/usr/include/root/TMVA/MethodBoost.h is in libroot-tmva-dev 5.34.30-0ubuntu8.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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// Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss,Or Cohen, Jan Therhaag, Eckhard von Toerne
/**********************************************************************************
* Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
* Package: TMVA *
* Class : MethodCompositeBase *
* Web : http://tmva.sourceforge.net *
* *
* Description: *
* Virtual base class for all MVA method *
* *
* Authors (alphabetical): *
* Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland *
* Peter Speckmayer <Peter.Speckmazer@cern.ch> - CERN, Switzerland *
* Joerg Stelzer <Joerg.Stelzer@cern.ch> - CERN, Switzerland *
* Helge Voss <Helge.Voss@cern.ch> - MPI-K Heidelberg, Germany *
* Jan Therhaag <Jan.Therhaag@cern.ch> - U of Bonn, Germany *
* Eckhard v. Toerne <evt@uni-bonn.de> - 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_MethodBoost
#define ROOT_TMVA_MethodBoost
//////////////////////////////////////////////////////////////////////////
// //
// MethodBoost //
// //
// Class for boosting a TMVA method //
// //
//////////////////////////////////////////////////////////////////////////
#include <iosfwd>
#include <vector>
#ifndef ROOT_TMVA_MethodBase
#include "TMVA/MethodBase.h"
#endif
#ifndef ROOT_TMVA_MethodCompositeBase
#include "TMVA/MethodCompositeBase.h"
#endif
namespace TMVA {
class Factory; // DSMTEST
class Reader; // DSMTEST
class DataSetManager; // DSMTEST
class MethodBoost : public MethodCompositeBase {
public :
// constructors
MethodBoost( const TString& jobName,
const TString& methodTitle,
DataSetInfo& theData,
const TString& theOption = "",
TDirectory* theTargetDir = NULL );
MethodBoost( DataSetInfo& dsi,
const TString& theWeightFile,
TDirectory* theTargetDir = NULL );
virtual ~MethodBoost( void );
virtual Bool_t HasAnalysisType( Types::EAnalysisType type, UInt_t numberClasses, UInt_t /*numberTargets*/ );
// training and boosting all the classifiers
void Train( void );
// ranking of input variables
const Ranking* CreateRanking();
// saves the name and options string of the boosted classifier
Bool_t BookMethod( Types::EMVA theMethod, TString methodTitle, TString theOption );
void SetBoostedMethodName ( TString methodName ) { fBoostedMethodName = methodName; }
Int_t GetBoostNum() { return fBoostNum; }
void CleanBoostOptions();
Double_t GetMvaValue( Double_t* err=0, Double_t* errUpper = 0 );
private :
// clean up
void ClearAll();
// print fit results
void PrintResults( const TString&, std::vector<Double_t>&, const Double_t ) const;
// initializing mostly monitoring tools of the boost process
void Init();
void InitHistos();
void CheckSetup();
void MonitorBoost( Types::EBoostStage stage, UInt_t methodIdx=0);
// the option handling methods
void DeclareOptions();
void DeclareCompatibilityOptions();
void ProcessOptions();
MethodBase* CurrentMethod(){return fCurrentMethod;}
UInt_t CurrentMethodIdx(){return fCurrentMethodIdx;}
// training a single classifier
void SingleTrain();
// calculating a boosting weight from the classifier, storing it in the next one
Double_t SingleBoost(MethodBase* method);
Double_t AdaBoost(MethodBase* method, Bool_t useYesNoLeaf );
Double_t Bagging();
// calculate weight of single method
Double_t CalcMethodWeight();
// return ROC integral on training/testing sample
Double_t GetBoostROCIntegral(Bool_t, Types::ETreeType, Bool_t CalcOverlapIntergral=kFALSE);
// writing the monitoring histograms and tree to a file
void WriteMonitoringHistosToFile( void ) const;
// write evaluation histograms into target file
virtual void WriteEvaluationHistosToFile(Types::ETreeType treetype);
// performs the MethodBase testing + testing of each boosted classifier
virtual void TestClassification();
// finding the MVA to cut between sig and bgd according to fMVACutPerc,fMVACutType
void FindMVACut(MethodBase* method);
// setting all the boost weights to 1
void ResetBoostWeights();
// creating the vectors of histogram for monitoring MVA response of each classifier
void CreateMVAHistorgrams();
// calculate MVA values of current trained method on training
// sample
void CalcMVAValues();
UInt_t fBoostNum; // Number of times the classifier is boosted
TString fBoostType; // string specifying the boost type
TString fTransformString; // min and max values for the classifier response
Bool_t fDetailedMonitoring; // produce detailed monitoring histograms (boost-wise)
Double_t fAdaBoostBeta; // ADA boost parameter, default is 1
UInt_t fRandomSeed; // seed for random number generator used for bagging
Double_t fBaggedSampleFraction;// rel.Size of bagged sample
TString fBoostedMethodName; // details of the boosted classifier
TString fBoostedMethodTitle; // title
TString fBoostedMethodOptions; // options
Bool_t fMonitorBoostedMethod; // monitor the MVA response of every classifier
// MVA output from each classifier over the training hist, using orignal events weights
std::vector< TH1* > fTrainSigMVAHist;
std::vector< TH1* > fTrainBgdMVAHist;
// MVA output from each classifier over the training hist, using boosted events weights
std::vector< TH1* > fBTrainSigMVAHist;
std::vector< TH1* > fBTrainBgdMVAHist;
// MVA output from each classifier over the testing hist
std::vector< TH1* > fTestSigMVAHist;
std::vector
< TH1* > fTestBgdMVAHist;
//monitoring tree/ntuple and it's variables
TTree* fMonitorTree; // tree to monitor values during the boosting
Double_t fBoostWeight; // the weight used to boost the next classifier
Double_t fMethodError; // estimation of the level error of the classifier
// analysing the train dataset
Double_t fROC_training; // roc integral of last trained method (on training sample)
// overlap integral of mva distributions for signal and
// background (training sample)
Double_t fOverlap_integral;
std::vector<Float_t> *fMVAvalues; // mva values for the last trained method
DataSetManager* fDataSetManager; // DSMTEST
friend class Factory; // DSMTEST
friend class Reader; // DSMTEST
TString fHistoricOption; //historic variable, only needed for "CompatibilityOptions"
Bool_t fHistoricBoolOption; //historic variable, only needed for "CompatibilityOptions"
protected:
// get help message text
void GetHelpMessage() const;
ClassDef(MethodBoost,0)
};
}
#endif
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