Create scorer.py

scorer.py

@@ -0,0 +1,52 @@
+from dataclasses import dataclass
+from typing import Dict, Any, List
+import re
+
+REQ = [
+    "platform_coherence_score",
+    "zone_pressure_asymmetry_index",
+    "vortex_system_integrity_flags",
+    "localized_collapse_zones",
+    "balance_shift_risk_score",
+]
+
+ZONE_HINT = ["floor_right", "floor_left", "diffuser", "front_wing", "none"]
+FLAG_HINT = ["ok", "fail", "weak", "stall", "transient"]
+
+@dataclass
+class ScoreResult:
+    score: float
+    details: Dict[str, Any]
+
+def _has_float(p: str) -> bool:
+    return bool(re.search(r"\b0\.\d+\b", p)) or "1.0" in p
+
+def _has_zone(p: str) -> bool:
+    return any(z in p for z in ZONE_HINT)
+
+def _has_flag(p: str) -> bool:
+    return any(f in p for f in FLAG_HINT)
+
+def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
+    p = (prediction or "").lower()
+    words_ok = len(p.split()) <= 1000
+
+    hits = sum(1 for k in REQ if k in p)
+    has_float = _has_float(p)
+    has_zone = _has_zone(p)
+    has_flag = _has_flag(p)
+
+    raw = (
+        0.20 * int(words_ok) +
+        0.60 * (hits / len(REQ)) +
+        0.10 * int(has_float) +
+        0.05 * int(has_zone) +
+        0.05 * int(has_flag)
+    )
+
+    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)}