Mercurial > repos > shellac > sam_consensus_v3
comparison env/lib/python3.9/site-packages/networkx/algorithms/bipartite/tests/test_cluster.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 networkx as nx | |
2 import pytest | |
3 from networkx.algorithms.bipartite.cluster import cc_dot, cc_min, cc_max | |
4 import networkx.algorithms.bipartite as bipartite | |
5 | |
6 | |
7 def test_pairwise_bipartite_cc_functions(): | |
8 # Test functions for different kinds of bipartite clustering coefficients | |
9 # between pairs of nodes using 3 example graphs from figure 5 p. 40 | |
10 # Latapy et al (2008) | |
11 G1 = nx.Graph([(0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (1, 5), (1, 6), (1, 7)]) | |
12 G2 = nx.Graph([(0, 2), (0, 3), (0, 4), (1, 3), (1, 4), (1, 5)]) | |
13 G3 = nx.Graph( | |
14 [(0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9)] | |
15 ) | |
16 result = { | |
17 0: [1 / 3.0, 2 / 3.0, 2 / 5.0], | |
18 1: [1 / 2.0, 2 / 3.0, 2 / 3.0], | |
19 2: [2 / 8.0, 2 / 5.0, 2 / 5.0], | |
20 } | |
21 for i, G in enumerate([G1, G2, G3]): | |
22 assert bipartite.is_bipartite(G) | |
23 assert cc_dot(set(G[0]), set(G[1])) == result[i][0] | |
24 assert cc_min(set(G[0]), set(G[1])) == result[i][1] | |
25 assert cc_max(set(G[0]), set(G[1])) == result[i][2] | |
26 | |
27 | |
28 def test_star_graph(): | |
29 G = nx.star_graph(3) | |
30 # all modes are the same | |
31 answer = {0: 0, 1: 1, 2: 1, 3: 1} | |
32 assert bipartite.clustering(G, mode="dot") == answer | |
33 assert bipartite.clustering(G, mode="min") == answer | |
34 assert bipartite.clustering(G, mode="max") == answer | |
35 | |
36 | |
37 def test_not_bipartite(): | |
38 with pytest.raises(nx.NetworkXError): | |
39 bipartite.clustering(nx.complete_graph(4)) | |
40 | |
41 | |
42 def test_bad_mode(): | |
43 with pytest.raises(nx.NetworkXError): | |
44 bipartite.clustering(nx.path_graph(4), mode="foo") | |
45 | |
46 | |
47 def test_path_graph(): | |
48 G = nx.path_graph(4) | |
49 answer = {0: 0.5, 1: 0.5, 2: 0.5, 3: 0.5} | |
50 assert bipartite.clustering(G, mode="dot") == answer | |
51 assert bipartite.clustering(G, mode="max") == answer | |
52 answer = {0: 1, 1: 1, 2: 1, 3: 1} | |
53 assert bipartite.clustering(G, mode="min") == answer | |
54 | |
55 | |
56 def test_average_path_graph(): | |
57 G = nx.path_graph(4) | |
58 assert bipartite.average_clustering(G, mode="dot") == 0.5 | |
59 assert bipartite.average_clustering(G, mode="max") == 0.5 | |
60 assert bipartite.average_clustering(G, mode="min") == 1 | |
61 | |
62 | |
63 def test_ra_clustering_davis(): | |
64 G = nx.davis_southern_women_graph() | |
65 cc4 = round(bipartite.robins_alexander_clustering(G), 3) | |
66 assert cc4 == 0.468 | |
67 | |
68 | |
69 def test_ra_clustering_square(): | |
70 G = nx.path_graph(4) | |
71 G.add_edge(0, 3) | |
72 assert bipartite.robins_alexander_clustering(G) == 1.0 | |
73 | |
74 | |
75 def test_ra_clustering_zero(): | |
76 G = nx.Graph() | |
77 assert bipartite.robins_alexander_clustering(G) == 0 | |
78 G.add_nodes_from(range(4)) | |
79 assert bipartite.robins_alexander_clustering(G) == 0 | |
80 G.add_edges_from([(0, 1), (2, 3), (3, 4)]) | |
81 assert bipartite.robins_alexander_clustering(G) == 0 | |
82 G.add_edge(1, 2) | |
83 assert bipartite.robins_alexander_clustering(G) == 0 |