"""评估已训练的DLinear模型"""

import sys
import os
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))

import torch
import json
from data.fetcher import BinanceDataFetcher
from data.preprocessor import CryptoPreprocessor, split_data
from data.dataset import create_dataloaders
from models.dlinear.model import DLinear
from training.evaluator import evaluate_model

def main():
    print("="*80)
    print("评估DLinear模型（真实数据）")
    print("="*80)

    # 加载数据
    print("\n加载数据...")
    fetcher = BinanceDataFetcher()
    df = fetcher.fetch_ohlcv('BTC', '1h', use_cache=True)

    preprocessor = CryptoPreprocessor(lookback=168, forecast_horizon=24)
    X, y, _ = preprocessor.preprocess_pipeline(df, task='classification', fit=True)

    train_data, val_data, test_data = split_data(X, y)
    _, _, test_loader = create_dataloaders(
        train_data, val_data, test_data,
        task='classification', batch_size=64
    )

    # 加载模型
    print("加载最佳模型...")
    model_path = './models/checkpoints/dlinear_btc_1h_real/best_model.pt'
    checkpoint = torch.load(model_path, map_location='cpu')
    model = DLinear(checkpoint['config'])
    model.load_state_dict(checkpoint['model_state_dict'])

    # 评估
    print("\n在测试集上评估...")
    device = 'mps' if torch.backends.mps.is_available() else 'cpu'
    metrics = evaluate_model(model, test_loader, 'classification', device)

    # 保存结果
    results_path = './models/checkpoints/dlinear_btc_1h_real/test_metrics.json'
    with open(results_path, 'w') as f:
        json.dump(metrics, f, indent=2)

    # 打印结果
    print("\n" + "="*80)
    print("测试集评估结果")
    print("="*80)
    print(f"\n📊 分类指标:")
    print(f"  准确率:  {metrics.get('accuracy', 0):.4f}")
    print(f"  F1分数:  {metrics.get('f1', 0):.4f}")
    print(f"  精确率:  {metrics.get('precision', 0):.4f}")
    print(f"  召回率:  {metrics.get('recall', 0):.4f}")

    if 'true_positives' in metrics:
        print(f"\n📈 混淆矩阵:")
        print(f"  真阳性:  {metrics['true_positives']}")
        print(f"  真阴性:  {metrics['true_negatives']}")
        print(f"  假阳性:  {metrics['false_positives']}")
        print(f"  假阴性:  {metrics['false_negatives']}")

    print(f"\n💰 回测结果:")
    print(f"  总收益率:    {metrics.get('backtest_total_return', 0)*100:.2f}%")
    print(f"  最终资金:    ${metrics.get('backtest_final_capital', 0):,.2f}")
    print(f"  夏普比率:    {metrics.get('backtest_sharpe_ratio', 0):.4f}")
    print(f"  最大回撤:    {metrics.get('backtest_max_drawdown', 0)*100:.2f}%")
    print(f"  胜率:        {metrics.get('backtest_win_rate', 0)*100:.2f}%")
    print(f"  交易次数:    {metrics.get('backtest_num_trades', 0)}")

    print("\n" + "="*80)
    print(f"✓ 结果已保存至：{results_path}")
    print("="*80)

if __name__ == "__main__":
    main()