Source code for gpflow.inducing_variables.multioutput.inducing_variables

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from typing import Sequence, Tuple

import tensorflow as tf

from ...experimental.check_shapes import Shape, check_shapes
from ..inducing_variables import InducingVariables


[docs]class MultioutputInducingVariables(InducingVariables): """ Multioutput Inducing Variables Base class for methods which define a collection of inducing variables which in some way can be grouped. The main example is where the inducing variables consist of outputs of various independent GPs. This can be because our model uses multiple independent GPs (SharedIndependent, SeparateIndependent) or because it is constructed from independent GPs (eg IndependentLatent, LinearCoregionalization). """ @property def inducing_variables(self) -> Tuple[InducingVariables, ...]: raise NotImplementedError
[docs]class FallbackSharedIndependentInducingVariables(MultioutputInducingVariables): """ Shared definition of inducing variables for each independent latent process. This class is designated to be used to: - provide a general interface for multioutput kernels constructed from independent latent processes, - only require the specification of Kuu and Kuf. All multioutput kernels constructed from independent latent processes allow the inducing variables to be specified in the latent processes, and a reasonably efficient method (i.e. one that takes advantage of the independence in the latent processes) can be specified quite generally by only requiring the following covariances: - Kuu: [L, M, M], - Kuf: [L, M, N, P]. In `gpflow/conditionals/multioutput/conditionals.py` we define a conditional() implementation for this combination. We specify this code path for all kernels which inherit from `IndependentLatentBase`. This set-up allows inference with any such kernel to be implemented by specifying only `Kuu()` and `Kuf()`. We call this the base class, since many multioutput GPs that are constructed from independent latent processes acutally allow even more efficient approximations. However, we include this code path, as it does not require specifying a new `conditional()` implementation. Here, we share the definition of inducing variables between all latent processes. """ @check_shapes( "inducing_variable: [M, D, 1]", ) def __init__(self, inducing_variable: InducingVariables): super().__init__() self.inducing_variable = inducing_variable @property # type: ignore[misc] # mypy doesn't like decorated properties. @check_shapes( "return: []", ) def num_inducing(self) -> tf.Tensor: return self.inducing_variable.num_inducing @property def inducing_variables(self) -> Tuple[InducingVariables]: return (self.inducing_variable,) @property def shape(self) -> Shape: inner = self.inducing_variable.shape if inner is None: return inner assert inner[2] == 1 return inner[:2] + (None,)
[docs]class FallbackSeparateIndependentInducingVariables(MultioutputInducingVariables): """ Separate set of inducing variables for each independent latent process. This class is designated to be used to: - provide a general interface for multioutput kernels constructed from independent latent processes, - only require the specification of Kuu and Kuf. All multioutput kernels constructed from independent latent processes allow the inducing variables to be specified in the latent processes, and a reasonably efficient method (i.e. one that takes advantage of the independence in the latent processes) can be specified quite generally by only requiring the following covariances: - Kuu: [L, M, M], - Kuf: [L, M, N, P]. In `gpflow/multioutput/conditionals.py` we define a conditional() implementation for this combination. We specify this code path for all kernels which inherit from `IndependentLatentBase`. This set-up allows inference with any such kernel to be implemented by specifying only `Kuu()` and `Kuf()`. We call this the base class, since many multioutput GPs that are constructed from independent latent processes acutally allow even more efficient approximations. However, we include this code path, as it does not require specifying a new `conditional()` implementation. We use a different definition of inducing variables for each latent process. Note: each object should have the same number of inducing variables, M. """ @check_shapes( "inducing_variable_list[all]: [M, D, 1]", ) def __init__(self, inducing_variable_list: Sequence[InducingVariables]): super().__init__() self.inducing_variable_list = inducing_variable_list @property # type: ignore[misc] # mypy doesn't like decorated properties. @check_shapes( "return: []", ) def num_inducing(self) -> tf.Tensor: return self.inducing_variable_list[0].num_inducing @property def inducing_variables(self) -> Tuple[InducingVariables, ...]: return tuple(self.inducing_variable_list) @property def shape(self) -> Shape: inner = self.inducing_variable_list[0].shape if inner is None: return inner assert inner[2] == 1 return inner[:2] + (len(self.inducing_variable_list),)
[docs]class SharedIndependentInducingVariables(FallbackSharedIndependentInducingVariables): """ Here, we define the same inducing variables as in the base class. However, this class is intended to be used without the constraints on the shapes that `Kuu()` and `Kuf()` return. This allows a custom `conditional()` to provide the most efficient implementation. """
[docs]class SeparateIndependentInducingVariables(FallbackSeparateIndependentInducingVariables): """ Here, we define the same inducing variables as in the base class. However, this class is intended to be used without the constraints on the shapes that `Kuu()` and `Kuf()` return. This allows a custom `conditional()` to provide the most efficient implementation. """