Source code for gpflow.kernels.changepoints

# Copyright 2017-2020 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 Optional, Sequence

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 ..utilities import positive
from .base import Combination, Kernel


[docs] class ChangePoints(Combination): r""" The ChangePoints kernel defines a fixed number of change-points along a 1d input space where different kernels govern different parts of the space. The kernel is by multiplication and addition of the base kernels with sigmoid functions (σ). A single change-point kernel is defined as:: K₁(x, x') * (1 - σ(x)) * (1 - σ(x')) + K₂(x, x') * σ(x) * σ(x') where K₁ is deactivated around the change-point and K₂ is activated. The single change-point version can be found in :cite:t:`lloyd2014`. Each sigmoid is a logistic function defined as:: σ(x) = 1 / (1 + exp{-s(x - x₀)}) parameterized by location "x₀" and steepness "s". The key reference is :cite:t:`lloyd2014`. """ @check_shapes( "locations: [n_change_points]", "steepness: [broadcast n_change_points]", ) def __init__( self, kernels: Sequence[Kernel], locations: TensorType, steepness: TensorType = 1.0, name: Optional[str] = None, ): """ :param kernels: list of kernels defining the different regimes :param locations: list of change-point locations in the 1d input space :param steepness: the steepness parameter(s) of the sigmoids, this can be common between them or decoupled """ if len(kernels) != len(locations) + 1: raise ValueError( "Number of kernels ({nk}) must be one more than the number of " "changepoint locations ({nl})".format(nk=len(kernels), nl=len(locations)) ) if isinstance(steepness, Sequence) and len(steepness) != len(locations): raise ValueError( "Dimension of steepness ({ns}) does not match number of changepoint " "locations ({nl})".format(ns=len(steepness), nl=len(locations)) ) super().__init__(kernels, name=name) self.locations = Parameter(locations) self.steepness = Parameter(steepness, transform=positive()) def _set_kernels(self, kernels: Sequence[Kernel]) -> None: # it is not clear how to flatten out nested change-points self.kernels = list(kernels) @inherit_check_shapes def K(self, X: tf.Tensor, X2: Optional[tf.Tensor] = None) -> tf.Tensor: cs(X, "[batch..., N, 1] # The `ChangePoints` kernel requires a 1D input space.") rank = tf.rank(X) - 2 batch = tf.shape(X)[:-2] N = tf.shape(X)[-2] Ncp = tf.shape(self.locations)[0] sig_X = cs(self._sigmoids(X), "[batch..., N, 1, Ncp]") if X2 is None: rank2 = 0 batch2 = tf.constant([], dtype=tf.int32) N2 = N sig_X2 = sig_X sig_X = cs( tf.reshape(sig_X, tf.concat([batch, [N, 1, Ncp]], 0)), "[batch..., N, 1, Ncp]" ) sig_X2 = cs( tf.reshape(sig_X2, tf.concat([batch, [1, N, Ncp]], 0)), "[batch..., 1, N, Ncp]" ) else: rank2 = tf.rank(X2) - 2 batch2 = tf.shape(X2)[:-2] N2 = tf.shape(X2)[-2] sig_X2 = cs(self._sigmoids(X2), "[batch2..., N2, 1, Ncp]") ones = tf.ones((rank,), dtype=tf.int32) ones2 = tf.ones((rank2,), dtype=tf.int32) sig_X = cs( tf.reshape(sig_X, tf.concat([batch, [N], ones2, [1, Ncp]], 0)), "[batch..., N, ..., 1, Ncp]", ) sig_X2 = cs( tf.reshape(sig_X2, tf.concat([ones, [1], batch2, [N2, Ncp]], 0)), "[..., 1, batch2..., N2, Ncp]", ) # `starters` are the sigmoids going from 0 -> 1, whilst `stoppers` go # from 1 -> 0. starters = cs(sig_X * sig_X2, "[batch..., N, batch2..., N2, Ncp]") stoppers = cs((1 - sig_X) * (1 - sig_X2), "[batch..., N, batch2..., N2, Ncp]") # prepend `starters` with ones and append ones to `stoppers` since the # first kernel has no start and the last kernel has no end ones = tf.ones(tf.concat([batch, [N], batch2, [N2, 1]], 0), dtype=X.dtype) starters = cs(tf.concat([ones, starters], axis=-1), "[batch..., N, batch2..., N2, Nkern]") stoppers = cs(tf.concat([stoppers, ones], axis=-1), "[batch..., N, batch2..., N2, Nkern]") # now combine with the underlying kernels kernel_stack = cs( tf.stack([k(X, X2) for k in self.kernels], axis=-1), "[batch..., N, batch2..., N2, Nkern]", ) return tf.reduce_sum(kernel_stack * starters * stoppers, axis=-1) @inherit_check_shapes def K_diag(self, X: tf.Tensor) -> tf.Tensor: cs(X, "[batch..., N, 1] # The `ChangePoints` kernel requires a 1D input space.") batch = tf.shape(X)[:-2] N = tf.shape(X)[-2] Ncp = tf.shape(self.locations)[0] sig_X = cs( tf.reshape(self._sigmoids(X), tf.concat([batch, [N, Ncp]], 0)), "[batch..., N, Ncp]" ) ones = tf.ones(tf.concat([batch, [N, 1]], 0), dtype=X.dtype) starters = cs(tf.concat([ones, sig_X * sig_X], axis=-1), "[batch..., N, Nkern]") stoppers = cs(tf.concat([(1 - sig_X) * (1 - sig_X), ones], axis=-1), "[batch..., N, Nkern]") kernel_stack = cs( tf.stack([k(X, full_cov=False) for k in self.kernels], axis=-1), "[batch..., N, Nkern]" ) return tf.reduce_sum(kernel_stack * starters * stoppers, axis=-1) @check_shapes( "X: [batch...]", "return: [batch..., Ncp]", ) def _sigmoids(self, X: tf.Tensor) -> tf.Tensor: locations = tf.sort(self.locations) # ensure locations are ordered locations = tf.reshape(locations, (-1,)) steepness = tf.reshape(self.steepness, (-1,)) return tf.sigmoid(steepness * (X[..., None] - locations))