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MSE calculation
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‎PWGHF/Core/HfAeToMseXicToPKPi.h‎

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/// \file HfAeToMseXicToPKPi.h
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/// \brief Class to compute the mse for the Autoencoder for Xic+ → p K- π+ analysis selections
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/// \author Maria Teresa Camerlingo
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#ifndef PWGHF_CORE_HFAETOMSEXICTOPKPI_H_
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#define PWGHF_CORE_HFAETOMSEXICTOPKPI_H_
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#include <vector>
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#include "PWGHF/Core/HfMlResponse.h"
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namespace o2::analysis
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{
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template <typename TypeOutputScore = float>
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class HfAeToMseXicToPKPi : public HfMlResponse<TypeOutputScore>
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{
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public:
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/// Default constructor
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HfAeToMseXicToPKPi() = default;
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/// Default destructor
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virtual ~HfAeToMseXicToPKPi() = default;
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std::vector<float> yScaled, yOutRescaled;
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//private :
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void setMinMaxScaling(std::vector<float>& yOut, std::vector<float> yIn, std::vector<float> scaleMin, std::vector<float> scaleMax)
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{ yOut.clear();//initial clear to avoid multiple filling if setMinMax o setScaling are called more than once
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for (size_t j = 0; j < yIn.size(); ++j)
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{ //for over the features
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//MinMax scaling of the input features
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LOG(debug)<<"--------------> MinMax scaling Debug \t"<<scaleMin.at(j)<<"\t"<<scaleMax.at(j);
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yOut.push_back((yIn.at(j) - scaleMin.at(j))/(scaleMax.at(j)- scaleMin.at(j)));
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LOG(debug)<<"Feature = "<<j<<" ----> input = "<<yIn.at(j)<<" scaled feature = "<< yOut.at(j);
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}
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}
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//---- External preprocessing scaling
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void setScaling(bool scaleFlag, int scaleType, /*input features of a candidate*/ std::vector<float> yIn, std::vector<float> scaleMin, std::vector<float> scaleMax){ //it takes the bool flag and scaling parameters configurables in taskXic
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yScaled.clear();
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if( scaleFlag == false){ LOG(debug)<<"No external preprocessing transformation will be applied";
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yScaled.assign(yIn.begin(), yIn.end());
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} else{
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if(scaleType == 1){
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LOG(debug)<<"MinMax scaling will be applied";
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setMinMaxScaling(yScaled, yIn, scaleMin, scaleMax);
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}//... with scaleType > 1 we could add other preprocessing trasformations
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}
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}
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std::vector<float> getPreprocessedFeatures(){
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for (size_t j = 0; j < yScaled.size(); ++j) LOG(debug)<<"Global scaled feature = "<< yScaled.at(j);
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return yScaled;
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}
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//Reverse preprocessing - output postprocessing
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void unsetMinMaxScaling(std::vector<float>& yOut, std::vector<float> yIn, std::vector<float> scaleMin, std::vector<float> scaleMax)
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{ yOut.clear();//initial clear to avoid multiple filling if setMinMax o setScaling are called more than once
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for (size_t j = 0; j < yIn.size(); ++j)
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{ //for over the features
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//MinMax scaling of the input features
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LOG(debug)<<"--------------> MinMax unscaling Debug \t"<<scaleMin.at(j)<<"\t"<<scaleMax.at(j);
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yOut.push_back(yIn.at(j)*(scaleMax.at(j)- scaleMin.at(j))+ scaleMin.at(j));
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LOG(debug)<<"Unscaling output = "<<j<<" ----> input = "<<yIn.at(j)<<" rescaled output = "<< yOut.at(j);
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}
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}
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void unsetScaling(bool scaleFlag, int scaleType, /*AE output*/ std::vector<float> yIn, std::vector<float> scaleMin, std::vector<float> scaleMax){ //it takes the bool flag and scaling parameters configurables in taskXic
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yOutRescaled.clear();
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if( scaleFlag == false){ LOG(debug)<<"No external preprocessing transformation will be applied";
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yOutRescaled.assign(yIn.begin(), yIn.end());
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} else{
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if(scaleType == 1){
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LOG(debug)<<"MinMax unscaling will be applied";
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unsetMinMaxScaling(yOutRescaled, yIn, scaleMin, scaleMax);
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}//... with scaleType > 1 we could add other preprocessing trasformations
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}
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}
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std::vector<float> getPostprocessedOutput(){
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for (size_t j = 0; j < yOutRescaled.size(); ++j) LOG(debug)<<"Global rescaled AE output = "<< yOutRescaled.at(j);
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return yOutRescaled;
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}
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//---- MSE function
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float getMse(std::vector<float> yTrue, std::vector<float> yPred){
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LOG(debug)<<"Inside getMse sizes "<<yTrue.size()<<"\t"<<yPred.size();
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float mse= 0.0f;
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float sum = 0.0f;
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for (size_t j = 0; j < yTrue.size(); ++j) LOG(debug)<<"Local Feature = "<<j<<" ----> input = "<<yTrue.at(j)<<" scaled feature = "<< yPred.at(j);
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std::vector<float> yTrueScaled = getPreprocessedFeatures();
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if( yTrue.size() != yPred.size()){
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LOG(debug)<< "size of input vector ="<<yTrue.size();
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LOG(debug)<< "size of AE output vector ="<< yPred.size();
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LOG(fatal) << "vectors of input and predictions don't have the same size";
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}
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else{//MSE
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for (size_t j = 0; j < yPred.size(); ++j) { //for over the features
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sum += pow(((yTrueScaled).at(j) - (yPred).at(j)), 2); //has dimensions
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LOG(debug)<<"getMse Local feature = "<<j<<" ----> input = "<<yTrueScaled.at(j)<<" AE prediction = "<< yPred.at(j);
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}
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mse = sum/yPred.size(); //MSE of a candidate
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LOG(debug)<<"Local mse "<<mse;
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}
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return mse;
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}
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}; //end of the class
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} // namespace o2::analysis
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#endif // PWGHF_CORE_HFAETOMSEXICTOPKPI_H_

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