def predict_f(self,
Xnew: InputData,
full_cov: bool = False,
full_output_cov: bool = False) -> MeanAndVariance:
"""
Compute the mean and variance of the latent function at some new points.
Note that this is very similar to the SGPR prediction, for which
there are notes in the SGPR notebook.
Note: This model does not allow full output covariances.
:param Xnew: points at which to predict
"""
if full_output_cov:
raise NotImplementedError
pX = DiagonalGaussian(self.X_data_mean, self.X_data_var)
Y_data = self.data
num_inducing = self.inducing_variable.num_inducing
psi1 = expectation(pX, (self.kernel, self.inducing_variable))
psi2 = tf.reduce_sum(
expectation(pX, (self.kernel, self.inducing_variable),
(self.kernel, self.inducing_variable)),
axis=0,
)
jitter = default_jitter()
Kus = covariances.Kuf(self.inducing_variable, self.kernel, Xnew)
sigma2 = self.likelihood.variance
sigma = tf.sqrt(sigma2)
L = tf.linalg.cholesky(
covariances.Kuu(self.inducing_variable, self.kernel,
jitter=jitter))
A = tf.linalg.triangular_solve(L, tf.transpose(psi1),
lower=True) / sigma
tmp = tf.linalg.triangular_solve(L, psi2, lower=True)
AAT = tf.linalg.triangular_solve(L, tf.transpose(tmp),
lower=True) / sigma2
B = AAT + tf.eye(num_inducing, dtype=default_float())
LB = tf.linalg.cholesky(B)
c = tf.linalg.triangular_solve(
LB, tf.linalg.matmul(A, Y_data), lower=True) / sigma
tmp1 = tf.linalg.triangular_solve(L, Kus, lower=True)
tmp2 = tf.linalg.triangular_solve(LB, tmp1, lower=True)
mean = tf.linalg.matmul(tmp2, c, transpose_a=True)
if full_cov:
var = (self.kernel(Xnew) +
tf.linalg.matmul(tmp2, tmp2, transpose_a=True) -
tf.linalg.matmul(tmp1, tmp1, transpose_a=True))
shape = tf.stack([1, 1, tf.shape(Y_data)[1]])
var = tf.tile(tf.expand_dims(var, 2), shape)
else:
var = (self.kernel(Xnew, full_cov=False) +
tf.reduce_sum(tf.square(tmp2), axis=0) -
tf.reduce_sum(tf.square(tmp1), axis=0))
shape = tf.stack([1, tf.shape(Y_data)[1]])
var = tf.tile(tf.expand_dims(var, 1), shape)
return mean + self.mean_function(Xnew), var
def __call__(self, Xnew, full_cov=False, full_output_cov=False):
q_mu = self.q_mu # M x K x O
q_sqrt = self.q_sqrt # K x O x M x M
Kuu = covariances.Kuu(self.inducing_variables,
self.kernel,
jitter=default_jitter()) # K x M x M
Kuf = covariances.Kuf(self.inducing_variables, self.kernel,
Xnew) # K x M x N
Knn = self.kernel.K(Xnew, full_output_cov=False)
def conditional_ND(self, X, full_cov=False):
"""
Computes q(f|m, S; X, Z) as defined in eq. 6-8 in the paper. Remember
that inducing point prior means are set to 0.
:param X:
:param full_cov:
:return:
"""
self.build_cholesky_if_needed()
Kuf = covs.Kuf(self.feature, self.kern, X)
# Compute the alpha term
alpha = tf.linalg.triangular_solve(self.Lu, Kuf, lower=True)
if not self.white:
alpha = tf.linalg.triangular_solve(tf.transpose(self.Lu),
alpha,
lower=False)
f_mean = tf.matmul(alpha, self.q_mu, transpose_a=True)
f_mean = f_mean + self.mean_function(X)
alpha_tiled = tf.tile(alpha[None, :, :], [self.num_outputs, 1, 1])
if self.white:
f_cov = -tf.eye(self.num_inducing,
dtype=gpflow.default_float())[None, :, :]
else:
f_cov = -self.Ku_tiled
if self.q_sqrt is not None:
S = tf.matmul(self.q_sqrt, self.q_sqrt,
transpose_b=True) # Inducing points prior covariance
f_cov += S
f_cov = tf.matmul(f_cov, alpha_tiled)
if full_cov:
# Shape [num_latent, num_X, num_X]
delta_cov = tf.matmul(alpha_tiled, f_cov, transpose_a=True)
Kff = self.kern.K(X)
else:
# Shape [num_latent, num_X]
delta_cov = tf.reduce_sum(alpha_tiled * f_cov, 1)
Kff = self.kern.K_diag(X)
# Shapes either [1, num_X] + [num_latent, num_X] or
# [1, num_X, num_X] + [num_latent, num_X, num_X]
f_cov = tf.expand_dims(Kff, 0) + delta_cov
f_cov = tf.transpose(f_cov)
return f_mean, f_cov
def _conditional_with_precompute(self, Xnew, full_cov, full_output_cov):
if full_output_cov:
raise NotImplementedError
Kuf = cov.Kuf(self.inducing_variable, self.kernel, Xnew) # still a Tensor
# construct the conditional mean
fmean = tf.matmul(Kuf, self.alpha, transpose_a=True)
num_func = tf.shape(self.alpha)[1] # K
Qinv_Kuf = tf.matmul(self.Qinv, Kuf)
# compute the covariance due to the conditioning
if full_cov:
fvar = self.kernel(Xnew) - tf.matmul(Kuf, Qinv_Kuf, transpose_a=True)
else:
KufT_Qinv_Kuf_diag = tf.reduce_sum(Kuf * Qinv_Kuf, axis=-2)
fvar = self.kernel(Xnew, full_cov=False) - KufT_Qinv_Kuf_diag
fvar = tf.transpose(fvar)
return fmean, fvar
def custom_predict_f(self,
Xnew: InputData,
full_cov: bool = False,
full_output_cov: bool = False) -> MeanAndVariance:
"""
Compute the mean and variance of the latent function at some new points.
Note that this is very similar to the SGPR prediction, for which
there are notes in the SGPR notebook.
Note: This model does not allow full output covariances.
:param Xnew: points at which to predict
"""
if full_output_cov:
raise NotImplementedError
Y_data = self.data
X_data_mean, X_data_var = self.encoder(Y_data)
pX = DiagonalGaussian(X_data_mean, X_data_var)
mu, cov = self.compute_qu()
jitter = default_jitter()
Kus = covariances.Kuf(self.inducing_variable, self.kernel, Xnew)
L = tf.linalg.cholesky(
covariances.Kuu(self.inducing_variable, self.kernel,
jitter=jitter))
var = cov
tmp1 = tf.linalg.triangular_solve(L, Kus, lower=True) #L^{-1} K_{us}
tmp2 = tf.linalg.triangular_solve(L, mu, lower=True) # L^{-1} m
mean = tf.linalg.matmul(
tmp1, tmp2, transpose_a=True
) #K_{su} L^{-T} L^{-1} m = K_{su} K_{uu}^{-1} m #ook
return mean + self.mean_function(Xnew), var
def conditional_vff(Xnew,
inducing_variable,
kernel,
f,
*,
full_cov=False,
full_output_cov=False,
q_sqrt=None,
white=False):
"""
- Xnew are the points of the data or minibatch, size N x D (tf.array, 2d)
- feat is an instance of features.InducingFeature that provides `Kuu` and `Kuf` methods
for Fourier features, this contains the limits of the bounding box and the frequencies
- f is the value (or mean value) of the features (i.e. the weights)
- q_sqrt (default None) is the Cholesky factor of the uncertainty about f
(to be propagated through the conditional as per the GPflow inducing-point implementation)
- white (defaults False) specifies whether the whitening has been applied
Given the GP represented by the inducing points specified in `feat`, produce the mean and
(co-)variance of the GP at the points Xnew.
Xnew :: N x D
Kuu :: M x M
Kuf :: M x N
f :: M x K, K = 1
q_sqrt :: K x M x M, with K = 1
"""
if full_output_cov:
raise NotImplementedError
# num_data = tf.shape(Xnew)[0] # M
num_func = tf.shape(f)[1] # K
Kuu = cov.Kuu(inducing_variable, kernel) # this is now a LinearOperator
Kuf = cov.Kuf(inducing_variable, kernel, Xnew) # still a Tensor
KuuInv_Kuf = Kuu.solve(Kuf)
# compute the covariance due to the conditioning
if full_cov:
fvar = kernel(Xnew) - tf.matmul(Kuf, KuuInv_Kuf, transpose_a=True)
shape = (num_func, 1, 1)
else:
KufT_KuuInv_Kuf_diag = tf.reduce_sum(Kuf * KuuInv_Kuf, axis=-2)
fvar = kernel(Xnew, full=False) - KufT_KuuInv_Kuf_diag
shape = (num_func, 1)
fvar = tf.expand_dims(fvar, 0) * tf.ones(
shape, dtype=gpflow.default_float()) # K x N x N or K x N
# another backsubstitution in the unwhitened case
if white:
raise NotImplementedError
A = KuuInv_Kuf
# construct the conditional mean
fmean = tf.matmul(A, f, transpose_a=True)
if q_sqrt is not None:
if q_sqrt.get_shape().ndims == 2:
# LTA = A * tf.expand_dims(q_sqrt, 2) # K x M x N
# won't work # make ticket for this?
raise NotImplementedError
elif q_sqrt.get_shape().ndims == 3:
# L = tf.matrix_band_part(tf.transpose(q_sqrt, (2, 0, 1)), -1, 0) # K x M x M
# K x M x N
# A_tiled = tf.expand_dims(A.get(), 0) * tf.ones((num_func, 1, 1), dtype=float_type)
# LTA = tf.matmul(L, A_tiled, transpose_a=True) # K x M x N
# TODO the following won't work for K > 1
assert q_sqrt.shape[0] == 1
# LTA = (A.T @ DenseMatrix(q_sqrt[:,:,0])).T.get()[None, :, :]
ATL = tf.matmul(A, q_sqrt, transpose_a=True)
else:
raise ValueError("Bad dimension for q_sqrt: %s" %
str(q_sqrt.get_shape().ndims))
if full_cov:
# fvar = fvar + tf.matmul(LTA, LTA, transpose_a=True) # K x N x N
fvar = fvar + tf.matmul(ATL, ATL, transpose_b=True) # K x N x N
else:
# fvar = fvar + tf.reduce_sum(tf.square(LTA), 1) # K x N
fvar = fvar + tf.reduce_sum(tf.square(ATL), 2) # K x N
fvar = tf.transpose(fvar) # N x K or N x N x K
return fmean, fvar
def _conditional_fused(self, Xnew, full_cov, full_output_cov):
"""
Xnew is a tensor with the points of the data or minibatch, shape N x D
"""
if full_output_cov:
raise NotImplementedError
f = self._q_dist.q_mu
q_sqrt = self._q_dist.q_sqrt
# num_data = tf.shape(Xnew)[0] # M
num_func = tf.shape(f)[1] # K
Kuu = cov.Kuu(self.X_data, self.kernel) # this is now a LinearOperator
Kuf = cov.Kuf(self.X_data, self.kernel, Xnew) # still a Tensor
KuuInv_Kuf = Kuu.solve(Kuf)
# compute the covariance due to the conditioning
if full_cov:
fvar = self.kernel(Xnew) - tf.matmul(
Kuf, KuuInv_Kuf, transpose_a=True)
shape = (num_func, 1, 1)
else:
KufT_KuuInv_Kuf_diag = tf.reduce_sum(Kuf * KuuInv_Kuf, axis=-2)
fvar = self.kernel(Xnew, full_cov=False) - KufT_KuuInv_Kuf_diag
shape = (num_func, 1)
fvar = tf.expand_dims(fvar, 0) * tf.ones(
shape, dtype=gpflow.default_float()) # K x N x N or K x N
if self.whiten:
raise NotImplementedError
A = KuuInv_Kuf
# construct the conditional mean
fmean = tf.matmul(A, f, transpose_a=True)
if q_sqrt is not None:
if q_sqrt.get_shape().ndims == 2:
# LTA = A * tf.expand_dims(q_sqrt, 2) # K x M x N
# won't work # make ticket for this?
raise NotImplementedError
elif q_sqrt.get_shape().ndims == 3:
# L = tf.matrix_band_part(tf.transpose(q_sqrt, (2, 0, 1)), -1, 0) # K x M x M
# K x M x N
# A_tiled = tf.expand_dims(A.get(), 0) * tf.ones((num_func, 1, 1), dtype=float_type)
# LTA = tf.matmul(L, A_tiled, transpose_a=True) # K x M x N
# TODO the following won't work for K > 1
assert q_sqrt.shape[0] == 1
# LTA = (A.T @ DenseMatrix(q_sqrt[:,:,0])).T.get()[None, :, :]
ATL = tf.matmul(A, q_sqrt, transpose_a=True)
else:
raise ValueError("Bad dimension for q_sqrt: %s" %
str(q_sqrt.get_shape().ndims))
if full_cov:
# fvar = fvar + tf.matmul(LTA, LTA, transpose_a=True) # K x N x N
fvar = fvar + tf.matmul(ATL, ATL,
transpose_b=True) # K x N x N
else:
# fvar = fvar + tf.reduce_sum(tf.square(LTA), 1) # K x N
fvar = fvar + tf.reduce_sum(tf.square(ATL), 2) # K x N
fvar = tf.transpose(fvar) # N x K or N x N x K
return fmean, fvar
def approx_conditional_ldf(
Xnew,
inducing_variable,
kernel,
f,
*,
full_cov=False,
full_output_cov=False,
q_sqrt=None,
white=True,
):
"""
- Xnew are the points of the data or minibatch, size N x D (tf.array, 2d)
- inducing_variable is an instance of inducing_variables.InducingVariable that provides
`Kuu` and `Kuf` methods for Laplacian Dirichlet features, this contains the limits of
the bounding box and the frequencies
- remainder_variable is another instance of inducing_variables.InducingVariable that specifies
the high frequency components not selected in inducing_variable.
- f is the value (or mean value) of the features (i.e. the weights)
- q_sqrt (default None) is the Cholesky factor of the uncertainty about f
(to be propagated through the conditional as per the GPflow inducing-point implementation)
- white (defaults False) specifies whether the whitening has been applied. LDF works a lot better,
when using vanilla gradients, if whitening has been applied, so it's the default option.
Given the GP represented by the inducing points specified in `inducing_variable`, produce the mean
and (co-)variance of the GP at the points Xnew.
Xnew :: N x D
Kuu :: M x M
Kuf :: M x N
f :: M x K, K = 1
q_sqrt :: K x M x M, with K = 1
"""
if full_output_cov:
raise NotImplementedError
# num_data = tf.shape(Xnew)[0] # M
num_func = tf.shape(f)[1] # K
Λ = cov.Kuu(inducing_variable, kernel) # this is now a LinearOperator
Φ = cov.Kuf(inducing_variable, kernel, Xnew) # still a Tensor
Λr = cov.Kuu(inducing_variable.remainder, kernel)
Φr = cov.Kuf(inducing_variable.remainder, kernel, Xnew)
# compute the covariance due to the conditioning
if full_cov:
fvar = tf.matmul(Φr, Λr.solve(Φr), transpose_a=True)
shape = (num_func, 1, 1)
else:
fvar = tf.reduce_sum(Φr * Λr.solve(Φr), -2)
shape = (num_func, 1)
fvar = tf.expand_dims(fvar, 0) * tf.ones(
shape, dtype=gpflow.default_float()) # K x N x N or K x N
# another backsubstitution in the unwhitened case
if white:
A = Λ.cholesky().solve(Φ)
else:
A = Λ.solve(Φ)
# construct the conditional mean
fmean = tf.matmul(A, f, transpose_a=True)
if q_sqrt is not None:
if q_sqrt.shape.ndims == 2:
# case for q_diag = True
LTA = Diag(q_sqrt) @ A # K x M x N
elif q_sqrt.shape.ndims == 3:
LTA = tf.matmul(q_sqrt, A, transpose_a=True)
else:
raise ValueError("Bad dimension for q_sqrt: %s" %
str(q_sqrt.get_shape().ndims))
if full_cov:
fvar = fvar + tf.matmul(LTA, LTA, transpose_a=True) # K x N x N
else:
fvar = fvar + tf.reduce_sum(tf.square(LTA), 1) # K x N
fvar = tf.transpose(fvar) # N x K or N x N x K
return fmean, fvar
