Removed NetworkX requirement for type checks, fixed docstring, added new docstrings, import cleanups

python/cugraph/cugraph/centrality/betweenness_centrality.py

@@ -16,8 +16,10 @@
 import cudf
 from cugraph.centrality import betweenness_centrality_wrapper
 from cugraph.centrality import edge_betweenness_centrality_wrapper
-from cugraph.utilities import df_edge_score_to_dictionary
-from cugraph.utilities import df_score_to_dictionary
+from cugraph.utilities import (df_edge_score_to_dictionary,
+                               df_score_to_dictionary,
+                               ensure_cugraph_obj_for_nx,
+                               )
 import cugraph
 
 
@@ -127,7 +129,7 @@ def betweenness_centrality(
     if result_dtype not in [np.float32, np.float64]:
         raise TypeError("result type can only be np.float32 or np.float64")
 
-    G, isNx = cugraph.utilities.check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     vertices = _initialize_vertices(G, k, seed)
 
@@ -249,7 +251,7 @@ def edge_betweenness_centrality(
     if result_dtype not in [np.float32, np.float64]:
         raise TypeError("result type can only be np.float32 or np.float64")
 
-    G, isNx = cugraph.utilities.check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
     vertices = _initialize_vertices(G, k, seed)
 
     df = edge_betweenness_centrality_wrapper.edge_betweenness_centrality(

python/cugraph/cugraph/centrality/katz_centrality.py

@@ -12,7 +12,9 @@
 # limitations under the License.
 
 from cugraph.centrality import katz_centrality_wrapper
-import cugraph
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               df_score_to_dictionary,
+                               )
 
 
 def katz_centrality(
@@ -112,7 +114,7 @@ def katz_centrality(
                 "currently not supported"
         )
 
-    G, isNx = cugraph.utilities.check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if nstart is not None:
         if G.renumbered is True:
@@ -130,7 +132,7 @@ def katz_centrality(
         df = G.unrenumber(df, "vertex")
 
     if isNx is True:
-        dict = cugraph.utilities.df_score_to_dictionary(df, 'katz_centrality')
+        dict = df_score_to_dictionary(df, 'katz_centrality')
         return dict
     else:
         return df

python/cugraph/cugraph/community/ecg.py

@@ -12,8 +12,9 @@
 # limitations under the License.
 
 from cugraph.community import ecg_wrapper
-from cugraph.utilities import check_nx_graph
-from cugraph.utilities import df_score_to_dictionary
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               df_score_to_dictionary,
+                               )
 
 
 def ecg(input_graph, min_weight=0.05, ensemble_size=16, weight=None):
@@ -70,7 +71,7 @@ def ecg(input_graph, min_weight=0.05, ensemble_size=16, weight=None):
 
     """
 
-    input_graph, isNx = check_nx_graph(input_graph, weight)
+    input_graph, isNx = ensure_cugraph_obj_for_nx(input_graph, weight)
 
     parts = ecg_wrapper.ecg(input_graph, min_weight, ensemble_size)
 

python/cugraph/cugraph/community/egonet.py

@@ -15,20 +15,17 @@
 import cudf
 from cugraph.utilities import (
     ensure_cugraph_obj,
-    import_optional,
+    is_nx_graph_type,
 )
 from cugraph.utilities import cugraph_to_nx
 
-# optional dependencies used for handling different input types
-nx = import_optional("networkx")
-
 
 def _convert_graph_to_output_type(G, input_type):
     """
     Given a cugraph.Graph, convert it to a new type appropriate for the
     graph algos in this module, based on input_type.
     """
-    if (nx is not None) and (input_type in [nx.Graph, nx.DiGraph]):
+    if is_nx_graph_type(input_type):
         return cugraph_to_nx(G)
 
     else:
@@ -40,7 +37,7 @@ def _convert_df_series_to_output_type(df, offsets, input_type):
     Given a cudf.DataFrame df, convert it to a new type appropriate for the
     graph algos in this module, based on input_type.
     """
-    if (nx is not None) and (input_type in [nx.Graph, nx.DiGraph]):
+    if is_nx_graph_type(input_type):
         return df.to_pandas(), offsets.values_host.tolist()
 
     else:

python/cugraph/cugraph/community/ktruss_subgraph.py

@@ -13,8 +13,9 @@
 
 from cugraph.community import ktruss_subgraph_wrapper
 from cugraph.structure.graph_classes import Graph
-from cugraph.utilities import check_nx_graph
-from cugraph.utilities import cugraph_to_nx
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               cugraph_to_nx,
+                               )
 
 from numba import cuda
 
@@ -67,7 +68,7 @@ def k_truss(G, k):
 
     _ensure_compatible_cuda_version()
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if isNx is True:
         k_sub = ktruss_subgraph(G, k)

python/cugraph/cugraph/community/leiden.py

@@ -13,8 +13,9 @@
 
 from cugraph.community import leiden_wrapper
 from cugraph.structure.graph_classes import Graph
-from cugraph.utilities import check_nx_graph
-from cugraph.utilities import df_score_to_dictionary
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               df_score_to_dictionary,
+                               )
 
 
 def leiden(G, max_iter=100, resolution=1.):
@@ -72,7 +73,7 @@ def leiden(G, max_iter=100, resolution=1.):
     >>> G.from_cudf_edgelist(M, source='0', destination='1')
     >>> parts, modularity_score = cugraph.leiden(G)
     """
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if type(G) is not Graph:
         raise Exception(f"input graph must be undirected was {type(G)}")

python/cugraph/cugraph/community/louvain.py

@@ -13,8 +13,9 @@
 
 from cugraph.community import louvain_wrapper
 from cugraph.structure.graph_classes import Graph
-from cugraph.utilities import check_nx_graph
-from cugraph.utilities import df_score_to_dictionary
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               df_score_to_dictionary,
+                               )
 
 
 def louvain(G, max_iter=100, resolution=1.):
@@ -73,7 +74,7 @@ def louvain(G, max_iter=100, resolution=1.):
     >>> parts, modularity_score = cugraph.louvain(G)
     """
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if type(G) is not Graph:
         raise Exception("input graph must be undirected")

python/cugraph/cugraph/community/spectral_clustering.py

@@ -12,8 +12,9 @@
 # limitations under the License.
 
 from cugraph.community import spectral_clustering_wrapper
-from cugraph.utilities import check_nx_graph
-from cugraph.utilities import df_score_to_dictionary
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               df_score_to_dictionary,
+                               )
 
 
 def spectralBalancedCutClustering(
@@ -75,7 +76,7 @@ def spectralBalancedCutClustering(
 
     # Error checking in C++ code
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     df = spectral_clustering_wrapper.spectralBalancedCutClustering(
         G,
@@ -152,7 +153,7 @@ def spectralModularityMaximizationClustering(
 
     # Error checking in C++ code
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     df = spectral_clustering_wrapper.spectralModularityMaximizationClustering(
         G,
@@ -222,7 +223,7 @@ def analyzeClustering_modularity(G, n_clusters, clustering,
     if type(cluster_col_name) is not str:
         raise Exception("cluster_col_name must be a string")
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if G.renumbered:
         clustering = G.add_internal_vertex_id(clustering,
@@ -288,7 +289,7 @@ def analyzeClustering_edge_cut(G, n_clusters, clustering,
     if type(cluster_col_name) is not str:
         raise Exception("cluster_col_name must be a string")
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if G.renumbered:
         clustering = G.add_internal_vertex_id(clustering,

python/cugraph/cugraph/community/subgraph_extraction.py

@@ -12,9 +12,11 @@
 # limitations under the License.
 
 from cugraph.community import subgraph_extraction_wrapper
-from cugraph.utilities import check_nx_graph
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               cugraph_to_nx,
+                               )
+
 import cudf
-from cugraph.utilities import cugraph_to_nx
 
 
 def subgraph(G, vertices):
@@ -53,7 +55,7 @@ def subgraph(G, vertices):
     >>> Sg = cugraph.subgraph(G, sverts)
     """
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if G.renumbered:
         if isinstance(vertices, cudf.DataFrame):

python/cugraph/cugraph/community/triangle_count.py

@@ -13,7 +13,7 @@
 
 from cugraph.community import triangle_count_wrapper
 from cugraph.structure.graph_classes import Graph
-from cugraph.utilities import check_nx_graph
+from cugraph.utilities import ensure_cugraph_obj_for_nx
 
 
 def triangles(G):
@@ -47,7 +47,7 @@ def triangles(G):
     >>> count = cugraph.triangles(G)
     """
 
-    G, _ = check_nx_graph(G)
+    G, _ = ensure_cugraph_obj_for_nx(G)
 
     if type(G) is not Graph:
         raise Exception("input graph must be undirected")

python/cugraph/cugraph/components/connectivity.py

@@ -16,30 +16,12 @@
                                ensure_cugraph_obj,
                                is_matrix_type,
                                is_cp_matrix_type,
-                               import_optional,
+                               is_nx_graph_type,
+                               cupy_package as cp,
                                )
 from cugraph.structure import Graph, DiGraph
 from cugraph.components import connectivity_wrapper
 
-# optional dependencies used for handling different input types
-nx = import_optional("networkx")
-
-cp = import_optional("cupy")
-cp_coo_matrix = import_optional("coo_matrix",
-                                import_from="cupyx.scipy.sparse.coo")
-cp_csr_matrix = import_optional("csr_matrix",
-                                import_from="cupyx.scipy.sparse.csr")
-cp_csc_matrix = import_optional("csc_matrix",
-                                import_from="cupyx.scipy.sparse.csc")
-
-sp = import_optional("scipy")
-sp_coo_matrix = import_optional("coo_matrix",
-                                import_from="scipy.sparse.coo")
-sp_csr_matrix = import_optional("csr_matrix",
-                                import_from="scipy.sparse.csr")
-sp_csc_matrix = import_optional("csc_matrix",
-                                import_from="scipy.sparse.csc")
-
 
 def _ensure_args(api_name, G, directed, connection, return_labels):
     """
@@ -49,8 +31,7 @@ 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 \
-       ((nx is not None) and (G_type in [nx.Graph, nx.DiGraph])):
+    if (G_type in [Graph, DiGraph]) 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")
@@ -92,7 +73,7 @@ def _convert_df_to_output_type(df, input_type, return_labels):
     if input_type in [Graph, DiGraph]:
         return df
 
-    elif (nx is not None) and (input_type in [nx.Graph, nx.DiGraph]):
+    elif is_nx_graph_type(input_type):
         return df_score_to_dictionary(df, "labels", "vertex")
 
     elif is_matrix_type(input_type):

python/cugraph/cugraph/cores/core_number.py

@@ -12,8 +12,9 @@
 # limitations under the License.
 
 from cugraph.cores import core_number_wrapper
-from cugraph.utilities import check_nx_graph
-from cugraph.utilities import df_score_to_dictionary
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               df_score_to_dictionary,
+                               )
 
 
 def core_number(G):
@@ -52,7 +53,7 @@ def core_number(G):
     >>> cn = cugraph.core_number(G)
     """
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     df = core_number_wrapper.core_number(G)
 

python/cugraph/cugraph/cores/k_core.py

@@ -12,8 +12,9 @@
 # limitations under the License.
 
 from cugraph.cores import k_core_wrapper, core_number_wrapper
-from cugraph.utilities import cugraph_to_nx
-from cugraph.utilities import check_nx_graph
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               cugraph_to_nx,
+                               )
 from cugraph.structure.graph_classes import Graph
 
 
@@ -59,7 +60,7 @@ def k_core(G, k=None, core_number=None):
     >>> KCoreGraph = cugraph.k_core(G)
     """
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     mytype = type(G)
     KCoreGraph = mytype()

python/cugraph/cugraph/generators/rmat.py

@@ -290,21 +290,21 @@ def rmat(
 
     Examples
     --------
-    import cugraph
-    from cugraph.generators import rmat
-
-    df = rmat(
-        scale,
-        (2**scale)*edgefactor,
-        0.1,
-        0.2,
-        0.3,
-        seed or 42,
-        clip_and_flip=False,
-        scramble_vertex_ids=True,
-        create_using=None,  # return edgelist instead of Graph instance
-        mg=False
-        )
+    >>> import cugraph
+    >>> from cugraph.generators import rmat
+    >>>
+    >>> df = rmat(
+    ...    scale,
+    ...    (2**scale)*edgefactor,
+    ...    0.1,
+    ...    0.2,
+    ...    0.3,
+    ...    seed or 42,
+    ...    clip_and_flip=False,
+    ...    scramble_vertex_ids=True,
+    ...    create_using=None,  # return edgelist instead of Graph instance
+    ...    mg=False
+    ... )
     """
 
     _ensure_args_rmat(scale, num_edges, a, b, c, seed, clip_and_flip,