Drop DiGraph

python/cugraph/cugraph/__init__.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, 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
@@ -30,11 +30,8 @@
 
 from cugraph.structure import (
     Graph,
-    DiGraph,
     MultiGraph,
-    MultiDiGraph,
     BiPartiteGraph,
-    BiPartiteDiGraph,
     from_edgelist,
     from_cudf_edgelist,
     from_pandas_edgelist,

python/cugraph/cugraph/community/subgraph_extraction.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, 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
@@ -12,7 +12,7 @@
 # limitations under the License.
 
 import cudf
-
+from cugraph.structure import Graph
 from cugraph.community import subgraph_extraction_wrapper
 from cugraph.utilities import (
     ensure_cugraph_obj_for_nx,
@@ -57,14 +57,15 @@ def subgraph(G, vertices):
     """
 
     G, isNx = ensure_cugraph_obj_for_nx(G)
+    directed = G.is_directed()
 
     if G.renumbered:
         if isinstance(vertices, cudf.DataFrame):
             vertices = G.lookup_internal_vertex_id(vertices, vertices.columns)
         else:
             vertices = G.lookup_internal_vertex_id(vertices)
 
-    result_graph = type(G)()
+    result_graph = Graph(directed=directed)
 
     df = subgraph_extraction_wrapper.subgraph(G, vertices)
     src_names = "src"

python/cugraph/cugraph/components/connectivity.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, 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
@@ -20,7 +20,7 @@
     is_nx_graph_type,
     cupy_package as cp,
 )
-from cugraph.structure import Graph, DiGraph
+from cugraph.structure import Graph
 from cugraph.components import connectivity_wrapper
 import cudf
 from pylibcugraph import weakly_connected_components as pylibcugraph_wcc
@@ -35,7 +35,7 @@ def _ensure_args(api_name, G, directed, connection, return_labels):
     """
     G_type = type(G)
     # Check for Graph-type inputs and set defaults if unset
-    if (G_type in [Graph, DiGraph]) or is_nx_graph_type(G_type):
+    if (G_type in [Graph]) or is_nx_graph_type(G_type):
         exc_value = "'%s' cannot be specified for a Graph-type input"
         if directed is not None:
             raise TypeError(exc_value % "directed")
@@ -71,7 +71,7 @@ def _convert_df_to_output_type(df, input_type, return_labels):
     graph algos in this module, based on input_type.
     return_labels is only used for return values from cupy/scipy input types.
     """
-    if input_type in [Graph, DiGraph]:
+    if input_type in [Graph]:
         return df
 
     elif is_nx_graph_type(input_type):
@@ -88,7 +88,7 @@ def _convert_df_to_output_type(df, input_type, return_labels):
         sorted_df = df.sort_values("vertex")
         if return_labels:
             if is_cp_matrix_type(input_type):
-                labels = cp.fromDlpack(sorted_df["labels"].to_dlpack())
+                labels = cp.from_dlpack(sorted_df["labels"].to_dlpack())
             else:
                 labels = sorted_df["labels"].to_numpy()
             return (n_components, labels)
@@ -121,7 +121,7 @@ def weakly_connected_components(G, directed=None, connection=None, return_labels
             For non-Graph-type (eg. sparse matrix) values of G only.
             Raises TypeError if used with a Graph object.
 
-        If True, then convert the input matrix to a cugraph.DiGraph
+        If True, then convert the input matrix to a Graph(directed=True)
         and only move from point i to point j along paths csgraph[i, j]. If
         False, then find the shortest path on an undirected graph: the
         algorithm can progress from point i to j along csgraph[i, j] or
@@ -231,7 +231,7 @@ def strongly_connected_components(
             For non-Graph-type (eg. sparse matrix) values of G only.
             Raises TypeError if used with a Graph object.
 
-        If True, then convert the input matrix to a cugraph.DiGraph
+        If True, then convert the input matrix to a Graph(directed=True)
         and only move from point i to point j along paths csgraph[i, j]. If
         False, then find the shortest path on an undirected graph: the
         algorithm can progress from point i to j along csgraph[i, j] or
@@ -323,7 +323,7 @@ def connected_components(G, directed=None, connection="weak", return_labels=None
             For non-Graph-type (eg. sparse matrix) values of G only. Raises
             TypeError if used with a Graph object.
 
-        If True, then convert the input matrix to a cugraph.DiGraph
+        If True, then convert the input matrix to a Graph(directed=True)
         and only move from point i to point j along paths csgraph[i, j]. If
         False, then find the shortest path on an undirected graph: the
         algorithm can progress from point i to j along csgraph[i, j] or

python/cugraph/cugraph/sampling/node2vec.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2022, NVIDIA CORPORATION.
+# Copyright (c) 2023, 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
@@ -38,7 +38,7 @@ def node2vec(G, start_vertices, max_depth=1, compress_result=True, p=1.0, q=1.0)
     Parameters
     ----------
     G : cuGraph.Graph or networkx.Graph
-        The graph can be either directed (DiGraph) or undirected (Graph).
+        The graph can be either directed or undirected.
         Weights in the graph are ignored.
 
     start_vertices: int or list or cudf.Series or cudf.DataFrame

python/cugraph/cugraph/structure/__init__.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, 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
@@ -13,11 +13,8 @@
 
 from cugraph.structure.graph_classes import (
     Graph,
-    DiGraph,
     MultiGraph,
-    MultiDiGraph,
     BiPartiteGraph,
-    BiPartiteDiGraph,
 )
 from cugraph.structure.graph_classes import (
     is_weighted,

python/cugraph/cugraph/structure/convert_matrix.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, 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
@@ -18,7 +18,7 @@
 import cudf
 import dask_cudf
 
-from cugraph.structure.graph_classes import DiGraph, Graph
+from cugraph.structure.graph_classes import Graph
 
 # optional dependencies used for handling different input types
 try:
@@ -91,8 +91,6 @@ def from_edgelist(
         elif isinstance(create_using, Graph):
             attrs = {"directed": create_using.is_directed()}
             G = type(create_using)(**attrs)
-        elif isinstance(create_using, DiGraph):
-            G = type(create_using)()
         elif type(create_using) is type(Graph):
             G = create_using()
         else:
@@ -159,8 +157,6 @@ def from_adjlist(offsets, indices, values=None, create_using=Graph):
     elif isinstance(create_using, Graph):
         attrs = {"directed": create_using.is_directed()}
         G = type(create_using)(**attrs)
-    elif isinstance(create_using, DiGraph):
-        G = type(create_using)()
     elif type(create_using) is type(Graph):
         G = create_using()
     else:
@@ -240,8 +236,6 @@ def from_cudf_edgelist(
     elif isinstance(create_using, Graph):
         attrs = {"directed": create_using.is_directed()}
         G = type(create_using)(**attrs)
-    elif isinstance(create_using, DiGraph):
-        G = type(create_using)()
     elif type(create_using) is type(Graph):
         G = create_using()
     else:
@@ -327,8 +321,6 @@ def from_pandas_edgelist(
     elif isinstance(create_using, Graph):
         attrs = {"directed": create_using.is_directed()}
         G = type(create_using)(**attrs)
-    elif isinstance(create_using, DiGraph):
-        G = type(create_using)()
     elif type(create_using) is type(Graph):
         G = create_using()
     else:
@@ -391,8 +383,6 @@ def from_pandas_adjacency(df, create_using=Graph):
     elif isinstance(create_using, Graph):
         attrs = {"directed": create_using.is_directed()}
         G = type(create_using)(**attrs)
-    elif isinstance(create_using, DiGraph):
-        G = type(create_using)()
     elif type(create_using) is type(Graph):
         G = create_using()
     else:
@@ -438,8 +428,6 @@ def from_numpy_array(A, create_using=Graph):
     elif isinstance(create_using, Graph):
         attrs = {"directed": create_using.is_directed()}
         G = type(create_using)(**attrs)
-    elif isinstance(create_using, DiGraph):
-        G = type(create_using)()
     elif type(create_using) is type(Graph):
         G = create_using()
     else:
@@ -484,8 +472,6 @@ def from_numpy_matrix(A, create_using=Graph):
     elif isinstance(create_using, Graph):
         attrs = {"directed": create_using.is_directed()}
         G = type(create_using)(**attrs)
-    elif isinstance(create_using, DiGraph):
-        G = type(create_using)()
     elif type(create_using) is type(Graph):
         G = create_using()
     else:

python/cugraph/cugraph/structure/graph_classes.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+# Copyright (c) 2021-2023, 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
@@ -19,7 +19,6 @@
 )
 import cudf
 import dask_cudf
-import warnings
 
 from cugraph.utilities.utils import import_optional
 
@@ -688,15 +687,6 @@ def add_nodes_from(self, nodes):
     # def properties():
 
 
-class DiGraph(Graph):
-    def __init__(self, m_graph=None):
-        warnings.warn(
-            "DiGraph is deprecated, use Graph(directed=True) instead",
-            DeprecationWarning,
-        )
-        super(DiGraph, self).__init__(m_graph, directed=True)
-
-
 class MultiGraph(Graph):
     """
     A Multigraph; a Graph containing more than one edge between vertex pairs.
@@ -714,16 +704,6 @@ def is_multigraph(self):
         return True
 
 
-class MultiDiGraph(MultiGraph):
-    def __init__(self):
-        warnings.warn(
-            "MultiDiGraph is deprecated,\
-                use MultiGraph(directed=True) instead",
-            DeprecationWarning,
-        )
-        super(MultiDiGraph, self).__init__(directed=True)
-
-
 class Tree(Graph):
     """
     A Tree
@@ -920,30 +900,6 @@ def is_bipartite(self):
         return True
 
 
-class BiPartiteDiGraph(BiPartiteGraph):
-    """
-    A Directed Bipartite Graph
-    """
-
-    def __init__(self):
-        warnings.warn(
-            "BiPartiteDiGraph is deprecated,\
- use BiPartiteGraph(directed=True) instead",
-            DeprecationWarning,
-        )
-        super(BiPartiteDiGraph, self).__init__(directed=True)
-
-
-class NPartiteDiGraph(NPartiteGraph):
-    def __init__(self):
-        warnings.warn(
-            "NPartiteDiGraph is deprecated,\
- use NPartiteGraph(directed=True) instead",
-            DeprecationWarning,
-        )
-        super(NPartiteGraph, self).__init__(directed=True)
-
-
 def is_directed(G):
     """
     Returns True if the graph is a directed graph.

python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+# Copyright (c) 2021-2023, 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
@@ -751,15 +751,13 @@ def convert_to_cudf(cp_arrays):
 
         return ddf
 
-    def to_directed(self, DiG):
+    def to_directed(self, G):
         """
         Return a directed representation of the graph.
-        This function sets the type of graph as DiGraph() and returns the
-        directed view.
 
         Returns
         -------
-        G : DiGraph
+        G : Graph(directed=True)
             A directed graph with the same nodes, and each edge (u,v,weights)
             replaced by two directed edges (u,v,weights) and (v,u,weights).
 

python/cugraph/cugraph/testing/utils.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+# Copyright (c) 2020-2023, 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
@@ -168,16 +168,16 @@ def create_obj_from_csv(
     Return an object based on obj_type populated with the contents of
     csv_file_name
     """
-    if obj_type in [cugraph.Graph, cugraph.DiGraph]:
+    if obj_type in [cugraph.Graph]:
         return generate_cugraph_graph_from_file(
             csv_file_name,
-            directed=(obj_type is cugraph.DiGraph),
+            directed=directed,
             edgevals=edgevals,
         )
     elif isinstance(obj_type, cugraph.Graph):
         return generate_cugraph_graph_from_file(
             csv_file_name,
-            directed=(obj_type.is_directed()),
+            directed=directed,
             edgevals=edgevals,
         )