dmlc--dgl
Hongzhi (Steve), Chen
704bcaf6dd
Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>
23 行
731 B
Python
23 行
731 B
Python
import dgl
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import torch as th
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class NegativeSampler(object):
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def __init__(self, g, k, neg_share=False, device=None):
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if device is None:
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device = g.device
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self.weights = g.in_degrees().float().to(device) ** 0.75
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self.k = k
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self.neg_share = neg_share
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def __call__(self, g, eids):
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src, _ = g.find_edges(eids)
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n = len(src)
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if self.neg_share and n % self.k == 0:
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dst = self.weights.multinomial(n, replacement=True)
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dst = dst.view(-1, 1, self.k).expand(-1, self.k, -1).flatten()
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else:
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dst = self.weights.multinomial(n * self.k, replacement=True)
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src = src.repeat_interleave(self.k)
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return src, dst
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