scorer.py

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

REQ = [
    "coupling_strength_index",
    "expected_coupling_band",
    "confidence_score",
]

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

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()) <= 600

    hits = sum(1 for k in REQ if k in p)
    floats_ok = sum(1 for k in REQ if _float01(p, k))
    has_band = "-" in p or "band" in p

    raw = (
        0.30 * int(words_ok) +
        0.40 * (hits / len(REQ)) +
        0.20 * (floats_ok / len(REQ)) +
        0.10 * int(has_band)
    )

    return ScoreResult(
        score=min(1.0, raw),
        details={"id": sample.get("id"), "hits": hits, "floats_ok": floats_ok},
    )

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)}