Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/sitepackages/networkx/algorithms/flow/tests/test_maxflow_large_graph.py @ 0:4f3585e2f14b draft default tip
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author  shellac 

date  Mon, 22 Mar 2021 18:12:50 +0000 
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0
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1 """Maximum flow algorithms test suite on large graphs. 
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2 """ 
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3 
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4 import os 
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5 import pytest 
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6 
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7 import networkx as nx 
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8 from networkx.algorithms.flow import build_flow_dict, build_residual_network 
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9 from networkx.algorithms.flow import boykov_kolmogorov 
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10 from networkx.algorithms.flow import dinitz 
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11 from networkx.algorithms.flow import edmonds_karp 
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12 from networkx.algorithms.flow import preflow_push 
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13 from networkx.algorithms.flow import shortest_augmenting_path 
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14 from networkx.testing import almost_equal 
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15 
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16 flow_funcs = [ 
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17 boykov_kolmogorov, 
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18 dinitz, 
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19 edmonds_karp, 
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20 preflow_push, 
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21 shortest_augmenting_path, 
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22 ] 
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23 
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24 
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25 def gen_pyramid(N): 
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26 # This graph admits a flow of value 1 for which every arc is at 
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27 # capacity (except the arcs incident to the sink which have 
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28 # infinite capacity). 
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29 G = nx.DiGraph() 
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30 
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31 for i in range(N  1): 
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32 cap = 1.0 / (i + 2) 
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33 for j in range(i + 1): 
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34 G.add_edge((i, j), (i + 1, j), capacity=cap) 
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35 cap = 1.0 / (i + 1)  cap 
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36 G.add_edge((i, j), (i + 1, j + 1), capacity=cap) 
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37 cap = 1.0 / (i + 2)  cap 
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38 
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39 for j in range(N): 
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40 G.add_edge((N  1, j), "t") 
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41 
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42 return G 
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43 
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44 
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45 def read_graph(name): 
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46 dirname = os.path.dirname(__file__) 
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47 path = os.path.join(dirname, name + ".gpickle.bz2") 
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48 return nx.read_gpickle(path) 
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49 
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50 
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51 def validate_flows(G, s, t, soln_value, R, flow_func): 
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52 flow_value = R.graph["flow_value"] 
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53 flow_dict = build_flow_dict(G, R) 
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54 errmsg = f"Assertion failed in function: {flow_func.__name__}" 
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55 assert soln_value == flow_value, errmsg 
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56 assert set(G) == set(flow_dict), errmsg 
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57 for u in G: 
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58 assert set(G[u]) == set(flow_dict[u]), errmsg 
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59 excess = {u: 0 for u in flow_dict} 
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60 for u in flow_dict: 
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61 for v, flow in flow_dict[u].items(): 
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62 assert flow <= G[u][v].get("capacity", float("inf")), errmsg 
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63 assert flow >= 0, errmsg 
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64 excess[u] = flow 
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65 excess[v] += flow 
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66 for u, exc in excess.items(): 
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67 if u == s: 
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68 assert exc == soln_value, errmsg 
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69 elif u == t: 
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70 assert exc == soln_value, errmsg 
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71 else: 
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72 assert exc == 0, errmsg 
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73 
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74 
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75 class TestMaxflowLargeGraph: 
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76 def test_complete_graph(self): 
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77 N = 50 
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78 G = nx.complete_graph(N) 
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79 nx.set_edge_attributes(G, 5, "capacity") 
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80 R = build_residual_network(G, "capacity") 
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81 kwargs = dict(residual=R) 
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82 
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83 for flow_func in flow_funcs: 
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84 kwargs["flow_func"] = flow_func 
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85 errmsg = f"Assertion failed in function: {flow_func.__name__}" 
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86 flow_value = nx.maximum_flow_value(G, 1, 2, **kwargs) 
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87 assert flow_value == 5 * (N  1), errmsg 
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88 
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89 def test_pyramid(self): 
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90 N = 10 
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91 # N = 100 # this gives a graph with 5051 nodes 
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92 G = gen_pyramid(N) 
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93 R = build_residual_network(G, "capacity") 
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94 kwargs = dict(residual=R) 
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95 
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96 for flow_func in flow_funcs: 
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97 kwargs["flow_func"] = flow_func 
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98 errmsg = f"Assertion failed in function: {flow_func.__name__}" 
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99 flow_value = nx.maximum_flow_value(G, (0, 0), "t", **kwargs) 
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100 assert almost_equal(flow_value, 1.0), errmsg 
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101 
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102 def test_gl1(self): 
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103 G = read_graph("gl1") 
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104 s = 1 
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105 t = len(G) 
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106 R = build_residual_network(G, "capacity") 
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107 kwargs = dict(residual=R) 
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108 
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109 # do one flow_func to save time 
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110 flow_func = flow_funcs[0] 
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111 validate_flows(G, s, t, 156545, flow_func(G, s, t, **kwargs), flow_func) 
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112 
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113 # for flow_func in flow_funcs: 
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114 # validate_flows(G, s, t, 156545, flow_func(G, s, t, **kwargs), 
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115 # flow_func) 
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116 
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117 @pytest.mark.slow 
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118 def test_gw1(self): 
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119 G = read_graph("gw1") 
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120 s = 1 
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121 t = len(G) 
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122 R = build_residual_network(G, "capacity") 
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123 kwargs = dict(residual=R) 
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124 
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125 for flow_func in flow_funcs: 
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126 validate_flows(G, s, t, 1202018, flow_func(G, s, t, **kwargs), flow_func) 
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127 
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128 def test_wlm3(self): 
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129 G = read_graph("wlm3") 
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130 s = 1 
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131 t = len(G) 
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132 R = build_residual_network(G, "capacity") 
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133 kwargs = dict(residual=R) 
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134 
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135 # do one flow_func to save time 
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136 flow_func = flow_funcs[0] 
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137 validate_flows(G, s, t, 11875108, flow_func(G, s, t, **kwargs), flow_func) 
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138 
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139 # for flow_func in flow_funcs: 
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140 # validate_flows(G, s, t, 11875108, flow_func(G, s, t, **kwargs), 
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141 # flow_func) 
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142 
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143 def test_preflow_push_global_relabel(self): 
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144 G = read_graph("gw1") 
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145 R = preflow_push(G, 1, len(G), global_relabel_freq=50) 
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146 assert R.graph["flow_value"] == 1202018 