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[ssmrd] implement with IBP prior

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This commit is contained in:
Zhenwen Dai 2015-05-22 14:29:53 +01:00
parent afa0621488
commit b5cac9af8c
3 changed files with 262 additions and 27 deletions
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26
GPy/models/ss_gplvm.py
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@ -15,12 +15,16 @@ class IBPPosterior(SpikeAndSlabPosterior):
'''
The SpikeAndSlab distribution for variational approximations.
'''
def __init__(self, means, variances, binary_prob, tau=None, name='latent space'):
def __init__(self, means, variances, binary_prob, tau=None, sharedX=False, name='latent space'):
"""
binary_prob : the probability of the distribution on the slab part.
"""
from ..core.parameterization.transformations import Logexp
super(IBPPosterior, self).__init__(means, variances, binary_prob, group_spike=True, name=name)
self.sharedX = sharedX
if sharedX:
self.mean.fix(warning=False)
self.variance.fix(warning=False)
self.tau = Param("tau_", np.ones((self.gamma_group.shape[0],2)), Logexp())
self.link_parameter(self.tau)
@ -83,7 +87,7 @@ class IBPPrior(VariationalPrior):
variational_posterior.mean.gradient -= gamma*mu/self.variance
variational_posterior.variance.gradient -= (1./self.variance - 1./S) * gamma /2.
from scipy.special import digamma,polygamma
dgamma = (np.log(gamma/(1.-gamma))+ digamma(tau[:,1])-digamma(tau[:,0]))/mu.shape[0]
dgamma = (np.log(gamma/(1.-gamma))+ digamma(tau[:,1])-digamma(tau[:,0]))/variational_posterior.num_data
variational_posterior.binary_prob.gradient -= dgamma+((np.square(mu)+S)/self.variance-np.log(S)+np.log(self.variance)-1.)/2.
ad = self.alpha/self.input_dim
common = (ad+2-tau[:,0]-tau[:,1])*polygamma(1,tau.sum(axis=1))
@ -103,9 +107,11 @@ class SSGPLVM(SparseGP_MPI):
"""
def __init__(self, Y, input_dim, X=None, X_variance=None, Gamma=None, init='PCA', num_inducing=10,
Z=None, kernel=None, inference_method=None, likelihood=None, name='Spike_and_Slab GPLVM', group_spike=False, IBP=False, alpha=2., tau=None, mpi_comm=None, pi=None, learnPi=False,normalizer=False, **kwargs):
Z=None, kernel=None, inference_method=None, likelihood=None, name='Spike_and_Slab GPLVM', group_spike=False, IBP=False, alpha=2., tau=None, mpi_comm=None, pi=None, learnPi=False,normalizer=False, sharedX=False, variational_prior=None,**kwargs):
self.group_spike = group_spike
self.init = init
self.sharedX = sharedX
if X == None:
from ..util.initialization import initialize_latent
@ -113,8 +119,6 @@ class SSGPLVM(SparseGP_MPI):
else:
fracs = np.ones(input_dim)
self.init = init
if X_variance is None: # The variance of the variational approximation (S)
X_variance = np.random.uniform(0,.1,X.shape)
@ -146,11 +150,11 @@ class SSGPLVM(SparseGP_MPI):
pi[:] = 0.5
if IBP:
self.variational_prior = IBPPrior(input_dim=input_dim, alpha=alpha)
X = IBPPosterior(X, X_variance, gamma, tau=tau)
self.variational_prior = IBPPrior(input_dim=input_dim, alpha=alpha) if variational_prior is None else variational_prior
X = IBPPosterior(X, X_variance, gamma, tau=tau,sharedX=sharedX)
else:
self.variational_prior = SpikeAndSlabPrior(pi=pi,learnPi=learnPi, group_spike=group_spike) # the prior probability of the latent binary variable b
X = SpikeAndSlabPosterior(X, X_variance, gamma, group_spike=group_spike)
self.variational_prior = SpikeAndSlabPrior(pi=pi,learnPi=learnPi, group_spike=group_spike) if variational_prior is None else variational_prior
X = SpikeAndSlabPosterior(X, X_variance, gamma, group_spike=group_spike,sharedX=sharedX)
super(SSGPLVM,self).__init__(X, Y, Z, kernel, likelihood, variational_prior=self.variational_prior, inference_method=inference_method, name=name, mpi_comm=mpi_comm, normalizer=normalizer, **kwargs)
self.link_parameter(self.X, index=0)
@ -163,8 +167,12 @@ class SSGPLVM(SparseGP_MPI):
"""Get the gradients of the posterior distribution of X in its specific form."""
return X.mean.gradient, X.variance.gradient, X.binary_prob.gradient
def _propogate_X_val(self):
pass
def parameters_changed(self):
self.X.propogate_val()
if self.sharedX: self._highest_parent_._propogate_X_val()
super(SSGPLVM,self).parameters_changed()
if isinstance(self.inference_method, VarDTC_minibatch):
self.X.collate_gradient()