MATLAB画ROC曲线，及计算AUC值

根据决策值和真实标签画ROC曲线，同时计算AUC的值

步骤：

1. 根据决策值和真实标签画ROC曲线，同时计算AUC的值:
2. 计算算法的决策函数值deci
3. 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$roc_y$
4. 根据$roc_y$分别对正负类样本进行累积分布$stack_x$，$stack_y$
5. 根据$stack_x$，$stack_y$计算RUC的值
6. $$AUC = \sum_{i=2}^{n}(stack_x(i)-stack_x(i-1))*stack_y(i)$$
7. 分别以$stack_x$，$stack_y$作为横坐标和纵坐标，画出RUC图

 

?

1 2 3 4 5 6 7 8 9 10 11 12 13 14

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

　　

 

代码来自林智仁网站：https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#roc_curve_for_binary_svm

?

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88

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,'-v\s+\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：

?

1 2 3

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