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

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

def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
    p = (prediction or "").lower()
    words_ok = len(p.split()) <= 520

    has_vector = "drift" in p or "fingerprint" in p
    has_axes = "axis" in p or "system" in p
    has_time = "temporal" in p or "onset" in p or "offset" in p
    has_reversible = "reversible" in p or "rebound" in p
    has_coherence = "coherence" in p or "net" in p

    raw = (
        0.15 * int(words_ok) +
        0.25 * int(has_vector) +
        0.20 * int(has_axes) +
        0.20 * int(has_time) +
        0.10 * int(has_reversible) +
        0.10 * int(has_coherence)
    )
    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)}