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
[docs]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']