MeasureDependency(triggers, required) is a data-declared 'A requires B' edge.
optimise_package gains a dependencies param: after the warm-start it injects any
dependency whose triggers intersect the selected measure-types, BEFORE the
whole-package re-score, so the dependency's (negative) SAP lands in the truthful
figure and the undershoot/repair decision (ADR-0016). Forced — injected
regardless of budget — but its cost counts toward package spend, so repair sees
less headroom. Repair candidates fold in any dependency they newly trigger, so
their marginal SAP-per-£ and incremental cost are truthful. The dependency never
competes in the optimiser pool. Returned selected includes the injected deps.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Slice 2 of #1160 — the ADR-0016 truth step on top of the warm-start
knapsack. optimise_package(groups, scorer, baseline_epc, budget,
target_sap) -> OptimisedPackage:
warm-start optimise() (role-1 signal) → re-score the chosen package on
the real scorer (role-2 truth) → while the true SAP undershoots
target_sap and budget remains, greedy-add the untreated-group Option
with the best *marginal* SAP-per-£ (re-scored, not the role-1 signal),
re-score, repeat until the target is met, nothing positive-marginal is
affordable, or the budget is spent.
`Scorer` is a structural Protocol (PackageScorer satisfies it) so the
repair loop is tested with a stub scorer — no calculator, runs on ARM.
The key case: role-1 under-counts roof so the warm-start skips it, the
re-score undershoots, and repair adds roof back to hit the target. 3
repair tests + the 6 core tests; pyright strict clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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>
