🐛 fix pyclustering conda imports

cdlib/algorithms/overlapping_partition.py

@@ -16,7 +16,6 @@
 from cdlib.algorithms.internal.SLPA_nx import slpa_nx
 from cdlib.algorithms.internal.multicom import MultiCom
 from cdlib.algorithms.internal.PercoMCV import percoMVC
-from cdlib.algorithms.internal.LPAM import LPAM
 from cdlib.algorithms.internal.core_exp import findCommunities as core_exp_find
 from cdlib.algorithms.internal.weightedCommunity import weightedCommunity
 from cdlib.algorithms.internal.LPANNI import LPANNI, GraphGenerator
@@ -73,6 +72,13 @@
     ASLPAw = None
     missing_packages.add("ASLPAw")
 
+try:
+    import pyclustering
+    from cdlib.algorithms.internal.LPAM import LPAM
+except ModuleNotFoundError:
+    LPAM = None
+    missing_packages.add("pyclustering")
+
 report_missing_packages(missing_packages)
 
 __all__ = [
@@ -1519,6 +1525,11 @@ def lpam(
     Alexander Ponomarenko, Leonidas Pitsoulis, Marat Shamshetdinov. "Link Partitioning Around Medoids". https://arxiv.org/abs/1907.08731
 
     """
+    if LPAM is None:
+        raise ModuleNotFoundError(
+            "Optional dependency not satisfied: install pyclustering (pip install pyclustering). Not available in CDlib Conda-based installation."
+        )
+
     g = convert_graph_formats(g_original, nx.Graph)
     return LPAM(graph=g, k=k, threshold=threshold, distance=distance, seed=seed)
 

cdlib/test/test_community_discovery_models.py

@@ -54,6 +54,11 @@
 except ModuleNotFoundError:
     by = None
 
+try:
+    from cdlib.algorithms.internal.LPAM import LPAM
+except ModuleNotFoundError:
+    LPAM = None
+
 
 def get_string_graph():
     g = nx.karate_club_graph()
@@ -778,13 +783,14 @@ def test_lpanni(self):
             self.assertEqual(type(coms.communities[0][0]), int)
 
     def test_lpam(self):
-        G = nx.karate_club_graph()
+        if LPAM is not None:
+            G = nx.karate_club_graph()
 
-        coms = algorithms.lpam(G, k=2, threshold=0.4, distance="amp")
-        self.assertEqual(type(coms.communities), list)
-        if len(coms.communities) > 0:
-            self.assertEqual(type(coms.communities[0]), list)
-            self.assertEqual(type(coms.communities[0][0]), int)
+            coms = algorithms.lpam(G, k=2, threshold=0.4, distance="amp")
+            self.assertEqual(type(coms.communities), list)
+            if len(coms.communities) > 0:
+                self.assertEqual(type(coms.communities[0]), list)
+                self.assertEqual(type(coms.communities[0][0]), int)
 
     def test_dcs(self):
         G = nx.karate_club_graph()