GPLinker_pytorch代码复现

GPLinker_pytorch是一个基于PyTorch实现的图神经网络模型，用于链接预测任务。以下是GPLinker_pytorch代码复现的步骤：

### 1. 环境准备
首先，确保你已经安装了必要的库和工具，包括PyTorch和相关的依赖项。

pip install torch torchvision

### 2. 数据准备
准备你的数据集。假设你的数据集包含节点特征、边信息和标签。你需要将数据转换为PyTorch张量。

import torch

# 示例数据
node_features = torch.randn(num_nodes, feature_dim)
edge_index = torch.tensor([[0, 1, 1, 2],
                           [1, 0, 2, 1]], dtype=torch.long)
edge_attr = torch.randn(num_edges, edge_dim)
labels = torch.tensor([0, 1, 1, 0], dtype=torch.long)

### 3. 模型定义
定义GPLinker模型。GPLinker通常包含多个图卷积层和一个链接预测层。

import torch.nn as nn
import torch.nn.functional as F

class GPLinker(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(GPLinker, self).__init__()
        self.conv1 = nn.Linear(input_dim, hidden_dim)
        self.conv2 = nn.Linear(hidden_dim, hidden_dim)
        self.link_pred = nn.Linear(hidden_dim, output_dim)
    
    def forward(self, x, edge_index):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        return self.link_pred(x)

model = GPLinker(input_dim=feature_dim, hidden_dim=hidden_dim, output_dim=num_classes)

### 4. 训练和评估
定义损失函数和优化器，并编写训练和评估循环。

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

for epoch in range(num_epochs):
    model.train()
    optimizer.zero_grad()
    out = model(node_features, edge_index)
    loss = criterion(out, labels)
    loss.backward()
    optimizer.step()
    
    if epoch % 10 == 0:
        model.eval()
        with torch.no_grad():
            out = model(node_features, edge_index)
            _, predicted = torch.max(out, dim=1)
            accuracy = (predicted == labels).sum().item() / labels.size(0)
            print(f'Epoch {epoch}, Loss: {loss.item()}, Accuracy: {accuracy}')

### 5. 保存和加载模型
训练完成后，可以保存模型参数，并在需要时加载。

# 保存模型
torch.save(model.state_dict(), 'gplinker_pytorch.pth')

# 加载模型
model = GPLinker(input_dim=feature_dim, hidden_dim=hidden_dim, output_dim=num_classes)
model.load_state_dict(torch.load('gplinker_pytorch.pth'))
model.eval()