The warm-start (and max-gain fallback) now price each forced Measure Dependency
the candidate triggers, not just inject it afterwards: optimise/optimise_min_cost
fold dependencies into each candidate's cost+gain via _augmented_cost_gain, and
optimise_package scores each dependency's true role-1 signal (_with_role1_signals)
instead of the 0.0 placeholder. This stops the min-cost objective (i) ignoring the
~£900 a wall drags in (a wall-free package reaching target can be cheaper) and
(ii) picking a small-gain wall whose mandatory ventilation (down to -5 SAP) makes
it net-negative, which repair cannot un-pick.
Budget is now a hard envelope: the constraint applies to the augmented (measure +
its ventilation) cost, so a wall that fits alone but whose ventilation would bust
the budget is DROPPED rather than forced over budget. This reverses the earlier
'forced regardless of budget' call (which made sense when selection was
ventilation-blind). Safety invariant intact — presence still injected on every
path; we just never recommend a wall we can't afford to ventilate. ADR-0016
amendment updated. 94 modelling+orchestration tests pass.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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>
Exact-enumeration sibling to optimise(): pick <=1 option per group to minimise
total cost subject to total gain >= target_gain and cost <= budget (None =
unconstrained). Ties broken toward higher gain ('recommend more'). Returns None
when no package within budget reaches the target (caller falls back to
max-gain); a non-positive target is met by the empty package. This is the
warm-start objective for an Increasing EPC goal per the ADR-0016 amendment
(least-cost-to-target, not max-gain). Dependency-blind for now; ventilation-aware
selection lands in a later slice.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
domain/modelling/ had grown to 15 flat modules. Group the behavioural ones into
subpackages — generators/ (wall/roof/floor Recommendation Generators), scoring/
(overlay applicator, package scorer, role-1/3 scoring), optimisation/ (optimiser
+ measure dependency) — and leave the shared value-object vocabulary
(recommendation, plan, scenario, product, contingencies, simulation) flat at the
top, since it is imported everywhere. Pure move + import-path rewrite across 89
import sites; no behaviour change. 136 pass, pyright strict clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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>
