MethodCuts.h

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// @(#)root/tmva $Id$
// Author: Andreas Hoecker, Matt Jachowski, Peter Speckmayer, Helge Voss, Kai Voss
 
/**********************************************************************************
 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis       *
 * Package: TMVA                                                                  *
 * Class  : MethodCuts                                                            *
 *                                             *
 *                                                                                *
 * Description:                                                                   *
 *      Multivariate optimisation of signal efficiency for given background       *
 *      efficiency, using rectangular minimum and maximum requirements on         *
 *      input variables                                                           *
 *                                                                                *
 * Authors (alphabetical):                                                        *
 *      Andreas Hoecker  <Andreas.Hocker@cern.ch> - CERN, Switzerland             *
 *      Matt Jachowski   <jachowski@stanford.edu> - Stanford University, USA      *
 *      Peter Speckmayer <speckmay@mail.cern.ch>  - CERN, Switzerland             *
 *      Helge Voss       <Helge.Voss@cern.ch>     - MPI-K Heidelberg, Germany     *
 *      Kai Voss         <Kai.Voss@cern.ch>       - U. of Victoria, Canada        *
 *                                                                                *
 * Copyright (c) 2005:                                                            *
 *      CERN, Switzerland                                                         *
 *      U. of Victoria, Canada                                                    *
 *      MPI-K Heidelberg, Germany                                                 *
 *      LAPP, Annecy, France                                                      *
 *                                                                                *
 * Redistribution and use in source and binary forms, with or without             *
 * modification, are permitted according to the terms listed in LICENSE           *
 * (see tmva/doc/LICENSE)                                          *
 **********************************************************************************/
 
#ifndef ROOT_TMVA_MethodCuts
#define ROOT_TMVA_MethodCuts
 
//////////////////////////////////////////////////////////////////////////
//                                                                      //
// MethodCuts                                                           //
//                                                                      //
// Multivariate optimisation of signal efficiency for given background  //
// efficiency, using rectangular minimum and maximum requirements on    //
// input variables                                                      //
//                                                                      //
//////////////////////////////////////////////////////////////////////////
 
#include <vector>
 
 
#include "TMVA/MethodBase.h"
#include "TMVA/BinarySearchTree.h"
#include "TMVA/PDF.h"
#include "TMatrixDfwd.h"
#include "IFitterTarget.h"
 
class TRandom;
 
namespace TMVA {
 
   class Interval;
 
   class MethodCuts : public MethodBase, public IFitterTarget {
 
   public:
 
      MethodCuts( const TString& jobName,
                  const TString& methodTitle,
                  DataSetInfo& theData,
                  const TString& theOption = "MC:150:10000:");
 
      MethodCuts( DataSetInfo& theData,
                  const TString& theWeightFile);
 
      // this is a workaround which is necessary since CINT is not capable of handling dynamic casts
      static MethodCuts* DynamicCast( IMethod* method ) { return dynamic_cast<MethodCuts*>(method); }
 
      virtual ~MethodCuts( void );
 
      virtual Bool_t HasAnalysisType( Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets );
 
      // training method
      void Train( void );
 
      using MethodBase::ReadWeightsFromStream;
 
      void AddWeightsXMLTo      ( void* parent ) const;
 
      void ReadWeightsFromStream( std::istream & i );
      void ReadWeightsFromXML   ( void* wghtnode );
 
      // calculate the MVA value (for CUTs this is just a dummy)
      Double_t GetMvaValue( Double_t* err = nullptr, Double_t* errUpper = nullptr );
 
      // write method specific histos to target file
      void WriteMonitoringHistosToFile( void ) const;
 
      // test the method
      void TestClassification();
 
      // also overwrite --> not computed for cuts
      Double_t GetSeparation  ( TH1*, TH1* ) const { return -1; }
      Double_t GetSeparation  ( PDF* = nullptr, PDF* = nullptr ) const { return -1; }
      Double_t GetSignificance( void )       const { return -1; }
      Double_t GetmuTransform ( TTree *)           { return -1; }
      Double_t GetEfficiency  ( const TString&, Types::ETreeType, Double_t& );
      Double_t GetTrainingEfficiency(const TString& );
 
      // rarity distributions (signal or background (default) is uniform in [0,1])
      Double_t GetRarity( Double_t, Types::ESBType ) const { return 0; }
 
      // accessors for Minuit
      Double_t ComputeEstimator( std::vector<Double_t> & );
 
      Double_t EstimatorFunction( std::vector<Double_t> & );
      Double_t EstimatorFunction( Int_t ievt1, Int_t ievt2 );
 
      void     SetTestSignalEfficiency( Double_t effS ) { fTestSignalEff = effS; }
 
      // retrieve cut values for given signal efficiency
      void     PrintCuts( Double_t effS ) const;
      Double_t GetCuts  ( Double_t effS, std::vector<Double_t>& cutMin, std::vector<Double_t>& cutMax ) const;
      Double_t GetCuts  ( Double_t effS, Double_t* cutMin, Double_t* cutMax ) const;
 
      // ranking of input variables (not available for cuts)
      const Ranking* CreateRanking() { return nullptr; }
 
      void DeclareOptions();
      void ProcessOptions();
 
      // maximum |cut| value
      static const Double_t fgMaxAbsCutVal;
 
      // no check of options at this place
      void CheckSetup() {}
 
   protected:
 
      // make ROOT-independent C++ class for classifier response (classifier-specific implementation)
      void MakeClassSpecific( std::ostream&, const TString& ) const;
 
      // get help message text
      void GetHelpMessage() const;
 
   private:
 
      // optimisation method
      enum EFitMethodType { kUseMonteCarlo = 0,
                            kUseGeneticAlgorithm,
                            kUseSimulatedAnnealing,
                            kUseMinuit,
                            kUseEventScan,
                            kUseMonteCarloEvents };
 
      // efficiency calculation method
      // - kUseEventSelection: computes efficiencies from given data sample
      // - kUsePDFs          : creates smoothed PDFs from data samples, and
      //                       uses this to compute efficiencies
      enum EEffMethod     { kUseEventSelection = 0,
                            kUsePDFs };
 
      // improve the Monte Carlo by providing some additional information
      enum EFitParameters { kNotEnforced = 0,
                            kForceMin,
                            kForceMax,
                            kForceSmart };
 
      // general
      TString                 fFitMethodS;         ///< chosen fit method (string)
      EFitMethodType          fFitMethod;          ///< chosen fit method
      TString                 fEffMethodS;         ///< chosen efficiency calculation method (string)
      EEffMethod              fEffMethod;          ///< chosen efficiency calculation method
      std::vector<EFitParameters>* fFitParams;     ///< vector for series of fit methods
      Double_t                fTestSignalEff;      ///< used to test optimized signal efficiency
      Double_t                fEffSMin;            ///< used to test optimized signal efficiency
      Double_t                fEffSMax;            ///< used to test optimized signal efficiency
      Double_t*               fCutRangeMin;        ///< minimum of allowed cut range
      Double_t*               fCutRangeMax;        ///< maximum of allowed cut range
      std::vector<Interval*>  fCutRange;           ///< allowed ranges for cut optimisation
 
      // for the use of the binary tree method
      BinarySearchTree*       fBinaryTreeS;
      BinarySearchTree*       fBinaryTreeB;
 
      // MC method
      Double_t**              fCutMin;             ///< minimum requirement
      Double_t**              fCutMax;             ///< maximum requirement
      Double_t*               fTmpCutMin;          ///< temporary minimum requirement
      Double_t*               fTmpCutMax;          ///< temporary maximum requirement
      TString*                fAllVarsI;           ///< what to do with variables
 
      // relevant for all methods
      Int_t                   fNpar;               ///< number of parameters in fit (default: 2*Nvar)
      Double_t                fEffRef;             ///< reference efficiency
      std::vector<Int_t>*     fRangeSign;          ///< used to match cuts to fit parameters (and vice versa)
      TRandom*                fRandom;             ///< random generator for MC optimisation method
 
      // basic statistics
      std::vector<Double_t>*  fMeanS;              ///< means of variables (signal)
      std::vector<Double_t>*  fMeanB;              ///< means of variables (background)
      std::vector<Double_t>*  fRmsS;               ///< RMSs of variables (signal)
      std::vector<Double_t>*  fRmsB;               ///< RMSs of variables (background)
 
      TH1*                    fEffBvsSLocal;       ///< intermediate eff. background versus eff signal histo
 
      // PDF section
      std::vector<TH1*>*      fVarHistS;           ///< reference histograms (signal)
      std::vector<TH1*>*      fVarHistB;           ///< reference histograms (background)
      std::vector<TH1*>*      fVarHistS_smooth;    ///< smoothed reference histograms (signal)
      std::vector<TH1*>*      fVarHistB_smooth;    ///< smoothed reference histograms (background)
      std::vector<PDF*>*      fVarPdfS;            ///< reference PDFs (signal)
      std::vector<PDF*>*      fVarPdfB;            ///< reference PDFs (background)
 
      // negative efficiencies
      Bool_t                  fNegEffWarning;      ///< flag risen in case of negative efficiency warning
 
 
      // the definition of fit parameters can be different from the actual
      // cut requirements; these functions provide the matching
      void     MatchParsToCuts( const std::vector<Double_t>&, Double_t*, Double_t* );
      void     MatchParsToCuts( Double_t*, Double_t*, Double_t* );
 
      void     MatchCutsToPars( std::vector<Double_t>&, Double_t*, Double_t* );
      void     MatchCutsToPars( std::vector<Double_t>&, Double_t**, Double_t**, Int_t ibin );
 
      // creates PDFs in case these are used to compute efficiencies
      // (corresponds to: EffMethod == kUsePDFs)
      void     CreateVariablePDFs( void );
 
      // returns signal and background efficiencies for given cuts - using event counting
      void     GetEffsfromSelection( Double_t* cutMin, Double_t* cutMax,
                                     Double_t& effS, Double_t& effB );
      // returns signal and background efficiencies for given cuts - using PDFs
      void     GetEffsfromPDFs( Double_t* cutMin, Double_t* cutMax,
                                Double_t& effS, Double_t& effB );
 
      // default initialisation method called by all constructors
      void     Init( void );
 
      ClassDef(MethodCuts,0);  // Multivariate optimisation of signal efficiency
   };
 
} // namespace TMVA
 
#endif