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|
import os
import os.path as osp
from typing import Callable, List, Optional
from torch_geometric.data import (
Data,
InMemoryDataset,
download_url,
extract_zip,
)
from torch_geometric.io import fs
class NeuroGraphDataset(InMemoryDataset):
r"""The NeuroGraph benchmark datasets from the
`"NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics"
<https://arxiv.org/abs/2306.06202>`_ paper.
:class:`NeuroGraphDataset` holds a collection of five neuroimaging graph
learning datasets that span multiple categories of demographics, mental
states, and cognitive traits.
See the `documentation
<https://neurograph.readthedocs.io/en/latest/NeuroGraph.html>`_ and the
`Github <https://github.com/Anwar-Said/NeuroGraph>`_ for more details.
+--------------------+---------+----------------------+
| Dataset | #Graphs | Task |
+====================+=========+======================+
| :obj:`HCPTask` | 7,443 | Graph Classification |
+--------------------+---------+----------------------+
| :obj:`HCPGender` | 1,078 | Graph Classification |
+--------------------+---------+----------------------+
| :obj:`HCPAge` | 1,065 | Graph Classification |
+--------------------+---------+----------------------+
| :obj:`HCPFI` | 1,071 | Graph Regression |
+--------------------+---------+----------------------+
| :obj:`HCPWM` | 1,078 | Graph Regression |
+--------------------+---------+----------------------+
Args:
root (str): Root directory where the dataset should be saved.
name (str): The name of the dataset (one of :obj:`"HCPGender"`,
:obj:`"HCPTask"`, :obj:`"HCPAge"`, :obj:`"HCPFI"`,
:obj:`"HCPWM"`).
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://vanderbilt.box.com/shared/static'
filenames = {
'HCPGender': 'r6hlz2arm7yiy6v6981cv2nzq3b0meax.zip',
'HCPTask': '8wzz4y17wpxg2stip7iybtmymnybwvma.zip',
'HCPAge': 'lzzks4472czy9f9vc8aikp7pdbknmtfe.zip',
'HCPWM': 'xtmpa6712fidi94x6kevpsddf9skuoxy.zip',
'HCPFI': 'g2md9h9snh7jh6eeay02k1kr9m4ido9f.zip',
}
def __init__(
self,
root: str,
name: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
assert name in self.filenames.keys()
self.name = name
super().__init__(root, transform, pre_transform, pre_filter,
force_reload=force_reload)
self.load(self.processed_paths[0])
@property
def raw_dir(self) -> str:
return osp.join(self.root, self.name, 'raw')
@property
def raw_file_names(self) -> str:
return 'data.pt'
@property
def processed_dir(self) -> str:
return osp.join(self.root, self.name, 'processed')
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self) -> None:
url = f'{self.url}/{self.filenames[self.name]}'
path = download_url(url, self.raw_dir)
extract_zip(path, self.raw_dir)
os.unlink(path)
os.rename(
osp.join(self.raw_dir, self.name, 'processed', f'{self.name}.pt'),
osp.join(self.raw_dir, 'data.pt'))
fs.rm(osp.join(self.raw_dir, self.name))
def process(self) -> None:
data, slices = fs.torch_load(self.raw_paths[0])
num_samples = slices['x'].size(0) - 1
data_list: List[Data] = []
for i in range(num_samples):
x = data.x[slices['x'][i]:slices['x'][i + 1]]
start = slices['edge_index'][i]
end = slices['edge_index'][i + 1]
edge_index = data.edge_index[:, start:end]
sample = Data(x=x, edge_index=edge_index, y=data.y[i])
if self.pre_filter is not None and not self.pre_filter(sample):
continue
if self.pre_transform is not None:
sample = self.pre_transform(sample)
data_list.append(sample)
self.save(data_list, self.processed_paths[0])
|
