Modified small mistakes in GetItemList and GetItem2Lists.

theano/sparse/basic.py

@@ -1000,6 +1000,7 @@ csc_from_dense = SparseFromDense('csc')
 :return: The same as `x` in a sparse matrix format.
 """
 
+
 # Indexing
 class GetItemList(gof.op.Op):
 
@@ -1037,6 +1038,14 @@ class GetItemList(gof.op.Op):
         return self.__class__.__name__
 
 get_item_list = GetItemList()
+"""Select row of sparse matrix, 
+returning them as a new sparse matrix.
+
+:param x: Sparse matrix.
+:param index: List of rows.
+
+:return: The corresponding rows in `x`.
+"""
 
 
 class GetItemListGrad(gof.op.Op):
@@ -1048,7 +1057,7 @@ class GetItemListGrad(gof.op.Op):
         return hash(type(self))
 
     def infer_shape(self, node, shapes):
-        return [(shapes[0][0], shapes[0][1])]
+        return [(shapes[0])]
 
     def make_node(self, x, index, gz):
         x = as_sparse_variable(x)
@@ -1060,6 +1069,11 @@ class GetItemListGrad(gof.op.Op):
         ind = tensor.as_tensor_variable(index)
         assert ind.ndim == 1
 
+        scipy_ver = [int(n) for n in scipy.__version__.split('.')[:2]]
+
+        if not scipy_ver >= [0, 13]:
+            raise NotImplemented("Scipy version is to old")
+
         return gof.Apply(self, [x, ind, gz], [x.type()])
 
     def perform(self, node, inp, (out, )):
@@ -1071,11 +1085,8 @@ class GetItemListGrad(gof.op.Op):
             y = scipy.sparse.csr_matrix((x.shape[0], x.shape[1]))
         else:
             y = scipy.sparse.csc_matrix((x.shape[0], x.shape[1]))
-        c = 0
-        for a in range(0, gz.shape[0]):
-            for b in range(0, gz.shape[1]):
-                y[(indices[c], b)] = gz[(a, b)]
-            c = c + 1
+        for a in range(0, len(indices)):
+                y[indices[a]] = gz[a]
 
         out[0] = y
 
@@ -1107,7 +1118,9 @@ class GetItem2Lists(gof.op.Op):
         ind1 = inp[1]
         ind2 = inp[2]
         out[0] = numpy.asarray(x[ind1, ind2]).flatten()
-
+        """Here scipy returns the corresponding elements in a matrix which isn't what we are aiming for.
+        Using asarray and flatten, out[0] becomes an array.
+        """
     def grad(self, inputs, g_outputs):
         x, ind1, ind2 = inputs
         gout, = g_outputs
@@ -1119,6 +1132,14 @@ class GetItem2Lists(gof.op.Op):
         return self.__class__.__name__
 
 get_item_2lists = GetItem2Lists()
+"""Select elements of sparse matrix, returning them in a vector.
+
+:param x: Sparse matrix.
+:param index: List of two lists, first list indicating the row 
+of each element and second list indicating its column.
+
+:return: The corresponding elements in `x`.
+"""
 
 
 class GetItem2ListsGrad(gof.op.Op):
@@ -1130,7 +1151,7 @@ class GetItem2ListsGrad(gof.op.Op):
         return hash(type(self))
 
     def infer_shape(self, node, shapes):
-        return [(shapes[0][0], shapes[0][1])]
+        return [(shapes[0])]
 
     def make_node(self, x, ind1, ind2, gz):
         x = as_sparse_variable(x)
@@ -1141,6 +1162,8 @@ class GetItem2ListsGrad(gof.op.Op):
         ind2 = tensor.as_tensor_variable(ind2)
         assert ind1.ndim == 1
         assert ind2.ndim == 1
+        assert "int" in ind1.dtype
+        assert "int" in ind2.dtype
 
         return gof.Apply(self, [x, ind1, ind2, gz], [x.type()])
 
@@ -1155,9 +1178,8 @@ class GetItem2ListsGrad(gof.op.Op):
         else:
             y = scipy.sparse.csc_matrix((x.shape[0], x.shape[1]))
         z = 0
-        for i in range(0, len(ind1)):
+        for z in range(0, len(ind1)):
             y[(ind1[z], ind2[z])] = gz[z]
-            z = z + 1
 
         out[0] = y
 

theano/sparse/tests/test_basic.py

@@ -2054,7 +2054,10 @@ class Test_getitem(unittest.TestCase):
 
         x, x_val = sparse_random_inputs("csr", (4,5), out_dtype="float64")
 
+        try:
             verify_grad_sparse(op_with_fixed_index, x_val)
+        except NotImplementedError, e:
+            assert "Scipy version is to old" in str(e)
 
     def test_GetItem2Lists(self):