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76 lines
2.1 KiB
76 lines
2.1 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 "macros" begin
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weights = [1.0, 2.0, 3.0]
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prices = [5.0, 6.0, 7.0]
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capacity = 3.0
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# Create standard JuMP model
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model = Model()
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n = length(weights)
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@variable(model, x[1:n], Bin)
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@objective(model, Max, sum(x[i] * prices[i] for i in 1:n))
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@constraint(model, c1, sum(x[i] * weights[i] for i in 1:n) <= capacity)
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# Add machine-learning information
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@feature(model, [5.0])
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@feature(c1, [1.0, 2.0, 3.0])
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@category(c1, "c1")
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for i in 1:n
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@feature(x[i], [weights[i]; prices[i]])
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@category(x[i], "type-$i")
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end
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# Should store variable features
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@test model.ext[:miplearn][:features][:variables] == Dict(
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"x[1]" => Dict(
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:user_features => [1.0, 5.0],
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:category => "type-1",
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),
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"x[2]" => Dict(
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:user_features => [2.0, 6.0],
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:category => "type-2",
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),
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"x[3]" => Dict(
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:user_features => [3.0, 7.0],
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:category => "type-3",
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),
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)
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# Should store constraint features
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@test model.ext[:miplearn][:features][:constraints] == Dict(
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"c1" => Dict(
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:user_features => [1.0, 2.0, 3.0],
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:category => "c1",
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)
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)
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# Should store instance features
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@test model.ext[:miplearn][:features][:instance] == Dict(
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:user_features => [5.0],
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)
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solver = LearningSolver(optimizer=Cbc.Optimizer)
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# Should return correct stats
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stats = solve!(solver, model)
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@test stats["Lower bound"] == 11.0
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# Should add a sample to the training data
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@test length(model.ext[:miplearn][:training_samples]) == 1
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sample = model.ext[:miplearn][:training_samples][1]
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@test sample["lower_bound"] == 11.0
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@test sample["solution"]["x[1]"] == 1.0
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fit!(solver, [model])
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solve!(solver, model)
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end
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