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06a66b3dd9
Heating sub-fields can't be field-moded without breaking system coherence, so the whole SapHeating cluster is now copied as a unit from a single coherent donor rather than inherited from the structural template: the neighbour matching the cohort's modal heating signature (main fuel + category + cylinder presence), most recent among the matches (recent cert = current system). Including cylinder presence in the signature is load-bearing — it protects has_hot_water_cylinder + cylinder_insulation (a bare fuel+cat signature regressed them). Corpus (150pc/514): heating_main_control 66.3 -> 73.9% (+7.6, the target), main_fuel 92.8 -> 96.9, category 90.7 -> 95.7, water_fuel 92.8 -> 96.3, water_code 88.5 -> 95.3, has_cylinder 81.1 -> 89.7, secondary 36.2 -> 42.0. SAP MAE vs lodged 7.08 -> 6.00 (calculator floor 1.57). cylinder_insulation -13.6 corpus (tiny-n) but +33pp on the fixture; AC requires control up + fuel/category hold + SAP not worsened, all met. Gate (36-target fixture): zero regression; ratcheted main_category 0.8889->0.9444, main_control 0.7500->0.8056, water_fuel 0.9167->0.9722, water_code 0.8889->0.9444, cylinder_insulation_type 0.1667->0.5000. This is the per-component heating method ([[feedback_per_component_best_method]]): coherent donor, never field-mode. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
109 lines
4.1 KiB
Python
109 lines
4.1 KiB
Python
"""Tier-1 ratcheting Component Accuracy gate (ADR-0030).
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Runs the calculator-free leave-one-out scorer over the committed, anonymised
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fixture and asserts each per-component classification rate / geometry residual is
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no worse than a committed baseline. Because the prediction is deterministic and
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the fixture is frozen, every run reproduces the same numbers exactly — so a
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failure means a real *regression* in prediction quality, never sample noise.
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The floors / ceilings are the currently-measured values and only ever **tighten**
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(the repo's no-tolerance-widening ethos applied to an aggregate): when prediction
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improves, ratchet the relevant floor up in the same change. The end-to-end
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SAP / carbon / PE guards are deliberately *not* here — they need the calculator,
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whose API-path residual is a separate workstream; the component floors are the
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real gate (ADR-0030).
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"""
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from pathlib import Path
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import pytest
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from domain.epc_prediction.validation import (
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ComponentAccuracy,
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evaluate_component_accuracy,
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)
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from harness.epc_prediction_corpus import load_corpus
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_FIXTURE = Path(__file__).parents[3] / "tests" / "fixtures" / "epc_prediction"
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# Minimum classification hit-rate per component (ratchet floors). Tighten — never
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# loosen — as prediction improves. Values are the measured rates over the frozen
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# 36-target fixture; a 1e-3 tolerance absorbs float rounding only.
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_RATE_FLOORS: dict[str, float] = {
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"wall_construction": 0.8889,
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"wall_insulation_type": 0.8333,
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"construction_age_band": 0.6389,
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"construction_age_band_pm1": 0.8333,
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"roof_construction": 0.7222,
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"floor_construction": 0.8125,
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"heating_main_fuel": 0.9722,
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"heating_main_category": 0.9444,
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"heating_main_control": 0.8056,
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"water_heating_fuel": 0.9722,
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"water_heating_code": 0.9444,
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"has_hot_water_cylinder": 0.8889,
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"cylinder_insulation_type": 0.5000,
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"secondary_heating_type": 0.0000,
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"roof_insulation_thickness": 0.4118,
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"floor_insulation": 0.9375,
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"has_room_in_roof": 0.8333,
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"modal_glazing_type": 0.5278,
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"has_pv": 1.0000,
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"solar_water_heating": 1.0000,
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}
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# Maximum mean absolute residual per numeric component (ratchet ceilings).
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# window_count is deliberately excluded — it is cosmetic for SAP (issue #1222):
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# the predicted picture clusters at a mapper-default 4 windows while actuals
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# spread 1-21, yet total_window_area (the SAP-relevant signal) stays tight.
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_RESIDUAL_CEILINGS: dict[str, float] = {
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"floor_area": 11.8983,
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"total_window_area": 4.4067,
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"building_parts": 0.3333,
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"door_count": 0.6389,
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}
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_TOLERANCE = 1e-3
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@pytest.fixture(scope="module")
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def accuracy() -> ComponentAccuracy:
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if not (_FIXTURE / "_index.json").exists():
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pytest.skip(f"no EPC Prediction fixture at {_FIXTURE}")
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return evaluate_component_accuracy(load_corpus(_FIXTURE))
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def test_fixture_yields_the_expected_target_count(
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accuracy: ComponentAccuracy,
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) -> None:
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# The frozen fixture must still produce its full set of SAP-10.2 targets — a
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# drop means the fixture or the target filter changed.
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assert accuracy.targets >= 36
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@pytest.mark.parametrize("component,floor", sorted(_RATE_FLOORS.items()))
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def test_classification_rate_does_not_regress(
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accuracy: ComponentAccuracy, component: str, floor: float
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) -> None:
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# Arrange / Act
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rate = accuracy.rate(component)
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# Assert — the component is still applicable and at or above its floor.
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assert rate is not None, f"{component} had no applicable targets"
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assert rate >= floor - _TOLERANCE, (
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f"{component} classification regressed: {rate:.4f} < floor {floor:.4f}"
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)
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@pytest.mark.parametrize("component,ceiling", sorted(_RESIDUAL_CEILINGS.items()))
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def test_residual_does_not_regress(
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accuracy: ComponentAccuracy, component: str, ceiling: float
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) -> None:
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# Arrange / Act
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mean_abs = accuracy.mean_abs_residual(component)
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# Assert — the mean absolute residual is at or below its ceiling.
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assert mean_abs is not None, f"{component} had no residuals"
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assert mean_abs <= ceiling + _TOLERANCE, (
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f"{component} residual regressed: {mean_abs:.4f} > ceiling {ceiling:.4f}"
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)
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