from typing import Callable, Optional
import torch
from torch_geometric.data import Data, InMemoryDataset, download_url
[docs]class QM7b(InMemoryDataset):
r"""The QM7b dataset from the `"MoleculeNet: A Benchmark for Molecular
Machine Learning" <https://arxiv.org/abs/1703.00564>`_ paper, consisting of
7,211 molecules with 14 regression targets.
Args:
root (str): Root directory where the dataset should be saved.
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
- #tasks
* - 7,211
- ~15.4
- ~245.0
- 0
- 14
"""
url = 'https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/qm7b.mat'
def __init__(
self,
root: str,
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)
self.load(self.processed_paths[0])
@property
def raw_file_names(self) -> str:
return 'qm7b.mat'
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self) -> None:
download_url(self.url, self.raw_dir)
def process(self) -> None:
from scipy.io import loadmat
data = loadmat(self.raw_paths[0])
coulomb_matrix = torch.from_numpy(data['X'])
target = torch.from_numpy(data['T']).to(torch.float)
data_list = []
for i in range(target.shape[0]):
edge_index = coulomb_matrix[i].nonzero(
as_tuple=False).t().contiguous()
edge_attr = coulomb_matrix[i, edge_index[0], edge_index[1]]
y = target[i].view(1, -1)
data = Data(edge_index=edge_index, edge_attr=edge_attr, y=y)
data.num_nodes = edge_index.max().item() + 1
data_list.append(data)
if self.pre_filter is not None:
data_list = [d for d in data_list if self.pre_filter(d)]
if self.pre_transform is not None:
data_list = [self.pre_transform(d) for d in data_list]
self.save(data_list, self.processed_paths[0])