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Predict objective function run times at a set of points



time = predictObjectiveEvaluationTime(results,XTable) returns estimated objective evaluation times at the points in XTable.


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This example shows how to estimate the objective function evaluation time in an optimized Bayesian model of SVM classification.

Create an optimized SVM model. For details of this model, see Optimize a Cross-Validated SVM Classifier Using bayesopt.

rng default
grnpop = mvnrnd([1,0],eye(2),10);
redpop = mvnrnd([0,1],eye(2),10);
redpts = zeros(100,2);
grnpts = redpts;
for i = 1:100
    grnpts(i,:) = mvnrnd(grnpop(randi(10),:),eye(2)*0.02);
    redpts(i,:) = mvnrnd(redpop(randi(10),:),eye(2)*0.02);
cdata = [grnpts;redpts];
grp = ones(200,1);
grp(101:200) = -1;
c = cvpartition(200,'KFold',10);
sigma = optimizableVariable('sigma',[1e-5,1e5],'Transform','log');
box = optimizableVariable('box',[1e-5,1e5],'Transform','log');
minfn = @(z)kfoldLoss(fitcsvm(cdata,grp,'CVPartition',c,...
results = bayesopt(minfn,[sigma,box],'IsObjectiveDeterministic',true,...

Figure contains an axes. The axes with title Objective function model contains 5 objects of type line, surface, contour. These objects represent Observed points, Model mean, Next point, Model minimum feasible.

Figure contains an axes. The axes with title Min objective vs. Number of function evaluations contains 2 objects of type line. These objects represent Min observed objective, Estimated min objective.

Predict the evaluation time for various points.

sigma = logspace(-5,5,11)';
box = 1e5*ones(size(sigma));
XTable = table(sigma,box);
time = predictObjectiveEvaluationTime(results,XTable);
ans=11×3 table
    sigma      box      time  
    ______    _____    _______

     1e-05    1e+05    0.23532
    0.0001    1e+05    0.23431
     0.001    1e+05    0.23457
      0.01    1e+05    0.23901
       0.1    1e+05     0.2714
         1    1e+05    0.51959
        10    1e+05     1.2671
       100    1e+05    0.91673
      1000    1e+05    0.40551
     10000    1e+05    0.25126
     1e+05    1e+05    0.21108

Input Arguments

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Bayesian optimization results, specified as a BayesianOptimization object.

Prediction points, specified as a table with D columns, where D is the number of variables in the problem. The function performs its predictions on these points.

Data Types: table

Output Arguments

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Estimated objective evaluation times, returned as an N-by-1 vector, where N is the number of rows of XTable. The estimated values are the means of the posterior distribution of the Gaussian process model of the evaluation times of the objective function.

Introduced in R2016b