feat: add Observable.weight and weighted_sum_filter#96
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jc-macdonald merged 1 commit intomainfrom Apr 17, 2026
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- Add weight: float = 1.0 to Observable (backward-compatible) - Propagate weights through extract_front, pareto_rank, hypervolume, igd_plus via new _normalize_objectives helper - Propagate weights through Study.front, Study.front_hypervolume, Study.summary, top_k_pareto_filter, and run_adaptive - Add weighted_sum_filter: scalarised ranking filter with min-max normalisation, direction-aware, compatible with Phase.filter_fn - Export weighted_sum_filter from trade_study.__init__ - 13 new tests covering weighted Pareto and weighted_sum_filter Closes #90, closes #91
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Summary
Adds weighted multi-objective support via two complementary features:
Observable.weight (#90)
weight: float = 1.0field onObservable(backward-compatible default)extract_front,pareto_rank,hypervolume,igd_plusStudy.front(),Study.front_hypervolume(),Study.summary(),top_k_pareto_filterrun_adaptive()(scales optuna objective values)weighted_sum_filter (#91)
weighted_sum_filter(weights, k)returning aPhase.filter_fn-compatible callabletrade_study.__init__Tests
test_pareto.py, 8 intest_study.py)Closes #90, closes #91