Source code for torch_geometric.datasets.gemsec

import os.path as osp
from typing import Callable, Optional

import numpy as np
import torch

from torch_geometric.data import Data, InMemoryDataset, download_url


[docs]class GemsecDeezer(InMemoryDataset): r"""The Deezer User Network datasets introduced in the `"GEMSEC: Graph Embedding with Self Clustering" <https://arxiv.org/abs/1802.03997>`_ paper. Nodes represent Deezer user and edges are mutual friendships. The task is multi-label multi-class node classification about the genres liked by the users. Args: root (str): Root directory where the dataset should be saved. name (str): The name of the dataset (:obj:`"HU"`, :obj:`"HR"`, :obj:`"RO"`). 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`) """ url = 'https://graphmining.ai/datasets/ptg/gemsec' def __init__( self, root: str, name: str, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None, force_reload: bool = False, ) -> None: self.name = name assert self.name in ['HU', 'HR', 'RO'] 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) -> str: return f'{self.name}.npz' @property def processed_file_names(self) -> str: return 'data.pt' def download(self) -> None: download_url(osp.join(self.url, self.name + '.npz'), self.raw_dir) def process(self) -> None: data = np.load(self.raw_paths[0], 'r', allow_pickle=True) y = torch.from_numpy(data['target']).to(torch.long) edge_index = torch.from_numpy(data['edges']).to(torch.long) edge_index = edge_index.t().contiguous() data = Data(y=y, edge_index=edge_index) if self.pre_transform is not None: data = self.pre_transform(data) self.save([data], self.processed_paths[0])