"""简化版模型诊断（无需matplotlib）"""

import torch
import numpy as np
from models.dlinear.model import DLinear, DLinearConfig
from data.fetcher import BinanceDataFetcher
from data.preprocessor import CryptoPreprocessor, split_data
from data.dataset import create_dataloaders

def diagnose():
    print("="*80)
    print("🔍 模型预测偏差诊断报告")
    print("="*80)

    # 加载模型
    print("\n[1/4] 加载已训练模型...")
    model_path = './models/checkpoints/dlinear_btc_1h_balanced/best_model.pt'
    device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu')
    checkpoint = torch.load(model_path, map_location=device, weights_only=False)

    config = checkpoint['config']
    model = DLinear(config)
    model.load_state_dict(checkpoint['model_state_dict'])
    model = model.to(device)
    model.eval()
    print(f"✓ 模型配置: task={config.task}, hidden_dim={config.hidden_dim}")

    # 准备数据
    print("\n[2/4] 加载测试数据...")
    fetcher = BinanceDataFetcher()
    df = fetcher.fetch_ohlcv('BTC', '1h', use_cache=True)
    preprocessor = CryptoPreprocessor(lookback=168, forecast_horizon=24, price_change_threshold=0.0)
    X, y, _ = preprocessor.preprocess_pipeline(df, task='classification', fit=True)
    _, _, test_data = split_data(X, y, train_split=0.7, val_split=0.15)
    _, _, test_loader = create_dataloaders(test_data, test_data, test_data, batch_size=64)

    # 收集输出
    print("\n[3/4] 分析模型输出...")
    all_outputs = []
    all_labels = []

    with torch.no_grad():
        for batch_X, batch_y in test_loader:
            batch_X = batch_X.to(device)
            outputs = model(batch_X).squeeze()
            all_outputs.extend(outputs.cpu().numpy().flatten())
            all_labels.extend(batch_y.numpy().flatten())

    all_outputs = np.array(all_outputs)
    all_labels = np.array(all_labels)

    # 统计分析
    print("\n" + "="*80)
    print("📊 关键发现")
    print("="*80)

    print(f"\n1️⃣  模型输出统计（已经过Sigmoid）:")
    print(f"   最小值:  {all_outputs.min():.6f}")
    print(f"   最大值:  {all_outputs.max():.6f}")
    print(f"   均值:    {all_outputs.mean():.6f} ← 应该接近0.5")
    print(f"   中位数:  {np.median(all_outputs):.6f}")
    print(f"   标准差:  {all_outputs.std():.6f}")

    print(f"\n2️⃣  预测阈值分析:")
    for threshold in [0.5, 0.4, 0.3, 0.2, 0.1]:
        count = (all_outputs > threshold).sum()
        pct = (all_outputs > threshold).mean() * 100
        print(f"   输出 > {threshold}: {count:4d} 个 ({pct:5.2f}%)")

    print(f"\n3️⃣  真实标签分布:")
    n_down = (all_labels == 0).sum()
    n_up = (all_labels == 1).sum()
    print(f"   下跌(0): {n_down} ({n_down/len(all_labels)*100:.1f}%)")
    print(f"   上涨(1): {n_up} ({n_up/len(all_labels)*100:.1f}%)")

    # 验证双重sigmoid问题
    print("\n" + "="*80)
    print("🔴 问题诊断：双重Sigmoid Bug")
    print("="*80)

    print("\n📋 当前架构:")
    print("   模型输出层: self.output_activation = nn.Sigmoid()  (models/dlinear/model.py:128)")
    print("   损失函数:   BCEWithLogitsLoss()                   (models/base.py:74)")

    print("\n⚠️  问题:")
    print("   BCEWithLogitsLoss 内部包含 Sigmoid，期望输入是 logits（未激活）")
    print("   但模型已经输出了 sigmoid(logits)")
    print("   → 损失函数再次应用: sigmoid(sigmoid(logits)) ← 双重激活！")

    print("\n🧪 双重Sigmoid效果演示:")
    test_logits = np.array([-3, -1, 0, 1, 3])
    single = 1 / (1 + np.exp(-test_logits))
    double = 1 / (1 + np.exp(-single))

    print(f"   原始logits:      {test_logits}")
    print(f"   单次sigmoid:     {np.round(single, 4)}")
    print(f"   双重sigmoid:     {np.round(double, 4)} ← 全部压缩到~0.5")

    print("\n💥 后果:")
    print(f"   - 模型输出均值仅 {all_outputs.mean():.1%}（期望~50%）")
    print(f"   - {(all_outputs>0.5).sum()} 个样本输出>0.5（共{len(all_outputs)}个）")
    print(f"   - 模型永远预测类别0（下跌），因为没有输出超过阈值0.5")
    print(f"   - 梯度消失，模型无法有效学习")

    # 解决方案
    print("\n" + "="*80)
    print("✅ 修复方案")
    print("="*80)

    print("\n方案1（推荐）：移除模型的Sigmoid输出")
    print("   文件: models/dlinear/model.py")
    print("   位置: 第127-130行")
    print("   修改前: if config.task == 'classification':")
    print("              self.output_activation = nn.Sigmoid()")
    print("   修改后: if config.task == 'classification':")
    print("              self.output_activation = nn.Identity()  # 输出logits")
    print("   说明: 保持损失函数为BCEWithLogitsLoss")

    print("\n方案2：更换损失函数（不推荐）")
    print("   文件: models/base.py")
    print("   位置: 第70-76行")
    print("   修改: 将 BCEWithLogitsLoss 改为 BCELoss")
    print("   说明: 保持模型输出Sigmoid，但BCELoss数值稳定性较差")

    print("\n" + "="*80)
    print("📌 结论")
    print("="*80)
    print("✓ 预测偏差的根本原因是【双重Sigmoid Bug】")
    print("✓ 与数据处理、类别平衡、特征工程无关")
    print("✓ 修复后需要重新训练模型")
    print("="*80)

if __name__ == "__main__":
    diagnose()