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77983caed8
Slice 1 of #1160. Recycles the GainOptimiser/CostOptimiser formulation (≤1 Option per Recommendation, maximise SAP gain subject to budget) as a clean typed DDD function — but as an exact pure-Python multiple-choice knapsack rather than the legacy `mip` MILP, since mip's CBC backend does not load on aarch64 (so the legacy solver path can't run / be tested here). At retrofit scale the candidate space Π(|group|+1) is tiny, so exhaustive enumeration is exact and instant; ADR-0016 only needs the knapsack as a warm-start signal anyway (the truthful figure comes from the whole-package re-score + repair, next slice). `optimise(groups, budget) -> list[ScoredOption]`: maximise total gain, tie-break toward lower cost, skip-per-group covers "select none". 6 tests (budget-bound selection, ≤1/group, unconstrained, budget-too-small, empty groups, partial-affordability); pyright strict clean. Multi-phase remains descoped (ADR-0005) — single-phase optimiser. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
123 lines
4.3 KiB
Python
123 lines
4.3 KiB
Python
"""Behaviour of the Optimiser core: a grouped-knapsack MILP over per-Option
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role-1 scores (ADR-0016). Picks at most one Option per Recommendation (disjoint
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groups, no cross-group constraints) to maximise total SAP gain subject to the
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Scenario budget. This is the warm-start *signal* — the truthful figure comes
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from the whole-package re-score + repair (a later slice); here we test the
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selection with synthetic scores and no calculator.
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"""
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from __future__ import annotations
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from domain.modelling.optimiser import ScoredOption, optimise
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from domain.modelling.recommendation import Cost, MeasureOption
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from domain.modelling.simulation import EpcSimulation
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def _scored(measure_type: str, *, gain: float, cost: float) -> ScoredOption:
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return ScoredOption(
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option=MeasureOption(
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measure_type=measure_type,
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description=measure_type,
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overlay=EpcSimulation(),
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cost=Cost(total=cost, contingency_rate=0.0),
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),
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sap_gain=gain,
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)
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def _selected_types(selection: list[ScoredOption]) -> set[str]:
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return {scored.option.measure_type for scored in selection}
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def test_grouped_knapsack_maximises_gain_within_budget() -> None:
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# Arrange — wall group has two mutually-exclusive options; roof + floor one
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# each. EWI has the best gain but is unaffordable alongside the rest.
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groups: list[list[ScoredOption]] = [
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[
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_scored("external_wall_insulation", gain=10.0, cost=8000.0),
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_scored("cavity_wall_insulation", gain=6.0, cost=1000.0),
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],
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[_scored("loft_insulation", gain=4.0, cost=1500.0)],
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[_scored("suspended_floor_insulation", gain=3.0, cost=2000.0)],
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]
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# Act
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selection: list[ScoredOption] = optimise(groups, budget=5000.0)
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# Assert — cavity + loft + floor (cost 4500, gain 13) beats any package
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# containing the 8000 EWI option within the 5000 budget.
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assert _selected_types(selection) == {
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"cavity_wall_insulation",
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"loft_insulation",
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"suspended_floor_insulation",
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}
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def test_picks_at_most_one_option_per_group() -> None:
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# Arrange — both wall options are individually affordable.
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groups: list[list[ScoredOption]] = [
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[
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_scored("external_wall_insulation", gain=10.0, cost=2000.0),
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_scored("cavity_wall_insulation", gain=6.0, cost=1000.0),
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],
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]
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# Act
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selection: list[ScoredOption] = optimise(groups, budget=10000.0)
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# Assert — never both treatments of the same wall; the higher-gain one wins.
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assert len(selection) == 1
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assert _selected_types(selection) == {"external_wall_insulation"}
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def test_no_budget_picks_the_best_option_in_every_group() -> None:
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# Arrange
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groups: list[list[ScoredOption]] = [
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[
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_scored("external_wall_insulation", gain=10.0, cost=8000.0),
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_scored("cavity_wall_insulation", gain=6.0, cost=1000.0),
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],
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[_scored("loft_insulation", gain=4.0, cost=1500.0)],
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]
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# Act — None budget = unconstrained.
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selection: list[ScoredOption] = optimise(groups, budget=None)
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# Assert
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assert _selected_types(selection) == {
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"external_wall_insulation",
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"loft_insulation",
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}
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def test_budget_too_small_for_any_option_selects_nothing() -> None:
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# Arrange
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groups: list[list[ScoredOption]] = [
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[_scored("cavity_wall_insulation", gain=6.0, cost=1000.0)],
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[_scored("loft_insulation", gain=4.0, cost=1500.0)],
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]
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# Act
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selection: list[ScoredOption] = optimise(groups, budget=500.0)
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# Assert — nothing affordable; selecting none is the optimum.
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assert selection == []
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def test_no_groups_selects_nothing() -> None:
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# Act / Assert
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assert optimise([], budget=10000.0) == []
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def test_within_budget_partial_selection_prefers_the_higher_gain_option() -> None:
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# Arrange — only one of the two fits the budget; pick the affordable best.
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groups: list[list[ScoredOption]] = [
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[_scored("external_wall_insulation", gain=10.0, cost=8000.0)],
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[_scored("loft_insulation", gain=4.0, cost=1500.0)],
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]
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# Act
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selection: list[ScoredOption] = optimise(groups, budget=2000.0)
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# Assert — EWI is unaffordable; loft alone is the best within £2000.
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assert _selected_types(selection) == {"loft_insulation"}
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