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
[docs]class OMDB(InMemoryDataset):
r"""The `Organic Materials Database (OMDB)
<https://omdb.mathub.io/dataset>`__ of bulk organic crystals.
Args:
root (string): 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`)
"""
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):
super().__init__(root, transform, pre_transform, pre_filter)
path = self.processed_paths[0] if train else self.processed_paths[1]
self.data, self.slices = torch.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):
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):
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]
torch.save(self.collate(train_data), self.processed_paths[0])
torch.save(self.collate(test_data), self.processed_paths[1])