nx-cugraph: rename plc= to _plc=

python/nx-cugraph/lint.yaml

@@ -50,7 +50,7 @@ repos:
       - id: black
       # - id: black-jupyter
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.13
+    rev: v0.1.14
     hooks:
       - id: ruff
         args: [--fix-only, --show-fixes]  # --unsafe-fixes]
@@ -62,7 +62,7 @@ repos:
         additional_dependencies: &flake8_dependencies
           # These versions need updated manually
           - flake8==7.0.0
-          - flake8-bugbear==23.12.2
+          - flake8-bugbear==24.1.17
           - flake8-simplify==0.21.0
   - repo: https://github.com/asottile/yesqa
     rev: v1.5.0
@@ -77,7 +77,7 @@ repos:
         additional_dependencies: [tomli]
         files: ^(nx_cugraph|docs)/
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.13
+    rev: v0.1.14
     hooks:
       - id: ruff
   - repo: https://github.com/pre-commit/pre-commit-hooks

python/nx-cugraph/nx_cugraph/algorithms/bipartite/basic.py

@@ -21,7 +21,7 @@
 ]
 
 
-@networkx_algorithm(plc="triangle_count", version_added="24.02")
+@networkx_algorithm(version_added="24.02", _plc="triangle_count")
 def is_bipartite(G):
     G = _to_graph(G)
     # Counting triangles may not be the fastest way to do this, but it is simple.

python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py

@@ -21,8 +21,8 @@
 @networkx_algorithm(
     is_incomplete=True,  # weight not supported
     is_different=True,  # RNG with seed is different
-    plc="betweenness_centrality",
     version_added="23.10",
+    _plc="betweenness_centrality",
 )
 def betweenness_centrality(
     G, k=None, normalized=True, weight=None, endpoints=False, seed=None
@@ -54,8 +54,8 @@ def _(G, k=None, normalized=True, weight=None, endpoints=False, seed=None):
 @networkx_algorithm(
     is_incomplete=True,  # weight not supported
     is_different=True,  # RNG with seed is different
-    plc="edge_betweenness_centrality",
     version_added="23.10",
+    _plc="edge_betweenness_centrality",
 )
 def edge_betweenness_centrality(G, k=None, normalized=True, weight=None, seed=None):
     """`weight` parameter is not yet supported, and RNG with seed may be different."""

python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py

@@ -29,8 +29,8 @@
 @networkx_algorithm(
     extra_params=_dtype_param,
     is_incomplete=True,  # nstart not supported
-    plc="eigenvector_centrality",
     version_added="23.12",
+    _plc="eigenvector_centrality",
 )
 def eigenvector_centrality(
     G, max_iter=100, tol=1.0e-6, nstart=None, weight=None, *, dtype=None

python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py

@@ -29,8 +29,8 @@
 @networkx_algorithm(
     extra_params=_dtype_param,
     is_incomplete=True,  # nstart and normalized=False not supported
-    plc="katz_centrality",
     version_added="23.12",
+    _plc="katz_centrality",
 )
 def katz_centrality(
     G,

python/nx-cugraph/nx_cugraph/algorithms/cluster.py

@@ -45,7 +45,7 @@ def _triangles(G, nodes, symmetrize=None):
 
 
 @not_implemented_for("directed")
-@networkx_algorithm(plc="triangle_count", version_added="24.02")
+@networkx_algorithm(version_added="24.02", _plc="triangle_count")
 def triangles(G, nodes=None):
     G = _to_undirected_graph(G)
     node_ids, triangles, is_single_node = _triangles(G, nodes)
@@ -57,9 +57,13 @@ def triangles(G, nodes=None):
 
 
 @not_implemented_for("directed")
-@networkx_algorithm(is_incomplete=True, plc="triangle_count", version_added="24.02")
+@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="triangle_count")
 def clustering(G, nodes=None, weight=None):
     """Directed graphs and `weight` parameter are not yet supported."""
+    if weight is not None:
+        raise NotImplementedError(
+            "Weighted implementation of clustering not currently supported"
+        )
     G = _to_undirected_graph(G)
     node_ids, triangles, is_single_node = _triangles(G, nodes)
     if len(G) == 0:
@@ -83,9 +87,13 @@ def _(G, nodes=None, weight=None):
 
 
 @not_implemented_for("directed")
-@networkx_algorithm(is_incomplete=True, plc="triangle_count", version_added="24.02")
+@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="triangle_count")
 def average_clustering(G, nodes=None, weight=None, count_zeros=True):
     """Directed graphs and `weight` parameter are not yet supported."""
+    if weight is not None:
+        raise NotImplementedError(
+            "Weighted implementation of average_clustering not currently supported"
+        )
     G = _to_undirected_graph(G)
     node_ids, triangles, is_single_node = _triangles(G, nodes)
     if len(G) == 0:
@@ -110,7 +118,7 @@ def _(G, nodes=None, weight=None, count_zeros=True):
 
 
 @not_implemented_for("directed")
-@networkx_algorithm(is_incomplete=True, plc="triangle_count", version_added="24.02")
+@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="triangle_count")
 def transitivity(G):
     """Directed graphs are not yet supported."""
     G = _to_undirected_graph(G)

python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py

@@ -36,8 +36,8 @@
     },
     is_incomplete=True,  # seed not supported; self-loops not supported
     is_different=True,  # RNG different
-    plc="louvain",
     version_added="23.10",
+    _plc="louvain",
 )
 def louvain_communities(
     G,

python/nx-cugraph/nx_cugraph/algorithms/components/connected.py

@@ -26,7 +26,7 @@
 
 
 @not_implemented_for("directed")
-@networkx_algorithm(plc="weakly_connected_components", version_added="23.12")
+@networkx_algorithm(version_added="23.12", _plc="weakly_connected_components")
 def number_connected_components(G):
     G = _to_undirected_graph(G)
     return _number_connected_components(G)
@@ -50,14 +50,11 @@ def _number_connected_components(G, symmetrize=None):
 @number_connected_components._can_run
 def _(G):
     # NetworkX <= 3.2.1 does not check directedness for us
-    try:
-        return not G.is_directed()
-    except Exception:
-        return False
+    return not G.is_directed()
 
 
 @not_implemented_for("directed")
-@networkx_algorithm(plc="weakly_connected_components", version_added="23.12")
+@networkx_algorithm(version_added="23.12", _plc="weakly_connected_components")
 def connected_components(G):
     G = _to_undirected_graph(G)
     return _connected_components(G)
@@ -80,7 +77,7 @@ def _connected_components(G, symmetrize=None):
 
 
 @not_implemented_for("directed")
-@networkx_algorithm(plc="weakly_connected_components", version_added="23.12")
+@networkx_algorithm(version_added="23.12", _plc="weakly_connected_components")
 def is_connected(G):
     G = _to_undirected_graph(G)
     return _is_connected(G)
@@ -106,7 +103,7 @@ def _is_connected(G, symmetrize=None):
 
 
 @not_implemented_for("directed")
-@networkx_algorithm(plc="weakly_connected_components", version_added="23.12")
+@networkx_algorithm(version_added="23.12", _plc="weakly_connected_components")
 def node_connected_component(G, n):
     # We could also do plain BFS from n
     G = _to_undirected_graph(G)

python/nx-cugraph/nx_cugraph/algorithms/components/strongly_connected.py

@@ -51,7 +51,7 @@ def _strongly_connected_components(G):
 
 
 @not_implemented_for("undirected")
-@networkx_algorithm(version_added="24.02", plc="strongly_connected_components")
+@networkx_algorithm(version_added="24.02", _plc="strongly_connected_components")
 def strongly_connected_components(G):
     G = _to_directed_graph(G)
     if G.src_indices.size == 0:
@@ -62,7 +62,7 @@ def strongly_connected_components(G):
 
 
 @not_implemented_for("undirected")
-@networkx_algorithm(version_added="24.02", plc="strongly_connected_components")
+@networkx_algorithm(version_added="24.02", _plc="strongly_connected_components")
 def number_strongly_connected_components(G):
     G = _to_directed_graph(G)
     if G.src_indices.size == 0:
@@ -72,7 +72,7 @@ def number_strongly_connected_components(G):
 
 
 @not_implemented_for("undirected")
-@networkx_algorithm(version_added="24.02", plc="strongly_connected_components")
+@networkx_algorithm(version_added="24.02", _plc="strongly_connected_components")
 def is_strongly_connected(G):
     G = _to_directed_graph(G)
     if len(G) == 0:

python/nx-cugraph/nx_cugraph/algorithms/components/weakly_connected.py

@@ -27,21 +27,21 @@
 
 
 @not_implemented_for("undirected")
-@networkx_algorithm(plc="weakly_connected_components", version_added="24.02")
+@networkx_algorithm(version_added="24.02", _plc="weakly_connected_components")
 def weakly_connected_components(G):
     G = _to_directed_graph(G)
     return _connected_components(G, symmetrize="union")
 
 
 @not_implemented_for("undirected")
-@networkx_algorithm(plc="weakly_connected_components", version_added="24.02")
+@networkx_algorithm(version_added="24.02", _plc="weakly_connected_components")
 def number_weakly_connected_components(G):
     G = _to_directed_graph(G)
     return _number_connected_components(G, symmetrize="union")
 
 
 @not_implemented_for("undirected")
-@networkx_algorithm(plc="weakly_connected_components", version_added="24.02")
+@networkx_algorithm(version_added="24.02", _plc="weakly_connected_components")
 def is_weakly_connected(G):
     G = _to_directed_graph(G)
     return _is_connected(G, symmetrize="union")

python/nx-cugraph/nx_cugraph/algorithms/core.py

@@ -28,7 +28,7 @@
 
 @not_implemented_for("directed")
 @not_implemented_for("multigraph")
-@networkx_algorithm(is_incomplete=True, plc="core_number", version_added="24.02")
+@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="core_number")
 def core_number(G):
     """Directed graphs are not yet supported."""
     G = _to_undirected_graph(G)
@@ -55,7 +55,7 @@ def _(G):
 
 @not_implemented_for("directed")
 @not_implemented_for("multigraph")
-@networkx_algorithm(is_incomplete=True, plc="k_truss_subgraph", version_added="23.12")
+@networkx_algorithm(is_incomplete=True, version_added="23.12", _plc="k_truss_subgraph")
 def k_truss(G, k):
     """
     Currently raises `NotImplementedError` for graphs with more than one connected

python/nx-cugraph/nx_cugraph/algorithms/dag.py

@@ -45,11 +45,11 @@ def _ancestors_and_descendants(G, source, *, is_ancestors):
     return G._nodearray_to_set(node_ids[mask])
 
 
-@networkx_algorithm(plc="bfs", version_added="24.02")
+@networkx_algorithm(version_added="24.02", _plc="bfs")
 def descendants(G, source):
     return _ancestors_and_descendants(G, source, is_ancestors=False)
 
 
-@networkx_algorithm(plc="bfs", version_added="24.02")
+@networkx_algorithm(version_added="24.02", _plc="bfs")
 def ancestors(G, source):
     return _ancestors_and_descendants(G, source, is_ancestors=True)

python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py

@@ -33,8 +33,8 @@
         ),
         **_dtype_param,
     },
-    plc="hits",
     version_added="23.12",
+    _plc="hits",
 )
 def hits(
     G,

python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py

@@ -29,8 +29,8 @@
 @networkx_algorithm(
     extra_params=_dtype_param,
     is_incomplete=True,  # dangling not supported
-    plc={"pagerank", "personalized_pagerank"},
     version_added="23.12",
+    _plc={"pagerank", "personalized_pagerank"},
 )
 def pagerank(
     G,

python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py

@@ -21,12 +21,12 @@
 __all__ = ["single_source_shortest_path_length", "single_target_shortest_path_length"]
 
 
-@networkx_algorithm(plc="bfs", version_added="23.12")
+@networkx_algorithm(version_added="23.12", _plc="bfs")
 def single_source_shortest_path_length(G, source, cutoff=None):
     return _single_shortest_path_length(G, source, cutoff, "Source")
 
 
-@networkx_algorithm(plc="bfs", version_added="23.12")
+@networkx_algorithm(version_added="23.12", _plc="bfs")
 def single_target_shortest_path_length(G, target, cutoff=None):
     return _single_shortest_path_length(G, target, cutoff, "Target")
 

python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py

@@ -57,9 +57,17 @@ def _bfs(G, source, *, depth_limit=None, reverse=False):
     return distances[mask], predecessors[mask], node_ids[mask]
 
 
-@networkx_algorithm(is_incomplete=True, plc="bfs", version_added="24.02")
+@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="bfs")
 def generic_bfs_edges(G, source, neighbors=None, depth_limit=None, sort_neighbors=None):
     """`neighbors` and `sort_neighbors` parameters are not yet supported."""
+    if neighbors is not None:
+        raise NotImplementedError(
+            "neighbors argument in generic_bfs_edges is not currently supported"
+        )
+    if sort_neighbors is not None:
+        raise NotImplementedError(
+            "sort_neighbors argument in generic_bfs_edges is not currently supported"
+        )
     return bfs_edges(source, depth_limit=depth_limit)
 
 
@@ -68,9 +76,13 @@ def _(G, source, neighbors=None, depth_limit=None, sort_neighbors=None):
     return neighbors is None and sort_neighbors is None
 
 
-@networkx_algorithm(is_incomplete=True, plc="bfs", version_added="24.02")
+@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="bfs")
 def bfs_edges(G, source, reverse=False, depth_limit=None, sort_neighbors=None):
     """`sort_neighbors` parameter is not yet supported."""
+    if sort_neighbors is not None:
+        raise NotImplementedError(
+            "sort_neighbors argument in bfs_edges is not currently supported"
+        )
     G = _check_G_and_source(G, source)
     if depth_limit is not None and depth_limit < 1:
         return
@@ -95,9 +107,13 @@ def _(G, source, reverse=False, depth_limit=None, sort_neighbors=None):
     return sort_neighbors is None
 
 
-@networkx_algorithm(is_incomplete=True, plc="bfs", version_added="24.02")
+@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="bfs")
 def bfs_tree(G, source, reverse=False, depth_limit=None, sort_neighbors=None):
     """`sort_neighbors` parameter is not yet supported."""
+    if sort_neighbors is not None:
+        raise NotImplementedError(
+            "sort_neighbors argument in bfs_tree is not currently supported"
+        )
     G = _check_G_and_source(G, source)
     if depth_limit is not None and depth_limit < 1:
         return nxcg.DiGraph.from_coo(
@@ -149,9 +165,13 @@ def _(G, source, reverse=False, depth_limit=None, sort_neighbors=None):
     return sort_neighbors is None
 
 
-@networkx_algorithm(is_incomplete=True, plc="bfs", version_added="24.02")
+@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="bfs")
 def bfs_successors(G, source, depth_limit=None, sort_neighbors=None):
     """`sort_neighbors` parameter is not yet supported."""
+    if sort_neighbors is not None:
+        raise NotImplementedError(
+            "sort_neighbors argument in bfs_successors is not currently supported"
+        )
     G = _check_G_and_source(G, source)
     if depth_limit is not None and depth_limit < 1:
         yield (source, [])
@@ -173,7 +193,7 @@ def _(G, source, depth_limit=None, sort_neighbors=None):
     return sort_neighbors is None
 
 
-@networkx_algorithm(plc="bfs", version_added="24.02")
+@networkx_algorithm(version_added="24.02", _plc="bfs")
 def bfs_layers(G, sources):
     G = _to_graph(G)
     if sources in G:
@@ -201,9 +221,13 @@ def bfs_layers(G, sources):
     return (G._nodearray_to_list(groups[key]) for key in range(len(groups)))
 
 
-@networkx_algorithm(is_incomplete=True, plc="bfs", version_added="24.02")
+@networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="bfs")
 def bfs_predecessors(G, source, depth_limit=None, sort_neighbors=None):
     """`sort_neighbors` parameter is not yet supported."""
+    if sort_neighbors is not None:
+        raise NotImplementedError(
+            "sort_neighbors argument in bfs_predecessors is not currently supported"
+        )
     G = _check_G_and_source(G, source)
     if depth_limit is not None and depth_limit < 1:
         return
@@ -227,7 +251,7 @@ def _(G, source, depth_limit=None, sort_neighbors=None):
     return sort_neighbors is None
 
 
-@networkx_algorithm(plc="bfs", version_added="24.02")
+@networkx_algorithm(version_added="24.02", _plc="bfs")
 def descendants_at_distance(G, source, distance):
     G = _check_G_and_source(G, source)
     if distance is None or distance < 0: