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import os
from typing import Callable, List, Optional
import torch
from torch_geometric.data import (
Data,
InMemoryDataset,
download_url,
extract_gz,
)
class EmailEUCore(InMemoryDataset):
r"""An e-mail communication network of a large European research
institution, taken from the `"Local Higher-order Graph Clustering"
<https://www-cs.stanford.edu/~jure/pubs/mappr-kdd17.pdf>`_ paper.
Nodes indicate members of the institution.
An edge between a pair of members indicates that they exchanged at least
one email.
Node labels indicate membership to one of the 42 departments.
Args:
root (str): Root directory where the dataset should be saved.
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`)
"""
urls = [
'https://snap.stanford.edu/data/email-Eu-core.txt.gz',
'https://snap.stanford.edu/data/email-Eu-core-department-labels.txt.gz'
]
def __init__(
self,
root: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
super().__init__(root, transform, pre_transform,
force_reload=force_reload)
self.load(self.processed_paths[0])
@property
def raw_file_names(self) -> List[str]:
return ['email-Eu-core.txt', 'email-Eu-core-department-labels.txt']
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self) -> None:
for url in self.urls:
path = download_url(url, self.raw_dir)
extract_gz(path, self.raw_dir)
os.unlink(path)
def process(self) -> None:
import pandas as pd
edge_index = pd.read_csv(self.raw_paths[0], sep=' ', header=None)
edge_index = torch.from_numpy(edge_index.values).t().contiguous()
y = pd.read_csv(self.raw_paths[1], sep=' ', header=None, usecols=[1])
y = torch.from_numpy(y.values).view(-1)
data = Data(edge_index=edge_index, y=y, num_nodes=y.size(0))
if self.pre_transform is not None:
data = self.pre_transform(data)
self.save([data], self.processed_paths[0])
|
