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Refactor tests into functional form (#220)
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‎test/C_API.jl

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# Copyright (c) 2013: Steven G. Johnson and contributors
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#
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# Use of this source code is governed by an MIT-style license that can be found
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# in the LICENSE.md file or at https://opensource.org/licenses/MIT.
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module TestCAPI
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using NLopt
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using Test
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function runtests()
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for name in names(@__MODULE__; all = true)
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if !startswith("$(name)", "test_")
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continue
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end
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@testset "$(name)" begin
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getfield(@__MODULE__, name)()
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end
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end
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return
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end
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function test_issue_163()
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opt = Opt(:LN_COBYLA, 2)
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opt.min_objective = (x, g) -> sum(x .^ 2)
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inequality_constraint!(opt, 2, (result, x, g) -> (result .= 1 .- x))
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(minf, minx, ret) = optimize(opt, [2.0, 2.0])
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@test minx ≈ [1.0, 1.0]
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return
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end
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function test_issue_132()
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opt = Opt(:LN_COBYLA, 2)
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err = ErrorException(
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"Getting `initial_step` is unsupported. Use " *
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"`initial_step(opt, x)` to access the initial step at a point `x`.",
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)
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@test_throws err opt.initial_step
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return
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end
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function test_issue_156_CapturedException()
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f(x, g = []) = (error("test error"); x[1]^2)
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opt = Opt(:LN_SBPLX, 1)
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opt.min_objective = f
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@test_throws CapturedException optimize(opt, [0.1234])
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@test NLopt.nlopt_exception === nothing
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try
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optimize(opt, [0.1234])
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catch e
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# Check that the backtrace is being printed
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@test length(sprint(show, e)) > 100
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end
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return
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end
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function test_issue_156_ForcedStop()
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f(x, g = []) = (throw(NLopt.ForcedStop()); x[1]^2)
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opt = Opt(:LN_SBPLX, 1)
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opt.min_objective = f
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fmin, xmin, ret = optimize(opt, [0.1234])
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@test ret == :FORCED_STOP
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@test NLopt.nlopt_exception === nothing
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return
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end
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function test_issue_156_no_error()
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f(x, g = []) = (x[1]^2)
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opt = Opt(:LN_SBPLX, 1)
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opt.min_objective = f
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fmin, xmin, ret = optimize(opt, [0.1234])
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@test ret ∈ (:SUCCESS, :FTOL_REACHED, :XTOL_REACHED)
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@test NLopt.nlopt_exception === nothing
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return
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end
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function test_invalid_algorithms()
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@test_throws ArgumentError("unknown algorithm BILL") Algorithm(:BILL)
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@test_throws ArgumentError("unknown algorithm BILL") Opt(:BILL, 420)
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return
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end
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function test_issue_133()
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function rosenbrock(x::Vector, grad::Vector)
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if length(grad) > 0
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grad[1] = -400 * x[1] * (x[2] - x[1]^2) - 2 * (1 - x[1])
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grad[2] = 200 * (x[2] - x[1]^2)
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end
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return (1 - x[1])^2 + 100 * (x[2] - x[1]^2)^2
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end
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function ineq01(x::Vector, grad::Vector)
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if length(grad) > 0
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grad[1] = 1
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grad[2] = 2
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end
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return x[1] + 2 * x[2] - 1
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end
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function ineq02(x::Vector, grad::Vector)
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if length(grad) > 0
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grad[1] = 2 * x[1]
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grad[2] = 1
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end
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return x[1]^2 + x[2] - 1
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end
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function ineq03(x::Vector, grad::Vector)
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if length(grad) > 0
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grad[1] = 2 * x[1]
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grad[2] = -1
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end
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return x[1]^2 - x[2] - 1
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end
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function eq01(x::Vector, grad::Vector)
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if length(grad) > 0
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grad[1] = 2
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grad[2] = 1
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end
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return 2 * x[1] + x[2] - 1
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end
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opt = Opt(:LD_SLSQP, 2)
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opt.lower_bounds = [0, -0.5]
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opt.upper_bounds = [1, 2]
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opt.xtol_rel = 1e-21
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opt.min_objective = rosenbrock
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opt.inequality_constraint = ineq01
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opt.inequality_constraint = ineq02
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opt.inequality_constraint = ineq03
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opt.equality_constraint = eq01
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(minf, minx, ret) = optimize(opt, [0.5, 0])
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println("got $minf at $minx with constraints (returned $ret)")
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@test minx[1] ≈ 0.4149 rtol = 1e-3
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@test minx[2] ≈ 0.1701 rtol = 1e-3
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remove_constraints!(opt)
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(minf, minx, ret) = optimize(opt, [0.5, 0])
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println("got $minf at $minx after removing constraints (returned $ret)")
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@test minx[1] ≈ 1 rtol = 1e-5
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@test minx[2] ≈ 1 rtol = 1e-5
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return
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end
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function test_tutorial()
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count = 0 # keep track of # function evaluations
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function myfunc(x::Vector, grad::Vector)
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if length(grad) > 0
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grad[1] = 0
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grad[2] = 0.5 / sqrt(x[2])
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end
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count::Int += 1
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println("f_$count($x)")
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return sqrt(x[2])
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end
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function myconstraint(x::Vector, grad::Vector, a, b)
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if length(grad) > 0
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grad[1] = 3a * (a * x[1] + b)^2
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grad[2] = -1
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end
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return (a * x[1] + b)^3 - x[2]
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end
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opt = Opt(:LD_MMA, 2)
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opt.lower_bounds = [-Inf, 0.0]
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opt.xtol_rel = 1e-4
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opt.min_objective = myfunc
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opt.inequality_constraint = (x, g) -> myconstraint(x, g, 2, 0)
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opt.inequality_constraint = (x, g) -> myconstraint(x, g, -1, 1)
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# test algorithm-parameter API
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opt.params["verbosity"] = 0
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opt.params["inner_maxeval"] = 10
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opt.params["dual_alg"] = NLopt.LD_MMA
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@test opt.params == Dict(
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"verbosity" => 0,
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"inner_maxeval" => 10,
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"dual_alg" => Int(NLopt.LD_MMA),
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)
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@test get(opt.params, "foobar", 3.14159) === 3.14159
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(minf, minx, ret) = optimize(opt, [1.234, 5.678])
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println("got $minf at $minx after $count iterations (returned $ret)")
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@test minx[1] ≈ 1 / 3 rtol = 1e-5
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@test minx[2] ≈ 8 / 27 rtol = 1e-5
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@test minf ≈ sqrt(8 / 27) rtol = 1e-5
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@test ret == :XTOL_REACHED
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@test opt.numevals == count
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return
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end
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end # module
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TestCAPI.runtests()

‎test/fix133.jl

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This file was deleted.

‎test/runtests.jl

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# Use of this source code is governed by an MIT-style license that can be found
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# in the LICENSE.md file or at https://opensource.org/licenses/MIT.
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include("tutorial.jl")
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include("fix133.jl")
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include("C_API.jl")
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include("MOI_wrapper.jl")
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using NLopt
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using Test
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@testset "Fix #163" begin
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opt = Opt(:LN_COBYLA, 2)
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opt.min_objective = (x, g) -> sum(x .^ 2)
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inequality_constraint!(opt, 2, (result, x, g) -> (result .= 1 .- x))
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(minf, minx, ret) = optimize(opt, [2.0, 2.0])
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@test minx ≈ [1.0, 1.0]
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end
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@testset "Fix #132" begin
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opt = Opt(:LN_COBYLA, 2)
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err = ErrorException(
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"Getting `initial_step` is unsupported. Use " *
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"`initial_step(opt, x)` to access the initial step at a point `x`.",
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)
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@test_throws err opt.initial_step
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end
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@testset "Fix #156" begin
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@testset "Test that CapturedException is thrown" begin
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f(x, g = []) = (error("test error"); x[1]^2)
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opt = Opt(:LN_SBPLX, 1)
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opt.min_objective = f
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@test_throws CapturedException optimize(opt, [0.1234])
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@test NLopt.nlopt_exception === nothing
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try
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optimize(opt, [0.1234])
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catch e
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# Check that the backtrace is being printed
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@test length(sprint(show, e)) > 100
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end
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end
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@testset "Test that ForcedStop does not rethrow" begin
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f(x, g = []) = (throw(NLopt.ForcedStop()); x[1]^2)
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opt = Opt(:LN_SBPLX, 1)
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opt.min_objective = f
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fmin, xmin, ret = optimize(opt, [0.1234])
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@test ret == :FORCED_STOP
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@test NLopt.nlopt_exception === nothing
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end
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@testset "Test that no error works correctly" begin
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f(x, g = []) = (x[1]^2)
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opt = Opt(:LN_SBPLX, 1)
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opt.min_objective = f
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fmin, xmin, ret = optimize(opt, [0.1234])
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@test ret ∈ (:SUCCESS, :FTOL_REACHED, :XTOL_REACHED)
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@test NLopt.nlopt_exception === nothing
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end
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end
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@testset "invalid algorithms" begin
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@test_throws ArgumentError("unknown algorithm BILL") Algorithm(:BILL)
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@test_throws ArgumentError("unknown algorithm BILL") Opt(:BILL, 420)
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end

‎test/tutorial.jl

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