Mercurial > repos > shellac > sam_consensus_v3
comparison env/lib/python3.9/site-packages/networkx/linalg/tests/test_modularity.py @ 0:4f3585e2f14b draft default tip
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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-1:000000000000 | 0:4f3585e2f14b |
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1 import pytest | |
2 | |
3 np = pytest.importorskip("numpy") | |
4 npt = pytest.importorskip("numpy.testing") | |
5 scipy = pytest.importorskip("scipy") | |
6 | |
7 import networkx as nx | |
8 from networkx.generators.degree_seq import havel_hakimi_graph | |
9 | |
10 | |
11 class TestModularity: | |
12 @classmethod | |
13 def setup_class(cls): | |
14 deg = [3, 2, 2, 1, 0] | |
15 cls.G = havel_hakimi_graph(deg) | |
16 # Graph used as an example in Sec. 4.1 of Langville and Meyer, | |
17 # "Google's PageRank and Beyond". (Used for test_directed_laplacian) | |
18 cls.DG = nx.DiGraph() | |
19 cls.DG.add_edges_from( | |
20 ( | |
21 (1, 2), | |
22 (1, 3), | |
23 (3, 1), | |
24 (3, 2), | |
25 (3, 5), | |
26 (4, 5), | |
27 (4, 6), | |
28 (5, 4), | |
29 (5, 6), | |
30 (6, 4), | |
31 ) | |
32 ) | |
33 | |
34 def test_modularity(self): | |
35 "Modularity matrix" | |
36 # fmt: off | |
37 B = np.array([[-1.125, 0.25, 0.25, 0.625, 0.], | |
38 [0.25, -0.5, 0.5, -0.25, 0.], | |
39 [0.25, 0.5, -0.5, -0.25, 0.], | |
40 [0.625, -0.25, -0.25, -0.125, 0.], | |
41 [0., 0., 0., 0., 0.]]) | |
42 # fmt: on | |
43 | |
44 permutation = [4, 0, 1, 2, 3] | |
45 npt.assert_equal(nx.modularity_matrix(self.G), B) | |
46 npt.assert_equal( | |
47 nx.modularity_matrix(self.G, nodelist=permutation), | |
48 B[np.ix_(permutation, permutation)], | |
49 ) | |
50 | |
51 def test_modularity_weight(self): | |
52 "Modularity matrix with weights" | |
53 # fmt: off | |
54 B = np.array([[-1.125, 0.25, 0.25, 0.625, 0.], | |
55 [0.25, -0.5, 0.5, -0.25, 0.], | |
56 [0.25, 0.5, -0.5, -0.25, 0.], | |
57 [0.625, -0.25, -0.25, -0.125, 0.], | |
58 [0., 0., 0., 0., 0.]]) | |
59 # fmt: on | |
60 | |
61 G_weighted = self.G.copy() | |
62 for n1, n2 in G_weighted.edges(): | |
63 G_weighted.edges[n1, n2]["weight"] = 0.5 | |
64 # The following test would fail in networkx 1.1 | |
65 npt.assert_equal(nx.modularity_matrix(G_weighted), B) | |
66 # The following test that the modularity matrix get rescaled accordingly | |
67 npt.assert_equal(nx.modularity_matrix(G_weighted, weight="weight"), 0.5 * B) | |
68 | |
69 def test_directed_modularity(self): | |
70 "Directed Modularity matrix" | |
71 # fmt: off | |
72 B = np.array([[-0.2, 0.6, 0.8, -0.4, -0.4, -0.4], | |
73 [0., 0., 0., 0., 0., 0.], | |
74 [0.7, 0.4, -0.3, -0.6, 0.4, -0.6], | |
75 [-0.2, -0.4, -0.2, -0.4, 0.6, 0.6], | |
76 [-0.2, -0.4, -0.2, 0.6, -0.4, 0.6], | |
77 [-0.1, -0.2, -0.1, 0.8, -0.2, -0.2]]) | |
78 # fmt: on | |
79 node_permutation = [5, 1, 2, 3, 4, 6] | |
80 idx_permutation = [4, 0, 1, 2, 3, 5] | |
81 mm = nx.directed_modularity_matrix(self.DG, nodelist=sorted(self.DG)) | |
82 npt.assert_equal(mm, B) | |
83 npt.assert_equal( | |
84 nx.directed_modularity_matrix(self.DG, nodelist=node_permutation), | |
85 B[np.ix_(idx_permutation, idx_permutation)], | |
86 ) |