[MRG] Remove some warnings from test suit #5283
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| sorted_array = np.arange(201, 300) | ||
| assert_true(np.any(sorted_array != ind[train])) | ||
| assert_true(np.any(np.arange(100) != ind[test])) | ||
| assert_true(np.any(np.arange(100, 200) != ind[test])) |
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np.arange(100) = [0, ..., 99] (100 elements)
np.arange(100, 200) = [100, ..., 199] (100 elements)
np.arange(101, 200) = [101, ..., 199] (99 elements)The test was asserting the arrays are different because of different sizes, and not because of the shuffling (as expected).
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besides LGTM |
| @@ -323,6 +322,7 @@ def test_cohen_kappa(): | |||
| assert_almost_equal(cohen_kappa_score(y1, y2), .8013, decimal=4) | |||
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| @ignore_warnings | |||
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What is the issue being raised here? Is ignoring the warning the only way?
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RuntimeWarning: Degrees of freedom <= 0 for slice
np.corrcoef raises a warning when dividing by zero, and return a NaN.
This test verifies that matthews_corrcoef transforms the NaN in zeros, so we can silence the warning
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Other than my comments, this LGTM. I don't think the best solution to warnings is just squelching them if they can be fixed, because in the future we can't see which warnings have to be fixed. |
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I fixed the warnings without using |
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With my comments addressed, this LGTM. |
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Thanks for the review ! |
[MRG] Remove some warnings from test suit
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thanks @TomDLT ! |
I removed some warnings from the test suit.
Issue #5089
Complementary work #5277