Source code for pykeen.sampling.bernoulli_negative_sampler

# -*- coding: utf-8 -*-

"""Negative sampling algorithm based on the work of [wang2014]_."""

import torch

from .negative_sampler import NegativeSampler
from ..triples import CoreTriplesFactory

__all__ = [

[docs]class BernoulliNegativeSampler(NegativeSampler): r"""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. """ def __init__( self, *, triples_factory: CoreTriplesFactory, **kwargs, ) -> None: """Initialize the bernoulli negative sampler with the given entities. :param triples_factory: The factory holding the positive training triples :param kwargs: Additional keyword based arguments passed to :class:`pykeen.sampling.NegativeSampler`. """ super().__init__(triples_factory=triples_factory, **kwargs) # Preprocessing: Compute corruption probabilities triples = triples_factory.mapped_triples head_rel_uniq, tail_count = torch.unique(triples[:, :2], return_counts=True, dim=0) rel_tail_uniq, head_count = torch.unique(triples[:, 1:], return_counts=True, dim=0) self.corrupt_head_probability = torch.empty( triples_factory.num_relations, device=triples_factory.mapped_triples.device, ) for r in range(triples_factory.num_relations): # compute tph, i.e. the average number of tail entities per head mask = head_rel_uniq[:, 1] == r tph = tail_count[mask].float().mean() # compute hpt, i.e. the average number of head entities per tail mask = rel_tail_uniq[:, 0] == r hpt = head_count[mask].float().mean() # Set parameter for Bernoulli distribution self.corrupt_head_probability[r] = tph / (tph + hpt)
[docs] def corrupt_batch(self, positive_batch: torch.LongTensor) -> torch.LongTensor: # noqa: D102 if self.num_negs_per_pos > 1: positive_batch = positive_batch.repeat_interleave(repeats=self.num_negs_per_pos, dim=0) # Bind number of negatives to sample num_negs = positive_batch.shape[0] # Copy positive batch for corruption. # Do not detach, as no gradients should flow into the indices. negative_batch = positive_batch.clone() device = positive_batch.device # Decide whether to corrupt head or tail head_corruption_probability = self.corrupt_head_probability[positive_batch[:, 1]] head_mask = torch.rand(num_negs, device=device) < # Tails are corrupted if heads are not corrupted tail_mask = ~head_mask # We at least make sure to not replace the triples by the original value # See below for explanation of why this is on a range of [0, num_entities - 1] index_max = self.num_entities - 1 # Randomly sample corruption. negative_entities = torch.randint( index_max, size=(num_negs,), device=positive_batch.device, ) # Replace heads negative_batch[head_mask, 0] = negative_entities[head_mask] # Replace tails negative_batch[tail_mask, 2] = negative_entities[tail_mask] # To make sure we don't replace the head by the original value # we shift all values greater or equal than the original value by one up # for that reason we choose the random value from [0, num_entities -1] negative_batch[head_mask, 0] += (negative_batch[head_mask, 0] >= positive_batch[head_mask, 0]).long() negative_batch[tail_mask, 2] += (negative_batch[tail_mask, 2] >= positive_batch[tail_mask, 2]).long() return negative_batch.view(-1, self.num_negs_per_pos, 3)