// @(#)root/tmva $Id: RuleFitParams.h 20882 2007-11-19 11:31:26Z rdm $ // Author: Andreas Hoecker, Joerg Stelzer, Fredrik Tegenfeldt, Helge Voss /********************************************************************************** * Project: TMVA - a Root-integrated toolkit for multivariate data analysis * * Package: TMVA * * Class : RuleFitParams * * Web : http://tmva.sourceforge.net * * * * Description: * * A class doing the actual fitting of a linear model using rules as * * base functions. * * Reference paper: 1.Gradient Directed Regularization * * Friedman, Popescu, 2004 * * 2.Predictive Learning with Rule Ensembles * * Friedman, Popescu, 2005 * * * * * * Authors (alphabetical): * * Fredrik Tegenfeldt - Iowa State U., USA * * Helge Voss - MPI-KP Heidelberg, Ger. * * * * Copyright (c) 2005: * * CERN, Switzerland * * Iowa State U. * * MPI-K Heidelberg, Germany * * * * 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_RuleFitParams #define ROOT_TMVA_RuleFitParams #if ROOT_VERSION_CODE >= 364802 #ifndef ROOT_TMathBase #include "TMathBase.h" #endif #else #ifndef ROOT_TMath #include "TMath.h" #endif #endif #ifndef ROOT_TMVA_Event #include "TMVA/Event.h" #endif #ifndef ROOT_TMVA_MsgLogger #include "TMVA/MsgLogger.h" #endif class TTree; namespace TMVA { class RuleEnsemble; class RuleFit; class RuleFitParams { public: RuleFitParams(); virtual ~RuleFitParams(); void Init(); // set message type void SetMsgType( EMsgType t ) { fLogger.SetMinType(t); } // set RuleFit ptr void SetRuleFit( RuleFit *rf ) { fRuleFit = rf; } // // GD path: set N(path steps) void SetGDNPathSteps( Int_t np ) { fGDNPathSteps = np; } // GD path: set path step size void SetGDPathStep( Double_t s ) { fGDPathStep = s; } // GD path: set tau search range void SetGDTauRange( Double_t t0, Double_t t1 ) { fGDTauMin = (t0>1.0 ? 1.0:(t0<0.0 ? 0.0:t0)); fGDTauMax = (t1>1.0 ? 1.0:(t1<0.0 ? 0.0:t1)); if (fGDTauMaxIsSignal() ? 1:-1); } // UInt_t GetPathIdx1() const { return fPathIdx1; } UInt_t GetPathIdx2() const { return fPathIdx2; } UInt_t GetPerfIdx1() const { return fPerfIdx1; } UInt_t GetPerfIdx2() const { return fPerfIdx2; } // Loss function; Huber loss eq 33 Double_t LossFunction( const Event& e ) const; // same but using evt idx (faster) Double_t LossFunction( UInt_t evtidx ) const; Double_t LossFunction( UInt_t evtidx, UInt_t itau ) const; // Empirical risk Double_t Risk(UInt_t ind1, UInt_t ind2, Double_t neff) const; Double_t Risk(UInt_t ind1, UInt_t ind2, Double_t neff, UInt_t itau) const; // Risk evaluation for fPathIdx and fPerfInd Double_t RiskPath() const { return Risk(fPathIdx1,fPathIdx2,fNEveEffPath); } Double_t RiskPerf() const { return Risk(fPerfIdx1,fPerfIdx2,fNEveEffPerf); } Double_t RiskPerf( UInt_t itau ) const { return Risk(fPerfIdx1,fPerfIdx2,fNEveEffPerf,itau); } // Risk evaluation for all tau UInt_t RiskPerfTst(); // Penalty function; Lasso function (eq 8) Double_t Penalty() const; // initialize GD path void InitGD(); // find best tau and return the number of scan steps used Int_t FindGDTau(); // make path for binary classification (squared-error ramp, sect 6 in ref 1) void MakeGDPath(); protected: // typedef of an Event const iterator typedef std::vector::const_iterator EventItr; // init ntuple void InitNtuple(); // calculate N(tau) in scan - limit to 100000. void CalcGDNTau() { fGDNTau = static_cast(1.0/fGDTauPrec)+1; if (fGDNTau>100000) fGDNTau=100000; } // fill ntuple with coefficient info void FillCoefficients(); // estimate the optimum scoring function void CalcFStar(); // estimate of binary error rate Double_t ErrorRateBin(); // estimate of scale average error rate Double_t ErrorRateReg(); // estimate 1-area under ROC Double_t ErrorRateRocRaw( std::vector & sFsig, std::vector & sFbkg ); Double_t ErrorRateRoc(); void ErrorRateRocTst(); // estimate optimism Double_t Optimism(); // make gradient vector (eq 44 in ref 1) void MakeGradientVector(); // Calculate the direction in parameter space (eq 25, ref 1) and update coeffs (eq 22, ref 1) void UpdateCoefficients(); // calculate average of responses of F Double_t CalcAverageResponse(); Double_t CalcAverageResponseOLD(); // calculate average of true response (initial estimate of a0) Double_t CalcAverageTruth(); // calculate the average of each variable over the range void EvaluateAverage(UInt_t ind1, UInt_t ind2, std::vector &avsel, std::vector &avrul); // evaluate using fPathIdx1,2 void EvaluateAveragePath() { EvaluateAverage( fPathIdx1, fPathIdx2, fAverageSelectorPath, fAverageRulePath ); } // evaluate using fPerfIdx1,2 void EvaluateAveragePerf() { EvaluateAverage( fPerfIdx1, fPerfIdx2, fAverageSelectorPerf, fAverageRulePerf ); } // the same as above but for the various tau void MakeTstGradientVector(); void UpdateTstCoefficients(); void CalcTstAverageResponse(); RuleFit * fRuleFit; // rule fit RuleEnsemble * fRuleEnsemble; // rule ensemble // UInt_t fNRules; // number of rules UInt_t fNLinear; // number of linear terms // // Event indecis for path/validation - TODO: should let the user decide // Now it is just a simple one-fold cross validation. // UInt_t fPathIdx1; // first event index for path search UInt_t fPathIdx2; // last event index for path search UInt_t fPerfIdx1; // first event index for performance evaluation UInt_t fPerfIdx2; // last event index for performance evaluation Double_t fNEveEffPath; // sum of weights for Path events Double_t fNEveEffPerf; // idem for Perf events std::vector fAverageSelectorPath; // average of each variable over the range fPathIdx1,2 std::vector fAverageRulePath; // average of each rule, same range std::vector fAverageSelectorPerf; // average of each variable over the range fPerfIdx1,2 std::vector fAverageRulePerf; // average of each rule, same range std::vector fGradVec; // gradient vector - dimension = number of rules in ensemble std::vector fGradVecLin; // gradient vector - dimension = number of variables std::vector< std::vector > fGradVecTst; // gradient vector - one per tau std::vector< std::vector > fGradVecLinTst; // gradient vector, linear terms - one per tau // std::vector fGDErrTst; // error rates per tau std::vector fGDErrTstOK; // error rate is sufficiently low std::vector< std::vector > fGDCoefTst; // rule coeffs - one per tau std::vector< std::vector > fGDCoefLinTst; // linear coeffs - one per tau std::vector fGDOfsTst; // offset per tau std::vector< Double_t > fGDTauVec; // the tau's UInt_t fGDNTauTstOK; // number of tau in the test-phase that are ok UInt_t fGDNTau; // number of tau-paths - calculated in SetGDTauPrec Double_t fGDTauPrec; // precision in tau UInt_t fGDTauScan; // number scan for tau-paths Double_t fGDTauMin; // min threshold parameter (tau in eq 26, ref 1) Double_t fGDTauMax; // max threshold parameter (tau in eq 26, ref 1) Double_t fGDTau; // selected threshold parameter (tau in eq 26, ref 1) Double_t fGDPathStep; // step size along path (delta nu in eq 22, ref 1) Int_t fGDNPathSteps; // number of path steps Double_t fGDErrScale; // stop scan at error = scale*errmin // Double_t fAverageTruth; // average truth, ie sum(y)/N, y=+-1 // std::vector fFstar; // vector of F*() - filled in CalcFStar() Double_t fFstarMedian; // median value of F*() using // TTree *fGDNtuple; // Gradient path ntuple, contains params for each step along the path Double_t fNTRisk; // GD path: risk Double_t fNTErrorRate; // GD path: error rate (or performance) Double_t fNTNuval; // GD path: value of nu Double_t fNTCoefRad; // GD path: 'radius' of all rulecoeffs Double_t fNTOffset; // GD path: model offset Double_t *fNTCoeff; // GD path: rule coefficients Double_t *fNTLinCoeff; // GD path: linear coefficients Double_t fsigave; // Sigma of current signal score function F(sig) Double_t fsigrms; // Rms of F(sig) Double_t fbkgave; // Average of F(bkg) Double_t fbkgrms; // Rms of F(bkg) private: mutable MsgLogger fLogger; // message logger }; // -------------------------------------------------------- class AbsValue { public: Bool_t operator()( Double_t first, Double_t second ) const { return TMath::Abs(first) < TMath::Abs(second); } }; } #endif