annotate env/lib/python3.9/site-packages/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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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=1.0e-08)
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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=1.0e-08, 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=1.0e-08)
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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=1.0e-08, 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=1.0e-08, 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) == {}