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36 lines
1.1 KiB
36 lines
1.1 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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using JuMP
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using MIPLearn
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using Cbc
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@testset "FileInstance" begin
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@testset "Solve" begin
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data = KnapsackData()
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filename = tempname()
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MIPLearn.save_data(filename, data)
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instance = FileInstance(filename, build_knapsack_model)
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solver = LearningSolver(Cbc.Optimizer)
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solve!(solver, instance)
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h5 = Hdf5Sample("$filename.h5")
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@test h5.get_scalar("mip_wallclock_time") > 0
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end
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@testset "Save and load data" begin
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filename = tempname()
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data = KnapsackData(
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weights = [5.0, 5.0, 5.0],
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prices = [1.0, 1.0, 1.0],
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capacity = 3.0,
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)
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MIPLearn.save_data(filename, data)
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loaded = MIPLearn.load_data(filename)
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@test loaded.weights == [5.0, 5.0, 5.0]
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@test loaded.prices == [1.0, 1.0, 1.0]
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@test loaded.capacity == 3.0
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end
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end
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