import pickle
import torch as th
import numpy as np
import dgl
import os
from dgl.data import DGLBuiltinDataset
from dgl.data.utils import idx2mask, load_graphs, save_graphs
__all__ = ['GTNDataset', 'IMDB4GTNDataset', 'ACM4GTNDataset', 'DBLP4GTNDataset']
[docs]
class GTNDataset(DGLBuiltinDataset):
r"""GTN Dataset.
It contains three datasets used in a NeurIPS'19 paper Graph Transformer Networks <https://arxiv.org/abs/1911.06455>,
which includes two citation network datasets DBLP and ACM, and a movie dataset
IMDB. DBLP contains three types of nodes (papers (P), authors (A), conferences (C)), four types of edges
(PA, AP, PC, CP), and research areas of authors as labels. ACM contains three types of nodes (papers
(P), authors (A), subject (S)), four types of edges (PA, AP, PS, SP), and categories of papers as labels.
Each node in the two datasets is represented as bag-of-words of keywords. On the other hand, IMDB
contains three types of nodes (movies (M), actors (A), and directors (D)) and labels are genres of
movies. Node features are given as bag-of-words representations of plots.
Dataset statistics:
Dataset Nodes Edges Edge type Features Training Validation Test
DBLP 18405 67946 4 334 800 400 2857
ACM 8994 25922 4 1902 600 300 2125
IMDB 12772 37288 4 1256 300 300 2339
Data source link: <https://drive.google.com/file/d/1qOZ3QjqWMIIvWjzrIdRe3EA4iKzPi6S5/view?usp=sharing>
Parameters
----------
name : str
Name of the dataset. Supported dataset names are 'dblp4GTN', 'acm4GTN' and 'imdb4GTN'.
raw_dir : str
Specifying the directory that will store the
downloaded data or the directory that
already stores the input data.
Default: ~/.dgl/
force_reload : bool
Whether to reload the dataset. Default: False
verbose : bool
Whether to print out progress information. Default: False
transform : callable, optional
A transform that takes in a :class:`~dgl.DGLGraph` object and returns
a transformed version. The :class:`~dgl.DGLGraph` object will be
transformed before every access.
Examples
--------
>>> dataset = GTNDataset(name='imdb4GTN')
>>> graph = dataset[0]
"""
def __init__(self, name, raw_dir=None, force_reload=False, verbose=False,
transform=None):
assert name in ['dblp4GTN', 'acm4GTN', 'imdb4GTN']
if name == 'dblp4GTN':
canonical_etypes = [('paper', 'paper-author', 'author'), ('author', 'author-paper', 'paper'),
('paper', 'paper-conference', 'conference'),
('conference', 'conference-paper', 'paper')]
target_ntype = 'author'
meta_paths_dict = {'APCPA': [('author', 'author-paper', 'paper'),
('paper', 'paper-conference', 'conference'),
('conference', 'conference-paper', 'paper'),
('paper', 'paper-author', 'author')],
'APA': [('author', 'author-paper', 'paper'),
('paper', 'paper-author', 'author')],
}
elif name == 'acm4GTN':
canonical_etypes = [('paper', 'paper-author', 'author'), ('author', 'author-paper', 'paper'),
('paper', 'paper-subject', 'subject'), ('subject', 'subject-paper', 'paper')]
target_ntype = 'paper'
meta_paths_dict = {'PAPSP': [('paper', 'paper-author', 'author'),
('author', 'author-paper', 'paper'),
('paper', 'paper-subject', 'subject'),
('subject', 'subject-paper', 'paper')],
'PAP': [('paper', 'paper-author', 'author'),
('author', 'author-paper', 'paper')],
'PSP': [('paper', 'paper-subject', 'subject'),
('subject', 'subject-paper', 'paper')]
}
elif name == 'imdb4GTN':
canonical_etypes = [('movie', 'movie-director', 'director'), ('director', 'director-movie', 'movie'),
('movie', 'movie-actor', 'actor'), ('actor', 'actor-movie', 'movie')]
target_ntype = 'movie'
meta_paths_dict = {'MAM': [('movie', 'movie-actor', 'actor'),
('actor', 'actor-movie', 'movie')],
'MDM': [('movie', 'movie-director', 'director'),
('director', 'director-movie', 'movie')]
}
else:
raise ValueError('Unsupported dataset name {}'.format(name))
self._canonical_etypes = canonical_etypes
self._target_ntype = target_ntype
self._meta_paths_dict = meta_paths_dict
super(GTNDataset, self).__init__(
name,
url='https://s3.cn-north-1.amazonaws.com.cn/dgl-data/dataset/openhgnn/{}.zip'.format(name),
raw_dir=raw_dir,
force_reload=force_reload, verbose=verbose, transform=transform)
def process(self):
target_ntype = self.target_ntype
canonical_etypes = self._canonical_etypes
if os.path.isfile(os.path.join(self.save_path, 'graph.bin')):# Has cache
graph_path = os.path.join(self.save_path, 'graph.bin')
gs, _ = load_graphs(graph_path)
g = gs[0]
else:
with open(self.raw_path + '/node_features.pkl', 'rb') as f:
node_features = pickle.load(f)
with open(self.raw_path + '/edges.pkl', 'rb') as f:
edges = pickle.load(f)
with open(self.raw_path + '/labels.pkl', 'rb') as f:
labels = pickle.load(f)
num_nodes = edges[0].shape[0]
assert len(canonical_etypes) == len(edges)
ntype_mask = dict()
ntype_idmap = dict()
ntypes = set()
data_dict = {}
# create dgl graph
for etype in canonical_etypes:
ntypes.add(etype[0])
ntypes.add(etype[2])
for ntype in ntypes:
ntype_mask[ntype] = np.zeros(num_nodes, dtype=bool)
ntype_idmap[ntype] = np.full(num_nodes, -1, dtype=int)
for i, etype in enumerate(canonical_etypes):
src_nodes = edges[i].nonzero()[0]
dst_nodes = edges[i].nonzero()[1]
src_ntype = etype[0]
dst_ntype = etype[2]
ntype_mask[src_ntype][src_nodes] = True
ntype_mask[dst_ntype][dst_nodes] = True
for ntype in ntypes:
ntype_idx = ntype_mask[ntype].nonzero()[0]
ntype_idmap[ntype][ntype_idx] = np.arange(ntype_idx.size)
for i, etype in enumerate(canonical_etypes):
src_nodes = edges[i].nonzero()[0]
dst_nodes = edges[i].nonzero()[1]
src_ntype = etype[0]
dst_ntype = etype[2]
data_dict[etype] = \
(th.from_numpy(ntype_idmap[src_ntype][src_nodes]).type(th.int64),
th.from_numpy(ntype_idmap[dst_ntype][dst_nodes]).type(th.int64))
g = dgl.heterograph(data_dict)
# split and label
all_label = np.full(g.num_nodes(target_ntype), -1, dtype=int)
for i, split in enumerate(['train', 'val', 'test']):
node = np.array(labels[i])[:, 0]
label = np.array(labels[i])[:, 1]
all_label[node] = label
g.nodes[target_ntype].data['{}_mask'.format(split)] = \
th.from_numpy(idx2mask(node, g.num_nodes(target_ntype))).type(th.bool)
g.nodes[target_ntype].data['label'] = th.from_numpy(all_label).type(th.long)
# node feature
node_features = th.from_numpy(node_features).type(th.FloatTensor)
for ntype in ntypes:
idx = ntype_mask[ntype].nonzero()[0]
g.nodes[ntype].data['h'] = node_features[idx]
self._g = g
self._num_classes = len(th.unique(self._g.nodes[self.target_ntype].data['label']))
self._in_dim = self._g.ndata['h'][self.target_ntype].shape[1]
def save(self):
graph_path = os.path.join(self.save_path, 'graph.bin')
save_graphs(graph_path, self._g)
def load(self):
graph_path = os.path.join(self.save_path, 'graph.bin')
gs, _ = load_graphs(graph_path)
self._g = gs[0]
self._num_classes = len(th.unique(self._g.nodes[self.target_ntype].data['label']))
self._in_dim = self._g.ndata['h'][self.target_ntype].shape[1]
def has_cache(self):
return os.path.isfile(os.path.join(self.save_path, 'graph.bin'))
@property
def target_ntype(self):
return self._target_ntype
@property
def category(self):
return self._target_ntype
@property
def num_classes(self):
return self._num_classes
@property
def meta_paths_dict(self):
return self._meta_paths_dict
@property
def in_dim(self):
return self._in_dim
def __getitem__(self, idx):
assert idx == 0
return self._g
def __len__(self):
return 1
class DBLP4GTNDataset(GTNDataset):
def __init__(self, raw_dir=None, force_reload=False, verbose=False, transform=None):
name = 'dblp4GTN'
super(DBLP4GTNDataset, self).__init__(name, raw_dir=raw_dir, force_reload=force_reload, verbose=verbose,
transform=transform)
class ACM4GTNDataset(GTNDataset):
def __init__(self, raw_dir=None, force_reload=False, verbose=False, transform=None):
name = 'acm4GTN'
super(ACM4GTNDataset, self).__init__(name, raw_dir=raw_dir, force_reload=force_reload, verbose=verbose,
transform=transform)
class IMDB4GTNDataset(GTNDataset):
def __init__(self, raw_dir=None, force_reload=False, verbose=False, transform=None):
name = 'imdb4GTN'
super(IMDB4GTNDataset, self).__init__(name, raw_dir=raw_dir, force_reload=force_reload, verbose=verbose,
transform=transform)