PyG框架：Graph Classification

训练GNN用来做Graph Classification

一、原理

1、根据Message Passing得到每个节点的node embedding

2、readout layer
把所有节点的node embedding聚合成整个图的graph embedding。
【文献中有很多种不同的readout layer，但最常用的是mean】

【跟Node Classification的区别】：是否把每个节点的node embedding聚合成一个graph embedding？

针对mini-batch，PyG框架有封装好的模块，torch_geometric.nn.global_mean_pool 可以分别将mini-batch中每个图的所有node embedding聚合成一个graph embedding（一个batch中有多少个图，就有多少个graph embedding）。一个batch的graph embedding矩阵的shape为：[batch_size，hidden_channels]。hidden_channels：一个graph embedding（向量）的长度

3、训练一个针对graph embedding的分类器

二、代码实现

PyG框架是什么？如何安装？可以参照官方文档or我的上一篇博客：https://blog.csdn.net/qq_38432089/article/details/122152640?spm=1001.2014.3001.5501
1、数据集准备

import torch
from torch_geometric.datasets import TUDataset# 1、数据集下载
dataset = TUDataset(root='data/TUDataset', name='MUTAG')# 查看数据集信息
print()
print(f'Dataset:{dataset}:')
print('====================')
print(f'Number of graphs:{len(dataset)}')
print(f'Number of features:{dataset.num_features}')
print(f'Number of classes:{dataset.num_classes}')data = dataset[0]  # Get the first graph object.print()
print(data)
print('=============================================================')# Gather some statistics about the first graph.
print(f'Number of nodes:{data.num_nodes}')
print(f'Number of edges:{data.num_edges}')
print(f'Average node degree:{data.num_edges / data.num_nodes:.2f}')
print(f'Has isolated nodes:{data.has_isolated_nodes()}')
print(f'Has self-loops:{data.has_self_loops()}')
print(f'Is undirected:{data.is_undirected()}')

# 2、训练集、测试集准备
torch.manual_seed(12345)
dataset = dataset.shuffle()
train_dataset = dataset[:150]
test_dataset = dataset[150:]
# 训练集、测试集数量
print(f'Number of training graphs:{len(train_dataset)}')
print(f'Number of test graphs:{len(test_dataset)}')

# 3、mini-batch
from torch_geometric.loader import DataLoadertrain_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)# 查看每个batch的信息：2⋅64+22=150 graphs.
for step, data in enumerate(train_loader):print(f'Step{step + 1}:')print('=======')print(f'Number of graphs in the current batch:{data.num_graphs}')print(data)print()

2、模型搭建

from torch.nn import Linear
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
from torch_geometric.nn import global_mean_poolclass GCN(torch.nn.Module):def __init__(self, hidden_channels):super(GCN, self).__init__()torch.manual_seed(12345)self.conv1 = GCNConv(dataset.num_node_features, hidden_channels)self.conv2 = GCNConv(hidden_channels, hidden_channels)self.conv3 = GCNConv(hidden_channels, hidden_channels)self.lin = Linear(hidden_channels, dataset.num_classes)def forward(self, x, edge_index, batch):# 1. Obtain node embeddingsx = self.conv1(x, edge_index)x = x.relu()x = self.conv2(x, edge_index)x = x.relu()x = self.conv3(x, edge_index)# 2. Readout layerx = global_mean_pool(x, batch)  # [batch_size, hidden_channels]# 3. Apply a final classifierx = F.dropout(x, p=0.5, training=self.training)x = self.lin(x)return xmodel = GCN(hidden_channels=64)
print(model)

model = GCN(hidden_channels=64)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
criterion = torch.nn.CrossEntropyLoss()def train():model.train()for data in train_loader:  # Iterate in batches over the training dataset.out = model(data.x, data.edge_index, data.batch)  # Perform a single forward pass.loss = criterion(out, data.y)  # Compute the loss.loss.backward()  # Derive gradients.optimizer.step()  # Update parameters based on gradients.optimizer.zero_grad()  # Clear gradients.def test(loader):model.eval()correct = 0for data in loader:  # Iterate in batches over the training/test dataset.out = model(data.x, data.edge_index, data.batch)  pred = out.argmax(dim=1)  # Use the class with highest probability.correct += int((pred == data.y).sum())  # Check against ground-truth labels.return correct / len(loader.dataset)  # Derive ratio of correct predictions.for epoch in range(1, 171):train()train_acc = test(train_loader)test_acc = test(test_loader)print(f'Epoch:{epoch:03d}, Train Acc:{train_acc:.4f}, Test Acc:{test_acc:.4f}')

运行结果：