Add paramter unsorted_indices and explicit_zero to sparse_random_inputs()

theano/sparse/tests/test_basic.py

@@ -80,7 +80,8 @@ def random_lil(shape, dtype, nnz):
     return rval
 
 
-def sparse_random_inputs(format, shape, n=1, out_dtype=None, p=0.5, gap=None):
+def sparse_random_inputs(format, shape, n=1, out_dtype=None, p=0.5, gap=None,
+                         explicit_zero=False, unsorted_indices=False):
     """Return a tuple containing everything needed to
     perform a test.
 
@@ -97,9 +98,15 @@ def sparse_random_inputs(format, shape, n=1, out_dtype=None, p=0.5, gap=None):
                 max, when `gap` = (`a`, `b`) it provide a sample
                 from [a, b[. If `None` is used, it provide [0, 1]
                 for float dtypes and [0, 50[ for integer dtypes.
-
+    :param explicit_zero: When True, we add explicit zero in the
+                          returned sparse matrix
+    :param unsorted_indices: when True, we make sure there is
+                             unsorted indices in the returned
+                             sparse matrix.
     :return: (variable, data) where both `variable`
              and `data` are list.
+
+    :note: explicit_zero and unsorted_indices was added in Theano 0.6rc4
     """
 
     if out_dtype is None:
@@ -136,6 +143,19 @@ def sparse_random_inputs(format, shape, n=1, out_dtype=None, p=0.5, gap=None):
                 for k in range(n)]
     data = [getattr(scipy.sparse, format + '_matrix')(_rand(), dtype=out_dtype)
             for k in range(n)]
+    if unsorted_indices:
+        for idx in range(n):
+            d = data[idx]
+            d = d[range(d.shape[0])]
+            assert not d.has_sorted_indices
+            data[idx] = d
+    if explicit_zero:
+        for idx in range(n):
+            assert data[idx].nnz > 1, (
+                "can't make a sparse matrix with explicit 0")
+            d_idx = numpy.random.randint(data[idx].nnz)
+            data[idx].data[d_idx] = 0
+
     #numpy 1.5.0 with scipy 0.9.0 have scipy.sparse.XXX_matrix return
     #typenum 10(ulonglong) instead of 8(uint64) event if they are the same!
     #Theano don't like ulonglong type_num
@@ -1845,17 +1865,11 @@ class Remove0Tester(utt.InferShapeTester):
             ('csr', scipy.sparse.csr_matrix), ]
 
         for format, matrix_class in configs:
-            # real
-            origin = (numpy.arange(9) + 1).reshape((3, 3))
-            origin.astype(config.floatX)
-            mat = matrix_class(origin).astype(theano.config.floatX)
-
-            mat[0, 1] = mat[1, 0] = mat[2, 2] = 0
-
-            assert mat.size == 9
+            (x,), (mat,) = sparse_random_inputs(format, (3, 4),
+                                        out_dtype=config.floatX,
+                                        explicit_zero=True)
+            assert 0 in mat.data
 
-            # symbolic
-            x = theano.sparse.SparseType(format=format, dtype=config.floatX)()
             # the In thingy has to be there because theano has as rule not
             # to optimize inputs
             f = theano.function([theano.In(x, borrow=True, mutable=True)],