Preserve zero-valued anonymous tuning metrics - #1603
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Why are these changes needed?
Returning 0 from an objective, or calling tune.report(0), drops the anonymous metric because report tests its truthiness. The default optimizer then raises KeyError: _metric instead of accepting the valid zero loss.
Use an explicit None check for the optional anonymous metric. Tests exercise returned and directly reported zero values, False, and positive/negative controls through a real tuning run.
Validation
6 regressions fail before the fix; 15 tests pass afterward across the new tests, test_sample.py, test_stop.py and test_reproducibility.py. Validation used Python 3.12 on CPU, with Optuna installed and without Ray. Full repository
pre-commit run --all-filespasses (pre-commit 3.7.1, compatible with the local Git version).Checks
Implementation and validation were performed with AI assistance.
Fixes #1602.