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

first hg version after svn

+function [FPrate, TPrate, AUC, thresholds] = computeROC(confidence, testClass)
+% function [FPrate, TPrate, AUC, thresholds] = computeROC(confidence, testClass)
+%
+% computeROC computes the data for an ROC curve based on a classifier's confidence output.
+% It returns the false positive rate and the true positive rate along with
+% the area under the ROC curve, and the list of thresholds.
+%
+% Inputs: 
+%           - confidence(i) is proportional to the probability that
+%             testClass(i) is positive
+%
+% testClass = 0 => target absent
+% testClass = 1 => target present
+%
+% Based on algorithms 2 and 4 from Tom Fawcett's paper "ROC Graphs: Notes and
+% Practical Considerations for Data Mining Researchers" (2003)
+% http://www.hpl.hp.com/techreports/2003/HPL-2003-4.pdf"
+%
+% Vlad Magdin, 21 Feb 2005
+
+% break ties in scores
+S = rand('state');
+rand('state',0);
+confidence = confidence + rand(size(confidence))*10^(-10);
+rand('state',S)
+[thresholds order] = sort(confidence, 'descend');
+testClass = testClass(order);
+
+%%% -- calculate TP/FP rates and totals -- %%%
+AUC = 0;
+faCnt = 0;
+tpCnt = 0;
+falseAlarms = zeros(1,size(thresholds,2));
+detections = zeros(1,size(thresholds,2));
+fPrev = -inf;
+faPrev = 0;
+tpPrev = 0;
+
+P = max(size(find(testClass==1)));
+N = max(size(find(testClass==0)));
+
+for i=1:length(thresholds)
+    if thresholds(i) ~= fPrev
+        falseAlarms(i) = faCnt;
+        detections(i) = tpCnt;
+
+        AUC = AUC + polyarea([faPrev faPrev faCnt/N faCnt/N],[0 tpPrev tpCnt/P 0]);
+
+        fPrev = thresholds(i);
+        faPrev = faCnt/N;
+        tpPrev = tpCnt/P;
+    end
+    
+    if testClass(i) == 1
+        tpCnt = tpCnt + 1;
+    else
+        faCnt = faCnt + 1;
+    end
+end
+
+AUC = AUC + polyarea([faPrev faPrev 1 1],[0 tpPrev 1 0]);
+
+FPrate = falseAlarms/N;
+TPrate = detections/P;