The heating-donor display synthesis reads donor.epc.main_heating, which has no
dataclass default — so a partial object.__new__ EpcPropertyData must set it.
test_validation's _comparable builder didn't, failing the two leave-one-out
scorer tests in CI (the full epc_prediction suite wasn't run pre-push).
main_heating_controls / sap_ventilation default to None via class attributes.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
A predicted EPC is seeded by deep-copying one representative neighbour's
structure. _template chose the member whose floor area was closest to the
cohort median, ignoring building-part labels. When that member's only part
was lodged with a null identifier (mapped to OTHER), the prediction had no
MAIN part and the modelling_e2e handler rejected it as "not predictable" —
discarding an otherwise-rich same-type cohort.
Restrict the template to MAIN-bearing members (median still over the whole
cohort); fall back to closest-on-size only when none are MAIN-bearing, so an
all-unlabelled cohort is left for the handler's MAIN-part guard to reject
rather than silently relabelling real data.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
A dwelling's heating is one conceptual system, but its fields are scattered
across EpcPropertyData (a gov-API schema mirror): the cluster on sap_heating, the
electricity tariff on sap_energy_source.meter_type, hot-water flags loose at top
level. Three places synthesise a heating system — Measure Options, Landlord
Overrides, EPC Prediction's donor — and each hand-copied a different ad-hoc
subset. The override and donor both dropped meter_type, so an electric-storage
system landed on the template's single-rate meter and billed overnight heat at
the peak rate: property 713406 scored SAP 13 (G) vs ~50 (E), inflating the HHRSH
measure to +45.8 and overshooting the plan to band A.
Establish a single Coherent Heating System boundary (CONTEXT.md) that every
synthesiser must cover, with a source-appropriate fill policy (ADR-0035):
- Override overlay *completes* the partial system the landlord named. Companion
fields are now DERIVED from the SAP code, not hand-attached per archetype: the
off-peak meter from the calculator's single off-peak classification (new
OFF_PEAK_IMPLYING_HEATING_CODES = SAP §12 Rules 1-2), and an unobserved storage
charge control defaults to the conservative manual control (Table 4e 2401). So
adding a heating archetype is just adding its code — companions can't be
forgotten. A contract test guards it (every off-peak code drags a Dual meter).
- Prediction's heating donor now *carries* the donor's meter_type alongside its
sap_heating cluster — the donor is already coherent.
Coherence is a synthesis-time obligation only; the calculator still scores a real
lodged cert exactly as lodged.
Verified on 713406: baseline 13 -> 47.8 (E), matching its recorded rating; the
phantom HHRSH recommendation is gone and the plan no longer overshoots to A.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Two review points from @dancafc:
1) Rename the `Comparable` dataclass → `ComparableProperty` (it models one
comparable *property*; the collection stays `ComparableProperties`). Applied
across domain, repositories, orchestration, harness, scripts, and tests with a
word-boundary rename so `ComparableProperties` is untouched.
2) Move `PredictionTarget` out of comparable_properties.py into prediction_target.py
(where `PredictionTargetAttributes` + `build_prediction_target` already live).
comparable_properties.py now imports it; no import cycle (prediction_target no
longer depends on comparable_properties). Importers updated.
92 tests pass across the touched suites; pyright strict clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
"One scorer, two harnesses" (ADR-0030): the committed gate, the local script,
and the future battle-test must run the *same* scoring. Extract it:
- domain/epc_prediction/validation.py — `iter_predictions` (the single
leave-one-out orchestration: latest-per-address hold-out, SAP-10.2 target
filter, all-vintage source) + `evaluate_component_accuracy` (calculator-free
ComponentAccuracy aggregation, the primary signal). Unit-tested.
- harness/epc_prediction_corpus.py — `load_corpus(dir)` IO: corpus dir ->
Comparable cohorts (maps payloads, carries address + registration_date).
validate_epc_prediction.py now just loads + calls the scorer for the component
section and iterates iter_predictions for the calculator-floored end-to-end.
Identical numbers (181 targets, SAP MAE 6.34) — behaviour-preserving.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
