Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/tests/test_all_random_functions.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 import random |
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5 |
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6 import networkx as nx |
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7 from networkx.algorithms import approximation as approx |
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8 from networkx.algorithms import threshold |
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9 |
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10 progress = 0 |
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11 |
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12 # store the random numbers after setting a global seed |
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13 np.random.seed(42) |
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14 np_rv = np.random.rand() |
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15 random.seed(42) |
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16 py_rv = random.random() |
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17 |
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18 |
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19 def t(f, *args, **kwds): |
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20 """call one function and check if global RNG changed""" |
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21 global progress |
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22 progress += 1 |
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23 print(progress, ",", end="") |
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24 |
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25 f(*args, **kwds) |
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26 |
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27 after_np_rv = np.random.rand() |
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28 # if np_rv != after_np_rv: |
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29 # print(np_rv, after_np_rv, "don't match np!") |
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30 assert np_rv == after_np_rv |
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31 np.random.seed(42) |
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32 |
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33 after_py_rv = random.random() |
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34 # if py_rv != after_py_rv: |
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35 # print(py_rv, after_py_rv, "don't match py!") |
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36 assert py_rv == after_py_rv |
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37 random.seed(42) |
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38 |
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39 |
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40 def run_all_random_functions(seed): |
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41 n = 20 |
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42 m = 10 |
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43 k = l = 2 |
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44 s = v = 10 |
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45 p = q = p1 = p2 = p_in = p_out = 0.4 |
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46 alpha = radius = theta = 0.75 |
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47 sizes = (20, 20, 10) |
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48 colors = [1, 2, 3] |
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49 G = nx.barbell_graph(12, 20) |
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50 deg_sequence = [3, 2, 1, 3, 2, 1, 3, 2, 1, 2, 1, 2, 1] |
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51 in_degree_sequence = w = sequence = aseq = bseq = deg_sequence |
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52 |
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53 # print("starting...") |
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54 t(nx.maximal_independent_set, G, seed=seed) |
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55 t(nx.rich_club_coefficient, G, seed=seed, normalized=False) |
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56 t(nx.random_reference, G, seed=seed) |
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57 t(nx.lattice_reference, G, seed=seed) |
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58 t(nx.sigma, G, 1, 2, seed=seed) |
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59 t(nx.omega, G, 1, 2, seed=seed) |
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60 # print("out of smallworld.py") |
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61 t(nx.double_edge_swap, G, seed=seed) |
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62 # print("starting connected_double_edge_swap") |
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63 t(nx.connected_double_edge_swap, nx.complete_graph(9), seed=seed) |
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64 # print("ending connected_double_edge_swap") |
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65 t(nx.random_layout, G, seed=seed) |
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66 t(nx.fruchterman_reingold_layout, G, seed=seed) |
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67 t(nx.algebraic_connectivity, G, seed=seed) |
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68 t(nx.fiedler_vector, G, seed=seed) |
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69 t(nx.spectral_ordering, G, seed=seed) |
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70 # print('starting average_clustering') |
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71 t(approx.average_clustering, G, seed=seed) |
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72 t(nx.betweenness_centrality, G, seed=seed) |
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73 t(nx.edge_betweenness_centrality, G, seed=seed) |
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74 t(nx.edge_betweenness, G, seed=seed) |
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75 t(nx.approximate_current_flow_betweenness_centrality, G, seed=seed) |
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76 # print("kernighan") |
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77 t(nx.algorithms.community.kernighan_lin_bisection, G, seed=seed) |
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78 # nx.algorithms.community.asyn_lpa_communities(G, seed=seed) |
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79 t(nx.algorithms.tree.greedy_branching, G, seed=seed) |
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80 t(nx.algorithms.tree.Edmonds, G, seed=seed) |
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81 # print('done with graph argument functions') |
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82 |
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83 t(nx.spectral_graph_forge, G, alpha, seed=seed) |
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84 t(nx.algorithms.community.asyn_fluidc, G, k, max_iter=1, seed=seed) |
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85 t( |
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86 nx.algorithms.connectivity.edge_augmentation.greedy_k_edge_augmentation, |
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87 G, |
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88 k, |
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89 seed=seed, |
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90 ) |
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91 t(nx.algorithms.coloring.strategy_random_sequential, G, colors, seed=seed) |
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92 |
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93 cs = ["d", "i", "i", "d", "d", "i"] |
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94 t(threshold.swap_d, cs, seed=seed) |
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95 t(nx.configuration_model, deg_sequence, seed=seed) |
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96 t( |
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97 nx.directed_configuration_model, |
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98 in_degree_sequence, |
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99 in_degree_sequence, |
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100 seed=seed, |
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101 ) |
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102 t(nx.expected_degree_graph, w, seed=seed) |
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103 t(nx.random_degree_sequence_graph, sequence, seed=seed) |
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104 joint_degrees = { |
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105 1: {4: 1}, |
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106 2: {2: 2, 3: 2, 4: 2}, |
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107 3: {2: 2, 4: 1}, |
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108 4: {1: 1, 2: 2, 3: 1}, |
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109 } |
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110 t(nx.joint_degree_graph, joint_degrees, seed=seed) |
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111 joint_degree_sequence = [ |
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112 (1, 0), |
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113 (1, 0), |
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114 (1, 0), |
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115 (2, 0), |
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116 (1, 0), |
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117 (2, 1), |
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118 (0, 1), |
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119 (0, 1), |
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120 ] |
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121 t(nx.random_clustered_graph, joint_degree_sequence, seed=seed) |
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122 constructor = [(3, 3, 0.5), (10, 10, 0.7)] |
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123 t(nx.random_shell_graph, constructor, seed=seed) |
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124 mapping = {1: 0.4, 2: 0.3, 3: 0.3} |
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125 t(nx.utils.random_weighted_sample, mapping, k, seed=seed) |
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126 t(nx.utils.weighted_choice, mapping, seed=seed) |
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127 t(nx.algorithms.bipartite.configuration_model, aseq, bseq, seed=seed) |
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128 t(nx.algorithms.bipartite.preferential_attachment_graph, aseq, p, seed=seed) |
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129 |
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130 def kernel_integral(u, w, z): |
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131 return z - w |
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132 |
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133 t(nx.random_kernel_graph, n, kernel_integral, seed=seed) |
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134 |
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135 sizes = [75, 75, 300] |
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136 probs = [[0.25, 0.05, 0.02], [0.05, 0.35, 0.07], [0.02, 0.07, 0.40]] |
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137 t(nx.stochastic_block_model, sizes, probs, seed=seed) |
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138 t(nx.random_partition_graph, sizes, p_in, p_out, seed=seed) |
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139 |
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140 # print("starting generator functions") |
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141 t(threshold.random_threshold_sequence, n, p, seed=seed) |
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142 t(nx.tournament.random_tournament, n, seed=seed) |
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143 t(nx.relaxed_caveman_graph, l, k, p, seed=seed) |
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144 t(nx.planted_partition_graph, l, k, p_in, p_out, seed=seed) |
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145 t(nx.gaussian_random_partition_graph, n, s, v, p_in, p_out, seed=seed) |
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146 t(nx.gn_graph, n, seed=seed) |
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147 t(nx.gnr_graph, n, p, seed=seed) |
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148 t(nx.gnc_graph, n, seed=seed) |
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149 t(nx.scale_free_graph, n, seed=seed) |
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150 t(nx.directed.random_uniform_k_out_graph, n, k, seed=seed) |
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151 t(nx.random_k_out_graph, n, k, alpha, seed=seed) |
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152 N = 1000 |
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153 t(nx.partial_duplication_graph, N, n, p, q, seed=seed) |
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154 t(nx.duplication_divergence_graph, n, p, seed=seed) |
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155 t(nx.random_geometric_graph, n, radius, seed=seed) |
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156 t(nx.soft_random_geometric_graph, n, radius, seed=seed) |
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157 t(nx.geographical_threshold_graph, n, theta, seed=seed) |
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158 t(nx.waxman_graph, n, seed=seed) |
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159 t(nx.navigable_small_world_graph, n, seed=seed) |
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160 t(nx.thresholded_random_geometric_graph, n, radius, theta, seed=seed) |
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161 t(nx.uniform_random_intersection_graph, n, m, p, seed=seed) |
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162 t(nx.k_random_intersection_graph, n, m, k, seed=seed) |
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163 |
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164 t(nx.general_random_intersection_graph, n, 2, [0.1, 0.5], seed=seed) |
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165 t(nx.fast_gnp_random_graph, n, p, seed=seed) |
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166 t(nx.gnp_random_graph, n, p, seed=seed) |
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167 t(nx.dense_gnm_random_graph, n, m, seed=seed) |
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168 t(nx.gnm_random_graph, n, m, seed=seed) |
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169 t(nx.newman_watts_strogatz_graph, n, k, p, seed=seed) |
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170 t(nx.watts_strogatz_graph, n, k, p, seed=seed) |
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171 t(nx.connected_watts_strogatz_graph, n, k, p, seed=seed) |
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172 t(nx.random_regular_graph, 3, n, seed=seed) |
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173 t(nx.barabasi_albert_graph, n, m, seed=seed) |
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174 t(nx.extended_barabasi_albert_graph, n, m, p, q, seed=seed) |
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175 t(nx.powerlaw_cluster_graph, n, m, p, seed=seed) |
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176 t(nx.random_lobster, n, p1, p2, seed=seed) |
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177 t(nx.random_powerlaw_tree, n, seed=seed, tries=5000) |
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178 t(nx.random_powerlaw_tree_sequence, 10, seed=seed, tries=5000) |
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179 t(nx.random_tree, n, seed=seed) |
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180 t(nx.utils.powerlaw_sequence, n, seed=seed) |
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181 t(nx.utils.zipf_rv, 2.3, seed=seed) |
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182 cdist = [0.2, 0.4, 0.5, 0.7, 0.9, 1.0] |
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183 t(nx.utils.discrete_sequence, n, cdistribution=cdist, seed=seed) |
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184 t(nx.algorithms.bipartite.random_graph, n, m, p, seed=seed) |
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185 t(nx.algorithms.bipartite.gnmk_random_graph, n, m, k, seed=seed) |
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186 LFR = nx.generators.LFR_benchmark_graph |
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187 t( |
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188 LFR, |
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189 25, |
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190 3, |
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191 1.5, |
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192 0.1, |
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193 average_degree=3, |
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194 min_community=10, |
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195 seed=seed, |
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196 max_community=20, |
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197 ) |
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198 t(nx.random_internet_as_graph, n, seed=seed) |
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199 # print("done") |
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200 |
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201 |
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202 # choose to test an integer seed, or whether a single RNG can be everywhere |
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203 # np_rng = np.random.RandomState(14) |
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204 # seed = np_rng |
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205 # seed = 14 |
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206 |
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207 |
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208 @pytest.mark.slow |
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209 # print("NetworkX Version:", nx.__version__) |
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210 def test_rng_interface(): |
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211 global progress |
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212 |
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213 # try different kinds of seeds |
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214 for seed in [14, np.random.RandomState(14)]: |
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215 np.random.seed(42) |
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216 random.seed(42) |
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217 run_all_random_functions(seed) |
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218 progress = 0 |
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219 |
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220 # check that both global RNGs are unaffected |
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221 after_np_rv = np.random.rand() |
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222 # if np_rv != after_np_rv: |
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223 # print(np_rv, after_np_rv, "don't match np!") |
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224 assert np_rv == after_np_rv |
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225 after_py_rv = random.random() |
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226 # if py_rv != after_py_rv: |
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227 # print(py_rv, after_py_rv, "don't match py!") |
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228 assert py_rv == after_py_rv |
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229 |
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230 |
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231 # print("\nDone testing seed:", seed) |
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232 |
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233 # test_rng_interface() |