# Training a GNN for Graph Classification¶

By the end of this tutorial, you will be able to

• Load a DGL-provided graph classification dataset.

• Understand what readout function does.

• Understand how to create and use a minibatch of graphs.

• Build a GNN-based graph classification model.

• Train and evaluate the model on a DGL-provided dataset.

(Time estimate: 18 minutes)

import dgl
import torch
import torch.nn as nn
import torch.nn.functional as F


## Overview of Graph Classification with GNN¶

Graph classification or regression requires a model to predict certain graph-level properties of a single graph given its node and edge features. Molecular property prediction is one particular application.

This tutorial shows how to train a graph classification model for a small dataset from the paper How Powerful Are Graph Neural Networks.

import dgl.data

# Generate a synthetic dataset with 10000 graphs, ranging from 10 to 500 nodes.
dataset = dgl.data.GINDataset('PROTEINS', self_loop=True)


The dataset is a set of graphs, each with node features and a single label. One can see the node feature dimensionality and the number of possible graph categories of GINDataset objects in dim_nfeats and gclasses attributes.

print('Node feature dimensionality:', dataset.dim_nfeats)
print('Number of graph categories:', dataset.gclasses)


Out:

Node feature dimensionality: 3
Number of graph categories: 2


A graph classification dataset usually contains two types of elements: a set of graphs, and their graph-level labels. Similar to an image classification task, when the dataset is large enough, we need to train with mini-batches. When you train a model for image classification or language modeling, you will use a DataLoader to iterate over the dataset. In DGL, you can use the GraphDataLoader.

You can also use various dataset samplers provided in torch.utils.data.sampler. For example, this tutorial creates a training GraphDataLoader and test GraphDataLoader, using SubsetRandomSampler to tell PyTorch to sample from only a subset of the dataset.

from dgl.dataloading import GraphDataLoader
from torch.utils.data.sampler import SubsetRandomSampler

num_examples = len(dataset)
num_train = int(num_examples * 0.8)

train_sampler = SubsetRandomSampler(torch.arange(num_train))
test_sampler = SubsetRandomSampler(torch.arange(num_train, num_examples))

dataset, sampler=train_sampler, batch_size=5, drop_last=False)
dataset, sampler=test_sampler, batch_size=5, drop_last=False)


You can try to iterate over the created GraphDataLoader and see what it gives:

it = iter(train_dataloader)
batch = next(it)
print(batch)


Out:

[Graph(num_nodes=233, num_edges=1175,
ndata_schemes={'attr': Scheme(shape=(3,), dtype=torch.float64), 'label': Scheme(shape=(), dtype=torch.int64)}
edata_schemes={}), tensor([0, 0, 0, 0, 0])]


As each element in dataset has a graph and a label, the GraphDataLoader will return two objects for each iteration. The first element is the batched graph, and the second element is simply a label vector representing the category of each graph in the mini-batch. Next, we’ll talked about the batched graph.

## A Batched Graph in DGL¶

In each mini-batch, the sampled graphs are combined into a single bigger batched graph via dgl.batch. The single bigger batched graph merges all original graphs as separately connected components, with the node and edge features concatenated. This bigger graph is also a DGLGraph instance (so you can still treat it as a normal DGLGraph object as in here). It however contains the information necessary for recovering the original graphs, such as the number of nodes and edges of each graph element.

batched_graph, labels = batch
print('Number of nodes for each graph element in the batch:', batched_graph.batch_num_nodes())
print('Number of edges for each graph element in the batch:', batched_graph.batch_num_edges())

# Recover the original graph elements from the minibatch
graphs = dgl.unbatch(batched_graph)
print('The original graphs in the minibatch:')
print(graphs)


Out:

Number of nodes for each graph element in the batch: tensor([ 31, 101,  38,  23,  40])
Number of edges for each graph element in the batch: tensor([135, 541, 192, 133, 174])
The original graphs in the minibatch:
[Graph(num_nodes=31, num_edges=135,
ndata_schemes={'attr': Scheme(shape=(3,), dtype=torch.float64), 'label': Scheme(shape=(), dtype=torch.int64)}
edata_schemes={}), Graph(num_nodes=101, num_edges=541,
ndata_schemes={'attr': Scheme(shape=(3,), dtype=torch.float64), 'label': Scheme(shape=(), dtype=torch.int64)}
edata_schemes={}), Graph(num_nodes=38, num_edges=192,
ndata_schemes={'attr': Scheme(shape=(3,), dtype=torch.float64), 'label': Scheme(shape=(), dtype=torch.int64)}
edata_schemes={}), Graph(num_nodes=23, num_edges=133,
ndata_schemes={'attr': Scheme(shape=(3,), dtype=torch.float64), 'label': Scheme(shape=(), dtype=torch.int64)}
edata_schemes={}), Graph(num_nodes=40, num_edges=174,
ndata_schemes={'attr': Scheme(shape=(3,), dtype=torch.float64), 'label': Scheme(shape=(), dtype=torch.int64)}
edata_schemes={})]


## Define Model¶

This tutorial will build a two-layer Graph Convolutional Network (GCN). Each of its layer computes new node representations by aggregating neighbor information. If you have gone through the introduction, you will notice two differences:

• Since the task is to predict a single category for the entire graph instead of for every node, you will need to aggregate the representations of all the nodes and potentially the edges to form a graph-level representation. Such process is more commonly referred as a readout. A simple choice is to average the node features of a graph with dgl.mean_nodes().

• The input graph to the model will be a batched graph yielded by the GraphDataLoader. The readout functions provided by DGL can handle batched graphs so that they will return one representation for each minibatch element.

from dgl.nn import GraphConv

class GCN(nn.Module):
def __init__(self, in_feats, h_feats, num_classes):
super(GCN, self).__init__()
self.conv1 = GraphConv(in_feats, h_feats)
self.conv2 = GraphConv(h_feats, num_classes)

def forward(self, g, in_feat):
h = self.conv1(g, in_feat)
h = F.relu(h)
h = self.conv2(g, h)
g.ndata['h'] = h
return dgl.mean_nodes(g, 'h')


## Training Loop¶

The training loop iterates over the training set with the GraphDataLoader object and computes the gradients, just like image classification or language modeling.

# Create the model with given dimensions
model = GCN(dataset.dim_nfeats, 16, dataset.gclasses)

for epoch in range(20):
pred = model(batched_graph, batched_graph.ndata['attr'].float())
loss = F.cross_entropy(pred, labels)
loss.backward()
optimizer.step()

num_correct = 0
num_tests = 0
pred = model(batched_graph, batched_graph.ndata['attr'].float())
num_correct += (pred.argmax(1) == labels).sum().item()
num_tests += len(labels)

print('Test accuracy:', num_correct / num_tests)


Out:

/home/ubuntu/.pyenv/versions/miniconda3-latest/lib/python3.7/site-packages/torch/autocast_mode.py:141: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling
warnings.warn('User provided device_type of \'cuda\', but CUDA is not available. Disabling')
Test accuracy: 0.07174887892376682


## What’s next¶

• See GIN example for an end-to-end graph classification model.

# Thumbnail credits: DGL
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