Create scorer.py

scorer.py

@@ -0,0 +1,43 @@
+from dataclasses import dataclass
+from typing import Dict, Any, List
+import re
+
+REQ = [
+    "current_regime_basin",
+    "adjacent_regime_targets",
+    "trigger_condition_set",
+    "transition_probability_24h",
+    "transition_probability_72h",
+    "projected_post_shift_structure",
+]
+
+@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 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.lower() in p)
+
+    has_numbers = _has_float(p)
+    has_prob = "24h" in p or "72h" in p
+
+    raw = (
+        0.20 * int(words_ok) +
+        0.55 * (hits / len(REQ)) +
+        0.15 * int(has_numbers) +
+        0.10 * int(has_prob)
+    )
+
+    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)}