Matlab画带星的ROC曲线

淡淡的烟草味﹌ 2023-06-10 10:25 26阅读 0赞

function [AUC betterthreshold MCCMAXThreshold Confuse_Matrix MCCs FPRs TPRs F1S] = ROC_2_Class_OneFile_WithStar(begin_threshold, end_threshold, step, ismaxmcc,classificationFileName, star_num, star_style)
    %  
    %   The format of the file with path 'classificationFileName':
    %       truelabel  proabilityOfFirstClass   proabilityOfSecondClass
    %           1           0.78                        0.22
    %           1           0.68                        0.32
    %           2           0.56                        0.44
    %           1           0.89                        0.11
    %           2           0.22                        0.78
    %           ....        ....                        ....
    %   ismaxmcc
    %  e.g., [AUC betterthreshold MCCMAXThreshold Confuse_Matrix MCCs FPRs TPRs F1S] = ROC_2_Class_OneFile_WithStar(0, 1, 0.01, 1, 'classificationFileName', 50, 'b--')
    %
    
    if nargin < 4, error('NNET:Arguments','Not enough input arguments.'); end
    if ((begin_threshold>end_threshold) || step < 0.0)
        error('NNET:Arguments','The pattern of a parameter is not right.');
    end
    
    A = load(classificationFileName);
    [H L] = size(A);
    
    maxTpr = 0;
    maxFpr = 0;
    balanceTpr = 0;
    balanceFpr = 0;
    MCCMAXThreshold = 0.5;
    MCCPREThreshold = 0.5;
    MCCs = [];
    FPRs = [];
    TPRs = [];
    F1S = [];
    tprs = [];
    fprs = [];
    th_fpr_5 = 0;
    th_fpr_6 = 0;
    th_fpr_7 = 0;
    th_fpr_8 = 0;
    th_fpr_9 = 0;
    th_fpr_10 = 0;
    maxMcc = -9999;
    maxPre = -9999;
    for threshold=begin_threshold:step:end_threshold
        total1_1 = 0;
        total1_2 = 0;
        total2_1 = 0;
        total2_2 = 0;
        for i=1:H
            if A(i, 2)>=threshold
                if 1==A(i, 1) 
                    total1_1 = total1_1 + 1; 
                else
                    total2_1 = total2_1 + 1; 
                end
            else 
                if 1==A(i, 1)  
                    total1_2 = total1_2 + 1;
                else
                    total2_2 = total2_2 + 1;
                end
            end
        end   %end_for_i
        tpr = 1.0*total1_1/(total1_1+total1_2);
        fpr = 1.0*total2_1/(total2_1+total2_2);
        
        pre = 1.0*total1_1/(total1_1+total2_1);
        
        f1_score = (2*tpr*pre) / (tpr + tpr);
        FPRs = [FPRs, fpr];
        TPRs = [TPRs, tpr];
        F1S = [F1S, f1_score];
        if fpr>=0.05
            if fpr<=0.055
                th_fpr_5 = threshold;
            end
        end
        
         if fpr>=0.06
            if fpr<=0.065
                th_fpr_6 = threshold;
            end
         end
        
        if fpr>=0.07
            if fpr<=0.075
                th_fpr_7 = threshold;
            end
         end
        
        if fpr>=0.08
            if fpr<=0.085
                th_fpr_8 = threshold;
            end
        end
        
        if fpr>=0.09
            if fpr<=0.095
                th_fpr_9 = threshold;
            end
        end
        
        if fpr>=0.10
            if fpr<=0.105
                th_fpr_10 = threshold;
            end
        end
        
        tprs = [tprs, tpr];
        fprs = [fprs, fpr];
        
        %calculate mcc
        mcc = (total1_1*total2_2 - total2_1*total1_2)/sqrt((total1_1+total2_1)*(total1_1+total1_2)*(total2_2+total2_1)*(total2_2+total1_2));
        MCCs = [MCCs, mcc];
        if mcc > maxMcc
            maxMcc = mcc;
            MCCMAXThreshold = threshold;
            
            maxTpr = tpr;
            maxFpr = fpr;
        end
        
        %calculate pre
        pre = total1_1/(total1_1+total2_1);
        if pre+mcc>maxPre+maxMcc
            maxPre=pre;
            MCCPREThreshold=threshold;
        end
        
    end 
    
    [L K] = size(tprs);
    
    wc = 1;
    tag = 0;
    for  x=1:K
        if 0~=tprs(1, x) && 1~=tprs(1,x) && 0~=fprs(1, x)&& 1~=fprs(1,x) && wc>abs(tprs(1, x)+fprs(1,x)-1)
            wc = abs(tprs(1, x)+fprs(1,x)-1);
            tag = x;
            
            balanceTpr = tprs(1, x);
            balanceFpr = fprs(1,x);
        end
    end
    
    betterthreshold = begin_threshold + tag*step;
    
    AUC = roc_curve(A(:, 2), A(:, 1), star_num, star_style);
    
    if 1 == ismaxmcc
    %     fprintf('Max Threshold = %.3f\n', MCCMAXThreshold);
        threshold = MCCMAXThreshold;
    elseif 0 == ismaxmcc
    %     fprintf('Balance Threshold = %.3f\n', betterthreshold);
        threshold = betterthreshold;
    elseif 5 == ismaxmcc
    %     fprintf('FPR=0.05 Threshold = %.3f\n', th_fpr_5);
        threshold = th_fpr_5;
    elseif 6 == ismaxmcc
    %     fprintf('FPR=0.06 Threshold = %.3f\n', th_fpr_6);
        threshold = th_fpr_6;
    elseif 7 == ismaxmcc
    %     fprintf('FPR=0.07 Threshold = %.3f\n', th_fpr_7);
        threshold = th_fpr_7;
    elseif 8 == ismaxmcc
    %     fprintf('FPR=0.08 Threshold = %.3f\n', th_fpr_8);
        threshold = th_fpr_8;
    elseif 9 == ismaxmcc
    %     fprintf('FPR=0.09 Threshold = %.3f\n', th_fpr_9);
        threshold = th_fpr_9;
    elseif 10 == ismaxmcc
    %     fprintf('FPR=0.10 Threshold = %.3f\n', th_fpr_10);
        threshold = th_fpr_10;
    end
    threshold 
    
    total1_1 = 0;
    total1_2 = 0;
    total2_1 = 0;
    total2_2 = 0;
    
    for i=1:H
        if A(i, 2)>=threshold 
            %disp(i);
            if 1==A(i, 1) 
                    total1_1 = total1_1 + 1;
            else
                    total2_1 = total2_1 + 1;
            end
        else 
            if 1==A(i, 1) 
               total1_2 = total1_2 + 1;
            else
               total2_2 = total2_2 + 1;
            end
        end
    end   %end_for_i
    Confuse_Matrix = [total1_1 total1_2; total2_1, total2_2];
    
    end
    
    function auc = roc_curve(deci,label_y, star_num, star_style)
        label_y(label_y~=1)=-1;
    	[tmp,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));
        
        step = floor( size(stack_x, 1)/star_num );
        select_indexes = 1:step:size(stack_x, 1);
        
    	plot(stack_x(select_indexes, :), stack_y(select_indexes, :), star_style);
        
        set(gca,'FontName','Times New Roman','FontSize', 12) %设置坐标轴字体大小，字型
    	xlabel('False Positive Rate', 'FontName', 'Times New Roman', 'FontSize', 20);
    	ylabel('True Positive Rate', 'FontName', 'Times New Roman', 'FontSize', 20);
    	title('ROC Curve', 'FontName', 'Times New Roman', 'FontSize', 20);    
    end

This matlab code can be used to draw a ROC curve with some stars.