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Merge branch 'params' of github.com:SheffieldML/GPy into params
Conflicts: GPy/core/sparse_gp.py
This commit is contained in:
Ricardo
commit
df50d2a990
7 changed files with 58 additions and 142 deletions
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@ -59,10 +59,10 @@ class GP(Model):
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self.parameters_changed()
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def parameters_changed(self):
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self.posterior = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y)
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self.posterior, self._log_marginal_likelihood, self._dL_dK = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y)
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def log_likelihood(self):
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return self.posterior.log_marginal
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return self._log_marginal_likelihood
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def dL_dtheta_K(self):
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return self.kern.dK_dtheta(self.posterior.dL_dK, self.X)
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@ -383,69 +383,6 @@ class Model(Parameterized):
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sgd.run()
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self.optimization_runs.append(sgd)
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def Laplace_covariance(self):
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"""return the covariance matrix of a Laplace approximation at the current (stationary) point."""
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# TODO add in the prior contributions for MAP estimation
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# TODO fix the hessian for tied, constrained and fixed components
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if hasattr(self, 'log_likelihood_hessian'):
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A = -self.log_likelihood_hessian()
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else:
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print "numerically calculating Hessian. please be patient!"
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x = self._get_params()
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def f(x):
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self._set_params(x)
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return self.log_likelihood()
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h = ndt.Hessian(f) # @UndefinedVariable
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A = -h(x)
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self._set_params(x)
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# check for almost zero components on the diagonal which screw up the cholesky
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aa = np.nonzero((np.diag(A) < 1e-6) & (np.diag(A) > 0.))[0]
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A[aa, aa] = 0.
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return A
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def Laplace_evidence(self):
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"""Returns an estiamte of the model evidence based on the Laplace approximation.
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Uses a numerical estimate of the Hessian if none is available analytically."""
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A = self.Laplace_covariance()
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try:
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hld = np.sum(np.log(np.diag(jitchol(A)[0])))
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except:
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return np.nan
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return 0.5 * self._get_params().size * np.log(2 * np.pi) + self.log_likelihood() - hld
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# def __str__(self):
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# s = Parameterized.__str__(self).split('\n')
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# #def __str__(self, names=None):
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# # if names is None:
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# # names = self._get_print_names()
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# #s = Parameterized.__str__(self, names=names).split('\n')
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# # add priors to the string
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# if self.priors is not None:
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# strs = [str(p) if p is not None else '' for p in self.priors]
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# else:
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# strs = [''] * len(self._get_params())
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# # strs = [''] * len(self._get_param_names())
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# # name_indices = self.grep_param_names("|".join(names))
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# # strs = np.array(strs)[name_indices]
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# width = np.array(max([len(p) for p in strs] + [5])) + 4
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#
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# log_like = self.log_likelihood()
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# log_prior = self.log_prior()
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# obj_funct = '\nLog-likelihood: {0:.3e}'.format(log_like)
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# if len(''.join(strs)) != 0:
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# obj_funct += ', Log prior: {0:.3e}, LL+prior = {0:.3e}'.format(log_prior, log_like + log_prior)
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# obj_funct += '\n\n'
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# s[0] = obj_funct + s[0]
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# s[0] += "|{h:^{col}}".format(h='prior', col=width)
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# s[1] += '-' * (width + 1)
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#
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# for p in range(2, len(strs) + 2):
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# s[p] += '|{prior:^{width}}'.format(prior=strs[p - 2], width=width)
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#
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# return '\n'.join(s)
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def checkgrad(self, target_param=None, verbose=False, step=1e-6, tolerance=1e-3):
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"""
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Check the gradient of the ,odel by comparing to a numerical
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@ -5,6 +5,7 @@ import numpy as np
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from ..util.linalg import mdot, tdot, symmetrify, backsub_both_sides, chol_inv, dtrtrs, dpotrs, dpotri
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from gp import GP
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from parameterization.param import Param
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from ..inference.latent_function_inference import varDTC
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class SparseGP(GP):
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"""
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@ -54,49 +55,16 @@ class SparseGP(GP):
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self.add_parameter(self.kern, gradient=self.dL_dtheta)
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self.add_parameter(self.likelihood, gradient=lambda:self.likelihood._gradients(partial=self.partial_for_likelihood))
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def parameters_changed(self):
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# kernel computations, using BGPLVM notation
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self.Kmm = self.kern.K(self.Z)
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self.posterior = self.inference_method.inference(self.kern, self.X, self.X_variance, self.Z, self.likelihood, self.Y)
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#The derivative of the bound wrt the inducing inputs Z
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self.Z.gradient = self.kern.dK_dX(self.dL_dKmm, self.Z)
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if self.has_uncertain_inputs:
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self.psi0 = self.kern.psi0(self.Z, self.X, self.X_variance)
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self.psi1 = self.kern.psi1(self.Z, self.X, self.X_variance)
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self.psi2 = self.kern.psi2(self.Z, self.X, self.X_variance)
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self.Z.gradient += self.kern.dpsi1_dZ(self.dL_dpsi1, self.Z, self.X, self.X_variance)
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self.Z.gradient += self.kern.dpsi2_dZ(self.dL_dpsi2, self.Z, self.X, self.X_variance)
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else:
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self.psi0 = self.kern.Kdiag(self.X)
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self.psi1 = self.kern.K(self.X, self.Z)
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self.psi2 = None
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#self.posterior = self.inference_method.inference(??)
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super(SparseGP, self).parameters_changed()
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def dL_dtheta(self):
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"""
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Compute and return the derivative of the log marginal likelihood wrt the parameters of the kernel
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"""
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dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm, self.Z)
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if self.has_uncertain_inputs:
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dL_dtheta += self.kern.dpsi0_dtheta(self.dL_dpsi0, self.Z, self.X, self.X_variance)
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dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1, self.Z, self.X, self.X_variance)
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dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2, self.Z, self.X, self.X_variance)
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else:
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dL_dtheta += self.kern.dK_dtheta(self.dL_dpsi1, self.X, self.Z)
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dL_dtheta += self.kern.dKdiag_dtheta(self.dL_dpsi0, self.X)
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return dL_dtheta
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def dL_dZ(self):
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"""
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The derivative of the bound wrt the inducing inputs Z
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"""
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dL_dZ = self.kern.dK_dX(self.dL_dKmm, self.Z)
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if self.has_uncertain_inputs:
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dL_dZ += self.kern.dpsi1_dZ(self.dL_dpsi1, self.Z, self.X, self.X_variance)
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dL_dZ += self.kern.dpsi2_dZ(self.dL_dpsi2, self.Z, self.X, self.X_variance)
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else:
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dL_dZ += self.kern.dK_dX(self.dL_dpsi1.T, self.Z, self.X)
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return dL_dZ
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self.Z.gradient += self.kern.dK_dX(self.dL_dpsi1.T, self.Z, self.X)
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def _raw_predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False):
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"""
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@ -53,6 +53,6 @@ class ExactGaussianInference(object):
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likelihood.update_gradients(np.diag(dL_dK))
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return Posterior(log_marginal, dL_dK, LW, alpha, K)
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return Posterior(LW, alpha, K), log_marginal, dL_dK
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@ -8,16 +8,14 @@ class Posterior(object):
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"""
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An object to represent a Gaussian posterior over latent function values.
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This may be computed exactly for Gaussian likelihoods, or approximated for
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non-Gaussian likelihoods.
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non-Gaussian likelihoods.
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The purpose of this class is to serve as an interface between the inference
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schemes and the model classes.
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schemes and the model classes.
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"""
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def __init__(self, log_marginal, dL_dK, woodbury_chol=None, woodbury_vector=None, K=None, mean=None, cov=None, K_chol=None):
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def __init__(self, woodbury_chol=None, woodbury_vector=None, K=None, mean=None, cov=None, K_chol=None):
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"""
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log_marginal: log p(Y|X)
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dL_dK: d/dK log p(Y|X)
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woodbury_chol : a lower triangular matrix L that satisfies posterior_covariance = K - K L^{-T} L^{-1} K
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woodbury_vector : a matrix (or vector, as Nx1 matrix) M which satisfies posterior_mean = K M
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K : the proir covariance (required for lazy computation of various quantities)
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@ -26,12 +24,10 @@ class Posterior(object):
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Not all of the above need to be supplied! You *must* supply:
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log_marginal
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dL_dK
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K (for lazy computation)
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You may supply either:
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cc
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woodbury_chol
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woodbury_vector
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@ -46,8 +42,6 @@ class Posterior(object):
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From the supplied quantities, all of the others will be computed on demand (lazy computation)
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"""
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#obligatory
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self.log_marginal = log_marginal
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self.dL_dK = dL_dK
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self._K = K
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if ((woodbury_chol is not None) and (woodbury_vector is not None) and (K is not None)) or ((mean is not None) and (cov is not None) and (K is not None)):
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@ -64,7 +58,7 @@ class Posterior(object):
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self._covariance = cov
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self._K_chol = K_chol
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#copmute this lazily
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#compute this lazily
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self._precision = None
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@property
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@ -2,10 +2,11 @@
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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from posterior import Posterior
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from .../util.linalg import pdinv, dpotrs, tdot
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from ...util.linalg import pdinv, dpotrs, tdot
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import numpy as np
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log_2_pi = np.log(2*np.pi)
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class DTCVar(object):
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class VarDTC(object):
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"""
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An object for inference when the likelihood is Gaussian, but we want to do sparse inference.
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@ -19,7 +20,7 @@ class DTCVar(object):
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self._YYTfactor_cache = caching.cache()
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self.const_jitter = 1e-6
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def get_YYTfactor(self, Y):
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def get_YYTfactor(self, Y):
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"""
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find a matrix L which satisfies LLT = YYT.
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@ -32,15 +33,29 @@ class DTCVar(object):
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#if Y in self.cache, return self.Cache[Y], else stor Y in cache and return L.
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raise NotImplementedError, 'TODO' #TODO
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def inference(self, Kmm, Kmn, Knn_diag, likelihood, Y):
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def inference(self, kern, X, X_variance, Z, likelihood, Y):
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num_inducing, num_data = Kmn.shape
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num_inducing, _ = Z.shape
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num_data, output_dim = Y.shape
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#see whether we're using variational uncertain inputs
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uncertain_inputs = (X_variance is None)
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# kernel computations, using BGPLVM notation
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Kmm = kern.K(Z)
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if uncertain_inputs:
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psi0 = kern.psi0(Z, X, X_variance)
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psi1 = kern.psi1(Z, X, X_variance)
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psi2 = kern.psi2(Z, X, X_variance)
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else:
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psi0 = kern.Kdiag(X)
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psi1 = kern.K(X, Z)
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#factor Kmm # TODO: cache?
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_Lm = jitchol(Kmm)
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Lm = jitchol(Kmm)
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# The rather complex computations of A
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if has_uncertain_inputs:
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if uncertain_inputs:
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if likelihood.is_heteroscedastic:
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psi2_beta = (psi2 * (likelihood.precision.flatten().reshape(num_data, 1, 1))).sum(0)
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else:
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@ -56,7 +71,7 @@ class DTCVar(object):
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tmp = psi1 * (np.sqrt(likelihood.precision.flatten().reshape(num_data, 1)))
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else:
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tmp = psi1 * (np.sqrt(likelihood.precision))
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tmp, _ = dtrtrs(_Lm, np.asfortranarray(tmp.T), lower=1)
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tmp, _ = dtrtrs(Lm, np.asfortranarray(tmp.T), lower=1)
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A = tdot(tmp)
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# factor B
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@ -67,11 +82,23 @@ class DTCVar(object):
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psi1Vf = np.dot(psi1.T, likelihood.VVT_factor)
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# back substutue C into psi1Vf
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tmp, info1 = dtrtrs(_Lm, np.asfortranarray(psi1Vf), lower=1, trans=0)
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tmp, info1 = dtrtrs(Lm, np.asfortranarray(psi1Vf), lower=1, trans=0)
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_LBi_Lmi_psi1Vf, _ = dtrtrs(LB, np.asfortranarray(tmp), lower=1, trans=0)
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# tmp, info2 = dpotrs(LB, tmp, lower=1)
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tmp, info2 = dtrtrs(LB, _LBi_Lmi_psi1Vf, lower=1, trans=1)
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Cpsi1Vf, info3 = dtrtrs(_Lm, tmp, lower=1, trans=1)
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Cpsi1Vf, info3 = dtrtrs(Lm, tmp, lower=1, trans=1)
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#compute log marginal likelihood
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if likelihood.is_heteroscedastic:
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A = -0.5 * num_data * output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(likelihood.precision)) - 0.5 * np.sum(likelihood.V * likelihood.Y)
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B = -0.5 * output_dim * (np.sum(likelihood.precision.flatten() * psi0) - np.trace(_A))
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else:
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A = -0.5 * num_data * output_dim * (np.log(2.*np.pi) - np.log(likelihood.precision)) - 0.5 * likelihood.precision * likelihood.trYYT
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B = -0.5 * output_dim * (np.sum(likelihood.precision * psi0) - np.trace(_A))
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C = -output_dim * (np.sum(np.log(np.diag(LB)))) # + 0.5 * num_inducing * np.log(sf2))
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D = 0.5 * data_fit
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log_marginal = A + B + C + D
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# Compute dL_dKmm
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tmp = tdot(_LBi_Lmi_psi1Vf)
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@ -80,12 +107,12 @@ class DTCVar(object):
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tmp = -0.5 * DBi_plus_BiPBi
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tmp += -0.5 * B * output_dim
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tmp += output_dim * np.eye(num_inducing)
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dL_dKmm = backsub_both_sides(_Lm, tmp)
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dL_dKmm = backsub_both_sides(Lm, tmp)
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# Compute dL_dpsi # FIXME: this is untested for the heterscedastic + uncertain inputs case
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dL_dpsi0 = -0.5 * output_dim * (likelihood.precision * np.ones([num_data, 1])).flatten()
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dL_dpsi1 = np.dot(likelihood.VVT_factor, Cpsi1Vf.T)
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dL_dpsi2_beta = 0.5 * backsub_both_sides(_Lm, output_dim * np.eye(num_inducing) - DBi_plus_BiPBi)
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dL_dpsi2_beta = 0.5 * backsub_both_sides(Lm, output_dim * np.eye(num_inducing) - DBi_plus_BiPBi)
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if likelihood.is_heteroscedastic:
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@ -117,7 +144,7 @@ class DTCVar(object):
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else:
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LBi = chol_inv(LB)
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Lmi_psi1, nil = dtrtrs(_Lm, np.asfortranarray(psi1.T), lower=1, trans=0)
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Lmi_psi1, nil = dtrtrs(Lm, np.asfortranarray(psi1.T), lower=1, trans=0)
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_LBi_Lmi_psi1, _ = dtrtrs(LB, np.asfortranarray(Lmi_psi1), lower=1, trans=0)
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@ -135,14 +162,4 @@ class DTCVar(object):
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partial_for_likelihood += 0.5 * output_dim * (psi0.sum() * likelihood.precision ** 2 - np.trace(_A) * likelihood.precision)
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partial_for_likelihood += likelihood.precision * (0.5 * np.sum(_A * DBi_plus_BiPBi) - data_fit)
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#def log_likelihood(self):
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if likelihood.is_heteroscedastic:
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A = -0.5 * num_data * output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(likelihood.precision)) - 0.5 * np.sum(likelihood.V * likelihood.Y)
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B = -0.5 * output_dim * (np.sum(likelihood.precision.flatten() * psi0) - np.trace(_A))
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else:
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A = -0.5 * num_data * output_dim * (np.log(2.*np.pi) - np.log(likelihood.precision)) - 0.5 * likelihood.precision * likelihood.trYYT
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B = -0.5 * output_dim * (np.sum(likelihood.precision * psi0) - np.trace(_A))
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C = -output_dim * (np.sum(np.log(np.diag(LB)))) # + 0.5 * num_inducing * np.log(sf2))
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D = 0.5 * data_fit
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return A + B + C + D + likelihood.Z
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@ -71,8 +71,8 @@ def align_subplots(N,M,xlim=None, ylim=None):
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removeUpperTicks()
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def align_subplot_array(axes,xlim=None, ylim=None):
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"""make all of the axes in the array hae the same limits, turn off unnecessary ticks
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"""
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Make all of the axes in the array hae the same limits, turn off unnecessary ticks
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use pb.subplots() to get an array of axes
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"""
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#find sensible xlim,ylim
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