scorer.py

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

REQ = [
    "pure_pharmacological_effect",
    "contextual_amplification_factor",
    "nocebo_risk_index",
    "signal_separation_confidence",
    "effect_stability_score",
]

@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 score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
    p = (prediction or "").lower()
    words_ok = len(p.split()) <= 900

    hits = sum(1 for k in REQ if k in p)
    floats_ok = sum(1 for k in REQ if _float01(p, k))

    has_split_logic = ("drug" in p and "placebo" in p) or "separation" in p
    has_nocebo_logic = "nocebo" in p or "adverse" in p or "expect" in p

    raw = (
        0.25 * int(words_ok) +
        0.40 * (hits / len(REQ)) +
        0.25 * (floats_ok / len(REQ)) +
        0.05 * int(has_split_logic) +
        0.05 * int(has_nocebo_logic)
    )

    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)}