Create scorer.py

scorer.py

@@ -0,0 +1,51 @@
+from dataclasses import dataclass
+from typing import Dict, Any, List
+import re
+
+@dataclass
+class ScoreResult:
+    score: float
+    details: Dict[str, Any]
+
+def _extract_float(text, key):
+    m = re.search(rf"{key}\s*[:=]\s*([0-9]*\.?[0-9]+)", text)
+    return float(m.group(1)) if m else None
+
+def _extract_int(text, key):
+    m = re.search(rf"{key}\s*[:=]\s*([0-9]+)", text)
+    return int(m.group(1)) if m else None
+
+def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
+    p = (prediction or "").lower()
+
+    drop = _extract_float(p, "correlation_drop")
+    risk = _extract_float(p, "immediate_failure_risk")
+    lap = _extract_int(p, "trigger_lap")
+
+    structure_hits = sum([
+        "trigger_event" in p,
+        "trigger_lap" in p,
+        "initiating_component" in p,
+        "correlation_drop" in p,
+        "immediate_failure_risk" in p
+    ])
+
+    numeric_ok = all(x is not None for x in [drop, risk])
+    lap_ok = lap is not None
+
+    raw = (
+        0.25 * int(numeric_ok) +
+        0.20 * int(lap_ok) +
+        0.35 * (structure_hits / 5) +
+        0.20 * int("trigger_event" in p)
+    )
+
+    return ScoreResult(score=min(1.0, raw), details={"id": sample.get("id")})
+
+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)
+    }