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1f26703dc5
Folds a haversine distance kernel into the categorical-mode weighting so a nearer neighbour counts for more — applied ONLY to the components that showed a clear distance signal in the corpus pre-check (age band, wall + floor construction, glazing: homes built/retrofitted together cluster). Roof construction showed no decay and is excluded; heating keeps its coherent donor. Predictor stays pure: weights come from target.coordinates vs each Comparable.coordinates (resolved at the boundary); geo is OFF when the target has no coords, neutral for a neighbour with none. Scale chosen on the harness: _GEO_SCALE_KM=0.1 is the gate-safe optimum (0.05 lifts the corpus more but regresses fixture floor_construction). Corpus (150pc/514, geo off->on): age 0.564->0.572, age_pm1 0.841->0.847, wall 0.902->0.912, floor_con 0.786->0.796, glazing 0.667->0.673; roof unchanged. Fixture: glazing 0.5278->0.5833 (floor ratcheted), all else held. Refactored recency into a reusable _recency_weights vector composed via _combine, so similarity/recency/geo factors multiply uniformly. Fixture ships a committed _coordinates.json (OGL OS OpenData; build script carries it from the corpus sidecar on rebuild) so the gate exercises geo without S3. This is the per-component method applied to geography ([[feedback_per_component_best_method]]). 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 | ||