rankroccurve.m

一个支持向量机的matlab工具箱

function [AUC,tpr,fpr,b]=rankroccurve(ypred,ytest)

% USAGE
%
% [AUC,tpr,fpr,WMW,b]=rankroccurve(ypred,ytest)
%
% the outputs
%
% AUC       Area under curve value
% tpr,fpr   true positive and false positive vector for ROC curve plotting purpose
% b         a new bias for the decision function. b can replace w0 and it has been
%           processed so that the decision function corresponds to the one where the roc
%            curve and the (1,0)-(0,1) diagonal meets
%

% 30/07/2004 A. Rakotomamonjy


eps=1e-7;
classcode=[1 -1];
w0=0;

ypredsorted=sort(ypred);
npos=length(find(ytest==classcode(1)));
nneg=length(find(ytest==classcode(2)));
n=npos+nneg;
  
i=1;
b=ypredsorted(1);
yaux=ypred-b+eps;
C=ConfusionMatrix(sign(yaux),ytest,classcode);
tp(i)=C(1,1);
fp(i)=C(2,1);

for i=1:n
    b=ypredsorted(i);
    yaux=ypred-b;
    C=ConfusionMatrix(sign(yaux),ytest,classcode);
    tp(i+1)=C(1,1);
    fp(i+1)=C(2,1);
end;
yaux=ypred-b-eps;
C=ConfusionMatrix(sign(yaux),ytest,classcode);
tp(i+2)=C(1,1);
fp(i+2)=C(2,1);
tpr=tp/npos;
fpr=fp/nneg;
difffpr=-diff(fpr);
AUC=sum(tpr(1:end-1).*difffpr);

%
% WMW statistics

% indpos=find(ytest==classcode(1));
% indneg=find(ytest==classcode(2));
% A=0;
% for i=1:npos
%     A=A+ sum( ypred(indpos(i))> ypred(indneg));
%     
% end;
% WMW=A/npos/nneg;


% calcul de b pour un cout erreur egale