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[vardtc] missing data handling and stochastic update in d
This commit is contained in:
Max Zwiessele
parent
3358d06e42
commit
26396939e5
6 changed files with 124 additions and 37 deletions
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@ -10,6 +10,8 @@ from parameterization.variational import VariationalPosterior
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import logging
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from GPy.inference.latent_function_inference.posterior import Posterior
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from GPy.inference.optimization.stochastics import SparseGPStochastics,\
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SparseGPMissing
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logger = logging.getLogger("sparse gp")
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class SparseGP(GP):
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@ -37,12 +39,7 @@ class SparseGP(GP):
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def __init__(self, X, Y, Z, kernel, likelihood, inference_method=None,
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name='sparse gp', Y_metadata=None, normalizer=False,
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missing_data=False):
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self.missing_data = missing_data
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if self.missing_data:
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self.ninan = ~np.isnan(Y)
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missing_data=False, stochastic=False, batchsize=1):
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#pick a sensible inference method
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if inference_method is None:
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if isinstance(likelihood, likelihoods.Gaussian):
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@ -56,6 +53,22 @@ class SparseGP(GP):
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self.num_inducing = Z.shape[0]
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GP.__init__(self, X, Y, kernel, likelihood, inference_method=inference_method, name=name, Y_metadata=Y_metadata, normalizer=normalizer)
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self.missing_data = missing_data
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if stochastic and missing_data:
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self.missing_data = True
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self.ninan = ~np.isnan(Y)
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self.stochastics = SparseGPStochastics(self, batchsize)
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elif stochastic and not missing_data:
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self.missing_data = False
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self.stochastics = SparseGPStochastics(self, batchsize)
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elif missing_data:
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self.missing_data = True
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self.ninan = ~np.isnan(Y)
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self.stochastics = SparseGPMissing(self)
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else:
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self.stochastics = False
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logger.info("Adding Z as parameter")
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self.link_parameter(self.Z, index=0)
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if self.missing_data:
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@ -71,6 +84,7 @@ class SparseGP(GP):
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print message,
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print ''
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self.posterior = None
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def has_uncertain_inputs(self):
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return isinstance(self.X, VariationalPosterior)
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@ -156,7 +170,31 @@ class SparseGP(GP):
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value_indices:
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dictionary holding indices for the update in full_values.
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if the key exists the update rule is:
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if the key exists the update rule is:def df(x):
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m.stochastics.do_stochastics()
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grads = m._grads(x)
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print '\r',
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message = "Lik: {: 6.4E} Grad: {: 6.4E} Dim: {} Lik: {} Len: {!s}".format(float(m.log_likelihood()), np.einsum('i,i->', grads, grads), m.stochastics.d, float(m.likelihood.variance), " ".join(["{:3.2E}".format(l) for l in m.kern.lengthscale.values]))
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print message,
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return grads
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def grad_stop(threshold):
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def inner(args):
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g = args['gradient']
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return np.sqrt(np.einsum('i,i->',g,g)) < threshold
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return inner
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def maxiter_stop(maxiter):
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def inner(args):
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return args['n_iter'] == maxiter
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return inner
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def optimize(m, maxiter=1000):
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#opt = climin.RmsProp(m.optimizer_array.copy(), df, 1e-6, decay=0.9, momentum=0.9, step_adapt=1e-7)
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opt = climin.Adadelta(m.optimizer_array.copy(), df, 1e-2, decay=0.9)
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ret = opt.minimize_until((grad_stop(.1), maxiter_stop(maxiter)))
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print
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return ret
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full_values[key][value_indices[key]] += current_values[key]
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"""
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for key in current_values.keys():
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@ -206,22 +244,29 @@ class SparseGP(GP):
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def _outer_loop_for_missing_data(self):
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Lm = None
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dL_dKmm = None
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Kmm = None
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self._log_marginal_likelihood = 0
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full_values = self._outer_init_full_values()
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woodbury_inv = np.zeros((self.num_inducing, self.num_inducing, self.output_dim))
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woodbury_vector = np.zeros((self.num_inducing, self.output_dim))
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m_f = lambda i: "Inference with missing data: {: >7.2%}".format(float(i+1)/self.output_dim)
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message = m_f(-1)
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print message,
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for d in xrange(self.output_dim):
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if self.posterior is None:
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woodbury_inv = np.zeros((self.num_inducing, self.num_inducing, self.output_dim))
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woodbury_vector = np.zeros((self.num_inducing, self.output_dim))
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else:
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woodbury_inv = self.posterior._woodbury_inv
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woodbury_vector = self.posterior._woodbury_vector
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if not self.stochastics:
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m_f = lambda i: "Inference with missing_data: {: >7.2%}".format(float(i+1)/self.output_dim)
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message = m_f(-1)
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print message,
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for d in self.stochastics.d:
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ninan = self.ninan[:, d]
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print ' '*(len(message)) + '\r',
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message = m_f(d)
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print message,
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if not self.stochastics:
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print ' '*(len(message)) + '\r',
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message = m_f(d)
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print message,
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posterior, log_marginal_likelihood, \
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grad_dict, current_values, value_indices = self._inner_parameters_changed(
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@ -236,19 +281,50 @@ class SparseGP(GP):
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Lm = posterior.K_chol
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dL_dKmm = grad_dict['dL_dKmm']
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Kmm = posterior._K
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woodbury_inv[:, :, d] = posterior.woodbury_inv
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woodbury_vector[:, d:d+1] = posterior.woodbury_vector
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self._log_marginal_likelihood += log_marginal_likelihood
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print ''
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if not self.stochastics:
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print ''
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self.posterior = Posterior(woodbury_inv=woodbury_inv, woodbury_vector=woodbury_vector,
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K=Kmm, mean=None, cov=None, K_chol=Lm)
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if self.posterior is None:
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self.posterior = Posterior(woodbury_inv=woodbury_inv, woodbury_vector=woodbury_vector,
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K=posterior._K, mean=None, cov=None, K_chol=posterior.K_chol)
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self._outer_values_update(full_values)
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def _outer_loop_without_missing_data(self):
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self._log_marginal_likelihood = 0
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if self.posterior is None:
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woodbury_inv = np.zeros((self.num_inducing, self.num_inducing, self.output_dim))
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woodbury_vector = np.zeros((self.num_inducing, self.output_dim))
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else:
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woodbury_inv = self.posterior._woodbury_inv
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woodbury_vector = self.posterior._woodbury_vector
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d = self.stochastics.d
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posterior, log_marginal_likelihood, \
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grad_dict, current_values, _ = self._inner_parameters_changed(
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self.kern, self.X,
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self.Z, self.likelihood,
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self.Y_normalized[:, d], self.Y_metadata)
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self.grad_dict = grad_dict
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self._log_marginal_likelihood += log_marginal_likelihood
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self._outer_values_update(current_values)
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woodbury_inv[:, :, d] = posterior.woodbury_inv[:, :, None]
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woodbury_vector[:, d] = posterior.woodbury_vector
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if self.posterior is None:
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self.posterior = Posterior(woodbury_inv=woodbury_inv, woodbury_vector=woodbury_vector,
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K=posterior._K, mean=None, cov=None, K_chol=posterior.K_chol)
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def parameters_changed(self):
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if self.missing_data:
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self._outer_loop_for_missing_data()
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elif self.stochastics:
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self._outer_loop_without_missing_data()
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else:
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self.posterior, self._log_marginal_likelihood, self.grad_dict, full_values, _ = self._inner_parameters_changed(self.kern, self.X, self.Z, self.likelihood, self.Y_normalized, self.Y_metadata)
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self._outer_values_update(full_values)
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