// 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; }
// 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 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; } } }
double StandardFrequentistDiscovery( const char* infile = "", const char* workspaceName = "channel1", const char* modelConfigNameSB = "ModelConfig", const char* dataName = "obsData", int toys = 1000, double poiValueForBackground = 0.0, double poiValueForSignal = 1.0 ) { // The workspace contains the model for s+b. The b model is "autogenerated" // by copying s+b and setting the one parameter of interest to zero. // To keep the script simple, multiple parameters of interest or different // functional forms of the b model are not supported. // for now, assume there is only one parameter of interest, and these are // its values: ///////////////////////////////////////////////////////////// // 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_channel1_GammaExample_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 -1; #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 -1; } ///////////////////////////////////////////////////////////// // Tutorial starts here //////////////////////////////////////////////////////////// TStopwatch *mn_t = new TStopwatch; mn_t->Start(); // get the workspace out of the file RooWorkspace* w = (RooWorkspace*) file->Get(workspaceName); if (!w) { cout << "workspace not found" << endl; return -1.0; } // get the modelConfig out of the file ModelConfig* mc = (ModelConfig*) w->obj(modelConfigNameSB); // get the data 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 -1.0; } RooRealVar* firstPOI = (RooRealVar*) mc->GetParametersOfInterest()->first(); firstPOI->setVal(poiValueForSignal); mc->SetSnapshot(*mc->GetParametersOfInterest()); // create null model ModelConfig *mcNull = mc->Clone("ModelConfigNull"); firstPOI->setVal(poiValueForBackground); mcNull->SetSnapshot(*(RooArgSet*)mcNull->GetParametersOfInterest()->snapshot()); // ---------------------------------------------------- // Configure a ProfileLikelihoodTestStat and a SimpleLikelihoodRatioTestStat // to use simultaneously with ToyMCSampler ProfileLikelihoodTestStat* plts = new ProfileLikelihoodTestStat(*mc->GetPdf()); plts->SetOneSidedDiscovery(true); plts->SetVarName( "q_{0}/2" ); // ---------------------------------------------------- // configure the ToyMCImportanceSampler with two test statistics ToyMCSampler toymcs(*plts, 50); // 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) { toymcs.SetNEventsPerToy(1); } else cout << "Not sure what to do about this model" << endl; } // We can use PROOF to speed things along in parallel // ProofConfig pc(*w, 2, "user@yourfavoriteproofcluster", false); ProofConfig pc(*w, 2, "", false); //toymcs.SetProofConfig(&pc); // enable proof // instantiate the calculator FrequentistCalculator freqCalc(*data, *mc, *mcNull, &toymcs); freqCalc.SetToys( toys,toys ); // null toys, alt toys // Run the calculator and print result HypoTestResult* freqCalcResult = freqCalc.GetHypoTest(); freqCalcResult->GetNullDistribution()->SetTitle( "b only" ); freqCalcResult->GetAltDistribution()->SetTitle( "s+b" ); freqCalcResult->Print(); double pvalue = freqCalcResult->NullPValue(); // stop timing mn_t->Stop(); cout << "total CPU time: " << mn_t->CpuTime() << endl; cout << "total real time: " << mn_t->RealTime() << endl; // plot TCanvas* c1 = new TCanvas(); HypoTestPlot *plot = new HypoTestPlot(*freqCalcResult, 100, -0.49, 9.51 ); plot->SetLogYaxis(true); // add chi2 to plot int nPOI = 1; TF1* f = new TF1("f", TString::Format("1*ROOT::Math::chisquared_pdf(2*x,%d,0)",nPOI), 0,20); f->SetLineColor( kBlack ); f->SetLineStyle( 7 ); plot->AddTF1( f, TString::Format("#chi^{2}(2x,%d)",nPOI) ); plot->Draw(); c1->SaveAs("standard_discovery_output.pdf"); return pvalue; }
void significance(RooWorkspace& w ) { ModelConfig* mc = (ModelConfig*)w.obj("mc"); RooDataSet* data = (RooDataSet*)w.data("data"); //data->Print(); // define the S+B snapshot (this is used for computing the expected significance) ModelConfig* sbModel = mc->Clone(); sbModel->SetName("S+B Model"); RooRealVar* poi = (RooRealVar*) sbModel->GetParametersOfInterest()->first(); poi->setVal(50); sbModel->SetSnapshot(*poi); ModelConfig * bModel = (ModelConfig*) sbModel->Clone(); bModel->SetName("B model"); poi->setVal(0); bModel->SetSnapshot(*poi); vector<double> masses; vector<double> p0values; vector<double> p0valuesExpected; vector<double> sigvalues; double massMin = 200; double massMax = 2500; int nbins = 100; // loop on the mass values for ( double mass=massMin; mass<=massMax; mass += (massMax-massMin)/nbins ) { w.var("mass")->setVal( mass ); // create the AsymptoticCalculator from data,alt model, null model AsymptoticCalculator * ac = new AsymptoticCalculator(*data, *sbModel, *bModel); ac->SetOneSidedDiscovery(true); // for one-side discovery test AsymptoticCalculator::SetPrintLevel(-1); // run the calculator HypoTestResult* asymCalcResult = ac->GetHypoTest(); asymCalcResult->Print(); double pvalue = asymCalcResult->NullPValue(); double sigvalue = asymCalcResult->Significance(); double expectedP0 = AsymptoticCalculator::GetExpectedPValues(asymCalcResult->NullPValue(),asymCalcResult->AlternatePValue(), 0, false); masses.push_back(mass); p0values.push_back(pvalue); p0valuesExpected.push_back(expectedP0); sigvalues.push_back(sigvalue); std::cout << "** Mass = " << mass << " p0-value = " << expectedP0 << " p-value = " << pvalue << " significance = " << sigvalue << std::endl; } TGraph* graph1 = new TGraph(masses.size(),&masses[0],&p0values[0]); TGraph* graph2 = new TGraph(masses.size(),&masses[0],&p0valuesExpected[0]); TGraph* graph3 = new TGraph(masses.size(),&masses[0],&sigvalues[0]); TCanvas* c2 = new TCanvas("c2","Significance", 900, 700); c2->Divide(1,2); c2->cd(1); graph1->SetMarkerStyle(10); //graph1->Draw("APC"); graph1->Draw("AC"); graph2->SetLineStyle(2); graph2->Draw("C"); graph1->GetXaxis()->SetTitle("Mass [GeV]"); graph1->GetYaxis()->SetTitle("p0 value"); graph1->SetTitle("P-value vs Mass"); graph1->SetMinimum(graph2->GetMinimum()); graph1->SetLineColor(kBlue); graph2->SetLineColor(kRed); gPad->SetLogy(true); c2->cd(2); graph3->SetMarkerStyle(10); graph3->Draw("AC"); graph3->SetLineStyle(1); graph3->SetLineColor(kRed); graph3->GetXaxis()->SetTitle("Mass [GeV]"); graph3->GetYaxis()->SetTitle("Significance"); graph3->SetTitle("Significance vs Mass"); gPad->SetLogy(false); c2->SaveAs("significance.pdf"); c2->SaveAs("significance.png"); }