根据决策值和真实标签画ROC曲线，同时计算AUC的值

步骤：

根据决策值和真实标签画ROC曲线，同时计算AUC的值:

计算算法的决策函数值deci

根据决策函数值deci对真实标签y进行降序排序，得到新的排序$roc_y$

根据$roc_y$分别对正负类样本进行累积分布$stack_x$，$stack_y$

根据$stack_x$，$stack_y$计算RUC的值

[AUC = sum_{i=2}^{n}(stack_x(i)-stack_x(i-1))*stack_y(i) ]

分别以$stack_x$，$stack_y$作为横坐标和纵坐标，画出RUC图

function auc = roc_curve(deci,label_y) %%deci=wx+b, label_y, true label

[val,ind] = sort(deci,'descend');

roc_y = label_y(ind);

stack_x = cumsum(roc_y == -1)/sum(roc_y == -1);

stack_y = cumsum(roc_y == 1)/sum(roc_y == 1);

auc = sum((stack_x(2:length(roc_y),1)-stack_x(1:length(roc_y)-1,1)).*stack_y(2:length(roc_y),1))

%Comment the above lines if using perfcurve of statistics toolbox

%[stack_x,stack_y,thre,auc]=perfcurve(label_y,deci,1);

plot(stack_x,stack_y);

xlabel('False Positive Rate');

ylabel('True Positive Rate');

title(['ROC curve of (AUC = ' num2str(auc) ' )']);

end

function auc = plotroc(y,x,params)

%plotroc draws the recevier operating characteristic(ROC) curve.

%

%auc = plotroc(training_label, training_instance [, libsvm_options -v cv_fold])

% Use cross-validation on training data to get decision values and plot ROC curve.

%

%auc = plotroc(testing_label, testing_instance, model)

% Use the given model to predict testing data and obtain decision values

% for ROC

%

% Example:

%

% load('heart_scale.mat');

% plotroc(heart_scale_label, heart_scale_inst,'-v 5');

%

%[y,x] = libsvmread('heart_scale');

% model = svmtrain(y,x);

% plotroc(y,x,model);

rand('state',0); % reset random seed

if nargin < 2

help plotroc

return

elseif isempty(y) | isempty(x)

error('Input data is empty');

elseif sum(y == 1) + sum(y == -1) ~= length(y)

error('ROC is only applicable to binary classes with labels 1, -1'); % check the trainig_file is binary

elseif exist('params') && ~ischar(params)

model = params;

[predict_label,mse,deci] = svmpredict(y,x,model) ;% the procedure for predicting

auc = roc_curve(deci*model.Label(1),y);

else

if ~exist('params')

params = [];

end

[param,fold] = proc_argv(params); % specify each parameter

if fold <= 1

error('The number of folds must be greater than 1');

else

[deci,label_y] = get_cv_deci(y,x,param,fold); % get the value of decision and label after cross-calidation

auc = roc_curve(deci,label_y); % plot ROC curve

end

end

end

function [resu,fold] = proc_argv(params)

resu=params;

fold=5;

if ~isempty(params) && ~isempty(regexp(params,'-v'))

[fold_val,fold_start,fold_end] = regexp(params,'-vs+d+','match','start','end');

if ~isempty(fold_val)

[temp1,fold] = strread([fold_val{:}],'%s %u');

resu([fold_start:fold_end]) = [];

else

error('Number of CV folds must be specified by "-v cv_fold"');

end

end

end

function [deci,label_y] = get_cv_deci(prob_y,prob_x,param,nr_fold)

l=length(prob_y);

deci = ones(l,1);

label_y = ones(l,1);

rand_ind = randperm(l);

for i=1:nr_fold % Cross training : folding

test_ind=rand_ind([floor((i-1)*l/nr_fold)+1:floor(i*l/nr_fold)]');

train_ind = [1:l]';

train_ind(test_ind) = [];

model = svmtrain(prob_y(train_ind),prob_x(train_ind,:),param);

[predict_label,mse,subdeci] = svmpredict(prob_y(test_ind),prob_x(test_ind,:),model);

deci(test_ind) = subdeci.*model.Label(1);

label_y(test_ind) = prob_y(test_ind);

end

end

function auc = roc_curve(deci,label_y) %%deci=wx+b, label_y, true label

[val,ind] = sort(deci,'descend');

roc_y = label_y(ind);

stack_x = cumsum(roc_y == -1)/sum(roc_y == -1);

stack_y = cumsum(roc_y == 1)/sum(roc_y == 1);

auc = sum((stack_x(2:length(roc_y),1)-stack_x(1:length(roc_y)-1,1)).*stack_y(2:length(roc_y),1))

%Comment the above lines if using perfcurve of statistics toolbox

%[stack_x,stack_y,thre,auc]=perfcurve(label_y,deci,1);

plot(stack_x,stack_y);

xlabel('False Positive Rate');

ylabel('True Positive Rate');

title(['ROC curve of (AUC = ' num2str(auc) ' )']);

end

调用：

[y,x] = libsvmread('heart_scale.txt');

model = svmtrain(y,x);

plotroc(y,x,model);