GlobalUniform(k, exclude_self_loops=True, replace=False)¶
Negative sampler that randomly chooses negative source-destination pairs according to a uniform distribution.
For each edge
(u, v)of type
(srctype, etype, dsttype), DGL generates at most
kpairs of negative edges
(u', v'), where
u'is chosen uniformly from all the nodes of type
v'is chosen uniformly from all the nodes of type
dsttype. The resulting edges will also have type
(srctype, etype, dsttype). DGL guarantees that the sampled pairs will not have edges in between.
k (int) – The desired number of negative samples to generate per edge.
exclude_self_loops (bool, optional) – Whether to exclude self-loops from negative samples. (Default: True)
replace (bool, optional) – Whether to sample with replacement. Setting it to True will make things faster. (Default: False)
This negative sampler will try to generate as many negative samples as possible, but it may rarely return less than
knegative samples per edge. This is more likely to happen if a graph is so small or dense that not many unique negative samples exist.
>>> g = dgl.graph(([0, 1, 2], [1, 2, 3])) >>> neg_sampler = dgl.dataloading.negative_sampler.GlobalUniform(2, True) >>> neg_sampler(g, torch.LongTensor([0, 1])) (tensor([0, 1, 3, 2]), tensor([2, 0, 2, 1]))
__init__(k, exclude_self_loops=True, replace=False)¶
Initialize self. See help(type(self)) for accurate signature.
__init__(k[, exclude_self_loops, replace])