Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/algorithms/tree/tests/test_mst.py @ 0:4f3585e2f14b draft default tip
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author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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1 """Unit tests for the :mod:`networkx.algorithms.tree.mst` module.""" |
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2 |
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3 import pytest |
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4 |
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5 import networkx as nx |
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6 from networkx.testing import assert_nodes_equal, assert_edges_equal |
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7 |
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8 |
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9 def test_unknown_algorithm(): |
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10 with pytest.raises(ValueError): |
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11 nx.minimum_spanning_tree(nx.Graph(), algorithm="random") |
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12 |
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13 |
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14 class MinimumSpanningTreeTestBase: |
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15 """Base class for test classes for minimum spanning tree algorithms. |
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16 |
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17 This class contains some common tests that will be inherited by |
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18 subclasses. Each subclass must have a class attribute |
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19 :data:`algorithm` that is a string representing the algorithm to |
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20 run, as described under the ``algorithm`` keyword argument for the |
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21 :func:`networkx.minimum_spanning_edges` function. Subclasses can |
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22 then implement any algorithm-specific tests. |
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23 |
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24 """ |
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25 |
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26 def setup_method(self, method): |
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27 """Creates an example graph and stores the expected minimum and |
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28 maximum spanning tree edges. |
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29 |
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30 """ |
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31 # This stores the class attribute `algorithm` in an instance attribute. |
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32 self.algo = self.algorithm |
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33 # This example graph comes from Wikipedia: |
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34 # https://en.wikipedia.org/wiki/Kruskal's_algorithm |
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35 edges = [ |
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36 (0, 1, 7), |
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37 (0, 3, 5), |
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38 (1, 2, 8), |
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39 (1, 3, 9), |
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40 (1, 4, 7), |
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41 (2, 4, 5), |
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42 (3, 4, 15), |
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43 (3, 5, 6), |
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44 (4, 5, 8), |
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45 (4, 6, 9), |
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46 (5, 6, 11), |
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47 ] |
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48 self.G = nx.Graph() |
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49 self.G.add_weighted_edges_from(edges) |
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50 self.minimum_spanning_edgelist = [ |
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51 (0, 1, {"weight": 7}), |
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52 (0, 3, {"weight": 5}), |
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53 (1, 4, {"weight": 7}), |
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54 (2, 4, {"weight": 5}), |
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55 (3, 5, {"weight": 6}), |
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56 (4, 6, {"weight": 9}), |
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57 ] |
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58 self.maximum_spanning_edgelist = [ |
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59 (0, 1, {"weight": 7}), |
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60 (1, 2, {"weight": 8}), |
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61 (1, 3, {"weight": 9}), |
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62 (3, 4, {"weight": 15}), |
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63 (4, 6, {"weight": 9}), |
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64 (5, 6, {"weight": 11}), |
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65 ] |
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66 |
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67 def test_minimum_edges(self): |
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68 edges = nx.minimum_spanning_edges(self.G, algorithm=self.algo) |
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69 # Edges from the spanning edges functions don't come in sorted |
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70 # orientation, so we need to sort each edge individually. |
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71 actual = sorted((min(u, v), max(u, v), d) for u, v, d in edges) |
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72 assert_edges_equal(actual, self.minimum_spanning_edgelist) |
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73 |
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74 def test_maximum_edges(self): |
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75 edges = nx.maximum_spanning_edges(self.G, algorithm=self.algo) |
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76 # Edges from the spanning edges functions don't come in sorted |
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77 # orientation, so we need to sort each edge individually. |
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78 actual = sorted((min(u, v), max(u, v), d) for u, v, d in edges) |
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79 assert_edges_equal(actual, self.maximum_spanning_edgelist) |
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80 |
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81 def test_without_data(self): |
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82 edges = nx.minimum_spanning_edges(self.G, algorithm=self.algo, data=False) |
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83 # Edges from the spanning edges functions don't come in sorted |
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84 # orientation, so we need to sort each edge individually. |
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85 actual = sorted((min(u, v), max(u, v)) for u, v in edges) |
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86 expected = [(u, v) for u, v, d in self.minimum_spanning_edgelist] |
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87 assert_edges_equal(actual, expected) |
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88 |
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89 def test_nan_weights(self): |
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90 # Edge weights NaN never appear in the spanning tree. see #2164 |
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91 G = self.G |
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92 G.add_edge(0, 12, weight=float("nan")) |
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93 edges = nx.minimum_spanning_edges( |
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94 G, algorithm=self.algo, data=False, ignore_nan=True |
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95 ) |
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96 actual = sorted((min(u, v), max(u, v)) for u, v in edges) |
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97 expected = [(u, v) for u, v, d in self.minimum_spanning_edgelist] |
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98 assert_edges_equal(actual, expected) |
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99 # Now test for raising exception |
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100 edges = nx.minimum_spanning_edges( |
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101 G, algorithm=self.algo, data=False, ignore_nan=False |
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102 ) |
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103 with pytest.raises(ValueError): |
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104 list(edges) |
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105 # test default for ignore_nan as False |
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106 edges = nx.minimum_spanning_edges(G, algorithm=self.algo, data=False) |
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107 with pytest.raises(ValueError): |
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108 list(edges) |
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109 |
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110 def test_nan_weights_order(self): |
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111 # now try again with a nan edge at the beginning of G.nodes |
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112 edges = [ |
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113 (0, 1, 7), |
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114 (0, 3, 5), |
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115 (1, 2, 8), |
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124 ] |
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125 G = nx.Graph() |
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126 G.add_weighted_edges_from([(u + 1, v + 1, wt) for u, v, wt in edges]) |
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127 G.add_edge(0, 7, weight=float("nan")) |
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128 edges = nx.minimum_spanning_edges( |
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129 G, algorithm=self.algo, data=False, ignore_nan=True |
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130 ) |
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131 actual = sorted((min(u, v), max(u, v)) for u, v in edges) |
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132 shift = [(u + 1, v + 1) for u, v, d in self.minimum_spanning_edgelist] |
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133 assert_edges_equal(actual, shift) |
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134 |
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135 def test_isolated_node(self): |
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136 # now try again with an isolated node |
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137 edges = [ |
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138 (0, 1, 7), |
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139 (0, 3, 5), |
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140 (1, 2, 8), |
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141 (1, 3, 9), |
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142 (1, 4, 7), |
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143 (2, 4, 5), |
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144 (3, 4, 15), |
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146 (4, 5, 8), |
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147 (4, 6, 9), |
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148 (5, 6, 11), |
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149 ] |
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150 G = nx.Graph() |
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151 G.add_weighted_edges_from([(u + 1, v + 1, wt) for u, v, wt in edges]) |
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152 G.add_node(0) |
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153 edges = nx.minimum_spanning_edges( |
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154 G, algorithm=self.algo, data=False, ignore_nan=True |
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155 ) |
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156 actual = sorted((min(u, v), max(u, v)) for u, v in edges) |
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157 shift = [(u + 1, v + 1) for u, v, d in self.minimum_spanning_edgelist] |
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158 assert_edges_equal(actual, shift) |
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159 |
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160 def test_minimum_tree(self): |
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161 T = nx.minimum_spanning_tree(self.G, algorithm=self.algo) |
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162 actual = sorted(T.edges(data=True)) |
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163 assert_edges_equal(actual, self.minimum_spanning_edgelist) |
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164 |
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165 def test_maximum_tree(self): |
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166 T = nx.maximum_spanning_tree(self.G, algorithm=self.algo) |
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167 actual = sorted(T.edges(data=True)) |
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168 assert_edges_equal(actual, self.maximum_spanning_edgelist) |
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169 |
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170 def test_disconnected(self): |
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171 G = nx.Graph([(0, 1, dict(weight=1)), (2, 3, dict(weight=2))]) |
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172 T = nx.minimum_spanning_tree(G, algorithm=self.algo) |
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173 assert_nodes_equal(list(T), list(range(4))) |
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174 assert_edges_equal(list(T.edges()), [(0, 1), (2, 3)]) |
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175 |
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176 def test_empty_graph(self): |
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177 G = nx.empty_graph(3) |
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178 T = nx.minimum_spanning_tree(G, algorithm=self.algo) |
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179 assert_nodes_equal(sorted(T), list(range(3))) |
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180 assert T.number_of_edges() == 0 |
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181 |
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182 def test_attributes(self): |
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183 G = nx.Graph() |
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184 G.add_edge(1, 2, weight=1, color="red", distance=7) |
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185 G.add_edge(2, 3, weight=1, color="green", distance=2) |
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186 G.add_edge(1, 3, weight=10, color="blue", distance=1) |
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187 G.graph["foo"] = "bar" |
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188 T = nx.minimum_spanning_tree(G, algorithm=self.algo) |
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189 assert T.graph == G.graph |
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190 assert_nodes_equal(T, G) |
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191 for u, v in T.edges(): |
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192 assert T.adj[u][v] == G.adj[u][v] |
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193 |
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194 def test_weight_attribute(self): |
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195 G = nx.Graph() |
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196 G.add_edge(0, 1, weight=1, distance=7) |
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197 G.add_edge(0, 2, weight=30, distance=1) |
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198 G.add_edge(1, 2, weight=1, distance=1) |
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199 G.add_node(3) |
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200 T = nx.minimum_spanning_tree(G, algorithm=self.algo, weight="distance") |
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201 assert_nodes_equal(sorted(T), list(range(4))) |
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202 assert_edges_equal(sorted(T.edges()), [(0, 2), (1, 2)]) |
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203 T = nx.maximum_spanning_tree(G, algorithm=self.algo, weight="distance") |
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204 assert_nodes_equal(sorted(T), list(range(4))) |
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205 assert_edges_equal(sorted(T.edges()), [(0, 1), (0, 2)]) |
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206 |
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207 |
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208 class TestBoruvka(MinimumSpanningTreeTestBase): |
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209 """Unit tests for computing a minimum (or maximum) spanning tree |
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210 using Borůvka's algorithm. |
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211 |
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212 """ |
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213 |
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214 algorithm = "boruvka" |
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215 |
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216 def test_unicode_name(self): |
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217 """Tests that using a Unicode string can correctly indicate |
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218 Borůvka's algorithm. |
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219 |
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220 """ |
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221 edges = nx.minimum_spanning_edges(self.G, algorithm="borůvka") |
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222 # Edges from the spanning edges functions don't come in sorted |
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223 # orientation, so we need to sort each edge individually. |
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224 actual = sorted((min(u, v), max(u, v), d) for u, v, d in edges) |
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225 assert_edges_equal(actual, self.minimum_spanning_edgelist) |
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226 |
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227 |
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228 class MultigraphMSTTestBase(MinimumSpanningTreeTestBase): |
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229 # Abstract class |
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230 |
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231 def test_multigraph_keys_min(self): |
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232 """Tests that the minimum spanning edges of a multigraph |
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233 preserves edge keys. |
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234 |
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235 """ |
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236 G = nx.MultiGraph() |
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237 G.add_edge(0, 1, key="a", weight=2) |
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238 G.add_edge(0, 1, key="b", weight=1) |
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239 min_edges = nx.minimum_spanning_edges |
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240 mst_edges = min_edges(G, algorithm=self.algo, data=False) |
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241 assert_edges_equal([(0, 1, "b")], list(mst_edges)) |
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242 |
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243 def test_multigraph_keys_max(self): |
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244 """Tests that the maximum spanning edges of a multigraph |
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245 preserves edge keys. |
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246 |
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247 """ |
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248 G = nx.MultiGraph() |
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249 G.add_edge(0, 1, key="a", weight=2) |
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250 G.add_edge(0, 1, key="b", weight=1) |
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251 max_edges = nx.maximum_spanning_edges |
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252 mst_edges = max_edges(G, algorithm=self.algo, data=False) |
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253 assert_edges_equal([(0, 1, "a")], list(mst_edges)) |
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254 |
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255 |
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256 class TestKruskal(MultigraphMSTTestBase): |
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257 """Unit tests for computing a minimum (or maximum) spanning tree |
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258 using Kruskal's algorithm. |
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259 |
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260 """ |
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261 |
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262 algorithm = "kruskal" |
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263 |
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264 |
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265 class TestPrim(MultigraphMSTTestBase): |
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266 """Unit tests for computing a minimum (or maximum) spanning tree |
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267 using Prim's algorithm. |
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268 |
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269 """ |
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270 |
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271 algorithm = "prim" |
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272 |
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273 def test_multigraph_keys_tree(self): |
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274 G = nx.MultiGraph() |
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275 G.add_edge(0, 1, key="a", weight=2) |
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276 G.add_edge(0, 1, key="b", weight=1) |
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277 T = nx.minimum_spanning_tree(G) |
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278 assert_edges_equal([(0, 1, 1)], list(T.edges(data="weight"))) |
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279 |
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280 def test_multigraph_keys_tree_max(self): |
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281 G = nx.MultiGraph() |
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282 G.add_edge(0, 1, key="a", weight=2) |
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283 G.add_edge(0, 1, key="b", weight=1) |
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284 T = nx.maximum_spanning_tree(G) |
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285 assert_edges_equal([(0, 1, 2)], list(T.edges(data="weight"))) |