PyFitterBase Class Reference

EW: As FitterBase is an abstract base class we have to do some gymnastics to get it to get it into python. More...

Inheritance diagram for PyFitterBase:

Collaboration diagram for PyFitterBase:

Public Member Functions
void	RunMCMC () override
	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. More...
	FitterBase (manager *const fitMan)
	Constructor. More...
Public Member Functions inherited from FitterBase
	FitterBase (manager *const fitMan)
	Constructor. More...
virtual	~FitterBase ()
	Destructor for the FitterBase class. More...
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. More...
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. More...
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. More...
void	RunLLHScan ()
	Perform a 1D likelihood scan. More...
void	GetStepScaleBasedOnLLHScan ()
	LLH scan is good first estimate of step scale. More...
void	Run2DLLHScan ()
	Perform a 2D likelihood scan. More...
void	RunSigmaVar ()
	Perform a 2D and 1D sigma var for all samples. More...
void	RunSigmaVarFD ()
	Perform a 1D sigma var for all samples. More...
virtual void	StartFromPreviousFit (const std::string &FitName)
	Allow to start from previous fit/chain. More...
std::string	GetName () const
	Get name of class. More...

Additional Inherited Members
Protected Member Functions inherited from FitterBase
void	ProcessMCMC ()
	Process MCMC output. More...
void	PrepareOutput ()
	Prepare the output file. More...
void	SaveOutput ()
	Save output and close files. More...
void	SanitiseInputs ()
	Remove obsolete memory and make other checks before fit starts. More...
void	SaveSettings ()
	Save the settings that the MCMC was run with. More...
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. More...
bool	CheckSkipParameter (const std::vector< std::string > &SkipVector, const std::string &ParamName) const
	KS: Check whether we want to skip parameter using skip vector. More...
void	CustomRange (const std::string &ParName, const double sigma, double &ParamShiftValue)
	For comparison with P-Theta we usually have to apply different parameter values then usual 1, 3 sigma. More...
Protected Attributes inherited from FitterBase
manager *	fitMan
	The manager. More...
unsigned int	step
	current state More...
double	logLCurr
	current likelihood More...
double	logLProp
	proposed likelihood More...
double	accProb
	current acceptance prob More...
int	accCount
	counts accepted steps More...
unsigned int	stepStart
	step start, by default 0 if we start from previous chain then it will be different More...
std::vector< double >	sample_llh
	store the llh breakdowns More...
std::vector< double >	syst_llh
	systematic llh breakdowns More...
std::vector< SampleHandlerBase * >	samples
	Sample holder. More...
unsigned int	TotalNSamples
	Total number of samples used. More...
std::vector< ParameterHandlerBase * >	systematics
	Systematic holder. More...
std::unique_ptr< TStopwatch >	clock
	tells global time how long fit took More...
std::unique_ptr< TStopwatch >	stepClock
	tells how long single step/fit iteration took More...
double	stepTime
	Time of single step. More...
std::unique_ptr< TRandom3 >	random
	Random number. More...
TFile *	outputFile
	Output. More...
TDirectory *	CovFolder
	Output cov folder. More...
TDirectory *	SampleFolder
	Output sample folder. More...
TTree *	outTree
	Output tree with posteriors. More...
int	auto_save
	auto save every N steps More...
bool	fTestLikelihood
	Necessary for some fitting algorithms like PSO. More...
bool	FileSaved
	Checks if file saved not repeat some operations. More...
bool	SettingsSaved
	Checks if setting saved not repeat some operations. More...
bool	OutputPrepared
	Checks if output prepared not repeat some operations. More...
std::string	AlgorithmName
	Name of fitting algorithm that is being used. More...

Detailed Description

EW: As FitterBase is an abstract base class we have to do some gymnastics to get it to get it into python.

Definition at line 14 of file fitters.cpp.

Member Function Documentation

FitterBase()

_MaCh3_Safe_Include_Start_ _MaCh3_Safe_Include_End_ FitterBase::FitterBase

Constructor.

Parameters

fitMan

A pointer to a manager object, which will handle all settings.

Definition at line 27 of file FitterBase.cpp.

                                          : fitMan(man) {
// *************************
  AlgorithmName = "";
  //Get mach3 modes from manager
  random = std::make_unique<TRandom3>(Get<int>(fitMan->raw()["General"]["Seed"], __FILE__, __LINE__));
 
  // Counter of the accepted # of steps
  accCount = 0;
  step = 0;
  stepStart = 0;
 
  clock = std::make_unique<TStopwatch>();
  stepClock = std::make_unique<TStopwatch>();
  #ifdef DEBUG
  // Fit summary and debug info
  debug = GetFromManager<bool>(fitMan->raw()["General"]["Debug"], false, __FILE__ , __LINE__);
  #endif
 
  auto outfile = Get<std::string>(fitMan->raw()["General"]["OutputFile"], __FILE__ , __LINE__);
  // Save output every auto_save steps
  //you don't want this too often https://root.cern/root/html606/TTree_8cxx_source.html#l01229
  auto_save = Get<int>(fitMan->raw()["General"]["MCMC"]["AutoSave"], __FILE__ , __LINE__);
 
  #ifdef MULTITHREAD
  //KS: TODO This should help with performance when saving entries to ROOT file. I didn't have time to validate hence commented out
  //Based on other tests it is really helpful
  //ROOT::EnableImplicitMT();
  #endif
  // Set the output file
  outputFile = M3::Open(outfile, "RECREATE", __FILE__, __LINE__);
  outputFile->cd();
  // Set output tree
  outTree = new TTree("posteriors", "Posterior_Distributions");
  // Auto-save every 200MB, the bigger the better https://root.cern/root/html606/TTree_8cxx_source.html#l01229
  outTree->SetAutoSave(-200E6);
 
  FileSaved = false;
  SettingsSaved = false;
  OutputPrepared = false;
 
  //Create TDirectory
  CovFolder = outputFile->mkdir("CovarianceFolder");
  outputFile->cd();
  SampleFolder = outputFile->mkdir("SampleFolder");
  outputFile->cd();
 
  #ifdef DEBUG
  // Prepare the output log file
  if (debug) debugFile.open((outfile+".log").c_str());
  #endif
 
  TotalNSamples = 0;
  fTestLikelihood = GetFromManager<bool>(fitMan->raw()["General"]["Fitter"]["FitTestLikelihood"], false, __FILE__ , __LINE__);
}

RunMCMC()

void PyFitterBase::RunMCMC ( )

inlineoverridevirtual

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.

Implements FitterBase.

Definition at line 20 of file fitters.cpp.

                            {
        PYBIND11_OVERRIDE_PURE_NAME(
            void,        /* Return type */
            FitterBase,  /* Parent class */
            "run",       /* Python name*/
            RunMCMC      /* Name of function in C++ (must match Python name) */
        );
    }

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

• python/fitters.cpp