mirror of
https://github.com/Hestia-Homes/Model.git
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51cdc25ce8
Per-component method, not a global template change: the predicted floor area is now the cohort median (the MAD-minimising point estimate of the target's size) rather than whichever structural template's own area. The calculator derives heat loss from building-part geometry, not this scalar, so decoupling them is safe and the scalar becomes a better size estimate. floor_area mean|.|: corpus (150pc/514 targets) 10.62 -> 10.48; fixture 12.2175 -> 11.8983 (ceiling ratcheted down). No other component moves. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
367 lines
14 KiB
Python
367 lines
14 KiB
Python
"""Behaviour of EPC Prediction synthesis (ADR-0029): turn the selected
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Comparable Properties into a predicted EpcPropertyData. Hybrid — copy a coherent
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representative template's structure (building parts, windows, geometry), set the
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homogeneous categoricals to the recency-weighted cohort mode, apply Landlord
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Overrides on top. Pure domain logic.
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"""
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from datetime import date
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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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SapBuildingPart,
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SapFloorDimension,
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SapWindow,
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)
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from domain.epc_prediction.comparable_properties import (
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Comparable,
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ComparableProperties,
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PredictionTarget,
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)
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from domain.epc_prediction.epc_prediction import (
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EpcPrediction,
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PredictionConfidence,
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)
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def _epc(
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*,
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building_parts: int = 1,
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floor_area: float = 80.0,
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wall_construction: Union[int, str] = 1,
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wall_insulation_type: Union[int, str] = 1,
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construction_age_band: str = "K",
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roof_construction: Optional[int] = 1,
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roof_insulation_thickness: Optional[Union[str, int]] = 100,
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floor_construction: Optional[int] = 1,
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floor_insulation: Optional[int] = 1,
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glazing_type: Union[int, str] = 3,
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) -> EpcPropertyData:
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epc: EpcPropertyData = object.__new__(EpcPropertyData)
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epc.property_type = "2"
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epc.built_form = "4"
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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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part.wall_insulation_type = wall_insulation_type
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part.construction_age_band = construction_age_band
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part.roof_construction = roof_construction
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part.roof_insulation_thickness = roof_insulation_thickness
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floor_dim: SapFloorDimension = object.__new__(SapFloorDimension)
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floor_dim.floor_construction = floor_construction
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floor_dim.floor_insulation = floor_insulation
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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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window: SapWindow = object.__new__(SapWindow)
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window.window_width = 1.0
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window.window_height = 1.0
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window.glazing_type = glazing_type
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epc.sap_windows = [window]
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return epc
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def _cohort(*epcs: EpcPropertyData) -> ComparableProperties:
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return ComparableProperties(
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members=tuple(
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Comparable(epc=e, certificate_number=str(i)) for i, e in enumerate(epcs)
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)
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)
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def _dated_cohort(
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*dated: tuple[EpcPropertyData, date],
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) -> ComparableProperties:
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return ComparableProperties(
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members=tuple(
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Comparable(epc=e, certificate_number=str(i), registration_date=d)
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for i, (e, d) in enumerate(dated)
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)
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)
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def test_predicts_a_picture_by_copying_a_representative_template() -> None:
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# Arrange — a single comparable with a distinctive structure (2 building
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# parts, 92 m²); with nothing else to go on it is the template.
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template = _epc(building_parts=2, floor_area=92.0)
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target = PredictionTarget(postcode="LS6 1AA", property_type="2")
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# Act
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predicted: EpcPropertyData = EpcPrediction().predict(target, _cohort(template))
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# Assert — the structure is copied wholesale (and it is a copy, not the same
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# object — the baseline must never be mutated).
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assert len(predicted.sap_building_parts) == 2
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assert predicted.total_floor_area_m2 == 92.0
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assert predicted is not template
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def test_template_is_the_member_closest_to_the_cohort_median_size() -> None:
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# Arrange — the cohort spans a wide range of sizes; members[0] is an atypical
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# tiny 20 m² outlier. A single neighbour's geometry is copied wholesale, so
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# the template must be the size-representative member (closest to the median),
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# not whoever happens to come first (ADR-0029 decision 4: closest on size).
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cohort = _cohort(
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_epc(floor_area=20.0),
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_epc(floor_area=80.0),
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_epc(floor_area=200.0),
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)
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# Act
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predicted: EpcPropertyData = EpcPrediction().predict(
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PredictionTarget(postcode="LS6 1AA", property_type="2"), cohort
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)
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# Assert — the 80 m² member (the median) seeds the structure, not the 20 m²
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# outlier sitting at members[0].
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assert predicted.total_floor_area_m2 == 80.0
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def test_sets_main_wall_construction_to_the_cohort_mode() -> None:
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# Arrange — the template (members[0]) is solid brick (2), but the cohort
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# majority is cavity (1). The homogeneous categorical should follow the mode,
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# not the one template, so the prediction is robust to an atypical template.
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cohort = _cohort(
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_epc(wall_construction=2),
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_epc(wall_construction=1),
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_epc(wall_construction=1),
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_epc(wall_construction=1),
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)
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# Act
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predicted: EpcPropertyData = EpcPrediction().predict(
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PredictionTarget(postcode="LS6 1AA", property_type="2"), cohort
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)
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# Assert — cavity (the mode) wins over the solid-brick template.
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assert predicted.sap_building_parts[0].wall_construction == 1
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def test_sets_the_other_homogeneous_categoricals_to_the_cohort_mode() -> None:
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# Arrange — the median-size template (members[0], 80 m²) is an atypical
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# outlier on every categorical; the cohort majority disagrees. Age band,
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# wall insulation, roof construction and floor construction are all
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# homogeneous categoricals, so each should follow its mode, not the one
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# template (ADR-0029 decision 4).
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cohort = _cohort(
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_epc(
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floor_area=80.0,
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construction_age_band="A",
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wall_insulation_type=9,
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roof_construction=7,
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floor_construction=7,
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),
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_epc(
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construction_age_band="K",
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wall_insulation_type=1,
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roof_construction=2,
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floor_construction=3,
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),
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_epc(
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construction_age_band="K",
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wall_insulation_type=1,
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roof_construction=2,
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floor_construction=3,
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),
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)
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# Act
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predicted: EpcPropertyData = EpcPrediction().predict(
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PredictionTarget(postcode="LS6 1AA", property_type="2"), cohort
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)
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# Assert — every categorical follows the cohort mode over the outlier
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# template.
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main = predicted.sap_building_parts[0]
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assert main.construction_age_band == "K"
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assert main.wall_insulation_type == 1
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assert main.roof_construction == 2
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assert main.sap_floor_dimensions[0].floor_construction == 3
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def test_modes_roof_and_floor_insulation() -> None:
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# Arrange — the median-size template (members[0]) is an outlier on roof
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# insulation thickness and floor insulation; the cohort majority disagrees.
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# These are independent fabric categoricals, so each should follow its
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# cohort mode like the construction categoricals do.
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cohort = _cohort(
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_epc(floor_area=80.0, roof_insulation_thickness=25, floor_insulation=9),
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_epc(roof_insulation_thickness=300, floor_insulation=2),
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_epc(roof_insulation_thickness=300, floor_insulation=2),
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)
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# Act
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predicted: EpcPropertyData = EpcPrediction().predict(
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PredictionTarget(postcode="LS6 1AA", property_type="2"), cohort
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)
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# Assert — each follows the cohort mode over the outlier template.
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main = predicted.sap_building_parts[0]
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assert main.roof_insulation_thickness == 300
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assert main.sap_floor_dimensions[0].floor_insulation == 2
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def test_recency_weights_roof_insulation_mode() -> None:
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# Arrange — an old majority (three 2015 certs at 100 mm) and a recent
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# minority (two 2025 certs at 300 mm). Roof insulation is topped up over
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# time, so the recent neighbours reflect the current state: the recency-
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# weighted mode must pick 300 over the plain-majority 100.
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cohort = _dated_cohort(
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(_epc(roof_insulation_thickness=100), date(2015, 1, 1)),
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(_epc(roof_insulation_thickness=100), date(2015, 1, 1)),
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(_epc(roof_insulation_thickness=100), date(2015, 1, 1)),
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(_epc(roof_insulation_thickness=300), date(2025, 1, 1)),
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(_epc(roof_insulation_thickness=300), date(2025, 1, 1)),
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)
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# Act
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predicted: EpcPropertyData = EpcPrediction().predict(
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PredictionTarget(postcode="LS6 1AA", property_type="2"), cohort
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)
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# Assert — recency overrides the stale majority.
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assert predicted.sap_building_parts[0].roof_insulation_thickness == 300
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def test_floor_area_is_the_cohort_median_not_the_templates_own_area() -> None:
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# Arrange — an even-sized cohort whose median (70) falls between members, so
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# the size-representative template (the first member closest to the median,
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# 60 m²) does not itself sit on the median. The predicted floor area is a
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# point estimate of the target's size, best served by the cohort median (the
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# MAD-minimising estimator), decoupled from whichever template seeds the
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# structure.
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cohort = _cohort(
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_epc(floor_area=40.0),
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_epc(floor_area=60.0),
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_epc(floor_area=80.0),
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_epc(floor_area=100.0),
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)
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# Act
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predicted: EpcPropertyData = EpcPrediction().predict(
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PredictionTarget(postcode="LS6 1AA", property_type="2"), cohort
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)
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# Assert — the floor area is the cohort median (70), not the template's 60.
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assert predicted.total_floor_area_m2 == 70.0
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def test_categorical_mode_leans_on_size_similar_neighbours() -> None:
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# Arrange — a count majority (three) carries wall-insulation 9, but two of
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# them are 400 m² size outliers; the cohort centre (median 100 m²) holds
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# wall-insulation 1. Physical-similarity weighting down-weights the outliers,
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# so the size-representative value 1 wins over the plain-count majority 9.
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cohort = _cohort(
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_epc(floor_area=100.0, wall_insulation_type=1),
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_epc(floor_area=100.0, wall_insulation_type=1),
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_epc(floor_area=100.0, wall_insulation_type=9),
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_epc(floor_area=400.0, wall_insulation_type=9),
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_epc(floor_area=400.0, wall_insulation_type=9),
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)
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# Act
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predicted: EpcPropertyData = EpcPrediction().predict(
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PredictionTarget(postcode="LS6 1AA", property_type="2"), cohort
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)
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# Assert — the size-similar value wins over the outlier-driven majority.
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assert predicted.sap_building_parts[0].wall_insulation_type == 1
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def test_categorical_mode_leans_on_age_similar_neighbours() -> None:
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# Arrange — same size throughout (so size weighting is neutral). A count
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# majority (three) carries wall-insulation 9, but two of them are age-band A
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# outliers while the cohort's modal band is K. Age-similarity weighting
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# down-weights the outliers, so the band-representative value 1 wins.
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cohort = _cohort(
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_epc(construction_age_band="K", wall_insulation_type=1),
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_epc(construction_age_band="K", wall_insulation_type=1),
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_epc(construction_age_band="K", wall_insulation_type=9),
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_epc(construction_age_band="A", wall_insulation_type=9),
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_epc(construction_age_band="A", wall_insulation_type=9),
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)
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# Act
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predicted: EpcPropertyData = EpcPrediction().predict(
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PredictionTarget(postcode="LS6 1AA", property_type="2"), cohort
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)
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# Assert — the age-similar value wins over the outlier-driven majority.
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assert predicted.sap_building_parts[0].wall_insulation_type == 1
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def test_confidence_reports_cohort_size_and_unanimous_agreement() -> None:
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# Arrange — a unanimous cohort: three neighbours, all cavity-walled (1).
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cohort = _cohort(
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_epc(wall_construction=1),
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_epc(wall_construction=1),
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_epc(wall_construction=1),
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)
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# Act
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confidence: PredictionConfidence = EpcPrediction().confidence(cohort)
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# Assert — three neighbours, total agreement on the wall construction.
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assert confidence.cohort_size == 3
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assert confidence.agreement("wall_construction") == 1.0
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def test_confidence_agreement_is_the_modal_share_of_the_cohort() -> None:
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# Arrange — three of four neighbours are cavity (1), one is solid brick (2),
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# so the cohort is split on the wall construction.
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cohort = _cohort(
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_epc(wall_construction=1),
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_epc(wall_construction=1),
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_epc(wall_construction=1),
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_epc(wall_construction=2),
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)
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# Act
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confidence: PredictionConfidence = EpcPrediction().confidence(cohort)
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# Assert — agreement is the modal value's share of the cohort: 3 of 4.
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share: Optional[float] = confidence.agreement("wall_construction")
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assert share is not None
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assert abs(share - 0.75) <= 1e-9
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def test_confidence_excludes_absent_component_values_from_the_denominator() -> None:
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# Arrange — two neighbours lodge a roof construction (both code 2); one lodges
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# none. The missing value must not dilute the agreement to 2/3.
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cohort = _cohort(
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_epc(roof_construction=2),
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_epc(roof_construction=2),
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_epc(roof_construction=None),
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)
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# Act
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confidence: PredictionConfidence = EpcPrediction().confidence(cohort)
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# Assert — agreement counts only the two present, unanimous values (1.0),
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# while the cohort size still reflects all three neighbours.
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share: Optional[float] = confidence.agreement("roof_construction")
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assert share is not None
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assert abs(share - 1.0) <= 1e-9
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assert confidence.cohort_size == 3
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def test_applies_a_known_wall_override_over_the_mode() -> None:
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# Arrange — the cohort mode is cavity (1), but we KNOW the target is solid
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# brick (2), a Landlord Override. The known value must win over the estimate.
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cohort = _cohort(
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_epc(wall_construction=1),
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_epc(wall_construction=1),
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_epc(wall_construction=1),
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)
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target = PredictionTarget(
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postcode="LS6 1AA", property_type="2", wall_construction=2
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)
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# Act
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predicted: EpcPropertyData = EpcPrediction().predict(target, cohort)
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# Assert — the known override overrides the cohort mode.
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assert predicted.sap_building_parts[0].wall_construction == 2
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