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除了节点级别的问题——节点分类、边级别的问题——链接预测之外#xff0c;还有整个图级别的问题——图分类。经过聚合、传递消息得到节点和边的新的表征后#xff0c;映射得到整个图的表征。
数据…
目录概述数据集定义Data LoaderDGL中的batched graph定义模型训练参考概述
除了节点级别的问题——节点分类、边级别的问题——链接预测之外还有整个图级别的问题——图分类。经过聚合、传递消息得到节点和边的新的表征后映射得到整个图的表征。
数据集
dataset dgl.data.GINDataset(PROTEINS, self_loopTrue)
g dataset[0]
print(g)
print(Node feature dimensionality:, dataset.dim_nfeats)
print(Number of graph categories:, dataset.gclasses)(Graph(num_nodes42, num_edges204,ndata_schemes{label: Scheme(shape(), dtypetorch.int64), attr: Scheme(shape(3,), dtypetorch.float32)}edata_schemes{}), tensor(0))
Node feature dimensionality: 3
Number of graph categories: 2共1113个图每个图中的节点的特征维度是3图的类别数是2.
定义Data Loader
from torch.utils.data.sampler import SubsetRandomSamplerfrom dgl.dataloading import GraphDataLoadernum_examples len(dataset)
num_train int(num_examples * 0.8)train_sampler SubsetRandomSampler(torch.arange(num_train))
test_sampler SubsetRandomSampler(torch.arange(num_train, num_examples))train_dataloader GraphDataLoader(dataset, samplertrain_sampler, batch_size5, drop_lastFalse
)
test_dataloader GraphDataLoader(dataset, samplertest_sampler, batch_size5, drop_lastFalse
)取80%用作训练集其余用作测试集 mini-batch操作取5个graph打包成一个大的batched graph
it iter(train_dataloader)
batch next(it)
print(batch)[Graph(num_nodes259, num_edges1201,ndata_schemes{label: Scheme(shape(), dtypetorch.int64), attr: Scheme(shape(3,), dtypetorch.float32)}edata_schemes{}), tensor([0, 1, 0, 0, 0])]DGL中的batched graph 在每个mini-batch里面batched graph是由dgl.batch对graph进行打包的
batched_graph, labels batch
print(Number of nodes for each graph element in the batch:,batched_graph.batch_num_nodes(),
)
print(Number of edges for each graph element in the batch:,batched_graph.batch_num_edges(),
)# Recover the original graph elements from the minibatch
graphs dgl.unbatch(batched_graph)
print(The original graphs in the minibatch:)
print(graphs)Number of nodes for each graph element in the batch: tensor([ 55, 16, 116, 31, 41])
Number of edges for each graph element in the batch: tensor([209, 70, 584, 153, 185])
The original graphs in the minibatch:
[Graph(num_nodes55, num_edges209,ndata_schemes{label: Scheme(shape(), dtypetorch.int64), attr: Scheme(shape(3,), dtypetorch.float32)}edata_schemes{}), Graph(num_nodes16, num_edges70,ndata_schemes{label: Scheme(shape(), dtypetorch.int64), attr: Scheme(shape(3,), dtypetorch.float32)}edata_schemes{}), Graph(num_nodes116, num_edges584,ndata_schemes{label: Scheme(shape(), dtypetorch.int64), attr: Scheme(shape(3,), dtypetorch.float32)}edata_schemes{}), Graph(num_nodes31, num_edges153,ndata_schemes{label: Scheme(shape(), dtypetorch.int64), attr: Scheme(shape(3,), dtypetorch.float32)}edata_schemes{}), Graph(num_nodes41, num_edges185,ndata_schemes{label: Scheme(shape(), dtypetorch.int64), attr: Scheme(shape(3,), dtypetorch.float32)}edata_schemes{})]定义模型
class GCN(nn.Module):def __init__(self, in_feats, h_feats, num_classes):super(GCN, self).__init__()self.conv1 GraphConv(in_feats, h_feats)self.conv2 GraphConv(h_feats, num_classes)def forward(self, g, in_feat):h self.conv1(g, in_feat)h F.relu(h)h self.conv2(g, h)g.ndata[h] hreturn dgl.mean_nodes(g, h)#取所有节点的h特征的平均值来表征整个图 readoutmodel GCN(dataset.dim_nfeats, 16, dataset.gclasses)
optimizer torch.optim.Adam(model.parameters(), lr0.01)一个batched graph中不同的图是完全分开的即没有边连接两个图所有消息传递函数仍然具有相同的结果和没有打包之前相比。 其次将对每个图分别执行readout功能。假设批次大小为B要聚合的特征维度为D则读取出的形状为B D。
训练
for epoch in range(20):num_correct 0num_trains 0for batched_graph, labels in train_dataloader:pred model(batched_graph, batched_graph.ndata[attr].float())loss F.cross_entropy(pred, labels)num_trains len(labels)num_correct (pred.argmax(1)labels).sum().item()optimizer.zero_grad()loss.backward()optimizer.step()print(train accuracy: , num_correct/num_trains)num_correct 0
num_tests 0
for batched_graph, labels in test_dataloader:pred model(batched_graph, batched_graph.ndata[attr].float())num_correct (pred.argmax(1)labels).sum().item()num_tests len(labels)print(Test accuracy: , num_correct/num_tests)train accuracy: 0.7404494382022472
train accuracy: 0.7426966292134831
train accuracy: 0.7471910112359551
train accuracy: 0.7539325842696629
train accuracy: 0.7584269662921348
train accuracy: 0.7674157303370787
train accuracy: 0.7629213483146068
train accuracy: 0.7617977528089888
train accuracy: 0.7584269662921348
train accuracy: 0.7707865168539326
train accuracy: 0.7629213483146068
train accuracy: 0.7651685393258427
train accuracy: 0.7629213483146068
train accuracy: 0.7561797752808989
train accuracy: 0.7606741573033707
train accuracy: 0.7584269662921348
train accuracy: 0.7617977528089888
train accuracy: 0.7707865168539326
train accuracy: 0.7629213483146068
train accuracy: 0.7539325842696629Test accuracy: 0.26905829596412556效果非常一般 明显过拟合 应该和没有边特征节点特征信息不足有关。
参考
https://docs.dgl.ai/tutorials/blitz/5_graph_classification.html
