Khalim Conn-Kowlessar 31ced27162 feat(modelling): surface the full candidate measure menu with per-measure cost ... The run only showed the measures the Optimiser selected, so a candidate it passed over (e.g. an ASHP it found too costly for the target band) and that measure's cost were invisible. Add `harness.console.candidate_recommendations` — every Generator Option with its per-Option cost, before optimisation — and have run_modelling_e2e print the full menu per property (flagging the selected Options), write a "cost per measure" section into the markdown, and emit a per-Option modelling_e2e_candidates.csv. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>		2026-06-16 15:03:26 +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	feat(modelling): surface the full candidate measure menu with per-measure cost	2026-06-16 15:03:26 +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): PR review — rename ComparableProperty, relocate PredictionTarget	2026-06-16 13:34:44 +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