TMVAClassification_Likelihood.class.C

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// Class: ReadLikelihood
// Automatically generated by MethodBase::MakeClass
//

/* configuration options =====================================================

#GEN -*-*-*-*-*-*-*-*-*-*-*- general info -*-*-*-*-*-*-*-*-*-*-*-

Method         : Likelihood::Likelihood
TMVA Release   : 4.2.1         [262657]
ROOT Release   : 6.12/07       [396295]
Creator        : sftnight
Date           : Sat Sep 29 23:25:13 2018
Host           : Linux ec-ubuntu-14-04-x86-64-2 3.13.0-157-generic #207-Ubuntu SMP Mon Aug 20 16:44:59 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux
Dir            : /mnt/build/workspace/root-makedoc-v612/rootspi/rdoc/src/v6-12-00-patches/documentation/doxygen
Training events: 2000
Analysis type  : [Classification]


#OPT -*-*-*-*-*-*-*-*-*-*-*-*- options -*-*-*-*-*-*-*-*-*-*-*-*-

# Set by User:
V: "False" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
H: "True" [Print method-specific help message]
TransformOutput: "True" [Transform likelihood output by inverse sigmoid function]
# Default:
VerbosityLevel: "Default" [Verbosity level]
VarTransform: "None" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
##


#VAR -*-*-*-*-*-*-*-*-*-*-*-* variables *-*-*-*-*-*-*-*-*-*-*-*-

NVar 4
var1+var2                     myvar1                        myvar1                        myvar1                                                          'F'    [-8.14423561096,7.26972866058]
var1-var2                     myvar2                        myvar2                        Expression 2                                                    'F'    [-3.96643972397,4.0258936882]
var3                          var3                          var3                          Variable 3                    units                             'F'    [-5.03730010986,4.27845287323]
var4                          var4                          var4                          Variable 4                    units                             'F'    [-5.95050764084,4.64035463333]
NSpec 2
var1*2                        spec1                         spec1                         Spectator 1                   units                             'F'    [-9.91655540466,8.67800140381]
var1*3                        spec2                         spec2                         Spectator 2                   units                             'F'    [-14.874833107,13.0170021057]


============================================================================ */

#include <array>
#include <vector>
#include <cmath>
#include <string>
#include <iostream>

#include <math.h>
#include <cstdlib>
#ifndef IClassifierReader__def
#define IClassifierReader__def

class IClassifierReader {

 public:

   // constructor
   IClassifierReader() : fStatusIsClean( true ) {}
   virtual ~IClassifierReader() {}

   // return classifier response
   virtual double GetMvaValue( const std::vector<double>& inputValues ) const = 0;

   // returns classifier status
   bool IsStatusClean() const { return fStatusIsClean; }

 protected:

   bool fStatusIsClean;
};

#endif

class ReadLikelihood : public IClassifierReader {

 public:

   // constructor
   ReadLikelihood( std::vector<std::string>& theInputVars ) 
      : IClassifierReader(),
        fClassName( "ReadLikelihood" ),
        fNvars( 4 ),
        fIsNormalised( false )
   {      
      // the training input variables
      const char* inputVars[] = { "var1+var2", "var1-var2", "var3", "var4" };

      // sanity checks
      if (theInputVars.size() <= 0) {
         std::cout << "Problem in class \"" << fClassName << "\": empty input vector" << std::endl;
         fStatusIsClean = false;
      }

      if (theInputVars.size() != fNvars) {
         std::cout << "Problem in class \"" << fClassName << "\": mismatch in number of input values: "
                   << theInputVars.size() << " != " << fNvars << std::endl;
         fStatusIsClean = false;
      }

      // validate input variables
      for (size_t ivar = 0; ivar < theInputVars.size(); ivar++) {
         if (theInputVars[ivar] != inputVars[ivar]) {
            std::cout << "Problem in class \"" << fClassName << "\": mismatch in input variable names" << std::endl
                      << " for variable [" << ivar << "]: " << theInputVars[ivar].c_str() << " != " << inputVars[ivar] << std::endl;
            fStatusIsClean = false;
         }
      }

      // initialize min and max vectors (for normalisation)
      fVmin[0] = 0;
      fVmax[0] = 0;
      fVmin[1] = 0;
      fVmax[1] = 0;
      fVmin[2] = 0;
      fVmax[2] = 0;
      fVmin[3] = 0;
      fVmax[3] = 0;

      // initialize input variable types
      fType[0] = 'F';
      fType[1] = 'F';
      fType[2] = 'F';
      fType[3] = 'F';

      // initialize constants
      Initialize();

   }

   // destructor
   virtual ~ReadLikelihood() {
      Clear(); // method-specific
   }

   // the classifier response
   // "inputValues" is a vector of input values in the same order as the 
   // variables given to the constructor
   double GetMvaValue( const std::vector<double>& inputValues ) const;

 private:

   // method-specific destructor
   void Clear();

   // common member variables
   const char* fClassName;

   const size_t fNvars;
   size_t GetNvar()           const { return fNvars; }
   char   GetType( int ivar ) const { return fType[ivar]; }

   // normalisation of input variables
   const bool fIsNormalised;
   bool IsNormalised() const { return fIsNormalised; }
   double fVmin[4];
   double fVmax[4];
   double NormVariable( double x, double xmin, double xmax ) const {
      // normalise to output range: [-1, 1]
      return 2*(x - xmin)/(xmax - xmin) - 1.0;
   }

   // type of input variable: 'F' or 'I'
   char   fType[4];

   // initialize internal variables
   void Initialize();
   double GetMvaValue__( const std::vector<double>& inputValues ) const;

   // private members (method specific)
   double       fEpsilon;
   static float fRefS[][10000]; // signal reference vector [nvars][max_nbins]
   static float fRefB[][10000]; // backgr reference vector [nvars][max_nbins]

// if a variable has its PDF encoded as a spline0 --> treat it like an Integer valued one
   bool    fHasDiscretPDF[4]; 
   int    fNbin[4]; // number of bins (discrete variables may have less bins)
   double    fHistMin[4]; 
   double    fHistMax[4]; 
   double TransformLikelihoodOutput( double, double ) const;
};

inline void ReadLikelihood::Initialize() 
{
   fEpsilon = 2.2250738585072e-305;
   fNbin[0] = 10000;
   fHistMin[0] = -8.14423561096191;
   fHistMax[0] = 7.2697286605835;
   fNbin[1] = 10000;
   fHistMin[1] = -3.96643972396851;
   fHistMax[1] = 4.0258936882019;
   fNbin[2] = 10000;
   fHistMin[2] = -5.03730010986328;
   fHistMax[2] = 4.27845287322998;
   fNbin[3] = 10000;
   fHistMin[3] = -5.95050764083862;
   fHistMax[3] = 4.6403546333313;
   fHasDiscretPDF[0] = false; 
   fHasDiscretPDF[1] = false; 
   fHasDiscretPDF[2] = false; 
   fHasDiscretPDF[3] = false; 
}

inline double ReadLikelihood::GetMvaValue__( const std::vector<double>& inputValues ) const
{
   double ps(1), pb(1);
   std::vector<double> inputValuesSig = inputValues;
   std::vector<double> inputValuesBgd = inputValues;
   for (size_t ivar = 0; ivar < GetNvar(); ivar++) {

      // dummy at present... will be used for variable transforms
      double x[2] = { inputValuesSig[ivar], inputValuesBgd[ivar] };

      for (int itype=0; itype < 2; itype++) {

         // interpolate linearly between adjacent bins
         // this is not useful for discrete variables (or forced Spline0)
         int bin = int((x[itype] - fHistMin[ivar])/(fHistMax[ivar] - fHistMin[ivar])*fNbin[ivar]) + 0;

         // since the test data sample is in general different from the training sample
         // it can happen that the min/max of the training sample are trespassed --> correct this
         if      (bin < 0) {
            bin = 0;
            x[itype] = fHistMin[ivar];
         }
         else if (bin >= fNbin[ivar]) {
            bin = fNbin[ivar]-1;
            x[itype] = fHistMax[ivar];
         }

         // find corresponding histogram from cached indices
         float ref = (itype == 0) ? fRefS[ivar][bin] : fRefB[ivar][bin];

         // sanity check
         if (ref < 0) {
            std::cout << "Fatal error in ReadLikelihood: bin entry < 0 ==> abort" << std::endl;
            std::exit(1);
         }

         double p = ref;

         if (GetType(ivar) != 'I' && !fHasDiscretPDF[ivar]) {
            float bincenter = (bin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];
            int nextbin = bin;
            if ((x[itype] > bincenter && bin != fNbin[ivar]-1) || bin == 0) 
               nextbin++;
            else
               nextbin--;  

            double refnext      = (itype == 0) ? fRefS[ivar][nextbin] : fRefB[ivar][nextbin];
            float nextbincenter = (nextbin + 0.5)/fNbin[ivar]*(fHistMax[ivar] - fHistMin[ivar]) + fHistMin[ivar];

            double dx = bincenter - nextbincenter;
            double dy = ref - refnext;
            p += (x[itype] - bincenter) * dy/dx;
         }

         if (p < fEpsilon) p = fEpsilon; // avoid zero response

         if (itype == 0) ps *= p;
         else            pb *= p;
      }            
   }     

   // the likelihood ratio (transform it ?)
   return TransformLikelihoodOutput( ps, pb );   
}

inline double ReadLikelihood::TransformLikelihoodOutput( double ps, double pb ) const
{
   // returns transformed or non-transformed output
   if (ps < fEpsilon) ps = fEpsilon;
   if (pb < fEpsilon) pb = fEpsilon;
   double r = ps/(ps + pb);
   if (r >= 1.0) r = 1. - 1.e-15;

   if (true) {
      // inverse Fermi function

      // sanity check
      if      (r <= 0.0) r = fEpsilon;
      else if (r >= 1.0) r = 1. - 1.e-15;

      double tau = 15.0;
      r = - log(1.0/r - 1.0)/tau;
   }

   return r;
}

// Clean up
inline void ReadLikelihood::Clear() 
{
   // nothing to clear
}

// signal map
float ReadLikelihood::fRefS[][10000] = 
{ 
   { 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 1.34420952355705e-15, 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}; 

// background map
float ReadLikelihood::fRefB[][10000] = 
{ 
   { 1.3629564e-05, 1.3600531e-05, 1.3571411e-05, 1.354242e-05, 1.3513431e-05, 1.3484484e-05, 1.3455494e-05, 1.3426636e-05, 1.3397732e-05, 1.3368915e-05, 1.3340099e-05, 1.3311283e-05, 1.328251e-05, 1.3253823e-05, 1.3225137e-05, 1.3196452e-05, 1.3167809e-05, 1.313921e-05, 1.3110654e-05, 1.3082098e-05, 1.3053499e-05, 1.3025029e-05, 1.2996604e-05, 1.2968135e-05, 1.2939709e-05, 1.2911326e-05, 1.2883032e-05, 1.2854692e-05, 1.2826354e-05, 1.27981e-05, 1.2769892e-05, 1.2741683e-05, 1.2713475e-05, 1.2685353e-05, 1.2657231e-05, 1.2629152e-05, 1.2601117e-05, 1.2573082e-05, 1.2545048e-05, 1.2517055e-05, 1.248915e-05, 1.2461245e-05, 1.243334e-05, 1.2405522e-05, 1.2377661e-05, 1.2349886e-05, 1.2322068e-05, 1.229438e-05, 1.2266649e-05, 1.2239005e-05, 1.2211273e-05, 1.2183716e-05, 1.2156113e-05, 1.2128557e-05, 1.2100955e-05, 1.2073484e-05, 1.2046056e-05, 1.2018586e-05, 1.1991115e-05, 1.1963774e-05, 1.1936433e-05, 1.1909093e-05, 1.1881752e-05, 1.1854499e-05, 1.1827287e-05, 1.1800033e-05, 1.1772867e-05, 1.1745656e-05, 1.1718575e-05, 1.1691451e-05, 1.1664415e-05, 1.1637334e-05, 1.161034e-05, 1.1583346e-05, 1.155644e-05, 1.152949e-05, 1.1502583e-05, 1.1475719e-05, 1.1448899e-05, 1.1422037e-05, 1.1395304e-05, 1.1368569e-05, 1.1341836e-05, 1.131519e-05, 1.1288457e-05, 1.1261854e-05, 1.1235251e-05, 1.1208692e-05, 1.1182088e-05, 1.1155616e-05, 1.11291e-05, 1.110267e-05, 1.107624e-05, 1.1049811e-05, 1.1023469e-05, 1.0997127e-05, 1.0970783e-05, 1.0944527e-05, 1.0918273e-05, 1.0892059e-05, 1.0865803e-05, 1.0839678e-05, 1.0813552e-05, 1.0787428e-05, 1.0761345e-05, 1.0735306e-05, 1.0709267e-05, 1.0683272e-05, 1.065732e-05, 1.0631368e-05, 1.0605459e-05, 1.0579593e-05, 1.0553728e-05, 1.0527951e-05, 1.0502129e-05, 1.0476393e-05, 1.0450658e-05, 1.0424967e-05, 1.0399275e-05, 1.0373627e-05, 1.0348065e-05, 1.0322417e-05, 1.0296899e-05, 1.0271381e-05, 1.0245863e-05, 1.0220432e-05, 1.0194956e-05, 1.016957e-05, 1.0144138e-05, 1.0118837e-05, 1.0093449e-05, 1.0068191e-05, 1.0042891e-05, 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}; 

   inline double ReadLikelihood::GetMvaValue( const std::vector<double>& inputValues ) const
   {
      // classifier response value
      double retval = 0;

      // classifier response, sanity check first
      if (!IsStatusClean()) {
         std::cout << "Problem in class \"" << fClassName << "\": cannot return classifier response"
                   << " because status is dirty" << std::endl;
         retval = 0;
      }
      else {
         if (IsNormalised()) {
            // normalise variables
            std::vector<double> iV;
            iV.reserve(inputValues.size());
            int ivar = 0;
            for (std::vector<double>::const_iterator varIt = inputValues.begin();
                 varIt != inputValues.end(); varIt++, ivar++) {
               iV.push_back(NormVariable( *varIt, fVmin[ivar], fVmax[ivar] ));
            }
            retval = GetMvaValue__( iV );
         }
         else {
            retval = GetMvaValue__( inputValues );
         }
      }

      return retval;
   }