mlx_graphs.datasets.SuperPixelDataset

class mlx_graphs.datasets.SuperPixelDataset(name: Literal['MNIST', 'CIFAR10'], split: Literal['train', 'test'], use_features: bool = False, base_dir: str | None = None)

MNIST and CIFAR10 superpixel datasets for graph classification tasks converted fromt the original MINST and CIFAR10 images.

The datasets were introduced in http://arxiv.org/abs/2003.00982.

Parameters:

• name (Literal['MNIST', 'CIFAR10']) – name of the selected dataset

• split (Literal['train', 'test']) – split of the dataset to load

• use_features (bool) – if True, the adjacency matrix is computed from superpixels locations and features. If False, only from superpixels locations. Defaults to False.

• base_dir (Optional[str]) – directory where to store the datasets

__init__(name: Literal['MNIST', 'CIFAR10'], split: Literal['train', 'test'], use_features: bool = False, base_dir: str | None = None)

Methods

__init__(name, split[, use_features, base_dir])
download()	Download the dataset at self.raw_path.
load()	Load the processed dataset
process()	Process the dataset and store data in self.data
save()	Save the processed dataset

Attributes

name	Name of the dataset
num_edge_classes	Returns the number of edge classes to predict.
num_edge_features	Returns the number of edge features.
num_graph_classes	Returns the number of graph classes to predict.
num_graph_features	Returns the number of graph features.
num_graphs	Returns the number of graphs in the dataset.
num_items	Returns the number of items in the dataset.
num_node_classes	Returns the number of node classes to predict.
num_node_features	Returns the number of node features.
processed_path	The path where raw files are stored.
raw_path	The path where raw files are stored.