Merge pull request #291 from pyt-team/update_readme_2

making code in the  readme more clear

README.md

@@ -50,19 +50,19 @@ from topomodelx.nn.simplicial.san import SAN
 from topomodelx.utils.sparse import from_sparse
 
 # Step 1: Load the Karate Club dataset
-dataset = tnx.datasets.karate_club(complex_type="simplicial")
+karate_club_complex = tnx.datasets.karate_club(complex_type="simplicial")
 
 # Step 2: Prepare Laplacians and node/edge features
-laplacian_down = from_sparse(dataset.down_laplacian_matrix(rank=1))
-laplacian_up = from_sparse(dataset.up_laplacian_matrix(rank=1))
-incidence_0_1 = from_sparse(dataset.incidence_matrix(rank=1))
+laplacian_down = from_sparse(karate_club_complex.down_laplacian_matrix(rank=1))
+laplacian_up = from_sparse(karate_club_complex.up_laplacian_matrix(rank=1))
+incidence_0_1 = from_sparse(karate_club_complex.incidence_matrix(rank=1))
 
-x_0 = torch.tensor(np.stack(list(dataset.get_simplex_attributes("node_feat").values())))
-x_1 = torch.tensor(np.stack(list(dataset.get_simplex_attributes("edge_feat").values())))
+x_0 = torch.tensor(np.stack(list(karate_club_complex.get_simplex_attributes("node_feat").values())))
+x_1 = torch.tensor(np.stack(list(karate_club_complex.get_simplex_attributes("edge_feat").values())))
 x = x_1 + torch.sparse.mm(incidence_0_1.T, x_0)
 
 # Step 3: Define the network
-class Network(torch.nn.Module):
+class TNN(torch.nn.Module):
     def __init__(self, in_channels, hidden_channels, out_channels):
         super().__init__()
         self.base_model = SAN(in_channels, hidden_channels, n_layers=2)
@@ -73,7 +73,7 @@ class Network(torch.nn.Module):
         return torch.sigmoid(self.linear(x))
 
 # Step 4: Initialize the network and perform a forward pass
-model = Network(in_channels=x.shape[-1], hidden_channels=16, out_channels=2)
+model = TNN(in_channels=x.shape[-1], hidden_channels=16, out_channels=2)
 y_hat_edge = model(x, laplacian_up=laplacian_up, laplacian_down=laplacian_down)
    ```