Create scorer.py

scorer.py

@@ -0,0 +1,40 @@
+import re
+from dataclasses import dataclass
+from typing import Dict, Any, List
+
+PATHWAYS = {"viral_triggered","stress_triggered","toxic_exposure","iatrogenic","mixed_or_unknown"}
+
+@dataclass
+class ScoreResult:
+    score: float
+    details: Dict[str, Any]
+
+def _extract_ints(text: str) -> List[int]:
+    return [int(x) for x in re.findall(r"\b\d{1,3}\b", text) if 0 <= int(x) <= 100]
+
+def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
+    p = (prediction or "").lower().strip()
+    words_ok = len(p.split()) <= 360
+
+    pathway_ok = any(x in p for x in PATHWAYS)
+    nums = _extract_ints(p)
+    has_dist = len(nums) >= 1
+
+    entry_ref = any(k in p for k in ["entry", "signature", "early", "onset", "phase"])
+    basin_ref = "basin" in p
+    evidence_ref = any(k in p for k in ["because", "shown by", "align", "matches", "differs"])
+
+    raw = (
+        0.25 * int(words_ok) +
+        0.30 * int(pathway_ok) +
+        0.20 * int(has_dist) +
+        0.10 * int(entry_ref) +
+        0.10 * int(basin_ref) +
+        0.05 * int(evidence_ref)
+    )
+    return ScoreResult(score=min(1.0, raw), details={"pathway_ok": pathway_ok, "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)}