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0001 // @(#)root/tmva $Id$ 0002 // Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss 0003 0004 /********************************************************************************** 0005 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis * 0006 * Package: TMVA * 0007 * Class : MethodDT (Boosted Decision Trees) * 0008 * * 0009 * * 0010 * Description: * 0011 * Analysis of Boosted Decision Trees * 0012 * * 0013 * Authors (alphabetical): * 0014 * Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland * 0015 * Helge Voss <Helge.Voss@cern.ch> - MPI-K Heidelberg, Germany * 0016 * Or Cohen <orcohenor@gmail.com> - Weizmann Inst., Israel * 0017 * * 0018 * Copyright (c) 2005: * 0019 * CERN, Switzerland * 0020 * MPI-K Heidelberg, Germany * 0021 * * 0022 * Redistribution and use in source and binary forms, with or without * 0023 * modification, are permitted according to the terms listed in LICENSE * 0024 * (see tmva/doc/LICENSE) * 0025 **********************************************************************************/ 0026 0027 #ifndef ROOT_TMVA_MethodDT 0028 #define ROOT_TMVA_MethodDT 0029 0030 ////////////////////////////////////////////////////////////////////////// 0031 // // 0032 // MethodDT // 0033 // // 0034 // Analysis of Single Decision Tree // 0035 // // 0036 ////////////////////////////////////////////////////////////////////////// 0037 0038 #include <vector> 0039 #include "TH1.h" 0040 #include "TH2.h" 0041 #include "TTree.h" 0042 #include "TMVA/MethodBase.h" 0043 #include "TMVA/DecisionTree.h" 0044 #include "TMVA/Event.h" 0045 0046 namespace TMVA { 0047 class MethodBoost; 0048 0049 class MethodDT : public MethodBase { 0050 public: 0051 MethodDT( const TString& jobName, 0052 const TString& methodTitle, 0053 DataSetInfo& theData, 0054 const TString& theOption = ""); 0055 0056 MethodDT( DataSetInfo& dsi, 0057 const TString& theWeightFile); 0058 0059 virtual ~MethodDT( void ); 0060 0061 virtual Bool_t HasAnalysisType( Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets ); 0062 0063 void Train( void ); 0064 0065 using MethodBase::ReadWeightsFromStream; 0066 0067 // write weights to file 0068 void AddWeightsXMLTo( void* parent ) const; 0069 0070 // read weights from file 0071 void ReadWeightsFromStream( std::istream& istr ); 0072 void ReadWeightsFromXML ( void* wghtnode ); 0073 0074 // calculate the MVA value 0075 Double_t GetMvaValue( Double_t* err = nullptr, Double_t* errUpper = nullptr ); 0076 0077 // the option handling methods 0078 void DeclareOptions(); 0079 void ProcessOptions(); 0080 void DeclareCompatibilityOptions(); 0081 0082 void GetHelpMessage() const; 0083 0084 // ranking of input variables 0085 const Ranking* CreateRanking(); 0086 0087 Double_t PruneTree( ); 0088 0089 Double_t TestTreeQuality( DecisionTree *dt ); 0090 0091 Double_t GetPruneStrength () { return fPruneStrength; } 0092 0093 void SetMinNodeSize(Double_t sizeInPercent); 0094 void SetMinNodeSize(TString sizeInPercent); 0095 0096 Int_t GetNNodesBeforePruning(){return fTree->GetNNodesBeforePruning();} 0097 Int_t GetNNodes(){return fTree->GetNNodes();} 0098 0099 private: 0100 // Init used in the various constructors 0101 void Init( void ); 0102 0103 private: 0104 0105 0106 std::vector<Event*> fEventSample; ///< the training events 0107 0108 DecisionTree* fTree; ///< the decision tree 0109 //options for the decision Tree 0110 SeparationBase *fSepType; ///< the separation used in node splitting 0111 TString fSepTypeS; ///< the separation (option string) used in node splitting 0112 Int_t fMinNodeEvents; ///< min number of events in node 0113 Float_t fMinNodeSize; ///< min percentage of training events in node 0114 TString fMinNodeSizeS; ///< string containing min percentage of training events in node 0115 0116 Int_t fNCuts; ///< grid used in cut applied in node splitting 0117 Bool_t fUseYesNoLeaf; ///< use sig or bkg classification in leave nodes or sig/bkg 0118 Double_t fNodePurityLimit; ///< purity limit for sig/bkg nodes 0119 UInt_t fMaxDepth; ///< max depth 0120 0121 0122 Double_t fErrorFraction; ///< ntuple var: misclassification error fraction 0123 Double_t fPruneStrength; ///< a parameter to set the "amount" of pruning..needs to be adjusted 0124 DecisionTree::EPruneMethod fPruneMethod; ///< method used for pruning 0125 TString fPruneMethodS; ///< prune method option String 0126 Bool_t fAutomatic; ///< use user given prune strength or automatically determined one using a validation sample 0127 Bool_t fRandomisedTrees; ///< choose a random subset of possible cut variables at each node during training 0128 Int_t fUseNvars; ///< the number of variables used in the randomised tree splitting 0129 Bool_t fUsePoissonNvars; ///< fUseNvars is used as a poisson mean, and the actual value of useNvars is at each step drawn form that distribution 0130 std::vector<Double_t> fVariableImportance; ///< the relative importance of the different variables 0131 0132 Double_t fDeltaPruneStrength; ///< step size in pruning, is adjusted according to experience of previous trees 0133 // debugging flags 0134 static const Int_t fgDebugLevel = 0; ///< debug level determining some printout/control plots etc. 0135 0136 0137 Bool_t fPruneBeforeBoost; ///< ancient variable, only needed for "CompatibilityOptions" 0138 0139 ClassDef(MethodDT,0); // Analysis of Decision Trees 0140 0141 }; 0142 } 0143 0144 #endif
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