def predictive(self, x_new, lik_noise=False):
# Matrices
q_m = self.q_m.detach().numpy()
q_L = torch.tril(self.q_L)
q_S = torch.mm(q_L, q_L.t()).detach().numpy()
Kuu = self.kernel.K(self.z, self.z).detach().numpy()
posterior = Posterior(mean=q_m, cov=q_S, K=Kuu, prior_mean=np.zeros(q_m.shape))
Kx = self.kernel.K(self.z, x_new).detach().numpy()
Kxx = self.kernel.K(x_new, x_new).detach().numpy()
# GP Predictive Posterior - mean + variance
gp_mu = np.dot(Kx.T, posterior.woodbury_vector)
Kxx = np.diag(Kxx)
gp_var = (Kxx - np.sum(np.dot(np.atleast_3d(posterior.woodbury_inv).T, Kx) * Kx[None, :, :], 1)).T
gp = gp_mu
if lik_noise:
gp_upper = gp_mu + 2 * np.sqrt(gp_var) + 2 * self.likelihood.sigma.detach().numpy()
gp_lower = gp_mu - 2 * np.sqrt(gp_var) - 2 * self.likelihood.sigma.detach().numpy()
else:
gp_upper = gp_mu + 2*np.sqrt(gp_var)
gp_lower = gp_mu - 2*np.sqrt(gp_var)
return gp, gp_upper, gp_lower
def _outer_loop_without_missing_data(self):
if self.posterior is None:
woodbury_inv = np.zeros(
(self.num_inducing, self.num_inducing, self.output_dim))
woodbury_vector = np.zeros((self.num_inducing, self.output_dim))
else:
woodbury_inv = self.posterior._woodbury_inv
woodbury_vector = self.posterior._woodbury_vector
d = self.stochastics.d[0][0]
posterior, log_marginal_likelihood, grad_dict = self._inner_parameters_changed(
self.kern, self.X, self.Z, self.likelihood,
self.Y_normalized[:, d], self.Y_metadata)
self.grad_dict = grad_dict
self._log_marginal_likelihood = log_marginal_likelihood
self._outer_values_update(self.grad_dict)
woodbury_inv[:, :, d] = posterior.woodbury_inv[:, :, None]
woodbury_vector[:, d] = posterior.woodbury_vector
if self.posterior is None:
self.posterior = Posterior(woodbury_inv=woodbury_inv,
woodbury_vector=woodbury_vector,
K=posterior._K,
mean=None,
cov=None,
K_chol=posterior.K_chol)
def _inference(K: np.ndarray, ga_approx: GaussianApproximation, cav_params: CavityParams, Z_tilde: float,
y: List[Tuple[int, float]], yc: List[List[Tuple[int, int]]]) -> Tuple[Posterior, int, Dict]:
"""
Compute the posterior approximation
:param K: prior covariance matrix
:param ga_approx: Gaussian approximation of the batches
:param cav_params: Cavity parameters of the posterior
:param Z_tilde: Log marginal likelihood
:param y: Direct observations as a list of tuples telling location index (row in X) and observation value.
:param yc: Batch comparisons in a list of lists of tuples. Each batch is a list and tuples tell the comparisons (winner index, loser index)
:return: A tuple consisting of the posterior approximation, log marginal likelihood and gradient dictionary
"""
log_marginal, post_params = _ep_marginal(K, ga_approx, Z_tilde,y,yc)
tau_tilde_root = sqrtm_block(ga_approx.tau, y,yc)
Sroot_tilde_K = np.dot(tau_tilde_root, K)
aux_alpha , _ = dpotrs(post_params.L, np.dot(Sroot_tilde_K, ga_approx.v), lower=1)
alpha = (ga_approx.v - np.dot(tau_tilde_root, aux_alpha))[:,None] #(K + Sigma^(\tilde))^(-1) /mu^(/tilde)
LWi, _ = dtrtrs(post_params.L, tau_tilde_root, lower=1)
Wi = np.dot(LWi.T,LWi)
symmetrify(Wi) #(K + Sigma^(\tilde))^(-1)
dL_dK = 0.5 * (tdot(alpha) - Wi)
dL_dthetaL = 0
return Posterior(woodbury_inv=np.asfortranarray(Wi), woodbury_vector=alpha, K=K), log_marginal, {'dL_dK':dL_dK, 'dL_dthetaL':dL_dthetaL, 'dL_dm':alpha}
def inference(self,
kern,
X,
W,
likelihood,
Y,
mean_function=None,
Y_metadata=None,
K=None,
variance=None,
Z_tilde=None):
"""
Returns a Posterior class containing essential quantities of the posterior
"""
if mean_function is None:
m = 0
else:
m = mean_function.f(X)
if variance is None:
variance = likelihood.gaussian_variance(Y_metadata)
YYT_factor = Y - m
if K is None:
K = kern.K(X)
Ky = K.copy()
diag.add(Ky, variance + 1e-8)
Wi, LW, LWi, W_logdet = pdinv(Ky)
alpha, _ = dpotrs(LW, YYT_factor, lower=1)
log_marginal = 0.5 * (-Y.size * log_2_pi - Y.shape[1] * W_logdet -
np.sum(alpha * YYT_factor))
if Z_tilde is not None:
# This is a correction term for the log marginal likelihood
# In EP this is log Z_tilde, which is the difference between the
# Gaussian marginal and Z_EP
log_marginal += Z_tilde
dL_dK = 0.5 * (tdot(alpha) - Y.shape[1] * Wi)
dL_dthetaL = likelihood.exact_inference_gradients(
np.diag(dL_dK), Y_metadata)
posterior_ = Posterior(woodbury_chol=LW, woodbury_vector=alpha, K=K)
return posterior_, log_marginal, {
'dL_dK': dL_dK,
'dL_dthetaL': dL_dthetaL,
'dL_dm': alpha
}, W_logdet
def _outer_loop_for_missing_data(self):
Lm = None
dL_dKmm = None
self._log_marginal_likelihood = 0
self.full_values = self._outer_init_full_values()
if self.posterior is None:
woodbury_inv = np.zeros(
(self.num_inducing, self.num_inducing, self.output_dim))
woodbury_vector = np.zeros((self.num_inducing, self.output_dim))
else:
woodbury_inv = self.posterior._woodbury_inv
woodbury_vector = self.posterior._woodbury_vector
if not self.stochastics:
m_f = lambda i: "Inference with missing_data: {: >7.2%}".format(
float(i + 1) / self.output_dim)
message = m_f(-1)
print message,
for d in self.stochastics.d:
ninan = self.ninan[:, d]
if not self.stochastics:
print ' ' * (len(message)) + '\r',
message = m_f(d)
print message,
posterior, log_marginal_likelihood, \
grad_dict, current_values, value_indices = self._inner_parameters_changed(
self.kern, self.X[ninan],
self.Z, self.likelihood,
self.Ylist[d], self.Y_metadata,
Lm, dL_dKmm,
subset_indices=dict(outputs=d, samples=ninan))
self._inner_take_over_or_update(self.full_values, current_values,
value_indices)
self._inner_values_update(current_values)
Lm = posterior.K_chol
dL_dKmm = grad_dict['dL_dKmm']
woodbury_inv[:, :, d] = posterior.woodbury_inv
woodbury_vector[:, d:d + 1] = posterior.woodbury_vector
self._log_marginal_likelihood += log_marginal_likelihood
if not self.stochastics:
print ''
if self.posterior is None:
self.posterior = Posterior(woodbury_inv=woodbury_inv,
woodbury_vector=woodbury_vector,
K=posterior._K,
mean=None,
cov=None,
K_chol=posterior.K_chol)
self._outer_values_update(self.full_values)
def vi_comparison(X: np.ndarray, y: List[Tuple[int, float]], yc: List[List[Tuple[int, int]]],
kern: GPy.kern.Kern, sigma2s: np.ndarray, alpha: np.ndarray, beta: np.ndarray,
max_iters: int=200, lr: float=1e-3, method: str='fr', optimize: str="adam",
get_logger: Callable=None) -> Tuple[Posterior, float, Dict, np.ndarray, np.ndarray]:
"""
:param X: All locations of both direct observations and batch comparisons
:param y: Direct observations in as a list of tuples telling location index (row in X) and observation value.
:param yc: Batch comparisons in a list of lists of tuples. Each batch is a list and tuples tell the comparisons (winner index, loser index)
:param kern: Prior covariance kernel
:param sigma2s: Noise variance of the observations
:param alpha: Initial values for alpha
:param beta: Initial values for beta
:param max_iter: macimum number of optimization iterations
:param method: full rank 'fr' or mean field 'mf' methods
:param optimize: optimization algorithm. adam or l-bfgs-B
:param get_logger: Function for receiving the legger where the prints are forwarded.
:return: A Tuple containing the posterior, log marginal likelihood, its gradients with respect to hyper parameters (not supported at the moment) and alpha and beta values
"""
if(method == 'fr'):
recompute_posterior = recompute_posterior_fr
s_to_l = dL_fr
else:
recompute_posterior = recompute_posterior_mf
s_to_l = dL_mf
K = kern.K(X)
K = K + 1e-6*np.identity(len(K))
N = X.shape[0]
X0 = np.r_[alpha, beta]
args = [K, sigma2s, y, yc, recompute_posterior, s_to_l]
if optimize is "adam":
X, log_marginal, _ = adam(log_lik, X0.flatten(), args, bounds=None, max_it=max_iters, get_logger=get_logger)
else:
res = sp.optimize.minimize(fun=log_lik,
x0=X0.flatten(),
args= args,
method='L-BFGS-B',
jac=True,
bounds=None
)
X = res.x.reshape(-1)
log_marginal = res.fun
alpha = X[:K.shape[0]].reshape(-1,1)
beta = X[K.shape[0]:].reshape(-1,1)
# Create posterior instance
m, L, L_inv, KL, dKL_db_, dKL_da_ = recompute_posterior(alpha, beta, K)
posterior = Posterior(mean=m, cov=L @ L.T, K=K)
grad_dict = {}# {'dL_dK': dF_dK - dKL_dK, 'dL_dthetaL':dL_dthetaL}
# return posterior, log_marginal, grad_dict
return posterior, log_marginal, grad_dict, alpha, beta
def _inference(K, ga_approx, cav_params, likelihood, Z_tilde, Y_metadata=None):
log_marginal, post_params = _ep_marginal(K, ga_approx, Z_tilde)
tau_tilde_root = np.sqrt(ga_approx.tau)
Sroot_tilde_K = tau_tilde_root[:,None] * K
aux_alpha , _ = dpotrs(post_params.L, np.dot(Sroot_tilde_K, ga_approx.v), lower=1)
alpha = (ga_approx.v - tau_tilde_root * aux_alpha)[:,None] #(K + Sigma^(\tilde))^(-1) /mu^(/tilde)
LWi, _ = dtrtrs(post_params.L, np.diag(tau_tilde_root), lower=1)
Wi = np.dot(LWi.T,LWi)
symmetrify(Wi) #(K + Sigma^(\tilde))^(-1)
dL_dK = 0.5 * (tdot(alpha) - Wi)
dL_dthetaL = 0 #likelihood.ep_gradients(Y, cav_params.tau, cav_params.v, np.diag(dL_dK), Y_metadata=Y_metadata, quad_mode='gh')
#temp2 = likelihood.ep_gradients(Y, cav_params.tau, cav_params.v, np.diag(dL_dK), Y_metadata=Y_metadata, quad_mode='naive')
#temp = likelihood.exact_inference_gradients(np.diag(dL_dK), Y_metadata = Y_metadata)
#print("exact: {}, approx: {}, Ztilde: {}, naive: {}".format(temp, dL_dthetaL, Z_tilde, temp2))
return Posterior(woodbury_inv=Wi, woodbury_vector=alpha, K=K), log_marginal, {'dL_dK':dL_dK, 'dL_dthetaL':dL_dthetaL, 'dL_dm':alpha}
def posteriors(self, q_u_means, q_u_chols, X, Y, Z, kern_list, likelihood,
B_list, Y_metadata):
"""
Description:
"""
####### Dimensions #######
D = likelihood.num_output_functions(Y_metadata)
Q = len(kern_list)
M = Z.shape[0]
T = len(Y)
dimensions = {'D': D, 'Q': Q, 'M': M, 'T': T}
####### Distributions #######
Kuu, Luu, Kuui = multi_output.latent_funs_cov(Z, kern_list)
p_U = pu(Kuu=Kuu, Luu=Luu, Kuui=Kuui)
q_U = qu(mu_u=q_u_means, chols_u=q_u_chols)
posteriors = []
f_index = Y_metadata['function_index'].flatten()
####### q(F) posterior computations #######
for d in range(D):
Xtask = X[f_index[d]]
q_fd = self.variational_q_fd(X=Xtask,
Z=Z,
q_U=q_U,
p_U=p_U,
kern_list=kern_list,
B=B_list,
N=Xtask.shape[0],
dims=dimensions,
d=d)
Knew_d = multi_output.function_covariance(X=Xtask,
B=B_list,
kernel_list=kern_list,
d=d)
posterior_fd = Posterior(mean=q_fd.m_fd.copy(),
cov=q_fd.S_fd.copy(),
K=Knew_d,
prior_mean=np.zeros(q_fd.m_fd.shape))
posteriors.append(posterior_fd)
return posteriors
def inference(self,
kern,
X,
Z,
likelihood,
Y,
indexD,
output_dim,
Y_metadata=None,
Lm=None,
dL_dKmm=None,
Kuu_sigma=None):
"""
The first phase of inference:
Compute: log-likelihood, dL_dKmm
Cached intermediate results: Kmm, KmmInv,
"""
input_dim = Z.shape[0]
uncertain_inputs = isinstance(X, VariationalPosterior)
beta = 1. / likelihood.variance
if len(beta) == 1:
beta = np.zeros(output_dim) + beta
beta_exp = np.zeros(indexD.shape[0])
for d in range(output_dim):
beta_exp[indexD == d] = beta[d]
psi0, psi1, psi2 = self.gatherPsiStat(kern, X, Z, Y, beta,
uncertain_inputs)
psi2_sum = (beta_exp[:, None, None] * psi2).sum(0) / output_dim
#======================================================================
# Compute Common Components
#======================================================================
Kmm = kern.K(Z).copy()
if Kuu_sigma is not None:
diag.add(Kmm, Kuu_sigma)
else:
diag.add(Kmm, self.const_jitter)
Lm = jitchol(Kmm)
logL = 0.
dL_dthetaL = np.zeros(output_dim)
dL_dKmm = np.zeros_like(Kmm)
dL_dpsi0 = np.zeros_like(psi0)
dL_dpsi1 = np.zeros_like(psi1)
dL_dpsi2 = np.zeros_like(psi2)
wv = np.empty((Kmm.shape[0], output_dim))
for d in range(output_dim):
idx_d = indexD == d
Y_d = Y[idx_d]
N_d = Y_d.shape[0]
beta_d = beta[d]
psi2_d = psi2[idx_d].sum(0) * beta_d
psi1Y = Y_d.T.dot(psi1[idx_d]) * beta_d
psi0_d = psi0[idx_d].sum() * beta_d
YRY_d = np.square(Y_d).sum() * beta_d
LmInvPsi2LmInvT = backsub_both_sides(Lm, psi2_d, 'right')
Lambda = np.eye(Kmm.shape[0]) + LmInvPsi2LmInvT
LL = jitchol(Lambda)
LmLL = Lm.dot(LL)
b = dtrtrs(LmLL, psi1Y.T)[0].T
bbt = np.square(b).sum()
v = dtrtrs(LmLL, b.T, trans=1)[0].T
LLinvPsi1TYYTPsi1LLinvT = tdot(b.T)
tmp = -backsub_both_sides(LL, LLinvPsi1TYYTPsi1LLinvT)
dL_dpsi2R = backsub_both_sides(Lm, tmp + np.eye(input_dim)) / 2
logL_R = -N_d * np.log(beta_d)
logL += -((N_d * log_2_pi + logL_R + psi0_d -
np.trace(LmInvPsi2LmInvT)) + YRY_d - bbt) / 2.
dL_dKmm += dL_dpsi2R - backsub_both_sides(Lm, LmInvPsi2LmInvT) / 2
dL_dthetaL[d:d +
1] = (YRY_d * beta_d + beta_d * psi0_d - N_d *
beta_d) / 2. - beta_d * (dL_dpsi2R * psi2_d).sum(
) - beta_d * np.trace(LLinvPsi1TYYTPsi1LLinvT)
dL_dpsi0[idx_d] = -beta_d / 2.
dL_dpsi1[idx_d] = beta_d * np.dot(Y_d, v)
dL_dpsi2[idx_d] = beta_d * dL_dpsi2R
wv[:, d] = v
LmInvPsi2LmInvT = backsub_both_sides(Lm, psi2_sum, 'right')
Lambda = np.eye(Kmm.shape[0]) + LmInvPsi2LmInvT
LL = jitchol(Lambda)
LmLL = Lm.dot(LL)
logdet_L = 2. * np.sum(np.log(np.diag(LL)))
dL_dpsi2R_common = dpotri(LmLL)[0] / -2.
dL_dpsi2 += dL_dpsi2R_common[None, :, :] * beta_exp[:, None, None]
for d in range(output_dim):
dL_dthetaL[d] += (dL_dpsi2R_common * psi2[indexD == d].sum(0)
).sum() * -beta[d] * beta[d]
dL_dKmm += dL_dpsi2R_common * output_dim
logL += -output_dim * logdet_L / 2.
#======================================================================
# Compute dL_dKmm
#======================================================================
# dL_dKmm = dL_dpsi2R - output_dim* backsub_both_sides(Lm, LmInvPsi2LmInvT)/2 #LmInv.T.dot(LmInvPsi2LmInvT).dot(LmInv)/2.
#======================================================================
# Compute the Posterior distribution of inducing points p(u|Y)
#======================================================================
LLInvLmT = dtrtrs(LL, Lm.T)[0]
cov = tdot(LLInvLmT.T)
wd_inv = backsub_both_sides(
Lm,
np.eye(input_dim) -
backsub_both_sides(LL, np.identity(input_dim), transpose='left'),
transpose='left')
post = Posterior(woodbury_inv=wd_inv,
woodbury_vector=wv,
K=Kmm,
mean=None,
cov=cov,
K_chol=Lm)
#======================================================================
# Compute dL_dthetaL for uncertian input and non-heter noise
#======================================================================
# for d in range(output_dim):
# dL_dthetaL[d:d+1] += - beta[d]*beta[d]*(dL_dpsi2R[None,:,:] * psi2[indexD==d]/output_dim).sum()
# dL_dthetaL += - (dL_dpsi2R[None,:,:] * psi2_sum*D beta*(dL_dpsi2R*psi2).sum()
#======================================================================
# Compute dL_dpsi
#======================================================================
if not uncertain_inputs:
dL_dpsi1 += (psi1[:, None, :] * dL_dpsi2).sum(2) * 2.
if uncertain_inputs:
grad_dict = {
'dL_dKmm': dL_dKmm,
'dL_dpsi0': dL_dpsi0,
'dL_dpsi1': dL_dpsi1,
'dL_dpsi2': dL_dpsi2,
'dL_dthetaL': dL_dthetaL
}
else:
grad_dict = {
'dL_dKmm': dL_dKmm,
'dL_dKdiag': dL_dpsi0,
'dL_dKnm': dL_dpsi1,
'dL_dthetaL': dL_dthetaL
}
return post, logL, grad_dict
def inference(self,
q_u_means,
q_u_chols,
X,
Y,
Z,
kern_list,
kern_list_Gdj,
kern_aux,
likelihood,
B_list,
Y_metadata,
KL_scale=1.0,
batch_scale=None,
predictive=False,
Gauss_Newton=False):
M = Z.shape[0]
T = len(Y)
if batch_scale is None:
batch_scale = [1.0] * T
Ntask = []
[Ntask.append(Y[t].shape[0]) for t in range(T)]
Q = len(kern_list)
D = likelihood.num_output_functions(Y_metadata)
Kuu, Luu, Kuui = util.latent_funs_cov(Z, kern_list)
p_U = pu(Kuu=Kuu, Luu=Luu, Kuui=Kuui)
q_U = qu(mu_u=q_u_means.copy(), chols_u=q_u_chols.copy())
S_u = np.empty((Q, M, M))
L_u = choleskies.flat_to_triang(q_U.chols_u.copy())
[np.dot(L_u[q, :, :], L_u[q, :, :].T, S_u[q, :, :]) for q in range(Q)]
Su_add_Kuu = np.zeros((Q, M, M))
Su_add_Kuu_chol = np.zeros((Q, M, M))
for q in range(Q):
Su_add_Kuu[q, :, :] = S_u[q, :, :] + Kuu[q, :, :]
Su_add_Kuu_chol[q, :, :] = linalg.jitchol(Su_add_Kuu[q, :, :])
# for every latent function f_d calculate q(f_d) and keep it as q(F):
q_F = []
posteriors_F = []
f_index = Y_metadata['function_index'].flatten()
d_index = Y_metadata['d_index'].flatten()
for d in range(D):
Xtask = X[f_index[d]]
q_fd, q_U = self.calculate_q_f(X=Xtask,
Z=Z,
q_U=q_U,
S_u=S_u,
p_U=p_U,
kern_list=kern_list,
kern_list_Gdj=kern_list_Gdj,
kern_aux=kern_aux,
B=B_list,
M=M,
N=Xtask.shape[0],
Q=Q,
D=D,
d=d)
# Posterior objects for output functions (used in prediction)
#I have to get rid of function below Posterior for it is not necessary
posterior_fd = Posterior(mean=q_fd.m_fd.copy(),
cov=q_fd.S_fd.copy(),
K=util.conv_function_covariance(
X=Xtask,
B=B_list,
kernel_list=kern_list,
kernel_list_Gdj=kern_list_Gdj,
kff_aux=kern_aux,
d=d),
prior_mean=np.zeros(q_fd.m_fd.shape))
posteriors_F.append(posterior_fd)
q_F.append(q_fd)
mu_F = []
v_F = []
for t in range(T):
mu_F_task = np.empty((X[t].shape[0], 1))
v_F_task = np.empty((X[t].shape[0], 1))
for d, q_fd in enumerate(q_F):
if f_index[d] == t:
mu_F_task = np.hstack((mu_F_task, q_fd.m_fd))
v_F_task = np.hstack((v_F_task, q_fd.v_fd))
mu_F.append(mu_F_task[:, 1:])
v_F.append(v_F_task[:, 1:])
# posterior_Fnew for predictive
if predictive:
return posteriors_F
# inference for rest of cases
else:
# Variational Expectations
VE = likelihood.var_exp(Y, mu_F, v_F, Y_metadata)
VE_dm, VE_dv = likelihood.var_exp_derivatives(
Y, mu_F, v_F, Y_metadata, Gauss_Newton)
for t in range(T):
VE[t] = VE[t] * batch_scale[t]
VE_dm[t] = VE_dm[t] * batch_scale[t]
VE_dv[t] = VE_dv[t] * batch_scale[t]
# KL Divergence
KL = self.calculate_KL(q_U=q_U,
Su_add_Kuu=Su_add_Kuu,
Su_add_Kuu_chol=Su_add_Kuu_chol,
p_U=p_U,
M=M,
Q=Q,
D=D)
# Log Marginal log(p(Y))
F = 0
for t in range(T):
F += VE[t].sum()
log_marginal = F - KL
# Gradients and Posteriors
dL_dS_u = []
dL_dmu_u = []
dL_dL_u = []
dL_dKmm = []
dL_dKmn = []
dL_dKdiag = []
posteriors = []
for q in range(Q):
(dL_dmu_q, dL_dL_q, dL_dS_q, posterior_q, dL_dKqq, dL_dKdq,
dL_dKdiag_q) = self.calculate_gradients(
q_U=q_U,
S_u=S_u,
Su_add_Kuu_chol=Su_add_Kuu_chol,
p_U=p_U,
q_F=q_F,
VE_dm=VE_dm,
VE_dv=VE_dv,
Ntask=Ntask,
M=M,
Q=Q,
D=D,
f_index=f_index,
d_index=d_index,
q=q)
dL_dmu_u.append(dL_dmu_q)
dL_dL_u.append(dL_dL_q)
dL_dS_u.append(dL_dS_q)
dL_dKmm.append(dL_dKqq)
dL_dKmn.append(dL_dKdq)
dL_dKdiag.append(dL_dKdiag_q)
posteriors.append(posterior_q)
gradients = {
'dL_dmu_u': dL_dmu_u,
'dL_dL_u': dL_dL_u,
'dL_dS_u': dL_dS_u,
'dL_dKmm': dL_dKmm,
'dL_dKmn': dL_dKmn,
'dL_dKdiag': dL_dKdiag
}
return log_marginal, gradients, posteriors, posteriors_F
def calculate_gradients(self, q_U, S_u, Su_add_Kuu_chol, p_U, q_F, VE_dm,
VE_dv, Ntask, M, Q, D, f_index, d_index, q):
"""
Calculates gradients of the Log-marginal distribution p(Y) wrt variational
parameters mu_q, S_q
"""
# Algebra for q(u) and p(u):
m_u = q_U.mu_u.copy()
#L_u = choleskies.flat_to_triang(q_U.chols_u.copy())
#S_u = np.empty((Q, M, M))
#[np.dot(L_u[q, :, :], L_u[q, :, :].T, S_u[q, :, :]) for q in range(Q)]
Kuu = p_U.Kuu.copy()
Luu = p_U.Luu.copy()
Kuui = p_U.Kuui.copy()
S_qi, _ = linalg.dpotri(np.asfortranarray(Su_add_Kuu_chol[q, :, :]))
if np.any(np.isinf(S_qi)):
raise ValueError("Sqi: Cholesky representation unstable")
# KL Terms
dKL_dmu_q = []
dKL_dKqq = 0
for d in range(D):
dKL_dmu_q.append(np.dot(Kuui[q, :, :], m_u[d][:, q, None])) #same
dKL_dKqq += -0.5 * S_qi + 0.5 * Kuui[q, :, :] - 0.5 * Kuui[q, :, :].dot(S_u[q, :, :]).dot(Kuui[q, :, :]) \
- 0.5 * np.dot(Kuui[q, :, :], np.dot(m_u[d][:, q, None], m_u[d][:, q, None].T)).dot(Kuui[q, :, :].T) # same
#dKL_dS_q = 0.5 * (Kuui[q,:,:] - S_qi) #old
dKL_dS_q = 0.5 * (Kuui[q, :, :] - S_qi) * D
# VE Terms
#dVE_dmu_q = np.zeros((M, 1))
dVE_dmu_q = []
dVE_dS_q = np.zeros((M, M))
dVE_dKqq = np.zeros((M, M))
dVE_dKqd = []
dVE_dKdiag = []
dL_dmu_q = []
for d, q_fd in enumerate(q_F):
Nt = Ntask[f_index[d]]
dVE_dmu_q.append(
np.dot(q_fd.Afdu[q, :, :].T,
VE_dm[f_index[d]][:, d_index[d]])[:, None])
dL_dmu_q.append(dVE_dmu_q[d] - dKL_dmu_q[d])
Adv = q_fd.Afdu[q, :, :].T * VE_dv[f_index[d]][:, d_index[d],
None].T
Adv = np.ascontiguousarray(Adv)
AdvA = np.dot(Adv.reshape(-1, Nt),
q_fd.Afdu[q, :, :]).reshape(M, M)
dVE_dS_q += AdvA
# Derivatives dKuquq
tmp_dv = np.dot(AdvA, S_u[q, :, :]).dot(Kuui[q, :, :])
dVE_dKqq += -tmp_dv - tmp_dv.T #+ AdvA last term not included in the derivative
Adm = np.dot(q_fd.Afdu[q, :, :].T, VE_dm[f_index[d]][:, d_index[d],
None])
dVE_dKqq += -np.dot(Adm,
np.dot(Kuui[q, :, :], m_u[d][:, q, None]).T)
# Derivatives dKuqfd
tmp = np.dot(S_u[q, :, :], Kuui[q, :, :])
tmp = 2. * tmp #2. * (tmp - np.eye(M)) # the term -2Adv not included
dve_kqd = np.dot(np.dot(Kuui[q, :, :], m_u[d][:, q, None]),
VE_dm[f_index[d]][:, d_index[d], None].T)
dve_kqd += np.dot(tmp.T, Adv)
dVE_dKqd.append(dve_kqd)
# Derivatives dKdiag
dVE_dKdiag.append(VE_dv[f_index[d]][:, d_index[d]])
dVE_dKqq = 0.5 * (dVE_dKqq + dVE_dKqq.T)
# Sum of VE and KL terms
#dL_dmu_q = dVE_dmu_q - dKL_dmu_q
dL_dS_q = dVE_dS_q - dKL_dS_q
dL_dKqq = dVE_dKqq - dKL_dKqq
dL_dKdq = dVE_dKqd
dL_dKdiag = dVE_dKdiag
# Pass S_q gradients to its low-triangular representation L_q
chol_u = q_U.chols_u.copy()
L_q = choleskies.flat_to_triang(chol_u[:, q:q + 1])
dL_dL_q = 2. * np.array(
[np.dot(a, b) for a, b in zip(dL_dS_q[None, :, :], L_q)])
dL_dL_q = choleskies.triang_to_flat(dL_dL_q)
# Posterior
posterior_q = []
for d in range(D):
posterior_q.append(
Posterior(mean=m_u[d][:, q, None],
cov=S_u[q, :, :] + Kuu[q, :, :],
K=Kuu[q, :, :],
prior_mean=np.zeros(m_u[d][:, q, None].shape)))
return dL_dmu_q, dL_dL_q, dL_dS_q, posterior_q, dL_dKqq, dL_dKdq, dL_dKdiag
def calculate_gradients(self, q_U, p_U, q_F, VE_dm, VE_dv, Ntask, M, Q, D,
f_index, d_index, j):
"""
Calculates gradients of the Log-marginal distribution p(Y) wrt variational
parameters mu_q, S_q
"""
# Algebra for q(u) and p(u):
m_u = q_U.mu_u.copy()
L_u = choleskies.flat_to_triang(q_U.chols_u.copy())
#S_u = np.empty((Q, M, M))
S_u = np.dot(
L_u[j, :, :], L_u[j, :, :].T
) #This could be done outside and recieve it to reduce computation
#[np.dot(L_u[q, :, :], L_u[q, :, :].T, S_u[q, :, :]) for q in range(Q)]
Kuu = p_U.Kuu.copy()
Luu = p_U.Luu.copy()
Kuui = p_U.Kuui.copy()
S_qi, _ = linalg.dpotri(np.asfortranarray(L_u[j, :, :]))
if np.any(np.isinf(S_qi)):
raise ValueError("Sqi: Cholesky representation unstable")
# KL Terms
dKL_dmu_j = np.dot(Kuui[j, :, :], m_u[:, j, None])
dKL_dS_j = 0.5 * (Kuui[j, :, :] - S_qi)
dKL_dKjj = 0.5 * Kuui[j,:,:] - 0.5 * Kuui[j,:,:].dot(S_u).dot(Kuui[j,:,:]) \
- 0.5 * np.dot(Kuui[j,:,:],np.dot(m_u[:, j, None],m_u[:, j, None].T)).dot(Kuui[j,:,:].T)
# VE Terms
dVE_dmu_j = np.zeros((M, 1))
dVE_dS_j = np.zeros((M, M))
dVE_dKjj = np.zeros((M, M))
dVE_dKjd = []
dVE_dKdiag = []
Nt = Ntask[f_index[j]]
dVE_dmu_j += np.dot(q_F[j].Afdu.T,
VE_dm[f_index[j]][:, d_index[j]])[:, None]
Adv = q_F[j].Afdu.T * VE_dv[f_index[j]][:, d_index[j], None].T
Adv = np.ascontiguousarray(Adv)
AdvA = np.dot(Adv.reshape(-1, Nt), q_F[j].Afdu).reshape(M, M)
dVE_dS_j += AdvA
# Derivatives dKuquq
tmp_dv = np.dot(AdvA, S_u).dot(Kuui[j, :, :])
dVE_dKjj += AdvA - tmp_dv - tmp_dv.T
Adm = np.dot(q_F[j].Afdu.T, VE_dm[f_index[j]][:, d_index[j], None])
dVE_dKjj += -np.dot(Adm, np.dot(Kuui[j, :, :], m_u[:, j, None]).T)
# Derivatives dKuqfd
tmp = np.dot(S_u, Kuui[j, :, :])
tmp = 2. * (tmp - np.eye(M))
dve_kjd = np.dot(np.dot(Kuui[j, :, :], m_u[:, j, None]),
VE_dm[f_index[j]][:, d_index[j], None].T)
dve_kjd += np.dot(tmp.T, Adv)
dVE_dKjd.append(dve_kjd)
# Derivatives dKdiag
dVE_dKdiag.append(VE_dv[f_index[j]][:, d_index[j]])
dVE_dKjj = 0.5 * (dVE_dKjj + dVE_dKjj.T)
# Sum of VE and KL terms
dL_dmu_j = dVE_dmu_j - dKL_dmu_j
dL_dS_j = dVE_dS_j - dKL_dS_j
dL_dKjj = dVE_dKjj - dKL_dKjj
dL_dKdj = dVE_dKjd[0].copy() #Here we just pass the unique position
dL_dKdiag = dVE_dKdiag[0].copy(
) #Here we just pass the unique position
# Pass S_q gradients to its low-triangular representation L_q
chol_u = q_U.chols_u.copy()
L_j = choleskies.flat_to_triang(chol_u[:, j:j + 1])
dL_dL_j = 2. * np.array(
[np.dot(a, b) for a, b in zip(dL_dS_j[None, :, :], L_j)])
dL_dL_j = choleskies.triang_to_flat(dL_dL_j)
# Posterior
posterior_j = Posterior(mean=m_u[:, j, None],
cov=S_u,
K=Kuu[j, :, :],
prior_mean=np.zeros(m_u[:, j, None].shape))
return dL_dmu_j, dL_dL_j, dL_dS_j, posterior_j, dL_dKjj, dL_dKdj, dL_dKdiag
def inference(self,
kern,
X,
Z,
likelihood,
Y,
Y_metadata=None,
Lm=None,
dL_dKmm=None):
"""
The first phase of inference:
Compute: log-likelihood, dL_dKmm
Cached intermediate results: Kmm, KmmInv,
"""
num_data, output_dim = Y.shape
input_dim = Z.shape[0]
uncertain_inputs = isinstance(X, VariationalPosterior)
uncertain_outputs = isinstance(Y, VariationalPosterior)
beta = 1. / np.fmax(likelihood.variance, 1e-6)
psi0, psi2, YRY, psi1, psi1Y, Shalf, psi1S = self.gatherPsiStat(
kern, X, Z, Y, beta, uncertain_inputs)
#======================================================================
# Compute Common Components
#======================================================================
Kmm = kern.K(Z).copy()
diag.add(Kmm, self.const_jitter)
Lm = jitchol(Kmm)
#LmInv = dtrtri(Lm)
if uncertain_inputs:
LmInvPsi2LmInvT = backsub_both_sides(Lm, psi2, 'right')
else:
LmInvPsi2LmInvT = tdot(dtrtrs(
Lm, psi1.T)[0]) / beta #tdot(psi1.dot(LmInv.T).T) /beta
Lambda = np.eye(Kmm.shape[0]) + LmInvPsi2LmInvT
LL = jitchol(Lambda)
LmLL = Lm.dot(LL)
# LLInv = dtrtri(LL)
# LmLLInv = LLInv.dot(LmInv)
logdet_L = 2. * np.sum(np.log(np.diag(LL)))
b = dtrtrs(LmLL, psi1Y.T)[0].T #psi1Y.dot(LmLLInv.T)
bbt = np.square(b).sum()
v = dtrtrs(LmLL, b.T, trans=1)[0].T #b.dot(LmLLInv)
LLinvPsi1TYYTPsi1LLinvT = tdot(b.T)
if psi1S is not None:
psi1SLLinv = dtrtrs(LmLL, psi1S.T)[0].T #psi1S.dot(LmLLInv.T)
bbt += np.square(psi1SLLinv).sum()
LLinvPsi1TYYTPsi1LLinvT += tdot(psi1SLLinv.T)
psi1SP = dtrtrs(LmLL, psi1SLLinv.T,
trans=1)[0].T #psi1SLLinv.dot(LmLLInv)
tmp = -backsub_both_sides(
LL, LLinvPsi1TYYTPsi1LLinvT + output_dim * np.eye(input_dim))
dL_dpsi2R = backsub_both_sides(
Lm, tmp + output_dim * np.eye(input_dim)) / 2
#tmp = -LLInv.T.dot(LLinvPsi1TYYTPsi1LLinvT+output_dim*np.eye(input_dim)).dot(LLInv)
#dL_dpsi2R = LmInv.T.dot(tmp+output_dim*np.eye(input_dim)).dot(LmInv)/2.
#======================================================================
# Compute log-likelihood
#======================================================================
logL_R = -num_data * np.log(beta)
logL = -(
output_dim *
(num_data * log_2_pi + logL_R + psi0 - np.trace(LmInvPsi2LmInvT)) +
YRY - bbt) / 2. - output_dim * logdet_L / 2.
#======================================================================
# Compute dL_dKmm
#======================================================================
dL_dKmm = dL_dpsi2R - output_dim * backsub_both_sides(
Lm,
LmInvPsi2LmInvT) / 2 #LmInv.T.dot(LmInvPsi2LmInvT).dot(LmInv)/2.
#======================================================================
# Compute the Posterior distribution of inducing points p(u|Y)
#======================================================================
wd_inv = backsub_both_sides(
Lm,
np.eye(input_dim) -
backsub_both_sides(LL, np.identity(input_dim), transpose='left'),
transpose='left')
post = Posterior(woodbury_inv=wd_inv,
woodbury_vector=v.T,
K=Kmm,
mean=None,
cov=None,
K_chol=Lm)
#======================================================================
# Compute dL_dthetaL for uncertian input and non-heter noise
#======================================================================
dL_dthetaL = (YRY * beta + beta * output_dim * psi0 - num_data *
output_dim * beta) / 2. - beta * (dL_dpsi2R * psi2).sum(
) - beta * np.trace(LLinvPsi1TYYTPsi1LLinvT)
#======================================================================
# Compute dL_dpsi
#======================================================================
dL_dpsi0 = -output_dim * (beta * np.ones((num_data, ))) / 2.
if uncertain_outputs:
m, s = Y.mean, Y.variance
dL_dpsi1 = beta * (np.dot(m, v) + Shalf[:, None] * psi1SP)
else:
dL_dpsi1 = beta * np.dot(Y, v)
if uncertain_inputs:
dL_dpsi2 = beta * dL_dpsi2R
else:
dL_dpsi1 += np.dot(psi1, dL_dpsi2R) * 2.
dL_dpsi2 = None
if uncertain_inputs:
grad_dict = {
'dL_dKmm': dL_dKmm,
'dL_dpsi0': dL_dpsi0,
'dL_dpsi1': dL_dpsi1,
'dL_dpsi2': dL_dpsi2,
'dL_dthetaL': dL_dthetaL
}
else:
grad_dict = {
'dL_dKmm': dL_dKmm,
'dL_dKdiag': dL_dpsi0,
'dL_dKnm': dL_dpsi1,
'dL_dthetaL': dL_dthetaL
}
if uncertain_outputs:
m, s = Y.mean, Y.variance
psi1LmiLLi = dtrtrs(LmLL, psi1.T)[0].T #psi1.dot(LmLLInv.T)
LLiLmipsi1Y = b.T
grad_dict['dL_dYmean'] = -m * beta + psi1LmiLLi.dot(LLiLmipsi1Y)
grad_dict['dL_dYvar'] = beta / -2. + np.square(psi1LmiLLi).sum(
axis=1) / 2
return post, logL, grad_dict
def inference(self,
kern,
X,
Z,
likelihood,
Y,
qU_mean,
qU_var,
Kuu_sigma=None):
"""
The SVI-VarDTC inference
"""
N, D, M, Q = Y.shape[0], Y.shape[1], Z.shape[0], Z.shape[1]
uncertain_inputs = isinstance(X, VariationalPosterior)
uncertain_outputs = isinstance(Y, VariationalPosterior)
beta = 1. / likelihood.variance
psi0, psi2, YRY, psi1, psi1Y = self.gatherPsiStat(
kern, X, Z, Y, beta, uncertain_inputs)
#======================================================================
# Compute Common Components
#======================================================================
Kuu = kern.K(Z).copy()
if Kuu_sigma is not None:
diag.add(Kuu, Kuu_sigma)
else:
diag.add(Kuu, self.const_jitter)
Lm = jitchol(Kuu)
mu, S = qU_mean, qU_var
Ls = jitchol(S)
LinvLs = dtrtrs(Lm, Ls)[0]
Linvmu = dtrtrs(Lm, mu)[0]
psi1YLinvT = dtrtrs(Lm, psi1Y.T)[0].T
self.mid = {'qU_L': Ls, 'LinvLu': LinvLs, 'L': Lm, 'Linvmu': Linvmu}
if uncertain_inputs:
LmInvPsi2LmInvT = backsub_both_sides(Lm, psi2, 'right')
else:
LmInvPsi2LmInvT = tdot(dtrtrs(Lm, psi1.T)[0]) / beta
LmInvSmuLmInvT = tdot(LinvLs) * D + tdot(Linvmu)
# logdet_L = np.sum(np.log(np.diag(Lm)))
# logdet_S = np.sum(np.log(np.diag(Ls)))
#======================================================================
# Compute log-likelihood
#======================================================================
logL_R = -N * np.log(beta)
logL = -N*D*log_2_pi/2. -D*logL_R/2. - D*psi0/2. - YRY/2. \
-(LmInvSmuLmInvT*LmInvPsi2LmInvT).sum()/2. + np.trace(LmInvPsi2LmInvT)*D/2.+(Linvmu*psi1YLinvT.T).sum()
#======================================================================
# Compute dL_dKmm
#======================================================================
tmp1 = backsub_both_sides(Lm, LmInvSmuLmInvT.dot(LmInvPsi2LmInvT),
'left')
tmp2 = Linvmu.dot(psi1YLinvT)
tmp3 = backsub_both_sides(Lm, -D * LmInvPsi2LmInvT - tmp2 - tmp2.T,
'left') / 2.
dL_dKmm = (tmp1 + tmp1.T) / 2. + tmp3
#======================================================================
# Compute dL_dthetaL for uncertian input and non-heter noise
#======================================================================
dL_dthetaL = -D * N * beta / 2. - (
-D * psi0 / 2. - YRY / 2. -
(LmInvSmuLmInvT * LmInvPsi2LmInvT).sum() / 2. +
np.trace(LmInvPsi2LmInvT) * D / 2. +
(Linvmu * psi1YLinvT.T).sum()) * beta
#======================================================================
# Compute dL_dqU
#======================================================================
tmp1 = backsub_both_sides(Lm, -LmInvPsi2LmInvT, 'left')
dL_dqU_mean = tmp1.dot(mu) + dtrtrs(Lm, psi1YLinvT.T, trans=1)[0]
dL_dqU_var = D / 2. * tmp1
#======================================================================
# Compute the Posterior distribution of inducing points p(u|Y)
#======================================================================
KuuInvmu = dtrtrs(Lm, Linvmu, trans=1)[0]
tmp = backsub_both_sides(Lm, np.eye(M) - tdot(LinvLs), 'left')
post = Posterior(woodbury_inv=tmp,
woodbury_vector=KuuInvmu,
K=Kuu,
mean=mu,
cov=S,
K_chol=Lm)
#======================================================================
# Compute dL_dpsi
#======================================================================
dL_dpsi0 = -D * (beta * np.ones((N, ))) / 2.
if uncertain_outputs:
dL_dpsi1 = Y.mean.dot(dtrtrs(Lm, Linvmu, trans=1)[0].T) * beta
else:
dL_dpsi1 = Y.dot(dtrtrs(Lm, Linvmu, trans=1)[0].T) * beta
dL_dpsi2 = beta * backsub_both_sides(Lm,
D * np.eye(M) - LmInvSmuLmInvT,
'left') / 2.
if not uncertain_inputs:
dL_dpsi1 += psi1.dot(dL_dpsi2 + dL_dpsi2.T) / beta
dL_dpsi2 = None
if uncertain_inputs:
grad_dict = {
'dL_dKmm': dL_dKmm,
'dL_dpsi0': dL_dpsi0,
'dL_dpsi1': dL_dpsi1,
'dL_dpsi2': dL_dpsi2,
'dL_dthetaL': dL_dthetaL,
'dL_dqU_mean': dL_dqU_mean,
'dL_dqU_var': dL_dqU_var
}
else:
grad_dict = {
'dL_dKmm': dL_dKmm,
'dL_dKdiag': dL_dpsi0,
'dL_dKnm': dL_dpsi1,
'dL_dthetaL': dL_dthetaL,
'dL_dqU_mean': dL_dqU_mean,
'dL_dqU_var': dL_dqU_var
}
if uncertain_outputs:
m, s = Y.mean, Y.variance
grad_dict['dL_dYmean'] = -m * beta + dtrtrs(Lm, psi1.T)[0].T.dot(
dtrtrs(Lm, mu)[0])
grad_dict['dL_dYvar'] = beta / -2.
return post, logL, grad_dict
def inference(self, kern, X, Z, likelihood, Y, Y_metadata=None, Lm=None, dL_dKmm=None, fixed_covs_kerns=None, **kw):
_, output_dim = Y.shape
uncertain_inputs = isinstance(X, VariationalPosterior)
#see whether we've got a different noise variance for each datum
beta = 1./np.fmax(likelihood.gaussian_variance(Y_metadata), 1e-6)
# VVT_factor is a matrix such that tdot(VVT_factor) = VVT...this is for efficiency!
#self.YYTfactor = self.get_YYTfactor(Y)
#VVT_factor = self.get_VVTfactor(self.YYTfactor, beta)
het_noise = beta.size > 1
if het_noise:
raise(NotImplementedError("Heteroscedastic noise not implemented, should be possible though, feel free to try implementing it :)"))
if beta.ndim == 1:
beta = beta[:, None]
# do the inference:
num_inducing = Z.shape[0]
num_data = Y.shape[0]
# kernel computations, using BGPLVM notation
Kmm = kern.K(Z).copy()
diag.add(Kmm, self.const_jitter)
if Lm is None:
Lm = jitchol(Kmm)
# The rather complex computations of A, and the psi stats
if uncertain_inputs:
psi0 = kern.psi0(Z, X)
psi1 = kern.psi1(Z, X)
if het_noise:
psi2_beta = np.sum([kern.psi2(Z,X[i:i+1,:]) * beta_i for i,beta_i in enumerate(beta)],0)
else:
psi2_beta = kern.psi2(Z,X) * beta
LmInv = dtrtri(Lm)
A = LmInv.dot(psi2_beta.dot(LmInv.T))
else:
psi0 = kern.Kdiag(X)
psi1 = kern.K(X, Z)
if het_noise:
tmp = psi1 * (np.sqrt(beta))
else:
tmp = psi1 * (np.sqrt(beta))
tmp, _ = dtrtrs(Lm, tmp.T, lower=1)
A = tdot(tmp)
# factor B
B = np.eye(num_inducing) + A
LB = jitchol(B)
# back substutue C into psi1Vf
#tmp, _ = dtrtrs(Lm, psi1.T.dot(VVT_factor), lower=1, trans=0)
#_LBi_Lmi_psi1Vf, _ = dtrtrs(LB, tmp, lower=1, trans=0)
#tmp, _ = dtrtrs(LB, _LBi_Lmi_psi1Vf, lower=1, trans=1)
#Cpsi1Vf, _ = dtrtrs(Lm, tmp, lower=1, trans=1)
# data fit and derivative of L w.r.t. Kmm
#delit = tdot(_LBi_Lmi_psi1Vf)
# Expose YYT to get additional covariates in (YYT + Kgg):
tmp, _ = dtrtrs(Lm, psi1.T, lower=1, trans=0)
_LBi_Lmi_psi1, _ = dtrtrs(LB, tmp, lower=1, trans=0)
tmp, _ = dtrtrs(LB, _LBi_Lmi_psi1, lower=1, trans=1)
Cpsi1, _ = dtrtrs(Lm, tmp, lower=1, trans=1)
# TODO: cache this:
# Compute fixed covariates covariance:
if fixed_covs_kerns is not None:
K_fixed = 0
for name, [cov, k] in fixed_covs_kerns.iteritems():
K_fixed += k.K(cov)
#trYYT = self.get_trYYT(Y)
YYT_covs = (tdot(Y) + K_fixed)
data_term = beta**2 * YYT_covs
trYYT_covs = np.trace(YYT_covs)
else:
data_term = beta**2 * tdot(Y)
trYYT_covs = self.get_trYYT(Y)
#trYYT = self.get_trYYT(Y)
delit = mdot(_LBi_Lmi_psi1, data_term, _LBi_Lmi_psi1.T)
data_fit = np.trace(delit)
DBi_plus_BiPBi = backsub_both_sides(LB, output_dim * np.eye(num_inducing) + delit)
if dL_dKmm is None:
delit = -0.5 * DBi_plus_BiPBi
delit += -0.5 * B * output_dim
delit += output_dim * np.eye(num_inducing)
# Compute dL_dKmm
dL_dKmm = backsub_both_sides(Lm, delit)
# derivatives of L w.r.t. psi
dL_dpsi0, dL_dpsi1, dL_dpsi2 = _compute_dL_dpsi(num_inducing, num_data, output_dim, beta, Lm,
data_term, Cpsi1, DBi_plus_BiPBi,
psi1, het_noise, uncertain_inputs)
# log marginal likelihood
log_marginal = _compute_log_marginal_likelihood(likelihood, num_data, output_dim, beta, het_noise,
psi0, A, LB, trYYT_covs, data_fit, Y)
if self.save_per_dim:
self.saved_vals = [psi0, A, LB, _LBi_Lmi_psi1, beta]
# No heteroscedastics, so no _LBi_Lmi_psi1Vf:
# For the interested reader, try implementing the heteroscedastic version, it should be possible
_LBi_Lmi_psi1Vf = None # Is just here for documentation, so you can see, what it was.
#noise derivatives
dL_dR = _compute_dL_dR(likelihood,
het_noise, uncertain_inputs, LB,
_LBi_Lmi_psi1Vf, DBi_plus_BiPBi, Lm, A,
psi0, psi1, beta,
data_fit, num_data, output_dim, trYYT_covs, Y, None)
dL_dthetaL = likelihood.exact_inference_gradients(dL_dR,Y_metadata)
#put the gradients in the right places
if uncertain_inputs:
grad_dict = {'dL_dKmm': dL_dKmm,
'dL_dpsi0':dL_dpsi0,
'dL_dpsi1':dL_dpsi1,
'dL_dpsi2':dL_dpsi2,
'dL_dthetaL':dL_dthetaL}
else:
grad_dict = {'dL_dKmm': dL_dKmm,
'dL_dKdiag':dL_dpsi0,
'dL_dKnm':dL_dpsi1,
'dL_dthetaL':dL_dthetaL}
if fixed_covs_kerns is not None:
# For now, we do not take the gradients, we can compute them,
# but the maximum likelihood solution is to switch off the additional covariates....
dL_dcovs = beta * np.eye(K_fixed.shape[0]) - beta**2*tdot(_LBi_Lmi_psi1.T)
grad_dict['dL_dcovs'] = -.5 * dL_dcovs
#get sufficient things for posterior prediction
#TODO: do we really want to do this in the loop?
if 1:
woodbury_vector = (beta*Cpsi1).dot(Y)
else:
import ipdb; ipdb.set_trace()
psi1V = np.dot(Y.T*beta, psi1).T
tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0)
tmp, _ = dpotrs(LB, tmp, lower=1)
woodbury_vector, _ = dtrtrs(Lm, tmp, lower=1, trans=1)
Bi, _ = dpotri(LB, lower=1)
symmetrify(Bi)
Bi = -dpotri(LB, lower=1)[0]
diag.add(Bi, 1)
woodbury_inv = backsub_both_sides(Lm, Bi)
#construct a posterior object
post = Posterior(woodbury_inv=woodbury_inv, woodbury_vector=woodbury_vector, K=Kmm, mean=None, cov=None, K_chol=Lm)
return post, log_marginal, grad_dict
def inference(self, q_u_means, q_u_chols, kern, X, Z, likelihood, Y, mean_functions, Y_metadata=None, KL_scale=1.0, batch_scale=1.0):
num_inducing = Z.shape[0]
num_data, num_outputs = Y.shape
num_latent_funcs = likelihood.request_num_latent_functions(Y)
#For each latent function, calculate some required values
latent_function_details = []
for latent_ind in range(num_latent_funcs):
q_u_meanj = q_u_means[:, latent_ind*num_outputs:(latent_ind+1)*num_outputs]
q_u_cholj = q_u_chols[:, latent_ind*num_outputs:(latent_ind+1)*num_outputs]
kernj = kern[latent_ind]
mean_functionj = mean_functions[latent_ind]
latent_detail = self.calculate_mu_var(X, Y, Z, q_u_meanj, q_u_cholj, kernj, mean_functionj, num_inducing, num_data, num_outputs)
latent_function_details.append(latent_detail)
mu = np.hstack([l.mu for l in latent_function_details])
v = np.hstack([l.v for l in latent_function_details])
#mu = [l.mu for l in latent_function_details]
#v = [l.v for l in latent_function_details]
#Hack shouldn't be necessary
#Y = np.hstack([Y]*num_latent_funcs)
#quadrature for the likelihood
F, dF_dmu, dF_dv, dF_dthetaL = likelihood.variational_expectations(Y, mu, v, Y_metadata=Y_metadata)
#for latent_ind in range(num_latent_functions):
#l.dF_dmu = dF_dmu[:, latent_ind][:, None]
#l.dF_dv = dF_dv[:, latent_ind][:, None]
#rescale the F term if working on a batch
F, dF_dmu, dF_dv = F*batch_scale, dF_dmu*batch_scale, dF_dv*batch_scale
if dF_dthetaL is not None:
dF_dthetaL = dF_dthetaL.sum(1).sum(1)*batch_scale
#sum (gradients of) expected likelihood and KL part
log_marginal = F.sum()
dL_dKmm = []
dL_dKmn = []
dL_dKdiag = []
dL_dm = []
dL_dchol = []
dL_dmfZ = []
dL_dmfX = []
posteriors = []
#For each latent function (and thus for each kernel the latent function uses)
#calculate the gradients and generate a posterior
for latent_ind in range(num_latent_funcs):
l = latent_function_details[latent_ind]
#q_u_meanj = q_u_means[:, latent_ind*num_outputs:(latent_ind+1)*num_outputs]
#q_u_cholj = q_u_chols[:, latent_ind*num_outputs:(latent_ind+1)*num_outputs]
dF_dmui = dF_dmu[:, latent_ind*num_outputs:(latent_ind+1)*num_outputs]
dF_dvi = dF_dv[:, latent_ind*num_outputs:(latent_ind+1)*num_outputs]
(log_marginal, dL_dKmmi, dL_dKmni, dL_dKdiagi,
dL_dmi, dL_dcholi, dL_dmfZi, dL_dmfXi) = self.calculate_gradients(log_marginal, l, dF_dmui, dF_dvi,
num_inducing, num_outputs, num_data)
posterior = Posterior(mean=l.q_u_mean, cov=l.S.T, K=l.Kmm, prior_mean=l.prior_mean_u)
dL_dKmm.append(dL_dKmmi)
dL_dKmn.append(dL_dKmni)
dL_dKdiag.append(dL_dKdiagi)
dL_dm.append(dL_dmi)
dL_dchol.append(dL_dcholi)
dL_dmfZ.append(dL_dmfZi)
dL_dmfX.append(dL_dmfXi)
posteriors.append(posterior)
grad_dict = {'dL_dKmm':dL_dKmm, 'dL_dKmn':dL_dKmn, 'dL_dKdiag': dL_dKdiag, 'dL_dm':dL_dm, 'dL_dchol':dL_dchol, 'dL_dthetaL':dF_dthetaL}
#If not all of the mean functions are null, fill out the others gradients with zeros
if not all(mean_function is None for mean_function in mean_functions):
for mean_function in mean_functions:
if mean_function is None:
grad_dict['dL_dmfZ'] = np.zeros(Z.shape)
grad_dict['dL_dmfX'] = np.zeros(X.shape)
else:
grad_dict['dL_dmfZ'] = dL_dmfZ
grad_dict['dL_dmfX'] = dL_dmfX
return posteriors, log_marginal, grad_dict
def inference(self,
kern,
X,
Z,
likelihood,
Y,
mean_function=None,
Y_metadata=None):
assert mean_function is None, "inference with a mean function not implemented"
num_inducing, _ = Z.shape
num_data, output_dim = Y.shape
#make sure the noise is not hetero
sigma_n = likelihood.gaussian_variance(Y_metadata)
if sigma_n.size > 1:
raise NotImplementedError(
"no hetero noise with this implementation of PEP")
Kmm = kern.K(Z)
Knn = kern.Kdiag(X)
Knm = kern.K(X, Z)
U = Knm
#factor Kmm
diag.add(Kmm, self.const_jitter)
Kmmi, L, Li, _ = pdinv(Kmm)
#compute beta_star, the effective noise precision
LiUT = np.dot(Li, U.T)
sigma_star = sigma_n + self.alpha * (Knn - np.sum(np.square(LiUT), 0))
beta_star = 1. / sigma_star
# Compute and factor A
A = tdot(LiUT * np.sqrt(beta_star)) + np.eye(num_inducing)
LA = jitchol(A)
# back substitute to get b, P, v
URiy = np.dot(U.T * beta_star, Y)
tmp, _ = dtrtrs(L, URiy, lower=1)
b, _ = dtrtrs(LA, tmp, lower=1)
tmp, _ = dtrtrs(LA, b, lower=1, trans=1)
v, _ = dtrtrs(L, tmp, lower=1, trans=1)
tmp, _ = dtrtrs(LA, Li, lower=1, trans=0)
P = tdot(tmp.T)
alpha_const_term = (1.0 - self.alpha) / self.alpha
#compute log marginal
log_marginal = -0.5*num_data*output_dim*np.log(2*np.pi) + \
-np.sum(np.log(np.diag(LA)))*output_dim + \
0.5*output_dim*(1+alpha_const_term)*np.sum(np.log(beta_star)) + \
-0.5*np.sum(np.square(Y.T*np.sqrt(beta_star))) + \
0.5*np.sum(np.square(b)) + 0.5*alpha_const_term*num_data*np.log(sigma_n)
#compute dL_dR
Uv = np.dot(U, v)
dL_dR = 0.5*(np.sum(U*np.dot(U,P), 1) - (1.0+alpha_const_term)/beta_star + np.sum(np.square(Y), 1) - 2.*np.sum(Uv*Y, 1) \
+ np.sum(np.square(Uv), 1))*beta_star**2
# Compute dL_dKmm
vvT_P = tdot(v.reshape(-1, 1)) + P
dL_dK = 0.5 * (Kmmi - vvT_P)
KiU = np.dot(Kmmi, U.T)
dL_dK += self.alpha * np.dot(KiU * dL_dR, KiU.T)
# Compute dL_dU
vY = np.dot(v.reshape(-1, 1), Y.T)
dL_dU = vY - np.dot(vvT_P, U.T)
dL_dU *= beta_star
dL_dU -= self.alpha * 2. * KiU * dL_dR
dL_dthetaL = likelihood.exact_inference_gradients(dL_dR)
dL_dthetaL += 0.5 * alpha_const_term * num_data / sigma_n
grad_dict = {
'dL_dKmm': dL_dK,
'dL_dKdiag': dL_dR * self.alpha,
'dL_dKnm': dL_dU.T,
'dL_dthetaL': dL_dthetaL
}
#construct a posterior object
post = Posterior(woodbury_inv=Kmmi - P,
woodbury_vector=v,
K=Kmm,
mean=None,
cov=None,
K_chol=L)
return post, log_marginal, grad_dict
def calculate_gradients(self, q_U, p_U_new, p_U_old, p_U_var, q_F, VE_dm, VE_dv, Ntask, M, Q, D, f_index, d_index,q):
"""
Calculates gradients of the Log-marginal distribution p(Y) wrt variational
parameters mu_q, S_q
"""
# Algebra for q(u):
m_u = q_U.mu_u.copy()
L_u = choleskies.flat_to_triang(q_U.chols_u.copy())
S_u = np.empty((Q, M, M))
[np.dot(L_u[q, :, :], L_u[q, :, :].T, S_u[q, :, :]) for q in range(Q)]
S_qi, _ = linalg.dpotri(np.asfortranarray(L_u[q, :, :]))
if np.any(np.isinf(S_qi)):
raise ValueError("Sqi: Cholesky representation unstable")
# Algebra for p(u)
Kuu_new = p_U_new.Kuu.copy()
Luu_new = p_U_new.Luu.copy()
Kuui_new = p_U_new.Kuui.copy()
Kuu_old = p_U_old.Kuu.copy()
Luu_old = p_U_old.Luu.copy()
Kuui_old = p_U_old.Kuui.copy()
Mu_var = p_U_var.Mu.copy()
Kuu_var = p_U_var.Kuu.copy()
Luu_var = p_U_var.Luu.copy()
Kuui_var = p_U_var.Kuui.copy()
# KL Terms
dKLnew_dmu_q = np.dot(Kuui_new[q,:,:], m_u[:, q, None])
dKLnew_dS_q = 0.5 * (Kuui_new[q,:,:] - S_qi)
dKLold_dmu_q = np.dot(Kuui_old[q,:,:], m_u[:, q, None])
dKLold_dS_q = 0.5 * (Kuui_old[q,:,:] - S_qi)
dKLvar_dmu_q = np.dot(Kuui_var[q,:,:], (m_u[:, q, None] - Mu_var[q, :, :])) # important!! (Eq. 69 MCB)
dKLvar_dS_q = 0.5 * (Kuui_var[q,:,:] - S_qi)
dKLnew_dKqq = 0.5 * Kuui_new[q,:,:] - 0.5 * Kuui_new[q,:,:].dot(S_u[q, :, :]).dot(Kuui_new[q,:,:]) \
- 0.5 * np.dot(Kuui_new[q,:,:],np.dot(m_u[:, q, None],m_u[:, q, None].T)).dot(Kuui_new[q,:,:].T)
dKLold_dKqq = 0.5 * Kuui_old[q,:,:] - 0.5 * Kuui_old[q,:,:].dot(S_u[q, :, :]).dot(Kuui_old[q,:,:]) \
- 0.5 * np.dot(Kuui_old[q,:,:],np.dot(m_u[:, q, None],m_u[:, q, None].T)).dot(Kuui_old[q,:,:].T)
#dKLvar_dKqq = 0.5 * Kuui_var[q,:,:] - 0.5 * Kuui_var[q,:,:].dot(S_u[q, :, :]).dot(Kuui_var[q,:,:]) \
# - 0.5 * np.dot(Kuui_var[q,:,:],np.dot(m_u[:, q, None],m_u[:, q, None].T)).dot(Kuui_var[q,:,:].T) \
# + 0.5 * np.dot(Kuui_var[q,:,:], np.dot(m_u[:,q,None], Mu_var[q,:,:].T)).dot(Kuui_var[q,:,:].T) \
# + 0.5 * np.dot(Kuui_var[q,:,:], np.dot(Mu_var[q,:,:], m_u[:,q,None].T)).dot(Kuui_var[q,:,:].T) \
# - 0.5 * np.dot(Kuui_var[q,:,:],np.dot(Mu_var[q,:,:], Mu_var[q,:,:].T)).dot(Kuui_var[q,:,:].T)
#KLvar += 0.5 * np.sum(Kuui_var[q, :, :] * S_u[q, :, :]) \
# + 0.5 * np.dot((Mu_var[q, :, :] - m_u[:, q, None]).T, np.dot(Kuui_var[q, :, :], (Mu_var[q, :, :] - m_u[:, q, None]))) \
# - 0.5 * M \
# + 0.5 * 2. * np.sum(np.log(np.abs(np.diag(Luu_var[q, :, :])))) \
# - 0.5 * 2. * np.sum(np.log(np.abs(np.diag(L_u[q, :, :]))))
#
# VE Terms
dVE_dmu_q = np.zeros((M, 1))
dVE_dS_q = np.zeros((M, M))
dVE_dKqq = np.zeros((M, M))
dVE_dKqd = []
dVE_dKdiag = []
for d, q_fd in enumerate(q_F):
Nt = Ntask[f_index[d]]
dVE_dmu_q += np.dot(q_fd.Afdu[q, :, :].T, VE_dm[f_index[d]][:,d_index[d]])[:, None]
Adv = q_fd.Afdu[q,:,:].T * VE_dv[f_index[d]][:,d_index[d],None].T
Adv = np.ascontiguousarray(Adv)
AdvA = np.dot(Adv.reshape(-1, Nt), q_fd.Afdu[q, :, :]).reshape(M, M)
dVE_dS_q += AdvA
# Derivatives dKuquq
tmp_dv = np.dot(AdvA, S_u[q, :, :]).dot(Kuui_new[q,:,:])
dVE_dKqq += AdvA - tmp_dv - tmp_dv.T
Adm = np.dot(q_fd.Afdu[q, :, :].T, VE_dm[f_index[d]][:,d_index[d],None])
dVE_dKqq += - np.dot(Adm, np.dot(Kuui_new[q,:,:], m_u[:, q, None]).T)
# Derivatives dKuqfd
tmp = np.dot(S_u[q, :, :], Kuui_new[q,:,:])
tmp = 2. * (tmp - np.eye(M))
dve_kqd = np.dot(np.dot(Kuui_new[q,:,:], m_u[:, q, None]), VE_dm[f_index[d]][:,d_index[d],None].T)
dve_kqd += np.dot(tmp.T, Adv)
dVE_dKqd.append(dve_kqd)
# Derivatives dKdiag
dVE_dKdiag.append(VE_dv[f_index[d]][:,d_index[d]])
dVE_dKqq = 0.5 * (dVE_dKqq + dVE_dKqq.T)
# Derivatives of variational parameters
dL_dmu_q = dVE_dmu_q - dKLnew_dmu_q + dKLold_dmu_q - dKLvar_dmu_q
dL_dS_q = dVE_dS_q - dKLnew_dS_q + dKLold_dS_q - dKLvar_dS_q
# Derivatives of prior hyperparameters
# if using Zgrad, dL_dKqq = dVE_dKqq - dKLnew_dKqq + dKLold_dKqq - dKLvar_dKqq
# otherwise for hyperparameters: dL_dKqq = dVE_dKqq - dKLnew_dKqq
dL_dKqq = dVE_dKqq - dKLnew_dKqq #+ dKLold_dKqq - dKLvar_dKqq # dKLold_dKqq sólo para Zgrad, dKLvar_dKqq to be done (for Zgrad)
dL_dKdq = dVE_dKqd
dL_dKdiag = dVE_dKdiag
# Pass S_q gradients to its low-triangular representation L_q
chol_u = q_U.chols_u.copy()
L_q = choleskies.flat_to_triang(chol_u[:,q:q+1])
dL_dL_q = 2. * np.array([np.dot(a, b) for a, b in zip(dL_dS_q[None,:,:], L_q)])
dL_dL_q = choleskies.triang_to_flat(dL_dL_q)
# Posterior
posterior_q = Posterior(mean=m_u[:, q, None], cov=S_u[q, :, :], K=Kuu_new[q,:,:], prior_mean=np.zeros(m_u[:, q, None].shape))
return dL_dmu_q, dL_dL_q, dL_dS_q, posterior_q, dL_dKqq, dL_dKdq, dL_dKdiag
def inference(self,
q_u_means,
q_u_chols,
X,
Y,
Z,
kern_list,
likelihood,
B_list,
Y_metadata,
KL_scale=1.0,
batch_scale=None):
M = Z.shape[0]
T = len(Y)
if batch_scale is None:
batch_scale = [1.0] * T
Ntask = []
[Ntask.append(Y[t].shape[0]) for t in range(T)]
Q = len(kern_list)
D = likelihood.num_output_functions(Y_metadata)
Kuu, Luu, Kuui = util.latent_funs_cov(Z, kern_list)
p_U = pu(Kuu=Kuu, Luu=Luu, Kuui=Kuui)
q_U = qu(mu_u=q_u_means, chols_u=q_u_chols)
# for every latent function f_d calculate q(f_d) and keep it as q(F):
q_F = []
posteriors_F = []
f_index = Y_metadata['function_index'].flatten()
d_index = Y_metadata['d_index'].flatten()
for d in range(D):
Xtask = X[f_index[d]]
q_fd = self.calculate_q_f(X=Xtask,
Z=Z,
q_U=q_U,
p_U=p_U,
kern_list=kern_list,
B=B_list,
M=M,
N=Xtask.shape[0],
Q=Q,
D=D,
d=d)
# Posterior objects for output functions (used in prediction)
posterior_fd = Posterior(mean=q_fd.m_fd.copy(),
cov=q_fd.S_fd.copy(),
K=util.function_covariance(
X=Xtask,
B=B_list,
kernel_list=kern_list,
d=d),
prior_mean=np.zeros(q_fd.m_fd.shape))
posteriors_F.append(posterior_fd)
q_F.append(q_fd)
mu_F = []
v_F = []
for t in range(T):
mu_F_task = np.empty((Y[t].shape[0], 1))
v_F_task = np.empty((Y[t].shape[0], 1))
for d, q_fd in enumerate(q_F):
if f_index[d] == t:
mu_F_task = np.hstack((mu_F_task, q_fd.m_fd))
v_F_task = np.hstack((v_F_task, q_fd.v_fd))
mu_F.append(mu_F_task[:, 1:])
v_F.append(v_F_task[:, 1:])
# Variational Expectations
VE = likelihood.var_exp(Y, mu_F, v_F, Y_metadata)
VE_dm, VE_dv = likelihood.var_exp_derivatives(Y, mu_F, v_F, Y_metadata)
for t in range(T):
VE[t] = VE[t] * batch_scale[t]
VE_dm[t] = VE_dm[t] * batch_scale[t]
VE_dv[t] = VE_dv[t] * batch_scale[t]
# KL Divergence
KL = self.calculate_KL(q_U=q_U, p_U=p_U, M=M, Q=Q)
# Log Marginal log(p(Y))
F = 0
for t in range(T):
F += VE[t].sum()
log_marginal = F - KL
# Gradients and Posteriors
dL_dmu_u = []
dL_dL_u = []
dL_dKmm = []
dL_dKmn = []
dL_dKdiag = []
posteriors = []
for q in range(Q):
(dL_dmu_q, dL_dL_q, posterior_q, dL_dKqq, dL_dKdq,
dL_dKdiag_q) = self.calculate_gradients(q_U=q_U,
p_U=p_U,
q_F=q_F,
VE_dm=VE_dm,
VE_dv=VE_dv,
Ntask=Ntask,
M=M,
Q=Q,
D=D,
f_index=f_index,
d_index=d_index,
q=q)
dL_dmu_u.append(dL_dmu_q)
dL_dL_u.append(dL_dL_q)
dL_dKmm.append(dL_dKqq)
dL_dKmn.append(dL_dKdq)
dL_dKdiag.append(dL_dKdiag_q)
posteriors.append(posterior_q)
gradients = {
'dL_dmu_u': dL_dmu_u,
'dL_dL_u': dL_dL_u,
'dL_dKmm': dL_dKmm,
'dL_dKmn': dL_dKmn,
'dL_dKdiag': dL_dKdiag
}
return log_marginal, gradients, posteriors, posteriors_F
def inference_root(self,
kern,
X,
Z,
likelihood,
Y,
Kuu_sigma=None,
Y_metadata=None,
Lm=None,
dL_dKmm=None):
"""
The first phase of inference:
Compute: log-likelihood, dL_dKmm
Cached intermediate results: Kmm, KmmInv,
"""
num_data, output_dim = Y.shape
input_dim = Z.shape[0]
num_data_total = allReduceArrays([np.int32(num_data)],
self.mpi_comm)[0]
uncertain_inputs = isinstance(X, VariationalPosterior)
uncertain_outputs = isinstance(Y, VariationalPosterior)
beta = 1. / np.fmax(likelihood.variance, 1e-6)
psi0, psi2, YRY, psi1, psi1Y, Shalf, psi1S = self.gatherPsiStat(
kern, X, Z, Y, beta, uncertain_inputs)
#======================================================================
# Compute Common Components
#======================================================================
try:
Kmm = kern.K(Z).copy()
if Kuu_sigma is not None:
diag.add(Kmm, Kuu_sigma)
else:
diag.add(Kmm, self.const_jitter)
Lm = jitchol(Kmm)
LmInv = dtrtri(Lm)
LmInvPsi2LmInvT = LmInv.dot(psi2.dot(LmInv.T))
Lambda = np.eye(Kmm.shape[0]) + LmInvPsi2LmInvT
LL = jitchol(Lambda)
LLInv = dtrtri(LL)
flag = np.zeros((1, ), dtype=np.int32)
self.mpi_comm.Bcast(flag, root=self.root)
except LinAlgError as e:
flag = np.ones((1, ), dtype=np.int32)
self.mpi_comm.Bcast(flag, root=self.root)
raise e
broadcastArrays([LmInv, LLInv], self.mpi_comm, self.root)
LmLLInv = LLInv.dot(LmInv)
logdet_L = 2. * np.sum(np.log(np.diag(LL)))
b = psi1Y.dot(LmLLInv.T)
bbt = np.square(b).sum()
v = b.dot(LmLLInv)
LLinvPsi1TYYTPsi1LLinvT = tdot(b.T)
if psi1S is not None:
psi1SLLinv = psi1S.dot(LmLLInv.T)
bbt_sum = np.square(psi1SLLinv).sum()
LLinvPsi1TYYTPsi1LLinvT_sum = tdot(psi1SLLinv.T)
bbt_sum, LLinvPsi1TYYTPsi1LLinvT_sum = reduceArrays(
[bbt_sum, LLinvPsi1TYYTPsi1LLinvT_sum], self.mpi_comm,
self.root)
bbt += bbt_sum
LLinvPsi1TYYTPsi1LLinvT += LLinvPsi1TYYTPsi1LLinvT_sum
psi1SP = psi1SLLinv.dot(LmLLInv)
tmp = -LLInv.T.dot(LLinvPsi1TYYTPsi1LLinvT +
output_dim * np.eye(input_dim)).dot(LLInv)
dL_dpsi2R = LmInv.T.dot(tmp +
output_dim * np.eye(input_dim)).dot(LmInv) / 2.
broadcastArrays([dL_dpsi2R], self.mpi_comm, self.root)
#======================================================================
# Compute log-likelihood
#======================================================================
logL_R = -num_data_total * np.log(beta)
logL = -(output_dim * (num_data_total * log_2_pi + logL_R + psi0 -
np.trace(LmInvPsi2LmInvT)) + YRY -
bbt) / 2. - output_dim * logdet_L / 2.
#======================================================================
# Compute dL_dKmm
#======================================================================
dL_dKmm = dL_dpsi2R - output_dim * LmInv.T.dot(LmInvPsi2LmInvT).dot(
LmInv) / 2.
#======================================================================
# Compute the Posterior distribution of inducing points p(u|Y)
#======================================================================
wd_inv = backsub_both_sides(
Lm,
np.eye(input_dim) -
backsub_both_sides(LL, np.identity(input_dim), transpose='left'),
transpose='left')
post = Posterior(woodbury_inv=wd_inv,
woodbury_vector=v.T,
K=Kmm,
mean=None,
cov=None,
K_chol=Lm)
#======================================================================
# Compute dL_dthetaL for uncertian input and non-heter noise
#======================================================================
dL_dthetaL = (YRY * beta + beta * output_dim * psi0 - num_data_total *
output_dim * beta) / 2. - beta * (dL_dpsi2R * psi2).sum(
) - beta * np.trace(LLinvPsi1TYYTPsi1LLinvT)
#======================================================================
# Compute dL_dpsi
#======================================================================
dL_dpsi0 = -output_dim * (beta * np.ones((num_data, ))) / 2.
if uncertain_outputs:
m, s = Y.mean, Y.variance
dL_dpsi1 = beta * (np.dot(m, v) + Shalf[:, None] * psi1SP)
else:
dL_dpsi1 = beta * np.dot(Y, v)
if uncertain_inputs:
dL_dpsi2 = beta * dL_dpsi2R
else:
dL_dpsi1 += np.dot(psi1, dL_dpsi2R) * 2.
dL_dpsi2 = None
if uncertain_inputs:
grad_dict = {
'dL_dKmm': dL_dKmm,
'dL_dpsi0': dL_dpsi0,
'dL_dpsi1': dL_dpsi1,
'dL_dpsi2': dL_dpsi2,
'dL_dthetaL': dL_dthetaL
}
else:
grad_dict = {
'dL_dKmm': dL_dKmm,
'dL_dKdiag': dL_dpsi0,
'dL_dKnm': dL_dpsi1,
'dL_dthetaL': dL_dthetaL
}
if uncertain_outputs:
m, s = Y.mean, Y.variance
psi1LmiLLi = psi1.dot(LmLLInv.T)
LLiLmipsi1Y = b.T
grad_dict['dL_dYmean'] = -m * beta + psi1LmiLLi.dot(LLiLmipsi1Y)
grad_dict['dL_dYvar'] = beta / -2. + np.square(psi1LmiLLi).sum(
axis=1) / 2
return post, logL, grad_dict
def incremental_inference(self,
kern,
X,
likelihood,
Y,
mean_function=None,
Y_metadata=None,
K=None,
variance=None,
Z_tilde=None):
# do incremental update
if mean_function is None:
m = 0
else:
m = mean_function.f(X)
if variance is None:
variance = likelihood.gaussian_variance(Y_metadata)
YYT_factor = Y - m
# K_tmp = kern.K(X, X[-1:])
K_inc = kern._K[:-1, -1]
K_inc2 = kern._K[-1:, -1]
# self._K = np.block([[self._K, K_inc], [K_inc.T, K_inc2]])
# Ky = K.copy()
jitter = variance[
-1] + 1e-8 # variance can be given for each point individually, in which case we just take the last point
# diag.add(Ky, jitter)
# LW_old = self._old_posterior.woodbury_chol
Wi, LW, LWi, W_logdet = pdinv_inc(self._old_LW, K_inc, K_inc2 + jitter,
self._old_Wi)
alpha, _ = dpotrs(LW, YYT_factor, lower=1)
log_marginal = 0.5 * (-Y.size * log_2_pi - Y.shape[1] * W_logdet -
np.sum(alpha * YYT_factor))
if Z_tilde is not None:
# This is a correction term for the log marginal likelihood
# In EP this is log Z_tilde, which is the difference between the
# Gaussian marginal and Z_EP
log_marginal += Z_tilde
dL_dK = 0.5 * (tdot(alpha) - Y.shape[1] * Wi)
dL_dthetaL = likelihood.exact_inference_gradients(
np.diag(dL_dK), Y_metadata)
self._old_LW = LW
self._old_Wi = Wi
posterior = Posterior(woodbury_chol=LW, woodbury_vector=alpha, K=K)
# TODO add logdet to posterior ?
return posterior, log_marginal, {
'dL_dK': dL_dK,
'dL_dthetaL': dL_dthetaL,
'dL_dm': alpha
}
def posteriors_F(self, Xnew, which_out=None):
# This function returns all the q(f*) associated to each output (It is the )
# We assume that Xnew can be a list of length equal to the number of likelihoods defined for the HetMOGP
# or Xnew can be a numpy array so that we can replicate it per each outout
if isinstance(Xnew, list):
Xmulti_all_new = Xnew
else:
Xmulti_all_new = []
for i in range(self.num_output_funcs):
Xmulti_all_new.append(Xnew.copy())
M = self.Z.shape[0]
Q = len(self.kern_list)
D = self.likelihood.num_output_functions(self.Y_metadata)
Kuu, Luu, Kuui = util.VIK_covariance(self.Z, self.kern_list,
self.kern_list_Tq, self.kern_aux)
p_U = pu(Kuu=Kuu, Luu=Luu, Kuui=Kuui)
q_U = qu(mu_u=self.q_u_means, chols_u=self.q_u_chols)
S_u = np.empty((Q, M, M))
L_u = choleskies.flat_to_triang(q_U.chols_u.copy())
[np.dot(L_u[q, :, :], L_u[q, :, :].T, S_u[q, :, :]) for q in range(Q)]
# for every latent function f_d calculate q(f_d) and keep it as q(F):
posteriors_F = []
f_index = self.Y_metadata['function_index'].flatten()
d_index = self.Y_metadata['d_index'].flatten()
if which_out is None:
indix_aux = f_index.copy()
else:
which_out = np.array(which_out)
indix_aux = -1 * np.ones_like(f_index)
for i in range(which_out.shape[0]):
posix = np.where(f_index == which_out[i])
indix_aux[posix] = f_index[posix].copy()
for d in range(D):
if f_index[d] == indix_aux[d]:
Xtask = Xmulti_all_new[f_index[d]]
q_fd, _ = self.inference_method.calculate_q_f(
X=Xtask,
Z=self.Z,
q_U=q_U,
S_u=S_u,
p_U=p_U,
kern_list=self.kern_list,
kern_list_Gdj=self.kern_list_Gdj,
kern_list_Tq=self.kern_list_Tq,
kern_aux=self.kern_aux,
B=self.B_list,
M=M,
N=Xtask.shape[0],
Q=Q,
D=D,
d=d)
# Posterior objects for output functions (used in prediction)
posterior_fd = Posterior(mean=q_fd.m_fd.copy(),
cov=q_fd.S_fd.copy(),
K=util.function_covariance(
X=Xtask,
B=self.B_list,
kernel_list=self.kern_list,
d=d),
prior_mean=np.zeros(q_fd.m_fd.shape))
posteriors_F.append(posterior_fd)
else:
#posteriors_F.append(fake_posterior)
posteriors_F.append([])
return posteriors_F
def _outer_loop_for_missing_data(self):
Lm = None
dL_dKmm = None
self._log_marginal_likelihood = 0
self.full_values = self._outer_init_full_values()
if self.posterior is None:
woodbury_inv = np.zeros(
(self.num_inducing, self.num_inducing, self.output_dim))
woodbury_vector = np.zeros((self.num_inducing, self.output_dim))
else:
woodbury_inv = self.posterior._woodbury_inv
woodbury_vector = self.posterior._woodbury_vector
if not self.stochastics:
m_f = lambda i: "Inference with missing_data: {: >7.2%}".format(
float(i + 1) / self.output_dim)
message = m_f(-1)
print(message, end=' ')
for d, ninan in self.stochastics.d:
if not self.stochastics:
print(' ' * (len(message)) + '\r', end=' ')
message = m_f(d)
print(message, end=' ')
psi0ni = self.psi0[ninan]
psi1ni = self.psi1[ninan]
if self.has_uncertain_inputs():
psi2ni = self.psi2[ninan]
value_indices = dict(outputs=d,
samples=ninan,
dL_dpsi0=ninan,
dL_dpsi1=ninan,
dL_dpsi2=ninan)
else:
psi2ni = None
value_indices = dict(outputs=d,
samples=ninan,
dL_dKdiag=ninan,
dL_dKnm=ninan)
posterior, log_marginal_likelihood, grad_dict = self._inner_parameters_changed(
self.kern,
self.X[ninan],
self.Z,
self.likelihood,
self.Y_normalized[ninan][:, d],
self.Y_metadata,
Lm,
dL_dKmm,
psi0=psi0ni,
psi1=psi1ni,
psi2=psi2ni)
# Fill out the full values by adding in the apporpriate grad_dict
# values
self._inner_take_over_or_update(self.full_values, grad_dict,
value_indices)
self._inner_values_update(grad_dict) # What is this for? -> MRD
woodbury_inv[:, :, d] = posterior.woodbury_inv[:, :, None]
woodbury_vector[:, d] = posterior.woodbury_vector
self._log_marginal_likelihood += log_marginal_likelihood
if not self.stochastics:
print('')
if self.posterior is None:
self.posterior = Posterior(woodbury_inv=woodbury_inv,
woodbury_vector=woodbury_vector,
K=posterior._K,
mean=None,
cov=None,
K_chol=posterior.K_chol)
self._outer_values_update(self.full_values)
if self.has_uncertain_inputs():
self.kern.return_psi2_n = False
