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Ricardo Andrade
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2 changed files with 0 additions and 243 deletions
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@ -8,7 +8,6 @@ from sparse_GP import sparse_GP
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from sparse_GP_regression import sparse_GP_regression
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from GPLVM import GPLVM
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from warped_GP import warpedGP
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# TODO: from generalized_FITC import generalized_FITC
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from sparse_GPLVM import sparse_GPLVM
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#from uncollapsed_sparse_GP import uncollapsed_sparse_GP
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from BGPLVM import Bayesian_GPLVM
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@ -1,242 +0,0 @@
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# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy as np
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import pylab as pb
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from scipy import stats, linalg
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from .. import kern
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from ..core import model
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from ..util.linalg import pdinv,mdot
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from ..util.plot import gpplot
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#from ..inference.Expectation_Propagation import FITC
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from ..likelihoods.EP import FITC
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from ..likelihoods import likelihood,probit
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class generalized_FITC(model):
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def __init__(self,X,likelihood,kernel=None,inducing=10,epsilon_ep=1e-3,powerep=[1.,1.]):
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"""
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Naish-Guzman, A. and Holden, S. (2008) implemantation of EP with FITC.
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:param X: input observations
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:param likelihood: Output's likelihood (likelihood class)
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:param kernel: a GPy kernel
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:param inducing: Either an array specifying the inducing points location or a scalar defining their number.
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:param epsilon_ep: EP convergence criterion, maximum squared difference allowed between mean updates to stop iterations (float)
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:param powerep: Power-EP parameters (eta,delta) - 2x1 numpy array (floats)
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"""
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assert isinstance(kernel,kern.kern)
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self.likelihood = likelihood
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self.Y = self.likelihood.Y
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self.kernel = kernel
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self.X = X
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self.N, self.D = self.X.shape
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assert self.Y.shape[0] == self.N
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if type(inducing) == int:
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self.M = inducing
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self.Z = (np.random.random_sample(self.D*self.M)*(self.X.max()-self.X.min())+self.X.min()).reshape(self.M,-1)
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elif type(inducing) == np.ndarray:
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self.Z = inducing
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self.M = self.Z.shape[0]
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self.eta,self.delta = powerep
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self.epsilon_ep = epsilon_ep
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self.jitter = 1e-12
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model.__init__(self)
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def _set_params(self,p):
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self.kernel._set_params_transformed(p[0:-self.Z.size])
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self.Z = p[-self.Z.size:].reshape(self.M,self.D)
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def _get_params(self):
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return np.hstack([self.kernel._get_params_transformed(),self.Z.flatten()])
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def _get_param_names(self):
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return self.kernel._get_param_names_transformed()+['iip_%i'%i for i in range(self.Z.size)]
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def approximate_likelihood(self):
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self.Kmm = self.kernel.K(self.Z)
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self.Knm = self.kernel.K(self.X,self.Z)
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self.Knn_diag = self.kernel.Kdiag(self.X)
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self.ep_approx = FITC(self.Kmm,self.likelihood,self.Knm.T,self.Knn_diag,epsilon=self.epsilon_ep,powerep=[self.eta,self.delta])
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self.ep_approx.fit_EP()
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def posterior_param(self):
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self.Knn_diag = self.kernel.Kdiag(self.X)
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self.Kmm = self.kernel.K(self.Z)
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self.Kmmi, self.Lmm, self.Lmmi, self.Kmm_logdet = pdinv(self.Kmm)
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self.Knm = self.kernel.K(self.X,self.Z)
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self.KmmiKmn = np.dot(self.Kmmi,self.Knm.T)
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self.Qnn = np.dot(self.Knm,self.KmmiKmn)
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self.Diag0 = self.Knn_diag - np.diag(self.Qnn)
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self.R0 = np.linalg.cholesky(self.Kmmi).T
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self.Taut = self.ep_approx.tau_tilde/(1.+ self.ep_approx.tau_tilde*self.Diag0)
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self.KmnTaut = self.Knm.T*self.Taut[None,:]
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self.KmnTautKnm = np.dot(self.KmnTaut, self.Knm)
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self.Woodbury_inv, self.Wood_L, self.Wood_Li, self.Woodbury_logdet = pdinv(self.Kmm + self.KmnTautKnm)
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self.Qnn_diag = self.Knn_diag - np.diag(self.Qnn) + 1./self.ep_approx.tau_tilde
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self.Qi = -np.dot(self.KmnTaut.T, np.dot(self.Woodbury_inv,self.KmnTaut)) + np.diag(self.Taut)
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self.hld = 0.5*np.sum(np.log(self.Diag0 + 1./self.ep_approx.tau_tilde)) - 0.5*self.Kmm_logdet + 0.5*self.Woodbury_logdet
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self.Diag = self.Diag0/(1.+ self.Diag0 * self.ep_approx.tau_tilde)
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self.P = (self.Diag / self.Diag0)[:,None] * self.Knm
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self.RPT0 = np.dot(self.R0,self.Knm.T)
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self.L = np.linalg.cholesky(np.eye(self.M) + np.dot(self.RPT0,(1./self.Diag0 - self.Diag/(self.Diag0**2))[:,None]*self.RPT0.T))
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self.R,info = linalg.flapack.dtrtrs(self.L,self.R0,lower=1)
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self.RPT = np.dot(self.R,self.P.T)
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self.Sigma = np.diag(self.Diag) + np.dot(self.RPT.T,self.RPT)
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self.w = self.Diag * self.ep_approx.v_tilde
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self.gamma = np.dot(self.R.T, np.dot(self.RPT,self.ep_approx.v_tilde))
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self.mu = self.w + np.dot(self.P,self.gamma)
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self.mu_tilde = (self.ep_approx.v_tilde/self.ep_approx.tau_tilde)[:,None]
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def log_likelihood(self):
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self.posterior_param()
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self.YYT = np.dot(self.mu_tilde,self.mu_tilde.T)
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A = -self.hld
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B = -.5*np.sum(self.Qi*self.YYT)
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C = sum(np.log(self.ep_approx.Z_hat))
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D = .5*np.sum(np.log(1./self.ep_approx.tau_tilde + 1./self.ep_approx.tau_))
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E = .5*np.sum((self.ep_approx.v_/self.ep_approx.tau_ - self.mu_tilde.flatten())**2/(1./self.ep_approx.tau_ + 1./self.ep_approx.tau_tilde))
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return A + B + C + D + E
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def _log_likelihood_gradients(self):
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dKmm_dtheta = self.kernel.dK_dtheta(self.Z)
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dKnn_dtheta = self.kernel.dK_dtheta(self.X)
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dKmn_dtheta = self.kernel.dK_dtheta(self.Z,self.X)
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dKmm_dZ = -self.kernel.dK_dX(self.Z)
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dKnm_dZ = -self.kernel.dK_dX(self.X,self.Z)
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tmp = [np.dot(dKmn_dtheta_i,self.KmmiKmn) for dKmn_dtheta_i in dKmn_dtheta.T]
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dQnn_dtheta = [tmp_i + tmp_i.T - np.dot(np.dot(self.KmmiKmn.T,dKmm_dtheta_i),self.KmmiKmn) for tmp_i,dKmm_dtheta_i in zip(tmp,dKmm_dtheta.T)]
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dDiag0_dtheta = [np.diag(dKnn_dtheta_i) - np.diag(dQnn_dtheta_i) for dKnn_dtheta_i,dQnn_dtheta_i in zip(dKnn_dtheta.T,dQnn_dtheta)]
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dQ_dtheta = [np.diag(dDiag0_dtheta_i) + dQnn_dtheta_i for dDiag0_dtheta_i,dQnn_dtheta_i in zip(dDiag0_dtheta,dQnn_dtheta)]
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dW_dtheta = [dKmm_dtheta_i + 2*np.dot(self.KmnTaut,dKmn_dtheta_i) - np.dot(self.KmnTaut*dDiag0_dtheta_i,self.KmnTaut.T) for dKmm_dtheta_i,dDiag0_dtheta_i,dKmn_dtheta_i in zip(dKmm_dtheta.T,dDiag0_dtheta,dKmn_dtheta.T)]
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QiY = np.dot(self.Qi, self.mu_tilde)
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QiYYQi = np.outer(QiY,QiY)
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WiKmnTaut = np.dot(self.Woodbury_inv,self.KmnTaut)
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K_Y = np.dot(self.KmmiKmn,QiY)
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# gradient - theta
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Atheta = [-0.5*np.dot(self.Taut,dDiag0_dtheta_i) + 0.5*np.sum(self.Kmmi*dKmm_dtheta_i) - 0.5*np.sum(self.Woodbury_inv*dW_dtheta_i) for dDiag0_dtheta_i,dKmm_dtheta_i,dW_dtheta_i in zip(dDiag0_dtheta,dKmm_dtheta.T,dW_dtheta)]
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Btheta = np.array([0.5*np.sum(QiYYQi*dQ_dtheta_i) for dQ_dtheta_i in dQ_dtheta])
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dL_dtheta = Atheta + Btheta
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# gradient - Z
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# Az
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dQnn_dZ_diag_a2 = (np.array([d[:,:,None]*self.KmmiKmn[:,:,None] for d in dKnm_dZ.transpose(2,0,1)]).reshape(self.D,self.M,self.N)).transpose(1,2,0)
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dQnn_dZ_diag_b2 = (np.array([(self.KmmiKmn*np.sum(d[:,:,None]*self.KmmiKmn,-2))[:,:,None] for d in dKmm_dZ.transpose(2,0,1)]).reshape(self.D,self.M,self.N)).transpose(1,2,0)
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dQnn_dZ_diag = dQnn_dZ_diag_a2 - dQnn_dZ_diag_b2
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d_hld_Diag1_dZ = -np.sum(np.dot(self.KmmiKmn*self.Taut,self.KmmiKmn.T)[:,:,None]*dKmm_dZ,-2) + np.sum((self.KmmiKmn*self.Taut)[:,:,None]*dKnm_dZ,-2)
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d_hld_Kmm_dZ = np.sum(self.Kmmi[:,:,None]*dKmm_dZ,-2)
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d_hld_W_dZ1 = np.sum(WiKmnTaut[:,:,None]*dKnm_dZ,-2)
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d_hld_W_dZ3 = np.sum(self.Woodbury_inv[:,:,None]*dKmm_dZ,-2)
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d_hld_W_dZ2 = np.array([np.sum(np.sum(WiKmnTaut.T*d[:,:,None]*self.KmnTaut.T,-2),-1) for d in dQnn_dZ_diag.transpose(2,0,1)]).T
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Az = d_hld_Diag1_dZ + d_hld_Kmm_dZ - d_hld_W_dZ1 - d_hld_W_dZ2 - d_hld_W_dZ3
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# Bz
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Bz2 = np.sum(np.dot(K_Y,QiY.T)[:,:,None]*dKnm_dZ,-2)
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Bz3 = - np.sum(np.dot(K_Y,K_Y.T)[:,:,None]*dKmm_dZ,-2)
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Bz1 = -np.array([np.sum((QiY**2)*d[:,:,None],-2) for d in dQnn_dZ_diag.transpose(2,0,1)]).reshape(self.D,self.M).T
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Bz = Bz1 + Bz2 + Bz3
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dL_dZ = (Az + Bz).flatten()
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return np.hstack([dL_dtheta, dL_dZ])
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def predict(self,X):
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"""
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Make a prediction for the vsGP model
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Arguments
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---------
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X : Input prediction data - Nx1 numpy array (floats)
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"""
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#TODO: check output dimensions
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K_x = self.kernel.K(self.Z,X)
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Kxx = self.kernel.K(X)
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#K_x = self.kernM.cross.K(X)
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# q(u|f) = N(u| R0i*mu_u*f, R0i*C*R0i.T)
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# Ci = I + (RPT0)Di(RPT0).T
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# C = I - [RPT0] * (D+[RPT0].T*[RPT0])^-1*[RPT0].T
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# = I - [RPT0] * (D + self.Qnn)^-1 * [RPT0].T
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# = I - [RPT0] * (U*U.T)^-1 * [RPT0].T
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# = I - V.T * V
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U = np.linalg.cholesky(np.diag(self.Diag0) + self.Qnn)
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V,info = linalg.flapack.dtrtrs(U,self.RPT0.T,lower=1)
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C = np.eye(self.M) - np.dot(V.T,V)
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mu_u = np.dot(C,self.RPT0)*(1./self.Diag0[None,:])
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#self.C = C
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#self.RPT0 = np.dot(self.R0,self.Knm.T) P0.T
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#self.mu_u = mu_u
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#self.U = U
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# q(u|y) = N(u| R0i*mu_H,R0i*Sigma_H*R0i.T)
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mu_H = np.dot(mu_u,self.mu)
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self.mu_H = mu_H
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Sigma_H = C + np.dot(mu_u,np.dot(self.Sigma,mu_u.T))
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# q(f_star|y) = N(f_star|mu_star,sigma2_star)
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KR0T = np.dot(K_x.T,self.R0.T)
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mu_star = np.dot(KR0T,mu_H)
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sigma2_star = Kxx + np.dot(KR0T,np.dot(Sigma_H - np.eye(self.M),KR0T.T))
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vdiag = np.diag(sigma2_star)
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# q(y_star|y) = non-gaussian posterior probability of class membership
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p = self.likelihood.predictive_mean(mu_star,vdiag)
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return mu_star,vdiag,p
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def plot(self):
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"""
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Plot the fitted model: training function values, inducing points used, mean estimate and confidence intervals.
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"""
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if self.X.shape[1]==1:
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pb.figure()
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xmin,xmax = np.r_[self.X,self.Z].min(),np.r_[self.X,self.Z].max()
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xmin, xmax = xmin-0.2*(xmax-xmin), xmax+0.2*(xmax-xmin)
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Xnew = np.linspace(xmin,xmax,100)[:,None]
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mu_f, var_f, mu_phi = self.predict(Xnew)
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self.mu_inducing,self.var_diag_inducing,self.phi_inducing = self.predict(self.Z)
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pb.subplot(211)
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self.likelihood.plot1Da(X_new=Xnew,Mean_new=mu_f,Var_new=var_f,X_u=self.Z,Mean_u=self.mu_inducing,Var_u=self.var_diag_inducing)
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pb.subplot(212)
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self.likelihood.plot1Db(self.X,Xnew,mu_phi,self.Z)
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elif self.X.shape[1]==2:
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pb.figure()
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x1min,x1max = self.X[:,0].min(0),self.X[:,0].max(0)
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x2min,x2max = self.X[:,1].min(0),self.X[:,1].max(0)
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x1min, x1max = x1min-0.2*(x1max-x1min), x1max+0.2*(x1max-x1min)
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x2min, x2max = x2min-0.2*(x2max-x2min), x2max+0.2*(x1max-x1min)
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axis1 = np.linspace(x1min,x1max,50)
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axis2 = np.linspace(x2min,x2max,50)
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XX1, XX2 = [e.flatten() for e in np.meshgrid(axis1,axis2)]
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Xnew = np.c_[XX1.flatten(),XX2.flatten()]
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f,v,p = self.predict(Xnew)
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self.likelihood.plot2D(self.X,Xnew,p,self.Z)
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else:
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raise NotImplementedError, "Cannot plot GPs with more than two input dimensions"
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def em(self,max_f_eval=1e4,epsilon=.1,plot_all=False): #TODO check this makes sense
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"""
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Fits sparse_EP and optimizes the hyperparametes iteratively until convergence is achieved.
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"""
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self.epsilon_em = epsilon
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log_likelihood_change = self.epsilon_em + 1.
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self.parameters_path = [self.kernel._get_params()]
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self.approximate_likelihood()
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self.site_approximations_path = [[self.ep_approx.tau_tilde,self.ep_approx.v_tilde]]
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self.inducing_inputs_path = [self.Z]
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self.log_likelihood_path = [self.log_likelihood()]
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iteration = 0
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while log_likelihood_change > self.epsilon_em:
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print 'EM iteration', iteration
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self.optimize(max_f_eval = max_f_eval)
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log_likelihood_new = self.log_likelihood()
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log_likelihood_change = log_likelihood_new - self.log_likelihood_path[-1]
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if log_likelihood_change < 0:
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print 'log_likelihood decrement'
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self.kernel._set_params_transformed(self.parameters_path[-1])
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self.kernM = self.kernel.copy()
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slef.kernM.expand_X(self.iducing_inputs_path[-1])
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self.__init__(self.kernel,self.likelihood,kernM=self.kernM,powerep=[self.eta,self.delta],epsilon_ep = self.epsilon_ep, epsilon_em = self.epsilon_em)
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else:
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self.approximate_likelihood()
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self.log_likelihood_path.append(self.log_likelihood())
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self.parameters_path.append(self.kernel._get_params())
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self.site_approximations_path.append([self.ep_approx.tau_tilde,self.ep_approx.v_tilde])
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self.inducing_inputs_path.append(self.Z)
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iteration += 1
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