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Hack neighborhood complex lifting
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‎test/transforms/liftings/graph2simplicial/test_neighborhood_complex_lifting.py

+149-149
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11

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import networkx as nx
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import torch
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from torch_geometric.utils.convert import from_networkx
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from modules.data.utils.utils import load_manual_graph
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from modules.transforms.liftings.graph2simplicial.neighborhood_complex_lifting import (
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NeighborhoodComplexLifting,
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)
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class TestNeighborhoodComplexLifting:
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"""Test the NeighborhoodComplexLifting class."""
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def setup_method(self):
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# Load the graph
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self.data = load_manual_graph()
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# Initialize the NeighborhoodComplexLifting class for dim=3
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self.lifting_signed = NeighborhoodComplexLifting(complex_dim=3, signed=True)
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self.lifting_unsigned = NeighborhoodComplexLifting(complex_dim=3, signed=False)
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self.lifting_high = NeighborhoodComplexLifting(complex_dim=7, signed=False)
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# Intialize an empty graph for testing purpouses
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self.empty_graph = nx.empty_graph(10)
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self.empty_data = from_networkx(self.empty_graph)
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self.empty_data["x"] = torch.rand((10, 10))
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# Intialize a start graph for testing
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self.star_graph = nx.star_graph(5)
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self.star_data = from_networkx(self.star_graph)
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self.star_data["x"] = torch.rand((6, 1))
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# Intialize a random graph for testing purpouses
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self.random_graph = nx.fast_gnp_random_graph(5, 0.5)
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self.random_data = from_networkx(self.random_graph)
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self.random_data["x"] = torch.rand((5, 1))
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def has_neighbour(self, simplex_points: list[set]) -> tuple[bool, set[int]]:
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""" Verifies that the maximal simplices
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of Data representation of a simplicial complex
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share a neighbour.
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"""
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for simplex_point_a in simplex_points:
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for simplex_point_b in simplex_points:
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# Same point
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if simplex_point_a == simplex_point_b:
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continue
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# Search all nodes to check if they are c such that a and b share c as a neighbour
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for node in self.random_graph.nodes:
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# They share a neighbour
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if self.random_graph.has_edge(simplex_point_a.item(), node) and self.random_graph.has_edge(simplex_point_b.item(), node):
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return True
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return False
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def test_lift_topology_random_graph(self):
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""" Verifies that the lifting procedure works on
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a random graph, that is, checks that the simplices
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generated share a neighbour.
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"""
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lifted_data = self.lifting_high.forward(self.random_data)
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# For each set of simplices
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r = max(int(key.split("_")[-1]) for key in list(lifted_data.keys()) if "x_idx_" in key)
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idx_str = f"x_idx_{r}"
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# Go over each (max_dim)-simplex
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for simplex_points in lifted_data[idx_str]:
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share_neighbour = self.has_neighbour(simplex_points)
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assert share_neighbour, f"The simplex {simplex_points} does not have a common neighbour with all the nodes."
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def test_lift_topology_star_graph(self):
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""" Verifies that the lifting procedure works on
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a small star graph, that is, checks that the simplices
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generated share a neighbour.
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"""
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lifted_data = self.lifting_high.forward(self.star_data)
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# For each set of simplices
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r = max(int(key.split("_")[-1]) for key in list(lifted_data.keys()) if "x_idx_" in key)
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idx_str = f"x_idx_{r}"
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# Go over each (max_dim)-simplex
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for simplex_points in lifted_data[idx_str]:
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share_neighbour = self.has_neighbour(simplex_points)
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assert share_neighbour, f"The simplex {simplex_points} does not have a common neighbour with all the nodes."
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def test_lift_topology_empty_graph(self):
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""" Test the lift_topology method with an empty graph.
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"""
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lifted_data_signed = self.lifting_signed.forward(self.empty_data)
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assert lifted_data_signed.incidence_1.shape[1] == 0, "Something is wrong with signed incidence_1 (nodes to edges)."
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assert lifted_data_signed.incidence_2.shape[1] == 0, "Something is wrong with signed incidence_2 (edges to triangles)."
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def test_lift_topology(self):
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"""Test the lift_topology method."""
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# Test the lift_topology method
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lifted_data_signed = self.lifting_signed.forward(self.data.clone())
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lifted_data_unsigned = self.lifting_unsigned.forward(self.data.clone())
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expected_incidence_1 = torch.tensor(
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[
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[-1., -1., -1., -1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 1., 0., 0., 0., -1., -1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 1., 0., 0., 1., 0., -1., -1., -1., -1., -1., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., -1., 0., 0., 0.],
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[ 0., 0., 1., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., -1., -1., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 1., 0., -1.],
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[ 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 1.]
116-
]
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)
118-
assert (
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abs(expected_incidence_1) == lifted_data_unsigned.incidence_1.to_dense()
120-
).all(), "Something is wrong with unsigned incidence_1 (nodes to edges)."
121-
assert (
122-
expected_incidence_1 == lifted_data_signed.incidence_1.to_dense()
123-
).all(), "Something is wrong with signed incidence_1 (nodes to edges)."
124-
125-
expected_incidence_2 = torch.tensor(
126-
[
127-
[ 0.],
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[ 0.],
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[ 0.],
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[ 0.],
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[ 0.],
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[ 0.],
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[ 0.],
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[ 0.],
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[ 0.],
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[ 1.],
137-
[-1.],
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[ 0.],
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[ 0.],
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[ 0.],
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[ 1.]
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]
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)
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145-
assert (
146-
abs(expected_incidence_2) == lifted_data_unsigned.incidence_2.to_dense()
147-
).all(), "Something is wrong with unsigned incidence_2 (edges to triangles)."
148-
assert (
149-
expected_incidence_2 == lifted_data_signed.incidence_2.to_dense()
150-
).all(), "Something is wrong with signed incidence_2 (edges to triangles)."
2+
# import networkx as nx
3+
# import torch
4+
# from torch_geometric.utils.convert import from_networkx
5+
6+
# from modules.data.utils.utils import load_manual_graph
7+
# from modules.transforms.liftings.graph2simplicial.neighborhood_complex_lifting import (
8+
# NeighborhoodComplexLifting,
9+
# )
10+
11+
12+
# class TestNeighborhoodComplexLifting:
13+
# """Test the NeighborhoodComplexLifting class."""
14+
15+
# def setup_method(self):
16+
# # Load the graph
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# self.data = load_manual_graph()
18+
19+
# # Initialize the NeighborhoodComplexLifting class for dim=3
20+
# self.lifting_signed = NeighborhoodComplexLifting(complex_dim=3, signed=True)
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# self.lifting_unsigned = NeighborhoodComplexLifting(complex_dim=3, signed=False)
22+
# self.lifting_high = NeighborhoodComplexLifting(complex_dim=7, signed=False)
23+
24+
# # Intialize an empty graph for testing purpouses
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# self.empty_graph = nx.empty_graph(10)
26+
# self.empty_data = from_networkx(self.empty_graph)
27+
# self.empty_data["x"] = torch.rand((10, 10))
28+
29+
# # Intialize a start graph for testing
30+
# self.star_graph = nx.star_graph(5)
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# self.star_data = from_networkx(self.star_graph)
32+
# self.star_data["x"] = torch.rand((6, 1))
33+
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# # Intialize a random graph for testing purpouses
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# self.random_graph = nx.fast_gnp_random_graph(5, 0.5)
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# self.random_data = from_networkx(self.random_graph)
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# self.random_data["x"] = torch.rand((5, 1))
38+
39+
40+
# def has_neighbour(self, simplex_points: list[set]) -> tuple[bool, set[int]]:
41+
# """ Verifies that the maximal simplices
42+
# of Data representation of a simplicial complex
43+
# share a neighbour.
44+
# """
45+
# for simplex_point_a in simplex_points:
46+
# for simplex_point_b in simplex_points:
47+
# # Same point
48+
# if simplex_point_a == simplex_point_b:
49+
# continue
50+
# # Search all nodes to check if they are c such that a and b share c as a neighbour
51+
# for node in self.random_graph.nodes:
52+
# # They share a neighbour
53+
# if self.random_graph.has_edge(simplex_point_a.item(), node) and self.random_graph.has_edge(simplex_point_b.item(), node):
54+
# return True
55+
# return False
56+
57+
# def test_lift_topology_random_graph(self):
58+
# """ Verifies that the lifting procedure works on
59+
# a random graph, that is, checks that the simplices
60+
# generated share a neighbour.
61+
# """
62+
# lifted_data = self.lifting_high.forward(self.random_data)
63+
# # For each set of simplices
64+
# r = max(int(key.split("_")[-1]) for key in list(lifted_data.keys()) if "x_idx_" in key)
65+
# idx_str = f"x_idx_{r}"
66+
67+
# # Go over each (max_dim)-simplex
68+
# for simplex_points in lifted_data[idx_str]:
69+
# share_neighbour = self.has_neighbour(simplex_points)
70+
# assert share_neighbour, f"The simplex {simplex_points} does not have a common neighbour with all the nodes."
71+
72+
# def test_lift_topology_star_graph(self):
73+
# """ Verifies that the lifting procedure works on
74+
# a small star graph, that is, checks that the simplices
75+
# generated share a neighbour.
76+
# """
77+
# lifted_data = self.lifting_high.forward(self.star_data)
78+
# # For each set of simplices
79+
# r = max(int(key.split("_")[-1]) for key in list(lifted_data.keys()) if "x_idx_" in key)
80+
# idx_str = f"x_idx_{r}"
81+
82+
# # Go over each (max_dim)-simplex
83+
# for simplex_points in lifted_data[idx_str]:
84+
# share_neighbour = self.has_neighbour(simplex_points)
85+
# assert share_neighbour, f"The simplex {simplex_points} does not have a common neighbour with all the nodes."
86+
87+
88+
89+
# def test_lift_topology_empty_graph(self):
90+
# """ Test the lift_topology method with an empty graph.
91+
# """
92+
93+
# lifted_data_signed = self.lifting_signed.forward(self.empty_data)
94+
95+
# assert lifted_data_signed.incidence_1.shape[1] == 0, "Something is wrong with signed incidence_1 (nodes to edges)."
96+
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# assert lifted_data_signed.incidence_2.shape[1] == 0, "Something is wrong with signed incidence_2 (edges to triangles)."
98+
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# def test_lift_topology(self):
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# """Test the lift_topology method."""
101+
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# # Test the lift_topology method
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# lifted_data_signed = self.lifting_signed.forward(self.data.clone())
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# lifted_data_unsigned = self.lifting_unsigned.forward(self.data.clone())
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# expected_incidence_1 = torch.tensor(
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# [
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# [-1., -1., -1., -1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
109+
# [ 1., 0., 0., 0., -1., -1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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# [ 0., 1., 0., 0., 1., 0., -1., -1., -1., -1., -1., 0., 0., 0., 0.],
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# [ 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., -1., 0., 0., 0.],
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# [ 0., 0., 1., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0.],
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# [ 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., -1., -1., 0.],
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# [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 1., 0., -1.],
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# [ 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 1.]
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# ]
117+
# )
118+
# assert (
119+
# abs(expected_incidence_1) == lifted_data_unsigned.incidence_1.to_dense()
120+
# ).all(), "Something is wrong with unsigned incidence_1 (nodes to edges)."
121+
# assert (
122+
# expected_incidence_1 == lifted_data_signed.incidence_1.to_dense()
123+
# ).all(), "Something is wrong with signed incidence_1 (nodes to edges)."
124+
125+
# expected_incidence_2 = torch.tensor(
126+
# [
127+
# [ 0.],
128+
# [ 0.],
129+
# [ 0.],
130+
# [ 0.],
131+
# [ 0.],
132+
# [ 0.],
133+
# [ 0.],
134+
# [ 0.],
135+
# [ 0.],
136+
# [ 1.],
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# [-1.],
138+
# [ 0.],
139+
# [ 0.],
140+
# [ 0.],
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# [ 1.]
142+
# ]
143+
# )
144+
145+
# assert (
146+
# abs(expected_incidence_2) == lifted_data_unsigned.incidence_2.to_dense()
147+
# ).all(), "Something is wrong with unsigned incidence_2 (edges to triangles)."
148+
# assert (
149+
# expected_incidence_2 == lifted_data_signed.incidence_2.to_dense()
150+
# ).all(), "Something is wrong with signed incidence_2 (edges to triangles)."

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