expose decompress to edgelist to the python API

python/cugraph/cugraph/structure/__init__.py

@@ -30,6 +30,7 @@
     replicate_cudf_dataframe,
     replicate_cudf_series,
 )
+from cugraph.structure.decompress_to_edgelist import decompress_to_edgelist
 from cugraph.structure.convert_matrix import (
     from_edgelist,
     from_cudf_edgelist,

python/cugraph/cugraph/structure/decompress_to_edgelist.py

@@ -0,0 +1,87 @@
+# 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
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import cudf
+from pylibcugraph import ResourceHandle
+from pylibcugraph import decompress_to_edgelist as pylibcugraph_decompress_to_edgelist
+
+from cugraph.structure import Graph
+
+
+def decompress_to_edgelist(
+    G: Graph,
+    do_expensive_check: bool
+) -> cudf.DataFrame:
+    """
+    Compute a subgraph of the existing graph including only the specified
+    vertices.  This algorithm works with both directed and undirected graphs
+    and does not actually traverse the edges, but instead simply pulls out any
+    edges that are incident on vertices that are both contained in the vertices
+    list.
+
+    If no subgraph can be extracted from the vertices provided, a 'None' value
+    will be returned.
+
+    Parameters
+    ----------
+    G : cugraph.Graph or networkx.Graph
+        The current implementation only supports weighted graphs.
+
+    do_expensive_check: bool
+
+    Returns
+    -------
+    edge_lists : cudf.DataFrame
+        Distributed GPU data frame containing all sources identifiers,
+        destination identifiers and if applicable edge weights, edge ids and
+        edge types
+
+    Examples
+    --------
+    >>> from cugraph.datasets import karate
+    >>> G = karate.get_graph(download=True)
+    >>> verts = np.zeros(3, dtype=np.int32)
+    >>> verts[0] = 0
+    >>> verts[1] = 1
+    >>> verts[2] = 2
+    >>> sverts = cudf.Series(verts)
+    >>> edgelist = cugraph.decompress_to_edgelist(G, False)
+
+    """
+
+
+    do_expensive_check = False
+    source, destination, weight, edge_ids, edge_type_ids = pylibcugraph_decompress_to_edgelist(
+        resource_handle=ResourceHandle(),
+        graph=G._plc_graph,
+        do_expensive_check=do_expensive_check
+    )
+
+    print("source = ", source)
+    print("detaination = ", destination)
+
+    df = cudf.DataFrame()
+    df["src"] = source
+    df["dst"] = destination
+    if weight is not None:
+        df["weight"] = weight
+    if edge_ids is not None:
+        df["edge_ids"] = edge_ids
+    if edge_type_ids is not None:
+        df["edge_type_ids"] = edge_type_ids
+
+    if G.renumbered:
+        df, _ = G.unrenumber(df, "src", get_column_names=True)
+        df, _ = G.unrenumber(df, "dst", get_column_names=True)
+
+    return df