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46 lines
2.0 KiB
46 lines
2.0 KiB
# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
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# Written by Alinson S. Xavier <axavier@anl.gov>
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from miplearn import LearningSolver
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from miplearn.problems.stab import MaxWeightStableSetInstance
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from miplearn.problems.stab import MaxWeightStableSetGenerator
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import networkx as nx
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import numpy as np
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from scipy.stats import uniform, randint
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def test_stab():
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graph = nx.cycle_graph(5)
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weights = [1., 2., 3., 4., 5.]
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instance = MaxWeightStableSetInstance(graph, weights)
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solver = LearningSolver()
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solver.solve(instance)
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assert instance.model.OBJ() == 8.
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def test_stab_generator_fixed_graph():
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from miplearn.problems.stab import MaxWeightStableSetGenerator
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gen = MaxWeightStableSetGenerator(w=uniform(loc=50., scale=10.),
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n=randint(low=10, high=11),
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density=uniform(loc=0.05, scale=0.),
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fix_graph=True)
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instances = gen.generate(1_000)
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weights = np.array([instance.weights for instance in instances])
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weights_avg_actual = np.round(np.average(weights, axis=0))
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weights_avg_expected = [55.0] * 10
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assert list(weights_avg_actual) == weights_avg_expected
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def test_stab_generator_random_graph():
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from miplearn.problems.stab import MaxWeightStableSetGenerator
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gen = MaxWeightStableSetGenerator(w=uniform(loc=50., scale=10.),
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n=randint(low=30, high=41),
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density=uniform(loc=0.5, scale=0.),
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fix_graph=False)
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instances = gen.generate(1_000)
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n_nodes = [instance.graph.number_of_nodes() for instance in instances]
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n_edges = [instance.graph.number_of_edges() for instance in instances]
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assert np.round(np.mean(n_nodes)) == 35.
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assert np.round(np.mean(n_edges), -1) == 300.
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