explain_graph_classification.py

# Import libararies
import numpy as np
import pandas as pd
import os

import torch_geometric.transforms as T
from torch_geometric.datasets import Planetoid
import matplotlib.pyplot as plt
import torch
import torch.nn.functional as F
from torch_geometric.nn import GraphConv
import torch_geometric
from torch.nn import Parameter
from torch_geometric.nn.conv import MessagePassing
import urllib.request
import tarfile
from torch.nn import Linear
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, GNNExplainer
from torch_geometric.nn import global_mean_pool
from torch_geometric.datasets import TUDataset
from torch_geometric.loader import DataLoader

# Load the dataset
dataset = TUDataset(root='data/TUDataset', name='MUTAG')

# print details about the graph
print(f'Dataset: {dataset}:')
print("Number of Graphs: ",len(dataset))
print("Number of Freatures: ", dataset.num_features)
print("Number of Classes: ", dataset.num_classes)

data= dataset[0]
print(data)
print("No. of nodes: ", data.num_nodes)
print("No. of Edges: ", data.num_edges)
print(f'Average node degree: {data.num_edges / 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()}')

# Create train and test dataset
torch.manual_seed(12345)
dataset = dataset.shuffle()

train_dataset = dataset[:50]
test_dataset = dataset[50:]

print(f'Number of training graphs: {len(train_dataset)}')
print(f'Number of test graphs: {len(test_dataset)}')

'''graphs in graph classification datasets are usually small, 
a good idea is to batch the graphs before inputting 
them into a Graph Neural Network to guarantee full GPU utilization__

_In pytorch Geometric adjacency matrices are stacked in a diagonal fashion 
(creating a giant graph that holds multiple isolated subgraphs), a
nd node and target features are simply concatenated in the node dimension:
'''


train_loader = DataLoader(train_dataset,  batch_size=64, shuffle= True)
test_loader= DataLoader(test_dataset,  batch_size=64, shuffle= False)

for step, data in enumerate(train_loader):
    print(f'Step {step + 1}:')
    print('=======')
    print(f'Number of graphs in the current batch: {data.num_graphs}')
    print(data)
    print()

# Build the model
class GNN(torch.nn.Module):
    def __init__(self, hidden_channels):
        super(GNN, self).__init__()
        torch.manual_seed(12345)
        self.conv1 = GraphConv(dataset.num_node_features, hidden_channels)
        self.conv2 = GraphConv(hidden_channels, hidden_channels)

        self.conv3 = GraphConv(hidden_channels, hidden_channels )
        self.lin = Linear(hidden_channels, dataset.num_classes)

    def forward(self, x, edge_index, batch):
        x = self.conv1(x, edge_index)
        x = x.relu()
        x = self.conv2(x, edge_index)
        x = x.relu()
        x = self.conv3(x, edge_index)

        x = global_mean_pool(x, batch)
        
        x = F.dropout(x, p=0.5, training=self.training)
        x = self.lin(x)
        
        return x
model = GNN(hidden_channels=64)
print(model)

# set the optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=0.02)

# set the loss function
criterion = torch.nn.CrossEntropyLoss()

# Creating the function to train the model
def train():
    model.train()

    for data in train_loader:  # Iterate in batches over the training dataset.
         out = model(data.x, data.edge_index, data.batch)  # Perform a single forward pass.
         loss = criterion(out, data.y)  # Compute the loss.
         loss.backward()  # Derive gradients.
         optimizer.step()  # Update parameters based on gradients.
         optimizer.zero_grad()  # Clear gradients.

# function to test the model
def test(loader):
     model.eval()

     correct = 0
     for data in loader:  # Iterate in batches over the training/test dataset.
         out = model(data.x, data.edge_index, data.batch)  
         pred = out.argmax(dim=1)  # Use the class with highest probability.
         correct += int((pred == data.y).sum())  # Check against ground-truth labels.
     return correct / len(loader.dataset)  # Derive ratio of correct predictions.

# Train the model for 150 epochs
for epoch in range(1, 160):
    train()
    train_acc = test(train_loader)
    test_acc = test(test_loader)
    if(epoch % 10 == 0):
          '''print(f'Epoch {epoch:>3} | Train Loss: {total_loss/len(train_loader):.3f} '
                f'| Train Acc: {acc/len(train_loader)*100:>6.2f}% | Val Loss: '
                f'{val_loss/len(train_loader):.2f} | Val Acc: '
                f'{val_acc/len(train_loader)*100:.2f}%')
          '''
          print(f'Epoch: {epoch:03d}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}')

#Explain the Graph
explainer = GNNExplainer(model, epochs=100,return_type='log_prob')
data = dataset[0]
node_feat_mask, edge_mask = explainer.explain_graph(data.x, data.edge_index)
ax, G = explainer.visualize_subgraph(-1,data.edge_index, edge_mask, data.y)
plt.show()