"""지표 집계 + 학습곡선 (H5, 계획서 G 지표)."""

import math
from dataclasses import dataclass
from typing import List

from eval_harness.simulator import EpisodeResult


@dataclass
class Aggregate:
    n: int
    success_rate: float
    mean_settled_ratio: float   # 평균 타결가 / 목표가
    mean_turns: float
    mean_reward: float
    reward_ci95: float          # 평균보상 95% 신뢰구간 반폭

    def as_row(self) -> dict:
        return {
            "n": self.n,
            "success_rate": round(self.success_rate, 3),
            "mean_settled_ratio": round(self.mean_settled_ratio, 3),
            "mean_turns": round(self.mean_turns, 2),
            "mean_reward": round(self.mean_reward, 4),
            "reward_ci95": round(self.reward_ci95, 4),
        }


def aggregate(results: List[EpisodeResult]) -> Aggregate:
    n = len(results)
    if n == 0:
        return Aggregate(0, 0, 0, 0, 0, 0)
    succ = sum(1 for r in results if r.success) / n
    settled = sum(r.settled_ratio for r in results) / n
    turns = sum(r.turns for r in results) / n
    rewards = [r.total_reward for r in results]
    mean_r = sum(rewards) / n
    var = sum((x - mean_r) ** 2 for x in rewards) / n if n > 1 else 0.0
    ci = 1.96 * math.sqrt(var / n) if n > 1 else 0.0
    return Aggregate(n, succ, settled, turns, mean_r, ci)


def learning_curve(results: List[EpisodeResult], buckets: int = 10) -> List[float]:
    """에피소드를 buckets 구간으로 나눠 구간별 평균 보상 (우상향이면 학습)."""
    n = len(results)
    if n == 0:
        return []
    size = max(1, n // buckets)
    curve = []
    for i in range(0, n, size):
        chunk = results[i:i + size]
        curve.append(round(sum(r.total_reward for r in chunk) / len(chunk), 4))
    return curve


def good_card_hit_rate(first_actions: List[int], good_actions: List[int], last_frac: float = 0.3) -> float:
    """후반 구간에서 첫 카드가 '좋은 카드'였던 비율 (학습 수렴 지표)."""
    if not first_actions:
        return 0.0
    tail = first_actions[max(0, int(len(first_actions) * (1 - last_frac))):]
    good = set(good_actions)
    return sum(1 for a in tail if a in good) / len(tail)


def hit_curve(first_actions: List[int], good_actions: List[int], buckets: int = 10) -> List[float]:
    """구간별 '좋은 카드 선택' 비율 (학습 전/후 개선곡선 — 세일즈용)."""
    n = len(first_actions)
    if n == 0:
        return []
    good = set(good_actions)
    size = max(1, n // buckets)
    curve = []
    for i in range(0, n, size):
        chunk = first_actions[i:i + size]
        curve.append(round(sum(1 for a in chunk if a in good) / len(chunk), 3))
    return curve