Khalim Conn-Kowlessar 2bf42d046e feat(modelling): optimise_package targets least-cost, falls back to max-gain ... Rewire the objective per the ADR-0016 amendment. With a target_sap (Increasing EPC): warm-start optimise_min_cost (cheapest package reaching target_gain = target_sap - baseline within budget) -> inject dependencies -> re-score -> repair toward target; if the warm-start is infeasible or the repaired package still falls short on the true score, fall back to max-gain-within-budget (best effort). Without a target_sap: max-gain (unchanged). The min-cost objective stops at the target without overshooting into a higher band; surplus budget is left unspent. Extracted _max_gain_package (no-target path + fallback) and _repair_to_target (inject + re-score + greedy repair). Dependency injection and the repair loop are preserved; all prior optimiser + dependency tests pass unchanged. Ventilation-aware *selection* is the next slice; injection is still post-warm-start here. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>		2026-06-03 15:43:06 +00:00
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__init__.py	refactor(modelling): group domain/modelling into generators/scoring/optimisation	2026-06-03 13:48:36 +00:00
measure_dependency.py	refactor(modelling): ventilation_dependency delegates to the generator + wraps	2026-06-03 14:04:17 +00:00
optimiser.py	feat(modelling): optimise_package targets least-cost, falls back to max-gain	2026-06-03 15:43:06 +00:00