scorer.py

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

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

def _extract_float(text, key):
    m = re.search(rf"{key}\s*[:=]\s*([0-9]*\.?[0-9]+)", text)
    return float(m.group(1)) if m else None

def _extract_int(text, key):
    m = re.search(rf"{key}\s*[:=]\s*([0-9]+)", text)
    return int(m.group(1)) if m else None

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

    drop = _extract_float(p, "correlation_drop")
    risk = _extract_float(p, "immediate_failure_risk")
    lap = _extract_int(p, "trigger_lap")

    structure_hits = sum([
        "trigger_event" in p,
        "trigger_lap" in p,
        "initiating_component" in p,
        "correlation_drop" in p,
        "immediate_failure_risk" in p
    ])

    numeric_ok = all(x is not None for x in [drop, risk])
    lap_ok = lap is not None

    raw = (
        0.25 * int(numeric_ok) +
        0.20 * int(lap_ok) +
        0.35 * (structure_hits / 5) +
        0.20 * int("trigger_event" in p)
    )

    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)
    }