TST use global_random_seed in sklearn/utils/tests/test_optimize.py #30112
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test_newton_cg uses global_random_seed fixture
test_optimize
marenwestermann
commented
Oct 19, 2024
| @@ -28,7 +28,7 @@ def grad_hess(x): | |||
| return grad(x), lambda x: A.T.dot(A.dot(x)) | |||
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| assert_array_almost_equal( | |||
| _newton_cg(grad_hess, func, grad, x0, tol=1e-10)[0], | |||
| _newton_cg(grad_hess, func, grad, x0, tol=1e-7)[0], | |||
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With a tolerance of 1e-10 I had two test failures. I was able to resolve these by reducing the threshold to 1e-7. It's not an optimal solution but the default of tol is 1e-4. So I think this solution should be acceptable.
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I think it's an okay solution. Both functions don't have the same parameters so it's hard to find the right combination such that they have the same behavior.
I replaced with assert_allclose and added a comment to add a bit more explanation.
test_newton_cg
test_newton_cg
jeremiedbb
approved these changes
Apr 10, 2025
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Reference Issues/PRs
towards #22827
What does this implement/fix? Explain your changes.
adds the global random seed fixture to the test
test_newton_cg.Any other comments?
I took over from the stale PR #26462