Source code for gpytorch.likelihoods.beta_likelihood

#!/usr/bin/env python3

import torch

from ..constraints import Positive
from ..distributions import base_distributions
from .likelihood import _OneDimensionalLikelihood

[docs]class BetaLikelihood(_OneDimensionalLikelihood): r""" A Beta likelihood for regressing over percentages. The Beta distribution is parameterized by :math:`\alpha > 0` and :math:`\beta > 0` parameters which roughly correspond to the number of prior positive and negative observations. We instead parameterize it through a mixture :math:`m \in [0, 1]` and scale :math:`s > 0` parameter. .. math:: \begin{equation*} \alpha = ms, \quad \beta = (1-m)s \end{equation*} The mixture parameter is the output of the GP passed through a logit function :math:`\sigma(\cdot)`. The scale parameter is learned. .. math:: p(y \mid f) = \text{Beta} \left( \sigma(f) s , (1 - \sigma(f)) s\right) :param batch_shape: The batch shape of the learned noise parameter (default: []). :type batch_shape: torch.Size, optional :param scale_prior: Prior for scale parameter :math:`s`. :type scale_prior: ~gpytorch.priors.Prior, optional :param scale_constraint: Constraint for scale parameter :math:`s`. :type scale_constraint: ~gpytorch.constraints.Interval, optional :var torch.Tensor scale: :math:`s` parameter (scale) """ def __init__(self, batch_shape=torch.Size([]), scale_prior=None, scale_constraint=None): super().__init__() if scale_constraint is None: scale_constraint = Positive() self.raw_scale = torch.nn.Parameter(torch.ones(*batch_shape, 1)) if scale_prior is not None: self.register_prior("scale_prior", scale_prior, lambda m: m.scale, lambda m, v: m._set_scale(v)) self.register_constraint("raw_scale", scale_constraint) @property def scale(self): return self.raw_scale_constraint.transform(self.raw_scale) @scale.setter def scale(self, value): self._set_scale(value) def _set_scale(self, value): if not torch.is_tensor(value): value = torch.as_tensor(value).to(self.raw_scale) self.initialize(raw_scale=self.raw_scale_constraint.inverse_transform(value)) def forward(self, function_samples, **kwargs): mixture = torch.sigmoid(function_samples) scale = self.scale alpha = mixture * scale + 1 beta = scale - alpha + 2 return base_distributions.Beta(concentration1=alpha, concentration0=beta)