Source code for torch_geometric.datasets.ba2motif_dataset

import pickle
from typing import Callable, List, Optional

import torch

from import Data, InMemoryDataset, download_url

[docs]class BA2MotifDataset(InMemoryDataset): r"""The synthetic BA-2motifs graph classification dataset for evaluating explainabilty algorithms, as described in the `"Parameterized Explainer for Graph Neural Network" <>`_ paper. :class:`~torch_geometric.datasets.BA2MotifDataset` contains 1000 random Barabasi-Albert (BA) graphs. Half of the graphs are attached with a :class:`~torch_geometric.datasets.motif_generator.HouseMotif`, and the rest are attached with a five-node :class:`~torch_geometric.datasets.motif_generator.CycleMotif`. The graphs are assigned to one of the two classes according to the type of attached motifs. This dataset is pre-computed from the official implementation. If you want to create own variations of it, you can make use of the :class:`~torch_geometric.datasets.ExplainerDataset`: .. code-block:: python import torch from torch_geometric.datasets import ExplainerDataset from torch_geometric.datasets.graph_generator import BAGraph from torch_geometric.datasets.motif_generator import HouseMotif from torch_geometric.datasets.motif_generator import CycleMotif dataset1 = ExplainerDataset( graph_generator=BAGraph(num_nodes=25, num_edges=1), motif_generator=HouseMotif(), num_motifs=1, num_graphs=500, ) dataset2 = ExplainerDataset( graph_generator=BAGraph(num_nodes=25, num_edges=1), motif_generator=CycleMotif(5), num_motifs=1, num_graphs=500, ) dataset =[dataset1, dataset2]) 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`) 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 * - #graphs - #nodes - #edges - #features - #classes * - 1000 - 25 - ~51.0 - 10 - 2 """ url = '' filename = 'BA-2motif.pkl' 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) -> str: return self.filename @property def processed_file_names(self) -> str: return '' def download(self) -> None: download_url(f'{self.url}/{self.filename}', self.raw_dir) def process(self) -> None: with open(self.raw_paths[0], 'rb') as f: adj, x, y = pickle.load(f) adjs = torch.from_numpy(adj) xs = torch.from_numpy(x).to(torch.float) ys = torch.from_numpy(y) data_list: List[Data] = [] for i in range(xs.size(0)): edge_index = adjs[i].nonzero().t() x = xs[i] y = int(ys[i].nonzero()) data = Data(x=x, edge_index=edge_index, y=y) if self.pre_transform is not None: data = self.pre_transform(data) data_list.append(data), self.processed_paths[0])