Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/algorithms/bipartite/tests/test_cluster.py @ 0:4f3585e2f14b draft default tip
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author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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1 import networkx as nx |
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2 import pytest |
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3 from networkx.algorithms.bipartite.cluster import cc_dot, cc_min, cc_max |
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4 import networkx.algorithms.bipartite as bipartite |
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5 |
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6 |
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7 def test_pairwise_bipartite_cc_functions(): |
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8 # Test functions for different kinds of bipartite clustering coefficients |
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9 # between pairs of nodes using 3 example graphs from figure 5 p. 40 |
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10 # Latapy et al (2008) |
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11 G1 = nx.Graph([(0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (1, 5), (1, 6), (1, 7)]) |
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12 G2 = nx.Graph([(0, 2), (0, 3), (0, 4), (1, 3), (1, 4), (1, 5)]) |
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13 G3 = nx.Graph( |
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14 [(0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9)] |
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15 ) |
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16 result = { |
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17 0: [1 / 3.0, 2 / 3.0, 2 / 5.0], |
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18 1: [1 / 2.0, 2 / 3.0, 2 / 3.0], |
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19 2: [2 / 8.0, 2 / 5.0, 2 / 5.0], |
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20 } |
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21 for i, G in enumerate([G1, G2, G3]): |
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22 assert bipartite.is_bipartite(G) |
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23 assert cc_dot(set(G[0]), set(G[1])) == result[i][0] |
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24 assert cc_min(set(G[0]), set(G[1])) == result[i][1] |
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25 assert cc_max(set(G[0]), set(G[1])) == result[i][2] |
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26 |
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27 |
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28 def test_star_graph(): |
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29 G = nx.star_graph(3) |
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30 # all modes are the same |
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31 answer = {0: 0, 1: 1, 2: 1, 3: 1} |
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32 assert bipartite.clustering(G, mode="dot") == answer |
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33 assert bipartite.clustering(G, mode="min") == answer |
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34 assert bipartite.clustering(G, mode="max") == answer |
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35 |
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36 |
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37 def test_not_bipartite(): |
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38 with pytest.raises(nx.NetworkXError): |
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39 bipartite.clustering(nx.complete_graph(4)) |
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40 |
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41 |
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42 def test_bad_mode(): |
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43 with pytest.raises(nx.NetworkXError): |
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44 bipartite.clustering(nx.path_graph(4), mode="foo") |
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45 |
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46 |
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47 def test_path_graph(): |
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48 G = nx.path_graph(4) |
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49 answer = {0: 0.5, 1: 0.5, 2: 0.5, 3: 0.5} |
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50 assert bipartite.clustering(G, mode="dot") == answer |
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51 assert bipartite.clustering(G, mode="max") == answer |
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52 answer = {0: 1, 1: 1, 2: 1, 3: 1} |
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53 assert bipartite.clustering(G, mode="min") == answer |
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54 |
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55 |
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56 def test_average_path_graph(): |
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57 G = nx.path_graph(4) |
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58 assert bipartite.average_clustering(G, mode="dot") == 0.5 |
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59 assert bipartite.average_clustering(G, mode="max") == 0.5 |
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60 assert bipartite.average_clustering(G, mode="min") == 1 |
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61 |
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62 |
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63 def test_ra_clustering_davis(): |
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64 G = nx.davis_southern_women_graph() |
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65 cc4 = round(bipartite.robins_alexander_clustering(G), 3) |
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66 assert cc4 == 0.468 |
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67 |
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68 |
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69 def test_ra_clustering_square(): |
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70 G = nx.path_graph(4) |
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71 G.add_edge(0, 3) |
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72 assert bipartite.robins_alexander_clustering(G) == 1.0 |
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73 |
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74 |
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75 def test_ra_clustering_zero(): |
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76 G = nx.Graph() |
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77 assert bipartite.robins_alexander_clustering(G) == 0 |
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78 G.add_nodes_from(range(4)) |
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79 assert bipartite.robins_alexander_clustering(G) == 0 |
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80 G.add_edges_from([(0, 1), (2, 3), (3, 4)]) |
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81 assert bipartite.robins_alexander_clustering(G) == 0 |
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82 G.add_edge(1, 2) |
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83 assert bipartite.robins_alexander_clustering(G) == 0 |