Fix Ridge sparse + sample_weight + intercept #22899
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| @pytest.mark.parametrize("solver", ["sparse_cg", "sag"]) | ||
| def test_ridge_sample_weights_dense_sparse(solver, global_random_seed): |
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Could this be merged with test_ridge_fit_intercept_sparse (and maybe also test_ridge_fit_intercept_sparse)? (take the best part of both).
It seems kind of duplicate.
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Right, I merged them into a single test. It allowed to find out that lbfgs also had the same issue. I fixed it as well
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This is great as it will resolve a long existing, severe bug! |
jeremiedbb
commented
Mar 19, 2022
| @@ -1362,33 +1362,41 @@ def test_n_iter(): | |||
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| @pytest.mark.parametrize("solver", ["sparse_cg", "lbfgs", "auto"]) | |||
| def test_ridge_fit_intercept_sparse(solver): | |||
| @pytest.mark.parametrize("with_sample_weight", [True, False]) | |||
| def test_ridge_fit_intercept_sparse(solver, with_sample_weight, global_random_seed): | |||
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tested with "all" seeds
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@agramfort @rth @TomDLT may be interested. |
ogrisel
reviewed
Mar 22, 2022
| For now only sparse_cg and lbfgs can correctly fit an intercept | ||
| with sparse X with default tol and max_iter. | ||
| 'sag' is tested separately in test_ridge_fit_intercept_sparse_sag because it | ||
| requires more iterations and should raise a warning if default max_iter is used. |
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I pushed a new commit to make this true with sample_weight != None. I tested it with "all" global_random_seed.
ogrisel
approved these changes
Mar 22, 2022
glemaitre
pushed a commit
to glemaitre/scikit-learn
that referenced
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Apr 6, 2022
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
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Fixes #15438
Same issue as in LinearRegression: in sparse X_offset needs the sample_weight rescaling.
The issue appears with solver = 'sparse-cg' and solver = 'lbfgs'.
I noticed that there's also an issue for 'sag' solver, but I'm not familiar with the code at all. Any help would be greatly appreciated. We can also leave that for a separate PR.ping @lorentzenchr