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47 lines
1.5 KiB
47 lines
1.5 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 Cbc
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using JuMP
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using MIPLearn
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@testset "LearningSolver" begin
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@testset "Model with annotations" begin
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model = build_knapsack_model()
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solver = LearningSolver(Cbc.Optimizer)
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instance = JuMPInstance(model)
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stats = solve!(solver, instance)
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@test stats["mip_lower_bound"] == 11.0
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@test length(instance.samples) == 1
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fit!(solver, [instance])
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solve!(solver, instance)
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end
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@testset "Model without annotations" begin
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model = build_knapsack_model()
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solver = LearningSolver(Cbc.Optimizer)
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instance = JuMPInstance(model)
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stats = solve!(solver, instance)
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@test stats["mip_lower_bound"] == 11.0
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end
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@testset "Save and load" begin
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solver = LearningSolver(Cbc.Optimizer)
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solver.py.components = "Placeholder"
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filename = tempname()
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save(filename, solver)
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@test isfile(filename)
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loaded = load_solver(filename)
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@test loaded.py.components == "Placeholder"
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end
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@testset "Discard output" begin
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instance = build_knapsack_file_instance()
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solver = LearningSolver(Cbc.Optimizer)
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solve!(solver, instance, discard_output = true)
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loaded = load_instance(instance.filename)
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@test length(loaded.samples) == 0
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end
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end
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