Source code for torch_geometric.datasets.wikics

import json
from itertools import chain

import torch
from torch_geometric.data import InMemoryDataset, Data, download_url


[docs]class WikiCS(InMemoryDataset): r"""The semi-supervised Wikipedia-based dataset from the `"Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks" <https://arxiv.org/abs/2007.02901>`_ paper, containing 11,701 nodes, 216,123 edges, 10 classes and 20 different training splits. Args: root (string): 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`) """ url = 'https://github.com/pmernyei/wiki-cs-dataset/raw/master/dataset' def __init__(self, root, transform=None, pre_transform=None): super(WikiCS, self).__init__(root, transform, pre_transform) self.data, self.slices = torch.load(self.processed_paths[0]) @property def raw_file_names(self): return ['data.json'] @property def processed_file_names(self): return 'data.pt' def download(self): for name in self.raw_file_names: download_url('{}/{}'.format(self.url, name), self.raw_dir) def process(self): 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)) edge_index = torch.tensor(edges, dtype=torch.long).t().contiguous() 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) torch.save(self.collate([data]), self.processed_paths[0]) def __repr__(self): return '{}()'.format(self.__class__.__name__)