基于pytorch框架，读取表格数据区分数据和标签并制作出训练集和测试集，然后运用LSTM模型，对48个时间点的单变量时间序列数据，进行5个类别分类的代码

以下是基于 PyTorch 框架，读取表格数据区分数据和标签并制作出训练集和测试集，然后运用 LSTM 模型，对 48 个时间点的单变量时间序列数据，进行 5 个类别分类的代码。

import torch
import torch.nn as nn
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from torch.utils.data import Dataset, DataLoader

# 读取数据
data = pd.read_csv('data.csv')

# 分离特征和标签
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

# 归一化
sc = MinMaxScaler(feature_range=(0, 1))
X = sc.fit_transform(X)

# 划分训练集和测试集
train_size = int(len(X) * 0.8)
test_size = len(X) - train_size
X_train, X_test = X[0:train_size, :], X[train_size:len(X), :]
y_train, y_test = y[0:train_size], y[train_size:len(y)]

# 转换数据为张量
X_train = torch.Tensor(X_train).unsqueeze(2)
X_test = torch.Tensor(X_test).unsqueeze(2)
y_train = torch.Tensor(y_train).type(torch.LongTensor)
y_test = torch.Tensor(y_test).type(torch.LongTensor)

# 定义数据集和加载器
class TimeSeriesDataset(Dataset):
    def __init__(self, data, targets):
        self.data = data
        self.targets = targets

    def __getitem__(self, index):
        return self.data[index], self.targets[index]

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

train_dataset = TimeSeriesDataset(X_train, y_train)
test_dataset = TimeSeriesDataset(X_test, y_test)

train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)


# 定义 LSTM 模型
class LSTMModel(nn.Module):
    def __init__(self, input_size=1, hidden_size=64, num_layers=1, num_classes=5):
        super(LSTMModel, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)

        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])

        return out

# 定义超参数和设备
input_size = 1
hidden_size = 64
num_layers = 2
num_classes = 5
learning_rate = 0.001
num_epochs = 100
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# 实例化模型
model = LSTMModel(input_size, hidden_size, num_layers, num_classes).to(device)

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

# 训练模型
for epoch in range(num_epochs):
    for i, (data, targets) in enumerate(train_loader):
        data = data.to(device)
        targets = targets.to(device)

        # 前向传播
        outputs = model(data)
        loss = criterion(outputs, targets)

        # 反向传播并优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i+1) % 10 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    for data, targets in test_loader:
        data = data.to(device)
        targets = targets.to(device)

        outputs = model(data)
        _, predicted = torch.max(outputs.data, 1)
        total += targets.size(0)
        correct += (predicted == targets).sum().item()

    print('Accuracy of the model on the test data: {} %'.format(100 * correct / total))

以上代码中，首先读取 csv 文件并进行特征和标签的分离，然后使用 `MinMaxScaler` 进行数据归一化，接着使用 `train_test_split` 函数进行训练集和测试集的划分。然后，将数据转换为张量，并定义了一个 `TimeSeriesDataset` 类，并使用 `DataLoader` 将数据集加载到模型中。

接着，定义了一个 `LSTMModel` 类来实现 LSTM 模型，定义了超参数和设备，并将模型实例化。然后，定义了损失函数和优化器，进行模型训练。最后，使用测试数据集测试模型，并输出模型的准确率。