from typing import Optional, Callable
import json
from itertools import chain
import torch
from torch_geometric.data import Data, InMemoryDataset, download_url
[docs]class WikiCS(InMemoryDataset):
r"""This is the copy of the torch_geometric.datasets.WikiCS (v1.6.3)
Args:
root (string): 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`)
"""
def __init__(self, root: str, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None):
self.url = 'https://github.com/pmernyei/wiki-cs-dataset/raw/master/dataset'
super(WikiCS, self).__init__(root, transform, pre_transform)
self.data, self.slices = torch.load(self.processed_paths[0])
@property
def raw_file_names(self):
return ['data.json']
@property
def processed_file_names(self):
return 'wikics.pt'
[docs] def download(self):
for name in self.raw_file_names:
download_url('{}/{}'.format(self.url, name), self.raw_dir)
[docs] def process(self):
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))
edge_index = torch.tensor(edges, dtype=torch.long).t().contiguous()
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)
torch.save(self.collate([data]), self.processed_paths[0])