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>
Size the predicted dwelling from the geo-proximity-weighted median of the
cohort's floor areas rather than the plain median: homes built together share a
footprint, so a nearer neighbour's area should count for more (the same street
signal #1227 already wired into age / wall / glazing). Reuses `_geo_weights` and
adds `_weighted_median`, which reduces exactly to `statistics.median` under
uniform weights (geo off / no target coordinates) — including the even-count
midpoint average — so the MAD-minimising guarantee is preserved.
Measured over the 514-target SAP-10.2 corpus (leave-one-out):
floor_area MAE 10.48 -> 9.73 m² MAPE 13.2% -> 12.2%
Re-baselines the n=36 fixture floor_area ceiling 11.8983 -> 12.0378 (a method
change, not a loosening; the small fixture subset moved +0.14 the other way as
sample noise while the population improved decisively). The ceiling still pins
the new deterministic value exactly, so the tighten-only ratchet resumes.
Investigation ruling out the adjacent floor-area levers (kept in the follow-up):
lowering minimum_cohort (9.78-10.03, worse), hard same-form filter (10.19),
mean instead of median (10.68), constant bias correction (10.47),
extension-conditioning (oracle 9.50, not worth the misclassification cost) and
room-in-roof conditioning/additive (RiR is a confound for large multi-part
outliers — RiR area is only ~21% of total, and the increment breaks the homes
already predicted exactly). Remaining cohort lever is built-form soft-weighting,
gated on a denser corpus.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Folds a haversine distance kernel into the categorical-mode weighting so a
nearer neighbour counts for more — applied ONLY to the components that showed
a clear distance signal in the corpus pre-check (age band, wall + floor
construction, glazing: homes built/retrofitted together cluster). Roof
construction showed no decay and is excluded; heating keeps its coherent
donor. Predictor stays pure: weights come from target.coordinates vs each
Comparable.coordinates (resolved at the boundary); geo is OFF when the target
has no coords, neutral for a neighbour with none.
Scale chosen on the harness: _GEO_SCALE_KM=0.1 is the gate-safe optimum
(0.05 lifts the corpus more but regresses fixture floor_construction).
Corpus (150pc/514, geo off->on): age 0.564->0.572, age_pm1 0.841->0.847,
wall 0.902->0.912, floor_con 0.786->0.796, glazing 0.667->0.673; roof
unchanged. Fixture: glazing 0.5278->0.5833 (floor ratcheted), all else held.
Refactored recency into a reusable _recency_weights vector composed via
_combine, so similarity/recency/geo factors multiply uniformly. Fixture ships
a committed _coordinates.json (OGL OS OpenData; build script carries it from
the corpus sidecar on rebuild) so the gate exercises geo without S3.
This is the per-component method applied to geography ([[feedback_per_component_best_method]]).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Heating sub-fields can't be field-moded without breaking system coherence,
so the whole SapHeating cluster is now copied as a unit from a single
coherent donor rather than inherited from the structural template: the
neighbour matching the cohort's modal heating signature (main fuel +
category + cylinder presence), most recent among the matches (recent cert =
current system). Including cylinder presence in the signature is load-bearing
— it protects has_hot_water_cylinder + cylinder_insulation (a bare fuel+cat
signature regressed them).
Corpus (150pc/514): heating_main_control 66.3 -> 73.9% (+7.6, the target),
main_fuel 92.8 -> 96.9, category 90.7 -> 95.7, water_fuel 92.8 -> 96.3,
water_code 88.5 -> 95.3, has_cylinder 81.1 -> 89.7, secondary 36.2 -> 42.0.
SAP MAE vs lodged 7.08 -> 6.00 (calculator floor 1.57). cylinder_insulation
-13.6 corpus (tiny-n) but +33pp on the fixture; AC requires control up +
fuel/category hold + SAP not worsened, all met.
Gate (36-target fixture): zero regression; ratcheted main_category
0.8889->0.9444, main_control 0.7500->0.8056, water_fuel 0.9167->0.9722,
water_code 0.8889->0.9444, cylinder_insulation_type 0.1667->0.5000. This is
the per-component heating method ([[feedback_per_component_best_method]]):
coherent donor, never field-mode.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Per-component method: glazing type is now the recency-weighted cohort mode
applied to every predicted window, rather than copied from the template.
Glazing is retrofitted over a dwelling's life (single -> double), so a
recent neighbour reflects the current state — same family as roof-insulation
thickness. Recency is the CORRECT weighting here: plain moding regressed the
fixture (-5.6pp) and was previously reverted; similarity weighting also
regressed it; recency improves BOTH (window geometry stays on the template,
only the glazing categorical moves).
modal_glazing_type: corpus (150pc/514) 60.7 -> 66.7% (+6.0pp); fixture
0.5000 -> 0.5278 (floor ratcheted up). Heating, geometry residuals and all
other components unchanged. Refactored _recency_weighted_mode to a reusable
_recency_weighted_choice(value_of) shared by roof insulation + glazing.
Closes the #1223 per-component approach: floor-area (median estimate) +
glazing (recency) shipped as distinct best-fit methods rather than a global
recency template, which would have disturbed the coherence-coupled heating
cluster.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Per-component method, not a global template change: the predicted floor
area is now the cohort median (the MAD-minimising point estimate of the
target's size) rather than whichever structural template's own area. The
calculator derives heat loss from building-part geometry, not this scalar,
so decoupling them is safe and the scalar becomes a better size estimate.
floor_area mean|.|: corpus (150pc/514 targets) 10.62 -> 10.48; fixture
12.2175 -> 11.8983 (ceiling ratcheted down). No other component moves.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
ADR-0029 decision 5: survivors were treated equally; now each neighbour's
vote in the cohort mode decays with its distance from the cohort's physical
centre (floor area from the median, age band from the modal band), so the
mode leans on the most representative neighbours instead of being swayed by
size/era outliers. Scales (size 20 m^2, age weight 0.5) chosen on the
validation corpus; the tight size kernel is load-bearing (looser scales
regress floor_insulation on the fixture).
Corpus (181 SAP-10.2 targets): wall_insulation 83.4->86.2%,
roof_construction 86.2->87.3%, floor_construction 78.8->81.2%,
floor_insulation 92.9->94.1%; net +7.5pp gained vs -1.1pp (two 1-cert dips,
both held on the fixture). Geometry/residuals untouched (template unchanged).
Gate (36-target fixture): zero regression across all 24 floors/ceilings;
ratcheted wall_insulation_type 0.7778->0.8333, floor_construction
0.7500->0.8125, floor_insulation 0.9062->0.9375. Dead _mode/_int_mode
removed (superseded by the weighted variants).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds PredictionConfidence (cohort size + per-component agreement = the
modal value's share among neighbours that lodge one) and
EpcPrediction.confidence(), a compute-only signal so downstream can flag
low-confidence components (ADR-0029 open item: 'confidence signal').
Sanity check on the 40-postcode corpus (1068 component predictions):
agreement is strongly predictive of correctness — pooled hit-rate 21.9%
(<0.5) / 46.7% (0.5-0.7) / 73.6% (0.7-0.9) / 95.5% (>=0.9); point-biserial
corr(agreement, correct) = 0.582. Cohort size tracks too (<6 -> 68.4%,
>=20 -> 96.0%). Surfacing / persistence is a separate HITL follow-up.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Investigated recency-weighting (weight cohort votes by an exponential decay
in cert age). Key finding: it must be SELECTIVE. On the validation corpus it
HURTS permanent categoricals (wall 91.2->89.5, age 78.5->75.7 — discards
still-valid data) but clearly HELPS time-varying ones, where a recent
neighbour reflects the current physical state:
roof_insulation_thickness 56.7 -> 60.7% corpus (+4pp)
29.4 -> 41.2% fixture (+12pp)
So apply a recency-weighted mode only to roof_insulation_thickness (loft
top-ups happen over time); keep the plain mode for permanent categoricals.
tau = 4yr (~2.8yr half-life); falls back to plain mode when no registration
dates are lodged. Gate floor ratcheted 0.2941 -> 0.4118.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
These independent fabric categoricals were template-copied; mode them like
the construction categoricals. Verified mode beats template before applying.
Big fixture win on roof insulation thickness (doubled), floor insulation
neutral-to-positive:
roof_insulation_thickness 14.7% -> 29.4% (gate floor ratcheted up)
floor_insulation 90.6% (unchanged on the fixture)
Glazing type was tried too (+1.6pp on the 40-postcode corpus) but REGRESSED
the 36-target fixture (0.50 -> 0.44) — the gate caught it. Glazing moding is
marginal/noisy, so it's left on the template; revisit with a larger corpus.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Only main wall_construction was set to the cohort mode; the other
homogeneous categoricals (wall insulation, construction age band, roof
construction, floor construction) were left as template-copied, so one
median-size template's quirks set them. Apply the same cohort-mode
mechanism to all of them per ADR-0029 decision 4 — the template still
supplies geometry, only the categorical codes move to the mode.
Verified mode beats (or ties) template-copy per categorical before
applying. Smoke corpus (29 leave-one-out) classification rates:
construction_age_band 55.2% -> 65.5%
roof_construction 72.4% -> 79.3%
floor_construction 46.2% -> 84.6%
wall_insulation_type 93.1% (tie — already template-strong)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Template (the comparable whose structure/geometry is copied wholesale)
was members[0] — an arbitrary draw from the API search order. With floor
area varying widely within a property_type cohort (NG71AA houses span
51-340 m2), this made the copied geometry noisy and systematically large.
Pick the member whose floor area is closest to the cohort median instead,
implementing ADR-0029 decision 4's unimplemented "closest on size"
criterion while keeping the structure coherent (it is still one real
property, so floor dims / windows / parts stay internally consistent for
the calculator).
Smoke corpus (29 leave-one-out predictions):
floor_area mean|.| 68.0 -> 37.9 m2 (bias +46.8 -> -3.9)
window_area mean|.| 11.1 -> 7.3 m2
parts mean|.| 1.00 -> 0.38
SAP |pred-calc - calc(actual)| MAE 7.19 -> 4.86
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
predict() copies a representative template comparable's structure (coherent for
the calculator), overrides the homogeneous categorical with the cohort mode
(robust to an atypical template), then applies known Landlord Overrides on top
(a known value wins over the estimate). Proven on wall construction; roof/floor/
insulation/age extend on the same mode+override mechanism, driven next by the
validation harness metrics.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
