Merge pull request #3128 from harlouci/flake8_sparse

theano/sparse/opt.py

@@ -12,6 +12,7 @@ from theano.sparse import (CSC, CSR, csm_properties,
 from theano.sparse import basic as sparse
 
 _is_sparse_variable = sparse._is_sparse_variable
+_is_dense = sparse._is_dense
 
 # This is tested in tests/test_opt.py:test_local_csm_properties_csm
 
@@ -47,7 +48,8 @@ def local_inplace_remove0(node):
         return [new_node]
     return False
 
-theano.compile.optdb.register('local_inplace_remove0',
+theano.compile.optdb.register(
+    'local_inplace_remove0',
     gof.TopoOptimizer(local_inplace_remove0,
                       failure_callback=gof.TopoOptimizer.warn_inplace),
     60, 'fast_run', 'inplace')
@@ -174,7 +176,8 @@ def local_inplace_addsd_ccode(node):
                                inplace=True)(*node.inputs)
         return [new_node]
     return False
-theano.compile.optdb.register('local_inplace_addsd_ccode',
+theano.compile.optdb.register(
+    'local_inplace_addsd_ccode',
     gof.TopoOptimizer(local_inplace_addsd_ccode,
                       failure_callback=gof.TopoOptimizer.warn_inplace),
     60, 'fast_run', 'inplace')
@@ -234,7 +237,8 @@ class StructuredDotCSC(gof.Op):
     def make_node(self, a_val, a_ind, a_ptr, a_nrows, b):
         dtype_out = scalar.upcast(a_val.type.dtype, b.type.dtype)
         r = gof.Apply(self, [a_val, a_ind, a_ptr, a_nrows, b],
-                [tensor.tensor(dtype_out, (False, b.type.broadcastable[1]))])
+                      [tensor.tensor(dtype_out,
+                                     (False, b.type.broadcastable[1]))])
         return r
 
     def perform(self, node, inputs, outputs):
@@ -243,7 +247,7 @@ class StructuredDotCSC(gof.Op):
         a = scipy.sparse.csc_matrix((a_val, a_ind, a_ptr),
                                     (a_nrows, b.shape[0]),
                                     copy=False)
-        #out[0] = a.dot(b)
+        # out[0] = a.dot(b)
         out[0] = theano._asarray(a * b, dtype=node.outputs[0].type.dtype)
         assert _is_dense(out[0])  # scipy 0.7 automatically converts to dense
 
@@ -427,17 +431,18 @@ class StructuredDotCSR(gof.Op):
     def make_node(self, a_val, a_ind, a_ptr, b):
         self.dtype_out = scalar.upcast(a_val.type.dtype, b.type.dtype)
         r = gof.Apply(self, [a_val, a_ind, a_ptr, b],
-                [tensor.tensor(self.dtype_out, (False,
-                                                b.type.broadcastable[1]))])
+                      [tensor.tensor(self.dtype_out,
+                                     (False, b.type.broadcastable[1]))])
         return r
 
     def perform(self, node, inputs, outputs):
         (a_val, a_ind, a_ptr, b) = inputs
         (out,) = outputs
-        a = scipy.sparse.csr_matrix((a_val, a_ind, a_ptr),
+        a = scipy.sparse.csr_matrix(
+            (a_val, a_ind, a_ptr),
             (len(a_ptr) - 1, b.shape[0]),
             copy=True)  # use view_map before setting this to False
-        #out[0] = a.dot(b)
+        # out[0] = a.dot(b)
         out[0] = a * b
         # scipy 0.7 automatically converts to dense, but not .6 sometimes
         assert _is_dense(out[0])
@@ -653,7 +658,8 @@ class UsmmCscDense(gof.Op):
         if dtype_out != z.type.dtype:
             z = tensor.cast(z, dtype_out)
 
-        r = gof.Apply(self, [alpha, x_val, x_ind, x_ptr, x_nrows, y, z],
+        r = gof.Apply(
+            self, [alpha, x_val, x_ind, x_ptr, x_nrows, y, z],
             [tensor.tensor(dtype_out, (False, y.type.broadcastable[1]))])
         return r
 
@@ -1019,7 +1025,7 @@ def local_csm_grad_c(node):
         return [csm_grad_c(*node.inputs)]
     return False
 # DISABLED AS IT IS BROKEN FOR UNSORTED INDICES!
-#register_specialize(local_csm_grad_c, 'cxx_only')
+# register_specialize(local_csm_grad_c, 'cxx_only')
 
 
 class MulSDCSC(gof.Op):
@@ -1572,7 +1578,7 @@ def local_structured_add_s_v(node):
         x, y = node.inputs
 
         x_is_sparse_variable = _is_sparse_variable(x)
-        #y_is_sparse_variable = _is_sparse_variable(y)
+        # y_is_sparse_variable = _is_sparse_variable(y)
 
         if x_is_sparse_variable:
             svar = x

theano/tests/test_flake8.py

@@ -228,10 +228,7 @@ whitelist_flake8 = [
     "misc/tests/test_pycuda_example.py",
     "misc/hooks/reindent.py",
     "misc/hooks/check_whitespace.py",
-    "sparse/type.py",
     "sparse/__init__.py",
-    "sparse/opt.py",
-    "sparse/basic.py",
     "sparse/tests/test_utils.py",
     "sparse/tests/test_opt.py",
     "sparse/tests/test_basic.py",