Enhancement: Add explicit UserWarning for NaN classes in macro-averaged metrics#558
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karttikjangid wants to merge 4 commits into
Open
Enhancement: Add explicit UserWarning for NaN classes in macro-averaged metrics#558karttikjangid wants to merge 4 commits into
karttikjangid wants to merge 4 commits into
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Hi team, the CI is failing on LiDAR tests due to an isinstance check against a mocked Open3D class in the headless runner. My changes are limited to metrics; would you like me to include a small guard to fix this CI bottleneck? |
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I have reinstated the original poetry files for reference in the last commit |
dpascualhe
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Jul 10, 2026
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Upon fixing the issues raised we will merge 😄
| missing_count = int(np.sum(nan_mask)) | ||
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| if missing_count > 0: | ||
| import warnings |
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raise the warning as part of logging (you can check out logging.warning in line 60 of rellis3d.py). At some point we will do a project-wide effort to actually settle down on a proper logging system
| f"Their {metric_name.upper()} evaluated to NaN and will be ignored in the macro-average." | ||
| ) | ||
| warnings.warn(msg, UserWarning) | ||
| return float(np.nanmean(metric(per_class=True))) |
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reuse here the per_class_values variable to avoid computing the metric twice
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Fixes #546
Following up on the discussion in #546, this PR adds a UserWarning during macro-averaged metric calculation.
When per-class metric values are NaN (for example, denominator-zero cases such as classes missing from the confusion matrix), they are ignored by np.nanmean as intended. This PR now surfaces a warning indicating how many classes were dropped for that metric so researchers are explicitly aware of the macro-average context.
All existing metric formulas and public APIs are unchanged. Feedback on the warning text format is welcome.