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import os.path as osp
from typing import Callable, List, Optional
import torch
from torch_geometric.data import Data, InMemoryDataset, download_url
from torch_geometric.utils import coalesce
class Airports(InMemoryDataset):
r"""The Airports dataset from the `"struc2vec: Learning Node
Representations from Structural Identity"
<https://arxiv.org/abs/1704.03165>`_ paper, where nodes denote airports
and labels correspond to activity levels.
Features are given by one-hot encoded node identifiers, as described in the
`"GraLSP: Graph Neural Networks with Local Structural Patterns"
` <https://arxiv.org/abs/1911.07675>`_ paper.
Args:
root (str): Root directory where the dataset should be saved.
name (str): The name of the dataset (:obj:`"USA"`, :obj:`"Brazil"`,
:obj:`"Europe"`).
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`)
"""
edge_url = ('https://github.com/leoribeiro/struc2vec/'
'raw/master/graph/{}-airports.edgelist')
label_url = ('https://github.com/leoribeiro/struc2vec/'
'raw/master/graph/labels-{}-airports.txt')
def __init__(
self,
root: str,
name: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
self.name = name.lower()
assert self.name in ['usa', 'brazil', 'europe']
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) -> List[str]:
return [
f'{self.name}-airports.edgelist',
f'labels-{self.name}-airports.txt',
]
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self) -> None:
download_url(self.edge_url.format(self.name), self.raw_dir)
download_url(self.label_url.format(self.name), self.raw_dir)
def process(self) -> None:
index_map, ys = {}, []
with open(self.raw_paths[1]) as f:
rows = f.read().split('\n')[1:-1]
for i, row in enumerate(rows):
idx, label = row.split()
index_map[int(idx)] = i
ys.append(int(label))
y = torch.tensor(ys, dtype=torch.long)
x = torch.eye(y.size(0))
edge_indices = []
with open(self.raw_paths[0]) as f:
rows = f.read().split('\n')[:-1]
for row in rows:
src, dst = row.split()
edge_indices.append([index_map[int(src)], index_map[int(dst)]])
edge_index = torch.tensor(edge_indices).t().contiguous()
edge_index = coalesce(edge_index, num_nodes=y.size(0))
data = Data(x=x, edge_index=edge_index, y=y)
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.name.capitalize()}Airports()'
|
