pyagc.transforms.RandomMaskFeat
- class RandomMaskFeat(p: float = 0.5, node_attrs: Optional[List[str]] = ['x'], edge_attrs: Optional[List[str]] = [], inplace: bool = False)[source]
Bases:
BaseTransformRandomly masks columns of node and/or edge feature tensors (functional name:
random_mask_feat), as described in the “Graph Contrastive Learning with Augmentations” paper.- Parameters:
p (float, optional) – The probability of masking a feature column. Default is
0.5.node_attrs (List[str], optional) – The names of node features to mask. Default is
["x"].edge_attrs (List[str], optional) – The names of edge features to mask. Default is
[].inplace (bool, optional) – If set to
False, will clone the input data object and feature tensors before applying the transform. Default isFalse.
- __init__(p: float = 0.5, node_attrs: Optional[List[str]] = ['x'], edge_attrs: Optional[List[str]] = [], inplace: bool = False)[source]
Methods
__init__([p, node_attrs, edge_attrs, inplace])forward(data)Applies random feature masking to node and edge features.
- forward(data: Union[Data, HeteroData]) Union[Data, HeteroData][source]
Applies random feature masking to node and edge features.
- Return type:
Union[Data,HeteroData]