Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/algorithms/centrality/tests/test_closeness_centrality.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 """ |
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2 Tests for closeness centrality. |
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3 """ |
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4 import pytest |
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5 import networkx as nx |
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6 from networkx.testing import almost_equal |
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7 |
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8 |
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9 class TestClosenessCentrality: |
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10 @classmethod |
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11 def setup_class(cls): |
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12 cls.K = nx.krackhardt_kite_graph() |
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13 cls.P3 = nx.path_graph(3) |
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14 cls.P4 = nx.path_graph(4) |
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15 cls.K5 = nx.complete_graph(5) |
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16 |
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17 cls.C4 = nx.cycle_graph(4) |
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18 cls.T = nx.balanced_tree(r=2, h=2) |
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19 cls.Gb = nx.Graph() |
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20 cls.Gb.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), (2, 4), (4, 5), (3, 5)]) |
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21 |
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22 F = nx.florentine_families_graph() |
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23 cls.F = F |
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24 |
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25 cls.LM = nx.les_miserables_graph() |
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26 |
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27 # Create random undirected, unweighted graph for testing incremental version |
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28 cls.undirected_G = nx.fast_gnp_random_graph(n=100, p=0.6, seed=123) |
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29 cls.undirected_G_cc = nx.closeness_centrality(cls.undirected_G) |
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30 |
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31 def test_wf_improved(self): |
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32 G = nx.union(self.P4, nx.path_graph([4, 5, 6])) |
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33 c = nx.closeness_centrality(G) |
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34 cwf = nx.closeness_centrality(G, wf_improved=False) |
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35 res = {0: 0.25, 1: 0.375, 2: 0.375, 3: 0.25, 4: 0.222, 5: 0.333, 6: 0.222} |
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36 wf_res = {0: 0.5, 1: 0.75, 2: 0.75, 3: 0.5, 4: 0.667, 5: 1.0, 6: 0.667} |
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37 for n in G: |
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38 assert almost_equal(c[n], res[n], places=3) |
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39 assert almost_equal(cwf[n], wf_res[n], places=3) |
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40 |
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41 def test_digraph(self): |
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42 G = nx.path_graph(3, create_using=nx.DiGraph()) |
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43 c = nx.closeness_centrality(G) |
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44 cr = nx.closeness_centrality(G.reverse()) |
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45 d = {0: 0.0, 1: 0.500, 2: 0.667} |
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46 dr = {0: 0.667, 1: 0.500, 2: 0.0} |
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47 for n in sorted(self.P3): |
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48 assert almost_equal(c[n], d[n], places=3) |
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49 assert almost_equal(cr[n], dr[n], places=3) |
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50 |
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51 def test_k5_closeness(self): |
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52 c = nx.closeness_centrality(self.K5) |
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53 d = {0: 1.000, 1: 1.000, 2: 1.000, 3: 1.000, 4: 1.000} |
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54 for n in sorted(self.K5): |
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55 assert almost_equal(c[n], d[n], places=3) |
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56 |
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57 def test_p3_closeness(self): |
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58 c = nx.closeness_centrality(self.P3) |
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59 d = {0: 0.667, 1: 1.000, 2: 0.667} |
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60 for n in sorted(self.P3): |
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61 assert almost_equal(c[n], d[n], places=3) |
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62 |
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63 def test_krackhardt_closeness(self): |
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64 c = nx.closeness_centrality(self.K) |
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65 d = { |
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66 0: 0.529, |
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67 1: 0.529, |
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68 2: 0.500, |
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69 3: 0.600, |
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70 4: 0.500, |
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71 5: 0.643, |
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72 6: 0.643, |
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73 7: 0.600, |
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74 8: 0.429, |
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75 9: 0.310, |
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76 } |
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77 for n in sorted(self.K): |
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78 assert almost_equal(c[n], d[n], places=3) |
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79 |
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80 def test_florentine_families_closeness(self): |
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81 c = nx.closeness_centrality(self.F) |
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82 d = { |
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83 "Acciaiuoli": 0.368, |
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84 "Albizzi": 0.483, |
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85 "Barbadori": 0.4375, |
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86 "Bischeri": 0.400, |
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87 "Castellani": 0.389, |
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88 "Ginori": 0.333, |
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89 "Guadagni": 0.467, |
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90 "Lamberteschi": 0.326, |
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91 "Medici": 0.560, |
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92 "Pazzi": 0.286, |
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93 "Peruzzi": 0.368, |
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94 "Ridolfi": 0.500, |
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95 "Salviati": 0.389, |
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96 "Strozzi": 0.4375, |
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97 "Tornabuoni": 0.483, |
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98 } |
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99 for n in sorted(self.F): |
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100 assert almost_equal(c[n], d[n], places=3) |
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101 |
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102 def test_les_miserables_closeness(self): |
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103 c = nx.closeness_centrality(self.LM) |
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104 d = { |
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105 "Napoleon": 0.302, |
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106 "Myriel": 0.429, |
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107 "MlleBaptistine": 0.413, |
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108 "MmeMagloire": 0.413, |
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109 "CountessDeLo": 0.302, |
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110 "Geborand": 0.302, |
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111 "Champtercier": 0.302, |
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112 "Cravatte": 0.302, |
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113 "Count": 0.302, |
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114 "OldMan": 0.302, |
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115 "Valjean": 0.644, |
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116 "Labarre": 0.394, |
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117 "Marguerite": 0.413, |
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118 "MmeDeR": 0.394, |
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119 "Isabeau": 0.394, |
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120 "Gervais": 0.394, |
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121 "Listolier": 0.341, |
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122 "Tholomyes": 0.392, |
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123 "Fameuil": 0.341, |
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124 "Blacheville": 0.341, |
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125 "Favourite": 0.341, |
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126 "Dahlia": 0.341, |
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127 "Zephine": 0.341, |
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128 "Fantine": 0.461, |
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129 "MmeThenardier": 0.461, |
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130 "Thenardier": 0.517, |
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131 "Cosette": 0.478, |
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132 "Javert": 0.517, |
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133 "Fauchelevent": 0.402, |
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134 "Bamatabois": 0.427, |
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135 "Perpetue": 0.318, |
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136 "Simplice": 0.418, |
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137 "Scaufflaire": 0.394, |
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138 "Woman1": 0.396, |
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139 "Judge": 0.404, |
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140 "Champmathieu": 0.404, |
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141 "Brevet": 0.404, |
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142 "Chenildieu": 0.404, |
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143 "Cochepaille": 0.404, |
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144 "Pontmercy": 0.373, |
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145 "Boulatruelle": 0.342, |
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146 "Eponine": 0.396, |
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147 "Anzelma": 0.352, |
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148 "Woman2": 0.402, |
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149 "MotherInnocent": 0.398, |
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150 "Gribier": 0.288, |
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151 "MmeBurgon": 0.344, |
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152 "Jondrette": 0.257, |
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153 "Gavroche": 0.514, |
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154 "Gillenormand": 0.442, |
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155 "Magnon": 0.335, |
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156 "MlleGillenormand": 0.442, |
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157 "MmePontmercy": 0.315, |
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158 "MlleVaubois": 0.308, |
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159 "LtGillenormand": 0.365, |
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160 "Marius": 0.531, |
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161 "BaronessT": 0.352, |
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162 "Mabeuf": 0.396, |
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163 "Enjolras": 0.481, |
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164 "Combeferre": 0.392, |
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165 "Prouvaire": 0.357, |
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166 "Feuilly": 0.392, |
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167 "Courfeyrac": 0.400, |
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168 "Bahorel": 0.394, |
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169 "Bossuet": 0.475, |
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170 "Joly": 0.394, |
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171 "Grantaire": 0.358, |
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172 "MotherPlutarch": 0.285, |
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173 "Gueulemer": 0.463, |
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174 "Babet": 0.463, |
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175 "Claquesous": 0.452, |
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176 "Montparnasse": 0.458, |
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177 "Toussaint": 0.402, |
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178 "Child1": 0.342, |
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179 "Child2": 0.342, |
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180 "Brujon": 0.380, |
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181 "MmeHucheloup": 0.353, |
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182 } |
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183 for n in sorted(self.LM): |
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184 assert almost_equal(c[n], d[n], places=3) |
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185 |
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186 def test_weighted_closeness(self): |
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187 edges = [ |
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188 ("s", "u", 10), |
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189 ("s", "x", 5), |
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190 ("u", "v", 1), |
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191 ("u", "x", 2), |
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192 ("v", "y", 1), |
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193 ("x", "u", 3), |
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194 ("x", "v", 5), |
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195 ("x", "y", 2), |
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196 ("y", "s", 7), |
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197 ("y", "v", 6), |
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198 ] |
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199 XG = nx.Graph() |
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200 XG.add_weighted_edges_from(edges) |
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201 c = nx.closeness_centrality(XG, distance="weight") |
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202 d = {"y": 0.200, "x": 0.286, "s": 0.138, "u": 0.235, "v": 0.200} |
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203 for n in sorted(XG): |
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204 assert almost_equal(c[n], d[n], places=3) |
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205 |
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206 # |
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207 # Tests for incremental closeness centrality. |
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208 # |
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209 @staticmethod |
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210 def pick_add_edge(g): |
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211 u = nx.utils.arbitrary_element(g) |
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212 possible_nodes = set(g.nodes()) |
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213 neighbors = list(g.neighbors(u)) + [u] |
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214 possible_nodes.difference_update(neighbors) |
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215 v = nx.utils.arbitrary_element(possible_nodes) |
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216 return (u, v) |
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217 |
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218 @staticmethod |
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219 def pick_remove_edge(g): |
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220 u = nx.utils.arbitrary_element(g) |
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221 possible_nodes = list(g.neighbors(u)) |
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222 v = nx.utils.arbitrary_element(possible_nodes) |
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223 return (u, v) |
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224 |
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225 def test_directed_raises(self): |
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226 with pytest.raises(nx.NetworkXNotImplemented): |
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227 dir_G = nx.gn_graph(n=5) |
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228 prev_cc = None |
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229 edge = self.pick_add_edge(dir_G) |
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230 insert = True |
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231 nx.incremental_closeness_centrality(dir_G, edge, prev_cc, insert) |
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232 |
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233 def test_wrong_size_prev_cc_raises(self): |
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234 with pytest.raises(nx.NetworkXError): |
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235 G = self.undirected_G.copy() |
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236 edge = self.pick_add_edge(G) |
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237 insert = True |
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238 prev_cc = self.undirected_G_cc.copy() |
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239 prev_cc.pop(0) |
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240 nx.incremental_closeness_centrality(G, edge, prev_cc, insert) |
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241 |
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242 def test_wrong_nodes_prev_cc_raises(self): |
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243 with pytest.raises(nx.NetworkXError): |
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244 G = self.undirected_G.copy() |
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245 edge = self.pick_add_edge(G) |
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246 insert = True |
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247 prev_cc = self.undirected_G_cc.copy() |
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248 num_nodes = len(prev_cc) |
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249 prev_cc.pop(0) |
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250 prev_cc[num_nodes] = 0.5 |
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251 nx.incremental_closeness_centrality(G, edge, prev_cc, insert) |
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252 |
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253 def test_zero_centrality(self): |
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254 G = nx.path_graph(3) |
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255 prev_cc = nx.closeness_centrality(G) |
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256 edge = self.pick_remove_edge(G) |
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257 test_cc = nx.incremental_closeness_centrality(G, edge, prev_cc, insertion=False) |
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258 G.remove_edges_from([edge]) |
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259 real_cc = nx.closeness_centrality(G) |
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260 shared_items = set(test_cc.items()) & set(real_cc.items()) |
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261 assert len(shared_items) == len(real_cc) |
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262 assert 0 in test_cc.values() |
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263 |
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264 def test_incremental(self): |
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265 # Check that incremental and regular give same output |
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266 G = self.undirected_G.copy() |
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267 prev_cc = None |
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268 for i in range(5): |
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269 if i % 2 == 0: |
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270 # Remove an edge |
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271 insert = False |
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272 edge = self.pick_remove_edge(G) |
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273 else: |
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274 # Add an edge |
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275 insert = True |
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276 edge = self.pick_add_edge(G) |
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277 |
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278 # start = timeit.default_timer() |
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279 test_cc = nx.incremental_closeness_centrality(G, edge, prev_cc, insert) |
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280 # inc_elapsed = (timeit.default_timer() - start) |
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281 # print(f"incremental time: {inc_elapsed}") |
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282 |
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283 if insert: |
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284 G.add_edges_from([edge]) |
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285 else: |
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286 G.remove_edges_from([edge]) |
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287 |
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288 # start = timeit.default_timer() |
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289 real_cc = nx.closeness_centrality(G) |
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290 # reg_elapsed = (timeit.default_timer() - start) |
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291 # print(f"regular time: {reg_elapsed}") |
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292 # Example output: |
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293 # incremental time: 0.208 |
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294 # regular time: 0.276 |
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295 # incremental time: 0.00683 |
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296 # regular time: 0.260 |
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297 # incremental time: 0.0224 |
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298 # regular time: 0.278 |
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299 # incremental time: 0.00804 |
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300 # regular time: 0.208 |
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301 # incremental time: 0.00947 |
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302 # regular time: 0.188 |
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303 |
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304 assert set(test_cc.items()) == set(real_cc.items()) |
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305 |
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306 prev_cc = test_cc |