scorer.py

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

REQ = [
    "failure_surface_type",
    "subgroup_affected",
    "intervention_route",
    "recalibration_needed",
    "protocol_reset_plan",
    "escalation_priority",
    "confidence_score",
]

@dataclass
class ScoreResult:
    score: float
    details: Dict[str, Any]

def _yesno(text: str, key: str) -> bool:
    return bool(re.search(rf"{key}\s*[:=]\s*(yes|no)\b", text))

def _float01(text: str, key: str) -> bool:
    return bool(re.search(rf"{key}\s*[:=]\s*(0\.\d+|1\.0)\b", text))

def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
    p = (prediction or "").lower()
    words_ok = len(p.split()) <= 1200

    hits = sum(1 for k in REQ if k in p)

    recal_ok = int(_yesno(p, "recalibration_needed"))
    conf_ok = int(_float01(p, "confidence_score"))

    priority_ok = int("escalation_priority" in p and any(x in p for x in [
        "low", "medium", "high", "critical"
    ]))

    raw = (
        0.20 * int(words_ok) +
        0.55 * (hits / len(REQ)) +
        0.10 * recal_ok +
        0.10 * conf_ok +
        0.05 * priority_ok
    )

    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)}