Source code for gpytorch.likelihoods.bernoulli_likelihood

#!/usr/bin/env python3

import warnings

import torch

from ..distributions import base_distributions
from ..functions import log_normal_cdf
from .likelihood import _OneDimensionalLikelihood


[docs]class BernoulliLikelihood(_OneDimensionalLikelihood): r""" Implements the Bernoulli likelihood used for GP classification, using Probit regression (i.e., the latent function is warped to be in [0,1] using the standard Normal CDF \Phi(x)). Given the identity \Phi(-x) = 1-\Phi(x), we can write the likelihood compactly as: .. math:: \begin{equation*} p(Y=y|f)=\Phi(yf) \end{equation*} """ def forward(self, function_samples, **kwargs): output_probs = base_distributions.Normal(0, 1).cdf(function_samples) return base_distributions.Bernoulli(probs=output_probs) def log_marginal(self, observations, function_dist, *args, **kwargs): marginal = self.marginal(function_dist, *args, **kwargs) return marginal.log_prob(observations) def marginal(self, function_dist, **kwargs): mean = function_dist.mean var = function_dist.variance link = mean.div(torch.sqrt(1 + var)) output_probs = base_distributions.Normal(0, 1).cdf(link) return base_distributions.Bernoulli(probs=output_probs) def expected_log_prob(self, observations, function_dist, *params, **kwargs): if torch.any(observations.eq(-1)): # Remove after 1.0 warnings.warn( "BernoulliLikelihood.expected_log_prob expects observations with labels in {0, 1}. " "Observations with labels in {-1, 1} are deprecated.", DeprecationWarning, ) else: observations = observations.mul(2).sub(1) # Custom function here so we can use log_normal_cdf rather than Normal.cdf # This is going to be less prone to overflow errors log_prob_lambda = lambda function_samples: log_normal_cdf(function_samples.mul(observations)) log_prob = self.quadrature(log_prob_lambda, function_dist) return log_prob