根据上述数据建立，lstm-transformer模型，不使用tansorflow帮我写出python代码

由于TensorFlow不是必需的，我们可以选择PyTorch库来构建LSTM-Transformer模型。以下是使用PyTorch实现的基本框架，不包含完整的数据预处理部分，假设你已经有了合适的数据格式（例如Pandas DataFrame）：

import torch
import torch.nn as nn
import torch.optim as optim

# 定义LSTM层
class LSTMBlock(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers):
        super(LSTMBlock, self).__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)

    def forward(self, x):
        h0, c0 = torch.zeros(num_layers, x.size(0), hidden_size).to(device), torch.zeros(num_layers, x.size(0), hidden_size).to(device)
        out, _ = self.lstm(x, (h0, c0))
        return out[:, -1, :]

# 定义Transformer模块
class TransformerBlock(nn.Module):
    def __init__(self, d_model, nhead, dim_feedforward, dropout=0.1):
        super(TransformerBlock, self).__init__()
        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)

    def forward(self, src):
        attn_output, _ = self.self_attn(src, src, src)
        attn_output = self.dropout(attn_output)
        residual = src
        src = self.linear2(self.dropout(torch.relu(self.linear1(attn_output))))
        src = src + residual
        return src

# 组合LSTM和Transformer
class LSTMTranformerModel(nn.Module):
    def __init__(self, input_size, hidden_size, transformer_hidden_size, num_layers, nhead, num_transformer_blocks):
        super().__init__()
        self.lstm = LSTMBlock(input_size, hidden_size, num_layers)
        self.transformer = nn.Sequential(*[TransformerBlock(hidden_size, nhead, transformer_hidden_size) for _ in range(num_transformer_blocks)])

    def forward(self, x):
        lstm_out = self.lstm(x)
        transformer_out = self.transformer(lstm_out.unsqueeze(1))  # Add time dimension
        return transformer_out.squeeze(1)

# 设定超参数
input_size = ...  # 根据你的输入数据特征设定
hidden_size = ...
transformer_hidden_size = ...
num_layers = ...
nhead = ...
num_transformer_blocks = ...

# 创建模型并设置设备（如GPU）
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = LSTMTranformerModel(input_size, hidden_size, transformer_hidden_size, num_layers, nhead, num_transformer_blocks).to(device)

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 假设train_loader是一个数据加载器
for data in train_loader:
    inputs, targets = data[0].to(device), data[1].to(device)
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

请注意，你需要根据实际的项目需求调整模型结构、超参数以及训练部分。此代码仅用于展示基本框架，实际应用中还需要添加数据预处理、批次处理等步骤。另外，如果你需要使用更复杂的模型，如自注意力机制，可能需要修改TransformerBlock部分。