ENH Array API support for f1_score

[OmarManzoor]

Reference Issues/PRs

Towards #26024

What does this implement/fix? Explain your changes.

• Adds array api support for f1_score and the functions related to it.

Any other comments?

CC: @ogrisel @betatim

[github-actions]

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[OmarManzoor]

@ogrisel Could you kindly have a look at this PR?

[ogrisel]

Overall this looks good to me. I am surprised it works without being very specific about device and dtypes, but as long as the tests (and they do), I am happy.

[ogrisel]

We should probably make sure that it matches the device of the inputs, no? It's curious that existing tests do not fail with PyTorch and MPS device (or cuda devices).

I am also wondering of whether we should convert to a specific dtype. However looking at the tests I never see any case where we pass non-integer sample weights. And even for integer weights, it's only done to check an error message, not to check an actual computation. So I am not sure our sample_weight support is correct, even outside of array API concerns.

I guess this is only indirectly tested by classification metrics that rely on multilabel_confusion_matrix internally. But then the array API compliance tests for F1 score do not fail with floating point weights (I just checked) and I am not sure why.

[ogrisel]

Here is the output of my cuda run on this PR (updated to check that boolean array indexing also works, but this should be orthogonal):

$ pytest -vlx -k "array_api and f1_score" sklearn/ 

================================================================================================== test session starts ===================================================================================================
platform linux -- Python 3.10.12, pytest-7.4.2, pluggy-1.3.0
collected 34881 items / 34863 deselected / 2 skipped / 18 selected                                                                                                                                                       

sklearn/metrics/tests/test_common.py::test_array_api_compliance[f1_score-check_array_api_binary_classification_metric-numpy-None-None] PASSED                                                                      [  5%]
sklearn/metrics/tests/test_common.py::test_array_api_compliance[f1_score-check_array_api_binary_classification_metric-array_api_strict-None-None] PASSED                                                           [ 11%]
sklearn/metrics/tests/test_common.py::test_array_api_compliance[f1_score-check_array_api_binary_classification_metric-cupy-None-None] PASSED                                                                       [ 16%]
sklearn/metrics/tests/test_common.py::test_array_api_compliance[f1_score-check_array_api_binary_classification_metric-cupy.array_api-None-None] PASSED                                                             [ 22%]
sklearn/metrics/tests/test_common.py::test_array_api_compliance[f1_score-check_array_api_binary_classification_metric-torch-cpu-float64] PASSED                                                                    [ 27%]
sklearn/metrics/tests/test_common.py::test_array_api_compliance[f1_score-check_array_api_binary_classification_metric-torch-cpu-float32] PASSED                                                                    [ 33%]
sklearn/metrics/tests/test_common.py::test_array_api_compliance[f1_score-check_array_api_binary_classification_metric-torch-cuda-float64] PASSED                                                                   [ 38%]
sklearn/metrics/tests/test_common.py::test_array_api_compliance[f1_score-check_array_api_binary_classification_metric-torch-cuda-float32] PASSED                                                                   [ 44%]
sklearn/metrics/tests/test_common.py::test_array_api_compliance[f1_score-check_array_api_binary_classification_metric-torch-mps-float32] SKIPPED (Skipping MPS device test because PYTORCH_ENABLE_MPS_FALLBACK...) [ 50%]
sklearn/metrics/tests/test_common.py::test_array_api_compliance[f1_score-check_array_api_multiclass_classification_metric-numpy-None-None] PASSED                                                                  [ 55%]
sklearn/metrics/tests/test_common.py::test_array_api_compliance[f1_score-check_array_api_multiclass_classification_metric-array_api_strict-None-None] PASSED                                                       [ 61%]
sklearn/metrics/tests/test_common.py::test_array_api_compliance[f1_score-check_array_api_multiclass_classification_metric-cupy-None-None] PASSED                                                                   [ 66%]
sklearn/metrics/tests/test_common.py::test_array_api_compliance[f1_score-check_array_api_multiclass_classification_metric-cupy.array_api-None-None] PASSED                                                         [ 72%]
sklearn/metrics/tests/test_common.py::test_array_api_compliance[f1_score-check_array_api_multiclass_classification_metric-torch-cpu-float64] PASSED                                                                [ 77%]
sklearn/metrics/tests/test_common.py::test_array_api_compliance[f1_score-check_array_api_multiclass_classification_metric-torch-cpu-float32] PASSED                                                                [ 83%]
sklearn/metrics/tests/test_common.py::test_array_api_compliance[f1_score-check_array_api_multiclass_classification_metric-torch-cuda-float64] PASSED                                                               [ 88%]
sklearn/metrics/tests/test_common.py::test_array_api_compliance[f1_score-check_array_api_multiclass_classification_metric-torch-cuda-float32] PASSED                                                               [ 94%]
sklearn/metrics/tests/test_common.py::test_array_api_compliance[f1_score-check_array_api_multiclass_classification_metric-torch-mps-float32] SKIPPED (Skipping MPS device test because PYTORCH_ENABLE_MPS_FALL...) [100%]

============================================================================= 16 passed, 4 skipped, 34863 deselected, 105 warnings in 15.59s =============================================================================

[OmarManzoor]

@ogrisel @betatim Does this look okay now?

[ogrisel]

I do not have time to finish the review today but here is some quick feedback:

[ogrisel]

I merged main to be able to launch the new CUDA GPU CI workflow on this PR:

• https://github.com/scikit-learn/scikit-learn/actions/runs/9387261905

EDIT: tests are green.

[ogrisel]

LGTM once the following is addressed:

[ogrisel]

@betatim this is ready for a second review.

[ogrisel]

I launched the CUDA GPU CI at:

• https://github.com/scikit-learn/scikit-learn/actions/runs/9402808393

EDIT: CUDA tests are green.