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Make LearningSolver working with JuMP

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This commit is contained in:
Alinson S. Xavier
2020-04-12 16:19:47 -05:00
parent 808e684c11
commit 72aa1b0cdf
3 changed files with 22 additions and 33 deletions
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2
src/julia/src/MIPLearn.jl
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@@ -32,6 +32,7 @@ end
function set_warm_start(self, solution)
for (basename, subsolution) in solution
for (idx, value) in subsolution
value != nothing || continue
var = self.basename_idx_to_var[basename, idx]
JuMP.set_start_value(var, value)
end
@@ -45,6 +46,7 @@ end
function fix(self, solution)
for (basename, subsolution) in solution
for (idx, value) in subsolution
value != nothing || continue
var = self.basename_idx_to_var[basename, idx]
JuMP.fix(var, value, force=true)
end

47
src/julia/test/learning_solver.jl
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@@ -13,38 +13,21 @@ using Gurobi
[505., 352., 458., 220.],
67.0)
model = instance.to_model()
solver = LearningSolver(solver=JuMPSolver(optimizer=optimizer))
solver = LearningSolver(solver=JuMPSolver(optimizer=optimizer),
mode="heuristic")
stats = solver.solve(instance, model)
@test stats["Lower bound"] == 1183.0
@test stats["Upper bound"] == 1183.0
@test stats["Sense"] == "max"
@test stats["Wallclock time"] > 0
# solution = solver.get_solution()
# @test solution["x[1]"] == 1.0
# @test solution["x[2]"] == 0.0
# @test solution["x[3]"] == 1.0
# @test solution["x[4]"] == 1.0
#
# stats = solver.solve_lp()
# @test round(stats["Optimal value"], digits=3) == 1287.923
#
# solution = solver.get_solution()
# @test round(solution["x[1]"], digits=3) == 1.000
# @test round(solution["x[2]"], digits=3) == 0.923
# @test round(solution["x[3]"], digits=3) == 1.000
# @test round(solution["x[4]"], digits=3) == 0.000
#
# solver.fix(Dict(
# "x[1]" => 1.0,
# "x[2]" => 0.0,
# "x[3]" => 0.0,
# "x[4]" => 1.0,
# ))
# stats = solver.solve()
# @test stats["Lower bound"] == 725.0
# @test stats["Upper bound"] == 725.0
@test instance.solution["x"]["1"] == 1.0
@test instance.solution["x"]["2"] == 0.0
@test instance.solution["x"]["3"] == 1.0
@test instance.solution["x"]["4"] == 1.0
@test instance.lower_bound == 1183.0
@test instance.upper_bound == 1183.0
@test round(instance.lp_solution["x"]["1"], digits=3) == 1.000
@test round(instance.lp_solution["x"]["2"], digits=3) == 0.923
@test round(instance.lp_solution["x"]["3"], digits=3) == 1.000
@test round(instance.lp_solution["x"]["4"], digits=3) == 0.000
@test round(instance.lp_value, digits=3) == 1287.923
solver.fit([instance])
solver.solve(instance)
end
end

6
src/julia/test/runtests.jl
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@@ -3,8 +3,12 @@
# Released under the modified BSD license. See COPYING.md for more details.
using Test
using PyCall
logging = pyimport("logging")
logging.basicConfig(format="%(levelname)10s %(message)s", level=logging.DEBUG)
@testset "MIPLearn" begin
include("jump_solver.jl")
#include("learning_solver.jl")
include("learning_solver.jl")
end