/*
* Prepares the workspace to be used by the hypothesis test calculator
*/
void workspace_preparer(char *signal_file_name, char *signal_hist_name_in_file, char *background_file_name, char *background_hist_name_in_file, char *data_file_name, char *data_hist_name_in_file, char *config_file) {
// Include the config_reader class.
TString path = gSystem->GetIncludePath();
path.Append(" -I/home/max/cern/cls/mario");
gSystem->SetIncludePath(path);
gROOT->LoadMacro("config_reader.cxx");
// RooWorkspace used to store values.
RooWorkspace * pWs = new RooWorkspace("ws");
// Create a config_reader (see source for details) to read the config
// file.
config_reader reader(config_file, pWs);
// Read MR and RR bounds from the config file.
double MR_lower = reader.find_double("MR_lower");
double MR_upper = reader.find_double("MR_upper");
double RR_lower = reader.find_double("RR_lower");
double RR_upper = reader.find_double("RR_upper");
double MR_initial = (MR_lower + MR_upper)/2;
double RR_initial = (RR_lower + RR_upper)/2;
// Define the Razor Variables
RooRealVar MR = RooRealVar("MR", "MR", MR_initial, MR_lower, MR_upper);
RooRealVar RR = RooRealVar("RSQ", "RSQ", RR_initial, RR_lower, RR_upper);
// Argument lists
RooArgList pdf_arg_list(MR, RR, "input_args_list");
RooArgSet pdf_arg_set(MR, RR, "input_pdf_args_set");
/***********************************************************************/
/* PART 1: IMPORTING SIGNAL AND BACKGROUND HISTOGRAMS */
/***********************************************************************/
/*
* Get the signal's unextended pdf by converting the TH2D in the file
* into a RooHistPdf
*/
TFile *signal_file = new TFile(signal_file_name);
TH2D *signal_hist = (TH2D *)signal_file->Get(signal_hist_name_in_file);
RooDataHist *signal_RooDataHist = new RooDataHist("signal_roodatahist",
"signal_roodatahist",
pdf_arg_list,
signal_hist);
RooHistPdf *unextended_sig_pdf = new RooHistPdf("unextended_sig_pdf",
"unextended_sig_pdf",
pdf_arg_set,
*signal_RooDataHist);
/*
* Repeat this process for the background.
*/
TFile *background_file = new TFile(background_file_name);
TH2D *background_hist =
(TH2D *)background_file->Get(background_hist_name_in_file);
RooDataHist *background_RooDataHist =
new RooDataHist("background_roodatahist", "background_roodatahist",
pdf_arg_list, background_hist);
RooHistPdf *unextended_bkg_pdf = new RooHistPdf("unextended_bkg_pdf",
"unextended_bkg_pdf",
pdf_arg_set,
*background_RooDataHist);
/*
* Now, we want to create the bprime variable, which represents the
* integral over the background-only sample. We will perform the
* integral automatically (that's why this is the only nuisance
* parameter declared in this file - its value can be determined from
* the input histograms).
*/
ostringstream bprime_string;
ostringstream bprime_pdf_string;
bprime_string << "bprime[" << background_hist->Integral() << ", 0, 999999999]";
bprime_pdf_string << "Poisson::bprime_pdf(bprime, " << background_hist->Integral() << ")";
pWs->factory(bprime_string.str().c_str());
pWs->factory(bprime_pdf_string.str().c_str());
/*
* This simple command will create all values from the config file
* with 'make:' at the beginning and a delimiter at the end (see config
* _reader if you don't know what a delimiter is). In other
* words, the luminosity, efficiency, transfer factors, and their pdfs
* are created from this command. The declarations are contained in the
* config file to be changed easily without having to modify this code.
*/
reader.factory_all();
/*
* Now, we want to create the extended pdfs from the unextended pdfs, as
* well as from the S and B values we manufactured in the config file.
* S and B are the values by which the signal and background pdfs,
* respectively, are extended. Recall that they were put in the
* workspace in the reader.facotry_all() command.
*/
RooAbsReal *S = pWs->function("S");
RooAbsReal *B = pWs->function("B");
RooExtendPdf *signalpart = new RooExtendPdf("signalpart", "signalpart",
*unextended_sig_pdf, *S);
RooExtendPdf *backgroundpart =
new RooExtendPdf("backgroundpart", "backgroundpart",
*unextended_bkg_pdf, *B);
RooArgList *pdf_list = new RooArgList(*signalpart, *backgroundpart,
"list");
// Add the signal and background pdfs to make a TotalPdf
RooAddPdf *TotalPdf = new RooAddPdf("TotalPdf", "TotalPdf", *pdf_list);
RooArgList *pdf_prod_list = new RooArgList(*TotalPdf,
*pWs->pdf("lumi_pdf"),
*pWs->pdf("eff_pdf"),
*pWs->pdf("rho_pdf"),
*pWs->pdf("bprime_pdf"));
// This creates the final model pdf.
RooProdPdf *model = new RooProdPdf("model", "model", *pdf_prod_list);
/*
* Up until now, we have been using the workspace pWs to contain all of
* our values. Now, all of our values that we require are in use in the
* RooProdPdf called "model". So, we need to import "model" into a
* RooWorkspace. To avoid recopying values into the rooworkspace, when
* the values may already be present (which can cause problems), we will
* simply create a new RooWorkspace to avoid confusion and problems. The
* new RooWorkspace is created here.
*/
RooWorkspace *newworkspace = new RooWorkspace("newws");
newworkspace->import(*model);
// Immediately delete pWs, so we don't accidentally use it again.
delete pWs;
// Show off the newworkspace
newworkspace->Print();
// observables
RooArgSet obs(*newworkspace->var("MR"), *newworkspace->var("RSQ"), "obs");
// global observables
RooArgSet globalObs(*newworkspace->var("nom_lumi"), *newworkspace->var("nom_eff"), *newworkspace->var("nom_rho"));
//fix global observables to their nominal values
newworkspace->var("nom_lumi")->setConstant();
newworkspace->var("nom_eff")->setConstant();
newworkspace->var("nom_rho")->setConstant();
//Set Parameters of interest
RooArgSet poi(*newworkspace->var("sigma"), "poi");
//Set Nuisnaces
RooArgSet nuis(*newworkspace->var("prime_lumi"), *newworkspace->var("prime_eff"), *newworkspace->var("prime_rho"), *newworkspace->var("bprime"));
// priors (for Bayesian calculation)
newworkspace->factory("Uniform::prior_signal(sigma)"); // for parameter of interest
newworkspace->factory("Uniform::prior_bg_b(bprime)"); // for data driven nuisance parameter
newworkspace->factory("PROD::prior(prior_signal,prior_bg_b)"); // total prior
//Observed data is pulled from histogram.
//TFile *data_file = new TFile(data_file_name);
TFile *data_file = new TFile(data_file_name);
TH2D *data_hist = (TH2D *)data_file->Get(data_hist_name_in_file);
RooDataHist *pData = new RooDataHist("data", "data", obs, data_hist);
newworkspace->import(*pData);
// Now, we will draw our data from a RooDataHist.
/*TFile *data_file = new TFile(data_file_name);
TTree *data_tree = (TTree *) data_file->Get(data_hist_name_in_file);
RooDataSet *pData = new RooDataSet("data", "data", data_tree, obs);
newworkspace->import(*pData);*/
// Craft the signal+background model
ModelConfig * pSbModel = new ModelConfig("SbModel");
pSbModel->SetWorkspace(*newworkspace);
pSbModel->SetPdf(*newworkspace->pdf("model"));
pSbModel->SetPriorPdf(*newworkspace->pdf("prior"));
pSbModel->SetParametersOfInterest(poi);
pSbModel->SetNuisanceParameters(nuis);
pSbModel->SetObservables(obs);
pSbModel->SetGlobalObservables(globalObs);
// set all but obs, poi and nuisance to const
SetConstants(newworkspace, pSbModel);
newworkspace->import(*pSbModel);
// background-only model
// use the same PDF as s+b, with sig=0
// POI value under the background hypothesis
// (We will set the value to 0 later)
Double_t poiValueForBModel = 0.0;
ModelConfig* pBModel = new ModelConfig(*(RooStats::ModelConfig *)newworkspace->obj("SbModel"));
pBModel->SetName("BModel");
pBModel->SetWorkspace(*newworkspace);
newworkspace->import(*pBModel);
// find global maximum with the signal+background model
// with conditional MLEs for nuisance parameters
// and save the parameter point snapshot in the Workspace
// - safer to keep a default name because some RooStats calculators
// will anticipate it
RooAbsReal * pNll = pSbModel->GetPdf()->createNLL(*pData);
RooAbsReal * pProfile = pNll->createProfile(RooArgSet());
pProfile->getVal(); // this will do fit and set POI and nuisance parameters to fitted values
RooArgSet * pPoiAndNuisance = new RooArgSet();
if(pSbModel->GetNuisanceParameters())
pPoiAndNuisance->add(*pSbModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pSbModel->GetParametersOfInterest());
cout << "\nWill save these parameter points that correspond to the fit to data" << endl;
pPoiAndNuisance->Print("v");
pSbModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// Find a parameter point for generating pseudo-data
// with the background-only data.
// Save the parameter point snapshot in the Workspace
pNll = pBModel->GetPdf()->createNLL(*pData);
pProfile = pNll->createProfile(poi);
((RooRealVar *)poi.first())->setVal(poiValueForBModel);
pProfile->getVal(); // this will do fit and set nuisance parameters to profiled values
pPoiAndNuisance = new RooArgSet();
if(pBModel->GetNuisanceParameters())
pPoiAndNuisance->add(*pBModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pBModel->GetParametersOfInterest());
cout << "\nShould use these parameter points to generate pseudo data for bkg only" << endl;
pPoiAndNuisance->Print("v");
pBModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// save workspace to file
newworkspace->writeToFile("ws_twobin.root");
// clean up
delete newworkspace;
delete pData;
delete pSbModel;
delete pBModel;
} // ----- end of tutorial ----------------------------------------
// internal routine to run the inverter
HypoTestInverterResult *
RooStats::HypoTestInvTool::RunInverter(RooWorkspace * w,
const char * modelSBName, const char * modelBName,
const char * dataName, int type, int testStatType,
bool useCLs, int npoints, double poimin, double poimax,
int ntoys,
bool useNumberCounting,
const char * nuisPriorName ){
std::cout << "Running HypoTestInverter on the workspace " << w->GetName() << std::endl;
w->Print();
RooAbsData * data = w->data(dataName);
if (!data) {
Error("StandardHypoTestDemo","Not existing data %s",dataName);
return 0;
}
else
std::cout << "Using data set " << dataName << std::endl;
if (mUseVectorStore) {
RooAbsData::setDefaultStorageType(RooAbsData::Vector);
data->convertToVectorStore() ;
}
// get models from WS
// get the modelConfig out of the file
ModelConfig* bModel = (ModelConfig*) w->obj(modelBName);
ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName);
if (!sbModel) {
Error("StandardHypoTestDemo","Not existing ModelConfig %s",modelSBName);
return 0;
}
// check the model
if (!sbModel->GetPdf()) {
Error("StandardHypoTestDemo","Model %s has no pdf ",modelSBName);
return 0;
}
if (!sbModel->GetParametersOfInterest()) {
Error("StandardHypoTestDemo","Model %s has no poi ",modelSBName);
return 0;
}
if (!sbModel->GetObservables()) {
Error("StandardHypoTestInvDemo","Model %s has no observables ",modelSBName);
return 0;
}
if (!sbModel->GetSnapshot() ) {
Info("StandardHypoTestInvDemo","Model %s has no snapshot - make one using model poi",modelSBName);
sbModel->SetSnapshot( *sbModel->GetParametersOfInterest() );
}
// case of no systematics
// remove nuisance parameters from model
if (noSystematics) {
const RooArgSet * nuisPar = sbModel->GetNuisanceParameters();
if (nuisPar && nuisPar->getSize() > 0) {
std::cout << "StandardHypoTestInvDemo" << " - Switch off all systematics by setting them constant to their initial values" << std::endl;
RooStats::SetAllConstant(*nuisPar);
}
if (bModel) {
const RooArgSet * bnuisPar = bModel->GetNuisanceParameters();
if (bnuisPar)
RooStats::SetAllConstant(*bnuisPar);
}
}
if (!bModel || bModel == sbModel) {
Info("StandardHypoTestInvDemo","The background model %s does not exist",modelBName);
Info("StandardHypoTestInvDemo","Copy it from ModelConfig %s and set POI to zero",modelSBName);
bModel = (ModelConfig*) sbModel->Clone();
bModel->SetName(TString(modelSBName)+TString("_with_poi_0"));
RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
if (!var) return 0;
double oldval = var->getVal();
var->setVal(0);
bModel->SetSnapshot( RooArgSet(*var) );
var->setVal(oldval);
}
else {
if (!bModel->GetSnapshot() ) {
Info("StandardHypoTestInvDemo","Model %s has no snapshot - make one using model poi and 0 values ",modelBName);
RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first());
if (var) {
double oldval = var->getVal();
var->setVal(0);
bModel->SetSnapshot( RooArgSet(*var) );
var->setVal(oldval);
}
else {
Error("StandardHypoTestInvDemo","Model %s has no valid poi",modelBName);
return 0;
}
}
}
// check model has global observables when there are nuisance pdf
// for the hybrid case the globobs are not needed
if (type != 1 ) {
bool hasNuisParam = (sbModel->GetNuisanceParameters() && sbModel->GetNuisanceParameters()->getSize() > 0);
bool hasGlobalObs = (sbModel->GetGlobalObservables() && sbModel->GetGlobalObservables()->getSize() > 0);
if (hasNuisParam && !hasGlobalObs ) {
// try to see if model has nuisance parameters first
RooAbsPdf * constrPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisanceConstraintPdf_sbmodel");
if (constrPdf) {
Warning("StandardHypoTestInvDemo","Model %s has nuisance parameters but no global observables associated",sbModel->GetName());
Warning("StandardHypoTestInvDemo","\tThe effect of the nuisance parameters will not be treated correctly ");
}
}
}
// run first a data fit
const RooArgSet * poiSet = sbModel->GetParametersOfInterest();
RooRealVar *poi = (RooRealVar*)poiSet->first();
std::cout << "StandardHypoTestInvDemo : POI initial value: " << poi->GetName() << " = " << poi->getVal() << std::endl;
// fit the data first (need to use constraint )
TStopwatch tw;
bool doFit = initialFit;
if (testStatType == 0 && initialFit == -1) doFit = false; // case of LEP test statistic
if (type == 3 && initialFit == -1) doFit = false; // case of Asymptoticcalculator with nominal Asimov
double poihat = 0;
if (minimizerType.size()==0) minimizerType = ROOT::Math::MinimizerOptions::DefaultMinimizerType();
else
ROOT::Math::MinimizerOptions::SetDefaultMinimizer(minimizerType.c_str());
Info("StandardHypoTestInvDemo","Using %s as minimizer for computing the test statistic",
ROOT::Math::MinimizerOptions::DefaultMinimizerType().c_str() );
if (doFit) {
// do the fit : By doing a fit the POI snapshot (for S+B) is set to the fit value
// and the nuisance parameters nominal values will be set to the fit value.
// This is relevant when using LEP test statistics
Info( "StandardHypoTestInvDemo"," Doing a first fit to the observed data ");
RooArgSet constrainParams;
if (sbModel->GetNuisanceParameters() ) constrainParams.add(*sbModel->GetNuisanceParameters());
RooStats::RemoveConstantParameters(&constrainParams);
tw.Start();
RooFitResult * fitres = sbModel->GetPdf()->fitTo(*data,InitialHesse(false), Hesse(false),
Minimizer(minimizerType.c_str(),"Migrad"), Strategy(0), PrintLevel(mPrintLevel), Constrain(constrainParams), Save(true) );
if (fitres->status() != 0) {
Warning("StandardHypoTestInvDemo","Fit to the model failed - try with strategy 1 and perform first an Hesse computation");
fitres = sbModel->GetPdf()->fitTo(*data,InitialHesse(true), Hesse(false),Minimizer(minimizerType.c_str(),"Migrad"), Strategy(1), PrintLevel(mPrintLevel+1), Constrain(constrainParams), Save(true) );
}
if (fitres->status() != 0)
Warning("StandardHypoTestInvDemo"," Fit still failed - continue anyway.....");
poihat = poi->getVal();
std::cout << "StandardHypoTestInvDemo - Best Fit value : " << poi->GetName() << " = "
<< poihat << " +/- " << poi->getError() << std::endl;
std::cout << "Time for fitting : "; tw.Print();
//save best fit value in the poi snapshot
sbModel->SetSnapshot(*sbModel->GetParametersOfInterest());
std::cout << "StandardHypoTestInvo: snapshot of S+B Model " << sbModel->GetName()
<< " is set to the best fit value" << std::endl;
}
// print a message in case of LEP test statistics because it affects result by doing or not doing a fit
if (testStatType == 0) {
if (!doFit)
Info("StandardHypoTestInvDemo","Using LEP test statistic - an initial fit is not done and the TS will use the nuisances at the model value");
else
Info("StandardHypoTestInvDemo","Using LEP test statistic - an initial fit has been done and the TS will use the nuisances at the best fit value");
}
// build test statistics and hypotest calculators for running the inverter
SimpleLikelihoodRatioTestStat slrts(*sbModel->GetPdf(),*bModel->GetPdf());
// null parameters must includes snapshot of poi plus the nuisance values
RooArgSet nullParams(*sbModel->GetSnapshot());
if (sbModel->GetNuisanceParameters()) nullParams.add(*sbModel->GetNuisanceParameters());
if (sbModel->GetSnapshot()) slrts.SetNullParameters(nullParams);
RooArgSet altParams(*bModel->GetSnapshot());
if (bModel->GetNuisanceParameters()) altParams.add(*bModel->GetNuisanceParameters());
if (bModel->GetSnapshot()) slrts.SetAltParameters(altParams);
// ratio of profile likelihood - need to pass snapshot for the alt
RatioOfProfiledLikelihoodsTestStat
ropl(*sbModel->GetPdf(), *bModel->GetPdf(), bModel->GetSnapshot());
ropl.SetSubtractMLE(false);
if (testStatType == 11) ropl.SetSubtractMLE(true);
ropl.SetPrintLevel(mPrintLevel);
ropl.SetMinimizer(minimizerType.c_str());
ProfileLikelihoodTestStat profll(*sbModel->GetPdf());
if (testStatType == 3) profll.SetOneSided(true);
if (testStatType == 4) profll.SetSigned(true);
profll.SetMinimizer(minimizerType.c_str());
profll.SetPrintLevel(mPrintLevel);
profll.SetReuseNLL(mOptimize);
slrts.SetReuseNLL(mOptimize);
ropl.SetReuseNLL(mOptimize);
if (mOptimize) {
profll.SetStrategy(0);
ropl.SetStrategy(0);
ROOT::Math::MinimizerOptions::SetDefaultStrategy(0);
}
if (mMaxPoi > 0) poi->setMax(mMaxPoi); // increase limit
MaxLikelihoodEstimateTestStat maxll(*sbModel->GetPdf(),*poi);
NumEventsTestStat nevtts;
AsymptoticCalculator::SetPrintLevel(mPrintLevel);
// create the HypoTest calculator class
HypoTestCalculatorGeneric * hc = 0;
if (type == 0) hc = new FrequentistCalculator(*data, *bModel, *sbModel);
else if (type == 1) hc = new HybridCalculator(*data, *bModel, *sbModel);
// else if (type == 2 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, false, mAsimovBins);
// else if (type == 3 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, true, mAsimovBins); // for using Asimov data generated with nominal values
else if (type == 2 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, false );
else if (type == 3 ) hc = new AsymptoticCalculator(*data, *bModel, *sbModel, true ); // for using Asimov data generated with nominal values
else {
Error("StandardHypoTestInvDemo","Invalid - calculator type = %d supported values are only :\n\t\t\t 0 (Frequentist) , 1 (Hybrid) , 2 (Asymptotic) ",type);
return 0;
}
// set the test statistic
TestStatistic * testStat = 0;
if (testStatType == 0) testStat = &slrts;
if (testStatType == 1 || testStatType == 11) testStat = &ropl;
if (testStatType == 2 || testStatType == 3 || testStatType == 4) testStat = &profll;
if (testStatType == 5) testStat = &maxll;
if (testStatType == 6) testStat = &nevtts;
if (testStat == 0) {
Error("StandardHypoTestInvDemo","Invalid - test statistic type = %d supported values are only :\n\t\t\t 0 (SLR) , 1 (Tevatron) , 2 (PLR), 3 (PLR1), 4(MLE)",testStatType);
return 0;
}
ToyMCSampler *toymcs = (ToyMCSampler*)hc->GetTestStatSampler();
if (toymcs && (type == 0 || type == 1) ) {
// look if pdf is number counting or extended
if (sbModel->GetPdf()->canBeExtended() ) {
if (useNumberCounting) Warning("StandardHypoTestInvDemo","Pdf is extended: but number counting flag is set: ignore it ");
}
else {
// for not extended pdf
if (!useNumberCounting ) {
int nEvents = data->numEntries();
Info("StandardHypoTestInvDemo","Pdf is not extended: number of events to generate taken from observed data set is %d",nEvents);
toymcs->SetNEventsPerToy(nEvents);
}
else {
Info("StandardHypoTestInvDemo","using a number counting pdf");
toymcs->SetNEventsPerToy(1);
}
}
toymcs->SetTestStatistic(testStat);
if (data->isWeighted() && !mGenerateBinned) {
Info("StandardHypoTestInvDemo","Data set is weighted, nentries = %d and sum of weights = %8.1f but toy generation is unbinned - it would be faster to set mGenerateBinned to true\n",data->numEntries(), data->sumEntries());
}
toymcs->SetGenerateBinned(mGenerateBinned);
toymcs->SetUseMultiGen(mOptimize);
if (mGenerateBinned && sbModel->GetObservables()->getSize() > 2) {
Warning("StandardHypoTestInvDemo","generate binned is activated but the number of ovservable is %d. Too much memory could be needed for allocating all the bins",sbModel->GetObservables()->getSize() );
}
// set the random seed if needed
if (mRandomSeed >= 0) RooRandom::randomGenerator()->SetSeed(mRandomSeed);
}
// specify if need to re-use same toys
if (reuseAltToys) {
hc->UseSameAltToys();
}
if (type == 1) {
HybridCalculator *hhc = dynamic_cast<HybridCalculator*> (hc);
assert(hhc);
hhc->SetToys(ntoys,ntoys/mNToysRatio); // can use less ntoys for b hypothesis
// remove global observables from ModelConfig (this is probably not needed anymore in 5.32)
bModel->SetGlobalObservables(RooArgSet() );
sbModel->SetGlobalObservables(RooArgSet() );
// check for nuisance prior pdf in case of nuisance parameters
if (bModel->GetNuisanceParameters() || sbModel->GetNuisanceParameters() ) {
// fix for using multigen (does not work in this case)
toymcs->SetUseMultiGen(false);
ToyMCSampler::SetAlwaysUseMultiGen(false);
RooAbsPdf * nuisPdf = 0;
if (nuisPriorName) nuisPdf = w->pdf(nuisPriorName);
// use prior defined first in bModel (then in SbModel)
if (!nuisPdf) {
Info("StandardHypoTestInvDemo","No nuisance pdf given for the HybridCalculator - try to deduce pdf from the model");
if (bModel->GetPdf() && bModel->GetObservables() )
nuisPdf = RooStats::MakeNuisancePdf(*bModel,"nuisancePdf_bmodel");
else
nuisPdf = RooStats::MakeNuisancePdf(*sbModel,"nuisancePdf_sbmodel");
}
if (!nuisPdf ) {
if (bModel->GetPriorPdf()) {
nuisPdf = bModel->GetPriorPdf();
Info("StandardHypoTestInvDemo","No nuisance pdf given - try to use %s that is defined as a prior pdf in the B model",nuisPdf->GetName());
}
else {
Error("StandardHypoTestInvDemo","Cannnot run Hybrid calculator because no prior on the nuisance parameter is specified or can be derived");
return 0;
}
}
assert(nuisPdf);
Info("StandardHypoTestInvDemo","Using as nuisance Pdf ... " );
nuisPdf->Print();
const RooArgSet * nuisParams = (bModel->GetNuisanceParameters() ) ? bModel->GetNuisanceParameters() : sbModel->GetNuisanceParameters();
RooArgSet * np = nuisPdf->getObservables(*nuisParams);
if (np->getSize() == 0) {
Warning("StandardHypoTestInvDemo","Prior nuisance does not depend on nuisance parameters. They will be smeared in their full range");
}
delete np;
hhc->ForcePriorNuisanceAlt(*nuisPdf);
hhc->ForcePriorNuisanceNull(*nuisPdf);
}
}
else if (type == 2 || type == 3) {
if (testStatType == 3) ((AsymptoticCalculator*) hc)->SetOneSided(true);
if (testStatType != 2 && testStatType != 3)
Warning("StandardHypoTestInvDemo","Only the PL test statistic can be used with AsymptoticCalculator - use by default a two-sided PL");
}
else if (type == 0 || type == 1)
((FrequentistCalculator*) hc)->SetToys(ntoys,ntoys/mNToysRatio);
// Get the result
RooMsgService::instance().getStream(1).removeTopic(RooFit::NumIntegration);
HypoTestInverter calc(*hc);
calc.SetConfidenceLevel(0.95);
calc.UseCLs(useCLs);
calc.SetVerbose(true);
// can speed up using proof-lite
if (mUseProof && mNWorkers > 1) {
ProofConfig pc(*w, mNWorkers, "", kFALSE);
toymcs->SetProofConfig(&pc); // enable proof
}
if (npoints > 0) {
if (poimin > poimax) {
// if no min/max given scan between MLE and +4 sigma
poimin = int(poihat);
poimax = int(poihat + 4 * poi->getError());
}
std::cout << "Doing a fixed scan in interval : " << poimin << " , " << poimax << std::endl;
calc.SetFixedScan(npoints,poimin,poimax);
}
else {
//poi->setMax(10*int( (poihat+ 10 *poi->getError() )/10 ) );
std::cout << "Doing an automatic scan in interval : " << poi->getMin() << " , " << poi->getMax() << std::endl;
}
tw.Start();
HypoTestInverterResult * r = calc.GetInterval();
std::cout << "Time to perform limit scan \n";
tw.Print();
if (mRebuild) {
calc.SetCloseProof(1);
tw.Start();
SamplingDistribution * limDist = calc.GetUpperLimitDistribution(true,mNToyToRebuild);
std::cout << "Time to rebuild distributions " << std::endl;
tw.Print();
if (limDist) {
std::cout << "expected up limit " << limDist->InverseCDF(0.5) << " +/- "
<< limDist->InverseCDF(0.16) << " "
<< limDist->InverseCDF(0.84) << "\n";
//update r to a new updated result object containing the rebuilt expected p-values distributions
// (it will not recompute the expected limit)
if (r) delete r; // need to delete previous object since GetInterval will return a cloned copy
r = calc.GetInterval();
}
else
std::cout << "ERROR : failed to re-build distributions " << std::endl;
}
return r;
}
void new_RA4(){
// let's time this challenging example
TStopwatch t;
t.Start();
// set RooFit random seed for reproducible results
RooRandom::randomGenerator()->SetSeed(4357);
// make model
RooWorkspace* wspace = new RooWorkspace("wspace");
wspace->factory("Gaussian::sigCons(prime_SigEff[0,-5,5], nom_SigEff[0,-5,5], 1)");
wspace->factory("expr::SigEff('1.0*pow(1.20,@0)',prime_SigEff)"); // // 1+-20%, 1.20=exp(20%)
wspace->factory("Poisson::on(non[0,50], sum::splusb(prod::SigUnc(s[0,0,50],SigEff),mainb[8.8,0,50],dilep[0.9,0,20],tau[2.3,0,20],QCD[0.,0,10],MC[0.1,0,4]))");
wspace->factory("Gaussian::mcCons(prime_rho[0,-5,5], nom_rho[0,-5,5], 1)");
wspace->factory("expr::rho('1.0*pow(1.39,@0)',prime_rho)"); // // 1+-39%
wspace->factory("Poisson::off(noff[0,200], prod::rhob(mainb,rho,mu_plus_e[0.74,0.01,10],1.08))");
wspace->factory("Gaussian::mcCons2(mu_plus_enom[0.74,0.01,4], mu_plus_e, sigmatwo[.05])");
wspace->factory("Gaussian::dilep_pred(dilep_nom[0.9,0,20], dilep, sigma3[2.2])");
wspace->factory("Gaussian::tau_pred(tau_nom[2.3,0,20], tau, sigma4[0.5])");
wspace->factory("Gaussian::QCD_pred(QCD_nom[0.0,0,10], QCD, sigma5[1.0])");
wspace->factory("Gaussian::MC_pred(MC_nom[0.1,0.01,4], MC, sigma7[0.14])");
wspace->factory("PROD::model(on,off,mcCons,mcCons2,sigCons,dilep_pred,tau_pred,QCD_pred,MC_pred)");
RooArgSet obs(*wspace->var("non"), *wspace->var("noff"), *wspace->var("mu_plus_enom"), *wspace->var("dilep_nom"), *wspace->var("tau_nom"), "obs");
obs.add(*wspace->var("QCD_nom")); obs.add(*wspace->var("MC_nom"));
RooArgSet globalObs(*wspace->var("nom_SigEff"), *wspace->var("nom_rho"), "global_obs");
// fix global observables to their nominal values
wspace->var("nom_SigEff")->setConstant();
wspace->var("nom_rho")->setConstant();
RooArgSet poi(*wspace->var("s"), "poi");
RooArgSet nuis(*wspace->var("mainb"), *wspace->var("prime_rho"), *wspace->var("prime_SigEff"), *wspace->var("mu_plus_e"), *wspace->var("dilep"), *wspace->var("tau"), "nuis");
nuis.add(*wspace->var("QCD")); nuis.add(*wspace->var("MC"));
wspace->factory("Uniform::prior_poi({s})");
wspace->factory("Uniform::prior_nuis({mainb,mu_plus_e,dilep,tau,QCD,MC})");
wspace->factory("PROD::prior(prior_poi,prior_nuis)");
wspace->var("non")->setVal(8); //observed
//wspace->var("non")->setVal(12); //expected observation
wspace->var("noff")->setVal(7); //observed events in control region
wspace->var("mu_plus_enom")->setVal(0.74);
wspace->var("dilep_nom")->setVal(0.9);
wspace->var("tau_nom")->setVal(2.3);
wspace->var("QCD")->setVal(0.0);
wspace->var("MC")->setVal(0.1);
RooDataSet * data = new RooDataSet("data","",obs);
data->add(obs);
wspace->import(*data);
/////////////////////////////////////////////////////
// Now the statistical tests
// model config
ModelConfig* pSbModel = new ModelConfig("SbModel");
pSbModel->SetWorkspace(*wspace);
pSbModel->SetPdf(*wspace->pdf("model"));
pSbModel->SetPriorPdf(*wspace->pdf("prior"));
pSbModel->SetParametersOfInterest(poi);
pSbModel->SetNuisanceParameters(nuis);
pSbModel->SetObservables(obs);
pSbModel->SetGlobalObservables(globalObs);
wspace->import(*pSbModel);
// set all but obs, poi and nuisance to const
SetConstants(wspace, pSbModel);
wspace->import(*pSbModel);
Double_t poiValueForBModel = 0.0;
ModelConfig* pBModel = new ModelConfig(*(RooStats::ModelConfig *)wspace->obj("SbModel"));
pBModel->SetName("BModel");
pBModel->SetWorkspace(*wspace);
wspace->import(*pBModel);
RooAbsReal * pNll = pSbModel->GetPdf()->createNLL(*data);
RooAbsReal * pProfile = pNll->createProfile(RooArgSet());
pProfile->getVal(); // this will do fit and set POI and nuisance parameters to fitted values
RooArgSet * pPoiAndNuisance = new RooArgSet();
//if(pSbModel->GetNuisanceParameters())
// pPoiAndNuisance->add(*pSbModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pSbModel->GetParametersOfInterest());
cout << "\nWill save these parameter points that correspond to the fit to data" << endl;
pPoiAndNuisance->Print("v");
pSbModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
pNll = pBModel->GetPdf()->createNLL(*data);
pProfile = pNll->createProfile(poi);
((RooRealVar *)poi.first())->setVal(poiValueForBModel);
pProfile->getVal(); // this will do fit and set nuisance parameters to profiled values
pPoiAndNuisance = new RooArgSet();
//if(pBModel->GetNuisanceParameters())
// pPoiAndNuisance->add(*pBModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pBModel->GetParametersOfInterest());
cout << "\nShould use these parameter points to generate pseudo data for bkg only" << endl;
pPoiAndNuisance->Print("v");
pBModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// inspect workspace
wspace->Print();
// save workspace to file
wspace->writeToFile("tight.root");
//wspace->writeToFile("tight_median.root");
// clean up
delete wspace;
delete data;
delete pSbModel;
delete pBModel;
}
// implementation
void TwoBinInstructional( void ){
// let's time this example
TStopwatch t;
t.Start();
// set RooFit random seed for reproducible results
RooRandom::randomGenerator()->SetSeed(4357);
// make model
RooWorkspace * pWs = new RooWorkspace("ws");
// derived from data
pWs->factory("xsec[0.2,0,2]"); // POI
pWs->factory("bg_b[10,0,50]"); // data driven nuisance
// predefined nuisances
pWs->factory("lumi[100,0,1000]");
pWs->factory("eff_a[0.2,0,1]");
pWs->factory("eff_b[0.05,0,1]");
pWs->factory("tau[0,1]");
pWs->factory("xsec_bg_a[0.05]"); // constant
pWs->var("xsec_bg_a")->setConstant(1);
// channel a (signal): lumi*xsec*eff_a + lumi*bg_a + tau*bg_b
pWs->factory("prod::sig_a(lumi,xsec,eff_a)");
pWs->factory("prod::bg_a(lumi,xsec_bg_a)");
pWs->factory("prod::tau_bg_b(tau, bg_b)");
pWs->factory("Poisson::pdf_a(na[14,0,100],sum::mu_a(sig_a,bg_a,tau_bg_b))");
// channel b (control): lumi*xsec*eff_b + bg_b
pWs->factory("prod::sig_b(lumi,xsec,eff_b)");
pWs->factory("Poisson::pdf_b(nb[11,0,100],sum::mu_b(sig_b,bg_b))");
// nuisance constraint terms (systematics)
pWs->factory("Lognormal::l_lumi(lumi,nom_lumi[100,0,1000],sum::kappa_lumi(1,d_lumi[0.1]))");
pWs->factory("Lognormal::l_eff_a(eff_a,nom_eff_a[0.20,0,1],sum::kappa_eff_a(1,d_eff_a[0.05]))");
pWs->factory("Lognormal::l_eff_b(eff_b,nom_eff_b[0.05,0,1],sum::kappa_eff_b(1,d_eff_b[0.05]))");
pWs->factory("Lognormal::l_tau(tau,nom_tau[0.50,0,1],sum::kappa_tau(1,d_tau[0.05]))");
//pWs->factory("Lognormal::l_bg_a(bg_a,nom_bg_a[0.05,0,1],sum::kappa_bg_a(1,d_bg_a[0.10]))");
// complete model PDF
pWs->factory("PROD::model(pdf_a,pdf_b,l_lumi,l_eff_a,l_eff_b,l_tau)");
// Now create sets of variables. Note that we could use the factory to
// create sets but in that case many of the sets would be duplicated
// when the ModelConfig objects are imported into the workspace. So,
// we create the sets outside the workspace, and only the needed ones
// will be automatically imported by ModelConfigs
// observables
RooArgSet obs(*pWs->var("na"), *pWs->var("nb"), "obs");
// global observables
RooArgSet globalObs(*pWs->var("nom_lumi"), *pWs->var("nom_eff_a"), *pWs->var("nom_eff_b"),
*pWs->var("nom_tau"),
"global_obs");
// parameters of interest
RooArgSet poi(*pWs->var("xsec"), "poi");
// nuisance parameters
RooArgSet nuis(*pWs->var("lumi"), *pWs->var("eff_a"), *pWs->var("eff_b"), *pWs->var("tau"), "nuis");
// priors (for Bayesian calculation)
pWs->factory("Uniform::prior_xsec(xsec)"); // for parameter of interest
pWs->factory("Uniform::prior_bg_b(bg_b)"); // for data driven nuisance parameter
pWs->factory("PROD::prior(prior_xsec,prior_bg_b)"); // total prior
// create data
pWs->var("na")->setVal(14);
pWs->var("nb")->setVal(11);
RooDataSet * pData = new RooDataSet("data","",obs);
pData->add(obs);
pWs->import(*pData);
//pData->Print();
// signal+background model
ModelConfig * pSbModel = new ModelConfig("SbModel");
pSbModel->SetWorkspace(*pWs);
pSbModel->SetPdf(*pWs->pdf("model"));
pSbModel->SetPriorPdf(*pWs->pdf("prior"));
pSbModel->SetParametersOfInterest(poi);
pSbModel->SetNuisanceParameters(nuis);
pSbModel->SetObservables(obs);
pSbModel->SetGlobalObservables(globalObs);
// set all but obs, poi and nuisance to const
SetConstants(pWs, pSbModel);
pWs->import(*pSbModel);
// background-only model
// use the same PDF as s+b, with xsec=0
// POI value under the background hypothesis
Double_t poiValueForBModel = 0.0;
ModelConfig* pBModel = new ModelConfig(*(RooStats::ModelConfig *)pWs->obj("SbModel"));
pBModel->SetName("BModel");
pBModel->SetWorkspace(*pWs);
pWs->import(*pBModel);
// find global maximum with the signal+background model
// with conditional MLEs for nuisance parameters
// and save the parameter point snapshot in the Workspace
// - safer to keep a default name because some RooStats calculators
// will anticipate it
RooAbsReal * pNll = pSbModel->GetPdf()->createNLL(*pData);
RooAbsReal * pProfile = pNll->createProfile(RooArgSet());
pProfile->getVal(); // this will do fit and set POI and nuisance parameters to fitted values
RooArgSet * pPoiAndNuisance = new RooArgSet();
if(pSbModel->GetNuisanceParameters())
pPoiAndNuisance->add(*pSbModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pSbModel->GetParametersOfInterest());
cout << "\nWill save these parameter points that correspond to the fit to data" << endl;
pPoiAndNuisance->Print("v");
pSbModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// Find a parameter point for generating pseudo-data
// with the background-only data.
// Save the parameter point snapshot in the Workspace
pNll = pBModel->GetPdf()->createNLL(*pData);
pProfile = pNll->createProfile(poi);
((RooRealVar *)poi.first())->setVal(poiValueForBModel);
pProfile->getVal(); // this will do fit and set nuisance parameters to profiled values
pPoiAndNuisance = new RooArgSet();
if(pBModel->GetNuisanceParameters())
pPoiAndNuisance->add(*pBModel->GetNuisanceParameters());
pPoiAndNuisance->add(*pBModel->GetParametersOfInterest());
cout << "\nShould use these parameter points to generate pseudo data for bkg only" << endl;
pPoiAndNuisance->Print("v");
pBModel->SetSnapshot(*pPoiAndNuisance);
delete pProfile;
delete pNll;
delete pPoiAndNuisance;
// inspect workspace
pWs->Print();
// save workspace to file
pWs->writeToFile("ws_twobin.root");
// clean up
delete pWs;
delete pData;
delete pSbModel;
delete pBModel;
} // ----- end of tutorial ----------------------------------------
void build_hbb_workspace1( const char* infile = "outputfiles/input-file.txt", const char* outfile = "outputfiles/ws.root" ) {
//-------------------------------------------------------------------------
//-- Create workspace and other RooStats things.
printf("\n\n Creating workspace.\n\n") ;
RooWorkspace workspace("ws") ;
workspace.autoImportClassCode(true) ;
globalObservables = new RooArgSet("globalObservables");
allNuisances = new RooArgSet("allNuisances");
allNuisancePdfs = new RooArgSet("allNuisancePdfs");
RooArgSet* observedParametersList = new RooArgSet("observables") ;
//-------------------------------------------------------------------------
printf("\n\n Reading input file: %s\n\n", infile ) ;
float fileVal ;
char pname[1000] ;
char formula[1000] ;
sprintf( pname, "bins_of_met" ) ;
if ( !getFileValue( infile, pname, fileVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
int bins_of_met = TMath::Nint( fileVal ) ;
//-- save bins_of_met in the workspace for convenience.
RooRealVar bom( "bins_of_met", "bins_of_met", bins_of_met, 0., 1000. ) ;
bom.setConstant(kTRUE) ;
workspace.import(bom) ;
//-- save bins_of_nb in the workspace for convenience.
RooRealVar bonb( "bins_of_nb", "bins_of_nb", bins_of_nb, 0., 1000. ) ;
bonb.setConstant(kTRUE) ;
workspace.import(bonb) ;
RooRealVar* rv_N_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooRealVar* rv_N_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooRealVar* rv_smc_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooRealVar* rv_smc_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_Rsigsb_corr[bins_of_nb][max_bins_of_met] ;
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "N_%db_msig_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, fileVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
rv_N_msig[nbi][mbi] = new RooRealVar( pname, pname, 0., 1.e6 ) ;
rv_N_msig[nbi][mbi] -> setVal( TMath::Nint(fileVal) ) ;
rv_N_msig[nbi][mbi] -> setConstant( kTRUE ) ;
observedParametersList -> add( *rv_N_msig[nbi][mbi] ) ;
sprintf( pname, "N_%db_msb_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, fileVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
rv_N_msb[nbi][mbi] = new RooRealVar( pname, pname, 0., 1.e6 ) ;
rv_N_msb[nbi][mbi] -> setVal( TMath::Nint(fileVal) ) ;
rv_N_msb[nbi][mbi] -> setConstant( kTRUE ) ;
observedParametersList -> add( *rv_N_msb[nbi][mbi] ) ;
sprintf( pname, "smc_%db_msig_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, fileVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
rv_smc_msig[nbi][mbi] = new RooRealVar( pname, pname, 0., 1.e6 ) ;
rv_smc_msig[nbi][mbi] -> setVal( TMath::Nint(fileVal) ) ;
rv_smc_msig[nbi][mbi] -> setConstant( kTRUE ) ;
sprintf( pname, "smc_%db_msb_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, fileVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
rv_smc_msb[nbi][mbi] = new RooRealVar( pname, pname, 0., 1.e6 ) ;
rv_smc_msb[nbi][mbi] -> setVal( TMath::Nint(fileVal) ) ;
rv_smc_msb[nbi][mbi] -> setConstant( kTRUE ) ;
float corrVal, corrSyst ;
sprintf( pname, "Rsigsb_syst_%db_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, corrSyst ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
sprintf( pname, "Rsigsb_corr_%db_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, corrVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
rv_Rsigsb_corr[nbi][mbi] = makeLognormalConstraint( pname, corrVal, corrSyst ) ;
} // mbi.
} // nbi.
//-- Finished reading input from file.
//-------------------------------------------------------------------------
printf("\n\n Creating and importing dataset into workspace.\n\n") ;
RooDataSet* dsObserved = new RooDataSet("hbb_observed_rds", "hbb observed data values", *observedParametersList ) ;
dsObserved -> add( *observedParametersList ) ;
workspace.import( *dsObserved ) ;
//-------------------------------------------------------------------------
//-- Define all floats.
printf("\n\n Defining all unconstrained floats (Ratios, signal strength).\n\n") ;
double R_msigmsb_initialval(0.15) ;
RooRealVar* rv_R_msigmsb[50] ;
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "R_msigmsb_met%d", mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_R_msigmsb[mbi] = new RooRealVar( pname, pname, R_msigmsb_initialval, 0., 3. ) ;
rv_R_msigmsb[mbi] -> setConstant( kFALSE ) ;
rv_R_msigmsb[mbi] -> Print() ;
} // mbi.
printf("\n") ;
sprintf( pname, "sig_strength" ) ;
RooRealVar* rv_sig_strength = new RooRealVar( pname, pname, 1.0, 0., 10. ) ;
rv_sig_strength -> setConstant(kFALSE) ;
rv_sig_strength -> Print() ;
printf(" %s\n\n", pname ) ;
//-------------------------------------------------------------------------
//-- Define all mu parameters.
printf("\n\n Defining mu parameters.\n\n") ;
RooAbsReal* rv_mu_bg_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_mu_bg_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_mu_sig_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_mu_sig_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "mu_bg_%db_msb_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_mu_bg_msb[nbi][mbi] = new RooRealVar( pname, pname, rv_N_msb[nbi][mbi] -> getVal(), 0., 1.e6 ) ;
rv_mu_bg_msb[nbi][mbi] -> Print() ;
sprintf( formula, "@0 * @1 * @2" ) ;
sprintf( pname, "mu_bg_%db_msig_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_mu_bg_msig[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_Rsigsb_corr[nbi][mbi], *rv_R_msigmsb[mbi], *rv_mu_bg_msb[nbi][mbi] ) ) ;
rv_mu_bg_msig[nbi][mbi] -> Print() ;
sprintf( formula, "@0 * @1" ) ;
sprintf( pname, "mu_sig_%db_msig_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_mu_sig_msig[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_sig_strength, *rv_smc_msig[nbi][mbi] ) ) ;
rv_mu_sig_msig[nbi][mbi] -> Print() ;
sprintf( formula, "@0 * @1" ) ;
sprintf( pname, "mu_sig_%db_msb_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_mu_sig_msb[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_sig_strength, *rv_smc_msb[nbi][mbi] ) ) ;
rv_mu_sig_msb[nbi][mbi] -> Print() ;
} // mbi.
} // nbi.
//-- Finished defining mu parameters.
//-------------------------------------------------------------------------
//-- Defining small n's
printf("\n\n Defining small n's.\n\n") ;
RooAbsReal* rv_n_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_n_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( formula, "@0 + @1" ) ;
sprintf( pname, "n_%db_msig_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_n_msig[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_mu_sig_msig[nbi][mbi], *rv_mu_bg_msig[nbi][mbi] ) ) ;
rv_n_msig[nbi][mbi] -> Print() ;
workspace.import( *rv_n_msig[nbi][mbi] ) ;
sprintf( pname, "n_%db_msb_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_n_msb[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_mu_sig_msb[nbi][mbi], *rv_mu_bg_msb[nbi][mbi] ) ) ;
rv_n_msb[nbi][mbi] -> Print() ;
workspace.import( *rv_n_msb[nbi][mbi] ) ;
} // mbi.
} // nbi.
//-------------------------------------------------------------------------
//-- Define the Poisson pdfs for the observables.
printf("\n\n Defining Poisson pdfs for the observables.\n\n") ;
RooAbsReal* rv_pdf_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_pdf_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooArgSet pdflist ;
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
for ( int mbi=0; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "pdf_%db_msig_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_pdf_msig[nbi][mbi] = new RooPoisson( pname, pname, *rv_N_msig[nbi][mbi], *rv_n_msig[nbi][mbi] ) ;
rv_pdf_msig[nbi][mbi] -> Print() ;
pdflist.add( *rv_pdf_msig[nbi][mbi] ) ;
sprintf( pname, "pdf_%db_msb_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_pdf_msb[nbi][mbi] = new RooPoisson( pname, pname, *rv_N_msb[nbi][mbi], *rv_n_msb[nbi][mbi] ) ;
rv_pdf_msb[nbi][mbi] -> Print() ;
pdflist.add( *rv_pdf_msb[nbi][mbi] ) ;
} // mbi.
} // nbi.
//-------------------------------------------------------------------------
//-- Build the likelihood.
printf("\n\n Building the likelihood.\n\n") ;
pdflist.add( *allNuisancePdfs ) ;
pdflist.Print() ;
printf("\n") ;
RooProdPdf* likelihood = new RooProdPdf( "likelihood", "hbb likelihood", pdflist ) ;
likelihood->Print() ;
//-------------------------------------------------------------------------
// printf("\n\n Running a test fit.\n\n") ;
// dsObserved -> Print() ;
// dsObserved -> printMultiline(cout, 1, kTRUE, "") ;
// printf("\n\n =============================================\n\n") ;
// likelihood -> fitTo( *dsObserved, PrintLevel(3), Hesse(0), Minos(0) ) ;
// printf("\n\n =============================================\n\n") ;
//-- Set up RooStats models.
printf("\n\n Setting up S+B model.\n\n") ;
RooArgSet poi( *rv_sig_strength, "poi" ) ;
RooUniform signal_prior( "signal_prior", "signal_prior", *rv_sig_strength ) ;
ModelConfig sbModel ("SbModel");
sbModel.SetWorkspace( workspace ) ;
sbModel.SetPdf( *likelihood ) ;
sbModel.SetParametersOfInterest( poi );
sbModel.SetPriorPdf(signal_prior);
sbModel.SetObservables( *observedParametersList );
sbModel.SetNuisanceParameters( *allNuisances );
sbModel.SetGlobalObservables( *globalObservables );
workspace.Print() ;
printf("\n\n Doing fit for S+B model.\n" ) ; fflush(stdout) ;
RooAbsReal* pNll = sbModel.GetPdf()->createNLL(*dsObserved);
RooAbsReal* pProfile = pNll->createProfile(RooArgSet());
pProfile->getVal();
RooArgSet* pPoiAndNuisance = new RooArgSet();
pPoiAndNuisance->add(*sbModel.GetParametersOfInterest());
if(sbModel.GetNuisanceParameters()) pPoiAndNuisance->add(*sbModel.GetNuisanceParameters());
printf("\n\n Will save these parameter points that correspond to the fit to data.\n\n") ; fflush(stdout) ;
pPoiAndNuisance->Print("v");
sbModel.SetSnapshot(*pPoiAndNuisance);
workspace.import (sbModel);
delete pProfile ;
delete pNll ;
delete pPoiAndNuisance ;
printf("\n\n Setting up BG-only model.\n\n") ;
ModelConfig bModel (*(RooStats::ModelConfig *)workspace.obj("SbModel"));
bModel.SetName("BModel");
bModel.SetWorkspace(workspace);
printf("\n\n Doing fit for BG-only model.\n" ) ; fflush(stdout) ;
pNll = bModel.GetPdf()->createNLL(*dsObserved);
pProfile = pNll->createProfile(*bModel.GetParametersOfInterest());
((RooRealVar *)(bModel.GetParametersOfInterest()->first()))->setVal(0.);
pProfile->getVal();
pPoiAndNuisance = new RooArgSet();
pPoiAndNuisance->add(*bModel.GetParametersOfInterest());
if(bModel.GetNuisanceParameters()) pPoiAndNuisance->add(*bModel.GetNuisanceParameters());
printf("\n\n Should use these parameter points to generate pseudo data for bkg only.\n\n") ; fflush(stdout) ;
pPoiAndNuisance->Print("v");
bModel.SetSnapshot(*pPoiAndNuisance);
workspace.import (bModel);
delete pProfile ;
delete pNll ;
delete pPoiAndNuisance ;
workspace.Print() ;
printf("\n\n Saving workspace in : %s\n\n", outfile ) ;
gSystem->Exec(" mkdir -p outputfiles " ) ;
workspace.writeToFile( outfile ) ;
} // build_hbb_workspace1.