import logging
import os
import os.path as osp
from collections import Counter
from typing import Any, Callable, List, Optional
import torch
from torch_geometric.data import (
Data,
HeteroData,
InMemoryDataset,
download_url,
extract_tar,
)
from torch_geometric.utils import index_sort
[docs]class Entities(InMemoryDataset):
r"""The relational entities networks :obj:`"AIFB"`, :obj:`"MUTAG"`,
:obj:`"BGS"` and :obj:`"AM"` from the `"Modeling Relational Data with Graph
Convolutional Networks" <https://arxiv.org/abs/1703.06103>`_ paper.
Training and test splits are given by node indices.
Args:
root (str): Root directory where the dataset should be saved.
name (str): The name of the dataset (:obj:`"AIFB"`, :obj:`"MUTAG"`,
:obj:`"BGS"`, :obj:`"AM"`).
hetero (bool, optional): If set to :obj:`True`, will save the dataset
as a :class:`~torch_geometric.data.HeteroData` object.
(default: :obj:`False`)
transform (callable, optional): A function/transform that takes in an
:obj:`torch_geometric.data.Data` 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 :obj:`torch_geometric.data.Data` 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`)
**STATS:**
.. list-table::
:widths: 10 10 10 10 10
:header-rows: 1
* - Name
- #nodes
- #edges
- #features
- #classes
* - AIFB
- 8,285
- 58,086
- 0
- 4
* - AM
- 1,666,764
- 11,976,642
- 0
- 11
* - MUTAG
- 23,644
- 148,454
- 0
- 2
* - BGS
- 333,845
- 1,832,398
- 0
- 2
"""
url = 'https://data.dgl.ai/dataset/{}.tgz'
def __init__(
self,
root: str,
name: str,
hetero: bool = False,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
self.name = name.lower()
self.hetero = hetero
assert self.name in ['aifb', 'am', 'mutag', 'bgs']
super().__init__(root, transform, pre_transform,
force_reload=force_reload)
if hetero:
self.load(self.processed_paths[0], data_cls=HeteroData)
else:
self.load(self.processed_paths[0], data_cls=Data)
@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 num_relations(self) -> int:
return int(self._data.edge_type.max()) + 1 # type: ignore
@property
def num_classes(self) -> int:
return int(self._data.train_y.max()) + 1 # type: ignore
@property
def raw_file_names(self) -> List[str]:
return [
f'{self.name}_stripped.nt.gz',
'completeDataset.tsv',
'trainingSet.tsv',
'testSet.tsv',
]
@property
def processed_file_names(self) -> str:
return 'hetero_data.pt' if self.hetero else 'data.pt'
def download(self) -> None:
path = download_url(self.url.format(self.name), self.root)
extract_tar(path, self.raw_dir)
os.unlink(path)
def process(self) -> None:
import gzip
import pandas as pd
import rdflib as rdf
graph_file, task_file, train_file, test_file = self.raw_paths
with hide_stdout():
g = rdf.Graph()
with gzip.open(graph_file, 'rb') as f:
g.parse(file=f, format='nt') # type: ignore
freq = Counter(g.predicates())
relations = sorted(set(g.predicates()), key=lambda p: -freq.get(p, 0))
subjects = set(g.subjects())
objects = set(g.objects())
nodes = list(subjects.union(objects))
N = len(nodes)
R = 2 * len(relations)
relations_dict = {rel: i for i, rel in enumerate(relations)}
nodes_dict = {str(node): i for i, node in enumerate(nodes)}
edges = []
for s, p, o in g.triples((None, None, None)):
src, dst = nodes_dict[str(s)], nodes_dict[str(o)]
rel = relations_dict[p]
edges.append([src, dst, 2 * rel])
edges.append([dst, src, 2 * rel + 1])
edge = torch.tensor(edges, dtype=torch.long).t().contiguous()
_, perm = index_sort(N * R * edge[0] + R * edge[1] + edge[2])
edge = edge[:, perm]
edge_index, edge_type = edge[:2], edge[2]
if self.name == 'am':
label_header = 'label_cateogory'
nodes_header = 'proxy'
elif self.name == 'aifb':
label_header = 'label_affiliation'
nodes_header = 'person'
elif self.name == 'mutag':
label_header = 'label_mutagenic'
nodes_header = 'bond'
elif self.name == 'bgs':
label_header = 'label_lithogenesis'
nodes_header = 'rock'
labels_df = pd.read_csv(task_file, sep='\t')
labels_set = set(labels_df[label_header].values.tolist())
labels_dict = {lab: i for i, lab in enumerate(list(labels_set))}
train_labels_df = pd.read_csv(train_file, sep='\t')
train_indices, train_labels = [], []
for nod, lab in zip(train_labels_df[nodes_header].values,
train_labels_df[label_header].values):
train_indices.append(nodes_dict[nod])
train_labels.append(labels_dict[lab])
train_idx = torch.tensor(train_indices, dtype=torch.long)
train_y = torch.tensor(train_labels, dtype=torch.long)
test_labels_df = pd.read_csv(test_file, sep='\t')
test_indices, test_labels = [], []
for nod, lab in zip(test_labels_df[nodes_header].values,
test_labels_df[label_header].values):
test_indices.append(nodes_dict[nod])
test_labels.append(labels_dict[lab])
test_idx = torch.tensor(test_indices, dtype=torch.long)
test_y = torch.tensor(test_labels, dtype=torch.long)
data = Data(edge_index=edge_index, edge_type=edge_type,
train_idx=train_idx, train_y=train_y, test_idx=test_idx,
test_y=test_y, num_nodes=N)
if self.hetero:
data = data.to_heterogeneous(node_type_names=['v'])
self.save([data], self.processed_paths[0])
def __repr__(self) -> str:
return f'{self.name.upper()}{self.__class__.__name__}()'
class hide_stdout:
def __enter__(self) -> None:
self.level = logging.getLogger().level
logging.getLogger().setLevel(logging.ERROR)
def __exit__(self, *args: Any) -> None:
logging.getLogger().setLevel(self.level)