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import json
import os
import os.path as osp
from collections import defaultdict
from typing import Callable, Dict, List, Optional
import torch
from torch_geometric.data import (
HeteroData,
InMemoryDataset,
download_google_url,
extract_zip,
)
class HGBDataset(InMemoryDataset):
r"""A variety of heterogeneous graph benchmark datasets from the
`"Are We Really Making Much Progress? Revisiting, Benchmarking, and
Refining Heterogeneous Graph Neural Networks"
<http://keg.cs.tsinghua.edu.cn/jietang/publications/
KDD21-Lv-et-al-HeterGNN.pdf>`_ paper.
.. note::
Test labels are randomly given to prevent data leakage issues.
If you want to obtain final test performance, you will need to submit
your model predictions to the
`HGB leaderboard <https://www.biendata.xyz/hgb/>`_.
Args:
root (str): Root directory where the dataset should be saved.
name (str): The name of the dataset (one of :obj:`"ACM"`,
:obj:`"DBLP"`, :obj:`"Freebase"`, :obj:`"IMDB"`)
transform (callable, optional): A function/transform that takes in an
:class:`torch_geometric.data.HeteroData` object and returns a
transformed version. The data object will be transformed before
every access. (default: :obj:`None`)
pre_transform (callable, optional): A function/transform that takes in
an :class:`torch_geometric.data.HeteroData` object and returns a
transformed version. The data object will be transformed before
being saved to disk. (default: :obj:`None`)
force_reload (bool, optional): Whether to re-process the dataset.
(default: :obj:`False`)
"""
names = {
'acm': 'ACM',
'dblp': 'DBLP',
'freebase': 'Freebase',
'imdb': 'IMDB',
}
file_ids = {
'acm': '1xbJ4QE9pcDJOcALv7dYhHDCPITX2Iddz',
'dblp': '1fLLoy559V7jJaQ_9mQEsC06VKd6Qd3SC',
'freebase': '1vw-uqbroJZfFsWpriC1CWbtHCJMGdWJ7',
'imdb': '18qXmmwKJBrEJxVQaYwKTL3Ny3fPqJeJ2',
}
def __init__(
self,
root: str,
name: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
self.name = name.lower()
assert self.name in set(self.names.keys())
super().__init__(root, transform, pre_transform,
force_reload=force_reload)
self.load(self.processed_paths[0], data_cls=HeteroData)
@property
def raw_dir(self) -> str:
return osp.join(self.root, self.name, 'raw')
@property
def processed_dir(self) -> str:
return osp.join(self.root, self.name, 'processed')
@property
def raw_file_names(self) -> List[str]:
x = ['info.dat', 'node.dat', 'link.dat', 'label.dat', 'label.dat.test']
return [osp.join(self.names[self.name], f) for f in x]
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self) -> None:
id = self.file_ids[self.name]
path = download_google_url(id, self.raw_dir, 'data.zip')
extract_zip(path, self.raw_dir)
os.unlink(path)
def process(self) -> None:
data = HeteroData()
# node_types = {0: 'paper', 1, 'author', ...}
# edge_types = {0: ('paper', 'cite', 'paper'), ...}
if self.name in ['acm', 'dblp', 'imdb']:
with open(self.raw_paths[0]) as f: # `info.dat`
info = json.load(f)
n_types = info['node.dat']['node type']
n_types = {int(k): v for k, v in n_types.items()}
e_types = info['link.dat']['link type']
e_types = {int(k): tuple(v.values()) for k, v in e_types.items()}
for key, (src, dst, rel) in e_types.items():
src, dst = n_types[int(src)], n_types[int(dst)]
rel = rel.split('-')[1]
rel = rel if rel != dst and rel[1:] != dst else 'to'
e_types[key] = (src, rel, dst)
num_classes = len(info['label.dat']['node type']['0'])
elif self.name in ['freebase']:
with open(self.raw_paths[0]) as f: # `info.dat`
info = f.read().split('\n')
start = info.index('TYPE\tMEANING') + 1
end = info[start:].index('')
n_types = [v.split('\t\t') for v in info[start:start + end]]
n_types = {int(k): v.lower() for k, v in n_types}
e_types = {}
start = info.index('LINK\tSTART\tEND\tMEANING') + 1
end = info[start:].index('')
for key, row in enumerate(info[start:start + end]):
row = row.split('\t')[1:]
src, dst, rel = (v for v in row if v != '')
src, dst = n_types[int(src)], n_types[int(dst)]
rel = rel.split('-')[1]
e_types[key] = (src, rel, dst)
else: # Link prediction:
raise NotImplementedError
# Extract node information:
mapping_dict = {} # Maps global node indices to local ones.
x_dict = defaultdict(list)
num_nodes_dict: Dict[str, int] = defaultdict(int)
with open(self.raw_paths[1]) as f: # `node.dat`
xs = [v.split('\t') for v in f.read().split('\n')[:-1]]
for x in xs:
n_id, n_type = int(x[0]), n_types[int(x[2])]
mapping_dict[n_id] = num_nodes_dict[n_type]
num_nodes_dict[n_type] += 1
if len(x) >= 4: # Extract features (in case they are given).
x_dict[n_type].append([float(v) for v in x[3].split(',')])
for n_type in n_types.values():
if len(x_dict[n_type]) == 0:
data[n_type].num_nodes = num_nodes_dict[n_type]
else:
data[n_type].x = torch.tensor(x_dict[n_type])
edge_index_dict = defaultdict(list)
edge_weight_dict = defaultdict(list)
with open(self.raw_paths[2]) as f: # `link.dat`
edges = [v.split('\t') for v in f.read().split('\n')[:-1]]
for src, dst, rel, weight in edges:
e_type = e_types[int(rel)]
src, dst = mapping_dict[int(src)], mapping_dict[int(dst)]
edge_index_dict[e_type].append([src, dst])
edge_weight_dict[e_type].append(float(weight))
for e_type in e_types.values():
edge_index = torch.tensor(edge_index_dict[e_type])
edge_weight = torch.tensor(edge_weight_dict[e_type])
data[e_type].edge_index = edge_index.t().contiguous()
# Only add "weighted" edgel to the graph:
if not torch.allclose(edge_weight, torch.ones_like(edge_weight)):
data[e_type].edge_weight = edge_weight
# Node classification:
if self.name in ['acm', 'dblp', 'freebase', 'imdb']:
with open(self.raw_paths[3]) as f: # `label.dat`
train_ys = [v.split('\t') for v in f.read().split('\n')[:-1]]
with open(self.raw_paths[4]) as f: # `label.dat.test`
test_ys = [v.split('\t') for v in f.read().split('\n')[:-1]]
for y in train_ys:
n_id, n_type = mapping_dict[int(y[0])], n_types[int(y[2])]
if not hasattr(data[n_type], 'y'):
num_nodes = data[n_type].num_nodes
if self.name in ['imdb']: # multi-label
data[n_type].y = torch.zeros((num_nodes, num_classes))
else:
data[n_type].y = torch.full((num_nodes, ), -1).long()
data[n_type].train_mask = torch.zeros(num_nodes).bool()
data[n_type].test_mask = torch.zeros(num_nodes).bool()
if data[n_type].y.dim() > 1: # multi-label
for v in y[3].split(','):
data[n_type].y[n_id, int(v)] = 1
else:
data[n_type].y[n_id] = int(y[3])
data[n_type].train_mask[n_id] = True
for y in test_ys:
n_id, n_type = mapping_dict[int(y[0])], n_types[int(y[2])]
if data[n_type].y.dim() > 1: # multi-label
for v in y[3].split(','):
data[n_type].y[n_id, int(v)] = 1
else:
data[n_type].y[n_id] = int(y[3])
data[n_type].test_mask[n_id] = True
else: # Link prediction:
raise NotImplementedError
if self.pre_transform is not None:
data = self.pre_transform(data)
self.save([data], self.processed_paths[0])
def __repr__(self) -> str:
return f'{self.names[self.name]}()'
|
