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>
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>
Pure-domain select_comparables: property type is an always-hard filter; built
form and known Landlord Overrides (e.g. solid brick) are conditioning filters on
the filter-then-relax ladder — applied while >= minimum_cohort survive, relaxed
otherwise (the mixed-street border case degrades gracefully). PredictionTarget
(known inputs) + Comparable (epc + register metadata) + ComparableProperties
(selected cohort). Weighting (recency x similarity) follows in the synthesis slice.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
