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41b5ce5057
ADR-0030 commits Component Accuracy to ~19 categorical components (5 today + 8 heating + glazing/renewables). Flat *_correct dataclass fields don't scale — each needs manual runner wiring. Collapse them into a single `categorical_hits: dict[str, Optional[bool]]` keyed by component name, which also matches the runner's name-keyed aggregation (now generic: it tallies whatever components the comparison reports). No behaviour change; the classification rates are identical (wall n 578->575 is the 3 certs whose actual wall is None, now correctly counted as not-applicable via _classify). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
100 lines
3.8 KiB
Python
100 lines
3.8 KiB
Python
"""Per-Property prediction comparison for the EPC Prediction validation harness
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(ADR-0029).
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`compare_prediction` scores a predicted `EpcPropertyData` against the actual one
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on the accuracy signals the leave-one-out harness aggregates: classification
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matches on the key categoricals (wall / roof / floor construction + insulation,
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construction age band) and residuals on the geometry (window area + count,
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building-parts count, floor area). Pure — the SAP residual is computed in the
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runner, which has the calculator and the lodged SAP.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Optional
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from datatypes.epc.domain.epc_property_data import EpcPropertyData, SapBuildingPart
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@dataclass(frozen=True)
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class PredictionComparison:
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"""One Property's prediction accuracy: per-component classification hits +
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geometry residuals (predicted − actual). `categorical_hits` maps a component
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name to its hit: True / False, or `None` ("not applicable") when the actual
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lodges no value there, so the harness can keep it out of the
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classification-rate denominator rather than score a free win. Keyed by name
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(not flat fields) so the component set can grow without reshaping the
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runner — see ADR-0030 Component Accuracy."""
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categorical_hits: dict[str, Optional[bool]]
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floor_area_residual: float
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building_parts_residual: int
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window_count_residual: int
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total_window_area_residual: float
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def _main(epc: EpcPropertyData) -> SapBuildingPart:
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return epc.sap_building_parts[0]
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def _main_floor_construction(epc: EpcPropertyData) -> Optional[int]:
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"""The main building part's ground-floor construction code, or None when no
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floor dimension is lodged."""
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dims = _main(epc).sap_floor_dimensions
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return dims[0].floor_construction if dims else None
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def _classify(predicted: object, actual: object) -> Optional[bool]:
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"""A categorical hit: None ("not applicable") when the actual is absent,
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else whether the predicted value matches it."""
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if actual is None:
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return None
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return predicted == actual
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def _total_window_area(epc: EpcPropertyData) -> float:
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return sum(w.window_width * w.window_height for w in epc.sap_windows)
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def compare_prediction(
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predicted: EpcPropertyData, actual: EpcPropertyData
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) -> PredictionComparison:
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"""Compare a predicted picture against the actual one, field by field. All
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residuals are signed, predicted − actual."""
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return PredictionComparison(
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categorical_hits={
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"wall_construction": _classify(
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_main(predicted).wall_construction,
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_main(actual).wall_construction,
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),
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"wall_insulation_type": _classify(
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_main(predicted).wall_insulation_type,
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_main(actual).wall_insulation_type,
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),
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"construction_age_band": _classify(
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_main(predicted).construction_age_band,
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_main(actual).construction_age_band,
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),
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"roof_construction": _classify(
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_main(predicted).roof_construction,
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_main(actual).roof_construction,
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),
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"floor_construction": _classify(
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_main_floor_construction(predicted),
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_main_floor_construction(actual),
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),
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},
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floor_area_residual=(
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predicted.total_floor_area_m2 - actual.total_floor_area_m2
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),
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building_parts_residual=(
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len(predicted.sap_building_parts) - len(actual.sap_building_parts)
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),
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window_count_residual=(
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len(predicted.sap_windows) - len(actual.sap_windows)
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),
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total_window_area_residual=(
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_total_window_area(predicted) - _total_window_area(actual)
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),
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)
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