Source code for torch_geometric.datasets.nell

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

from torch_geometric.data import InMemoryDataset, download_url, extract_tar
from torch_geometric.io import fs, read_planetoid_data


[docs]class NELL(InMemoryDataset): r"""The NELL dataset, a knowledge graph from the `"Toward an Architecture for Never-Ending Language Learning" <https://www.cs.cmu.edu/~acarlson/papers/carlson-aaai10.pdf>`_ paper. The dataset is processed as in the `"Revisiting Semi-Supervised Learning with Graph Embeddings" <https://arxiv.org/abs/1603.08861>`_ paper. .. note:: Entity nodes are described by sparse feature vectors of type :class:`torch.sparse_csr_tensor`. 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`) **STATS:** .. list-table:: :widths: 10 10 10 10 :header-rows: 1 * - #nodes - #edges - #features - #classes * - 65,755 - 251,550 - 61,278 - 186 """ url = 'http://www.cs.cmu.edu/~zhiliny/data/nell_data.tar.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]: names = ['x', 'tx', 'allx', 'y', 'ty', 'ally', 'graph', 'test.index'] return [f'ind.nell.0.001.{name}' for name in names] @property def processed_file_names(self) -> str: return 'data.pt' def download(self) -> None: path = download_url(self.url, self.root) extract_tar(path, self.root) os.unlink(path) fs.rm(self.raw_dir) os.rename(osp.join(self.root, 'nell_data'), self.raw_dir) def process(self) -> None: data = read_planetoid_data(self.raw_dir, 'nell.0.001') data = data if self.pre_transform is None else self.pre_transform(data) self.save([data], self.processed_paths[0])