from typing import Optional, Callable
import os.path as osp
from torch_sparse import coalesce
import torch
import numpy as np
from torch_geometric.data import Data, InMemoryDataset, download_url
[docs]class WikipediaNetwork(InMemoryDataset):
r"""The code is modified from torch_geometric.datasets.WikipediaNetwork (v1.6.3)
Args:
root (string): Root directory where the dataset should be saved.
name (string): The name of the dataset (:obj:`"Cornell"`,
:obj:`"Chameleon"` :obj:`"Squirrel"`).
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, name: str, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None):
self.name = name.lower()
assert self.name in ['chameleon', 'squirrel']
self.url = ('https://raw.githubusercontent.com/graphdml-uiuc-jlu/'
'geom-gcn/f1fc0d14b3b019c562737240d06ec83b07d16a8f')
super(WikipediaNetwork, self).__init__(root, transform, pre_transform)
self.data, self.slices = torch.load(self.processed_paths[0])
@property
def raw_dir(self):
return osp.join(self.root, self.name, 'raw')
@property
def processed_dir(self):
return osp.join(self.root, self.name, 'processed')
@property
def raw_file_names(self):
return ['out1_node_feature_label.txt', 'out1_graph_edges.txt'] + [
'{}_split_0.6_0.2_{}.npz'.format(self.name, i) for i in range(10)
]
@property
def processed_file_names(self):
return 'wikipedianetwork.pt'
[docs] def download(self):
for f in self.raw_file_names[:2]:
download_url(f'{self.url}/new_data/{self.name}/{f}', self.raw_dir)
for f in self.raw_file_names[2:]:
download_url(f'{self.url}/splits/{f}', self.raw_dir)
[docs] def process(self):
with open(self.raw_paths[0], 'r') as f:
data = f.read().split('\n')[1:-1]
x = [[float(v) for v in r.split('\t')[1].split(',')] for r in data]
x = torch.tensor(x, dtype=torch.float)
y = [int(r.split('\t')[2]) for r in data]
y = torch.tensor(y, dtype=torch.float)
with open(self.raw_paths[1], 'r') as f:
data = f.read().split('\n')[1:-1]
data = [[int(v) for v in r.split('\t')] for r in data]
edge_index = torch.tensor(data, dtype=torch.long).t().contiguous()
edge_index, _ = coalesce(edge_index, None, x.size(0), x.size(0))
train_masks, val_masks, test_masks = [], [], []
for f in self.raw_paths[2:]:
tmp = np.load(f)
train_masks += [torch.from_numpy(tmp['train_mask']).to(torch.bool)]
val_masks += [torch.from_numpy(tmp['val_mask']).to(torch.bool)]
test_masks += [torch.from_numpy(tmp['test_mask']).to(torch.bool)]
train_mask = torch.stack(train_masks, dim=1)
val_mask = torch.stack(val_masks, dim=1)
test_mask = torch.stack(test_masks, dim=1)
data = Data(x=x, edge_index=edge_index, y=y, train_mask=train_mask,
val_mask=val_mask, test_mask=test_mask)
data = data if self.pre_transform is None else self.pre_transform(data)
torch.save(self.collate([data]), self.processed_paths[0])