⬇️ markov_clustering moved to optional

cdlib/algorithms/crisp_partition.py

@@ -38,8 +38,6 @@
 
 import warnings
 
-import markov_clustering as mc
-
 # from chinese_whispers import chinese_whispers as cw
 # from chinese_whispers import aggregate_clusters
 from thresholdclustering.thresholdclustering import best_partition as th_best_partition
@@ -91,6 +89,13 @@
     missing_packages.add("bayanpy")
     by = None
 
+
+try:
+    import markov_clustering as mc
+except ModuleNotFoundError:
+    missing_packages.add("markov_clustering")
+    by = None
+
 # try:
 #    import karateclub
 # except ModuleNotFoundError:
@@ -1669,6 +1674,13 @@ def markov_clustering(
     .. note:: Reference implementation: https://github.com/GuyAllard/markov_clustering
     """
 
+    if mc is None:
+        raise Exception(
+            "===================================================== \n"
+            "The markov clustering algorithm seems not to be installed (or incorrectly installed). \n"
+            "Please resolve with: pip install markov_clustering"
+        )
+
     g = convert_graph_formats(g_original, nx.Graph)
     g, maps = nx_node_integer_mapping(g)
 

cdlib/algorithms/internal/RSC.py

@@ -34,8 +34,8 @@ def __regularized_laplacian_matrix(adj_matrix, tau):
 
     # diags will be zero at points where there is no edge and/or the node you are at
     #  ignore the error and make it zero later
-    with scipy.errstate(divide="ignore"):
-        diags_sqrt = 1.0 / scipy.sqrt(diags)
+    #with scipy.errstate(divide="ignore"):
+    diags_sqrt = 1.0 / scipy.sqrt(diags)
     diags_sqrt[scipy.isinf(diags_sqrt)] = 0
     D = scipy.sparse.spdiags(diags_sqrt, [0], m, n, format="csr")
 

requirements.txt

@@ -10,7 +10,6 @@ seaborn
 pandas
 eva_lcd
 bimlpa
-markov_clustering
 python-igraph>=0.10
 angelcommunity
 pooch

requirements_optional.txt

@@ -3,4 +3,5 @@ networkit
 pycombo
 leidenalg
 infomap>=1.3.0
-wurlitzer>=1.0.2
+wurlitzer>=1.0.2
+markov_clustering