numpy.random.choice is always available from numpy 1.7 onwards

theano/tensor/raw_random.py

@@ -647,11 +647,6 @@ def choice(random_state, size=None, a=2, replace=True, p=None, ndim=None,
     If size is None, a scalar will be returned.
 
     """
-    # numpy.random.choice is only available for numpy versions >= 1.7
-    major, minor, _ = numpy.version.short_version.split('.')
-    if (int(major), int(minor)) < (1, 7):
-        raise ImportError('choice requires at NumPy version >= 1.7 '
-                          '(%s)' % numpy.__version__)
     a = tensor.as_tensor_variable(a)
     if isinstance(replace, bool):
         replace = tensor.constant(replace, dtype='int8')

theano/tensor/tests/test_raw_random.py

@@ -459,12 +459,6 @@ class T_random_function(utt.InferShapeTester):
     def test_choice(self):
         """Test that raw_random.choice generates the same
         results as numpy."""
-        # numpy.random.choice is only available for numpy versions >= 1.7
-        major, minor, _ = numpy.version.short_version.split('.')
-        if (int(major), int(minor)) < (1, 7):
-            raise utt.SkipTest('choice requires at NumPy version >= 1.7 '
-                               '(%s)' % numpy.__version__)
-        
         # Check over two calls to see if the random state is correctly updated.
         rng_R = random_state_type()
         # Use non-default parameters, and larger dimensions because of

theano/tensor/tests/test_shared_randomstreams.py

@@ -190,12 +190,6 @@ class T_SharedRandomStreams(unittest.TestCase):
     
     def test_choice(self):
         """Test that RandomStreams.choice generates the same results as numpy"""
-        # numpy.random.choice is only available for numpy versions >= 1.7
-        major, minor, _ = numpy.version.short_version.split('.')
-        if (int(major), int(minor)) < (1, 7):
-            raise utt.SkipTest('choice requires at NumPy version >= 1.7 '
-                               '(%s)' % numpy.__version__)
-        
         # Check over two calls to see if the random state is correctly updated.
         random = RandomStreams(utt.fetch_seed())
         fn = function([], random.choice((11, 8), 10, 1, 0))