from typing import Optional, Callable, List
import os
import torch
from torch_geometric.data import (Data, InMemoryDataset, download_url,
extract_tar)
[docs]class PolBlogs(InMemoryDataset):
r"""The Political Blogs dataset from the `"The Political Blogosphere and
the 2004 US Election: Divided they Blog"
<https://dl.acm.org/doi/10.1145/1134271.1134277>`_ paper.
:class:`Polblogs` is a graph with 1,490 vertices (representing political
blogs) and 19,025 edges (links between blogs).
The links are automatically extracted from a crawl of the front page of the
blog.
Each vertex receives a label indicating the political leaning of the blog:
liberal or conservative.
Args:
root (string): 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`)
"""
url = 'https://netset.telecom-paris.fr/datasets/polblogs.tar.gz'
def __init__(self, root: str = None, transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None):
super().__init__(root, transform, pre_transform)
self.data, self.slices = torch.load(self.processed_paths[0])
@property
def raw_file_names(self) -> List[str]:
return ['adjacency.csv', 'labels.csv']
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self):
path = download_url(self.url, self.raw_dir)
extract_tar(path, self.raw_dir)
os.unlink(path)
def process(self):
import pandas as pd
edge_index = pd.read_csv(self.raw_paths[0], header=None)
edge_index = torch.from_numpy(edge_index.values).t().contiguous()
y = pd.read_csv(self.raw_paths[1], header=None)
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)
torch.save(self.collate([data]), self.processed_paths[0])