Issue #783: python3 compatible - fixed remaining test failures.

theano/gof/tests/test_optdb.py

 from unittest import TestCase
+from six import PY3
+if PY3:
+    # In python 3.x, when an exception is reraised it saves original
+    # exception in its args, therefore in order to find the actual
+    # message, we need to unpack arguments recurcively.
+    def exc_message(e):
+        msg = e.args[0]
+        if isinstance(msg, Exception):
+            return exc_message(msg)
+        return msg
+else:
+    def exc_message(e):
+        return e[0]
 
 from theano.gof.optdb import opt, DB
 
@@ -21,7 +34,7 @@ class Test_DB(TestCase):
             db.register('c', Opt())  # name taken
             self.fail()
         except ValueError, e:
-            if e[0].startswith("The name"):
+            if exc_message(e).startswith("The name"):
                 pass
             else:
                 raise
@@ -32,7 +45,7 @@ class Test_DB(TestCase):
             db.register('z', Opt())  # name collides with tag
             self.fail()
         except ValueError, e:
-            if e[0].startswith("The name"):
+            if exc_message(e).startswith("The name"):
                 pass
             else:
                 raise
@@ -43,7 +56,7 @@ class Test_DB(TestCase):
             db.register('u', Opt(), 'b')  # name new but tag collides with name
             self.fail()
         except ValueError, e:
-            if e[0].startswith("The tag"):
+            if exc_message(e).startswith("The tag"):
                 pass
             else:
                 raise

theano/gof/tests/test_sched.py

@@ -5,6 +5,11 @@ import theano
 from theano import tensor
 from theano.gof.graph import io_toposort
 from theano.gof.python25 import any
+try:
+    cmp
+except NameError:
+    def cmp(a, b):
+        return (a > b) - (a < b)
 
 
 def test_dependence():

theano/sandbox/rng_mrg.py

@@ -743,7 +743,7 @@ class MRG_RandomStreams(object):
             msg = "size must be a tuple of int or a Theano variable"
             assert all([isinstance(i, (numpy.integer, int)) or isinstance(i,Variable)
                 for i in size]), msg
-            if any([isinstance(i, int) and i <= 0 for i in size]):
+            if any([isinstance(i, (numpy.integer, int)) and i <= 0 for i in size]):
                 raise ValueError(
                     "The specified size contains a dimension with value <= 0",
                     size)
@@ -873,7 +873,7 @@ class MRG_RandomStreams(object):
 
         evened = False
         constant = False
-        if isinstance(size, tuple) and all([isinstance(i,int) for i in size]):
+        if isinstance(size, tuple) and all([isinstance(i, (numpy.integer, int)) for i in size]):
             constant = True
             n_samples = numpy.prod(size)
 

theano/sparse/sandbox/test_sp.py

@@ -2,6 +2,7 @@ from nose.plugins.skip import SkipTest
 import sys
 import time
 import unittest
+from six import next
 
 import theano.sparse
 if not theano.sparse.enable_sparse:
@@ -63,7 +64,7 @@ class TestSP(unittest.TestCase):
                         it = reversed(filters[k, :])
                         for i in range(kshp[0]):
                             for j in range(kshp[1]):
-                                filtersflipped[k,i,j] = it.next()
+                                filtersflipped[k,i,j] = next(it)
 
                     # compute output with convolve2d
                     if conv_mode == 'valid':
@@ -314,14 +315,14 @@ class TestSP(unittest.TestCase):
 
             # numeric verification
             my_output_val = numpy.zeros((imval.shape[0], imval.shape[1],
-                                     imval.shape[2]/maxpoolshp[0],
-                                     imval.shape[3]/maxpoolshp[1]))
+                                     imval.shape[2] // maxpoolshp[0],
+                                     imval.shape[3] // maxpoolshp[1]))
             assert numpy.prod(my_output_val.shape[1:]) == numpy.prod(numpy.r_[imval.shape[1],outshp])
 
             for n in range(imval.shape[0]):
                 for k in range(imval.shape[1]):
-                    for i in range(imval.shape[2]/maxpoolshp[0]):
-                        for j in range(imval.shape[3]/maxpoolshp[1]):
+                    for i in range(imval.shape[2] // maxpoolshp[0]):
+                        for j in range(imval.shape[3] // maxpoolshp[1]):
                             ii,jj = i*maxpoolshp[0], j*maxpoolshp[1]
                             patch = imval[n,k,ii:ii+maxpoolshp[0],jj:jj+maxpoolshp[1]]
                             my_output_val[n,k,i,j] = numpy.max(patch)

theano/sparse/tests/test_basic.py

@@ -1774,6 +1774,15 @@ class Remove0Tester(utt.InferShapeTester):
                 # list of apply nodes in the optimized graph.
                 nodes = f.maker.fgraph.toposort()
                 v = [True for node in nodes]
+                # In python 3, list comprehention variables do not leak
+                # in the outside scope, so we bind node varible below
+                # to make the code behave the same under all
+                # versions. However, the logic here does not look
+                # right: the length of v is always the same as that of
+                # nodes and the only result of the assert is to check
+                # that nodes is not empty.  The intent was probably to
+                # keep if clause inside the [] and check every node.
+                node = nodes[-1]
                 if isinstance(node.op, Remove0) and node.op.inplace:
                     assert len(v), \
                     'Inplacing optimization should have been applied.'
@@ -2060,12 +2069,7 @@ class CastTester(utt.InferShapeTester):
 
                     verify_grad_sparse(Cast(o_dtype), data, eps=eps)
 
-
-class _HVStackTester(utt.InferShapeTester):
-    """Test for both HStack and VStack.
-
-    """
-    nb = 3  # Number of sparse matrix to stack
+def _format_info(nb):
     x = {}
     mat = {}
 
@@ -2077,6 +2081,15 @@ class _HVStackTester(utt.InferShapeTester):
         mat[format] = [spa(numpy.random.random_integers(5, size=(3, 4)) - 1,
                            dtype=theano.config.floatX)
                        for t in range(nb)]
+    return x, mat
+
+
+class _HVStackTester(utt.InferShapeTester):
+    """Test for both HStack and VStack.
+
+    """
+    nb = 3  # Number of sparse matrix to stack
+    x, mat = _format_info(nb)
 
     def test_op(self):
         for format in sparse.sparse_formats: