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[missing data] general implementation for subsetting data

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This commit is contained in:
Max Zwiessele 2014-10-08 12:03:51 +01:00
parent d93fac8c13
commit fa7807ee6f
7 changed files with 329 additions and 108 deletions
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64
GPy/models/bayesian_gplvm.py
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@ -25,7 +25,9 @@ class BayesianGPLVM(SparseGP_MPI):
"""
def __init__(self, Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
Z=None, kernel=None, inference_method=None, likelihood=None, name='bayesian gplvm', mpi_comm=None, normalizer=None):
Z=None, kernel=None, inference_method=None, likelihood=None,
name='bayesian gplvm', mpi_comm=None, normalizer=None,
missing_data=False):
self.mpi_comm = mpi_comm
self.__IN_OPTIMIZATION__ = False
@ -59,24 +61,23 @@ class BayesianGPLVM(SparseGP_MPI):
X = NormalPosterior(X, X_variance)
if inference_method is None:
inan = np.isnan(Y)
if np.any(inan):
from ..inference.latent_function_inference.var_dtc import VarDTCMissingData
self.logger.debug("creating inference_method with var_dtc missing data")
inference_method = VarDTCMissingData(inan=inan)
elif mpi_comm is not None:
if mpi_comm is not None:
inference_method = VarDTC_minibatch(mpi_comm=mpi_comm)
else:
from ..inference.latent_function_inference.var_dtc import VarDTC
self.logger.debug("creating inference_method var_dtc")
inference_method = VarDTC()
inference_method = VarDTC(limit=1 if not missing_data else Y.shape[1])
if isinstance(inference_method,VarDTC_minibatch):
inference_method.mpi_comm = mpi_comm
if kernel.useGPU and isinstance(inference_method, VarDTC_GPU):
kernel.psicomp.GPU_direct = True
super(BayesianGPLVM,self).__init__(X, Y, Z, kernel, likelihood=likelihood, name=name, inference_method=inference_method, normalizer=normalizer, mpi_comm=mpi_comm, variational_prior=self.variational_prior)
super(BayesianGPLVM,self).__init__(X, Y, Z, kernel, likelihood=likelihood,
name=name, inference_method=inference_method,
normalizer=normalizer, mpi_comm=mpi_comm,
variational_prior=self.variational_prior,
missing_data=missing_data)
def set_X_gradients(self, X, X_grad):
"""Set the gradients of the posterior distribution of X in its specific form."""
@ -86,15 +87,48 @@ class BayesianGPLVM(SparseGP_MPI):
"""Get the gradients of the posterior distribution of X in its specific form."""
return X.mean.gradient, X.variance.gradient
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(BayesianGPLVM, self)._inner_parameters_changed(kern, X, Z, likelihood, Y, Y_metadata, Lm=Lm, dL_dKmm=dL_dKmm, subset_indices=subset_indices)
log_marginal_likelihood -= self.variational_prior.KL_divergence(X)
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'])
X.mean.gradient[:] = 0
X.variance.gradient[:] = 0
self.variational_prior.update_gradients_KL(X)
current_values['meangrad'] += X.mean.gradient
current_values['vargrad'] += X.variance.gradient
value_indices['meangrad'] = subset_indices['samples']
value_indices['vargrad'] = subset_indices['samples']
return posterior, log_marginal_likelihood, grad_dict, current_values, value_indices
def _outer_values_update(self, full_values):
"""
Here you put the values, which were collected before in the right places.
E.g. set the gradients of parameters, etc.
"""
super(BayesianGPLVM, self)._outer_values_update(full_values)
self.X.mean.gradient = full_values['meangrad']
self.X.variance.gradient = full_values['vargrad']
def _outer_init_full_values(self):
return dict(meangrad=np.zeros(self.X.mean.shape),
vargrad=np.zeros(self.X.variance.shape))
def parameters_changed(self):
super(BayesianGPLVM,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)
#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'])
#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])
@ -113,7 +147,7 @@ class BayesianGPLVM(SparseGP_MPI):
# -----------------------------------------------
# update for the KL divergence
self.variational_prior.update_gradients_KL(self.X)
#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,
@ -178,7 +212,7 @@ class BayesianGPLVM(SparseGP_MPI):
"""
dmu_dX = np.zeros_like(Xnew)
for i in range(self.Z.shape[0]):
dmu_dX += self.kern.gradients_X(self.Cpsi1Vf[i:i + 1, :], Xnew, self.Z[i:i + 1, :])
dmu_dX += self.kern.gradients_X(self.grad_dict['dL_dpsi1'][i:i + 1, :], Xnew, self.Z[i:i + 1, :])
return dmu_dX
def dmu_dXnew(self, Xnew):
@ -189,7 +223,7 @@ class BayesianGPLVM(SparseGP_MPI):
ones = np.ones((1, 1))
for i in range(self.Z.shape[0]):
gradients_X[:, i] = self.kern.gradients_X(ones, Xnew, self.Z[i:i + 1, :]).sum(-1)
return np.dot(gradients_X, self.Cpsi1Vf)
return np.dot(gradients_X, self.grad_dict['dL_dpsi1'])
def plot_steepest_gradient_map(self, *args, ** kwargs):
"""