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77983caed8
Slice 1 of #1160. Recycles the GainOptimiser/CostOptimiser formulation (≤1 Option per Recommendation, maximise SAP gain subject to budget) as a clean typed DDD function — but as an exact pure-Python multiple-choice knapsack rather than the legacy `mip` MILP, since mip's CBC backend does not load on aarch64 (so the legacy solver path can't run / be tested here). At retrofit scale the candidate space Π(|group|+1) is tiny, so exhaustive enumeration is exact and instant; ADR-0016 only needs the knapsack as a warm-start signal anyway (the truthful figure comes from the whole-package re-score + repair, next slice). `optimise(groups, budget) -> list[ScoredOption]`: maximise total gain, tie-break toward lower cost, skip-per-group covers "select none". 6 tests (budget-bound selection, ≤1/group, unconstrained, budget-too-small, empty groups, partial-affordability); pyright strict clean. Multi-phase remains descoped (ADR-0005) — single-phase optimiser. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| .. | ||
| addresses | ||
| data_transformation | ||
| epc | ||
| fuel_rates | ||
| geospatial | ||
| modelling | ||
| property | ||
| property_baseline | ||
| sap10_calculator | ||
| sap10_ml | ||
| tasks | ||
| building_geometry.py | ||
| postcode.py | ||