pep8

theano/sandbox/tests/test_multinomial.py

@@ -8,11 +8,11 @@ from theano.sandbox import multinomial
 from theano.compile.mode import get_default_mode, predefined_linkers
 import theano.sandbox.cuda as cuda
 import theano.tests.unittest_tools as utt
-import six.moves.cPickle as pickle
 import os
 from theano.compat import PY3
 from theano.misc.pkl_utils import CompatUnpickler
 
+
 def get_mode(gpu):
     mode = get_default_mode()
     mode = copy.copy(mode)
@@ -37,14 +37,14 @@ def test_n_samples_1():
     u = tensor.fvector()
     n = tensor.iscalar()
     m = multinomial.MultinomialFromUniform('auto')(p, u, n)
-    
+
     f = function([p, u, n], m, allow_input_downcast=True)
 
     numpy.random.seed(12345)
     for i in [1, 5, 10, 100, 1000, 10000]:
-        uni = numpy.random.rand(2*i).astype(config.floatX)
+        uni = numpy.random.rand(2 * i).astype(config.floatX)
         res = f([[1.0, 0.0], [0.0, 1.0]], uni, i)
-        utt.assert_allclose(res, [[i*1.0, 0.0], [0.0, i*1.0]])
+        utt.assert_allclose(res, [[i * 1.0, 0.0], [0.0, i * 1.0]])
 
 
 def test_n_samples_2():
@@ -52,24 +52,26 @@ def test_n_samples_2():
     u = tensor.fvector()
     n = tensor.iscalar()
     m = multinomial.MultinomialFromUniform('auto')(p, u, n)
-    
+
     f = function([p, u, n], m, allow_input_downcast=True)
 
     numpy.random.seed(12345)
     for i in [1, 5, 10, 100, 1000]:
         uni = numpy.random.rand(i).astype(config.floatX)
-        pvals = numpy.random.randint(1,1000,(1,1000)).astype(config.floatX)
+        pvals = numpy.random.randint(1, 1000, (1, 1000)).astype(config.floatX)
         pvals /= pvals.sum(1)
         res = f(pvals, uni, i)
         assert res.sum() == i
 
     for i in [1, 5, 10, 100, 1000]:
         uni = numpy.random.rand(i).astype(config.floatX)
-        pvals = numpy.random.randint(1,1000000,(1,1000000)).astype(config.floatX)
+        pvals = numpy.random.randint(
+            1, 1000000, (1, 1000000)).astype(config.floatX)
         pvals /= pvals.sum(1)
         res = f(pvals, uni, i)
         assert res.sum() == i
 
+
 def test_n_samples_compatibility():
     """
     This test checks if the new change to MultinomialFromUniform is still compatible
@@ -83,28 +85,30 @@ def test_n_samples_compatibility():
     pickle.dump([X, samples], open("multinomial_test_graph.pkl", "w"))
     """
     folder = os.path.dirname(os.path.abspath(__file__))
-    with open(os.path.join(folder, "multinomial_test_graph.pkl"), "rb") as pkl_file:
+    with open(os.path.join(folder, "multinomial_test_graph.pkl"),
+              "rb") as pkl_file:
         if PY3:
             u = CompatUnpickler(pkl_file, encoding="latin1")
         else:
             u = CompatUnpickler(pkl_file)
         X, samples = u.load()
         f = theano.function([X], samples)
-        res = f(numpy.random.randn(20,10))
+        res = f(numpy.random.randn(20, 10))
         assert numpy.all(res.sum(axis=1) == 1)
 
+
 def test_multinomial_0():
     # This tests the MultinomialFromUniform Op directly, not going through the
     # multinomial() call in GPU random generation.
 
     p = tensor.fmatrix()
     u = tensor.fvector()
-    
+
     m = multinomial.MultinomialFromUniform('auto')(p, u)
 
     def body(mode, gpu):
         # the m*2 allows the multinomial to reuse output
-        f = function([p, u], m*2, allow_input_downcast=True, mode=mode)
+        f = function([p, u], m * 2, allow_input_downcast=True, mode=mode)
 
         if gpu:
             assert any([type(node.op) is multinomial.GpuMultinomialFromUniform
@@ -112,7 +116,7 @@ def test_multinomial_0():
 
         # test that both first and second samples can be drawn
         utt.assert_allclose(f([[1, 0], [0, 1]], [.1, .1]),
-                              [[2, 0], [0, 2]])
+                            [[2, 0], [0, 2]])
 
         # test that both second labels can be drawn
         r = f([[.2, .8], [.3, .7]], [.31, .31])
@@ -140,12 +144,12 @@ def test_multinomial_large():
         p = tensor.fmatrix()
         u = tensor.fvector()
         m = multinomial.MultinomialFromUniform('auto')(p, u)
-        f = function([p, u], m*2, allow_input_downcast=True, mode=mode)
+        f = function([p, u], m * 2, allow_input_downcast=True, mode=mode)
         if gpu:
             assert any([type(node.op) is multinomial.GpuMultinomialFromUniform
                         for node in f.maker.fgraph.toposort()])
 
-        pval = numpy.arange(10000 * 4, dtype='float32').reshape((10000, 4))+0.1
+        pval = numpy.arange(10000 * 4, dtype='float32').reshape((10000, 4)) + 0.1
         pval = pval / pval.sum(axis=1)[:, None]
         uval = numpy.ones_like(pval[:, 0]) * 0.5
         mval = f(pval, uval)
@@ -160,7 +164,7 @@ def test_multinomial_large():
         else:
             raise NotImplementedError(config.cast_policy)
         utt.assert_allclose(mval.sum(axis=1), 2)
-        asdf = numpy.asarray([0, 0, 2, 0])+0*pval
+        asdf = numpy.asarray([0, 0, 2, 0]) + 0 * pval
         utt.assert_allclose(mval, asdf)  # broadcast over all rows
     run_with_c(body)
     if cuda.cuda_available:
@@ -201,10 +205,10 @@ def test_gpu_opt():
     f = function([p, u], m_gpu, allow_input_downcast=True, mode=get_mode(True))
     assert any([type(node.op) is multinomial.GpuMultinomialFromUniform
                 for node in f.maker.fgraph.toposort()])
-    pval = numpy.arange(10000 * 4, dtype='float32').reshape((10000, 4))+0.1
+    pval = numpy.arange(10000 * 4, dtype='float32').reshape((10000, 4)) + 0.1
     pval = pval / pval.sum(axis=1)[:, None]
     uval = numpy.ones_like(pval[:, 0]) * 0.5
-    mval = f(pval, uval)
+    f(pval, uval)
 
     # Test with a row, it was failing in the past.
     r = tensor.frow()
@@ -215,7 +219,7 @@ def test_gpu_opt():
     f = function([r, u], m_gpu, allow_input_downcast=True, mode=get_mode(True))
     assert any([type(node.op) is multinomial.GpuMultinomialFromUniform
                 for node in f.maker.fgraph.toposort()])
-    pval = numpy.arange(1 * 4, dtype='float32').reshape((1, 4))+0.1
+    pval = numpy.arange(1 * 4, dtype='float32').reshape((1, 4)) + 0.1
     pval = pval / pval.sum(axis=1)[:, None]
     uval = numpy.ones_like(pval[:, 0]) * 0.5
-    mval2 = f(pval, uval)
+    f(pval, uval)

theano/tests/test_flake8.py

@@ -104,7 +104,6 @@ whitelist_flake8 = [
     "sandbox/tests/test_scan.py",
     "sandbox/tests/test_rng_mrg.py",
     "sandbox/tests/test_neighbourhoods.py",
-    "sandbox/tests/test_multinomial.py",
     "sandbox/tests/__init__.py",
     "sandbox/cuda/var.py",
     "sandbox/cuda/GpuConvGrad3D.py",