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nFold_BaggedC45_withFeatureselection.m
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function [stats, feature_scores]= nFold_BaggedC45_withFeatureselection(data_set,data_labels,feature_list,shuffle,n,nIter,Subsets)
% Using n-fold subset selection and C4.5 decision tree classifier
% Input:
% data_set: data
% data_labels: labels
% shuffle: 1 for random, 0 for non-random partition (Default: 1)
% n: Number of folds to your cross-validation (Default: 3)
% nIter: Number of cross-validation iterations (Default: 1)
% Subsets: pass your own training and testing subsets & labels (Default:
% computer will generate using 'nFold')
%
% Output:
% stats: struct containing TP, FP, TN, FN, etc.
% data_set = eigVec_graphfeats;
% data_labels = labels(:);
% classifier = 'SVM';
% shuffle = 1;
% n = 3;
if nargin < 7
Subsets = {};
end
if nargin < 6
nIter = 1;
end
if nargin < 5
n = 4; % 3-fold cross-validation
end
if nargin < 4
shuffle = 1; % randomized
end
% if any(~xor(data_labels == 1, data_labels == -1)), error('Labels must be 1 and -1'); end
feature_scores=zeros(size(data_set,2),1);
stats = struct; %cell(1,nIter);
for j=1:nIter
fprintf('Iteration: %i\n',j);
% reset total statistics
Ttp = 0; Ttn = 0; Tfp = 0; Tfn = 0; decision=zeros(size(data_labels)); prediction=zeros(size(data_labels));
if isempty(Subsets)
[tra tes]=GenerateSubsets('nFold',data_set,data_labels,shuffle,n);
else
tra = Subsets{j}.training;
tes = Subsets{j}.testing;
end
for i=1:n
fprintf(['Fold # ' num2str(i) '\n']);
training_set = data_set(tra{i},:);
testing_set = data_set(tes{i},:);
training_labels = data_labels(tra{i});
testing_labels = data_labels(tes{i});
%%% do feature selection on the fly
K=5;
%% using mrmr
% map the data in [0,100] and all integers
% dataw_discrete=makeDataDiscrete(training_set);
% setAll=1:size(training_set,2);
% [idx_TTest] = mrmr_mid_d(dataw_discrete(:,setAll), training_labels, K);
% setTopF_mRMR=feature_list(idx_TTest)';
%% using ttest
[TTidx,confidence] = prunefeatures_new(training_set, training_labels, 'ttestp');
% find(confidence<0.05);
% [TTidx,confidence] = prunefeatures_new(training_set, training_labels, 'wilcoxon');
% [Wilcoxonidx,confidence] = prunefeatures_new(dataw, labels, 'wilcoxon');
idx_TTest=TTidx(confidence<0.05);
if isempty(idx_TTest)
idx_TTest=TTidx(1:5);
end
%%% lock down top features with low correlation
set_candiF=Lpick_top_n_features_with_pvalue_correlation(training_set,idx_TTest,9);
set_fff=feature_list(set_candiF)';
idx_TTest=set_candiF;
%% using forward sequential
% [TTidx,confidence] = prunefeatures_new(training_set, training_labels, 'ttestp');
% % set_potentialF_idx= confidence<0.05;
% set_potentialF_idx=1:100;%find(confidence<0.02);
% % set_potentialF_idx=find(confidence<0.5);
% % lulu
% fs1=TTidx(set_potentialF_idx);
%
% threefoldCVP = cvpartition(training_labels,'kfold',3);
%
% % fun = @(XT,yT,Xt,yt)...
% % (sum(~strcmp(yt,classify(Xt,XT,yT,'quadratic'))));
% % try
% % fun = @(XT,yT,Xt,yt)...
% % (sum(~strcmp(yt,classify(Xt,XT,yT,'linear'))));
% %
% % fsLocal = sequentialfs(fun,training_set(:,fs1),training_labels','cv',threefoldCVP);
% % catch
% % fun = @(XT,yT,Xt,yt)...
% % (sum(~strcmp(yt,classify(Xt,XT,yT,'diaglinear'))));
% % fsLocal = sequentialfs(fun,training_set(:,fs1),training_labels','cv',threefoldCVP);
% % end
% %
% % fun = @(XT,yT,Xt,yt)...
% % (sum(~strcmp(yt,classify(Xt,XT,yT,'diaglinear'))));
% fun = @(training_set,training_labels,testing_set,testing_labels)...
% (sum(~strcmp(testing_labels,classf_RandomForest(training_set,testing_set,training_labels,testing_labels))));
%
% fsLocal = sequentialfs(fun,training_set(:,fs1),training_labels','cv',threefoldCVP);
%
% idx_TTest=fs1(fsLocal);
% feature_list(idx_TTest)
%% test on the testing set
% a=setTopF_TTest{1};b=setTopF_TTest{2};
% strcmp
% interr=intersect(a,b);
% add one value on the piceked features
feature_scores(idx_TTest)=feature_scores(idx_TTest)+1;
% [temp_stats,methodstring] = Classify( 'SVM', training_set(:,idx_TTest) , testing_set(:,idx_TTest), training_labels(:), testing_labels(:));
[temp_stats,methodstring] = Classify( 'BaggedC45', training_set(:,idx_TTest) , testing_set(:,idx_TTest), training_labels(:), testing_labels(:));
% [temp_stats,methodstring] = Classify( 'QDA', training_set(:,idx_TTest) , testing_set(:,idx_TTest), training_labels(:), testing_labels(:));
Ttp = Ttp + temp_stats.tp;
Ttn = Ttn + temp_stats.tn;
Tfp = Tfp + temp_stats.fp;
Tfn = Tfn + temp_stats.fn;
decision(tes{i}) = temp_stats.decision;
% decision(tes{i}) = temp_stats.prediction >= temp_stats.threshold;
prediction(tes{i}) = temp_stats.prediction;
end
decision(decision==0) = -1;
% output statistics
if numel(unique(data_labels))>1 %numel(unique(testing_labels))>1
if n == 1
[FPR,TPR,T,AUC,OPTROCPT,~,~] = perfcurve(data_labels(tes{i}),prediction(tes{i}),1);
else
[FPR,TPR,T,AUC,OPTROCPT,~,~] = perfcurve(data_labels,prediction,1);
end
stats(j).AUC = AUC;
stats(j).TPR = TPR;
stats(j).FPR = FPR;
else
stats(j).AUC = [];
stats(j).TPR = [];
stats(j).FPR = [];
end
stats(j).tp = Ttp;
stats(j).tn = Ttn;
stats(j).fp = Tfp;
stats(j).fn = Tfn;
stats(j).acc = (Ttp+Ttn)/(Ttp+Ttn+Tfp+Tfn);
stats(j).ppv = Ttp/(Ttp+Tfp);
stats(j).sens = Ttp/(Ttp+Tfn);
stats(j).spec = Ttn/(Tfp+Ttn);
stats(j).subsets.training = tra;
stats(j).subsets.testing = tes;
stats(j).labels = data_labels;
stats(j).decision = decision;
stats(j).prediction = prediction;
Pre = ((Ttp+Tfp)*(Ttp+Tfn) + (Ttn+Tfn)*(Ttn+Tfp)) / (Ttp+Ttn+Tfp+Tfn)^2;
stats(j).kappa = (stats(j).acc - Pre) / (1 - Pre);
end