add test using karate dataset

python/cugraph-dgl/tests/test_utils.py

@@ -22,6 +22,7 @@
     create_homogeneous_sampled_graphs_from_dataframe,
     _get_source_destination_range,
     _create_homogeneous_cugraph_dgl_nn_sparse_graph,
+    create_homogeneous_sampled_graphs_from_dataframe_csc,
 )
 from cugraph.utilities.utils import import_optional
 
@@ -50,6 +51,23 @@ def get_dummy_sampled_df():
     return df
 
 
+def get_dummy_sampled_df_csc():
+    df_dict = dict(
+        minors=np.array(
+            [1, 1, 2, 1, 0, 3, 1, 3, 2, 3, 2, 4, 0, 1, 1, 0, 3, 2], dtype=np.int32
+        ),
+        major_offsets=np.arange(19, dtype=np.int64),
+        map=np.array(
+            [26, 29, 33, 22, 23, 32, 18, 29, 33, 33, 8, 30, 32], dtype=np.int32
+        ),
+        renumber_map_offsets=np.array([0, 4, 9, 13], dtype=np.int64),
+        label_hop_offsets=np.array([0, 1, 3, 6, 7, 9, 13, 14, 16, 18], dtype=np.int64),
+    )
+
+    # convert values to Series so that NaNs are padded automatically
+    return cudf.DataFrame({k: cudf.Series(v) for k, v in df_dict.items()})
+
+
 def test_get_renumber_map():
 
     sampled_df = get_dummy_sampled_df()
@@ -176,3 +194,13 @@ def test__create_homogeneous_cugraph_dgl_nn_sparse_graph():
     assert sparse_graph.num_src_nodes() == 2
     assert sparse_graph.num_dst_nodes() == seednodes_range + 1
     assert isinstance(sparse_graph, cugraph_dgl.nn.SparseGraph)
+
+
+def test_create_homogeneous_sampled_graphs_from_dataframe_csc():
+    df = get_dummy_sampled_df_csc()
+    batches = create_homogeneous_sampled_graphs_from_dataframe_csc(df)
+
+    assert len(batches) == 3
+    assert torch.equal(batches[0][0], torch.IntTensor([26, 29, 33, 22]).cuda())
+    assert torch.equal(batches[1][0], torch.IntTensor([23, 32, 18, 29, 33]).cuda())
+    assert torch.equal(batches[2][0], torch.IntTensor([33, 8, 30, 32]).cuda())