FIX: Remove additional useles stuff

Author: martin-carrasco

modules/transforms/data_manipulations/manipulations.py

@@ -305,85 +305,4 @@ def forward(self, data: torch_geometric.data.Data):
             if key not in self.parameters["keep_fields"]:
                 del data[key]
 
-        return data
-
-class AlexanderDual(torch_geometric.transforms.BaseTransform):
-    r"""The Alexander dual of a Simplicial Complex `\sigma` denoted
-    `\bar{\sigma}` is the simplicial complex whose simplices are the 
-    complements of the simplices of `\sigma`.
-
-    Parameters
-    ----------
-    **kwargs : optional
-        Parameters for the transform.
-    """
-
-    def __init__(self, **kwargs):
-        super().__init__()
-        self.type = "alexander_dual"
-        self.parameters = kwargs
-
-    def forward(self, data: torch_geometric.data.Data):
-        r"""Apply the transform to the input data.
-
-        Parameters
-        ----------
-        data : torch_geometric.data.Data
-            The input data.
-
-        Returns
-        -------
-        torch_geometric.data.Data
-            The transformed data.
-        """
-
-        G = to_networkx(data)
-        S = SimplicialComplex(simplices=G)
-        S_star = SimplicialComplex()
-
-        nodes = set(S.nodes)
-        for s in S.simplices:
-            s_bar = frozenset(nodes - set(s))
-            if s_bar not in S:
-                S_star.add_simplex(s)
-
-        S_star
-
-        return data
-
-
-class RandomGraph(torch_geometric.transforms.BaseTransform):
-    r"""A transform that generates a random graph from the input data.
-
-    Parameters
-    ----------
-    **kwargs : optional
-        Parameters for the transform.
-    """
-
-    def __init__(self, **kwargs):
-        super().__init__()
-        self.type = "random_graph"
-        self.parameters = kwargs
-
-    def forward(self, data: torch_geometric.data.Data):
-        r"""Apply the transform to the input data.
-
-        Parameters
-        ----------
-        data : torch_geometric.data.Data
-            The input data.
-
-        Returns
-        -------
-        torch_geometric.data.Data
-            The transformed data.
-        """
-
-        G = to_networkx(data)
-        n = G.number_of_nodes()
-        p = self.parameters["p"]
-        G = nx.fast_gnp_random_graph(n, p)
-        data = torch_geometric.utils.from_networkx(G)
-
-        return data
+        return data