More relative import fixes for Python 3 compatibility

GPy/core/gp.py

@@ -4,11 +4,11 @@
 import numpy as np
 import sys
 from .. import kern
-from model import Model
-from parameterization import ObsAr
+from .model import Model
+from .parameterization import ObsAr
 from .. import likelihoods
 from ..inference.latent_function_inference import exact_gaussian_inference, expectation_propagation
-from parameterization.variational import VariationalPosterior
+from .parameterization.variational import VariationalPosterior
 
 import logging
 from GPy.util.normalizer import MeanNorm

GPy/core/mapping.py

@@ -2,7 +2,7 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import sys
-from parameterization import Parameterized
+from .parameterization import Parameterized
 import numpy as np
 
 class Mapping(Parameterized):
@@ -74,7 +74,7 @@ class Bijective_mapping(Mapping):
         """Inverse mapping from output domain of the function to the inputs."""
         raise NotImplementedError
 
-from model import Model
+from .model import Model
 
 class Mapping_check_model(Model):
     """

GPy/core/model.py

@@ -5,7 +5,7 @@
 from .. import likelihoods
 from ..inference import optimization
 from ..util.misc import opt_wrapper
-from parameterization import Parameterized
+from .parameterization import Parameterized
 import multiprocessing as mp
 import numpy as np
 from numpy.linalg.linalg import LinAlgError

GPy/core/parameterization/index_operations.py

@@ -3,7 +3,7 @@
 
 import numpy
 from numpy.lib.function_base import vectorize
-from lists_and_dicts import IntArrayDict
+from .lists_and_dicts import IntArrayDict
 
 def extract_properties_to_index(index, props):
     prop_index = dict()

GPy/core/parameterization/lists_and_dicts.py

@@ -75,7 +75,7 @@ class ObserverList(object):
 
     def __str__(self):
         from . import ObsAr, Param
-        from parameter_core import Parameterizable
+        from .parameter_core import Parameterizable
         ret = []
         curr_p = None
         

GPy/core/parameterization/observable.py

@@ -12,7 +12,7 @@ class Observable(object):
     """
     def __init__(self, *args, **kwargs):
         super(Observable, self).__init__()
-        from lists_and_dicts import ObserverList
+        from .lists_and_dicts import ObserverList
         self.observers = ObserverList()
         self._update_on = True
 

GPy/core/parameterization/observable_array.py

@@ -3,8 +3,8 @@
 
 
 import numpy as np
-from parameter_core import Pickleable
-from observable import Observable
+from .parameter_core import Pickleable
+from .observable import Observable
 
 class ObsAr(np.ndarray, Pickleable, Observable):
     """
@@ -39,7 +39,7 @@ class ObsAr(np.ndarray, Pickleable, Observable):
         return self.view(np.ndarray)
 
     def copy(self):
-        from lists_and_dicts import ObserverList
+        from .lists_and_dicts import ObserverList
         memo = {}
         memo[id(self)] = self
         memo[id(self.observers)] = ObserverList()

GPy/core/parameterization/param.py

@@ -4,8 +4,8 @@
 import itertools
 import numpy
 np = numpy
-from parameter_core import Parameterizable, adjust_name_for_printing, Pickleable
-from observable_array import ObsAr
+from .parameter_core import Parameterizable, adjust_name_for_printing, Pickleable
+from .observable_array import ObsAr
 
 ###### printing
 __constraints_name__ = "Constraint"
@@ -156,7 +156,7 @@ class Param(Parameterizable, ObsAr):
     #===========================================================================
     @property
     def is_fixed(self):
-        from transformations import __fixed__
+        from .transformations import __fixed__
         return self.constraints[__fixed__].size == self.size
 
     def _get_original(self, param):
@@ -313,7 +313,7 @@ class ParamConcatenation(object):
         See :py:class:`GPy.core.parameter.Param` for more details on constraining.
         """
         # self.params = params
-        from lists_and_dicts import ArrayList
+        from .lists_and_dicts import ArrayList
         self.params = ArrayList([])
         for p in params:
             for p in p.flattened_parameters:

GPy/core/parameterization/parameter_core.py

@@ -13,11 +13,11 @@ Observable Pattern for patameterization
 
 """
 
-from transformations import Transformation,Logexp, NegativeLogexp, Logistic, __fixed__, FIXED, UNFIXED
+from .transformations import Transformation,Logexp, NegativeLogexp, Logistic, __fixed__, FIXED, UNFIXED
 import numpy as np
 import re
 import logging
-from updateable import Updateable
+from .updateable import Updateable
 
 class HierarchyError(Exception):
     """
@@ -170,7 +170,7 @@ class Pickleable(object):
 
     def __setstate__(self, state):
         self.__dict__.update(state)
-        from lists_and_dicts import ObserverList
+        from .lists_and_dicts import ObserverList
         self.observers = ObserverList()
         self._setup_observers()
         self._optimizer_copy_transformed = False
@@ -268,7 +268,7 @@ class Indexable(Nameable, Updateable):
     def __init__(self, name, default_constraint=None, *a, **kw):
         super(Indexable, self).__init__(name=name, *a, **kw)
         self._default_constraint_ = default_constraint
-        from index_operations import ParameterIndexOperations
+        from .index_operations import ParameterIndexOperations
         self.constraints = ParameterIndexOperations()
         self.priors = ParameterIndexOperations()
         if self._default_constraint_ is not None:
@@ -310,7 +310,7 @@ class Indexable(Nameable, Updateable):
         that is an int array, containing the indexes for the flattened
         param inside this parameterized logic.
         """
-        from param import ParamConcatenation
+        from .param import ParamConcatenation
         if isinstance(param, ParamConcatenation):
             return np.hstack((self._raveled_index_for(p) for p in param.params))
         return param._raveled_index() + self._offset_for(param)
@@ -407,7 +407,7 @@ class Indexable(Nameable, Updateable):
         repriorized = self.unset_priors()
         self._add_to_index_operations(self.priors, repriorized, prior, warning)
 
-        from domains import _REAL, _POSITIVE, _NEGATIVE
+        from .domains import _REAL, _POSITIVE, _NEGATIVE
         if prior.domain is _POSITIVE:
             self.constrain_positive(warning)
         elif prior.domain is _NEGATIVE:
@@ -536,7 +536,7 @@ class Indexable(Nameable, Updateable):
         update the constraints and priors view, so that
         constraining is automized for the parent.
         """
-        from index_operations import ParameterIndexOperationsView
+        from .index_operations import ParameterIndexOperationsView
         #if getattr(self, "_in_init_"):
             #import ipdb;ipdb.set_trace()
             #self.constraints.update(param.constraints, start)

GPy/core/parameterization/parameterized.py

@@ -5,8 +5,8 @@
 import numpy; np = numpy
 import itertools
 from re import compile, _pattern_type
-from param import ParamConcatenation
-from parameter_core import HierarchyError, Parameterizable, adjust_name_for_printing
+from .param import ParamConcatenation
+from .parameter_core import HierarchyError, Parameterizable, adjust_name_for_printing
 
 import logging
 from GPy.core.parameterization.index_operations import ParameterIndexOperationsView

GPy/core/parameterization/priors.py

@@ -5,7 +5,7 @@
 import numpy as np
 from scipy.special import gammaln, digamma
 from ...util.linalg import pdinv
-from domains import _REAL, _POSITIVE
+from .domains import _REAL, _POSITIVE
 import warnings
 import weakref
 

GPy/core/parameterization/ties_and_remappings.py

@@ -2,8 +2,8 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
-from parameterized import Parameterized
-from param import Param
+from .parameterized import Parameterized
+from .param import Param
 
 class Remapping(Parameterized):
     def mapping(self):

GPy/core/parameterization/transformations.py

@@ -3,7 +3,7 @@
 
 
 import numpy as np
-from domains import _POSITIVE,_NEGATIVE, _BOUNDED
+from .domains import _POSITIVE,_NEGATIVE, _BOUNDED
 import weakref
 
 import sys

GPy/core/parameterization/updateable.py

@@ -3,7 +3,7 @@ Created on 11 Nov 2014
 
 @author: maxz
 '''
-from observable import Observable
+from .observable import Observable
 
 
 class Updateable(Observable):

GPy/core/parameterization/variational.py

@@ -5,9 +5,9 @@ Created on 6 Nov 2013
 '''
 
 import numpy as np
-from parameterized import Parameterized
-from param import Param
-from transformations import Logexp, Logistic,__fixed__
+from .parameterized import Parameterized
+from .param import Param
+from .transformations import Logexp, Logistic,__fixed__
 from GPy.util.misc import param_to_array
 from GPy.util.caching import Cache_this
 

GPy/core/sparse_gp.py

@@ -2,11 +2,11 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
-from gp import GP
-from parameterization.param import Param
+from .gp import GP
+from .parameterization.param import Param
 from ..inference.latent_function_inference import var_dtc
 from .. import likelihoods
-from parameterization.variational import VariationalPosterior, NormalPosterior
+from .parameterization.variational import VariationalPosterior, NormalPosterior
 from ..util.linalg import mdot
 
 import logging

GPy/core/sparse_gp_mpi.py

@@ -2,7 +2,7 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
-from sparse_gp import SparseGP
+from .sparse_gp import SparseGP
 from numpy.linalg.linalg import LinAlgError
 from ..inference.latent_function_inference.var_dtc_parallel import update_gradients, VarDTC_minibatch
 

GPy/core/svgp.py

@@ -3,8 +3,8 @@
 
 import numpy as np
 from ..util import choleskies
-from sparse_gp import SparseGP
-from parameterization.param import Param
+from .sparse_gp import SparseGP
+from .parameterization.param import Param
 from ..inference.latent_function_inference import SVGP as svgp_inf
 
 

GPy/likelihoods/bernoulli.py

@@ -3,8 +3,8 @@
 
 import numpy as np
 from ..util.univariate_Gaussian import std_norm_pdf, std_norm_cdf
-import link_functions
-from likelihood import Likelihood
+from . import link_functions
+from .likelihood import Likelihood
 from scipy import stats
 
 class Bernoulli(Likelihood):

GPy/likelihoods/exponential.py

@@ -5,8 +5,8 @@
 import numpy as np
 from scipy import stats,special
 import scipy as sp
-import link_functions
-from likelihood import Likelihood
+from . import link_functions
+from .likelihood import Likelihood
 
 class Exponential(Likelihood):
     """

GPy/likelihoods/gamma.py

@@ -6,8 +6,8 @@ import numpy as np
 from scipy import stats,special
 import scipy as sp
 from ..core.parameterization import Param
-import link_functions
-from likelihood import Likelihood
+from . import link_functions
+from .likelihood import Likelihood
 
 class Gamma(Likelihood):
     """

GPy/likelihoods/gaussian.py

@@ -13,8 +13,8 @@ James 11/12/13
 
 import numpy as np
 from scipy import stats, special
-import link_functions
-from likelihood import Likelihood
+from . import link_functions
+from .likelihood import Likelihood
 from ..core.parameterization import Param
 from ..core.parameterization.transformations import Logexp
 from scipy import stats

GPy/likelihoods/likelihood.py

@@ -4,7 +4,7 @@
 import numpy as np
 from scipy import stats,special
 import scipy as sp
-import link_functions
+from . import link_functions
 from ..util.misc import chain_1, chain_2, chain_3
 from scipy.integrate import quad
 import warnings

GPy/likelihoods/mixed_noise.py

@@ -3,9 +3,9 @@
 
 import numpy as np
 from scipy import stats, special
-import link_functions
-from likelihood import Likelihood
-from gaussian import Gaussian
+from . import link_functions
+from .likelihood import Likelihood
+from .gaussian import Gaussian
 from ..core.parameterization import Param
 from ..core.parameterization.transformations import Logexp
 from ..core.parameterization import Parameterized

GPy/likelihoods/poisson.py

@@ -5,8 +5,8 @@ from __future__ import division
 import numpy as np
 from scipy import stats,special
 import scipy as sp
-import link_functions
-from likelihood import Likelihood
+from . import link_functions
+from .likelihood import Likelihood
 
 class Poisson(Likelihood):
     """

GPy/likelihoods/student_t.py

@@ -4,10 +4,10 @@
 import numpy as np
 from scipy import stats, special
 import scipy as sp
-import link_functions
+from . import link_functions
 from scipy import stats, integrate
 from scipy.special import gammaln, gamma
-from likelihood import Likelihood
+from .likelihood import Likelihood
 from ..core.parameterization import Param
 from ..core.parameterization.transformations import Logexp
 

GPy/util/choleskies.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 from scipy import weave
-import linalg
+from . import linalg
 
 
 def safe_root(N):

GPy/util/datasets.py

@@ -11,7 +11,7 @@ import datetime
 import json
 import re
 
-from config import *
+from .config import *
 
 ipython_available=True
 try:

GPy/util/linalg.py

@@ -13,7 +13,7 @@ from ctypes import byref, c_char, c_int, c_double # TODO
 import scipy
 import warnings
 import os
-from config import config
+from .config import config
 import logging
 
 _scipyversion = np.float64((scipy.__version__).split('.')[:2])

GPy/util/ln_diff_erfs.py

@@ -6,7 +6,7 @@ try:
     from scipy.special import erfcx, erf
 except ImportError:
     from scipy.special import erf
-    from erfcx import erfcx
+    from .erfcx import erfcx
 
 import numpy as np
 

GPy/util/misc.py

@@ -2,7 +2,7 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
-from config import *
+from .config import *
 
 def chain_1(df_dg, dg_dx):
     """