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import os.path as osp
from typing import Callable, List, Optional
import torch
from torch_geometric.data import Data, InMemoryDataset, download_url
class RelLinkPredDataset(InMemoryDataset):
r"""The relational link prediction datasets from the
`"Modeling Relational Data with Graph Convolutional Networks"
<https://arxiv.org/abs/1703.06103>`_ paper.
Training and test splits are given by sets of triplets.
Args:
root (str): Root directory where the dataset should be saved.
name (str): The name of the dataset (:obj:`"FB15k-237"`).
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
:header-rows: 1
* - #nodes
- #edges
- #features
- #classes
* - 14,541
- 544,230
- 0
- 0
"""
urls = {
'FB15k-237': ('https://raw.githubusercontent.com/MichSchli/'
'RelationPrediction/master/data/FB-Toutanova')
}
def __init__(
self,
root: str,
name: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
self.name = name
assert name in ['FB15k-237']
super().__init__(root, transform, pre_transform,
force_reload=force_reload)
self.load(self.processed_paths[0])
@property
def num_relations(self) -> int:
return int(self._data.edge_type.max()) + 1 # type: ignore
@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 processed_file_names(self) -> str:
return 'data.pt'
@property
def raw_file_names(self) -> List[str]:
return [
'entities.dict', 'relations.dict', 'test.txt', 'train.txt',
'valid.txt'
]
def download(self) -> None:
for file_name in self.raw_file_names:
download_url(f'{self.urls[self.name]}/{file_name}', self.raw_dir)
def process(self) -> None:
with open(osp.join(self.raw_dir, 'entities.dict')) as f:
lines = [row.split('\t') for row in f.read().split('\n')[:-1]]
entities_dict = {key: int(value) for value, key in lines}
with open(osp.join(self.raw_dir, 'relations.dict')) as f:
lines = [row.split('\t') for row in f.read().split('\n')[:-1]]
relations_dict = {key: int(value) for value, key in lines}
kwargs = {}
for split in ['train', 'valid', 'test']:
with open(osp.join(self.raw_dir, f'{split}.txt')) as f:
lines = [row.split('\t') for row in f.read().split('\n')[:-1]]
src = [entities_dict[row[0]] for row in lines]
rel = [relations_dict[row[1]] for row in lines]
dst = [entities_dict[row[2]] for row in lines]
kwargs[f'{split}_edge_index'] = torch.tensor([src, dst])
kwargs[f'{split}_edge_type'] = torch.tensor(rel)
# For message passing, we add reverse edges and types to the graph:
row, col = kwargs['train_edge_index']
edge_type = kwargs['train_edge_type']
row, col = torch.cat([row, col], dim=0), torch.cat([col, row], dim=0)
edge_index = torch.stack([row, col], dim=0)
edge_type = torch.cat([edge_type, edge_type + len(relations_dict)])
data = Data(num_nodes=len(entities_dict), edge_index=edge_index,
edge_type=edge_type, **kwargs)
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.name}()'
|
