Revision

**51101649daadcc59584c013f4040d2eb559667fb**authored by Jesper Nielsen on**04 July 2022, 09:18:06 UTC**, committed by GitHub on**04 July 2022, 09:18:06 UTC****1 parent**174939c

multilatent.py

```
# Copyright 2020 The GPflow Contributors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Callable, Optional, Type
import tensorflow as tf
import tensorflow_probability as tfp
from ..base import TensorType
from ..utilities import positive
from .base import QuadratureLikelihood
class MultiLatentLikelihood(QuadratureLikelihood):
r"""
A Likelihood which assumes that a single dimensional observation is driven
by multiple latent GPs.
Note that this implementation does not allow for taking into account
covariance between outputs.
"""
def __init__(self, latent_dim: int, **kwargs: Any) -> None:
super().__init__(
latent_dim=latent_dim,
observation_dim=1,
**kwargs,
)
class MultiLatentTFPConditional(MultiLatentLikelihood):
"""
MultiLatent likelihood where the conditional distribution
is given by a TensorFlow Probability Distribution.
"""
def __init__(
self,
latent_dim: int,
conditional_distribution: Callable[..., tfp.distributions.Distribution],
**kwargs: Any,
):
"""
:param latent_dim: number of arguments to the `conditional_distribution` callable
:param conditional_distribution: function from Fs to a tfp Distribution,
where Fs has shape [..., latent_dim]
"""
super().__init__(latent_dim, **kwargs)
self.conditional_distribution = conditional_distribution
def _log_prob(self, Fs: TensorType, Y: TensorType) -> tf.Tensor:
"""
The log probability density log p(Y|F)
:param F: function evaluation Tensor, with shape [..., latent_dim]
:param Y: observation Tensor, with shape [..., 1]:
:returns: log pdf, with shape [...]
"""
return tf.squeeze(self.conditional_distribution(Fs).log_prob(Y), -1)
def _conditional_mean(self, Fs: TensorType) -> tf.Tensor:
"""
The conditional marginal mean of Y|F: [E(Y₁|F)]
:param Fs: function evaluation Tensor, with shape [..., latent_dim]
:returns: mean [..., 1]
"""
return self.conditional_distribution(Fs).mean()
def _conditional_variance(self, Fs: TensorType) -> tf.Tensor:
"""
The conditional marginal variance of Y|F: [Var(Y₁|F)]
:param Fs: function evaluation Tensor, with shape [..., latent_dim]
:returns: variance [..., 1]
"""
return self.conditional_distribution(Fs).variance()
class HeteroskedasticTFPConditional(MultiLatentTFPConditional):
"""
Heteroskedastic Likelihood where the conditional distribution
is given by a TensorFlow Probability Distribution.
The `loc` and `scale` of the distribution are given by a
two-dimensional multi-output GP.
"""
def __init__(
self,
distribution_class: Type[tfp.distributions.Distribution] = tfp.distributions.Normal,
scale_transform: Optional[tfp.bijectors.Bijector] = None,
**kwargs: Any,
) -> None:
"""
:param distribution_class: distribution class parameterized by `loc` and `scale`
as first and second argument, respectively.
:param scale_transform: callable/bijector applied to the latent
function modelling the scale to ensure its positivity.
Typically, `tf.exp` or `tf.softplus`, but can be any function f: R -> R^+. Defaults to exp if not explicitly specified.
"""
if scale_transform is None:
scale_transform = positive(base="exp")
self.scale_transform = scale_transform
def conditional_distribution(Fs: TensorType) -> tfp.distributions.Distribution:
tf.debugging.assert_equal(tf.shape(Fs)[-1], 2)
loc = Fs[..., :1]
scale = self.scale_transform(Fs[..., 1:])
return distribution_class(loc, scale)
super().__init__(
latent_dim=2,
conditional_distribution=conditional_distribution,
**kwargs,
)
```

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