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import pickle
from typing import Callable, List, Optional
import numpy as np
import torch
from torch_geometric.data import Data, InMemoryDataset, download_url
class BAMultiShapesDataset(InMemoryDataset):
r"""The synthetic BA-Multi-Shapes graph classification dataset for
evaluating explainabilty algorithms, as described in the
`"Global Explainability of GNNs via Logic Combination of Learned Concepts"
<https://arxiv.org/abs/2210.07147>`_ paper.
Given three atomic motifs, namely House (H), Wheel (W), and Grid (G),
:class:`~torch_geometric.datasets.BAMultiShapesDataset` contains 1,000
graphs where each graph is obtained by attaching the motifs to a random
Barabasi-Albert (BA) as follows:
* class 0: :math:`\emptyset \lor H \lor W \lor G \lor \{ H, W, G \}`
* class 1: :math:`(H \land W) \lor (H \land G) \lor (W \land G)`
This dataset is pre-computed from the official implementation.
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`)
pre_filter (callable, optional): A function that takes in an
:obj:`torch_geometric.data.Data` object and returns a boolean
value, indicating whether the data object should be included in the
final dataset. (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
- 40
- ~87.0
- 10
- 2
"""
url = ('https://github.com/steveazzolin/gnn_logic_global_expl/raw/master/'
'datasets/BAMultiShapes/BAMultiShapes.pkl')
def __init__(
self,
root: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
super().__init__(root, transform, pre_transform, pre_filter,
force_reload=force_reload)
self.load(self.processed_paths[0])
@property
def raw_file_names(self) -> str:
return 'BAMultiShapes.pkl'
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self) -> None:
download_url(self.url, self.raw_dir)
def process(self) -> None:
with open(self.raw_paths[0], 'rb') as f:
adjs, xs, ys = pickle.load(f)
data_list: List[Data] = []
for adj, x, y in zip(adjs, xs, ys):
edge_index = torch.from_numpy(adj).nonzero().t()
x = torch.from_numpy(np.array(x)).to(torch.float)
data = Data(x=x, edge_index=edge_index, y=y)
if self.pre_filter is not None and not self.pre_filter(data):
continue
if self.pre_transform is not None:
data = self.pre_transform(data)
data_list.append(data)
self.save(data_list, self.processed_paths[0])
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