import torch
from torch_geometric.data import InMemoryDataset, download_url
from torch_geometric.io import read_planetoid_data
[docs]class Planetoid(InMemoryDataset):
r"""The citation network datasets "Cora", "CiteSeer" and "PubMed" from the
`"Revisiting Semi-Supervised Learning with Graph Embeddings"
<https://arxiv.org/abs/1603.08861>`_ paper.
Nodes represent documents and edges represent citation links.
Training, validation and test splits are given by binary masks.
Args:
root (string): Root directory where the dataset should be saved.
name (string): The name of the dataset (:obj:`"Cora"`,
:obj:`"CiteSeer"`, :obj:`"PubMed"`).
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`)
"""
url = 'https://github.com/kimiyoung/planetoid/raw/master/data'
def __init__(self, root, name, transform=None, pre_transform=None):
self.name = name
super(Planetoid, self).__init__(root, transform, pre_transform)
self.data, self.slices = torch.load(self.processed_paths[0])
@property
def raw_file_names(self):
names = ['x', 'tx', 'allx', 'y', 'ty', 'ally', 'graph', 'test.index']
return ['ind.{}.{}'.format(self.name.lower(), name) for name in names]
@property
def processed_file_names(self):
return 'data.pt'
def download(self):
for name in self.raw_file_names:
download_url('{}/{}'.format(self.url, name), self.raw_dir)
def process(self):
data = read_planetoid_data(self.raw_dir, self.name)
data = data if self.pre_transform is None else self.pre_transform(data)
torch.save(self.collate([data]), self.processed_paths[0])
def __repr__(self):
return '{}()'.format(self.name)