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
|
import os.path as osp
from typing import Callable, List, Optional
import numpy as np
import torch
from torch_geometric.data import Data, InMemoryDataset, extract_tar
class OMDB(InMemoryDataset):
r"""The `Organic Materials Database (OMDB)
<https://omdb.mathub.io/dataset>`__ of bulk organic crystals.
Args:
root (str): Root directory where the dataset should be saved.
train (bool, optional): If :obj:`True`, loads the training dataset,
otherwise the test dataset. (default: :obj:`True`)
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`)
"""
url = 'https://omdb.mathub.io/dataset'
def __init__(
self,
root: str,
train: bool = True,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
super().__init__(root, transform, pre_transform, pre_filter,
force_reload=force_reload)
path = self.processed_paths[0] if train else self.processed_paths[1]
self.load(path)
@property
def raw_file_names(self) -> str:
return 'OMDB-GAP1_v1.1.tar.gz'
@property
def processed_file_names(self) -> List[str]:
return ['train_data.pt', 'test_data.pt']
def download(self) -> None:
raise RuntimeError(
f"Dataset not found. Please download '{self.raw_file_names}' from "
f"'{self.url}' and move it to '{self.raw_dir}'")
def process(self) -> None:
from ase.io import read
extract_tar(self.raw_paths[0], self.raw_dir, log=False)
materials = read(osp.join(self.raw_dir, 'structures.xyz'), index=':')
bandgaps = np.loadtxt(osp.join(self.raw_dir, 'bandgaps.csv'))
data_list = []
for material, bandgap in zip(materials, bandgaps):
pos = torch.from_numpy(material.get_positions()).to(torch.float)
z = torch.from_numpy(material.get_atomic_numbers()).to(torch.int64)
y = torch.tensor([float(bandgap)])
data_list.append(Data(z=z, pos=pos, y=y))
train_data = data_list[:10000]
test_data = data_list[10000:]
if self.pre_filter is not None:
train_data = [d for d in train_data if self.pre_filter(d)]
test_data = [d for d in test_data if self.pre_filter(d)]
if self.pre_transform is not None:
train_data = [self.pre_transform(d) for d in train_data]
test_data = [self.pre_transform(d) for d in test_data]
self.save(train_data, self.processed_paths[0])
self.save(test_data, self.processed_paths[1])
|
