Virtual base Class for all MVA method.

MethodBase hosts several specific evaluation methods.

The kind of MVA that provides optimal performance in an analysis strongly depends on the particular application. The evaluation factory provides a number of numerical benchmark results to directly assess the performance of the MVA training on the independent test sample. These are:

The signal efficiency at three representative background efficiencies (which is 1 − rejection).
The significance of an MVA estimator, defined by the difference between the MVA mean values for signal and background, divided by the quadratic sum of their root mean squares.
The separation of an MVA x, defined by the integral
\[ \frac{1}{2} \int \frac{(S(x) - B(x))^2}{(S(x) + B(x))} dx \]
where \( S(x) \) and \( B(x) \) are the signal and background distributions, respectively. The separation is zero for identical signal and background MVA shapes, and it is one for disjunctive shapes.
The average, \( \int x \mu (S(x)) dx \), of the signal \( \mu_{transform} \). The \( \mu_{transform} \) of an MVA denotes the transformation that yields a uniform background distribution. In this way, the signal distributions \( S(x) \) can be directly compared among the various MVAs. The stronger \( S(x) \) peaks towards one, the better is the discrimination of the MVA. The \( \mu_{transform} \) is documented here.

The MVA standard output also prints the linear correlation coefficients between signal and background, which can be useful to eliminate variables that exhibit too strong correlations.

Definition at line 111 of file MethodBase.h.

Public Types
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 Member Functions
	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)

virtual const Ranking *	CreateRanking ()=0

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

virtual void	DeclareOptions ()=0

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)

virtual Double_t	GetMvaValue (Double_t errLower=nullptr, Double_t errUpper=nullptr)=0

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

virtual void	Init ()=0

void	InitIPythonInteractive ()

virtual TClass *	IsA () const

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

virtual void	ProcessOptions ()=0

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

virtual void	Streamer (TBuffer &)

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

virtual void	Train ()=0

bool	TrainingEnded ()

void	TrainMethod ()

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

virtual void	WriteMonitoringHistosToFile () const
	write special monitoring histograms to file dummy implementation here --------------—

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 ()

virtual Bool_t	HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)=0

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.

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)
	Operator delete [].

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::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.

Public Attributes
Bool_t	fSetupCompleted

TrainingHistory	fTrainHistory

Protected Member Functions
virtual void	AddWeightsXMLTo (void *parent) const =0

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	MakeClassSpecific (std::ostream &, const TString &="") const

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

void	NoErrorCalc (Double_t const err, Double_t const errUpper)

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

virtual void	ReadWeightsFromStream (TFile &)

virtual void	ReadWeightsFromXML (void *wghtnode)=0

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::IMethod
virtual void	GetHelpMessage () const =0

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 ()

Protected Attributes
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

Private Types
enum	ECutOrientation { kNegative = -1 , kPositive = +1 }

Private Member Functions
void	AddClassesXMLTo (void *parent) const
	write class info to XML

virtual void	AddClassifierOutput (Types::ETreeType type)
	prepare tree branch with the method's discriminating variable

virtual void	AddClassifierOutputProb (Types::ETreeType type)
	prepare tree branch with the method's discriminating variable

void	AddInfoItem (void *gi, const TString &name, const TString &value) const
	xml writing

virtual void	AddMulticlassOutput (Types::ETreeType type)
	prepare tree branch with the method's discriminating variable

virtual void	AddRegressionOutput (Types::ETreeType type)
	prepare tree branch with the method's discriminating variable

void	AddSpectatorsXMLTo (void *parent) const
	write spectator info to XML

void	AddTargetsXMLTo (void *parent) const
	write target info to XML

void	AddVarsXMLTo (void *parent) const
	write variable info to XML

void	CreateMVAPdfs ()
	Create PDFs of the MVA output variables.

void	DeclareBaseOptions ()
	define the options (their key words) that can be set in the option string here the options valid for ALL MVA methods are declared.

ECutOrientation	GetCutOrientation () const

Bool_t	GetLine (std::istream &fin, char *buf)
	reads one line from the input stream checks for certain keywords and interprets the line if keywords are found

virtual Double_t	GetValueForRoot (Double_t)
	returns efficiency as function of cut

void	InitBase ()
	default initialization called by all constructors

void	ProcessBaseOptions ()
	the option string is decoded, for available options see "DeclareOptions"

void	ReadClassesFromXML (void *clsnode)
	read number of classes from XML

void	ReadSpectatorsFromXML (void *specnode)
	read spectator info from XML

void	ReadStateFromXML (void *parent)

void	ReadTargetsFromXML (void *tarnode)
	read target info from XML

void	ReadVariablesFromXML (void *varnode)
	read variable info from XML

void	ReadVarsFromStream (std::istream &istr)
	Read the variables (name, min, max) for a given data transformation method from the stream.

void	ResetThisBase ()

void	WriteStateToStream (std::ostream &tf) const
	general method used in writing the header of the weight files where the used variables, variable transformation type etc.

void	WriteStateToXML (void *parent) const
	general method used in writing the header of the weight files where the used variables, variable transformation type etc.

void	WriteVarsToStream (std::ostream &tf, const TString &prefix="") const
	write the list of variables (name, min, max) for a given data transformation method to the stream

Private Attributes
TDirectory *	fBaseDir

Bool_t	fConstructedFromWeightFile

ECutOrientation	fCutOrientation

DataSetInfo &	fDataSetInfo

PDF *	fDefaultPDF
	default PDF definitions

TH1 *	fEffS
	efficiency histogram for rootfinder

std::vector< const std::vector< TMVA::Event * > * >	fEventCollections

TFile *	fFile

TString	fFileDir
	unix sub-directory for weight files (default: DataLoader's Name + "weights")

Bool_t	fHasMVAPdfs
	MVA Pdfs are created for this classifier.

Bool_t	fHelp
	help flag

Bool_t	fIgnoreNegWeightsInTraining
	If true, events with negative weights are not used in training.

TString	fJobName

Double_t	fMeanB
	mean (background)

Double_t	fMeanS
	mean (signal)

TDirectory *	fMethodBaseDir

TString	fMethodName

Types::EMVA	fMethodType

Bool_t	fModelPersistence

PDF *	fMVAPdfB
	background MVA PDF

PDF *	fMVAPdfS
	signal MVA PDF

Int_t	fNbinsMVAPdf

Bool_t	fNormalise

Int_t	fNsmoothMVAPdf

TString	fParentDir
	method parent name, like booster name

Double_t	fRmsB
	RMS (background)

Double_t	fRmsS
	RMS (signal)

UInt_t	fROOTTrainingVersion

Double_t	fSignalReferenceCut
	the data set information (sometimes needed)

Double_t	fSignalReferenceCutOrientation

Bool_t	fSilentFile

PDF *	fSplB
	PDFs of MVA distribution (background)

TSpline *	fSpleffBvsS
	splines for signal eff. versus background eff.

TSpline1 *	fSplRefB

TSpline1 *	fSplRefS

PDF *	fSplS
	PDFs of MVA distribution (signal)

PDF *	fSplTrainB
	PDFs of training MVA distribution (background)

TSpline *	fSplTrainEffBvsS
	splines for training signal eff. versus background eff.

TSpline1 *	fSplTrainRefB

TSpline1 *	fSplTrainRefS

PDF *	fSplTrainS
	PDFs of training MVA distribution (signal)

Double_t	fTestTime

TString	fTestvar

UInt_t	fTMVATrainingVersion

Double_t	fTrainTime

TransformationHandler	fTransformation
	the list of transformations

TransformationHandler *	fTransformationPointer
	pointer to the rest of transformations

Bool_t	fTxtWeightsOnly

Bool_t	fUseDecorr

Types::ESBType	fVariableTransformType

TString	fVariableTransformTypeString

TString	fVarTransformString
	labels variable transform method

Bool_t	fVerbose
	verbose flag

EMsgType	fVerbosityLevel
	verbosity level

TString	fVerbosityLevelString
	verbosity level (user input string)

TString	fWeightFile
	weight file name

Double_t	fXmax
	maximum (signal and background)

Double_t	fXmin
	minimum (signal and background)

Friends
class	CrossValidation

class	Experimental::Classification

class	Factory

class	MethodBoost

class	MethodCategory

class	MethodCompositeBase

class	MethodCrossValidation

class	MethodCuts

class	RootFinder

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

#include <TMVA/MethodBase.h>

Inheritance diagram for TMVA::MethodBase:

[legend]

Member Enumeration Documentation

◆ ECutOrientation

enum TMVA::MethodBase::ECutOrientation

private

Enumerator
kNegative
kPositive

Definition at line 551 of file MethodBase.h.

◆ EWeightFileType

enum TMVA::MethodBase::EWeightFileType

Enumerator
kROOT
kTEXT

Definition at line 122 of file MethodBase.h.

Constructor & Destructor Documentation

◆ MethodBase() [1/2]

TMVA::MethodBase::MethodBase	(	const TString &	jobName,
		Types::EMVA	methodType,
		const TString &	methodTitle,
		DataSetInfo &	dsi,
		const TString &	theOption = `""`
	)

standard constructor

Definition at line 237 of file MethodBase.cxx.

◆ MethodBase() [2/2]

TMVA::MethodBase::MethodBase	(	Types::EMVA	methodType,
		DataSetInfo &	dsi,
		const TString &	weightFile
	)

constructor used for Testing + Application of the MVA, only (no training), using given WeightFiles

Definition at line 303 of file MethodBase.cxx.

◆ ~MethodBase()

TMVA::MethodBase::~MethodBase ( void )

virtual

destructor

Definition at line 364 of file MethodBase.cxx.

Member Function Documentation

◆ AddClassesXMLTo()

void TMVA::MethodBase::AddClassesXMLTo ( void * parent ) const

private

write class info to XML

Definition at line 1802 of file MethodBase.cxx.

◆ AddClassifierOutput()

void TMVA::MethodBase::AddClassifierOutput ( Types::ETreeType type )

privatevirtual

prepare tree branch with the method's discriminating variable

Definition at line 868 of file MethodBase.cxx.

◆ AddClassifierOutputProb()

void TMVA::MethodBase::AddClassifierOutputProb ( Types::ETreeType type )

privatevirtual

prepare tree branch with the method's discriminating variable

Definition at line 950 of file MethodBase.cxx.

◆ AddInfoItem()

void TMVA::MethodBase::AddInfoItem	(	void *	gi,
		const TString &	name,
		const TString &	value
	)		const

private

xml writing

Definition at line 1307 of file MethodBase.cxx.

◆ AddMulticlassOutput()

void TMVA::MethodBase::AddMulticlassOutput ( Types::ETreeType type )

privatevirtual

prepare tree branch with the method's discriminating variable

Definition at line 794 of file MethodBase.cxx.

◆ AddOutput()

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

Definition at line 1316 of file MethodBase.cxx.

◆ AddRegressionOutput()

void TMVA::MethodBase::AddRegressionOutput ( Types::ETreeType type )

privatevirtual

prepare tree branch with the method's discriminating variable

Definition at line 744 of file MethodBase.cxx.

◆ AddSpectatorsXMLTo()

void TMVA::MethodBase::AddSpectatorsXMLTo ( void * parent ) const

private

write spectator info to XML

Definition at line 1779 of file MethodBase.cxx.

◆ AddTargetsXMLTo()

void TMVA::MethodBase::AddTargetsXMLTo ( void * parent ) const

private

write target info to XML

Definition at line 1822 of file MethodBase.cxx.

◆ AddVarsXMLTo()

void TMVA::MethodBase::AddVarsXMLTo ( void * parent ) const

private

write variable info to XML

Definition at line 1763 of file MethodBase.cxx.

◆ AddWeightsXMLTo()

virtual void TMVA::MethodBase::AddWeightsXMLTo ( void * parent ) const

protectedpure virtual

◆ BaseDir()

TDirectory * TMVA::MethodBase::BaseDir ( ) const

returns the ROOT directory where info/histograms etc of the corresponding MVA method instance are stored

Definition at line 1981 of file MethodBase.cxx.

◆ CheckSetup()

void TMVA::MethodBase::CheckSetup ( )

virtual

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

Reimplemented in TMVA::MethodBoost, TMVA::MethodCuts, and TMVA::MethodFDA.

Definition at line 433 of file MethodBase.cxx.

◆ Class()

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

static

Returns: TClass describing this class

◆ Class_Name()

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

static

Returns: Name of this class

◆ Class_Version()

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

inlinestaticconstexpr

Returns: Version of this class

Definition at line 731 of file MethodBase.h.

◆ CreateMVAPdfs()

void TMVA::MethodBase::CreateMVAPdfs ( )

private

Create PDFs of the MVA output variables.

Definition at line 2186 of file MethodBase.cxx.

◆ CreateRanking()

virtual const Ranking * TMVA::MethodBase::CreateRanking ( )

pure virtual

◆ Data()

DataSet * TMVA::MethodBase::Data ( ) const

inline

Definition at line 409 of file MethodBase.h.

◆ DataInfo()

DataSetInfo & TMVA::MethodBase::DataInfo ( ) const

inline

Definition at line 410 of file MethodBase.h.

◆ DeclareBaseOptions()

void TMVA::MethodBase::DeclareBaseOptions ( )

private

define the options (their key words) that can be set in the option string here the options valid for ALL MVA methods are declared.

know options:

VariableTransform=None,Decorrelated,PCA to use transformed variables instead of the original ones
VariableTransformType=Signal,Background which decorrelation matrix to use in the method. Only the Likelihood Method can make proper use of independent transformations of signal and background
fNbinsMVAPdf = 50 Number of bins used to create a PDF of MVA
fNsmoothMVAPdf = 2 Number of times a histogram is smoothed before creating the PDF
fHasMVAPdfs create PDFs for the MVA outputs
V for Verbose output (!V) for non verbos
H for Help message

Definition at line 509 of file MethodBase.cxx.

◆ DeclareCompatibilityOptions()

void TMVA::MethodBase::DeclareCompatibilityOptions ( )

virtual

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 in TMVA::MethodBDT, TMVA::MethodBoost, TMVA::MethodCrossValidation, TMVA::MethodDT, TMVA::MethodKNN, TMVA::MethodLikelihood, TMVA::MethodPDEFoam, and TMVA::MethodSVM.

Definition at line 596 of file MethodBase.cxx.

◆ DeclareOptions()

virtual void TMVA::MethodBase::DeclareOptions ( )

pure virtual

◆ DeclFileName()

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

inlinestatic

Returns: Name of the file containing the class declaration

Definition at line 731 of file MethodBase.h.

◆ DisableWriting()

void TMVA::MethodBase::DisableWriting ( Bool_t setter )

inline

Definition at line 442 of file MethodBase.h.

◆ DoMulticlass()

Bool_t TMVA::MethodBase::DoMulticlass ( ) const

inline

Definition at line 439 of file MethodBase.h.

◆ DoRegression()

Bool_t TMVA::MethodBase::DoRegression ( ) const

inline

Definition at line 438 of file MethodBase.h.

◆ ExitFromTraining()

void TMVA::MethodBase::ExitFromTraining ( )

inline

Definition at line 464 of file MethodBase.h.

◆ GetAnalysisType()

Types::EAnalysisType TMVA::MethodBase::GetAnalysisType ( ) const

inline

Definition at line 437 of file MethodBase.h.

◆ GetCurrentIter()

UInt_t TMVA::MethodBase::GetCurrentIter ( )

inline

Definition at line 481 of file MethodBase.h.

◆ GetCutOrientation()

ECutOrientation TMVA::MethodBase::GetCutOrientation ( ) const

inlineprivate

Definition at line 552 of file MethodBase.h.

◆ GetDataMvaValues()

std::vector< Double_t > TMVA::MethodBase::GetDataMvaValues	(	DataSet *	data = `nullptr`,
		Long64_t	firstEvt = `0`,
		Long64_t	lastEvt = `-1`,
		Bool_t	logProgress = `false`
	)

protectedvirtual

get all the MVA values for the events of the given Data type

Definition at line 939 of file MethodBase.cxx.

◆ GetEfficiency()

Double_t TMVA::MethodBase::GetEfficiency	(	const TString &	theString,
		Types::ETreeType	type,
		Double_t &	effSerr
	)

virtual

fill background efficiency (resp.

rejection) versus signal efficiency plots returns signal efficiency at background efficiency indicated in theString

Reimplemented in TMVA::MethodCuts.

Definition at line 2303 of file MethodBase.cxx.

◆ GetEvent() [1/4]

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

inline

Definition at line 751 of file MethodBase.h.

◆ GetEvent() [2/4]

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

inline

Definition at line 746 of file MethodBase.h.

◆ GetEvent() [3/4]

const TMVA::Event * TMVA::MethodBase::GetEvent ( Long64_t ievt ) const

inline

Definition at line 759 of file MethodBase.h.

◆ GetEvent() [4/4]

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

inline

Definition at line 765 of file MethodBase.h.

◆ GetEventCollection()

const std::vector< TMVA::Event * > & TMVA::MethodBase::GetEventCollection ( Types::ETreeType type )

returns the event collection (i.e.

the dataset) TRANSFORMED using the classifiers specific Variable Transformation (e.g. Decorr or Decorr:Gauss:Decorr)

Definition at line 3348 of file MethodBase.cxx.

◆ GetFile()

TFile * TMVA::MethodBase::GetFile ( ) const

inline

Definition at line 370 of file MethodBase.h.

◆ GetInputLabel()

const TString & TMVA::MethodBase::GetInputLabel ( Int_t i ) const

inline

Definition at line 350 of file MethodBase.h.

◆ GetInputTitle()

const char * TMVA::MethodBase::GetInputTitle ( Int_t i ) const

inline

Definition at line 351 of file MethodBase.h.

◆ GetInputVar()

const TString & TMVA::MethodBase::GetInputVar ( Int_t i ) const

inline

Definition at line 349 of file MethodBase.h.

◆ GetInteractiveTrainingError()

TMultiGraph * TMVA::MethodBase::GetInteractiveTrainingError ( )

inline

Definition at line 461 of file MethodBase.h.

◆ GetInternalVarName()

const TString & TMVA::MethodBase::GetInternalVarName ( Int_t ivar ) const

inlineprotected

Definition at line 510 of file MethodBase.h.

◆ GetJobName()

const TString & TMVA::MethodBase::GetJobName ( ) const

inline

Definition at line 330 of file MethodBase.h.

◆ GetKSTrainingVsTest()

Double_t TMVA::MethodBase::GetKSTrainingVsTest	(	Char_t	SorB,
		TString	opt = `"X"`
	)

virtual

Definition at line 3393 of file MethodBase.cxx.

◆ GetLine()

Bool_t TMVA::MethodBase::GetLine	(	std::istream &	fin,
		char *	buf
	)

private

reads one line from the input stream checks for certain keywords and interprets the line if keywords are found

Definition at line 2143 of file MethodBase.cxx.

◆ GetMaximumSignificance()

Double_t TMVA::MethodBase::GetMaximumSignificance	(	Double_t	SignalEvents,
		Double_t	BackgroundEvents,
		Double_t &	optimal_significance_value
	)		const

virtual

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

Definition at line 2887 of file MethodBase.cxx.

◆ GetMaxIter()

UInt_t TMVA::MethodBase::GetMaxIter ( )

inline

Definition at line 478 of file MethodBase.h.

◆ GetMean()

Double_t TMVA::MethodBase::GetMean ( Int_t ivar ) const

inline

Definition at line 354 of file MethodBase.h.

◆ GetMethodName()

const TString & TMVA::MethodBase::GetMethodName ( ) const

inline

Definition at line 331 of file MethodBase.h.

◆ GetMethodType()

Types::EMVA TMVA::MethodBase::GetMethodType ( ) const

inline

Definition at line 333 of file MethodBase.h.

◆ GetMethodTypeName()

TString TMVA::MethodBase::GetMethodTypeName ( ) const

inline

Definition at line 332 of file MethodBase.h.

◆ GetMulticlassConfusionMatrix()

TMatrixD TMVA::MethodBase::GetMulticlassConfusionMatrix	(	Double_t	effB,
		Types::ETreeType	type
	)

virtual

Construct a confusion matrix for a multiclass classifier.

The confusion matrix compares, in turn, each class agaist all other classes in a pair-wise fashion. In rows with index \( k_r = 0 ... K \), \( k_r \) is considered signal for the sake of comparison and for each column \( k_c = 0 ... K \) the corresponding class is considered background.

Note that the diagonal elements will be returned as NaN since this will compare a class against itself.

See also: TMVA::ResultsMulticlass::GetConfusionMatrix

Parameters

[in]	effB	The background efficiency for which to evaluate.
[in]	type	The data set on which to evaluate (training, testing ...).

Returns: A matrix containing signal efficiencies for the given background efficiency. The diagonal elements are NaN since this measure is meaningless (comparing a class against itself).

Definition at line 2751 of file MethodBase.cxx.

◆ GetMulticlassEfficiency()

std::vector< Float_t > TMVA::MethodBase::GetMulticlassEfficiency ( std::vector< std::vector< Float_t > > & purity )

virtual

Definition at line 2704 of file MethodBase.cxx.

◆ GetMulticlassTrainingEfficiency()

std::vector< Float_t > TMVA::MethodBase::GetMulticlassTrainingEfficiency ( std::vector< std::vector< Float_t > > & purity )

virtual

Definition at line 2716 of file MethodBase.cxx.

◆ GetMulticlassValues()

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

inlinevirtual

Reimplemented in TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyKeras, TMVA::MethodPyRandomForest, TMVA::MethodPyTorch, TMVA::MethodANNBase, TMVA::MethodBDT, TMVA::MethodCategory, TMVA::MethodCrossValidation, TMVA::MethodDL, TMVA::MethodDNN, TMVA::MethodFDA, and TMVA::MethodPDEFoam.

Definition at line 227 of file MethodBase.h.

◆ GetMvaValue() [1/2]

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

Definition at line 843 of file MethodBase.cxx.

◆ GetMvaValue() [2/2]

virtual Double_t TMVA::MethodBase::GetMvaValue	(	Double_t *	errLower = `nullptr`,
		Double_t *	errUpper = `nullptr`
	)

pure virtual

◆ GetMvaValues()

std::vector< Double_t > TMVA::MethodBase::GetMvaValues	(	Long64_t	firstEvt = `0`,
		Long64_t	lastEvt = `-1`,
		Bool_t	logProgress = `false`
	)

protectedvirtual

get all the MVA values for the events of the current Data type

Reimplemented in TMVA::MethodPyKeras, TMVA::MethodPyTorch, TMVA::MethodDL, TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyRandomForest, TMVA::MethodC50, TMVA::MethodRSNNS, TMVA::MethodRSVM, TMVA::MethodRXGB, TMVA::MethodCategory, and TMVA::PyMethodBase.

Definition at line 897 of file MethodBase.cxx.

◆ GetName()

const char * TMVA::MethodBase::GetName ( ) const

inlinevirtual

Implements TMVA::IMethod.

Definition at line 334 of file MethodBase.h.

◆ GetNEvents()

UInt_t TMVA::MethodBase::GetNEvents ( ) const

inline

Definition at line 416 of file MethodBase.h.

◆ GetNTargets()

UInt_t TMVA::MethodBase::GetNTargets ( ) const

inline

Definition at line 346 of file MethodBase.h.

◆ GetNvar()

UInt_t TMVA::MethodBase::GetNvar ( ) const

inline

Definition at line 344 of file MethodBase.h.

◆ GetNVariables()

UInt_t TMVA::MethodBase::GetNVariables ( ) const

inline

Definition at line 345 of file MethodBase.h.

◆ GetOriginalVarName()

const TString & TMVA::MethodBase::GetOriginalVarName ( Int_t ivar ) const

inlineprotected

Definition at line 511 of file MethodBase.h.

◆ GetProba() [1/2]

Double_t TMVA::MethodBase::GetProba ( const Event * ev )

virtual

Definition at line 2248 of file MethodBase.cxx.

◆ GetProba() [2/2]

Double_t TMVA::MethodBase::GetProba	(	Double_t	mvaVal,
		Double_t	ap_sig
	)

virtual

compute likelihood ratio

Definition at line 2265 of file MethodBase.cxx.

◆ GetProbaName()

const TString TMVA::MethodBase::GetProbaName ( ) const

inline

Definition at line 336 of file MethodBase.h.

◆ GetRarity()

Double_t TMVA::MethodBase::GetRarity	(	Double_t	mvaVal,
		Types::ESBType	reftype = `Types::kBackground`
	)		const

virtual

compute rarity:

\[ R(x) = \int_{[-\infty..x]} { PDF(x') dx' } \]

where PDF(x) is the PDF of the classifier's signal or background distribution

Reimplemented in TMVA::MethodCuts.

Definition at line 2286 of file MethodBase.cxx.

◆ GetRegressionDeviation()

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

virtual

Definition at line 724 of file MethodBase.cxx.

◆ GetRegressionValues() [1/2]

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

inlinevirtual

Reimplemented in TMVA::MethodPyKeras, TMVA::MethodPyTorch, TMVA::MethodANNBase, TMVA::MethodBDT, TMVA::MethodCategory, TMVA::MethodCrossValidation, TMVA::MethodDL, TMVA::MethodDNN, TMVA::MethodFDA, TMVA::MethodKNN, TMVA::MethodLD, TMVA::MethodPDEFoam, TMVA::MethodPDERS, and TMVA::MethodSVM.

Definition at line 221 of file MethodBase.h.

◆ GetRegressionValues() [2/2]

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

inline

Definition at line 214 of file MethodBase.h.

◆ GetRMS()

Double_t TMVA::MethodBase::GetRMS ( Int_t ivar ) const

inline

Definition at line 355 of file MethodBase.h.

◆ GetROCIntegral() [1/2]

Double_t TMVA::MethodBase::GetROCIntegral	(	PDF *	pdfS = `nullptr`,
		PDF *	pdfB = `nullptr`
	)		const

virtual

calculate the area (integral) under the ROC curve as a overall quality measure of the classification

Definition at line 2857 of file MethodBase.cxx.

◆ GetROCIntegral() [2/2]

Double_t TMVA::MethodBase::GetROCIntegral	(	TH1D *	histS,
		TH1D *	histB
	)		const

virtual

calculate the area (integral) under the ROC curve as a overall quality measure of the classification

Definition at line 2823 of file MethodBase.cxx.

◆ GetSeparation() [1/2]

Double_t TMVA::MethodBase::GetSeparation	(	PDF *	pdfS = `nullptr`,
		PDF *	pdfB = `nullptr`
	)		const

virtual

compute "separation" defined as

\[ <s2> = \frac{1}{2} \int_{-\infty}^{+\infty} { \frac{(S(x) - B(x))^2}{(S(x) + B(x))} dx } \]

Reimplemented in TMVA::MethodCuts.

Definition at line 2801 of file MethodBase.cxx.

◆ GetSeparation() [2/2]

Double_t TMVA::MethodBase::GetSeparation	(	TH1 *	histoS,
		TH1 *	histoB
	)		const

virtual

compute "separation" defined as

\[ <s2> = \frac{1}{2} \int_{-\infty}^{+\infty} { \frac{(S(x) - B(x))^2}{(S(x) + B(x))} dx } \]

Reimplemented in TMVA::MethodCuts.

Definition at line 2790 of file MethodBase.cxx.

◆ GetSignalReferenceCut()

Double_t TMVA::MethodBase::GetSignalReferenceCut ( ) const

inline

Definition at line 360 of file MethodBase.h.

◆ GetSignalReferenceCutOrientation()

Double_t TMVA::MethodBase::GetSignalReferenceCutOrientation ( ) const

inline

Definition at line 361 of file MethodBase.h.

◆ GetSignificance()

Double_t TMVA::MethodBase::GetSignificance ( void ) const

virtual

compute significance of mean difference

\[ significance = \frac{|<S> - <B>|}{\sqrt{RMS_{S2} + RMS_{B2}}} \]

Reimplemented in TMVA::MethodCuts.

Definition at line 2777 of file MethodBase.cxx.

◆ GetTestingEvent()

const TMVA::Event * TMVA::MethodBase::GetTestingEvent ( Long64_t ievt ) const

inline

Definition at line 777 of file MethodBase.h.

◆ GetTestTime()

Double_t TMVA::MethodBase::GetTestTime ( ) const

inline

Definition at line 166 of file MethodBase.h.

◆ GetTestvarName()

const TString & TMVA::MethodBase::GetTestvarName ( ) const

inline

Definition at line 335 of file MethodBase.h.

◆ GetTrainingEfficiency()

Double_t TMVA::MethodBase::GetTrainingEfficiency ( const TString & theString )

virtual

Reimplemented in TMVA::MethodCuts.

Definition at line 2529 of file MethodBase.cxx.

◆ GetTrainingEvent()

const TMVA::Event * TMVA::MethodBase::GetTrainingEvent ( Long64_t ievt ) const

inline

Definition at line 771 of file MethodBase.h.

◆ GetTrainingHistory()

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

inlinevirtual

Definition at line 233 of file MethodBase.h.

◆ GetTrainingROOTVersionCode()

UInt_t TMVA::MethodBase::GetTrainingROOTVersionCode ( ) const

inline

Definition at line 390 of file MethodBase.h.

◆ GetTrainingROOTVersionString()

TString TMVA::MethodBase::GetTrainingROOTVersionString ( ) const

calculates the ROOT version string from the training version code on the fly

Definition at line 3382 of file MethodBase.cxx.

◆ GetTrainingTMVAVersionCode()

UInt_t TMVA::MethodBase::GetTrainingTMVAVersionCode ( ) const

inline

Definition at line 389 of file MethodBase.h.

◆ GetTrainingTMVAVersionString()

TString TMVA::MethodBase::GetTrainingTMVAVersionString ( ) const

calculates the TMVA version string from the training version code on the fly

Definition at line 3370 of file MethodBase.cxx.

◆ GetTrainTime()

Double_t TMVA::MethodBase::GetTrainTime ( ) const

inline

Definition at line 162 of file MethodBase.h.

◆ GetTransformationHandler() [1/2]

TransformationHandler & TMVA::MethodBase::GetTransformationHandler ( Bool_t takeReroutedIfAvailable = true )

inline

Definition at line 394 of file MethodBase.h.

◆ GetTransformationHandler() [2/2]

const TransformationHandler & TMVA::MethodBase::GetTransformationHandler ( Bool_t takeReroutedIfAvailable = true ) const

inline

Definition at line 398 of file MethodBase.h.

◆ GetValueForRoot()

Double_t TMVA::MethodBase::GetValueForRoot ( Double_t theCut )

privatevirtual

returns efficiency as function of cut

Definition at line 3321 of file MethodBase.cxx.

◆ GetWeightFileDir()

const TString & TMVA::MethodBase::GetWeightFileDir ( ) const

inlineprotected

Definition at line 492 of file MethodBase.h.

◆ GetWeightFileName()

TString TMVA::MethodBase::GetWeightFileName ( ) const

retrieve weight file name

Definition at line 2077 of file MethodBase.cxx.

◆ GetXmax()

Double_t TMVA::MethodBase::GetXmax ( Int_t ivar ) const

inline

Definition at line 357 of file MethodBase.h.

◆ GetXmin()

Double_t TMVA::MethodBase::GetXmin ( Int_t ivar ) const

inline

Definition at line 356 of file MethodBase.h.

◆ HasMVAPdfs()

Bool_t TMVA::MethodBase::HasMVAPdfs ( ) const

inline

Definition at line 435 of file MethodBase.h.

◆ HasTrainingTree()

Bool_t TMVA::MethodBase::HasTrainingTree ( ) const

inlineprotected

Definition at line 513 of file MethodBase.h.

◆ Help()

Bool_t TMVA::MethodBase::Help ( ) const

inlineprotected

Definition at line 504 of file MethodBase.h.

◆ IgnoreEventsWithNegWeightsInTraining()

Bool_t TMVA::MethodBase::IgnoreEventsWithNegWeightsInTraining ( ) const

inlineprotected

Definition at line 686 of file MethodBase.h.

◆ Init()

virtual void TMVA::MethodBase::Init ( )

pure virtual

◆ InitBase()

void TMVA::MethodBase::InitBase ( )

private

default initialization called by all constructors

Definition at line 441 of file MethodBase.cxx.

◆ InitIPythonInteractive()

void TMVA::MethodBase::InitIPythonInteractive ( )

inline

Definition at line 455 of file MethodBase.h.

◆ IsA()

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

inlinevirtual

Returns: TClass describing current object

Reimplemented from TMVA::IMethod.

Reimplemented in TMVA::MethodPyAdaBoost, TMVA::PyMethodBase, TMVA::RMethodBase, TMVA::MethodANNBase, TMVA::MethodBayesClassifier, TMVA::MethodBDT, TMVA::MethodBoost, TMVA::MethodCategory, TMVA::MethodCFMlpANN, TMVA::MethodCompositeBase, TMVA::MethodCrossValidation, TMVA::MethodCuts, TMVA::MethodDL, TMVA::MethodDNN, TMVA::MethodDT, TMVA::MethodFDA, TMVA::MethodFisher, TMVA::MethodHMatrix, TMVA::MethodKNN, TMVA::MethodLD, TMVA::MethodLikelihood, TMVA::MethodMLP, TMVA::MethodPDEFoam, TMVA::MethodPDERS, TMVA::MethodRuleFit, TMVA::MethodSVM, and TMVA::MethodTMlpANN.

Definition at line 731 of file MethodBase.h.

◆ IsConstructedFromWeightFile()

Bool_t TMVA::MethodBase::IsConstructedFromWeightFile ( ) const

inlineprotected

Definition at line 540 of file MethodBase.h.

◆ IsModelPersistence()

Bool_t TMVA::MethodBase::IsModelPersistence ( ) const

inline

Definition at line 383 of file MethodBase.h.

◆ IsNormalised()

Bool_t TMVA::MethodBase::IsNormalised ( ) const

inlineprotected

Definition at line 496 of file MethodBase.h.

◆ IsSignalLike() [1/2]

Bool_t TMVA::MethodBase::IsSignalLike ( )

virtual

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

Definition at line 854 of file MethodBase.cxx.

◆ IsSignalLike() [2/2]

Bool_t TMVA::MethodBase::IsSignalLike ( Double_t mvaVal )

virtual

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

Definition at line 861 of file MethodBase.cxx.

◆ IsSilentFile()

Bool_t TMVA::MethodBase::IsSilentFile ( ) const

inline

Definition at line 379 of file MethodBase.h.

◆ MakeClass()

void TMVA::MethodBase::MakeClass ( const TString & classFileName = TString("") ) const

virtual

create reader class for method (classification only at present)

Implements TMVA::IMethod.

Reimplemented in TMVA::MethodCategory, TMVA::MethodC50, TMVA::MethodRXGB, and TMVA::MethodTMlpANN.

Definition at line 3004 of file MethodBase.cxx.

◆ MakeClassSpecific()

virtual void TMVA::MethodBase::MakeClassSpecific	(	std::ostream &	,
		const TString &	= `""`
	)		const

inlineprotectedvirtual

Implements TMVA::IMethod.

Reimplemented in TMVA::MethodANNBase, TMVA::MethodBayesClassifier, TMVA::MethodBDT, TMVA::MethodCFMlpANN, TMVA::MethodCrossValidation, TMVA::MethodCuts, TMVA::MethodDNN, TMVA::MethodFDA, TMVA::MethodFisher, TMVA::MethodHMatrix, TMVA::MethodKNN, TMVA::MethodLD, TMVA::MethodLikelihood, TMVA::MethodMLP, TMVA::MethodPDEFoam, TMVA::MethodPDERS, TMVA::MethodRuleFit, TMVA::MethodSVM, and TMVA::MethodTMlpANN.

Definition at line 520 of file MethodBase.h.

◆ MakeClassSpecificHeader()

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

inlineprotectedvirtual

Reimplemented in TMVA::MethodBDT, TMVA::MethodCrossValidation, TMVA::MethodCFMlpANN, and TMVA::MethodLikelihood.

Definition at line 523 of file MethodBase.h.

◆ MethodBaseDir()

TDirectory * TMVA::MethodBase::MethodBaseDir ( ) const

returns the ROOT directory where all instances of the corresponding MVA method are stored

Definition at line 2021 of file MethodBase.cxx.

◆ NoErrorCalc()

void TMVA::MethodBase::NoErrorCalc	(	Double_t *const	err,
		Double_t *const	errUpper
	)

protected

Definition at line 836 of file MethodBase.cxx.

◆ OptimizeTuningParameters()

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

virtual

call the Optimizer with the set of parameters and ranges that are meant to be tuned.

Reimplemented in TMVA::MethodBDT, and TMVA::MethodSVM.

Definition at line 623 of file MethodBase.cxx.

◆ PrintHelpMessage()

void TMVA::MethodBase::PrintHelpMessage ( ) const

virtual

prints out method-specific help method

Implements TMVA::IMethod.

Definition at line 3265 of file MethodBase.cxx.

◆ ProcessBaseOptions()

void TMVA::MethodBase::ProcessBaseOptions ( )

private

the option string is decoded, for available options see "DeclareOptions"

Definition at line 540 of file MethodBase.cxx.

◆ ProcessOptions()

virtual void TMVA::MethodBase::ProcessOptions ( )

pure virtual

◆ ProcessSetup()

void TMVA::MethodBase::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)

Definition at line 423 of file MethodBase.cxx.

◆ ReadClassesFromXML()

void TMVA::MethodBase::ReadClassesFromXML ( void * clsnode )

private

read number of classes from XML

Definition at line 1918 of file MethodBase.cxx.

◆ ReadSpectatorsFromXML()

void TMVA::MethodBase::ReadSpectatorsFromXML ( void * specnode )

private

read spectator info from XML

Definition at line 1878 of file MethodBase.cxx.

◆ ReadStateFromFile()

void TMVA::MethodBase::ReadStateFromFile ( )

Function to write options and weights to file.

Definition at line 1427 of file MethodBase.cxx.

◆ ReadStateFromStream() [1/2]

void TMVA::MethodBase::ReadStateFromStream ( std::istream & tf )

read the header from the weight files of the different MVA methods

Definition at line 1591 of file MethodBase.cxx.

◆ ReadStateFromStream() [2/2]

void TMVA::MethodBase::ReadStateFromStream ( TFile & rf )

write reference MVA distributions (and other information) to a ROOT type weight file

Definition at line 1386 of file MethodBase.cxx.

◆ ReadStateFromXML()

void TMVA::MethodBase::ReadStateFromXML ( void * parent )

private

Definition at line 1481 of file MethodBase.cxx.

◆ ReadStateFromXMLString()

void TMVA::MethodBase::ReadStateFromXMLString ( const char * xmlstr )

for reading from memory

Definition at line 1470 of file MethodBase.cxx.

◆ ReadTargetsFromXML()

void TMVA::MethodBase::ReadTargetsFromXML ( void * tarnode )

private

read target info from XML

Definition at line 1960 of file MethodBase.cxx.

◆ ReadVariablesFromXML()

void TMVA::MethodBase::ReadVariablesFromXML ( void * varnode )

private

read variable info from XML

Definition at line 1838 of file MethodBase.cxx.

◆ ReadVarsFromStream()

void TMVA::MethodBase::ReadVarsFromStream ( std::istream & istr )

private

Read the variables (name, min, max) for a given data transformation method from the stream.

In the stream we only expect the limits which will be set

Definition at line 1726 of file MethodBase.cxx.

◆ ReadWeightsFromStream() [1/2]

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

protectedpure virtual

Implements TMVA::IMethod.

Implemented in TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyKeras, TMVA::MethodPyRandomForest, TMVA::MethodPyTorch, TMVA::MethodC50, TMVA::MethodRSNNS, TMVA::MethodRSVM, TMVA::MethodRXGB, TMVA::MethodDL, TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyRandomForest, TMVA::PyMethodBase, TMVA::MethodC50, TMVA::MethodRSNNS, TMVA::MethodRSVM, TMVA::MethodRXGB, TMVA::RMethodBase, TMVA::MethodANNBase, TMVA::MethodBayesClassifier, TMVA::MethodBDT, TMVA::MethodCFMlpANN, TMVA::MethodCompositeBase, TMVA::MethodCrossValidation, TMVA::MethodCuts, TMVA::MethodDL, TMVA::MethodDNN, TMVA::MethodDT, TMVA::MethodFDA, TMVA::MethodFisher, TMVA::MethodHMatrix, TMVA::MethodKNN, TMVA::MethodLD, TMVA::MethodPDEFoam, TMVA::MethodRuleFit, TMVA::MethodSVM, TMVA::MethodTMlpANN, TMVA::MethodCuts, TMVA::MethodDNN, TMVA::MethodFDA, TMVA::MethodFisher, TMVA::MethodLD, TMVA::MethodPDEFoam, TMVA::MethodANNBase, TMVA::MethodBayesClassifier, TMVA::MethodBDT, TMVA::MethodCFMlpANN, TMVA::MethodCompositeBase, TMVA::MethodCrossValidation, TMVA::MethodDT, TMVA::MethodHMatrix, TMVA::MethodKNN, TMVA::MethodLikelihood, TMVA::MethodPDERS, TMVA::MethodRuleFit, TMVA::MethodSVM, and TMVA::MethodTMlpANN.

◆ ReadWeightsFromStream() [2/2]

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

inlineprotectedvirtual

◆ ReadWeightsFromXML()

virtual void TMVA::MethodBase::ReadWeightsFromXML ( void * wghtnode )

protectedpure virtual

◆ RerouteTransformationHandler()

void TMVA::MethodBase::RerouteTransformationHandler ( TransformationHandler * fTargetTransformation )

inline

Definition at line 403 of file MethodBase.h.

◆ Reset()

virtual void TMVA::MethodBase::Reset ( void )

inlinevirtual

Reimplemented in TMVA::MethodPDEFoam, TMVA::MethodBDT, TMVA::MethodCrossValidation, and TMVA::MethodSVM.

Definition at line 193 of file MethodBase.h.

◆ ResetThisBase()

void TMVA::MethodBase::ResetThisBase ( )

private

◆ SetAnalysisType()

virtual void TMVA::MethodBase::SetAnalysisType ( Types::EAnalysisType type )

inlinevirtual

Definition at line 436 of file MethodBase.h.

◆ SetBaseDir()

void TMVA::MethodBase::SetBaseDir ( TDirectory * methodDir )

inline

Definition at line 373 of file MethodBase.h.

◆ SetFile()

void TMVA::MethodBase::SetFile ( TFile * file )

inline

Definition at line 375 of file MethodBase.h.

◆ SetMethodBaseDir()

void TMVA::MethodBase::SetMethodBaseDir ( TDirectory * methodDir )

inline

Definition at line 374 of file MethodBase.h.

◆ SetMethodDir()

void TMVA::MethodBase::SetMethodDir ( TDirectory * methodDir )

inline

Definition at line 372 of file MethodBase.h.

◆ SetModelPersistence()

void TMVA::MethodBase::SetModelPersistence ( Bool_t status )

inline

Definition at line 382 of file MethodBase.h.

◆ SetNormalised()

void TMVA::MethodBase::SetNormalised ( Bool_t norm )

inlineprotected

Definition at line 497 of file MethodBase.h.

◆ SetSignalReferenceCut()

void TMVA::MethodBase::SetSignalReferenceCut ( Double_t cut )

inline

Definition at line 364 of file MethodBase.h.

◆ SetSignalReferenceCutOrientation()

void TMVA::MethodBase::SetSignalReferenceCutOrientation ( Double_t cutOrientation )

inline

Definition at line 365 of file MethodBase.h.

◆ SetSilentFile()

void TMVA::MethodBase::SetSilentFile ( Bool_t status )

inline

Definition at line 378 of file MethodBase.h.

◆ SetTestTime()

void TMVA::MethodBase::SetTestTime ( Double_t testTime )

inline

Definition at line 165 of file MethodBase.h.

◆ SetTestvarName()

void TMVA::MethodBase::SetTestvarName ( const TString & v = "" )

inline

Definition at line 341 of file MethodBase.h.

◆ SetTrainTime()

void TMVA::MethodBase::SetTrainTime ( Double_t trainTime )

inline

Definition at line 161 of file MethodBase.h.

◆ SetTuneParameters()

void TMVA::MethodBase::SetTuneParameters ( std::map< TString, Double_t > tuneParameters )

virtual

set the tuning parameters according to the argument This is just a dummy .

. have a look at the MethodBDT how you could perhaps implement the same thing for the other Classifiers..

Reimplemented in TMVA::MethodBDT, and TMVA::MethodSVM.

Definition at line 644 of file MethodBase.cxx.

◆ SetupMethod()

void TMVA::MethodBase::SetupMethod ( )

setup of methods

Definition at line 406 of file MethodBase.cxx.

◆ SetWeightFileDir()

void TMVA::MethodBase::SetWeightFileDir ( TString fileDir )

protected

set directory of weight file

Definition at line 2060 of file MethodBase.cxx.

◆ SetWeightFileName()

void TMVA::MethodBase::SetWeightFileName ( TString theWeightFile )

protected

set the weight file name (depreciated)

Definition at line 2069 of file MethodBase.cxx.

◆ Statistics()

void TMVA::MethodBase::Statistics	(	Types::ETreeType	treeType,
		const TString &	theVarName,
		Double_t &	meanS,
		Double_t &	meanB,
		Double_t &	rmsS,
		Double_t &	rmsB,
		Double_t &	xmin,
		Double_t &	xmax
	)

protected

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

Definition at line 2943 of file MethodBase.cxx.

◆ Streamer()

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

virtual

◆ StreamerNVirtual()

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

inline

Definition at line 731 of file MethodBase.h.

◆ TestClassification()

void TMVA::MethodBase::TestClassification ( )

virtual

initialization

Reimplemented in TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyKeras, TMVA::MethodPyRandomForest, TMVA::MethodPyTorch, TMVA::MethodC50, TMVA::MethodRSNNS, TMVA::MethodRSVM, TMVA::MethodRXGB, TMVA::MethodBoost, and TMVA::MethodCuts.

Definition at line 1126 of file MethodBase.cxx.

◆ TestMulticlass()

void TMVA::MethodBase::TestMulticlass ( )

virtual

test multiclass classification

Definition at line 1099 of file MethodBase.cxx.

◆ TestRegression()

void TMVA::MethodBase::TestRegression	(	Double_t &	bias,
		Double_t &	biasT,
		Double_t &	dev,
		Double_t &	devT,
		Double_t &	rms,
		Double_t &	rmsT,
		Double_t &	mInf,
		Double_t &	mInfT,
		Double_t &	corr,
		Types::ETreeType	type
	)

virtual

calculate <sum-of-deviation-squared> of regression output versus "true" value from test sample

bias = average deviation
dev = average absolute deviation
rms = rms of deviation

Definition at line 991 of file MethodBase.cxx.

◆ Train()

virtual void TMVA::MethodBase::Train ( )

pure virtual

◆ TrainingEnded()

bool TMVA::MethodBase::TrainingEnded ( )

inline

Definition at line 469 of file MethodBase.h.

◆ TrainMethod()

void TMVA::MethodBase::TrainMethod ( )

Definition at line 650 of file MethodBase.cxx.

◆ TxtWeightsOnly()

Bool_t TMVA::MethodBase::TxtWeightsOnly ( ) const

inlineprotected

Definition at line 534 of file MethodBase.h.

◆ Verbose()

Bool_t TMVA::MethodBase::Verbose ( ) const

inlineprotected

Definition at line 503 of file MethodBase.h.

◆ WriteEvaluationHistosToFile()

void TMVA::MethodBase::WriteEvaluationHistosToFile ( Types::ETreeType treetype )

virtual

writes all MVA evaluation histograms to file

Reimplemented in TMVA::MethodBoost.

Definition at line 2095 of file MethodBase.cxx.

◆ WriteMonitoringHistosToFile()

void TMVA::MethodBase::WriteMonitoringHistosToFile ( void ) const

virtual

write special monitoring histograms to file dummy implementation here --------------—

Implements TMVA::IMethod.

Reimplemented in TMVA::MethodANNBase, TMVA::MethodLikelihood, TMVA::MethodBDT, TMVA::MethodBoost, TMVA::MethodCrossValidation, TMVA::MethodCuts, and TMVA::MethodRuleFit.

Definition at line 2134 of file MethodBase.cxx.

◆ WriteStateToFile()

void TMVA::MethodBase::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

Definition at line 1405 of file MethodBase.cxx.

◆ WriteStateToStream()

void TMVA::MethodBase::WriteStateToStream ( std::ostream & tf ) const

private

general method used in writing the header of the weight files where the used variables, variable transformation type etc.

is specified

Definition at line 1268 of file MethodBase.cxx.

◆ WriteStateToXML()

void TMVA::MethodBase::WriteStateToXML ( void * parent ) const

private

general method used in writing the header of the weight files where the used variables, variable transformation type etc.

is specified

Definition at line 1332 of file MethodBase.cxx.

◆ WriteVarsToStream()

void TMVA::MethodBase::WriteVarsToStream	(	std::ostream &	tf,
		const TString &	prefix = `""`
	)		const

private

write the list of variables (name, min, max) for a given data transformation method to the stream

Definition at line 1711 of file MethodBase.cxx.

Friends And Related Symbol Documentation

◆ CrossValidation

friend class CrossValidation

friend

Definition at line 113 of file MethodBase.h.

◆ Experimental::Classification

friend class Experimental::Classification

friend

Definition at line 118 of file MethodBase.h.

◆ Factory

friend class Factory

friend

Definition at line 114 of file MethodBase.h.

◆ MethodBoost

friend class MethodBoost

friend

Definition at line 116 of file MethodBase.h.

◆ MethodCategory

friend class MethodCategory

friend

Definition at line 269 of file MethodBase.h.

◆ MethodCompositeBase

friend class MethodCompositeBase

friend

Definition at line 270 of file MethodBase.h.

◆ MethodCrossValidation

friend class MethodCrossValidation

friend

Definition at line 117 of file MethodBase.h.

◆ MethodCuts

friend class MethodCuts

friend

Definition at line 603 of file MethodBase.h.

◆ RootFinder

friend class RootFinder

friend

Definition at line 115 of file MethodBase.h.

Member Data Documentation

◆ fAnalysisType

Types::EAnalysisType TMVA::MethodBase::fAnalysisType

protected

Definition at line 595 of file MethodBase.h.

◆ fBackgroundClass

UInt_t TMVA::MethodBase::fBackgroundClass

protected

Definition at line 690 of file MethodBase.h.

◆ fBaseDir

TDirectory* TMVA::MethodBase::fBaseDir

private

Definition at line 625 of file MethodBase.h.

◆ fConstructedFromWeightFile

Bool_t TMVA::MethodBase::fConstructedFromWeightFile

private

Definition at line 620 of file MethodBase.h.

◆ fCutOrientation

ECutOrientation TMVA::MethodBase::fCutOrientation

private

Definition at line 699 of file MethodBase.h.

◆ fDataSetInfo

DataSetInfo& TMVA::MethodBase::fDataSetInfo

private

Definition at line 607 of file MethodBase.h.

◆ fDefaultPDF

PDF* TMVA::MethodBase::fDefaultPDF

private

default PDF definitions

Definition at line 644 of file MethodBase.h.

◆ fEffS

TH1* TMVA::MethodBase::fEffS

private

efficiency histogram for rootfinder

Definition at line 642 of file MethodBase.h.

◆ fEventCollections

std::vector<const std::vector<TMVA::Event*>*> TMVA::MethodBase::fEventCollections

mutableprivate

Definition at line 708 of file MethodBase.h.

◆ fExitFromTraining

bool TMVA::MethodBase::fExitFromTraining = false

protected

Definition at line 449 of file MethodBase.h.

◆ fFile

TFile* TMVA::MethodBase::fFile

private

Definition at line 628 of file MethodBase.h.

◆ fFileDir

TString TMVA::MethodBase::fFileDir

private

unix sub-directory for weight files (default: DataLoader's Name + "weights")

Definition at line 637 of file MethodBase.h.

◆ fHasMVAPdfs

Bool_t TMVA::MethodBase::fHasMVAPdfs

private

MVA Pdfs are created for this classifier.

Definition at line 680 of file MethodBase.h.

◆ fHelp

Bool_t TMVA::MethodBase::fHelp

private

help flag

Definition at line 679 of file MethodBase.h.

◆ fIgnoreNegWeightsInTraining

Bool_t TMVA::MethodBase::fIgnoreNegWeightsInTraining

private

If true, events with negative weights are not used in training.

Definition at line 682 of file MethodBase.h.

◆ fInputVars

std::vector<TString>* TMVA::MethodBase::fInputVars

protected

Definition at line 588 of file MethodBase.h.

◆ fInteractive

IPythonInteractive* TMVA::MethodBase::fInteractive = nullptr

protected

temporary dataset used when evaluating on a different data (used by MethodCategory::GetMvaValues)

Definition at line 448 of file MethodBase.h.

◆ fIPyCurrentIter

UInt_t TMVA::MethodBase::fIPyCurrentIter = 0

protected

Definition at line 450 of file MethodBase.h.

◆ fIPyMaxIter

UInt_t TMVA::MethodBase::fIPyMaxIter = 0

protected

Definition at line 450 of file MethodBase.h.

◆ fJobName

TString TMVA::MethodBase::fJobName

private

Definition at line 614 of file MethodBase.h.

◆ fMeanB

Double_t TMVA::MethodBase::fMeanB

private

mean (background)

Definition at line 662 of file MethodBase.h.

◆ fMeanS

Double_t TMVA::MethodBase::fMeanS

private

mean (signal)

Definition at line 661 of file MethodBase.h.

◆ fMethodBaseDir

TDirectory* TMVA::MethodBase::fMethodBaseDir

mutableprivate

Definition at line 626 of file MethodBase.h.

◆ fMethodName

TString TMVA::MethodBase::fMethodName

private

Definition at line 615 of file MethodBase.h.

◆ fMethodType

Types::EMVA TMVA::MethodBase::fMethodType

private

Definition at line 616 of file MethodBase.h.

◆ fModelPersistence

Bool_t TMVA::MethodBase::fModelPersistence

private

Definition at line 633 of file MethodBase.h.

◆ fMulticlassReturnVal

std::vector<Float_t>* TMVA::MethodBase::fMulticlassReturnVal

protected

Definition at line 598 of file MethodBase.h.

◆ fMVAPdfB

PDF* TMVA::MethodBase::fMVAPdfB

private

background MVA PDF

Definition at line 646 of file MethodBase.h.

◆ fMVAPdfS

PDF* TMVA::MethodBase::fMVAPdfS

private

signal MVA PDF

Definition at line 645 of file MethodBase.h.

◆ fNbins

Int_t TMVA::MethodBase::fNbins

protected

Definition at line 591 of file MethodBase.h.

◆ fNbinsH

Int_t TMVA::MethodBase::fNbinsH

protected

Definition at line 593 of file MethodBase.h.

◆ fNbinsMVAoutput

Int_t TMVA::MethodBase::fNbinsMVAoutput

protected

Definition at line 592 of file MethodBase.h.

◆ fNbinsMVAPdf

Int_t TMVA::MethodBase::fNbinsMVAPdf

private

Definition at line 726 of file MethodBase.h.

◆ fNormalise

Bool_t TMVA::MethodBase::fNormalise

private

Definition at line 722 of file MethodBase.h.

◆ fNsmoothMVAPdf

Int_t TMVA::MethodBase::fNsmoothMVAPdf

private

Definition at line 727 of file MethodBase.h.

◆ fParentDir

TString TMVA::MethodBase::fParentDir

private

method parent name, like booster name

Definition at line 635 of file MethodBase.h.

◆ fRanking

Ranking* TMVA::MethodBase::fRanking

protected

Definition at line 587 of file MethodBase.h.

◆ fRegressionReturnVal

std::vector<Float_t>* TMVA::MethodBase::fRegressionReturnVal

protected

Definition at line 597 of file MethodBase.h.

◆ fResults

Results* TMVA::MethodBase::fResults

protected

Definition at line 730 of file MethodBase.h.

◆ fRmsB

Double_t TMVA::MethodBase::fRmsB

private

RMS (background)

Definition at line 664 of file MethodBase.h.

◆ fRmsS

Double_t TMVA::MethodBase::fRmsS

private

RMS (signal)

Definition at line 663 of file MethodBase.h.

◆ fROOTTrainingVersion

UInt_t TMVA::MethodBase::fROOTTrainingVersion

private

Definition at line 619 of file MethodBase.h.

◆ fSetupCompleted

Bool_t TMVA::MethodBase::fSetupCompleted

Definition at line 711 of file MethodBase.h.

◆ fSignalClass

UInt_t TMVA::MethodBase::fSignalClass

protected

Definition at line 689 of file MethodBase.h.

◆ fSignalReferenceCut

Double_t TMVA::MethodBase::fSignalReferenceCut

private

the data set information (sometimes needed)

Definition at line 609 of file MethodBase.h.

◆ fSignalReferenceCutOrientation

Double_t TMVA::MethodBase::fSignalReferenceCutOrientation

private

Definition at line 610 of file MethodBase.h.

◆ fSilentFile

Bool_t TMVA::MethodBase::fSilentFile

private

Definition at line 631 of file MethodBase.h.

◆ fSplB

PDF* TMVA::MethodBase::fSplB

private

PDFs of MVA distribution (background)

Definition at line 651 of file MethodBase.h.

◆ fSpleffBvsS

TSpline* TMVA::MethodBase::fSpleffBvsS

private

splines for signal eff. versus background eff.

Definition at line 652 of file MethodBase.h.

◆ fSplRefB

TSpline1* TMVA::MethodBase::fSplRefB

private

Definition at line 703 of file MethodBase.h.

◆ fSplRefS

TSpline1* TMVA::MethodBase::fSplRefS

private

Definition at line 702 of file MethodBase.h.

◆ fSplS

PDF* TMVA::MethodBase::fSplS

private

PDFs of MVA distribution (signal)

Definition at line 650 of file MethodBase.h.

◆ fSplTrainB

PDF* TMVA::MethodBase::fSplTrainB

private

PDFs of training MVA distribution (background)

Definition at line 655 of file MethodBase.h.

◆ fSplTrainEffBvsS

TSpline* TMVA::MethodBase::fSplTrainEffBvsS

private

splines for training signal eff. versus background eff.

Definition at line 656 of file MethodBase.h.

◆ fSplTrainRefB

TSpline1* TMVA::MethodBase::fSplTrainRefB

private

Definition at line 706 of file MethodBase.h.

◆ fSplTrainRefS

TSpline1* TMVA::MethodBase::fSplTrainRefS

private

Definition at line 705 of file MethodBase.h.

◆ fSplTrainS

PDF* TMVA::MethodBase::fSplTrainS

private

PDFs of training MVA distribution (signal)

Definition at line 654 of file MethodBase.h.

◆ fTestTime

Double_t TMVA::MethodBase::fTestTime

private

Definition at line 696 of file MethodBase.h.

◆ fTestvar

TString TMVA::MethodBase::fTestvar

private

Definition at line 617 of file MethodBase.h.

◆ fTmpData

DataSet* TMVA::MethodBase::fTmpData = nullptr

protected

temporary event when testing on a different DataSet than the own one

Definition at line 446 of file MethodBase.h.

◆ fTmpEvent

const Event* TMVA::MethodBase::fTmpEvent

mutableprotected

Definition at line 445 of file MethodBase.h.

◆ fTMVATrainingVersion

UInt_t TMVA::MethodBase::fTMVATrainingVersion

private

Definition at line 618 of file MethodBase.h.

◆ fTrainHistory

TrainingHistory TMVA::MethodBase::fTrainHistory

Definition at line 425 of file MethodBase.h.

◆ fTrainTime

Double_t TMVA::MethodBase::fTrainTime

private

Definition at line 695 of file MethodBase.h.

◆ fTransformation

TransformationHandler TMVA::MethodBase::fTransformation

private

the list of transformations

Definition at line 672 of file MethodBase.h.

◆ fTransformationPointer

TransformationHandler* TMVA::MethodBase::fTransformationPointer

private

pointer to the rest of transformations

Definition at line 671 of file MethodBase.h.

◆ fTxtWeightsOnly

Bool_t TMVA::MethodBase::fTxtWeightsOnly

private

Definition at line 725 of file MethodBase.h.

◆ fUseDecorr

Bool_t TMVA::MethodBase::fUseDecorr

private

Definition at line 723 of file MethodBase.h.

◆ fVariableTransformType

Types::ESBType TMVA::MethodBase::fVariableTransformType

private

Definition at line 611 of file MethodBase.h.

◆ fVariableTransformTypeString

TString TMVA::MethodBase::fVariableTransformTypeString

private

Definition at line 724 of file MethodBase.h.

◆ fVarTransformString

TString TMVA::MethodBase::fVarTransformString

private

labels variable transform method

Definition at line 669 of file MethodBase.h.

◆ fVerbose

Bool_t TMVA::MethodBase::fVerbose

private

verbose flag

Definition at line 676 of file MethodBase.h.

◆ fVerbosityLevel

EMsgType TMVA::MethodBase::fVerbosityLevel

private

verbosity level

Definition at line 678 of file MethodBase.h.

◆ fVerbosityLevelString

TString TMVA::MethodBase::fVerbosityLevelString

private

verbosity level (user input string)

Definition at line 677 of file MethodBase.h.

◆ fWeightFile

TString TMVA::MethodBase::fWeightFile

private

weight file name

Definition at line 638 of file MethodBase.h.

◆ fXmax

Double_t TMVA::MethodBase::fXmax

private

maximum (signal and background)

Definition at line 666 of file MethodBase.h.

◆ fXmin

Double_t TMVA::MethodBase::fXmin

private

minimum (signal and background)

Definition at line 665 of file MethodBase.h.

Libraries for TMVA::MethodBase:

[legend]

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

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

Public Types

Public Member Functions

Static Public Member Functions

Public Attributes

Protected Member Functions

Protected Attributes

Private Types

Private Member Functions

Private Attributes

Friends

Additional Inherited Members

Member Enumeration Documentation

◆ ECutOrientation

◆ EWeightFileType

Constructor & Destructor Documentation

◆ MethodBase() [1/2]

◆ MethodBase() [2/2]

◆ ~MethodBase()

Member Function Documentation

◆ AddClassesXMLTo()

◆ AddClassifierOutput()

◆ AddClassifierOutputProb()

◆ AddInfoItem()

◆ AddMulticlassOutput()

◆ AddOutput()

◆ AddRegressionOutput()

◆ AddSpectatorsXMLTo()

◆ AddTargetsXMLTo()

◆ AddVarsXMLTo()

◆ AddWeightsXMLTo()

◆ BaseDir()

◆ CheckSetup()

◆ Class()

◆ Class_Name()

◆ Class_Version()

◆ CreateMVAPdfs()

◆ CreateRanking()

◆ Data()

◆ DataInfo()

◆ DeclareBaseOptions()

◆ DeclareCompatibilityOptions()

◆ DeclareOptions()

◆ DeclFileName()

◆ DisableWriting()

◆ DoMulticlass()

◆ DoRegression()

◆ ExitFromTraining()

◆ GetAnalysisType()

◆ GetCurrentIter()

◆ GetCutOrientation()

◆ GetDataMvaValues()

◆ GetEfficiency()

◆ GetEvent() [1/4]

◆ GetEvent() [2/4]

◆ GetEvent() [3/4]

◆ GetEvent() [4/4]

◆ GetEventCollection()

◆ GetFile()

◆ GetInputLabel()

◆ GetInputTitle()

◆ GetInputVar()

◆ GetInteractiveTrainingError()

◆ GetInternalVarName()

◆ GetJobName()

◆ GetKSTrainingVsTest()

◆ GetLine()

◆ GetMaximumSignificance()

◆ GetMaxIter()

◆ GetMean()

◆ GetMethodName()

◆ GetMethodType()

◆ GetMethodTypeName()

◆ GetMulticlassConfusionMatrix()

◆ GetMulticlassEfficiency()

◆ GetMulticlassTrainingEfficiency()

◆ GetMulticlassValues()

◆ GetMvaValue() [1/2]

◆ GetMvaValue() [2/2]

◆ GetMvaValues()

◆ GetName()