scorer.py

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

REQ = [
    "optimal_endpoint_mix",
    "rwe_generation_plan",
    "pricing_stability_zone",
    "access_probability",
    "restriction_burden_forecast",
    "coverage_stability_horizon",
    "confidence_score",
]

ZONES = ["wide", "moderate", "narrow"]

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

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

def _zone_ok(p: str) -> bool:
    m = re.search(r"pricing_stability_zone\s*[:=]\s*([a-z\-]+)", p)
    return bool(m and m.group(1) in ZONES)

def _horizon_ok(p: str) -> bool:
    return bool(re.search(r"coverage_stability_horizon\s*[:=]\s*\d+(\.\d+)?\s*(m|mo|mos|y|yr|yrs)\b", p))

def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
    p = (prediction or "").lower()
    words_ok = len(p.split()) <= 950

    hits = sum(1 for k in REQ if k in p)
    ap_ok = int(_float01(p, "access_probability"))
    conf_ok = int(_float01(p, "confidence_score"))
    zone_ok = int(_zone_ok(p))
    hz_ok = int(_horizon_ok(p))

    raw = (
        0.22 * int(words_ok) +
        0.40 * (hits / len(REQ)) +
        0.14 * ap_ok +
        0.08 * conf_ok +
        0.08 * zone_ok +
        0.08 * hz_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)}