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feat(epc-prediction): score the heating components (ADR-0030 Component Accuracy)
Heating is the dominant SAP lever (ablating it to actual cut the SAP error ~7 -> ~4.5) yet was entirely unscored. Add the heating group to compare_prediction's categorical_hits: main fuel / category / control (off the primary MainHeatingDetail), water-heating fuel / code, has-cylinder, cylinder insulation, secondary heating (off SapHeating). Template-copied baseline on the 40-postcode corpus (no predictor change yet — this just makes the signal visible): heating_main_fuel 93.4% heating_main_category 92.7% water_heating_fuel/code 91.7% / 92.4% heating_main_control 62.1% <- weak has_hot_water_cylinder 78.5% cylinder_insulation_type 35.8% (n=120) <- weak secondary_heating_type 16.8% (n=125) <- weak Fuel/category predict well from the template; controls, cylinder, and secondary heating are poor and now drive the next predictor slices. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Khalim Conn-Kowlessar
parent
41b5ce5057
commit
cd43c52cf9
2 changed files with 139 additions and 23 deletions
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@ -14,7 +14,11 @@ 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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from datatypes.epc.domain.epc_property_data import (
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EpcPropertyData,
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MainHeatingDetail,
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SapBuildingPart,
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)
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@dataclass(frozen=True)
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@ -53,6 +57,53 @@ def _classify(predicted: object, actual: object) -> Optional[bool]:
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return predicted == actual
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def _main_heating_detail(epc: EpcPropertyData) -> Optional[MainHeatingDetail]:
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"""The primary heating system's detail row, or None when none is lodged."""
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details = epc.sap_heating.main_heating_details
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return details[0] if details else None
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def _heating_hits(
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predicted: EpcPropertyData, actual: EpcPropertyData
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) -> dict[str, Optional[bool]]:
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"""Classification hits for the heating components — the dominant SAP lever
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(ADR-0030). Main-system fields come off the primary `MainHeatingDetail`;
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hot-water + secondary fields off `SapHeating`."""
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pred_main = _main_heating_detail(predicted)
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actual_main = _main_heating_detail(actual)
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pred_h = predicted.sap_heating
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actual_h = actual.sap_heating
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return {
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"heating_main_fuel": _classify(
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getattr(pred_main, "main_fuel_type", None),
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getattr(actual_main, "main_fuel_type", None),
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),
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"heating_main_category": _classify(
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getattr(pred_main, "main_heating_category", None),
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getattr(actual_main, "main_heating_category", None),
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),
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"heating_main_control": _classify(
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getattr(pred_main, "main_heating_control", None),
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getattr(actual_main, "main_heating_control", None),
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),
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"water_heating_fuel": _classify(
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pred_h.water_heating_fuel, actual_h.water_heating_fuel
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),
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"water_heating_code": _classify(
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pred_h.water_heating_code, actual_h.water_heating_code
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),
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"has_hot_water_cylinder": _classify(
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predicted.has_hot_water_cylinder, actual.has_hot_water_cylinder
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),
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"cylinder_insulation_type": _classify(
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pred_h.cylinder_insulation_type, actual_h.cylinder_insulation_type
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),
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"secondary_heating_type": _classify(
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pred_h.secondary_heating_type, actual_h.secondary_heating_type
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),
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}
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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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@ -62,29 +113,30 @@ def compare_prediction(
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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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fabric_hits: dict[str, Optional[bool]] = {
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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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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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categorical_hits={**fabric_hits, **_heating_hits(predicted, actual)},
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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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@ -9,8 +9,10 @@ from typing import Optional, Union
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from datatypes.epc.domain.epc_property_data import (
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EpcPropertyData,
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MainHeatingDetail,
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SapBuildingPart,
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SapFloorDimension,
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SapHeating,
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SapWindow,
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)
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from domain.epc_prediction.prediction_comparison import compare_prediction
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@ -26,6 +28,14 @@ def _epc(
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floor_area: float = 80.0,
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building_parts: int = 1,
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windows: Optional[list[tuple[float, float]]] = None,
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main_fuel_type: Optional[int] = 20,
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main_heating_category: Optional[int] = 2,
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main_heating_control: Optional[Union[int, str]] = 2100,
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water_heating_fuel: Optional[int] = 20,
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water_heating_code: Optional[int] = 901,
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has_hot_water_cylinder: bool = True,
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cylinder_insulation_type: Optional[Union[int, str]] = 1,
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secondary_heating_type: Optional[Union[int, str]] = None,
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) -> EpcPropertyData:
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epc: EpcPropertyData = object.__new__(EpcPropertyData)
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epc.total_floor_area_m2 = floor_area
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@ -41,6 +51,18 @@ def _epc(
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part.sap_floor_dimensions = [floor_dim]
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parts.append(part)
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epc.sap_building_parts = parts
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detail: MainHeatingDetail = object.__new__(MainHeatingDetail)
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detail.main_fuel_type = main_fuel_type
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detail.main_heating_category = main_heating_category
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detail.main_heating_control = main_heating_control
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heating: SapHeating = object.__new__(SapHeating)
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heating.main_heating_details = [detail]
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heating.water_heating_fuel = water_heating_fuel
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heating.water_heating_code = water_heating_code
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heating.cylinder_insulation_type = cylinder_insulation_type
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heating.secondary_heating_type = secondary_heating_type
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epc.sap_heating = heating
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epc.has_hot_water_cylinder = has_hot_water_cylinder
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sap_windows: list[SapWindow] = []
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for width, height in windows or []:
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w: SapWindow = object.__new__(SapWindow)
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@ -101,6 +123,48 @@ def test_classifies_the_extra_homogeneous_categoricals() -> None:
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assert comparison.categorical_hits["floor_construction"] is True
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def test_classifies_the_heating_components() -> None:
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# Arrange — predicted and actual agree on everything heating except the main
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# fuel (predicted oil 28, actual gas 20) and secondary heating (predicted
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# none, actual a wood stove 693). Heating is the dominant SAP lever, so each
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# heating component is scored (ADR-0030 Component Accuracy).
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predicted = _epc(
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main_fuel_type=28,
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main_heating_category=2,
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main_heating_control=2100,
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water_heating_fuel=20,
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water_heating_code=901,
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has_hot_water_cylinder=True,
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cylinder_insulation_type=1,
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secondary_heating_type=None,
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)
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actual = _epc(
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main_fuel_type=20,
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main_heating_category=2,
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main_heating_control=2100,
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water_heating_fuel=20,
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water_heating_code=901,
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has_hot_water_cylinder=True,
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cylinder_insulation_type=1,
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secondary_heating_type=693,
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)
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# Act
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hits = compare_prediction(predicted, actual).categorical_hits
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# Assert
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assert hits["heating_main_fuel"] is False
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assert hits["heating_main_category"] is True
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assert hits["heating_main_control"] is True
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assert hits["water_heating_fuel"] is True
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assert hits["water_heating_code"] is True
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assert hits["has_hot_water_cylinder"] is True
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assert hits["cylinder_insulation_type"] is True
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# Secondary heating is absent in the prediction but present in the actual —
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# a real miss (predicted None ≠ actual 693), not "not applicable".
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assert hits["secondary_heating_type"] is False
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def test_categorical_hit_is_not_applicable_when_actual_is_absent() -> None:
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# Arrange — the actual lodges no roof construction (a flat under another
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# dwelling). A hit there is not applicable, not a free win, so it must not
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