Source code for gpytorch.likelihoods.student_t_likelihood

#!/usr/bin/env python3

from typing import Any, Optional

import torch
from torch import Tensor
from torch.distributions import StudentT

from ..constraints import GreaterThan, Interval, Positive
from ..distributions import base_distributions
from ..priors import Prior
from .likelihood import _OneDimensionalLikelihood

[docs]class StudentTLikelihood(_OneDimensionalLikelihood): r""" A Student T likelihood/noise model for GP regression. It has two learnable parameters: :math:`\nu` - the degrees of freedom, and :math:`\sigma^2` - the noise :param batch_shape: The batch shape of the learned noise parameter (default: []). :param noise_prior: Prior for noise parameter :math:`\sigma^2`. :param noise_constraint: Constraint for noise parameter :math:`\sigma^2`. :param deg_free_prior: Prior for deg_free parameter :math:`\nu`. :param deg_free_constraint: Constraint for deg_free parameter :math:`\nu`. :var torch.Tensor deg_free: :math:`\nu` parameter (degrees of freedom) :var torch.Tensor noise: :math:`\sigma^2` parameter (noise) """ def __init__( self, batch_shape: torch.Size = torch.Size([]), deg_free_prior: Optional[Prior] = None, deg_free_constraint: Optional[Interval] = None, noise_prior: Optional[Prior] = None, noise_constraint: Optional[Interval] = None, ) -> None: super().__init__() if deg_free_constraint is None: deg_free_constraint = GreaterThan(2) if noise_constraint is None: noise_constraint = Positive() self.raw_deg_free = torch.nn.Parameter(torch.zeros(*batch_shape, 1)) self.raw_noise = torch.nn.Parameter(torch.zeros(*batch_shape, 1)) if noise_prior is not None: self.register_prior("noise_prior", noise_prior, lambda m: m.noise, lambda m, v: m._set_noise(v)) self.register_constraint("raw_noise", noise_constraint) if deg_free_prior is not None: self.register_prior("deg_free_prior", deg_free_prior, lambda m: m.deg_free, lambda m, v: m._set_deg_free(v)) self.register_constraint("raw_deg_free", deg_free_constraint) # Rough initialization self.initialize(deg_free=7) @property def deg_free(self) -> Tensor: return self.raw_deg_free_constraint.transform(self.raw_deg_free) @deg_free.setter def deg_free(self, value: Tensor) -> None: self._set_deg_free(value) def _set_deg_free(self, value: Tensor) -> None: if not torch.is_tensor(value): value = torch.as_tensor(value).to(self.raw_deg_free) self.initialize(raw_deg_free=self.raw_deg_free_constraint.inverse_transform(value)) @property def noise(self) -> Tensor: return self.raw_noise_constraint.transform(self.raw_noise) @noise.setter def noise(self, value: Tensor) -> None: self._set_noise(value) def _set_noise(self, value: Tensor) -> None: if not torch.is_tensor(value): value = torch.as_tensor(value).to(self.raw_noise) self.initialize(raw_noise=self.raw_noise_constraint.inverse_transform(value)) def forward(self, function_samples: Tensor, *args: Any, **kwargs: Any) -> StudentT: return base_distributions.StudentT(df=self.deg_free, loc=function_samples, scale=self.noise.sqrt())