StandardHypoTestDemo.C: Standard tutorial macro for hypothesis test (for computing the discovery significance) using all

// Standard tutorial macro for hypothesis test (for computing the discovery significance) using all 
// RooStats hypotheiss tests calculators and test statistics  
//
//
//Author:  L. Moneta
//
// Usage: 
//
// root>.L StandardHypoTestDemo.C
// root> StandardHypoTestDemo("fileName","workspace name","S+B modelconfig name","B model name","data set name",calculator type, test statistic type, //                             number of toys)
//
//
// type = 0 Freq calculator 
// type = 1 Hybrid calculator
// type = 2 Asymptotic calculator  
// type = 3 Asymptotic calculator using nominal Asimov data sets (not using fitted parameter values but nominal ones)
//
// testStatType = 0 LEP
//              = 1 Tevatron 
//              = 2 Profile Likelihood
//              = 3 Profile Likelihood one sided (i.e. = 0 if mu_hat < 0)
//


#include "TFile.h"
#include "RooWorkspace.h"
#include "RooAbsPdf.h"
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooStats/ModelConfig.h"
#include "RooRandom.h"
#include "TGraphErrors.h"
#include "TGraphAsymmErrors.h"
#include "TCanvas.h"
#include "TLine.h"
#include "TSystem.h"
#include "TROOT.h"

#include "RooStats/AsymptoticCalculator.h"
#include "RooStats/HybridCalculator.h"
#include "RooStats/FrequentistCalculator.h"
#include "RooStats/ToyMCSampler.h"
#include "RooStats/HypoTestPlot.h"

#include "RooStats/NumEventsTestStat.h"
#include "RooStats/ProfileLikelihoodTestStat.h"
#include "RooStats/SimpleLikelihoodRatioTestStat.h"
#include "RooStats/RatioOfProfiledLikelihoodsTestStat.h"
#include "RooStats/MaxLikelihoodEstimateTestStat.h"

#include "RooStats/HypoTestInverter.h"
#include "RooStats/HypoTestInverterResult.h"
#include "RooStats/HypoTestInverterPlot.h"

using namespace RooFit;
using namespace RooStats;


bool noSystematics = false;              // force all systematics to be off (i.e. set all nuisance parameters as constat
double nToysRatio = 4;                   // ratio Ntoys Null/ntoys ALT
double poiValue = -1;                    // change poi snapshot value for S+B model (needed for expected p0 values)
int  printLevel=0;
bool generateBinned = false;             // for binned generation

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";
      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;
  }
  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; 
         
      }
   }

}