from typing import Optional, Callable
import torch
import numpy as np
import scipy.sparse as sp
from torch_geometric.data import Data, InMemoryDataset, download_url
[docs]class DIGRAC_real_data(InMemoryDataset):
r"""Data loader for the data sets used in the
`DIGRAC: Digraph Clustering Based on Flow Imbalance" <https://proceedings.mlr.press/v198/he22b.html>`_ paper.
Args:
name (str): Name of the data set, choices are: 'blog', 'wikitalk', 'migration', 'lead_lag"+str(year) (year from 2001 to 2019).
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`)
"""
def __init__(self, name: str, root: str, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None):
self.name = name
url = ('https://github.com/SherylHYX/pytorch_geometric_signed_directed/raw/main/datasets/'+name+'.npz')
self.url = url
super().__init__(root, transform, pre_transform)
self.data, self.slices = torch.load(self.processed_paths[0])
@property
def raw_file_names(self):
return [self.name+'.npz']
@property
def processed_file_names(self):
return [self.name+'.pt']
[docs] def download(self):
download_url(self.url, self.raw_dir)
[docs] def process(self):
adj = sp.load_npz(self.raw_dir+'/'+self.name+'.npz')
coo = adj.tocoo()
values = torch.from_numpy(coo.data).float()
indices = np.vstack((coo.row, coo.col))
indices = torch.from_numpy(indices).long()
data = Data(num_nodes=indices.max().item() + 1,
edge_index=indices, edge_weight=values)
if self.pre_transform is not None:
data = self.pre_transform(data)
data, slices = self.collate([data])
torch.save((data, slices), self.processed_paths[0])