1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
|
import os.path as osp
from typing import Callable, List, Optional
import numpy as np
import torch
from torch_geometric.data import InMemoryDataset
from torch_geometric.io import fs, read_planetoid_data
class Planetoid(InMemoryDataset):
r"""The citation network datasets :obj:`"Cora"`, :obj:`"CiteSeer"` and
:obj:`"PubMed"` from the `"Revisiting Semi-Supervised Learning with Graph
Embeddings" <https://arxiv.org/abs/1603.08861>`_ paper.
Nodes represent documents and edges represent citation links.
Training, validation and test splits are given by binary masks.
Args:
root (str): Root directory where the dataset should be saved.
name (str): The name of the dataset (:obj:`"Cora"`, :obj:`"CiteSeer"`,
:obj:`"PubMed"`).
split (str, optional): The type of dataset split (:obj:`"public"`,
:obj:`"full"`, :obj:`"geom-gcn"`, :obj:`"random"`).
If set to :obj:`"public"`, the split will be the public fixed split
from the `"Revisiting Semi-Supervised Learning with Graph
Embeddings" <https://arxiv.org/abs/1603.08861>`_ paper.
If set to :obj:`"full"`, all nodes except those in the validation
and test sets will be used for training (as in the
`"FastGCN: Fast Learning with Graph Convolutional Networks via
Importance Sampling" <https://arxiv.org/abs/1801.10247>`_ paper).
If set to :obj:`"geom-gcn"`, the 10 public fixed splits from the
`"Geom-GCN: Geometric Graph Convolutional Networks"
<https://openreview.net/forum?id=S1e2agrFvS>`_ paper are given.
If set to :obj:`"random"`, train, validation, and test sets will be
randomly generated, according to :obj:`num_train_per_class`,
:obj:`num_val` and :obj:`num_test`. (default: :obj:`"public"`)
num_train_per_class (int, optional): The number of training samples
per class in case of :obj:`"random"` split. (default: :obj:`20`)
num_val (int, optional): The number of validation samples in case of
:obj:`"random"` split. (default: :obj:`500`)
num_test (int, optional): The number of test samples in case of
:obj:`"random"` split. (default: :obj:`1000`)
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
* - Cora
- 2,708
- 10,556
- 1,433
- 7
* - CiteSeer
- 3,327
- 9,104
- 3,703
- 6
* - PubMed
- 19,717
- 88,648
- 500
- 3
"""
url = 'https://github.com/kimiyoung/planetoid/raw/master/data'
geom_gcn_url = ('https://raw.githubusercontent.com/graphdml-uiuc-jlu/'
'geom-gcn/master')
def __init__(
self,
root: str,
name: str,
split: str = "public",
num_train_per_class: int = 20,
num_val: int = 500,
num_test: int = 1000,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
self.name = name
self.split = split.lower()
assert self.split in ['public', 'full', 'geom-gcn', 'random']
super().__init__(root, transform, pre_transform,
force_reload=force_reload)
self.load(self.processed_paths[0])
if split == 'full':
data = self.get(0)
data.train_mask.fill_(True)
data.train_mask[data.val_mask | data.test_mask] = False
self.data, self.slices = self.collate([data])
elif split == 'random':
data = self.get(0)
data.train_mask.fill_(False)
for c in range(self.num_classes):
idx = (data.y == c).nonzero(as_tuple=False).view(-1)
idx = idx[torch.randperm(idx.size(0))[:num_train_per_class]]
data.train_mask[idx] = True
remaining = (~data.train_mask).nonzero(as_tuple=False).view(-1)
remaining = remaining[torch.randperm(remaining.size(0))]
data.val_mask.fill_(False)
data.val_mask[remaining[:num_val]] = True
data.test_mask.fill_(False)
data.test_mask[remaining[num_val:num_val + num_test]] = True
self.data, self.slices = self.collate([data])
@property
def raw_dir(self) -> str:
if self.split == 'geom-gcn':
return osp.join(self.root, self.name, 'geom-gcn', 'raw')
return osp.join(self.root, self.name, 'raw')
@property
def processed_dir(self) -> str:
if self.split == 'geom-gcn':
return osp.join(self.root, self.name, 'geom-gcn', 'processed')
return osp.join(self.root, self.name, 'processed')
@property
def raw_file_names(self) -> List[str]:
names = ['x', 'tx', 'allx', 'y', 'ty', 'ally', 'graph', 'test.index']
return [f'ind.{self.name.lower()}.{name}' for name in names]
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self) -> None:
for name in self.raw_file_names:
fs.cp(f'{self.url}/{name}', self.raw_dir)
if self.split == 'geom-gcn':
for i in range(10):
url = f'{self.geom_gcn_url}/splits/{self.name.lower()}'
fs.cp(f'{url}_split_0.6_0.2_{i}.npz', self.raw_dir)
def process(self) -> None:
data = read_planetoid_data(self.raw_dir, self.name)
if self.split == 'geom-gcn':
train_masks, val_masks, test_masks = [], [], []
for i in range(10):
name = f'{self.name.lower()}_split_0.6_0.2_{i}.npz'
splits = np.load(osp.join(self.raw_dir, name))
train_masks.append(torch.from_numpy(splits['train_mask']))
val_masks.append(torch.from_numpy(splits['val_mask']))
test_masks.append(torch.from_numpy(splits['test_mask']))
data.train_mask = torch.stack(train_masks, dim=1)
data.val_mask = torch.stack(val_masks, dim=1)
data.test_mask = torch.stack(test_masks, dim=1)
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}()'
|
