Create scorer.py

scorer.py

@@ -0,0 +1,51 @@
+from dataclasses import dataclass
+from typing import Dict, Any, List
+import re
+
+REQ = [
+    "crowded_agent_type",
+    "capacity_utilization_score",
+    "reversal_risk_score",
+    "unwind_trigger_conditions",
+    "time_to_capacity_event_hours",
+]
+
+AGENTS = ["cta_trend", "options_hedging", "distressed_seller", "market_maker", "fundamental_growth"]
+
+@dataclass
+class ScoreResult:
+    score: float
+    details: Dict[str, Any]
+
+def _has_float_0_1(p: str) -> bool:
+    return bool(re.search(r"\b0\.\d+\b", p)) or "1.0" in p
+
+def _has_agent(p: str) -> bool:
+    return any(a in p for a in AGENTS)
+
+def _has_time(p: str) -> bool:
+    return "hour" in p or bool(re.search(r"\b\d+\b", p))
+
+def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
+    p = (prediction or "").lower()
+    words_ok = len(p.split()) <= 900
+
+    hits = sum(1 for k in REQ if k in p)
+    has_nums = _has_float_0_1(p)
+    has_agent = _has_agent(p)
+    has_time = _has_time(p)
+
+    raw = (
+        0.20 * int(words_ok) +
+        0.60 * (hits / len(REQ)) +
+        0.10 * int(has_nums) +
+        0.05 * int(has_agent) +
+        0.05 * int(has_time)
+    )
+
+    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)}