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https://github.com/Hestia-Homes/Model.git
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f3ad6343a3
Pure compare_prediction (TDD): wall-construction classification hit + signed residuals on floor area, window count, total window area, building-parts count. Plus validate_epc_prediction.py (IO plumbing): drops each cert from its postcode cohort, predicts from the rest on guaranteed inputs only, aggregates the metrics, and reports SAP three ways (pred-calc vs lodged / vs calc-on-actual / vs the neighbour-mean baseline). Smoke run: wall 90.9%, floor-area mean|·| 42.6 m2 (a real signal — template-copied floor area is noisy), SAP pred-calc edges baseline. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
101 lines
3.3 KiB
Python
101 lines
3.3 KiB
Python
"""Behaviour of the per-Property prediction comparison (ADR-0029): given a
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predicted EpcPropertyData and the actual one, report the accuracy signals the
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validation harness aggregates — classification matches on the key categoricals
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and residuals on the geometry. Pure; SAP residual is computed in the runner
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(it needs the calculator + lodged SAP).
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"""
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from typing import Optional
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from datatypes.epc.domain.epc_property_data import (
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EpcPropertyData,
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SapBuildingPart,
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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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def _epc(
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*,
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wall_construction: int = 1,
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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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) -> 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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parts: list[SapBuildingPart] = []
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for _ in range(building_parts):
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part: SapBuildingPart = object.__new__(SapBuildingPart)
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part.wall_construction = wall_construction
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parts.append(part)
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epc.sap_building_parts = parts
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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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w.window_width = width
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w.window_height = height
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sap_windows.append(w)
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epc.sap_windows = sap_windows
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return epc
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def test_flags_a_correct_main_wall_construction_classification() -> None:
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# Arrange — predicted and actual agree on cavity (1).
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predicted = _epc(wall_construction=1)
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actual = _epc(wall_construction=1)
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# Act
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comparison = compare_prediction(predicted, actual)
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# Assert
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assert comparison.wall_construction_correct is True
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def test_flags_an_incorrect_main_wall_construction_classification() -> None:
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# Arrange — predicted cavity (1), actual solid brick (2).
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predicted = _epc(wall_construction=1)
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actual = _epc(wall_construction=2)
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# Act
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comparison = compare_prediction(predicted, actual)
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# Assert
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assert comparison.wall_construction_correct is False
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def test_reports_the_floor_area_residual_as_predicted_minus_actual() -> None:
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# Arrange — predicted 90 m², actual 100 m² (a 10 m² under-prediction).
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predicted = _epc(floor_area=90.0)
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actual = _epc(floor_area=100.0)
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# Act
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comparison = compare_prediction(predicted, actual)
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# Assert — signed residual, predicted − actual.
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assert abs(comparison.floor_area_residual - (-10.0)) <= 1e-9
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def test_reports_the_building_parts_count_residual() -> None:
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# Arrange — predicted a single part; the actual has a main + an extension.
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predicted = _epc(building_parts=1)
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actual = _epc(building_parts=2)
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# Act
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comparison = compare_prediction(predicted, actual)
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# Assert — predicted − actual.
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assert comparison.building_parts_residual == -1
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def test_reports_window_count_and_total_area_residuals() -> None:
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# Arrange — predicted 2 windows (3 m² total); actual 1 window (1 m²).
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predicted = _epc(windows=[(1.0, 1.0), (2.0, 1.0)])
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actual = _epc(windows=[(1.0, 1.0)])
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# Act
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comparison = compare_prediction(predicted, actual)
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# Assert
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assert comparison.window_count_residual == 1
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assert abs(comparison.total_window_area_residual - 2.0) <= 1e-9
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