📝 documentation fix

cdlib/algorithms/bipartite_clustering.py

@@ -60,7 +60,7 @@ def bimlpa(g_original: object, theta: float = 0.3, lambd: int = 7) -> BiNodeClus
 
     >>> from cdlib import algorithms
     >>> import networkx as nx
-    >>> G = nx.algorithms.bipartite.generators.random_graph(100, 20, 0.1)
+    >>> G = nx.algorithms.bipartite.random_graph(50, 50, 0.25)
     >>> coms = algorithms.bimlpa(G)
 
     :References:
@@ -288,7 +288,7 @@ def condor(g_original: object) -> BiNodeClustering:
     ========== ======== ======== =========
     Undirected Directed Weighted Bipartite
     ========== ======== ======== =========
-    Yes        No       No       Yes
+    Yes        No       Yes      Yes
     ========== ======== ======== =========
 
     :param g_original: a networkx/igraph object
@@ -298,7 +298,7 @@ def condor(g_original: object) -> BiNodeClustering:
 
     >>> from cdlib import algorithms
     >>> import networkx as nx
-    >>> G = nx.karate_club_graph()
+    >>> G = nx.algorithms.bipartite.random_graph(50, 50, 0.25)
     >>> coms = algorithms.condor(G)
 
     :References:

cdlib/algorithms/crisp_partition.py

@@ -2752,7 +2752,7 @@ def ricci_community(
 
 def spectral(
     g_original: object,
-    kmax: int,
+    kmax: int = 2,
     projection_on_smaller_class: bool = True,
     scaler: Callable = None,
 ) -> NodeClustering:
@@ -2768,11 +2768,11 @@ def spectral(
     ========== ======== ======== =========
     Undirected Directed Weighted Bipartite
     ========== ======== ======== =========
-    Yes        No       No       Yes
+    Yes        No       No       No
     ========== ======== ======== =========
 
     :param g_original: a networkx/igraph object
-    :param kmax: maximum number of desired communities
+    :param kmax: maximum number of desired communities (mandatory). Default 2.
     :param projection_on_smaller_class: a boolean value that if True then it project a bipartite network in the smallest class of node. (default is True)
     :param scaler: the function to scale the fielder’s vector to apply KMeans
     :return: NodeClustering object
@@ -2783,7 +2783,7 @@ def spectral(
     >>> from cdlib import algorithms
     >>> import networkx as nx
     >>> G = nx.karate_club_graph()
-    >>> coms = algorithms.spectral(G)
+    >>> coms = algorithms.spectral(G, kmax=2)
 
     :References:
 

cdlib/algorithms/internal/pycondor.py

@@ -7,7 +7,6 @@
     raise("This module requires the igraph package. Please install it with pip install python-igraph.")
 
 
-
 def condor_object(net):
     """Initialization of the condor object. The function gets a network in edgelist format encoded in a pandas
     dataframe. Returns a dictionary with an igraph network, names of the targets and regulators, list of edges,

cdlib/test/test_community_discovery_models.py

@@ -653,9 +653,9 @@ def test_belief(self):
                 self.assertEqual(type(communities.communities[0][0]), int)
 
     def test_CPM_Bipartite(self):
-        g = ig.Graph.Erdos_Renyi(n=80, m=600)
-        g.vs["type"] = 0
-        g.vs[15:]["type"] = 1
+
+        g = nx.algorithms.bipartite.random_graph(50, 50, 0.25)
+
         if leidenalg is None:
             return
         coms = algorithms.CPM_Bipartite(g, 0.3)
@@ -909,7 +909,7 @@ def test_endntm(self):
     def test_scd(self):
         G = nx.karate_club_graph()
 
-        coms = algorithms.spectral(G, kmax=4)
+        coms = algorithms.spectral(G, kmax=2)
         self.assertEqual(type(coms.communities), list)
         if len(coms.communities) > 0:
             self.assertEqual(type(coms.communities[0]), list)