Update scorer.py

scorer.py

@@ -0,0 +1,36 @@
+from dataclasses import dataclass
+from typing import Dict, Any, List
+
+GRADIENTS = {"flat", "shallow", "moderate", "moderate_to_steep", "steep", "flat_to_positive"}
+
+@dataclass
+class ScoreResult:
+    score: float
+    details: Dict[str, Any]
+
+def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
+    p = (prediction or "").lower()
+    words_ok = len(p.split()) <= 800
+
+    has_traj = "trajectory" in p or "stressed" in p or "decoupling" in p or "collapse" in p
+    has_gradient = any(g in p for g in GRADIENTS) or "gradient" in p
+    has_levers = ">" in p or "rank" in p or "leverage" in p
+    has_gain = "coherence" in p and ("gain" in p or "medium" in p or "high" in p or "low" in p)
+    has_tradeoffs = "tradeoff" in p or "cost" in p or "risk" in p
+    has_monitor = "monitor" in p or "indicator" in p or "metric" in p
+
+    raw = (
+        0.15 * int(words_ok) +
+        0.20 * int(has_traj) +
+        0.20 * int(has_gradient) +
+        0.20 * int(has_levers) +
+        0.10 * int(has_gain) +
+        0.10 * int(has_tradeoffs) +
+        0.05 * int(has_monitor)
+    )
+    return ScoreResult(score=min(1.0, raw), details={"id": sample.get("id")})
+
+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)}