[paramz] fully integrated all tests running

GPy/models/bayesian_gplvm.py

@@ -5,7 +5,7 @@ import numpy as np
 from .. import kern
 from ..core.sparse_gp_mpi import SparseGP_MPI
 from ..likelihoods import Gaussian
-from ..core.parameterization.variational import NormalPosterior, NormalPrior
+from ..core.variational import NormalPosterior, NormalPrior
 from ..inference.latent_function_inference.var_dtc_parallel import VarDTC_minibatch
 import logging
 

GPy/models/bayesian_gplvm_minibatch.py

@@ -2,14 +2,12 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
+import logging
 from .. import kern
 from ..likelihoods import Gaussian
-from ..core.parameterization.variational import NormalPosterior, NormalPrior
-from ..inference.latent_function_inference.var_dtc_parallel import VarDTC_minibatch
-import logging
-from GPy.models.sparse_gp_minibatch import SparseGPMiniBatch
-from GPy.core.parameterization.param import Param
-from GPy.core.parameterization.observable_array import ObsAr
+from ..core.variational import NormalPosterior, NormalPrior
+from .sparse_gp_minibatch import SparseGPMiniBatch
+from ..core.parameterization.param import Param
 
 class BayesianGPLVMMiniBatch(SparseGPMiniBatch):
     """

GPy/models/dpgplvm.py

@@ -1,10 +1,7 @@
 # Copyright (c) 2015 the GPy Austhors (see AUTHORS.txt)
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-import numpy as np
-from .. import kern
 from .bayesian_gplvm import BayesianGPLVM
-from ..core.parameterization.variational import NormalPosterior, NormalPrior
 
 class DPBayesianGPLVM(BayesianGPLVM):
     """

GPy/models/gp_kronecker_gaussian_regression.py

@@ -2,11 +2,11 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
-from GPy.core.probabilistic_model import Model
-from ..core.parameterization import ObsAr
+from ..core import ProbabilisticModel
+from paramz import ObsAr
 from .. import likelihoods
 
-class GPKroneckerGaussianRegression(Model):
+class GPKroneckerGaussianRegression(ProbabilisticModel):
     """
     Kronecker GP regression
 
@@ -29,7 +29,7 @@ class GPKroneckerGaussianRegression(Model):
 
     """
     def __init__(self, X1, X2, Y, kern1, kern2, noise_var=1., name='KGPR'):
-        Model.__init__(self, name=name)
+        ProbabilisticModel.__init__(self, name=name)
         # accept the construction arguments
         self.X1 = ObsAr(X1)
         self.X2 = ObsAr(X2)

GPy/models/gp_var_gauss.py

@@ -3,8 +3,6 @@
 
 import numpy as np
 from ..core import GP
-from ..core.parameterization import ObsAr
-from .. import kern
 from ..core.parameterization.param import Param
 from ..inference.latent_function_inference import VarGauss
 

GPy/models/gradient_checker.py

@@ -1,11 +1,11 @@
 # ## Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-from GPy.core.probabilistic_model import Model
-import itertools
 import numpy
-from ..core.parameterization import Param
 np = numpy
+
+from ..core.parameterization import Param
+from ..core.probabilistic_model import ProbabilisticModel
 from ..util.block_matrices import get_blocks, get_block_shapes, unblock, get_blocks_3d, get_block_shapes_3d
 
 def get_shape(x):
@@ -21,7 +21,7 @@ def at_least_one_element(x):
 def flatten_if_needed(x):
     return numpy.atleast_1d(x).flatten()
 
-class GradientChecker(Model):
+class GradientChecker(ProbabilisticModel):
 
     def __init__(self, f, df, x0, names=None, *args, **kwargs):
         """
@@ -62,7 +62,7 @@ class GradientChecker(Model):
                 grad.randomize()
                 grad.checkgrad(verbose=1)
         """
-        Model.__init__(self, 'GradientChecker')
+        super(GradientChecker, self).__init__(name='GradientChecker')
         if isinstance(x0, (list, tuple)) and names is None:
             self.shapes = [get_shape(xi) for xi in x0]
             self.names = ['X{i}'.format(i=i) for i in range(len(x0))]

GPy/models/mrd.py

@@ -5,18 +5,14 @@ import numpy as np
 import itertools, logging
 
 from ..kern import Kern
-from ..core.parameterization.variational import NormalPosterior, NormalPrior
-from ..core.parameterization import Param, Parameterized
-from ..core.parameterization.observable_array import ObsAr
+from ..core.variational import NormalPrior
+from ..core.parameterization import Param
+from paramz import ObsAr
 from ..inference.latent_function_inference.var_dtc import VarDTC
 from ..inference.latent_function_inference import InferenceMethodList
 from ..likelihoods import Gaussian
 from ..util.initialization import initialize_latent
-from ..core.sparse_gp import SparseGP, GP
-from GPy.core.parameterization.variational import VariationalPosterior
 from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
-from GPy.models.bayesian_gplvm import BayesianGPLVM
-from GPy.models.sparse_gp_minibatch import SparseGPMiniBatch
 
 class MRD(BayesianGPLVMMiniBatch):
     """

GPy/models/one_vs_all_classification.py

@@ -1,7 +1,6 @@
 # Copyright (c) 2013, the GPy Authors (see AUTHORS.txt)
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-from ..core import GP
 from . import SparseGPClassification
 from .. import likelihoods
 from .. import kern

GPy/models/sparse_gp_classification.py

@@ -62,7 +62,7 @@ class SparseGPClassificationUncertainInput(SparseGP):
     .. Note:: Multiple independent outputs are allowed using columns of Y
     """
     def __init__(self, X, X_variance, Y, kernel=None, Z=None, num_inducing=10, Y_metadata=None, normalizer=None):
-        from ..core.parameterization.variational import NormalPosterior
+        from ..core.variational import NormalPosterior
         if kernel is None:
             kernel = kern.RBF(X.shape[1])
 

GPy/models/sparse_gp_coregionalized_regression.py

@@ -4,7 +4,6 @@
 import numpy as np
 from ..core import SparseGP
 from ..inference.latent_function_inference import VarDTC
-from .. import likelihoods
 from .. import kern
 from .. import util
 

GPy/models/sparse_gp_minibatch.py

@@ -4,18 +4,15 @@
 from __future__ import print_function
 import numpy as np
 from ..core.parameterization.param import Param
+from ..core.variational import VariationalPosterior
 from ..core.sparse_gp import SparseGP
 from ..core.gp import GP
 from ..inference.latent_function_inference import var_dtc
 from .. import likelihoods
-from ..core.parameterization.variational import VariationalPosterior
 
 import logging
-from GPy.inference.latent_function_inference.posterior import Posterior
-from GPy.inference.optimization.stochastics import SparseGPStochastics,\
-    SparseGPMissing
-#no stochastics.py file added! from GPy.inference.optimization.stochastics import SparseGPStochastics,\
-    #SparseGPMissing
+from ..inference.latent_function_inference.posterior import Posterior
+from ..inference.optimization.stochastics import SparseGPStochastics, SparseGPMissing
 logger = logging.getLogger("sparse gp")
 
 class SparseGPMiniBatch(SparseGP):

GPy/models/sparse_gp_regression.py

@@ -3,12 +3,11 @@
 
 
 import numpy as np
-from ..core import SparseGP
 from ..core.sparse_gp_mpi import SparseGP_MPI
 from .. import likelihoods
 from .. import kern
 from ..inference.latent_function_inference import VarDTC
-from ..core.parameterization.variational import NormalPosterior
+from ..core.variational import NormalPosterior
 
 class SparseGPRegression(SparseGP_MPI):
     """

GPy/models/sparse_gplvm.py

@@ -2,9 +2,8 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 
-import numpy as np
 import sys
-from GPy.models.sparse_gp_regression import SparseGPRegression
+from .sparse_gp_regression import SparseGPRegression
 
 class SparseGPLVM(SparseGPRegression):
     """

GPy/models/ss_gplvm.py

@@ -7,7 +7,7 @@ from ..core.sparse_gp_mpi import SparseGP_MPI
 from .. import kern
 from ..core.parameterization import Param
 from ..likelihoods import Gaussian
-from ..core.parameterization.variational import SpikeAndSlabPrior, SpikeAndSlabPosterior,VariationalPrior
+from ..core.variational import SpikeAndSlabPrior, SpikeAndSlabPosterior,VariationalPrior
 from ..inference.latent_function_inference.var_dtc_parallel import update_gradients, VarDTC_minibatch
 from ..kern.src.psi_comp.ssrbf_psi_gpucomp import PSICOMP_SSRBF_GPU
 
@@ -19,7 +19,7 @@ class IBPPosterior(SpikeAndSlabPosterior):
         """
         binary_prob : the probability of the distribution on the slab part.
         """
-        from ..core.parameterization.transformations import Logexp
+        from paramz.transformations import Logexp
         super(IBPPosterior, self).__init__(means, variances, binary_prob, group_spike=True, name=name)
         self.sharedX = sharedX
         if sharedX:
@@ -60,7 +60,7 @@ class IBPPosterior(SpikeAndSlabPosterior):
 class IBPPrior(VariationalPrior):
     def __init__(self, input_dim, alpha =2., name='IBPPrior', **kw):
         super(IBPPrior, self).__init__(name=name, **kw)
-        from ..core.parameterization.transformations import Logexp, __fixed__  
+        from paramz.transformations import Logexp, __fixed__  
         self.input_dim = input_dim
         self.variance = 1.
         self.alpha = Param('alpha', alpha, __fixed__)

GPy/models/ss_mrd.py

@@ -3,15 +3,15 @@ The Maniforld Relevance Determination model with the spike-and-slab prior
 """
 
 import numpy as np
-from ..core import Model
+from ..core import ProbabilisticModel
 from .ss_gplvm import SSGPLVM
-from ..core.parameterization.variational import SpikeAndSlabPrior,NormalPosterior,VariationalPrior
+from ..core.variational import SpikeAndSlabPrior,NormalPosterior,VariationalPrior
 from ..util.misc import param_to_array
 from ..kern import RBF
 from ..core import Param
 from numpy.linalg.linalg import LinAlgError
 
-class SSMRD(Model):
+class SSMRD(ProbabilisticModel):
     
     def __init__(self, Ylist, input_dim, X=None, X_variance=None, Gammas=None, initx = 'PCA_concat', initz = 'permute', 
                  num_inducing=10, Zs=None, kernels=None, inference_methods=None, likelihoods=None, group_spike=True,
@@ -117,13 +117,13 @@ class SSMRD(Model):
                 Gammas.append(gamma)
         return X, X_variance, Gammas, fracs
 
-    @Model.optimizer_array.setter
+    @ProbabilisticModel.optimizer_array.setter
     def optimizer_array(self, p):
         if self.mpi_comm != None:
             if self._IN_OPTIMIZATION_ and self.mpi_comm.rank==0:
                 self.mpi_comm.Bcast(np.int32(1),root=0)
             self.mpi_comm.Bcast(p, root=0)        
-        Model.optimizer_array.fset(self,p)
+        ProbabilisticModel.optimizer_array.fset(self,p)
         
     def optimize(self, optimizer=None, start=None, **kwargs):
         self._IN_OPTIMIZATION_ = True
@@ -214,7 +214,7 @@ class SpikeAndSlabPrior_SSMRD(SpikeAndSlabPrior):
 class IBPPrior_SSMRD(VariationalPrior):
     def __init__(self, nModels, input_dim, alpha =2., tau=None, name='IBPPrior', **kw):
         super(IBPPrior_SSMRD, self).__init__(name=name, **kw)
-        from ..core.parameterization.transformations import Logexp, __fixed__  
+        from paramz.transformations import Logexp, __fixed__  
         self.nModels = nModels
         self._b_prob_all = 0.5
         self.input_dim = input_dim