# BernoulliNegativeSampler

class BernoulliNegativeSampler(*, mapped_triples, **kwargs)[source]

An implementation of the Bernoulli negative sampling approach proposed by [wang2014].

The probability of corrupting the head $$h$$ or tail $$t$$ in a relation $$(h,r,t) \in \mathcal{K}$$ is determined by global properties of the relation $$r$$:

• $$r$$ is one-to-many (e.g. motherOf): a higher probability is assigned to replace $$h$$

• $$r$$ is many-to-one (e.g. bornIn): a higher probability is assigned to replace $$t$$.

More precisely, for each relation $$r \in \mathcal{R}$$, the average number of tails per head (tph) and heads per tail (hpt) are first computed.

Then, the head corruption probability $$p_r$$ is defined as $$p_r = \frac{tph}{tph + hpt}$$. The tail corruption probability is defined as $$1 - p_r = \frac{hpt}{tph + hpt}$$.

For each triple $$(h,r,t) \in \mathcal{K}$$, the head is corrupted with probability $$p_r$$ and the tail is corrupted with probability $$1 - p_r$$.

If filtered is set to True, all proposed corrupted triples that also exist as actual positive triples $$(h,r,t) \in \mathcal{K}$$ will be removed.

Initialize the bernoulli negative sampler with the given entities.

Parameters:

Methods Summary

 corrupt_batch(positive_batch) Generate negative samples from the positive batch without application of any filter.

Methods Documentation

corrupt_batch(positive_batch)[source]

Generate negative samples from the positive batch without application of any filter.

Parameters:

positive_batch (LongTensor) – shape: (*batch_dims, 3) The positive triples.

Return type:

LongTensor

Returns:

shape: (*batch_dims, num_negs_per_pos, 3) The negative triples. result[*bi, :, :] contains the negative examples generated from positive_batch[*bi, :].