Merge pull request #4144 from ChihebTrabelsi/ccw

theano/tests/diverse_tests.py

@@ -47,18 +47,18 @@ class T_scipy(unittest.TestCase):
         b = shared(numpy.zeros(()))
 
         # Construct Theano expression graph
-        p_1 = 1 / (1 + T.exp(-T.dot(x, w)-b))
-        xent = -y*T.log(p_1) - (1-y)*T.log(1-p_1)
+        p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b))
+        xent = -y * T.log(p_1) - (1 - y) * T.log(1 - p_1)
         prediction = p_1 > 0.5
-        cost = xent.mean() + 0.01*(w**2).sum()
+        cost = xent.mean() + 0.01 * (w ** 2).sum()
         gw, gb = T.grad(cost, [w, b])
 
         # Compile expressions to functions
         train = function(
             inputs=[x, y],
             outputs=[prediction, xent],
-            updates=[(w, w-0.1*gw), (b, b-0.1*gb)])
-        predict = function(inputs=[x], outputs=prediction)
+            updates=[(w, w - 0.1 * gw), (b, b - 0.1 * gb)])
+        function(inputs=[x], outputs=prediction)
 
         N = 4
         feats = 100

theano/tests/record.py

 from __future__ import print_function
+from theano.compile import Mode
+import theano
+from theano.printing import hex_digest
+
 __authors__ = "Ian Goodfellow"
 __credits__ = ["Ian Goodfellow"]
 __license__ = "3-clause BSD"
 __maintainer__ = "Ian Goodfellow"
 __email__ = "goodfeli@iro"
 
-from theano.compile import Mode
-import theano
-from theano.printing import hex_digest
-
 
 class MismatchError(Exception):
     """
@@ -96,12 +96,12 @@ class Record(object):
                 msg = 'Replay detected mismatch.\n'
                 msg += ' I wanted to write:\n'
                 if len(line) > 100:
-                    msg += line[0:100]+'...'
+                    msg += line[0:100] + '...'
                 else:
                     msg += line
                 msg += '\nwhen previous job wrote:\n'
                 if len(old_line) > 100:
-                    msg += old_line[0:100]+'...'
+                    msg += old_line[0:100] + '...'
                 else:
                     msg += old_line
                 raise MismatchError(msg)
@@ -198,7 +198,7 @@ class RecordMode(Mode):
             except MismatchError as e:
                 print('Got this MismatchError:')
                 print(e)
-                print('while processing node i='+str(i)+':')
+                print('while processing node i=' + str(i) + ':')
                 print('str(node):', str(node))
                 print('Symbolic inputs: ')
                 for elem in node.inputs:
@@ -208,7 +208,7 @@ class RecordMode(Mode):
                     assert isinstance(elem, list)
                     elem, = elem
                     print(str(elem))
-                print('function name: '+node.fgraph.name)
+                print('function name: ' + node.fgraph.name)
                 raise MismatchError("Non-determinism detected by WrapLinker")
 
         def callback(i, node, fn):
@@ -228,16 +228,15 @@ class RecordMode(Mode):
                                 for elem in self.known_fgraphs])
                 self.known_fgraphs.add(fgraph)
                 num_app = len(fgraph.apply_nodes)
-                line = 'Function '+fgraph.name+' has ' + str(num_app) \
+                line = 'Function ' + fgraph.name + ' has ' + str(num_app) \
                        + ' apply nodes.\n'
                 handle_line(line, i, node, fn)
 
-            line = 'Function name: '+fgraph.name + '\n'
+            line = 'Function name: ' + fgraph.name + '\n'
             handle_line(line, i, node, fn)
-            line = 'Node '+str(i)+':'+str(node)+'\n'
+            line = 'Node ' + str(i) + ':' + str(node) + '\n'
             handle_line(line, i, node, fn)
-            assert all([isinstance(x, list)
-                        and len(x) == 1 for x in fn.inputs])
+            assert all([isinstance(x, list) and len(x) == 1 for x in fn.inputs])
 
             def digest(x):
                 x = x[0]

theano/tests/run_tests_in_batch.py

 #!/usr/bin/env python
 from __future__ import print_function
 
+import datetime
+import os
+import subprocess
+import sys
+import time
+from six.moves import xrange
+import six.moves.cPickle as pickle
+
+import theano
+from theano.misc.windows import output_subprocess_Popen
+
 __authors__ = "Olivier Delalleau, Eric Larsen"
 __contact__ = "delallea@iro"
 
@@ -55,18 +66,6 @@ nosetests.
 """
 
 
-import datetime
-import os
-import subprocess
-import sys
-import time
-from six.moves import xrange
-import six.moves.cPickle as pickle
-
-import theano
-from theano.misc.windows import output_subprocess_Popen
-
-
 def main(stdout=None, stderr=None, argv=None, theano_nose=None,
          batch_size=None, time_profile=False, display_batch_output=False):
     """
@@ -95,7 +94,7 @@ def main(stdout=None, stderr=None, argv=None, theano_nose=None,
         argv = sys.argv
     if theano_nose is None:
         # If Theano is installed with pip/easy_install, it can be in the
-    #*/lib/python2.7/site-packages/theano, but theano-nose in */bin
+        # */lib/python2.7/site-packages/theano, but theano-nose in */bin
         for i in range(1, 5):
             path = theano.__path__[0]
             for _ in range(i):
@@ -145,8 +144,7 @@ def run(stdout, stderr, argv, theano_nose, batch_size, time_profile,
     # Using sys.executable, so that the same Python version is used.
     python = sys.executable
     rval = subprocess.call(
-        ([python, theano_nose, '--collect-only', '--with-id']
-         + argv),
+        ([python, theano_nose, '--collect-only', '--with-id'] + argv),
         stdin=dummy_in.fileno(),
         stdout=stdout.fileno(),
         stderr=stderr.fileno())
@@ -215,9 +213,7 @@ def run(stdout, stderr, argv, theano_nose, batch_size, time_profile,
             stdout.flush()
             stderr.flush()
             subprocess.call(
-                ([python, theano_nose, '-v', '--with-id']
-                 + failed
-                 + argv),
+                ([python, theano_nose, '-v', '--with-id'] + failed + argv),
                 stdin=dummy_in.fileno(),
                 stdout=stdout.fileno(),
                 stderr=stderr.fileno())
@@ -252,7 +248,6 @@ def run(stdout, stderr, argv, theano_nose, batch_size, time_profile,
         # iterating through tests
         # initializing master profiling list and raw log
         prof_master_nosort = []
-        prof_rawlog = []
         dummy_out = open(os.devnull, 'w')
         path_rawlog = os.path.join(sav_dir, 'timeprof_rawlog')
         stamp = str(datetime.datetime.now()) + '\n\n'
@@ -280,9 +275,9 @@ def run(stdout, stderr, argv, theano_nose, batch_size, time_profile,
                         time.ctime(), test_id, data["ids"][test_id]))
                     f_rawlog.flush()
 
-                    p_out = output_subprocess_Popen(
-                        ([python, theano_nose, '-v', '--with-id']
-                         + [str(test_id)] + argv +
+                    p_out = output_subprocess_Popen(([python, theano_nose, '-v', '--with-id'] +
+                                                     [str(test_id)] +
+                                                     argv +
                                                      ['--disabdocstring']))
                     # the previous option calls a custom Nosetests plugin
                     # precluding automatic sustitution of doc. string for
@@ -361,5 +356,6 @@ def run(stdout, stderr, argv, theano_nose, batch_size, time_profile,
 
             # end of saving nosort
 
+
 if __name__ == '__main__':
     sys.exit(main())

theano/tests/test_2nd_order_grads.py

 """
 Test for jacobian/hessian functions in Theano
 """
-import unittest
 from six.moves import xrange
 from theano.tests import unittest_tools as utt
-from theano import function
 import theano
 from theano import tensor
 import numpy
@@ -151,17 +149,14 @@ def test_jacobian_disconnected_inputs():
     """
     v1 = tensor.vector()
     v2 = tensor.vector()
-    jacobian_v = theano.gradient.jacobian(1 + v1, v2,
-        disconnected_inputs='ignore')
+    jacobian_v = theano.gradient.jacobian(1 + v1, v2, disconnected_inputs='ignore')
     func_v = theano.function([v1, v2], jacobian_v)
     val = numpy.arange(4.0).astype(theano.config.floatX)
     assert numpy.allclose(func_v(val, val), numpy.zeros((4, 4)))
 
     s1 = tensor.scalar()
     s2 = tensor.scalar()
-    jacobian_s = theano.gradient.jacobian(1 + s1, s2,
-        disconnected_inputs='ignore')
+    jacobian_s = theano.gradient.jacobian(1 + s1, s2, disconnected_inputs='ignore')
     func_s = theano.function([s2], jacobian_s)
     val = numpy.array(1.0).astype(theano.config.floatX)
     assert numpy.allclose(func_s(val), numpy.zeros(1))
-

theano/tests/test_config.py

@@ -33,8 +33,7 @@ class T_config(unittest.TestCase):
         THEANO_FLAGS_DICT['T_config.test_invalid_default_b'] = 'ok'
         # This should succeed since we defined a proper value, even
         # though the default was invalid.
-        AddConfigVar(
-                'T_config.test_invalid_default_b',
+        AddConfigVar('T_config.test_invalid_default_b',
                      doc='unittest',
                      configparam=ConfigParam('invalid', filter=filter),
                      in_c_key=False)

theano/tests/test_determinism.py

@@ -4,7 +4,6 @@ from theano.tests import disturb_mem
 import numpy as np
 import theano
 from theano.printing import var_descriptor
-from nose.plugins.skip import SkipTest
 
 from theano import config, shared
 from six import StringIO
@@ -24,10 +23,6 @@ def test_determinism_1():
     # change.
     # This specific script is capable of catching a bug where
     # FunctionGraph.toposort was non-deterministic.
-    try:
-        import hashlib
-    except ImportError:
-        raise SkipTest('python version too old to do this test')
 
     def run(replay, log=None):
 

theano/tests/test_flake8.py

@@ -27,19 +27,7 @@ ignore = ('E501', 'E123', 'E133')
 whitelist_flake8 = [
     "compat/six.py",  # This is bundled code that will be deleted, don't fix it
     "__init__.py",
-    "tests/test_gradient.py",
-    "tests/test_config.py",
-    "tests/diverse_tests.py",
-    "tests/test_rop.py",
-    "tests/test_2nd_order_grads.py",
-    "tests/run_tests_in_batch.py",
-    "tests/test_record.py",
     "tests/__init__.py",
-    "tests/test_updates.py",
-    "tests/test_pickle_unpickle_theano_fn.py",
-    "tests/test_determinism.py",
-    "tests/record.py",
-    "tests/unittest_tools.py",
     "compile/__init__.py",
     "compile/profiling.py",
     "compile/tests/test_builders.py",

theano/tests/test_gradient.py

@@ -37,6 +37,7 @@ class testgrad_sources_inputs(unittest.TestCase):
         """Test that it is not ok to return None from op.grad()"""
         class retNone(gof.op.Op):
             __props__ = ()
+
             def make_node(self):
                 inputs = [theano.tensor.vector()]
                 outputs = [theano.tensor.vector()]
@@ -54,6 +55,7 @@ class testgrad_sources_inputs(unittest.TestCase):
         """
         class retOne(gof.op.Op):
             __props__ = ()
+
             def make_node(self, *inputs):
                 outputs = [theano.tensor.vector()]
                 return gof.Apply(self, inputs, outputs)
@@ -66,8 +68,7 @@ class testgrad_sources_inputs(unittest.TestCase):
         a1 = retOne().make_node(i)
         grad_sources_inputs([(a1.out, one)], None)
         a2 = retOne().make_node(i, j)
-        self.assertRaises(ValueError, grad_sources_inputs,
-                [(a2.out, one)], None)
+        self.assertRaises(ValueError, grad_sources_inputs, [(a2.out, one)], None)
 
     def test_1in_1out(self):
         """Test grad is called correctly for a 1-to-1 op"""
@@ -75,6 +76,7 @@ class testgrad_sources_inputs(unittest.TestCase):
 
         class O(gof.op.Op):
             __props__ = ()
+
             def make_node(self):
                 inputs = [theano.tensor.matrix()]
                 outputs = [theano.tensor.matrix()]
@@ -92,6 +94,7 @@ class testgrad_sources_inputs(unittest.TestCase):
 
         class O(gof.op.Op):
             __props__ = ()
+
             def make_node(self):
                 inputs = [theano.tensor.matrix()]
                 outputs = [theano.tensor.scalar(), theano.tensor.scalar()]
@@ -112,6 +115,7 @@ class testgrad_sources_inputs(unittest.TestCase):
 
         class O(gof.op.Op):
             __props__ = ()
+
             def make_node(self):
                 inputs = [theano.tensor.scalar(), theano.tensor.scalar()]
                 outputs = [theano.tensor.matrix()]
@@ -133,6 +137,7 @@ class testgrad_sources_inputs(unittest.TestCase):
 
         class O(gof.op.Op):
             __props__ = ()
+
             def make_node(self):
                 inputs = [theano.tensor.matrix(), theano.tensor.matrix()]
                 outputs = [theano.tensor.matrix(), theano.tensor.matrix()]
@@ -153,27 +158,25 @@ class test_grad(unittest.TestCase):
         # in the graph
         a = theano.tensor.vector()
         b = theano.gradient.grad_not_implemented(theano.tensor.add, 0, a)
-        self.assertRaises(TypeError, theano.function,
-                [a], b, on_unused_input='ignore')
+        self.assertRaises(TypeError, theano.function, [a], b, on_unused_input='ignore')
 
     def test_undefined_grad_func(self):
         # tests that function compilation catches undefined grads in the graph
         a = theano.tensor.vector()
         b = theano.gradient.grad_undefined(theano.tensor.add, 0, a)
-        self.assertRaises(TypeError, theano.function,
-                [a], b, on_unused_input='ignore')
+        self.assertRaises(TypeError, theano.function, [a], b, on_unused_input='ignore')
 
     def test_unimplemented_grad_grad(self):
         # tests that unimplemented grads are caught in the grad method
 
         class DummyOp(gof.Op):
             __props__ = ()
+
             def make_node(self, x):
                 return gof.Apply(self, [x], [x.type()])
 
             def grad(self, inputs, output_grads):
-                return [theano.gradient.grad_not_implemented(
-                            self, 0, inputs[0])]
+                return [theano.gradient.grad_not_implemented(self, 0, inputs[0])]
 
         a = theano.tensor.scalar()
         b = DummyOp()(a)
@@ -324,8 +327,7 @@ class test_grad(unittest.TestCase):
         int_result = int_func(X, W, b)
         float_result = float_func(np.cast[float_type](X), W, b)
 
-        assert np.allclose(int_result, float_result), (
-                int_result, float_result)
+        assert np.allclose(int_result, float_result), (int_result, float_result)
 
     def test_grad_disconnected(self):
 
@@ -358,9 +360,9 @@ class test_grad(unittest.TestCase):
         # x is connected to f but not to g
         class Op1(theano.gof.Op):
             __props__ = ()
+
             def make_node(self, x):
-                return theano.Apply(self, inputs=[x],
-                        outputs=[x.type(), theano.tensor.scalar()])
+                return theano.Apply(self, inputs=[x], outputs=[x.type(), theano.tensor.scalar()])
 
             def connection_pattern(self, node):
                 return [[True, False]]
@@ -372,9 +374,9 @@ class test_grad(unittest.TestCase):
         # Its gradient with respect to g is not defined
         class Op2(theano.gof.Op):
             __props__ = ()
+
             def make_node(self, f, g):
-                return theano.Apply(self, inputs=[f, g],
-                        outputs=[theano.tensor.scalar()])
+                return theano.Apply(self, inputs=[f, g], outputs=[theano.tensor.scalar()])
 
             def grad(self, inputs, output_grads):
                 return [inputs[0].zeros_like(), NullType()()]
@@ -430,8 +432,9 @@ class test_grad(unittest.TestCase):
         g_x, g_one = f(1, .5)
 
         if not np.allclose(g_x, g_one):
-            raise AssertionError("Gradient using consider constant is " + str(g_x)\
-                    + " but gradient with respect to the same Constant is " + \
+            raise AssertionError("Gradient using consider constant is " +
+                                 str(g_x) +
+                                 " but gradient with respect to the same Constant is " +
                                  str(g_one))
 
 
@@ -456,18 +459,12 @@ def test_known_grads():
     cost = theano.tensor.sqr(y)
     cost.name = 'cost'
 
-    layers = [
-            [cost],
-            [y],
-            [ct, p],
-            [ct, x, ft],
-            [coeffs, t, full_range, x]
-            ]
+    layers = [[cost], [y], [ct, p], [ct, x, ft], [coeffs, t, full_range, x]]
 
     inputs = [coeffs, t, x]
 
     rng = np.random.RandomState([2012, 11, 15])
-    values = [rng.randn(10), rng.randint(10), rng.randn() ]
+    values = [rng.randn(10), rng.randint(10), rng.randn()]
     values = [np.cast[ipt.dtype](value) for ipt, value in zip(inputs, values)]
 
     true_grads = theano.tensor.grad(cost, inputs, disconnected_inputs='ignore')
@@ -478,8 +475,7 @@ def test_known_grads():
         print('Testing by separately computing ', layer)
         first = theano.tensor.grad(cost, layer, disconnected_inputs='ignore')
         known = dict(izip(layer, first))
-        full = theano.tensor.grad(cost=None,
-                known_grads=known, wrt=inputs, disconnected_inputs='ignore')
+        full = theano.tensor.grad(cost=None, known_grads=known, wrt=inputs, disconnected_inputs='ignore')
         full = theano.function(inputs, full)
         full = full(*values)
         assert len(true_grads) == len(full)
@@ -508,7 +504,7 @@ def test_dxdx():
     x = theano.tensor.iscalar()
     g = theano.tensor.grad(x, x)
 
-    g = g.eval({ x : 12 })
+    g = g.eval({x: 12})
 
     assert np.allclose(g, 1.)
 
@@ -520,9 +516,7 @@ def test_known_grads_integers():
     x = theano.tensor.iscalar()
     g_expected = theano.tensor.scalar()
 
-    g_grad = theano.gradient.grad(cost=None,
-            known_grads={x : g_expected},
-            wrt=x)
+    g_grad = theano.gradient.grad(cost=None, known_grads={x: g_expected}, wrt=x)
 
     f = theano.function([g_expected], g_grad)
 
@@ -547,7 +541,7 @@ def test_undefined_cost_grad():
         cost = x + y
         assert cost.dtype in theano.tensor.discrete_dtypes
         try:
-            grads = theano.tensor.grad(cost, [x, y], known_grads={cost: NullType()() })
+            theano.tensor.grad(cost, [x, y], known_grads={cost: NullType()()})
         except theano.gradient.NullTypeGradError:
             return
         raise AssertionError("An undefined gradient has been ignored.")
@@ -566,8 +560,7 @@ def test_disconnected_cost_grad():
         cost = x + y
         assert cost.dtype in theano.tensor.discrete_dtypes
         try:
-            grads = theano.tensor.grad(cost, [x, y], known_grads={cost: gradient.DisconnectedType()() },
-                    disconnected_inputs='raise')
+            theano.tensor.grad(cost, [x, y], known_grads={cost: gradient.DisconnectedType()()}, disconnected_inputs='raise')
         except theano.gradient.DisconnectedInputError:
             return
         raise AssertionError("A disconnected gradient has been ignored.")
@@ -768,7 +761,7 @@ class TestDisconnectedGrad(unittest.TestCase):
                           gradient.grad, y.sum(), b)
 
         # This MUST NOT raise a DisconnectedInputError error.
-        z = gradient.grad(y.sum(), a)
+        gradient.grad(y.sum(), a)
 
 
 def test_grad_clip():

theano/tests/test_pickle_unpickle_theano_fn.py

@@ -12,15 +12,15 @@ The config option is in configdefaults.py
 
 This note is written by Li Yao.
 """
-import unittest
 import numpy
 import six.moves.cPickle as pickle
-from theano.compat import DictMixin, OrderedDict
+from theano.compat import OrderedDict
 import theano
 import theano.tensor as T
 
 floatX = 'float32'
 
+
 def test_pickle_unpickle_with_reoptimization():
     mode = theano.config.mode
     if mode in ["DEBUG_MODE", "DebugMode"]:

theano/tests/test_record.py

-from theano.tests.record import *
+from theano.tests.record import Record, MismatchError, RecordMode
 from theano import function
 from six.moves import xrange, StringIO
 from theano.tensor import iscalar
@@ -20,12 +20,12 @@ def test_record_good():
     num_lines = 10
 
     for i in xrange(num_lines):
-        recorder.handle_line(str(i)+'\n')
+        recorder.handle_line(str(i) + '\n')
 
     # Make sure they were recorded correctly
     output_value = output.getvalue()
 
-    assert output_value == ''.join(str(i)+'\n' for i in xrange(num_lines))
+    assert output_value == ''.join(str(i) + '\n' for i in xrange(num_lines))
 
     # Make sure that the playback functionality doesn't raise any errors
     # when we repeat them
@@ -34,7 +34,7 @@ def test_record_good():
     playback_checker = Record(file_object=output, replay=True)
 
     for i in xrange(num_lines):
-        playback_checker.handle_line(str(i)+'\n')
+        playback_checker.handle_line(str(i) + '\n')
 
 
 def test_record_bad():
@@ -51,7 +51,7 @@ def test_record_bad():
     num_lines = 10
 
     for i in xrange(num_lines):
-        recorder.handle_line(str(i)+'\n')
+        recorder.handle_line(str(i) + '\n')
 
     # Make sure that the playback functionality doesn't raise any errors
     # when we repeat some of them
@@ -61,7 +61,7 @@ def test_record_bad():
     playback_checker = Record(file_object=output, replay=True)
 
     for i in xrange(num_lines // 2):
-        playback_checker.handle_line(str(i)+'\n')
+        playback_checker.handle_line(str(i) + '\n')
 
     # Make sure it raises an error when we deviate from the recorded sequence
     try:
@@ -92,7 +92,7 @@ def test_record_mode_good():
     num_lines = 10
 
     for i in xrange(num_lines):
-        recorder.handle_line(str(i)+'\n')
+        recorder.handle_line(str(i) + '\n')
         f(i)
 
     # Make sure that the playback functionality doesn't raise any errors
@@ -108,7 +108,7 @@ def test_record_mode_good():
     f = function([i], i, mode=playback_mode, name='f')
 
     for i in xrange(num_lines):
-        playback_checker.handle_line(str(i)+'\n')
+        playback_checker.handle_line(str(i) + '\n')
         f(i)
 
 
@@ -132,7 +132,7 @@ def test_record_mode_bad():
     num_lines = 10
 
     for i in xrange(num_lines):
-        recorder.handle_line(str(i)+'\n')
+        recorder.handle_line(str(i) + '\n')
         f(i)
 
     # Make sure that the playback functionality doesn't raise any errors
@@ -148,7 +148,7 @@ def test_record_mode_bad():
     f = function([i], i, mode=playback_mode, name='f')
 
     for i in xrange(num_lines // 2):
-        playback_checker.handle_line(str(i)+'\n')
+        playback_checker.handle_line(str(i) + '\n')
         f(i)
 
     # Make sure a wrong event causes a MismatchError

theano/tests/test_rop.py

@@ -21,7 +21,6 @@ import numpy
 from theano.gof import Op, Apply
 from theano.gradient import grad_undefined
 from theano.tests.unittest_tools import SkipTest
-from theano.tensor.signal.pool import Pool
 from theano.tensor.nnet import conv, conv2d
 
 '''
@@ -75,7 +74,7 @@ class RopLop_checker(unittest.TestCase):
         test that an error is raised."""
         raised = False
         try:
-            tmp = tensor.Rop(y, self.x, self.v)
+            tensor.Rop(y, self.x, self.v)
         except ValueError:
             raised = True
         if not raised:
@@ -108,7 +107,7 @@ class RopLop_checker(unittest.TestCase):
                            theano.config.floatX)
         yv = tensor.Rop(y, self.mx, self.mv)
         rop_f = function([self.mx, self.mv], yv, on_unused_input='ignore')
-        sy, _ = theano.scan(lambda i, y, x, v: \
+        sy, _ = theano.scan(lambda i, y, x, v:
                             (tensor.grad(y[i], x) * v).sum(),
                             sequences=tensor.arange(y.shape[0]),
                             non_sequences=[y, self.mx, self.mv])
@@ -119,11 +118,9 @@ class RopLop_checker(unittest.TestCase):
 
         assert numpy.allclose(v1, v2), ('ROP mismatch: %s %s' % (v1, v2))
 
-        self.check_nondiff_rop(theano.clone(y,
-                                    replace={self.mx: break_op(self.mx)}))
+        self.check_nondiff_rop(theano.clone(y, replace={self.mx: break_op(self.mx)}))
 
-        vv = numpy.asarray(self.rng.uniform(size=out_shape),
-                           theano.config.floatX)
+        vv = numpy.asarray(self.rng.uniform(size=out_shape), theano.config.floatX)
         yv = tensor.Lop(y, self.mx, self.v)
         lop_f = function([self.mx, self.v], yv)
 
@@ -160,8 +157,7 @@ class RopLop_checker(unittest.TestCase):
         assert numpy.allclose(v1, v2), ('ROP mismatch: %s %s' % (v1, v2))
         known_fail = False
         try:
-            self.check_nondiff_rop(theano.clone(y,
-                                replace={self.x: break_op(self.x)}))
+            self.check_nondiff_rop(theano.clone(y, replace={self.x: break_op(self.x)}))
         except AssertionError:
             known_fail = True
 
@@ -285,27 +281,22 @@ class test_RopLop(RopLop_checker):
                 yv = tensor.Rop(output, [input, filters], [ev_input, ev_filters])
                 rop_f = function([input, filters, ev_input, ev_filters],
                                  yv, on_unused_input='ignore')
-                sy, _ = theano.scan(
-                    lambda i, y, x1, x2, v1, v2:
-                        (tensor.grad(y[i], x1) * v1).sum() + \
+                sy, _ = theano.scan(lambda i, y, x1, x2, v1, v2:
+                                    (tensor.grad(y[i], x1) * v1).sum() +
                                     (tensor.grad(y[i], x2) * v2).sum(),
                                     sequences=tensor.arange(output.shape[0]),
                                     non_sequences=[output, input, filters,
                                                    ev_input, ev_filters])
                 scan_f = function([input, filters, ev_input, ev_filters], sy,
                                   on_unused_input='ignore')
-
                 dtype = theano.config.floatX
                 image_data = numpy.random.random(image_shape).astype(dtype)
                 filter_data = numpy.random.random(filter_shape).astype(dtype)
                 ev_image_data = numpy.random.random(image_shape).astype(dtype)
                 ev_filter_data = numpy.random.random(filter_shape).astype(dtype)
-                v1 = rop_f(image_data, filter_data, ev_image_data,
-                        ev_filter_data)
-                v2 = scan_f(image_data, filter_data, ev_image_data,
-                            ev_filter_data)
-                assert numpy.allclose(v1, v2), ("Rop mismatch: %s %s" %
-                                            (v1, v2))
+                v1 = rop_f(image_data, filter_data, ev_image_data, ev_filter_data)
+                v2 = scan_f(image_data, filter_data, ev_image_data, ev_filter_data)
+                assert numpy.allclose(v1, v2), ("Rop mismatch: %s %s" % (v1, v2))
 
     def test_join(self):
         tv = numpy.asarray(self.rng.uniform(size=(10,)),
@@ -353,10 +344,8 @@ class test_RopLop(RopLop_checker):
         self.check_rop_lop(out1d, self.in_shape[0])
 
         # Alloc of x into a 3-D tensor, flattened
-        out3d = tensor.alloc(self.x,
-                self.mat_in_shape[0], self.mat_in_shape[1], self.in_shape[0])
-        self.check_rop_lop(out3d.flatten(),
-                self.mat_in_shape[0] * self.mat_in_shape[1] * self.in_shape[0])
+        out3d = tensor.alloc(self.x, self.mat_in_shape[0], self.mat_in_shape[1], self.in_shape[0])
+        self.check_rop_lop(out3d.flatten(), self.mat_in_shape[0] * self.mat_in_shape[1] * self.in_shape[0])
 
     def test_invalid_input(self):
         success = False

theano/tests/test_updates.py

@@ -14,10 +14,8 @@ class test_ifelse(unittest.TestCase):
         OrderedUpdates({sv: 3})
 
     def test_updates_setitem(self):
-        ok = True
 
         up = OrderedUpdates()
-        sv = theano.shared('asdf')
 
         # keys have to be SharedVariables
         self.assertRaises(TypeError, up.__setitem__, 5, 7)

theano/tests/unittest_tools.py

@@ -51,8 +51,7 @@ def fetch_seed(pseed=None):
         else:
             seed = None
     except ValueError:
-        print(('Error: config.unittests.rseed contains '
-                              'invalid seed, using None instead'), file=sys.stderr)
+        print(('Error: config.unittests.rseed contains ' 'invalid seed, using None instead'), file=sys.stderr)
         seed = None
 
     return seed
@@ -66,8 +65,7 @@ def seed_rng(pseed=None):
 
     seed = fetch_seed(pseed)
     if pseed and pseed != seed:
-        print('Warning: using seed given by config.unittests.rseed=%i'\
-                'instead of seed %i given as parameter' % (seed, pseed), file=sys.stderr)
+        print('Warning: using seed given by config.unittests.rseed=%i' 'instead of seed %i given as parameter' % (seed, pseed), file=sys.stderr)
     numpy.random.seed(seed)
     return seed