StandardBayesianNumericalDemo.C File Reference

Detailed Description

Standard demo of the numerical Bayesian calculator

This is a standard demo that can be used with any ROOT file prepared in the standard way. You specify:

• name for input ROOT file
• name of workspace inside ROOT file that holds model and data
• name of ModelConfig that specifies details for calculator tools
• name of dataset

With default parameters the macro will attempt to run the standard hist2workspace example and read the ROOT file that it produces.

The actual heart of the demo is only about 10 lines long.

The BayesianCalculator is based on Bayes's theorem and performs the integration using ROOT's numeric integration utilities

 
[#1] INFO:ObjectHandling -- RooWorkspace::import(combined) importing RooUniform::prior
[#1] INFO:Minimization -- p.d.f. provides expected number of events, including extended term in likelihood.
[#1] INFO:Minimization --  Including the following constraint terms in minimization: (alpha_syst2Constraint,alpha_syst3Constraint,gamma_stat_channel1_bin_0_constraint,gamma_stat_channel1_bin_1_constraint)
[#1] INFO:Minimization -- The following global observables have been defined and their values are taken from the model: (nominalLumi,nom_alpha_syst1,nom_alpha_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
RooAbsTestStatistic::initSimMode: creating slave calculator #0 for state channel1 (2 dataset entries)
[#1] INFO:Fitting -- RooAbsTestStatistic::initSimMode: created 1 slave calculators.
[#1] INFO:Eval -- BayesianCalculator::GetPosteriorFunction :  nll value 15.6103 poi value = 1
[#1] INFO:Eval -- BayesianCalculator::GetPosteriorFunction : minimum of NLL vs POI for POI =  1.12121 min NLL = 15.5792
[#0] WARNING:InputArguments -- RooRealBinding: The function prior does not depend on the parameter alpha_syst2. Note that passing copies of the parameters is not supported.
[#0] WARNING:InputArguments -- RooRealBinding: The function prior does not depend on the parameter alpha_syst3. Note that passing copies of the parameters is not supported.
[#0] WARNING:InputArguments -- RooRealBinding: The function prior does not depend on the parameter gamma_stat_channel1_bin_0. Note that passing copies of the parameters is not supported.
[#0] WARNING:InputArguments -- RooRealBinding: The function prior does not depend on the parameter gamma_stat_channel1_bin_1. Note that passing copies of the parameters is not supported.
[#1] INFO:InputArguments -- BayesianCalculator:GetInterval Compute the interval from the posterior cdf 
[#0] WARNING:InputArguments -- RooRealBinding: The function prior does not depend on the parameter alpha_syst2. Note that passing copies of the parameters is not supported.
[#0] WARNING:InputArguments -- RooRealBinding: The function prior does not depend on the parameter alpha_syst3. Note that passing copies of the parameters is not supported.
[#0] WARNING:InputArguments -- RooRealBinding: The function prior does not depend on the parameter gamma_stat_channel1_bin_0. Note that passing copies of the parameters is not supported.
[#0] WARNING:InputArguments -- RooRealBinding: The function prior does not depend on the parameter gamma_stat_channel1_bin_1. Note that passing copies of the parameters is not supported.
[#1] INFO:NumericIntegration -- PosteriorCdfFunction - integral of posterior = 0.0560849 +/- 0.000423427
[#0] WARNING:Eval -- BayesianCalculator::GetInterval : 315 errors reported in evaluating log-likelihood function 
[#1] INFO:Eval -- BayesianCalculator::GetInterval - found a valid interval : [0.170773 , 2.33979 ]
 
>>>> RESULT : 95% interval on SigXsecOverSM is : [0.170773, 2.33979] 

 
#include "TFile.h"
#include "TROOT.h"
#include "RooWorkspace.h"
#include "RooAbsData.h"
#include "RooRealVar.h"
 
#include "RooUniform.h"
#include "RooStats/ModelConfig.h"
#include "RooStats/BayesianCalculator.h"
#include "RooStats/SimpleInterval.h"
#include "RooStats/RooStatsUtils.h"
#include "RooPlot.h"
#include "TSystem.h"
 
#include <cassert>
 
using namespace RooFit;
using namespace RooStats;
 
struct BayesianNumericalOptions {
 
   double confLevel = 0.95;      // interval CL
   TString integrationType = ""; // integration Type (default is adaptive (numerical integration)
   // possible values are "TOYMC" (toy MC integration, work when nuisances have a constraints pdf)
   //  "VEGAS" , "MISER", or "PLAIN"  (these are all possible MC integration)
   int nToys =
      10000; // number of toys used for the MC integrations - for Vegas should be probably set to an higher value
   bool scanPosterior =
      false; // flag to compute interval by scanning posterior (it is more robust but maybe less precise)
   bool plotPosterior = false; // plot posterior function after having computed the interval
   int nScanPoints = 50; // number of points for scanning the posterior (if scanPosterior = false it is used only for
                         // plotting). Use by default a low value to speed-up tutorial
   int intervalType = 1; // type of interval (0 is shortest, 1 central, 2 upper limit)
   double maxPOI = -999; // force a different value of POI for doing the scan (default is given value)
   double nSigmaNuisance = -1; // force integration of nuisance parameters to be within nSigma of their error (do first
                               // a model fit to find nuisance error)
};
 
BayesianNumericalOptions optBayes;
 
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;
   bool plotPosterior = optBayes.plotPosterior;
   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);
      std::unique_ptr<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 iterations (i.e. number of toys for MC integrations)
   }
 
   // in case of toyMC make a nuisance 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 interval on the first Parameter of Interest
   cout << "\n>>>> RESULT : " << confLevel * 100 << "% interval on " << poi->GetName() << " is : ["
        << interval->LowerLimit() << ", " << interval->UpperLimit() << "] " << endl;
 
   // end in case plotting is not requested
   if (!plotPosterior)
      return;
 
   // 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();
}

Author

Kyle Cranmer

Definition in file StandardBayesianNumericalDemo.C.