gusucode.com > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM源码程序 > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM\LS_SVMlab\roc.m
function [AREA,SE,RESULT_S,FPR_ROC,TPR_ROC,TNa,TPa,FNa,FPa]=roc(RESULT,CLASS,fig)
% Receiver Operating Characteristic (ROC) curve of a binary classifier
%
% >> [area, se, deltab, oneMinusSpec, sens, TN, TP, FN, FP] = roc(Zt, Y)
%
% The ROC curve shows the separation abilities of a binary
% classifier: by iteratively setting the possible classifier
% thresholds, the dataset is tested on misclassifications. As a
% result, a plot is shown where the various outcomes are
% described. If the plot has a surface of 1 on test data, a
% perfectly separating classifier is found (on that particular
% dataset), if the area equals 0.5, the classifier has no
% discriminative power at all. In general, this function can be
% called with the latent variables Zt and the corresponding class labels Yclass
%
% >> Zt = [-.7 Yclass = [-1
% .3 -1
% 1.5 1
% ... ..
% -.2] 1]
% >> roc(Zt, Yclass)
%
% For use in LS-SVMlab, a shorthand notation allows making the ROC
% curve on the training data. Implicit training and simulation of
% the latent values simplifies the call.
%
% >> roc({X,Y,'classifier',gam,sig2,kernel})
% >> roc(model)
%
%
% Full syntax
%
% 1. Standard call (LS-SVMlab independent):
%
% >> [area, se, deltab, oneMinusSpec, sens, TN, TP, FN, FP] = roc(Zt, Y)
% >> [area, se, deltab, oneMinusSpec, sens, TN, TP, FN, FP] = roc(Zt, Y, figure)
%
% Outputs
% area(*) : Area under the ROC curve
% se(*) : Standard deviation of the residuals
% deltab(*) : N x 1 different thresholds value
% oneMinusSpec(*) : 1-Specificity of each threshold value
% sens(*) : Sensitivity for each threshold value
% TN(*) : Number of true negative predictions
% TP(*) : Number of true positive predictions
% FN(*) : Number of false negative predictions
% FP(*) : Number of false positive predictions
% Inputs
% Zt : N x 1 latent values of the predicted outputs
% Y : N x 1 of true class labels
% figure(*) : 'figure'(*) or 'nofigure'
%
%
% 2. Using the functional interface for the LS-SVMs:
%
% >> [area, se, deltab, oneMinusSpec, sens, TN, TP, FN, FP] = roc({X,Y,'classifier',gam,sig2,kernel})
% >> [area, se, deltab, oneMinusSpec, sens, TN, TP, FN, FP] = roc({X,Y,'classifier',gam,sig2,kernel}, figure)
%
% Outputs
% area(*) : Area under the ROC curve
% se(*) : Standard deviation of the residuals
% deltab(*) : Different thresholds
% oneMinusSpec(*) : 1-Specificity of each threshold value
% sens(*) : Sensibility for each threshold value
% TN(*) : Number of true negative predictions
% TP(*) : Number of true positive predictions
% FN(*) : Number of false negative predictions
% FP(*) : Number of false positive predictions
% Inputs
% X : N x d matrix with the inputs of the training data
% Y : N x 1 vector with the outputs of the training data
% type : 'function estimation' ('f') or 'classifier' ('c')
% gam : Regularization parameter
% sig2 : Kernel parameter (bandwidth in the case of the 'RBF_kernel')
% kernel(*) : Kernel type (by default 'RBF_kernel')
% preprocess(*) : 'preprocess'(*) or 'original'
% figure(*) : 'figure'(*) or 'nofigure'
%
%
% 3. Using the object oriented interface for the LS-SVMs:
%
% >> [area, se, deltab, oneMinusSpec, sens, TN, TP, FN, FP] = roc(model)
% >> [area, se, deltab, oneMinusSpec, sens, TN, TP, FN, FP] = roc(model, figure)
%
% Outputs
% area(*) : Area under the ROC curve
% se(*) : Standard deviation of the residuals
% deltab(*) : N x 1 vector with different thresholds
% oneMinusSpec(*) 1-Specificity of each threshold value
% sens(*) : Sensibility for each threshold value
% TN(*) : Number of true negative predictions
% TP(*) : Number of true positive predictions
% FN(*) : Number of false negative predictions
% FP(*) : Number of false positive predictions
% Inputs
% model : Object oriented representation of the LS-SVM model
% figure(*) : 'figure'(*) or 'nofigure'
%
% See also:
% deltablssvm, trainlssvm
% Copyright (c) 2002, KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab
eval('fig;','fig=''figure'';');
%
% roc(model)
%
if iscell(RESULT),
RESULT = initlssvm(RESULT{:});
end
if isstruct(RESULT),
model = RESULT;
if model.type~='c',
error(' ROC only possible for classification...');
end
RESULT = latentlssvm(model,postlssvm(model,model.xtrain));
if size(RESULT,2)>1,
warning(' ROC only possible for binary classification...');
end
CLASS = codelssvm(model,model.ytrain);
end
if min(CLASS)~=-1 | max(CLASS)~=1,
warning('Class labels need to be -1 or 1');
end
if prod(size(CLASS))~=prod(size(RESULT)),
warning('Number of elements in Zt and Y must be equal;');
end
%
% roc(RESPONS, CLASS)
%
FI=find(isfinite(RESULT));
RESULT=(RESULT(FI));
CLASS=CLASS(FI);
NRSAM=size(RESULT,1);
NN=sum(CLASS==-1);
NP=sum(CLASS==1);
[RESULT_S,I]=sort(RESULT);
CLASS_S=CLASS(I);
TH=RESULT_S(NRSAM);
SAMNR=NRSAM;
TP=0; TPa = [];
FP=0; FPa = [];
TN=NN; TNa = [];
FN=NP; FNa = [];
TPR=0;
FPR=0;
AREA=0;
Q1B=0;
Q2B=0;
THRES_ROC=[TH];
TPR_ROC=[TPR];
FPR_ROC=[FPR];
SPEC_ROC=[TN/(FP+TN)];
ACC_ROC=[(TP+TN)/(NN+NP)];
PPV_ROC=[NaN];
NPV_ROC=[TN/(TN+FN)];
while ~isempty(TH)
DELTA=CLASS_S(find(RESULT_S==TH));
DFP=sum(DELTA==-1);
DTP=sum(DELTA==1);
TN=TN-DFP;
AREA=AREA + DFP*TP + 0.5*DFP*DTP;
Q2B=Q2B+DTP*((TN^2)+(TN*DFP)+((1/3)*(DFP^2)));
Q1B=Q1B+DFP*((TP^2)+(TP*DTP)+((1/3)*(DTP^2)));
FP=FP+DFP;
TP=TP+DTP;
FN=FN-DTP;
TPR=TP/(TP+FN);
FPR=FP/(FP+TN);
SAMNR=max(find(RESULT_S<TH));
TH=RESULT_S(SAMNR);
TPR_ROC=[TPR_ROC ; TPR];
FPR_ROC=[FPR_ROC ; FPR];
THRES_ROC=[THRES_ROC ; TH];
SPEC_ROC=[SPEC_ROC ; TN/(FP+TN)];
ACC_ROC=[ACC_ROC ; (TP+TN)/(NN+NP)];
if (TP+FP)==0
PPV_ROC=[PPV_ROC ; NaN];
else
PPV_ROC=[PPV_ROC ; TP/(TP+FP)];
end
if (TN+FN)==0
NPV_ROC=[NPV_ROC ; NaN];
else
NPV_ROC=[NPV_ROC ; TN/(TN+FN)];
end
TPa = [TPa TP];
TNa = [TNa TN];
FPa = [FPa FP];
FNa = [FNa FN];
end
THRES_ROC=[THRES_ROC ; -1];
AREA=AREA/(NN*NP);
Q2=Q2B/((NN^2)*NP);
Q1=Q1B/(NN*(NP^2));
%Q1=AREA/(2-AREA);
%Q2=2*(AREA^2)/(1+AREA);
SE=sqrt((AREA*(1-AREA) + (NP-1)*(Q1-(AREA^2)) + (NN-1)*(Q2-(AREA^2)))/(NN*NP));
if fig(1)=='f',
figure
%fill([1 FPR_ROC 0],[0 TPR_ROC 0]','b');drawnow;
plot(FPR_ROC,TPR_ROC,'b-','linewidth',2);
title(['Receiver Operating Characteristic curve, area=' num2str(AREA) ...
', std = ',num2str(SE)]);
xlabel('1 - Specificity');
ylabel('Sensitivity');
end
FPR_ROC = FPR_ROC(2:end);
TPR_ROC = TPR_ROC(2:end);