Create scorer.py

scorer.py

@@ -0,0 +1,54 @@
+from dataclasses import dataclass
+from typing import Dict, Any, List
+import re
+
+REQ = [
+    "trigger_event_type",
+    "trigger_location_zone",
+    "collapse_onset_latency",
+    "coherence_recovery_time",
+    "collapse_spread_risk",
+    "disturbance_to_balance_shift_score",
+]
+
+EVENT_HINT = ["dirty_air", "kerb", "crosswind", "damage", "ride_height", "clean_air"]
+ZONE_HINT = ["floor_right", "floor_left", "diffuser_rear", "front_wing"]
+NUM_HINT = re.compile(r"\b\d+(\.\d+)?\b")
+
+@dataclass
+class ScoreResult:
+    score: float
+    details: Dict[str, Any]
+
+def _has_number(p: str) -> bool:
+    return bool(NUM_HINT.search(p))
+
+def _has_event(p: str) -> bool:
+    return any(e in p for e in EVENT_HINT)
+
+def _has_zone(p: str) -> bool:
+    return any(z in p for z in ZONE_HINT)
+
+def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
+    p = (prediction or "").lower()
+    words_ok = len(p.split()) <= 1100
+
+    hits = sum(1 for k in REQ if k in p)
+    has_number = _has_number(p)
+    has_event = _has_event(p)
+    has_zone = _has_zone(p)
+
+    raw = (
+        0.20 * int(words_ok) +
+        0.60 * (hits / len(REQ)) +
+        0.10 * int(has_number) +
+        0.05 * int(has_event) +
+        0.05 * int(has_zone)
+    )
+
+    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)}