LikelihoodFit Class Reference

Implementation of base Likelihood Fit class, it is mostly responsible for likelihood calculation while actual use depend on classes which inherits. More...

#include <Fitters/LikelihoodFit.h>

Inheritance diagram for LikelihoodFit:

Collaboration diagram for LikelihoodFit:

Public Member Functions
	LikelihoodFit (manager *const fitMan)
	Constructor.
virtual	~LikelihoodFit ()
	Destructor.
virtual double	CalcChi2 (const double *x)
	Chi2 calculation over all included samples and syst objects.
int	GetNPars ()
	Get total number of params, this sums over all covariance objects.
virtual void	RunMCMC ()=0
	Implementation of fitting algorithm.
virtual std::string	GetName () const
	Get name of class.
Public Member Functions inherited from FitterBase
	FitterBase (manager *const fitMan)
	Constructor.
virtual	~FitterBase ()
	Destructor for the FitterBase class.
void	AddSampleHandler (SampleHandlerBase *sample)
	This function adds a sample PDF object to the analysis framework. The sample PDF object will be utilized in fitting procedures or likelihood scans.
void	AddSystObj (ParameterHandlerBase *cov)
	This function adds a Covariance object to the analysis framework. The Covariance object will be utilized in fitting procedures or likelihood scans.
virtual void	RunMCMC ()=0
	The specific fitting algorithm implemented in this function depends on the derived class. It could be Markov Chain Monte Carlo (MCMC), MinuitFit, or another algorithm.
void	DragRace (const int NLaps=100)
	Calculates the required time for each sample or covariance object in a drag race simulation. Inspired by Dan's feature.
void	RunLLHScan ()
	Perform a 1D likelihood scan.
void	GetStepScaleBasedOnLLHScan ()
	LLH scan is good first estimate of step scale.
void	Run2DLLHScan ()
	Perform a 2D likelihood scan.
void	RunSigmaVar ()
	Perform a 2D and 1D sigma var for all samples.
virtual void	StartFromPreviousFit (const std::string &FitName)
	Allow to start from previous fit/chain.
virtual std::string	GetName () const
	Get name of class.

Protected Member Functions
void	PrepareFit ()
	prepare output and perform sanity checks
Protected Member Functions inherited from FitterBase
void	ProcessMCMC ()
	Process MCMC output.
void	PrepareOutput ()
	Prepare the output file.
void	SaveOutput ()
	Save output and close files.
void	SanitiseInputs ()
	Remove obsolete memory and make other checks before fit starts.
void	SaveSettings ()
	Save the settings that the MCMC was run with.
bool	GetScaneRange (std::map< std::string, std::vector< double > > &scanRanges)
	YSP: Set up a mapping to store parameters with user-specified ranges, suggested by D. Barrow.
bool	CheckSkipParameter (const std::vector< std::string > &SkipVector, const std::string &ParamName) const
	KS: Check whether we want to skip parameter using skip vector.

Protected Attributes
int	NPars
	Number of all parameters from all covariances.
int	NParsPCA
	Number of all parameters from all covariances in PCA base.
bool	fMirroring
	Flag telling if mirroring is used or not.
Protected Attributes inherited from FitterBase
manager *	fitMan
	The manager.
unsigned int	step
	current state
double	logLCurr
	current likelihood
double	logLProp
	proposed likelihood
double	accProb
	current acceptance prob
int	accCount
	counts accepted steps
int	stepStart
	step start if restarting
std::vector< double >	sample_llh
	store the llh breakdowns
std::vector< double >	syst_llh
	systematic llh breakdowns
std::vector< SampleHandlerBase * >	samples
	Sample holder.
unsigned int	TotalNSamples
	Total number of samples used.
std::vector< ParameterHandlerBase * >	systematics
	Systematic holder.
std::unique_ptr< TStopwatch >	clock
	tells global time how long fit took
std::unique_ptr< TStopwatch >	stepClock
	tells how long single step/fit iteration took
double	stepTime
	Time of single step.
std::unique_ptr< TRandom3 >	random
	Random number.
TFile *	outputFile
	Output.
TDirectory *	CovFolder
	Output cov folder.
TDirectory *	SampleFolder
	Output sample folder.
TTree *	outTree
	Output tree with posteriors.
int	auto_save
	auto save every N steps
bool	fTestLikelihood
	Necessary for some fitting algorithms like PSO.
bool	FileSaved
	Checks if file saved not repeat some operations.
bool	SettingsSaved
	Checks if setting saved not repeat some operations.
bool	OutputPrepared
	Checks if output prepared not repeat some operations.

Detailed Description

Implementation of base Likelihood Fit class, it is mostly responsible for likelihood calculation while actual use depend on classes which inherits.

Definition at line 6 of file LikelihoodFit.h.

Constructor & Destructor Documentation

LikelihoodFit()

LikelihoodFit::LikelihoodFit

(

manager *const

fitMan

)

Constructor.

Definition at line 5 of file LikelihoodFit.cpp.

                                         : FitterBase(man) {
// *******************
  NPars = 0;
  NParsPCA = 0;
  fMirroring = GetFromManager<bool>(fitMan->raw()["General"]["Fitter"]["Mirroring"], false);
  if(fMirroring) MACH3LOG_INFO("Mirroring enabled");
}

~LikelihoodFit()

LikelihoodFit::~LikelihoodFit ( )

virtual

Destructor.

Definition at line 16 of file LikelihoodFit.cpp.

                              {
// *************************
 
}

Member Function Documentation

CalcChi2()

double LikelihoodFit::CalcChi2 ( const double *  x )

virtual

Chi2 calculation over all included samples and syst objects.

Reimplemented in PSO.

Definition at line 40 of file LikelihoodFit.cpp.

                                              {
// *******************
  if (step % 10000 == 0) {
    MACH3LOG_INFO("Iteration {}", step);
  }
 
  stepClock->Start();
 
  int ParCounter = 0;
  double llh = 0;
  for (std::vector<ParameterHandlerBase*>::iterator it = systematics.begin(); it != systematics.end(); ++it)
  {
    if(!(*it)->IsPCA())
    {
      std::vector<double> pars;
      const int NumPar = (*it)->GetNumParams();
      //KS: Avoid push back as they are slow
      pars.resize(NumPar);
      for(int i = 0; i < NumPar; ++i, ++ParCounter)
      {
        double ParVal = x[ParCounter];
        //KS: Basically apply mirroring for parameters out of bounds
        if(fMirroring)
        {
          if(ParVal < (*it)->GetLowerBound(i))
          {
            ParVal = (*it)->GetLowerBound(i) + ((*it)->GetLowerBound(i) - ParVal);
          }
          else if (ParVal > (*it)->GetUpperBound(i))
          {
            ParVal = (*it)->GetUpperBound(i) - ( ParVal - (*it)->GetUpperBound(i));
          }
        }
        pars[i] = ParVal;
      }
      (*it)->SetParameters(pars);
    }
    else
    {
      std::vector<double> pars;
      const int NumPar = (*it)->GetNParameters();
      //KS: Avoid push back as they are slow
      pars.resize(NumPar);
      for(int i = 0; i < NumPar; ++i, ++ParCounter)
      {
        double ParVal = x[ParCounter];
        //KS: Basically apply mirroring for parameters out of bounds
        pars[i] = ParVal;
      }
      (*it)->GetPCAHandler()->SetParametersPCA(pars);
    }
    (*it)->AcceptStep();
  }
 
  // Loop over the systematics and propose the initial step
  int stdIt = 0;
  for (std::vector<ParameterHandlerBase*>::iterator it = systematics.begin(); it != systematics.end(); ++it, ++stdIt)
  {
    //GetLikelihood will return LargeL if out of bounds, for minimizers this is not the problem, while calcLikelihood will return actual likelihood
    syst_llh[stdIt] = (*it)->CalcLikelihood();
    llh += syst_llh[stdIt];
    #ifdef DEBUG
    if (debug) debugFile << "LLH after " << systematics[stdIt]->GetName() << " " << llh << std::endl;
    #endif
  }
  // Could multi-thread this
  // But since sample reweight is multi-threaded it's probably better to do that
  for (size_t i = 0; i < samples.size(); i++)
  {
    samples[i]->Reweight();
  }
 
  //DB for atmospheric event by event sample migration, need to fully reweight all samples to allow event passing prior to likelihood evaluation
  for (size_t i = 0; i < samples.size(); i++) {
    // Get the sample likelihoods and add them
    sample_llh[i] = samples[i]->GetLikelihood();
    llh += sample_llh[i];
    #ifdef DEBUG
    if (debug) debugFile << "LLH after sample " << i << " " << llh << std::endl;
    #endif
  }
 
  // Save the proposed likelihood (class member)
  logLProp = llh;
  logLCurr = llh;
  accProb = 1;
 
  stepClock->Stop();
  stepTime = stepClock->RealTime();
 
  // Write step to output tree
  outTree->Fill();
 
  // Auto save the output
  if (step % auto_save == 0) outTree->AutoSave();
  step++;
  accCount++;
 
  llh = 2.0*llh;
  return llh;
}

GetName()

virtual std::string LikelihoodFit::GetName ( ) const

inlinevirtual

Get name of class.

Reimplemented from FitterBase.

Reimplemented in MinuitFit, and PSO.

Definition at line 22 of file LikelihoodFit.h.

{return "LikelihoodFit";};

GetNPars()

int LikelihoodFit::GetNPars ( )

inline

Get total number of params, this sums over all covariance objects.

Definition at line 16 of file LikelihoodFit.h.

{return NPars;};

PrepareFit()

void LikelihoodFit::PrepareFit ( )

protected

prepare output and perform sanity checks

Definition at line 22 of file LikelihoodFit.cpp.

                               {
// *******************
  // Save the settings into the output file
  SaveSettings();
 
  // Prepare the output branches
  PrepareOutput();
  
  for (size_t s = 0; s < systematics.size(); ++s) {
    NPars += systematics[s]->GetNumParams();
    NParsPCA += systematics[s]->GetNParameters();
  }
 
  //KS: If PCA is note enabled NParsPCA == NPars
  MACH3LOG_INFO("Total number of parameters {}", NParsPCA);
}

RunMCMC()

virtual void LikelihoodFit::RunMCMC ( )

pure virtual

Implementation of fitting algorithm.

Implements FitterBase.

Implemented in MinuitFit, PSO, and PyLikelihoodFit.

Member Data Documentation

fMirroring

bool LikelihoodFit::fMirroring

protected

Flag telling if mirroring is used or not.

Definition at line 32 of file LikelihoodFit.h.

NPars

int LikelihoodFit::NPars

protected

Number of all parameters from all covariances.

Definition at line 28 of file LikelihoodFit.h.

NParsPCA

int LikelihoodFit::NParsPCA

protected

Number of all parameters from all covariances in PCA base.

Definition at line 30 of file LikelihoodFit.h.

---

The documentation for this class was generated from the following files:

• Fitters/LikelihoodFit.h
• Fitters/LikelihoodFit.cpp