# UniformNegativeSamplerο

class dgl.graphbolt.UniformNegativeSampler(datapipe, graph, negative_ratio)[source]ο

Sample negative destination nodes for each source node based on a uniform distribution.

Functional name: `sample_uniform_negative`.

Itβs important to note that the term βnegativeβ refers to false negatives, indicating that the sampled pairs are not ensured to be absent in the graph. For each edge `(u, v)`, it is supposed to generate negative_ratio pairs of negative edges `(u, v')`, where `v'` is chosen uniformly from all the nodes in the graph.

Parameters:
• datapipe (DataPipe) β The datapipe.

• graph (FusedCSCSamplingGraph) β The graph on which to perform negative sampling.

• negative_ratio (int) β The proportion of negative samples to positive samples.

Examples

```>>> from dgl import graphbolt as gb
>>> indptr = torch.LongTensor([0, 1, 2, 3, 4])
>>> indices = torch.LongTensor([1, 2, 3, 0])
>>> graph = gb.fused_csc_sampling_graph(indptr, indices)
>>> seeds = torch.tensor([[0, 1], [1, 2], [2, 3], [3, 0]])
>>> item_set = gb.ItemSet(seeds, names="seeds")
>>> item_sampler = gb.ItemSampler(
...     item_set, batch_size=4,)
>>> neg_sampler = gb.UniformNegativeSampler(
...     item_sampler, graph, 2)
>>> for minibatch in neg_sampler:
...       print(minibatch.seeds)
...       print(minibatch.labels)
...       print(minibatch.indexes)
tensor([[0, 1], [1, 2], [2, 3], [3, 0], [0, 1], [0, 3], [1, 1], [1, 2],
[2, 1], [2, 0], [3, 0], [3, 2]])
tensor([1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0.])
tensor([0, 1, 2, 3, 0, 0, 1, 1, 2, 2, 3, 3])
```