Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/algorithms/tests/test_similarity.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 pytest |
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2 |
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3 import networkx as nx |
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4 from networkx.algorithms.similarity import ( |
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5 graph_edit_distance, |
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6 optimal_edit_paths, |
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7 optimize_graph_edit_distance, |
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8 ) |
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9 from networkx.generators.classic import ( |
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10 circular_ladder_graph, |
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11 cycle_graph, |
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12 path_graph, |
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13 wheel_graph, |
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14 ) |
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15 |
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16 |
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17 def nmatch(n1, n2): |
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18 return n1 == n2 |
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19 |
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20 |
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21 def ematch(e1, e2): |
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22 return e1 == e2 |
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23 |
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24 |
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25 def getCanonical(): |
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26 G = nx.Graph() |
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27 G.add_node("A", label="A") |
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28 G.add_node("B", label="B") |
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29 G.add_node("C", label="C") |
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30 G.add_node("D", label="D") |
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31 G.add_edge("A", "B", label="a-b") |
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32 G.add_edge("B", "C", label="b-c") |
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33 G.add_edge("B", "D", label="b-d") |
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34 return G |
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35 |
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36 |
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37 class TestSimilarity: |
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38 @classmethod |
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39 def setup_class(cls): |
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40 global numpy |
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41 global scipy |
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42 numpy = pytest.importorskip("numpy") |
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43 scipy = pytest.importorskip("scipy") |
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44 |
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45 def test_graph_edit_distance_roots_and_timeout(self): |
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46 G0 = nx.star_graph(5) |
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47 G1 = G0.copy() |
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48 pytest.raises(ValueError, graph_edit_distance, G0, G1, roots=[2]) |
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49 pytest.raises(ValueError, graph_edit_distance, G0, G1, roots=[2, 3, 4]) |
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50 pytest.raises(nx.NodeNotFound, graph_edit_distance, G0, G1, roots=(9, 3)) |
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51 pytest.raises(nx.NodeNotFound, graph_edit_distance, G0, G1, roots=(3, 9)) |
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52 pytest.raises(nx.NodeNotFound, graph_edit_distance, G0, G1, roots=(9, 9)) |
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53 assert graph_edit_distance(G0, G1, roots=(1, 2)) == 0 |
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54 assert graph_edit_distance(G0, G1, roots=(0, 1)) == 8 |
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55 assert graph_edit_distance(G0, G1, roots=(1, 2), timeout=5) == 0 |
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56 assert graph_edit_distance(G0, G1, roots=(0, 1), timeout=5) == 8 |
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57 assert graph_edit_distance(G0, G1, roots=(0, 1), timeout=0.0001) is None |
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58 # test raise on 0 timeout |
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59 pytest.raises(nx.NetworkXError, graph_edit_distance, G0, G1, timeout=0) |
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60 |
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61 def test_graph_edit_distance(self): |
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62 G0 = nx.Graph() |
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63 G1 = path_graph(6) |
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64 G2 = cycle_graph(6) |
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65 G3 = wheel_graph(7) |
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66 |
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67 assert graph_edit_distance(G0, G0) == 0 |
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68 assert graph_edit_distance(G0, G1) == 11 |
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69 assert graph_edit_distance(G1, G0) == 11 |
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70 assert graph_edit_distance(G0, G2) == 12 |
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71 assert graph_edit_distance(G2, G0) == 12 |
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72 assert graph_edit_distance(G0, G3) == 19 |
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73 assert graph_edit_distance(G3, G0) == 19 |
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74 |
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75 assert graph_edit_distance(G1, G1) == 0 |
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76 assert graph_edit_distance(G1, G2) == 1 |
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77 assert graph_edit_distance(G2, G1) == 1 |
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78 assert graph_edit_distance(G1, G3) == 8 |
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79 assert graph_edit_distance(G3, G1) == 8 |
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80 |
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81 assert graph_edit_distance(G2, G2) == 0 |
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82 assert graph_edit_distance(G2, G3) == 7 |
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83 assert graph_edit_distance(G3, G2) == 7 |
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84 |
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85 assert graph_edit_distance(G3, G3) == 0 |
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86 |
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87 def test_graph_edit_distance_node_match(self): |
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88 G1 = cycle_graph(5) |
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89 G2 = cycle_graph(5) |
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90 for n, attr in G1.nodes.items(): |
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91 attr["color"] = "red" if n % 2 == 0 else "blue" |
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92 for n, attr in G2.nodes.items(): |
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93 attr["color"] = "red" if n % 2 == 1 else "blue" |
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94 assert graph_edit_distance(G1, G2) == 0 |
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95 assert ( |
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96 graph_edit_distance( |
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97 G1, G2, node_match=lambda n1, n2: n1["color"] == n2["color"] |
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98 ) |
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99 == 1 |
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100 ) |
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101 |
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102 def test_graph_edit_distance_edge_match(self): |
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103 G1 = path_graph(6) |
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104 G2 = path_graph(6) |
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105 for e, attr in G1.edges.items(): |
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106 attr["color"] = "red" if min(e) % 2 == 0 else "blue" |
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107 for e, attr in G2.edges.items(): |
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108 attr["color"] = "red" if min(e) // 3 == 0 else "blue" |
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109 assert graph_edit_distance(G1, G2) == 0 |
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110 assert ( |
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111 graph_edit_distance( |
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112 G1, G2, edge_match=lambda e1, e2: e1["color"] == e2["color"] |
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113 ) |
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114 == 2 |
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115 ) |
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116 |
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117 def test_graph_edit_distance_node_cost(self): |
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118 G1 = path_graph(6) |
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119 G2 = path_graph(6) |
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120 for n, attr in G1.nodes.items(): |
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121 attr["color"] = "red" if n % 2 == 0 else "blue" |
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122 for n, attr in G2.nodes.items(): |
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123 attr["color"] = "red" if n % 2 == 1 else "blue" |
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124 |
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125 def node_subst_cost(uattr, vattr): |
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126 if uattr["color"] == vattr["color"]: |
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127 return 1 |
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128 else: |
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129 return 10 |
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130 |
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131 def node_del_cost(attr): |
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132 if attr["color"] == "blue": |
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133 return 20 |
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134 else: |
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135 return 50 |
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136 |
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137 def node_ins_cost(attr): |
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138 if attr["color"] == "blue": |
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139 return 40 |
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140 else: |
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141 return 100 |
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142 |
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143 assert ( |
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144 graph_edit_distance( |
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145 G1, |
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146 G2, |
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147 node_subst_cost=node_subst_cost, |
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148 node_del_cost=node_del_cost, |
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149 node_ins_cost=node_ins_cost, |
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150 ) |
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151 == 6 |
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152 ) |
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153 |
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154 def test_graph_edit_distance_edge_cost(self): |
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155 G1 = path_graph(6) |
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156 G2 = path_graph(6) |
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157 for e, attr in G1.edges.items(): |
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158 attr["color"] = "red" if min(e) % 2 == 0 else "blue" |
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159 for e, attr in G2.edges.items(): |
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160 attr["color"] = "red" if min(e) // 3 == 0 else "blue" |
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161 |
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162 def edge_subst_cost(gattr, hattr): |
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163 if gattr["color"] == hattr["color"]: |
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164 return 0.01 |
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165 else: |
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166 return 0.1 |
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167 |
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168 def edge_del_cost(attr): |
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169 if attr["color"] == "blue": |
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170 return 0.2 |
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171 else: |
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172 return 0.5 |
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173 |
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174 def edge_ins_cost(attr): |
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175 if attr["color"] == "blue": |
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176 return 0.4 |
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177 else: |
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178 return 1.0 |
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179 |
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180 assert ( |
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181 graph_edit_distance( |
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182 G1, |
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183 G2, |
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184 edge_subst_cost=edge_subst_cost, |
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185 edge_del_cost=edge_del_cost, |
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186 edge_ins_cost=edge_ins_cost, |
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187 ) |
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188 == 0.23 |
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189 ) |
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190 |
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191 def test_graph_edit_distance_upper_bound(self): |
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192 G1 = circular_ladder_graph(2) |
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193 G2 = circular_ladder_graph(6) |
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194 assert graph_edit_distance(G1, G2, upper_bound=5) is None |
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195 assert graph_edit_distance(G1, G2, upper_bound=24) == 22 |
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196 assert graph_edit_distance(G1, G2) == 22 |
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197 |
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198 def test_optimal_edit_paths(self): |
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199 G1 = path_graph(3) |
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200 G2 = cycle_graph(3) |
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201 paths, cost = optimal_edit_paths(G1, G2) |
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202 assert cost == 1 |
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203 assert len(paths) == 6 |
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204 |
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205 def canonical(vertex_path, edge_path): |
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206 return ( |
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207 tuple(sorted(vertex_path)), |
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208 tuple(sorted(edge_path, key=lambda x: (None in x, x))), |
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209 ) |
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210 |
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211 expected_paths = [ |
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212 ( |
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213 [(0, 0), (1, 1), (2, 2)], |
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214 [((0, 1), (0, 1)), ((1, 2), (1, 2)), (None, (0, 2))], |
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215 ), |
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216 ( |
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217 [(0, 0), (1, 2), (2, 1)], |
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218 [((0, 1), (0, 2)), ((1, 2), (1, 2)), (None, (0, 1))], |
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219 ), |
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220 ( |
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221 [(0, 1), (1, 0), (2, 2)], |
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222 [((0, 1), (0, 1)), ((1, 2), (0, 2)), (None, (1, 2))], |
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223 ), |
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224 ( |
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225 [(0, 1), (1, 2), (2, 0)], |
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226 [((0, 1), (1, 2)), ((1, 2), (0, 2)), (None, (0, 1))], |
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227 ), |
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228 ( |
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229 [(0, 2), (1, 0), (2, 1)], |
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230 [((0, 1), (0, 2)), ((1, 2), (0, 1)), (None, (1, 2))], |
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231 ), |
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232 ( |
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233 [(0, 2), (1, 1), (2, 0)], |
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234 [((0, 1), (1, 2)), ((1, 2), (0, 1)), (None, (0, 2))], |
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235 ), |
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236 ] |
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237 assert {canonical(*p) for p in paths} == {canonical(*p) for p in expected_paths} |
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238 |
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239 def test_optimize_graph_edit_distance(self): |
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240 G1 = circular_ladder_graph(2) |
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241 G2 = circular_ladder_graph(6) |
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242 bestcost = 1000 |
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243 for cost in optimize_graph_edit_distance(G1, G2): |
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244 assert cost < bestcost |
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245 bestcost = cost |
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246 assert bestcost == 22 |
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247 |
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248 # def test_graph_edit_distance_bigger(self): |
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249 # G1 = circular_ladder_graph(12) |
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250 # G2 = circular_ladder_graph(16) |
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251 # assert_equal(graph_edit_distance(G1, G2), 22) |
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252 |
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253 def test_selfloops(self): |
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254 G0 = nx.Graph() |
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255 G1 = nx.Graph() |
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256 G1.add_edges_from((("A", "A"), ("A", "B"))) |
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257 G2 = nx.Graph() |
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258 G2.add_edges_from((("A", "B"), ("B", "B"))) |
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259 G3 = nx.Graph() |
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260 G3.add_edges_from((("A", "A"), ("A", "B"), ("B", "B"))) |
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261 |
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262 assert graph_edit_distance(G0, G0) == 0 |
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263 assert graph_edit_distance(G0, G1) == 4 |
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264 assert graph_edit_distance(G1, G0) == 4 |
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265 assert graph_edit_distance(G0, G2) == 4 |
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266 assert graph_edit_distance(G2, G0) == 4 |
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267 assert graph_edit_distance(G0, G3) == 5 |
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268 assert graph_edit_distance(G3, G0) == 5 |
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269 |
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270 assert graph_edit_distance(G1, G1) == 0 |
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271 assert graph_edit_distance(G1, G2) == 0 |
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272 assert graph_edit_distance(G2, G1) == 0 |
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273 assert graph_edit_distance(G1, G3) == 1 |
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274 assert graph_edit_distance(G3, G1) == 1 |
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275 |
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276 assert graph_edit_distance(G2, G2) == 0 |
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277 assert graph_edit_distance(G2, G3) == 1 |
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278 assert graph_edit_distance(G3, G2) == 1 |
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279 |
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280 assert graph_edit_distance(G3, G3) == 0 |
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281 |
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282 def test_digraph(self): |
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283 G0 = nx.DiGraph() |
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284 G1 = nx.DiGraph() |
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285 G1.add_edges_from((("A", "B"), ("B", "C"), ("C", "D"), ("D", "A"))) |
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286 G2 = nx.DiGraph() |
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287 G2.add_edges_from((("A", "B"), ("B", "C"), ("C", "D"), ("A", "D"))) |
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288 G3 = nx.DiGraph() |
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289 G3.add_edges_from((("A", "B"), ("A", "C"), ("B", "D"), ("C", "D"))) |
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290 |
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291 assert graph_edit_distance(G0, G0) == 0 |
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292 assert graph_edit_distance(G0, G1) == 8 |
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293 assert graph_edit_distance(G1, G0) == 8 |
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294 assert graph_edit_distance(G0, G2) == 8 |
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295 assert graph_edit_distance(G2, G0) == 8 |
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296 assert graph_edit_distance(G0, G3) == 8 |
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297 assert graph_edit_distance(G3, G0) == 8 |
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298 |
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299 assert graph_edit_distance(G1, G1) == 0 |
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300 assert graph_edit_distance(G1, G2) == 2 |
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301 assert graph_edit_distance(G2, G1) == 2 |
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302 assert graph_edit_distance(G1, G3) == 4 |
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303 assert graph_edit_distance(G3, G1) == 4 |
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304 |
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305 assert graph_edit_distance(G2, G2) == 0 |
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306 assert graph_edit_distance(G2, G3) == 2 |
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307 assert graph_edit_distance(G3, G2) == 2 |
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308 |
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309 assert graph_edit_distance(G3, G3) == 0 |
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310 |
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311 def test_multigraph(self): |
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312 G0 = nx.MultiGraph() |
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313 G1 = nx.MultiGraph() |
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314 G1.add_edges_from((("A", "B"), ("B", "C"), ("A", "C"))) |
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315 G2 = nx.MultiGraph() |
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316 G2.add_edges_from((("A", "B"), ("B", "C"), ("B", "C"), ("A", "C"))) |
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317 G3 = nx.MultiGraph() |
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318 G3.add_edges_from((("A", "B"), ("B", "C"), ("A", "C"), ("A", "C"), ("A", "C"))) |
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319 |
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320 assert graph_edit_distance(G0, G0) == 0 |
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321 assert graph_edit_distance(G0, G1) == 6 |
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322 assert graph_edit_distance(G1, G0) == 6 |
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323 assert graph_edit_distance(G0, G2) == 7 |
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324 assert graph_edit_distance(G2, G0) == 7 |
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325 assert graph_edit_distance(G0, G3) == 8 |
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326 assert graph_edit_distance(G3, G0) == 8 |
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327 |
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328 assert graph_edit_distance(G1, G1) == 0 |
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329 assert graph_edit_distance(G1, G2) == 1 |
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330 assert graph_edit_distance(G2, G1) == 1 |
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331 assert graph_edit_distance(G1, G3) == 2 |
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332 assert graph_edit_distance(G3, G1) == 2 |
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333 |
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334 assert graph_edit_distance(G2, G2) == 0 |
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335 assert graph_edit_distance(G2, G3) == 1 |
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336 assert graph_edit_distance(G3, G2) == 1 |
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337 |
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338 assert graph_edit_distance(G3, G3) == 0 |
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339 |
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340 def test_multidigraph(self): |
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341 G1 = nx.MultiDiGraph() |
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342 G1.add_edges_from( |
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343 ( |
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344 ("hardware", "kernel"), |
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345 ("kernel", "hardware"), |
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346 ("kernel", "userspace"), |
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347 ("userspace", "kernel"), |
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348 ) |
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349 ) |
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350 G2 = nx.MultiDiGraph() |
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351 G2.add_edges_from( |
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352 ( |
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353 ("winter", "spring"), |
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354 ("spring", "summer"), |
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355 ("summer", "autumn"), |
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356 ("autumn", "winter"), |
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357 ) |
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358 ) |
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359 |
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360 assert graph_edit_distance(G1, G2) == 5 |
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361 assert graph_edit_distance(G2, G1) == 5 |
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362 |
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363 # by https://github.com/jfbeaumont |
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364 def testCopy(self): |
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365 G = nx.Graph() |
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366 G.add_node("A", label="A") |
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367 G.add_node("B", label="B") |
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368 G.add_edge("A", "B", label="a-b") |
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369 assert ( |
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370 graph_edit_distance(G, G.copy(), node_match=nmatch, edge_match=ematch) == 0 |
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371 ) |
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372 |
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373 def testSame(self): |
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374 G1 = nx.Graph() |
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375 G1.add_node("A", label="A") |
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376 G1.add_node("B", label="B") |
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377 G1.add_edge("A", "B", label="a-b") |
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378 G2 = nx.Graph() |
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379 G2.add_node("A", label="A") |
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380 G2.add_node("B", label="B") |
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381 G2.add_edge("A", "B", label="a-b") |
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382 assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 0 |
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383 |
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384 def testOneEdgeLabelDiff(self): |
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385 G1 = nx.Graph() |
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386 G1.add_node("A", label="A") |
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387 G1.add_node("B", label="B") |
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388 G1.add_edge("A", "B", label="a-b") |
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389 G2 = nx.Graph() |
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390 G2.add_node("A", label="A") |
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391 G2.add_node("B", label="B") |
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392 G2.add_edge("A", "B", label="bad") |
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393 assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1 |
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394 |
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395 def testOneNodeLabelDiff(self): |
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396 G1 = nx.Graph() |
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397 G1.add_node("A", label="A") |
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398 G1.add_node("B", label="B") |
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399 G1.add_edge("A", "B", label="a-b") |
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400 G2 = nx.Graph() |
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401 G2.add_node("A", label="Z") |
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402 G2.add_node("B", label="B") |
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403 G2.add_edge("A", "B", label="a-b") |
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404 assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1 |
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405 |
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406 def testOneExtraNode(self): |
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407 G1 = nx.Graph() |
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408 G1.add_node("A", label="A") |
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409 G1.add_node("B", label="B") |
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410 G1.add_edge("A", "B", label="a-b") |
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411 G2 = nx.Graph() |
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412 G2.add_node("A", label="A") |
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413 G2.add_node("B", label="B") |
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414 G2.add_edge("A", "B", label="a-b") |
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415 G2.add_node("C", label="C") |
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416 assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1 |
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417 |
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418 def testOneExtraEdge(self): |
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419 G1 = nx.Graph() |
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420 G1.add_node("A", label="A") |
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421 G1.add_node("B", label="B") |
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422 G1.add_node("C", label="C") |
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423 G1.add_node("C", label="C") |
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424 G1.add_edge("A", "B", label="a-b") |
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425 G2 = nx.Graph() |
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426 G2.add_node("A", label="A") |
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427 G2.add_node("B", label="B") |
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428 G2.add_node("C", label="C") |
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429 G2.add_edge("A", "B", label="a-b") |
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430 G2.add_edge("A", "C", label="a-c") |
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431 assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1 |
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432 |
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433 def testOneExtraNodeAndEdge(self): |
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434 G1 = nx.Graph() |
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435 G1.add_node("A", label="A") |
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436 G1.add_node("B", label="B") |
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437 G1.add_edge("A", "B", label="a-b") |
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438 G2 = nx.Graph() |
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439 G2.add_node("A", label="A") |
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440 G2.add_node("B", label="B") |
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441 G2.add_node("C", label="C") |
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442 G2.add_edge("A", "B", label="a-b") |
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443 G2.add_edge("A", "C", label="a-c") |
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444 assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 2 |
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445 |
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446 def testGraph1(self): |
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447 G1 = getCanonical() |
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448 G2 = nx.Graph() |
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449 G2.add_node("A", label="A") |
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450 G2.add_node("B", label="B") |
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451 G2.add_node("D", label="D") |
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452 G2.add_node("E", label="E") |
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453 G2.add_edge("A", "B", label="a-b") |
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454 G2.add_edge("B", "D", label="b-d") |
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455 G2.add_edge("D", "E", label="d-e") |
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456 assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 3 |
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457 |
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458 def testGraph2(self): |
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459 G1 = getCanonical() |
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460 G2 = nx.Graph() |
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461 G2.add_node("A", label="A") |
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462 G2.add_node("B", label="B") |
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463 G2.add_node("C", label="C") |
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464 G2.add_node("D", label="D") |
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465 G2.add_node("E", label="E") |
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466 G2.add_edge("A", "B", label="a-b") |
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467 G2.add_edge("B", "C", label="b-c") |
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468 G2.add_edge("C", "D", label="c-d") |
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469 G2.add_edge("C", "E", label="c-e") |
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470 assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 4 |
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471 |
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472 def testGraph3(self): |
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473 G1 = getCanonical() |
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474 G2 = nx.Graph() |
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475 G2.add_node("A", label="A") |
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476 G2.add_node("B", label="B") |
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477 G2.add_node("C", label="C") |
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478 G2.add_node("D", label="D") |
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479 G2.add_node("E", label="E") |
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480 G2.add_node("F", label="F") |
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481 G2.add_node("G", label="G") |
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482 G2.add_edge("A", "C", label="a-c") |
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483 G2.add_edge("A", "D", label="a-d") |
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484 G2.add_edge("D", "E", label="d-e") |
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485 G2.add_edge("D", "F", label="d-f") |
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486 G2.add_edge("D", "G", label="d-g") |
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487 G2.add_edge("E", "B", label="e-b") |
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488 assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 12 |
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489 |
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490 def testGraph4(self): |
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491 G1 = getCanonical() |
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492 G2 = nx.Graph() |
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493 G2.add_node("A", label="A") |
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494 G2.add_node("B", label="B") |
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495 G2.add_node("C", label="C") |
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496 G2.add_node("D", label="D") |
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497 G2.add_edge("A", "B", label="a-b") |
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498 G2.add_edge("B", "C", label="b-c") |
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499 G2.add_edge("C", "D", label="c-d") |
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500 assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 2 |
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501 |
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502 def testGraph4_a(self): |
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503 G1 = getCanonical() |
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504 G2 = nx.Graph() |
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505 G2.add_node("A", label="A") |
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506 G2.add_node("B", label="B") |
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507 G2.add_node("C", label="C") |
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508 G2.add_node("D", label="D") |
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509 G2.add_edge("A", "B", label="a-b") |
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510 G2.add_edge("B", "C", label="b-c") |
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511 G2.add_edge("A", "D", label="a-d") |
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512 assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 2 |
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513 |
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514 def testGraph4_b(self): |
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515 G1 = getCanonical() |
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516 G2 = nx.Graph() |
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517 G2.add_node("A", label="A") |
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518 G2.add_node("B", label="B") |
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519 G2.add_node("C", label="C") |
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520 G2.add_node("D", label="D") |
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521 G2.add_edge("A", "B", label="a-b") |
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522 G2.add_edge("B", "C", label="b-c") |
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523 G2.add_edge("B", "D", label="bad") |
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524 assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1 |
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525 |
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526 def test_simrank_no_source_no_target(self): |
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527 G = nx.cycle_graph(5) |
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528 expected = { |
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529 0: { |
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530 0: 1, |
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531 1: 0.3951219505902448, |
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532 2: 0.5707317069281646, |
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533 3: 0.5707317069281646, |
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534 4: 0.3951219505902449, |
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535 }, |
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536 1: { |
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537 0: 0.3951219505902448, |
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538 1: 1, |
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539 2: 0.3951219505902449, |
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540 3: 0.5707317069281646, |
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541 4: 0.5707317069281646, |
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542 }, |
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543 2: { |
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544 0: 0.5707317069281646, |
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545 1: 0.3951219505902449, |
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546 2: 1, |
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547 3: 0.3951219505902449, |
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548 4: 0.5707317069281646, |
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549 }, |
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550 3: { |
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551 0: 0.5707317069281646, |
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552 1: 0.5707317069281646, |
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553 2: 0.3951219505902449, |
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554 3: 1, |
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555 4: 0.3951219505902449, |
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556 }, |
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557 4: { |
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558 0: 0.3951219505902449, |
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559 1: 0.5707317069281646, |
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560 2: 0.5707317069281646, |
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561 3: 0.3951219505902449, |
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562 4: 1, |
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563 }, |
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564 } |
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565 actual = nx.simrank_similarity(G) |
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566 assert expected == actual |
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567 |
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568 # For a DiGraph test, use the first graph from the paper cited in |
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569 # the docs: https://dl.acm.org/doi/pdf/10.1145/775047.775126 |
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570 G = nx.DiGraph() |
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571 G.add_node(0, label="Univ") |
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572 G.add_node(1, label="ProfA") |
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573 G.add_node(2, label="ProfB") |
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574 G.add_node(3, label="StudentA") |
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575 G.add_node(4, label="StudentB") |
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576 G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 4), (4, 2), (3, 0)]) |
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577 |
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578 expected = { |
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579 0: {0: 1, 1: 0.0, 2: 0.1323363991265798, 3: 0.0, 4: 0.03387811817640443}, |
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580 1: {0: 0.0, 1: 1, 2: 0.4135512472705618, 3: 0.0, 4: 0.10586911930126384}, |
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581 2: { |
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582 0: 0.1323363991265798, |
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583 1: 0.4135512472705618, |
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584 2: 1, |
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585 3: 0.04234764772050554, |
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586 4: 0.08822426608438655, |
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587 }, |
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588 3: {0: 0.0, 1: 0.0, 2: 0.04234764772050554, 3: 1, 4: 0.3308409978164495}, |
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589 4: { |
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590 0: 0.03387811817640443, |
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591 1: 0.10586911930126384, |
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592 2: 0.08822426608438655, |
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593 3: 0.3308409978164495, |
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594 4: 1, |
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595 }, |
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596 } |
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597 # Use the importance_factor from the paper to get the same numbers. |
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598 actual = nx.algorithms.similarity.simrank_similarity(G, importance_factor=0.8) |
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599 assert expected == actual |
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600 |
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601 def test_simrank_source_no_target(self): |
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602 G = nx.cycle_graph(5) |
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603 expected = { |
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604 0: 1, |
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605 1: 0.3951219505902448, |
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606 2: 0.5707317069281646, |
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607 3: 0.5707317069281646, |
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608 4: 0.3951219505902449, |
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609 } |
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610 actual = nx.simrank_similarity(G, source=0) |
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611 assert expected == actual |
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612 |
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613 # For a DiGraph test, use the first graph from the paper cited in |
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614 # the docs: https://dl.acm.org/doi/pdf/10.1145/775047.775126 |
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615 G = nx.DiGraph() |
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616 G.add_node(0, label="Univ") |
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617 G.add_node(1, label="ProfA") |
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618 G.add_node(2, label="ProfB") |
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619 G.add_node(3, label="StudentA") |
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620 G.add_node(4, label="StudentB") |
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621 G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 4), (4, 2), (3, 0)]) |
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622 |
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parents:
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623 expected = {0: 1, 1: 0.0, 2: 0.1323363991265798, 3: 0.0, 4: 0.03387811817640443} |
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624 # Use the importance_factor from the paper to get the same numbers. |
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625 actual = nx.algorithms.similarity.simrank_similarity( |
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626 G, importance_factor=0.8, source=0 |
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|
627 ) |
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628 assert expected == actual |
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629 |
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630 def test_simrank_source_and_target(self): |
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631 G = nx.cycle_graph(5) |
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632 expected = 1 |
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633 actual = nx.simrank_similarity(G, source=0, target=0) |
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|
634 |
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parents:
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635 # For a DiGraph test, use the first graph from the paper cited in |
4f3585e2f14b
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
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parents:
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636 # the docs: https://dl.acm.org/doi/pdf/10.1145/775047.775126 |
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637 G = nx.DiGraph() |
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638 G.add_node(0, label="Univ") |
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639 G.add_node(1, label="ProfA") |
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640 G.add_node(2, label="ProfB") |
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641 G.add_node(3, label="StudentA") |
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parents:
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642 G.add_node(4, label="StudentB") |
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parents:
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643 G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 4), (4, 2), (3, 0)]) |
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parents:
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|
644 |
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645 expected = 0.1323363991265798 |
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parents:
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646 # Use the importance_factor from the paper to get the same numbers. |
4f3585e2f14b
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
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parents:
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647 # Use the pair (0,2) because (0,0) and (0,1) have trivial results. |
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648 actual = nx.algorithms.similarity.simrank_similarity( |
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649 G, importance_factor=0.8, source=0, target=2 |
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|
650 ) |
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651 assert expected == actual |
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652 |
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653 def test_simrank_numpy_no_source_no_target(self): |
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654 G = nx.cycle_graph(5) |
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655 expected = numpy.array( |
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656 [ |
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657 [ |
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658 1.0, |
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659 0.3947180735764555, |
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660 0.570482097206368, |
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661 0.570482097206368, |
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662 0.3947180735764555, |
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663 ], |
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664 [ |
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665 0.3947180735764555, |
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666 1.0, |
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667 0.3947180735764555, |
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668 0.570482097206368, |
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669 0.570482097206368, |
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670 ], |
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671 [ |
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672 0.570482097206368, |
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673 0.3947180735764555, |
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674 1.0, |
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675 0.3947180735764555, |
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676 0.570482097206368, |
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677 ], |
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679 0.570482097206368, |
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680 0.570482097206368, |
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681 0.3947180735764555, |
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682 1.0, |
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683 0.3947180735764555, |
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684 ], |
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685 [ |
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686 0.3947180735764555, |
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687 0.570482097206368, |
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688 0.570482097206368, |
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689 0.3947180735764555, |
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690 1.0, |
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691 ], |
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692 ] |
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693 ) |
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694 actual = nx.simrank_similarity_numpy(G) |
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695 numpy.testing.assert_allclose(expected, actual, atol=1e-7) |
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696 |
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697 def test_simrank_numpy_source_no_target(self): |
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698 G = nx.cycle_graph(5) |
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699 expected = numpy.array( |
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700 [ |
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701 1.0, |
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702 0.3947180735764555, |
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703 0.570482097206368, |
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704 0.570482097206368, |
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705 0.3947180735764555, |
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706 ] |
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707 ) |
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708 actual = nx.simrank_similarity_numpy(G, source=0) |
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709 numpy.testing.assert_allclose(expected, actual, atol=1e-7) |
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710 |
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711 def test_simrank_numpy_source_and_target(self): |
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712 G = nx.cycle_graph(5) |
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713 expected = 1.0 |
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714 actual = nx.simrank_similarity_numpy(G, source=0, target=0) |
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715 numpy.testing.assert_allclose(expected, actual, atol=1e-7) |