Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/tests/test_convert_numpy.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 np_assert_equal = np.testing.assert_equal |
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5 |
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6 import networkx as nx |
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7 from networkx.generators.classic import barbell_graph, cycle_graph, path_graph |
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8 from networkx.testing.utils import assert_graphs_equal |
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9 |
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10 |
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11 class TestConvertNumpy: |
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12 def setup_method(self): |
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13 self.G1 = barbell_graph(10, 3) |
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14 self.G2 = cycle_graph(10, create_using=nx.DiGraph) |
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15 |
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16 self.G3 = self.create_weighted(nx.Graph()) |
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17 self.G4 = self.create_weighted(nx.DiGraph()) |
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18 |
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19 def test_exceptions(self): |
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20 G = np.array("a") |
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21 pytest.raises(nx.NetworkXError, nx.to_networkx_graph, G) |
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22 |
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23 def create_weighted(self, G): |
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24 g = cycle_graph(4) |
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25 G.add_nodes_from(g) |
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26 G.add_weighted_edges_from((u, v, 10 + u) for u, v in g.edges()) |
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27 return G |
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28 |
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29 def assert_equal(self, G1, G2): |
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30 assert sorted(G1.nodes()) == sorted(G2.nodes()) |
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31 assert sorted(G1.edges()) == sorted(G2.edges()) |
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32 |
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33 def identity_conversion(self, G, A, create_using): |
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34 assert A.sum() > 0 |
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35 GG = nx.from_numpy_matrix(A, create_using=create_using) |
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36 self.assert_equal(G, GG) |
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37 GW = nx.to_networkx_graph(A, create_using=create_using) |
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38 self.assert_equal(G, GW) |
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39 GI = nx.empty_graph(0, create_using).__class__(A) |
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40 self.assert_equal(G, GI) |
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41 |
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42 def test_shape(self): |
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43 "Conversion from non-square array." |
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44 A = np.array([[1, 2, 3], [4, 5, 6]]) |
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45 pytest.raises(nx.NetworkXError, nx.from_numpy_matrix, A) |
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46 |
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47 def test_identity_graph_matrix(self): |
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48 "Conversion from graph to matrix to graph." |
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49 A = nx.to_numpy_matrix(self.G1) |
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50 self.identity_conversion(self.G1, A, nx.Graph()) |
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51 |
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52 def test_identity_graph_array(self): |
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53 "Conversion from graph to array to graph." |
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54 A = nx.to_numpy_matrix(self.G1) |
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55 A = np.asarray(A) |
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56 self.identity_conversion(self.G1, A, nx.Graph()) |
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57 |
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58 def test_identity_digraph_matrix(self): |
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59 """Conversion from digraph to matrix to digraph.""" |
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60 A = nx.to_numpy_matrix(self.G2) |
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61 self.identity_conversion(self.G2, A, nx.DiGraph()) |
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62 |
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63 def test_identity_digraph_array(self): |
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64 """Conversion from digraph to array to digraph.""" |
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65 A = nx.to_numpy_matrix(self.G2) |
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66 A = np.asarray(A) |
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67 self.identity_conversion(self.G2, A, nx.DiGraph()) |
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68 |
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69 def test_identity_weighted_graph_matrix(self): |
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70 """Conversion from weighted graph to matrix to weighted graph.""" |
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71 A = nx.to_numpy_matrix(self.G3) |
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72 self.identity_conversion(self.G3, A, nx.Graph()) |
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73 |
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74 def test_identity_weighted_graph_array(self): |
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75 """Conversion from weighted graph to array to weighted graph.""" |
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76 A = nx.to_numpy_matrix(self.G3) |
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77 A = np.asarray(A) |
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78 self.identity_conversion(self.G3, A, nx.Graph()) |
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79 |
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80 def test_identity_weighted_digraph_matrix(self): |
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81 """Conversion from weighted digraph to matrix to weighted digraph.""" |
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82 A = nx.to_numpy_matrix(self.G4) |
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83 self.identity_conversion(self.G4, A, nx.DiGraph()) |
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84 |
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85 def test_identity_weighted_digraph_array(self): |
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86 """Conversion from weighted digraph to array to weighted digraph.""" |
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87 A = nx.to_numpy_matrix(self.G4) |
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88 A = np.asarray(A) |
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89 self.identity_conversion(self.G4, A, nx.DiGraph()) |
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90 |
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91 def test_nodelist(self): |
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92 """Conversion from graph to matrix to graph with nodelist.""" |
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93 P4 = path_graph(4) |
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94 P3 = path_graph(3) |
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95 nodelist = list(P3) |
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96 A = nx.to_numpy_matrix(P4, nodelist=nodelist) |
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97 GA = nx.Graph(A) |
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98 self.assert_equal(GA, P3) |
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99 |
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100 # Make nodelist ambiguous by containing duplicates. |
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101 nodelist += [nodelist[0]] |
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102 pytest.raises(nx.NetworkXError, nx.to_numpy_matrix, P3, nodelist=nodelist) |
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103 |
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104 def test_weight_keyword(self): |
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105 WP4 = nx.Graph() |
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106 WP4.add_edges_from((n, n + 1, dict(weight=0.5, other=0.3)) for n in range(3)) |
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107 P4 = path_graph(4) |
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108 A = nx.to_numpy_matrix(P4) |
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109 np_assert_equal(A, nx.to_numpy_matrix(WP4, weight=None)) |
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110 np_assert_equal(0.5 * A, nx.to_numpy_matrix(WP4)) |
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111 np_assert_equal(0.3 * A, nx.to_numpy_matrix(WP4, weight="other")) |
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112 |
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113 def test_from_numpy_matrix_type(self): |
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114 A = np.matrix([[1]]) |
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115 G = nx.from_numpy_matrix(A) |
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116 assert type(G[0][0]["weight"]) == int |
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117 |
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118 A = np.matrix([[1]]).astype(np.float) |
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119 G = nx.from_numpy_matrix(A) |
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120 assert type(G[0][0]["weight"]) == float |
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121 |
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122 A = np.matrix([[1]]).astype(np.str) |
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123 G = nx.from_numpy_matrix(A) |
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124 assert type(G[0][0]["weight"]) == str |
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125 |
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126 A = np.matrix([[1]]).astype(np.bool) |
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127 G = nx.from_numpy_matrix(A) |
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128 assert type(G[0][0]["weight"]) == bool |
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129 |
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130 A = np.matrix([[1]]).astype(np.complex) |
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131 G = nx.from_numpy_matrix(A) |
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132 assert type(G[0][0]["weight"]) == complex |
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133 |
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134 A = np.matrix([[1]]).astype(np.object) |
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135 pytest.raises(TypeError, nx.from_numpy_matrix, A) |
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136 |
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137 G = nx.cycle_graph(3) |
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138 A = nx.adj_matrix(G).todense() |
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139 H = nx.from_numpy_matrix(A) |
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140 assert all(type(m) == int and type(n) == int for m, n in H.edges()) |
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141 H = nx.from_numpy_array(A) |
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142 assert all(type(m) == int and type(n) == int for m, n in H.edges()) |
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143 |
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144 def test_from_numpy_matrix_dtype(self): |
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145 dt = [("weight", float), ("cost", int)] |
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146 A = np.matrix([[(1.0, 2)]], dtype=dt) |
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147 G = nx.from_numpy_matrix(A) |
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148 assert type(G[0][0]["weight"]) == float |
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149 assert type(G[0][0]["cost"]) == int |
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150 assert G[0][0]["cost"] == 2 |
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151 assert G[0][0]["weight"] == 1.0 |
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152 |
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153 def test_to_numpy_recarray(self): |
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154 G = nx.Graph() |
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155 G.add_edge(1, 2, weight=7.0, cost=5) |
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156 A = nx.to_numpy_recarray(G, dtype=[("weight", float), ("cost", int)]) |
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157 assert sorted(A.dtype.names) == ["cost", "weight"] |
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158 assert A.weight[0, 1] == 7.0 |
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159 assert A.weight[0, 0] == 0.0 |
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160 assert A.cost[0, 1] == 5 |
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161 assert A.cost[0, 0] == 0 |
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162 |
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163 def test_numpy_multigraph(self): |
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164 G = nx.MultiGraph() |
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165 G.add_edge(1, 2, weight=7) |
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166 G.add_edge(1, 2, weight=70) |
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167 A = nx.to_numpy_matrix(G) |
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168 assert A[1, 0] == 77 |
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169 A = nx.to_numpy_matrix(G, multigraph_weight=min) |
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170 assert A[1, 0] == 7 |
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171 A = nx.to_numpy_matrix(G, multigraph_weight=max) |
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172 assert A[1, 0] == 70 |
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173 |
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174 def test_from_numpy_matrix_parallel_edges(self): |
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175 """Tests that the :func:`networkx.from_numpy_matrix` function |
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176 interprets integer weights as the number of parallel edges when |
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177 creating a multigraph. |
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178 |
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179 """ |
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180 A = np.matrix([[1, 1], [1, 2]]) |
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181 # First, with a simple graph, each integer entry in the adjacency |
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182 # matrix is interpreted as the weight of a single edge in the graph. |
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183 expected = nx.DiGraph() |
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184 edges = [(0, 0), (0, 1), (1, 0)] |
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185 expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges]) |
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186 expected.add_edge(1, 1, weight=2) |
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187 actual = nx.from_numpy_matrix(A, parallel_edges=True, create_using=nx.DiGraph) |
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188 assert_graphs_equal(actual, expected) |
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189 actual = nx.from_numpy_matrix(A, parallel_edges=False, create_using=nx.DiGraph) |
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190 assert_graphs_equal(actual, expected) |
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191 # Now each integer entry in the adjacency matrix is interpreted as the |
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192 # number of parallel edges in the graph if the appropriate keyword |
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193 # argument is specified. |
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194 edges = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 1)] |
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195 expected = nx.MultiDiGraph() |
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196 expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges]) |
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197 actual = nx.from_numpy_matrix( |
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198 A, parallel_edges=True, create_using=nx.MultiDiGraph |
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199 ) |
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200 assert_graphs_equal(actual, expected) |
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201 expected = nx.MultiDiGraph() |
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202 expected.add_edges_from(set(edges), weight=1) |
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203 # The sole self-loop (edge 0) on vertex 1 should have weight 2. |
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204 expected[1][1][0]["weight"] = 2 |
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205 actual = nx.from_numpy_matrix( |
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206 A, parallel_edges=False, create_using=nx.MultiDiGraph |
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207 ) |
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208 assert_graphs_equal(actual, expected) |
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209 |
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210 def test_symmetric(self): |
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211 """Tests that a symmetric matrix has edges added only once to an |
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212 undirected multigraph when using :func:`networkx.from_numpy_matrix`. |
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213 |
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214 """ |
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215 A = np.matrix([[0, 1], [1, 0]]) |
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216 G = nx.from_numpy_matrix(A, create_using=nx.MultiGraph) |
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217 expected = nx.MultiGraph() |
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218 expected.add_edge(0, 1, weight=1) |
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219 assert_graphs_equal(G, expected) |
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220 |
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221 def test_dtype_int_graph(self): |
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222 """Test that setting dtype int actually gives an integer matrix. |
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223 |
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224 For more information, see GitHub pull request #1363. |
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225 |
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226 """ |
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227 G = nx.complete_graph(3) |
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228 A = nx.to_numpy_matrix(G, dtype=int) |
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229 assert A.dtype == int |
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230 |
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231 def test_dtype_int_multigraph(self): |
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232 """Test that setting dtype int actually gives an integer matrix. |
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233 |
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234 For more information, see GitHub pull request #1363. |
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235 |
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236 """ |
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237 G = nx.MultiGraph(nx.complete_graph(3)) |
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238 A = nx.to_numpy_matrix(G, dtype=int) |
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239 assert A.dtype == int |
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240 |
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241 |
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242 class TestConvertNumpyArray: |
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243 def setup_method(self): |
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244 self.G1 = barbell_graph(10, 3) |
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245 self.G2 = cycle_graph(10, create_using=nx.DiGraph) |
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246 self.G3 = self.create_weighted(nx.Graph()) |
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247 self.G4 = self.create_weighted(nx.DiGraph()) |
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248 |
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249 def create_weighted(self, G): |
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250 g = cycle_graph(4) |
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251 G.add_nodes_from(g) |
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252 G.add_weighted_edges_from((u, v, 10 + u) for u, v in g.edges()) |
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253 return G |
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254 |
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255 def assert_equal(self, G1, G2): |
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256 assert sorted(G1.nodes()) == sorted(G2.nodes()) |
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257 assert sorted(G1.edges()) == sorted(G2.edges()) |
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258 |
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259 def identity_conversion(self, G, A, create_using): |
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260 assert A.sum() > 0 |
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261 GG = nx.from_numpy_array(A, create_using=create_using) |
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262 self.assert_equal(G, GG) |
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263 GW = nx.to_networkx_graph(A, create_using=create_using) |
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264 self.assert_equal(G, GW) |
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265 GI = nx.empty_graph(0, create_using).__class__(A) |
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266 self.assert_equal(G, GI) |
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267 |
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268 def test_shape(self): |
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269 "Conversion from non-square array." |
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270 A = np.array([[1, 2, 3], [4, 5, 6]]) |
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271 pytest.raises(nx.NetworkXError, nx.from_numpy_array, A) |
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272 |
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273 def test_identity_graph_array(self): |
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274 "Conversion from graph to array to graph." |
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275 A = nx.to_numpy_array(self.G1) |
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276 self.identity_conversion(self.G1, A, nx.Graph()) |
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277 |
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278 def test_identity_digraph_array(self): |
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279 """Conversion from digraph to array to digraph.""" |
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280 A = nx.to_numpy_array(self.G2) |
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281 self.identity_conversion(self.G2, A, nx.DiGraph()) |
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282 |
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283 def test_identity_weighted_graph_array(self): |
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284 """Conversion from weighted graph to array to weighted graph.""" |
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285 A = nx.to_numpy_array(self.G3) |
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286 self.identity_conversion(self.G3, A, nx.Graph()) |
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287 |
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288 def test_identity_weighted_digraph_array(self): |
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289 """Conversion from weighted digraph to array to weighted digraph.""" |
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290 A = nx.to_numpy_array(self.G4) |
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291 self.identity_conversion(self.G4, A, nx.DiGraph()) |
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292 |
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293 def test_nodelist(self): |
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294 """Conversion from graph to array to graph with nodelist.""" |
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295 P4 = path_graph(4) |
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296 P3 = path_graph(3) |
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297 nodelist = list(P3) |
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298 A = nx.to_numpy_array(P4, nodelist=nodelist) |
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299 GA = nx.Graph(A) |
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300 self.assert_equal(GA, P3) |
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301 |
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302 # Make nodelist ambiguous by containing duplicates. |
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303 nodelist += [nodelist[0]] |
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304 pytest.raises(nx.NetworkXError, nx.to_numpy_array, P3, nodelist=nodelist) |
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305 |
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306 def test_weight_keyword(self): |
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307 WP4 = nx.Graph() |
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308 WP4.add_edges_from((n, n + 1, dict(weight=0.5, other=0.3)) for n in range(3)) |
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309 P4 = path_graph(4) |
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310 A = nx.to_numpy_array(P4) |
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311 np_assert_equal(A, nx.to_numpy_array(WP4, weight=None)) |
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312 np_assert_equal(0.5 * A, nx.to_numpy_array(WP4)) |
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313 np_assert_equal(0.3 * A, nx.to_numpy_array(WP4, weight="other")) |
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314 |
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315 def test_from_numpy_array_type(self): |
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316 A = np.array([[1]]) |
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317 G = nx.from_numpy_array(A) |
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318 assert type(G[0][0]["weight"]) == int |
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319 |
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320 A = np.array([[1]]).astype(np.float) |
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321 G = nx.from_numpy_array(A) |
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322 assert type(G[0][0]["weight"]) == float |
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323 |
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324 A = np.array([[1]]).astype(np.str) |
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325 G = nx.from_numpy_array(A) |
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326 assert type(G[0][0]["weight"]) == str |
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327 |
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328 A = np.array([[1]]).astype(np.bool) |
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329 G = nx.from_numpy_array(A) |
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330 assert type(G[0][0]["weight"]) == bool |
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331 |
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332 A = np.array([[1]]).astype(np.complex) |
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333 G = nx.from_numpy_array(A) |
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334 assert type(G[0][0]["weight"]) == complex |
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335 |
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336 A = np.array([[1]]).astype(np.object) |
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337 pytest.raises(TypeError, nx.from_numpy_array, A) |
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338 |
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339 def test_from_numpy_array_dtype(self): |
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340 dt = [("weight", float), ("cost", int)] |
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341 A = np.array([[(1.0, 2)]], dtype=dt) |
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342 G = nx.from_numpy_array(A) |
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343 assert type(G[0][0]["weight"]) == float |
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344 assert type(G[0][0]["cost"]) == int |
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345 assert G[0][0]["cost"] == 2 |
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346 assert G[0][0]["weight"] == 1.0 |
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347 |
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348 def test_to_numpy_recarray(self): |
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349 G = nx.Graph() |
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350 G.add_edge(1, 2, weight=7.0, cost=5) |
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351 A = nx.to_numpy_recarray(G, dtype=[("weight", float), ("cost", int)]) |
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352 assert sorted(A.dtype.names) == ["cost", "weight"] |
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353 assert A.weight[0, 1] == 7.0 |
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354 assert A.weight[0, 0] == 0.0 |
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355 assert A.cost[0, 1] == 5 |
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356 assert A.cost[0, 0] == 0 |
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357 |
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358 def test_numpy_multigraph(self): |
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359 G = nx.MultiGraph() |
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360 G.add_edge(1, 2, weight=7) |
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361 G.add_edge(1, 2, weight=70) |
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362 A = nx.to_numpy_array(G) |
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363 assert A[1, 0] == 77 |
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364 A = nx.to_numpy_array(G, multigraph_weight=min) |
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365 assert A[1, 0] == 7 |
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366 A = nx.to_numpy_array(G, multigraph_weight=max) |
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367 assert A[1, 0] == 70 |
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368 |
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369 def test_from_numpy_array_parallel_edges(self): |
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370 """Tests that the :func:`networkx.from_numpy_array` function |
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371 interprets integer weights as the number of parallel edges when |
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372 creating a multigraph. |
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373 |
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374 """ |
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375 A = np.array([[1, 1], [1, 2]]) |
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376 # First, with a simple graph, each integer entry in the adjacency |
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377 # matrix is interpreted as the weight of a single edge in the graph. |
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378 expected = nx.DiGraph() |
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379 edges = [(0, 0), (0, 1), (1, 0)] |
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380 expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges]) |
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381 expected.add_edge(1, 1, weight=2) |
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382 actual = nx.from_numpy_array(A, parallel_edges=True, create_using=nx.DiGraph) |
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383 assert_graphs_equal(actual, expected) |
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384 actual = nx.from_numpy_array(A, parallel_edges=False, create_using=nx.DiGraph) |
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385 assert_graphs_equal(actual, expected) |
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386 # Now each integer entry in the adjacency matrix is interpreted as the |
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387 # number of parallel edges in the graph if the appropriate keyword |
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388 # argument is specified. |
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389 edges = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 1)] |
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390 expected = nx.MultiDiGraph() |
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391 expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges]) |
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392 actual = nx.from_numpy_array( |
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393 A, parallel_edges=True, create_using=nx.MultiDiGraph |
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394 ) |
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395 assert_graphs_equal(actual, expected) |
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396 expected = nx.MultiDiGraph() |
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397 expected.add_edges_from(set(edges), weight=1) |
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398 # The sole self-loop (edge 0) on vertex 1 should have weight 2. |
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399 expected[1][1][0]["weight"] = 2 |
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400 actual = nx.from_numpy_array( |
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401 A, parallel_edges=False, create_using=nx.MultiDiGraph |
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402 ) |
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403 assert_graphs_equal(actual, expected) |
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404 |
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405 def test_symmetric(self): |
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406 """Tests that a symmetric array has edges added only once to an |
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407 undirected multigraph when using :func:`networkx.from_numpy_array`. |
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408 |
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409 """ |
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410 A = np.array([[0, 1], [1, 0]]) |
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411 G = nx.from_numpy_array(A, create_using=nx.MultiGraph) |
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412 expected = nx.MultiGraph() |
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413 expected.add_edge(0, 1, weight=1) |
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414 assert_graphs_equal(G, expected) |
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415 |
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416 def test_dtype_int_graph(self): |
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417 """Test that setting dtype int actually gives an integer array. |
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418 |
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419 For more information, see GitHub pull request #1363. |
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420 |
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421 """ |
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422 G = nx.complete_graph(3) |
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423 A = nx.to_numpy_array(G, dtype=int) |
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424 assert A.dtype == int |
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425 |
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426 def test_dtype_int_multigraph(self): |
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427 """Test that setting dtype int actually gives an integer array. |
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428 |
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429 For more information, see GitHub pull request #1363. |
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430 |
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431 """ |
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"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
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432 G = nx.MultiGraph(nx.complete_graph(3)) |
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"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
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433 A = nx.to_numpy_array(G, dtype=int) |
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"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
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434 assert A.dtype == int |