class BaseSampler(data: Graph, device: torch.device, loss_info: Dict)

Bases: ABC

Base class for sampling of positive and negative edges for unsupervised loss function

Parameters:

• data – (Graph): Input dataset

• device – (device): Either ‘cuda’ or ‘cpu’

• loss_info – (dict): Dict of parameters of unsupervised loss function

abstract sample(batch: torch_geometric.typing.Tensor) → torch_geometric.typing.Tensor

Sample edges. Must be implemented

Parameters:

batch – (Batch): Nodes for sampling positive edges for them

class BaseSamplerWithNegative(data: Graph, device: torch.device, loss_info: Dict)

Bases: BaseSampler

Sampler for negative edges

Parameters:

• data – (Graph): Input Graph data

• device – (device): Either ‘cuda’ or ‘cpu’

sample(batch: torch_geometric.data.Batch) → Tuple[torch_geometric.typing.Tensor, torch_geometric.typing.Tensor]

Sample positive and negative edges for batch nodes

Parameters:

batch – (Batch): Nodes for positive and negative sampling from them

Returns:

(Tensor, Tensor): positive and negative samples