# Copyright 2018-2020 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.
import abc
from typing import Optional, Sequence, Tuple
import tensorflow as tf
from check_shapes import check_shape as cs
from check_shapes import check_shapes, inherit_check_shapes
from ...base import Parameter, TensorType
from ..base import Combination, Kernel
[docs]class MultioutputKernel(Kernel):
"""
Multi Output Kernel class.
This kernel can represent correlation between outputs of different datapoints.
The `full_output_cov` argument holds whether the kernel should calculate
the covariance between the outputs. In case there is no correlation but
`full_output_cov` is set to True the covariance matrix will be filled with zeros
until the appropriate size is reached.
"""
@property
@abc.abstractmethod
def num_latent_gps(self) -> int:
"""The number of latent GPs in the multioutput kernel"""
raise NotImplementedError
@property
@abc.abstractmethod
def latent_kernels(self) -> Tuple[Kernel, ...]:
"""The underlying kernels in the multioutput kernel"""
raise NotImplementedError
[docs] @abc.abstractmethod
@check_shapes(
"X: [batch..., N, D]",
"X2: [batch2..., N2, D]",
"return: [batch..., N, P, batch2..., N2, P] if full_output_cov and (X2 is not None)",
"return: [P, batch..., N, batch2..., N2] if not full_output_cov and (X2 is not None)",
"return: [batch..., N, P, N, P] if full_output_cov and (X2 is None)",
"return: [P, batch..., N, N] if not full_output_cov and (X2 is None)",
)
def K(
self, X: TensorType, X2: Optional[TensorType] = None, full_output_cov: bool = True
) -> tf.Tensor:
"""
Returns the correlation of f(X) and f(X2), where f(.) can be multi-dimensional.
:param X: data matrix
:param X2: data matrix
:param full_output_cov: calculate correlation between outputs.
:return: cov[f(X), f(X2)]
"""
raise NotImplementedError
[docs] @abc.abstractmethod
@check_shapes(
"X: [batch..., N, D]",
"return: [batch..., N, P, P] if full_output_cov",
"return: [batch..., N, P] if not full_output_cov",
)
def K_diag(self, X: TensorType, full_output_cov: bool = True) -> tf.Tensor:
"""
Returns the correlation of f(X) and f(X), where f(.) can be multi-dimensional.
:param X: data matrix
:param full_output_cov: calculate correlation between outputs.
:return: var[f(X)]
"""
raise NotImplementedError
@check_shapes(
"X: [batch..., N, D]",
"X2: [batch2..., N2, D]",
"return: [batch..., N, P, batch2..., N2, P] if full_cov and full_output_cov and (X2 is not None)",
"return: [P, batch..., N, batch2..., N2] if full_cov and (not full_output_cov) and (X2 is not None)",
"return: [batch..., N, P, N, P] if full_cov and full_output_cov and (X2 is None)",
"return: [P, batch..., N, N] if full_cov and (not full_output_cov) and (X2 is None)",
"return: [batch..., N, P, P] if (not full_cov) and full_output_cov and (X2 is None)",
"return: [batch..., N, P] if (not full_cov) and (not full_output_cov) and (X2 is None)",
)
def __call__(
self,
X: TensorType,
X2: Optional[TensorType] = None,
*,
full_cov: bool = False,
full_output_cov: bool = True,
presliced: bool = False,
) -> tf.Tensor:
if not presliced:
X, X2 = self.slice(X, X2)
if not full_cov and X2 is not None:
raise ValueError(
"Ambiguous inputs: passing in `X2` is not compatible with `full_cov=False`."
)
if not full_cov:
return self.K_diag(X, full_output_cov=full_output_cov)
return self.K(X, X2, full_output_cov=full_output_cov)
[docs]class SharedIndependent(MultioutputKernel):
"""
- Shared: we use the same kernel for each latent GP
- Independent: Latents are uncorrelated a priori.
.. warning::
This class is created only for testing and comparison purposes.
Use `gpflow.kernels` instead for more efficient code.
"""
def __init__(self, kernel: Kernel, output_dim: int) -> None:
super().__init__()
self.kernel = kernel
self.output_dim = output_dim
@property
def num_latent_gps(self) -> int:
# In this case number of latent GPs (L) == output_dim (P)
return self.output_dim
@property
def latent_kernels(self) -> Tuple[Kernel, ...]:
"""The underlying kernels in the multioutput kernel"""
return (self.kernel,)
[docs] @inherit_check_shapes
def K(
self, X: TensorType, X2: Optional[TensorType] = None, full_output_cov: bool = True
) -> tf.Tensor:
K = self.kernel.K(X, X2)
rank = tf.rank(X) - 1
if X2 is None:
cs(K, "[batch..., N, N]")
ones = tf.ones((rank,), dtype=tf.int32)
if full_output_cov:
multiples = tf.concat([ones, [1, self.output_dim]], 0)
Ks = cs(tf.tile(K[..., None], multiples), "[batch..., N, N, P]")
perm = tf.concat(
[
tf.range(rank),
[rank + 1, rank, rank + 2],
],
0,
)
return cs(tf.transpose(tf.linalg.diag(Ks), perm), "[batch..., N, P, N, P]")
else:
multiples = tf.concat([[self.output_dim], ones, [1]], 0)
return cs(tf.tile(K[None, ...], multiples), "[P, batch..., N, N]")
else:
cs(K, "[batch..., N, batch2..., N2]")
rank2 = tf.rank(X2) - 1
ones12 = tf.ones((rank + rank2,), dtype=tf.int32)
if full_output_cov:
multiples = tf.concat([ones12, [self.output_dim]], 0)
Ks = cs(tf.tile(K[..., None], multiples), "[batch..., N, batch2..., N2, P]")
perm = tf.concat(
[
tf.range(rank),
[rank + rank2],
rank + tf.range(rank2),
[rank + rank2 + 1],
],
0,
)
return cs(
tf.transpose(tf.linalg.diag(Ks), perm), "[batch..., N, P, batch2..., N2, P]"
)
else:
multiples = tf.concat([[self.output_dim], ones12], 0)
return cs(tf.tile(K[None, ...], multiples), "[P, batch..., N, batch2..., N2]")
[docs] @inherit_check_shapes
def K_diag(self, X: TensorType, full_output_cov: bool = True) -> tf.Tensor:
K = cs(self.kernel.K_diag(X), "[batch..., N]")
rank = tf.rank(X) - 1
ones = tf.ones((rank,), dtype=tf.int32)
multiples = tf.concat([ones, [self.output_dim]], 0)
Ks = cs(tf.tile(K[..., None], multiples), "[batch..., N, P]")
return tf.linalg.diag(Ks) if full_output_cov else Ks
[docs]class SeparateIndependent(MultioutputKernel, Combination):
"""
- Separate: we use different kernel for each output latent
- Independent: Latents are uncorrelated a priori.
"""
def __init__(self, kernels: Sequence[Kernel], name: Optional[str] = None) -> None:
super().__init__(kernels=kernels, name=name)
@property
def num_latent_gps(self) -> int:
return len(self.kernels)
@property
def latent_kernels(self) -> Tuple[Kernel, ...]:
"""The underlying kernels in the multioutput kernel"""
return tuple(self.kernels)
[docs] @inherit_check_shapes
def K(
self, X: TensorType, X2: Optional[TensorType] = None, full_output_cov: bool = True
) -> tf.Tensor:
rank = tf.rank(X) - 1
if X2 is None:
if full_output_cov:
Kxxs = cs(
tf.stack([k.K(X, X2) for k in self.kernels], axis=-1), "[batch..., N, N, P]"
)
perm = tf.concat(
[
tf.range(rank),
[rank + 1, rank, rank + 2],
],
0,
)
return cs(tf.transpose(tf.linalg.diag(Kxxs), perm), "[batch..., N, P, N, P]")
else:
return cs(
tf.stack([k.K(X, X2) for k in self.kernels], axis=0), "[P, batch..., N, N]"
)
else:
rank2 = tf.rank(X2) - 1
if full_output_cov:
Kxxs = cs(
tf.stack([k.K(X, X2) for k in self.kernels], axis=-1),
"[batch..., N, batch2..., N2, P]",
)
perm = tf.concat(
[
tf.range(rank),
[rank + rank2],
rank + tf.range(rank2),
[rank + rank2 + 1],
],
0,
)
return cs(
tf.transpose(tf.linalg.diag(Kxxs), perm), "[batch..., N, P, batch2..., N2, P]"
)
else:
return cs(
tf.stack([k.K(X, X2) for k in self.kernels], axis=0),
"[P, batch..., N, batch2..., N2]",
)
[docs] @inherit_check_shapes
def K_diag(self, X: TensorType, full_output_cov: bool = False) -> tf.Tensor:
stacked = cs(tf.stack([k.K_diag(X) for k in self.kernels], axis=-1), "[batch..., N, P]")
if full_output_cov:
return cs(tf.linalg.diag(stacked), "[batch..., N, P, P]")
else:
return stacked
[docs]class IndependentLatent(MultioutputKernel):
"""
Base class for multioutput kernels that are constructed from independent
latent Gaussian processes.
It should always be possible to specify inducing variables for such kernels
that give a block-diagonal Kuu, which can be represented as a [L, M, M]
tensor. A reasonable (but not optimal) inference procedure can be specified
by placing the inducing points in the latent processes and simply computing
Kuu [L, M, M] and Kuf [N, P, M, L] and using `fallback_independent_latent_
conditional()`. This can be specified by using `Fallback{Separate|Shared}
IndependentInducingVariables`.
"""
@abc.abstractmethod
@check_shapes(
"X: [batch..., N, D]",
"X2: [batch2..., N2, D]",
"return: [L, batch..., N, batch2..., N2]",
)
def Kgg(self, X: TensorType, X2: TensorType) -> tf.Tensor:
raise NotImplementedError
[docs]class LinearCoregionalization(IndependentLatent, Combination):
"""
Linear mixing of the latent GPs to form the output.
"""
@check_shapes(
"W: [P, L]",
)
def __init__(self, kernels: Sequence[Kernel], W: TensorType, name: Optional[str] = None):
Combination.__init__(self, kernels=kernels, name=name)
self.W = Parameter(W)
@property
def num_latent_gps(self) -> int:
return self.W.shape[-1] # type: ignore[no-any-return] # L
@property
def latent_kernels(self) -> Tuple[Kernel, ...]:
"""The underlying kernels in the multioutput kernel"""
return tuple(self.kernels)
@inherit_check_shapes
def Kgg(self, X: TensorType, X2: TensorType) -> tf.Tensor:
return cs(
tf.stack([k.K(X, X2) for k in self.kernels], axis=0), "[L, batch..., N, batch2..., M]"
)
[docs] @inherit_check_shapes
def K(
self, X: TensorType, X2: Optional[TensorType] = None, full_output_cov: bool = True
) -> tf.Tensor:
Kxx = self.Kgg(X, X2)
if X2 is None:
cs(Kxx, "[L, batch..., N, N]")
rank = tf.rank(X) - 1
ones = tf.ones((rank + 1,), dtype=tf.int32)
P = tf.shape(self.W)[0]
L = tf.shape(self.W)[1]
W_broadcast = cs(
tf.reshape(self.W, tf.concat([[P, L], ones], 0)), "[P, L, broadcast batch..., 1, 1]"
)
KxxW = cs(Kxx[None, ...] * W_broadcast, "[P, L, batch..., N, N]")
if full_output_cov:
# return tf.einsum('lnm,kl,ql->nkmq', Kxx, self.W, self.W)
WKxxW = cs(tf.tensordot(self.W, KxxW, [[1], [1]]), "[P, P, batch..., N, N]")
perm = tf.concat(
[
2 + tf.range(rank),
[0, 2 + rank, 1],
],
0,
)
return cs(tf.transpose(WKxxW, perm), "[batch..., N, P, N, P]")
else:
cs(Kxx, "[L, batch..., N, batch2..., N2]")
rank = tf.rank(X) - 1
rank2 = tf.rank(X2) - 1
ones12 = tf.ones((rank + rank2,), dtype=tf.int32)
P = tf.shape(self.W)[0]
L = tf.shape(self.W)[1]
W_broadcast = cs(
tf.reshape(self.W, tf.concat([[P, L], ones12], 0)),
"[P, L, broadcast batch..., 1, broadcast batch2..., 1]",
)
KxxW = cs(Kxx[None, ...] * W_broadcast, "[P, L, batch..., N, batch2..., N2]")
if full_output_cov:
# return tf.einsum('lnm,kl,ql->nkmq', Kxx, self.W, self.W)
WKxxW = cs(
tf.tensordot(self.W, KxxW, [[1], [1]]), "[P, P, batch..., N, batch2..., N2]"
)
perm = tf.concat(
[
2 + tf.range(rank),
[0],
2 + rank + tf.range(rank2),
[1],
],
0,
)
return cs(tf.transpose(WKxxW, perm), "[batch..., N, P, batch2..., N2, P]")
# return tf.einsum('lnm,kl,kl->knm', Kxx, self.W, self.W)
return tf.reduce_sum(W_broadcast * KxxW, axis=1)
[docs] @inherit_check_shapes
def K_diag(self, X: TensorType, full_output_cov: bool = True) -> tf.Tensor:
K = cs(tf.stack([k.K_diag(X) for k in self.kernels], axis=-1), "[batch..., N, L]")
rank = tf.rank(X) - 1
ones = tf.ones((rank,), dtype=tf.int32)
if full_output_cov:
# Can currently not use einsum due to unknown shape from `tf.stack()`
# return tf.einsum('nl,lk,lq->nkq', K, self.W, self.W)
Wt = cs(tf.transpose(self.W), "[L, P]")
L = tf.shape(Wt)[0]
P = tf.shape(Wt)[1]
return cs(
tf.reduce_sum(
cs(K[..., None, None], "[batch..., N, L, 1, 1]")
* cs(tf.reshape(Wt, tf.concat([ones, [L, P, 1]], 0)), "[..., L, P, 1]")
* cs(tf.reshape(Wt, tf.concat([ones, [L, 1, P]], 0)), "[..., L, 1, P]"),
axis=-3,
),
"[batch..., N, P, P]",
)
else:
# return tf.einsum('nl,lk,lk->nkq', K, self.W, self.W)
return cs(tf.linalg.matmul(K, self.W ** 2.0, transpose_b=True), "[batch..., N, P]")