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Reorganize Julia package

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pull/3/head
Alinson S. Xavier 6 years ago
parent 72aa1b0cdf
commit 1fefca584e
6 changed files with 179 additions and 168 deletions
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174
src/julia/src/MIPLearn.jl
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@ -4,172 +4,14 @@
__precompile__(false) __precompile__(false)
module MIPLearn module MIPLearn
using PyCall
miplearn = pyimport("miplearn")
Instance = miplearn.Instance
LearningSolver = miplearn.LearningSolver
InternalSolver = miplearn.solvers.internal.InternalSolver
using JuMP include("jump_solver.jl")
using PyCall include("knapsack.jl")
using MathOptInterface
const MOI = MathOptInterface
miplearn = pyimport("miplearn")
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 "JuMPSolver: add_constraint not implemented"
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
end
end
function clear_warm_start(self)
@error "JuMPSolver: clear_warm_start not implemented"
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
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)]
JuMP.set_optimizer(self.model, self.optimizer)
end
function solve(self; tee=false)
JuMP.optimize!(self.model)
self._update_solution()
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,
"Wallclock time" => JuMP.solve_time(self.model),
"Nodes" => 1,
"Log" => nothing,
"Warm start value" => nothing)
end
function solve_lp(self; tee=false)
for var in self.bin_vars
JuMP.unset_binary(var)
JuMP.set_upper_bound(var, 1.0)
JuMP.set_lower_bound(var, 0.0)
end
JuMP.optimize!(self.model)
obj_value = JuMP.objective_value(self.model)
self._update_solution()
for var in self.bin_vars
JuMP.set_binary(var)
end
return Dict("Optimal value" => obj_value)
end
function get_solution(self)
return self.solution
end
function _update_solution(self)
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 "JuMPSolver: set_gap_tolerance not implemented"
end
function set_node_limit(self)
@error "JuMPSolver: set_node_limit not implemented"
end
function set_threads(self, threads)
@error "JuMPSolver: set_threads not implemented"
end
function set_time_limit(self, time_limit)
JuMP.set_time_limit_sec(self.model, 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 LearningSolver, JuMPSolver, KnapsackInstance
export Instance, LearningSolver, InternalSolver, JuMPSolver
end # module end # module

137
src/julia/src/jump_solver.jl
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@ -0,0 +1,137 @@
# 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 JuMP
using MathOptInterface
const MOI = MathOptInterface
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 "JuMPSolver: add_constraint not implemented"
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
end
end
function clear_warm_start(self)
@error "JuMPSolver: clear_warm_start not implemented"
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
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)]
JuMP.set_optimizer(self.model, self.optimizer)
end
function solve(self; tee=false)
JuMP.optimize!(self.model)
self._update_solution()
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,
"Wallclock time" => JuMP.solve_time(self.model),
"Nodes" => 1,
"Log" => nothing,
"Warm start value" => nothing)
end
function solve_lp(self; tee=false)
for var in self.bin_vars
JuMP.unset_binary(var)
JuMP.set_upper_bound(var, 1.0)
JuMP.set_lower_bound(var, 0.0)
end
JuMP.optimize!(self.model)
obj_value = JuMP.objective_value(self.model)
self._update_solution()
for var in self.bin_vars
JuMP.set_binary(var)
end
return Dict("Optimal value" => obj_value)
end
function get_solution(self)
return self.solution
end
function _update_solution(self)
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 "JuMPSolver: set_gap_tolerance not implemented"
end
function set_node_limit(self)
@error "JuMPSolver: set_node_limit not implemented"
end
function set_threads(self, threads)
@error "JuMPSolver: set_threads not implemented"
end
function set_time_limit(self, time_limit)
JuMP.set_time_limit_sec(self.model, time_limit)
end
end

32
src/julia/src/knapsack.jl
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@ -0,0 +1,32 @@
# 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 PyCall
@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 KnapsackInstance

0
src/julia/test/jump_solver.jl → src/julia/test/jump_solver_test.jl
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0
src/julia/test/learning_solver.jl → src/julia/test/learning_solver_test.jl
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4
src/julia/test/runtests.jl
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@ -9,6 +9,6 @@ logging = pyimport("logging")
logging.basicConfig(format="%(levelname)10s %(message)s", level=logging.DEBUG) logging.basicConfig(format="%(levelname)10s %(message)s", level=logging.DEBUG)
@testset "MIPLearn" begin @testset "MIPLearn" begin
include("jump_solver.jl") include("jump_solver_test.jl")
include("learning_solver.jl") include("learning_solver_test.jl")
end end
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