Create scorer.py

scorer.py

@@ -0,0 +1,42 @@
+from dataclasses import dataclass
+from typing import Dict, Any, List
+import re
+
+REQ = [
+    "coupling_strength_index",
+    "expected_coupling_band",
+    "confidence_score",
+]
+
+@dataclass
+class ScoreResult:
+    score: float
+    details: Dict[str, Any]
+
+def _float01(text: str, key: str) -> bool:
+    return bool(re.search(rf"{key}\s*[:=]\s*(0\.\d+|1\.0)\b", text))
+
+def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
+    p = (prediction or "").lower()
+    words_ok = len(p.split()) <= 600
+
+    hits = sum(1 for k in REQ if k in p)
+    floats_ok = sum(1 for k in REQ if _float01(p, k))
+    has_band = "-" in p or "band" in p
+
+    raw = (
+        0.30 * int(words_ok) +
+        0.40 * (hits / len(REQ)) +
+        0.20 * (floats_ok / len(REQ)) +
+        0.10 * int(has_band)
+    )
+
+    return ScoreResult(
+        score=min(1.0, raw),
+        details={"id": sample.get("id"), "hits": hits, "floats_ok": floats_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)}