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__ = [
"BernoulliNegativeSampler",
]

[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)