Fix a few pep8 errors

theano/sandbox/__init__.py

+from . import opt

theano/sandbox/blocksparse.py

@@ -16,6 +16,7 @@ class SparseBlockGemv(Op):
                 for i in range(h.shape[1]):
                     o[b, j, :] += numpy.dot(h[b, i], W[iIdx[b, i], oIdx[b, j]])
 
+    where b, h, W, o iIdx, oIdx are defined in the docstring of make_node.
     .. image:: ../../images/blocksparse.png
     """
 
@@ -29,7 +30,7 @@ class SparseBlockGemv(Op):
     def make_node(self, o, W, h, inputIdx, outputIdx):
         """
         Compute the dot product of the specified pieces of vectors
-        and matrices. 
+        and matrices.
 
         Parameters
         ----------
@@ -76,7 +77,7 @@ class SparseBlockGemv(Op):
         assert outputIdx.type.dtype in discrete_dtypes
 
         output = o.type.__class__(dtype=o.type.dtype,
-                                  broadcastable=(False,)*o.ndim)()
+                                  broadcastable=(False,) * o.ndim)()
 
         return Apply(self, [o, W, h, inputIdx, outputIdx], [output])
 
@@ -100,7 +101,6 @@ class SparseBlockGemv(Op):
                                "grad of outputIdx makes no sense")]
 
 
-
 class SparseBlockOuter(Op):
     """
     This computes the outer product of two sets of pieces of vectors
@@ -121,7 +121,7 @@ class SparseBlockOuter(Op):
         """
 
         Compute the dot product of the specified pieces of vectors
-        and matrices. 
+        and matrices.
 
         Parameters
         ----------
@@ -154,7 +154,7 @@ class SparseBlockOuter(Op):
             alpha = one
 
         output = o.type.__class__(dtype=o.type.dtype,
-                                  broadcastable=(False,)*o.ndim)()
+                                  broadcastable=(False,) * o.ndim)()
 
         return Apply(self, [o, x, y, xIdx, yIdx, alpha],
                      [output])

theano/sandbox/cuda/nnet.py

@@ -220,7 +220,8 @@ class GpuCrossentropySoftmaxArgmax1HotWithBias(GpuOp):
         # return ()
         return (4,)
 
-gpu_crossentropy_softmax_argmax_1hot_with_bias = GpuCrossentropySoftmaxArgmax1HotWithBias()
+gpu_crossentropy_softmax_argmax_1hot_with_bias = \
+    GpuCrossentropySoftmaxArgmax1HotWithBias()
 
 
 class GpuCrossentropySoftmax1HotWithBiasDx(GpuOp):
@@ -391,7 +392,8 @@ class GpuCrossentropySoftmax1HotWithBiasDx(GpuOp):
         }
         """ % locals()
 
-gpu_crossentropy_softmax_1hot_with_bias_dx = GpuCrossentropySoftmax1HotWithBiasDx()
+gpu_crossentropy_softmax_1hot_with_bias_dx = \
+    GpuCrossentropySoftmax1HotWithBiasDx()
 
 
 class GpuSoftmax(GpuOp):

theano/sandbox/cuda/tests/test_blocksparse.py

@@ -7,13 +7,13 @@ import theano.tests.unittest_tools as utt
 import theano.sandbox.tests.test_blocksparse
 
 import theano.sandbox.cuda as cuda_ndarray
-if not cuda_ndarray.cuda_available:
-    raise SkipTest('Optional package cuda disabled')
 from theano.sandbox.cuda.blocksparse import (GpuSparseBlockOuter,
                                              gpu_sparse_block_gemv,
                                              gpu_sparse_block_outer)
 from theano.sandbox.cuda.var import float32_shared_constructor
 
+if not cuda_ndarray.cuda_available:
+    raise SkipTest('Optional package cuda disabled')
 
 if theano.config.mode == 'FAST_COMPILE':
     mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')

theano/sandbox/cuda/tests/test_opt.py

@@ -766,11 +766,10 @@ def test_blocksparse_gpu_outer_opt():
 
     o = sparse_block_dot(W, h, iIdx, b, oIdx)
 
-    theano.printing.debugprint(tensor.grad(o.sum(),wrt=W))
-
-    f = theano.function([W, h, iIdx, b, oIdx], [o, tensor.grad(o.sum(),wrt=W)], 
+    f = theano.function([W, h, iIdx, b, oIdx], [o, tensor.grad(o.sum(),
+                                                               wrt=W)],
                         mode=mode_with_gpu)
-    
+
     assert isinstance(f.maker.fgraph.toposort()[-2].op, GpuSparseBlockOuter)
 
 
@@ -785,10 +784,8 @@ class test_diag(theano.tensor.tests.test_nlinalg.test_diag):
               self).__init__(name)
 
 
-
 if __name__ == '__main__':
     test_gpualloc()
     test_opt_gpujoin_onlyajoin()
     test_opt_gpujoin_joinvectors_elemwise_then_minusone()
     test_opt_gpujoin_joinvectors_negativeaxes()
-

theano/sandbox/opt.py

@@ -126,7 +126,7 @@ def register_meta_opt(op_class, db_name, position, *args):
         idx = bisect.bisect_left((positions, local_meta_opt),
                                  op_class.registered_opts)
         op_class.registered_opts.insert(idx,
-                (positions, local_meta_opt.__name__))
+                                        (positions, local_meta_opt.__name__))
 
         return local_meta_opt
 

theano/sandbox/tests/test_blocksparse.py

@@ -2,7 +2,6 @@
     Tests for block sparse dot
 """
 import unittest
-import time
 
 import numpy
 from numpy.random import randn
@@ -11,8 +10,8 @@ import theano
 from theano import tensor
 import theano.tests.unittest_tools as utt
 
-from theano.sandbox.blocksparse import sparse_block_dot, cpu_sparse_block_gemv, \
-    cpu_sparse_block_outer
+from theano.sandbox.blocksparse import sparse_block_dot, \
+    cpu_sparse_block_gemv, cpu_sparse_block_outer
 
 
 class BlockSparse_Gemv_and_Outer(unittest.TestCase):
@@ -94,6 +93,9 @@ class BlockSparse_Gemv_and_Outer(unittest.TestCase):
 
     @staticmethod
     def gemv_numpy2(o, W, h, iIdx, oIdx):
+        """
+        Other implementation
+        """
         from numpy import ix_
         for b in range(o.shape[0]):
             w = W[ix_(iIdx[b], oIdx[b])].swapaxes(1, 2)
@@ -103,9 +105,14 @@ class BlockSparse_Gemv_and_Outer(unittest.TestCase):
 
     @staticmethod
     def gemv_numpy3(o, W, h, iIdx, oIdx):
+        """
+        Other implementation
+        """
         from numpy import ix_
         for b in range(o.shape[0]):
             w = W[ix_(iIdx[b], oIdx[b])]
+            # The next three lines do the same operation. The last one is the
+            # fastest
             # o[b] += (h[b][:, None, :, None] * w).sum(axis=(0, 2))
             # o[b] += numpy.tensordot(h[b], w, [(0,1),(0,2)])
             o[b] += numpy.einsum('ik,ijkl', h[b], w)
@@ -135,27 +142,6 @@ class BlockSparse_Gemv_and_Outer(unittest.TestCase):
 
         return weight, input, inputIndice, bias, outputIndice
 
-    @staticmethod
-    def compare():
-        W_val, h_val, iIdx_val, b_val, oIdx_val = \
-            BlockSparse_Gemv_and_Outer.gemv_data2()
-
-        start = time.clock()
-        ref_out = BlockSparse_Gemv_and_Outer.gemv_numpy(
-             b_val.take(oIdx_val, axis=0), W_val, h_val, iIdx_val, oIdx_val)
-        v1 = time.clock()
-        ref_out_2 = BlockSparse_Gemv_and_Outer.gemv_numpy2(
-             b_val.take(oIdx_val, axis=0), W_val, h_val, iIdx_val, oIdx_val)
-        v2 = time.clock()
-        ref_out_3 = BlockSparse_Gemv_and_Outer.gemv_numpy3(
-             b_val.take(oIdx_val, axis=0), W_val, h_val, iIdx_val, oIdx_val)
-        v3 = time.clock()
-
-        print v1 - start
-        print v2 - v1
-        print v3 - v2
-        # utt.assert_allclose(ref_out, ref_out_2)
-
     @staticmethod
     def outer_numpy(o, x, y, xIdx, yIdx):
         for b in range(x.shape[0]):
@@ -185,7 +171,7 @@ class BlockSparse_Gemv_and_Outer(unittest.TestCase):
         th_out = f(W_val, h_val, iIdx_val, b_val, oIdx_val)
 
         ref_out = BlockSparse_Gemv_and_Outer.gemv_numpy(
-             b_val.take(oIdx_val, axis=0), W_val, h_val, iIdx_val, oIdx_val)
+            b_val.take(oIdx_val, axis=0), W_val, h_val, iIdx_val, oIdx_val)
 
         utt.assert_allclose(ref_out, th_out)
 
@@ -208,7 +194,7 @@ class BlockSparse_Gemv_and_Outer(unittest.TestCase):
 
         th_out = f(W_val, h_val, iIdx_val, b_val, oIdx_val)
         ref_out = BlockSparse_Gemv_and_Outer.gemv_numpy(
-             b_val.take(oIdx_val, axis=0), W_val, h_val, iIdx_val, oIdx_val)
+            b_val.take(oIdx_val, axis=0), W_val, h_val, iIdx_val, oIdx_val)
 
         utt.assert_allclose(ref_out, th_out)
 
@@ -224,9 +210,10 @@ class BlockSparse_Gemv_and_Outer(unittest.TestCase):
         oIdx = tensor.imatrix()
 
         o = self.gemv_op(b.take(oIdx, axis=0),
-            tensor.DimShuffle((False, False, False, False),
-                              (0, 1, 3, 2))(tensor.as_tensor_variable(W)),
-            h, iIdx, oIdx)
+                         tensor.DimShuffle((False, False, False, False),
+                                           (0, 1, 3, 2))
+                         (tensor.as_tensor_variable(W)),
+                         h, iIdx, oIdx)
 
         f = theano.function([W, h, iIdx, b, oIdx], o, mode=self.mode)
 
@@ -236,7 +223,7 @@ class BlockSparse_Gemv_and_Outer(unittest.TestCase):
         th_out = f(numpy.swapaxes(W_val, 2, 3), h_val, iIdx_val, b_val,
                    oIdx_val)
         ref_out = BlockSparse_Gemv_and_Outer.gemv_numpy(
-             b_val.take(oIdx_val, axis=0), W_val, h_val, iIdx_val, oIdx_val)
+            b_val.take(oIdx_val, axis=0), W_val, h_val, iIdx_val, oIdx_val)
 
         utt.assert_allclose(ref_out, th_out)
 

theano/sandbox/tests/test_multinomial.py

@@ -4,7 +4,7 @@ import numpy
 
 import theano
 from theano import config, function, tensor
-from . import multinomial
+from theano.sandbox import multinomial
 from theano.compile.mode import get_default_mode, predefined_linkers
 import theano.sandbox.cuda as cuda
 

theano/sandbox/tests/test_opt.py

 import theano
 from theano import tensor
-from theano.sandbox.blocksparse import CpuSparseBlockGemv, CpuSparseBlockOuter, sparse_block_dot
+from theano.sandbox.blocksparse import CpuSparseBlockGemv, \
+    CpuSparseBlockOuter, sparse_block_dot
 
 
 def test_blocksparse_cpu_gemv_opt():
@@ -26,8 +27,9 @@ def test_blocksparse_cpu_outer_opt():
 
     o = sparse_block_dot(W, h, iIdx, b, oIdx)
 
-    theano.printing.debugprint(tensor.grad(o.sum(),wrt=W))
+    theano.printing.debugprint(tensor.grad(o.sum(), wrt=W))
+
+    f = theano.function([W, h, iIdx, b, oIdx],
+                        [o, tensor.grad(o.sum(), wrt=W)])
 
-    f = theano.function([W, h, iIdx, b, oIdx], [o, tensor.grad(o.sum(),wrt=W)])
-    
     assert isinstance(f.maker.fgraph.toposort()[-1].op, CpuSparseBlockOuter)

theano/sandbox/tests/test_scan.py

 import theano
 import numpy
-from . import scan
+from theano.sandbox import scan
 
 
 def test_001():

theano/sandbox/tests/test_theano_object.py

 from __future__ import print_function
-from .theano_object import *
+from theano.sandbox.theano_object import *
 
 
 RUN_TESTS = False

theano/tests/test_flake8.py

@@ -98,14 +98,15 @@ whitelist_flake8 = [
     "tensor/nnet/tests/test_sigm.py",
     "scalar/__init__.py",
     "scalar/tests/test_basic.py",
-    "sandbox/test_theano_object.py",
-    "sandbox/test_scan.py",
+    "sandbox/tests/test_theano_object.py",
+    "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/rng_mrg.py",
     "sandbox/theano_object.py",
     "sandbox/scan.py",
-    "sandbox/test_multinomial.py",
-    "sandbox/test_rng_mrg.py",
-    "sandbox/test_neighbourhoods.py",
     "sandbox/symbolic_module.py",
     "sandbox/conv.py",
     "sandbox/debug.py",