GraphSAGE项目练手
# 导包from torch_geometric.datasets import Planetoid
from torch_geometric.loader import NeighborLoader
from torch_geometric.utils import to_networkx
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import torch
import torch.nn.functional as F
from torch_geometric.nn import SAGEConv
# 导入PubMed数据集
dataset = Planetoid(root='',name='Pubmed')
data = dataset
# 邻居采样
# 使用NeighborLoader 来完成这一任务。
# 保留目的节点的10个邻居 和 其邻居的10个邻居， 对60个目的节点进行分组，每16个目的节点为一组
# 进行采样
train_loader = NeighborLoader(
data,# 数据源
num_neighbors=, # 每一层采样的邻居采样量，第一层5，第二层10
batch_size=16,
input_nodes=data.train_mask # 60个训练目的节点
)
# 遍历数据检验
# for i,subgraph in enumerate(train_loader):
# print(f'Subgraph{i}:{subgraph}')
# 子图可视化
# fig = plt.figure(figsize=(16,16))
# for idx,(subdata,pos) in enumerate(zip(train_loader,)):
# G = to_networkx(subdata,to_undirected=True)
# ax = fig.add_subplot(pos)
# ax.set_title(f'Subgraph{idx},fonts=24')
# plt.axis('off')
# nx.draw_networkx(G,pos=nx.spring_layout(G),with_labels=False,node_color=subdata.y)
# plt.show()
# 实现准确率评估模型
defaccuracy(pre_y,y):
return ((pre_y==y).sum() / len(y)).item()
# 定义GraphSAGE
class GraphSAGE(torch.nn.Module):
def __init__(self,dim_in,dim_h,dim_out):
super().__init__()
self.sage1 = SAGEConv(dim_in,dim_h)
self.sage2= SAGEConv(dim_h,dim_out)
def forward(self,x,edge_index):
h = self.sage1(x,edge_index)
h = torch.relu(h)
h = F.dropout(h,p=0.5,training=self.training)
h = self.sage2(h,edge_index)
return h
# 使用小批量训练，Fit函数要修改为先循环epoch次，然后循环批数据，以在每个批数据上训练epoch次
def fit(self,loader,epochs):
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(self.parameters(),lr=0.01)
self.train()
for epoch in range(epochs+1):
total_loss = 0
acc = 0
val_loss = 0
val_acc = 0
for batch in loader:
optimizer.zero_grad()
out = self(batch.x, batch.edge_index)
loss = criterion(out, batch.y)
total_loss += loss.item()
acc += accuracy(out.argmax(dim=1), batch.y)
loss.backward()
optimizer.step()
# Validation
val_loss += criterion(out, batch.y)
val_acc += accuracy(out.argmax(dim=1), batch.y)
if epoch % 20 == 0:
print(f'Epoch {epoch:>3} | Train Loss: {loss/len(loader):.3f} | Train Acc: {acc/len(loader)*100:>6.2f}% | Val Loss: {val_loss/len(train_loader):.2f} | Val Acc: {val_acc/len(train_loader)*100:.2f}%')
@torch.no_grad()
def test(self, data):
self.eval()
out = self(data.x, data.edge_index)
acc = accuracy(out.argmax(dim=1), data.y)
return acc
# Create GraphSAGE
graphsage = GraphSAGE(dataset.num_features, 64, dataset.num_classes)
print(graphsage)
# Train
graphsage.fit(train_loader, 200)
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