Adapt to new TopoNetX import convention

test/nn/simplicial/test_dist2cycle.py

@@ -1,7 +1,8 @@
 """Unit tests for Dist2Cycke Model."""
+
 import numpy as np
+import toponetx as tnx
 import torch
-from toponetx.classes import SimplicialComplex
 
 from topomodelx.nn.simplicial.dist2cycle import Dist2Cycle
 
@@ -15,7 +16,7 @@ def test_forward(self):
         face_set = [[2, 3, 4], [2, 4, 5]]
 
         torch.manual_seed(42)
-        simplicial_complex = SimplicialComplex(edge_set + face_set)
+        simplicial_complex = tnx.SimplicialComplex(edge_set + face_set)
         laplacian_down_1 = simplicial_complex.down_laplacian_matrix(rank=1).todense()
         adjacency_1 = simplicial_complex.adjacency_matrix(rank=1).todense()
         laplacian_down_1_inv = np.linalg.pinv(

test/nn/simplicial/test_hsn.py

@@ -1,7 +1,8 @@
 """Unit tests for HSN Model."""
+
 import numpy as np
+import toponetx as tnx
 import torch
-from toponetx.classes import SimplicialComplex
 
 from topomodelx.nn.simplicial.hsn import HSN
 
@@ -15,7 +16,7 @@ def test_forward(self):
         face_set = [[2, 3, 4], [2, 4, 5]]
 
         torch.manual_seed(42)
-        simplicial_complex = SimplicialComplex(edge_set + face_set)
+        simplicial_complex = tnx.SimplicialComplex(edge_set + face_set)
         laplacian_down_1 = simplicial_complex.down_laplacian_matrix(rank=1).todense()
         adjacency_1 = simplicial_complex.adjacency_matrix(rank=1).todense()
         laplacian_down_1_inv = np.linalg.pinv(

test/nn/simplicial/test_san.py

@@ -1,9 +1,10 @@
 """Unit tests for SAN Model."""
+
 import itertools
 import random
 
+import toponetx as tnx
 import torch
-from toponetx.classes import SimplicialComplex
 
 from topomodelx.nn.simplicial.san import SAN
 from topomodelx.utils.sparse import from_sparse
@@ -28,7 +29,7 @@ def test_forward(self):
         )
         random.shuffle(all_combinations)
         selected_combinations = all_combinations[:faces]
-        simplicial_complex = SimplicialComplex()
+        simplicial_complex = tnx.SimplicialComplex()
         for simplex in selected_combinations:
             simplicial_complex.add_simplex(simplex)
         x_1 = torch.randn(35, 2)

test/nn/simplicial/test_sca_cmps.py

@@ -1,9 +1,10 @@
 """Unit tests for SCA Model."""
+
 import itertools
 import random
 
+import toponetx as tnx
 import torch
-from toponetx.classes import SimplicialComplex
 
 from topomodelx.nn.simplicial.sca_cmps import SCACMPS
 from topomodelx.utils.sparse import from_sparse
@@ -28,7 +29,7 @@ def test_forward(self):
         )
         random.shuffle(all_combinations)
         selected_combinations = all_combinations[:faces]
-        simplicial_complex = SimplicialComplex()
+        simplicial_complex = tnx.SimplicialComplex()
         for simplex in selected_combinations:
             simplicial_complex.add_simplex(simplex)
 

test/nn/simplicial/test_sccn.py

@@ -1,9 +1,10 @@
 """Unit tests for SCCN Model."""
+
 import itertools
 import random
 
+import toponetx as tnx
 import torch
-from toponetx.classes import SimplicialComplex
 
 from topomodelx.nn.simplicial.sccn import SCCN
 from topomodelx.utils.sparse import from_sparse
@@ -28,7 +29,7 @@ def test_forward(self):
         )
         random.shuffle(all_combinations)
         selected_combinations = all_combinations[:faces]
-        simplicial_complex = SimplicialComplex()
+        simplicial_complex = tnx.SimplicialComplex()
         for simplex in selected_combinations:
             simplicial_complex.add_simplex(simplex)
 

test/nn/simplicial/test_sccnn.py

@@ -1,9 +1,10 @@
 """Unit tests for SCCNN Model."""
+
 import itertools
 import random
 
+import toponetx as tnx
 import torch
-from toponetx.classes import SimplicialComplex
 
 from topomodelx.nn.simplicial.sccnn import SCCNN
 from topomodelx.utils.sparse import from_sparse
@@ -28,7 +29,7 @@ def test_forward(self):
         )
         random.shuffle(all_combinations)
         selected_combinations = all_combinations[:faces]
-        simplicial_complex = SimplicialComplex()
+        simplicial_complex = tnx.SimplicialComplex()
         for simplex in selected_combinations:
             simplicial_complex.add_simplex(simplex)
         # Some nodes might not be selected at all in the combinations above

test/nn/simplicial/test_scconv.py

@@ -1,9 +1,10 @@
 """Unit tests for SCCNN Model."""
+
 import itertools
 import random
 
+import toponetx as tnx
 import torch
-from toponetx.classes import SimplicialComplex
 
 from topomodelx.nn.simplicial.scconv import SCConv
 from topomodelx.utils.sparse import from_sparse
@@ -28,7 +29,7 @@ def test_forward(self):
         )
         random.shuffle(all_combinations)
         selected_combinations = all_combinations[:faces]
-        simplicial_complex = SimplicialComplex()
+        simplicial_complex = tnx.SimplicialComplex()
         for simplex in selected_combinations:
             simplicial_complex.add_simplex(simplex)
         # Some nodes might not be selected at all in the combinations above

test/nn/simplicial/test_scn2.py

@@ -1,9 +1,10 @@
 """Unit tests for SCN2 Model."""
+
 import itertools
 import random
 
+import toponetx as tnx
 import torch
-from toponetx.classes import SimplicialComplex
 
 from topomodelx.nn.simplicial.scn2 import SCN2
 from topomodelx.utils.sparse import from_sparse
@@ -28,7 +29,7 @@ def test_forward(self):
         )
         random.shuffle(all_combinations)
         selected_combinations = all_combinations[:faces]
-        simplicial_complex = SimplicialComplex()
+        simplicial_complex = tnx.SimplicialComplex()
         for simplex in selected_combinations:
             simplicial_complex.add_simplex(simplex)
 

test/nn/simplicial/test_scnn.py

@@ -1,10 +1,11 @@
 """Unit tests for SCNN Model."""
+
 import itertools
 import random
 
 import numpy as np
+import toponetx as tnx
 import torch
-from toponetx.classes import SimplicialComplex
 
 from topomodelx.nn.simplicial.scnn import SCNN
 from topomodelx.utils.sparse import from_sparse
@@ -29,7 +30,7 @@ def test_forward(self):
         )
         random.shuffle(all_combinations)
         selected_combinations = all_combinations[:faces]
-        simplicial_complex = SimplicialComplex()
+        simplicial_complex = tnx.SimplicialComplex()
         for simplex in selected_combinations:
             simplicial_complex.add_simplex(simplex)
         x_1 = torch.randn(simplicial_complex.shape[1], 2)

topomodelx/nn/simplicial/scone.py

@@ -1,12 +1,13 @@
 """Neural network implementation of classification using SCoNe."""
+
 import random
 from itertools import product
 
 import networkx as nx
 import numpy as np
+import toponetx as tnx
 import torch
 from scipy.spatial import Delaunay, distance
-from toponetx.classes.simplicial_complex import SimplicialComplex
 from torch import nn
 from torch.utils.data.dataset import Dataset
 
@@ -15,7 +16,7 @@
 
 def generate_complex(
     N: int = 100, *, rng: np.random.Generator | None = None
-) -> tuple[SimplicialComplex, np.ndarray]:
+) -> tuple[tnx.SimplicialComplex, np.ndarray]:
     """Generate a simplicial complex as described.
 
     Generate a simplicial complex of dimension 2 as follows:
@@ -58,13 +59,13 @@ def generate_complex(
         for j in range(3):
             simplices[i][j] = idx_dict[simplices[i][j]]
 
-    sc = SimplicialComplex(simplices)
+    sc = tnx.SimplicialComplex(simplices)
     coords = points[list(indices_included)]
     return sc, coords
 
 
 def generate_trajectories(
-    sc: SimplicialComplex, coords: np.ndarray, n_max: int = 1000
+    sc: tnx.SimplicialComplex, coords: np.ndarray, n_max: int = 1000
 ) -> list[list[int]]:
     """Generate trajectories from nodes in the lower left corner to the upper right corner connected through a node in the middle."""
     # Get indices for start points in the lower left corner, mid points in the center region and end points in the upper right corner.
@@ -98,7 +99,9 @@ def generate_trajectories(
 class TrajectoriesDataset(Dataset):
     """Create a dataset of trajectories."""
 
-    def __init__(self, sc: SimplicialComplex, trajectories: list[list[int]]) -> None:
+    def __init__(
+        self, sc: tnx.SimplicialComplex, trajectories: list[list[int]]
+    ) -> None:
         self.trajectories = trajectories
         self.sc = sc
         self.adjacency = torch.Tensor(sc.adjacency_matrix(0).toarray())