Create scorer.py

scorer.py

@@ -0,0 +1,50 @@
+import re
+from dataclasses import dataclass
+from typing import Dict, Any, List
+
+SYSTEMS = {
+    "sleep_circadian",
+    "autonomic",
+    "immune_inflammatory",
+    "metabolic",
+    "neurocognitive",
+    "gut_microbiome",
+    "behavior_load",
+    "subjective_experience",
+}
+
+VECTOR_KEYS = ["direction=", "magnitude=", "velocity=", "coupling_loss=", "onset_time=", "cross_modal_consensus="]
+
+@dataclass
+class ScoreResult:
+    score: float
+    details: Dict[str, Any]
+
+def _ints(text: str) -> List[int]:
+    return [int(x) for x in re.findall(r"\b\d{1,3}\b", text) if 0 <= int(x) <= 100]
+
+def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
+    p = (prediction or "").lower().strip()
+    words_ok = len(p.split()) <= 360
+
+    keys_ok = sum(1 for k in VECTOR_KEYS if k in p) >= 4
+    sys_ok = any(s in p for s in SYSTEMS)
+
+    nums = _ints(p)
+    has_sev = len(nums) >= 1
+
+    evidence_ref = any(k in p for k in ["envelope", "beyond", "break", "coupling", "predicts", "decouples"])
+
+    raw = (
+        0.20 * int(words_ok) +
+        0.30 * int(keys_ok) +
+        0.20 * int(sys_ok) +
+        0.20 * int(has_sev) +
+        0.10 * int(evidence_ref)
+    )
+    return ScoreResult(score=min(1.0, raw), details={"id": sample.get("id"), "keys_ok": keys_ok, "sys_ok": sys_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)}