Enable FutureWarnings and DeprecationWarnings as errors in cugraph

python/cugraph/cugraph/community/egonet.py

@@ -199,10 +199,10 @@ def batched_ego_graphs(G, seeds, radius=1, center=True, undirected=None, distanc
     --------
     >>> from cugraph.datasets import karate
     >>> G = karate.get_graph(download=True)
-    >>> b_ego_graph, offsets = cugraph.batched_ego_graphs(G, seeds=[1,5],
+    >>> b_ego_graph, offsets = cugraph.batched_ego_graphs(G, seeds=[1,5],  # doctest: +SKIP
     ...                                                   radius=2)
 
-    """
+    """  # noqa:E501
     warning_msg = "This function is deprecated. Batched support for multiple vertices \
          will be added to `ego_graph`"
     warnings.warn(warning_msg, DeprecationWarning)

python/cugraph/cugraph/community/ktruss_subgraph.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -95,11 +95,11 @@ def k_truss(
     G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if isNx is True:
-        k_sub = ktruss_subgraph(G, k)
+        k_sub = ktruss_subgraph(G, k, use_weights=False)
         S = cugraph_to_nx(k_sub)
         return S
     else:
-        return ktruss_subgraph(G, k)
+        return ktruss_subgraph(G, k, use_weights=False)
 
 
 # FIXME: merge this function with k_truss
@@ -174,7 +174,7 @@ def ktruss_subgraph(
     --------
     >>> from cugraph.datasets import karate
     >>> G = karate.get_graph(download=True)
-    >>> k_subgraph = cugraph.ktruss_subgraph(G, 3)
+    >>> k_subgraph = cugraph.ktruss_subgraph(G, 3, use_weights=False)
 
     """
 

python/cugraph/cugraph/community/subgraph_extraction.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -64,7 +64,7 @@ def subgraph(
     >>> verts[1] = 1
     >>> verts[2] = 2
     >>> sverts = cudf.Series(verts)
-    >>> Sg = cugraph.subgraph(G, sverts)
+    >>> Sg = cugraph.subgraph(G, sverts)  # doctest: +SKIP
 
     """
 

python/cugraph/cugraph/dask/centrality/betweenness_centrality.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2022-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -204,7 +204,7 @@ def betweenness_centrality(
     >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/..
     >>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv")
     >>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv",
-    ...                          chunksize=chunksize, delimiter=" ",
+    ...                          blocksize=chunksize, delimiter=" ",
     ...                          names=["src", "dst", "value"],
     ...                          dtype=["int32", "int32", "float32"])
     >>> dg = cugraph.Graph(directed=True)
@@ -362,7 +362,7 @@ def edge_betweenness_centrality(
     >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/..
     >>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv")
     >>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv",
-    ...                          chunksize=chunksize, delimiter=" ",
+    ...                          blocksize=chunksize, delimiter=" ",
     ...                          names=["src", "dst", "value"],
     ...                          dtype=["int32", "int32", "float32"])
     >>> dg = cugraph.Graph(directed=True)

python/cugraph/cugraph/dask/centrality/eigenvector_centrality.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2022-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -104,7 +104,7 @@ def eigenvector_centrality(input_graph, max_iter=100, tol=1.0e-6):
     >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/..
     >>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv")
     >>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv",
-    ...                          chunksize=chunksize, delimiter=" ",
+    ...                          blocksize=chunksize, delimiter=" ",
     ...                          names=["src", "dst", "value"],
     ...                          dtype=["int32", "int32", "float32"])
     >>> dg = cugraph.Graph()

python/cugraph/cugraph/dask/centrality/katz_centrality.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2022-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -133,7 +133,7 @@ def katz_centrality(
     >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/..
     >>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv")
     >>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv",
-    ...                          chunksize=chunksize, delimiter=" ",
+    ...                          blocksize=chunksize, delimiter=" ",
     ...                          names=["src", "dst", "value"],
     ...                          dtype=["int32", "int32", "float32"])
     >>> dg = cugraph.Graph(directed=True)

python/cugraph/cugraph/dask/community/leiden.py

@@ -132,7 +132,7 @@ def leiden(
     >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/..
     >>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv")
     >>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv",
-    ...                          chunksize=chunksize, delimiter=" ",
+    ...                          blocksize=chunksize, delimiter=" ",
     ...                          names=["src", "dst", "value"],
     ...                          dtype=["int32", "int32", "float32"])
     >>> dg = cugraph.Graph()

python/cugraph/cugraph/dask/community/louvain.py

@@ -136,7 +136,7 @@ def louvain(
     >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/..
     >>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv")
     >>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv",
-    ...                          chunksize=chunksize, delimiter=" ",
+    ...                          blocksize=chunksize, delimiter=" ",
     ...                          names=["src", "dst", "value"],
     ...                          dtype=["int32", "int32", "float32"])
     >>> dg = cugraph.Graph()

python/cugraph/cugraph/dask/components/connectivity.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+# Copyright (c) 2021-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -78,7 +78,7 @@ def weakly_connected_components(input_graph):
     >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/..
     >>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv")
     >>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv",
-    ...                          chunksize=chunksize, delimiter=" ",
+    ...                          blocksize=chunksize, delimiter=" ",
     ...                          names=["src", "dst", "value"],
     ...                          dtype=["int32", "int32", "float32"])
     >>> dg = cugraph.Graph(directed=False)

python/cugraph/cugraph/dask/cores/k_core.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2022-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -109,7 +109,7 @@ def k_core(input_graph, k=None, core_number=None, degree_type="bidirectional"):
     >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/..
     >>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv")
     >>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv",
-    ...                          chunksize=chunksize, delimiter=" ",
+    ...                          blocksize=chunksize, delimiter=" ",
     ...                          names=["src", "dst", "value"],
     ...                          dtype=["int32", "int32", "float32"])
     >>> dg = cugraph.Graph(directed=False)

python/cugraph/cugraph/dask/link_analysis/hits.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+# Copyright (c) 2022-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -118,7 +118,7 @@ def hits(input_graph, tol=1.0e-5, max_iter=100, nstart=None, normalized=True):
     >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/..
     >>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv")
     >>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv",
-    ...                          chunksize=chunksize, delimiter=" ",
+    ...                          blocksize=chunksize, delimiter=" ",
     ...                          names=["src", "dst", "value"],
     ...                          dtype=["int32", "int32", "float32"])
     >>> dg = cugraph.Graph(directed=True)

python/cugraph/cugraph/dask/link_analysis/pagerank.py

@@ -295,7 +295,7 @@ def pagerank(
     >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/..
     >>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv")
     >>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv",
-    ...                          chunksize=chunksize, delimiter=" ",
+    ...                          blocksize=chunksize, delimiter=" ",
     ...                          names=["src", "dst", "value"],
     ...                          dtype=["int32", "int32", "float32"])
     >>> dg = cugraph.Graph(directed=True)

python/cugraph/cugraph/dask/traversal/bfs.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -106,7 +106,7 @@ def bfs(input_graph, start, depth_limit=None, return_distances=True, check_start
     >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/..
     >>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv")
     >>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv",
-    ...                          chunksize=chunksize, delimiter=" ",
+    ...                          blocksize=chunksize, delimiter=" ",
     ...                          names=["src", "dst", "value"],
     ...                          dtype=["int32", "int32", "float32"])
     >>> dg = cugraph.Graph(directed=True)

python/cugraph/cugraph/dask/traversal/sssp.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -89,7 +89,7 @@ def sssp(input_graph, source, cutoff=None, check_source=True):
     >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/..
     >>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv")
     >>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv",
-    ...                          chunksize=chunksize, delimiter=" ",
+    ...                          blocksize=chunksize, delimiter=" ",
     ...                          names=["src", "dst", "value"],
     ...                          dtype=["int32", "int32", "float32"])
     >>> dg = cugraph.Graph(directed=True)

python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -259,18 +259,24 @@ def flush(self) -> None:
         start_time_sample_call = time.perf_counter()
 
         # Call uniform neighbor sample
-        output = sample_fn(
-            self.__graph,
-            **self.__sample_call_args,
-            start_list=self.__batches[[self.start_col_name, self.batch_col_name]][
-                batch_id_filter
-            ],
-            with_batch_ids=True,
-            with_edge_properties=True,
-            return_offsets=True,
-            renumber=self.__renumber,
-            # use_legacy_names=False,
-        )
+        with warnings.catch_warnings():
+            # TODO: Address the following uniform_neighbor_sample deprecations
+            # with_edge_properties
+            # include_hop_column
+            # use_legacy_names
+            warnings.simplefilter("ignore", FutureWarning)
+            output = sample_fn(
+                self.__graph,
+                **self.__sample_call_args,
+                start_list=self.__batches[[self.start_col_name, self.batch_col_name]][
+                    batch_id_filter
+                ],
+                with_batch_ids=True,
+                with_edge_properties=True,
+                return_offsets=True,
+                renumber=self.__renumber,
+                # use_legacy_names=False,
+            )
 
         if self.__renumber:
             samples, offsets, renumber_map = output

python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py

@@ -131,7 +131,7 @@ def __from_edgelist(
         edge_id=None,
         edge_type=None,
         renumber=True,
-        legacy_renum_only=True,
+        legacy_renum_only=False,
         store_transposed=False,
     ):
         if legacy_renum_only:
@@ -261,14 +261,16 @@ def __from_edgelist(
         # will be dropped unless the graph is a MultiGraph(Not Implemented yet)
         # TODO: Update Symmetrize to work on Graph and/or DataFrame
         if edge_attr is not None:
-            source_col, dest_col, value_col = symmetrize(
-                elist,
-                source,
-                destination,
-                edge_attr,
-                multi=self.properties.multi_edge,  # Deprecated parameter
-                symmetrize=not self.properties.directed,
-            )
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", "Multi is deprecated", FutureWarning)
+                source_col, dest_col, value_col = symmetrize(
+                    elist,
+                    source,
+                    destination,
+                    edge_attr,
+                    multi=self.properties.multi_edge,  # Deprecated parameter
+                    symmetrize=not self.properties.directed,
+                )
 
             if isinstance(value_col, cudf.DataFrame):
                 value_dict = {}
@@ -277,13 +279,15 @@ def __from_edgelist(
                 value_col = value_dict
         else:
             value_col = None
-            source_col, dest_col = symmetrize(
-                elist,
-                source,
-                destination,
-                multi=self.properties.multi_edge,  # Deprecated parameter
-                symmetrize=not self.properties.directed,
-            )
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", "Multi is deprecated", FutureWarning)
+                source_col, dest_col = symmetrize(
+                    elist,
+                    source,
+                    destination,
+                    multi=self.properties.multi_edge,  # Deprecated parameter
+                    symmetrize=not self.properties.directed,
+                )
 
         if isinstance(value_col, dict):
             value_col = {

python/cugraph/cugraph/structure/symmetrize.py

@@ -73,7 +73,7 @@ def symmetrize_df(
     >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/..
     >>> M = cudf.read_csv(datasets_path / 'karate.csv', delimiter=' ',
     ...                   dtype=['int32', 'int32', 'float32'], header=None)
-    >>> sym_df = symmetrize_df(M, '0', '1')
+    >>> sym_df = symmetrize_df(M, '0', '1', multi=True)
 
     """
     if not isinstance(src_name, list):
@@ -159,7 +159,7 @@ def symmetrize_ddf(
     >>> # Init a DASK Cluster
     >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/..
     >>> # chunksize = dcg.get_chunksize(datasets / 'karate.csv')
-    >>> # ddf = dask_cudf.read_csv(datasets/'karate.csv', chunksize=chunksize,
+    >>> # ddf = dask_cudf.read_csv(datasets/'karate.csv', blocksize=chunksize,
     >>> #                          delimiter=' ',
     >>> #                          names=['src', 'dst', 'weight'],
     >>> #                          dtype=['int32', 'int32', 'float32'])
@@ -256,7 +256,7 @@ def symmetrize(
     >>> df['sources'] = cudf.Series(M['0'])
     >>> df['destinations'] = cudf.Series(M['1'])
     >>> df['values'] = cudf.Series(M['2'])
-    >>> src, dst, val = symmetrize(df, 'sources', 'destinations', 'values')
+    >>> src, dst, val = symmetrize(df, 'sources', 'destinations', 'values', multi=True)
 
     """
 

python/cugraph/cugraph/testing/utils.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+# Copyright (c) 2020-2024, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -245,7 +245,7 @@ def read_dask_cudf_csv_file(csv_file, read_weights_in_sp=True, single_partition=
             chunksize = os.path.getsize(csv_file)
             return dask_cudf.read_csv(
                 csv_file,
-                chunksize=chunksize,
+                blocksize=chunksize,
                 delimiter=" ",
                 names=["src", "dst", "weight"],
                 dtype=["int32", "int32", "float32"],
@@ -264,7 +264,7 @@ def read_dask_cudf_csv_file(csv_file, read_weights_in_sp=True, single_partition=
             chunksize = os.path.getsize(csv_file)
             return dask_cudf.read_csv(
                 csv_file,
-                chunksize=chunksize,
+                blocksize=chunksize,
                 delimiter=" ",
                 names=["src", "dst", "weight"],
                 dtype=["int32", "int32", "float32"],

python/cugraph/cugraph/tests/comms/test_comms_mg.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+# Copyright (c) 2020-2024, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -53,7 +53,7 @@ def test_dask_mg_pagerank(dask_client, directed):
 
     ddf1 = dask_cudf.read_csv(
         input_data_path1,
-        chunksize=chunksize1,
+        blocksize=chunksize1,
         delimiter=" ",
         names=["src", "dst", "value"],
         dtype=["int32", "int32", "float32"],
@@ -66,7 +66,7 @@ def test_dask_mg_pagerank(dask_client, directed):
 
     ddf2 = dask_cudf.read_csv(
         input_data_path2,
-        chunksize=chunksize2,
+        blocksize=chunksize2,
         delimiter=" ",
         names=["src", "dst", "value"],
         dtype=["int32", "int32", "float32"],