Use estimator tags to improve sparse error handling #6151
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dantegd
added
improvement
wphicks
requested changes
Dec 11, 2024
| @pytest.mark.parametrize("estimator_name", list(estimators.keys())) | ||
| def test_sparse_support(estimator_name): | ||
| X_sparse = csr_matrix([[0, 1], [1, 0]]) | ||
| print(X_sparse.shape[0]) |
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Suggested change
| print(X_sparse.shape[0]) |
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| @pytest.mark.parametrize("estimator_name", list(estimators.keys())) | ||
| def test_sparse_support(estimator_name): |
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How would we feel about adding a test that confirms that an estimator without sparse support raises the correct exception on sparse input?
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Added them in a separate file to test vanilla cuML exceptions
…h-sparse-handling
…s, and remove them from exception test
wphicks
approved these changes
Dec 12, 2024
| @@ -122,13 +122,19 @@ def custom_weights(distances): | |||
| assert acc > 0.7, "Accuracy should be reasonable with custom weights" | |||
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| @pytest.mark.xfail( | |||
| reason="cuML and sklearn don't have matching exceptions yet" | |||
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/merge |
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