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[ssgplvm] group spike

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Zhenwen Dai 2015-05-21 11:33:37 +01:00
parent 7188e92efb
commit b8508cc20c
2 changed files with 39 additions and 14 deletions
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41
GPy/core/parameterization/variational.py
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@ -36,8 +36,9 @@ class NormalPrior(VariationalPrior):
variational_posterior.variance.gradient -= (1. - (1. / (variational_posterior.variance))) * 0.5 variational_posterior.variance.gradient -= (1. - (1. / (variational_posterior.variance))) * 0.5
class SpikeAndSlabPrior(VariationalPrior): class SpikeAndSlabPrior(VariationalPrior):
def __init__(self, pi=None, learnPi=False, variance = 1.0, name='SpikeAndSlabPrior', **kw): def __init__(self, pi=None, learnPi=False, variance = 1.0, group_spike=False, name='SpikeAndSlabPrior', **kw):
super(SpikeAndSlabPrior, self).__init__(name=name, **kw) super(SpikeAndSlabPrior, self).__init__(name=name, **kw)
self.group_spike = group_spike
self.variance = Param('variance',variance) self.variance = Param('variance',variance)
self.learnPi = learnPi self.learnPi = learnPi
if learnPi: if learnPi:
@ -50,7 +51,10 @@ class SpikeAndSlabPrior(VariationalPrior):
def KL_divergence(self, variational_posterior): def KL_divergence(self, variational_posterior):
mu = variational_posterior.mean mu = variational_posterior.mean
S = variational_posterior.variance S = variational_posterior.variance
gamma = variational_posterior.gamma.values if self.group_spike:
gamma = variational_posterior.gamma.values[0]
else:
gamma = variational_posterior.gamma.values
if len(self.pi.shape)==2: if len(self.pi.shape)==2:
idx = np.unique(variational_posterior.gamma._raveled_index()/gamma.shape[-1]) idx = np.unique(variational_posterior.gamma._raveled_index()/gamma.shape[-1])
pi = self.pi[idx] pi = self.pi[idx]
@ -65,14 +69,21 @@ class SpikeAndSlabPrior(VariationalPrior):
def update_gradients_KL(self, variational_posterior): def update_gradients_KL(self, variational_posterior):
mu = variational_posterior.mean mu = variational_posterior.mean
S = variational_posterior.variance S = variational_posterior.variance
gamma = variational_posterior.gamma.values if self.group_spike:
gamma = variational_posterior.gamma.values[0]
else:
gamma = variational_posterior.gamma.values
if len(self.pi.shape)==2: if len(self.pi.shape)==2:
idx = np.unique(variational_posterior.gamma._raveled_index()/gamma.shape[-1]) idx = np.unique(variational_posterior.gamma._raveled_index()/gamma.shape[-1])
pi = self.pi[idx] pi = self.pi[idx]
else: else:
pi = self.pi pi = self.pi
variational_posterior.binary_prob.gradient -= np.log((1-pi)/pi*gamma/(1.-gamma))+((np.square(mu)+S)/self.variance-np.log(S)+np.log(self.variance)-1.)/2. if self.group_spike:
dgamma = np.log((1-pi)/pi*gamma/(1.-gamma))/mu.shape[0]
else:
dgamma = np.log((1-pi)/pi*gamma/(1.-gamma))
variational_posterior.binary_prob.gradient -= dgamma+((np.square(mu)+S)/self.variance-np.log(S)+np.log(self.variance)-1.)/2.
mu.gradient -= gamma*mu/self.variance mu.gradient -= gamma*mu/self.variance
S.gradient -= (1./self.variance - 1./S) * gamma /2. S.gradient -= (1./self.variance - 1./S) * gamma /2.
if self.learnPi: if self.learnPi:
@ -154,13 +165,27 @@ class SpikeAndSlabPosterior(VariationalPosterior):
''' '''
The SpikeAndSlab distribution for variational approximations. The SpikeAndSlab distribution for variational approximations.
''' '''
def __init__(self, means, variances, binary_prob, name='latent space'): def __init__(self, means, variances, binary_prob, group_spike=False, name='latent space'):
""" """
binary_prob : the probability of the distribution on the slab part. binary_prob : the probability of the distribution on the slab part.
""" """
super(SpikeAndSlabPosterior, self).__init__(means, variances, name) super(SpikeAndSlabPosterior, self).__init__(means, variances, name)
self.gamma = Param("binary_prob",binary_prob,Logistic(0.,1.)) self.group_spike = group_spike
self.link_parameter(self.gamma) if group_spike:
self.gamma_group = Param("binary_prob_group",binary_prob.mean(axis=0),Logistic(0.,1.))
self.gamma = Param("binary_prob",binary_prob, __fixed__)
self.link_parameters(self.gamma_group,self.gamma)
else:
self.gamma = Param("binary_prob",binary_prob,Logistic(0.,1.))
self.link_parameter(self.gamma)
def propogate_val(self):
if self.group_spike:
self.gamma.param_array.values.reshape(self.gamma.shape)[:] = self.gamma_group.values
def collate_gradient(self):
if self.group_spike:
self.gamma_group.gradient = self.gamma.gradient.reshape(self.gamma.shape).sum(axis=0)
def set_gradients(self, grad): def set_gradients(self, grad):
self.mean.gradient, self.variance.gradient, self.gamma.gradient = grad self.mean.gradient, self.variance.gradient, self.gamma.gradient = grad

12
GPy/models/ss_gplvm.py
View file

@ -103,7 +103,7 @@ class SSGPLVM(SparseGP_MPI):
""" """
def __init__(self, Y, input_dim, X=None, X_variance=None, Gamma=None, init='PCA', num_inducing=10, 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, mpi_comm=None, pi=None, learnPi=True,normalizer=False, **kwargs): Z=None, kernel=None, inference_method=None, likelihood=None, name='Spike_and_Slab GPLVM', group_spike=False, mpi_comm=None, pi=None, learnPi=False,normalizer=False, **kwargs):
self.group_spike = group_spike self.group_spike = group_spike
@ -144,15 +144,12 @@ class SSGPLVM(SparseGP_MPI):
if pi is None: if pi is None:
pi = np.empty((input_dim)) pi = np.empty((input_dim))
pi[:] = 0.5 pi[:] = 0.5
self.variational_prior = SpikeAndSlabPrior(pi=pi,learnPi=learnPi) # the prior probability of the latent binary variable b 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) X = SpikeAndSlabPosterior(X, X_variance, gamma, group_spike=group_spike)
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) 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) self.link_parameter(self.X, index=0)
if self.group_spike:
[self.X.gamma[:,i].tie('tieGamma'+str(i)) for i in range(self.X.gamma.shape[1])] # Tie columns together
def set_X_gradients(self, X, X_grad): def set_X_gradients(self, X, X_grad):
"""Set the gradients of the posterior distribution of X in its specific form.""" """Set the gradients of the posterior distribution of X in its specific form."""
@ -163,8 +160,10 @@ class SSGPLVM(SparseGP_MPI):
return X.mean.gradient, X.variance.gradient, X.binary_prob.gradient return X.mean.gradient, X.variance.gradient, X.binary_prob.gradient
def parameters_changed(self): def parameters_changed(self):
self.X.propogate_val()
super(SSGPLVM,self).parameters_changed() super(SSGPLVM,self).parameters_changed()
if isinstance(self.inference_method, VarDTC_minibatch): if isinstance(self.inference_method, VarDTC_minibatch):
self.X.collate_gradient()
return return
self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X) self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X)
@ -173,6 +172,7 @@ class SSGPLVM(SparseGP_MPI):
# update for the KL divergence # update for the KL divergence
self.variational_prior.update_gradients_KL(self.X) self.variational_prior.update_gradients_KL(self.X)
self.X.collate_gradient()
def input_sensitivity(self): def input_sensitivity(self):
if self.kern.ARD: if self.kern.ARD: