Fix _nxver comparisons for variable length tuples

nx_cugraph/algorithms/bipartite/generators.py

@@ -49,7 +49,7 @@ def complete_bipartite_graph(n1, n2, create_using=None):
         nodes.extend(range(n2) if nodes2 is None else nodes2)
         if len(set(nodes)) != len(nodes):
             raise nx.NetworkXError("Inputs n1 and n2 must contain distinct nodes")
-    if _nxver <= (3, 3):
+    if _nxver < (3, 4):
         name = f"complete_bipartite_graph({orig_n1}, {orig_n2})"
     else:
         name = f"complete_bipartite_graph({n1}, {n2})"

nx_cugraph/algorithms/community/louvain.py

@@ -27,7 +27,7 @@
 __all__ = ["louvain_communities"]
 
 # max_level argument was added to NetworkX 3.3
-if _nxver <= (3, 2):
+if _nxver < (3, 3):
     _max_level_param = {
         "max_level : int, optional": (
             "Upper limit of the number of macro-iterations (max: 500)."

nx_cugraph/algorithms/core.py

@@ -64,7 +64,7 @@ def k_truss(G, k):
     else:
         is_compat_graph = False
     if nxcg.number_of_selfloops(G) > 0:
-        if _nxver <= (3, 2):
+        if _nxver < (3, 3):
             exc_class = nx.NetworkXError
         else:
             exc_class = nx.NetworkXNotImplemented

nx_cugraph/algorithms/link_analysis/hits_alg.py

@@ -54,7 +54,7 @@ def hits(
     if nstart is not None:
         nstart = G._dict_to_nodearray(nstart, 0, dtype)
     if max_iter <= 0:
-        if _nxver <= (3, 2):
+        if _nxver < (3, 3):
             raise ValueError("`maxiter` must be a positive integer.")
         raise nx.PowerIterationFailedConvergence(max_iter)
     try:

nx_cugraph/algorithms/shortest_paths/generic.py

@@ -58,7 +58,7 @@ def shortest_path(
                 paths = nxcg.all_pairs_dijkstra_path(G, weight=weight, dtype=dtype)
             else:  # method == 'bellman-ford':
                 paths = nxcg.all_pairs_bellman_ford_path(G, weight=weight, dtype=dtype)
-            if _nxver <= (3, 4):
+            if _nxver < (3, 5):
                 paths = dict(paths)
         # To target
         elif method == "unweighted":
@@ -130,7 +130,7 @@ def shortest_path_length(
         # To target
         elif method == "unweighted":
             lengths = nxcg.single_target_shortest_path_length(G, target)
-            if _nxver <= (3, 4):
+            if _nxver < (3, 5):
                 lengths = dict(lengths)
         elif method == "dijkstra":
             lengths = nxcg.single_source_dijkstra_path_length(

nx_cugraph/algorithms/shortest_paths/unweighted.py

@@ -44,7 +44,7 @@ def single_source_shortest_path_length(G, source, cutoff=None):
 def single_target_shortest_path_length(G, target, cutoff=None):
     G = _to_graph(G)
     rv = _bfs(G, target, cutoff, "Target", return_type="length")
-    if _nxver <= (3, 4):
+    if _nxver < (3, 5):
         return iter(rv.items())
     return rv
 
@@ -62,7 +62,7 @@ def bidirectional_shortest_path(G, source, target):
     # TODO PERF: do bidirectional traversal in core
     G = _to_graph(G)
     if source not in G or target not in G:
-        if _nxver <= (3, 3):
+        if _nxver < (3, 4):
             raise nx.NodeNotFound(
                 f"Either source {source} or target {target} is not in G"
             )

nx_cugraph/algorithms/traversal/breadth_first_search.py

@@ -58,7 +58,7 @@ def _bfs(G, source, *, depth_limit=None, reverse=False):
     return distances[mask], predecessors[mask], node_ids[mask]
 
 
-if _nxver <= (3, 3):
+if _nxver < (3, 4):
 
     @networkx_algorithm(is_incomplete=True, version_added="24.02", _plc="bfs")
     def generic_bfs_edges(

nx_cugraph/convert_matrix.py

@@ -140,7 +140,7 @@ def from_pandas_edgelist(
         and (
             # In nx <= 3.3, `edge_key` was ignored if `edge_attr` is None
             edge_attr is not None
-            or _nxver > (3, 3)
+            or _nxver >= (3, 4)
         )
     ):
         try:

nx_cugraph/interface.py

@@ -228,7 +228,7 @@ def key(testpath):
                 }
             )
 
-        if _nxver <= (3, 2):
+        if _nxver < (3, 3):
             xfail.update(
                 {
                     # NetworkX versions prior to 3.2.1 have tests written to
@@ -265,7 +265,7 @@ def key(testpath):
                 }
             )
 
-        if _nxver <= (3, 1):
+        if _nxver < (3, 2):
             # MAINT: networkx 3.0, 3.1
             # NetworkX 3.2 added the ability to "fallback to nx" if backend algorithms
             # raise NotImplementedError or `can_run` returns False. The tests below

nx_cugraph/utils/decorators.py

@@ -80,7 +80,7 @@ def __new__(
                 _plc=_plc,
             )
         instance = object.__new__(cls)
-        if nodes_or_number is not None and _nxver > (3, 2):
+        if nodes_or_number is not None and _nxver >= (3, 3):
             func = nx.utils.decorators.nodes_or_number(nodes_or_number)(func)
         # update_wrapper sets __wrapped__, which will be used for the signature
         update_wrapper(instance, func)
@@ -121,7 +121,7 @@ def __new__(
         # Set methods so they are in __dict__
         instance._can_run = instance._can_run
         instance._should_run = instance._should_run
-        if nodes_or_number is not None and _nxver <= (3, 2):
+        if nodes_or_number is not None and _nxver < (3, 3):
             instance = nx.utils.decorators.nodes_or_number(nodes_or_number)(instance)
         return instance