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fix: RTEA and add AbstractSurrogate as a union type #508

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fix: RTEA and add AbstractSurrogate as a union type #508

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bcfd603
fix: RTEA and add AbstractSurrogate as a union type
sathvikbhagavan Feb 10, 2025
433cbc9
refactor: make SurrogatesSVM work with data with tuples
sathvikbhagavan Feb 10, 2025
1bf564e
test: uncomment optimization tests
sathvikbhagavan Feb 10, 2025
48442ab
test: import SurrogatesSVM
sathvikbhagavan Feb 10, 2025
1c2feaa
chore: fix typo
sathvikbhagavan Feb 10, 2025
57a5484
test: SurrogatesRandomForest before MOE
sathvikbhagavan Feb 10, 2025
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7 changes: 4 additions & 3 deletions lib/SurrogatesSVM/src/SurrogatesSVM.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@ function SVMSurrogate(x, y, lb, ub)
end
else
for j in eachindex(x)
X[j, :] = x[j]
X[j, :] .= x[j]
end
end
model = LIBSVM.fit!(SVC(), X, y)
Expand All @@ -46,7 +46,7 @@ end

function (svmsurr::SVMSurrogate)(val)
n = length(val)
return LIBSVM.predict(svmsurr.model, reshape(val, 1, n))[1]
return LIBSVM.predict(svmsurr.model, reshape(collect(val), 1, n))[1]
end

"""
Expand All @@ -65,7 +65,8 @@ function SurrogatesBase.update!(svmsurr::SVMSurrogate, x_new, y_new)
svmsurr.model = LIBSVM.fit!(
SVC(), reshape(svmsurr.x, length(svmsurr.x), 1), svmsurr.y)
else
svmsurr.model = LIBSVM.fit!(SVC(), transpose(reduce(hcat, svmsurr.x)), svmsurr.y)
svmsurr.model = LIBSVM.fit!(
SVC(), transpose(reduce(hcat, collect.(svmsurr.x))), svmsurr.y)
end
end

Expand Down
11 changes: 6 additions & 5 deletions lib/SurrogatesSVM/test/runtests.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -25,16 +25,17 @@ using SafeTestsets
obj_N = x -> x[1]^2 * x[2]
lb = [0.0, 0.0]
ub = [10.0, 10.0]
x = collect.(sample(100, lb, ub, RandomSample()))
x = sample(100, lb, ub, RandomSample())
y = obj_N.(x)
svm = LIBSVM.fit!(SVC(), transpose(reduce(hcat, x)), y)
svm = LIBSVM.fit!(SVC(), transpose(reduce(hcat, collect.(x))), y)
my_svm_ND = SVMSurrogate(x, y, lb, ub)
x_test = [5.0, 1.2]
val = my_svm_ND(x_test)
@test LIBSVM.predict(svm, reshape(x_test, 1, 2))[1] == val
update!(my_svm_ND, [[1.0, 1.0]], [1.0])
update!(my_svm_ND, [[1.2, 1.2], [1.5, 1.5]], [1.728, 3.375])
svm = LIBSVM.fit!(SVC(), transpose(reduce(hcat, my_svm_ND.x)), my_svm_ND.y)
update!(my_svm_ND, [(1.0, 1.0)], [1.0])
update!(my_svm_ND, [(1.2, 1.2), (1.5, 1.5)], [1.728, 3.375])
svm = LIBSVM.fit!(
SVC(), transpose(reduce(hcat, collect.(my_svm_ND.x))), my_svm_ND.y)
x_test = [1.0, 1.0]
val = my_svm_ND(x_test)
@test LIBSVM.predict(svm, reshape(x_test, 1, 2))[1] == val
Expand Down
68 changes: 35 additions & 33 deletions src/Optimization.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,7 @@ struct RTEA{K, Z, P, N, S} <: SurrogateOptimizationAlgorithm
end

function merit_function(
point, w, surr::AbstractDeterministicSurrogate, s_max, s_min, d_max, d_min,
point, w, surr::AbstractSurrogate, s_max, s_min, d_max, d_min,
box_size)
if length(point) == 1
D_x = box_size + 1
Expand Down Expand Up @@ -85,7 +85,7 @@ a few values to achieve both exploitation and exploration.
When w is close to zero, we do pure exploration, while w close to 1 corresponds to exploitation.
"""
function surrogate_optimize(
obj::Function, ::SRBF, lb, ub, surr::AbstractDeterministicSurrogate,
obj::Function, ::SRBF, lb, ub, surr::AbstractSurrogate,
sample_type::SamplingAlgorithm; maxiters = 100,
num_new_samples = 100, needs_gradient = false)
scale = 0.2
Expand Down Expand Up @@ -234,10 +234,10 @@ end

"""
SRBF 1D:
surrogate_optimize(obj::Function,::SRBF,lb::Number,ub::Number,surr::AbstractDeterministicSurrogate,sample_type::SamplingAlgorithm;maxiters=100,num_new_samples=100)
surrogate_optimize(obj::Function,::SRBF,lb::Number,ub::Number,surr::AbstractSurrogate,sample_type::SamplingAlgorithm;maxiters=100,num_new_samples=100)
"""
function surrogate_optimize(obj::Function, ::SRBF, lb::Number, ub::Number,
surr::AbstractDeterministicSurrogate, sample_type::SamplingAlgorithm;
surr::AbstractSurrogate, sample_type::SamplingAlgorithm;
maxiters = 100, num_new_samples = 100)
#Suggested by:
#https://www.mathworks.com/help/gads/surrogate-optimization-algorithm.html
Expand Down Expand Up @@ -293,8 +293,10 @@ function surrogate_optimize(obj::Function, ::SRBF, lb::Number, ub::Number,
end
end
#3) Evaluate merit function at the sampled points
evaluation_of_merit_function = merit_function.(new_sample, w, surr, s_max,
s_min, d_max, d_min, box_size)
evaluation_of_merit_function = map(
x -> merit_function(x, w, surr, s_max,
s_min, d_max, d_min, box_size),
new_sample)

new_addition = false
adaptive_point_x = zero(eltype(new_sample[1]))
Expand Down Expand Up @@ -377,7 +379,7 @@ end

# Ask SRBF ND
function potential_optimal_points(
::SRBF, strategy, lb, ub, surr::AbstractDeterministicSurrogate,
::SRBF, strategy, lb, ub, surr::AbstractSurrogate,
sample_type::SamplingAlgorithm, n_parallel;
num_new_samples = 500)
scale = 0.2
Expand Down Expand Up @@ -484,7 +486,7 @@ end

# Ask SRBF 1D
function potential_optimal_points(::SRBF, strategy, lb::Number, ub::Number,
surr::AbstractDeterministicSurrogate,
surr::AbstractSurrogate,
sample_type::SamplingAlgorithm, n_parallel;
num_new_samples = 500)
scale = 0.2
Expand Down Expand Up @@ -935,7 +937,7 @@ function adjust_step_size(sigma_n, sigma_min, C_success, t_success, C_fail, t_fa
end

function select_evaluation_point_1D(
new_points1, surr1::AbstractDeterministicSurrogate, numb_iters,
new_points1, surr1::AbstractSurrogate, numb_iters,
maxiters)
v = [0.3, 0.5, 0.8, 0.95]
k = 4
Expand Down Expand Up @@ -990,13 +992,13 @@ function select_evaluation_point_1D(
end

"""
surrogate_optimize(obj::Function,::DYCORS,lb::Number,ub::Number,surr1::AbstractDeterministicSurrogate,sample_type::SamplingAlgorithm;maxiters=100,num_new_samples=100)
surrogate_optimize(obj::Function,::DYCORS,lb::Number,ub::Number,surr1::AbstractSurrogate,sample_type::SamplingAlgorithm;maxiters=100,num_new_samples=100)

DYCORS optimization method in 1D, following closely: Combining radial basis function
surrogates and dynamic coordinate search in high-dimensional expensive black-box optimization".
"""
function surrogate_optimize(obj::Function, ::DYCORS, lb::Number, ub::Number,
surr1::AbstractDeterministicSurrogate, sample_type::SamplingAlgorithm;
surr1::AbstractSurrogate, sample_type::SamplingAlgorithm;
maxiters = 100, num_new_samples = 100)
x_best = argmin(surr1.y)
y_best = minimum(surr1.y)
Expand Down Expand Up @@ -1056,7 +1058,7 @@ function surrogate_optimize(obj::Function, ::DYCORS, lb::Number, ub::Number,
end

function select_evaluation_point_ND(
new_points, surrn::AbstractDeterministicSurrogate, numb_iters,
new_points, surrn::AbstractSurrogate, numb_iters,
maxiters)
v = [0.3, 0.5, 0.8, 0.95]
k = 4
Expand Down Expand Up @@ -1110,7 +1112,7 @@ function select_evaluation_point_ND(
end

"""
surrogate_optimize(obj::Function,::DYCORS,lb::Number,ub::Number,surr1::AbstractDeterministicSurrogate,sample_type::SamplingAlgorithm;maxiters=100,num_new_samples=100)
surrogate_optimize(obj::Function,::DYCORS,lb::Number,ub::Number,surr1::AbstractSurrogate,sample_type::SamplingAlgorithm;maxiters=100,num_new_samples=100)

This is an implementation of the DYCORS strategy by Regis and Shoemaker:
Rommel G Regis and Christine A Shoemaker.
Expand All @@ -1124,7 +1126,7 @@ to perturb a given coordinate and decrease this probability after each function
evaluation, so fewer coordinates are perturbed later in the optimization.
"""
function surrogate_optimize(
obj::Function, ::DYCORS, lb, ub, surrn::AbstractDeterministicSurrogate,
obj::Function, ::DYCORS, lb, ub, surrn::AbstractSurrogate,
sample_type::SamplingAlgorithm; maxiters = 100,
num_new_samples = 100)
x_best = collect(surrn.x[argmin(surrn.y)])
Expand Down Expand Up @@ -1210,7 +1212,7 @@ function obj2_1D(value, points)
return min
end

function I_tier_ranking_1D(P, surrSOP::AbstractDeterministicSurrogate)
function I_tier_ranking_1D(P, surrSOP::AbstractSurrogate)
#obj1 = objective_function
#obj2 = obj2_1D
Fronts = Dict{Int, Array{eltype(surrSOP.x[1]), 1}}()
Expand Down Expand Up @@ -1259,7 +1261,7 @@ function I_tier_ranking_1D(P, surrSOP::AbstractDeterministicSurrogate)
return F
end

function II_tier_ranking_1D(D::Dict, srg::AbstractDeterministicSurrogate)
function II_tier_ranking_1D(D::Dict, srg::AbstractSurrogate)
for i in 1:length(D)
pos = []
yn = []
Expand Down Expand Up @@ -1303,7 +1305,7 @@ function Hypervolume_Pareto_improving(f1_new, f2_new, Pareto_set)
end

"""
surrogate_optimize(obj::Function,::SOP,lb::Number,ub::Number,surr::AbstractDeterministicSurrogate,sample_type::SamplingAlgorithm;maxiters=100,num_new_samples=100)
surrogate_optimize(obj::Function,::SOP,lb::Number,ub::Number,surr::AbstractSurrogate,sample_type::SamplingAlgorithm;maxiters=100,num_new_samples=100)

SOP Surrogate optimization method, following closely the following papers:

Expand All @@ -1313,7 +1315,7 @@ SOP Surrogate optimization method, following closely the following papers:
#Suggested number of new_samples = min(500*d,5000)
"""
function surrogate_optimize(obj::Function, sop1::SOP, lb::Number, ub::Number,
surrSOP::AbstractDeterministicSurrogate, sample_type::SamplingAlgorithm;
surrSOP::AbstractSurrogate, sample_type::SamplingAlgorithm;
maxiters = 100, num_new_samples = min(500 * 1, 5000))
d = length(lb)
N_fail = 3
Expand Down Expand Up @@ -1497,7 +1499,7 @@ function obj2_ND(value, points)
return min
end

function I_tier_ranking_ND(P, surrSOPD::AbstractDeterministicSurrogate)
function I_tier_ranking_ND(P, surrSOPD::AbstractSurrogate)
#obj1 = objective_function
#obj2 = obj2_1D
Fronts = Dict{Int, Array{eltype(surrSOPD.x), 1}}()
Expand Down Expand Up @@ -1546,7 +1548,7 @@ function I_tier_ranking_ND(P, surrSOPD::AbstractDeterministicSurrogate)
return F
end

function II_tier_ranking_ND(D::Dict, srgD::AbstractDeterministicSurrogate)
function II_tier_ranking_ND(D::Dict, srgD::AbstractSurrogate)
for i in 1:length(D)
pos = []
yn = []
Expand All @@ -1560,7 +1562,7 @@ function II_tier_ranking_ND(D::Dict, srgD::AbstractDeterministicSurrogate)
end

function surrogate_optimize(
obj::Function, sopd::SOP, lb, ub, surrSOPD::AbstractDeterministicSurrogate,
obj::Function, sopd::SOP, lb, ub, surrSOPD::AbstractSurrogate,
sample_type::SamplingAlgorithm; maxiters = 100,
num_new_samples = min(500 * length(lb), 5000))
d = length(lb)
Expand Down Expand Up @@ -1758,7 +1760,7 @@ function _nonDominatedSorting(arr::Array{Float64, 2})
end

function surrogate_optimize(obj::Function, sbm::SMB, lb::Number, ub::Number,
surrSMB::AbstractDeterministicSurrogate, sample_type::SamplingAlgorithm;
surrSMB::AbstractSurrogate, sample_type::SamplingAlgorithm;
maxiters = 100, n_new_look = 1000)
#obj contains a function for each output dimension
dim_out = length(surrSMB.y[1])
Expand Down Expand Up @@ -1798,7 +1800,7 @@ function surrogate_optimize(obj::Function, sbm::SMB, lb::Number, ub::Number,
end

function surrogate_optimize(
obj::Function, smb::SMB, lb, ub, surrSMBND::AbstractDeterministicSurrogate,
obj::Function, smb::SMB, lb, ub, surrSMBND::AbstractSurrogate,
sample_type::SamplingAlgorithm; maxiters = 100,
n_new_look = 1000)
#obj contains a function for each output dimension
Expand Down Expand Up @@ -1840,7 +1842,7 @@ end
# RTEA (Noisy model based multi objective optimization + standard rtea by fieldsen), use this for very noisy objective functions because there are a lot of re-evaluations

function surrogate_optimize(obj, rtea::RTEA, lb::Number, ub::Number,
surrRTEA::AbstractDeterministicSurrogate, sample_type::SamplingAlgorithm;
surrRTEA::AbstractSurrogate, sample_type::SamplingAlgorithm;
maxiters = 100, n_new_look = 1000)
Z = rtea.z
K = rtea.k
Expand Down Expand Up @@ -1892,8 +1894,8 @@ function surrogate_optimize(obj, rtea::RTEA, lb::Number, ub::Number,

#update pareto
new_to_pareto = false
counter = zeros(Int, dim_out)
for i in 1:length(pareto_set)
counter = zeros(Int, dim_out)
#compare the y_new values to pareto, if there is at least one entry where it dominates all the others, then it can be in pareto
for l in 1:dim_out
if y_new[l] < pareto_front[i][l]
Expand All @@ -1911,13 +1913,13 @@ function surrogate_optimize(obj, rtea::RTEA, lb::Number, ub::Number,
push!(pareto_front, y_new)
push!(number_of_revaluations, 0)
end
update!(surrRTEA, new_x, new_y)
update!(surrRTEA, x_new, y_new)
end
for k in 1:K
val, pos = findmin(number_of_revaluations)
x_r = pareto_set[pos]
y_r = obj(x_r)
number_of_revaluations[pos] = number_of_revaluations + 1
number_of_revaluations[pos] = number_of_revaluations[pos] + 1
#check if it is again in the pareto set or not, if not eliminate it from pareto
still_in_pareto = false
for i in 1:length(pareto_set)
Expand All @@ -1937,7 +1939,7 @@ function surrogate_optimize(obj, rtea::RTEA, lb::Number, ub::Number,
#remove from pareto
deleteat!(pareto_set, pos)
deleteat!(pareto_front, pos)
deleteat!(number_of_revaluationsm, pos)
deleteat!(number_of_revaluations, pos)
end
end
iter = iter + 1
Expand All @@ -1946,7 +1948,7 @@ function surrogate_optimize(obj, rtea::RTEA, lb::Number, ub::Number,
end

function surrogate_optimize(
obj, rtea::RTEA, lb, ub, surrRTEAND::AbstractDeterministicSurrogate,
obj, rtea::RTEA, lb, ub, surrRTEAND::AbstractSurrogate,
sample_type::SamplingAlgorithm; maxiters = 100,
n_new_look = 1000)
Z = rtea.z
Expand Down Expand Up @@ -2000,8 +2002,8 @@ function surrogate_optimize(

#update pareto
new_to_pareto = false
counter = zeros(Int, dim_out)
for i in 1:length(pareto_set)
counter = zeros(Int, dim_out)
#compare the y_new values to pareto, if there is at least one entry where it dominates all the others, then it can be in pareto
for l in 1:dim_out
if y_new[l] < pareto_front[i][l]
Expand All @@ -2019,13 +2021,13 @@ function surrogate_optimize(
push!(pareto_front, y_new)
push!(number_of_revaluations, 0)
end
update!(surrRTEAND, new_x, new_y)
update!(surrRTEAND, x_new, y_new)
end
for k in 1:K
val, pos = findmin(number_of_revaluations)
x_r = pareto_set[pos]
y_r = obj(x_r)
number_of_revaluations[pos] = number_of_revaluations + 1
number_of_revaluations[pos] = number_of_revaluations[pos] + 1
#check if it is again in the pareto set or not, if not eliminate it from pareto
still_in_pareto = false
for i in 1:length(pareto_set)
Expand All @@ -2045,7 +2047,7 @@ function surrogate_optimize(
#remove from pareto
deleteat!(pareto_set, pos)
deleteat!(pareto_front, pos)
deleteat!(number_of_revaluationsm, pos)
deleteat!(number_of_revaluations, pos)
end
end
iter = iter + 1
Expand Down
5 changes: 3 additions & 2 deletions src/Surrogates.jl
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Expand Up @@ -4,9 +4,10 @@ using LinearAlgebra
using Distributions
using GLM
using ExtendableSparse
using SurrogatesBase: SurrogatesBase, update!, AbstractDeterministicSurrogate
using SurrogatesBase: SurrogatesBase, update!, AbstractDeterministicSurrogate,
AbstractStochasticSurrogate

abstract type AbstractSurrogate <: Function end
const AbstractSurrogate = Union{AbstractDeterministicSurrogate, AbstractStochasticSurrogate}
include("utils.jl")
include("Radials.jl")
include("Kriging.jl")
Expand Down
2 changes: 1 addition & 1 deletion src/VariableFidelity.jl
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Expand Up @@ -145,7 +145,7 @@ function (varfid::VariableFidelitySurrogate)(val)
end

"""
add_point!(varfid::VariableFidelitySurrogate,x_new,y_new)
update!(varfid::VariableFidelitySurrogate,x_new,y_new)

I expect to add low fidelity data to the surrogate.
"""
Expand Down
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