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41 lines
1.4 KiB
41 lines
1.4 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2021, 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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import os.path
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from scipy.stats import randint
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from miplearn.benchmark import BenchmarkRunner
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from miplearn.problems.stab import MaxWeightStableSetGenerator
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from miplearn.solvers.learning import LearningSolver
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def test_benchmark() -> None:
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for n_jobs in [1, 4]:
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# Generate training and test instances
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generator = MaxWeightStableSetGenerator(n=randint(low=25, high=26))
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train_instances = generator.generate(5)
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test_instances = generator.generate(3)
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# Solve training instances
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training_solver = LearningSolver()
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training_solver.parallel_solve(train_instances, n_jobs=n_jobs) # type: ignore
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# Benchmark
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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.fit(train_instances) # type: ignore
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benchmark.parallel_solve(
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test_instances, # type: ignore
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n_jobs=n_jobs,
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n_trials=2,
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)
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assert benchmark.results.values.shape == (12, 20)
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benchmark.write_csv("/tmp/benchmark.csv")
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assert os.path.isfile("/tmp/benchmark.csv")
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