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47 lines
1.2 KiB
47 lines
1.2 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 CSV
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using DataFrames
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using Gurobi
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@testset "BenchmarkRunner" begin
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# Initialie instances and generate training data
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instances = [
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build_knapsack_file_instance(),
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build_knapsack_file_instance(),
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]
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parallel_solve!(
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LearningSolver(Gurobi.Optimizer),
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instances,
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)
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# Fit and benchmark
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benchmark = BenchmarkRunner(
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solvers=Dict(
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"baseline" => LearningSolver(
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Gurobi.Optimizer,
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components=[],
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),
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"ml-exact" => LearningSolver(
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Gurobi.Optimizer,
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),
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"ml-heur" => LearningSolver(
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Gurobi.Optimizer,
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mode="heuristic",
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),
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),
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)
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fit!(benchmark, instances)
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parallel_solve!(benchmark, instances, n_trials=1)
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# Write CSV
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csv_filename = tempname()
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write_csv!(benchmark, csv_filename)
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@test isfile(csv_filename)
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csv = DataFrame(CSV.File(csv_filename))
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@test size(csv)[1] == 6
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end
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