Make test_ridge_regression_dtype_stability less random #13816
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Note that this test in one of the 2 tests that currently fail on the travis CRON job: https://travis-ci.org/scikit-learn/scikit-learn/builds/529275776 |
NicolasHug
approved these changes
May 7, 2019
jnothman
reviewed
May 8, 2019
| @@ -998,16 +998,18 @@ def test_dtype_match_cholesky(): | |||
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| @pytest.mark.parametrize( | |||
| 'solver', ['svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga']) | |||
| def test_ridge_regression_dtype_stability(solver): | |||
| random_state = np.random.RandomState(0) | |||
| @pytest.mark.parametrize('seed', range(1)) | |||
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It's meant to make it easy to edit this test to run on 100s of different seeds. We probably need a more generic way to achieve this with a fixture.
agramfort
approved these changes
May 8, 2019
jnothman
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May 8, 2019
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We could have a random_seed pytest.fixture that is different on different
ci?
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koenvandevelde
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to koenvandevelde/scikit-learn
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Jul 12, 2019
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As reported in #13350 (comment) a new test introduced in #13302 tend to fail randomly when changing the seed (also one of the rng used internally was a module level singleton which caused the tests to be dependent on other tests).
I changed the test criterion to work with a wide variety of random problems (I tried all
seed in range(100)).I have observed a few catastrophic failures with
sparse_cginfloat32: some coefs could have very large magnitude. Maybe we should disable float32 bit data forsparse_cg.