Source code for torch_geometric.datasets.citation_full

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

from torch_geometric.data import InMemoryDataset, download_url
from torch_geometric.io import read_npz


[docs]class CitationFull(InMemoryDataset): r"""The full citation network datasets from the `"Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking" <https://arxiv.org/abs/1707.03815>`_ paper. Nodes represent documents and edges represent citation links. Datasets include :obj:`"Cora"`, :obj:`"Cora_ML"`, :obj:`"CiteSeer"`, :obj:`"DBLP"`, :obj:`"PubMed"`. Args: root (str): Root directory where the dataset should be saved. name (str): The name of the dataset (:obj:`"Cora"`, :obj:`"Cora_ML"` :obj:`"CiteSeer"`, :obj:`"DBLP"`, :obj:`"PubMed"`). 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`) to_undirected (bool, optional): Whether the original graph is converted to an undirected one. (default: :obj:`True`) force_reload (bool, optional): Whether to re-process the dataset. (default: :obj:`False`) **STATS:** .. list-table:: :widths: 10 10 10 10 10 :header-rows: 1 * - Name - #nodes - #edges - #features - #classes * - Cora - 19,793 - 126,842 - 8,710 - 70 * - Cora_ML - 2,995 - 16,316 - 2,879 - 7 * - CiteSeer - 4,230 - 10,674 - 602 - 6 * - DBLP - 17,716 - 105,734 - 1,639 - 4 * - PubMed - 19,717 - 88,648 - 500 - 3 """ url = 'https://github.com/abojchevski/graph2gauss/raw/master/data/{}.npz' def __init__( self, root: str, name: str, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None, to_undirected: bool = True, force_reload: bool = False, ) -> None: self.name = name.lower() self.to_undirected = to_undirected assert self.name in ['cora', 'cora_ml', 'citeseer', 'dblp', 'pubmed'] 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: suffix = 'undirected' if self.to_undirected else 'directed' return f'data_{suffix}.pt' def download(self) -> None: download_url(self.url.format(self.name), self.raw_dir) def process(self) -> None: data = read_npz(self.raw_paths[0], to_undirected=self.to_undirected) 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()}Full()'
[docs]class CoraFull(CitationFull): r"""Alias for :class:`~torch_geometric.datasets.CitationFull` with :obj:`name="Cora"`. **STATS:** .. list-table:: :widths: 10 10 10 10 :header-rows: 1 * - #nodes - #edges - #features - #classes * - 19,793 - 126,842 - 8,710 - 70 """ def __init__( self, root: str, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None, ) -> None: super().__init__(root, 'cora', transform, pre_transform) def download(self) -> None: super().download() def process(self) -> None: super().process()