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Replace tuples; make it work with plain JuMP models

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This commit is contained in:
Alinson S. Xavier
2021-05-20 12:02:29 -05:00
parent 241d1d4157
commit f94eb598c9
5 changed files with 99 additions and 76 deletions
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82
test/modeling/learning_solver_test.jl
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@@ -6,43 +6,55 @@ using JuMP
using MIPLearn
using Gurobi
@testset "macros" begin
weights = [1.0, 2.0, 3.0]
prices = [5.0, 6.0, 7.0]
capacity = 3.0
@testset "LearningSolver" begin
@testset "model with annotations" begin
# Create standard JuMP model
weights = [1.0, 2.0, 3.0]
prices = [5.0, 6.0, 7.0]
capacity = 3.0
model = Model()
# Create standard JuMP model
model = Model()
n = length(weights)
@variable(model, x[1:n], Bin)
@objective(model, Max, sum(x[i] * prices[i] for i in 1:n))
@constraint(model, c1, sum(x[i] * weights[i] for i in 1:n) <= capacity)
n = length(weights)
@variable(model, x[1:n], Bin)
@objective(model, Max, sum(x[i] * prices[i] for i in 1:n))
@constraint(model, c1, sum(x[i] * weights[i] for i in 1:n) <= capacity)
# Add ML information to the model
@feature(model, [5.0])
@feature(c1, [1.0, 2.0, 3.0])
@category(c1, "c1")
for i in 1:n
@feature(x[i], [weights[i]; prices[i]])
@category(x[i], "type-$i")
# Add ML information to the model
@feature(model, [5.0])
@feature(c1, [1.0, 2.0, 3.0])
@category(c1, "c1")
for i in 1:n
@feature(x[i], [weights[i]; prices[i]])
@category(x[i], "type-$i")
end
# Should store ML information
@test model.ext[:miplearn][:variable_features][x[1]] == [1.0, 5.0]
@test model.ext[:miplearn][:variable_features][x[2]] == [2.0, 6.0]
@test model.ext[:miplearn][:variable_features][x[3]] == [3.0, 7.0]
@test model.ext[:miplearn][:variable_categories][x[1]] == "type-1"
@test model.ext[:miplearn][:variable_categories][x[2]] == "type-2"
@test model.ext[:miplearn][:variable_categories][x[3]] == "type-3"
@test model.ext[:miplearn][:constraint_features][c1] == [1.0, 2.0, 3.0]
@test model.ext[:miplearn][:constraint_categories][c1] == "c1"
@test model.ext[:miplearn][:instance_features] == [5.0]
solver = LearningSolver(Gurobi.Optimizer)
instance = JuMPInstance(model)
stats = solve!(solver, instance)
@test stats["mip_lower_bound"] == 11.0
@test length(instance.py.samples) == 1
fit!(solver, [instance])
solve!(solver, instance)
end
# Should store ML information
@test model.ext[:miplearn][:variable_features][x[1]] == [1.0, 5.0]
@test model.ext[:miplearn][:variable_features][x[2]] == [2.0, 6.0]
@test model.ext[:miplearn][:variable_features][x[3]] == [3.0, 7.0]
@test model.ext[:miplearn][:variable_categories][x[1]] == "type-1"
@test model.ext[:miplearn][:variable_categories][x[2]] == "type-2"
@test model.ext[:miplearn][:variable_categories][x[3]] == "type-3"
@test model.ext[:miplearn][:constraint_features][c1] == [1.0, 2.0, 3.0]
@test model.ext[:miplearn][:constraint_categories][c1] == "c1"
@test model.ext[:miplearn][:instance_features] == [5.0]
solver = LearningSolver(Gurobi.Optimizer)
instance = JuMPInstance(model)
stats = solve!(solver, instance)
@test stats["mip_lower_bound"] == 11.0
@test length(instance.py.samples) == 1
fit!(solver, [instance])
solve!(solver, instance)
@testset "plain model" begin
model = Model()
@variable(model, x, Bin)
@variable(model, y, Bin)
@objective(model, Max, x + y)
solver = LearningSolver(Gurobi.Optimizer)
instance = JuMPInstance(model)
stats = solve!(solver, instance)
end
end