import json
import warnings
from itertools import chain
from typing import Callable, List, Optional
import torch
from torch_geometric.data import Data, InMemoryDataset, download_url
from torch_geometric.utils import to_undirected
[docs]class WikiCS(InMemoryDataset):
r"""The semi-supervised Wikipedia-based dataset from the
`"Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks"
<https://arxiv.org/abs/2007.02901>`_ paper, containing 11,701 nodes,
216,123 edges, 10 classes and 20 different training splits.
Args:
root (str): Root directory where the dataset should be saved.
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`)
is_undirected (bool, optional): Whether the graph is undirected.
(default: :obj:`True`)
force_reload (bool, optional): Whether to re-process the dataset.
(default: :obj:`False`)
"""
url = 'https://github.com/pmernyei/wiki-cs-dataset/raw/master/dataset'
def __init__(
self,
root: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
is_undirected: Optional[bool] = None,
force_reload: bool = False,
) -> None:
if is_undirected is None:
warnings.warn(
f"The {self.__class__.__name__} dataset now returns an "
f"undirected graph by default. Please explicitly specify "
f"'is_undirected=False' to restore the old behavior.")
is_undirected = True
self.is_undirected = is_undirected
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 ['data.json']
@property
def processed_file_names(self) -> str:
return 'data_undirected.pt' if self.is_undirected else 'data.pt'
def download(self) -> None:
for name in self.raw_file_names:
download_url(f'{self.url}/{name}', self.raw_dir)
def process(self) -> None:
with open(self.raw_paths[0], 'r') as f:
data = json.load(f)
x = torch.tensor(data['features'], dtype=torch.float)
y = torch.tensor(data['labels'], dtype=torch.long)
edges = [[(i, j) for j in js] for i, js in enumerate(data['links'])]
edges = list(chain(*edges)) # type: ignore
edge_index = torch.tensor(edges, dtype=torch.long).t().contiguous()
if self.is_undirected:
edge_index = to_undirected(edge_index, num_nodes=x.size(0))
train_mask = torch.tensor(data['train_masks'], dtype=torch.bool)
train_mask = train_mask.t().contiguous()
val_mask = torch.tensor(data['val_masks'], dtype=torch.bool)
val_mask = val_mask.t().contiguous()
test_mask = torch.tensor(data['test_mask'], dtype=torch.bool)
stopping_mask = torch.tensor(data['stopping_masks'], dtype=torch.bool)
stopping_mask = stopping_mask.t().contiguous()
data = Data(x=x, y=y, edge_index=edge_index, train_mask=train_mask,
val_mask=val_mask, test_mask=test_mask,
stopping_mask=stopping_mask)
if self.pre_transform is not None:
data = self.pre_transform(data)
self.save([data], self.processed_paths[0])