// @(#)root/tmva $Id: MethodCuts.h,v 1.9 2006/11/20 15:35:28 brun Exp $ // 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 * * Web : http://tmva.sourceforge.net * * * * 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 * * (http://tmva.sourceforge.net/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 // // // ////////////////////////////////////////////////////////////////////////// #ifndef ROOT_TMVA_MethodBase #include "TMVA/MethodBase.h" #endif #ifndef ROOT_TMVA_BinarySearchTree #include "TMVA/BinarySearchTree.h" #endif #ifndef ROOT_TMVA_PDF #include "TMVA/PDF.h" #endif #ifndef ROOT_TMVA_GeneticBase #include "TMVA/GeneticBase.h" #endif #ifndef ROOT_TMVA_TMatrixD #include "TMatrixD.h" #endif class TRandom; namespace TMVA { class MethodCuts : public MethodBase { public: MethodCuts( TString jobName, TString methodTitle, DataSet& theData, TString theOption = "MC:150:10000:", TDirectory* theTargetFile = 0 ); MethodCuts( DataSet& theData, TString theWeightFile, TDirectory* theTargetDir = NULL ); virtual ~MethodCuts( void ); // training method virtual void Train( void ); // write weights to file virtual void WriteWeightsToStream( ostream& o ) const; // read weights from file virtual void ReadWeightsFromStream( istream& istr ); // calculate the MVA value (for CUTs this is just a dummy) virtual Double_t GetMvaValue(); // write method specific histos to target file virtual void WriteMonitoringHistosToFile( void ) const; // test the method virtual void Test( TTree* theTestTree ); // also overwrite: virtual Double_t GetSignificance( void ) { return 0; } virtual Double_t GetSeparation ( void ) { return 0; } virtual Double_t GetmuTransform ( TTree *) { return 0; } virtual Double_t GetEfficiency ( TString, TTree *); virtual Double_t GetTrainingEfficiency ( TString ); // accessors for Minuit Double_t ComputeEstimator( const std::vector<Double_t> & ); void SetTestSignalEfficiency( Double_t eff ) { fTestSignalEff = eff; } // static pointer to this object static MethodCuts* ThisCuts( void ) { return fgThisCuts; } // original version of above // ranking of input variables const Ranking* CreateRanking() { return 0; } virtual void DeclareOptions(); virtual void ProcessOptions(); protected: private: // determines type of data to be optimised enum EConstrainType { kConstrainEffS = 0, kConstrainEffB } fConstrainType; // optimisation method enum EFitMethodType { kUseMonteCarlo = 0, kUseGeneticAlgorithm, kUseSimulatedAnnealing }; // 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, kForceVerySmart }; // 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 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 // for the use of the binary tree method BinarySearchTree* fBinaryTreeS; BinarySearchTree* fBinaryTreeB; // GA (genetic algorithm) options Int_t fGA_nsteps; // GA settings: number of steps Int_t fGA_cycles; // GA settings: number of pre-calc steps Int_t fGA_popSize; // GA settings: population size Int_t fGA_SC_steps; // GA settings: SC_steps Int_t fGA_SC_offsteps; // GA settings: SC_offsteps Double_t fGA_SC_factor; // GA settings: SC_factor // SA (simulated annealing) options Int_t fSA_MaxCalls; // max number of FCN calls Double_t fSA_TemperatureGradient; // starting value for temperature gradient Bool_t fSA_UseAdaptiveTemperature; // compute temperature steps on the fly Double_t fSA_InitialTemperature; // initial temperature (depends on FCN) Double_t fSA_MinTemperature; // minimum temperature before SA quit Double_t fSA_Eps; // relative required FCN accuracy at minimum Int_t fSA_NFunLoops; // number of FCN loops Int_t fSA_NEps; // test parameter // MC method Int_t fNRandCuts; // number of random cut samplings Double_t** fCutMin; // minimum requirement Double_t** fCutMax; // maximum requirement Double_t* fTmpCutMin; // temporary minimum requirement Double_t* fTmpCutMax; // temporary maximum requirement TString fAllVars; TString fAllVarsI[10]; // relevant for all methods Int_t fNpar; // number of parameters in fit (default: 2*Nvar) Double_t fEffRef; // reference efficiency vector<Int_t>* fRangeSign; // used to match cuts to fit parameters (and vice versa) TRandom* fRandom; // random generator for MC optimisation method // basic statistics vector<Double_t>* fMeanS; // means of variables (signal) vector<Double_t>* fMeanB; // means of variables (background) vector<Double_t>* fRmsS; // RMSs of variables (signal) vector<Double_t>* fRmsB; // RMSs of variables (background) vector<Double_t>* fXmin; // minimum values of variables vector<Double_t>* fXmax; // maximum values of variables TH1* fEffBvsSLocal; // intermediate eff. background versus eff signal histo // PDF section vector<TH1*>* fVarHistS; // reference histograms (signal) vector<TH1*>* fVarHistB; // reference histograms (background) vector<TH1*>* fVarHistS_smooth; // smoothed reference histograms (signal) vector<TH1*>* fVarHistB_smooth; // smoothed reference histograms (background) vector<PDF*>* fVarPdfS; // reference PDFs (signal) vector<PDF*>* fVarPdfB; // reference PDFs (background) static MethodCuts* fgThisCuts; // used for function reference (GA) // 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( Double_t*, Double_t*, Double_t* ); // creates PDFs in case these are used to compute efficiencies // (corresponds to: EffMethod == kUsePDFs) void CreateVariablePDFs( void ); // checks ordering of variables in vectors Bool_t SanityChecks( 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 InitCuts( void ); ClassDef(MethodCuts,0) // Multivariate optimisation of signal efficiency ; }; } // namespace TMVA #endif