predictObjectiveEvaluationTime

Predict objective function run times at a set of points

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Syntax

time = predictObjectiveEvaluationTime(results,XTable)

Description

example

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

Examples

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Open Live Script

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);
end
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,...
    'KernelFunction','rbf','BoxConstraint',z.box,...
    'KernelScale',z.sigma));
results = bayesopt(minfn,[sigma,box],'IsObjectiveDeterministic',true,...
    'AcquisitionFunctionName','expected-improvement-plus','Verbose',0);

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);
[XTable,table(time)]
ans=11×3 table
    sigma      box      time  
    ______    _____    _______

     1e-05    1e+05    0.19622
    0.0001    1e+05    0.19345
     0.001    1e+05     0.1741
      0.01    1e+05    0.14076
       0.1    1e+05    0.13461
         1    1e+05    0.42671
        10    1e+05     1.6299
       100    1e+05    0.58799
      1000    1e+05     0.1402
     10000    1e+05    0.15671
     1e+05    1e+05    0.17544

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.

See Also

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Introduced in R2016b

Statistics and Machine Learning Toolbox Documentation

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