#!/usr/bin/env python3
import math
import warnings
from copy import deepcopy
from typing import Any, Optional, Tuple, Union
import torch
from linear_operator.operators import LinearOperator, MaskedLinearOperator, ZeroLinearOperator, DiagLinearOperator
from torch import Tensor
from torch.distributions import Distribution
from .. import settings
from ..constraints import Interval
from ..distributions import base_distributions, QExponential, MultivariateQExponential
from ..priors import Prior
from ..utils.warnings import QEPInputWarning
from .likelihood import Likelihood
from .noise_models import FixedNoise, HomoskedasticNoise, Noise
class _QExponentialLikelihoodBase(Likelihood):
"""Base class for QExponential Likelihoods, supporting general heteroskedastic noise models."""
has_analytic_marginal = True
def __init__(self, noise_covar: Union[Noise, FixedNoise], **kwargs: Any) -> None:
super().__init__()
param_transform = kwargs.get("param_transform")
if param_transform is not None:
warnings.warn(
"The 'param_transform' argument is now deprecated. If you want to use a different "
"transformaton, specify a different 'noise_constraint' instead.",
DeprecationWarning,
)
self.noise_covar = noise_covar
self.power = kwargs.pop('power', torch.tensor(2.0))
self.miu = kwargs.pop('miu', False) # marginally identical but uncorrelated
def _shaped_noise_covar(self, base_shape: torch.Size, *params: Any, **kwargs: Any) -> Union[Tensor, LinearOperator]:
return self.noise_covar(*params, shape=base_shape, **kwargs)
def expected_log_prob(self, target: Tensor, input: MultivariateQExponential, *params: Any, **kwargs: Any) -> Tensor:
noise = self._shaped_noise_covar(input.mean.shape, *params, **kwargs).diagonal(dim1=-1, dim2=-2)
# Potentially reshape the noise to deal with the multitask case
noise = noise.view(*noise.shape[:-1], *input.event_shape)
# Handle NaN values if enabled
nan_policy = settings.observation_nan_policy.value()
if nan_policy == "mask":
observed = settings.observation_nan_policy._get_observed(target, input.event_shape)
input = MultivariateQExponential(
mean=input.mean[..., observed],
covariance_matrix=MaskedLinearOperator(
input.lazy_covariance_matrix, observed.reshape(-1), observed.reshape(-1)
),
power=input.power
)
noise = noise[..., observed]
target = target[..., observed]
elif nan_policy == "fill":
missing = torch.isnan(target)
target = settings.observation_nan_policy._fill_tensor(target)
mean, variance, power = input.mean, input.variance, self.power
trace_plus_inv_quad_form = ((target - mean).square() + variance) / noise # <r>
res = noise.log() + math.log(2 * math.pi) #+ trace_plus_inv_quad_form**(power/2.)
# res = res.mul(-0.5)
# if power!=2: res += 0.5 * (power/2.-1) * trace_plus_inv_quad_form.log() + torch.log(power/2.) # exact value is intractable; only lower bound is provided.
if nan_policy == "fill":
# res = res * ~missing
trace_plus_inv_quad_form = trace_plus_inv_quad_form * ~missing
# Do appropriate summation for multitask QExponential likelihoods
num_event_dim = len(input.event_shape)
if num_event_dim > 1 and self.miu:
res = res.sum(list(range(-1, -num_event_dim, -1)))
trace_plus_inv_quad_form = trace_plus_inv_quad_form.sum(list(range(-1, -num_event_dim, -1)))
res = res + trace_plus_inv_quad_form**(power/2.)
res = res.mul(-0.5)
if power!=2: res += 0.5 * (power/2.-1) * trace_plus_inv_quad_form.log() * (torch.tensor(input.event_shape[-1:-num_event_dim:-1]).prod() if self.miu else 1.0) + torch.log(power/2.) # exact value is intractable; only lower bound is provided.
if num_event_dim > 1 and not self.miu:
res = res.sum(list(range(-1, -num_event_dim, -1)))
if 'reduction' in kwargs:
res = eval('res.'+kwargs.pop('reduction'))
return res
def forward(self, function_samples: Tensor, *params: Any, **kwargs: Any) -> QExponential:
noise = self._shaped_noise_covar(function_samples.shape, *params, **kwargs).diagonal(dim1=-1, dim2=-2)
return QExponential(function_samples, noise.sqrt(), self.power)
def log_marginal(
self, observations: Tensor, function_dist: MultivariateQExponential, *params: Any, **kwargs: Any
) -> Tensor:
marginal = self.marginal(function_dist, *params, **kwargs)
# Handle NaN values if enabled
nan_policy = settings.observation_nan_policy.value()
if nan_policy == "mask":
observed = settings.observation_nan_policy._get_observed(observations, marginal.event_shape)
marginal = MultivariateQExponential(
mean=marginal.mean[..., observed],
covariance_matrix=MaskedLinearOperator(
marginal.lazy_covariance_matrix, observed.reshape(-1), observed.reshape(-1)
),
power=marginal.power
)
observations = observations[..., observed]
elif nan_policy == "fill":
missing = torch.isnan(observations)
observations = settings.observation_nan_policy._fill_tensor(observations)
if self.miu:
if type(marginal) is MultivariateQExponential:
marginal = marginal.to_data_uncorrelated_dist()
else:
marginal.lazy_covariance_matrix = DiagLinearOperator(marginal.lazy_covariance_matrix.diagonal(dim1=-1, dim2=-2))
res = marginal.log_prob(observations)/marginal.event_shape[0]
else:
# We're making everything conditionally independent
indep_dist = QExponential(marginal.mean, marginal.variance.clamp_min(1e-8).sqrt(), marginal.power)
res = indep_dist.log_prob(observations)
if nan_policy == "fill":
res = res * ~missing
# Do appropriate summation for multitask QExponential likelihoods
num_event_dim = len(marginal.event_shape)
if num_event_dim > 1 and not self.miu:
res = res.sum(list(range(-1, -num_event_dim, -1)))
return res
def marginal(self, function_dist: MultivariateQExponential, *params: Any, **kwargs: Any) -> MultivariateQExponential:
mean, covar = function_dist.mean, function_dist.lazy_covariance_matrix
noise_covar = self._shaped_noise_covar(mean.shape, *params, **kwargs)
full_covar = covar + noise_covar
return function_dist.__class__(mean, full_covar, self.power)
[docs]class QExponentialLikelihood(_QExponentialLikelihoodBase):
r"""
The standard likelihood for regression.
Assumes a standard homoskedastic noise model:
.. math::
p(y \mid f) = f + \epsilon, \quad \epsilon \sim \textrm{q-ED} (0, \sigma^2)
where :math:`\sigma^2` is a noise parameter.
.. note::
This likelihood can be used for exact or approximate inference.
.. note::
QExponentialLikelihood has an analytic marginal distribution.
:param noise_prior: Prior for noise parameter :math:`\sigma^2`.
:param noise_constraint: Constraint for noise parameter :math:`\sigma^2`.
:param batch_shape: The batch shape of the learned noise parameter (default: []).
:param kwargs: power (default: 2.0), miu (default: False).
:ivar torch.Tensor noise: :math:`\sigma^2` parameter (noise)
"""
def __init__(
self,
noise_prior: Optional[Prior] = None,
noise_constraint: Optional[Interval] = None,
batch_shape: torch.Size = torch.Size(),
**kwargs: Any,
) -> None:
noise_covar = HomoskedasticNoise(
noise_prior=noise_prior, noise_constraint=noise_constraint, batch_shape=batch_shape
)
super().__init__(noise_covar=noise_covar, **kwargs)
@property
def noise(self) -> Tensor:
return self.noise_covar.noise
@noise.setter
def noise(self, value: Tensor) -> None:
self.noise_covar.initialize(noise=value)
@property
def raw_noise(self) -> Tensor:
return self.noise_covar.raw_noise
@raw_noise.setter
def raw_noise(self, value: Tensor) -> None:
self.noise_covar.initialize(raw_noise=value)
[docs] def marginal(self, function_dist: MultivariateQExponential, *args: Any, **kwargs: Any) -> MultivariateQExponential:
r"""
:return: Analytic marginal :math:`p(\mathbf y)`.
"""
return super().marginal(function_dist, *args, **kwargs)
[docs]class QExponentialLikelihoodWithMissingObs(QExponentialLikelihood):
r"""
The standard likelihood for regression with support for missing values.
Assumes a standard homoskedastic noise model:
.. math::
p(y \mid f) = f + \epsilon, \quad \epsilon \sim \textrm{q-ED} (0, \sigma^2)
where :math:`\sigma^2` is a noise parameter. Values of y that are nan do
not impact the likelihood calculation.
.. note::
This likelihood can be used for exact or approximate inference.
.. warning::
This likelihood is deprecated in favor of :class:`gpytorch.settings.observation_nan_policy`.
:param noise_prior: Prior for noise parameter :math:`\sigma^2`.
:type noise_prior: ~gpytorch.priors.Prior, optional
:param noise_constraint: Constraint for noise parameter :math:`\sigma^2`.
:type noise_constraint: ~gpytorch.constraints.Interval, optional
:param batch_shape: The batch shape of the learned noise parameter (default: []).
:type batch_shape: torch.Size, optional
:var torch.Tensor noise: :math:`\sigma^2` parameter (noise)
.. note::
QExponentialLikelihoodWithMissingObs has an analytic marginal distribution.
"""
MISSING_VALUE_FILL: float = -999.0
def __init__(self, **kwargs: Any) -> None:
warnings.warn(
"QExponentialLikelihoodWithMissingObs is replaced by qpytorch.settings.observation_nan_policy('fill').",
DeprecationWarning,
)
super().__init__(**kwargs)
def _get_masked_obs(self, x: Tensor) -> Tuple[Tensor, Tensor]:
missing_idx = x.isnan()
x_masked = x.masked_fill(missing_idx, self.MISSING_VALUE_FILL)
return missing_idx, x_masked
def expected_log_prob(self, target: Tensor, input: MultivariateQExponential, *params: Any, **kwargs: Any) -> Tensor:
missing_idx, target = self._get_masked_obs(target)
res = super().expected_log_prob(target, input, *params, **kwargs)
return res * ~missing_idx
def log_marginal(
self, observations: Tensor, function_dist: MultivariateQExponential, *params: Any, **kwargs: Any
) -> Tensor:
missing_idx, observations = self._get_masked_obs(observations)
res = super().log_marginal(observations, function_dist, *params, **kwargs)
return res * ~missing_idx
[docs] def marginal(self, function_dist: MultivariateQExponential, *args: Any, **kwargs: Any) -> MultivariateQExponential:
r"""
:return: Analytic marginal :math:`p(\mathbf y)`.
"""
return super().marginal(function_dist, *args, **kwargs)
[docs]class FixedNoiseQExponentialLikelihood(_QExponentialLikelihoodBase):
r"""
A Likelihood that assumes fixed heteroscedastic noise. This is useful when you have fixed, known observation
noise for each training example.
Note that this likelihood takes an additional argument when you call it, `noise`, that adds a specified amount
of noise to the passed MultivariateQExponential. This allows for adding known observational noise to test data.
.. note::
This likelihood can be used for exact or approximate inference.
:param noise: Known observation noise (variance) for each training example.
:type noise: torch.Tensor (... x N)
:param learn_additional_noise: Set to true if you additionally want to
learn added diagonal noise, similar to QExponentialLikelihood.
:type learn_additional_noise: bool, optional
:param batch_shape: The batch shape of the learned noise parameter (default
[]) if :obj:`learn_additional_noise=True`.
:type batch_shape: torch.Size, optional
:var torch.Tensor noise: :math:`\sigma^2` parameter (noise)
.. note::
FixedNoiseQExponentialLikelihood has an analytic marginal distribution.
Example:
>>> train_x = torch.randn(55, 2)
>>> noises = torch.ones(55) * 0.01
>>> likelihood = FixedNoiseQExponentialLikelihood(noise=noises, learn_additional_noise=True)
>>> pred_y = likelihood(qep_model(train_x))
>>>
>>> test_x = torch.randn(21, 2)
>>> test_noises = torch.ones(21) * 0.02
>>> pred_y = likelihood(qep_model(test_x), noise=test_noises)
"""
def __init__(
self,
noise: Tensor,
learn_additional_noise: Optional[bool] = False,
batch_shape: Optional[torch.Size] = torch.Size(),
**kwargs: Any,
) -> None:
super().__init__(noise_covar=FixedNoise(noise=noise), **kwargs)
self.second_noise_covar: Optional[HomoskedasticNoise] = None
if learn_additional_noise:
noise_prior = kwargs.get("noise_prior", None)
noise_constraint = kwargs.get("noise_constraint", None)
self.second_noise_covar = HomoskedasticNoise(
noise_prior=noise_prior, noise_constraint=noise_constraint, batch_shape=batch_shape
)
@property
def noise(self) -> Tensor:
return self.noise_covar.noise + self.second_noise
@noise.setter
def noise(self, value: Tensor) -> None:
self.noise_covar.initialize(noise=value)
@property
def second_noise(self) -> Union[float, Tensor]:
if self.second_noise_covar is None:
return 0.0
else:
return self.second_noise_covar.noise
@second_noise.setter
def second_noise(self, value: Tensor) -> None:
if self.second_noise_covar is None:
raise RuntimeError(
"Attempting to set secondary learned noise for FixedNoiseQExponentialLikelihood, "
"but learn_additional_noise must have been False!"
)
self.second_noise_covar.initialize(noise=value)
def get_fantasy_likelihood(self, **kwargs: Any) -> "FixedNoiseQExponentialLikelihood":
if "noise" not in kwargs:
raise RuntimeError("FixedNoiseQExponentialLikelihood.fantasize requires a `noise` kwarg")
old_noise_covar = self.noise_covar
self.noise_covar = None # pyre-fixme[8]
fantasy_liklihood = deepcopy(self)
self.noise_covar = old_noise_covar
old_noise = old_noise_covar.noise
new_noise = kwargs.get("noise")
if old_noise.dim() != new_noise.dim():
old_noise = old_noise.expand(*new_noise.shape[:-1], old_noise.shape[-1])
fantasy_liklihood.noise_covar = FixedNoise(noise=torch.cat([old_noise, new_noise], -1))
return fantasy_liklihood
def _shaped_noise_covar(self, base_shape: torch.Size, *params: Any, **kwargs: Any) -> Union[Tensor, LinearOperator]:
if len(params) > 0:
# we can infer the shape from the params
shape = None
else:
# here shape[:-1] is the batch shape requested, and shape[-1] is `n`, the number of points
shape = base_shape
res = self.noise_covar(*params, shape=shape, **kwargs)
if self.second_noise_covar is not None:
res = res + self.second_noise_covar(*params, shape=shape, **kwargs)
elif isinstance(res, ZeroLinearOperator):
warnings.warn(
"You have passed data through a FixedNoiseQExponentialLikelihood that did not match the size "
"of the fixed noise, *and* you did not specify noise. This is treated as a no-op.",
QEPInputWarning,
)
return res
[docs] def marginal(self, function_dist: MultivariateQExponential, *args: Any, **kwargs: Any) -> MultivariateQExponential:
r"""
:return: Analytic marginal :math:`p(\mathbf y)`.
"""
return super().marginal(function_dist, *args, **kwargs)
[docs]class QExponentialDirichletClassificationLikelihood(FixedNoiseQExponentialLikelihood):
r"""
A classification likelihood that treats the labels as regression targets with fixed heteroscedastic noise.
From Milios et al, NeurIPS, 2018 [https://arxiv.org/abs/1805.10915].
.. note::
This likelihood can be used for exact or approximate inference.
:param targets: (... x N) Classification labels.
:param alpha_epsilon: Tuning parameter for the scaling of the likeihood targets. We'd suggest 0.01 or setting
via cross-validation.
:param learn_additional_noise: Set to true if you additionally want to
learn added diagonal noise, similar to QExponentialLikelihood.
:param batch_shape: The batch shape of the learned noise parameter (default
[]) if :obj:`learn_additional_noise=True`.
:ivar torch.Tensor noise: :math:`\sigma^2` parameter (noise)
.. note::
DirichletClassificationLikelihood has an analytic marginal distribution.
Example:
>>> train_x = torch.randn(55, 1)
>>> labels = torch.round(train_x).long()
>>> likelihood = DirichletClassificationLikelihood(targets=labels, learn_additional_noise=True)
>>> pred_y = likelihood(qep_model(train_x))
>>>
>>> test_x = torch.randn(21, 1)
>>> test_labels = torch.round(test_x).long()
>>> pred_y = likelihood(qep_model(test_x), targets=labels)
"""
def _prepare_targets(
self, targets: Tensor, num_classes: Optional = None, alpha_epsilon: float = 0.01, dtype: torch.dtype = torch.float
) -> Tuple[Tensor, Tensor, int]:
if num_classes is None: num_classes = int(targets.max() + 1)
# set alpha = \alpha_\epsilon
alpha = alpha_epsilon * torch.ones(targets.shape[-1], num_classes, device=targets.device, dtype=dtype)
# alpha[class_labels] = 1 + \alpha_\epsilon
alpha[torch.arange(len(targets)), targets] = alpha[torch.arange(len(targets)), targets] + 1.0
# sigma^2 = log(1 / alpha + 1)
sigma2_i = torch.log(alpha.reciprocal() + 1.0)
# y = log(alpha) - 0.5 * sigma^2
transformed_targets = alpha.log() - 0.5 * sigma2_i
return sigma2_i.transpose(-2, -1).type(dtype), transformed_targets.type(dtype), num_classes
def __init__(
self,
targets: Tensor,
alpha_epsilon: float = 0.01,
learn_additional_noise: Optional[bool] = False,
batch_shape: torch.Size = torch.Size(),
dtype: torch.dtype = torch.float,
**kwargs: Any,
) -> None:
sigma2_labels, transformed_targets, num_classes = self._prepare_targets(
targets, alpha_epsilon=alpha_epsilon, dtype=dtype
)
super().__init__(
noise=sigma2_labels,
learn_additional_noise=learn_additional_noise,
batch_shape=torch.Size((num_classes,)),
**kwargs,
)
self.transformed_targets: Tensor = transformed_targets.transpose(-2, -1)
self.num_classes: int = num_classes
self.targets: Tensor = targets
self.alpha_epsilon: float = alpha_epsilon
def get_fantasy_likelihood(self, **kwargs: Any) -> "DirichletClassificationLikelihood":
# we assume that the number of classes does not change.
if "targets" not in kwargs:
raise RuntimeError("FixedNoiseQExponentialLikelihood.fantasize requires a `targets` kwarg")
old_noise_covar = self.noise_covar
self.noise_covar = None # pyre-fixme[8]
fantasy_liklihood = deepcopy(self)
self.noise_covar = old_noise_covar
old_noise = old_noise_covar.noise
new_targets = kwargs.get("noise")
new_noise, new_targets, _ = fantasy_liklihood._prepare_targets(new_targets, self.alpha_epsilon)
fantasy_liklihood.targets = torch.cat([fantasy_liklihood.targets, new_targets], -1)
if old_noise.dim() != new_noise.dim():
old_noise = old_noise.expand(*new_noise.shape[:-1], old_noise.shape[-1])
fantasy_liklihood.noise_covar = FixedNoise(noise=torch.cat([old_noise, new_noise], -1))
return fantasy_liklihood
[docs] def marginal(self, function_dist: MultivariateQExponential, *args: Any, **kwargs: Any) -> MultivariateQExponential:
r"""
:return: Analytic marginal :math:`p(\mathbf y)`.
"""
return super().marginal(function_dist, *args, **kwargs)
def __call__(self, input: Union[Tensor, MultivariateQExponential], *args: Any, **kwargs: Any) -> Distribution:
if "targets" in kwargs:
targets = kwargs.pop("targets")
dtype = self.transformed_targets.dtype
new_noise, _, _ = self._prepare_targets(targets, dtype=dtype)
kwargs["noise"] = new_noise
return super().__call__(input, *args, **kwargs)