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0.2.0 GST
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libraries/GST/GST.cpp
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226
libraries/GST/GST.cpp
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//
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// FILE: GST.cpp
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// VERSION: 0.1.1
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// PURPOSE: Arduino library for Gold Standard Test metrics
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// URL: https://github.com/RobTillaart/GST
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// https://en.wikipedia.org/wiki/Sensitivity_and_specificity
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// https://en.wikipedia.org/wiki/Confusion_matrix
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//
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// formula's based upon Wikipedia.
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#include "GST.h"
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GST::GST()
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{
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clearAll();
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};
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///////////////////////////////////////////////////////
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//
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// INPUT FUNCTIONS
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//
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void GST::setTruePositive(float v)
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{
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TP = v;
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AP = TP + FN;
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};
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void GST::setTrueNegative(float v)
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{
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TN = v;
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AN = TN + FP;
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};
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void GST::setFalsePositive(float v)
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{
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FP = v;
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AN = TN + FP;
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};
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void GST::setFalseNegative(float v)
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{
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FN = v;
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AP = TP + FN;
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};
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void GST::clearAll()
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{
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AP = 0;
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AN = 0;
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TP = 0;
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TN = 0;
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FP = 0;
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FN = 0;
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}
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// These are used for updating the test matrix
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float GST::addTruePositive(float v)
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{
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TP += v;
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AP = TP + FN;
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return TP;
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};
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float GST::addTrueNegative(float v)
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{
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TN += v;
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AN = TN + FP;
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return TN;
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};
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float GST::addFalsePositive(float v)
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{
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FP += v;
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AN = TN + FP;
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return FP;
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};
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float GST::addFalseNegative(float v)
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{
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FN += v;
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AP = TP + FN;
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return FN;
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};
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///////////////////////////////////////////////////////
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//
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// OUTPUT FUNCTIONS I
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//
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float GST::getTruePositive() { return TP; };
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float GST::getTrueNegative() { return TN; };
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float GST::getFalsePositive() { return FP; };
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float GST::getFalseNegative() { return FN; };
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float GST::getTotal() { return AP + AN; };
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float GST::getActualPositive() { return AP; };
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float GST::getActualNegative() { return AN; };
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float GST::getTestedPositive() { return TP + FP; };
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float GST::getTestedNegative() { return TN + FN; };
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float GST::sensitivity() { return TPR(); };
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float GST::specificity() { return TNR(); };
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float GST::truePositiveRate() { return TPR(); };
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float GST::TPR() { return TP / AP; };
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float GST::trueNegativeRate() { return TNR(); };
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float GST::TNR() { return TN / AN; };
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float GST::falseNegativeRate() { return FNR(); };
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float GST::FNR() { return FN / AP; };
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float GST::falsePositiveRate() { return FPR(); };
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float GST::FPR() { return FP / AN; };
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///////////////////////////////////////////////////////
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//
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// OUTPUT FUNCTIONS II
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//
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float GST::positivePredictiveValue() { return PPV(); };
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float GST::PPV() { return TP / (TP + FP); };
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float GST::negativePredictiveValue() { return NPV(); };
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float GST::NPV() { return TN / (TN + FN); };
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float GST::falseDiscoveryRate() { return FDR(); };
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float GST::FDR() { return FP / (TP + FP); };
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float GST::falseOmissionRate() { return FOR(); };
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float GST::FOR() { return FN / (TN + FN); };
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float GST::positiveLikelihoodRatio() { return LRplus(); };
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float GST::LRplus() { return TPR() / FPR(); };
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float GST::negativeLikelihoodRatio() { return LRminus(); };
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float GST::LRminus() { return FNR() / TNR(); };
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float GST::prevalenceThreshold()
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{
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return sqrt(FPR()) / (sqrt(TPR()) + sqrt(FPR()));
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};
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float GST::threatScore()
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{
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return TP / (TP + FN + FP);
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};
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float GST::criticalSuccessIndex()
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{
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return threatScore();
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};
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float GST::prevalence()
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{
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return AP / (AP + AN);
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};
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float GST::accuracy()
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{
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return (TP + TN) / (AP + AN);
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};
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float GST::balancedAccuracy()
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{
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return (TPR() + TNR()) * 0.5;
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};
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float GST::F1Score()
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{
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return (2 * TP) / (2 * TP + FP + FN);
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};
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float GST::MatthewsCorrelationCoefficient() { return MCC(); };
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float GST::phi() { return MCC(); };
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float GST::MCC()
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{
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return (TP*TN - FP*FN)/sqrt((TP+FP) * (TP+FN) * (TN+FP) * (TN+FN));
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};
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float GST::FowlkesMallowsIndex() { return FM(); };
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float GST::FM()
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{
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return sqrt(PPV()*TPR());
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};
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float GST::BookmakerInformedness() { return BM(); };
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float GST::BM()
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{
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return TPR() + TNR() - 1;
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};
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float GST::markedness() { return MK(); };
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float GST::deltaP() { return MK(); };
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float GST::MK()
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{
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return PPV() + NPV() - 1;
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};
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float GST::diagnosticOddsRatio() { return DOR(); };
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float GST::DOR()
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{
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return LRplus() / LRminus();
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};
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// -- END OF FILE --
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@ -1,110 +1,116 @@
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#pragma once
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//
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// FILE: GST.h
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// VERSION: 0.1.0
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// VERSION: 0.1.1
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// PURPOSE: Arduino library for Gold Standard Test metrics
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// URL: https://github.com/RobTillaart/GST
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// https://en.wikipedia.org/wiki/Sensitivity_and_specificity
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// https://en.wikipedia.org/wiki/Confusion_matrix
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//
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// formula's based upon wikipedia.
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// formula's based upon Wikipedia.
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#define GST_LIB_VERSION (F("0.1.1"))
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#define GST_LIB_VERSION (F("0.1.0"))
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#include "Arduino.h"
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class GST
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{
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public:
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GST() {};
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GST();
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// These 4 need to be filled in.
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void setTruePositive(float v) { TP = v; P = TP + FN; };
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void setTrueNegative(float v) { TN = v; N = TN + FP; };
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void setFalsePositive(float v) { FP = v; N = TN + FP; };
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void setFalseNegative(float v) { FN = v; P = TP + FN; };
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// These four values of the matrix need to be set to get started.
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void setTruePositive(float v = 0);
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void setTrueNegative(float v = 0);
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void setFalsePositive(float v = 0);
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void setFalseNegative(float v = 0);
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void clearAll();
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float getTruePositive() { return TP; };
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float getTrueNegative() { return TN; };
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float getFalsePositive() { return FP; };
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float getFalseNegative() { return FN; };
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float getTotal() { return P + N; };
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float getActualPositive() { return P; };
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float getActualNegative() { return N; };
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float getTestedPositive() { return TP + FP; };
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float getTestedNegative() { return TN + FN; };
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float sensitivity() { return TPR(); };
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float specificity() { return TNR(); };
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// These are used for updating the test matrix
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float addTruePositive(float v);
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float addTrueNegative(float v);
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float addFalsePositive(float v);
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float addFalseNegative(float v);
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// Output functions I
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float getTruePositive();
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float getTrueNegative();
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float getFalsePositive();
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float getFalseNegative();
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// true positive rate
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float TPR() { return TP / P; };
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// true negative rate
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float TNR() { return TN / N; };
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float getTotal();
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float getActualPositive();
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float getActualNegative();
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float getTestedPositive();
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float getTestedNegative();
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// false negative rate
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float FNR() { return FN / (FN + TP); };
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// false positive rate
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float FPR() { return FP / (FP + TN); };
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float sensitivity();
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float specificity();
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// positive predictive value
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float PPV() { return TP / (TP + FP); };
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// negative predictive value
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float NPV() { return TN / (TN + FN); };
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// false discovery rate
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float FDR() { return FP / (FP + TP); };
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// false omission rate
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float FOR() { return FN / (FN + TN); };
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float truePositiveRate();
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float TPR();
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float trueNegativeRate();
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float TNR();
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float falseNegativeRate();
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float FNR();
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float falsePositiveRate();
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float FPR();
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// positive likelihood ratio
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float LRplus() { return TPR() / FPR(); };
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// negative likelihood ratio
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float LRminus() { return FNR() / TNR(); };
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// Output functions II
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float positivePredictiveValue();
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float PPV();
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float negativePredictiveValue();
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float NPV();
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float falseDiscoveryRate();
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float FDR();
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float falseOmissionRate();
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float FOR();
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float prevalenceThreshold() { return sqrt(FPR()) / (sqrt(TPR()) + sqrt(FPR())); };
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float threatScore() { return TP / (TP + FN + FP); };
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float criticalSuccessIndex() { return threatScore(); };
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float positiveLikelihoodRatio();
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float LRplus();
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float negativeLikelihoodRatio();
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float LRminus();
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float prevalence() { return P / (P + N); };
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float accuracy() { return (TP + TN) / (P + N); };
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float balancedAccuracy() { return (TPR() + TNR()) / 2; };
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float F1Score() { return (2 * TP)/(2 * TP + FP + FN); };
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float prevalenceThreshold();
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float threatScore();
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float criticalSuccessIndex();
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float prevalence();
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float accuracy();
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float balancedAccuracy();
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float F1Score();
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// Matthews correlation coefficient
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float MCC() { return (TP*TN-FP*FN)/sqrt((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN)); };
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float phi() { return MCC(); };
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// Fowlkes–Mallows index
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float FM() { return sqrt(PPV()*TPR()); };
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// Bookmaker informedness
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float BM() { return TPR() + TNR() - 1; };
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// markedness
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float MK() { return PPV() + NPV() - 1; };
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float deltaP() { return MK(); };
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// diagnostic odds ratio
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float DOR() { return LRplus() / LRminus(); };
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float MatthewsCorrelationCoefficient();
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float phi();
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float MCC();
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float FowlkesMallowsIndex();
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float FM();
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float BookmakerInformedness();
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float BM();
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float markedness();
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float deltaP();
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float MK();
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float diagnosticOddsRatio();
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float DOR();
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private:
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float P = 0;
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float N = 0;
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float TP = 0;
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float TN = 0;
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float FP = 0;
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float FN = 0;
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float AP; // actual positive
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float AN; // actual negative
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float TP; // true positive
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float TN; // true negative
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float FP; // false positive
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float FN; // false positive
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};
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@ -13,30 +13,140 @@ Arduino library for Gold Standard Test metrics.
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## Description
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The GST library is **experimental**.
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Note: **experimental**
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The GST library is an implementation of the **Gold Standard Test**.
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#### Links
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These sites describe the functions in more detail.
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- https://en.wikipedia.org/wiki/Sensitivity_and_specificity
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- https://en.wikipedia.org/wiki/Confusion_matrix
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#### Performance
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The math functions are from pretty straightforward to rather complex.
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It is possible to optimize functions with intermediate values if needed.
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However the right way to optimize depends on the way the library is used.
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#### Related libraries
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- https://github.com/RobTillaart/Statistic
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## Interface
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See .h file
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See .h file for all functions. Many function exist in a long descriptive name and an acronym version. Here only the long names are given.
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For the definitions please check - https://en.wikipedia.org/wiki/Sensitivity_and_specificity or
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https://en.wikipedia.org/wiki/Confusion_matrix
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### Input functions
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These four numbers should all be set before output functions make sense.
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The parameter **value** is typical absolute value measured or counted.
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If the parameter is omitted, the default 0 will be used to reset the value.
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- **void setTruePositive(float value = 0)** set the internal TP value.
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- **void setTrueNegative(float value = 0)** set the internal TN value.
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- **void setFalsePositive(float value = 0)** set the internal FP value.
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- **void setFalseNegative(float value = 0)** set the internal FN value.
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- **void clearAll()** reset all the above to 0.
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In tests one often want to increase / change the numbers.
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This can be done with the **addTruePositive()** etc functions.
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After every addition all output functions can be called.
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- **float addTruePositive(float value)** increases the internal TP value.
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Use a negative value to decrease.
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Returns the new value of TP.
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- **float addTrueNegative(float value)** increases the internal TN value.
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Use a negative value to decrease.
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Returns the new value of TN.
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- **float addFalsePositive(float value)** increases the internal FP value.
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Use a negative value to decrease.
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Returns the new value of FP.
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- **float addFalseNegative(float value)** increases the internal FN value.
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Use a negative value to decrease.
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Returns the new value of FN.
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### Output functions I
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Basic output
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- **float getTruePositive()** returns internal TP.
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- **float getTrueNegative()** returns internal TN.
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- **float getFalsePositive()** returns internal FP.
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- **float getFalseNegative()** returns internal FN.
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- **float getTotal()** returns total of four numbers.
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- **float getActualPositive()**
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- **float getActualNegative()**
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- **float getTestedPositive()**
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- **float getTestedNegative()**
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- **float sensitivity()** equals truePositiveRate().
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- **float specificity()** equals trueNegativeRate()
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- **float truePositiveRate()**
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- **float trueNegativeRate()**
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- **float falseNegativeRate()**
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- **float falsePositiveRate()**
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### Output functions II
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These are the more 'complex' functions.
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Read the Wikipedia pages for their uses.
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- **float positivePredictiveValue()**
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- **float negativePredictiveValue()**
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- **float falseDiscoveryRate()**
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- **float falseOmissionRate()**
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- **float positiveLikelihoodRatio()**
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- **float negativeLikelihoodRatio()**
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- **float prevalenceThreshold()**
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- **float threatScore()**
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- **float criticalSuccessIndex()**
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- **float prevalence()**
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- **float accuracy()**
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- **float balancedAccuracy()**
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- **float F1Score()**
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- **float MatthewsCorrelationCoefficient()**
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- **float FowlkesMallowsIndex()**
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- **float BookmakerInformedness()**
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- **float markedness()**
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- **float deltaP()**
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- **float diagnosticOddsRatio()**
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## Future
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- documentation
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- improve
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- more links?
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- improve documentation
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- add functions
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- percentage functions for TP TN FP and FN?
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- test
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- complete the CI test coverage.
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- examples
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- add real life examples.
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- combination with a sensor? batch testing?
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- code
|
||||
- full name functions instead of acronyms. (wrap?)
|
||||
- is GST a good class name?
|
||||
|
@ -2,7 +2,18 @@
|
||||
# GST Changelog
|
||||
|
||||
|
||||
## 0.1.1 2022-06-08
|
||||
|
||||
- add **addTruePositive()** etc functions.
|
||||
- add defaults for **setTruePositive(value = 0)** etc functions
|
||||
- add long descriptive names for the short functions.
|
||||
- add derived class GoldenStandardTest, for descriptive name
|
||||
- added some documentation
|
||||
- split off GST.cpp file, prevent - https://github.com/RobTillaart/CRC/issues/21
|
||||
|
||||
|
||||
## 0.1.0 2022-02-25
|
||||
|
||||
- initial version
|
||||
-
|
||||
|
||||
|
121
libraries/GST/examples/GST_add_runtime/GST_add_runtime.ino
Normal file
121
libraries/GST/examples/GST_add_runtime/GST_add_runtime.ino
Normal file
@ -0,0 +1,121 @@
|
||||
// FILE: GST_add_runtime.ino
|
||||
// AUTHOR: Rob Tillaart
|
||||
// PURPOSE: demo
|
||||
// URL: https://github.com/RobTillaart/GST
|
||||
|
||||
|
||||
#include "Arduino.h"
|
||||
#include "GST.h"
|
||||
|
||||
|
||||
GST gst;
|
||||
|
||||
|
||||
void setup()
|
||||
{
|
||||
Serial.begin(115200);
|
||||
while (!Serial);
|
||||
Serial.println(__FILE__);
|
||||
|
||||
gst.setTruePositive(0);
|
||||
gst.setTrueNegative(0);
|
||||
gst.setFalsePositive(0);
|
||||
gst.setFalseNegative(0);
|
||||
|
||||
}
|
||||
|
||||
void loop()
|
||||
{
|
||||
// simulate a test result
|
||||
delay(500);
|
||||
int score = random(4);
|
||||
switch (score)
|
||||
{
|
||||
case 0:
|
||||
gst.addTruePositive(1);
|
||||
break;
|
||||
case 1:
|
||||
gst.addTrueNegative(1);
|
||||
break;
|
||||
case 2:
|
||||
gst.addFalsePositive(1);
|
||||
break;
|
||||
case 3:
|
||||
gst.addFalseNegative(1);
|
||||
break;
|
||||
}
|
||||
confusion_matrix();
|
||||
// confusion_matrix_normalized();
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
void confusion_matrix()
|
||||
{
|
||||
Serial.println();
|
||||
Serial.println(__FUNCTION__);
|
||||
Serial.println();
|
||||
|
||||
// PRINTED IN A MATRIX
|
||||
Serial.print("\t");
|
||||
Serial.print(gst.getTotal());
|
||||
Serial.print("\t");
|
||||
Serial.print(gst.getTestedPositive());
|
||||
Serial.print("\t");
|
||||
Serial.println(gst.getTestedNegative());
|
||||
|
||||
Serial.print("\t");
|
||||
Serial.print(gst.getActualPositive());
|
||||
Serial.print("\t");
|
||||
Serial.print(gst.getTruePositive());
|
||||
Serial.print("\t");
|
||||
Serial.println(gst.getFalseNegative());
|
||||
|
||||
Serial.print("\t");
|
||||
Serial.print(gst.getActualNegative());
|
||||
Serial.print("\t");
|
||||
Serial.print(gst.getFalsePositive());
|
||||
Serial.print("\t");
|
||||
Serial.println(gst.getTrueNegative());
|
||||
|
||||
Serial.println();
|
||||
Serial.print("\tSensitivity:\t");
|
||||
Serial.println(gst.sensitivity(), 4);
|
||||
Serial.print("\tSpecificity:\t");
|
||||
Serial.println(gst.specificity(), 4);
|
||||
}
|
||||
|
||||
|
||||
void confusion_matrix_normalized()
|
||||
{
|
||||
Serial.println();
|
||||
Serial.println(__FUNCTION__);
|
||||
Serial.println();
|
||||
|
||||
// PRINTED IN A MATRIX
|
||||
Serial.print("\t");
|
||||
Serial.print("100.00%");
|
||||
Serial.print("\t");
|
||||
Serial.print(gst.getTestedPositive());
|
||||
Serial.print("\t");
|
||||
Serial.println(gst.getTestedNegative());
|
||||
|
||||
Serial.print("\t");
|
||||
Serial.print(gst.getActualPositive());
|
||||
Serial.print("\t");
|
||||
Serial.print(gst.TPR(), 4);
|
||||
Serial.print("\t");
|
||||
Serial.println(gst.FNR(), 4);
|
||||
|
||||
Serial.print("\t");
|
||||
Serial.print(gst.getActualNegative());
|
||||
Serial.print("\t");
|
||||
Serial.print(gst.FPR(), 4);
|
||||
Serial.print("\t");
|
||||
Serial.println(gst.TNR(), 4);
|
||||
}
|
||||
|
||||
|
||||
|
||||
// -- END OF FILE --
|
@ -9,6 +9,12 @@ setTruePositive KEYWORD2
|
||||
setTrueNegative KEYWORD2
|
||||
setFalsePositive KEYWORD2
|
||||
setFalseNegative KEYWORD2
|
||||
clearAll KEYWORD2
|
||||
|
||||
addTruePositive KEYWORD2
|
||||
addTrueNegative KEYWORD2
|
||||
addFalsePositive KEYWORD2
|
||||
addFalseNegative KEYWORD2
|
||||
|
||||
getTruePositive KEYWORD2
|
||||
getTrueNegative KEYWORD2
|
||||
@ -60,6 +66,29 @@ deltaP KEYWORD2
|
||||
DOR KEYWORD2
|
||||
|
||||
|
||||
truePositiveRate KEYWORD2
|
||||
trueNegativeRate KEYWORD2
|
||||
falseNegativeRate KEYWORD2
|
||||
falsePositiveRate KEYWORD2
|
||||
|
||||
|
||||
positivePredictiveValue KEYWORD2
|
||||
negativePredictiveValue KEYWORD2
|
||||
falseDiscoveryRate KEYWORD2
|
||||
falseOmissionRate KEYWORD2
|
||||
|
||||
|
||||
positiveLikelihoodRatio KEYWORD2
|
||||
negativeLikelihoodRatio KEYWORD2
|
||||
|
||||
|
||||
MatthewsCorrelationCoefficient KEYWORD2
|
||||
FowlkesMallowsIndex KEYWORD2
|
||||
BookmakerInformedness KEYWORD2
|
||||
markedness KEYWORD2
|
||||
diagnosticOddsRatio KEYWORD2
|
||||
|
||||
|
||||
# Constants (LITERAL1)
|
||||
GST_LIB_VERSION LITERAL1
|
||||
|
||||
|
@ -15,7 +15,7 @@
|
||||
"type": "git",
|
||||
"url": "https://github.com/RobTillaart/GST.git"
|
||||
},
|
||||
"version": "0.1.0",
|
||||
"version": "0.1.1",
|
||||
"license": "MIT",
|
||||
"frameworks": "arduino",
|
||||
"platforms": "*",
|
||||
|
@ -1,5 +1,5 @@
|
||||
name=GST
|
||||
version=0.1.0
|
||||
version=0.1.1
|
||||
author=Rob Tillaart <rob.tillaart@gmail.com>
|
||||
maintainer=Rob Tillaart <rob.tillaart@gmail.com>
|
||||
sentence=Arduino library for Golden Standard Test, confusion matrix.
|
||||
|
Loading…
Reference in New Issue
Block a user