// @(#)root/tmva $Id: DecisionTreeNode.h 21079 2007-11-27 20:08:49Z brun $ // Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss /********************************************************************************** * Project: TMVA - a Root-integrated toolkit for multivariate data analysis * * Package: TMVA * * Class : DecisionTreeNode * * Web : http://tmva.sourceforge.net * * * * Description: * * Node for the Decision Tree * * * * Authors (alphabetical): * * Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland * * Xavier Prudent <prudent@lapp.in2p3.fr> - LAPP, France * * 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_DecisionTreeNode #define ROOT_TMVA_DecisionTreeNode ////////////////////////////////////////////////////////////////////////// // // // DecisionTreeNode // // // // Node for the Decision Tree // // // ////////////////////////////////////////////////////////////////////////// #ifndef ROOT_TMVA_Node #include "TMVA/Node.h" #endif using std::string; namespace TMVA { class Event; class DecisionTreeNode: public Node { public: // constructor of an essentially "empty" node floating in space DecisionTreeNode (); // constructor of a daughter node as a daughter of 'p' DecisionTreeNode (Node* p, char pos); // copy constructor DecisionTreeNode (const DecisionTreeNode &n, DecisionTreeNode* parent = NULL); virtual ~DecisionTreeNode(){} // test event if it decends the tree at this node to the right virtual Bool_t GoesRight( const Event & ) const; // test event if it decends the tree at this node to the left virtual Bool_t GoesLeft ( const Event & ) const; // set index of variable used for discrimination at this node void SetSelector( Short_t i) { fSelector = i; } // return index of variable used for discrimination at this node Short_t GetSelector() const { return fSelector; } // set the cut value applied at this node void SetCutValue ( Double_t c ) { fCutValue = c; } // return the cut value applied at this node Double_t GetCutValue ( void ) const { return fCutValue; } // set true: if event variable > cutValue ==> signal , false otherwise void SetCutType( Bool_t t ) { fCutType = t; } // return kTRUE: Cuts select signal, kFALSE: Cuts select bkg Bool_t GetCutType( void ) const { return fCutType; } // set node type: 1 signal node, -1 bkg leave, 0 intermediate Node void SetNodeType( Int_t t ) { fNodeType = t;} // return node type: 1 signal node, -1 bkg leave, 0 intermediate Node Int_t GetNodeType( void ) const { return fNodeType; } //return S/(S+B) (purity) at this node (from training) Double_t GetPurity( void ) const ; // set the sum of the signal weights in the node void SetNSigEvents( Double_t s ) { fNSigEvents = s; } // set the sum of the backgr weights in the node void SetNBkgEvents( Double_t b ) { fNBkgEvents = b; } // set the number of events that entered the node (during training) void SetNEvents( Double_t nev ){ fNEvents =nev ; } // set the sum of the unweighted signal events in the node void SetNSigEvents_unweighted( Double_t s ) { fNSigEvents_unweighted = s; } // set the sum of the unweighted backgr events in the node void SetNBkgEvents_unweighted( Double_t b ) { fNBkgEvents_unweighted = b; } // set the number of unweighted events that entered the node (during training) void SetNEvents_unweighted( Double_t nev ){ fNEvents_unweighted =nev ; } // increment the sum of the signal weights in the node void IncrementNSigEvents( Double_t s ) { fNSigEvents += s; } // increment the sum of the backgr weights in the node void IncrementNBkgEvents( Double_t b ) { fNBkgEvents += b; } // increment the number of events that entered the node (during training) void IncrementNEvents( Double_t nev ){ fNEvents +=nev ; } // increment the sum of the signal weights in the node void IncrementNSigEvents_unweighted( ) { fNSigEvents_unweighted += 1; } // increment the sum of the backgr weights in the node void IncrementNBkgEvents_unweighted( ) { fNBkgEvents_unweighted += 1; } // increment the number of events that entered the node (during training) void IncrementNEvents_unweighted( ){ fNEvents_unweighted +=1 ; } // return the sum of the signal weights in the node Double_t GetNSigEvents( void ) const { return fNSigEvents; } // return the sum of the backgr weights in the node Double_t GetNBkgEvents( void ) const { return fNBkgEvents; } // return the number of events that entered the node (during training) Double_t GetNEvents( void ) const { return fNEvents; } // return the sum of unweighted signal weights in the node Double_t GetNSigEvents_unweighted( void ) const { return fNSigEvents_unweighted; } // return the sum of unweighted backgr weights in the node Double_t GetNBkgEvents_unweighted( void ) const { return fNBkgEvents_unweighted; } // return the number of unweighted events that entered the node (during training) Double_t GetNEvents_unweighted( void ) const { return fNEvents_unweighted; } // set the choosen index, measure of "purity" (separation between S and B) AT this node void SetSeparationIndex( Double_t sep ){ fSeparationIndex =sep ; } // return the separation index AT this node Double_t GetSeparationIndex( void ) const { return fSeparationIndex; } // set the separation, or information gained BY this nodes selection void SetSeparationGain( Double_t sep ){ fSeparationGain =sep ; } // return the gain in separation obtained by this nodes selection Double_t GetSeparationGain( void ) const { return fSeparationGain; } // printout of the node virtual void Print( ostream& os ) const; //recursively print the node and its daughters (--> print the 'tree') virtual void PrintRec( ostream& os ) const; //recursively clear the nodes content (S/N etc, but not the cut criteria) void ClearNodeAndAllDaughters(); ULong_t GetSequence() const {return fSequence;} void SetSequence(ULong_t s) {fSequence=s;} private: Bool_t ReadDataRecord( istream& is ); Double_t fCutValue; // cut value appplied on this node to discriminate bkg against sig Bool_t fCutType; // true: if event variable > cutValue ==> signal , false otherwise Short_t fSelector; // index of variable used in node selection (decision tree) Double_t fNSigEvents; // sum of weights of signal event in the node Double_t fNBkgEvents; // sum of weights of backgr event in the node Double_t fNEvents; // number of events in that entered the node (during training) Double_t fNSigEvents_unweighted; // sum of signal event in the node Double_t fNBkgEvents_unweighted; // sum of backgr event in the node Double_t fNEvents_unweighted; // number of events in that entered the node (during training) Double_t fSeparationIndex; // measure of "purity" (separation between S and B) AT this node Double_t fSeparationGain; // measure of "purity", separation, or information gained BY this nodes selection Int_t fNodeType; // Type of node: -1 == Bkg-leaf, 1 == Signal-leaf, 0 = internal ULong_t fSequence; // bit coded left right sequence to reach the node static MsgLogger* fgLogger; // static because there is a huge number of nodes... ClassDef(DecisionTreeNode,0) // Node for the Decision Tree }; } // namespace TMVA #endif