feat(fusion): map modality predictions to per-disease signals

src/fusion/modality.py

@@ -0,0 +1,18 @@
+"""Convert a modality classifier's probability vector into a signed signal."""
+from __future__ import annotations
+
+from src.fusion.types import ModalityPrediction
+
+
+def signal_for_disease(pred: ModalityPrediction, disease: str) -> float | None:
+    """Return signal in [-1, 1] for `disease`, or None if the model has no
+    matching class.
+
+    A class matches if its `label_text` equals `disease` case-insensitively.
+    Signal = 2 * P(disease) - 1.
+    """
+    target = disease.strip().lower()
+    for cls in pred.probabilities:
+        if cls.label_text.strip().lower() == target:
+            return 2.0 * cls.probability - 1.0
+    return None

tests/fusion/test_modality.py

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+"""Tests for src.fusion.modality — turn ModalityPrediction into a per-disease signal."""
+from __future__ import annotations
+
+import pytest
+
+from src.fusion.modality import signal_for_disease
+from src.fusion.types import ModalityClassProb, ModalityPrediction
+
+
+def _pred(probs: dict[str, float]) -> ModalityPrediction:
+    items = [ModalityClassProb(label_text=k, probability=v) for k, v in probs.items()]
+    top = max(items, key=lambda p: p.probability)
+    return ModalityPrediction(
+        label_text=top.label_text,
+        label=list(probs).index(top.label_text),
+        confidence=top.probability,
+        probabilities=items,
+    )
+
+
+class TestSignalForDisease:
+    def test_disease_class_present_high_prob(self) -> None:
+        pred = _pred({"control": 0.1, "alzheimers": 0.9})
+        sig = signal_for_disease(pred, disease="alzheimers")
+        assert sig == pytest.approx(0.8)
+
+    def test_disease_class_present_low_prob(self) -> None:
+        pred = _pred({"control": 0.95, "alzheimers": 0.05})
+        sig = signal_for_disease(pred, disease="alzheimers")
+        assert sig == pytest.approx(-0.9)
+
+    def test_disease_class_absent_returns_none(self) -> None:
+        pred = _pred({"control": 0.4, "parkinsons": 0.6})
+        sig = signal_for_disease(pred, disease="alzheimers")
+        assert sig is None
+
+    def test_label_alias_matches_case_insensitively(self) -> None:
+        pred = _pred({"Control": 0.2, "ALZHEIMERS": 0.8})
+        sig = signal_for_disease(pred, disease="alzheimers")
+        assert sig == pytest.approx(0.6)