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003defcf55
A focused sibling to audit-ara-portfolio: that skill audits baselines/plans/SAP; this one audits the *recommendations themselves* — why a measure was or wasn't offered. Motivated by the portfolio-814 review (Khalim's HHRSH-on-community- heating, missing-HHRSH, missing-secondary-heating-removal, and a neighbour split). Adds: - .claude/skills/find-weird-recommendations/SKILL.md — scan -> neighbour scan -> live re-model deep-dive -> root-cause -> codify, with a seeded known-bug catalogue and the query-safety rules inherited from audit-ara-portfolio. - scripts/audit/anomalies.py: new `plan-stops-short-of-goal` HIGH check — the default plan ends below the goal band on an unlimited-budget scenario (the deterministic worklist for "why didn't this get recommended X"). Adds scenario_budget to the bundle/query so budget-capped scenarios are excluded. - scripts/audit/neighbour_divergence.py: groups a portfolio by (postcode, property_type, built_form) and flags effective-SAP outliers vs the cohort median. Never touches the 26m-row recommendation table, so it is safe portfolio-wide. - Tests for both (12 passing). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
76 lines
2.2 KiB
Python
76 lines
2.2 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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) -> 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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)
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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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