-py311h00856b1_1.conda#4d1715777d0485d6e455b64f72794aa4 +https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.29-py311h00856b1_0.conda#f231b6f51b2154ac92fe26b874dafca2 https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.9-py311hf0fb5b6_5.conda#ec7e45bc76d9d0b69a74a2075932b8e8 https://conda.anaconda.org/conda-forge/linux-64/pytorch-1.13.1-cpu_py311h410fd25_1.conda#ddd2fadddf89e3dc3d541a2537fce010 -https://conda.anaconda.org/conda-forge/linux-64/scipy-1.13.0-py311h517d4fd_1.conda#a86b8bea39e292a23b2cf9a750f49ea1 +https://conda.anaconda.org/conda-forge/linux-64/scipy-1.13.1-py311h517d4fd_0.conda#764b0e055f59dbd7d114d32b8c6e55e6 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.8.4-py311ha4ca890_2.conda#0848e2084cbb57014f232f48568561af https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.1.0-py311h5510f57_1.conda#734865cccfb0a27b433ea31bd178d0e3 https://conda.anaconda.org/conda-forge/linux-64/pyarrow-12.0.1-py311h39c9aba_8_cpu.conda#587370a25bb2c50cce90909ce20d38b8 diff --git a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock index 827eaabd3aaac..833ce559e02b9 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: osx-64 -# input_hash: 134a56cb648e4588930416968ccdd27a7b83285f04712b032c6d07291b51b526 +# input_hash: e7c2bc2b07721ef735f30d3b1cf0b2a780b5bf5c138d9d18ad174611bfbd32bf @EXPLICIT https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.8-h10d778d_5.conda#6097a6ca9ada32699b5fc4312dd6ef18 https://conda.anaconda.org/conda-forge/osx-64/ca-certificates-2024.2.2-h8857fd0_0.conda#f2eacee8c33c43692f1ccfd33d0f50b1 @@ -29,8 +29,8 @@ https://conda.anaconda.org/conda-forge/osx-64/libpng-1.6.43-h92b6c6a_0.conda#65d https://conda.anaconda.org/conda-forge/osx-64/libsqlite-3.45.3-h92b6c6a_0.conda#68e462226209f35182ef66eda0f794ff https://conda.anaconda.org/conda-forge/osx-64/libxcb-1.15-hb7f2c08_0.conda#5513f57e0238c87c12dffedbcc9c1a4a https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.12.7-h3e169fe_0.conda#4c04ba47fdd2ebecc1d3b6a77534d9ef -https://conda.anaconda.org/conda-forge/osx-64/llvm-openmp-18.1.5-h39e0ece_0.conda#ee12a644568269838b91f901b2537425 -https://conda.anaconda.org/conda-forge/osx-64/openssl-3.3.0-h87427d6_2.conda#fa4859f58b1810d77089b0482c886da0 +https://conda.anaconda.org/conda-forge/osx-64/llvm-openmp-18.1.6-h15ab845_0.conda#065f974bc7afcef3f94df56394e16154 +https://conda.anaconda.org/conda-forge/osx-64/openssl-3.3.0-h87427d6_3.conda#ec504fefb403644d893adffb6e7a2dbe https://conda.anaconda.org/conda-forge/osx-64/readline-8.2-h9e318b2_1.conda#f17f77f2acf4d344734bda76829ce14e https://conda.anaconda.org/conda-forge/osx-64/tk-8.6.13-h1abcd95_1.conda#bf830ba5afc507c6232d4ef0fb1a882d https://conda.anaconda.org/conda-forge/osx-64/zlib-1.2.13-h8a1eda9_5.conda#75a8a98b1c4671c5d2897975731da42d @@ -78,8 +78,8 @@ https://conda.anaconda.org/conda-forge/osx-64/tornado-6.4-py312h41838bb_0.conda# https://conda.anaconda.org/conda-forge/noarch/wheel-0.43.0-pyhd8ed1ab_1.conda#0b5293a157c2b5cd513dd1b03d8d3aae https://conda.anaconda.org/conda-forge/osx-64/cctools_osx-64-986-ha1c5b94_0.conda#a8951de2506df5649f5a3295fdfd9f2c https://conda.anaconda.org/conda-forge/osx-64/clang-16-16.0.6-default_h7151d67_6.conda#1c298568c30efe7d9369c7c15b748461 -https://conda.anaconda.org/conda-forge/osx-64/coverage-7.5.1-py312h520dd33_0.conda#afc8c7b237683760a3c35e49bcc04deb -https://conda.anaconda.org/conda-forge/osx-64/fonttools-4.51.0-py312h41838bb_0.conda#ebe40134b860cf704ddaf81f684f95a5 +https://conda.anaconda.org/conda-forge/osx-64/coverage-7.5.2-py312hbd25219_0.conda#7af2cf0238c403ef3f92abdcda6151bf +https://conda.anaconda.org/conda-forge/osx-64/fonttools-4.52.1-py312hbd25219_0.conda#e830ecb59e43bcaabc98fa945e1985a3 https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/osx-64/lcms2-2.16-ha2f27b4_0.conda#1442db8f03517834843666c422238c9b https://conda.anaconda.org/conda-forge/osx-64/ld64-711-ha02d983_0.conda#3ae4930ec076735cce481e906f5192e0 @@ -112,17 +112,17 @@ https://conda.anaconda.org/conda-forge/osx-64/blas-devel-3.9.0-20_osx64_mkl.cond https://conda.anaconda.org/conda-forge/osx-64/compiler-rt-16.0.6-ha38d28d_2.conda#3b9e8c5c63b8e86234f499490acd85c2 https://conda.anaconda.org/conda-forge/osx-64/contourpy-1.2.1-py312h9230928_0.conda#079df34ce7c71259cfdd394645370891 https://conda.anaconda.org/conda-forge/osx-64/pandas-2.2.2-py312h1171441_1.conda#240737937f1f046b0e03ecc11ac4ec98 -https://conda.anaconda.org/conda-forge/osx-64/scipy-1.13.0-py312h741d2f9_1.conda#c416453a8ea3b38d823fe8dcecdb6a12 +https://conda.anaconda.org/conda-forge/osx-64/scipy-1.13.1-py312hb9702fa_0.conda#46cb49e67c33f8340a09e49e69adf195 https://conda.anaconda.org/conda-forge/osx-64/blas-2.120-mkl.conda#b041a7677a412f3d925d8208936cb1e2 -https://conda.anaconda.org/conda-forge/osx-64/clang_impl_osx-64-16.0.6-h8787910_14.conda#fc1a7d3f1bf236f63c58bab6e36844cb +https://conda.anaconda.org/conda-forge/osx-64/clang_impl_osx-64-16.0.6-h8787910_15.conda#4604a48f32935cd5d6e6a48e818b7eb0 https://conda.anaconda.org/conda-forge/osx-64/matplotlib-base-3.8.4-py312hb6d62fa_2.conda#6c5cf505d118f4b58961191fd5e0d030 https://conda.anaconda.org/conda-forge/osx-64/pyamg-5.1.0-py312h44e70fa_1.conda#ffbfe3b3d5e9675541ee516badfb7729 -https://conda.anaconda.org/conda-forge/osx-64/clang_osx-64-16.0.6-hb91bd55_14.conda#3d0d9c725912bb0cb4cd301d2a5d31d7 +https://conda.anaconda.org/conda-forge/osx-64/clang_osx-64-16.0.6-hb91bd55_15.conda#dc667dff0fefcb19ab8318e9518011f3 https://conda.anaconda.org/conda-forge/osx-64/matplotlib-3.8.4-py312hb401068_2.conda#456c057a3e2dcac3d02f4b9d25e277f5 https://conda.anaconda.org/conda-forge/osx-64/c-compiler-1.7.0-h282daa2_1.conda#d27411cb82bc1b76b9f487da6ae97f1d -https://conda.anaconda.org/conda-forge/osx-64/clangxx_impl_osx-64-16.0.6-h6d92fbe_14.conda#66b9f06d5f0d0ea47ffcb3a9ca65774a +https://conda.anaconda.org/conda-forge/osx-64/clangxx_impl_osx-64-16.0.6-h6d92fbe_15.conda#0e9dfa5ea554d23a3ad1f373470f84a2 https://conda.anaconda.org/conda-forge/osx-64/gfortran_osx-64-12.3.0-h18f7dce_1.conda#436af2384c47aedb94af78a128e174f1 -https://conda.anaconda.org/conda-forge/osx-64/clangxx_osx-64-16.0.6-hb91bd55_14.conda#a4504c1a7beab8875d6f765941e77248 +https://conda.anaconda.org/conda-forge/osx-64/clangxx_osx-64-16.0.6-hb91bd55_15.conda#3d3b8b7b99770601759ef8bc23264df2 https://conda.anaconda.org/conda-forge/osx-64/gfortran-12.3.0-h2c809b3_1.conda#c48adbaa8944234b80ef287c37e329b0 https://conda.anaconda.org/conda-forge/osx-64/cxx-compiler-1.7.0-h7728843_1.conda#e04cb15a20553b973dd068c2dc81d682 https://conda.anaconda.org/conda-forge/osx-64/fortran-compiler-1.7.0-h6c2ab21_1.conda#48319058089f492d5059e04494b81ed9 diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock index 418cf7a32a350..956622fe0008e 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: osx-64 -# input_hash: 197b213ba4980b3e95dcd438da09eb9b55e4bf5a4d8e6d3ddab29275aa22556a +# input_hash: 33a102d2ccde4e14e315a98b50340349af349f802403dd49589375b2b889f2d3 @EXPLICIT https://repo.anaconda.com/pkgs/main/osx-64/blas-1.0-mkl.conda#cb2c87e85ac8e0ceae776d26d4214c8a https://repo.anaconda.com/pkgs/main/osx-64/bzip2-1.0.8-h6c40b1e_6.conda#96224786021d0765ce05818fa3c59bdb @@ -65,7 +65,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/numpy-base-1.26.4-py312h6f81483_0.con https://repo.anaconda.com/pkgs/main/osx-64/pillow-10.3.0-py312h6c40b1e_0.conda#fe883fa4247d35fe6de49f713529ca02 https://repo.anaconda.com/pkgs/main/osx-64/pip-24.0-py312hecd8cb5_0.conda#7a8e0b1d3742ddf1c8aa97fbaa158039 https://repo.anaconda.com/pkgs/main/osx-64/pytest-7.4.0-py312hecd8cb5_0.conda#b816a2439ba9b87524aec74d58e55b0a -https://repo.anaconda.com/pkgs/main/osx-64/python-dateutil-2.9.0post0-py312hecd8cb5_0.conda#b3ed54eb118325785284dd18bfceca19 +https://repo.anaconda.com/pkgs/main/osx-64/python-dateutil-2.9.0post0-py312hecd8cb5_2.conda#1047dde28f78127dd9f6121e882926dd https://repo.anaconda.com/pkgs/main/osx-64/pytest-cov-4.1.0-py312hecd8cb5_1.conda#a33a24eb20359f464938e75b2f57e23a https://repo.anaconda.com/pkgs/main/osx-64/pytest-xdist-3.5.0-py312hecd8cb5_0.conda#d1ecfb3691cceecb1f16bcfdf0b67bb5 https://repo.anaconda.com/pkgs/main/osx-64/bottleneck-1.3.7-py312h32608ca_0.conda#f96a01eba5ea542cf9c7cc8d77447627 diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index c841fae4fe6b5..fb409d975c5a4 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: af52e4ce613b7668e1e28daaea07461722275d345395a5eaced4e07a16998179 +# input_hash: 37f8029b6bb116e0d5856093424791a8c1ddc3f493e20fcb5d02cd32d516523d @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2024.3.11-h06a4308_0.conda#08529eb3504712baabcbda266a19feb7 @@ -12,7 +12,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d28 https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464 https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_1.conda#70646cc713f0c43926cfdcfe9b695fe0 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c -https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.13-h7f8727e_1.conda#d1d1fc47640fe0d9f7fa64c0a054bfd8 +https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.13-h7f8727e_2.conda#0019453c25f5e72129f99236e60febaa https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_1.conda#1562802f843297ee776a50b9329597ed https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e @@ -32,7 +32,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py39h06a4308_0.conda#7f8ce # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 # pip exceptiongroup @ https://files.pythonhosted.org/packages/01/90/79fe92dd413a9cab314ef5c591b5aa9b9ba787ae4cadab75055b0ae00b33/exceptiongroup-1.2.1-py3-none-any.whl#sha256=5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc -# pip fonttools @ https://files.pythonhosted.org/packages/8b/c6/636f008104908a93b80419f756be755bb91df4b8a0c88d5158bb52c82c3a/fonttools-4.51.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=0d145976194a5242fdd22df18a1b451481a88071feadf251221af110ca8f00ce +# pip fonttools @ https://files.pythonhosted.org/packages/22/56/8b2ffe792b0a11d481ed797e0c246ee914b86370e317476c84d4fc537d95/fonttools-4.52.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ee2a8c1101d06cc8fca7851dceb67afd53dd6fc0288bacaa632e647bc5afff58 # pip idna @ https://files.pythonhosted.org/packages/e5/3e/741d8c82801c347547f8a2a06aa57dbb1992be9e948df2ea0eda2c8b79e8/idna-3.7-py3-none-any.whl#sha256=82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0 # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b # pip iniconfig @ https://files.pythonhosted.org/packages/ef/a6/62565a6e1cf69e10f5727360368e451d4b7f58beeac6173dc9db836a5b46/iniconfig-2.0.0-py3-none-any.whl#sha256=b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374 @@ -62,9 +62,9 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py39h06a4308_0.conda#7f8ce # pip tomli @ https://files.pythonhosted.org/packages/97/75/10a9ebee3fd790d20926a90a2547f0bf78f371b2f13aa822c759680ca7b9/tomli-2.0.1-py3-none-any.whl#sha256=939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc # pip tzdata @ https://files.pythonhosted.org/packages/65/58/f9c9e6be752e9fcb8b6a0ee9fb87e6e7a1f6bcab2cdc73f02bb7ba91ada0/tzdata-2024.1-py2.py3-none-any.whl#sha256=9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252 # pip urllib3 @ https://files.pythonhosted.org/packages/a2/73/a68704750a7679d0b6d3ad7aa8d4da8e14e151ae82e6fee774e6e0d05ec8/urllib3-2.2.1-py3-none-any.whl#sha256=450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d -# pip zipp @ https://files.pythonhosted.org/packages/da/55/a03fd7240714916507e1fcf7ae355bd9d9ed2e6db492595f1a67f61681be/zipp-3.18.2-py3-none-any.whl#sha256=dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e +# pip zipp @ https://files.pythonhosted.org/packages/7f/2d/670176f39d6613af2908b5bc31c9974588208de1fcc4117dfae08623d188/zipp-3.19.0-py3-none-any.whl#sha256=96dc6ad62f1441bcaccef23b274ec471518daf4fbbc580341204936a5a3dddec # pip contourpy @ https://files.pythonhosted.org/packages/31/a2/2f12e3a6e45935ff694654b710961b03310b0e1ec997ee9f416d3c873f87/contourpy-1.2.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=e1d59258c3c67c865435d8fbeb35f8c59b8bef3d6f46c1f29f6123556af28445 -# pip coverage @ https://files.pythonhosted.org/packages/c1/50/b7d6f236c20334b0378ed88078e830640a64ad8eb9f11f818b2af34d00c0/coverage-7.5.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=d21918e9ef11edf36764b93101e2ae8cc82aa5efdc7c5a4e9c6c35a48496d601 +# pip coverage @ https://files.pythonhosted.org/packages/b2/31/aa090d5e5a4e1a0e2d517f73a737a6d4b4975ca1f2b9cea9cb985b3ef307/coverage-7.5.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=24bb4c7859a3f757a116521d4d3a8a82befad56ea1bdacd17d6aafd113b0071e # pip imageio @ https://files.pythonhosted.org/packages/a3/b6/39c7dad203d9984225f47e0aa39ac3ba3a47c77a02d0ef2a7be691855a06/imageio-2.34.1-py3-none-any.whl#sha256=408c1d4d62f72c9e8347e7d1ca9bc11d8673328af3913868db3b828e28b40a4c # pip importlib-metadata @ https://files.pythonhosted.org/packages/2d/0a/679461c511447ffaf176567d5c496d1de27cbe34a87df6677d7171b2fbd4/importlib_metadata-7.1.0-py3-none-any.whl#sha256=30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570 # pip importlib-resources @ https://files.pythonhosted.org/packages/75/06/4df55e1b7b112d183f65db9503bff189e97179b256e1ea450a3c365241e0/importlib_resources-6.4.0-py3-none-any.whl#sha256=50d10f043df931902d4194ea07ec57960f66a80449ff867bfe782b4c486ba78c @@ -74,8 +74,8 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py39h06a4308_0.conda#7f8ce # pip pytest @ https://files.pythonhosted.org/packages/b4/c1/27a1274b73712232328cb5115030057b7dec377f36a518c83f2e01d4f305/pytest-8.2.1-py3-none-any.whl#sha256=faccc5d332b8c3719f40283d0d44aa5cf101cec36f88cde9ed8f2bc0538612b1 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/c3/20/748e38b466e0819491f0ce6e90ebe4184966ee304fe483e2c414b0f4ef07/requests-2.32.2-py3-none-any.whl#sha256=fc06670dd0ed212426dfeb94fc1b983d917c4f9847c863f313c9dfaaffb7c23c -# pip scipy @ https://files.pythonhosted.org/packages/c6/ba/a778e6c0020d728c119b0379805a357135fe8c9bc87fdb7e0750ca11319f/scipy-1.13.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=28e286bf9ac422d6beb559bc61312c348ca9b0f0dae0d7c5afde7f722d6ea13d -# pip tifffile @ https://files.pythonhosted.org/packages/c1/79/29d0fa40017f7b749ce344759dcc21e2ec9bbb81fc69ca2ce06e261f83f0/tifffile-2024.5.10-py3-none-any.whl#sha256=4154f091aa24d4e75bfad9ab2d5424a68c70e67b8220188066dc61946d4551bd +# pip scipy @ https://files.pythonhosted.org/packages/35/f5/d0ad1a96f80962ba65e2ce1de6a1e59edecd1f0a7b55990ed208848012e0/scipy-1.13.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=637e98dcf185ba7f8e663e122ebf908c4702420477ae52a04f9908707456ba4d +# pip tifffile @ https://files.pythonhosted.org/packages/d9/6c/740c07588434e86028c24b0653c1eb6b46904d9ce585a20f07590620ec41/tifffile-2024.5.22-py3-none-any.whl#sha256=e281781c15d7d197d7e12749849c965651413aa905f97a48b0f84bd90a3b4c6f # pip lightgbm @ https://files.pythonhosted.org/packages/ba/11/cb8b67f3cbdca05b59a032bb57963d4fe8c8d18c3870f30bed005b7f174d/lightgbm-4.3.0-py3-none-manylinux_2_28_x86_64.whl#sha256=104496a3404cb2452d3412cbddcfbfadbef9c372ea91e3a9b8794bcc5183bf07 # pip matplotlib @ https://files.pythonhosted.org/packages/d3/6d/45837c5b3d0005a5a9b04729b218a16bf3aa195701c6b33b2cc39ae943b6/matplotlib-3.9.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=81c40af649d19c85f8073e25e5806926986806fa6d54be506fbf02aef47d5a89 # pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 diff --git a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock index 8cecfc60c4c9d..deb9f11010bd9 100644 --- a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 244b6a064d3785ea62baaf9436848821d153846b455c2976f5e811182e848c83 +# input_hash: be0f080ab1974d224952262cd9179ff999d10108421d7e7ded2435e6f08edf0e @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-openblas.conda#9ddfcaef10d79366c90128f5dc444be8 @@ -28,7 +28,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/libwebp-base-1.3.2-h5eee18b_0.conda https://repo.anaconda.com/pkgs/main/linux-64/libxcb-1.15-h7f8727e_0.conda#ada518dcadd6aaee9aae47ba9a671553 https://repo.anaconda.com/pkgs/main/linux-64/lz4-c-1.9.4-h6a678d5_1.conda#2ee58861f2b92b868ce761abb831819d https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c -https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.13-h7f8727e_1.conda#d1d1fc47640fe0d9f7fa64c0a054bfd8 +https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.13-h7f8727e_2.conda#0019453c25f5e72129f99236e60febaa https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_1.conda#1562802f843297ee776a50b9329597ed https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e @@ -85,7 +85,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/gstreamer-1.14.1-h5eee18b_1.conda#f https://repo.anaconda.com/pkgs/main/linux-64/numpy-1.21.6-py39hac523dd_1.conda#f379f92039f666828a193fadd18c9819 https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py39h06a4308_0.conda#7f8ce3af15cfecd12e4dda8c5cef5fb7 https://repo.anaconda.com/pkgs/main/linux-64/pytest-7.4.0-py39h06a4308_0.conda#99d92a7a39f7e615de84f8cc5606c49a -https://repo.anaconda.com/pkgs/main/linux-64/python-dateutil-2.9.0post0-py39h06a4308_0.conda#bb2c65e53e610ec258e03771cd79ad17 +https://repo.anaconda.com/pkgs/main/linux-64/python-dateutil-2.9.0post0-py39h06a4308_2.conda#d6566c6d6a0140e45f22787ae58ed6e8 https://repo.anaconda.com/pkgs/main/linux-64/sip-6.7.12-py39h6a678d5_0.conda#6988a3e12fcacfedcac523c1e4c3167c https://repo.anaconda.com/pkgs/main/linux-64/gst-plugins-base-1.14.1-h6a678d5_1.conda#afd9cbe949d670d24cc0a007aaec1fe1 https://repo.anaconda.com/pkgs/main/linux-64/matplotlib-base-3.3.4-py39h62a2d02_0.conda#dbab28222c740af8e21a3e5e2882c178 diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock index 4b517f825d4fb..f4c2f51b1ea88 100644 --- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock @@ -1,9 +1,9 @@ # Generated by conda-lock. # platform: win-64 -# input_hash: 9b46e8490ff2c4c93104b20d3bffb2d04120288c8c8680bef8376ebd9d9b8eb3 +# input_hash: ea607aaeb7b1d1f8a1f821a9f505b3601083a218ec4763e2d72d3d3d800e718c @EXPLICIT https://conda.anaconda.org/conda-forge/win-64/ca-certificates-2024.2.2-h56e8100_0.conda#63da060240ab8087b60d1357051ea7d6 -https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2024.1.0-h57928b3_965.conda#c66eb2fd33b999ccc258aef85689758e +https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2024.1.0-h57928b3_966.conda#35d7ea07ad6c878bd7240d2d6c1b8657 https://conda.anaconda.org/conda-forge/win-64/mkl-include-2024.1.0-h66d3029_692.conda#60233966dc7c0261c9a443120b43c477 https://conda.anaconda.org/conda-forge/win-64/msys2-conda-epoch-20160418-1.tar.bz2#b0309b72560df66f71a9d5e34a5efdfa https://conda.anaconda.org/conda-forge/win-64/python_abi-3.9-4_cp39.conda#948b0d93d4ab1372d8fd45e1560afd47 @@ -29,7 +29,7 @@ https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.4.0-hcfcfb64_0.cond https://conda.anaconda.org/conda-forge/win-64/libzlib-1.2.13-hcfcfb64_5.conda#5fdb9c6a113b6b6cb5e517fd972d5f41 https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libgfortran-5.3.0-6.tar.bz2#066552ac6b907ec6d72c0ddab29050dc https://conda.anaconda.org/conda-forge/win-64/ninja-1.12.1-hc790b64_0.conda#a557dde55343e03c68cd7e29e7f87279 -https://conda.anaconda.org/conda-forge/win-64/openssl-3.3.0-h2466b09_2.conda#67d5f8749ee4ae3c4060d7a13fc8872f +https://conda.anaconda.org/conda-forge/win-64/openssl-3.3.0-h2466b09_3.conda#d7fec5d3bb8fc0c8e266bf1ad350cec5 https://conda.anaconda.org/conda-forge/win-64/pthreads-win32-2.9.1-hfa6e2cd_3.tar.bz2#e2da8758d7d51ff6aa78a14dfb9dbed4 https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h5226925_1.conda#fc048363eb8f03cd1737600a5d08aafe https://conda.anaconda.org/conda-forge/win-64/xz-5.2.6-h8d14728_0.tar.bz2#515d77642eaa3639413c6b1bc3f94219 @@ -54,7 +54,7 @@ https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_0.conda#1 https://conda.anaconda.org/conda-forge/win-64/freetype-2.12.1-hdaf720e_2.conda#3761b23693f768dc75a8fd0a73ca053f https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda#f800d2da156d08e289b14e87e43c1ae5 https://conda.anaconda.org/conda-forge/win-64/kiwisolver-1.4.5-py39h1f6ef14_1.conda#4fc5bd0a7b535252028c647cc27d6c87 -https://conda.anaconda.org/conda-forge/win-64/libclang13-18.1.5-default_hf64faad_0.conda#8a662434c6be1f40e2d5d2506d05a41d +https://conda.anaconda.org/conda-forge/win-64/libclang13-18.1.6-default_hf64faad_0.conda#cb8ef624b31ede2c69f0e33135d00110 https://conda.anaconda.org/conda-forge/win-64/libglib-2.80.2-h0df6a38_0.conda#ef9ae80bb2a15aee7a30180c057678ea https://conda.anaconda.org/conda-forge/win-64/libhwloc-2.10.0-default_h8125262_1001.conda#e761885eb4c181074d172220d46319a0 https://conda.anaconda.org/conda-forge/win-64/libintl-devel-0.22.5-h5728263_2.conda#a2ad82fae23975e4ccbfab2847d31d48 @@ -77,7 +77,7 @@ https://conda.anaconda.org/conda-forge/win-64/xorg-libxau-1.0.11-hcd874cb_0.cond https://conda.anaconda.org/conda-forge/win-64/xorg-libxdmcp-1.1.3-hcd874cb_0.tar.bz2#46878ebb6b9cbd8afcf8088d7ef00ece https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda#2e4d6bc0b14e10f895fc6791a7d9b26a https://conda.anaconda.org/conda-forge/win-64/brotli-1.1.0-hcfcfb64_1.conda#f47f6db2528e38321fb00ae31674c133 -https://conda.anaconda.org/conda-forge/win-64/coverage-7.5.1-py39ha55e580_0.conda#e8f43ea91f0f17d92d5575cfab41a42f +https://conda.anaconda.org/conda-forge/win-64/coverage-7.5.2-py39ha55e580_0.conda#efb1e63bf5157f005afcc40778efaff5 https://conda.anaconda.org/conda-forge/win-64/glib-tools-2.80.2-h2f9d560_0.conda#42fc785d9db7ab051a206fbf882ecf2e https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1ab_0.conda#c5d3907ad8bd7bf557521a1833cf7e6d https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f @@ -91,7 +91,7 @@ https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/win-64/sip-6.7.12-py39h99910a6_0.conda#0cc5774390ada632ed7975203057c91c https://conda.anaconda.org/conda-forge/win-64/tbb-2021.12.0-hc790b64_1.conda#e98333643abc739ebea1bac97a479828 -https://conda.anaconda.org/conda-forge/win-64/fonttools-4.51.0-py39ha55989b_0.conda#5d19302bab29e347116b743e793aa7d6 +https://conda.anaconda.org/conda-forge/win-64/fonttools-4.52.1-py39ha55e580_0.conda#781c66ea2eeed910c0f1abc5ccc4a079 https://conda.anaconda.org/conda-forge/win-64/glib-2.80.2-h0df6a38_0.conda#a728ca6f04c33ecb0f39eeda5fbd0e23 https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.0-pyhd8ed1ab_0.conda#dcbadab7a68738a028e195ab68ab2d2e https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 @@ -112,7 +112,7 @@ https://conda.anaconda.org/conda-forge/win-64/qt-main-5.15.8-hcef0176_21.conda#7 https://conda.anaconda.org/conda-forge/win-64/blas-devel-3.9.0-22_win64_mkl.conda#adeb834f3b7b06f3d77cd90b7c9d08f0 https://conda.anaconda.org/conda-forge/win-64/contourpy-1.2.1-py39h1f6ef14_0.conda#03e25c6bae87f4f9595337255b44b0fb https://conda.anaconda.org/conda-forge/win-64/pyqt-5.15.9-py39hb77abff_5.conda#5ed899124a51958336371ff01482b8fd -https://conda.anaconda.org/conda-forge/win-64/scipy-1.13.0-py39h1a10956_1.conda#5624ccefd670072fc86b2cd4ffdc6c44 +https://conda.anaconda.org/conda-forge/win-64/scipy-1.13.1-py39h1a10956_0.conda#9f8e571406af04d2f5fdcbecec704505 https://conda.anaconda.org/conda-forge/win-64/blas-2.122-mkl.conda#aee642435696de144ddf91dc02101cf8 https://conda.anaconda.org/conda-forge/win-64/matplotlib-base-3.8.4-py39he1095e7_2.conda#5c813b5da86f186d8026b6de6429c212 https://conda.anaconda.org/conda-forge/win-64/matplotlib-3.8.4-py39hcbf5309_2.conda#1ecee90b529cb69ec4e95add23323110 diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock index 48dd5ab3c3021..66cd5eced566b 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: be96a61e1a8c45c41b04e6e30fafbbf78e5b05202b348a702c726eced7c7df48 +# input_hash: 3974f9847d888a2fd37ba5fcfb76cb09bba4c9b84b6200932500fc94e3b0c4ae @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.2.2-hbcca054_0.conda#2f4327a1cbe7f022401b236e915a5fef @@ -8,7 +8,7 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f -https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h55db66e_0.conda#10569984e7db886e4f1abc2b47ad79a1 +https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_1.conda#33b7851c39c25da14f6a233a8ccbeeca https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-hc0a3c3a_7.conda#53ebd4c833fa01cb2c6353e99f905406 https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.9-4_cp39.conda#bfe4b3259a8ac6cdf0037752904da6a7 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 @@ -46,7 +46,7 @@ https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_2.conda#b8934d399b56d73e323403e183d009c5 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_3.conda#12ea6d0d4ed54530eaed18e4835c1f7c https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a @@ -86,10 +86,10 @@ https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.2-h659d440_0.conda#cd9 https://conda.anaconda.org/conda-forge/linux-64/libglib-2.80.2-hf974151_0.conda#72724f6a78ecb15559396966226d5838 https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a https://conda.anaconda.org/conda-forge/linux-64/libllvm15-15.0.7-hb3ce162_4.conda#8a35df3cbc0c8b12cc8af9473ae75eef -https://conda.anaconda.org/conda-forge/linux-64/libllvm18-18.1.5-hb77312f_0.conda#efd221d3668077ca067a206269418dec +https://conda.anaconda.org/conda-forge/linux-64/libllvm18-18.1.6-hb77312f_0.conda#1246fc4b9f4db452e69cc297967d4b3e https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.27-pthreads_h413a1c8_0.conda#a356024784da6dfd4683dc5ecf45b155 https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-h1dd3fc0_3.conda#66f03896ffbe1a110ffda05c7a856504 -https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.5-ha31de31_0.conda#b923cdb6e567ada84f991ffcc5848afb +https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.6-ha31de31_0.conda#8e9ad283cf953ebb4e6d1db9633b8344 https://conda.anaconda.org/conda-forge/linux-64/mysql-libs-8.3.0-hca2cd23_4.conda#1b50eebe2a738a3146c154d2eceaa8b6 https://conda.anaconda.org/conda-forge/linux-64/nss-3.100-hca3bf56_0.conda#949c4a82290ee58b3c970cef4bcfd4ad https://conda.anaconda.org/conda-forge/linux-64/python-3.9.19-h0755675_0_cpython.conda#d9ee3647fbd9e8595b8df759b2bbefb8 @@ -120,7 +120,7 @@ https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.5-py39h7633fee_1. https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.16-hb7c19ff_0.conda#51bb7010fc86f70eee639b4bb7a894f5 https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-22_linux64_openblas.conda#1a2a0cd3153464fee6646f3dd6dad9b8 https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp15-15.0.7-default_h127d8a8_5.conda#d0a9633b53cdc319b8a1a532ae7822b8 -https://conda.anaconda.org/conda-forge/linux-64/libclang13-18.1.5-default_h5d6823c_0.conda#60c39a00b694c98da03f67a3ba1d7499 +https://conda.anaconda.org/conda-forge/linux-64/libclang13-18.1.6-default_h5d6823c_0.conda#fbe666f653068958eb27f549cb12f202 https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-h4637d8d_4.conda#d4529f4dff3057982a7617c7ac58fde3 https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda#ee48bf17cc83a00f59ca1494d5646869 https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.49-h4f305b6_0.conda#dfcfd72c7a430d3616763ecfbefe4ca9 @@ -155,7 +155,7 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.11-hd590300_ https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda#2e4d6bc0b14e10f895fc6791a7d9b26a https://conda.anaconda.org/conda-forge/noarch/babel-2.14.0-pyhd8ed1ab_0.conda#9669586875baeced8fc30c0826c3270e https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-h3faef2a_0.conda#f907bb958910dc404647326ca80c263e -https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.51.0-py39hd1e30aa_0.conda#79f5dd8778873faa54e8f7b2729fe8a6 +https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.52.1-py39hd3abc70_0.conda#66b6088c2446c25e714829a289070499 https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.2-hf974151_0.conda#d427988dc3dbd0a4c136f52db356cc6a https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-7.1.0-pyha770c72_0.conda#0896606848b2dc5cebdf111b6543aa04 https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1ab_0.conda#c5d3907ad8bd7bf557521a1833cf7e6d @@ -189,7 +189,7 @@ https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py39h7633fee_0.c https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.3-h9ad1361_0.conda#8fb0e954c616bb0f9389efac4b4ed44b https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.2-py39hfc16268_1.conda#8b23d2b425035a7468d17e6fe1d54124 https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-client-17.0-hb77b528_0.conda#07f45f1be1c25345faddb8db0de8039b -https://conda.anaconda.org/conda-forge/linux-64/scipy-1.13.0-py39haf93ffa_1.conda#57ce54e228e3fbc60e42fa368eff3251 +https://conda.anaconda.org/conda-forge/linux-64/scipy-1.13.1-py39haf93ffa_0.conda#492a2cd65862d16a4aaf535ae9ccb761 https://conda.anaconda.org/conda-forge/linux-64/blas-2.122-openblas.conda#5065468105542a8b23ea47bd8b6fa55f https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.8.4-py39h10d1fc8_2.conda#c9fb6571b93b1dd490ea627af7344f36 https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.1.0-py39h85c637f_1.conda#b2b15112d019e27e62f9433e31607d08 diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index 4ef53ff72bc3f..1652c4a4f395d 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 46e3ce252a769181cca2ee8df6e2bfd3f6749900d7863cc5a3b41c300daa0380 +# input_hash: f6f3862aafcafa139a322e498517c3db58e1b8db95f1b1ca8c18f5b70d446dc9 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.2.2-hbcca054_0.conda#2f4327a1cbe7f022401b236e915a5fef @@ -9,7 +9,7 @@ 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https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-hc0a3c3a_7.conda#53ebd4c833fa01cb2c6353e99f905406 @@ -18,9 +18,9 @@ https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#1610 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.2.0-h77fa898_7.conda#abf3fec87c2563697defa759dec3d639 https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.12-he073ed8_17.conda#595db67e32b276298ff3d94d07d47fbf -https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.40-ha885e6a_0.conda#800a4c872b5bc06fa83888d112fe6c4f +https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.40-ha1999f0_1.conda#e901545940ebdc5c40017fab53642b3c https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab -https://conda.anaconda.org/conda-forge/linux-64/binutils-2.40-h4852527_0.conda#a05c7712be80622934f7011e0a1d43fc +https://conda.anaconda.org/conda-forge/linux-64/binutils-2.40-h4852527_1.conda#dfaea5684bbbbf0b64a4c31f984d0661 https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.40-hdade7a5_3.conda#2d9a60578bc28469d9aeef9aea5520c3 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793 https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h77fa898_7.conda#72ec1b1b04c4d15d4204ece1ecea5978 @@ -63,7 +63,7 @@ https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_2.conda#b8934d399b56d73e323403e183d009c5 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_3.conda#12ea6d0d4ed54530eaed18e4835c1f7c https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.6.6-he8a937b_2.conda#77d9955b4abddb811cb8ab1aa7d743e4 @@ -116,10 +116,10 @@ https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.2-h659d440_0.conda#cd9 https://conda.anaconda.org/conda-forge/linux-64/libglib-2.80.2-hf974151_0.conda#72724f6a78ecb15559396966226d5838 https://conda.anaconda.org/conda-forge/linux-64/libjxl-0.10.2-hcae5a98_0.conda#901db891e1e21afd8524cd636a8c8e3b https://conda.anaconda.org/conda-forge/linux-64/libllvm15-15.0.7-hb3ce162_4.conda#8a35df3cbc0c8b12cc8af9473ae75eef -https://conda.anaconda.org/conda-forge/linux-64/libllvm18-18.1.5-hb77312f_0.conda#efd221d3668077ca067a206269418dec +https://conda.anaconda.org/conda-forge/linux-64/libllvm18-18.1.6-hb77312f_0.conda#1246fc4b9f4db452e69cc297967d4b3e https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.27-pthreads_h413a1c8_0.conda#a356024784da6dfd4683dc5ecf45b155 https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-h1dd3fc0_3.conda#66f03896ffbe1a110ffda05c7a856504 -https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.5-ha31de31_0.conda#b923cdb6e567ada84f991ffcc5848afb +https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.6-ha31de31_0.conda#8e9ad283cf953ebb4e6d1db9633b8344 https://conda.anaconda.org/conda-forge/linux-64/mysql-libs-8.3.0-hca2cd23_4.conda#1b50eebe2a738a3146c154d2eceaa8b6 https://conda.anaconda.org/conda-forge/linux-64/nss-3.100-hca3bf56_0.conda#949c4a82290ee58b3c970cef4bcfd4ad https://conda.anaconda.org/conda-forge/linux-64/python-3.9.19-h0755675_0_cpython.conda#d9ee3647fbd9e8595b8df759b2bbefb8 @@ -154,7 +154,7 @@ https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.5-py39h7633fee_1. https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.16-hb7c19ff_0.conda#51bb7010fc86f70eee639b4bb7a894f5 https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-22_linux64_openblas.conda#1a2a0cd3153464fee6646f3dd6dad9b8 https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp15-15.0.7-default_h127d8a8_5.conda#d0a9633b53cdc319b8a1a532ae7822b8 -https://conda.anaconda.org/conda-forge/linux-64/libclang13-18.1.5-default_h5d6823c_0.conda#60c39a00b694c98da03f67a3ba1d7499 +https://conda.anaconda.org/conda-forge/linux-64/libclang13-18.1.6-default_h5d6823c_0.conda#fbe666f653068958eb27f549cb12f202 https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-h4637d8d_4.conda#d4529f4dff3057982a7617c7ac58fde3 https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda#ee48bf17cc83a00f59ca1494d5646869 https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.49-h4f305b6_0.conda#dfcfd72c7a430d3616763ecfbefe4ca9 @@ -199,7 +199,7 @@ https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.12.3-pyha770c72_0 https://conda.anaconda.org/conda-forge/linux-64/brunsli-0.1-h9c3ff4c_0.tar.bz2#c1ac6229d0bfd14f8354ff9ad2a26cad https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-h3faef2a_0.conda#f907bb958910dc404647326ca80c263e https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.7.0-h00ab1b0_1.conda#28de2e073db9ca9b72858bee9fb6f571 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https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.6-pyhd8ed1ab_0.conda#a5b55d1cb110cdcedc748b5c3e16e687 -https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.26-py39ha963410_1.conda#da14fdc7c8400f6b6505b710dd129636 +https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.29-py39ha963410_0.conda#fb87101281e618d404e70cad4e5c5c88 https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.1-pyhd8ed1ab_0.conda#d15917f33140f8d2ac9ca44db7ec8a25 https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-client-17.0-hb77b528_0.conda#07f45f1be1c25345faddb8db0de8039b https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.4.1-py39h44dd56e_1.conda#d037c20e3da2e85f03ebd20ad480c359 -https://conda.anaconda.org/conda-forge/linux-64/scipy-1.13.0-py39haf93ffa_1.conda#57ce54e228e3fbc60e42fa368eff3251 +https://conda.anaconda.org/conda-forge/linux-64/scipy-1.13.1-py39haf93ffa_0.conda#492a2cd65862d16a4aaf535ae9ccb761 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https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.22.0-py39hddac248_2.conda#8d502a4d2cbe5a45ff35ca8af8cbec0a https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_2.conda#b713b116feaf98acdba93ad4d7f90ca1 @@ -299,7 +299,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip webcolors @ https://files.pythonhosted.org/packages/d5/e1/3e9013159b4cbb71df9bd7611cbf90dc2c621c8aeeb677fc41dad72f2261/webcolors-1.13-py3-none-any.whl#sha256=29bc7e8752c0a1bd4a1f03c14d6e6a72e93d82193738fa860cbff59d0fcc11bf # pip webencodings @ https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl#sha256=a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78 # pip websocket-client @ https://files.pythonhosted.org/packages/5a/84/44687a29792a70e111c5c477230a72c4b957d88d16141199bf9acb7537a3/websocket_client-1.8.0-py3-none-any.whl#sha256=17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526 -# pip anyio @ https://files.pythonhosted.org/packages/14/fd/2f20c40b45e4fb4324834aea24bd4afdf1143390242c0b33774da0e2e34f/anyio-4.3.0-py3-none-any.whl#sha256=048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8 +# pip anyio @ https://files.pythonhosted.org/packages/7b/a2/10639a79341f6c019dedc95bd48a4928eed9f1d1197f4c04f546fc7ae0ff/anyio-4.4.0-py3-none-any.whl#sha256=c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7 # pip arrow @ https://files.pythonhosted.org/packages/f8/ed/e97229a566617f2ae958a6b13e7cc0f585470eac730a73e9e82c32a3cdd2/arrow-1.3.0-py3-none-any.whl#sha256=c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80 # pip bleach @ https://files.pythonhosted.org/packages/ea/63/da7237f805089ecc28a3f36bca6a21c31fcbc2eb380f3b8f1be3312abd14/bleach-6.1.0-py3-none-any.whl#sha256=3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6 # pip cffi @ https://files.pythonhosted.org/packages/ea/ac/e9e77bc385729035143e54cc8c4785bd480eaca9df17565963556b0b7a93/cffi-1.16.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=8f8e709127c6c77446a8c0a8c8bf3c8ee706a06cd44b1e827c3e6a2ee6b8c098 @@ -318,12 +318,12 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip pyzmq @ https://files.pythonhosted.org/packages/64/b8/1c181c13e118cabccfd25bd3e169e44958c649180b0d78b798a66899e08b/pyzmq-26.0.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=b3cd31f859b662ac5d7f4226ec7d8bd60384fa037fc02aee6ff0b53ba29a3ba8 # pip argon2-cffi @ 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https://files.pythonhosted.org/packages/71/2c/bd797dc46a7281d43444c79ff312d4f8d27d41a0de05f48cad81c7939966/jupyterlite_sphinx-0.15.0-py3-none-any.whl#sha256=344d1f9ee5a20b141a4a4139874eae30a68216f0c995d03ea2e3b3e9d29c4cd5 diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index 7a5a65ba03d41..b9754f92f73e3 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: b285d0ba8ec3865f6ac4f6f990060713f430995f71bf0b3a4c94a6610defc5b9 +# input_hash: 5d01f25ac5aaf25812f967948c86aafae507b69a57ec6ba448270e1b598886af @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 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https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h77fa898_7.conda#72ec1b1b04c4d15d4204ece1ecea5978 @@ -55,7 +55,7 @@ https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_2.conda#b8934d399b56d73e323403e183d009c5 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_3.conda#12ea6d0d4ed54530eaed18e4835c1f7c https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a @@ -98,9 +98,9 @@ https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.2-h659d440_0.conda#cd9 https://conda.anaconda.org/conda-forge/linux-64/libglib-2.80.2-hf974151_0.conda#72724f6a78ecb15559396966226d5838 https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.10.0-default_h5622ce7_1001.conda#fc2d5b79c2d3f8568fbab31db7ae02f3 https://conda.anaconda.org/conda-forge/linux-64/libllvm15-15.0.7-hb3ce162_4.conda#8a35df3cbc0c8b12cc8af9473ae75eef -https://conda.anaconda.org/conda-forge/linux-64/libllvm18-18.1.5-hb77312f_0.conda#efd221d3668077ca067a206269418dec +https://conda.anaconda.org/conda-forge/linux-64/libllvm18-18.1.6-hb77312f_0.conda#1246fc4b9f4db452e69cc297967d4b3e https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-h1dd3fc0_3.conda#66f03896ffbe1a110ffda05c7a856504 -https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.5-ha31de31_0.conda#b923cdb6e567ada84f991ffcc5848afb +https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.6-ha31de31_0.conda#8e9ad283cf953ebb4e6d1db9633b8344 https://conda.anaconda.org/conda-forge/linux-64/mysql-libs-8.3.0-hca2cd23_4.conda#1b50eebe2a738a3146c154d2eceaa8b6 https://conda.anaconda.org/conda-forge/linux-64/nss-3.100-hca3bf56_0.conda#949c4a82290ee58b3c970cef4bcfd4ad https://conda.anaconda.org/conda-forge/linux-64/python-3.9.19-h0755675_0_cpython.conda#d9ee3647fbd9e8595b8df759b2bbefb8 @@ -137,7 +137,7 @@ https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.5-py39h7633fee_1.conda#c9f74d717e5a2847a9f8b779c54130f2 https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.16-hb7c19ff_0.conda#51bb7010fc86f70eee639b4bb7a894f5 https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp15-15.0.7-default_h127d8a8_5.conda#d0a9633b53cdc319b8a1a532ae7822b8 -https://conda.anaconda.org/conda-forge/linux-64/libclang13-18.1.5-default_h5d6823c_0.conda#60c39a00b694c98da03f67a3ba1d7499 +https://conda.anaconda.org/conda-forge/linux-64/libclang13-18.1.6-default_h5d6823c_0.conda#fbe666f653068958eb27f549cb12f202 https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-h4637d8d_4.conda#d4529f4dff3057982a7617c7ac58fde3 https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda#ee48bf17cc83a00f59ca1494d5646869 https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.49-h4f305b6_0.conda#dfcfd72c7a430d3616763ecfbefe4ca9 @@ -190,7 +190,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.cond https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.7.0-h662e7e4_0.conda#b32c0da42b1f24a98577bb3d7fc0b995 https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhd8ed1ab_0.tar.bz2#8b45f9f2b2f7a98b0ec179c8991a4a9b https://conda.anaconda.org/conda-forge/noarch/meson-1.4.0-pyhd8ed1ab_0.conda#52a0660cfa40b45bf254ecc3374cb2e0 -https://conda.anaconda.org/conda-forge/linux-64/mkl-2024.1.0-ha957f24_692.conda#e7f5c5cda17c6f5047db27d44367c19d +https://conda.anaconda.org/conda-forge/linux-64/mkl-2024.1.0-ha957f24_693.conda#ff0f4abf6f94e36a918f1ef4dbeb9769 https://conda.anaconda.org/conda-forge/noarch/partd-1.4.2-pyhd8ed1ab_0.conda#0badf9c54e24cecfb0ad2f99d680c163 https://conda.anaconda.org/conda-forge/linux-64/pillow-10.3.0-py39h90c7501_0.conda#1e3b6af9592be71ce19f0a6aae05d97b https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 @@ -208,7 +208,7 @@ https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-7.1.0-hd8ed1ab_ https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-22_linux64_mkl.conda#eb6deb4ba6f92ea3f31c09cb8b764738 https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-255-h3516f8a_1.conda#3366af27f0b593544a6cd453c7932ac5 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 -https://conda.anaconda.org/conda-forge/linux-64/mkl-devel-2024.1.0-ha770c72_692.conda#56142862a71bcfdd6ef2ce95c8e90755 +https://conda.anaconda.org/conda-forge/linux-64/mkl-devel-2024.1.0-ha770c72_693.conda#7f422e2cf549a3fb920c95288393870d https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5.conda#93aff412f3e49fdb43361c0215cbd72d https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b https://conda.anaconda.org/conda-forge/noarch/requests-2.32.2-pyhd8ed1ab_0.conda#e1643b34b19df8c028a4f00bf5df58a6 From 163b4edce0840a481a15fa5f425bed00dfef6bda Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Morais?= <15629444+ojoaomorais@users.noreply.github.com> Date: Tue, 28 May 2024 00:37:01 -0300 Subject: [PATCH 068/275] DOC Removing warnings from plot_sgd_iris example (#29121) --- examples/linear_model/plot_sgd_iris.py | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/linear_model/plot_sgd_iris.py b/examples/linear_model/plot_sgd_iris.py index 5d9b923f9b444..838f612d4659c 100644 --- a/examples/linear_model/plot_sgd_iris.py +++ b/examples/linear_model/plot_sgd_iris.py @@ -58,7 +58,6 @@ X[idx, 1], c=color, label=iris.target_names[i], - cmap=plt.cm.Paired, edgecolor="black", s=20, ) From a4b14cb4a8fc46ee2599c309f60b90b5f8a16c38 Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Tue, 28 May 2024 07:41:24 +0200 Subject: [PATCH 069/275] FIX always return None as device when array API dispatch is disabled (#29119) --- doc/whats_new/v1.5.rst | 10 +++++++++- sklearn/utils/_array_api.py | 13 +++++++++---- sklearn/utils/tests/test_array_api.py | 26 ++++++++++++++++++++++++++ 3 files changed, 44 insertions(+), 5 deletions(-) diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst index 8286f4044941f..60b8dadc97373 100644 --- a/doc/whats_new/v1.5.rst +++ b/doc/whats_new/v1.5.rst @@ -23,12 +23,20 @@ Version 1.5.1 Changelog --------- +:mod:`sklearn.metrics` +...................... + +- |Fix| Fix a regression in :func:`metrics.r2_score`. Passing torch CPU tensors + with array API dispatched disabled would complain about non-CPU devices + instead of implicitly converting those inputs as regular NumPy arrays. + :pr:`29119` by :user:`Olivier Grisel`. + :mod:`sklearn.model_selection` .............................. - |Fix| Fix a regression in :class:`model_selection.GridSearchCV` for parameter grids that have heterogeneous parameter values. - :pr:`29078` by :user:`Loïc Estève ` + :pr:`29078` by :user:`Loïc Estève `. .. _changes_1_5: diff --git a/sklearn/utils/_array_api.py b/sklearn/utils/_array_api.py index 7c3fd12ad4dee..70b32ded33a7e 100644 --- a/sklearn/utils/_array_api.py +++ b/sklearn/utils/_array_api.py @@ -560,10 +560,15 @@ def get_namespace_and_device(*array_list, remove_none=True, remove_types=(str,)) skip_remove_kwargs = dict(remove_none=False, remove_types=[]) - return ( - *get_namespace(*array_list, **skip_remove_kwargs), - device(*array_list, **skip_remove_kwargs), - ) + xp, is_array_api = get_namespace(*array_list, **skip_remove_kwargs) + if is_array_api: + return ( + xp, + is_array_api, + device(*array_list, **skip_remove_kwargs), + ) + else: + return xp, False, None def _expit(X, xp=None): diff --git a/sklearn/utils/tests/test_array_api.py b/sklearn/utils/tests/test_array_api.py index d0b368cd7fe91..f14d143ac993d 100644 --- a/sklearn/utils/tests/test_array_api.py +++ b/sklearn/utils/tests/test_array_api.py @@ -21,6 +21,7 @@ _ravel, device, get_namespace, + get_namespace_and_device, indexing_dtype, supported_float_dtypes, yield_namespace_device_dtype_combinations, @@ -504,3 +505,28 @@ def test_indexing_dtype(namespace, _device, _dtype): assert indexing_dtype(xp) == xp.int32 else: assert indexing_dtype(xp) == xp.int64 + + +def test_get_namespace_and_device(): + # Use torch as a library with custom Device objects: + torch = pytest.importorskip("torch") + xp_torch = pytest.importorskip("array_api_compat.torch") + some_torch_tensor = torch.arange(3, device="cpu") + some_numpy_array = numpy.arange(3) + + # When dispatch is disabled, get_namespace_and_device should return the + # default NumPy wrapper namespace and no device. Our code will handle such + # inputs via the usual __array__ interface without attempting to dispatch + # via the array API. + namespace, is_array_api, device = get_namespace_and_device(some_torch_tensor) + assert namespace is get_namespace(some_numpy_array)[0] + assert not is_array_api + assert device is None + + # Otherwise, expose the torch namespace and device via array API compat + # wrapper. + with config_context(array_api_dispatch=True): + namespace, is_array_api, device = get_namespace_and_device(some_torch_tensor) + assert namespace is xp_torch + assert is_array_api + assert device == some_torch_tensor.device From 4c86080a45bd1aeb0487ddf634aa72bb8842df1c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Morais?= <15629444+ojoaomorais@users.noreply.github.com> Date: Tue, 28 May 2024 06:19:48 -0300 Subject: [PATCH 070/275] DOC Removing warnings from plot_logistic_multinomial examples (#29120) --- examples/linear_model/plot_logistic_multinomial.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/examples/linear_model/plot_logistic_multinomial.py b/examples/linear_model/plot_logistic_multinomial.py index c332aecea2ce7..c21cfd2523b2f 100644 --- a/examples/linear_model/plot_logistic_multinomial.py +++ b/examples/linear_model/plot_logistic_multinomial.py @@ -46,9 +46,7 @@ colors = "bry" for i, color in zip(clf.classes_, colors): idx = np.where(y == i) - plt.scatter( - X[idx, 0], X[idx, 1], c=color, cmap=plt.cm.Paired, edgecolor="black", s=20 - ) + plt.scatter(X[idx, 0], X[idx, 1], c=color, edgecolor="black", s=20) # Plot the three one-against-all classifiers xmin, xmax = plt.xlim() From 510a6b0e1e0cbaec6c5da1843568a35797b72004 Mon Sep 17 00:00:00 2001 From: Yao Xiao <108576690+Charlie-XIAO@users.noreply.github.com> Date: Tue, 28 May 2024 18:54:56 +0800 Subject: [PATCH 071/275] DOC improve contributing docs (#29126) --- doc/developers/contributing.rst | 474 +++++++++++++++++--------------- 1 file changed, 249 insertions(+), 225 deletions(-) diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst index 402711dcd1bf3..a9dc7cb305f61 100644 --- a/doc/developers/contributing.rst +++ b/doc/developers/contributing.rst @@ -7,12 +7,9 @@ Contributing .. currentmodule:: sklearn This project is a community effort, and everyone is welcome to -contribute. - -The project is hosted on https://github.com/scikit-learn/scikit-learn - +contribute. It is hosted on https://github.com/scikit-learn/scikit-learn. The decision making process and governance structure of scikit-learn is laid -out in the governance document: :ref:`governance`. +out in :ref:`governance`. Scikit-learn is somewhat :ref:`selective ` when it comes to adding new algorithms, and the best way to contribute and to help the project @@ -70,10 +67,12 @@ link to it from your website, or simply star to say "I use it": .. raw:: html - Star - +

    + + +

    In case a contribution/issue involves changes to the API principles or changes to dependencies or supported versions, it must be backed by a @@ -100,9 +99,8 @@ and follows the decision-making process outlined in :ref:`governance`. * `matplotlib `__ * and so on. - Look for issues marked "help wanted" or similar. - Helping these projects may help Scikit-learn too. - See also :ref:`related_projects`. + Look for issues marked "help wanted" or similar. Helping these projects may help + scikit-learn too. See also :ref:`related_projects`. Submitting a bug report or a feature request ============================================ @@ -135,17 +133,15 @@ following rules before submitting: How to make a good bug report ----------------------------- -When you submit an issue to `Github +When you submit an issue to `GitHub `__, please do your best to follow these guidelines! This will make it a lot easier to provide you with good feedback: - The ideal bug report contains a :ref:`short reproducible code snippet - `, this way - anyone can try to reproduce the bug easily (see `this - `_ for more details). If your snippet is - longer than around 50 lines, please link to a `gist - `_ or a github repo. + `, this way anyone can try to reproduce the bug easily. If your + snippet is longer than around 50 lines, please link to a `Gist + `_ or a GitHub repo. - If not feasible to include a reproducible snippet, please be specific about what **estimators and/or functions are involved and the shape of the data**. @@ -154,18 +150,18 @@ feedback: - Please include your **operating system type and version number**, as well as your **Python, scikit-learn, numpy, and scipy versions**. This information - can be found by running the following code snippet:: + can be found by running: - >>> import sklearn - >>> sklearn.show_versions() # doctest: +SKIP + .. prompt:: bash + + python -c "import sklearn; sklearn.show_versions()" - Please ensure all **code snippets and error messages are formatted in appropriate code blocks**. See `Creating and highlighting code blocks `_ for more details. -If you want to help curate issues, read :ref:`the following -`. +If you want to help curate issues, read about :ref:`bug_triaging`. Contributing code ================= @@ -241,7 +237,7 @@ how to set up your git repository: 3. Clone your fork of the scikit-learn repo from your GitHub account to your local disk: - .. prompt:: bash $ + .. prompt:: bash git clone git@github.com:YourLogin/scikit-learn.git # add --depth 1 if your connection is slow cd scikit-learn @@ -251,7 +247,7 @@ how to set up your git repository: 5. Install the development dependencies: - .. prompt:: bash $ + .. prompt:: bash pip install pytest pytest-cov ruff mypy numpydoc black==24.3.0 @@ -261,12 +257,14 @@ how to set up your git repository: scikit-learn repository, which you can use to keep your repository synchronized with the latest changes: - .. prompt:: bash $ + .. prompt:: bash git remote add upstream git@github.com:scikit-learn/scikit-learn.git 7. Check that the `upstream` and `origin` remote aliases are configured correctly - by running `git remote -v` which should display:: + by running `git remote -v` which should display: + + .. code-block:: text origin git@github.com:YourLogin/scikit-learn.git (fetch) origin git@github.com:YourLogin/scikit-learn.git (push) @@ -282,7 +280,7 @@ The next steps now describe the process of modifying code and submitting a PR: 8. Synchronize your ``main`` branch with the ``upstream/main`` branch, more details on `GitHub Docs `_: - .. prompt:: bash $ + .. prompt:: bash git checkout main git fetch upstream @@ -290,7 +288,7 @@ The next steps now describe the process of modifying code and submitting a PR: 9. Create a feature branch to hold your development changes: - .. prompt:: bash $ + .. prompt:: bash git checkout -b my_feature @@ -300,7 +298,7 @@ The next steps now describe the process of modifying code and submitting a PR: 10. (**Optional**) Install `pre-commit `_ to run code style checks before each commit: - .. prompt:: bash $ + .. prompt:: bash pip install pre-commit pre-commit install @@ -312,7 +310,7 @@ The next steps now describe the process of modifying code and submitting a PR: do the version control. When you're done editing, add changed files using ``git add`` and then ``git commit``: - .. prompt:: bash $ + .. prompt:: bash git add modified_files git commit @@ -320,7 +318,7 @@ The next steps now describe the process of modifying code and submitting a PR: to record your changes in Git, then push the changes to your GitHub account with: - .. prompt:: bash $ + .. prompt:: bash git push -u origin my_feature @@ -333,7 +331,7 @@ The next steps now describe the process of modifying code and submitting a PR: It is often helpful to keep your local feature branch synchronized with the latest changes of the main scikit-learn repository: -.. prompt:: bash $ +.. prompt:: bash git fetch upstream git merge upstream/main @@ -343,7 +341,7 @@ Subsequently, you might need to solve the conflicts. You can refer to the line `_. -.. topic:: Learning git: +.. topic:: Learning Git The `Git documentation `_ and http://try.github.io are excellent resources to get started with git, @@ -355,19 +353,18 @@ Pull request checklist ---------------------- Before a PR can be merged, it needs to be approved by two core developers. -Please prefix the title of your pull request with ``[MRG]`` if the -contribution is complete and should be subjected to a detailed review. An -incomplete contribution -- where you expect to do more work before receiving -a full review -- should be prefixed ``[WIP]`` (to indicate a work in -progress) and changed to ``[MRG]`` when it matures. WIPs may be useful to: +An incomplete contribution -- where you expect to do more work before receiving +a full review -- should be marked as a `draft pull request +`__ +and changed to "ready for review" when it matures. Draft PRs may be useful to: indicate you are working on something to avoid duplicated work, request -broad review of functionality or API, or seek collaborators. WIPs often +broad review of functionality or API, or seek collaborators. Draft PRs often benefit from the inclusion of a `task list `_ in the PR description. In order to ease the reviewing process, we recommend that your contribution -complies with the following rules before marking a PR as ``[MRG]``. The +complies with the following rules before marking a PR as "ready for review". The **bolded** ones are especially important: 1. **Give your pull request a helpful title** that summarizes what your @@ -414,13 +411,11 @@ complies with the following rules before marking a PR as ``[MRG]``. The non-regression tests should fail for the code base in the ``main`` branch and pass for the PR code. - 5. Follow the :ref:`coding-guidelines`. - -6. When applicable, use the validation tools and scripts in the - ``sklearn.utils`` submodule. A list of utility routines available - for developers can be found in the :ref:`developers-utils` page. +6. When applicable, use the validation tools and scripts in the :mod:`sklearn.utils` + module. A list of utility routines available for developers can be found in the + :ref:`developers-utils` page. 7. Often pull requests resolve one or more other issues (or pull requests). If merging your pull request means that some other issues/PRs should @@ -429,61 +424,59 @@ complies with the following rules before marking a PR as ``[MRG]``. The (e.g., ``Fixes #1234``; multiple issues/PRs are allowed as long as each one is preceded by a keyword). Upon merging, those issues/PRs will automatically be closed by GitHub. If your pull request is simply - related to some other issues/PRs, create a link to them without using - the keywords (e.g., ``See also #1234``). + related to some other issues/PRs, or it only partially resolves the target + issue, create a link to them without using the keywords (e.g., ``Towards #1234``). 8. PRs should often substantiate the change, through benchmarks of - performance and efficiency (see :ref:`monitoring_performances`) or through - examples of usage. Examples also illustrate the features and intricacies of - the library to users. Have a look at other examples in the `examples/ - `_ - directory for reference. Examples should demonstrate why the new - functionality is useful in practice and, if possible, compare it to other - methods available in scikit-learn. + performance and efficiency (see :ref:`monitoring_performances`) or through + examples of usage. Examples also illustrate the features and intricacies of + the library to users. Have a look at other examples in the `examples/ + `_ + directory for reference. Examples should demonstrate why the new + functionality is useful in practice and, if possible, compare it to other + methods available in scikit-learn. 9. New features have some maintenance overhead. We expect PR authors - to take part in the maintenance for the code they submit, at least - initially. New features need to be illustrated with narrative - documentation in the user guide, with small code snippets. - If relevant, please also add references in the literature, with PDF links - when possible. + to take part in the maintenance for the code they submit, at least + initially. New features need to be illustrated with narrative + documentation in the user guide, with small code snippets. + If relevant, please also add references in the literature, with PDF links + when possible. 10. The user guide should also include expected time and space complexity of the algorithm and scalability, e.g. "this algorithm can scale to a large number of samples > 100000, but does not scale in dimensionality: - n_features is expected to be lower than 100". + `n_features` is expected to be lower than 100". You can also check our :ref:`code_review` to get an idea of what reviewers will expect. You can check for common programming errors with the following tools: -* Code with a good unittest coverage (at least 80%, better 100%), check - with: +* Code with a good unit test coverage (at least 80%, better 100%), check with: - .. prompt:: bash $ + .. prompt:: bash pip install pytest pytest-cov - pytest --cov sklearn path/to/tests_for_package + pytest --cov sklearn path/to/tests - see also :ref:`testing_coverage` + See also :ref:`testing_coverage`. - Run static analysis with `mypy`: +* Run static analysis with `mypy`: - .. prompt:: bash $ + .. prompt:: bash mypy sklearn - must not produce new errors in your pull request. Using `# type: ignore` + This must not produce new errors in your pull request. Using `# type: ignore` annotation can be a workaround for a few cases that are not supported by mypy, in particular, - - when importing C or Cython modules - - on properties with decorators + - when importing C or Cython modules, + - on properties with decorators. Bonus points for contributions that include a performance analysis with a benchmark script and profiling output (see :ref:`monitoring_performances`). - Also check out the :ref:`performance-howto` guide for more details on profiling and Cython optimizations. @@ -494,7 +487,7 @@ profiling and Cython optimizations. on all new contributions will get the overall code base quality in the right direction. -.. note:: +.. seealso:: For two very well documented and more detailed guides on development workflow, please pay a visit to the `Scipy Development Workflow @@ -546,9 +539,7 @@ Stalled pull requests As contributing a feature can be a lengthy process, some pull requests appear inactive but unfinished. In such a case, taking -them over is a great service for the project. - -A good etiquette to take over is: +them over is a great service for the project. A good etiquette to take over is: * **Determine if a PR is stalled** @@ -615,33 +606,32 @@ the contributor become familiar with the contribution workflow, and for the core devs to become acquainted with the contributor; besides which, we frequently underestimate how easy an issue is to solve! -.. topic:: good first issue tag - - A great way to start contributing to scikit-learn is to pick an item from - the list of `good first issues - `_ - in the issue tracker. Resolving these issues allow you to start contributing - to the project without much prior knowledge. If you have already contributed - to scikit-learn, you should look at Easy issues instead. +- **Good first issue tag** -.. topic:: Easy tag + A great way to start contributing to scikit-learn is to pick an item from + the list of `good first issues + `_ + in the issue tracker. Resolving these issues allow you to start contributing + to the project without much prior knowledge. If you have already contributed + to scikit-learn, you should look at Easy issues instead. - If you have already contributed to scikit-learn, another great way to contribute - to scikit-learn is to pick an item from the list of `Easy issues - `_ in the issue - tracker. Your assistance in this area will be greatly appreciated by the - more experienced developers as it helps free up their time to concentrate on - other issues. +- **Easy tag** -.. topic:: help wanted tag + If you have already contributed to scikit-learn, another great way to contribute + to scikit-learn is to pick an item from the list of `Easy issues + `_ in the issue + tracker. Your assistance in this area will be greatly appreciated by the + more experienced developers as it helps free up their time to concentrate on + other issues. - We often use the help wanted tag to mark issues regardless of difficulty. Additionally, - we use the help wanted tag to mark Pull Requests which have been abandoned - by their original contributor and are available for someone to pick up where the original - contributor left off. The list of issues with the help wanted tag can be found - `here `_. +- **Help wanted tag** - Note that not all issues which need contributors will have this tag. + We often use the help wanted tag to mark issues regardless of difficulty. + Additionally, we use the help wanted tag to mark Pull Requests which have been + abandoned by their original contributor and are available for someone to pick up where + the original contributor left off. The list of issues with the help wanted tag can be + found `here `_. + Note that not all issues which need contributors will have this tag. .. _contribute_documentation: @@ -650,44 +640,49 @@ Documentation We are glad to accept any sort of documentation: -* **function/method/class docstrings** (also known as "API documentation") - - these describe what the object does and details any parameters, attributes and - methods. Docstrings live alongside the code in - `sklearn/ `_. -* **user guide** - these provide more detailed information about the algorithms +* **Function/method/class docstrings:** Also known as "API documentation", these + describe what the object does and details any parameters, attributes and + methods. Docstrings live alongside the code in `sklearn/ + `_, and are generated + generated according to `doc/api_reference.py + `_. To + add, update, remove, or deprecate a public API that is listed in :ref:`api_ref`, this + is the place to look at. +* **User guide:** These provide more detailed information about the algorithms implemented in scikit-learn and generally live in the root `doc/ `_ directory - and - `doc/modules/ `_. -* **tutorials** - these introduce various statistical learning and machine learning + and `doc/modules/ `_. +* **Tutorials:** These introduce various statistical learning and machine learning concepts and are located in `doc/tutorial `_. -* **examples** - these provide full code examples that may demonstrate the use +* **Examples:** These provide full code examples that may demonstrate the use of scikit-learn modules, compare different algorithms or discuss their - interpretation etc. Examples live in - `examples/ `_ -* **other reStructuredText documents** - provide various other - useful information (e.g., the :ref:`contributing` guide) and live in + interpretation, etc. Examples live in + `examples/ `_. +* **Other reStructuredText documents:** These provide various other useful information + (e.g., the :ref:`contributing` guide) and live in `doc/ `_. .. dropdown:: Guidelines for writing docstrings * When documenting the parameters and attributes, here is a list of some - well-formatted examples:: + well-formatted examples + + .. code-block:: text n_clusters : int, default=3 The number of clusters detected by the algorithm. - some_param : {'hello', 'goodbye'}, bool or int, default=True + some_param : {"hello", "goodbye"}, bool or int, default=True The parameter description goes here, which can be either a string literal (either `hello` or `goodbye`), a bool, or an int. The default value is True. - array_parameter : {array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples,) + array_parameter : {array-like, sparse matrix} of shape (n_samples, n_features) \ + or (n_samples,) This parameter accepts data in either of the mentioned forms, with one - of the mentioned shapes. The default value is - `np.ones(shape=(n_samples,))`. + of the mentioned shapes. The default value is `np.ones(shape=(n_samples,))`. list_param : list of int @@ -727,7 +722,9 @@ We are glad to accept any sort of documentation: * Add "See Also" in docstrings for related classes/functions. * "See Also" in docstrings should be one line per reference, with a colon and an - explanation, for example:: + explanation, for example: + + .. code-block:: text See Also -------- @@ -769,7 +766,9 @@ We are glad to accept any sort of documentation: backticks should be used nowadays. * Too much information makes it difficult for users to access the content they - are interested in. Use dropdowns to factorize it by using the following syntax:: + are interested in. Use dropdowns to factorize it by using the following syntax + + .. code-block:: rst .. dropdown:: Dropdown title @@ -808,13 +807,13 @@ We are glad to accept any sort of documentation: use the sphinx directives `:arxiv:` or `:doi:`. For example, see references in :ref:`Spectral Clustering Graphs `. - * For "References" in docstrings, see the Silhouette Coefficient - (:func:`sklearn.metrics.silhouette_score`). + * For the "References" section in docstrings, see + :func:`sklearn.metrics.silhouette_score` as an example. * To cross-reference to other pages in the scikit-learn documentation use the reStructuredText cross-referencing syntax: - * Section - to link to an arbitrary section in the documentation, use + * **Section:** to link to an arbitrary section in the documentation, use reference labels (see `Sphinx docs `_). For example: @@ -834,13 +833,13 @@ We are glad to accept any sort of documentation: existing cross references and external links pointing to specific sections in the scikit-learn documentation. - * Glossary - linking to a term in the :ref:`glossary`: + * **Glossary:** linking to a term in the :ref:`glossary`: .. code-block:: rst :term:`cross_validation` - * Function - to link to the documentation of a function, use the full import + * **Function:** to link to the documentation of a function, use the full import path to the function: .. code-block:: rst @@ -857,8 +856,8 @@ We are glad to accept any sort of documentation: :func:`cross_val_score` - * Class - to link to documentation of a class, use the full import path to the - class, unless there is a 'currentmodule' directive in the document above + * **Class:** to link to documentation of a class, use the full import path to the + class, unless there is a `.. currentmodule::` directive in the document above (see above): .. code-block:: rst @@ -867,8 +866,12 @@ We are glad to accept any sort of documentation: You can edit the documentation using any text editor, and then generate the HTML output by following :ref:`building_documentation`. The resulting HTML files -will be placed in ``_build/html/stable`` and are viewable in a web browser, for -instance by opening the local ``_build/html/stable/index.html`` file. +will be placed in ``_build/html/`` and are viewable in a web browser, for instance by +opening the local ``_build/html/index.html`` file or by running a local server + +.. prompt:: bash + + python -m http.server -d _build/html .. _building_documentation: @@ -879,15 +882,14 @@ Building the documentation **Before submitting a pull request check if your modifications have introduced new sphinx warnings by building the documentation locally and try to fix them.** -First, make sure you have :ref:`properly installed ` -the development version. +First, make sure you have :ref:`properly installed ` the +development version. On top of that, building the documentation requires installing some +additional packages: .. packaging is not needed once setuptools starts shipping packaging>=17.0 -Building the documentation requires installing some additional packages: - -.. prompt:: bash $ +.. prompt:: bash pip install sphinx sphinx-gallery numpydoc matplotlib Pillow pandas \ polars scikit-image packaging seaborn sphinx-prompt \ @@ -897,14 +899,14 @@ Building the documentation requires installing some additional packages: To build the documentation, you need to be in the ``doc`` folder: -.. prompt:: bash $ +.. prompt:: bash cd doc In the vast majority of cases, you only need to generate the full web site, without the example gallery: -.. prompt:: bash $ +.. prompt:: bash make @@ -913,25 +915,22 @@ and are viewable in a web browser, for instance by opening the local ``_build/html/stable/index.html`` file. To also generate the example gallery you can use: -.. prompt:: bash $ +.. prompt:: bash make html -This will run all the examples, which takes a while. If you only want to -generate a few examples, you can use: +This will run all the examples, which takes a while. If you only want to generate a few +examples, which is particularly useful if you are modifying only a few examples, you can +use: -.. prompt:: bash $ +.. prompt:: bash EXAMPLES_PATTERN=your_regex_goes_here make html -This is particularly useful if you are modifying a few examples. - -Set the environment variable `NO_MATHJAX=1` if you intend to view -the documentation in an offline setting. +Set the environment variable `NO_MATHJAX=1` if you intend to view the documentation in +an offline setting. To build the PDF manual, run: -To build the PDF manual, run: - -.. prompt:: bash $ +.. prompt:: bash make latexpdf @@ -977,18 +976,17 @@ subpackages. For a more detailed `pytest` workflow, please refer to the We expect code coverage of new features to be at least around 90%. -.. dropdown:: Writing matplotlib related tests +.. dropdown:: Writing matplotlib-related tests Test fixtures ensure that a set of tests will be executing with the appropriate - initialization and cleanup. The scikit-learn test suite implements a fixture - which can be used with ``matplotlib``. + initialization and cleanup. The scikit-learn test suite implements a ``pyplot`` + fixture which can be used with ``matplotlib``. - ``pyplot`` - The ``pyplot`` fixture should be used when a test function is dealing with - ``matplotlib``. ``matplotlib`` is a soft dependency and is not required. - This fixture is in charge of skipping the tests if ``matplotlib`` is not - installed. In addition, figures created during the tests will be - automatically closed once the test function has been executed. + The ``pyplot`` fixture should be used when a test function is dealing with + ``matplotlib``. ``matplotlib`` is a soft dependency and is not required. + This fixture is in charge of skipping the tests if ``matplotlib`` is not + installed. In addition, figures created during the tests will be + automatically closed once the test function has been executed. To use this fixture in a test function, one needs to pass it as an argument:: @@ -999,13 +997,13 @@ We expect code coverage of new features to be at least around 90%. .. dropdown:: Workflow to improve test coverage To test code coverage, you need to install the `coverage - `_ package in addition to pytest. + `_ package in addition to `pytest`. - 1. Run 'make test-coverage'. The output lists for each file the line - numbers that are not tested. + 1. Run `make test-coverage`. The output lists for each file the line + numbers that are not tested. 2. Find a low hanging fruit, looking at which lines are not tested, - write or adapt a test specifically for these lines. + write or adapt a test specifically for these lines. 3. Loop. @@ -1021,8 +1019,9 @@ When proposing changes to the existing code base, it's important to make sure that they don't introduce performance regressions. Scikit-learn uses `asv benchmarks `_ to monitor the performance of a selection of common estimators and functions. You can view -these benchmarks on the `scikit-learn benchmark page `_. -The corresponding benchmark suite can be found in the `scikit-learn/asv_benchmarks` directory. +these benchmarks on the `scikit-learn benchmark page +`_. +The corresponding benchmark suite can be found in the `asv_benchmarks/` directory. To use all features of asv, you will need either `conda` or `virtualenv`. For more details please check the `asv installation webpage @@ -1030,20 +1029,20 @@ more details please check the `asv installation webpage First of all you need to install the development version of asv: -.. prompt:: bash $ +.. prompt:: bash pip install git+https://github.com/airspeed-velocity/asv and change your directory to `asv_benchmarks/`: -.. prompt:: bash $ +.. prompt:: bash - cd asv_benchmarks/ + cd asv_benchmarks The benchmark suite is configured to run against your local clone of scikit-learn. Make sure it is up to date: -.. prompt:: bash $ +.. prompt:: bash git fetch upstream @@ -1051,20 +1050,20 @@ In the benchmark suite, the benchmarks are organized following the same structure as scikit-learn. For example, you can compare the performance of a specific estimator between ``upstream/main`` and the branch you are working on: -.. prompt:: bash $ +.. prompt:: bash asv continuous -b LogisticRegression upstream/main HEAD The command uses conda by default for creating the benchmark environments. If you want to use virtualenv instead, use the `-E` flag: -.. prompt:: bash $ +.. prompt:: bash asv continuous -E virtualenv -b LogisticRegression upstream/main HEAD You can also specify a whole module to benchmark: -.. prompt:: bash $ +.. prompt:: bash asv continuous -b linear_model upstream/main HEAD @@ -1074,7 +1073,7 @@ the `-f` flag. To run the full benchmark suite, simply remove the `-b` flag : -.. prompt:: bash $ +.. prompt:: bash asv continuous upstream/main HEAD @@ -1084,14 +1083,14 @@ expression for a more complex subset of benchmarks to run. To run the benchmarks without comparing to another branch, use the `run` command: -.. prompt:: bash $ +.. prompt:: bash asv run -b linear_model HEAD^! You can also run the benchmark suite using the version of scikit-learn already installed in your current Python environment: -.. prompt:: bash $ +.. prompt:: bash asv run --python=same @@ -1100,20 +1099,20 @@ avoid creating a new environment each time you run the benchmarks. By default the results are not saved when using an existing installation. To save the results you must specify a commit hash: -.. prompt:: bash $ +.. prompt:: bash asv run --python=same --set-commit-hash= Benchmarks are saved and organized by machine, environment and commit. To see the list of all saved benchmarks: -.. prompt:: bash $ +.. prompt:: bash asv show and to see the report of a specific run: -.. prompt:: bash $ +.. prompt:: bash asv show @@ -1136,11 +1135,11 @@ All issues and pull requests on the `GitHub issue tracker `_ should have (at least) one of the following tags: -:Bug / Crash: +:Bug: Something is happening that clearly shouldn't happen. Wrong results as well as unexpected errors from estimators go here. -:Cleanup / Enhancement: +:Enhancement: Improving performance, usability, consistency. :Documentation: @@ -1151,7 +1150,7 @@ should have (at least) one of the following tags: There are four other tags to help new contributors: -:good first issue: +:Good first issue: This issue is ideal for a first contribution to scikit-learn. Ask for help if the formulation is unclear. If you have already contributed to scikit-learn, look at Easy issues instead. @@ -1163,7 +1162,7 @@ There are four other tags to help new contributors: Might need some knowledge of machine learning or the package, but is still approachable for someone new to the project. -:help wanted: +:Help wanted: This tag marks an issue which currently lacks a contributor or a PR that needs another contributor to take over the work. These issues can range in difficulty, and may not be approachable @@ -1180,12 +1179,15 @@ Maintaining backwards compatibility Deprecation ----------- -If any publicly accessible method, function, attribute or parameter -is renamed, we still support the old one for two releases and issue -a deprecation warning when it is called/passed/accessed. -E.g., if the function ``zero_one`` is renamed to ``zero_one_loss``, -we add the decorator ``deprecated`` (from ``sklearn.utils``) -to ``zero_one`` and call ``zero_one_loss`` from that function:: +If any publicly accessible class, function, method, attribute or parameter is renamed, +we still support the old one for two releases and issue a deprecation warning when it is +called, passed, or accessed. + +.. rubric:: Deprecating a class or a function + +Suppose the function ``zero_one`` is renamed to ``zero_one_loss``, we add the decorator +:class:`utils.deprecated` to ``zero_one`` and call ``zero_one_loss`` from that +function:: from ..utils import deprecated @@ -1193,36 +1195,47 @@ to ``zero_one`` and call ``zero_one_loss`` from that function:: # actual implementation pass - @deprecated("Function 'zero_one' was renamed to 'zero_one_loss' " - "in version 0.13 and will be removed in release 0.15. " - "Default behavior is changed from 'normalize=False' to " - "'normalize=True'") + @deprecated( + "Function `zero_one` was renamed to `zero_one_loss` in 0.13 and will be " + "removed in 0.15. Default behavior is changed from `normalize=False` to " + "`normalize=True`" + ) def zero_one(y_true, y_pred, normalize=False): return zero_one_loss(y_true, y_pred, normalize) -If an attribute is to be deprecated, -use the decorator ``deprecated`` on a property. Please note that the -``deprecated`` decorator should be placed before the ``property`` -decorator for the docstrings to be rendered properly. -E.g., renaming an attribute ``labels_`` to ``classes_`` can be done as:: +One also needs to move ``zero_one`` from ``API_REFERENCE`` to +``DEPRECATED_API_REFERENCE`` and add ``zero_one_loss`` to ``API_REFERENCE`` in the +``doc/api_reference.py`` file to reflect the changes in :ref:`api_ref`. + +.. rubric:: Deprecating an attribute or a method + +If an attribute or a method is to be deprecated, use the decorator +:class:`~utils.deprecated` on the property. Please note that the +:class:`~utils.deprecated` decorator should be placed before the ``property`` decorator +if there is one, so that the docstrings can be rendered properly. For instance, renaming +an attribute ``labels_`` to ``classes_`` can be done as:: - @deprecated("Attribute `labels_` was deprecated in version 0.13 and " - "will be removed in 0.15. Use `classes_` instead") + @deprecated( + "Attribute `labels_` was deprecated in 0.13 and will be removed in 0.15. Use " + "`classes_` instead" + ) @property def labels_(self): return self.classes_ -If a parameter has to be deprecated, a ``FutureWarning`` warning -must be raised too. -In the following example, k is deprecated and renamed to n_clusters:: +.. rubric:: Deprecating a parameter + +If a parameter has to be deprecated, a ``FutureWarning`` warning must be raised +manually. In the following example, ``k`` is deprecated and renamed to n_clusters:: import warnings - def example_function(n_clusters=8, k='deprecated'): - if k != 'deprecated': - warnings.warn("'k' was renamed to n_clusters in version 0.13 and " - "will be removed in 0.15.", - FutureWarning) + def example_function(n_clusters=8, k="deprecated"): + if k != "deprecated": + warnings.warn( + "`k` was renamed to `n_clusters` in 0.13 and will be removed in 0.15", + FutureWarning, + ) n_clusters = k When the change is in a class, we validate and raise warning in ``fit``:: @@ -1235,10 +1248,11 @@ When the change is in a class, we validate and raise warning in ``fit``:: self.k = k def fit(self, X, y): - if self.k != 'deprecated': - warnings.warn("'k' was renamed to n_clusters in version 0.13 and " - "will be removed in 0.15.", - FutureWarning) + if self.k != "deprecated": + warnings.warn( + "`k` was renamed to `n_clusters` in 0.13 and will be removed in 0.15.", + FutureWarning, + ) self._n_clusters = self.k else: self._n_clusters = self.n_clusters @@ -1252,9 +1266,14 @@ adapt their code to the new behaviour. For example, if the deprecation happened in version 0.18-dev, the message should say it happened in version 0.18 and the old behavior will be removed in version 0.20. +The warning message should also include a brief explanation of the change and point +users to an alternative. + In addition, a deprecation note should be added in the docstring, recalling the same information as the deprecation warning as explained above. Use the -``.. deprecated::`` directive:: +``.. deprecated::`` directive: + +.. code-block:: rst .. deprecated:: 0.13 ``k`` was renamed to ``n_clusters`` in version 0.13 and will be removed @@ -1270,7 +1289,7 @@ Change the default value of a parameter --------------------------------------- If the default value of a parameter needs to be changed, please replace the -default value with a specific value (e.g., ``warn``) and raise +default value with a specific value (e.g., ``"warn"``) and raise ``FutureWarning`` when users are using the default value. The following example assumes that the current version is 0.20 and that we change the default value of ``n_clusters`` from 5 (old default for 0.20) to 10 @@ -1278,10 +1297,12 @@ default value of ``n_clusters`` from 5 (old default for 0.20) to 10 import warnings - def example_function(n_clusters='warn'): - if n_clusters == 'warn': - warnings.warn("The default value of n_clusters will change from " - "5 to 10 in 0.22.", FutureWarning) + def example_function(n_clusters="warn"): + if n_clusters == "warn": + warnings.warn( + "The default value of `n_clusters` will change from 5 to 10 in 0.22.", + FutureWarning, + ) n_clusters = 5 When the change is in a class, we validate and raise warning in ``fit``:: @@ -1289,22 +1310,26 @@ When the change is in a class, we validate and raise warning in ``fit``:: import warnings class ExampleEstimator: - def __init__(self, n_clusters='warn'): + def __init__(self, n_clusters="warn"): self.n_clusters = n_clusters def fit(self, X, y): - if self.n_clusters == 'warn': - warnings.warn("The default value of n_clusters will change from " - "5 to 10 in 0.22.", FutureWarning) - self._n_clusters = 5 + if self.n_clusters == "warn": + warnings.warn( + "The default value of `n_clusters` will change from 5 to 10 in 0.22.", + FutureWarning, + ) + self._n_clusters = 5 Similar to deprecations, the warning message should always give both the version in which the change happened and the version in which the old behavior will be removed. The parameter description in the docstring needs to be updated accordingly by adding -a `versionchanged` directive with the old and new default value, pointing to the -version when the change will be effective:: +a ``versionchanged`` directive with the old and new default value, pointing to the +version when the change will be effective: + +.. code-block:: rst .. versionchanged:: 0.22 The default value for `n_clusters` will change from 5 to 10 in version 0.22. @@ -1314,12 +1339,11 @@ not in other cases. The warning should be caught in all other tests (using e.g., ``@pytest.mark.filterwarnings``), and there should be no warning in the examples. -.. currentmodule:: sklearn - .. _code_review: Code Review Guidelines ====================== + Reviewing code contributed to the project as PRs is a crucial component of scikit-learn development. We encourage anyone to start reviewing code of other developers. The code review process is often highly educational for everybody @@ -1438,9 +1462,9 @@ make this task easier and faster (in no particular order). relevant, and which are not. In scikit-learn **a lot** of input checking is performed, especially at the beginning of the :term:`fit` methods. Sometimes, only a very small portion of the code is doing the actual job. - For example looking at the ``fit()`` method of + For example looking at the :meth:`~linear_model.LinearRegression.fit` method of :class:`~linear_model.LinearRegression`, what you're looking for - might just be the call the ``scipy.linalg.lstsq``, but it is buried into + might just be the call the :func:`scipy.linalg.lstsq`, but it is buried into multiple lines of input checking and the handling of different kinds of parameters. - Due to the use of `Inheritance @@ -1470,7 +1494,7 @@ make this task easier and faster (in no particular order). IDE goes a long way towards digesting the code base. Being able to quickly jump (or *peek*) to a function/class/attribute definition helps a lot. So does being able to quickly see where a given name is used in a file. - - `git `_ also has some built-in killer + - `Git `_ also has some built-in killer features. It is often useful to understand how a file changed over time, using e.g. ``git blame`` (`manual `_). This can also be done directly @@ -1482,7 +1506,7 @@ make this task easier and faster (in no particular order). - Configure `git blame` to ignore the commit that migrated the code style to `black`. - .. prompt:: bash $ + .. prompt:: bash git config blame.ignoreRevsFile .git-blame-ignore-revs From df01bd7df1b89c7c48dc0e7bf08893f2e01c8416 Mon Sep 17 00:00:00 2001 From: Yao Xiao <108576690+Charlie-XIAO@users.noreply.github.com> Date: Wed, 29 May 2024 18:00:06 +0800 Subject: [PATCH 072/275] DOC reflect deprecations in 1.5 on API page (#29125) --- doc/api_reference.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/doc/api_reference.py b/doc/api_reference.py index 1aa6455fb7e44..d22f2d5aa8d13 100644 --- a/doc/api_reference.py +++ b/doc/api_reference.py @@ -1284,8 +1284,6 @@ def _get_submodule(module_name, submodule_name): "autosummary": [ "parallel.Parallel", "parallel.delayed", - "parallel_backend", - "register_parallel_backend", ], }, ], @@ -1328,4 +1326,9 @@ def _get_submodule(module_name, submodule_name): } """ -DEPRECATED_API_REFERENCE = {} # type: ignore +DEPRECATED_API_REFERENCE = { + "1.7": [ + "utils.parallel_backend", + "utils.register_parallel_backend", + ] +} # type: ignore From e5d81777244c6c93cac6ab4764f5e69b6f2854c5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Fri, 31 May 2024 09:39:56 +0200 Subject: [PATCH 073/275] DOC Remove unneeded html_show_search_summary since True is the default (#29139) --- doc/conf.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/doc/conf.py b/doc/conf.py index f025c77dcce0c..5e328ebd69b7b 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -456,9 +456,6 @@ def add_js_css_files(app, pagename, templatename, context, doctree): # See https://github.com/scikit-learn/scikit-learn/pull/22550 html_context["is_devrelease"] = parsed_version.is_devrelease -# Not showing the search summary makes the search page load faster. -html_show_search_summary = True - # -- Options for LaTeX output ------------------------------------------------ latex_elements = { From a2d0f5a2ed1955e3c3f729db166d947cff6a29dc Mon Sep 17 00:00:00 2001 From: Yao Xiao <108576690+Charlie-XIAO@users.noreply.github.com> Date: Fri, 31 May 2024 15:41:13 +0800 Subject: [PATCH 074/275] DOC bump `pydata-sphinx-theme` to 0.15.3 (#29134) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- build_tools/circle/doc_linux-64_conda.lock | 17 +- .../doc_min_dependencies_environment.yml | 2 +- .../doc_min_dependencies_linux-64_conda.lock | 13 +- doc/conf.py | 4 +- doc/js/searchtools.js | 628 ------------------ doc/scss/api-search.scss | 9 +- doc/scss/index.scss | 1 + doc/scss/install.scss | 27 +- pyproject.toml | 2 +- sklearn/_min_dependencies.py | 2 +- 10 files changed, 30 insertions(+), 675 deletions(-) delete mode 100644 doc/js/searchtools.js diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index 1652c4a4f395d..0d925375b643b 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -56,7 +56,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-12.3.0-hb8811af_7.c https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.conda#b26e8aa824079e1be0294e7152ca4559 https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc -https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.13-hd590300_5.conda#f36c115f1ee199da648e0597ec2047ad +https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.13-h4ab18f5_6.conda#27329162c0dc732bcf67a4e0cd488125 https://conda.anaconda.org/conda-forge/linux-64/libzopfli-1.0.3-h9c3ff4c_0.tar.bz2#c66fe2d123249af7651ebde8984c51c2 https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda#318b08df404f9c9be5712aaa5a6f0bb0 https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9160cdeb523a1b20cf8d2a0bf821f45d @@ -101,7 +101,7 @@ https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.43-hcad00b1_0.conda#829 https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda#47d31b792659ce70f470b5c82fdfb7a4 https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda#d453b98d9c83e71da0741bb0ff4d76bc https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.4-h7391055_0.conda#93ee23f12bc2e684548181256edd2cf6 -https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.13-hd590300_5.conda#68c34ec6149623be41a1933ab996a209 +https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.13-h4ab18f5_6.conda#559d338a4234c2ad6e676f460a093e67 https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda#4d056880988120e29d75bfff282e0f45 https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.5-hc2324a3_1.conda#11d76bee958b1989bd1ac6ee7372ea6d https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hd590300_1.conda#39f910d205726805a958da408ca194ba @@ -199,7 +199,7 @@ https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.12.3-pyha770c72_0 https://conda.anaconda.org/conda-forge/linux-64/brunsli-0.1-h9c3ff4c_0.tar.bz2#c1ac6229d0bfd14f8354ff9ad2a26cad https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-h3faef2a_0.conda#f907bb958910dc404647326ca80c263e https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.7.0-h00ab1b0_1.conda#28de2e073db9ca9b72858bee9fb6f571 -https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.52.1-py39hd3abc70_0.conda#66b6088c2446c25e714829a289070499 +https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.52.4-py39hd3abc70_0.conda#2e309d4c5736d32dfb1a1afccb4fea66 https://conda.anaconda.org/conda-forge/linux-64/fortran-compiler-1.7.0-heb67821_1.conda#cf4b0e7c4c78bb0662aed9b27c414a3c https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.2-hf974151_0.conda#d427988dc3dbd0a4c136f52db356cc6a https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-7.1.0-pyha770c72_0.conda#0896606848b2dc5cebdf111b6543aa04 @@ -220,10 +220,9 @@ https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1a https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb -https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.11.0-hd8ed1ab_0.conda#471e3988f8ca5e9eb3ce6be7eac3bcee https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.1-pyhd8ed1ab_0.conda#08807a87fa7af10754d46f63b368e016 https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_1.conda#d8d07866ac3b5b6937213c89a1874f08 -https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.3-haf2f30d_0.conda#f3df87cc9ef0b5113bff55aefcbcafd5 +https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.4-haf2f30d_0.conda#926c2c7ee7a0b48d6d70783a33f7bc80 https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.5.0-hfac3d4d_0.conda#f5126317dd0ce0ba26945e411ecc6960 https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.0-pyhd8ed1ab_0.conda#dcbadab7a68738a028e195ab68ab2d2e https://conda.anaconda.org/conda-forge/noarch/lazy_loader-0.4-pyhd8ed1ab_0.conda#a284ff318fbdb0dd83928275b4b6087c @@ -236,12 +235,12 @@ https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.co https://conda.anaconda.org/conda-forge/noarch/requests-2.32.2-pyhd8ed1ab_0.conda#e1643b34b19df8c028a4f00bf5df58a6 https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-22_linux64_openblas.conda#63ddb593595c9cf5eb08d3de54d66df8 https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py39h7633fee_0.conda#bdc188e59857d6efab332714e0d01d93 -https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.3-h9ad1361_0.conda#8fb0e954c616bb0f9389efac4b4ed44b -https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.1.1-py39ha98d97a_6.conda#9ada409e8a8202f848abfed8e4e3f6be +https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.4-h9ad1361_0.conda#147cce520ec59367549fd0d96d404213 +https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.1.1-py39hbbab4d9_7.conda#ade05a8093fc3a23c5637f433706141e https://conda.anaconda.org/conda-forge/noarch/imageio-2.34.1-pyh4b66e23_0.conda#bcf6a6f4c6889ca083e8d33afbafb8d5 https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.2-py39hfc16268_1.conda#8b23d2b425035a7468d17e6fe1d54124 https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.6-pyhd8ed1ab_0.conda#a5b55d1cb110cdcedc748b5c3e16e687 -https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.29-py39ha963410_0.conda#fb87101281e618d404e70cad4e5c5c88 +https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.30-py39ha963410_0.conda#322084e8890afc27fcca6df7a528df25 https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.1-pyhd8ed1ab_0.conda#d15917f33140f8d2ac9ca44db7ec8a25 https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-client-17.0-hb77b528_0.conda#07f45f1be1c25345faddb8db0de8039b https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.4.1-py39h44dd56e_1.conda#d037c20e3da2e85f03ebd20ad480c359 @@ -258,7 +257,7 @@ https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_2.c https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.8.4-py39hf3d152e_2.conda#bd956c7563b6a6b27521b83623c74e22 https://conda.anaconda.org/conda-forge/noarch/seaborn-0.13.2-hd8ed1ab_2.conda#a79d8797f62715255308d92d3a91ef2e https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.7.0-pyhd8ed1ab_0.conda#1ad3afced398492586ca1bef70328be4 -https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.15.2-pyhd8ed1ab_0.conda#ce99859070b0e17ccc63234ca58f3ed8 +https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.15.3-pyhd8ed1ab_0.conda#55e445f4fcb07f2471fb0e1102d36488 https://conda.anaconda.org/conda-forge/noarch/sphinx-copybutton-0.5.2-pyhd8ed1ab_0.conda#ac832cc43adc79118cf6e23f1f9b8995 https://conda.anaconda.org/conda-forge/noarch/sphinx-design-0.5.0-pyhd8ed1ab_0.conda#264b3c697fa9cdade87eb0abe4440d54 https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.16.0-pyhd8ed1ab_0.conda#add28691ee89e875b190eda07929d5d4 diff --git a/build_tools/circle/doc_min_dependencies_environment.yml b/build_tools/circle/doc_min_dependencies_environment.yml index e4824024381f4..e27c3a700fdad 100644 --- a/build_tools/circle/doc_min_dependencies_environment.yml +++ b/build_tools/circle/doc_min_dependencies_environment.yml @@ -34,7 +34,7 @@ dependencies: - pooch=1.6.0 # min - sphinx-remove-toctrees=1.0.0.post1 # min - sphinx-design=0.5.0 # min - - pydata-sphinx-theme=0.15.2 # min + - pydata-sphinx-theme=0.15.3 # min - pip - pip: - sphinxext-opengraph==0.9.1 # min diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index b9754f92f73e3..3708a86ff6a7a 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 5d01f25ac5aaf25812f967948c86aafae507b69a57ec6ba448270e1b598886af +# input_hash: aa64e81a701c97b7c4cf149f108c3ca59fc65572bfda79dbaeb2d093afc8a665 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.2.2-hbcca054_0.conda#2f4327a1cbe7f022401b236e915a5fef @@ -49,7 +49,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-12.3.0-hb8811af_7.c https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.conda#b26e8aa824079e1be0294e7152ca4559 https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc -https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.13-hd590300_5.conda#f36c115f1ee199da648e0597ec2047ad +https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.13-h4ab18f5_6.conda#27329162c0dc732bcf67a4e0cd488125 https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda#318b08df404f9c9be5712aaa5a6f0bb0 https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9160cdeb523a1b20cf8d2a0bf821f45d https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 @@ -86,7 +86,7 @@ https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.43-hcad00b1_0.conda#829 https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda#47d31b792659ce70f470b5c82fdfb7a4 https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda#d453b98d9c83e71da0741bb0ff4d76bc https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.4-h7391055_0.conda#93ee23f12bc2e684548181256edd2cf6 -https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.13-hd590300_5.conda#68c34ec6149623be41a1933ab996a209 +https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.13-h4ab18f5_6.conda#559d338a4234c2ad6e676f460a093e67 https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda#4d056880988120e29d75bfff282e0f45 https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-h267a509_2.conda#9ae35c3d96db2c94ce0cef86efdfa2cb https://conda.anaconda.org/conda-forge/linux-64/gcc-12.3.0-h915e2ae_7.conda#84b1c5cebd0a0443f3d7f90a4be93fc6 @@ -199,10 +199,9 @@ https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1a https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb -https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.11.0-hd8ed1ab_0.conda#471e3988f8ca5e9eb3ce6be7eac3bcee https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.1-pyhd8ed1ab_0.conda#08807a87fa7af10754d46f63b368e016 https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_1.conda#d8d07866ac3b5b6937213c89a1874f08 -https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.3-haf2f30d_0.conda#f3df87cc9ef0b5113bff55aefcbcafd5 +https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.4-haf2f30d_0.conda#926c2c7ee7a0b48d6d70783a33f7bc80 https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.5.0-hfac3d4d_0.conda#f5126317dd0ce0ba26945e411ecc6960 https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-7.1.0-hd8ed1ab_0.conda#6ef2b72d291b39e479d7694efa2b2b98 https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-22_linux64_mkl.conda#eb6deb4ba6f92ea3f31c09cb8b764738 @@ -213,7 +212,7 @@ https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b https://conda.anaconda.org/conda-forge/noarch/requests-2.32.2-pyhd8ed1ab_0.conda#e1643b34b19df8c028a4f00bf5df58a6 https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.5.1-pyhd8ed1ab_0.conda#d4f60ccc5421472d2583efd9ce39d8b1 -https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.3-h9ad1361_0.conda#8fb0e954c616bb0f9389efac4b4ed44b +https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.4-h9ad1361_0.conda#147cce520ec59367549fd0d96d404213 https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-22_linux64_mkl.conda#d6f942423116553f068b2f2d93ffea2e https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-22_linux64_mkl.conda#4edf2e7ce63920e4f539d12e32fb478e https://conda.anaconda.org/conda-forge/noarch/pooch-1.6.0-pyhd8ed1ab_0.tar.bz2#6429e1d1091c51f626b5dcfdd38bf429 @@ -240,7 +239,7 @@ https://conda.anaconda.org/conda-forge/noarch/tifffile-2020.6.3-py_0.tar.bz2#1fb https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.17.2-py39hde0f152_4.tar.bz2#2a58a7e382317b03f023b2fddf40f8a1 https://conda.anaconda.org/conda-forge/noarch/seaborn-0.12.2-hd8ed1ab_0.conda#50847a47c07812f88581081c620f5160 https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.2-pyhd8ed1ab_0.tar.bz2#025ad7ca2c7f65007ab6b6f5d93a56eb -https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.15.2-pyhd8ed1ab_0.conda#ce99859070b0e17ccc63234ca58f3ed8 +https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.15.3-pyhd8ed1ab_0.conda#55e445f4fcb07f2471fb0e1102d36488 https://conda.anaconda.org/conda-forge/noarch/sphinx-copybutton-0.5.2-pyhd8ed1ab_0.conda#ac832cc43adc79118cf6e23f1f9b8995 https://conda.anaconda.org/conda-forge/noarch/sphinx-design-0.5.0-pyhd8ed1ab_0.conda#264b3c697fa9cdade87eb0abe4440d54 https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.16.0-pyhd8ed1ab_0.conda#add28691ee89e875b190eda07929d5d4 diff --git a/doc/conf.py b/doc/conf.py index 5e328ebd69b7b..db79bfe3f7865 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -247,8 +247,8 @@ # check_switcher may be set to False if docbuild pipeline fails. See # https://pydata-sphinx-theme.readthedocs.io/en/stable/user_guide/version-dropdown.html#configure-switcher-json-url "check_switcher": True, - "pygment_light_style": "tango", - "pygment_dark_style": "monokai", + "pygments_light_style": "tango", + "pygments_dark_style": "monokai", "logo": { "alt_text": "scikit-learn homepage", "image_relative": "logos/scikit-learn-logo-small.png", diff --git a/doc/js/searchtools.js b/doc/js/searchtools.js deleted file mode 100644 index ca53342fb4788..0000000000000 --- a/doc/js/searchtools.js +++ /dev/null @@ -1,628 +0,0 @@ -/* - * searchtools.js - * ~~~~~~~~~~~~~~~~ - * - * This is vendored from Sphinx 7.3.7 (subject to formatting with `prettier`). The - * original file is available at: - * https://github.com/sphinx-doc/sphinx/blob/v7.3.7/sphinx/themes/basic/static/searchtools.js - * - * This file is used to override the default `searchtools.js`. Every occurrence of - * `[role="main"]` is replaced with `#main-content` to make searching summary work with - * `pydata-sphinx-theme==0.15.2`. After upgrading to `pydata-sphinx-theme>=0.15.3` this - * file should be no longer needed. - * - * Sphinx JavaScript utilities for the full-text search. - * - * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ -"use strict"; - -/** - * Simple result scoring code. - */ -if (typeof Scorer === "undefined") { - var Scorer = { - // Implement the following function to further tweak the score for each result - // The function takes a result array [docname, title, anchor, descr, score, filename] - // and returns the new score. - /* - score: result => { - const [docname, title, anchor, descr, score, filename] = result - return score - }, - */ - - // query matches the full name of an object - objNameMatch: 11, - // or matches in the last dotted part of the object name - objPartialMatch: 6, - // Additive scores depending on the priority of the object - objPrio: { - 0: 15, // used to be importantResults - 1: 5, // used to be objectResults - 2: -5, // used to be unimportantResults - }, - // Used when the priority is not in the mapping. - objPrioDefault: 0, - - // query found in title - title: 15, - partialTitle: 7, - // query found in terms - term: 5, - partialTerm: 2, - }; -} - -const _removeChildren = (element) => { - while (element && element.lastChild) element.removeChild(element.lastChild); -}; - -/** - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping - */ -const _escapeRegExp = (string) => - string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string - -const _displayItem = (item, searchTerms, highlightTerms) => { - const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; - const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; - const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; - const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; - const contentRoot = document.documentElement.dataset.content_root; - - const [docName, title, anchor, descr, score, _filename] = item; - - let listItem = document.createElement("li"); - let requestUrl; - let linkUrl; - if (docBuilder === "dirhtml") { - // dirhtml builder - let dirname = docName + "/"; - if (dirname.match(/\/index\/$/)) - dirname = dirname.substring(0, dirname.length - 6); - else if (dirname === "index/") dirname = ""; - requestUrl = contentRoot + dirname; - linkUrl = requestUrl; - } else { - // normal html builders - requestUrl = contentRoot + docName + docFileSuffix; - linkUrl = docName + docLinkSuffix; - } - let linkEl = listItem.appendChild(document.createElement("a")); - linkEl.href = linkUrl + anchor; - linkEl.dataset.score = score; - linkEl.innerHTML = title; - if (descr) { - listItem.appendChild(document.createElement("span")).innerHTML = - " (" + descr + ")"; - // highlight search terms in the description - if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js - highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); - } - else if (showSearchSummary) - fetch(requestUrl) - .then((responseData) => responseData.text()) - .then((data) => { - if (data) - listItem.appendChild( - Search.makeSearchSummary(data, searchTerms, anchor) - ); - // highlight search terms in the summary - if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js - highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); - }); - Search.output.appendChild(listItem); -}; -const _finishSearch = (resultCount) => { - Search.stopPulse(); - Search.title.innerText = _("Search Results"); - if (!resultCount) - Search.status.innerText = Documentation.gettext( - "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." - ); - else - Search.status.innerText = _( - "Search finished, found ${resultCount} page(s) matching the search query." - ).replace('${resultCount}', resultCount); -}; -const _displayNextItem = ( - results, - resultCount, - searchTerms, - highlightTerms, -) => { - // results left, load the summary and display it - // this is intended to be dynamic (don't sub resultsCount) - if (results.length) { - _displayItem(results.pop(), searchTerms, highlightTerms); - setTimeout( - () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), - 5 - ); - } - // search finished, update title and status message - else _finishSearch(resultCount); -}; -// Helper function used by query() to order search results. -// Each input is an array of [docname, title, anchor, descr, score, filename]. -// Order the results by score (in opposite order of appearance, since the -// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. -const _orderResultsByScoreThenName = (a, b) => { - const leftScore = a[4]; - const rightScore = b[4]; - if (leftScore === rightScore) { - // same score: sort alphabetically - const leftTitle = a[1].toLowerCase(); - const rightTitle = b[1].toLowerCase(); - if (leftTitle === rightTitle) return 0; - return leftTitle > rightTitle ? -1 : 1; // inverted is intentional - } - return leftScore > rightScore ? 1 : -1; -}; - -/** - * Default splitQuery function. Can be overridden in ``sphinx.search`` with a - * custom function per language. - * - * The regular expression works by splitting the string on consecutive characters - * that are not Unicode letters, numbers, underscores, or emoji characters. - * This is the same as ``\W+`` in Python, preserving the surrogate pair area. - */ -if (typeof splitQuery === "undefined") { - var splitQuery = (query) => query - .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) - .filter(term => term) // remove remaining empty strings -} - -/** - * Search Module - */ -const Search = { - _index: null, - _queued_query: null, - _pulse_status: -1, - - htmlToText: (htmlString, anchor) => { - const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); - for (const removalQuery of [".headerlinks", "script", "style"]) { - htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); - } - if (anchor) { - const anchorContent = htmlElement.querySelector(`#main-content ${anchor}`); - if (anchorContent) return anchorContent.textContent; - - console.warn( - `Anchored content block not found. Sphinx search tries to obtain it via DOM query '#main-content ${anchor}'. Check your theme or template.` - ); - } - - // if anchor not specified or not found, fall back to main content - const docContent = htmlElement.querySelector("#main-content"); - if (docContent) return docContent.textContent; - - console.warn( - "Content block not found. Sphinx search tries to obtain it via DOM query '#main-content'. Check your theme or template." - ); - return ""; - }, - - init: () => { - const query = new URLSearchParams(window.location.search).get("q"); - document - .querySelectorAll('input[name="q"]') - .forEach((el) => (el.value = query)); - if (query) Search.performSearch(query); - }, - - loadIndex: (url) => - (document.body.appendChild(document.createElement("script")).src = url), - - setIndex: (index) => { - Search._index = index; - if (Search._queued_query !== null) { - const query = Search._queued_query; - Search._queued_query = null; - Search.query(query); - } - }, - - hasIndex: () => Search._index !== null, - - deferQuery: (query) => (Search._queued_query = query), - - stopPulse: () => (Search._pulse_status = -1), - - startPulse: () => { - if (Search._pulse_status >= 0) return; - - const pulse = () => { - Search._pulse_status = (Search._pulse_status + 1) % 4; - Search.dots.innerText = ".".repeat(Search._pulse_status); - if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); - }; - pulse(); - }, - - /** - * perform a search for something (or wait until index is loaded) - */ - performSearch: (query) => { - // create the required interface elements - const searchText = document.createElement("h2"); - searchText.textContent = _("Searching"); - const searchSummary = document.createElement("p"); - searchSummary.classList.add("search-summary"); - searchSummary.innerText = ""; - const searchList = document.createElement("ul"); - searchList.classList.add("search"); - - const out = document.getElementById("search-results"); - Search.title = out.appendChild(searchText); - Search.dots = Search.title.appendChild(document.createElement("span")); - Search.status = out.appendChild(searchSummary); - Search.output = out.appendChild(searchList); - - const searchProgress = document.getElementById("search-progress"); - // Some themes don't use the search progress node - if (searchProgress) { - searchProgress.innerText = _("Preparing search..."); - } - Search.startPulse(); - - // index already loaded, the browser was quick! - if (Search.hasIndex()) Search.query(query); - else Search.deferQuery(query); - }, - - _parseQuery: (query) => { - // stem the search terms and add them to the correct list - const stemmer = new Stemmer(); - const searchTerms = new Set(); - const excludedTerms = new Set(); - const highlightTerms = new Set(); - const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); - splitQuery(query.trim()).forEach((queryTerm) => { - const queryTermLower = queryTerm.toLowerCase(); - - // maybe skip this "word" - // stopwords array is from language_data.js - if ( - stopwords.indexOf(queryTermLower) !== -1 || - queryTerm.match(/^\d+$/) - ) - return; - - // stem the word - let word = stemmer.stemWord(queryTermLower); - // select the correct list - if (word[0] === "-") excludedTerms.add(word.substr(1)); - else { - searchTerms.add(word); - highlightTerms.add(queryTermLower); - } - }); - - if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js - localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) - } - - // console.debug("SEARCH: searching for:"); - // console.info("required: ", [...searchTerms]); - // console.info("excluded: ", [...excludedTerms]); - - return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; - }, - - /** - * execute search (requires search index to be loaded) - */ - _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - const allTitles = Search._index.alltitles; - const indexEntries = Search._index.indexentries; - - // Collect multiple result groups to be sorted separately and then ordered. - // Each is an array of [docname, title, anchor, descr, score, filename]. - const normalResults = []; - const nonMainIndexResults = []; - - _removeChildren(document.getElementById("search-progress")); - - const queryLower = query.toLowerCase().trim(); - for (const [title, foundTitles] of Object.entries(allTitles)) { - if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { - for (const [file, id] of foundTitles) { - let score = Math.round(100 * queryLower.length / title.length) - normalResults.push([ - docNames[file], - titles[file] !== title ? `${titles[file]} > ${title}` : title, - id !== null ? "#" + id : "", - null, - score, - filenames[file], - ]); - } - } - } - - // search for explicit entries in index directives - for (const [entry, foundEntries] of Object.entries(indexEntries)) { - if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { - for (const [file, id, isMain] of foundEntries) { - const score = Math.round(100 * queryLower.length / entry.length); - const result = [ - docNames[file], - titles[file], - id ? "#" + id : "", - null, - score, - filenames[file], - ]; - if (isMain) { - normalResults.push(result); - } else { - nonMainIndexResults.push(result); - } - } - } - } - - // lookup as object - objectTerms.forEach((term) => - normalResults.push(...Search.performObjectSearch(term, objectTerms)) - ); - - // lookup as search terms in fulltext - normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); - - // let the scorer override scores with a custom scoring function - if (Scorer.score) { - normalResults.forEach((item) => (item[4] = Scorer.score(item))); - nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); - } - - // Sort each group of results by score and then alphabetically by name. - normalResults.sort(_orderResultsByScoreThenName); - nonMainIndexResults.sort(_orderResultsByScoreThenName); - - // Combine the result groups in (reverse) order. - // Non-main index entries are typically arbitrary cross-references, - // so display them after other results. - let results = [...nonMainIndexResults, ...normalResults]; - - // remove duplicate search results - // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept - let seen = new Set(); - results = results.reverse().reduce((acc, result) => { - let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); - if (!seen.has(resultStr)) { - acc.push(result); - seen.add(resultStr); - } - return acc; - }, []); - - return results.reverse(); - }, - - query: (query) => { - const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); - const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); - - // for debugging - //Search.lastresults = results.slice(); // a copy - // console.info("search results:", Search.lastresults); - - // print the results - _displayNextItem(results, results.length, searchTerms, highlightTerms); - }, - - /** - * search for object names - */ - performObjectSearch: (object, objectTerms) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const objects = Search._index.objects; - const objNames = Search._index.objnames; - const titles = Search._index.titles; - - const results = []; - - const objectSearchCallback = (prefix, match) => { - const name = match[4] - const fullname = (prefix ? prefix + "." : "") + name; - const fullnameLower = fullname.toLowerCase(); - if (fullnameLower.indexOf(object) < 0) return; - - let score = 0; - const parts = fullnameLower.split("."); - - // check for different match types: exact matches of full name or - // "last name" (i.e. last dotted part) - if (fullnameLower === object || parts.slice(-1)[0] === object) - score += Scorer.objNameMatch; - else if (parts.slice(-1)[0].indexOf(object) > -1) - score += Scorer.objPartialMatch; // matches in last name - - const objName = objNames[match[1]][2]; - const title = titles[match[0]]; - - // If more than one term searched for, we require other words to be - // found in the name/title/description - const otherTerms = new Set(objectTerms); - otherTerms.delete(object); - if (otherTerms.size > 0) { - const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); - if ( - [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) - ) - return; - } - - let anchor = match[3]; - if (anchor === "") anchor = fullname; - else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; - - const descr = objName + _(", in ") + title; - - // add custom score for some objects according to scorer - if (Scorer.objPrio.hasOwnProperty(match[2])) - score += Scorer.objPrio[match[2]]; - else score += Scorer.objPrioDefault; - - results.push([ - docNames[match[0]], - fullname, - "#" + anchor, - descr, - score, - filenames[match[0]], - ]); - }; - Object.keys(objects).forEach((prefix) => - objects[prefix].forEach((array) => - objectSearchCallback(prefix, array) - ) - ); - return results; - }, - - /** - * search for full-text terms in the index - */ - performTermsSearch: (searchTerms, excludedTerms) => { - // prepare search - const terms = Search._index.terms; - const titleTerms = Search._index.titleterms; - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - - const scoreMap = new Map(); - const fileMap = new Map(); - - // perform the search on the required terms - searchTerms.forEach((word) => { - const files = []; - const arr = [ - { files: terms[word], score: Scorer.term }, - { files: titleTerms[word], score: Scorer.title }, - ]; - // add support for partial matches - if (word.length > 2) { - const escapedWord = _escapeRegExp(word); - if (!terms.hasOwnProperty(word)) { - Object.keys(terms).forEach((term) => { - if (term.match(escapedWord)) - arr.push({ files: terms[term], score: Scorer.partialTerm }); - }); - } - if (!titleTerms.hasOwnProperty(word)) { - Object.keys(titleTerms).forEach((term) => { - if (term.match(escapedWord)) - arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); - }); - } - } - - // no match but word was a required one - if (arr.every((record) => record.files === undefined)) return; - - // found search word in contents - arr.forEach((record) => { - if (record.files === undefined) return; - - let recordFiles = record.files; - if (recordFiles.length === undefined) recordFiles = [recordFiles]; - files.push(...recordFiles); - - // set score for the word in each file - recordFiles.forEach((file) => { - if (!scoreMap.has(file)) scoreMap.set(file, {}); - scoreMap.get(file)[word] = record.score; - }); - }); - - // create the mapping - files.forEach((file) => { - if (!fileMap.has(file)) fileMap.set(file, [word]); - else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); - }); - }); - - // now check if the files don't contain excluded terms - const results = []; - for (const [file, wordList] of fileMap) { - // check if all requirements are matched - - // as search terms with length < 3 are discarded - const filteredTermCount = [...searchTerms].filter( - (term) => term.length > 2 - ).length; - if ( - wordList.length !== searchTerms.size && - wordList.length !== filteredTermCount - ) - continue; - - // ensure that none of the excluded terms is in the search result - if ( - [...excludedTerms].some( - (term) => - terms[term] === file || - titleTerms[term] === file || - (terms[term] || []).includes(file) || - (titleTerms[term] || []).includes(file) - ) - ) - break; - - // select one (max) score for the file. - const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); - // add result to the result list - results.push([ - docNames[file], - titles[file], - "", - null, - score, - filenames[file], - ]); - } - return results; - }, - - /** - * helper function to return a node containing the - * search summary for a given text. keywords is a list - * of stemmed words. - */ - makeSearchSummary: (htmlText, keywords, anchor) => { - const text = Search.htmlToText(htmlText, anchor); - if (text === "") return null; - - const textLower = text.toLowerCase(); - const actualStartPosition = [...keywords] - .map((k) => textLower.indexOf(k.toLowerCase())) - .filter((i) => i > -1) - .slice(-1)[0]; - const startWithContext = Math.max(actualStartPosition - 120, 0); - - const top = startWithContext === 0 ? "" : "..."; - const tail = startWithContext + 240 < text.length ? "..." : ""; - - let summary = document.createElement("p"); - summary.classList.add("context"); - summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; - - return summary; - }, -}; - -_ready(Search.init); diff --git a/doc/scss/api-search.scss b/doc/scss/api-search.scss index 5e9bbfdcf27ba..51cf15f92c1cb 100644 --- a/doc/scss/api-search.scss +++ b/doc/scss/api-search.scss @@ -28,10 +28,7 @@ table.dataTable { th { - // Fix border color of the header - border-color: inherit; - - // Disabled the bottom margin of

    in table cells to avoid making it too tall + // Avoid table header being too tall p { margin-bottom: 0; } @@ -53,9 +50,9 @@ } } - // Fix border color of the last row + // Unset bottom border of the last row tr:last-child > * { - border-bottom-color: var(--bs-table-border-color) !important; + border-bottom: unset !important; } } diff --git a/doc/scss/index.scss b/doc/scss/index.scss index 4e3f371f236d4..c3bb8e86b41c6 100644 --- a/doc/scss/index.scss +++ b/doc/scss/index.scss @@ -90,6 +90,7 @@ div.sk-landing-body { .card-body { p { margin-bottom: 0.8rem; + color: var(--pst-color-text-base); } .sk-card-title { diff --git a/doc/scss/install.scss b/doc/scss/install.scss index 965b3d589e86d..92e201f00a107 100644 --- a/doc/scss/install.scss +++ b/doc/scss/install.scss @@ -6,28 +6,15 @@ * https://sass-lang.com/guide/ */ -.install-instructions .sd-tab-set { - .sd-tab-content { - box-shadow: 0 -0.0625rem var(--pst-color-border); - padding: 0.5rem 0 0 0; +.install-instructions .sd-tab-set > label.sd-tab-label { + margin: 0; + text-align: center; - > p:first-child { - margin-top: 1.25rem !important; - } + &.tab-6 { + width: 50% !important; } - > label.sd-tab-label { - border-top: none !important; - padding: 0 0 0.1rem 0; - margin: 0; - text-align: center; - - &.tab-6 { - width: 50% !important; - } - - &.tab-4 { - width: calc(100% / 3) !important; - } + &.tab-4 { + width: calc(100% / 3) !important; } } diff --git a/pyproject.toml b/pyproject.toml index 224b4ac5bad20..22e9b808dd8e1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -65,7 +65,7 @@ docs = [ "polars>=0.20.23", "sphinx-design>=0.5.0", "sphinxcontrib-sass>=0.3.4", - "pydata-sphinx-theme>=0.15.2", + "pydata-sphinx-theme>=0.15.3", "sphinx-remove-toctrees>=1.0.0.post1", ] examples = [ diff --git a/sklearn/_min_dependencies.py b/sklearn/_min_dependencies.py index 05899cac96faf..9c108791b45bc 100644 --- a/sklearn/_min_dependencies.py +++ b/sklearn/_min_dependencies.py @@ -47,7 +47,7 @@ "sphinxcontrib-sass": ("0.3.4", "docs"), "sphinx-remove-toctrees": ("1.0.0.post1", "docs"), "sphinx-design": ("0.5.0", "docs"), - "pydata-sphinx-theme": ("0.15.2", "docs"), + "pydata-sphinx-theme": ("0.15.3", "docs"), # XXX: Pin conda-lock to the latest released version (needs manual update # from time to time) "conda-lock": ("2.5.6", "maintenance"), From 6451c993372062fef0cdf48016ede77297aee1c9 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 3 Jun 2024 09:34:40 +0200 Subject: [PATCH 075/275] :lock: :robot: CI Update lock files for cirrus-arm CI build(s) :lock: :robot: (#29164) Co-authored-by: Lock file bot --- .../cirrus/pymin_conda_forge_linux-aarch64_conda.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock index 5d0c005fbdb17..7c36386889867 100644 --- a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock +++ b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock @@ -2,7 +2,7 @@ # platform: linux-aarch64 # input_hash: 2d8c526ab7c0c2f0ca509bfec3f035e5bd33b8096f194f0747f167c8aff66383 @EXPLICIT -https://conda.anaconda.org/conda-forge/linux-aarch64/ca-certificates-2024.2.2-hcefe29a_0.conda#57c226edb90c4e973b9b7503537dd339 +https://conda.anaconda.org/conda-forge/linux-aarch64/ca-certificates-2024.6.2-hcefe29a_0.conda#3ef6b1a30375f8a973a593698e317191 https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.40-h9fc2d93_1.conda#8a90b17a5d12ba55b7d90534f38579d0 https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-ng-13.2.0-h3f4de04_7.conda#2a54872c7fab2db99b0074212d8efe64 https://conda.anaconda.org/conda-forge/linux-aarch64/python_abi-3.9-4_cp39.conda#c191905a08694e4a5cb1238e90233878 @@ -20,7 +20,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/libnsl-2.0.1-h31becfc_0.con https://conda.anaconda.org/conda-forge/linux-aarch64/libuuid-2.38.1-hb4cce97_0.conda#000e30b09db0b7c775b21695dff30969 https://conda.anaconda.org/conda-forge/linux-aarch64/libwebp-base-1.4.0-h31becfc_0.conda#5fd7ab3e5f382c70607fbac6335e6e19 https://conda.anaconda.org/conda-forge/linux-aarch64/libxcrypt-4.4.36-h31becfc_1.conda#b4df5d7d4b63579d081fd3a4cf99740e -https://conda.anaconda.org/conda-forge/linux-aarch64/libzlib-1.2.13-h31becfc_5.conda#b213aa87eea9491ef7b129179322e955 +https://conda.anaconda.org/conda-forge/linux-aarch64/libzlib-1.3.1-h68df207_1.conda#b13fb82f88902e34dd0638cd7d378c21 https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.5-h0425590_0.conda#38362af7bfac0efef69675acee564458 https://conda.anaconda.org/conda-forge/linux-aarch64/ninja-1.12.1-h70be974_0.conda#216635cea46498d8045c7cf0f03eaf72 https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.3.0-h68df207_3.conda#f2dec4814ac9649cd3d3e474cd0b9a58 @@ -70,7 +70,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/tornado-6.4-py39h7cc1d5f_0. https://conda.anaconda.org/conda-forge/linux-aarch64/unicodedata2-15.1.0-py39h898b7ef_0.conda#8c072c9329aeea97a46005625267a851 https://conda.anaconda.org/conda-forge/noarch/wheel-0.43.0-pyhd8ed1ab_1.conda#0b5293a157c2b5cd513dd1b03d8d3aae https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda#2e4d6bc0b14e10f895fc6791a7d9b26a -https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.52.1-py39he257ee7_0.conda#06188260ef5320c7d6eb4f1467224bb0 +https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.53.0-py39he257ee7_0.conda#de1dc44f6b1a4393b37f4264d8e31e2a https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1ab_0.conda#c5d3907ad8bd7bf557521a1833cf7e6d https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-22_linuxaarch64_openblas.conda#fbe7fe553f2cc78a0311e009b26f180d From ce3b753e960d6af3f5520533c1ed8f28767a31a7 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 3 Jun 2024 09:34:52 +0200 Subject: [PATCH 076/275] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#29165) Co-authored-by: Lock file bot --- .../azure/pylatest_pip_scipy_dev_linux-64_conda.lock | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index 370dd97916518..c7212167a0b5e 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -28,16 +28,16 @@ https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py312h06a4308_0.conda# https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py312h06a4308_0.conda#6d9697bb8b9f3212be10b3b8e01a12b9 # pip alabaster @ https://files.pythonhosted.org/packages/32/34/d4e1c02d3bee589efb5dfa17f88ea08bdb3e3eac12bc475462aec52ed223/alabaster-0.7.16-py3-none-any.whl#sha256=b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92 # pip babel @ https://files.pythonhosted.org/packages/27/45/377f7e32a5c93d94cd56542349b34efab5ca3f9e2fd5a68c5e93169aa32d/Babel-2.15.0-py3-none-any.whl#sha256=08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb -# pip certifi @ https://files.pythonhosted.org/packages/ba/06/a07f096c664aeb9f01624f858c3add0a4e913d6c96257acb4fce61e7de14/certifi-2024.2.2-py3-none-any.whl#sha256=dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1 +# pip certifi @ https://files.pythonhosted.org/packages/5b/11/1e78951465b4a225519b8c3ad29769c49e0d8d157a070f681d5b6d64737f/certifi-2024.6.2-py3-none-any.whl#sha256=ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56 # pip charset-normalizer @ https://files.pythonhosted.org/packages/ee/fb/14d30eb4956408ee3ae09ad34299131fb383c47df355ddb428a7331cfa1e/charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b -# pip coverage @ https://files.pythonhosted.org/packages/36/2e/f52bd5573fdf4b527ed0255ec8efc5fae77f5d0b086c41abae6eb175b426/coverage-7.5.2-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=3bb5b92a0ab3d22dfdbfe845e2fef92717b067bdf41a5b68c7e3e857c0cff1a4 +# pip coverage @ https://files.pythonhosted.org/packages/0e/39/c44111cfc5e40fc1681a41b96911fba6560b51172b59c204b08b75d58495/coverage-7.5.3-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b1196e13c45e327d6cd0b6e471530a1882f1017eb83c6229fc613cd1a11b53cd # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc # pip idna @ https://files.pythonhosted.org/packages/e5/3e/741d8c82801c347547f8a2a06aa57dbb1992be9e948df2ea0eda2c8b79e8/idna-3.7-py3-none-any.whl#sha256=82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0 # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b # pip iniconfig @ https://files.pythonhosted.org/packages/ef/a6/62565a6e1cf69e10f5727360368e451d4b7f58beeac6173dc9db836a5b46/iniconfig-2.0.0-py3-none-any.whl#sha256=b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374 # pip markupsafe @ https://files.pythonhosted.org/packages/0a/0d/2454f072fae3b5a137c119abf15465d1771319dfe9e4acbb31722a0fff91/MarkupSafe-2.1.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=f5dfb42c4604dddc8e4305050aa6deb084540643ed5804d7455b5df8fe16f5e5 -# pip meson @ https://files.pythonhosted.org/packages/33/75/b1a37fa7b2dbca8c0dbb04d5cdd7e2720c8ef6febe41b4a74866350e041c/meson-1.4.0-py3-none-any.whl#sha256=476a458d51fcfa322a6bdc64da5138997c542d08e6b2e49b9fa68c46fd7c4475 +# pip meson @ https://files.pythonhosted.org/packages/44/b2/d4433391a7c5e94a39b50ca7295a8ceba736e7c72c455752a60122f52453/meson-1.4.1-py3-none-any.whl#sha256=d5acc3abae2dad3c70ddcbd10acac92b78b144d34d43f40f5b8ac31dfd8a826a # pip ninja @ https://files.pythonhosted.org/packages/6d/92/8d7aebd4430ab5ff65df2bfee6d5745f95c004284db2d8ca76dcbfd9de47/ninja-1.11.1.1-py2.py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl#sha256=84502ec98f02a037a169c4b0d5d86075eaf6afc55e1879003d6cab51ced2ea4b # pip packaging @ https://files.pythonhosted.org/packages/49/df/1fceb2f8900f8639e278b056416d49134fb8d84c5942ffaa01ad34782422/packaging-24.0-py3-none-any.whl#sha256=2ddfb553fdf02fb784c234c7ba6ccc288296ceabec964ad2eae3777778130bc5 # pip platformdirs @ https://files.pythonhosted.org/packages/68/13/2aa1f0e1364feb2c9ef45302f387ac0bd81484e9c9a4c5688a322fbdfd08/platformdirs-4.2.2-py3-none-any.whl#sha256=2d7a1657e36a80ea911db832a8a6ece5ee53d8de21edd5cc5879af6530b1bfee @@ -58,7 +58,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py312h06a4308_0.conda#6d96 # pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526 # pip pytest @ https://files.pythonhosted.org/packages/b4/c1/27a1274b73712232328cb5115030057b7dec377f36a518c83f2e01d4f305/pytest-8.2.1-py3-none-any.whl#sha256=faccc5d332b8c3719f40283d0d44aa5cf101cec36f88cde9ed8f2bc0538612b1 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 -# pip requests @ https://files.pythonhosted.org/packages/c3/20/748e38b466e0819491f0ce6e90ebe4184966ee304fe483e2c414b0f4ef07/requests-2.32.2-py3-none-any.whl#sha256=fc06670dd0ed212426dfeb94fc1b983d917c4f9847c863f313c9dfaaffb7c23c +# pip requests @ https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl#sha256=70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6 # pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 # pip pooch @ https://files.pythonhosted.org/packages/f4/72/8ae0f1ba4ce6a4f6d4d01a60a9fdf690fde188c45c1872b0b4ddb0607ace/pooch-1.8.1-py3-none-any.whl#sha256=6b56611ac320c239faece1ac51a60b25796792599ce5c0b1bb87bf01df55e0a9 # pip pytest-cov @ https://files.pythonhosted.org/packages/78/3a/af5b4fa5961d9a1e6237b530eb87dd04aea6eb83da09d2a4073d81b54ccf/pytest_cov-5.0.0-py3-none-any.whl#sha256=4f0764a1219df53214206bf1feea4633c3b558a2925c8b59f144f682861ce652 From eec146484db39872fd41ee9af762433556cacccd Mon Sep 17 00:00:00 2001 From: baggiponte <57922983+baggiponte@users.noreply.github.com> Date: Mon, 3 Jun 2024 10:34:58 +0200 Subject: [PATCH 077/275] DOC Fix typo in logistic regression predict_proba docstring (#29162) Co-authored-by: Guillaume Lemaitre --- sklearn/linear_model/_logistic.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/linear_model/_logistic.py b/sklearn/linear_model/_logistic.py index 055ccc1c6a202..2530151658236 100644 --- a/sklearn/linear_model/_logistic.py +++ b/sklearn/linear_model/_logistic.py @@ -1403,7 +1403,7 @@ def predict_proba(self, X): the softmax function is used to find the predicted probability of each class. Else use a one-vs-rest approach, i.e. calculate the probability - of each class assuming it to be positive using the logistic function. + of each class assuming it to be positive using the logistic function and normalize these values across all the classes. Parameters From 62893e4fbfecb68b9356a16d261d7292d369b884 Mon Sep 17 00:00:00 2001 From: yareyaredesuyo <31843888+yareyaredesuyo@users.noreply.github.com> Date: Mon, 3 Jun 2024 18:52:48 +0900 Subject: [PATCH 078/275] DOC fix digits dataset link (#29161) --- doc/tutorial/basic/tutorial.rst | 2 +- examples/datasets/plot_digits_last_image.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/tutorial/basic/tutorial.rst b/doc/tutorial/basic/tutorial.rst index 27dddb4e0e909..f63f958146ea8 100644 --- a/doc/tutorial/basic/tutorial.rst +++ b/doc/tutorial/basic/tutorial.rst @@ -76,7 +76,7 @@ Loading an example dataset `scikit-learn` comes with a few standard datasets, for instance the `iris `_ and `digits -`_ +`_ datasets for classification and the `diabetes dataset `_ for regression. diff --git a/examples/datasets/plot_digits_last_image.py b/examples/datasets/plot_digits_last_image.py index 0fde32cc674a8..fd051a3ad29bf 100644 --- a/examples/datasets/plot_digits_last_image.py +++ b/examples/datasets/plot_digits_last_image.py @@ -9,7 +9,7 @@ first transform it into a feature vector with length 64. See `here -`_ +`_ for more information about this dataset. """ From a51312c8626d3e346d729dca2df61ba899a8918d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Mon, 3 Jun 2024 13:53:16 +0200 Subject: [PATCH 079/275] BLD Use work-around for partial recompilation when building with Meson (#29140) --- sklearn/_loss/meson.build | 6 +- sklearn/linear_model/meson.build | 8 ++- .../_pairwise_distances_reduction/meson.build | 72 +++++++++++++------ sklearn/metrics/meson.build | 8 ++- sklearn/neighbors/meson.build | 8 ++- sklearn/utils/meson.build | 8 ++- 6 files changed, 78 insertions(+), 32 deletions(-) diff --git a/sklearn/_loss/meson.build b/sklearn/_loss/meson.build index 7802d1643df18..7978fa56139c6 100644 --- a/sklearn/_loss/meson.build +++ b/sklearn/_loss/meson.build @@ -8,11 +8,15 @@ _loss_pyx = custom_target( output: '_loss.pyx', input: '_loss.pyx.tp', command: [py, tempita, '@INPUT@', '-o', '@OUTDIR@'], + # TODO in principle this should go in py.exension_module below. This is + # temporary work-around for dependency issue with .pyx.tp files. For more + # details, see https://github.com/mesonbuild/meson/issues/13212 + depends: _loss_cython_tree, ) py.extension_module( '_loss', - [_loss_pyx, _loss_cython_tree], + _loss_pyx, cython_args: cython_args, install: true, subdir: 'sklearn/_loss', diff --git a/sklearn/linear_model/meson.build b/sklearn/linear_model/meson.build index 1a40cea39b648..e58b88f46c8f8 100644 --- a/sklearn/linear_model/meson.build +++ b/sklearn/linear_model/meson.build @@ -19,11 +19,15 @@ foreach name: name_list name + '_pyx', output: name + '.pyx', input: name + '.pyx.tp', - command: [py, tempita, '@INPUT@', '-o', '@OUTDIR@'] + command: [py, tempita, '@INPUT@', '-o', '@OUTDIR@'], + # TODO in principle this should go in py.exension_module below. This is + # temporary work-around for dependency issue with .pyx.tp files. For more + # details, see https://github.com/mesonbuild/meson/issues/13212 + depends: [linear_model_cython_tree, utils_cython_tree], ) py.extension_module( name, - [pyx, linear_model_cython_tree, utils_cython_tree], + pyx, cython_args: cython_args, subdir: 'sklearn/linear_model', install: true diff --git a/sklearn/metrics/_pairwise_distances_reduction/meson.build b/sklearn/metrics/_pairwise_distances_reduction/meson.build index ced94fd8b694e..52ea6062da26b 100644 --- a/sklearn/metrics/_pairwise_distances_reduction/meson.build +++ b/sklearn/metrics/_pairwise_distances_reduction/meson.build @@ -31,11 +31,14 @@ _datasets_pair_pyx = custom_target( output: '_datasets_pair.pyx', input: '_datasets_pair.pyx.tp', command: [py, tempita, '@INPUT@', '-o', '@OUTDIR@'], + # TODO in principle this should go in py.exension_module below. This is + # temporary work-around for dependency issue with .pyx.tp files. For more + # details, see https://github.com/mesonbuild/meson/issues/13212 + depends: [_datasets_pair_pxd, _pairwise_distances_reduction_cython_tree, utils_cython_tree], ) _datasets_pair = py.extension_module( '_datasets_pair', - [_datasets_pair_pxd, _datasets_pair_pyx, - _pairwise_distances_reduction_cython_tree, utils_cython_tree], + _datasets_pair_pyx, dependencies: [np_dep, openmp_dep], override_options: ['cython_language=cpp'], cython_args: cython_args, @@ -54,12 +57,15 @@ _base_pyx = custom_target( output: '_base.pyx', input: '_base.pyx.tp', command: [py, tempita, '@INPUT@', '-o', '@OUTDIR@'], + # TODO in principle this should go in py.exension_module below. This is + # temporary work-around for dependency issue with .pyx.tp files. For more + # details, see https://github.com/mesonbuild/meson/issues/13212 + depends: [_base_pxd, _pairwise_distances_reduction_cython_tree, + _datasets_pair_pxd, utils_cython_tree], ) _base = py.extension_module( '_base', - [_base_pxd, _base_pyx, - _pairwise_distances_reduction_cython_tree, - _datasets_pair_pxd, utils_cython_tree], + _base_pyx, dependencies: [np_dep, openmp_dep], override_options: ['cython_language=cpp'], cython_args: cython_args, @@ -78,11 +84,16 @@ _middle_term_computer_pyx = custom_target( output: '_middle_term_computer.pyx', input: '_middle_term_computer.pyx.tp', command: [py, tempita, '@INPUT@', '-o', '@OUTDIR@'], + # TODO in principle this should go in py.exension_module below. This is + # temporary work-around for dependency issue with .pyx.tp files. For more + # details, see https://github.com/mesonbuild/meson/issues/13212 + depends: [_middle_term_computer_pxd, + _pairwise_distances_reduction_cython_tree, + utils_cython_tree], ) _middle_term_computer = py.extension_module( '_middle_term_computer', - [_middle_term_computer_pxd, _middle_term_computer_pyx, - _pairwise_distances_reduction_cython_tree, utils_cython_tree], + _middle_term_computer_pyx, dependencies: [np_dep, openmp_dep], override_options: ['cython_language=cpp'], cython_args: cython_args, @@ -101,13 +112,16 @@ _argkmin_pyx = custom_target( output: '_argkmin.pyx', input: '_argkmin.pyx.tp', command: [py, tempita, '@INPUT@', '-o', '@OUTDIR@'], - ) + # TODO in principle this should go in py.exension_module below. This is + # temporary work-around for dependency issue with .pyx.tp files. For more + # details, see https://github.com/mesonbuild/meson/issues/13212 + depends: [_argkmin_pxd, + _pairwise_distances_reduction_cython_tree, + _datasets_pair_pxd, _base_pxd, _middle_term_computer_pxd], + ) _argkmin = py.extension_module( '_argkmin', - [_argkmin_pxd, _argkmin_pyx, - _pairwise_distances_reduction_cython_tree, - _datasets_pair_pxd, _base_pxd, _middle_term_computer_pxd, - utils_cython_tree], + _argkmin_pyx, dependencies: [np_dep, openmp_dep], override_options: ['cython_language=cpp'], cython_args: cython_args, @@ -126,12 +140,16 @@ _radius_neighbors_pyx = custom_target( output: '_radius_neighbors.pyx', input: '_radius_neighbors.pyx.tp', command: [py, tempita, '@INPUT@', '-o', '@OUTDIR@'], - ) + # TODO in principle this should go in py.exension_module below. This is + # temporary work-around for dependency issue with .pyx.tp files. For more + # details, see https://github.com/mesonbuild/meson/issues/13212 + depends: [_radius_neighbors_pxd, + _datasets_pair_pxd, _base_pxd, _middle_term_computer_pxd, + _pairwise_distances_reduction_cython_tree, utils_cython_tree], +) _radius_neighbors = py.extension_module( '_radius_neighbors', - [_radius_neighbors_pxd, _radius_neighbors_pyx, - _datasets_pair_pxd, _base_pxd, _middle_term_computer_pxd, - _pairwise_distances_reduction_cython_tree, utils_cython_tree], + _radius_neighbors_pyx, dependencies: [np_dep, openmp_dep], override_options: ['cython_language=cpp'], cython_args: cython_args, @@ -144,12 +162,16 @@ _argkmin_classmode_pyx = custom_target( output: '_argkmin_classmode.pyx', input: '_argkmin_classmode.pyx.tp', command: [py, tempita, '@INPUT@', '-o', '@OUTDIR@'], + # TODO in principle this should go in py.exension_module below. This is + # temporary work-around for dependency issue with .pyx.tp files. For more + # details, see https://github.com/mesonbuild/meson/issues/13212 + depends: [_classmode_pxd, + _argkmin_pxd, _pairwise_distances_reduction_cython_tree, + _datasets_pair_pxd, _base_pxd, _middle_term_computer_pxd, utils_cython_tree], ) _argkmin_classmode = py.extension_module( '_argkmin_classmode', - [_argkmin_classmode_pyx, _classmode_pxd, - _argkmin_pxd, _pairwise_distances_reduction_cython_tree, - _datasets_pair_pxd, _base_pxd, _middle_term_computer_pxd, utils_cython_tree], + _argkmin_classmode_pyx, dependencies: [np_dep], override_options: ['cython_language=cpp'], cython_args: cython_args, @@ -166,13 +188,17 @@ _radius_neighbors_classmode_pyx = custom_target( output: '_radius_neighbors_classmode.pyx', input: '_radius_neighbors_classmode.pyx.tp', command: [py, tempita, '@INPUT@', '-o', '@OUTDIR@'], + # TODO in principle this should go in py.exension_module below. This is + # temporary work-around for dependency issue with .pyx.tp files. For more + # details, see https://github.com/mesonbuild/meson/issues/13212 + depends: [_classmode_pxd, + _middle_term_computer_pxd, _radius_neighbors_pxd, + _pairwise_distances_reduction_cython_tree, + _datasets_pair_pxd, _base_pxd, utils_cython_tree], ) _radius_neighbors_classmode = py.extension_module( '_radius_neighbors_classmode', - [_radius_neighbors_classmode_pyx, _classmode_pxd, - _middle_term_computer_pxd, _radius_neighbors_pxd, - _pairwise_distances_reduction_cython_tree, - _datasets_pair_pxd, _base_pxd, utils_cython_tree], + _radius_neighbors_classmode_pyx, dependencies: [np_dep], override_options: ['cython_language=cpp'], cython_args: cython_args, diff --git a/sklearn/metrics/meson.build b/sklearn/metrics/meson.build index 24101fb435939..ef7b202c6f89c 100644 --- a/sklearn/metrics/meson.build +++ b/sklearn/metrics/meson.build @@ -22,12 +22,16 @@ _dist_metrics_pyx = custom_target( '_dist_metrics_pyx', output: '_dist_metrics.pyx', input: '_dist_metrics.pyx.tp', - command: [py, tempita, '@INPUT@', '-o', '@OUTDIR@'] + command: [py, tempita, '@INPUT@', '-o', '@OUTDIR@'], + # TODO in principle this should go in py.exension_module below. This is + # temporary work-around for dependency issue with .pyx.tp files. For more + # details, see https://github.com/mesonbuild/meson/issues/13212 + depends: metrics_cython_tree, ) _dist_metrics = py.extension_module( '_dist_metrics', - [_dist_metrics_pyx, metrics_cython_tree], + _dist_metrics_pyx, dependencies: [np_dep], cython_args: cython_args, subdir: 'sklearn/metrics', diff --git a/sklearn/neighbors/meson.build b/sklearn/neighbors/meson.build index 7e361be25052e..22f81d597948b 100644 --- a/sklearn/neighbors/meson.build +++ b/sklearn/neighbors/meson.build @@ -20,11 +20,15 @@ foreach name: name_list name + '_pyx', output: name + '.pyx', input: name + '.pyx.tp', - command: [py, tempita, '@INPUT@', '-o', '@OUTDIR@'] + command: [py, tempita, '@INPUT@', '-o', '@OUTDIR@'], + # TODO in principle this should go in py.exension_module below. This is + # temporary work-around for dependency issue with .pyx.tp files. For more + # details, see https://github.com/mesonbuild/meson/issues/13212 + depends: [neighbors_cython_tree, utils_cython_tree, metrics_cython_tree], ) py.extension_module( name, - [pyx, neighbors_cython_tree, utils_cython_tree, metrics_cython_tree], + pyx, dependencies: [np_dep], cython_args: cython_args, subdir: 'sklearn/neighbors', diff --git a/sklearn/utils/meson.build b/sklearn/utils/meson.build index df74d4c24a411..9bbfc01b7b6bf 100644 --- a/sklearn/utils/meson.build +++ b/sklearn/utils/meson.build @@ -62,11 +62,15 @@ foreach name: util_extension_names name + '_pyx', output: name + '.pyx', input: name + '.pyx.tp', - command: [py, tempita, '@INPUT@', '-o', '@OUTDIR@'] + command: [py, tempita, '@INPUT@', '-o', '@OUTDIR@'], + # TODO in principle this should go in py.exension_module below. This is + # temporary work-around for dependency issue with .pyx.tp files. For more + # details, see https://github.com/mesonbuild/meson/issues/13212 + depends: [pxd, utils_cython_tree], ) py.extension_module( name, - [pxd, pyx, utils_cython_tree], + pyx, cython_args: cython_args, subdir: 'sklearn/utils', install: true From 6b2185843ad702de7c43dee973ca472bf49a15e8 Mon Sep 17 00:00:00 2001 From: Audrey Flanders <114197067+aflanders7@users.noreply.github.com> Date: Wed, 5 Jun 2024 01:33:20 -0400 Subject: [PATCH 080/275] DOC Remove warning from plot adaboost twoclass example (#29188) --- examples/ensemble/plot_adaboost_twoclass.py | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/ensemble/plot_adaboost_twoclass.py b/examples/ensemble/plot_adaboost_twoclass.py index d1e89c47b7fcf..cb070d66a58d8 100644 --- a/examples/ensemble/plot_adaboost_twoclass.py +++ b/examples/ensemble/plot_adaboost_twoclass.py @@ -74,7 +74,6 @@ X[idx, 0], X[idx, 1], c=c, - cmap=plt.cm.Paired, s=20, edgecolor="k", label="Class %s" % n, From c52c626d50ac3c1924e75b57d97ec426b3aad54d Mon Sep 17 00:00:00 2001 From: Audrey Flanders <114197067+aflanders7@users.noreply.github.com> Date: Wed, 5 Jun 2024 01:34:40 -0400 Subject: [PATCH 081/275] DOC Remove warning from plot svm margin example (#29187) --- examples/svm/plot_svm_margin.py | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/svm/plot_svm_margin.py b/examples/svm/plot_svm_margin.py index b8253264a4ad0..72d9608bbbe44 100644 --- a/examples/svm/plot_svm_margin.py +++ b/examples/svm/plot_svm_margin.py @@ -63,7 +63,6 @@ facecolors="none", zorder=10, edgecolors="k", - cmap=plt.get_cmap("RdBu"), ) plt.scatter( X[:, 0], X[:, 1], c=Y, zorder=10, cmap=plt.get_cmap("RdBu"), edgecolors="k" From d29b2a0d8608b77fd8e174b9018c216fec263711 Mon Sep 17 00:00:00 2001 From: "Thomas J. Fan" Date: Tue, 4 Jun 2024 22:39:19 -0700 Subject: [PATCH 082/275] MAINT Update pyodide to 0.26.0 (#29181) --- azure-pipelines.yml | 6 +++--- doc/jupyter-lite.json | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/azure-pipelines.yml b/azure-pipelines.yml index 9b0e8c2259f19..f17063681360d 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -126,9 +126,9 @@ jobs: # Need to match Python version and Emscripten version for the correct # Pyodide version. For example, for Pyodide version 0.25.1, see # https://github.com/pyodide/pyodide/blob/0.25.1/Makefile.envs - PYODIDE_VERSION: '0.25.1' - EMSCRIPTEN_VERSION: '3.1.46' - PYTHON_VERSION: '3.11.3' + PYODIDE_VERSION: '0.26.0' + EMSCRIPTEN_VERSION: '3.1.58' + PYTHON_VERSION: '3.12.1' dependsOn: [git_commit, linting] condition: | diff --git a/doc/jupyter-lite.json b/doc/jupyter-lite.json index e582ad81eb541..65ec9ca3006dc 100644 --- a/doc/jupyter-lite.json +++ b/doc/jupyter-lite.json @@ -3,7 +3,7 @@ "jupyter-config-data": { "litePluginSettings": { "@jupyterlite/pyodide-kernel-extension:kernel": { - "pyodideUrl": "https://cdn.jsdelivr.net/pyodide/v0.25.0/full/pyodide.js" + "pyodideUrl": "https://cdn.jsdelivr.net/pyodide/v0.26.0/full/pyodide.js" } } } From 19aaa192d8d6268b7ffe7dc638cb1c0ea6d14b46 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Wed, 5 Jun 2024 01:50:16 -0700 Subject: [PATCH 083/275] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#29184) Co-authored-by: Lock file bot --- build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index c7212167a0b5e..138c863b520f5 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -56,7 +56,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py312h06a4308_0.conda#6d96 # pip urllib3 @ https://files.pythonhosted.org/packages/a2/73/a68704750a7679d0b6d3ad7aa8d4da8e14e151ae82e6fee774e6e0d05ec8/urllib3-2.2.1-py3-none-any.whl#sha256=450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d # pip jinja2 @ https://files.pythonhosted.org/packages/31/80/3a54838c3fb461f6fec263ebf3a3a41771bd05190238de3486aae8540c36/jinja2-3.1.4-py3-none-any.whl#sha256=bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d # pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526 -# pip pytest @ https://files.pythonhosted.org/packages/b4/c1/27a1274b73712232328cb5115030057b7dec377f36a518c83f2e01d4f305/pytest-8.2.1-py3-none-any.whl#sha256=faccc5d332b8c3719f40283d0d44aa5cf101cec36f88cde9ed8f2bc0538612b1 +# pip pytest @ https://files.pythonhosted.org/packages/4e/e7/81ebdd666d3bff6670d27349b5053605d83d55548e6bd5711f3b0ae7dd23/pytest-8.2.2-py3-none-any.whl#sha256=c434598117762e2bd304e526244f67bf66bbd7b5d6cf22138be51ff661980343 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl#sha256=70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6 # pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 From 40a13dacad9f1da42fccd60f115cb8fc6c7cf38a Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Wed, 5 Jun 2024 01:50:40 -0700 Subject: [PATCH 084/275] :lock: :robot: CI Update lock files for cirrus-arm CI build(s) :lock: :robot: (#29183) Co-authored-by: Lock file bot --- build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock index 7c36386889867..8df4e01434186 100644 --- a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock +++ b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock @@ -3,7 +3,7 @@ # input_hash: 2d8c526ab7c0c2f0ca509bfec3f035e5bd33b8096f194f0747f167c8aff66383 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-aarch64/ca-certificates-2024.6.2-hcefe29a_0.conda#3ef6b1a30375f8a973a593698e317191 -https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.40-h9fc2d93_1.conda#8a90b17a5d12ba55b7d90534f38579d0 +https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.40-h9fc2d93_2.conda#4db8582be880caa8946573950c3e0e8b https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-ng-13.2.0-h3f4de04_7.conda#2a54872c7fab2db99b0074212d8efe64 https://conda.anaconda.org/conda-forge/linux-aarch64/python_abi-3.9-4_cp39.conda#c191905a08694e4a5cb1238e90233878 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 @@ -79,7 +79,7 @@ https://conda.anaconda.org/conda-forge/noarch/meson-1.4.0-pyhd8ed1ab_0.conda#52a https://conda.anaconda.org/conda-forge/linux-aarch64/pillow-10.3.0-py39h71661b1_0.conda#dae548b7b537d7ef796d1d4c38a55319 https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 -https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 +https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.2-pyhd8ed1ab_0.conda#0f3f49c22c7ef3a1195fa61dad3c43be https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.0-pyhd8ed1ab_0.conda#dcbadab7a68738a028e195ab68ab2d2e https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-22_linuxaarch64_openblas.conda#5acf669e0be669f30f4b813d2ecda7b8 From 05db17e382902a084ebcc8142dedd02b74cc8a88 Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Wed, 5 Jun 2024 12:01:31 +0200 Subject: [PATCH 085/275] DOC remove tutorials (#29104) Co-authored-by: Yao Xiao <108576690+Charlie-XIAO@users.noreply.github.com> --- doc/conf.py | 1 + doc/conftest.py | 6 - doc/developers/contributing.rst | 6 +- doc/getting_started.rst | 5 +- .../README.md => images/ml_map.README.rst} | 0 doc/index.rst.template | 1 - .../index.rst => machine_learning_map.rst} | 6 +- doc/presentations.rst | 7 +- doc/tutorial/basic/tutorial.rst | 351 ----------- doc/tutorial/common_includes/info.txt | 3 - doc/tutorial/index.rst | 26 - doc/tutorial/statistical_inference/index.rst | 34 - .../statistical_inference/model_selection.rst | 315 ---------- .../putting_together.rst | 62 -- .../statistical_inference/settings.rst | 92 --- .../supervised_learning.rst | 528 ---------------- .../unsupervised_learning.rst | 297 --------- doc/tutorial/text_analytics/.gitignore | 25 - .../data/languages/fetch_data.py | 103 --- .../data/movie_reviews/fetch_data.py | 33 - .../exercise_01_language_train_model.py | 62 -- .../skeletons/exercise_02_sentiment.py | 63 -- .../exercise_01_language_train_model.py | 70 --- .../solutions/exercise_02_sentiment.py | 79 --- .../solutions/generate_skeletons.py | 38 -- .../text_analytics/working_with_text_data.rst | 586 ------------------ doc/user_guide.rst | 2 + doc/whats_new/v0.15.rst | 2 +- pyproject.toml | 2 - 29 files changed, 12 insertions(+), 2793 deletions(-) rename doc/{tutorial/machine_learning_map/README.md => images/ml_map.README.rst} (100%) rename doc/{tutorial/machine_learning_map/index.rst => machine_learning_map.rst} (92%) delete mode 100644 doc/tutorial/basic/tutorial.rst delete mode 100644 doc/tutorial/common_includes/info.txt delete mode 100644 doc/tutorial/index.rst delete mode 100644 doc/tutorial/statistical_inference/index.rst delete mode 100644 doc/tutorial/statistical_inference/model_selection.rst delete mode 100644 doc/tutorial/statistical_inference/putting_together.rst delete mode 100644 doc/tutorial/statistical_inference/settings.rst delete mode 100644 doc/tutorial/statistical_inference/supervised_learning.rst delete mode 100644 doc/tutorial/statistical_inference/unsupervised_learning.rst delete mode 100644 doc/tutorial/text_analytics/.gitignore delete mode 100644 doc/tutorial/text_analytics/data/languages/fetch_data.py delete mode 100644 doc/tutorial/text_analytics/data/movie_reviews/fetch_data.py delete mode 100644 doc/tutorial/text_analytics/skeletons/exercise_01_language_train_model.py delete mode 100644 doc/tutorial/text_analytics/skeletons/exercise_02_sentiment.py delete mode 100644 doc/tutorial/text_analytics/solutions/exercise_01_language_train_model.py delete mode 100644 doc/tutorial/text_analytics/solutions/exercise_02_sentiment.py delete mode 100644 doc/tutorial/text_analytics/solutions/generate_skeletons.py delete mode 100644 doc/tutorial/text_analytics/working_with_text_data.rst diff --git a/doc/conf.py b/doc/conf.py index db79bfe3f7865..9923a24260267 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -448,6 +448,7 @@ def add_js_css_files(app, pagename, templatename, context, doctree): "auto_examples/exercises/plot_cv_digits.py": ( "auto_examples/model_selection/plot_nested_cross_validation_iris.py" ), + "tutorial/machine_learning_map/index.html": "machine_learning_map/index.html", } html_context["redirects"] = redirects for old_link in redirects: diff --git a/doc/conftest.py b/doc/conftest.py index d66148ccc553f..7081e8b8bf698 100644 --- a/doc/conftest.py +++ b/doc/conftest.py @@ -128,10 +128,6 @@ def pytest_runtest_setup(item): setup_rcv1() elif fname.endswith("datasets/twenty_newsgroups.rst") or is_index: setup_twenty_newsgroups() - elif ( - fname.endswith("tutorial/text_analytics/working_with_text_data.rst") or is_index - ): - setup_working_with_text_data() elif fname.endswith("modules/compose.rst") or is_index: setup_compose() elif fname.endswith("datasets/loading_other_datasets.rst"): @@ -148,8 +144,6 @@ def pytest_runtest_setup(item): rst_files_requiring_matplotlib = [ "modules/partial_dependence.rst", "modules/tree.rst", - "tutorial/statistical_inference/settings.rst", - "tutorial/statistical_inference/supervised_learning.rst", ] for each in rst_files_requiring_matplotlib: if fname.endswith(each): diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst index a9dc7cb305f61..32f6c786b1051 100644 --- a/doc/developers/contributing.rst +++ b/doc/developers/contributing.rst @@ -651,10 +651,8 @@ We are glad to accept any sort of documentation: * **User guide:** These provide more detailed information about the algorithms implemented in scikit-learn and generally live in the root `doc/ `_ directory - and `doc/modules/ `_. -* **Tutorials:** These introduce various statistical learning and machine learning - concepts and are located in - `doc/tutorial `_. + and + `doc/modules/ `_. * **Examples:** These provide full code examples that may demonstrate the use of scikit-learn modules, compare different algorithms or discuss their interpretation, etc. Examples live in diff --git a/doc/getting_started.rst b/doc/getting_started.rst index cd4d953db1b8a..14e0178f0826b 100644 --- a/doc/getting_started.rst +++ b/doc/getting_started.rst @@ -53,6 +53,8 @@ new data. You don't need to re-train the estimator:: >>> clf.predict([[4, 5, 6], [14, 15, 16]]) # predict classes of new data array([0, 1]) +You can check :ref:`ml_map` on how to choose the right model for your use case. + Transformers and pre-processors ------------------------------- @@ -227,6 +229,3 @@ provide. You can also find an exhaustive list of the public API in the You can also look at our numerous :ref:`examples ` that illustrate the use of ``scikit-learn`` in many different contexts. - -The :ref:`tutorials ` also contain additional learning -resources. diff --git a/doc/tutorial/machine_learning_map/README.md b/doc/images/ml_map.README.rst similarity index 100% rename from doc/tutorial/machine_learning_map/README.md rename to doc/images/ml_map.README.rst diff --git a/doc/index.rst.template b/doc/index.rst.template index df058f5fb6185..f1f1f49836515 100644 --- a/doc/index.rst.template +++ b/doc/index.rst.template @@ -13,7 +13,6 @@ auto_examples/index Community getting_started - Tutorials whats_new Glossary Development <{{ development_link }}> diff --git a/doc/tutorial/machine_learning_map/index.rst b/doc/machine_learning_map.rst similarity index 92% rename from doc/tutorial/machine_learning_map/index.rst rename to doc/machine_learning_map.rst index e3d6531d05bd5..a03bb963cb046 100644 --- a/doc/tutorial/machine_learning_map/index.rst +++ b/doc/machine_learning_map.rst @@ -14,7 +14,7 @@ problems with regard to which estimators to try on your data. Click on any estim the chart below to see its documentation. The 😭 emoji is to be read as "if this estimator does not achieve the desired outcome, then follow the arrow and try the next one". Use scroll wheel to zoom in and out, and click and drag to pan around. You can -also download the chart: :download:`ml_map.svg <../../images/ml_map.svg>`. +also download the chart: :download:`ml_map.svg `. .. raw:: html @@ -36,7 +36,7 @@ also download the chart: :download:`ml_map.svg <../../images/ml_map.svg>`. } - + +>>>>>>> 34db65a3ad (DOC use pydata-sphinx-theme for the website (#29038)) In case a contribution/issue involves changes to the API principles or changes to dependencies or supported versions, it must be backed by a @@ -751,6 +758,7 @@ We are glad to accept any sort of documentation: * Add one or two snippets of code in "Example" section to show how it can be used. +.. dropdown:: Guidelines for writing references .. dropdown:: Guidelines for writing the user guide and other reStructuredText documents @@ -786,7 +794,7 @@ We are glad to accept any sort of documentation: * Too much information makes it difficult for users to access the content they are interested in. Use dropdowns to factorize it by using the following syntax - .. code-block:: rst + .. _my-section: .. dropdown:: Dropdown title diff --git a/doc/tutorial/machine_learning_map/README.md b/doc/tutorial/machine_learning_map/README.md new file mode 100644 index 0000000000000..006b1e5e1a38c --- /dev/null +++ b/doc/tutorial/machine_learning_map/README.md @@ -0,0 +1,17 @@ +The scikit-learn machine learning cheat sheet was originally created by Andreas Mueller: +https://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html + +The current version of the chart is located at `doc/images/ml_map.svg` in SVG+XML +format, created using [draw.io](https://draw.io/). To edit the chart, open the file in +draw.io, make changes, and export as SVG with the same filename. Export configurations +are: + +- Zoom: 100% +- Border width: 15 +- Size: Diagram +- Transparent Background: False +- Appearance: Light + +Each node in the chart that contains an estimator should have a link, where the root +directory is at `../../`. Note that after exporting the SVG, the links may be prefixed +with e.g. `https://app.diagrams.net/`. Remember to check and remove them. From 5731b70e77c51f7d79b91c79da8321474e97e148 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 9 Sep 2024 11:33:06 +0200 Subject: [PATCH 131/275] MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081) --- ...latest_conda_forge_mkl_linux-64_conda.lock | 12 ++++++++++++ ...pylatest_conda_forge_mkl_osx-64_conda.lock | 14 ++++++++++++++ ...test_conda_mkl_no_openmp_osx-64_conda.lock | 13 +++++++++++++ ...st_pip_openblas_pandas_linux-64_conda.lock | 12 ++++++++++++ ...pylatest_pip_scipy_dev_linux-64_conda.lock | 10 ++++++++++ ...onda_defaults_openblas_linux-64_conda.lock | 4 ++++ .../pymin_conda_forge_mkl_win-64_conda.lock | 19 +++++++++++++++++++ ...e_openblas_ubuntu_2204_linux-64_conda.lock | 16 ++++++++++++++++ build_tools/azure/ubuntu_atlas_lock.txt | 4 ++++ build_tools/circle/doc_linux-64_conda.lock | 16 ++++++++++++++++ .../doc_min_dependencies_linux-64_conda.lock | 16 ++++++++++++++++ ...pymin_conda_forge_linux-aarch64_conda.lock | 17 +++++++++++++++++ 12 files changed, 153 insertions(+) diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index bef3a59d9df8c..9b9003842886f 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -1,6 +1,10 @@ # Generated by conda-lock. # platform: linux-64 +<<<<<<< HEAD # input_hash: 50fed47bc507d9ee3dbf5ff7a2247cb88944928bd5797e534ebdf8ece2d858ec +======= +# input_hash: 29e3d76c436ee8cb86d5976aa41ef8822f991c469202b8d12fc27e43576ec805 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.6.2-hbcca054_0.conda#847c3c2905cc467cea52c24f9cfa8080 @@ -54,7 +58,11 @@ https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-h4ab18f5_0.conda#a41fa0e391cc9e0d6b78ac69ca047a6c +======= +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_2.conda#b8934d399b56d73e323403e183d009c5 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/rdma-core-28.9-h59595ed_1.conda#aeffb7c06b5f65e55e6c637408dc4100 @@ -185,7 +193,11 @@ https://conda.anaconda.org/conda-forge/linux-64/mkl-2022.2.1-h84fe81f_16997.cond https://conda.anaconda.org/conda-forge/linux-64/pillow-10.3.0-py311h18e6fac_0.conda#6c520a9d36c9d7270988c7a6c360d6d4 https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.2-pyhd8ed1ab_0.conda#0f3f49c22c7ef3a1195fa61dad3c43be +======= +https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py311hb755f60_0.conda#02336abab4cb5dd794010ef53c54bd09 https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.3.14-hf3aad02_1.conda#a968ffa7e9fe0c257628033d393e512f diff --git a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock index c400171503ecd..796bb53d0aa80 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock @@ -1,6 +1,10 @@ # Generated by conda-lock. # platform: osx-64 +<<<<<<< HEAD # input_hash: e7c2bc2b07721ef735f30d3b1cf0b2a780b5bf5c138d9d18ad174611bfbd32bf +======= +# input_hash: 134a56cb648e4588930416968ccdd27a7b83285f04712b032c6d07291b51b526 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) @EXPLICIT https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.8-h10d778d_5.conda#6097a6ca9ada32699b5fc4312dd6ef18 https://conda.anaconda.org/conda-forge/osx-64/ca-certificates-2024.6.2-h8857fd0_0.conda#3c23a8cab15ae51ebc9efdc229fccecf @@ -25,9 +29,15 @@ https://conda.anaconda.org/conda-forge/osx-64/libbrotlidec-1.1.0-h0dc2134_1.cond https://conda.anaconda.org/conda-forge/osx-64/libbrotlienc-1.1.0-h0dc2134_1.conda#8a421fe09c6187f0eb5e2338a8a8be6d https://conda.anaconda.org/conda-forge/osx-64/libcxx-17.0.6-h88467a6_0.conda#0fe355aecb8d24b8bc07c763209adbd9 https://conda.anaconda.org/conda-forge/osx-64/libxcb-1.15-hb7f2c08_0.conda#5513f57e0238c87c12dffedbcc9c1a4a +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/osx-64/libzlib-1.3.1-h87427d6_1.conda#b7575b5aa92108dcc9aaab0f05f2dbce https://conda.anaconda.org/conda-forge/osx-64/llvm-openmp-18.1.7-h15ab845_0.conda#57440310d92e93efd808c75fec50f94d https://conda.anaconda.org/conda-forge/osx-64/openssl-3.3.1-h87427d6_0.conda#1bdad93ae01353340f194c5d879745db +======= +https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.12.7-h3e169fe_0.conda#4c04ba47fdd2ebecc1d3b6a77534d9ef +https://conda.anaconda.org/conda-forge/osx-64/llvm-openmp-18.1.5-h39e0ece_0.conda#ee12a644568269838b91f901b2537425 +https://conda.anaconda.org/conda-forge/osx-64/openssl-3.3.0-h87427d6_2.conda#fa4859f58b1810d77089b0482c886da0 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/osx-64/readline-8.2-h9e318b2_1.conda#f17f77f2acf4d344734bda76829ce14e https://conda.anaconda.org/conda-forge/osx-64/brotli-bin-1.1.0-h0dc2134_1.conda#ece565c215adcc47fc1db4e651ee094b https://conda.anaconda.org/conda-forge/osx-64/gmp-6.3.0-h73e2aa4_1.conda#92f8d748d95d97f92fc26cfac9bb5b6e @@ -98,7 +108,11 @@ https://conda.anaconda.org/conda-forge/osx-64/mkl-2023.2.0-h54c2260_50500.conda# https://conda.anaconda.org/conda-forge/osx-64/pillow-10.3.0-py312h0c923fa_0.conda#6f0591ae972e9b815739da3392fbb3c3 https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.2-pyhd8ed1ab_0.conda#0f3f49c22c7ef3a1195fa61dad3c43be +======= +https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/osx-64/cctools-986-h40f6528_0.conda#b7a2ca0062a6ee8bc4e83ec887bef942 https://conda.anaconda.org/conda-forge/osx-64/clang-16.0.6-hd4457cd_7.conda#0f91e4c1d9d85887db66ddbc185d65d4 diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock index d38d4fcd82d6f..deb62a8584f52 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock @@ -1,6 +1,10 @@ # Generated by conda-lock. # platform: osx-64 +<<<<<<< HEAD # input_hash: 33a102d2ccde4e14e315a98b50340349af349f802403dd49589375b2b889f2d3 +======= +# input_hash: 197b213ba4980b3e95dcd438da09eb9b55e4bf5a4d8e6d3ddab29275aa22556a +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) @EXPLICIT https://repo.anaconda.com/pkgs/main/osx-64/blas-1.0-mkl.conda#cb2c87e85ac8e0ceae776d26d4214c8a https://repo.anaconda.com/pkgs/main/osx-64/bzip2-1.0.8-h6c40b1e_6.conda#96224786021d0765ce05818fa3c59bdb @@ -64,8 +68,13 @@ https://repo.anaconda.com/pkgs/main/osx-64/fonttools-4.51.0-py312h6c40b1e_0.cond https://repo.anaconda.com/pkgs/main/osx-64/numpy-base-1.26.4-py312h6f81483_0.conda#87f73efbf26ab2e2ea7c32481a71bd47 https://repo.anaconda.com/pkgs/main/osx-64/pillow-10.3.0-py312h6c40b1e_0.conda#fe883fa4247d35fe6de49f713529ca02 https://repo.anaconda.com/pkgs/main/osx-64/pip-24.0-py312hecd8cb5_0.conda#7a8e0b1d3742ddf1c8aa97fbaa158039 +<<<<<<< HEAD https://repo.anaconda.com/pkgs/main/osx-64/pytest-7.4.4-py312hecd8cb5_0.conda#d4dda983900b045cd27ae836cad670de https://repo.anaconda.com/pkgs/main/osx-64/python-dateutil-2.9.0post0-py312hecd8cb5_2.conda#1047dde28f78127dd9f6121e882926dd +======= +https://repo.anaconda.com/pkgs/main/osx-64/pytest-7.4.0-py312hecd8cb5_0.conda#b816a2439ba9b87524aec74d58e55b0a +https://repo.anaconda.com/pkgs/main/osx-64/python-dateutil-2.9.0post0-py312hecd8cb5_0.conda#b3ed54eb118325785284dd18bfceca19 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://repo.anaconda.com/pkgs/main/osx-64/pytest-cov-4.1.0-py312hecd8cb5_1.conda#a33a24eb20359f464938e75b2f57e23a https://repo.anaconda.com/pkgs/main/osx-64/pytest-xdist-3.5.0-py312hecd8cb5_0.conda#d1ecfb3691cceecb1f16bcfdf0b67bb5 https://repo.anaconda.com/pkgs/main/osx-64/bottleneck-1.3.7-py312h32608ca_0.conda#f96a01eba5ea542cf9c7cc8d77447627 @@ -80,7 +89,11 @@ https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.11.4-py312h81688c2_0.conda#7d https://repo.anaconda.com/pkgs/main/osx-64/pandas-2.2.1-py312he282a81_0.conda#021b70a1e40efb75b89eb8ebdb347132 https://repo.anaconda.com/pkgs/main/osx-64/pyamg-4.2.3-py312h44cbcf4_0.conda#3bdc7be74087b3a5a83c520a74e1e8eb # pip cython @ https://files.pythonhosted.org/packages/d5/6d/06c08d75adb98cdf72af18801e193d22580cc86ca553610f430f18ea26b3/Cython-3.0.10-cp312-cp312-macosx_10_9_x86_64.whl#sha256=8f2864ab5fcd27a346f0b50f901ebeb8f60b25a60a575ccfd982e7f3e9674914 +<<<<<<< HEAD # pip meson @ 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af52e4ce613b7668e1e28daaea07461722275d345395a5eaced4e07a16998179 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2024.3.11-h06a4308_0.conda#08529eb3504712baabcbda266a19feb7 @@ -75,11 +79,19 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py39h06a4308_0.conda#7f8ce # pip jinja2 @ https://files.pythonhosted.org/packages/31/80/3a54838c3fb461f6fec263ebf3a3a41771bd05190238de3486aae8540c36/jinja2-3.1.4-py3-none-any.whl#sha256=bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d # pip lazy-loader @ https://files.pythonhosted.org/packages/83/60/d497a310bde3f01cb805196ac61b7ad6dc5dcf8dce66634dc34364b20b4f/lazy_loader-0.4-py3-none-any.whl#sha256=342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc # pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526 +<<<<<<< HEAD # pip pytest @ https://files.pythonhosted.org/packages/4e/e7/81ebdd666d3bff6670d27349b5053605d83d55548e6bd5711f3b0ae7dd23/pytest-8.2.2-py3-none-any.whl#sha256=c434598117762e2bd304e526244f67bf66bbd7b5d6cf22138be51ff661980343 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl#sha256=70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6 # pip scipy @ 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https://files.pythonhosted.org/packages/ba/11/cb8b67f3cbdca05b59a032bb57963d4fe8c8d18c3870f30bed005b7f174d/lightgbm-4.3.0-py3-none-manylinux_2_28_x86_64.whl#sha256=104496a3404cb2452d3412cbddcfbfadbef9c372ea91e3a9b8794bcc5183bf07 # pip matplotlib @ https://files.pythonhosted.org/packages/d3/6d/45837c5b3d0005a5a9b04729b218a16bf3aa195701c6b33b2cc39ae943b6/matplotlib-3.9.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=81c40af649d19c85f8073e25e5806926986806fa6d54be506fbf02aef47d5a89 # pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index 502d425d2adb7..79bcfe1b3842d 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -1,6 +1,10 @@ # Generated by conda-lock. # platform: linux-64 +<<<<<<< HEAD # input_hash: 8a4a203136d97ff3b2c8657fce2dd2228215bfbf9c1cfbe271e401f934bdf1a7 +======= +# input_hash: 893691ececb72bbf81101535204fa5694d898312941037a946e81dcb5cc2c322 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2024.3.11-h06a4308_0.conda#08529eb3504712baabcbda266a19feb7 @@ -56,9 +60,15 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py312h06a4308_0.conda#6d96 # pip urllib3 @ https://files.pythonhosted.org/packages/ca/1c/89ffc63a9605b583d5df2be791a27bc1a42b7c32bab68d3c8f2f73a98cd4/urllib3-2.2.2-py3-none-any.whl#sha256=a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472 # pip jinja2 @ https://files.pythonhosted.org/packages/31/80/3a54838c3fb461f6fec263ebf3a3a41771bd05190238de3486aae8540c36/jinja2-3.1.4-py3-none-any.whl#sha256=bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d # pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526 +<<<<<<< HEAD # pip pytest @ https://files.pythonhosted.org/packages/4e/e7/81ebdd666d3bff6670d27349b5053605d83d55548e6bd5711f3b0ae7dd23/pytest-8.2.2-py3-none-any.whl#sha256=c434598117762e2bd304e526244f67bf66bbd7b5d6cf22138be51ff661980343 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl#sha256=70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6 +======= +# pip pytest @ https://files.pythonhosted.org/packages/b4/c1/27a1274b73712232328cb5115030057b7dec377f36a518c83f2e01d4f305/pytest-8.2.1-py3-none-any.whl#sha256=faccc5d332b8c3719f40283d0d44aa5cf101cec36f88cde9ed8f2bc0538612b1 +# pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 +# pip requests @ https://files.pythonhosted.org/packages/c3/20/748e38b466e0819491f0ce6e90ebe4184966ee304fe483e2c414b0f4ef07/requests-2.32.2-py3-none-any.whl#sha256=fc06670dd0ed212426dfeb94fc1b983d917c4f9847c863f313c9dfaaffb7c23c +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) # pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 # pip pooch @ https://files.pythonhosted.org/packages/a8/87/77cc11c7a9ea9fd05503def69e3d18605852cd0d4b0d3b8f15bbeb3ef1d1/pooch-1.8.2-py3-none-any.whl#sha256=3529a57096f7198778a5ceefd5ac3ef0e4d06a6ddaf9fc2d609b806f25302c47 # pip pytest-cov @ https://files.pythonhosted.org/packages/78/3a/af5b4fa5961d9a1e6237b530eb87dd04aea6eb83da09d2a4073d81b54ccf/pytest_cov-5.0.0-py3-none-any.whl#sha256=4f0764a1219df53214206bf1feea4633c3b558a2925c8b59f144f682861ce652 diff --git a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock index a83d0cef3e063..13c6509413ee9 100644 --- a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock @@ -1,6 +1,10 @@ # Generated by conda-lock. # platform: linux-64 +<<<<<<< HEAD # input_hash: be0f080ab1974d224952262cd9179ff999d10108421d7e7ded2435e6f08edf0e +======= +# input_hash: 244b6a064d3785ea62baaf9436848821d153846b455c2976f5e811182e848c83 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-openblas.conda#9ddfcaef10d79366c90128f5dc444be8 diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock index 5c59745ab86d4..e07f1d748f73c 100644 --- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock @@ -1,6 +1,10 @@ # Generated by conda-lock. # platform: win-64 +<<<<<<< HEAD # input_hash: ea607aaeb7b1d1f8a1f821a9f505b3601083a218ec4763e2d72d3d3d800e718c +======= +# input_hash: 9b46e8490ff2c4c93104b20d3bffb2d04120288c8c8680bef8376ebd9d9b8eb3 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) @EXPLICIT https://conda.anaconda.org/conda-forge/win-64/ca-certificates-2024.6.2-h56e8100_0.conda#12a3a2b3a00a21bbb390d4de5ad8dd0f https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2024.1.0-h57928b3_966.conda#35d7ea07ad6c878bd7240d2d6c1b8657 @@ -11,10 +15,17 @@ https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#1610 https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.22621.0-h57928b3_0.tar.bz2#72608f6cd3e5898229c3ea16deb1ac43 https://conda.anaconda.org/conda-forge/win-64/m2w64-gmp-6.1.0-2.tar.bz2#53a1c73e1e3d185516d7e3af177596d9 https://conda.anaconda.org/conda-forge/win-64/m2w64-libwinpthread-git-5.0.0.4634.697f757-2.tar.bz2#774130a326dee16f1ceb05cc687ee4f0 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.40.33810-ha82c5b3_20.conda#e39cc4c34c53654ec939558993d9dc5b https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libs-core-5.3.0-7.tar.bz2#4289d80fb4d272f1f3b56cfe87ac90bd https://conda.anaconda.org/conda-forge/win-64/vc-14.3-h8a93ad2_20.conda#8558f367e1d7700554f7cdb823c46faf https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.40.33810-h3bf8584_20.conda#c21f1b4a3a30bbc3ef35a50957578e0e +======= +https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.38.33135-h835141b_20.conda#e971b35a5765862fabc4ba6e5ddf9470 +https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libs-core-5.3.0-7.tar.bz2#4289d80fb4d272f1f3b56cfe87ac90bd +https://conda.anaconda.org/conda-forge/win-64/vc-14.3-ha32ba9b_20.conda#2abfb5cb1b9d41a50f765d60f0be563d +https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.38.33135-h22015db_20.conda#bb4f5ab332e46e1b022d8842e72905b1 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/win-64/bzip2-1.0.8-hcfcfb64_5.conda#26eb8ca6ea332b675e11704cce84a3be https://conda.anaconda.org/conda-forge/win-64/icu-73.2-h63175ca_0.conda#0f47d9e3192d9e09ae300da0d28e0f56 https://conda.anaconda.org/conda-forge/win-64/lerc-4.0.0-h63175ca_0.tar.bz2#1900cb3cab5055833cfddb0ba233b074 @@ -29,7 +40,11 @@ https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.4.0-hcfcfb64_0.cond https://conda.anaconda.org/conda-forge/win-64/libzlib-1.3.1-h2466b09_1.conda#d4483ca8afc57ddf1f6dded53b36c17f https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libgfortran-5.3.0-6.tar.bz2#066552ac6b907ec6d72c0ddab29050dc https://conda.anaconda.org/conda-forge/win-64/ninja-1.12.1-hc790b64_0.conda#a557dde55343e03c68cd7e29e7f87279 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/win-64/openssl-3.3.1-h2466b09_0.conda#27fe798366ef3a81715b13eedf699e2f +======= +https://conda.anaconda.org/conda-forge/win-64/openssl-3.3.0-h2466b09_2.conda#67d5f8749ee4ae3c4060d7a13fc8872f +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/win-64/pthreads-win32-2.9.1-hfa6e2cd_3.tar.bz2#e2da8758d7d51ff6aa78a14dfb9dbed4 https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h5226925_1.conda#fc048363eb8f03cd1737600a5d08aafe https://conda.anaconda.org/conda-forge/win-64/xz-5.2.6-h8d14728_0.tar.bz2#515d77642eaa3639413c6b1bc3f94219 @@ -87,7 +102,11 @@ https://conda.anaconda.org/conda-forge/noarch/meson-1.4.0-pyhd8ed1ab_0.conda#52a https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.2-h3d672ee_0.conda#7e7099ad94ac3b599808950cec30ad4e https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.2-pyhd8ed1ab_0.conda#0f3f49c22c7ef3a1195fa61dad3c43be +======= +https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/win-64/sip-6.7.12-py39h99910a6_0.conda#0cc5774390ada632ed7975203057c91c https://conda.anaconda.org/conda-forge/win-64/tbb-2021.12.0-hc790b64_1.conda#e98333643abc739ebea1bac97a479828 diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock index 9aa3a50a4ee7f..09bbbbb4018b8 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock @@ -1,6 +1,10 @@ # Generated by conda-lock. # platform: linux-64 +<<<<<<< HEAD # input_hash: 3974f9847d888a2fd37ba5fcfb76cb09bba4c9b84b6200932500fc94e3b0c4ae +======= +# input_hash: be96a61e1a8c45c41b04e6e30fafbbf78e5b05202b348a702c726eced7c7df48 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.6.2-hbcca054_0.conda#847c3c2905cc467cea52c24f9cfa8080 @@ -46,7 +50,11 @@ https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-h4ab18f5_0.conda#a41fa0e391cc9e0d6b78ac69ca047a6c +======= +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_2.conda#b8934d399b56d73e323403e183d009c5 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a @@ -170,7 +178,11 @@ https://conda.anaconda.org/conda-forge/noarch/meson-1.4.0-pyhd8ed1ab_0.conda#52a https://conda.anaconda.org/conda-forge/linux-64/pillow-10.3.0-py39h90c7501_0.conda#1e3b6af9592be71ce19f0a6aae05d97b https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.2-pyhd8ed1ab_0.conda#0f3f49c22c7ef3a1195fa61dad3c43be +======= +https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.1-pyhd8ed1ab_0.conda#08807a87fa7af10754d46f63b368e016 @@ -183,7 +195,11 @@ https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.c https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py39h474f0d3_0.conda#aa265f5697237aa13cc10f53fa8acc4f https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5.conda#93aff412f3e49fdb43361c0215cbd72d https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda#5ede4753180c7a550a443c430dc8ab52 +======= +https://conda.anaconda.org/conda-forge/noarch/requests-2.32.2-pyhd8ed1ab_0.conda#e1643b34b19df8c028a4f00bf5df58a6 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-22_linux64_openblas.conda#63ddb593595c9cf5eb08d3de54d66df8 https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py39h7633fee_0.conda#bdc188e59857d6efab332714e0d01d93 <<<<<<< HEAD diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt index 3fa5995aafd1e..950999ab0b49b 100644 --- a/build_tools/azure/ubuntu_atlas_lock.txt +++ b/build_tools/azure/ubuntu_atlas_lock.txt @@ -29,7 +29,11 @@ pluggy==1.5.0 # via pytest pyproject-metadata==0.8.0 # via meson-python +<<<<<<< HEAD pytest==8.2.2 +======= +pytest==8.2.1 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) # via # -r build_tools/azure/ubuntu_atlas_requirements.txt # pytest-xdist diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index 18b521b853aca..fd029d6a58078 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -1,10 +1,14 @@ # Generated by conda-lock. # platform: linux-64 <<<<<<< HEAD +<<<<<<< HEAD # input_hash: f6f3862aafcafa139a322e498517c3db58e1b8db95f1b1ca8c18f5b70d446dc9 ======= # input_hash: beab3d7262ec74c4ef8c9050098de8b9fe7910606e7bd4ff52687972bff35868 >>>>>>> 34db65a3ad (DOC use pydata-sphinx-theme for the website (#29038)) +======= +# input_hash: 46e3ce252a769181cca2ee8df6e2bfd3f6749900d7863cc5a3b41c300daa0380 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.6.2-hbcca054_0.conda#847c3c2905cc467cea52c24f9cfa8080 @@ -67,7 +71,11 @@ https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-h4ab18f5_0.conda#a41fa0e391cc9e0d6b78ac69ca047a6c +======= +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_2.conda#b8934d399b56d73e323403e183d009c5 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.6.6-he8a937b_2.conda#77d9955b4abddb811cb8ab1aa7d743e4 @@ -225,7 +233,11 @@ https://conda.anaconda.org/conda-forge/linux-64/pillow-10.3.0-py39h90c7501_0.con https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/plotly-5.22.0-pyhd8ed1ab_0.conda#5b409a5f738e7d76c2b426eddb7e9956 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.2-pyhd8ed1ab_0.conda#0f3f49c22c7ef3a1195fa61dad3c43be +======= +https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.11.0-hd8ed1ab_0.conda#471e3988f8ca5e9eb3ce6be7eac3bcee @@ -241,7 +253,11 @@ https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.c https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py39h474f0d3_0.conda#aa265f5697237aa13cc10f53fa8acc4f https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5.conda#93aff412f3e49fdb43361c0215cbd72d https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda#5ede4753180c7a550a443c430dc8ab52 +======= +https://conda.anaconda.org/conda-forge/noarch/requests-2.32.2-pyhd8ed1ab_0.conda#e1643b34b19df8c028a4f00bf5df58a6 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-22_linux64_openblas.conda#63ddb593595c9cf5eb08d3de54d66df8 https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py39h7633fee_0.conda#bdc188e59857d6efab332714e0d01d93 https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.4-h9ad1361_0.conda#147cce520ec59367549fd0d96d404213 diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index 3fcefe17522be..409fcf597d72c 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -1,10 +1,14 @@ # Generated by conda-lock. # platform: linux-64 <<<<<<< HEAD +<<<<<<< HEAD # input_hash: aa64e81a701c97b7c4cf149f108c3ca59fc65572bfda79dbaeb2d093afc8a665 ======= # input_hash: 6c9ff93ed18fe7c2e8387a4c3d7104701555959a32e797c1cb83593137afe155 >>>>>>> 34db65a3ad (DOC use pydata-sphinx-theme for the website (#29038)) +======= +# input_hash: b285d0ba8ec3865f6ac4f6f990060713f430995f71bf0b3a4c94a6610defc5b9 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.6.2-hbcca054_0.conda#847c3c2905cc467cea52c24f9cfa8080 @@ -59,7 +63,11 @@ https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-h4ab18f5_0.conda#a41fa0e391cc9e0d6b78ac69ca047a6c +======= +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_2.conda#b8934d399b56d73e323403e183d009c5 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a @@ -208,7 +216,11 @@ https://conda.anaconda.org/conda-forge/linux-64/pillow-10.3.0-py39h90c7501_0.con https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/plotly-5.14.0-pyhd8ed1ab_0.conda#6a7bcc42ef58dd6cf3da9333ea102433 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.2-pyhd8ed1ab_0.conda#0f3f49c22c7ef3a1195fa61dad3c43be +======= +https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.11.0-hd8ed1ab_0.conda#471e3988f8ca5e9eb3ce6be7eac3bcee @@ -224,11 +236,15 @@ https://conda.anaconda.org/conda-forge/linux-64/mkl-devel-2024.1.0-ha770c72_693. https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5.conda#93aff412f3e49fdb43361c0215cbd72d https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b <<<<<<< HEAD +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda#5ede4753180c7a550a443c430dc8ab52 https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.5.2-pyhd8ed1ab_0.conda#1a57a819915e1c169b74933720b138f2 https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.4-h9ad1361_0.conda#147cce520ec59367549fd0d96d404213 ======= https://conda.anaconda.org/conda-forge/noarch/requests-2.31.0-pyhd8ed1ab_0.conda#a30144e4156cdbb236f99ebb49828f8b +======= +https://conda.anaconda.org/conda-forge/noarch/requests-2.32.2-pyhd8ed1ab_0.conda#e1643b34b19df8c028a4f00bf5df58a6 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.5.1-pyhd8ed1ab_0.conda#d4f60ccc5421472d2583efd9ce39d8b1 https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.3-h9ad1361_0.conda#8fb0e954c616bb0f9389efac4b4ed44b >>>>>>> d03c351780 (:lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29053)) diff --git a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock index edc83e3689ccc..96aeb68c6d922 100644 --- a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock +++ b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock @@ -1,6 +1,10 @@ # Generated by conda-lock. # platform: linux-aarch64 +<<<<<<< HEAD # input_hash: 2d8c526ab7c0c2f0ca509bfec3f035e5bd33b8096f194f0747f167c8aff66383 +======= +# input_hash: bd97deca0a3c9c77631f0ee5b9933ef1a3dd81165c7ad29467c0f9504350507e +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) @EXPLICIT https://conda.anaconda.org/conda-forge/linux-aarch64/ca-certificates-2024.6.2-hcefe29a_0.conda#3ef6b1a30375f8a973a593698e317191 https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.40-h9fc2d93_7.conda#1b0feef706f4d03eff0b76626ead64fc @@ -21,7 +25,12 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/libwebp-base-1.4.0-h31becfc https://conda.anaconda.org/conda-forge/linux-aarch64/libxcrypt-4.4.36-h31becfc_1.conda#b4df5d7d4b63579d081fd3a4cf99740e https://conda.anaconda.org/conda-forge/linux-aarch64/libzlib-1.3.1-h68df207_1.conda#b13fb82f88902e34dd0638cd7d378c21 https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.5-h0425590_0.conda#38362af7bfac0efef69675acee564458 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.3.1-h68df207_1.conda#8349df397000d7a7acb514d97879fe09 +======= +https://conda.anaconda.org/conda-forge/linux-aarch64/ninja-1.12.1-h70be974_0.conda#216635cea46498d8045c7cf0f03eaf72 +https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.3.0-h68df207_2.conda#052eb8fb4acf496390ae7c5f2fd71d69 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/linux-aarch64/pthread-stubs-0.4-hb9de7d4_1001.tar.bz2#d0183ec6ce0b5aaa3486df25fa5f0ded https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxau-1.0.11-h31becfc_0.conda#13de34f69cb73165dbe08c1e9148bedb https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxdmcp-1.1.3-h3557bc0_0.tar.bz2#a6c9016ae1ca5c47a3603ed4cd65fedd @@ -62,7 +71,11 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/openjpeg-2.5.2-h0d9d63b_0.c https://conda.anaconda.org/conda-forge/noarch/packaging-24.1-pyhd8ed1ab_0.conda#cbe1bb1f21567018ce595d9c2be0f0db https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda#d3483c8fc2dc2cc3f5cf43e26d60cabf https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.2-pyhd8ed1ab_0.conda#b9a4dacf97241704529131a0dfc0494f +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/noarch/setuptools-70.1.1-pyhd8ed1ab_0.conda#985e9e86e1b0fc75a74a9bfab9309ef7 +======= +https://conda.anaconda.org/conda-forge/noarch/setuptools-70.0.0-pyhd8ed1ab_0.conda#c8ddb4f34a208df4dd42509a0f6a1c89 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96 @@ -79,7 +92,11 @@ https://conda.anaconda.org/conda-forge/noarch/meson-1.4.1-pyhd8ed1ab_0.conda#714 https://conda.anaconda.org/conda-forge/linux-aarch64/pillow-10.3.0-py39h4a8821f_1.conda#12b4464ffbad162e28b7bf6e81129501 https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.2-pyhd8ed1ab_0.conda#0f3f49c22c7ef3a1195fa61dad3c43be +======= +https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.0-pyhd8ed1ab_0.conda#dcbadab7a68738a028e195ab68ab2d2e https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-22_linuxaarch64_openblas.conda#5acf669e0be669f30f4b813d2ecda7b8 From 3395bf3c37a729b1de71526fcf14a64f3536812e Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 9 Sep 2024 11:44:31 +0200 Subject: [PATCH 132/275] Bump the actions group across 1 directory with 4 updates (#29214) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/check-changelog.yml | 2 +- .github/workflows/check-sdist.yml | 2 +- .github/workflows/labeler-title-regex.yml | 2 +- .github/workflows/lint.yml | 4 ++-- .github/workflows/publish_pypi.yml | 2 +- .github/workflows/update-lock-files.yml | 2 +- .github/workflows/update_tracking_issue.yml | 2 +- .github/workflows/wheels.yml | 8 ++++---- 8 files changed, 12 insertions(+), 12 deletions(-) diff --git a/.github/workflows/check-changelog.yml b/.github/workflows/check-changelog.yml index d5bfc8ef0f430..2c0792136a204 100644 --- a/.github/workflows/check-changelog.yml +++ b/.github/workflows/check-changelog.yml @@ -16,7 +16,7 @@ jobs: run: | echo "PR_NUMBER=${{ github.event.pull_request.number }}" >> $GITHUB_ENV echo "TAGGED_MILESTONE=${{ github.event.pull_request.milestone.title }}" >> $GITHUB_ENV - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 with: fetch-depth: '0' - name: Check the changelog entry diff --git a/.github/workflows/check-sdist.yml b/.github/workflows/check-sdist.yml index c02af711bdb6c..81a13294bdd96 100644 --- a/.github/workflows/check-sdist.yml +++ b/.github/workflows/check-sdist.yml @@ -11,7 +11,7 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: python-version: '3.9' diff --git a/.github/workflows/labeler-title-regex.yml b/.github/workflows/labeler-title-regex.yml index 10195eca13a73..03de57d66ddb9 100644 --- a/.github/workflows/labeler-title-regex.yml +++ b/.github/workflows/labeler-title-regex.yml @@ -15,7 +15,7 @@ jobs: labeler: runs-on: ubuntu-20.04 steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: python-version: '3.9' diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index 30dd632289b6e..e2de3bbde583b 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -20,7 +20,7 @@ jobs: steps: - name: Checkout code - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: ref: ${{ github.event.pull_request.head.sha }} @@ -72,7 +72,7 @@ jobs: steps: - name: Checkout code - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v5 diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml index b8940ae133ad9..79ab2f4a6381a 100644 --- a/.github/workflows/publish_pypi.yml +++ b/.github/workflows/publish_pypi.yml @@ -18,7 +18,7 @@ jobs: # IMPORTANT: this permission is mandatory for trusted publishing id-token: write steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: python-version: '3.8' diff --git a/.github/workflows/update-lock-files.yml b/.github/workflows/update-lock-files.yml index 50d62c85d00a6..669a1e88b024e 100644 --- a/.github/workflows/update-lock-files.yml +++ b/.github/workflows/update-lock-files.yml @@ -43,7 +43,7 @@ jobs: - name: Create Pull Request id: cpr - uses: peter-evans/create-pull-request@v5 + uses: peter-evans/create-pull-request@v6 with: token: ${{ secrets.BOT_GITHUB_TOKEN }} push-to-fork: scikit-learn-bot/scikit-learn diff --git a/.github/workflows/update_tracking_issue.yml b/.github/workflows/update_tracking_issue.yml index d4538fe6848d8..2039089654fea 100644 --- a/.github/workflows/update_tracking_issue.yml +++ b/.github/workflows/update_tracking_issue.yml @@ -26,7 +26,7 @@ jobs: runs-on: ubuntu-latest if: github.repository == 'scikit-learn/scikit-learn' && github.event_name == 'schedule' steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: python-version: '3.9' diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml index 632425dcd046e..606062e81937b 100644 --- a/.github/workflows/wheels.yml +++ b/.github/workflows/wheels.yml @@ -32,7 +32,7 @@ jobs: steps: - name: Checkout scikit-learn - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: ref: ${{ github.event.pull_request.head.sha }} @@ -117,7 +117,7 @@ jobs: steps: - name: Checkout scikit-learn - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Setup Python uses: actions/setup-python@v5 @@ -198,7 +198,7 @@ jobs: steps: - name: Checkout scikit-learn - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Setup Python uses: actions/setup-python@v5 @@ -232,7 +232,7 @@ jobs: steps: - name: Checkout scikit-learn - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Download artifacts uses: actions/download-artifact@v4 From 452dbe5efcc5162b5adbe6bfaab604f0e5bf4c6b Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 9 Sep 2024 11:49:20 +0200 Subject: [PATCH 133/275] CI Use development Cython wheel rather than building from source (#29223) --- build_tools/azure/install.sh | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh index 9a6a526fafa46..f61d85e1478f5 100755 --- a/build_tools/azure/install.sh +++ b/build_tools/azure/install.sh @@ -50,7 +50,7 @@ pre_python_environment_install() { check_packages_dev_version() { for package in $@; do package_version=$(python -c "import $package; print($package.__version__)") - if ! [[ $package_version =~ "dev" ]]; then + if [[ $package_version =~ "^[.0-9]+$" ]]; then echo "$package is not a development version: $package_version" exit 1 fi @@ -71,18 +71,25 @@ python_environment_install_and_activate() { python -m venv $VIRTUALENV source $VIRTUALENV/bin/activate pip install -r "${LOCK_FILE}" + # TODO for now need pip 24.1b1 to find free-threaded wheels + pip install -U --pre pip + # TODO When there are CPython 3.13 free-threaded wheels for numpy, + # scipy and cython move them to + # build_tools/azure/cpython_free_threaded_requirements.txt. For now we + # install them from scientific-python-nightly-wheels + dev_anaconda_url=https://pypi.anaconda.org/scientific-python-nightly-wheels/simple + dev_packages="numpy scipy Cython" + pip install --pre --upgrade --timeout=60 --extra-index $dev_anaconda_url $dev_packages fi if [[ "$DISTRIB" == "conda-pip-scipy-dev" ]]; then echo "Installing development dependency wheels" dev_anaconda_url=https://pypi.anaconda.org/scientific-python-nightly-wheels/simple - dev_packages="numpy scipy pandas" + dev_packages="numpy scipy pandas Cython" pip install --pre --upgrade --timeout=60 --extra-index $dev_anaconda_url $dev_packages check_packages_dev_version $dev_packages - echo "Installing Cython from latest sources" - pip install https://github.com/cython/cython/archive/master.zip echo "Installing joblib from latest sources" pip install https://github.com/joblib/joblib/archive/master.zip echo "Installing pillow from latest sources" From a8bc8da76af5db91715e551ea111b0df3577029a Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 9 Sep 2024 11:51:46 +0200 Subject: [PATCH 134/275] FIX Lazy instantiate the ThreadpoolController (#29235) --- doc/whats_new/v1.5.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst index 20184bbd2a551..0d54414120f58 100644 --- a/doc/whats_new/v1.5.rst +++ b/doc/whats_new/v1.5.rst @@ -40,7 +40,6 @@ Changelog where the full input data was copied for each transformer when `n_jobs > 1`. :pr:`29330` by :user:`Jérémie du Boisberranger `. -:mod:`sklearn.metrics` ...................... - |Fix| Fix a regression in :func:`metrics.r2_score`. Passing torch CPU tensors From f6141354f92be398c13ad1f81f667df6702ef334 Mon Sep 17 00:00:00 2001 From: Tialo <65392801+Tialo@users.noreply.github.com> Date: Thu, 20 Jun 2024 12:56:49 +0300 Subject: [PATCH 135/275] FIX `zero_one_loss` breaks with multilabel and Array API (#29269) --- doc/whats_new/v1.5.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst index 0d54414120f58..20184bbd2a551 100644 --- a/doc/whats_new/v1.5.rst +++ b/doc/whats_new/v1.5.rst @@ -40,6 +40,7 @@ Changelog where the full input data was copied for each transformer when `n_jobs > 1`. :pr:`29330` by :user:`Jérémie du Boisberranger `. +:mod:`sklearn.metrics` ...................... - |Fix| Fix a regression in :func:`metrics.r2_score`. Passing torch CPU tensors From 511fb4d1090ab1a42e8d8a5460c699d84a913f5b Mon Sep 17 00:00:00 2001 From: Adam Li Date: Mon, 1 Jul 2024 23:57:36 -0400 Subject: [PATCH 136/275] DOC Fix tree explanation of `tree_.value` in example (#29331) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> Co-authored-by: Loïc Estève --- doc/whats_new/v1.4.rst | 9 ++++++ examples/tree/plot_unveil_tree_structure.py | 35 +++++++++++++++------ 2 files changed, 34 insertions(+), 10 deletions(-) diff --git a/doc/whats_new/v1.4.rst b/doc/whats_new/v1.4.rst index 7865ff38adb79..96715e49277b3 100644 --- a/doc/whats_new/v1.4.rst +++ b/doc/whats_new/v1.4.rst @@ -29,6 +29,15 @@ Version 1.4.1 **February 2024** +Changed models +-------------- + +- |API| The `tree_.value` attribute in :class:`tree.DecisionTreeClassifier`, + :class:`tree.DecisionTreeRegressor`, :class:`tree.ExtraTreeClassifier` and + :class:`tree.ExtraTreeRegressor` changed from an weighted absolute count + of number of samples to a weighted fraction of the total number of samples. + :pr:`27639` by :user:`Samuel Ronsin `. + Metadata Routing ---------------- diff --git a/examples/tree/plot_unveil_tree_structure.py b/examples/tree/plot_unveil_tree_structure.py index 19b7c643ec0f7..508c3c304f4d9 100644 --- a/examples/tree/plot_unveil_tree_structure.py +++ b/examples/tree/plot_unveil_tree_structure.py @@ -68,7 +68,8 @@ # - ``weighted_n_node_samples[i]``: the weighted number of training samples # reaching node ``i`` # - ``value[i, j, k]``: the summary of the training samples that reached node i for -# output j and class k (for regression tree, class is set to 1). +# output j and class k (for regression tree, class is set to 1). See below +# for more information about ``value``. # # Using the arrays, we can traverse the tree structure to compute various # properties. Below, we will compute the depth of each node and whether or not @@ -108,7 +109,7 @@ if is_leaves[i]: print( "{space}node={node} is a leaf node with value={value}.".format( - space=node_depth[i] * "\t", node=i, value=values[i] + space=node_depth[i] * "\t", node=i, value=np.around(values[i], 3) ) ) else: @@ -122,7 +123,7 @@ feature=feature[i], threshold=threshold[i], right=children_right[i], - value=values[i], + value=np.around(values[i], 3), ) ) @@ -130,16 +131,28 @@ # What is the values array used here? # ----------------------------------- # The `tree_.value` array is a 3D array of shape -# [``n_nodes``, ``n_classes``, ``n_outputs``] which provides the count of samples -# reaching a node for each class and for each output. Each node has a ``value`` -# array which is the number of weighted samples reaching this -# node for each output and class. +# [``n_nodes``, ``n_classes``, ``n_outputs``] which provides the proportion of samples +# reaching a node for each class and for each output. +# Each node has a ``value`` array which is the proportion of weighted samples reaching +# this node for each output and class with respect to the parent node. +# +# One could convert this to the absolute weighted number of samples reaching a node, +# by multiplying this number by `tree_.weighted_n_node_samples[node_idx]` for the +# given node. Note sample weights are not used in this example, so the weighted +# number of samples is the number of samples reaching the node because each sample +# has a weight of 1 by default. # # For example, in the above tree built on the iris dataset, the root node has -# ``value = [37, 34, 41]``, indicating there are 37 samples +# ``value = [0.33, 0.304, 0.366]`` indicating there are 33% of class 0 samples, +# 30.4% of class 1 samples, and 36.6% of class 2 samples at the root node. One can +# convert this to the absolute number of samples by multiplying by the number of +# samples reaching the root node, which is `tree_.weighted_n_node_samples[0]`. +# Then the root node has ``value = [37, 34, 41]``, indicating there are 37 samples # of class 0, 34 samples of class 1, and 41 samples of class 2 at the root node. +# # Traversing the tree, the samples are split and as a result, the ``value`` array -# reaching each node changes. The left child of the root node has ``value = [37, 0, 0]`` +# reaching each node changes. The left child of the root node has ``value = [1., 0, 0]`` +# (or ``value = [37, 0, 0]`` when converted to the absolute number of samples) # because all 37 samples in the left child node are from class 0. # # Note: In this example, `n_outputs=1`, but the tree classifier can also handle @@ -148,8 +161,10 @@ ############################################################################## # We can compare the above output to the plot of the decision tree. +# Here, we show the proportions of samples of each class that reach each +# node corresponding to the actual elements of `tree_.value` array. -tree.plot_tree(clf) +tree.plot_tree(clf, proportion=True) plt.show() ############################################################################## From 70ac14bdc281a03651df387e9f6462064ebdee93 Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Tue, 2 Jul 2024 18:56:13 +1000 Subject: [PATCH 137/275] DOC Improve `plot_precision_recall` (#28967) --- .../model_selection/plot_precision_recall.py | 56 +++++++++---------- 1 file changed, 28 insertions(+), 28 deletions(-) diff --git a/examples/model_selection/plot_precision_recall.py b/examples/model_selection/plot_precision_recall.py index 19a93c7324cbb..6934f0f49ddde 100644 --- a/examples/model_selection/plot_precision_recall.py +++ b/examples/model_selection/plot_precision_recall.py @@ -7,44 +7,42 @@ Precision-Recall is a useful measure of success of prediction when the classes are very imbalanced. In information retrieval, precision is a -measure of result relevancy, while recall is a measure of how many truly -relevant results are returned. - -The precision-recall curve shows the tradeoff between precision and -recall for different threshold. A high area under the curve represents -both high recall and high precision, where high precision relates to a -low false positive rate, and high recall relates to a low false negative -rate. High scores for both show that the classifier is returning accurate -results (high precision), as well as returning a majority of all positive -results (high recall). - -A system with high recall but low precision returns many results, but most of -its predicted labels are incorrect when compared to the training labels. A -system with high precision but low recall is just the opposite, returning very -few results, but most of its predicted labels are correct when compared to the -training labels. An ideal system with high precision and high recall will -return many results, with all results labeled correctly. +measure of the fraction of relevant items among actually returned items while recall +is a measure of the fraction of items that were returned among all items that should +have been returned. 'Relevancy' here refers to items that are +postively labeled, i.e., true positives and false negatives. Precision (:math:`P`) is defined as the number of true positives (:math:`T_p`) over the number of true positives plus the number of false positives (:math:`F_p`). -:math:`P = \\frac{T_p}{T_p+F_p}` +.. math:: + P = \\frac{T_p}{T_p+F_p} Recall (:math:`R`) is defined as the number of true positives (:math:`T_p`) over the number of true positives plus the number of false negatives (:math:`F_n`). -:math:`R = \\frac{T_p}{T_p + F_n}` +.. math:: + R = \\frac{T_p}{T_p + F_n} -These quantities are also related to the :math:`F_1` score, which is the -harmonic mean of precision and recall. Thus, we can compute the :math:`F_1` -using the following formula: +The precision-recall curve shows the tradeoff between precision and +recall for different thresholds. A high area under the curve represents +both high recall and high precision. High precision is achieved by having +few false positives in the returned results, and high recall is achieved by +having few false negatives in the relevant results. +High scores for both show that the classifier is returning +accurate results (high precision), as well as returning a majority of all relevant +results (high recall). -:math:`F_1 = \\frac{2T_p}{2T_p + F_p + F_n}` +A system with high recall but low precision returns most of the relevant items, but +the proportion of returned results that are incorrectly labeled is high. A +system with high precision but low recall is just the opposite, returning very +few of the relevant items, but most of its predicted labels are correct when compared +to the actual labels. An ideal system with high precision and high recall will +return most of the relevant items, with most results labeled correctly. -Note that the precision may not decrease with recall. The -definition of precision (:math:`\\frac{T_p}{T_p + F_p}`) shows that lowering +The definition of precision (:math:`\\frac{T_p}{T_p + F_p}`) shows that lowering the threshold of a classifier may increase the denominator, by increasing the number of results returned. If the threshold was previously set too high, the new results may all be true positives, which will increase precision. If the @@ -52,10 +50,12 @@ will introduce false positives, decreasing precision. Recall is defined as :math:`\\frac{T_p}{T_p+F_n}`, where :math:`T_p+F_n` does -not depend on the classifier threshold. This means that lowering the classifier +not depend on the classifier threshold. Changing the classifier threshold can only +change the numerator, :math:`T_p`. Lowering the classifier threshold may increase recall, by increasing the number of true positive results. It is also possible that lowering the threshold may leave recall -unchanged, while the precision fluctuates. +unchanged, while the precision fluctuates. Thus, precision does not necessarily +decrease with recall. The relationship between recall and precision can be observed in the stairstep area of the plot - at the edges of these steps a small change @@ -82,7 +82,7 @@ average precision to multi-class or multi-label classification, it is necessary to binarize the output. One curve can be drawn per label, but one can also draw a precision-recall curve by considering each element of the label indicator -matrix as a binary prediction (micro-averaging). +matrix as a binary prediction (:ref:`micro-averaging `). .. note:: From fe97533a9dc4f573dfc84202250c287a8bc4e08c Mon Sep 17 00:00:00 2001 From: Yao Xiao <108576690+Charlie-XIAO@users.noreply.github.com> Date: Wed, 3 Jul 2024 10:27:37 +0800 Subject: [PATCH 138/275] DOC tweak appearance of installation instructions (#29160) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- doc/install.rst | 73 +++++++++++++++++------------- doc/install_instructions_conda.rst | 17 +++++++ doc/scss/install.scss | 54 +++++++++++++++++++--- 3 files changed, 105 insertions(+), 39 deletions(-) create mode 100644 doc/install_instructions_conda.rst diff --git a/doc/install.rst b/doc/install.rst index be924b012ce65..3d11b506473ee 100644 --- a/doc/install.rst +++ b/doc/install.rst @@ -33,16 +33,17 @@ Installing the latest release .. div:: install-instructions .. tab-set:: + :class: tabs-os - .. tab-item:: pip - :class-label: tab-6 - :sync: packager-pip + .. tab-item:: Windows + :class-label: tab-4 .. tab-set:: + :class: tabs-package-manager - .. tab-item:: Windows - :class-label: tab-4 - :sync: os-windows + .. tab-item:: pip + :class-label: tab-6 + :sync: package-manager-pip Install the 64-bit version of Python 3, for instance from the `official website `__. @@ -66,9 +67,21 @@ Installing the latest release python -m pip freeze # show all installed packages in the environment python -c "import sklearn; sklearn.show_versions()" - .. tab-item:: macOS - :class-label: tab-4 - :sync: os-macos + .. tab-item:: conda + :class-label: tab-6 + :sync: package-manager-conda + + .. include:: ./install_instructions_conda.rst + + .. tab-item:: MacOS + :class-label: tab-4 + + .. tab-set:: + :class: tabs-package-manager + + .. tab-item:: pip + :class-label: tab-6 + :sync: package-manager-pip Install Python 3 using `homebrew `_ (`brew install python`) or by manually installing the package from the `official website @@ -93,9 +106,21 @@ Installing the latest release python -m pip freeze # show all installed packages in the environment python -c "import sklearn; sklearn.show_versions()" - .. tab-item:: Linux - :class-label: tab-4 - :sync: os-linux + .. tab-item:: conda + :class-label: tab-6 + :sync: package-manager-conda + + .. include:: ./install_instructions_conda.rst + + .. tab-item:: Linux + :class-label: tab-4 + + .. tab-set:: + :class: tabs-package-manager + + .. tab-item:: pip + :class-label: tab-6 + :sync: package-manager-pip Python 3 is usually installed by default on most Linux distributions. To check if you have it installed, try: @@ -127,28 +152,12 @@ Installing the latest release python3 -m pip freeze # show all installed packages in the environment python3 -c "import sklearn; sklearn.show_versions()" - .. tab-item:: conda - :class-label: tab-6 - :sync: packager-conda - - Install conda using the `Anaconda or miniconda installers - `__ - or the `miniforge installers - `__ (no administrator - permission required for any of those). Then run: - - .. prompt:: bash - - conda create -n sklearn-env -c conda-forge scikit-learn - conda activate sklearn-env - - In order to check your installation, you can use: + .. tab-item:: conda + :class-label: tab-6 + :sync: package-manager-conda - .. prompt:: bash + .. include:: ./install_instructions_conda.rst - conda list scikit-learn # show scikit-learn version and location - conda list # show all installed packages in the environment - python -c "import sklearn; sklearn.show_versions()" Using an isolated environment such as pip venv or conda makes it possible to install a specific version of scikit-learn with pip or conda and its dependencies diff --git a/doc/install_instructions_conda.rst b/doc/install_instructions_conda.rst new file mode 100644 index 0000000000000..284a6925eeba9 --- /dev/null +++ b/doc/install_instructions_conda.rst @@ -0,0 +1,17 @@ +Install conda using the `Anaconda or miniconda installers +`__ or the +`miniforge installers `__ (no +administrator permission required for any of those). Then run: + +.. prompt:: bash + + conda create -n sklearn-env -c conda-forge scikit-learn + conda activate sklearn-env + +In order to check your installation, you can use: + +.. prompt:: bash + + conda list scikit-learn # show scikit-learn version and location + conda list # show all installed packages in the environment + python -c "import sklearn; sklearn.show_versions()" diff --git a/doc/scss/install.scss b/doc/scss/install.scss index 92e201f00a107..cb9c5357dea57 100644 --- a/doc/scss/install.scss +++ b/doc/scss/install.scss @@ -6,15 +6,55 @@ * https://sass-lang.com/guide/ */ -.install-instructions .sd-tab-set > label.sd-tab-label { - margin: 0; - text-align: center; +.install-instructions .sd-tab-set { + .sd-tab-content { + padding: 0.5rem 0 0 0; // Vertical gap between the two sets of nested tabs + background-color: transparent; + border: none; - &.tab-6 { - width: 50% !important; + p:first-child { + margin-top: 1rem !important; + } } - &.tab-4 { - width: calc(100% / 3) !important; + > label.sd-tab-label { + margin: 0 3px; // Horizontal gap within the same set of tabs + display: flex; + align-items: center; + justify-content: center; + border-radius: 5px !important; + + &.tab-6 { + width: calc((100% - var(--tab-caption-width, 0%)) / 2 - 6px) !important; + } + + &.tab-4 { + width: calc((100% - var(--tab-caption-width, 0%)) / 3 - 6px) !important; + } + } + + > input:checked + label.sd-tab-label { + transform: unset; + border: 2px solid var(--pst-color-primary); + } + + // Show tab captions on large screens + @media screen and (min-width: 960px) { + --tab-caption-width: 20%; + + &::before { + width: var(--tab-caption-width); + display: flex; + align-items: center; + font-weight: bold; + } + + &.tabs-os::before { + content: "Operating System"; + } + + &.tabs-packager::before { + content: "Package Manager"; + } } } From cc9392854cb563029956bd8dbf5f7ad7eb2ff3d1 Mon Sep 17 00:00:00 2001 From: Yao Xiao <108576690+Charlie-XIAO@users.noreply.github.com> Date: Wed, 3 Jul 2024 16:59:03 +0800 Subject: [PATCH 139/275] DOC fix the caption of installation tabs (#29393) --- doc/scss/install.scss | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/scss/install.scss b/doc/scss/install.scss index cb9c5357dea57..5f4fc406bff7d 100644 --- a/doc/scss/install.scss +++ b/doc/scss/install.scss @@ -53,7 +53,7 @@ content: "Operating System"; } - &.tabs-packager::before { + &.tabs-package-manager::before { content: "Package Manager"; } } From 9013e8e2ce11a35964efb49bd210a5a21b8cd510 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?= Date: Thu, 4 Jul 2024 05:42:24 +0200 Subject: [PATCH 140/275] DOC Rephrase Datasets guide introduction (#29405) --- doc/datasets.rst | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/doc/datasets.rst b/doc/datasets.rst index ee767e5843256..d381e4152990d 100644 --- a/doc/datasets.rst +++ b/doc/datasets.rst @@ -6,12 +6,9 @@ Dataset loading utilities .. currentmodule:: sklearn.datasets -The ``sklearn.datasets`` package embeds some small toy datasets -as introduced in the :ref:`Getting Started ` section. - -This package also features helpers to fetch larger datasets commonly -used by the machine learning community to benchmark algorithms on data -that comes from the 'real world'. +The ``sklearn.datasets`` package embeds some small toy datasets and provides helpers +to fetch larger datasets commonly used by the machine learning community to benchmark +algorithms on data that comes from the 'real world'. To evaluate the impact of the scale of the dataset (``n_samples`` and ``n_features``) while controlling the statistical properties of the data From 40dcc8998827962a1cbdfd8015dfdf812feee106 Mon Sep 17 00:00:00 2001 From: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> Date: Thu, 4 Jul 2024 09:17:16 +0200 Subject: [PATCH 141/275] DOC Update funding information (#29356) Co-authored-by: ArturoAmorQ --- doc/about.rst | 18 ++++++++++++------ doc/templates/index.html | 1 - 2 files changed, 12 insertions(+), 7 deletions(-) diff --git a/doc/about.rst b/doc/about.rst index 47d57e4737318..2edf06af32005 100644 --- a/doc/about.rst +++ b/doc/about.rst @@ -174,8 +174,9 @@ The project would like to thank the following funders. .. div:: text-box - `:probabl. `_ funds Adrin Jalali, Arturo Amor, François Goupil, - Guillaume Lemaitre, Jérémie du Boisberranger, Olivier Grisel, and Stefanie Senger. + `:probabl. `_ employs Adrin Jalali, Arturo Amor, + François Goupil, Guillaume Lemaitre, Jérémie du Boisberranger, Loïc Estève, + Olivier Grisel, and Stefanie Senger. .. div:: image-box @@ -196,9 +197,6 @@ The project would like to thank the following funders. .. |dataiku| image:: images/dataiku.png :target: https://www.dataiku.com/ -.. |hf| image:: images/huggingface_logo-noborder.png - :target: https://huggingface.co - .. |nvidia| image:: images/nvidia.png :target: https://www.nvidia.com @@ -243,7 +241,7 @@ The project would like to thank the following funders. +----------+-----------+ | |axa| | |bnp| | +----------+-----------+ - | |nvidia| | |hf| | + | |nvidia| | +----------+-----------+ | |dataiku| | +----------+-----------+ @@ -499,6 +497,9 @@ the past: .. |aphp| image:: images/logo_APHP_text.png :target: https://aphp.fr/ +.. |hf| image:: images/huggingface_logo-noborder.png + :target: https://huggingface.co + .. raw:: html .. div:: install-instructions diff --git a/doc/scss/custom.scss b/doc/scss/custom.scss index 6af234b871ed6..5256a6c83befb 100644 --- a/doc/scss/custom.scss +++ b/doc/scss/custom.scss @@ -111,6 +111,52 @@ details.sd-dropdown { } } +/* Tabs (sphinx-design) */ + +.sd-tab-set { + --tab-caption-width: 0%; // No tab caption by default + margin-top: 1.5rem; + + &::before { + // Set `content` for tab caption + width: var(--tab-caption-width); + display: flex; + align-items: center; + font-weight: bold; + } + + .sd-tab-content { + padding: 0.5rem 0 0 0 !important; + background-color: transparent !important; + border: none !important; + + > p:first-child { + margin-top: 1rem !important; + } + } + + > label.sd-tab-label { + margin: 0 3px; + display: flex; + align-items: center; + justify-content: center; + border-radius: 5px !important; + + &.tab-6 { + width: calc((100% - var(--tab-caption-width)) / 2 - 6px) !important; + } + + &.tab-4 { + width: calc((100% - var(--tab-caption-width)) / 3 - 6px) !important; + } + } + + > input:checked + label.sd-tab-label { + transform: unset; + border: 2px solid var(--pst-color-primary); + } +} + /* scikit-learn buttons */ a.btn { diff --git a/doc/scss/install.scss b/doc/scss/install.scss deleted file mode 100644 index 5f4fc406bff7d..0000000000000 --- a/doc/scss/install.scss +++ /dev/null @@ -1,60 +0,0 @@ -/** - * Styling for the installation page, including overriding some default styling of - * sphinx-design. This style sheet should be included only for the install page. - * - * This file is compiled into styles/install.css by sphinxcontrib.sass, see: - * https://sass-lang.com/guide/ - */ - -.install-instructions .sd-tab-set { - .sd-tab-content { - padding: 0.5rem 0 0 0; // Vertical gap between the two sets of nested tabs - background-color: transparent; - border: none; - - p:first-child { - margin-top: 1rem !important; - } - } - - > label.sd-tab-label { - margin: 0 3px; // Horizontal gap within the same set of tabs - display: flex; - align-items: center; - justify-content: center; - border-radius: 5px !important; - - &.tab-6 { - width: calc((100% - var(--tab-caption-width, 0%)) / 2 - 6px) !important; - } - - &.tab-4 { - width: calc((100% - var(--tab-caption-width, 0%)) / 3 - 6px) !important; - } - } - - > input:checked + label.sd-tab-label { - transform: unset; - border: 2px solid var(--pst-color-primary); - } - - // Show tab captions on large screens - @media screen and (min-width: 960px) { - --tab-caption-width: 20%; - - &::before { - width: var(--tab-caption-width); - display: flex; - align-items: center; - font-weight: bold; - } - - &.tabs-os::before { - content: "Operating System"; - } - - &.tabs-package-manager::before { - content: "Package Manager"; - } - } -} From 0402ada97335b3bbcb142c6de51fd94165e4e4bd Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Tue, 23 Jul 2024 02:41:40 +1000 Subject: [PATCH 179/275] DOC Add note on cv splits in `CalibratedClassifierCV` missing classes (#29449) --- doc/modules/calibration.rst | 20 +++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/doc/modules/calibration.rst b/doc/modules/calibration.rst index a2bfa152d2b26..ad183aa79c6c4 100644 --- a/doc/modules/calibration.rst +++ b/doc/modules/calibration.rst @@ -149,9 +149,14 @@ The :class:`CalibratedClassifierCV` class is used to calibrate a classifier. unbiased data is always used to fit the calibrator. The data is split into k `(train_set, test_set)` couples (as determined by `cv`). When `ensemble=True` (default), the following procedure is repeated independently for each -cross-validation split: a clone of `base_estimator` is first trained on the -train subset. Then its predictions on the test subset are used to fit a -calibrator (either a sigmoid or isotonic regressor). This results in an +cross-validation split: + +1. a clone of `base_estimator` is trained on the train subset +2. the trained `base_estimator` makes predictions on the test subset +3. the predictions are used to fit a calibrator (either a sigmoid or isotonic + regressor) (when the data is multiclass, a calibrator is fit for every class) + +This results in an ensemble of k `(classifier, calibrator)` couples where each calibrator maps the output of its corresponding classifier into [0, 1]. Each couple is exposed in the `calibrated_classifiers_` attribute, where each entry is a calibrated @@ -162,6 +167,15 @@ predicted probabilities of the `k` estimators in the `calibrated_classifiers_` list. The output of :term:`predict` is the class that has the highest probability. +It is important to choose `cv` carefully when using `ensemble=True`. +All classes should be present in both train and test subsets for every split. +When a class is absent in the train subset, the predicted probability for that +class will default to 0 for the `(classifier, calibrator)` couple of that split. +This skews the :term:`predict_proba` as it averages across all couples. +When a class is absent in the test subset, the calibrator for that class +(within the `(classifier, calibrator)` couple of that split) is +fit on data with no positive class. This results in ineffective calibration. + When `ensemble=False`, cross-validation is used to obtain 'unbiased' predictions for all the data, via :func:`~sklearn.model_selection.cross_val_predict`. From f96bfc2586fd62be16b8e4f271f2bf8289dcbf05 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 9 Sep 2024 14:19:39 +0200 Subject: [PATCH 180/275] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29486) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Lock file bot Co-authored-by: Loïc Estève --- build_tools/azure/debian_atlas_32bit_lock.txt | 4 +- ...latest_conda_forge_mkl_linux-64_conda.lock | 222 +++++++++--------- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 91 ++----- ...latest_conda_mkl_no_openmp_environment.yml | 1 + ...test_conda_mkl_no_openmp_osx-64_conda.lock | 27 +-- ...st_pip_openblas_pandas_linux-64_conda.lock | 20 +- ...onda_defaults_openblas_linux-64_conda.lock | 23 +- .../pymin_conda_forge_mkl_win-64_conda.lock | 70 ++---- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 90 ++----- build_tools/azure/ubuntu_atlas_lock.txt | 10 +- build_tools/circle/doc_linux-64_conda.lock | 184 ++++----------- .../doc_min_dependencies_linux-64_conda.lock | 50 ++-- .../update_environments_and_lock_files.py | 5 +- sklearn/utils/deprecation.py | 2 +- 14 files changed, 280 insertions(+), 519 deletions(-) diff --git a/build_tools/azure/debian_atlas_32bit_lock.txt b/build_tools/azure/debian_atlas_32bit_lock.txt index 2fd38ec1c8f81..960d9ae329823 100644 --- a/build_tools/azure/debian_atlas_32bit_lock.txt +++ b/build_tools/azure/debian_atlas_32bit_lock.txt @@ -6,7 +6,7 @@ # attrs==23.2.0 # via pytest -coverage==7.5.4 +coverage==7.6.0 # via pytest-cov cython==3.0.10 # via -r build_tools/azure/debian_atlas_32bit_requirements.txt @@ -14,7 +14,7 @@ iniconfig==2.0.0 # via pytest joblib==1.2.0 # via -r build_tools/azure/debian_atlas_32bit_requirements.txt -meson==1.4.1 +meson==1.5.0 # via meson-python meson-python==0.16.0 # via -r build_tools/azure/debian_atlas_32bit_requirements.txt diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock 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https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.7.4-hbcca054_0.conda#23ab7665c5f63cfb9f1f6195256daac6 @@ -17,7 +9,8 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed3 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_7.conda#b80f2f396ca2c28b8c14c437a4ed1e74 -https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-4_cp311.conda#d786502c97404c94d7d58d258a445a65 +https://conda.anaconda.org/conda-forge/linux-64/mkl-include-2023.2.0-h84fe81f_50496.conda#7af9fd0b2d7219f4a4200a34561340f6 +https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.12-4_cp312.conda#dccc2d142812964fcc6abdc97b672dff 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CI build(s) :lock: :robot: (#29053)) -https://conda.anaconda.org/conda-forge/osx-64/matplotlib-3.8.4-py312hb401068_2.conda#456c057a3e2dcac3d02f4b9d25e277f5 +https://conda.anaconda.org/conda-forge/osx-64/clang_osx-64-16.0.6-hb91bd55_18.conda#fd48bd52766dc748842ae785a96d547c +https://conda.anaconda.org/conda-forge/osx-64/matplotlib-3.9.1-py312hb401068_0.conda#4938ff12b9ec103d5caca8c4666955c3 https://conda.anaconda.org/conda-forge/osx-64/c-compiler-1.7.0-h282daa2_1.conda#d27411cb82bc1b76b9f487da6ae97f1d -https://conda.anaconda.org/conda-forge/osx-64/clangxx_impl_osx-64-16.0.6-h6d92fbe_16.conda#55fb2d5cbc9ec490347b1f797536fba8 +https://conda.anaconda.org/conda-forge/osx-64/clangxx_impl_osx-64-16.0.6-h6d92fbe_18.conda#6caeea3e1c0af451118c19894448d4a0 https://conda.anaconda.org/conda-forge/osx-64/gfortran_osx-64-12.3.0-h18f7dce_1.conda#436af2384c47aedb94af78a128e174f1 -https://conda.anaconda.org/conda-forge/osx-64/clangxx_osx-64-16.0.6-hb91bd55_16.conda#5879c43528a2601d04d64c5f9fdf3033 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b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock index f51b7c73d8587..ad56be7b9584a 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock @@ -1,18 +1,10 @@ # Generated by conda-lock. # platform: osx-64 -<<<<<<< HEAD -<<<<<<< HEAD -# input_hash: 33a102d2ccde4e14e315a98b50340349af349f802403dd49589375b2b889f2d3 -======= -# input_hash: 197b213ba4980b3e95dcd438da09eb9b55e4bf5a4d8e6d3ddab29275aa22556a ->>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) -======= -# input_hash: 197b213ba4980b3e95dcd438da09eb9b55e4bf5a4d8e6d3ddab29275aa22556a ->>>>>>> 0dba98f1eb (MNT Update main-ci lock files (#29388)) +# input_hash: 3e9254c5d9da20709b9379b68863a871e29920e1e97554971f4c32ab37e85be9 @EXPLICIT https://repo.anaconda.com/pkgs/main/osx-64/blas-1.0-mkl.conda#cb2c87e85ac8e0ceae776d26d4214c8a https://repo.anaconda.com/pkgs/main/osx-64/bzip2-1.0.8-h6c40b1e_6.conda#96224786021d0765ce05818fa3c59bdb -https://repo.anaconda.com/pkgs/main/osx-64/ca-certificates-2024.3.11-hecd8cb5_0.conda#a2e29a11940c66baf9942912096fad5f +https://repo.anaconda.com/pkgs/main/osx-64/ca-certificates-2024.7.2-hecd8cb5_0.conda#297cfad0c0eac53e5ac75674828eedd9 https://repo.anaconda.com/pkgs/main/osx-64/jpeg-9e-h6c40b1e_1.conda#fc3e61fa41309946c9283fe8737d7f41 https://repo.anaconda.com/pkgs/main/osx-64/libbrotlicommon-1.0.9-h6c40b1e_8.conda#8e86dfa34b08bc664b19e1499e5465b8 https://repo.anaconda.com/pkgs/main/osx-64/libcxx-14.0.6-h9765a3e_0.conda#387757bb354ae9042370452cd0fb5627 @@ -56,7 +48,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/kiwisolver-1.4.4-py312hcec6c5f_0.cond https://repo.anaconda.com/pkgs/main/osx-64/lcms2-2.12-hf1fd2bf_0.conda#697aba7a3308226df7a93ccfeae16ffa https://repo.anaconda.com/pkgs/main/osx-64/mkl-service-2.4.0-py312h6c40b1e_1.conda#b1ef860be9043b35c5e8d9388b858514 https://repo.anaconda.com/pkgs/main/osx-64/ninja-1.10.2-hecd8cb5_5.conda#a0043b325fb08db82477ae433668e684 -https://repo.anaconda.com/pkgs/main/osx-64/openjpeg-2.4.0-h7231236_1.conda#90f2c6bc7fe1384a4c1ba069a101ad9e +https://repo.anaconda.com/pkgs/main/osx-64/openjpeg-2.4.0-h7231236_2.conda#e7cd7f1cdc309f7e32cedd73803536e0 https://repo.anaconda.com/pkgs/main/osx-64/packaging-24.1-py312hecd8cb5_0.conda#6130dafc4d26d55e93ceab460d2a72b5 https://repo.anaconda.com/pkgs/main/osx-64/pluggy-1.0.0-py312hecd8cb5_1.conda#647fada22f1697691fdee90b52c99bcb https://repo.anaconda.com/pkgs/main/osx-64/pyparsing-3.0.9-py312hecd8cb5_0.conda#d85cf2b81c6d9326a57a6418e14db258 @@ -70,15 +62,10 @@ https://repo.anaconda.com/pkgs/main/osx-64/unicodedata2-15.1.0-py312h6c40b1e_0.c https://repo.anaconda.com/pkgs/main/osx-64/wheel-0.43.0-py312hecd8cb5_0.conda#c0bdd5748b170523232e8ad1d667136c https://repo.anaconda.com/pkgs/main/osx-64/fonttools-4.51.0-py312h6c40b1e_0.conda#8f55fa86b73e8a7f4403503f9b7a9959 https://repo.anaconda.com/pkgs/main/osx-64/numpy-base-1.26.4-py312h6f81483_0.conda#87f73efbf26ab2e2ea7c32481a71bd47 -https://repo.anaconda.com/pkgs/main/osx-64/pillow-10.3.0-py312h6c40b1e_0.conda#fe883fa4247d35fe6de49f713529ca02 +https://repo.anaconda.com/pkgs/main/osx-64/pillow-10.4.0-py312h46256e1_0.conda#486a21e17faf0611e454c0e7faf0bcbc https://repo.anaconda.com/pkgs/main/osx-64/pip-24.0-py312hecd8cb5_0.conda#7a8e0b1d3742ddf1c8aa97fbaa158039 -<<<<<<< HEAD https://repo.anaconda.com/pkgs/main/osx-64/pytest-7.4.4-py312hecd8cb5_0.conda#d4dda983900b045cd27ae836cad670de https://repo.anaconda.com/pkgs/main/osx-64/python-dateutil-2.9.0post0-py312hecd8cb5_2.conda#1047dde28f78127dd9f6121e882926dd -======= -https://repo.anaconda.com/pkgs/main/osx-64/pytest-7.4.0-py312hecd8cb5_0.conda#b816a2439ba9b87524aec74d58e55b0a -https://repo.anaconda.com/pkgs/main/osx-64/python-dateutil-2.9.0post0-py312hecd8cb5_0.conda#b3ed54eb118325785284dd18bfceca19 ->>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) https://repo.anaconda.com/pkgs/main/osx-64/pytest-cov-4.1.0-py312hecd8cb5_1.conda#a33a24eb20359f464938e75b2f57e23a https://repo.anaconda.com/pkgs/main/osx-64/pytest-xdist-3.5.0-py312hecd8cb5_0.conda#d1ecfb3691cceecb1f16bcfdf0b67bb5 https://repo.anaconda.com/pkgs/main/osx-64/bottleneck-1.3.7-py312h32608ca_0.conda#f96a01eba5ea542cf9c7cc8d77447627 @@ -93,11 +80,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.11.4-py312h81688c2_0.conda#7d https://repo.anaconda.com/pkgs/main/osx-64/pandas-2.2.2-py312h77d3abe_0.conda#463868c40d8ff98bec263f1fd57a8d97 https://repo.anaconda.com/pkgs/main/osx-64/pyamg-4.2.3-py312h44cbcf4_0.conda#3bdc7be74087b3a5a83c520a74e1e8eb # pip cython @ https://files.pythonhosted.org/packages/d5/6d/06c08d75adb98cdf72af18801e193d22580cc86ca553610f430f18ea26b3/Cython-3.0.10-cp312-cp312-macosx_10_9_x86_64.whl#sha256=8f2864ab5fcd27a346f0b50f901ebeb8f60b25a60a575ccfd982e7f3e9674914 -<<<<<<< HEAD -# pip meson @ https://files.pythonhosted.org/packages/44/b2/d4433391a7c5e94a39b50ca7295a8ceba736e7c72c455752a60122f52453/meson-1.4.1-py3-none-any.whl#sha256=d5acc3abae2dad3c70ddcbd10acac92b78b144d34d43f40f5b8ac31dfd8a826a -======= -# pip meson @ https://files.pythonhosted.org/packages/33/75/b1a37fa7b2dbca8c0dbb04d5cdd7e2720c8ef6febe41b4a74866350e041c/meson-1.4.0-py3-none-any.whl#sha256=476a458d51fcfa322a6bdc64da5138997c542d08e6b2e49b9fa68c46fd7c4475 ->>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) +# pip meson @ https://files.pythonhosted.org/packages/1d/8d/b83d525907c00c5e22a9cae832bbd958310518ae6ad1dc7e01b69abbb117/meson-1.4.2.tar.gz#sha256=ea2546a26f4a171a741c1fd036f22c9c804d6198e3259f1df588e01f842dd69f # pip threadpoolctl @ https://files.pythonhosted.org/packages/4b/2c/ffbf7a134b9ab11a67b0cf0726453cedd9c5043a4fe7a35d1cefa9a1bcfb/threadpoolctl-3.5.0-py3-none-any.whl#sha256=56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467 # pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526 # pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index 8145a497caa86..9dfa936fe97ac 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -1,9 +1,9 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 11829a15aa51e3a3ad9479d8b5b953c0af47dd8e86d725e58d55cbcfe68c6d5e +# input_hash: 893e5f90e655d6606d6b7e308c1099125012b25c3444b5a4240d44b184531e00 @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 -https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2024.3.11-h06a4308_0.conda#08529eb3504712baabcbda266a19feb7 +https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2024.7.2-h06a4308_0.conda#5c6799c01e9be4c7ba294f6530b2d562 https://repo.anaconda.com/pkgs/main/linux-64/ld_impl_linux-64-2.38-h1181459_1.conda#68eedfd9c06f2b0e6888d8db345b7f5b https://repo.anaconda.com/pkgs/main/noarch/tzdata-2024a-h04d1e81_0.conda#452af53adae0a5b06eb5d05c707b2f25 https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda#b372c0eea9b60732fdae4b817a63c8cd @@ -30,7 +30,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py311h06a4308_0.conda#84ae # pip babel @ https://files.pythonhosted.org/packages/27/45/377f7e32a5c93d94cd56542349b34efab5ca3f9e2fd5a68c5e93169aa32d/Babel-2.15.0-py3-none-any.whl#sha256=08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb # pip certifi @ https://files.pythonhosted.org/packages/1c/d5/c84e1a17bf61d4df64ca866a1c9a913874b4e9bdc131ec689a0ad013fb36/certifi-2024.7.4-py3-none-any.whl#sha256=c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90 # pip charset-normalizer @ https://files.pythonhosted.org/packages/40/26/f35951c45070edc957ba40a5b1db3cf60a9dbb1b350c2d5bef03e01e61de/charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8 -# pip coverage @ https://files.pythonhosted.org/packages/1e/62/e33595d35c9fa7cbcca5df2c3745b595532ec94b68c49ca2877629c4aca1/coverage-7.5.4-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ed550e7442f278af76d9d65af48069f1fb84c9f745ae249c1a183c1e9d1b025c +# pip coverage @ https://files.pythonhosted.org/packages/39/33/da23d8bfdfdc5c221d2c9a2f169f63a4e04b9a0327c1c67777157155e4d6/coverage-7.6.0-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=03cafe82c1b32b770a29fd6de923625ccac3185a54a5e66606da26d105f37dac # pip cycler @ https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl#sha256=85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 # pip cython @ https://files.pythonhosted.org/packages/45/82/077c13035d6f45d8b8b74d67e9f73f2bfc54ef8d1f79572790f6f7d2b4f5/Cython-3.0.10-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=38d40fa1324ac47c04483d151f5e092406a147eac88a18aec789cf01c089c3f2 # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 @@ -42,10 +42,10 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py311h06a4308_0.conda#84ae # pip joblib @ https://files.pythonhosted.org/packages/91/29/df4b9b42f2be0b623cbd5e2140cafcaa2bef0759a00b7b70104dcfe2fb51/joblib-1.4.2-py3-none-any.whl#sha256=06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6 # pip kiwisolver @ https://files.pythonhosted.org/packages/17/ba/17a706b232308e65f57deeccae503c268292e6a091313f6ce833a23093ea/kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=040c1aebeda72197ef477a906782b5ab0d387642e93bda547336b8957c61022e # pip markupsafe @ https://files.pythonhosted.org/packages/97/18/c30da5e7a0e7f4603abfc6780574131221d9148f323752c2755d48abad30/MarkupSafe-2.1.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b91c037585eba9095565a3556f611e3cbfaa42ca1e865f7b8015fe5c7336d5a5 -# pip meson @ https://files.pythonhosted.org/packages/44/b2/d4433391a7c5e94a39b50ca7295a8ceba736e7c72c455752a60122f52453/meson-1.4.1-py3-none-any.whl#sha256=d5acc3abae2dad3c70ddcbd10acac92b78b144d34d43f40f5b8ac31dfd8a826a +# pip meson @ https://files.pythonhosted.org/packages/c4/cd/47e45d3abada2e1edb9e2ca9334be186d2e7f97a01b09b5b82799c4d7bd3/meson-1.5.0-py3-none-any.whl#sha256=52b34f4903b882df52ad0d533146d4b992c018ea77399f825579737672ae7b20 # pip networkx @ https://files.pythonhosted.org/packages/38/e9/5f72929373e1a0e8d142a130f3f97e6ff920070f87f91c4e13e40e0fba5a/networkx-3.3-py3-none-any.whl#sha256=28575580c6ebdaf4505b22c6256a2b9de86b316dc63ba9e93abde3d78dfdbcf2 # pip ninja @ https://files.pythonhosted.org/packages/6d/92/8d7aebd4430ab5ff65df2bfee6d5745f95c004284db2d8ca76dcbfd9de47/ninja-1.11.1.1-py2.py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl#sha256=84502ec98f02a037a169c4b0d5d86075eaf6afc55e1879003d6cab51ced2ea4b -# pip numpy @ https://files.pythonhosted.org/packages/d1/27/2a7bd6855dc717aeec5f553073a3c426b9c816126555f8e616392eab856b/numpy-2.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=a7039a136017eaa92c1848152827e1424701532ca8e8967fe480fe1569dae581 +# pip numpy @ https://files.pythonhosted.org/packages/ef/27/39622993e8688a1f05898a3c3b2836b856f79c06637ebd4b71cb35cc9b18/numpy-2.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=15eb4eca47d36ec3f78cde0a3a2ee24cf05ca7396ef808dda2c0ddad7c2bde67 # pip packaging @ https://files.pythonhosted.org/packages/08/aa/cc0199a5f0ad350994d660967a8efb233fe0416e4639146c089643407ce6/packaging-24.1-py3-none-any.whl#sha256=5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124 # pip pillow @ https://files.pythonhosted.org/packages/ba/e5/8c68ff608a4203085158cff5cc2a3c534ec384536d9438c405ed6370d080/pillow-10.4.0-cp311-cp311-manylinux_2_28_x86_64.whl#sha256=76a911dfe51a36041f2e756b00f96ed84677cdeb75d25c767f296c1c1eda1319 # pip pluggy @ https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl#sha256=44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669 @@ -56,9 +56,9 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py311h06a4308_0.conda#84ae # pip snowballstemmer @ https://files.pythonhosted.org/packages/ed/dc/c02e01294f7265e63a7315fe086dd1df7dacb9f840a804da846b96d01b96/snowballstemmer-2.2.0-py2.py3-none-any.whl#sha256=c8e1716e83cc398ae16824e5572ae04e0d9fc2c6b985fb0f900f5f0c96ecba1a # pip sphinxcontrib-applehelp @ https://files.pythonhosted.org/packages/56/89/fea3fbf6785b388e6cb8a1beaf62f96e80b37311bdeed6e133388a732426/sphinxcontrib_applehelp-1.0.8-py3-none-any.whl#sha256=cb61eb0ec1b61f349e5cc36b2028e9e7ca765be05e49641c97241274753067b4 # pip sphinxcontrib-devhelp @ https://files.pythonhosted.org/packages/a0/52/1049d918d1d1c72857d285c3f0c64c1cbe0be394ce1c93a3d2aa4f39fe3b/sphinxcontrib_devhelp-1.0.6-py3-none-any.whl#sha256=6485d09629944511c893fa11355bda18b742b83a2b181f9a009f7e500595c90f -# pip sphinxcontrib-htmlhelp @ https://files.pythonhosted.org/packages/c2/e9/74c4cda5b409af3222fda38f0774e616011bc935f639dbc0da5ca2d1be7d/sphinxcontrib_htmlhelp-2.0.5-py3-none-any.whl#sha256=393f04f112b4d2f53d93448d4bce35842f62b307ccdc549ec1585e950bc35e04 +# pip sphinxcontrib-htmlhelp @ https://files.pythonhosted.org/packages/a4/b4/6ebbdc57b5b216b400b355f34ef669e9b6b5c31a6ede8cf5ac36f9e8fc0c/sphinxcontrib_htmlhelp-2.0.6-py3-none-any.whl#sha256=1b9af5a2671a61410a868fce050cab7ca393c218e6205cbc7f590136f207395c # pip sphinxcontrib-jsmath @ https://files.pythonhosted.org/packages/c2/42/4c8646762ee83602e3fb3fbe774c2fac12f317deb0b5dbeeedd2d3ba4b77/sphinxcontrib_jsmath-1.0.1-py2.py3-none-any.whl#sha256=2ec2eaebfb78f3f2078e73666b1415417a116cc848b72e5172e596c871103178 -# pip sphinxcontrib-qthelp @ https://files.pythonhosted.org/packages/80/b3/1beac14a88654d2e5120d0143b49be5ad450b86eb1963523d8dbdcc51eb2/sphinxcontrib_qthelp-1.0.7-py3-none-any.whl#sha256=e2ae3b5c492d58fcbd73281fbd27e34b8393ec34a073c792642cd8e529288182 +# pip sphinxcontrib-qthelp @ https://files.pythonhosted.org/packages/cf/91/cec9416d27ebe9b8aa83f014a1ac8402c729aed791da67704e10bb2c8f33/sphinxcontrib_qthelp-1.0.8-py3-none-any.whl#sha256=323d6acc4189af76dfe94edd2a27d458902319b60fcca2aeef3b2180c106a75f # pip sphinxcontrib-serializinghtml @ https://files.pythonhosted.org/packages/38/24/228bb903ea87b9e08ab33470e6102402a644127108c7117ac9c00d849f82/sphinxcontrib_serializinghtml-1.1.10-py3-none-any.whl#sha256=326369b8df80a7d2d8d7f99aa5ac577f51ea51556ed974e7716cfd4fca3f6cb7 # pip tabulate @ https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl#sha256=024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f # pip threadpoolctl @ https://files.pythonhosted.org/packages/4b/2c/ffbf7a134b9ab11a67b0cf0726453cedd9c5043a4fe7a35d1cefa9a1bcfb/threadpoolctl-3.5.0-py3-none-any.whl#sha256=56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467 @@ -70,11 +70,11 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py311h06a4308_0.conda#84ae # pip jinja2 @ https://files.pythonhosted.org/packages/31/80/3a54838c3fb461f6fec263ebf3a3a41771bd05190238de3486aae8540c36/jinja2-3.1.4-py3-none-any.whl#sha256=bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d # pip lazy-loader @ https://files.pythonhosted.org/packages/83/60/d497a310bde3f01cb805196ac61b7ad6dc5dcf8dce66634dc34364b20b4f/lazy_loader-0.4-py3-none-any.whl#sha256=342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc # pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526 -# pip pytest @ https://files.pythonhosted.org/packages/4e/e7/81ebdd666d3bff6670d27349b5053605d83d55548e6bd5711f3b0ae7dd23/pytest-8.2.2-py3-none-any.whl#sha256=c434598117762e2bd304e526244f67bf66bbd7b5d6cf22138be51ff661980343 +# pip pytest @ https://files.pythonhosted.org/packages/b9/77/ccea391104f576a6e54a54334fc26d29c28aa0ec85714c781fbd2c34ac86/pytest-8.3.1-py3-none-any.whl#sha256=e9600ccf4f563976e2c99fa02c7624ab938296551f280835ee6516df8bc4ae8c # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl#sha256=70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6 # pip scipy @ https://files.pythonhosted.org/packages/89/bb/80c9c98d887c855710fd31fc5ae5574133e98203b3475b07579251803662/scipy-1.14.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=9e3154691b9f7ed73778d746da2df67a19d046a6c8087c8b385bc4cdb2cfca74 -# pip tifffile @ https://files.pythonhosted.org/packages/d2/d7/ca95f347442e82700f591f3608e336596ee607daecbcad6a7ebd16ff5de4/tifffile-2024.7.2-py3-none-any.whl#sha256=5a2ee608c9cc1f2e044d943dacebddc71d4827b6fad150ef4c644b7aefbe2d1a +# pip tifffile @ https://files.pythonhosted.org/packages/b0/54/3aef476dc3820ab7b002d832c4b77fa9b7cb81b773917e0e7d6aa3aedcd5/tifffile-2024.7.21-py3-none-any.whl#sha256=7f335b5d6ca49401fe0f1d87deb206f5dae47297e47b1ed52a676d05d6d26798 # pip lightgbm @ https://files.pythonhosted.org/packages/f2/3d/4f152cf694aec100ab63b4a5547f2dbfbea59ab39d9375c89bed9775e47d/lightgbm-4.4.0-py3-none-manylinux_2_28_x86_64.whl#sha256=8700b41f637717d36763a282d280b8d4722a87103030b7f0f373b96da0225022 # pip matplotlib @ https://files.pythonhosted.org/packages/b8/63/cef838d92c1918ae28afd12b8aeaa9c104a0686cf6447aa0546f7c6dd1f0/matplotlib-3.9.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ab38a4f3772523179b2f772103d8030215b318fef6360cb40558f585bf3d017f # pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 @@ -83,5 +83,5 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py311h06a4308_0.conda#84ae # pip pytest-cov @ https://files.pythonhosted.org/packages/78/3a/af5b4fa5961d9a1e6237b530eb87dd04aea6eb83da09d2a4073d81b54ccf/pytest_cov-5.0.0-py3-none-any.whl#sha256=4f0764a1219df53214206bf1feea4633c3b558a2925c8b59f144f682861ce652 # pip pytest-xdist @ https://files.pythonhosted.org/packages/6d/82/1d96bf03ee4c0fdc3c0cbe61470070e659ca78dc0086fb88b66c185e2449/pytest_xdist-3.6.1-py3-none-any.whl#sha256=9ed4adfb68a016610848639bb7e02c9352d5d9f03d04809919e2dafc3be4cca7 # pip scikit-image @ https://files.pythonhosted.org/packages/ad/96/138484302b8ec9a69cdf65e8d4ab47a640a3b1a8ea3c437e1da3e1a5a6b8/scikit_image-0.24.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=fa27b3a0dbad807b966b8db2d78da734cb812ca4787f7fbb143764800ce2fa9c -# pip sphinx @ https://files.pythonhosted.org/packages/b4/fa/130c32ed94cf270e3d0b9ded16fb7b2c8fea86fa7263c29a696a30c1dde7/sphinx-7.3.7-py3-none-any.whl#sha256=413f75440be4cacf328f580b4274ada4565fb2187d696a84970c23f77b64d8c3 +# pip sphinx @ https://files.pythonhosted.org/packages/0d/ef/153f6803c5d5f8917dbb7f7fcf6d34a871ede3296fa89c2c703f5f8a6c8e/sphinx-7.4.7-py3-none-any.whl#sha256=c2419e2135d11f1951cd994d6eb18a1835bd8fdd8429f9ca375dc1f3281bd239 # pip numpydoc @ https://files.pythonhosted.org/packages/f0/fa/dcfe0f65660661db757ee9ebd84e170ff98edd5d80235f62457d9088f85f/numpydoc-1.7.0-py3-none-any.whl#sha256=5a56419d931310d79a06cfc2a126d1558700feeb9b4f3d8dcae1a8134be829c9 diff --git a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock index 20021009e9539..114a61e8d48b7 100644 --- a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock @@ -1,18 +1,10 @@ # Generated by conda-lock. # platform: linux-64 -<<<<<<< HEAD -<<<<<<< HEAD -# input_hash: be0f080ab1974d224952262cd9179ff999d10108421d7e7ded2435e6f08edf0e -======= -# input_hash: 244b6a064d3785ea62baaf9436848821d153846b455c2976f5e811182e848c83 ->>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) -======= -# input_hash: 244b6a064d3785ea62baaf9436848821d153846b455c2976f5e811182e848c83 ->>>>>>> 0dba98f1eb (MNT Update main-ci lock files (#29388)) +# input_hash: 04b8b77032ffbfbea2a84fe8c3e8a5c93a45d1025824f14bc95379a659e60e4b @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-openblas.conda#9ddfcaef10d79366c90128f5dc444be8 -https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2024.3.11-h06a4308_0.conda#08529eb3504712baabcbda266a19feb7 +https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2024.7.2-h06a4308_0.conda#5c6799c01e9be4c7ba294f6530b2d562 https://repo.anaconda.com/pkgs/main/linux-64/ld_impl_linux-64-2.38-h1181459_1.conda#68eedfd9c06f2b0e6888d8db345b7f5b https://repo.anaconda.com/pkgs/main/linux-64/libgfortran5-11.2.0-h1234567_1.conda#36a01a8c30e0cadf0d3e842c50b73f3b https://repo.anaconda.com/pkgs/main/noarch/tzdata-2024a-h04d1e81_0.conda#452af53adae0a5b06eb5d05c707b2f25 @@ -36,6 +28,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/libwebp-base-1.3.2-h5eee18b_0.conda https://repo.anaconda.com/pkgs/main/linux-64/libxcb-1.15-h7f8727e_0.conda#ada518dcadd6aaee9aae47ba9a671553 https://repo.anaconda.com/pkgs/main/linux-64/lz4-c-1.9.4-h6a678d5_1.conda#2ee58861f2b92b868ce761abb831819d https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c +https://repo.anaconda.com/pkgs/main/linux-64/ninja-base-1.10.2-hd09550d_5.conda#09dcbad622d58caaeefe46cd399f0a76 https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.14-h5eee18b_0.conda#37b6dad6aa49000a4230a9f0cad172f6 https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_1.conda#1562802f843297ee776a50b9329597ed https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25 @@ -62,9 +55,9 @@ https://repo.anaconda.com/pkgs/main/linux-64/glib-tools-2.78.4-h6a678d5_0.conda# https://repo.anaconda.com/pkgs/main/linux-64/lcms2-2.12-h3be6417_0.conda#719db47afba9f6586eecb5eacac70bff https://repo.anaconda.com/pkgs/main/linux-64/libclang-14.0.6-default_hc6dbbc7_1.conda#8f12583c4027b2861cff470f6b8837c4 https://repo.anaconda.com/pkgs/main/linux-64/libpq-12.17-hdbd6064_0.conda#6bed363e25859faff66bf546a11c10e8 -https://repo.anaconda.com/pkgs/main/linux-64/openjpeg-2.4.0-h9ca470c_1.conda#dfd4b36eb8ddaffeca0ab412de63c3e2 +https://repo.anaconda.com/pkgs/main/linux-64/openjpeg-2.4.0-h9ca470c_2.conda#a612b3a0adb199eba6860b551e23f4d2 https://repo.anaconda.com/pkgs/main/linux-64/python-3.9.19-h955ad1f_1.conda#4b453281859c293c9d577271f3b18a0d -https://repo.anaconda.com/pkgs/main/linux-64/certifi-2024.6.2-py39h06a4308_0.conda#738daf43271605d7291ecae0e8cac41c +https://repo.anaconda.com/pkgs/main/linux-64/certifi-2024.7.4-py39h06a4308_0.conda#add87fa3b69a43e4e9ea1e619b267c4b https://repo.anaconda.com/pkgs/main/noarch/cycler-0.11.0-pyhd3eb1b0_0.conda#f5e365d2cdb66d547eb8c3ab93843aab https://repo.anaconda.com/pkgs/main/linux-64/cython-3.0.10-py39h5eee18b_0.conda#1419a658ed2b4d5c3ac1964f33143b64 https://repo.anaconda.com/pkgs/main/linux-64/exceptiongroup-1.2.0-py39h06a4308_0.conda#960e2cb83ac5134df8e593a130aa11af @@ -74,9 +67,10 @@ https://repo.anaconda.com/pkgs/main/noarch/iniconfig-1.1.1-pyhd3eb1b0_0.tar.bz2# https://repo.anaconda.com/pkgs/main/linux-64/joblib-1.2.0-py39h06a4308_0.conda#ac1f5687d70aa1128cbecb26bc9e559d https://repo.anaconda.com/pkgs/main/linux-64/kiwisolver-1.4.4-py39h6a678d5_0.conda#3d57aedbfbd054ce57fb3c1e4448828c https://repo.anaconda.com/pkgs/main/linux-64/mysql-5.7.24-h721c034_2.conda#dfc19ca2466d275c4c1f73b62c57f37b +https://repo.anaconda.com/pkgs/main/linux-64/ninja-1.10.2-h06a4308_5.conda#6fc219bbc4c8dbb9060b5b7fe31ae83d https://repo.anaconda.com/pkgs/main/linux-64/numpy-base-1.21.6-py39h375b286_1.conda#0061d9193658774ab79fc85d143a94fc https://repo.anaconda.com/pkgs/main/linux-64/packaging-24.1-py39h06a4308_0.conda#e80d41ffc9450162ef10cbbb9b4ec7e9 -https://repo.anaconda.com/pkgs/main/linux-64/pillow-10.3.0-py39h5eee18b_0.conda#b346d6c71267c1553b6c18d3db5fdf6d +https://repo.anaconda.com/pkgs/main/linux-64/pillow-10.4.0-py39h5eee18b_0.conda#f67fdf77f7f039b58a3dfaf58c2d3014 https://repo.anaconda.com/pkgs/main/linux-64/pluggy-1.0.0-py39h06a4308_1.conda#fb4fed11ed43cf727dbd51883cc1d9fa https://repo.anaconda.com/pkgs/main/linux-64/ply-3.11-py39h06a4308_0.conda#6c89bf6d2fdf6d24126e34cb83fd10f1 https://repo.anaconda.com/pkgs/main/linux-64/pyparsing-3.0.9-py39h06a4308_0.conda#3a0537468e59760404f63b4f04369828 @@ -104,4 +98,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pyamg-4.2.3-py39h79cecc1_0.conda#af https://repo.anaconda.com/pkgs/main/linux-64/qt-main-5.15.2-h53bd1ea_10.conda#bd0c79e82df6323f638bdcb871891b61 https://repo.anaconda.com/pkgs/main/linux-64/pyqt-5.15.10-py39h6a678d5_0.conda#52da5ff9b1144b078d2f41bab0b213f2 https://repo.anaconda.com/pkgs/main/linux-64/matplotlib-3.3.4-py39h06a4308_0.conda#384fc5e01ebfcf30e7161119d3029b5a +# pip meson @ https://files.pythonhosted.org/packages/c4/cd/47e45d3abada2e1edb9e2ca9334be186d2e7f97a01b09b5b82799c4d7bd3/meson-1.5.0-py3-none-any.whl#sha256=52b34f4903b882df52ad0d533146d4b992c018ea77399f825579737672ae7b20 # pip threadpoolctl @ https://files.pythonhosted.org/packages/61/cf/6e354304bcb9c6413c4e02a747b600061c21d38ba51e7e544ac7bc66aecc/threadpoolctl-3.1.0-py3-none-any.whl#sha256=8b99adda265feb6773280df41eece7b2e6561b772d21ffd52e372f999024907b +# pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526 +# pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock index 564f4866bece7..0f94c8c06ddd7 100644 --- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock @@ -1,17 +1,9 @@ # Generated by conda-lock. # platform: win-64 -<<<<<<< HEAD -<<<<<<< HEAD # input_hash: ea607aaeb7b1d1f8a1f821a9f505b3601083a218ec4763e2d72d3d3d800e718c -======= -# input_hash: 9b46e8490ff2c4c93104b20d3bffb2d04120288c8c8680bef8376ebd9d9b8eb3 ->>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) -======= -# input_hash: 9b46e8490ff2c4c93104b20d3bffb2d04120288c8c8680bef8376ebd9d9b8eb3 ->>>>>>> 0dba98f1eb (MNT Update main-ci lock files (#29388)) @EXPLICIT https://conda.anaconda.org/conda-forge/win-64/ca-certificates-2024.7.4-h56e8100_0.conda#9caa97c9504072cd060cf0a3142cc0ed -https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2024.2.0-h57928b3_979.conda#192b0028299eebbc8d88624764df61f5 +https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2024.2.0-h57928b3_980.conda#9c28c39e64871a0adef7d1195bd58655 https://conda.anaconda.org/conda-forge/win-64/mkl-include-2024.1.0-h66d3029_692.conda#60233966dc7c0261c9a443120b43c477 https://conda.anaconda.org/conda-forge/win-64/msys2-conda-epoch-20160418-1.tar.bz2#b0309b72560df66f71a9d5e34a5efdfa https://conda.anaconda.org/conda-forge/win-64/python_abi-3.9-4_cp39.conda#948b0d93d4ab1372d8fd45e1560afd47 @@ -19,18 +11,11 @@ https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#1610 https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.22621.0-h57928b3_0.tar.bz2#72608f6cd3e5898229c3ea16deb1ac43 https://conda.anaconda.org/conda-forge/win-64/m2w64-gmp-6.1.0-2.tar.bz2#53a1c73e1e3d185516d7e3af177596d9 https://conda.anaconda.org/conda-forge/win-64/m2w64-libwinpthread-git-5.0.0.4634.697f757-2.tar.bz2#774130a326dee16f1ceb05cc687ee4f0 -<<<<<<< HEAD https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.40.33810-ha82c5b3_20.conda#e39cc4c34c53654ec939558993d9dc5b https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libs-core-5.3.0-7.tar.bz2#4289d80fb4d272f1f3b56cfe87ac90bd https://conda.anaconda.org/conda-forge/win-64/vc-14.3-h8a93ad2_20.conda#8558f367e1d7700554f7cdb823c46faf 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-https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 -<<<<<<< HEAD -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-h4ab18f5_0.conda#a41fa0e391cc9e0d6b78ac69ca047a6c -======= -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_2.conda#b8934d399b56d73e323403e183d009c5 ->>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) -https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123 -======= -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-h4ab18f5_1.conda#b1e9d076f14e8d776213fd5047b4c3d9 ->>>>>>> 0dba98f1eb (MNT Update main-ci lock files (#29388)) +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-h4bc722e_2.conda#e1b454497f9f7c1147fdde4b53f1b512 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.1-hd590300_0.conda#b462a33c0be1421532f28bfe8f4a7514 @@ -86,11 +67,12 @@ https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h9c3ff4c_0.tar.b https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.16-hd590300_0.conda#151cba22b85a989c2d6ef9633ffee1e4 https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda#318b08df404f9c9be5712aaa5a6f0bb0 https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9160cdeb523a1b20cf8d2a0bf821f45d -https://conda.anaconda.org/conda-forge/linux-64/mysql-common-8.3.0-hf1915f5_4.conda#784a4df6676c581ca624fbe460703a6d +https://conda.anaconda.org/conda-forge/linux-64/mysql-common-8.3.0-h70512c7_5.conda#4b652e3e572cbb3f297e77c96313faea https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.44-h0f59acf_0.conda#3914f7ac1761dce57102c72ca7c35d01 https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123 +https://conda.anaconda.org/conda-forge/linux-64/qhull-2020.2-h434a139_5.conda#353823361b1d27eb3960efb076dfcaf6 https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda#47d31b792659ce70f470b5c82fdfb7a4 https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda#d453b98d9c83e71da0741bb0ff4d76bc https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.4-h7391055_0.conda#93ee23f12bc2e684548181256edd2cf6 @@ -102,11 +84,11 @@ https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda#3f4 https://conda.anaconda.org/conda-forge/linux-64/libasprintf-devel-0.22.5-h661eb56_2.conda#02e41ab5834dcdcc8590cf29d9526f50 https://conda.anaconda.org/conda-forge/linux-64/libglib-2.80.3-h8a4344b_1.conda#6ea440297aacee4893f02ad759e6ffbc https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a -https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.27-pthreads_h413a1c8_0.conda#a356024784da6dfd4683dc5ecf45b155 +https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.27-pthreads_hac2b453_1.conda#ae05ece66d3924ac3d48b4aa3fa96cec https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-h1dd3fc0_3.conda#66f03896ffbe1a110ffda05c7a856504 -https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.7-hc051c1a_1.conda#340278ded8b0dc3a73f3660bbb0adbc6 +https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.7-h4c95cb1_3.conda#0ac9aff6010a7751961c8e4b863a40e7 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https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py39h3d6467e_1.conda#c48418c8b35f1d59ae9ae1174812b40a https://conda.anaconda.org/conda-forge/linux-64/ccache-4.10.1-h065aff2_0.conda#d6b48c138e0c8170a6fe9c136e063540 -https://conda.anaconda.org/conda-forge/noarch/certifi-2024.6.2-pyhd8ed1ab_0.conda#8821ec1c8fcdc9e1d291d7b9f6e9968a +https://conda.anaconda.org/conda-forge/noarch/certifi-2024.7.4-pyhd8ed1ab_0.conda#24e7fd6ca65997938fff9e5ab6f653e4 https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.3.2-pyhd8ed1ab_0.conda#7f4a9e3fcff3f6356ae99244a014da6a https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99 https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda#5cd86562580f274031ede6aa6aa24441 https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.10-py39h3d6467e_0.conda#76b5d215fb735a6dc43010ffbe78040e 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-https://conda.anaconda.org/conda-forge/noarch/requests-2.32.2-pyhd8ed1ab_0.conda#e1643b34b19df8c028a4f00bf5df58a6 ->>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) -https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-22_linux64_openblas.conda#63ddb593595c9cf5eb08d3de54d66df8 -https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py39h7633fee_0.conda#bdc188e59857d6efab332714e0d01d93 -<<<<<<< HEAD -https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.4-h9ad1361_0.conda#147cce520ec59367549fd0d96d404213 -======= -https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.3-h9ad1361_0.conda#8fb0e954c616bb0f9389efac4b4ed44b ->>>>>>> d03c351780 (:lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29053)) -======= https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_0.conda#b39568655c127a9c4a44d178ac99b6d0 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https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-1.0.8-pyhd8ed1ab_0.conda#611a35a27914fac3aa37611a6fe40bb5 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-1.0.6-pyhd8ed1ab_0.conda#d7e4954df0d3aea2eacc7835ad12671d -https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.0.5-pyhd8ed1ab_0.conda#7e1e7437273682ada2ed5e9e9714b140 -https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-1.0.7-pyhd8ed1ab_0.conda#26acae54b06f178681bfb551760f5dd1 -https://conda.anaconda.org/conda-forge/noarch/sphinx-7.3.7-pyhd8ed1ab_0.conda#7b1465205e28d75d2c0e1a868ee00a67 +https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.0.6-pyhd8ed1ab_0.conda#d6f4b617daa8c677f60c06a3a61e2743 +https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-1.0.8-pyhd8ed1ab_0.conda#179912c661d6aa9fe794e81c854f8d9f +https://conda.anaconda.org/conda-forge/noarch/sphinx-7.4.7-pyhd8ed1ab_0.conda#c568e260463da2528ecfd7c5a0b41bbd https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda#e507335cb4ca9cff4c3d0fa9cdab255e diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt index 950999ab0b49b..0c370ebe27db1 100644 --- a/build_tools/azure/ubuntu_atlas_lock.txt +++ b/build_tools/azure/ubuntu_atlas_lock.txt @@ -6,7 +6,7 @@ # cython==3.0.10 # via -r build_tools/azure/ubuntu_atlas_requirements.txt -exceptiongroup==1.2.1 +exceptiongroup==1.2.2 # via pytest execnet==2.1.1 # via pytest-xdist @@ -14,7 +14,7 @@ iniconfig==2.0.0 # via pytest joblib==1.2.0 # via -r build_tools/azure/ubuntu_atlas_requirements.txt -meson==1.4.1 +meson==1.5.0 # via meson-python meson-python==0.16.0 # via -r build_tools/azure/ubuntu_atlas_requirements.txt @@ -29,11 +29,7 @@ pluggy==1.5.0 # via pytest pyproject-metadata==0.8.0 # via meson-python -<<<<<<< HEAD -pytest==8.2.2 -======= -pytest==8.2.1 ->>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) +pytest==8.3.1 # via # -r build_tools/azure/ubuntu_atlas_requirements.txt # pytest-xdist diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index b73ae0bb520db..915930504493a 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -1,43 +1,32 @@ # Generated by conda-lock. # platform: linux-64 -<<<<<<< HEAD -<<<<<<< HEAD -<<<<<<< HEAD -# input_hash: f6f3862aafcafa139a322e498517c3db58e1b8db95f1b1ca8c18f5b70d446dc9 -======= -# input_hash: beab3d7262ec74c4ef8c9050098de8b9fe7910606e7bd4ff52687972bff35868 ->>>>>>> 34db65a3ad (DOC use pydata-sphinx-theme for the website (#29038)) -======= -# input_hash: 46e3ce252a769181cca2ee8df6e2bfd3f6749900d7863cc5a3b41c300daa0380 ->>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) -======= -# input_hash: 9a2a865e0d1dd470f9dab2a8b0fab036ee049da39187276fee78752b182d944d ->>>>>>> 0dba98f1eb (MNT Update main-ci lock files (#29388)) +# input_hash: 92691d82bff911619dfa8492dcfc15ec0f09ee96f9dc2d23f1e40004149ffefe @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 +https://conda.anaconda.org/conda-forge/noarch/_sysroot_linux-64_curr_repodata_hack-3-h69a702a_16.conda#1c005af0c6ff22814b7c52ee448d4bea https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.7.4-hbcca054_0.conda#23ab7665c5f63cfb9f1f6195256daac6 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb 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+https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-12.4.0-ha4f9413_100.conda#cc5767cb4e052330106536a9fb34f077 https://conda.anaconda.org/conda-forge/linux-64/libgomp-14.1.0-h77fa898_0.conda#ae061a5ed5f05818acdf9adab72c146d -https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-12.3.0-h6b66f73_113.conda#3706e34877bd82d04cb1e9e9baeb2739 -https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.12-he073ed8_17.conda#595db67e32b276298ff3d94d07d47fbf -https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.40-ha1999f0_7.conda#3f840c7ed70a96b5ebde8044b2f36f32 +https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-12.4.0-ha4f9413_100.conda#0351f91f429a046542bba7255438fa04 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab +https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.17-h4a8ded7_16.conda#223fe8a3ff6d5e78484a9d58eb34d055 +https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.40-ha1999f0_7.conda#3f840c7ed70a96b5ebde8044b2f36f32 https://conda.anaconda.org/conda-forge/linux-64/binutils-2.40-h4852527_7.conda#df53aa8418f8c289ae9b9665986034f8 -https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.40-hb3c18ed_9.conda#bb3fb8553a669828501e80d13b6bd744 +https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.40-hb3c18ed_0.conda#f152f00b4c709e88cd88af1fb50a70b4 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793 https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.1.0-h77fa898_0.conda#ca0fad6a41ddaef54a153b78eccb5037 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.12-h4ab18f5_0.conda#7ed427f0871fd41cb1d9c17727c17589 https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2#d9c69a24ad678ffce24c6543a0176b00 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sniffio @ https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl#sha256=2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2 # pip traitlets @ https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl#sha256=b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f @@ -405,17 +322,12 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip jupyter-client @ https://files.pythonhosted.org/packages/cf/d3/c4bb02580bc0db807edb9a29b2d0c56031be1ef0d804336deb2699a470f6/jupyter_client-8.6.2-py3-none-any.whl#sha256=50cbc5c66fd1b8f65ecb66bc490ab73217993632809b6e505687de18e9dea39f # pip jupyter-server-terminals @ 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https://files.pythonhosted.org/packages/42/ce/87fadd7eaa01caaa564d3345025b983f72b4200abc82245068bd2664fb56/jupyterlite_pyodide_kernel-0.3.2-py3-none-any.whl#sha256=ae600571fa755b6fd7a2633a171de3fe490f2b1264bef32cdd7e8c34c95cd5ff # pip jupyter-events @ https://files.pythonhosted.org/packages/a5/94/059180ea70a9a326e1815176b2370da56376da347a796f8c4f0b830208ef/jupyter_events-0.10.0-py3-none-any.whl#sha256=4b72130875e59d57716d327ea70d3ebc3af1944d3717e5a498b8a06c6c159960 # pip nbformat @ https://files.pythonhosted.org/packages/a9/82/0340caa499416c78e5d8f5f05947ae4bc3cba53c9f038ab6e9ed964e22f1/nbformat-5.10.4-py3-none-any.whl#sha256=3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b # pip nbclient @ https://files.pythonhosted.org/packages/66/e8/00517a23d3eeaed0513e718fbc94aab26eaa1758f5690fc8578839791c79/nbclient-0.10.0-py3-none-any.whl#sha256=f13e3529332a1f1f81d82a53210322476a168bb7090a0289c795fe9cc11c9d3f # pip nbconvert @ https://files.pythonhosted.org/packages/b8/bb/bb5b6a515d1584aa2fd89965b11db6632e4bdc69495a52374bcc36e56cfa/nbconvert-7.16.4-py3-none-any.whl#sha256=05873c620fe520b6322bf8a5ad562692343fe3452abda5765c7a34b7d1aa3eb3 -<<<<<<< HEAD -# pip jupyter-server @ https://files.pythonhosted.org/packages/26/f5/be75c159deda5b54e15cf54029915ad28337fcfef402d671566c45f9e61f/jupyter_server-2.14.1-py3-none-any.whl#sha256=16f7177c3a4ea8fe37784e2d31271981a812f0b2874af17339031dc3510cc2a5 -# pip jupyterlab-server @ https://files.pythonhosted.org/packages/cb/46/d5ffd7c0f63db4e9f0982c3d58efeea10fc5f47e79fb328431df78843772/jupyterlab_server-2.27.2-py3-none-any.whl#sha256=54aa2d64fd86383b5438d9f0c032f043c4d8c0264b8af9f60bd061157466ea43 -======= -# pip jupyter-server @ https://files.pythonhosted.org/packages/07/46/6bb926b3bf878bf687b952fb6a4c09d014b4575a25960f2cd1a61793763f/jupyter_server-2.14.0-py3-none-any.whl#sha256=fb6be52c713e80e004fac34b35a0990d6d36ba06fd0a2b2ed82b899143a64210 -# pip jupyterlab-server @ https://files.pythonhosted.org/packages/2f/b9/ed4ecad7cf1863a64920dc4c19b0376628b5d6bd28d2ec1e00cbac4ba2fb/jupyterlab_server-2.27.1-py3-none-any.whl#sha256=f5e26156e5258b24d532c84e7c74cc212e203bff93eb856f81c24c16daeecc75 ->>>>>>> d03c351780 (:lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29053)) -# pip jupyterlite-sphinx @ https://files.pythonhosted.org/packages/71/2c/bd797dc46a7281d43444c79ff312d4f8d27d41a0de05f48cad81c7939966/jupyterlite_sphinx-0.15.0-py3-none-any.whl#sha256=344d1f9ee5a20b141a4a4139874eae30a68216f0c995d03ea2e3b3e9d29c4cd5 +# pip jupyter-server @ https://files.pythonhosted.org/packages/57/e1/085edea6187a127ca8ea053eb01f4e1792d778b4d192c74d32eb6730fed6/jupyter_server-2.14.2-py3-none-any.whl#sha256=47ff506127c2f7851a17bf4713434208fc490955d0e8632e95014a9a9afbeefd +# pip jupyterlab-server @ https://files.pythonhosted.org/packages/54/09/2032e7d15c544a0e3cd831c51d77a8ca57f7555b2e1b2922142eddb02a84/jupyterlab_server-2.27.3-py3-none-any.whl#sha256=e697488f66c3db49df675158a77b3b017520d772c6e1548c7d9bcc5df7944ee4 +# pip jupyterlite-sphinx @ https://files.pythonhosted.org/packages/06/4a/6db8e63becb9443f792c2b169bf40cef123125cabfe9cc8152824f2cc81d/jupyterlite_sphinx-0.16.2-py3-none-any.whl#sha256=02c981a501f00cf3237742cdfc0972855815a2440f4b3b18833aa8dcad0e7e4f diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index 4ac0cafb6c0cc..28263647280f0 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -15,14 +15,14 @@ https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.9-4_cp39.conda#bfe4 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 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https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.17-h4a8ded7_16.conda#223fe8a3ff6d5e78484a9d58eb34d055 https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.40-ha1999f0_7.conda#3f840c7ed70a96b5ebde8044b2f36f32 https://conda.anaconda.org/conda-forge/linux-64/binutils-2.40-h4852527_7.conda#df53aa8418f8c289ae9b9665986034f8 -https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.40-hb3c18ed_9.conda#bb3fb8553a669828501e80d13b6bd744 +https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.40-hb3c18ed_0.conda#f152f00b4c709e88cd88af1fb50a70b4 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793 https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.1.0-h77fa898_0.conda#ca0fad6a41ddaef54a153b78eccb5037 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.12-h4ab18f5_0.conda#7ed427f0871fd41cb1d9c17727c17589 @@ -51,7 +51,7 @@ 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https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-255-h3516f8a_1.conda#3366af27f0b593544a6cd453c7932ac5 @@ -249,7 +249,7 @@ https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-22_linux64_mkl.conda#aa0a5a70e1c957d5911e76ac98e471e1 https://conda.anaconda.org/conda-forge/linux-64/numpy-1.19.5-py39hd249d9e_3.tar.bz2#0cf333996ebdeeba8d1c8c1c0ee9eff9 https://conda.anaconda.org/conda-forge/noarch/pooch-1.6.0-pyhd8ed1ab_0.tar.bz2#6429e1d1091c51f626b5dcfdd38bf429 -https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.8-ha2b5568_22.conda#15de976572f24032540236006d6d0e9f +https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.8-h320f8da_24.conda#bec111b67cb8dc63277c6af65d214044 https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-22_linux64_mkl.conda#3cb0e51433c88d2f4cdfb50c5c08a683 https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py39h34cef29_2.conda#d3ee926e63ebd5b44ebc984dff020305 https://conda.anaconda.org/conda-forge/noarch/imageio-2.34.2-pyh12aca89_0.conda#97ad994fae55dce96bd397054b32e41a @@ -277,8 +277,8 @@ https://conda.anaconda.org/conda-forge/noarch/sphinx-prompt-1.4.0-pyhd8ed1ab_0.t https://conda.anaconda.org/conda-forge/noarch/sphinx-remove-toctrees-1.0.0.post1-pyhd8ed1ab_0.conda#6dee8412218288a17f99f2cfffab334d https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-1.0.8-pyhd8ed1ab_0.conda#611a35a27914fac3aa37611a6fe40bb5 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-1.0.6-pyhd8ed1ab_0.conda#d7e4954df0d3aea2eacc7835ad12671d -https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.0.5-pyhd8ed1ab_0.conda#7e1e7437273682ada2ed5e9e9714b140 -https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-1.0.7-pyhd8ed1ab_0.conda#26acae54b06f178681bfb551760f5dd1 +https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.0.6-pyhd8ed1ab_0.conda#d6f4b617daa8c677f60c06a3a61e2743 +https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-1.0.8-pyhd8ed1ab_0.conda#179912c661d6aa9fe794e81c854f8d9f https://conda.anaconda.org/conda-forge/noarch/sphinx-7.3.7-pyhd8ed1ab_0.conda#7b1465205e28d75d2c0e1a868ee00a67 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda#e507335cb4ca9cff4c3d0fa9cdab255e # pip libsass @ https://files.pythonhosted.org/packages/fd/5a/eb5b62641df0459a3291fc206cf5bd669c0feed7814dded8edef4ade8512/libsass-0.23.0-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=4a218406d605f325d234e4678bd57126a66a88841cb95bee2caeafdc6f138306 diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index 3a92c52a87304..cfa130186d5fd 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -146,10 +146,13 @@ def remove_from(alist, to_remove): # TODO: release scipy constraint when 1.13 is available in the "default" # channel. "scipy": "<1.12", + # TODO temporary to avoid a timeout in the no-OpenMP build, see + # https://github.com/scikit-learn/scikit-learn/pull/29486#issuecomment-2242359516 + "meson": "<1.5", }, # TODO: put cython, threadpoolctl and meson-python back to conda # dependencies when required version is available on the main channel - "pip_dependencies": ["cython", "threadpoolctl", "meson-python"], + "pip_dependencies": ["cython", "threadpoolctl", "meson-python", "meson"], }, { "name": "pymin_conda_defaults_openblas", diff --git a/sklearn/utils/deprecation.py b/sklearn/utils/deprecation.py index a3225597701c7..f72f8b8124f28 100644 --- a/sklearn/utils/deprecation.py +++ b/sklearn/utils/deprecation.py @@ -97,7 +97,7 @@ def _decorate_property(self, prop): msg = self.extra @property - @functools.wraps(prop) + @functools.wraps(prop.fget) def wrapped(*args, **kwargs): warnings.warn(msg, category=FutureWarning) return prop.fget(*args, **kwargs) From 91d8dec1cece7a3332d783c68049633ead62337e Mon Sep 17 00:00:00 2001 From: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> Date: Tue, 23 Jul 2024 14:47:07 +0200 Subject: [PATCH 181/275] DOC Add quantile loss to user guide on HGBT regression (#29063) Co-authored-by: ArturoAmorQ Co-authored-by: Guillaume Lemaitre Co-authored-by: Olivier Grisel --- doc/modules/ensemble.rst | 23 +++++++++++++++-------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst index 58ac09583ea6c..3160ff24d57f3 100644 --- a/doc/modules/ensemble.rst +++ b/doc/modules/ensemble.rst @@ -98,14 +98,21 @@ controls the number of iterations of the boosting process:: >>> clf.score(X_test, y_test) 0.8965 -Available losses for regression are 'squared_error', -'absolute_error', which is less sensitive to outliers, and -'poisson', which is well suited to model counts and frequencies. For -classification, 'log_loss' is the only option. For binary classification it uses the -binary log loss, also known as binomial deviance or binary cross-entropy. For -`n_classes >= 3`, it uses the multi-class log loss function, with multinomial deviance -and categorical cross-entropy as alternative names. The appropriate loss version is -selected based on :term:`y` passed to :term:`fit`. +Available losses for **regression** are: + +- 'squared_error', which is the default loss; +- 'absolute_error', which is less sensitive to outliers than the squared error; +- 'gamma', which is well suited to model strictly positive outcomes; +- 'poisson', which is well suited to model counts and frequencies; +- 'quantile', which allows for estimating a conditional quantile that can later + be used to obtain prediction intervals. + +For **classification**, 'log_loss' is the only option. For binary classification +it uses the binary log loss, also known as binomial deviance or binary +cross-entropy. For `n_classes >= 3`, it uses the multi-class log loss function, +with multinomial deviance and categorical cross-entropy as alternative names. +The appropriate loss version is selected based on :term:`y` passed to +:term:`fit`. The size of the trees can be controlled through the ``max_leaf_nodes``, ``max_depth``, and ``min_samples_leaf`` parameters. From 474d7b72fd8c3b974dd1af87dc7d8a34aa506394 Mon Sep 17 00:00:00 2001 From: Yao Xiao <108576690+Charlie-XIAO@users.noreply.github.com> Date: Tue, 23 Jul 2024 21:38:35 +0800 Subject: [PATCH 182/275] DOC fix links in the machine learning map (#29544) --- doc/images/ml_map.svg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/images/ml_map.svg b/doc/images/ml_map.svg index 7c587cef011b9..2dedc6cf054df 100644 --- a/doc/images/ml_map.svg +++ b/doc/images/ml_map.svg @@ -1,4 +1,4 @@ -
    START
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    predicting a
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    just
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    predicting
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    Linear
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    text
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    <10K
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    NO
    NO
    NO
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    YES
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    MeanShift
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    YES
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    MiniBatch
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    few features
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    ElasticNet
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    YES
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    RidgeRegression
    RidgeRegression
    SVR(kernel="linear")
    SVR(kernel="linea...
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    SVR(kernel="rbf")
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    scikit-learn
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    predicting a
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    predicting...
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    do you have
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    do you hav...
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    YES
    predicting a
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    NO
    just
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    NO
    NO
    predicting
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    NO
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    SGD...
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    Linear
    SVC
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    text
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    SVC
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    known
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    NO
    <10K
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    NO
    NO
    NO
    NO
    YES
    YES
    MeanShift
    MeanShift
    VBGMM
    VBGMM
    YES
    YES
    MiniBatch
    KMeans
    MiniBatch...
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    NO
    clustering
    clustering
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    Spectral
    Clustering
    Spectral...
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    😭
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    <100K
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    few features
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    ElasticNet
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    YES
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    RidgeRegression
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    SVR(kernel="linea...
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    NO
    SVR(kernel="rbf")
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    Ensemble
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    Ensemble...
    😭
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    regression
    regression
    Ramdomized
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    <10K
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    <10K...
    😭
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    Kernel
    Approximation
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    IsoMap
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    Spectral
    Embedding
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Text is not SVG - cannot display     From 281ed1247eb7d6922c4cecb75cbbd7e0124da68c Mon Sep 17 00:00:00 2001 From: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> Date: Tue, 23 Jul 2024 16:30:16 +0200 Subject: [PATCH 183/275] MNT Update environment and lockfile for build doc with matplotlib unpinned (#29548) --- build_tools/circle/doc_environment.yml | 2 +- build_tools/circle/doc_linux-64_conda.lock | 19 ++++++++++--------- .../update_environments_and_lock_files.py | 7 ------- 3 files changed, 11 insertions(+), 17 deletions(-) diff --git a/build_tools/circle/doc_environment.yml b/build_tools/circle/doc_environment.yml index 4f0e41927c784..ea930fadcb528 100644 --- a/build_tools/circle/doc_environment.yml +++ b/build_tools/circle/doc_environment.yml @@ -11,7 +11,7 @@ dependencies: - cython - joblib - threadpoolctl - - matplotlib<3.9 + - matplotlib - pandas - pyamg - pytest diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index 915930504493a..d1c0fe1e6ddf2 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 92691d82bff911619dfa8492dcfc15ec0f09ee96f9dc2d23f1e40004149ffefe +# input_hash: f6f3862aafcafa139a322e498517c3db58e1b8db95f1b1ca8c18f5b70d446dc9 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/_sysroot_linux-64_curr_repodata_hack-3-h69a702a_16.conda#1c005af0c6ff22814b7c52ee448d4bea @@ -91,6 +91,7 @@ https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3a https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.44-h0f59acf_0.conda#3914f7ac1761dce57102c72ca7c35d01 https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123 +https://conda.anaconda.org/conda-forge/linux-64/qhull-2020.2-h434a139_5.conda#353823361b1d27eb3960efb076dfcaf6 https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda#47d31b792659ce70f470b5c82fdfb7a4 https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.1-ha2e4443_0.conda#6b7dcc7349efd123d493d2dbe85a045f https://conda.anaconda.org/conda-forge/linux-64/svt-av1-2.1.2-hac33072_0.conda#06c5dec4ebb47213b648a6c4dc8400d6 @@ -148,7 +149,7 @@ https://conda.anaconda.org/conda-forge/noarch/imagesize-1.4.1-pyhd8ed1ab_0.tar.b https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda#f800d2da156d08e289b14e87e43c1ae5 https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.5-py39h7633fee_1.conda#c9f74d717e5a2847a9f8b779c54130f2 https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.16-hb7c19ff_0.conda#51bb7010fc86f70eee639b4bb7a894f5 -https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-22_linux64_openblas.conda#1a2a0cd3153464fee6646f3dd6dad9b8 +https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-23_linux64_openblas.conda#96c8450a40aa2b9733073a9460de972c https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-h4637d8d_4.conda#d4529f4dff3057982a7617c7ac58fde3 https://conda.anaconda.org/conda-forge/linux-64/libllvm15-15.0.7-hb3ce162_4.conda#8a35df3cbc0c8b12cc8af9473ae75eef https://conda.anaconda.org/conda-forge/linux-64/libllvm18-18.1.8-h8b73ec9_1.conda#16d94b3586ef3558e5a583598524deb4 @@ -206,12 +207,12 @@ https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.0.0-pyha770c7 https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1ab_0.conda#c5d3907ad8bd7bf557521a1833cf7e6d https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda#7b86ecb7d3557821c649b3c31e3eb9f2 https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f -https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-22_linux64_openblas.conda#4b31699e0ec5de64d5896e580389c9a1 +https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-23_linux64_openblas.conda#eede29b40efa878cbe5bdcb767e97310 https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp15-15.0.7-default_h127d8a8_5.conda#d0a9633b53cdc319b8a1a532ae7822b8 https://conda.anaconda.org/conda-forge/linux-64/libclang13-18.1.8-default_h9def88c_1.conda#04c8c481b30c3fe62bec148fa4a75857 https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda#ee48bf17cc83a00f59ca1494d5646869 https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.50-h4f305b6_0.conda#0d7ff1a8e69565ca3add6925e18e708f -https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-22_linux64_openblas.conda#b083767b6c877e24ee597d93b87ab838 +https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-23_linux64_openblas.conda#2af0879961951987e464722fd00ec1e0 https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.7.0-h2c5496b_1.conda#e2eaefa4de2b7237af7c907b8bbc760a https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhd8ed1ab_0.tar.bz2#8b45f9f2b2f7a98b0ec179c8991a4a9b https://conda.anaconda.org/conda-forge/noarch/meson-1.5.0-pyhd8ed1ab_0.conda#9d971c5bf99aed063664d6650e7e7ed8 @@ -229,14 +230,14 @@ https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-9.0.0-hfac3d4d_0.conda# https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.0-pyhd8ed1ab_0.conda#dcbadab7a68738a028e195ab68ab2d2e https://conda.anaconda.org/conda-forge/noarch/lazy_loader-0.4-pyhd8ed1ab_0.conda#a284ff318fbdb0dd83928275b4b6087c https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-1.11.0-h4ab18f5_1.conda#14858a47d4cc995892e79f2b340682d7 -https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-22_linux64_openblas.conda#1fd156abd41a4992835952f6f4d951d0 +https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-23_linux64_openblas.conda#89d7bcdb1e9a72a73e36d8e29d2a2beb https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.conda#ef1910918dd895516a769ed36b5b3a4e https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 https://conda.anaconda.org/conda-forge/linux-64/numpy-2.0.0-py39ha0965c0_0.conda#b411be2728ba5711fc9bcdb0efa2db71 https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5.conda#93aff412f3e49fdb43361c0215cbd72d https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_0.conda#b39568655c127a9c4a44d178ac99b6d0 https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py39h623c9ba_0.conda#a19d023682384c637cb356d270c276c0 -https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-22_linux64_openblas.conda#63ddb593595c9cf5eb08d3de54d66df8 +https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-23_linux64_openblas.conda#08b43a5c3d6cc13aeb69bd2cbc293196 https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_1.conda#d8d07866ac3b5b6937213c89a1874f08 https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py39h7633fee_0.conda#bdc188e59857d6efab332714e0d01d93 https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.5-hbaaba92_0.conda#4a485842570569ba754863b2c083b346 @@ -249,8 +250,8 @@ https://conda.anaconda.org/conda-forge/linux-64/polars-1.2.1-py39h883198d_0.cond https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.6.0-py39hd92a3bb_0.conda#32e26e16f60c568b17a82e3033a4d309 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.13.1-py39haf93ffa_0.conda#492a2cd65862d16a4aaf535ae9ccb761 https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.2-pyhd8ed1ab_1.conda#e804c43f58255e977093a2298e442bb8 -https://conda.anaconda.org/conda-forge/linux-64/blas-2.122-openblas.conda#5065468105542a8b23ea47bd8b6fa55f -https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.8.4-py39h10d1fc8_2.conda#c9fb6571b93b1dd490ea627af7344f36 +https://conda.anaconda.org/conda-forge/linux-64/blas-2.123-openblas.conda#7f4b3ea1cdd6e50dca2a226abda6e2d9 +https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.9.1-py39hd75cb13_0.conda#31dbb5b27008fabf74d67da272f5e002 https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-client-17.0-hb77b528_0.conda#07f45f1be1c25345faddb8db0de8039b https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.2.1-py39h85c637f_0.conda#0bfaf33b7ebdbadc77bf9a67e281c0b1 https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda#5ede4753180c7a550a443c430dc8ab52 @@ -262,7 +263,7 @@ https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.24.0-py39hfc16268 https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_2.conda#b713b116feaf98acdba93ad4d7f90ca1 https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.9-py39h52134e7_5.conda#e1f148e57d071b09187719df86f513c1 https://conda.anaconda.org/conda-forge/noarch/seaborn-0.13.2-hd8ed1ab_2.conda#a79d8797f62715255308d92d3a91ef2e -https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.8.4-py39hf3d152e_2.conda#bd956c7563b6a6b27521b83623c74e22 +https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.9.1-py39hf3d152e_0.conda#9d2e01516067eadfa9a3471778991e9d https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.7.0-pyhd8ed1ab_1.conda#66798cbfdcb003d9fbccd92cd08eb3ac https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.15.4-pyhd8ed1ab_0.conda#c7c50dd5192caa58a05e6a4248a27acb https://conda.anaconda.org/conda-forge/noarch/sphinx-copybutton-0.5.2-pyhd8ed1ab_0.conda#ac832cc43adc79118cf6e23f1f9b8995 diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index cfa130186d5fd..20517bbc925b5 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -375,13 +375,6 @@ def remove_from(alist, to_remove): ], "package_constraints": { "python": "3.9", - # TODO: this needs to be adapted when matplotlib 3.11 is out. In - # the meantime, this avoids a warning in matplotlib 3.9 boxplot - # labels has been renamed to tick_labels. Possible options: - # - bump minimum matplotlib supported versions to 3.9 at one point - # - complicate the example code to do the right thing depending on - # maplotlib version - "matplotlib": "<3.9", }, }, { From 510c6123fee774472e829325256a1ed6b01b4273 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Thu, 25 Jul 2024 10:29:37 +0200 Subject: [PATCH 184/275] CI Use correct Python version in lock-file update script for Debian 32bit build (#29541) --- build_tools/update_environments_and_lock_files.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index 20517bbc925b5..d44e00a45941e 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -415,7 +415,7 @@ def remove_from(alist, to_remove): "cython": "min", }, # same Python version as in debian-32 build - "python_version": "3.9.2", + "python_version": "3.11.2", }, { "name": "ubuntu_atlas", From 494d1c0972dcffed92b566ed0056304e175f530b Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Thu, 25 Jul 2024 10:42:05 +0200 Subject: [PATCH 185/275] DOC add persistence methods table (#29215) Co-authored-by: Olivier Grisel --- doc/model_persistence.rst | 41 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) diff --git a/doc/model_persistence.rst b/doc/model_persistence.rst index cd5347d302123..604f7640d41fc 100644 --- a/doc/model_persistence.rst +++ b/doc/model_persistence.rst @@ -4,6 +4,47 @@ Model persistence ================= +.. list-table:: Summary of model persistence methods + :widths: 25 50 50 + :header-rows: 1 + + * - Persistence method + - Pros + - Risks / Cons + * - :ref:`ONNX ` + - * Serve models without a Python environment + * Serving and training environments independent of one another + * Most secure option + - * Not all scikit-learn models are supported + * Custom estimators require more work to support + * Original Python object is lost and cannot be reconstructed + * - :ref:`skops_persistence` + - * More secure than `pickle` based formats + * Contents can be partly validated without loading + - * Not as fast as `pickle` based formats + * Supports less types than `pickle` based formats + * Requires the same environment as the training environment + * - :mod:`pickle` + - * Native to Python + * Can serialize most Python objects + * Efficient memory usage with `protocol=5` + - * Loading can execute arbitrary code + * Requires the same environment as the training environment + * - :mod:`joblib` + - * Efficient memory usage + * Supports memory mapping + * Easy shortcuts for compression and decompression + - * Pickle based format + * Loading can execute arbitrary code + * Requires the same environment as the training environment + * - `cloudpickle`_ + - * Can serialize non-packaged, custom Python code + * Comparable loading efficiency as :mod:`pickle` with `protocol=5` + - * Pickle based format + * Loading can execute arbitrary code + * No forward compatibility guarantees + * Requires the same environment as the training environment + After training a scikit-learn model, it is desirable to have a way to persist the model for future use without having to retrain. Based on your use-case, there are a few different ways to persist a scikit-learn model, and here we From 2a7125018e523bad49c3676d8926a7e7e003b1e7 Mon Sep 17 00:00:00 2001 From: Roberto Rosati <24945128+bioruffo@users.noreply.github.com> Date: Thu, 25 Jul 2024 08:43:55 -0300 Subject: [PATCH 186/275] DOC fixed typo in _rfe.py (#29559) --- sklearn/feature_selection/_rfe.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/feature_selection/_rfe.py b/sklearn/feature_selection/_rfe.py index 7c5cd8d45b8d1..d02e9ba874400 100644 --- a/sklearn/feature_selection/_rfe.py +++ b/sklearn/feature_selection/_rfe.py @@ -529,7 +529,7 @@ class RFECV(RFE): For integer/None inputs, if ``y`` is binary or multiclass, :class:`~sklearn.model_selection.StratifiedKFold` is used. If the - estimator is a classifier or if ``y`` is neither binary nor multiclass, + estimator is not a classifier or if ``y`` is neither binary nor multiclass, :class:`~sklearn.model_selection.KFold` is used. Refer :ref:`User Guide ` for the various From bcea3ca1f8f67bbb0c6e8e9c9fc8e1d6ec72d773 Mon Sep 17 00:00:00 2001 From: renaissance0ne <151532311+renaissance0ne@users.noreply.github.com> Date: Mon, 29 Jul 2024 13:48:59 +0530 Subject: [PATCH 187/275] DOC Added link to plot_mean_shift.py example to _mean_shift.py (#29566) --- sklearn/cluster/_mean_shift.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/sklearn/cluster/_mean_shift.py b/sklearn/cluster/_mean_shift.py index a99a607f3cf0d..9710e0941a158 100644 --- a/sklearn/cluster/_mean_shift.py +++ b/sklearn/cluster/_mean_shift.py @@ -218,8 +218,8 @@ def mean_shift( Notes ----- - For an example, see :ref:`examples/cluster/plot_mean_shift.py - `. + For a usage example, see + :ref:`sphx_glr_auto_examples_cluster_plot_mean_shift.py`. Examples -------- @@ -310,6 +310,9 @@ class MeanShift(ClusterMixin, BaseEstimator): Seeding is performed using a binning technique for scalability. + For an example of how to use MeanShift clustering, refer to: + :ref:`sphx_glr_auto_examples_cluster_plot_mean_shift.py`. + Read more in the :ref:`User Guide `. Parameters From a57680cc7b0cfe29e602b8a384b18124d51adc48 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 29 Jul 2024 01:40:53 -0700 Subject: [PATCH 188/275] :lock: :robot: CI Update lock files for cirrus-arm CI build(s) :lock: :robot: (#29574) Co-authored-by: Lock file bot --- .../pymin_conda_forge_linux-aarch64_conda.lock | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock index 9b5010ce3588f..ed956180b06e4 100644 --- a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock +++ b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock @@ -56,7 +56,7 @@ https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_0.conda#1 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda#f800d2da156d08e289b14e87e43c1ae5 https://conda.anaconda.org/conda-forge/linux-aarch64/kiwisolver-1.4.5-py39had2cf8c_1.conda#ddb99610f7b950fdd5ff2aff19136363 https://conda.anaconda.org/conda-forge/linux-aarch64/lcms2-2.16-h922389a_0.conda#ffdd8267a04c515e7ce69c727b051414 -https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.9.0-22_linuxaarch64_openblas.conda#068ab33f2382cda4dd0b72a715ad33b5 +https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.9.0-23_linuxaarch64_openblas.conda#3ac1ad627e1a07fae62556d6aabafdfd https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2#2ba8498c1018c1e9c61eb99b973dfe19 https://conda.anaconda.org/conda-forge/linux-aarch64/openblas-0.3.27-pthreads_hd33deab_1.conda#70c0aa7d1dd049fffae952bfe8f2c4e9 https://conda.anaconda.org/conda-forge/linux-aarch64/openjpeg-2.5.2-h0d9d63b_0.conda#fd2898519e839d5ceb778343f39a3176 @@ -74,22 +74,22 @@ https://conda.anaconda.org/conda-forge/noarch/zipp-3.19.2-pyhd8ed1ab_0.conda#498 https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.53.1-py39he257ee7_0.conda#e30df3a3431af304f87bbd0cd07d5674 https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1ab_0.conda#c5d3907ad8bd7bf557521a1833cf7e6d https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f -https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-22_linuxaarch64_openblas.conda#fbe7fe553f2cc78a0311e009b26f180d -https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.9.0-22_linuxaarch64_openblas.conda#8c709d281609792c39b1d5c0241f90f1 -https://conda.anaconda.org/conda-forge/noarch/meson-1.5.0-pyhd8ed1ab_0.conda#9d971c5bf99aed063664d6650e7e7ed8 +https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-23_linuxaarch64_openblas.conda#65a4f18036c0f5419146fddee6653a96 +https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.9.0-23_linuxaarch64_openblas.conda#85c4fec3847027ca7402f3bd7d2de4c1 +https://conda.anaconda.org/conda-forge/noarch/meson-1.5.1-pyhd8ed1ab_0.conda#a87a68d26db0c5bea5cab1f8d7772112 https://conda.anaconda.org/conda-forge/linux-aarch64/pillow-10.4.0-py39h4a8821f_0.conda#318861157594972acc05a8715d3018a8 https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 -https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.1-pyhd8ed1ab_0.conda#b6a3ab8559a42070c6b6c3063faea1ed +https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.2-pyhd8ed1ab_0.conda#e010a224b90f1f623a917c35addbb924 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.0-pyhd8ed1ab_0.conda#dcbadab7a68738a028e195ab68ab2d2e -https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-22_linuxaarch64_openblas.conda#5acf669e0be669f30f4b813d2ecda7b8 +https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-23_linuxaarch64_openblas.conda#d71af7934d6dcef05a3c9b0379e1cdfa https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 -https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-2.0.0-py39hcdcdb6f_0.conda#8ba5676e972e45d1c0d947f169c6e914 +https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-2.0.1-py39hcdcdb6f_0.conda#e97d4cba6dd293bb795baddad9ddae02 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_0.conda#b39568655c127a9c4a44d178ac99b6d0 -https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-22_linuxaarch64_openblas.conda#a5b77b6c6807661afd716f33e85814b3 +https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-23_linuxaarch64_openblas.conda#0270f72a50c9d64fb8b67ae6681011c8 https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.2.1-py39hd16970a_0.conda#66b9718539ecdd38876b0176c315bcad https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.13.1-py39hb921187_0.conda#1aac9080de661e03d286f18fb71e5240 -https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.122-openblas.conda#65bc48b3bc85f8eeeab54311443a83aa +https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.123-openblas.conda#43772c0a1ae8f29c9a223c21fd89262b https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.9.1-py39hf3ba65a_0.conda#1bd99011ababf4f9d0976271b23b179f https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-3.9.1-py39ha65689a_0.conda#677115251fc0a0cd1665318573e2307a From 9901702ccbbf1584cae428f231b2e696d7e42aca Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 29 Jul 2024 01:41:50 -0700 Subject: [PATCH 189/275] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#29575) Co-authored-by: Lock file bot --- .../pylatest_pip_scipy_dev_linux-64_conda.lock | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index b6d75c41ecb8f..3fe15f2eada17 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -37,7 +37,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py312h06a4308_0.conda#6d96 # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b # pip iniconfig @ https://files.pythonhosted.org/packages/ef/a6/62565a6e1cf69e10f5727360368e451d4b7f58beeac6173dc9db836a5b46/iniconfig-2.0.0-py3-none-any.whl#sha256=b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374 # pip markupsafe @ https://files.pythonhosted.org/packages/0a/0d/2454f072fae3b5a137c119abf15465d1771319dfe9e4acbb31722a0fff91/MarkupSafe-2.1.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=f5dfb42c4604dddc8e4305050aa6deb084540643ed5804d7455b5df8fe16f5e5 -# pip meson @ https://files.pythonhosted.org/packages/c4/cd/47e45d3abada2e1edb9e2ca9334be186d2e7f97a01b09b5b82799c4d7bd3/meson-1.5.0-py3-none-any.whl#sha256=52b34f4903b882df52ad0d533146d4b992c018ea77399f825579737672ae7b20 +# pip meson @ https://files.pythonhosted.org/packages/b7/33/513a9ca4fd5892463abb38592105b78fd425214f7983033633e2e48cbd30/meson-1.5.1-py3-none-any.whl#sha256=5531e24e6cfd6000bf1c712793cf28dff032841370b1a3b941a894e4fde46e5a # pip ninja @ https://files.pythonhosted.org/packages/6d/92/8d7aebd4430ab5ff65df2bfee6d5745f95c004284db2d8ca76dcbfd9de47/ninja-1.11.1.1-py2.py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl#sha256=84502ec98f02a037a169c4b0d5d86075eaf6afc55e1879003d6cab51ced2ea4b # pip packaging @ https://files.pythonhosted.org/packages/08/aa/cc0199a5f0ad350994d660967a8efb233fe0416e4639146c089643407ce6/packaging-24.1-py3-none-any.whl#sha256=5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124 # pip platformdirs @ https://files.pythonhosted.org/packages/68/13/2aa1f0e1364feb2c9ef45302f387ac0bd81484e9c9a4c5688a322fbdfd08/platformdirs-4.2.2-py3-none-any.whl#sha256=2d7a1657e36a80ea911db832a8a6ece5ee53d8de21edd5cc5879af6530b1bfee @@ -45,18 +45,18 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py312h06a4308_0.conda#6d96 # pip pygments @ https://files.pythonhosted.org/packages/f7/3f/01c8b82017c199075f8f788d0d906b9ffbbc5a47dc9918a945e13d5a2bda/pygments-2.18.0-py3-none-any.whl#sha256=b8e6aca0523f3ab76fee51799c488e38782ac06eafcf95e7ba832985c8e7b13a # pip six @ https://files.pythonhosted.org/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl#sha256=8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254 # pip snowballstemmer @ https://files.pythonhosted.org/packages/ed/dc/c02e01294f7265e63a7315fe086dd1df7dacb9f840a804da846b96d01b96/snowballstemmer-2.2.0-py2.py3-none-any.whl#sha256=c8e1716e83cc398ae16824e5572ae04e0d9fc2c6b985fb0f900f5f0c96ecba1a -# pip sphinxcontrib-applehelp @ https://files.pythonhosted.org/packages/56/89/fea3fbf6785b388e6cb8a1beaf62f96e80b37311bdeed6e133388a732426/sphinxcontrib_applehelp-1.0.8-py3-none-any.whl#sha256=cb61eb0ec1b61f349e5cc36b2028e9e7ca765be05e49641c97241274753067b4 -# pip sphinxcontrib-devhelp @ https://files.pythonhosted.org/packages/a0/52/1049d918d1d1c72857d285c3f0c64c1cbe0be394ce1c93a3d2aa4f39fe3b/sphinxcontrib_devhelp-1.0.6-py3-none-any.whl#sha256=6485d09629944511c893fa11355bda18b742b83a2b181f9a009f7e500595c90f -# pip sphinxcontrib-htmlhelp @ https://files.pythonhosted.org/packages/a4/b4/6ebbdc57b5b216b400b355f34ef669e9b6b5c31a6ede8cf5ac36f9e8fc0c/sphinxcontrib_htmlhelp-2.0.6-py3-none-any.whl#sha256=1b9af5a2671a61410a868fce050cab7ca393c218e6205cbc7f590136f207395c +# pip sphinxcontrib-applehelp @ https://files.pythonhosted.org/packages/5d/85/9ebeae2f76e9e77b952f4b274c27238156eae7979c5421fba91a28f4970d/sphinxcontrib_applehelp-2.0.0-py3-none-any.whl#sha256=4cd3f0ec4ac5dd9c17ec65e9ab272c9b867ea77425228e68ecf08d6b28ddbdb5 +# pip sphinxcontrib-devhelp @ https://files.pythonhosted.org/packages/35/7a/987e583882f985fe4d7323774889ec58049171828b58c2217e7f79cdf44e/sphinxcontrib_devhelp-2.0.0-py3-none-any.whl#sha256=aefb8b83854e4b0998877524d1029fd3e6879210422ee3780459e28a1f03a8a2 +# pip sphinxcontrib-htmlhelp @ https://files.pythonhosted.org/packages/0a/7b/18a8c0bcec9182c05a0b3ec2a776bba4ead82750a55ff798e8d406dae604/sphinxcontrib_htmlhelp-2.1.0-py3-none-any.whl#sha256=166759820b47002d22914d64a075ce08f4c46818e17cfc9470a9786b759b19f8 # pip sphinxcontrib-jsmath @ https://files.pythonhosted.org/packages/c2/42/4c8646762ee83602e3fb3fbe774c2fac12f317deb0b5dbeeedd2d3ba4b77/sphinxcontrib_jsmath-1.0.1-py2.py3-none-any.whl#sha256=2ec2eaebfb78f3f2078e73666b1415417a116cc848b72e5172e596c871103178 -# pip sphinxcontrib-qthelp @ https://files.pythonhosted.org/packages/cf/91/cec9416d27ebe9b8aa83f014a1ac8402c729aed791da67704e10bb2c8f33/sphinxcontrib_qthelp-1.0.8-py3-none-any.whl#sha256=323d6acc4189af76dfe94edd2a27d458902319b60fcca2aeef3b2180c106a75f -# pip sphinxcontrib-serializinghtml @ https://files.pythonhosted.org/packages/38/24/228bb903ea87b9e08ab33470e6102402a644127108c7117ac9c00d849f82/sphinxcontrib_serializinghtml-1.1.10-py3-none-any.whl#sha256=326369b8df80a7d2d8d7f99aa5ac577f51ea51556ed974e7716cfd4fca3f6cb7 +# pip sphinxcontrib-qthelp @ https://files.pythonhosted.org/packages/27/83/859ecdd180cacc13b1f7e857abf8582a64552ea7a061057a6c716e790fce/sphinxcontrib_qthelp-2.0.0-py3-none-any.whl#sha256=b18a828cdba941ccd6ee8445dbe72ffa3ef8cbe7505d8cd1fa0d42d3f2d5f3eb +# pip sphinxcontrib-serializinghtml @ https://files.pythonhosted.org/packages/52/a7/d2782e4e3f77c8450f727ba74a8f12756d5ba823d81b941f1b04da9d033a/sphinxcontrib_serializinghtml-2.0.0-py3-none-any.whl#sha256=6e2cb0eef194e10c27ec0023bfeb25badbbb5868244cf5bc5bdc04e4464bf331 # pip tabulate @ https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl#sha256=024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f # pip threadpoolctl @ https://files.pythonhosted.org/packages/4b/2c/ffbf7a134b9ab11a67b0cf0726453cedd9c5043a4fe7a35d1cefa9a1bcfb/threadpoolctl-3.5.0-py3-none-any.whl#sha256=56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467 # pip urllib3 @ https://files.pythonhosted.org/packages/ca/1c/89ffc63a9605b583d5df2be791a27bc1a42b7c32bab68d3c8f2f73a98cd4/urllib3-2.2.2-py3-none-any.whl#sha256=a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472 # pip jinja2 @ https://files.pythonhosted.org/packages/31/80/3a54838c3fb461f6fec263ebf3a3a41771bd05190238de3486aae8540c36/jinja2-3.1.4-py3-none-any.whl#sha256=bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d # pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526 -# pip pytest @ https://files.pythonhosted.org/packages/b9/77/ccea391104f576a6e54a54334fc26d29c28aa0ec85714c781fbd2c34ac86/pytest-8.3.1-py3-none-any.whl#sha256=e9600ccf4f563976e2c99fa02c7624ab938296551f280835ee6516df8bc4ae8c +# pip pytest @ https://files.pythonhosted.org/packages/0f/f9/cf155cf32ca7d6fa3601bc4c5dd19086af4b320b706919d48a4c79081cf9/pytest-8.3.2-py3-none-any.whl#sha256=4ba08f9ae7dcf84ded419494d229b48d0903ea6407b030eaec46df5e6a73bba5 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl#sha256=70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6 # pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 From 5cd1c830f98e880508bd2711e297ddc2f9cf9876 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 29 Jul 2024 02:19:58 -0700 Subject: [PATCH 190/275] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29577) Co-authored-by: Lock file bot --- build_tools/azure/debian_atlas_32bit_lock.txt | 8 ++--- ...latest_conda_forge_mkl_linux-64_conda.lock | 36 +++++++++---------- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 10 +++--- ...test_conda_mkl_no_openmp_osx-64_conda.lock | 4 +-- ...st_pip_openblas_pandas_linux-64_conda.lock | 20 +++++------ ...onda_defaults_openblas_linux-64_conda.lock | 4 +-- .../pymin_conda_forge_mkl_win-64_conda.lock | 24 ++++++------- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 22 ++++++------ build_tools/azure/ubuntu_atlas_lock.txt | 4 +-- build_tools/circle/doc_linux-64_conda.lock | 20 +++++------ .../doc_min_dependencies_linux-64_conda.lock | 20 +++++------ 11 files changed, 85 insertions(+), 87 deletions(-) diff --git a/build_tools/azure/debian_atlas_32bit_lock.txt b/build_tools/azure/debian_atlas_32bit_lock.txt index 960d9ae329823..d9d6da6394dfb 100644 --- a/build_tools/azure/debian_atlas_32bit_lock.txt +++ b/build_tools/azure/debian_atlas_32bit_lock.txt @@ -1,5 +1,5 @@ # -# This file is autogenerated by pip-compile with Python 3.9 +# This file is autogenerated by pip-compile with Python 3.11 # by the following command: # # pip-compile --output-file=build_tools/azure/debian_atlas_32bit_lock.txt build_tools/azure/debian_atlas_32bit_requirements.txt @@ -14,7 +14,7 @@ iniconfig==2.0.0 # via pytest joblib==1.2.0 # via -r build_tools/azure/debian_atlas_32bit_requirements.txt -meson==1.5.0 +meson==1.5.1 # via meson-python meson-python==0.16.0 # via -r build_tools/azure/debian_atlas_32bit_requirements.txt @@ -40,6 +40,4 @@ pytest-cov==2.9.0 threadpoolctl==3.1.0 # via -r build_tools/azure/debian_atlas_32bit_requirements.txt tomli==2.0.1 - # via - # meson-python - # pytest + # via pytest diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index 81d820f94e504..72c211ed8d51d 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -20,7 +20,7 @@ https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.12-h4ab18f5_0.conda https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2#d9c69a24ad678ffce24c6543a0176b00 https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.23-h4ab18f5_0.conda#94d61ae2b2b701008a9d52ce6bbead27 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 -https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.32.2-h4bc722e_0.conda#8024af1ee7078e37fa3101c0a0296af2 +https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.32.3-h4bc722e_0.conda#7624e34ee6baebfc80d67bac76cc9d9d https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.22.5-h59595ed_2.conda#985f2f453fb72408d6b6f1be0f324033 https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3 https://conda.anaconda.org/conda-forge/linux-64/lame-3.100-h166bdaf_1003.tar.bz2#a8832b479f93521a9e7b5b743803be51 @@ -111,7 +111,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.19.0-hb90f79a_1.cond https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-h1dd3fc0_3.conda#66f03896ffbe1a110ffda05c7a856504 https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.7-h4c95cb1_3.conda#0ac9aff6010a7751961c8e4b863a40e7 https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.8-hf5423f3_0.conda#322be9d39e030673e105b0abb320514e -https://conda.anaconda.org/conda-forge/linux-64/mpfr-4.2.1-h9458935_1.conda#8083b20f566639c22f78bcd6ca35b276 +https://conda.anaconda.org/conda-forge/linux-64/mpfr-4.2.1-h38ae2d0_2.conda#168e18a2bba4f8520e6c5e38982f5847 https://conda.anaconda.org/conda-forge/linux-64/mysql-libs-8.3.0-ha479ceb_5.conda#82776ee8145b9d1fd6546604de4b351d https://conda.anaconda.org/conda-forge/linux-64/nss-3.102-h593d115_0.conda#40e5e48c55a45621c4399ca9236406b7 https://conda.anaconda.org/conda-forge/linux-64/python-3.12.4-h194c7f8_0_cpython.conda#d73490214f536cccb5819e9873048c92 @@ -120,7 +120,7 @@ https://conda.anaconda.org/conda-forge/linux-64/xcb-util-keysyms-0.4.1-hb711507_ https://conda.anaconda.org/conda-forge/linux-64/xcb-util-renderutil-0.3.10-hb711507_0.conda#0e0cbe0564d03a99afd5fd7b362feecd https://conda.anaconda.org/conda-forge/linux-64/xcb-util-wm-0.4.2-hb711507_0.conda#608e0ef8256b81d04456e8d211eee3e8 https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.9-hb711507_1.conda#4a6d410296d7e39f00bacdee7df046e9 -https://conda.anaconda.org/conda-forge/noarch/array-api-compat-1.7.1-pyhd8ed1ab_0.conda#8791d81c38f676a7c08c76546800bf70 +https://conda.anaconda.org/conda-forge/noarch/array-api-compat-1.8-pyhd8ed1ab_0.conda#1178a75b8f6f260ac4b4436979754278 https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.4.2-h7671281_15.conda#3b45b0da170f515de8be68155e14955a 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https://conda.anaconda.org/conda-forge/noarch/compiler-rt_osx-64-16.0.6-ha38d28d_2.conda#7a46507edc35c6c8818db0adaf8d787f https://conda.anaconda.org/conda-forge/osx-64/liblapacke-3.9.0-20_osx64_mkl.conda#124ae8e384268a8da66f1d64114a1eda -https://conda.anaconda.org/conda-forge/osx-64/numpy-2.0.0-py312h8813227_0.conda#814dba0ed2de85d89e09a5b811024a86 +https://conda.anaconda.org/conda-forge/osx-64/numpy-2.0.1-py312h8813227_0.conda#7f239fbf9d9355f86529a35af0b24d29 https://conda.anaconda.org/conda-forge/osx-64/blas-devel-3.9.0-20_osx64_mkl.conda#cc3260179093918b801e373c6e888e02 https://conda.anaconda.org/conda-forge/osx-64/compiler-rt-16.0.6-ha38d28d_2.conda#3b9e8c5c63b8e86234f499490acd85c2 https://conda.anaconda.org/conda-forge/osx-64/contourpy-1.2.1-py312h9230928_0.conda#079df34ce7c71259cfdd394645370891 diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock index ad56be7b9584a..f9827208ac958 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock @@ -1,11 +1,11 @@ # Generated by conda-lock. # platform: osx-64 -# input_hash: 3e9254c5d9da20709b9379b68863a871e29920e1e97554971f4c32ab37e85be9 +# input_hash: 5845cb6c37d0833b4f2f61e276bf4f86f98aa0e19d9c74bfbc8ddcc73d251139 @EXPLICIT https://repo.anaconda.com/pkgs/main/osx-64/blas-1.0-mkl.conda#cb2c87e85ac8e0ceae776d26d4214c8a https://repo.anaconda.com/pkgs/main/osx-64/bzip2-1.0.8-h6c40b1e_6.conda#96224786021d0765ce05818fa3c59bdb https://repo.anaconda.com/pkgs/main/osx-64/ca-certificates-2024.7.2-hecd8cb5_0.conda#297cfad0c0eac53e5ac75674828eedd9 -https://repo.anaconda.com/pkgs/main/osx-64/jpeg-9e-h6c40b1e_1.conda#fc3e61fa41309946c9283fe8737d7f41 +https://repo.anaconda.com/pkgs/main/osx-64/jpeg-9e-h46256e1_2.conda#5f0bfd93528771ebc3e340ac1c91a4cd https://repo.anaconda.com/pkgs/main/osx-64/libbrotlicommon-1.0.9-h6c40b1e_8.conda#8e86dfa34b08bc664b19e1499e5465b8 https://repo.anaconda.com/pkgs/main/osx-64/libcxx-14.0.6-h9765a3e_0.conda#387757bb354ae9042370452cd0fb5627 https://repo.anaconda.com/pkgs/main/osx-64/libdeflate-1.17-hb664fd8_1.conda#b6116b8db33ea6a5b5287dae70d4a913 diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index 9dfa936fe97ac..6644bfb55e798 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -26,7 +26,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/setuptools-69.5.1-py311h06a4308_0.c https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py311h06a4308_0.conda#ec915b5ff89bdbcea7ef943d9e296967 https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py311h06a4308_0.conda#84aef4db159f0daf63751d87d7d6ca56 # pip alabaster @ https://files.pythonhosted.org/packages/32/34/d4e1c02d3bee589efb5dfa17f88ea08bdb3e3eac12bc475462aec52ed223/alabaster-0.7.16-py3-none-any.whl#sha256=b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92 -# pip array-api-compat @ https://files.pythonhosted.org/packages/05/ae/2f11031bb9f819f6efaaa66b720b37928fbb0087161fcbae3465ae374a18/array_api_compat-1.7.1-py3-none-any.whl#sha256=6974f51775972f39edbca39e08f1c2e43c51401c093a0fea5ac7159875095d8a +# pip array-api-compat @ https://files.pythonhosted.org/packages/0f/22/8228be1d3c6d4ffcf05cd89872ce65c1317b2af98d34b9d89b247d8d49cb/array_api_compat-1.8-py3-none-any.whl#sha256=140204454086264d37263bc4afe1182b428353e94e9edcc38d17b009863c982d # pip babel @ https://files.pythonhosted.org/packages/27/45/377f7e32a5c93d94cd56542349b34efab5ca3f9e2fd5a68c5e93169aa32d/Babel-2.15.0-py3-none-any.whl#sha256=08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb # pip certifi @ https://files.pythonhosted.org/packages/1c/d5/c84e1a17bf61d4df64ca866a1c9a913874b4e9bdc131ec689a0ad013fb36/certifi-2024.7.4-py3-none-any.whl#sha256=c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90 # pip charset-normalizer @ https://files.pythonhosted.org/packages/40/26/f35951c45070edc957ba40a5b1db3cf60a9dbb1b350c2d5bef03e01e61de/charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8 @@ -42,7 +42,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py311h06a4308_0.conda#84ae # pip joblib @ https://files.pythonhosted.org/packages/91/29/df4b9b42f2be0b623cbd5e2140cafcaa2bef0759a00b7b70104dcfe2fb51/joblib-1.4.2-py3-none-any.whl#sha256=06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6 # pip kiwisolver @ https://files.pythonhosted.org/packages/17/ba/17a706b232308e65f57deeccae503c268292e6a091313f6ce833a23093ea/kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=040c1aebeda72197ef477a906782b5ab0d387642e93bda547336b8957c61022e # pip markupsafe @ https://files.pythonhosted.org/packages/97/18/c30da5e7a0e7f4603abfc6780574131221d9148f323752c2755d48abad30/MarkupSafe-2.1.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b91c037585eba9095565a3556f611e3cbfaa42ca1e865f7b8015fe5c7336d5a5 -# pip meson @ https://files.pythonhosted.org/packages/c4/cd/47e45d3abada2e1edb9e2ca9334be186d2e7f97a01b09b5b82799c4d7bd3/meson-1.5.0-py3-none-any.whl#sha256=52b34f4903b882df52ad0d533146d4b992c018ea77399f825579737672ae7b20 +# pip meson @ https://files.pythonhosted.org/packages/b7/33/513a9ca4fd5892463abb38592105b78fd425214f7983033633e2e48cbd30/meson-1.5.1-py3-none-any.whl#sha256=5531e24e6cfd6000bf1c712793cf28dff032841370b1a3b941a894e4fde46e5a # pip networkx @ https://files.pythonhosted.org/packages/38/e9/5f72929373e1a0e8d142a130f3f97e6ff920070f87f91c4e13e40e0fba5a/networkx-3.3-py3-none-any.whl#sha256=28575580c6ebdaf4505b22c6256a2b9de86b316dc63ba9e93abde3d78dfdbcf2 # pip ninja @ https://files.pythonhosted.org/packages/6d/92/8d7aebd4430ab5ff65df2bfee6d5745f95c004284db2d8ca76dcbfd9de47/ninja-1.11.1.1-py2.py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl#sha256=84502ec98f02a037a169c4b0d5d86075eaf6afc55e1879003d6cab51ced2ea4b # pip numpy @ https://files.pythonhosted.org/packages/ef/27/39622993e8688a1f05898a3c3b2836b856f79c06637ebd4b71cb35cc9b18/numpy-2.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=15eb4eca47d36ec3f78cde0a3a2ee24cf05ca7396ef808dda2c0ddad7c2bde67 @@ -54,12 +54,12 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py311h06a4308_0.conda#84ae # pip pytz @ https://files.pythonhosted.org/packages/9c/3d/a121f284241f08268b21359bd425f7d4825cffc5ac5cd0e1b3d82ffd2b10/pytz-2024.1-py2.py3-none-any.whl#sha256=328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319 # pip six @ https://files.pythonhosted.org/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl#sha256=8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254 # pip snowballstemmer @ https://files.pythonhosted.org/packages/ed/dc/c02e01294f7265e63a7315fe086dd1df7dacb9f840a804da846b96d01b96/snowballstemmer-2.2.0-py2.py3-none-any.whl#sha256=c8e1716e83cc398ae16824e5572ae04e0d9fc2c6b985fb0f900f5f0c96ecba1a -# pip sphinxcontrib-applehelp @ https://files.pythonhosted.org/packages/56/89/fea3fbf6785b388e6cb8a1beaf62f96e80b37311bdeed6e133388a732426/sphinxcontrib_applehelp-1.0.8-py3-none-any.whl#sha256=cb61eb0ec1b61f349e5cc36b2028e9e7ca765be05e49641c97241274753067b4 -# pip sphinxcontrib-devhelp @ https://files.pythonhosted.org/packages/a0/52/1049d918d1d1c72857d285c3f0c64c1cbe0be394ce1c93a3d2aa4f39fe3b/sphinxcontrib_devhelp-1.0.6-py3-none-any.whl#sha256=6485d09629944511c893fa11355bda18b742b83a2b181f9a009f7e500595c90f -# pip sphinxcontrib-htmlhelp @ https://files.pythonhosted.org/packages/a4/b4/6ebbdc57b5b216b400b355f34ef669e9b6b5c31a6ede8cf5ac36f9e8fc0c/sphinxcontrib_htmlhelp-2.0.6-py3-none-any.whl#sha256=1b9af5a2671a61410a868fce050cab7ca393c218e6205cbc7f590136f207395c +# pip sphinxcontrib-applehelp @ https://files.pythonhosted.org/packages/5d/85/9ebeae2f76e9e77b952f4b274c27238156eae7979c5421fba91a28f4970d/sphinxcontrib_applehelp-2.0.0-py3-none-any.whl#sha256=4cd3f0ec4ac5dd9c17ec65e9ab272c9b867ea77425228e68ecf08d6b28ddbdb5 +# pip sphinxcontrib-devhelp @ https://files.pythonhosted.org/packages/35/7a/987e583882f985fe4d7323774889ec58049171828b58c2217e7f79cdf44e/sphinxcontrib_devhelp-2.0.0-py3-none-any.whl#sha256=aefb8b83854e4b0998877524d1029fd3e6879210422ee3780459e28a1f03a8a2 +# pip sphinxcontrib-htmlhelp @ https://files.pythonhosted.org/packages/0a/7b/18a8c0bcec9182c05a0b3ec2a776bba4ead82750a55ff798e8d406dae604/sphinxcontrib_htmlhelp-2.1.0-py3-none-any.whl#sha256=166759820b47002d22914d64a075ce08f4c46818e17cfc9470a9786b759b19f8 # pip sphinxcontrib-jsmath @ https://files.pythonhosted.org/packages/c2/42/4c8646762ee83602e3fb3fbe774c2fac12f317deb0b5dbeeedd2d3ba4b77/sphinxcontrib_jsmath-1.0.1-py2.py3-none-any.whl#sha256=2ec2eaebfb78f3f2078e73666b1415417a116cc848b72e5172e596c871103178 -# pip sphinxcontrib-qthelp @ https://files.pythonhosted.org/packages/cf/91/cec9416d27ebe9b8aa83f014a1ac8402c729aed791da67704e10bb2c8f33/sphinxcontrib_qthelp-1.0.8-py3-none-any.whl#sha256=323d6acc4189af76dfe94edd2a27d458902319b60fcca2aeef3b2180c106a75f -# pip sphinxcontrib-serializinghtml @ https://files.pythonhosted.org/packages/38/24/228bb903ea87b9e08ab33470e6102402a644127108c7117ac9c00d849f82/sphinxcontrib_serializinghtml-1.1.10-py3-none-any.whl#sha256=326369b8df80a7d2d8d7f99aa5ac577f51ea51556ed974e7716cfd4fca3f6cb7 +# pip sphinxcontrib-qthelp @ https://files.pythonhosted.org/packages/27/83/859ecdd180cacc13b1f7e857abf8582a64552ea7a061057a6c716e790fce/sphinxcontrib_qthelp-2.0.0-py3-none-any.whl#sha256=b18a828cdba941ccd6ee8445dbe72ffa3ef8cbe7505d8cd1fa0d42d3f2d5f3eb +# pip sphinxcontrib-serializinghtml @ https://files.pythonhosted.org/packages/52/a7/d2782e4e3f77c8450f727ba74a8f12756d5ba823d81b941f1b04da9d033a/sphinxcontrib_serializinghtml-2.0.0-py3-none-any.whl#sha256=6e2cb0eef194e10c27ec0023bfeb25badbbb5868244cf5bc5bdc04e4464bf331 # pip tabulate @ https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl#sha256=024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f # pip threadpoolctl @ https://files.pythonhosted.org/packages/4b/2c/ffbf7a134b9ab11a67b0cf0726453cedd9c5043a4fe7a35d1cefa9a1bcfb/threadpoolctl-3.5.0-py3-none-any.whl#sha256=56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467 # pip tzdata @ https://files.pythonhosted.org/packages/65/58/f9c9e6be752e9fcb8b6a0ee9fb87e6e7a1f6bcab2cdc73f02bb7ba91ada0/tzdata-2024.1-py2.py3-none-any.whl#sha256=9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252 @@ -70,12 +70,12 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py311h06a4308_0.conda#84ae # pip jinja2 @ https://files.pythonhosted.org/packages/31/80/3a54838c3fb461f6fec263ebf3a3a41771bd05190238de3486aae8540c36/jinja2-3.1.4-py3-none-any.whl#sha256=bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d # pip lazy-loader @ 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https://files.pythonhosted.org/packages/f2/3d/4f152cf694aec100ab63b4a5547f2dbfbea59ab39d9375c89bed9775e47d/lightgbm-4.4.0-py3-none-manylinux_2_28_x86_64.whl#sha256=8700b41f637717d36763a282d280b8d4722a87103030b7f0f373b96da0225022 +# pip tifffile @ https://files.pythonhosted.org/packages/05/a9/f7b3fd6c73e0ac29e7f9c9c86c3b7367b182a3dd60495da7b8129e6df681/tifffile-2024.7.24-py3-none-any.whl#sha256=f5cce1a915c37bc44ae4a792e3b42c07a30a3fa88406f5c6060a3de076487ed1 +# pip lightgbm @ https://files.pythonhosted.org/packages/4e/19/1b928cad70a4e1a3e2c37d5417ca2182510f2451eaadb6c91cd9ec692cae/lightgbm-4.5.0-py3-none-manylinux_2_28_x86_64.whl#sha256=960a0e7c077de0ca3053f1325d3edfc92ea815acf5176adcacdea0f635aeef9b # pip matplotlib @ https://files.pythonhosted.org/packages/b8/63/cef838d92c1918ae28afd12b8aeaa9c104a0686cf6447aa0546f7c6dd1f0/matplotlib-3.9.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ab38a4f3772523179b2f772103d8030215b318fef6360cb40558f585bf3d017f # pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 # pip pandas @ https://files.pythonhosted.org/packages/fc/a5/4d82be566f069d7a9a702dcdf6f9106df0e0b042e738043c0cc7ddd7e3f6/pandas-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=6d2123dc9ad6a814bcdea0f099885276b31b24f7edf40f6cdbc0912672e22eee diff --git a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock index 114a61e8d48b7..abc6bd3f94998 100644 --- a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock @@ -17,7 +17,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h5eee18b_6.conda#f21a3f 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https://repo.anaconda.com/pkgs/main/linux-64/qt-main-5.15.2-h53bd1ea_10.conda#bd0c79e82df6323f638bdcb871891b61 https://repo.anaconda.com/pkgs/main/linux-64/pyqt-5.15.10-py39h6a678d5_0.conda#52da5ff9b1144b078d2f41bab0b213f2 https://repo.anaconda.com/pkgs/main/linux-64/matplotlib-3.3.4-py39h06a4308_0.conda#384fc5e01ebfcf30e7161119d3029b5a -# pip meson @ https://files.pythonhosted.org/packages/c4/cd/47e45d3abada2e1edb9e2ca9334be186d2e7f97a01b09b5b82799c4d7bd3/meson-1.5.0-py3-none-any.whl#sha256=52b34f4903b882df52ad0d533146d4b992c018ea77399f825579737672ae7b20 +# pip meson @ https://files.pythonhosted.org/packages/b7/33/513a9ca4fd5892463abb38592105b78fd425214f7983033633e2e48cbd30/meson-1.5.1-py3-none-any.whl#sha256=5531e24e6cfd6000bf1c712793cf28dff032841370b1a3b941a894e4fde46e5a # pip threadpoolctl @ 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+https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.2-pyhd8ed1ab_0.conda#e010a224b90f1f623a917c35addbb924 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/win-64/sip-6.7.12-py39h99910a6_0.conda#0cc5774390ada632ed7975203057c91c https://conda.anaconda.org/conda-forge/win-64/tbb-2021.12.0-hc790b64_3.conda#a16e2a639e87c554abee5192ce6ee308 @@ -96,24 +96,24 @@ https://conda.anaconda.org/conda-forge/win-64/fonttools-4.53.1-py39ha55e580_0.co https://conda.anaconda.org/conda-forge/win-64/glib-2.80.3-h7025463_1.conda#13ce8fd2eb07f41c7108f7ad7bb0062e https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.0-pyhd8ed1ab_0.conda#dcbadab7a68738a028e195ab68ab2d2e https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 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b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock @@ -119,7 +119,7 @@ https://conda.anaconda.org/conda-forge/noarch/imagesize-1.4.1-pyhd8ed1ab_0.tar.b https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda#f800d2da156d08e289b14e87e43c1ae5 https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.5-py39h7633fee_1.conda#c9f74d717e5a2847a9f8b779c54130f2 https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.16-hb7c19ff_0.conda#51bb7010fc86f70eee639b4bb7a894f5 -https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-22_linux64_openblas.conda#1a2a0cd3153464fee6646f3dd6dad9b8 +https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-23_linux64_openblas.conda#96c8450a40aa2b9733073a9460de972c https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-h4637d8d_4.conda#d4529f4dff3057982a7617c7ac58fde3 https://conda.anaconda.org/conda-forge/linux-64/libllvm15-15.0.7-hb3ce162_4.conda#8a35df3cbc0c8b12cc8af9473ae75eef https://conda.anaconda.org/conda-forge/linux-64/libllvm18-18.1.8-h8b73ec9_1.conda#16d94b3586ef3558e5a583598524deb4 @@ -159,43 +159,43 @@ https://conda.anaconda.org/conda-forge/linux-64/cffi-1.16.0-py39h7a31438_0.conda https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.53.1-py39hcd6043d_0.conda#297804eca6ea16a835a869699095de1c https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.3-h8a4344b_1.conda#a3acc4920c9ca19cb6b295028d606477 https://conda.anaconda.org/conda-forge/noarch/h2-4.1.0-pyhd8ed1ab_0.tar.bz2#b748fbf7060927a6e82df7cb5ee8f097 -https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.0.0-pyha770c72_0.conda#3286556cdd99048d198f72c3f6f69103 +https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.2.0-pyha770c72_0.conda#c261d14fc7f49cdd403868998a18c318 https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1ab_0.conda#c5d3907ad8bd7bf557521a1833cf7e6d https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda#7b86ecb7d3557821c649b3c31e3eb9f2 https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f -https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-22_linux64_openblas.conda#4b31699e0ec5de64d5896e580389c9a1 +https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-23_linux64_openblas.conda#eede29b40efa878cbe5bdcb767e97310 https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp15-15.0.7-default_h127d8a8_5.conda#d0a9633b53cdc319b8a1a532ae7822b8 https://conda.anaconda.org/conda-forge/linux-64/libclang13-18.1.8-default_h9def88c_1.conda#04c8c481b30c3fe62bec148fa4a75857 https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda#ee48bf17cc83a00f59ca1494d5646869 https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.50-h4f305b6_0.conda#0d7ff1a8e69565ca3add6925e18e708f -https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-22_linux64_openblas.conda#b083767b6c877e24ee597d93b87ab838 +https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-23_linux64_openblas.conda#2af0879961951987e464722fd00ec1e0 https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.7.0-h2c5496b_1.conda#e2eaefa4de2b7237af7c907b8bbc760a -https://conda.anaconda.org/conda-forge/noarch/meson-1.5.0-pyhd8ed1ab_0.conda#9d971c5bf99aed063664d6650e7e7ed8 +https://conda.anaconda.org/conda-forge/noarch/meson-1.5.1-pyhd8ed1ab_0.conda#a87a68d26db0c5bea5cab1f8d7772112 https://conda.anaconda.org/conda-forge/linux-64/pillow-10.4.0-py39h16a7006_0.conda#d9a6b19174a6cf5185296b16f781951f https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 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-https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-22_linux64_openblas.conda#1fd156abd41a4992835952f6f4d951d0 +https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-23_linux64_openblas.conda#89d7bcdb1e9a72a73e36d8e29d2a2beb https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.conda#ef1910918dd895516a769ed36b5b3a4e https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 -https://conda.anaconda.org/conda-forge/linux-64/numpy-2.0.0-py39ha0965c0_0.conda#b411be2728ba5711fc9bcdb0efa2db71 +https://conda.anaconda.org/conda-forge/linux-64/numpy-2.0.1-py39h2fd3214_0.conda#2c69819400d3318cf74f831811ab066f https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5.conda#93aff412f3e49fdb43361c0215cbd72d https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_0.conda#b39568655c127a9c4a44d178ac99b6d0 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https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.2-pyhd8ed1ab_1.conda#e804c43f58255e977093a2298e442bb8 -https://conda.anaconda.org/conda-forge/linux-64/blas-2.122-openblas.conda#5065468105542a8b23ea47bd8b6fa55f +https://conda.anaconda.org/conda-forge/linux-64/blas-2.123-openblas.conda#7f4b3ea1cdd6e50dca2a226abda6e2d9 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.9.1-py39hd75cb13_0.conda#31dbb5b27008fabf74d67da272f5e002 https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-client-17.0-hb77b528_0.conda#07f45f1be1c25345faddb8db0de8039b https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.2.1-py39h85c637f_0.conda#0bfaf33b7ebdbadc77bf9a67e281c0b1 @@ -203,7 +203,7 @@ https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.8-h320f8da_24.conda#bec111b67cb8dc63277c6af65d214044 https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.9-py39h52134e7_5.conda#e1f148e57d071b09187719df86f513c1 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.9.1-py39hf3d152e_0.conda#9d2e01516067eadfa9a3471778991e9d -https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.7.0-pyhd8ed1ab_1.conda#66798cbfdcb003d9fbccd92cd08eb3ac +https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.7.0-pyhd8ed1ab_3.conda#4f5a67d2176fe024a7d83b3eb09b8e13 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-1.0.8-pyhd8ed1ab_0.conda#611a35a27914fac3aa37611a6fe40bb5 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-1.0.6-pyhd8ed1ab_0.conda#d7e4954df0d3aea2eacc7835ad12671d https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.0.6-pyhd8ed1ab_0.conda#d6f4b617daa8c677f60c06a3a61e2743 diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt index 0c370ebe27db1..4e4283d56104e 100644 --- a/build_tools/azure/ubuntu_atlas_lock.txt +++ b/build_tools/azure/ubuntu_atlas_lock.txt @@ -14,7 +14,7 @@ iniconfig==2.0.0 # via pytest joblib==1.2.0 # via -r build_tools/azure/ubuntu_atlas_requirements.txt -meson==1.5.0 +meson==1.5.1 # via meson-python meson-python==0.16.0 # via -r build_tools/azure/ubuntu_atlas_requirements.txt @@ -29,7 +29,7 @@ pluggy==1.5.0 # via pytest pyproject-metadata==0.8.0 # via meson-python -pytest==8.3.1 +pytest==8.3.2 # via # -r build_tools/azure/ubuntu_atlas_requirements.txt # pytest-xdist diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index d1c0fe1e6ddf2..786b50b1eae24 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -203,7 +203,7 @@ https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.3-h8a4344b_1.conda#a3a https://conda.anaconda.org/conda-forge/linux-64/gxx-12.4.0-h236703b_0.conda#56cefffbce52071b597fd3eb9208adc9 https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-12.4.0-h8489865_0.conda#5cf73d936678e6805da39b8ba6be263c https://conda.anaconda.org/conda-forge/noarch/h2-4.1.0-pyhd8ed1ab_0.tar.bz2#b748fbf7060927a6e82df7cb5ee8f097 -https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.0.0-pyha770c72_0.conda#3286556cdd99048d198f72c3f6f69103 +https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.2.0-pyha770c72_0.conda#c261d14fc7f49cdd403868998a18c318 https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1ab_0.conda#c5d3907ad8bd7bf557521a1833cf7e6d https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda#7b86ecb7d3557821c649b3c31e3eb9f2 https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f @@ -215,12 +215,12 @@ https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.50-h4f305b6_0.con https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-23_linux64_openblas.conda#2af0879961951987e464722fd00ec1e0 https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.7.0-h2c5496b_1.conda#e2eaefa4de2b7237af7c907b8bbc760a https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhd8ed1ab_0.tar.bz2#8b45f9f2b2f7a98b0ec179c8991a4a9b -https://conda.anaconda.org/conda-forge/noarch/meson-1.5.0-pyhd8ed1ab_0.conda#9d971c5bf99aed063664d6650e7e7ed8 +https://conda.anaconda.org/conda-forge/noarch/meson-1.5.1-pyhd8ed1ab_0.conda#a87a68d26db0c5bea5cab1f8d7772112 https://conda.anaconda.org/conda-forge/linux-64/pillow-10.4.0-py39h16a7006_0.conda#d9a6b19174a6cf5185296b16f781951f https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 -https://conda.anaconda.org/conda-forge/noarch/plotly-5.22.0-pyhd8ed1ab_0.conda#5b409a5f738e7d76c2b426eddb7e9956 +https://conda.anaconda.org/conda-forge/noarch/plotly-5.23.0-pyhd8ed1ab_0.conda#41e535b9e479c72a6bffc69a4c85837c https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 -https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.1-pyhd8ed1ab_0.conda#b6a3ab8559a42070c6b6c3063faea1ed +https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.2-pyhd8ed1ab_0.conda#e010a224b90f1f623a917c35addbb924 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.7.0-h00ab1b0_1.conda#28de2e073db9ca9b72858bee9fb6f571 @@ -233,7 +233,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-1.11.0-h4ab18f5_1.cond https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-23_linux64_openblas.conda#89d7bcdb1e9a72a73e36d8e29d2a2beb https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.conda#ef1910918dd895516a769ed36b5b3a4e https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 -https://conda.anaconda.org/conda-forge/linux-64/numpy-2.0.0-py39ha0965c0_0.conda#b411be2728ba5711fc9bcdb0efa2db71 +https://conda.anaconda.org/conda-forge/linux-64/numpy-2.0.1-py39h2fd3214_0.conda#2c69819400d3318cf74f831811ab066f https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5.conda#93aff412f3e49fdb43361c0215cbd72d https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_0.conda#b39568655c127a9c4a44d178ac99b6d0 https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py39h623c9ba_0.conda#a19d023682384c637cb356d270c276c0 @@ -264,7 +264,7 @@ https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_2.c https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.9-py39h52134e7_5.conda#e1f148e57d071b09187719df86f513c1 https://conda.anaconda.org/conda-forge/noarch/seaborn-0.13.2-hd8ed1ab_2.conda#a79d8797f62715255308d92d3a91ef2e https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.9.1-py39hf3d152e_0.conda#9d2e01516067eadfa9a3471778991e9d -https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.7.0-pyhd8ed1ab_1.conda#66798cbfdcb003d9fbccd92cd08eb3ac +https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.7.0-pyhd8ed1ab_3.conda#4f5a67d2176fe024a7d83b3eb09b8e13 https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.15.4-pyhd8ed1ab_0.conda#c7c50dd5192caa58a05e6a4248a27acb https://conda.anaconda.org/conda-forge/noarch/sphinx-copybutton-0.5.2-pyhd8ed1ab_0.conda#ac832cc43adc79118cf6e23f1f9b8995 https://conda.anaconda.org/conda-forge/noarch/sphinx-design-0.6.0-pyhd8ed1ab_0.conda#b04f3c04e4f7939c6207dc0c0355f468 @@ -296,7 +296,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip python-json-logger @ https://files.pythonhosted.org/packages/35/a6/145655273568ee78a581e734cf35beb9e33a370b29c5d3c8fee3744de29f/python_json_logger-2.0.7-py3-none-any.whl#sha256=f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd # pip pyyaml @ https://files.pythonhosted.org/packages/7d/39/472f2554a0f1e825bd7c5afc11c817cd7a2f3657460f7159f691fbb37c51/PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c # pip rfc3986-validator @ https://files.pythonhosted.org/packages/9e/51/17023c0f8f1869d8806b979a2bffa3f861f26a3f1a66b094288323fba52f/rfc3986_validator-0.1.1-py2.py3-none-any.whl#sha256=2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9 -# pip rpds-py @ 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https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-12.4.0-h8489865_0.conda#5cf73d936678e6805da39b8ba6be263c https://conda.anaconda.org/conda-forge/noarch/h2-4.1.0-pyhd8ed1ab_0.tar.bz2#b748fbf7060927a6e82df7cb5ee8f097 -https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.0.0-pyha770c72_0.conda#3286556cdd99048d198f72c3f6f69103 +https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.2.0-pyha770c72_0.conda#c261d14fc7f49cdd403868998a18c318 https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda#7b86ecb7d3557821c649b3c31e3eb9f2 https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp15-15.0.7-default_h127d8a8_5.conda#d0a9633b53cdc319b8a1a532ae7822b8 @@ -213,13 +213,13 @@ https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda#e https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.50-h4f305b6_0.conda#0d7ff1a8e69565ca3add6925e18e708f https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.7.0-h2c5496b_1.conda#e2eaefa4de2b7237af7c907b8bbc760a https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhd8ed1ab_0.tar.bz2#8b45f9f2b2f7a98b0ec179c8991a4a9b -https://conda.anaconda.org/conda-forge/noarch/meson-1.5.0-pyhd8ed1ab_0.conda#9d971c5bf99aed063664d6650e7e7ed8 +https://conda.anaconda.org/conda-forge/noarch/meson-1.5.1-pyhd8ed1ab_0.conda#a87a68d26db0c5bea5cab1f8d7772112 https://conda.anaconda.org/conda-forge/noarch/partd-1.4.2-pyhd8ed1ab_0.conda#0badf9c54e24cecfb0ad2f99d680c163 https://conda.anaconda.org/conda-forge/linux-64/pillow-10.4.0-py39h16a7006_0.conda#d9a6b19174a6cf5185296b16f781951f https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/plotly-5.14.0-pyhd8ed1ab_0.conda#6a7bcc42ef58dd6cf3da9333ea102433 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 -https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.1-pyhd8ed1ab_0.conda#b6a3ab8559a42070c6b6c3063faea1ed +https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.2-pyhd8ed1ab_0.conda#e010a224b90f1f623a917c35addbb924 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.12.0-h434a139_3.conda#c667c11d1e488a38220ede8a34441bff @@ -227,7 +227,7 @@ https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.7.0-h00ab1b0_1.co https://conda.anaconda.org/conda-forge/linux-64/fortran-compiler-1.7.0-heb67821_1.conda#cf4b0e7c4c78bb0662aed9b27c414a3c https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.5-haf2f30d_0.conda#c5252c02592373fa8caf5a5327165a89 https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-9.0.0-hfac3d4d_0.conda#c7b47c64af53e8ecee01d101eeab2342 -https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-8.0.0-hd8ed1ab_0.conda#5f8c8ebbe6413a7838cf6ecf14d5d31b +https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-8.2.0-hd8ed1ab_0.conda#0fd030dce707a6654472cf7619b0b01b https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-1.11.0-h4ab18f5_1.conda#14858a47d4cc995892e79f2b340682d7 https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.conda#ef1910918dd895516a769ed36b5b3a4e https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 @@ -238,19 +238,19 @@ https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py39h623c9ba_0. https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_1.conda#d8d07866ac3b5b6937213c89a1874f08 https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.7.1-pyhd8ed1ab_0.conda#80f7ce024289c333fdc5ad54a194fc86 https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.5-hbaaba92_0.conda#4a485842570569ba754863b2c083b346 -https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-22_linux64_mkl.conda#eb6deb4ba6f92ea3f31c09cb8b764738 +https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-23_linux64_mkl.conda#5bdaf561cf48f95093dedaa665083874 https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-255-h3516f8a_1.conda#3366af27f0b593544a6cd453c7932ac5 https://conda.anaconda.org/conda-forge/linux-64/mkl-devel-2024.1.0-ha770c72_693.conda#7f422e2cf549a3fb920c95288393870d https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.2-pyhd8ed1ab_1.conda#e804c43f58255e977093a2298e442bb8 -https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-22_linux64_mkl.conda#d6f942423116553f068b2f2d93ffea2e -https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-22_linux64_mkl.conda#4edf2e7ce63920e4f539d12e32fb478e +https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-23_linux64_mkl.conda#e0219f401906533e26346d8634ef35f4 +https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-23_linux64_mkl.conda#157fa1f6fa92aa21c7d55f3c701afc9f https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-client-17.0-hb77b528_0.conda#07f45f1be1c25345faddb8db0de8039b https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda#5ede4753180c7a550a443c430dc8ab52 -https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-22_linux64_mkl.conda#aa0a5a70e1c957d5911e76ac98e471e1 +https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-23_linux64_mkl.conda#11f5d6ba1108647192fffe2baa042094 https://conda.anaconda.org/conda-forge/linux-64/numpy-1.19.5-py39hd249d9e_3.tar.bz2#0cf333996ebdeeba8d1c8c1c0ee9eff9 https://conda.anaconda.org/conda-forge/noarch/pooch-1.6.0-pyhd8ed1ab_0.tar.bz2#6429e1d1091c51f626b5dcfdd38bf429 https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.8-h320f8da_24.conda#bec111b67cb8dc63277c6af65d214044 -https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-22_linux64_mkl.conda#3cb0e51433c88d2f4cdfb50c5c08a683 +https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-23_linux64_mkl.conda#c8f8d0ebf2e7fd3a90ec68e3bb008995 https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py39h34cef29_2.conda#d3ee926e63ebd5b44ebc984dff020305 https://conda.anaconda.org/conda-forge/noarch/imageio-2.34.2-pyh12aca89_0.conda#97ad994fae55dce96bd397054b32e41a https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.3.4-py39h2fa2bec_0.tar.bz2#9ec0b2186fab9121c54f4844f93ee5b7 @@ -260,7 +260,7 @@ https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.23-py39ha963410_0.co https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.9-py39h52134e7_5.conda#e1f148e57d071b09187719df86f513c1 https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.6.0-py39hd92a3bb_0.conda#32e26e16f60c568b17a82e3033a4d309 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.6.0-py39hee8e79c_0.tar.bz2#3afcb78281836e61351a2924f3230060 -https://conda.anaconda.org/conda-forge/linux-64/blas-2.122-mkl.conda#ead856637ff8a7feba572e2cf23b453b +https://conda.anaconda.org/conda-forge/linux-64/blas-2.123-mkl.conda#f11a9d598ec5b312fad52789fa415c66 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.3.4-py39hf3d152e_0.tar.bz2#cbaec993375a908bbe506dc7328d747c https://conda.anaconda.org/conda-forge/linux-64/pyamg-4.2.3-py39hac2352c_1.tar.bz2#6fb0628d6195d8b6caa2422d09296399 https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.12.2-pyhd8ed1ab_0.conda#cf88f3a1c11536bc3c10c14ad00ccc42 From c6800358426c795af03e237d308377e61ae92a6a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 30 Jul 2024 12:09:55 +0200 Subject: [PATCH 191/275] CI Use conda-forge for min-dependencies build and add polars and pandas (#29502) Co-authored-by: Tim Head --- azure-pipelines.yml | 6 +- ...onda_defaults_openblas_linux-64_conda.lock | 104 ----------- ...openblas_min_dependencies_environment.yml} | 15 +- ...nblas_min_dependencies_linux-64_conda.lock | 172 ++++++++++++++++++ .../doc_min_dependencies_environment.yml | 2 +- .../doc_min_dependencies_linux-64_conda.lock | 14 +- .../update_environments_and_lock_files.py | 20 +- pyproject.toml | 5 +- sklearn/_min_dependencies.py | 2 +- sklearn/utils/_set_output.py | 5 +- sklearn/utils/fixes.py | 24 +++ 11 files changed, 232 insertions(+), 137 deletions(-) delete mode 100644 build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock rename build_tools/azure/{pymin_conda_defaults_openblas_environment.yml => pymin_conda_forge_openblas_min_dependencies_environment.yml} (69%) create mode 100644 build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock diff --git a/azure-pipelines.yml b/azure-pipelines.yml index 1f8819d264d77..c792db42ab9a1 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -232,10 +232,10 @@ jobs: not(contains(dependencies['git_commit']['outputs']['commit.message'], '[ci skip]')) ) matrix: - # Linux + Python 3.9 build with OpenBLAS and without pandas - pymin_conda_defaults_openblas: + # Linux + Python 3.9 build with minimum supported version of dependencies + pymin_conda_forge_openblas_min_dependencies: DISTRIB: 'conda' - LOCK_FILE: './build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock' + LOCK_FILE: './build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock' # Enable debug Cython directives to capture IndexError exceptions in # combination with the -Werror::pytest.PytestUnraisableExceptionWarning # flag for pytest. diff --git a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock deleted file mode 100644 index abc6bd3f94998..0000000000000 --- a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock +++ /dev/null @@ -1,104 +0,0 @@ -# Generated by conda-lock. -# platform: linux-64 -# input_hash: 04b8b77032ffbfbea2a84fe8c3e8a5c93a45d1025824f14bc95379a659e60e4b -@EXPLICIT -https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 -https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-openblas.conda#9ddfcaef10d79366c90128f5dc444be8 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-https://repo.anaconda.com/pkgs/main/linux-64/sip-6.7.12-py39h6a678d5_0.conda#6988a3e12fcacfedcac523c1e4c3167c -https://repo.anaconda.com/pkgs/main/linux-64/gst-plugins-base-1.14.1-h6a678d5_1.conda#afd9cbe949d670d24cc0a007aaec1fe1 -https://repo.anaconda.com/pkgs/main/linux-64/matplotlib-base-3.3.4-py39h62a2d02_0.conda#dbab28222c740af8e21a3e5e2882c178 -https://repo.anaconda.com/pkgs/main/linux-64/pytest-cov-4.1.0-py39h06a4308_1.conda#8f41fce21670b120bf7fa8a7883380d9 -https://repo.anaconda.com/pkgs/main/linux-64/pytest-xdist-3.5.0-py39h06a4308_0.conda#e1d7ffcb1ee2ed9a84800f5c4bbbd7ae -https://repo.anaconda.com/pkgs/main/linux-64/scipy-1.7.3-py39hf838250_2.conda#0667ea5ac14d35e26da19a0f068739da -https://repo.anaconda.com/pkgs/main/linux-64/pyamg-4.2.3-py39h79cecc1_0.conda#afc634da8b81dc504179d53d334e6e55 -https://repo.anaconda.com/pkgs/main/linux-64/qt-main-5.15.2-h53bd1ea_10.conda#bd0c79e82df6323f638bdcb871891b61 -https://repo.anaconda.com/pkgs/main/linux-64/pyqt-5.15.10-py39h6a678d5_0.conda#52da5ff9b1144b078d2f41bab0b213f2 -https://repo.anaconda.com/pkgs/main/linux-64/matplotlib-3.3.4-py39h06a4308_0.conda#384fc5e01ebfcf30e7161119d3029b5a -# pip meson @ https://files.pythonhosted.org/packages/b7/33/513a9ca4fd5892463abb38592105b78fd425214f7983033633e2e48cbd30/meson-1.5.1-py3-none-any.whl#sha256=5531e24e6cfd6000bf1c712793cf28dff032841370b1a3b941a894e4fde46e5a -# pip threadpoolctl @ https://files.pythonhosted.org/packages/61/cf/6e354304bcb9c6413c4e02a747b600061c21d38ba51e7e544ac7bc66aecc/threadpoolctl-3.1.0-py3-none-any.whl#sha256=8b99adda265feb6773280df41eece7b2e6561b772d21ffd52e372f999024907b -# pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526 -# pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 diff --git a/build_tools/azure/pymin_conda_defaults_openblas_environment.yml b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml similarity index 69% rename from build_tools/azure/pymin_conda_defaults_openblas_environment.yml rename to build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml index a82ba18e27980..a1bda8231e958 100644 --- a/build_tools/azure/pymin_conda_defaults_openblas_environment.yml +++ b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml @@ -2,22 +2,25 @@ # following script to centralize the configuration for CI builds: # build_tools/update_environments_and_lock_files.py channels: - - defaults + - conda-forge dependencies: - python=3.9 - - numpy=1.21 + - numpy=1.19.5 # min - blas[build=openblas] - - scipy=1.7 + - scipy=1.6.0 # min - cython=3.0.10 # min - joblib=1.2.0 # min + - threadpoolctl=3.1.0 # min - matplotlib=3.3.4 # min + - pandas=1.1.5 # min - pyamg - pytest - pytest-xdist - pillow + - pip + - ninja + - meson-python=0.16.0 # min - pytest-cov - coverage - ccache - - pip - - pip: - - threadpoolctl==3.1.0 # min + - polars=0.20.30 # min diff --git a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock new file mode 100644 index 0000000000000..d0148aebaabcb --- /dev/null +++ b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock @@ -0,0 +1,172 @@ +# Generated by conda-lock. +# platform: linux-64 +# input_hash: 14996a352b0e8e849fc4282e5e6d1bb05e15a036e54586c87a0d30b2792b1c6b +@EXPLICIT +https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 +https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.7.4-hbcca054_0.conda#23ab7665c5f63cfb9f1f6195256daac6 +https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 +https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 +https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb +https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f +https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_7.conda#b80f2f396ca2c28b8c14c437a4ed1e74 +https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.9-4_cp39.conda#bfe4b3259a8ac6cdf0037752904da6a7 +https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 +https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 +https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab +https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.1.0-h77fa898_0.conda#ca0fad6a41ddaef54a153b78eccb5037 +https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.12-h4ab18f5_0.conda#7ed427f0871fd41cb1d9c17727c17589 +https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2#d9c69a24ad678ffce24c6543a0176b00 +https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 +https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.22.5-h59595ed_2.conda#985f2f453fb72408d6b6f1be0f324033 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a/build_tools/circle/doc_min_dependencies_environment.yml +++ b/build_tools/circle/doc_min_dependencies_environment.yml @@ -30,7 +30,7 @@ dependencies: - numpydoc=1.2.0 # min - sphinx-prompt=1.4.0 # min - plotly=5.14.0 # min - - polars=0.20.23 # min + - polars=0.20.30 # min - pooch=1.6.0 # min - sphinx-remove-toctrees=1.0.0.post1 # min - sphinx-design=0.6.0 # min diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index 01f79e505f6d8..49df34c068d09 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: c9e2f9de85f55e6ab811a43c7c2d9b5d7e61a09e812b195c8d11b698b7eac1dd +# input_hash: e41a5bd0186b0b4cf7693057a75793fbd25b1862071b26e8cb937b22dd61d244 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/_sysroot_linux-64_curr_repodata_hack-3-h69a702a_16.conda#1c005af0c6ff22814b7c52ee448d4bea @@ -177,7 +177,7 @@ https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5 https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-2.2.0-pyhd8ed1ab_0.tar.bz2#4d22a9315e78c6827f806065957d566e https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.5-pyhd8ed1ab_1.conda#3f144b2c34f8cb5a9abd9ed23a39c561 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_0.conda#da1d979339e2714c30a8e806a33ec087 -https://conda.anaconda.org/conda-forge/noarch/tenacity-8.5.0-pyhd8ed1ab_0.conda#354cbc1244395cabbaec2617906d3a27 +https://conda.anaconda.org/conda-forge/noarch/tenacity-9.0.0-pyhd8ed1ab_0.conda#42af51ad3b654ece73572628ad2882ae https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_0.tar.bz2#f832c45a477c78bebd107098db465095 https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96 @@ -256,7 +256,7 @@ https://conda.anaconda.org/conda-forge/noarch/imageio-2.34.2-pyh12aca89_0.conda# https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.3.4-py39h2fa2bec_0.tar.bz2#9ec0b2186fab9121c54f4844f93ee5b7 https://conda.anaconda.org/conda-forge/linux-64/pandas-1.1.5-py39hde0f152_0.tar.bz2#79fc4b5b3a865b90dd3701cecf1ad33c https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.6-pyhd8ed1ab_0.conda#a5b55d1cb110cdcedc748b5c3e16e687 -https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.23-py39ha963410_0.conda#4871f09d653e979d598d2d4cd5fa868d +https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.30-py39ha963410_0.conda#322084e8890afc27fcca6df7a528df25 https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.9-py39h52134e7_5.conda#e1f148e57d071b09187719df86f513c1 https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.6.0-py39hd92a3bb_0.conda#32e26e16f60c568b17a82e3033a4d309 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.6.0-py39hee8e79c_0.tar.bz2#3afcb78281836e61351a2924f3230060 @@ -275,10 +275,10 @@ https://conda.anaconda.org/conda-forge/noarch/sphinx-design-0.6.0-pyhd8ed1ab_0.c https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.16.0-pyhd8ed1ab_0.conda#add28691ee89e875b190eda07929d5d4 https://conda.anaconda.org/conda-forge/noarch/sphinx-prompt-1.4.0-pyhd8ed1ab_0.tar.bz2#88ee91e8679603f2a5bd036d52919cc2 https://conda.anaconda.org/conda-forge/noarch/sphinx-remove-toctrees-1.0.0.post1-pyhd8ed1ab_0.conda#6dee8412218288a17f99f2cfffab334d -https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-1.0.8-pyhd8ed1ab_0.conda#611a35a27914fac3aa37611a6fe40bb5 -https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-1.0.6-pyhd8ed1ab_0.conda#d7e4954df0d3aea2eacc7835ad12671d -https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.0.6-pyhd8ed1ab_0.conda#d6f4b617daa8c677f60c06a3a61e2743 -https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-1.0.8-pyhd8ed1ab_0.conda#179912c661d6aa9fe794e81c854f8d9f +https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-2.0.0-pyhd8ed1ab_0.conda#9075bd8c033f0257122300db914e49c9 +https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-2.0.0-pyhd8ed1ab_0.conda#b3bcc38c471ebb738854f52a36059b48 +https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8ed1ab_0.conda#e25640d692c02e8acfff0372f547e940 +https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_0.conda#d6e5ea5fe00164ac6c2dcc5d76a42192 https://conda.anaconda.org/conda-forge/noarch/sphinx-7.3.7-pyhd8ed1ab_0.conda#7b1465205e28d75d2c0e1a868ee00a67 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda#e507335cb4ca9cff4c3d0fa9cdab255e # pip libsass @ https://files.pythonhosted.org/packages/fd/5a/eb5b62641df0459a3291fc206cf5bd669c0feed7814dded8edef4ade8512/libsass-0.23.0-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=4a218406d605f325d234e4678bd57126a66a88841cb95bee2caeafdc6f138306 diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index d44e00a45941e..1bf603873a73c 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -155,30 +155,26 @@ def remove_from(alist, to_remove): "pip_dependencies": ["cython", "threadpoolctl", "meson-python", "meson"], }, { - "name": "pymin_conda_defaults_openblas", + "name": "pymin_conda_forge_openblas_min_dependencies", "type": "conda", "tag": "main-ci", "folder": "build_tools/azure", "platform": "linux-64", - "channel": "defaults", - "conda_dependencies": remove_from( - common_dependencies, - ["pandas", "threadpoolctl", "pip", "ninja", "meson-python"], - ) - + ["ccache"], + "channels": ["conda-forge"], + "conda_dependencies": common_dependencies + ["ccache", "polars"], "package_constraints": { "python": "3.9", "blas": "[build=openblas]", - "numpy": "1.21", # the min version is not available on the defaults channel - "scipy": "1.7", # the min version has some low level crashes + "numpy": "min", + "scipy": "min", "matplotlib": "min", "cython": "min", "joblib": "min", "threadpoolctl": "min", + "meson-python": "min", + "pandas": "min", + "polars": "min", }, - # TODO: put pip dependencies back to conda dependencies when required - # version is available on the defaults channel. - "pip_dependencies": ["threadpoolctl"], }, { "name": "pymin_conda_forge_openblas_ubuntu_2204", diff --git a/pyproject.toml b/pyproject.toml index 0c61946753700..d5af6d80af75b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -62,7 +62,8 @@ docs = [ "sphinx-prompt>=1.4.0", "sphinxext-opengraph>=0.9.1", "plotly>=5.14.0", - "polars>=0.20.23", + "polars>=0.20.30", + "sphinx-design>=0.5.0", "sphinx-design>=0.6.0", "sphinxcontrib-sass>=0.3.4", "pydata-sphinx-theme>=0.15.3", @@ -86,7 +87,7 @@ tests = [ "black>=24.3.0", "mypy>=1.9", "pyamg>=4.0.0", - "polars>=0.20.23", + "polars>=0.20.30", "pyarrow>=12.0.0", "numpydoc>=1.2.0", "pooch>=1.6.0", diff --git a/sklearn/_min_dependencies.py b/sklearn/_min_dependencies.py index 2003ccdea1abc..40a1e60b87d45 100644 --- a/sklearn/_min_dependencies.py +++ b/sklearn/_min_dependencies.py @@ -33,7 +33,7 @@ "black": ("24.3.0", "tests"), "mypy": ("1.9", "tests"), "pyamg": ("4.0.0", "tests"), - "polars": ("0.20.23", "docs, tests"), + "polars": ("0.20.30", "docs, tests"), "pyarrow": ("12.0.0", "tests"), "sphinx": ("7.3.7", "docs"), "sphinx-copybutton": ("0.5.2", "docs"), diff --git a/sklearn/utils/_set_output.py b/sklearn/utils/_set_output.py index 42757dbb00fae..216cb061de9be 100644 --- a/sklearn/utils/_set_output.py +++ b/sklearn/utils/_set_output.py @@ -7,6 +7,7 @@ from .._config import get_config from ._available_if import available_if +from .fixes import _create_pandas_dataframe_from_non_pandas_container def check_library_installed(library): @@ -128,7 +129,9 @@ def create_container(self, X_output, X_original, columns, inplace=True): # We don't pass columns here because it would intend columns selection # instead of renaming. - X_output = pd.DataFrame(X_output, index=index, copy=not inplace) + X_output = _create_pandas_dataframe_from_non_pandas_container( + X=X_output, index=index, copy=not inplace + ) if columns is not None: return self.rename_columns(X_output, columns) diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py index d0ac208ed5e0b..f15ed273f6c2a 100644 --- a/sklearn/utils/fixes.py +++ b/sklearn/utils/fixes.py @@ -13,12 +13,18 @@ import platform import struct +import sys import numpy as np import scipy import scipy.sparse.linalg import scipy.stats +try: + import pandas as pd +except ImportError: + pd = None + from ..externals._packaging.version import parse as parse_version from .parallel import _get_threadpool_controller @@ -414,3 +420,21 @@ def _in_unstable_openblas_configuration(): # See discussions in https://github.com/numpy/numpy/issues/19411 return True # pragma: no cover return False + + +# TODO: remove when pandas >= 1.4 is the minimum supported version +if pd is not None and parse_version(pd.__version__) < parse_version("1.4"): + + def _create_pandas_dataframe_from_non_pandas_container(X, *, index, copy): + pl = sys.modules.get("polars") + if pl is None or not isinstance(X, pl.DataFrame): + return pd.DataFrame(X, index=index, copy=copy) + + # Bug in pandas<1.4: when constructing a pandas DataFrame from a polars + # DataFrame, the data is transposed ... + return pd.DataFrame(X.to_numpy(), index=index, copy=copy) + +else: + + def _create_pandas_dataframe_from_non_pandas_container(X, *, index, copy): + return pd.DataFrame(X, index=index, copy=copy) From 608155001e93bd8e37392230ce9dbb61435a3496 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 30 Jul 2024 13:17:04 +0200 Subject: [PATCH 192/275] BLD Do not require meson-python when using meson directly (#29578) --- sklearn/meson.build | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/sklearn/meson.build b/sklearn/meson.build index b9ecb5afad898..4bf896fcdeaef 100644 --- a/sklearn/meson.build +++ b/sklearn/meson.build @@ -31,13 +31,6 @@ if not meson.is_cross_build() error('scikit-learn requires Cython>=' + cython_min_version + ', got ' + cython.version() + ' instead') endif - meson_python_version = run_command(py, - ['-c', 'import mesonpy; print(mesonpy.__version__)'], check: true).stdout().strip() - meson_python_min_version = run_command(py, ['_min_dependencies.py', 'meson-python'], check: true).stdout().strip() - if not meson_python_version.version_compare('>=' + meson_python_min_version) - error('scikit-learn requires meson-python>=' + meson_python_min_version + ', got ' + meson_python_version + ' instead') - endif - numpy_version = run_command(py, ['-c', 'import numpy; print(numpy.__version__)'], check: true).stdout().strip() numpy_min_version = run_command(py, ['_min_dependencies.py', 'numpy'], check: true).stdout().strip() @@ -51,6 +44,20 @@ if not meson.is_cross_build() if not scipy_version.version_compare('>=' + scipy_min_version) error('scikit-learn requires scipy>=' + scipy_min_version + ', got ' + scipy_version + ' instead') endif + + # meson-python is required only when going through pip. Using meson directly + # should not check meson-python version. + meson_python_version_command_result = run_command(py, + ['-c', 'import mesonpy; print(mesonpy.__version__)'], check: false) + meson_python_installed = meson_python_version_command_result.returncode() == 0 + if meson_python_installed + meson_python_version = meson_python_version_command_result.stdout().strip() + meson_python_min_version = run_command(py, ['_min_dependencies.py', 'meson-python'], check: true).stdout().strip() + if not meson_python_version.version_compare('>=' + meson_python_min_version) + error('scikit-learn requires meson-python>=' + meson_python_min_version + ', got ' + meson_python_version + ' instead') + endif + endif + endif # Adapted from scipy, each project seems to have its own tweaks for this. One From 8e9640d2957f71e204c10410722b186fc4cbaadc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Thu, 1 Aug 2024 07:30:38 +0200 Subject: [PATCH 193/275] CI Only use wheels when installing from scientific-python-nightly-wheels (#29598) --- build_tools/azure/install.sh | 4 ++-- build_tools/wheels/build_wheels.sh | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh index f61d85e1478f5..5625197971195 100755 --- a/build_tools/azure/install.sh +++ b/build_tools/azure/install.sh @@ -79,14 +79,14 @@ python_environment_install_and_activate() { # install them from scientific-python-nightly-wheels dev_anaconda_url=https://pypi.anaconda.org/scientific-python-nightly-wheels/simple dev_packages="numpy scipy Cython" - pip install --pre --upgrade --timeout=60 --extra-index $dev_anaconda_url $dev_packages + pip install --pre --upgrade --timeout=60 --extra-index $dev_anaconda_url $dev_packages --only-binary :all: fi if [[ "$DISTRIB" == "conda-pip-scipy-dev" ]]; then echo "Installing development dependency wheels" dev_anaconda_url=https://pypi.anaconda.org/scientific-python-nightly-wheels/simple dev_packages="numpy scipy pandas Cython" - pip install --pre --upgrade --timeout=60 --extra-index $dev_anaconda_url $dev_packages + pip install --pre --upgrade --timeout=60 --extra-index $dev_anaconda_url $dev_packages --only-binary :all: check_packages_dev_version $dev_packages diff --git a/build_tools/wheels/build_wheels.sh b/build_tools/wheels/build_wheels.sh index 085ad887c1aec..f2ed8495ec11f 100755 --- a/build_tools/wheels/build_wheels.sh +++ b/build_tools/wheels/build_wheels.sh @@ -53,7 +53,7 @@ if [[ "$CIBW_FREE_THREADED_SUPPORT" =~ [tT]rue ]]; then # Numpy, scipy, Cython only have free-threaded wheels on scientific-python-nightly-wheels # TODO: remove this after CPython 3.13 is released (scheduled October 2024) # and our dependencies have free-threaded wheels on PyPI - export CIBW_BUILD_FRONTEND='pip; args: --pre --extra-index-url "https://pypi.anaconda.org/scientific-python-nightly-wheels/simple"' + export CIBW_BUILD_FRONTEND='pip; args: --pre --extra-index-url "https://pypi.anaconda.org/scientific-python-nightly-wheels/simple" --only-binary :all:' fi # The version of the built dependencies are specified From 3aed4d705b7c6af4379f1f47b53271934011e94d Mon Sep 17 00:00:00 2001 From: alexqiao Date: Fri, 2 Aug 2024 15:26:37 +0800 Subject: [PATCH 194/275] DOC Add plot stock market example to API docs (#29602) --- sklearn/covariance/_graph_lasso.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/sklearn/covariance/_graph_lasso.py b/sklearn/covariance/_graph_lasso.py index 75bfc396340c9..953d50f3012a5 100644 --- a/sklearn/covariance/_graph_lasso.py +++ b/sklearn/covariance/_graph_lasso.py @@ -391,6 +391,9 @@ def __init__( class GraphicalLasso(BaseGraphicalLasso): """Sparse inverse covariance estimation with an l1-penalized estimator. + For a usage example see + :ref:`sphx_glr_auto_examples_applications_plot_stock_market.py`. + Read more in the :ref:`User Guide `. .. versionchanged:: v0.20 From 8ee8bac95d9805b1780f3e0abaf10f6cb245f253 Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Fri, 2 Aug 2024 19:39:04 +0200 Subject: [PATCH 195/275] Fix seed sensitivity of test_fastica_eigh_low_rank_warning (#29612) --- sklearn/decomposition/_fastica.py | 2 +- sklearn/decomposition/tests/test_fastica.py | 9 +++++++-- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/sklearn/decomposition/_fastica.py b/sklearn/decomposition/_fastica.py index a4f36e5ba87db..c875d830eafb7 100644 --- a/sklearn/decomposition/_fastica.py +++ b/sklearn/decomposition/_fastica.py @@ -606,7 +606,7 @@ def g(x, fun_args): # Faster when num_samples >> n_features d, u = linalg.eigh(XT.dot(X)) sort_indices = np.argsort(d)[::-1] - eps = np.finfo(d.dtype).eps + eps = np.finfo(d.dtype).eps * 10 degenerate_idx = d < eps if np.any(degenerate_idx): warnings.warn( diff --git a/sklearn/decomposition/tests/test_fastica.py b/sklearn/decomposition/tests/test_fastica.py index bd7a35bb8a96f..0066d9faf17f2 100644 --- a/sklearn/decomposition/tests/test_fastica.py +++ b/sklearn/decomposition/tests/test_fastica.py @@ -13,7 +13,7 @@ from sklearn.decomposition import PCA, FastICA, fastica from sklearn.decomposition._fastica import _gs_decorrelation from sklearn.exceptions import ConvergenceWarning -from sklearn.utils._testing import assert_allclose +from sklearn.utils._testing import assert_allclose, ignore_warnings def center_and_norm(x, axis=-1): @@ -448,5 +448,10 @@ def test_fastica_eigh_low_rank_warning(global_random_seed): X = A @ A.T ica = FastICA(random_state=0, whiten="unit-variance", whiten_solver="eigh") msg = "There are some small singular values" + with pytest.warns(UserWarning, match=msg): - ica.fit(X) + with ignore_warnings(category=ConvergenceWarning): + # The FastICA solver may not converge for some data with specific + # random seeds but this happens after the whiten step so this is + # not want we want to test here. + ica.fit(X) From d6bef4165262e2257ed8c85bee7a858aeffe0420 Mon Sep 17 00:00:00 2001 From: Adeyemi Biola <52200708+decorouz@users.noreply.github.com> Date: Fri, 2 Aug 2024 21:23:13 +0100 Subject: [PATCH 196/275] Replaced instance of affinity with metric (#29608) --- sklearn/cluster/_agglomerative.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/cluster/_agglomerative.py b/sklearn/cluster/_agglomerative.py index 55215d2a0312c..bec45b6a206bc 100644 --- a/sklearn/cluster/_agglomerative.py +++ b/sklearn/cluster/_agglomerative.py @@ -996,7 +996,7 @@ def _fit(self, X): ---------- X : ndarray of shape (n_samples, n_features) or (n_samples, n_samples) Training instances to cluster, or distances between instances if - ``affinity='precomputed'``. + ``metric='precomputed'``. Returns ------- From 87514891875f7990c4ae0c205f0cdf7071bb10de Mon Sep 17 00:00:00 2001 From: Lars <34921098+der-Lehmann@users.noreply.github.com> Date: Sat, 3 Aug 2024 14:35:11 +0200 Subject: [PATCH 197/275] DOC Fix typo (#29618) --- doc/model_persistence.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/model_persistence.rst b/doc/model_persistence.rst index 604f7640d41fc..afa460a2fcea3 100644 --- a/doc/model_persistence.rst +++ b/doc/model_persistence.rst @@ -119,7 +119,7 @@ persist and plan to serve the model: to get predictions. This environment can be minimal and does not necessarily even require Python to be installed to load the model and compute predictions. Also note that `onnxruntime` typically requires much less RAM - than Python to to compute predictions from small models. + than Python to compute predictions from small models. - :mod:`skops.io`, :mod:`pickle`, :mod:`joblib`, `cloudpickle`_: You need a Python environment with the appropriate dependencies installed to load the From cda1fdd798cb890710898b0e967656c9fcb1c5ad Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 5 Aug 2024 02:28:38 -0700 Subject: [PATCH 198/275] :lock: :robot: CI Update lock files for cirrus-arm CI build(s) :lock: :robot: (#29622) Co-authored-by: Lock file bot --- ...pymin_conda_forge_linux-aarch64_conda.lock | 67 +++++++++++++++++-- 1 file changed, 61 insertions(+), 6 deletions(-) diff --git a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock index ed956180b06e4..cb2d117efcd76 100644 --- a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock +++ 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https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.9.0-23_linuxaarch64_openblas.conda#85c4fec3847027ca7402f3bd7d2de4c1 -https://conda.anaconda.org/conda-forge/noarch/meson-1.5.1-pyhd8ed1ab_0.conda#a87a68d26db0c5bea5cab1f8d7772112 +https://conda.anaconda.org/conda-forge/linux-aarch64/libxkbcommon-1.7.0-h46f2afe_1.conda#78a24e611ab9c09c518f519be49c2e46 +https://conda.anaconda.org/conda-forge/noarch/meson-1.5.1-pyhd8ed1ab_1.conda#979087ee59bea1355f991a3b738af64e https://conda.anaconda.org/conda-forge/linux-aarch64/pillow-10.4.0-py39h4a8821f_0.conda#318861157594972acc05a8715d3018a8 -https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 +https://conda.anaconda.org/conda-forge/noarch/pip-24.2-pyhd8ed1ab_0.conda#6721aef6bfe5937abe70181545dd2c51 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.2-pyhd8ed1ab_0.conda#e010a224b90f1f623a917c35addbb924 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c +https://conda.anaconda.org/conda-forge/linux-aarch64/xcb-util-cursor-0.1.4-h68df207_2.conda#9a1c7ed78dff58f6c7b22981ab5c9d39 +https://conda.anaconda.org/conda-forge/linux-aarch64/harfbuzz-9.0.0-hbf49d6b_1.conda#ceb458f664cab8550fcd74fff26451db https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.0-pyhd8ed1ab_0.conda#dcbadab7a68738a028e195ab68ab2d2e https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-23_linuxaarch64_openblas.conda#d71af7934d6dcef05a3c9b0379e1cdfa https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 @@ -89,7 +142,9 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-2.0.1-py39hcdcdb6f_0. https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_0.conda#b39568655c127a9c4a44d178ac99b6d0 https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-23_linuxaarch64_openblas.conda#0270f72a50c9d64fb8b67ae6681011c8 https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.2.1-py39hd16970a_0.conda#66b9718539ecdd38876b0176c315bcad +https://conda.anaconda.org/conda-forge/linux-aarch64/qt6-main-6.7.2-h288a8fd_4.conda#f6771673fad8025bb1d4dd765bc3caad https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.13.1-py39hb921187_0.conda#1aac9080de661e03d286f18fb71e5240 https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.123-openblas.conda#43772c0a1ae8f29c9a223c21fd89262b -https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.9.1-py39hf3ba65a_0.conda#1bd99011ababf4f9d0976271b23b179f -https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-3.9.1-py39ha65689a_0.conda#677115251fc0a0cd1665318573e2307a +https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.9.1-py39hf3ba65a_2.conda#2c71adc96eab781c8cd8d1cfc64d5bc7 +https://conda.anaconda.org/conda-forge/linux-aarch64/pyside6-6.7.2-py39hb23dda1_1.conda#0e0b29e8b60171e7c8d5652b955a50fd +https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-3.9.1-py39ha65689a_2.conda#6f6879438411334f90c06aca5e2cb6b7 From d524f0a10f813f3482c680405b54baa44a40829d Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 5 Aug 2024 02:29:04 -0700 Subject: [PATCH 199/275] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#29623) Co-authored-by: Lock file bot --- .../azure/pylatest_pip_scipy_dev_linux-64_conda.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index 3fe15f2eada17..afecc31b579cf 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -26,11 +26,11 @@ https://repo.anaconda.com/pkgs/main/linux-64/python-3.12.4-h5148396_1.conda#7863 https://repo.anaconda.com/pkgs/main/linux-64/setuptools-69.5.1-py312h06a4308_0.conda#ce85d9a864a73e0b12d31a97733c9fca https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py312h06a4308_0.conda#18d5f3b68a175c72576876db4afc9e9e https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py312h06a4308_0.conda#6d9697bb8b9f3212be10b3b8e01a12b9 -# pip alabaster @ https://files.pythonhosted.org/packages/32/34/d4e1c02d3bee589efb5dfa17f88ea08bdb3e3eac12bc475462aec52ed223/alabaster-0.7.16-py3-none-any.whl#sha256=b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92 +# pip alabaster @ https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl#sha256=fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b # pip babel @ https://files.pythonhosted.org/packages/27/45/377f7e32a5c93d94cd56542349b34efab5ca3f9e2fd5a68c5e93169aa32d/Babel-2.15.0-py3-none-any.whl#sha256=08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb # pip certifi @ https://files.pythonhosted.org/packages/1c/d5/c84e1a17bf61d4df64ca866a1c9a913874b4e9bdc131ec689a0ad013fb36/certifi-2024.7.4-py3-none-any.whl#sha256=c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90 # pip charset-normalizer @ https://files.pythonhosted.org/packages/ee/fb/14d30eb4956408ee3ae09ad34299131fb383c47df355ddb428a7331cfa1e/charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b -# pip coverage @ https://files.pythonhosted.org/packages/f2/aa/0419103c357bfd95a65d7b2e2249f9f1d79194241c5e87819cd81d36b96c/coverage-7.6.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=0086cd4fc71b7d485ac93ca4239c8f75732c2ae3ba83f6be1c9be59d9e2c6382 +# pip coverage @ https://files.pythonhosted.org/packages/1f/0f/c890339dd605f3ebc269543247bdd43b703cce6825b5ed42ff5f2d6122c7/coverage-7.6.1-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c44fee9975f04b33331cb8eb272827111efc8930cfd582e0320613263ca849ca # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc # pip idna @ https://files.pythonhosted.org/packages/e5/3e/741d8c82801c347547f8a2a06aa57dbb1992be9e948df2ea0eda2c8b79e8/idna-3.7-py3-none-any.whl#sha256=82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0 @@ -63,5 +63,5 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py312h06a4308_0.conda#6d96 # pip pooch @ https://files.pythonhosted.org/packages/a8/87/77cc11c7a9ea9fd05503def69e3d18605852cd0d4b0d3b8f15bbeb3ef1d1/pooch-1.8.2-py3-none-any.whl#sha256=3529a57096f7198778a5ceefd5ac3ef0e4d06a6ddaf9fc2d609b806f25302c47 # pip pytest-cov @ https://files.pythonhosted.org/packages/78/3a/af5b4fa5961d9a1e6237b530eb87dd04aea6eb83da09d2a4073d81b54ccf/pytest_cov-5.0.0-py3-none-any.whl#sha256=4f0764a1219df53214206bf1feea4633c3b558a2925c8b59f144f682861ce652 # pip pytest-xdist @ https://files.pythonhosted.org/packages/6d/82/1d96bf03ee4c0fdc3c0cbe61470070e659ca78dc0086fb88b66c185e2449/pytest_xdist-3.6.1-py3-none-any.whl#sha256=9ed4adfb68a016610848639bb7e02c9352d5d9f03d04809919e2dafc3be4cca7 -# pip sphinx @ https://files.pythonhosted.org/packages/0d/ef/153f6803c5d5f8917dbb7f7fcf6d34a871ede3296fa89c2c703f5f8a6c8e/sphinx-7.4.7-py3-none-any.whl#sha256=c2419e2135d11f1951cd994d6eb18a1835bd8fdd8429f9ca375dc1f3281bd239 +# pip sphinx @ https://files.pythonhosted.org/packages/4d/61/2ad169c6ff1226b46e50da0e44671592dbc6d840a52034a0193a99b28579/sphinx-8.0.2-py3-none-any.whl#sha256=56173572ae6c1b9a38911786e206a110c9749116745873feae4f9ce88e59391d # pip numpydoc @ https://files.pythonhosted.org/packages/f0/fa/dcfe0f65660661db757ee9ebd84e170ff98edd5d80235f62457d9088f85f/numpydoc-1.7.0-py3-none-any.whl#sha256=5a56419d931310d79a06cfc2a126d1558700feeb9b4f3d8dcae1a8134be829c9 From 846f6e03e626a0459149eac3b2a9b7d0edf56414 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 5 Aug 2024 02:30:06 -0700 Subject: [PATCH 200/275] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29625) Co-authored-by: Lock file bot --- build_tools/azure/debian_atlas_32bit_lock.txt | 4 +- ...latest_conda_forge_mkl_linux-64_conda.lock | 80 ++++++----------- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 24 ++--- ...st_pip_openblas_pandas_linux-64_conda.lock | 6 +- .../pymin_conda_forge_mkl_win-64_conda.lock | 46 +++++----- ...nblas_min_dependencies_linux-64_conda.lock | 17 ++-- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 73 +++++---------- build_tools/circle/doc_linux-64_conda.lock | 88 +++++++------------ .../doc_min_dependencies_linux-64_conda.lock | 19 ++-- 9 files changed, 144 insertions(+), 213 deletions(-) diff --git a/build_tools/azure/debian_atlas_32bit_lock.txt b/build_tools/azure/debian_atlas_32bit_lock.txt index d9d6da6394dfb..64513a4be3866 100644 --- a/build_tools/azure/debian_atlas_32bit_lock.txt +++ b/build_tools/azure/debian_atlas_32bit_lock.txt @@ -4,9 +4,9 @@ # # pip-compile --output-file=build_tools/azure/debian_atlas_32bit_lock.txt build_tools/azure/debian_atlas_32bit_requirements.txt # -attrs==23.2.0 +attrs==24.1.0 # via pytest -coverage==7.6.0 +coverage==7.6.1 # via pytest-cov cython==3.0.10 # via -r build_tools/azure/debian_atlas_32bit_requirements.txt diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index 72c211ed8d51d..d25420e46b309 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -17,25 +17,20 @@ https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2# https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793 https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.1.0-h77fa898_0.conda#ca0fad6a41ddaef54a153b78eccb5037 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.12-h4ab18f5_0.conda#7ed427f0871fd41cb1d9c17727c17589 -https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2#d9c69a24ad678ffce24c6543a0176b00 https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.23-h4ab18f5_0.conda#94d61ae2b2b701008a9d52ce6bbead27 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.32.3-h4bc722e_0.conda#7624e34ee6baebfc80d67bac76cc9d9d -https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.22.5-h59595ed_2.conda#985f2f453fb72408d6b6f1be0f324033 https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3 -https://conda.anaconda.org/conda-forge/linux-64/lame-3.100-h166bdaf_1003.tar.bz2#a8832b479f93521a9e7b5b743803be51 https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hd590300_1.conda#aec6c91c7371c26392a06708a73c70e5 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.20-hd590300_0.conda#8e88f9389f1165d7c0936fe40d9a9a79 https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda#172bf1cd1ff8629f2b1179945ed45055 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.2-h59595ed_0.conda#e7ba12deb7020dd080c6c70e7b6f6a3d https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2#d645c6d2ac96843a2bfaccd2d62b3ac3 -https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-0.22.5-h59595ed_2.conda#172bcc51059416e7ce99e7b528cede83 https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.1.0-hc5f4f2c_0.conda#6456c2620c990cd8dde2428a27ba0bc5 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https://conda.anaconda.org/conda-forge/osx-64/tbb-2021.12.0-h3c5361c_3.conda#b0cada4d5a4cf1cbf8598b86231b5958 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_0.tar.bz2#f832c45a477c78bebd107098db465095 https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96 https://conda.anaconda.org/conda-forge/osx-64/tornado-6.4.1-py312hbd25219_0.conda#5a40db69b327c71511248f8186965bd3 -https://conda.anaconda.org/conda-forge/noarch/wheel-0.43.0-pyhd8ed1ab_1.conda#0b5293a157c2b5cd513dd1b03d8d3aae +https://conda.anaconda.org/conda-forge/noarch/wheel-0.44.0-pyhd8ed1ab_0.conda#d44e3b085abcaef02983c6305b84b584 https://conda.anaconda.org/conda-forge/osx-64/ccache-4.10.1-hee5fd93_0.conda#09898bb80e196695cea9e07402cff215 -https://conda.anaconda.org/conda-forge/osx-64/cctools_osx-64-986-ha1c5b94_0.conda#a8951de2506df5649f5a3295fdfd9f2c +https://conda.anaconda.org/conda-forge/osx-64/cctools_osx-64-986-h303a5ab_3.conda#3fc65d01538ca026f662f2b13dacc35e https://conda.anaconda.org/conda-forge/osx-64/clang-16-16.0.6-default_h0c94c6a_11.conda#ba17dcbffdd79fc381eba4125d83fa03 -https://conda.anaconda.org/conda-forge/osx-64/coverage-7.6.0-py312hbd25219_0.conda#8ed4889999caaa8d3b1b3ba939b2fae5 +https://conda.anaconda.org/conda-forge/osx-64/coverage-7.6.1-py312hbd25219_0.conda#17ee8821c9b8cd8f7ae752f4a57fbf56 https://conda.anaconda.org/conda-forge/osx-64/fonttools-4.53.1-py312hbd25219_0.conda#56b85d2b2f034ed31feaaa0b90c37b7f https://conda.anaconda.org/conda-forge/osx-64/gfortran_impl_osx-64-12.3.0-hc328e78_3.conda#b3d751dc7073bbfdfa9d863e39b9685d https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f -https://conda.anaconda.org/conda-forge/osx-64/ld64-711-ha02d983_0.conda#3ae4930ec076735cce481e906f5192e0 -https://conda.anaconda.org/conda-forge/noarch/meson-1.5.1-pyhd8ed1ab_0.conda#a87a68d26db0c5bea5cab1f8d7772112 +https://conda.anaconda.org/conda-forge/osx-64/ld64-711-ha02d983_3.conda#c28c578f9791983a2a9dd480d120d562 +https://conda.anaconda.org/conda-forge/noarch/meson-1.5.1-pyhd8ed1ab_1.conda#979087ee59bea1355f991a3b738af64e https://conda.anaconda.org/conda-forge/osx-64/mkl-2023.2.0-h54c2260_50500.conda#0a342ccdc79e4fcd359245ac51941e7b https://conda.anaconda.org/conda-forge/osx-64/pillow-10.4.0-py312hbd70edc_0.conda#8d55e92fa6380ac8c245f253b096fefd -https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 +https://conda.anaconda.org/conda-forge/noarch/pip-24.2-pyhd8ed1ab_0.conda#6721aef6bfe5937abe70181545dd2c51 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.2-pyhd8ed1ab_0.conda#e010a224b90f1f623a917c35addbb924 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c -https://conda.anaconda.org/conda-forge/osx-64/cctools-986-h40f6528_0.conda#b7a2ca0062a6ee8bc4e83ec887bef942 +https://conda.anaconda.org/conda-forge/osx-64/cctools-986-h40f6528_3.conda#9dd9cb9edfe3c3437c28e495a3b67517 https://conda.anaconda.org/conda-forge/osx-64/clang-16.0.6-default_h179603d_11.conda#29c8b527d8b8fac52f5e2cf6abfcdc93 https://conda.anaconda.org/conda-forge/osx-64/libblas-3.9.0-20_osx64_mkl.conda#160fdc97a51d66d51dc782fb67d35205 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 @@ -116,10 +116,10 @@ https://conda.anaconda.org/conda-forge/osx-64/pandas-2.2.2-py312h1171441_1.conda https://conda.anaconda.org/conda-forge/osx-64/scipy-1.14.0-py312hb9702fa_1.conda#9899db3cf8965c3aecab3daf5227d3eb https://conda.anaconda.org/conda-forge/osx-64/blas-2.120-mkl.conda#b041a7677a412f3d925d8208936cb1e2 https://conda.anaconda.org/conda-forge/osx-64/clang_impl_osx-64-16.0.6-h8787910_18.conda#12f8213141de7f6750b237eb933bfe40 -https://conda.anaconda.org/conda-forge/osx-64/matplotlib-base-3.9.1-py312h0d5aeb7_0.conda#824194f775dbc413b1dc44d58f3249db +https://conda.anaconda.org/conda-forge/osx-64/matplotlib-base-3.9.1-py312h0d5aeb7_2.conda#0aece95a1cd3b77990022d3e0f37c6aa https://conda.anaconda.org/conda-forge/osx-64/pyamg-5.2.1-py312h44e70fa_0.conda#a7c77239f0135d30cbba0164922aa861 https://conda.anaconda.org/conda-forge/osx-64/clang_osx-64-16.0.6-hb91bd55_18.conda#fd48bd52766dc748842ae785a96d547c -https://conda.anaconda.org/conda-forge/osx-64/matplotlib-3.9.1-py312hb401068_0.conda#4938ff12b9ec103d5caca8c4666955c3 +https://conda.anaconda.org/conda-forge/osx-64/matplotlib-3.9.1-py312hb401068_2.conda#1ead575881ba176014aad8dfac07d1b1 https://conda.anaconda.org/conda-forge/osx-64/c-compiler-1.7.0-h282daa2_1.conda#d27411cb82bc1b76b9f487da6ae97f1d https://conda.anaconda.org/conda-forge/osx-64/clangxx_impl_osx-64-16.0.6-h6d92fbe_18.conda#6caeea3e1c0af451118c19894448d4a0 https://conda.anaconda.org/conda-forge/osx-64/gfortran_osx-64-12.3.0-h18f7dce_1.conda#436af2384c47aedb94af78a128e174f1 diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index 6644bfb55e798..b79b8cd5ea6de 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -25,12 +25,12 @@ https://repo.anaconda.com/pkgs/main/linux-64/python-3.11.9-h955ad1f_0.conda#5668 https://repo.anaconda.com/pkgs/main/linux-64/setuptools-69.5.1-py311h06a4308_0.conda#0989470c81841dfcb22c7bbb40f543c5 https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py311h06a4308_0.conda#ec915b5ff89bdbcea7ef943d9e296967 https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py311h06a4308_0.conda#84aef4db159f0daf63751d87d7d6ca56 -# pip alabaster @ https://files.pythonhosted.org/packages/32/34/d4e1c02d3bee589efb5dfa17f88ea08bdb3e3eac12bc475462aec52ed223/alabaster-0.7.16-py3-none-any.whl#sha256=b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92 +# pip alabaster @ https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl#sha256=fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b # pip array-api-compat @ https://files.pythonhosted.org/packages/0f/22/8228be1d3c6d4ffcf05cd89872ce65c1317b2af98d34b9d89b247d8d49cb/array_api_compat-1.8-py3-none-any.whl#sha256=140204454086264d37263bc4afe1182b428353e94e9edcc38d17b009863c982d # pip babel @ https://files.pythonhosted.org/packages/27/45/377f7e32a5c93d94cd56542349b34efab5ca3f9e2fd5a68c5e93169aa32d/Babel-2.15.0-py3-none-any.whl#sha256=08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb # pip certifi @ https://files.pythonhosted.org/packages/1c/d5/c84e1a17bf61d4df64ca866a1c9a913874b4e9bdc131ec689a0ad013fb36/certifi-2024.7.4-py3-none-any.whl#sha256=c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90 # pip charset-normalizer @ https://files.pythonhosted.org/packages/40/26/f35951c45070edc957ba40a5b1db3cf60a9dbb1b350c2d5bef03e01e61de/charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8 -# pip coverage @ https://files.pythonhosted.org/packages/39/33/da23d8bfdfdc5c221d2c9a2f169f63a4e04b9a0327c1c67777157155e4d6/coverage-7.6.0-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=03cafe82c1b32b770a29fd6de923625ccac3185a54a5e66606da26d105f37dac +# pip coverage @ https://files.pythonhosted.org/packages/14/6f/8351b465febb4dbc1ca9929505202db909c5a635c6fdf33e089bbc3d7d85/coverage-7.6.1-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=0c0420b573964c760df9e9e86d1a9a622d0d27f417e1a949a8a66dd7bcee7bc6 # pip cycler @ https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl#sha256=85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 # pip cython @ https://files.pythonhosted.org/packages/45/82/077c13035d6f45d8b8b74d67e9f73f2bfc54ef8d1f79572790f6f7d2b4f5/Cython-3.0.10-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=38d40fa1324ac47c04483d151f5e092406a147eac88a18aec789cf01c089c3f2 # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 @@ -83,5 +83,5 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py311h06a4308_0.conda#84ae # pip pytest-cov @ https://files.pythonhosted.org/packages/78/3a/af5b4fa5961d9a1e6237b530eb87dd04aea6eb83da09d2a4073d81b54ccf/pytest_cov-5.0.0-py3-none-any.whl#sha256=4f0764a1219df53214206bf1feea4633c3b558a2925c8b59f144f682861ce652 # pip pytest-xdist @ https://files.pythonhosted.org/packages/6d/82/1d96bf03ee4c0fdc3c0cbe61470070e659ca78dc0086fb88b66c185e2449/pytest_xdist-3.6.1-py3-none-any.whl#sha256=9ed4adfb68a016610848639bb7e02c9352d5d9f03d04809919e2dafc3be4cca7 # pip scikit-image @ https://files.pythonhosted.org/packages/ad/96/138484302b8ec9a69cdf65e8d4ab47a640a3b1a8ea3c437e1da3e1a5a6b8/scikit_image-0.24.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=fa27b3a0dbad807b966b8db2d78da734cb812ca4787f7fbb143764800ce2fa9c -# pip sphinx @ https://files.pythonhosted.org/packages/0d/ef/153f6803c5d5f8917dbb7f7fcf6d34a871ede3296fa89c2c703f5f8a6c8e/sphinx-7.4.7-py3-none-any.whl#sha256=c2419e2135d11f1951cd994d6eb18a1835bd8fdd8429f9ca375dc1f3281bd239 +# pip sphinx @ https://files.pythonhosted.org/packages/4d/61/2ad169c6ff1226b46e50da0e44671592dbc6d840a52034a0193a99b28579/sphinx-8.0.2-py3-none-any.whl#sha256=56173572ae6c1b9a38911786e206a110c9749116745873feae4f9ce88e59391d # pip numpydoc @ https://files.pythonhosted.org/packages/f0/fa/dcfe0f65660661db757ee9ebd84e170ff98edd5d80235f62457d9088f85f/numpydoc-1.7.0-py3-none-any.whl#sha256=5a56419d931310d79a06cfc2a126d1558700feeb9b4f3d8dcae1a8134be829c9 diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock index 5df3e6bd397fe..ae91423d25ea1 100644 --- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock @@ -3,33 +3,43 @@ # input_hash: ea607aaeb7b1d1f8a1f821a9f505b3601083a218ec4763e2d72d3d3d800e718c @EXPLICIT https://conda.anaconda.org/conda-forge/win-64/ca-certificates-2024.7.4-h56e8100_0.conda#9caa97c9504072cd060cf0a3142cc0ed +https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 +https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 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a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock index d0148aebaabcb..3f7ea06a3891b 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 14996a352b0e8e849fc4282e5e6d1bb05e15a036e54586c87a0d30b2792b1c6b +# input_hash: da804213459d72ef5fa344326a71a64386dfb5085c8e0b582527e8337cecca32 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.7.4-hbcca054_0.conda#23ab7665c5f63cfb9f1f6195256daac6 @@ -84,7 +84,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-h1dd3fc0_3.conda#6 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https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.2-pyhd8ed1ab_0.conda#e010a224b90f1f623a917c35addbb924 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb -https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.5-haf2f30d_0.conda#c5252c02592373fa8caf5a5327165a89 +https://conda.anaconda.org/conda-forge/linux-64/xorg-libxxf86vm-1.1.5-h4bc722e_1.conda#0c90ad87101001080484b91bd9d2cdef +https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.6-haf2f30d_0.conda#a15d7b21e4b7b82b87ba04c3b46c1317 https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-9.0.0-hfac3d4d_0.conda#c7b47c64af53e8ecee01d101eeab2342 https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-1.11.0-h4ab18f5_1.conda#14858a47d4cc995892e79f2b340682d7 https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-20_linux64_openblas.conda#05c5862c7dc25e65ba6c471d96429dae @@ -158,7 +159,7 @@ https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5 https://conda.anaconda.org/conda-forge/noarch/pytest-cov-5.0.0-pyhd8ed1ab_0.conda#c54c0107057d67ddf077751339ec2c63 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_0.conda#b39568655c127a9c4a44d178ac99b6d0 https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-20_linux64_openblas.conda#9932a1d4e9ecf2d35fb19475446e361e -https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.5-hbaaba92_0.conda#4a485842570569ba754863b2c083b346 +https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.6-hbaaba92_0.conda#b22ffc80ac9af846df60b2640c98fea4 https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-255-h3516f8a_1.conda#3366af27f0b593544a6cd453c7932ac5 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.3.4-py39h2fa2bec_0.tar.bz2#9ec0b2186fab9121c54f4844f93ee5b7 https://conda.anaconda.org/conda-forge/linux-64/pandas-1.1.5-py39hde0f152_0.tar.bz2#79fc4b5b3a865b90dd3701cecf1ad33c diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock index edceb11fedacc..b35e7a0764bb1 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock @@ -16,22 +16,17 @@ https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2# https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793 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-https://conda.anaconda.org/conda-forge/linux-64/libogg-1.3.5-h4ab18f5_0.conda#601bfb4b3c6f0b844443bb81a56651e0 -https://conda.anaconda.org/conda-forge/linux-64/libopus-1.3.1-h7f98852_1.tar.bz2#15345e56d527b330e1cacbdf58676e8f +https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hd590300_0.conda#48f4330bfcd959c3cfb704d424903c82 https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-14.1.0-hc0a3c3a_0.conda#1cb187a157136398ddbaae90713e2498 https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.conda#b26e8aa824079e1be0294e7152ca4559 @@ -46,50 +41,42 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.11-hd590300_0.co https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.3-h7f98852_0.tar.bz2#be93aabceefa2fac576e971aef407908 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@ https://files.pythonhosted.org/packages/a9/82/0340caa499416c78e5d8f5f05947ae4bc3cba53c9f038ab6e9ed964e22f1/nbformat-5.10.4-py3-none-any.whl#sha256=3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b # pip nbclient @ https://files.pythonhosted.org/packages/66/e8/00517a23d3eeaed0513e718fbc94aab26eaa1758f5690fc8578839791c79/nbclient-0.10.0-py3-none-any.whl#sha256=f13e3529332a1f1f81d82a53210322476a168bb7090a0289c795fe9cc11c9d3f # pip nbconvert @ https://files.pythonhosted.org/packages/b8/bb/bb5b6a515d1584aa2fd89965b11db6632e4bdc69495a52374bcc36e56cfa/nbconvert-7.16.4-py3-none-any.whl#sha256=05873c620fe520b6322bf8a5ad562692343fe3452abda5765c7a34b7d1aa3eb3 # pip jupyter-server @ https://files.pythonhosted.org/packages/57/e1/085edea6187a127ca8ea053eb01f4e1792d778b4d192c74d32eb6730fed6/jupyter_server-2.14.2-py3-none-any.whl#sha256=47ff506127c2f7851a17bf4713434208fc490955d0e8632e95014a9a9afbeefd # pip jupyterlab-server @ https://files.pythonhosted.org/packages/54/09/2032e7d15c544a0e3cd831c51d77a8ca57f7555b2e1b2922142eddb02a84/jupyterlab_server-2.27.3-py3-none-any.whl#sha256=e697488f66c3db49df675158a77b3b017520d772c6e1548c7d9bcc5df7944ee4 -# pip jupyterlite-sphinx @ https://files.pythonhosted.org/packages/38/c9/5f1142c005cf8d75830b10029e53f074324bc85cfca1f1d0f22a207b771c/jupyterlite_sphinx-0.9.3-py3-none-any.whl#sha256=be6332d16490ea2fa90b78187a2c5e1c357195966a25741d60b1790346571041 +# pip jupyterlite-sphinx @ https://files.pythonhosted.org/packages/ce/a4/f91fa06fb9d345e7e9fe38c9f5cc12b5de24741ad13ec82e9769396d7a8c/jupyterlite_sphinx-0.16.3-py3-none-any.whl#sha256=0e6d976f831fbdfd12e15adf2f3bbcbfd59705fdf5546948956c5ce7928026a8 diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index 49df34c068d09..1c28f0399ef47 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: e41a5bd0186b0b4cf7693057a75793fbd25b1862071b26e8cb937b22dd61d244 +# input_hash: 5c8271fa578693d01da90fd519c56b90a86857bdb8b28024e74528f3d937d440 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/_sysroot_linux-64_curr_repodata_hack-3-h69a702a_16.conda#1c005af0c6ff22814b7c52ee448d4bea @@ -104,19 +104,19 @@ https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.2.1-he02047a_0.conda#8 https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda#4d056880988120e29d75bfff282e0f45 https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.6-hef167b5_0.conda#54fe76ab3d0189acaef95156874db7f9 https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hd590300_1.conda#39f910d205726805a958da408ca194ba 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https://conda.anaconda.org/conda-forge/linux-64/libglib-2.80.3-h8a4344b_1.conda#6ea440297aacee4893f02ad759e6ffbc https://conda.anaconda.org/conda-forge/linux-64/libjxl-0.10.3-h66b40c8_0.conda#a394f85083195ab8aa33911f40d76870 https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-h1dd3fc0_3.conda#66f03896ffbe1a110ffda05c7a856504 https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.7-h4c95cb1_3.conda#0ac9aff6010a7751961c8e4b863a40e7 https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.8-hf5423f3_0.conda#322be9d39e030673e105b0abb320514e https://conda.anaconda.org/conda-forge/linux-64/mysql-libs-8.3.0-ha479ceb_5.conda#82776ee8145b9d1fd6546604de4b351d -https://conda.anaconda.org/conda-forge/linux-64/nss-3.102-h593d115_0.conda#40e5e48c55a45621c4399ca9236406b7 +https://conda.anaconda.org/conda-forge/linux-64/nss-3.103-h593d115_0.conda#233bfe41968d6fb04eba9258bb5061ad https://conda.anaconda.org/conda-forge/linux-64/python-3.9.19-h0755675_0_cpython.conda#d9ee3647fbd9e8595b8df759b2bbefb8 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-0.4.1-hb711507_2.conda#8637c3e5821654d0edf97e2b0404b443 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-keysyms-0.4.1-hb711507_0.conda#ad748ccca349aec3e91743e08b5e2b50 @@ -184,7 +184,7 @@ https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5 https://conda.anaconda.org/conda-forge/noarch/toolz-0.12.1-pyhd8ed1ab_0.conda#2fcb582444635e2c402e8569bb94e039 https://conda.anaconda.org/conda-forge/linux-64/tornado-6.4.1-py39hd3abc70_0.conda#c183e99f9320e5e2d0f9c43efcb3fb22 https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.12.2-pyha770c72_0.conda#ebe6952715e1d5eb567eeebf25250fa7 -https://conda.anaconda.org/conda-forge/noarch/wheel-0.43.0-pyhd8ed1ab_1.conda#0b5293a157c2b5cd513dd1b03d8d3aae +https://conda.anaconda.org/conda-forge/noarch/wheel-0.44.0-pyhd8ed1ab_0.conda#d44e3b085abcaef02983c6305b84b584 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-image-0.4.0-hb711507_2.conda#a0901183f08b6c7107aab109733a3c91 https://conda.anaconda.org/conda-forge/linux-64/xkeyboard-config-2.42-h4ab18f5_0.conda#b193af204da1bfb8c13882d131a14bd2 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.4-h0b41bf4_2.conda#82b6df12252e6f32402b96dacc656fec @@ -213,19 +213,20 @@ https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda#e https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.50-h4f305b6_0.conda#0d7ff1a8e69565ca3add6925e18e708f https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.7.0-h2c5496b_1.conda#e2eaefa4de2b7237af7c907b8bbc760a https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhd8ed1ab_0.tar.bz2#8b45f9f2b2f7a98b0ec179c8991a4a9b -https://conda.anaconda.org/conda-forge/noarch/meson-1.5.1-pyhd8ed1ab_0.conda#a87a68d26db0c5bea5cab1f8d7772112 +https://conda.anaconda.org/conda-forge/noarch/meson-1.5.1-pyhd8ed1ab_1.conda#979087ee59bea1355f991a3b738af64e https://conda.anaconda.org/conda-forge/noarch/partd-1.4.2-pyhd8ed1ab_0.conda#0badf9c54e24cecfb0ad2f99d680c163 https://conda.anaconda.org/conda-forge/linux-64/pillow-10.4.0-py39h16a7006_0.conda#d9a6b19174a6cf5185296b16f781951f -https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 +https://conda.anaconda.org/conda-forge/noarch/pip-24.2-pyhd8ed1ab_0.conda#6721aef6bfe5937abe70181545dd2c51 https://conda.anaconda.org/conda-forge/noarch/plotly-5.14.0-pyhd8ed1ab_0.conda#6a7bcc42ef58dd6cf3da9333ea102433 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.2-pyhd8ed1ab_0.conda#e010a224b90f1f623a917c35addbb924 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.12.0-h434a139_3.conda#c667c11d1e488a38220ede8a34441bff +https://conda.anaconda.org/conda-forge/linux-64/xorg-libxxf86vm-1.1.5-h4bc722e_1.conda#0c90ad87101001080484b91bd9d2cdef https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.7.0-h00ab1b0_1.conda#28de2e073db9ca9b72858bee9fb6f571 https://conda.anaconda.org/conda-forge/linux-64/fortran-compiler-1.7.0-heb67821_1.conda#cf4b0e7c4c78bb0662aed9b27c414a3c -https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.5-haf2f30d_0.conda#c5252c02592373fa8caf5a5327165a89 +https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.6-haf2f30d_0.conda#a15d7b21e4b7b82b87ba04c3b46c1317 https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-9.0.0-hfac3d4d_0.conda#c7b47c64af53e8ecee01d101eeab2342 https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-8.2.0-hd8ed1ab_0.conda#0fd030dce707a6654472cf7619b0b01b https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-1.11.0-h4ab18f5_1.conda#14858a47d4cc995892e79f2b340682d7 @@ -237,7 +238,7 @@ https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_0.co https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py39h623c9ba_0.conda#a19d023682384c637cb356d270c276c0 https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_1.conda#d8d07866ac3b5b6937213c89a1874f08 https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.7.1-pyhd8ed1ab_0.conda#80f7ce024289c333fdc5ad54a194fc86 -https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.5-hbaaba92_0.conda#4a485842570569ba754863b2c083b346 +https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.6-hbaaba92_0.conda#b22ffc80ac9af846df60b2640c98fea4 https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-23_linux64_mkl.conda#5bdaf561cf48f95093dedaa665083874 https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-255-h3516f8a_1.conda#3366af27f0b593544a6cd453c7932ac5 https://conda.anaconda.org/conda-forge/linux-64/mkl-devel-2024.1.0-ha770c72_693.conda#7f422e2cf549a3fb920c95288393870d From 52e81fa8b9630f3aa3eaf2ebc580cc978f555db7 Mon Sep 17 00:00:00 2001 From: EmilyXinyi <52259856+EmilyXinyi@users.noreply.github.com> Date: Mon, 5 Aug 2024 13:32:53 -0400 Subject: [PATCH 201/275] update the doctests to be compatible with numpy>=2 (#29613) Co-authored-by: Olivier Grisel --- sklearn/cluster/_mean_shift.py | 2 +- sklearn/conftest.py | 5 +- sklearn/covariance/_shrunk_covariance.py | 8 +-- sklearn/datasets/_base.py | 6 +- sklearn/datasets/_samples_generator.py | 15 +++-- sklearn/decomposition/_dict_learning.py | 16 ++--- sklearn/decomposition/_sparse_pca.py | 4 +- sklearn/feature_selection/_from_model.py | 2 +- sklearn/isotonic.py | 4 +- sklearn/linear_model/_base.py | 2 +- sklearn/linear_model/_coordinate_descent.py | 2 +- sklearn/linear_model/_glm/glm.py | 12 ++-- sklearn/linear_model/_least_angle.py | 4 +- sklearn/linear_model/_omp.py | 2 +- sklearn/linear_model/_quantile.py | 2 +- sklearn/linear_model/_ridge.py | 4 +- sklearn/manifold/_mds.py | 2 +- sklearn/metrics/_classification.py | 72 ++++++++++----------- sklearn/metrics/_ranking.py | 42 ++++++------ sklearn/metrics/_regression.py | 44 ++++++------- sklearn/metrics/cluster/_bicluster.py | 2 +- sklearn/metrics/cluster/_supervised.py | 18 +++--- sklearn/metrics/cluster/_unsupervised.py | 6 +- sklearn/model_selection/_search.py | 2 +- sklearn/preprocessing/_label.py | 4 +- sklearn/preprocessing/_target_encoder.py | 2 +- sklearn/random_projection.py | 4 +- sklearn/utils/extmath.py | 2 +- 28 files changed, 147 insertions(+), 143 deletions(-) diff --git a/sklearn/cluster/_mean_shift.py b/sklearn/cluster/_mean_shift.py index 9710e0941a158..cfff674b4c30b 100644 --- a/sklearn/cluster/_mean_shift.py +++ b/sklearn/cluster/_mean_shift.py @@ -84,7 +84,7 @@ def estimate_bandwidth(X, *, quantile=0.3, n_samples=None, random_state=0, n_job >>> X = np.array([[1, 1], [2, 1], [1, 0], ... [4, 7], [3, 5], [3, 6]]) >>> estimate_bandwidth(X, quantile=0.5) - 1.61... + np.float64(1.61...) """ X = check_array(X) diff --git a/sklearn/conftest.py b/sklearn/conftest.py index 79fd695fb64f1..92a5c3279bde1 100644 --- a/sklearn/conftest.py +++ b/sklearn/conftest.py @@ -207,7 +207,10 @@ def pytest_collection_modifyitems(config, items): ) skip_doctests = True - if np_base_version >= parse_version("2"): + if np_base_version < parse_version("2"): + # TODO: configure numpy to output scalar arrays as regular Python scalars + # once possible to improve readability of the tests docstrings. + # https://numpy.org/neps/nep-0051-scalar-representation.html#implementation reason = "Due to NEP 51 numpy scalar repr has changed in numpy 2" skip_doctests = True diff --git a/sklearn/covariance/_shrunk_covariance.py b/sklearn/covariance/_shrunk_covariance.py index caf23ffaf687e..95d12ec4cff24 100644 --- a/sklearn/covariance/_shrunk_covariance.py +++ b/sklearn/covariance/_shrunk_covariance.py @@ -338,7 +338,7 @@ def ledoit_wolf_shrinkage(X, assume_centered=False, block_size=1000): >>> X = rng.multivariate_normal(mean=[0, 0], cov=real_cov, size=50) >>> shrinkage_coefficient = ledoit_wolf_shrinkage(X) >>> shrinkage_coefficient - 0.23... + np.float64(0.23...) """ X = check_array(X) # for only one feature, the result is the same whatever the shrinkage @@ -455,7 +455,7 @@ def ledoit_wolf(X, *, assume_centered=False, block_size=1000): array([[0.44..., 0.16...], [0.16..., 0.80...]]) >>> shrinkage - 0.23... + np.float64(0.23...) """ estimator = LedoitWolf( assume_centered=assume_centered, @@ -675,7 +675,7 @@ def oas(X, *, assume_centered=False): array([[0.7533..., 0.2763...], [0.2763..., 0.3964...]]) >>> shrinkage - 0.0195... + np.float64(0.0195...) """ estimator = OAS( assume_centered=assume_centered, @@ -781,7 +781,7 @@ class OAS(EmpiricalCovariance): array([[ 1.7833..., -1.2431... ], [-1.2431..., 3.3889...]]) >>> oas.shrinkage_ - 0.0195... + np.float64(0.0195...) """ @_fit_context(prefer_skip_nested_validation=True) diff --git a/sklearn/datasets/_base.py b/sklearn/datasets/_base.py index aa145384c042d..62296490580bb 100644 --- a/sklearn/datasets/_base.py +++ b/sklearn/datasets/_base.py @@ -568,7 +568,7 @@ def load_wine(*, return_X_y=False, as_frame=False): >>> data.target[[10, 80, 140]] array([0, 1, 2]) >>> list(data.target_names) - ['class_0', 'class_1', 'class_2'] + [np.str_('class_0'), np.str_('class_1'), np.str_('class_2')] """ data, target, target_names, fdescr = load_csv_data( @@ -702,7 +702,7 @@ def load_iris(*, return_X_y=False, as_frame=False): >>> data.target[[10, 25, 50]] array([0, 0, 1]) >>> list(data.target_names) - ['setosa', 'versicolor', 'virginica'] + [np.str_('setosa'), np.str_('versicolor'), np.str_('virginica')] See :ref:`sphx_glr_auto_examples_datasets_plot_iris_dataset.py` for a more detailed example of how to work with the iris dataset. @@ -830,7 +830,7 @@ def load_breast_cancer(*, return_X_y=False, as_frame=False): >>> data.target[[10, 50, 85]] array([0, 1, 0]) >>> list(data.target_names) - ['malignant', 'benign'] + [np.str_('malignant'), np.str_('benign')] """ data_file_name = "breast_cancer.csv" data, target, target_names, fdescr = load_csv_data( diff --git a/sklearn/datasets/_samples_generator.py b/sklearn/datasets/_samples_generator.py index e4fabcd892d7e..4d24bf620cacf 100644 --- a/sklearn/datasets/_samples_generator.py +++ b/sklearn/datasets/_samples_generator.py @@ -205,7 +205,7 @@ def make_classification( >>> y.shape (100,) >>> list(y[:5]) - [0, 0, 1, 1, 0] + [np.int64(0), np.int64(0), np.int64(1), np.int64(1), np.int64(0)] """ generator = check_random_state(random_state) @@ -557,7 +557,8 @@ def make_hastie_10_2(n_samples=12000, *, random_state=None): >>> y.shape (24000,) >>> list(y[:5]) - [-1.0, 1.0, -1.0, 1.0, -1.0] + [np.float64(-1.0), np.float64(1.0), np.float64(-1.0), np.float64(1.0), + np.float64(-1.0)] """ rs = check_random_state(random_state) @@ -797,7 +798,7 @@ def make_circles( >>> y.shape (100,) >>> list(y[:5]) - [1, 1, 1, 0, 0] + [np.int64(1), np.int64(1), np.int64(1), np.int64(0), np.int64(0)] """ if isinstance(n_samples, numbers.Integral): n_samples_out = n_samples // 2 @@ -1169,7 +1170,7 @@ def make_friedman1(n_samples=100, n_features=10, *, noise=0.0, random_state=None >>> y.shape (100,) >>> list(y[:3]) - [16.8..., 5.8..., 9.4...] + [np.float64(16.8...), np.float64(5.8...), np.float64(9.4...)] """ generator = check_random_state(random_state) @@ -1251,7 +1252,7 @@ def make_friedman2(n_samples=100, *, noise=0.0, random_state=None): >>> y.shape (100,) >>> list(y[:3]) - [1229.4..., 27.0..., 65.6...] + [np.float64(1229.4...), np.float64(27.0...), np.float64(65.6...)] """ generator = check_random_state(random_state) @@ -1335,7 +1336,7 @@ def make_friedman3(n_samples=100, *, noise=0.0, random_state=None): >>> y.shape (100,) >>> list(y[:3]) - [1.5..., 0.9..., 0.4...] + [np.float64(1.5...), np.float64(0.9...), np.float64(0.4...)] """ generator = check_random_state(random_state) @@ -2050,7 +2051,7 @@ def make_gaussian_quantiles( >>> y.shape (100,) >>> list(y[:5]) - [2, 0, 1, 0, 2] + [np.int64(2), np.int64(0), np.int64(1), np.int64(0), np.int64(2)] """ if n_samples < n_classes: raise ValueError("n_samples must be at least n_classes") diff --git a/sklearn/decomposition/_dict_learning.py b/sklearn/decomposition/_dict_learning.py index 267e1cbfe756b..a2e3496e43941 100644 --- a/sklearn/decomposition/_dict_learning.py +++ b/sklearn/decomposition/_dict_learning.py @@ -847,7 +847,7 @@ def dict_learning_online( We can check the level of sparsity of `U`: >>> np.mean(U == 0) - 0.53... + np.float64(0.53...) We can compare the average squared euclidean norm of the reconstruction error of the sparse coded signal relative to the squared euclidean norm of @@ -855,7 +855,7 @@ def dict_learning_online( >>> X_hat = U @ V >>> np.mean(np.sum((X_hat - X) ** 2, axis=1) / np.sum(X ** 2, axis=1)) - 0.05... + np.float64(0.05...) """ # TODO(1.6): remove in 1.6 if max_iter is None: @@ -1049,7 +1049,7 @@ def dict_learning( We can check the level of sparsity of `U`: >>> np.mean(U == 0) - 0.6... + np.float64(0.6...) We can compare the average squared euclidean norm of the reconstruction error of the sparse coded signal relative to the squared euclidean norm of @@ -1057,7 +1057,7 @@ def dict_learning( >>> X_hat = U @ V >>> np.mean(np.sum((X_hat - X) ** 2, axis=1) / np.sum(X ** 2, axis=1)) - 0.01... + np.float64(0.01...) """ estimator = DictionaryLearning( n_components=n_components, @@ -1551,7 +1551,7 @@ class DictionaryLearning(_BaseSparseCoding, BaseEstimator): We can check the level of sparsity of `X_transformed`: >>> np.mean(X_transformed == 0) - 0.52... + np.float64(0.52...) We can compare the average squared euclidean norm of the reconstruction error of the sparse coded signal relative to the squared euclidean norm of @@ -1559,7 +1559,7 @@ class DictionaryLearning(_BaseSparseCoding, BaseEstimator): >>> X_hat = X_transformed @ dict_learner.components_ >>> np.mean(np.sum((X_hat - X) ** 2, axis=1) / np.sum(X ** 2, axis=1)) - 0.05... + np.float64(0.05...) """ _parameter_constraints: dict = { @@ -1914,7 +1914,7 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator): We can check the level of sparsity of `X_transformed`: >>> np.mean(X_transformed == 0) > 0.5 - True + np.True_ We can compare the average squared euclidean norm of the reconstruction error of the sparse coded signal relative to the squared euclidean norm of @@ -1922,7 +1922,7 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator): >>> X_hat = X_transformed @ dict_learner.components_ >>> np.mean(np.sum((X_hat - X) ** 2, axis=1) / np.sum(X ** 2, axis=1)) - 0.052... + np.float64(0.052...) """ _parameter_constraints: dict = { diff --git a/sklearn/decomposition/_sparse_pca.py b/sklearn/decomposition/_sparse_pca.py index b284e784d4466..39f01325a1515 100644 --- a/sklearn/decomposition/_sparse_pca.py +++ b/sklearn/decomposition/_sparse_pca.py @@ -267,7 +267,7 @@ class SparsePCA(_BaseSparsePCA): (200, 5) >>> # most values in the components_ are zero (sparsity) >>> np.mean(transformer.components_ == 0) - 0.9666... + np.float64(0.9666...) """ _parameter_constraints: dict = { @@ -473,7 +473,7 @@ class MiniBatchSparsePCA(_BaseSparsePCA): (200, 5) >>> # most values in the components_ are zero (sparsity) >>> np.mean(transformer.components_ == 0) - 0.9... + np.float64(0.9...) """ _parameter_constraints: dict = { diff --git a/sklearn/feature_selection/_from_model.py b/sklearn/feature_selection/_from_model.py index 46c2b9ebbb163..b6c3fc9ed5601 100644 --- a/sklearn/feature_selection/_from_model.py +++ b/sklearn/feature_selection/_from_model.py @@ -219,7 +219,7 @@ class SelectFromModel(MetaEstimatorMixin, SelectorMixin, BaseEstimator): >>> selector.estimator_.coef_ array([[-0.3252..., 0.8345..., 0.4976...]]) >>> selector.threshold_ - 0.55249... + np.float64(0.55249...) >>> selector.get_support() array([False, True, False]) >>> selector.transform(X) diff --git a/sklearn/isotonic.py b/sklearn/isotonic.py index b77ffc04185c0..71ca0b177f13c 100644 --- a/sklearn/isotonic.py +++ b/sklearn/isotonic.py @@ -66,10 +66,10 @@ def check_increasing(x, y): >>> from sklearn.isotonic import check_increasing >>> x, y = [1, 2, 3, 4, 5], [2, 4, 6, 8, 10] >>> check_increasing(x, y) - True + np.True_ >>> y = [10, 8, 6, 4, 2] >>> check_increasing(x, y) - False + np.False_ """ # Calculate Spearman rho estimate and set return accordingly. diff --git a/sklearn/linear_model/_base.py b/sklearn/linear_model/_base.py index 56beba93f6f98..02a72355e3602 100644 --- a/sklearn/linear_model/_base.py +++ b/sklearn/linear_model/_base.py @@ -553,7 +553,7 @@ class LinearRegression(MultiOutputMixin, RegressorMixin, LinearModel): >>> reg.coef_ array([1., 2.]) >>> reg.intercept_ - 3.0... + np.float64(3.0...) >>> reg.predict(np.array([[3, 5]])) array([16.]) """ diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py index 9987304652b08..b7e5e36d498c2 100644 --- a/sklearn/linear_model/_coordinate_descent.py +++ b/sklearn/linear_model/_coordinate_descent.py @@ -3131,7 +3131,7 @@ class MultiTaskLassoCV(RegressorMixin, LinearModelCV): >>> r2_score(y, reg.predict(X)) 0.9994... >>> reg.alpha_ - 0.5713... + np.float64(0.5713...) >>> reg.predict(X[:1,]) array([[153.7971..., 94.9015...]]) """ diff --git a/sklearn/linear_model/_glm/glm.py b/sklearn/linear_model/_glm/glm.py index 14caa4fd733c2..92d1aaa6077e6 100644 --- a/sklearn/linear_model/_glm/glm.py +++ b/sklearn/linear_model/_glm/glm.py @@ -552,11 +552,11 @@ class PoissonRegressor(_GeneralizedLinearRegressor): >>> clf.fit(X, y) PoissonRegressor() >>> clf.score(X, y) - 0.990... + np.float64(0.990...) >>> clf.coef_ array([0.121..., 0.158...]) >>> clf.intercept_ - 2.088... + np.float64(2.088...) >>> clf.predict([[1, 1], [3, 4]]) array([10.676..., 21.875...]) """ @@ -684,11 +684,11 @@ class GammaRegressor(_GeneralizedLinearRegressor): >>> clf.fit(X, y) GammaRegressor() >>> clf.score(X, y) - 0.773... + np.float64(0.773...) >>> clf.coef_ array([0.072..., 0.066...]) >>> clf.intercept_ - 2.896... + np.float64(2.896...) >>> clf.predict([[1, 0], [2, 8]]) array([19.483..., 35.795...]) """ @@ -846,11 +846,11 @@ class TweedieRegressor(_GeneralizedLinearRegressor): >>> clf.fit(X, y) TweedieRegressor() >>> clf.score(X, y) - 0.839... + np.float64(0.839...) >>> clf.coef_ array([0.599..., 0.299...]) >>> clf.intercept_ - 1.600... + np.float64(1.600...) >>> clf.predict([[1, 1], [3, 4]]) array([2.500..., 4.599...]) """ diff --git a/sklearn/linear_model/_least_angle.py b/sklearn/linear_model/_least_angle.py index 5f0715135d57b..7d5b11431c583 100644 --- a/sklearn/linear_model/_least_angle.py +++ b/sklearn/linear_model/_least_angle.py @@ -1646,7 +1646,7 @@ class LarsCV(Lars): >>> reg.score(X, y) 0.9996... >>> reg.alpha_ - 0.2961... + np.float64(0.2961...) >>> reg.predict(X[:1,]) array([154.3996...]) """ @@ -1986,7 +1986,7 @@ class LassoLarsCV(LarsCV): >>> reg.score(X, y) 0.9993... >>> reg.alpha_ - 0.3972... + np.float64(0.3972...) >>> reg.predict(X[:1,]) array([-78.4831...]) """ diff --git a/sklearn/linear_model/_omp.py b/sklearn/linear_model/_omp.py index f52ef553eab4c..03549b46b385d 100644 --- a/sklearn/linear_model/_omp.py +++ b/sklearn/linear_model/_omp.py @@ -996,7 +996,7 @@ class OrthogonalMatchingPursuitCV(RegressorMixin, LinearModel): >>> reg.score(X, y) 0.9991... >>> reg.n_nonzero_coefs_ - 10 + np.int64(10) >>> reg.predict(X[:1,]) array([-78.3854...]) """ diff --git a/sklearn/linear_model/_quantile.py b/sklearn/linear_model/_quantile.py index 33451d8640bff..021328eb361ff 100644 --- a/sklearn/linear_model/_quantile.py +++ b/sklearn/linear_model/_quantile.py @@ -104,7 +104,7 @@ class QuantileRegressor(LinearModel, RegressorMixin, BaseEstimator): >>> solver = "highs" if sp_version >= parse_version("1.6.0") else "interior-point" >>> reg = QuantileRegressor(quantile=0.8, solver=solver).fit(X, y) >>> np.mean(y <= reg.predict(X)) - 0.8 + np.float64(0.8) """ _parameter_constraints: dict = { diff --git a/sklearn/linear_model/_ridge.py b/sklearn/linear_model/_ridge.py index 7890875a76032..5960d6c7cb170 100644 --- a/sklearn/linear_model/_ridge.py +++ b/sklearn/linear_model/_ridge.py @@ -573,9 +573,9 @@ def ridge_regression( >>> y = 2.0 * X[:, 0] - 1.0 * X[:, 1] + 0.1 * rng.standard_normal(100) >>> coef, intercept = ridge_regression(X, y, alpha=1.0, return_intercept=True) >>> list(coef) - [1.9..., -1.0..., -0.0..., -0.0...] + [np.float64(1.9...), np.float64(-1.0...), np.float64(-0.0...), np.float64(-0.0...)] >>> intercept - -0.0... + np.float64(-0.0...) """ return _ridge_regression( X, diff --git a/sklearn/manifold/_mds.py b/sklearn/manifold/_mds.py index 760336da52e9f..9732f2556f2ba 100644 --- a/sklearn/manifold/_mds.py +++ b/sklearn/manifold/_mds.py @@ -322,7 +322,7 @@ def smacof( [ 1.74..., -0.75...], [-1.79..., 1.83...]]) >>> stress - 0.0012... + np.float64(0.0012...) """ dissimilarities = check_array(dissimilarities) diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py index 703fe18b0d67c..3f11d95c0c7ab 100644 --- a/sklearn/metrics/_classification.py +++ b/sklearn/metrics/_classification.py @@ -337,7 +337,7 @@ def confusion_matrix( >>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel() >>> (tn, fp, fn, tp) - (0, 2, 1, 1) + (np.int64(0), np.int64(2), np.int64(1), np.int64(1)) """ y_type, y_true, y_pred = _check_targets(y_true, y_pred) if y_type not in ("binary", "multiclass"): @@ -708,7 +708,7 @@ class labels [2]_. >>> y1 = ["negative", "positive", "negative", "neutral", "positive"] >>> y2 = ["negative", "positive", "negative", "neutral", "negative"] >>> cohen_kappa_score(y1, y2) - 0.6875 + np.float64(0.6875) """ confusion = confusion_matrix(y1, y2, labels=labels, sample_weight=sample_weight) n_classes = confusion.shape[0] @@ -867,19 +867,19 @@ def jaccard_score( In the binary case: >>> jaccard_score(y_true[0], y_pred[0]) - 0.6666... + np.float64(0.6666...) In the 2D comparison case (e.g. image similarity): >>> jaccard_score(y_true, y_pred, average="micro") - 0.6 + np.float64(0.6) In the multilabel case: >>> jaccard_score(y_true, y_pred, average='samples') - 0.5833... + np.float64(0.5833...) >>> jaccard_score(y_true, y_pred, average='macro') - 0.6666... + np.float64(0.6666...) >>> jaccard_score(y_true, y_pred, average=None) array([0.5, 0.5, 1. ]) @@ -998,7 +998,7 @@ def matthews_corrcoef(y_true, y_pred, *, sample_weight=None): >>> y_true = [+1, +1, +1, -1] >>> y_pred = [+1, -1, +1, +1] >>> matthews_corrcoef(y_true, y_pred) - -0.33... + np.float64(-0.33...) """ y_type, y_true, y_pred = _check_targets(y_true, y_pred) check_consistent_length(y_true, y_pred, sample_weight) @@ -1266,11 +1266,11 @@ def f1_score( >>> y_true = [0, 1, 2, 0, 1, 2] >>> y_pred = [0, 2, 1, 0, 0, 1] >>> f1_score(y_true, y_pred, average='macro') - 0.26... + np.float64(0.26...) >>> f1_score(y_true, y_pred, average='micro') - 0.33... + np.float64(0.33...) >>> f1_score(y_true, y_pred, average='weighted') - 0.26... + np.float64(0.26...) >>> f1_score(y_true, y_pred, average=None) array([0.8, 0. , 0. ]) @@ -1278,9 +1278,9 @@ def f1_score( >>> y_true_empty = [0, 0, 0, 0, 0, 0] >>> y_pred_empty = [0, 0, 0, 0, 0, 0] >>> f1_score(y_true_empty, y_pred_empty) - 0.0... + np.float64(0.0...) >>> f1_score(y_true_empty, y_pred_empty, zero_division=1.0) - 1.0... + np.float64(1.0...) >>> f1_score(y_true_empty, y_pred_empty, zero_division=np.nan) nan... @@ -1469,17 +1469,17 @@ def fbeta_score( >>> y_true = [0, 1, 2, 0, 1, 2] >>> y_pred = [0, 2, 1, 0, 0, 1] >>> fbeta_score(y_true, y_pred, average='macro', beta=0.5) - 0.23... + np.float64(0.23...) >>> fbeta_score(y_true, y_pred, average='micro', beta=0.5) - 0.33... + np.float64(0.33...) >>> fbeta_score(y_true, y_pred, average='weighted', beta=0.5) - 0.23... + np.float64(0.23...) >>> fbeta_score(y_true, y_pred, average=None, beta=0.5) array([0.71..., 0. , 0. ]) >>> y_pred_empty = [0, 0, 0, 0, 0, 0] >>> fbeta_score(y_true, y_pred_empty, ... average="macro", zero_division=np.nan, beta=0.5) - 0.12... + np.float64(0.12...) """ _, _, f, _ = precision_recall_fscore_support( @@ -1770,11 +1770,11 @@ def precision_recall_fscore_support( >>> y_true = np.array(['cat', 'dog', 'pig', 'cat', 'dog', 'pig']) >>> y_pred = np.array(['cat', 'pig', 'dog', 'cat', 'cat', 'dog']) >>> precision_recall_fscore_support(y_true, y_pred, average='macro') - (0.22..., 0.33..., 0.26..., None) + (np.float64(0.22...), np.float64(0.33...), np.float64(0.26...), None) >>> precision_recall_fscore_support(y_true, y_pred, average='micro') - (0.33..., 0.33..., 0.33..., None) + (np.float64(0.33...), np.float64(0.33...), np.float64(0.33...), None) >>> precision_recall_fscore_support(y_true, y_pred, average='weighted') - (0.22..., 0.33..., 0.26..., None) + (np.float64(0.22...), np.float64(0.33...), np.float64(0.26...), None) It is possible to compute per-label precisions, recalls, F1-scores and supports instead of averaging: @@ -1955,15 +1955,15 @@ class after being classified as negative. This is the case when the >>> import numpy as np >>> from sklearn.metrics import class_likelihood_ratios >>> class_likelihood_ratios([0, 1, 0, 1, 0], [1, 1, 0, 0, 0]) - (1.5, 0.75) + (np.float64(1.5), np.float64(0.75)) >>> y_true = np.array(["non-cat", "cat", "non-cat", "cat", "non-cat"]) >>> y_pred = np.array(["cat", "cat", "non-cat", "non-cat", "non-cat"]) >>> class_likelihood_ratios(y_true, y_pred) - (1.33..., 0.66...) + (np.float64(1.33...), np.float64(0.66...)) >>> y_true = np.array(["non-zebra", "zebra", "non-zebra", "zebra", "non-zebra"]) >>> y_pred = np.array(["zebra", "zebra", "non-zebra", "non-zebra", "non-zebra"]) >>> class_likelihood_ratios(y_true, y_pred) - (1.5, 0.75) + (np.float64(1.5), np.float64(0.75)) To avoid ambiguities, use the notation `labels=[negative_class, positive_class]` @@ -1971,7 +1971,7 @@ class after being classified as negative. This is the case when the >>> y_true = np.array(["non-cat", "cat", "non-cat", "cat", "non-cat"]) >>> y_pred = np.array(["cat", "cat", "non-cat", "non-cat", "non-cat"]) >>> class_likelihood_ratios(y_true, y_pred, labels=["non-cat", "cat"]) - (1.5, 0.75) + (np.float64(1.5), np.float64(0.75)) """ y_type, y_true, y_pred = _check_targets(y_true, y_pred) @@ -2180,11 +2180,11 @@ def precision_score( >>> y_true = [0, 1, 2, 0, 1, 2] >>> y_pred = [0, 2, 1, 0, 0, 1] >>> precision_score(y_true, y_pred, average='macro') - 0.22... + np.float64(0.22...) >>> precision_score(y_true, y_pred, average='micro') - 0.33... + np.float64(0.33...) >>> precision_score(y_true, y_pred, average='weighted') - 0.22... + np.float64(0.22...) >>> precision_score(y_true, y_pred, average=None) array([0.66..., 0. , 0. ]) >>> y_pred = [0, 0, 0, 0, 0, 0] @@ -2361,11 +2361,11 @@ def recall_score( >>> y_true = [0, 1, 2, 0, 1, 2] >>> y_pred = [0, 2, 1, 0, 0, 1] >>> recall_score(y_true, y_pred, average='macro') - 0.33... + np.float64(0.33...) >>> recall_score(y_true, y_pred, average='micro') - 0.33... + np.float64(0.33...) >>> recall_score(y_true, y_pred, average='weighted') - 0.33... + np.float64(0.33...) >>> recall_score(y_true, y_pred, average=None) array([1., 0., 0.]) >>> y_true = [0, 0, 0, 0, 0, 0] @@ -2471,7 +2471,7 @@ def balanced_accuracy_score(y_true, y_pred, *, sample_weight=None, adjusted=Fals >>> y_true = [0, 1, 0, 0, 1, 0] >>> y_pred = [0, 1, 0, 0, 0, 1] >>> balanced_accuracy_score(y_true, y_pred) - 0.625 + np.float64(0.625) """ C = confusion_matrix(y_true, y_pred, sample_weight=sample_weight) with np.errstate(divide="ignore", invalid="ignore"): @@ -3059,7 +3059,7 @@ def hinge_loss(y_true, pred_decision, *, labels=None, sample_weight=None): >>> pred_decision array([-2.18..., 2.36..., 0.09...]) >>> hinge_loss([-1, 1, 1], pred_decision) - 0.30... + np.float64(0.30...) In the multiclass case: @@ -3073,7 +3073,7 @@ def hinge_loss(y_true, pred_decision, *, labels=None, sample_weight=None): >>> pred_decision = est.decision_function([[-1], [2], [3]]) >>> y_true = [0, 2, 3] >>> hinge_loss(y_true, pred_decision, labels=labels) - 0.56... + np.float64(0.56...) """ check_consistent_length(y_true, pred_decision, sample_weight) pred_decision = check_array(pred_decision, ensure_2d=False) @@ -3219,13 +3219,13 @@ def brier_score_loss( >>> y_true_categorical = np.array(["spam", "ham", "ham", "spam"]) >>> y_prob = np.array([0.1, 0.9, 0.8, 0.3]) >>> brier_score_loss(y_true, y_prob) - 0.037... + np.float64(0.037...) >>> brier_score_loss(y_true, 1-y_prob, pos_label=0) - 0.037... + np.float64(0.037...) >>> brier_score_loss(y_true_categorical, y_prob, pos_label="ham") - 0.037... + np.float64(0.037...) >>> brier_score_loss(y_true, np.array(y_prob) > 0.5) - 0.0 + np.float64(0.0) """ # TODO(1.7): remove in 1.7 and reset y_proba to be required # Note: validate params will raise an error if y_prob is not array-like, diff --git a/sklearn/metrics/_ranking.py b/sklearn/metrics/_ranking.py index 6a53fb542fd32..29594ec949b19 100644 --- a/sklearn/metrics/_ranking.py +++ b/sklearn/metrics/_ranking.py @@ -85,7 +85,7 @@ def auc(x, y): >>> pred = np.array([0.1, 0.4, 0.35, 0.8]) >>> fpr, tpr, thresholds = metrics.roc_curve(y, pred, pos_label=2) >>> metrics.auc(fpr, tpr) - 0.75 + np.float64(0.75) """ check_consistent_length(x, y) x = column_or_1d(x) @@ -210,7 +210,7 @@ def average_precision_score( >>> y_true = np.array([0, 0, 1, 1]) >>> y_scores = np.array([0.1, 0.4, 0.35, 0.8]) >>> average_precision_score(y_true, y_scores) - 0.83... + np.float64(0.83...) >>> y_true = np.array([0, 0, 1, 1, 2, 2]) >>> y_scores = np.array([ ... [0.7, 0.2, 0.1], @@ -221,7 +221,7 @@ def average_precision_score( ... [0.1, 0.2, 0.7], ... ]) >>> average_precision_score(y_true, y_scores) - 0.77... + np.float64(0.77...) """ def _binary_uninterpolated_average_precision( @@ -582,9 +582,9 @@ class scores must correspond to the order of ``labels``, >>> X, y = load_breast_cancer(return_X_y=True) >>> clf = LogisticRegression(solver="liblinear", random_state=0).fit(X, y) >>> roc_auc_score(y, clf.predict_proba(X)[:, 1]) - 0.99... + np.float64(0.99...) >>> roc_auc_score(y, clf.decision_function(X)) - 0.99... + np.float64(0.99...) Multiclass case: @@ -592,7 +592,7 @@ class scores must correspond to the order of ``labels``, >>> X, y = load_iris(return_X_y=True) >>> clf = LogisticRegression(solver="liblinear").fit(X, y) >>> roc_auc_score(y, clf.predict_proba(X), multi_class='ovr') - 0.99... + np.float64(0.99...) Multilabel case: @@ -1241,7 +1241,7 @@ def label_ranking_average_precision_score(y_true, y_score, *, sample_weight=None >>> y_true = np.array([[1, 0, 0], [0, 0, 1]]) >>> y_score = np.array([[0.75, 0.5, 1], [1, 0.2, 0.1]]) >>> label_ranking_average_precision_score(y_true, y_score) - 0.416... + np.float64(0.416...) """ check_consistent_length(y_true, y_score, sample_weight) y_true = check_array(y_true, ensure_2d=False, accept_sparse="csr") @@ -1344,7 +1344,7 @@ def coverage_error(y_true, y_score, *, sample_weight=None): >>> y_true = [[1, 0, 0], [0, 1, 1]] >>> y_score = [[1, 0, 0], [0, 1, 1]] >>> coverage_error(y_true, y_score) - 1.5 + np.float64(1.5) """ y_true = check_array(y_true, ensure_2d=True) y_score = check_array(y_score, ensure_2d=True) @@ -1421,7 +1421,7 @@ def label_ranking_loss(y_true, y_score, *, sample_weight=None): >>> y_true = [[1, 0, 0], [0, 0, 1]] >>> y_score = [[0.75, 0.5, 1], [1, 0.2, 0.1]] >>> label_ranking_loss(y_true, y_score) - 0.75... + np.float64(0.75...) """ y_true = check_array(y_true, ensure_2d=False, accept_sparse="csr") y_score = check_array(y_score, ensure_2d=False) @@ -1677,22 +1677,22 @@ def dcg_score( >>> # we predict scores for the answers >>> scores = np.asarray([[.1, .2, .3, 4, 70]]) >>> dcg_score(true_relevance, scores) - 9.49... + np.float64(9.49...) >>> # we can set k to truncate the sum; only top k answers contribute >>> dcg_score(true_relevance, scores, k=2) - 5.63... + np.float64(5.63...) >>> # now we have some ties in our prediction >>> scores = np.asarray([[1, 0, 0, 0, 1]]) >>> # by default ties are averaged, so here we get the average true >>> # relevance of our top predictions: (10 + 5) / 2 = 7.5 >>> dcg_score(true_relevance, scores, k=1) - 7.5 + np.float64(7.5) >>> # we can choose to ignore ties for faster results, but only >>> # if we know there aren't ties in our scores, otherwise we get >>> # wrong results: >>> dcg_score(true_relevance, ... scores, k=1, ignore_ties=True) - 5.0 + np.float64(5.0) """ y_true = check_array(y_true, ensure_2d=False) y_score = check_array(y_score, ensure_2d=False) @@ -1837,29 +1837,29 @@ def ndcg_score(y_true, y_score, *, k=None, sample_weight=None, ignore_ties=False >>> # we predict some scores (relevance) for the answers >>> scores = np.asarray([[.1, .2, .3, 4, 70]]) >>> ndcg_score(true_relevance, scores) - 0.69... + np.float64(0.69...) >>> scores = np.asarray([[.05, 1.1, 1., .5, .0]]) >>> ndcg_score(true_relevance, scores) - 0.49... + np.float64(0.49...) >>> # we can set k to truncate the sum; only top k answers contribute. >>> ndcg_score(true_relevance, scores, k=4) - 0.35... + np.float64(0.35...) >>> # the normalization takes k into account so a perfect answer >>> # would still get 1.0 >>> ndcg_score(true_relevance, true_relevance, k=4) - 1.0... + np.float64(1.0...) >>> # now we have some ties in our prediction >>> scores = np.asarray([[1, 0, 0, 0, 1]]) >>> # by default ties are averaged, so here we get the average (normalized) >>> # true relevance of our top predictions: (10 / 10 + 5 / 10) / 2 = .75 >>> ndcg_score(true_relevance, scores, k=1) - 0.75... + np.float64(0.75...) >>> # we can choose to ignore ties for faster results, but only >>> # if we know there aren't ties in our scores, otherwise we get >>> # wrong results: >>> ndcg_score(true_relevance, ... scores, k=1, ignore_ties=True) - 0.5... + np.float64(0.5...) """ y_true = check_array(y_true, ensure_2d=False) y_score = check_array(y_score, ensure_2d=False) @@ -1962,10 +1962,10 @@ def top_k_accuracy_score( ... [0.2, 0.4, 0.3], # 2 is in top 2 ... [0.7, 0.2, 0.1]]) # 2 isn't in top 2 >>> top_k_accuracy_score(y_true, y_score, k=2) - 0.75 + np.float64(0.75) >>> # Not normalizing gives the number of "correctly" classified samples >>> top_k_accuracy_score(y_true, y_score, k=2, normalize=False) - 3 + np.int64(3) """ y_true = check_array(y_true, ensure_2d=False, dtype=None) y_true = column_or_1d(y_true) diff --git a/sklearn/metrics/_regression.py b/sklearn/metrics/_regression.py index b5605f18803ab..704838ab16d67 100644 --- a/sklearn/metrics/_regression.py +++ b/sklearn/metrics/_regression.py @@ -287,17 +287,17 @@ def mean_pinball_loss( >>> from sklearn.metrics import mean_pinball_loss >>> y_true = [1, 2, 3] >>> mean_pinball_loss(y_true, [0, 2, 3], alpha=0.1) - 0.03... + np.float64(0.03...) >>> mean_pinball_loss(y_true, [1, 2, 4], alpha=0.1) - 0.3... + np.float64(0.3...) >>> mean_pinball_loss(y_true, [0, 2, 3], alpha=0.9) - 0.3... + np.float64(0.3...) >>> mean_pinball_loss(y_true, [1, 2, 4], alpha=0.9) - 0.03... + np.float64(0.03...) >>> mean_pinball_loss(y_true, y_true, alpha=0.1) - 0.0 + np.float64(0.0) >>> mean_pinball_loss(y_true, y_true, alpha=0.9) - 0.0 + np.float64(0.0) """ y_type, y_true, y_pred, multioutput = _check_reg_targets( y_true, y_pred, multioutput @@ -571,11 +571,11 @@ def root_mean_squared_error( >>> y_true = [3, -0.5, 2, 7] >>> y_pred = [2.5, 0.0, 2, 8] >>> root_mean_squared_error(y_true, y_pred) - 0.612... + np.float64(0.612...) >>> y_true = [[0.5, 1],[-1, 1],[7, -6]] >>> y_pred = [[0, 2],[-1, 2],[8, -5]] >>> root_mean_squared_error(y_true, y_pred) - 0.822... + np.float64(0.822...) """ output_errors = np.sqrt( mean_squared_error( @@ -759,7 +759,7 @@ def root_mean_squared_log_error( >>> y_true = [3, 5, 2.5, 7] >>> y_pred = [2.5, 5, 4, 8] >>> root_mean_squared_log_error(y_true, y_pred) - 0.199... + np.float64(0.199...) """ _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput) check_consistent_length(y_true, y_pred, sample_weight) @@ -833,15 +833,15 @@ def median_absolute_error( >>> y_true = [3, -0.5, 2, 7] >>> y_pred = [2.5, 0.0, 2, 8] >>> median_absolute_error(y_true, y_pred) - 0.5 + np.float64(0.5) >>> y_true = [[0.5, 1], [-1, 1], [7, -6]] >>> y_pred = [[0, 2], [-1, 2], [8, -5]] >>> median_absolute_error(y_true, y_pred) - 0.75 + np.float64(0.75) >>> median_absolute_error(y_true, y_pred, multioutput='raw_values') array([0.5, 1. ]) >>> median_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7]) - 0.85 + np.float64(0.85) """ y_type, y_true, y_pred, multioutput = _check_reg_targets( y_true, y_pred, multioutput @@ -1266,7 +1266,7 @@ def max_error(y_true, y_pred): >>> y_true = [3, 2, 7, 1] >>> y_pred = [4, 2, 7, 1] >>> max_error(y_true, y_pred) - 1 + np.int64(1) """ y_type, y_true, y_pred, _ = _check_reg_targets(y_true, y_pred, None) if y_type == "continuous-multioutput": @@ -1683,13 +1683,13 @@ def d2_pinball_score( >>> y_true = [1, 2, 3] >>> y_pred = [1, 3, 3] >>> d2_pinball_score(y_true, y_pred) - 0.5 + np.float64(0.5) >>> d2_pinball_score(y_true, y_pred, alpha=0.9) - 0.772... + np.float64(0.772...) >>> d2_pinball_score(y_true, y_pred, alpha=0.1) - -1.045... + np.float64(-1.045...) >>> d2_pinball_score(y_true, y_true, alpha=0.1) - 1.0 + np.float64(1.0) """ y_type, y_true, y_pred, multioutput = _check_reg_targets( y_true, y_pred, multioutput @@ -1826,25 +1826,25 @@ def d2_absolute_error_score( >>> y_true = [3, -0.5, 2, 7] >>> y_pred = [2.5, 0.0, 2, 8] >>> d2_absolute_error_score(y_true, y_pred) - 0.764... + np.float64(0.764...) >>> y_true = [[0.5, 1], [-1, 1], [7, -6]] >>> y_pred = [[0, 2], [-1, 2], [8, -5]] >>> d2_absolute_error_score(y_true, y_pred, multioutput='uniform_average') - 0.691... + np.float64(0.691...) >>> d2_absolute_error_score(y_true, y_pred, multioutput='raw_values') array([0.8125 , 0.57142857]) >>> y_true = [1, 2, 3] >>> y_pred = [1, 2, 3] >>> d2_absolute_error_score(y_true, y_pred) - 1.0 + np.float64(1.0) >>> y_true = [1, 2, 3] >>> y_pred = [2, 2, 2] >>> d2_absolute_error_score(y_true, y_pred) - 0.0 + np.float64(0.0) >>> y_true = [1, 2, 3] >>> y_pred = [3, 2, 1] >>> d2_absolute_error_score(y_true, y_pred) - -1.0 + np.float64(-1.0) """ return d2_pinball_score( y_true, y_pred, sample_weight=sample_weight, alpha=0.5, multioutput=multioutput diff --git a/sklearn/metrics/cluster/_bicluster.py b/sklearn/metrics/cluster/_bicluster.py index ad2153878b37e..0d1d3820b8ab3 100644 --- a/sklearn/metrics/cluster/_bicluster.py +++ b/sklearn/metrics/cluster/_bicluster.py @@ -100,7 +100,7 @@ def consensus_score(a, b, *, similarity="jaccard"): >>> a = ([[True, False], [False, True]], [[False, True], [True, False]]) >>> b = ([[False, True], [True, False]], [[True, False], [False, True]]) >>> consensus_score(a, b, similarity='jaccard') - 1.0 + np.float64(1.0) """ if similarity == "jaccard": similarity = _jaccard diff --git a/sklearn/metrics/cluster/_supervised.py b/sklearn/metrics/cluster/_supervised.py index 1f72eae3725f6..ac47c83a488a3 100644 --- a/sklearn/metrics/cluster/_supervised.py +++ b/sklearn/metrics/cluster/_supervised.py @@ -328,7 +328,7 @@ def rand_score(labels_true, labels_pred): are complete but may not always be pure, hence penalized: >>> rand_score([0, 0, 1, 2], [0, 0, 1, 1]) - 0.83... + np.float64(0.83...) """ contingency = pair_confusion_matrix(labels_true, labels_pred) numerator = contingency.diagonal().sum() @@ -524,7 +524,7 @@ def homogeneity_completeness_v_measure(labels_true, labels_pred, *, beta=1.0): >>> from sklearn.metrics import homogeneity_completeness_v_measure >>> y_true, y_pred = [0, 0, 1, 1, 2, 2], [0, 0, 1, 2, 2, 2] >>> homogeneity_completeness_v_measure(y_true, y_pred) - (0.71..., 0.77..., 0.73...) + (np.float64(0.71...), np.float64(0.77...), np.float64(0.73...)) """ labels_true, labels_pred = check_clusterings(labels_true, labels_pred) @@ -608,7 +608,7 @@ def homogeneity_score(labels_true, labels_pred): >>> from sklearn.metrics.cluster import homogeneity_score >>> homogeneity_score([0, 0, 1, 1], [1, 1, 0, 0]) - 1.0 + np.float64(1.0) Non-perfect labelings that further split classes into more clusters can be perfectly homogeneous:: @@ -684,7 +684,7 @@ def completeness_score(labels_true, labels_pred): >>> from sklearn.metrics.cluster import completeness_score >>> completeness_score([0, 0, 1, 1], [1, 1, 0, 0]) - 1.0 + np.float64(1.0) Non-perfect labelings that assign all classes members to the same clusters are still complete:: @@ -773,9 +773,9 @@ def v_measure_score(labels_true, labels_pred, *, beta=1.0): >>> from sklearn.metrics.cluster import v_measure_score >>> v_measure_score([0, 0, 1, 1], [0, 0, 1, 1]) - 1.0 + np.float64(1.0) >>> v_measure_score([0, 0, 1, 1], [1, 1, 0, 0]) - 1.0 + np.float64(1.0) Labelings that assign all classes members to the same clusters are complete but not homogeneous, hence penalized:: @@ -881,7 +881,7 @@ def mutual_info_score(labels_true, labels_pred, *, contingency=None): >>> labels_true = [0, 1, 1, 0, 1, 0] >>> labels_pred = [0, 1, 0, 0, 1, 1] >>> mutual_info_score(labels_true, labels_pred) - 0.056... + np.float64(0.056...) """ if contingency is None: labels_true, labels_pred = check_clusterings(labels_true, labels_pred) @@ -1238,9 +1238,9 @@ def fowlkes_mallows_score(labels_true, labels_pred, *, sparse=False): >>> from sklearn.metrics.cluster import fowlkes_mallows_score >>> fowlkes_mallows_score([0, 0, 1, 1], [0, 0, 1, 1]) - 1.0 + np.float64(1.0) >>> fowlkes_mallows_score([0, 0, 1, 1], [1, 1, 0, 0]) - 1.0 + np.float64(1.0) If classes members are completely split across different clusters, the assignment is totally random, hence the FMI is null:: diff --git a/sklearn/metrics/cluster/_unsupervised.py b/sklearn/metrics/cluster/_unsupervised.py index 8e032b971d54e..36a12ddadc5ea 100644 --- a/sklearn/metrics/cluster/_unsupervised.py +++ b/sklearn/metrics/cluster/_unsupervised.py @@ -128,7 +128,7 @@ def silhouette_score( >>> X, y = make_blobs(random_state=42) >>> kmeans = KMeans(n_clusters=2, random_state=42) >>> silhouette_score(X, kmeans.fit_predict(X)) - 0.49... + np.float64(0.49...) """ if sample_size is not None: X, labels = check_X_y(X, labels, accept_sparse=["csc", "csr"]) @@ -363,7 +363,7 @@ def calinski_harabasz_score(X, labels): >>> X, _ = make_blobs(random_state=0) >>> kmeans = KMeans(n_clusters=3, random_state=0,).fit(X) >>> calinski_harabasz_score(X, kmeans.labels_) - 114.8... + np.float64(114.8...) """ X, labels = check_X_y(X, labels) le = LabelEncoder() @@ -438,7 +438,7 @@ def davies_bouldin_score(X, labels): >>> X = [[0, 1], [1, 1], [3, 4]] >>> labels = [0, 0, 1] >>> davies_bouldin_score(X, labels) - 0.12... + np.float64(0.12...) """ X, labels = check_X_y(X, labels) le = LabelEncoder() diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py index 43fab5fce6e63..150e6bd989d40 100644 --- a/sklearn/model_selection/_search.py +++ b/sklearn/model_selection/_search.py @@ -1911,7 +1911,7 @@ class RandomizedSearchCV(BaseSearchCV): >>> clf = RandomizedSearchCV(logistic, distributions, random_state=0) >>> search = clf.fit(iris.data, iris.target) >>> search.best_params_ - {'C': 2..., 'penalty': 'l1'} + {'C': np.float64(2...), 'penalty': 'l1'} """ _required_parameters = ["estimator", "param_distributions"] diff --git a/sklearn/preprocessing/_label.py b/sklearn/preprocessing/_label.py index 301dc19bb1985..98684ac3c5506 100644 --- a/sklearn/preprocessing/_label.py +++ b/sklearn/preprocessing/_label.py @@ -74,11 +74,11 @@ class LabelEncoder(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None): >>> le.fit(["paris", "paris", "tokyo", "amsterdam"]) LabelEncoder() >>> list(le.classes_) - ['amsterdam', 'paris', 'tokyo'] + [np.str_('amsterdam'), np.str_('paris'), np.str_('tokyo')] >>> le.transform(["tokyo", "tokyo", "paris"]) array([2, 2, 1]...) >>> list(le.inverse_transform([2, 2, 1])) - ['tokyo', 'tokyo', 'paris'] + [np.str_('tokyo'), np.str_('tokyo'), np.str_('paris')] """ def fit(self, y): diff --git a/sklearn/preprocessing/_target_encoder.py b/sklearn/preprocessing/_target_encoder.py index b3b7c3d5e7bd9..5a4e24c8bc5f5 100644 --- a/sklearn/preprocessing/_target_encoder.py +++ b/sklearn/preprocessing/_target_encoder.py @@ -172,7 +172,7 @@ class TargetEncoder(OneToOneFeatureMixin, _BaseEncoder): >>> # encodings: >>> enc_high_smooth = TargetEncoder(smooth=5000.0).fit(X, y) >>> enc_high_smooth.target_mean_ - 44... + np.float64(44...) >>> enc_high_smooth.encodings_ [array([44..., 44..., 44...])] diff --git a/sklearn/random_projection.py b/sklearn/random_projection.py index c6f7057396d4a..5c228ade8068d 100644 --- a/sklearn/random_projection.py +++ b/sklearn/random_projection.py @@ -119,7 +119,7 @@ def johnson_lindenstrauss_min_dim(n_samples, *, eps=0.1): -------- >>> from sklearn.random_projection import johnson_lindenstrauss_min_dim >>> johnson_lindenstrauss_min_dim(1e6, eps=0.5) - 663 + np.int64(663) >>> johnson_lindenstrauss_min_dim(1e6, eps=[0.5, 0.1, 0.01]) array([ 663, 11841, 1112658]) @@ -736,7 +736,7 @@ class SparseRandomProjection(BaseRandomProjection): (25, 2759) >>> # very few components are non-zero >>> np.mean(transformer.components_ != 0) - 0.0182... + np.float64(0.0182...) """ _parameter_constraints: dict = { diff --git a/sklearn/utils/extmath.py b/sklearn/utils/extmath.py index 638423e1ebc54..63b24d665597c 100644 --- a/sklearn/utils/extmath.py +++ b/sklearn/utils/extmath.py @@ -121,7 +121,7 @@ def fast_logdet(A): >>> from sklearn.utils.extmath import fast_logdet >>> a = np.array([[5, 1], [2, 8]]) >>> fast_logdet(a) - 3.6375861597263857 + np.float64(3.6375861597263857) """ xp, _ = get_namespace(A) sign, ld = xp.linalg.slogdet(A) From 64a264e136c86e3bace2d7269d61211661bd6dbb Mon Sep 17 00:00:00 2001 From: nbrown-ScottLogic <142215175+nbrown-ScottLogic@users.noreply.github.com> Date: Tue, 6 Aug 2024 14:54:13 +0100 Subject: [PATCH 202/275] DOC add link to plot outlier detection wine to docs (#29237) Co-authored-by: Adrin Jalali --- doc/modules/outlier_detection.rst | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/doc/modules/outlier_detection.rst b/doc/modules/outlier_detection.rst index 0c6891ed119bd..a8740a68d5586 100644 --- a/doc/modules/outlier_detection.rst +++ b/doc/modules/outlier_detection.rst @@ -246,6 +246,10 @@ This strategy is illustrated below. (:class:`covariance.MinCovDet`) of location and covariance to assess the degree of outlyingness of an observation. +* See :ref:`sphx_glr_auto_examples_applications_plot_outlier_detection_wine.py` + for an example of robust covariance estimation on a real data set. + + .. rubric:: References * Rousseeuw, P.J., Van Driessen, K. "A fast algorithm for the minimum From 6dd63fd38cdd4109ecc2ee02db0c511004f583f5 Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Sat, 10 Aug 2024 23:50:37 +1000 Subject: [PATCH 203/275] FIX Add error when `LeaveOneOut` used in `CalibratedClassifierCV` (#29545) --- doc/whats_new/v1.5.rst | 7 ++++ sklearn/calibration.py | 9 ++++- sklearn/tests/test_calibration.py | 62 +++++++++++++++++++------------ 3 files changed, 54 insertions(+), 24 deletions(-) diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst index 059875eec12d6..b5542a0d1cf5f 100644 --- a/doc/whats_new/v1.5.rst +++ b/doc/whats_new/v1.5.rst @@ -23,6 +23,13 @@ Version 1.5.2 Changelog --------- +:mod:`sklearn.calibration` +.......................... + +- |Fix| Raise error when :class:`~sklearn.model_selection.LeaveOneOut` used in + `cv`, matching what would happen if `KFold(n_splits=n_samples)` was used. + :pr:`29545` by :user:`Lucy Liu ` + :mod:`sklearn.compose` ...................... diff --git a/sklearn/calibration.py b/sklearn/calibration.py index 4fb0c508dba71..18395fb77219c 100644 --- a/sklearn/calibration.py +++ b/sklearn/calibration.py @@ -28,7 +28,7 @@ clone, ) from .isotonic import IsotonicRegression -from .model_selection import check_cv, cross_val_predict +from .model_selection import LeaveOneOut, check_cv, cross_val_predict from .preprocessing import LabelEncoder, label_binarize from .svm import LinearSVC from .utils import ( @@ -394,6 +394,13 @@ def fit(self, X, y, sample_weight=None, **fit_params): "cross-validation but provided less than " f"{n_folds} examples for at least one class." ) + if isinstance(self.cv, LeaveOneOut): + raise ValueError( + "LeaveOneOut cross-validation does not allow" + "all classes to be present in test splits. " + "Please use a cross-validation generator that allows " + "all classes to appear in every test and train split." + ) cv = check_cv(self.cv, y, classifier=True) if self.ensemble: diff --git a/sklearn/tests/test_calibration.py b/sklearn/tests/test_calibration.py index 833ef2ea7e558..94ef295b7619c 100644 --- a/sklearn/tests/test_calibration.py +++ b/sklearn/tests/test_calibration.py @@ -146,6 +146,20 @@ def test_calibration_cv_splitter(data, ensemble): assert len(calib_clf.calibrated_classifiers_) == expected_n_clf +def test_calibration_cv_nfold(data): + # Check error raised when number of examples per class less than nfold + X, y = data + + kfold = KFold(n_splits=101) + calib_clf = CalibratedClassifierCV(cv=kfold, ensemble=True) + with pytest.raises(ValueError, match="Requesting 101-fold cross-validation"): + calib_clf.fit(X, y) + + calib_clf = CalibratedClassifierCV(cv=LeaveOneOut(), ensemble=True) + with pytest.raises(ValueError, match="LeaveOneOut cross-validation does"): + calib_clf.fit(X, y) + + @pytest.mark.parametrize("method", ["sigmoid", "isotonic"]) @pytest.mark.parametrize("ensemble", [True, False]) def test_sample_weight(data, method, ensemble): @@ -423,45 +437,47 @@ def test_calibration_nan_imputer(ensemble): @pytest.mark.parametrize("ensemble", [True, False]) def test_calibration_prob_sum(ensemble): - # Test that sum of probabilities is 1. A non-regression test for - # issue #7796 - num_classes = 2 - X, y = make_classification(n_samples=10, n_features=5, n_classes=num_classes) + # Test that sum of probabilities is (max) 1. A non-regression test for + # issue #7796 - when test has fewer classes than train + X, _ = make_classification(n_samples=10, n_features=5, n_classes=2) + y = [1, 1, 1, 1, 1, 0, 0, 0, 0, 0] clf = LinearSVC(C=1.0, random_state=7) + # In the first and last fold, test will have 1 class while train will have 2 clf_prob = CalibratedClassifierCV( - clf, method="sigmoid", cv=LeaveOneOut(), ensemble=ensemble + clf, method="sigmoid", cv=KFold(n_splits=3), ensemble=ensemble ) clf_prob.fit(X, y) - - probs = clf_prob.predict_proba(X) - assert_array_almost_equal(probs.sum(axis=1), np.ones(probs.shape[0])) + assert_allclose(clf_prob.predict_proba(X).sum(axis=1), 1.0) @pytest.mark.parametrize("ensemble", [True, False]) def test_calibration_less_classes(ensemble): # Test to check calibration works fine when train set in a test-train # split does not contain all classes - # Since this test uses LOO, at each iteration train set will not contain a - # class label - X = np.random.randn(10, 5) - y = np.arange(10) - clf = LinearSVC(C=1.0, random_state=7) + # In 1st split, train is missing class 0 + # In 3rd split, train is missing class 3 + X = np.random.randn(12, 5) + y = [0, 0, 0, 1] + [1, 1, 2, 2] + [2, 3, 3, 3] + clf = DecisionTreeClassifier(random_state=7) cal_clf = CalibratedClassifierCV( - clf, method="sigmoid", cv=LeaveOneOut(), ensemble=ensemble + clf, method="sigmoid", cv=KFold(3), ensemble=ensemble ) cal_clf.fit(X, y) - for i, calibrated_classifier in enumerate(cal_clf.calibrated_classifiers_): - proba = calibrated_classifier.predict_proba(X) - if ensemble: + if ensemble: + classes = np.arange(4) + for calib_i, class_i in zip([0, 2], [0, 3]): + proba = cal_clf.calibrated_classifiers_[calib_i].predict_proba(X) # Check that the unobserved class has proba=0 - assert_array_equal(proba[:, i], np.zeros(len(y))) + assert_array_equal(proba[:, class_i], np.zeros(len(y))) # Check for all other classes proba>0 - assert np.all(proba[:, :i] > 0) - assert np.all(proba[:, i + 1 :] > 0) - else: - # Check `proba` are all 1/n_classes - assert np.allclose(proba, 1 / proba.shape[0]) + assert np.all(proba[:, classes != class_i] > 0) + + # When `ensemble=False`, `cross_val_predict` is used to compute predictions + # to fit only one `calibrated_classifiers_` + else: + proba = cal_clf.calibrated_classifiers_[0].predict_proba(X) + assert_array_almost_equal(proba.sum(axis=1), np.ones(proba.shape[0])) @pytest.mark.parametrize( From 4236f109d8fb80fff1d61416ff17bfe3da7764e1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Sat, 10 Aug 2024 15:55:44 +0200 Subject: [PATCH 204/275] BLD Remove confusing default Meson buildtype (#29594) --- meson.build | 1 - 1 file changed, 1 deletion(-) diff --git a/meson.build b/meson.build index 9902d3fe189d2..3f14108f77998 100644 --- a/meson.build +++ b/meson.build @@ -5,7 +5,6 @@ project( license: 'BSD-3', meson_version: '>= 1.1.0', default_options: [ - 'buildtype=debugoptimized', 'c_std=c11', 'cpp_std=c++14', ], From d9335bfae73650f24de73eff84c231c0de1f0d02 Mon Sep 17 00:00:00 2001 From: ParsifalXu <43930877+ParsifalXu@users.noreply.github.com> Date: Sun, 11 Aug 2024 07:20:18 +0800 Subject: [PATCH 205/275] Updating documentation of `SequentialFeatureSelector` (#29468) Co-authored-by: Adrin Jalali Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> Co-authored-by: Thomas J. Fan --- sklearn/feature_selection/_sequential.py | 5 ++++- sklearn/feature_selection/tests/test_sequential.py | 2 +- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/sklearn/feature_selection/_sequential.py b/sklearn/feature_selection/_sequential.py index 9c393724f9cea..91e9410fc657c 100644 --- a/sklearn/feature_selection/_sequential.py +++ b/sklearn/feature_selection/_sequential.py @@ -58,6 +58,7 @@ class SequentialFeatureSelector( consecutive feature additions or removals, stop adding or removing. `tol` can be negative when removing features using `direction="backward"`. + `tol` is required to be strictly positive when doing forward selection. It can be useful to reduce the number of features at the cost of a small decrease in the score. @@ -230,7 +231,9 @@ def fit(self, X, y=None): self.n_features_to_select_ = int(n_features * self.n_features_to_select) if self.tol is not None and self.tol < 0 and self.direction == "forward": - raise ValueError("tol must be positive when doing forward selection") + raise ValueError( + "tol must be strictly positive when doing forward selection" + ) cv = check_cv(self.cv, y, classifier=is_classifier(self.estimator)) diff --git a/sklearn/feature_selection/tests/test_sequential.py b/sklearn/feature_selection/tests/test_sequential.py index 82d65c55a0195..e1e0e2cdde938 100644 --- a/sklearn/feature_selection/tests/test_sequential.py +++ b/sklearn/feature_selection/tests/test_sequential.py @@ -278,7 +278,7 @@ def test_forward_neg_tol_error(): tol=-1e-3, ) - with pytest.raises(ValueError, match="tol must be positive"): + with pytest.raises(ValueError, match="tol must be strictly positive"): sfs.fit(X, y) From c0149cfa329e8474376485f71f654e2e0f817cc8 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 12 Aug 2024 10:32:59 +0200 Subject: [PATCH 206/275] DOC add CZI and Wellcome Trust as funders (#29646) Co-authored-by: Thomas J. Fan --- doc/about.rst | 31 +++++++++++++++++++++++++++- doc/images/czi-small.png | Bin 0 -> 16114 bytes doc/images/czi.png | Bin 0 -> 11788 bytes doc/images/czi_logo.svg | 19 ----------------- doc/images/wellcome-trust-small.png | Bin 0 -> 7109 bytes doc/images/wellcome-trust.png | Bin 0 -> 6512 bytes doc/templates/index.html | 2 ++ 7 files changed, 32 insertions(+), 20 deletions(-) create mode 100644 doc/images/czi-small.png create mode 100644 doc/images/czi.png delete mode 100644 doc/images/czi_logo.svg create mode 100644 doc/images/wellcome-trust-small.png create mode 100644 doc/images/wellcome-trust.png diff --git a/doc/about.rst b/doc/about.rst index 0fa34e1a9cf30..840049114d9cf 100644 --- a/doc/about.rst +++ b/doc/about.rst @@ -290,6 +290,35 @@ The project would like to thank the following funders. ........... +.. |czi| image:: images/czi.png + :target: https://chanzuckerberg.com + +.. |wellcome| image:: images/wellcome-trust.png + :target: https://wellcome.org/ + +.. div:: sk-text-image-grid-small + + .. div:: text-box + + `The Chan-Zuckerberg Initiative `_ and + `Wellcome Trust `_ fund scikit-learn through the + `Essential Open Source Software for Science (EOSS) `_ + cycle 6. + + It supports Lucy Liu and diversity & inclusion initiatives that will + be announced in the future. + + .. div:: image-box + + .. table:: + :class: image-subtable + + +----------+----------------+ + | |czi| | |wellcome| | + +----------+----------------+ + +........... + .. div:: sk-text-image-grid-small .. div:: text-box @@ -453,7 +482,7 @@ Past Sponsors .. div:: image-box - .. image:: images/czi_logo.svg + .. image:: images/czi.png :target: https://chanzuckerberg.com ...................... diff --git a/doc/images/czi-small.png b/doc/images/czi-small.png new file mode 100644 index 0000000000000000000000000000000000000000..7a6c81acb44a0722a3d0c35d44d994b63dc0ae80 GIT binary patch literal 16114 zcmeHtWl&sA*X`i$?(XjH?(Ps6U~qQ{?ry=|CAdp)Cj^%S_uy_p!X?k^b-zDf)xH1T znL0D)^xk{z-m6#lOjmW3szHl~jNKru=ikLcf1*12QrI06I(`O&pUT(Mdprv_bk^KLw5zFtfp5_e8C zbUwM$bA^$w$$_(G1es*K8oym+1iYRYPlfL(DV83jHSHVQHT;@FSrmSIdOf!LVtgS& zvf0}~Lc(Jla4CrSNStSE>!1JTz2;}M^=s)DonMz~?ghz6LV(o;(#O{$@r$@%>AhdT zZXBD7u;75-TN?;Aumu(chjh!Hm~`UXGV zkqJY!=!@Kpk3XzoNaneIvVC=)Wm;Z$e)i9IU>~Y*zFL1i8uY6U-^tGJ z$3Mlq+m2@`3=4zu%gErlta!tZ>3Xn#@R-+f_(C{+8y~~>VLPtp#rWgPmH5xG{oJNI z+Zx>T^kQ~IJ!NBWY-s17yD~@2mp^xZ1~l6C1s^fHf`ZrMf1*=#EVu2+Y~%A^4h=of zEE(!uK)G+4Oh$J#z%xeGnT}6&* z_RpF2_xn1!TSF6{t2e|z-DiTb#Bo3&?*g$NL zVtAbOcu=(%_tdfLj%G%jxNn~^+$}2Gh6{dMU!F43tN5YbUy5Z!653ird5hm(@4t;* z3q!j2aIRhL|A0=@YC-iyu7`O^C~v$I_M-xc&un8){&R{uLirWN4b|-@-!a=~e(@7f zST~*>!|d@&WUL}Fgw$5m32>gPB*jb zn&yBZ<++)np+r!KT-qjmL@{T4j+fFAM5I|It6v*`*rE7M5XtW<1c{*eX1VFzlb~+O zkGr$i?~{{!eQBGxM;^kyK~sbLgN$(`k6-Q6C}K5A3N3=AZXT8Zu6y3R3tqWNGsiJw z1BsFEg+5~5u1_XYLJ9+#s>QT(4H|pO;4jxp5O7ln78F>Be2|_%(q#4-9-fnM50Qg? 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    Who uses scikit-learn?

    + +
    2. From f1f58c392de3f6349751a1fb61caee9a6b25184a Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 12 Aug 2024 01:34:05 -0700 Subject: [PATCH 207/275] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29659) Co-authored-by: Lock file bot --- build_tools/azure/debian_atlas_32bit_lock.txt | 2 +- ...latest_conda_forge_mkl_linux-64_conda.lock | 56 +++++++++---------- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 18 +++--- ...test_conda_mkl_no_openmp_osx-64_conda.lock | 8 +-- ...st_pip_openblas_pandas_linux-64_conda.lock | 12 ++-- .../pymin_conda_forge_mkl_win-64_conda.lock | 12 ++-- ...nblas_min_dependencies_linux-64_conda.lock | 16 +++--- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 20 +++---- build_tools/circle/doc_linux-64_conda.lock | 36 ++++++------ .../doc_min_dependencies_linux-64_conda.lock | 26 ++++----- 10 files changed, 103 insertions(+), 103 deletions(-) diff --git a/build_tools/azure/debian_atlas_32bit_lock.txt b/build_tools/azure/debian_atlas_32bit_lock.txt index 64513a4be3866..6e407243fc695 100644 --- a/build_tools/azure/debian_atlas_32bit_lock.txt +++ b/build_tools/azure/debian_atlas_32bit_lock.txt @@ -4,7 +4,7 @@ # # pip-compile --output-file=build_tools/azure/debian_atlas_32bit_lock.txt build_tools/azure/debian_atlas_32bit_requirements.txt # -attrs==24.1.0 +attrs==24.2.0 # via pytest coverage==7.6.1 # via pytest-cov diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index d25420e46b309..e54faa3011313 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -22,7 +22,7 @@ https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62e https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.32.3-h4bc722e_0.conda#7624e34ee6baebfc80d67bac76cc9d9d https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3 https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hd590300_1.conda#aec6c91c7371c26392a06708a73c70e5 -https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.20-hd590300_0.conda#8e88f9389f1165d7c0936fe40d9a9a79 +https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.21-h4bc722e_0.conda#36ce76665bf67f5aac36be7a0d21b7f3 https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda#172bf1cd1ff8629f2b1179945ed45055 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.2-h59595ed_0.conda#e7ba12deb7020dd080c6c70e7b6f6a3d https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2#d645c6d2ac96843a2bfaccd2d62b3ac3 @@ -50,7 +50,7 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-renderproto-0.11.1-h7f98852 https://conda.anaconda.org/conda-forge/linux-64/xorg-xextproto-7.3.0-h0b41bf4_1003.conda#bce9f945da8ad2ae9b1d7165a64d0f87 https://conda.anaconda.org/conda-forge/linux-64/xorg-xproto-7.0.31-h7f98852_1007.tar.bz2#b4a4381d54784606820704f7b5f05a15 https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2#2161070d867d1b1204ea749c8eec4ef0 -https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.7.1-h87b94db_1.conda#2d76d2cfdcfe2d5c3883d33d8be919e7 +https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.7.2-h87b94db_0.conda#8623f26fa29df281dc69ebdb41df0a25 https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.2.18-he027950_7.conda#11e5cb0b426772974f6416545baee0ce https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.1.16-he027950_3.conda#adbf0c44ca88a3cded175cd809a106b6 https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.1.18-he027950_7.conda#95611b325a9728ed68b8f7eef2dd3feb @@ -81,43 +81,43 @@ 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https://repo.anaconda.com/pkgs/main/osx-64/kiwisolver-1.4.4-py312hcec6c5f_0.cond https://repo.anaconda.com/pkgs/main/osx-64/lcms2-2.12-hf1fd2bf_0.conda#697aba7a3308226df7a93ccfeae16ffa https://repo.anaconda.com/pkgs/main/osx-64/mkl-service-2.4.0-py312h6c40b1e_1.conda#b1ef860be9043b35c5e8d9388b858514 https://repo.anaconda.com/pkgs/main/osx-64/ninja-1.10.2-hecd8cb5_5.conda#a0043b325fb08db82477ae433668e684 -https://repo.anaconda.com/pkgs/main/osx-64/openjpeg-2.4.0-h7231236_2.conda#e7cd7f1cdc309f7e32cedd73803536e0 +https://repo.anaconda.com/pkgs/main/osx-64/openjpeg-2.5.2-hbf2204d_0.conda#8463f11309271a93d615450382761470 https://repo.anaconda.com/pkgs/main/osx-64/packaging-24.1-py312hecd8cb5_0.conda#6130dafc4d26d55e93ceab460d2a72b5 https://repo.anaconda.com/pkgs/main/osx-64/pluggy-1.0.0-py312hecd8cb5_1.conda#647fada22f1697691fdee90b52c99bcb https://repo.anaconda.com/pkgs/main/osx-64/pyparsing-3.0.9-py312hecd8cb5_0.conda#d85cf2b81c6d9326a57a6418e14db258 https://repo.anaconda.com/pkgs/main/noarch/python-tzdata-2023.3-pyhd3eb1b0_0.conda#479c037de0186d114b9911158427624e https://repo.anaconda.com/pkgs/main/osx-64/pytz-2024.1-py312hecd8cb5_0.conda#2b28ec0e0d07f5c0c701f75200b1e8b6 -https://repo.anaconda.com/pkgs/main/osx-64/setuptools-69.5.1-py312hecd8cb5_0.conda#5c7c7ef1e0762e3ca1f543d28310946f +https://repo.anaconda.com/pkgs/main/osx-64/setuptools-72.1.0-py312hecd8cb5_0.conda#dff219f3528a6e8ad235c48a29cd6dbe https://repo.anaconda.com/pkgs/main/noarch/six-1.16.0-pyhd3eb1b0_1.conda#34586824d411d36af2fa40e799c172d0 https://repo.anaconda.com/pkgs/main/noarch/toml-0.10.2-pyhd3eb1b0_0.conda#cda05f5f6d8509529d1a2743288d197a https://repo.anaconda.com/pkgs/main/osx-64/tornado-6.4.1-py312h46256e1_0.conda#ff2efd781e1b1af38284aeda9d676d42 @@ -79,7 +79,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/numexpr-2.8.7-py312hac873b0_0.conda#6 https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.11.4-py312h81688c2_0.conda#7d57b4c21a9261f97fa511e0940c5d93 https://repo.anaconda.com/pkgs/main/osx-64/pandas-2.2.2-py312h77d3abe_0.conda#463868c40d8ff98bec263f1fd57a8d97 https://repo.anaconda.com/pkgs/main/osx-64/pyamg-4.2.3-py312h44cbcf4_0.conda#3bdc7be74087b3a5a83c520a74e1e8eb -# pip cython @ https://files.pythonhosted.org/packages/d5/6d/06c08d75adb98cdf72af18801e193d22580cc86ca553610f430f18ea26b3/Cython-3.0.10-cp312-cp312-macosx_10_9_x86_64.whl#sha256=8f2864ab5fcd27a346f0b50f901ebeb8f60b25a60a575ccfd982e7f3e9674914 +# pip cython @ https://files.pythonhosted.org/packages/58/50/fbb23239efe2183e4eaf76689270d6f5b3bbcf9be9ad1eb97cc34349e6fc/Cython-3.0.11-cp312-cp312-macosx_10_9_x86_64.whl#sha256=11996c40c32abf843ba652a6d53cb15944c88d91f91fc4e6f0028f5df8a8f8a1 # pip meson @ https://files.pythonhosted.org/packages/1d/8d/b83d525907c00c5e22a9cae832bbd958310518ae6ad1dc7e01b69abbb117/meson-1.4.2.tar.gz#sha256=ea2546a26f4a171a741c1fd036f22c9c804d6198e3259f1df588e01f842dd69f # pip threadpoolctl @ https://files.pythonhosted.org/packages/4b/2c/ffbf7a134b9ab11a67b0cf0726453cedd9c5043a4fe7a35d1cefa9a1bcfb/threadpoolctl-3.5.0-py3-none-any.whl#sha256=56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467 # pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526 diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index b79b8cd5ea6de..7cc0fbf4c197e 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -22,17 +22,17 @@ https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be421 https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.14-h39e8969_0.conda#78dbc5e3c69143ebc037fc5d5b22e597 https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda#acf93d6aceb74d6110e20b44cc45939e https://repo.anaconda.com/pkgs/main/linux-64/python-3.11.9-h955ad1f_0.conda#5668a8845dd35bbbc9663c8f217a2ab8 -https://repo.anaconda.com/pkgs/main/linux-64/setuptools-69.5.1-py311h06a4308_0.conda#0989470c81841dfcb22c7bbb40f543c5 +https://repo.anaconda.com/pkgs/main/linux-64/setuptools-72.1.0-py311h06a4308_0.conda#58a35dba367429761d046074dcfa8b19 https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py311h06a4308_0.conda#ec915b5ff89bdbcea7ef943d9e296967 https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py311h06a4308_0.conda#84aef4db159f0daf63751d87d7d6ca56 # pip alabaster @ https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl#sha256=fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b # pip array-api-compat @ https://files.pythonhosted.org/packages/0f/22/8228be1d3c6d4ffcf05cd89872ce65c1317b2af98d34b9d89b247d8d49cb/array_api_compat-1.8-py3-none-any.whl#sha256=140204454086264d37263bc4afe1182b428353e94e9edcc38d17b009863c982d -# pip babel @ https://files.pythonhosted.org/packages/27/45/377f7e32a5c93d94cd56542349b34efab5ca3f9e2fd5a68c5e93169aa32d/Babel-2.15.0-py3-none-any.whl#sha256=08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb +# pip babel @ https://files.pythonhosted.org/packages/ed/20/bc79bc575ba2e2a7f70e8a1155618bb1301eaa5132a8271373a6903f73f8/babel-2.16.0-py3-none-any.whl#sha256=368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b # pip certifi @ https://files.pythonhosted.org/packages/1c/d5/c84e1a17bf61d4df64ca866a1c9a913874b4e9bdc131ec689a0ad013fb36/certifi-2024.7.4-py3-none-any.whl#sha256=c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90 # pip charset-normalizer @ https://files.pythonhosted.org/packages/40/26/f35951c45070edc957ba40a5b1db3cf60a9dbb1b350c2d5bef03e01e61de/charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8 # pip coverage @ https://files.pythonhosted.org/packages/14/6f/8351b465febb4dbc1ca9929505202db909c5a635c6fdf33e089bbc3d7d85/coverage-7.6.1-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=0c0420b573964c760df9e9e86d1a9a622d0d27f417e1a949a8a66dd7bcee7bc6 # pip cycler @ https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl#sha256=85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 -# pip cython @ https://files.pythonhosted.org/packages/45/82/077c13035d6f45d8b8b74d67e9f73f2bfc54ef8d1f79572790f6f7d2b4f5/Cython-3.0.10-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=38d40fa1324ac47c04483d151f5e092406a147eac88a18aec789cf01c089c3f2 +# pip cython @ https://files.pythonhosted.org/packages/93/03/e330b241ad8aa12bb9d98b58fb76d4eb7dcbe747479aab5c29fce937b9e7/Cython-3.0.11-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=3999fb52d3328a6a5e8c63122b0a8bd110dfcdb98dda585a3def1426b991cba7 # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc # pip fonttools @ https://files.pythonhosted.org/packages/a4/22/0a0ad59d9367997fd74a00ad2e88d10559122e09f105e94d34c155aecc0a/fonttools-4.53.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=bee32ea8765e859670c4447b0817514ca79054463b6b79784b08a8df3a4d78e3 @@ -74,9 +74,9 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py311h06a4308_0.conda#84ae # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl#sha256=70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6 # pip scipy @ https://files.pythonhosted.org/packages/89/bb/80c9c98d887c855710fd31fc5ae5574133e98203b3475b07579251803662/scipy-1.14.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=9e3154691b9f7ed73778d746da2df67a19d046a6c8087c8b385bc4cdb2cfca74 -# pip tifffile @ https://files.pythonhosted.org/packages/05/a9/f7b3fd6c73e0ac29e7f9c9c86c3b7367b182a3dd60495da7b8129e6df681/tifffile-2024.7.24-py3-none-any.whl#sha256=f5cce1a915c37bc44ae4a792e3b42c07a30a3fa88406f5c6060a3de076487ed1 +# pip tifffile @ https://files.pythonhosted.org/packages/fd/3a/6ec0327e238253a2b7adab0e542763fd639c4b3cef63b135a74ef3f454a7/tifffile-2024.8.10-py3-none-any.whl#sha256=1c224564fa92e7e9f9a0ed65880b2ece97c3f0d10029ffbebfa5e62b3f6b343d # pip lightgbm @ https://files.pythonhosted.org/packages/4e/19/1b928cad70a4e1a3e2c37d5417ca2182510f2451eaadb6c91cd9ec692cae/lightgbm-4.5.0-py3-none-manylinux_2_28_x86_64.whl#sha256=960a0e7c077de0ca3053f1325d3edfc92ea815acf5176adcacdea0f635aeef9b -# pip matplotlib @ https://files.pythonhosted.org/packages/b8/63/cef838d92c1918ae28afd12b8aeaa9c104a0686cf6447aa0546f7c6dd1f0/matplotlib-3.9.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ab38a4f3772523179b2f772103d8030215b318fef6360cb40558f585bf3d017f +# pip matplotlib @ https://files.pythonhosted.org/packages/a5/8b/90fae9c1b34ef3252003c26b15e8cb26b83701e34e5acf6430919c2c5c89/matplotlib-3.9.1.post1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=89eb7e89e2b57856533c5c98f018aa3254fa3789fcd86d5f80077b9034a54c9a # pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 # pip pandas @ https://files.pythonhosted.org/packages/fc/a5/4d82be566f069d7a9a702dcdf6f9106df0e0b042e738043c0cc7ddd7e3f6/pandas-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=6d2123dc9ad6a814bcdea0f099885276b31b24f7edf40f6cdbc0912672e22eee # pip pyamg @ https://files.pythonhosted.org/packages/d3/e8/6898b3b791f369605012e896ed903b6626f3bd1208c6a647d7219c070209/pyamg-5.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=679a5904eac3a4880288c8c0e6a29f110a2627ea15a443a4e9d5997c7dc5fab6 @@ -84,4 +84,4 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py311h06a4308_0.conda#84ae # pip pytest-xdist @ https://files.pythonhosted.org/packages/6d/82/1d96bf03ee4c0fdc3c0cbe61470070e659ca78dc0086fb88b66c185e2449/pytest_xdist-3.6.1-py3-none-any.whl#sha256=9ed4adfb68a016610848639bb7e02c9352d5d9f03d04809919e2dafc3be4cca7 # pip scikit-image @ https://files.pythonhosted.org/packages/ad/96/138484302b8ec9a69cdf65e8d4ab47a640a3b1a8ea3c437e1da3e1a5a6b8/scikit_image-0.24.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=fa27b3a0dbad807b966b8db2d78da734cb812ca4787f7fbb143764800ce2fa9c # pip sphinx @ https://files.pythonhosted.org/packages/4d/61/2ad169c6ff1226b46e50da0e44671592dbc6d840a52034a0193a99b28579/sphinx-8.0.2-py3-none-any.whl#sha256=56173572ae6c1b9a38911786e206a110c9749116745873feae4f9ce88e59391d -# pip numpydoc @ https://files.pythonhosted.org/packages/f0/fa/dcfe0f65660661db757ee9ebd84e170ff98edd5d80235f62457d9088f85f/numpydoc-1.7.0-py3-none-any.whl#sha256=5a56419d931310d79a06cfc2a126d1558700feeb9b4f3d8dcae1a8134be829c9 +# pip numpydoc @ https://files.pythonhosted.org/packages/6c/45/56d99ba9366476cd8548527667f01869279cedb9e66b28eb4dfb27701679/numpydoc-1.8.0-py3-none-any.whl#sha256=72024c7fd5e17375dec3608a27c03303e8ad00c81292667955c6fea7a3ccf541 diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock index ae91423d25ea1..feae35c24526a 100644 --- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock @@ -29,7 +29,7 @@ https://conda.anaconda.org/conda-forge/win-64/graphite2-1.3.13-h63175ca_1003.con https://conda.anaconda.org/conda-forge/win-64/icu-75.1-he0c23c2_0.conda#8579b6bb8d18be7c0b27fb08adeeeb40 https://conda.anaconda.org/conda-forge/win-64/lerc-4.0.0-h63175ca_0.tar.bz2#1900cb3cab5055833cfddb0ba233b074 https://conda.anaconda.org/conda-forge/win-64/libbrotlicommon-1.1.0-hcfcfb64_1.conda#f77f319fb82980166569e1280d5b2864 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https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-h267a509_2.conda#9ae35c3d96db2c94ce0cef86efdfa2cb https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda#3f43953b7d3fb3aaa1d0d0723d91e368 https://conda.anaconda.org/conda-forge/linux-64/libasprintf-devel-0.22.5-h661eb56_2.conda#02e41ab5834dcdcc8590cf29d9526f50 -https://conda.anaconda.org/conda-forge/linux-64/libglib-2.80.3-h8a4344b_1.conda#6ea440297aacee4893f02ad759e6ffbc +https://conda.anaconda.org/conda-forge/linux-64/libglib-2.80.3-h315aac3_2.conda#b0143a3e98136a680b728fdf9b42a258 https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.25-pthreads_h413a1c8_0.conda#d172b34a443b95f86089e8229ddc9a17 -https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-h1dd3fc0_3.conda#66f03896ffbe1a110ffda05c7a856504 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https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.11-hd590300_0.conda#ed67c36f215b310412b2af935bf3e530 https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-hbb29018_2.conda#b6d90276c5aee9b4407dd94eb0cd40a8 https://conda.anaconda.org/conda-forge/linux-64/coverage-7.6.1-py39hcd6043d_0.conda#daab0ee8e85e258281e2b2dd74ebe0bb -https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.3-h8a4344b_1.conda#a3acc4920c9ca19cb6b295028d606477 +https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.3-h315aac3_2.conda#00e0da7e4fceb5449f3ddd2bf6b2c351 https://conda.anaconda.org/conda-forge/noarch/joblib-1.2.0-pyhd8ed1ab_0.tar.bz2#7583652522d71ad78ba536bba06940eb https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-20_linux64_openblas.conda#36d486d72ab64ffea932329a1d3729a3 https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp15-15.0.7-default_h127d8a8_5.conda#d0a9633b53cdc319b8a1a532ae7822b8 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https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.53.1-py39hcd6043d_0.conda#297804eca6ea16a835a869699095de1c https://conda.anaconda.org/conda-forge/linux-64/gfortran-12.4.0-h236703b_0.conda#581156aeb9b903f5425d5dd963d56ec1 https://conda.anaconda.org/conda-forge/linux-64/gfortran_linux-64-12.4.0-hd748a6a_0.conda#6fd80632f36e5a3934af2600bcbb2b2d @@ -191,8 +191,8 @@ https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1 https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda#7b86ecb7d3557821c649b3c31e3eb9f2 https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-23_linux64_openblas.conda#eede29b40efa878cbe5bdcb767e97310 -https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp18.1-18.1.8-default_hf981a13_1.conda#1cd622f71ea159cc8c9c416568a34f0a -https://conda.anaconda.org/conda-forge/linux-64/libclang13-18.1.8-default_h9def88c_1.conda#04c8c481b30c3fe62bec148fa4a75857 +https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp18.1-18.1.8-default_hf981a13_2.conda#b0f8c590aa86d9bee5987082f7f15bdf +https://conda.anaconda.org/conda-forge/linux-64/libclang13-18.1.8-default_h9def88c_2.conda#ba2d12adbea9de311297f2b577f4bb86 https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-23_linux64_openblas.conda#2af0879961951987e464722fd00ec1e0 https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.7.0-h2c5496b_1.conda#e2eaefa4de2b7237af7c907b8bbc760a https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhd8ed1ab_0.tar.bz2#8b45f9f2b2f7a98b0ec179c8991a4a9b @@ -217,7 +217,7 @@ https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py39h623c9ba_0. https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-23_linux64_openblas.conda#08b43a5c3d6cc13aeb69bd2cbc293196 https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_1.conda#d8d07866ac3b5b6937213c89a1874f08 https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py39h7633fee_0.conda#bdc188e59857d6efab332714e0d01d93 -https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py39h34cef29_2.conda#d3ee926e63ebd5b44ebc984dff020305 +https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py39h9d013fb_3.conda#f3bcbaa497af215e86d966244d683289 https://conda.anaconda.org/conda-forge/noarch/imageio-2.34.2-pyh12aca89_0.conda#97ad994fae55dce96bd397054b32e41a https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.2-py39hfc16268_1.conda#8b23d2b425035a7468d17e6fe1d54124 https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.6-pyhd8ed1ab_0.conda#a5b55d1cb110cdcedc748b5c3e16e687 @@ -229,7 +229,7 @@ https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.2-pyhd8ed1ab_1.conda#e https://conda.anaconda.org/conda-forge/linux-64/blas-2.123-openblas.conda#7f4b3ea1cdd6e50dca2a226abda6e2d9 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.9.1-py39h0565ad7_2.conda#bdde79163fde321b3dddac0c08dd6134 https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.2.1-py39h85c637f_0.conda#0bfaf33b7ebdbadc77bf9a67e281c0b1 -https://conda.anaconda.org/conda-forge/linux-64/pyside6-6.7.2-py39h8242bd1_1.conda#27964496b9996f7453f8b45ea72acc7a +https://conda.anaconda.org/conda-forge/linux-64/pyside6-6.7.2-py39h8242bd1_2.conda#e5c6995331893cf9fcaab45d11e343ff https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda#5ede4753180c7a550a443c430dc8ab52 https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.2-py39hd92a3bb_0.conda#2f6c03d60e71f13d92d511b06193f007 https://conda.anaconda.org/conda-forge/noarch/tifffile-2024.6.18-pyhd8ed1ab_0.conda#7c3077529bfe3b86f9425d526d73bd24 @@ -242,7 +242,7 @@ https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.7.0-pyhd8ed1ab_3.conda# https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.15.4-pyhd8ed1ab_0.conda#c7c50dd5192caa58a05e6a4248a27acb https://conda.anaconda.org/conda-forge/noarch/sphinx-copybutton-0.5.2-pyhd8ed1ab_0.conda#ac832cc43adc79118cf6e23f1f9b8995 https://conda.anaconda.org/conda-forge/noarch/sphinx-design-0.6.1-pyhd8ed1ab_0.conda#51b2433e4a223b14defee96d3caf9bab -https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.17.0-pyhd8ed1ab_0.conda#952c3c12f751861ae704080aab566c5a +https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.17.1-pyhd8ed1ab_0.conda#0adfccc6e7269a29a63c1c8ee3c6d8ba https://conda.anaconda.org/conda-forge/noarch/sphinx-prompt-1.4.0-pyhd8ed1ab_0.tar.bz2#88ee91e8679603f2a5bd036d52919cc2 https://conda.anaconda.org/conda-forge/noarch/sphinx-remove-toctrees-1.0.0.post1-pyhd8ed1ab_0.conda#6dee8412218288a17f99f2cfffab334d https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-2.0.0-pyhd8ed1ab_0.conda#9075bd8c033f0257122300db914e49c9 @@ -252,7 +252,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed https://conda.anaconda.org/conda-forge/noarch/sphinx-7.4.7-pyhd8ed1ab_0.conda#c568e260463da2528ecfd7c5a0b41bbd https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda#e507335cb4ca9cff4c3d0fa9cdab255e https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1ab_0.conda#286283e05a1eff606f55e7cd70f6d7f7 -# pip attrs @ https://files.pythonhosted.org/packages/9b/2b/913eda7a67f7bea7496c1a8e1666f48aa9f15520da79368e4ec1109e2690/attrs-24.1.0-py3-none-any.whl#sha256=377b47448cb61fea38533f671fba0d0f8a96fd58facd4dc518e3dac9dbea0905 +# pip attrs @ https://files.pythonhosted.org/packages/6a/21/5b6702a7f963e95456c0de2d495f67bf5fd62840ac655dc451586d23d39a/attrs-24.2.0-py3-none-any.whl#sha256=81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2 # pip cloudpickle @ https://files.pythonhosted.org/packages/96/43/dae06432d0c4b1dc9e9149ad37b4ca8384cf6eb7700cd9215b177b914f0a/cloudpickle-3.0.0-py3-none-any.whl#sha256=246ee7d0c295602a036e86369c77fecda4ab17b506496730f2f576d9016fd9c7 # pip defusedxml @ https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl#sha256=a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61 # pip fastjsonschema @ https://files.pythonhosted.org/packages/6d/ca/086311cdfc017ec964b2436fe0c98c1f4efcb7e4c328956a22456e497655/fastjsonschema-2.20.0-py3-none-any.whl#sha256=5875f0b0fa7a0043a91e93a9b8f793bcbbba9691e7fd83dca95c28ba26d21f0a @@ -268,15 +268,15 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip prometheus-client @ https://files.pythonhosted.org/packages/c7/98/745b810d822103adca2df8decd4c0bbe839ba7ad3511af3f0d09692fc0f0/prometheus_client-0.20.0-py3-none-any.whl#sha256=cde524a85bce83ca359cc837f28b8c0db5cac7aa653a588fd7e84ba061c329e7 # pip ptyprocess @ https://files.pythonhosted.org/packages/22/a6/858897256d0deac81a172289110f31629fc4cee19b6f01283303e18c8db3/ptyprocess-0.7.0-py2.py3-none-any.whl#sha256=4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35 # pip python-json-logger @ https://files.pythonhosted.org/packages/35/a6/145655273568ee78a581e734cf35beb9e33a370b29c5d3c8fee3744de29f/python_json_logger-2.0.7-py3-none-any.whl#sha256=f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd -# pip pyyaml @ https://files.pythonhosted.org/packages/7d/39/472f2554a0f1e825bd7c5afc11c817cd7a2f3657460f7159f691fbb37c51/PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c +# pip pyyaml @ https://files.pythonhosted.org/packages/3d/32/e7bd8535d22ea2874cef6a81021ba019474ace0d13a4819c2a4bce79bd6a/PyYAML-6.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=3b1fdb9dc17f5a7677423d508ab4f243a726dea51fa5e70992e59a7411c89d19 # pip rfc3986-validator @ https://files.pythonhosted.org/packages/9e/51/17023c0f8f1869d8806b979a2bffa3f861f26a3f1a66b094288323fba52f/rfc3986_validator-0.1.1-py2.py3-none-any.whl#sha256=2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9 -# pip rpds-py @ https://files.pythonhosted.org/packages/9d/9f/683f61c2541da8e98d9d4612c7282ce5a6b169573df3262274fdf3ba94a8/rpds_py-0.19.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=f09529d2332264a902688031a83c19de8fda5eb5881e44233286b9c9ec91856d +# pip rpds-py @ https://files.pythonhosted.org/packages/04/d8/e73d56b1908a6c0e3e5982365eb293170cd458cc25a19363f69c76e00fd2/rpds_py-0.20.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b4c29cbbba378759ac5786730d1c3cb4ec6f8ababf5c42a9ce303dc4b3d08cda # pip send2trash @ https://files.pythonhosted.org/packages/40/b0/4562db6223154aa4e22f939003cb92514c79f3d4dccca3444253fd17f902/Send2Trash-1.8.3-py3-none-any.whl#sha256=0c31227e0bd08961c7665474a3d1ef7193929fedda4233843689baa056be46c9 # pip sniffio @ https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl#sha256=2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2 # pip traitlets @ https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl#sha256=b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f # pip types-python-dateutil @ https://files.pythonhosted.org/packages/c7/1b/af4f4c4f3f7339a4b7eb3c0ab13416db98f8ac09de3399129ee5fdfa282b/types_python_dateutil-2.9.0.20240316-py3-none-any.whl#sha256=6b8cb66d960771ce5ff974e9dd45e38facb81718cc1e208b10b1baccbfdbee3b # pip uri-template @ https://files.pythonhosted.org/packages/e7/00/3fca040d7cf8a32776d3d81a00c8ee7457e00f80c649f1e4a863c8321ae9/uri_template-1.3.0-py3-none-any.whl#sha256=a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363 -# pip webcolors @ https://files.pythonhosted.org/packages/3b/45/0c30e10a2ac52606476394e4ba11cf3b12ba5823e7fbb9167f80eee6000a/webcolors-24.6.0-py3-none-any.whl#sha256=8cf5bc7e28defd1d48b9e83d5fc30741328305a8195c29a8e668fa45586568a1 +# pip webcolors @ https://files.pythonhosted.org/packages/f0/33/12020ba99beaff91682b28dc0bbf0345bbc3244a4afbae7644e4fa348f23/webcolors-24.8.0-py3-none-any.whl#sha256=fc4c3b59358ada164552084a8ebee637c221e4059267d0f8325b3b560f6c7f0a # pip webencodings @ https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl#sha256=a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78 # pip websocket-client @ https://files.pythonhosted.org/packages/5a/84/44687a29792a70e111c5c477230a72c4b957d88d16141199bf9acb7537a3/websocket_client-1.8.0-py3-none-any.whl#sha256=17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526 # pip anyio @ https://files.pythonhosted.org/packages/7b/a2/10639a79341f6c019dedc95bd48a4928eed9f1d1197f4c04f546fc7ae0ff/anyio-4.4.0-py3-none-any.whl#sha256=c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7 @@ -305,4 +305,4 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip nbconvert @ https://files.pythonhosted.org/packages/b8/bb/bb5b6a515d1584aa2fd89965b11db6632e4bdc69495a52374bcc36e56cfa/nbconvert-7.16.4-py3-none-any.whl#sha256=05873c620fe520b6322bf8a5ad562692343fe3452abda5765c7a34b7d1aa3eb3 # pip jupyter-server @ https://files.pythonhosted.org/packages/57/e1/085edea6187a127ca8ea053eb01f4e1792d778b4d192c74d32eb6730fed6/jupyter_server-2.14.2-py3-none-any.whl#sha256=47ff506127c2f7851a17bf4713434208fc490955d0e8632e95014a9a9afbeefd # pip jupyterlab-server @ https://files.pythonhosted.org/packages/54/09/2032e7d15c544a0e3cd831c51d77a8ca57f7555b2e1b2922142eddb02a84/jupyterlab_server-2.27.3-py3-none-any.whl#sha256=e697488f66c3db49df675158a77b3b017520d772c6e1548c7d9bcc5df7944ee4 -# pip jupyterlite-sphinx @ https://files.pythonhosted.org/packages/ce/a4/f91fa06fb9d345e7e9fe38c9f5cc12b5de24741ad13ec82e9769396d7a8c/jupyterlite_sphinx-0.16.3-py3-none-any.whl#sha256=0e6d976f831fbdfd12e15adf2f3bbcbfd59705fdf5546948956c5ce7928026a8 +# pip jupyterlite-sphinx @ https://files.pythonhosted.org/packages/f6/71/d7fa0b7d802f359539019dfe2ec9e4b0b11b14ce815748b5adc8d28bb283/jupyterlite_sphinx-0.16.5-py3-none-any.whl#sha256=9429bfd0310d18c3cd4273e342a7e67e5a07b6baf21b150c26a54fae1b2a0077 diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index 1c28f0399ef47..286c970c75940 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -35,7 +35,7 @@ https://conda.anaconda.org/conda-forge/linux-64/jxrlib-1.1-hd590300_3.conda#5aea https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3 https://conda.anaconda.org/conda-forge/linux-64/lame-3.100-h166bdaf_1003.tar.bz2#a8832b479f93521a9e7b5b743803be51 https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hd590300_1.conda#aec6c91c7371c26392a06708a73c70e5 -https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.20-hd590300_0.conda#8e88f9389f1165d7c0936fe40d9a9a79 +https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.21-h4bc722e_0.conda#36ce76665bf67f5aac36be7a0d21b7f3 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.2-h59595ed_0.conda#e7ba12deb7020dd080c6c70e7b6f6a3d https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2#d645c6d2ac96843a2bfaccd2d62b3ac3 https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-0.22.5-h59595ed_2.conda#172bcc51059416e7ce99e7b528cede83 @@ -110,9 +110,9 @@ https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-12.4.0-hb2e57f 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https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.7.0-h2c5496b_1.conda#e2eaefa4de2b7237af7c907b8bbc760a @@ -237,7 +237,7 @@ https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_0.conda#b39568655c127a9c4a44d178ac99b6d0 https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py39h623c9ba_0.conda#a19d023682384c637cb356d270c276c0 https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_1.conda#d8d07866ac3b5b6937213c89a1874f08 -https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.7.1-pyhd8ed1ab_0.conda#80f7ce024289c333fdc5ad54a194fc86 +https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.8.0-pyhd8ed1ab_0.conda#bf68bf9ff9a18f1b17aa8c817225aee0 https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.6-hbaaba92_0.conda#b22ffc80ac9af846df60b2640c98fea4 https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-23_linux64_mkl.conda#5bdaf561cf48f95093dedaa665083874 https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-255-h3516f8a_1.conda#3366af27f0b593544a6cd453c7932ac5 @@ -252,7 +252,7 @@ https://conda.anaconda.org/conda-forge/linux-64/numpy-1.19.5-py39hd249d9e_3.tar. https://conda.anaconda.org/conda-forge/noarch/pooch-1.6.0-pyhd8ed1ab_0.tar.bz2#6429e1d1091c51f626b5dcfdd38bf429 https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.8-h320f8da_24.conda#bec111b67cb8dc63277c6af65d214044 https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-23_linux64_mkl.conda#c8f8d0ebf2e7fd3a90ec68e3bb008995 -https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py39h34cef29_2.conda#d3ee926e63ebd5b44ebc984dff020305 +https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py39h9d013fb_3.conda#f3bcbaa497af215e86d966244d683289 https://conda.anaconda.org/conda-forge/noarch/imageio-2.34.2-pyh12aca89_0.conda#97ad994fae55dce96bd397054b32e41a https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.3.4-py39h2fa2bec_0.tar.bz2#9ec0b2186fab9121c54f4844f93ee5b7 https://conda.anaconda.org/conda-forge/linux-64/pandas-1.1.5-py39hde0f152_0.tar.bz2#79fc4b5b3a865b90dd3701cecf1ad33c From afd482111ce3ddce721d1fdf17792a64a2049d77 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 12 Aug 2024 01:35:20 -0700 Subject: [PATCH 208/275] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#29657) Co-authored-by: Lock file bot --- .../azure/pylatest_pip_scipy_dev_linux-64_conda.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index afecc31b579cf..5c84b2119f2e8 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -23,11 +23,11 @@ https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be421 https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.14-h39e8969_0.conda#78dbc5e3c69143ebc037fc5d5b22e597 https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda#acf93d6aceb74d6110e20b44cc45939e https://repo.anaconda.com/pkgs/main/linux-64/python-3.12.4-h5148396_1.conda#7863dc035441267f7b617f080c933671 -https://repo.anaconda.com/pkgs/main/linux-64/setuptools-69.5.1-py312h06a4308_0.conda#ce85d9a864a73e0b12d31a97733c9fca +https://repo.anaconda.com/pkgs/main/linux-64/setuptools-72.1.0-py312h06a4308_0.conda#bab64ac5186aa07014788baf1fbe3ca9 https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py312h06a4308_0.conda#18d5f3b68a175c72576876db4afc9e9e https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py312h06a4308_0.conda#6d9697bb8b9f3212be10b3b8e01a12b9 # pip alabaster @ https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl#sha256=fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b -# pip babel @ https://files.pythonhosted.org/packages/27/45/377f7e32a5c93d94cd56542349b34efab5ca3f9e2fd5a68c5e93169aa32d/Babel-2.15.0-py3-none-any.whl#sha256=08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb +# pip babel @ https://files.pythonhosted.org/packages/ed/20/bc79bc575ba2e2a7f70e8a1155618bb1301eaa5132a8271373a6903f73f8/babel-2.16.0-py3-none-any.whl#sha256=368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b # pip certifi @ https://files.pythonhosted.org/packages/1c/d5/c84e1a17bf61d4df64ca866a1c9a913874b4e9bdc131ec689a0ad013fb36/certifi-2024.7.4-py3-none-any.whl#sha256=c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90 # pip charset-normalizer @ https://files.pythonhosted.org/packages/ee/fb/14d30eb4956408ee3ae09ad34299131fb383c47df355ddb428a7331cfa1e/charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b # pip coverage @ https://files.pythonhosted.org/packages/1f/0f/c890339dd605f3ebc269543247bdd43b703cce6825b5ed42ff5f2d6122c7/coverage-7.6.1-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c44fee9975f04b33331cb8eb272827111efc8930cfd582e0320613263ca849ca @@ -64,4 +64,4 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py312h06a4308_0.conda#6d96 # pip pytest-cov @ https://files.pythonhosted.org/packages/78/3a/af5b4fa5961d9a1e6237b530eb87dd04aea6eb83da09d2a4073d81b54ccf/pytest_cov-5.0.0-py3-none-any.whl#sha256=4f0764a1219df53214206bf1feea4633c3b558a2925c8b59f144f682861ce652 # pip pytest-xdist @ https://files.pythonhosted.org/packages/6d/82/1d96bf03ee4c0fdc3c0cbe61470070e659ca78dc0086fb88b66c185e2449/pytest_xdist-3.6.1-py3-none-any.whl#sha256=9ed4adfb68a016610848639bb7e02c9352d5d9f03d04809919e2dafc3be4cca7 # pip sphinx @ https://files.pythonhosted.org/packages/4d/61/2ad169c6ff1226b46e50da0e44671592dbc6d840a52034a0193a99b28579/sphinx-8.0.2-py3-none-any.whl#sha256=56173572ae6c1b9a38911786e206a110c9749116745873feae4f9ce88e59391d -# pip numpydoc @ https://files.pythonhosted.org/packages/f0/fa/dcfe0f65660661db757ee9ebd84e170ff98edd5d80235f62457d9088f85f/numpydoc-1.7.0-py3-none-any.whl#sha256=5a56419d931310d79a06cfc2a126d1558700feeb9b4f3d8dcae1a8134be829c9 +# pip numpydoc @ https://files.pythonhosted.org/packages/6c/45/56d99ba9366476cd8548527667f01869279cedb9e66b28eb4dfb27701679/numpydoc-1.8.0-py3-none-any.whl#sha256=72024c7fd5e17375dec3608a27c03303e8ad00c81292667955c6fea7a3ccf541 From 3328bc0b49dc148926fe86780df233eaf2c89f5b Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 12 Aug 2024 02:18:57 -0700 Subject: [PATCH 209/275] :lock: :robot: CI Update lock files for cirrus-arm CI build(s) :lock: :robot: (#29656) Co-authored-by: Lock file bot --- .../pymin_conda_forge_linux-aarch64_conda.lock | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock index cb2d117efcd76..9ce2c67444c6d 100644 --- a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock +++ b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock @@ -18,7 +18,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/alsa-lib-1.2.12-h68df207_0. https://conda.anaconda.org/conda-forge/linux-aarch64/bzip2-1.0.8-h68df207_7.conda#56398c28220513b9ea13d7b450acfb20 https://conda.anaconda.org/conda-forge/linux-aarch64/keyutils-1.6.1-h4e544f5_0.tar.bz2#1f24853e59c68892452ef94ddd8afd4b https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlicommon-1.1.0-h31becfc_1.conda#1b219fd801eddb7a94df5bd001053ad9 -https://conda.anaconda.org/conda-forge/linux-aarch64/libdeflate-1.20-h31becfc_0.conda#018592a3d691662f451f89d0de474a20 +https://conda.anaconda.org/conda-forge/linux-aarch64/libdeflate-1.21-h68df207_0.conda#806c74df6dcf96adea47c7829b264f80 https://conda.anaconda.org/conda-forge/linux-aarch64/libexpat-2.6.2-h2f0025b_0.conda#1b9f46b804a2c3c5d7fd6a80b77c35f9 https://conda.anaconda.org/conda-forge/linux-aarch64/libffi-3.4.2-h3557bc0_5.tar.bz2#dddd85f4d52121fab0a8b099c5e06501 https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran5-14.1.0-h9420597_0.conda#b907b29b964b8ebd7be215e47a659179 @@ -69,10 +69,10 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/zstd-1.5.6-h02f22dd_0.conda https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-bin-1.1.0-h31becfc_1.conda#9e4a13596ab651ea8d77aae023d0ce3f https://conda.anaconda.org/conda-forge/linux-aarch64/freetype-2.12.1-hf0a5ef3_2.conda#a5ab74c5bd158c3d5532b66d8d83d907 https://conda.anaconda.org/conda-forge/linux-aarch64/krb5-1.21.3-h50a48e9_0.conda#29c10432a2ca1472b53f299ffb2ffa37 -https://conda.anaconda.org/conda-forge/linux-aarch64/libglib-2.80.3-haee52c6_1.conda#50ed8a077706cfe3da719deb71001f2c +https://conda.anaconda.org/conda-forge/linux-aarch64/libglib-2.80.3-haee52c6_2.conda#937a787ab5789a1e0c818b9545b6deb9 https://conda.anaconda.org/conda-forge/linux-aarch64/libhiredis-1.0.2-h05efe27_0.tar.bz2#a87f068744fd20334cd41489eb163bee https://conda.anaconda.org/conda-forge/linux-aarch64/libopenblas-0.3.27-pthreads_h076ed1e_1.conda#cc0a15e3a6f92f454b6132ca6aca8e8d -https://conda.anaconda.org/conda-forge/linux-aarch64/libtiff-4.6.0-hf980d43_3.conda#b6f3abf5726ae33094bee238b4eb492f +https://conda.anaconda.org/conda-forge/linux-aarch64/libtiff-4.6.0-h395e79b_4.conda#07ac339fcab2d44ddfd9b8ac58e80a05 https://conda.anaconda.org/conda-forge/linux-aarch64/libxml2-2.12.7-h00a45b3_4.conda#d25c3e16ee77cd25342e4e235424c758 https://conda.anaconda.org/conda-forge/linux-aarch64/llvm-openmp-18.1.8-hb063fc5_0.conda#f0cf07feda9ed87092833cd8fca012f5 https://conda.anaconda.org/conda-forge/linux-aarch64/mysql-libs-8.3.0-h0c23661_5.conda#c5447423bf6ba4f4ad398033bd66998f @@ -87,7 +87,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/ccache-4.10.1-ha3bccff_0.co https://conda.anaconda.org/conda-forge/noarch/certifi-2024.7.4-pyhd8ed1ab_0.conda#24e7fd6ca65997938fff9e5ab6f653e4 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99 https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda#5cd86562580f274031ede6aa6aa24441 -https://conda.anaconda.org/conda-forge/linux-aarch64/cython-3.0.10-py39h387a81e_0.conda#0e917a89f77c978d152099357bd75b22 +https://conda.anaconda.org/conda-forge/linux-aarch64/cython-3.0.11-py39h6e76b30_0.conda#7b2bd72eeb9a59b13090b02f4a534168 https://conda.anaconda.org/conda-forge/linux-aarch64/dbus-1.13.6-h12b9eeb_3.tar.bz2#f3d63805602166bac09386741e00935e https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_0.conda#d02ae936e42063ca46af6cdad2dbd1e0 https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_0.conda#15dda3cdbf330abfe9f555d22f66db46 @@ -97,8 +97,8 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/kiwisolver-1.4.5-py39had2cf https://conda.anaconda.org/conda-forge/linux-aarch64/lcms2-2.16-h922389a_0.conda#ffdd8267a04c515e7ce69c727b051414 https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.9.0-23_linuxaarch64_openblas.conda#3ac1ad627e1a07fae62556d6aabafdfd https://conda.anaconda.org/conda-forge/linux-aarch64/libcups-2.3.3-h405e4a8_4.conda#d42c670b0c96c1795fd859d5e0275a55 -https://conda.anaconda.org/conda-forge/linux-aarch64/libllvm18-18.1.8-h36f4c5c_1.conda#4807ee3558305d0e7634fd4be0f6cfbc -https://conda.anaconda.org/conda-forge/linux-aarch64/libpq-16.3-hcf0348d_0.conda#7dd46e914b037824b9a9629ca6586fc3 +https://conda.anaconda.org/conda-forge/linux-aarch64/libllvm18-18.1.8-h36f4c5c_2.conda#e42436ab11417326ca4c317a9a78124b +https://conda.anaconda.org/conda-forge/linux-aarch64/libpq-16.4-hcf0348d_0.conda#d7a3cef9193c842d8621869affb3e069 https://conda.anaconda.org/conda-forge/linux-aarch64/libxslt-1.1.39-h1cc9640_0.conda#13e1d3f9188e85c6d59a98651aced002 https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2#2ba8498c1018c1e9c61eb99b973dfe19 https://conda.anaconda.org/conda-forge/linux-aarch64/openblas-0.3.27-pthreads_hd33deab_1.conda#70c0aa7d1dd049fffae952bfe8f2c4e9 @@ -123,8 +123,8 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.53.1-py39he257e https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1ab_0.conda#c5d3907ad8bd7bf557521a1833cf7e6d https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-23_linuxaarch64_openblas.conda#65a4f18036c0f5419146fddee6653a96 -https://conda.anaconda.org/conda-forge/linux-aarch64/libclang-cpp18.1-18.1.8-default_h14d1da3_1.conda#b8c48ff5a2c8fac7885ca558202d0bd4 -https://conda.anaconda.org/conda-forge/linux-aarch64/libclang13-18.1.8-default_h465fbfb_1.conda#b472fe26d5032bed56caf31064580fb9 +https://conda.anaconda.org/conda-forge/linux-aarch64/libclang-cpp18.1-18.1.8-default_h14d1da3_2.conda#ed0dd9fe9fb649dc19593919df0afd43 +https://conda.anaconda.org/conda-forge/linux-aarch64/libclang13-18.1.8-default_h465fbfb_2.conda#940ece4a5d753f0cb6ee27219bcd814a https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.9.0-23_linuxaarch64_openblas.conda#85c4fec3847027ca7402f3bd7d2de4c1 https://conda.anaconda.org/conda-forge/linux-aarch64/libxkbcommon-1.7.0-h46f2afe_1.conda#78a24e611ab9c09c518f519be49c2e46 https://conda.anaconda.org/conda-forge/noarch/meson-1.5.1-pyhd8ed1ab_1.conda#979087ee59bea1355f991a3b738af64e @@ -146,5 +146,5 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/qt6-main-6.7.2-h288a8fd_4.c https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.13.1-py39hb921187_0.conda#1aac9080de661e03d286f18fb71e5240 https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.123-openblas.conda#43772c0a1ae8f29c9a223c21fd89262b https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.9.1-py39hf3ba65a_2.conda#2c71adc96eab781c8cd8d1cfc64d5bc7 -https://conda.anaconda.org/conda-forge/linux-aarch64/pyside6-6.7.2-py39hb23dda1_1.conda#0e0b29e8b60171e7c8d5652b955a50fd +https://conda.anaconda.org/conda-forge/linux-aarch64/pyside6-6.7.2-py39hb23dda1_2.conda#f4e3d54705d9aaddc4cadf200c30f330 https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-3.9.1-py39ha65689a_2.conda#6f6879438411334f90c06aca5e2cb6b7 From ad7f3abbb9324afc654096c86cd55c17455988a3 Mon Sep 17 00:00:00 2001 From: Evelyn Date: Tue, 13 Aug 2024 02:56:50 -0400 Subject: [PATCH 210/275] DOC Copyedit species names in species distribution modeling example (#29654) --- .../plot_species_distribution_modeling.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/applications/plot_species_distribution_modeling.py b/examples/applications/plot_species_distribution_modeling.py index bdf50918840c2..612123c149897 100644 --- a/examples/applications/plot_species_distribution_modeling.py +++ b/examples/applications/plot_species_distribution_modeling.py @@ -17,13 +17,13 @@ The two species are: - - `"Bradypus variegatus" - `_ , - the Brown-throated Sloth. + - `Bradypus variegatus + `_, + the brown-throated sloth. - - `"Microryzomys minutus" - `_ , - also known as the Forest Small Rice Rat, a rodent that lives in Peru, + - `Microryzomys minutus + `_, + also known as the forest small rice rat, a rodent that lives in Peru, Colombia, Ecuador, Peru, and Venezuela. References From 5615a59d8d43112ec1e5640beb59424767eeb4cc Mon Sep 17 00:00:00 2001 From: bme-git Date: Tue, 13 Aug 2024 12:13:39 +0200 Subject: [PATCH 211/275] DOC Update svm.rst - Add default setting 'ovr' to SVC/NuSVC multi-class classification description. (#29363) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- doc/modules/svm.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/modules/svm.rst b/doc/modules/svm.rst index 47115e43a89e0..99e66e1dd69ce 100644 --- a/doc/modules/svm.rst +++ b/doc/modules/svm.rst @@ -125,7 +125,8 @@ classifiers are constructed and each one trains data from two classes. To provide a consistent interface with other classifiers, the ``decision_function_shape`` option allows to monotonically transform the results of the "one-versus-one" classifiers to a "one-vs-rest" decision -function of shape ``(n_samples, n_classes)``. +function of shape ``(n_samples, n_classes)``, which is the default setting +of the parameter (default='ovr'). >>> X = [[0], [1], [2], [3]] >>> Y = [0, 1, 2, 3] From 94c2e8fb08b0c14a9e8b016813cd142437f1eafc Mon Sep 17 00:00:00 2001 From: dinga92 Date: Tue, 13 Aug 2024 13:19:06 +0200 Subject: [PATCH 212/275] TST change y creation in the check_estimators_dtypes test so that it is not identical to a column in X (#29080) Co-authored-by: Adrin Jalali --- sklearn/utils/estimator_checks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py index 77c7c505a1dac..2d2833cc1c649 100644 --- a/sklearn/utils/estimator_checks.py +++ b/sklearn/utils/estimator_checks.py @@ -1919,7 +1919,7 @@ def check_estimators_dtypes(name, estimator_orig): X_train_64 = X_train_32.astype(np.float64) X_train_int_64 = X_train_32.astype(np.int64) X_train_int_32 = X_train_32.astype(np.int32) - y = X_train_int_64[:, 0] + y = np.array([1, 2] * 10, dtype=np.int64) y = _enforce_estimator_tags_y(estimator_orig, y) methods = ["predict", "transform", "decision_function", "predict_proba"] From 3d3ae27fc1be039e49c4d1ff0b5c4520d07cdc7f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C5=A0t=C4=9Bp=C3=A1n=20Sr=C5=A1e=C5=88?= Date: Tue, 13 Aug 2024 16:02:13 +0200 Subject: [PATCH 213/275] DOC improved documentation for BaseSearchCV fit method - precomputed kernel or distance matrices (#29586) Co-authored-by: Stepan Srsen --- sklearn/model_selection/_search.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py index 150e6bd989d40..c6ae9823faf2a 100644 --- a/sklearn/model_selection/_search.py +++ b/sklearn/model_selection/_search.py @@ -893,9 +893,10 @@ def fit(self, X, y=None, **params): Parameters ---------- - X : array-like of shape (n_samples, n_features) - Training vector, where `n_samples` is the number of samples and - `n_features` is the number of features. + X : array-like of shape (n_samples, n_features) or (n_samples, n_samples) + Training vectors, where `n_samples` is the number of samples and + `n_features` is the number of features. For precomputed kernel or + distance matrix, the expected shape of X is (n_samples, n_samples). y : array-like of shape (n_samples, n_output) \ or (n_samples,), default=None From 4d8c93dcceb894978a7052d3cd5038bcc6231033 Mon Sep 17 00:00:00 2001 From: Rahil Parikh <75483881+rprkh@users.noreply.github.com> Date: Tue, 13 Aug 2024 22:20:37 +0530 Subject: [PATCH 214/275] DOC include note for searching for optimal parameters with successive halving (#25645) --- doc/modules/grid_search.rst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/doc/modules/grid_search.rst b/doc/modules/grid_search.rst index 12ee76d8e4d39..ee567c8e497e2 100644 --- a/doc/modules/grid_search.rst +++ b/doc/modules/grid_search.rst @@ -188,6 +188,11 @@ iteration, which will be allocated more resources. For parameter tuning, the resource is typically the number of training samples, but it can also be an arbitrary numeric parameter such as `n_estimators` in a random forest. +.. note:: + + The resource increase chosen should be large enough so that a large improvement + in scores is obtained when taking into account statistical significance. + As illustrated in the figure below, only a subset of candidates 'survive' until the last iteration. These are the candidates that have consistently ranked among the top-scoring candidates across all iterations. From 66fc80556236cb9d148406005c22181d5bc22276 Mon Sep 17 00:00:00 2001 From: Steffen Schneider Date: Fri, 16 Aug 2024 11:29:02 +0200 Subject: [PATCH 215/275] DOC fix outdated github remote link in advanced installation guideline (#29680) --- doc/developers/advanced_installation.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/developers/advanced_installation.rst b/doc/developers/advanced_installation.rst index 88521c6c51867..04313a43754d5 100644 --- a/doc/developers/advanced_installation.rst +++ b/doc/developers/advanced_installation.rst @@ -52,7 +52,7 @@ feature, code or documentation improvement). .. prompt:: bash $ - git clone git://github.com/scikit-learn/scikit-learn.git # add --depth 1 if your connection is slow + git clone git@github.com:scikit-learn/scikit-learn.git # add --depth 1 if your connection is slow cd scikit-learn If you plan on submitting a pull-request, you should clone from your fork From 04b0f26537a303b340465baa5e9a273ad8089a05 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 19 Aug 2024 02:03:02 -0700 Subject: [PATCH 216/275] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29690) Co-authored-by: Lock file bot --- ...latest_conda_forge_mkl_linux-64_conda.lock | 60 +++++++++---------- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 14 ++--- ...test_conda_mkl_no_openmp_osx-64_conda.lock | 2 +- ...st_pip_openblas_pandas_linux-64_conda.lock | 8 +-- .../pymin_conda_forge_mkl_win-64_conda.lock | 18 +++--- ...nblas_min_dependencies_linux-64_conda.lock | 18 +++--- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 18 +++--- build_tools/circle/doc_linux-64_conda.lock | 25 ++++---- .../doc_min_dependencies_linux-64_conda.lock | 20 +++---- 9 files changed, 92 insertions(+), 91 deletions(-) diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index e54faa3011313..26bd6fdeb8edf 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -10,16 +10,16 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77 https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_7.conda#b80f2f396ca2c28b8c14c437a4ed1e74 https://conda.anaconda.org/conda-forge/linux-64/mkl-include-2023.2.0-h84fe81f_50496.conda#7af9fd0b2d7219f4a4200a34561340f6 -https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.12-4_cp312.conda#dccc2d142812964fcc6abdc97b672dff +https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.12-5_cp312.conda#0424ae29b104430108f5218a66db7260 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 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a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock index 3b8066be2568c..29e222fc9ff76 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock @@ -13,7 +13,7 @@ https://conda.anaconda.org/conda-forge/osx-64/libwebp-base-1.4.0-h10d778d_0.cond https://conda.anaconda.org/conda-forge/osx-64/mkl-include-2023.2.0-h6bab518_50500.conda#835abb8ded5e26f23ea6996259c7972e https://conda.anaconda.org/conda-forge/osx-64/ncurses-6.5-h5846eda_0.conda#02a888433d165c99bf09784a7b14d900 https://conda.anaconda.org/conda-forge/osx-64/pthread-stubs-0.4-hc929b4f_1001.tar.bz2#addd19059de62181cd11ae8f4ef26084 -https://conda.anaconda.org/conda-forge/osx-64/python_abi-3.12-4_cp312.conda#87201ac4314b911b74197e588cca3639 +https://conda.anaconda.org/conda-forge/osx-64/python_abi-3.12-5_cp312.conda#c34dd4920e0addf7cfcc725809f25d8e https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 https://conda.anaconda.org/conda-forge/osx-64/xorg-libxau-1.0.11-h0dc2134_0.conda#9566b4c29274125b0266d0177b5eb97b https://conda.anaconda.org/conda-forge/osx-64/xorg-libxdmcp-1.1.3-h35c211d_0.tar.bz2#86ac76d6bf1cbb9621943eb3bd9ae36e @@ -22,11 +22,11 @@ https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.8-hfdf4475_7.conda#7ed43 https://conda.anaconda.org/conda-forge/osx-64/icu-75.1-h120a0e1_0.conda#d68d48a3060eb5abdc1cdc8e2a3a5966 https://conda.anaconda.org/conda-forge/osx-64/libbrotlidec-1.1.0-h0dc2134_1.conda#9ee0bab91b2ca579e10353738be36063 https://conda.anaconda.org/conda-forge/osx-64/libbrotlienc-1.1.0-h0dc2134_1.conda#8a421fe09c6187f0eb5e2338a8a8be6d -https://conda.anaconda.org/conda-forge/osx-64/libcxx-18.1.8-heced48a_2.conda#8c8198f9e93fcc0fd359ff37b4a8cd2d +https://conda.anaconda.org/conda-forge/osx-64/libcxx-18.1.8-heced48a_4.conda#7e13da1296840905452340fca10a625b https://conda.anaconda.org/conda-forge/osx-64/libdeflate-1.21-hfdf4475_0.conda#88409b23a5585c15d52de0073f3c9c61 https://conda.anaconda.org/conda-forge/osx-64/libxcb-1.16-h0dc2134_0.conda#07e80289d4ba724f37b4b6f001f88fbe https://conda.anaconda.org/conda-forge/osx-64/libzlib-1.3.1-h87427d6_1.conda#b7575b5aa92108dcc9aaab0f05f2dbce -https://conda.anaconda.org/conda-forge/osx-64/llvm-openmp-18.1.8-h15ab845_0.conda#2c3c6c8aaf8728f87326964a82fdc7d8 +https://conda.anaconda.org/conda-forge/osx-64/llvm-openmp-18.1.8-h15ab845_1.conda#ad0afa524866cc1c08b436865d0ae484 https://conda.anaconda.org/conda-forge/osx-64/openssl-3.3.1-h87427d6_2.conda#3f3dbeedbee31e257866407d9dea1ff5 https://conda.anaconda.org/conda-forge/osx-64/readline-8.2-h9e318b2_1.conda#f17f77f2acf4d344734bda76829ce14e https://conda.anaconda.org/conda-forge/osx-64/brotli-bin-1.1.0-h0dc2134_1.conda#ece565c215adcc47fc1db4e651ee094b @@ -108,18 +108,18 @@ https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.9.0-20_osx64_mkl.conda# https://conda.anaconda.org/conda-forge/osx-64/liblapack-3.9.0-20_osx64_mkl.conda#58f08e12ad487fac4a08f90ff0b87aec https://conda.anaconda.org/conda-forge/noarch/compiler-rt_osx-64-16.0.6-ha38d28d_2.conda#7a46507edc35c6c8818db0adaf8d787f https://conda.anaconda.org/conda-forge/osx-64/liblapacke-3.9.0-20_osx64_mkl.conda#124ae8e384268a8da66f1d64114a1eda -https://conda.anaconda.org/conda-forge/osx-64/numpy-2.0.1-py312h8813227_0.conda#7f239fbf9d9355f86529a35af0b24d29 +https://conda.anaconda.org/conda-forge/osx-64/numpy-2.1.0-py312h8813227_0.conda#437bc6e9dcd5612d123a9c99b2988040 https://conda.anaconda.org/conda-forge/osx-64/blas-devel-3.9.0-20_osx64_mkl.conda#cc3260179093918b801e373c6e888e02 https://conda.anaconda.org/conda-forge/osx-64/compiler-rt-16.0.6-ha38d28d_2.conda#3b9e8c5c63b8e86234f499490acd85c2 https://conda.anaconda.org/conda-forge/osx-64/contourpy-1.2.1-py312h9230928_0.conda#079df34ce7c71259cfdd394645370891 https://conda.anaconda.org/conda-forge/osx-64/pandas-2.2.2-py312h1171441_1.conda#240737937f1f046b0e03ecc11ac4ec98 -https://conda.anaconda.org/conda-forge/osx-64/scipy-1.14.0-py312hb9702fa_1.conda#9899db3cf8965c3aecab3daf5227d3eb +https://conda.anaconda.org/conda-forge/osx-64/scipy-1.14.0-py312hb9702fa_2.conda#610311c8f21dbbf294157f03922e9ca8 https://conda.anaconda.org/conda-forge/osx-64/blas-2.120-mkl.conda#b041a7677a412f3d925d8208936cb1e2 https://conda.anaconda.org/conda-forge/osx-64/clang_impl_osx-64-16.0.6-h8787910_19.conda#64155ef139280e8c181dad866dea2980 -https://conda.anaconda.org/conda-forge/osx-64/matplotlib-base-3.9.1-py312h0d5aeb7_2.conda#0aece95a1cd3b77990022d3e0f37c6aa +https://conda.anaconda.org/conda-forge/osx-64/matplotlib-base-3.9.2-py312h0d5aeb7_0.conda#0c73a08429d20f15fa8b28083ec04cc9 https://conda.anaconda.org/conda-forge/osx-64/pyamg-5.2.1-py312h44e70fa_0.conda#a7c77239f0135d30cbba0164922aa861 https://conda.anaconda.org/conda-forge/osx-64/clang_osx-64-16.0.6-hb91bd55_19.conda#760ecbc6f4b6cecbe440b0080626286f -https://conda.anaconda.org/conda-forge/osx-64/matplotlib-3.9.1-py312hb401068_2.conda#1ead575881ba176014aad8dfac07d1b1 +https://conda.anaconda.org/conda-forge/osx-64/matplotlib-3.9.2-py312hb401068_0.conda#f468fd4f10632ff2500482118a3d4ace https://conda.anaconda.org/conda-forge/osx-64/c-compiler-1.7.0-h282daa2_1.conda#d27411cb82bc1b76b9f487da6ae97f1d https://conda.anaconda.org/conda-forge/osx-64/clangxx_impl_osx-64-16.0.6-h6d92fbe_19.conda#9ffa16e2bd7eb5b8b1a0d19185710cd3 https://conda.anaconda.org/conda-forge/osx-64/gfortran_osx-64-12.3.0-h18f7dce_1.conda#436af2384c47aedb94af78a128e174f1 diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock index b994b147ae513..8a7c31bba3125 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock @@ -63,7 +63,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/wheel-0.43.0-py312hecd8cb5_0.conda#c0 https://repo.anaconda.com/pkgs/main/osx-64/fonttools-4.51.0-py312h6c40b1e_0.conda#8f55fa86b73e8a7f4403503f9b7a9959 https://repo.anaconda.com/pkgs/main/osx-64/numpy-base-1.26.4-py312h6f81483_0.conda#87f73efbf26ab2e2ea7c32481a71bd47 https://repo.anaconda.com/pkgs/main/osx-64/pillow-10.4.0-py312h46256e1_0.conda#486a21e17faf0611e454c0e7faf0bcbc -https://repo.anaconda.com/pkgs/main/osx-64/pip-24.0-py312hecd8cb5_0.conda#7a8e0b1d3742ddf1c8aa97fbaa158039 +https://repo.anaconda.com/pkgs/main/osx-64/pip-24.2-py312hecd8cb5_0.conda#35119ef238299ccf29b25889fd466139 https://repo.anaconda.com/pkgs/main/osx-64/pytest-7.4.4-py312hecd8cb5_0.conda#d4dda983900b045cd27ae836cad670de https://repo.anaconda.com/pkgs/main/osx-64/python-dateutil-2.9.0post0-py312hecd8cb5_2.conda#1047dde28f78127dd9f6121e882926dd https://repo.anaconda.com/pkgs/main/osx-64/pytest-cov-4.1.0-py312hecd8cb5_1.conda#a33a24eb20359f464938e75b2f57e23a diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index 7cc0fbf4c197e..8f54469e2d1b1 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -24,7 +24,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda#acf9 https://repo.anaconda.com/pkgs/main/linux-64/python-3.11.9-h955ad1f_0.conda#5668a8845dd35bbbc9663c8f217a2ab8 https://repo.anaconda.com/pkgs/main/linux-64/setuptools-72.1.0-py311h06a4308_0.conda#58a35dba367429761d046074dcfa8b19 https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py311h06a4308_0.conda#ec915b5ff89bdbcea7ef943d9e296967 -https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py311h06a4308_0.conda#84aef4db159f0daf63751d87d7d6ca56 +https://repo.anaconda.com/pkgs/main/linux-64/pip-24.2-py311h06a4308_0.conda#eff3ec695130b6912d64997edbc0db16 # pip alabaster @ https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl#sha256=fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b # pip array-api-compat @ https://files.pythonhosted.org/packages/0f/22/8228be1d3c6d4ffcf05cd89872ce65c1317b2af98d34b9d89b247d8d49cb/array_api_compat-1.8-py3-none-any.whl#sha256=140204454086264d37263bc4afe1182b428353e94e9edcc38d17b009863c982d # pip babel @ https://files.pythonhosted.org/packages/ed/20/bc79bc575ba2e2a7f70e8a1155618bb1301eaa5132a8271373a6903f73f8/babel-2.16.0-py3-none-any.whl#sha256=368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b @@ -45,7 +45,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py311h06a4308_0.conda#84ae # pip meson @ https://files.pythonhosted.org/packages/b7/33/513a9ca4fd5892463abb38592105b78fd425214f7983033633e2e48cbd30/meson-1.5.1-py3-none-any.whl#sha256=5531e24e6cfd6000bf1c712793cf28dff032841370b1a3b941a894e4fde46e5a # pip networkx @ https://files.pythonhosted.org/packages/38/e9/5f72929373e1a0e8d142a130f3f97e6ff920070f87f91c4e13e40e0fba5a/networkx-3.3-py3-none-any.whl#sha256=28575580c6ebdaf4505b22c6256a2b9de86b316dc63ba9e93abde3d78dfdbcf2 # pip ninja @ https://files.pythonhosted.org/packages/6d/92/8d7aebd4430ab5ff65df2bfee6d5745f95c004284db2d8ca76dcbfd9de47/ninja-1.11.1.1-py2.py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl#sha256=84502ec98f02a037a169c4b0d5d86075eaf6afc55e1879003d6cab51ced2ea4b -# pip numpy @ https://files.pythonhosted.org/packages/ef/27/39622993e8688a1f05898a3c3b2836b856f79c06637ebd4b71cb35cc9b18/numpy-2.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=15eb4eca47d36ec3f78cde0a3a2ee24cf05ca7396ef808dda2c0ddad7c2bde67 +# pip numpy @ https://files.pythonhosted.org/packages/7b/93/831b4c5b4355210827b3de34f539297e1833c39a68c26a8b454d8cf9f5ed/numpy-2.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=f5ebbf9fbdabed208d4ecd2e1dfd2c0741af2f876e7ae522c2537d404ca895c3 # pip packaging @ https://files.pythonhosted.org/packages/08/aa/cc0199a5f0ad350994d660967a8efb233fe0416e4639146c089643407ce6/packaging-24.1-py3-none-any.whl#sha256=5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124 # pip pillow @ https://files.pythonhosted.org/packages/ba/e5/8c68ff608a4203085158cff5cc2a3c534ec384536d9438c405ed6370d080/pillow-10.4.0-cp311-cp311-manylinux_2_28_x86_64.whl#sha256=76a911dfe51a36041f2e756b00f96ed84677cdeb75d25c767f296c1c1eda1319 # pip pluggy @ https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl#sha256=44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669 @@ -66,7 +66,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py311h06a4308_0.conda#84ae # pip urllib3 @ https://files.pythonhosted.org/packages/ca/1c/89ffc63a9605b583d5df2be791a27bc1a42b7c32bab68d3c8f2f73a98cd4/urllib3-2.2.2-py3-none-any.whl#sha256=a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472 # pip array-api-strict @ https://files.pythonhosted.org/packages/08/06/aba69bce257fd1cda0d1db616c12728af0f46878a5cc1923fcbb94201947/array_api_strict-2.0.1-py3-none-any.whl#sha256=f74cbf0d0c182fcb45c5ee7f28f9c7b77e6281610dfbbdd63be60b1a5a7872b3 # pip contourpy @ https://files.pythonhosted.org/packages/ee/c0/9bd123d676eb61750e116a2cd915b06483fc406143cfc36c7f263f0f5368/contourpy-1.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=d4492d82b3bc7fbb7e3610747b159869468079fe149ec5c4d771fa1f614a14df -# pip imageio @ https://files.pythonhosted.org/packages/3d/84/f1647217231f6cc46883e5d26e870cc3e1520d458ecd52d6df750810d53c/imageio-2.34.2-py3-none-any.whl#sha256=a0bb27ec9d5bab36a9f4835e51b21d2cb099e1f78451441f94687ff3404b79f8 +# pip imageio @ https://files.pythonhosted.org/packages/1e/b7/02adac4e42a691008b5cfb31db98c190e1fc348d1521b9be4429f9454ed1/imageio-2.35.1-py3-none-any.whl#sha256=6eb2e5244e7a16b85c10b5c2fe0f7bf961b40fcb9f1a9fd1bd1d2c2f8fb3cd65 # pip jinja2 @ https://files.pythonhosted.org/packages/31/80/3a54838c3fb461f6fec263ebf3a3a41771bd05190238de3486aae8540c36/jinja2-3.1.4-py3-none-any.whl#sha256=bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d # pip lazy-loader @ https://files.pythonhosted.org/packages/83/60/d497a310bde3f01cb805196ac61b7ad6dc5dcf8dce66634dc34364b20b4f/lazy_loader-0.4-py3-none-any.whl#sha256=342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc # pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526 @@ -76,7 +76,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py311h06a4308_0.conda#84ae # pip scipy @ https://files.pythonhosted.org/packages/89/bb/80c9c98d887c855710fd31fc5ae5574133e98203b3475b07579251803662/scipy-1.14.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=9e3154691b9f7ed73778d746da2df67a19d046a6c8087c8b385bc4cdb2cfca74 # pip tifffile @ https://files.pythonhosted.org/packages/fd/3a/6ec0327e238253a2b7adab0e542763fd639c4b3cef63b135a74ef3f454a7/tifffile-2024.8.10-py3-none-any.whl#sha256=1c224564fa92e7e9f9a0ed65880b2ece97c3f0d10029ffbebfa5e62b3f6b343d # pip lightgbm @ https://files.pythonhosted.org/packages/4e/19/1b928cad70a4e1a3e2c37d5417ca2182510f2451eaadb6c91cd9ec692cae/lightgbm-4.5.0-py3-none-manylinux_2_28_x86_64.whl#sha256=960a0e7c077de0ca3053f1325d3edfc92ea815acf5176adcacdea0f635aeef9b -# pip matplotlib @ https://files.pythonhosted.org/packages/a5/8b/90fae9c1b34ef3252003c26b15e8cb26b83701e34e5acf6430919c2c5c89/matplotlib-3.9.1.post1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=89eb7e89e2b57856533c5c98f018aa3254fa3789fcd86d5f80077b9034a54c9a +# pip matplotlib @ https://files.pythonhosted.org/packages/01/75/6c7ce560e95714a10fcbb3367d1304975a1a3e620f72af28921b796403f3/matplotlib-3.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=8912ef7c2362f7193b5819d17dae8629b34a95c58603d781329712ada83f9447 # pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 # pip pandas @ https://files.pythonhosted.org/packages/fc/a5/4d82be566f069d7a9a702dcdf6f9106df0e0b042e738043c0cc7ddd7e3f6/pandas-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=6d2123dc9ad6a814bcdea0f099885276b31b24f7edf40f6cdbc0912672e22eee # pip pyamg @ https://files.pythonhosted.org/packages/d3/e8/6898b3b791f369605012e896ed903b6626f3bd1208c6a647d7219c070209/pyamg-5.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=679a5904eac3a4880288c8c0e6a29f110a2627ea15a443a4e9d5997c7dc5fab6 diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock index feae35c24526a..48e75610b28f2 100644 --- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock @@ -7,11 +7,11 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f -https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2024.2.0-h57928b3_980.conda#9c28c39e64871a0adef7d1195bd58655 +https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2024.2.1-h57928b3_1083.conda#2d89243bfb53652c182a7c73182cce4f https://conda.anaconda.org/conda-forge/win-64/libexpat-2.6.2-h63175ca_0.conda#bc592d03f62779511d392c175dcece64 https://conda.anaconda.org/conda-forge/win-64/mkl-include-2024.1.0-h66d3029_694.conda#1f80971a50e69c1f7af15707619df49e https://conda.anaconda.org/conda-forge/win-64/msys2-conda-epoch-20160418-1.tar.bz2#b0309b72560df66f71a9d5e34a5efdfa -https://conda.anaconda.org/conda-forge/win-64/python_abi-3.9-4_cp39.conda#948b0d93d4ab1372d8fd45e1560afd47 +https://conda.anaconda.org/conda-forge/win-64/python_abi-3.9-5_cp39.conda#86ba1bbcf9b259d1592201f3c345c810 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.22621.0-h57928b3_0.tar.bz2#72608f6cd3e5898229c3ea16deb1ac43 https://conda.anaconda.org/conda-forge/win-64/expat-2.6.2-h63175ca_0.conda#52f9dec6758ceb8ce0ea8af9fa13eb1a @@ -47,11 +47,11 @@ https://conda.anaconda.org/conda-forge/win-64/xz-5.2.6-h8d14728_0.tar.bz2#515d77 https://conda.anaconda.org/conda-forge/win-64/krb5-1.21.3-hdf4eb48_0.conda#31aec030344e962fbd7dbbbbd68e60a9 https://conda.anaconda.org/conda-forge/win-64/libbrotlidec-1.1.0-hcfcfb64_1.conda#19ce3e1dacc7912b3d6ff40690ba9ae0 https://conda.anaconda.org/conda-forge/win-64/libbrotlienc-1.1.0-hcfcfb64_1.conda#71e890a0b361fd58743a13f77e1506b7 -https://conda.anaconda.org/conda-forge/win-64/libintl-0.22.5-h5728263_2.conda#aa622c938af057adc119f8b8eecada01 +https://conda.anaconda.org/conda-forge/win-64/libintl-0.22.5-h5728263_3.conda#2cf0cf76cc15d360dfa2f17fd6cf9772 https://conda.anaconda.org/conda-forge/win-64/libpng-1.6.43-h19919ed_0.conda#77e398acc32617a0384553aea29e866b https://conda.anaconda.org/conda-forge/win-64/libxml2-2.12.7-h0f24e4e_4.conda#ed4d301f0d2149b34deb9c4fecafd836 https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libs-5.3.0-7.tar.bz2#fe759119b8b3bfa720b8762c6fdc35de -https://conda.anaconda.org/conda-forge/win-64/pcre2-10.44-h3d7b363_0.conda#007d07ab5027e0bf49f6fa660a9f89a0 +https://conda.anaconda.org/conda-forge/win-64/pcre2-10.44-h3d7b363_2.conda#a3a3baddcfb8c80db84bec3cb7746fb8 https://conda.anaconda.org/conda-forge/win-64/python-3.9.19-h4de0772_0_cpython.conda#b6999bc275e0e6beae7b1c8ea0be1e85 https://conda.anaconda.org/conda-forge/win-64/zlib-1.3.1-h2466b09_1.conda#f8e0a35bf6df768ad87ed7bbbc36ab04 https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.6-h0ea2cb4_0.conda#9a17230f95733c04dc40a2b1e5491d74 @@ -85,11 +85,11 @@ https://conda.anaconda.org/conda-forge/win-64/unicodedata2-15.1.0-py39ha55989b_0 https://conda.anaconda.org/conda-forge/noarch/wheel-0.44.0-pyhd8ed1ab_0.conda#d44e3b085abcaef02983c6305b84b584 https://conda.anaconda.org/conda-forge/win-64/xorg-libxau-1.0.11-hcd874cb_0.conda#c46ba8712093cb0114404ae8a7582e1a https://conda.anaconda.org/conda-forge/win-64/xorg-libxdmcp-1.1.3-hcd874cb_0.tar.bz2#46878ebb6b9cbd8afcf8088d7ef00ece -https://conda.anaconda.org/conda-forge/noarch/zipp-3.19.2-pyhd8ed1ab_0.conda#49808e59df5535116f6878b2a820d6f4 +https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.0-pyhd8ed1ab_0.conda#05b6bcb391b5be17374f7ad0aeedc479 https://conda.anaconda.org/conda-forge/win-64/brotli-1.1.0-hcfcfb64_1.conda#f47f6db2528e38321fb00ae31674c133 https://conda.anaconda.org/conda-forge/win-64/coverage-7.6.1-py39ha55e580_0.conda#a9c63313e61e510e8f8bca90794eee73 https://conda.anaconda.org/conda-forge/win-64/fontconfig-2.14.2-hbde0cde_0.conda#08767992f1a4f1336a257af1241034bd -https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1ab_0.conda#c5d3907ad8bd7bf557521a1833cf7e6d +https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.2-pyhd8ed1ab_0.conda#ff64113bd700cf0f892ebf4b223e56aa https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/win-64/lcms2-2.16-h67d730c_0.conda#d3592435917b62a8becff3a60db674f6 https://conda.anaconda.org/conda-forge/win-64/libxcb-1.16-hcd874cb_0.conda#7c1217d3b075f195ab17370f2d550f5d @@ -102,7 +102,7 @@ https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0 https://conda.anaconda.org/conda-forge/win-64/tbb-2021.12.0-hc790b64_3.conda#a16e2a639e87c554abee5192ce6ee308 https://conda.anaconda.org/conda-forge/win-64/cairo-1.18.0-h32b962e_3.conda#8f43723a4925c51e55c2d81725a97db4 https://conda.anaconda.org/conda-forge/win-64/fonttools-4.53.1-py39ha55e580_0.conda#81bbae03542e491178a620a45ad0b474 -https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.0-pyhd8ed1ab_0.conda#dcbadab7a68738a028e195ab68ab2d2e +https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.2-pyhd8ed1ab_0.conda#af1f82a5ea4f16b1482165f93b11399d https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 https://conda.anaconda.org/conda-forge/win-64/mkl-2024.1.0-h66d3029_694.conda#a17423859d3fb912c8f2e9797603ddb6 https://conda.anaconda.org/conda-forge/win-64/pillow-10.4.0-py39hfa8c767_0.conda#7b24bccfb14f05019c8a488d4ee084a8 @@ -121,5 +121,5 @@ https://conda.anaconda.org/conda-forge/win-64/blas-devel-3.9.0-23_win64_mkl.cond https://conda.anaconda.org/conda-forge/win-64/contourpy-1.2.1-py39h1f6ef14_0.conda#03e25c6bae87f4f9595337255b44b0fb https://conda.anaconda.org/conda-forge/win-64/scipy-1.13.1-py39h1a10956_0.conda#9f8e571406af04d2f5fdcbecec704505 https://conda.anaconda.org/conda-forge/win-64/blas-2.123-mkl.conda#0d089770a9bc073da806864c60a0a173 -https://conda.anaconda.org/conda-forge/win-64/matplotlib-base-3.9.1-py39h5376392_2.conda#5542b333ad1c031d2462834f8559c769 -https://conda.anaconda.org/conda-forge/win-64/matplotlib-3.9.1-py39hcbf5309_2.conda#e162629338b6b4db1b75dbd917d2dca6 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b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -11,7 +11,7 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77 https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_7.conda#b80f2f396ca2c28b8c14c437a4ed1e74 https://conda.anaconda.org/conda-forge/linux-64/mkl-include-2024.1.0-ha957f24_693.conda#249c91c2186d236c6d180342241db2ec -https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.9-4_cp39.conda#bfe4b3259a8ac6cdf0037752904da6a7 +https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.9-5_cp39.conda#40363a30db350596b5f225d0d5a33328 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 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https://conda.anaconda.org/conda-forge/linux-64/jxrlib-1.1-hd590300_3.conda#5aeabe88534ea4169d4c49998f293d6c https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3 @@ -38,7 +38,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hd590300_1 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.21-h4bc722e_0.conda#36ce76665bf67f5aac36be7a0d21b7f3 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.2-h59595ed_0.conda#e7ba12deb7020dd080c6c70e7b6f6a3d https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2#d645c6d2ac96843a2bfaccd2d62b3ac3 -https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-0.22.5-h59595ed_2.conda#172bcc51059416e7ce99e7b528cede83 +https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-0.22.5-he02047a_3.conda#efab66b82ec976930b96d62a976de8e7 https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.1.0-hc5f4f2c_0.conda#6456c2620c990cd8dde2428a27ba0bc5 https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda#d66573916ffcf376178462f1b61c941e https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda#ea25936bb4080d843790b586850f82b8 @@ -71,13 +71,13 @@ https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h59595ed_1003.c https://conda.anaconda.org/conda-forge/linux-64/icu-73.2-h59595ed_0.conda#cc47e1facc155f91abd89b11e48e72ff https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2#76bbff344f0134279f225174e9064c8f https://conda.anaconda.org/conda-forge/linux-64/libaec-1.1.3-h59595ed_0.conda#5e97e271911b8b2001a8b71860c32faa -https://conda.anaconda.org/conda-forge/linux-64/libasprintf-0.22.5-h661eb56_2.conda#dd197c968bf9760bba0031888d431ede +https://conda.anaconda.org/conda-forge/linux-64/libasprintf-0.22.5-he8f35ee_3.conda#4fab9799da9571266d05ca5503330655 https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hd590300_1.conda#f07002e225d7a60a694d42a7bf5ff53f https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.1.0-hd590300_1.conda#5fc11c6020d421960607d821310fcd4d https://conda.anaconda.org/conda-forge/linux-64/libcap-2.69-h0f662aa_0.conda#25cb5999faa414e5ccb2c1388f62d3d5 https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20191231-he28a2e2_2.tar.bz2#4d331e44109e3f0e19b4cb8f9b82f3e1 https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda#a1cfcc585f0c42bf8d5546bb1dfb668d -https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-devel-0.22.5-h59595ed_2.conda#b63d9b6da3653179a278077f0de20014 +https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-devel-0.22.5-he02047a_3.conda#9aba7960731e6b4547b3a52f812ed801 https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-14.1.0-h69a702a_0.conda#f4ca84fbd6d06b0a052fb2d5b96dde41 https://conda.anaconda.org/conda-forge/linux-64/libhwy-1.1.0-h00ab1b0_0.conda#88928158ccfe797eac29ef5e03f7d23d https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.43-h2797004_0.conda#009981dd9cfcaa4dbfa25ffaed86bcae @@ -91,7 +91,7 @@ https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9 https://conda.anaconda.org/conda-forge/linux-64/mysql-common-8.3.0-h70512c7_5.conda#4b652e3e572cbb3f297e77c96313faea https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 -https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.44-h0f59acf_0.conda#3914f7ac1761dce57102c72ca7c35d01 +https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.44-hba22ea6_2.conda#df359c09c41cd186fffb93a2d87aa6f5 https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123 https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda#47d31b792659ce70f470b5c82fdfb7a4 https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.1-ha2e4443_0.conda#6b7dcc7349efd123d493d2dbe85a045f @@ -108,13 +108,13 @@ https://conda.anaconda.org/conda-forge/linux-64/c-blosc2-2.15.1-hc57e6cf_0.conda https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-h267a509_2.conda#9ae35c3d96db2c94ce0cef86efdfa2cb https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-12.4.0-hb2e57f8_0.conda#61f3e74c92b7c44191143a661f821bab https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda#3f43953b7d3fb3aaa1d0d0723d91e368 -https://conda.anaconda.org/conda-forge/linux-64/libasprintf-devel-0.22.5-h661eb56_2.conda#02e41ab5834dcdcc8590cf29d9526f50 +https://conda.anaconda.org/conda-forge/linux-64/libasprintf-devel-0.22.5-he8f35ee_3.conda#1091193789bb830127ed067a9e01ac57 https://conda.anaconda.org/conda-forge/linux-64/libavif16-1.1.1-h9b56c87_0.conda#cb7355212240e92dcf9c73cb1f10e4a9 https://conda.anaconda.org/conda-forge/linux-64/libglib-2.80.3-h315aac3_2.conda#b0143a3e98136a680b728fdf9b42a258 https://conda.anaconda.org/conda-forge/linux-64/libjxl-0.10.3-h66b40c8_0.conda#a394f85083195ab8aa33911f40d76870 https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-h46a8edc_4.conda#a7e3a62981350e232e0e7345b5aea580 https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.7-h4c95cb1_3.conda#0ac9aff6010a7751961c8e4b863a40e7 -https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.8-hf5423f3_0.conda#322be9d39e030673e105b0abb320514e +https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.8-hf5423f3_1.conda#8782406a10201b67bd6476ca70cf92a8 https://conda.anaconda.org/conda-forge/linux-64/mysql-libs-8.3.0-ha479ceb_5.conda#82776ee8145b9d1fd6546604de4b351d https://conda.anaconda.org/conda-forge/linux-64/nss-3.103-h593d115_0.conda#233bfe41968d6fb04eba9258bb5061ad https://conda.anaconda.org/conda-forge/linux-64/python-3.9.19-h0755675_0_cpython.conda#d9ee3647fbd9e8595b8df759b2bbefb8 @@ -142,7 +142,7 @@ https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.14.2-h14ed4e7_0.con https://conda.anaconda.org/conda-forge/noarch/fsspec-2024.6.1-pyhff2d567_0.conda#996bf792cdb8c0ac38ff54b9fde56841 https://conda.anaconda.org/conda-forge/linux-64/gcc-12.4.0-h236703b_0.conda#9485dc28dccde81b12e17f9bdda18f14 https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-12.4.0-h6b7512a_0.conda#fec7117a58f5becf76b43dec55064ff9 -https://conda.anaconda.org/conda-forge/linux-64/gettext-0.22.5-h59595ed_2.conda#219ba82e95d7614cf7140d2a4afc0926 +https://conda.anaconda.org/conda-forge/linux-64/gettext-0.22.5-he02047a_3.conda#c7f243bbaea97cd6ea1edd693270100e https://conda.anaconda.org/conda-forge/linux-64/gfortran_impl_linux-64-12.4.0-hc568b83_0.conda#bf4f9ad129a9a8dc86cce6626697d413 https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.80.3-h8fdd7da_2.conda#9958a1f8faba35260e6b68e3a7bc88d6 https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-12.4.0-h613a52c_0.conda#0740149e4653caebd1d2f6bbf84a1720 @@ -189,7 +189,7 @@ https://conda.anaconda.org/conda-forge/linux-64/xcb-util-image-0.4.0-hb711507_2. https://conda.anaconda.org/conda-forge/linux-64/xkeyboard-config-2.42-h4ab18f5_0.conda#b193af204da1bfb8c13882d131a14bd2 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.4-h0b41bf4_2.conda#82b6df12252e6f32402b96dacc656fec https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.11-hd590300_0.conda#ed67c36f215b310412b2af935bf3e530 -https://conda.anaconda.org/conda-forge/noarch/zipp-3.19.2-pyhd8ed1ab_0.conda#49808e59df5535116f6878b2a820d6f4 +https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.0-pyhd8ed1ab_0.conda#05b6bcb391b5be17374f7ad0aeedc479 https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.5-pyhd8ed1ab_0.conda#1bb1ef9806a9a20872434f58b3e7fc1a https://conda.anaconda.org/conda-forge/noarch/babel-2.14.0-pyhd8ed1ab_0.conda#9669586875baeced8fc30c0826c3270e https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.12.3-pyha770c72_0.conda#332493000404d8411859539a5a630865 From 992be835c1c56a3c0ff15e8237449e727f75325b Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 19 Aug 2024 02:03:25 -0700 Subject: [PATCH 217/275] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#29688) Co-authored-by: Lock file bot --- build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index 5c84b2119f2e8..31e29c4d4b9e2 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -25,7 +25,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda#acf9 https://repo.anaconda.com/pkgs/main/linux-64/python-3.12.4-h5148396_1.conda#7863dc035441267f7b617f080c933671 https://repo.anaconda.com/pkgs/main/linux-64/setuptools-72.1.0-py312h06a4308_0.conda#bab64ac5186aa07014788baf1fbe3ca9 https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py312h06a4308_0.conda#18d5f3b68a175c72576876db4afc9e9e -https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py312h06a4308_0.conda#6d9697bb8b9f3212be10b3b8e01a12b9 +https://repo.anaconda.com/pkgs/main/linux-64/pip-24.2-py312h06a4308_0.conda#798cbea8112672434d0cd7551f8fc4b9 # pip alabaster @ https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl#sha256=fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b # pip babel @ https://files.pythonhosted.org/packages/ed/20/bc79bc575ba2e2a7f70e8a1155618bb1301eaa5132a8271373a6903f73f8/babel-2.16.0-py3-none-any.whl#sha256=368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b # pip certifi @ https://files.pythonhosted.org/packages/1c/d5/c84e1a17bf61d4df64ca866a1c9a913874b4e9bdc131ec689a0ad013fb36/certifi-2024.7.4-py3-none-any.whl#sha256=c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90 From e8d974f6bff2ce3705be47be9cb60349ffd7f5d4 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 19 Aug 2024 02:03:57 -0700 Subject: [PATCH 218/275] :lock: :robot: CI Update lock files for cirrus-arm CI build(s) :lock: :robot: (#29687) Co-authored-by: Lock file bot --- .../pymin_conda_forge_linux-aarch64_conda.lock | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock index 9ce2c67444c6d..edb5b2fd745ad 100644 --- a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock +++ b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock @@ -8,7 +8,7 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed3 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.40-h9fc2d93_7.conda#1b0feef706f4d03eff0b76626ead64fc -https://conda.anaconda.org/conda-forge/linux-aarch64/python_abi-3.9-4_cp39.conda#c191905a08694e4a5cb1238e90233878 +https://conda.anaconda.org/conda-forge/linux-aarch64/python_abi-3.9-5_cp39.conda#2d2843f11ec622f556137d72d9c72d89 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab @@ -57,7 +57,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/libsqlite-3.46.0-hf51ef55_0 https://conda.anaconda.org/conda-forge/linux-aarch64/libxcb-1.16-h7935292_0.conda#93c0136e9cba96657339dfe25fba4da7 https://conda.anaconda.org/conda-forge/linux-aarch64/mysql-common-8.3.0-h940b476_5.conda#f027f6c56a5ee03d21e6e32c963e2fbd https://conda.anaconda.org/conda-forge/linux-aarch64/ninja-1.12.1-h70be974_0.conda#216635cea46498d8045c7cf0f03eaf72 -https://conda.anaconda.org/conda-forge/linux-aarch64/pcre2-10.44-h070dd5b_0.conda#e5c5c5acdd1f52508f5e9938b454ae5d +https://conda.anaconda.org/conda-forge/linux-aarch64/pcre2-10.44-h070dd5b_2.conda#94022de9682cb1a0bb18a99cbc3541b3 https://conda.anaconda.org/conda-forge/linux-aarch64/pixman-0.43.4-h2f0025b_0.conda#81b2ddea4b0eca188da9c5a7aa4b0cff https://conda.anaconda.org/conda-forge/linux-aarch64/qhull-2020.2-h70be974_5.conda#bb138086d938e2b64f5f364945793ebf https://conda.anaconda.org/conda-forge/linux-aarch64/readline-8.2-h8fc344f_1.conda#105eb1e16bf83bfb2eb380a48032b655 @@ -74,7 +74,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/libhiredis-1.0.2-h05efe27_0 https://conda.anaconda.org/conda-forge/linux-aarch64/libopenblas-0.3.27-pthreads_h076ed1e_1.conda#cc0a15e3a6f92f454b6132ca6aca8e8d https://conda.anaconda.org/conda-forge/linux-aarch64/libtiff-4.6.0-h395e79b_4.conda#07ac339fcab2d44ddfd9b8ac58e80a05 https://conda.anaconda.org/conda-forge/linux-aarch64/libxml2-2.12.7-h00a45b3_4.conda#d25c3e16ee77cd25342e4e235424c758 -https://conda.anaconda.org/conda-forge/linux-aarch64/llvm-openmp-18.1.8-hb063fc5_0.conda#f0cf07feda9ed87092833cd8fca012f5 +https://conda.anaconda.org/conda-forge/linux-aarch64/llvm-openmp-18.1.8-hb063fc5_1.conda#1656aad33930dad81fdc552fe8b44e13 https://conda.anaconda.org/conda-forge/linux-aarch64/mysql-libs-8.3.0-h0c23661_5.conda#c5447423bf6ba4f4ad398033bd66998f https://conda.anaconda.org/conda-forge/linux-aarch64/python-3.9.19-h4ac3b42_0_cpython.conda#1501507cd9451472ec8900d587ce872f https://conda.anaconda.org/conda-forge/linux-aarch64/xcb-util-0.4.1-h5c728e9_2.conda#b4cf8ba6cff9cdf1249bcfe1314222b0 @@ -117,10 +117,10 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/xcb-util-image-0.4.0-h5c728 https://conda.anaconda.org/conda-forge/linux-aarch64/xkeyboard-config-2.42-h68df207_0.conda#910ed255de2a0ec218a3c3db12d20a4d https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxext-1.3.4-h2a766a3_2.conda#0cea7d840c8eeaa4e349e0b4775c826d https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxrender-0.9.11-h7935292_0.conda#8c96b84f7fb97a3cd533a14dbdcd6626 -https://conda.anaconda.org/conda-forge/noarch/zipp-3.19.2-pyhd8ed1ab_0.conda#49808e59df5535116f6878b2a820d6f4 +https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.0-pyhd8ed1ab_0.conda#05b6bcb391b5be17374f7ad0aeedc479 https://conda.anaconda.org/conda-forge/linux-aarch64/cairo-1.18.0-hdb1a16f_3.conda#080659f02bf2202c57f1cda4f9e51f21 https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.53.1-py39he257ee7_0.conda#e30df3a3431af304f87bbd0cd07d5674 -https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1ab_0.conda#c5d3907ad8bd7bf557521a1833cf7e6d +https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.2-pyhd8ed1ab_0.conda#ff64113bd700cf0f892ebf4b223e56aa https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-23_linuxaarch64_openblas.conda#65a4f18036c0f5419146fddee6653a96 https://conda.anaconda.org/conda-forge/linux-aarch64/libclang-cpp18.1-18.1.8-default_h14d1da3_2.conda#ed0dd9fe9fb649dc19593919df0afd43 @@ -135,7 +135,7 @@ https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.2-pyhd8ed1ab_0.conda#e0 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-aarch64/xcb-util-cursor-0.1.4-h68df207_2.conda#9a1c7ed78dff58f6c7b22981ab5c9d39 https://conda.anaconda.org/conda-forge/linux-aarch64/harfbuzz-9.0.0-hbf49d6b_1.conda#ceb458f664cab8550fcd74fff26451db -https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.0-pyhd8ed1ab_0.conda#dcbadab7a68738a028e195ab68ab2d2e +https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.2-pyhd8ed1ab_0.conda#af1f82a5ea4f16b1482165f93b11399d https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-23_linuxaarch64_openblas.conda#d71af7934d6dcef05a3c9b0379e1cdfa https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-2.0.1-py39hcdcdb6f_0.conda#e97d4cba6dd293bb795baddad9ddae02 @@ -145,6 +145,6 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.2.1-py39hd16970 https://conda.anaconda.org/conda-forge/linux-aarch64/qt6-main-6.7.2-h288a8fd_4.conda#f6771673fad8025bb1d4dd765bc3caad https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.13.1-py39hb921187_0.conda#1aac9080de661e03d286f18fb71e5240 https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.123-openblas.conda#43772c0a1ae8f29c9a223c21fd89262b -https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.9.1-py39hf3ba65a_2.conda#2c71adc96eab781c8cd8d1cfc64d5bc7 +https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.9.2-py39hf3ba65a_0.conda#45cb5c6c0ffab8fce4070bdd481fe2d3 https://conda.anaconda.org/conda-forge/linux-aarch64/pyside6-6.7.2-py39hb23dda1_2.conda#f4e3d54705d9aaddc4cadf200c30f330 -https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-3.9.1-py39ha65689a_2.conda#6f6879438411334f90c06aca5e2cb6b7 +https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-3.9.2-py39ha65689a_0.conda#ab4bdeddf031205e3789f14685b5c8a0 From fe8b6643805fefc7420ec7191147065f1f2f103e Mon Sep 17 00:00:00 2001 From: Aisha <41858301+aisha-als@users.noreply.github.com> Date: Tue, 20 Aug 2024 15:43:22 +0100 Subject: [PATCH 219/275] DOC Add CITATION.cff (#29581) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- CITATION.cff | 48 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 CITATION.cff diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000000000..c3e367c124f81 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,48 @@ +cff-version: 1.2.0 +title: scikit-learn +type: software +authors: + - name: "The scikit-learn developers" +message: "If you use scikit-learn in a scientific publication, we would appreciate citations to the following paper:" +preferred-citation: + type: article + title: "Scikit-learn: Machine Learning in Python" + authors: + - family-names: "Pedregosa" + given-names: "Fabian" + - family-names: "Varoquaux" + given-names: "Gaël" + - family-names: "Gramfort" + given-names: "Alexandre" + - family-names: "Michel" + given-names: "Vincent" + - family-names: "Thirion" + given-names: "Bertrand" + - family-names: "Grisel" + given-names: "Olivier" + - family-names: "Blondel" + given-names: "Mathieu" + - family-names: "Prettenhofer" + given-names: "Peter" + - family-names: "Weiss" + given-names: "Ron" + - family-names: "Dubourg" + given-names: "Vincent" + - family-names: "Vanderplas" + given-names: "Jake" + - family-names: "Passos" + given-names: "Alexandre" + - family-names: "Cournapeau" + given-names: "David" + - family-names: "Brucher" + given-names: "Matthieu" + - family-names: "Perrot" + given-names: "Matthieu" + - family-names: "Duchesnay" + given-names: "Édouard" + journal: "Journal of Machine Learning Research" + volume: 12 + start: 2825 + end: 2830 + year: 2011 + url: "https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html" From 77d168fb66c8fd8aed82a451eda76059585dc292 Mon Sep 17 00:00:00 2001 From: Tushar Parimi <93556280+tusharparimi@users.noreply.github.com> Date: Wed, 21 Aug 2024 02:44:07 -0700 Subject: [PATCH 220/275] DOC add link to plot_ransac example in _ransac (#29636) --- sklearn/linear_model/_ransac.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/sklearn/linear_model/_ransac.py b/sklearn/linear_model/_ransac.py index b6bf7b082fc5e..46276632d6b45 100644 --- a/sklearn/linear_model/_ransac.py +++ b/sklearn/linear_model/_ransac.py @@ -252,6 +252,9 @@ class RANSACRegressor( 0.9885... >>> reg.predict(X[:1,]) array([-31.9417...]) + + For a more detailed example, see + :ref:`sphx_glr_auto_examples_linear_model_plot_ransac.py` """ # noqa: E501 _parameter_constraints: dict = { From a4b7b52005abde46c2f97de819155253c174c816 Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Fri, 23 Aug 2024 20:15:22 +1000 Subject: [PATCH 221/275] DOC Add note on overlapping test sets in `GroupShuffleSplit` (#29676) --- sklearn/model_selection/_split.py | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/sklearn/model_selection/_split.py b/sklearn/model_selection/_split.py index 216034715c5bb..9ae5c8ff44812 100644 --- a/sklearn/model_selection/_split.py +++ b/sklearn/model_selection/_split.py @@ -1892,8 +1892,9 @@ class ShuffleSplit(_UnsupportedGroupCVMixin, BaseShuffleSplit): Yields indices to split data into training and test sets. Note: contrary to other cross-validation strategies, random splits - do not guarantee that all folds will be different, although this is - still very likely for sizeable datasets. + do not guarantee that test sets across all folds will be mutually exclusive, + and might include overlapping samples. However, this is still very likely for + sizeable datasets. Read more in the :ref:`User Guide `. @@ -2009,6 +2010,11 @@ class GroupShuffleSplit(GroupsConsumerMixin, BaseShuffleSplit): ``LeavePGroupsOut(p=10)`` would be ``GroupShuffleSplit(test_size=10, n_splits=100)``. + Contrary to other cross-validation strategies, the random splits + do not guarantee that test sets across all folds will be mutually exclusive, + and might include overlapping samples. However, this is still very likely for + sizeable datasets. + Note: The parameters ``test_size`` and ``train_size`` refer to groups, and not to samples as in :class:`ShuffleSplit`. @@ -2136,13 +2142,14 @@ class StratifiedShuffleSplit(BaseShuffleSplit): Provides train/test indices to split data in train/test sets. - This cross-validation object is a merge of StratifiedKFold and - ShuffleSplit, which returns stratified randomized folds. The folds + This cross-validation object is a merge of :class:`StratifiedKFold` and + :class:`ShuffleSplit`, which returns stratified randomized folds. The folds are made by preserving the percentage of samples for each class. - Note: like the ShuffleSplit strategy, stratified random splits - do not guarantee that all folds will be different, although this is - still very likely for sizeable datasets. + Note: like the :class:`ShuffleSplit` strategy, stratified random splits + do not guarantee that test sets across all folds will be mutually exclusive, + and might include overlapping samples. However, this is still very likely for + sizeable datasets. Read more in the :ref:`User Guide `. From 79a5787917de4bb45b099c98b44f2bad6ad3f2f0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C5=A0t=C4=9Bp=C3=A1n=20Sr=C5=A1e=C5=88?= Date: Fri, 23 Aug 2024 14:48:26 +0200 Subject: [PATCH 222/275] DOC clarify that n_jobs argument uses threading in pairwise_distances and pairwise_kernels (#29693) Co-authored-by: Stepan Srsen Co-authored-by: Thomas J. Fan --- sklearn/metrics/pairwise.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py index e3faffa77ae51..7c2fcbfc369b3 100644 --- a/sklearn/metrics/pairwise.py +++ b/sklearn/metrics/pairwise.py @@ -1883,7 +1883,7 @@ def _dist_wrapper(dist_func, dist_matrix, slice_, *args, **kwargs): def _parallel_pairwise(X, Y, func, n_jobs, **kwds): """Break the pairwise matrix in n_jobs even slices - and compute them in parallel.""" + and compute them using multithreading.""" if Y is None: Y = X @@ -2272,8 +2272,8 @@ def pairwise_distances( n_jobs : int, default=None The number of jobs to use for the computation. This works by breaking - down the pairwise matrix into n_jobs even slices and computing them in - parallel. + down the pairwise matrix into n_jobs even slices and computing them + using multithreading. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` @@ -2517,8 +2517,8 @@ def pairwise_kernels( n_jobs : int, default=None The number of jobs to use for the computation. This works by breaking - down the pairwise matrix into n_jobs even slices and computing them in - parallel. + down the pairwise matrix into n_jobs even slices and computing them + using multithreading. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` From bea4e21c4e79353ffa318be4d031173a6eb4e577 Mon Sep 17 00:00:00 2001 From: Alberto Torres Date: Mon, 26 Aug 2024 16:00:11 +0200 Subject: [PATCH 223/275] DOC fix y documentation in subclasses of LinearModelCV (#29708) Co-authored-by: Adrin Jalali --- sklearn/linear_model/_coordinate_descent.py | 84 ++++++++++++++++++++- 1 file changed, 82 insertions(+), 2 deletions(-) diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py index b7e5e36d498c2..3996c994bfa3b 100644 --- a/sklearn/linear_model/_coordinate_descent.py +++ b/sklearn/linear_model/_coordinate_descent.py @@ -2087,6 +2087,46 @@ def _is_multitask(self): def _more_tags(self): return {"multioutput": False} + def fit(self, X, y, sample_weight=None, **params): + """Fit Lasso model with coordinate descent. + + Fit is on grid of alphas and best alpha estimated by cross-validation. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + Training data. Pass directly as Fortran-contiguous data + to avoid unnecessary memory duplication. If y is mono-output, + X can be sparse. Note that large sparse matrices and arrays + requiring `int64` indices are not accepted. + + y : array-like of shape (n_samples,) + Target values. + + sample_weight : float or array-like of shape (n_samples,), \ + default=None + Sample weights used for fitting and evaluation of the weighted + mean squared error of each cv-fold. Note that the cross validated + MSE that is finally used to find the best model is the unweighted + mean over the (weighted) MSEs of each test fold. + + **params : dict, default=None + Parameters to be passed to the CV splitter. + + .. versionadded:: 1.4 + Only available if `enable_metadata_routing=True`, + which can be set by using + ``sklearn.set_config(enable_metadata_routing=True)``. + See :ref:`Metadata Routing User Guide ` for + more details. + + Returns + ------- + self : object + Returns an instance of fitted model. + """ + return super().fit(X, y, sample_weight=sample_weight, **params) + class ElasticNetCV(RegressorMixin, LinearModelCV): """Elastic Net model with iterative fitting along a regularization path. @@ -2326,6 +2366,46 @@ def _is_multitask(self): def _more_tags(self): return {"multioutput": False} + def fit(self, X, y, sample_weight=None, **params): + """Fit ElasticNet model with coordinate descent. + + Fit is on grid of alphas and best alpha estimated by cross-validation. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + Training data. Pass directly as Fortran-contiguous data + to avoid unnecessary memory duplication. If y is mono-output, + X can be sparse. Note that large sparse matrices and arrays + requiring `int64` indices are not accepted. + + y : array-like of shape (n_samples,) + Target values. + + sample_weight : float or array-like of shape (n_samples,), \ + default=None + Sample weights used for fitting and evaluation of the weighted + mean squared error of each cv-fold. Note that the cross validated + MSE that is finally used to find the best model is the unweighted + mean over the (weighted) MSEs of each test fold. + + **params : dict, default=None + Parameters to be passed to the CV splitter. + + .. versionadded:: 1.4 + Only available if `enable_metadata_routing=True`, + which can be set by using + ``sklearn.set_config(enable_metadata_routing=True)``. + See :ref:`Metadata Routing User Guide ` for + more details. + + Returns + ------- + self : object + Returns an instance of fitted model. + """ + return super().fit(X, y, sample_weight=sample_weight, **params) + ############################################################################### # Multi Task ElasticNet and Lasso models (with joint feature selection) @@ -2947,7 +3027,7 @@ def _more_tags(self): return {"multioutput_only": True} # This is necessary as LinearModelCV now supports sample_weight while - # MultiTaskElasticNet does not (yet). + # MultiTaskElasticNetCV does not (yet). def fit(self, X, y, **params): """Fit MultiTaskElasticNet model with coordinate descent. @@ -3185,7 +3265,7 @@ def _more_tags(self): return {"multioutput_only": True} # This is necessary as LinearModelCV now supports sample_weight while - # MultiTaskElasticNet does not (yet). + # MultiTaskLassoCV does not (yet). def fit(self, X, y, **params): """Fit MultiTaskLasso model with coordinate descent. From 004f860abbc111f7767ed345a6477cfe947cbf62 Mon Sep 17 00:00:00 2001 From: Tim Head Date: Mon, 26 Aug 2024 20:19:45 +0200 Subject: [PATCH 224/275] DOC Point users to miniforge as the way to get conda (#29723) --- doc/install_instructions_conda.rst | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/doc/install_instructions_conda.rst b/doc/install_instructions_conda.rst index 284a6925eeba9..fe1c14bbb78d3 100644 --- a/doc/install_instructions_conda.rst +++ b/doc/install_instructions_conda.rst @@ -1,7 +1,6 @@ -Install conda using the `Anaconda or miniconda installers -`__ or the +Install conda using the `miniforge installers `__ (no -administrator permission required for any of those). Then run: +administrator permission required). Then run: .. prompt:: bash From 5411b7c91e2dc381fcdc8fda3375df3f65272e05 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 26 Aug 2024 11:20:14 -0700 Subject: [PATCH 225/275] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29721) Co-authored-by: Lock file bot --- ...latest_conda_forge_mkl_linux-64_conda.lock | 65 +++++++++++-------- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 10 +-- ...st_pip_openblas_pandas_linux-64_conda.lock | 8 +-- .../pymin_conda_forge_mkl_win-64_conda.lock | 14 ++-- ...nblas_min_dependencies_linux-64_conda.lock | 6 +- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 35 ++++++---- build_tools/circle/doc_linux-64_conda.lock | 43 +++++++----- .../doc_min_dependencies_linux-64_conda.lock | 14 ++-- 8 files changed, 114 insertions(+), 81 deletions(-) diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index 26bd6fdeb8edf..c72eceb0ae54d 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -13,13 +13,15 @@ https://conda.anaconda.org/conda-forge/linux-64/mkl-include-2023.2.0-h84fe81f_50 https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.12-5_cp312.conda#0424ae29b104430108f5218a66db7260 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 +https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_0.conda#e46b5ae31282252e0525713e34ffbe2b https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab +https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_0.conda#35e52d19547cb3265a09c49de146a5ae https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793 https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.1.0-h77fa898_0.conda#ca0fad6a41ddaef54a153b78eccb5037 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.12-h4ab18f5_0.conda#7ed427f0871fd41cb1d9c17727c17589 https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.27-h4bc722e_0.conda#817119e8a21a45d325f65d0d54710052 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 -https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.33.0-ha66036c_0.conda#b6927f788e85267beef6cbb292aaebdd +https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.33.1-heb4867d_0.conda#0d3c60291342c0c025db231353376dfb https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3 https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hd590300_1.conda#aec6c91c7371c26392a06708a73c70e5 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.21-h4bc722e_0.conda#36ce76665bf67f5aac36be7a0d21b7f3 @@ -39,19 +41,20 @@ https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.co https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-h4ab18f5_1.conda#57d7dc60e9325e3de37ff8dffd18e814 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-h4bc722e_2.conda#e1b454497f9f7c1147fdde4b53f1b512 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-hb9d3cd8_3.conda#6c566a46baae794daf34775d41eb180a https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 -https://conda.anaconda.org/conda-forge/linux-64/sleef-3.6.1-h3400bea_1.conda#ac00525f47c9fd0e0456a64caef525a6 +https://conda.anaconda.org/conda-forge/linux-64/xorg-inputproto-2.3.2-h7f98852_1002.tar.bz2#bcd1b3396ec6960cbc1d2855a9e60b2b https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.1-hd590300_0.conda#b462a33c0be1421532f28bfe8f4a7514 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.11-hd590300_0.conda#2c80dc38fface310c9bd81b17037fee5 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.3-h7f98852_0.tar.bz2#be93aabceefa2fac576e971aef407908 +https://conda.anaconda.org/conda-forge/linux-64/xorg-recordproto-1.14.2-h7f98852_1002.tar.bz2#2f835e6c386e73c6faaddfe9eda67e98 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https://conda.anaconda.org/conda-forge/osx-64/blas-devel-3.9.0-20_osx64_mkl.cond https://conda.anaconda.org/conda-forge/osx-64/compiler-rt-16.0.6-ha38d28d_2.conda#3b9e8c5c63b8e86234f499490acd85c2 https://conda.anaconda.org/conda-forge/osx-64/contourpy-1.2.1-py312h9230928_0.conda#079df34ce7c71259cfdd394645370891 https://conda.anaconda.org/conda-forge/osx-64/pandas-2.2.2-py312h1171441_1.conda#240737937f1f046b0e03ecc11ac4ec98 -https://conda.anaconda.org/conda-forge/osx-64/scipy-1.14.0-py312hb9702fa_2.conda#610311c8f21dbbf294157f03922e9ca8 +https://conda.anaconda.org/conda-forge/osx-64/scipy-1.14.1-py312he82a568_0.conda#dd3c55da62964fcadf27771e1928e67f https://conda.anaconda.org/conda-forge/osx-64/blas-2.120-mkl.conda#b041a7677a412f3d925d8208936cb1e2 https://conda.anaconda.org/conda-forge/osx-64/clang_impl_osx-64-16.0.6-h8787910_19.conda#64155ef139280e8c181dad866dea2980 https://conda.anaconda.org/conda-forge/osx-64/matplotlib-base-3.9.2-py312h0d5aeb7_0.conda#0c73a08429d20f15fa8b28083ec04cc9 diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index 8f54469e2d1b1..f1afa482db7a3 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -36,7 +36,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.2-py311h06a4308_0.conda#eff3 # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc # pip fonttools @ https://files.pythonhosted.org/packages/a4/22/0a0ad59d9367997fd74a00ad2e88d10559122e09f105e94d34c155aecc0a/fonttools-4.53.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=bee32ea8765e859670c4447b0817514ca79054463b6b79784b08a8df3a4d78e3 -# pip idna @ https://files.pythonhosted.org/packages/e5/3e/741d8c82801c347547f8a2a06aa57dbb1992be9e948df2ea0eda2c8b79e8/idna-3.7-py3-none-any.whl#sha256=82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0 +# pip idna @ https://files.pythonhosted.org/packages/22/7e/d71db821f177828df9dea8c42ac46473366f191be53080e552e628aad991/idna-3.8-py3-none-any.whl#sha256=050b4e5baadcd44d760cedbd2b8e639f2ff89bbc7a5730fcc662954303377aac # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b # pip iniconfig @ https://files.pythonhosted.org/packages/ef/a6/62565a6e1cf69e10f5727360368e451d4b7f58beeac6173dc9db836a5b46/iniconfig-2.0.0-py3-none-any.whl#sha256=b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374 # pip joblib @ https://files.pythonhosted.org/packages/91/29/df4b9b42f2be0b623cbd5e2140cafcaa2bef0759a00b7b70104dcfe2fb51/joblib-1.4.2-py3-none-any.whl#sha256=06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6 @@ -50,7 +50,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.2-py311h06a4308_0.conda#eff3 # pip pillow @ https://files.pythonhosted.org/packages/ba/e5/8c68ff608a4203085158cff5cc2a3c534ec384536d9438c405ed6370d080/pillow-10.4.0-cp311-cp311-manylinux_2_28_x86_64.whl#sha256=76a911dfe51a36041f2e756b00f96ed84677cdeb75d25c767f296c1c1eda1319 # pip pluggy @ https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl#sha256=44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669 # pip pygments @ https://files.pythonhosted.org/packages/f7/3f/01c8b82017c199075f8f788d0d906b9ffbbc5a47dc9918a945e13d5a2bda/pygments-2.18.0-py3-none-any.whl#sha256=b8e6aca0523f3ab76fee51799c488e38782ac06eafcf95e7ba832985c8e7b13a -# pip pyparsing @ https://files.pythonhosted.org/packages/9d/ea/6d76df31432a0e6fdf81681a895f009a4bb47b3c39036db3e1b528191d52/pyparsing-3.1.2-py3-none-any.whl#sha256=f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742 +# pip pyparsing @ https://files.pythonhosted.org/packages/e5/0c/0e3c05b1c87bb6a1c76d281b0f35e78d2d80ac91b5f8f524cebf77f51049/pyparsing-3.1.4-py3-none-any.whl#sha256=a6a7ee4235a3f944aa1fa2249307708f893fe5717dc603503c6c7969c070fb7c # pip pytz @ https://files.pythonhosted.org/packages/9c/3d/a121f284241f08268b21359bd425f7d4825cffc5ac5cd0e1b3d82ffd2b10/pytz-2024.1-py2.py3-none-any.whl#sha256=328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319 # pip six @ https://files.pythonhosted.org/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl#sha256=8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254 # pip snowballstemmer @ https://files.pythonhosted.org/packages/ed/dc/c02e01294f7265e63a7315fe086dd1df7dacb9f840a804da846b96d01b96/snowballstemmer-2.2.0-py2.py3-none-any.whl#sha256=c8e1716e83cc398ae16824e5572ae04e0d9fc2c6b985fb0f900f5f0c96ecba1a @@ -73,8 +73,8 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.2-py311h06a4308_0.conda#eff3 # pip pytest @ https://files.pythonhosted.org/packages/0f/f9/cf155cf32ca7d6fa3601bc4c5dd19086af4b320b706919d48a4c79081cf9/pytest-8.3.2-py3-none-any.whl#sha256=4ba08f9ae7dcf84ded419494d229b48d0903ea6407b030eaec46df5e6a73bba5 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl#sha256=70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6 -# pip scipy @ https://files.pythonhosted.org/packages/89/bb/80c9c98d887c855710fd31fc5ae5574133e98203b3475b07579251803662/scipy-1.14.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=9e3154691b9f7ed73778d746da2df67a19d046a6c8087c8b385bc4cdb2cfca74 -# pip tifffile @ https://files.pythonhosted.org/packages/fd/3a/6ec0327e238253a2b7adab0e542763fd639c4b3cef63b135a74ef3f454a7/tifffile-2024.8.10-py3-none-any.whl#sha256=1c224564fa92e7e9f9a0ed65880b2ece97c3f0d10029ffbebfa5e62b3f6b343d +# pip scipy @ https://files.pythonhosted.org/packages/93/6b/701776d4bd6bdd9b629c387b5140f006185bd8ddea16788a44434376b98f/scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=fef8c87f8abfb884dac04e97824b61299880c43f4ce675dd2cbeadd3c9b466d2 +# pip tifffile @ https://files.pythonhosted.org/packages/e1/82/e3d0b9720345f9057e736b305d22809e5b80143c76f2266e2a1bf57ad2cd/tifffile-2024.8.24-py3-none-any.whl#sha256=40faba20cb0af05c0eb500eda63244dd81500360e1518ff4548eb61ce3943099 # pip lightgbm @ https://files.pythonhosted.org/packages/4e/19/1b928cad70a4e1a3e2c37d5417ca2182510f2451eaadb6c91cd9ec692cae/lightgbm-4.5.0-py3-none-manylinux_2_28_x86_64.whl#sha256=960a0e7c077de0ca3053f1325d3edfc92ea815acf5176adcacdea0f635aeef9b # pip matplotlib @ https://files.pythonhosted.org/packages/01/75/6c7ce560e95714a10fcbb3367d1304975a1a3e620f72af28921b796403f3/matplotlib-3.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=8912ef7c2362f7193b5819d17dae8629b34a95c58603d781329712ada83f9447 # pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock index 48e75610b28f2..eb81eaedb8802 100644 --- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock @@ -38,7 +38,7 @@ https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.4.0-hcfcfb64_0.cond https://conda.anaconda.org/conda-forge/win-64/libzlib-1.3.1-h2466b09_1.conda#d4483ca8afc57ddf1f6dded53b36c17f https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libgfortran-5.3.0-6.tar.bz2#066552ac6b907ec6d72c0ddab29050dc https://conda.anaconda.org/conda-forge/win-64/ninja-1.12.1-hc790b64_0.conda#a557dde55343e03c68cd7e29e7f87279 -https://conda.anaconda.org/conda-forge/win-64/openssl-3.3.1-h2466b09_2.conda#375dbc2a4d5a2e4c738703207e8e368b +https://conda.anaconda.org/conda-forge/win-64/openssl-3.3.1-h2466b09_3.conda#c6ebd3a1a2b393e040ca71c9f9ef8d97 https://conda.anaconda.org/conda-forge/win-64/pixman-0.43.4-h63175ca_0.conda#b98135614135d5f458b75ab9ebb9558c https://conda.anaconda.org/conda-forge/win-64/pthreads-win32-2.9.1-hfa6e2cd_3.tar.bz2#e2da8758d7d51ff6aa78a14dfb9dbed4 https://conda.anaconda.org/conda-forge/win-64/qhull-2020.2-hc790b64_5.conda#854fbdff64b572b5c0b470f334d34c11 @@ -74,8 +74,8 @@ https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2 https://conda.anaconda.org/conda-forge/noarch/packaging-24.1-pyhd8ed1ab_0.conda#cbe1bb1f21567018ce595d9c2be0f0db https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda#d3483c8fc2dc2cc3f5cf43e26d60cabf https://conda.anaconda.org/conda-forge/win-64/pthread-stubs-0.4-hcd874cb_1001.tar.bz2#a1f820480193ea83582b13249a7e7bd9 -https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.2-pyhd8ed1ab_0.conda#b9a4dacf97241704529131a0dfc0494f -https://conda.anaconda.org/conda-forge/noarch/setuptools-72.1.0-pyhd8ed1ab_0.conda#e06d4c26df4f958a8d38696f2c344d15 +https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.4-pyhd8ed1ab_0.conda#4d91352a50949d049cf9714c8563d433 +https://conda.anaconda.org/conda-forge/noarch/setuptools-72.2.0-pyhd8ed1ab_0.conda#1462aa8b243aad09ef5d0841c745eb89 https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_0.tar.bz2#f832c45a477c78bebd107098db465095 @@ -89,10 +89,10 @@ https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.0-pyhd8ed1ab_0.conda#05b https://conda.anaconda.org/conda-forge/win-64/brotli-1.1.0-hcfcfb64_1.conda#f47f6db2528e38321fb00ae31674c133 https://conda.anaconda.org/conda-forge/win-64/coverage-7.6.1-py39ha55e580_0.conda#a9c63313e61e510e8f8bca90794eee73 https://conda.anaconda.org/conda-forge/win-64/fontconfig-2.14.2-hbde0cde_0.conda#08767992f1a4f1336a257af1241034bd -https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.2-pyhd8ed1ab_0.conda#ff64113bd700cf0f892ebf4b223e56aa +https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.4-pyhd8ed1ab_0.conda#99aa3edd3f452d61c305a30e78140513 https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/win-64/lcms2-2.16-h67d730c_0.conda#d3592435917b62a8becff3a60db674f6 -https://conda.anaconda.org/conda-forge/win-64/libxcb-1.16-hcd874cb_0.conda#7c1217d3b075f195ab17370f2d550f5d +https://conda.anaconda.org/conda-forge/win-64/libxcb-1.16-h013a479_1.conda#f0b599acdc82d5bc7e3b105833e7c5c8 https://conda.anaconda.org/conda-forge/noarch/meson-1.5.1-pyhd8ed1ab_1.conda#979087ee59bea1355f991a3b738af64e https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.2-h3d672ee_0.conda#7e7099ad94ac3b599808950cec30ad4e https://conda.anaconda.org/conda-forge/noarch/pip-24.2-pyhd8ed1ab_0.conda#6721aef6bfe5937abe70181545dd2c51 @@ -102,7 +102,7 @@ https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0 https://conda.anaconda.org/conda-forge/win-64/tbb-2021.12.0-hc790b64_3.conda#a16e2a639e87c554abee5192ce6ee308 https://conda.anaconda.org/conda-forge/win-64/cairo-1.18.0-h32b962e_3.conda#8f43723a4925c51e55c2d81725a97db4 https://conda.anaconda.org/conda-forge/win-64/fonttools-4.53.1-py39ha55e580_0.conda#81bbae03542e491178a620a45ad0b474 -https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.2-pyhd8ed1ab_0.conda#af1f82a5ea4f16b1482165f93b11399d +https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.4-pyhd8ed1ab_0.conda#c62e775953b6b65f2079c9ee2a62813c https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 https://conda.anaconda.org/conda-forge/win-64/mkl-2024.1.0-h66d3029_694.conda#a17423859d3fb912c8f2e9797603ddb6 https://conda.anaconda.org/conda-forge/win-64/pillow-10.4.0-py39hfa8c767_0.conda#7b24bccfb14f05019c8a488d4ee084a8 @@ -113,7 +113,7 @@ https://conda.anaconda.org/conda-forge/win-64/libblas-3.9.0-23_win64_mkl.conda#6 https://conda.anaconda.org/conda-forge/win-64/mkl-devel-2024.1.0-h57928b3_694.conda#cb1406a70154cdef203167c6a95f6351 https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-23_win64_mkl.conda#7ffb5b336cefd2e6d1e00ac1f7c9f2c9 https://conda.anaconda.org/conda-forge/win-64/liblapack-3.9.0-23_win64_mkl.conda#3580796ab7b7d68143f45d4d94d866b7 -https://conda.anaconda.org/conda-forge/win-64/qt6-main-6.7.2-hbb46ec1_4.conda#11c572c84b282f085c0379d6b5a6db19 +https://conda.anaconda.org/conda-forge/win-64/qt6-main-6.7.2-hbb46ec1_5.conda#e14fa5fe2da0bf8cc30d06314ce6ce33 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https://conda.anaconda.org/conda-forge/noarch/seaborn-0.13.2-hd8ed1ab_2.conda#a7 https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.8.0-pyhd8ed1ab_0.conda#0a5522bdd3983c52102e75d1307ad8c4 https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.15.4-pyhd8ed1ab_0.conda#c7c50dd5192caa58a05e6a4248a27acb https://conda.anaconda.org/conda-forge/noarch/sphinx-copybutton-0.5.2-pyhd8ed1ab_0.conda#ac832cc43adc79118cf6e23f1f9b8995 -https://conda.anaconda.org/conda-forge/noarch/sphinx-design-0.6.1-pyhd8ed1ab_0.conda#51b2433e4a223b14defee96d3caf9bab +https://conda.anaconda.org/conda-forge/noarch/sphinx-design-0.6.1-pyhd8ed1ab_1.conda#db0f1eb28b6df3a11e89437597309009 https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.17.1-pyhd8ed1ab_0.conda#0adfccc6e7269a29a63c1c8ee3c6d8ba https://conda.anaconda.org/conda-forge/noarch/sphinx-prompt-1.4.0-pyhd8ed1ab_0.tar.bz2#88ee91e8679603f2a5bd036d52919cc2 https://conda.anaconda.org/conda-forge/noarch/sphinx-remove-toctrees-1.0.0.post1-pyhd8ed1ab_0.conda#6dee8412218288a17f99f2cfffab334d @@ -275,7 +286,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip send2trash @ https://files.pythonhosted.org/packages/40/b0/4562db6223154aa4e22f939003cb92514c79f3d4dccca3444253fd17f902/Send2Trash-1.8.3-py3-none-any.whl#sha256=0c31227e0bd08961c7665474a3d1ef7193929fedda4233843689baa056be46c9 # pip sniffio @ https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl#sha256=2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2 # pip traitlets @ https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl#sha256=b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f -# pip types-python-dateutil @ https://files.pythonhosted.org/packages/c7/1b/af4f4c4f3f7339a4b7eb3c0ab13416db98f8ac09de3399129ee5fdfa282b/types_python_dateutil-2.9.0.20240316-py3-none-any.whl#sha256=6b8cb66d960771ce5ff974e9dd45e38facb81718cc1e208b10b1baccbfdbee3b +# pip types-python-dateutil @ https://files.pythonhosted.org/packages/45/ba/2a4750156272f180f8209f87656ae92e0aeb14f9864976aa90cbd9f21eda/types_python_dateutil-2.9.0.20240821-py3-none-any.whl#sha256=f5889fcb4e63ed4aaa379b44f93c32593d50b9a94c9a60a0c854d8cc3511cd57 # pip uri-template @ https://files.pythonhosted.org/packages/e7/00/3fca040d7cf8a32776d3d81a00c8ee7457e00f80c649f1e4a863c8321ae9/uri_template-1.3.0-py3-none-any.whl#sha256=a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363 # pip webcolors @ https://files.pythonhosted.org/packages/f0/33/12020ba99beaff91682b28dc0bbf0345bbc3244a4afbae7644e4fa348f23/webcolors-24.8.0-py3-none-any.whl#sha256=fc4c3b59358ada164552084a8ebee637c221e4059267d0f8325b3b560f6c7f0a # pip webencodings @ https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl#sha256=a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78 @@ -286,7 +297,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip bleach @ https://files.pythonhosted.org/packages/ea/63/da7237f805089ecc28a3f36bca6a21c31fcbc2eb380f3b8f1be3312abd14/bleach-6.1.0-py3-none-any.whl#sha256=3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6 # pip doit @ https://files.pythonhosted.org/packages/44/83/a2960d2c975836daa629a73995134fd86520c101412578c57da3d2aa71ee/doit-0.36.0-py3-none-any.whl#sha256=ebc285f6666871b5300091c26eafdff3de968a6bd60ea35dd1e3fc6f2e32479a # pip jupyter-core @ https://files.pythonhosted.org/packages/c9/fb/108ecd1fe961941959ad0ee4e12ee7b8b1477247f30b1fdfd83ceaf017f0/jupyter_core-5.7.2-py3-none-any.whl#sha256=4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409 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https://files.pythonhosted.org/packages/7b/44/4e421b96b67b2daff264473f7465db72fbdf36a07e05494f50300cc7b0c6/rfc3339_validator-0.1.4-py2.py3-none-any.whl#sha256=24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa # pip sphinxcontrib-sass @ https://files.pythonhosted.org/packages/2e/87/7c2eb08e3ca1d6baae32c0a5e005330fe1cec93a36aa085e714c3b3a3c7d/sphinxcontrib_sass-0.3.4-py2.py3-none-any.whl#sha256=a0c79a44ae8b8935c02dc340ebe40c9e002c839331201c899dc93708970c355a diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index bb3da6a06564a..12a7fa04058c7 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -51,7 +51,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.co https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-h4ab18f5_1.conda#57d7dc60e9325e3de37ff8dffd18e814 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-h4bc722e_2.conda#e1b454497f9f7c1147fdde4b53f1b512 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-hb9d3cd8_3.conda#6c566a46baae794daf34775d41eb180a https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.6.6-he8a937b_2.conda#77d9955b4abddb811cb8ab1aa7d743e4 https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a @@ -84,7 +84,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.43-h2797004_0.conda#0 https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-12.4.0-h46f95d5_0.conda#23f5c8ad2a46976a9eee4d21392fa421 https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.46.0-hde9e2c9_0.conda#18aa975d2094c34aef978060ae7da7d8 https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h9c3ff4c_0.tar.bz2#309dec04b70a3cc0f1e84a4013683bc0 -https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.16-hd590300_0.conda#151cba22b85a989c2d6ef9633ffee1e4 +https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.16-hb9d3cd8_1.conda#3601598f0db0470af28985e3e7ad0158 https://conda.anaconda.org/conda-forge/linux-64/libzopfli-1.0.3-h9c3ff4c_0.tar.bz2#c66fe2d123249af7651ebde8984c51c2 https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda#318b08df404f9c9be5712aaa5a6f0bb0 https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9160cdeb523a1b20cf8d2a0bf821f45d @@ -148,7 +148,7 @@ https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.80.3-h8fdd7da_2.con https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-12.4.0-h613a52c_0.conda#0740149e4653caebd1d2f6bbf84a1720 https://conda.anaconda.org/conda-forge/noarch/hpack-4.0.0-pyh9f0ad1d_0.tar.bz2#914d6646c4dbb1fd3ff539830a12fd71 https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.0.1-pyhd8ed1ab_0.tar.bz2#9f765cbfab6870c8435b9eefecd7a1f4 -https://conda.anaconda.org/conda-forge/noarch/idna-3.7-pyhd8ed1ab_0.conda#c0cc1420498b17414d8617d0b9f506ca +https://conda.anaconda.org/conda-forge/noarch/idna-3.8-pyhd8ed1ab_0.conda#99e164522f6bdf23c177c8d9ae63f975 https://conda.anaconda.org/conda-forge/noarch/imagesize-1.4.1-pyhd8ed1ab_0.tar.bz2#7de5386c8fea29e76b303f37dde4c352 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda#f800d2da156d08e289b14e87e43c1ae5 https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.5-py39h7633fee_1.conda#c9f74d717e5a2847a9f8b779c54130f2 @@ -168,7 +168,7 @@ https://conda.anaconda.org/conda-forge/noarch/ply-3.11-pyhd8ed1ab_2.conda#18c6de https://conda.anaconda.org/conda-forge/linux-64/psutil-6.0.0-py39hd3abc70_0.conda#984987a2ef8c931691ad0d7fbb8ef3ca https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyhd8ed1ab_0.conda#844d9eb3b43095b031874477f7d70088 https://conda.anaconda.org/conda-forge/noarch/pygments-2.18.0-pyhd8ed1ab_0.conda#b7f5c092b8f9800150d998a71b76d5a1 -https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.2-pyhd8ed1ab_0.conda#b9a4dacf97241704529131a0dfc0494f +https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.4-pyhd8ed1ab_0.conda#4d91352a50949d049cf9714c8563d433 https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2#2a7de29fb590ca14b5243c4c812c8025 https://conda.anaconda.org/conda-forge/noarch/pytz-2024.1-pyhd8ed1ab_0.conda#3eeeeb9e4827ace8c0c1419c85d590ad https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.2-py39hcd6043d_0.conda#40f1dd93ac87fff4b776d6fb8033ddb9 @@ -204,7 +204,7 @@ https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.3-h315aac3_2.conda#00e https://conda.anaconda.org/conda-forge/linux-64/gxx-12.4.0-h236703b_0.conda#56cefffbce52071b597fd3eb9208adc9 https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-12.4.0-h8489865_0.conda#5cf73d936678e6805da39b8ba6be263c https://conda.anaconda.org/conda-forge/noarch/h2-4.1.0-pyhd8ed1ab_0.tar.bz2#b748fbf7060927a6e82df7cb5ee8f097 -https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.2.0-pyha770c72_0.conda#c261d14fc7f49cdd403868998a18c318 +https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.4.0-pyha770c72_0.conda#6e3dbc422d3749ad72659243d6ac8b2b https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda#7b86ecb7d3557821c649b3c31e3eb9f2 https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp15-15.0.7-default_h127d8a8_5.conda#d0a9633b53cdc319b8a1a532ae7822b8 @@ -228,7 +228,7 @@ https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.7.0-h00ab1b0_1.co https://conda.anaconda.org/conda-forge/linux-64/fortran-compiler-1.7.0-heb67821_1.conda#cf4b0e7c4c78bb0662aed9b27c414a3c https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.6-haf2f30d_0.conda#a15d7b21e4b7b82b87ba04c3b46c1317 https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-9.0.0-hfac3d4d_0.conda#c7b47c64af53e8ecee01d101eeab2342 -https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-8.2.0-hd8ed1ab_0.conda#0fd030dce707a6654472cf7619b0b01b +https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-8.4.0-hd8ed1ab_0.conda#01b7411c765c3d863dcc920207f258bd https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-1.11.0-h4ab18f5_1.conda#14858a47d4cc995892e79f2b340682d7 https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.conda#ef1910918dd895516a769ed36b5b3a4e https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 @@ -253,7 +253,7 @@ https://conda.anaconda.org/conda-forge/noarch/pooch-1.6.0-pyhd8ed1ab_0.tar.bz2#6 https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.8-h320f8da_24.conda#bec111b67cb8dc63277c6af65d214044 https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-23_linux64_mkl.conda#c8f8d0ebf2e7fd3a90ec68e3bb008995 https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py39h9d013fb_3.conda#f3bcbaa497af215e86d966244d683289 -https://conda.anaconda.org/conda-forge/noarch/imageio-2.34.2-pyh12aca89_0.conda#97ad994fae55dce96bd397054b32e41a +https://conda.anaconda.org/conda-forge/noarch/imageio-2.35.1-pyh12aca89_0.conda#b03ff3631329c8ef17bae35d2bb216f7 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.3.4-py39h2fa2bec_0.tar.bz2#9ec0b2186fab9121c54f4844f93ee5b7 https://conda.anaconda.org/conda-forge/linux-64/pandas-1.1.5-py39hde0f152_0.tar.bz2#79fc4b5b3a865b90dd3701cecf1ad33c https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.6-pyhd8ed1ab_0.conda#a5b55d1cb110cdcedc748b5c3e16e687 From 2f805adc0b173105b959a1eaa048477719979b1f Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 26 Aug 2024 11:21:10 -0700 Subject: [PATCH 226/275] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#29719) Co-authored-by: Lock file bot --- build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index 31e29c4d4b9e2..dffc09ccb459b 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -33,7 +33,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.2-py312h06a4308_0.conda#798c # pip coverage @ https://files.pythonhosted.org/packages/1f/0f/c890339dd605f3ebc269543247bdd43b703cce6825b5ed42ff5f2d6122c7/coverage-7.6.1-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c44fee9975f04b33331cb8eb272827111efc8930cfd582e0320613263ca849ca # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc -# pip idna @ https://files.pythonhosted.org/packages/e5/3e/741d8c82801c347547f8a2a06aa57dbb1992be9e948df2ea0eda2c8b79e8/idna-3.7-py3-none-any.whl#sha256=82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0 +# pip idna @ https://files.pythonhosted.org/packages/22/7e/d71db821f177828df9dea8c42ac46473366f191be53080e552e628aad991/idna-3.8-py3-none-any.whl#sha256=050b4e5baadcd44d760cedbd2b8e639f2ff89bbc7a5730fcc662954303377aac # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b # pip iniconfig @ https://files.pythonhosted.org/packages/ef/a6/62565a6e1cf69e10f5727360368e451d4b7f58beeac6173dc9db836a5b46/iniconfig-2.0.0-py3-none-any.whl#sha256=b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374 # pip markupsafe @ https://files.pythonhosted.org/packages/0a/0d/2454f072fae3b5a137c119abf15465d1771319dfe9e4acbb31722a0fff91/MarkupSafe-2.1.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=f5dfb42c4604dddc8e4305050aa6deb084540643ed5804d7455b5df8fe16f5e5 From 8111fe270b42d8c3d6acabeb28ed1a083f07b704 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 26 Aug 2024 11:21:46 -0700 Subject: [PATCH 227/275] :lock: :robot: CI Update lock files for cirrus-arm CI build(s) :lock: :robot: (#29718) Co-authored-by: Lock file bot --- .../pymin_conda_forge_linux-aarch64_conda.lock | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock index edb5b2fd745ad..84f1dc22035d6 100644 --- a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock +++ b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock @@ -32,7 +32,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/libwebp-base-1.4.0-h31becfc https://conda.anaconda.org/conda-forge/linux-aarch64/libxcrypt-4.4.36-h31becfc_1.conda#b4df5d7d4b63579d081fd3a4cf99740e https://conda.anaconda.org/conda-forge/linux-aarch64/libzlib-1.3.1-h68df207_1.conda#b13fb82f88902e34dd0638cd7d378c21 https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.5-h0425590_0.conda#38362af7bfac0efef69675acee564458 -https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.3.1-h68df207_2.conda#e53f74e640d477466e04bae394b0d163 +https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.3.1-h86ecc28_3.conda#7f591390401ad65781372240424ab7fc https://conda.anaconda.org/conda-forge/linux-aarch64/pthread-stubs-0.4-hb9de7d4_1001.tar.bz2#d0183ec6ce0b5aaa3486df25fa5f0ded https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-kbproto-1.0.7-h3557bc0_1002.tar.bz2#ec8ce6b3dac3945a4010559a6284b755 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libice-1.1.1-h7935292_0.conda#025968e2637bca910b9b3e7f6743beff @@ -54,7 +54,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/libedit-3.1.20191231-he28a2 https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran-ng-14.1.0-he9431aa_0.conda#a50ae662c1e7f26f0f2c99e31d1bf614 https://conda.anaconda.org/conda-forge/linux-aarch64/libpng-1.6.43-h194ca79_0.conda#1123e504d9254dd9494267ab9aba95f0 https://conda.anaconda.org/conda-forge/linux-aarch64/libsqlite-3.46.0-hf51ef55_0.conda#a8ae63fd6fb7d007f74ef3df95e5edf3 -https://conda.anaconda.org/conda-forge/linux-aarch64/libxcb-1.16-h7935292_0.conda#93c0136e9cba96657339dfe25fba4da7 +https://conda.anaconda.org/conda-forge/linux-aarch64/libxcb-1.16-h57736b2_1.conda#8d502f235bf4f3ce1f288cb1ff3a90b6 https://conda.anaconda.org/conda-forge/linux-aarch64/mysql-common-8.3.0-h940b476_5.conda#f027f6c56a5ee03d21e6e32c963e2fbd https://conda.anaconda.org/conda-forge/linux-aarch64/ninja-1.12.1-h70be974_0.conda#216635cea46498d8045c7cf0f03eaf72 https://conda.anaconda.org/conda-forge/linux-aarch64/pcre2-10.44-h070dd5b_2.conda#94022de9682cb1a0bb18a99cbc3541b3 @@ -62,7 +62,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/pixman-0.43.4-h2f0025b_0.co https://conda.anaconda.org/conda-forge/linux-aarch64/qhull-2020.2-h70be974_5.conda#bb138086d938e2b64f5f364945793ebf https://conda.anaconda.org/conda-forge/linux-aarch64/readline-8.2-h8fc344f_1.conda#105eb1e16bf83bfb2eb380a48032b655 https://conda.anaconda.org/conda-forge/linux-aarch64/tk-8.6.13-h194ca79_0.conda#f75105e0585851f818e0009dd1dde4dc -https://conda.anaconda.org/conda-forge/linux-aarch64/wayland-1.23.0-hc89ecf9_0.conda#ea40919919b262d072199c1f8ba37cb6 +https://conda.anaconda.org/conda-forge/linux-aarch64/wayland-1.23.1-h698ed42_0.conda#2661f9252065051914f1cdf5835e7430 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libsm-1.2.4-h5a01bc2_0.conda#d788eca20ecd63bad8eea7219e5c5fb7 https://conda.anaconda.org/conda-forge/linux-aarch64/zlib-1.3.1-h68df207_1.conda#6031f9e32654fbdb9fdba406ab980517 https://conda.anaconda.org/conda-forge/linux-aarch64/zstd-1.5.6-h02f22dd_0.conda#be8d5f8cf21aed237b8b182ea86b3dd6 @@ -105,8 +105,8 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/openblas-0.3.27-pthreads_hd https://conda.anaconda.org/conda-forge/linux-aarch64/openjpeg-2.5.2-h0d9d63b_0.conda#fd2898519e839d5ceb778343f39a3176 https://conda.anaconda.org/conda-forge/noarch/packaging-24.1-pyhd8ed1ab_0.conda#cbe1bb1f21567018ce595d9c2be0f0db https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda#d3483c8fc2dc2cc3f5cf43e26d60cabf -https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.2-pyhd8ed1ab_0.conda#b9a4dacf97241704529131a0dfc0494f -https://conda.anaconda.org/conda-forge/noarch/setuptools-72.1.0-pyhd8ed1ab_0.conda#e06d4c26df4f958a8d38696f2c344d15 +https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.4-pyhd8ed1ab_0.conda#4d91352a50949d049cf9714c8563d433 +https://conda.anaconda.org/conda-forge/noarch/setuptools-72.2.0-pyhd8ed1ab_0.conda#1462aa8b243aad09ef5d0841c745eb89 https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96 @@ -120,7 +120,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxrender-0.9.11-h793 https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.0-pyhd8ed1ab_0.conda#05b6bcb391b5be17374f7ad0aeedc479 https://conda.anaconda.org/conda-forge/linux-aarch64/cairo-1.18.0-hdb1a16f_3.conda#080659f02bf2202c57f1cda4f9e51f21 https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.53.1-py39he257ee7_0.conda#e30df3a3431af304f87bbd0cd07d5674 -https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.2-pyhd8ed1ab_0.conda#ff64113bd700cf0f892ebf4b223e56aa +https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.4-pyhd8ed1ab_0.conda#99aa3edd3f452d61c305a30e78140513 https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-23_linuxaarch64_openblas.conda#65a4f18036c0f5419146fddee6653a96 https://conda.anaconda.org/conda-forge/linux-aarch64/libclang-cpp18.1-18.1.8-default_h14d1da3_2.conda#ed0dd9fe9fb649dc19593919df0afd43 @@ -135,7 +135,7 @@ https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.2-pyhd8ed1ab_0.conda#e0 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-aarch64/xcb-util-cursor-0.1.4-h68df207_2.conda#9a1c7ed78dff58f6c7b22981ab5c9d39 https://conda.anaconda.org/conda-forge/linux-aarch64/harfbuzz-9.0.0-hbf49d6b_1.conda#ceb458f664cab8550fcd74fff26451db -https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.2-pyhd8ed1ab_0.conda#af1f82a5ea4f16b1482165f93b11399d +https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.4-pyhd8ed1ab_0.conda#c62e775953b6b65f2079c9ee2a62813c https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-23_linuxaarch64_openblas.conda#d71af7934d6dcef05a3c9b0379e1cdfa https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-2.0.1-py39hcdcdb6f_0.conda#e97d4cba6dd293bb795baddad9ddae02 From 7234c0081f61769d7f277376417653c6417e41c7 Mon Sep 17 00:00:00 2001 From: David Matthew Cherney <103840964+davidcherney@users.noreply.github.com> Date: Tue, 27 Aug 2024 10:11:33 -0600 Subject: [PATCH 228/275] added link to examples_manifold_plot_compare_methods.py to manifold.MDS doc string (#29714) --- sklearn/manifold/_mds.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/sklearn/manifold/_mds.py b/sklearn/manifold/_mds.py index 9732f2556f2ba..d7e4877bf2f34 100644 --- a/sklearn/manifold/_mds.py +++ b/sklearn/manifold/_mds.py @@ -524,8 +524,11 @@ class MDS(BaseEstimator): >>> X_transformed.shape (100, 2) - For a more detailed example of usage, see: - :ref:`sphx_glr_auto_examples_manifold_plot_mds.py` + For a more detailed example of usage, see + :ref:`sphx_glr_auto_examples_manifold_plot_mds.py`. + + For a comparison of manifold learning techniques, see + :ref:`sphx_glr_auto_examples_manifold_plot_compare_methods.py`. """ _parameter_constraints: dict = { From 6d7f58f1af097aba97d20ea5600f2d1d4f45bcc3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Thu, 29 Aug 2024 11:44:41 +0200 Subject: [PATCH 229/275] BLD Add missing OpenMP dependencies in relevant meson.build (#29694) --- doc/whats_new/v1.5.rst | 8 ++++++++ sklearn/_loss/meson.build | 1 + sklearn/manifold/meson.build | 2 +- sklearn/metrics/_pairwise_distances_reduction/meson.build | 4 ++-- sklearn/metrics/meson.build | 1 + sklearn/utils/meson.build | 2 +- 6 files changed, 14 insertions(+), 4 deletions(-) diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst index b5542a0d1cf5f..05ea928358377 100644 --- a/doc/whats_new/v1.5.rst +++ b/doc/whats_new/v1.5.rst @@ -20,6 +20,14 @@ Version 1.5.2 **release date of 1.5.2** +Changes impacting many modules +------------------------------ + +- |Fix| Fixed performance regression in a few Cython modules in + `sklearn._loss`, `sklearn.manifold`, `sklearn.metrics` and `sklearn.utils`, + which were built without OpenMP support. + :pr:`29694` by :user:`Loïc Estèvce `. + Changelog --------- diff --git a/sklearn/_loss/meson.build b/sklearn/_loss/meson.build index 7978fa56139c6..bb187fd03f71b 100644 --- a/sklearn/_loss/meson.build +++ b/sklearn/_loss/meson.build @@ -17,6 +17,7 @@ _loss_pyx = custom_target( py.extension_module( '_loss', _loss_pyx, + dependencies: [openmp_dep], cython_args: cython_args, install: true, subdir: 'sklearn/_loss', diff --git a/sklearn/manifold/meson.build b/sklearn/manifold/meson.build index b112f63dd4f2d..ee83e8afc5019 100644 --- a/sklearn/manifold/meson.build +++ b/sklearn/manifold/meson.build @@ -9,7 +9,7 @@ py.extension_module( py.extension_module( '_barnes_hut_tsne', '_barnes_hut_tsne.pyx', - dependencies: [np_dep], + dependencies: [np_dep, openmp_dep], cython_args: cython_args, subdir: 'sklearn/manifold', install: true diff --git a/sklearn/metrics/_pairwise_distances_reduction/meson.build b/sklearn/metrics/_pairwise_distances_reduction/meson.build index 52ea6062da26b..878b29e869f5e 100644 --- a/sklearn/metrics/_pairwise_distances_reduction/meson.build +++ b/sklearn/metrics/_pairwise_distances_reduction/meson.build @@ -172,7 +172,7 @@ _argkmin_classmode_pyx = custom_target( _argkmin_classmode = py.extension_module( '_argkmin_classmode', _argkmin_classmode_pyx, - dependencies: [np_dep], + dependencies: [np_dep, openmp_dep], override_options: ['cython_language=cpp'], cython_args: cython_args, # XXX: for some reason -fno-sized-deallocation is needed otherwise there is @@ -199,7 +199,7 @@ _radius_neighbors_classmode_pyx = custom_target( _radius_neighbors_classmode = py.extension_module( '_radius_neighbors_classmode', _radius_neighbors_classmode_pyx, - dependencies: [np_dep], + dependencies: [np_dep, openmp_dep], override_options: ['cython_language=cpp'], cython_args: cython_args, subdir: 'sklearn/metrics/_pairwise_distances_reduction', diff --git a/sklearn/metrics/meson.build b/sklearn/metrics/meson.build index ef7b202c6f89c..2e01572144707 100644 --- a/sklearn/metrics/meson.build +++ b/sklearn/metrics/meson.build @@ -41,6 +41,7 @@ _dist_metrics = py.extension_module( py.extension_module( '_pairwise_fast', ['_pairwise_fast.pyx', metrics_cython_tree], + dependencies: [openmp_dep], cython_args: cython_args, subdir: 'sklearn/metrics', install: true diff --git a/sklearn/utils/meson.build b/sklearn/utils/meson.build index 9bbfc01b7b6bf..c7a6102b956e8 100644 --- a/sklearn/utils/meson.build +++ b/sklearn/utils/meson.build @@ -18,7 +18,7 @@ utils_extension_metadata = { 'sparsefuncs_fast': {'sources': ['sparsefuncs_fast.pyx']}, '_cython_blas': {'sources': ['_cython_blas.pyx']}, - 'arrayfuncs': {'sources': ['arrayfuncs.pyx']}, + 'arrayfuncs': {'sources': ['arrayfuncs.pyx'], 'dependencies': [openmp_dep]}, 'murmurhash': { 'sources': ['murmurhash.pyx', 'src' / 'MurmurHash3.cpp'], }, From 21705cc1a8212585d749feb622591c6084f0a2b5 Mon Sep 17 00:00:00 2001 From: Arif Qodari Date: Fri, 30 Aug 2024 17:23:55 +0700 Subject: [PATCH 230/275] DOC Fix Definitions of False Positive and False Negative in Fowlkes-Mallows scores (#29685) --- doc/modules/clustering.rst | 27 +++++++++++++++----------- sklearn/metrics/cluster/_supervised.py | 12 ++++++------ 2 files changed, 22 insertions(+), 17 deletions(-) diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst index b72b8f5ed0312..3a055abb65c8b 100644 --- a/doc/modules/clustering.rst +++ b/doc/modules/clustering.rst @@ -1791,20 +1791,25 @@ homogeneous but not complete:: Fowlkes-Mallows scores ---------------------- -The Fowlkes-Mallows index (:func:`sklearn.metrics.fowlkes_mallows_score`) can be -used when the ground truth class assignments of the samples is known. The -Fowlkes-Mallows score FMI is defined as the geometric mean of the -pairwise precision and recall: +The original Fowlkes-Mallows index (FMI) was intended to measure the similarity +between two clustering results, which is inherently an unsupervised comparison. +The supervised adaptation of the Fowlkes-Mallows index +(as implemented in :func:`sklearn.metrics.fowlkes_mallows_score`) can be used +when the ground truth class assignments of the samples are known. +The FMI is defined as the geometric mean of the pairwise precision and recall: .. math:: \text{FMI} = \frac{\text{TP}}{\sqrt{(\text{TP} + \text{FP}) (\text{TP} + \text{FN})}} -Where ``TP`` is the number of **True Positive** (i.e. the number of pair -of points that belong to the same clusters in both the true labels and the -predicted labels), ``FP`` is the number of **False Positive** (i.e. the number -of pair of points that belong to the same clusters in the true labels and not -in the predicted labels) and ``FN`` is the number of **False Negative** (i.e. the -number of pair of points that belongs in the same clusters in the predicted -labels and not in the true labels). +In the above formula: + +* ``TP`` (**True Positive**): The number of pairs of points that are clustered together + both in the true labels and in the predicted labels. + +* ``FP`` (**False Positive**): The number of pairs of points that are clustered together + in the predicted labels but not in the true labels. + +* ``FN`` (**False Negative**): The number of pairs of points that are clustered together + in the true labels but not in the predicted labels. The score ranges from 0 to 1. A high value indicates a good similarity between two clusters. diff --git a/sklearn/metrics/cluster/_supervised.py b/sklearn/metrics/cluster/_supervised.py index ac47c83a488a3..e11eca535cbcd 100644 --- a/sklearn/metrics/cluster/_supervised.py +++ b/sklearn/metrics/cluster/_supervised.py @@ -1191,13 +1191,13 @@ def fowlkes_mallows_score(labels_true, labels_pred, *, sparse=False): FMI = TP / sqrt((TP + FP) * (TP + FN)) - Where ``TP`` is the number of **True Positive** (i.e. the number of pair of - points that belongs in the same clusters in both ``labels_true`` and + Where ``TP`` is the number of **True Positive** (i.e. the number of pairs of + points that belong to the same cluster in both ``labels_true`` and ``labels_pred``), ``FP`` is the number of **False Positive** (i.e. the - number of pair of points that belongs in the same clusters in - ``labels_true`` and not in ``labels_pred``) and ``FN`` is the number of - **False Negative** (i.e. the number of pair of points that belongs in the - same clusters in ``labels_pred`` and not in ``labels_True``). + number of pairs of points that belong to the same cluster in + ``labels_pred`` but not in ``labels_true``) and ``FN`` is the number of + **False Negative** (i.e. the number of pairs of points that belong to the + same cluster in ``labels_true`` but not in ``labels_pred``). The score ranges from 0 to 1. A high value indicates a good similarity between two clusters. From 2ec6816b7ee25d0af033a274f6a182548d6bd88f Mon Sep 17 00:00:00 2001 From: Ilya Komarov Date: Fri, 30 Aug 2024 12:54:21 +0200 Subject: [PATCH 231/275] DOC Clarify Sphinx build instructions (#29743) Co-authored-by: Tim Head --- doc/developers/contributing.rst | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst index 03c0addacb156..4f5bda71372e5 100644 --- a/doc/developers/contributing.rst +++ b/doc/developers/contributing.rst @@ -929,8 +929,8 @@ To build the documentation, you need to be in the ``doc`` folder: cd doc -In the vast majority of cases, you only need to generate the full web site, -without the example gallery: +In the vast majority of cases, you only need to generate the web site without +the example gallery: .. prompt:: bash @@ -945,13 +945,16 @@ To also generate the example gallery you can use: make html -This will run all the examples, which takes a while. If you only want to generate a few -examples, which is particularly useful if you are modifying only a few examples, you can -use: +This will run all the examples, which takes a while. If you only want to generate +images for a few examples, you can pass their names (or parts of) to the build command. +The line below will run all examples with names starting with `plot_calibration`: .. prompt:: bash - EXAMPLES_PATTERN=your_regex_goes_here make html + EXAMPLES_PATTERN="plot_calibration" make html + +You can also pass regular expressions to gain more advanced control over examples +you want run. Set the environment variable `NO_MATHJAX=1` if you intend to view the documentation in an offline setting. To build the PDF manual, run: From 8a1fe4cb72797b6cdf5e6f0a60a7bf4f0c01db0b Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Fri, 30 Aug 2024 15:13:14 +0200 Subject: [PATCH 232/275] MNT Fix E721 linting issues to do type comparisons with is (#29501) --- .../plot_tweedie_regression_insurance_claims.py | 2 +- sklearn/cluster/_optics.py | 2 +- sklearn/cluster/tests/test_dbscan.py | 2 +- sklearn/linear_model/tests/test_ridge.py | 4 ++-- sklearn/metrics/pairwise.py | 2 +- sklearn/model_selection/_split.py | 2 +- sklearn/model_selection/tests/test_validation.py | 8 ++++---- sklearn/utils/estimator_checks.py | 2 +- sklearn/utils/tests/test_validation.py | 2 +- 9 files changed, 13 insertions(+), 13 deletions(-) diff --git a/examples/linear_model/plot_tweedie_regression_insurance_claims.py b/examples/linear_model/plot_tweedie_regression_insurance_claims.py index 31a91fb37c766..b18702bdef2b6 100644 --- a/examples/linear_model/plot_tweedie_regression_insurance_claims.py +++ b/examples/linear_model/plot_tweedie_regression_insurance_claims.py @@ -79,7 +79,7 @@ def load_mtpl2(n_samples=None): df["ClaimAmount"] = df["ClaimAmount"].fillna(0) # unquote string fields - for column_name in df.columns[df.dtypes.values == object]: + for column_name in df.columns[[t is object for t in df.dtypes.values]]: df[column_name] = df[column_name].str.strip("'") return df.iloc[:n_samples] diff --git a/sklearn/cluster/_optics.py b/sklearn/cluster/_optics.py index b2a0c4d642a00..70eee67b0a98b 100755 --- a/sklearn/cluster/_optics.py +++ b/sklearn/cluster/_optics.py @@ -324,7 +324,7 @@ def fit(self, X, y=None): Returns a fitted instance of self. """ dtype = bool if self.metric in PAIRWISE_BOOLEAN_FUNCTIONS else float - if dtype == bool and X.dtype != bool: + if dtype is bool and X.dtype != bool: msg = ( "Data will be converted to boolean for" f" metric {self.metric}, to avoid this warning," diff --git a/sklearn/cluster/tests/test_dbscan.py b/sklearn/cluster/tests/test_dbscan.py index d42cc2b17d518..556f89312d2fc 100644 --- a/sklearn/cluster/tests/test_dbscan.py +++ b/sklearn/cluster/tests/test_dbscan.py @@ -291,7 +291,7 @@ def test_input_validation(): def test_pickle(): obj = DBSCAN() s = pickle.dumps(obj) - assert type(pickle.loads(s)) == obj.__class__ + assert type(pickle.loads(s)) is obj.__class__ def test_boundaries(): diff --git a/sklearn/linear_model/tests/test_ridge.py b/sklearn/linear_model/tests/test_ridge.py index 167ce0bac4cba..9be28cac141b1 100644 --- a/sklearn/linear_model/tests/test_ridge.py +++ b/sklearn/linear_model/tests/test_ridge.py @@ -1020,7 +1020,7 @@ def _test_ridge_cv(sparse_container): ridge_cv.predict(X) assert len(ridge_cv.coef_.shape) == 1 - assert type(ridge_cv.intercept_) == np.float64 + assert type(ridge_cv.intercept_) is np.float64 cv = KFold(5) ridge_cv.set_params(cv=cv) @@ -1028,7 +1028,7 @@ def _test_ridge_cv(sparse_container): ridge_cv.predict(X) assert len(ridge_cv.coef_.shape) == 1 - assert type(ridge_cv.intercept_) == np.float64 + assert type(ridge_cv.intercept_) is np.float64 @pytest.mark.parametrize( diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py index 7c2fcbfc369b3..0892ee8c91c5a 100644 --- a/sklearn/metrics/pairwise.py +++ b/sklearn/metrics/pairwise.py @@ -2356,7 +2356,7 @@ def pairwise_distances( dtype = bool if metric in PAIRWISE_BOOLEAN_FUNCTIONS else "infer_float" - if dtype == bool and (X.dtype != bool or (Y is not None and Y.dtype != bool)): + if dtype is bool and (X.dtype != bool or (Y is not None and Y.dtype != bool)): msg = "Data was converted to boolean for metric %s" % metric warnings.warn(msg, DataConversionWarning) diff --git a/sklearn/model_selection/_split.py b/sklearn/model_selection/_split.py index 9ae5c8ff44812..35c9ed7878146 100644 --- a/sklearn/model_selection/_split.py +++ b/sklearn/model_selection/_split.py @@ -2900,7 +2900,7 @@ def _build_repr(self): value = getattr(self, key, None) if value is None and hasattr(self, "cvargs"): value = self.cvargs.get(key, None) - if len(w) and w[0].category == FutureWarning: + if len(w) and w[0].category is FutureWarning: # if the parameter is deprecated, don't show it continue finally: diff --git a/sklearn/model_selection/tests/test_validation.py b/sklearn/model_selection/tests/test_validation.py index a1a860b243249..0fd15eaa5c657 100644 --- a/sklearn/model_selection/tests/test_validation.py +++ b/sklearn/model_selection/tests/test_validation.py @@ -586,10 +586,10 @@ def custom_scorer(clf, X, y): ) # Make sure all the arrays are of np.ndarray type - assert type(cv_results["test_r2"]) == np.ndarray - assert type(cv_results["test_neg_mean_squared_error"]) == np.ndarray - assert type(cv_results["fit_time"]) == np.ndarray - assert type(cv_results["score_time"]) == np.ndarray + assert isinstance(cv_results["test_r2"], np.ndarray) + assert isinstance(cv_results["test_neg_mean_squared_error"], np.ndarray) + assert isinstance(cv_results["fit_time"], np.ndarray) + assert isinstance(cv_results["score_time"], np.ndarray) # Ensure all the times are within sane limits assert np.all(cv_results["fit_time"] >= 0) diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py index 2d2833cc1c649..360f204dac912 100644 --- a/sklearn/utils/estimator_checks.py +++ b/sklearn/utils/estimator_checks.py @@ -1506,7 +1506,7 @@ def _apply_on_subsets(func, X): result_by_batch = [func(batch.reshape(1, n_features)) for batch in X] # func can output tuple (e.g. score_samples) - if type(result_full) == tuple: + if isinstance(result_full, tuple): result_full = result_full[0] result_by_batch = list(map(lambda x: x[0], result_by_batch)) diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py index 5bde51ae514d9..d3646b783fc15 100644 --- a/sklearn/utils/tests/test_validation.py +++ b/sklearn/utils/tests/test_validation.py @@ -1341,7 +1341,7 @@ def test_check_scalar_invalid( include_boundaries=include_boundaries, ) assert str(raised_error.value) == str(err_msg) - assert type(raised_error.value) == type(err_msg) + assert isinstance(raised_error.value, type(err_msg)) _psd_cases_valid = { From 9fc95f55ec9a0fafcf7a07149d3dd79c8a78bc12 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Fri, 30 Aug 2024 16:35:55 +0200 Subject: [PATCH 233/275] DOC Tweak documentation build doc (#29749) Co-authored-by: Tim Head --- doc/developers/contributing.rst | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst index 4f5bda71372e5..a78974c694256 100644 --- a/doc/developers/contributing.rst +++ b/doc/developers/contributing.rst @@ -945,16 +945,14 @@ To also generate the example gallery you can use: make html -This will run all the examples, which takes a while. If you only want to generate -images for a few examples, you can pass their names (or parts of) to the build command. -The line below will run all examples with names starting with `plot_calibration`: +This will run all the examples, which takes a while. You can also run only a few examples based on their file names. +Here is a way to run all examples with filenames containing `plot_calibration`: .. prompt:: bash EXAMPLES_PATTERN="plot_calibration" make html -You can also pass regular expressions to gain more advanced control over examples -you want run. +You can use regular expressions for more advanced use cases. Set the environment variable `NO_MATHJAX=1` if you intend to view the documentation in an offline setting. To build the PDF manual, run: From 55845bef792caef2154116f0be5005e51f1405f1 Mon Sep 17 00:00:00 2001 From: rwelsch427 Date: Fri, 30 Aug 2024 17:06:56 +0200 Subject: [PATCH 234/275] DOC Add links to calibration examples (#29745) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Ralph Welsch Co-authored-by: Loïc Estève --- sklearn/calibration.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/sklearn/calibration.py b/sklearn/calibration.py index 18395fb77219c..ad6d8bfd0559c 100644 --- a/sklearn/calibration.py +++ b/sklearn/calibration.py @@ -88,6 +88,11 @@ class CalibratedClassifierCV(ClassifierMixin, MetaEstimatorMixin, BaseEstimator) `estimator` if it exists, else on :term:`predict_proba`. Read more in the :ref:`User Guide `. + In order to learn more on the CalibratedClassifierCV class, see the + following calibration examples: + :ref:`sphx_glr_auto_examples_calibration_plot_calibration.py`, + :ref:`sphx_glr_auto_examples_calibration_plot_calibration_curve.py`, and + :ref:`sphx_glr_auto_examples_calibration_plot_calibration_multiclass.py`. Parameters ---------- @@ -1051,6 +1056,9 @@ class CalibrationDisplay(_BinaryClassifierCurveDisplayMixin): Read more about calibration in the :ref:`User Guide ` and more about the scikit-learn visualization API in :ref:`visualizations`. + For an example on how to use the visualization, see + :ref:`sphx_glr_auto_examples_calibration_plot_calibration_curve.py`. + .. versionadded:: 1.0 Parameters From 3d03f7c80f83c04e34325fc7da93f6b3f3afb75c Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Sat, 31 Aug 2024 20:38:35 +1000 Subject: [PATCH 235/275] DOC Clarify when `class_of_interest` required in `DecisionBoundaryDisplay` (#29733) --- sklearn/inspection/_plot/decision_boundary.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/sklearn/inspection/_plot/decision_boundary.py b/sklearn/inspection/_plot/decision_boundary.py index 92e1a2527400e..87c97be848d33 100644 --- a/sklearn/inspection/_plot/decision_boundary.py +++ b/sklearn/inspection/_plot/decision_boundary.py @@ -30,8 +30,8 @@ def _check_boundary_response_method(estimator, response_method, class_of_interes :term:`decision_function`, :term:`predict_proba`, :term:`predict`. class_of_interest : int, float, bool, str or None - The class considered when plotting the decision. If the label is specified, it - is then possible to plot the decision boundary in multiclass settings. + The class considered when plotting the decision. Cannot be None if + multiclass and `response_method` is 'predict_proba' or 'decision_function'. .. versionadded:: 1.4 @@ -257,8 +257,9 @@ def from_estimator( class_of_interest : int, float, bool or str, default=None The class considered when plotting the decision. If None, `estimator.classes_[1]` is considered as the positive class - for binary classifiers. For multiclass classifiers, passing - an explicit value for `class_of_interest` is mandatory. + for binary classifiers. Must have an explicit value for + multiclass classifiers when `response_method` is 'predict_proba' + or 'decision_function'. .. versionadded:: 1.4 From 4168e73ec897f9e661f47a37e91b8d103a238b7f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Sun, 1 Sep 2024 08:08:30 +0200 Subject: [PATCH 236/275] TST Fix lfw doctest failure due to NumPy repr (#29753) --- sklearn/datasets/_lfw.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/datasets/_lfw.py b/sklearn/datasets/_lfw.py index be72baa981da7..a394b8ca7b2d5 100644 --- a/sklearn/datasets/_lfw.py +++ b/sklearn/datasets/_lfw.py @@ -595,7 +595,7 @@ def fetch_lfw_pairs( >>> from sklearn.datasets import fetch_lfw_pairs >>> lfw_pairs_train = fetch_lfw_pairs(subset='train') >>> list(lfw_pairs_train.target_names) - ['Different persons', 'Same person'] + [np.str_('Different persons'), np.str_('Same person')] >>> lfw_pairs_train.pairs.shape (2200, 2, 62, 47) >>> lfw_pairs_train.data.shape From 5d50fb9582c5e73d067ff3866a0803d0cb4ece71 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 2 Sep 2024 02:05:27 -0700 Subject: [PATCH 237/275] :lock: :robot: CI Update lock files for cirrus-arm CI build(s) :lock: :robot: (#29764) Co-authored-by: Lock file bot --- ...pymin_conda_forge_linux-aarch64_conda.lock | 39 ++++++++++--------- 1 file changed, 21 insertions(+), 18 deletions(-) diff --git a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock index 84f1dc22035d6..7b73c2ff406ce 100644 --- a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock +++ b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock @@ -2,18 +2,21 @@ # platform: linux-aarch64 # input_hash: 2d8c526ab7c0c2f0ca509bfec3f035e5bd33b8096f194f0747f167c8aff66383 @EXPLICIT -https://conda.anaconda.org/conda-forge/linux-aarch64/ca-certificates-2024.7.4-hcefe29a_0.conda#c4c784a1336d72fff54f6b207f3dd75f +https://conda.anaconda.org/conda-forge/linux-aarch64/ca-certificates-2024.8.30-hcefe29a_0.conda#70e57e8f59d2c98f86b49c69e5074be5 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.40-h9fc2d93_7.conda#1b0feef706f4d03eff0b76626ead64fc https://conda.anaconda.org/conda-forge/linux-aarch64/python_abi-3.9-5_cp39.conda#2d2843f11ec622f556137d72d9c72d89 -https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 +https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h8827d51_1.conda#8bfdead4e0fff0383ae4c9c50d0531bd https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-aarch64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#98a1185182fec3c434069fa74e6473d6 -https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-ng-14.1.0-he277a41_0.conda#47ecd1292a3fd78b616640b35dd9632c +https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-14.1.0-he277a41_1.conda#2cb475709e327bb76f74645784582e6a +https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-ng-14.1.0-he9431aa_1.conda#842a1a0cf6f995091734a723e5d291ef +https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran5-14.1.0-h9420597_1.conda#f30cf31e474062ea51481d4181ee15df +https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-14.1.0-h3f4de04_1.conda#6c2afef2109372440a90c566bcb6391c https://conda.anaconda.org/conda-forge/linux-aarch64/alsa-lib-1.2.12-h68df207_0.conda#65448d015f05afb3c68ea92d0483a466 https://conda.anaconda.org/conda-forge/linux-aarch64/bzip2-1.0.8-h68df207_7.conda#56398c28220513b9ea13d7b450acfb20 https://conda.anaconda.org/conda-forge/linux-aarch64/keyutils-1.6.1-h4e544f5_0.tar.bz2#1f24853e59c68892452ef94ddd8afd4b @@ -21,17 +24,17 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlicommon-1.1.0-h31be https://conda.anaconda.org/conda-forge/linux-aarch64/libdeflate-1.21-h68df207_0.conda#806c74df6dcf96adea47c7829b264f80 https://conda.anaconda.org/conda-forge/linux-aarch64/libexpat-2.6.2-h2f0025b_0.conda#1b9f46b804a2c3c5d7fd6a80b77c35f9 https://conda.anaconda.org/conda-forge/linux-aarch64/libffi-3.4.2-h3557bc0_5.tar.bz2#dddd85f4d52121fab0a8b099c5e06501 -https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran5-14.1.0-h9420597_0.conda#b907b29b964b8ebd7be215e47a659179 +https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran-14.1.0-he9431aa_1.conda#c0b5e52811ae0997f9df25a99846eb9e https://conda.anaconda.org/conda-forge/linux-aarch64/libiconv-1.17-h31becfc_2.conda#9a8eb13f14de7d761555a98712e6df65 https://conda.anaconda.org/conda-forge/linux-aarch64/libjpeg-turbo-3.0.0-h31becfc_1.conda#ed24e702928be089d9ba3f05618515c6 https://conda.anaconda.org/conda-forge/linux-aarch64/libnsl-2.0.1-h31becfc_0.conda#c14f32510f694e3185704d89967ec422 https://conda.anaconda.org/conda-forge/linux-aarch64/libpciaccess-0.18-h31becfc_0.conda#6d48179630f00e8c9ad9e30879ce1e54 -https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-ng-14.1.0-h3f4de04_0.conda#2f84852b723ac4389eb188db695526bb +https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-ng-14.1.0-hf1166c9_1.conda#51f54efdd1d2ed5d7e9c67381b75fdb1 https://conda.anaconda.org/conda-forge/linux-aarch64/libuuid-2.38.1-hb4cce97_0.conda#000e30b09db0b7c775b21695dff30969 https://conda.anaconda.org/conda-forge/linux-aarch64/libwebp-base-1.4.0-h31becfc_0.conda#5fd7ab3e5f382c70607fbac6335e6e19 https://conda.anaconda.org/conda-forge/linux-aarch64/libxcrypt-4.4.36-h31becfc_1.conda#b4df5d7d4b63579d081fd3a4cf99740e https://conda.anaconda.org/conda-forge/linux-aarch64/libzlib-1.3.1-h68df207_1.conda#b13fb82f88902e34dd0638cd7d378c21 -https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.5-h0425590_0.conda#38362af7bfac0efef69675acee564458 +https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.5-hcccb83c_1.conda#91d49c85cacd92caa40cf375ef72a25d https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.3.1-h86ecc28_3.conda#7f591390401ad65781372240424ab7fc https://conda.anaconda.org/conda-forge/linux-aarch64/pthread-stubs-0.4-hb9de7d4_1001.tar.bz2#d0183ec6ce0b5aaa3486df25fa5f0ded https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-kbproto-1.0.7-h3557bc0_1002.tar.bz2#ec8ce6b3dac3945a4010559a6284b755 @@ -49,9 +52,9 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/icu-75.1-hf9b3779_0.conda#2 https://conda.anaconda.org/conda-forge/linux-aarch64/lerc-4.0.0-h4de3ea5_0.tar.bz2#1a0ffc65e03ce81559dbcb0695ad1476 https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlidec-1.1.0-h31becfc_1.conda#8db7cff89510bec0b863a0a8ee6a7bce https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlienc-1.1.0-h31becfc_1.conda#ad3d3a826b5848d99936e4466ebbaa26 -https://conda.anaconda.org/conda-forge/linux-aarch64/libdrm-2.4.122-h68df207_0.conda#c868401cb9c775757fe7392ef8606feb +https://conda.anaconda.org/conda-forge/linux-aarch64/libdrm-2.4.123-h86ecc28_0.conda#4e3c67f6999ea7ccac41611f930d19d4 https://conda.anaconda.org/conda-forge/linux-aarch64/libedit-3.1.20191231-he28a2e2_2.tar.bz2#29371161d77933a54fccf1bb66b96529 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https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.2-pyhd8ed1ab_0.conda#e010a224b90f1f623a917c35addbb924 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c @@ -138,10 +141,10 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/harfbuzz-9.0.0-hbf49d6b_1.c https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.4-pyhd8ed1ab_0.conda#c62e775953b6b65f2079c9ee2a62813c https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-23_linuxaarch64_openblas.conda#d71af7934d6dcef05a3c9b0379e1cdfa https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 -https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-2.0.1-py39hcdcdb6f_0.conda#e97d4cba6dd293bb795baddad9ddae02 +https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-2.0.2-py39h4a34e27_0.conda#4d6edcc002364ced01e4fc947832eee6 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_0.conda#b39568655c127a9c4a44d178ac99b6d0 https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-23_linuxaarch64_openblas.conda#0270f72a50c9d64fb8b67ae6681011c8 -https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.2.1-py39hd16970a_0.conda#66b9718539ecdd38876b0176c315bcad +https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.3.0-py39hbd2ca3f_0.conda#d21904acee235eeb3898b26e6d35c2c6 https://conda.anaconda.org/conda-forge/linux-aarch64/qt6-main-6.7.2-h288a8fd_4.conda#f6771673fad8025bb1d4dd765bc3caad https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.13.1-py39hb921187_0.conda#1aac9080de661e03d286f18fb71e5240 https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.123-openblas.conda#43772c0a1ae8f29c9a223c21fd89262b From 192c57a94227b518e756d1353755be23413228e7 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 2 Sep 2024 02:19:49 -0700 Subject: [PATCH 238/275] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29767) Co-authored-by: Lock file bot --- ...latest_conda_forge_mkl_linux-64_conda.lock | 73 ++++++++-------- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 35 ++++---- ...st_pip_openblas_pandas_linux-64_conda.lock | 6 +- .../pymin_conda_forge_mkl_win-64_conda.lock | 26 +++--- ...nblas_min_dependencies_linux-64_conda.lock | 33 +++---- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 43 ++++----- build_tools/circle/doc_linux-64_conda.lock | 87 ++++++++++--------- .../doc_min_dependencies_linux-64_conda.lock | 79 +++++++++-------- 8 files changed, 199 insertions(+), 183 deletions(-) diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index c72eceb0ae54d..24f01e6d22063 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -3,7 +3,7 @@ # input_hash: 93ee312868bc5df4bdc9b2ef07f938f6a5922dfe2375c4963a7c63d19c5d87f6 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 -https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.7.4-hbcca054_0.conda#23ab7665c5f63cfb9f1f6195256daac6 +https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.8.30-hbcca054_0.conda#c27d1c142233b5bc9ca570c6e2e0c244 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb @@ -11,13 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# pip alabaster @ https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl#sha256=fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b # pip array-api-compat @ https://files.pythonhosted.org/packages/0f/22/8228be1d3c6d4ffcf05cd89872ce65c1317b2af98d34b9d89b247d8d49cb/array_api_compat-1.8-py3-none-any.whl#sha256=140204454086264d37263bc4afe1182b428353e94e9edcc38d17b009863c982d # pip babel @ https://files.pythonhosted.org/packages/ed/20/bc79bc575ba2e2a7f70e8a1155618bb1301eaa5132a8271373a6903f73f8/babel-2.16.0-py3-none-any.whl#sha256=368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b -# pip certifi @ https://files.pythonhosted.org/packages/1c/d5/c84e1a17bf61d4df64ca866a1c9a913874b4e9bdc131ec689a0ad013fb36/certifi-2024.7.4-py3-none-any.whl#sha256=c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90 +# pip certifi @ https://files.pythonhosted.org/packages/12/90/3c9ff0512038035f59d279fddeb79f5f1eccd8859f06d6163c58798b9487/certifi-2024.8.30-py3-none-any.whl#sha256=922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8 # pip charset-normalizer @ https://files.pythonhosted.org/packages/40/26/f35951c45070edc957ba40a5b1db3cf60a9dbb1b350c2d5bef03e01e61de/charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8 # pip coverage @ https://files.pythonhosted.org/packages/14/6f/8351b465febb4dbc1ca9929505202db909c5a635c6fdf33e089bbc3d7d85/coverage-7.6.1-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=0c0420b573964c760df9e9e86d1a9a622d0d27f417e1a949a8a66dd7bcee7bc6 # pip cycler @ https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl#sha256=85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 @@ -65,7 +65,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.2-py311h06a4308_0.conda#eff3 # pip tzdata @ https://files.pythonhosted.org/packages/65/58/f9c9e6be752e9fcb8b6a0ee9fb87e6e7a1f6bcab2cdc73f02bb7ba91ada0/tzdata-2024.1-py2.py3-none-any.whl#sha256=9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252 # pip urllib3 @ https://files.pythonhosted.org/packages/ca/1c/89ffc63a9605b583d5df2be791a27bc1a42b7c32bab68d3c8f2f73a98cd4/urllib3-2.2.2-py3-none-any.whl#sha256=a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472 # pip array-api-strict @ https://files.pythonhosted.org/packages/08/06/aba69bce257fd1cda0d1db616c12728af0f46878a5cc1923fcbb94201947/array_api_strict-2.0.1-py3-none-any.whl#sha256=f74cbf0d0c182fcb45c5ee7f28f9c7b77e6281610dfbbdd63be60b1a5a7872b3 -# pip contourpy @ https://files.pythonhosted.org/packages/ee/c0/9bd123d676eb61750e116a2cd915b06483fc406143cfc36c7f263f0f5368/contourpy-1.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=d4492d82b3bc7fbb7e3610747b159869468079fe149ec5c4d771fa1f614a14df +# pip contourpy @ https://files.pythonhosted.org/packages/03/33/003065374f38894cdf1040cef474ad0546368eea7e3a51d48b8a423961f8/contourpy-1.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=637f674226be46f6ba372fd29d9523dd977a291f66ab2a74fbeb5530bb3f445d # pip imageio @ https://files.pythonhosted.org/packages/1e/b7/02adac4e42a691008b5cfb31db98c190e1fc348d1521b9be4429f9454ed1/imageio-2.35.1-py3-none-any.whl#sha256=6eb2e5244e7a16b85c10b5c2fe0f7bf961b40fcb9f1a9fd1bd1d2c2f8fb3cd65 # pip jinja2 @ https://files.pythonhosted.org/packages/31/80/3a54838c3fb461f6fec263ebf3a3a41771bd05190238de3486aae8540c36/jinja2-3.1.4-py3-none-any.whl#sha256=bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d # pip lazy-loader @ https://files.pythonhosted.org/packages/83/60/d497a310bde3f01cb805196ac61b7ad6dc5dcf8dce66634dc34364b20b4f/lazy_loader-0.4-py3-none-any.whl#sha256=342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc @@ -74,7 +74,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.2-py311h06a4308_0.conda#eff3 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl#sha256=70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6 # pip scipy @ https://files.pythonhosted.org/packages/93/6b/701776d4bd6bdd9b629c387b5140f006185bd8ddea16788a44434376b98f/scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=fef8c87f8abfb884dac04e97824b61299880c43f4ce675dd2cbeadd3c9b466d2 -# pip tifffile @ https://files.pythonhosted.org/packages/e1/82/e3d0b9720345f9057e736b305d22809e5b80143c76f2266e2a1bf57ad2cd/tifffile-2024.8.24-py3-none-any.whl#sha256=40faba20cb0af05c0eb500eda63244dd81500360e1518ff4548eb61ce3943099 +# pip tifffile @ https://files.pythonhosted.org/packages/3a/4f/73714b1c1d339b1545cac28764e39f88c69468b5e10e51f327f9aa9d55b9/tifffile-2024.8.30-py3-none-any.whl#sha256=8bc59a8f02a2665cd50a910ec64961c5373bee0b8850ec89d3b7b485bf7be7ad # pip lightgbm @ https://files.pythonhosted.org/packages/4e/19/1b928cad70a4e1a3e2c37d5417ca2182510f2451eaadb6c91cd9ec692cae/lightgbm-4.5.0-py3-none-manylinux_2_28_x86_64.whl#sha256=960a0e7c077de0ca3053f1325d3edfc92ea815acf5176adcacdea0f635aeef9b # pip matplotlib @ https://files.pythonhosted.org/packages/01/75/6c7ce560e95714a10fcbb3367d1304975a1a3e620f72af28921b796403f3/matplotlib-3.9.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=8912ef7c2362f7193b5819d17dae8629b34a95c58603d781329712ada83f9447 # pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock index eb81eaedb8802..dafb9c14c58df 100644 --- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock @@ -2,7 +2,7 @@ # platform: win-64 # input_hash: ea607aaeb7b1d1f8a1f821a9f505b3601083a218ec4763e2d72d3d3d800e718c @EXPLICIT -https://conda.anaconda.org/conda-forge/win-64/ca-certificates-2024.7.4-h56e8100_0.conda#9caa97c9504072cd060cf0a3142cc0ed +https://conda.anaconda.org/conda-forge/win-64/ca-certificates-2024.8.30-h56e8100_0.conda#4c4fd67c18619be5aa65dc5b6c72e490 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb @@ -12,13 +12,13 @@ https://conda.anaconda.org/conda-forge/win-64/libexpat-2.6.2-h63175ca_0.conda#bc https://conda.anaconda.org/conda-forge/win-64/mkl-include-2024.1.0-h66d3029_694.conda#1f80971a50e69c1f7af15707619df49e https://conda.anaconda.org/conda-forge/win-64/msys2-conda-epoch-20160418-1.tar.bz2#b0309b72560df66f71a9d5e34a5efdfa https://conda.anaconda.org/conda-forge/win-64/python_abi-3.9-5_cp39.conda#86ba1bbcf9b259d1592201f3c345c810 -https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 +https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h8827d51_1.conda#8bfdead4e0fff0383ae4c9c50d0531bd https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.22621.0-h57928b3_0.tar.bz2#72608f6cd3e5898229c3ea16deb1ac43 https://conda.anaconda.org/conda-forge/win-64/expat-2.6.2-h63175ca_0.conda#52f9dec6758ceb8ce0ea8af9fa13eb1a https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 https://conda.anaconda.org/conda-forge/win-64/m2w64-gmp-6.1.0-2.tar.bz2#53a1c73e1e3d185516d7e3af177596d9 https://conda.anaconda.org/conda-forge/win-64/m2w64-libwinpthread-git-5.0.0.4634.697f757-2.tar.bz2#774130a326dee16f1ceb05cc687ee4f0 -https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.40.33810-ha82c5b3_20.conda#e39cc4c34c53654ec939558993d9dc5b +https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.40.33810-hcc2c482_20.conda#ad33c7cd933d69b9dee0f48317cdf137 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libs-core-5.3.0-7.tar.bz2#4289d80fb4d272f1f3b56cfe87ac90bd https://conda.anaconda.org/conda-forge/win-64/vc-14.3-h8a93ad2_20.conda#8558f367e1d7700554f7cdb823c46faf @@ -59,13 +59,13 @@ https://conda.anaconda.org/conda-forge/win-64/brotli-bin-1.1.0-hcfcfb64_1.conda# https://conda.anaconda.org/conda-forge/noarch/certifi-2024.7.4-pyhd8ed1ab_0.conda#24e7fd6ca65997938fff9e5ab6f653e4 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99 https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda#5cd86562580f274031ede6aa6aa24441 -https://conda.anaconda.org/conda-forge/win-64/cython-3.0.11-py39ha51f57c_0.conda#d7dfdb0e5fa3cc89807fc77fe6173c4d +https://conda.anaconda.org/conda-forge/win-64/cython-3.0.11-py39ha51f57c_1.conda#2de4603ad5a9676c698f9709f1428c7a https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_0.conda#d02ae936e42063ca46af6cdad2dbd1e0 https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_0.conda#15dda3cdbf330abfe9f555d22f66db46 https://conda.anaconda.org/conda-forge/win-64/freetype-2.12.1-hdaf720e_2.conda#3761b23693f768dc75a8fd0a73ca053f https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda#f800d2da156d08e289b14e87e43c1ae5 -https://conda.anaconda.org/conda-forge/win-64/kiwisolver-1.4.5-py39h1f6ef14_1.conda#4fc5bd0a7b535252028c647cc27d6c87 -https://conda.anaconda.org/conda-forge/win-64/libclang13-18.1.8-default_ha5278ca_2.conda#8185207d3f7e59474870cc79e4f9eaa5 +https://conda.anaconda.org/conda-forge/win-64/kiwisolver-1.4.5-py39h2b77a98_2.conda#c5cd596a9db4d6790a2548ac8a205b21 +https://conda.anaconda.org/conda-forge/win-64/libclang13-18.1.8-default_ha5278ca_3.conda#cbf7af56fbfab1d0d4bc863ae99a32d3 https://conda.anaconda.org/conda-forge/win-64/libglib-2.80.3-h7025463_2.conda#b60894793e7e4a555027bfb4e4ed1d54 https://conda.anaconda.org/conda-forge/win-64/libhwloc-2.11.1-default_h8125262_1000.conda#933bad6e4658157f1aec9b171374fde2 https://conda.anaconda.org/conda-forge/win-64/libtiff-4.6.0-hb151862_4.conda#7d35d9aa8f051d548116039f5813c8ec @@ -80,14 +80,14 @@ https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_0.tar.bz2#f832c45a477c78bebd107098db465095 https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96 -https://conda.anaconda.org/conda-forge/win-64/tornado-6.4.1-py39ha55e580_0.conda#7d1e87f3036af858ce7e248489c3faec +https://conda.anaconda.org/conda-forge/win-64/tornado-6.4.1-py39ha55e580_1.conda#4a93d22ed5b2cede80fbee7f7f775a9d https://conda.anaconda.org/conda-forge/win-64/unicodedata2-15.1.0-py39ha55989b_0.conda#20ec896e8d97f2ff8be1124e624dc8f2 https://conda.anaconda.org/conda-forge/noarch/wheel-0.44.0-pyhd8ed1ab_0.conda#d44e3b085abcaef02983c6305b84b584 https://conda.anaconda.org/conda-forge/win-64/xorg-libxau-1.0.11-hcd874cb_0.conda#c46ba8712093cb0114404ae8a7582e1a https://conda.anaconda.org/conda-forge/win-64/xorg-libxdmcp-1.1.3-hcd874cb_0.tar.bz2#46878ebb6b9cbd8afcf8088d7ef00ece -https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.0-pyhd8ed1ab_0.conda#05b6bcb391b5be17374f7ad0aeedc479 +https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.1-pyhd8ed1ab_0.conda#74a4befb4b38897e19a107693e49da20 https://conda.anaconda.org/conda-forge/win-64/brotli-1.1.0-hcfcfb64_1.conda#f47f6db2528e38321fb00ae31674c133 -https://conda.anaconda.org/conda-forge/win-64/coverage-7.6.1-py39ha55e580_0.conda#a9c63313e61e510e8f8bca90794eee73 +https://conda.anaconda.org/conda-forge/win-64/coverage-7.6.1-py39ha55e580_1.conda#762cd375d661c49065ddaba3fd9e6259 https://conda.anaconda.org/conda-forge/win-64/fontconfig-2.14.2-hbde0cde_0.conda#08767992f1a4f1336a257af1241034bd https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.4-pyhd8ed1ab_0.conda#99aa3edd3f452d61c305a30e78140513 https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f @@ -95,11 +95,11 @@ https://conda.anaconda.org/conda-forge/win-64/lcms2-2.16-h67d730c_0.conda#d35924 https://conda.anaconda.org/conda-forge/win-64/libxcb-1.16-h013a479_1.conda#f0b599acdc82d5bc7e3b105833e7c5c8 https://conda.anaconda.org/conda-forge/noarch/meson-1.5.1-pyhd8ed1ab_1.conda#979087ee59bea1355f991a3b738af64e https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.2-h3d672ee_0.conda#7e7099ad94ac3b599808950cec30ad4e -https://conda.anaconda.org/conda-forge/noarch/pip-24.2-pyhd8ed1ab_0.conda#6721aef6bfe5937abe70181545dd2c51 +https://conda.anaconda.org/conda-forge/noarch/pip-24.2-pyh8b19718_1.conda#6c78fbb8ddfd64bcb55b5cbafd2d2c43 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.2-pyhd8ed1ab_0.conda#e010a224b90f1f623a917c35addbb924 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c -https://conda.anaconda.org/conda-forge/win-64/tbb-2021.12.0-hc790b64_3.conda#a16e2a639e87c554abee5192ce6ee308 +https://conda.anaconda.org/conda-forge/win-64/tbb-2021.12.0-hc790b64_4.conda#bce92c19a6cb64b47866b7271363f747 https://conda.anaconda.org/conda-forge/win-64/cairo-1.18.0-h32b962e_3.conda#8f43723a4925c51e55c2d81725a97db4 https://conda.anaconda.org/conda-forge/win-64/fonttools-4.53.1-py39ha55e580_0.conda#81bbae03542e491178a620a45ad0b474 https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.4-pyhd8ed1ab_0.conda#c62e775953b6b65f2079c9ee2a62813c @@ -115,10 +115,10 @@ https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-23_win64_mkl.conda# https://conda.anaconda.org/conda-forge/win-64/liblapack-3.9.0-23_win64_mkl.conda#3580796ab7b7d68143f45d4d94d866b7 https://conda.anaconda.org/conda-forge/win-64/qt6-main-6.7.2-hbb46ec1_5.conda#e14fa5fe2da0bf8cc30d06314ce6ce33 https://conda.anaconda.org/conda-forge/win-64/liblapacke-3.9.0-23_win64_mkl.conda#f6e2619d4359c6806b97b3d405193741 -https://conda.anaconda.org/conda-forge/win-64/numpy-2.0.1-py39h60232e0_0.conda#abb4185f8ac60eeb9b450757197da7ac +https://conda.anaconda.org/conda-forge/win-64/numpy-2.0.2-py39h60232e0_0.conda#13c59f25f5d4ad7d1c677667555f6547 https://conda.anaconda.org/conda-forge/win-64/pyside6-6.7.2-py39h0285922_2.conda#12004e14d1835eca43c4207841c24e4f https://conda.anaconda.org/conda-forge/win-64/blas-devel-3.9.0-23_win64_mkl.conda#5fd0882b94fa827533f51cc8c2e04392 -https://conda.anaconda.org/conda-forge/win-64/contourpy-1.2.1-py39h1f6ef14_0.conda#03e25c6bae87f4f9595337255b44b0fb +https://conda.anaconda.org/conda-forge/win-64/contourpy-1.3.0-py39h2b77a98_0.conda#17c8b9d02a09b301f3eea85b3e966f23 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-https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.0-pyhd8ed1ab_0.conda#05b6bcb391b5be17374f7ad0aeedc479 +https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.1-pyhd8ed1ab_0.conda#74a4befb4b38897e19a107693e49da20 https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.5-pyhd8ed1ab_0.conda#1bb1ef9806a9a20872434f58b3e7fc1a https://conda.anaconda.org/conda-forge/noarch/babel-2.14.0-pyhd8ed1ab_0.conda#9669586875baeced8fc30c0826c3270e https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.12.3-pyha770c72_0.conda#332493000404d8411859539a5a630865 https://conda.anaconda.org/conda-forge/linux-64/brunsli-0.1-h9c3ff4c_0.tar.bz2#c1ac6229d0bfd14f8354ff9ad2a26cad -https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.7.0-hd590300_1.conda#e9dffe1056994133616378309f932d77 https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-hbb29018_2.conda#b6d90276c5aee9b4407dd94eb0cd40a8 -https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.0-py39h49a4b6b_0.conda#278cc676a7e939cf2561ce4a5cfaa484 +https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.0-py39h15c3d72_1.conda#26236d9306b1a33b079df356ad4d07ee +https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.7.0-h00ab1b0_1.conda#28de2e073db9ca9b72858bee9fb6f571 https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.12.3-py39hd1e30aa_0.conda#dc0fb8e157c7caba4c98f1e1f9d2e5f4 -https://conda.anaconda.org/conda-forge/linux-64/gfortran-12.4.0-h236703b_0.conda#581156aeb9b903f5425d5dd963d56ec1 -https://conda.anaconda.org/conda-forge/linux-64/gfortran_linux-64-12.4.0-hd748a6a_0.conda#6fd80632f36e5a3934af2600bcbb2b2d +https://conda.anaconda.org/conda-forge/linux-64/fortran-compiler-1.7.0-heb67821_1.conda#cf4b0e7c4c78bb0662aed9b27c414a3c https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.3-h315aac3_2.conda#00e0da7e4fceb5449f3ddd2bf6b2c351 -https://conda.anaconda.org/conda-forge/linux-64/gxx-12.4.0-h236703b_0.conda#56cefffbce52071b597fd3eb9208adc9 -https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-12.4.0-h8489865_0.conda#5cf73d936678e6805da39b8ba6be263c https://conda.anaconda.org/conda-forge/noarch/h2-4.1.0-pyhd8ed1ab_0.tar.bz2#b748fbf7060927a6e82df7cb5ee8f097 https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.4.0-pyha770c72_0.conda#6e3dbc422d3749ad72659243d6ac8b2b https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda#7b86ecb7d3557821c649b3c31e3eb9f2 https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp15-15.0.7-default_h127d8a8_5.conda#d0a9633b53cdc319b8a1a532ae7822b8 -https://conda.anaconda.org/conda-forge/linux-64/libclang13-18.1.8-default_h9def88c_2.conda#ba2d12adbea9de311297f2b577f4bb86 +https://conda.anaconda.org/conda-forge/linux-64/libclang13-18.1.8-default_h9def88c_3.conda#8b10a801bd45383d6e0dd286c6814238 https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda#ee48bf17cc83a00f59ca1494d5646869 https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.50-h4f305b6_0.conda#0d7ff1a8e69565ca3add6925e18e708f https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.7.0-h2c5496b_1.conda#e2eaefa4de2b7237af7c907b8bbc760a @@ -216,16 +221,15 @@ https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhd8ed1ab_ https://conda.anaconda.org/conda-forge/noarch/meson-1.5.1-pyhd8ed1ab_1.conda#979087ee59bea1355f991a3b738af64e https://conda.anaconda.org/conda-forge/noarch/partd-1.4.2-pyhd8ed1ab_0.conda#0badf9c54e24cecfb0ad2f99d680c163 https://conda.anaconda.org/conda-forge/linux-64/pillow-10.4.0-py39h16a7006_0.conda#d9a6b19174a6cf5185296b16f781951f -https://conda.anaconda.org/conda-forge/noarch/pip-24.2-pyhd8ed1ab_0.conda#6721aef6bfe5937abe70181545dd2c51 +https://conda.anaconda.org/conda-forge/noarch/pip-24.2-pyh8b19718_1.conda#6c78fbb8ddfd64bcb55b5cbafd2d2c43 https://conda.anaconda.org/conda-forge/noarch/plotly-5.14.0-pyhd8ed1ab_0.conda#6a7bcc42ef58dd6cf3da9333ea102433 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.2-pyhd8ed1ab_0.conda#e010a224b90f1f623a917c35addbb924 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb -https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.12.0-h434a139_3.conda#c667c11d1e488a38220ede8a34441bff +https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.12.0-h84d6215_4.conda#1fa72fdeb88f538018612ce2ed9fc789 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxxf86vm-1.1.5-h4bc722e_1.conda#0c90ad87101001080484b91bd9d2cdef -https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.7.0-h00ab1b0_1.conda#28de2e073db9ca9b72858bee9fb6f571 -https://conda.anaconda.org/conda-forge/linux-64/fortran-compiler-1.7.0-heb67821_1.conda#cf4b0e7c4c78bb0662aed9b27c414a3c +https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_1.conda#d8d07866ac3b5b6937213c89a1874f08 https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.6-haf2f30d_0.conda#a15d7b21e4b7b82b87ba04c3b46c1317 https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-9.0.0-hfac3d4d_0.conda#c7b47c64af53e8ecee01d101eeab2342 https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-8.4.0-hd8ed1ab_0.conda#01b7411c765c3d863dcc920207f258bd @@ -236,11 +240,10 @@ https://conda.anaconda.org/conda-forge/linux-64/mkl-2024.1.0-ha957f24_693.conda# https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5.conda#93aff412f3e49fdb43361c0215cbd72d https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_0.conda#b39568655c127a9c4a44d178ac99b6d0 https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py39h623c9ba_0.conda#a19d023682384c637cb356d270c276c0 -https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_1.conda#d8d07866ac3b5b6937213c89a1874f08 https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.8.0-pyhd8ed1ab_0.conda#bf68bf9ff9a18f1b17aa8c817225aee0 https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.6-hbaaba92_0.conda#b22ffc80ac9af846df60b2640c98fea4 https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-23_linux64_mkl.conda#5bdaf561cf48f95093dedaa665083874 -https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-255-h3516f8a_1.conda#3366af27f0b593544a6cd453c7932ac5 +https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-256.5-hb6d7363_0.conda#3b3912077a5515b2a39bda92008bc2c3 https://conda.anaconda.org/conda-forge/linux-64/mkl-devel-2024.1.0-ha770c72_693.conda#7f422e2cf549a3fb920c95288393870d https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.2-pyhd8ed1ab_1.conda#e804c43f58255e977093a2298e442bb8 https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-23_linux64_mkl.conda#e0219f401906533e26346d8634ef35f4 From 7a08041016aa5cb96ca2b5a75ba8cdb07bfe7a53 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 2 Sep 2024 13:48:31 +0200 Subject: [PATCH 239/275] Bump pypa/gh-action-pypi-publish from 1.9.0 to 1.10.0 in the actions group (#29761) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/publish_pypi.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml index ef70ea4d97d13..394dcfd408ac2 100644 --- a/.github/workflows/publish_pypi.yml +++ b/.github/workflows/publish_pypi.yml @@ -39,10 +39,10 @@ jobs: run: | python build_tools/github/check_wheels.py - name: Publish package to TestPyPI - uses: pypa/gh-action-pypi-publish@ec4db0b4ddc65acdf4bff5fa45ac92d78b56bdf0 # v1.9.0 + uses: pypa/gh-action-pypi-publish@8a08d616893759ef8e1aa1f2785787c0b97e20d6 # v1.10.0 with: repository_url: https://test.pypi.org/legacy/ if: ${{ github.event.inputs.pypi_repo == 'testpypi' }} - name: Publish package to PyPI - uses: pypa/gh-action-pypi-publish@ec4db0b4ddc65acdf4bff5fa45ac92d78b56bdf0 # v1.9.0 + uses: pypa/gh-action-pypi-publish@8a08d616893759ef8e1aa1f2785787c0b97e20d6 # v1.10.0 if: ${{ github.event.inputs.pypi_repo == 'pypi' }} From 9aa2749e7f9b22ee7d299d7fd42c9f80d09beea4 Mon Sep 17 00:00:00 2001 From: Reshama Shaikh Date: Tue, 3 Sep 2024 16:16:58 -0400 Subject: [PATCH 240/275] DOC update wording for MAPE formula (#29775) --- doc/modules/model_evaluation.rst | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst index eff6684458deb..46ebd053bef1b 100644 --- a/doc/modules/model_evaluation.rst +++ b/doc/modules/model_evaluation.rst @@ -2485,6 +2485,22 @@ the small magnitude values and only reflected the error in prediction of highest magnitude value. But that problem is resolved in case of MAPE because it calculates relative percentage error with respect to actual output. +.. note:: + + The MAPE formula here represents a relative error and outputs a value in the + range [0, 1]. It is not a percentage in the range [0, 100] and a value of 100 + does not mean 100% but 1e2. The motivation for the MAPE formula here to be in + the range [0, 1] is to be consistent with other error metrics in scikit-learn + such as `accuracy_score`. + + To obtain the mean absolute percentage error as per the Wikipedia formula, + multiply the `mean_absolute_percentage_error` computed here by 100. + +.. dropdown:: References + + * `Wikipedia entry for Mean Absolute Percentage Error + `_ + .. _median_absolute_error: Median absolute error From 520b0babde9a9839fd47b0df4b034e53e608a90c Mon Sep 17 00:00:00 2001 From: brdav <40954221+brdav@users.noreply.github.com> Date: Wed, 4 Sep 2024 09:21:10 +0200 Subject: [PATCH 241/275] EXA Fix axis scaling in example `plot_ica_vs_pca.py` (#29726) --- examples/decomposition/plot_ica_vs_pca.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/examples/decomposition/plot_ica_vs_pca.py b/examples/decomposition/plot_ica_vs_pca.py index 07f6327e9922f..18010e5358971 100644 --- a/examples/decomposition/plot_ica_vs_pca.py +++ b/examples/decomposition/plot_ica_vs_pca.py @@ -67,8 +67,7 @@ def plot_samples(S, axis_list=None): ) if axis_list is not None: for axis, color, label in axis_list: - axis /= axis.std() - x_axis, y_axis = axis + x_axis, y_axis = axis / axis.std() plt.quiver( (0, 0), (0, 0), @@ -81,10 +80,11 @@ def plot_samples(S, axis_list=None): label=label, ) - plt.hlines(0, -3, 3) - plt.vlines(0, -3, 3) - plt.xlim(-3, 3) + plt.hlines(0, -5, 5, color="black", linewidth=0.5) + plt.vlines(0, -3, 3, color="black", linewidth=0.5) + plt.xlim(-5, 5) plt.ylim(-3, 3) + plt.gca().set_aspect("equal") plt.xlabel("x") plt.ylabel("y") @@ -97,13 +97,13 @@ def plot_samples(S, axis_list=None): axis_list = [(pca.components_.T, "orange", "PCA"), (ica.mixing_, "red", "ICA")] plt.subplot(2, 2, 2) plot_samples(X / np.std(X), axis_list=axis_list) -legend = plt.legend(loc="lower right") +legend = plt.legend(loc="upper left") legend.set_zorder(100) plt.title("Observations") plt.subplot(2, 2, 3) -plot_samples(S_pca_ / np.std(S_pca_, axis=0)) +plot_samples(S_pca_ / np.std(S_pca_)) plt.title("PCA recovered signals") plt.subplot(2, 2, 4) From 9913ed677c7e542db8f79d42ba12d8c0bacea6d1 Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Wed, 4 Sep 2024 11:28:13 +0200 Subject: [PATCH 242/275] FIX update debian 32 bit CI config to avoid a SIMD related bug in numpy 1.24.2 on some GitHub Actions CI workers (#29771) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- azure-pipelines.yml | 6 +-- build_tools/azure/debian_32bit_lock.txt | 37 ++++++++++++++++ ...ents.txt => debian_32bit_requirements.txt} | 10 ++--- build_tools/azure/debian_atlas_32bit_lock.txt | 43 ------------------- build_tools/azure/install.sh | 5 ++- build_tools/azure/test_script.sh | 6 +++ .../update_environments_and_lock_files.py | 15 ++----- sklearn/svm/tests/test_svm.py | 10 ++++- 8 files changed, 67 insertions(+), 65 deletions(-) create mode 100644 build_tools/azure/debian_32bit_lock.txt rename build_tools/azure/{debian_atlas_32bit_requirements.txt => debian_32bit_requirements.txt} (65%) delete mode 100644 build_tools/azure/debian_atlas_32bit_lock.txt diff --git a/azure-pipelines.yml b/azure-pipelines.yml index c792db42ab9a1..6348ab2393288 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -270,11 +270,11 @@ jobs: not(contains(dependencies['git_commit']['outputs']['commit.message'], '[ci skip]')) ) matrix: - debian_atlas_32bit: - DOCKER_CONTAINER: 'i386/debian:11.2' + debian_32bit: + DOCKER_CONTAINER: 'i386/debian:trixie' DISTRIB: 'debian-32' COVERAGE: "true" - LOCK_FILE: './build_tools/azure/debian_atlas_32bit_lock.txt' + LOCK_FILE: './build_tools/azure/debian_32bit_lock.txt' # disable pytest xdist due to unknown bug with 32-bit container PYTEST_XDIST_VERSION: 'none' SKLEARN_TESTS_GLOBAL_RANDOM_SEED: '4' # non-default seed diff --git a/build_tools/azure/debian_32bit_lock.txt b/build_tools/azure/debian_32bit_lock.txt new file mode 100644 index 0000000000000..4778cc05a91fa --- /dev/null +++ b/build_tools/azure/debian_32bit_lock.txt @@ -0,0 +1,37 @@ +# +# This file is autogenerated by pip-compile with Python 3.12 +# by the following command: +# +# pip-compile --output-file=build_tools/azure/debian_32bit_lock.txt build_tools/azure/debian_32bit_requirements.txt +# +coverage[toml]==7.6.1 + # via pytest-cov +cython==3.0.11 + # via -r build_tools/azure/debian_32bit_requirements.txt +iniconfig==2.0.0 + # via pytest +joblib==1.4.2 + # via -r build_tools/azure/debian_32bit_requirements.txt +meson==1.5.1 + # via meson-python +meson-python==0.16.0 + # via -r build_tools/azure/debian_32bit_requirements.txt +ninja==1.11.1.1 + # via -r build_tools/azure/debian_32bit_requirements.txt +packaging==24.1 + # via + # meson-python + # pyproject-metadata + # pytest +pluggy==1.5.0 + # via pytest +pyproject-metadata==0.8.0 + # via meson-python +pytest==8.3.2 + # via + # -r build_tools/azure/debian_32bit_requirements.txt + # pytest-cov +pytest-cov==5.0.0 + # via -r build_tools/azure/debian_32bit_requirements.txt +threadpoolctl==3.5.0 + # via -r build_tools/azure/debian_32bit_requirements.txt diff --git a/build_tools/azure/debian_atlas_32bit_requirements.txt b/build_tools/azure/debian_32bit_requirements.txt similarity index 65% rename from build_tools/azure/debian_atlas_32bit_requirements.txt rename to build_tools/azure/debian_32bit_requirements.txt index 615193a71fc6b..6dcf67d11c58d 100644 --- a/build_tools/azure/debian_atlas_32bit_requirements.txt +++ b/build_tools/azure/debian_32bit_requirements.txt @@ -1,10 +1,10 @@ # DO NOT EDIT: this file is generated from the specification found in the # following script to centralize the configuration for CI builds: # build_tools/update_environments_and_lock_files.py -cython==3.0.10 # min -joblib==1.2.0 # min -threadpoolctl==3.1.0 -pytest==7.1.2 # min -pytest-cov==2.9.0 # min +cython +joblib +threadpoolctl +pytest +pytest-cov ninja meson-python diff --git a/build_tools/azure/debian_atlas_32bit_lock.txt b/build_tools/azure/debian_atlas_32bit_lock.txt deleted file mode 100644 index 6e407243fc695..0000000000000 --- a/build_tools/azure/debian_atlas_32bit_lock.txt +++ /dev/null @@ -1,43 +0,0 @@ -# -# This file is autogenerated by pip-compile with Python 3.11 -# by the following command: -# -# pip-compile --output-file=build_tools/azure/debian_atlas_32bit_lock.txt build_tools/azure/debian_atlas_32bit_requirements.txt -# -attrs==24.2.0 - # via pytest -coverage==7.6.1 - # via pytest-cov -cython==3.0.10 - # via -r build_tools/azure/debian_atlas_32bit_requirements.txt -iniconfig==2.0.0 - # via pytest -joblib==1.2.0 - # via -r build_tools/azure/debian_atlas_32bit_requirements.txt -meson==1.5.1 - # via meson-python -meson-python==0.16.0 - # via -r build_tools/azure/debian_atlas_32bit_requirements.txt -ninja==1.11.1.1 - # via -r build_tools/azure/debian_atlas_32bit_requirements.txt -packaging==24.1 - # via - # meson-python - # pyproject-metadata - # pytest -pluggy==1.5.0 - # via pytest -py==1.11.0 - # via pytest -pyproject-metadata==0.8.0 - # via meson-python -pytest==7.1.2 - # via - # -r build_tools/azure/debian_atlas_32bit_requirements.txt - # pytest-cov -pytest-cov==2.9.0 - # via -r build_tools/azure/debian_atlas_32bit_requirements.txt -threadpoolctl==3.1.0 - # via -r build_tools/azure/debian_atlas_32bit_requirements.txt -tomli==2.0.1 - # via pytest diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh index 5625197971195..3bb62a8c3cf5d 100755 --- a/build_tools/azure/install.sh +++ b/build_tools/azure/install.sh @@ -24,6 +24,9 @@ setup_ccache() { done export PATH="${CCACHE_LINKS_DIR}:${PATH}" ccache -M 256M + + # Zeroing statistics so that ccache statistics are shown only for this build + ccache -z fi } @@ -36,7 +39,7 @@ pre_python_environment_install() { elif [[ "$DISTRIB" == "debian-32" ]]; then apt-get update apt-get install -y python3-dev python3-numpy python3-scipy \ - python3-matplotlib libatlas3-base libatlas-base-dev \ + python3-matplotlib libopenblas-dev \ python3-virtualenv python3-pandas ccache git elif [[ "$DISTRIB" == "conda-pypy3" ]]; then diff --git a/build_tools/azure/test_script.sh b/build_tools/azure/test_script.sh index faf48e27efefb..777c61f304bea 100755 --- a/build_tools/azure/test_script.sh +++ b/build_tools/azure/test_script.sh @@ -60,6 +60,12 @@ if [[ -n "$SELECTED_TESTS" ]]; then export SKLEARN_TESTS_GLOBAL_RANDOM_SEED="all" fi +if which lscpu ; then + lscpu +else + echo "Could not inspect CPU architecture." +fi + TEST_CMD="$TEST_CMD --pyargs sklearn" if [[ "$DISTRIB" == "conda-pypy3" ]]; then # Run only common tests for PyPy. Running the full test suite uses too diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index 1bf603873a73c..a91cd4b658263 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -389,7 +389,7 @@ def remove_from(alist, to_remove): }, }, { - "name": "debian_atlas_32bit", + "name": "debian_32bit", "type": "pip", "tag": "main-ci", "folder": "build_tools/azure", @@ -402,16 +402,9 @@ def remove_from(alist, to_remove): "ninja", "meson-python", ], - "package_constraints": { - "joblib": "min", - "threadpoolctl": "3.1.0", - "pytest": "min", - "pytest-cov": "min", - # no pytest-xdist because it causes issue on 32bit - "cython": "min", - }, - # same Python version as in debian-32 build - "python_version": "3.11.2", + # Python version from the python3 APT package in the debian-32 docker + # image. + "python_version": "3.12.5", }, { "name": "ubuntu_atlas", diff --git a/sklearn/svm/tests/test_svm.py b/sklearn/svm/tests/test_svm.py index 2735dc0651d89..5d41a8cf399b1 100644 --- a/sklearn/svm/tests/test_svm.py +++ b/sklearn/svm/tests/test_svm.py @@ -36,8 +36,7 @@ ) from sklearn.svm._classes import _validate_dual_parameter from sklearn.utils import check_random_state, shuffle -from sklearn.utils._testing import ignore_warnings -from sklearn.utils.fixes import CSR_CONTAINERS, LIL_CONTAINERS +from sklearn.utils.fixes import _IS_32BIT, CSR_CONTAINERS, LIL_CONTAINERS from sklearn.utils.validation import _num_samples # toy sample @@ -1203,6 +1202,13 @@ def test_svc_ovr_tie_breaking(SVCClass): """Test if predict breaks ties in OVR mode. Related issue: https://github.com/scikit-learn/scikit-learn/issues/8277 """ + if SVCClass.__name__ == "NuSVC" and _IS_32BIT: + # XXX: known failure to be investigated. Either the code needs to be + # fixed or the test itself might need to be made less sensitive to + # random changes in test data and rounding errors more generally. + # https://github.com/scikit-learn/scikit-learn/issues/29633 + pytest.xfail("Failing test on 32bit OS") + X, y = make_blobs(random_state=0, n_samples=20, n_features=2) xs = np.linspace(X[:, 0].min(), X[:, 0].max(), 100) From 0121b828f0e755121ec28ff9350ed0da10ae9e08 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Wed, 4 Sep 2024 04:59:59 -0700 Subject: [PATCH 243/275] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#29765) Co-authored-by: Lock file bot Co-authored-by: Olivier Grisel --- build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index dffc09ccb459b..84da2623cd7de 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -28,7 +28,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py312h06a4308_0.conda# https://repo.anaconda.com/pkgs/main/linux-64/pip-24.2-py312h06a4308_0.conda#798cbea8112672434d0cd7551f8fc4b9 # pip alabaster @ https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl#sha256=fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b # pip babel @ https://files.pythonhosted.org/packages/ed/20/bc79bc575ba2e2a7f70e8a1155618bb1301eaa5132a8271373a6903f73f8/babel-2.16.0-py3-none-any.whl#sha256=368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b -# pip certifi @ https://files.pythonhosted.org/packages/1c/d5/c84e1a17bf61d4df64ca866a1c9a913874b4e9bdc131ec689a0ad013fb36/certifi-2024.7.4-py3-none-any.whl#sha256=c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90 +# pip certifi @ https://files.pythonhosted.org/packages/12/90/3c9ff0512038035f59d279fddeb79f5f1eccd8859f06d6163c58798b9487/certifi-2024.8.30-py3-none-any.whl#sha256=922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8 # pip charset-normalizer @ https://files.pythonhosted.org/packages/ee/fb/14d30eb4956408ee3ae09ad34299131fb383c47df355ddb428a7331cfa1e/charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b # pip coverage @ https://files.pythonhosted.org/packages/1f/0f/c890339dd605f3ebc269543247bdd43b703cce6825b5ed42ff5f2d6122c7/coverage-7.6.1-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c44fee9975f04b33331cb8eb272827111efc8930cfd582e0320613263ca849ca # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 From c502f7390882bdfc56ffad4927ce001bfa366648 Mon Sep 17 00:00:00 2001 From: Marco Wolsza Date: Wed, 4 Sep 2024 19:06:58 +0200 Subject: [PATCH 244/275] DOC Improve readability in `2.1.2. Variational Bayesian Gaussian Mixture` dropdown (#29777) --- doc/modules/mixture.rst | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/doc/modules/mixture.rst b/doc/modules/mixture.rst index 1fd72c3158336..1dec761ae813d 100644 --- a/doc/modules/mixture.rst +++ b/doc/modules/mixture.rst @@ -279,7 +279,7 @@ from the two resulting mixtures. .. rubric:: Pros - :Automatic selection: when ``weight_concentration_prior`` is small enough and + :Automatic selection: When ``weight_concentration_prior`` is small enough and ``n_components`` is larger than what is found necessary by the model, the Variational Bayesian mixture model has a natural tendency to set some mixture weights values close to zero. This makes it possible to let the model choose @@ -288,26 +288,26 @@ from the two resulting mixtures. active components is very application specific and is typically ill-defined in a data exploration setting. - :Less sensitivity to the number of parameters: unlike finite models, which will + :Less sensitivity to the number of parameters: Unlike finite models, which will almost always use all components as much as they can, and hence will produce wildly different solutions for different numbers of components, the variational inference with a Dirichlet process prior (``weight_concentration_prior_type='dirichlet_process'``) won't change much with changes to the parameters, leading to more stability and less tuning. - :Regularization: due to the incorporation of prior information, + :Regularization: Due to the incorporation of prior information, variational solutions have less pathological special cases than expectation-maximization solutions. .. rubric:: Cons - :Speed: the extra parametrization necessary for variational inference makes + :Speed: The extra parametrization necessary for variational inference makes inference slower, although not by much. - :Hyperparameters: this algorithm needs an extra hyperparameter + :Hyperparameters: This algorithm needs an extra hyperparameter that might need experimental tuning via cross-validation. - :Bias: there are many implicit biases in the inference algorithms (and also in + :Bias: There are many implicit biases in the inference algorithms (and also in the Dirichlet process if used), and whenever there is a mismatch between these biases and the data it might be possible to fit better models using a finite mixture. From 0c36d7e1e9468621c6f98ab4cf87f1db2f3b6a96 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Wed, 4 Sep 2024 20:12:18 +0200 Subject: [PATCH 245/275] FIX accept infinite C in SVC and SVR (#29780) Co-authored-by: Olivier Grisel --- doc/whats_new/v1.5.rst | 7 +++++++ sklearn/svm/_base.py | 2 +- sklearn/svm/tests/test_svm.py | 20 +++++++++++++++++++- 3 files changed, 27 insertions(+), 2 deletions(-) diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst index 05ea928358377..10af85bb80bb1 100644 --- a/doc/whats_new/v1.5.rst +++ b/doc/whats_new/v1.5.rst @@ -45,6 +45,13 @@ Changelog transform output is set to `pandas` or `polars`, since it isn't a transformer. :pr:`29401` by :user:`Stefanie Senger `. +:mod:`sklearn.svm` +.................. + +- |Fix| Fixed a regression in :class:`svm.SVC` and :class:`svm.SVR` such that we accept + `C=float("inf")`. + :pr:`29780` by :user:`Guillaume Lemaitre `. + .. _changes_1_5_1: Version 1.5.1 diff --git a/sklearn/svm/_base.py b/sklearn/svm/_base.py index 47d4027c50754..01f9e19be1155 100644 --- a/sklearn/svm/_base.py +++ b/sklearn/svm/_base.py @@ -82,7 +82,7 @@ class BaseLibSVM(BaseEstimator, metaclass=ABCMeta): ], "coef0": [Interval(Real, None, None, closed="neither")], "tol": [Interval(Real, 0.0, None, closed="neither")], - "C": [Interval(Real, 0.0, None, closed="neither")], + "C": [Interval(Real, 0.0, None, closed="right")], "nu": [Interval(Real, 0.0, 1.0, closed="right")], "epsilon": [Interval(Real, 0.0, None, closed="left")], "shrinking": ["boolean"], diff --git a/sklearn/svm/tests/test_svm.py b/sklearn/svm/tests/test_svm.py index 5d41a8cf399b1..68438a9f8196d 100644 --- a/sklearn/svm/tests/test_svm.py +++ b/sklearn/svm/tests/test_svm.py @@ -14,7 +14,7 @@ ) from sklearn import base, datasets, linear_model, metrics, svm -from sklearn.datasets import make_blobs, make_classification +from sklearn.datasets import make_blobs, make_classification, make_regression from sklearn.exceptions import ( ConvergenceWarning, NotFittedError, @@ -1422,3 +1422,21 @@ def test_dual_auto_edge_cases(): "auto", "squared_hinge", "l1", "ovr", np.asarray(X).T ) assert dual is False # only supports False + + +@pytest.mark.parametrize( + "Estimator, make_dataset", + [(svm.SVC, make_classification), (svm.SVR, make_regression)], +) +@pytest.mark.parametrize("C_inf", [np.inf, float("inf")]) +def test_svm_with_infinite_C(Estimator, make_dataset, C_inf, global_random_seed): + """Check that we can pass `C=inf` that is equivalent to a very large C value. + + Non-regression test for + https://github.com/scikit-learn/scikit-learn/issues/29772 + """ + X, y = make_dataset(random_state=global_random_seed) + estimator_C_inf = Estimator(C=C_inf).fit(X, y) + estimator_C_large = Estimator(C=1e10).fit(X, y) + + assert_allclose(estimator_C_large.predict(X), estimator_C_inf.predict(X)) From a49b8e47cb1aea5828135c3a48e97d3a0cfac24d Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Thu, 5 Sep 2024 10:57:16 +0200 Subject: [PATCH 246/275] DOC update the list of related projects (#29786) --- doc/related_projects.rst | 71 ---------------------------------------- 1 file changed, 71 deletions(-) diff --git a/doc/related_projects.rst b/doc/related_projects.rst index e6d0bd83f0a16..2d2fd02917ad2 100644 --- a/doc/related_projects.rst +++ b/doc/related_projects.rst @@ -19,14 +19,6 @@ Interoperability and framework enhancements These tools adapt scikit-learn for use with other technologies or otherwise enhance the functionality of scikit-learn's estimators. -**Data formats** - -- `sklearn_pandas `_ bridge for - scikit-learn pipelines and pandas data frame with dedicated transformers. - -- `sklearn_xarray `_ provides - compatibility of scikit-learn estimators with xarray data structures. - **Auto-ML** - `auto-sklearn `_ @@ -48,13 +40,6 @@ enhance the functionality of scikit-learn's estimators. transforming temporal and relational datasets into feature matrices for machine learning. -- `Neuraxle `_ - A library for building neat pipelines, providing the right abstractions to - both ease research, development, and deployment of machine learning - applications. Compatible with deep learning frameworks and scikit-learn API, - it can stream minibatches, use data checkpoints, build funky pipelines, and - serialize models with custom per-step savers. - - `EvalML `_ EvalML is an AutoML library which builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions. @@ -85,10 +70,6 @@ enhance the functionality of scikit-learn's estimators. - `dtreeviz `_ A python library for decision tree visualization and model interpretation. -- `eli5 `_ A library for - debugging/inspecting machine learning models and explaining their - predictions. - - `sklearn-evaluation `_ Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis. Visual analysis, model @@ -98,17 +79,6 @@ enhance the functionality of scikit-learn's estimators. custom matplotlib visualizers for scikit-learn estimators to support visual feature analysis, model selection, evaluation, and diagnostics. -**Model selection** - -- `scikit-optimize `_ - A library to minimize (very) expensive and noisy black-box functions. It - implements several methods for sequential model-based optimization, and - includes a replacement for ``GridSearchCV`` or ``RandomizedSearchCV`` to do - cross-validated parameter search using any of these strategies. - -- `sklearn-deap `_ Use evolutionary - algorithms instead of gridsearch in scikit-learn. - **Model export for production** - `sklearn-onnx `_ Serialization of many @@ -124,22 +94,10 @@ enhance the functionality of scikit-learn's estimators. into PMML with the help of `JPMML-SkLearn `_ library. -- `sklearn-porter `_ - Transpile trained scikit-learn models to C, Java, Javascript and others. - -- `m2cgen `_ - A lightweight library which allows to transpile trained machine learning - models including many scikit-learn estimators into a native code of C, Java, - Go, R, PHP, Dart, Haskell, Rust and many other programming languages. - - `treelite `_ Compiles tree-based ensemble models into C code for minimizing prediction latency. -- `micromlgen `_ - MicroML brings Machine Learning algorithms to microcontrollers. - Supports several scikit-learn classifiers by transpiling them to C code. - - `emlearn `_ Implements scikit-learn estimators in C99 for embedded devices and microcontrollers. Supports several classifier, regression and outlier detection models. @@ -202,18 +160,9 @@ Note scikit-learn own modern gradient boosting estimators - `HMMLearn `_ Implementation of hidden markov models that was previously part of scikit-learn. -- `PyStruct `_ General conditional random fields - and structured prediction. - - `pomegranate `_ Probabilistic modelling for Python, with an emphasis on hidden Markov models. -- `sklearn-crfsuite `_ - Linear-chain conditional random fields - (`CRFsuite `_ wrapper with - sklearn-like API). - - **Deep neural networks etc.** - `skorch `_ A scikit-learn compatible @@ -246,13 +195,6 @@ Note scikit-learn own modern gradient boosting estimators **Other regression and classification** -- `ML-Ensemble `_ Generalized - ensemble learning (stacking, blending, subsemble, deep ensembles, - etc.). - -- `lightning `_ Fast - state-of-the-art linear model solvers (SDCA, AdaGrad, SVRG, SAG, etc...). - - `py-earth `_ Multivariate adaptive regression splines @@ -262,12 +204,6 @@ Note scikit-learn own modern gradient boosting estimators - `scikit-multilearn `_ Multi-label classification with focus on label space manipulation. -- `seglearn `_ Time series and sequence - learning using sliding window segmentation. - -- `fastFM `_ Fast factorization machine - implementation compatible with scikit-learn - **Decomposition and clustering** - `lda `_: Fast implementation of latent @@ -286,10 +222,6 @@ Note scikit-learn own modern gradient boosting estimators Linkage clustering algorithms for robust variable density clustering. As of scikit-learn version 1.3.0, there is :class:`~sklearn.cluster.HDBSCAN`. -- `spherecluster `_ Spherical - K-means and mixture of von Mises Fisher clustering routines for data on the - unit hypersphere. - **Pre-processing** - `categorical-encoding @@ -349,9 +281,6 @@ Recommendation Engine packages - `lightfm `_ A Python/Cython implementation of a hybrid recommender system. -- `OpenRec `_ TensorFlow-based - neural-network inspired recommendation algorithms. - - `Surprise Lib `_ Library for explicit feedback datasets. From 746d292f92a457a62a7a67d1f5dfb1b16b72e7d9 Mon Sep 17 00:00:00 2001 From: Piotr Date: Thu, 5 Sep 2024 12:06:13 +0200 Subject: [PATCH 247/275] DOC add MLJAR AutoML in related projects (#29728) Co-authored-by: Guillaume Lemaitre --- doc/related_projects.rst | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/doc/related_projects.rst b/doc/related_projects.rst index 2d2fd02917ad2..4814cddb75ea9 100644 --- a/doc/related_projects.rst +++ b/doc/related_projects.rst @@ -46,6 +46,10 @@ enhance the functionality of scikit-learn's estimators. It incorporates multiple modeling libraries under one API, and the objects that EvalML creates use an sklearn-compatible API. +- `MLJAR AutoML `_ + Python package for AutoML on Tabular Data with Feature Engineering, + Hyper-Parameters Tuning, Explanations and Automatic Documentation. + **Experimentation and model registry frameworks** - `MLFlow `_ MLflow is an open source platform to manage the ML From 7b7f1e61e6a5997b655cd7f9e3afce0b5a0838f8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=B4me=20Dock=C3=A8s?= Date: Thu, 5 Sep 2024 14:14:52 +0200 Subject: [PATCH 248/275] DOC Add dropdowns to User Guide section 3.2, "Tuning the hyper-parameters of an estimator" (#27631) --- doc/modules/grid_search.rst | 628 ++++++++++++++++++------------------ 1 file changed, 314 insertions(+), 314 deletions(-) diff --git a/doc/modules/grid_search.rst b/doc/modules/grid_search.rst index ee567c8e497e2..f91f070e644be 100644 --- a/doc/modules/grid_search.rst +++ b/doc/modules/grid_search.rst @@ -74,8 +74,10 @@ evaluated and the best combination is retained. .. rubric:: Examples -- See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py` for an example of - Grid Search computation on the digits dataset. +- See :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py` + for an example of Grid Search within a cross validation loop on the iris + dataset. This is the best practice for evaluating the performance of a + model with grid search. - See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` for an example of Grid Search coupling parameters from a text documents feature @@ -83,24 +85,28 @@ evaluated and the best combination is retained. classifier (here a linear SVM trained with SGD with either elastic net or L2 penalty) using a :class:`~sklearn.pipeline.Pipeline` instance. -- See :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py` - for an example of Grid Search within a cross validation loop on the iris - dataset. This is the best practice for evaluating the performance of a - model with grid search. -- See :ref:`sphx_glr_auto_examples_model_selection_plot_multi_metric_evaluation.py` - for an example of :class:`GridSearchCV` being used to evaluate multiple - metrics simultaneously. +.. dropdown:: Advanced examples + + - See :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py` + for an example of Grid Search within a cross validation loop on the iris + dataset. This is the best practice for evaluating the performance of a + model with grid search. -- See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py` - for an example of using ``refit=callable`` interface in - :class:`GridSearchCV`. The example shows how this interface adds certain - amount of flexibility in identifying the "best" estimator. This interface - can also be used in multiple metrics evaluation. + - See :ref:`sphx_glr_auto_examples_model_selection_plot_multi_metric_evaluation.py` + for an example of :class:`GridSearchCV` being used to evaluate multiple + metrics simultaneously. + + - See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py` + for an example of using ``refit=callable`` interface in + :class:`GridSearchCV`. The example shows how this interface adds certain + amount of flexibility in identifying the "best" estimator. This interface + can also be used in multiple metrics evaluation. + + - See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_stats.py` + for an example of how to do a statistical comparison on the outputs of + :class:`GridSearchCV`. -- See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_stats.py` - for an example of how to do a statistical comparison on the outputs of - :class:`GridSearchCV`. .. _randomized_parameter_search: @@ -204,7 +210,7 @@ here the number of samples. :align: center We here briefly describe the main parameters, but each parameter and their -interactions are described in more details in the sections below. The +interactions are described more in detail in the dropdown section below. The ``factor`` (> 1) parameter controls the rate at which the resources grow, and the rate at which the number of candidates decreases. In each iteration, the number of resources per candidate is multiplied by ``factor`` and the number @@ -221,9 +227,7 @@ These estimators are still **experimental**: their predictions and their API might change without any deprecation cycle. To use them, you need to explicitly import ``enable_halving_search_cv``:: - >>> # explicitly require this experimental feature >>> from sklearn.experimental import enable_halving_search_cv # noqa - >>> # now you can import normally from model_selection >>> from sklearn.model_selection import HalvingGridSearchCV >>> from sklearn.model_selection import HalvingRandomSearchCV @@ -232,268 +236,263 @@ need to explicitly import ``enable_halving_search_cv``:: * :ref:`sphx_glr_auto_examples_model_selection_plot_successive_halving_heatmap.py` * :ref:`sphx_glr_auto_examples_model_selection_plot_successive_halving_iterations.py` -Choosing ``min_resources`` and the number of candidates -------------------------------------------------------- - -Beside ``factor``, the two main parameters that influence the behaviour of a -successive halving search are the ``min_resources`` parameter, and the -number of candidates (or parameter combinations) that are evaluated. -``min_resources`` is the amount of resources allocated at the first -iteration for each candidate. The number of candidates is specified directly -in :class:`HalvingRandomSearchCV`, and is determined from the ``param_grid`` -parameter of :class:`HalvingGridSearchCV`. - -Consider a case where the resource is the number of samples, and where we -have 1000 samples. In theory, with ``min_resources=10`` and ``factor=2``, we -are able to run **at most** 7 iterations with the following number of -samples: ``[10, 20, 40, 80, 160, 320, 640]``. - -But depending on the number of candidates, we might run less than 7 -iterations: if we start with a **small** number of candidates, the last -iteration might use less than 640 samples, which means not using all the -available resources (samples). For example if we start with 5 candidates, we -only need 2 iterations: 5 candidates for the first iteration, then -`5 // 2 = 2` candidates at the second iteration, after which we know which -candidate performs the best (so we don't need a third one). We would only be -using at most 20 samples which is a waste since we have 1000 samples at our -disposal. On the other hand, if we start with a **high** number of -candidates, we might end up with a lot of candidates at the last iteration, -which may not always be ideal: it means that many candidates will run with -the full resources, basically reducing the procedure to standard search. - -In the case of :class:`HalvingRandomSearchCV`, the number of candidates is set -by default such that the last iteration uses as much of the available -resources as possible. For :class:`HalvingGridSearchCV`, the number of -candidates is determined by the `param_grid` parameter. Changing the value of -``min_resources`` will impact the number of possible iterations, and as a -result will also have an effect on the ideal number of candidates. - -Another consideration when choosing ``min_resources`` is whether or not it -is easy to discriminate between good and bad candidates with a small amount -of resources. For example, if you need a lot of samples to distinguish -between good and bad parameters, a high ``min_resources`` is recommended. On -the other hand if the distinction is clear even with a small amount of -samples, then a small ``min_resources`` may be preferable since it would -speed up the computation. - -Notice in the example above that the last iteration does not use the maximum -amount of resources available: 1000 samples are available, yet only 640 are -used, at most. By default, both :class:`HalvingRandomSearchCV` and -:class:`HalvingGridSearchCV` try to use as many resources as possible in the -last iteration, with the constraint that this amount of resources must be a -multiple of both `min_resources` and `factor` (this constraint will be clear -in the next section). :class:`HalvingRandomSearchCV` achieves this by -sampling the right amount of candidates, while :class:`HalvingGridSearchCV` -achieves this by properly setting `min_resources`. Please see -:ref:`exhausting_the_resources` for details. - -.. _amount_of_resource_and_number_of_candidates: - -Amount of resource and number of candidates at each iteration -------------------------------------------------------------- - -At any iteration `i`, each candidate is allocated a given amount of resources -which we denote `n_resources_i`. This quantity is controlled by the -parameters ``factor`` and ``min_resources`` as follows (`factor` is strictly -greater than 1):: - - n_resources_i = factor**i * min_resources, - -or equivalently:: - - n_resources_{i+1} = n_resources_i * factor - -where ``min_resources == n_resources_0`` is the amount of resources used at -the first iteration. ``factor`` also defines the proportions of candidates -that will be selected for the next iteration:: - - n_candidates_i = n_candidates // (factor ** i) - -or equivalently:: - - n_candidates_0 = n_candidates - n_candidates_{i+1} = n_candidates_i // factor - -So in the first iteration, we use ``min_resources`` resources -``n_candidates`` times. In the second iteration, we use ``min_resources * -factor`` resources ``n_candidates // factor`` times. The third again -multiplies the resources per candidate and divides the number of candidates. -This process stops when the maximum amount of resource per candidate is -reached, or when we have identified the best candidate. The best candidate -is identified at the iteration that is evaluating `factor` or less candidates -(see just below for an explanation). - -Here is an example with ``min_resources=3`` and ``factor=2``, starting with -70 candidates: - -+-----------------------+-----------------------+ -| ``n_resources_i`` | ``n_candidates_i`` | -+=======================+=======================+ -| 3 (=min_resources) | 70 (=n_candidates) | -+-----------------------+-----------------------+ -| 3 * 2 = 6 | 70 // 2 = 35 | -+-----------------------+-----------------------+ -| 6 * 2 = 12 | 35 // 2 = 17 | -+-----------------------+-----------------------+ -| 12 * 2 = 24 | 17 // 2 = 8 | -+-----------------------+-----------------------+ -| 24 * 2 = 48 | 8 // 2 = 4 | -+-----------------------+-----------------------+ -| 48 * 2 = 96 | 4 // 2 = 2 | -+-----------------------+-----------------------+ - -We can note that: - -- the process stops at the first iteration which evaluates `factor=2` - candidates: the best candidate is the best out of these 2 candidates. It - is not necessary to run an additional iteration, since it would only - evaluate one candidate (namely the best one, which we have already - identified). For this reason, in general, we want the last iteration to - run at most ``factor`` candidates. If the last iteration evaluates more - than `factor` candidates, then this last iteration reduces to a regular - search (as in :class:`RandomizedSearchCV` or :class:`GridSearchCV`). -- each ``n_resources_i`` is a multiple of both ``factor`` and - ``min_resources`` (which is confirmed by its definition above). - -The amount of resources that is used at each iteration can be found in the -`n_resources_` attribute. - -Choosing a resource -------------------- - -By default, the resource is defined in terms of number of samples. That is, -each iteration will use an increasing amount of samples to train on. You can -however manually specify a parameter to use as the resource with the -``resource`` parameter. Here is an example where the resource is defined in -terms of the number of estimators of a random forest:: - - >>> from sklearn.datasets import make_classification - >>> from sklearn.ensemble import RandomForestClassifier - >>> from sklearn.experimental import enable_halving_search_cv # noqa - >>> from sklearn.model_selection import HalvingGridSearchCV - >>> import pandas as pd - >>> - >>> param_grid = {'max_depth': [3, 5, 10], - ... 'min_samples_split': [2, 5, 10]} - >>> base_estimator = RandomForestClassifier(random_state=0) - >>> X, y = make_classification(n_samples=1000, random_state=0) - >>> sh = HalvingGridSearchCV(base_estimator, param_grid, cv=5, - ... factor=2, resource='n_estimators', - ... max_resources=30).fit(X, y) - >>> sh.best_estimator_ - RandomForestClassifier(max_depth=5, n_estimators=24, random_state=0) - -Note that it is not possible to budget on a parameter that is part of the -parameter grid. - -.. _exhausting_the_resources: - -Exhausting the available resources ----------------------------------- - -As mentioned above, the number of resources that is used at each iteration -depends on the `min_resources` parameter. -If you have a lot of resources available but start with a low number of -resources, some of them might be wasted (i.e. not used):: - - >>> from sklearn.datasets import make_classification - >>> from sklearn.svm import SVC - >>> from sklearn.experimental import enable_halving_search_cv # noqa - >>> from sklearn.model_selection import HalvingGridSearchCV - >>> import pandas as pd - >>> param_grid= {'kernel': ('linear', 'rbf'), - ... 'C': [1, 10, 100]} - >>> base_estimator = SVC(gamma='scale') - >>> X, y = make_classification(n_samples=1000) - >>> sh = HalvingGridSearchCV(base_estimator, param_grid, cv=5, - ... factor=2, min_resources=20).fit(X, y) - >>> sh.n_resources_ - [20, 40, 80] - -The search process will only use 80 resources at most, while our maximum -amount of available resources is ``n_samples=1000``. Here, we have -``min_resources = r_0 = 20``. - -For :class:`HalvingGridSearchCV`, by default, the `min_resources` parameter -is set to 'exhaust'. This means that `min_resources` is automatically set -such that the last iteration can use as many resources as possible, within -the `max_resources` limit:: - - >>> sh = HalvingGridSearchCV(base_estimator, param_grid, cv=5, - ... factor=2, min_resources='exhaust').fit(X, y) - >>> sh.n_resources_ - [250, 500, 1000] - -`min_resources` was here automatically set to 250, which results in the last -iteration using all the resources. The exact value that is used depends on -the number of candidate parameter, on `max_resources` and on `factor`. - -For :class:`HalvingRandomSearchCV`, exhausting the resources can be done in 2 -ways: - -- by setting `min_resources='exhaust'`, just like for - :class:`HalvingGridSearchCV`; -- by setting `n_candidates='exhaust'`. - -Both options are mutually exclusive: using `min_resources='exhaust'` requires -knowing the number of candidates, and symmetrically `n_candidates='exhaust'` -requires knowing `min_resources`. - -In general, exhausting the total number of resources leads to a better final -candidate parameter, and is slightly more time-intensive. +The sections below dive into technical aspects of successive halving. + +.. dropdown:: Choosing ``min_resources`` and the number of candidates + + Beside ``factor``, the two main parameters that influence the behaviour of a + successive halving search are the ``min_resources`` parameter, and the + number of candidates (or parameter combinations) that are evaluated. + ``min_resources`` is the amount of resources allocated at the first + iteration for each candidate. The number of candidates is specified directly + in :class:`HalvingRandomSearchCV`, and is determined from the ``param_grid`` + parameter of :class:`HalvingGridSearchCV`. + + Consider a case where the resource is the number of samples, and where we + have 1000 samples. In theory, with ``min_resources=10`` and ``factor=2``, we + are able to run **at most** 7 iterations with the following number of + samples: ``[10, 20, 40, 80, 160, 320, 640]``. + + But depending on the number of candidates, we might run less than 7 + iterations: if we start with a **small** number of candidates, the last + iteration might use less than 640 samples, which means not using all the + available resources (samples). For example if we start with 5 candidates, we + only need 2 iterations: 5 candidates for the first iteration, then + `5 // 2 = 2` candidates at the second iteration, after which we know which + candidate performs the best (so we don't need a third one). We would only be + using at most 20 samples which is a waste since we have 1000 samples at our + disposal. On the other hand, if we start with a **high** number of + candidates, we might end up with a lot of candidates at the last iteration, + which may not always be ideal: it means that many candidates will run with + the full resources, basically reducing the procedure to standard search. + + In the case of :class:`HalvingRandomSearchCV`, the number of candidates is set + by default such that the last iteration uses as much of the available + resources as possible. For :class:`HalvingGridSearchCV`, the number of + candidates is determined by the `param_grid` parameter. Changing the value of + ``min_resources`` will impact the number of possible iterations, and as a + result will also have an effect on the ideal number of candidates. + + Another consideration when choosing ``min_resources`` is whether or not it + is easy to discriminate between good and bad candidates with a small amount + of resources. For example, if you need a lot of samples to distinguish + between good and bad parameters, a high ``min_resources`` is recommended. On + the other hand if the distinction is clear even with a small amount of + samples, then a small ``min_resources`` may be preferable since it would + speed up the computation. + + Notice in the example above that the last iteration does not use the maximum + amount of resources available: 1000 samples are available, yet only 640 are + used, at most. By default, both :class:`HalvingRandomSearchCV` and + :class:`HalvingGridSearchCV` try to use as many resources as possible in the + last iteration, with the constraint that this amount of resources must be a + multiple of both `min_resources` and `factor` (this constraint will be clear + in the next section). :class:`HalvingRandomSearchCV` achieves this by + sampling the right amount of candidates, while :class:`HalvingGridSearchCV` + achieves this by properly setting `min_resources`. + + +.. dropdown:: Amount of resource and number of candidates at each iteration + + At any iteration `i`, each candidate is allocated a given amount of resources + which we denote `n_resources_i`. This quantity is controlled by the + parameters ``factor`` and ``min_resources`` as follows (`factor` is strictly + greater than 1):: + + n_resources_i = factor**i * min_resources, + + or equivalently:: + + n_resources_{i+1} = n_resources_i * factor + + where ``min_resources == n_resources_0`` is the amount of resources used at + the first iteration. ``factor`` also defines the proportions of candidates + that will be selected for the next iteration:: + + n_candidates_i = n_candidates // (factor ** i) + + or equivalently:: + + n_candidates_0 = n_candidates + n_candidates_{i+1} = n_candidates_i // factor + + So in the first iteration, we use ``min_resources`` resources + ``n_candidates`` times. In the second iteration, we use ``min_resources * + factor`` resources ``n_candidates // factor`` times. The third again + multiplies the resources per candidate and divides the number of candidates. + This process stops when the maximum amount of resource per candidate is + reached, or when we have identified the best candidate. The best candidate + is identified at the iteration that is evaluating `factor` or less candidates + (see just below for an explanation). + + Here is an example with ``min_resources=3`` and ``factor=2``, starting with + 70 candidates: + + +-----------------------+-----------------------+ + | ``n_resources_i`` | ``n_candidates_i`` | + +=======================+=======================+ + | 3 (=min_resources) | 70 (=n_candidates) | + +-----------------------+-----------------------+ + | 3 * 2 = 6 | 70 // 2 = 35 | + +-----------------------+-----------------------+ + | 6 * 2 = 12 | 35 // 2 = 17 | + +-----------------------+-----------------------+ + | 12 * 2 = 24 | 17 // 2 = 8 | + +-----------------------+-----------------------+ + | 24 * 2 = 48 | 8 // 2 = 4 | + +-----------------------+-----------------------+ + | 48 * 2 = 96 | 4 // 2 = 2 | + +-----------------------+-----------------------+ + + We can note that: + + - the process stops at the first iteration which evaluates `factor=2` + candidates: the best candidate is the best out of these 2 candidates. It + is not necessary to run an additional iteration, since it would only + evaluate one candidate (namely the best one, which we have already + identified). For this reason, in general, we want the last iteration to + run at most ``factor`` candidates. If the last iteration evaluates more + than `factor` candidates, then this last iteration reduces to a regular + search (as in :class:`RandomizedSearchCV` or :class:`GridSearchCV`). + - each ``n_resources_i`` is a multiple of both ``factor`` and + ``min_resources`` (which is confirmed by its definition above). + + The amount of resources that is used at each iteration can be found in the + `n_resources_` attribute. + +.. dropdown:: Choosing a resource + + By default, the resource is defined in terms of number of samples. That is, + each iteration will use an increasing amount of samples to train on. You can + however manually specify a parameter to use as the resource with the + ``resource`` parameter. Here is an example where the resource is defined in + terms of the number of estimators of a random forest:: + + >>> from sklearn.datasets import make_classification + >>> from sklearn.ensemble import RandomForestClassifier + >>> from sklearn.experimental import enable_halving_search_cv # noqa + >>> from sklearn.model_selection import HalvingGridSearchCV + >>> import pandas as pd + >>> param_grid = {'max_depth': [3, 5, 10], + ... 'min_samples_split': [2, 5, 10]} + >>> base_estimator = RandomForestClassifier(random_state=0) + >>> X, y = make_classification(n_samples=1000, random_state=0) + >>> sh = HalvingGridSearchCV(base_estimator, param_grid, cv=5, + ... factor=2, resource='n_estimators', + ... max_resources=30).fit(X, y) + >>> sh.best_estimator_ + RandomForestClassifier(max_depth=5, n_estimators=24, random_state=0) + + Note that it is not possible to budget on a parameter that is part of the + parameter grid. + + +.. dropdown:: Exhausting the available resources + + As mentioned above, the number of resources that is used at each iteration + depends on the `min_resources` parameter. + If you have a lot of resources available but start with a low number of + resources, some of them might be wasted (i.e. not used):: + + >>> from sklearn.datasets import make_classification + >>> from sklearn.svm import SVC + >>> from sklearn.experimental import enable_halving_search_cv # noqa + >>> from sklearn.model_selection import HalvingGridSearchCV + >>> import pandas as pd + >>> param_grid= {'kernel': ('linear', 'rbf'), + ... 'C': [1, 10, 100]} + >>> base_estimator = SVC(gamma='scale') + >>> X, y = make_classification(n_samples=1000) + >>> sh = HalvingGridSearchCV(base_estimator, param_grid, cv=5, + ... factor=2, min_resources=20).fit(X, y) + >>> sh.n_resources_ + [20, 40, 80] + + The search process will only use 80 resources at most, while our maximum + amount of available resources is ``n_samples=1000``. Here, we have + ``min_resources = r_0 = 20``. + + For :class:`HalvingGridSearchCV`, by default, the `min_resources` parameter + is set to 'exhaust'. This means that `min_resources` is automatically set + such that the last iteration can use as many resources as possible, within + the `max_resources` limit:: + + >>> sh = HalvingGridSearchCV(base_estimator, param_grid, cv=5, + ... factor=2, min_resources='exhaust').fit(X, y) + >>> sh.n_resources_ + [250, 500, 1000] + + `min_resources` was here automatically set to 250, which results in the last + iteration using all the resources. The exact value that is used depends on + the number of candidate parameter, on `max_resources` and on `factor`. + + For :class:`HalvingRandomSearchCV`, exhausting the resources can be done in 2 + ways: + + - by setting `min_resources='exhaust'`, just like for + :class:`HalvingGridSearchCV`; + - by setting `n_candidates='exhaust'`. + + Both options are mutually exclusive: using `min_resources='exhaust'` requires + knowing the number of candidates, and symmetrically `n_candidates='exhaust'` + requires knowing `min_resources`. + + In general, exhausting the total number of resources leads to a better final + candidate parameter, and is slightly more time-intensive. .. _aggressive_elimination: Aggressive elimination of candidates ------------------------------------ -Ideally, we want the last iteration to evaluate ``factor`` candidates (see -:ref:`amount_of_resource_and_number_of_candidates`). We then just have to -pick the best one. When the number of available resources is small with -respect to the number of candidates, the last iteration may have to evaluate -more than ``factor`` candidates:: - - >>> from sklearn.datasets import make_classification - >>> from sklearn.svm import SVC - >>> from sklearn.experimental import enable_halving_search_cv # noqa - >>> from sklearn.model_selection import HalvingGridSearchCV - >>> import pandas as pd - >>> - >>> - >>> param_grid = {'kernel': ('linear', 'rbf'), - ... 'C': [1, 10, 100]} - >>> base_estimator = SVC(gamma='scale') - >>> X, y = make_classification(n_samples=1000) - >>> sh = HalvingGridSearchCV(base_estimator, param_grid, cv=5, - ... factor=2, max_resources=40, - ... aggressive_elimination=False).fit(X, y) - >>> sh.n_resources_ - [20, 40] - >>> sh.n_candidates_ - [6, 3] - -Since we cannot use more than ``max_resources=40`` resources, the process -has to stop at the second iteration which evaluates more than ``factor=2`` -candidates. - Using the ``aggressive_elimination`` parameter, you can force the search process to end up with less than ``factor`` candidates at the last -iteration. To do this, the process will eliminate as many candidates as -necessary using ``min_resources`` resources:: - - >>> sh = HalvingGridSearchCV(base_estimator, param_grid, cv=5, - ... factor=2, - ... max_resources=40, - ... aggressive_elimination=True, - ... ).fit(X, y) - >>> sh.n_resources_ - [20, 20, 40] - >>> sh.n_candidates_ - [6, 3, 2] - -Notice that we end with 2 candidates at the last iteration since we have -eliminated enough candidates during the first iterations, using ``n_resources = -min_resources = 20``. +iteration. + +.. dropdown:: Code example of aggressive elimination + + Ideally, we want the last iteration to evaluate ``factor`` candidates. We + then just have to pick the best one. When the number of available resources is + small with respect to the number of candidates, the last iteration may have to + evaluate more than ``factor`` candidates:: + + >>> from sklearn.datasets import make_classification + >>> from sklearn.svm import SVC + >>> from sklearn.experimental import enable_halving_search_cv # noqa + >>> from sklearn.model_selection import HalvingGridSearchCV + >>> import pandas as pd + >>> param_grid = {'kernel': ('linear', 'rbf'), + ... 'C': [1, 10, 100]} + >>> base_estimator = SVC(gamma='scale') + >>> X, y = make_classification(n_samples=1000) + >>> sh = HalvingGridSearchCV(base_estimator, param_grid, cv=5, + ... factor=2, max_resources=40, + ... aggressive_elimination=False).fit(X, y) + >>> sh.n_resources_ + [20, 40] + >>> sh.n_candidates_ + [6, 3] + + Since we cannot use more than ``max_resources=40`` resources, the process + has to stop at the second iteration which evaluates more than ``factor=2`` + candidates. + + When using ``aggressive_elimination``, the process will eliminate as many + candidates as necessary using ``min_resources`` resources:: + + >>> sh = HalvingGridSearchCV(base_estimator, param_grid, cv=5, + ... factor=2, + ... max_resources=40, + ... aggressive_elimination=True, + ... ).fit(X, y) + >>> sh.n_resources_ + [20, 20, 40] + >>> sh.n_candidates_ + [6, 3, 2] + + Notice that we end with 2 candidates at the last iteration since we have + eliminated enough candidates during the first iterations, using ``n_resources = + min_resources = 20``. .. _successive_halving_cv_results: @@ -507,42 +506,44 @@ pd.DataFrame(est.cv_results_)``. The ``cv_results_`` attribute of to that of :class:`GridSearchCV` and :class:`RandomizedSearchCV`, with additional information related to the successive halving process. -Here is an example with some of the columns of a (truncated) dataframe: - -==== ====== =============== ================= ======================================================================================== - .. iter n_resources mean_test_score params -==== ====== =============== ================= ======================================================================================== - 0 0 125 0.983667 {'criterion': 'log_loss', 'max_depth': None, 'max_features': 9, 'min_samples_split': 5} - 1 0 125 0.983667 {'criterion': 'gini', 'max_depth': None, 'max_features': 8, 'min_samples_split': 7} - 2 0 125 0.983667 {'criterion': 'gini', 'max_depth': None, 'max_features': 10, 'min_samples_split': 10} - 3 0 125 0.983667 {'criterion': 'log_loss', 'max_depth': None, 'max_features': 6, 'min_samples_split': 6} - ... ... ... ... ... - 15 2 500 0.951958 {'criterion': 'log_loss', 'max_depth': None, 'max_features': 9, 'min_samples_split': 10} - 16 2 500 0.947958 {'criterion': 'gini', 'max_depth': None, 'max_features': 10, 'min_samples_split': 10} - 17 2 500 0.951958 {'criterion': 'gini', 'max_depth': None, 'max_features': 10, 'min_samples_split': 4} - 18 3 1000 0.961009 {'criterion': 'log_loss', 'max_depth': None, 'max_features': 9, 'min_samples_split': 10} - 19 3 1000 0.955989 {'criterion': 'gini', 'max_depth': None, 'max_features': 10, 'min_samples_split': 4} -==== ====== =============== ================= ======================================================================================== - -Each row corresponds to a given parameter combination (a candidate) and a given -iteration. The iteration is given by the ``iter`` column. The ``n_resources`` -column tells you how many resources were used. - -In the example above, the best parameter combination is ``{'criterion': -'log_loss', 'max_depth': None, 'max_features': 9, 'min_samples_split': 10}`` -since it has reached the last iteration (3) with the highest score: -0.96. +.. dropdown:: Example of a (truncated) output dataframe: -.. rubric:: References + ==== ====== =============== ================= ======================================================================================== + .. iter n_resources mean_test_score params + ==== ====== =============== ================= ======================================================================================== + 0 0 125 0.983667 {'criterion': 'log_loss', 'max_depth': None, 'max_features': 9, 'min_samples_split': 5} + 1 0 125 0.983667 {'criterion': 'gini', 'max_depth': None, 'max_features': 8, 'min_samples_split': 7} + 2 0 125 0.983667 {'criterion': 'gini', 'max_depth': None, 'max_features': 10, 'min_samples_split': 10} + 3 0 125 0.983667 {'criterion': 'log_loss', 'max_depth': None, 'max_features': 6, 'min_samples_split': 6} + ... ... ... ... ... + 15 2 500 0.951958 {'criterion': 'log_loss', 'max_depth': None, 'max_features': 9, 'min_samples_split': 10} + 16 2 500 0.947958 {'criterion': 'gini', 'max_depth': None, 'max_features': 10, 'min_samples_split': 10} + 17 2 500 0.951958 {'criterion': 'gini', 'max_depth': None, 'max_features': 10, 'min_samples_split': 4} + 18 3 1000 0.961009 {'criterion': 'log_loss', 'max_depth': None, 'max_features': 9, 'min_samples_split': 10} + 19 3 1000 0.955989 {'criterion': 'gini', 'max_depth': None, 'max_features': 10, 'min_samples_split': 4} + ==== ====== =============== ================= ======================================================================================== + + Each row corresponds to a given parameter combination (a candidate) and a given + iteration. The iteration is given by the ``iter`` column. The ``n_resources`` + column tells you how many resources were used. + + In the example above, the best parameter combination is ``{'criterion': + 'log_loss', 'max_depth': None, 'max_features': 9, 'min_samples_split': 10}`` + since it has reached the last iteration (3) with the highest score: + 0.96. + + .. rubric:: References + + .. [1] K. Jamieson, A. Talwalkar, + `Non-stochastic Best Arm Identification and Hyperparameter + Optimization `_, in + proc. of Machine Learning Research, 2016. + + .. [2] L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, A. Talwalkar, + :arxiv:`Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization + <1603.06560>`, in Machine Learning Research 18, 2018. -.. [1] K. Jamieson, A. Talwalkar, - `Non-stochastic Best Arm Identification and Hyperparameter - Optimization `_, in - proc. of Machine Learning Research, 2016. -.. [2] L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, A. Talwalkar, - :arxiv:`Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization - <1603.06560>`, in Machine Learning Research 18, 2018. .. _grid_search_tips: @@ -611,7 +612,7 @@ parameters of composite or nested estimators such as >>> search = GridSearchCV(calibrated_forest, param_grid, cv=5) >>> search.fit(X, y) GridSearchCV(cv=5, - estimator=CalibratedClassifierCV(...), + estimator=CalibratedClassifierCV(estimator=RandomForestClassifier(n_estimators=10)), param_grid={'estimator__max_depth': [2, 4, 6, 8]}) Here, ```` is the parameter name of the nested estimator, @@ -660,12 +661,11 @@ entry for :term:`n_jobs`. Robustness to failure --------------------- -Some parameter settings may result in a failure to ``fit`` one or more folds -of the data. By default, this will cause the entire search to fail, even if -some parameter settings could be fully evaluated. Setting ``error_score=0`` -(or `=np.nan`) will make the procedure robust to such failure, issuing a -warning and setting the score for that fold to 0 (or `nan`), but completing -the search. +Some parameter settings may result in a failure to ``fit`` one or more folds of +the data. By default, the score for those settings will be `np.nan`. This can +be controlled by setting `error_score="raise"` to raise an exception if one fit +fails, or for example `error_score=0` to set another value for the score of +failing parameter combinations. .. _alternative_cv: From b16544234b8e3ffcb32bfb10abbc299fc18f7b79 Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Thu, 5 Sep 2024 18:30:50 +0200 Subject: [PATCH 249/275] DOC Improve the plot_gpy_noisy.py example (#29788) Co-authored-by: Guillaume Lemaitre --- examples/gaussian_process/plot_gpr_noisy.py | 82 +++++++++++++++------ 1 file changed, 59 insertions(+), 23 deletions(-) diff --git a/examples/gaussian_process/plot_gpr_noisy.py b/examples/gaussian_process/plot_gpr_noisy.py index 31d3b149aa47f..f61c594634b41 100644 --- a/examples/gaussian_process/plot_gpr_noisy.py +++ b/examples/gaussian_process/plot_gpr_noisy.py @@ -34,7 +34,7 @@ def target_generator(X, add_noise=False): # %% # Let's have a look to the target generator where we will not add any noise to # observe the signal that we would like to predict. -X = np.linspace(0, 5, num=30).reshape(-1, 1) +X = np.linspace(0, 5, num=80).reshape(-1, 1) y = target_generator(X, add_noise=False) # %% @@ -89,7 +89,7 @@ def target_generator(X, add_noise=False): from sklearn.gaussian_process.kernels import RBF, WhiteKernel kernel = 1.0 * RBF(length_scale=1e1, length_scale_bounds=(1e-2, 1e3)) + WhiteKernel( - noise_level=1, noise_level_bounds=(1e-5, 1e1) + noise_level=1, noise_level_bounds=(1e-10, 1e1) ) gpr = GaussianProcessRegressor(kernel=kernel, alpha=0.0) gpr.fit(X_train, y_train) @@ -98,7 +98,7 @@ def target_generator(X, add_noise=False): # %% plt.plot(X, y, label="Expected signal") plt.scatter(x=X_train[:, 0], y=y_train, color="black", alpha=0.4, label="Observations") -plt.errorbar(X, y_mean, y_std) +plt.errorbar(X, y_mean, y_std, label="Posterior mean ± std") plt.legend() plt.xlabel("X") plt.ylabel("y") @@ -110,15 +110,18 @@ def target_generator(X, add_noise=False): fontsize=8, ) # %% -# We see that the optimum kernel found still have a high noise level and -# an even larger length scale. Furthermore, we observe that the -# model does not provide faithful predictions. +# We see that the optimum kernel found still has a high noise level and an even +# larger length scale. The length scale reaches the maximum bound that we +# allowed for this parameter and we got a warning as a result. # -# Now, we will initialize the -# :class:`~sklearn.gaussian_process.kernels.RBF` with a -# larger `length_scale` and the -# :class:`~sklearn.gaussian_process.kernels.WhiteKernel` -# with a smaller noise level lower bound. +# More importantly, we observe that the model does not provide useful +# predictions: the mean prediction seems to be constant: it does not follow the +# expected noise-free signal. +# +# Now, we will initialize the :class:`~sklearn.gaussian_process.kernels.RBF` +# with a larger `length_scale` initial value and the +# :class:`~sklearn.gaussian_process.kernels.WhiteKernel` with a smaller initial +# noise level lower while keeping the parameter bounds unchanged. kernel = 1.0 * RBF(length_scale=1e-1, length_scale_bounds=(1e-2, 1e3)) + WhiteKernel( noise_level=1e-2, noise_level_bounds=(1e-10, 1e1) ) @@ -129,7 +132,7 @@ def target_generator(X, add_noise=False): # %% plt.plot(X, y, label="Expected signal") plt.scatter(x=X_train[:, 0], y=y_train, color="black", alpha=0.4, label="Observations") -plt.errorbar(X, y_mean, y_std) +plt.errorbar(X, y_mean, y_std, label="Posterior mean ± std") plt.legend() plt.xlabel("X") plt.ylabel("y") @@ -154,21 +157,19 @@ def target_generator(X, add_noise=False): # for different hyperparameters to get a sense of the local minima. from matplotlib.colors import LogNorm -length_scale = np.logspace(-2, 4, num=50) -noise_level = np.logspace(-2, 1, num=50) +length_scale = np.logspace(-2, 4, num=80) +noise_level = np.logspace(-2, 1, num=80) length_scale_grid, noise_level_grid = np.meshgrid(length_scale, noise_level) log_marginal_likelihood = [ gpr.log_marginal_likelihood(theta=np.log([0.36, scale, noise])) for scale, noise in zip(length_scale_grid.ravel(), noise_level_grid.ravel()) ] -log_marginal_likelihood = np.reshape( - log_marginal_likelihood, newshape=noise_level_grid.shape -) +log_marginal_likelihood = np.reshape(log_marginal_likelihood, noise_level_grid.shape) # %% vmin, vmax = (-log_marginal_likelihood).min(), 50 -level = np.around(np.logspace(np.log10(vmin), np.log10(vmax), num=50), decimals=1) +level = np.around(np.logspace(np.log10(vmin), np.log10(vmax), num=20), decimals=1) plt.contour( length_scale_grid, noise_level_grid, @@ -185,8 +186,43 @@ def target_generator(X, add_noise=False): plt.show() # %% -# We see that there are two local minima that correspond to the combination -# of hyperparameters previously found. Depending on the initial values for the -# hyperparameters, the gradient-based optimization might converge whether or -# not to the best model. It is thus important to repeat the optimization -# several times for different initializations. +# +# We see that there are two local minima that correspond to the combination of +# hyperparameters previously found. Depending on the initial values for the +# hyperparameters, the gradient-based optimization might or might not +# converge to the best model. It is thus important to repeat the optimization +# several times for different initializations. This can be done by setting the +# `n_restarts_optimizer` parameter of the +# :class:`~sklearn.gaussian_process.GaussianProcessRegressor` class. +# +# Let's try again to fit our model with the bad initial values but this time +# with 10 random restarts. + +kernel = 1.0 * RBF(length_scale=1e1, length_scale_bounds=(1e-2, 1e3)) + WhiteKernel( + noise_level=1, noise_level_bounds=(1e-10, 1e1) +) +gpr = GaussianProcessRegressor( + kernel=kernel, alpha=0.0, n_restarts_optimizer=10, random_state=0 +) +gpr.fit(X_train, y_train) +y_mean, y_std = gpr.predict(X, return_std=True) + +# %% +plt.plot(X, y, label="Expected signal") +plt.scatter(x=X_train[:, 0], y=y_train, color="black", alpha=0.4, label="Observations") +plt.errorbar(X, y_mean, y_std, label="Posterior mean ± std") +plt.legend() +plt.xlabel("X") +plt.ylabel("y") +_ = plt.title( + ( + f"Initial: {kernel}\nOptimum: {gpr.kernel_}\nLog-Marginal-Likelihood: " + f"{gpr.log_marginal_likelihood(gpr.kernel_.theta)}" + ), + fontsize=8, +) + +# %% +# +# As we hoped, random restarts allow the optimization to find the best set +# of hyperparameters despite the bad initial values. From 0c7073da5c14da35ca2f0cb6310ec2b71c6361d0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Thu, 5 Sep 2024 18:47:43 +0200 Subject: [PATCH 250/275] BLD Add Meson OpenMP checks (#29762) Co-authored-by: Thomas J. Fan Co-authored-by: Olivier Grisel --- azure-pipelines.yml | 5 + .../check-meson-openmp-dependencies.py | 172 ++++++++++++++++++ sklearn/cluster/meson.build | 10 +- .../_hist_gradient_boosting/meson.build | 12 +- .../_pairwise_distances_reduction/meson.build | 4 +- 5 files changed, 190 insertions(+), 13 deletions(-) create mode 100644 build_tools/check-meson-openmp-dependencies.py diff --git a/azure-pipelines.yml b/azure-pipelines.yml index 6348ab2393288..4497436eef0de 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -40,6 +40,11 @@ jobs: - bash: | ./build_tools/linting.sh displayName: Run linters + - bash: | + pip install ninja meson scipy + python build_tools/check-meson-openmp-dependencies.py + displayName: Run Meson OpenMP checks + - template: build_tools/azure/posix.yml parameters: diff --git a/build_tools/check-meson-openmp-dependencies.py b/build_tools/check-meson-openmp-dependencies.py new file mode 100644 index 0000000000000..43a7426494160 --- /dev/null +++ b/build_tools/check-meson-openmp-dependencies.py @@ -0,0 +1,172 @@ +""" +Check that OpenMP dependencies are correctly defined in meson.build files. + +This is based on trying to make sure the the following two things match: +- the Cython files using OpenMP (based on a git grep regex) +- the Cython extension modules that are built with OpenMP compiler flags (based + on meson introspect json output) +""" + +import json +import re +import subprocess +from pathlib import Path + + +def has_source_openmp_flags(target_source): + return any("openmp" in arg for arg in target_source["parameters"]) + + +def has_openmp_flags(target): + """Return whether target sources use OpenMP flags. + + Make sure that both compiler and linker source use OpenMP. + Look at `get_meson_info` docstring to see what `target` looks like. + """ + target_sources = target["target_sources"] + + target_use_openmp_flags = any( + has_source_openmp_flags(target_source) for target_source in target_sources + ) + + if not target_use_openmp_flags: + return False + + # When the target use OpenMP we expect a compiler + linker source and we + # want to make sure that both the compiler and the linker use OpenMP + assert len(target_sources) == 2 + compiler_source, linker_source = target_sources + assert "compiler" in compiler_source + assert "linker" in linker_source + + compiler_use_openmp_flags = any( + "openmp" in arg for arg in compiler_source["parameters"] + ) + linker_use_openmp_flags = any( + "openmp" in arg for arg in linker_source["parameters"] + ) + + assert compiler_use_openmp_flags == linker_use_openmp_flags + return compiler_use_openmp_flags + + +def get_canonical_name_meson(target, build_path): + """Return a name based on generated shared library. + + The goal is to return a name that can be easily matched with the output + from `git_grep_info`. + + Look at `get_meson_info` docstring to see what `target` looks like. + """ + # Expect a list with one element with the name of the shared library + assert len(target["filename"]) == 1 + shared_library_path = Path(target["filename"][0]) + shared_library_relative_path = shared_library_path.relative_to( + build_path.absolute() + ) + # Needed on Windows to match git grep output + rel_path = shared_library_relative_path.as_posix() + # OS-specific naming of the shared library .cpython- on POSIX and + # something like .cp312- on Windows + pattern = r"\.(cpython|cp\d+)-.+" + return re.sub(pattern, "", str(rel_path)) + + +def get_canonical_name_git_grep(filename): + """Return name based on filename. + + The goal is to return a name that can easily be matched with the output + from `get_meson_info`. + """ + return re.sub(r"\.pyx(\.tp)?", "", filename) + + +def get_meson_info(): + """Return names of extension that use OpenMP based on meson introspect output. + + The meson introspect json info is a list of targets where a target is a dict + that looks like this (parts not used in this script are not shown for simplicity): + { + 'name': '_k_means_elkan.cpython-312-x86_64-linux-gnu', + 'filename': [ + '/sklearn/cluster/_k_means_elkan.cpython-312-x86_64-linux-gnu.so' + ], + 'target_sources': [ + { + 'compiler': ['ccache', 'cc'], + 'parameters': [ + '-Wall', + '-std=c11', + '-fopenmp', + ... + ], + ... + }, + { + 'linker': ['cc'], + 'parameters': [ + '-shared', + '-fPIC', + '-fopenmp', + ... + ] + } + ] + } + """ + build_path = Path("build/introspect") + subprocess.check_call(["meson", "setup", build_path, "--reconfigure"]) + + json_out = subprocess.check_output( + ["meson", "introspect", build_path, "--targets"], text=True + ) + target_list = json.loads(json_out) + meson_targets = [target for target in target_list if has_openmp_flags(target)] + + return [get_canonical_name_meson(each, build_path) for each in meson_targets] + + +def get_git_grep_info(): + """Return names of extensions that use OpenMP based on git grep regex.""" + git_grep_filenames = subprocess.check_output( + ["git", "grep", "-lP", "cython.*parallel|_openmp_helpers"], text=True + ).splitlines() + git_grep_filenames = [f for f in git_grep_filenames if ".pyx" in f] + + return [get_canonical_name_git_grep(each) for each in git_grep_filenames] + + +def main(): + from_meson = set(get_meson_info()) + from_git_grep = set(get_git_grep_info()) + + only_in_git_grep = from_git_grep - from_meson + only_in_meson = from_meson - from_git_grep + + msg = "" + if only_in_git_grep: + only_in_git_grep_msg = "\n".join( + [f" {each}" for each in sorted(only_in_git_grep)] + ) + msg += ( + "Some Cython files use OpenMP," + " but their meson.build is missing the openmp_dep dependency:\n" + f"{only_in_git_grep_msg}\n\n" + ) + + if only_in_meson: + only_in_meson_msg = "\n".join([f" {each}" for each in sorted(only_in_meson)]) + msg += ( + "Some Cython files do not use OpenMP," + " you should remove openmp_dep from their meson.build:\n" + f"{only_in_meson_msg}\n\n" + ) + + if from_meson != from_git_grep: + raise ValueError( + f"Some issues have been found in Meson OpenMP dependencies:\n\n{msg}" + ) + + +if __name__ == "__main__": + main() diff --git a/sklearn/cluster/meson.build b/sklearn/cluster/meson.build index afc066797a659..9031d11d56319 100644 --- a/sklearn/cluster/meson.build +++ b/sklearn/cluster/meson.build @@ -5,20 +5,20 @@ cluster_extension_metadata = { {'sources': ['_hierarchical_fast.pyx', metrics_cython_tree], 'override_options': ['cython_language=cpp']}, '_k_means_common': - {'sources': ['_k_means_common.pyx']}, + {'sources': ['_k_means_common.pyx'], 'dependencies': [openmp_dep]}, '_k_means_lloyd': - {'sources': ['_k_means_lloyd.pyx']}, + {'sources': ['_k_means_lloyd.pyx'], 'dependencies': [openmp_dep]}, '_k_means_elkan': - {'sources': ['_k_means_elkan.pyx']}, + {'sources': ['_k_means_elkan.pyx'], 'dependencies': [openmp_dep]}, '_k_means_minibatch': - {'sources': ['_k_means_minibatch.pyx']}, + {'sources': ['_k_means_minibatch.pyx'], 'dependencies': [openmp_dep]}, } foreach ext_name, ext_dict : cluster_extension_metadata py.extension_module( ext_name, [ext_dict.get('sources'), utils_cython_tree], - dependencies: [np_dep, openmp_dep], + dependencies: [np_dep] + ext_dict.get('dependencies', []), override_options : ext_dict.get('override_options', []), cython_args: cython_args, subdir: 'sklearn/cluster', diff --git a/sklearn/ensemble/_hist_gradient_boosting/meson.build b/sklearn/ensemble/_hist_gradient_boosting/meson.build index 70327fb15c3d3..362bd5efb82d5 100644 --- a/sklearn/ensemble/_hist_gradient_boosting/meson.build +++ b/sklearn/ensemble/_hist_gradient_boosting/meson.build @@ -1,9 +1,9 @@ hist_gradient_boosting_extension_metadata = { - '_gradient_boosting': {'sources': ['_gradient_boosting.pyx']}, - 'histogram': {'sources': ['histogram.pyx']}, - 'splitting': {'sources': ['splitting.pyx']}, - '_binning': {'sources': ['_binning.pyx']}, - '_predictor': {'sources': ['_predictor.pyx']}, + '_gradient_boosting': {'sources': ['_gradient_boosting.pyx'], 'dependencies': [openmp_dep]}, + 'histogram': {'sources': ['histogram.pyx'], 'dependencies': [openmp_dep]}, + 'splitting': {'sources': ['splitting.pyx'], 'dependencies': [openmp_dep]}, + '_binning': {'sources': ['_binning.pyx'], 'dependencies': [openmp_dep]}, + '_predictor': {'sources': ['_predictor.pyx'], 'dependencies': [openmp_dep]}, '_bitset': {'sources': ['_bitset.pyx']}, 'common': {'sources': ['common.pyx']}, } @@ -12,7 +12,7 @@ foreach ext_name, ext_dict : hist_gradient_boosting_extension_metadata py.extension_module( ext_name, ext_dict.get('sources'), - dependencies: [openmp_dep], + dependencies: ext_dict.get('dependencies', []), cython_args: cython_args, subdir: 'sklearn/ensemble/_hist_gradient_boosting', install: true diff --git a/sklearn/metrics/_pairwise_distances_reduction/meson.build b/sklearn/metrics/_pairwise_distances_reduction/meson.build index 878b29e869f5e..76760ac271cef 100644 --- a/sklearn/metrics/_pairwise_distances_reduction/meson.build +++ b/sklearn/metrics/_pairwise_distances_reduction/meson.build @@ -39,7 +39,7 @@ _datasets_pair_pyx = custom_target( _datasets_pair = py.extension_module( '_datasets_pair', _datasets_pair_pyx, - dependencies: [np_dep, openmp_dep], + dependencies: [np_dep], override_options: ['cython_language=cpp'], cython_args: cython_args, subdir: 'sklearn/metrics/_pairwise_distances_reduction', @@ -94,7 +94,7 @@ _middle_term_computer_pyx = custom_target( _middle_term_computer = py.extension_module( '_middle_term_computer', _middle_term_computer_pyx, - dependencies: [np_dep, openmp_dep], + dependencies: [np_dep], override_options: ['cython_language=cpp'], cython_args: cython_args, subdir: 'sklearn/metrics/_pairwise_distances_reduction', From 201cfded113b5c5dcde73ebb6b26075028c0c2c9 Mon Sep 17 00:00:00 2001 From: Yao Xiao <108576690+Charlie-XIAO@users.noreply.github.com> Date: Thu, 5 Sep 2024 12:59:53 -0400 Subject: [PATCH 251/275] DOC `GradientBoosting*` will not implement monotonic constraints, use `HistGradientBoosting*` instead (#27516) --- sklearn/ensemble/_gb.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/sklearn/ensemble/_gb.py b/sklearn/ensemble/_gb.py index 18d89b21f5512..0ce85a43bd310 100644 --- a/sklearn/ensemble/_gb.py +++ b/sklearn/ensemble/_gb.py @@ -1124,8 +1124,9 @@ class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting): classification is a special case where only a single regression tree is induced. - :class:`sklearn.ensemble.HistGradientBoostingClassifier` is a much faster - variant of this algorithm for intermediate datasets (`n_samples >= 10_000`). + :class:`~sklearn.ensemble.HistGradientBoostingClassifier` is a much faster variant + of this algorithm for intermediate and large datasets (`n_samples >= 10_000`) and + supports monotonic constraints. Read more in the :ref:`User Guide `. @@ -1727,8 +1728,9 @@ class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting): each stage a regression tree is fit on the negative gradient of the given loss function. - :class:`sklearn.ensemble.HistGradientBoostingRegressor` is a much faster - variant of this algorithm for intermediate datasets (`n_samples >= 10_000`). + :class:`~sklearn.ensemble.HistGradientBoostingRegressor` is a much faster variant + of this algorithm for intermediate and large datasets (`n_samples >= 10_000`) and + supports monotonic constraints. Read more in the :ref:`User Guide `. From 7a50457045daa4351304d88303afe525d9eec45c Mon Sep 17 00:00:00 2001 From: Cailean Carter Date: Thu, 5 Sep 2024 18:34:45 +0100 Subject: [PATCH 252/275] DOC update KNNImputer `metric` parameter by describing the expected interface when passing a callable (#29776) Co-authored-by: Guillaume Lemaitre --- sklearn/impute/_knn.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/sklearn/impute/_knn.py b/sklearn/impute/_knn.py index 59aba2b2f2d38..da5f4a81a7095 100644 --- a/sklearn/impute/_knn.py +++ b/sklearn/impute/_knn.py @@ -56,9 +56,9 @@ class KNNImputer(_BaseImputer): - 'nan_euclidean' - callable : a user-defined function which conforms to the definition - of ``_pairwise_callable(X, Y, metric, **kwds)``. The function - accepts two arrays, X and Y, and a `missing_values` keyword in - `kwds` and returns a scalar distance value. + of ``func_metric(x, y, *, missing_values=np.nan)``. `x` and `y` + corresponds to a row (i.e. 1-D arrays) of `X` and `Y`, respectively. + The callable should returns a scalar distance value. copy : bool, default=True If True, a copy of X will be created. If False, imputation will From f5e981e53597a3eee23a2adefd9619462a519ba0 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Thu, 5 Sep 2024 20:46:39 +0200 Subject: [PATCH 253/275] FIX improve doc and test for HTMLDocumentationLinkMixin (#29774) Co-authored-by: Adrin Jalali --- sklearn/utils/_estimator_html_repr.py | 30 ++++++++--- .../utils/tests/test_estimator_html_repr.py | 54 +++++++++++++++++-- 2 files changed, 73 insertions(+), 11 deletions(-) diff --git a/sklearn/utils/_estimator_html_repr.py b/sklearn/utils/_estimator_html_repr.py index 5e465234f516b..31f3a2b213e01 100644 --- a/sklearn/utils/_estimator_html_repr.py +++ b/sklearn/utils/_estimator_html_repr.py @@ -427,15 +427,31 @@ class _HTMLDocumentationLinkMixin: Examples -------- If the default values for `_doc_link_module`, `_doc_link_template` are not suitable, - then you can override them: + then you can override them and provide a method to generate the URL parameters: >>> from sklearn.base import BaseEstimator - >>> estimator = BaseEstimator() - >>> estimator._doc_link_template = "https://website.com/{single_param}.html" + >>> doc_link_template = "https://address.local/{single_param}.html" >>> def url_param_generator(estimator): ... return {"single_param": estimator.__class__.__name__} - >>> estimator._doc_link_url_param_generator = url_param_generator + >>> class MyEstimator(BaseEstimator): + ... # use "builtins" since it is the associated module when declaring + ... # the class in a docstring + ... _doc_link_module = "builtins" + ... _doc_link_template = doc_link_template + ... _doc_link_url_param_generator = url_param_generator + >>> estimator = MyEstimator() >>> estimator._get_doc_link() - 'https://website.com/BaseEstimator.html' + 'https://address.local/MyEstimator.html' + + If instead of overriding the attributes inside the class definition, you want to + override a class instance, you can use `types.MethodType` to bind the method to the + instance: + >>> import types + >>> estimator = BaseEstimator() + >>> estimator._doc_link_template = doc_link_template + >>> estimator._doc_link_url_param_generator = types.MethodType( + ... url_param_generator, estimator) + >>> estimator._get_doc_link() + 'https://address.local/BaseEstimator.html' """ _doc_link_module = "sklearn" @@ -491,6 +507,4 @@ def _get_doc_link(self): return self._doc_link_template.format( estimator_module=estimator_module, estimator_name=estimator_name ) - return self._doc_link_template.format( - **self._doc_link_url_param_generator(self) - ) + return self._doc_link_template.format(**self._doc_link_url_param_generator()) diff --git a/sklearn/utils/tests/test_estimator_html_repr.py b/sklearn/utils/tests/test_estimator_html_repr.py index d59658998432d..67aff63799ec3 100644 --- a/sklearn/utils/tests/test_estimator_html_repr.py +++ b/sklearn/utils/tests/test_estimator_html_repr.py @@ -1,6 +1,7 @@ import html import locale import re +import types from contextlib import closing from io import StringIO from unittest.mock import patch @@ -443,7 +444,9 @@ def test_html_documentation_link_mixin_sklearn(mock_version): ("prefix.mypackage.mymodule.submodule", "prefix.mypackage.mymodule.submodule"), ], ) -def test_html_documentation_link_mixin_get_doc_link(module_path, expected_module): +def test_html_documentation_link_mixin_get_doc_link_instance( + module_path, expected_module +): """Check the behaviour of the `_get_doc_link` with various parameter.""" class FooBar(_HTMLDocumentationLinkMixin): @@ -459,6 +462,32 @@ class FooBar(_HTMLDocumentationLinkMixin): assert est._get_doc_link() == f"https://website.com/{expected_module}.FooBar.html" +@pytest.mark.parametrize( + "module_path,expected_module", + [ + ("prefix.mymodule", "prefix.mymodule"), + ("prefix._mymodule", "prefix"), + ("prefix.mypackage._mymodule", "prefix.mypackage"), + ("prefix.mypackage._mymodule.submodule", "prefix.mypackage"), + ("prefix.mypackage.mymodule.submodule", "prefix.mypackage.mymodule.submodule"), + ], +) +def test_html_documentation_link_mixin_get_doc_link_class(module_path, expected_module): + """Check the behaviour of the `_get_doc_link` when `_doc_link_module` and + `_doc_link_template` are defined at the class level and not at the instance + level.""" + + class FooBar(_HTMLDocumentationLinkMixin): + _doc_link_module = "prefix" + _doc_link_template = ( + "https://website.com/{estimator_module}.{estimator_name}.html" + ) + + FooBar.__module__ = module_path + est = FooBar() + assert est._get_doc_link() == f"https://website.com/{expected_module}.FooBar.html" + + def test_html_documentation_link_mixin_get_doc_link_out_of_library(): """Check the behaviour of the `_get_doc_link` with various parameter.""" mixin = _HTMLDocumentationLinkMixin() @@ -469,7 +498,7 @@ def test_html_documentation_link_mixin_get_doc_link_out_of_library(): assert mixin._get_doc_link() == "" -def test_html_documentation_link_mixin_doc_link_url_param_generator(): +def test_html_documentation_link_mixin_doc_link_url_param_generator_instance(): mixin = _HTMLDocumentationLinkMixin() # we can bypass the generation by providing our own callable mixin._doc_link_template = ( @@ -482,11 +511,30 @@ def url_param_generator(estimator): "another_variable": "value_2", } - mixin._doc_link_url_param_generator = url_param_generator + mixin._doc_link_url_param_generator = types.MethodType(url_param_generator, mixin) assert mixin._get_doc_link() == "https://website.com/value_1.value_2.html" +def test_html_documentation_link_mixin_doc_link_url_param_generator_class(): + # we can bypass the generation by providing our own callable + + def url_param_generator(estimator): + return { + "my_own_variable": "value_1", + "another_variable": "value_2", + } + + class FooBar(_HTMLDocumentationLinkMixin): + _doc_link_template = ( + "https://website.com/{my_own_variable}.{another_variable}.html" + ) + _doc_link_url_param_generator = url_param_generator + + estimator = FooBar() + assert estimator._get_doc_link() == "https://website.com/value_1.value_2.html" + + @pytest.fixture def set_non_utf8_locale(): """Pytest fixture to set non utf-8 locale during the test. From 5a369dbad825dbadc2952f5725db5e5e3081d115 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 9 Sep 2024 00:39:50 -0700 Subject: [PATCH 254/275] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29811) Co-authored-by: Lock file bot --- ...latest_conda_forge_mkl_linux-64_conda.lock | 62 ++++++------- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 90 +++++++++---------- ...test_conda_mkl_no_openmp_osx-64_conda.lock | 10 +-- ...st_pip_openblas_pandas_linux-64_conda.lock | 6 +- .../pymin_conda_forge_mkl_win-64_conda.lock | 34 +++---- ...nblas_min_dependencies_linux-64_conda.lock | 20 ++--- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 40 ++++----- build_tools/circle/doc_linux-64_conda.lock | 86 +++++++++--------- .../doc_min_dependencies_linux-64_conda.lock | 80 ++++++++--------- 9 files changed, 214 insertions(+), 214 deletions(-) diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index 24f01e6d22063..3579a586bbfa1 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -18,18 +18,23 @@ https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2# https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_0.conda#35e52d19547cb3265a09c49de146a5ae https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793 https://conda.anaconda.org/conda-forge/linux-64/libgcc-14.1.0-h77fa898_1.conda#002ef4463dd1e2b44a94a4ace468f5d2 +https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_2.conda#41b599ed2b02abcfdd84302bff174b23 +https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.3-h5888daf_0.conda#59f4c43bb1b5ef1c71946ff2cbf59524 https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.1.0-h69a702a_1.conda#1efc0ad219877a73ef977af7dbb51f17 https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.1.0-hc5f4f2c_1.conda#10a0cef64b784d6ab6da50ebca4e984d https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-14.1.0-hc0a3c3a_1.conda#9dbb9699ea467983ba8a4ba89b08b066 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.2-hb9d3cd8_0.conda#4d638782050ab6faa27275bed57e9b4e https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.12-h4ab18f5_0.conda#7ed427f0871fd41cb1d9c17727c17589 https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.27-h4bc722e_0.conda#817119e8a21a45d325f65d0d54710052 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.33.1-heb4867d_0.conda#0d3c60291342c0c025db231353376dfb +https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.3-h5888daf_0.conda#6595440079bed734b113de44ffd3cd0a https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3 -https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hd590300_1.conda#aec6c91c7371c26392a06708a73c70e5 +https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hb9d3cd8_2.conda#9566f0bd264fbd463002e759b8a82401 +https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.1.0-hb9d3cd8_2.conda#06f70867945ea6a84d35836af780f1de https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.21-h4bc722e_0.conda#36ce76665bf67f5aac36be7a0d21b7f3 https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda#172bf1cd1ff8629f2b1179945ed45055 -https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.2-h59595ed_0.conda#e7ba12deb7020dd080c6c70e7b6f6a3d +https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda#a1cfcc585f0c42bf8d5546bb1dfb668d https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2#d645c6d2ac96843a2bfaccd2d62b3ac3 https://conda.anaconda.org/conda-forge/linux-64/libgfortran-14.1.0-h69a702a_1.conda#591e631bc1ae62c64f2ab4f66178c097 https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda#d66573916ffcf376178462f1b61c941e @@ -44,8 +49,8 @@ https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.co https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-h4ab18f5_1.conda#57d7dc60e9325e3de37ff8dffd18e814 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-he02047a_1.conda#70caf8bb6cf39a0b6b7efc885f51c0fe -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-hb9d3cd8_3.conda#6c566a46baae794daf34775d41eb180a https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 +https://conda.anaconda.org/conda-forge/linux-64/s2n-1.5.2-h7b32b05_0.conda#daf6322364fe6fc46c515d4d3d0051c2 https://conda.anaconda.org/conda-forge/linux-64/xorg-inputproto-2.3.2-h7f98852_1002.tar.bz2#bcd1b3396ec6960cbc1d2855a9e60b2b https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.1-hd590300_0.conda#b462a33c0be1421532f28bfe8f4a7514 @@ -60,25 +65,23 @@ https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.7.4-h2abdd08_0.conda https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.2.19-haa50ccc_0.conda#00c38c49d0befb632f686cf67ee8c9f5 https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.1.19-h038f3f9_2.conda#6861cab6cddb5d713cb3db95c838d30f https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.1.18-h038f3f9_10.conda#4bf9c8fcf2bb6793c55e5c5758b9b011 +https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hb9d3cd8_2.conda#c63b5e52939e795ba8d26e35d767a843 https://conda.anaconda.org/conda-forge/linux-64/double-conversion-3.3.0-h59595ed_0.conda#c2f83a5ddadadcdb08fe05863295ee97 -https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.2-h59595ed_0.conda#53fb86322bdb89496d7579fe3f02fd61 https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-he1b5a44_1004.tar.bz2#cddaf2c63ea4a5901cf09524c490ecdc https://conda.anaconda.org/conda-forge/linux-64/gmp-6.3.0-hac33072_2.conda#c94a5994ef49749880a8139cf9afcbe1 https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h59595ed_1003.conda#f87c7b7c2cb45f323ffbce941c78ab7c https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda#8b189310083baabfb622af68fd9d3ae3 https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2#76bbff344f0134279f225174e9064c8f https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240116.2-cxx17_he02047a_1.conda#c48fc56ec03229f294176923c3265c05 -https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hd590300_1.conda#f07002e225d7a60a694d42a7bf5ff53f -https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.1.0-hd590300_1.conda#5fc11c6020d421960607d821310fcd4d https://conda.anaconda.org/conda-forge/linux-64/libcrc32c-1.1.2-h9c3ff4c_0.tar.bz2#c965a5aa0d5c1c37ffc62dff36e28400 https://conda.anaconda.org/conda-forge/linux-64/libdrm-2.4.123-hb9d3cd8_0.conda#ee605e794bdc14e2b7f84c4faa0d8c2c https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20191231-he28a2e2_2.tar.bz2#4d331e44109e3f0e19b4cb8f9b82f3e1 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https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e @@ -40,12 +40,12 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.2-py311h06a4308_0.conda#eff3 # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b # pip iniconfig @ https://files.pythonhosted.org/packages/ef/a6/62565a6e1cf69e10f5727360368e451d4b7f58beeac6173dc9db836a5b46/iniconfig-2.0.0-py3-none-any.whl#sha256=b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374 # pip joblib @ https://files.pythonhosted.org/packages/91/29/df4b9b42f2be0b623cbd5e2140cafcaa2bef0759a00b7b70104dcfe2fb51/joblib-1.4.2-py3-none-any.whl#sha256=06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6 -# pip kiwisolver @ https://files.pythonhosted.org/packages/17/ba/17a706b232308e65f57deeccae503c268292e6a091313f6ce833a23093ea/kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=040c1aebeda72197ef477a906782b5ab0d387642e93bda547336b8957c61022e +# pip kiwisolver @ https://files.pythonhosted.org/packages/a7/4b/2db7af3ed3af7c35f388d5f53c28e155cd402a55432d800c543dc6deb731/kiwisolver-1.4.7-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=18077b53dc3bb490e330669a99920c5e6a496889ae8c63b58fbc57c3d7f33a18 # pip markupsafe @ https://files.pythonhosted.org/packages/97/18/c30da5e7a0e7f4603abfc6780574131221d9148f323752c2755d48abad30/MarkupSafe-2.1.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b91c037585eba9095565a3556f611e3cbfaa42ca1e865f7b8015fe5c7336d5a5 # pip meson @ https://files.pythonhosted.org/packages/b7/33/513a9ca4fd5892463abb38592105b78fd425214f7983033633e2e48cbd30/meson-1.5.1-py3-none-any.whl#sha256=5531e24e6cfd6000bf1c712793cf28dff032841370b1a3b941a894e4fde46e5a # pip networkx @ https://files.pythonhosted.org/packages/38/e9/5f72929373e1a0e8d142a130f3f97e6ff920070f87f91c4e13e40e0fba5a/networkx-3.3-py3-none-any.whl#sha256=28575580c6ebdaf4505b22c6256a2b9de86b316dc63ba9e93abde3d78dfdbcf2 # pip ninja @ https://files.pythonhosted.org/packages/6d/92/8d7aebd4430ab5ff65df2bfee6d5745f95c004284db2d8ca76dcbfd9de47/ninja-1.11.1.1-py2.py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl#sha256=84502ec98f02a037a169c4b0d5d86075eaf6afc55e1879003d6cab51ced2ea4b -# pip numpy @ https://files.pythonhosted.org/packages/7b/93/831b4c5b4355210827b3de34f539297e1833c39a68c26a8b454d8cf9f5ed/numpy-2.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=f5ebbf9fbdabed208d4ecd2e1dfd2c0741af2f876e7ae522c2537d404ca895c3 +# pip numpy @ https://files.pythonhosted.org/packages/d9/37/108d692f7e2544b9ae972c7bfa06c26717871c273ccec86470bc3132b04d/numpy-2.1.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=d51fc141ddbe3f919e91a096ec739f49d686df8af254b2053ba21a910ae518bf # pip packaging @ https://files.pythonhosted.org/packages/08/aa/cc0199a5f0ad350994d660967a8efb233fe0416e4639146c089643407ce6/packaging-24.1-py3-none-any.whl#sha256=5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124 # pip pillow @ https://files.pythonhosted.org/packages/ba/e5/8c68ff608a4203085158cff5cc2a3c534ec384536d9438c405ed6370d080/pillow-10.4.0-cp311-cp311-manylinux_2_28_x86_64.whl#sha256=76a911dfe51a36041f2e756b00f96ed84677cdeb75d25c767f296c1c1eda1319 # pip pluggy @ https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl#sha256=44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669 diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock index dafb9c14c58df..efe058b97f331 100644 --- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock @@ -8,13 +8,11 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed3 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2024.2.1-h57928b3_1083.conda#2d89243bfb53652c182a7c73182cce4f -https://conda.anaconda.org/conda-forge/win-64/libexpat-2.6.2-h63175ca_0.conda#bc592d03f62779511d392c175dcece64 https://conda.anaconda.org/conda-forge/win-64/mkl-include-2024.1.0-h66d3029_694.conda#1f80971a50e69c1f7af15707619df49e https://conda.anaconda.org/conda-forge/win-64/msys2-conda-epoch-20160418-1.tar.bz2#b0309b72560df66f71a9d5e34a5efdfa https://conda.anaconda.org/conda-forge/win-64/python_abi-3.9-5_cp39.conda#86ba1bbcf9b259d1592201f3c345c810 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h8827d51_1.conda#8bfdead4e0fff0383ae4c9c50d0531bd https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.22621.0-h57928b3_0.tar.bz2#72608f6cd3e5898229c3ea16deb1ac43 -https://conda.anaconda.org/conda-forge/win-64/expat-2.6.2-h63175ca_0.conda#52f9dec6758ceb8ce0ea8af9fa13eb1a https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 https://conda.anaconda.org/conda-forge/win-64/m2w64-gmp-6.1.0-2.tar.bz2#53a1c73e1e3d185516d7e3af177596d9 https://conda.anaconda.org/conda-forge/win-64/m2w64-libwinpthread-git-5.0.0.4634.697f757-2.tar.bz2#774130a326dee16f1ceb05cc687ee4f0 @@ -28,25 +26,27 @@ 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https://conda.anaconda.org/conda-forge/win-64/libiconv-1.17-hcfcfb64_2.conda#e1eb10b1cca179f2baa3601e4efc8712 https://conda.anaconda.org/conda-forge/win-64/libjpeg-turbo-3.0.0-hcfcfb64_1.conda#3f1b948619c45b1ca714d60c7389092c -https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.46.0-h2466b09_0.conda#951b0a3a463932e17414cd9f047fa03d +https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.46.1-h2466b09_0.conda#8a7c1ad01f58623bfbae8d601db7cf3b https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.4.0-hcfcfb64_0.conda#abd61d0ab127ec5cd68f62c2969e6f34 https://conda.anaconda.org/conda-forge/win-64/libzlib-1.3.1-h2466b09_1.conda#d4483ca8afc57ddf1f6dded53b36c17f https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libgfortran-5.3.0-6.tar.bz2#066552ac6b907ec6d72c0ddab29050dc https://conda.anaconda.org/conda-forge/win-64/ninja-1.12.1-hc790b64_0.conda#a557dde55343e03c68cd7e29e7f87279 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-https://conda.anaconda.org/conda-forge/win-64/libbrotlidec-1.1.0-hcfcfb64_1.conda#19ce3e1dacc7912b3d6ff40690ba9ae0 -https://conda.anaconda.org/conda-forge/win-64/libbrotlienc-1.1.0-hcfcfb64_1.conda#71e890a0b361fd58743a13f77e1506b7 +https://conda.anaconda.org/conda-forge/win-64/libbrotlidec-1.1.0-h2466b09_2.conda#9bae75ce723fa34e98e239d21d752a7e +https://conda.anaconda.org/conda-forge/win-64/libbrotlienc-1.1.0-h2466b09_2.conda#85741a24d97954a991e55e34bc55990b https://conda.anaconda.org/conda-forge/win-64/libintl-0.22.5-h5728263_3.conda#2cf0cf76cc15d360dfa2f17fd6cf9772 https://conda.anaconda.org/conda-forge/win-64/libpng-1.6.43-h19919ed_0.conda#77e398acc32617a0384553aea29e866b https://conda.anaconda.org/conda-forge/win-64/libxml2-2.12.7-h0f24e4e_4.conda#ed4d301f0d2149b34deb9c4fecafd836 @@ -55,17 +55,17 @@ https://conda.anaconda.org/conda-forge/win-64/pcre2-10.44-h3d7b363_2.conda#a3a3b https://conda.anaconda.org/conda-forge/win-64/python-3.9.19-h4de0772_0_cpython.conda#b6999bc275e0e6beae7b1c8ea0be1e85 https://conda.anaconda.org/conda-forge/win-64/zlib-1.3.1-h2466b09_1.conda#f8e0a35bf6df768ad87ed7bbbc36ab04 https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.6-h0ea2cb4_0.conda#9a17230f95733c04dc40a2b1e5491d74 -https://conda.anaconda.org/conda-forge/win-64/brotli-bin-1.1.0-hcfcfb64_1.conda#0105229d7c5fabaa840043a86c10ec64 -https://conda.anaconda.org/conda-forge/noarch/certifi-2024.7.4-pyhd8ed1ab_0.conda#24e7fd6ca65997938fff9e5ab6f653e4 +https://conda.anaconda.org/conda-forge/win-64/brotli-bin-1.1.0-h2466b09_2.conda#d22534a9be5771fc58eb7564947f669d +https://conda.anaconda.org/conda-forge/noarch/certifi-2024.8.30-pyhd8ed1ab_0.conda#12f7d00853807b0531775e9be891cb11 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99 https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda#5cd86562580f274031ede6aa6aa24441 -https://conda.anaconda.org/conda-forge/win-64/cython-3.0.11-py39ha51f57c_1.conda#2de4603ad5a9676c698f9709f1428c7a +https://conda.anaconda.org/conda-forge/win-64/cython-3.0.11-py39ha51f57c_2.conda#a84fdf498fa757da15a1bd0dd967f8ed https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_0.conda#d02ae936e42063ca46af6cdad2dbd1e0 https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_0.conda#15dda3cdbf330abfe9f555d22f66db46 https://conda.anaconda.org/conda-forge/win-64/freetype-2.12.1-hdaf720e_2.conda#3761b23693f768dc75a8fd0a73ca053f https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda#f800d2da156d08e289b14e87e43c1ae5 -https://conda.anaconda.org/conda-forge/win-64/kiwisolver-1.4.5-py39h2b77a98_2.conda#c5cd596a9db4d6790a2548ac8a205b21 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https://conda.anaconda.org/conda-forge/linux-64/xcb-util-cursor-0.1.4-h4ab18f5_2.conda#79e46d4a6ccecb7ee1912042958a8758 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxi-1.7.10-h4bc722e_1.conda#749baebe7e2ff3360630e069175e528b https://conda.anaconda.org/conda-forge/linux-64/xorg-libxxf86vm-1.1.5-h4bc722e_1.conda#0c90ad87101001080484b91bd9d2cdef -https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_1.conda#d8d07866ac3b5b6937213c89a1874f08 +https://conda.anaconda.org/conda-forge/linux-64/compilers-1.8.0-ha770c72_0.conda#e08e569c1b7e923654d1fe9e76dadb3d https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-9.0.0-hda332d3_1.conda#76b32dcf243444aea9c6b804bcfa40b8 https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.4-pyhd8ed1ab_0.conda#c62e775953b6b65f2079c9ee2a62813c https://conda.anaconda.org/conda-forge/noarch/lazy-loader-0.4-pyhd8ed1ab_1.conda#4809b9f4c6ce106d443c3f90b8e10db2 @@ -228,9 +228,9 @@ 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+https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.0-py39h74842e3_1.conda#7f97d682c5c7fdd49a8ddd995636a6ad https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py39h9d013fb_3.conda#f3bcbaa497af215e86d966244d683289 https://conda.anaconda.org/conda-forge/noarch/imageio-2.35.1-pyh12aca89_0.conda#b03ff3631329c8ef17bae35d2bb216f7 https://conda.anaconda.org/conda-forge/noarch/lazy_loader-0.4-pyhd8ed1ab_1.conda#ec6f70b8a5242936567d4f886726a372 @@ -289,7 +289,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip send2trash @ https://files.pythonhosted.org/packages/40/b0/4562db6223154aa4e22f939003cb92514c79f3d4dccca3444253fd17f902/Send2Trash-1.8.3-py3-none-any.whl#sha256=0c31227e0bd08961c7665474a3d1ef7193929fedda4233843689baa056be46c9 # pip sniffio @ https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl#sha256=2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2 # pip traitlets @ https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl#sha256=b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f -# pip types-python-dateutil @ https://files.pythonhosted.org/packages/45/ba/2a4750156272f180f8209f87656ae92e0aeb14f9864976aa90cbd9f21eda/types_python_dateutil-2.9.0.20240821-py3-none-any.whl#sha256=f5889fcb4e63ed4aaa379b44f93c32593d50b9a94c9a60a0c854d8cc3511cd57 +# pip types-python-dateutil @ https://files.pythonhosted.org/packages/aa/4c/5c684b333135a6fb085bb5a5bdfd962937f80bec06745a88fd551e29f4d9/types_python_dateutil-2.9.0.20240906-py3-none-any.whl#sha256=27c8cc2d058ccb14946eebcaaa503088f4f6dbc4fb6093d3d456a49aef2753f6 # pip uri-template @ https://files.pythonhosted.org/packages/e7/00/3fca040d7cf8a32776d3d81a00c8ee7457e00f80c649f1e4a863c8321ae9/uri_template-1.3.0-py3-none-any.whl#sha256=a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363 # pip webcolors @ https://files.pythonhosted.org/packages/f0/33/12020ba99beaff91682b28dc0bbf0345bbc3244a4afbae7644e4fa348f23/webcolors-24.8.0-py3-none-any.whl#sha256=fc4c3b59358ada164552084a8ebee637c221e4059267d0f8325b3b560f6c7f0a # pip webencodings @ https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl#sha256=a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78 @@ -311,9 +311,9 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip jsonschema-specifications @ https://files.pythonhosted.org/packages/ee/07/44bd408781594c4d0a027666ef27fab1e441b109dc3b76b4f836f8fd04fe/jsonschema_specifications-2023.12.1-py3-none-any.whl#sha256=87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c # pip jupyter-client @ https://files.pythonhosted.org/packages/cf/d3/c4bb02580bc0db807edb9a29b2d0c56031be1ef0d804336deb2699a470f6/jupyter_client-8.6.2-py3-none-any.whl#sha256=50cbc5c66fd1b8f65ecb66bc490ab73217993632809b6e505687de18e9dea39f # pip jupyter-server-terminals @ https://files.pythonhosted.org/packages/07/2d/2b32cdbe8d2a602f697a649798554e4f072115438e92249624e532e8aca6/jupyter_server_terminals-0.5.3-py3-none-any.whl#sha256=41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa -# pip jupyterlite-core @ https://files.pythonhosted.org/packages/e1/e5/87d8eea7c9bdcbcc903481fe83fd9b674917b7156f802a9d8fb2bb603f79/jupyterlite_core-0.4.0-py3-none-any.whl#sha256=5035240760c58a52fb035ad5de3d4ab180817c12a7245a5a18ea6a84f51a752e +# pip jupyterlite-core @ https://files.pythonhosted.org/packages/5c/da/20f28adfa38f3a89b69fc3fa803591c5016b2ca905fad8c25954067fea92/jupyterlite_core-0.4.1-py3-none-any.whl#sha256=62dba3adf1e379596c629226389353992804cd8d2c5907dcddee06218c6d8cdd # pip jsonschema @ https://files.pythonhosted.org/packages/69/4a/4f9dbeb84e8850557c02365a0eee0649abe5eb1d84af92a25731c6c0f922/jsonschema-4.23.0-py3-none-any.whl#sha256=fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566 -# pip jupyterlite-pyodide-kernel @ https://files.pythonhosted.org/packages/2e/dd/b4dfb2ffc0c67e1547946a075af4e1408c52f65fb6b537ba1dcfb5d7dce6/jupyterlite_pyodide_kernel-0.4.1-py3-none-any.whl#sha256=3fd496c31a9da5aed5c2bb2e3e0e61d0a58fd3f6f74f87411950e2b28132930f +# pip jupyterlite-pyodide-kernel @ https://files.pythonhosted.org/packages/9a/38/8d94eb15014a8c1107128b8bfb88101f28b39628eee5cdc2daacbe92b82e/jupyterlite_pyodide_kernel-0.4.2-py3-none-any.whl#sha256=d78fd12f1ac08eb98c55b476275b53e7d011fb46a01c631ed182da3f00d5895a # pip jupyter-events @ https://files.pythonhosted.org/packages/a5/94/059180ea70a9a326e1815176b2370da56376da347a796f8c4f0b830208ef/jupyter_events-0.10.0-py3-none-any.whl#sha256=4b72130875e59d57716d327ea70d3ebc3af1944d3717e5a498b8a06c6c159960 # pip nbformat @ https://files.pythonhosted.org/packages/a9/82/0340caa499416c78e5d8f5f05947ae4bc3cba53c9f038ab6e9ed964e22f1/nbformat-5.10.4-py3-none-any.whl#sha256=3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b # pip nbclient @ https://files.pythonhosted.org/packages/66/e8/00517a23d3eeaed0513e718fbc94aab26eaa1758f5690fc8578839791c79/nbclient-0.10.0-py3-none-any.whl#sha256=f13e3529332a1f1f81d82a53210322476a168bb7090a0289c795fe9cc11c9d3f diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index fc8019728a181..2779e25b8a454 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ 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https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.12-h4ab18f5_0.conda#7ed427f0871fd41cb1d9c17727c17589 https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2#d9c69a24ad678ffce24c6543a0176b00 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda#418c6ca5929a611cbd69204907a83995 +https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.3-h5888daf_0.conda#6595440079bed734b113de44ffd3cd0a https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.22.5-he02047a_3.conda#fcd2016d1d299f654f81021e27496818 https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.2-hd590300_0.conda#3bf7b9fd5a7136126e0234db4b87c8b6 https://conda.anaconda.org/conda-forge/linux-64/jxrlib-1.1-hd590300_3.conda#5aeabe88534ea4169d4c49998f293d6c 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https://conda.anaconda.org/conda-forge/linux-64/setuptools-59.8.0-py39hf3d152e_1.tar.bz2#4252d0c211566a9f65149ba7f6e87aa4 https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2 https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-2.2.0-pyhd8ed1ab_0.tar.bz2#4d22a9315e78c6827f806065957d566e @@ -201,19 +202,18 @@ https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.1-pyhd8ed1ab_0.conda#74a https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.5-pyhd8ed1ab_0.conda#1bb1ef9806a9a20872434f58b3e7fc1a https://conda.anaconda.org/conda-forge/noarch/babel-2.14.0-pyhd8ed1ab_0.conda#9669586875baeced8fc30c0826c3270e https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.12.3-pyha770c72_0.conda#332493000404d8411859539a5a630865 -https://conda.anaconda.org/conda-forge/linux-64/brunsli-0.1-h9c3ff4c_0.tar.bz2#c1ac6229d0bfd14f8354ff9ad2a26cad 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https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.3-h315aac3_2.conda#00e0da7e4fceb5449f3ddd2bf6b2c351 https://conda.anaconda.org/conda-forge/noarch/h2-4.1.0-pyhd8ed1ab_0.tar.bz2#b748fbf7060927a6e82df7cb5ee8f097 https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.4.0-pyha770c72_0.conda#6e3dbc422d3749ad72659243d6ac8b2b https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda#7b86ecb7d3557821c649b3c31e3eb9f2 https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp15-15.0.7-default_h127d8a8_5.conda#d0a9633b53cdc319b8a1a532ae7822b8 -https://conda.anaconda.org/conda-forge/linux-64/libclang13-18.1.8-default_h9def88c_3.conda#8b10a801bd45383d6e0dd286c6814238 +https://conda.anaconda.org/conda-forge/linux-64/libclang13-18.1.8-default_h9def88c_4.conda#7e3f831d4ae9820999418821be65ff67 https://conda.anaconda.org/conda-forge/linux-64/libflac-1.4.3-h59595ed_0.conda#ee48bf17cc83a00f59ca1494d5646869 https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.50-h4f305b6_0.conda#0d7ff1a8e69565ca3add6925e18e708f https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.7.0-h2c5496b_1.conda#e2eaefa4de2b7237af7c907b8bbc760a @@ -227,10 +227,10 @@ https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1a https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.2-pyhd8ed1ab_0.conda#e010a224b90f1f623a917c35addbb924 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb -https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.12.0-h84d6215_4.conda#1fa72fdeb88f538018612ce2ed9fc789 +https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.13.0-h84d6215_0.conda#ee6f7fd1e76061ef1fa307d41fa86a96 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxxf86vm-1.1.5-h4bc722e_1.conda#0c90ad87101001080484b91bd9d2cdef -https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_1.conda#d8d07866ac3b5b6937213c89a1874f08 -https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.6-haf2f30d_0.conda#a15d7b21e4b7b82b87ba04c3b46c1317 +https://conda.anaconda.org/conda-forge/linux-64/compilers-1.8.0-ha770c72_0.conda#e08e569c1b7e923654d1fe9e76dadb3d +https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.7-hf3bb09a_0.conda#c78bc4ef0afb3cd2365d9973c71fc876 https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-9.0.0-hfac3d4d_0.conda#c7b47c64af53e8ecee01d101eeab2342 https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-8.4.0-hd8ed1ab_0.conda#01b7411c765c3d863dcc920207f258bd https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-1.11.0-h4ab18f5_1.conda#14858a47d4cc995892e79f2b340682d7 @@ -239,9 +239,9 @@ https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.c https://conda.anaconda.org/conda-forge/linux-64/mkl-2024.1.0-ha957f24_693.conda#ff0f4abf6f94e36a918f1ef4dbeb9769 https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5.conda#93aff412f3e49fdb43361c0215cbd72d https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_0.conda#b39568655c127a9c4a44d178ac99b6d0 -https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py39h623c9ba_0.conda#a19d023682384c637cb356d270c276c0 +https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py39h08a7858_1.conda#cd9fa334e11886738f17254f52210bc3 https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.8.0-pyhd8ed1ab_0.conda#bf68bf9ff9a18f1b17aa8c817225aee0 -https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.6-hbaaba92_0.conda#b22ffc80ac9af846df60b2640c98fea4 +https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.7-h0a52356_0.conda#d368425fbd031a2f8e801a40c3415c72 https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-23_linux64_mkl.conda#5bdaf561cf48f95093dedaa665083874 https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-256.5-hb6d7363_0.conda#3b3912077a5515b2a39bda92008bc2c3 https://conda.anaconda.org/conda-forge/linux-64/mkl-devel-2024.1.0-ha770c72_693.conda#7f422e2cf549a3fb920c95288393870d From 0c87b51a684ee88ce2f963258f263772cf81d4f2 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 9 Sep 2024 00:40:55 -0700 Subject: [PATCH 255/275] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#29808) Co-authored-by: Lock file bot --- .../azure/pylatest_pip_scipy_dev_linux-64_conda.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index 84da2623cd7de..9da78736760b2 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -11,11 +11,11 @@ https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-11.2.0-h1234567_1.cond https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d281e9c2192cb3fa425655a8defb85 https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464 https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h5eee18b_6.conda#f21a3ff51c1b271977f53ce956a69297 -https://repo.anaconda.com/pkgs/main/linux-64/expat-2.6.2-h6a678d5_0.conda#55049db2772dae035f6b8a95f72b5970 +https://repo.anaconda.com/pkgs/main/linux-64/expat-2.6.3-h6a678d5_0.conda#5e184279ccb8b85331093305cb548f5c https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_1.conda#70646cc713f0c43926cfdcfe9b695fe0 https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.41.5-h5eee18b_0.conda#4a6a2354414c9080327274aa514e5299 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c -https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.14-h5eee18b_0.conda#37b6dad6aa49000a4230a9f0cad172f6 +https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.15-h5eee18b_0.conda#019e501b69841c6d4aeaef3b8619a678 https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_1.conda#1562802f843297ee776a50b9329597ed https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e @@ -40,7 +40,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.2-py312h06a4308_0.conda#798c # pip meson @ https://files.pythonhosted.org/packages/b7/33/513a9ca4fd5892463abb38592105b78fd425214f7983033633e2e48cbd30/meson-1.5.1-py3-none-any.whl#sha256=5531e24e6cfd6000bf1c712793cf28dff032841370b1a3b941a894e4fde46e5a # pip ninja @ https://files.pythonhosted.org/packages/6d/92/8d7aebd4430ab5ff65df2bfee6d5745f95c004284db2d8ca76dcbfd9de47/ninja-1.11.1.1-py2.py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl#sha256=84502ec98f02a037a169c4b0d5d86075eaf6afc55e1879003d6cab51ced2ea4b # pip packaging @ https://files.pythonhosted.org/packages/08/aa/cc0199a5f0ad350994d660967a8efb233fe0416e4639146c089643407ce6/packaging-24.1-py3-none-any.whl#sha256=5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124 -# pip platformdirs @ https://files.pythonhosted.org/packages/68/13/2aa1f0e1364feb2c9ef45302f387ac0bd81484e9c9a4c5688a322fbdfd08/platformdirs-4.2.2-py3-none-any.whl#sha256=2d7a1657e36a80ea911db832a8a6ece5ee53d8de21edd5cc5879af6530b1bfee +# pip platformdirs @ https://files.pythonhosted.org/packages/da/8b/d497999c4017b80678017ddce745cf675489c110681ad3c84a55eddfd3e7/platformdirs-4.3.2-py3-none-any.whl#sha256=eb1c8582560b34ed4ba105009a4badf7f6f85768b30126f351328507b2beb617 # pip pluggy @ https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl#sha256=44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669 # pip pygments @ https://files.pythonhosted.org/packages/f7/3f/01c8b82017c199075f8f788d0d906b9ffbbc5a47dc9918a945e13d5a2bda/pygments-2.18.0-py3-none-any.whl#sha256=b8e6aca0523f3ab76fee51799c488e38782ac06eafcf95e7ba832985c8e7b13a # pip six @ https://files.pythonhosted.org/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl#sha256=8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254 From cc09359868665f77eb86a4fb9438b72cad25daa5 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 9 Sep 2024 00:41:40 -0700 Subject: [PATCH 256/275] :lock: :robot: CI Update lock files for cirrus-arm CI build(s) :lock: :robot: (#29809) Co-authored-by: Lock file bot --- ...pymin_conda_forge_linux-aarch64_conda.lock | 34 +++++++++---------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock index 7b73c2ff406ce..cef592f5fa906 100644 --- a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock +++ b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock @@ -14,15 +14,19 @@ https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-aarch64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#98a1185182fec3c434069fa74e6473d6 https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-14.1.0-he277a41_1.conda#2cb475709e327bb76f74645784582e6a +https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlicommon-1.1.0-h86ecc28_2.conda#3ee026955c688f551a9999840cff4c67 +https://conda.anaconda.org/conda-forge/linux-aarch64/libexpat-2.6.3-h5ad3122_0.conda#1d2b842bb76e268625e8ee8d0a9fe8c3 https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-ng-14.1.0-he9431aa_1.conda#842a1a0cf6f995091734a723e5d291ef https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran5-14.1.0-h9420597_1.conda#f30cf31e474062ea51481d4181ee15df https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-14.1.0-h3f4de04_1.conda#6c2afef2109372440a90c566bcb6391c +https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.3.2-h86ecc28_0.conda#9e1e477b3f8ee3789297883faffa708b https://conda.anaconda.org/conda-forge/linux-aarch64/alsa-lib-1.2.12-h68df207_0.conda#65448d015f05afb3c68ea92d0483a466 https://conda.anaconda.org/conda-forge/linux-aarch64/bzip2-1.0.8-h68df207_7.conda#56398c28220513b9ea13d7b450acfb20 +https://conda.anaconda.org/conda-forge/linux-aarch64/expat-2.6.3-h5ad3122_0.conda#901a44b341632b0c233756ed5abcd78b https://conda.anaconda.org/conda-forge/linux-aarch64/keyutils-1.6.1-h4e544f5_0.tar.bz2#1f24853e59c68892452ef94ddd8afd4b -https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlicommon-1.1.0-h31becfc_1.conda#1b219fd801eddb7a94df5bd001053ad9 +https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlidec-1.1.0-h86ecc28_2.conda#e64d0f3b59c7c4047446b97a8624a72d +https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlienc-1.1.0-h86ecc28_2.conda#0e9bd365480c72b25c71a448257b537d https://conda.anaconda.org/conda-forge/linux-aarch64/libdeflate-1.21-h68df207_0.conda#806c74df6dcf96adea47c7829b264f80 -https://conda.anaconda.org/conda-forge/linux-aarch64/libexpat-2.6.2-h2f0025b_0.conda#1b9f46b804a2c3c5d7fd6a80b77c35f9 https://conda.anaconda.org/conda-forge/linux-aarch64/libffi-3.4.2-h3557bc0_5.tar.bz2#dddd85f4d52121fab0a8b099c5e06501 https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran-14.1.0-he9431aa_1.conda#c0b5e52811ae0997f9df25a99846eb9e https://conda.anaconda.org/conda-forge/linux-aarch64/libiconv-1.17-h31becfc_2.conda#9a8eb13f14de7d761555a98712e6df65 @@ -35,7 +39,6 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/libwebp-base-1.4.0-h31becfc https://conda.anaconda.org/conda-forge/linux-aarch64/libxcrypt-4.4.36-h31becfc_1.conda#b4df5d7d4b63579d081fd3a4cf99740e https://conda.anaconda.org/conda-forge/linux-aarch64/libzlib-1.3.1-h68df207_1.conda#b13fb82f88902e34dd0638cd7d378c21 https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.5-hcccb83c_1.conda#91d49c85cacd92caa40cf375ef72a25d -https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.3.1-h86ecc28_3.conda#7f591390401ad65781372240424ab7fc https://conda.anaconda.org/conda-forge/linux-aarch64/pthread-stubs-0.4-hb9de7d4_1001.tar.bz2#d0183ec6ce0b5aaa3486df25fa5f0ded https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-kbproto-1.0.7-h3557bc0_1002.tar.bz2#ec8ce6b3dac3945a4010559a6284b755 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libice-1.1.1-h7935292_0.conda#025968e2637bca910b9b3e7f6743beff @@ -45,18 +48,16 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-renderproto-0.11.1-h35 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-xextproto-7.3.0-h2a766a3_1003.conda#32de1e4422c986e3b6eff59e7edc4d04 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-xproto-7.0.31-h3557bc0_1007.tar.bz2#987e98faa0ad2c667bbea6b6aae260bc https://conda.anaconda.org/conda-forge/linux-aarch64/xz-5.2.6-h9cdd2b7_0.tar.bz2#83baad393a31d59c20b63ba4da6592df +https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-bin-1.1.0-h86ecc28_2.conda#7d48b185fe1f722f8cda4539bb931f85 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https://conda.anaconda.org/conda-forge/linux-aarch64/libedit-3.1.20191231-he28a2e2_2.tar.bz2#29371161d77933a54fccf1bb66b96529 https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran-ng-14.1.0-he9431aa_1.conda#494514d173c7a4eb00957dc203b4d784 https://conda.anaconda.org/conda-forge/linux-aarch64/libpng-1.6.43-h194ca79_0.conda#1123e504d9254dd9494267ab9aba95f0 -https://conda.anaconda.org/conda-forge/linux-aarch64/libsqlite-3.46.0-hf51ef55_0.conda#a8ae63fd6fb7d007f74ef3df95e5edf3 +https://conda.anaconda.org/conda-forge/linux-aarch64/libsqlite-3.46.1-hc4a20ef_0.conda#cd559337c1bd9545ecbeaad017e7d878 https://conda.anaconda.org/conda-forge/linux-aarch64/libxcb-1.16-h57736b2_1.conda#8d502f235bf4f3ce1f288cb1ff3a90b6 https://conda.anaconda.org/conda-forge/linux-aarch64/mysql-common-8.3.0-h940b476_5.conda#f027f6c56a5ee03d21e6e32c963e2fbd https://conda.anaconda.org/conda-forge/linux-aarch64/ninja-1.12.1-h70be974_0.conda#216635cea46498d8045c7cf0f03eaf72 @@ -69,7 +70,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/wayland-1.23.1-h698ed42_0.c https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libsm-1.2.4-h5a01bc2_0.conda#d788eca20ecd63bad8eea7219e5c5fb7 https://conda.anaconda.org/conda-forge/linux-aarch64/zlib-1.3.1-h68df207_1.conda#6031f9e32654fbdb9fdba406ab980517 https://conda.anaconda.org/conda-forge/linux-aarch64/zstd-1.5.6-h02f22dd_0.conda#be8d5f8cf21aed237b8b182ea86b3dd6 -https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-bin-1.1.0-h31becfc_1.conda#9e4a13596ab651ea8d77aae023d0ce3f +https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-1.1.0-h86ecc28_2.conda#5094acc34eb173f74205c0b55f0dd4a4 https://conda.anaconda.org/conda-forge/linux-aarch64/freetype-2.12.1-hf0a5ef3_2.conda#a5ab74c5bd158c3d5532b66d8d83d907 https://conda.anaconda.org/conda-forge/linux-aarch64/krb5-1.21.3-h50a48e9_0.conda#29c10432a2ca1472b53f299ffb2ffa37 https://conda.anaconda.org/conda-forge/linux-aarch64/libglib-2.80.3-haee52c6_2.conda#937a787ab5789a1e0c818b9545b6deb9 @@ -85,18 +86,17 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/xcb-util-keysyms-0.4.1-h5c7 https://conda.anaconda.org/conda-forge/linux-aarch64/xcb-util-renderutil-0.3.10-h5c728e9_0.conda#7beeda4223c5484ef72d89fb66b7e8c1 https://conda.anaconda.org/conda-forge/linux-aarch64/xcb-util-wm-0.4.2-h5c728e9_0.conda#f14dcda6894722e421da2b7dcffb0b78 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libx11-1.8.9-h08be655_1.conda#66470f69e83673153ef02a2ebc018915 -https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-1.1.0-h31becfc_1.conda#e41f5862ac746428407f3fd44d2ed01f https://conda.anaconda.org/conda-forge/linux-aarch64/ccache-4.10.1-ha3bccff_0.conda#7cd24a038d2727b5e6377975237a6cfa -https://conda.anaconda.org/conda-forge/noarch/certifi-2024.7.4-pyhd8ed1ab_0.conda#24e7fd6ca65997938fff9e5ab6f653e4 +https://conda.anaconda.org/conda-forge/noarch/certifi-2024.8.30-pyhd8ed1ab_0.conda#12f7d00853807b0531775e9be891cb11 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99 https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda#5cd86562580f274031ede6aa6aa24441 -https://conda.anaconda.org/conda-forge/linux-aarch64/cython-3.0.11-py39h7dbf29c_1.conda#17fd68c0aa4dbeb08303742cebc896fd +https://conda.anaconda.org/conda-forge/linux-aarch64/cython-3.0.11-py39h7dbf29c_2.conda#10e704f42cb0c74a29a2f48da784c9e9 https://conda.anaconda.org/conda-forge/linux-aarch64/dbus-1.13.6-h12b9eeb_3.tar.bz2#f3d63805602166bac09386741e00935e https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_0.conda#d02ae936e42063ca46af6cdad2dbd1e0 https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_0.conda#15dda3cdbf330abfe9f555d22f66db46 https://conda.anaconda.org/conda-forge/linux-aarch64/fontconfig-2.14.2-ha9a116f_0.conda#6d2d19ea85f9d41534cd28fdefd59a25 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda#f800d2da156d08e289b14e87e43c1ae5 -https://conda.anaconda.org/conda-forge/linux-aarch64/kiwisolver-1.4.5-py39h78c8b8d_2.conda#10f50ee6c230bfa2ba09e9bb6de05af6 +https://conda.anaconda.org/conda-forge/linux-aarch64/kiwisolver-1.4.7-py39h78c8b8d_0.conda#8dc5516dd121089f14c1a557ecec3224 https://conda.anaconda.org/conda-forge/linux-aarch64/lcms2-2.16-h922389a_0.conda#ffdd8267a04c515e7ce69c727b051414 https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.9.0-23_linuxaarch64_openblas.conda#3ac1ad627e1a07fae62556d6aabafdfd https://conda.anaconda.org/conda-forge/linux-aarch64/libcups-2.3.3-h405e4a8_4.conda#d42c670b0c96c1795fd859d5e0275a55 @@ -109,7 +109,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/openjpeg-2.5.2-h0d9d63b_0.c https://conda.anaconda.org/conda-forge/noarch/packaging-24.1-pyhd8ed1ab_0.conda#cbe1bb1f21567018ce595d9c2be0f0db https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda#d3483c8fc2dc2cc3f5cf43e26d60cabf https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.4-pyhd8ed1ab_0.conda#4d91352a50949d049cf9714c8563d433 -https://conda.anaconda.org/conda-forge/noarch/setuptools-72.2.0-pyhd8ed1ab_0.conda#1462aa8b243aad09ef5d0841c745eb89 +https://conda.anaconda.org/conda-forge/noarch/setuptools-73.0.1-pyhd8ed1ab_0.conda#f0b618d7673d1b2464f600b34d912f6f https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96 @@ -122,12 +122,12 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxext-1.3.4-h2a766a3 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxrender-0.9.11-h7935292_0.conda#8c96b84f7fb97a3cd533a14dbdcd6626 https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.1-pyhd8ed1ab_0.conda#74a4befb4b38897e19a107693e49da20 https://conda.anaconda.org/conda-forge/linux-aarch64/cairo-1.18.0-hdb1a16f_3.conda#080659f02bf2202c57f1cda4f9e51f21 -https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.53.1-py39he257ee7_0.conda#e30df3a3431af304f87bbd0cd07d5674 +https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.53.1-py39h060674a_1.conda#e45b07efca11dedaa27ab4131485541f https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.4-pyhd8ed1ab_0.conda#99aa3edd3f452d61c305a30e78140513 https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-23_linuxaarch64_openblas.conda#65a4f18036c0f5419146fddee6653a96 -https://conda.anaconda.org/conda-forge/linux-aarch64/libclang-cpp18.1-18.1.8-default_h14d1da3_3.conda#6cbdc5d3581cab35472125394a26c3f4 -https://conda.anaconda.org/conda-forge/linux-aarch64/libclang13-18.1.8-default_h465fbfb_3.conda#478068aedf049fb4ae66754cba1cbe73 +https://conda.anaconda.org/conda-forge/linux-aarch64/libclang-cpp18.1-18.1.8-default_h14d1da3_4.conda#8bcade3ee01ba095a3fa42fec64261ae +https://conda.anaconda.org/conda-forge/linux-aarch64/libclang13-18.1.8-default_h465fbfb_4.conda#71334cadd10653dfd8eafc57cef45820 https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.9.0-23_linuxaarch64_openblas.conda#85c4fec3847027ca7402f3bd7d2de4c1 https://conda.anaconda.org/conda-forge/linux-aarch64/libxkbcommon-1.7.0-h46f2afe_1.conda#78a24e611ab9c09c518f519be49c2e46 https://conda.anaconda.org/conda-forge/noarch/meson-1.5.1-pyhd8ed1ab_1.conda#979087ee59bea1355f991a3b738af64e @@ -144,7 +144,7 @@ https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.c https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-2.0.2-py39h4a34e27_0.conda#4d6edcc002364ced01e4fc947832eee6 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_0.conda#b39568655c127a9c4a44d178ac99b6d0 https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-23_linuxaarch64_openblas.conda#0270f72a50c9d64fb8b67ae6681011c8 -https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.3.0-py39hbd2ca3f_0.conda#d21904acee235eeb3898b26e6d35c2c6 +https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.3.0-py39hbd2ca3f_1.conda#bbd779f52a946eb629a6a786aa150bde https://conda.anaconda.org/conda-forge/linux-aarch64/qt6-main-6.7.2-h288a8fd_4.conda#f6771673fad8025bb1d4dd765bc3caad https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.13.1-py39hb921187_0.conda#1aac9080de661e03d286f18fb71e5240 https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.123-openblas.conda#43772c0a1ae8f29c9a223c21fd89262b From 428e1481b0d8928926833a678c6ca4eec3ef7532 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 9 Sep 2024 14:54:57 +0200 Subject: [PATCH 257/275] Solve import error --- sklearn/svm/tests/test_svm.py | 1 + 1 file changed, 1 insertion(+) diff --git a/sklearn/svm/tests/test_svm.py b/sklearn/svm/tests/test_svm.py index 68438a9f8196d..a40e10286bdef 100644 --- a/sklearn/svm/tests/test_svm.py +++ b/sklearn/svm/tests/test_svm.py @@ -36,6 +36,7 @@ ) from sklearn.svm._classes import _validate_dual_parameter from sklearn.utils import check_random_state, shuffle +from sklearn.utils._testing import ignore_warnings from sklearn.utils.fixes import _IS_32BIT, CSR_CONTAINERS, LIL_CONTAINERS from sklearn.utils.validation import _num_samples From b5b5017954c30404bc44c0c788df62c92037a257 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 9 Sep 2024 16:56:47 +0200 Subject: [PATCH 258/275] MAINT update lock file --- ...latest_conda_forge_mkl_linux-64_conda.lock | 142 +++++++----------- build_tools/azure/pypy3_linux-64_conda.lock | 122 +++++++-------- .../update_environments_and_lock_files.py | 2 +- 3 files changed, 122 insertions(+), 144 deletions(-) diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index 3579a586bbfa1..3394858626338 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 93ee312868bc5df4bdc9b2ef07f938f6a5922dfe2375c4963a7c63d19c5d87f6 +# input_hash: 50fed47bc507d9ee3dbf5ff7a2247cb88944928bd5797e534ebdf8ece2d858ec @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.8.30-hbcca054_0.conda#c27d1c142233b5bc9ca570c6e2e0c244 @@ -9,8 +9,7 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed3 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_7.conda#b80f2f396ca2c28b8c14c437a4ed1e74 -https://conda.anaconda.org/conda-forge/linux-64/mkl-include-2023.2.0-h84fe81f_50496.conda#7af9fd0b2d7219f4a4200a34561340f6 -https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.12-5_cp312.conda#0424ae29b104430108f5218a66db7260 +https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-5_cp311.conda#139a8d40c8a2f430df31048949e450de https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h8827d51_1.conda#8bfdead4e0fff0383ae4c9c50d0531bd https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_0.conda#e46b5ae31282252e0525713e34ffbe2b @@ -18,20 +17,18 @@ https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2# https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_0.conda#35e52d19547cb3265a09c49de146a5ae https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793 https://conda.anaconda.org/conda-forge/linux-64/libgcc-14.1.0-h77fa898_1.conda#002ef4463dd1e2b44a94a4ace468f5d2 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+https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.0-hd590300_0.conda#71b89db63b5b504e7afc8ad901172e1e https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.33.1-heb4867d_0.conda#0d3c60291342c0c025db231353376dfb https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.3-h5888daf_0.conda#6595440079bed734b113de44ffd3cd0a https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3 -https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hb9d3cd8_2.conda#9566f0bd264fbd463002e759b8a82401 -https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.1.0-hb9d3cd8_2.conda#06f70867945ea6a84d35836af780f1de +https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.0.9-h166bdaf_9.conda#61641e239f96eae2b8492dc7e755828c https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.21-h4bc722e_0.conda#36ce76665bf67f5aac36be7a0d21b7f3 https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda#172bf1cd1ff8629f2b1179945ed45055 https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda#a1cfcc585f0c42bf8d5546bb1dfb668d @@ -40,17 +37,17 @@ https://conda.anaconda.org/conda-forge/linux-64/libgfortran-14.1.0-h69a702a_1.co https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda#d66573916ffcf376178462f1b61c941e https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda#ea25936bb4080d843790b586850f82b8 https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda#30fd6e37fe21f86f4bd26d6ee73eeec7 +https://conda.anaconda.org/conda-forge/linux-64/libnuma-2.0.18-h4ab18f5_2.conda#a263760479dbc7bc1f3df12707bd90dc https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hd590300_0.conda#48f4330bfcd959c3cfb704d424903c82 https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-14.1.0-h4852527_1.conda#bd2598399a70bb86d8218e95548d735e https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.8.0-h166bdaf_0.tar.bz2#ede4266dc02e875fe1ea77b25dd43747 https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b -https://conda.anaconda.org/conda-forge/linux-64/libuv-1.48.0-hd590300_0.conda#7e8b914b1062dd4386e3de4d82a3ead6 https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.conda#b26e8aa824079e1be0294e7152ca4559 https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-h4ab18f5_1.conda#57d7dc60e9325e3de37ff8dffd18e814 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-he02047a_1.conda#70caf8bb6cf39a0b6b7efc885f51c0fe https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 -https://conda.anaconda.org/conda-forge/linux-64/s2n-1.5.2-h7b32b05_0.conda#daf6322364fe6fc46c515d4d3d0051c2 +https://conda.anaconda.org/conda-forge/linux-64/s2n-1.3.49-h06160fa_0.conda#1d78349eb26366ecc034a4afe70a8534 https://conda.anaconda.org/conda-forge/linux-64/xorg-inputproto-2.3.2-h7f98852_1002.tar.bz2#bcd1b3396ec6960cbc1d2855a9e60b2b https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.1-hd590300_0.conda#b462a33c0be1421532f28bfe8f4a7514 @@ -61,27 +58,28 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-renderproto-0.11.1-h7f98852 https://conda.anaconda.org/conda-forge/linux-64/xorg-xextproto-7.3.0-h0b41bf4_1003.conda#bce9f945da8ad2ae9b1d7165a64d0f87 https://conda.anaconda.org/conda-forge/linux-64/xorg-xproto-7.0.31-h7f98852_1007.tar.bz2#b4a4381d54784606820704f7b5f05a15 https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2#2161070d867d1b1204ea749c8eec4ef0 -https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.7.4-h2abdd08_0.conda#006ee3bee3d0428e1b43b47ef1cffbc6 -https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.2.19-haa50ccc_0.conda#00c38c49d0befb632f686cf67ee8c9f5 -https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.1.19-h038f3f9_2.conda#6861cab6cddb5d713cb3db95c838d30f -https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.1.18-h038f3f9_10.conda#4bf9c8fcf2bb6793c55e5c5758b9b011 -https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hb9d3cd8_2.conda#c63b5e52939e795ba8d26e35d767a843 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a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index a91cd4b658263..2a5a353b6b314 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -160,7 +160,7 @@ def remove_from(alist, to_remove): "tag": "main-ci", "folder": "build_tools/azure", "platform": "linux-64", - "channels": ["conda-forge"], + "channel": "conda-forge", "conda_dependencies": common_dependencies + ["ccache", "polars"], "package_constraints": { "python": "3.9", From 04b71d208ce55417c904818e47f41890b960ee5b Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 9 Sep 2024 17:19:00 +0200 Subject: [PATCH 259/275] FIX solve conflict git --- doc/developers/contributing.rst | 7 ------- 1 file changed, 7 deletions(-) diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst index a78974c694256..5bfaec3e2555f 100644 --- a/doc/developers/contributing.rst +++ b/doc/developers/contributing.rst @@ -67,19 +67,12 @@ link to it from your website, or simply star to say "I use it": .. raw:: html -<<<<<<< HEAD

    -======= - Star - ->>>>>>> 34db65a3ad (DOC use pydata-sphinx-theme for the website (#29038)) In case a contribution/issue involves changes to the API principles or changes to dependencies or supported versions, it must be backed by a From 8ade4f56d2f8ee57fcc691a8f069daca53b17891 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 9 Sep 2024 17:44:13 +0200 Subject: [PATCH 260/275] MAINT bump from 1.5.1 to 1.5.2 --- sklearn/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/__init__.py b/sklearn/__init__.py index 58ff42284e698..38e79e2d3abd8 100644 --- a/sklearn/__init__.py +++ b/sklearn/__init__.py @@ -39,7 +39,7 @@ # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer. # 'X.Y.dev0' is the canonical version of 'X.Y.dev' # -__version__ = "1.5.1" +__version__ = "1.5.2" # On OSX, we can get a runtime error due to multiple OpenMP libraries loaded From c993dd29e4fe8cee32f62e286b3399b89c51916c Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 9 Sep 2024 18:24:28 +0200 Subject: [PATCH 261/275] DOC update repr for NumPy 2.0 --- sklearn/metrics/_regression.py | 40 +++++++++++++++++----------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/sklearn/metrics/_regression.py b/sklearn/metrics/_regression.py index 704838ab16d67..f9d8717198394 100644 --- a/sklearn/metrics/_regression.py +++ b/sklearn/metrics/_regression.py @@ -203,15 +203,15 @@ def mean_absolute_error( >>> y_true = [3, -0.5, 2, 7] >>> y_pred = [2.5, 0.0, 2, 8] >>> mean_absolute_error(y_true, y_pred) - 0.5 + np.float64(0.5) >>> y_true = [[0.5, 1], [-1, 1], [7, -6]] >>> y_pred = [[0, 2], [-1, 2], [8, -5]] >>> mean_absolute_error(y_true, y_pred) - 0.75 + np.float64(0.75) >>> mean_absolute_error(y_true, y_pred, multioutput='raw_values') array([0.5, 1. ]) >>> mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7]) - 0.85... + np.float64(0.85...) """ y_type, y_true, y_pred, multioutput = _check_reg_targets( y_true, y_pred, multioutput @@ -382,19 +382,19 @@ def mean_absolute_percentage_error( >>> y_true = [3, -0.5, 2, 7] >>> y_pred = [2.5, 0.0, 2, 8] >>> mean_absolute_percentage_error(y_true, y_pred) - 0.3273... + np.float64(0.3273...) >>> y_true = [[0.5, 1], [-1, 1], [7, -6]] >>> y_pred = [[0, 2], [-1, 2], [8, -5]] >>> mean_absolute_percentage_error(y_true, y_pred) - 0.5515... + np.float64(0.5515...) >>> mean_absolute_percentage_error(y_true, y_pred, multioutput=[0.3, 0.7]) - 0.6198... + np.float64(0.6198...) >>> # the value when some element of the y_true is zero is arbitrarily high because >>> # of the division by epsilon >>> y_true = [1., 0., 2.4, 7.] >>> y_pred = [1.2, 0.1, 2.4, 8.] >>> mean_absolute_percentage_error(y_true, y_pred) - 112589990684262.48 + np.float64(112589990684262.48) """ y_type, y_true, y_pred, multioutput = _check_reg_targets( y_true, y_pred, multioutput @@ -477,15 +477,15 @@ def mean_squared_error( >>> y_true = [3, -0.5, 2, 7] >>> y_pred = [2.5, 0.0, 2, 8] >>> mean_squared_error(y_true, y_pred) - 0.375 + np.float64(0.375) >>> y_true = [[0.5, 1],[-1, 1],[7, -6]] >>> y_pred = [[0, 2],[-1, 2],[8, -5]] >>> mean_squared_error(y_true, y_pred) - 0.708... + np.float64(0.708...) >>> mean_squared_error(y_true, y_pred, multioutput='raw_values') array([0.41666667, 1. ]) >>> mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7]) - 0.825... + np.float64(0.825...) """ # TODO(1.6): remove if squared != "deprecated": @@ -660,15 +660,15 @@ def mean_squared_log_error( >>> y_true = [3, 5, 2.5, 7] >>> y_pred = [2.5, 5, 4, 8] >>> mean_squared_log_error(y_true, y_pred) - 0.039... + np.float64(0.039...) >>> y_true = [[0.5, 1], [1, 2], [7, 6]] >>> y_pred = [[0.5, 2], [1, 2.5], [8, 8]] >>> mean_squared_log_error(y_true, y_pred) - 0.044... + np.float64(0.044...) >>> mean_squared_log_error(y_true, y_pred, multioutput='raw_values') array([0.00462428, 0.08377444]) >>> mean_squared_log_error(y_true, y_pred, multioutput=[0.3, 0.7]) - 0.060... + np.float64(0.060...) """ # TODO(1.6): remove if squared != "deprecated": @@ -1361,7 +1361,7 @@ def mean_tweedie_deviance(y_true, y_pred, *, sample_weight=None, power=0): >>> y_true = [2, 0, 1, 4] >>> y_pred = [0.5, 0.5, 2., 2.] >>> mean_tweedie_deviance(y_true, y_pred, power=1) - 1.4260... + np.float64(1.4260...) """ y_type, y_true, y_pred, _ = _check_reg_targets( y_true, y_pred, None, dtype=[np.float64, np.float32] @@ -1437,7 +1437,7 @@ def mean_poisson_deviance(y_true, y_pred, *, sample_weight=None): >>> y_true = [2, 0, 1, 4] >>> y_pred = [0.5, 0.5, 2., 2.] >>> mean_poisson_deviance(y_true, y_pred) - 1.4260... + np.float64(1.4260...) """ return mean_tweedie_deviance(y_true, y_pred, sample_weight=sample_weight, power=1) @@ -1481,7 +1481,7 @@ def mean_gamma_deviance(y_true, y_pred, *, sample_weight=None): >>> y_true = [2, 0.5, 1, 4] >>> y_pred = [0.5, 0.5, 2., 2.] >>> mean_gamma_deviance(y_true, y_pred) - 1.0568... + np.float64(1.0568...) """ return mean_tweedie_deviance(y_true, y_pred, sample_weight=sample_weight, power=2) @@ -1567,13 +1567,13 @@ def d2_tweedie_score(y_true, y_pred, *, sample_weight=None, power=0): >>> y_true = [0.5, 1, 2.5, 7] >>> y_pred = [1, 1, 5, 3.5] >>> d2_tweedie_score(y_true, y_pred) - 0.285... + np.float64(0.285...) >>> d2_tweedie_score(y_true, y_pred, power=1) - 0.487... + np.float64(0.487...) >>> d2_tweedie_score(y_true, y_pred, power=2) - 0.630... + np.float64(0.630...) >>> d2_tweedie_score(y_true, y_true, power=2) - 1.0 + np.float64(1.0) """ y_type, y_true, y_pred, _ = _check_reg_targets( y_true, y_pred, None, dtype=[np.float64, np.float32] From c735641b5b2caeb8268c4dc658f96d76cf36aa91 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 9 Sep 2024 18:38:46 +0200 Subject: [PATCH 262/275] MAINT install setuptools for debian-32bits --- build_tools/azure/install.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build_tools/azure/install.sh b/build_tools/azure/install.sh index 3bb62a8c3cf5d..c5dd4e07de6fe 100755 --- a/build_tools/azure/install.sh +++ b/build_tools/azure/install.sh @@ -38,7 +38,7 @@ pre_python_environment_install() { elif [[ "$DISTRIB" == "debian-32" ]]; then apt-get update - apt-get install -y python3-dev python3-numpy python3-scipy \ + apt-get install -y python3-dev python3-setuptools python3-numpy python3-scipy \ python3-matplotlib libopenblas-dev \ python3-virtualenv python3-pandas ccache git From 2e79f521fe0ab3edd5b1a312ab15ac4e22c3bc55 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 9 Sep 2024 18:47:25 +0200 Subject: [PATCH 263/275] DOC fix entry in changelog for backport happening in 1.5.2 (#29815) --- doc/whats_new/v1.5.rst | 21 ++++++++++++++++++--- 1 file changed, 18 insertions(+), 3 deletions(-) diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst index 10af85bb80bb1..31671717bf473 100644 --- a/doc/whats_new/v1.5.rst +++ b/doc/whats_new/v1.5.rst @@ -45,6 +45,22 @@ Changelog transform output is set to `pandas` or `polars`, since it isn't a transformer. :pr:`29401` by :user:`Stefanie Senger `. +:mod:`sklearn.decomposition` +............................ + +- |Fix| Increase rank defficiency threshold in the whitening step of + :class:`decomposition.FastICA` with `whiten_solver="eigh"` to improve the + platform-agnosticity of the estimator. + :pr:`29612` by :user:`Olivier Grisel `. + +:mod:`sklearn.metrics` +...................... + +- |Fix| Fix a regression in :func:`metrics.accuracy_score` and in + :func:`metrics.zero_one_loss` causing an error for Array API dispatch with multilabel + inputs. + :pr:`29336` by :user:`Edoardo Abati `. + :mod:`sklearn.svm` .................. @@ -87,11 +103,10 @@ Changelog instead of implicitly converting those inputs as regular NumPy arrays. :pr:`29119` by :user:`Olivier Grisel`. -- |Fix| Fix a regression in :func:`metrics.accuracy_score` and in +- |Fix| Fix a regression in :func:`metrics.zero_one_loss` causing an error for Array API dispatch with multilabel inputs. - :pr:`29269` by :user:`Yaroslav Korobko ` and - :pr:`29336` by :user:`Edoardo Abati `. + :pr:`29269` by :user:`Yaroslav Korobko `. :mod:`sklearn.model_selection` .............................. From 4d838dc5d52aab22016d403f987d7c58fc373576 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 9 Sep 2024 20:25:47 +0200 Subject: [PATCH 264/275] TST fix tolerance as in #29400 --- sklearn/ensemble/tests/test_forest.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/sklearn/ensemble/tests/test_forest.py b/sklearn/ensemble/tests/test_forest.py index 2468f8fc5b590..a36e73cd301a2 100644 --- a/sklearn/ensemble/tests/test_forest.py +++ b/sklearn/ensemble/tests/test_forest.py @@ -515,7 +515,8 @@ def test_forest_classifier_oob( test_score = classifier.score(X_test, y_test) assert classifier.oob_score_ >= lower_bound_accuracy - assert abs(test_score - classifier.oob_score_) <= 0.1 + abs_diff = abs(test_score - classifier.oob_score_) + assert abs_diff <= 0.11, f"{abs_diff=} is greater than 0.11" assert hasattr(classifier, "oob_score_") assert not hasattr(classifier, "oob_prediction_") From c119c7e09d4be52b7d4b61f53555e41f55c58553 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 9 Sep 2024 20:28:59 +0200 Subject: [PATCH 265/275] DOC add orphan option to developers/index.rst --- doc/developers/index.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/doc/developers/index.rst b/doc/developers/index.rst index cca77b6a015c9..c4307ec826b50 100644 --- a/doc/developers/index.rst +++ b/doc/developers/index.rst @@ -1,3 +1,5 @@ +:orphan: + .. _developers_guide: ================= From 40c7416925b1a41aa36d02f9fa4aa00622fd6970 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Tue, 10 Sep 2024 10:31:07 +0200 Subject: [PATCH 266/275] DOC update the list of contributors for 1.5.2 (#29819) --- doc/whats_new/v1.5.rst | 61 ++++++++++++++++++++++++------------------ 1 file changed, 35 insertions(+), 26 deletions(-) diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst index 31671717bf473..dc283d38f646a 100644 --- a/doc/whats_new/v1.5.rst +++ b/doc/whats_new/v1.5.rst @@ -676,29 +676,38 @@ Changelog Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.4, including: -101AlexMartin, Abdulaziz Aloqeely, Adam J. Stewart, Adam Li, Adarsh Wase, Adrin -Jalali, Advik Sinha, Akash Srivastava, Akihiro Kuno, Alan Guedes, Alexis -IMBERT, Ana Paula Gomes, Anderson Nelson, Andrei Dzis, Arnaud Capitaine, Arturo -Amor, Aswathavicky, Bharat Raghunathan, Brendan Lu, Bruno, Cemlyn, Christian -Lorentzen, Christian Veenhuis, Cindy Liang, Claudio Salvatore Arcidiacono, -Connor Boyle, Conrad Stevens, crispinlogan, davidleon123, DerWeh, Dipan Banik, -Duarte São José, DUONG, Eddie Bergman, Edoardo Abati, Egehan Gunduz, Emad -Izadifar, Erich Schubert, Filip Karlo Došilović, Franck Charras, Gael -Varoquaux, Gönül Aycı, Guillaume Lemaitre, Gyeongjae Choi, Harmanan Kohli, -Hong Xiang Yue, Ian Faust, itsaphel, Ivan Wiryadi, Jack Bowyer, Javier Marin -Tur, Jérémie du Boisberranger, Jérôme Dockès, Jiawei Zhang, Joel Nothman, -Johanna Bayer, John Cant, John Hopfensperger, jpcars, jpienaar-tuks, Julian -Libiseller-Egger, Julien Jerphanion, KanchiMoe, Kaushik Amar Das, keyber, -Koustav Ghosh, kraktus, Krsto Proroković, ldwy4, LeoGrin, lihaitao, Linus -Sommer, Loic Esteve, Lucy Liu, Lukas Geiger, manasimj, Manuel Labbé, Manuel -Morales, Marco Edward Gorelli, Maren Westermann, Marija Vlajic, Mark Elliot, -Mateusz Sokół, Mavs, Michael Higgins, Michael Mayer, miguelcsilva, Miki -Watanabe, Mohammed Hamdy, myenugula, Nathan Goldbaum, Naziya Mahimkar, Neto, -Olivier Grisel, Omar Salman, Patrick Wang, Pierre de Fréminville, Priyash -Shah, Puneeth K, Rahil Parikh, raisadz, Raj Pulapakura, Ralf Gommers, Ralph -Urlus, Randolf Scholz, Reshama Shaikh, Richard Barnes, Rodrigo Romero, Saad -Mahmood, Salim Dohri, Sandip Dutta, SarahRemus, scikit-learn-bot, Shaharyar -Choudhry, Shubham, sperret6, Stefanie Senger, Suha Siddiqui, Thanh Lam DANG, -thebabush, Thomas J. Fan, Thomas Lazarus, Thomas Li, Tialo, Tim Head, Tuhin -Sharma, VarunChaduvula, Vineet Joshi, virchan, Waël Boukhobza, Weyb, Will -Dean, Xavier Beltran, Xiao Yuan, Xuefeng Xu, Yao Xiao +101AlexMartin, Abdulaziz Aloqeely, Adam J. Stewart, Adam Li, Adarsh Wase, +Adeyemi Biola, Aditi Juneja, Adrin Jalali, Advik Sinha, Aisha, Akash +Srivastava, Akihiro Kuno, Alan Guedes, Alberto Torres, Alexis IMBERT, alexqiao, +Ana Paula Gomes, Anderson Nelson, Andrei Dzis, Arif Qodari, Arnaud Capitaine, +Arturo Amor, Aswathavicky, Audrey Flanders, awwwyan, baggiponte, Bharat +Raghunathan, bme-git, brdav, Brendan Lu, Brigitta Sipőcz, Bruno, Cailean +Carter, Cemlyn, Christian Lorentzen, Christian Veenhuis, Cindy Liang, Claudio +Salvatore Arcidiacono, Connor Boyle, Conrad Stevens, crispinlogan, David +Matthew Cherney, Davide Chicco, davidleon123, dependabot[bot], DerWeh, dinga92, +Dipan Banik, Drew Craeton, Duarte São José, DUONG, Eddie Bergman, Edoardo +Abati, Egehan Gunduz, Emad Izadifar, EmilyXinyi, Erich Schubert, Evelyn, Filip +Karlo Došilović, Franck Charras, Gael Varoquaux, Gönül Aycı, Guillaume +Lemaitre, Gyeongjae Choi, Harmanan Kohli, Hong Xiang Yue, Ian Faust, Ilya +Komarov, itsaphel, Ivan Wiryadi, Jack Bowyer, Javier Marin Tur, Jérémie du +Boisberranger, Jérôme Dockès, Jiawei Zhang, João Morais, Joe Cainey, Joel +Nothman, Johanna Bayer, John Cant, John Enblom, John Hopfensperger, jpcars, +jpienaar-tuks, Julian Chan, Julian Libiseller-Egger, Julien Jerphanion, +KanchiMoe, Kaushik Amar Das, keyber, Koustav Ghosh, kraktus, Krsto Proroković, +Lars, ldwy4, LeoGrin, lihaitao, Linus Sommer, Loic Esteve, Lucy Liu, Lukas +Geiger, m-maggi, manasimj, Manuel Labbé, Manuel Morales, Marco Edward Gorelli, +Marco Wolsza, Maren Westermann, Marija Vlajic, Mark Elliot, Martin Helm, +Mateusz Sokół, mathurinm, Mavs, Michael Dawson, Michael Higgins, Michael Mayer, +miguelcsilva, Miki Watanabe, Mohammed Hamdy, myenugula, Nathan Goldbaum, Naziya +Mahimkar, nbrown-ScottLogic, Neto, Nithish Bolleddula, notPlancha, Olivier +Grisel, Omar Salman, ParsifalXu, Patrick Wang, Pierre de Fréminville, Piotr, +Priyank Shroff, Priyansh Gupta, Priyash Shah, Puneeth K, Rahil Parikh, raisadz, +Raj Pulapakura, Ralf Gommers, Ralph Urlus, Randolf Scholz, renaissance0ne, +Reshama Shaikh, Richard Barnes, Robert Pollak, Roberto Rosati, Rodrigo Romero, +rwelsch427, Saad Mahmood, Salim Dohri, Sandip Dutta, SarahRemus, +scikit-learn-bot, Shaharyar Choudhry, Shubham, sperret6, Stefanie Senger, +Steffen Schneider, Suha Siddiqui, Thanh Lam DANG, thebabush, Thomas, Thomas J. +Fan, Thomas Lazarus, Tialo, Tim Head, Tuhin Sharma, Tushar Parimi, +VarunChaduvula, Vineet Joshi, virchan, Waël Boukhobza, Weyb, Will Dean, Xavier +Beltran, Xiao Yuan, Xuefeng Xu, Yao Xiao, yareyaredesuyo, Ziad Amerr, Štěpán +Sršeň From 156ef141f3b270edb06c8ae9af37c55253c0aabe Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Wed, 11 Sep 2024 11:00:08 +0200 Subject: [PATCH 267/275] [cd build] trigger ci/cd From f5aac217372759d4d35d69934199be878c3bcc65 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Tue, 10 Sep 2024 10:34:05 +0200 Subject: [PATCH 268/275] DOC update date for the 1.5.2 release (#29816) --- doc/templates/index.html | 1 + doc/whats_new/v1.5.rst | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/templates/index.html b/doc/templates/index.html index c99d45ff1321f..2893718365e2e 100644 --- a/doc/templates/index.html +++ b/doc/templates/index.html @@ -207,6 +207,7 @@

    News

    • On-going development: scikit-learn 1.6 (Changelog).
      • +
    • September 2024. scikit-learn 1.5.2 is available for download (Changelog).
    • July 2024. scikit-learn 1.5.1 is available for download (Changelog).
    • May 2024. scikit-learn 1.5.0 is available for download (Changelog).
    • April 2024. scikit-learn 1.4.2 is available for download (Changelog).
      • diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst index dc283d38f646a..bcea1edf6bb83 100644 --- a/doc/whats_new/v1.5.rst +++ b/doc/whats_new/v1.5.rst @@ -18,7 +18,7 @@ For a short description of the main highlights of the release, please refer to Version 1.5.2 ============= -**release date of 1.5.2** +**September 2024** Changes impacting many modules ------------------------------ From ddc170d41e5554aa8508511d81ba00d80dd331b6 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Thu, 19 Sep 2024 08:52:33 +0200 Subject: [PATCH 269/275] MAINT redirect the URL of the machine learning map (#29879) (#29885) --- doc/conf.py | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/conf.py b/doc/conf.py index ab88d72d66e5e..92a4fcceaee3f 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -428,6 +428,7 @@ def add_js_css_files(app, pagename, templatename, context, doctree): "contents": "index", "preface": "index", "modules/classes": "api/index", + "tutorial/machine_learning_map/index": "machine_learning_map", "auto_examples/feature_selection/plot_permutation_test_for_classification": ( "auto_examples/model_selection/plot_permutation_tests_for_classification" ), From 870081cce8df152bf724a7846b05dee206653e37 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Wed, 2 Oct 2024 17:15:21 +0200 Subject: [PATCH 270/275] REL scikit-learn 1.5.2 for Python 3.13 (#29987) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- .github/workflows/wheels.yml | 25 ++++++++++- build_tools/cirrus/arm_wheel.yml | 5 +++ build_tools/github/Windows | 13 ------ .../github/build_minimal_windows_image.sh | 42 ++++++++++++++----- build_tools/wheels/build_wheels.sh | 7 ++++ build_tools/wheels/cibw_before_test.sh | 19 +++++++++ build_tools/wheels/test_wheels.sh | 8 ++++ 7 files changed, 94 insertions(+), 25 deletions(-) delete mode 100644 build_tools/github/Windows create mode 100755 build_tools/wheels/cibw_before_test.sh diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml index 606062e81937b..4d0d1e420e813 100644 --- a/.github/workflows/wheels.yml +++ b/.github/workflows/wheels.yml @@ -65,6 +65,11 @@ jobs: - os: windows-latest python: 312 platform_id: win_amd64 + - os: windows-latest + python: 313 + platform_id: win_amd64 + # TODO: remove next line when Python 3.13 is released + prerelease_pythons: True # Linux 64 bit manylinux2014 - os: ubuntu-latest @@ -86,6 +91,10 @@ jobs: python: 312 platform_id: manylinux_x86_64 manylinux_image: manylinux2014 + - os: ubuntu-latest + python: 313 + platform_id: manylinux_x86_64 + manylinux_image: manylinux2014 # MacOS x86_64 - os: macos-12 @@ -100,6 +109,9 @@ jobs: - os: macos-12 python: 312 platform_id: macosx_x86_64 + - os: macos-12 + python: 313 + platform_id: macosx_x86_64 # MacOS arm64 - os: macos-14 @@ -114,6 +126,9 @@ jobs: - os: macos-14 python: 312 platform_id: macosx_arm64 + - os: macos-14 + python: 313 + platform_id: macosx_arm64 steps: - name: Checkout scikit-learn @@ -154,7 +169,8 @@ jobs: - name: Build and test wheels env: - CIBW_PRERELEASE_PYTHONS: ${{ matrix.prerelease }} + CIBW_PRERELEASE_PYTHONS: ${{ matrix.prerelease_pythons }} + CIBW_FREE_THREADED_SUPPORT: ${{ matrix.free_threaded_support }} CIBW_ENVIRONMENT: SKLEARN_SKIP_NETWORK_TESTS=1 SKLEARN_BUILD_PARALLEL=3 CIBW_BUILD: cp${{ matrix.python }}-${{ matrix.platform_id }} @@ -167,7 +183,12 @@ jobs: CIBW_CONFIG_SETTINGS_WINDOWS: "setup-args=--vsenv" CIBW_REPAIR_WHEEL_COMMAND_WINDOWS: bash build_tools/github/repair_windows_wheels.sh {wheel} {dest_dir} CIBW_BEFORE_TEST_WINDOWS: bash build_tools/github/build_minimal_windows_image.sh ${{ matrix.python }} - CIBW_TEST_REQUIRES: pytest pandas ${{ matrix.python == 312 && 'numpy>=2.0.0rc2' || '' }} + CIBW_BEFORE_TEST: bash {project}/build_tools/wheels/cibw_before_test.sh + CIBW_TEST_REQUIRES: pytest pandas + # On Windows, we use a custom Docker image and CIBW_TEST_REQUIRES_WINDOWS + # does not make sense because it would install dependencies in the host + # rather than inside the Docker image + CIBW_TEST_REQUIRES_WINDOWS: "" CIBW_TEST_COMMAND: bash {project}/build_tools/wheels/test_wheels.sh CIBW_TEST_COMMAND_WINDOWS: bash {project}/build_tools/github/test_windows_wheels.sh ${{ matrix.python }} CIBW_BUILD_VERBOSITY: 1 diff --git a/build_tools/cirrus/arm_wheel.yml b/build_tools/cirrus/arm_wheel.yml index c3dfcfbc53ad9..7ce7ac41961cb 100644 --- a/build_tools/cirrus/arm_wheel.yml +++ b/build_tools/cirrus/arm_wheel.yml @@ -29,6 +29,11 @@ linux_arm64_wheel_task: CIBW_TEST_SKIP: "*_aarch64" - env: CIBW_BUILD: cp312-manylinux_aarch64 + - env: + CIBW_BUILD: cp313-manylinux_aarch64 + # TODO remove next line when Python 3.13 is relased and add + # CIBW_TEST_SKIP for Python 3.12 above + CIBW_TEST_SKIP: "*_aarch64" cibuildwheel_script: - apt install -y python3 python-is-python3 diff --git a/build_tools/github/Windows b/build_tools/github/Windows deleted file mode 100644 index a9971aa525581..0000000000000 --- a/build_tools/github/Windows +++ /dev/null @@ -1,13 +0,0 @@ -# Get the Python version of the base image from a build argument -ARG PYTHON_VERSION -FROM winamd64/python:$PYTHON_VERSION-windowsservercore - -ARG WHEEL_NAME -ARG CIBW_TEST_REQUIRES - -# Copy and install the Windows wheel -COPY $WHEEL_NAME $WHEEL_NAME -RUN pip install $env:WHEEL_NAME - -# Install the testing dependencies -RUN pip install $env:CIBW_TEST_REQUIRES.split(" ") diff --git a/build_tools/github/build_minimal_windows_image.sh b/build_tools/github/build_minimal_windows_image.sh index 2995b6906c535..adac06f02bb9a 100755 --- a/build_tools/github/build_minimal_windows_image.sh +++ b/build_tools/github/build_minimal_windows_image.sh @@ -11,15 +11,37 @@ WHEEL_NAME=$(basename $WHEEL_PATH) cp $WHEEL_PATH $WHEEL_NAME -# Dot the Python version for identyfing the base Docker image -PYTHON_VERSION=$(echo ${PYTHON_VERSION:0:1}.${PYTHON_VERSION:1:2}) +# Dot the Python version for identifying the base Docker image +PYTHON_DOCKER_IMAGE_PART=$(echo ${PYTHON_VERSION:0:1}.${PYTHON_VERSION:1:2}) -if [[ "$CIBW_PRERELEASE_PYTHONS" == "True" ]]; then - PYTHON_VERSION="$PYTHON_VERSION-rc" +if [[ "$CIBW_PRERELEASE_PYTHONS" =~ [tT]rue ]]; then + PYTHON_DOCKER_IMAGE_PART="${PYTHON_DOCKER_IMAGE_PART}-rc" fi -# Build a minimal Windows Docker image for testing the wheels -docker build --build-arg PYTHON_VERSION=$PYTHON_VERSION \ - --build-arg WHEEL_NAME=$WHEEL_NAME \ - --build-arg CIBW_TEST_REQUIRES="$CIBW_TEST_REQUIRES" \ - -f build_tools/github/Windows \ - -t scikit-learn/minimal-windows . + +# We could have all of the following logic in a Dockerfile but it's a lot +# easier to do it in bash rather than figure out how to do it in Powershell +# inside the Dockerfile ... +DOCKER_IMAGE="winamd64/python:${PYTHON_DOCKER_IMAGE_PART}-windowsservercore" +MNT_FOLDER="C:/mnt" +CONTAINER_ID=$(docker run -it -v "$(cygpath -w $PWD):$MNT_FOLDER" -d $DOCKER_IMAGE) + +function exec_inside_container() { + docker exec $CONTAINER_ID powershell -Command $1 +} + +exec_inside_container "python -m pip install $MNT_FOLDER/$WHEEL_NAME" + +if [[ "$PYTHON_VERSION" == "313" ]]; then + # TODO: remove when pandas has a release with python 3.13 wheels + # First install numpy release + exec_inside_container "python -m pip install numpy" + # Then install pandas-dev + exec_inside_container "python -m pip install --pre --extra-index https://pypi.anaconda.org/scientific-python-nightly-wheels/simple pandas --only-binary :all:" +fi + +exec_inside_container "python -m pip install $CIBW_TEST_REQUIRES" + +# Save container state to scikit-learn/minimal-windows image. On Windows the +# container needs to be stopped first. +docker stop $CONTAINER_ID +docker commit $CONTAINER_ID scikit-learn/minimal-windows diff --git a/build_tools/wheels/build_wheels.sh b/build_tools/wheels/build_wheels.sh index f2ed8495ec11f..1ff3ed282f775 100755 --- a/build_tools/wheels/build_wheels.sh +++ b/build_tools/wheels/build_wheels.sh @@ -56,6 +56,13 @@ if [[ "$CIBW_FREE_THREADED_SUPPORT" =~ [tT]rue ]]; then export CIBW_BUILD_FRONTEND='pip; args: --pre --extra-index-url "https://pypi.anaconda.org/scientific-python-nightly-wheels/simple" --only-binary :all:' fi +if [[ "$CIBW_FREE_THREADED_SUPPORT" =~ [tT]rue ]]; then + # Numpy, scipy, Cython only have free-threaded wheels on scientific-python-nightly-wheels + # TODO: remove this after CPython 3.13 is released (scheduled October 2024) + # and our dependencies have free-threaded wheels on PyPI + export CIBW_BUILD_FRONTEND='pip; args: --pre --extra-index-url "https://pypi.anaconda.org/scientific-python-nightly-wheels/simple"' +fi + # The version of the built dependencies are specified # in the pyproject.toml file, while the tests are run # against the most recent version of the dependencies diff --git a/build_tools/wheels/cibw_before_test.sh b/build_tools/wheels/cibw_before_test.sh new file mode 100755 index 0000000000000..193a3890530b4 --- /dev/null +++ b/build_tools/wheels/cibw_before_test.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +set -e +set -x + +FREE_THREADED_BUILD="$(python -c"import sysconfig; print(bool(sysconfig.get_config_var('Py_GIL_DISABLED')))")" +PY_VERSION=$(python -c 'import sys; print(f"{sys.version_info.major}{sys.version_info.minor}")') + +if [[ $FREE_THREADED_BUILD == "True" ]]; then + # TODO: remove when numpy, scipy and pandas have releases with free-threaded wheels + python -m pip install --pre --extra-index https://pypi.anaconda.org/scientific-python-nightly-wheels/simple numpy scipy pandas --only-binary :all: + +elif [[ "$PY_VERSION" == "313" ]]; then + # TODO: remove when pandas has a release with python 3.13 wheels + # First install numpy release + python -m pip install numpy --only-binary :all: + # Then install pandas-dev + python -m pip install --pre --extra-index https://pypi.anaconda.org/scientific-python-nightly-wheels/simple pandas --only-binary :all: +fi diff --git a/build_tools/wheels/test_wheels.sh b/build_tools/wheels/test_wheels.sh index e8cdf4b3ea8a2..da2c458c52903 100755 --- a/build_tools/wheels/test_wheels.sh +++ b/build_tools/wheels/test_wheels.sh @@ -6,6 +6,14 @@ set -x python -c "import joblib; print(f'Number of cores (physical): \ {joblib.cpu_count()} ({joblib.cpu_count(only_physical_cores=True)})')" +FREE_THREADED_BUILD="$(python -c"import sysconfig; print(bool(sysconfig.get_config_var('Py_GIL_DISABLED')))")" +if [[ $FREE_THREADED_BUILD == "True" ]]; then + # TODO: delete when importing numpy no longer enables the GIL + # setting to zero ensures the GIL is disabled while running the + # tests under free-threaded python + export PYTHON_GIL=0 +fi + # Test that there are no links to system libraries in the # threadpoolctl output section of the show_versions output: python -c "import sklearn; sklearn.show_versions()" From d5082d32de2797f9594c9477f2810c743560a1f1 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Wed, 2 Oct 2024 17:16:55 +0200 Subject: [PATCH 271/275] [cd build] trigger wheels builder From 97012457483bd550147a3e7d8cffdcf4d487512f Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Fri, 4 Oct 2024 10:58:26 +0200 Subject: [PATCH 272/275] DOC update scikit-learn contributors table (#30003) --- doc/communication_team.rst | 2 +- doc/maintainers.rst | 4 ++++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/doc/communication_team.rst b/doc/communication_team.rst index 30e4f1169cfc9..fb9666f0b42f7 100644 --- a/doc/communication_team.rst +++ b/doc/communication_team.rst @@ -7,7 +7,7 @@

      -

      Lauren Burke

      +

      Lauren Burke-McCarthy


      diff --git a/doc/maintainers.rst b/doc/maintainers.rst index 0ba69d8afa60d..72ba579ec63c9 100644 --- a/doc/maintainers.rst +++ b/doc/maintainers.rst @@ -54,6 +54,10 @@

      Guillaume Lemaitre

      +
      +

      Adam Li

      +
      +

      Christian Lorentzen

      From 6e9039160f0dfc3153643143af4cfdca941d2045 Mon Sep 17 00:00:00 2001 From: Inessa Pawson Date: Thu, 17 Oct 2024 13:18:28 -0400 Subject: [PATCH 273/275] DOC Remove the 2024 user survey announcement (#30091) --- doc/conf.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/doc/conf.py b/doc/conf.py index 92a4fcceaee3f..0da7518ac1342 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -283,10 +283,7 @@ # Use :html_theme.sidebar_secondary.remove: for file-wide removal "secondary_sidebar_items": {"**": ["page-toc", "sourcelink"]}, "show_version_warning_banner": True, - "announcement": ( - 'Help us make ' - "scikit-learn better! The 2024 user survey is now live." - ), + "announcement": None, } # Add any paths that contain custom themes here, relative to this directory. From c632e6d5f7ee9267793f6ed63f370c1b3a0cea1a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Wed, 18 Dec 2024 17:31:33 +0100 Subject: [PATCH 274/275] MNT Fetch script from main branch in lint.yml (#30505) --- .github/workflows/lint.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index e2de3bbde583b..0ef75cdcce660 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -31,6 +31,7 @@ jobs: - name: Install dependencies run: | + curl https://raw.githubusercontent.com/${{ github.repository }}/main/build_tools/shared.sh --retry 5 -o ./build_tools/shared.sh source build_tools/shared.sh # Include pytest compatibility with mypy pip install pytest $(get_dep ruff min) $(get_dep mypy min) $(get_dep black min) cython-lint From 8bf604961e3014102b6155fc84370676a1b856df Mon Sep 17 00:00:00 2001 From: Tim Head Date: Tue, 28 Jan 2025 18:16:22 +0100 Subject: [PATCH 275/275] Backport docs updates (conda-forge link, canonical URL) to 1.5.x (#30728) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- build_tools/circle/build_doc.sh | 2 +- doc/conf.py | 5 +++++ doc/developers/advanced_installation.rst | 7 ++++--- doc/install_instructions_conda.rst | 2 +- 4 files changed, 11 insertions(+), 5 deletions(-) diff --git a/build_tools/circle/build_doc.sh b/build_tools/circle/build_doc.sh index 014ac0fac8d7a..1938bd504f2c1 100755 --- a/build_tools/circle/build_doc.sh +++ b/build_tools/circle/build_doc.sh @@ -179,7 +179,7 @@ show_installed_libraries # Set parallelism to 3 to overlap IO bound tasks with CPU bound tasks on CI # workers with 2 cores when building the compiled extensions of scikit-learn. export SKLEARN_BUILD_PARALLEL=3 -pip install -e . --no-build-isolation +pip install -e . --no-build-isolation --config-settings=compile-args="-j4" echo "ccache build summary:" ccache -s diff --git a/doc/conf.py b/doc/conf.py index 0da7518ac1342..8a8cc1b313acc 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -207,6 +207,11 @@ # Sphinx are currently 'default' and 'sphinxdoc'. html_theme = "pydata_sphinx_theme" +# This config option is used to generate the canonical links in the header +# of every page. The canonical link is needed to prevent search engines from +# returning results pointing to old scikit-learn versions. +html_baseurl = "https://scikit-learn.org/stable/" + # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. diff --git a/doc/developers/advanced_installation.rst b/doc/developers/advanced_installation.rst index 04313a43754d5..9490d1c05de18 100644 --- a/doc/developers/advanced_installation.rst +++ b/doc/developers/advanced_installation.rst @@ -59,7 +59,7 @@ feature, code or documentation improvement). instead. #. Install a recent version of Python (3.9 or later at the time of writing) for - instance using Miniforge3_. Miniforge provides a conda-based distribution of + instance using Condaforge_. Conda-forge provides a conda-based distribution of Python and the most popular scientific libraries. If you installed Python with conda, we recommend to create a dedicated @@ -255,8 +255,8 @@ to enable OpenMP support: For Apple Silicon M1 hardware, only the conda-forge method below is known to work at the time of writing (January 2021). You can install the `macos/arm64` -distribution of conda using the `miniforge installer -`_ +distribution of conda using the `conda-forge installer +`_ macOS compilers from conda-forge ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -480,6 +480,7 @@ the base system and these steps will not be necessary. .. _virtualenv: https://docs.python.org/3/tutorial/venv.html .. _conda environment: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html .. _Miniforge3: https://github.com/conda-forge/miniforge#miniforge3 +.. _Condaforge: https://conda-forge.org/download/ Alternative compilers ===================== diff --git a/doc/install_instructions_conda.rst b/doc/install_instructions_conda.rst index fe1c14bbb78d3..0b5a57b747021 100644 --- a/doc/install_instructions_conda.rst +++ b/doc/install_instructions_conda.rst @@ -1,5 +1,5 @@ Install conda using the -`miniforge installers `__ (no +`conda-forge installers `__ (no administrator permission required). Then run: .. prompt:: bash