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import pickle
from typing import Callable, List, Optional
import torch
from torch_geometric.data import Data, InMemoryDataset, download_url
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" <https://arxiv.org/abs/2011.04573>`_ 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 = torch.utils.data.ConcatDataset([dataset1, dataset2])
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`)
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 = 'https://github.com/flyingdoog/PGExplainer/raw/master/dataset'
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 'data.pt'
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.save(data_list, self.processed_paths[0])
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