Create scorer.py

scorer.py

@@ -0,0 +1,49 @@
+import re
+from dataclasses import dataclass
+from typing import Dict, Any, List
+
+RHYTHMS = {
+    "circadian",
+    "weekly_load",
+    "menstrual_cycle",
+    "seasonal",
+    "post_infection_recovery",
+    "training_cycle",
+    "medication_cycle",
+    "none_detected",
+}
+
+@dataclass
+class ScoreResult:
+    score: float
+    details: Dict[str, Any]
+
+def _has(t: str, pats: List[str]) -> bool:
+    t = (t or "").lower()
+    return any(re.search(p, t) for p in pats)
+
+def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
+    p = (prediction or "").lower().strip()
+    words_ok = len(p.split()) <= 320
+
+    signature_ref = _has(p, [r"signature=", r"personal_baseline_signature", r"baseline signature"])
+    envelope_ref = _has(p, [r"envelope=", r"variability", r"±", r"range"])
+    rhythm_ok = any(r in p for r in RHYTHMS)
+
+    coupling_ref = _has(p, [r"coupl", r"predict", r"tracks", r"rebound", r"anchors"])
+    marker_ref = _has(p, [r"rhr", r"hrv", r"crp", r"sleep", r"hba1c", r"tsh", r"ferritin"])
+
+    raw = (
+        0.25 * int(words_ok) +
+        0.25 * int(signature_ref) +
+        0.20 * int(envelope_ref) +
+        0.15 * int(rhythm_ok) +
+        0.10 * int(coupling_ref) +
+        0.05 * int(marker_ref)
+    )
+    return ScoreResult(score=min(1.0, raw), details={"id": sample.get("id"), "rhythm_ok": rhythm_ok})
+
+def aggregate(results: List[ScoreResult]) -> Dict[str, Any]:
+    if not results:
+        return {"mean": 0.0, "n": 0}
+    return {"mean": sum(r.score for r in results) / len(results), "n": len(results)}