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Reference Guide
StandardBayesianNumericalDemo.C File Reference

Detailed Description

View in nbviewer Open in SWAN 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

␛[1mRooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby␛[0m
Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University
All rights reserved, please read http://roofit.sourceforge.net/license.txt
[#1] INFO:ObjectHandling -- RooWorkspace::import(combined) importing RooUniform::prior
[#1] INFO:Minization -- p.d.f. provides expected number of events, including extended term in likelihood.
[#1] INFO:Minization -- createNLL: caching constraint set under name CONSTR_OF_PDF_simPdf_FOR_OBS_channelCat:obs_x_channel1 with 4 entries
[#1] INFO:Minization -- 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:Minization -- The following global observables have been defined: (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:NumericIntegration -- RooRealIntegral::init(channel1_model_Int[obs_x_channel1]) using numeric integrator RooBinIntegrator to calculate Int(obs_x_channel1)
[#1] INFO:Fitting -- RooAbsTestStatistic::initSimMode: created 1 slave calculators.
[#1] INFO:NumericIntegration -- RooRealIntegral::init(channel1_model_Int[obs_x_channel1]) using numeric integrator RooBinIntegrator to calculate Int(obs_x_channel1)
[#1] INFO:Eval -- BayesianCalculator::GetPosteriorFunction : nll value -1033.75 poi value = 1
[#1] INFO:Eval -- BayesianCalculator::GetPosteriorFunction : minimum of NLL vs POI for POI = 1.12121 min NLL = -1033.78
[#1] INFO:InputArguments -- BayesianCalculator:GetInterval Compute the interval from the posterior cdf
[#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 "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;
// 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;
// -------------------------------------------------------
// Tutorial starts here
// -------------------------------------------------------
// get the workspace out of the file
RooWorkspace *w = (RooWorkspace *)file->Get(workspaceName);
if (!w) {
cout << "workspace not found" << endl;
// 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) {
cout << "data or ModelConfig was not found" << endl;
// ------------------------------------------
// 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());
// do without systematics
// mc->SetNuisanceParameters(RooArgSet() );
if (nSigmaNuisance > 0) {
RooAbsPdf *pdf = mc->GetPdf();
RooFitResult *res =
RooArgList nuisPar(*mc->GetNuisanceParameters());
for (int i = 0; i < nuisPar.getSize(); ++i) {
RooRealVar *v = dynamic_cast<RooRealVar *>(&nuisPar[i]);
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;
scanPosterior = true; // for ToyMC the posterior is scanned anyway so used given points
// compute interval by scanning the posterior function
if (scanPosterior)
RooRealVar *poi = (RooRealVar *)mc->GetParametersOfInterest()->first();
if (maxPOI != -999 && maxPOI > poi->getMin())
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)
// 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
cout << "\nDrawing plot of posterior function....." << endl;
// always plot using numer of scan points
RooPlot *plot = bayesianCalc.GetPosteriorPlot();
#define gROOT
Definition: TROOT.h:406
R__EXTERN TSystem * gSystem
Definition: TSystem.h:556
static const std::string & DefaultMinimizerType()
virtual void Print(Option_t *options=0) const
Print the object to the defaultPrintStream().
Definition: RooAbsArg.h:302
RooAbsData is the common abstract base class for binned and unbinned datasets.
Definition: RooAbsData.h:44
virtual RooFitResult * fitTo(RooAbsData &data, const RooCmdArg &arg1=RooCmdArg::none(), const RooCmdArg &arg2=RooCmdArg::none(), const RooCmdArg &arg3=RooCmdArg::none(), const RooCmdArg &arg4=RooCmdArg::none(), const RooCmdArg &arg5=RooCmdArg::none(), const RooCmdArg &arg6=RooCmdArg::none(), const RooCmdArg &arg7=RooCmdArg::none(), const RooCmdArg &arg8=RooCmdArg::none())
Fit PDF to given dataset.
Definition: RooAbsPdf.cxx:1254
virtual Double_t getMin(const char *name=0) const
Get miniminum of currently defined range.
static void setEvalErrorLoggingMode(ErrorLoggingMode m)
Set evaluation error logging mode.
RooArgList is a container object that can hold multiple RooAbsArg objects.
Definition: RooArgList.h:21
RooFitResult is a container class to hold the input and output of a PDF fit to a dataset.
Definition: RooFitResult.h:40
virtual void Print(Option_t *options=0) const
Print TNamed name and title.
Definition: RooFitResult.h:66
A RooPlot is a plot frame and a container for graphics objects within that frame.
Definition: RooPlot.h:44
virtual void Draw(Option_t *options=0)
Draw this plot and all of the elements it contains.
Definition: RooPlot.cxx:712
RooRealVar represents a variable that can be changed from the outside.
Definition: RooRealVar.h:35
void setMax(const char *name, Double_t value)
Set maximum of name range to given value.
Definition: RooRealVar.cxx:496
Flat p.d.f.
Definition: RooUniform.h:24
The RooWorkspace is a persistable container for RooFit projects.
Definition: RooWorkspace.h:43
RooAbsData * data(const char *name) const
Retrieve dataset (binned or unbinned) with given name. A null pointer is returned if not found.
void Print(Option_t *opts=0) const
Print contents of the workspace.
Bool_t import(const RooAbsArg &arg, const RooCmdArg &arg1=RooCmdArg(), const RooCmdArg &arg2=RooCmdArg(), const RooCmdArg &arg3=RooCmdArg(), const RooCmdArg &arg4=RooCmdArg(), const RooCmdArg &arg5=RooCmdArg(), const RooCmdArg &arg6=RooCmdArg(), const RooCmdArg &arg7=RooCmdArg(), const RooCmdArg &arg8=RooCmdArg(), const RooCmdArg &arg9=RooCmdArg())
Import a RooAbsArg object, e.g.
TObject * obj(const char *name) const
Return any type of object (RooAbsArg, RooAbsData or generic object) with given name)
RooAbsPdf * pdf(const char *name) const
Retrieve p.d.f (RooAbsPdf) with given name. A null pointer is returned if not found.
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format.
Definition: TFile.h:53
static TFile * Open(const char *name, Option_t *option="", const char *ftitle="", Int_t compress=ROOT::RCompressionSetting::EDefaults::kUseCompiledDefault, Int_t netopt=0)
Create / open a file.
Definition: TFile.cxx:3942
virtual const char * GetName() const
Returns name of object.
Definition: TNamed.h:47
Basic string class.
Definition: TString.h:131
Bool_t IsNull() const
Definition: TString.h:402
Bool_t Contains(const char *pat, ECaseCompare cmp=kExact) const
Definition: TString.h:619
virtual Bool_t AccessPathName(const char *path, EAccessMode mode=kFileExists)
Returns FALSE if one can access a file using the specified access mode.
Definition: TSystem.cxx:1291
RooCmdArg Hesse(Bool_t flag=kTRUE)
RooCmdArg Save(Bool_t flag=kTRUE)
RooCmdArg PrintLevel(Int_t code)
RooCmdArg Minimizer(const char *type, const char *alg=0)
The namespace RooFit contains mostly switches that change the behaviour of functions of PDFs (or othe...
Namespace for the RooStats classes.
Definition: Asimov.h:19
RooAbsPdf * MakeNuisancePdf(RooAbsPdf &pdf, const RooArgSet &observables, const char *name)
Short_t Max(Short_t a, Short_t b)
Definition: TMathBase.h:212
Short_t Min(Short_t a, Short_t b)
Definition: TMathBase.h:180
Definition: file.py:1
Kyle Cranmer

Definition in file StandardBayesianNumericalDemo.C.