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classify_test_ds2.m
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classify_test_ds2.m
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function classify_test_ds2(featureSet, featSel, classifier, param1, param2, param3)
%function classify_test_ds2(featureSet, featSel, classifier, C, gamma)
% Classificação de Arritmia. Treino o modelo com DS1 e valida em DS2.
%
% >> featureSet: conjunto de características
% Pode assumir os valores:
% 'YuCN','YuFourier', '2005Chazal', '2005Guler', 2005Song, 2006Yu, 2007Yu, 2010YeCoimbra, VCGComplexNet
%
% >> featSel: vetor para seleção de características
% Deve ser no formato: fs = [1 1 0 0 0 1 0 0 1], do dimensão das
% características onde:
% 1 - usar a característica
% 0 - ignorar a característica
% passe um vetor vazio para utilizar todas as características
%
% >> classifier: classificador a ser utilizado
% Pode assumir os valores:
%
% 'SVM', 'LD', 'MLP', 'PNN'
%
% param1
% C: parâmetro para SVM
% 0: escolhe default (0.05)
% -1: utiliza script para grid selection
%
% param2
% gamma: parâmetro para SVM
% 0: escolhe default (1/8*num features)
%
% Autor: Eduardo Luz
%
if nargin < 4
%C = 0;
%gamma = 0; % default
% default
param1 = 0;
param2 = 0;
param3 = 0;
elseif nargin < 5
param2 = 0;
param3 = 0;
elseif nargin < 6
param3 = 0;
end
s = char(featureSet);
if (~isempty(featSel))
numfs = size(featSel,2);
fileNamed = ['results\',s,'\',classifier,'_','_test_ds2_results_FS_' num2str(numfs) '.tex'];
else
fileNamed = ['results\',s,'\',classifier,'_','_test_ds2_results_allFeatures.tex'];
end
arq = fopen(fileNamed,'w');
% Tabela latex dos resultados
fprintf(arq,'\\documentclass{article}\n');
fprintf(arq,'\\usepackage{graphicx}\n');
fprintf(arq,'\\usepackage[latin1]{inputenc}\n');
fprintf(arq,'\\usepackage{tabularx}\n');
fprintf(arq,'\\usepackage{multirow}\n');
fprintf(arq,'\\newcommand{\\citep}{\\cite}\n');
fprintf(arq,'\\newcommand{\\citet}{\\cite}\n');
fprintf(arq,'\\newcommand{\\TFigure}{Fig.}\n');
fprintf(arq,'\\begin{document}\n');
fprintf(arq,'\n');
fprintf(arq,'\\begin{table*} \n');
fprintf(arq,'\\footnotesize \n');
s2 = char('\\caption{Tebela de resultados por paciente ');
s2 = [s2 s(10:end-1) '} \n'];
fprintf(arq,s2);
%fprintf(arq,' \\caption{Tebela dos registros do método} \n');
fprintf(arq,' \\label{tab:regtable} \n');
fprintf(arq,' \\begin{center} \n');
fprintf(arq,' \\begin{tabular}{|c|c|c|c|c|c|c|} \n');
fprintf(arq,' \\hline \n');
fprintf(arq,' Registro & Acc & N Se/+P/FPR & SVEB Se/+P/FPR & VEB Se/+P/FPR & F Se/+P/FPR & Q Se/+P/FPR \\\\ \n');
fprintf(arq,' \\hline \n');
% iniciliza variaveis
finalCM = zeros(5,5);
% Inicializa os registros
DS1 = {'101';'106';'108';'109';'112';'114';'115';'116';'118';'119';'122';'124';'201';'203';'205';'207';'208';'209';'215';'220';'223';'230'};
DS2 = {'100';'103';'105';'111';'113';'117';'121';'123';'200';'202';'210';'212';'213';'214';'219';'221';'222';'228';'231';'232';'233';'234'};
%DS1 = {'101';'106'};
%DS2 = {'100';'103'};
% imprime a tabela de registros em arquvo .tex
printRegisterTable(['features\' featureSet '\']);
printRegisterTable_15_classes(['features\' featureSet '\']);
fprintf('\n Registro | Acc | N_Se SVEB_Se VEB_Se N+P SVEB+P VEB+P \n')
test_da = [];
train_ds = [];
for j=1:size(DS1,1)
train_ds(j) = str2double(DS1(j));
end
first_time=1;
for k=1:size(DS2,1) % numero de registros
test_ds = str2double(DS2(k));
%% Carrega os dados
feat_folder = ['features\' featureSet '\'];
[p1d p1t p2d p2t] = loadDataAAMI(0,feat_folder,train_ds,test_ds);
if ~isempty(featSel)
fsel = find(featSel==1);
p1d = p1d(:,fsel) ;
p2d = p2d(:,fsel) ;
end
%primeira etapa com DS1 treino DS2 teste
fs1.train = p1d;
fs1.test = p2d;
target.train = p1t;
target.test = p2t;
if(strcmp(classifier,'LD'))
%[fs1.train fs1.test] = normaliza(fs1.train, fs1.test);
elseif(strcmp(classifier,'PNN'))
% normalização feita em classifyPNN(..)
else
[fs1.train, scale_factor] = mapminmax(fs1.train');
fs1.test = mapminmax('apply',fs1.test',scale_factor);
fs1.train = fs1.train';
fs1.test = fs1.test';
end
%% aplica o classificador
if(strcmp(classifier,'LD'))
cd('LD_classifier')
cm1 = ld_Classifier_chazal(fs1,fs1,target);
%fprintf('\n----------------------- Classificador LD ---------------------\n')
cd ..
elseif(strcmp(classifier,'SVM'))
cd('svm')
tic
if(first_time==1)
first_time=0;
if param1 == -1
[newData newTarget] = undersampling_all_class(20, fs1.train,target.train);
[best_c,best_g,best_cv,hC] = parameter_optimization(newData,newTarget);
else
if param1 == 0
best_c=0.05;
else
best_c=param1;
end
if param2 ==0
best_g=1/(8*size(fs1.train,2));
else
best_g= param2;
end
end
%[newData newTarget] = SMOTE(fs1.train, target.train, 2, 4000, 5);
[cm1 model] = svm_Classifier(fs1.train,target.train,fs1.test,target.test,best_c,best_g);
else
testingLabels = [];
targetsTest = [];
% formata para uso em SVM - para cada classe
for t=1:size(target.test,2)
targetsTest(find(target.test(:,t)==1),t)=t;
end
testingLabels = sum(targetsTest');
[predict_label, accuracy, dec_values] = svmpredict(testingLabels', fs1.test, model); % test the training data
%cm1 = confusionmat(testingLabels',predict_label);
clear predict_matrix;
for i=1: size(predict_label,1)
switch(predict_label(i))
case 1,
predict_matrix(i,:) = [1 0 0 0 0];
case 2,
predict_matrix(i,:) = [0 1 0 0 0];
case 3,
predict_matrix(i,:) = [0 0 1 0 0];
case 4,
predict_matrix(i,:) = [0 0 0 1 0];
case 5,
predict_matrix(i,:) = [0 0 0 0 1];
end
end
clear label_matrix;
for i=1: size(testingLabels,2)
switch(testingLabels(i))
case 1,
label_matrix(i,:) = [1 0 0 0 0];
case 2,
label_matrix(i,:) = [0 1 0 0 0];
case 3,
label_matrix(i,:) = [0 0 1 0 0];
case 4,
label_matrix(i,:) = [0 0 0 1 0];
case 5,
label_matrix(i,:) = [0 0 0 0 1];
end
end
%[C,CM,IND,PER] = CONFUSION(TARGETS,OUTPUTS)
[c cm1] = confusion(label_matrix', predict_matrix');
end
toc
%fprintf('\n----------------------- Classificador SVM
%---------------------\n')
cd ..
%elseif(strcmp(classifier,'PNN'))
% cd pnn
%
% [acc1 sensitivityN1 sensitivitySVEB1 sensitivityVEB1 sensitivityF1 sensitivityQ1 specificitySVEB1 specificityVEB1 specificityF1 specificityQ1] = pnn_Classifier(fs.train,target.train,fs.test,target.test);
% [acc2 sensitivityN2 sensitivitySVEB2 sensitivityVEB2 sensitivityF2
% sensitivityQ2 specificitySVEB2 specificityVEB2 specificityF2 specificityQ2] = svm_Classifier(fs.train,target.train,fs.test,target.test);%
% cd ..
elseif(strcmp(classifier,'MLP'))
cd('mlp')
tic
if(first_time==1)
first_time=0;
[cm1 net1 net2 net3 net4 combNet] = mlp_Classifier(fs1.train,target.train,fs1.test,target.test);
else
%% Testa o método com input2 e target2 (dados de teste)
outputs1 = sim(net1,fs1.test');
outputs2 = sim(net2,fs1.test');
outputs3 = sim(net3,fs1.test');
outputs4 = sim(net4,fs1.test');
newInputTest = [outputs1;outputs2;outputs3;outputs4];
predictedTarget2 = sim(combNet,newInputTest);
% cria matriz de confusão
% c = total accuracy
% cm = matriz de confusão
% per = S x 3 matrix, where each ith row represents the percentage of false
% negatives, false positives, and true positives for the ith category
[c,cm1,ind,per] = confusion(target.test',predictedTarget2);
end
%fprintf('\n----------------------- Classificador MLP comb ---------------------\n')
toc
cd ..
elseif(strcmp(classifier,'PNN'))
cd('pnn')
if(first_time==1)
first_time=0;
if param1 == 0
spread = 0.1;
else
spread = param1;
end
[cm1 net] = classifyPNN(fs1.train,target.train,fs1.test,target.test, spread);
else
%formata para pnn
testingLabels = [];
targetsTest = [];
for t=1:size(target.test,2)
targetsTest(find(target.test(:,t)==1),t)=t;
end
testingLabels = sum(targetsTest');
for(i=1:length(fs1.test(:,1)))
for(j=1:length(fs1.test(1,:)))
%normaliza tb a matriz de teste
fs1.test(i,j) = tansig((fs1.test(i,j)-mean(fs1.test(i,:)))/std(fs1.test(i,:)));
end
end
predict_label = sim(net,fs1.test');
%[c cm] = confusion(target.test', predict_label);
predict_label = predict_label';
clear predict_matrix;
predict_matrix=[];
for t=1:5
predict_matrix(find(predict_label(:,t)==1),t)=1;
end
clear label_matrix;
label_matrix=[];
for i=1: size(testingLabels,2)
switch(testingLabels(i))
case 1,
label_matrix(i,:) = [1 0 0 0 0];
case 2,
label_matrix(i,:) = [0 1 0 0 0];
case 3,
label_matrix(i,:) = [0 0 1 0 0];
case 4,
label_matrix(i,:) = [0 0 0 1 0];
case 5,
label_matrix(i,:) = [0 0 0 0 1];
end
end
%[C,CM,IND,PER] = CONFUSION(TARGETS,OUTPUTS)
[c cm1] = confusion(label_matrix', predict_matrix');
if size(cm1,1)<5
cm_N = zeros(5,5);
for i=1:5
for j=1:5
if i <= size(cm1,1) && j <= size(cm1,2)
cm_N(i,j) = cm1(i,j);
end
end
end
cm1 = cm_N;
end
end
cd ..
end
% Calcula estatíSticas
% Calcula estatíSticas
cm1
finalCM = finalCM + cm1;
acc_num=0;
acc_den=0;
den1=0;
den2=0;
num=0;
t=0;
if(size(cm1,1)>=1)
t = 1;
num = cm1(t,t);
den1 = sum(cm1(t,:));
den2 = sum(cm1(:,t));
TN = sum(sum(cm1(:,:))) - den1 - den2 + cm1(t,t);
FP = den2 - cm1(t,t);
if(den1~=0)
sensitivityN = (num/den1) * 100;
else
sensitivityN = -1;
end
if(den2~=0)
specificityN = (num/den2) * 100;
else
specificityN = 0;
end
if(TN + FP > 0)
FPR_N = 100*FP/(TN+FP);
else
FPR_N=0;
end
acc_num = acc_num + num;
acc_den = acc_den + den1;
else
sensitivityN = -1;
specificityN = -1;
FPR_N=-1;
end
% caso especial para LD classifier
%size(cm1,1)==5
if(size(cm1,1)>=2)
t = 2;
num = cm1(t,t);
den1 = sum(cm1(t,:));
den2 = sum(cm1(:,t));
TN = sum(sum(cm1(:,:))) - den1 - den2 + cm1(t,t);
FP = den2 - cm1(t,t);
if(den1~=0)
sensitivitySVEB = (num/den1) * 100;
else
sensitivitySVEB = -1;
end
if(den2~=0)
specificitySVEB = (num/den2) * 100;
else
specificitySVEB = -1;
end
if(TN + FP > 0)
FPR_SVEB = 100*FP/(TN+FP);
else
FPR_SVEB=-1;
end
acc_num = acc_num + num;
acc_den = acc_den + den1;
else
sensitivitySVEB = -1;
specificitySVEB = -1;
FPR_SVEB=-1;
end
if(size(cm1,1)>=3)
t = 3;
num = cm1(t,t);
den1 = sum(cm1(t,:));
den2 = sum(cm1(:,t));
TN = sum(sum(cm1(:,:))) - den1 - den2 + cm1(t,t);
FP = den2 - cm1(t,t);
if(den1~=0)
sensitivityVEB = (num/den1) * 100;
else
sensitivityVEB = -1;
end
if(den2~=0)
specificityVEB = (num/den2) * 100;
else
specificityVEB = -1;
end
if(TN + FP > 0)
FPR_VEB = 100*FP/(TN+FP);
else
FPR_VEB=-1;
end
acc_num = acc_num + num;
acc_den = acc_den + den1;
else
sensitivityVEB = -1;
specificityVEB = -1;
FPR_VEB=-1;
end
if(size(cm1,1)>=4)
t = 4;
num = cm1(t,t);
den1 = sum(cm1(t,:));
den2 = sum(cm1(:,t));
TN = sum(sum(cm1(:,:))) - den1 - den2 + cm1(t,t);
FP = den2 - cm1(t,t);
if(den1~=0)
sensitivityF = (num/den1) * 100;
else
sensitivityF = -1;
end
if(den2~=0)
specificityF = (num/den2) * 100;
else
specificityF = -1;
end
if(TN + FP > 0)
FPR_F = 100*FP/(TN+FP);
else
FPR_F=-1;
end
acc_num = acc_num + num;
acc_den = acc_den + den1;
else
sensitivityF = -1;
specificityF = -1;
FPR_F=-1;
end
if(size(cm1,1)>=5)
t = 5;
num = cm1(t,t);
den1 = sum(cm1(t,:));
den2 = sum(cm1(:,t));
TN = sum(sum(cm1(:,:))) - den1 - den2 + cm1(t,t);
FP = den2 - cm1(t,t);
if(den1~=0)
sensitivityQ = (num/den1) * 100;
else
sensitivityQ = -1;
end
if(den2~=0)
specificityQ = (num/den2) * 100;
else
specificityQ = -1;
end
if(TN + FP > 0)
FPR_Q = 100*FP/(TN+FP);
else
FPR_Q=-1;
end
acc_num = acc_num + num;
acc_den = acc_den + den1;
else
sensitivityQ = -1;
specificityQ = -1;
FPR_Q=-1;
end
fprintf(arq,'%6d & %6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f \\\\ \n',...
str2double(DS2(k)), 100*acc_num/acc_den, sensitivityN,specificityN,FPR_N,sensitivitySVEB,specificitySVEB,FPR_SVEB,sensitivityVEB,specificityVEB,FPR_VEB,...
sensitivityF,specificityF,FPR_F, sensitivityQ,specificityQ,FPR_Q);
fprintf(arq,'\n');
fprintf('\n');
fprintf('Registro=%6.0f | Acc=%6.1f |', str2double(DS2(k)), 100*acc_num/acc_den);
fprintf(' N_Se=%6.1f SVEB_Se=%6.1f VEB_Se=%6.1f N+P=%6.1f SVEB+P=%6.1f VEB+P=%6.1f \n\n',...
sensitivityN, sensitivitySVEB,sensitivityVEB, specificityN, specificitySVEB,specificityVEB)
end % for t
fprintf(arq,' \\hline \n');
%fprintf(arq,' \\hline \n');
TN = sum(sum(finalCM(:,:))) - sum(finalCM(1,:)) - sum(finalCM(:,1)) + finalCM(1,1);
FP = sum(finalCM(:,1)) - finalCM(1,1);
FPR_N = FP/(TN+FP);
TN = sum(sum(finalCM(:,:))) - sum(finalCM(2,:)) - sum(finalCM(:,2)) + finalCM(2,2);
FP = sum(finalCM(:,2)) - finalCM(2,2);
FPR_SVEB = FP/(TN+FP);
TN = sum(sum(finalCM(:,:))) - sum(finalCM(3,:)) - sum(finalCM(:,3)) + finalCM(3,3) ;
FP = sum(finalCM(:,3)) - finalCM(3,3);
FPR_VEB = FP/(TN+FP);
if size(finalCM,1) > 3
TN = sum(sum(finalCM(:,:))) - sum(finalCM(4,:)) - sum(finalCM(:,4)) + finalCM(4,4);
FP = sum(finalCM(:,4)) - finalCM(4,4);
FPR_F = FP/(TN+FP);
TN = sum(sum(finalCM(:,:))) - sum(finalCM(5,:)) - sum(finalCM(:,5)) + finalCM(5,5);
FP = sum(finalCM(:,5)) - finalCM(5,5);
FPR_Q = FP/(TN+FP);
fprintf(arq, ' Gross & %6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f \\\\ \n',...
100*(finalCM(1,1)+finalCM(2,2)+finalCM(3,3)+finalCM(4,4)+finalCM(5,5))/(sum(finalCM(1,:))+sum(finalCM(2,:))+sum(finalCM(3,:))+sum(finalCM(4,:))+sum(finalCM(5,:))) ,...
100*finalCM(1,1)/sum(finalCM(1,:)), 100*finalCM(1,1)/sum(finalCM(:,1)),100*FPR_N,...
100*finalCM(2,2)/sum(finalCM(2,:)),100*finalCM(2,2)/sum(finalCM(1:end-1,2)),100*FPR_SVEB,...
100*finalCM(3,3)/sum(finalCM(3,:)),100*finalCM(3,3)/sum(finalCM(1:end-2,3)),100*FPR_VEB,...
100*finalCM(4,4)/sum(finalCM(4,:)),100*finalCM(4,4)/sum(finalCM(:,4)),100*FPR_F,...
100*finalCM(4,4)/sum(finalCM(5,:)),100*finalCM(5,5)/sum(finalCM(:,5)),100*FPR_Q);
else
fprintf(arq, ' Gross & %6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f & %6.1f/%6.1f/%6.1f & - & - \\\\ \n',...
100*(finalCM(1,1)+finalCM(2,2)+finalCM(3,3))/(sum(finalCM(1,:))+sum(finalCM(2,:))+sum(finalCM(3,:))) ,...
100*finalCM(1,1)/sum(finalCM(1,:)), 100*finalCM(1,1)/sum(finalCM(:,1)),100*FPR_N,...
100*finalCM(2,2)/sum(finalCM(2,:)),100*finalCM(2,2)/sum(finalCM(:,2)),100*FPR_SVEB,...
100*finalCM(3,3)/sum(finalCM(3,:)),100*finalCM(3,3)/sum(finalCM(:,3)),100*FPR_VEB);
end
fprintf(arq,' \\hline \n');
fprintf(arq,' \\end{tabular} \n');
fprintf(arq,' \\end{center} \n');
fprintf(arq,'\\end{table*} \n');
fprintf(arq,'\n');
fprintf(arq,'\n');
fprintf(arq,'\n');
fprintf(arq,'\\begin{table*} \n');
fprintf(arq,' \\caption{Matriz e confusão} \n');
fprintf(arq,' \\label{tab:regtable} \n');
fprintf(arq,' \\begin{center} \n');
fprintf(arq,' \\begin{tabular}{|');
for tt=1:size(finalCM,1)
fprintf(arq,'c|');
end
fprintf(arq,'} \n');
fprintf(arq,'\n');
fprintf(arq,' \\hline \n');
for tt=1:size(finalCM,1)
for uu=1:size(finalCM,2)
if uu==5
fprintf(arq,'%6.0f',finalCM(tt,uu));
else
fprintf(arq,'%6.0f & ',finalCM(tt,uu));
end
end
fprintf(arq,' \\\\ \n');
fprintf(arq,' \\hline \n');
end
fprintf(arq,' \\hline \n');
fprintf(arq,' \\end{tabular} \n');
fprintf(arq,' \\end{center} \n');
fprintf(arq,'\\end{table*} \n');
fprintf(arq,'\n');
if(strcmp(classifier,'SVM'))
fprintf(arq,'SVM parameters - C = %6.2f, gamma= %6.2f',best_c, best_g);
end
fprintf(arq,'\n');
fprintf(arq,'\\end{document}\n');
fclose(arq);
fprintf('\n Gross Statistics:\n');
fprintf(' Gross & %6.1f & %6.1f & %6.1f & %6.1f & %6.1f & %6.1f & %6.1f & %6.1f & %6.1f & %6.1f & %6.1f \\\\ \n',...
100*(finalCM(1,1)+finalCM(2,2)+finalCM(3,3)+finalCM(4,4)+finalCM(5,5))/(sum(finalCM(1,:))+sum(finalCM(2,:))+sum(finalCM(3,:))+sum(finalCM(4,:))+sum(finalCM(5,:))) ,...
100*finalCM(1,1)/sum(finalCM(1,:)), 100*finalCM(2,2)/sum(finalCM(2,:)), 100*finalCM(3,3)/sum(finalCM(3,:)), 100*finalCM(4,4)/sum(finalCM(4,:)), 100*finalCM(5,5)/sum(finalCM(5,:)),...
100*finalCM(1,1)/sum(finalCM(:,1)), 100*finalCM(2,2)/sum(finalCM(:,2)), 100*finalCM(3,3)/sum(finalCM(:,3)), 100*finalCM(4,4)/sum(finalCM(:,4)), 100*finalCM(5,5)/sum(finalCM(:,5)));
end