Likelihood analysis ("non-parametric approach")

Also implemented is a "diagonalized likelihood approach", which improves over the uncorrelated likelihood approach by transforming linearly the input variables into a diagonal space, using the square-root of the covariance matrix

The method of maximum likelihood is the most straightforward, and certainly among the most elegant multivariate analyser approaches. We define the likelihood ratio, \( R_L \), for event \( i \), by:

\[ R_L(i) = \frac{L_S(i)}{L_B(i) + L_B(i)} \]

Here the signal and background likelihoods, \( L_S \), \( L_B \), are products of the corresponding probability densities, \( p_S \), \( p_B \), of the \( N_{var} \) discriminating variables used in the MVA:

\[ L_S(i) \ \prod_{j=1}^{N_{var}} p_{Sj} (i) \]

and accordingly for \( L_B \). In practise, TMVA uses polynomial splines to estimate the probability density functions (PDF) obtained from the distributions of the training variables.

Note that in TMVA the output of the likelihood ratio is transformed by:

\[ R_L(i) \to R'_L(i) = -\frac{1}{\tau} ln(R_L^{-1}(i) -1) \]

to avoid the occurrence of heavy peaks at \( R_L = 0.1 \) .

Decorrelated (or "diagonalized") Likelihood

The biggest drawback of the Likelihood approach is that it assumes that the discriminant variables are uncorrelated. If it were the case, it can be proven that the discrimination obtained by the above likelihood ratio is optimal, ie, no other method can beat it. However, in most practical applications of MVAs correlations are present.

Linear correlations, measured from the training sample, can be taken into account in a straightforward manner through the square-root of the covariance matrix. The square-root of a matrix \( C \) is the matrix \( C′ \) that multiplied with itself yields \( C \): \( C \)= \( C′C′ \). We compute the square-root matrix (SQM) by means of diagonalising ( \( D \)) the covariance matrix:

\[ D = S^TCS \Rightarrow C' = S \sqrt{DS^T} \]

and the linear transformation of the linearly correlated into the uncorrelated variables space is then given by multiplying the measured variable tuple by the inverse of the SQM. Note that these transformations are performed for both signal and background separately, since the correlation pattern is not the same in the two samples.

The above diagonalisation is complete for linearly correlated, Gaussian distributed variables only. In real-world examples this is not often the case, so that only little additional information may be recovered by the diagonalisation procedure. In these cases, non-linear methods must be applied.

Definition at line 61 of file MethodLikelihood.h.

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

	MethodLikelihood (DataSetInfo &theData, const TString &theWeightFile)
	construct likelihood references from file

virtual	~MethodLikelihood ()
	destructor

void	AddWeightsXMLTo (void *parent) const
	write weights to XML

const Ranking *	CreateRanking ()
	computes ranking of input variables

Double_t	GetMvaValue (Double_t err=nullptr, Double_t errUpper=nullptr)
	returns the likelihood estimator for signal fill a new Likelihood branch into the testTree

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

virtual TClass *	IsA () const

void	ReadWeightsFromStream (std::istream &istr)
	read weight info from file nothing to do for this method

void	ReadWeightsFromStream (TFile &istr)
	read reference PDF from ROOT file

void	ReadWeightsFromXML (void *wghtnode)
	read weights from XML

virtual void	Streamer (TBuffer &)

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

void	Train ()
	create reference distributions (PDFs) from signal and background events: fill histograms and smooth them; if decorrelation is required, compute corresponding square-root matrices the reference histograms require the correct boundaries.

void	WriteMonitoringHistosToFile () const
	write histograms and PDFs to file for monitoring purposes

virtual void	WriteOptionsToStream (std::ostream &o, const TString &prefix) const
	write options to stream

void	WriteWeightsToStream (TFile &rf) const
	write reference PDFs to ROOT file

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

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	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	GetHelpMessage () const
	get help message text

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

void	MakeClassSpecificHeader (std::ostream &, const TString &="") const
	write specific header of the classifier (mostly include files)

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

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

void	Init ()
	default initialisation called by all constructors

void	ProcessOptions ()
	process user options reference cut value to distinguish signal-like from background-like events

Double_t	TransformLikelihoodOutput (Double_t ps, Double_t pb) const
	returns transformed or non-transformed output

Private Attributes
Int_t	fAverageEvtPerBin
	average events per bin; used to calculate fNbins

Int_t *	fAverageEvtPerBinVarB
	average events per bin; used to calculate fNbins

Int_t *	fAverageEvtPerBinVarS
	average events per bin; used to calculate fNbins

TString	fBorderMethodString
	the method to take care about "border" effects (string)

PDF *	fDefaultPDFLik
	pdf that contains default definitions

Int_t	fDropVariable
	for ranking test

Double_t	fEpsilon
	minimum number of likelihood (to avoid zero)

std::vector< TH1 * > *	fHistBgd
	background PDFs (histograms)

std::vector< TH1 * > *	fHistBgd_smooth
	background PDFs (smoothed histograms)

std::vector< TH1 * > *	fHistSig
	signal PDFs (histograms)

std::vector< TH1 * > *	fHistSig_smooth
	signal PDFs (smoothed histograms)

TString *	fInterpolateString
	which interpolation method used for reference histograms (individual for each variable)

Float_t	fKDEfineFactor
	fine tuning factor for Adaptive KDE

TString	fKDEiterString
	Number of iterations (string)

TString	fKDEtypeString
	Kernel type to use for KDE (string)

Int_t	fNsmooth
	number of smooth passes

Int_t *	fNsmoothVarB
	number of smooth passes

Int_t *	fNsmoothVarS
	number of smooth passes

std::vector< PDF * > *	fPDFBgd
	list of PDFs (background)

std::vector< PDF * > *	fPDFSig
	list of PDFs (signal)

Bool_t	fTransformLikelihoodOutput
	likelihood output is sigmoid-transformed

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/MethodLikelihood.h>

Inheritance diagram for TMVA::MethodLikelihood:

[legend]

Constructor & Destructor Documentation

◆ MethodLikelihood() [1/2]

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

standard constructor

Definition at line 142 of file MethodLikelihood.cxx.

◆ MethodLikelihood() [2/2]

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

construct likelihood references from file

Definition at line 171 of file MethodLikelihood.cxx.

◆ ~MethodLikelihood()

TMVA::MethodLikelihood::~MethodLikelihood ( void )

virtual

destructor

Definition at line 198 of file MethodLikelihood.cxx.

Member Function Documentation

◆ AddWeightsXMLTo()

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

virtual

write weights to XML

Implements TMVA::MethodBase.

Definition at line 583 of file MethodLikelihood.cxx.

◆ Class()

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

static

Returns: TClass describing this class

◆ Class_Name()

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

static

Returns: Name of this class

◆ Class_Version()

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

inlinestaticconstexpr

Returns: Version of this class

Definition at line 154 of file MethodLikelihood.h.

◆ CreateRanking()

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

virtual

computes ranking of input variables

Implements TMVA::MethodBase.

Definition at line 607 of file MethodLikelihood.cxx.

◆ DeclareCompatibilityOptions()

void TMVA::MethodLikelihood::DeclareCompatibilityOptions ( )

protectedvirtual

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

Reimplemented from TMVA::MethodBase.

Definition at line 274 of file MethodLikelihood.cxx.

◆ DeclareOptions()

void TMVA::MethodLikelihood::DeclareOptions ( )

privatevirtual

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

TransformOutput <bool> transform (often strongly peaked) likelihood output through sigmoid inversion

Implements TMVA::MethodBase.

Definition at line 243 of file MethodLikelihood.cxx.

◆ DeclFileName()

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

inlinestatic

Returns: Name of the file containing the class declaration

Definition at line 154 of file MethodLikelihood.h.

◆ GetHelpMessage()

void TMVA::MethodLikelihood::GetHelpMessage ( ) const

protectedvirtual

get help message text

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

Implements TMVA::IMethod.

Definition at line 997 of file MethodLikelihood.cxx.

◆ GetMvaValue()

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

virtual

returns the likelihood estimator for signal fill a new Likelihood branch into the testTree

Implements TMVA::MethodBase.

Definition at line 453 of file MethodLikelihood.cxx.

◆ HasAnalysisType()

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

virtual

FDA can handle classification with 2 classes.

Implements TMVA::IMethod.

Definition at line 216 of file MethodLikelihood.cxx.

◆ Init()

void TMVA::MethodLikelihood::Init ( void )

privatevirtual

default initialisation called by all constructors

Implements TMVA::MethodBase.

Definition at line 226 of file MethodLikelihood.cxx.

◆ IsA()

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

inlinevirtual

Returns: TClass describing current object

Reimplemented from TMVA::MethodBase.

Definition at line 154 of file MethodLikelihood.h.

◆ MakeClassSpecific()

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

protectedvirtual

write specific classifier response

Reimplemented from TMVA::MethodBase.

Definition at line 793 of file MethodLikelihood.cxx.

◆ MakeClassSpecificHeader()

void TMVA::MethodLikelihood::MakeClassSpecificHeader	(	std::ostream &	fout,
		const TString &	= `""`
	)		const

protectedvirtual

write specific header of the classifier (mostly include files)

Reimplemented from TMVA::MethodBase.

Definition at line 784 of file MethodLikelihood.cxx.

◆ ProcessOptions()

void TMVA::MethodLikelihood::ProcessOptions ( )

privatevirtual

process user options reference cut value to distinguish signal-like from background-like events

Implements TMVA::MethodBase.

Definition at line 316 of file MethodLikelihood.cxx.

◆ ReadWeightsFromStream() [1/2]

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

virtual

read weight info from file nothing to do for this method

Implements TMVA::MethodBase.

Definition at line 699 of file MethodLikelihood.cxx.

◆ ReadWeightsFromStream() [2/2]

void TMVA::MethodLikelihood::ReadWeightsFromStream ( TFile & istr )

virtual

read reference PDF from ROOT file

Reimplemented from TMVA::MethodBase.

Definition at line 721 of file MethodLikelihood.cxx.

◆ ReadWeightsFromXML()

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

virtual

read weights from XML

Implements TMVA::MethodBase.

Definition at line 669 of file MethodLikelihood.cxx.

◆ Streamer()

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

virtual

Reimplemented from TMVA::MethodBase.

◆ StreamerNVirtual()

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

inline

Definition at line 154 of file MethodLikelihood.h.

◆ Train()

void TMVA::MethodLikelihood::Train ( void )

virtual

create reference distributions (PDFs) from signal and background events: fill histograms and smooth them; if decorrelation is required, compute corresponding square-root matrices the reference histograms require the correct boundaries.

Since in Likelihood classification the transformations are applied using both classes, also the corresponding boundaries need to take this into account

Implements TMVA::MethodBase.

Definition at line 335 of file MethodLikelihood.cxx.

◆ TransformLikelihoodOutput()

Double_t TMVA::MethodLikelihood::TransformLikelihoodOutput	(	Double_t	ps,
		Double_t	pb
	)		const

private

returns transformed or non-transformed output

Definition at line 535 of file MethodLikelihood.cxx.

◆ WriteMonitoringHistosToFile()

void TMVA::MethodLikelihood::WriteMonitoringHistosToFile ( void ) const

virtual

write histograms and PDFs to file for monitoring purposes

Reimplemented from TMVA::MethodBase.

Definition at line 736 of file MethodLikelihood.cxx.

◆ WriteOptionsToStream()

void TMVA::MethodLikelihood::WriteOptionsToStream	(	std::ostream &	o,
		const TString &	prefix
	)		const

virtual

write options to stream

Definition at line 559 of file MethodLikelihood.cxx.

◆ WriteWeightsToStream()

void TMVA::MethodLikelihood::WriteWeightsToStream ( TFile & rf ) const

write reference PDFs to ROOT file

Definition at line 657 of file MethodLikelihood.cxx.

Member Data Documentation

◆ fAverageEvtPerBin

Int_t TMVA::MethodLikelihood::fAverageEvtPerBin

private

average events per bin; used to calculate fNbins

Definition at line 145 of file MethodLikelihood.h.

◆ fAverageEvtPerBinVarB

Int_t* TMVA::MethodLikelihood::fAverageEvtPerBinVarB

private

average events per bin; used to calculate fNbins

Definition at line 147 of file MethodLikelihood.h.

◆ fAverageEvtPerBinVarS

Int_t* TMVA::MethodLikelihood::fAverageEvtPerBinVarS

private

average events per bin; used to calculate fNbins

Definition at line 146 of file MethodLikelihood.h.

◆ fBorderMethodString

TString TMVA::MethodLikelihood::fBorderMethodString

private

the method to take care about "border" effects (string)

Definition at line 148 of file MethodLikelihood.h.

◆ fDefaultPDFLik

PDF* TMVA::MethodLikelihood::fDefaultPDFLik

private

pdf that contains default definitions

Definition at line 135 of file MethodLikelihood.h.

◆ fDropVariable

Int_t TMVA::MethodLikelihood::fDropVariable

private

for ranking test

Definition at line 128 of file MethodLikelihood.h.

◆ fEpsilon

Double_t TMVA::MethodLikelihood::fEpsilon

private

minimum number of likelihood (to avoid zero)

Definition at line 125 of file MethodLikelihood.h.

◆ fHistBgd

std::vector<TH1*>* TMVA::MethodLikelihood::fHistBgd

private

background PDFs (histograms)

Definition at line 131 of file MethodLikelihood.h.

◆ fHistBgd_smooth

std::vector<TH1*>* TMVA::MethodLikelihood::fHistBgd_smooth

private

background PDFs (smoothed histograms)

Definition at line 133 of file MethodLikelihood.h.

◆ fHistSig

std::vector<TH1*>* TMVA::MethodLikelihood::fHistSig

private

signal PDFs (histograms)

Definition at line 130 of file MethodLikelihood.h.

◆ fHistSig_smooth

std::vector<TH1*>* TMVA::MethodLikelihood::fHistSig_smooth

private

signal PDFs (smoothed histograms)

Definition at line 132 of file MethodLikelihood.h.

◆ fInterpolateString

TString* TMVA::MethodLikelihood::fInterpolateString

private

which interpolation method used for reference histograms (individual for each variable)

Definition at line 152 of file MethodLikelihood.h.

◆ fKDEfineFactor

Float_t TMVA::MethodLikelihood::fKDEfineFactor

private

fine tuning factor for Adaptive KDE

Definition at line 149 of file MethodLikelihood.h.

◆ fKDEiterString

TString TMVA::MethodLikelihood::fKDEiterString

private

Number of iterations (string)

Definition at line 150 of file MethodLikelihood.h.

◆ fKDEtypeString

TString TMVA::MethodLikelihood::fKDEtypeString

private

Kernel type to use for KDE (string)

Definition at line 151 of file MethodLikelihood.h.

◆ fNsmooth

Int_t TMVA::MethodLikelihood::fNsmooth

private

number of smooth passes

Definition at line 142 of file MethodLikelihood.h.

◆ fNsmoothVarB

Int_t* TMVA::MethodLikelihood::fNsmoothVarB

private

number of smooth passes

Definition at line 144 of file MethodLikelihood.h.

◆ fNsmoothVarS

Int_t* TMVA::MethodLikelihood::fNsmoothVarS

private

number of smooth passes

Definition at line 143 of file MethodLikelihood.h.

◆ fPDFBgd

std::vector<PDF*>* TMVA::MethodLikelihood::fPDFBgd

private

list of PDFs (background)

Definition at line 137 of file MethodLikelihood.h.

◆ fPDFSig

std::vector<PDF*>* TMVA::MethodLikelihood::fPDFSig

private

list of PDFs (signal)

Definition at line 136 of file MethodLikelihood.h.

◆ fTransformLikelihoodOutput

Bool_t TMVA::MethodLikelihood::fTransformLikelihoodOutput

private

likelihood output is sigmoid-transformed

Definition at line 126 of file MethodLikelihood.h.

Libraries for TMVA::MethodLikelihood:

[legend]

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

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

Decorrelated (or "diagonalized") Likelihood

Public Member Functions

Static Public Member Functions

Protected Member Functions

Private Member Functions

Private Attributes

Additional Inherited Members

Constructor & Destructor Documentation

◆ MethodLikelihood() [1/2]

◆ MethodLikelihood() [2/2]

◆ ~MethodLikelihood()

Member Function Documentation

◆ AddWeightsXMLTo()

◆ Class()

◆ Class_Name()

◆ Class_Version()

◆ CreateRanking()

◆ DeclareCompatibilityOptions()

◆ DeclareOptions()

◆ DeclFileName()

◆ GetHelpMessage()

◆ GetMvaValue()

◆ HasAnalysisType()

◆ Init()

◆ IsA()

◆ MakeClassSpecific()

◆ MakeClassSpecificHeader()

◆ ProcessOptions()

◆ ReadWeightsFromStream() [1/2]

◆ ReadWeightsFromStream() [2/2]

◆ ReadWeightsFromXML()

◆ Streamer()

◆ StreamerNVirtual()

◆ Train()

◆ TransformLikelihoodOutput()

◆ WriteMonitoringHistosToFile()

◆ WriteOptionsToStream()

◆ WriteWeightsToStream()

Member Data Documentation

◆ fAverageEvtPerBin

◆ fAverageEvtPerBinVarB

◆ fAverageEvtPerBinVarS

◆ fBorderMethodString

◆ fDefaultPDFLik

◆ fDropVariable

◆ fEpsilon

◆ fHistBgd

◆ fHistBgd_smooth

◆ fHistSig

◆ fHistSig_smooth

◆ fInterpolateString

◆ fKDEfineFactor

◆ fKDEiterString

◆ fKDEtypeString

◆ fNsmooth

◆ fNsmoothVarB

◆ fNsmoothVarS

◆ fPDFBgd

◆ fPDFSig

◆ fTransformLikelihoodOutput