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41 lines
1.5 KiB
41 lines
1.5 KiB
# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright © 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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# Written by Alinson S. Xavier <axavier@anl.gov>
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from miplearn import LearningSolver, BenchmarkRunner, KnnWarmStartPredictor
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from miplearn.problems.stab import MaxWeightStableSetGenerator
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from scipy.stats import randint
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import numpy as np
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import pyomo.environ as pe
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import os.path
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def test_benchmark():
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# Generate training and test instances
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train_instances = MaxWeightStableSetGenerator(n=randint(low=25, high=26)).generate(5)
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test_instances = MaxWeightStableSetGenerator(n=randint(low=25, high=26)).generate(3)
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# Training phase...
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training_solver = LearningSolver()
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training_solver.parallel_solve(train_instances, n_jobs=10)
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training_solver.fit()
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training_solver.save_state("data.bin")
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# Test phase...
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test_solvers = {
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"Strategy A": LearningSolver(),
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"Strategy B": LearningSolver(),
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}
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benchmark = BenchmarkRunner(test_solvers)
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benchmark.load_state("data.bin")
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benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2)
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assert benchmark.raw_results().values.shape == (12,12)
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benchmark.save_results("/tmp/benchmark.csv")
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assert os.path.isfile("/tmp/benchmark.csv")
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benchmark = BenchmarkRunner(test_solvers)
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benchmark.load_results("/tmp/benchmark.csv")
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assert benchmark.raw_results().values.shape == (12,12)
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