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d762b25808
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> |
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| .. | ||
| addresses | ||
| billing | ||
| data_transformation | ||
| epc | ||
| epc_prediction | ||
| fuel_rates | ||
| geospatial | ||
| magicplan | ||
| modelling | ||
| property | ||
| property_baseline | ||
| sap10_calculator | ||
| sap10_ml | ||
| tasks | ||
| building_geometry.py | ||
| postcode.py | ||