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Perf now accepts score_func/error_func as a single callable or a list of callables (both can be given simultaneously to mix score-type and error-type measures), building a composite statistic evaluated on the same paired bootstrap samples. Every metrics.py wrapper exposes a .measure() factory tagged with its BiB direction so measures compose, e.g. score_func=[f1_score.measure(average='macro'), recall_score.measure(average='macro')]. Bump version to 0.2.0. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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Summary
Perfacceptsscore_func/error_funcas a single callable or a list of callables (both can be given simultaneously to mix score-type and error-type measures into one instance), building a composite statistic evaluated on the same paired bootstrap samples — implements the design plan from Plan: Multi-measure API for Perf/Difference (e.g. macro-F1 + macro-recall together) #30.metrics.pywrapper exposes a.measure()factory tagged with itsBiBdirection, so metrics compose, e.g.score_func=[f1_score.measure(average='macro'), recall_score.measure(average='macro')].Perf.best,Perf.difference(),Difference.p_value(), anddataframe()/plot()handle per-columnBiB/naming for the multi-measure case; single-measure usage is unchanged (existing tests pass unmodified).Closes #30.
Test plan
pytest CompStats— 65 passed (52 pre-existing + 13 new), no regressionsPerf/Difference/plot()/dataframe()with real classifiersBiBsign logic inbest/difference()/p_value()🤖 Generated with Claude Code