# 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 :math:\Phi(x)). Given the identity
:math:\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
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))