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Fix flaky test_scores_for_identical_examples unit test fails #1079

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merged 4 commits into from
Apr 6, 2024
Merged

Fix flaky test_scores_for_identical_examples unit test fails #1079

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4f31a8b
Fix: np.isclose atol parameter
gogetron Apr 1, 2024
d3833de
Add: correct_precision_errors
gogetron Apr 4, 2024
830338b
Add: test_scores_for_identical_examples_across_rows
gogetron Apr 4, 2024
1ca5ef6
Fix: include p when metric is minkowski
gogetron Apr 4, 2024
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Fix: np.isclose atol parameter
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@gogetron
gogetron committed Apr 1, 2024
commit 4f31a8bc7f997b84cfbc1ff63073f579039cfc8e
2 changes: 1 addition & 1 deletion cleanlab/internal/outlier.py
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Expand Up @@ -70,6 +70,6 @@ def transform_distances_to_scores(
ood_features_scores: np.ndarray = np.exp(-1 * avg_distances / scaling_factor * t)

# Set scores to 1 if the average distance is close to 0
inds = np.isclose(avg_distances, 0)
inds = np.isclose(avg_distances, 0, atol=5e-6)
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I don't think we should be changing our source code to fix unit tests. Should instead fix the unit test. This atol value was added here for a reason, but will let @elisno share how to best proceed

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It turns out tthat he default atol was a bit too small for the euclidean distance, so we explicitly set the atol parameter based how large the floating-point error can get for the distance computations.

It's still not 100% verified how this happens on only a few platforms for the euclidean distances, but we're confident that we can put a MUCH lower tolerance for the cosine metric.
The takeaway is: A single atol value won't work for different types of datasets, we need to set it carefully when comparing values against 0.

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It's not within the scope of this PR, but we may want to look at similar things for the near-duplicate check.

ood_features_scores[inds] = 1.0
return ood_features_scores
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