scorer.py

from dataclasses import dataclass
from typing import Dict, Any, List

@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()) <= 700

    has_subgroup = "subgroup" in p or "responder" in p
    has_non = "non" in p and "responder" in p
    has_gate = "gate" in p or "context" in p or "only if" in p
    has_class = any(k in p for k in ["masked", "stage", "adherence", "toxicity", "context"])
    has_salvage = "salvage" in p or "niche" in p or "use in" in p
    has_plan = "plan" in p or "trial" in p or "verify" in p

    raw = (
        0.15 * int(words_ok) +
        0.25 * int(has_subgroup) +
        0.10 * int(has_non) +
        0.20 * int(has_gate) +
        0.10 * int(has_class) +
        0.10 * int(has_salvage) +
        0.10 * int(has_plan)
    )
    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)}