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Author SHA1 Message Date
Khalim Conn-Kowlessar
af501fce0e feat(modelling): ventilation-aware selection — price the forced dependency in
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>
2026-06-03 16:16:26 +00:00
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
Khalim Conn-Kowlessar
05a4f5f84a feat(modelling): optimise_min_cost — least-cost-to-target selector (#1152 follow-up)
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>
2026-06-03 15:31:26 +00:00
Khalim Conn-Kowlessar
02afc04ce2 refactor(modelling): ventilation_dependency delegates to the generator + wraps
measure_dependency.py now owns only the selection semantics: the trigger set and
the forced-edge wrapping. It delegates production (detection + pricing) to
recommend_ventilation and wraps the returned Recommendation into the
MeasureDependency, picking the cheapest Option (one MEV today; readies the seam
for MEV-c / MVHR). The orchestrator's _measure_dependencies call is unchanged.
Trimmed the now-redundant option-detail assertions — those live in
test_ventilation_recommendation. 138 pass, behaviour-preserving.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-03 14:04:17 +00:00
Khalim Conn-Kowlessar
84ec6da032 refactor(modelling): group domain/modelling into generators/scoring/optimisation
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>
2026-06-03 13:48:36 +00:00