Exception fixes for Python 3 compat

GPy/core/mapping.py

@@ -60,7 +60,7 @@ class Mapping(Parameterized):
             from ..plotting.matplot_dep import models_plots
             mapping_plots.plot_mapping(self,*args)
         else:
-            raise NameError, "matplotlib package has not been imported."
+            raise NameError("matplotlib package has not been imported.")
 
 class Bijective_mapping(Mapping):
     """
@@ -111,7 +111,7 @@ class Mapping_check_model(Model):
         return (self.dL_df*self.mapping.f(self.X)).sum()
 
     def _log_likelihood_gradients(self):
-        raise NotImplementedError, "This needs to be implemented to use the Mapping_check_model class."
+        raise NotImplementedError("This needs to be implemented to use the Mapping_check_model class.")
 
 class Mapping_check_df_dtheta(Mapping_check_model):
     """This class allows gradient checks for the gradient of a mapping with respect to parameters. """

GPy/core/model.py

@@ -30,7 +30,7 @@ class Model(Parameterized):
         self.add_observer(self.tie, self.tie._parameters_changed_notification, priority=-500)
 
     def log_likelihood(self):
-        raise NotImplementedError, "this needs to be implemented to use the model class"
+        raise NotImplementedError("this needs to be implemented to use the model class")
     def _log_likelihood_gradients(self):
         return self.gradient.copy()
 
@@ -119,7 +119,7 @@ class Model(Parameterized):
 
         DEPRECATED.
         """
-        raise DeprecationWarning, 'parameters now have default constraints'
+        raise DeprecationWarning('parameters now have default constraints')
 
     def objective_function(self):
         """

GPy/core/parameterization/lists_and_dicts.py

@@ -32,7 +32,7 @@ class ArrayList(list):
             if el is item:
                 return index
             index += 1
-        raise ValueError, "{} is not in list".format(item)
+        raise ValueError("{} is not in list".format(item))
     pass
 
 class ObserverList(object):

GPy/core/parameterization/parameter_core.py

@@ -36,7 +36,7 @@ def adjust_name_for_printing(name):
         name = name.replace("/", "_l_").replace("@", '_at_')
         name = name.replace("(", "_of_").replace(")", "")
         if re.match(r'^[a-zA-Z_][a-zA-Z0-9-_]*$', name) is None:
-            raise NameError, "name {} converted to {} cannot be further converted to valid python variable name!".format(name2, name)
+            raise NameError("name {} converted to {} cannot be further converted to valid python variable name!".format(name2, name))
         return name
     return ''
 
@@ -65,13 +65,13 @@ class Parentable(object):
         Gets called, when the parent changed, so we can adjust our
         inner attributes according to the new parent.
         """
-        raise NotImplementedError, "shouldnt happen, Parentable objects need to be able to change their parent"
+        raise NotImplementedError("shouldnt happen, Parentable objects need to be able to change their parent")
 
     def _disconnect_parent(self, *args, **kw):
         """
         Disconnect this object from its parent
         """
-        raise NotImplementedError, "Abstract superclass"
+        raise NotImplementedError("Abstract superclass")
 
     @property
     def _highest_parent_(self):
@@ -214,7 +214,7 @@ class Gradcheckable(Pickleable, Parentable):
         Perform the checkgrad on the model.
         TODO: this can be done more efficiently, when doing it inside here
         """
-        raise HierarchyError, "This parameter is not in a model with a likelihood, and, therefore, cannot be gradient checked!"
+        raise HierarchyError("This parameter is not in a model with a likelihood, and, therefore, cannot be gradient checked!")
 
 class Nameable(Gradcheckable):
     """
@@ -652,10 +652,10 @@ class OptimizationHandlable(Indexable):
         self.trigger_update()
 
     def _get_params_transformed(self):
-        raise DeprecationWarning, "_get|set_params{_optimizer_copy_transformed} is deprecated, use self.optimizer array insetad!"
+        raise DeprecationWarning("_get|set_params{_optimizer_copy_transformed} is deprecated, use self.optimizer array insetad!")
 #
     def _set_params_transformed(self, p):
-        raise DeprecationWarning, "_get|set_params{_optimizer_copy_transformed} is deprecated, use self.optimizer array insetad!"
+        raise DeprecationWarning("_get|set_params{_optimizer_copy_transformed} is deprecated, use self.optimizer array insetad!")
 
     def _trigger_params_changed(self, trigger_parent=True):
         """
@@ -701,7 +701,7 @@ class OptimizationHandlable(Indexable):
         Return the number of parameters of this parameter_handle.
         Param objects will always return 0.
         """
-        raise NotImplemented, "Abstract, please implement in respective classes"
+        raise NotImplemented("Abstract, please implement in respective classes")
 
     def parameter_names(self, add_self=False, adjust_for_printing=False, recursive=True):
         """

GPy/core/parameterization/parameterized.py

@@ -131,7 +131,7 @@ class Parameterized(Parameterizable):
             if param.has_parent():
                 def visit(parent, self):
                     if parent is self:
-                        raise HierarchyError, "You cannot add a parameter twice into the hierarchy"
+                        raise HierarchyError("You cannot add a parameter twice into the hierarchy")
                 param.traverse_parents(visit, self)
                 param._parent_.unlink_parameter(param)
             # make sure the size is set
@@ -173,7 +173,7 @@ class Parameterized(Parameterizable):
                 self._highest_parent_._connect_fixes()
 
         else:
-            raise HierarchyError, """Parameter exists already, try making a copy"""
+            raise HierarchyError("""Parameter exists already, try making a copy""")
 
 
     def link_parameters(self, *parameters):
@@ -189,9 +189,9 @@ class Parameterized(Parameterizable):
         """
         if not param in self.parameters:
             try:
-                raise RuntimeError, "{} does not belong to this object {}, remove parameters directly from their respective parents".format(param._short(), self.name)
+                raise RuntimeError("{} does not belong to this object {}, remove parameters directly from their respective parents".format(param._short(), self.name))
             except AttributeError:
-                raise RuntimeError, "{} does not seem to be a parameter, remove parameters directly from their respective parents".format(str(param))
+                raise RuntimeError("{} does not seem to be a parameter, remove parameters directly from their respective parents".format(str(param)))
 
         start = sum([p.size for p in self.parameters[:param._parent_index_]])
         self._remove_parameter_name(param)
@@ -215,9 +215,9 @@ class Parameterized(Parameterizable):
         self._highest_parent_._notify_parent_change()
 
     def add_parameter(self, *args, **kwargs):
-        raise DeprecationWarning, "add_parameter was renamed to link_parameter to avoid confusion of setting variables, use link_parameter instead"
+        raise DeprecationWarning("add_parameter was renamed to link_parameter to avoid confusion of setting variables, use link_parameter instead")
     def remove_parameter(self, *args, **kwargs):
-        raise DeprecationWarning, "remove_parameter was renamed to unlink_parameter to avoid confusion of setting variables, use unlink_parameter instead"
+        raise DeprecationWarning("remove_parameter was renamed to unlink_parameter to avoid confusion of setting variables, use unlink_parameter instead")
 
     def _connect_parameters(self, ignore_added_names=False):
         # connect parameterlist to this parameterized object
@@ -237,7 +237,7 @@ class Parameterized(Parameterizable):
         self._param_slices_ = []
         for i, p in enumerate(self.parameters):
             if not p.param_array.flags['C_CONTIGUOUS']:
-                raise ValueError, "This should not happen! Please write an email to the developers with the code, which reproduces this error. All parameter arrays must be C_CONTIGUOUS"
+                raise ValueError("This should not happen! Please write an email to the developers with the code, which reproduces this error. All parameter arrays must be C_CONTIGUOUS")
 
             p._parent_ = self
             p._parent_index_ = i
@@ -279,7 +279,7 @@ class Parameterized(Parameterizable):
         else:
             if paramlist is None:
                 paramlist = self.grep_param_names(name)
-            if len(paramlist) < 1: raise AttributeError, name
+            if len(paramlist) < 1: raise AttributeError(name)
             if len(paramlist) == 1:
                 if isinstance(paramlist[-1], Parameterized):
                     paramlist = paramlist[-1].flattened_parameters
@@ -295,7 +295,7 @@ class Parameterized(Parameterizable):
             try:
                 self.param_array[name] = value
             except:
-                raise ValueError, "Setting by slice or index only allowed with array-like"
+                raise ValueError("Setting by slice or index only allowed with array-like")
             self.trigger_update()
         else:
             try: param = self.__getitem__(name, paramlist)

GPy/core/parameterization/variational.py

@@ -16,13 +16,13 @@ class VariationalPrior(Parameterized):
         super(VariationalPrior, self).__init__(name=name, **kw)
 
     def KL_divergence(self, variational_posterior):
-        raise NotImplementedError, "override this for variational inference of latent space"
+        raise NotImplementedError("override this for variational inference of latent space")
 
     def update_gradients_KL(self, variational_posterior):
         """
         updates the gradients for mean and variance **in place**
         """
-        raise NotImplementedError, "override this for variational inference of latent space"
+        raise NotImplementedError("override this for variational inference of latent space")
 
 class NormalPrior(VariationalPrior):
     def KL_divergence(self, variational_posterior):

GPy/core/sparse_gp.py

@@ -48,7 +48,7 @@ class SparseGP(GP):
                 inference_method = var_dtc.VarDTC(limit=1 if not self.missing_data else Y.shape[1])
             else:
                 #inference_method = ??
-                raise NotImplementedError, "what to do what to do?"
+                raise NotImplementedError("what to do what to do?")
             print("defaulting to ", inference_method, "for latent function inference")
 
         self.Z = Param('inducing inputs', Z)

GPy/core/symbolic.py

@@ -223,7 +223,7 @@ class Symbolic_core():
 
     def code_gradients_cacheable(self, function, variable):
         if variable not in self.cacheable:
-            raise RuntimeError, variable + ' must be a cacheable.'
+            raise RuntimeError(variable + ' must be a cacheable.')
         lcode = 'gradients_' + variable + ' = np.zeros_like(' + variable + ')\n'
         lcode += 'self.update_cache(' + ', '.join(self.cacheable) + ')\n'
         for i, theta in enumerate(self.variables[variable]):