GraphSAGE代码实现多分类任务,要求包括训练和测试,训练集测试集使用随机数
时间: 2023-10-21 09:07:13 浏览: 47
GraphSAGE是一种用于节点分类的图神经网络模型,可以通过聚合节点邻居信息来学习节点的表征向量。下面是使用PyTorch实现GraphSAGE进行多分类任务的代码,包括数据准备、模型构建、训练和测试。
首先,我们需要导入相关的库:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from sklearn.metrics import f1_score
```
接下来,我们定义一个函数来生成随机的图数据:
```python
def generate_graph(num_nodes, num_edges, num_classes, features_dim):
adj = np.zeros((num_nodes, num_nodes))
features = np.zeros((num_nodes, features_dim))
labels = np.random.randint(num_classes, size=num_nodes)
# Generate edges randomly
edges = np.random.randint(num_nodes, size=(2, num_edges))
for i in range(num_edges):
adj[edges[0][i], edges[1][i]] = 1
adj[edges[1][i], edges[0][i]] = 1
# Generate features randomly
for i in range(num_nodes):
features[i] = np.random.randn(features_dim)
return adj, features, labels
```
然后,我们可以使用这个函数来生成训练集和测试集:
```python
train_adj, train_features, train_labels = generate_graph(1000, 5000, 10, 16)
test_adj, test_features, test_labels = generate_graph(500, 2500, 10, 16)
```
现在,我们可以定义GraphSAGE模型。这里我们使用两层GCN,每一层都包括聚合、线性变换和激活函数。在最后一层,我们使用softmax激活函数来将节点表征向量转化为类别概率。
```python
class GraphSAGE(nn.Module):
def __init__(self, in_features, hidden_size, out_features):
super(GraphSAGE, self).__init__()
self.fc1 = nn.Linear(in_features*2, hidden_size)
self.fc2 = nn.Linear(hidden_size, out_features)
def forward(self, adj, features):
# Compute neighborhood embeddings
h = torch.mm(adj, features)
h = torch.cat((features, h), dim=1)
h = torch.relu(self.fc1(h))
h = self.fc2(h)
# Compute class probabilities
logits = h
probs = torch.softmax(logits, dim=1)
return probs
```
接下来,我们定义训练函数。在每个epoch中,我们使用训练集的数据来更新模型参数。每个batch的大小为256,使用交叉熵损失函数来计算损失,使用Adam优化器来更新模型参数。
```python
def train(model, adj, features, labels, optimizer):
model.train()
# Shuffle data randomly
idx = np.random.permutation(len(labels))
adj = adj[idx]
features = features[idx]
labels = labels[idx]
loss_history = []
for i in range(0, len(labels), 256):
adj_batch = torch.tensor(adj[i:i+256], dtype=torch.float32)
features_batch = torch.tensor(features[i:i+256], dtype=torch.float32)
labels_batch = torch.tensor(labels[i:i+256], dtype=torch.long)
optimizer.zero_grad()
probs = model(adj_batch, features_batch)
loss = nn.CrossEntropyLoss()(probs, labels_batch)
loss.backward()
optimizer.step()
loss_history.append(loss.item())
return np.mean(loss_history)
```
最后,我们定义测试函数。在测试函数中,我们使用测试集的数据来评估模型的性能。计算预测结果的F1分数。
```python
def test(model, adj, features, labels):
model.eval()
with torch.no_grad():
adj = torch.tensor(adj, dtype=torch.float32)
features = torch.tensor(features, dtype=torch.float32)
labels = torch.tensor(labels, dtype=torch.long)
probs = model(adj, features)
preds = torch.argmax(probs, dim=1).numpy()
f1 = f1_score(labels.numpy(), preds, average='micro')
return f1
```
现在我们可以开始训练模型了。在每个epoch中,我们先使用训练集的数据训练模型,然后使用测试集的数据评估模型的性能。
```python
model = GraphSAGE(16, 64, 10)
optimizer = optim.Adam(model.parameters())
for epoch in range(10):
train_loss = train(model, train_adj, train_features, train_labels, optimizer)
test_f1 = test(model, test_adj, test_features, test_labels)
print("Epoch {:03d}: train loss {:.4f}, test F1 {:.4f}".format(epoch, train_loss, test_f1))
```
在训练完成后,我们可以使用模型来预测新的节点类别。
```python
new_adj, new_features, _ = generate_graph(10, 20, 10, 16)
probs = model(torch.tensor(new_adj, dtype=torch.float32), torch.tensor(new_features, dtype=torch.float32))
preds = torch.argmax(probs, dim=1).numpy()
print(preds)
```
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