/usr/include/root/RooStats/ToyMCSampler.h is in libroot-roofit-dev 5.34.19+dfsg-1.2.
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// Author: Sven Kreiss and Kyle Cranmer June 2010
// Author: Kyle Cranmer, Lorenzo Moneta, Gregory Schott, Wouter Verkerke
// Additions and modifications by Mario Pelliccioni
/*************************************************************************
* Copyright (C) 1995-2008, Rene Brun and Fons Rademakers. *
* All rights reserved. *
* *
* For the licensing terms see $ROOTSYS/LICENSE. *
* For the list of contributors see $ROOTSYS/README/CREDITS. *
*************************************************************************/
#ifndef ROOSTATS_ToyMCSampler
#define ROOSTATS_ToyMCSampler
//_________________________________________________
/*
BEGIN_HTML
<p>
ToyMCSampler is an implementation of the TestStatSampler interface.
It generates Toy Monte Carlo for a given parameter point and evaluates a
TestStatistic.
</p>
<p>
For parallel runs, ToyMCSampler can be given an instance of ProofConfig
and then run in parallel using proof or proof-lite. Internally, it uses
ToyMCStudy with the RooStudyManager.
</p>
END_HTML
*/
//
#ifndef ROOT_Rtypes
#include "Rtypes.h"
#endif
#include <vector>
#include <sstream>
#include "RooStats/TestStatSampler.h"
#include "RooStats/SamplingDistribution.h"
#include "RooStats/TestStatistic.h"
#include "RooStats/ModelConfig.h"
#include "RooStats/ProofConfig.h"
#include "RooWorkspace.h"
#include "RooMsgService.h"
#include "RooAbsPdf.h"
#include "RooRealVar.h"
#include "RooDataSet.h"
namespace RooStats {
class DetailedOutputAggregator;
// only used inside ToyMCSampler, ie "private" in the cxx file
class NuisanceParametersSampler {
// Helper for ToyMCSampler. Handles all of the nuisance parameter related
// functions. Once instantiated, it gives a new nuisance parameter point
// at each call to nextPoint(...).
public:
NuisanceParametersSampler(RooAbsPdf *prior=NULL, const RooArgSet *parameters=NULL, Int_t nToys=1000, Bool_t asimov=kFALSE) :
fPrior(prior),
fParams(parameters),
fNToys(nToys),
fExpected(asimov),
fPoints(NULL),
fIndex(0)
{
if(prior) Refresh();
}
virtual ~NuisanceParametersSampler() {
if(fPoints) { delete fPoints; fPoints = NULL; }
}
void NextPoint(RooArgSet& nuisPoint, Double_t& weight);
protected:
void Refresh();
private:
RooAbsPdf *fPrior; // prior for nuisance parameters
const RooArgSet *fParams; // nuisance parameters
Int_t fNToys;
Bool_t fExpected;
RooAbsData *fPoints; // generated nuisance parameter points
Int_t fIndex; // current index in fPoints array
};
class ToyMCSampler: public TestStatSampler {
public:
ToyMCSampler();
ToyMCSampler(TestStatistic &ts, Int_t ntoys);
virtual ~ToyMCSampler();
static void SetAlwaysUseMultiGen(Bool_t flag);
void SetUseMultiGen(Bool_t flag) { fUseMultiGen = flag ; }
// main interface
virtual SamplingDistribution* GetSamplingDistribution(RooArgSet& paramPoint);
virtual RooDataSet* GetSamplingDistributions(RooArgSet& paramPoint);
virtual RooDataSet* GetSamplingDistributionsSingleWorker(RooArgSet& paramPoint);
virtual SamplingDistribution* AppendSamplingDistribution(
RooArgSet& allParameters,
SamplingDistribution* last,
Int_t additionalMC
);
// The pdf can be NULL in which case the density from SetPdf()
// is used. The snapshot and TestStatistic is also optional.
virtual void AddTestStatistic(TestStatistic* t = NULL) {
if( t == NULL ) {
oocoutI((TObject*)0,InputArguments) << "No test statistic given. Doing nothing." << std::endl;
return;
}
//if( t == NULL && fTestStatistics.size() >= 1 ) t = fTestStatistics[0];
fTestStatistics.push_back( t );
}
// generates toy data
// without weight
virtual RooAbsData* GenerateToyData(RooArgSet& paramPoint, RooAbsPdf& pdf) const {
if(fExpectedNuisancePar) oocoutE((TObject*)NULL,InputArguments) << "ToyMCSampler: using expected nuisance parameters but ignoring weight. Use GetSamplingDistribution(paramPoint, weight) instead." << std::endl;
double weight;
return GenerateToyData(paramPoint, weight, pdf);
}
virtual RooAbsData* GenerateToyData(RooArgSet& paramPoint) const { return GenerateToyData(paramPoint,*fPdf); }
// with weight
virtual RooAbsData* GenerateToyData(RooArgSet& paramPoint, double& weight, RooAbsPdf& pdf) const;
virtual RooAbsData* GenerateToyData(RooArgSet& paramPoint, double& weight) const { return GenerateToyData(paramPoint,weight,*fPdf); }
// generate global observables
virtual void GenerateGlobalObservables(RooAbsPdf& pdf) const;
// Main interface to evaluate the test statistic on a dataset
virtual Double_t EvaluateTestStatistic(RooAbsData& data, RooArgSet& nullPOI, int i ) {
return fTestStatistics[i]->Evaluate(data, nullPOI);
}
virtual Double_t EvaluateTestStatistic(RooAbsData& data, RooArgSet& nullPOI) { return EvaluateTestStatistic( data,nullPOI, 0 ); }
virtual RooArgList* EvaluateAllTestStatistics(RooAbsData& data, const RooArgSet& poi);
virtual TestStatistic* GetTestStatistic(unsigned int i) const {
if( fTestStatistics.size() <= i ) return NULL;
return fTestStatistics[i];
}
virtual TestStatistic* GetTestStatistic(void) const { return GetTestStatistic(0); }
virtual Double_t ConfidenceLevel() const { return 1. - fSize; }
virtual void Initialize(
RooAbsArg& /*testStatistic*/,
RooArgSet& /*paramsOfInterest*/,
RooArgSet& /*nuisanceParameters*/
) {}
virtual Int_t GetNToys(void) { return fNToys; }
virtual void SetNToys(const Int_t ntoy) { fNToys = ntoy; }
virtual void SetNEventsPerToy(const Int_t nevents) {
// Forces n events even for extended PDFs. Set NEvents=0 to
// use the Poisson distributed events from the extended PDF.
fNEvents = nevents;
}
// Set the Pdf, add to the the workspace if not already there
virtual void SetParametersForTestStat(const RooArgSet& nullpoi) {
if( fParametersForTestStat ) delete fParametersForTestStat;
fParametersForTestStat = (const RooArgSet*)nullpoi.snapshot();
}
virtual void SetPdf(RooAbsPdf& pdf) { fPdf = &pdf; ClearCache(); }
// How to randomize the prior. Set to NULL to deactivate randomization.
virtual void SetPriorNuisance(RooAbsPdf* pdf) {
fPriorNuisance = pdf;
if (fNuisanceParametersSampler) {
delete fNuisanceParametersSampler;
fNuisanceParametersSampler = NULL;
}
}
// specify the nuisance parameters (eg. the rest of the parameters)
virtual void SetNuisanceParameters(const RooArgSet& np) { fNuisancePars = &np; }
// specify the observables in the dataset (needed to evaluate the test statistic)
virtual void SetObservables(const RooArgSet& o) { fObservables = &o; }
// specify the conditional observables
virtual void SetGlobalObservables(const RooArgSet& o) { fGlobalObservables = &o; }
// set the size of the test (rate of Type I error) ( Eg. 0.05 for a 95% Confidence Interval)
virtual void SetTestSize(Double_t size) { fSize = size; }
// set the confidence level for the interval (eg. 0.95 for a 95% Confidence Interval)
virtual void SetConfidenceLevel(Double_t cl) { fSize = 1. - cl; }
// Set the TestStatistic (want the argument to be a function of the data & parameter points
virtual void SetTestStatistic(TestStatistic *testStatistic, unsigned int i) {
if( fTestStatistics.size() < i ) {
oocoutE((TObject*)NULL,InputArguments) << "Cannot set test statistic for this index." << std::endl;
return;
}
if( fTestStatistics.size() == i)
fTestStatistics.push_back(testStatistic);
else
fTestStatistics[i] = testStatistic;
}
virtual void SetTestStatistic(TestStatistic *t) { return SetTestStatistic(t,0); }
virtual void SetExpectedNuisancePar(Bool_t i = kTRUE) { fExpectedNuisancePar = i; }
virtual void SetAsimovNuisancePar(Bool_t i = kTRUE) { fExpectedNuisancePar = i; }
// Checks for sufficient information to do a GetSamplingDistribution(...).
Bool_t CheckConfig(void);
// control to use bin data generation (=> see RooFit::AllBinned() option)
void SetGenerateBinned(bool binned = true) { fGenerateBinned = binned; }
// name of the tag for individual components to be generated binned (=> see RooFit::GenBinned() option)
void SetGenerateBinnedTag( const char* binnedTag = "" ) { fGenerateBinnedTag = binnedTag; }
// set auto binned generation (=> see RooFit::AutoBinned() option)
void SetGenerateAutoBinned( Bool_t autoBinned = kTRUE ) { fGenerateAutoBinned = autoBinned; }
// Set the name of the sampling distribution used for plotting
void SetSamplingDistName(const char* name) { if(name) fSamplingDistName = name; }
std::string GetSamplingDistName(void) { return fSamplingDistName; }
// This option forces a maximum number of total toys.
void SetMaxToys(Double_t t) { fMaxToys = t; }
void SetToysLeftTail(Double_t toys, Double_t threshold) {
fToysInTails = toys;
fAdaptiveLowLimit = threshold;
fAdaptiveHighLimit = RooNumber::infinity();
}
void SetToysRightTail(Double_t toys, Double_t threshold) {
fToysInTails = toys;
fAdaptiveHighLimit = threshold;
fAdaptiveLowLimit = -RooNumber::infinity();
}
void SetToysBothTails(Double_t toys, Double_t low_threshold, Double_t high_threshold) {
fToysInTails = toys;
fAdaptiveHighLimit = high_threshold;
fAdaptiveLowLimit = low_threshold;
}
// calling with argument or NULL deactivates proof
void SetProofConfig(ProofConfig *pc = NULL) { fProofConfig = pc; }
void SetProtoData(const RooDataSet* d) { fProtoData = d; }
protected:
const RooArgList* EvaluateAllTestStatistics(RooAbsData& data, const RooArgSet& poi, DetailedOutputAggregator& detOutAgg);
// helper for GenerateToyData
RooAbsData* Generate(RooAbsPdf &pdf, RooArgSet &observables, const RooDataSet *protoData=NULL, int forceEvents=0) const;
// helper method for clearing the cache
virtual void ClearCache();
// densities, snapshots, and test statistics to reweight to
RooAbsPdf *fPdf; // model (can be alt or null)
const RooArgSet* fParametersForTestStat;
std::vector<TestStatistic*> fTestStatistics;
std::string fSamplingDistName; // name of the model
RooAbsPdf *fPriorNuisance; // prior pdf for nuisance parameters
const RooArgSet *fNuisancePars;
const RooArgSet *fObservables;
const RooArgSet *fGlobalObservables;
Int_t fNToys; // number of toys to generate
Int_t fNEvents; // number of events per toy (may be ignored depending on settings)
Double_t fSize;
Bool_t fExpectedNuisancePar; // whether to use expectation values for nuisance parameters (ie Asimov data set)
Bool_t fGenerateBinned;
TString fGenerateBinnedTag;
Bool_t fGenerateAutoBinned;
// minimum no of toys in tails for adaptive sampling
// (taking weights into account, therefore double)
// Default: 0.0 which means no adaptive sampling
Double_t fToysInTails;
// maximum no of toys
// (taking weights into account, therefore double)
Double_t fMaxToys;
// tails
Double_t fAdaptiveLowLimit;
Double_t fAdaptiveHighLimit;
const RooDataSet *fProtoData; // in dev
ProofConfig *fProofConfig; //!
mutable NuisanceParametersSampler *fNuisanceParametersSampler; //!
// objects below cache information and are mutable and non-persistent
mutable RooArgSet* _allVars ; //!
mutable std::list<RooAbsPdf*> _pdfList ; //!
mutable std::list<RooArgSet*> _obsList ; //!
mutable std::list<RooAbsPdf::GenSpec*> _gsList ; //!
mutable RooAbsPdf::GenSpec* _gs1 ; //! GenSpec #1
mutable RooAbsPdf::GenSpec* _gs2 ; //! GenSpec #2
mutable RooAbsPdf::GenSpec* _gs3 ; //! GenSpec #3
mutable RooAbsPdf::GenSpec* _gs4 ; //! GenSpec #4
static Bool_t fgAlwaysUseMultiGen ; // Use PrepareMultiGen always
Bool_t fUseMultiGen ; // Use PrepareMultiGen?
protected:
ClassDef(ToyMCSampler,3) // A simple implementation of the TestStatSampler interface
};
}
#endif
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