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Make Julia's solution format consistent with Python's

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This commit is contained in:
Alinson S. Xavier
2020-04-12 15:56:59 -05:00
parent ad7a1839d4
commit 808e684c11
5 changed files with 111 additions and 39 deletions
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48
src/julia/src/MIPLearn.jl
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@@ -15,36 +15,49 @@ Instance = miplearn.Instance
LearningSolver = miplearn.LearningSolver
InternalSolver = miplearn.solvers.internal.InternalSolver
function varname_split(varname::String)
m = match(r"([^[]*)\[(.*)\]", varname)
return m.captures[1], m.captures[2]
end
@pydef mutable struct JuMPSolver <: InternalSolver
function __init__(self; optimizer=CPLEX.Optimizer)
self.optimizer = optimizer
end
function add_constraint(self, constraint)
@error "Not implemented"
@error "JuMPSolver: add_constraint not implemented"
end
function set_warm_start(self, solution)
for (varname, value) in solution
var = JuMP.variable_by_name(self.model, varname)
JuMP.set_start_value(var, value)
for (basename, subsolution) in solution
for (idx, value) in subsolution
var = self.basename_idx_to_var[basename, idx]
JuMP.set_start_value(var, value)
end
end
end
function clear_warm_start(self)
@error "Not implemented"
@error "JuMPSolver: clear_warm_start not implemented"
end
function fix(self, solution)
for (varname, value) in solution
var = JuMP.variable_by_name(self.model, varname)
JuMP.fix(var, value, force=true)
for (basename, subsolution) in solution
for (idx, value) in subsolution
var = self.basename_idx_to_var[basename, idx]
JuMP.fix(var, value, force=true)
end
end
end
function set_instance(self, instance, model)
self.instance = instance
self.model = model
self.var_to_basename_idx = Dict(var => varname_split(JuMP.name(var))
for var in JuMP.all_variables(self.model))
self.basename_idx_to_var = Dict(varname_split(JuMP.name(var)) => var
for var in JuMP.all_variables(self.model))
self.bin_vars = [var
for var in JuMP.all_variables(self.model)
if JuMP.is_binary(var)]
@@ -102,20 +115,27 @@ InternalSolver = miplearn.solvers.internal.InternalSolver
end
function _update_solution(self)
self.solution = Dict(JuMP.name(var) => JuMP.value(var)
for var in JuMP.all_variables(self.model))
solution = Dict()
for var in JuMP.all_variables(self.model)
basename, idx = self.var_to_basename_idx[var]
if !haskey(solution, basename)
solution[basename] = Dict()
end
solution[basename][idx] = JuMP.value(var)
end
self.solution = solution
end
function set_gap_tolerance(self, gap_tolerance)
@error "Not implemented"
@error "JuMPSolver: set_gap_tolerance not implemented"
end
function set_node_limit(self)
@error "Not implemented"
@error "JuMPSolver: set_node_limit not implemented"
end
function set_threads(self, threads)
@error "Not implemented"
@error "JuMPSolver: set_threads not implemented"
end
function set_time_limit(self, time_limit)
@@ -148,6 +168,6 @@ end
end
end
export JuMPSolver, KnapsackInstance
export LearningSolver, JuMPSolver, KnapsackInstance
end # module

45
src/julia/test/jump_solver.jl
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@@ -7,6 +7,11 @@ using MIPLearn
using CPLEX
using Gurobi
@testset "varname_split" begin
@test MIPLearn.varname_split("x[1]") == ("x", "1")
end
@testset "JuMPSolver" begin
for optimizer in [CPLEX.Optimizer, Gurobi.Optimizer]
instance = KnapsackInstance([23., 26., 20., 18.],
@@ -17,12 +22,12 @@ using Gurobi
solver = JuMPSolver(optimizer=optimizer)
solver.set_instance(instance, model)
solver.set_time_limit(30)
solver.set_warm_start(Dict(
"x[1]" => 1.0,
"x[2]" => 0.0,
"x[3]" => 0.0,
"x[4]" => 1.0,
))
solver.set_warm_start(Dict("x" => Dict(
"1" => 1.0,
"2" => 0.0,
"3" => 0.0,
"4" => 1.0,
)))
stats = solver.solve()
@test stats["Lower bound"] == 1183.0
@@ -31,26 +36,26 @@ using Gurobi
@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
@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
@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,
))
solver.fix(Dict("x" => Dict(
"1" => 1.0,
"2" => 0.0,
"3" => 0.0,
"4" => 1.0,
)))
stats = solver.solve()
@test stats["Lower bound"] == 725.0
@test stats["Upper bound"] == 725.0

50
src/julia/test/learning_solver.jl Normal file
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@@ -0,0 +1,50 @@
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using Test
using MIPLearn
using CPLEX
using Gurobi
@testset "LearningSolver" begin
for optimizer in [CPLEX.Optimizer, Gurobi.Optimizer]
instance = KnapsackInstance([23., 26., 20., 18.],
[505., 352., 458., 220.],
67.0)
model = instance.to_model()
solver = LearningSolver(solver=JuMPSolver(optimizer=optimizer))
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
end
end

4
src/julia/test/runtests.jl
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@@ -3,10 +3,8 @@
# Released under the modified BSD license. See COPYING.md for more details.
using Test
using MIPLearn
using CPLEX
using Gurobi
@testset "MIPLearn" begin
include("jump_solver.jl")
#include("learning_solver.jl")
end

3
src/python/miplearn/solvers/learning.py
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@@ -79,9 +79,8 @@ class LearningSolver:
solver = GurobiSolver()
elif callable(self.internal_solver_factory):
solver = self.internal_solver_factory()
assert isinstance(solver, InternalSolver)
else:
raise Exception("solver %s not supported" % self.internal_solver_factory)
solver = self.internal_solver_factory
solver.set_threads(self.threads)
if self.time_limit is not None:
solver.set_time_limit(self.time_limit)