Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/sitepackages/networkx/tests/test_convert_scipy.py @ 0:4f3585e2f14b draft default tip
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
author  shellac 

date  Mon, 22 Mar 2021 18:12:50 +0000 
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0
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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.testing import assert_graphs_equal 
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5 from networkx.generators.classic import barbell_graph, cycle_graph, path_graph 
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6 
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7 
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8 class TestConvertNumpy: 
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9 @classmethod 
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10 def setup_class(cls): 
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11 global np, sp, sparse, np_assert_equal 
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12 np = pytest.importorskip("numpy") 
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13 sp = pytest.importorskip("scipy") 
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14 sparse = sp.sparse 
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15 np_assert_equal = np.testing.assert_equal 
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16 
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17 def setup_method(self): 
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18 self.G1 = barbell_graph(10, 3) 
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19 self.G2 = cycle_graph(10, create_using=nx.DiGraph) 
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20 
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21 self.G3 = self.create_weighted(nx.Graph()) 
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22 self.G4 = self.create_weighted(nx.DiGraph()) 
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23 
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24 def test_exceptions(self): 
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25 class G: 
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26 format = None 
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27 
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28 pytest.raises(nx.NetworkXError, nx.to_networkx_graph, G) 
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29 
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30 def create_weighted(self, G): 
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31 g = cycle_graph(4) 
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32 e = list(g.edges()) 
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33 source = [u for u, v in e] 
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34 dest = [v for u, v in e] 
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35 weight = [s + 10 for s in source] 
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36 ex = zip(source, dest, weight) 
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37 G.add_weighted_edges_from(ex) 
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38 return G 
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39 
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40 def assert_isomorphic(self, G1, G2): 
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41 assert nx.is_isomorphic(G1, G2) 
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42 
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43 def identity_conversion(self, G, A, create_using): 
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44 GG = nx.from_scipy_sparse_matrix(A, create_using=create_using) 
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45 self.assert_isomorphic(G, GG) 
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46 
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47 GW = nx.to_networkx_graph(A, create_using=create_using) 
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48 self.assert_isomorphic(G, GW) 
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49 
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50 GI = nx.empty_graph(0, create_using).__class__(A) 
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51 self.assert_isomorphic(G, GI) 
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52 
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53 ACSR = A.tocsr() 
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54 GI = nx.empty_graph(0, create_using).__class__(ACSR) 
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55 self.assert_isomorphic(G, GI) 
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56 
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57 ACOO = A.tocoo() 
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58 GI = nx.empty_graph(0, create_using).__class__(ACOO) 
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59 self.assert_isomorphic(G, GI) 
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60 
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61 ACSC = A.tocsc() 
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62 GI = nx.empty_graph(0, create_using).__class__(ACSC) 
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63 self.assert_isomorphic(G, GI) 
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64 
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65 AD = A.todense() 
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66 GI = nx.empty_graph(0, create_using).__class__(AD) 
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67 self.assert_isomorphic(G, GI) 
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68 
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69 AA = A.toarray() 
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70 GI = nx.empty_graph(0, create_using).__class__(AA) 
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71 self.assert_isomorphic(G, GI) 
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72 
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73 def test_shape(self): 
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74 "Conversion from nonsquare sparse array." 
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75 A = sp.sparse.lil_matrix([[1, 2, 3], [4, 5, 6]]) 
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76 pytest.raises(nx.NetworkXError, nx.from_scipy_sparse_matrix, A) 
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77 
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78 def test_identity_graph_matrix(self): 
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79 "Conversion from graph to sparse matrix to graph." 
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80 A = nx.to_scipy_sparse_matrix(self.G1) 
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81 self.identity_conversion(self.G1, A, nx.Graph()) 
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82 
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83 def test_identity_digraph_matrix(self): 
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84 "Conversion from digraph to sparse matrix to digraph." 
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85 A = nx.to_scipy_sparse_matrix(self.G2) 
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86 self.identity_conversion(self.G2, A, nx.DiGraph()) 
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87 
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88 def test_identity_weighted_graph_matrix(self): 
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89 """Conversion from weighted graph to sparse matrix to weighted graph.""" 
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90 A = nx.to_scipy_sparse_matrix(self.G3) 
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91 self.identity_conversion(self.G3, A, nx.Graph()) 
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92 
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93 def test_identity_weighted_digraph_matrix(self): 
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94 """Conversion from weighted digraph to sparse matrix to weighted digraph.""" 
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95 A = nx.to_scipy_sparse_matrix(self.G4) 
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96 self.identity_conversion(self.G4, A, nx.DiGraph()) 
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97 
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98 def test_nodelist(self): 
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99 """Conversion from graph to sparse matrix to graph with nodelist.""" 
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100 P4 = path_graph(4) 
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101 P3 = path_graph(3) 
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102 nodelist = list(P3.nodes()) 
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103 A = nx.to_scipy_sparse_matrix(P4, nodelist=nodelist) 
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104 GA = nx.Graph(A) 
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105 self.assert_isomorphic(GA, P3) 
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106 
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107 # Make nodelist ambiguous by containing duplicates. 
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108 nodelist += [nodelist[0]] 
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109 pytest.raises(nx.NetworkXError, nx.to_numpy_matrix, P3, nodelist=nodelist) 
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110 
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111 def test_weight_keyword(self): 
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112 WP4 = nx.Graph() 
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113 WP4.add_edges_from((n, n + 1, dict(weight=0.5, other=0.3)) for n in range(3)) 
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114 P4 = path_graph(4) 
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115 A = nx.to_scipy_sparse_matrix(P4) 
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116 np_assert_equal( 
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117 A.todense(), nx.to_scipy_sparse_matrix(WP4, weight=None).todense() 
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118 ) 
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119 np_assert_equal(0.5 * A.todense(), nx.to_scipy_sparse_matrix(WP4).todense()) 
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120 np_assert_equal( 
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121 0.3 * A.todense(), nx.to_scipy_sparse_matrix(WP4, weight="other").todense() 
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122 ) 
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123 
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124 def test_format_keyword(self): 
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125 WP4 = nx.Graph() 
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126 WP4.add_edges_from((n, n + 1, dict(weight=0.5, other=0.3)) for n in range(3)) 
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127 P4 = path_graph(4) 
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128 A = nx.to_scipy_sparse_matrix(P4, format="csr") 
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129 np_assert_equal( 
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130 A.todense(), nx.to_scipy_sparse_matrix(WP4, weight=None).todense() 
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131 ) 
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132 
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133 A = nx.to_scipy_sparse_matrix(P4, format="csc") 
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134 np_assert_equal( 
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135 A.todense(), nx.to_scipy_sparse_matrix(WP4, weight=None).todense() 
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136 ) 
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137 
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138 A = nx.to_scipy_sparse_matrix(P4, format="coo") 
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139 np_assert_equal( 
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140 A.todense(), nx.to_scipy_sparse_matrix(WP4, weight=None).todense() 
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141 ) 
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142 
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143 A = nx.to_scipy_sparse_matrix(P4, format="bsr") 
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144 np_assert_equal( 
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145 A.todense(), nx.to_scipy_sparse_matrix(WP4, weight=None).todense() 
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146 ) 
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147 
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148 A = nx.to_scipy_sparse_matrix(P4, format="lil") 
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149 np_assert_equal( 
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150 A.todense(), nx.to_scipy_sparse_matrix(WP4, weight=None).todense() 
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151 ) 
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152 
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153 A = nx.to_scipy_sparse_matrix(P4, format="dia") 
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154 np_assert_equal( 
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155 A.todense(), nx.to_scipy_sparse_matrix(WP4, weight=None).todense() 
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156 ) 
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157 
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158 A = nx.to_scipy_sparse_matrix(P4, format="dok") 
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159 np_assert_equal( 
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160 A.todense(), nx.to_scipy_sparse_matrix(WP4, weight=None).todense() 
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161 ) 
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162 
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163 def test_format_keyword_raise(self): 
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164 with pytest.raises(nx.NetworkXError): 
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165 WP4 = nx.Graph() 
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166 WP4.add_edges_from( 
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167 (n, n + 1, dict(weight=0.5, other=0.3)) for n in range(3) 
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168 ) 
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169 P4 = path_graph(4) 
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170 nx.to_scipy_sparse_matrix(P4, format="any_other") 
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171 
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172 def test_null_raise(self): 
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173 with pytest.raises(nx.NetworkXError): 
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174 nx.to_scipy_sparse_matrix(nx.Graph()) 
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175 
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176 def test_empty(self): 
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177 G = nx.Graph() 
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178 G.add_node(1) 
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179 M = nx.to_scipy_sparse_matrix(G) 
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180 np_assert_equal(M.todense(), np.matrix([[0]])) 
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181 
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182 def test_ordering(self): 
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183 G = nx.DiGraph() 
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184 G.add_edge(1, 2) 
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185 G.add_edge(2, 3) 
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186 G.add_edge(3, 1) 
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187 M = nx.to_scipy_sparse_matrix(G, nodelist=[3, 2, 1]) 
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188 np_assert_equal(M.todense(), np.matrix([[0, 0, 1], [1, 0, 0], [0, 1, 0]])) 
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189 
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190 def test_selfloop_graph(self): 
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191 G = nx.Graph([(1, 1)]) 
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192 M = nx.to_scipy_sparse_matrix(G) 
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193 np_assert_equal(M.todense(), np.matrix([[1]])) 
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194 
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195 G.add_edges_from([(2, 3), (3, 4)]) 
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196 M = nx.to_scipy_sparse_matrix(G, nodelist=[2, 3, 4]) 
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197 np_assert_equal(M.todense(), np.matrix([[0, 1, 0], [1, 0, 1], [0, 1, 0]])) 
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198 
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199 def test_selfloop_digraph(self): 
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200 G = nx.DiGraph([(1, 1)]) 
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201 M = nx.to_scipy_sparse_matrix(G) 
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202 np_assert_equal(M.todense(), np.matrix([[1]])) 
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203 
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204 G.add_edges_from([(2, 3), (3, 4)]) 
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205 M = nx.to_scipy_sparse_matrix(G, nodelist=[2, 3, 4]) 
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206 np_assert_equal(M.todense(), np.matrix([[0, 1, 0], [0, 0, 1], [0, 0, 0]])) 
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207 
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208 def test_from_scipy_sparse_matrix_parallel_edges(self): 
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209 """Tests that the :func:`networkx.from_scipy_sparse_matrix` function 
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210 interprets integer weights as the number of parallel edges when 
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211 creating a multigraph. 
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212 
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213 """ 
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214 A = sparse.csr_matrix([[1, 1], [1, 2]]) 
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215 # First, with a simple graph, each integer entry in the adjacency 
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216 # matrix is interpreted as the weight of a single edge in the graph. 
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217 expected = nx.DiGraph() 
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218 edges = [(0, 0), (0, 1), (1, 0)] 
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219 expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges]) 
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220 expected.add_edge(1, 1, weight=2) 
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221 actual = nx.from_scipy_sparse_matrix( 
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222 A, parallel_edges=True, create_using=nx.DiGraph 
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223 ) 
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224 assert_graphs_equal(actual, expected) 
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225 actual = nx.from_scipy_sparse_matrix( 
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226 A, parallel_edges=False, create_using=nx.DiGraph 
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227 ) 
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228 assert_graphs_equal(actual, expected) 
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229 # Now each integer entry in the adjacency matrix is interpreted as the 
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230 # number of parallel edges in the graph if the appropriate keyword 
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231 # argument is specified. 
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232 edges = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 1)] 
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233 expected = nx.MultiDiGraph() 
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234 expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges]) 
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235 actual = nx.from_scipy_sparse_matrix( 
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236 A, parallel_edges=True, create_using=nx.MultiDiGraph 
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237 ) 
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238 assert_graphs_equal(actual, expected) 
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239 expected = nx.MultiDiGraph() 
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240 expected.add_edges_from(set(edges), weight=1) 
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241 # The sole selfloop (edge 0) on vertex 1 should have weight 2. 
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242 expected[1][1][0]["weight"] = 2 
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243 actual = nx.from_scipy_sparse_matrix( 
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244 A, parallel_edges=False, create_using=nx.MultiDiGraph 
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245 ) 
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246 assert_graphs_equal(actual, expected) 
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247 
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248 def test_symmetric(self): 
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249 """Tests that a symmetric matrix has edges added only once to an 
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250 undirected multigraph when using 
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251 :func:`networkx.from_scipy_sparse_matrix`. 
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252 
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253 """ 
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254 A = sparse.csr_matrix([[0, 1], [1, 0]]) 
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255 G = nx.from_scipy_sparse_matrix(A, create_using=nx.MultiGraph) 
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256 expected = nx.MultiGraph() 
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257 expected.add_edge(0, 1, weight=1) 
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258 assert_graphs_equal(G, expected) 