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49 lines
1.7 KiB
49 lines
1.7 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 Gurobi
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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 ML information to the model
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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 ML information
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@test model.ext[:miplearn][:variable_features][x[1]] == [1.0, 5.0]
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@test model.ext[:miplearn][:variable_features][x[2]] == [2.0, 6.0]
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@test model.ext[:miplearn][:variable_features][x[3]] == [3.0, 7.0]
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@test model.ext[:miplearn][:variable_categories][x[1]] == "type-1"
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@test model.ext[:miplearn][:variable_categories][x[2]] == "type-2"
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@test model.ext[:miplearn][:variable_categories][x[3]] == "type-3"
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@test model.ext[:miplearn][:constraint_features][c1] == [1.0, 2.0, 3.0]
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@test model.ext[:miplearn][:constraint_categories][c1] == "c1"
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@test model.ext[:miplearn][:instance_features] == [5.0]
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solver = LearningSolver(Gurobi.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.py.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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