Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/linalg/tests/test_graphmatrix.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 np = pytest.importorskip("numpy") |
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4 npt = pytest.importorskip("numpy.testing") |
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5 scipy = pytest.importorskip("scipy") |
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6 |
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7 import networkx as nx |
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8 from networkx.generators.degree_seq import havel_hakimi_graph |
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9 from networkx.exception import NetworkXError |
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10 |
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11 |
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12 def test_incidence_matrix_simple(): |
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13 deg = [3, 2, 2, 1, 0] |
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14 G = havel_hakimi_graph(deg) |
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15 deg = [(1, 0), (1, 0), (1, 0), (2, 0), (1, 0), (2, 1), (0, 1), (0, 1)] |
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16 MG = nx.random_clustered_graph(deg, seed=42) |
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17 |
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18 I = nx.incidence_matrix(G).todense().astype(int) |
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19 # fmt: off |
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20 expected = np.array( |
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21 [[1, 1, 1, 0], |
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22 [0, 1, 0, 1], |
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23 [1, 0, 0, 1], |
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24 [0, 0, 1, 0], |
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25 [0, 0, 0, 0]] |
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26 ) |
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27 # fmt: on |
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28 npt.assert_equal(I, expected) |
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29 |
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30 I = nx.incidence_matrix(MG).todense().astype(int) |
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31 # fmt: off |
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32 expected = np.array( |
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33 [[1, 0, 0, 0, 0, 0, 0], |
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34 [1, 0, 0, 0, 0, 0, 0], |
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35 [0, 1, 0, 0, 0, 0, 0], |
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36 [0, 0, 0, 0, 0, 0, 0], |
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37 [0, 1, 0, 0, 0, 0, 0], |
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38 [0, 0, 0, 0, 1, 1, 0], |
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39 [0, 0, 0, 0, 0, 1, 1], |
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40 [0, 0, 0, 0, 1, 0, 1]] |
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41 ) |
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42 # fmt: on |
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43 npt.assert_equal(I, expected) |
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44 |
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45 with pytest.raises(NetworkXError): |
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46 nx.incidence_matrix(G, nodelist=[0, 1]) |
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47 |
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48 |
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49 class TestGraphMatrix: |
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50 @classmethod |
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51 def setup_class(cls): |
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52 deg = [3, 2, 2, 1, 0] |
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53 cls.G = havel_hakimi_graph(deg) |
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54 # fmt: off |
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55 cls.OI = np.array( |
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56 [[-1, -1, -1, 0], |
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57 [1, 0, 0, -1], |
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58 [0, 1, 0, 1], |
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59 [0, 0, 1, 0], |
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60 [0, 0, 0, 0]] |
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61 ) |
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62 cls.A = np.array( |
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63 [[0, 1, 1, 1, 0], |
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64 [1, 0, 1, 0, 0], |
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65 [1, 1, 0, 0, 0], |
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66 [1, 0, 0, 0, 0], |
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67 [0, 0, 0, 0, 0]] |
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68 ) |
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69 # fmt: on |
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70 cls.WG = havel_hakimi_graph(deg) |
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71 cls.WG.add_edges_from( |
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72 (u, v, {"weight": 0.5, "other": 0.3}) for (u, v) in cls.G.edges() |
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73 ) |
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74 # fmt: off |
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75 cls.WA = np.array( |
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76 [[0, 0.5, 0.5, 0.5, 0], |
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77 [0.5, 0, 0.5, 0, 0], |
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78 [0.5, 0.5, 0, 0, 0], |
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79 [0.5, 0, 0, 0, 0], |
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80 [0, 0, 0, 0, 0]] |
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81 ) |
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82 # fmt: on |
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83 cls.MG = nx.MultiGraph(cls.G) |
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84 cls.MG2 = cls.MG.copy() |
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85 cls.MG2.add_edge(0, 1) |
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86 # fmt: off |
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87 cls.MG2A = np.array( |
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88 [[0, 2, 1, 1, 0], |
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89 [2, 0, 1, 0, 0], |
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90 [1, 1, 0, 0, 0], |
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91 [1, 0, 0, 0, 0], |
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92 [0, 0, 0, 0, 0]] |
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93 ) |
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94 cls.MGOI = np.array( |
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95 [[-1, -1, -1, -1, 0], |
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96 [1, 1, 0, 0, -1], |
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97 [0, 0, 1, 0, 1], |
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98 [0, 0, 0, 1, 0], |
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99 [0, 0, 0, 0, 0]] |
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100 ) |
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101 # fmt: on |
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102 cls.no_edges_G = nx.Graph([(1, 2), (3, 2, {"weight": 8})]) |
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103 cls.no_edges_A = np.array([[0, 0], [0, 0]]) |
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104 |
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105 def test_incidence_matrix(self): |
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106 "Conversion to incidence matrix" |
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107 I = ( |
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108 nx.incidence_matrix( |
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109 self.G, |
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110 nodelist=sorted(self.G), |
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111 edgelist=sorted(self.G.edges()), |
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112 oriented=True, |
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113 ) |
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114 .todense() |
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115 .astype(int) |
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116 ) |
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117 npt.assert_equal(I, self.OI) |
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118 |
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119 I = ( |
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120 nx.incidence_matrix( |
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121 self.G, |
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122 nodelist=sorted(self.G), |
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123 edgelist=sorted(self.G.edges()), |
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124 oriented=False, |
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125 ) |
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126 .todense() |
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127 .astype(int) |
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128 ) |
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129 npt.assert_equal(I, np.abs(self.OI)) |
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130 |
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131 I = ( |
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132 nx.incidence_matrix( |
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133 self.MG, |
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134 nodelist=sorted(self.MG), |
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135 edgelist=sorted(self.MG.edges()), |
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136 oriented=True, |
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137 ) |
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138 .todense() |
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139 .astype(int) |
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140 ) |
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141 npt.assert_equal(I, self.OI) |
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142 |
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143 I = ( |
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144 nx.incidence_matrix( |
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145 self.MG, |
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146 nodelist=sorted(self.MG), |
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147 edgelist=sorted(self.MG.edges()), |
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148 oriented=False, |
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149 ) |
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150 .todense() |
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151 .astype(int) |
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152 ) |
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153 npt.assert_equal(I, np.abs(self.OI)) |
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154 |
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155 I = ( |
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156 nx.incidence_matrix( |
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157 self.MG2, |
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158 nodelist=sorted(self.MG2), |
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159 edgelist=sorted(self.MG2.edges()), |
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160 oriented=True, |
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161 ) |
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162 .todense() |
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163 .astype(int) |
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164 ) |
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165 npt.assert_equal(I, self.MGOI) |
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166 |
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167 I = ( |
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168 nx.incidence_matrix( |
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169 self.MG2, |
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170 nodelist=sorted(self.MG), |
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171 edgelist=sorted(self.MG2.edges()), |
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172 oriented=False, |
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173 ) |
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174 .todense() |
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175 .astype(int) |
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176 ) |
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177 npt.assert_equal(I, np.abs(self.MGOI)) |
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178 |
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179 def test_weighted_incidence_matrix(self): |
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180 I = ( |
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181 nx.incidence_matrix( |
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182 self.WG, |
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183 nodelist=sorted(self.WG), |
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184 edgelist=sorted(self.WG.edges()), |
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185 oriented=True, |
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186 ) |
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187 .todense() |
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188 .astype(int) |
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189 ) |
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190 npt.assert_equal(I, self.OI) |
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191 |
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192 I = ( |
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193 nx.incidence_matrix( |
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194 self.WG, |
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195 nodelist=sorted(self.WG), |
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196 edgelist=sorted(self.WG.edges()), |
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197 oriented=False, |
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198 ) |
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199 .todense() |
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200 .astype(int) |
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201 ) |
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202 npt.assert_equal(I, np.abs(self.OI)) |
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203 |
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204 # npt.assert_equal(nx.incidence_matrix(self.WG,oriented=True, |
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205 # weight='weight').todense(),0.5*self.OI) |
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206 # npt.assert_equal(nx.incidence_matrix(self.WG,weight='weight').todense(), |
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207 # np.abs(0.5*self.OI)) |
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208 # npt.assert_equal(nx.incidence_matrix(self.WG,oriented=True,weight='other').todense(), |
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209 # 0.3*self.OI) |
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210 |
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211 I = nx.incidence_matrix( |
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212 self.WG, |
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213 nodelist=sorted(self.WG), |
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214 edgelist=sorted(self.WG.edges()), |
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215 oriented=True, |
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216 weight="weight", |
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217 ).todense() |
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218 npt.assert_equal(I, 0.5 * self.OI) |
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219 |
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220 I = nx.incidence_matrix( |
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221 self.WG, |
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222 nodelist=sorted(self.WG), |
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223 edgelist=sorted(self.WG.edges()), |
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224 oriented=False, |
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225 weight="weight", |
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226 ).todense() |
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227 npt.assert_equal(I, np.abs(0.5 * self.OI)) |
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228 |
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229 I = nx.incidence_matrix( |
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230 self.WG, |
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231 nodelist=sorted(self.WG), |
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232 edgelist=sorted(self.WG.edges()), |
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233 oriented=True, |
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234 weight="other", |
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235 ).todense() |
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236 npt.assert_equal(I, 0.3 * self.OI) |
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237 |
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238 # WMG=nx.MultiGraph(self.WG) |
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239 # WMG.add_edge(0,1,weight=0.5,other=0.3) |
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240 # npt.assert_equal(nx.incidence_matrix(WMG,weight='weight').todense(), |
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241 # np.abs(0.5*self.MGOI)) |
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242 # npt.assert_equal(nx.incidence_matrix(WMG,weight='weight',oriented=True).todense(), |
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243 # 0.5*self.MGOI) |
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244 # npt.assert_equal(nx.incidence_matrix(WMG,weight='other',oriented=True).todense(), |
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245 # 0.3*self.MGOI) |
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246 |
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247 WMG = nx.MultiGraph(self.WG) |
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248 WMG.add_edge(0, 1, weight=0.5, other=0.3) |
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249 |
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250 I = nx.incidence_matrix( |
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251 WMG, |
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252 nodelist=sorted(WMG), |
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253 edgelist=sorted(WMG.edges(keys=True)), |
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254 oriented=True, |
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255 weight="weight", |
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256 ).todense() |
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257 npt.assert_equal(I, 0.5 * self.MGOI) |
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258 |
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259 I = nx.incidence_matrix( |
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260 WMG, |
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261 nodelist=sorted(WMG), |
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262 edgelist=sorted(WMG.edges(keys=True)), |
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263 oriented=False, |
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264 weight="weight", |
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265 ).todense() |
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266 npt.assert_equal(I, np.abs(0.5 * self.MGOI)) |
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267 |
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268 I = nx.incidence_matrix( |
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269 WMG, |
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270 nodelist=sorted(WMG), |
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271 edgelist=sorted(WMG.edges(keys=True)), |
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272 oriented=True, |
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273 weight="other", |
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274 ).todense() |
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275 npt.assert_equal(I, 0.3 * self.MGOI) |
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276 |
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277 def test_adjacency_matrix(self): |
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278 "Conversion to adjacency matrix" |
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279 npt.assert_equal(nx.adj_matrix(self.G).todense(), self.A) |
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280 npt.assert_equal(nx.adj_matrix(self.MG).todense(), self.A) |
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281 npt.assert_equal(nx.adj_matrix(self.MG2).todense(), self.MG2A) |
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282 npt.assert_equal( |
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283 nx.adj_matrix(self.G, nodelist=[0, 1]).todense(), self.A[:2, :2] |
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284 ) |
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285 npt.assert_equal(nx.adj_matrix(self.WG).todense(), self.WA) |
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286 npt.assert_equal(nx.adj_matrix(self.WG, weight=None).todense(), self.A) |
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287 npt.assert_equal(nx.adj_matrix(self.MG2, weight=None).todense(), self.MG2A) |
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288 npt.assert_equal( |
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289 nx.adj_matrix(self.WG, weight="other").todense(), 0.6 * self.WA |
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290 ) |
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291 npt.assert_equal( |
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292 nx.adj_matrix(self.no_edges_G, nodelist=[1, 3]).todense(), self.no_edges_A |
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293 ) |