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
|
import os
import os.path as osp
import pickle
from typing import Callable, List, Optional
import torch
from torch_geometric.data import (
Data,
InMemoryDataset,
download_url,
extract_zip,
)
from torch_geometric.io import fs
class AQSOL(InMemoryDataset):
r"""The AQSOL dataset from the `Benchmarking Graph Neural Networks
<http://arxiv.org/abs/2003.00982>`_ paper based on
`AqSolDB <https://www.nature.com/articles/s41597-019-0151-1>`_, a
standardized database of 9,982 molecular graphs with their aqueous
solubility values, collected from 9 different data sources.
The aqueous solubility targets are collected from experimental measurements
and standardized to LogS units in AqSolDB. These final values denote the
property to regress in the :class:`AQSOL` dataset. After filtering out few
graphs with no bonds/edges, the total number of molecular graphs is 9,833.
For each molecular graph, the node features are the types of heavy atoms
and the edge features are the types of bonds between them, similar as in
the :class:`~torch_geometric.datasets.ZINC` dataset.
Args:
root (str): Root directory where the dataset should be saved.
split (str, optional): If :obj:`"train"`, loads the training dataset.
If :obj:`"val"`, loads the validation dataset.
If :obj:`"test"`, loads the test dataset.
(default: :obj:`"train"`)
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`)
pre_filter (callable, optional): A function that takes in an
:obj:`torch_geometric.data.Data` object and returns a boolean
value, indicating whether the data object should be included in
the final dataset. (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
* - #graphs
- #nodes
- #edges
- #features
- #classes
* - 9,833
- ~17.6
- ~35.8
- 1
- 1
"""
url = 'https://www.dropbox.com/s/lzu9lmukwov12kt/aqsol_graph_raw.zip?dl=1'
def __init__(
self,
root: str,
split: str = 'train',
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
force_reload: bool = False,
):
assert split in ['train', 'val', 'test']
super().__init__(root, transform, pre_transform, pre_filter,
force_reload=force_reload)
path = osp.join(self.processed_dir, f'{split}.pt')
self.load(path)
@property
def raw_file_names(self) -> List[str]:
return [
'train.pickle', 'val.pickle', 'test.pickle', 'atom_dict.pickle',
'bond_dict.pickle'
]
@property
def processed_file_names(self) -> List[str]:
return ['train.pt', 'val.pt', 'test.pt']
def download(self) -> None:
fs.rm(self.raw_dir)
path = download_url(self.url, self.root)
extract_zip(path, self.root)
os.rename(osp.join(self.root, 'asqol_graph_raw'), self.raw_dir)
os.unlink(path)
def process(self) -> None:
for raw_path, path in zip(self.raw_paths, self.processed_paths):
with open(raw_path, 'rb') as f:
graphs = pickle.load(f)
data_list: List[Data] = []
for graph in graphs:
x, edge_attr, edge_index, y = graph
x = torch.from_numpy(x)
edge_attr = torch.from_numpy(edge_attr)
edge_index = torch.from_numpy(edge_index)
y = torch.tensor([y]).float()
if edge_index.numel() == 0:
continue # Skipping for graphs with no bonds/edges.
data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr,
y=y)
if self.pre_filter is not None and not self.pre_filter(data):
continue
if self.pre_transform is not None:
data = self.pre_transform(data)
data_list.append(data)
self.save(data_list, path)
def atoms(self) -> List[str]:
return [
'Br', 'C', 'N', 'O', 'Cl', 'Zn', 'F', 'P', 'S', 'Na', 'Al', 'Si',
'Mo', 'Ca', 'W', 'Pb', 'B', 'V', 'Co', 'Mg', 'Bi', 'Fe', 'Ba', 'K',
'Ti', 'Sn', 'Cd', 'I', 'Re', 'Sr', 'H', 'Cu', 'Ni', 'Lu', 'Pr',
'Te', 'Ce', 'Nd', 'Gd', 'Zr', 'Mn', 'As', 'Hg', 'Sb', 'Cr', 'Se',
'La', 'Dy', 'Y', 'Pd', 'Ag', 'In', 'Li', 'Rh', 'Nb', 'Hf', 'Cs',
'Ru', 'Au', 'Sm', 'Ta', 'Pt', 'Ir', 'Be', 'Ge'
]
def bonds(self) -> List[str]:
return ['NONE', 'SINGLE', 'DOUBLE', 'AROMATIC', 'TRIPLE']
|
