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nFold_AnyClassifier_withFeatureselection_v5.m
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% v5 can use balanced train set in CV
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Input:
% data_set: data
% data_labels: labels
% feature_list: the feature name list in cell
% para:
% parameter like what classifier you use, the number of top feature
% para.classifier='LDA';
% para.num_top_feature=5;
% para.featureranking='wilcoxon';
% para.correlation_factor=.9;
% 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.
% The function is written by Cheng Lu @2016
% example here:
% para.balanced_trainset=1;
% para.get_balance_sens_spec=1;
% para.feature_score_method='weighted';
% para.classifier='BaggedC45';
% para.num_top_feature=6;
% para.featureranking='wilcoxon';
% para.correlation_factor=.99;
% [resultImbalancedC45,feat_scores] = nFold_AnyClassifier_withFeatureselection_v5(data_cLoCoM_train_truncated_583(label_SC_AD,:),double(label_KRAS(label_SC_AD)),...
% feature_list_truncated,para,1,5,10);
% (c) Edited by Cheng Lu,
% Biomedical Engineering,
% Case Western Reserve Univeristy, cleveland, OH. Aug, 2016
% If you have any problem feel free to contact me.
% Please address questions or comments to: [email protected]
% Terms of use: You are free to copy,
% distribute, display, and use this work, under the following
% conditions. (1) You must give the original authors credit. (2) You may
% not use or redistribute this work for commercial purposes. (3) You may
% not alter, transform, or build upon this work. (4) For any reuse or
% distribution, you must make clear to others the license terms of this
% work. (5) Any of these conditions can be waived if you get permission
% from the authors.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% v4 can return the balance acc
function [stats, feature_scores]= nFold_AnyClassifier_withFeatureselection_v5(data_set,data_labels,feature_list,para,shuffle,n,nIter,Subsets)
data_labels=double(data_labels);
if nargin < 8
Subsets = {};
end
if nargin < 7
nIter = 1;
end
if nargin < 6
n = 4; % 3-fold cross-validation
end
if nargin < 5
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);
if size(data_set,1)~=length(data_labels)
error('the size of the feature data should be the same as the label data!!!');
end
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;
if isempty(Subsets)
if para.balanced_trainset
[tra tes]=GenerateSubsets('nFold_balanced_trainset',data_set,data_labels,shuffle,n);
% train=tra{1}; test=tes{1};
%intersect(train,test)
else
[tra tes]=GenerateSubsets('nFold',data_set,data_labels,shuffle,n);
end
decision=zeros(size(data_labels)); prediction=zeros(size(data_labels));
else
tra{1} = Subsets{j}.training;
tes{1} = Subsets{j}.testing;
% decision=zeros(size(tes{1})); prediction=zeros(size(tes{1}));
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});%sum(training_labels) sum(~training_labels)
testing_labels = data_labels(tes{i});%sum(testing_labels) sum(~testing_labels)
%%% do feature selection on the fly
%% using mrmr
if strcmp(para.featureranking,'mrmr')
% map the data in to binary values 0 1
dataw_discrete=makeDataDiscrete_mrmr(training_set);
% dataw_discrete=training_set>t; check check check
setAll=1:size(training_set,2);
[idx_TTest] = mrmr_mid_d(dataw_discrete(:,setAll), training_labels, para.num_top_feature);
end
%% using random forest
if strcmp(para.featureranking,'rf')
options = statset('UseParallel','never','UseSubstreams','never');
B = TreeBagger(50,training_set,training_labels,'FBoot',0.667, 'oobpred','on','OOBVarImp', 'on', 'Method','classification','NVarToSample','all','NPrint',4,'Options',options);
variableimportance = B.OOBPermutedVarDeltaError;
[t,idx]=sort(variableimportance,'descend');
idx_TTest=idx(1:para.num_top_feature);
end
if strcmp(para.featureranking,'ttest') | strcmp(para.featureranking,'wilcoxon')
%% using ttest
if strcmp(para.featureranking,'ttest')
[TTidx,confidence] = prunefeatures_new(training_set, training_labels, 'ttestp');
% idx_TTest=TTidx(confidence<0.05);
% if isempty(idx_TTest)
idx_TTest=TTidx(1:min(para.num_top_feature*8,size(data_set,2)));
% end
end
if strcmp(para.featureranking,'wilcoxon')
[TTidx,confidence] = prunefeatures_new(training_set, training_labels, 'wilcoxon');
% idx_TTest=TTidx(confidence<0.5);
% if isempty(idx_TTest)
idx_TTest=TTidx(1:min(para.num_top_feature*8,size(data_set,2)));
% end
end
%%% lock down top features with low correlation
set_candiF=Lpick_top_n_features_with_pvalue_correlation(training_set,idx_TTest,para.num_top_feature,para.correlation_factor);
set_fff=feature_list(set_candiF)'; % training_set(:,373)
idx_TTest=set_candiF;
end
%% test on the testing set
% a=setTopF_TTest{1};b=setTopF_TTest{2};
% strcmp
% interr=intersect(a,b);
if strcmp(para.feature_score_method,'addone')
% add one value on the piceked features
feature_scores(idx_TTest)=feature_scores(idx_TTest)+1;
end
if strcmp(para.feature_score_method,'weighted')
feature_scores(idx_TTest)=feature_scores(idx_TTest)+ linspace( para.num_top_feature ,1, length(idx_TTest))';
end
fprintf('on the fold, %d features are picked\n', length(idx_TTest));
try
if strcmp(para.classifier,'BaggedC45')
[temp_stats,methodstring] = Classify( 'BaggedC45', training_set(:,idx_TTest) , testing_set(:,idx_TTest), training_labels(:), testing_labels(:));
end
if strcmp(para.classifier,'QDA')|| strcmp(para.classifier,'qda')
[temp_stats,methodstring] = Classify( 'QDA', training_set(:,idx_TTest) , testing_set(:,idx_TTest), training_labels(:), testing_labels(:));
end
if strcmp(para.classifier,'LDA') ||strcmp(para.classifier,'lda')
[temp_stats,methodstring] = Classify( 'LDA', training_set(:,idx_TTest) , testing_set(:,idx_TTest), training_labels(:), testing_labels(:));
end
if strcmp(para.classifier,'SVM')||strcmp(para.classifier,'svm')
if exist('para.params','var')
params.kernel=para.params.kernel;
params.c_range=para.params.c_range;
params.g_range=para.params.g_range;
params.cvfolds=para.params.cvfolds;
[temp_stats,methodstring] = Classify( 'SVM', training_set(:,idx_TTest) , testing_set(:,idx_TTest), training_labels(:), testing_labels(:),params);
else
[temp_stats,methodstring] = Classify( 'SVM', training_set(:,idx_TTest) , testing_set(:,idx_TTest), training_labels(:), testing_labels(:));
end
temp_stats.decision=temp_stats.predicted_labels;
temp_stats.prediction=temp_stats.prob_estimates(:,1);
end
catch
display('Error while using LDA or QDA, the training data is linear dependent, use Random Forest for this fold instead\n');
[temp_stats,methodstring] = Classify( 'BaggedC45', training_set(:,idx_TTest) , testing_set(:,idx_TTest), training_labels(:), testing_labels(:));
end
Ttp = Ttp + temp_stats.tp;
Ttn = Ttn + temp_stats.tn;
Tfp = Tfp + temp_stats.fp;
Tfn = Tfn + temp_stats.fn;
if ~isempty(Subsets)
stats=temp_stats;
return;
end
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);
% get a blance sens and spec to report
if para.get_balance_sens_spec
spe=1-FPR;
labels=stats(j).labels;
balanceAcc=(spe+TPR)/2;
[~,maxIdx]=max(balanceAcc);
stats(j).sens=TPR(maxIdx);
stats(j).spec=1-FPR(maxIdx);
stats(j).tp=round(stats(j).sens*sum(labels));
stats(j).tn=round(stats(j).spec*sum(~labels));
stats(j).fp=sum(~labels)-stats(j).tn;
stats(j).fn=sum(labels)-stats(j).tp;
stats(j).acc=(stats(j).tp+stats(j).tn)/length(labels);
%% modeified other metrics if neccesary !!
end
end