Refactoring + Improving Tutorials

configs/transforms/liftings/graph2hypergraph/knn_lifting.yaml

@@ -1,5 +1,5 @@
 transform_type: 'lifting'
 transform_name: "HypergraphKNNLifting"
-k_value: 2
+k_value: 3
 loop: True
 feature_lifting: ProjectionSum

modules/io/load/loaders.py → modules/data/load/loaders.py

@@ -5,8 +5,9 @@
 import torch_geometric
 from omegaconf import DictConfig
 
-from modules.io.load.base import AbstractLoader
-from modules.io.utils.utils import (
+from modules.data.load.base import AbstractLoader
+from modules.data.utils.custom_dataset import CustomDataset
+from modules.data.utils.utils import (
     load_cell_complex_dataset,
     load_hypergraph_pickle_dataset,
     load_manual_graph,
@@ -102,7 +103,8 @@ def load(self) -> torch_geometric.data.Dataset:
             dataset = datasets[0] + datasets[1] + datasets[2]
 
         elif self.parameters.data_name in ["manual"]:
-            dataset = load_manual_graph()
+            data = load_manual_graph()
+            dataset = CustomDataset([data], self.data_dir)
 
         else:
             raise NotImplementedError(

modules/io/preprocess/preprocessor.py → modules/data/preprocess/preprocessor.py

@@ -3,7 +3,7 @@
 
 import torch_geometric
 
-from modules.io.utils.utils import ensure_serializable, make_hash
+from modules.data.utils.utils import ensure_serializable, make_hash
 from modules.transforms.data_transform import DataTransform
 
 

modules/data/utils/custom_dataset.py

@@ -0,0 +1,15 @@
+import torch_geometric
+
+
+class CustomDataset(torch_geometric.data.InMemoryDataset):
+    def __init__(self, data_list, data_dir, transform=None):
+        self.data_list = data_list
+        super().__init__(data_dir, transform)
+        self.load(self.processed_paths[0])
+
+    @property
+    def processed_file_names(self):
+        return "data.pt"
+
+    def process(self):
+        self.save(self.data_list, self.processed_paths[0])

modules/io/utils/utils.py → modules/data/utils/utils.py

@@ -50,16 +50,16 @@ def get_complex_connectivity(complex, max_rank, signed=False):
                 )
             except ValueError:
                 if connectivity_info == "incidence":
-                    connectivity[f"{connectivity_info}_{rank_idx}"] = (
-                        generate_zero_sparse_connectivity(
-                            m=practical_shape[rank_idx - 1], n=practical_shape[rank_idx]
-                        )
+                    connectivity[
+                        f"{connectivity_info}_{rank_idx}"
+                    ] = generate_zero_sparse_connectivity(
+                        m=practical_shape[rank_idx - 1], n=practical_shape[rank_idx]
                     )
                 else:
-                    connectivity[f"{connectivity_info}_{rank_idx}"] = (
-                        generate_zero_sparse_connectivity(
-                            m=practical_shape[rank_idx], n=practical_shape[rank_idx]
-                        )
+                    connectivity[
+                        f"{connectivity_info}_{rank_idx}"
+                    ] = generate_zero_sparse_connectivity(
+                        m=practical_shape[rank_idx], n=practical_shape[rank_idx]
                     )
     connectivity["shape"] = practical_shape
     return connectivity

modules/models/cell/cwn.py

@@ -15,9 +15,13 @@ class CWNModel(torch.nn.Module):
     """
 
     def __init__(self, model_config, dataset_config):
-        in_channels_0 = dataset_config["num_features"]
-        in_channels_1 = dataset_config["num_features"]
-        in_channels_2 = dataset_config["num_features"]
+        in_channels_0 = (
+            dataset_config["num_features"]
+            if isinstance(dataset_config["num_features"], int)
+            else dataset_config["num_features"][0]
+        )
+        in_channels_1 = in_channels_0
+        in_channels_2 = in_channels_0
         hidden_channels = model_config["hidden_channels"]
         out_channels = dataset_config["num_classes"]
         n_layers = model_config["n_layers"]

modules/models/hypergraph/unigcn.py

@@ -15,7 +15,11 @@ class UniGCNModel(torch.nn.Module):
     """
 
     def __init__(self, model_config, dataset_config):
-        in_channels = dataset_config["num_features"]
+        in_channels = (
+            dataset_config["num_features"]
+            if isinstance(dataset_config["num_features"], int)
+            else dataset_config["num_features"][0]
+        )
         hidden_channels = model_config["hidden_channels"]
         out_channels = dataset_config["num_classes"]
         n_layers = model_config["n_layers"]

modules/models/simplicial/san.py

@@ -15,7 +15,11 @@ class SANModel(torch.nn.Module):
     """
 
     def __init__(self, model_config, dataset_config):
-        in_channels = dataset_config["num_features"]
+        in_channels = (
+            dataset_config["num_features"]
+            if isinstance(dataset_config["num_features"], int)
+            else dataset_config["num_features"][0]
+        )
         hidden_channels = model_config["hidden_channels"]
         out_channels = dataset_config["num_classes"]
         n_layers = model_config["n_layers"]

modules/transforms/liftings/graph2cell/base.py

@@ -2,7 +2,7 @@
 import torch
 from toponetx.classes import CellComplex
 
-from modules.io.utils.utils import get_complex_connectivity
+from modules.data.utils.utils import get_complex_connectivity
 from modules.transforms.liftings.lifting import GraphLifting
 
 

modules/transforms/liftings/graph2simplicial/base.py

@@ -2,7 +2,7 @@
 import torch
 from toponetx.classes import SimplicialComplex
 
-from modules.io.utils.utils import get_complex_connectivity
+from modules.data.utils.utils import get_complex_connectivity
 from modules.transforms.liftings.lifting import GraphLifting