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qm9_dataset.py
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qm9_dataset.py
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import os
import os.path as osp
import numpy as np
from tqdm import tqdm
import torch
from sklearn.utils import shuffle
from rdkit import Chem
from torch_geometric.data import Data, DataLoader, InMemoryDataset, download_url, extract_zip
HAR2EV = 27.211386246
KCALMOL2EV = 0.04336414
conversion = torch.tensor([
1., 1., HAR2EV, HAR2EV, HAR2EV, 1., HAR2EV, HAR2EV, HAR2EV, HAR2EV, HAR2EV,
1., KCALMOL2EV, KCALMOL2EV, KCALMOL2EV, KCALMOL2EV, 1., 1., 1.
])
types = {'H': 0, 'C': 1, 'N': 2, 'O': 3, 'F': 4}
class QM93D(InMemoryDataset):
r"""
A `Pytorch Geometric <https://pytorch-geometric.readthedocs.io/en/latest/index.html>`_ data interface for :obj:`QM9` dataset
which is from `"Quantum chemistry structures and properties of 134 kilo molecules" <https://www.nature.com/articles/sdata201422>`_ paper.
It connsists of about 130,000 equilibrium molecules with 12 regression targets:
:obj:`mu`, :obj:`alpha`, :obj:`homo`, :obj:`lumo`, :obj:`gap`, :obj:`r2`, :obj:`zpve`, :obj:`U0`, :obj:`U`, :obj:`H`, :obj:`G`, :obj:`Cv`.
Each molecule includes complete spatial information for the single low energy conformation of the atoms in the molecule.
.. note::
Based on the code of `QM9 in Pytorch Geometric <https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/datasets/qm9.html#QM9>`_.
Args:
root (string): the dataset folder will be located at root/qm9.
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`)
Example:
--------
>>> dataset = QM93D()
>>> target = 'mu'
>>> dataset.data.y = dataset.data[target]
>>> split_idx = dataset.get_idx_split(len(dataset.data.y), train_size=110000, valid_size=10000, seed=42)
>>> train_dataset, valid_dataset, test_dataset = dataset[split_idx['train']], dataset[split_idx['valid']], dataset[split_idx['test']]
>>> train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
>>> data = next(iter(train_loader))
>>> data
Batch(Cv=[32], G=[32], H=[32], U=[32], U0=[32], alpha=[32], batch=[579], gap=[32], homo=[32], lumo=[32], mu=[32], pos=[579, 3], ptr=[33], r2=[32], y=[32], z=[579], zpve=[32])
Where the attributes of the output data indicates:
* :obj:`z`: The atom type.
* :obj:`pos`: The 3D position for atoms.
* :obj:`y`: The target property for the graph (molecule).
* :obj:`batch`: The assignment vector which maps each node to its respective graph identifier and can help reconstructe single graphs
"""
def __init__(self, root = 'dataset/', transform = None, pre_transform = None, pre_filter = None):
self.raw_url = ('https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/'
'molnet_publish/qm9.zip')
self.raw_url2 = 'https://ndownloader.figshare.com/files/3195404'
self.folder = osp.join(root, 'qm9')
super(QM93D, self).__init__(self.folder, transform, pre_transform, pre_filter)
self.data, self.slices = torch.load(self.processed_paths[0])
@property
def raw_file_names(self):
return ['gdb9.sdf', 'gdb9.sdf.csv', 'uncharacterized.txt']
@property
def processed_file_names(self):
return 'qm9_pyg.pt'
def download(self):
file_path = download_url(self.raw_url, self.raw_dir)
extract_zip(file_path, self.raw_dir)
os.unlink(file_path)
file_path = download_url(self.raw_url2, self.raw_dir)
os.rename(osp.join(self.raw_dir, '3195404'),
osp.join(self.raw_dir, 'uncharacterized.txt'))
def process(self):
with open(self.raw_paths[1], 'r') as f:
target = [[float(x) for x in line.split(',')[1:20]]
for line in f.read().split('\n')[1:-1]]
y = torch.tensor(target, dtype=torch.float)
y = torch.cat([y[:, 3:], y[:, :3]], dim=-1)
y = y * conversion.view(1, -1)
with open(self.raw_paths[2], 'r') as f:
skip = [int(x.split()[0]) - 1 for x in f.read().split('\n')[9:-2]]
suppl = Chem.SDMolSupplier(self.raw_paths[0], removeHs=False,
sanitize=False)
data_list = []
for i, mol in enumerate(tqdm(suppl)):
if i in skip:
continue
conf = mol.GetConformer()
pos = conf.GetPositions()
pos = torch.tensor(pos, dtype=torch.float)
posc = pos - pos.mean(dim=0)
atomic_number = []
for atom in mol.GetAtoms():
atomic_number.append(atom.GetAtomicNum())
z = torch.tensor(atomic_number, dtype=torch.long)
data = Data(
z=z,
pos=pos,
posc=posc,
y=y[i].unsqueeze(0),
mu=y[i][0], alpha=y[i][1], homo=y[i][2], lumo=y[i][3], gap=y[i][4], r2=y[i][5], zpve=y[i][6], U0=y[i][7], U=y[i][12], H=y[i][13], G=y[i][14], Cv=y[i][15]
)
data_list.append(data)
if self.pre_filter is not None:
data_list = [data for data in data_list if self.pre_filter(data)]
if self.pre_transform is not None:
data_list = [self.pre_transform(data) for data in data_list]
data, slices = self.collate(data_list)
print('Saving...')
torch.save((data, slices), self.processed_paths[0])
def get_idx_split(self, data_size, train_size, valid_size, seed):
ids = shuffle(range(data_size), random_state=seed)
train_idx, val_idx, test_idx = torch.tensor(ids[:train_size]), torch.tensor(ids[train_size:train_size + valid_size]), torch.tensor(ids[train_size + valid_size:])
split_dict = {'train':train_idx, 'valid':val_idx, 'test':test_idx}
return split_dict
if __name__ == '__main__':
dataset = QM93D(root='dataset/')
print(dataset)
print(dataset.data.z.shape)
print(dataset.data.pos.shape)
target = 'mu'
dataset.data.y = dataset.data[target]
print(dataset.data.y.shape)
print(dataset.data.y)
print(dataset.data.mu)
split_idx = dataset.get_idx_split(len(dataset.data.y), train_size=110000, valid_size=10000, seed=42)
print(split_idx)
print(dataset[split_idx['train']])
train_dataset, valid_dataset, test_dataset = dataset[split_idx['train']], dataset[split_idx['valid']], dataset[split_idx['test']]
train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True)
data = next(iter(train_loader))
print(data)