帮我写一段GNN预测用户违约概率的python代码

好的，以下是一段简单的GNN预测用户违约概率的Python代码，供您参考：

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

# 定义GNN模型
class GNN(nn.Module):
    def __init__(self, in_feats, hidden_feats, out_feats):
        super(GNN, self).__init__()
        self.conv1 = dgl.nn.GraphConv(in_feats, hidden_feats)
        self.conv2 = dgl.nn.GraphConv(hidden_feats, out_feats)
    
    def forward(self, g, features):
        h = self.conv1(g, features)
        h = F.relu(h)
        h = self.conv2(g, h)
        return h

# 构建图数据
g = dgl.DGLGraph()
g.add_nodes(num_nodes)  # 添加节点数量
g.add_edges(src, dst)  # 添加边

# 定义特征和标签
features = torch.randn(num_nodes, num_features)
labels = torch.tensor(y_train)

# 定义模型和优化器
model = GNN(num_features, hidden_feats, num_classes)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)

# 训练模型
model.train()
for epoch in range(num_epochs):
    logits = model(g, features)
    loss = F.cross_entropy(logits[train_mask], labels[train_mask])
    
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

# 预测测试集
model.eval()
with torch.no_grad():
    logits = model(g, features)
    pred = logits[test_mask].argmax(dim=1)
    accuracy = (pred == labels[test_mask]).float().mean().item()
    
print("Accuracy: {:.4f}".format(accuracy))

需要根据具体问题进行参数配置，例如节点数量、特征数量、隐藏层节点数量、类别数量、训练集和测试集的掩码等。同时，还需要根据具体情况处理数据、构建图数据等。