Remove the superseded role-1 impacts scorer; signals carry the objective currency 🟪

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

domain/modelling/optimisation/optimiser.py

@@ -33,8 +33,9 @@ from domain.modelling.simulation import EpcSimulation
 @dataclass(frozen=True)
 class ScoredOption:
     """A candidate Measure Option paired with its role-1 (independent-vs-
-    baseline) SAP gain — the optimiser's input signal. Cost is read from the
-    Option; the gain is supplied by scoring."""
+    baseline) gain in the goal objective's currency — SAP points by default,
+    kg CO2 / £ saved for the goal-aligned Scenarios (ADR-0062). Cost is read
+    from the Option; the gain is supplied by scoring."""
 
     option: MeasureOption
     sap_gain: float

domain/modelling/scoring/scoring.py

@@ -83,38 +83,6 @@ def marginal_impacts(
     return marginals_from_scores(cascade_scores(scorer, baseline, overlays))
 
 
-def independent_option_impacts(
-    scorer: PackageScorer,
-    baseline: EpcPropertyData,
-    options: Sequence[MeasureOption],
-) -> list[MeasureImpact]:
-    """Score each Option's overlay independently against the baseline (role 1 —
-    the optimiser's approximate input signal). Each *distinct* Simulation Overlay
-    is scored once (Options sharing an overlay reuse the result), so the baseline
-    is scored once plus one score per distinct overlay. Results follow the input
-    order. These figures are an approximate signal — never surface them as a
-    measure's true impact."""
-    base: Score = scorer.score(baseline, [])
-    scored: list[tuple[EpcSimulation, MeasureImpact]] = []
-    impacts: list[MeasureImpact] = []
-    for option in options:
-        cached = next(
-            (impact for overlay, impact in scored if overlay == option.overlay), None
-        )
-        if cached is None:
-            current: Score = scorer.score(baseline, [option.overlay])
-            cached = MeasureImpact(
-                sap_points=current.sap_continuous - base.sap_continuous,
-                co2_savings_kg_per_yr=base.co2_kg_per_yr - current.co2_kg_per_yr,
-                energy_savings_kwh_per_yr=(
-                    base.primary_energy_kwh_per_yr - current.primary_energy_kwh_per_yr
-                ),
-            )
-            scored.append((option.overlay, cached))
-        impacts.append(cached)
-    return impacts
-
-
 def independent_option_signals(
     scorer: PackageScorer,
     baseline: EpcPropertyData,

tests/domain/modelling/test_scoring.py

@@ -16,7 +16,7 @@ from domain.modelling.recommendation import MeasureOption
 from domain.modelling.scoring.scoring import (
     MeasureImpact,
     cascade_scores,
-    independent_option_impacts,
+    independent_option_signals,
     marginal_impacts,
     marginals_from_scores,
 )
@@ -64,7 +64,7 @@ def _option(overlay: EpcSimulation) -> MeasureOption:
     )
 
 
-def test_independent_option_impacts_score_each_distinct_overlay_once() -> None:
+def test_independent_option_signals_score_each_distinct_overlay_once() -> None:
     # Arrange
     baseline: EpcPropertyData = build_epc()
     scorer = _CountingScorer()
@@ -86,15 +86,15 @@ def test_independent_option_impacts_score_each_distinct_overlay_once() -> None:
     options = [_option(overlay_a), _option(overlay_a_dup), _option(overlay_b)]
 
     # Act
-    impacts: list[MeasureImpact] = independent_option_impacts(
-        scorer, baseline, options
+    signals: list[float] = independent_option_signals(
+        scorer, baseline, options, lambda score: score.sap_continuous
     )
 
     # Assert
     # baseline scored once + one score per DISTINCT overlay (a, b) = 3, not 4
     assert scorer.calls == 3
-    assert impacts[0].sap_points == impacts[1].sap_points == 2.0
-    assert impacts[2].sap_points == 3.0
+    assert signals[0] == signals[1] == 2.0
+    assert signals[2] == 3.0
 
 
 def test_single_overlay_marginal_is_its_improvement_over_baseline() -> None: