// 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; }
/* * 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 ----------------------------------------
// 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 compute_p0(const char* inFileName, const char* wsName = "combined", const char* modelConfigName = "ModelConfig", const char* dataName = "obsData", const char* asimov1DataName = "asimovData_1", const char* conditional1Snapshot = "conditionalGlobs_1", const char* nominalSnapshot = "nominalGlobs", string smass = "130", string folder = "test") { double mass; stringstream massStr; massStr << smass; massStr >> mass; double mu_profile_value = 1; // mu value to profile the obs data at wbefore generating the expected bool doConditional = 1; // do conditional expected data bool remakeData = 0; // handle unphysical pdf cases in H->ZZ->4l bool doUncap = 1; // uncap p0 bool doInj = 0; // setup the poi for injection study (zero is faster if you're not) bool doObs = 1; // compute median significance bool doMedian = 1; // compute observed significance TStopwatch timer; timer.Start(); TFile f(inFileName); RooWorkspace* ws = (RooWorkspace*)f.Get(wsName); if (!ws) { cout << "ERROR::Workspace: " << wsName << " doesn't exist!" << endl; return; } ModelConfig* mc = (ModelConfig*)ws->obj(modelConfigName); if (!mc) { cout << "ERROR::ModelConfig: " << modelConfigName << " doesn't exist!" << endl; return; } RooDataSet* data = (RooDataSet*)ws->data(dataName); if (!data) { cout << "ERROR::Dataset: " << dataName << " doesn't exist!" << endl; return; } mc->GetNuisanceParameters()->Print("v"); ROOT::Math::MinimizerOptions::SetDefaultMinimizer("Minuit2"); ROOT::Math::MinimizerOptions::SetDefaultStrategy(0); ROOT::Math::MinimizerOptions::SetDefaultPrintLevel(1); cout << "Setting max function calls" << endl; ws->loadSnapshot("conditionalNuis_0"); RooArgSet nuis(*mc->GetNuisanceParameters()); RooRealVar* mu = (RooRealVar*)mc->GetParametersOfInterest()->first(); RooAbsPdf* pdf = mc->GetPdf(); string condSnapshot(conditional1Snapshot); RooArgSet nuis_tmp2 = *mc->GetNuisanceParameters(); RooNLLVar* obs_nll = doObs ? (RooNLLVar*)pdf->createNLL(*data, Constrain(nuis_tmp2)) : NULL; RooDataSet* asimovData1 = (RooDataSet*)ws->data(asimov1DataName); RooRealVar* emb = (RooRealVar*)mc->GetNuisanceParameters()->find("ATLAS_EMB"); if (!asimovData1 || (string(inFileName).find("ic10") != string::npos && emb)) { if (emb) emb->setVal(0.7); cout << "Asimov data doesn't exist! Please, allow me to build one for you..." << endl; string mu_str, mu_prof_str; asimovData1 = makeAsimovData(mc, doConditional, ws, obs_nll, 1, &mu_str, &mu_prof_str, mu_profile_value, true); condSnapshot="conditionalGlobs"+mu_prof_str; } if (!doUncap) mu->setRange(0, 40); else mu->setRange(-40, 40); RooAbsPdf* pdf = mc->GetPdf(); RooArgSet nuis_tmp1 = *mc->GetNuisanceParameters(); RooNLLVar* asimov_nll = (RooNLLVar*)pdf->createNLL(*asimovData1, Constrain(nuis_tmp1)); //do asimov mu->setVal(1); mu->setConstant(0); if (!doInj) mu->setConstant(1); int status,sign; double med_sig=0,obs_sig=0,asimov_q0=0,obs_q0=0; if (doMedian) { ws->loadSnapshot(condSnapshot.c_str()); if (doInj) ws->loadSnapshot("conditionalNuis_inj"); else ws->loadSnapshot("conditionalNuis_1"); mc->GetGlobalObservables()->Print("v"); mu->setVal(0); mu->setConstant(1); status = minimize(asimov_nll, ws); if (status < 0) { cout << "Retrying with conditional snapshot at mu=1" << endl; ws->loadSnapshot("conditionalNuis_0"); status = minimize(asimov_nll, ws); if (status >= 0) cout << "Success!" << endl; } double asimov_nll_cond = asimov_nll->getVal(); mu->setVal(1); if (doInj) ws->loadSnapshot("conditionalNuis_inj"); else ws->loadSnapshot("conditionalNuis_1"); if (doInj) mu->setConstant(0); status = minimize(asimov_nll, ws); if (status < 0) { cout << "Retrying with conditional snapshot at mu=1" << endl; ws->loadSnapshot("conditionalNuis_0"); status = minimize(asimov_nll, ws); if (status >= 0) cout << "Success!" << endl; } double asimov_nll_min = asimov_nll->getVal(); asimov_q0 = 2*(asimov_nll_cond - asimov_nll_min); if (doUncap && mu->getVal() < 0) asimov_q0 = -asimov_q0; sign = int(asimov_q0 != 0 ? asimov_q0/fabs(asimov_q0) : 0); med_sig = sign*sqrt(fabs(asimov_q0)); ws->loadSnapshot(nominalSnapshot); } if (doObs) { ws->loadSnapshot("conditionalNuis_0"); mu->setVal(0); mu->setConstant(1); status = minimize(obs_nll, ws); if (status < 0) { cout << "Retrying with conditional snapshot at mu=1" << endl; ws->loadSnapshot("conditionalNuis_0"); status = minimize(obs_nll, ws); if (status >= 0) cout << "Success!" << endl; } double obs_nll_cond = obs_nll->getVal(); mu->setConstant(0); status = minimize(obs_nll, ws); if (status < 0) { cout << "Retrying with conditional snapshot at mu=1" << endl; ws->loadSnapshot("conditionalNuis_0"); status = minimize(obs_nll, ws); if (status >= 0) cout << "Success!" << endl; } double obs_nll_min = obs_nll->getVal(); obs_q0 = 2*(obs_nll_cond - obs_nll_min); if (doUncap && mu->getVal() < 0) obs_q0 = -obs_q0; sign = int(obs_q0 == 0 ? 0 : obs_q0 / fabs(obs_q0)); if (!doUncap && (obs_q0 < 0 && obs_q0 > -0.1 || mu->getVal() < 0.001)) obs_sig = 0; else obs_sig = sign*sqrt(fabs(obs_q0)); } // Report results cout << "obs: " << obs_sig << endl; cout << "Observed significance: " << obs_sig << endl; cout << "Corresponding to a p-value of " << (1-ROOT::Math::gaussian_cdf( obs_sig )) << endl; if (med_sig) { cout << "Median test stat val: " << asimov_q0 << endl; cout << "Median significance: " << med_sig << endl; } f.Close(); stringstream fileName; fileName << "root-files/" << folder << "/" << mass << ".root"; system(("mkdir -vp root-files/" + folder).c_str()); TFile f2(fileName.str().c_str(),"recreate"); TH1D* h_hypo = new TH1D("hypo","hypo",2,0,2); h_hypo->SetBinContent(1, obs_sig); h_hypo->SetBinContent(2, med_sig); f2.Write(); f2.Close(); timer.Stop(); timer.Print(); }
// internal routine to run the inverter HypoTestInverterResult * RunInverter(RooWorkspace * w, const char * modelSBName, const char * modelBName, const char * dataName, int type, int testStatType, int npoints, double poimin, double poimax, int ntoys, bool useCls ) { std::cout << "Running HypoTestInverter on the workspace " << w->GetName() << std::endl; w->Print(); RooAbsData * data = w->data(dataName); if (!data) { Error("RA2bHypoTestDemo","Not existing data %s",dataName); return 0; } else std::cout << "Using data set " << dataName << std::endl; // 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("RA2bHypoTestDemo","Not existing ModelConfig %s",modelSBName); return 0; } // check the model if (!sbModel->GetPdf()) { Error("RA2bHypoTestDemo","Model %s has no pdf ",modelSBName); return 0; } if (!sbModel->GetParametersOfInterest()) { Error("RA2bHypoTestDemo","Model %s has no poi ",modelSBName); return 0; } if (!sbModel->GetParametersOfInterest()) { Error("RA2bHypoTestInvDemo","Model %s has no poi ",modelSBName); return 0; } if (!sbModel->GetSnapshot() ) { Info("RA2bHypoTestInvDemo","Model %s has no snapshot - make one using model poi",modelSBName); sbModel->SetSnapshot( *sbModel->GetParametersOfInterest() ); } if (!bModel || bModel == sbModel) { Info("RA2bHypoTestInvDemo","The background model %s does not exist",modelBName); Info("RA2bHypoTestInvDemo","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("RA2bHypoTestInvDemo","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("RA2bHypoTestInvDemo","Model %s has no valid poi",modelBName); return 0; } } } SimpleLikelihoodRatioTestStat slrts(*sbModel->GetPdf(),*bModel->GetPdf()); if (sbModel->GetSnapshot()) slrts.SetNullParameters(*sbModel->GetSnapshot()); if (bModel->GetSnapshot()) slrts.SetAltParameters(*bModel->GetSnapshot()); // ratio of profile likelihood - need to pass snapshot for the alt RatioOfProfiledLikelihoodsTestStat ropl(*sbModel->GetPdf(), *bModel->GetPdf(), bModel->GetSnapshot()); ropl.SetSubtractMLE(false); //MyProfileLikelihoodTestStat profll(*sbModel->GetPdf()); ProfileLikelihoodTestStat profll(*sbModel->GetPdf()); if (testStatType == 3) profll.SetOneSided(1); if (optimize) profll.SetReuseNLL(true); TestStatistic * testStat = &slrts; if (testStatType == 1) testStat = &ropl; if (testStatType == 2 || testStatType == 3) testStat = &profll; HypoTestCalculatorGeneric * hc = 0; if (type == 0) hc = new FrequentistCalculator(*data, *bModel, *sbModel); else hc = new HybridCalculator(*data, *bModel, *sbModel); ToyMCSampler *toymcs = (ToyMCSampler*)hc->GetTestStatSampler(); //=== DEBUG ///// toymcs->SetWS( w ) ; //=== DEBUG toymcs->SetNEventsPerToy(1); toymcs->SetTestStatistic(testStat); if (optimize) toymcs->SetUseMultiGen(true); if (type == 1) { HybridCalculator *hhc = (HybridCalculator*) hc; hhc->SetToys(ntoys,ntoys); // check for nuisance prior pdf if (bModel->GetPriorPdf() && sbModel->GetPriorPdf() ) { hhc->ForcePriorNuisanceAlt(*bModel->GetPriorPdf()); hhc->ForcePriorNuisanceNull(*sbModel->GetPriorPdf()); } else { if (bModel->GetNuisanceParameters() || sbModel->GetNuisanceParameters() ) { Error("RA2bHypoTestInvDemo","Cannnot run Hybrid calculator because no prior on the nuisance parameter is specified"); return 0; } } } else ((FrequentistCalculator*) hc)->SetToys(ntoys,ntoys); // Get the result RooMsgService::instance().getStream(1).removeTopic(RooFit::NumIntegration); TStopwatch tw; tw.Start(); const RooArgSet * poiSet = sbModel->GetParametersOfInterest(); RooRealVar *poi = (RooRealVar*)poiSet->first(); // fit the data first sbModel->GetPdf()->fitTo(*data); double poihat = poi->getVal(); HypoTestInverter calc(*hc); calc.SetConfidenceLevel(0.95); calc.UseCLs(useCls); calc.SetVerbose(true); // can speed up using proof-lite if (useProof && nworkers > 1) { ProofConfig pc(*w, nworkers, "", kFALSE); toymcs->SetProofConfig(&pc); // enable proof } printf(" npoints = %d, poimin = %7.2f, poimax = %7.2f\n\n", npoints, poimin, poimax ) ; cout << flush ; if ( npoints==1 ) { std::cout << "Evaluating one point : " << poimax << std::endl; calc.RunOnePoint(poimax); } else 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; } cout << "\n\n right before calc.GetInterval(), ntoys = " << ntoys << " \n\n" << flush ; HypoTestInverterResult * r = calc.GetInterval(); return r; }
void OneSidedFrequentistUpperLimitWithBands(const char* infile = "", const char* workspaceName = "combined", const char* modelConfigName = "ModelConfig", const char* dataName = "obsData") { double confidenceLevel=0.95; int nPointsToScan = 20; int nToyMC = 200; // ------------------------------------------------------- // First part is just to access a user-defined file // or create the standard example file if it doesn't exist const char* filename = ""; if (!strcmp(infile,"")) { filename = "results/example_combined_GaussExample_model.root"; bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code // if file does not exists generate with histfactory if (!fileExist) { #ifdef _WIN32 cout << "HistFactory file cannot be generated on Windows - exit" << endl; return; #endif // Normally this would be run on the command line cout <<"will run standard hist2workspace example"<<endl; gROOT->ProcessLine(".! prepareHistFactory ."); gROOT->ProcessLine(".! hist2workspace config/example.xml"); cout <<"\n\n---------------------"<<endl; cout <<"Done creating example input"<<endl; cout <<"---------------------\n\n"<<endl; } } else filename = infile; // Try to open the file TFile *file = TFile::Open(filename); // if input file was specified byt not found, quit if(!file ){ cout <<"StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl; return; } // ------------------------------------------------------- // Now get the data and workspace // get the workspace out of the file RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName); if(!w){ cout <<"workspace not found" << endl; return; } // get the modelConfig out of the file ModelConfig* mc = (ModelConfig*) w->obj(modelConfigName); // get the modelConfig out of the file RooAbsData* data = w->data(dataName); // make sure ingredients are found if(!data || !mc){ w->Print(); cout << "data or ModelConfig was not found" <<endl; return; } // ------------------------------------------------------- // Now get the POI for convenience // you may want to adjust the range of your POI RooRealVar* firstPOI = (RooRealVar*) mc->GetParametersOfInterest()->first(); /* firstPOI->setMin(0);*/ /* firstPOI->setMax(10);*/ // -------------------------------------------- // Create and use the FeldmanCousins tool // to find and plot the 95% confidence interval // on the parameter of interest as specified // in the model config // REMEMBER, we will change the test statistic // so this is NOT a Feldman-Cousins interval FeldmanCousins fc(*data,*mc); fc.SetConfidenceLevel(confidenceLevel); /* fc.AdditionalNToysFactor(0.25); // degrade/improve sampling that defines confidence belt*/ /* fc.UseAdaptiveSampling(true); // speed it up a bit, don't use for expected limits*/ fc.SetNBins(nPointsToScan); // set how many points per parameter of interest to scan fc.CreateConfBelt(true); // save the information in the belt for plotting // ------------------------------------------------------- // Feldman-Cousins is a unified limit by definition // but the tool takes care of a few things for us like which values // of the nuisance parameters should be used to generate toys. // so let's just change the test statistic and realize this is // no longer "Feldman-Cousins" but is a fully frequentist Neyman-Construction. /* ProfileLikelihoodTestStatModified onesided(*mc->GetPdf());*/ /* fc.GetTestStatSampler()->SetTestStatistic(&onesided);*/ /* ((ToyMCSampler*) fc.GetTestStatSampler())->SetGenerateBinned(true); */ ToyMCSampler* toymcsampler = (ToyMCSampler*) fc.GetTestStatSampler(); ProfileLikelihoodTestStat* testStat = dynamic_cast<ProfileLikelihoodTestStat*>(toymcsampler->GetTestStatistic()); testStat->SetOneSided(true); // Since this tool needs to throw toy MC the PDF needs to be // extended or the tool needs to know how many entries in a dataset // per pseudo experiment. // In the 'number counting form' where the entries in the dataset // are counts, and not values of discriminating variables, the // datasets typically only have one entry and the PDF is not // extended. if(!mc->GetPdf()->canBeExtended()){ if(data->numEntries()==1) fc.FluctuateNumDataEntries(false); else cout <<"Not sure what to do about this model" <<endl; } // We can use PROOF to speed things along in parallel // However, the test statistic has to be installed on the workers // so either turn off PROOF or include the modified test statistic // in your `$ROOTSYS/roofit/roostats/inc` directory, // add the additional line to the LinkDef.h file, // and recompile root. if (useProof) { ProofConfig pc(*w, nworkers, "", false); toymcsampler->SetProofConfig(&pc); // enable proof } if(mc->GetGlobalObservables()){ cout << "will use global observables for unconditional ensemble"<<endl; mc->GetGlobalObservables()->Print(); toymcsampler->SetGlobalObservables(*mc->GetGlobalObservables()); } // Now get the interval PointSetInterval* interval = fc.GetInterval(); ConfidenceBelt* belt = fc.GetConfidenceBelt(); // print out the interval on the first Parameter of Interest cout << "\n95% interval on " <<firstPOI->GetName()<<" is : ["<< interval->LowerLimit(*firstPOI) << ", "<< interval->UpperLimit(*firstPOI) <<"] "<<endl; // get observed UL and value of test statistic evaluated there RooArgSet tmpPOI(*firstPOI); double observedUL = interval->UpperLimit(*firstPOI); firstPOI->setVal(observedUL); double obsTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*data,tmpPOI); // Ask the calculator which points were scanned RooDataSet* parameterScan = (RooDataSet*) fc.GetPointsToScan(); RooArgSet* tmpPoint; // make a histogram of parameter vs. threshold TH1F* histOfThresholds = new TH1F("histOfThresholds","", parameterScan->numEntries(), firstPOI->getMin(), firstPOI->getMax()); histOfThresholds->GetXaxis()->SetTitle(firstPOI->GetName()); histOfThresholds->GetYaxis()->SetTitle("Threshold"); // loop through the points that were tested and ask confidence belt // what the upper/lower thresholds were. // For FeldmanCousins, the lower cut off is always 0 for(Int_t i=0; i<parameterScan->numEntries(); ++i){ tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp"); //cout <<"get threshold"<<endl; double arMax = belt->GetAcceptanceRegionMax(*tmpPoint); double poiVal = tmpPoint->getRealValue(firstPOI->GetName()) ; histOfThresholds->Fill(poiVal,arMax); } TCanvas* c1 = new TCanvas(); c1->Divide(2); c1->cd(1); histOfThresholds->SetMinimum(0); histOfThresholds->Draw(); c1->cd(2); // ------------------------------------------------------- // Now we generate the expected bands and power-constraint // First: find parameter point for mu=0, with conditional MLEs for nuisance parameters RooAbsReal* nll = mc->GetPdf()->createNLL(*data); RooAbsReal* profile = nll->createProfile(*mc->GetParametersOfInterest()); firstPOI->setVal(0.); profile->getVal(); // this will do fit and set nuisance parameters to profiled values RooArgSet* poiAndNuisance = new RooArgSet(); if(mc->GetNuisanceParameters()) poiAndNuisance->add(*mc->GetNuisanceParameters()); poiAndNuisance->add(*mc->GetParametersOfInterest()); w->saveSnapshot("paramsToGenerateData",*poiAndNuisance); RooArgSet* paramsToGenerateData = (RooArgSet*) poiAndNuisance->snapshot(); cout << "\nWill use these parameter points to generate pseudo data for bkg only" << endl; paramsToGenerateData->Print("v"); RooArgSet unconditionalObs; unconditionalObs.add(*mc->GetObservables()); unconditionalObs.add(*mc->GetGlobalObservables()); // comment this out for the original conditional ensemble double CLb=0; double CLbinclusive=0; // Now we generate background only and find distribution of upper limits TH1F* histOfUL = new TH1F("histOfUL","",100,0,firstPOI->getMax()); histOfUL->GetXaxis()->SetTitle("Upper Limit (background only)"); histOfUL->GetYaxis()->SetTitle("Entries"); for(int imc=0; imc<nToyMC; ++imc){ // set parameters back to values for generating pseudo data // cout << "\n get current nuis, set vals, print again" << endl; w->loadSnapshot("paramsToGenerateData"); // poiAndNuisance->Print("v"); RooDataSet* toyData = 0; // now generate a toy dataset if(!mc->GetPdf()->canBeExtended()){ if(data->numEntries()==1) toyData = mc->GetPdf()->generate(*mc->GetObservables(),1); else cout <<"Not sure what to do about this model" <<endl; } else{ // cout << "generating extended dataset"<<endl; toyData = mc->GetPdf()->generate(*mc->GetObservables(),Extended()); } // generate global observables // need to be careful for simpdf // RooDataSet* globalData = mc->GetPdf()->generate(*mc->GetGlobalObservables(),1); RooSimultaneous* simPdf = dynamic_cast<RooSimultaneous*>(mc->GetPdf()); if(!simPdf){ RooDataSet *one = mc->GetPdf()->generate(*mc->GetGlobalObservables(), 1); const RooArgSet *values = one->get(); RooArgSet *allVars = mc->GetPdf()->getVariables(); *allVars = *values; delete allVars; delete values; delete one; } else { //try fix for sim pdf TIterator* iter = simPdf->indexCat().typeIterator() ; RooCatType* tt = NULL; while((tt=(RooCatType*) iter->Next())) { // Get pdf associated with state from simpdf RooAbsPdf* pdftmp = simPdf->getPdf(tt->GetName()) ; // Generate only global variables defined by the pdf associated with this state RooArgSet* globtmp = pdftmp->getObservables(*mc->GetGlobalObservables()) ; RooDataSet* tmp = pdftmp->generate(*globtmp,1) ; // Transfer values to output placeholder *globtmp = *tmp->get(0) ; // Cleanup delete globtmp ; delete tmp ; } } // globalData->Print("v"); // unconditionalObs = *globalData->get(); // mc->GetGlobalObservables()->Print("v"); // delete globalData; // cout << "toy data = " << endl; // toyData->get()->Print("v"); // get test stat at observed UL in observed data firstPOI->setVal(observedUL); double toyTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI); // toyData->get()->Print("v"); // cout <<"obsTSatObsUL " <<obsTSatObsUL << "toyTS " << toyTSatObsUL << endl; if(obsTSatObsUL < toyTSatObsUL) // not sure about <= part yet CLb+= (1.)/nToyMC; if(obsTSatObsUL <= toyTSatObsUL) // not sure about <= part yet CLbinclusive+= (1.)/nToyMC; // loop over points in belt to find upper limit for this toy data double thisUL = 0; for(Int_t i=0; i<parameterScan->numEntries(); ++i){ tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp"); double arMax = belt->GetAcceptanceRegionMax(*tmpPoint); firstPOI->setVal( tmpPoint->getRealValue(firstPOI->GetName()) ); // double thisTS = profile->getVal(); double thisTS = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI); // cout << "poi = " << firstPOI->getVal() // << " max is " << arMax << " this profile = " << thisTS << endl; // cout << "thisTS = " << thisTS<<endl; if(thisTS<=arMax){ thisUL = firstPOI->getVal(); } else{ break; } } /* // loop over points in belt to find upper limit for this toy data double thisUL = 0; for(Int_t i=0; i<histOfThresholds->GetNbinsX(); ++i){ tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp"); cout <<"---------------- "<<i<<endl; tmpPoint->Print("v"); cout << "from hist " << histOfThresholds->GetBinCenter(i+1) <<endl; double arMax = histOfThresholds->GetBinContent(i+1); // cout << " threhold from Hist = aMax " << arMax<<endl; // double arMax2 = belt->GetAcceptanceRegionMax(*tmpPoint); // cout << "from scan arMax2 = "<< arMax2 << endl; // not the same due to TH1F not TH1D // cout << "scan - hist" << arMax2-arMax << endl; firstPOI->setVal( histOfThresholds->GetBinCenter(i+1)); // double thisTS = profile->getVal(); double thisTS = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI); // cout << "poi = " << firstPOI->getVal() // << " max is " << arMax << " this profile = " << thisTS << endl; // cout << "thisTS = " << thisTS<<endl; // NOTE: need to add a small epsilon term for single precision vs. double precision if(thisTS<=arMax + 1e-7){ thisUL = firstPOI->getVal(); } else{ break; } } */ histOfUL->Fill(thisUL); // for few events, data is often the same, and UL is often the same // cout << "thisUL = " << thisUL<<endl; delete toyData; } histOfUL->Draw(); c1->SaveAs("one-sided_upper_limit_output.pdf"); // if you want to see a plot of the sampling distribution for a particular scan point: /* SamplingDistPlot sampPlot; int indexInScan = 0; tmpPoint = (RooArgSet*) parameterScan->get(indexInScan)->clone("temp"); firstPOI->setVal( tmpPoint->getRealValue(firstPOI->GetName()) ); toymcsampler->SetParametersForTestStat(tmpPOI); SamplingDistribution* samp = toymcsampler->GetSamplingDistribution(*tmpPoint); sampPlot.AddSamplingDistribution(samp); sampPlot.Draw(); */ // Now find bands and power constraint Double_t* bins = histOfUL->GetIntegral(); TH1F* cumulative = (TH1F*) histOfUL->Clone("cumulative"); cumulative->SetContent(bins); double band2sigDown, band1sigDown, bandMedian, band1sigUp,band2sigUp; for(int i=1; i<=cumulative->GetNbinsX(); ++i){ if(bins[i]<RooStats::SignificanceToPValue(2)) band2sigDown=cumulative->GetBinCenter(i); if(bins[i]<RooStats::SignificanceToPValue(1)) band1sigDown=cumulative->GetBinCenter(i); if(bins[i]<0.5) bandMedian=cumulative->GetBinCenter(i); if(bins[i]<RooStats::SignificanceToPValue(-1)) band1sigUp=cumulative->GetBinCenter(i); if(bins[i]<RooStats::SignificanceToPValue(-2)) band2sigUp=cumulative->GetBinCenter(i); } cout << "-2 sigma band " << band2sigDown << endl; cout << "-1 sigma band " << band1sigDown << " [Power Constraint)]" << endl; cout << "median of band " << bandMedian << endl; cout << "+1 sigma band " << band1sigUp << endl; cout << "+2 sigma band " << band2sigUp << endl; // print out the interval on the first Parameter of Interest cout << "\nobserved 95% upper-limit "<< interval->UpperLimit(*firstPOI) <<endl; cout << "CLb strict [P(toy>obs|0)] for observed 95% upper-limit "<< CLb <<endl; cout << "CLb inclusive [P(toy>=obs|0)] for observed 95% upper-limit "<< CLbinclusive <<endl; delete profile; delete nll; }
void runQ(const char* inFileName, const char* wsName = "combined", const char* modelConfigName = "ModelConfig", const char* dataName = "obsData", const char* asimov0DataName = "asimovData_0", const char* conditional0Snapshot = "conditionalGlobs_0", const char* asimov1DataName = "asimovData_1", const char* conditional1Snapshot = "conditionalGlobs_1", const char* nominalSnapshot = "nominalGlobs", string smass = "130", string folder = "test") { double mass; stringstream massStr; massStr << smass; massStr >> mass; bool errFast = 0; bool goFast = 1; bool remakeData = 1; bool doRightSided = 1; bool doInj = 0; bool doObs = 1; bool doMedian = 1; TStopwatch timer; timer.Start(); TFile f(inFileName); RooWorkspace* ws = (RooWorkspace*)f.Get(wsName); if (!ws) { cout << "ERROR::Workspace: " << wsName << " doesn't exist!" << endl; return; } ModelConfig* mc = (ModelConfig*)ws->obj(modelConfigName); if (!mc) { cout << "ERROR::ModelConfig: " << modelConfigName << " doesn't exist!" << endl; return; } RooDataSet* data = (RooDataSet*)ws->data(dataName); if (!data) { cout << "ERROR::Dataset: " << dataName << " doesn't exist!" << endl; return; } mc->GetNuisanceParameters()->Print("v"); RooNLLVar::SetIgnoreZeroEntries(1); ROOT::Math::MinimizerOptions::SetDefaultMinimizer("Minuit2"); ROOT::Math::MinimizerOptions::SetDefaultStrategy(0); ROOT::Math::MinimizerOptions::SetDefaultPrintLevel(1); cout << "Setting max function calls" << endl; //ROOT::Math::MinimizerOptions::SetDefaultMaxFunctionCalls(20000); RooMinimizer::SetMaxFunctionCalls(10000); ws->loadSnapshot("conditionalNuis_0"); RooArgSet nuis(*mc->GetNuisanceParameters()); RooRealVar* mu = (RooRealVar*)mc->GetParametersOfInterest()->first(); if (string(mc->GetPdf()->ClassName()) == "RooSimultaneous" && remakeData) { RooSimultaneous* simPdf = (RooSimultaneous*)mc->GetPdf(); double min_mu; data = makeData(data, simPdf, mc->GetObservables(), mu, mass, min_mu); } RooDataSet* asimovData0 = (RooDataSet*)ws->data(asimov0DataName); if (!asimovData0) { cout << "Asimov data doesn't exist! Please, allow me to build one for you..." << endl; makeAsimovData(mc, true, ws, mc->GetPdf(), data, 1); ws->Print(); asimovData0 = (RooDataSet*)ws->data("asimovData_0"); } RooDataSet* asimovData1 = (RooDataSet*)ws->data(asimov1DataName); if (!asimovData1) { cout << "Asimov data doesn't exist! Please, allow me to build one for you..." << endl; makeAsimovData(mc, true, ws, mc->GetPdf(), data, 0); ws->Print(); asimovData1 = (RooDataSet*)ws->data("asimovData_1"); } if (!doRightSided) mu->setRange(0, 40); else mu->setRange(-40, 40); bool old = false; if (old) { mu->setVal(0); RooArgSet poi(*mu); ProfileLikelihoodTestStat_modified asimov_testStat_sig(*mc->GetPdf()); asimov_testStat_sig.SetRightSided(doRightSided); asimov_testStat_sig.SetNuis(&nuis); if (!doInj) asimov_testStat_sig.SetDoAsimov(true, 1); asimov_testStat_sig.SetWorkspace(ws); ProfileLikelihoodTestStat_modified testStat(*mc->GetPdf()); testStat.SetRightSided(doRightSided); testStat.SetNuis(&nuis); testStat.SetWorkspace(ws); //RooMinimizerFcn::SetOverrideEverything(true); double med_sig = 0; double med_testStat_val = 0; //gRandom->SetSeed(1); //RooRandom::randomGenerator()->SetSeed(1); RooNLLVar::SetIgnoreZeroEntries(1); if (asimov1DataName != "" && doMedian) { mu->setVal(0); if (!doInj) mu->setRange(0, 2); ws->loadSnapshot("conditionalNuis_0"); asimov_testStat_sig.SetLoadUncondSnapshot("conditionalNuis_1"); if (string(conditional1Snapshot) != "") ws->loadSnapshot(conditional1Snapshot); med_testStat_val = 2*asimov_testStat_sig.Evaluate(*asimovData1, poi); if (med_testStat_val < 0 && !doInj) { mu->setVal(0); med_testStat_val = 2*asimov_testStat_sig.Evaluate(*asimovData1, poi); // just try again } int sign = med_testStat_val != 0 ? med_testStat_val/fabs(med_testStat_val) : 0; med_sig = sign*sqrt(fabs(med_testStat_val)); if (string(nominalSnapshot) != "") ws->loadSnapshot(nominalSnapshot); if (!doRightSided) mu->setRange(0, 40); else mu->setRange(-40, 40); } RooNLLVar::SetIgnoreZeroEntries(0); //gRandom->SetSeed(1); //RooRandom::randomGenerator()->SetSeed(1); //RooMinimizerFcn::SetOverrideEverything(false); cout << "med test stat: " << med_testStat_val << endl; ws->loadSnapshot("nominalGlobs"); ws->loadSnapshot("conditionalNuis_0"); mu->setVal(0); testStat.SetWorkspace(ws); testStat.SetLoadUncondSnapshot("ucmles"); double obsTestStat_val = doObs ? 2*testStat.Evaluate(*data, poi) : 0; cout << "obs test stat: " << obsTestStat_val << endl; // obsTestStat_val = 2*testStat.Evaluate(*data, poi); // cout << "obs test stat: " << obsTestStat_val << endl; // obsTestStat_val = 2*testStat.Evaluate(*data, poi); // cout << "obs test stat: " << obsTestStat_val << endl; double obs_sig; int sign = obsTestStat_val == 0 ? 0 : obsTestStat_val / fabs(obsTestStat_val); if (!doRightSided && (obsTestStat_val < 0 && obsTestStat_val > -0.1 || mu->getVal() < 0.001)) obs_sig = 0; else obs_sig = sign*sqrt(fabs(obsTestStat_val)); if (obs_sig != obs_sig) //nan, do by hand { cout << "Obs test stat gave nan: try by hand" << endl; mu->setVal(0); mu->setConstant(1); mc->GetPdf()->fitTo(*data, Hesse(0), Minos(0), PrintLevel(-1), Constrain(*mc->GetNuisanceParameters())); mu->setConstant(0); double L_0 = mc->GetPdf()->getVal(); //mu->setVal(0); //mu->setConstant(1); mc->GetPdf()->fitTo(*data, Hesse(0), Minos(0), PrintLevel(-1), Constrain(*mc->GetNuisanceParameters())); //mu->setConstant(0); double L_muhat = mc->GetPdf()->getVal(); cout << "L_0: " << L_0 << ", L_muhat: " << L_muhat << endl; obs_sig = sqrt(-2*TMath::Log(L_0/L_muhat)); //still nan if (obs_sig != obs_sig && fabs(L_0 - L_muhat) < 0.000001) obs_sig = 0; } cout << "obs: " << obs_sig << endl; cout << "Observed significance: " << obs_sig << endl; if (med_sig) { cout << "Median test stat val: " << med_testStat_val << endl; cout << "Median significance: " << med_sig << endl; } f.Close(); stringstream fileName; fileName << "root_files/" << folder << "/" << mass << ".root"; system(("mkdir -vp root_files/" + folder).c_str()); TFile f2(fileName.str().c_str(),"recreate"); // stringstream fileName; // fileName << "results_sig/" << mass << ".root"; // system("mkdir results_sig"); // TFile f(fileName.str().c_str(),"recreate"); TH1D* h_hypo = new TH1D("hypo","hypo",2,0,2); h_hypo->SetBinContent(1, obs_sig); h_hypo->SetBinContent(2, med_sig); f2.Write(); f2.Close(); //mc->GetPdf()->fitTo(*data, PrintLevel(0)); timer.Stop(); timer.Print(); } else { RooAbsPdf* pdf = mc->GetPdf(); RooArgSet nuis_tmp1 = *mc->GetNuisanceParameters(); RooNLLVar* asimov_nll0 = (RooNLLVar*)pdf->createNLL(*asimovData0, Constrain(nuis_tmp1)); RooArgSet nuis_tmp2 = *mc->GetNuisanceParameters(); RooNLLVar* asimov_nll1 = (RooNLLVar*)pdf->createNLL(*asimovData1, Constrain(nuis_tmp2)); RooArgSet nuis_tmp3 = *mc->GetNuisanceParameters(); RooNLLVar* obs_nll = (RooNLLVar*)pdf->createNLL(*data, Constrain(nuis_tmp3)); //do asimov int status; //get sigma_b ws->loadSnapshot(conditional0Snapshot); status = ws->loadSnapshot("conditionalNuis_0"); if (status != 0 && goFast) errFast = 1; mu->setVal(0); mu->setConstant(1); status = goFast ? 0 : minimize(asimov_nll0, ws); if (status < 0) { cout << "Retrying" << endl; //ws->loadSnapshot("conditionalNuis_0"); status = minimize(asimov_nll0, ws); if (status >= 0) cout << "Success!" << endl; } double asimov0_nll0 = asimov_nll0->getVal(); mu->setVal(1); ws->loadSnapshot("conditionalNuis_1"); status = minimize(asimov_nll0, ws); if (status < 0) { cout << "Retrying" << endl; //ws->loadSnapshot("conditionalNuis_0"); status = minimize(asimov_nll0, ws); if (status >= 0) cout << "Success!" << endl; } double asimov0_nll1 = asimov_nll0->getVal(); double asimov0_q = 2*(asimov0_nll1 - asimov0_nll0); double sigma_b = sqrt(1./asimov0_q); ws->loadSnapshot(nominalSnapshot); //get sigma_sb ws->loadSnapshot(conditional1Snapshot); ws->loadSnapshot("conditionalNuis_0"); mu->setVal(0); mu->setConstant(1); status = minimize(asimov_nll1, ws); if (status < 0) { cout << "Retrying" << endl; //ws->loadSnapshot("conditionalNuis_0"); status = minimize(asimov_nll1, ws); if (status >= 0) cout << "Success!" << endl; } double asimov1_nll0 = asimov_nll1->getVal(); mu->setVal(1); status = ws->loadSnapshot("conditionalNuis_1"); if (status != 0 && goFast) errFast = 1; status = goFast ? 0 : minimize(asimov_nll1, ws); if (status < 0) { cout << "Retrying" << endl; //ws->loadSnapshot("conditionalNuis_0"); status = minimize(asimov_nll1, ws); if (status >= 0) cout << "Success!" << endl; } double asimov1_nll1 = asimov_nll1->getVal(); double asimov1_q = 2*(asimov1_nll1 - asimov1_nll0); double sigma_sb = sqrt(-1./asimov1_q); ws->loadSnapshot(nominalSnapshot); //do obs mu->setVal(0); status = ws->loadSnapshot("conditionalNuis_0"); if (status != 0 && goFast) errFast = 1; mu->setConstant(1); status = goFast ? 0 : minimize(obs_nll, ws); if (status < 0) { cout << "Retrying with conditional snapshot at mu=1" << endl; ws->loadSnapshot("conditionalNuis_0"); status = minimize(obs_nll, ws); if (status >= 0) cout << "Success!" << endl; } double obs_nll0 = obs_nll->getVal(); status = ws->loadSnapshot("conditionalNuis_1"); if (status != 0 && goFast) errFast = 1; mu->setVal(1); status = goFast ? 0 : minimize(obs_nll, ws); if (status < 0) { cout << "Retrying with conditional snapshot at mu=1" << endl; ws->loadSnapshot("conditionalNuis_0"); status = minimize(obs_nll, ws); if (status >= 0) cout << "Success!" << endl; } double obs_nll1 = obs_nll->getVal(); double obs_q = 2*(obs_nll1 - obs_nll0); double Zobs = (1./sigma_b/sigma_b - obs_q) / (2./sigma_b); double Zexp = (1./sigma_b/sigma_b - asimov1_q) / (2./sigma_b); double pb_obs = 1-ROOT::Math::gaussian_cdf(Zobs); double pb_exp = 1-ROOT::Math::gaussian_cdf(Zexp); cout << "asimov0_q = " << asimov0_q << endl; cout << "asimov1_q = " << asimov1_q << endl; cout << "obs_q = " << obs_q << endl; cout << "sigma_b = " << sigma_b << endl; cout << "sigma_sb = " << sigma_sb << endl; cout << "Z obs = " << Zobs << endl; cout << "Z exp = " << Zexp << endl; f.Close(); stringstream fileName; fileName << "root_files/" << folder << "/" << mass << ".root"; system(("mkdir -vp root_files/" + folder).c_str()); TFile f2(fileName.str().c_str(),"recreate"); TH1D* h_hypo = new TH1D("hypo_tev","hypo_tev",2,0,2); h_hypo->SetBinContent(1, pb_obs); h_hypo->SetBinContent(2, pb_exp); f2.Write(); f2.Close(); stringstream fileName3; fileName3 << "root_files/" << folder << "_llr/" << mass << ".root"; system(("mkdir -vp root_files/" + folder + "_llr").c_str()); TFile f3(fileName3.str().c_str(),"recreate"); TH1D* h_hypo3 = new TH1D("hypo_llr","hypo_llr",7,0,7); h_hypo3->SetBinContent(1, -obs_q); h_hypo3->SetBinContent(2, -asimov1_q); h_hypo3->SetBinContent(3, -asimov0_q); h_hypo3->SetBinContent(4, -asimov0_q-2*2/sigma_b); h_hypo3->SetBinContent(5, -asimov0_q-1*2/sigma_b); h_hypo3->SetBinContent(6, -asimov0_q+1*2/sigma_b); h_hypo3->SetBinContent(7, -asimov0_q+2*2/sigma_b); f3.Write(); f3.Close(); timer.Stop(); timer.Print(); } }
TH1D* runSig(RooWorkspace* ws, const char* modelConfigName = "ModelConfig", const char* dataName = "obsData", const char* asimov1DataName = "asimovData_1", const char* conditional1Snapshot = "conditionalGlobs_1", const char* nominalSnapshot = "nominalGlobs") { string defaultMinimizer = "Minuit"; // or "Minuit" int defaultStrategy = 2; // Minimization strategy. 0-2. 0 = fastest, least robust. 2 = slowest, most robust double mu_profile_value = 1; // mu value to profile the obs data at wbefore generating the expected bool doUncap = 1; // uncap p0 bool doInj = 0; // setup the poi for injection study (zero is faster if you're not) bool doMedian = 1; // compute median significance bool isBlind = 0; // Dont look at observed data bool doConditional = !isBlind; // do conditional expected data bool doObs = !isBlind; // compute observed significance TStopwatch timer; timer.Start(); if (!ws) { cout << "ERROR::Workspace is NULL!" << endl; return NULL; } ModelConfig* mc = (ModelConfig*)ws->obj(modelConfigName); if (!mc) { cout << "ERROR::ModelConfig: " << modelConfigName << " doesn't exist!" << endl; return NULL; } RooDataSet* data = (RooDataSet*)ws->data(dataName); if (!data) { cout << "ERROR::Dataset: " << dataName << " doesn't exist!" << endl; return NULL; } mc->GetNuisanceParameters()->Print("v"); //RooNLLVar::SetIgnoreZeroEntries(1); ROOT::Math::MinimizerOptions::SetDefaultMinimizer(defaultMinimizer.c_str()); ROOT::Math::MinimizerOptions::SetDefaultStrategy(defaultStrategy); ROOT::Math::MinimizerOptions::SetDefaultPrintLevel(-1); // cout << "Setting max function calls" << endl; //ROOT::Math::MinimizerOptions::SetDefaultMaxFunctionCalls(20000); //RooMinimizer::SetMaxFunctionCalls(10000); ws->loadSnapshot("conditionalNuis_0"); RooArgSet nuis(*mc->GetNuisanceParameters()); RooRealVar* mu = (RooRealVar*)mc->GetParametersOfInterest()->first(); RooAbsPdf* pdf_temp = mc->GetPdf(); string condSnapshot(conditional1Snapshot); RooArgSet nuis_tmp2 = *mc->GetNuisanceParameters(); RooNLLVar* obs_nll = doObs ? (RooNLLVar*)pdf_temp->createNLL(*data, Constrain(nuis_tmp2)) : NULL; RooDataSet* asimovData1 = (RooDataSet*)ws->data(asimov1DataName); if (!asimovData1) { cout << "Asimov data doesn't exist! Please, allow me to build one for you..." << endl; string mu_str, mu_prof_str; asimovData1 = makeAsimovData(mc, doConditional, ws, obs_nll, 1, &mu_str, &mu_prof_str, mu_profile_value, true); condSnapshot="conditionalGlobs"+mu_prof_str; //makeAsimovData(mc, true, ws, mc->GetPdf(), data, 0); //ws->Print(); //asimovData1 = (RooDataSet*)ws->data("asimovData_1"); } if (!doUncap) mu->setRange(0, 40); else mu->setRange(-40, 40); RooAbsPdf* pdf = mc->GetPdf(); RooArgSet nuis_tmp1 = *mc->GetNuisanceParameters(); RooNLLVar* asimov_nll = (RooNLLVar*)pdf->createNLL(*asimovData1, Constrain(nuis_tmp1)); //do asimov mu->setVal(1); mu->setConstant(0); if (!doInj) mu->setConstant(1); int status,sign; double med_sig=0,obs_sig=0,asimov_q0=0,obs_q0=0; if (doMedian) { ws->loadSnapshot(condSnapshot.c_str()); if (doInj) ws->loadSnapshot("conditionalNuis_inj"); else ws->loadSnapshot("conditionalNuis_1"); mc->GetGlobalObservables()->Print("v"); mu->setVal(0); mu->setConstant(1); status = minimize(asimov_nll, ws); if (status >= 0) cout << "Success!" << endl; if (status < 0) { cout << "Retrying with conditional snapshot at mu=1" << endl; ws->loadSnapshot("conditionalNuis_0"); status = minimize(asimov_nll, ws); if (status >= 0) cout << "Success!" << endl; } double asimov_nll_cond = asimov_nll->getVal(); mu->setVal(1); if (doInj) ws->loadSnapshot("conditionalNuis_inj"); else ws->loadSnapshot("conditionalNuis_1"); if (doInj) mu->setConstant(0); status = minimize(asimov_nll, ws); if (status >= 0) cout << "Success!" << endl; if (status < 0) { cout << "Retrying with conditional snapshot at mu=1" << endl; ws->loadSnapshot("conditionalNuis_0"); status = minimize(asimov_nll, ws); if (status >= 0) cout << "Success!" << endl; } double asimov_nll_min = asimov_nll->getVal(); asimov_q0 = 2*(asimov_nll_cond - asimov_nll_min); if (doUncap && mu->getVal() < 0) asimov_q0 = -asimov_q0; sign = int(asimov_q0 != 0 ? asimov_q0/fabs(asimov_q0) : 0); med_sig = sign*sqrt(fabs(asimov_q0)); ws->loadSnapshot(nominalSnapshot); } if (doObs) { ws->loadSnapshot("conditionalNuis_0"); mu->setVal(0); mu->setConstant(1); status = minimize(obs_nll, ws); if (status < 0) { cout << "Retrying with conditional snapshot at mu=1" << endl; ws->loadSnapshot("conditionalNuis_0"); status = minimize(obs_nll, ws); if (status >= 0) cout << "Success!" << endl; } double obs_nll_cond = obs_nll->getVal(); //ws->loadSnapshot("ucmles"); mu->setConstant(0); status = minimize(obs_nll, ws); if (status < 0) { cout << "Retrying with conditional snapshot at mu=1" << endl; ws->loadSnapshot("conditionalNuis_0"); status = minimize(obs_nll, ws); if (status >= 0) cout << "Success!" << endl; } double obs_nll_min = obs_nll->getVal(); obs_q0 = 2*(obs_nll_cond - obs_nll_min); if (doUncap && mu->getVal() < 0) obs_q0 = -obs_q0; sign = int(obs_q0 == 0 ? 0 : obs_q0 / fabs(obs_q0)); if (!doUncap && ((obs_q0 < 0 && obs_q0 > -0.1) || mu->getVal() < 0.001)) obs_sig = 0; else obs_sig = sign*sqrt(fabs(obs_q0)); } cout << "obs: " << obs_sig << endl; cout << "Observed significance: " << obs_sig << endl; if (med_sig) { cout << "Median test stat val: " << asimov_q0 << endl; cout << "Median significance: " << med_sig << endl; } TH1D* h_hypo = new TH1D("hypo","hypo",2,0,2); h_hypo->SetBinContent(1, obs_sig); h_hypo->SetBinContent(2, med_sig); timer.Stop(); timer.Print(); return h_hypo; }
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.
void StandardHypoTestDemo(const char* infile = "", const char* workspaceName = "combined", const char* modelSBName = "ModelConfig", const char* modelBName = "", const char* dataName = "obsData", int calcType = 0, // 0 freq 1 hybrid, 2 asymptotic int testStatType = 3, // 0 LEP, 1 TeV, 2 LHC, 3 LHC - one sided int ntoys = 5000, bool useNC = false, const char * nuisPriorName = 0) { /* Other Parameter to pass in tutorial apart from standard for filename, ws, modelconfig and data type = 0 Freq calculator type = 1 Hybrid calculator type = 2 Asymptotic calculator testStatType = 0 LEP = 1 Tevatron = 2 Profile Likelihood = 3 Profile Likelihood one sided (i.e. = 0 if mu < mu_hat) ntoys: number of toys to use useNumberCounting: set to true when using number counting events nuisPriorName: name of prior for the nnuisance. This is often expressed as constraint term in the global model It is needed only when using the HybridCalculator (type=1) If not given by default the prior pdf from ModelConfig is used. extra options are available as global paramwters of the macro. They major ones are: generateBinned generate binned data sets for toys (default is false) - be careful not to activate with a too large (>=3) number of observables nToyRatio ratio of S+B/B toys (default is 2) printLevel */ // disable - can cause some problems //ToyMCSampler::SetAlwaysUseMultiGen(true); SimpleLikelihoodRatioTestStat::SetAlwaysReuseNLL(true); ProfileLikelihoodTestStat::SetAlwaysReuseNLL(true); RatioOfProfiledLikelihoodsTestStat::SetAlwaysReuseNLL(true); //RooRandom::randomGenerator()->SetSeed(0); // to change minimizers // ROOT::Math::MinimizerOptions::SetDefaultStrategy(0); // ROOT::Math::MinimizerOptions::SetDefaultMinimizer("Minuit2"); // ROOT::Math::MinimizerOptions::SetDefaultTolerance(1); ///////////////////////////////////////////////////////////// // First part is just to access a user-defined file // or create the standard example file if it doesn't exist //////////////////////////////////////////////////////////// const char* filename = ""; if (!strcmp(infile,"")) filename = "results/example_combined_GaussExample_model.root"; else filename = infile; // Check if example input file exists TFile *file = TFile::Open(filename); // if input file was specified byt not found, quit if(!file && strcmp(infile,"")){ cout <<"file not found" << endl; return; } // if default file not found, try to create it if(!file ){ // Normally this would be run on the command line cout <<"will run standard hist2workspace example"<<endl; gROOT->ProcessLine(".! prepareHistFactory ."); gROOT->ProcessLine(".! hist2workspace config/example.xml"); cout <<"\n\n---------------------"<<endl; cout <<"Done creating example input"<<endl; cout <<"---------------------\n\n"<<endl; } // now try to access the file again file = TFile::Open(filename); if(!file){ // if it is still not there, then we can't continue cout << "Not able to run hist2workspace to create example input" <<endl; return; } ///////////////////////////////////////////////////////////// // Tutorial starts here //////////////////////////////////////////////////////////// // get the workspace out of the file RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName); if(!w){ cout <<"workspace not found" << endl; return; } w->Print(); // get the modelConfig out of the file ModelConfig* sbModel = (ModelConfig*) w->obj(modelSBName); // get the modelConfig out of the file RooAbsData* data = w->data(dataName); // make sure ingredients are found if(!data || !sbModel){ w->Print(); cout << "data or ModelConfig was not found" <<endl; return; } // make b model ModelConfig* bModel = (ModelConfig*) w->obj(modelBName); // 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 ) { 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("B_only")); RooRealVar * var = dynamic_cast<RooRealVar*>(bModel->GetParametersOfInterest()->first()); if (!var) return; double oldval = var->getVal(); var->setVal(0); //bModel->SetSnapshot( RooArgSet(*var, *w->var("lumi")) ); bModel->SetSnapshot( RooArgSet(*var) ); var->setVal(oldval); } if (!sbModel->GetSnapshot() || poiValue > 0) { Info("StandardHypoTestDemo","Model %s has no snapshot - make one using model poi",modelSBName); RooRealVar * var = dynamic_cast<RooRealVar*>(sbModel->GetParametersOfInterest()->first()); if (!var) return; double oldval = var->getVal(); if (poiValue > 0) var->setVal(poiValue); //sbModel->SetSnapshot( RooArgSet(*var, *w->var("lumi") ) ); sbModel->SetSnapshot( RooArgSet(*var) ); if (poiValue > 0) var->setVal(oldval); //sbModel->SetSnapshot( *sbModel->GetParametersOfInterest() ); } // part 1, hypothesis testing SimpleLikelihoodRatioTestStat * slrts = new SimpleLikelihoodRatioTestStat(*bModel->GetPdf(), *sbModel->GetPdf()); // null parameters must includes snapshot of poi plus the nuisance values RooArgSet nullParams(*bModel->GetSnapshot()); if (bModel->GetNuisanceParameters()) nullParams.add(*bModel->GetNuisanceParameters()); slrts->SetNullParameters(nullParams); RooArgSet altParams(*sbModel->GetSnapshot()); if (sbModel->GetNuisanceParameters()) altParams.add(*sbModel->GetNuisanceParameters()); slrts->SetAltParameters(altParams); ProfileLikelihoodTestStat * profll = new ProfileLikelihoodTestStat(*bModel->GetPdf()); RatioOfProfiledLikelihoodsTestStat * ropl = new RatioOfProfiledLikelihoodsTestStat(*bModel->GetPdf(), *sbModel->GetPdf(), sbModel->GetSnapshot()); ropl->SetSubtractMLE(false); if (testStatType == 3) profll->SetOneSidedDiscovery(1); profll->SetPrintLevel(printLevel); // profll.SetReuseNLL(mOptimize); // slrts.SetReuseNLL(mOptimize); // ropl.SetReuseNLL(mOptimize); AsymptoticCalculator::SetPrintLevel(printLevel); HypoTestCalculatorGeneric * hypoCalc = 0; // note here Null is B and Alt is S+B if (calcType == 0) hypoCalc = new FrequentistCalculator(*data, *sbModel, *bModel); else if (calcType == 1) hypoCalc= new HybridCalculator(*data, *sbModel, *bModel); else if (calcType == 2) hypoCalc= new AsymptoticCalculator(*data, *sbModel, *bModel); if (calcType == 0) ((FrequentistCalculator*)hypoCalc)->SetToys(ntoys, ntoys/nToysRatio); if (calcType == 1) ((HybridCalculator*)hypoCalc)->SetToys(ntoys, ntoys/nToysRatio); if (calcType == 2 ) { if (testStatType == 3) ((AsymptoticCalculator*) hypoCalc)->SetOneSidedDiscovery(true); if (testStatType != 2 && testStatType != 3) Warning("StandardHypoTestDemo","Only the PL test statistic can be used with AsymptoticCalculator - use by default a two-sided PL"); } // check for nuisance prior pdf in case of nuisance parameters if (calcType == 1 && (bModel->GetNuisanceParameters() || sbModel->GetNuisanceParameters() )) { RooAbsPdf * nuisPdf = 0; if (nuisPriorName) nuisPdf = w->pdf(nuisPriorName); // use prior defined first in bModel (then in SbModel) if (!nuisPdf) { Info("StandardHypoTestDemo","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("StandardHypoTestDemo","No nuisance pdf given - try to use %s that is defined as a prior pdf in the B model",nuisPdf->GetName()); } else { Error("StandardHypoTestDemo","Cannnot run Hybrid calculator because no prior on the nuisance parameter is specified or can be derived"); return; } } assert(nuisPdf); Info("StandardHypoTestDemo","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("StandardHypoTestDemo","Prior nuisance does not depend on nuisance parameters. They will be smeared in their full range"); } delete np; ((HybridCalculator*)hypoCalc)->ForcePriorNuisanceAlt(*nuisPdf); ((HybridCalculator*)hypoCalc)->ForcePriorNuisanceNull(*nuisPdf); } // hypoCalc->ForcePriorNuisanceAlt(*sbModel->GetPriorPdf()); // hypoCalc->ForcePriorNuisanceNull(*bModel->GetPriorPdf()); ToyMCSampler * sampler = (ToyMCSampler *)hypoCalc->GetTestStatSampler(); if (sampler && (calcType == 0 || calcType == 1) ) { // look if pdf is number counting or extended if (sbModel->GetPdf()->canBeExtended() ) { if (useNC) Warning("StandardHypoTestDemo","Pdf is extended: but number counting flag is set: ignore it "); } else { // for not extended pdf if (!useNC) { int nEvents = data->numEntries(); Info("StandardHypoTestDemo","Pdf is not extended: number of events to generate taken from observed data set is %d",nEvents); sampler->SetNEventsPerToy(nEvents); } else { Info("StandardHypoTestDemo","using a number counting pdf"); sampler->SetNEventsPerToy(1); } } if (data->isWeighted() && !generateBinned) { Info("StandardHypoTestDemo","Data set is weighted, nentries = %d and sum of weights = %8.1f but toy generation is unbinned - it would be faster to set generateBinned to true\n",data->numEntries(), data->sumEntries()); } if (generateBinned) sampler->SetGenerateBinned(generateBinned); // set the test statistic if (testStatType == 0) sampler->SetTestStatistic(slrts); if (testStatType == 1) sampler->SetTestStatistic(ropl); if (testStatType == 2 || testStatType == 3) sampler->SetTestStatistic(profll); } HypoTestResult * htr = hypoCalc->GetHypoTest(); htr->SetPValueIsRightTail(true); htr->SetBackgroundAsAlt(false); htr->Print(); // how to get meaningfull CLs at this point? delete sampler; delete slrts; delete ropl; delete profll; if (calcType != 2) { HypoTestPlot * plot = new HypoTestPlot(*htr,100); plot->SetLogYaxis(true); plot->Draw(); } else { std::cout << "Asymptotic results " << std::endl; } // look at expected significances // found median of S+B distribution if (calcType != 2) { SamplingDistribution * altDist = htr->GetAltDistribution(); HypoTestResult htExp("Expected Result"); htExp.Append(htr); // find quantiles in alt (S+B) distribution double p[5]; double q[5]; for (int i = 0; i < 5; ++i) { double sig = -2 + i; p[i] = ROOT::Math::normal_cdf(sig,1); } std::vector<double> values = altDist->GetSamplingDistribution(); TMath::Quantiles( values.size(), 5, &values[0], q, p, false); for (int i = 0; i < 5; ++i) { htExp.SetTestStatisticData( q[i] ); double sig = -2 + i; std::cout << " Expected p -value and significance at " << sig << " sigma = " << htExp.NullPValue() << " significance " << htExp.Significance() << " sigma " << std::endl; } } else { // case of asymptotic calculator for (int i = 0; i < 5; ++i) { double sig = -2 + i; // sigma is inverted here double pval = AsymptoticCalculator::GetExpectedPValues( htr->NullPValue(), htr->AlternatePValue(), -sig, false); std::cout << " Expected p -value and significance at " << sig << " sigma = " << pval << " significance " << ROOT::Math::normal_quantile_c(pval,1) << " sigma " << std::endl; } } }
result fit_toy(RooWorkspace* wspace, int n, const RooArgSet* globals) { RooRandom::randomGenerator()->SetSeed(0); // TFile f(filename); // RooWorkspace *wspace = (RooWorkspace*)f.Get("combined"); ModelConfig* model = (ModelConfig*)wspace->obj("ModelConfig"); RooAbsPdf* pdf; pdf = model->GetPdf(); RooAbsPdf* top_constraint = (RooAbsPdf*)wspace->obj("top_ratio_constraint"); RooAbsPdf* vv_constraint = (RooAbsPdf*)wspace->obj("vv_ratio_constraint"); RooAbsPdf* top_vv_constraint_sf = (RooAbsPdf*)wspace->obj("top_vv_ratio_sf_constraint"); RooAbsPdf* top_vv_constraint_of = (RooAbsPdf*)wspace->obj("top_vv_ratio_of_constraint"); // generate constraint global observables RooRealVar *nom_top_ratio = (RooRealVar*)wspace->obj("nom_top_ratio"); nom_top_ratio->setRange(0, 100); RooRealVar *nom_vv_ratio = (RooRealVar*)wspace->obj("nom_vv_ratio"); nom_vv_ratio->setRange(0,100); RooRealVar *nom_top_vv_ratio_sf = (RooRealVar*)wspace->obj("nom_top_vv_ratio_sf"); nom_top_vv_ratio_sf->setRange(0,100); RooRealVar *nom_top_vv_ratio_of = (RooRealVar*)wspace->obj("nom_top_vv_ratio_of"); nom_top_vv_ratio_of->setRange(0,100); RooDataSet *nom_top_generated = top_constraint->generateSimGlobal(RooArgSet(*nom_top_ratio), 1); nom_top_ratio->setVal(((RooRealVar*)nom_top_generated->get(0)->find("nom_top_ratio"))->getVal()); RooDataSet *nom_vv_generated = vv_constraint->generateSimGlobal(RooArgSet(*nom_vv_ratio), 1); nom_vv_ratio->setVal(((RooRealVar*)nom_vv_generated->get(0)->find("nom_vv_ratio"))->getVal()); RooDataSet *nom_top_vv_sf_generated = top_vv_constraint_sf->generateSimGlobal(RooArgSet(*nom_top_vv_ratio_sf), 1); nom_top_vv_ratio_sf->setVal(((RooRealVar*)nom_top_vv_sf_generated->get(0)->find("nom_top_vv_ratio_sf"))->getVal()); RooDataSet *nom_top_vv_of_generated = top_vv_constraint_of->generateSimGlobal(RooArgSet(*nom_top_vv_ratio_of), 1); nom_top_vv_ratio_of->setVal(((RooRealVar*)nom_top_vv_of_generated->get(0)->find("nom_top_vv_ratio_of"))->getVal()); NumEventsTestStat* dummy = new NumEventsTestStat(*pdf); ToyMCSampler* mc = new ToyMCSampler(*dummy, 1); mc->SetPdf(*pdf); mc->SetObservables(*model->GetObservables()); mc->SetGlobalObservables(*globals); mc->SetNuisanceParameters(*model->GetNuisanceParameters()); mc->SetParametersForTestStat(*model->GetParametersOfInterest()); mc->SetNEventsPerToy(n); RooArgSet constr; constr.add(*(model->GetNuisanceParameters())); RemoveConstantParameters(&constr); RooDataSet* toy_data = (RooDataSet*)mc->GenerateToyData(*const_cast<RooArgSet*>(model->GetSnapshot())); RooFitResult *res = pdf->fitTo(*toy_data, Constrain(constr), PrintLevel(0), Save(), Range("fitRange"), InitialHesse(), ExternalConstraints(RooArgSet(*top_constraint, *vv_constraint, *top_vv_constraint_sf, *top_vv_constraint_of))); result yield = get_results(wspace, res); yield.of.generated_sum.val = toy_data->sumEntries("(channelCat==channelCat::of) & (obs_x_of>120)"); yield.sf.generated_sum.val = toy_data->sumEntries("(channelCat==channelCat::sf) & (obs_x_sf>120)"); delete mc; delete dummy; // f.Close(); return yield; }
void StandardHistFactoryPlotsWithCategories(const char* infile = "", const char* workspaceName = "combined", const char* modelConfigName = "ModelConfig", const char* dataName = "obsData"){ double nSigmaToVary=5.; double muVal=0; bool doFit=false; // ------------------------------------------------------- // First part is just to access a user-defined file // or create the standard example file if it doesn't exist const char* filename = ""; if (!strcmp(infile,"")) { filename = "results/example_combined_GaussExample_model.root"; bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code // if file does not exists generate with histfactory if (!fileExist) { #ifdef _WIN32 cout << "HistFactory file cannot be generated on Windows - exit" << endl; return; #endif // Normally this would be run on the command line cout <<"will run standard hist2workspace example"<<endl; gROOT->ProcessLine(".! prepareHistFactory ."); gROOT->ProcessLine(".! hist2workspace config/example.xml"); cout <<"\n\n---------------------"<<endl; cout <<"Done creating example input"<<endl; cout <<"---------------------\n\n"<<endl; } } else filename = infile; // Try to open the file TFile *file = TFile::Open(filename); // if input file was specified byt not found, quit if(!file ){ cout <<"StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl; return; } // ------------------------------------------------------- // Tutorial starts here // ------------------------------------------------------- // get the workspace out of the file RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName); if(!w){ cout <<"workspace not found" << endl; return; } // get the modelConfig out of the file ModelConfig* mc = (ModelConfig*) w->obj(modelConfigName); // get the modelConfig out of the file RooAbsData* data = w->data(dataName); // make sure ingredients are found if(!data || !mc){ w->Print(); cout << "data or ModelConfig was not found" <<endl; return; } // ------------------------------------------------------- // now use the profile inspector RooRealVar* obs = (RooRealVar*)mc->GetObservables()->first(); TList* list = new TList(); RooRealVar * firstPOI = dynamic_cast<RooRealVar*>(mc->GetParametersOfInterest()->first()); firstPOI->setVal(muVal); // firstPOI->setConstant(); if(doFit){ mc->GetPdf()->fitTo(*data); } // ------------------------------------------------------- mc->GetNuisanceParameters()->Print("v"); int nPlotsMax = 1000; cout <<" check expectedData by category"<<endl; RooDataSet* simData=NULL; RooSimultaneous* simPdf = NULL; if(strcmp(mc->GetPdf()->ClassName(),"RooSimultaneous")==0){ cout <<"Is a simultaneous PDF"<<endl; simPdf = (RooSimultaneous *)(mc->GetPdf()); } else { cout <<"Is not a simultaneous PDF"<<endl; } if(doFit) { RooCategory* channelCat = (RooCategory*) (&simPdf->indexCat()); TIterator* iter = channelCat->typeIterator() ; RooCatType* tt = NULL; tt=(RooCatType*) iter->Next(); RooAbsPdf* pdftmp = ((RooSimultaneous*)mc->GetPdf())->getPdf(tt->GetName()) ; RooArgSet* obstmp = pdftmp->getObservables(*mc->GetObservables()) ; obs = ((RooRealVar*)obstmp->first()); RooPlot* frame = obs->frame(); cout <<Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())<<endl; cout << tt->GetName() << " " << channelCat->getLabel() <<endl; data->plotOn(frame,MarkerSize(1),Cut(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())),DataError(RooAbsData::None)); Double_t normCount = data->sumEntries(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())) ; pdftmp->plotOn(frame,LineWidth(2.),Normalization(normCount,RooAbsReal::NumEvent)) ; frame->Draw(); cout <<"expected events = " << mc->GetPdf()->expectedEvents(*data->get()) <<endl; return; } int nPlots=0; if(!simPdf){ TIterator* it = mc->GetNuisanceParameters()->createIterator(); RooRealVar* var = NULL; while( (var = (RooRealVar*) it->Next()) != NULL){ RooPlot* frame = obs->frame(); frame->SetYTitle(var->GetName()); data->plotOn(frame,MarkerSize(1)); var->setVal(0); mc->GetPdf()->plotOn(frame,LineWidth(1.)); var->setVal(1); mc->GetPdf()->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(1)); var->setVal(-1); mc->GetPdf()->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(1)); list->Add(frame); var->setVal(0); } } else { RooCategory* channelCat = (RooCategory*) (&simPdf->indexCat()); // TIterator* iter = simPdf->indexCat().typeIterator() ; TIterator* iter = channelCat->typeIterator() ; RooCatType* tt = NULL; while(nPlots<nPlotsMax && (tt=(RooCatType*) iter->Next())) { cout << "on type " << tt->GetName() << " " << endl; // Get pdf associated with state from simpdf RooAbsPdf* pdftmp = simPdf->getPdf(tt->GetName()) ; // Generate observables defined by the pdf associated with this state RooArgSet* obstmp = pdftmp->getObservables(*mc->GetObservables()) ; // obstmp->Print(); obs = ((RooRealVar*)obstmp->first()); TIterator* it = mc->GetNuisanceParameters()->createIterator(); RooRealVar* var = NULL; while(nPlots<nPlotsMax && (var = (RooRealVar*) it->Next())){ TCanvas* c2 = new TCanvas("c2"); RooPlot* frame = obs->frame(); frame->SetName(Form("frame%d",nPlots)); frame->SetYTitle(var->GetName()); cout <<Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())<<endl; cout << tt->GetName() << " " << channelCat->getLabel() <<endl; data->plotOn(frame,MarkerSize(1),Cut(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())),DataError(RooAbsData::None)); Double_t normCount = data->sumEntries(Form("%s==%s::%s",channelCat->GetName(),channelCat->GetName(),tt->GetName())) ; if(strcmp(var->GetName(),"Lumi")==0){ cout <<"working on lumi"<<endl; var->setVal(w->var("nominalLumi")->getVal()); var->Print(); } else{ var->setVal(0); } // w->allVars().Print("v"); // mc->GetNuisanceParameters()->Print("v"); // pdftmp->plotOn(frame,LineWidth(2.)); // mc->GetPdf()->plotOn(frame,LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data)); //pdftmp->plotOn(frame,LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data)); normCount = pdftmp->expectedEvents(*obs); pdftmp->plotOn(frame,LineWidth(2.),Normalization(normCount,RooAbsReal::NumEvent)) ; if(strcmp(var->GetName(),"Lumi")==0){ cout <<"working on lumi"<<endl; var->setVal(w->var("nominalLumi")->getVal()+0.05); var->Print(); } else{ var->setVal(nSigmaToVary); } // pdftmp->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(2)); // mc->GetPdf()->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data)); //pdftmp->plotOn(frame,LineColor(kRed),LineStyle(kDashed),LineWidth(2.),Slice(*channelCat,tt->GetName()),ProjWData(*data)); normCount = pdftmp->expectedEvents(*obs); pdftmp->plotOn(frame,LineWidth(2.),LineColor(kRed),LineStyle(kDashed),Normalization(normCount,RooAbsReal::NumEvent)) ; if(strcmp(var->GetName(),"Lumi")==0){ cout <<"working on lumi"<<endl; var->setVal(w->var("nominalLumi")->getVal()-0.05); var->Print(); } else{ var->setVal(-nSigmaToVary); } // pdftmp->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(2)); // mc->GetPdf()->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(2),Slice(*channelCat,tt->GetName()),ProjWData(*data)); //pdftmp->plotOn(frame,LineColor(kGreen),LineStyle(kDashed),LineWidth(2),Slice(*channelCat,tt->GetName()),ProjWData(*data)); normCount = pdftmp->expectedEvents(*obs); pdftmp->plotOn(frame,LineWidth(2.),LineColor(kGreen),LineStyle(kDashed),Normalization(normCount,RooAbsReal::NumEvent)) ; // set them back to normal if(strcmp(var->GetName(),"Lumi")==0){ cout <<"working on lumi"<<endl; var->setVal(w->var("nominalLumi")->getVal()); var->Print(); } else{ var->setVal(0); } list->Add(frame); // quit making plots ++nPlots; frame->Draw(); c2->SaveAs(Form("%s_%s_%s.pdf",tt->GetName(),obs->GetName(),var->GetName())); delete c2; } } } // ------------------------------------------------------- // now make plots TCanvas* c1 = new TCanvas("c1","ProfileInspectorDemo",800,200); if(list->GetSize()>4){ double n = list->GetSize(); int nx = (int)sqrt(n) ; int ny = TMath::CeilNint(n/nx); nx = TMath::CeilNint( sqrt(n) ); c1->Divide(ny,nx); } else c1->Divide(list->GetSize()); for(int i=0; i<list->GetSize(); ++i){ c1->cd(i+1); list->At(i)->Draw(); } }
void StandardTestStatDistributionDemo(const char* infile = "", const char* workspaceName = "combined", const char* modelConfigName = "ModelConfig", const char* dataName = "obsData"){ // the number of toy MC used to generate the distribution int nToyMC = 1000; // The parameter below is needed for asymptotic distribution to be chi-square, // but set to false if your model is not numerically stable if mu<0 bool allowNegativeMu=true; ///////////////////////////////////////////////////////////// // First part is just to access a user-defined file // or create the standard example file if it doesn't exist //////////////////////////////////////////////////////////// const char* filename = ""; if (!strcmp(infile,"")) { filename = "results/example_combined_GaussExample_model.root"; bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code // if file does not exists generate with histfactory if (!fileExist) { #ifdef _WIN32 cout << "HistFactory file cannot be generated on Windows - exit" << endl; return; #endif // Normally this would be run on the command line cout <<"will run standard hist2workspace example"<<endl; gROOT->ProcessLine(".! prepareHistFactory ."); gROOT->ProcessLine(".! hist2workspace config/example.xml"); cout <<"\n\n---------------------"<<endl; cout <<"Done creating example input"<<endl; cout <<"---------------------\n\n"<<endl; } } else filename = infile; // Try to open the file TFile *file = TFile::Open(filename); // if input file was specified byt not found, quit if(!file ){ cout <<"StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl; return; } ///////////////////////////////////////////////////////////// // Now get the data and workspace //////////////////////////////////////////////////////////// // get the workspace out of the file RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName); if(!w){ cout <<"workspace not found" << endl; return; } // get the modelConfig out of the file ModelConfig* mc = (ModelConfig*) w->obj(modelConfigName); // get the modelConfig out of the file RooAbsData* data = w->data(dataName); // make sure ingredients are found if(!data || !mc){ w->Print(); cout << "data or ModelConfig was not found" <<endl; return; } mc->Print(); ///////////////////////////////////////////////////////////// // Now find the upper limit based on the asymptotic results //////////////////////////////////////////////////////////// RooRealVar* firstPOI = (RooRealVar*) mc->GetParametersOfInterest()->first(); ProfileLikelihoodCalculator plc(*data,*mc); LikelihoodInterval* interval = plc.GetInterval(); double plcUpperLimit = interval->UpperLimit(*firstPOI); delete interval; cout << "\n\n--------------------------------------"<<endl; cout <<"Will generate sampling distribution at " << firstPOI->GetName() << " = " << plcUpperLimit <<endl; int nPOI = mc->GetParametersOfInterest()->getSize(); if(nPOI>1){ cout <<"not sure what to do with other parameters of interest, but here are their values"<<endl; mc->GetParametersOfInterest()->Print("v"); } ///////////////////////////////////////////// // create thte test stat sampler ProfileLikelihoodTestStat ts(*mc->GetPdf()); // to avoid effects from boundary and simplify asymptotic comparison, set min=-max if(allowNegativeMu) firstPOI->setMin(-1*firstPOI->getMax()); // temporary RooArgSet RooArgSet poi; poi.add(*mc->GetParametersOfInterest()); // create and configure the ToyMCSampler ToyMCSampler sampler(ts,nToyMC); sampler.SetPdf(*mc->GetPdf()); sampler.SetObservables(*mc->GetObservables()); sampler.SetGlobalObservables(*mc->GetGlobalObservables()); if(!mc->GetPdf()->canBeExtended() && (data->numEntries()==1)){ cout << "tell it to use 1 event"<<endl; sampler.SetNEventsPerToy(1); } firstPOI->setVal(plcUpperLimit); // set POI value for generation sampler.SetParametersForTestStat(*mc->GetParametersOfInterest()); // set POI value for evaluation if (useProof) { ProofConfig pc(*w, nworkers, "",false); sampler.SetProofConfig(&pc); // enable proof } firstPOI->setVal(plcUpperLimit); RooArgSet allParameters; allParameters.add(*mc->GetParametersOfInterest()); allParameters.add(*mc->GetNuisanceParameters()); allParameters.Print("v"); SamplingDistribution* sampDist = sampler.GetSamplingDistribution(allParameters); SamplingDistPlot plot; plot.AddSamplingDistribution(sampDist); plot.GetTH1F(sampDist)->GetYaxis()->SetTitle(Form("f(-log #lambda(#mu=%.2f) | #mu=%.2f)",plcUpperLimit,plcUpperLimit)); plot.SetAxisTitle(Form("-log #lambda(#mu=%.2f)",plcUpperLimit)); TCanvas* c1 = new TCanvas("c1"); c1->SetLogy(); plot.Draw(); double min = plot.GetTH1F(sampDist)->GetXaxis()->GetXmin(); double max = plot.GetTH1F(sampDist)->GetXaxis()->GetXmax(); TF1* f = new TF1("f",Form("2*ROOT::Math::chisquared_pdf(2*x,%d,0)",nPOI),min,max); f->Draw("same"); c1->SaveAs("standard_test_stat_distribution.pdf"); }
void splitws(string inFolderName, double mass, string channel) { cout << "Splitting workspace in " << channel << endl; int flatInterpCode = 4; int shapeInterpCode = 4; bool do2011 = 0; if (inFolderName.find("2011") != string::npos) do2011 = 1; bool conditionalAsimov = 0; bool doData = 1; //if (inFolderName.find("_blind_") != string::npos) { //conditionalAsimov = 0; //} //else { //conditionalAsimov = 1; //} set<string> channelNames; if (channel == "01j") { channelNames.insert("em_signalLike1_0j"+string(!do2011?"_2012":"")); channelNames.insert("em_signalLike2_0j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike1_0j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike2_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_AfrecSR_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_ASR_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_AfrecSR_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_ASR_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CfrecZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CfrecZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":"")); channelNames.insert("em_signalLike1_1j"+string(!do2011?"_2012":"")); channelNames.insert("em_signalLike2_1j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike1_1j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike2_1j"+string(!do2011?"_2012":"")); channelNames.insert("SF_AfrecSR_1j"+string(!do2011?"_2012":"")); channelNames.insert("SF_ASR_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_AfrecSR_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_ASR_1j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CfrecZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CfrecZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":"")); } else if (channel == "0j") { channelNames.insert("em_signalLike1_0j"+string(!do2011?"_2012":"")); channelNames.insert("em_signalLike2_0j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike1_0j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike2_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_AfrecSR_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_ASR_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_AfrecSR_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_ASR_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CfrecZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CfrecZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":"")); } else if (channel == "1j") { channelNames.insert("em_signalLike1_1j"+string(!do2011?"_2012":"")); channelNames.insert("em_signalLike2_1j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike1_1j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike2_1j"+string(!do2011?"_2012":"")); channelNames.insert("SF_AfrecSR_1j"+string(!do2011?"_2012":"")); channelNames.insert("SF_ASR_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_AfrecSR_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_ASR_1j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CfrecZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CfrecZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":"")); } else if (channel == "OF01j") { channelNames.insert("em_signalLike1_0j"+string(!do2011?"_2012":"")); channelNames.insert("em_signalLike2_0j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike1_0j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike2_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_sscr_0j"+string(!do2011?"_2012":"")); channelNames.insert("em_signalLike1_1j"+string(!do2011?"_2012":"")); channelNames.insert("em_signalLike2_1j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike1_1j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike2_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_sscr_1j"+string(!do2011?"_2012":"")); } else if (channel == "OF0j") { channelNames.insert("em_signalLike1_0j"+string(!do2011?"_2012":"")); channelNames.insert("em_signalLike2_0j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike1_0j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike2_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_sscr_0j"+string(!do2011?"_2012":"")); } else if (channel == "OF1j") { channelNames.insert("em_signalLike1_1j"+string(!do2011?"_2012":"")); channelNames.insert("em_signalLike2_1j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike1_1j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike2_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_sscr_1j"+string(!do2011?"_2012":"")); } else if (channel == "SF01j") { channelNames.insert("SF_AfrecSR_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_ASR_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_AfrecSR_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_ASR_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CfrecZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CfrecZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_AfrecSR_1j"+string(!do2011?"_2012":"")); channelNames.insert("SF_ASR_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_AfrecSR_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_ASR_1j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CfrecZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CfrecZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":"")); } else if (channel == "SF0j") { channelNames.insert("SF_AfrecSR_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_ASR_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_AfrecSR_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_ASR_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CfrecZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CfrecZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":"")); } else if (channel == "SF1j") { channelNames.insert("SF_AfrecSR_1j"+string(!do2011?"_2012":"")); channelNames.insert("SF_ASR_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_AfrecSR_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_ASR_1j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CfrecZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CfrecZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":"")); } else if (channel == "2j") { channelNames.insert("em_signalLike1_2j"+string(!do2011?"_2012":"")); channelNames.insert("ee_signalLike1_2j"+string(!do2011?"_2012":"")); channelNames.insert("SF_topbox_2j"+string(!do2011?"_2012":"")); } else if (channel == "OF2j") { channelNames.insert("em_signalLike1_2j"+string(!do2011?"_2012":"")); channelNames.insert("SF_topbox_2j"+string(!do2011?"_2012":"")); } else if (channel == "SF2j") { channelNames.insert("ee_signalLike1_2j"+string(!do2011?"_2012":"")); channelNames.insert("SF_topbox_2j"+string(!do2011?"_2012":"")); } else if (channel == "OF") { channelNames.insert("em_signalLike1_0j"+string(!do2011?"_2012":"")); channelNames.insert("em_signalLike2_0j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike1_0j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike2_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":"")); channelNames.insert("em_signalLike1_1j"+string(!do2011?"_2012":"")); channelNames.insert("em_signalLike2_1j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike1_1j"+string(!do2011?"_2012":"")); channelNames.insert("me_signalLike2_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":"")); channelNames.insert("em_signalLike1_2j"+string(!do2011?"_2012":"")); channelNames.insert("SF_topbox_2j"+string(!do2011?"_2012":"")); } else if (channel == "SF") { channelNames.insert("SF_AfrecSR_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_ASR_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_AfrecSR_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_ASR_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CfrecZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CfrecZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CZpeak_0j"+string(!do2011?"_2012":"")); channelNames.insert("OF_mainControl_0j"+string(!do2011?"_2012":"")); channelNames.insert("SF_AfrecSR_1j"+string(!do2011?"_2012":"")); channelNames.insert("SF_ASR_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_AfrecSR_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_ASR_1j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CfrecZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("SF_CZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CfrecZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_CZpeak_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_mainControl_1j"+string(!do2011?"_2012":"")); channelNames.insert("OF_topbox_1j"+string(!do2011?"_2012":"")); channelNames.insert("ee_signalLike1_2j"+string(!do2011?"_2012":"")); channelNames.insert("SF_topbox_2j"+string(!do2011?"_2012":"")); } else { cout << "Channel " << channel << " not defined. Please check!" << endl; exit(1); } // bool fix = 1; stringstream inFileName; inFileName << "workspaces/" << inFolderName << "/" << mass << ".root"; TFile f(inFileName.str().c_str()); RooWorkspace* w = (RooWorkspace*)f.Get("combWS"); if (!w) w = (RooWorkspace*)f.Get("combined"); RooDataSet* data = (RooDataSet*)w->data("combData"); if (!data) data = (RooDataSet*)w->data("obsData"); ModelConfig* mc = (ModelConfig*)w->obj("ModelConfig"); RooRealVar* weightVar = w->var("weightVar"); RooRealVar* mu = (RooRealVar*)mc->GetParametersOfInterest()->first(); if (!mu) mu = w->var("SigXsecOverSM"); const RooArgSet* mc_obs = mc->GetObservables(); const RooArgSet* mc_nuis = mc->GetNuisanceParameters(); const RooArgSet* mc_globs = mc->GetGlobalObservables(); const RooArgSet* mc_poi = mc->GetParametersOfInterest(); RooArgSet nuis = *mc_nuis; RooArgSet antiNuis = *mc_nuis; RooArgSet globs = *mc_globs; RooArgSet antiGlobs = *mc_globs; RooArgSet allParams; RooSimultaneous* simPdf = (RooSimultaneous*)mc->GetPdf(); RooCategory* cat = (RooCategory*)&simPdf->indexCat(); RooArgSet nuis_tmp = nuis; RooArgSet fullConstraints = *simPdf->getAllConstraints(*mc_obs,nuis_tmp,false); vector<string> foundChannels; vector<string> skippedChannels; cout << "Getting constraints" << endl; map<string, RooDataSet*> data_map; map<string, RooAbsPdf*> pdf_map; RooCategory* decCat = new RooCategory("dec_channel","dec_channel"); // int i = 0; TIterator* catItr = cat->typeIterator(); RooCatType* type; RooArgSet allConstraints; while ((type = (RooCatType*)catItr->Next())) { RooAbsPdf* pdf = simPdf->getPdf(type->GetName()); string typeName(type->GetName()); if (channelNames.size() && channelNames.find(typeName) == channelNames.end()) { skippedChannels.push_back(typeName); continue; } cout << "On channel " << type->GetName() << endl; foundChannels.push_back(typeName); decCat->defineType(type->GetName()); // pdf->getParameters(*data)->Print("v"); RooArgSet nuis_tmp1 = nuis; RooArgSet nuis_tmp2 = nuis; RooArgSet* constraints = pdf->getAllConstraints(*mc_obs, nuis_tmp1, true); constraints->Print(); allConstraints.add(*constraints); } catItr->Reset(); while ((type = (RooCatType*)catItr->Next())) { RooAbsPdf* pdf = simPdf->getPdf(type->GetName()); string typeName(type->GetName()); cout << "Considering type " << typeName << endl; if (channelNames.size() && channelNames.find(typeName) == channelNames.end()) continue; cout << "On channel " << type->GetName() << endl; RooArgSet nuis_tmp1 = nuis; RooArgSet nuis_tmp2 = nuis; RooArgSet* constraints = pdf->getAllConstraints(*mc_obs, nuis_tmp1, true); cout << "Adding pdf to map: " << typeName << " = " << pdf->GetName() << endl; pdf_map[typeName] = pdf; RooProdPdf prod("prod","prod",*constraints); RooArgSet* params = pdf->getParameters(*data); antiNuis.remove(*params); antiGlobs.remove(*params); allParams.add(*params); // cout << type->GetName() << endl; } // return; RooArgSet decNuis; TIterator* nuiItr = mc_nuis->createIterator(); TIterator* parItr = allParams.createIterator(); RooAbsArg* nui, *par; while ((par = (RooAbsArg*)parItr->Next())) { nuiItr->Reset(); while ((nui = (RooAbsArg*)nuiItr->Next())) { if (par == nui) decNuis.add(*nui); } } RooArgSet decGlobs; TIterator* globItr = mc_globs->createIterator(); parItr->Reset(); RooAbsArg* glob; while ((par = (RooAbsArg*)parItr->Next())) { globItr->Reset(); while ((glob = (RooAbsArg*)globItr->Next())) { if (par == glob) decGlobs.add(*glob); } } // antiNuis.Print(); // nuis.Print(); // globs.Print(); // i = 0; TList* datalist = data->split(*cat, true); TIterator* dataItr = datalist->MakeIterator(); RooAbsData* ds; while ((ds = (RooAbsData*)dataItr->Next())) { string typeName(ds->GetName()); if (channelNames.size() && channelNames.find(typeName) == channelNames.end()) continue; cout << "Adding dataset to map: " << ds->GetName() << endl; data_map[string(ds->GetName())] = (RooDataSet*)ds; cout << ds->GetName() << endl; } RooSimultaneous* decPdf = new RooSimultaneous("decPdf","decPdf",pdf_map,*decCat); RooArgSet decObs = *decPdf->getObservables(data); // decObs.add(*(RooAbsArg*)weightVar); decObs.add(*(RooAbsArg*)decCat); decObs.Print(); nuis.remove(antiNuis); globs.remove(antiGlobs); // nuis.Print("v"); RooDataSet* decData = new RooDataSet("obsData","obsData",RooArgSet(decObs,*(RooAbsArg*)weightVar),Index(*decCat),Import(data_map),WeightVar(*weightVar)); decData->Print(); RooArgSet poi(*(RooAbsArg*)mu); RooWorkspace decWS("combined"); ModelConfig decMC("ModelConfig",&decWS); decMC.SetPdf(*decPdf); decMC.SetObservables(decObs); decMC.SetNuisanceParameters(decNuis); decMC.SetGlobalObservables(decGlobs); decMC.SetParametersOfInterest(poi); decMC.Print(); decWS.import(*decPdf); decWS.import(decMC); decWS.import(*decData); // decWS.Print(); ModelConfig* mcInWs = (ModelConfig*)decWS.obj("ModelConfig"); decPdf = (RooSimultaneous*)mcInWs->GetPdf(); // setup(mcInWs); // return; mcInWs->GetNuisanceParameters()->Print("v"); mcInWs->GetGlobalObservables()->Print("v"); // decData->tree()->Scan("*"); // Make asimov data RooArgSet funcs = decWS.allFunctions(); TIterator* it = funcs.createIterator(); TObject* tempObj = 0; while((tempObj=it->Next())) { FlexibleInterpVar* flex = dynamic_cast<FlexibleInterpVar*>(tempObj); if(flex) { flex->setAllInterpCodes(flatInterpCode); } PiecewiseInterpolation* piece = dynamic_cast<PiecewiseInterpolation*>(tempObj); if(piece) { piece->setAllInterpCodes(shapeInterpCode); } } RooDataSet* dataInWs = (RooDataSet*)decWS.data("obsData"); makeAsimovData(mcInWs, conditionalAsimov && doData, &decWS, mcInWs->GetPdf(), dataInWs, 0); makeAsimovData(mcInWs, conditionalAsimov && doData, &decWS, mcInWs->GetPdf(), dataInWs, 1); makeAsimovData(mcInWs, conditionalAsimov && doData, &decWS, mcInWs->GetPdf(), dataInWs, 2); system(("mkdir -vp workspaces/"+inFolderName+"_"+channel).c_str()); stringstream outFileName; outFileName << "workspaces/" << inFolderName << "_" << channel << "/" << mass << ".root"; cout << "Exporting" << endl; decWS.writeToFile(outFileName.str().c_str()); cout << "\nIncluded the following channels: " << endl; for (int i=0;i<(int)foundChannels.size();i++) { cout << "-> " << foundChannels[i] << endl; } cout << "\nSkipping the following channels: " << endl; for (int i=0;i<(int)skippedChannels.size();i++) { cout << "-> " << skippedChannels[i] << endl; } cout << "Done" << endl; // decPdf->fitTo(*decData, Hesse(0), Minos(0), PrintLevel(0)); }
void OneSidedFrequentistUpperLimitWithBands_intermediate(const char* infile = "", const char* workspaceName = "combined", const char* modelConfigName = "ModelConfig", const char* dataName = "obsData"){ double confidenceLevel=0.95; // degrade/improve number of pseudo-experiments used to define the confidence belt. // value of 1 corresponds to default number of toys in the tail, which is 50/(1-confidenceLevel) double additionalToysFac = 1.; int nPointsToScan = 30; // number of steps in the parameter of interest int nToyMC = 100; // number of toys used to define the expected limit and band TStopwatch t; t.Start(); ///////////////////////////////////////////////////////////// // First part is just to access a user-defined file // or create the standard example file if it doesn't exist //////////////////////////////////////////////////////////// const char* filename = ""; if (!strcmp(infile,"")) filename = "results/example_combined_GaussExample_model.root"; else filename = infile; // Check if example input file exists TFile *file = TFile::Open(filename); // if input file was specified byt not found, quit if(!file && strcmp(infile,"")){ cout <<"file not found" << endl; return; } // if default file not found, try to create it if(!file ){ // Normally this would be run on the command line cout <<"will run standard hist2workspace example"<<endl; gROOT->ProcessLine(".! prepareHistFactory ."); gROOT->ProcessLine(".! hist2workspace config/example.xml"); cout <<"\n\n---------------------"<<endl; cout <<"Done creating example input"<<endl; cout <<"---------------------\n\n"<<endl; } // now try to access the file again file = TFile::Open(filename); if(!file){ // if it is still not there, then we can't continue cout << "Not able to run hist2workspace to create example input" <<endl; return; } ///////////////////////////////////////////////////////////// // Now get the data and workspace //////////////////////////////////////////////////////////// // get the workspace out of the file RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName); if(!w){ cout <<"workspace not found" << endl; return; } // get the modelConfig out of the file ModelConfig* mc = (ModelConfig*) w->obj(modelConfigName); // get the modelConfig out of the file RooAbsData* data = w->data(dataName); // make sure ingredients are found if(!data || !mc){ w->Print(); cout << "data or ModelConfig was not found" <<endl; return; } cout << "Found data and ModelConfig:" <<endl; mc->Print(); ///////////////////////////////////////////////////////////// // Now get the POI for convenience // you may want to adjust the range of your POI //////////////////////////////////////////////////////////// RooRealVar* firstPOI = (RooRealVar*) mc->GetParametersOfInterest()->first(); // firstPOI->setMin(0); // firstPOI->setMax(10); ///////////////////////////////////////////// // create and use the FeldmanCousins tool // to find and plot the 95% confidence interval // on the parameter of interest as specified // in the model config // REMEMBER, we will change the test statistic // so this is NOT a Feldman-Cousins interval FeldmanCousins fc(*data,*mc); fc.SetConfidenceLevel(confidenceLevel); fc.AdditionalNToysFactor(additionalToysFac); // improve sampling that defines confidence belt // fc.UseAdaptiveSampling(true); // speed it up a bit, but don't use for expectd limits fc.SetNBins(nPointsToScan); // set how many points per parameter of interest to scan fc.CreateConfBelt(true); // save the information in the belt for plotting ///////////////////////////////////////////// // Feldman-Cousins is a unified limit by definition // but the tool takes care of a few things for us like which values // of the nuisance parameters should be used to generate toys. // so let's just change the test statistic and realize this is // no longer "Feldman-Cousins" but is a fully frequentist Neyman-Construction. // ProfileLikelihoodTestStatModified onesided(*mc->GetPdf()); // fc.GetTestStatSampler()->SetTestStatistic(&onesided); // ((ToyMCSampler*) fc.GetTestStatSampler())->SetGenerateBinned(true); ToyMCSampler* toymcsampler = (ToyMCSampler*) fc.GetTestStatSampler(); ProfileLikelihoodTestStat* testStat = dynamic_cast<ProfileLikelihoodTestStat*>(toymcsampler->GetTestStatistic()); testStat->SetOneSided(true); // test speedups: testStat->SetReuseNLL(true); // toymcsampler->setUseMultiGen(true); // not fully validated // Since this tool needs to throw toy MC the PDF needs to be // extended or the tool needs to know how many entries in a dataset // per pseudo experiment. // In the 'number counting form' where the entries in the dataset // are counts, and not values of discriminating variables, the // datasets typically only have one entry and the PDF is not // extended. if(!mc->GetPdf()->canBeExtended()){ if(data->numEntries()==1) fc.FluctuateNumDataEntries(false); else cout <<"Not sure what to do about this model" <<endl; } // We can use PROOF to speed things along in parallel ProofConfig pc(*w, 4, "",false); if(mc->GetGlobalObservables()){ cout << "will use global observables for unconditional ensemble"<<endl; mc->GetGlobalObservables()->Print(); toymcsampler->SetGlobalObservables(*mc->GetGlobalObservables()); } toymcsampler->SetProofConfig(&pc); // enable proof // Now get the interval PointSetInterval* interval = fc.GetInterval(); ConfidenceBelt* belt = fc.GetConfidenceBelt(); // print out the iterval on the first Parameter of Interest cout << "\n95% interval on " <<firstPOI->GetName()<<" is : ["<< interval->LowerLimit(*firstPOI) << ", "<< interval->UpperLimit(*firstPOI) <<"] "<<endl; // get observed UL and value of test statistic evaluated there RooArgSet tmpPOI(*firstPOI); double observedUL = interval->UpperLimit(*firstPOI); firstPOI->setVal(observedUL); double obsTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*data,tmpPOI); // Ask the calculator which points were scanned RooDataSet* parameterScan = (RooDataSet*) fc.GetPointsToScan(); RooArgSet* tmpPoint; // make a histogram of parameter vs. threshold TH1F* histOfThresholds = new TH1F("histOfThresholds","", parameterScan->numEntries(), firstPOI->getMin(), firstPOI->getMax()); histOfThresholds->GetXaxis()->SetTitle(firstPOI->GetName()); histOfThresholds->GetYaxis()->SetTitle("Threshold"); // loop through the points that were tested and ask confidence belt // what the upper/lower thresholds were. // For FeldmanCousins, the lower cut off is always 0 for(Int_t i=0; i<parameterScan->numEntries(); ++i){ tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp"); double arMax = belt->GetAcceptanceRegionMax(*tmpPoint); double poiVal = tmpPoint->getRealValue(firstPOI->GetName()) ; histOfThresholds->Fill(poiVal,arMax); } TCanvas* c1 = new TCanvas(); c1->Divide(2); c1->cd(1); histOfThresholds->SetMinimum(0); histOfThresholds->Draw(); c1->cd(2); ///////////////////////////////////////////////////////////// // Now we generate the expected bands and power-constriant //////////////////////////////////////////////////////////// // First: find parameter point for mu=0, with conditional MLEs for nuisance parameters RooAbsReal* nll = mc->GetPdf()->createNLL(*data); RooAbsReal* profile = nll->createProfile(*mc->GetParametersOfInterest()); firstPOI->setVal(0.); profile->getVal(); // this will do fit and set nuisance parameters to profiled values RooArgSet* poiAndNuisance = new RooArgSet(); if(mc->GetNuisanceParameters()) poiAndNuisance->add(*mc->GetNuisanceParameters()); poiAndNuisance->add(*mc->GetParametersOfInterest()); w->saveSnapshot("paramsToGenerateData",*poiAndNuisance); RooArgSet* paramsToGenerateData = (RooArgSet*) poiAndNuisance->snapshot(); cout << "\nWill use these parameter points to generate pseudo data for bkg only" << endl; paramsToGenerateData->Print("v"); double CLb=0; double CLbinclusive=0; // Now we generate background only and find distribution of upper limits TH1F* histOfUL = new TH1F("histOfUL","",100,0,firstPOI->getMax()); histOfUL->GetXaxis()->SetTitle("Upper Limit (background only)"); histOfUL->GetYaxis()->SetTitle("Entries"); for(int imc=0; imc<nToyMC; ++imc){ // set parameters back to values for generating pseudo data w->loadSnapshot("paramsToGenerateData"); // in 5.30 there is a nicer way to generate toy data & randomize global obs RooAbsData* toyData = toymcsampler->GenerateToyData(*paramsToGenerateData); // get test stat at observed UL in observed data firstPOI->setVal(observedUL); double toyTSatObsUL = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI); // toyData->get()->Print("v"); // cout <<"obsTSatObsUL " <<obsTSatObsUL << "toyTS " << toyTSatObsUL << endl; if(obsTSatObsUL < toyTSatObsUL) // (should be checked) CLb+= (1.)/nToyMC; if(obsTSatObsUL <= toyTSatObsUL) // (should be checked) CLbinclusive+= (1.)/nToyMC; // loop over points in belt to find upper limit for this toy data double thisUL = 0; for(Int_t i=0; i<parameterScan->numEntries(); ++i){ tmpPoint = (RooArgSet*) parameterScan->get(i)->clone("temp"); double arMax = belt->GetAcceptanceRegionMax(*tmpPoint); firstPOI->setVal( tmpPoint->getRealValue(firstPOI->GetName()) ); double thisTS = fc.GetTestStatSampler()->EvaluateTestStatistic(*toyData,tmpPOI); if(thisTS<=arMax){ thisUL = firstPOI->getVal(); } else{ break; } } histOfUL->Fill(thisUL); delete toyData; } histOfUL->Draw(); c1->SaveAs("one-sided_upper_limit_output.pdf"); // if you want to see a plot of the sampling distribution for a particular scan point: // Now find bands and power constraint Double_t* bins = histOfUL->GetIntegral(); TH1F* cumulative = (TH1F*) histOfUL->Clone("cumulative"); cumulative->SetContent(bins); double band2sigDown=0, band1sigDown=0, bandMedian=0, band1sigUp=0,band2sigUp=0; for(int i=1; i<=cumulative->GetNbinsX(); ++i){ if(bins[i]<RooStats::SignificanceToPValue(2)) band2sigDown=cumulative->GetBinCenter(i); if(bins[i]<RooStats::SignificanceToPValue(1)) band1sigDown=cumulative->GetBinCenter(i); if(bins[i]<0.5) bandMedian=cumulative->GetBinCenter(i); if(bins[i]<RooStats::SignificanceToPValue(-1)) band1sigUp=cumulative->GetBinCenter(i); if(bins[i]<RooStats::SignificanceToPValue(-2)) band2sigUp=cumulative->GetBinCenter(i); } t.Stop(); t.Print(); cout << "-2 sigma band " << band2sigDown << endl; cout << "-1 sigma band " << band1sigDown << endl; cout << "median of band " << bandMedian << " [Power Constriant)]" << endl; cout << "+1 sigma band " << band1sigUp << endl; cout << "+2 sigma band " << band2sigUp << endl; // print out the iterval on the first Parameter of Interest cout << "\nobserved 95% upper-limit "<< interval->UpperLimit(*firstPOI) <<endl; cout << "CLb strict [P(toy>obs|0)] for observed 95% upper-limit "<< CLb <<endl; cout << "CLb inclusive [P(toy>=obs|0)] for observed 95% upper-limit "<< CLbinclusive <<endl; delete profile; delete nll; }
void StandardBayesianNumericalDemo(const char* infile = "", const char* workspaceName = "combined", const char* modelConfigName = "ModelConfig", const char* dataName = "obsData") { // option definitions double confLevel = optBayes.confLevel; TString integrationType = optBayes.integrationType; int nToys = optBayes.nToys; bool scanPosterior = optBayes.scanPosterior; int nScanPoints = optBayes.nScanPoints; int intervalType = optBayes.intervalType; int maxPOI = optBayes.maxPOI; double nSigmaNuisance = optBayes.nSigmaNuisance; ///////////////////////////////////////////////////////////// // First part is just to access a user-defined file // or create the standard example file if it doesn't exist //////////////////////////////////////////////////////////// const char* filename = ""; if (!strcmp(infile,"")) { filename = "results/example_combined_GaussExample_model.root"; bool fileExist = !gSystem->AccessPathName(filename); // note opposite return code // if file does not exists generate with histfactory if (!fileExist) { #ifdef _WIN32 cout << "HistFactory file cannot be generated on Windows - exit" << endl; return; #endif // Normally this would be run on the command line cout <<"will run standard hist2workspace example"<<endl; gROOT->ProcessLine(".! prepareHistFactory ."); gROOT->ProcessLine(".! hist2workspace config/example.xml"); cout <<"\n\n---------------------"<<endl; cout <<"Done creating example input"<<endl; cout <<"---------------------\n\n"<<endl; } } else filename = infile; // Try to open the file TFile *file = TFile::Open(filename); // if input file was specified byt not found, quit if(!file ){ cout <<"StandardRooStatsDemoMacro: Input file " << filename << " is not found" << endl; return; } ///////////////////////////////////////////////////////////// // Tutorial starts here //////////////////////////////////////////////////////////// // get the workspace out of the file RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName); if(!w){ cout <<"workspace not found" << endl; return; } // get the modelConfig out of the file ModelConfig* mc = (ModelConfig*) w->obj(modelConfigName); // get the modelConfig out of the file RooAbsData* data = w->data(dataName); // make sure ingredients are found if(!data || !mc){ w->Print(); cout << "data or ModelConfig was not found" <<endl; return; } ///////////////////////////////////////////// // create and use the BayesianCalculator // to find and plot the 95% credible interval // on the parameter of interest as specified // in the model config // before we do that, we must specify our prior // it belongs in the model config, but it may not have // been specified RooUniform prior("prior","",*mc->GetParametersOfInterest()); w->import(prior); mc->SetPriorPdf(*w->pdf("prior")); // do without systematics //mc->SetNuisanceParameters(RooArgSet() ); if (nSigmaNuisance > 0) { RooAbsPdf * pdf = mc->GetPdf(); assert(pdf); RooFitResult * res = pdf->fitTo(*data, Save(true), Minimizer(ROOT::Math::MinimizerOptions::DefaultMinimizerType().c_str()), Hesse(true), PrintLevel(ROOT::Math::MinimizerOptions::DefaultPrintLevel()-1) ); res->Print(); RooArgList nuisPar(*mc->GetNuisanceParameters()); for (int i = 0; i < nuisPar.getSize(); ++i) { RooRealVar * v = dynamic_cast<RooRealVar*> (&nuisPar[i] ); assert( v); v->setMin( TMath::Max( v->getMin(), v->getVal() - nSigmaNuisance * v->getError() ) ); v->setMax( TMath::Min( v->getMax(), v->getVal() + nSigmaNuisance * v->getError() ) ); std::cout << "setting interval for nuisance " << v->GetName() << " : [ " << v->getMin() << " , " << v->getMax() << " ]" << std::endl; } } BayesianCalculator bayesianCalc(*data,*mc); bayesianCalc.SetConfidenceLevel(confLevel); // 95% interval // default of the calculator is central interval. here use shortest , central or upper limit depending on input // doing a shortest interval might require a longer time since it requires a scan of the posterior function if (intervalType == 0) bayesianCalc.SetShortestInterval(); // for shortest interval if (intervalType == 1) bayesianCalc.SetLeftSideTailFraction(0.5); // for central interval if (intervalType == 2) bayesianCalc.SetLeftSideTailFraction(0.); // for upper limit if (!integrationType.IsNull() ) { bayesianCalc.SetIntegrationType(integrationType); // set integrationType bayesianCalc.SetNumIters(nToys); // set number of ietrations (i.e. number of toys for MC integrations) } // in case of toyMC make a nnuisance pdf if (integrationType.Contains("TOYMC") ) { RooAbsPdf * nuisPdf = RooStats::MakeNuisancePdf(*mc, "nuisance_pdf"); cout << "using TOYMC integration: make nuisance pdf from the model " << std::endl; nuisPdf->Print(); bayesianCalc.ForceNuisancePdf(*nuisPdf); scanPosterior = true; // for ToyMC the posterior is scanned anyway so used given points } // compute interval by scanning the posterior function if (scanPosterior) bayesianCalc.SetScanOfPosterior(nScanPoints); RooRealVar* poi = (RooRealVar*) mc->GetParametersOfInterest()->first(); if (maxPOI != -999 && maxPOI > poi->getMin()) poi->setMax(maxPOI); SimpleInterval* interval = bayesianCalc.GetInterval(); // print out the iterval on the first Parameter of Interest cout << "\n>>>> RESULT : " << confLevel*100 << "% interval on " << poi->GetName()<<" is : ["<< interval->LowerLimit() << ", "<< interval->UpperLimit() <<"] "<<endl; // make a plot // since plotting may take a long time (it requires evaluating // the posterior in many points) this command will speed up // by reducing the number of points to plot - do 50 // ignore errors of PDF if is zero RooAbsReal::setEvalErrorLoggingMode(RooAbsReal::Ignore) ; cout << "\nDrawing plot of posterior function....." << endl; // always plot using numer of scan points bayesianCalc.SetScanOfPosterior(nScanPoints); RooPlot * plot = bayesianCalc.GetPosteriorPlot(); plot->Draw(); }
void ws_cls_hybrid1_ag( const char* wsfile = "output-files/expected-ws-lm9-2BL.root", bool isBgonlyStudy=false, double poiVal = 150.0, int nToys=100, bool makeTtree=true, int verbLevel=0 ) { TTree* toytt(0x0) ; TFile* ttfile(0x0) ; int tt_gen_Nsig ; int tt_gen_Nsb ; int tt_gen_Nsig_sl ; int tt_gen_Nsb_sl ; int tt_gen_Nsig_ldp ; int tt_gen_Nsb_ldp ; int tt_gen_Nsig_ee ; int tt_gen_Nsb_ee ; int tt_gen_Nsig_mm ; int tt_gen_Nsb_mm ; double tt_testStat ; double tt_dataTestStat ; double tt_hypo_mu_susy_sig ; char ttname[1000] ; char tttitle[1000] ; if ( makeTtree ) { ttfile = gDirectory->GetFile() ; if ( ttfile == 0x0 ) { printf("\n\n\n *** asked for a ttree but no open file???\n\n") ; return ; } if ( isBgonlyStudy ) { sprintf( ttname, "toytt_%.0f_bgo", poiVal ) ; sprintf( tttitle, "Toy study for background only, mu_susy_sig = %.0f", poiVal ) ; } else { sprintf( ttname, "toytt_%.0f_spb", poiVal ) ; sprintf( tttitle, "Toy study for signal+background, mu_susy_sig = %.0f", poiVal ) ; } printf("\n\n Creating TTree : %s : %s\n\n", ttname, tttitle ) ; gDirectory->pwd() ; gDirectory->ls() ; toytt = new TTree( ttname, tttitle ) ; gDirectory->ls() ; toytt -> Branch( "gen_Nsig" , &tt_gen_Nsig , "gen_Nsig/I" ) ; toytt -> Branch( "gen_Nsb" , &tt_gen_Nsb , "gen_Nsb/I" ) ; toytt -> Branch( "gen_Nsig_sl" , &tt_gen_Nsig_sl , "gen_Nsig_sl/I" ) ; toytt -> Branch( "gen_Nsb_sl" , &tt_gen_Nsb_sl , "gen_Nsb_sl/I" ) ; toytt -> Branch( "gen_Nsig_ldp" , &tt_gen_Nsig_ldp , "gen_Nsig_ldp/I" ) ; toytt -> Branch( "gen_Nsb_ldp" , &tt_gen_Nsb_ldp , "gen_Nsb_ldp/I" ) ; toytt -> Branch( "gen_Nsig_ee" , &tt_gen_Nsig_ee , "gen_Nsig_ee/I" ) ; toytt -> Branch( "gen_Nsb_ee" , &tt_gen_Nsb_ee , "gen_Nsb_ee/I" ) ; toytt -> Branch( "gen_Nsig_mm" , &tt_gen_Nsig_mm , "gen_Nsig_mm/I" ) ; toytt -> Branch( "gen_Nsb_mm" , &tt_gen_Nsb_mm , "gen_Nsb_mm/I" ) ; toytt -> Branch( "testStat" , &tt_testStat , "testStat/D" ) ; toytt -> Branch( "dataTestStat" , &tt_dataTestStat , "dataTestStat/D" ) ; toytt -> Branch( "hypo_mu_susy_sig" , &tt_hypo_mu_susy_sig , "hypo_mu_susy_sig/D" ) ; } //--- Tell RooFit to shut up about anything less important than an ERROR. RooMsgService::instance().setGlobalKillBelow(RooFit::ERROR) ; random_ng = new TRandom2(12345) ; /// char sel[100] ; /// if ( strstr( wsfile, "1BL" ) != 0 ) { /// sprintf( sel, "1BL" ) ; /// } else if ( strstr( wsfile, "2BL" ) != 0 ) { /// sprintf( sel, "2BL" ) ; /// } else if ( strstr( wsfile, "3B" ) != 0 ) { /// sprintf( sel, "3B" ) ; /// } else if ( strstr( wsfile, "1BT" ) != 0 ) { /// sprintf( sel, "1BT" ) ; /// } else if ( strstr( wsfile, "2BT" ) != 0 ) { /// sprintf( sel, "2BT" ) ; /// } else { /// printf("\n\n\n *** can't figure out which selection this is. I quit.\n\n" ) ; /// return ; /// } /// printf("\n\n selection is %s\n\n", sel ) ; TFile* wstf = new TFile( wsfile ) ; RooWorkspace* ws = dynamic_cast<RooWorkspace*>( wstf->Get("ws") ); ws->Print() ; RooDataSet* rds = (RooDataSet*) ws->obj( "ra2b_observed_rds" ) ; printf("\n\n\n ===== RooDataSet ====================\n\n") ; rds->Print() ; rds->printMultiline(cout, 1, kTRUE, "") ; ModelConfig* modelConfig = (ModelConfig*) ws->obj( "SbModel" ) ; RooAbsPdf* likelihood = modelConfig->GetPdf() ; const RooArgSet* nuisanceParameters = modelConfig->GetNuisanceParameters() ; RooRealVar* rrv_mu_susy_sig = ws->var("mu_susy_sig") ; if ( rrv_mu_susy_sig == 0x0 ) { printf("\n\n\n *** can't find mu_susy_sig in workspace. Quitting.\n\n\n") ; return ; } //// printf("\n\n\n ===== Doing a fit ====================\n\n") ; //// RooFitResult* preFitResult = likelihood->fitTo( *rds, Save(true) ) ; //// const RooArgList preFitFloatVals = preFitResult->floatParsFinal() ; //// { //// TIterator* parIter = preFitFloatVals.createIterator() ; //// while ( RooRealVar* par = (RooRealVar*) parIter->Next() ) { //// printf(" %20s : %8.2f\n", par->GetName(), par->getVal() ) ; //// } //// } //--- Get pointers to the model predictions of the observables. rfv_n_sig = ws->function("n_sig") ; rfv_n_sb = ws->function("n_sb") ; rfv_n_sig_sl = ws->function("n_sig_sl") ; rfv_n_sb_sl = ws->function("n_sb_sl") ; rfv_n_sig_ldp = ws->function("n_sig_ldp") ; rfv_n_sb_ldp = ws->function("n_sb_ldp") ; rfv_n_sig_ee = ws->function("n_sig_ee") ; rfv_n_sb_ee = ws->function("n_sb_ee") ; rfv_n_sig_mm = ws->function("n_sig_mm") ; rfv_n_sb_mm = ws->function("n_sb_mm") ; if ( rfv_n_sig == 0x0 ) { printf("\n\n\n *** can't find n_sig in workspace. Quitting.\n\n\n") ; return ; } if ( rfv_n_sb == 0x0 ) { printf("\n\n\n *** can't find n_sb in workspace. Quitting.\n\n\n") ; return ; } if ( rfv_n_sig_sl == 0x0 ) { printf("\n\n\n *** can't find n_sig_sl in workspace. Quitting.\n\n\n") ; return ; } if ( rfv_n_sb_sl == 0x0 ) { printf("\n\n\n *** can't find n_sb_sl in workspace. Quitting.\n\n\n") ; return ; } if ( rfv_n_sig_ldp == 0x0 ) { printf("\n\n\n *** can't find n_sig_ldp in workspace. Quitting.\n\n\n") ; return ; } if ( rfv_n_sb_ldp == 0x0 ) { printf("\n\n\n *** can't find n_sb_ldp in workspace. Quitting.\n\n\n") ; return ; } if ( rfv_n_sig_ee == 0x0 ) { printf("\n\n\n *** can't find n_sig_ee in workspace. Quitting.\n\n\n") ; return ; } if ( rfv_n_sb_ee == 0x0 ) { printf("\n\n\n *** can't find n_sb_ee in workspace. Quitting.\n\n\n") ; return ; } if ( rfv_n_sig_mm == 0x0 ) { printf("\n\n\n *** can't find n_sig_mm in workspace. Quitting.\n\n\n") ; return ; } if ( rfv_n_sb_mm == 0x0 ) { printf("\n\n\n *** can't find n_sb_mm in workspace. Quitting.\n\n\n") ; return ; } //--- Get pointers to the observables. const RooArgSet* dsras = rds->get() ; TIterator* obsIter = dsras->createIterator() ; while ( RooRealVar* obs = (RooRealVar*) obsIter->Next() ) { if ( strcmp( obs->GetName(), "Nsig" ) == 0 ) { rrv_Nsig = obs ; } if ( strcmp( obs->GetName(), "Nsb" ) == 0 ) { rrv_Nsb = obs ; } if ( strcmp( obs->GetName(), "Nsig_sl" ) == 0 ) { rrv_Nsig_sl = obs ; } if ( strcmp( obs->GetName(), "Nsb_sl" ) == 0 ) { rrv_Nsb_sl = obs ; } if ( strcmp( obs->GetName(), "Nsig_ldp" ) == 0 ) { rrv_Nsig_ldp = obs ; } if ( strcmp( obs->GetName(), "Nsb_ldp" ) == 0 ) { rrv_Nsb_ldp = obs ; } if ( strcmp( obs->GetName(), "Nsig_ee" ) == 0 ) { rrv_Nsig_ee = obs ; } if ( strcmp( obs->GetName(), "Nsb_ee" ) == 0 ) { rrv_Nsb_ee = obs ; } if ( strcmp( obs->GetName(), "Nsig_mm" ) == 0 ) { rrv_Nsig_mm = obs ; } if ( strcmp( obs->GetName(), "Nsb_mm" ) == 0 ) { rrv_Nsb_mm = obs ; } } if ( rrv_Nsig == 0x0 ) { printf("\n\n\n *** can't find Nsig in dataset. Quitting.\n\n\n") ; return ; } if ( rrv_Nsb == 0x0 ) { printf("\n\n\n *** can't find Nsb in dataset. Quitting.\n\n\n") ; return ; } if ( rrv_Nsig_sl == 0x0 ) { printf("\n\n\n *** can't find Nsig_sl in dataset. Quitting.\n\n\n") ; return ; } if ( rrv_Nsb_sl == 0x0 ) { printf("\n\n\n *** can't find Nsb_sl in dataset. Quitting.\n\n\n") ; return ; } if ( rrv_Nsig_ldp == 0x0 ) { printf("\n\n\n *** can't find Nsig_ldp in dataset. Quitting.\n\n\n") ; return ; } if ( rrv_Nsb_ldp == 0x0 ) { printf("\n\n\n *** can't find Nsb_ldp in dataset. Quitting.\n\n\n") ; return ; } if ( rrv_Nsig_ee == 0x0 ) { printf("\n\n\n *** can't find Nsig_ee in dataset. Quitting.\n\n\n") ; return ; } if ( rrv_Nsb_ee == 0x0 ) { printf("\n\n\n *** can't find Nsb_ee in dataset. Quitting.\n\n\n") ; return ; } if ( rrv_Nsig_mm == 0x0 ) { printf("\n\n\n *** can't find Nsig_mm in dataset. Quitting.\n\n\n") ; return ; } if ( rrv_Nsb_mm == 0x0 ) { printf("\n\n\n *** can't find Nsb_mm in dataset. Quitting.\n\n\n") ; return ; } printf("\n\n\n === Model values for observables\n\n") ; printObservables() ; //--- save the actual values of the observables. saveObservables() ; //--- evaluate the test stat on the data: fit with susy floating. rrv_mu_susy_sig->setVal( poiVal ) ; rrv_mu_susy_sig->setConstant( kTRUE ) ; printf("\n\n\n ====== Fitting the data with susy fixed.\n\n") ; RooFitResult* dataFitResultSusyFixed = likelihood->fitTo(*rds, Save(true)); int dataSusyFixedFitCovQual = dataFitResultSusyFixed->covQual() ; if ( dataSusyFixedFitCovQual != 3 ) { printf("\n\n\n *** Failed fit! Cov qual %d. Quitting.\n\n", dataSusyFixedFitCovQual ) ; return ; } double dataFitSusyFixedNll = dataFitResultSusyFixed->minNll() ; rrv_mu_susy_sig->setVal( 0.0 ) ; rrv_mu_susy_sig->setConstant( kFALSE ) ; printf("\n\n\n ====== Fitting the data with susy floating.\n\n") ; RooFitResult* dataFitResultSusyFloat = likelihood->fitTo(*rds, Save(true)); int dataSusyFloatFitCovQual = dataFitResultSusyFloat->covQual() ; if ( dataSusyFloatFitCovQual != 3 ) { printf("\n\n\n *** Failed fit! Cov qual %d. Quitting.\n\n", dataSusyFloatFitCovQual ) ; return ; } double dataFitSusyFloatNll = dataFitResultSusyFloat->minNll() ; double dataTestStat = 2.*( dataFitSusyFixedNll - dataFitSusyFloatNll) ; printf("\n\n\n Data value of test stat : %8.2f\n", dataTestStat ) ; printf("\n\n\n === Nuisance parameters\n\n") ; { int npi(0) ; TIterator* npIter = nuisanceParameters->createIterator() ; while ( RooRealVar* np_rrv = (RooRealVar*) npIter->Next() ) { np_initial_val[npi] = np_rrv->getVal() ; //--- I am assuming that the order of the NPs in the iterator does not change. TString npname( np_rrv->GetName() ) ; npname.ReplaceAll("_prim","") ; RooAbsReal* np_rfv = ws->function( npname ) ; TString pdfname( np_rrv->GetName() ) ; pdfname.ReplaceAll("_prim","") ; pdfname.Prepend("pdf_") ; RooAbsPdf* np_pdf = ws->pdf( pdfname ) ; if ( np_pdf == 0x0 ) { printf("\n\n *** Can't find nuisance parameter pdf with name %s.\n\n", pdfname.Data() ) ; } if ( np_rfv != 0x0 ) { printf(" %20s : %8.2f , %20s, %8.2f\n", np_rrv->GetName(), np_rrv->getVal(), np_rfv->GetName(), np_rfv->getVal() ) ; } else { printf(" %20s : %8.2f\n", np_rrv->GetName(), np_rrv->getVal() ) ; } npi++ ; } // np_rrv iterator. np_count = npi ; } tt_dataTestStat = dataTestStat ; tt_hypo_mu_susy_sig = poiVal ; printf("\n\n\n === Doing the toys\n\n") ; int nToyOK(0) ; int nToyWorseThanData(0) ; for ( int ti=0; ti<nToys; ti++ ) { printf("\n\n\n ======= Toy %4d\n\n\n", ti ) ; //--- 1) pick values for the nuisance parameters from the PDFs and fix them. { TIterator* npIter = nuisanceParameters->createIterator() ; while ( RooRealVar* np_rrv = (RooRealVar*) npIter->Next() ) { TString pdfname( np_rrv->GetName() ) ; pdfname.ReplaceAll("_prim","") ; pdfname.Prepend("pdf_") ; RooAbsPdf* np_pdf = ws->pdf( pdfname ) ; if ( np_pdf == 0x0 ) { printf("\n\n *** Can't find nuisance parameter pdf with name %s.\n\n", pdfname.Data() ) ; return ; } RooDataSet* nprds = np_pdf->generate( RooArgSet(*np_rrv) ,1) ; const RooArgSet* npdsras = nprds->get() ; TIterator* valIter = npdsras->createIterator() ; RooRealVar* val = (RooRealVar*) valIter->Next() ; //--- reset the value of the nuisance parameter and fix it for the toy model definition fit. np_rrv->setVal( val->getVal() ) ; np_rrv->setConstant( kTRUE ) ; TString npname( np_rrv->GetName() ) ; npname.ReplaceAll("_prim","") ; RooAbsReal* np_rfv = ws->function( npname ) ; if ( verbLevel > 0 ) { if ( np_rfv != 0x0 ) { printf(" %20s : %8.2f , %15s, %8.3f\n", val->GetName(), val->getVal(), np_rfv->GetName(), np_rfv->getVal() ) ; } else if ( strstr( npname.Data(), "eff_sf" ) != 0 ) { np_rfv = ws->function( "eff_sf_sig" ) ; RooAbsReal* np_rfv2 = ws->function( "eff_sf_sb" ) ; printf(" %20s : %8.2f , %15s, %8.3f , %15s, %8.3f\n", val->GetName(), val->getVal(), np_rfv->GetName(), np_rfv->getVal(), np_rfv2->GetName(), np_rfv2->getVal() ) ; } else if ( strstr( npname.Data(), "sf_ll" ) != 0 ) { np_rfv = ws->function( "sf_ee" ) ; RooAbsReal* np_rfv2 = ws->function( "sf_mm" ) ; printf(" %20s : %8.2f , %15s, %8.3f , %15s, %8.3f\n", val->GetName(), val->getVal(), np_rfv->GetName(), np_rfv->getVal(), np_rfv2->GetName(), np_rfv2->getVal() ) ; } else { printf(" %20s : %8.2f\n", val->GetName(), val->getVal() ) ; } } delete nprds ; } // np_rrv iterator } //--- 2) Fit the dataset with these values for the nuisance parameters. if ( isBgonlyStudy ) { //-- fit with susy yield fixed to zero. rrv_mu_susy_sig -> setVal( 0. ) ; if ( verbLevel > 0 ) { printf("\n Setting mu_susy_sig to zero.\n\n") ; } } else { //-- fit with susy yield fixed to predicted value. rrv_mu_susy_sig -> setVal( poiVal ) ; if ( verbLevel > 0 ) { printf("\n Setting mu_susy_sig to %8.1f.\n\n", poiVal) ; } } rrv_mu_susy_sig->setConstant( kTRUE ) ; if ( verbLevel > 0 ) { printf("\n\n") ; printf(" Fitting with these values for the observables to define the model for toy generation.\n") ; rds->printMultiline(cout, 1, kTRUE, "") ; printf("\n\n") ; } RooFitResult* toyModelDefinitionFitResult(0x0) ; if ( verbLevel < 2 ) { toyModelDefinitionFitResult = likelihood->fitTo(*rds, Save(true), PrintLevel(-1)); } else { toyModelDefinitionFitResult = likelihood->fitTo(*rds, Save(true)); } int toyModelDefFitCovQual = toyModelDefinitionFitResult->covQual() ; if ( verbLevel > 0 ) { printf("\n fit covariance matrix quality: %d\n\n", toyModelDefFitCovQual ) ; } if ( toyModelDefFitCovQual != 3 ) { printf("\n\n\n *** Bad toy model definition fit. Cov qual %d. Aborting this toy.\n\n\n", toyModelDefFitCovQual ) ; continue ; } delete toyModelDefinitionFitResult ; if ( verbLevel > 0 ) { printf("\n\n\n === Model values for observables. These will be used to generate the toy dataset.\n\n") ; printObservables() ; } //--- 3) Generate a new set of observables based on this model. generateObservables() ; printf("\n\n\n Generated dataset\n") ; rds->Print() ; rds->printMultiline(cout, 1, kTRUE, "") ; //--- Apparently, I need to make a new RooDataSet... Resetting the // values in the old one doesn't stick. If you do likelihood->fitTo(*rds), it // uses the original values, not the reset ones, in the fit. RooArgSet toyFitobservedParametersList ; toyFitobservedParametersList.add( *rrv_Nsig ) ; toyFitobservedParametersList.add( *rrv_Nsb ) ; toyFitobservedParametersList.add( *rrv_Nsig_sl ) ; toyFitobservedParametersList.add( *rrv_Nsb_sl ) ; toyFitobservedParametersList.add( *rrv_Nsig_ldp ) ; toyFitobservedParametersList.add( *rrv_Nsb_ldp ) ; toyFitobservedParametersList.add( *rrv_Nsig_ee ) ; toyFitobservedParametersList.add( *rrv_Nsb_ee ) ; toyFitobservedParametersList.add( *rrv_Nsig_mm ) ; toyFitobservedParametersList.add( *rrv_Nsb_mm ) ; RooDataSet* toyFitdsObserved = new RooDataSet("toyfit_ra2b_observed_rds", "RA2b toy observed data values", toyFitobservedParametersList ) ; toyFitdsObserved->add( toyFitobservedParametersList ) ; //--- 4) Reset and free the nuisance parameters. { if ( verbLevel > 0 ) { printf("\n\n") ; } int npi(0) ; TIterator* npIter = nuisanceParameters->createIterator() ; while ( RooRealVar* np_rrv = (RooRealVar*) npIter->Next() ) { np_rrv -> setVal( np_initial_val[npi] ) ; // assuming that the order in the iterator does not change. np_rrv -> setConstant( kFALSE ) ; npi++ ; if ( verbLevel > 0 ) { printf(" reset %20s to %8.2f and freed it.\n", np_rrv->GetName() , np_rrv->getVal() ) ; } } // np_rrv iterator. if ( verbLevel > 0 ) { printf("\n\n") ; } } //--- 5a) Evaluate the test statistic: Fit with susy yield floating to get the absolute maximum log likelihood. if ( verbLevel > 0 ) { printf("\n\n Evaluating the test statistic for this toy. Fitting with susy floating.\n\n") ; } rrv_mu_susy_sig->setVal( 0.0 ) ; rrv_mu_susy_sig->setConstant( kFALSE ) ; if ( verbLevel > 0 ) { printf("\n toy dataset\n\n") ; toyFitdsObserved->printMultiline(cout, 1, kTRUE, "") ; } /////---- nfg. Need to create a new dataset ---------- /////RooFitResult* maxLikelihoodFitResult = likelihood->fitTo(*rds, Save(true), PrintLevel(-1)); /////RooFitResult* maxLikelihoodFitResult = likelihood->fitTo(*rds, Save(true)); /////-------------- RooFitResult* maxLikelihoodFitResult(0x0) ; if ( verbLevel < 2 ) { maxLikelihoodFitResult = likelihood->fitTo(*toyFitdsObserved, Save(true), PrintLevel(-1)); } else { maxLikelihoodFitResult = likelihood->fitTo(*toyFitdsObserved, Save(true)); } if ( verbLevel > 0 ) { printObservables() ; } int mlFitCovQual = maxLikelihoodFitResult->covQual() ; if ( verbLevel > 0 ) { printf("\n fit covariance matrix quality: %d , -log likelihood %f\n\n", mlFitCovQual, maxLikelihoodFitResult->minNll() ) ; } if ( mlFitCovQual != 3 ) { printf("\n\n\n *** Bad maximum likelihood fit (susy floating). Cov qual %d. Aborting this toy.\n\n\n", mlFitCovQual ) ; continue ; } double maxL_susyFloat = maxLikelihoodFitResult->minNll() ; double maxL_mu_susy_sig = rrv_mu_susy_sig->getVal() ; delete maxLikelihoodFitResult ; //--- 5b) Evaluate the test statistic: Fit with susy yield fixed to hypothesis value. // This is only necessary if the maximum likelihood fit value of the susy yield // is less than the hypothesis value (to get a one-sided limit). double testStat(0.0) ; double maxL_susyFixed(0.0) ; if ( maxL_mu_susy_sig < poiVal ) { if ( verbLevel > 0 ) { printf("\n\n Evaluating the test statistic for this toy. Fitting with susy fixed to %8.2f.\n\n", poiVal ) ; } rrv_mu_susy_sig->setVal( poiVal ) ; rrv_mu_susy_sig->setConstant( kTRUE ) ; if ( verbLevel > 0 ) { printf("\n toy dataset\n\n") ; rds->printMultiline(cout, 1, kTRUE, "") ; } ////--------- nfg. need to make a new dataset --------------- ////RooFitResult* susyFixedFitResult = likelihood->fitTo(*rds, Save(true), PrintLevel(-1)); ////RooFitResult* susyFixedFitResult = likelihood->fitTo(*rds, Save(true)); ////----------------------------- RooFitResult* susyFixedFitResult(0x0) ; if ( verbLevel < 2 ) { susyFixedFitResult = likelihood->fitTo(*toyFitdsObserved, Save(true), PrintLevel(-1)); } else { susyFixedFitResult = likelihood->fitTo(*toyFitdsObserved, Save(true)); } if ( verbLevel > 0 ) { printObservables() ; } int susyFixedFitCovQual = susyFixedFitResult->covQual() ; if ( verbLevel > 0 ) { printf("\n fit covariance matrix quality: %d , -log likelihood %f\n\n", susyFixedFitCovQual, susyFixedFitResult->minNll() ) ; } if ( susyFixedFitCovQual != 3 ) { printf("\n\n\n *** Bad maximum likelihood fit (susy fixed). Cov qual %d. Aborting this toy.\n\n\n", susyFixedFitCovQual ) ; continue ; } maxL_susyFixed = susyFixedFitResult->minNll() ; testStat = 2. * (maxL_susyFixed - maxL_susyFloat) ; delete susyFixedFitResult ; } else { if ( verbLevel > 0 ) { printf("\n\n Floating value of susy yield greater than hypo value (%8.2f > %8.2f). Setting test stat to zero.\n\n", maxL_mu_susy_sig, poiVal ) ; } testStat = 0.0 ; } printf(" --- test stat for toy %4d : %8.2f\n", ti, testStat ) ; nToyOK++ ; if ( testStat > dataTestStat ) { nToyWorseThanData++ ; } if ( makeTtree ) { tt_testStat = testStat ; tt_gen_Nsig = rrv_Nsig->getVal() ; tt_gen_Nsb = rrv_Nsb->getVal() ; tt_gen_Nsig_sl = rrv_Nsig_sl->getVal() ; tt_gen_Nsb_sl = rrv_Nsb_sl->getVal() ; tt_gen_Nsig_ldp = rrv_Nsig_ldp->getVal() ; tt_gen_Nsb_ldp = rrv_Nsb_ldp->getVal() ; tt_gen_Nsig_ee = rrv_Nsig_ee->getVal() ; tt_gen_Nsb_ee = rrv_Nsb_ee->getVal() ; tt_gen_Nsig_mm = rrv_Nsig_mm->getVal() ; tt_gen_Nsb_mm = rrv_Nsb_mm->getVal() ; toytt->Fill() ; } //--- *) reset things for the next toy. resetObservables() ; delete toyFitdsObserved ; } // ti. wstf->Close() ; printf("\n\n\n") ; if ( nToyOK == 0 ) { printf("\n\n\n *** All toys bad !?!?!\n\n\n") ; return ; } double pValue = (1.0*nToyWorseThanData) / (1.0*nToyOK) ; if ( isBgonlyStudy ) { printf("\n\n\n p-value result, BG-only , poi=%3.0f : %4d / %4d = %6.3f\n\n\n\n", poiVal, nToyWorseThanData, nToyOK, pValue ) ; } else { printf("\n\n\n p-value result, S-plus-B, poi=%3.0f : %4d / %4d = %6.3f\n\n\n\n", poiVal, nToyWorseThanData, nToyOK, pValue ) ; } if ( makeTtree ) { printf("\n\n Writing TTree : %s : %s\n\n", ttname, tttitle ) ; ttfile->cd() ; toytt->Write() ; } } // ws_cls_hybrid1