Grid search in svm

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SP 2022-7-20

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1764040-grid-search-in-svm

评论： Walter Roberson 2022-7-22

Hi,

I am having training data (train.mat) and testing data (test.mat), I need to perform grid search in this. How can I do svm training with this? Kindly help me with this. Thanks in advance.

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Walter Roberson 2022-7-20

What is to be searched by the grid method?

SP 2022-7-20

I need to do svm hyperparameter tuning using grid search method.

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Answer 1

Walter Roberson 2022-7-20

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1764040-grid-search-in-svm#answer_1011630

在 MATLAB Online 中打开

Nbox = 15;    %change these as desired
Nkern = 10;   %change these as desired
bcvec = logspace(-3, 3, Nbox);
ksvec = logspace(-3, 3, Nkern);
goodness = zeros(Nbox, Nkern);
for ksidx = 1 : Nkern
    ks = ksvec(ksidx);
    for bcidx = 1 : Nbox
        bc = bcvec(bcidx);
        mdl = fitcsvm(Training_Data, Training_Class, ...
            'Standardize', true, ...
            'KernelFunction','rbf', ...
            'BoxConstraint', bc, ...
            'KernelScale', ks);
        predictions = predict(mdl, Test_Data);
        correct_matches = nnz(predictions == Test_Class);
        goodness(bcidx, ksidx) = correct_matches;
    end
end
best_score = max(goodness(:));
[bcidx, ksidx] = find(goodness == best_score);
best_bc = bcvec(bcidx);
best_ks = ksvec(ksidx);
best_fits = table(best_bc, best_ks, 'VariableNames', {'BoxConstraint', 'KernelScale'});

The output will be a table, best_fits, listing all of the combinations of BoxConstraint and KernelScale that together lead to the best scores discovered, where "score" here is determined [in this code] solely by the number of correct matches.

You would have to change how the goodness is calculated if you wanted to be concerned about aspects such as balancing the accuracies of the various classes. With the current code, if it was (for example) 100% successful in matching the largest class, and 0% successful in all of the other classes, then the goodness calculated by this code might be as high as it gets, even though it might be terrible for the other classes. You would probably be better off calculating the goodness some other way.

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Walter Roberson 2022-7-20

编辑：Walter Roberson 2022-7-20

在 MATLAB Online 中打开

temp = load('train.mat');
Training_Data = temp.VariableThatContainsTrainingData;
Training_Class = temp.VariableThatContainsTrainingClass;
temp = load('test.mat');
Test_Data = temp.VariableThatContainsTestData;
Test_Class = temp.VariableThatContainsTestClass;

I do not know the variable names in your train.mat and test.mat so I had to fill in placeholders like VariableThatContainsTrainingData .

Also it is not uncommon that the data and class information are combined, often with the class information being the last column of the data. In that case it might look more like

temp = load('train.mat');
Training_Data = temp.VariableThatContainsData(:,1:end-1);
Training_Class = temp.VariableThatContainsData(:,end);
temp = load('test.mat');
Test_Data = temp.VariableThatContainsData(:,1:end-1);
Test_Class = temp.VariableThatContainsData(:,end);

The rest of the code is a straight-forward double-nested loop, going through all defined combinations of BoxConstraint and KernelScale, and looking for the combination that produces the best score.

The only thing unusual about the code is that it would be common for people to assume that there is only one combination that produces the maximum score, but I programmed in the possibility that several combinations each produce the maximum score.

It is not clear to me why you do not just turn on hyperparameter optimization and let the built-in optimization code take care of the situation.

SP 2022-7-21

I have attached my mat files. It consists of matrix values. The mat files consists of 30 columns in which first 15 columns (1 to 15 columns) belongs to Class_1 and rest of the 15 columns (16 to 30 columns) belongs to Class_2.

Walter Roberson 2022-7-22

在 MATLAB Online 中打开

temp = load('train.mat');
Training_Data1 = temp.finaltrain(:,1:end/2);
Training_Data2 = temp.finaltrain(:,end/2+1:end);
Training_Data = [Training_Data1; Training_Data2];
Training_Class = [ones(size(Training_Data1, 1), 1); 2*ones(size(Training_Data2,1),1)];
temp = load('test.mat');
Test_Data1 = temp.finaltest(:,1:end/2);
Test_Data2 = temp.finaltest(:,end/2+1:end);
Test_Data = [Test_Data1; Test_Data2];
Test_Class = [ones(size(Test_Data1, 1), 1); 2*ones(size(Test_Data2,1),1)];
Nbox = 25;    %change these as desired
Nkern = 20;   %change these as desired
bcvec = logspace(-3, 3, Nbox);
ksvec = logspace(-3, 3, Nkern);
goodness = zeros(Nbox, Nkern);
wb = waitbar(0, 'please wait, processing kernel scales');
cleanMe = onCleanup(@() delete(wb));
for ksidx = 1 : Nkern
    waitbar(ksidx/Nkern, wb);
    ks = ksvec(ksidx);
    for bcidx = 1 : Nbox
        bc = bcvec(bcidx);
        mdl = fitcsvm(Training_Data, Training_Class, ...
            'Standardize', true, ...
            'KernelFunction','rbf', ...
            'BoxConstraint', bc, ...
            'KernelScale', ks);
        predictions = predict(mdl, Test_Data);
        correct_matches = nnz(predictions == Test_Class);
        goodness(bcidx, ksidx) = correct_matches;
    end
end
clear cleanMe  %get rid of waitbar
best_score = max(goodness(:));
[bcidx, ksidx] = find(goodness == best_score);
best_bc = bcvec(bcidx);
best_ks = ksvec(ksidx);
best_fits = table(best_bc, best_ks, 'VariableNames', {'BoxConstraint', 'KernelScale'});
best_fits

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