J Friedman's RuleFit method.

Definition at line 48 of file MethodRuleFit.h.

Public Member Functions
	MethodRuleFit (const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption="")
	standard constructor

	MethodRuleFit (DataSetInfo &theData, const TString &theWeightFile)
	constructor from weight file

virtual	~MethodRuleFit (void)
	destructor

void	AddWeightsXMLTo (void *parent) const
	add the rules to XML node

const Ranking *	CreateRanking ()
	computes ranking of input variables

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

Double_t	GetGDErrScale () const

Int_t	GetGDNPathSteps () const

Double_t	GetGDPathEveFrac () const

Double_t	GetGDPathStep () const

Double_t	GetGDValidEveFrac () const

Double_t	GetLinQuantile () const

Double_t	GetMaxFracNEve () const

TDirectory *	GetMethodBaseDir () const

Double_t	GetMinFracNEve () const

Double_t	GetMvaValue (Double_t err=nullptr, Double_t errUpper=nullptr)
	returns MVA value for given event

Int_t	GetNCuts () const

Int_t	GetNTrees () const

TMVA::DecisionTree::EPruneMethod	GetPruneMethod () const

Double_t	GetPruneStrength () const

Int_t	GetRFNendnodes () const

Int_t	GetRFNrules () const

const TString	GetRFWorkDir () const

const RuleFit *	GetRuleFitConstPtr () const

RuleFit *	GetRuleFitPtr ()

SeparationBase *	GetSeparationBase () const

const SeparationBase *	GetSeparationBaseConst () const

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

Double_t	GetTreeEveFrac () const

virtual Bool_t	HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t)
	RuleFit can handle classification with 2 classes.

virtual TClass *	IsA () const

virtual void	ReadWeightsFromStream (std::istream &)=0

void	ReadWeightsFromStream (std::istream &istr)
	read rules from an std::istream

virtual void	ReadWeightsFromStream (TFile &)

void	ReadWeightsFromXML (void *wghtnode)
	read rules from XML node

virtual void	Streamer (TBuffer &)

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

void	Train (void)

Bool_t	UseBoost () const

void	WriteMonitoringHistosToFile (void) const
	write special monitoring histograms to file (here ntuple)

Public Member Functions inherited from TMVA::MethodBase
	MethodBase (const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="")
	standard constructor

	MethodBase (Types::EMVA methodType, DataSetInfo &dsi, const TString &weightFile)
	constructor used for Testing + Application of the MVA, only (no training), using given WeightFiles

virtual	~MethodBase ()
	destructor

void	AddOutput (Types::ETreeType type, Types::EAnalysisType analysisType)

TDirectory *	BaseDir () const
	returns the ROOT directory where info/histograms etc of the corresponding MVA method instance are stored

virtual void	CheckSetup ()
	check may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase)

DataSet *	Data () const

DataSetInfo &	DataInfo () const

virtual void	DeclareCompatibilityOptions ()
	options that are used ONLY for the READER to ensure backward compatibility they are hence without any effect (the reader is only reading the training options that HAD been used at the training of the .xml weight file at hand

void	DisableWriting (Bool_t setter)

Bool_t	DoMulticlass () const

Bool_t	DoRegression () const

void	ExitFromTraining ()

Types::EAnalysisType	GetAnalysisType () const

UInt_t	GetCurrentIter ()

virtual Double_t	GetEfficiency (const TString &, Types::ETreeType, Double_t &err)
	fill background efficiency (resp.

const Event *	GetEvent () const

const Event *	GetEvent (const TMVA::Event *ev) const

const Event *	GetEvent (Long64_t ievt) const

const Event *	GetEvent (Long64_t ievt, Types::ETreeType type) const

const std::vector< TMVA::Event * > &	GetEventCollection (Types::ETreeType type)
	returns the event collection (i.e.

TFile *	GetFile () const

const TString &	GetInputLabel (Int_t i) const

const char *	GetInputTitle (Int_t i) const

const TString &	GetInputVar (Int_t i) const

TMultiGraph *	GetInteractiveTrainingError ()

const TString &	GetJobName () const

virtual Double_t	GetKSTrainingVsTest (Char_t SorB, TString opt="X")

virtual Double_t	GetMaximumSignificance (Double_t SignalEvents, Double_t BackgroundEvents, Double_t &optimal_significance_value) const
	plot significance, \( \frac{S}{\sqrt{S^2 + B^2}} \), curve for given number of signal and background events; returns cut for maximum significance also returned via reference is the maximum significance

UInt_t	GetMaxIter ()

Double_t	GetMean (Int_t ivar) const

const TString &	GetMethodName () const

Types::EMVA	GetMethodType () const

TString	GetMethodTypeName () const

virtual TMatrixD	GetMulticlassConfusionMatrix (Double_t effB, Types::ETreeType type)
	Construct a confusion matrix for a multiclass classifier.

virtual std::vector< Float_t >	GetMulticlassEfficiency (std::vector< std::vector< Float_t > > &purity)

virtual std::vector< Float_t >	GetMulticlassTrainingEfficiency (std::vector< std::vector< Float_t > > &purity)

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

Double_t	GetMvaValue (const TMVA::Event const ev, Double_t err=nullptr, Double_t *errUpper=nullptr)

const char *	GetName () const

UInt_t	GetNEvents () const

UInt_t	GetNTargets () const

UInt_t	GetNvar () const

UInt_t	GetNVariables () const

virtual Double_t	GetProba (const Event *ev)

virtual Double_t	GetProba (Double_t mvaVal, Double_t ap_sig)
	compute likelihood ratio

const TString	GetProbaName () const

virtual Double_t	GetRarity (Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const
	compute rarity:

virtual void	GetRegressionDeviation (UInt_t tgtNum, Types::ETreeType type, Double_t &stddev, Double_t &stddev90Percent) const

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

const std::vector< Float_t > &	GetRegressionValues (const TMVA::Event *const ev)

Double_t	GetRMS (Int_t ivar) const

virtual Double_t	GetROCIntegral (PDF pdfS=nullptr, PDF pdfB=nullptr) const
	calculate the area (integral) under the ROC curve as a overall quality measure of the classification

virtual Double_t	GetROCIntegral (TH1D histS, TH1D histB) const
	calculate the area (integral) under the ROC curve as a overall quality measure of the classification

virtual Double_t	GetSeparation (PDF pdfS=nullptr, PDF pdfB=nullptr) const
	compute "separation" defined as

virtual Double_t	GetSeparation (TH1 , TH1 ) const
	compute "separation" defined as

Double_t	GetSignalReferenceCut () const

Double_t	GetSignalReferenceCutOrientation () const

virtual Double_t	GetSignificance () const
	compute significance of mean difference

const Event *	GetTestingEvent (Long64_t ievt) const

Double_t	GetTestTime () const

const TString &	GetTestvarName () const

virtual Double_t	GetTrainingEfficiency (const TString &)

const Event *	GetTrainingEvent (Long64_t ievt) const

virtual const std::vector< Float_t > &	GetTrainingHistory (const char *)

UInt_t	GetTrainingROOTVersionCode () const

TString	GetTrainingROOTVersionString () const
	calculates the ROOT version string from the training version code on the fly

UInt_t	GetTrainingTMVAVersionCode () const

TString	GetTrainingTMVAVersionString () const
	calculates the TMVA version string from the training version code on the fly

Double_t	GetTrainTime () const

TransformationHandler &	GetTransformationHandler (Bool_t takeReroutedIfAvailable=true)

const TransformationHandler &	GetTransformationHandler (Bool_t takeReroutedIfAvailable=true) const

TString	GetWeightFileName () const
	retrieve weight file name

Double_t	GetXmax (Int_t ivar) const

Double_t	GetXmin (Int_t ivar) const

Bool_t	HasMVAPdfs () const

void	InitIPythonInteractive ()

Bool_t	IsModelPersistence () const

virtual Bool_t	IsSignalLike ()
	uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for a quick determination if an event would be selected as signal or background

virtual Bool_t	IsSignalLike (Double_t mvaVal)
	uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for a quick determination if an event with this mva output value would be selected as signal or background

Bool_t	IsSilentFile () const

virtual void	MakeClass (const TString &classFileName=TString("")) const
	create reader class for method (classification only at present)

TDirectory *	MethodBaseDir () const
	returns the ROOT directory where all instances of the corresponding MVA method are stored

virtual std::map< TString, Double_t >	OptimizeTuningParameters (TString fomType="ROCIntegral", TString fitType="FitGA")
	call the Optimizer with the set of parameters and ranges that are meant to be tuned.

void	PrintHelpMessage () const
	prints out method-specific help method

void	ProcessSetup ()
	process all options the "CheckForUnusedOptions" is done in an independent call, since it may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase)

void	ReadStateFromFile ()
	Function to write options and weights to file.

void	ReadStateFromStream (std::istream &tf)
	read the header from the weight files of the different MVA methods

void	ReadStateFromStream (TFile &rf)
	write reference MVA distributions (and other information) to a ROOT type weight file

void	ReadStateFromXMLString (const char *xmlstr)
	for reading from memory

void	RerouteTransformationHandler (TransformationHandler *fTargetTransformation)

virtual void	Reset ()

virtual void	SetAnalysisType (Types::EAnalysisType type)

void	SetBaseDir (TDirectory *methodDir)

void	SetFile (TFile *file)

void	SetMethodBaseDir (TDirectory *methodDir)

void	SetMethodDir (TDirectory *methodDir)

void	SetModelPersistence (Bool_t status)

void	SetSignalReferenceCut (Double_t cut)

void	SetSignalReferenceCutOrientation (Double_t cutOrientation)

void	SetSilentFile (Bool_t status)

void	SetTestTime (Double_t testTime)

void	SetTestvarName (const TString &v="")

void	SetTrainTime (Double_t trainTime)

virtual void	SetTuneParameters (std::map< TString, Double_t > tuneParameters)
	set the tuning parameters according to the argument This is just a dummy .

void	SetupMethod ()
	setup of methods

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

virtual void	TestClassification ()
	initialization

virtual void	TestMulticlass ()
	test multiclass classification

virtual void	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, Types::ETreeType type)
	calculate <sum-of-deviation-squared> of regression output versus "true" value from test sample

bool	TrainingEnded ()

void	TrainMethod ()

virtual void	WriteEvaluationHistosToFile (Types::ETreeType treetype)
	writes all MVA evaluation histograms to file

void	WriteStateToFile () const
	write options and weights to file note that each one text file for the main configuration information and one ROOT file for ROOT objects are created

Public Member Functions inherited from TMVA::IMethod
	IMethod ()

virtual	~IMethod ()

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

Public Member Functions inherited from TMVA::Configurable
	Configurable (const TString &theOption="")
	constructor

virtual	~Configurable ()
	default destructor

void	AddOptionsXMLTo (void *parent) const
	write options to XML file

template<class T >
void	AddPreDefVal (const T &)

template<class T >
void	AddPreDefVal (const TString &optname, const T &)

void	CheckForUnusedOptions () const
	checks for unused options in option string

template<class T >
TMVA::OptionBase *	DeclareOptionRef (T &ref, const TString &name, const TString &desc)

template<class T >
OptionBase *	DeclareOptionRef (T &ref, const TString &name, const TString &desc="")

template<class T >
TMVA::OptionBase *	DeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc)

template<class T >
OptionBase *	DeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc="")

const char *	GetConfigDescription () const

const char *	GetConfigName () const

const TString &	GetOptions () const

MsgLogger &	Log () const

virtual void	ParseOptions ()
	options parser

void	PrintOptions () const
	prints out the options set in the options string and the defaults

void	ReadOptionsFromStream (std::istream &istr)
	read option back from the weight file

void	ReadOptionsFromXML (void *node)

void	SetConfigDescription (const char *d)

void	SetConfigName (const char *n)

void	SetMsgType (EMsgType t)

void	SetOptions (const TString &s)

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

void	WriteOptionsToStream (std::ostream &o, const TString &prefix) const
	write options to output stream (e.g. in writing the MVA weight files

Public Member Functions inherited from TNamed
	TNamed ()

	TNamed (const char name, const char title)

	TNamed (const TNamed &named)
	TNamed copy ctor.

	TNamed (const TString &name, const TString &title)

virtual	~TNamed ()
	TNamed destructor.

void	Clear (Option_t *option="") override
	Set name and title to empty strings ("").

TObject *	Clone (const char *newname="") const override
	Make a clone of an object using the Streamer facility.

Int_t	Compare (const TObject *obj) const override
	Compare two TNamed objects.

void	Copy (TObject &named) const override
	Copy this to obj.

virtual void	FillBuffer (char *&buffer)
	Encode TNamed into output buffer.

const char *	GetName () const override
	Returns name of object.

const char *	GetTitle () const override
	Returns title of object.

ULong_t	Hash () const override
	Return hash value for this object.

TClass *	IsA () const override

Bool_t	IsSortable () const override

void	ls (Option_t *option="") const override
	List TNamed name and title.

TNamed &	operator= (const TNamed &rhs)
	TNamed assignment operator.

void	Print (Option_t *option="") const override
	Print TNamed name and title.

virtual void	SetName (const char *name)
	Set the name of the TNamed.

virtual void	SetNameTitle (const char name, const char title)
	Set all the TNamed parameters (name and title).

virtual void	SetTitle (const char *title="")
	Set the title of the TNamed.

virtual Int_t	Sizeof () const
	Return size of the TNamed part of the TObject.

void	Streamer (TBuffer &) override
	Stream an object of class TObject.

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

Public Member Functions inherited from TObject
	TObject ()
	TObject constructor.

	TObject (const TObject &object)
	TObject copy ctor.

virtual	~TObject ()
	TObject destructor.

void	AbstractMethod (const char *method) const
	Use this method to implement an "abstract" method that you don't want to leave purely abstract.

virtual void	AppendPad (Option_t *option="")
	Append graphics object to current pad.

virtual void	Browse (TBrowser *b)
	Browse object. May be overridden for another default action.

ULong_t	CheckedHash ()
	Check and record whether this class has a consistent Hash/RecursiveRemove setup (*) and then return the regular Hash value for this object.

virtual const char *	ClassName () const
	Returns name of class to which the object belongs.

virtual void	Delete (Option_t *option="")
	Delete this object.

virtual Int_t	DistancetoPrimitive (Int_t px, Int_t py)
	Computes distance from point (px,py) to the object.

virtual void	Draw (Option_t *option="")
	Default Draw method for all objects.

virtual void	DrawClass () const
	Draw class inheritance tree of the class to which this object belongs.

virtual TObject *	DrawClone (Option_t *option="") const
	Draw a clone of this object in the current selected pad with: `gROOT->SetSelectedPad(c1)`.

virtual void	Dump () const
	Dump contents of object on stdout.

virtual void	Error (const char method, const char msgfmt,...) const
	Issue error message.

virtual void	Execute (const char method, const char params, Int_t *error=nullptr)
	Execute method on this object with the given parameter string, e.g.

virtual void	Execute (TMethod method, TObjArray params, Int_t *error=nullptr)
	Execute method on this object with parameters stored in the TObjArray.

virtual void	ExecuteEvent (Int_t event, Int_t px, Int_t py)
	Execute action corresponding to an event at (px,py).

virtual void	Fatal (const char method, const char msgfmt,...) const
	Issue fatal error message.

virtual TObject *	FindObject (const char *name) const
	Must be redefined in derived classes.

virtual TObject *	FindObject (const TObject *obj) const
	Must be redefined in derived classes.

virtual Option_t *	GetDrawOption () const
	Get option used by the graphics system to draw this object.

virtual const char *	GetIconName () const
	Returns mime type name of object.

virtual char *	GetObjectInfo (Int_t px, Int_t py) const
	Returns string containing info about the object at position (px,py).

virtual Option_t *	GetOption () const

virtual UInt_t	GetUniqueID () const
	Return the unique object id.

virtual Bool_t	HandleTimer (TTimer *timer)
	Execute action in response of a timer timing out.

Bool_t	HasInconsistentHash () const
	Return true is the type of this object is known to have an inconsistent setup for Hash and RecursiveRemove (i.e.

virtual void	Info (const char method, const char msgfmt,...) const
	Issue info message.

virtual Bool_t	InheritsFrom (const char *classname) const
	Returns kTRUE if object inherits from class "classname".

virtual Bool_t	InheritsFrom (const TClass *cl) const
	Returns kTRUE if object inherits from TClass cl.

virtual void	Inspect () const
	Dump contents of this object in a graphics canvas.

void	InvertBit (UInt_t f)

Bool_t	IsDestructed () const
	IsDestructed.

virtual Bool_t	IsEqual (const TObject *obj) const
	Default equal comparison (objects are equal if they have the same address in memory).

virtual Bool_t	IsFolder () const
	Returns kTRUE in case object contains browsable objects (like containers or lists of other objects).

R__ALWAYS_INLINE Bool_t	IsOnHeap () const

R__ALWAYS_INLINE Bool_t	IsZombie () const

void	MayNotUse (const char *method) const
	Use this method to signal that a method (defined in a base class) may not be called in a derived class (in principle against good design since a child class should not provide less functionality than its parent, however, sometimes it is necessary).

virtual Bool_t	Notify ()
	This method must be overridden to handle object notification (the base implementation is no-op).

void	Obsolete (const char method, const char asOfVers, const char *removedFromVers) const
	Use this method to declare a method obsolete.

void	operator delete (void *ptr)
	Operator delete.

void	operator delete (void ptr, void vp)
	Only called by placement new when throwing an exception.

void	operator delete[] (void *ptr)
	Operator delete [].

void	operator delete[] (void ptr, void vp)
	Only called by placement new[] when throwing an exception.

void *	operator new (size_t sz)

void *	operator new (size_t sz, void *vp)

void *	operator new[] (size_t sz)

void *	operator new[] (size_t sz, void *vp)

TObject &	operator= (const TObject &rhs)
	TObject assignment operator.

virtual void	Paint (Option_t *option="")
	This method must be overridden if a class wants to paint itself.

virtual void	Pop ()
	Pop on object drawn in a pad to the top of the display list.

virtual Int_t	Read (const char *name)
	Read contents of object with specified name from the current directory.

virtual void	RecursiveRemove (TObject *obj)
	Recursively remove this object from a list.

void	ResetBit (UInt_t f)

virtual void	SaveAs (const char filename="", Option_t option="") const
	Save this object in the file specified by filename.

virtual void	SavePrimitive (std::ostream &out, Option_t *option="")
	Save a primitive as a C++ statement(s) on output stream "out".

void	SetBit (UInt_t f)

void	SetBit (UInt_t f, Bool_t set)
	Set or unset the user status bits as specified in f.

virtual void	SetDrawOption (Option_t *option="")
	Set drawing option for object.

virtual void	SetUniqueID (UInt_t uid)
	Set the unique object id.

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

virtual void	SysError (const char method, const char msgfmt,...) const
	Issue system error message.

R__ALWAYS_INLINE Bool_t	TestBit (UInt_t f) const

Int_t	TestBits (UInt_t f) const

virtual void	UseCurrentStyle ()
	Set current style settings in this object This function is called when either TCanvas::UseCurrentStyle or TROOT::ForceStyle have been invoked.

virtual void	Warning (const char method, const char msgfmt,...) const
	Issue warning message.

virtual Int_t	Write (const char *name=nullptr, Int_t option=0, Int_t bufsize=0)
	Write this object to the current directory.

virtual Int_t	Write (const char *name=nullptr, Int_t option=0, Int_t bufsize=0) const
	Write this object to the current directory.

Static Public Member Functions
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Static Public Member Functions inherited from TMVA::MethodBase
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Static Public Member Functions inherited from TMVA::IMethod
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Static Public Member Functions inherited from TMVA::Configurable
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Static Public Member Functions inherited from TNamed
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Static Public Member Functions inherited from TObject
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

static Longptr_t	GetDtorOnly ()
	Return destructor only flag.

static Bool_t	GetObjectStat ()
	Get status of object stat flag.

static void	SetDtorOnly (void *obj)
	Set destructor only flag.

static void	SetObjectStat (Bool_t stat)
	Turn on/off tracking of objects in the TObjectTable.

Protected Member Functions
void	GetHelpMessage () const
	get help message text

void	Init (void)
	default initialization

void	InitEventSample (void)
	write all Events from the Tree into a vector of Events, that are more easily manipulated.

void	InitMonitorNtuple ()
	initialize the monitoring ntuple

void	MakeClassLinear (std::ostream &) const
	print out the linear terms

void	MakeClassRuleCuts (std::ostream &) const
	print out the rule cuts

void	MakeClassSpecific (std::ostream &, const TString &) const
	write specific classifier response

void	TrainJFRuleFit ()
	training of rules using Jerome Friedmans implementation

void	TrainTMVARuleFit ()
	training of rules using TMVA implementation

Protected Member Functions inherited from TMVA::MethodBase
virtual std::vector< Double_t >	GetDataMvaValues (DataSet *data=nullptr, Long64_t firstEvt=0, Long64_t lastEvt=-1, Bool_t logProgress=false)
	get all the MVA values for the events of the given Data type

const TString &	GetInternalVarName (Int_t ivar) const

virtual std::vector< Double_t >	GetMvaValues (Long64_t firstEvt=0, Long64_t lastEvt=-1, Bool_t logProgress=false)
	get all the MVA values for the events of the current Data type

const TString &	GetOriginalVarName (Int_t ivar) const

const TString &	GetWeightFileDir () const

Bool_t	HasTrainingTree () const

Bool_t	Help () const

Bool_t	IgnoreEventsWithNegWeightsInTraining () const

Bool_t	IsConstructedFromWeightFile () const

Bool_t	IsNormalised () const

virtual void	MakeClassSpecificHeader (std::ostream &, const TString &="") const

void	NoErrorCalc (Double_t const err, Double_t const errUpper)

void	SetNormalised (Bool_t norm)

void	SetWeightFileDir (TString fileDir)
	set directory of weight file

void	SetWeightFileName (TString)
	set the weight file name (depreciated)

void	Statistics (Types::ETreeType treeType, const TString &theVarName, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &)
	calculates rms,mean, xmin, xmax of the event variable this can be either done for the variables as they are or for normalised variables (in the range of 0-1) if "norm" is set to kTRUE

Bool_t	TxtWeightsOnly () const

Bool_t	Verbose () const

Protected Member Functions inherited from TMVA::Configurable
void	EnableLooseOptions (Bool_t b=kTRUE)

const TString &	GetReferenceFile () const

Bool_t	LooseOptionCheckingEnabled () const

void	ResetSetFlag ()
	resets the IsSet flag for all declare options to be called before options are read from stream

void	WriteOptionsReferenceToFile ()
	write complete options to output stream

Protected Member Functions inherited from TObject
virtual void	DoError (int level, const char location, const char fmt, va_list va) const
	Interface to ErrorHandler (protected).

void	MakeZombie ()

Private Member Functions
void	DeclareOptions ()
	define the options (their key words) that can be set in the option string know options.

void	ProcessOptions ()
	process the options specified by the user

template<typename T >
Int_t	VerifyRange (const T &var, const T &vmin, const T &vmax)

template<typename T >
Bool_t	VerifyRange (MsgLogger &mlog, const char *varstr, T &var, const T &vmin, const T &vmax)

template<typename T >
Bool_t	VerifyRange (MsgLogger &mlog, const char *varstr, T &var, const T &vmin, const T &vmax, const T &vdef)

Private Attributes
std::vector< TMVA::Event * >	fEventSample
	the complete training sample

std::vector< DecisionTree * >	fForest
	the forest

TString	fForestTypeS
	forest generation: how the trees are generated

Double_t	fGDErrScale
	GD path: stop.

Int_t	fGDNPathSteps
	GD path: number of steps.

Double_t	fGDPathEveFrac
	GD path: fraction of subsamples used for the fitting.

Double_t	fGDPathStep
	GD path: step size in path.

Double_t	fGDTau
	GD path: def threshold fraction [0..1].

Double_t	fGDTauMax
	GD path: max threshold fraction [0..1].

Double_t	fGDTauMin
	GD path: min threshold fraction [0..1].

Double_t	fGDTauPrec
	GD path: precision of estimated tau.

UInt_t	fGDTauScan
	GD path: number of points to scan.

Double_t	fGDValidEveFrac
	GD path: fraction of subsamples used for the fitting.

Double_t	fLinQuantile
	quantile cut to remove outliers - see RuleEnsemble

Double_t	fMaxFracNEve
	ditto max

Double_t	fMinFracNEve
	min fraction of number events

Double_t	fMinimp
	rule/linear: minimum importance

TString	fModelTypeS
	rule ensemble: which model (rule,linear or both)

TTree *	fMonitorNtuple
	pointer to monitor rule ntuple

Int_t	fNCuts
	grid used in cut applied in node splitting

Double_t	fNTCoefficient
	ntuple: rule coefficient

Double_t	fNTImportance
	ntuple: rule importance

Int_t	fNTNcuts
	ntuple: rule number of cuts

Int_t	fNTNvars
	ntuple: rule number of vars

Double_t	fNTPbb
	ntuple: rule P(tag b, true b)

Double_t	fNTPbs
	ntuple: rule P(tag b, true s)

Double_t	fNTPsb
	ntuple: rule P(tag s, true b)

Double_t	fNTPss
	ntuple: rule P(tag s, true s)

Double_t	fNTPtag
	ntuple: rule P(tag)

Int_t	fNTrees
	number of trees in forest

Double_t	fNTSSB
	ntuple: rule S/(S+B)

Double_t	fNTSupport
	ntuple: rule support

Int_t	fNTType
	ntuple: rule type (+1->signal, -1->bkg)

TMVA::DecisionTree::EPruneMethod	fPruneMethod
	forest generation: method used for pruning - see DecisionTree

TString	fPruneMethodS
	forest generation: prune method - see DecisionTree

Double_t	fPruneStrength
	forest generation: prune strength - see DecisionTree

Int_t	fRFNendnodes
	max number of rules (only Friedmans module)

Int_t	fRFNrules
	max number of rules (only Friedmans module)

TString	fRFWorkDir
	working directory from Friedmans module

RuleFit	fRuleFit
	RuleFit instance.

TString	fRuleFitModuleS
	which rulefit module to use

Double_t	fRuleMinDist
	rule min distance - see RuleEnsemble

SeparationBase *	fSepType
	the separation used in node splitting

TString	fSepTypeS
	forest generation: separation type - see DecisionTree

Double_t	fSignalFraction
	scalefactor for bkg events to modify initial s/b fraction in training data

Double_t	fTreeEveFrac
	fraction of events used for training each tree

Bool_t	fUseBoost
	use boosted events for forest generation

Bool_t	fUseRuleFitJF
	if true interface with J.Friedmans RuleFit module

Additional Inherited Members
Public Types inherited from TMVA::MethodBase
enum	EWeightFileType { kROOT =0 , kTEXT }

Public Types inherited from TObject
enum	{ kIsOnHeap = 0x01000000 , kNotDeleted = 0x02000000 , kZombie = 0x04000000 , kInconsistent = 0x08000000 , kBitMask = 0x00ffffff }

enum	{ kSingleKey = (1ULL << ( 0 )) , kOverwrite = (1ULL << ( 1 )) , kWriteDelete = (1ULL << ( 2 )) }

enum	EDeprecatedStatusBits { kObjInCanvas = (1ULL << ( 3 )) }

enum	EStatusBits { kCanDelete = (1ULL << ( 0 )) , kMustCleanup = (1ULL << ( 3 )) , kIsReferenced = (1ULL << ( 4 )) , kHasUUID = (1ULL << ( 5 )) , kCannotPick = (1ULL << ( 6 )) , kNoContextMenu = (1ULL << ( 8 )) , kInvalidObject = (1ULL << ( 13 )) }

Public Attributes inherited from TMVA::MethodBase
Bool_t	fSetupCompleted

TrainingHistory	fTrainHistory

Protected Types inherited from TObject
enum	{ kOnlyPrepStep = (1ULL << ( 3 )) }

Protected Attributes inherited from TMVA::MethodBase
Types::EAnalysisType	fAnalysisType

UInt_t	fBackgroundClass

bool	fExitFromTraining = false

std::vector< TString > *	fInputVars

IPythonInteractive *	fInteractive = nullptr
	temporary dataset used when evaluating on a different data (used by MethodCategory::GetMvaValues)

UInt_t	fIPyCurrentIter = 0

UInt_t	fIPyMaxIter = 0

std::vector< Float_t > *	fMulticlassReturnVal

Int_t	fNbins

Int_t	fNbinsH

Int_t	fNbinsMVAoutput

Ranking *	fRanking

std::vector< Float_t > *	fRegressionReturnVal

Results *	fResults

UInt_t	fSignalClass

DataSet *	fTmpData = nullptr
	temporary event when testing on a different DataSet than the own one

const Event *	fTmpEvent

Protected Attributes inherited from TMVA::Configurable
MsgLogger *	fLogger
	! message logger

Protected Attributes inherited from TNamed
TString	fName

TString	fTitle

#include <TMVA/MethodRuleFit.h>

Inheritance diagram for TMVA::MethodRuleFit:

[legend]

Constructor & Destructor Documentation

◆ MethodRuleFit() [1/2]

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

standard constructor

Definition at line 70 of file MethodRuleFit.cxx.

◆ MethodRuleFit() [2/2]

TMVA::MethodRuleFit::MethodRuleFit	(	DataSetInfo &	theData,
		const TString &	theWeightFile
	)

constructor from weight file

Definition at line 120 of file MethodRuleFit.cxx.

◆ ~MethodRuleFit()

TMVA::MethodRuleFit::~MethodRuleFit ( void )

virtual

destructor

Definition at line 168 of file MethodRuleFit.cxx.

Member Function Documentation

◆ AddWeightsXMLTo()

void TMVA::MethodRuleFit::AddWeightsXMLTo ( void * parent ) const

virtual

add the rules to XML node

Implements TMVA::MethodBase.

Definition at line 593 of file MethodRuleFit.cxx.

◆ Class()

static TClass * TMVA::MethodRuleFit::Class ( )

static

Returns: TClass describing this class

◆ Class_Name()

static const char * TMVA::MethodRuleFit::Class_Name ( )

static

Returns: Name of this class

◆ Class_Version()

static constexpr Version_t TMVA::MethodRuleFit::Class_Version ( )

inlinestaticconstexpr

Returns: Version of this class

Definition at line 210 of file MethodRuleFit.h.

◆ CreateRanking()

const TMVA::Ranking * TMVA::MethodRuleFit::CreateRanking ( )

virtual

computes ranking of input variables

Implements TMVA::MethodBase.

Definition at line 578 of file MethodRuleFit.cxx.

◆ DeclareOptions()

void TMVA::MethodRuleFit::DeclareOptions ( )

privatevirtual

define the options (their key words) that can be set in the option string know options.

general

RuleFitModule <string> available values are:
- RFTMVA - use TMVA implementation
- RFFriedman - use Friedmans original implementation

Path search (fitting)

GDTau <float> gradient-directed path: fit threshold, default
GDTauPrec <float> gradient-directed path: precision of estimated tau
GDStep <float> gradient-directed path: step size
GDNSteps <float> gradient-directed path: number of steps
GDErrScale <float> stop scan when error>scale*errmin

Tree generation

fEventsMin <float> minimum fraction of events in a splittable node
fEventsMax <float> maximum fraction of events in a splittable node
nTrees <float> number of trees in forest.
ForestType <string> available values are:
- Random - create forest using random subsample and only random variables subset at each node
- AdaBoost - create forest with boosted events

Model creation

RuleMinDist <float> min distance allowed between rules
MinImp <float> minimum rule importance accepted
Model <string> model to be used available values are:
- ModRuleLinear <default>
- ModRule
- ModLinear

Friedmans module

RFWorkDir <string> directory where Friedmans module (rf_go.exe) is installed
RFNrules <int> maximum number of rules allowed
RFNendnodes <int> average number of end nodes in the forest of trees

Implements TMVA::MethodBase.

Definition at line 228 of file MethodRuleFit.cxx.

◆ DeclFileName()

static const char * TMVA::MethodRuleFit::DeclFileName ( )

inlinestatic

Returns: Name of the file containing the class declaration

Definition at line 210 of file MethodRuleFit.h.

◆ GetForest()

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

inline

Definition at line 92 of file MethodRuleFit.h.

◆ GetGDErrScale()

Double_t TMVA::MethodRuleFit::GetGDErrScale ( ) const

inline

Definition at line 105 of file MethodRuleFit.h.

◆ GetGDNPathSteps()

Int_t TMVA::MethodRuleFit::GetGDNPathSteps ( ) const

inline

Definition at line 103 of file MethodRuleFit.h.

◆ GetGDPathEveFrac()

Double_t TMVA::MethodRuleFit::GetGDPathEveFrac ( ) const

inline

Definition at line 106 of file MethodRuleFit.h.

◆ GetGDPathStep()

Double_t TMVA::MethodRuleFit::GetGDPathStep ( ) const

inline

Definition at line 104 of file MethodRuleFit.h.

◆ GetGDValidEveFrac()

Double_t TMVA::MethodRuleFit::GetGDValidEveFrac ( ) const

inline

Definition at line 107 of file MethodRuleFit.h.

◆ GetHelpMessage()

void TMVA::MethodRuleFit::GetHelpMessage ( ) const

protectedvirtual

get help message text

typical length of text line: "|--------------------------------------------------------------|"

Implements TMVA::IMethod.

Definition at line 751 of file MethodRuleFit.cxx.

◆ GetLinQuantile()

Double_t TMVA::MethodRuleFit::GetLinQuantile ( ) const

inline

Definition at line 109 of file MethodRuleFit.h.

◆ GetMaxFracNEve()

Double_t TMVA::MethodRuleFit::GetMaxFracNEve ( ) const

inline

Definition at line 100 of file MethodRuleFit.h.

◆ GetMethodBaseDir()

TDirectory * TMVA::MethodRuleFit::GetMethodBaseDir ( ) const

inline

Definition at line 90 of file MethodRuleFit.h.

◆ GetMinFracNEve()

Double_t TMVA::MethodRuleFit::GetMinFracNEve ( ) const

inline

Definition at line 99 of file MethodRuleFit.h.

◆ GetMvaValue()

Double_t TMVA::MethodRuleFit::GetMvaValue	(	Double_t *	err = `nullptr`,
		Double_t *	errUpper = `nullptr`
	)

virtual

returns MVA value for given event

Implements TMVA::MethodBase.

Definition at line 617 of file MethodRuleFit.cxx.

◆ GetNCuts()

Int_t TMVA::MethodRuleFit::GetNCuts ( ) const

inline

Definition at line 101 of file MethodRuleFit.h.

◆ GetNTrees()

Int_t TMVA::MethodRuleFit::GetNTrees ( ) const

inline

Definition at line 93 of file MethodRuleFit.h.

◆ GetPruneMethod()

TMVA::DecisionTree::EPruneMethod TMVA::MethodRuleFit::GetPruneMethod ( ) const

inline

Definition at line 97 of file MethodRuleFit.h.

◆ GetPruneStrength()

Double_t TMVA::MethodRuleFit::GetPruneStrength ( ) const

inline

Definition at line 98 of file MethodRuleFit.h.

◆ GetRFNendnodes()

Int_t TMVA::MethodRuleFit::GetRFNendnodes ( ) const

inline

Definition at line 113 of file MethodRuleFit.h.

◆ GetRFNrules()

Int_t TMVA::MethodRuleFit::GetRFNrules ( ) const

inline

Definition at line 112 of file MethodRuleFit.h.

◆ GetRFWorkDir()

const TString TMVA::MethodRuleFit::GetRFWorkDir ( ) const

inline

Definition at line 111 of file MethodRuleFit.h.

◆ GetRuleFitConstPtr()

const RuleFit * TMVA::MethodRuleFit::GetRuleFitConstPtr ( ) const

inline

Definition at line 89 of file MethodRuleFit.h.

◆ GetRuleFitPtr()

RuleFit * TMVA::MethodRuleFit::GetRuleFitPtr ( )

inline

Definition at line 88 of file MethodRuleFit.h.

◆ GetSeparationBase()

SeparationBase * TMVA::MethodRuleFit::GetSeparationBase ( ) const

inline

Definition at line 96 of file MethodRuleFit.h.

◆ GetSeparationBaseConst()

const SeparationBase * TMVA::MethodRuleFit::GetSeparationBaseConst ( ) const

inline

Definition at line 95 of file MethodRuleFit.h.

◆ GetTrainingEvents()

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

inline

Definition at line 91 of file MethodRuleFit.h.

◆ GetTreeEveFrac()

Double_t TMVA::MethodRuleFit::GetTreeEveFrac ( ) const

inline

Definition at line 94 of file MethodRuleFit.h.

◆ HasAnalysisType()

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

virtual

RuleFit can handle classification with 2 classes.

Implements TMVA::IMethod.

Definition at line 177 of file MethodRuleFit.cxx.

◆ Init()

void TMVA::MethodRuleFit::Init ( void )

protectedvirtual

default initialization

Implements TMVA::MethodBase.

Definition at line 396 of file MethodRuleFit.cxx.

◆ InitEventSample()

void TMVA::MethodRuleFit::InitEventSample ( void )

protected

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

This method should never be called without existing trainingTree, as it the vector of events from the ROOT training tree

Definition at line 421 of file MethodRuleFit.cxx.

◆ InitMonitorNtuple()

void TMVA::MethodRuleFit::InitMonitorNtuple ( )

protected

initialize the monitoring ntuple

Definition at line 375 of file MethodRuleFit.cxx.

◆ IsA()

virtual TClass * TMVA::MethodRuleFit::IsA ( ) const

inlinevirtual

Returns: TClass describing current object

Reimplemented from TMVA::MethodBase.

Definition at line 210 of file MethodRuleFit.h.

◆ MakeClassLinear()

void TMVA::MethodRuleFit::MakeClassLinear ( std::ostream & fout ) const

protected

print out the linear terms

Definition at line 715 of file MethodRuleFit.cxx.

◆ MakeClassRuleCuts()

void TMVA::MethodRuleFit::MakeClassRuleCuts ( std::ostream & fout ) const

protected

print out the rule cuts

Definition at line 657 of file MethodRuleFit.cxx.

◆ MakeClassSpecific()

void TMVA::MethodRuleFit::MakeClassSpecific	(	std::ostream &	fout,
		const TString &	className
	)		const

protectedvirtual

write specific classifier response

Reimplemented from TMVA::MethodBase.

Definition at line 638 of file MethodRuleFit.cxx.

◆ ProcessOptions()

void TMVA::MethodRuleFit::ProcessOptions ( )

privatevirtual

process the options specified by the user

Implements TMVA::MethodBase.

Definition at line 266 of file MethodRuleFit.cxx.

◆ ReadWeightsFromStream() [1/3]

virtual void TMVA::MethodBase::ReadWeightsFromStream ( std::istream & )

virtual

Implements TMVA::MethodBase.

◆ ReadWeightsFromStream() [2/3]

void TMVA::MethodRuleFit::ReadWeightsFromStream ( std::istream & istr )

virtual

read rules from an std::istream

Implements TMVA::MethodBase.

Definition at line 601 of file MethodRuleFit.cxx.

◆ ReadWeightsFromStream() [3/3]

virtual void TMVA::MethodBase::ReadWeightsFromStream ( TFile & )

inlinevirtual

Reimplemented from TMVA::MethodBase.

Definition at line 266 of file MethodBase.h.

◆ ReadWeightsFromXML()

void TMVA::MethodRuleFit::ReadWeightsFromXML ( void * wghtnode )

virtual

read rules from XML node

Implements TMVA::MethodBase.

Definition at line 609 of file MethodRuleFit.cxx.

◆ Streamer()

virtual void TMVA::MethodRuleFit::Streamer ( TBuffer & )

virtual

Reimplemented from TMVA::MethodBase.

◆ StreamerNVirtual()

void TMVA::MethodRuleFit::StreamerNVirtual ( TBuffer & ClassDef_StreamerNVirtual_b )

inline

Definition at line 210 of file MethodRuleFit.h.

◆ Train()

void TMVA::MethodRuleFit::Train ( void )

virtual

Implements TMVA::MethodBase.

Definition at line 443 of file MethodRuleFit.cxx.

◆ TrainJFRuleFit()

void TMVA::MethodRuleFit::TrainJFRuleFit ( void )

protected

training of rules using Jerome Friedmans implementation

Definition at line 535 of file MethodRuleFit.cxx.

◆ TrainTMVARuleFit()

void TMVA::MethodRuleFit::TrainTMVARuleFit ( void )

protected

training of rules using TMVA implementation

Definition at line 467 of file MethodRuleFit.cxx.

◆ UseBoost()

Bool_t TMVA::MethodRuleFit::UseBoost ( ) const

inline

Definition at line 85 of file MethodRuleFit.h.

◆ VerifyRange() [1/3]

template<typename T >

Int_t TMVA::MethodRuleFit::VerifyRange	(	const T &	var,
		const T &	vmin,
		const T &	vmax
	)

inlineprivate

Definition at line 218 of file MethodRuleFit.h.

◆ VerifyRange() [2/3]

template<typename T >

Bool_t TMVA::MethodRuleFit::VerifyRange	(	TMVA::MsgLogger &	mlog,
		const char *	varstr,
		T &	var,
		const T &	vmin,
		const T &	vmax
	)

inlineprivate

Definition at line 228 of file MethodRuleFit.h.

◆ VerifyRange() [3/3]

template<typename T >

Bool_t TMVA::MethodRuleFit::VerifyRange	(	TMVA::MsgLogger &	mlog,
		const char *	varstr,
		T &	var,
		const T &	vmin,
		const T &	vmax,
		const T &	vdef
	)

inlineprivate

Definition at line 250 of file MethodRuleFit.h.

◆ WriteMonitoringHistosToFile()

void TMVA::MethodRuleFit::WriteMonitoringHistosToFile ( void ) const

virtual

write special monitoring histograms to file (here ntuple)

Reimplemented from TMVA::MethodBase.

Definition at line 628 of file MethodRuleFit.cxx.

Member Data Documentation

◆ fEventSample

std::vector<TMVA::Event *> TMVA::MethodRuleFit::fEventSample

private

the complete training sample

Definition at line 156 of file MethodRuleFit.h.

◆ fForest

std::vector<DecisionTree *> TMVA::MethodRuleFit::fForest

private

the forest

Definition at line 180 of file MethodRuleFit.h.

◆ fForestTypeS

TString TMVA::MethodRuleFit::fForestTypeS

private

forest generation: how the trees are generated

Definition at line 191 of file MethodRuleFit.h.

◆ fGDErrScale

Double_t TMVA::MethodRuleFit::fGDErrScale

private

GD path: stop.

Definition at line 203 of file MethodRuleFit.h.

◆ fGDNPathSteps

Int_t TMVA::MethodRuleFit::fGDNPathSteps

private

GD path: number of steps.

Definition at line 202 of file MethodRuleFit.h.

◆ fGDPathEveFrac

Double_t TMVA::MethodRuleFit::fGDPathEveFrac

private

GD path: fraction of subsamples used for the fitting.

Definition at line 194 of file MethodRuleFit.h.

◆ fGDPathStep

Double_t TMVA::MethodRuleFit::fGDPathStep

private

GD path: step size in path.

Definition at line 201 of file MethodRuleFit.h.

◆ fGDTau

Double_t TMVA::MethodRuleFit::fGDTau

private

GD path: def threshold fraction [0..1].

Definition at line 196 of file MethodRuleFit.h.

◆ fGDTauMax

Double_t TMVA::MethodRuleFit::fGDTauMax

private

GD path: max threshold fraction [0..1].

Definition at line 199 of file MethodRuleFit.h.

◆ fGDTauMin

Double_t TMVA::MethodRuleFit::fGDTauMin

private

GD path: min threshold fraction [0..1].

Definition at line 198 of file MethodRuleFit.h.

◆ fGDTauPrec

Double_t TMVA::MethodRuleFit::fGDTauPrec

private

GD path: precision of estimated tau.

Definition at line 197 of file MethodRuleFit.h.

◆ fGDTauScan

UInt_t TMVA::MethodRuleFit::fGDTauScan

private

GD path: number of points to scan.

Definition at line 200 of file MethodRuleFit.h.

◆ fGDValidEveFrac

Double_t TMVA::MethodRuleFit::fGDValidEveFrac

private

GD path: fraction of subsamples used for the fitting.

Definition at line 195 of file MethodRuleFit.h.

◆ fLinQuantile

Double_t TMVA::MethodRuleFit::fLinQuantile

private

quantile cut to remove outliers - see RuleEnsemble

Definition at line 208 of file MethodRuleFit.h.

◆ fMaxFracNEve

Double_t TMVA::MethodRuleFit::fMaxFracNEve

private

ditto max

Definition at line 185 of file MethodRuleFit.h.

◆ fMinFracNEve

Double_t TMVA::MethodRuleFit::fMinFracNEve

private

min fraction of number events

Definition at line 184 of file MethodRuleFit.h.

◆ fMinimp

Double_t TMVA::MethodRuleFit::fMinimp

private

rule/linear: minimum importance

Definition at line 204 of file MethodRuleFit.h.

◆ fModelTypeS

TString TMVA::MethodRuleFit::fModelTypeS

private

rule ensemble: which model (rule,linear or both)

Definition at line 206 of file MethodRuleFit.h.

◆ fMonitorNtuple

TTree* TMVA::MethodRuleFit::fMonitorNtuple

private

pointer to monitor rule ntuple

Definition at line 160 of file MethodRuleFit.h.

◆ fNCuts

Int_t TMVA::MethodRuleFit::fNCuts

private

grid used in cut applied in node splitting

Definition at line 186 of file MethodRuleFit.h.

◆ fNTCoefficient

Double_t TMVA::MethodRuleFit::fNTCoefficient

private

ntuple: rule coefficient

Definition at line 162 of file MethodRuleFit.h.

◆ fNTImportance

Double_t TMVA::MethodRuleFit::fNTImportance

private

ntuple: rule importance

Definition at line 161 of file MethodRuleFit.h.

◆ fNTNcuts

Int_t TMVA::MethodRuleFit::fNTNcuts

private

ntuple: rule number of cuts

Definition at line 164 of file MethodRuleFit.h.

◆ fNTNvars

Int_t TMVA::MethodRuleFit::fNTNvars

private

ntuple: rule number of vars

Definition at line 165 of file MethodRuleFit.h.

◆ fNTPbb

Double_t TMVA::MethodRuleFit::fNTPbb

private

ntuple: rule P(tag b, true b)

Definition at line 170 of file MethodRuleFit.h.

◆ fNTPbs

Double_t TMVA::MethodRuleFit::fNTPbs

private

ntuple: rule P(tag b, true s)

Definition at line 169 of file MethodRuleFit.h.

◆ fNTPsb

Double_t TMVA::MethodRuleFit::fNTPsb

private

ntuple: rule P(tag s, true b)

Definition at line 168 of file MethodRuleFit.h.

◆ fNTPss

Double_t TMVA::MethodRuleFit::fNTPss

private

ntuple: rule P(tag s, true s)

Definition at line 167 of file MethodRuleFit.h.

◆ fNTPtag

Double_t TMVA::MethodRuleFit::fNTPtag

private

ntuple: rule P(tag)

Definition at line 166 of file MethodRuleFit.h.

◆ fNTrees

Int_t TMVA::MethodRuleFit::fNTrees

private

number of trees in forest

Definition at line 181 of file MethodRuleFit.h.

◆ fNTSSB

Double_t TMVA::MethodRuleFit::fNTSSB

private

ntuple: rule S/(S+B)

Definition at line 171 of file MethodRuleFit.h.

◆ fNTSupport

Double_t TMVA::MethodRuleFit::fNTSupport

private

ntuple: rule support

Definition at line 163 of file MethodRuleFit.h.

◆ fNTType

Int_t TMVA::MethodRuleFit::fNTType

private

ntuple: rule type (+1->signal, -1->bkg)

Definition at line 172 of file MethodRuleFit.h.

◆ fPruneMethod

TMVA::DecisionTree::EPruneMethod TMVA::MethodRuleFit::fPruneMethod

private

forest generation: method used for pruning - see DecisionTree

Definition at line 189 of file MethodRuleFit.h.

◆ fPruneMethodS

TString TMVA::MethodRuleFit::fPruneMethodS

private

forest generation: prune method - see DecisionTree

Definition at line 188 of file MethodRuleFit.h.

◆ fPruneStrength

Double_t TMVA::MethodRuleFit::fPruneStrength

private

forest generation: prune strength - see DecisionTree

Definition at line 190 of file MethodRuleFit.h.

◆ fRFNendnodes

Int_t TMVA::MethodRuleFit::fRFNendnodes

private

max number of rules (only Friedmans module)

Definition at line 179 of file MethodRuleFit.h.

◆ fRFNrules

Int_t TMVA::MethodRuleFit::fRFNrules

private

max number of rules (only Friedmans module)

Definition at line 178 of file MethodRuleFit.h.

◆ fRFWorkDir

TString TMVA::MethodRuleFit::fRFWorkDir

private

working directory from Friedmans module

Definition at line 177 of file MethodRuleFit.h.

◆ fRuleFit

RuleFit TMVA::MethodRuleFit::fRuleFit

private

RuleFit instance.

Definition at line 155 of file MethodRuleFit.h.

◆ fRuleFitModuleS

TString TMVA::MethodRuleFit::fRuleFitModuleS

private

which rulefit module to use

Definition at line 175 of file MethodRuleFit.h.

◆ fRuleMinDist

Double_t TMVA::MethodRuleFit::fRuleMinDist

private

rule min distance - see RuleEnsemble

Definition at line 207 of file MethodRuleFit.h.

◆ fSepType

SeparationBase* TMVA::MethodRuleFit::fSepType

private

the separation used in node splitting

Definition at line 183 of file MethodRuleFit.h.

◆ fSepTypeS

TString TMVA::MethodRuleFit::fSepTypeS

private

forest generation: separation type - see DecisionTree

Definition at line 187 of file MethodRuleFit.h.

◆ fSignalFraction

Double_t TMVA::MethodRuleFit::fSignalFraction

private

scalefactor for bkg events to modify initial s/b fraction in training data

Definition at line 157 of file MethodRuleFit.h.

◆ fTreeEveFrac

Double_t TMVA::MethodRuleFit::fTreeEveFrac

private

fraction of events used for training each tree

Definition at line 182 of file MethodRuleFit.h.

◆ fUseBoost

Bool_t TMVA::MethodRuleFit::fUseBoost

private

use boosted events for forest generation

Definition at line 192 of file MethodRuleFit.h.

◆ fUseRuleFitJF

Bool_t TMVA::MethodRuleFit::fUseRuleFitJF

private

if true interface with J.Friedmans RuleFit module

Definition at line 176 of file MethodRuleFit.h.

Libraries for TMVA::MethodRuleFit:

[legend]

The documentation for this class was generated from the following files:

tmva/tmva/inc/TMVA/MethodRuleFit.h
tmva/tmva/src/MethodRuleFit.cxx

Public Member Functions

Static Public Member Functions

Protected Member Functions

Private Member Functions

Private Attributes

Additional Inherited Members

Constructor & Destructor Documentation

◆ MethodRuleFit() [1/2]

◆ MethodRuleFit() [2/2]

◆ ~MethodRuleFit()

Member Function Documentation

◆ AddWeightsXMLTo()

◆ Class()

◆ Class_Name()

◆ Class_Version()

◆ CreateRanking()

◆ DeclareOptions()

general

Path search (fitting)

Tree generation

Model creation

Friedmans module

◆ DeclFileName()

◆ GetForest()

◆ GetGDErrScale()

◆ GetGDNPathSteps()

◆ GetGDPathEveFrac()

◆ GetGDPathStep()

◆ GetGDValidEveFrac()

◆ GetHelpMessage()

◆ GetLinQuantile()

◆ GetMaxFracNEve()

◆ GetMethodBaseDir()

◆ GetMinFracNEve()

◆ GetMvaValue()

◆ GetNCuts()

◆ GetNTrees()

◆ GetPruneMethod()

◆ GetPruneStrength()

◆ GetRFNendnodes()

◆ GetRFNrules()

◆ GetRFWorkDir()

◆ GetRuleFitConstPtr()

◆ GetRuleFitPtr()

◆ GetSeparationBase()

◆ GetSeparationBaseConst()

◆ GetTrainingEvents()

◆ GetTreeEveFrac()

◆ HasAnalysisType()

◆ Init()

◆ InitEventSample()

◆ InitMonitorNtuple()

◆ IsA()

◆ MakeClassLinear()

◆ MakeClassRuleCuts()

◆ MakeClassSpecific()

◆ ProcessOptions()

◆ ReadWeightsFromStream() [1/3]

◆ ReadWeightsFromStream() [2/3]

◆ ReadWeightsFromStream() [3/3]

◆ ReadWeightsFromXML()

◆ Streamer()

◆ StreamerNVirtual()

◆ Train()

◆ TrainJFRuleFit()

◆ TrainTMVARuleFit()

◆ UseBoost()

◆ VerifyRange() [1/3]

◆ VerifyRange() [2/3]

◆ VerifyRange() [3/3]

◆ WriteMonitoringHistosToFile()

Member Data Documentation

◆ fEventSample

◆ fForest

◆ fForestTypeS

◆ fGDErrScale

◆ fGDNPathSteps

◆ fGDPathEveFrac

◆ fGDPathStep

◆ fGDTau