Move tests of sp_sum.

theano/sparse/basic.py

@@ -1287,11 +1287,11 @@ neg = Neg()
 
 
 class SpSum(gof.op.Op):
-    """
-    TODO: rewrite
+    """TODO: rewrite
     Scale each columns of a sparse matrix by the
     corresponding element of a dense vector
     """
+
     axis = None
     sparse_grad = False
 
@@ -1351,7 +1351,7 @@ class SpSum(gof.op.Op):
             if self.axis == 0:
                 if x.format == 'csc':
                     z[0] = numpy.asarray(x.sum(axis=self.axis)).reshape(
-                        (x.shape[1],))
+                        (x.shape[1], ))
                 else:
                     z[0] = numpy.asarray(x.asformat(x.format).sum(
                             axis=self.axis)).reshape((x.shape[1],))

theano/sparse/sandbox/test_sp.py

@@ -362,72 +362,6 @@ class TestSP(unittest.TestCase):
                     # symbolic stuff
                     utt.verify_grad(d, [kvals])
 
-    def test_sp_sum(self):
-        from theano.sparse import SpSum
-
-        # TODO: test both grad.
-        rng = numpy.random.RandomState(42)
-        from theano.sparse.basic import SparseFromDense,DenseFromSparse
-        cases = [("csc", scipy.sparse.csc_matrix), ("csr", scipy.sparse.csr_matrix)]
-
-        for format, cast in cases:
-
-            #print 'format: %(format)s' % locals()
-            x = theano.sparse.SparseType(format=format,
-                                         dtype=theano.config.floatX)()
-            x_data = numpy.arange(20).reshape(5,4).astype(theano.config.floatX)
-
-            # Sum on all axis
-            #print 'sum on all axis...'
-            z = theano.sparse.sandbox.sp.sp_sum(x)
-            assert z.type.broadcastable == ()
-            f = theano.function([x], z)
-            x_val = cast(x_data)
-            out = f(x_val)
-            expected = x_val.sum()
-            assert out == expected
-
-            # Sum on axis 0
-            #print 'sum on axis 0...'
-            z = theano.sparse.sandbox.sp.sp_sum(x, axis=0)
-            assert z.type.broadcastable == (False,)
-            f = theano.function([x], z)
-            x_val = cast(x_data)
-            out = f(x_val)
-            expected = x_val.sum(axis=0)
-            assert (out == expected).all()
-
-            # Sum on axis 1
-            #print 'sum on axis 1...'
-            z = theano.sparse.sandbox.sp.sp_sum(x, axis=1)
-            assert z.type.broadcastable == (False,)
-            f = theano.function([x], z)
-            x_val = cast(x_data)
-            out = f(x_val)
-            expected = numpy.asarray(x_val.sum(axis=1)).reshape(x_val.shape[0])
-            assert (out == expected).all()
-
-            # Sparse gradient on Sum on all axis
-            # unfinished, and suspended until verify_grad get fixed
-            if False:
-#                print 'grad on sum on all axis...'
-                def fun(x):
-                    ## verify_grad does not handle sparse data, so here's some casting as a workaround.
-                    # x is a dense matrix: make it sparse
-                    sparse_var = SparseFromDense(format)(x)
-                    # apply op
-                    dense_sum = theano.sparse.sandbox.sp.SpSum(axis=None, sparse_grad=False)(sparse_var)
-                    return dense_sum
-                    # cast back to dense so that verify_grad can work
-                    dense_sum = theano.sparse.DenseFromSparse()(sparse_sum)
-                    return dense_sum
-                x_val = x_data.copy()
-#                print type(x_val)
-                import pdb;pdb.set_trace()
-                tensor.verify_grad(fun, [x_val], rng=rng)
-                #utt.verify_grad(SpSum(axis=None), [x_val])
-#                print 'ok'
-
 
 def test_diag():
     m = theano.sparse.csc_matrix()

theano/sparse/tests/test_basic.py

@@ -1329,6 +1329,73 @@ def test_size():
         check()
 
 
+def test_sp_sum():
+    from theano.sparse import SpSum
+
+    # TODO: test both grad.
+    rng = numpy.random.RandomState(42)
+    from theano.sparse.basic import SparseFromDense,DenseFromSparse
+    cases = [("csc", scipy.sparse.csc_matrix), ("csr", scipy.sparse.csr_matrix)]
+
+    for format, cast in cases:
+
+        #print 'format: %(format)s' % locals()
+        x = theano.sparse.SparseType(format=format,
+                                     dtype=theano.config.floatX)()
+        x_data = numpy.arange(20).reshape(5,4).astype(theano.config.floatX)
+
+        # Sum on all axis
+        #print 'sum on all axis...'
+        z = theano.sparse.sp_sum(x)
+        assert z.type.broadcastable == ()
+        f = theano.function([x], z)
+        x_val = cast(x_data)
+        out = f(x_val)
+        expected = x_val.sum()
+        assert out == expected
+
+        # Sum on axis 0
+        #print 'sum on axis 0...'
+        z = theano.sparse.sp_sum(x, axis=0)
+        assert z.type.broadcastable == (False,)
+        f = theano.function([x], z)
+        x_val = cast(x_data)
+        out = f(x_val)
+        expected = x_val.sum(axis=0)
+        assert (out == expected).all()
+
+        # Sum on axis 1
+        #print 'sum on axis 1...'
+        z = theano.sparse.sp_sum(x, axis=1)
+        assert z.type.broadcastable == (False,)
+        f = theano.function([x], z)
+        x_val = cast(x_data)
+        out = f(x_val)
+        expected = numpy.asarray(x_val.sum(axis=1)).reshape(x_val.shape[0])
+        assert (out == expected).all()
+
+        # Sparse gradient on Sum on all axis
+        # unfinished, and suspended until verify_grad get fixed
+        if False:
+            #                print 'grad on sum on all axis...'
+            def fun(x):
+                ## verify_grad does not handle sparse data, so here's some casting as a workaround.
+                # x is a dense matrix: make it sparse
+                sparse_var = SparseFromDense(format)(x)
+                # apply op
+                dense_sum = theano.sparse.SpSum(axis=None, sparse_grad=False)(sparse_var)
+                return dense_sum
+                # cast back to dense so that verify_grad can work
+                dense_sum = theano.sparse.DenseFromSparse()(sparse_sum)
+                return dense_sum
+            x_val = x_data.copy()
+            #                print type(x_val)
+            import pdb;pdb.set_trace()
+            tensor.verify_grad(fun, [x_val], rng=rng)
+            #utt.verify_grad(SpSum(axis=None), [x_val])
+            #                print 'ok'
+
+
 class Remove0Tester(utt.InferShapeTester):
     def setUp(self):
         super(Remove0Tester, self).setUp()