Create scorer.py

scorer.py

@@ -0,0 +1,56 @@
+from dataclasses import dataclass
+from typing import Dict, Any, List
+import re
+
+REQ = [
+    "loop_coherence_index",
+    "resonance_stability_band",
+    "control_lag_profile",
+    "correction_efficiency_score",
+    "baseline_deviation",
+]
+
+BANDS = ["stable","fragile","unstable"]
+
+@dataclass
+class ScoreResult:
+    score: float
+    details: Dict[str, Any]
+
+def _f(p: str, key: str):
+    m = re.search(rf"{key}\s*[:=]\s*(0\.\d+|1\.0)\b", p)
+    return float(m.group(1)) if m else None
+
+def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
+    p = (prediction or "").lower()
+    words_ok = len(p.split()) <= 800
+
+    hits = sum(1 for k in REQ if k in p)
+
+    idx = _f(p,"loop_coherence_index")
+    eff = _f(p,"correction_efficiency_score")
+    dev = _f(p,"baseline_deviation")
+
+    numeric_ok = int(
+        idx is not None and 0<=idx<=1 and
+        eff is not None and 0<=eff<=1 and
+        dev is not None and 0<=dev<=1
+    )
+
+    band_ok = int("resonance_stability_band" in p and any(b in p for b in BANDS))
+    lag_ok = int("control_lag_profile" in p)
+
+    raw = (
+        0.2 * int(words_ok) +
+        0.4 * (hits/len(REQ)) +
+        0.25 * numeric_ok +
+        0.1 * band_ok +
+        0.05 * lag_ok
+    )
+
+    return ScoreResult(score=min(1.0,raw), details={"id": sample.get("id"),"hits":hits})
+
+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)}