"""评估正演代理模型集成并估计预测不确定性。

脚本按多个随机种子加载同结构模型，计算集成均值预测、成员间标准差和逐样本误差，
导出不确定性-误差相关性统计、散点图和高不确定性样本，用于判断集成方差是否可作为
自动拟合候选筛选或风险提示信号。
"""
# pylint: disable=import-error,wrong-import-position
# pylint: disable=too-many-locals,too-many-arguments,too-many-positional-arguments
# pylint: disable=too-many-statements


from __future__ import annotations

import argparse
import csv
import json
import sys
from pathlib import Path

ROOT = Path(__file__).resolve().parents[1]
sys.path.append(str(ROOT))

import joblib
import matplotlib.pyplot as plt
import numpy as np
import torch

from src.common.experiment_paths import normalize_tag, processed_path_for_tag
from src.models.forward_surrogate import ForwardSurrogate


def parse_seed_list(seed_text: str) -> list[int]:
    """解析逗号分隔的随机种子列表，用于训练或评估模型集成。"""
    seeds = []
    for item in str(seed_text).split(","):
        item = item.strip()
        if not item:
            continue
        seeds.append(int(item))
    if not seeds:
        raise ValueError("至少需要一个 seed")
    return seeds


def default_model_root(tag: str | None, use_schedule: bool) -> Path:
    """根据实验标签推导集成模型成员所在的根目录。"""
    suffix = "" if use_schedule else "_no_schedule"
    if tag:
        return Path("models") / f"forward_surrogate_{tag}_ensemble{suffix}"
    return Path("models") / f"forward_surrogate_ensemble{suffix}"


def default_output_dir(tag: str | None, use_schedule: bool) -> Path:
    """根据实验标签生成当前分析脚本默认的输出目录。"""
    suffix = "" if use_schedule else "_no_schedule"
    if tag:
        return Path("results") / f"evaluation_{tag}_ensemble_uq{suffix}"
    return Path("results") / f"evaluation_ensemble_uq{suffix}"


def parse_args() -> argparse.Namespace:
    """解析深度集成评估所需的数据、成员模型清单、样本数量和绘图数量。"""
    parser = argparse.ArgumentParser(description="Evaluate deep-ensemble UQ for forward surrogate")
    parser.add_argument("--processed", type=str, default=None, help="Processed dataset path")
    parser.add_argument("--tag", type=str, default=None, help="Experiment tag")
    parser.add_argument(
        "--model-root",
        type=str,
        default=None,
        help="Root dir that contains seed_* members",
    )
    parser.add_argument("--output-dir", type=str, default=None, help="Evaluation output dir")
    parser.add_argument("--seeds", type=str, default="41,42,43", help="Comma-separated seed list")
    parser.add_argument("--no-schedule", action="store_true")
    parser.add_argument("--n-top-uncertain-plots", type=int, default=5)
    return parser.parse_args()


def calc_metrics(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    eps_range: float = 1e-3,
    eps_var: float = 1e-6,
) -> dict:
    """计算 RMSE、MAE、Bias、NRMSE、R2 等回归指标。"""
    err = y_pred - y_true
    mse = np.mean(err**2)
    rmse = float(np.sqrt(mse))
    mae = float(np.mean(np.abs(err)))
    bias = float(np.mean(err))
    value_range = float(np.max(y_true) - np.min(y_true))
    ss_tot = float(np.sum((y_true - np.mean(y_true)) ** 2))
    ss_res = float(np.sum(err**2))
    valid_nrmse = value_range > eps_range
    valid_r2 = ss_tot > eps_var
    nrmse = float(rmse / value_range) if valid_nrmse else np.nan
    r2 = float(1.0 - ss_res / ss_tot) if valid_r2 else np.nan
    return {
        "rmse": rmse,
        "mae": mae,
        "bias": bias,
        "abs_bias": float(abs(bias)),
        "nrmse": nrmse,
        "r2": r2,
        "valid_nrmse": bool(valid_nrmse),
        "valid_r2": bool(valid_r2),
    }


def infer_curve_layout(meta: dict, curve_dim: int) -> dict:
    """从元数据读取曲线分段布局；旧数据没有布局时按压力/导数/斜率三等分回退。"""
    curve_layout = meta.get("curve_layout")
    if curve_layout is not None:
        return curve_layout
    n_time_points = curve_dim // 3
    return {
        "n_time_points": int(n_time_points),
        "parts": [
            {"name": "log_pressure", "start": 0, "end": n_time_points},
            {"name": "log_derivative", "start": n_time_points, "end": 2 * n_time_points},
            {"name": "slope", "start": 2 * n_time_points, "end": 3 * n_time_points},
        ],
    }


def split_curve_by_layout(curve: np.ndarray, layout: dict) -> dict[str, np.ndarray]:
    """按照 curve_layout 将拼接曲线拆成 log_pressure、log_derivative 和 slope 三段。"""
    parts: dict[str, np.ndarray] = {}
    for part in layout["parts"]:
        start = int(part["start"])
        end = int(part["end"])
        parts[str(part["name"])] = curve[start:end]
    return parts


def load_member(checkpoint_path: Path, device: torch.device) -> tuple[ForwardSurrogate, dict]:
    """加载集成中的一个模型成员、对应归一化器和模型配置。"""
    checkpoint = torch.load(checkpoint_path, map_location="cpu")
    model = ForwardSurrogate(
        param_dim=int(checkpoint["param_dim"]),
        schedule_dim=int(checkpoint["schedule_dim"]),
        curve_dim=int(checkpoint["curve_dim"]),
        hidden_dim=int(checkpoint["hidden_dim"]),
        dropout=float(checkpoint["dropout"]),
        use_schedule=bool(checkpoint.get("use_schedule", True)),
    ).to(device)
    model.load_state_dict(checkpoint["model_state_dict"])
    model.eval()
    return model, checkpoint


def safe_mean(x: np.ndarray) -> float:
    """计算忽略 NaN 后的均值；没有有效数据时返回 NaN。"""
    return float(np.mean(x)) if x.size else np.nan


def safe_median(x: np.ndarray) -> float:
    """计算忽略 NaN 后的中位数；没有有效数据时返回 NaN。"""
    return float(np.median(x)) if x.size else np.nan


def safe_percentile(x: np.ndarray, q: float) -> float:
    """计算忽略 NaN 后的百分位数；没有有效数据时返回 NaN。"""
    return float(np.percentile(x, q)) if x.size else np.nan


def pearson_corr(x: np.ndarray, y: np.ndarray) -> float:
    """计算 Pearson 相关系数；样本数不足或方差为零时返回 NaN。"""
    if x.size == 0 or y.size == 0:
        return np.nan
    if np.allclose(np.std(x), 0.0) or np.allclose(np.std(y), 0.0):
        return np.nan
    return float(np.corrcoef(x, y)[0, 1])


def plot_uncertainty_scatter(sample_rows: list[dict], output_path: Path) -> None:
    """绘制预测不确定性与真实误差的散点图，检查二者是否相关。"""
    rmse = np.array([row["overall_rmse"] for row in sample_rows], dtype=np.float64)
    unc = np.array([row["unc_mean_std"] for row in sample_rows], dtype=np.float64)
    plt.figure(figsize=(7, 5))
    plt.scatter(unc, rmse, s=10, alpha=0.35)
    plt.xlabel("Predictive Uncertainty (mean std)")
    plt.ylabel("Overall RMSE")
    plt.title(f"Uncertainty vs Error | Pearson={pearson_corr(unc, rmse):.4f}")
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig(output_path, dpi=150, bbox_inches="tight")
    plt.close()


def plot_uncertain_sample(
    idx: int,
    curve_true: np.ndarray,
    curve_mean: np.ndarray,
    curve_std: np.ndarray,
    curve_layout: dict,
    output_dir: Path,
    unc_score: float,
    rmse: float,
) -> None:
    """绘制高不确定性样本的真实曲线、集成均值和成员分布。"""
    true_parts = split_curve_by_layout(curve_true, curve_layout)
    mean_parts = split_curve_by_layout(curve_mean, curve_layout)
    std_parts = split_curve_by_layout(curve_std, curve_layout)

    title_map = {
        "log_pressure": "Log Pressure",
        "log_derivative": "Log |Derivative|",
        "slope": "Slope of Log Pressure vs Log Time",
    }

    fig, axes = plt.subplots(3, 1, figsize=(12, 10))
    fig.suptitle(
        f"High-Uncertainty Sample #{idx} | unc_mean_std={unc_score:.4f}, overall_rmse={rmse:.4f}"
    )

    for ax, name in zip(axes, ["log_pressure", "log_derivative", "slope"]):
        y_true = true_parts[name]
        y_mean = mean_parts[name]
        y_std = std_parts[name]
        x = np.arange(len(y_true))
        ax.plot(x, y_true, label="True", linewidth=2)
        ax.plot(x, y_mean, label="Ensemble mean", linewidth=2)
        ax.fill_between(
            x,
            y_mean - 2.0 * y_std,
            y_mean + 2.0 * y_std,
            alpha=0.2,
            label="mean ± 2 std",
        )
        ax.set_title(title_map[name])
        ax.grid(True, alpha=0.3)
        ax.legend()

    plt.tight_layout(rect=[0, 0, 1, 0.96])
    plt.savefig(output_dir / f"top_uncertain_sample_{idx:04d}.png", dpi=150, bbox_inches="tight")
    plt.close()


def main() -> None:
    """汇总多个代理模型成员的均值和方差，用于测试集误差与不确定性评估。"""
    args = parse_args()
    tag = normalize_tag(args.tag)
    use_schedule = not args.no_schedule
    seeds = parse_seed_list(args.seeds)

    processed_path = (
        Path(args.processed)
        if args.processed is not None
        else processed_path_for_tag(tag)
    )
    model_root = (
        Path(args.model_root)
        if args.model_root is not None
        else default_model_root(tag, use_schedule)
    )
    output_dir = (
        Path(args.output_dir)
        if args.output_dir is not None
        else default_output_dir(tag, use_schedule)
    )
    output_dir.mkdir(parents=True, exist_ok=True)

    # 集成评估必须使用同一份 processed 数据，保证各成员输入标准化口径一致。
    data = joblib.load(processed_path)
    x_params_test = data["X_params_test"]
    x_schedule_test = data["X_schedule_test"]
    y_curve_test = data["Y_curve_test"]
    scaler_curve = data["scaler_curve"]
    curve_layout = infer_curve_layout(data["meta"], int(data["meta"]["curve_dim"]))

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    members: list[tuple[int, ForwardSurrogate]] = []
    member_paths = []
    first_use_schedule = None

    for seed in seeds:
        ckpt_path = model_root / f"seed_{seed}" / "forward_surrogate_best.pt"
        model, checkpoint = load_member(ckpt_path, device)
        member_paths.append(str(ckpt_path))
        members.append((seed, model))
        cur_use_schedule = bool(checkpoint.get("use_schedule", True))
        if first_use_schedule is None:
            first_use_schedule = cur_use_schedule
        elif first_use_schedule != cur_use_schedule:
            # 混用带/不带 schedule 的模型会导致输入含义不同，不能直接求均值和方差。
            raise RuntimeError("Ensemble 成员的 use_schedule 设置不一致")

    all_true = []
    all_mean = []
    all_std = []
    sample_rows = []

    with torch.no_grad():
        for idx in range(len(x_params_test)):
            params_t = torch.tensor(
                x_params_test[idx : idx + 1],
                dtype=torch.float32,
                device=device,
            )
            schedule_t = torch.tensor(
                x_schedule_test[idx : idx + 1],
                dtype=torch.float32,
                device=device,
            )
            member_preds = []
            for _, model in members:
                # 每个成员独立预测后先反标准化；集成均值和标准差都在原始曲线尺度上计算。
                if first_use_schedule:
                    pred_scaled = model(params_t, schedule_t).cpu().numpy()
                else:
                    pred_scaled = model(params_t, None).cpu().numpy()
                pred = scaler_curve.inverse_transform(pred_scaled)[0].astype(np.float32)
                member_preds.append(pred)

            member_preds = np.stack(member_preds, axis=0)
            curve_true = scaler_curve.inverse_transform(
                y_curve_test[idx : idx + 1]
            )[0].astype(np.float32)
            curve_mean = member_preds.mean(axis=0).astype(np.float32)
            curve_std = member_preds.std(axis=0, ddof=0).astype(np.float32)

            metrics = calc_metrics(curve_true, curve_mean)
            parts_std = split_curve_by_layout(curve_std, curve_layout)

            # std 作为经验不确定性指标，后续会和真实 RMSE 做相关性分析。
            sample_rows.append(
                {
                    "idx": idx,
                    "overall_rmse": metrics["rmse"],
                    "overall_mae": metrics["mae"],
                    "overall_bias": metrics["bias"],
                    "overall_r2": metrics["r2"],
                    "unc_mean_std": float(np.mean(curve_std)),
                    "unc_max_std": float(np.max(curve_std)),
                    "unc_log_pressure_mean_std": float(np.mean(parts_std["log_pressure"])),
                    "unc_log_derivative_mean_std": float(np.mean(parts_std["log_derivative"])),
                    "unc_slope_mean_std": float(np.mean(parts_std["slope"])),
                }
            )

            all_true.append(curve_true)
            all_mean.append(curve_mean)
            all_std.append(curve_std)

    all_true = np.stack(all_true, axis=0)
    all_mean = np.stack(all_mean, axis=0)
    all_std = np.stack(all_std, axis=0)

    overall_metrics = [calc_metrics(t, p) for t, p in zip(all_true, all_mean)]
    rmse = np.array([m["rmse"] for m in overall_metrics], dtype=np.float64)
    mae = np.array([m["mae"] for m in overall_metrics], dtype=np.float64)
    r2_valid = np.array([m["r2"] for m in overall_metrics if m["valid_r2"]], dtype=np.float64)
    unc = np.array([row["unc_mean_std"] for row in sample_rows], dtype=np.float64)

    # 聚合输出分为预测质量和不确定性质量两部分，方便单独比较 ensemble 是否有价值。
    summary = {
        "ensemble": {
            "member_count": len(members),
            "member_paths": member_paths,
            "use_schedule": bool(first_use_schedule),
            "processed_path": str(processed_path),
        },
        "prediction": {
            "rmse_mean": safe_mean(rmse),
            "rmse_median": safe_median(rmse),
            "rmse_p90": safe_percentile(rmse, 90),
            "mae_mean": safe_mean(mae),
            "mae_median": safe_median(mae),
            "r2_mean_valid": safe_mean(r2_valid),
            "r2_median_valid": safe_median(r2_valid),
        },
        "uncertainty": {
            "unc_mean": safe_mean(unc),
            "unc_median": safe_median(unc),
            "unc_p90": safe_percentile(unc, 90),
            "unc_vs_rmse_pearson": pearson_corr(unc, rmse),
        },
    }

    with open(output_dir / "ensemble_uq_summary.json", "w", encoding="utf-8") as f:
        json.dump(summary, f, ensure_ascii=False, indent=2)

    with open(
        output_dir / "sample_uncertainty_metrics.csv",
        "w",
        newline="",
        encoding="utf-8-sig",
    ) as f:
        writer = csv.DictWriter(f, fieldnames=list(sample_rows[0].keys()))
        writer.writeheader()
        writer.writerows(sample_rows)

    plot_uncertainty_scatter(sample_rows, output_dir / "uncertainty_vs_error.png")

    top_k = min(args.n_top_uncertain_plots, len(sample_rows))
    top_uncertain = sorted(sample_rows, key=lambda row: row["unc_mean_std"], reverse=True)[:top_k]
    # 只绘制最高不确定性的样本，重点检查模型成员分歧最大的区域。
    for row in top_uncertain:
        idx = int(row["idx"])
        plot_uncertain_sample(
            idx=idx,
            curve_true=all_true[idx],
            curve_mean=all_mean[idx],
            curve_std=all_std[idx],
            curve_layout=curve_layout,
            output_dir=output_dir,
            unc_score=float(row["unc_mean_std"]),
            rmse=float(row["overall_rmse"]),
        )

    print("Ensemble UQ evaluation complete.")
    print(f"Output dir: {output_dir}")
    print(f"RMSE mean={summary['prediction']['rmse_mean']:.6f}")
    print(f"UQ-RMSE Pearson={summary['uncertainty']['unc_vs_rmse_pearson']:.6f}")


if __name__ == "__main__":
    main()