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The neighbour SAP-divergence cohort was (postcode, property_type, built_form), which mixed electric- and gas-heated dwellings. Electricity scores materially lower in SAP than mains gas for identical fabric, so a mixed-fuel postcode produced cross-fuel false outliers — an electric dwelling flagged only for being electric among gas neighbours. Add the main-heating fuel class to the cohort key (electricity variants — 29/30 + off-peak 31-40 — collapse to one class; every other code is its own bucket). Now every flagged divergence is a same-fuel comparison worth investigating. On portfolio 814 this refined 20 raw divergences to 17 same-fuel ones while keeping the genuine within-fuel outliers (e.g. the all-electric WC2B 4AW flats at SAP 26/38 vs a cohort median of 67). Reaches the fuel via epc_main_heating_detail through the indexed property_id — still never touches the recommendation table. Surfaced by the portfolio-814 recommendation audit (Work item B, #1388). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
109 lines
3.6 KiB
Python
109 lines
3.6 KiB
Python
from typing import Optional
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from scripts.audit.neighbour_divergence import Neighbour, find_divergences
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def _n(
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property_id: int,
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*,
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effective_sap: Optional[float],
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postcode: str = "AB1 2CD",
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property_type: str = "Flat",
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built_form: str = "Mid-Terrace",
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floor_area_m2: float = 60.0,
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main_fuel_type: Optional[int] = 26, # default: mains gas
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) -> Neighbour:
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return Neighbour(
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property_id=property_id,
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uprn=None,
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postcode=postcode,
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property_type=property_type,
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built_form=built_form,
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floor_area_m2=floor_area_m2,
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effective_sap=effective_sap,
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effective_band=None,
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main_fuel_type=main_fuel_type,
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)
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class TestFindDivergences:
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def test_flags_the_outlier_neighbour(self) -> None:
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# Arrange — three identical-cohort flats, one sits a clear band below.
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cohort = [
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_n(1, effective_sap=70.0),
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_n(2, effective_sap=72.0),
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_n(3, effective_sap=50.0), # Δ-20 vs median 70
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]
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# Act
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result = find_divergences(cohort, min_gap=12.0)
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# Assert — only the outlier is flagged.
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assert [d.property_id for d in result] == [3]
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def test_silent_when_cohort_agrees(self) -> None:
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# Arrange — neighbours within a few SAP points of each other.
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cohort = [_n(1, effective_sap=70.0), _n(2, effective_sap=68.0)]
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# Act
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result = find_divergences(cohort, min_gap=12.0)
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# Assert
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assert result == []
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def test_singletons_never_flagged(self) -> None:
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# Arrange — one flat here, one house there: neither has a peer to diverge from.
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lonely = [
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_n(1, effective_sap=30.0, property_type="Flat"),
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_n(2, effective_sap=90.0, property_type="House"),
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]
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# Act
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result = find_divergences(lonely, min_gap=12.0)
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# Assert
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assert result == []
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def test_different_postcode_is_a_different_cohort(self) -> None:
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# Arrange — same type/form but different postcodes: not neighbours.
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split = [
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_n(1, effective_sap=40.0, postcode="AB1 2CD"),
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_n(2, effective_sap=80.0, postcode="ZZ9 9ZZ"),
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]
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# Act
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result = find_divergences(split, min_gap=12.0)
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# Assert
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assert result == []
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def test_electric_and_gas_are_separate_cohorts(self) -> None:
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# Arrange — same postcode/type/form, but an electric dwelling (fuel 29)
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# scores a band below its gas neighbours (fuel 26). Electricity is
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# inherently lower-SAP, so this is expected stock, not a divergence.
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mixed = [
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_n(1, effective_sap=70.0, main_fuel_type=26), # gas
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_n(2, effective_sap=72.0, main_fuel_type=26), # gas
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_n(3, effective_sap=52.0, main_fuel_type=29), # electric — its own cohort
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]
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# Act
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result = find_divergences(mixed, min_gap=12.0)
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# Assert — the electric singleton has no same-fuel peers, so nothing fires.
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assert result == []
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def test_electric_outlier_still_flagged_within_its_own_fuel_cohort(self) -> None:
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# Arrange — three electric dwellings; one genuinely diverges. Keying on
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# fuel must not blind the detector to real within-fuel outliers.
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electric = [
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_n(1, effective_sap=55.0, main_fuel_type=29),
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_n(2, effective_sap=57.0, main_fuel_type=30), # 30 also electric-class
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_n(3, effective_sap=30.0, main_fuel_type=29), # Δ-25 vs median
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]
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# Act
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result = find_divergences(electric, min_gap=12.0)
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# Assert
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assert [d.property_id for d in result] == [3]
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