From 8a2d5ffa0512873a75da608eb14832253979ec44 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Mon, 2 Sep 2024 14:22:30 +0200 Subject: [PATCH 001/148] DOC Announce 2024 user survey on the stable doc (#29769) Co-authored-by: Inessa Pawson Co-authored-by: Paul Juma Otieno <103896399+otieno-juma@users.noreply.github.com> Co-authored-by: Guillaume Lemaitre --- doc/conf.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/doc/conf.py b/doc/conf.py index 9923a24260267..ac88fc39782c6 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -278,7 +278,10 @@ # Use :html_theme.sidebar_secondary.remove: for file-wide removal "secondary_sidebar_items": {"**": ["page-toc", "sourcelink"]}, "show_version_warning_banner": True, - "announcement": None, + "announcement": ( + 'Help us make ' + "scikit-learn better! The 2024 user survey is now live." + ), } # Add any paths that contain custom themes here, relative to this directory. From 5b3206794f29a4acd9a4168296e77ce2a7ecc550 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 9 Sep 2024 11:22:26 +0200 Subject: [PATCH 002/148] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29053) Co-authored-by: Lock file bot --- .../pylatest_conda_forge_mkl_linux-64_conda.lock | 7 +++++++ .../pylatest_conda_forge_mkl_osx-64_conda.lock | 14 ++++++++++++++ ...ylatest_pip_openblas_pandas_linux-64_conda.lock | 4 ++++ .../azure/pymin_conda_forge_mkl_win-64_conda.lock | 4 ++++ ..._forge_openblas_ubuntu_2204_linux-64_conda.lock | 8 ++++++++ build_tools/circle/doc_linux-64_conda.lock | 14 ++++++++++++++ .../doc_min_dependencies_linux-64_conda.lock | 10 ++++++++++ 7 files changed, 61 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 09249ad17160b..bef3a59d9df8c 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 @@ -210,10 +210,17 @@ 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 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.31-py311h00856b1_0.conda#4f1cc2c95c25fe838acabfa8dc0d48ff 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-1.13.1-py311h517d4fd_0.conda#764b0e055f59dbd7d114d32b8c6e55e6 +======= +https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.26-py311h00856b1_1.conda#4d1715777d0485d6e455b64f72794aa4 +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 +>>>>>>> d03c351780 (:lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29053)) 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 4b7200379d4fe..c400171503ecd 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 @@ -34,9 +34,14 @@ https://conda.anaconda.org/conda-forge/osx-64/gmp-6.3.0-h73e2aa4_1.conda#92f8d74 https://conda.anaconda.org/conda-forge/osx-64/isl-0.26-imath32_h2e86a7b_101.conda#d06222822a9144918333346f145b68c6 https://conda.anaconda.org/conda-forge/osx-64/lerc-4.0.0-hb486fe8_0.tar.bz2#f9d6a4c82889d5ecedec1d90eb673c55 https://conda.anaconda.org/conda-forge/osx-64/libgfortran5-13.2.0-h2873a65_3.conda#e4fb4d23ec2870ff3c40d10afe305aec +<<<<<<< HEAD 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/libxml2-2.12.7-h3e169fe_1.conda#ddb63049aa7bd9f08f2cdc5a1c144d1a +======= +https://conda.anaconda.org/conda-forge/osx-64/libhwloc-2.10.0-default_h456cccd_1001.conda#d2dc768b14cdf226a30a8eab15641305 +https://conda.anaconda.org/conda-forge/osx-64/libllvm16-16.0.6-hbedff68_3.conda#8fd56c0adc07a37f93bd44aa61a97c90 +>>>>>>> d03c351780 (:lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29053)) https://conda.anaconda.org/conda-forge/osx-64/ninja-1.12.1-h3c5361c_0.conda#a0ebabd021c8191aeb82793fe43cfdcb https://conda.anaconda.org/conda-forge/osx-64/sigtool-0.1.3-h88f4db0_0.tar.bz2#fbfb84b9de9a6939cb165c02c69b1865 https://conda.anaconda.org/conda-forge/osx-64/tapi-1100.0.11-h9ce4665_0.tar.bz2#f9ff42ccf809a21ba6f8607f8de36108 @@ -112,12 +117,21 @@ 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 +<<<<<<< HEAD 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_16.conda#c50c939d1bf9785561220b2cfbb98cb9 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_16.conda#b5dacba087761db21ba9eb69b2c1718b +======= +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-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 +>>>>>>> d03c351780 (:lock: :robot: CI Update lock files for main 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/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 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 449ca9a70121d..895ac637be4d9 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 @@ -62,7 +62,11 @@ 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 +<<<<<<< HEAD # pip zipp @ https://files.pythonhosted.org/packages/20/38/f5c473fe9b90c8debdd29ea68d5add0289f1936d6f923b6b9cc0b931194c/zipp-3.19.2-py3-none-any.whl#sha256=f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c +======= +# pip zipp @ https://files.pythonhosted.org/packages/da/55/a03fd7240714916507e1fcf7ae355bd9d9ed2e6db492595f1a67f61681be/zipp-3.18.2-py3-none-any.whl#sha256=dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e +>>>>>>> d03c351780 (:lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29053)) # 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/07/e0/0e30ca5c6c5bcae86df9583c30807ff26e0b991e76f266b81224410663e4/coverage-7.5.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=2e079c9ec772fedbade9d7ebc36202a1d9ef7291bc9b3a024ca395c4d52853d7 # pip imageio @ https://files.pythonhosted.org/packages/a3/b6/39c7dad203d9984225f47e0aa39ac3ba3a47c77a02d0ef2a7be691855a06/imageio-2.34.1-py3-none-any.whl#sha256=408c1d4d62f72c9e8347e7d1ca9bc11d8673328af3913868db3b828e28b40a4c 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 e6fe3ab2d07dd..5c59745ab86d4 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 @@ -91,7 +91,11 @@ https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.2-pyhd8ed1ab_0.conda#0f 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 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/win-64/fonttools-4.53.0-py39ha55e580_0.conda#7c4625b8a1013dd22e924f1fa9fbc605 +======= +https://conda.anaconda.org/conda-forge/win-64/fonttools-4.51.0-py39ha55989b_0.conda#5d19302bab29e347116b743e793aa7d6 +>>>>>>> d03c351780 (:lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29053)) 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 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 6c03c413bb212..9aa3a50a4ee7f 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 @@ -186,7 +186,11 @@ https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.co https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda#5ede4753180c7a550a443c430dc8ab52 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/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-1.13.1-py39haf93ffa_0.conda#492a2cd65862d16a4aaf535ae9ccb761 @@ -196,7 +200,11 @@ https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.1.0-py39h85c637f_1.conda 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/pyqt-5.15.9-py39h52134e7_5.conda#e1f148e57d071b09187719df86f513c1 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.8.4-py39hf3d152e_2.conda#bd956c7563b6a6b27521b83623c74e22 +<<<<<<< HEAD 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_0.conda#1ad3afced398492586ca1bef70328be4 +>>>>>>> d03c351780 (:lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29053)) 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 diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index 57908c8c4026f..5a17119bf702d 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -240,8 +240,13 @@ https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.6.1-py39hbbab4d 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 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.31-py39ha963410_0.conda#ef7ffefe34eae8f69a2ed0cdf2a27678 https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.2-pyhd8ed1ab_0.conda#8dab97d8a9616e07d779782995710aed +======= +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 +>>>>>>> d03c351780 (:lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29053)) 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-1.13.1-py39haf93ffa_0.conda#492a2cd65862d16a4aaf535ae9ccb761 @@ -250,7 +255,11 @@ https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.8.4-py39h10d1f 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 https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.2-py39hd92a3bb_0.conda#2f6c03d60e71f13d92d511b06193f007 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/noarch/tifffile-2024.5.22-pyhd8ed1ab_0.conda#3930cabe8ca8c8594026fa8768cae75c +======= +https://conda.anaconda.org/conda-forge/noarch/tifffile-2024.5.10-pyhd8ed1ab_0.conda#125438a8b679e4c08ee8f244177216c9 +>>>>>>> d03c351780 (:lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29053)) https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.9-py39h52134e7_5.conda#e1f148e57d071b09187719df86f513c1 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 @@ -323,6 +332,11 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # 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 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 f445fe196a6da..62adb69fbb97b 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -125,7 +125,11 @@ 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 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/noarch/fsspec-2024.6.0-pyhff2d567_0.conda#ad6af3f92e71b1579ac2362b6cf29105 +======= +https://conda.anaconda.org/conda-forge/noarch/fsspec-2024.5.0-pyhff2d567_0.conda#d73e9932511ef7670b2cc0ebd9dfbd30 +>>>>>>> d03c351780 (:lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#29053)) 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-h5877db1_4.conda#aa5735fc506449d6bc2ec27bc066364e https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.80.2-hb6ce0ca_0.conda#a965aeaf060289528a3fbe09326edae2 @@ -210,9 +214,15 @@ 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_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 +<<<<<<< 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/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)) 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 From 20b620f1265064f98531b1415604674baa63f404 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Mon, 9 Sep 2024 11:32:38 +0200 Subject: [PATCH 003/148] 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 --- build_tools/circle/doc_linux-64_conda.lock | 14 ++++++++++++++ .../doc_min_dependencies_linux-64_conda.lock | 13 +++++++++++++ doc/developers/contributing.rst | 10 +++++++++- doc/tutorial/machine_learning_map/README.md | 17 +++++++++++++++++ 4 files changed, 53 insertions(+), 1 deletion(-) create mode 100644 doc/tutorial/machine_learning_map/README.md diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index 5a17119bf702d..18b521b853aca 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -1,6 +1,10 @@ # Generated by conda-lock. # platform: linux-64 +<<<<<<< HEAD # input_hash: f6f3862aafcafa139a322e498517c3db58e1b8db95f1b1ca8c18f5b70d446dc9 +======= +# input_hash: beab3d7262ec74c4ef8c9050098de8b9fe7910606e7bd4ff52687972bff35868 +>>>>>>> 34db65a3ad (DOC use pydata-sphinx-theme for the website (#29038)) @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 @@ -192,7 +196,11 @@ https://conda.anaconda.org/conda-forge/linux-64/xcb-util-image-0.4.0-h8ee46fc_1. 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 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/noarch/zipp-3.18.1-pyhd8ed1ab_0.conda#c77c4aabc01b156a8cb4395f0233d335 +======= +https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda#2e4d6bc0b14e10f895fc6791a7d9b26a +>>>>>>> 34db65a3ad (DOC use pydata-sphinx-theme for the website (#29038)) 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 @@ -220,6 +228,7 @@ https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1a 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/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-1.24.4-haf2f30d_0.conda#926c2c7ee7a0b48d6d70783a33f7bc80 @@ -265,8 +274,13 @@ https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.22.0-py39hddac248 https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_2.conda#b713b116feaf98acdba93ad4d7f90ca1 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 +<<<<<<< HEAD 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-0.15.3-pyhd8ed1ab_0.conda#55e445f4fcb07f2471fb0e1102d36488 +======= +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 +>>>>>>> 34db65a3ad (DOC use pydata-sphinx-theme for the website (#29038)) 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_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index 62adb69fbb97b..3fcefe17522be 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 +1,10 @@ # Generated by conda-lock. # platform: linux-64 +<<<<<<< HEAD # input_hash: aa64e81a701c97b7c4cf149f108c3ca59fc65572bfda79dbaeb2d093afc8a665 +======= +# input_hash: 6c9ff93ed18fe7c2e8387a4c3d7104701555959a32e797c1cb83593137afe155 +>>>>>>> 34db65a3ad (DOC use pydata-sphinx-theme for the website (#29038)) @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 @@ -177,7 +181,11 @@ https://conda.anaconda.org/conda-forge/linux-64/xcb-util-image-0.4.0-h8ee46fc_1. 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 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/noarch/zipp-3.18.1-pyhd8ed1ab_0.conda#c77c4aabc01b156a8cb4395f0233d335 +======= +https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda#2e4d6bc0b14e10f895fc6791a7d9b26a +>>>>>>> 34db65a3ad (DOC use pydata-sphinx-theme for the website (#29038)) 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 @@ -203,6 +211,7 @@ https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1a 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/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-1.24.4-haf2f30d_0.conda#926c2c7ee7a0b48d6d70783a33f7bc80 @@ -249,7 +258,11 @@ 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 +<<<<<<< HEAD https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.15.3-pyhd8ed1ab_0.conda#55e445f4fcb07f2471fb0e1102d36488 +======= +https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.15.2-pyhd8ed1ab_0.conda#ce99859070b0e17ccc63234ca58f3ed8 +>>>>>>> 34db65a3ad (DOC use pydata-sphinx-theme for the website (#29038)) 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/developers/contributing.rst b/doc/developers/contributing.rst index 3184d71426c95..03c0addacb156 100644 --- a/doc/developers/contributing.rst +++ b/doc/developers/contributing.rst @@ -67,12 +67,19 @@ 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 @@ -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 004/148] 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 @ 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 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 895ac637be4d9..d09b283767601 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 +1,10 @@ # Generated by conda-lock. # platform: linux-64 +<<<<<<< HEAD # input_hash: 37f8029b6bb116e0d5856093424791a8c1ddc3f493e20fcb5d02cd32d516523d +======= +# input_hash: 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 @ 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 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 +>>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) # 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/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 005/148] 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 006/148] 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 007/148] 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 008/148] 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 009/148] 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 010/148] 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 011/148] 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 012/148] 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 013/148] 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 014/148] 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 052/148] 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 053/148] :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 b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index a1faa40f2baad..81d820f94e504 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 @@ -1,14 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -<<<<<<< HEAD -<<<<<<< HEAD -# input_hash: 50fed47bc507d9ee3dbf5ff7a2247cb88944928bd5797e534ebdf8ece2d858ec -======= -# input_hash: 29e3d76c436ee8cb86d5976aa41ef8822f991c469202b8d12fc27e43576ec805 ->>>>>>> a63b021310 (MAINT Require meson-python >= 0.16 and remove temporary work-arounds (#29081)) -======= -# input_hash: 29e3d76c436ee8cb86d5976aa41ef8822f991c469202b8d12fc27e43576ec805 ->>>>>>> 0dba98f1eb (MNT Update main-ci lock files (#29388)) +# 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 @@ -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 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 @@ -25,13 +18,13 @@ https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar 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-0.9.0-hd590300_0.conda#71b89db63b5b504e7afc8ad901172e1e -https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hd590300_5.conda#69b8b6202a07720f448be700e300ccf4 -https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.28.1-hd590300_0.conda#dcde58ff9a1f30b0037a2315d1846d1f +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/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-1.0.9-h166bdaf_9.conda#61641e239f96eae2b8492dc7e755828c +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 @@ -41,30 +34,19 @@ https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.1.0-hc5f4f2c_0.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 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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 +https://conda.anaconda.org/conda-forge/osx-64/clangxx_osx-64-16.0.6-hb91bd55_18.conda#0d120b5e06d2ea6c9103f2017be1ff22 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_environment.yml b/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml index 7e85b28b3f6c4..4752e10556a92 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml @@ -25,3 +25,4 @@ dependencies: - cython - threadpoolctl - meson-python + - meson<1.5 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 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 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/bzip2-1.0.8-h2466b09_7.conda#276e7ffe9ffe39688abc665ef0f45596 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 https://conda.anaconda.org/conda-forge/win-64/libbrotlicommon-1.1.0-hcfcfb64_1.conda#f77f319fb82980166569e1280d5b2864 @@ -44,16 +29,9 @@ 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 -<<<<<<< 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/openssl-3.3.1-h2466b09_1.conda#aa36aca82d1ffd26bee88ac7dc9e1ee3 ->>>>>>> 0dba98f1eb (MNT Update main-ci lock files (#29388)) +https://conda.anaconda.org/conda-forge/win-64/openssl-3.3.1-h2466b09_2.conda#375dbc2a4d5a2e4c738703207e8e368b 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 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 https://conda.anaconda.org/conda-forge/win-64/krb5-1.21.3-hdf4eb48_0.conda#31aec030344e962fbd7dbbbbd68e60a9 @@ -62,24 +40,24 @@ 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/libxml2-2.12.7-h283a6d9_1.conda#7ab2653cc21c44a1370ef3b409261b3d +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/python-3.9.19-h4de0772_0_cpython.conda#b6999bc275e0e6beae7b1c8ea0be1e85 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-2024.6.2-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/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-3.0.10-py39h99910a6_0.conda#8ebc2fca8a6840d0694f37e698f4e59c -https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.0-pyhd8ed1ab_2.conda#8d652ea2ee8eaee02ed8dc820bc794aa +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_0.conda#2f4204ba38a8654b132e5ae03287efb8 +https://conda.anaconda.org/conda-forge/win-64/libclang13-18.1.8-default_ha5278ca_1.conda#30a167d5b69555fbf39192a23e40df52 https://conda.anaconda.org/conda-forge/win-64/libglib-2.80.3-h7025463_1.conda#53c80e0ed9a3905ca7047c03756a5caa -https://conda.anaconda.org/conda-forge/win-64/libhwloc-2.11.0-default_h8125262_1000.conda#065e86390dcd9304259ad8b627f724bd +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/libintl-devel-0.22.5-h5728263_2.conda#a2ad82fae23975e4ccbfab2847d31d48 https://conda.anaconda.org/conda-forge/win-64/libtiff-4.6.0-hddb2be6_3.conda#6d1828c9039929e2f185c5fa9d133018 https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2#2ba8498c1018c1e9c61eb99b973dfe19 @@ -88,7 +66,7 @@ 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-70.1.1-pyhd8ed1ab_0.conda#985e9e86e1b0fc75a74a9bfab9309ef7 +https://conda.anaconda.org/conda-forge/noarch/setuptools-71.0.4-pyhd8ed1ab_0.conda#ee78ac9c720d0d02fcfd420866b82ab1 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 @@ -100,36 +78,22 @@ 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-3.19.2-pyhd8ed1ab_0.conda#49808e59df5535116f6878b2a820d6f4 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-7.5.4-py39ha55e580_0.conda#1407b17a896e4d64913f72a99c607353 +https://conda.anaconda.org/conda-forge/win-64/coverage-7.6.0-py39ha55e580_0.conda#c6331a8b8505391abf8a12b90c8e449c https://conda.anaconda.org/conda-forge/win-64/glib-tools-2.80.3-h4394cf3_1.conda#12d270a5f8b8ae0a9536c1960f21e0aa 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 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https://conda.anaconda.org/conda-forge/win-64/mkl-2024.1.0-h66d3029_692.conda#b43ec7ed045323edeff31e348eea8652 @@ -145,11 +109,11 @@ https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-22_win64_mkl.conda# https://conda.anaconda.org/conda-forge/win-64/liblapack-3.9.0-22_win64_mkl.conda#c752cc2af9f3d8d7b2fdebb915a33ef7 https://conda.anaconda.org/conda-forge/win-64/liblapacke-3.9.0-22_win64_mkl.conda#db33ffa4bae1d2f6d5602afaa048bf6b https://conda.anaconda.org/conda-forge/win-64/numpy-2.0.0-py39h60232e0_0.conda#a0e44751d8bc67ef2932d832304872a9 -https://conda.anaconda.org/conda-forge/win-64/qt-main-5.15.8-h06adc49_22.conda#919650dc31edac6a17a99157aec4f87c +https://conda.anaconda.org/conda-forge/win-64/qt-main-5.15.8-h06adc49_23.conda#1f6a464e4fc36114ac7286d1db8d260e https://conda.anaconda.org/conda-forge/win-64/blas-devel-3.9.0-22_win64_mkl.conda#adeb834f3b7b06f3d77cd90b7c9d08f0 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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 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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 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-https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.5.0-hfac3d4d_0.conda#f5126317dd0ce0ba26945e411ecc6960 +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/linux-64/libgcrypt-1.11.0-h4ab18f5_0.conda#0a00e32cabe3e571c0611387e7bc2042 +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 https://conda.anaconda.org/conda-forge/linux-64/mkl-2024.1.0-ha957f24_693.conda#ff0f4abf6f94e36a918f1ef4dbeb9769 @@ -236,7 +236,7 @@ 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-2024.7.0-pyhd8ed1ab_0.conda#755e47653ae38f5c50f1435af756e844 +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/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 054/148] 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 055/148] 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
START
>50
samples
>50...
get
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NO
NO
predicting a
category
predicting...
YES
YES
do you have
labeled
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do you hav...
YES
YES
predicting a
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predicting...
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NO
just
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NO
predicting
structure
predicting...
NO
NO
tough
luck
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<100K
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Linear
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text
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text...
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Kernel
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KNeighbors
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SVC
SVC
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😭
😭
Naive
Bayes
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YES
YES
classification
classification
number of
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NO
<10K
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NO
NO
YES
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MeanShift
MeanShift
VBGMM
VBGMM
YES
YES
MiniBatch
KMeans
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NO
clustering
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YES
YES
Spectral
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Spectral...
GMM
GMM
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<100K
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YES
few features
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few features...
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YES
SGD
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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
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Ensemble...
😭
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regression
regression
Ramdomized
PCA
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YES
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😭
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NO
IsoMap
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Spectral
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YES
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LLE
😭
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dimensionality
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scikit-learn
algorithm cheat sheet
scikit-learn...Text is not SVG - cannot display +
START
START
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>50...
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predicting a
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predicting...
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YES
do you have
labeled
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do you hav...
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YES
predicting a
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NO
just
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just...
NO
NO
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tough
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NO
Linear
SVC
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YES
text
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😭
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NO
SVC
SVC
Ensemble
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Ensemble...
😭
😭
Naive
Bayes
Naive...
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YES
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classification
number of
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known
number of...
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NO
<10K
samples
<10K...
<10K
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NO
NO
NO
NO
YES
YES
MeanShift
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VBGMM
VBGMM
YES
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MiniBatch
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NO
clustering
clustering
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YES
YES
Spectral
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Spectral...
GMM
GMM
😭
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<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
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😭
😭
regression
regression
Ramdomized
PCA
Ramdomized...
YES
YES
<10K
samples
<10K...
😭
😭
Kernel
Approximation
Kernel...
NO
NO
IsoMap
IsoMap
Spectral
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YES
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LLE
😭
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dimensionality
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scikit-learn
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scikit-learn...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 056/148] 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 057/148] 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 058/148] 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 059/148] 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 060/148] 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 061/148] :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 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9901702ccbbf1584cae428f231b2e696d7e42aca Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 29 Jul 2024 01:41:50 -0700 Subject: [PATCH 062/148] :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 @ 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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 @ 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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 @ 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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 063/148] :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 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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 https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.8.2-he17ee6b_6.conda#4e3d1bb2ade85619ac2163e695c2cc1b https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hd590300_1.conda#f27a24d46e3ea7b70a1f98e50c62508f @@ -141,7 +141,7 @@ 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-py312h8572e83_1.conda#c1e71f2bc05d8e8e033aefac2c490d05 https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.16-hb7c19ff_0.conda#51bb7010fc86f70eee639b4bb7a894f5 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_1.conda#b8afb3e3cb3423cc445cf611ab95fdb0 +https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.9.0-hdb1bdb2_0.conda#5badfbdb2688d8aaca7bd3c98d557b97 https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.11.1-default_hecaa2ac_1000.conda#f54aeebefb5c5ff84eca4fb05ca8aa3a 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 @@ -174,7 +174,7 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.4-h0b41bf4_2.co 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/aws-c-auth-0.7.22-hbd3ac97_10.conda#7ca4abcc98c7521c02f4e8809bbe40df https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.10.4-hcd6a914_8.conda#b81c45867558446640306507498b2c6b -https://conda.anaconda.org/conda-forge/linux-64/azure-core-cpp-1.12.0-h830ed8b_0.conda#320d066f9cad598854f4af32c7c82931 +https://conda.anaconda.org/conda-forge/linux-64/azure-core-cpp-1.13.0-h935415a_0.conda#debd1677c2fea41eb2233a260f48a298 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-7.6.0-py312h41a817b_0.conda#66c68c204a3eaabc3b4221f1c4bcebbe https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.53.1-py312h41a817b_0.conda#da921c56bcf69a8b97216ecec0cc4015 @@ -188,17 +188,17 @@ 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/libgrpc-1.62.2-h15f2491_0.conda#8dabe607748cb3d7002ad73cd06f1325 https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.7.0-h2c5496b_1.conda#e2eaefa4de2b7237af7c907b8bbc760a 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https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py312h30efb56_0.conda#32633871002ee9902f747d2236e0d122 https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.12.0-h434a139_3.conda#c667c11d1e488a38220ede8a34441bff https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.6.0-h365ddd8_2.conda#22339cf124753bafda336167f80e7860 -https://conda.anaconda.org/conda-forge/linux-64/azure-identity-cpp-1.8.0-hdb0d106_1.conda#a297ffb4b505f51d0f58352c5c13971b -https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.6.0-he3f277c_1.conda#8a10bb068b138dd473300b5fe34a1865 +https://conda.anaconda.org/conda-forge/linux-64/azure-identity-cpp-1.8.0-hd126650_2.conda#36df3cf05459de5d0a41c77c4329634b +https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.7.0-h10ac4d7_1.conda#ab6d507ad16dbe2157920451d662e4a1 https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.5-haf2f30d_0.conda#c5252c02592373fa8caf5a5327165a89 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-https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-16.1.0-he02047a_14_cpu.conda#4381fff40c7bcf7ca0142ed4b0120dbc +https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-17.0.0-he02047a_2_cpu.conda#241bbbd938197304409716fa9510b5f2 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.9.1-py312h9201f00_0.conda#e1dc3a7d999666f5c58cbb391940e235 https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.2.1-py312h389efb2_0.conda#37038b979f8be9666d90a852879368fb https://conda.anaconda.org/conda-forge/linux-64/pytorch-cpu-2.1.2-cpu_mkl_py312he2922ba_104.conda#d258a5ab0b958cbdd0573f5ca2ef8895 -https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-16.1.0-hc9a23c6_14_cpu.conda#c35b4b76394d7414888fd4d66d7ae96a +https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-17.0.0-hc9a23c6_2_cpu.conda#7c6bbc213f37b593c6a90a36b371e48d https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.9.1-py312h7900ff3_0.conda#a5031dbd62fa2f33e180f5d7f331b6ea -https://conda.anaconda.org/conda-forge/linux-64/pyarrow-16.1.0-py312h9cebb41_4.conda#2097b6ae7186e10c9aab1228636b804f +https://conda.anaconda.org/conda-forge/linux-64/pyarrow-17.0.0-py312h9cebb41_0.conda#2ae7d9f1d04309e421b5feb4ab8bede5 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 22357821f011e..d57f326975eee 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 @@ -23,7 +23,7 @@ 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-hef8daea_0.conda#4101cde4241c92aeac310d65e6791579 +https://conda.anaconda.org/conda-forge/osx-64/libcxx-18.1.8-hef8daea_1.conda#8309952890f89dbd4283a18633ecdfe3 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 @@ -50,7 +50,7 @@ https://conda.anaconda.org/conda-forge/osx-64/libgfortran-5.0.0-13_2_0_h97931a8_ https://conda.anaconda.org/conda-forge/osx-64/libhwloc-2.11.1-default_h456cccd_1000.conda#a14989f6bbea46e6ec4521a403f63ff2 https://conda.anaconda.org/conda-forge/osx-64/libllvm16-16.0.6-hbedff68_3.conda#8fd56c0adc07a37f93bd44aa61a97c90 https://conda.anaconda.org/conda-forge/osx-64/libtiff-4.6.0-h129831d_3.conda#568593071d2e6cea7b5fc1f75bfa10ca -https://conda.anaconda.org/conda-forge/osx-64/mpfr-4.2.1-h4f6b447_1.conda#b90df08f0deb2f58631447c1462c92a7 +https://conda.anaconda.org/conda-forge/osx-64/mpfr-4.2.1-hc80595b_2.conda#fc9b5179824146b67ad5a0b053b253ff https://conda.anaconda.org/conda-forge/osx-64/python-3.12.4-h37a9e06_0_cpython.conda#94e2b77992f580ac6b7a4fc9b53018b3 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 @@ -89,12 +89,12 @@ https://conda.anaconda.org/conda-forge/osx-64/fonttools-4.53.1-py312hbd25219_0.c 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-1.5.0-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/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/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/osx-64/cctools-986-h40f6528_0.conda#b7a2ca0062a6ee8bc4e83ec887bef942 https://conda.anaconda.org/conda-forge/osx-64/clang-16.0.6-default_h179603d_11.conda#29c8b527d8b8fac52f5e2cf6abfcdc93 @@ -108,7 +108,7 @@ 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-2.0.0-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 @ 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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 @ 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/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 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/b0/54/3aef476dc3820ab7b002d832c4b77fa9b7cb81b773917e0e7d6aa3aedcd5/tifffile-2024.7.21-py3-none-any.whl#sha256=7f335b5d6ca49401fe0f1d87deb206f5dae47297e47b1ed52a676d05d6d26798 -# pip lightgbm @ 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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 https://repo.anaconda.com/pkgs/main/linux-64/expat-2.6.2-h6a678d5_0.conda#55049db2772dae035f6b8a95f72b5970 https://repo.anaconda.com/pkgs/main/linux-64/fftw-3.3.9-h5eee18b_2.conda#db1df41113accc18ec59a99f1631bfcd https://repo.anaconda.com/pkgs/main/linux-64/icu-73.1-h6a678d5_0.conda#6d09df641fc23f7d277a04dc7ea32dd4 -https://repo.anaconda.com/pkgs/main/linux-64/jpeg-9e-h5eee18b_1.conda#ac373800fda872108412d1ccfe3fa572 +https://repo.anaconda.com/pkgs/main/linux-64/jpeg-9e-h5eee18b_2.conda#5662b75d7140dea1fb183348af40bc49 https://repo.anaconda.com/pkgs/main/linux-64/lerc-3.0-h295c915_0.conda#b97309770412f10bed8d9448f6f98f87 https://repo.anaconda.com/pkgs/main/linux-64/libdeflate-1.17-h5eee18b_1.conda#82831ef0b6c9595382d74e0c281f6742 https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_1.conda#70646cc713f0c43926cfdcfe9b695fe0 @@ -98,7 +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 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_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock index 0f94c8c06ddd7..5df3e6bd397fe 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 @@ -4,7 +4,7 @@ @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_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/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/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 @@ -84,11 +84,11 @@ https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1 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/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/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-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/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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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 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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 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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://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl#sha256=b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f @@ -322,13 +322,13 @@ 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/05/d2/1d59d9a70d684b1eb3eb3a0b80a36b4e1d691e94af5d53aee56b1ad5240b/jupyterlite_core-0.3.0-py3-none-any.whl#sha256=247cc34ae6fedda41b15ce4778997164508b2039bc92480665cadfe955193467 +# pip jupyterlite-core @ https://files.pythonhosted.org/packages/e1/e5/87d8eea7c9bdcbcc903481fe83fd9b674917b7156f802a9d8fb2bb603f79/jupyterlite_core-0.4.0-py3-none-any.whl#sha256=5035240760c58a52fb035ad5de3d4ab180817c12a7245a5a18ea6a84f51a752e # 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/42/ce/87fadd7eaa01caaa564d3345025b983f72b4200abc82245068bd2664fb56/jupyterlite_pyodide_kernel-0.3.2-py3-none-any.whl#sha256=ae600571fa755b6fd7a2633a171de3fe490f2b1264bef32cdd7e8c34c95cd5ff +# pip jupyterlite-pyodide-kernel @ https://files.pythonhosted.org/packages/a5/f4/2a26ab5f964bb7453ffc2f86fcbff7f8afc2f0438e2a2fe9e84a09a68d07/jupyterlite_pyodide_kernel-0.4.0-py3-none-any.whl#sha256=2f2c801d3912cb6ca553ca44e10ab2bb0c5089a2d7d7b368aab34865f5df63c7 # 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 # 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 +# pip jupyterlite-sphinx @ https://files.pythonhosted.org/packages/38/c9/5f1142c005cf8d75830b10029e53f074324bc85cfca1f1d0f22a207b771c/jupyterlite_sphinx-0.9.3-py3-none-any.whl#sha256=be6332d16490ea2fa90b78187a2c5e1c357195966a25741d60b1790346571041 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 28263647280f0..01f79e505f6d8 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -204,7 +204,7 @@ 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/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 064/148] 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 -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 -https://repo.anaconda.com/pkgs/main/linux-64/libgfortran-ng-11.2.0-h00389a5_1.conda#7429b67ab7b1d7cb99b9d1f3ddaec6e3 -https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda#b372c0eea9b60732fdae4b817a63c8cd -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 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-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-2024.7.4-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 -https://repo.anaconda.com/pkgs/main/noarch/execnet-1.9.0-pyhd3eb1b0_0.conda#f895937671af67cebb8af617494b3513 -https://repo.anaconda.com/pkgs/main/linux-64/glib-2.78.4-h6a678d5_0.conda#045ff487547f7b2b7ff01648681b8ebe -https://repo.anaconda.com/pkgs/main/noarch/iniconfig-1.1.1-pyhd3eb1b0_0.tar.bz2#e40edff2c5708f342cef43c7f280c507 -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-10.4.0-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 -https://repo.anaconda.com/pkgs/main/linux-64/pyqt5-sip-12.13.0-py39h5eee18b_0.conda#256840c3841b52346ea5743be8490ede -https://repo.anaconda.com/pkgs/main/linux-64/setuptools-69.5.1-py39h06a4308_0.conda#3eb144d481b39c0fbbced789dd9b76b3 -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/linux-64/tomli-2.0.1-py39h06a4308_0.conda#b06dffe7ddca2645ed72f5116f0a087d -https://repo.anaconda.com/pkgs/main/linux-64/tornado-6.4.1-py39h5eee18b_0.conda#ec35d759636a13f934dd1610082bdbd0 -https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py39h06a4308_0.conda#40bb60408c7433d767fd8c65b35bc4a0 -https://repo.anaconda.com/pkgs/main/linux-64/coverage-7.2.2-py39h5eee18b_0.conda#e9da151b7e1f56be2cb569c65949a1d2 -https://repo.anaconda.com/pkgs/main/linux-64/dbus-1.13.18-hb2f20db_0.conda#6a6a6f1391f807847404344489ef6cf4 -https://repo.anaconda.com/pkgs/main/linux-64/gstreamer-1.14.1-h5eee18b_1.conda#f2f26e6f869b5d87f41bd059fae47c3e -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-7.4.4-py39h06a4308_0.conda#05ff36245067ecb0b6796a4f31a81f1c -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 -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 +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/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/libgettextpo-0.22.5-h59595ed_2.conda#172bcc51059416e7ce99e7b528cede83 +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 +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-1.3.5-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/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 +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/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 +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-renderproto-0.11.1-h7f98852_1002.tar.bz2#06feff3d2634e3097ce2fe681474b534 +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-xf86vidmodeproto-2.3.1-h7f98852_1002.tar.bz2#3ceea9668625c18f19530de98b15d5b0 +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/expat-2.6.2-h59595ed_0.conda#53fb86322bdb89496d7579fe3f02fd61 +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-73.2-h59595ed_0.conda#cc47e1facc155f91abd89b11e48e72ff 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+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-2.120-openblas.conda#c8f6916a81a340650078171b1d852574 +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-4.2.3-py39hac2352c_1.tar.bz2#6fb0628d6195d8b6caa2422d09296399 +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-3.3.4-py39hf3d152e_0.tar.bz2#cbaec993375a908bbe506dc7328d747c diff --git a/build_tools/circle/doc_min_dependencies_environment.yml b/build_tools/circle/doc_min_dependencies_environment.yml index 84cfe2fc53d49..4ccadd4439958 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=1.4.0 # 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 065/148] 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 066/148] 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 067/148] 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 068/148] 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 069/148] 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 070/148] 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 071/148] :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/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-71.0.4-pyhd8ed1ab_0.conda#ee78ac9c720d0d02fcfd420866b82ab1 +https://conda.anaconda.org/conda-forge/noarch/setuptools-72.1.0-pyhd8ed1ab_0.conda#e06d4c26df4f958a8d38696f2c344d15 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 https://conda.anaconda.org/conda-forge/linux-aarch64/tornado-6.4.1-py39ha3e8b56_0.conda#60ad0fcecca6af49fe5888a408618d8a 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/wheel-0.44.0-pyhd8ed1ab_0.conda#d44e3b085abcaef02983c6305b84b584 +https://conda.anaconda.org/conda-forge/linux-aarch64/xcb-util-image-0.4.0-h5c728e9_2.conda#b82e5c78dbbfa931980e8bfe83bce913 +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/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/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/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 072/148] :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 073/148] :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 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https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b @@ -53,40 +48,35 @@ 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 https://conda.anaconda.org/conda-forge/linux-64/xorg-renderproto-0.11.1-h7f98852_1002.tar.bz2#06feff3d2634e3097ce2fe681474b534 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-xf86vidmodeproto-2.3.1-h7f98852_1002.tar.bz2#3ceea9668625c18f19530de98b15d5b0 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 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https://conda.anaconda.org/conda-forge/osx-64/llvm-tools-16.0.6-hbedff68_3.conda#e9356b0807462e8f84c1384a8da539a5 @@ -73,30 +73,30 @@ 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-71.0.4-pyhd8ed1ab_0.conda#ee78ac9c720d0d02fcfd420866b82ab1 +https://conda.anaconda.org/conda-forge/noarch/setuptools-72.1.0-pyhd8ed1ab_0.conda#e06d4c26df4f958a8d38696f2c344d15 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_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 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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/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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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/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 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/bzip2-1.0.8-h2466b09_7.conda#276e7ffe9ffe39688abc665ef0f45596 -https://conda.anaconda.org/conda-forge/win-64/icu-73.2-h63175ca_0.conda#0f47d9e3192d9e09ae300da0d28e0f56 +https://conda.anaconda.org/conda-forge/win-64/double-conversion-3.3.0-h63175ca_0.conda#1a8bc18b24014167b2184c5afbe6037e +https://conda.anaconda.org/conda-forge/win-64/graphite2-1.3.13-h63175ca_1003.conda#3194499ee7d1a67404a87d0eefdd92c6 +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 https://conda.anaconda.org/conda-forge/win-64/libdeflate-1.20-hcfcfb64_0.conda#b12b5bde5eb201a1df75e49320cc938a https://conda.anaconda.org/conda-forge/win-64/libffi-3.4.2-h8ffe710_5.tar.bz2#2c96d1b6915b408893f9472569dee135 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/libogg-1.3.5-h2466b09_0.conda#44a4d173e62c5ed6d715f18ae7c46b7a https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.46.0-h2466b09_0.conda#951b0a3a463932e17414cd9f047fa03d 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 https://conda.anaconda.org/conda-forge/win-64/openssl-3.3.1-h2466b09_2.conda#375dbc2a4d5a2e4c738703207e8e368b +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 https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h5226925_1.conda#fc048363eb8f03cd1737600a5d08aafe @@ -39,11 +49,11 @@ https://conda.anaconda.org/conda-forge/win-64/libbrotlidec-1.1.0-hcfcfb64_1.cond 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/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/libxml2-2.12.7-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/python-3.9.19-h4de0772_0_cpython.conda#b6999bc275e0e6beae7b1c8ea0be1e85 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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/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 -# 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 pyzmq @ https://files.pythonhosted.org/packages/e1/5c/c8c1db048dd121ca4298c4ac09c9b2c235b4cb3bd1ff8d4c60c52b40e356/pyzmq-26.1.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=5a6ed52f0b9bf8dcc64cc82cce0607a3dfed1dbb7e8c6f282adfccc7be9781de # 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 @@ -324,11 +298,11 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # 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 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/a5/f4/2a26ab5f964bb7453ffc2f86fcbff7f8afc2f0438e2a2fe9e84a09a68d07/jupyterlite_pyodide_kernel-0.4.0-py3-none-any.whl#sha256=2f2c801d3912cb6ca553ca44e10ab2bb0c5089a2d7d7b368aab34865f5df63c7 +# 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 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 # 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 -https://conda.anaconda.org/conda-forge/linux-64/c-blosc2-2.15.0-h6d6b9e4_1.conda#0dbd746357ef08ceb6c732c391e6a98c +https://conda.anaconda.org/conda-forge/linux-64/c-blosc2-2.15.1-hc57e6cf_0.conda#5f84961d86d0ef78851cb34f9d5e31fe 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/libavif16-1.1.0-h9b56c87_0.conda#ab39000b12375e3a30ee79fea996e3c5 +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-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 074/148] 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 075/148] 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 076/148] 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 077/148] 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 078/148] 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 079/148] 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?

    + + From f1f58c392de3f6349751a1fb61caee9a6b25184a Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 12 Aug 2024 01:34:05 -0700 Subject: [PATCH 080/148] :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 @@ https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.44-h0f59acf_0.conda#391 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/s2n-1.4.17-he19d79f_0.conda#e25ac9bf10f8e6aa67727b1cdbe762ef +https://conda.anaconda.org/conda-forge/linux-64/s2n-1.4.19-h3400bea_0.conda#7d6818f07e4471d471be9b4252d7b54c https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.1-ha2e4443_0.conda#6b7dcc7349efd123d493d2dbe85a045f 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/wayland-1.23.0-h5291e77_0.conda#c13ca0abd5d1d31d0eebcf86d51da8a4 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-1.3.1-h4ab18f5_1.conda#9653f1bf3766164d0e65fa723cabbc54 https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda#4d056880988120e29d75bfff282e0f45 -https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.14.10-h826b7d6_1.conda#6961646dded770513a781de4cd5c1fe1 +https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.14.18-h6ea103f_1.conda#b0da9b0d46def0a1190790e623f246d3 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/glog-0.7.1-hbabe93e_0.conda#ff862eebdfeb2fd048ae9dc92510baca 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b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock index 2425532b1bb73..3b8066be2568c 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 @@ -4,7 +4,6 @@ @EXPLICIT https://conda.anaconda.org/conda-forge/osx-64/ca-certificates-2024.7.4-h8857fd0_0.conda#7df874a4b05b2d2b82826190170eaa0f https://conda.anaconda.org/conda-forge/osx-64/libbrotlicommon-1.1.0-h0dc2134_1.conda#9e6c31441c9aa24e41ace40d6151aab6 -https://conda.anaconda.org/conda-forge/osx-64/libdeflate-1.20-h49d49c5_0.conda#d46104f6a896a0bc6a1d37b88b2edf5c https://conda.anaconda.org/conda-forge/osx-64/libexpat-2.6.2-h73e2aa4_0.conda#3d1d51c8f716d97c864d12f7af329526 https://conda.anaconda.org/conda-forge/osx-64/libffi-3.4.2-h0d85af4_5.tar.bz2#ccb34fb14960ad8b125962d3d79b31a9 https://conda.anaconda.org/conda-forge/noarch/libgfortran-devel_osx-64-12.3.0-h0b6f5ec_3.conda#39eeea5454333825d72202fae2d5e0b8 @@ -23,7 +22,8 @@ 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https://conda.anaconda.org/conda-forge/osx-64/llvm-openmp-18.1.8-h15ab845_0.conda#2c3c6c8aaf8728f87326964a82fdc7d8 @@ -49,13 +49,13 @@ https://conda.anaconda.org/conda-forge/osx-64/freetype-2.12.1-h60636b9_2.conda#2 https://conda.anaconda.org/conda-forge/osx-64/libgfortran-5.0.0-13_2_0_h97931a8_3.conda#0b6e23a012ee7a9a5f6b244f5a92c1d5 https://conda.anaconda.org/conda-forge/osx-64/libhwloc-2.11.1-default_h456cccd_1000.conda#a14989f6bbea46e6ec4521a403f63ff2 https://conda.anaconda.org/conda-forge/osx-64/libllvm16-16.0.6-hbedff68_3.conda#8fd56c0adc07a37f93bd44aa61a97c90 -https://conda.anaconda.org/conda-forge/osx-64/libtiff-4.6.0-h129831d_3.conda#568593071d2e6cea7b5fc1f75bfa10ca +https://conda.anaconda.org/conda-forge/osx-64/libtiff-4.6.0-h603087a_4.conda#362626a2aacb976ec89c91b99bfab30b https://conda.anaconda.org/conda-forge/osx-64/mpfr-4.2.1-hc80595b_2.conda#fc9b5179824146b67ad5a0b053b253ff -https://conda.anaconda.org/conda-forge/osx-64/python-3.12.4-h37a9e06_0_cpython.conda#94e2b77992f580ac6b7a4fc9b53018b3 +https://conda.anaconda.org/conda-forge/osx-64/python-3.12.5-h37a9e06_0_cpython.conda#517cb4e16466f8d96ba2a72897d14c48 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/osx-64/cython-3.0.10-py312hede676d_0.conda#3008aa88f0dc67e7144734b16e331ee4 +https://conda.anaconda.org/conda-forge/osx-64/cython-3.0.11-py312h28f332c_0.conda#4ab9ee64007a1e4a79b38e4de31aa2fc 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/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda#f800d2da156d08e289b14e87e43c1ae5 @@ -115,15 +115,15 @@ https://conda.anaconda.org/conda-forge/osx-64/contourpy-1.2.1-py312h9230928_0.co 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/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/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/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/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/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/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 -https://conda.anaconda.org/conda-forge/osx-64/clangxx_osx-64-16.0.6-hb91bd55_18.conda#0d120b5e06d2ea6c9103f2017be1ff22 +https://conda.anaconda.org/conda-forge/osx-64/clangxx_osx-64-16.0.6-hb91bd55_19.conda#81d40fad4c14cc7a893f2e274647c7a4 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 f9827208ac958..b994b147ae513 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 @@ -5,7 +5,7 @@ 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-h46256e1_2.conda#5f0bfd93528771ebc3e340ac1c91a4cd +https://repo.anaconda.com/pkgs/main/osx-64/jpeg-9e-h46256e1_3.conda#b1d9769eac428e11f5f922531a1da2e0 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 @@ -48,13 +48,13 @@ 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 -https://conda.anaconda.org/conda-forge/win-64/libdeflate-1.20-hcfcfb64_0.conda#b12b5bde5eb201a1df75e49320cc938a +https://conda.anaconda.org/conda-forge/win-64/libdeflate-1.21-h2466b09_0.conda#4ebe2206ebf4bf38f6084ad836110361 https://conda.anaconda.org/conda-forge/win-64/libffi-3.4.2-h8ffe710_5.tar.bz2#2c96d1b6915b408893f9472569dee135 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 @@ -59,16 +59,16 @@ 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-3.0.10-py39h99910a6_0.conda#8ebc2fca8a6840d0694f37e698f4e59c +https://conda.anaconda.org/conda-forge/win-64/cython-3.0.11-py39ha51f57c_0.conda#d7dfdb0e5fa3cc89807fc77fe6173c4d 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_1.conda#30a167d5b69555fbf39192a23e40df52 -https://conda.anaconda.org/conda-forge/win-64/libglib-2.80.3-h7025463_1.conda#53c80e0ed9a3905ca7047c03756a5caa +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/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-hddb2be6_3.conda#6d1828c9039929e2f185c5fa9d133018 +https://conda.anaconda.org/conda-forge/win-64/libtiff-4.6.0-hb151862_4.conda#7d35d9aa8f051d548116039f5813c8ec https://conda.anaconda.org/conda-forge/win-64/libxslt-1.1.39-h3df6e99_0.conda#279ee338c9b34871d578cb3c7aa68f70 https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2#2ba8498c1018c1e9c61eb99b973dfe19 https://conda.anaconda.org/conda-forge/noarch/packaging-24.1-pyhd8ed1ab_0.conda#cbe1bb1f21567018ce595d9c2be0f0db @@ -116,7 +116,7 @@ https://conda.anaconda.org/conda-forge/win-64/liblapack-3.9.0-23_win64_mkl.conda 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/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/pyside6-6.7.2-py39h0285922_1.conda#f1e4e1f964077cce3d44bbfd94686a78 +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/scipy-1.13.1-py39h1a10956_0.conda#9f8e571406af04d2f5fdcbecec704505 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 index 3f7ea06a3891b..264049d4abb3a 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 @@ -21,7 +21,7 @@ https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62e https://conda.anaconda.org/conda-forge/linux-64/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/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 @@ -77,10 +77,10 @@ https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda#4d05 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/gcc_linux-64-12.4.0-h6b7512a_0.conda#fec7117a58f5becf76b43dec55064ff9 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/gxx_impl_linux-64-12.4.0-h557a472_0.conda#77076175ffd18ef618470991cc38c540 +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 @@ -137,8 +137,8 @@ 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-3.9.0-23_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/libllvm18-18.1.8-h8b73ec9_1.conda#16d94b3586ef3558e5a583598524deb4 -https://conda.anaconda.org/conda-forge/linux-64/libpq-16.3-ha72fbe1_0.conda#bac737ae28b79cfbafd515258d97d29e +https://conda.anaconda.org/conda-forge/linux-64/libllvm18-18.1.8-h8b73ec9_2.conda#2e25bb2f53e4a48873a936f8ef53e592 +https://conda.anaconda.org/conda-forge/linux-64/libpq-16.4-h482b261_0.conda#0f74c5581623f860e7baca042d9d7139 https://conda.anaconda.org/conda-forge/linux-64/libxslt-1.1.39-h76b75d6_0.conda#e71f31f8cfb0a91439f2086fc8aa0461 https://conda.anaconda.org/conda-forge/linux-64/markupsafe-2.1.5-py39hd1e30aa_0.conda#9a9a22eb1f83c44953319ee3b027769f https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2#2ba8498c1018c1e9c61eb99b973dfe19 @@ -179,7 +179,7 @@ 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/c-compiler-1.7.0-hd590300_1.conda#e9dffe1056994133616378309f932d77 https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-hebfffa5_3.conda#fceaedf1cdbcb02df9699a0d9b005292 -https://conda.anaconda.org/conda-forge/linux-64/cffi-1.16.0-py39h7a31438_0.conda#ac992767d7f8ed2cb27e71e78f0fb2d7 +https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.0-py39h49a4b6b_0.conda#278cc676a7e939cf2561ce4a5cfaa484 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 @ 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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 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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/libavif16-1.1.1-h9b56c87_0.conda#cb7355212240e92dcf9c73cb1f10e4a9 -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/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/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/mysql-libs-8.3.0-ha479ceb_5.conda#82776ee8145b9d1fd6546604de4b351d @@ -144,8 +144,8 @@ https://conda.anaconda.org/conda-forge/linux-64/gcc-12.4.0-h236703b_0.conda#9485 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/gfortran_impl_linux-64-12.4.0-hc568b83_0.conda#bf4f9ad129a9a8dc86cce6626697d413 -https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.80.3-h73ef956_1.conda#99701cdc9a25a333d15265d1d243b2dc -https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-12.4.0-h557a472_0.conda#77076175ffd18ef618470991cc38c540 +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 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 @@ -156,8 +156,8 @@ https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.16-hb7c19ff_0.conda#51bb https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-h4637d8d_4.conda#d4529f4dff3057982a7617c7ac58fde3 https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.11.1-default_hecaa2ac_1000.conda#f54aeebefb5c5ff84eca4fb05ca8aa3a 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 -https://conda.anaconda.org/conda-forge/linux-64/libpq-16.3-ha72fbe1_0.conda#bac737ae28b79cfbafd515258d97d29e +https://conda.anaconda.org/conda-forge/linux-64/libllvm18-18.1.8-h8b73ec9_2.conda#2e25bb2f53e4a48873a936f8ef53e592 +https://conda.anaconda.org/conda-forge/linux-64/libpq-16.4-h482b261_0.conda#0f74c5581623f860e7baca042d9d7139 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 @@ -171,7 +171,7 @@ https://conda.anaconda.org/conda-forge/noarch/pygments-2.18.0-pyhd8ed1ab_0.conda 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 -https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.1-py39hd1e30aa_1.conda#37218233bcdc310e4fde6453bc1b40d8 +https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.2-py39hcd6043d_0.conda#40f1dd93ac87fff4b776d6fb8033ddb9 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 @@ -196,11 +196,11 @@ 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/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-1.16.0-py39h7a31438_0.conda#ac992767d7f8ed2cb27e71e78f0fb2d7 +https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.0-py39h49a4b6b_0.conda#278cc676a7e939cf2561ce4a5cfaa484 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/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/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 @@ -208,7 +208,7 @@ https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.2.0-pyha770c7 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_1.conda#04c8c481b30c3fe62bec148fa4a75857 +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/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 @@ -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 081/148] :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 082/148] :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 083/148] 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 084/148] 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 085/148] 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 086/148] 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 087/148] 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 088/148] 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 089/148] :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 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/aws-c-common-0.9.23-h4ab18f5_0.conda#94d61ae2b2b701008a9d52ce6bbead27 +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-1.32.3-h4bc722e_0.conda#7624e34ee6baebfc80d67bac76cc9d9d +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/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 @@ -50,10 +50,10 @@ 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 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+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/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 @@ -77,18 +77,18 @@ https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.16-hd590300_0.conda#151 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/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/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/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/s2n-1.4.19-h3400bea_0.conda#7d6818f07e4471d471be9b4252d7b54c +https://conda.anaconda.org/conda-forge/linux-64/s2n-1.5.0-h3400bea_0.conda#5f17883266c5312a1fc73583f28ebae5 https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.1-ha2e4443_0.conda#6b7dcc7349efd123d493d2dbe85a045f 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/wayland-1.23.0-h5291e77_0.conda#c13ca0abd5d1d31d0eebcf86d51da8a4 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-1.3.1-h4ab18f5_1.conda#9653f1bf3766164d0e65fa723cabbc54 https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda#4d056880988120e29d75bfff282e0f45 -https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.14.18-h6ea103f_1.conda#b0da9b0d46def0a1190790e623f246d3 +https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.14.18-h0040ed1_5.conda#2f6316f09b3152fecc2d34ab69508e6a 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/glog-0.7.1-hbabe93e_0.conda#ff862eebdfeb2fd048ae9dc92510baca @@ -97,10 +97,10 @@ https://conda.anaconda.org/conda-forge/linux-64/libglib-2.80.3-h315aac3_2.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/libprotobuf-4.25.3-h08a7969_0.conda#6945825cebd2aeb16af4c69d97c32c13 https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2023.09.01-h5a48ba9_2.conda#41c69fba59d495e8cf5ffda48a607e35 -https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.19.0-hb90f79a_1.conda#8cdb7d41faa0260875ba92414c487e2d +https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.20.0-hb90f79a_0.conda#9ce07c1750e779c9d4cc968047f78b0d 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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 +++ 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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 090/148] :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 091/148] :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 092/148] 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 093/148] 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 094/148] 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 095/148] 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 096/148] 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 097/148] 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 098/148] :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 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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 +++ 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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 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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 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/pyside6-6.7.2-py39h0285922_2.conda#12004e14d1835eca43c4207841c24e4f 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 index 579fe44e33a63..6173ded60ae0f 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 @@ -37,7 +37,7 @@ 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 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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 df0f4cb506789..86af9ecba1886 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 @@ -12,7 +12,9 @@ https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_7 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 +https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_0.conda#e46b5ae31282252e0525713e34ffbe2b 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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/libgl-1.7.0-ha4b6fd6_0.conda#3deca8c25851196c28d1c84dd4ae9149 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 @@ -204,27 +212,30 @@ 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/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/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/harfbuzz-9.0.0-hda332d3_1.conda#76b32dcf243444aea9c6b804bcfa40b8 -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/lazy-loader-0.4-pyhd8ed1ab_1.conda#4809b9f4c6ce106d443c3f90b8e10db2 https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-23_linux64_openblas.conda#89d7bcdb1e9a72a73e36d8e29d2a2beb 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-2.0.1-py39h2fd3214_0.conda#2c69819400d3318cf74f831811ab066f https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_0.conda#b39568655c127a9c4a44d178ac99b6d0 +https://conda.anaconda.org/conda-forge/linux-64/xorg-libxtst-1.2.5-h4bc722e_0.conda#185159d666308204eca00295599b0a5c 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-3.9.0-23_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-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/noarch/lazy_loader-0.4-pyhd8ed1ab_1.conda#ec6f70b8a5242936567d4f886726a372 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-1.5.0-py39h883198d_0.conda#859218b56a47b0bbb752a4e7f2e4074f https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.6.0-py39hd92a3bb_0.conda#32e26e16f60c568b17a82e3033a4d309 -https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.7.2-hb12f9c5_4.conda#5dd4fddb73e5e4fef38ef54f35c155cd +https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.7.2-hb12f9c5_5.conda#8c662388c2418f293266f5e7f50df7d7 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-2.123-openblas.conda#7f4b3ea1cdd6e50dca2a226abda6e2d9 @@ -242,7 +253,7 @@ 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 -# pip pyzmq @ https://files.pythonhosted.org/packages/e1/5c/c8c1db048dd121ca4298c4ac09c9b2c235b4cb3bd1ff8d4c60c52b40e356/pyzmq-26.1.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=5a6ed52f0b9bf8dcc64cc82cce0607a3dfed1dbb7e8c6f282adfccc7be9781de +# pip pyzmq @ https://files.pythonhosted.org/packages/6e/bd/3ff3e1172f12f55769793a3a334e956ec2886805ebfb2f64756b6b5c6a1a/pyzmq-26.2.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=05590cdbc6b902101d0e65d6a4780af14dc22914cc6ab995d99b85af45362cc9 # 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 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 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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 101/148] 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 102/148] 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 103/148] 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 104/148] 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 105/148] 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 106/148] 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 107/148] 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 108/148] 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 109/148] 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 110/148] :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 -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/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/libxcb-1.16-h57736b2_1.conda#8d502f235bf4f3ce1f288cb1ff3a90b6 @@ -87,18 +90,18 @@ 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-3.0.11-py39h6e76b30_0.conda#7b2bd72eeb9a59b13090b02f4a534168 +https://conda.anaconda.org/conda-forge/linux-aarch64/cython-3.0.11-py39h7dbf29c_1.conda#17fd68c0aa4dbeb08303742cebc896fd 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 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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 @@ -110,26 +113,26 @@ https://conda.anaconda.org/conda-forge/noarch/setuptools-72.2.0-pyhd8ed1ab_0.con 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 -https://conda.anaconda.org/conda-forge/linux-aarch64/tornado-6.4.1-py39ha3e8b56_0.conda#60ad0fcecca6af49fe5888a408618d8a 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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 111/148] :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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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/libgcc-14.1.0-h77fa898_1.conda#002ef4463dd1e2b44a94a4ace468f5d2 +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/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 @@ -28,19 +31,19 @@ https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.21-h4bc722e_0.conda 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/libgfortran5-14.1.0-hc5f4f2c_0.conda#6456c2620c990cd8dde2428a27ba0bc5 +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 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/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/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 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https://conda.anaconda.org/conda-forge/linux-64/xorg-inputproto-2.3.2-h7f98852_1002.tar.bz2#bcd1b3396ec6960cbc1d2855a9e60b2b @@ -53,7 +56,7 @@ 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-0.7.3-h8dac057_2.conda#577509458a061ddc9b089602ac6e1e98 +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 @@ -68,10 +71,10 @@ https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240116.2-cxx17_he020 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-2.4.122-h4ab18f5_0.conda#bbfc4dbe5e97b385ef088f354d65e563 +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 https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda#a1cfcc585f0c42bf8d5546bb1dfb668d -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/libgfortran-ng-14.1.0-h69a702a_1.conda#16cec94c5992d7f42ae3f9fa8b25df8d https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.58.0-h47da74e_1.conda#700ac6ea6d53d5510591c4344d5c989a 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-3.46.0-hde9e2c9_0.conda#18aa975d2094c34aef978060ae7da7d8 @@ -93,7 +96,7 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-fixesproto-5.0-h7f98852_100 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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-24.2-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/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/clang-16.0.6-default_h179603d_13.conda#b501f33eddd693b062ba7a2d6bf9eccb 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 https://conda.anaconda.org/conda-forge/osx-64/mkl-devel-2023.2.0-h694c41f_50500.conda#1b4d0235ef253a1e19459351badf4f9f 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/osx-64/clangxx-16.0.6-default_h179603d_11.conda#8c2055146f68eb4c3b0da893a8bed33c +https://conda.anaconda.org/conda-forge/osx-64/clangxx-16.0.6-default_h179603d_13.conda#8934fd8e83d051adcaba71fcbed9ecf0 https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.9.0-20_osx64_mkl.conda#51089a4865eb4aec2bc5c7468bd07f9f 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 @@ -111,7 +112,7 @@ https://conda.anaconda.org/conda-forge/osx-64/liblapacke-3.9.0-20_osx64_mkl.cond 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/contourpy-1.3.0-py312hc5c4d5f_0.conda#85509cca727804577d8252eaf8bad230 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-1.14.1-py312he82a568_0.conda#dd3c55da62964fcadf27771e1928e67f https://conda.anaconda.org/conda-forge/osx-64/blas-2.120-mkl.conda#b041a7677a412f3d925d8208936cb1e2 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 f1afa482db7a3..4a0213ac637fe 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 @@ -28,7 +28,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.2-py311h06a4308_0.conda#eff3 # 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 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+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 @@ 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-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 112/148] 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 113/148] 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 114/148] 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 115/148] 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 116/148] :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 117/148] 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 118/148] 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 119/148] 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 120/148] 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 121/148] 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 122/148] 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 123/148] 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 124/148] 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 125/148] 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 126/148] 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 127/148] :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 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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 8a7c31bba3125..04c06e437881c 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 @@ -17,7 +17,7 @@ https://repo.anaconda.com/pkgs/main/noarch/tzdata-2024a-h04d1e81_0.conda#452af53 https://repo.anaconda.com/pkgs/main/osx-64/xz-5.4.6-h6c40b1e_1.conda#b40d69768d28133d8be1843def4f82f5 https://repo.anaconda.com/pkgs/main/osx-64/zlib-1.2.13-h4b97444_1.conda#38e35f7c817fac0973034bfce6706ec2 https://repo.anaconda.com/pkgs/main/osx-64/ccache-3.7.9-hf120daa_0.conda#a01515a32e721c51d631283f991bc8ea -https://repo.anaconda.com/pkgs/main/osx-64/expat-2.6.2-hcec6c5f_0.conda#c748234dd7e242784198ab038372cb0c +https://repo.anaconda.com/pkgs/main/osx-64/expat-2.6.3-h6d0c2b6_0.conda#7cfb1a4651369640118e6ee80198e682 https://repo.anaconda.com/pkgs/main/osx-64/intel-openmp-2023.1.0-ha357a0b_43548.conda#ba8a89ffe593eb88e4c01334753c40c3 https://repo.anaconda.com/pkgs/main/osx-64/lerc-3.0-he9d5cce_0.conda#aec2c3dbef836849c9260f05be04f3db https://repo.anaconda.com/pkgs/main/osx-64/libbrotlidec-1.0.9-h6c40b1e_8.conda#6338cd7779e614fc16d835990e627e04 @@ -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-3.0.14-h46256e1_0.conda#d722280df65b3308e1b8b1b7777a3305 +https://repo.anaconda.com/pkgs/main/osx-64/openssl-3.0.15-h46256e1_0.conda#3286ae31653124afad386b813a5d17da 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 @@ -51,7 +51,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/ninja-1.10.2-hecd8cb5_5.conda#a0043b3 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/osx-64/pyparsing-3.1.2-py312hecd8cb5_0.conda#645e2108165e45a3a385f0e11d1748a1 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-72.1.0-py312hecd8cb5_0.conda#dff219f3528a6e8ad235c48a29cd6dbe @@ -70,8 +70,8 @@ https://repo.anaconda.com/pkgs/main/osx-64/pytest-cov-4.1.0-py312hecd8cb5_1.cond 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 https://repo.anaconda.com/pkgs/main/osx-64/contourpy-1.2.0-py312ha357a0b_0.conda#57d384ad07152375b40a6293f79e3f0c -https://repo.anaconda.com/pkgs/main/osx-64/matplotlib-3.8.4-py312hecd8cb5_0.conda#6886c230c2ec2f47621b5cca4c7d493a -https://repo.anaconda.com/pkgs/main/osx-64/matplotlib-base-3.8.4-py312h7f12edd_0.conda#a4eee14a4dcaa89b306ca33d2d479fa4 +https://repo.anaconda.com/pkgs/main/osx-64/matplotlib-3.9.2-py312hecd8cb5_0.conda#4a0c6fbe79aefa058fddc09690772afa +https://repo.anaconda.com/pkgs/main/osx-64/matplotlib-base-3.9.2-py312ha7ebc0d_0.conda#a5396c401f535238325577ab702ac32a https://repo.anaconda.com/pkgs/main/osx-64/mkl_fft-1.3.8-py312h6c40b1e_0.conda#d59d01b940493f2b6a84aac922fd0c76 https://repo.anaconda.com/pkgs/main/osx-64/mkl_random-1.2.4-py312ha357a0b_0.conda#c1ea9c8eee79a5af3399f3c31be0e9c6 https://repo.anaconda.com/pkgs/main/osx-64/numpy-1.26.4-py312hac873b0_0.conda#3150bac1e382156f82a153229e1ebd06 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 4a0213ac637fe..d3e309f445f96 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 @@ -14,7 +14,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h5eee18b_6.conda#f21a3f 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 @@ -40,12 +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://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 @ 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-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 128/148] :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 129/148] :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 https://conda.anaconda.org/conda-forge/linux-aarch64/double-conversion-3.3.0-h2f0025b_0.conda#3b34b29f68d60abc1ce132b87f5a213c -https://conda.anaconda.org/conda-forge/linux-aarch64/expat-2.6.2-h2f0025b_0.conda#6d31100ba1e12773b4f1ef0693fb0169 https://conda.anaconda.org/conda-forge/linux-aarch64/graphite2-1.3.13-h2f0025b_1003.conda#f33009add6a08358bc12d114ceec1304 https://conda.anaconda.org/conda-forge/linux-aarch64/icu-75.1-hf9b3779_0.conda#268203e8b983fddb6412b36f2024e75c 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-2.4.123-h86ecc28_0.conda#4e3c67f6999ea7ccac41611f930d19d4 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 130/148] 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 131/148] 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 -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/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 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+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/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/meson-1.5.1-pyhd8ed1ab_1.conda#979087ee59bea1355f991a3b738af64e +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-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8 +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/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/linux-64/blas-2.123-openblas.conda#7f4b3ea1cdd6e50dca2a226abda6e2d9 +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/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-py39h6fb8a73_2.conda#3212f51613e10b3ee319f3f2bf8ee5a8 -https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.8.4-py39h4162558_2.conda#05babd7bae196648bfc6b7e3d9ea7630 +https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.2.1-py39h3c335be_0.conda#571fd9aeec6fe1b68280363f5b028f0a +https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_0.conda#b39568655c127a9c4a44d178ac99b6d0 +https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.9.2-py39h32a45fc_0.conda#8d50b28459357250dd7d2e3540413226 +https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.9.2-py39h4162558_0.conda#e6bfb3e2d9e2ae8300795692a710e0be diff --git 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 132/148] 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 133/148] 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 134/148] 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 135/148] 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 136/148] 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 137/148] 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 138/148] 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 139/148] 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 140/148] [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 141/148] 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 142/148] 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 143/148] 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 144/148] [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 145/148] 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 146/148] 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 147/148] 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 148/148] 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