Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/algorithms/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 import networkx as nx |
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2 |
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3 |
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4 class TestTriangles: |
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5 def test_empty(self): |
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6 G = nx.Graph() |
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7 assert list(nx.triangles(G).values()) == [] |
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8 |
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9 def test_path(self): |
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10 G = nx.path_graph(10) |
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11 assert list(nx.triangles(G).values()) == [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] |
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12 assert nx.triangles(G) == { |
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13 0: 0, |
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14 1: 0, |
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15 2: 0, |
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16 3: 0, |
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17 4: 0, |
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18 5: 0, |
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19 6: 0, |
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20 7: 0, |
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21 8: 0, |
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22 9: 0, |
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23 } |
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24 |
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25 def test_cubical(self): |
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26 G = nx.cubical_graph() |
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27 assert list(nx.triangles(G).values()) == [0, 0, 0, 0, 0, 0, 0, 0] |
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28 assert nx.triangles(G, 1) == 0 |
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29 assert list(nx.triangles(G, [1, 2]).values()) == [0, 0] |
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30 assert nx.triangles(G, 1) == 0 |
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31 assert nx.triangles(G, [1, 2]) == {1: 0, 2: 0} |
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32 |
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33 def test_k5(self): |
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34 G = nx.complete_graph(5) |
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35 assert list(nx.triangles(G).values()) == [6, 6, 6, 6, 6] |
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36 assert sum(nx.triangles(G).values()) / 3.0 == 10 |
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37 assert nx.triangles(G, 1) == 6 |
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38 G.remove_edge(1, 2) |
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39 assert list(nx.triangles(G).values()) == [5, 3, 3, 5, 5] |
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40 assert nx.triangles(G, 1) == 3 |
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41 |
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42 |
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43 class TestDirectedClustering: |
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44 def test_clustering(self): |
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45 G = nx.DiGraph() |
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46 assert list(nx.clustering(G).values()) == [] |
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47 assert nx.clustering(G) == {} |
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48 |
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49 def test_path(self): |
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50 G = nx.path_graph(10, create_using=nx.DiGraph()) |
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51 assert list(nx.clustering(G).values()) == [ |
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52 0.0, |
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53 0.0, |
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54 0.0, |
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55 0.0, |
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56 0.0, |
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57 0.0, |
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58 0.0, |
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59 0.0, |
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60 0.0, |
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61 0.0, |
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62 ] |
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63 assert nx.clustering(G) == { |
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64 0: 0.0, |
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65 1: 0.0, |
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66 2: 0.0, |
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67 3: 0.0, |
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68 4: 0.0, |
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69 5: 0.0, |
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70 6: 0.0, |
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71 7: 0.0, |
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72 8: 0.0, |
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73 9: 0.0, |
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74 } |
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75 |
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76 def test_k5(self): |
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77 G = nx.complete_graph(5, create_using=nx.DiGraph()) |
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78 assert list(nx.clustering(G).values()) == [1, 1, 1, 1, 1] |
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79 assert nx.average_clustering(G) == 1 |
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80 G.remove_edge(1, 2) |
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81 assert list(nx.clustering(G).values()) == [ |
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82 11.0 / 12.0, |
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83 1.0, |
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84 1.0, |
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85 11.0 / 12.0, |
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86 11.0 / 12.0, |
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87 ] |
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88 assert nx.clustering(G, [1, 4]) == {1: 1.0, 4: 11.0 / 12.0} |
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89 G.remove_edge(2, 1) |
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90 assert list(nx.clustering(G).values()) == [ |
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91 5.0 / 6.0, |
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92 1.0, |
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93 1.0, |
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94 5.0 / 6.0, |
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95 5.0 / 6.0, |
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96 ] |
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97 assert nx.clustering(G, [1, 4]) == {1: 1.0, 4: 0.83333333333333337} |
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98 |
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99 def test_triangle_and_edge(self): |
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100 G = nx.cycle_graph(3, create_using=nx.DiGraph()) |
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101 G.add_edge(0, 4) |
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102 assert nx.clustering(G)[0] == 1.0 / 6.0 |
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103 |
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104 |
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105 class TestDirectedWeightedClustering: |
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106 def test_clustering(self): |
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107 G = nx.DiGraph() |
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108 assert list(nx.clustering(G, weight="weight").values()) == [] |
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109 assert nx.clustering(G) == {} |
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110 |
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111 def test_path(self): |
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112 G = nx.path_graph(10, create_using=nx.DiGraph()) |
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113 assert list(nx.clustering(G, weight="weight").values()) == [ |
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114 0.0, |
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115 0.0, |
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116 0.0, |
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117 0.0, |
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118 0.0, |
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119 0.0, |
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120 0.0, |
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121 0.0, |
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122 0.0, |
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123 0.0, |
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124 ] |
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125 assert nx.clustering(G, weight="weight") == { |
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126 0: 0.0, |
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128 2: 0.0, |
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129 3: 0.0, |
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130 4: 0.0, |
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131 5: 0.0, |
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132 6: 0.0, |
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133 7: 0.0, |
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134 8: 0.0, |
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135 9: 0.0, |
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136 } |
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137 |
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138 def test_k5(self): |
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139 G = nx.complete_graph(5, create_using=nx.DiGraph()) |
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140 assert list(nx.clustering(G, weight="weight").values()) == [1, 1, 1, 1, 1] |
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141 assert nx.average_clustering(G, weight="weight") == 1 |
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142 G.remove_edge(1, 2) |
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143 assert list(nx.clustering(G, weight="weight").values()) == [ |
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144 11.0 / 12.0, |
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145 1.0, |
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146 1.0, |
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147 11.0 / 12.0, |
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148 11.0 / 12.0, |
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149 ] |
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150 assert nx.clustering(G, [1, 4], weight="weight") == {1: 1.0, 4: 11.0 / 12.0} |
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151 G.remove_edge(2, 1) |
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152 assert list(nx.clustering(G, weight="weight").values()) == [ |
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153 5.0 / 6.0, |
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154 1.0, |
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155 1.0, |
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156 5.0 / 6.0, |
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157 5.0 / 6.0, |
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158 ] |
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159 assert nx.clustering(G, [1, 4], weight="weight") == { |
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160 1: 1.0, |
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161 4: 0.83333333333333337, |
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162 } |
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163 |
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164 def test_triangle_and_edge(self): |
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165 G = nx.cycle_graph(3, create_using=nx.DiGraph()) |
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166 G.add_edge(0, 4, weight=2) |
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167 assert nx.clustering(G)[0] == 1.0 / 6.0 |
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168 assert nx.clustering(G, weight="weight")[0] == 1.0 / 12.0 |
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169 |
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170 |
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171 class TestWeightedClustering: |
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172 def test_clustering(self): |
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173 G = nx.Graph() |
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174 assert list(nx.clustering(G, weight="weight").values()) == [] |
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175 assert nx.clustering(G) == {} |
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176 |
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177 def test_path(self): |
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178 G = nx.path_graph(10) |
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179 assert list(nx.clustering(G, weight="weight").values()) == [ |
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180 0.0, |
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181 0.0, |
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182 0.0, |
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183 0.0, |
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184 0.0, |
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185 0.0, |
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186 0.0, |
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187 0.0, |
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188 0.0, |
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189 0.0, |
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190 ] |
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191 assert nx.clustering(G, weight="weight") == { |
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192 0: 0.0, |
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193 1: 0.0, |
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194 2: 0.0, |
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195 3: 0.0, |
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196 4: 0.0, |
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197 5: 0.0, |
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198 6: 0.0, |
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199 7: 0.0, |
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200 8: 0.0, |
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201 9: 0.0, |
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202 } |
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203 |
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204 def test_cubical(self): |
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205 G = nx.cubical_graph() |
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206 assert list(nx.clustering(G, weight="weight").values()) == [ |
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207 0, |
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208 0, |
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209 0, |
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210 0, |
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211 0, |
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212 0, |
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213 0, |
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214 0, |
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215 ] |
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216 assert nx.clustering(G, 1) == 0 |
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217 assert list(nx.clustering(G, [1, 2], weight="weight").values()) == [0, 0] |
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218 assert nx.clustering(G, 1, weight="weight") == 0 |
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219 assert nx.clustering(G, [1, 2], weight="weight") == {1: 0, 2: 0} |
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220 |
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221 def test_k5(self): |
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222 G = nx.complete_graph(5) |
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223 assert list(nx.clustering(G, weight="weight").values()) == [1, 1, 1, 1, 1] |
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224 assert nx.average_clustering(G, weight="weight") == 1 |
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225 G.remove_edge(1, 2) |
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226 assert list(nx.clustering(G, weight="weight").values()) == [ |
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227 5.0 / 6.0, |
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228 1.0, |
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229 1.0, |
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230 5.0 / 6.0, |
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231 5.0 / 6.0, |
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232 ] |
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233 assert nx.clustering(G, [1, 4], weight="weight") == { |
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234 1: 1.0, |
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235 4: 0.83333333333333337, |
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236 } |
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237 |
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238 def test_triangle_and_edge(self): |
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239 G = nx.cycle_graph(3) |
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240 G.add_edge(0, 4, weight=2) |
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241 assert nx.clustering(G)[0] == 1.0 / 3.0 |
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242 assert nx.clustering(G, weight="weight")[0] == 1.0 / 6.0 |
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243 |
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244 |
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245 class TestClustering: |
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246 def test_clustering(self): |
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247 G = nx.Graph() |
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248 assert list(nx.clustering(G).values()) == [] |
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249 assert nx.clustering(G) == {} |
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250 |
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251 def test_path(self): |
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252 G = nx.path_graph(10) |
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253 assert list(nx.clustering(G).values()) == [ |
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254 0.0, |
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255 0.0, |
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256 0.0, |
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257 0.0, |
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258 0.0, |
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259 0.0, |
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260 0.0, |
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261 0.0, |
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262 0.0, |
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263 0.0, |
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264 ] |
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265 assert nx.clustering(G) == { |
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266 0: 0.0, |
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267 1: 0.0, |
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268 2: 0.0, |
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269 3: 0.0, |
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270 4: 0.0, |
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271 5: 0.0, |
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272 6: 0.0, |
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273 7: 0.0, |
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274 8: 0.0, |
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275 9: 0.0, |
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276 } |
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277 |
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278 def test_cubical(self): |
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279 G = nx.cubical_graph() |
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280 assert list(nx.clustering(G).values()) == [0, 0, 0, 0, 0, 0, 0, 0] |
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281 assert nx.clustering(G, 1) == 0 |
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282 assert list(nx.clustering(G, [1, 2]).values()) == [0, 0] |
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283 assert nx.clustering(G, 1) == 0 |
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284 assert nx.clustering(G, [1, 2]) == {1: 0, 2: 0} |
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285 |
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286 def test_k5(self): |
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287 G = nx.complete_graph(5) |
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288 assert list(nx.clustering(G).values()) == [1, 1, 1, 1, 1] |
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289 assert nx.average_clustering(G) == 1 |
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290 G.remove_edge(1, 2) |
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291 assert list(nx.clustering(G).values()) == [ |
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292 5.0 / 6.0, |
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293 1.0, |
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294 1.0, |
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295 5.0 / 6.0, |
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296 5.0 / 6.0, |
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297 ] |
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298 assert nx.clustering(G, [1, 4]) == {1: 1.0, 4: 0.83333333333333337} |
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299 |
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300 |
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301 class TestTransitivity: |
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302 def test_transitivity(self): |
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303 G = nx.Graph() |
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304 assert nx.transitivity(G) == 0.0 |
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305 |
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306 def test_path(self): |
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307 G = nx.path_graph(10) |
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308 assert nx.transitivity(G) == 0.0 |
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309 |
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310 def test_cubical(self): |
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311 G = nx.cubical_graph() |
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312 assert nx.transitivity(G) == 0.0 |
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313 |
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314 def test_k5(self): |
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315 G = nx.complete_graph(5) |
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316 assert nx.transitivity(G) == 1.0 |
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317 G.remove_edge(1, 2) |
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318 assert nx.transitivity(G) == 0.875 |
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319 |
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320 |
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321 class TestSquareClustering: |
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322 def test_clustering(self): |
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323 G = nx.Graph() |
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324 assert list(nx.square_clustering(G).values()) == [] |
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325 assert nx.square_clustering(G) == {} |
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326 |
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327 def test_path(self): |
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328 G = nx.path_graph(10) |
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329 assert list(nx.square_clustering(G).values()) == [ |
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330 0.0, |
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331 0.0, |
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332 0.0, |
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333 0.0, |
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334 0.0, |
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335 0.0, |
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336 0.0, |
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337 0.0, |
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338 0.0, |
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339 0.0, |
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340 ] |
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341 assert nx.square_clustering(G) == { |
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342 0: 0.0, |
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343 1: 0.0, |
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344 2: 0.0, |
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345 3: 0.0, |
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346 4: 0.0, |
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347 5: 0.0, |
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348 6: 0.0, |
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349 7: 0.0, |
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350 8: 0.0, |
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351 9: 0.0, |
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352 } |
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353 |
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354 def test_cubical(self): |
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355 G = nx.cubical_graph() |
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356 assert list(nx.square_clustering(G).values()) == [ |
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357 0.5, |
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358 0.5, |
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359 0.5, |
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360 0.5, |
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361 0.5, |
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362 0.5, |
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363 0.5, |
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364 0.5, |
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365 ] |
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366 assert list(nx.square_clustering(G, [1, 2]).values()) == [0.5, 0.5] |
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367 assert nx.square_clustering(G, [1])[1] == 0.5 |
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368 assert nx.square_clustering(G, [1, 2]) == {1: 0.5, 2: 0.5} |
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369 |
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370 def test_k5(self): |
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371 G = nx.complete_graph(5) |
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372 assert list(nx.square_clustering(G).values()) == [1, 1, 1, 1, 1] |
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373 |
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374 def test_bipartite_k5(self): |
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375 G = nx.complete_bipartite_graph(5, 5) |
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376 assert list(nx.square_clustering(G).values()) == [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] |
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377 |
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378 def test_lind_square_clustering(self): |
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379 """Test C4 for figure 1 Lind et al (2005)""" |
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380 G = nx.Graph( |
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381 [ |
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382 (1, 2), |
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383 (1, 3), |
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384 (1, 6), |
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385 (1, 7), |
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386 (2, 4), |
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387 (2, 5), |
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388 (3, 4), |
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389 (3, 5), |
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390 (6, 7), |
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391 (7, 8), |
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392 (6, 8), |
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393 (7, 9), |
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394 (7, 10), |
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395 (6, 11), |
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396 (6, 12), |
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397 (2, 13), |
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398 (2, 14), |
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399 (3, 15), |
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400 (3, 16), |
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401 ] |
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402 ) |
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403 G1 = G.subgraph([1, 2, 3, 4, 5, 13, 14, 15, 16]) |
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404 G2 = G.subgraph([1, 6, 7, 8, 9, 10, 11, 12]) |
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405 assert nx.square_clustering(G, [1])[1] == 3 / 75.0 |
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406 assert nx.square_clustering(G1, [1])[1] == 2 / 6.0 |
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407 assert nx.square_clustering(G2, [1])[1] == 1 / 5.0 |
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408 |
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409 |
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410 def test_average_clustering(): |
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411 G = nx.cycle_graph(3) |
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412 G.add_edge(2, 3) |
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413 assert nx.average_clustering(G) == (1 + 1 + 1 / 3.0) / 4.0 |
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414 assert nx.average_clustering(G, count_zeros=True) == (1 + 1 + 1 / 3.0) / 4.0 |
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415 assert nx.average_clustering(G, count_zeros=False) == (1 + 1 + 1 / 3.0) / 3.0 |
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416 |
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417 |
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418 class TestGeneralizedDegree: |
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419 def test_generalized_degree(self): |
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420 G = nx.Graph() |
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421 assert nx.generalized_degree(G) == {} |
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422 |
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423 def test_path(self): |
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424 G = nx.path_graph(5) |
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425 assert nx.generalized_degree(G, 0) == {0: 1} |
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426 assert nx.generalized_degree(G, 1) == {0: 2} |
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427 |
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428 def test_cubical(self): |
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429 G = nx.cubical_graph() |
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430 assert nx.generalized_degree(G, 0) == {0: 3} |
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431 |
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432 def test_k5(self): |
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433 G = nx.complete_graph(5) |
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434 assert nx.generalized_degree(G, 0) == {3: 4} |
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435 G.remove_edge(0, 1) |
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436 assert nx.generalized_degree(G, 0) == {2: 3} |