已有训练集1000*32、训练标签1000*1和测试集100*32、测试标签100*1的.csv文件，写一个ARMA卷积图卷积神经网络的python的torch代码

首先，我们需要导入必要的库：

import torch
import torch.nn as nn
import pandas as pd
import numpy as np

接着，我们需要读取训练集和测试集的csv文件，并将数据转换为PyTorch张量：

train_data = pd.read_csv("train_data.csv")
train_labels = pd.read_csv("train_labels.csv")
test_data = pd.read_csv("test_data.csv")
test_labels = pd.read_csv("test_labels.csv")

train_data = torch.tensor(train_data.values)
train_labels = torch.tensor(train_labels.values)
test_data = torch.tensor(test_data.values)
test_labels = torch.tensor(test_labels.values)

然后，我们可以定义一个ARMA卷积层：

class ARMAConv1d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, p, q):
        super(ARMAConv1d, self).__init__()

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.p = p
        self.q = q

        self.ar_coef = nn.Parameter(torch.randn(out_channels, p))
        self.ma_coef = nn.Parameter(torch.randn(out_channels, q))
        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size)

    def forward(self, x):
        b, c, l = x.shape

        ar_coef = self.ar_coef.unsqueeze(2).expand(-1, -1, self.kernel_size)
        ma_coef = self.ma_coef.unsqueeze(2).expand(-1, -1, self.kernel_size)

        for i in range(self.kernel_size, l):
            ar_term = torch.einsum('ijk,bck->bcij', ar_coef, x[:, :, i - self.kernel_size:i])
            ma_term = torch.einsum('ijk,bck->bcij', ma_coef, x[:, :, i - self.kernel_size:i])
            x[:, :, i] = torch.relu(self.conv(x[:, :, i-self.kernel_size:i]) + ar_term + ma_term)

        return x

该层包含一个ARMA滤波器和一个一维卷积层。在前向传递过程中，我们将ARMA滤波器应用于输入数据，然后将其送入卷积层中。

接下来，我们可以定义一个包含两个ARMA卷积层和一个全连接层的神经网络：

class ARMACNN(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, p, q, hidden_units):
        super(ARMACNN, self).__init__()

        self.conv1 = ARMAConv1d(in_channels, out_channels, kernel_size, p, q)
        self.conv2 = ARMAConv1d(out_channels, out_channels, kernel_size, p, q)
        self.fc = nn.Linear(out_channels, hidden_units)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.mean(dim=2)
        x = self.fc(x)

        return x

最后，我们可以定义一些超参数和训练过程：

in_channels = 32
out_channels = 64
kernel_size = 5
p = 2
q = 2
hidden_units = 10
learning_rate = 0.001
num_epochs = 100

model = ARMACNN(in_channels, out_channels, kernel_size, p, q, hidden_units)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.CrossEntropyLoss()

for epoch in range(num_epochs):
    optimizer.zero_grad()
    outputs = model(train_data.float())
    loss = criterion(outputs, train_labels.squeeze().long())
    loss.backward()
    optimizer.step()

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

with torch.no_grad():
    correct = 0
    total = 0
    outputs = model(test_data.float())
    _, predicted = torch.max(outputs.data, 1)
    total = test_labels.size(0)
    correct = (predicted == test_labels.squeeze().long()).sum().item()

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

这段代码中，我们使用Adam优化器和交叉熵损失函数进行训练，并在测试集上计算模型的准确度。

注意，这里我们需要将数据转换为浮点型张量，以便能够在模型中进行计算。

完整代码如下：