Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/algorithms/centrality/tests/test_katz_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 import math |
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2 |
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3 import networkx as nx |
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4 from networkx.testing import almost_equal |
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5 import pytest |
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6 |
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7 |
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8 class TestKatzCentrality: |
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9 def test_K5(self): |
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10 """Katz centrality: K5""" |
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11 G = nx.complete_graph(5) |
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12 alpha = 0.1 |
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13 b = nx.katz_centrality(G, alpha) |
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14 v = math.sqrt(1 / 5.0) |
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15 b_answer = dict.fromkeys(G, v) |
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16 for n in sorted(G): |
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17 assert almost_equal(b[n], b_answer[n]) |
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18 nstart = {n: 1 for n in G} |
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19 b = nx.katz_centrality(G, alpha, nstart=nstart) |
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20 for n in sorted(G): |
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21 assert almost_equal(b[n], b_answer[n]) |
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22 |
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23 def test_P3(self): |
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24 """Katz centrality: P3""" |
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25 alpha = 0.1 |
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26 G = nx.path_graph(3) |
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27 b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449, 2: 0.5598852584152162} |
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28 b = nx.katz_centrality(G, alpha) |
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29 for n in sorted(G): |
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30 assert almost_equal(b[n], b_answer[n], places=4) |
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31 |
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32 def test_maxiter(self): |
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33 with pytest.raises(nx.PowerIterationFailedConvergence): |
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34 alpha = 0.1 |
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35 G = nx.path_graph(3) |
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36 max_iter = 0 |
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37 try: |
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38 b = nx.katz_centrality(G, alpha, max_iter=max_iter) |
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39 except nx.NetworkXError as e: |
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40 assert str(max_iter) in e.args[0], "max_iter value not in error msg" |
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41 raise # So that the decorater sees the exception. |
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42 |
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43 def test_beta_as_scalar(self): |
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44 alpha = 0.1 |
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45 beta = 0.1 |
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46 b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449, 2: 0.5598852584152162} |
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47 G = nx.path_graph(3) |
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48 b = nx.katz_centrality(G, alpha, beta) |
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49 for n in sorted(G): |
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50 assert almost_equal(b[n], b_answer[n], places=4) |
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51 |
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52 def test_beta_as_dict(self): |
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53 alpha = 0.1 |
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54 beta = {0: 1.0, 1: 1.0, 2: 1.0} |
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55 b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449, 2: 0.5598852584152162} |
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56 G = nx.path_graph(3) |
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57 b = nx.katz_centrality(G, alpha, beta) |
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58 for n in sorted(G): |
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59 assert almost_equal(b[n], b_answer[n], places=4) |
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60 |
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61 def test_multiple_alpha(self): |
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62 alpha_list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6] |
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63 for alpha in alpha_list: |
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64 b_answer = { |
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65 0.1: { |
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66 0: 0.5598852584152165, |
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67 1: 0.6107839182711449, |
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68 2: 0.5598852584152162, |
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69 }, |
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70 0.2: { |
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71 0: 0.5454545454545454, |
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72 1: 0.6363636363636365, |
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73 2: 0.5454545454545454, |
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74 }, |
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75 0.3: { |
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76 0: 0.5333964609104419, |
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77 1: 0.6564879518897746, |
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78 2: 0.5333964609104419, |
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79 }, |
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80 0.4: { |
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81 0: 0.5232045649263551, |
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82 1: 0.6726915834767423, |
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83 2: 0.5232045649263551, |
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84 }, |
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85 0.5: { |
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86 0: 0.5144957746691622, |
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87 1: 0.6859943117075809, |
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88 2: 0.5144957746691622, |
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89 }, |
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90 0.6: { |
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91 0: 0.5069794004195823, |
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92 1: 0.6970966755769258, |
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93 2: 0.5069794004195823, |
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94 }, |
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95 } |
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96 G = nx.path_graph(3) |
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97 b = nx.katz_centrality(G, alpha) |
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98 for n in sorted(G): |
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99 assert almost_equal(b[n], b_answer[alpha][n], places=4) |
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100 |
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101 def test_multigraph(self): |
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102 with pytest.raises(nx.NetworkXException): |
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103 e = nx.katz_centrality(nx.MultiGraph(), 0.1) |
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104 |
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105 def test_empty(self): |
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106 e = nx.katz_centrality(nx.Graph(), 0.1) |
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107 assert e == {} |
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108 |
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109 def test_bad_beta(self): |
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110 with pytest.raises(nx.NetworkXException): |
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111 G = nx.Graph([(0, 1)]) |
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112 beta = {0: 77} |
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113 e = nx.katz_centrality(G, 0.1, beta=beta) |
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114 |
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115 def test_bad_beta_numbe(self): |
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116 with pytest.raises(nx.NetworkXException): |
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117 G = nx.Graph([(0, 1)]) |
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118 e = nx.katz_centrality(G, 0.1, beta="foo") |
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119 |
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120 |
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121 class TestKatzCentralityNumpy: |
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122 @classmethod |
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123 def setup_class(cls): |
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124 global np |
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125 np = pytest.importorskip("numpy") |
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126 scipy = pytest.importorskip("scipy") |
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127 |
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128 def test_K5(self): |
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129 """Katz centrality: K5""" |
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130 G = nx.complete_graph(5) |
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131 alpha = 0.1 |
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132 b = nx.katz_centrality(G, alpha) |
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133 v = math.sqrt(1 / 5.0) |
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134 b_answer = dict.fromkeys(G, v) |
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135 for n in sorted(G): |
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136 assert almost_equal(b[n], b_answer[n]) |
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137 nstart = {n: 1 for n in G} |
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138 b = nx.eigenvector_centrality_numpy(G) |
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139 for n in sorted(G): |
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140 assert almost_equal(b[n], b_answer[n], places=3) |
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141 |
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142 def test_P3(self): |
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143 """Katz centrality: P3""" |
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144 alpha = 0.1 |
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145 G = nx.path_graph(3) |
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146 b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449, 2: 0.5598852584152162} |
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147 b = nx.katz_centrality_numpy(G, alpha) |
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148 for n in sorted(G): |
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149 assert almost_equal(b[n], b_answer[n], places=4) |
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150 |
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151 def test_beta_as_scalar(self): |
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152 alpha = 0.1 |
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153 beta = 0.1 |
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154 b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449, 2: 0.5598852584152162} |
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155 G = nx.path_graph(3) |
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156 b = nx.katz_centrality_numpy(G, alpha, beta) |
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157 for n in sorted(G): |
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158 assert almost_equal(b[n], b_answer[n], places=4) |
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159 |
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160 def test_beta_as_dict(self): |
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161 alpha = 0.1 |
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162 beta = {0: 1.0, 1: 1.0, 2: 1.0} |
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163 b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449, 2: 0.5598852584152162} |
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164 G = nx.path_graph(3) |
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165 b = nx.katz_centrality_numpy(G, alpha, beta) |
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166 for n in sorted(G): |
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167 assert almost_equal(b[n], b_answer[n], places=4) |
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168 |
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169 def test_multiple_alpha(self): |
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170 alpha_list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6] |
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171 for alpha in alpha_list: |
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172 b_answer = { |
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173 0.1: { |
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174 0: 0.5598852584152165, |
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175 1: 0.6107839182711449, |
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176 2: 0.5598852584152162, |
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177 }, |
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178 0.2: { |
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179 0: 0.5454545454545454, |
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180 1: 0.6363636363636365, |
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181 2: 0.5454545454545454, |
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182 }, |
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183 0.3: { |
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184 0: 0.5333964609104419, |
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185 1: 0.6564879518897746, |
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186 2: 0.5333964609104419, |
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187 }, |
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188 0.4: { |
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189 0: 0.5232045649263551, |
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190 1: 0.6726915834767423, |
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191 2: 0.5232045649263551, |
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192 }, |
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193 0.5: { |
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194 0: 0.5144957746691622, |
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195 1: 0.6859943117075809, |
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196 2: 0.5144957746691622, |
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197 }, |
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198 0.6: { |
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199 0: 0.5069794004195823, |
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200 1: 0.6970966755769258, |
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201 2: 0.5069794004195823, |
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202 }, |
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203 } |
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204 G = nx.path_graph(3) |
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205 b = nx.katz_centrality_numpy(G, alpha) |
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206 for n in sorted(G): |
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207 assert almost_equal(b[n], b_answer[alpha][n], places=4) |
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208 |
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209 def test_multigraph(self): |
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210 with pytest.raises(nx.NetworkXException): |
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211 e = nx.katz_centrality(nx.MultiGraph(), 0.1) |
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212 |
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213 def test_empty(self): |
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214 e = nx.katz_centrality(nx.Graph(), 0.1) |
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215 assert e == {} |
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216 |
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217 def test_bad_beta(self): |
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218 with pytest.raises(nx.NetworkXException): |
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219 G = nx.Graph([(0, 1)]) |
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220 beta = {0: 77} |
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221 e = nx.katz_centrality_numpy(G, 0.1, beta=beta) |
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222 |
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223 def test_bad_beta_numbe(self): |
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224 with pytest.raises(nx.NetworkXException): |
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225 G = nx.Graph([(0, 1)]) |
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226 e = nx.katz_centrality_numpy(G, 0.1, beta="foo") |
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227 |
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228 def test_K5_unweighted(self): |
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229 """Katz centrality: K5""" |
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230 G = nx.complete_graph(5) |
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231 alpha = 0.1 |
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232 b = nx.katz_centrality(G, alpha, weight=None) |
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233 v = math.sqrt(1 / 5.0) |
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234 b_answer = dict.fromkeys(G, v) |
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235 for n in sorted(G): |
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236 assert almost_equal(b[n], b_answer[n]) |
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237 nstart = {n: 1 for n in G} |
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238 b = nx.eigenvector_centrality_numpy(G, weight=None) |
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239 for n in sorted(G): |
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240 assert almost_equal(b[n], b_answer[n], places=3) |
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241 |
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242 def test_P3_unweighted(self): |
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243 """Katz centrality: P3""" |
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244 alpha = 0.1 |
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245 G = nx.path_graph(3) |
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246 b_answer = {0: 0.5598852584152165, 1: 0.6107839182711449, 2: 0.5598852584152162} |
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247 b = nx.katz_centrality_numpy(G, alpha, weight=None) |
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248 for n in sorted(G): |
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249 assert almost_equal(b[n], b_answer[n], places=4) |
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250 |
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251 |
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252 class TestKatzCentralityDirected: |
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253 @classmethod |
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254 def setup_class(cls): |
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255 G = nx.DiGraph() |
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256 edges = [ |
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257 (1, 2), |
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258 (1, 3), |
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259 (2, 4), |
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260 (3, 2), |
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261 (3, 5), |
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262 (4, 2), |
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263 (4, 5), |
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264 (4, 6), |
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265 (5, 6), |
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266 (5, 7), |
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267 (5, 8), |
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268 (6, 8), |
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269 (7, 1), |
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270 (7, 5), |
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271 (7, 8), |
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272 (8, 6), |
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273 (8, 7), |
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274 ] |
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275 G.add_edges_from(edges, weight=2.0) |
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276 cls.G = G.reverse() |
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277 cls.G.alpha = 0.1 |
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278 cls.G.evc = [ |
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279 0.3289589783189635, |
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280 0.2832077296243516, |
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281 0.3425906003685471, |
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282 0.3970420865198392, |
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283 0.41074871061646284, |
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284 0.272257430756461, |
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285 0.4201989685435462, |
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286 0.34229059218038554, |
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287 ] |
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288 |
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289 H = nx.DiGraph(edges) |
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290 cls.H = G.reverse() |
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291 cls.H.alpha = 0.1 |
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292 cls.H.evc = [ |
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293 0.3289589783189635, |
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294 0.2832077296243516, |
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295 0.3425906003685471, |
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296 0.3970420865198392, |
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297 0.41074871061646284, |
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298 0.272257430756461, |
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299 0.4201989685435462, |
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300 0.34229059218038554, |
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301 ] |
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302 |
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303 def test_katz_centrality_weighted(self): |
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304 G = self.G |
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305 alpha = self.G.alpha |
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306 p = nx.katz_centrality(G, alpha, weight="weight") |
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307 for (a, b) in zip(list(p.values()), self.G.evc): |
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308 assert almost_equal(a, b) |
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309 |
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310 def test_katz_centrality_unweighted(self): |
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311 H = self.H |
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312 alpha = self.H.alpha |
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313 p = nx.katz_centrality(H, alpha, weight="weight") |
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314 for (a, b) in zip(list(p.values()), self.H.evc): |
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315 assert almost_equal(a, b) |
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316 |
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317 |
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318 class TestKatzCentralityDirectedNumpy(TestKatzCentralityDirected): |
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319 @classmethod |
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320 def setup_class(cls): |
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321 global np |
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322 np = pytest.importorskip("numpy") |
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323 scipy = pytest.importorskip("scipy") |
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324 |
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325 def test_katz_centrality_weighted(self): |
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326 G = self.G |
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327 alpha = self.G.alpha |
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328 p = nx.katz_centrality_numpy(G, alpha, weight="weight") |
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329 for (a, b) in zip(list(p.values()), self.G.evc): |
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330 assert almost_equal(a, b) |
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331 |
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332 def test_katz_centrality_unweighted(self): |
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333 H = self.H |
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334 alpha = self.H.alpha |
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335 p = nx.katz_centrality_numpy(H, alpha, weight="weight") |
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336 for (a, b) in zip(list(p.values()), self.H.evc): |
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337 assert almost_equal(a, b) |
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338 |
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339 |
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340 class TestKatzEigenvectorVKatz: |
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341 @classmethod |
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342 def setup_class(cls): |
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343 global np |
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344 global eigvals |
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345 np = pytest.importorskip("numpy") |
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346 scipy = pytest.importorskip("scipy") |
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347 from numpy.linalg import eigvals |
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348 |
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349 def test_eigenvector_v_katz_random(self): |
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350 G = nx.gnp_random_graph(10, 0.5, seed=1234) |
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351 l = float(max(eigvals(nx.adjacency_matrix(G).todense()))) |
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352 e = nx.eigenvector_centrality_numpy(G) |
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353 k = nx.katz_centrality_numpy(G, 1.0 / l) |
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354 for n in G: |
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355 assert almost_equal(e[n], k[n]) |