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class TMVA::DataSet

Function Members (Methods)

public:

virtual	~DataSet()
void	AddBackgroundTree(TTree* tr, Double_t weight = 1.0, TMVA::Types::ETreeType tt = Types::kMaxTreeType)
void	AddSignalTree(TTree* tr, Double_t weight = 1.0, TMVA::Types::ETreeType tt = Types::kMaxTreeType)
void	AddVariable(const TString& expression, char varType = 'F', void* external = 0)
void	AddVariable(const TString& expression, Double_t min, Double_t max, char varType, void* external = 0)
const TMVA::TreeInfo&	BackgroundTreeInfo(Int_t i) const
TDirectory*	BaseRootDir() const
void	ClearBackgroundTreeList()
void	ClearSignalTreeList()
const TMatrixD*	CorrelationMatrix(TMVA::Types::ESBType sigbgd) const
const TCut&	CutBkg() const
const char*	CutBkgS() const
const TCut&	CutSig() const
const char*	CutSigS() const
TMVA::DataSet	DataSet()
TMVA::VariableTransformBase*	FindTransform(TMVA::Types::EVariableTransform transform) const
Int_t	FindVar(const TString& var) const
void	GetCorrelationMatrix(Bool_t isSignal, TMatrixDBase* mat)
void	GetCovarianceMatrix(Bool_t isSignal, TMatrixDBase*, Bool_t norm = kFALSE)
UInt_t	GetCurrentEvtIdx() const
TMVA::Event&	GetEvent()
const TMVA::Event&	GetEvent() const
const TString&	GetExpression(Int_t i) const
const TString&	GetInternalVarName(Int_t i) const
TTree*	GetMultiCutTestTree() const
const char*	GetName() const
Int_t	GetNEvtBkgdTest() const
Int_t	GetNEvtBkgdTrain() const
Int_t	GetNEvtSigTest() const
Int_t	GetNEvtSigTrain() const
Int_t	GetNEvtTest() const
Int_t	GetNEvtTrain() const
UInt_t	GetNVariables() const
TTree*	GetTestTree() const
TTree*	GetTrainingTree() const
TMVA::VariableTransformBase*	GetTransform(TMVA::Types::EVariableTransform transform)
vector<TMVA::VariableInfo,allocator<TMVA::VariableInfo> >&	GetVariableInfos()
char	GetVarType(Int_t i) const
Bool_t	HasCuts() const
Bool_t	HasNegativeEventWeights() const
TDirectory*	LocalRootDir() const
UInt_t	NBackgroundTrees() const
UInt_t	NSignalTrees() const
void	PrepareForTrainingAndTesting(const TString& splitOpt)
Bool_t	ReadEvent(TTree* tr, Long64_t evidx) const
Bool_t	ReadTestEvent(Long64_t evidx) const
Bool_t	ReadTrainingEvent(Long64_t evidx) const
void	ResetBranchAndEventAddresses(TTree*)
void	ResetCurrentTree()
void	SetBackgroundWeightExpression(const TString& expr)
void	SetBaseRootDir(TDirectory* dir)
void	SetCuts(const TString& scut, const TString& bcut)
void	SetCuts(const TCut& scut, const TCut& bcut)
void	SetLocalRootDir(TDirectory* dir)
void	SetMultiCut(const TString& cut)
void	SetMultiCut(const TCut& cut)
void	SetMultiCutTestTree(TTree* tr)
void	SetSignalWeightExpression(const TString& expr)
void	SetTestTree(TTree* tr)
void	SetTrainingTree(TTree* tr)
void	SetVerbose(Bool_t v = kTRUE)
const TMVA::TreeInfo&	SignalTreeInfo(Int_t i) const

private:

void	ChangeToNewTree(TTree* tr, Int_t sb)
void	PrintCorrelationMatrix(TTree* theTree)
Bool_t	Verbose()

Data Members

private:

TDirectory*	fBaseRootDir	! the base directory, usually the root dir of a ROOT-file
UInt_t	fCurrentEvtIdx	! the current event (to avoid reading of the same event)
TTree*	fCurrentTree	! the tree, events are currently read from
TCut	fCutBkg	the pretraining cut
TTreeFormula*	fCutBkgF	the pretraining cut as formula
TCut	fCutSig	the pretraining cut
TTreeFormula*	fCutSigF	the pretraining cut as formula
UInt_t	fDataStats[2][3]	! statistics of the dataset for training/test tree
TMatrixD*	fDecorrMatrix[2]	! Decorrelation matrix [signal/background]
TMVA::Event*	fEvent	! the event
Bool_t	fExplicitTrainTest[2]	! if set to true the user has specified training and testing data explicitly
Bool_t	fHasNegativeEventWeights	true if at least one signal or bkg event has negative weight
vector<TTreeFormula*>	fInputVarFormulas	local formulas of the same
TDirectory*	fLocalRootDir	! the current directory, where things are created
TMVA::MsgLogger	fLogger	! message logger
TCut	fMultiCut	phase-space cut
TTree*	fMultiCutTestTree	! tree used for testing of multicut method
TTree*	fTestTree	! tree used for testing
TTree*	fTrainingTree	! tree used for training
vector<TMVA::TreeInfo>	fTreeCollection[2]	! list of signal and background trees/weights
map<TMVA::Types::EVariableTransform,TMVA::VariableTransformBase*>	fVarTransforms	! Registered variable transformations
vector<TString>	fVariableStrings	! list of variable expressions
vector<TMVA::VariableInfo>	fVariables	! list of variable expressions/internal names
Bool_t	fVerbose	! Verbosity
TString	fWeightExp[2]	! the input formula string that is the weight
TTreeFormula*	fWeightFormula[2]	! local weight formula

Class Charts

Inheritance Inherited Members Includes Libraries

Function documentation

DataSet()

 constructor

~DataSet()

 destructor

Bool_t ReadEvent(TTree* tr, Long64_t evidx) const

 read event from a tree into memory
 after the reading the event transformation is called

TMVA::VariableTransformBase* FindTransform(TMVA::Types::EVariableTransform transform) const

 finds transformation in map

TMVA::VariableTransformBase* GetTransform(TMVA::Types::EVariableTransform transform)

 retrieves transformation

void ResetBranchAndEventAddresses(TTree* )

 resets all branch adresses of the tree given as parameter
 to the event memory

void AddSignalTree(TTree* tr, Double_t weight = 1.0, TMVA::Types::ETreeType tt = Types::kMaxTreeType)

 add a signal tree to the dataset to be used as input

void AddBackgroundTree(TTree* tr, Double_t weight = 1.0, TMVA::Types::ETreeType tt = Types::kMaxTreeType)

 add a background tree to the dataset to be used as input

void AddVariable(const TString& expression, char varType = 'F', void* external = 0)

 add a variable (can be a complex expression) to the set of variables used in
 the MV analysis

void AddVariable(const TString& expression, Double_t min, Double_t max, char varType, void* external = 0)

 add a variable (can be a complex expression) to the set of variables used in
 the MV analysis

Int_t FindVar(const TString& var) const

 find variable by name

void PrepareForTrainingAndTesting(const TString& splitOpt)

void ChangeToNewTree(TTree* tr, Int_t sb)

 While the data gets copied into the local training and testing
 trees, the input tree can change (for intance when changing from
 signal to background tree, or using TChains as input) The
 TTreeFormulas, that hold the input expressions need to be
 reassociated with the new tree, which is done here

void PrintCorrelationMatrix(TTree* theTree)

 calculates the correlation matrices for signal and background,
 prints them to standard output, and fills 2D histograms

void GetCorrelationMatrix(Bool_t isSignal, TMatrixDBase* mat)

 computes correlation matrix for variables "theVars" in tree;
 "theType" defines the required event "type"
 ("type" variable must be present in tree)

void GetCovarianceMatrix(Bool_t isSignal, TMatrixDBase* , Bool_t norm = kFALSE)

 compute covariance matrix

const char* GetName()

{ return "DataSet"; }

UInt_t NSignalTrees()

{ return fTreeCollection[Types::kSignal].size(); }

UInt_t NBackgroundTrees()

{ return fTreeCollection[Types::kBackground].size(); }

const TreeInfo& SignalTreeInfo(Int_t i) const

{ return fTreeCollection[Types::kSignal][i]; }

const TreeInfo& BackgroundTreeInfo(Int_t i) const

{ return fTreeCollection[Types::kBackground][i]; }

void ClearSignalTreeList()

{ fTreeCollection[Types::kSignal].clear(); }

void ClearBackgroundTreeList()

{ fTreeCollection[Types::kBackground].clear(); }

std::vector<VariableInfo>& GetVariableInfos()

{ return fVariables; }

UInt_t GetNVariables()

{ return fVariables.size(); }

char GetVarType(Int_t i) const

{ return fVariables[i].GetVarType(); }

const TString& GetExpression(Int_t i) const

{ return fVariables[i].GetExpression(); }

const TString& GetInternalVarName(Int_t i) const

{ return fVariables[i].GetInternalVarName(); }

void SetCuts( const TString& scut, const TString& bcut )

 the cut

{ SetCuts(TCut(scut), TCut(bcut)); }

void SetCuts( const TCut& scut, const TCut& bcut )

{ fCutSig = scut; fCutBkg = bcut; }

void SetMultiCut( const TString& cut )

{ SetMultiCut(TCut(cut)); }

void SetMultiCut( const TCut& cut )

{ fMultiCut = cut; }

const TCut& CutSig()

{ return fCutSig; }

const TCut& CutBkg()

{ return fCutBkg; }

const char* CutSigS()

{ return fCutSig.GetTitle(); }

const char* CutBkgS()

{ return fCutBkg.GetTitle(); }

Bool_t HasCuts()

{ return TString(CutSig()) != "" || TString(CutBkg()) != ""; }

TTree* GetTrainingTree()

 the internal trees

{ return fTrainingTree; }

TTree* GetTestTree()

{ return fTestTree; }

TTree* GetMultiCutTestTree()

{ return fMultiCutTestTree; }

void SetTrainingTree(TTree* tr)

{ fTrainingTree = tr; }

void SetTestTree(TTree* tr)

{ fTestTree = tr; }

void SetMultiCutTestTree(TTree* tr)

{ fMultiCutTestTree = tr; }

TDirectory* LocalRootDir()

 ROOT stuff

{ return fLocalRootDir; }

TDirectory* BaseRootDir()

{ return fBaseRootDir; }

void SetBaseRootDir(TDirectory* dir)

{ fBaseRootDir = dir; }

void SetLocalRootDir(TDirectory* dir)

{ fLocalRootDir = dir; }

void SetVerbose(Bool_t v = kTRUE)

{ fVerbose = v; }

Bool_t ReadTrainingEvent(Long64_t evidx) const

{ return ReadEvent(GetTrainingTree(), evidx ); }

Bool_t ReadTestEvent(Long64_t evidx) const

{ return ReadEvent(GetTestTree(), evidx ); }

TMVA::Event& GetEvent()

{ if (fEvent==0) fEvent = new TMVA::Event(fVariables); return *fEvent; }

UInt_t GetCurrentEvtIdx()

{ return fCurrentEvtIdx; }

const TMVA::Event& GetEvent()

{ return *fEvent; }

const TMatrixD* CorrelationMatrix(TMVA::Types::ESBType sigbgd) const

 correlation matrix

{ return fDecorrMatrix[sigbgd]; }

void SetSignalWeightExpression(const TString& expr)

 the weight

{ fWeightExp[Types::kSignal] = expr; }

void SetBackgroundWeightExpression(const TString& expr)

{ fWeightExp[Types::kBackground] = expr; }

Bool_t HasNegativeEventWeights()

{ return fHasNegativeEventWeights; }

Int_t GetNEvtTrain()

 some dataset stats

{ return fDataStats[Types::kTraining][Types::kSBBoth]; }

Int_t GetNEvtSigTrain()

{ return fDataStats[Types::kTraining][Types::kSignal]; }

Int_t GetNEvtBkgdTrain()

{ return fDataStats[Types::kTraining][Types::kBackground]; }

Int_t GetNEvtTest()

{ return fDataStats[Types::kTesting][Types::kSBBoth]; }

Int_t GetNEvtSigTest()

{ return fDataStats[Types::kTesting][Types::kSignal]; }

Int_t GetNEvtBkgdTest()

{ return fDataStats[Types::kTesting][Types::kBackground]; }

void ResetCurrentTree()

{ fCurrentTree = 0; }

Bool_t Verbose()

 verbosity

{ return fVerbose; }

Author: Andreas Hoecker, Joerg Stelzer, Helge Voss
Last change: root/tmva $Id: DataSet.h 26050 2008-11-01 09:18:41Z brun $
Last generated: 2008-11-01 10:21
Copyright (c) 2006: *

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