StandardFrequentistDiscovery.C File Reference

Detailed Description

StandardFrequentistDiscovery

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.

Processing /mnt/build/workspace/root-makedoc-v614/rootspi/rdoc/src/v6-14-00-patches/tutorials/roostats/StandardFrequentistDiscovery.C...

RooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby 
                Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University
                All rights reserved, please read http://roofit.sourceforge.net/license.txt


=== Using the following for ModelConfigNull ===
Observables:             RooArgSet:: = (obs_x_channel1)
Parameters of Interest:  RooArgSet:: = (SigXsecOverSM)
Nuisance Parameters:     RooArgSet:: = (alpha_syst3,beta_syst2,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
Global Observables:      RooArgSet:: = (nom_beta_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
PDF:                     RooProdPdf::model_channel1[ lumiConstraint * alpha_syst1Constraint * beta_syst2Constraint * alpha_syst3Constraint * gamma_stat_channel1_bin_0_constraint * gamma_stat_channel1_bin_1_constraint * channel1_model(obs_x_channel1) ] = 0.208662
Snapshot:                
  1) 0x3bfeba0 RooRealVar:: SigXsecOverSM = 0  L(0 - 3)  "SigXsecOverSM"


=== Using the following for ModelConfig ===
Observables:             RooArgSet:: = (obs_x_channel1)
Parameters of Interest:  RooArgSet:: = (SigXsecOverSM)
Nuisance Parameters:     RooArgSet:: = (alpha_syst3,beta_syst2,gamma_stat_channel1_bin_0,gamma_stat_channel1_bin_1)
Global Observables:      RooArgSet:: = (nom_beta_syst2,nom_alpha_syst3,nom_gamma_stat_channel1_bin_0,nom_gamma_stat_channel1_bin_1)
PDF:                     RooProdPdf::model_channel1[ lumiConstraint * alpha_syst1Constraint * beta_syst2Constraint * alpha_syst3Constraint * gamma_stat_channel1_bin_0_constraint * gamma_stat_channel1_bin_1_constraint * channel1_model(obs_x_channel1) ] = 0.208662
Snapshot:                
  1) 0x3bfeba0 RooRealVar:: SigXsecOverSM = 1  L(0 - 3)  "SigXsecOverSM"

[#0] PROGRESS:Generation -- Test Statistic on data: 2.00338
[#1] INFO:InputArguments -- Profiling conditional MLEs for Null.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Null.
[#0] PROGRESS:Generation -- generated toys: 500 / 1000
[#1] INFO:InputArguments -- Profiling conditional MLEs for Alt.
[#1] INFO:InputArguments -- Using a ToyMCSampler. Now configuring for Alt.
[#0] PROGRESS:Generation -- generated toys: 500 / 1000

Results HypoTestCalculator_result: 
 - Null p-value = 0.515 +/- 0.0158043
 - Significance = -0.0376083 +/- 0.0396435 sigma
 - Number of Alt toys: 1000
 - Number of Null toys: 1000
 - Test statistic evaluated on data: 2.00338
 - CL_b: 0.515 +/- 0.0158043
 - CL_s+b: 0.767 +/- 0.0133683
 - CL_s: 1.48932 +/- 0.0525612
total CPU time: 16.22
total real time: 16.2358
(double) 0.51500000

#include "TFile.h"
#include "TROOT.h"
#include "TH1F.h"
#include "TF1.h"
#include "TCanvas.h"
#include "TStopwatch.h"

#include "RooWorkspace.h"
#include "RooAbsData.h"
#include "RooRandom.h"
#include "RooRealSumPdf.h"
#include "RooNumIntConfig.h"

#include "RooStats/ModelConfig.h"
#include "RooStats/ToyMCImportanceSampler.h"
#include "RooStats/HypoTestResult.h"
#include "RooStats/HypoTestPlot.h"
#include "RooStats/SamplingDistribution.h"
#include "RooStats/ProfileLikelihoodTestStat.h"
#include "RooStats/SimpleLikelihoodRatioTestStat.h"
#include "RooStats/ProfileLikelihoodCalculator.h"
#include "RooStats/LikelihoodInterval.h"
#include "RooStats/LikelihoodIntervalPlot.h"

#include "RooStats/FrequentistCalculator.h"
#include "TSystem.h"

#include <vector>

using namespace RooFit;
using namespace RooStats;


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;
}

Authors

Sven Kreiss, Kyle Cranmer

Definition in file StandardFrequentistDiscovery.C.