FeatMaskΒΆ
-
class
dgl.transforms.
FeatMask
(p=0.5, node_feat_names=None, edge_feat_names=None)[source]ΒΆ Bases:
dgl.transforms.module.BaseTransform
Randomly mask columns of the node and edge feature tensors, as described in Graph Contrastive Learning with Augmentations.
- Parameters
p (float, optional) β Probability of masking a column of a feature tensor. Default: 0.5.
node_feat_names (list[str], optional) β The names of the node feature tensors to be masked. Default: None, which will not mask any node feature tensor.
edge_feat_names (list[str], optional) β The names of the edge features to be masked. Default: None, which will not mask any edge feature tensor.
Example
The following example uses PyTorch backend.
>>> import dgl >>> import torch >>> from dgl import FeatMask
Case1 : Mask node and edge feature tensors of a homogeneous graph.
>>> transform = FeatMask(node_feat_names=['h'], edge_feat_names=['w']) >>> g = dgl.rand_graph(5, 10) >>> g.ndata['h'] = torch.ones((g.num_nodes(), 10)) >>> g.edata['w'] = torch.ones((g.num_edges(), 10))
>>> g = transform(g) >>> print(g.ndata['h']) tensor([[0., 0., 1., 1., 0., 0., 1., 1., 1., 0.], [0., 0., 1., 1., 0., 0., 1., 1., 1., 0.], [0., 0., 1., 1., 0., 0., 1., 1., 1., 0.], [0., 0., 1., 1., 0., 0., 1., 1., 1., 0.], [0., 0., 1., 1., 0., 0., 1., 1., 1., 0.]]) >>> print(g.edata['w']) tensor([[1., 1., 0., 1., 0., 1., 0., 0., 0., 1.], [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.], [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.], [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.], [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.], [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.], [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.], [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.], [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.], [1., 1., 0., 1., 0., 1., 0., 0., 0., 1.]])
Case2 : Mask node and edge feature tensors of a heterogeneous graph.
>>> g = dgl.heterograph({ ... ('user', 'follows', 'user'): (torch.tensor([1, 2]), torch.tensor([3, 4])), ... ('player', 'plays', 'game'): (torch.tensor([2, 2]), torch.tensor([1, 1])) ... }) >>> g.ndata['h'] = {'game': torch.ones(2, 5), 'player': torch.ones(3, 5)} >>> g.edata['w'] = {('user', 'follows', 'user'): torch.ones(2, 5)} >>> print(g.ndata['h']['game']) tensor([[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.]]) >>> print(g.edata['w'][('user', 'follows', 'user')]) tensor([[1., 1., 1., 1., 1.], [1., 1., 1., 1., 1.]]) >>> g = transform(g) >>> print(g.ndata['h']['game']) tensor([[1., 1., 0., 1., 0.], [1., 1., 0., 1., 0.]]) >>> print(g.edata['w'][('user', 'follows', 'user')]) tensor([[0., 1., 0., 1., 0.], [0., 1., 0., 1., 0.]])