How to use bayesopt function to predict the optimal parameters for the experiment?

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I have a list of X values (like experimental parameters) and corresponding Y values (the experimental results), as well as X variable ranges, how can I use the bayesopt() function to predict the optimal X parameters to get the best Y value, since most of the examples on the internet are about find the optimal hyperparameters of a algorithm, which is a litte different from my problem. I don't know how to deal with the object function, since I don't have a object function, my data are experimental parameters and experimental results. I hope to use the existing data to predict the optimal parameters. I find some examples, and write a program, but it works not well as I expected, here is the program,
the examples that I refered,
X = unifrnd(0,10,30,3);
Y = unifrnd(0,2000,30,1);
sat1 = optimizableVariable('s1',[0,10],'Type','real');
sat2 = optimizableVariable('s2',[0,10],'Type','real');
sat3 = optimizableVariable('s3',[0,10],'Type','real');
var=[sat1,sat2 sat3];
initialXList = table;
initialXList.s1 = X(:,1);
initialXList.s2 = X(:,2);
initialXList.s3 = X(:,3);
initialObjList = Y;
dummyFunc = @(Tbl)0;
bayesObject = bayesopt(dummyFunc,var,...
'InitialX',initialXList,...
'InitialObjective',initialObjList,...
'MaxObjectiveEvaluations',50, ...
'Verbose',1);
  1 Comment
xu supeng
xu supeng on 24 Jun 2020
Edited: xu supeng on 24 Jun 2020
I have a thought, but maybe it's not easy to realize. Firstly, use a group of X and corresponding Y to train a model with optimal parameters, e.g. fitcknn algorithm (to get the optimal hyperparameters), then, use the model as objective function, and use bayesopt() to select the next 20 X values from the X ranges, and the correspoing Y values, and use the 20 group of [X, Y] as experimental data to train the algorithm again, repeat this process agin and again, finally, get the optimal results, I will do it, if anyone can give me suggestions, I would thanks very much, since I am not familar with algorithm, and which algorithm to choose should have quite influence on the final results~
Note, the [X, Y] are totally experimental results, so there isn't obvious relationship between them~
A simple case, but it seems the bayesopt() function can't use the trained model Md1, why? what's the problem~
rng default
X = unifrnd(0,10,30,3);
Y = unifrnd(0,2000,30,1);
Mdl = fitcknn(X,Y,'OptimizeHyperparameters','auto',...
'HyperparameterOptimizationOptions',...
struct('AcquisitionFunctionName','expected-improvement-plus'));
sat1 = optimizableVariable('s1',[0,10],'Type','real');
sat2 = optimizableVariable('s2',[0,10],'Type','real');
sat3 = optimizableVariable('s3',[0,10],'Type','real');
var=[sat1,sat2 sat3];
Fun = @(z)predict(Mdl,[z.s1,z.s2,z.s3]);
bayesObject = bayesopt(Fun,var,...
'MaxObjectiveEvaluations',20, ...
'Verbose',1);

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Accepted Answer

Alan Weiss
Alan Weiss on 24 Jun 2020
It soiunds to me as if there are two steps to your problem:
  1. Fit a parameterized function to some data. So you might have some function like y = A(1)exp(-A(2)*x + A(3)*x^2 and you first need to fit your A values to the given (x,y) data. It really is up to you to decide on a suitable parameterized function.
  2. Minimize the resulting function. Once you have a parameterized curve or surface y = f(x) with known function f, use the normal optimization procedures to find the location of the minimum.
You can use bayesopt for either or both steps, or use some other optimizer for either or both steps.
Good luck,
Alan Weiss
MATLAB mathematical toolbox documentation
  10 Comments
Alan Weiss
Alan Weiss on 21 Feb 2022
Thanks for the clarifications. I cannot help you, though; I obviously know nothing about Julia, and the problems you encounter seem to be related to using Julia.
Alan Weiss
MATLAB mathematical toolbox documentation

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More Answers (2)

xu supeng
xu supeng on 6 Jul 2020
Hi, guys
I already fix this problem last week, the problem is I try to use the fitted objective function to estimated the next Y values, but actually the function is a prediction function, it can tell you the next optimal Xi place where you have the largest opportunity to found the optimal Yi value, and you need to do experiment or simulation with Xi to get the real Yi value and put it into the database and updates the fitted objective function, then it works quite well, here I share the codes and the results ~
clc;
clear;
tic;
%%%Colom 1 ~ 6 is the X parameters, Colom 7 is the Y values
Data =[5.6207 3.4326 0.99076 -0.5681 2.2269 -0.76352 -2560.6
9.4475 5.1669 8.8368 -1.5091 4.6748 -1.2324 -738.19
6.2045 3.1342 5.3344 -1.0461 -4.807 -2.4016 -3044.6
9.1965 5.4567 2.741 4.9886 -2.2476 -1.2381 -427.06
5.5178 2.9224 0.47097 -4.7451 -4.3422 2.582 -1363.8
1.4587 6.82 9.6364 -0.55175 -4.2558 4.4485 -5265.8
9.003 1.3252 0.91798 3.956 1.3036 2.8979 -788.03
3.658 4.7395 9.7259 3.8048 -0.8229 0.097753 -2241.8
3.661 9.6049 9.3967 -0.52114 1.8493 -3.1426 -2594.9
7.538 6.3236 3.6793 -0.27248 -2.4987 2.9187 -3992.8
2.8826 2.9379 7.6082 -4.3078 3.5267 -0.76454 -1637.5
2.5475 0.41993 2.1689 3.0568 -0.5199 -2.6993 -971.66
9.8318 9.7541 7.1499 -1.069 -0.10125 2.48 -3309.2
5.0904 0.4727 6.0548 -3.1414 -1.1608 2.6665 -2494.9
5.3166 9.3089 3.4795 1.1064 1.6673 3.2222 -3272.3
4.8304 0.35989 1.7593 4.4904 -4.7422 1.9583 -421.03
5.5926 7.7423 7.4898 1.6932 3.9333 3.2528 -911.6
8.5082 8.2249 6.6189 4.0599 1.9319 -0.82999 -6993
7.0123 4.0614 2.0719 -2.5927 -3.013 -1.769 -1800.4
3.6205 2.0072 4.4781 -1.2097 3.811 2.8131 -1086.1];
X = Data(:,1:6);
Y = Data(:,7);
for ii=1:500 %%% 10 groups of Loop, each Loop 20 iterations based on the former X Y data, since the initial [X Y] values are also included in the iteration numbers
% disp([X,Y]);
disp(ii);
Len=length(Y);
% disp(Len);
%%% trained with fitrgp to find the optimal hyperparameters for X Y
Md1 = fitrgp(X,Y,'OptimizeHyperparameters','all','HyperparameterOptimizationOptions',struct('ShowPlots',false,'Verbose',0));
% disp([Md1.BasisFunction,' ',Md1.KernelFunction,' ',num2str(Md1.Sigma)]);
sat1 = optimizableVariable('s1',[0,10],'Type','real');
sat2 = optimizableVariable('s2',[0,10],'Type','real');
sat3 = optimizableVariable('s3',[0,10],'Type','real');
delta1 = optimizableVariable('d1',[-5,5],'Type','real');
delta2 = optimizableVariable('d2',[-5,5],'Type','real');
delta3 = optimizableVariable('d3',[-5,5],'Type','real'); %% set the variable
var=[sat1 sat2 sat3 delta1 delta2 delta3];
initialXList = table;
initialXList.s1 = X(:,1);
initialXList.s2 = X(:,2);
initialXList.s3 = X(:,3);
initialXList.d1 = X(:,4);
initialXList.d2 = X(:,5);
initialXList.d3 = X(:,6);
initialObjList = Y; %%% set te initial X Y values
bayesObject = bayesopt(@(tbl)mdlfun(tbl,Md1),var,...
'MaxObjectiveEvaluations',Len+1,... %%%
'InitialX',initialXList,...
'InitialObjective',initialObjList,...
'PlotFcn',{},...
'Verbose',0);
if mod(ii,5)==0
disp([bayesObject.XAtMinObjective array2table(bayesObject.MinObjective)]);
disp([bayesObject.XAtMinEstimatedObjective array2table(bayesObject.MinEstimatedObjective)]);
end
Results(ii,:) = [table2array(bayesObject.XAtMinObjective) bayesObject.MinObjective table2array(bayesObject.XAtMinEstimatedObjective) bayesObject.MinEstimatedObjective];
X=table2array(bayesObject.XTrace);
Yplusone=Updates(X(end,:)); %%% Updates the database with the optimal Xi parameters found by the fitted objective function
if Yplusone>0
Yplusone=0;
end
Y=bayesObject.ObjectiveTrace;
Y(end)=Yplusone;
end
figure;
plot(Results(:,7),'Linewidth',1.5);
hold on
plot(Results(:,14),'o');
xlabel('Number of iteration','FontSize',20);
ylabel('Slope','FontSize',20);
set(gca,'FontSize',20,'LineWidth',1.5);
toc;
function f = mdlfun(tbl,mdl)
sat1 = tbl.s1;
sat2 = tbl.s2;
sat3 = tbl.s3;
delta1 = tbl.d1;
delta2 = tbl.d2;
delta3 = tbl.d3;
var = [sat1 sat2 sat3 delta1 delta2 delta3];
f = predict(mdl,var);
end
both fitrgp and fitrlinear works well, but fitrlinear is much faster than the fitrgp~~~
  2 Comments
xu supeng
xu supeng on 19 Oct 2022
Sorry, this is my initial program and is not clean or simple, if you are still interested, use the following example:
clc;
clear;
tic;
% define initial X value, but I didn't use initial Y value
X=[
53.999 34.843 36.974 51.649 2 1 1 2 -4.1921 4.4694 -3.6857 -3.2385
7.9335 72.09 39.635 53.757 2 1 1 2 -3.7329 4.6742 -2.6008 -2.9756
32.198 29.476 16.324 59.771 2 1 1 2 -4.1383 5.1433 -3.368 -3.3614
19.269 91.109 36.935 76.83 2 1 1 2 -4.0158 5.0661 -3.0922 -1.6616
];
% set the variable
pow1 = optimizableVariable('p1',[0,100],'Type','real');
pow2 = optimizableVariable('p2',[0,100],'Type','real');
pow3 = optimizableVariable('p3',[0,100],'Type','real');
pow4 = optimizableVariable('p4',[0,100],'Type','real');
pol1 = optimizableVariable('po1',[1,2],'Type','integer');
pol2 = optimizableVariable('po2',[1,2],'Type','integer');
pol3 = optimizableVariable('po3',[1,2],'Type','integer');
pol4 = optimizableVariable('po4',[1,2],'Type','integer');
det1 = optimizableVariable('d1',[-10,10],'Type','real');
det2 = optimizableVariable('d2',[-10,10],'Type','real');
det3 = optimizableVariable('d3',[-10,10],'Type','real');
det4 = optimizableVariable('d4',[-10,10],'Type','real');
var=[pow1 pow2 pow3 pow4 pol1 pol2 pol3 pol4 det1 det2 det3 det4];
initialXList = table;
initialXList.p1 = X(:,1);
initialXList.p2 = X(:,2);
initialXList.p3 = X(:,3);
initialXList.p4 = X(:,4);
initialXList.po1 = X(:,5);
initialXList.po2 = X(:,6);
initialXList.po3 = X(:,7);
initialXList.po4 = X(:,8);
initialXList.d1 = X(:,9);
initialXList.d2 = X(:,10);
initialXList.d3 = X(:,11);
initialXList.d4 = X(:,12);
bayesObject = bayesopt(@(tbl)mdlfun(tbl),var,...
'MaxObjectiveEvaluations',1000,... % optimizaiton numbers
'IsObjectiveDeterministic',true,...
'ExplorationRatio',0.5,... % exploration degree, default value is 0.5
'InitialX',initialXList,... % start from the initial value
'UseParallel',true); % use parallel
figure;
plot(bayesObject.ObjectiveTrace,'o');
hold on;
plot(-bayesObject.ObjectiveMinimumTrace,'Linewidth',2);
xlabel('Iteration times','FontSize',20);
ylabel('Capture velocity (m/s)','FontSize',20);
set(gca,'FontSize',20,'LineWidth',1.5);
toc;
function f = mdlfun(tbl)
pow1 = tbl.p1;
pow2 = tbl.p2;
pow3 = tbl.p3;
pow4 = tbl.p4;
pol1 = tbl.po1;
pol2 = tbl.po2;
pol3 = tbl.po3;
pol4 = tbl.po4;
det1= tbl.d1;
det2= tbl.d2;
det3= tbl.d3;
det4= tbl.d4;
var = [pow1 pow2 pow3 pow4 pol1 pol2 pol3 pol4 det1 det2 det3 det4];
f = black_box_function(var); % black box function defined by yourself, once input a group of X, it will return a Y value
end

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M Mirrashid
M Mirrashid on 5 Jun 2022

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