Made corrections from review.

theano/sparse/sandbox/sp2.py

@@ -76,8 +76,9 @@ class HStack(gof.op.Op):
     """Stack sparse matrices horizontally (column wise).
 
     :param blocks: Sequence of sparse array of compatible shape.
-    :param format: String representing the output format.
-    :param dtype: Output dtype.
+    :param format: String representing the output format. Defaul
+                   is csc.
+    :param dtype: Output dtype. Must be specified.
 
     :return: The concatenation of the sparse arrays column wise.
 
@@ -86,15 +87,15 @@ class HStack(gof.op.Op):
     - The grad implemented is regular, i.e. not structured.
     """
 
-    def __init__(self, format=None, dtype=None):
+    def __init__(self, dtype, format=None):
         if format is None:
             self.format = 'csc'
         else:
             self.format = format
+
         if dtype is None:
-            self.dtype = theano.config.floatX
-        else:
-            self.dtype = dtype
+            raise ValueError('The output dtype must be specified.')
+        self.dtype = dtype
 
     def __eq__(self, other):
         return (type(self) == type(other) and
@@ -152,26 +153,29 @@ def hstack(blocks, format=None, dtype=None):
     This wrap the method hstack from scipy.
 
     :param blocks: List of sparse array of compatible shape.
-    :param format: String representing the output format.
-    :param dtype: Output dtype.
+    :param format: String representing the output format. Defaul
+                   is csc.
+    :param dtype: Output dtype. Must be specified.
 
     :return: The concatenation of the sparse array column wise.
 
     :note:
     - The number of line of the sparse matrix must agree.
     - The grad implemented is regular, i.e. not structured.
-
     """
 
-    return HStack(format=format, dtype=dtype)(*blocks)
+    if dtype is None:
+        raise ValueError('The output dtype must be specified.')
+    return HStack(dtype, format=format)(*blocks)
 
 
 class VStack(HStack):
     """Stack sparse matrices vertically (row wise).
 
     :param blocks: Sequence of sparse array of compatible shape.
-    :param format: String representing the output format.
-    :param dtype: Output dtype.
+    :param format: String representing the output format. Defaul
+                   is csc.
+    :param dtype: Output dtype. Must be specified.
 
     :return: The concatenation of the sparse arrays row wise.
 
@@ -211,13 +215,14 @@ class VStack(HStack):
         return [(d, ins_shapes[0][1])]
 
 
-def hstack(blocks, format=None, dtype=None):
+def vstack(blocks, format=None, dtype=None):
     """Stack sparse matrices vertically (row wise).
 
     This wrap the method vstack from scipy.
 
     :param blocks: List of sparse array of compatible shape.
-    :param format: String representing the output format.
+    :param format: String representing the output format. Defaul
+                   is csc.
     :param dtype: Output dtype.
 
     :return: The concatenation of the sparse array row wise.
@@ -226,6 +231,9 @@ def hstack(blocks, format=None, dtype=None):
     - The number of column of the sparse matrix must agree.
     - The grad implemented is regular, i.e. not structured.
     """
+
+    if dtype is None:
+        raise ValueError('The output dtype must be specified.')
     return VStack(format=format, dtype=dtype)(*blocks)
 
 
@@ -270,11 +278,8 @@ class AddSSData(gof.op.Op):
     def grad(self, inputs, (gz, )):
         is_continuous = [(i.dtype in sparse.continuous_dtypes)
                          for i in inputs]
-
-        if all(is_continuous):
-            return [gz, gz]
-        else:
-            return [None] * len(inputs)
+        derivative = {True: gz, False: None}
+        return [derivative[b] for b in is_continuous]
 
     def infer_shape(self, node, ins_shapes):
         return [ins_shapes[0]]

theano/sparse/tests/test_sp2.py

@@ -163,7 +163,8 @@ class _HVStackTester(utt.InferShapeTester):
     def test_infer_shape(self):
         for format in sparse.sparse_formats:
             self._compile_and_check(self.x[format],
-                                    [self.op_class()(*self.x[format])],
+                                    [self.op_class(theano.config.floatX)
+                                     (*self.x[format])],
                                     self.mat[format],
                                     self.op_class)
 
@@ -171,15 +172,11 @@ class _HVStackTester(utt.InferShapeTester):
         for format in sparse.sparse_formats:
             for out_f in sparse.sparse_formats:
                 for dtype in sparse.float_dtypes:
-                    eps = None
-                    if dtype == 'float32':
-                        eps = 7e-4
-
                     verify_grad_sparse(
                         self.op_class(format=out_f, dtype=dtype),
                         self.mat[format],
                         structured=False,
-                        eps=eps)
+                        eps=7e-4)
 
 
 def _hv_switch(op, expected_function):
@@ -195,10 +192,6 @@ def _hv_switch(op, expected_function):
 
         def expected_f(self, a, format=None, dtype=None):
             return expected_function(a, format, dtype)
-
-        def setUp(self):
-            super(XStackTester, self).setUp()
-
     return XStackTester
 
 HStackTester = _hv_switch(S2.HStack, sp.hstack)