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extend inference X for all gp models

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Zhenwen Dai 2014-11-03 16:04:15 +00:00
parent 3eb2fa3a8f
commit 1840b7e6b8
5 changed files with 78 additions and 37 deletions
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16
GPy/core/gp.py
View file

@ -354,3 +354,19 @@ class GP(Model):
print "KeyboardInterrupt caught, calling on_optimization_end() to round things up"
self.inference_method.on_optimization_end()
raise
def infer_newX(self, Y_new, optimize=True, ):
"""
Infer the distribution of X for the new observed data *Y_new*.
:param model: the GPy model used in inference
:type model: GPy.core.Model
:param Y_new: the new observed data for inference
:type Y_new: numpy.ndarray
:param optimize: whether to optimize the location of new X (True by default)
:type optimize: boolean
:return: a tuple containing the estimated posterior distribution of X and the model that optimize X
:rtype: (GPy.core.parameterization.variational.VariationalPosterior, GPy.core.Model)
"""
from ..inference.latent_function_inference.inferenceX import infer_newX
return infer_newX(self, Y_new, optimize=optimize)

69
GPy/inference/latent_function_inference/inferenceX.py
View file

@ -51,8 +51,18 @@ class InferenceX(Model):
self.kern.GPU(True)
from copy import deepcopy
self.posterior = deepcopy(model.posterior)
self.variational_prior = model.variational_prior.copy()
self.Z = model.Z.copy()
if hasattr(model, 'variational_prior'):
self.uncertain_input = True
self.variational_prior = model.variational_prior.copy()
else:
self.uncertain_input = False
if hasattr(model, 'inducing_inputs'):
self.sparse_gp = True
self.Z = model.Z.copy()
else:
self.sparse_gp = False
self.uncertain_input = False
self.Z = model.X.copy()
self.Y = Y
self.X = self._init_X(model, Y, init=init)
self.compute_dL()
@ -72,6 +82,8 @@ class InferenceX(Model):
dist = -2.*Y_new.dot(Y.T) + np.square(Y_new).sum(axis=1)[:,None]+ np.square(Y).sum(axis=1)[None,:]
elif init=='NCC':
dist = Y_new.dot(Y.T)
elif init=='rand':
dist = np.random.rand(Y_new.shape[0],Y.shape[0])
idx = dist.argmin(axis=1)
from ...models import SSGPLVM
@ -81,7 +93,11 @@ class InferenceX(Model):
if model.group_spike:
X.gamma.fix()
else:
X = variational.NormalPosterior(param_to_array(model.X.mean[idx]), param_to_array(model.X.variance[idx]))
if self.uncertain_input and self.sparse_gp:
X = variational.NormalPosterior(param_to_array(model.X.mean[idx]), param_to_array(model.X.variance[idx]))
else:
from ...core import Param
X = Param('latent mean',param_to_array(model.X[idx]).copy())
return X
@ -99,29 +115,42 @@ class InferenceX(Model):
else:
self.dL_dpsi2 = beta*(output_dim*self.posterior.woodbury_inv - np.einsum('md,od->mo',wv, wv))/2.
self.dL_dpsi1 = beta*np.dot(self.Y, wv.T)
self.dL_dpsi0 = -beta/2.* np.ones(self.Y.shape[0]) #self.dL_dpsi0[:] = 0
self.dL_dpsi0 = -beta/2.* np.ones(self.Y.shape[0])
def parameters_changed(self):
psi0 = self.kern.psi0(self.Z, self.X)
psi1 = self.kern.psi1(self.Z, self.X)
psi2 = self.kern.psi2(self.Z, self.X)
if self.uncertain_input:
psi0 = self.kern.psi0(self.Z, self.X)
psi1 = self.kern.psi1(self.Z, self.X)
psi2 = self.kern.psi2(self.Z, self.X)
else:
psi0 = self.kern.Kdiag(self.X)
psi1 = self.kern.K(self.X, self.Z)
psi2 = np.dot(psi1.T,psi1)
self._log_marginal_likelihood = (self.dL_dpsi2*psi2).sum()+(self.dL_dpsi1*psi1).sum()+(self.dL_dpsi0*psi0).sum()
X_grad = self.kern.gradients_qX_expectations(variational_posterior=self.X, Z=self.Z, dL_dpsi0=self.dL_dpsi0, dL_dpsi1=self.dL_dpsi1, dL_dpsi2=self.dL_dpsi2)
self.X.set_gradients(X_grad)
from ...core.parameterization.variational import SpikeAndSlabPrior
if isinstance(self.variational_prior, SpikeAndSlabPrior):
# Update Log-likelihood
KL_div = self.variational_prior.KL_divergence(self.X, N=self.Y.shape[0])
# update for the KL divergence
self.variational_prior.update_gradients_KL(self.X, N=self.Y.shape[0])
if self.uncertain_input:
X_grad = self.kern.gradients_qX_expectations(variational_posterior=self.X, Z=self.Z, dL_dpsi0=self.dL_dpsi0, dL_dpsi1=self.dL_dpsi1, dL_dpsi2=self.dL_dpsi2)
self.X.set_gradients(X_grad)
else:
# Update Log-likelihood
KL_div = self.variational_prior.KL_divergence(self.X)
# update for the KL divergence
self.variational_prior.update_gradients_KL(self.X)
self._log_marginal_likelihood += -KL_div
dL_dpsi1 = self.dL_dpsi1 + 2.*np.dot(psi1,self.dL_dpsi2)
X_grad = self.kern.gradients_X_diag(self.dL_dpsi0, self.X)
X_grad += self.kern.gradients_X(dL_dpsi1, self.X, self.Z)
self.X.gradient = X_grad
if self.uncertain_input:
from ...core.parameterization.variational import SpikeAndSlabPrior
if isinstance(self.variational_prior, SpikeAndSlabPrior):
# Update Log-likelihood
KL_div = self.variational_prior.KL_divergence(self.X, N=self.Y.shape[0])
# update for the KL divergence
self.variational_prior.update_gradients_KL(self.X, N=self.Y.shape[0])
else:
# Update Log-likelihood
KL_div = self.variational_prior.KL_divergence(self.X)
# update for the KL divergence
self.variational_prior.update_gradients_KL(self.X)
self._log_marginal_likelihood += -KL_div
def log_likelihood(self):
return self._log_marginal_likelihood

16
GPy/models/bayesian_gplvm.py
View file

@ -141,22 +141,6 @@ class BayesianGPLVM(SparseGP_MPI):
resolution, ax, marker, s,
fignum, plot_inducing, legend,
plot_limits, aspect, updates, predict_kwargs, imshow_kwargs)
def infer_newX(self, Y_new, optimize=True, ):
"""
Infer the distribution of X for the new observed data *Y_new*.
:param model: the GPy model used in inference
:type model: GPy.core.Model
:param Y_new: the new observed data for inference
:type Y_new: numpy.ndarray
:param optimize: whether to optimize the location of new X (True by default)
:type optimize: boolean
:return: a tuple containing the estimated posterior distribution of X and the model that optimize X
:rtype: (GPy.core.parameterization.variational.VariationalPosterior, GPy.core.Model)
"""
from ..inference.latent_function_inference.inferenceX import infer_newX
return infer_newX(self, Y_new, optimize=optimize)
def do_test_latents(self, Y):
"""

3
GPy/models/sparse_gplvm.py
View file

@ -26,7 +26,8 @@ class SparseGPLVM(SparseGPRegression):
def parameters_changed(self):
super(SparseGPLVM, self).parameters_changed()
self.X.gradient = self.kern.gradients_X(self.grad_dict['dL_dKnm'], self.X, self.Z)
self.X.gradient = self.kern.gradients_X_diag(self.grad_dict['dL_dKdiag'], self.X)
self.X.gradient += self.kern.gradients_X(self.grad_dict['dL_dKnm'], self.X, self.Z)
def plot_latent(self, labels=None, which_indices=None,
resolution=50, ax=None, marker='o', s=40,

11
GPy/testing/inference_tests.py
View file

@ -65,6 +65,17 @@ class InferenceXTestCase(unittest.TestCase):
self.assertTrue(np.allclose(m.X.mean, mi.X.mean))
self.assertTrue(np.allclose(m.X.variance, mi.X.variance))
def test_inferenceX_GPLVM(self):
Ys = self.genData()
m = GPy.models.GPLVM(Ys[0],3,kernel=GPy.kern.RBF(3,ARD=True))
x,mi = m.infer_newX(m.Y, optimize=False)
self.assertTrue(mi.checkgrad())
# m.optimize(max_iters=10000)
# x,mi = m.infer_newX(m.Y)
# self.assertTrue(np.allclose(m.X, x))
if __name__ == "__main__":