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
mean_functions.py
``````# Copyright 2016-2020 The GPflow Contributors. All Rights Reserved.
#
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

"""
Throughout GPflow, by default, latent functions being modelled with Gaussian
processes are assumed to have zero mean, f ~ GP(0, k(x,x')).

In some cases we may wish to model only the deviation from a fixed function
with a Gaussian process.  For flexibility this fixed function could be both
input dependent and parameterised function, μ(x; θ),
with some unknown parameters θ, resulting in f ~ GP(μ(x;θ), k(x,x')).

The GPflow :class:`MeanFunction <gpflow.mean_functions.MeanFunction>` class
allows this to be done whilst additionally learning parameters of the
parametric function.
"""

from typing import Collection, Optional

import numpy as np
import tensorflow as tf

from .base import Module, Parameter, TensorType
from .config import default_float, default_int
from .experimental.check_shapes import check_shapes, inherit_check_shapes

class MeanFunction(Module):
"""
The base mean function class.
To implement a mean function, write the __call__ method. This takes a
tensor X and returns a tensor m(X). In accordance with the GPflow
standard, each row of X represents one datum, and each row of Y is computed
independently for each row of X.

MeanFunction classes can have parameters, see the Linear class for an
example.
"""

@check_shapes(
"X: [batch..., D]",
"return: [batch..., Q]",
)
def __call__(self, X: TensorType) -> tf.Tensor:
raise NotImplementedError("Implement the __call__ method for this mean function")

def __add__(self, other: "MeanFunction") -> "MeanFunction":

def __mul__(self, other: "MeanFunction") -> "MeanFunction":
return Product(self, other)

class Linear(MeanFunction):
"""
y_i = A x_i + b
"""

@check_shapes(
)
def __init__(self, A: TensorType = None, b: TensorType = None) -> None:
"""
A is a matrix which maps each element of X to Y, b is an additive
constant.
"""
MeanFunction.__init__(self)
A = np.ones((1, 1), dtype=default_float()) if A is None else A
b = np.zeros(1, dtype=default_float()) if b is None else b
self.A = Parameter(np.atleast_2d(A))
self.b = Parameter(b)

@inherit_check_shapes
def __call__(self, X: TensorType) -> tf.Tensor:
return tf.tensordot(X, self.A, [[-1], [0]]) + self.b

class Identity(Linear):
"""
y_i = x_i
"""

# The many type-ignores in this class is because we replace a field in the super class with a
# property, which mypy doesn't like.

def __init__(self, input_dim: Optional[int] = None) -> None:
Linear.__init__(self)
self.input_dim = input_dim

@inherit_check_shapes
def __call__(self, X: TensorType) -> tf.Tensor:
return X

@property
def A(self) -> tf.Tensor:  # type: ignore[override]
if self.input_dim is None:
raise ValueError(
"An input_dim needs to be specified when using the "
"`Identity` mean function in combination with expectations."
)
return tf.eye(self.input_dim, dtype=default_float())

@property
def b(self) -> tf.Tensor:  # type: ignore[override]
if self.input_dim is None:
raise ValueError(
"An input_dim needs to be specified when using the "
"`Identity` mean function in combination with expectations."
)

return tf.zeros(self.input_dim, dtype=default_float())

@A.setter  # type: ignore[attr-defined, no-redef]
def A(self, A: tf.Tensor) -> None:
pass

@b.setter  # type: ignore[attr-defined, no-redef]
def b(self, b: tf.Tensor) -> None:
pass

class Constant(MeanFunction):
@check_shapes(
)
def __init__(self, c: TensorType = None) -> None:
super().__init__()
c = np.zeros(1) if c is None else c
self.c = Parameter(c)

@inherit_check_shapes
def __call__(self, X: TensorType) -> tf.Tensor:
tile_shape = tf.concat(
[tf.shape(X)[:-1], [1]],
axis=0,
)
reshape_shape = tf.concat(
[tf.ones(shape=(tf.rank(X) - 1), dtype=default_int()), [-1]],
axis=0,
)
return tf.tile(tf.reshape(self.c, reshape_shape), tile_shape)

class Zero(Constant):
def __init__(self, output_dim: int = 1) -> None:
Constant.__init__(self)
self.output_dim = output_dim
del self.c

@inherit_check_shapes
def __call__(self, X: TensorType) -> tf.Tensor:
output_shape = tf.concat([tf.shape(X)[:-1], [self.output_dim]], axis=0)
return tf.zeros(output_shape, dtype=X.dtype)

class SwitchedMeanFunction(MeanFunction):
"""
This class enables to use different (independent) mean_functions respective
to the data 'label'.
We assume the 'label' is stored in the extra column of X.
"""

def __init__(self, meanfunction_list: Collection[MeanFunction]) -> None:
super().__init__()
for m in meanfunction_list:
assert isinstance(m, MeanFunction)
self.meanfunctions = meanfunction_list

@inherit_check_shapes
def __call__(self, X: TensorType) -> tf.Tensor:
ind = tf.gather(tf.transpose(X), tf.shape(X)[1] - 1)  # ind = X[:,-1]
ind = tf.cast(ind, tf.int32)
X = tf.transpose(
tf.gather(tf.transpose(X), tf.range(0, tf.shape(X)[1] - 1))
)  # X = X[:,:-1]

# split up X into chunks corresponding to the relevant likelihoods
x_list = tf.dynamic_partition(X, ind, len(self.meanfunctions))
# apply the likelihood-function to each section of the data
results = [m(x) for x, m in zip(x_list, self.meanfunctions)]
# stitch the results back together
partitions = tf.dynamic_partition(tf.range(0, tf.size(ind)), ind, len(self.meanfunctions))
return tf.dynamic_stitch(partitions, results)

def __init__(self, first_part: MeanFunction, second_part: MeanFunction) -> None:
MeanFunction.__init__(self)

@inherit_check_shapes
def __call__(self, X: TensorType) -> tf.Tensor:

class Product(MeanFunction):
def __init__(self, first_part: MeanFunction, second_part: MeanFunction):
MeanFunction.__init__(self)

self.prod_1 = first_part
self.prod_2 = second_part

@inherit_check_shapes
def __call__(self, X: TensorType) -> tf.Tensor:
return tf.multiply(self.prod_1(X), self.prod_2(X))
``````

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