Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/sitepackages/networkx/algorithms/link_analysis/tests/test_pagerank.py @ 0:4f3585e2f14b draft default tip
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
author  shellac 

date  Mon, 22 Mar 2021 18:12:50 +0000 
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0
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1 import random 
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2 
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3 import networkx 
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4 import pytest 
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5 
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6 numpy = pytest.importorskip("numpy") 
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7 scipy = pytest.importorskip("scipy") 
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8 
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9 from networkx.testing import almost_equal 
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10 
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11 # Example from 
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12 # A. Langville and C. Meyer, "A survey of eigenvector methods of web 
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13 # information retrieval." http://citeseer.ist.psu.edu/713792.html 
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14 
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15 
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16 class TestPageRank: 
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17 @classmethod 
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18 def setup_class(cls): 
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19 G = networkx.DiGraph() 
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20 edges = [ 
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21 (1, 2), 
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22 (1, 3), 
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23 # 2 is a dangling node 
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24 (3, 1), 
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25 (3, 2), 
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26 (3, 5), 
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27 (4, 5), 
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28 (4, 6), 
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29 (5, 4), 
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30 (5, 6), 
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31 (6, 4), 
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32 ] 
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33 G.add_edges_from(edges) 
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34 cls.G = G 
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35 cls.G.pagerank = dict( 
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36 zip( 
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37 sorted(G), 
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38 [ 
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39 0.03721197, 
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40 0.05395735, 
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41 0.04150565, 
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42 0.37508082, 
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43 0.20599833, 
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44 0.28624589, 
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45 ], 
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46 ) 
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47 ) 
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48 cls.dangling_node_index = 1 
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49 cls.dangling_edges = {1: 2, 2: 3, 3: 0, 4: 0, 5: 0, 6: 0} 
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50 cls.G.dangling_pagerank = dict( 
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51 zip( 
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52 sorted(G), 
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53 [0.10844518, 0.18618601, 0.0710892, 0.2683668, 0.15919783, 0.20671497], 
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54 ) 
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55 ) 
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56 
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57 def test_pagerank(self): 
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58 G = self.G 
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59 p = networkx.pagerank(G, alpha=0.9, tol=1.0e08) 
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60 for n in G: 
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61 assert almost_equal(p[n], G.pagerank[n], places=4) 
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62 
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63 nstart = {n: random.random() for n in G} 
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64 p = networkx.pagerank(G, alpha=0.9, tol=1.0e08, nstart=nstart) 
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65 for n in G: 
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66 assert almost_equal(p[n], G.pagerank[n], places=4) 
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67 
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68 def test_pagerank_max_iter(self): 
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69 with pytest.raises(networkx.PowerIterationFailedConvergence): 
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70 networkx.pagerank(self.G, max_iter=0) 
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71 
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72 def test_numpy_pagerank(self): 
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73 G = self.G 
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74 p = networkx.pagerank_numpy(G, alpha=0.9) 
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75 for n in G: 
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76 assert almost_equal(p[n], G.pagerank[n], places=4) 
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77 personalize = {n: random.random() for n in G} 
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78 p = networkx.pagerank_numpy(G, alpha=0.9, personalization=personalize) 
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79 
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80 def test_google_matrix(self): 
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81 G = self.G 
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82 M = networkx.google_matrix(G, alpha=0.9, nodelist=sorted(G)) 
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83 e, ev = numpy.linalg.eig(M.T) 
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84 p = numpy.array(ev[:, 0] / ev[:, 0].sum())[:, 0] 
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85 for (a, b) in zip(p, self.G.pagerank.values()): 
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86 assert almost_equal(a, b) 
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87 
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88 def test_personalization(self): 
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89 G = networkx.complete_graph(4) 
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90 personalize = {0: 1, 1: 1, 2: 4, 3: 4} 
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91 answer = { 
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92 0: 0.23246732615667579, 
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93 1: 0.23246732615667579, 
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94 2: 0.267532673843324, 
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95 3: 0.2675326738433241, 
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96 } 
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97 p = networkx.pagerank(G, alpha=0.85, personalization=personalize) 
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98 for n in G: 
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99 assert almost_equal(p[n], answer[n], places=4) 
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100 
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101 def test_zero_personalization_vector(self): 
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102 G = networkx.complete_graph(4) 
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103 personalize = {0: 0, 1: 0, 2: 0, 3: 0} 
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104 pytest.raises( 
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105 ZeroDivisionError, networkx.pagerank, G, personalization=personalize 
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106 ) 
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107 
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108 def test_one_nonzero_personalization_value(self): 
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109 G = networkx.complete_graph(4) 
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110 personalize = {0: 0, 1: 0, 2: 0, 3: 1} 
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111 answer = { 
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112 0: 0.22077931820379187, 
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113 1: 0.22077931820379187, 
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114 2: 0.22077931820379187, 
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115 3: 0.3376620453886241, 
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116 } 
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117 p = networkx.pagerank(G, alpha=0.85, personalization=personalize) 
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118 for n in G: 
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119 assert almost_equal(p[n], answer[n], places=4) 
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120 
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121 def test_incomplete_personalization(self): 
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122 G = networkx.complete_graph(4) 
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123 personalize = {3: 1} 
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124 answer = { 
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125 0: 0.22077931820379187, 
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126 1: 0.22077931820379187, 
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127 2: 0.22077931820379187, 
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128 3: 0.3376620453886241, 
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129 } 
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130 p = networkx.pagerank(G, alpha=0.85, personalization=personalize) 
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131 for n in G: 
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132 assert almost_equal(p[n], answer[n], places=4) 
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133 
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134 def test_dangling_matrix(self): 
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135 """ 
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136 Tests that the google_matrix doesn't change except for the dangling 
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137 nodes. 
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138 """ 
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139 G = self.G 
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140 dangling = self.dangling_edges 
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141 dangling_sum = float(sum(dangling.values())) 
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142 M1 = networkx.google_matrix(G, personalization=dangling) 
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143 M2 = networkx.google_matrix(G, personalization=dangling, dangling=dangling) 
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144 for i in range(len(G)): 
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145 for j in range(len(G)): 
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146 if i == self.dangling_node_index and (j + 1) in dangling: 
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147 assert almost_equal( 
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148 M2[i, j], dangling[j + 1] / dangling_sum, places=4 
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149 ) 
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150 else: 
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151 assert almost_equal(M2[i, j], M1[i, j], places=4) 
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152 
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153 def test_dangling_pagerank(self): 
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154 pr = networkx.pagerank(self.G, dangling=self.dangling_edges) 
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155 for n in self.G: 
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156 assert almost_equal(pr[n], self.G.dangling_pagerank[n], places=4) 
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157 
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158 def test_dangling_numpy_pagerank(self): 
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159 pr = networkx.pagerank_numpy(self.G, dangling=self.dangling_edges) 
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160 for n in self.G: 
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161 assert almost_equal(pr[n], self.G.dangling_pagerank[n], places=4) 
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162 
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163 def test_empty(self): 
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164 G = networkx.Graph() 
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165 assert networkx.pagerank(G) == {} 
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166 assert networkx.pagerank_numpy(G) == {} 
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167 assert networkx.google_matrix(G).shape == (0, 0) 
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168 
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169 
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170 class TestPageRankScipy(TestPageRank): 
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171 def test_scipy_pagerank(self): 
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172 G = self.G 
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173 p = networkx.pagerank_scipy(G, alpha=0.9, tol=1.0e08) 
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174 for n in G: 
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175 assert almost_equal(p[n], G.pagerank[n], places=4) 
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176 personalize = {n: random.random() for n in G} 
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177 p = networkx.pagerank_scipy( 
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178 G, alpha=0.9, tol=1.0e08, personalization=personalize 
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179 ) 
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180 
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181 nstart = {n: random.random() for n in G} 
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182 p = networkx.pagerank_scipy(G, alpha=0.9, tol=1.0e08, nstart=nstart) 
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183 for n in G: 
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184 assert almost_equal(p[n], G.pagerank[n], places=4) 
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185 
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186 def test_scipy_pagerank_max_iter(self): 
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187 with pytest.raises(networkx.PowerIterationFailedConvergence): 
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188 networkx.pagerank_scipy(self.G, max_iter=0) 
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189 
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190 def test_dangling_scipy_pagerank(self): 
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191 pr = networkx.pagerank_scipy(self.G, dangling=self.dangling_edges) 
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192 for n in self.G: 
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193 assert almost_equal(pr[n], self.G.dangling_pagerank[n], places=4) 
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194 
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195 def test_empty_scipy(self): 
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196 G = networkx.Graph() 
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197 assert networkx.pagerank_scipy(G) == {} 