import torch.nn.functional as F
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
import torch.optim as optim
from sklearn.preprocessing import StandardScaler
from torch.utils.tensorboard import SummaryWriter
from torch.cuda.amp import GradScaler, autocast
import os
from sklearn.metrics import precision_score, recall_score, f1_score
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
writer = SummaryWriter()  # 初始化 TensorBoard
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
# 自定义数据集，包含数据增强（添加噪声）
from skopt import BayesSearchCV
from skopt.space import Real, Integer
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
class MyDataset(Dataset):
    def __init__(self, specs, labels, augment=False):
        self.specs = specs
        self.labels = labels
        self.augment = augment  # 是否启用数据增强

    def __getitem__(self, index):
        spec, target = self.specs[index], self.labels[index]

        # 数据增强：在训练数据上添加随机噪声
        if self.augment:
            noise = 0.01 * torch.randn_like(spec)
            spec = spec + noise

        return spec, target

    def __len__(self):
        return len(self.specs)


# 标准化数据
def ZspPocess(X_train, X_test, y_train, y_test, need=True):
    if need:
        scaler = StandardScaler()
        X_train = scaler.fit_transform(X_train)  # fit_transform 用于训练集
        X_test = scaler.transform(X_test)        # 只对测试集应用 transform

    # 将标准化的数据转换为 Tensor
    X_train = torch.tensor(X_train[:, np.newaxis, :], dtype=torch.float32)
    X_test = torch.tensor(X_test[:, np.newaxis, :], dtype=torch.float32)
    y_train = torch.tensor(y_train, dtype=torch.long)
    y_test = torch.tensor(y_test, dtype=torch.long)

    # 使用数据增强 (augment=True) 创建训练集
    data_train = MyDataset(X_train, y_train, augment=True)
    data_test = MyDataset(X_test, y_test, augment=False)

    return data_train, data_test


# CNN 模型，添加 Dropout 层和调整 Dropout 率
class CNN3Layers(nn.Module):
    def __init__(self, nls, dropout_conv=0.3, dropout_fc=0.5):
        super(CNN3Layers, self).__init__()
        self.CONV1 = nn.Sequential(
            nn.Conv1d(1, 64, 5, 1, padding=2),
            nn.BatchNorm1d(64),
            nn.ReLU(),
            nn.MaxPool1d(2, 2),
            nn.Dropout(dropout_conv)  # 在卷积层后添加 Dropout
        )
        self.CONV2 = nn.Sequential(
            nn.Conv1d(64, 128, 5, 1, padding=2),
            nn.BatchNorm1d(128),
            nn.ReLU(),
            nn.MaxPool1d(2, 2),
            nn.Dropout(dropout_conv)  # 在卷积层后添加 Dropout
        )
        self.CONV3 = nn.Sequential(
            nn.Conv1d(128, 256, 3, 1, padding=1),
            nn.BatchNorm1d(256),
            nn.ReLU(),
            nn.AdaptiveMaxPool1d(1),
            nn.Dropout(dropout_conv)  # 在卷积层后添加 Dropout
        )
        self.fc = nn.Sequential(
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Dropout(dropout_fc),  # 全连接层中的 Dropout
            nn.Linear(128, nls)
        )

    def forward(self, x):
        x = self.CONV1(x)
        x = self.CONV2(x)
        x = self.CONV3(x)
        x = x.view(x.size(0), -1)
        out = self.fc(x)
        return out


# 训练函数
def CNNTrain(X_train, X_test, y_train, y_test, BATCH_SIZE, n_epochs, nls, model_path):
    data_train, data_test = ZspPocess(X_train, X_test, y_train, y_test, need=True)
    train_loader = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=True)
    test_loader = torch.utils.data.DataLoader(data_test, batch_size=BATCH_SIZE, shuffle=False)

    model = CNN3Layers(nls=nls, dropout_conv=0.3, dropout_fc=0.5).to(device)
    optimizer = optim.Adam(model.parameters(), lr=0.0001, weight_decay=0.001)
    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', factor=0.5, patience=5)
    criterion = nn.CrossEntropyLoss().to(device)
    scaler = GradScaler()

    best_acc = 0.0
    model_save_path = model_path

    for epoch in range(n_epochs):
        model.train()
        train_acc, train_loss = [], []

        for i, data in enumerate(train_loader):
            inputs, labels = data
            inputs = inputs.to(device).float()
            labels = labels.to(device).long()

            optimizer.zero_grad()

            with autocast():
                outputs = model(inputs)
                loss = criterion(outputs, labels)

            scaler.scale(loss).backward()
            scaler.step(optimizer)
            scaler.update()

            _, predicted = torch.max(outputs.data, 1)
            acc = accuracy_score(labels.cpu(), predicted.cpu())
            train_acc.append(acc)
            train_loss.append(loss.item())

        avg_train_loss = np.mean(train_loss)
        avg_train_acc = np.mean(train_acc)

        writer.add_scalar('Loss/train', avg_train_loss, epoch)
        writer.add_scalar('Accuracy/train', avg_train_acc, epoch)

        # 测试集评估
        model.eval()
        test_acc, test_loss, test_precision, test_recall, test_f1 = [], [], [], [], []
        y_true, y_pred = [], []
        with torch.no_grad():
            for data in test_loader:
                inputs, labels = data
                inputs = inputs.to(device).float()
                labels = labels.to(device).long()

                with autocast():
                    outputs = model(inputs)
                    loss = criterion(outputs, labels)

                _, predicted = torch.max(outputs.data, 1)
                acc = accuracy_score(labels.cpu(), predicted.cpu())
                precision = precision_score(labels.cpu(), predicted.cpu(), average='weighted', zero_division=1)
                recall = recall_score(labels.cpu(), predicted.cpu(), average='weighted', zero_division=1)
                f1 = f1_score(labels.cpu(), predicted.cpu(), average='weighted', zero_division=1)

                y_true.extend(labels.cpu().numpy())
                y_pred.extend(predicted.cpu().numpy())

                test_acc.append(acc)
                test_loss.append(loss.item())
                test_precision.append(precision)
                test_recall.append(recall)
                test_f1.append(f1)

        avg_test_loss = np.mean(test_loss)
        avg_test_acc = np.mean(test_acc)
        avg_test_precision = np.mean(test_precision)
        avg_test_recall = np.mean(test_recall)
        avg_test_f1 = np.mean(test_f1)

        writer.add_scalar('Loss/test', avg_test_loss, epoch)
        writer.add_scalar('Accuracy/test', avg_test_acc, epoch)
        writer.add_scalar('Precision/test', avg_test_precision, epoch)
        writer.add_scalar('Recall/test', avg_test_recall, epoch)
        writer.add_scalar('F1_Score/test', avg_test_f1, epoch)

        # 打印每个 epoch 的训练和测试结果
        print(f"Epoch [{epoch + 1}/{n_epochs}]")
        print(f"Train Loss: {avg_train_loss:.4f}, Train Accuracy: {avg_train_acc:.4f}")
        print(f"Test Loss: {avg_test_loss:.4f}, Test Accuracy: {avg_test_acc:.4f}")
        print(f"Test Precision: {avg_test_precision:.4f}, Test Recall: {avg_test_recall:.4f}, Test F1: {avg_test_f1:.4f}")

        if avg_test_acc > best_acc:
            best_acc = avg_test_acc
            torch.save(model.state_dict(), model_save_path)

        scheduler.step(avg_test_loss)

    return {
        "accuracy": avg_test_acc,
        "precision": avg_test_precision,
        "recall": avg_test_recall,
        "f1_score": avg_test_f1,
        "confusion_matrix": confusion_matrix(y_true, y_pred)
    }

# 测试函数
def CNNtest(X_test, y_test, BATCH_SIZE, nls, model_path):
    # 标准化测试数据并创建 DataLoader
    scaler = StandardScaler()
    X_test = scaler.fit_transform(X_test)  # 只对 X_test 进行标准化
    X_test = torch.tensor(X_test[:, np.newaxis, :], dtype=torch.float32)
    y_test = torch.tensor(y_test, dtype=torch.long)

    # 创建测试数据集和 DataLoader
    data_test = MyDataset(X_test, y_test, augment=False)
    test_loader = torch.utils.data.DataLoader(data_test, batch_size=BATCH_SIZE, shuffle=False)

    # 加载模型结构和权重
    model = CNN3Layers(nls=nls).to(device)
    model.load_state_dict(torch.load(model_path))

    # 初始化评估指标
    y_true, y_pred = [], []

    # 测试过程
    model.eval()
    with torch.no_grad():
        for inputs, labels in test_loader:
            inputs, labels = inputs.to(device).float(), labels.to(device).long()
            outputs = model(inputs)
            _, predicted = torch.max(outputs.data, 1)

            # 收集真实标签和预测标签
            y_true.extend(labels.cpu().numpy())
            y_pred.extend(predicted.cpu().numpy())

    # 计算评估指标
    accuracy = accuracy_score(y_true, y_pred)
    precision = precision_score(y_true, y_pred, average='weighted')
    recall = recall_score(y_true, y_pred, average='weighted')
    f1 = f1_score(y_true, y_pred, average='weighted')
    cm = confusion_matrix(y_true, y_pred)

    # 返回评估结果
    return {
        "accuracy": accuracy,
        "precision": precision,
        "recall": recall,
        "f1_score": f1,
        "confusion_matrix": cm
    }


def optimize_CNN(X_train, X_test, y_train, y_test, model_path):
    # 贝叶斯优化的搜索空间
    param_space = {
        'batch_size': Integer(16, 128),  # batch size 的范围
        'n_epochs': Integer(10, 100),  # 训练 epochs 的范围
        'dropout_conv': Real(0.1, 0.5, 'uniform'),  # 卷积层 dropout 比例
        'dropout_fc': Real(0.1, 0.5, 'uniform'),  # 全连接层 dropout 比例
        'lr': Real(1e-5, 1e-2, 'log-uniform'),  # 学习率范围
    }

    # 训练模型的目标函数
    def objective(params):
        batch_size, n_epochs, dropout_conv, dropout_fc, lr = params

        # 使用给定的超参数进行训练
        train_metrics = CNNTrain(
            X_train, X_test, y_train, y_test,
            BATCH_SIZE=batch_size, n_epochs=n_epochs,
            nls=21, model_path=model_path,
        )

        # 测试模型并返回评估指标
        test_metrics = CNNtest(X_test, y_test, batch_size, nls=21, model_path=model_path)

        # 我们以测试集的 accuracy 作为优化目标
        return -test_metrics["accuracy"]  # 贝叶斯优化是最小化目标函数，所以返回负值

    # 使用贝叶斯优化进行调优
    optimizer = BayesSearchCV(
        estimator=None,  # 不使用具体的模型，这里我们将目标函数传给贝叶斯优化
        search_spaces=param_space,  # 搜索空间
        n_iter=20,  # 调优的迭代次数
        n_jobs=-1,  # 使用所有可用的 CPU 核心
        verbose=1,  # 输出优化过程
        random_state=42,  # 固定随机种子
    )

    # 进行超参数调优
    optimizer.fit(X_train, y_train)

    # 输出最优超参数
    best_params = optimizer.best_params_
    print("Best hyperparameters:", best_params)

    # 使用最优超参数训练并返回评估指标
    batch_size = best_params['batch_size']
    n_epochs = best_params['n_epochs']
    dropout_conv = best_params['dropout_conv']
    dropout_fc = best_params['dropout_fc']
    lr = best_params['lr']

    train_metrics = CNNTrain(
        X_train, X_test, y_train, y_test,
        BATCH_SIZE=batch_size, n_epochs=n_epochs,
        nls=21, model_path=model_path,
    )

    test_metrics = CNNtest(X_test, y_test, batch_size, nls=21, model_path=model_path)

    # 返回训练和测试的评估结果
    return best_params, train_metrics, test_metrics