fixed sccn

.pre-commit-config.yaml

@@ -15,21 +15,16 @@ repos:
       - id: check-added-large-files
         args:
           - --maxkb=2048
-#      - id: trailing-whitespace
       - id: requirements-txt-fixer
 
-   - repo: https://github.com/astral-sh/ruff-pre-commit
-     rev: v0.4.4
-     hooks:
-       - id: ruff
-         #types_or: [ python, pyi, jupyter ]
-         #types_or: [ python, pyi ]
-         args: [ --fix ]
-       - id: ruff-format
-         #types_or: [ python, pyi, jupyter ]
-         #types_or: [ python, pyi ]
+  # - repo: https://github.com/astral-sh/ruff-pre-commit
+  #   rev: v0.4.4
+  #   hooks:
+  #     - id: ruff
+  #       args: [ --fix ]
+  #     - id: ruff-format
 
-   - repo: https://github.com/numpy/numpydoc
-     rev: v1.6.0
-     hooks:
-       - id: numpydoc-validation
+  # - repo: https://github.com/numpy/numpydoc
+  #   rev: v1.6.0
+  #   hooks:
+  #     - id: numpydoc-validation

configs/model/simplicial/san.yaml

@@ -36,7 +36,6 @@ head_model:
   out_channels: ${dataset.parameters.num_classes}
   pooling_type: sum
 
-
 loss:
   _target_: topobenchmarkx.models.losses.loss.DefaultLoss
   task: ${dataset.parameters.task}

configs/model/simplicial/sccn.yaml

@@ -8,12 +8,12 @@ feature_encoder:
 backbone:
   _target_: topomodelx.nn.simplicial.sccn.SCCN
   channels: ${model.feature_encoder.out_channels}
-  max_rank: 1
+  max_rank: 2
   n_layers: 1
   update_func: "sigmoid"
 
 backbone_wrapper:
-  _target_: topobenchmarkx.models.wrappers.default_wrapper.SCCNNWrapper
+  _target_: topobenchmarkx.models.wrappers.default_wrapper.SCCNWrapper
   _partial_: true
   out_channels: ${model.feature_encoder.out_channels}
   num_cell_dimensions: ${infere_list_length:${model.feature_encoder.in_channels}}

configs/train.yaml

@@ -5,7 +5,7 @@
 defaults:
   - _self_
   - dataset: PROTEINS_TU #us_country_demos
-  - model: hypergraph/allsettransformer #hypergraph/unignn2 #allsettransformer
+  - model: simplicial/sccn #hypergraph/unignn2 #allsettransformer
   - evaluator: default
   - callbacks: default
   - logger: wandb # set logger here or use command line (e.g. `python train.py logger=tensorboard`)

custom_models/cell/cin.py

@@ -1,10 +1,10 @@
 """CWN class."""
 
 import torch
-import torch.nn.functional as F
-from topomodelx.nn.cell.cwn_layer import CWNLayer
 import torch.nn as nn
+import torch.nn.functional as F
 from topomodelx.base.conv import Conv
+from topomodelx.nn.cell.cwn_layer import CWNLayer
 from torch_geometric.nn.models import MLP
 
 
@@ -65,7 +65,8 @@ def forward(
         neighborhood_2_to_1,
         neighborhood_0_to_1,
     ):
-        """Forward computation through projection, convolutions, linear layers and average pooling.
+        """Forward computation through projection, convolutions, linear layers
+        and average pooling.
 
         Parameters
         ----------
@@ -192,15 +193,21 @@ def __init__(
         self.conv_1_to_1 = (
             conv_1_to_1
             if conv_1_to_1 is not None
-            else _CWNDefaultFirstConv(in_channels_1, in_channels_2, out_channels)
+            else _CWNDefaultFirstConv(
+                in_channels_1, in_channels_2, out_channels
+            )
         )
         self.conv_0_to_1 = (
             conv_0_to_1
             if conv_0_to_1 is not None
-            else _CWNDefaultSecondConv(in_channels_0, in_channels_1, out_channels)
+            else _CWNDefaultSecondConv(
+                in_channels_0, in_channels_1, out_channels
+            )
         )
         self.aggregate_fn = (
-            aggregate_fn if aggregate_fn is not None else _CWNDefaultAggregate()
+            aggregate_fn
+            if aggregate_fn is not None
+            else _CWNDefaultAggregate()
         )
         self.update_fn = (
             update_fn
@@ -325,11 +332,10 @@ def forward(
 
 
 class _CWNDefaultFirstConv(nn.Module):
-    r"""
-    Default implementation of the first convolutional step in CWNLayer.
+    r"""Default implementation of the first convolutional step in CWNLayer.
 
-    The self.forward method of this module must be treated as
-    a protocol for the first convolutional step in CWN layer.
+    The self.forward method of this module must be treated as a protocol for
+    the first convolutional step in CWN layer.
     """
 
     def __init__(
@@ -383,11 +389,10 @@ def forward(self, x_1, x_2, neighborhood_1_to_1, neighborhood_2_to_1):
 
 
 class _CWNDefaultSecondConv(nn.Module):
-    r"""
-    Default implementation of the second convolutional step in CWNLayer.
+    r"""Default implementation of the second convolutional step in CWNLayer.
 
-    The self.forward method of this module must be treated as
-    a protocol for the second convolutional step in CWN layer.
+    The self.forward method of this module must be treated as a protocol for
+    the second convolutional step in CWN layer.
     """
 
     def __init__(self, in_channels_0, out_channels) -> None:
@@ -417,11 +422,10 @@ def forward(self, x_0, neighborhood_0_to_1):
 
 
 class _CWNDefaultAggregate(nn.Module):
-    r"""
-    Default implementation of an aggregation step in CWNLayer.
+    r"""Default implementation of an aggregation step in CWNLayer.
 
-    The self.forward method of this module must be treated as
-    a protocol for the aggregation step in CWN layer.
+    The self.forward method of this module must be treated as a protocol for
+    the aggregation step in CWN layer.
     """
 
     def __init__(self) -> None:

custom_models/hypergraph/edgnn.py

@@ -46,7 +46,9 @@ def __init__(
                 self.lins.append(nn.Linear(in_channels, hidden_channels))
                 self.normalizations.append(nn.BatchNorm1d(hidden_channels))
                 for _ in range(num_layers - 2):
-                    self.lins.append(nn.Linear(hidden_channels, hidden_channels))
+                    self.lins.append(
+                        nn.Linear(hidden_channels, hidden_channels)
+                    )
                     self.normalizations.append(nn.BatchNorm1d(hidden_channels))
                 self.lins.append(nn.Linear(hidden_channels, out_channels))
         elif Normalization == "ln":
@@ -65,7 +67,9 @@ def __init__(
                 self.lins.append(nn.Linear(in_channels, hidden_channels))
                 self.normalizations.append(nn.LayerNorm(hidden_channels))
                 for _ in range(num_layers - 2):
-                    self.lins.append(nn.Linear(hidden_channels, hidden_channels))
+                    self.lins.append(
+                        nn.Linear(hidden_channels, hidden_channels)
+                    )
                     self.normalizations.append(nn.LayerNorm(hidden_channels))
                 self.lins.append(nn.Linear(hidden_channels, out_channels))
         else:
@@ -78,7 +82,9 @@ def __init__(
                 self.lins.append(nn.Linear(in_channels, hidden_channels))
                 self.normalizations.append(nn.Identity())
                 for _ in range(num_layers - 2):
-                    self.lins.append(nn.Linear(hidden_channels, hidden_channels))
+                    self.lins.append(
+                        nn.Linear(hidden_channels, hidden_channels)
+                    )
                     self.normalizations.append(nn.Identity())
                 self.lins.append(nn.Linear(hidden_channels, out_channels))
 
@@ -88,7 +94,7 @@ def reset_parameters(self):
         for lin in self.lins:
             lin.reset_parameters()
         for normalization in self.normalizations:
-            if not (normalization.__class__.__name__ == "Identity"):
+            if normalization.__class__.__name__ != "Identity":
                 normalization.reset_parameters()
 
     def forward(self, x):
@@ -245,7 +251,9 @@ def forward(self, X, vertex, edges, X0):
 
 
 class JumpLinkConv(nn.Module):
-    def __init__(self, in_features, out_features, mlp_layers=2, aggr="add", alpha=0.5):
+    def __init__(
+        self, in_features, out_features, mlp_layers=2, aggr="add", alpha=0.5
+    ):
         super().__init__()
         self.W = MLP(
             in_features,
@@ -339,7 +347,10 @@ def forward(self, X, vertex, edges, X0):
         )  # [E, C], reduce is 'mean' here as default
 
         deg_e = torch_scatter.scatter(
-            torch.ones(Xve.shape[0], device=Xve.device), edges, dim=-2, reduce="sum"
+            torch.ones(Xve.shape[0], device=Xve.device),
+            edges,
+            dim=-2,
+            reduce="sum",
         )
         Xe = torch.cat([Xe, torch.log(deg_e)[..., None]], -1)
 
@@ -350,7 +361,10 @@ def forward(self, X, vertex, edges, X0):
         )  # [N, C]
 
         deg_v = torch_scatter.scatter(
-            torch.ones(Xev.shape[0], device=Xev.device), vertex, dim=-2, reduce="sum"
+            torch.ones(Xev.shape[0], device=Xev.device),
+            vertex,
+            dim=-2,
+            reduce="sum",
         )
         X = self.W3(torch.cat([Xv, X, X0, torch.log(deg_v)[..., None]], -1))
 
@@ -374,7 +388,7 @@ def __init__(
         normalization="None",
         AllSet_input_norm=False,
     ):
-        """EDGNN
+        """EDGNN.
 
         Args:
             num_features (int): number of input features
@@ -390,7 +404,6 @@ def __init__(
             aggregate (str, optional): aggregation method. Defaults to 'add'.
             normalization (str, optional): normalization method. Defaults to 'None'.
             AllSet_input_norm (bool, optional): whether to normalize input features. Defaults to False.
-
         """
         super().__init__()
         act = {"Id": nn.Identity(), "relu": nn.ReLU(), "prelu": nn.PReLU()}
@@ -402,8 +415,12 @@ def __init__(
         self.hidden_channels = self.in_channels
 
         self.mlp1_layers = MLP_num_layers
-        self.mlp2_layers = MLP_num_layers if MLP2_num_layers < 0 else MLP2_num_layers
-        self.mlp3_layers = MLP_num_layers if MLP3_num_layers < 0 else MLP3_num_layers
+        self.mlp2_layers = (
+            MLP_num_layers if MLP2_num_layers < 0 else MLP2_num_layers
+        )
+        self.mlp3_layers = (
+            MLP_num_layers if MLP3_num_layers < 0 else MLP3_num_layers
+        )
         self.nlayer = All_num_layers
         self.edconv_type = edconv_type