Merge pull request #5454 from bscellier/import_numpy_gof

theano/gof/cc.py

@@ -11,7 +11,7 @@ import os
 import sys
 import logging
 
-import numpy
+import numpy as np
 
 import theano
 from theano import config
@@ -1347,7 +1347,7 @@ class CLinker(link.Linker):
         # We must always add the numpy ABI version here as
         # DynamicModule always add the include <numpy/arrayobject.h>
         sig.append('NPY_ABI_VERSION=0x%X' %
-                   numpy.core.multiarray._get_ndarray_c_version())
+                   np.core.multiarray._get_ndarray_c_version())
         if c_compiler:
             sig.append('c_compiler_str=' + c_compiler.version_str())
 

theano/gof/cmodule.py

@@ -20,7 +20,7 @@ import platform
 import distutils.sysconfig
 import warnings
 
-import numpy.distutils  # TODO: TensorType should handle this
+import numpy as np  # TODO: TensorType should handle nunpy.distutils
 
 import theano
 from theano.compat import PY3, decode, decode_iter
@@ -1578,7 +1578,7 @@ def get_gcc_shared_library_arg():
 
 
 def std_include_dirs():
-    numpy_inc_dirs = numpy.distutils.misc_util.get_numpy_include_dirs()
+    numpy_inc_dirs = np.distutils.misc_util.get_numpy_include_dirs()
     py_inc = distutils.sysconfig.get_python_inc()
     py_plat_spec_inc = distutils.sysconfig.get_python_inc(plat_specific=True)
     python_inc_dirs = ([py_inc] if py_inc == py_plat_spec_inc

theano/gof/compiledir.py

@@ -4,7 +4,7 @@ import logging
 import os
 import shutil
 
-import numpy
+import numpy as np
 
 import theano
 from six import string_types, iteritems
@@ -42,7 +42,7 @@ def cleanup():
                         have_npy_abi_version = False
                         have_c_compiler = False
                         for obj in flatten(key):
-                            if isinstance(obj, numpy.ndarray):
+                            if isinstance(obj, np.ndarray):
                                 # Reuse have_npy_abi_version to
                                 # force the removing of key
                                 have_npy_abi_version = False

theano/gof/graph.py

@@ -481,12 +481,12 @@ class Variable(Node):
         Examples
         --------
 
-        >>> import numpy
+        >>> import numpy as np
         >>> import theano.tensor as T
         >>> x = T.dscalar('x')
         >>> y = T.dscalar('y')
         >>> z = x + y
-        >>> numpy.allclose(z.eval({x : 16.3, y : 12.1}), 28.4)
+        >>> np.allclose(z.eval({x : 16.3, y : 12.1}), 28.4)
         True
 
         We passed :func:`eval` a dictionary mapping symbolic theano

theano/gof/link.py

@@ -7,7 +7,7 @@ from copy import copy, deepcopy
 from sys import getsizeof
 import sys
 import traceback
-import numpy
+import numpy as np
 
 import theano
 from theano.compat import izip
@@ -236,11 +236,11 @@ def raise_with_op(node, thunk=None, exc_info=None, storage_map=None):
             # storage_map_item[3]: bytes
             if hasattr(storage_map[k][0], 'dtype'):
                 dtype = storage_map[k][0].dtype
-                storage_map_item.append(numpy.dtype(dtype).itemsize)
+                storage_map_item.append(np.dtype(dtype).itemsize)
                 if shapeinfo is None:
                     storage_map_item.append(-1)
                 else:
-                    sz = numpy.dtype(dtype).itemsize * numpy.prod(shapeinfo)
+                    sz = np.dtype(dtype).itemsize * np.prod(shapeinfo)
                     storage_map_item.append(sz)
                     total_size += sz
                     if not k.owner:

theano/gof/op.py

@@ -9,7 +9,7 @@ from __future__ import absolute_import, print_function, division
 
 import inspect
 import logging
-import numpy
+import numpy as np
 import os
 import re
 import sys
@@ -1430,7 +1430,7 @@ class COp(Op):
                     (macro_name, macro_value))
                 undef_macros.append(undef_template % macro_name)
 
-                d = numpy.dtype(v.dtype)
+                d = np.dtype(v.dtype)
 
                 macro_name = "TYPENUM_" + vname
                 macro_value = d.num

theano/gof/opt.py

@@ -15,7 +15,7 @@ import time
 import warnings
 import traceback
 
-import numpy
+import numpy as np
 
 import theano
 from theano import config
@@ -1695,8 +1695,7 @@ class PatternSub(LocalOptimizer):
                     u = u.merge(expr, v)
             elif (isinstance(pattern, (integer_types, float)) and
                     isinstance(expr, graph.Constant)):
-                if numpy.all(
-                        theano.tensor.constant(pattern).value == expr.value):
+                if np.all(theano.tensor.constant(pattern).value == expr.value):
                     return u
                 else:
                     return retry_with_equiv()

theano/gof/tests/test_cc.py

@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
 
 from nose.plugins.skip import SkipTest
 
-import numpy
+import numpy as np
 
 import theano
 from theano.gof.link import PerformLinker
@@ -211,16 +211,16 @@ def test_clinker_literal_cache():
     A = theano.tensor.matrix()
     input1 = theano.tensor.vector()
 
-    normal_svd = numpy.array([[5.936276e+01, -4.664007e-07, -2.56265e-06],
-                              [-4.664007e-07, 9.468691e-01, -3.18862e-02],
-                              [-2.562651e-06, -3.188625e-02, 1.05226e+00]],
-                             dtype=theano.config.floatX)
+    normal_svd = np.array([[5.936276e+01, -4.664007e-07, -2.56265e-06],
+                           [-4.664007e-07, 9.468691e-01, -3.18862e-02],
+                           [-2.562651e-06, -3.188625e-02, 1.05226e+00]],
+                          dtype=theano.config.floatX)
 
-    orientationi = numpy.array([59.36276866, 1.06116353, 0.93797339],
-                               dtype=theano.config.floatX)
+    orientationi = np.array([59.36276866, 1.06116353, 0.93797339],
+                            dtype=theano.config.floatX)
 
-    for out1 in [A - input1[0] * numpy.identity(3),
-                 input1[0] * numpy.identity(3)]:
+    for out1 in [A - input1[0] * np.identity(3),
+                 input1[0] * np.identity(3)]:
         benchmark = theano.function(
             inputs=[A, input1],
             outputs=[out1],
@@ -421,7 +421,7 @@ def test_shared_input_output():
     g0 = g(0)
     assert f0 == g0 == 5, (f0, g0)
 
-    vstate = theano.shared(numpy.zeros(3, dtype='int32'))
+    vstate = theano.shared(np.zeros(3, dtype='int32'))
     vstate.name = 'vstate'
     fv = theano.function([inc], vstate, updates=[(vstate, vstate + inc)],
                          mode=mode)
@@ -430,21 +430,21 @@ def test_shared_input_output():
     # Initial value
     fv0 = fv(0)
     gv0 = gv(0)
-    assert numpy.all(fv0 == 0), fv0
-    assert numpy.all(gv0 == 0), gv0
+    assert np.all(fv0 == 0), fv0
+    assert np.all(gv0 == 0), gv0
 
     # Increment state via f, returns the previous value.
     fv2 = fv(2)
-    assert numpy.all(fv2 == fv0), (fv2, fv0)
+    assert np.all(fv2 == fv0), (fv2, fv0)
     fv0 = fv(0)
     gv0 = gv(0)
-    assert numpy.all(fv0 == 2), fv0
-    assert numpy.all(gv0 == 2), gv0
+    assert np.all(fv0 == 2), fv0
+    assert np.all(gv0 == 2), gv0
 
     # Increment state via g, returns the previous value
     gv3 = gv(3)
-    assert numpy.all(gv3 == gv0), (gv3, gv0)
+    assert np.all(gv3 == gv0), (gv3, gv0)
     fv0 = fv(0)
     gv0 = gv(0)
-    assert numpy.all(fv0 == 5), fv0
-    assert numpy.all(gv0 == 5), gv0
+    assert np.all(fv0 == 5), fv0
+    assert np.all(gv0 == 5), gv0

theano/gof/tests/test_cmodule.py

@@ -6,7 +6,7 @@ deterministic based on the input type and the op.
 """
 from __future__ import absolute_import, print_function, division
 
-import numpy
+import numpy as np
 
 import theano
 from theano.gof.cmodule import GCC_compiler
@@ -26,7 +26,7 @@ class MyOp(theano.compile.ops.DeepCopyOp):
         itype = node.inputs[0].type.__class__
         if itype in self.c_code_and_version:
             code, version = self.c_code_and_version[itype]
-            rand = numpy.random.rand()
+            rand = np.random.rand()
             return ("""printf("%(rand)s\\n");""" + code) % locals()
         # Else, no C code
         return super(theano.compile.ops.DeepCopyOp, self).c_code(
@@ -47,7 +47,7 @@ def test_inter_process_cache():
 
     x, y = theano.tensor.dvectors('xy')
     f = theano.function([x, y], [MyOp()(x), MyOp()(y)])
-    f(numpy.arange(60), numpy.arange(60))
+    f(np.arange(60), np.arange(60))
     if theano.config.mode == 'FAST_COMPILE' or theano.config.cxx == "":
         assert MyOp.nb_called == 0
     else:
@@ -56,7 +56,7 @@ def test_inter_process_cache():
     # What if we compile a new function with new variables?
     x, y = theano.tensor.dvectors('xy')
     f = theano.function([x, y], [MyOp()(x), MyOp()(y)])
-    f(numpy.arange(60), numpy.arange(60))
+    f(np.arange(60), np.arange(60))
     if theano.config.mode == 'FAST_COMPILE' or theano.config.cxx == "":
         assert MyOp.nb_called == 0
     else:

theano/gof/tests/test_compute_test_value.py

@@ -4,7 +4,7 @@ import sys
 import traceback
 import warnings
 
-import numpy
+import numpy as np
 from nose.plugins.skip import SkipTest
 import unittest
 
@@ -44,9 +44,9 @@ class TestComputeTestValue(unittest.TestCase):
             theano.config.compute_test_value = 'raise'
 
             x = T.matrix('x')
-            x.tag.test_value = numpy.random.rand(3, 4).astype(config.floatX)
+            x.tag.test_value = np.random.rand(3, 4).astype(config.floatX)
             y = T.matrix('y')
-            y.tag.test_value = numpy.random.rand(4, 5).astype(config.floatX)
+            y.tag.test_value = np.random.rand(4, 5).astype(config.floatX)
 
             # should work
             z = T.dot(x, y)
@@ -56,7 +56,7 @@ class TestComputeTestValue(unittest.TestCase):
                              z.tag.test_value)
 
             # this test should fail
-            y.tag.test_value = numpy.random.rand(6, 5).astype(config.floatX)
+            y.tag.test_value = np.random.rand(6, 5).astype(config.floatX)
             self.assertRaises(ValueError, T.dot, x, y)
         finally:
             theano.config.compute_test_value = orig_compute_test_value
@@ -66,7 +66,7 @@ class TestComputeTestValue(unittest.TestCase):
         try:
             x = T.matrix('x')
             y = T.matrix('y')
-            y.tag.test_value = numpy.random.rand(4, 5).astype(config.floatX)
+            y.tag.test_value = np.random.rand(4, 5).astype(config.floatX)
 
             # should skip computation of test value
             theano.config.compute_test_value = 'off'
@@ -96,11 +96,11 @@ class TestComputeTestValue(unittest.TestCase):
             theano.config.compute_test_value = 'raise'
 
             x = T.matrix('x')
-            x.tag.test_value = numpy.random.rand(3, 4).astype(config.floatX)
+            x.tag.test_value = np.random.rand(3, 4).astype(config.floatX)
             y = T.matrix('y')
-            y.tag.test_value = numpy.random.rand(4, 5).astype(config.floatX)
+            y.tag.test_value = np.random.rand(4, 5).astype(config.floatX)
 
-            z = theano.shared(numpy.random.rand(5, 6).astype(config.floatX))
+            z = theano.shared(np.random.rand(5, 6).astype(config.floatX))
 
             # should work
             out = T.dot(T.dot(x, y), z)
@@ -114,7 +114,7 @@ class TestComputeTestValue(unittest.TestCase):
                 return T.dot(T.dot(x, y), z)
 
             # this test should fail
-            z.set_value(numpy.random.rand(7, 6).astype(config.floatX))
+            z.set_value(np.random.rand(7, 6).astype(config.floatX))
             self.assertRaises(ValueError, f, x, y, z)
         finally:
             theano.config.compute_test_value = orig_compute_test_value
@@ -125,8 +125,8 @@ class TestComputeTestValue(unittest.TestCase):
             theano.config.compute_test_value = 'raise'
 
             x = T.matrix('x')
-            x.tag.test_value = numpy.random.rand(3, 4).astype(config.floatX)
-            y = theano.shared(numpy.random.rand(4, 6).astype(config.floatX),
+            x.tag.test_value = np.random.rand(3, 4).astype(config.floatX)
+            y = theano.shared(np.random.rand(4, 6).astype(config.floatX),
                               'y')
 
             # should work
@@ -136,7 +136,7 @@ class TestComputeTestValue(unittest.TestCase):
             assert _allclose(f(x.tag.test_value), z.tag.test_value)
 
             # this test should fail
-            y.set_value(numpy.random.rand(5, 6).astype(config.floatX))
+            y.set_value(np.random.rand(5, 6).astype(config.floatX))
             self.assertRaises(ValueError, T.dot, x, y)
         finally:
             theano.config.compute_test_value = orig_compute_test_value
@@ -146,8 +146,8 @@ class TestComputeTestValue(unittest.TestCase):
         try:
             theano.config.compute_test_value = 'raise'
 
-            x = numpy.random.rand(2, 3).astype(config.floatX)
-            y = theano.shared(numpy.random.rand(3, 6).astype(config.floatX),
+            x = np.random.rand(2, 3).astype(config.floatX)
+            y = theano.shared(np.random.rand(3, 6).astype(config.floatX),
                               'y')
 
             # should work
@@ -157,7 +157,7 @@ class TestComputeTestValue(unittest.TestCase):
             assert _allclose(f(), z.tag.test_value)
 
             # this test should fail
-            x = numpy.random.rand(2, 4).astype(config.floatX)
+            x = np.random.rand(2, 4).astype(config.floatX)
             self.assertRaises(ValueError, T.dot, x, y)
         finally:
             theano.config.compute_test_value = orig_compute_test_value
@@ -167,7 +167,7 @@ class TestComputeTestValue(unittest.TestCase):
         try:
             theano.config.compute_test_value = 'raise'
 
-            x = theano.shared(numpy.random.rand(0, 6).astype(config.floatX),
+            x = theano.shared(np.random.rand(0, 6).astype(config.floatX),
                               'x')
 
             # should work
@@ -184,8 +184,8 @@ class TestComputeTestValue(unittest.TestCase):
         try:
             theano.config.compute_test_value = 'raise'
 
-            x = T.constant(numpy.random.rand(2, 3), dtype=config.floatX)
-            y = theano.shared(numpy.random.rand(3, 6).astype(config.floatX),
+            x = T.constant(np.random.rand(2, 3), dtype=config.floatX)
+            y = theano.shared(np.random.rand(3, 6).astype(config.floatX),
                               'y')
 
             # should work
@@ -195,7 +195,7 @@ class TestComputeTestValue(unittest.TestCase):
             assert _allclose(f(), z.tag.test_value)
 
             # this test should fail
-            x = T.constant(numpy.random.rand(2, 4), dtype=config.floatX)
+            x = T.constant(np.random.rand(2, 4), dtype=config.floatX)
             self.assertRaises(ValueError, T.dot, x, y)
         finally:
             theano.config.compute_test_value = orig_compute_test_value
@@ -207,9 +207,9 @@ class TestComputeTestValue(unittest.TestCase):
 
             x = T.fmatrix('x')
             # Incorrect dtype (float64) for test_value
-            x.tag.test_value = numpy.random.rand(3, 4)
+            x.tag.test_value = np.random.rand(3, 4)
             y = T.dmatrix('y')
-            y.tag.test_value = numpy.random.rand(4, 5)
+            y.tag.test_value = np.random.rand(4, 5)
 
             self.assertRaises(TypeError, T.dot, x, y)
         finally:
@@ -222,9 +222,9 @@ class TestComputeTestValue(unittest.TestCase):
         try:
             config.compute_test_value = "raise"
             x = T.matrix()
-            x.tag.test_value = numpy.zeros((2, 3), dtype=config.floatX)
+            x.tag.test_value = np.zeros((2, 3), dtype=config.floatX)
             y = T.matrix()
-            y.tag.test_value = numpy.zeros((2, 2), dtype=config.floatX)
+            y.tag.test_value = np.zeros((2, 2), dtype=config.floatX)
             self.assertRaises(ValueError, x.__mul__, y)
         finally:
             theano.config.compute_test_value = orig_compute_test_value
@@ -240,7 +240,7 @@ class TestComputeTestValue(unittest.TestCase):
             k = T.iscalar("k")
             A = T.vector("A")
             k.tag.test_value = 3
-            A.tag.test_value = numpy.random.rand(5).astype(config.floatX)
+            A.tag.test_value = np.random.rand(5).astype(config.floatX)
 
             def fx(prior_result, A):
                 return prior_result * A
@@ -267,7 +267,7 @@ class TestComputeTestValue(unittest.TestCase):
             k = T.iscalar("k")
             A = T.matrix("A")
             k.tag.test_value = 3
-            A.tag.test_value = numpy.random.rand(5, 3).astype(config.floatX)
+            A.tag.test_value = np.random.rand(5, 3).astype(config.floatX)
 
             def fx(prior_result, A):
                 return T.dot(prior_result, A)
@@ -304,7 +304,7 @@ class TestComputeTestValue(unittest.TestCase):
             k = T.iscalar("k")
             A = T.matrix("A")
             k.tag.test_value = 3
-            A.tag.test_value = numpy.random.rand(5, 3).astype(config.floatX)
+            A.tag.test_value = np.random.rand(5, 3).astype(config.floatX)
 
             def fx(prior_result, A):
                 return T.dot(prior_result, A)
@@ -400,7 +400,7 @@ class TestComputeTestValue(unittest.TestCase):
         try:
             theano.config.compute_test_value = 'raise'
             init_Mu1 = theano.shared(
-                numpy.zeros((5,), dtype=config.floatX)).dimshuffle('x', 0)
+                np.zeros((5,), dtype=config.floatX)).dimshuffle('x', 0)
 
             theano.function([], outputs=[init_Mu1])
         finally:

theano/gof/tests/test_graph.py

@@ -4,7 +4,7 @@ import pickle
 import unittest
 
 from nose.plugins.skip import SkipTest
-import numpy
+import numpy as np
 
 from theano import (
     sparse,
@@ -362,7 +362,7 @@ class TestAutoName:
         r1 = tensor.TensorType(dtype='int32', broadcastable=())('myvar')
         r2 = tensor.TensorVariable(tensor.TensorType(dtype='int32',
                                                      broadcastable=()))
-        r3 = shared(numpy.random.randn(3, 4))
+        r3 = shared(np.random.randn(3, 4))
         assert r1.auto_name == "auto_" + str(autoname_id)
         assert r2.auto_name == "auto_" + str(autoname_id + 1)
         assert r3.auto_name == "auto_" + str(autoname_id + 2)

theano/gof/tests/test_graph_opt_caching.py

 from __future__ import absolute_import, print_function, division
 import os
-import numpy
+import numpy as np
 import theano
 import theano.tensor as T
 
@@ -19,20 +19,20 @@ def test_graph_opt_caching():
         theano.config.cache_optimizations = True
         a = T.fmatrix('a')
         b = T.fmatrix('b')
-        c = theano.shared(numpy.ones((10, 10), dtype=floatX))
-        d = theano.shared(numpy.ones((10, 10), dtype=floatX))
+        c = theano.shared(np.ones((10, 10), dtype=floatX))
+        d = theano.shared(np.ones((10, 10), dtype=floatX))
         e = T.sum(T.sum(T.sum(a ** 2 + b) + c) + d)
         f1 = theano.function([a, b], e, mode=mode)
 
         m = T.fmatrix('x1')
         n = T.fmatrix('x2')
-        p = theano.shared(numpy.ones((10, 10), dtype=floatX))
-        q = theano.shared(numpy.ones((10, 10), dtype=floatX))
+        p = theano.shared(np.ones((10, 10), dtype=floatX))
+        q = theano.shared(np.ones((10, 10), dtype=floatX))
         j = T.sum(T.sum(T.sum(m ** 2 + n) + p) + q)
         f2 = theano.function([m, n], j, mode=mode)
 
-        in1 = numpy.ones((10, 10), dtype=floatX)
-        in2 = numpy.ones((10, 10), dtype=floatX)
+        in1 = np.ones((10, 10), dtype=floatX)
+        in2 = np.ones((10, 10), dtype=floatX)
         assert f1(in1, in2) == f2(in1, in2)
     finally:
         theano.config.cache_optimizations = default

theano/gof/tests/test_lazy.py

 from __future__ import absolute_import, print_function, division
 from copy import deepcopy
 
-import numpy
+import numpy as np
 
 import theano
 from theano.gof.op import PureOp
@@ -154,13 +154,13 @@ def more_complex_test():
                                                  optimizer='fast_run'))
     if theano.config.vm.lazy is False:
         try:
-            f(1, 0, numpy.array(10, dtype=x1.dtype), 0)
+            f(1, 0, np.array(10, dtype=x1.dtype), 0)
             assert False
         except NotImplementedOp.E:
             pass
     else:
-        print(f(1, 0, numpy.array(10, dtype=x1.dtype), 0))
-        assert f(1, 0, numpy.array(10, dtype=x1.dtype), 0) == 20.5
+        print(f(1, 0, np.array(10, dtype=x1.dtype), 0))
+        assert f(1, 0, np.array(10, dtype=x1.dtype), 0) == 20.5
     print('... passed')
 
 if __name__ == '__main__':

theano/gof/tests/test_link.py

@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
 from copy import deepcopy
 import unittest
 
-import numpy
+import numpy as np
 
 import theano
 from theano.gof import graph
@@ -203,18 +203,18 @@ def test_container_deepcopy():
     # It seam that numpy.asarray(0.).astype(floatX) can return a numpy
     # scalar with some NumPy Version. So we call numpy.asarray with
     # the dtype parameter.
-    v = numpy.asarray(0., dtype=theano.config.floatX)
-    assert isinstance(v, numpy.ndarray), type(v)
+    v = np.asarray(0., dtype=theano.config.floatX)
+    assert isinstance(v, np.ndarray), type(v)
     for readonly in [True, False]:
         c = Container(t, [v], readonly=readonly)
-        assert isinstance(c.storage[0], numpy.ndarray), (c.storage[0],
-                                                         type(c.storage[0]))
+        assert isinstance(c.storage[0], np.ndarray), (c.storage[0],
+                                                      type(c.storage[0]))
         assert c.storage[0].dtype == v.dtype, (c.storage[0].dtype, v.dtype)
         assert c.storage[0].dtype == c.type.dtype, (c.storage[0].dtype,
                                                     c.type.dtype)
         d = deepcopy(c)
-        assert isinstance(d.storage[0], numpy.ndarray), (d.storage[0],
-                                                         type(d.storage[0]))
+        assert isinstance(d.storage[0], np.ndarray), (d.storage[0],
+                                                      type(d.storage[0]))
         assert d.storage[0].dtype == v.dtype, (d.storage[0].dtype, v.dtype)
         assert d.storage[0].dtype == c.type.dtype, (d.storage[0].dtype,
                                                     c.type.dtype)

theano/gof/tests/test_op.py

@@ -2,7 +2,7 @@ from __future__ import absolute_import, print_function, division
 import unittest
 
 from nose.plugins.skip import SkipTest
-import numpy
+import numpy as np
 
 import theano
 import theano.gof.op as op
@@ -181,7 +181,7 @@ class TestMakeThunk(unittest.TestCase):
                           o.owner.op.c_code,
                           o.owner, 'o', ['x'], 'z', {'fail': ''})
 
-        storage_map = {i: [numpy.int32(3)],
+        storage_map = {i: [np.int32(3)],
                        o: [None]}
         compute_map = {i: [True],
                        o: [False]}
@@ -218,7 +218,7 @@ class TestMakeThunk(unittest.TestCase):
                           o.owner.op.perform,
                           o.owner, 0, [None])
 
-        storage_map = {i: [numpy.int32(3)],
+        storage_map = {i: [np.int32(3)],
                        o: [None]}
         compute_map = {i: [True],
                        o: [False]}
@@ -251,9 +251,9 @@ class TestMakeThunk(unittest.TestCase):
 
         x_input = T.dmatrix('x_input')
         f = theano.function([x_input], DoubleOp()(x_input))
-        inp = numpy.random.rand(5, 4)
+        inp = np.random.rand(5, 4)
         out = f(inp)
-        assert numpy.allclose(inp * 2, out)
+        assert np.allclose(inp * 2, out)
 
 
 def test_test_value_python_objects():
@@ -262,33 +262,33 @@ def test_test_value_python_objects():
 
 
 def test_test_value_ndarray():
-    x = numpy.zeros((5, 5))
+    x = np.zeros((5, 5))
     v = op.get_test_value(x)
     assert (v == x).all()
 
 
 def test_test_value_constant():
-    x = T.as_tensor_variable(numpy.zeros((5, 5)))
+    x = T.as_tensor_variable(np.zeros((5, 5)))
     v = op.get_test_value(x)
 
-    assert numpy.all(v == numpy.zeros((5, 5)))
+    assert np.all(v == np.zeros((5, 5)))
 
 
 def test_test_value_shared():
-    x = shared(numpy.zeros((5, 5)))
+    x = shared(np.zeros((5, 5)))
     v = op.get_test_value(x)
 
-    assert numpy.all(v == numpy.zeros((5, 5)))
+    assert np.all(v == np.zeros((5, 5)))
 
 
 def test_test_value_op():
     try:
         prev_value = config.compute_test_value
         config.compute_test_value = 'raise'
-        x = T.log(numpy.ones((5, 5)))
+        x = T.log(np.ones((5, 5)))
         v = op.get_test_value(x)
 
-        assert numpy.allclose(v, numpy.zeros((5, 5)))
+        assert np.allclose(v, np.zeros((5, 5)))
     finally:
         config.compute_test_value = prev_value
 
@@ -337,8 +337,8 @@ def test_get_debug_values_success():
             config.compute_test_value = mode
 
             x = T.vector()
-            x.tag.test_value = numpy.zeros((4,), dtype=config.floatX)
-            y = numpy.zeros((5, 5))
+            x.tag.test_value = np.zeros((4,), dtype=config.floatX)
+            y = np.zeros((5, 5))
 
             iters = 0
 

theano/gof/tests/test_types.py

 from __future__ import absolute_import, print_function, division
-import numpy
+import numpy as np
 
 import theano
 from theano import Op, Apply
@@ -72,7 +72,7 @@ def test_cdata():
     # This should be a passthrough function for vectors
     f = theano.function([i], i2, mode=mode)
 
-    v = numpy.random.randn(9).astype('float32')
+    v = np.random.randn(9).astype('float32')
 
     v2 = f(v)
     assert (v2 == v).all()

theano/gof/tests/test_vm.py

@@ -5,7 +5,7 @@ import time
 import unittest
 
 from nose.plugins.skip import SkipTest
-import numpy
+import numpy as np
 from six import itervalues
 
 from theano import function
@@ -92,7 +92,7 @@ def test_speed():
     def time_numpy():
         steps_a = 5
         steps_b = 100
-        x = numpy.asarray([2.0, 3.0], dtype=theano.config.floatX)
+        x = np.asarray([2.0, 3.0], dtype=theano.config.floatX)
 
         numpy_version(x, steps_a)
         t0 = time.time()
@@ -195,7 +195,6 @@ def test_speed_lazy():
 
 
 def test_partial_function():
-    import numpy as np
     from theano.tests import unittest_tools as utt
 
     def check_partial_function(linker_name):
@@ -234,7 +233,7 @@ def test_partial_function_with_updates():
 
     def check_updates(linker_name):
         x = tensor.lscalar('input')
-        y = theano.shared(numpy.asarray(1, 'int64'), name='global')
+        y = theano.shared(np.asarray(1, 'int64'), name='global')
         f = theano.function([x], [x, x + 34], updates=[(y, x + 1)], mode=Mode(
             optimizer=None, linker=linker_name))
         g = theano.function([x], [x - 6], updates=[(y, y + 3)], mode=Mode(
@@ -283,7 +282,7 @@ if run_memory_usage_tests:
     def test_leak2():
         import theano.sandbox.cuda as cuda
         for i in xrange(1000000):
-            n = numpy.asarray([2.3, 4.5], dtype='f')
+            n = np.asarray([2.3, 4.5], dtype='f')
             c = sys.getrefcount(n)
             a = cuda.CudaNdarray(n)
             a.sum()
@@ -338,7 +337,7 @@ if run_memory_usage_tests:
             f_a = function([x], a,
                            mode=Mode(optimizer=None,
                                      linker=linker()))
-            inp = numpy.random.rand(1000000)
+            inp = np.random.rand(1000000)
             for i in xrange(100):
                 f_a(inp)
             if 0:  # this doesn't seem to work, prints 0 for everything
@@ -375,7 +374,7 @@ if run_memory_usage_tests:
             f_a = function([x], a,
                            mode=Mode(optimizer=None,
                                      linker=linker()))
-            inp = numpy.random.rand(1000000)
+            inp = np.random.rand(1000000)
             for i in xrange(500):
                 f_a(inp)
         print(1)

theano/gof/utils.py

@@ -3,7 +3,7 @@ import linecache
 import sys
 import traceback
 
-import numpy
+import numpy as np
 from six import iteritems, integer_types, string_types, with_metaclass
 from six.moves import StringIO
 
@@ -561,8 +561,8 @@ else:
         try:
             return hashlib.md5(msg).hexdigest()
         except TypeError:
-            assert isinstance(msg, numpy.ndarray)
-            return hashlib.md5(numpy.getbuffer(msg)).hexdigest()
+            assert isinstance(msg, np.ndarray)
+            return hashlib.md5(np.getbuffer(msg)).hexdigest()
 
 
 def hash_from_file(file_path):