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A dwelling's heating is one conceptual system, but its fields are scattered across EpcPropertyData (a gov-API schema mirror): the cluster on sap_heating, the electricity tariff on sap_energy_source.meter_type, hot-water flags loose at top level. Three places synthesise a heating system — Measure Options, Landlord Overrides, EPC Prediction's donor — and each hand-copied a different ad-hoc subset. The override and donor both dropped meter_type, so an electric-storage system landed on the template's single-rate meter and billed overnight heat at the peak rate: property 713406 scored SAP 13 (G) vs ~50 (E), inflating the HHRSH measure to +45.8 and overshooting the plan to band A. Establish a single Coherent Heating System boundary (CONTEXT.md) that every synthesiser must cover, with a source-appropriate fill policy (ADR-0035): - Override overlay *completes* the partial system the landlord named. Companion fields are now DERIVED from the SAP code, not hand-attached per archetype: the off-peak meter from the calculator's single off-peak classification (new OFF_PEAK_IMPLYING_HEATING_CODES = SAP §12 Rules 1-2), and an unobserved storage charge control defaults to the conservative manual control (Table 4e 2401). So adding a heating archetype is just adding its code — companions can't be forgotten. A contract test guards it (every off-peak code drags a Dual meter). - Prediction's heating donor now *carries* the donor's meter_type alongside its sap_heating cluster — the donor is already coherent. Coherence is a synthesis-time obligation only; the calculator still scores a real lodged cert exactly as lodged. Verified on 713406: baseline 13 -> 47.8 (E), matching its recorded rating; the phantom HHRSH recommendation is gone and the plan no longer overshoots to A. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
124 lines
4.3 KiB
Python
124 lines
4.3 KiB
Python
"""Behaviour of the Component Accuracy leave-one-out scorer (ADR-0030): given
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loaded postcode cohorts, hold out each SAP 10.2 target, predict it from its
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all-vintage neighbours, and aggregate the per-component hits + residuals. Pure
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(no IO, no calculator) — corpus loading is the caller's job.
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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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MainHeatingDetail,
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SapBuildingPart,
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SapEnergySource,
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SapFloorDimension,
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SapHeating,
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)
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from domain.epc_prediction.comparable_properties import ComparableProperty
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from domain.epc_prediction.validation import evaluate_component_accuracy
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def _comparable(
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*,
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certificate_number: str,
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address: str,
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sap_version: float,
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wall_construction: Union[int, str] = 1,
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registration_date: Optional[date] = None,
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) -> ComparableProperty:
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"""A ComparableProperty carrying a fully-populated opaque EpcPropertyData — every
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field the predictor + comparison read (the partial-instance idiom)."""
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epc: EpcPropertyData = object.__new__(EpcPropertyData)
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epc.sap_version = sap_version
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epc.postcode = "LS6 1AA"
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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 = 80.0
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epc.door_count = 2
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epc.solar_water_heating = False
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epc.has_hot_water_cylinder = True
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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 = 1
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part.construction_age_band = "K"
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part.roof_construction = 1
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part.roof_insulation_thickness = 100
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part.sap_room_in_roof = None
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floor_dim: SapFloorDimension = object.__new__(SapFloorDimension)
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floor_dim.floor_construction = 1
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floor_dim.floor_insulation = 1
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part.sap_floor_dimensions = [floor_dim]
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epc.sap_building_parts = [part]
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epc.sap_windows = []
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detail: MainHeatingDetail = object.__new__(MainHeatingDetail)
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detail.main_fuel_type = 20
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detail.main_heating_category = 2
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detail.main_heating_control = 2100
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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 = 20
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heating.water_heating_code = 901
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heating.cylinder_insulation_type = 1
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heating.secondary_heating_type = None
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epc.sap_heating = heating
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energy: SapEnergySource = object.__new__(SapEnergySource)
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energy.photovoltaic_supply = None
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energy.photovoltaic_arrays = None
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energy.meter_type = "2"
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epc.sap_energy_source = energy
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return ComparableProperty(
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epc=epc,
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certificate_number=certificate_number,
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address=address,
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registration_date=registration_date,
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)
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def test_scores_only_sap_10_2_targets() -> None:
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# Arrange — a cohort of two distinct addresses: one SAP 10.2, one older
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# (SAP 9.94). Only the 10.2 cert is a valid held-out target; the older one
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# is kept as source evidence (its components are still valid).
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cohort = [
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_comparable(
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certificate_number="A", address="1 THE ROW", sap_version=10.2
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),
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_comparable(
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certificate_number="B", address="2 THE ROW", sap_version=9.94
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),
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]
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# Act
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accuracy = evaluate_component_accuracy([cohort])
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# Assert — exactly one target scored (the 10.2 cert), predicted from the
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# older neighbour; the older cert was never held out.
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assert accuracy.targets == 1
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assert accuracy.rate("wall_construction") == 1.0
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def test_aggregates_a_wall_classification_miss() -> None:
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# Arrange — the 10.2 target is solid brick (2); its only neighbour (the
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# source) is cavity (1), so the predicted mode misses the wall.
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cohort = [
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_comparable(
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certificate_number="A",
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address="1 THE ROW",
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sap_version=10.2,
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wall_construction=2,
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),
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_comparable(
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certificate_number="B",
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address="2 THE ROW",
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sap_version=10.2,
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wall_construction=1,
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),
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]
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# Act
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accuracy = evaluate_component_accuracy([cohort])
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# Assert — both are 10.2 targets, and each is predicted from the other (the
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# opposite wall), so wall_construction is missed both times.
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assert accuracy.targets == 2
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assert accuracy.rate("wall_construction") == 0.0
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