StandardFeldmanCousinsDemo.C

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/// \file
/// \ingroup tutorial_roostats
/// \notebook -js
/// Standard demo of the Feldman-Cousins calculator
/// StandardFeldmanCousinsDemo
///
/// 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 FeldmanCousins tools is a classical frequentist calculation
/// based on the Neyman Construction.  The test statistic can be
/// generalized for nuisance parameters by using the profile likelihood ratio.
/// But unlike the ProfileLikelihoodCalculator, this tool explicitly
/// builds the sampling distribution of the test statistic via toy Monte Carlo.
///
/// \macro_image
/// \macro_output
/// \macro_code
///
/// \author Kyle Cranmer

#include "TFile.h"
#include "TROOT.h"
#include "TH1F.h"
#include "TSystem.h"

#include "RooWorkspace.h"
#include "RooAbsData.h"

#include "RooStats/ModelConfig.h"
#include "RooStats/FeldmanCousins.h"
#include "RooStats/ToyMCSampler.h"
#include "RooStats/PointSetInterval.h"
#include "RooStats/ConfidenceBelt.h"

using namespace RooFit;
using namespace RooStats;

void StandardFeldmanCousinsDemo(const char *infile = "", const char *workspaceName = "combined",
                                const char *modelConfigName = "ModelConfig", const char *dataName = "obsData")
{

   // -------------------------------------------------------
   // 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 FeldmanCousins tool
   // to find and plot the 95% confidence interval
   // on the parameter of interest as specified
   // in the model config
   FeldmanCousins fc(*data, *mc);
   fc.SetConfidenceLevel(0.95); // 95% interval
   // fc.AdditionalNToysFactor(0.1); // to speed up the result
   fc.UseAdaptiveSampling(true); // speed it up a bit
   fc.SetNBins(10);              // set how many points per parameter of interest to scan
   fc.CreateConfBelt(true);      // save the information in the belt for plotting

   // 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)
         fc.FluctuateNumDataEntries(false);
      else
         cout << "Not sure what to do about this model" << endl;
   }

   // We can use PROOF to speed things along in parallel
   //  ProofConfig pc(*w, 1, "workers=4", kFALSE);
   //  ToyMCSampler*  toymcsampler = (ToyMCSampler*) fc.GetTestStatSampler();
   //  toymcsampler->SetProofConfig(&pc); // enable proof

   // Now get the interval
   PointSetInterval *interval = fc.GetInterval();
   ConfidenceBelt *belt = fc.GetConfidenceBelt();

   // print out the interval on the first Parameter of Interest
   RooRealVar *firstPOI = (RooRealVar *)mc->GetParametersOfInterest()->first();
   cout << "\n95% interval on " << firstPOI->GetName() << " is : [" << interval->LowerLimit(*firstPOI) << ", "
        << interval->UpperLimit(*firstPOI) << "] " << endl;

   // ---------------------------------------------
   // No nice plots yet, so plot the belt by hand

   // Ask the calculator which points were scanned
   RooDataSet *parameterScan = (RooDataSet *)fc.GetPointsToScan();
   RooArgSet *tmpPoint;

   // make a histogram of parameter vs. threshold
   TH1F *histOfThresholds =
      new TH1F("histOfThresholds", "", parameterScan->numEntries(), firstPOI->getMin(), firstPOI->getMax());

   // loop through the points that were tested and ask confidence belt
   // what the upper/lower thresholds were.
   // For FeldmanCousins, the lower cut off is always 0
   for (Int_t i = 0; i < parameterScan->numEntries(); ++i) {
      tmpPoint = (RooArgSet *)parameterScan->get(i)->clone("temp");
      double arMax = belt->GetAcceptanceRegionMax(*tmpPoint);
      double arMin = belt->GetAcceptanceRegionMax(*tmpPoint);
      double poiVal = tmpPoint->getRealValue(firstPOI->GetName());
      histOfThresholds->Fill(poiVal, arMax);
   }
   histOfThresholds->SetMinimum(0);
   histOfThresholds->Draw();
}