"""
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 `__.
#
# Loading Data
# ------------
#
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)
######################################################################
# Defining Data Loader
# --------------------
#
# 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))
train_dataloader = GraphDataLoader(
dataset, sampler=train_sampler, batch_size=5, drop_last=False)
test_dataloader = GraphDataLoader(
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)
######################################################################
# 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 <2_dglgraph.ipynb>`__). 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)
######################################################################
# 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
# :doc:`introduction <1_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)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
for epoch in range(20):
for batched_graph, labels in train_dataloader:
pred = model(batched_graph, batched_graph.ndata['attr'].float())
loss = F.cross_entropy(pred, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
num_correct = 0
num_tests = 0
for batched_graph, labels in test_dataloader:
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)
######################################################################
# What’s next
# -----------
#
# - See `GIN
# example `__
# for an end-to-end graph classification model.
#
# Thumbnail credits: DGL
# sphinx_gallery_thumbnail_path = '_static/blitz_5_graph_classification.png'