2、点分类任务

1、Cora dataset(数据集描述：Yang et al. (2016))

• 论文引用数据集，每一个点有1433维向量
• 最终要对每个点进行7分类任务(每个类别只有20个点有标注)

from torch_geometric.datasets import Planetoid#下载数据集用的
from torch_geometric.transforms import NormalizeFeatures

dataset = Planetoid(root='data/Planetoid', name='Cora', transform=NormalizeFeatures())#transform预处理

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 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'Number of training nodes: {data.train_mask.sum()}')
print(f'Training node label rate: {int(data.train_mask.sum()) / 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()}')

Dataset: Cora():
======================
Number of graphs: 1
Number of features: 1433
Number of classes: 7

Data(x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708])
===========================================================================================================
Number of nodes: 2708
Number of edges: 10556
Average node degree: 3.90
Number of training nodes: 140
Training node label rate: 0.05
Has isolated nodes: False
Has self-loops: False
Is undirected: True

• val_mask和test_mask分别表示这个点需要被用到哪个集中

# 可视化部分
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE

def visualize(h, color):
    z = TSNE(n_components=2).fit_transform(h.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()

2、试试直接用传统的全连接层会咋样(Multi-layer Perception Network)

import torch
from torch.nn import Linear
import torch.nn.functional as F


class MLP(torch.nn.Module):
    def __init__(self, hidden_channels):
        super().__init__()
        torch.manual_seed(12345)
        self.lin1 = Linear(dataset.num_features, hidden_channels)
        self.lin2 = Linear(hidden_channels, dataset.num_classes)

    def forward(self, x):
        x = self.lin1(x)
        x = x.relu()
        x = F.dropout(x, p=0.5, training=self.training)
        x = self.lin2(x)
        return x

model = MLP(hidden_channels=16)
print(model)

MLP(
  (lin1): Linear(in_features=1433, out_features=16, bias=True)
  (lin2): Linear(in_features=16, out_features=7, bias=True)
)

model = MLP(hidden_channels=16)
criterion = torch.nn.CrossEntropyLoss()  # Define loss criterion.
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)  # Define optimizer.

def train():
    model.train()
    optimizer.zero_grad()  # Clear gradients.
    out = model(data.x)  # Perform a single forward pass.
    loss = criterion(out[data.train_mask], data.y[data.train_mask])  # Compute the loss solely based on the training nodes.
    loss.backward()  # Derive gradients.
    optimizer.step()  # Update parameters based on gradients.
    return loss

def test():
    model.eval()
    out = model(data.x)
    pred = out.argmax(dim=1)  # Use the class with highest probability.
    test_correct = pred[data.test_mask] == data.y[data.test_mask]  # Check against ground-truth labels.
    test_acc = int(test_correct.sum()) / int(data.test_mask.sum())  # Derive ratio of correct predictions.
    return test_acc

for epoch in range(1, 210):
    loss = train()
print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}')

Epoch: 209, Loss: 0.3570

准确率计算

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

Test Accuracy: 0.5890

3、Graph Neural Network (GNN)

将全连接层替换成GCN层

from torch_geometric.nn import GCNConv


class GCN(torch.nn.Module):
    def __init__(self, hidden_channels):
        super().__init__()
        torch.manual_seed(1234567)
        self.conv1 = GCNConv(dataset.num_features, hidden_channels)
        self.conv2 = GCNConv(hidden_channels, dataset.num_classes)

    def forward(self, x, edge_index):
        x = self.conv1(x, edge_index)
        x = x.relu()
        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)

GCN(
  (conv1): GCNConv(1433, 16)
  (conv2): GCNConv(16, 7)
)

可视化时由于输出是7维向量，所以降维成2维进行展示

model = GCN(hidden_channels=16)
model.eval()

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

训练GCN模型

model = GCN(hidden_channels=16)
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)  
    loss = criterion(out[data.train_mask], data.y[data.train_mask])  
    loss.backward() 
    optimizer.step()  
    return loss

def test():
    model.eval()
    out = model(data.x, data.edge_index)
    pred = out.argmax(dim=1)  
    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


for epoch in range(1, 101):
    loss = train()
print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}')

Epoch: 100, Loss: 0.5799

准确率计算

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

Test Accuracy: 0.8150

从59%到81%，这个提升还是蛮大的；训练后的可视化展示如下：

model.eval()

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