Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/algorithms/bipartite/tests/test_generators.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 import networkx as nx |
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3 from ..generators import ( |
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4 alternating_havel_hakimi_graph, |
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5 complete_bipartite_graph, |
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6 configuration_model, |
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7 gnmk_random_graph, |
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8 havel_hakimi_graph, |
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9 preferential_attachment_graph, |
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10 random_graph, |
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11 reverse_havel_hakimi_graph, |
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12 ) |
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13 |
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14 """ |
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15 Generators - Bipartite |
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16 ---------------------- |
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17 """ |
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18 |
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19 |
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20 class TestGeneratorsBipartite: |
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21 def test_complete_bipartite_graph(self): |
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22 G = complete_bipartite_graph(0, 0) |
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23 assert nx.is_isomorphic(G, nx.null_graph()) |
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24 |
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25 for i in [1, 5]: |
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26 G = complete_bipartite_graph(i, 0) |
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27 assert nx.is_isomorphic(G, nx.empty_graph(i)) |
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28 G = complete_bipartite_graph(0, i) |
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29 assert nx.is_isomorphic(G, nx.empty_graph(i)) |
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30 |
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31 G = complete_bipartite_graph(2, 2) |
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32 assert nx.is_isomorphic(G, nx.cycle_graph(4)) |
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33 |
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34 G = complete_bipartite_graph(1, 5) |
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35 assert nx.is_isomorphic(G, nx.star_graph(5)) |
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36 |
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37 G = complete_bipartite_graph(5, 1) |
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38 assert nx.is_isomorphic(G, nx.star_graph(5)) |
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39 |
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40 # complete_bipartite_graph(m1,m2) is a connected graph with |
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41 # m1+m2 nodes and m1*m2 edges |
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42 for m1, m2 in [(5, 11), (7, 3)]: |
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43 G = complete_bipartite_graph(m1, m2) |
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44 assert nx.number_of_nodes(G) == m1 + m2 |
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45 assert nx.number_of_edges(G) == m1 * m2 |
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46 |
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47 pytest.raises( |
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48 nx.NetworkXError, complete_bipartite_graph, 7, 3, create_using=nx.DiGraph |
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49 ) |
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50 pytest.raises( |
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51 nx.NetworkXError, complete_bipartite_graph, 7, 3, create_using=nx.DiGraph |
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52 ) |
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53 pytest.raises( |
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54 nx.NetworkXError, |
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55 complete_bipartite_graph, |
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56 7, |
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57 3, |
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58 create_using=nx.MultiDiGraph, |
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59 ) |
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60 |
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61 mG = complete_bipartite_graph(7, 3, create_using=nx.MultiGraph) |
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62 assert mG.is_multigraph() |
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63 assert sorted(mG.edges()) == sorted(G.edges()) |
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64 |
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65 mG = complete_bipartite_graph(7, 3, create_using=nx.MultiGraph) |
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66 assert mG.is_multigraph() |
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67 assert sorted(mG.edges()) == sorted(G.edges()) |
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68 |
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69 mG = complete_bipartite_graph(7, 3) # default to Graph |
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70 assert sorted(mG.edges()) == sorted(G.edges()) |
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71 assert not mG.is_multigraph() |
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72 assert not mG.is_directed() |
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73 |
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74 # specify nodes rather than number of nodes |
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75 G = complete_bipartite_graph([1, 2], ["a", "b"]) |
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76 has_edges = ( |
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77 G.has_edge(1, "a") |
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78 & G.has_edge(1, "b") |
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79 & G.has_edge(2, "a") |
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80 & G.has_edge(2, "b") |
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81 ) |
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82 assert has_edges |
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83 assert G.size() == 4 |
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84 |
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85 def test_configuration_model(self): |
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86 aseq = [] |
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87 bseq = [] |
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88 G = configuration_model(aseq, bseq) |
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89 assert len(G) == 0 |
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90 |
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91 aseq = [0, 0] |
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92 bseq = [0, 0] |
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93 G = configuration_model(aseq, bseq) |
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94 assert len(G) == 4 |
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95 assert G.number_of_edges() == 0 |
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96 |
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97 aseq = [3, 3, 3, 3] |
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98 bseq = [2, 2, 2, 2, 2] |
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99 pytest.raises(nx.NetworkXError, configuration_model, aseq, bseq) |
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100 |
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101 aseq = [3, 3, 3, 3] |
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102 bseq = [2, 2, 2, 2, 2, 2] |
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103 G = configuration_model(aseq, bseq) |
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104 assert sorted(d for n, d in G.degree()) == [2, 2, 2, 2, 2, 2, 3, 3, 3, 3] |
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105 |
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106 aseq = [2, 2, 2, 2, 2, 2] |
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107 bseq = [3, 3, 3, 3] |
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108 G = configuration_model(aseq, bseq) |
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109 assert sorted(d for n, d in G.degree()) == [2, 2, 2, 2, 2, 2, 3, 3, 3, 3] |
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110 |
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111 aseq = [2, 2, 2, 1, 1, 1] |
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112 bseq = [3, 3, 3] |
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113 G = configuration_model(aseq, bseq) |
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114 assert G.is_multigraph() |
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115 assert not G.is_directed() |
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116 assert sorted(d for n, d in G.degree()) == [1, 1, 1, 2, 2, 2, 3, 3, 3] |
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117 |
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118 GU = nx.project(nx.Graph(G), range(len(aseq))) |
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119 assert GU.number_of_nodes() == 6 |
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120 |
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121 GD = nx.project(nx.Graph(G), range(len(aseq), len(aseq) + len(bseq))) |
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122 assert GD.number_of_nodes() == 3 |
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123 |
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124 G = reverse_havel_hakimi_graph(aseq, bseq, create_using=nx.Graph) |
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125 assert not G.is_multigraph() |
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126 assert not G.is_directed() |
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127 |
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128 pytest.raises( |
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129 nx.NetworkXError, configuration_model, aseq, bseq, create_using=nx.DiGraph() |
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130 ) |
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131 pytest.raises( |
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132 nx.NetworkXError, configuration_model, aseq, bseq, create_using=nx.DiGraph |
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133 ) |
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134 pytest.raises( |
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135 nx.NetworkXError, |
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136 configuration_model, |
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137 aseq, |
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138 bseq, |
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139 create_using=nx.MultiDiGraph, |
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140 ) |
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141 |
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142 def test_havel_hakimi_graph(self): |
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143 aseq = [] |
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144 bseq = [] |
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145 G = havel_hakimi_graph(aseq, bseq) |
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146 assert len(G) == 0 |
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147 |
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148 aseq = [0, 0] |
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149 bseq = [0, 0] |
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150 G = havel_hakimi_graph(aseq, bseq) |
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151 assert len(G) == 4 |
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152 assert G.number_of_edges() == 0 |
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153 |
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154 aseq = [3, 3, 3, 3] |
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155 bseq = [2, 2, 2, 2, 2] |
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156 pytest.raises(nx.NetworkXError, havel_hakimi_graph, aseq, bseq) |
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157 |
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158 bseq = [2, 2, 2, 2, 2, 2] |
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159 G = havel_hakimi_graph(aseq, bseq) |
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160 assert sorted(d for n, d in G.degree()) == [2, 2, 2, 2, 2, 2, 3, 3, 3, 3] |
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161 |
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162 aseq = [2, 2, 2, 2, 2, 2] |
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163 bseq = [3, 3, 3, 3] |
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164 G = havel_hakimi_graph(aseq, bseq) |
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165 assert G.is_multigraph() |
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166 assert not G.is_directed() |
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167 assert sorted(d for n, d in G.degree()) == [2, 2, 2, 2, 2, 2, 3, 3, 3, 3] |
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168 |
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169 GU = nx.project(nx.Graph(G), range(len(aseq))) |
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170 assert GU.number_of_nodes() == 6 |
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171 |
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172 GD = nx.project(nx.Graph(G), range(len(aseq), len(aseq) + len(bseq))) |
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173 assert GD.number_of_nodes() == 4 |
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174 |
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175 G = reverse_havel_hakimi_graph(aseq, bseq, create_using=nx.Graph) |
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176 assert not G.is_multigraph() |
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177 assert not G.is_directed() |
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178 |
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179 pytest.raises( |
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180 nx.NetworkXError, havel_hakimi_graph, aseq, bseq, create_using=nx.DiGraph |
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181 ) |
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182 pytest.raises( |
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183 nx.NetworkXError, havel_hakimi_graph, aseq, bseq, create_using=nx.DiGraph |
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184 ) |
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185 pytest.raises( |
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186 nx.NetworkXError, |
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187 havel_hakimi_graph, |
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188 aseq, |
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189 bseq, |
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190 create_using=nx.MultiDiGraph, |
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191 ) |
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192 |
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193 def test_reverse_havel_hakimi_graph(self): |
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194 aseq = [] |
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195 bseq = [] |
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196 G = reverse_havel_hakimi_graph(aseq, bseq) |
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197 assert len(G) == 0 |
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198 |
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199 aseq = [0, 0] |
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200 bseq = [0, 0] |
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201 G = reverse_havel_hakimi_graph(aseq, bseq) |
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202 assert len(G) == 4 |
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203 assert G.number_of_edges() == 0 |
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204 |
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205 aseq = [3, 3, 3, 3] |
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206 bseq = [2, 2, 2, 2, 2] |
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207 pytest.raises(nx.NetworkXError, reverse_havel_hakimi_graph, aseq, bseq) |
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208 |
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209 bseq = [2, 2, 2, 2, 2, 2] |
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210 G = reverse_havel_hakimi_graph(aseq, bseq) |
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211 assert sorted(d for n, d in G.degree()) == [2, 2, 2, 2, 2, 2, 3, 3, 3, 3] |
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212 |
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213 aseq = [2, 2, 2, 2, 2, 2] |
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214 bseq = [3, 3, 3, 3] |
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215 G = reverse_havel_hakimi_graph(aseq, bseq) |
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216 assert sorted(d for n, d in G.degree()) == [2, 2, 2, 2, 2, 2, 3, 3, 3, 3] |
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217 |
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218 aseq = [2, 2, 2, 1, 1, 1] |
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219 bseq = [3, 3, 3] |
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220 G = reverse_havel_hakimi_graph(aseq, bseq) |
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221 assert G.is_multigraph() |
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222 assert not G.is_directed() |
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223 assert sorted(d for n, d in G.degree()) == [1, 1, 1, 2, 2, 2, 3, 3, 3] |
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224 |
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225 GU = nx.project(nx.Graph(G), range(len(aseq))) |
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226 assert GU.number_of_nodes() == 6 |
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227 |
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228 GD = nx.project(nx.Graph(G), range(len(aseq), len(aseq) + len(bseq))) |
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229 assert GD.number_of_nodes() == 3 |
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230 |
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231 G = reverse_havel_hakimi_graph(aseq, bseq, create_using=nx.Graph) |
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232 assert not G.is_multigraph() |
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233 assert not G.is_directed() |
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234 |
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235 pytest.raises( |
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236 nx.NetworkXError, |
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237 reverse_havel_hakimi_graph, |
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238 aseq, |
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239 bseq, |
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240 create_using=nx.DiGraph, |
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241 ) |
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242 pytest.raises( |
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243 nx.NetworkXError, |
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244 reverse_havel_hakimi_graph, |
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245 aseq, |
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246 bseq, |
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247 create_using=nx.DiGraph, |
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248 ) |
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249 pytest.raises( |
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250 nx.NetworkXError, |
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251 reverse_havel_hakimi_graph, |
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252 aseq, |
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253 bseq, |
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254 create_using=nx.MultiDiGraph, |
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255 ) |
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256 |
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257 def test_alternating_havel_hakimi_graph(self): |
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258 aseq = [] |
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259 bseq = [] |
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260 G = alternating_havel_hakimi_graph(aseq, bseq) |
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261 assert len(G) == 0 |
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262 |
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263 aseq = [0, 0] |
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264 bseq = [0, 0] |
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265 G = alternating_havel_hakimi_graph(aseq, bseq) |
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266 assert len(G) == 4 |
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267 assert G.number_of_edges() == 0 |
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268 |
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269 aseq = [3, 3, 3, 3] |
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270 bseq = [2, 2, 2, 2, 2] |
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271 pytest.raises(nx.NetworkXError, alternating_havel_hakimi_graph, aseq, bseq) |
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272 |
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273 bseq = [2, 2, 2, 2, 2, 2] |
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274 G = alternating_havel_hakimi_graph(aseq, bseq) |
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275 assert sorted(d for n, d in G.degree()) == [2, 2, 2, 2, 2, 2, 3, 3, 3, 3] |
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276 |
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277 aseq = [2, 2, 2, 2, 2, 2] |
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278 bseq = [3, 3, 3, 3] |
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279 G = alternating_havel_hakimi_graph(aseq, bseq) |
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280 assert sorted(d for n, d in G.degree()) == [2, 2, 2, 2, 2, 2, 3, 3, 3, 3] |
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281 |
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282 aseq = [2, 2, 2, 1, 1, 1] |
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283 bseq = [3, 3, 3] |
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284 G = alternating_havel_hakimi_graph(aseq, bseq) |
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285 assert G.is_multigraph() |
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286 assert not G.is_directed() |
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287 assert sorted(d for n, d in G.degree()) == [1, 1, 1, 2, 2, 2, 3, 3, 3] |
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288 |
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289 GU = nx.project(nx.Graph(G), range(len(aseq))) |
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290 assert GU.number_of_nodes() == 6 |
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291 |
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292 GD = nx.project(nx.Graph(G), range(len(aseq), len(aseq) + len(bseq))) |
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293 assert GD.number_of_nodes() == 3 |
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294 |
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295 G = reverse_havel_hakimi_graph(aseq, bseq, create_using=nx.Graph) |
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296 assert not G.is_multigraph() |
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297 assert not G.is_directed() |
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298 |
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299 pytest.raises( |
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300 nx.NetworkXError, |
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301 alternating_havel_hakimi_graph, |
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302 aseq, |
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303 bseq, |
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304 create_using=nx.DiGraph, |
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305 ) |
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306 pytest.raises( |
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307 nx.NetworkXError, |
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308 alternating_havel_hakimi_graph, |
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309 aseq, |
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310 bseq, |
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311 create_using=nx.DiGraph, |
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312 ) |
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313 pytest.raises( |
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314 nx.NetworkXError, |
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315 alternating_havel_hakimi_graph, |
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316 aseq, |
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317 bseq, |
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318 create_using=nx.MultiDiGraph, |
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319 ) |
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320 |
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321 def test_preferential_attachment(self): |
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322 aseq = [3, 2, 1, 1] |
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323 G = preferential_attachment_graph(aseq, 0.5) |
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324 assert G.is_multigraph() |
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325 assert not G.is_directed() |
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326 |
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327 G = preferential_attachment_graph(aseq, 0.5, create_using=nx.Graph) |
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328 assert not G.is_multigraph() |
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329 assert not G.is_directed() |
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330 |
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331 pytest.raises( |
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332 nx.NetworkXError, |
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333 preferential_attachment_graph, |
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334 aseq, |
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335 0.5, |
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336 create_using=nx.DiGraph(), |
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337 ) |
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338 pytest.raises( |
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339 nx.NetworkXError, |
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340 preferential_attachment_graph, |
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341 aseq, |
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342 0.5, |
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343 create_using=nx.DiGraph(), |
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344 ) |
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345 pytest.raises( |
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346 nx.NetworkXError, |
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347 preferential_attachment_graph, |
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348 aseq, |
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349 0.5, |
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350 create_using=nx.DiGraph(), |
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351 ) |
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352 |
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353 def test_random_graph(self): |
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354 n = 10 |
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355 m = 20 |
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356 G = random_graph(n, m, 0.9) |
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357 assert len(G) == 30 |
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358 assert nx.is_bipartite(G) |
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359 X, Y = nx.algorithms.bipartite.sets(G) |
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360 assert set(range(n)) == X |
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361 assert set(range(n, n + m)) == Y |
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362 |
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363 def test_random_digraph(self): |
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364 n = 10 |
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365 m = 20 |
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366 G = random_graph(n, m, 0.9, directed=True) |
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367 assert len(G) == 30 |
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368 assert nx.is_bipartite(G) |
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369 X, Y = nx.algorithms.bipartite.sets(G) |
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370 assert set(range(n)) == X |
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371 assert set(range(n, n + m)) == Y |
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372 |
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373 def test_gnmk_random_graph(self): |
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374 n = 10 |
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375 m = 20 |
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376 edges = 100 |
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377 # set seed because sometimes it is not connected |
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378 # which raises an error in bipartite.sets(G) below. |
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379 G = gnmk_random_graph(n, m, edges, seed=1234) |
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380 assert len(G) == n + m |
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381 assert nx.is_bipartite(G) |
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382 X, Y = nx.algorithms.bipartite.sets(G) |
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383 # print(X) |
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384 assert set(range(n)) == X |
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385 assert set(range(n, n + m)) == Y |
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386 assert edges == len(list(G.edges())) |
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387 |
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388 def test_gnmk_random_graph_complete(self): |
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389 n = 10 |
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390 m = 20 |
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391 edges = 200 |
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392 G = gnmk_random_graph(n, m, edges) |
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393 assert len(G) == n + m |
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394 assert nx.is_bipartite(G) |
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395 X, Y = nx.algorithms.bipartite.sets(G) |
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396 # print(X) |
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397 assert set(range(n)) == X |
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398 assert set(range(n, n + m)) == Y |
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399 assert edges == len(list(G.edges())) |