ANL-CEEESA
/
MIPLearn
mirror of https://github.com/ANL-CEEESA/MIPLearn.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Julia: move code from test to main package

Browse Source
pull/3/head
Alinson S. Xavier 6 years ago
parent 0d6c08099d
commit 54d2a9a0a7
2 changed files with 136 additions and 131 deletions
Show Stats Download Patch File Download Diff File

103
src/julia/src/MIPLearn.jl
Unescape View File

@ -2,5 +2,108 @@
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
__precompile__(false)
module MIPLearn
using JuMP
using Gurobi
using PyCall
using MathOptInterface
const MOI = MathOptInterface
miplearn = pyimport("miplearn")
Instance = miplearn.Instance
LearningSolver = miplearn.LearningSolver
InternalSolver = miplearn.solvers.internal.InternalSolver
@pydef mutable struct JuMPSolver <: InternalSolver
function add_constraint(self, constraint)
end
function set_warm_start(self, solution)
end
function clear_warm_start(self)
end
function fix(self, solution)
end
function set_instance(self, instance, model)
self.instance = instance
self.model = model
end
function solve(self; tee=false)
JuMP.set_optimizer(self.model, Gurobi.Optimizer)
JuMP.optimize!(self.model)
primal_bound = JuMP.objective_value(self.model)
dual_bound = JuMP.objective_bound(self.model)
if JuMP.objective_sense(self.model) == MOI.MIN_SENSE
sense = "min"
lower_bound = dual_bound
upper_bound = primal_bound
else
sense = "max"
lower_bound = primal_bound
upper_bound = dual_bound
end
@show primal_bound, dual_bound
return Dict("Lower bound" => lower_bound,
"Upper bound" => upper_bound,
"Sense" => sense)
end
function solve_lp(self; tee=false)
end
function get_solution(self)
return Dict(JuMP.name(var) => JuMP.value(var)
for var in JuMP.all_variables(self.model))
end
function set_gap_tolerance(self, gap_tolerance)
end
function set_node_limit(self)
end
function set_threads(self, threads)
end
function set_time_limit(self, time_limit)
end
end
@pydef mutable struct KnapsackInstance <: Instance
function __init__(self, weights, prices, capacity)
self.weights = weights
self.prices = prices
self.capacity = capacity
end
function to_model(self)
model = Model()
n = length(self.weights)
@variable(model, x[1:n], Bin)
@objective(model, Max, sum(x[i] * self.prices[i] for i in 1:n))
@constraint(model, sum(x[i] * self.weights[i] for i in 1:n) <= self.capacity)
return model
end
function get_instance_features(self)
return [0.]
end
function get_variable_features(self, var, index)
return [0.]
end
end
export JuMPSolver, KnapsackInstance
end # module

162
src/julia/test/runtests.jl
Unescape View File

@ -2,136 +2,38 @@
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using MIPLearn
using Test
using JuMP
using Gurobi
using PyCall
using MathOptInterface
const MOI = MathOptInterface
miplearn = pyimport("miplearn")
Instance = miplearn.Instance
LearningSolver = miplearn.LearningSolver
InternalSolver = miplearn.solvers.internal.InternalSolver
@pydef mutable struct JuMPSolver <: InternalSolver
function add_constraint(self, constraint)
end
function clear_warm_start(self)
end
function fix(self, solution)
end
function set_gap_tolerance(self, gap_tolerance)
end
function set_instance(self, instance, model)
self.instance = instance
self.model = model
end
function set_node_limit(self)
end
function set_threads(self, threads)
end
function set_time_limit(self, time_limit)
end
function set_warm_start(self, solution)
end
function solve(self; tee=false)
JuMP.set_optimizer(self.model, Gurobi.Optimizer)
JuMP.optimize!(self.model)
primal_bound = JuMP.objective_value(self.model)
dual_bound = JuMP.objective_bound(self.model)
if JuMP.objective_sense(self.model) == MOI.MIN_SENSE
sense = "min"
lower_bound = dual_bound
upper_bound = primal_bound
else
sense = "max"
lower_bound = primal_bound
upper_bound = dual_bound
end
@show primal_bound, dual_bound
return Dict("Lower bound" => lower_bound,
"Upper bound" => upper_bound,
"Sense" => sense)
end
function solve_lp(self; tee=false)
end
function get_solution(self)
return Dict(JuMP.name(var) => JuMP.value(var)
for var in JuMP.all_variables(self.model))
end
end
@pydef mutable struct KnapsackInstance <: Instance
function __init__(self, weights, prices, capacity)
self.weights = weights
self.prices = prices
self.capacity = capacity
end
function to_model(self)
model = Model()
n = length(self.weights)
@variable(model, x[1:n], Bin)
@objective(model, Max, sum(x[i] * self.prices[i] for i in 1:n))
@constraint(model, sum(x[i] * self.weights[i] for i in 1:n) <= instance.capacity)
return model
end
function get_instance_features(self)
return [0.]
end
using MIPLearn
function get_variable_features(self, var, index)
@show var
return [0.]
end
@testset "MIPLearn" begin
instance = KnapsackInstance([23., 26., 20., 18.],
[505., 352., 458., 220.],
67.0)
model = instance.to_model()
solver = JuMPSolver()
solver.set_instance(instance, model)
stats = solver.solve()
# assert len(stats["Log"]) > 100
@test stats["Lower bound"] == 1183.0
@test stats["Upper bound"] == 1183.0
@test stats["Sense"] == "max"
# @test isinstance(stats["Wallclock time"], float)
# @test isinstance(stats["Nodes"], int)
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"], 3) == 1287.923
#
# solution = solver.get_solution()
# @test round(solution["x"][0], 3) == 1.000
# @test round(solution["x"][1], 3) == 0.923
# @test round(solution["x"][2], 3) == 1.000
# @test round(solution["x"][3], 3) == 0.000
end
instance = KnapsackInstance([23., 26., 20., 18.],
[505., 352., 458., 220.],
67.0)
model = instance.to_model()
solver = JuMPSolver()
solver.set_instance(instance, model)
stats = solver.solve()
# assert len(stats["Log"]) > 100
@test stats["Lower bound"] == 1183.0
@test stats["Upper bound"] == 1183.0
@test stats["Sense"] == "max"
# @test isinstance(stats["Wallclock time"], float)
# @test isinstance(stats["Nodes"], int)
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"], 3) == 1287.923
#
# solution = solver.get_solution()
# @test round(solution["x"][0], 3) == 1.000
# @test round(solution["x"][1], 3) == 0.923
# @test round(solution["x"][2], 3) == 1.000
# @test round(solution["x"][3], 3) == 0.000

Write Preview
Loading…
Cancel
Save