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[vardtc] sparse gplvm in bayesian gplvm minibatch

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Max Zwiessele 2014-12-03 08:35:41 +00:00
parent 865d8e3851
commit 4fc006f45d
2 changed files with 57 additions and 66 deletions
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55
GPy/models/bayesian_gplvm_minibatch.py
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@ -8,6 +8,7 @@ from ..core.parameterization.variational import NormalPosterior, NormalPrior
from ..inference.latent_function_inference.var_dtc_parallel import VarDTC_minibatch
import logging
from GPy.models.sparse_gp_minibatch import SparseGPMiniBatch
from GPy.core.parameterization.param import Param
class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
"""
@ -35,15 +36,20 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
self.init = init
if X_variance is None:
self.logger.info("initializing latent space variance ~ uniform(0,.1)")
X_variance = np.random.uniform(0,.1,X.shape)
if Z is None:
self.logger.info("initializing inducing inputs")
Z = np.random.permutation(X.copy())[:num_inducing]
assert Z.shape[1] == X.shape[1]
if X_variance == False:
self.logger.info('no variance on X, activating sparse GPLVM')
X = Param("latent space", X)
elif X_variance is None:
self.logger.info("initializing latent space variance ~ uniform(0,.1)")
X_variance = np.random.uniform(0,.1,X.shape)
self.variational_prior = NormalPrior()
X = NormalPosterior(X, X_variance)
if kernel is None:
self.logger.info("initializing kernel RBF")
kernel = kern.RBF(input_dim, lengthscale=1./fracs, ARD=True) #+ kern.Bias(input_dim) + kern.White(input_dim)
@ -51,9 +57,6 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
if likelihood is None:
likelihood = Gaussian()
self.variational_prior = NormalPrior()
X = NormalPosterior(X, X_variance)
self.kl_factr = 1.
if inference_method is None:
@ -83,20 +86,26 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
def _inner_parameters_changed(self, kern, X, Z, likelihood, Y, Y_metadata, Lm=None, dL_dKmm=None, subset_indices=None):
posterior, log_marginal_likelihood, grad_dict, current_values, value_indices = super(BayesianGPLVMMiniBatch, self)._inner_parameters_changed(kern, X, Z, likelihood, Y, Y_metadata, Lm=Lm, dL_dKmm=dL_dKmm, subset_indices=subset_indices)
if self.has_uncertain_inputs():
current_values['meangrad'], current_values['vargrad'] = self.kern.gradients_qX_expectations(
variational_posterior=X,
Z=Z, dL_dpsi0=grad_dict['dL_dpsi0'],
dL_dpsi1=grad_dict['dL_dpsi1'],
dL_dpsi2=grad_dict['dL_dpsi2'])
else:
current_values['Xgrad'] = self.kern.gradients_X(grad_dict['dL_dKnm'], X, Z)
current_values['Xgrad'] += self.kern.gradients_X_diag(grad_dict['dL_dKdiag'], X)
if subset_indices is not None:
value_indices['Xgrad'] = subset_indices['samples']
kl_fctr = self.kl_factr
if self.has_uncertain_inputs():
if self.missing_data:
d = self.output_dim
log_marginal_likelihood -= kl_fctr*self.variational_prior.KL_divergence(X)/d
else:
log_marginal_likelihood -= kl_fctr*self.variational_prior.KL_divergence(X)
# Subsetting Variational Posterior objects, makes the gradients
# empty. We need them to be 0 though:
X.mean.gradient[:] = 0
@ -121,42 +130,24 @@ class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
E.g. set the gradients of parameters, etc.
"""
super(BayesianGPLVMMiniBatch, self)._outer_values_update(full_values)
if self.has_uncertain_inputs():
self.X.mean.gradient = full_values['meangrad']
self.X.variance.gradient = full_values['vargrad']
else:
self.X.gradient = full_values['Xgrad']
def _outer_init_full_values(self):
if self.has_uncertain_inputs():
return dict(meangrad=np.zeros(self.X.mean.shape),
vargrad=np.zeros(self.X.variance.shape))
else:
return dict(Xgrad=np.zeros(self.X.shape))
def parameters_changed(self):
super(BayesianGPLVMMiniBatch,self).parameters_changed()
if isinstance(self.inference_method, VarDTC_minibatch):
return
#super(BayesianGPLVM, self).parameters_changed()
#self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X)
#self.X.mean.gradient, self.X.variance.gradient = self.kern.gradients_qX_expectations(variational_posterior=self.X, Z=self.Z, dL_dpsi0=self.grad_dict['dL_dpsi0'], dL_dpsi1=self.grad_dict['dL_dpsi1'], dL_dpsi2=self.grad_dict['dL_dpsi2'])
# This is testing code -------------------------
# i = np.random.randint(self.X.shape[0])
# X_ = self.X.mean
# which = np.sqrt(((X_ - X_[i:i+1])**2).sum(1)).argsort()>(max(0, self.X.shape[0]-51))
# _, _, grad_dict = self.inference_method.inference(self.kern, self.X[which], self.Z, self.likelihood, self.Y[which], self.Y_metadata)
# grad = self.kern.gradients_qX_expectations(variational_posterior=self.X[which], Z=self.Z, dL_dpsi0=grad_dict['dL_dpsi0'], dL_dpsi1=grad_dict['dL_dpsi1'], dL_dpsi2=grad_dict['dL_dpsi2'])
#
# self.X.mean.gradient[:] = 0
# self.X.variance.gradient[:] = 0
# self.X.mean.gradient[which] = grad[0]
# self.X.variance.gradient[which] = grad[1]
# update for the KL divergence
# self.variational_prior.update_gradients_KL(self.X, which)
# -----------------------------------------------
# update for the KL divergence
#self.variational_prior.update_gradients_KL(self.X)
def plot_latent(self, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40,
fignum=None, plot_inducing=True, legend=True,

8
GPy/models/mrd.py
View file

@ -111,9 +111,6 @@ class MRD(BayesianGPLVMMiniBatch):
assert all([isinstance(k, Kern) for k in kernel]), "invalid kernel object detected!"
kernels = kernel
if X_variance is None:
X_variance = np.random.uniform(0.1, 0.2, X.shape)
self.variational_prior = NormalPrior()
#self.X = NormalPosterior(X, X_variance)
@ -174,10 +171,13 @@ class MRD(BayesianGPLVMMiniBatch):
self.Z.gradient[:] += b.full_values['Zgrad']
grad_dict = b.full_values
if self.has_uncertain_inputs():
self.X.mean.gradient += grad_dict['meangrad']
self.X.variance.gradient += grad_dict['vargrad']
else:
self.X.gradient += grad_dict['Xgrad']
if isinstance(self.X, VariationalPosterior):
if self.has_uncertain_inputs():
# update for the KL divergence
self.variational_prior.update_gradients_KL(self.X)
self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X)