Source code for torch_geometric.datasets.planetoid

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

import numpy as np
import torch

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


[docs]class Planetoid(InMemoryDataset): r"""The citation network datasets :obj:`"Cora"`, :obj:`"CiteSeer"` and :obj:`"PubMed"` from the `"Revisiting Semi-Supervised Learning with Graph Embeddings" <https://arxiv.org/abs/1603.08861>`_ paper. Nodes represent documents and edges represent citation links. Training, validation and test splits are given by binary masks. Args: root (str): Root directory where the dataset should be saved. name (str): The name of the dataset (:obj:`"Cora"`, :obj:`"CiteSeer"`, :obj:`"PubMed"`). split (str, optional): The type of dataset split (:obj:`"public"`, :obj:`"full"`, :obj:`"geom-gcn"`, :obj:`"random"`). If set to :obj:`"public"`, the split will be the public fixed split from the `"Revisiting Semi-Supervised Learning with Graph Embeddings" <https://arxiv.org/abs/1603.08861>`_ paper. If set to :obj:`"full"`, all nodes except those in the validation and test sets will be used for training (as in the `"FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling" <https://arxiv.org/abs/1801.10247>`_ paper). If set to :obj:`"geom-gcn"`, the 10 public fixed splits from the `"Geom-GCN: Geometric Graph Convolutional Networks" <https://openreview.net/forum?id=S1e2agrFvS>`_ paper are given. If set to :obj:`"random"`, train, validation, and test sets will be randomly generated, according to :obj:`num_train_per_class`, :obj:`num_val` and :obj:`num_test`. (default: :obj:`"public"`) num_train_per_class (int, optional): The number of training samples per class in case of :obj:`"random"` split. (default: :obj:`20`) num_val (int, optional): The number of validation samples in case of :obj:`"random"` split. (default: :obj:`500`) num_test (int, optional): The number of test samples in case of :obj:`"random"` split. (default: :obj:`1000`) 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 10 :header-rows: 1 * - Name - #nodes - #edges - #features - #classes * - Cora - 2,708 - 10,556 - 1,433 - 7 * - CiteSeer - 3,327 - 9,104 - 3,703 - 6 * - PubMed - 19,717 - 88,648 - 500 - 3 """ url = 'https://github.com/kimiyoung/planetoid/raw/master/data' geom_gcn_url = ('https://raw.githubusercontent.com/graphdml-uiuc-jlu/' 'geom-gcn/master') def __init__( self, root: str, name: str, split: str = "public", num_train_per_class: int = 20, num_val: int = 500, num_test: int = 1000, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None, force_reload: bool = False, ) -> None: self.name = name self.split = split.lower() assert self.split in ['public', 'full', 'geom-gcn', 'random'] super().__init__(root, transform, pre_transform, force_reload=force_reload) self.load(self.processed_paths[0]) if split == 'full': data = self.get(0) data.train_mask.fill_(True) data.train_mask[data.val_mask | data.test_mask] = False self.data, self.slices = self.collate([data]) elif split == 'random': data = self.get(0) data.train_mask.fill_(False) for c in range(self.num_classes): idx = (data.y == c).nonzero(as_tuple=False).view(-1) idx = idx[torch.randperm(idx.size(0))[:num_train_per_class]] data.train_mask[idx] = True remaining = (~data.train_mask).nonzero(as_tuple=False).view(-1) remaining = remaining[torch.randperm(remaining.size(0))] data.val_mask.fill_(False) data.val_mask[remaining[:num_val]] = True data.test_mask.fill_(False) data.test_mask[remaining[num_val:num_val + num_test]] = True self.data, self.slices = self.collate([data]) @property def raw_dir(self) -> str: if self.split == 'geom-gcn': return osp.join(self.root, self.name, 'geom-gcn', 'raw') return osp.join(self.root, self.name, 'raw') @property def processed_dir(self) -> str: if self.split == 'geom-gcn': return osp.join(self.root, self.name, 'geom-gcn', 'processed') return osp.join(self.root, self.name, 'processed') @property def raw_file_names(self) -> List[str]: names = ['x', 'tx', 'allx', 'y', 'ty', 'ally', 'graph', 'test.index'] return [f'ind.{self.name.lower()}.{name}' for name in names] @property def processed_file_names(self) -> str: return 'data.pt' def download(self) -> None: for name in self.raw_file_names: fs.cp(f'{self.url}/{name}', self.raw_dir) if self.split == 'geom-gcn': for i in range(10): url = f'{self.geom_gcn_url}/splits/{self.name.lower()}' fs.cp(f'{url}_split_0.6_0.2_{i}.npz', self.raw_dir) def process(self) -> None: data = read_planetoid_data(self.raw_dir, self.name) if self.split == 'geom-gcn': train_masks, val_masks, test_masks = [], [], [] for i in range(10): name = f'{self.name.lower()}_split_0.6_0.2_{i}.npz' splits = np.load(osp.join(self.raw_dir, name)) train_masks.append(torch.from_numpy(splits['train_mask'])) val_masks.append(torch.from_numpy(splits['val_mask'])) test_masks.append(torch.from_numpy(splits['test_mask'])) data.train_mask = torch.stack(train_masks, dim=1) data.val_mask = torch.stack(val_masks, dim=1) data.test_mask = torch.stack(test_masks, dim=1) 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}()'