[vardtc] missing data handling and stochastic update in d

GPy/core/sparse_gp.py

@@ -10,6 +10,8 @@ from parameterization.variational import VariationalPosterior
 
 import logging
 from GPy.inference.latent_function_inference.posterior import Posterior
+from GPy.inference.optimization.stochastics import SparseGPStochastics,\
+    SparseGPMissing
 logger = logging.getLogger("sparse gp")
 
 class SparseGP(GP):
@@ -37,12 +39,7 @@ class SparseGP(GP):
 
     def __init__(self, X, Y, Z, kernel, likelihood, inference_method=None,
                  name='sparse gp', Y_metadata=None, normalizer=False,
-                 missing_data=False):
+                 missing_data=False, stochastic=False, batchsize=1):
-
-        self.missing_data = missing_data
-        if self.missing_data:
-            self.ninan = ~np.isnan(Y)
-
         #pick a sensible inference method
         if inference_method is None:
             if isinstance(likelihood, likelihoods.Gaussian):
@@ -56,6 +53,22 @@ class SparseGP(GP):
         self.num_inducing = Z.shape[0]
 
         GP.__init__(self, X, Y, kernel, likelihood, inference_method=inference_method, name=name, Y_metadata=Y_metadata, normalizer=normalizer)
+        self.missing_data = missing_data
+
+        if stochastic and missing_data:
+            self.missing_data = True
+            self.ninan = ~np.isnan(Y)
+            self.stochastics = SparseGPStochastics(self, batchsize)
+        elif stochastic and not missing_data:
+            self.missing_data = False
+            self.stochastics = SparseGPStochastics(self, batchsize)
+        elif missing_data:
+            self.missing_data = True
+            self.ninan = ~np.isnan(Y)
+            self.stochastics = SparseGPMissing(self)
+        else:
+            self.stochastics = False
+
         logger.info("Adding Z as parameter")
         self.link_parameter(self.Z, index=0)
         if self.missing_data:
@@ -71,6 +84,7 @@ class SparseGP(GP):
                 print message,
             print ''
 
+        self.posterior = None
 
     def has_uncertain_inputs(self):
         return isinstance(self.X, VariationalPosterior)
@@ -156,7 +170,31 @@ class SparseGP(GP):
 
         value_indices:
             dictionary holding indices for the update in full_values.
-            if the key exists the update rule is:
+            if the key exists the update rule is:def df(x):
+    m.stochastics.do_stochastics()
+    grads = m._grads(x)
+    print '\r',
+    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]))
+    print message,
+    return grads
+
+def grad_stop(threshold):
+    def inner(args):
+        g = args['gradient']
+        return np.sqrt(np.einsum('i,i->',g,g)) < threshold
+    return inner
+
+def maxiter_stop(maxiter):
+    def inner(args):
+        return args['n_iter'] == maxiter
+    return inner
+
+def optimize(m, maxiter=1000):
+    #opt = climin.RmsProp(m.optimizer_array.copy(), df, 1e-6, decay=0.9, momentum=0.9, step_adapt=1e-7)
+    opt = climin.Adadelta(m.optimizer_array.copy(), df, 1e-2, decay=0.9)
+    ret = opt.minimize_until((grad_stop(.1), maxiter_stop(maxiter)))
+    print
+    return ret
             full_values[key][value_indices[key]] += current_values[key]
         """
         for key in current_values.keys():
@@ -206,19 +244,26 @@ class SparseGP(GP):
     def _outer_loop_for_missing_data(self):
         Lm = None
         dL_dKmm = None
-        Kmm = None
 
         self._log_marginal_likelihood = 0
         full_values = self._outer_init_full_values()
 
+        if self.posterior is None:
             woodbury_inv = np.zeros((self.num_inducing, self.num_inducing, self.output_dim))
             woodbury_vector = np.zeros((self.num_inducing, self.output_dim))
-        m_f = lambda i: "Inference with missing data: {: >7.2%}".format(float(i+1)/self.output_dim)
+        else:
+            woodbury_inv = self.posterior._woodbury_inv
+            woodbury_vector = self.posterior._woodbury_vector
+
+        if not self.stochastics:
+            m_f = lambda i: "Inference with missing_data: {: >7.2%}".format(float(i+1)/self.output_dim)
             message = m_f(-1)
             print message,
-        for d in xrange(self.output_dim):
+
+        for d in self.stochastics.d:
             ninan = self.ninan[:, d]
 
+            if not self.stochastics:
                 print ' '*(len(message)) + '\r',
                 message = m_f(d)
                 print message,
@@ -236,19 +281,50 @@ class SparseGP(GP):
 
             Lm = posterior.K_chol
             dL_dKmm = grad_dict['dL_dKmm']
-            Kmm = posterior._K
             woodbury_inv[:, :, d] = posterior.woodbury_inv
             woodbury_vector[:, d:d+1] = posterior.woodbury_vector
             self._log_marginal_likelihood += log_marginal_likelihood
+        if not self.stochastics:
             print ''
 
+        if self.posterior is None:
             self.posterior = Posterior(woodbury_inv=woodbury_inv, woodbury_vector=woodbury_vector,
-                                   K=Kmm, mean=None, cov=None, K_chol=Lm)
+                                   K=posterior._K, mean=None, cov=None, K_chol=posterior.K_chol)
         self._outer_values_update(full_values)
 
+    def _outer_loop_without_missing_data(self):
+        self._log_marginal_likelihood = 0
+
+        if self.posterior is None:
+            woodbury_inv = np.zeros((self.num_inducing, self.num_inducing, self.output_dim))
+            woodbury_vector = np.zeros((self.num_inducing, self.output_dim))
+        else:
+            woodbury_inv = self.posterior._woodbury_inv
+            woodbury_vector = self.posterior._woodbury_vector
+
+        d = self.stochastics.d
+        posterior, log_marginal_likelihood, \
+            grad_dict, current_values, _ = self._inner_parameters_changed(
+                            self.kern, self.X,
+                            self.Z, self.likelihood,
+                            self.Y_normalized[:, d], self.Y_metadata)
+        self.grad_dict = grad_dict
+
+        self._log_marginal_likelihood += log_marginal_likelihood
+
+        self._outer_values_update(current_values)
+
+        woodbury_inv[:, :, d] = posterior.woodbury_inv[:, :, None]
+        woodbury_vector[:, d] = posterior.woodbury_vector
+        if self.posterior is None:
+            self.posterior = Posterior(woodbury_inv=woodbury_inv, woodbury_vector=woodbury_vector,
+                                   K=posterior._K, mean=None, cov=None, K_chol=posterior.K_chol)
+
     def parameters_changed(self):
         if self.missing_data:
             self._outer_loop_for_missing_data()
+        elif self.stochastics:
+            self._outer_loop_without_missing_data()
         else:
             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)
             self._outer_values_update(full_values)

GPy/core/sparse_gp_mpi.py

@@ -34,7 +34,8 @@ class SparseGP_MPI(SparseGP):
 
     """
 
-    def __init__(self, X, Y, Z, kernel, likelihood, variational_prior=None, inference_method=None, name='sparse gp mpi', Y_metadata=None, mpi_comm=None, normalizer=False, missing_data=False):
+    def __init__(self, X, Y, Z, kernel, likelihood, variational_prior=None, inference_method=None, name='sparse gp mpi', Y_metadata=None, mpi_comm=None, normalizer=False,
+                 missing_data=False, stochastic=False, batchsize=1):
         self._IN_OPTIMIZATION_ = False
         if mpi_comm != None:
             if inference_method is None:
@@ -42,7 +43,8 @@ class SparseGP_MPI(SparseGP):
             else:
                 assert isinstance(inference_method, VarDTC_minibatch), 'inference_method has to support MPI!'
 
-        super(SparseGP_MPI, self).__init__(X, Y, Z, kernel, likelihood, inference_method=inference_method, name=name, Y_metadata=Y_metadata, normalizer=normalizer, missing_data=missing_data)
+        super(SparseGP_MPI, self).__init__(X, Y, Z, kernel, likelihood, inference_method=inference_method, name=name, Y_metadata=Y_metadata, normalizer=normalizer,
+                                           missing_data=missing_data, stochastic=stochastic, batchsize=batchsize)
         self.update_model(False)
         self.link_parameter(self.X, index=0)
         if variational_prior is not None:

GPy/examples/dimensionality_reduction.py

@@ -208,12 +208,12 @@ def _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim=False):
     Q_signal = 4
     import GPy
     import numpy as np
-    np.random.seed(0)
+    np.random.seed(3000)
 
-    k = GPy.kern.Matern32(Q_signal, 1., lengthscale=np.random.uniform(1,6,Q_signal), ARD=1)
+    k = GPy.kern.Matern32(Q_signal, 10., lengthscale=1+(np.random.uniform(1,6,Q_signal)), ARD=1)
     t = np.c_[[np.linspace(-1,5,N) for _ in range(Q_signal)]].T
     K = k.K(t)
-    s1, s2, s3, sS = np.random.multivariate_normal(np.zeros(K.shape[0]), K, size=(4))[:,:,None]
+    s2, s1, s3, sS = np.random.multivariate_normal(np.zeros(K.shape[0]), K, size=(4))[:,:,None]
 
     Y1, Y2, Y3, S1, S2, S3 = _generate_high_dimensional_output(D1, D2, D3, s1, s2, s3, sS)
 
@@ -360,7 +360,6 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
                       ):
     from GPy import kern
     from GPy.models import BayesianGPLVM
-    from GPy.inference.latent_function_inference.var_dtc import VarDTCMissingData
 
     D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
     _, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)

GPy/models/bayesian_gplvm.py

@@ -27,7 +27,7 @@ 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,
-                 missing_data=False):
+                 missing_data=False, stochastic=False, batchsize=1):
         self.mpi_comm = mpi_comm
         self.__IN_OPTIMIZATION__ = False
 
@@ -77,7 +77,8 @@ class BayesianGPLVM(SparseGP_MPI):
                                            name=name, inference_method=inference_method,
                                            normalizer=normalizer, mpi_comm=mpi_comm,
                                            variational_prior=self.variational_prior,
-                                           missing_data=missing_data)
+                                           missing_data=missing_data, stochastic=stochastic,
+                                           batchsize=batchsize)
 
     def set_X_gradients(self, X, X_grad):
         """Set the gradients of the posterior distribution of X in its specific form."""
@@ -90,7 +91,12 @@ class BayesianGPLVM(SparseGP_MPI):
     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)
+        kl_fctr = 1.
+        if self.missing_data:
+            d = self.output_dim
+            log_marginal_likelihood -= kl_fctr*self.variational_prior.KL_divergence(X)/d
+        else:
+            log_marginal_likelihood -= kl_fctr*self.variational_prior.KL_divergence(X)
 
         current_values['meangrad'], current_values['vargrad'] = self.kern.gradients_qX_expectations(
                                             variational_posterior=X,
@@ -104,8 +110,12 @@ class BayesianGPLVM(SparseGP_MPI):
         X.variance.gradient[:] = 0
 
         self.variational_prior.update_gradients_KL(X)
-        current_values['meangrad'] += X.mean.gradient
+        if self.missing_data:
-        current_values['vargrad'] += X.variance.gradient
+            current_values['meangrad'] += kl_fctr*X.mean.gradient/d
+            current_values['vargrad'] += kl_fctr*X.variance.gradient/d
+        else:
+            current_values['meangrad'] += kl_fctr*X.mean.gradient
+            current_values['vargrad'] += kl_fctr*X.variance.gradient
 
         if subset_indices is not None:
             value_indices['meangrad'] = subset_indices['samples']

GPy/testing/model_tests.py

@@ -139,16 +139,16 @@ class MiscTests(unittest.TestCase):
         np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
         m.kern.randomize()
         m2.kern[''] = m.kern[:]
-        np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
+        np.testing.assert_almost_equal(m.log_likelihood(), m2.log_likelihood())
         m.kern.randomize()
         m2.kern[:] = m.kern[:]
-        np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
+        np.testing.assert_almost_equal(m.log_likelihood(), m2.log_likelihood())
         m.kern.randomize()
         m2.kern[''] = m.kern['']
-        np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
+        np.testing.assert_almost_equal(m.log_likelihood(), m2.log_likelihood())
         m.kern.randomize()
         m2.kern[:] = m.kern[''].values()
-        np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
+        np.testing.assert_almost_equal(m.log_likelihood(), m2.log_likelihood())
 
     def test_big_model(self):
         m = GPy.examples.dimensionality_reduction.mrd_simulation(optimize=0, plot=0, plot_sim=0)

GPy/util/initialization.py

@@ -13,7 +13,7 @@ def initialize_latent(init, input_dim, Y):
         p = pca(Y)
         PC = p.project(Y, min(input_dim, Y.shape[1]))
         Xr[:PC.shape[0], :PC.shape[1]] = PC
-        var = p.fracs[:input_dim]
+        var = .1*p.fracs[:input_dim]
     else:
         var = Xr.var(0)