Source code for GPy.inference.latent_function_inference.pep

from .posterior import Posterior
from ...util.linalg import jitchol, tdot, dtrtrs, dtrtri, pdinv
from ...util import diag
import numpy as np
from . import LatentFunctionInference
log_2_pi = np.log(2*np.pi)

[docs]class PEP(LatentFunctionInference): ''' Sparse Gaussian processes using Power-Expectation Propagation for regression: alpha \approx 0 gives VarDTC and alpha = 1 gives FITC Reference: A Unifying Framework for Sparse Gaussian Process Approximation using Power Expectation Propagation, https://arxiv.org/abs/1605.07066 ''' const_jitter = 1e-6 def __init__(self, alpha): super(PEP, self).__init__() self.alpha = alpha
[docs] 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