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MIPLearn.jl/test/utils/benchmark_test.jl

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# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using Cbc
using CSV
using DataFrames
@testset "BenchmarkRunner" begin
@info "Building training data..."
instances = [
build_knapsack_file_instance(),
build_knapsack_file_instance(),
]
stats = parallel_solve!(
LearningSolver(Cbc.Optimizer),
instances,
)
@test length(stats) == 2
@test stats[1] !== nothing
@test stats[2] !== nothing
benchmark = BenchmarkRunner(
solvers=Dict(
"baseline" => LearningSolver(
Cbc.Optimizer,
components=[],
),
"ml-exact" => LearningSolver(
Cbc.Optimizer,
),
"ml-heur" => LearningSolver(
Cbc.Optimizer,
mode="heuristic",
),
),
)
@info "Fitting..."
fit!(benchmark, instances)
@info "Benchmarking..."
parallel_solve!(benchmark, instances, n_trials=2)
csv_filename = tempname()
write_csv!(benchmark, csv_filename)
@test isfile(csv_filename)
csv = DataFrame(CSV.File(csv_filename))
@test size(csv)[1] == 12
end