Khalim Conn-Kowlessar 027ee1fba3 refactor(epc-prediction): extract shared leave-one-out scorer + corpus loader (ADR-0030) ... "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>		2026-06-14 09:12:08 +00:00
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__init__.py	feat(modelling): sense-check table for a Plan in the DB-less harness	2026-06-04 08:06:53 +00:00
cohort.py	feat(modelling): turnkey offline cohort script (tables + CSV)	2026-06-04 09:30:53 +00:00
console.py	17.1 and 18 done by claude	2026-06-12 12:52:36 +00:00
epc_bulk.py	feat(modelling): sample a year from the EPC bulk export, offline-ready	2026-06-04 12:20:57 +00:00
epc_prediction_corpus.py	refactor(epc-prediction): extract shared leave-one-out scorer + corpus loader (ADR-0030)	2026-06-14 09:12:08 +00:00
plan_table.py	feat(modelling): wire Valuation Uplift onto the Plan	2026-06-04 08:59:04 +00:00
report.py	feat(modelling): wire secondary-heating-removal into the pipeline (ADR-0028)	2026-06-11 16:04:07 +00:00
sample_catalogue.json	feat(modelling): cost data for secondary-heating-removal (ADR-0028)	2026-06-11 13:51:16 +00:00