TMVAClassification_LikelihoodPCA.class.C

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

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

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

Method         : Likelihood::LikelihoodPCA
TMVA Release   : 4.2.1         [262657]
ROOT Release   : 6.10/09       [395785]
Creator        : sftnight
Date           : Thu May 31 12:04:22 2018
Host           : Linux SFT-ubuntu-1710-1 4.13.0-31-generic #34-Ubuntu SMP Fri Jan 19 16:34:46 UTC 2018 x86_64 x86_64 x86_64 GNU/Linux
Dir            : /mnt/build/workspace/root-makedoc-v610/rootspi/rdoc/src/v6-10-00-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)]
VarTransform: "PCA" [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)"]
H: "False" [Print method-specific help message]
TransformOutput: "False" [Transform likelihood output by inverse sigmoid function]
# Default:
VerbosityLevel: "Default" [Verbosity level]
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 <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 ReadLikelihoodPCA : public IClassifierReader {

 public:

   // constructor
   ReadLikelihoodPCA( std::vector<std::string>& theInputVars ) 
      : IClassifierReader(),
        fClassName( "ReadLikelihoodPCA" ),
        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] = -11.1978998184204;
      fVmax[0] = 8.98580265045166;
      fVmin[1] = -4.05419635772705;
      fVmax[1] = 3.94173216819763;
      fVmin[2] = -2.01150608062744;
      fVmax[2] = 1.96753799915314;
      fVmin[3] = -0.997752070426941;
      fVmax[3] = 1.02176344394684;

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

      // initialize constants
      Initialize();

      // initialize transformation
      InitTransform();
   }

   // destructor
   virtual ~ReadLikelihoodPCA() {
      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();

   // input variable transformation

   void X2P_1( const double*, double*, int ) const;
   double fMeanValues_1[3][4];
   double fEigenVectors_1[3][4][4];

   void InitTransform_1();
   void Transform_1( std::vector<double> & iv, int sigOrBgd ) const;
   void InitTransform();
   void Transform( std::vector<double> & iv, int sigOrBgd ) const;

   // 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 ReadLikelihoodPCA::Initialize() 
{
   fEpsilon = 2.2250738585072e-305;
   fNbin[0] = 10000;
   fHistMin[0] = -11.8069553375244;
   fHistMax[0] = 9.62459087371826;
   fNbin[1] = 10000;
   fHistMin[1] = -4.05161714553833;
   fHistMax[1] = 4.07033824920654;
   fNbin[2] = 10000;
   fHistMin[2] = -2.12039041519165;
   fHistMax[2] = 2.23738646507263;
   fNbin[3] = 10000;
   fHistMin[3] = -1.23021447658539;
   fHistMax[3] = 1.54229879379272;
   fHasDiscretPDF[0] = false; 
   fHasDiscretPDF[1] = false; 
   fHasDiscretPDF[2] = false; 
   fHasDiscretPDF[3] = false; 
}

inline double ReadLikelihoodPCA::GetMvaValue__( const std::vector<double>& inputValues ) const
{
   double ps(1), pb(1);
   std::vector<double> inputValuesSig = inputValues;
   std::vector<double> inputValuesBgd = inputValues;
   Transform(inputValuesSig,0);
   Transform(inputValuesBgd,1);
   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 ReadLikelihoodPCA: 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 ReadLikelihoodPCA::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 (false) {
      // 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 ReadLikelihoodPCA::Clear() 
{
   // nothing to clear
}

// signal map
float ReadLikelihoodPCA::fRefS[][10000] = 
{ 
   { 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 9.42994898035516e-16, 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7.34183086504789e-15, 7.34183086504789e-15, 7.34183086504789e-15, 7.34183086504789e-15   }, 
}; 

// background map
float ReadLikelihoodPCA::fRefB[][10000] = 
{ 
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}; 

   inline double ReadLikelihoodPCA::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;
   }

//_______________________________________________________________________
inline void ReadLikelihoodPCA::X2P_1( const double* x, double* p, int index ) const
{
   // Calculate the principal components from the original data vector
   // x, and return it in p (function extracted from TPrincipal::X2P)
   // It's the users responsibility to make sure that both x and p are
   // of the right size (i.e., memory must be allocated for p).
   const int nVar = 4;

   for (int i = 0; i < nVar; i++) {
      p[i] = 0;
      for (int j = 0; j < nVar; j++) p[i] += (x[j] - fMeanValues_1[index][j]) * fEigenVectors_1[index][j][i];
   }
}

//_______________________________________________________________________
inline void ReadLikelihoodPCA::InitTransform_1()
{
   // PCA transformation, initialisation
   // initialise vector of mean values
   fMeanValues_1[0][0] = 0.501171251774;
   fMeanValues_1[0][1] = -0.0994528753422;
   fMeanValues_1[0][2] = 0.404633918432;
   fMeanValues_1[0][3] = 0.801902735948;
   fMeanValues_1[1][0] = -0.13187061622;
   fMeanValues_1[1][1] = 0.0861246706024;
   fMeanValues_1[1][2] = -0.193617885351;
   fMeanValues_1[1][3] = -0.245611558282;
   fMeanValues_1[2][0] = 0.184650317777;
   fMeanValues_1[2][1] = -0.00666410236992;
   fMeanValues_1[2][2] = 0.105508016541;
   fMeanValues_1[2][3] = 0.278145588833;

   // initialise matrix of eigenvectors
   fEigenVectors_1[0][0][0] = 0.769850496029;
   fEigenVectors_1[0][0][1] = 0.0214560882009;
   fEigenVectors_1[0][0][2] = 0.53366216298;
   fEigenVectors_1[0][0][3] = 0.349391679706;
   fEigenVectors_1[0][1][0] = -0.0261975664757;
   fEigenVectors_1[0][1][1] = 0.993735389308;
   fEigenVectors_1[0][1][2] = -0.0610151208007;
   fEigenVectors_1[0][1][3] = 0.0898933733973;
   fEigenVectors_1[0][2][0] = 0.406874796009;
   fEigenVectors_1[0][2][1] = -0.07414675704;
   fEigenVectors_1[0][2][2] = -0.829585758536;
   fEigenVectors_1[0][2][3] = 0.375156804586;
   fEigenVectors_1[0][3][0] = 0.491016091027;
   fEigenVectors_1[0][3][1] = 0.0808199897049;
   fEigenVectors_1[0][3][2] = -0.152542846675;
   fEigenVectors_1[0][3][3] = -0.853874702485;
   fEigenVectors_1[1][0][0] = 0.747072878467;
   fEigenVectors_1[1][0][1] = -0.0309636701488;
   fEigenVectors_1[1][0][2] = 0.550364978004;
   fEigenVectors_1[1][0][3] = 0.371512794364;
   fEigenVectors_1[1][1][0] = 0.0365121539727;
   fEigenVectors_1[1][1][1] = 0.990509185752;
   fEigenVectors_1[1][1][2] = -0.0698472228143;
   fEigenVectors_1[1][1][3] = 0.112604533739;
   fEigenVectors_1[1][2][0] = 0.409718983323;
   fEigenVectors_1[1][2][1] = -0.116337090967;
   fEigenVectors_1[1][2][2] = -0.820328626956;
   fEigenVectors_1[1][2][3] = 0.381650337045;
   fEigenVectors_1[1][3][0] = 0.522187065691;
   fEigenVectors_1[1][3][1] = 0.0663210776248;
   fEigenVectors_1[1][3][2] = -0.138854961196;
   fEigenVectors_1[1][3][3] = -0.838833405891;
   fEigenVectors_1[2][0][0] = 0.744343580161;
   fEigenVectors_1[2][0][1] = 0.00738265903333;
   fEigenVectors_1[2][0][2] = -0.597855936426;
   fEigenVectors_1[2][0][3] = 0.297433034983;
   fEigenVectors_1[2][1][0] = 0.0249331241684;
   fEigenVectors_1[2][1][1] = -0.993034846946;
   fEigenVectors_1[2][1][2] = 0.0657955964205;
   fEigenVectors_1[2][1][3] = 0.0945043467932;
   fEigenVectors_1[2][2][0] = 0.411076013343;
   fEigenVectors_1[2][2][1] = 0.107578409358;
   fEigenVectors_1[2][2][2] = 0.758746350422;
   fEigenVectors_1[2][2][3] = 0.493707780793;
   fEigenVectors_1[2][3][0] = 0.525687630868;
   fEigenVectors_1[2][3][1] = -0.047478152212;
   fEigenVectors_1[2][3][2] = 0.250086374066;
   fEigenVectors_1[2][3][3] = -0.811698925293;
}

//_______________________________________________________________________
inline void ReadLikelihoodPCA::Transform_1( std::vector<double>& iv, int cls ) const
{
   // PCA transformation
   const int nVar = 4;
   double *dv = new double[nVar];
   double *rv = new double[nVar];
   if (cls < 0 || cls > 2) {
       if (2 > 1 ) cls = 2;
       else cls = 2;
   }

   // define the indices of the variables which are transformed by this transformation
   static std::vector<int> indicesGet;
   static std::vector<int> indicesPut;

   if ( indicesGet.empty() ) { 
      indicesGet.reserve(fNvars);
      indicesGet.push_back( 0);
      indicesGet.push_back( 1);
      indicesGet.push_back( 2);
      indicesGet.push_back( 3);
   } 
   if ( indicesPut.empty() ) { 
      indicesPut.reserve(fNvars);
      indicesPut.push_back( 0);
      indicesPut.push_back( 1);
      indicesPut.push_back( 2);
      indicesPut.push_back( 3);
   } 

   for (int ivar=0; ivar<nVar; ivar++) dv[ivar] = iv[indicesGet.at(ivar)];

   // Perform PCA and put it into PCAed events tree
   this->X2P_1( dv, rv, cls );
   for (int ivar=0; ivar<nVar; ivar++) iv[indicesPut.at(ivar)] = rv[ivar];

   delete [] dv;
   delete [] rv;
}

//_______________________________________________________________________
inline void ReadLikelihoodPCA::InitTransform()
{
   InitTransform_1();
}

//_______________________________________________________________________
inline void ReadLikelihoodPCA::Transform( std::vector<double>& iv, int sigOrBgd ) const
{
   Transform_1( iv, sigOrBgd );
}