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()) <= 420

    has_disc = "discovery" in p or "cohort" in p
    has_target = "target" in p or "population" in p
    has_failure = any(k in p for k in ["fail","break","collapse","invalid"])
    has_missing = "no" in p and "data" in p or "missing" in p
    has_risk = any(k in p for k in ["harm","risk","misclass","inequit"])

    raw = (
        0.20 * int(words_ok) +
        0.25 * int(has_disc) +
        0.25 * int(has_target) +
        0.20 * int(has_failure) +
        0.10 * int(has_risk)
    )
    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)}