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the slow test suite is now up-to-date again
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@robertfeldt
robertfeldt committed Sep 22, 2016
1 parent dcbffe1 commit d249ab8
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2 changes: 1 addition & 1 deletion test/problems/common.jl
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Expand Up @@ -2,7 +2,7 @@ using BlackBoxOptim

function fitness_for_opt(problem, numDimensions, populationSize, numSteps, method)

problem = fixed_dim_problem(problem, numDimensions)
problem = instantiate(problem, numDimensions)

println("\n$(problem.name), n = $(numdims(problem)), optimizer = $(string(method))")

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72 changes: 35 additions & 37 deletions test/problems/test_optimize_single_objective_problems.jl
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@@ -1,44 +1,42 @@
include("common.jl")

facts("Optimize single objective problems in 5, 10, and 30 dimensions with DE") do
simple_problems = ["Sphere", "Schwefel2.22", "Schwefel2.22"]
for problem in simple_problems
context(problem) do
p = BlackBoxOptim.example_problems[problem]
@testset "Optimize single objective problems in 5, 10, and 30 dimensions with DE" begin
simple_problems = ["Sphere", "Schwefel2.22", "Schwefel2.22"]
@testset "Simple problem $pr" for pr in simple_problems
p = BlackBoxOptim.example_problems[pr]

@fact fitness_for_opt(p, 5, 20, 5e3, :de_rand_1_bin) < 0.01 --> true
@fact fitness_for_opt(p, 5, 20, 5e3, :de_rand_1_bin_radiuslimited) < 0.01 --> true
@test fitness_for_opt(p, 5, 20, 5e3, :de_rand_1_bin) < 0.01
@test fitness_for_opt(p, 5, 20, 5e3, :de_rand_1_bin_radiuslimited) < 0.01

@fact fitness_for_opt(p, 10, 20, 1e4, :de_rand_1_bin) < 0.01 --> true
@fact fitness_for_opt(p, 10, 20, 1e4, :de_rand_1_bin_radiuslimited) < 0.01 --> true
@test fitness_for_opt(p, 10, 20, 1e4, :de_rand_1_bin) < 0.01
@test fitness_for_opt(p, 10, 20, 1e4, :de_rand_1_bin_radiuslimited) < 0.01

@fact fitness_for_opt(p, 30, 25, 3e4, :de_rand_1_bin) < 0.01 --> true
@fact fitness_for_opt(p, 30, 25, 3e4, :de_rand_1_bin_radiuslimited) < 0.01 --> true
@test fitness_for_opt(p, 30, 25, 3e4, :de_rand_1_bin) < 0.01
@test fitness_for_opt(p, 30, 25, 3e4, :de_rand_1_bin_radiuslimited) < 0.01
end

@testset "Schwefel1.2" begin
problem = "Schwefel1.2"
p = BlackBoxOptim.example_problems[problem]
@test fitness_for_opt(p, 5, 20, 5e3, :de_rand_1_bin_radiuslimited) < 0.01
@test fitness_for_opt(p, 10, 50, 5e4, :de_rand_1_bin_radiuslimited) < 0.01

#DE/rand/1/bin seems to have troubles...
@test fitness_for_opt(p, 30, 50, 2e5, :de_rand_1_bin_radiuslimited) < 10.0
@test fitness_for_opt(p, 30, 50, 2e5, :adaptive_de_rand_1_bin) < 10.0
@test fitness_for_opt(p, 30, 50, 2e5, :adaptive_de_rand_1_bin_radiuslimited) < 10.0
end

@testset "Rosenbrock" begin
problem = "Rosenbrock"
p = BlackBoxOptim.example_problems[problem]
@test fitness_for_opt(p, 5, 20, 1e4, :de_rand_1_bin_radiuslimited) < 100.0
@test fitness_for_opt(p, 10, 20, 5e4, :de_rand_1_bin_radiuslimited) < 100.0
@test fitness_for_opt(p, 30, 40, 2e5, :de_rand_1_bin_radiuslimited) < 100.0

@test fitness_for_opt(p, 30, 40, 2e5, :adaptive_de_rand_1_bin) < 100.0
@test fitness_for_opt(p, 30, 40, 2e5, :adaptive_de_rand_1_bin_radiuslimited) < 100.0

@test fitness_for_opt(p, 50, 40, 3e5, :adaptive_de_rand_1_bin_radiuslimited) < 100.0
end
end

context("Schwefel1.2") do
problem = "Schwefel1.2"
p = BlackBoxOptim.example_problems[problem]
@fact fitness_for_opt(p, 5, 20, 5e3, :de_rand_1_bin_radiuslimited) < 0.01 --> true
@fact fitness_for_opt(p, 10, 50, 5e4, :de_rand_1_bin_radiuslimited) < 0.01 --> true

#DE/rand/1/bin seems to have troubles...
@fact fitness_for_opt(p, 30, 50, 2e5, :de_rand_1_bin_radiuslimited) < 10.0 --> true
@fact fitness_for_opt(p, 30, 50, 2e5, :adaptive_de_rand_1_bin) < 10.0 --> true
@fact fitness_for_opt(p, 30, 50, 2e5, :adaptive_de_rand_1_bin_radiuslimited) < 10.0 --> true
end

context("Rosenbrock") do
problem = "Rosenbrock"
p = BlackBoxOptim.example_problems[problem]
@fact fitness_for_opt(p, 5, 20, 1e4, :de_rand_1_bin_radiuslimited) < 100.0 --> true
@fact fitness_for_opt(p, 10, 20, 5e4, :de_rand_1_bin_radiuslimited) < 100.0 --> true
@fact fitness_for_opt(p, 30, 40, 2e5, :de_rand_1_bin_radiuslimited) < 100.0 --> true

@fact fitness_for_opt(p, 30, 40, 2e5, :adaptive_de_rand_1_bin) < 100.0 --> true
@fact fitness_for_opt(p, 30, 40, 2e5, :adaptive_de_rand_1_bin_radiuslimited) < 100.0 --> true

@fact fitness_for_opt(p, 50, 40, 3e5, :adaptive_de_rand_1_bin_radiuslimited) < 100.0 --> true
end
end
6 changes: 4 additions & 2 deletions test/runslowtests.jl
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Expand Up @@ -5,6 +5,8 @@ my_slow_tests = [
"test_bboptimize.jl"
]

for t in my_slow_tests
include(t)
@testset "BlackBoxOptim long-running test suite" begin
for t in my_slow_tests
include(t)
end
end
96 changes: 52 additions & 44 deletions test/test_bboptimize.jl
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@@ -1,61 +1,69 @@
function rosenbrock2d(x)
return (1.0 - x[1])^2 + 100.0 * (x[2] - x[1]^2)^2
return (1.0 - x[1])^2 + 100.0 * (x[2] - x[1]^2)^2
end

function rosenbrock(x)
return( sum( 100*( x[2:end] - x[1:end-1].^2 ).^2 + ( x[1:end-1] - 1 ).^2 ) )
return( sum( 100*( x[2:end] - x[1:end-1].^2 ).^2 + ( x[1:end-1] - 1 ).^2 ) )
end

facts("bboptimize") do
context("example 1 from README") do
res = bboptimize(rosenbrock2d; SearchRange = (-5.0, 5.0), NumDimensions = 2, TraceMode = :silent)
@fact best_fitness(res) < 0.001 --> true
end
@testset "bboptimize" begin
@testset "example 1 from README" begin
res = bboptimize(rosenbrock2d; SearchRange = (-5.0, 5.0), NumDimensions = 2, TraceMode = :silent)
@test best_fitness(res) < 0.001
end

context("example 2 from README") do
res = bboptimize(rosenbrock2d; SearchRange = [(-5.0, 5.0), (-2.0, 2.0)], TraceMode = :silent)
@fact best_fitness(res) < 0.001 --> true
end
@testset "example 2 from README" begin
res = bboptimize(rosenbrock2d; SearchRange = [(-5.0, 5.0), (-2.0, 2.0)], TraceMode = :silent)
@test best_fitness(res) < 0.001
end

context("example 3 from README") do
res = bboptimize(rosenbrock2d; SearchRange = (-5.0, 5.0), NumDimensions = 2, method = :de_rand_1_bin, TraceMode = :silent)
@fact best_fitness(res) < 0.001 --> true
end
@testset "example 3 from README" begin
res = bboptimize(rosenbrock2d; SearchRange = (-5.0, 5.0), NumDimensions = 2, method = :de_rand_1_bin, TraceMode = :silent)
@test best_fitness(res) < 0.001
end

context("example 4 from README") do
res = bboptimize(rosenbrock2d; SearchRange = (-5.0, 5.0), NumDimensions = 2,
Method = :random_search, MaxTime = 10.0, TraceMode = :silent)
@fact best_fitness(res) < 0.2 --> true
end
@testset "example 4 from README" begin
res = bboptimize(rosenbrock2d; SearchRange = (-5.0, 5.0), NumDimensions = 2,
Method = :random_search, MaxTime = 10.0, TraceMode = :silent)
@test best_fitness(res) < 0.2
end

context("example 5 from README") do
BlackBoxOptim.compare_optimizers(rosenbrock; SearchRange = (-5.0, 5.0), NumDimensions = 3,
MaxTime = 2.0, TraceMode = :compact)
end
@testset "example 5 from README" begin
res = BlackBoxOptim.compare_optimizers(rosenbrock; SearchRange = (-5.0, 5.0), NumDimensions = 3,
MaxTime = 2.0, TraceMode = :compact)

context("run one longer example in case there is problem with the reporting in long runs") do
res = bboptimize(rosenbrock2d; SearchRange = (-5.0, 5.0), NumDimensions = 2,
Method = :de_rand_1_bin, TraceMode = :silent, MaxSteps = 25001)
@fact best_fitness(res) < 0.001 --> true
end
# We at least expect the DE optimizers and DX-NES to come out better than random search
idx_adaptive_de = findfirst(t -> t[1] == :adaptive_de_rand_1_bin, res)
idx_random = findfirst(t -> t[1] == :random_search, res)
@test idx_adaptive_de < idx_random

context("Fixed-dimensional problem takes precedence over search range and related params") do
prob = BlackBoxOptim.minimization_problem((x) -> sum(x), "no name", (10.0, 20.0), 3)
res = bboptimize(prob; SearchRange = (0.0, 2.0), NumDimensions = 2, TraceMode = :silent)
xbest = best_candidate(res)
@fact length(xbest) --> 3
@fact xbest[1] >= 10.0 --> true
@fact xbest[2] >= 10.0 --> true
@fact xbest[3] >= 10.0 --> true
end
idx_dxnes = findfirst(t -> t[1] == :dxnes, res)
@test idx_dxnes < idx_random
end

context("fault handling: anydimensional problem") do
@fact_throws bboptimize(BlackBoxOptim.anydim_problem("dummy", (x) -> sum(x), 0.0:1.0))
end
@testset "run one longer example in case there is problem with the reporting in long runs" begin
res = bboptimize(rosenbrock2d; SearchRange = (-5.0, 5.0), NumDimensions = 2,
Method = :de_rand_1_bin, TraceMode = :silent, MaxSteps = 25001)
@test best_fitness(res) < 0.001
end

context("fault handling: func & search range but not num dimensions") do
@fact_throws BlackBoxOptim.setup_problem((x) -> sum(x); search_range = (0.0, 1.0))
end
@testset "Fixed-dimensional problem takes precedence over search range and related params" begin
prob = BlackBoxOptim.minimization_problem((x) -> sum(x), "no name", (10.0, 20.0), 3)
res = bboptimize(prob; SearchRange = (0.0, 2.0), NumDimensions = 2, TraceMode = :silent)
xbest = best_candidate(res)
@test length(xbest) == 3
@test xbest[1] >= 10.0
@test xbest[2] >= 10.0
@test xbest[3] >= 10.0
end

#@testset "fault handling: anydimensional problem" begin
# @test_throws ArgumentError bboptimize(BlackBoxOptim.anydim_problem("dummy", (x) -> sum(x), 0.0:1.0))
#end

@testset "fault handling: func & search range but not num dimensions" begin
@test_throws ArgumentError BlackBoxOptim.setup_problem((x) -> sum(x); search_range = (0.0, 1.0))
end

# context("restarting an optimizer again and again should gradually improve") do
# optimizer, problem, params = BlackBoxOptim.setup_bboptimize(rosenbrock2d,
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