SampleHandlerInterface.cpp

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#include "SampleHandlerInterface.h"
 
// ***************************************************************************
SampleHandlerInterface::SampleHandlerInterface() {
// ***************************************************************************
  nSamples = 0;
}
 
// ***************************************************************************
SampleHandlerInterface::~SampleHandlerInterface() {
// ***************************************************************************
}
 
// ***************************************************************************
// Poisson likelihood calc for data and MC event rates
double SampleHandlerInterface::GetPoissonLLH(const double data, const double mc) const {
// ***************************************************************************
  // Return MC if there are no data, returns 0 for data == 0 && mc == 0  
  if ( data == 0 ) return mc;
 
  // If there are some data, but the prediction falls below the MC bound => return Poisson LogL for the low MC bound
  if ( mc < M3::_LOW_MC_BOUND_ ) {
    if ( data > M3::_LOW_MC_BOUND_ ) return ( M3::_LOW_MC_BOUND_ - data + data * std::log( data/M3::_LOW_MC_BOUND_ ) );
    else if ( data >= mc ) return 0.;
  }
  
  // Otherwise, just return usual Poisson LogL using Stirling's approximation
  // http://hyperphysics.phy-astr.gsu.edu/hbase/math/stirling.html
  return ( mc - data + data * std::log( data / mc ) );
}
 
// *************************
// data is data, mc is mc, w2 is Sum(w_{i}^2) (sum of weights squared), which is sigma^2_{MC stats}
double SampleHandlerInterface::GetTestStatLLH(const double data, const double mc, const double w2) const {
// *************************
  switch (fTestStatistic)
  {
    //CW: Not full Barlow-Beeston or what is referred to as "light": we're not introducing any more parameters
    // Assume the MC has a Gaussian distribution around generated
    // As in https://arxiv.org/abs/1103.0354 eq 10, 11
    //CW: Calculate the Barlow-Beeston likelihood contribution from MC statistics
    // Assumes the beta scaling parameters are Gaussian distributed
    // Follows arXiv:1103.0354 section 5 and equation 8, 9, 10, 11 on page 4/5
    // Essentially solves equation 11
    case (kBarlowBeeston):
    {
      // The MC used in the likelihood calculation is allowed to be changed by Barlow Beeston beta parameters
      double newmc = mc;
 
      // If MC falls below the low MC bound, use low MC bound for newmc
      if ( mc < M3::_LOW_MC_BOUND_ ) {
        if ( data > M3::_LOW_MC_BOUND_ ) newmc = M3::_LOW_MC_BOUND_;
        else if ( data >= mc ) return 0.;
      }
        
      // Barlow-Beeston uses fractional uncertainty on MC, so sqrt(sum[w^2])/mc
      const double fractional = std::sqrt( w2 ) / newmc;
      // fractional^2 to avoid doing same operation several times
      const double fractional2 = fractional * fractional;
      // b in quadratic equation
      const double temp = newmc * fractional2 - 1;
      // b^2 - 4ac in quadratic equation
      const double temp2 = temp * temp + 4 * data * fractional2;
      if ( temp2 < 0 ) {
        MACH3LOG_ERROR("Negative square root in Barlow Beeston coefficient calculation!");
        throw MaCh3Exception(__FILE__ , __LINE__ );
      }
      // Solve for the positive beta
      const double beta = ( -1 * temp + sqrt( temp2 ) ) / 2.;
 
      // If there is no data, test-stat shall return only MC*beta
      double stat = mc * beta;
      // With data, test-stat shall return LogL for newMC*beta which includes the low MC bound
      if ( data > 0 ) {
        newmc *= beta;
        stat = newmc - data + data * std::log( data / newmc );
      }
 
      // Now, MC stat penalty
      // The penalty from MC statistics using Conways approach (https://cds.cern.ch/record/1333496?)
      double penalty = 0;
      if ( fractional > 0 )  penalty = ( beta - 1 ) * ( beta - 1 ) / ( 2 * fractional2 );
 
      // Returns test-stat plus the MC stat penalty 
      return stat+penalty;
    }
    break;
    //KS: Alternative calculation of Barlow-Beeston following Hans Dembinski and Ahmed Abdelmottele arXiv:2206.12346v2
    case (kDembinskiAbdelmotteleb):
    {
      //KS: code follows authors implementation from:
      //https://github.com/scikit-hep/iminuit/blob/059d06b00cae097ebf340b218b4eb57357111df8/src/iminuit/cost.py#L274-L300
      
      // If there is no MC stat error for any reason, return Poisson LogL
      if ( w2 == 0 ) return GetPoissonLLH(data,mc);
      
      // The MC can be changed
      double newmc = mc;
      
      // If MC falls below the low MC bound, use low MC bound for newmc
      if ( mc < M3::_LOW_MC_BOUND_ ) {
        if ( data > M3::_LOW_MC_BOUND_ ) newmc = M3::_LOW_MC_BOUND_;
        else if ( data >= mc ) return 0.;
      }
      
      //the so-called effective count
      const double k = newmc * newmc / w2;
      //Calculate beta which is scaling factor between true and generated MC
      const double beta = ( data + k ) / ( newmc + k );
 
      newmc *= beta;
      
      // And penalise the movement in beta relative the mc uncertainty
      const double penalty = k * beta - k + k * std::log( k / ( k * beta ) );
 
      // If there are no data, this shall return newmc
      double stat = newmc;
      // All likelihood calculations may use the bare Poisson likelihood, so calculate here
      // Only if there are some data
      if ( data > 0 ) stat = newmc - data + data * std::log( data / newmc );
 
      // Return the statistical contribution and penalty
      return stat+penalty;
    }
    break;
    //CW: Also try the IceCube likelihood
    // It does not modify the MC content
    // https://arxiv.org/abs/1901.04645
    // Argüelles, C.A., Schneider, A. & Yuan, T. J. High Energ. Phys. (2019) 2019: 30. https://doi.org/10.1007/JHEP06(2019)030
    // We essentially construct eq 3.16 and take the logarithm
    // in eq 3.16, mu is MC, sigma2 is w2, k is data
    case (kIceCube):
    {
      // IceCube low MC bound is implemented to return Poisson(data, _LOW_MC_BOUND_)
      // up until the IceCube(data, mc) test-statistic is less than Poisson(data, _LOW_MC_BOUND_)
      // The 0 MC limit is set to Poisson(data, 0.) as there is no way to get a non-diverging and reasonable guess on w2
 
      // If there is 0 MC uncertainty (technically also when MC is 0) => Return Poisson(data, mc)
      if ( w2 == 0 ) return GetPoissonLLH(data,mc);
      
      // Auxiliary variables
      const long double b = mc / w2;
      const long double a = mc * b + 1;
      
      // Use C99's implementation of log of gamma function to not be C++11 dependent
      const double stat = double( -1 * ( a * logl( b ) + lgammal( data + a ) - lgammal( data + 1 ) - ( ( data + a ) * log1pl( b ) ) - lgammal( a ) ) );
 
      // Check whether the stat is more than Poisson-like bound for low mc (mc < data)
      // TN: I believe this might get some extra optimization
      if ( mc <= data ) {
        if ( data <= M3::_LOW_MC_BOUND_ ) return 0.;
        const double poisson = GetPoissonLLH(data, M3::_LOW_MC_BOUND_);
        if ( stat > poisson ) return poisson;
      }
 
      // Otherwise, return IceCube test-stat
      return stat;
    }
    break;
    //KS: Pearson works on assumption that event distribution in each bin is described by a Gaussian which in our case is not fulfilled for all bins, hence use it at your own risk
    case (kPearson):
    {
      //KS: 2 is because this function returns -LLH not -2LLH
      // With no data return the MC/2.
      if ( data == 0 ) return mc/2.;
      
      // If MC is lower than the low MC bound, return the test-stat at the bound
      if ( mc < M3::_LOW_MC_BOUND_ ) {
        if ( data > M3::_LOW_MC_BOUND_ ) return ( data - M3::_LOW_MC_BOUND_ ) * ( data - M3::_LOW_MC_BOUND_ ) / ( 2. * M3::_LOW_MC_BOUND_ ); 
        else if ( data >= mc ) return 0.;
      }
      
      // Return the Pearson metric
      return ( data - mc ) * ( data - mc ) / ( 2 * mc );
    }
    break;
    case (kPoisson):
    {
      //Just call GetPoissonLLH which doesn't take in weights
      //and is a Poisson likelihood comparison.
      return GetPoissonLLH(data, mc);
    }
    break;
    case TestStatistic::kNTestStatistics:
      MACH3LOG_ERROR("kNTestStatistics is not a valid TestStatistic!");
      throw MaCh3Exception(__FILE__, __LINE__);
    default:
    MACH3LOG_ERROR("Couldn't find TestStatistic {} exiting!", static_cast<int>(fTestStatistic));
    throw MaCh3Exception(__FILE__ , __LINE__ );
  } // end switch
}
 
// ***************************************************************************
// CW: Silence cout and cerr. Last is risky but psyche persists on spamming both
void SampleHandlerInterface::QuietPlease() {
// ***************************************************************************
  #if MACH3_DEBUG > 0
  return;
  #else
  buf = std::cout.rdbuf();
  errbuf = std::cerr.rdbuf();
  std::cout.rdbuf( nullptr );
  std::cerr.rdbuf( nullptr );
  #endif
}
 
// ***************************************************************************
// CW: Reset cout and cerr
void SampleHandlerInterface::NowTalk() {
// ***************************************************************************
  #if MACH3_DEBUG > 0
  return;
  #else
  std::cout.rdbuf(buf);
  std::cerr.rdbuf(errbuf);
  #endif
}