ENH Allow for appropriate dtype us in `preprocessing.PolynomialFeatures` for sparse matrices #23731

Micky774 · 2022-06-22T18:07:41Z

Reference Issues/PRs

Fixes #16803
Fixes #17554
Resolves #19676 (stalled)
Resolves #20524 (stalled)

What does this implement/fix? Explain your changes.

PR #20524: Calculates number of non-zero terms for each degree (row-wise) and creates dense arrays for data/indices/indptr to pass to Cython _csr_polynomial_expansion. Since the size is known a-priori, the appropriate dtype can be used during construction. The use of fused types in _csr_polynomial_expansion allows for only the minimally sufficient index dtype to be used, decreasing wasted memory when int32 is sufficient.

This PR: reconciles w/ main and makes minor changes.

Any other comments?

The full functionality of this PR is really only enabled in scipy_version>1.8 since it depends on an upstream bug fix

…(bis) Fixes scikit-learn#19676

…nto csr_polynomial

ogrisel

Thanks for the PR. Here is a first pass of feedback.

doc/whats_new/v1.2.rst

sklearn/preprocessing/_csr_polynomial_expansion.pyx

sklearn/preprocessing/_polynomial.py

sklearn/preprocessing/tests/test_polynomial.py

Co-authored-by: Olivier Grisel <[email protected]>

sklearn/preprocessing/_polynomial.py

sklearn/preprocessing/_csr_polynomial_expansion.pyx

sklearn/preprocessing/tests/test_polynomial.py

sklearn/preprocessing/_polynomial.py

Co-authored-by: Olivier Grisel <[email protected]>

jjerphan

Thank you, @Micky774.

A few comments.

Note that a specific dtype and C type have been added for sparse matrices indices.

scikit-learn/sklearn/utils/_typedefs.pxd

Lines 19 to 28 in b157ac7

    
           # scipy matrices indices dtype (namely for indptr and indices arrays) 
        
           # 
        
           #   Note that indices might need to be represented as cnp.int64_t. 
        
           #   Currently, we use Cython classes which do not handle fused types 
        
           #   so we hardcode this type to cnp.int32_t, supporting all but edge 
        
           #   cases. 
        
           # 
        
           # TODO: support cnp.int64_t for this case 
        
           # See: https://github.com/scikit-learn/scikit-learn/issues/23653 
        
           ctypedef cnp.int32_t SPARSE_INDEX_TYPE_t

Could we propagate this here for semantics?

sklearn/preprocessing/_csr_polynomial_expansion.pyx

sklearn/preprocessing/tests/test_polynomial.py

thomasjpfan

LGTM

sklearn/preprocessing/_polynomial.py

Co-authored-by: Thomas J. Fan <[email protected]>

Micky774 · 2023-04-24T15:03:23Z

@ogrisel wondering if you could take another look

ogrisel

Thanks for the update. Here are some more comments for a build problem that I am still investigating:

ogrisel · 2023-04-25T08:35:00Z

sklearn/preprocessing/tests/test_polynomial.py

+    # On Windows, scikit-learn is typically compiled with MSVC that
+    # does not support int128 arithmetic (at the time of writing):
+    # https://stackoverflow.com/a/6761962/163740
+    if sys.maxsize <= 2**32 or sys.platform == "win32":


The sys.maxsize <= 2**32 part was unexpected to me but I checked:

https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=54466&view=ms.vss-test-web.build-test-results-tab

and added "Passed" to the status filer and test_sizeof_LARGEST_INT_t to the name filter and indeed it passed on our 32 bit linux build (with gcc).

So I think we need to check if this test would also pass with clang on 32 bit linux (at least manually, we don't necessarily need to configure a new CI entry for this).

clang (or clang-cl) on windows would be challenging to test because I believe there is not documented way to use this compiler to build Python extensions at this time.

I tried to the following debian docker image for i386 (32 bit).

docker run -ti -v `pwd`:/io --platform linux/i386 i386/debian:11.2 bash

Then I installed build dependencies:

apt update && apt install clang python3-pip python3-dev build-essential python3-scipy

and then changed update-alternatives --config cc and update-alternatives --config c++ to point to clang version 11.0.1-2 instead of gcc (not sure if it's needed) and also set the following env variables:

export CC="clang" export CXX="clang++"

and then installed cython with pip3 and built scikit-learn with in editable mode with --no-build-isolation.

The beginning of the build works fine, but when it reaches this error:

(...) clang -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -ffile-prefix-map=/build/python3.9-hfHQKB/python3.9-3.9.2=. -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -ffile-prefix-map=/build/python3.9-hfHQKB/python3.9-3.9.2=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION -I/usr/include/python3.9 -c sklearn/preprocessing/_csr_polynomial_expansion.c -o build/temp.linux-i686-3.9/sklearn/preprocessing/_csr_polynomial_expansion.o -g0 -O2 -fopenmp sklearn/preprocessing/_csr_polynomial_expansion.c:767:25: error: expected parameter declarator typedef _BitInt(128) LARGEST_INT_t; ^ sklearn/preprocessing/_csr_polynomial_expansion.c:767:25: error: expected ')' sklearn/preprocessing/_csr_polynomial_expansion.c:767:24: note: to match this '(' typedef _BitInt(128) LARGEST_INT_t; ^ sklearn/preprocessing/_csr_polynomial_expansion.c:767:30: error: expected function body after function declarator typedef _BitInt(128) LARGEST_INT_t; ^ sklearn/preprocessing/_csr_polynomial_expansion.c:2061:22: error: unknown type name 'LARGEST_INT_t' static CYTHON_INLINE LARGEST_INT_t __Pyx_pow_LARGEST_INT_t(LARGEST_INT_t, LARGEST_INT_t); ^ sklearn/preprocessing/_csr_polynomial_expansion.c:2061:75: error: redefinition of parameter 'LARGEST_INT_t' static CYTHON_INLINE LARGEST_INT_t __Pyx_pow_LARGEST_INT_t(LARGEST_INT_t, LARGEST_INT_t); ^ sklearn/preprocessing/_csr_polynomial_expansion.c:2061:60: error: a parameter list without types is only allowed in a function definition static CYTHON_INLINE LARGEST_INT_t __Pyx_pow_LARGEST_INT_t(LARGEST_INT_t, LARGEST_INT_t); ^ sklearn/preprocessing/_csr_polynomial_expansion.c:2079:22: error: unknown type name 'LARGEST_INT_t' static CYTHON_INLINE LARGEST_INT_t __Pyx_PyInt_As_LARGEST_INT_t(PyObject *); ^ sklearn/preprocessing/_csr_polynomial_expansion.c:2091:63: error: unknown type name 'LARGEST_INT_t' static CYTHON_INLINE PyObject* __Pyx_PyInt_From_LARGEST_INT_t(LARGEST_INT_t value); ^ sklearn/preprocessing/_csr_polynomial_expansion.c:2149:154: error: redefinition of parameter 'LARGEST_INT_t' static CYTHON_INLINE __pyx_t_7sklearn_5utils_9_typedefs_int64_t __pyx_f_7sklearn_13preprocessing_25_csr_polynomial_expansion__deg2_column(LARGEST_INT_t, LARGEST_INT_t, LARGEST_INT_t, __pyx_t_7sklearn_13preprocessing_25_csr_polynomial_expansion_FLAG_t); /*proto*/ ^ sklearn/preprocessing/_csr_polynomial_expansion.c:2149:169: error: redefinition of parameter 'LARGEST_INT_t' static CYTHON_INLINE __pyx_t_7sklearn_5utils_9_typedefs_int64_t __pyx_f_7sklearn_13preprocessing_25_csr_polynomial_expansion__deg2_column(LARGEST_INT_t, LARGEST_INT_t, LARGEST_INT_t, __pyx_t_7sklearn_13preprocessing_25_csr_polynomial_expansion_FLAG_t); /*proto*/ ^ sklearn/preprocessing/_csr_polynomial_expansion.c:2149:184: error: unexpected type name '__pyx_t_7sklearn_13preprocessing_25_csr_polynomial_expansion_FLAG_t': expected identifier static CYTHON_INLINE __pyx_t_7sklearn_5utils_9_typedefs_int64_t __pyx_f_7sklearn_13preprocessing_25_csr_polynomial_expansion__deg2_column(LARGEST_INT_t, LARGEST_INT_t, LARGEST_INT_t, __pyx_t_7sklearn_13preprocessing_25_csr_polynomial_expansion_FLAG_t); /*proto*/ ^ sklearn/preprocessing/_csr_polynomial_expansion.c:2149:139: error: a parameter list without types is only allowed in a function definition static CYTHON_INLINE __pyx_t_7sklearn_5utils_9_typedefs_int64_t __pyx_f_7sklearn_13preprocessing_25_csr_polynomial_expansion__deg2_column(LARGEST_INT_t, LARGEST_INT_t, LARGEST_INT_t, __pyx_t_7sklearn_13preprocessing_25_csr_polynomial_expansion_FLAG_t); /*proto*/ ^ sklearn/preprocessing/_csr_polynomial_expansion.c:2150:154: error: redefinition of parameter 'LARGEST_INT_t' static CYTHON_INLINE __pyx_t_7sklearn_5utils_9_typedefs_int64_t __pyx_f_7sklearn_13preprocessing_25_csr_polynomial_expansion__deg3_column(LARGEST_INT_t, LARGEST_INT_t, LARGEST_INT_t, LARGEST_INT_t, __pyx_t_7sklearn_13preprocessing_25_csr_polynomial_expansion_FLAG_t); /*proto*/ ^ sklearn/preprocessing/_csr_polynomial_expansion.c:2150:169: error: redefinition of parameter 'LARGEST_INT_t' static CYTHON_INLINE __pyx_t_7sklearn_5utils_9_typedefs_int64_t __pyx_f_7sklearn_13preprocessing_25_csr_polynomial_expansion__deg3_column(LARGEST_INT_t, LARGEST_INT_t, LARGEST_INT_t, LARGEST_INT_t, __pyx_t_7sklearn_13preprocessing_25_csr_polynomial_expansion_FLAG_t); /*proto*/ ^ sklearn/preprocessing/_csr_polynomial_expansion.c:2150:184: error: redefinition of parameter 'LARGEST_INT_t' static CYTHON_INLINE __pyx_t_7sklearn_5utils_9_typedefs_int64_t __pyx_f_7sklearn_13preprocessing_25_csr_polynomial_expansion__deg3_column(LARGEST_INT_t, LARGEST_INT_t, LARGEST_INT_t, LARGEST_INT_t, __pyx_t_7sklearn_13preprocessing_25_csr_polynomial_expansion_FLAG_t); /*proto*/ ^ sklearn/preprocessing/_csr_polynomial_expansion.c:2150:199: error: unexpected type name '__pyx_t_7sklearn_13preprocessing_25_csr_polynomial_expansion_FLAG_t': expected identifier static CYTHON_INLINE __pyx_t_7sklearn_5utils_9_typedefs_int64_t __pyx_f_7sklearn_13preprocessing_25_csr_polynomial_expansion__deg3_column(LARGEST_INT_t, LARGEST_INT_t, LARGEST_INT_t, LARGEST_INT_t, __pyx_t_7sklearn_13preprocessing_25_csr_polynomial_expansion_FLAG_t); /*proto*/ ^ sklearn/preprocessing/_csr_polynomial_expansion.c:2150:139: error: a parameter list without types is only allowed in a function definition static CYTHON_INLINE __pyx_t_7sklearn_5utils_9_typedefs_int64_t __pyx_f_7sklearn_13preprocessing_25_csr_polynomial_expansion__deg3_column(LARGEST_INT_t, LARGEST_INT_t, LARGEST_INT_t, LARGEST_INT_t, __pyx_t_7sklearn_13preprocessing_25_csr_polynomial_expansion_FLAG_t); /*proto*/ ^ sklearn/preprocessing/_csr_polynomial_expansion.c:2151:146: error: unexpected type name '__pyx_t_7sklearn_13preprocessing_25_csr_polynomial_expansion_FLAG_t': expected identifier static __pyx_t_7sklearn_5utils_9_typedefs_int64_t __pyx_f_7sklearn_13preprocessing_25_csr_polynomial_expansion__calc_expanded_nnz(LARGEST_INT_t, __pyx_t_7sklearn_13preprocessing_25_csr_polynomial_expansion_FLAG_t, LARGEST_INT_t, int __pyx_skip_dispatch); /*proto*/ ^ sklearn/preprocessing/_csr_polynomial_expansion.c:2151:215: error: redefinition of parameter 'LARGEST_INT_t' static __pyx_t_7sklearn_5utils_9_typedefs_int64_t __pyx_f_7sklearn_13preprocessing_25_csr_polynomial_expansion__calc_expanded_nnz(LARGEST_INT_t, __pyx_t_7sklearn_13preprocessing_25_csr_polynomial_expansion_FLAG_t, LARGEST_INT_t, int __pyx_skip_dispatch); /*proto*/

The first error seems to be triggering the other errors. But this code works with clang on macos. Maybe the version of clang (11) is too old?

I can try with a more recent version of debian or ubuntu. I used the same docker container as the one we use on the CI.

Actually maybe the problem above was caused by previously generated .c files. I need to try again from a clean build folder to make sure.

I tried again on 32 bit linux on armv7 because I could find more recent python / clang versions for that platform and now the build works. But the resulting _csr_polynomial_expansion compiled extension is still broken:

root@4af20c1dad16:/io# pytest -lxk test_sizeof_LARGEST_INT_t sklearn/preprocessing/tests/test_polynomial.py ImportError while loading conftest '/io/sklearn/conftest.py'. sklearn/conftest.py:18: in <module> from sklearn.datasets import fetch_20newsgroups sklearn/datasets/__init__.py:8: in <module> from ._base import load_breast_cancer sklearn/datasets/_base.py:20: in <module> from ..preprocessing import scale sklearn/preprocessing/__init__.py:38: in <module> from ._polynomial import PolynomialFeatures sklearn/preprocessing/_polynomial.py:22: in <module> from ._csr_polynomial_expansion import ( E ImportError: /io/sklearn/preprocessing/_csr_polynomial_expansion.cpython-310-arm-linux-gnueabihf.so: undefined symbol: __divti3

Indeed I think we need to first check for 32bit systems before eagerly trying __int128 or _BitInt(128) since neither are representable on 32bit systems. Probably the most portable way would be to check sizeof(void*) == 8 right? I'll implement that soon.

Indeed:

If you compile for a 32-bit architecture like ARM, or x86 with -m32, no 128-bit integer type is supported with even the newest version of any of these compilers. So you need to detect support before using, if it's possible for your code to work at all without it.

from: https://stackoverflow.com/a/54815033/163740

I think we should also link to that SO answer which is quite a good reference for this topic.

Please also add a TODO note such that once all platform have C23 compilers, we should instead switch to the new way to do this:

SO C23 will let you typedef unsigned _BitInt(128) u128, modeled on clang's feature originally called _ExtInt() which works even on 32-bit machines; see a brief intro to it. Current GCC -std=gnu2x doesn't even support that syntax yet.

This will require a few years, but then it should work both for 32-bit and 64-bit target architectures.

Note that the clang + 32-bit target is no just a theoretical possibility: emscripten / WASM / pyodide is a concrete platform that is built on that combo. I think @lesteve has the required tooling to test this PR on that target.

BTW @lesteve if you do so, please copy and paste the command lines you use to do this because I am curious but too lazy to RTFM ;)

Quick and dirty testing of this PR on Pyodide seems to indicate that the test_sizeof_LARGEST_INT_t fails but the other added tests pass:

>>> pytest.main(['--pyargs', 'sklearn.preprocessing', '-k', 'test_csr_polynomial_expansion_index_overflow_non_reg ression or test_csr_polynomial_expansion_too_large_to_index or LARGEST_INT', '-v']) ============================= test session starts ============================== platform emscripten -- Python 3.11.3, pytest-7.3.1, pluggy-1.0.0 -- /home/pyodide/this.program cachedir: .pytest_cache rootdir: /home/pyodide collecting ... collected 1098 items / 1089 deselected / 9 selected tests/test_polynomial.py::test_csr_polynomial_expansion_index_overflow_non_regression[True-True] PASSED [ 11%] tests/test_polynomial.py::test_csr_polynomial_expansion_index_overflow_non_regression[True-False] PASSED [ 22%] tests/test_polynomial.py::test_csr_polynomial_expansion_index_overflow_non_regression[False-True] PASSED [ 33%] tests/test_polynomial.py::test_csr_polynomial_expansion_index_overflow_non_regression[False-False] PASSED [ 44%] tests/test_polynomial.py::test_csr_polynomial_expansion_too_large_to_index[True-True] PASSED [ 55%] tests/test_polynomial.py::test_csr_polynomial_expansion_too_large_to_index[True-False] PASSED [ 66%] tests/test_polynomial.py::test_csr_polynomial_expansion_too_large_to_index[False-True] PASSED [ 77%] tests/test_polynomial.py::test_csr_polynomial_expansion_too_large_to_index[False-False] PASSED [ 88%] tests/test_polynomial.py::test_sizeof_LARGEST_INT_t FAILED [100%] =================================== FAILURES =================================== __________________________ test_sizeof_LARGEST_INT_t ___________________________ def test_sizeof_LARGEST_INT_t(): # On Windows, scikit-learn is typically compiled with MSVC that # does not support int128 arithmetic (at the time of writing): # https://stackoverflow.com/a/6761962/163740 if sys.maxsize <= 2**32 or sys.platform == "win32": expected_size = 8 else: expected_size = 16 > assert _get_sizeof_LARGEST_INT_t() == expected_size E assert 16 == 8 E + where 16 = _get_sizeof_LARGEST_INT_t() /lib/python3.11/site-packages/sklearn/preprocessing/tests/test_polynomial.py:1094: AssertionError =========================== short test summary info ============================ FAILED tests/test_polynomial.py::test_sizeof_LARGEST_INT_t - assert 16 == 8 ================= 1 failed, 8 passed, 1089 deselected in 1.03s =================

Note that the clang + 32-bit target is no just a theoretical possibility: emscripten / WASM / pyodide is a concrete platform that is built on that combo.

By the way I am not sure __clang__ is defined in the Pyodide use case, I think __EMSCRIPTEN__ is used instead.

BTW @lesteve if you do so, please copy and paste the command lines you use to do this because I am curious but too lazy to RTFM ;)

Set-up the correct emscripten version (it has to match the version Pyodide was built with namely 3.1.32 for Pyodide stable, 0.23.1 at the time of writing, see https://github.com/pyodide/pyodide/blob/0.23.1/Makefile.envs):

git clone https://github.com/emscripten-core/emsdk.git cd emsdk ./emsdk install 3.1.32 ./emsdk activate 3.1.32 source ./emsdk_env.sh

Build a Pyodide wheel, this should work from the scikit-learn repo root:

pip install pyodide-build pyodide build

Now you need to use this wheel and I would say one of the possibility is something like this:

put this wheel in a github repo

use the stable Pyodide console

in the console use await micropip.install('https://cdn.jsdelivr.net/gh/<owner>/<repo>/path/to/wheel.whl') (the cdn.jsdelivr.net/gh is a CORS proxy if you are wondering)

Okay so I have:

Added a check for __EMSCRIPTEN__ as an alternative to __clang__

Updated the runtime test for largest integer type to account for the odd (but beneficial) exception that is emscripten

Also when trying to build pydodide/emscripten for myself, I get the following error. I'm not sure what's going on here 🤷 :

* Creating virtualenv isolated environment... * Installing packages in isolated environment... (Cython>=0.29.24, setuptools<=61.0, wheel) * Getting dependencies for wheel... Partial import of sklearn during the build process. running egg_info writing scikit_learn.egg-info/PKG-INFO writing dependency_links to scikit_learn.egg-info/dependency_links.txt writing requirements to scikit_learn.egg-info/requires.txt writing top-level names to scikit_learn.egg-info/top_level.txt reading manifest file 'scikit_learn.egg-info/SOURCES.txt' reading manifest template 'MANIFEST.in' warning: no previously-included files matching '*' found under directory 'asv_benchmarks' warning: no previously-included files matching '*' found under directory 'benchmarks' warning: no previously-included files matching '*' found under directory 'build_tools' warning: no previously-included files matching '*' found under directory 'maint_tools' warning: no previously-included files matching '*' found under directory 'benchmarks' warning: no previously-included files matching '*' found under directory '.binder' warning: no previously-included files matching '*' found under directory '.circleci' warning: no previously-included files found matching '.codecov.yml' warning: no previously-included files found matching '.git-blame-ignore-revs' warning: no previously-included files found matching '.mailmap' warning: no previously-included files found matching '.pre-commit-config.yaml' warning: no previously-included files found matching 'azure-pipelines.yml' warning: no previously-included files found matching 'CODE_OF_CONDUCT.md' warning: no previously-included files found matching 'CONTRIBUTING.md' warning: no previously-included files found matching 'SECURITY.md' warning: no previously-included files found matching 'PULL_REQUEST_TEMPLATE.md' adding license file 'COPYING' writing manifest file 'scikit_learn.egg-info/SOURCES.txt' * Installing packages in isolated environment... (wheel) * Building wheel... Partial import of sklearn during the build process. Traceback (most recent call last): File "/tmp/build-env-mnfj07nc/lib/python3.11/site-packages/numpy/core/__init__.py", line 23, in <module> from . import multiarray File "/tmp/build-env-mnfj07nc/lib/python3.11/site-packages/numpy/core/multiarray.py", line 10, in <module> from . import overrides File "/tmp/build-env-mnfj07nc/lib/python3.11/site-packages/numpy/core/overrides.py", line 6, in <module> from numpy.core._multiarray_umath import ( ModuleNotFoundError: No module named 'numpy.core._multiarray_umath' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "setup.py", line 221, in check_package_status module = importlib.import_module(package) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/importlib/__init__.py", line 126, in import_module return _bootstrap._gcd_import(name[level:], package, level) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "<frozen importlib._bootstrap>", line 1206, in _gcd_import File "<frozen importlib._bootstrap>", line 1178, in _find_and_load File "<frozen importlib._bootstrap>", line 1149, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 690, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 940, in exec_module File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed File "/tmp/build-env-mnfj07nc/lib/python3.11/site-packages/numpy/__init__.py", line 141, in <module> from . import core File "/tmp/build-env-mnfj07nc/lib/python3.11/site-packages/numpy/core/__init__.py", line 49, in <module> raise ImportError(msg) ImportError: IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! Importing the numpy C-extensions failed. This error can happen for many reasons, often due to issues with your setup or how NumPy was installed. We have compiled some common reasons and troubleshooting tips at: https://numpy.org/devdocs/user/troubleshooting-importerror.html Please note and check the following: * The Python version is: Python3.11 from "/tmp/build-env-mnfj07nc/bin/python" * The NumPy version is: "1.24.2" and make sure that they are the versions you expect. Please carefully study the documentation linked above for further help. Original error was: No module named 'numpy.core._multiarray_umath' Traceback (most recent call last): File "/usr/local/lib/python3.11/site-packages/pep517/in_process/_in_process.py", line 351, in <module> main() File "/usr/local/lib/python3.11/site-packages/pep517/in_process/_in_process.py", line 333, in main json_out['return_val'] = hook(**hook_input['kwargs']) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/site-packages/pep517/in_process/_in_process.py", line 249, in build_wheel return _build_backend().build_wheel(wheel_directory, config_settings, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/tmp/build-env-mnfj07nc/lib/python3.11/site-packages/setuptools/build_meta.py", line 244, in build_wheel return self._build_with_temp_dir(['bdist_wheel'], '.whl', ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/tmp/build-env-mnfj07nc/lib/python3.11/site-packages/setuptools/build_meta.py", line 229, in _build_with_temp_dir self.run_setup() File "/tmp/build-env-mnfj07nc/lib/python3.11/site-packages/setuptools/build_meta.py", line 282, in run_setup self).run_setup(setup_script=setup_script) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/tmp/build-env-mnfj07nc/lib/python3.11/site-packages/setuptools/build_meta.py", line 174, in run_setup exec(compile(code, __file__, 'exec'), locals()) File "setup.py", line 655, in <module> setup_package() File "setup.py", line 645, in setup_package check_package_status("numpy", min_deps.NUMPY_MIN_VERSION) File "setup.py", line 248, in check_package_status raise ImportError( ImportError: numpy is not installed. scikit-learn requires numpy >= 1.17.3. Installation instructions are available on the scikit-learn website: http://scikit-learn.org/stable/install.html ERROR Backend subproccess exited when trying to invoke build_wheel

sklearn/preprocessing/_csr_polynomial_expansion.pyx

sklearn/preprocessing/_polynomial.py

jjerphan · 2023-05-01T16:35:47Z

@Micky774: is this PR reviewable now? If it is not the case, could you ping me when it is? :)

Micky774 · 2023-05-01T16:44:41Z

@Micky774: is this PR reviewable now? If it is not the case, could you ping me when it is? :)

It should be good for review! The last large point of interest was this thread: #23731 (comment)

which hopefully is resolved through: #23731 (comment)

Let me know if you have any questions or concerns

ogrisel

Alright, LGTM. If the wasm env triggers a new failure we can fix it up in a follow-up PRs.

At some point we will introduce a pyodide CI entry but later :)

ogrisel · 2023-05-04T19:31:07Z

Thanks @Micky774!

jjerphan · 2023-05-04T19:34:38Z

Thank you all (and especially @Micky774) for your efforts in this rocambolesque journey!

Micky774 · 2023-05-04T19:36:24Z

It was definitely quite a tale of bugs (old and new) and niche features/interactions 😅

I really appreciate the patience and support of the reviewers as well ❤️

Edit: I should have known better than to say at the beginning of this PR "oh it shouldn't be too bad"

ogrisel · 2023-05-05T09:51:56Z

I tried to build scikit-learn in a local emscrypten sdk on macos and I get a build failure (seemingly unrelated to this PR, it fails when building sklearn/utils/_typedefs.cpython-311-wasm32-emscripten.so).

I suspect we might still want to test the code of this PR on pyodide because, in retrospect the emscrypten condition seems a bit counterintuitive to me.

* MAINT Clean deprecated losses in (hist) gradient boosting for 1.3 (scikit-learn#25834) * MAINT Clean deprecation of normalize in calibration_curve for 1.3 (scikit-learn#25833) * BLD Clean command removes generated from cython templates (scikit-learn#25839) * PERF Implement `PairwiseDistancesReduction` backend for `KNeighbors.predict_proba` (scikit-learn#24076) Signed-off-by: Julien Jerphanion <[email protected]> Co-authored-by: Julien Jerphanion <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> * MAINT Added Parameter Validation for datasets.make_circles (scikit-learn#25848) Co-authored-by: jeremiedbb <[email protected]> * MNT use a single job by default with sphinx build (scikit-learn#25836) * BLD Generate warning automatically for templated cython files (scikit-learn#25842) * MAINT parameter validation for sklearn.datasets.fetch_lfw_people (scikit-learn#25820) Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameters validation for metrics.fbeta_score (scikit-learn#25841) * TST add global_random_seed fixture to sklearn/covariance/tests/test_robust_covariance.py (scikit-learn#25821) * MAINT Parameter validation for linear_model.orthogonal_mp (scikit-learn#25817) * TST activate common tests for TSNE (scikit-learn#25374) * CI Update lock files (scikit-learn#25849) * MAINT Added Parameter Validation for metrics.mean_gamma_deviance (scikit-learn#25853) * MAINT Parameters validation for feature_selection.mutual_info_regression (scikit-learn#25850) * MAINT parameter validation metrics.class_likelihood_ratios (scikit-learn#25863) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Ensure disjoint interval constraints (scikit-learn#25797) * MAINT Parameters validation for utils.gen_batches (scikit-learn#25864) * TST use global_random_seed in test_dict_vectorizer.py (scikit-learn#24533) * TST use global_random_seed in test_pls.py (scikit-learn#24526) Co-authored-by: jeremiedbb <[email protected]> * TST use global_random_seed in test_gpc.py (scikit-learn#24600) Co-authored-by: jeremiedbb <[email protected]> * DOC Fix overlapping plot axis in bench_sample_without_replacement.py (scikit-learn#25870) * MAINT Use contiguous memoryviews in _random.pyx (scikit-learn#25871) * MAINT parameter validation sklearn.datasets.fetch_lfw_pair (scikit-learn#25857) * MAINT Parameters validation for metrics.classification_report (scikit-learn#25868) * Empty commit * DOC fix docstring dtype parameter in OrdinalEncoder (scikit-learn#25877) * MAINT Clean up depreacted "log" loss of SGDClassifier for 1.3 (scikit-learn#25865) * ENH Adds TargetEncoder (scikit-learn#25334) Co-authored-by: Andreas Mueller <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Jovan Stojanovic <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]> * CI make it possible to cancel running Azure jobs (scikit-learn#25876) * MAINT Clean-up deprecated if_delegate_has_method for 1.3 (scikit-learn#25879) * MAINT Parameter validation for tree.export_text (scikit-learn#25867) * DOC impact of `tol` for solvers in RidgeClassifier (scikit-learn#25530) * MAINT Parameters validation for metrics.hinge_loss (scikit-learn#25880) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for metrics.ndcg_score (scikit-learn#25885) * ENH KMeans initialization account for sample weights (scikit-learn#25752) Co-authored-by: jeremiedbb <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]> Co-authored-by: Jérémie du Boisberranger <[email protected]> * TST use global_random_seed in sklearn/tests/test_dummy.py (scikit-learn#25884) * DOC improve calibration user guide (scikit-learn#25687) * ENH Support for sparse matrices added to `sklearn.metrics.silhouette_samples` (scikit-learn#24677) Co-authored-by: Sahil Gupta <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]> * MAINT validate_params for plot_tree (scikit-learn#25882) Co-authored-by: Itay <[email protected]> * MAINT add missing space in error message in SVM (scikit-learn#25913) * FIX Adds requires_y tag to TargetEncoder (scikit-learn#25917) * MAINT Consistent cython types continued (scikit-learn#25810) * TST Speed-up common tests of DictionaryLearning (scikit-learn#25892) * TST Speed-up test_dbscan_optics_parity (scikit-learn#25893) * ENH add np.nan option for zero_division in precision/recall/f-score (scikit-learn#25531) Co-authored-by: Guillaume Lemaitre <[email protected]> * MAINT Parameters validation for datasets.make_low_rank_matrix (scikit-learn#25901) * MAINT Parameter validation for metrics.cluster.adjusted_mutual_info_score (scikit-learn#25898) Co-authored-by: Jérémie du Boisberranger <[email protected]> * TST Speed-up test_partial_dependence.test_output_shape (scikit-learn#25895) Co-authored-by: Thomas J. Fan <[email protected]> * MAINT Parameters validation for datasets.make_regression (scikit-learn#25899) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for metrics.mean_squared_log_error (scikit-learn#25924) * TST Use global_random_seed in tests/test_naive_bayes.py (scikit-learn#25890) * TST add global_random_seed fixture to sklearn/datasets/tests/test_covtype.py (scikit-learn#25904) Co-authored-by: Jérémie du Boisberranger <[email protected]> Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameters validation for datasets.make_multilabel_classification (scikit-learn#25920) * Fixed feature mapping typo (scikit-learn#25934) * MAINT switch to newer codecov uploader (scikit-learn#25919) Co-authored-by: Loïc Estève <[email protected]> * TST Speed-up test suite when using pytest-xdist (scikit-learn#25918) * DOC update license year to 2023 (scikit-learn#25936) * FIX Remove spurious feature names warning in IsolationForest (scikit-learn#25931) * TST fix unstable test_newrand_set_seed (scikit-learn#25940) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Clean-up deprecated max_features="auto" in trees/forests/gb (scikit-learn#25941) * MAINT LogisticRegression informative error msg when penaly=elasticnet and l1_ratio is None (scikit-learn#25925) Co-authored-by: jeremiedbb <[email protected]> * MAINT Clean-up remaining SGDClassifier(loss="log") (scikit-learn#25938) * FIX Fixes pandas extension arrays in check_array (scikit-learn#25813) * FIX Fixes pandas extension arrays with objects in check_array (scikit-learn#25814) * CI Disable pytest-xdist in pylatest_pip_openblas_pandas build (scikit-learn#25943) * MAINT remove deprecated call to resources.content (scikit-learn#25951) * DOC note on calibration impact on ranking (scikit-learn#25900) * Remove loguniform fix, use scipy.stats instead (scikit-learn#24665) Co-authored-by: Olivier Grisel <[email protected]> * MAINT Fix broken links in cluster.dbscan module (scikit-learn#25958) * DOC Fix lars Xy shape (scikit-learn#25952) * ENH Add drop_intermediate parameter to metrics.precision_recall_curve (scikit-learn#24668) Co-authored-by: Guillaume Lemaitre <[email protected]> * FIX improve error message when computing NDCG with a single document (scikit-learn#25672) Co-authored-by: Guillaume Lemaitre <[email protected]> * MAINT introduce _get_response_values and _check_response_methods (scikit-learn#23073) Co-authored-by: Thomas J. Fan <[email protected]> Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Extend message for large sparse matrices support (scikit-learn#25961) Co-authored-by: Meekail Zain <[email protected]> * MAINT Parameters validation for datasets.make_gaussian_quantiles (scikit-learn#25959) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.metrics.d2_tweedie_score (scikit-learn#25975) * MAINT Parameters validation for datasets.make_hastie_10_2 (scikit-learn#25967) * MAINT Parameters validation for preprocessing.minmax_scale (scikit-learn#25962) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for datasets.make_checkerboard (scikit-learn#25955) * MAINT Parameters validation for datasets.make_biclusters (scikit-learn#25945) * MAINT Parameters validation for datasets.make_moons (scikit-learn#25971) * DOC replace deviance by loss in docstring of GradientBoosting (scikit-learn#25968) * MAINT Fix broken link in feature_selection/_univariate_selection.py (scikit-learn#25984) * DOC Update model_persistence.rst to fix skops example (scikit-learn#25993) Co-authored-by: adrinjalali <[email protected]> * DOC Specified meaning for max_patches=None in extract_patches_2d (scikit-learn#25996) * DOC document that last step is never cached in pipeline (scikit-learn#25995) Co-authored-by: Guillaume Lemaitre <[email protected]> * FIX SequentialFeatureSelector throws IndexError when cv is a generator (scikit-learn#25973) * ENH Adds infrequent categories support to OrdinalEncoder (scikit-learn#25677) Co-authored-by: Tim Head <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Andreas Mueller <[email protected]> * MAINT make plot_digits_denoising deterministic by fixing random state (scikit-learn#26004) * DOC improve example of PatchExtractor (scikit-learn#26002) * MAINT Parameters validation for datasets.make_friedman2 (scikit-learn#25986) * MAINT Parameters validation for datasets.make_friedman3 (scikit-learn#25989) * MAINT Parameters validation for datasets.make_sparse_uncorrelated (scikit-learn#26001) * MAINT Parameters validation for datasets.make_spd_matrix (scikit-learn#26003) * MAINT Parameters validation for datasets.make_sparse_spd_matrix (scikit-learn#26009) * DOC Added the meanings of default=None for PatchExtractor parameters (scikit-learn#26005) * MAINT remove unecessary check covered by parameter validation framework (scikit-learn#26014) * MAINT Consistent cython types from _typedefs (scikit-learn#25942) Co-authored-by: Julien Jerphanion <[email protected]> * MAINT Parameters validation for datasets.make_swiss_roll (scikit-learn#26020) * MAINT Parameters validation for datasets.make_s_curve (scikit-learn#26022) * MAINT Parameters validation for datasets.make_blobs (scikit-learn#25983) Co-authored-by: Guillaume Lemaitre <[email protected]> * DOC fix SplineTransformer include_bias docstring (scikit-learn#26018) * ENH RocCurveDisplay add option to plot chance level (scikit-learn#25987) * DOC show from_estimator and from_predictions for Displays (scikit-learn#25994) * EXA Fix rst in plot_partial_dependence (scikit-learn#26028) * CI Adds coverage to docker jobs on Azure (scikit-learn#26027) Co-authored-by: Julien Jerphanion <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> * API Replace `n_iter` in `Bayesian Ridge` and `ARDRegression` (scikit-learn#25697) Co-authored-by: Guillaume Lemaitre <[email protected]> * CLN Make _NumPyAPIWrapper naming consistent to _ArrayAPIWrapper (scikit-learn#26039) * CI disable coverage on Windows to keep CI times reasonable (scikit-learn#26052) * DOC Use Scientific Python Plausible instance for analytics (scikit-learn#25547) * MAINT Parameters validation for sklearn.preprocessing.scale (scikit-learn#26036) * MAINT Parameters validation for sklearn.metrics.pairwise.haversine_distances (scikit-learn#26047) * MAINT Parameters validation for sklearn.metrics.pairwise.laplacian_kernel (scikit-learn#26048) * MAINT Parameters validation for sklearn.metrics.pairwise.linear_kernel (scikit-learn#26049) * MAINT Parameters validation for sklearn.metrics.silhouette_samples (scikit-learn#26053) * MAINT Parameters validation for sklearn.preprocessing.add_dummy_feature (scikit-learn#26058) * Added Parameter Validation for metrics.cluster.normalized_mutual_info_score() (scikit-learn#26060) * DOC Typos in HistGradientBoosting documentation (scikit-learn#26057) * TST add global_random_seed fixture to sklearn/datasets/tests/test_rcv1.py (scikit-learn#26043) * MAINT Parameters validation for sklearn.metrics.pairwise.cosine_similarity (scikit-learn#26006) Co-authored-by: Jérémie du Boisberranger <[email protected]> * ENH Adds isdtype to Array API wrapper (scikit-learn#26029) * MAINT Parameters validation for sklearn.metrics.silhouette_score (scikit-learn#26054) Co-authored-by: Jérémie du Boisberranger <[email protected]> * FIX fix spelling mistake in _NumPyAPIWrapper (scikit-learn#26064) * CI ignore more non-library Python files in codecov (scikit-learn#26059) * MAINT Parameters validation for sklearn.metrics.pairwise.cosine_distances (scikit-learn#26046) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Introduce BinaryClassifierCurveDisplayMixin (scikit-learn#25969) Co-authored-by: Jérémie du Boisberranger <[email protected]> * ENH Forces shape to be tuple when using Array API's reshape (scikit-learn#26030) Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Tim Head <[email protected]> * MAINT Parameters validation for sklearn.metrics.pairwise.paired_euclidean_distances (scikit-learn#26073) * MAINT Parameters validation for sklearn.metrics.pairwise.paired_manhattan_distances (scikit-learn#26074) * MAINT Parameters validation for sklearn.metrics.pairwise.paired_cosine_distances (scikit-learn#26075) * MAINT Parameters validation for sklearn.preprocessing.binarize (scikit-learn#26076) * MAINT Parameters validation for metrics.explained_variance_score (scikit-learn#26079) * DOC use correct template name for displays (scikit-learn#26081) * MAINT Parameters validation for sklearn.preprocessing.maxabs_scale (scikit-learn#26077) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.preprocessing.label_binarize (scikit-learn#26078) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT parameter validation for d2_absolute_error_score (scikit-learn#26066) Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameter validation for roc_auc_score (scikit-learn#26007) Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameters validation for sklearn.preprocessing.normalize (scikit-learn#26069) Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameter validation for metrics.cluster.fowlkes_mallows_score (scikit-learn#26080) Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameters validation for compose.make_column_transformer (scikit-learn#25897) Co-authored-by: jeremiedbb <[email protected]> * MAINT Parameters validation for sklearn.metrics.pairwise.polynomial_kernel (scikit-learn#26070) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.metrics.pairwise.rbf_kernel (scikit-learn#26071) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.metrics.pairwise.sigmoid_kernel (scikit-learn#26072) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Param validation: constraint for numeric missing values (scikit-learn#26085) * FIX Adds support for negative values in categorical features in gradient boosting (scikit-learn#25629) Co-authored-by: Julien Jerphanion <[email protected]> Co-authored-by: Tim Head <[email protected]> * MAINT Fix C warning in Cython module splitting.pyx (scikit-learn#26051) * MNT Updates _isotonic.pyx to use memoryviews instead of `cnp.ndarray` (scikit-learn#26068) * FIX Fixes memory regression for inspecting extension arrays (scikit-learn#26106) * PERF set openmp to use only physical cores by default (scikit-learn#26082) * MNT Update black to 23.3.0 (scikit-learn#26110) * MNT Adds black commit to git-blame-ignore-revs (scikit-learn#26111) * MAINT Parameters validation for sklearn.metrics.pair_confusion_matrix (scikit-learn#26107) * MAINT Parameters validation for sklearn.metrics.mean_poisson_deviance (scikit-learn#26104) * DOC Use notebook style in plot_lof_outlier_detection.py (scikit-learn#26017) Co-authored-by: Jérémie du Boisberranger <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]> * MAINT utils._fast_dict uses types from utils._typedefs (scikit-learn#26025) * DOC remove sparse-matrix for `y` in ElasticNet (scikit-learn#26127) * ENH add exponential loss (scikit-learn#25965) * MAINT Parameters validation for sklearn.preprocessing.robust_scale (scikit-learn#26086) * MAINT Parameters validation for sklearn.datasets.fetch_rcv1 (scikit-learn#26126) * MAINT Parameters validation for sklearn.metrics.adjusted_rand_score (scikit-learn#26134) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.metrics.calinski_harabasz_score (scikit-learn#26135) * MAINT Parameters validation for sklearn.metrics.davies_bouldin_score (scikit-learn#26136) * MAINT: remove `from numpy.math cimport` statements (scikit-learn#26143) * MAINT Parameters validation for sklearn.inspection.permutation_importance (scikit-learn#26145) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.metrics.cluster.homogeneity_completeness_v_measure (scikit-learn#26137) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.metrics.rand_score (scikit-learn#26138) Co-authored-by: Jérémie du Boisberranger <[email protected]> * DOC update comment in metrics/tests/test_classification.py (scikit-learn#26150) * CI small cleanup of Cirrus CI test script (scikit-learn#26168) * MAINT remove deprecated is_categorical_dtype (scikit-learn#26156) * DOC Add skforecast to related projects page (scikit-learn#26133) Co-authored-by: Thomas J. Fan <[email protected]> * FIX Keeps namedtuple's class when transform returns a tuple (scikit-learn#26121) * DOC corrected letter case for better readability in sklearn/metrics/_classification.py / (scikit-learn#26169) * MAINT Parameters validation for sklearn.preprocessing.power_transform (scikit-learn#26142) * FIX `roc_auc_score` now uses `y_prob` instead of `y_pred` (scikit-learn#26155) * MAINT Parameters validation for sklearn.datasets.load_iris (scikit-learn#26177) * MAINT Parameters validation for sklearn.datasets.load_diabetes (scikit-learn#26166) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.datasets.load_breast_cancer (scikit-learn#26165) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.metrics.cluster.entropy (scikit-learn#26162) * MAINT Parameters validation for sklearn.datasets.fetch_species_distributions (scikit-learn#26161) Co-authored-by: Jérémie du Boisberranger <[email protected]> * ASV Fix tol in SGDRegressorBenchmark (scikit-learn#26146) Co-authored-by: jeremie du boisberranger <[email protected]> * MNT use api.openml.org URLs for fetch_openml (scikit-learn#26171) * MAINT Parameters validation for sklearn.utils.resample (scikit-learn#26139) * MAINT make it explicit that additive_chi2_kernel does not accept sparse matrix (scikit-learn#26178) * MNT fix circleci link in README.rst (scikit-learn#26183) * CI Fix circleci artifact redirector action (scikit-learn#26181) * GOV introduce rights for groups as discussed in SLEP019 (scikit-learn#25753) Co-authored-by: Julien <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]> * MAINT Parameters validation for sklearn.neighbors.sort_graph_by_row_values (scikit-learn#26173) Co-authored-by: Jérémie du Boisberranger <[email protected]> * FIX improve convergence criterion for LogisticRegression(penalty="l1", solver='liblinear') (scikit-learn#25214) Co-authored-by: Thomas J. Fan <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> * MAINT Fix several typos in src and doc files (scikit-learn#26187) * PERF fix overhead of _rescale_data in LinearRegression (scikit-learn#26207) * ENH add Huber loss (scikit-learn#25966) * MAINT Refactor GraphicalLasso and graphical_lasso (scikit-learn#26033) Co-authored-by: Guillaume Lemaitre <[email protected]> Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Cython linting (scikit-learn#25861) * DOC Add JupyterLite button in example gallery (scikit-learn#25887) * MAINT Parameters validation for sklearn.covariance.ledoit_wolf_shrinkage (scikit-learn#26200) * MAINT Parameters validation for sklearn.datasets.load_linnerud (scikit-learn#26199) * MAINT Parameters validation for sklearn.datasets.load_wine (scikit-learn#26196) * DOC Added redirect to Provost paper + minor refactor (scikit-learn#26223) * MAINT Parameter Validation for `covariance.graphical_lasso` (scikit-learn#25053) Co-authored-by: Guillaume Lemaitre <[email protected]> Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.datasets.load_digits (scikit-learn#26195) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.preprocessing.quantile_transform (scikit-learn#26144) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.model_selection.cross_validate (scikit-learn#26129) Co-authored-by: jeremiedbb <[email protected]> * DOC Adds TargetEncoder example explaining the internal CV (scikit-learn#26185) Co-authored-by: Tim Head <[email protected]> * spelling mistake corrected in documentation for script `plot_document_clustering.py` (scikit-learn#26228) Co-authored-by: Olivier Grisel <[email protected]> * FIX possible UnboundLocalError in fetch_openml (scikit-learn#26236) * ENH Adds PyTorch support to LinearDiscriminantAnalysis (scikit-learn#25956) Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Tim Head <[email protected]> * MNT Use fixed version of Pyodide (scikit-learn#26247) * MNT Reset transform_output default in example to fix doc build build (scikit-learn#26269) * DOC Update example plot_nearest_centroid.py (scikit-learn#26263) * MNT reduce JupyterLite build size (scikit-learn#26246) * DOC term -> meth in GradientBoosting (scikit-learn#26225) * MNT speed-up html-noplot build (scikit-learn#26245) Co-authored-by: Thomas J. Fan <[email protected]> * MNT Use copy=False when creating DataFrames (scikit-learn#26272) * MAINT Parameters validation for sklearn.model_selection.permutation_test_score (scikit-learn#26230) * MAINT Parameters validation for sklearn.datasets.clear_data_home (scikit-learn#26259) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.datasets.load_files (scikit-learn#26203) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.datasets.get_data_home (scikit-learn#26260) Co-authored-by: Jérémie du Boisberranger <[email protected]> * DOC Fix y-axis plot labels in permutation test score example (scikit-learn#26240) * MAINT cython-lint ignores asv_benchmarks (scikit-learn#26282) * MAINT Parameter validation for metrics.cluster._supervised (scikit-learn#26258) Co-authored-by: Jérémie du Boisberranger <[email protected]> * DOC Improve docstring for tol in SequentialFeatureSelector (scikit-learn#26271) * MAINT Parameters validation for sklearn.datasets.load_sample_image (scikit-learn#26226) Co-authored-by: Jérémie du Boisberranger <[email protected]> * DOC Consistent param type for pos_label (scikit-learn#26237) * DOC Minor grammar fix to imputation docs (scikit-learn#26283) * MAINT Parameters validation for sklearn.calibration.calibration_curve (scikit-learn#26198) Co-authored-by: jeremie du boisberranger <[email protected]> * MAINT Parameters validation for sklearn.inspection.partial_dependence (scikit-learn#26209) Co-authored-by: jeremie du boisberranger <[email protected]> * MAINT Parameters validation for sklearn.model_selection.validation_curve (scikit-learn#26229) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MAINT Parameters validation for sklearn.model_selection.learning_curve (scikit-learn#26227) Co-authored-by: jeremie du boisberranger <[email protected]> * MNT Remove deprecated pandas.api.types.is_sparse (scikit-learn#26287) * CI Use Trusted Publishers for uploading wheels to PyPI (scikit-learn#26249) * MAINT Parameters validation for sklearn.metrics.pairwise.manhattan_distances (scikit-learn#26122) * PERF revert openmp use in csr_row_norms (scikit-learn#26275) * MAINT Parameters validation for metrics.check_scoring (scikit-learn#26041) Co-authored-by: Jérémie du Boisberranger <[email protected]> * MNT Improve error message when checking classification target is of a non-regression type (scikit-learn#26281) Co-authored-by: Adrin Jalali <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]> * DOC fix link to User Guide encoder_infrequent_categories (scikit-learn#26309) * MNT remove unused args in _predict_regression_tree_inplace_fast_dense (scikit-learn#26314) * ENH Adds missing value support for trees (scikit-learn#23595) Co-authored-by: Tim Head <[email protected]> Co-authored-by: Julien Jerphanion <[email protected]> * CLN Clean up logic in validate_data and cast_to_ndarray (scikit-learn#26300) * MAINT refactor scorer using _get_response_values (scikit-learn#26037) Co-authored-by: Jérémie du Boisberranger <[email protected]> Co-authored-by: Adrin Jalali <[email protected]> * DOC Add HGBDT to "see also" section of random forests (scikit-learn#26319) Co-authored-by: ArturoAmorQ <[email protected]> Co-authored-by: Tim Head <[email protected]> * MNT Bump Github Action labeler version to use newer Node (scikit-learn#26302) * FIX thresholds should not exceed 1.0 with probabilities in `roc_curve` (scikit-learn#26194) Co-authored-by: Olivier Grisel <[email protected]> * ENH Allow for appropriate dtype us in `preprocessing.PolynomialFeatures` for sparse matrices (scikit-learn#23731) Co-authored-by: Aleksandr Kokhaniukov <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Julien Jerphanion <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]> * DOC Fix minor typo (scikit-learn#26327) * MAINT bump minimum version for pytest (scikit-learn#26184) Co-authored-by: Loïc Estève <[email protected]> Co-authored-by: Adrin Jalali <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> * DOC fix return type in isotonic_regression (scikit-learn#26332) * FIX fix available_if for MultiOutputRegressor.partial_fit (scikit-learn#26333) Co-authored-by: Guillaume Lemaitre <[email protected]> * FIX make pipeline pass check_estimator (scikit-learn#26325) * FEA Add multiclass support to `average_precision_score` (scikit-learn#24769) Co-authored-by: Geoffrey <[email protected]> Co-authored-by: gbolmier <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]> --------- Signed-off-by: Julien Jerphanion <[email protected]> Co-authored-by: Jérémie du Boisberranger <[email protected]> Co-authored-by: Meekail Zain <[email protected]> Co-authored-by: Julien Jerphanion <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: zeeshan lone <[email protected]> Co-authored-by: jeremiedbb <[email protected]> Co-authored-by: Adrin Jalali <[email protected]> Co-authored-by: Shiva chauhan <[email protected]> Co-authored-by: AymericBasset <[email protected]> Co-authored-by: Maren Westermann <[email protected]> Co-authored-by: Nishu Choudhary <[email protected]> Co-authored-by: Guillaume Lemaitre <[email protected]> Co-authored-by: Loïc Estève <[email protected]> Co-authored-by: Benedek Harsanyi <[email protected]> Co-authored-by: Pooja Subramaniam <[email protected]> Co-authored-by: Rushil Desai <[email protected]> Co-authored-by: Xiao Yuan <[email protected]> Co-authored-by: Omar Salman <[email protected]> Co-authored-by: 2357juan <[email protected]> Co-authored-by: Théophile Baranger <[email protected]> Co-authored-by: Thomas J. Fan <[email protected]> Co-authored-by: Andreas Mueller <[email protected]> Co-authored-by: Jovan Stojanovic <[email protected]> Co-authored-by: Rahil Parikh <[email protected]> Co-authored-by: Bharat Raghunathan <[email protected]> Co-authored-by: Sortofamudkip <[email protected]> Co-authored-by: Gleb Levitski <[email protected]> Co-authored-by: Christian Lorentzen <[email protected]> Co-authored-by: Ashwin Mathur <[email protected]> Co-authored-by: Sahil Gupta <[email protected]> Co-authored-by: Veghit <[email protected]> Co-authored-by: Itay <[email protected]> Co-authored-by: precondition <[email protected]> Co-authored-by: Marc Torrellas Socastro <[email protected]> Co-authored-by: Dominic Fox <[email protected]> Co-authored-by: futurewarning <[email protected]> Co-authored-by: Yao Xiao <[email protected]> Co-authored-by: Joey Ortiz <[email protected]> Co-authored-by: Tim Head <[email protected]> Co-authored-by: Christian Veenhuis <[email protected]> Co-authored-by: adienes <[email protected]> Co-authored-by: Dave Berenbaum <[email protected]> Co-authored-by: Lene Preuss <[email protected]> Co-authored-by: A.H.Mansouri <[email protected]> Co-authored-by: Boris Feld <[email protected]> Co-authored-by: Carla J <[email protected]> Co-authored-by: windiana42 <[email protected]> Co-authored-by: mdarii <[email protected]> Co-authored-by: murezzda <[email protected]> Co-authored-by: Peter Piontek <[email protected]> Co-authored-by: John Pangas <[email protected]> Co-authored-by: Dmitry Nesterov <[email protected]> Co-authored-by: Yuchen Zhou <[email protected]> Co-authored-by: Ekaterina Butyugina <[email protected]> Co-authored-by: Jiawei Zhang <[email protected]> Co-authored-by: Ansam Zedan <[email protected]> Co-authored-by: genvalen <[email protected]> Co-authored-by: farhan khan <[email protected]> Co-authored-by: Arturo Amor <[email protected]> Co-authored-by: Jiawei Zhang <[email protected]> Co-authored-by: Ralf Gommers <[email protected]> Co-authored-by: Jessicakk0711 <[email protected]> Co-authored-by: Ankur Singh <[email protected]> Co-authored-by: Seoeun(Sun☀️) Hong <[email protected]> Co-authored-by: Nightwalkx <[email protected]> Co-authored-by: VIGNESH D <[email protected]> Co-authored-by: Vincent-violet <[email protected]> Co-authored-by: Elabonga Atuo <[email protected]> Co-authored-by: Tom Dupré la Tour <[email protected]> Co-authored-by: André Pedersen <[email protected]> Co-authored-by: Ashish Dutt <[email protected]> Co-authored-by: Phil <[email protected]> Co-authored-by: Stanislav (Stanley) Modrak <[email protected]> Co-authored-by: hujiahong726 <[email protected]> Co-authored-by: James Dean <[email protected]> Co-authored-by: ArturoAmorQ <[email protected]> Co-authored-by: Aleksandr Kokhaniukov <[email protected]> Co-authored-by: c-git <[email protected]> Co-authored-by: annegnx <[email protected]> Co-authored-by: Geoffrey <[email protected]> Co-authored-by: gbolmier <[email protected]>

niuk-a and others added 6 commits July 13, 2021 15:54

[WIP] FIX index overflow error in sparse matrix polynomial expansion …

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…(bis) Fixes scikit-learn#19676

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Micky774 changed the title ~~ENH Allow for appropriate dtype us in preprocessing.PolynomialFeatures::_csr_polynomial_expansion~~ ENH Allow for appropriate dtype us in preprocessing.PolynomialFeatures for sparse matrices Jun 22, 2022

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Remove indices downcasting for sparse arrays scipy/scipy#18509

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	# scipy matrices indices dtype (namely for indptr and indices arrays)
	#
	# Note that indices might need to be represented as cnp.int64_t.
	# Currently, we use Cython classes which do not handle fused types
	# so we hardcode this type to cnp.int32_t, supporting all but edge
	# cases.
	#
	# TODO: support cnp.int64_t for this case
	# See: https://github.com/scikit-learn/scikit-learn/issues/23653
	ctypedef cnp.int32_t SPARSE_INDEX_TYPE_t

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ENH Allow for appropriate dtype us in preprocessing.PolynomialFeatures for sparse matrices #23731

ENH Allow for appropriate dtype us in preprocessing.PolynomialFeatures for sparse matrices #23731

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ENH Allow for appropriate dtype us in `preprocessing.PolynomialFeatures` for sparse matrices #23731

ENH Allow for appropriate dtype us in `preprocessing.PolynomialFeatures` for sparse matrices #23731

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