Adds coordinates: Optional[Coordinates] to Comparable and PredictionTarget
(data carriers — the pure predictor stays IO-free), and wires load_corpus to
read an optional _coordinates.json sidecar ({uprn: [lon, lat]}) and populate
each Comparable from its cert's uprn; iter_predictions threads the held-out
target's coordinates through. Absent sidecar -> geo-weighting stays off (no
behaviour change yet — weighting lands next slice). fetch_corpus_coordinates
now writes the sidecar into the corpus dir. load_corpus populates 99% of
corpus comparables.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The committed gate needs frozen, reproducible data without dumping real UK
addresses into the repo. Add:
- harness anonymise_payload + stable_hash: hash street address + cert number
into opaque, dedup-stable tokens; blank secondary address lines + post_town;
keep postcode + all component/lodged fields (gov data is OGL). Unit-tested.
- scripts/build_epc_prediction_fixture.py: curate qualifying postcodes (>=1
SAP 10.2 target + >=2 distinct addresses) from the local scratch corpus,
anonymise, freeze under tests/fixtures/epc_prediction/.
- The frozen fixture: 15 postcodes / 280 certs / 36 SAP-10.2 targets.
Verified no plaintext address_line_1 and post_town all blank.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
