# 4.3 Process data¶

(中文版)

One can implement the data processing code in function process(), and it assumes that the raw data is located in self.raw_dir already. There are typically three types of tasks in machine learning on graphs: graph classification, node classification, and link prediction. This section will show how to process datasets related to these tasks.

The section focuses on the standard way to process graphs, features and masks. It will use builtin datasets as examples and skip the implementations for building graphs from files, but add links to the detailed implementations. Please refer to 1.4 Creating Graphs from External Sources to see a complete guide on how to build graphs from external sources.

## Processing Graph Classification datasets¶

Graph classification datasets are almost the same as most datasets in typical machine learning tasks, where mini-batch training is used. So one can process the raw data to a list of dgl.DGLGraph objects and a list of label tensors. In addition, if the raw data has been split into several files, one can add a parameter split to load specific part of the data.

Take QM7bDataset as example:

from dgl.data import DGLDataset

class QM7bDataset(DGLDataset):
_url = 'http://deepchem.io.s3-website-us-west-1.amazonaws.com/' \
'datasets/qm7b.mat'
_sha1_str = '4102c744bb9d6fd7b40ac67a300e49cd87e28392'

super(QM7bDataset, self).__init__(name='qm7b',
url=self._url,
raw_dir=raw_dir,
verbose=verbose)

def process(self):
mat_path = self.raw_path + '.mat'
# process data to a list of graphs and a list of labels

def __getitem__(self, idx):
""" Get graph and label by index

Parameters
----------
idx : int
Item index

Returns
-------
(dgl.DGLGraph, Tensor)
"""
return self.graphs[idx], self.label[idx]

def __len__(self):
"""Number of graphs in the dataset"""
return len(self.graphs)


In process(), the raw data is processed to a list of graphs and a list of labels. One must implement __getitem__(idx) and __len__() for iteration. DGL recommends making __getitem__(idx) return a tuple (graph, label) as above. Please check the QM7bDataset source code for details of self._load_graph() and __getitem__.

One can also add properties to the class to indicate some useful information of the dataset. In QM7bDataset, one can add a property num_tasks to indicate the total number of prediction tasks in this multi-task dataset:

@property
"""Number of labels for each graph, i.e. number of prediction tasks."""
return 14


After all these coding, one can finally use QM7bDataset as follows:

import dgl
import torch

dataset = QM7bDataset()

# training
for epoch in range(100):
pass


A complete guide for training graph classification models can be found in 5.4 Graph Classification.

For more examples of graph classification datasets, please refer to DGL’s builtin graph classification datasets:

• gindataset

• minigcdataset

• qm7bdata

• tudata

## Processing Node Classification datasets¶

Different from graph classification, node classification is typically on a single graph. As such, splits of the dataset are on the nodes of the graph. DGL recommends using node masks to specify the splits. The section uses builtin dataset CitationGraphDataset as an example:

In addition, DGL recommends re-arrange the nodes and edges so that nodes near to each other have IDs in a close range. The procedure could improve the locality to access a node’s neighbors, which may benefit follow-up computation and analysis conducted on the graph. DGL provides an API called dgl.reorder_graph() for this purpose. Please refer to process() part in below example for more details.

from dgl.data import DGLBuiltinDataset
from dgl.data.utils import _get_dgl_url

class CitationGraphDataset(DGLBuiltinDataset):
_urls = {
'cora_v2' : 'dataset/cora_v2.zip',
'citeseer' : 'dataset/citeseer.zip',
'pubmed' : 'dataset/pubmed.zip',
}

def __init__(self, name, raw_dir=None, force_reload=False, verbose=True):
assert name.lower() in ['cora', 'citeseer', 'pubmed']
if name.lower() == 'cora':
name = 'cora_v2'
url = _get_dgl_url(self._urls[name])
super(CitationGraphDataset, self).__init__(name,
url=url,
raw_dir=raw_dir,
verbose=verbose)

def process(self):
# Skip some processing code
# === data processing skipped ===

# build graph
g = dgl.graph(graph)
# node labels
g.ndata['label'] = torch.tensor(labels)
# node features
g.ndata['feat'] = torch.tensor(_preprocess_features(features),
dtype=F.data_type_dict['float32'])
self._labels = labels
# reorder graph to obtain better locality.
self._g = dgl.reorder_graph(g)

def __getitem__(self, idx):
assert idx == 0, "This dataset has only one graph"
return self._g

def __len__(self):
return 1


For brevity, this section skips some code in process() to highlight the key part for processing node classification dataset: splitting masks. Node features and node labels are stored in g.ndata. For detailed implementation, please refer to CitationGraphDataset source code.

Note that the implementations of __getitem__(idx) and __len__() are changed as well, since there is often only one graph for node classification tasks. The masks are bool tensors in PyTorch and TensorFlow, and float tensors in MXNet.

The section uses a subclass of CitationGraphDataset, dgl.data.CiteseerGraphDataset, to show the usage of it:

# load data
dataset = CiteseerGraphDataset(raw_dir='')
graph = dataset[0]

# get node features
feats = graph.ndata['feat']

# get labels
labels = graph.ndata['label']


A complete guide for training node classification models can be found in 5.1 Node Classification/Regression.

For more examples of node classification datasets, please refer to DGL’s builtin datasets:

• citationdata

• corafulldata

• coauthordata

• karateclubdata

• ppidata

• redditdata

• sbmdata

• sstdata

• rdfdata