Check format of input matrix in Louvain

sknetwork/clustering/louvain.py

@@ -14,8 +14,8 @@
 from sknetwork.clustering.base import BaseClustering
 from sknetwork.clustering.louvain_core import optimize_core
 from sknetwork.clustering.postprocess import reindex_labels
-from sknetwork.utils.check import check_random_state, get_probs
-from sknetwork.utils.format import check_format, get_adjacency, directed2undirected
+from sknetwork.utils.check import check_format, check_random_state, get_probs
+from sknetwork.utils.format import get_adjacency, directed2undirected
 from sknetwork.utils.membership import get_membership
 from sknetwork.log import Log
 
@@ -193,7 +193,6 @@ def _pre_processing(self, input_matrix, force_bipartite):
         self._init_vars()
 
         # adjacency matrix
-        input_matrix = check_format(input_matrix)
         force_directed = self.modularity == 'dugue'
         adjacency, self.bipartite = get_adjacency(input_matrix, force_directed=force_directed,
                                                   force_bipartite=force_bipartite)
@@ -266,6 +265,7 @@ def fit(self, input_matrix: Union[sparse.csr_matrix, np.ndarray], force_bipartit
         -------
         self : :class:`Louvain`
         """
+        input_matrix = check_format(input_matrix)
         adjacency, out_weights, in_weights, membership, index = self._pre_processing(input_matrix, force_bipartite)
         n = adjacency.shape[0]
         count = 0

sknetwork/clustering/tests/test_louvain.py

@@ -17,6 +17,12 @@ def test_disconnected(self):
         labels = Louvain().fit_predict(adjacency)
         self.assertEqual(len(labels), n)
 
+    def test_format(self):
+        adjacency = test_graph()
+        n = adjacency.shape[0]
+        labels = Louvain().fit_predict(adjacency.toarray())
+        self.assertEqual(len(labels), n)
+
     def test_modularity(self):
         adjacency = karate_club()
         louvain_d = Louvain(modularity='dugue')