RooStats::ModelConfig * Tprime::SetBModel( void ) {
//
// Define model config and parameter snapshot to describe the b model.
// Import to workspace.
//
std::string legend = "[Tprime::SetBModel]: ";
// full signal+background model
//RooStats::ModelConfig * pSBModel = (RooStats::ModelConfig *)pWs->genobj("ModelConfig");
// let's make the b model (bg-only) from the alt model (s+b) with xsec=0
//RooStats::ModelConfig * pBModel =
// new RooStats::ModelConfig(*(RooStats::ModelConfig *)pWs->genobj("ModelConfig"));
RooStats::ModelConfig * _sbModel = (RooStats::ModelConfig *)pWs->genobj("ModelConfig");
RooStats::ModelConfig * pBModel = _sbModel->Clone("BModel");
//pBModel->SetName("BModel");
pBModel->SetWorkspace(*pWs);
//pBModel->SetParametersOfInterest(RooArgSet());
pWs->import(*pBModel);
// set POI to the b model value and take snapshot
RooRealVar * pPoi = (RooRealVar *)pBModel->GetParametersOfInterest()->first();
pPoi->setVal(0.0);
pBModel->SetSnapshot(*pPoi);
pBModel->Print();
return pBModel;
}
RooAbsData * Tprime::GetPseudoData( void ) {
//
// Generate pseudo data, return a pointer.
// Class member pointer data set to point to the dataset.
// Caller does not take ownership.
//
static int n_toys = 0;
// legend for printouts
std::string legend = "[Tprime::GetPseudoData()]: ";
delete data;
// We will use ToyMCSampler to generate pseudo-data (and test statistic, eventually)
// We are responsible for randomizing nuisances and global observables,
// ToyMCSampler only generates observables (as of ROOT 5.30.00-rc1 and before)
// MC sampler and test statistic
if(n_toys == 0) { // on first entry
// get B model config from workspace
RooStats::ModelConfig * pBModel = (RooStats::ModelConfig *)pWs->obj("BModel");
pBModel->SetWorkspace(*pWs);
// get parameter of interest set
//const RooArgSet * poi = pSbModel->GetParametersOfInterest();
//RooStats::TestStatistic * pTestStatistic = new RooStats::ProfileLikelihoodTestStat(*pBModel->GetPdf());
//RooStats::ToyMCSampler toymcs(*pTestStatistic, 1);
pTestStatistic = new RooStats::ProfileLikelihoodTestStat(*pBModel->GetPdf());
pToyMcSampler = new RooStats::ToyMCSampler(*pTestStatistic, 1);
pToyMcSampler->SetPdf(*pBModel->GetPdf());
pToyMcSampler->SetObservables(*pBModel->GetObservables());
pToyMcSampler->SetParametersForTestStat(*pBModel->GetParametersOfInterest()); // just POI
pToyMcSampler->SetGlobalObservables(*pBModel->GetGlobalObservables());
}
// load parameter point
pWs->loadSnapshot("parametersToGenerateData");
RooArgSet dummySet;
data = pToyMcSampler->GenerateToyData(dummySet);
std::cout << legend << "generated the following background-only pseudo-data:" << std::endl;
data->Print();
// count number of generated toys
++n_toys;
return data;
}
Int_t Tprime::RunMcmc( std::string channel, // ejets, mujets, combined
std::string mode, // observed, expected
double peak, // resonance mass
std::string suffix, // suffix for output file names
Int_t ntoys, // number of pseudoexperiments for expected limit
Int_t mcmc_iter, // number of MCMC iterations
Int_t mcmc_burnin, // number of MCMC burn in steps to be discarded
std::string inputdir // input dir name
) {
//
// Bayesian MCMC calculation
//
std::string legend = "[tprime::RunMcmc()]: ";
// print out inputs
std::cout << legend << std::endl;
std::cout << legend << "Input parameters specified. Some of them are not used and defaults are entered" << std::endl;
std::cout << legend << "------------------------------" << std::endl;
std::cout << legend << "channel: " << channel << std::endl;
std::cout << legend << "mode: " << mode << std::endl;
std::cout << legend << "input directory: " << inputdir << std::endl;
std::cout << legend << "resonance peak mass: " << peak << std::endl;
std::cout << legend << "suffix: ->" << suffix << "<-" << std::endl;
std::cout << legend << "number of pseudo-experiments: "<< ntoys << std::endl;
std::cout << legend << std::endl;
std::cout << legend << "Bayesian MCMC parameters" << std::endl;
std::cout << legend << "------------------------------" << std::endl;
std::cout << legend << "number of iterations: " << mcmc_iter << std::endl;
std::cout << legend << "number of burn-in steps to discard: " << mcmc_burnin << std::endl;
std::cout << legend << std::endl;
// compose the workspace file name
char buf[1024];
sprintf(buf, "%sresults_%04.0f/tprime_%s_tprimeCrossSection_model.root", inputdir.c_str(), peak, channel.c_str());
std::string _file = buf;
std::cout << legend << "guessed name of the file with the workspace: >" << _file << "<" << std::endl;
//load workspace
LoadWorkspace(_file, channel);
// change POI range
double poiUpper = GetPoiUpper(channel, peak);
std::cout << legend << "setting POI range to [0; " << poiUpper << "]" << std::endl;
pWs->var("xsec")->setRange(0.0, poiUpper);
// timer
TStopwatch t;
t.Start();
int pe_counter = 0;
std::vector<Double_t> _limits;
while (pe_counter < ntoys) {
// for mass limit, add k-factor systematics to the nsig systematics
// FIXME: this is a correlated part of the uncertainty!!!
// - different uncertainties for graviton and Z' models
if ( mode.find("mass_") != std::string::npos ) {
std::cout << legend << std::endl;
std::cout << legend << "this a mass limit calculation," << std::endl;
std::cout << legend << "I would add k-factor uncertainty to the nsig uncertainty" << std::endl;
std::cout << legend << "I would do it " << ntoys << " times, so one can average. " << pe_counter+1 << " of " << ntoys << std::endl;
std::cout << legend << "Not implemented yet " << std::endl;
std::cout << legend << std::endl;
//Double_t kfactor_err = GetKfactorUncertainty(peak, mode);
//double nsig_kappa = ws->var("nsig_kappa_dimuon")->getVal();
//nsig_kappa = sqrt(nsig_kappa*nsig_kappa + kfactor_err*kfactor_err);
//ws->var("nsig_kappa_dimuon")->setVal(nsig_kappa);
//ntoys = 1;
}
else if ( mode.find("expected") != std::string::npos ) {
std::cout << legend << std::endl;
std::cout << legend << "this is pseudoexperiment " << pe_counter+1 << " of " << ntoys << std::endl;
std::cout << legend << "for the expected limit estimate" << std::endl;
std::cout << legend << std::endl;
// prepare PE data
GetPseudoData();
}
else { // "regular" observed limit
std::cout << legend << std::endl;
std::cout << legend << "calculating an observed limit..." << std::endl;
std::cout << legend << "I will do it " << ntoys << " times, so one can average. " << pe_counter+1 << " of " << ntoys << std::endl;
std::cout << legend << std::endl;
GetWorkspaceData("obsData");
//ntoys = 1;
}
mcInt = GetMcmcInterval(0.95, // conf level
mcmc_iter, // number of iterations
mcmc_burnin, // number of burn-in to discard
0.0, // left side tail fraction, 0 for upper limit
100); // number of bins in posterior, only for plotting
++pe_counter;
if (!mcInt) {
continue;
}
else {
std::string _outfile = "tprime_"+channel+"_xsec_mcmc_limit_" + suffix + ".ascii";
printMcmcUpperLimit( peak, _outfile );
// limits for averaging/medianing
RooStats::ModelConfig * pSbModel = GetModelConfig("ModelConfig");
RooRealVar * firstPOI = (RooRealVar*) pSbModel->GetParametersOfInterest()->first();
double _limit = mcInt->UpperLimit(*firstPOI);
_limits.push_back(_limit);
} // end of valid mcInt block
} // end of while
// write median limit to a file
if (_limits.size() > 0) {
Double_t _median_limit = TMath::Median(_limits.size(), &_limits[0]);
std::vector<Double_t> _mass_limit;
_mass_limit.push_back(peak);
_mass_limit.push_back(_median_limit);
std::string _outfile = "tprime_"+channel+"_xsec_mcmc_median_limit_" + suffix + ".ascii";
PrintToFile(_outfile, _mass_limit, "# mass median_limit");
}
std::string _outfile = "tprime_"+channel+"_xsec_mcmc_posterior_" + suffix + ".pdf";
makeMcmcPosteriorPlot( _outfile );
// timer
t.Print();
return 0;
}
Int_t Tprime::SetParameterPoints( std::string sbModelName,
std::string bModelName ) {
//
// Fit the data with S+B model.
// Make a snapshot of the S+B parameter point.
// Profile with POI=0.
// Make a snapshot of the B parameter point
// (B model is the S+B model with POI=0
//
Double_t poi_value_for_b_model = 0.0;
// get S+B model config from workspace
RooStats::ModelConfig * pSbModel = (RooStats::ModelConfig *)pWs->obj(sbModelName.c_str());
pSbModel->SetWorkspace(*pWs);
// get parameter of interest set
const RooArgSet * poi = pSbModel->GetParametersOfInterest();
// get B model config from workspace
RooStats::ModelConfig * pBModel = (RooStats::ModelConfig *)pWs->obj(bModelName.c_str());
pBModel->SetWorkspace(*pWs);
// make sure that data has been loaded
if (!data) return -1;
// find parameter point for global maximum with the S+B model,
// with conditional MLEs for nuisance parameters
// and save the parameter point snapshot in the Workspace
RooAbsReal * nll = pSbModel->GetPdf()->createNLL(*data);
RooAbsReal * profile = nll->createProfile(RooArgSet());
profile->getVal(); // this will do fit and set POI and nuisance parameters to fitted values
RooArgSet * poiAndNuisance = new RooArgSet();
if(pSbModel->GetNuisanceParameters())
poiAndNuisance->add(*pSbModel->GetNuisanceParameters());
poiAndNuisance->add(*pSbModel->GetParametersOfInterest());
pWs->defineSet("SPlusBModelParameters", *poiAndNuisance);
pWs->saveSnapshot("SPlusBFitParameters",*poiAndNuisance);
pSbModel->SetSnapshot(*poi);
RooArgSet * sbModelFitParams = (RooArgSet *)poiAndNuisance->snapshot();
cout << "\nWill save these parameter points that correspond to the fit to data" << endl;
sbModelFitParams->Print("v");
delete profile;
delete nll;
delete poiAndNuisance;
delete sbModelFitParams;
//
// Find a parameter point for generating pseudo-data
// with the background-only data.
// Save the parameter point snapshot in the Workspace
nll = pBModel->GetPdf()->createNLL(*data);
profile = nll->createProfile(*poi);
((RooRealVar *)poi->first())->setVal(poi_value_for_b_model);
profile->getVal(); // this will do fit and set nuisance parameters to profiled values
poiAndNuisance = new RooArgSet();
if(pBModel->GetNuisanceParameters())
poiAndNuisance->add(*pBModel->GetNuisanceParameters());
poiAndNuisance->add(*pBModel->GetParametersOfInterest());
pWs->defineSet("parameterPointToGenerateData", *poiAndNuisance);
pWs->saveSnapshot("parametersToGenerateData",*poiAndNuisance);
pBModel->SetSnapshot(*poi);
RooArgSet * paramsToGenerateData = (RooArgSet *)poiAndNuisance->snapshot();
cout << "\nShould use these parameter points to generate pseudo data for bkg only" << endl;
paramsToGenerateData->Print("v");
delete profile;
delete nll;
delete poiAndNuisance;
delete paramsToGenerateData;
return 0;
}
