/usr/include/root/TMVA/Factory.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, Peter Speckmayer, Joerg Stelzer, Helge Voss, Kai Voss, Eckhard von Toerne, Jan Therhaag
/**********************************************************************************
* Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
* Package: TMVA *
* Class : Factory *
* Web : http://tmva.sourceforge.net *
* *
* Description: *
* This is the main MVA steering class: it creates (books) all MVA methods, *
* and guides them through the training, testing and evaluation phases. *
* *
* Authors (alphabetical): *
* Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland *
* Joerg Stelzer <stelzer@cern.ch> - DESY, Germany *
* Peter Speckmayer <peter.speckmayer@cern.ch> - CERN, Switzerland *
* Jan Therhaag <Jan.Therhaag@cern.ch> - U of Bonn, Germany *
* Eckhard v. Toerne <evt@uni-bonn.de> - U of Bonn, Germany *
* Helge Voss <Helge.Voss@cern.ch> - MPI-K Heidelberg, Germany *
* Kai Voss <Kai.Voss@cern.ch> - U. of Victoria, Canada *
* *
* 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_Factory
#define ROOT_TMVA_Factory
//////////////////////////////////////////////////////////////////////////
// //
// Factory //
// //
// This is the main MVA steering class: it creates all MVA methods, //
// and guides them through the training, testing and evaluation //
// phases //
// //
//////////////////////////////////////////////////////////////////////////
#include <string>
#include <vector>
#include <map>
#ifndef ROOT_TCut
#include "TCut.h"
#endif
#ifndef ROOT_TMVA_Configurable
#include "TMVA/Configurable.h"
#endif
#ifndef ROOT_TMVA_Types
#include "TMVA/Types.h"
#endif
#ifndef ROOT_TMVA_DataSet
#include "TMVA/DataSet.h"
#endif
class TFile;
class TTree;
class TDirectory;
namespace TMVA {
class IMethod;
class MethodBase;
class DataInputHandler;
class DataSetInfo;
class DataSetManager;
class VariableTransformBase;
class Factory : public Configurable {
public:
typedef std::vector<IMethod*> MVector;
// no default constructor
Factory( TString theJobName, TFile* theTargetFile, TString theOption = "" );
// default destructor
virtual ~Factory();
virtual const char* GetName() const { return "Factory"; }
// add events to training and testing trees
void AddSignalTrainingEvent ( const std::vector<Double_t>& event, Double_t weight = 1.0 );
void AddBackgroundTrainingEvent( const std::vector<Double_t>& event, Double_t weight = 1.0 );
void AddSignalTestEvent ( const std::vector<Double_t>& event, Double_t weight = 1.0 );
void AddBackgroundTestEvent ( const std::vector<Double_t>& event, Double_t weight = 1.0 );
void AddTrainingEvent( const TString& className, const std::vector<Double_t>& event, Double_t weight );
void AddTestEvent ( const TString& className, const std::vector<Double_t>& event, Double_t weight );
void AddEvent ( const TString& className, Types::ETreeType tt, const std::vector<Double_t>& event, Double_t weight );
Bool_t UserAssignEvents(UInt_t clIndex);
TTree* CreateEventAssignTrees( const TString& name );
DataSetInfo& AddDataSet( DataSetInfo& );
DataSetInfo& AddDataSet( const TString& );
// special case: signal/background
// Data input related
void SetInputTrees( const TString& signalFileName, const TString& backgroundFileName,
Double_t signalWeight=1.0, Double_t backgroundWeight=1.0 );
void SetInputTrees( TTree* inputTree, const TCut& SigCut, const TCut& BgCut );
// Set input trees at once
void SetInputTrees( TTree* signal, TTree* background,
Double_t signalWeight=1.0, Double_t backgroundWeight=1.0) ;
void AddSignalTree( TTree* signal, Double_t weight=1.0, Types::ETreeType treetype = Types::kMaxTreeType );
void AddSignalTree( TString datFileS, Double_t weight=1.0, Types::ETreeType treetype = Types::kMaxTreeType );
void AddSignalTree( TTree* signal, Double_t weight, const TString& treetype );
// ... depreciated, kept for backwards compatibility
void SetSignalTree( TTree* signal, Double_t weight=1.0);
void AddBackgroundTree( TTree* background, Double_t weight=1.0, Types::ETreeType treetype = Types::kMaxTreeType );
void AddBackgroundTree( TString datFileB, Double_t weight=1.0, Types::ETreeType treetype = Types::kMaxTreeType );
void AddBackgroundTree( TTree* background, Double_t weight, const TString & treetype );
// ... depreciated, kept for backwards compatibility
void SetBackgroundTree( TTree* background, Double_t weight=1.0 );
void SetSignalWeightExpression( const TString& variable );
void SetBackgroundWeightExpression( const TString& variable );
// special case: regression
void AddRegressionTree( TTree* tree, Double_t weight = 1.0,
Types::ETreeType treetype = Types::kMaxTreeType ) {
AddTree( tree, "Regression", weight, "", treetype );
}
// general
// Data input related
void SetTree( TTree* tree, const TString& className, Double_t weight ); // depreciated
void AddTree( TTree* tree, const TString& className, Double_t weight=1.0,
const TCut& cut = "",
Types::ETreeType tt = Types::kMaxTreeType );
void AddTree( TTree* tree, const TString& className, Double_t weight, const TCut& cut, const TString& treeType );
// set input variable
void SetInputVariables ( std::vector<TString>* theVariables ); // depreciated
void AddVariable ( const TString& expression, const TString& title, const TString& unit,
char type='F', Double_t min = 0, Double_t max = 0 );
void AddVariable ( const TString& expression, char type='F',
Double_t min = 0, Double_t max = 0 );
void AddTarget ( const TString& expression, const TString& title = "", const TString& unit = "",
Double_t min = 0, Double_t max = 0 );
void AddRegressionTarget( const TString& expression, const TString& title = "", const TString& unit = "",
Double_t min = 0, Double_t max = 0 )
{
AddTarget( expression, title, unit, min, max );
}
void AddSpectator ( const TString& expression, const TString& title = "", const TString& unit = "",
Double_t min = 0, Double_t max = 0 );
// set weight for class
void SetWeightExpression( const TString& variable, const TString& className = "" );
// set cut for class
void SetCut( const TString& cut, const TString& className = "" );
void SetCut( const TCut& cut, const TString& className = "" );
void AddCut( const TString& cut, const TString& className = "" );
void AddCut( const TCut& cut, const TString& className = "" );
// prepare input tree for training
void PrepareTrainingAndTestTree( const TCut& cut, const TString& splitOpt );
void PrepareTrainingAndTestTree( TCut sigcut, TCut bkgcut, const TString& splitOpt );
// ... deprecated, kept for backwards compatibility
void PrepareTrainingAndTestTree( const TCut& cut, Int_t Ntrain, Int_t Ntest = -1 );
void PrepareTrainingAndTestTree( const TCut& cut, Int_t NsigTrain, Int_t NbkgTrain, Int_t NsigTest, Int_t NbkgTest,
const TString& otherOpt="SplitMode=Random:!V" );
MethodBase* BookMethod( TString theMethodName, TString methodTitle, TString theOption = "" );
MethodBase* BookMethod( Types::EMVA theMethod, TString methodTitle, TString theOption = "" );
MethodBase* BookMethod( TMVA::Types::EMVA /*theMethod*/,
TString /*methodTitle*/,
TString /*methodOption*/,
TMVA::Types::EMVA /*theComposite*/,
TString /*compositeOption = ""*/ ) { return 0; }
// optimize all booked methods (well, if desired by the method)
void OptimizeAllMethods (TString fomType="ROCIntegral", TString fitType="FitGA");
void OptimizeAllMethodsForClassification(TString fomType="ROCIntegral", TString fitType="FitGA") { OptimizeAllMethods(fomType,fitType); }
void OptimizeAllMethodsForRegression (TString fomType="ROCIntegral", TString fitType="FitGA") { OptimizeAllMethods(fomType,fitType); }
// training for all booked methods
void TrainAllMethods ();
void TrainAllMethodsForClassification( void ) { TrainAllMethods(); }
void TrainAllMethodsForRegression ( void ) { TrainAllMethods(); }
// testing
void TestAllMethods();
// performance evaluation
void EvaluateAllMethods( void );
void EvaluateAllVariables( TString options = "" );
// delete all methods and reset the method vector
void DeleteAllMethods( void );
// accessors
IMethod* GetMethod( const TString& title ) const;
Bool_t Verbose( void ) const { return fVerbose; }
void SetVerbose( Bool_t v=kTRUE );
// make ROOT-independent C++ class for classifier response
// (classifier-specific implementation)
// If no classifier name is given, help messages for all booked
// classifiers are printed
virtual void MakeClass( const TString& methodTitle = "" ) const;
// prints classifier-specific hepl messages, dedicated to
// help with the optimisation and configuration options tuning.
// If no classifier name is given, help messages for all booked
// classifiers are printed
void PrintHelpMessage( const TString& methodTitle = "" ) const;
static TDirectory* RootBaseDir() { return (TDirectory*)fgTargetFile; }
private:
// the beautiful greeting message
void Greetings();
void WriteDataInformation();
DataInputHandler& DataInput() { return *fDataInputHandler; }
DataSetInfo& DefaultDataSetInfo();
void SetInputTreesFromEventAssignTrees();
private:
// data members
DataSetManager* fDataSetManager; // DSMTEST
static TFile* fgTargetFile; //! ROOT output file
DataInputHandler* fDataInputHandler;
std::vector<TMVA::VariableTransformBase*> fDefaultTrfs; //! list of transformations on default DataSet
// cd to local directory
TString fOptions; //! option string given by construction (presently only "V")
TString fTransformations; //! List of transformations to test
Bool_t fVerbose; //! verbose mode
MVector fMethods; //! all MVA methods
TString fJobName; //! jobname, used as extension in weight file names
// flag determining the way training and test data are assigned to Factory
enum DataAssignType { kUndefined = 0,
kAssignTrees,
kAssignEvents };
DataAssignType fDataAssignType; //! flags for data assigning
std::vector<TTree*> fTrainAssignTree; //! for each class: tmp tree if user wants to assign the events directly
std::vector<TTree*> fTestAssignTree; //! for each class: tmp tree if user wants to assign the events directly
Int_t fATreeType; // type of event (=classIndex)
Float_t fATreeWeight; // weight of the event
Float_t* fATreeEvent; // event variables
Types::EAnalysisType fAnalysisType; //! the training type
protected:
ClassDef(Factory,0) // The factory creates all MVA methods, and performs their training and testing
};
} // namespace TMVA
#endif
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