Review findings on PR #1526, behaviour-preserving:
- No fabric groups, or phase 1 committed nothing → delegate to the plain
optimise_package: the phase-2 prefix would be empty and its re-scoring
would reproduce the role-1 signals the groups already carry, one full
SAP-calculator run per Option for zero effect (the common case on
already-insulated stock).
- Phase 1 consumed every group → return the fabric package instead of a
phase-2 pass that can only select the empty package.
- _rescored_groups takes start_sap from the caller: the post-fabric
baseline score is the phase-1 package score already in hand, not a
fresh calculator run.
- fabric_types → phase_one_types: the set holds everything phase 1
committed, injected dependencies included — that inclusion is what the
outstanding-dependencies filter relies on, so the name must not invite
narrowing it to FABRIC_MEASURE_TYPES.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Tighten the recommendation/plan vocabulary off generic str:
MeasureOption.measure_type and PlanMeasure.measure_type are now MeasureType
(also _GlazingTarget.measure_type, MeasureDependency.triggers ->
frozenset[MeasureType], and the optimiser's chosen/required-type locals).
Because MeasureType is a StrEnum the change is transparent to persistence
(the `recommendation` varchar column), the optimiser group-by key, and every
`== "solar_pv"`-style comparison — so pyright now enforces the enum at every
construction site with no runtime behaviour change.
The catalogue boundary stays str: ProductRepository.get(measure_type: str)
and Product.measure_type are unchanged (they map arbitrary DB/JSON rows), so
the fake product repos in tests need no edit. Test construction helpers coerce
their str arg via MeasureType(...); direct constructions use members.
Suite green: tests/domain/modelling + orchestration + harness 253 pass + 3
xfail; pyright clean on production + tests (pre-existing moto + property-
override-rowcount baselines untouched).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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>
2026-06-03 13:48:36 +00:00
Renamed from domain/modelling/optimiser.py (Browse further)
