Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/algorithms/bipartite/tests/test_basic.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 pytest |
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2 |
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3 import networkx as nx |
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4 from networkx.algorithms import bipartite |
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5 |
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6 |
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7 class TestBipartiteBasic: |
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8 def test_is_bipartite(self): |
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9 assert bipartite.is_bipartite(nx.path_graph(4)) |
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10 assert bipartite.is_bipartite(nx.DiGraph([(1, 0)])) |
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11 assert not bipartite.is_bipartite(nx.complete_graph(3)) |
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12 |
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13 def test_bipartite_color(self): |
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14 G = nx.path_graph(4) |
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15 c = bipartite.color(G) |
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16 assert c == {0: 1, 1: 0, 2: 1, 3: 0} |
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17 |
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18 def test_not_bipartite_color(self): |
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19 with pytest.raises(nx.NetworkXError): |
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20 c = bipartite.color(nx.complete_graph(4)) |
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21 |
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22 def test_bipartite_directed(self): |
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23 G = bipartite.random_graph(10, 10, 0.1, directed=True) |
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24 assert bipartite.is_bipartite(G) |
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25 |
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26 def test_bipartite_sets(self): |
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27 G = nx.path_graph(4) |
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28 X, Y = bipartite.sets(G) |
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29 assert X == {0, 2} |
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30 assert Y == {1, 3} |
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31 |
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32 def test_bipartite_sets_directed(self): |
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33 G = nx.path_graph(4) |
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34 D = G.to_directed() |
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35 X, Y = bipartite.sets(D) |
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36 assert X == {0, 2} |
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37 assert Y == {1, 3} |
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38 |
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39 def test_bipartite_sets_given_top_nodes(self): |
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40 G = nx.path_graph(4) |
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41 top_nodes = [0, 2] |
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42 X, Y = bipartite.sets(G, top_nodes) |
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43 assert X == {0, 2} |
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44 assert Y == {1, 3} |
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45 |
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46 def test_bipartite_sets_disconnected(self): |
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47 with pytest.raises(nx.AmbiguousSolution): |
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48 G = nx.path_graph(4) |
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49 G.add_edges_from([(5, 6), (6, 7)]) |
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50 X, Y = bipartite.sets(G) |
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51 |
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52 def test_is_bipartite_node_set(self): |
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53 G = nx.path_graph(4) |
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54 assert bipartite.is_bipartite_node_set(G, [0, 2]) |
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55 assert bipartite.is_bipartite_node_set(G, [1, 3]) |
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56 assert not bipartite.is_bipartite_node_set(G, [1, 2]) |
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57 G.add_edge(10, 20) |
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58 assert bipartite.is_bipartite_node_set(G, [0, 2, 10]) |
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59 assert bipartite.is_bipartite_node_set(G, [0, 2, 20]) |
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60 assert bipartite.is_bipartite_node_set(G, [1, 3, 10]) |
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61 assert bipartite.is_bipartite_node_set(G, [1, 3, 20]) |
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62 |
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63 def test_bipartite_density(self): |
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64 G = nx.path_graph(5) |
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65 X, Y = bipartite.sets(G) |
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66 density = float(len(list(G.edges()))) / (len(X) * len(Y)) |
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67 assert bipartite.density(G, X) == density |
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68 D = nx.DiGraph(G.edges()) |
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69 assert bipartite.density(D, X) == density / 2.0 |
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70 assert bipartite.density(nx.Graph(), {}) == 0.0 |
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71 |
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72 def test_bipartite_degrees(self): |
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73 G = nx.path_graph(5) |
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74 X = {1, 3} |
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75 Y = {0, 2, 4} |
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76 u, d = bipartite.degrees(G, Y) |
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77 assert dict(u) == {1: 2, 3: 2} |
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78 assert dict(d) == {0: 1, 2: 2, 4: 1} |
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79 |
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80 def test_bipartite_weighted_degrees(self): |
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81 G = nx.path_graph(5) |
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82 G.add_edge(0, 1, weight=0.1, other=0.2) |
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83 X = {1, 3} |
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84 Y = {0, 2, 4} |
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85 u, d = bipartite.degrees(G, Y, weight="weight") |
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86 assert dict(u) == {1: 1.1, 3: 2} |
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87 assert dict(d) == {0: 0.1, 2: 2, 4: 1} |
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88 u, d = bipartite.degrees(G, Y, weight="other") |
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89 assert dict(u) == {1: 1.2, 3: 2} |
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90 assert dict(d) == {0: 0.2, 2: 2, 4: 1} |
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91 |
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92 def test_biadjacency_matrix_weight(self): |
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93 scipy = pytest.importorskip("scipy") |
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94 G = nx.path_graph(5) |
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95 G.add_edge(0, 1, weight=2, other=4) |
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96 X = [1, 3] |
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97 Y = [0, 2, 4] |
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98 M = bipartite.biadjacency_matrix(G, X, weight="weight") |
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99 assert M[0, 0] == 2 |
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100 M = bipartite.biadjacency_matrix(G, X, weight="other") |
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101 assert M[0, 0] == 4 |
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102 |
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103 def test_biadjacency_matrix(self): |
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104 scipy = pytest.importorskip("scipy") |
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105 tops = [2, 5, 10] |
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106 bots = [5, 10, 15] |
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107 for i in range(len(tops)): |
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108 G = bipartite.random_graph(tops[i], bots[i], 0.2) |
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109 top = [n for n, d in G.nodes(data=True) if d["bipartite"] == 0] |
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110 M = bipartite.biadjacency_matrix(G, top) |
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111 assert M.shape[0] == tops[i] |
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112 assert M.shape[1] == bots[i] |
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113 |
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114 def test_biadjacency_matrix_order(self): |
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115 scipy = pytest.importorskip("scipy") |
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116 G = nx.path_graph(5) |
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117 G.add_edge(0, 1, weight=2) |
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118 X = [3, 1] |
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119 Y = [4, 2, 0] |
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120 M = bipartite.biadjacency_matrix(G, X, Y, weight="weight") |
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121 assert M[1, 2] == 2 |