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

REQ = [
    "optimal_context_shift_set",
    "projected_response_gain",
    "placebo_amplification_ceiling",
    "nocebo_suppression_gain",
    "context_sensitivity_index",
    "forecast_confidence",
]

@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_context_logic = "context" in p or "expect" in p or "clinician" in p
    has_gain_logic = "gain" in p or "increase" in p or "optimiz" in p

    raw = (
        0.25 * int(words_ok) +
        0.40 * (hits / len(REQ)) +
        0.25 * (floats_ok / len(REQ)) +
        0.05 * int(has_context_logic) +
        0.05 * int(has_gain_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)}