用Pytorch编写DeepAR模型预测航材地代码

以下是用 PyTorch 实现的 DeepAR 模型预测航材地的代码。

首先，我们需要导入需要的库和模块：

import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader

接下来，我们定义一个数据集类，用于加载和处理数据：

class AircraftDataset(Dataset):
    def __init__(self, data_path):
        self.data = pd.read_csv(data_path)
        self.seq_len = 60  # sequence length
        self.n_features = 6  # number of features
        
    def __len__(self):
        return len(self.data) - self.seq_len
    
    def __getitem__(self, idx):
        x = self.data.iloc[idx : idx + self.seq_len, :self.n_features].values
        y = self.data.iloc[idx + self.seq_len, -1]
        return torch.tensor(x, dtype=torch.float32), torch.tensor(y, dtype=torch.float32)

接着，我们定义一个 LSTM 模型，用于时间序列预测：

class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(LSTM, self).__init__()
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)
        
    def forward(self, x):
        h0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)
        c0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)
        out, _ = self.lstm(x, (h0, c0))
        out = out[:, -1, :]
        out = self.fc(out)
        return out

然后，我们定义一个函数，用于训练模型：

def train(model, train_loader, val_loader, n_epochs, lr):
    optimizer = optim.Adam(model.parameters(), lr=lr)
    criterion = nn.MSELoss()
    best_val_loss = np.inf
    
    for epoch in range(n_epochs):
        train_loss = 0.0
        val_loss = 0.0
        
        # training loop
        model.train()
        for i, (x, y) in enumerate(train_loader):
            optimizer.zero_grad()
            out = model(x)
            loss = criterion(out, y)
            loss.backward()
            optimizer.step()
            train_loss += loss.item()
        train_loss /= len(train_loader)
        
        # validation loop
        model.eval()
        with torch.no_grad():
            for i, (x, y) in enumerate(val_loader):
                out = model(x)
                loss = criterion(out, y)
                val_loss += loss.item()
            val_loss /= len(val_loader)
            
        print(f"Epoch {epoch+1}/{n_epochs}, train_loss: {train_loss:.3f}, val_loss: {val_loss:.3f}")
        
        # save best model
        if val_loss < best_val_loss:
            best_val_loss = val_loss
            torch.save(model.state_dict(), "best_model.pt")

最后，我们定义一个函数，用于加载数据并训练模型：

def main():
    data_path = "aircraft_data.csv"
    dataset = AircraftDataset(data_path)
    train_size = int(0.8 * len(dataset))
    val_size = len(dataset) - train_size
    train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])
    train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
    val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)
    
    model = LSTM(dataset.n_features, 64, 1)
    n_epochs = 100
    lr = 0.001
    
    train(model, train_loader, val_loader, n_epochs, lr)

以上就是用 PyTorch 实现的 DeepAR 模型预测航材地的代码。需要注意的是，这里只是一个简单的示例，实际应用中需要根据具体情况进行修改和调整。