JCOMPAREHISTOGRAMS::JTestEffectiveLogLikelihoodRatio Class Reference

Implementation of the effective log-likelihood ratio test. More...

#include <JTestEffectiveLogLikelihoodRatio.hh>

Inheritance diagram for JCOMPAREHISTOGRAMS::JTestEffectiveLogLikelihoodRatio:

Public Member Functions
	JTestEffectiveLogLikelihoodRatio ()
	Default constructor.
void	test (const TObject *o1, const TObject *o2) override
	Applies a log-likelihood ratio test to the two given histograms.
std::istream &	read (std::istream &in) override
	Read test parameters from input.
virtual std::ostream &	write (std::ostream &out, const char delimiter=' ', const bool onlyFailures=false) const
	Write test result to output.
virtual void	save (TFile *f, const std::string &path, const bool onlyFailures=false) const
	Writes the test result to root file.
const std::string &	getTestName () const
	Get test name.
const std::string &	getResultType () const
	Get result type.

Static Public Member Functions
static double	getNLL (const double k, const double n, const double ne)
	Get negative log-likelihood (i.e.

Protected Attributes
const std::string	testName
	test name
const std::string	resultType
	test result type

Private Attributes
double	threshold
	threshold chi-square to decide if test is passed.

Detailed Description

Implementation of the effective log-likelihood ratio test.

The first histogram is treated as an Asimov dataset corresponding to a given null hypothesis, which is compared to an alternative hypothesis given by the second histogram.

This likelihood in this method is taken from the article: "A binned likelihood for stochastic models" JHEP, Volume 2019, article number 30, (2019), by C.A. Argüelles, A. Schneider and T. Yuan. DOI: 10.1007/JHEP06(2019)030

Definition at line 31 of file JTestEffectiveLogLikelihoodRatio.hh.

Constructor & Destructor Documentation

JTestEffectiveLogLikelihoodRatio()

JCOMPAREHISTOGRAMS::JTestEffectiveLogLikelihoodRatio::JTestEffectiveLogLikelihoodRatio ( )

inline

Default constructor.

Definition at line 39 of file JTestEffectiveLogLikelihoodRatio.hh.

                                       :
      JTest_t("Effective_NLLR", "NLLR"),
      threshold(0.0)
    {}

Member Function Documentation

getNLL()

static double JCOMPAREHISTOGRAMS::JTestEffectiveLogLikelihoodRatio::getNLL ( const double k, const double n, const double ne )

inlinestatic

Get negative log-likelihood (i.e.

).

Parameters

k	number of observed events
n	model prediction
ne	model prediction error

Definition at line 52 of file JTestEffectiveLogLikelihoodRatio.hh.

    {
      using namespace JPP;
      
      static const double epsilon = std::numeric_limits<double>::min();
      
      if (k < 0.0) {
        THROW(JValueOutOfRange, "JTestEffectiveLogLikelihoodRatio::getNLL(): Invalid number of events: " << k);
      }
 
      if (n < 0.0) {
        THROW(JValueOutOfRange, "JTestEffectiveLogLikelihoodRatio::getNLL(): Invalid model prediction: " << n);
      }
            
      double M = epsilon; // Relative bin variance of given model
 
      if (n > 0.0 && ne > 0.0) {
        M = n/ne/ne;
      } else if (n > 0.0) {
        THROW(JDivisionByZero, "JTestEffectiveLogLikelihoodRatio::getNLL(): Invalid effective bin content (bin content error is zero)");
      }
     
      const double N = n*M; // Effective bin content
 
      return -2.0 * ((N + 1) * log(M)         -
                     (k + N + 1) * log(1 + M) +
                     lgamma(k + N + 1)        -
                     lgamma(N + 1)            -
                     lgamma(k + 1));
    }

test()

void JCOMPAREHISTOGRAMS::JTestEffectiveLogLikelihoodRatio::test ( const TObject * o1, const TObject * o2 )

inlineoverridevirtual

Applies a log-likelihood ratio test to the two given histograms.

The first histogram is treated as an Asimov dataset corresponding to a given null hypothesis.
The second histogram is treated as the expectation for the alternative hypothesis, to which the null hypothesis is compared.

Parameters

o1	First histogram
o2	Second histogram

Implements JCOMPAREHISTOGRAMS::JTest_t.

Definition at line 94 of file JTestEffectiveLogLikelihoodRatio.hh.

    {
      using namespace std;
      using namespace JPP;
      
      const TH1* h1 =  dynamic_cast<const TH1*>(o1);
      const TH1* h2 =  dynamic_cast<const TH1*>(o2);
 
      if (h1 == NULL || h2 == NULL) {
        THROW(JValueOutOfRange, "JTestEffectiveLogLikelihood::test(): Could not cast given TObjects to TH1.");
      }
 
      if(h1->GetNbinsX() != h2->GetNbinsX() ||
         h1->GetNbinsY() != h2->GetNbinsY() ||
         h1->GetNbinsZ() != h2->GetNbinsZ()) {
        THROW(JValueOutOfRange, "JTestEffectiveLogLikelihood::test(): Histograms with different binning. The objects: " <<
              h1->GetName() << " and " << h2->GetName() << " can not be compared." << endl);
      }
 
      TH1* h3 = (TH1*) h1->Clone(h1->GetName() == h2->GetName() ?
                                 MAKE_CSTRING(h1->GetName()                            << "_" << testName) :
                                 MAKE_CSTRING(h1->GetName() << "_VS_" << h2->GetName() << "_" << testName));
 
      h3->Reset();
 
      double LnLratio = 0.0;
 
      for (int i=1 ; i <= h1->GetNbinsX() ; ++i) {
        for (int j=1 ; j <= h1->GetNbinsY() ; ++j) {
          for (int k=1 ; k <= h1->GetNbinsZ() ; ++k) {
 
            const double n1 = h1->GetBinContent(i,j,k);
            const double n2 = h2->GetBinContent(i,j,k);
 
            const double e1 = h1->GetBinError(i,j,k);
            const double e2 = h2->GetBinError(i,j,k);
            
            double contribution = 0.0;
            
            if        (n1 > 0.0) {
              contribution = (getNLL(n1, n2, e2) -
                              getNLL(n1, n1, e1));
            } else if (n2 > 0.0) {
              contribution =  getNLL(n1, n2, e2);
            } // Skip bins with zero data and zero prediction
 
            h3->SetBinContent(i,j,k, contribution);
            
            LnLratio += contribution;
          }
        }
      }
 
      const bool passed = (LnLratio < threshold);
 
      const JResultTitle title(testName, resultType, passed, LnLratio);
 
      h3->SetTitle(title.getTitle().c_str());
      h3->GetYaxis()->SetTitle(resultType.c_str());
 
      const JTestResult r (testName,
                           JRootObjectID(MAKE_STRING(h1->GetDirectory()->GetPath() << h1->GetName())),
                           JRootObjectID(MAKE_STRING(h2->GetDirectory()->GetPath() << h1->GetName())),
                           resultType, LnLratio, threshold, h3, passed);
      
      this->push_back(r);
    }

read()

std::istream & JCOMPAREHISTOGRAMS::JTestEffectiveLogLikelihoodRatio::read ( std::istream & in )

inlineoverridevirtual

Read test parameters from input.

Parameters

in	input stream

Returns

input stream

Implements JCOMPAREHISTOGRAMS::JTest_t.

Definition at line 169 of file JTestEffectiveLogLikelihoodRatio.hh.

    {
      using namespace JPP;
      
      in >> threshold;
 
      if (!(threshold > 0.0)) {
        THROW(JValueOutOfRange, "JTestEffectiveLogLikelihoodRatio::read(): Given chi-square threshold " << threshold << " is invalid");
      }
       
      return in;
    }

write()

virtual std::ostream & JCOMPAREHISTOGRAMS::JTest_t::write ( std::ostream & out, const char delimiter = ' ', const bool onlyFailures = false ) const

inlinevirtualinherited

Write test result to output.

Parameters

out	output stream
delimiter	field delimiter
onlyFailures	if true, write only failures.

Returns

output stream

Definition at line 84 of file JTest_t.hh.

    {
      using namespace std;
      using namespace JPP;
 
      for (vector<JTestResult>::const_iterator r = this->begin() ; r != this->end() ; ++r) {
 
        if (onlyFailures && r->passed) { continue; }
        
        print(out, *r, delimiter, true);
      }
      
      return out;
    }

save()

virtual void JCOMPAREHISTOGRAMS::JTest_t::save ( TFile * f, const std::string & path, const bool onlyFailures = false ) const

inlinevirtualinherited

Writes the test result to root file.

Parameters

f	A ROOT file
path	Path in root file.
onlyFailures	If true, write only failures.

Definition at line 108 of file JTest_t.hh.

    {
      using namespace std;
      using namespace JPP;
 
      if (f->GetDirectory(path.c_str())==0) {
        f->mkdir(path.c_str());
      }
      
      f->cd(path.c_str());
 
      for (vector<JTestResult>::const_iterator r = this->begin() ; r != this->end() ; ++r) {
 
        if (onlyFailures && r->passed) { continue; }
        
        r->obj->Write();
      }
    }

getTestName()

const std::string & JCOMPAREHISTOGRAMS::JTest_t::getTestName ( ) const

inlineinherited

Get test name.

Returns

test name

Definition at line 135 of file JTest_t.hh.

    {
      return testName;
    }

getResultType()

const std::string & JCOMPAREHISTOGRAMS::JTest_t::getResultType ( ) const

inlineinherited

Get result type.

Returns

result type

Definition at line 146 of file JTest_t.hh.

    {
      return resultType;
    }

Member Data Documentation

threshold

double JCOMPAREHISTOGRAMS::JTestEffectiveLogLikelihoodRatio::threshold

private

threshold chi-square to decide if test is passed.

Definition at line 184 of file JTestEffectiveLogLikelihoodRatio.hh.

testName

const std::string JCOMPAREHISTOGRAMS::JTest_t::testName

protectedinherited

test name

Definition at line 180 of file JTest_t.hh.

resultType

const std::string JCOMPAREHISTOGRAMS::JTest_t::resultType

protectedinherited

test result type

Definition at line 181 of file JTest_t.hh.

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The documentation for this class was generated from the following file:

• JTestEffectiveLogLikelihoodRatio.hh