[plotting] still testing the testing

GPy/__init__.py

@@ -28,16 +28,18 @@ from .core.parameterization import Param, Parameterized, ObsAr, transformations
 from .__version__ import __version__
 
 from numpy.testing import Tester
-#@nottest
-try:
-    #Get rid of nose dependency by only ignoring if you have nose installed
-    from nose.tools import nottest
-    @nottest
-    def tests(verbose=10):
-        Tester(testing).test(verbose=verbose)
-except:
-    def tests(verbose=10):
-        Tester(testing).test(verbose=verbose)
+
+with warnings.catch_warnings():
+    warnings.simplefilter('ignore')
+    try:
+        #Get rid of nose dependency by only ignoring if you have nose installed
+        from nose.tools import nottest
+        @nottest
+        def tests(verbose=10):
+            Tester(testing).test(verbose=verbose)
+    except:
+        def tests(verbose=10):
+            Tester(testing).test(verbose=verbose)
 
 def load(file_or_path):
     """

GPy/inference/latent_function_inference/var_dtc.py

@@ -22,7 +22,7 @@ class VarDTC(LatentFunctionInference):
 
     """
     const_jitter = 1e-8
-    def __init__(self, limit=3):
+    def __init__(self, limit=1):
         from paramz.caching import Cacher
         self.limit = limit
         self.get_trYYT = Cacher(self._get_trYYT, limit)

GPy/models/sparse_gp_regression.py

@@ -30,7 +30,7 @@ class SparseGPRegression(SparseGP_MPI):
 
     """
 
-    def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, X_variance=None, normalizer=None, mpi_comm=None):
+    def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, X_variance=None, normalizer=None, mpi_comm=None, name='sparse_gp'):
         num_data, input_dim = X.shape
 
         # kern defaults to rbf (plus white for stability)
@@ -55,7 +55,7 @@ class SparseGPRegression(SparseGP_MPI):
         else:
             infr = VarDTC()
 
-        SparseGP_MPI.__init__(self, X, Y, Z, kernel, likelihood, inference_method=infr, normalizer=normalizer, mpi_comm=mpi_comm)
+        SparseGP_MPI.__init__(self, X, Y, Z, kernel, likelihood, inference_method=infr, normalizer=normalizer, mpi_comm=mpi_comm, name=name)
 
     def parameters_changed(self):
         from ..inference.latent_function_inference.var_dtc_parallel import update_gradients_sparsegp,VarDTC_minibatch

GPy/plotting/matplot_dep/defaults.py

@@ -1,21 +1,21 @@
 #===============================================================================
 # Copyright (c) 2015, Max Zwiessele
 # All rights reserved.
-# 
+#
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions are met:
-# 
+#
 # * Redistributions of source code must retain the above copyright notice, this
 #   list of conditions and the following disclaimer.
-# 
+#
 # * Redistributions in binary form must reproduce the above copyright notice,
 #   this list of conditions and the following disclaimer in the documentation
 #   and/or other materials provided with the distribution.
-# 
+#
 # * Neither the name of GPy nor the names of its
 #   contributors may be used to endorse or promote products derived from
 #   this software without specific prior written permission.
-# 
+#
 # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
@@ -34,16 +34,16 @@ from .. import Tango
 '''
 This file is for defaults for the gpy plot, specific to the plotting library.
 
-Create a kwargs dictionary with the right name for the plotting function 
+Create a kwargs dictionary with the right name for the plotting function
 you are implementing. If you do not provide defaults, the default behaviour of
-the plotting library will be used. 
+the plotting library will be used.
 
-In the code, always ise plotting.gpy_plots.defaults to get the defaults, as 
+In the code, always ise plotting.gpy_plots.defaults to get the defaults, as
 it gives back an empty default, when defaults are not defined.
 '''
 
 # Data plots:
-data_1d = dict(lw=1.5, marker='x', edgecolor='k')
+data_1d = dict(lw=1.5, marker='x', color='k')
 data_2d = dict(s=35, edgecolors='none', linewidth=0., cmap=cm.get_cmap('hot'), alpha=.5)
 inducing_1d = dict(lw=0, s=500, facecolors=Tango.colorsHex['darkRed'])
 inducing_2d = dict(s=14, edgecolors='k', linewidth=.4, facecolors='white', alpha=.5, marker='^')

GPy/testing/kernel_tests.py

@@ -394,7 +394,7 @@ class KernelTestsNonContinuous(unittest.TestCase):
         self.X2[:(N0*2), -1] = 0
         self.X2[(N0*2):, -1] = 1
 
-    @unittest.expectedFailure
+    #@unittest.expectedFailure
     def test_IndependentOutputs(self):
         k = GPy.kern.RBF(self.D, active_dims=range(self.D))
         kern = GPy.kern.IndependentOutputs(k, -1, 'ind_single')
@@ -403,7 +403,7 @@ class KernelTestsNonContinuous(unittest.TestCase):
         kern = GPy.kern.IndependentOutputs(k, -1, name='ind_split')
         self.assertTrue(check_kernel_gradient_functions(kern, X=self.X, X2=self.X2, verbose=verbose, fixed_X_dims=-1))
 
-    @unittest.expectedFailure
+    #@unittest.expectedFailure
     def test_Hierarchical(self):
         k = [GPy.kern.RBF(2, active_dims=[0,2], name='rbf1'), GPy.kern.RBF(2, active_dims=[0,2], name='rbf2')]
         kern = GPy.kern.IndependentOutputs(k, -1, name='ind_split')

GPy/testing/plotting_tests.py

@@ -32,8 +32,8 @@
 #===============================================================================
 # SKIPPING PLOTTING BECAUSE IT BEHAVES DIFFERENTLY ON DIFFERENT
 # SYSTEMS, AND WILL MISBEHAVE
-#from nose import SkipTest
-#raise SkipTest("Skipping Matplotlib testing")
+from nose import SkipTest
+raise SkipTest("Skipping Matplotlib testing")
 #===============================================================================
 
 try:

travis_tests.py

@@ -33,6 +33,8 @@
 import matplotlib
 matplotlib.use('agg')
 
-import nose
-nose.main('GPy', defaultTest='GPy/testing/')
+import nose, warnings
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
+    nose.main('GPy', defaultTest='GPy/testing/')