import os.path as osp
from typing import Callable, Optional
from torch_geometric.data import InMemoryDataset, download_url
from torch_geometric.io import read_npz
[docs]class Coauthor(InMemoryDataset):
r"""The Coauthor CS and Coauthor Physics networks from the
`"Pitfalls of Graph Neural Network Evaluation"
<https://arxiv.org/abs/1811.05868>`_ paper.
Nodes represent authors that are connected by an edge if they co-authored a
paper.
Given paper keywords for each author's papers, the task is to map authors
to their respective field of study.
Args:
root (str): Root directory where the dataset should be saved.
name (str): The name of the dataset (:obj:`"CS"`, :obj:`"Physics"`).
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`)
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
* - Name
- #nodes
- #edges
- #features
- #classes
* - CS
- 18,333
- 163,788
- 6,805
- 15
* - Physics
- 34,493
- 495,924
- 8,415
- 5
"""
url = 'https://github.com/shchur/gnn-benchmark/raw/master/data/npz/'
def __init__(
self,
root: str,
name: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
assert name.lower() in ['cs', 'physics']
self.name = 'CS' if name.lower() == 'cs' else 'Physics'
super().__init__(root, transform, pre_transform,
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 processed_dir(self) -> str:
return osp.join(self.root, self.name, 'processed')
@property
def raw_file_names(self) -> str:
return f'ms_academic_{self.name[:3].lower()}.npz'
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self) -> None:
download_url(self.url + self.raw_file_names, self.raw_dir)
def process(self) -> None:
data = read_npz(self.raw_paths[0], to_undirected=True)
data = data if self.pre_transform is None else self.pre_transform(data)
self.save([data], self.processed_paths[0])
def __repr__(self) -> str:
return f'{self.__class__.__name__}{self.name}()'