Add per-group loss multipliers for SparseCalibrationWeights #38
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Adds normalize_groups and group_multipliers parameters to SparseCalibrationWeights.fit(), allowing decoupled control over within-group normalization and per-group loss importance weighting. Closes #37 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
normalize_groupsparameter to decouple group identification from size-based normalizationgroup_multipliersparameter for arbitrary per-group loss scalingverbose=TrueCloses #37
Test plan
test_group_multipliers_without_normalization— multipliers work without normalizationtest_group_multipliers_with_normalization— multipliers compose with normalizationtest_group_multipliers_requires_target_groups— ValueError without target_groupstest_group_multipliers_invalid_group_id— ValueError on unknown group IDtest_normalize_groups_false_uniform_weights— no normalization behaves like no groups🤖 Generated with Claude Code