Fix karateclub warning about self-loops

test/classes/test_cell2vec.py

@@ -12,7 +12,7 @@ class TestCell2Vec:
     def test_fit_and_get_embedding(self):
         """Test get_embedding."""
         # Create a small complex
-        cx = tnx.classes.CellComplex([[1, 2, 3, 4], [3, 4, 5, 6, 7, 8]], ranks=2)
+        cx = tnx.CellComplex([[1, 2, 3, 4], [3, 4, 5, 6, 7, 8]], ranks=2)
 
         # Create a Cell2Vec object
         dc = Cell2Vec(dimensions=5)

topoembedx/classes/cell2vec.py

@@ -6,6 +6,7 @@
 import numpy as np
 import toponetx as tnx
 from karateclub import Node2Vec
+from scipy.sparse import csr_matrix
 
 from topoembedx.neighborhood import neighborhood_from_complex
 
@@ -50,7 +51,7 @@ class Cell2Vec(Node2Vec):
         Random seed value.
     """
 
-    A: np.ndarray
+    A: csr_matrix
     ind: list
 
     def fit(
@@ -94,6 +95,7 @@ def fit(
         )
 
         g = nx.from_numpy_array(self.A)
+        g.add_edges_from((index, index) for index in range(g.number_of_nodes()))
 
         super().fit(g)
 

topoembedx/classes/cell_diff2vec.py

@@ -6,6 +6,7 @@
 import numpy as np
 import toponetx as tnx
 from karateclub import Diff2Vec
+from scipy.sparse import csr_matrix
 
 from topoembedx.neighborhood import neighborhood_from_complex
 
@@ -35,7 +36,7 @@ class CellDiff2Vec(Diff2Vec):
         Random seed value.
     """
 
-    A: np.ndarray
+    A: csr_matrix
     ind: list
 
     def fit(
@@ -78,11 +79,14 @@ def fit(
             complex, neighborhood_type, neighborhood_dim
         )
 
+        self.A.setdiag(1)
         g = nx.from_numpy_array(self.A)
+
         if self.diffusion_cover > g.number_of_nodes():
             raise ValueError(
                 "The diffusion_cover is too large for the size of the graph."
             )
+
         super().fit(g)
 
     def get_embedding(self, get_dict: bool = False) -> dict | np.ndarray:

topoembedx/classes/deepcell.py

@@ -6,6 +6,7 @@
 import numpy as np
 import toponetx as tnx
 from karateclub import DeepWalk
+from scipy.sparse import csr_matrix
 
 from topoembedx.neighborhood import neighborhood_from_complex
 
@@ -35,7 +36,7 @@ class DeepCell(DeepWalk):
         Random seed to use for reproducibility.
     """
 
-    A: np.ndarray
+    A: csr_matrix
     ind: list
 
     def fit(
@@ -78,6 +79,7 @@ def fit(
             complex, neighborhood_type, neighborhood_dim
         )
 
+        self.A.setdiag(1)
         g = nx.from_numpy_array(self.A)
 
         super().fit(g)

topoembedx/classes/higher_order_laplacian_eigenmaps.py

@@ -6,6 +6,7 @@
 import numpy as np
 import toponetx as tnx
 from karateclub import LaplacianEigenmaps
+from scipy.sparse import csr_matrix
 
 from topoembedx.neighborhood import neighborhood_from_complex
 
@@ -23,7 +24,7 @@ class HigherOrderLaplacianEigenmaps(LaplacianEigenmaps):
         Random seed value.
     """
 
-    A: np.ndarray
+    A: csr_matrix
     ind: list
 
     def __init__(
@@ -75,6 +76,7 @@ def fit(
             complex, neighborhood_type, neighborhood_dim
         )
 
+        self.A.setdiag(1)
         g = nx.from_numpy_array(self.A)
 
         super().fit(g)

topoembedx/classes/hoglee.py

@@ -6,6 +6,7 @@
 import numpy as np
 import toponetx as tnx
 from karateclub import GLEE
+from scipy.sparse import csr_matrix
 
 from topoembedx.neighborhood import neighborhood_from_complex
 
@@ -21,7 +22,7 @@ class HOGLEE(GLEE):
         Random seed value. Defaults to 42.
     """
 
-    A: np.ndarray
+    A: csr_matrix
     ind: list
 
     def fit(
@@ -64,6 +65,7 @@ def fit(
             complex, neighborhood_type, neighborhood_dim
         )
 
+        self.A.setdiag(1)
         g = nx.from_numpy_array(self.A)
 
         super().fit(g)

topoembedx/neighborhood.py

@@ -2,15 +2,15 @@
 
 from typing import Literal
 
-import numpy as np
 import toponetx as tnx
+from scipy.sparse import csr_matrix
 
 
 def neighborhood_from_complex(
     complex: tnx.Complex,
     neighborhood_type: Literal["adj", "coadj"] = "adj",
     neighborhood_dim=None,
-) -> tuple[list, np.ndarray]:
+) -> tuple[list, csr_matrix]:
     """Compute the neighborhood of a complex.
 
     This function returns the indices and matrix for the neighborhood specified
@@ -44,7 +44,7 @@ def neighborhood_from_complex(
     -------
     ind : list
         A list of the indices for the nodes in the neighborhood.
-    A : ndarray
+    A : scipy.sparse.csr_matrix
         The matrix representing the neighborhood.
 
     Raises