annotate toolboxes/FullBNT-1.0.7/KPMtools/computeROC.m @ 0:e9a9cd732c1e tip

first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
parents
children
rev   line source
wolffd@0 1 function [FPrate, TPrate, AUC, thresholds] = computeROC(confidence, testClass)
wolffd@0 2 % function [FPrate, TPrate, AUC, thresholds] = computeROC(confidence, testClass)
wolffd@0 3 %
wolffd@0 4 % computeROC computes the data for an ROC curve based on a classifier's confidence output.
wolffd@0 5 % It returns the false positive rate and the true positive rate along with
wolffd@0 6 % the area under the ROC curve, and the list of thresholds.
wolffd@0 7 %
wolffd@0 8 % Inputs:
wolffd@0 9 % - confidence(i) is proportional to the probability that
wolffd@0 10 % testClass(i) is positive
wolffd@0 11 %
wolffd@0 12 % testClass = 0 => target absent
wolffd@0 13 % testClass = 1 => target present
wolffd@0 14 %
wolffd@0 15 % Based on algorithms 2 and 4 from Tom Fawcett's paper "ROC Graphs: Notes and
wolffd@0 16 % Practical Considerations for Data Mining Researchers" (2003)
wolffd@0 17 % http://www.hpl.hp.com/techreports/2003/HPL-2003-4.pdf"
wolffd@0 18 %
wolffd@0 19 % Vlad Magdin, 21 Feb 2005
wolffd@0 20
wolffd@0 21 % break ties in scores
wolffd@0 22 S = rand('state');
wolffd@0 23 rand('state',0);
wolffd@0 24 confidence = confidence + rand(size(confidence))*10^(-10);
wolffd@0 25 rand('state',S)
wolffd@0 26 [thresholds order] = sort(confidence, 'descend');
wolffd@0 27 testClass = testClass(order);
wolffd@0 28
wolffd@0 29 %%% -- calculate TP/FP rates and totals -- %%%
wolffd@0 30 AUC = 0;
wolffd@0 31 faCnt = 0;
wolffd@0 32 tpCnt = 0;
wolffd@0 33 falseAlarms = zeros(1,size(thresholds,2));
wolffd@0 34 detections = zeros(1,size(thresholds,2));
wolffd@0 35 fPrev = -inf;
wolffd@0 36 faPrev = 0;
wolffd@0 37 tpPrev = 0;
wolffd@0 38
wolffd@0 39 P = max(size(find(testClass==1)));
wolffd@0 40 N = max(size(find(testClass==0)));
wolffd@0 41
wolffd@0 42 for i=1:length(thresholds)
wolffd@0 43 if thresholds(i) ~= fPrev
wolffd@0 44 falseAlarms(i) = faCnt;
wolffd@0 45 detections(i) = tpCnt;
wolffd@0 46
wolffd@0 47 AUC = AUC + polyarea([faPrev faPrev faCnt/N faCnt/N],[0 tpPrev tpCnt/P 0]);
wolffd@0 48
wolffd@0 49 fPrev = thresholds(i);
wolffd@0 50 faPrev = faCnt/N;
wolffd@0 51 tpPrev = tpCnt/P;
wolffd@0 52 end
wolffd@0 53
wolffd@0 54 if testClass(i) == 1
wolffd@0 55 tpCnt = tpCnt + 1;
wolffd@0 56 else
wolffd@0 57 faCnt = faCnt + 1;
wolffd@0 58 end
wolffd@0 59 end
wolffd@0 60
wolffd@0 61 AUC = AUC + polyarea([faPrev faPrev 1 1],[0 tpPrev 1 0]);
wolffd@0 62
wolffd@0 63 FPrate = falseAlarms/N;
wolffd@0 64 TPrate = detections/P;