// @(#)root/tmva $Id$ // Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss, Jan Therhaag /********************************************************************************** * Project: TMVA - a Root-integrated toolkit for multivariate data analysis * * Package: TMVA * * Class : MethodBDT (Boosted Decision Trees) * * Web : http://tmva.sourceforge.net * * * * Description: * * Analysis of Boosted Decision Trees * * * * Authors (alphabetical): * * Andreas Hoecker - CERN, Switzerland * * Helge Voss - MPI-K Heidelberg, Germany * * Kai Voss - U. of Victoria, Canada * * Doug Schouten - Simon Fraser U., Canada * * Jan Therhaag - U. of Bonn, Germany * * * * Copyright (c) 2005-2011: * * CERN, Switzerland * * U. of Victoria, Canada * * MPI-K Heidelberg, Germany * * U. of Bonn, 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_MethodBDT #define ROOT_TMVA_MethodBDT ////////////////////////////////////////////////////////////////////////// // // // MethodBDT // // // // Analysis of Boosted Decision Trees // // // ////////////////////////////////////////////////////////////////////////// #include #ifndef ROOT_TH2 #include "TH2.h" #endif #ifndef ROOT_TTree #include "TTree.h" #endif #ifndef ROOT_TMVA_MethodBase #include "TMVA/MethodBase.h" #endif #ifndef ROOT_TMVA_DecisionTree #include "TMVA/DecisionTree.h" #endif #ifndef ROOT_TMVA_Event #include "TMVA/Event.h" #endif namespace TMVA { class SeparationBase; class MethodBDT : public MethodBase { public: // constructor for training and reading MethodBDT( const TString& jobName, const TString& methodTitle, DataSetInfo& theData, const TString& theOption = "", TDirectory* theTargetDir = 0 ); // constructor for calculating BDT-MVA using previously generatad decision trees MethodBDT( DataSetInfo& theData, const TString& theWeightFile, TDirectory* theTargetDir = NULL ); virtual ~MethodBDT( void ); virtual Bool_t HasAnalysisType( Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets ); // write all Events from the Tree into a vector of Events, that are // more easily manipulated void InitEventSample(); // optimize tuning parameters virtual std::map OptimizeTuningParameters(TString fomType="ROCIntegral", TString fitType="FitGA"); virtual void SetTuneParameters(std::map tuneParameters); // training method void Train( void ); // revoke training void Reset( void ); using MethodBase::ReadWeightsFromStream; // write weights to file void AddWeightsXMLTo( void* parent ) const; // read weights from file void ReadWeightsFromStream( std::istream& istr ); void ReadWeightsFromXML(void* parent); // write method specific histos to target file void WriteMonitoringHistosToFile( void ) const; // calculate the MVA value Double_t GetMvaValue( Double_t* err = 0, Double_t* errUpper = 0); // get the actual forest size (might be less than fNTrees, the requested one, if boosting is stopped early UInt_t GetNTrees() const {return fForest.size();} private: Double_t GetMvaValue( Double_t* err, Double_t* errUpper, UInt_t useNTrees ); Double_t PrivateGetMvaValue( const TMVA::Event *ev, Double_t* err=0, Double_t* errUpper=0, UInt_t useNTrees=0 ); void BoostMonitor(Int_t iTree); public: const std::vector& GetMulticlassValues(); // regression response const std::vector& GetRegressionValues(); // apply the boost algorithm to a tree in the collection Double_t Boost( std::vector&, DecisionTree *dt, UInt_t cls = 0); // ranking of input variables const Ranking* CreateRanking(); // the option handling methods void DeclareOptions(); void ProcessOptions(); void SetMaxDepth(Int_t d){fMaxDepth = d;} void SetMinNodeSize(Double_t sizeInPercent); void SetMinNodeSize(TString sizeInPercent); void SetNTrees(Int_t d){fNTrees = d;} void SetAdaBoostBeta(Double_t b){fAdaBoostBeta = b;} void SetNodePurityLimit(Double_t l){fNodePurityLimit = l;} void SetShrinkage(Double_t s){fShrinkage = s;} void SetUseNvars(Int_t n){fUseNvars = n;} void SetBaggedSampleFraction(Double_t f){fBaggedSampleFraction = f;} // get the forest inline const std::vector & GetForest() const; // get the forest inline const std::vector & GetTrainingEvents() const; inline const std::vector & GetBoostWeights() const; //return the individual relative variable importance std::vector GetVariableImportance(); Double_t GetVariableImportance(UInt_t ivar); Double_t TestTreeQuality( DecisionTree *dt ); // make ROOT-independent C++ class for classifier response (classifier-specific implementation) void MakeClassSpecific( std::ostream&, const TString& ) const; // header and auxiliary classes void MakeClassSpecificHeader( std::ostream&, const TString& ) const; void MakeClassInstantiateNode( DecisionTreeNode *n, std::ostream& fout, const TString& className ) const; void GetHelpMessage() const; protected: void DeclareCompatibilityOptions(); private: // Init used in the various constructors void Init( void ); void PreProcessNegativeEventWeights(); // boosting algorithm (adaptive boosting) Double_t AdaBoost( std::vector&, DecisionTree *dt ); // boosting algorithm (adaptive boosting with cost matrix) Double_t AdaCost( std::vector&, DecisionTree *dt ); // boosting as a random re-weighting Double_t Bagging( ); // boosting special for regression Double_t RegBoost( std::vector&, DecisionTree *dt ); // adaboost adapted to regression Double_t AdaBoostR2( std::vector&, DecisionTree *dt ); // binomial likelihood gradient boost for classification // (see Friedman: "Greedy Function Approximation: a Gradient Boosting Machine" // Technical report, Dept. of Statistics, Stanford University) Double_t GradBoost( std::vector&, DecisionTree *dt, UInt_t cls = 0); Double_t GradBoostRegression(std::vector&, DecisionTree *dt ); void InitGradBoost( std::vector&); void UpdateTargets( std::vector&, UInt_t cls = 0); void UpdateTargetsRegression( std::vector&,Bool_t first=kFALSE); Double_t GetGradBoostMVA(const TMVA::Event *e, UInt_t nTrees); void GetBaggedSubSample(std::vector&); Double_t GetWeightedQuantile(std::vector > vec, const Double_t quantile, const Double_t SumOfWeights = 0.0); std::vector fEventSample; // the training events std::vector fValidationSample;// the Validation events std::vector fSubSample; // subsample for bagged grad boost std::vector *fTrainSample; // pointer to sample actually used in training (fEventSample or fSubSample) for example Int_t fNTrees; // number of decision trees requested std::vector fForest; // the collection of decision trees std::vector fBoostWeights; // the weights applied in the individual boosts Double_t fSigToBkgFraction;// Signal to Background fraction assumed during training TString fBoostType; // string specifying the boost type Double_t fAdaBoostBeta; // beta parameter for AdaBoost algorithm TString fAdaBoostR2Loss; // loss type used in AdaBoostR2 (Linear,Quadratic or Exponential) Double_t fTransitionPoint; // break-down point for gradient regression Double_t fShrinkage; // learning rate for gradient boost; Bool_t fBaggedBoost; // turn bagging in combination with boost on/off Bool_t fBaggedGradBoost; // turn bagging in combination with grad boost on/off Double_t fSumOfWeights; // sum of all event weights std::map< const TMVA::Event*, std::pair > fWeightedResiduals; // weighted regression residuals std::map< const TMVA::Event*,std::vector > fResiduals; // individual event residuals for gradient boost //options for the decision Tree SeparationBase *fSepType; // the separation used in node splitting TString fSepTypeS; // the separation (option string) used in node splitting Int_t fMinNodeEvents; // min number of events in node Float_t fMinNodeSize; // min percentage of training events in node TString fMinNodeSizeS; // string containing min percentage of training events in node Int_t fNCuts; // grid used in cut applied in node splitting Bool_t fUseFisherCuts; // use multivariate splits using the Fisher criterium Double_t fMinLinCorrForFisher; // the minimum linear correlation between two variables demanded for use in fisher criterium in node splitting Bool_t fUseExclusiveVars; // individual variables already used in fisher criterium are not anymore analysed individually for node splitting Bool_t fUseYesNoLeaf; // use sig or bkg classification in leave nodes or sig/bkg Double_t fNodePurityLimit; // purity limit for sig/bkg nodes UInt_t fNNodesMax; // max # of nodes UInt_t fMaxDepth; // max depth DecisionTree::EPruneMethod fPruneMethod; // method used for prunig TString fPruneMethodS; // prune method option String Double_t fPruneStrength; // a parameter to set the "amount" of pruning..needs to be adjusted Double_t fFValidationEvents; // fraction of events to use for pruning Bool_t fAutomatic; // use user given prune strength or automatically determined one using a validation sample Bool_t fRandomisedTrees; // choose a random subset of possible cut variables at each node during training UInt_t fUseNvars; // the number of variables used in the randomised tree splitting Bool_t fUsePoissonNvars; // use "fUseNvars" not as fixed number but as mean of a possion distr. in each split UInt_t fUseNTrainEvents; // number of randomly picked training events used in randomised (and bagged) trees Double_t fBaggedSampleFraction; // relative size of bagged event sample to original sample size TString fNegWeightTreatment; // variable that holds the option of how to treat negative event weights in training Bool_t fNoNegWeightsInTraining; // ignore negative event weights in the training Bool_t fInverseBoostNegWeights; // boost ev. with neg. weights with 1/boostweight rathre than boostweight Bool_t fPairNegWeightsGlobal; // pair ev. with neg. and pos. weights in traning sample and "annihilate" them Bool_t fTrainWithNegWeights; // yes there are negative event weights and we don't ignore them Bool_t fDoBoostMonitor; //create control plot with ROC integral vs tree number //some histograms for monitoring TTree* fMonitorNtuple; // monitoring ntuple Int_t fITree; // ntuple var: ith tree Double_t fBoostWeight; // ntuple var: boost weight Double_t fErrorFraction; // ntuple var: misclassification error fraction Double_t fCss; // Cost factor Double_t fCts_sb; // Cost factor Double_t fCtb_ss; // Cost factor Double_t fCbb; // Cost factor Bool_t fDoPreselection; // do or do not perform automatic pre-selection of 100% eff. cuts std::vector fVariableImportance; // the relative importance of the different variables void DeterminePreselectionCuts(const std::vector& eventSample); Double_t ApplyPreselectionCuts(const Event* ev); std::vector fLowSigCut; std::vector fLowBkgCut; std::vector fHighSigCut; std::vector fHighBkgCut; std::vector fIsLowSigCut; std::vector fIsLowBkgCut; std::vector fIsHighSigCut; std::vector fIsHighBkgCut; Bool_t fHistoricBool; //historic variable, only needed for "CompatibilityOptions" // debugging flags static const Int_t fgDebugLevel; // debug level determining some printout/control plots etc. // for backward compatibility ClassDef(MethodBDT,0) // Analysis of Boosted Decision Trees }; } // namespace TMVA const std::vector& TMVA::MethodBDT::GetForest() const { return fForest; } const std::vector & TMVA::MethodBDT::GetTrainingEvents() const { return fEventSample; } const std::vector& TMVA::MethodBDT::GetBoostWeights() const { return fBoostWeights; } #endif