[人工智能] datawhalechina-GNN组队学习作业：PyG不同模块在PyG数据集上的应用

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[人工智能]datawhalechina-GNN组队学习作业：PyG不同模块在PyG数据集上的应用

作者:>

MLP、GCN、GAT在数据集citeseer等上的节点分类任务

算是GNN的helloworld，直接上代码，注释很详细

# -*- coding: utf-8 -*-
"""
Created on Fri Feb 18 19:10:05 2022

@author: lz
"""

from torch_geometric.datasets import Planetoid
from torch_geometric.transforms import NormalizeFeatures

dataset = Planetoid(root = 'dataset', name='CiteSeer', transform=NormalizeFeatures())

print()
print(f'Dataset:{dataset}')
print(f'Number of Graph:{len(dataset)}')
print(f'Number of features:{dataset.num_features}')
print(f'Number of classes:{dataset.num_classes}')

data = dataset[0]

print()
print(data)

print()
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'Number of training nodes:{data.train_mask.sum()}')
print(f'Training node label rate:{data.train_mask.sum() / data.num_nodes:.2f}')
print(f'Contains isolated nodes:{data.has_isolated_nodes()}')
print(f'Contains self-loops:{data.has_self_loops()}')
print(f'Is undirected:{data.is_undirected()}')

'''
可视化节点表征分布的方法
'''
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
def visualize(h, color):
    z = TSNE(n_components=2).fit_transform(out.detach().cpu().numpy())
    plt.figure(figsize=(10,10))
    plt.xticks([])
    plt.yticks([])
    plt.scatter(z[:, 0], z[:, 1], s=70, c=color, cmap="Set2")
    plt.show()
    
'''
MLP神经网络的构造
'''
import torch
from torch.nn import Module
from torch.nn import Linear
import torch.nn.functional as F

class MLP(Module):
    def __init__(self, hidden_channels):
        super(MLP, self).__init__()
        torch.manual_seed(12345)
        self.lin1 = Linear(dataset.num_features, hidden_channels)#dataset.num?不应该是dataset[0].num?难道dataset也有Num属性？
        self.lin2 = Linear(hidden_channels, dataset.num_classes)
        
    def forward(self, x):
        x = self.lin1(x)#等价于 self.lin1.forward(x),还是module call的forward
        relu = torch.nn.ReLU(inplace = True)
        x = relu(x)
        x = F.dropout(x, p=0.5, training=self.training)#!
        x = self.lin2(x)
        return x
    
model = MLP(hidden_channels=16)
print()
print('MLP神经网络的构造')
print(model)
        
print()
print('利用交叉熵损失和Adam优化器来训练这个简单的MLP神经网络')
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay= 5e-4)

def train():
    model.train()#!
    optimizer.zero_grad()
    out = model(data.x)
    loss = criterion(out[data.train_mask], data.y[data.train_mask])
    loss.backward()#!
    optimizer.step()
    return loss

print('开始训练')

for epoch in range(1, 201):
    loss = train()
    print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}')
    

print('看看测试集上的表现')

def test():
    model.eval()#!
    out = model(data.x)
    pred = out.argmax(dim = 1)#选择概率最大的类
    test_correct = pred[data.test_mask] == data.y[data.test_mask]#检查标签是否正确
    test_acc = int(test_correct.sum()) / int(data.test_mask.sum()) 
    return test_acc

test_acc = test()
print(f'Test Accuracy:{test_acc:.4f}')
   
print('将MLP中的torch.nn.Linear 替换为torch_geometric.nn.GCNConv，我们就可以得到一个GCN网络')     
from torch_geometric.nn import GCNConv

class GCN(Module):
    def __init__(self, hidden_channels):
        super(GCN, self).__init__()
        torch.manual_seed(12345)
        self.conv1 = GCNConv(dataset.num_features, hidden_channels)#dataset.num?不应该是dataset[0].num?难道dataset也有Num属性？
        self.conv2 = GCNConv(hidden_channels, dataset.num_classes)
        
    def forward(self, x, edge_index):
        x = self.conv1(x, edge_index)#等价于 self.lin1.forward(x),还是module call的forward
        relu = torch.nn.ReLU(inplace = True)
        x = relu(x)
        x = F.dropout(x, p=0.5, training=self.training)#!
        x = self.conv2(x, edge_index)
        return x
    
model = GCN(hidden_channels=16)
print(model)        
     
print()
print('可视化未经训练的GCN生成的节点表征')
model.eval()   

out = model(data.x, data.edge_index)
visualize(out, color=data.y)
   
print()     
print('训练GCN图神经网络')
optimizer = torch.optim.Adam(model.parameters(), lr = 0.01, weight_decay=5e-4)
criterion = torch.nn.CrossEntropyLoss()

def train():
    model.train()
    optimizer.zero_grad()
    out = model(data.x, data.edge_index)#进行一次正向计算
    loss = criterion(out[data.train_mask], data.y[data.train_mask])
    loss.backward()
    optimizer.step()
    return loss

for epoch in range(1, 201):
    loss = train()
    print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}')
        
print('测试集上的准确性')
def test():
    model.eval()
    out = model(data.x, data.edge_index)
    pred = out.argmax(dim = 1)#选择概率最大的类
    test_correct = pred[data.test_mask] == data.y[data.test_mask]
    test_acc = int(test_correct.sum()) / int(data.test_mask.sum())
    return test_acc

test_acc = test()
print(f'Test Accuracy: {test_acc:.4f}')

print()
print('可视化训练后的GCN生成的节点表征')
model.eval()

out = model(data.x, data.edge_index)
visualize(out, color=data.y)

print()
print('将MLP中的torch.nn.Linear 替换为torch_geometric.nn.GCNConv，我们就可以得到一个GCN网络')     
from torch_geometric.nn import GATConv
class GAT(Module):
    def __init__(self, hidden_channels):
        super(GAT, self).__init__()
        torch.manual_seed(12345)
        self.conv1 = GATConv(dataset.num_features, hidden_channels)#dataset.num?不应该是dataset[0].num?难道dataset也有Num属性？
        self.conv2 = GATConv(hidden_channels, dataset.num_classes)
        
    def forward(self, x, edge_index):
        x = self.conv1(x, edge_index)#等价于 self.lin1.forward(x),还是module call的forward
        relu = torch.nn.ReLU(inplace = True)
        x = relu(x)
        x = F.dropout(x, p=0.5, training=self.training)#!
        x = self.conv2(x, edge_index)
        return x
    
model = GAT(hidden_channels=16)
print(model)

print()     
print('训练GAT图神经网络')
optimizer = torch.optim.Adam(model.parameters(), lr = 0.01, weight_decay=5e-4)
criterion = torch.nn.CrossEntropyLoss()

def train():
    model.train()
    optimizer.zero_grad()
    out = model(data.x, data.edge_index)#进行一次正向计算
    loss = criterion(out[data.train_mask], data.y[data.train_mask])
    loss.backward()
    optimizer.step()
    return loss

for epoch in range(1, 201):
    loss = train()
    print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}')
        
print('测试集上的准确性')
def test():
    model.eval()
    out = model(data.x, data.edge_index)
    pred = out.argmax(dim = 1)#选择概率最大的类
    test_correct = pred[data.test_mask] == data.y[data.test_mask]
    test_acc = int(test_correct.sum()) / int(data.test_mask.sum())
    return test_acc

test_acc = test()
print(f'Test Accuracy: {test_acc:.4f}')

print()
print('可视化训练后的GAT生成的节点表征')
model.eval()

out = model(data.x, data.edge_index)
visualize(out, color=data.y)