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class TMVA::MethodBDT: public TMVA::MethodBase


MethodBDT

Analysis of Boosted Decision Trees

Function Members (Methods)

public:

virtual	~MethodBDT()
void	TObject::AbstractMethod(const char* method) const
void	TMVA::Configurable::AddOptionsXMLTo(void* parent) const
void	TMVA::MethodBase::AddOutput(TMVA::Types::ETreeType type, TMVA::Types::EAnalysisType analysisType)
virtual void	AddWeightsXMLTo(void* parent) const
virtual void	TObject::AppendPad(Option_t* option = "")
TDirectory*	TMVA::MethodBase::BaseDir() const
Double_t	Boost(vector<const TMVA::Event>&, TMVA::DecisionTree dt, UInt_t cls = 0)
virtual void	TObject::Browse(TBrowser* b)
void	TMVA::Configurable::CheckForUnusedOptions() const
virtual void	TMVA::MethodBase::CheckSetup()
static TClass*	Class()
virtual const char*	TObject::ClassName() const
virtual void	TNamed::Clear(Option_t* option = "")
virtual TObject*	TNamed::Clone(const char* newname = "") const
virtual Int_t	TNamed::Compare(const TObject* obj) const
TMVA::Configurable	TMVA::Configurable::Configurable(const TString& theOption = "")
TMVA::Configurable	TMVA::Configurable::Configurable(const TMVA::Configurable&)
virtual void	TNamed::Copy(TObject& named) const
virtual const TMVA::Ranking*	CreateRanking()
TMVA::DataSet*	TMVA::MethodBase::Data() const
TMVA::DataSetInfo&	TMVA::MethodBase::DataInfo() const
virtual void	DeclareOptions()
virtual void	TObject::Delete(Option_t* option = "")MENU
void	TMVA::MethodBase::DisableWriting(Bool_t setter)
virtual Int_t	TObject::DistancetoPrimitive(Int_t px, Int_t py)
Bool_t	TMVA::MethodBase::DoMulticlass() const
Bool_t	TMVA::MethodBase::DoRegression() const
virtual void	TObject::Draw(Option_t* option = "")
virtual void	TObject::DrawClass() constMENU
virtual TObject*	TObject::DrawClone(Option_t* option = "") constMENU
virtual void	TObject::Dump() constMENU
virtual void	TObject::Error(const char* method, const char* msgfmt) const
virtual void	TObject::Execute(const char* method, const char* params, Int_t* error = 0)
virtual void	TObject::Execute(TMethod* method, TObjArray* params, Int_t* error = 0)
virtual void	TObject::ExecuteEvent(Int_t event, Int_t px, Int_t py)
void	TMVA::MethodBase::ExitFromTraining()
virtual void	TObject::Fatal(const char* method, const char* msgfmt) const
virtual void	TNamed::FillBuffer(char*& buffer)
virtual TObject*	TObject::FindObject(const char* name) const
virtual TObject*	TObject::FindObject(const TObject* obj) const
TMVA::Types::EAnalysisType	TMVA::MethodBase::GetAnalysisType() const
const vector<double>&	GetBoostWeights() const
const char*	TMVA::Configurable::GetConfigDescription() const
const char*	TMVA::Configurable::GetConfigName() const
UInt_t	TMVA::MethodBase::GetCurrentIter()
virtual Option_t*	TObject::GetDrawOption() const
static Long_t	TObject::GetDtorOnly()
virtual Double_t	TMVA::MethodBase::GetEfficiency(const TString&, TMVA::Types::ETreeType, Double_t& err)
const TMVA::Event*	TMVA::MethodBase::GetEvent() const
const TMVA::Event*	TMVA::MethodBase::GetEvent(const TMVA::Event* ev) const
const TMVA::Event*	TMVA::MethodBase::GetEvent(Long64_t ievt) const
const TMVA::Event*	TMVA::MethodBase::GetEvent(Long64_t ievt, TMVA::Types::ETreeType type) const
const vector<TMVA::Event*>&	TMVA::MethodBase::GetEventCollection(TMVA::Types::ETreeType type)
TFile*	TMVA::MethodBase::GetFile() const
const vector<TMVA::DecisionTree*>&	GetForest() const
virtual void	GetHelpMessage() const
virtual const char*	TObject::GetIconName() const
const TString&	TMVA::MethodBase::GetInputLabel(Int_t i) const
const char*	TMVA::MethodBase::GetInputTitle(Int_t i) const
const TString&	TMVA::MethodBase::GetInputVar(Int_t i) const
TMultiGraph*	TMVA::MethodBase::GetInteractiveTrainingError()
const TString&	TMVA::MethodBase::GetJobName() const
virtual Double_t	TMVA::MethodBase::GetKSTrainingVsTest(Char_t SorB, TString opt = "X")
virtual Double_t	TMVA::MethodBase::GetMaximumSignificance(Double_t SignalEvents, Double_t BackgroundEvents, Double_t& optimal_significance_value) const
UInt_t	TMVA::MethodBase::GetMaxIter()
Double_t	TMVA::MethodBase::GetMean(Int_t ivar) const
const TString&	TMVA::MethodBase::GetMethodName() const
TMVA::Types::EMVA	TMVA::MethodBase::GetMethodType() const
TString	TMVA::MethodBase::GetMethodTypeName() const
virtual vector<Float_t>	TMVA::MethodBase::GetMulticlassEfficiency(vector<vector<Float_t> >& purity)
virtual vector<Float_t>	TMVA::MethodBase::GetMulticlassTrainingEfficiency(vector<vector<Float_t> >& purity)
virtual const vector<Float_t>&	GetMulticlassValues()
virtual Double_t	GetMvaValue(Double_t* err = 0, Double_t* errUpper = 0)
virtual const char*	TMVA::MethodBase::GetName() const
UInt_t	TMVA::MethodBase::GetNEvents() const
UInt_t	TMVA::MethodBase::GetNTargets() const
UInt_t	GetNTrees() const
UInt_t	TMVA::MethodBase::GetNvar() const
UInt_t	TMVA::MethodBase::GetNVariables() const
virtual char*	TObject::GetObjectInfo(Int_t px, Int_t py) const
static Bool_t	TObject::GetObjectStat()
virtual Option_t*	TObject::GetOption() const
const TString&	TMVA::Configurable::GetOptions() const
virtual Double_t	TMVA::MethodBase::GetProba(const TMVA::Event* ev)
virtual Double_t	TMVA::MethodBase::GetProba(Double_t mvaVal, Double_t ap_sig)
const TString	TMVA::MethodBase::GetProbaName() const
virtual Double_t	TMVA::MethodBase::GetRarity(Double_t mvaVal, TMVA::Types::ESBType reftype = Types::kBackground) const
virtual void	TMVA::MethodBase::GetRegressionDeviation(UInt_t tgtNum, TMVA::Types::ETreeType type, Double_t& stddev, Double_t& stddev90Percent) const
virtual const vector<Float_t>&	GetRegressionValues()
Double_t	TMVA::MethodBase::GetRMS(Int_t ivar) const
virtual Double_t	TMVA::MethodBase::GetROCIntegral(TH1D* histS, TH1D* histB) const
virtual Double_t	TMVA::MethodBase::GetROCIntegral(TMVA::PDF* pdfS = 0, TMVA::PDF* pdfB = 0) const
virtual Double_t	TMVA::MethodBase::GetSeparation(TH1, TH1) const
virtual Double_t	TMVA::MethodBase::GetSeparation(TMVA::PDF* pdfS = 0, TMVA::PDF* pdfB = 0) const
Double_t	TMVA::MethodBase::GetSignalReferenceCut() const
Double_t	TMVA::MethodBase::GetSignalReferenceCutOrientation() const
virtual Double_t	TMVA::MethodBase::GetSignificance() const
const TMVA::Event*	TMVA::MethodBase::GetTestingEvent(Long64_t ievt) const
Double_t	TMVA::MethodBase::GetTestTime() const
const TString&	TMVA::MethodBase::GetTestvarName() const
virtual const char*	TNamed::GetTitle() const
virtual Double_t	TMVA::MethodBase::GetTrainingEfficiency(const TString&)
const TMVA::Event*	TMVA::MethodBase::GetTrainingEvent(Long64_t ievt) const
const vector<const TMVA::Event*>&	GetTrainingEvents() const
UInt_t	TMVA::MethodBase::GetTrainingROOTVersionCode() const
TString	TMVA::MethodBase::GetTrainingROOTVersionString() const
UInt_t	TMVA::MethodBase::GetTrainingTMVAVersionCode() const
TString	TMVA::MethodBase::GetTrainingTMVAVersionString() const
Double_t	TMVA::MethodBase::GetTrainTime() const
TMVA::TransformationHandler&	TMVA::MethodBase::GetTransformationHandler(Bool_t takeReroutedIfAvailable = true)
const TMVA::TransformationHandler&	TMVA::MethodBase::GetTransformationHandler(Bool_t takeReroutedIfAvailable = true) const
virtual UInt_t	TObject::GetUniqueID() const
vector<Double_t>	GetVariableImportance()
Double_t	GetVariableImportance(UInt_t ivar)
TString	TMVA::MethodBase::GetWeightFileName() const
Double_t	TMVA::MethodBase::GetXmax(Int_t ivar) const
Double_t	TMVA::MethodBase::GetXmin(Int_t ivar) const
virtual Bool_t	TObject::HandleTimer(TTimer* timer)
virtual Bool_t	HasAnalysisType(TMVA::Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)
virtual ULong_t	TNamed::Hash() const
Bool_t	TMVA::MethodBase::HasMVAPdfs() const
TMVA::IMethod	TMVA::IMethod::IMethod()
TMVA::IMethod	TMVA::IMethod::IMethod(const TMVA::IMethod&)
virtual void	TObject::Info(const char* method, const char* msgfmt) const
virtual Bool_t	TObject::InheritsFrom(const char* classname) const
virtual Bool_t	TObject::InheritsFrom(const TClass* cl) const
void	InitEventSample()
void	TMVA::MethodBase::InitIPythonInteractive()
virtual void	TObject::Inspect() constMENU
void	TObject::InvertBit(UInt_t f)
virtual TClass*	IsA() const
virtual Bool_t	TObject::IsEqual(const TObject* obj) const
virtual Bool_t	TObject::IsFolder() const
Bool_t	TMVA::MethodBase::IsModelPersistence()
Bool_t	TObject::IsOnHeap() const
virtual Bool_t	TMVA::MethodBase::IsSignalLike()
virtual Bool_t	TMVA::MethodBase::IsSignalLike(Double_t mvaVal)
Bool_t	TMVA::MethodBase::IsSilentFile()
virtual Bool_t	TNamed::IsSortable() const
Bool_t	TObject::IsZombie() const
TMVA::MsgLogger&	TMVA::Configurable::Log() const
virtual void	TNamed::ls(Option_t* option = "") const
virtual void	TMVA::MethodBase::MakeClass(const TString& classFileName = TString("")) const
void	MakeClassInstantiateNode(TMVA::DecisionTreeNode* n, ostream& fout, const TString& className) const
virtual void	MakeClassSpecific(ostream&, const TString&) const
virtual void	MakeClassSpecificHeader(ostream&, const TString&) const
void	TObject::MayNotUse(const char* method) const
TMVA::MethodBase	TMVA::MethodBase::MethodBase(const TMVA::MethodBase&)
TMVA::MethodBase	TMVA::MethodBase::MethodBase(TMVA::Types::EMVA methodType, TMVA::DataSetInfo& dsi, const TString& weightFile)
TMVA::MethodBase	TMVA::MethodBase::MethodBase(const TString& jobName, TMVA::Types::EMVA methodType, const TString& methodTitle, TMVA::DataSetInfo& dsi, const TString& theOption = "")
TDirectory*	TMVA::MethodBase::MethodBaseDir() const
TMVA::MethodBDT	MethodBDT(const TMVA::MethodBDT&)
TMVA::MethodBDT	MethodBDT(TMVA::DataSetInfo& theData, const TString& theWeightFile)
TMVA::MethodBDT	MethodBDT(const TString& jobName, const TString& methodTitle, TMVA::DataSetInfo& theData, const TString& theOption = "")
virtual Bool_t	TObject::Notify()
void	TObject::Obsolete(const char* method, const char* asOfVers, const char* removedFromVers) const
void	TObject::operator delete(void* ptr)
void	TObject::operator delete(void* ptr, void* vp)
void	TObject::operator delete[](void* ptr)
void	TObject::operator delete[](void* ptr, void* vp)
void*	TObject::operator new(size_t sz)
void*	TObject::operator new(size_t sz, void* vp)
void*	TObject::operator new[](size_t sz)
void*	TObject::operator new[](size_t sz, void* vp)
TMVA::MethodBDT&	operator=(const TMVA::MethodBDT&)
virtual map<TString,Double_t>	OptimizeTuningParameters(TString fomType = "ROCIntegral", TString fitType = "FitGA")
virtual void	TObject::Paint(Option_t* option = "")
virtual void	TMVA::Configurable::ParseOptions()
virtual void	TObject::Pop()
virtual void	TNamed::Print(Option_t* option = "") const
virtual void	TMVA::MethodBase::PrintHelpMessage() const
void	TMVA::Configurable::PrintOptions() const
virtual void	ProcessOptions()
void	TMVA::MethodBase::ProcessSetup()
virtual Int_t	TObject::Read(const char* name)
void	TMVA::Configurable::ReadOptionsFromStream(istream& istr)
void	TMVA::Configurable::ReadOptionsFromXML(void* node)
void	TMVA::MethodBase::ReadStateFromFile()
void	TMVA::MethodBase::ReadStateFromStream(istream& tf)
void	TMVA::MethodBase::ReadStateFromStream(TFile& rf)
void	TMVA::MethodBase::ReadStateFromXMLString(const char* xmlstr)
virtual void	ReadWeightsFromStream(istream& istr)
virtual void	ReadWeightsFromXML(void* parent)
virtual void	TObject::RecursiveRemove(TObject* obj)
void	TMVA::MethodBase::RerouteTransformationHandler(TMVA::TransformationHandler* fTargetTransformation)
virtual void	Reset()
void	TObject::ResetBit(UInt_t f)
virtual void	TObject::SaveAs(const char* filename = "", Option_t* option = "") constMENU
virtual void	TObject::SavePrimitive(ostream& out, Option_t* option = "")
void	SetAdaBoostBeta(Double_t b)
virtual void	TMVA::MethodBase::SetAnalysisType(TMVA::Types::EAnalysisType type)
void	SetBaggedSampleFraction(Double_t f)
void	TMVA::MethodBase::SetBaseDir(TDirectory* methodDir)
void	TObject::SetBit(UInt_t f)
void	TObject::SetBit(UInt_t f, Bool_t set)
void	TMVA::Configurable::SetConfigDescription(const char* d)
void	TMVA::Configurable::SetConfigName(const char* n)
virtual void	TObject::SetDrawOption(Option_t* option = "")MENU
static void	TObject::SetDtorOnly(void* obj)
void	TMVA::MethodBase::SetFile(TFile* file)
void	SetMaxDepth(Int_t d)
void	TMVA::MethodBase::SetMethodBaseDir(TDirectory* methodDir)
void	TMVA::MethodBase::SetMethodDir(TDirectory* methodDir)
void	SetMinNodeSize(Double_t sizeInPercent)
void	SetMinNodeSize(TString sizeInPercent)
void	TMVA::MethodBase::SetModelPersistence(Bool_t status)
void	TMVA::Configurable::SetMsgType(TMVA::EMsgType t)
virtual void	TNamed::SetName(const char* name)MENU
virtual void	TNamed::SetNameTitle(const char* name, const char* title)
void	SetNodePurityLimit(Double_t l)
void	SetNTrees(Int_t d)
static void	TObject::SetObjectStat(Bool_t stat)
void	TMVA::Configurable::SetOptions(const TString& s)
void	SetShrinkage(Double_t s)
void	TMVA::MethodBase::SetSignalReferenceCut(Double_t cut)
void	TMVA::MethodBase::SetSignalReferenceCutOrientation(Double_t cutOrientation)
void	TMVA::MethodBase::SetSilentFile(Bool_t status)
void	TMVA::MethodBase::SetTestTime(Double_t testTime)
void	TMVA::MethodBase::SetTestvarName(const TString& v = "")
virtual void	TNamed::SetTitle(const char* title = "")MENU
void	TMVA::MethodBase::SetTrainTime(Double_t trainTime)
virtual void	SetTuneParameters(map<TString,Double_t> tuneParameters)
virtual void	TObject::SetUniqueID(UInt_t uid)
void	TMVA::MethodBase::SetupMethod()
void	SetUseNvars(Int_t n)
virtual void	ShowMembers(TMemberInspector& insp) const
virtual Int_t	TNamed::Sizeof() const
virtual void	Streamer(TBuffer&)
void	StreamerNVirtual(TBuffer& ClassDef_StreamerNVirtual_b)
virtual void	TObject::SysError(const char* method, const char* msgfmt) const
Bool_t	TObject::TestBit(UInt_t f) const
Int_t	TObject::TestBits(UInt_t f) const
virtual void	TMVA::MethodBase::TestClassification()
virtual void	TMVA::MethodBase::TestMulticlass()
virtual void	TMVA::MethodBase::TestRegression(Double_t& bias, Double_t& biasT, Double_t& dev, Double_t& devT, Double_t& rms, Double_t& rmsT, Double_t& mInf, Double_t& mInfT, Double_t& corr, TMVA::Types::ETreeType type)
Double_t	TestTreeQuality(TMVA::DecisionTree* dt)
virtual void	Train()
bool	TMVA::MethodBase::TrainingEnded()
void	TMVA::MethodBase::TrainMethod()
virtual void	TObject::UseCurrentStyle()
virtual void	TObject::Warning(const char* method, const char* msgfmt) const
virtual Int_t	TObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0)
virtual Int_t	TObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0) const
virtual void	TMVA::MethodBase::WriteEvaluationHistosToFile(TMVA::Types::ETreeType treetype)
virtual void	WriteMonitoringHistosToFile() const
void	TMVA::Configurable::WriteOptionsToStream(ostream& o, const TString& prefix) const
void	TMVA::MethodBase::WriteStateToFile() const

protected:

virtual void	DeclareCompatibilityOptions()
virtual void	TObject::DoError(int level, const char* location, const char* fmt, va_list va) const
void	TMVA::Configurable::EnableLooseOptions(Bool_t b = kTRUE)
const TString&	TMVA::MethodBase::GetInternalVarName(Int_t ivar) const
virtual vector<Double_t>	TMVA::MethodBase::GetMvaValues(Long64_t firstEvt = 0, Long64_t lastEvt = -1, Bool_t logProgress = false)
const TString&	TMVA::MethodBase::GetOriginalVarName(Int_t ivar) const
const TString&	TMVA::Configurable::GetReferenceFile() const
const TString&	TMVA::MethodBase::GetWeightFileDir() const
Bool_t	TMVA::MethodBase::HasTrainingTree() const
Bool_t	TMVA::MethodBase::Help() const
Bool_t	TMVA::MethodBase::IgnoreEventsWithNegWeightsInTraining() const
Bool_t	TMVA::MethodBase::IsConstructedFromWeightFile() const
Bool_t	TMVA::MethodBase::IsNormalised() const
Bool_t	TMVA::Configurable::LooseOptionCheckingEnabled() const
void	TObject::MakeZombie()
void	TMVA::MethodBase::NoErrorCalc(Double_tconst err, Double_tconst errUpper)
void	TMVA::Configurable::ResetSetFlag()
void	TMVA::MethodBase::SetNormalised(Bool_t norm)
void	TMVA::MethodBase::SetWeightFileDir(TString fileDir)
void	TMVA::MethodBase::SetWeightFileName(TString)
void	TMVA::MethodBase::Statistics(TMVA::Types::ETreeType treeType, const TString& theVarName, Double_t&, Double_t&, Double_t&, Double_t&, Double_t&, Double_t&)
Bool_t	TMVA::MethodBase::TxtWeightsOnly() const
Bool_t	TMVA::MethodBase::Verbose() const
void	TMVA::Configurable::WriteOptionsReferenceToFile()

private:

Double_t	AdaBoost(vector<const TMVA::Event>&, TMVA::DecisionTree dt)
Double_t	AdaBoostR2(vector<const TMVA::Event>&, TMVA::DecisionTree dt)
Double_t	AdaCost(vector<const TMVA::Event>&, TMVA::DecisionTree dt)
Double_t	ApplyPreselectionCuts(const TMVA::Event* ev)
Double_t	Bagging()
void	BoostMonitor(Int_t iTree)
void	DeterminePreselectionCuts(const vector<const TMVA::Event*>& eventSample)
void	GetBaggedSubSample(vector<const TMVA::Event*>&)
Double_t	GetGradBoostMVA(const TMVA::Event* e, UInt_t nTrees)
Double_t	GetMvaValue(Double_t* err, Double_t* errUpper, UInt_t useNTrees)
Double_t	GradBoost(vector<const TMVA::Event>&, TMVA::DecisionTree dt, UInt_t cls = 0)
Double_t	GradBoostRegression(vector<const TMVA::Event>&, TMVA::DecisionTree dt)
virtual void	Init()
void	InitGradBoost(vector<const TMVA::Event*>&)
void	PreProcessNegativeEventWeights()
Double_t	PrivateGetMvaValue(const TMVA::Event* ev, Double_t* err = 0, Double_t* errUpper = 0, UInt_t useNTrees = 0)
Double_t	RegBoost(vector<const TMVA::Event>&, TMVA::DecisionTree dt)
void	UpdateTargets(vector<const TMVA::Event*>&, UInt_t cls = 0)
void	UpdateTargetsRegression(vector<const TMVA::Event*>&, Bool_t first = kFALSE)

Data Members

public:

Bool_t	TMVA::MethodBase::fSetupCompleted	is method setup
const TMVA::Event*	TMVA::MethodBase::fTmpEvent	! temporary event when testing on a different DataSet than the own one
static TObject::(anonymous)	TObject::kBitMask
static TObject::EStatusBits	TObject::kCanDelete
static TObject::EStatusBits	TObject::kCannotPick
static TObject::EStatusBits	TObject::kHasUUID
static TObject::EStatusBits	TObject::kInvalidObject
static TObject::(anonymous)	TObject::kIsOnHeap
static TObject::EStatusBits	TObject::kIsReferenced
static TObject::EStatusBits	TObject::kMustCleanup
static TObject::EStatusBits	TObject::kNoContextMenu
static TObject::(anonymous)	TObject::kNotDeleted
static TObject::EStatusBits	TObject::kObjInCanvas
static TObject::(anonymous)	TObject::kOverwrite
static TMVA::MethodBase::EWeightFileType	TMVA::MethodBase::kROOT
static TObject::(anonymous)	TObject::kSingleKey
static TMVA::MethodBase::EWeightFileType	TMVA::MethodBase::kTEXT
static TObject::(anonymous)	TObject::kWriteDelete
static TObject::(anonymous)	TObject::kZombie

protected:

TMVA::Types::EAnalysisType	TMVA::MethodBase::fAnalysisType	method-mode : true --> regression, false --> classification
UInt_t	TMVA::MethodBase::fBackgroundClass	index of the Background-class
bool	TMVA::MethodBase::fExitFromTraining
UInt_t	TMVA::MethodBase::fIPyCurrentIter
UInt_t	TMVA::MethodBase::fIPyMaxIter
vector<TString>*	TMVA::MethodBase::fInputVars	vector of input variables used in MVA
TMVA::IPythonInteractive*	TMVA::MethodBase::fInteractive
TMVA::MsgLogger*	TMVA::Configurable::fLogger	! message logger
vector<Float_t>*	TMVA::MethodBase::fMulticlassReturnVal	holds the return-values for the multiclass classification
TString	TNamed::fName	object identifier
Int_t	TMVA::MethodBase::fNbins	number of bins in input variable histograms
Int_t	TMVA::MethodBase::fNbinsH	number of bins in evaluation histograms
Int_t	TMVA::MethodBase::fNbinsMVAoutput	number of bins in MVA output histograms
TMVA::Ranking*	TMVA::MethodBase::fRanking	pointer to ranking object (created by derived classifiers)
vector<Float_t>*	TMVA::MethodBase::fRegressionReturnVal	holds the return-values for the regression
TMVA::Results*	TMVA::MethodBase::fResults
UInt_t	TMVA::MethodBase::fSignalClass	index of the Signal-class
TString	TNamed::fTitle	object title

private:

Double_t	fAdaBoostBeta	beta parameter for AdaBoost algorithm
TString	fAdaBoostR2Loss	loss type used in AdaBoostR2 (Linear,Quadratic or Exponential)
Bool_t	fAutomatic	use user given prune strength or automatically determined one using a validation sample
Bool_t	fBaggedBoost	turn bagging in combination with boost on/off
Bool_t	fBaggedGradBoost	turn bagging in combination with grad boost on/off
Double_t	fBaggedSampleFraction	relative size of bagged event sample to original sample size
TString	fBoostType	string specifying the boost type
Double_t	fBoostWeight	ntuple var: boost weight
vector<double>	fBoostWeights	the weights applied in the individual boosts
Double_t	fCbb	Cost factor
Double_t	fCss	Cost factor
Double_t	fCtb_ss	Cost factor
Double_t	fCts_sb	Cost factor
Bool_t	fDoBoostMonitor	create control plot with ROC integral vs tree number
Bool_t	fDoPreselection	do or do not perform automatic pre-selection of 100% eff. cuts
Double_t	fErrorFraction	ntuple var: misclassification error fraction
vector<const TMVA::Event*>	fEventSample	the training events
Double_t	fFValidationEvents	fraction of events to use for pruning
vector<TMVA::DecisionTree*>	fForest	the collection of decision trees
vector<Double_t>	fHighBkgCut
vector<Double_t>	fHighSigCut
Bool_t	fHistoricBool	historic variable, only needed for "CompatibilityOptions"
Double_t	fHuberQuantile	the option string determining the quantile for the Huber Loss Function
Int_t	fITree	ntuple var: ith tree
Bool_t	fInverseBoostNegWeights	boost ev. with neg. weights with 1/boostweight rathre than boostweight
vector<Bool_t>	fIsHighBkgCut
vector<Bool_t>	fIsHighSigCut
vector<Bool_t>	fIsLowBkgCut
vector<Bool_t>	fIsLowSigCut
map<const TMVA::Event*,TMVA::LossFunctionEventInfo>	fLossFunctionEventInfo	map event to true value, predicted value, and weight
vector<Double_t>	fLowBkgCut
vector<Double_t>	fLowSigCut
UInt_t	fMaxDepth	max depth
Double_t	fMinLinCorrForFisher	the minimum linear correlation between two variables demanded for use in fisher criterium in node splitting
Int_t	fMinNodeEvents	min number of events in node
Float_t	fMinNodeSize	min percentage of training events in node
TString	fMinNodeSizeS	string containing min percentage of training events in node
TTree*	fMonitorNtuple	monitoring ntuple
Int_t	fNCuts	grid used in cut applied in node splitting
UInt_t	fNNodesMax	max # of nodes
Int_t	fNTrees	number of decision trees requested
TString	fNegWeightTreatment	variable that holds the option of how to treat negative event weights in training
Bool_t	fNoNegWeightsInTraining	ignore negative event weights in the training
Double_t	fNodePurityLimit	purity limit for sig/bkg nodes
Bool_t	fPairNegWeightsGlobal	pair ev. with neg. and pos. weights in traning sample and "annihilate" them
TMVA::DecisionTree::EPruneMethod	fPruneMethod	method used for prunig
TString	fPruneMethodS	prune method option String
Double_t	fPruneStrength	a parameter to set the "amount" of pruning..needs to be adjusted
Bool_t	fRandomisedTrees	choose a random subset of possible cut variables at each node during training
TMVA::LossFunctionBDT*	fRegressionLossFunctionBDTG
TString	fRegressionLossFunctionBDTGS	the option string determining the loss function for BDT regression
map<const TMVA::Event*,vector<double> >	fResiduals	individual event residuals for gradient boost
TMVA::SeparationBase*	fSepType	the separation used in node splitting
TString	fSepTypeS	the separation (option string) used in node splitting
Double_t	fShrinkage	learning rate for gradient boost;
Double_t	fSigToBkgFraction	Signal to Background fraction assumed during training
Bool_t	fSkipNormalization	true for skipping normalization at initialization of trees
vector<const TMVA::Event*>	fSubSample	subsample for bagged grad boost
vector<const TMVA::Event>	fTrainSample	pointer to sample actually used in training (fEventSample or fSubSample) for example
Bool_t	fTrainWithNegWeights	yes there are negative event weights and we don't ignore them
Bool_t	fUseExclusiveVars	individual variables already used in fisher criterium are not anymore analysed individually for node splitting
Bool_t	fUseFisherCuts	use multivariate splits using the Fisher criterium
UInt_t	fUseNTrainEvents	number of randomly picked training events used in randomised (and bagged) trees
UInt_t	fUseNvars	the number of variables used in the randomised tree splitting
Bool_t	fUsePoissonNvars	use "fUseNvars" not as fixed number but as mean of a possion distr. in each split
Bool_t	fUseYesNoLeaf	use sig or bkg classification in leave nodes or sig/bkg
vector<const TMVA::Event*>	fValidationSample	the Validation events
vector<Double_t>	fVariableImportance	the relative importance of the different variables
static const Int_t	fgDebugLevel	debug level determining some printout/control plots etc.

Class Charts

Inheritance Chart:

TMVA::IMethod

TObject

←

TNamed

←

TMVA::Configurable

←

TMVA::MethodBase

←

TMVA::MethodBDT

Function documentation

const std::vector<TMVA::DecisionTree*>& GetForest() const

{ return fForest; }

const std::vector<const TMVA::Event*> & GetTrainingEvents() const

{ return fEventSample; }

const std::vector<double>& GetBoostWeights() const

{ return fBoostWeights; }

MethodBDT(const TString& jobName, const TString& methodTitle, TMVA::DataSetInfo& theData, const TString& theOption = "")

 constructor for training and reading

virtual ~MethodBDT( void )

 constructor for calculating BDT-MVA using previously generatad decision trees

Bool_t HasAnalysisType(TMVA::Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)

void InitEventSample()

 write all Events from the Tree into a vector of Events, that are
 more easily manipulated

std::map<TString,Double_t> OptimizeTuningParameters(TString fomType = "ROCIntegral", TString fitType = "FitGA")

 optimize tuning parameters

void SetTuneParameters(map<TString,Double_t> tuneParameters)

void Train( void )

 training method

void Reset( void )

 revoke training

void AddWeightsXMLTo(void* parent) const

 write weights to file

void ReadWeightsFromStream(istream& istr)

 read weights from file

void ReadWeightsFromXML(void* parent)

void WriteMonitoringHistosToFile( void )

 write method specific histos to target file

Double_t GetMvaValue(Double_t* err = 0, Double_t* errUpper = 0)

 calculate the MVA value

UInt_t GetNTrees() const

 get the actual forest size (might be less than fNTrees, the requested one, if boosting is stopped early

{return fForest.size();}

Double_t GetMvaValue(Double_t* err, Double_t* errUpper, UInt_t useNTrees)

Double_t PrivateGetMvaValue(const TMVA::Event* ev, Double_t* err = 0, Double_t* errUpper = 0, UInt_t useNTrees = 0)

void BoostMonitor(Int_t iTree)

const std::vector<Float_t>& GetMulticlassValues()

const std::vector<Float_t>& GetRegressionValues()

 regression response

Double_t Boost(vector<const TMVA::Event*>& , TMVA::DecisionTree* dt, UInt_t cls = 0)

 apply the boost algorithm to a tree in the collection

const Ranking* CreateRanking()

 ranking of input variables

void DeclareOptions()

 the option handling methods

void ProcessOptions()

void SetMaxDepth(Int_t d)

{fMaxDepth = d;}

void SetMinNodeSize(Double_t sizeInPercent)

void SetMinNodeSize(TString sizeInPercent)

void SetNTrees(Int_t d)

{fNTrees = d;}

void SetAdaBoostBeta(Double_t b)

{fAdaBoostBeta = b;}

void SetNodePurityLimit(Double_t l)

{fNodePurityLimit = l;}

void SetShrinkage(Double_t s)

{fShrinkage = s;}

void SetUseNvars(Int_t n)

{fUseNvars = n;}

void SetBaggedSampleFraction(Double_t f)

{fBaggedSampleFraction = f;}

std::vector<Double_t> GetVariableImportance()

return the individual relative variable importance

Double_t GetVariableImportance(UInt_t ivar)

Double_t TestTreeQuality(TMVA::DecisionTree* dt)

void MakeClassSpecific(ostream& , const TString& ) const

 make ROOT-independent C++ class for classifier response (classifier-specific implementation)

void MakeClassSpecificHeader(ostream& , const TString& ) const

 header and auxiliary classes

void MakeClassInstantiateNode(TMVA::DecisionTreeNode* n, ostream& fout, const TString& className) const

void DeclareCompatibilityOptions()

void Init( void )

 Init used in the various constructors

void PreProcessNegativeEventWeights()

Double_t AdaBoost(vector<const TMVA::Event*>& , TMVA::DecisionTree* dt)

 boosting algorithm (adaptive boosting)

Double_t AdaCost(vector<const TMVA::Event*>& , TMVA::DecisionTree* dt)

 boosting algorithm (adaptive boosting with cost matrix)

Double_t Bagging()

 boosting as a random re-weighting

Double_t RegBoost(vector<const TMVA::Event*>& , TMVA::DecisionTree* dt)

 boosting special for regression

Double_t AdaBoostR2(vector<const TMVA::Event*>& , TMVA::DecisionTree* dt)

 adaboost adapted to regression

Double_t GradBoost(vector<const TMVA::Event*>& , TMVA::DecisionTree* dt, UInt_t cls = 0)

 binomial likelihood gradient boost for classification
 (see Friedman: "Greedy Function Approximation: a Gradient Boosting Machine"
 Technical report, Dept. of Statistics, Stanford University)

Double_t GradBoostRegression(vector<const TMVA::Event*>& , TMVA::DecisionTree* dt)

void InitGradBoost(vector<const TMVA::Event*>& )

void UpdateTargets(vector<const TMVA::Event*>& , UInt_t cls = 0)

void UpdateTargetsRegression(vector<const TMVA::Event*>& , Bool_t first = kFALSE)

Double_t GetGradBoostMVA(const TMVA::Event* e, UInt_t nTrees)

void GetBaggedSubSample(vector<const TMVA::Event*>& )

void DeterminePreselectionCuts(const vector<const TMVA::Event*>& eventSample)

Double_t ApplyPreselectionCuts(const TMVA::Event* ev)