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import os
import os.path as osp
from typing import Callable, Dict, List, Optional
import torch
from torch_geometric.data import (
Data,
InMemoryDataset,
download_url,
extract_tar,
)
from torch_geometric.io import fs
class Wikidata5M(InMemoryDataset):
r"""The Wikidata-5M dataset from the `"KEPLER: A Unified Model for
Knowledge Embedding and Pre-trained Language Representation"
<https://arxiv.org/abs/1911.06136>`_ paper,
containing 4,594,485 entities, 822 relations,
20,614,279 train triples, 5,163 validation triples, and 5,133 test triples.
`Wikidata-5M <https://deepgraphlearning.github.io/project/wikidata5m>`_
is a large-scale knowledge graph dataset with aligned corpus
extracted form Wikidata.
Args:
root (str): Root directory where the dataset should be saved.
setting (str, optional):
If :obj:`"transductive"`, loads the transductive dataset.
If :obj:`"inductive"`, loads the inductive dataset.
(default: :obj:`"transductive"`)
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`)
"""
def __init__(
self,
root: str,
setting: str = 'transductive',
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
if setting not in {'transductive', 'inductive'}:
raise ValueError(f"Invalid 'setting' argument (got '{setting}')")
self.setting = setting
self.urls = [
('https://www.dropbox.com/s/7jp4ib8zo3i6m10/'
'wikidata5m_text.txt.gz?dl=1'),
'https://uni-bielefeld.sciebo.de/s/yuBKzBxsEc9j3hy/download',
]
if self.setting == 'inductive':
self.urls.append('https://www.dropbox.com/s/csed3cgal3m7rzo/'
'wikidata5m_inductive.tar.gz?dl=1')
else:
self.urls.append('https://www.dropbox.com/s/6sbhm0rwo4l73jq/'
'wikidata5m_transductive.tar.gz?dl=1')
super().__init__(root, transform, pre_transform,
force_reload=force_reload)
self.load(self.processed_paths[0])
@property
def raw_file_names(self) -> List[str]:
return [
'wikidata5m_text.txt.gz',
'download',
f'wikidata5m_{self.setting}_train.txt',
f'wikidata5m_{self.setting}_valid.txt',
f'wikidata5m_{self.setting}_test.txt',
]
@property
def processed_file_names(self) -> str:
return f'{self.setting}_data.pt'
def download(self) -> None:
for url in self.urls:
download_url(url, self.raw_dir)
path = osp.join(self.raw_dir, f'wikidata5m_{self.setting}.tar.gz')
extract_tar(path, self.raw_dir)
os.remove(path)
def process(self) -> None:
import gzip
entity_to_id: Dict[str, int] = {}
with gzip.open(self.raw_paths[0], 'rt') as f:
for i, line in enumerate(f):
values = line.strip().split('\t')
entity_to_id[values[0]] = i
x = fs.torch_load(self.raw_paths[1])
edge_indices = []
edge_types = []
split_indices = []
rel_to_id: Dict[str, int] = {}
for split, path in enumerate(self.raw_paths[2:]):
with open(path) as f:
for line in f:
head, rel, tail = line[:-1].split('\t')
src = entity_to_id[head]
dst = entity_to_id[tail]
edge_indices.append([src, dst])
if rel not in rel_to_id:
rel_to_id[rel] = len(rel_to_id)
edge_types.append(rel_to_id[rel])
split_indices.append(split)
edge_index = torch.tensor(edge_indices).t().contiguous()
edge_type = torch.tensor(edge_types)
split_index = torch.tensor(split_indices)
data = Data(
x=x,
edge_index=edge_index,
edge_type=edge_type,
train_mask=split_index == 0,
val_mask=split_index == 1,
test_mask=split_index == 2,
)
if self.pre_transform is not None:
data = self.pre_transform(data)
self.save([data], self.processed_paths[0])
|
