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>
Adds a floor_area line giving MAE (m2), MAPE (% of actual), and the typical
(median actual) size, so the absolute error reads relative to dwelling size.
Corpus: MAE 10.48 m2 / MAPE 13.2% / typical 61 m2.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The validation report showed only the SAP calculator floor (calc(actual) vs
lodged), so the headline PEI MAE (~40 kWh/m2) read as prediction error when
much of it is the calculator's own API-path residual. Adds the CO2 + PEI
floors alongside SAP.
Diagnostic (150pc/514): PEI floor MAE 15.73 (calc(actual) vs lodged) vs SAP
floor 1.57; calc(actual)/lodged PEI ratio ~1.06 (mean +10.7, ~+6% over-
estimate). That RULES OUT the suspected gross unit/definition mismatch (a
unit bug would be ~2x/3.6x, not 1.06) and reframes #1228: the PEI gap is a
modest calculator bias (~16 floor, calc-branch) plus a larger prediction-
sensitivity term (~24) — PEI is far more prediction-sensitive than SAP.
CO2 floor 0.20 t. Script-only; no gate impact.
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>
Make the leave-one-out runner ADR-0030-compliant:
- Hold out only SAP 10.2 targets (sap_version == 10.2) — the source cohort
keeps every vintage (components are methodology-agnostic).
- Label Component Accuracy as the PRIMARY, calculator-independent section.
- End-to-end vs API-lodged (SECONDARY, calculator-FLOORED): add CO2 (tonnes)
and PEI (kWh/m2) alongside SAP, using the canonical performance.py mapping
(co2_kg/1000; primary_energy_kwh_per_m2).
- Add the attribution readout calc(actual) vs lodged SAP — the calculator
floor the end-to-end can reach.
- Drop the neighbour-mean-of-lodged-SAP baseline (mixes SAP versions —
rejected by ADR-0030).
On the 181 SAP-10.2 targets: component rates are higher than the all-vintage
view (age band 60.9 -> 78.5%, floor_area mean|.| 12.7 -> 8.4). End-to-end SAP
MAE 6.34 vs the calc(actual) floor of 3.25 — ~half the gap is the known
API-path calculator residual, not prediction error.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
ADR-0030 commits Component Accuracy to ~19 categorical components (5 today
+ 8 heating + glazing/renewables). Flat *_correct dataclass fields don't
scale — each needs manual runner wiring. Collapse them into a single
`categorical_hits: dict[str, Optional[bool]]` keyed by component name, which
also matches the runner's name-keyed aggregation (now generic: it tallies
whatever components the comparison reports). No behaviour change; the
classification rates are identical (wall n 578->575 is the 3 certs whose
actual wall is None, now correctly counted as not-applicable via _classify).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The register lists every historical lodgement, so a postcode cohort
contains the same physical address many times (LS61AA: 15 certs / 11
addresses; NG71AA: 15 / 9 — "FLAT 3" appears 3x in each). Two
consequences:
- Production: a re-lodged neighbour was counting up to 3x towards the
cohort mode. select_comparables now dedupes candidates to the latest
cert per address (one comparable per real neighbour) — Comparable
gains address + registration_date (the register metadata its docstring
already anticipated, read straight off the cached payload).
- Validation: leave-one-out leaked — predicting a flat from a near-
identical re-lodgement of itself. The harness now holds out a whole
address (excludes every sibling cert) and evaluates on the latest cert
per address (the best ground truth).
Removing the leak gives the honest numbers (19 distinct addresses):
wall_construction 93.1% -> 89.5%
construction_age_band 65.5% -> 52.6%
roof_construction 79.3% -> 68.4%
floor_area mean|.| 37.9 -> 52.6 m2
The earlier figures were inflated by self-leakage; these are the real
accuracy to beat.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The comparison only scored main wall_construction; everything else the
predictor produces (by template-copy) went unmeasured. Extend
compare_prediction to the rest of the ADR-0029 homogeneous categoricals —
wall insulation type, construction age band, roof construction, floor
construction — and aggregate per-categorical classification rates in the
runner. A categorical hit is "not applicable" (None, excluded from the
denominator) when the actual lodges no value, so absent-roof flats don't
score free wins.
Smoke corpus (29 leave-one-out, all but wall are template-copied today):
wall_construction 93.1%
wall_insulation_type 93.1%
construction_age_band 55.2% <- loud; candidate for cohort-mode
roof_construction 72.4%
floor_construction 46.2% (n=13)
These numbers drive the next slice (extend cohort-mode coverage).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Pure compare_prediction (TDD): wall-construction classification hit + signed
residuals on floor area, window count, total window area, building-parts count.
Plus validate_epc_prediction.py (IO plumbing): drops each cert from its postcode
cohort, predicts from the rest on guaranteed inputs only, aggregates the metrics,
and reports SAP three ways (pred-calc vs lodged / vs calc-on-actual / vs the
neighbour-mean baseline). Smoke run: wall 90.9%, floor-area mean|·| 42.6 m2 (a
real signal — template-copied floor area is noisy), SAP pred-calc edges baseline.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
