import os.path as osp
from typing import Callable, List, Optional
import numpy as np
import torch
from torch_geometric.data import Data, InMemoryDataset, download_url
from torch_geometric.utils import coalesce
[docs]class WebKB(InMemoryDataset):
r"""The WebKB datasets used in the
`"Geom-GCN: Geometric Graph Convolutional Networks"
<https://openreview.net/forum?id=S1e2agrFvS>`_ paper.
Nodes represent web pages and edges represent hyperlinks between them.
Node features are the bag-of-words representation of web pages.
The task is to classify the nodes into one of the five categories, student,
project, course, staff, and faculty.
Args:
root (str): Root directory where the dataset should be saved.
name (str): The name of the dataset (:obj:`"Cornell"`, :obj:`"Texas"`,
:obj:`"Wisconsin"`).
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 10
:header-rows: 1
* - Name
- #nodes
- #edges
- #features
- #classes
* - Cornell
- 183
- 298
- 1,703
- 5
* - Texas
- 183
- 325
- 1,703
- 5
* - Wisconsin
- 251
- 515
- 1,703
- 5
"""
url = 'https://raw.githubusercontent.com/graphdml-uiuc-jlu/geom-gcn/master'
def __init__(
self,
root: str,
name: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
self.name = name.lower()
assert self.name in ['cornell', 'texas', 'wisconsin']
super().__init__(root, transform, pre_transform,
force_reload=force_reload)
self.load(self.processed_paths[0])
@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 raw_file_names(self) -> List[str]:
out = ['out1_node_feature_label.txt', 'out1_graph_edges.txt']
out += [f'{self.name}_split_0.6_0.2_{i}.npz' for i in range(10)]
return out
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self) -> None:
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)
def process(self) -> None:
with open(self.raw_paths[0], 'r') as f:
lines = f.read().split('\n')[1:-1]
xs = [[float(value) for value in line.split('\t')[1].split(',')]
for line in lines]
x = torch.tensor(xs, dtype=torch.float)
ys = [int(line.split('\t')[2]) for line in lines]
y = torch.tensor(ys, dtype=torch.long)
with open(self.raw_paths[1], 'r') as f:
lines = f.read().split('\n')[1:-1]
edge_indices = [[int(value) for value in line.split('\t')]
for line in lines]
edge_index = torch.tensor(edge_indices).t().contiguous()
edge_index = coalesce(edge_index, num_nodes=x.size(0))
train_masks, val_masks, test_masks = [], [], []
for path in self.raw_paths[2:]:
tmp = np.load(path)
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)
self.save([data], self.processed_paths[0])
def __repr__(self) -> str:
return f'{self.name}()'