wehub-resource-sync
87 lines
3.1 KiB
Python
87 lines
3.1 KiB
Python
"""Uniform negative sampler for GraphBolt."""
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import torch
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from torch.utils.data import functional_datapipe
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from ..negative_sampler import NegativeSampler
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__all__ = ["UniformNegativeSampler"]
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@functional_datapipe("sample_uniform_negative")
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class UniformNegativeSampler(NegativeSampler):
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"""Sample negative destination nodes for each source node based on a uniform
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distribution.
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Functional name: :obj:`sample_uniform_negative`.
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It's important to note that the term 'negative' refers to false negatives,
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indicating that the sampled pairs are not ensured to be absent in the graph.
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For each edge ``(u, v)``, it is supposed to generate `negative_ratio` pairs
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of negative edges ``(u, v')``, where ``v'`` is chosen uniformly from all
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the nodes in the graph.
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Parameters
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----------
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datapipe : DataPipe
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The datapipe.
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graph : FusedCSCSamplingGraph
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The graph on which to perform negative sampling.
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negative_ratio : int
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The proportion of negative samples to positive samples.
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Examples
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--------
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>>> from dgl import graphbolt as gb
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>>> indptr = torch.LongTensor([0, 1, 2, 3, 4])
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>>> indices = torch.LongTensor([1, 2, 3, 0])
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>>> graph = gb.fused_csc_sampling_graph(indptr, indices)
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>>> seeds = torch.tensor([[0, 1], [1, 2], [2, 3], [3, 0]])
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>>> item_set = gb.ItemSet(seeds, names="seeds")
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>>> item_sampler = gb.ItemSampler(
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... item_set, batch_size=4,)
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>>> neg_sampler = gb.UniformNegativeSampler(
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... item_sampler, graph, 2)
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>>> for minibatch in neg_sampler:
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... print(minibatch.seeds)
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... print(minibatch.labels)
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... print(minibatch.indexes)
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tensor([[0, 1], [1, 2], [2, 3], [3, 0], [0, 1], [0, 3], [1, 1], [1, 2],
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[2, 1], [2, 0], [3, 0], [3, 2]])
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tensor([1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0.])
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tensor([0, 1, 2, 3, 0, 0, 1, 1, 2, 2, 3, 3])
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"""
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def __init__(
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self,
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datapipe,
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graph,
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negative_ratio,
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):
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super().__init__(datapipe, negative_ratio)
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self.graph = graph
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def _sample_with_etype(self, seeds, etype=None):
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assert seeds.ndim == 2 and seeds.shape[1] == 2, (
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"Only tensor with shape N*2 is supported for negative"
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+ f" sampling, but got {seeds.shape}."
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)
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# Sample negative edges, and concatenate positive edges with them.
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all_seeds = self.graph.sample_negative_edges_uniform(
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etype,
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seeds,
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self.negative_ratio,
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)
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# Construct indexes for all node pairs.
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pos_num = seeds.shape[0]
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negative_ratio = self.negative_ratio
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pos_indexes = torch.arange(0, pos_num, device=all_seeds.device)
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neg_indexes = pos_indexes.repeat_interleave(negative_ratio)
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indexes = torch.cat((pos_indexes, neg_indexes))
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# Construct labels for all node pairs.
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neg_num = all_seeds.shape[0] - pos_num
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labels = torch.empty(pos_num + neg_num, device=all_seeds.device)
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labels[:pos_num] = 1
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labels[pos_num:] = 0
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return all_seeds, labels, indexes
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