Source code for torch_geometric.datasets.wikics

import json
import warnings
from itertools import chain
from typing import Callable, List, Optional

import torch

from import Data, InMemoryDataset, download_url
from torch_geometric.utils import to_undirected

[docs]class WikiCS(InMemoryDataset): r"""The semi-supervised Wikipedia-based dataset from the `"Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks" <>`_ paper, containing 11,701 nodes, 216,123 edges, 10 classes and 20 different training splits. Args: root (str): Root directory where the dataset should be saved. transform (callable, optional): A function/transform that takes in an :obj:`` 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:`` object and returns a transformed version. The data object will be transformed before being saved to disk. (default: :obj:`None`) is_undirected (bool, optional): Whether the graph is undirected. (default: :obj:`True`) force_reload (bool, optional): Whether to re-process the dataset. (default: :obj:`False`) """ url = '' def __init__( self, root: str, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None, is_undirected: Optional[bool] = None, force_reload: bool = False, ) -> None: if is_undirected is None: warnings.warn( f"The {self.__class__.__name__} dataset now returns an " f"undirected graph by default. Please explicitly specify " f"'is_undirected=False' to restore the old behavior.") is_undirected = True self.is_undirected = is_undirected super().__init__(root, transform, pre_transform, force_reload=force_reload) self.load(self.processed_paths[0]) @property def raw_file_names(self) -> List[str]: return ['data.json'] @property def processed_file_names(self) -> str: return '' if self.is_undirected else '' def download(self) -> None: for name in self.raw_file_names: download_url(f'{self.url}/{name}', self.raw_dir) def process(self) -> None: with open(self.raw_paths[0], 'r') as f: data = json.load(f) x = torch.tensor(data['features'], dtype=torch.float) y = torch.tensor(data['labels'], dtype=torch.long) edges = [[(i, j) for j in js] for i, js in enumerate(data['links'])] edges = list(chain(*edges)) # type: ignore edge_index = torch.tensor(edges, dtype=torch.long).t().contiguous() if self.is_undirected: edge_index = to_undirected(edge_index, num_nodes=x.size(0)) train_mask = torch.tensor(data['train_masks'], dtype=torch.bool) train_mask = train_mask.t().contiguous() val_mask = torch.tensor(data['val_masks'], dtype=torch.bool) val_mask = val_mask.t().contiguous() test_mask = torch.tensor(data['test_mask'], dtype=torch.bool) stopping_mask = torch.tensor(data['stopping_masks'], dtype=torch.bool) stopping_mask = stopping_mask.t().contiguous() data = Data(x=x, y=y, edge_index=edge_index, train_mask=train_mask, val_mask=val_mask, test_mask=test_mask, stopping_mask=stopping_mask) if self.pre_transform is not None: data = self.pre_transform(data)[data], self.processed_paths[0])