Started CI

.github/workflows/lint.yml

@@ -0,0 +1,14 @@
+# name: Linting
+
+# on:
+#   push:
+#     branches: [ main,github-actions-test ]
+#   pull_request:
+#     branches: [ main ]
+
+# jobs:
+#   ruff:
+#     runs-on: ubuntu-latest
+#     steps:
+#       - uses: actions/checkout@v3
+#       - uses: chartboost/ruff-action@v1

.pre-commit-config.yaml

@@ -1,10 +1,11 @@
-default_language_version :
-  python : python3
+default_language_version:
+  python: python3.10
+
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.0.1
     hooks:
-      - id: check-byte-order-marker
+      - id: fix-byte-order-marker
       - id: check-case-conflict
       - id: check-merge-conflict
       - id: check-yaml
@@ -17,25 +18,16 @@ repos:
       - id: trailing-whitespace
       - id: requirements-txt-fixer
 
-  - repo: https://github.com/psf/black
-    rev: 23.3.0
-    hooks:
-      - id: black-jupyter
-
-  - repo: https://github.com/pycqa/isort
-    rev: 5.12.0
-    hooks:
-      - id : isort
-        args : ["--profile=black", "--filter-files"]
-
-  - repo: https://github.com/asottile/blacken-docs
-    rev: 1.13.0
-    hooks:
-    -   id: blacken-docs
-        additional_dependencies: [black==23.3.0]
+  # - repo: https://github.com/astral-sh/ruff-pre-commit
+  #   rev: v0.1.14
+  #   hooks:
+  #     - id: ruff
+  #       types_or: [ python, pyi, jupyter ]
+  #       args: [ --fix ]
+  #     - id: ruff-format
+  #       types_or: [ python, pyi, jupyter ]
 
-  # - repo: https://github.com/pycqa/flake8
-  #   rev: 6.0.0
+  # - repo: https://github.com/numpy/numpydoc
+  #   rev: v1.6.0
   #   hooks:
-  #   -   id: flake8
-  #       additional_dependencies: [flake8-docstrings, Flake8-pyproject]
+  #     - id: numpydoc-validation

configs/train.yaml

@@ -5,7 +5,7 @@
 defaults:
   - _self_
   - dataset: ZINC
-  - model: hypergraph/unignn2 #simplicial/scn #
+  - model: cell/cwn #hypergraph/unignn2 #
   - 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

@@ -2,7 +2,6 @@
 
 import torch
 import torch.nn.functional as F
-
 from topomodelx.nn.cell.cwn_layer import CWNLayer
 
 
@@ -104,18 +103,18 @@ def forward(
             )
 
         return x_0, x_1, x_2
-    
+
 
 #### LAYERs ####
-    
+
 """Implementation of CWN layer from Bodnar et al.: Weisfeiler and Lehman Go Cellular: CW Networks."""
 
 import torch.nn as nn
 import torch.nn.functional as F
-
 from topomodelx.base.conv import Conv
 from torch_geometric.nn.models import MLP
 
+
 class CWNLayer(nn.Module):
     r"""Layer of a CW Network (CWN).
 
@@ -188,11 +187,10 @@ def __init__(
         conv_0_to_1=None,
         aggregate_fn=None,
         update_fn=None,
-        eps=0.01
+        eps=0.01,
     ) -> None:
         super().__init__()
-
-
+
         self.conv_1_to_1 = (
             conv_1_to_1
             if conv_1_to_1 is not None
@@ -213,15 +211,15 @@ def __init__(
         )
         self.mlp_arrow = MLP(
             [in_channels_1, in_channels_1, in_channels_1],
-            act='relu',
+            act="relu",
             act_first=False,
             norm=torch.nn.BatchNorm1d(out_channels),
             # norm_kwargs=self.norm_kwargs,
         )
 
         self.mlp = MLP(
             [in_channels_2 + in_channels_2, out_channels, out_channels],
-            act='relu',
+            act="relu",
             act_first=False,
             norm=torch.nn.BatchNorm1d(out_channels),
             # norm_kwargs=self.norm_kwargs,
@@ -316,11 +314,15 @@ def forward(
         )
         x_convolved_1_to_1 = self.mlp_arrow(x_convolved_1_to_1)
 
-        # 
-        x_convolved_0_to_1 = (1 + self.eps) * x_1 + self.conv_0_to_1(x_0, neighborhood_0_to_1)
+        #
+        x_convolved_0_to_1 = (1 + self.eps) * x_1 + self.conv_0_to_1(
+            x_0, neighborhood_0_to_1
+        )
 
-        x_aggregated = self.mlp(torch.cat([x_convolved_0_to_1, x_convolved_1_to_1], dim=-1))
-        #x_aggregated = self.aggregate_fn(x_convolved_1_to_1, x_convolved_0_to_1)
+        x_aggregated = self.mlp(
+            torch.cat([x_convolved_0_to_1, x_convolved_1_to_1], dim=-1)
+        )
+        # x_aggregated = self.aggregate_fn(x_convolved_1_to_1, x_convolved_0_to_1)
         return self.update_fn(x_aggregated, x_1)
 
 
@@ -332,7 +334,9 @@ class _CWNDefaultFirstConv(nn.Module):
     a protocol for the first convolutional step in CWN layer.
     """
 
-    def __init__(self, in_channels_1, in_channels_2, out_channels, eps: float = 0.) -> None:
+    def __init__(
+        self, in_channels_1, in_channels_2, out_channels, eps: float = 0.0
+    ) -> None:
         super().__init__()
         self.conv_1_to_1 = Conv(
             in_channels_1, out_channels, aggr_norm=False, update_func=None
@@ -343,14 +347,14 @@ def __init__(self, in_channels_1, in_channels_2, out_channels, eps: float = 0.)
 
         self.mlp = MLP(
             [in_channels_1 + in_channels_2, out_channels, out_channels],
-            act='relu',
+            act="relu",
             act_first=False,
             norm=torch.nn.BatchNorm1d(out_channels),
             # norm_kwargs=self.norm_kwargs,
         )
 
         self.eps = torch.nn.Parameter(torch.Tensor([eps]))
-        
+
     def forward(self, x_1, x_2, neighborhood_1_to_1, neighborhood_2_to_1):
         r"""Forward pass.
 
@@ -370,12 +374,10 @@ def forward(self, x_1, x_2, neighborhood_1_to_1, neighborhood_2_to_1):
         torch.Tensor, shape = (n_{r}_cells, out_channels)
             Updated representations on the r-cells.
         """
-        # 
+        #
         x_up = F.elu(self.conv_1_to_1(x_1, neighborhood_1_to_1))
         x_up = (1 + self.eps) * x_1 + x_up
 
-
-
         x_coboundary = F.elu(self.conv_2_to_1(x_2, neighborhood_2_to_1))
         x_coboundary = (1 + self.eps) * x_1 + x_coboundary
 
@@ -467,4 +469,4 @@ def forward(self, x, x_prev=None):
         torch.Tensor, shape = (n_{r}_cells, out_channels)
             Updated representations on the r-cells.
         """
-        return F.elu(self.transform(x))
+        return F.elu(self.transform(x))

custom_models/simplicial/sccnn.py

@@ -1,6 +1,6 @@
 """SCCNN implementation for complex classification."""
-import torch
 
+import torch
 from topomodelx.nn.simplicial.sccnn_layer import SCCNNLayer
 
 
@@ -93,8 +93,9 @@ def forward(self, x_all, laplacian_all, incidence_all):
             x_all = layer(x_all, laplacian_all, incidence_all)
 
         return x_all
-
-# Layer 
+
+
+# Layer
 """Simplicial Complex Convolutional Neural Network Layer."""
 import torch
 from torch.nn.parameter import Parameter
@@ -223,7 +224,7 @@ def __init__(
             torch.Tensor(
                 self.in_channels_1,
                 self.out_channels_1,
-                6*conv_order + 3,
+                6 * conv_order + 3,
             )
         )
 
@@ -236,16 +237,17 @@ def __init__(
                 torch.Tensor(
                     self.in_channels_2,
                     self.out_channels_2,
-                    4*conv_order + 2, # in the future for arbitrary sc_order we should have this 6*conv_order + 3,
+                    4 * conv_order
+                    + 2,  # in the future for arbitrary sc_order we should have this 6*conv_order + 3,
                 )
             )
-    
+
         elif sc_order == 2:
             self.weight_2 = Parameter(
                 torch.Tensor(
                     self.in_channels_2,
                     self.out_channels_2,
-                    4*conv_order + 2,
+                    4 * conv_order + 2,
                 )
             )
 
@@ -410,33 +412,35 @@ def forward(self, x_all, laplacian_all, incidence_all):
         """
         Convolution in the node space
         """
-        #-----------Logic to obtain update for 0-cells --------
+        # -----------Logic to obtain update for 0-cells --------
         # x_identity_0 = torch.unsqueeze(identity_0 @ x_0, 2)
         # x_0_to_0 = self.chebyshev_conv(laplacian_0, self.conv_order, x_0)
         # x_0_to_0 = torch.cat((x_identity_0, x_0_to_0), 2)
-        
+
         x_0_laplacian = self.chebyshev_conv(laplacian_0, self.conv_order, x_0)
         x_0_to_0 = torch.cat([x_0.unsqueeze(2), x_0_laplacian], dim=2)
-        #-------------------
+        # -------------------
 
         # x_1_to_0 = torch.mm(b1, x_1)
         # x_1_to_0_identity = torch.unsqueeze(identity_0 @ x_1_to_0, 2)
         # x_1_to_0 = self.chebyshev_conv(laplacian_0, self.conv_order, x_1_to_0)
         # x_1_to_0 = torch.cat((x_1_to_0_identity, x_1_to_0), 2)
 
         x_1_to_0_upper = torch.mm(b1, x_1)
-        x_1_to_0_laplacian = self.chebyshev_conv(laplacian_0, self.conv_order, x_1_to_0_upper)
+        x_1_to_0_laplacian = self.chebyshev_conv(
+            laplacian_0, self.conv_order, x_1_to_0_upper
+        )
         x_1_to_0 = torch.cat([x_1_to_0_upper.unsqueeze(2), x_1_to_0_laplacian], dim=2)
-        #-------------------
+        # -------------------
 
         x_0_all = torch.cat((x_0_to_0, x_1_to_0), 2)
 
-        #-------------------
+        # -------------------
         """
         Convolution in the edge space
         """
 
-        #-----------Logic to obtain update for 1-cells --------
+        # -----------Logic to obtain update for 1-cells --------
         # x_identity_1 = torch.unsqueeze(identity_1 @ x_1, 2)
         # x_1_down = self.chebyshev_conv(laplacian_down_1, self.conv_order, x_1)
         # x_1_up = self.chebyshev_conv(laplacian_up_1, self.conv_order, x_1)
@@ -446,25 +450,25 @@ def forward(self, x_all, laplacian_all, incidence_all):
         x_1_up = self.chebyshev_conv(laplacian_down_1, self.conv_order, x_1)
         x_1_to_1 = torch.cat((x_1.unsqueeze(2), x_1_down, x_1_up), 2)
 
-        #-------------------
+        # -------------------
 
         # x_0_to_1 = torch.mm(b1.T, x_0)
         # x_0_to_1_identity = torch.unsqueeze(identity_1 @ x_0_to_1, 2)
         # x_0_to_1 = self.chebyshev_conv(laplacian_down_1, self.conv_order, x_0_to_1)
         # x_0_to_1 = torch.cat((x_0_to_1_identity, x_0_to_1), 2)
-        
+
         # Lower projection
-        x_0_1_lower = torch.mm(b1.T, x_0) 
-        
+        x_0_1_lower = torch.mm(b1.T, x_0)
+
         # Calculate lowwer chebyshev_conv
         x_0_1_down = self.chebyshev_conv(laplacian_down_1, self.conv_order, x_0_1_lower)
-        
+
         # Calculate upper chebyshev_conv (Note: in case of signed incidence should be always zero)
         x_0_1_up = self.chebyshev_conv(laplacian_up_1, self.conv_order, x_0_1_lower)
-        
+
         # Concatenate output of filters
-        x_0_to_1 = torch.cat([x_0_1_lower.unsqueeze(2),x_0_1_down, x_0_1_up], dim=2)
-        #-------------------
+        x_0_to_1 = torch.cat([x_0_1_lower.unsqueeze(2), x_0_1_down, x_0_1_up], dim=2)
+        # -------------------
 
         # x_2_to_1 = torch.mm(b2, x_2)
         # x_2_to_1_identity = torch.unsqueeze(identity_1 @ x_2_to_1, 2)
@@ -475,20 +479,20 @@ def forward(self, x_all, laplacian_all, incidence_all):
 
         # Calculate lowwer chebyshev_conv (Note: In case of signed incidence should be always zero)
         x_2_1_down = self.chebyshev_conv(laplacian_down_1, self.conv_order, x_2_1_upper)
-        
+
         # Calculate upper chebyshev_conv
         x_2_1_up = self.chebyshev_conv(laplacian_up_1, self.conv_order, x_2_1_upper)
 
         x_2_to_1 = torch.cat([x_2_1_upper.unsqueeze(2), x_2_1_down, x_2_1_up], dim=2)
 
-        #-------------------
+        # -------------------
         x_1_all = torch.cat((x_0_to_1, x_1_to_1, x_2_to_1), 2)
 
         """
         convolution in the face (triangle) space, depending on the SC order,
         the exact form maybe a little different
         """
-        #-------------------Logic to obtain update for 2-cells --------
+        # -------------------Logic to obtain update for 2-cells --------
         # x_identity_2 = torch.unsqueeze(identity_2 @ x_2, 2)
 
         # if self.sc_order == 2:
@@ -502,7 +506,7 @@ def forward(self, x_all, laplacian_all, incidence_all):
         x_2_up = self.chebyshev_conv(laplacian_up_2, self.conv_order, x_2)
         x_2_to_2 = torch.cat((x_2.unsqueeze(2), x_2_down, x_2_up), 2)
 
-        #-------------------
+        # -------------------
 
         # x_1_to_2 = torch.mm(b2.T, x_1)
         # x_1_to_2_identity = torch.unsqueeze(identity_2 @ x_1_to_2, 2)
@@ -525,13 +529,13 @@ def forward(self, x_all, laplacian_all, incidence_all):
 
         # x_3_to_2 = torch.cat([x_3_2_upper.unsueeze(2), x_3_2_down, x_3_2_up], dim=2)
 
-        #-------------------
-        
+        # -------------------
+
         x_2_all = torch.cat([x_1_to_2, x_2_to_2], dim=2)
         # The final version of this model should have the following line
         # x_2_all = torch.cat([x_1_to_2, x_2_to_2, x_3_to_2], dim=2)
 
-        #-------------------
+        # -------------------
 
         # Need to check that this einsums are correct
         y_0 = torch.einsum("nik,iok->no", x_0_all, self.weight_0)
@@ -541,4 +545,4 @@ def forward(self, x_all, laplacian_all, incidence_all):
         if self.update_func is None:
             return y_0, y_1, y_2
 
-        return self.update(y_0), self.update(y_1), self.update(y_2)
+        return self.update(y_0), self.update(y_1), self.update(y_2)