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mEnsembleTree.m
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% Ensemble Tree (10/12/2020)
function ET = mEnsembleTree(feat,label,opts)
% Default
num_split = 50;
kfold = 10;
tf = 2;
if isfield(opts,'kfold'), kfold = opts.kfold; end
if isfield(opts,'ho'), ho = opts.ho; end
if isfield(opts,'tf'), tf = opts.tf; end
if isfield(opts,'nSplit'), num_split = opts.nSplit; end
% [Hold-out]
if tf == 1
fold = cvpartition(label,'HoldOut',ho);
% Call train & test data
xtrain = feat(fold.training,:); ytrain = label(fold.training);
xtest = feat(fold.test,:); ytest2 = label(fold.test);
% Train model
Temp = templateTree('MaxNumSplits',num_split);
Model = fitcensemble(xtrain,ytrain,'Learners',Temp,...
'Method','Bag');
% Test
pred2 = predict(Model,xtest);
% Accuracy
Afold = sum(pred2 == ytest2) / length(ytest2);
% [Cross-validation]
elseif tf == 2
% [Cross-validation]
fold = cvpartition(label,'KFold',kfold);
Afold = zeros(kfold,1);
pred2 = [];
ytest2 = [];
for i = 1:kfold
% Call train & test data
trainIdx = fold.training(i); testIdx = fold.test(i);
xtrain = feat(trainIdx,:); ytrain = label(trainIdx);
xtest = feat(testIdx,:); ytest = label(testIdx);
% Train model
Temp = templateTree('MaxNumSplits',num_split);
Model = fitcensemble(xtrain,ytrain,'Learners',Temp,...
'Method','Bag');
% Test
pred = predict(Model,xtest); clear Model
% Accuracy
Afold(i) = sum(pred == ytest) / length(ytest);
% Store temporary
pred2 = [pred2(1:end); pred];
ytest2 = [ytest2(1:end); ytest];
end
% [Leave one out]
elseif tf == 3
fold = cvpartition(label,'LeaveOut');
% Size of data
num_data = length(label);
Afold = zeros(num_data,1);
pred2 = [];
ytest2 = [];
for i = 1:num_data
% Call train & test data
trainIdx = fold.training(i); testIdx = fold.test(i);
xtrain = feat(trainIdx,:); ytrain = label(trainIdx);
xtest = feat(testIdx,:); ytest = label(testIdx);
% Train model
Temp = templateTree('MaxNumSplits',num_split);
Model = fitcensemble(xtrain,ytrain,'Learners',Temp,...
'Method','Bag');
% Test
pred = predict(Model,xtest); clear Model
% Accuracy
Afold(i) = sum(pred == ytest) / length(ytest);
% Store temporary
pred2 = [pred2(1:end); pred];
ytest2 = [ytest2(1:end); ytest];
end
end
% Confusion matrix
confmat = confusionmat(ytest2,pred2);
% Overall accuracy
acc = mean(Afold);
% Store
ET.acc = acc;
ET.con = confmat;
if tf==1
fprintf('\n Accuracy (ET-HO): %g %%',100 * acc);
elseif tf == 2
fprintf('\n Accuracy (ET-CV): %g %%',100 * acc);
elseif tf == 3
fprintf('\n Accuracy (ET-LOO): %g %%',100 * acc);
end
end