// @(#)root/tmva $Id$ // Author: Andreas Hoecker, Joerg Stelzer, Fredrik Tegenfeldt, Helge Voss /********************************************************************************** * Project: TMVA - a Root-integrated toolkit for multivariate data analysis * * Package: TMVA * * Class : RuleFit * * Web : http://tmva.sourceforge.net * * * * Description: * * A class implementing various fits of rule ensembles * * * * 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_RuleFit #define ROOT_TMVA_RuleFit #include #ifndef ROOT_TMVA_DecisionTree #include "TMVA/DecisionTree.h" #endif #ifndef ROOT_TMVA_RuleEnsemble #include "TMVA/RuleEnsemble.h" #endif #ifndef ROOT_TMVA_RuleFitParams #include "TMVA/RuleFitParams.h" #endif #ifndef ROOT_TMVA_Event #include "TMVA/Event.h" #endif namespace TMVA { class MethodBase; class MethodRuleFit; class MsgLogger; class RuleFit { public: // main constructor RuleFit( const TMVA::MethodBase *rfbase ); // empty constructor RuleFit( void ); virtual ~RuleFit( void ); void InitNEveEff(); void InitPtrs( const TMVA::MethodBase *rfbase ); void Initialize( const TMVA::MethodBase *rfbase ); void SetMsgType( EMsgType t ); void SetTrainingEvents( const std::vector & el ); void ReshuffleEvents() { std::random_shuffle(fTrainingEventsRndm.begin(),fTrainingEventsRndm.end()); } void SetMethodBase( const MethodBase *rfbase ); // make the forest of trees for rule generation void MakeForest(); // build a tree void BuildTree( TMVA::DecisionTree *dt ); // save event weights void SaveEventWeights(); // restore saved event weights void RestoreEventWeights(); // boost events based on the given tree void Boost( TMVA::DecisionTree *dt ); // calculate and print some statistics on the given forest void ForestStatistics(); // calculate the discriminating variable for the given event Double_t EvalEvent( const Event& e ); // calculate sum of Double_t CalcWeightSum( const std::vector *events, UInt_t neve=0 ); // do the fitting of the coefficients void FitCoefficients(); // calculate variable and rule importance from a set of events void CalcImportance(); // set usage of linear term void SetModelLinear() { fRuleEnsemble.SetModelLinear(); } // set usage of rules void SetModelRules() { fRuleEnsemble.SetModelRules(); } // set usage of linear term void SetModelFull() { fRuleEnsemble.SetModelFull(); } // set minimum importance allowed void SetImportanceCut( Double_t minimp=0 ) { fRuleEnsemble.SetImportanceCut(minimp); } // set minimum rule distance - see RuleEnsemble void SetRuleMinDist( Double_t d ) { fRuleEnsemble.SetRuleMinDist(d); } // set path related parameters void SetGDTau( Double_t t=0.0 ) { fRuleFitParams.SetGDTau(t); } void SetGDPathStep( Double_t s=0.01 ) { fRuleFitParams.SetGDPathStep(s); } void SetGDNPathSteps( Int_t n=100 ) { fRuleFitParams.SetGDNPathSteps(n); } // make visualization histograms void SetVisHistsUseImp( Bool_t f ) { fVisHistsUseImp = f; } void UseImportanceVisHists() { fVisHistsUseImp = kTRUE; } void UseCoefficientsVisHists() { fVisHistsUseImp = kFALSE; } void MakeVisHists(); void FillVisHistCut(const Rule * rule, std::vector & hlist); void FillVisHistCorr(const Rule * rule, std::vector & hlist); void FillCut(TH2F* h2,const TMVA::Rule *rule,Int_t vind); void FillLin(TH2F* h2,Int_t vind); void FillCorr(TH2F* h2,const TMVA::Rule *rule,Int_t v1, Int_t v2); void NormVisHists(std::vector & hlist); void MakeDebugHists(); Bool_t GetCorrVars(TString & title, TString & var1, TString & var2); // accessors UInt_t GetNTreeSample() const { return fNTreeSample; } Double_t GetNEveEff() const { return fNEveEffTrain; } // reweighted number of events = sum(wi) const Event* GetTrainingEvent(UInt_t i) const { return static_cast< const Event *>(fTrainingEvents[i]); } Double_t GetTrainingEventWeight(UInt_t i) const { return fTrainingEvents[i]->GetWeight(); } // const Event* GetTrainingEvent(UInt_t i, UInt_t isub) const { return &(fTrainingEvents[fSubsampleEvents[isub]])[i]; } const std::vector< const TMVA::Event * > & GetTrainingEvents() const { return fTrainingEvents; } // const std::vector< Int_t > & GetSubsampleEvents() const { return fSubsampleEvents; } // void GetSubsampleEvents(Int_t sub, UInt_t & ibeg, UInt_t & iend) const; void GetRndmSampleEvents(std::vector< const TMVA::Event * > & evevec, UInt_t nevents); // const std::vector< const TMVA::DecisionTree *> & GetForest() const { return fForest; } const RuleEnsemble & GetRuleEnsemble() const { return fRuleEnsemble; } RuleEnsemble * GetRuleEnsemblePtr() { return &fRuleEnsemble; } const RuleFitParams & GetRuleFitParams() const { return fRuleFitParams; } RuleFitParams * GetRuleFitParamsPtr() { return &fRuleFitParams; } const MethodRuleFit * GetMethodRuleFit() const { return fMethodRuleFit; } const MethodBase * GetMethodBase() const { return fMethodBase; } private: // copy constructor RuleFit( const RuleFit & other ); // copy method void Copy( const RuleFit & other ); std::vector fTrainingEvents; // all training events std::vector fTrainingEventsRndm; // idem, but randomly shuffled std::vector fEventWeights; // original weights of the events - follows fTrainingEvents UInt_t fNTreeSample; // number of events in sub sample = frac*neve Double_t fNEveEffTrain; // reweighted number of events = sum(wi) std::vector< const TMVA::DecisionTree *> fForest; // the input forest of decision trees RuleEnsemble fRuleEnsemble; // the ensemble of rules RuleFitParams fRuleFitParams; // fit rule parameters const MethodRuleFit *fMethodRuleFit; // pointer the method which initialized this RuleFit instance const MethodBase *fMethodBase; // pointer the method base which initialized this RuleFit instance Bool_t fVisHistsUseImp; // if true, use importance as weight; else coef in vis hists mutable MsgLogger* fLogger; // message logger MsgLogger& Log() const { return *fLogger; } static const Int_t randSEED = 0; // set to 1 for debugging purposes or to zero for random seeds ClassDef(RuleFit,0) // Calculations for Friedman's RuleFit method }; } #endif