Add force_bipartite option in hierarchical algorithms

sknetwork/hierarchy/louvain_hierarchy.py

@@ -128,21 +128,23 @@ def _recursive_louvain(self, adjacency: Union[sparse.csr_matrix, np.ndarray], de
                 tree.append(self._recursive_louvain(adjacency_cluster, depth - 1, nodes_cluster))
             return tree
 
-    def fit(self, input_matrix: Union[sparse.csr_matrix, np.ndarray]) -> 'LouvainIteration':
+    def fit(self, input_matrix: Union[sparse.csr_matrix, np.ndarray], force_bipartite: bool = False) \
+            -> 'LouvainIteration':
         """Fit algorithm to data.
 
         Parameters
         ----------
         input_matrix : sparse.csr_matrix, np.ndarray
             Adjacency matrix or biadjacency matrix of the graph.
+        force_bipartite :
+            If ``True``, force the input matrix to be considered as a biadjacency matrix.
 
         Returns
         -------
         self: :class:`LouvainIteration`
         """
         self._init_vars()
-        input_matrix = check_format(input_matrix)
-        adjacency, self.bipartite = get_adjacency(input_matrix)
+        adjacency, self.bipartite = get_adjacency(input_matrix, force_bipartite=force_bipartite)
         tree = self._recursive_louvain(adjacency, self.depth)
         dendrogram, _ = get_dendrogram(tree)
         dendrogram = np.array(dendrogram)
@@ -243,21 +245,23 @@ def _get_hierarchy(self, adjacency: Union[sparse.csr_matrix, np.ndarray]):
                 labels_unique = np.unique(labels)
         return tree
 
-    def fit(self, input_matrix: Union[sparse.csr_matrix, np.ndarray]) -> 'LouvainHierarchy':
+    def fit(self, input_matrix: Union[sparse.csr_matrix, np.ndarray], force_bipartite: bool = False) \
+            -> 'LouvainHierarchy':
         """Fit algorithm to data.
 
         Parameters
         ----------
         input_matrix : sparse.csr_matrix, np.ndarray
             Adjacency matrix or biadjacency matrix of the graph.
+        force_bipartite :
+            If ``True``, force the input matrix to be considered as a biadjacency matrix.
 
         Returns
         -------
         self: :class:`LouvainHierarchy`
         """
         self._init_vars()
-        input_matrix = check_format(input_matrix)
-        adjacency, self.bipartite = get_adjacency(input_matrix)
+        adjacency, self.bipartite = get_adjacency(input_matrix, force_bipartite=force_bipartite)
         tree = self._get_hierarchy(adjacency)
         dendrogram, _ = get_dendrogram(tree)
         dendrogram = np.array(dendrogram)

sknetwork/hierarchy/paris.pyx

@@ -212,13 +212,15 @@ class Paris(BaseHierarchy):
 
     @cython.boundscheck(False)
     @cython.wraparound(False)
-    def fit(self, input_matrix: Union[sparse.csr_matrix, np.ndarray]) -> 'Paris':
+    def fit(self, input_matrix: Union[sparse.csr_matrix, np.ndarray], force_bipartite: bool = False) -> 'Paris':
         """Agglomerative clustering using the nearest neighbor chain.
 
         Parameters
         ----------
         input_matrix : sparse.csr_matrix, np.ndarray
             Adjacency matrix or biadjacency matrix of the graph.
+        force_bipartite :
+            If ``True``, force the input matrix to be considered as a biadjacency matrix.
 
         Returns
         -------
@@ -227,8 +229,7 @@ class Paris(BaseHierarchy):
         self._init_vars()
 
         # input
-        input_matrix = check_format(input_matrix)
-        adjacency, self.bipartite = get_adjacency(input_matrix)
+        adjacency, self.bipartite = get_adjacency(input_matrix, force_bipartite=force_bipartite)
 
         weights = self.weights
         out_weights = get_probs(weights, adjacency)

sknetwork/hierarchy/tests/test_metrics.py

@@ -17,7 +17,7 @@ class TestMetrics(unittest.TestCase):
 
     def setUp(self):
         self.paris = Paris()
-        self.louvain_hierarchy = LouvainIteration()
+        self.louvain_iteration = LouvainIteration()
 
     def test_undirected(self):
         adjacency = cyclic_graph(3)
@@ -31,7 +31,7 @@ def test_undirected(self):
         self.assertAlmostEqual(dasgupta_cost(adjacency, dendrogram), 4.26, 2)
         self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.573, 2)
         self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram), 0.304, 2)
-        dendrogram = self.louvain_hierarchy.fit_transform(adjacency)
+        dendrogram = self.louvain_iteration.fit_transform(adjacency)
         self.assertAlmostEqual(dasgupta_cost(adjacency, dendrogram), 4.43, 2)
         self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.555, 2)
         self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram), 0.286, 2)
@@ -41,7 +41,7 @@ def test_directed(self):
         dendrogram = self.paris.fit_transform(adjacency)
         self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.566, 2)
         self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram), 0.318, 2)
-        dendrogram = self.louvain_hierarchy.fit_transform(adjacency)
+        dendrogram = self.louvain_iteration.fit_transform(adjacency)
         self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.55, 2)
         self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram), 0.313, 2)
 
@@ -50,7 +50,7 @@ def test_disconnected(self):
         dendrogram = self.paris.fit_transform(adjacency)
         self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.682, 2)
         self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram), 0.464, 2)
-        dendrogram = self.louvain_hierarchy.fit_transform(adjacency)
+        dendrogram = self.louvain_iteration.fit_transform(adjacency)
         self.assertAlmostEqual(dasgupta_score(adjacency, dendrogram), 0.670, 2)
         self.assertAlmostEqual(tree_sampling_divergence(adjacency, dendrogram), 0.594, 2)