[util] tests for util/debug.py

GPy/core/parameterization/__init__.py

@@ -3,7 +3,7 @@
 
 from .param import Param
 from .parameterized import Parameterized
-from paramz import transformations
+from . import transformations
 
 from paramz.core import lists_and_dicts, index_operations, observable_array, observable
 from paramz import ties_and_remappings, ObsAr

GPy/testing/util_tests.py

@@ -0,0 +1,49 @@
+#===============================================================================
+# Copyright (c) 2016, 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.testing.util_tests 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
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#===============================================================================
+
+import unittest, numpy as np
+
+class TestDebug(unittest.TestCase):
+    def test_checkFinite(self):
+        from GPy.util.debug import checkFinite
+        array = np.random.normal(0, 1, 100).reshape(25,4)
+        self.assertTrue(checkFinite(array, name='test'))
+        
+        array[np.random.binomial(1, .3, array.shape).astype(bool)] = np.nan
+        self.assertFalse(checkFinite(array))
+
+    def test_checkFullRank(self):
+        from GPy.util.debug import checkFullRank
+        from GPy.util.linalg import tdot
+        array = np.random.normal(0, 1, 100).reshape(25,4)
+        self.assertFalse(checkFullRank(tdot(array), name='test'))
+        
+        array = np.random.normal(0, 1, (25,25))
+        self.assertTrue(checkFullRank(tdot(array)))

GPy/util/debug.py

@@ -22,7 +22,7 @@ def checkFullRank(m, tol=1e-10, name=None, force_check=False):
         name = 'Matrix with ID['+str(id(m))+']'
     assert len(m.shape)==2 and m.shape[0]==m.shape[1], 'The input of checkFullRank has to be a square matrix!'
 
-    if not force_check and m.shape[0]>=10000:
+    if not force_check and m.shape[0]>=10000: # pragma: no cover
         print('The size of '+name+'is too big to check (>=10000)!')
         return True
 

GPy/util/functions.py

@@ -1,27 +1,31 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 import numpy as np
-from scipy.special import erf, erfc, erfcx
+from scipy import special
+from scipy.special import erfcx
 import sys
 epsilon = sys.float_info.epsilon
 lim_val = -np.log(epsilon) 
 
-def logisticln(x):
+def logisticln(x): # pragma: no cover
     return np.where(x<lim_val, np.where(x>-lim_val, -np.log(1+np.exp(-x)), -x), -np.log(1+epsilon))
 
-def logistic(x):
-    return np.where(x<lim_val, np.where(x>-lim_val, 1/(1+np.exp(-x)), epsilon/(epsilon+1)), 1/(1+epsilon))
+def logistic(x): # pragma: no cover
+    return special.expit(x)
+    #return np.where(x<lim_val, np.where(x>-lim_val, 1/(1+np.exp(-x)), epsilon/(epsilon+1)), 1/(1+epsilon))
 
-def normcdf(x):
-    g=0.5*erfc(-x/np.sqrt(2))
-    return np.where(g==0, epsilon, np.where(g==1, 1-epsilon, g)) 
+def normcdf(x): # pragma: no cover
+    return special.ndtr(x)
+    #g=0.5*erfc(-x/np.sqrt(2))
+    #return np.where(g==0, epsilon, np.where(g==1, 1-epsilon, g)) 
 
-def normcdfln(x):
-    return np.where(x < 0, -.5*x*x + np.log(.5) + np.log(erfcx(-x/np.sqrt(2))), np.log(normcdf(x)))
+def normcdfln(x): # pragma: no cover
+    return special.log_ndtr(x)
+    #return np.where(x < 0, -.5*x*x + np.log(.5) + np.log(erfcx(-x/np.sqrt(2))), np.log(normcdf(x)))
 
-def clip_exp(x):
+def clip_exp(x): # pragma: no cover
     return np.where(x<lim_val, np.where(x>-lim_val, np.exp(x), epsilon), 1/epsilon)
 
-def differfln(x0, x1):
+def differfln(x0, x1): # pragma: no cover
     # this is a, hopefully!, a numerically more stable variant of log(erf(x0)-erf(x1)) = log(erfc(x1)-erfc(x0)).
     return np.where(x0>x1, -x1*x1 + np.log(erfcx(x1)-np.exp(-x0**2+x1**2)*erfcx(x0)), -x0*x0 + np.log(np.exp(-x1**2+x0**2)*erfcx(x1) - erfcx(x0)))