Add initial redesign of liftings maps

test/transforms/liftings/simplicial/test_SimplicialCliqueLifting.py

@@ -3,21 +3,24 @@
 import torch
 
 from topobenchmarkx.transforms.liftings.graph2simplicial import (
-    SimplicialCliqueLifting,
+    SimplicialCliqueLifting
 )
-
+from topobenchmarkx.transforms.converters import Data2NxGraph, Complex2Dict
+from topobenchmarkx.transforms.liftings.base import LiftingTransform
 
 class TestSimplicialCliqueLifting:
     """Test the SimplicialCliqueLifting class."""
 
     def setup_method(self):
         # Initialise the SimplicialCliqueLifting class
-        self.lifting_signed = SimplicialCliqueLifting(
-            complex_dim=3, signed=True
-        )
-        self.lifting_unsigned = SimplicialCliqueLifting(
-            complex_dim=3, signed=False
-        )
+        data2graph = Data2NxGraph()
+        simplicial2dict_signed = Complex2Dict(signed=True)
+        simplicial2dict_unsigned = Complex2Dict(signed=False)
+
+        lifting_map = SimplicialCliqueLifting(complex_dim=3)
+
+        self.lifting_signed = LiftingTransform(data2graph, simplicial2dict_signed, lifting_map)
+        self.lifting_unsigned  = LiftingTransform(data2graph, simplicial2dict_unsigned, lifting_map)
 
     def test_lift_topology(self, simple_graph_1):
         """Test the lift_topology method."""

topobenchmarkx/complex.py

@@ -0,0 +1,85 @@
+import torch
+
+
+class PlainComplex:
+    def __init__(
+        self,
+        incidence,
+        down_laplacian,
+        up_laplacian,
+        adjacency,
+        coadjacency,
+        hodge_laplacian,
+        features=None,
+    ):
+        # TODO: allow None with nice error message if callable?
+
+        # TODO: make this private? do not allow for changes in these values?
+        self.incidence = incidence
+        self.down_laplacian = down_laplacian
+        self.up_laplacian = up_laplacian
+        self.adjacency = adjacency
+        self.coadjacency = coadjacency
+        self.hodge_laplacian = hodge_laplacian
+
+        if features is None:
+            features = [None for _ in range(len(self.incidence))]
+        else:
+            for rank, dim in enumerate(self.shape):
+                # TODO: make error message more informative
+                if (
+                    features[rank] is not None
+                    and features[rank].shape[0] != dim
+                ):
+                    raise ValueError("Features have wrong shape.")
+
+        self.features = features
+
+    @property
+    def shape(self):
+        """Shape of the complex.
+
+        Returns
+        -------
+        list[int]
+        """
+        return [incidence.shape[-1] for incidence in self.incidence]
+
+    @property
+    def max_rank(self):
+        """Maximum rank of the complex.
+
+        Returns
+        -------
+        int
+        """
+        return len(self.incidence)
+
+    def update_features(self, rank, values):
+        """Update features.
+
+        Parameters
+        ----------
+        rank : int
+            Rank of simplices the features belong to.
+        values : array-like
+            New features for the rank-simplices.
+        """
+        self.features[rank] = values
+
+    def reset_features(self):
+        """Reset features."""
+        self.features = [None for _ in self.features]
+
+    def propagate_values(self, rank, values):
+        """Propagate features from a rank to an upper one.
+
+        Parameters
+        ----------
+        rank : int
+            Rank of the simplices the values belong to.
+        values : array-like
+            Features for the rank-simplices.
+        """
+        # TODO: can be made much better
+        return torch.matmul(torch.abs(self.incidence[rank + 1].t()), values)