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Merged
merged 11 commits into from
Jan 26, 2021
Merged

Add output struct #101

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22c6a40
Add output struct
mkg33 Jan 7, 2021
3b1fd5b
Update solve.jl
mkg33 Jan 7, 2021
b14200b
Update solve.jl
mkg33 Jan 7, 2021
6dc9495
Fix maxiters as int
mkg33 Jan 7, 2021
e10df8f
Update solve.jl
mkg33 Jan 18, 2021
94d37b7
Update solve.jl
mkg33 Jan 18, 2021
0350306
Update solve.jl
mkg33 Jan 18, 2021
4a902b4
Fix maxiters in CMAEvolutionaryOpt
Vaibhavdixit02 Jan 19, 2021
2cbfd2a
Added Evolutionary output
mkg33 Jan 25, 2021
dad951f
Fixed Evolutionary output
mkg33 Jan 25, 2021
17bed8c
Update solve.jl
mkg33 Jan 25, 2021
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290 changes: 117 additions & 173 deletions src/solve.jl
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Original file line number Diff line number Diff line change
@@ -1,3 +1,32 @@
abstract type AbstractOptimizationSolution end #experimental; comments welcome
mutable struct OptimizationSolution{O, Tx, Tf, Tls, Tsb} <: AbstractOptimizationSolution
method::O
initial_x::Tx
minimizer::Tx
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we should have a getproperty for this to be .u to match the others.

minimum::Tf
iterations::Int
iteration_converged::Bool
ls_success::Tls
time_run::Float64
stopped_by::Tsb
end

function Base.show(io::IO, r::AbstractOptimizationSolution)
take = Iterators.take
failure_string = "failure"
if isa(r.ls_success, Bool) && !r.ls_success
failure_string *= " (line search failed)"
end

@printf io " * Status: %s\n\n" r.iteration_converged ? "success" : failure_string
@printf io " * Candidate solution\n"
@printf io " Final objective value: %e\n" r.minimum
@printf io "\n"
@printf io " * Found with\n"
@printf io " Algorithm: %s\n" r.method
return
end

struct NullData end
const DEFAULT_DATA = Iterators.cycle((NullData(),))
Base.iterate(::NullData, i=1) = nothing
Expand Down Expand Up @@ -124,34 +153,15 @@ function __solve(prob::OptimizationProblem, opt, data = DEFAULT_DATA;

_time = time()

Optim.MultivariateOptimizationResults(opt,
prob.u0,# initial_x,
θ, #pick_best_x(f_incr_pick, state),
save_best ? first(min_err) : first(x), # pick_best_f(f_incr_pick, state, d),
maxiters, #iteration,
maxiters >= maxiters, #iteration == options.iterations,
true, # x_converged,
0.0,#T(options.x_tol),
0.0,#T(options.x_tol),
NaN,# x_abschange(state),
NaN,# x_abschange(state),
true,# f_converged,
0.0,#T(options.f_tol),
0.0,#T(options.f_tol),
NaN,#f_abschange(d, state),
NaN,#f_abschange(d, state),
true,#g_converged,
0.0,#T(options.g_tol),
NaN,#g_residual(d),
false, #f_increased,
nothing,
maxiters,
maxiters,
0,
true,
NaN,
_time-t0,
NamedTuple())
OptimizationSolution(opt,
prob.u0,# initial_x,
θ, #pick_best_x(f_incr_pick, state),
save_best ? first(min_err) : first(x), # pick_best_f(f_incr_pick, state, d),
maxiters, #iteration,
maxiters >= maxiters, #iteration == options.iterations,
true,
_time-t0,
NamedTuple())
end


Expand Down Expand Up @@ -384,34 +394,16 @@ function __init__()
bboptre = !(isnothing(maxiters)) ? BlackBoxOptim.bboptimize(_loss;Method = opt.method, SearchRange = [(prob.lb[i], prob.ub[i]) for i in 1:length(prob.lb)], MaxSteps = maxiters, CallbackFunction = _cb, CallbackInterval = 0.0, kwargs...) : BlackBoxOptim.bboptimize(_loss;Method = opt.method, SearchRange = [(prob.lb[i], prob.ub[i]) for i in 1:length(prob.lb)], CallbackFunction = _cb, CallbackInterval = 0.0, kwargs...)


Optim.MultivariateOptimizationResults(opt.method,
[NaN],# initial_x,
BlackBoxOptim.best_candidate(bboptre), #pick_best_x(f_incr_pick, state),
BlackBoxOptim.best_fitness(bboptre), # pick_best_f(f_incr_pick, state, d),
bboptre.iterations, #iteration,
!(isnothing(maxiters)) ? bboptre.iterations >= maxiters : true, #iteration == options.iterations,
false, # x_converged,
0.0,#T(options.x_tol),
0.0,#T(options.x_tol),
NaN,# x_abschange(state),
NaN,# x_abschange(state),
false,# f_converged,
0.0,#T(options.f_tol),
0.0,#T(options.f_tol),
NaN,#f_abschange(d, state),
NaN,#f_abschange(d, state),
false,#g_converged,
0.0,#T(options.g_tol),
NaN,#g_residual(d),
false, #f_increased,
nothing,
bboptre.f_calls,
0,
0,
true,
NaN,
bboptre.elapsed_time,
NamedTuple())
OptimizationSolution(opt.method,
[NaN],# initial_x,
BlackBoxOptim.best_candidate(bboptre), #pick_best_x(f_incr_pick, state),
BlackBoxOptim.best_fitness(bboptre), # pick_best_f(f_incr_pick, state, d),
bboptre.iterations, #iteration,
!(isnothing(maxiters)) ? bboptre.iterations >= maxiters : true, #iteration == options.iterations,
true,
bboptre.elapsed_time,
NamedTuple())

end
end

Expand Down Expand Up @@ -461,38 +453,19 @@ function __init__()
end
end

t0= time()
t0 = time()
(minf,minx,ret) = NLopt.optimize(opt, prob.u0)
_time = time()

Optim.MultivariateOptimizationResults(opt,
prob.u0,# initial_x,
minx, #pick_best_x(f_incr_pick, state),
minf, # pick_best_f(f_incr_pick, state, d),
Int(opt.numevals), #iteration,
!(isnothing(maxiters)) ? opt.numevals >= maxiters : true, #iteration == options.iterations,
false, # x_converged,
0.0,#T(options.x_tol),
0.0,#T(options.x_tol),
NaN,# x_abschange(state),
NaN,# x_abschange(state),
false,# f_converged,
0.0,#T(options.f_tol),
0.0,#T(options.f_tol),
NaN,#f_abschange(d, state),
NaN,#f_abschange(d, state),
false,#g_converged,
0.0,#T(options.g_tol),
NaN,#g_residual(d),
false, #f_increased,
nothing,
Int(opt.numevals),
0,
0,
ret,
NaN,
_time-t0,
NamedTuple())
OptimizationSolution(opt.algorithm,
prob.u0,# initial_x,
minx, #pick_best_x(f_incr_pick, state),
minf, # pick_best_f(f_incr_pick, state, d),
Int(opt.numevals), #iteration,
!(isnothing(maxiters)) ? opt.numevals >= maxiters : true, #iteration == options.iterations,
ret,
_time-t0,
NamedTuple())
end
end

Expand Down Expand Up @@ -526,34 +499,15 @@ function __init__()

t1 = time()

Optim.MultivariateOptimizationResults(opt,
[NaN],# initial_x,
p.location, #pick_best_x(f_incr_pick, state),
p.value, # pick_best_f(f_incr_pick, state, d),
0, #iteration,
false, #iteration == options.iterations,
false, # x_converged,
0.0,#T(options.x_tol),
0.0,#T(options.x_tol),
NaN,# x_abschange(state),
NaN,# x_abschange(state),
false,# f_converged,
0.0,#T(options.f_tol),
0.0,#T(options.f_tol),
NaN,#f_abschange(d, state),
NaN,#f_abschange(d, state),
false,#g_converged,
0.0,#T(options.g_tol),
NaN,#g_residual(d),
false, #f_increased,
nothing,
0,
0,
0,
true,
NaN,
t1 - t0,
NamedTuple())
OptimizationSolution(opt,
[NaN],# initial_x,
p.location, #pick_best_x(f_incr_pick, state),
p.value, # pick_best_f(f_incr_pick, state, d),
local_maxiters,
local_maxiters>=opt.maxeval, #not sure if that's correct
true,
t1 - t0,
NamedTuple())
end
end

Expand Down Expand Up @@ -588,34 +542,15 @@ function __init__()
box = minimum(root)
t1 = time()

Optim.MultivariateOptimizationResults(opt,
[NaN],# initial_x,
QuadDIRECT.position(box, x0), #pick_best_x(f_incr_pick, state),
QuadDIRECT.value(box), # pick_best_f(f_incr_pick, state, d),
0, #iteration,
false, #iteration == options.iterations,
false, # x_converged,
0.0,#T(options.x_tol),
0.0,#T(options.x_tol),
NaN,# x_abschange(state),
NaN,# x_abschange(state),
false,# f_converged,
0.0,#T(options.f_tol),
0.0,#T(options.f_tol),
NaN,#f_abschange(d, state),
NaN,#f_abschange(d, state),
false,#g_converged,
0.0,#T(options.g_tol),
NaN,#g_residual(d),
false, #f_increased,
nothing,
0,
0,
0,
true,
NaN,
t1 - t0,
NamedTuple())
OptimizationSolution(opt,
[NaN],# initial_x,
QuadDIRECT.position(box, x0), #pick_best_x(f_incr_pick, state),
QuadDIRECT.value(box), # pick_best_f(f_incr_pick, state, d),
!(isnothing(maxiters)) ? maxiters : 0,
box.qnconverged, #not sure if that's correct
true,
t1 - t0,
NamedTuple())
end
end

Expand Down Expand Up @@ -657,16 +592,29 @@ function __init__()
return first(x)
end

Evolutionary.optimize(_loss, prob.u0, opt, !isnothing(maxiters) ? Evolutionary.Options(;iterations = maxiters, callback = _cb, kwargs...)
t0 = time()

result = Evolutionary.optimize(_loss, prob.u0, opt, !isnothing(maxiters) ? Evolutionary.Options(;iterations = maxiters, callback = _cb, kwargs...)
: Evolutionary.Options(;callback = _cb, kwargs...))
t1 = time()

OptimizationSolution(summary(result),
prob.u0, #initial_x
Evolutionary.minimizer(result), #pick_best_x
minimum(result), #pick_best_f
Evolutionary.iterations(result), #iteration
Evolutionary.converged(result), #convergence status
true,
t1 - t0,
NamedTuple())
end
end
@require CMAEvolutionStrategy="8d3b24bd-414e-49e0-94fb-163cc3a3e411" begin

struct CMAEvolutionStrategyOpt end

function __solve(prob::OptimizationProblem, opt::CMAEvolutionStrategyOpt, data = DEFAULT_DATA;
cb = (args...) -> (false),
cb = (args...) -> (false), maxiters = nothing,
progress = false, kwargs...)
local x, cur, state

Expand All @@ -690,36 +638,32 @@ function __init__()
return first(x)
end

result = CMAEvolutionStrategy.minimize(_loss, prob.u0, 0.1; lower = prob.lb, upper = prob.ub, kwargs...)

Optim.MultivariateOptimizationResults(opt,
prob.u0,# initial_x,
result.logger.xbest[end], #pick_best_x(f_incr_pick, state),
result.logger.fbest[end], # pick_best_f(f_incr_pick, state, d),
0, #iteration,
false, #iteration == options.iterations,
false, # x_converged,
0.0,#T(options.x_tol),
0.0,#T(options.x_tol),
NaN,# x_abschange(state),
NaN,# x_abschange(state),
false,# f_converged,
0.0,#T(options.f_tol),
0.0,#T(options.f_tol),
NaN,#f_abschange(d, state),
NaN,#f_abschange(d, state),
false,#g_converged,
0.0,#T(options.g_tol),
NaN,#g_residual(d),
false, #f_increased,
nothing,
0,
0,
0,
true,
NaN,
result.logger.times[end] - result.logger.times[1],
NamedTuple())

if !(isnothing(maxiters)) && maxiters <= 0.0
error("The number of maxiters has to be a non-negative and non-zero number.")
elseif !(isnothing(maxiters))
maxiters = convert(Int, maxiters)
end

result = CMAEvolutionStrategy.minimize(_loss, prob.u0, 0.1; lower = prob.lb, upper = prob.ub, maxiter = maxiters, kwargs...)
CMAEvolutionStrategy.print_header(result)
CMAEvolutionStrategy.print_result(result)
println("\n")
criterion = true

if (result.stop.reason === :maxtime) #this is an arbitrary choice of convergence (based on the stop.reason values)
criterion = false
end

OptimizationSolution(opt,
prob.u0,# initial_x,
result.logger.xbest[end], #pick_best_x(f_incr_pick, state),
result.logger.fbest[end], # pick_best_f(f_incr_pick, state, d),
length(result.logger.fbest),
criterion,
true,
result.logger.times[end] - result.logger.times[1],
NamedTuple())
end
end
end