From 9e73617c37d5242c855d4f011fdc05de15c8d03d Mon Sep 17 00:00:00 2001 From: Tingyu Wang Date: Thu, 28 Sep 2023 09:55:18 -0700 Subject: [PATCH] add test using karate dataset --- python/cugraph-dgl/tests/test_utils.py | 28 ++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/python/cugraph-dgl/tests/test_utils.py b/python/cugraph-dgl/tests/test_utils.py index 740db59ce7f..4be66758b43 100644 --- a/python/cugraph-dgl/tests/test_utils.py +++ b/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())