Create scorer.py

scorer.py

@@ -0,0 +1,56 @@
+from dataclasses import dataclass
+from typing import Dict, Any, List
+import re
+
+REQ = [
+    "optimal_endpoint_mix",
+    "rwe_generation_plan",
+    "pricing_stability_zone",
+    "access_probability",
+    "restriction_burden_forecast",
+    "coverage_stability_horizon",
+    "confidence_score",
+]
+
+ZONES = ["wide", "moderate", "narrow"]
+
+@dataclass
+class ScoreResult:
+    score: float
+    details: Dict[str, Any]
+
+def _float01(p: str, key: str) -> bool:
+    return bool(re.search(rf"{key}\s*[:=]\s*(0\.\d+|1\.0)\b", p))
+
+def _zone_ok(p: str) -> bool:
+    m = re.search(r"pricing_stability_zone\s*[:=]\s*([a-z\-]+)", p)
+    return bool(m and m.group(1) in ZONES)
+
+def _horizon_ok(p: str) -> bool:
+    return bool(re.search(r"coverage_stability_horizon\s*[:=]\s*\d+(\.\d+)?\s*(m|mo|mos|y|yr|yrs)\b", p))
+
+def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
+    p = (prediction or "").lower()
+    words_ok = len(p.split()) <= 950
+
+    hits = sum(1 for k in REQ if k in p)
+    ap_ok = int(_float01(p, "access_probability"))
+    conf_ok = int(_float01(p, "confidence_score"))
+    zone_ok = int(_zone_ok(p))
+    hz_ok = int(_horizon_ok(p))
+
+    raw = (
+        0.22 * int(words_ok) +
+        0.40 * (hits / len(REQ)) +
+        0.14 * ap_ok +
+        0.08 * conf_ok +
+        0.08 * zone_ok +
+        0.08 * hz_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)}