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84ec6da032
domain/modelling/ had grown to 15 flat modules. Group the behavioural ones into subpackages — generators/ (wall/roof/floor Recommendation Generators), scoring/ (overlay applicator, package scorer, role-1/3 scoring), optimisation/ (optimiser + measure dependency) — and leave the shared value-object vocabulary (recommendation, plan, scenario, product, contingencies, simulation) flat at the top, since it is imported everywhere. Pure move + import-path rewrite across 89 import sites; no behaviour change. 136 pass, pyright strict clean. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
91 lines
3.8 KiB
Python
91 lines
3.8 KiB
Python
"""Per-measure scoring — the telescoping marginal cascade (ADR-0016).
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`marginal_impacts` applies overlays one at a time in the given order and
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reports each measure's marginal contribution. It serves two of the three
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scoring roles:
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- role 1 (per-Option optimiser signal): call per Option as a 1-element
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sequence -> its independent-vs-baseline impact;
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- role 3 (final-package display attribution): call once with the selected
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overlays in best-practice order -> per-measure impacts that telescope
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exactly to the whole-package total.
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Per-Option (role 1) figures are an approximate signal and must not be surfaced
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as a measure's true impact — only the final-package cascade (role 3) is
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truthful. The whole-package re-score (role 2) is `PackageScorer.score` directly.
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"""
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from dataclasses import dataclass
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from typing import Sequence
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from datatypes.epc.domain.epc_property_data import EpcPropertyData
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from domain.modelling.scoring.package_scorer import PackageScorer, Score
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from domain.modelling.recommendation import MeasureOption
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from domain.modelling.simulation import EpcSimulation
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@dataclass(frozen=True)
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class MeasureImpact:
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"""One measure's marginal contribution, signed so positive is always an
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improvement: `sap_points` is the SAP gain; the savings are reductions
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(baseline-at-this-step minus the new value)."""
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sap_points: float
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co2_savings_kg_per_yr: float
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energy_savings_kwh_per_yr: float
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def marginal_impacts(
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scorer: PackageScorer,
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baseline: EpcPropertyData,
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overlays: Sequence[EpcSimulation],
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) -> list[MeasureImpact]:
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"""Apply overlays cumulatively in order; return each one's marginal impact
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over the running state. The marginals telescope to the whole-package total."""
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impacts: list[MeasureImpact] = []
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previous: Score = scorer.score(baseline, [])
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for index in range(len(overlays)):
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current: Score = scorer.score(baseline, list(overlays[: index + 1]))
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impacts.append(
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MeasureImpact(
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sap_points=current.sap_continuous - previous.sap_continuous,
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co2_savings_kg_per_yr=previous.co2_kg_per_yr - current.co2_kg_per_yr,
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energy_savings_kwh_per_yr=(
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previous.primary_energy_kwh_per_yr
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- current.primary_energy_kwh_per_yr
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),
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)
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)
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previous = current
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return impacts
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def independent_option_impacts(
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scorer: PackageScorer,
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baseline: EpcPropertyData,
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options: Sequence[MeasureOption],
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) -> list[MeasureImpact]:
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"""Score each Option's overlay independently against the baseline (role 1 —
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the optimiser's approximate input signal). Each *distinct* Simulation Overlay
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is scored once (Options sharing an overlay reuse the result), so the baseline
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is scored once plus one score per distinct overlay. Results follow the input
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order. These figures are an approximate signal — never surface them as a
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measure's true impact."""
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base: Score = scorer.score(baseline, [])
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scored: list[tuple[EpcSimulation, MeasureImpact]] = []
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impacts: list[MeasureImpact] = []
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for option in options:
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cached = next(
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(impact for overlay, impact in scored if overlay == option.overlay), None
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)
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if cached is None:
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current: Score = scorer.score(baseline, [option.overlay])
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cached = MeasureImpact(
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sap_points=current.sap_continuous - base.sap_continuous,
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co2_savings_kg_per_yr=base.co2_kg_per_yr - current.co2_kg_per_yr,
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energy_savings_kwh_per_yr=(
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base.primary_energy_kwh_per_yr - current.primary_energy_kwh_per_yr
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),
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)
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scored.append((option.overlay, cached))
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impacts.append(cached)
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return impacts
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