TMVA::MethodBDT Class Reference

Analysis of Boosted Decision Trees.

Boosted decision trees have been successfully used in High Energy Physics analysis for example by the MiniBooNE experiment (Yang-Roe-Zhu, physics/0508045). In Boosted Decision Trees, the selection is done on a majority vote on the result of several decision trees, which are all derived from the same training sample by supplying different event weights during the training.

Decision trees:

Successive decision nodes are used to categorize the events out of the sample as either signal or background. Each node uses only a single discriminating variable to decide if the event is signal-like ("goes right") or background-like ("goes left"). This forms a tree like structure with "baskets" at the end (leave nodes), and an event is classified as either signal or background according to whether the basket where it ends up has been classified signal or background during the training. Training of a decision tree is the process to define the "cut criteria" for each node. The training starts with the root node. Here one takes the full training event sample and selects the variable and corresponding cut value that gives the best separation between signal and background at this stage. Using this cut criterion, the sample is then divided into two subsamples, a signal-like (right) and a background-like (left) sample. Two new nodes are then created for each of the two sub-samples and they are constructed using the same mechanism as described for the root node. The devision is stopped once a certain node has reached either a minimum number of events, or a minimum or maximum signal purity. These leave nodes are then called "signal" or "background" if they contain more signal respective background events from the training sample.

Boosting:

The idea behind adaptive boosting (AdaBoost) is, that signal events from the training sample, that end up in a background node (and vice versa) are given a larger weight than events that are in the correct leave node. This results in a re-weighed training event sample, with which then a new decision tree can be developed. The boosting can be applied several times (typically 100-500 times) and one ends up with a set of decision trees (a forest). Gradient boosting works more like a function expansion approach, where each tree corresponds to a summand. The parameters for each summand (tree) are determined by the minimization of a error function (binomial log- likelihood for classification and Huber loss for regression). A greedy algorithm is used, which means, that only one tree is modified at a time, while the other trees stay fixed.

Bagging:

In this particular variant of the Boosted Decision Trees the boosting is not done on the basis of previous training results, but by a simple stochastic re-sampling of the initial training event sample.

Random Trees:

Similar to the "Random Forests" from Leo Breiman and Adele Cutler, it uses the bagging algorithm together and bases the determination of the best node-split during the training on a random subset of variables only which is individually chosen for each split.

Analysis:

Applying an individual decision tree to a test event results in a classification of the event as either signal or background. For the boosted decision tree selection, an event is successively subjected to the whole set of decision trees and depending on how often it is classified as signal, a "likelihood" estimator is constructed for the event being signal or background. The value of this estimator is the one which is then used to select the events from an event sample, and the cut value on this estimator defines the efficiency and purity of the selection.

Definition at line 63 of file MethodBDT.h.

Public Types
enum	{ kSingleKey = (1ULL << (0)) , kOverwrite = (1ULL << (1)) , kWriteDelete = (1ULL << (2)) }
enum	{   kIsOnHeap = 0x01000000 , kNotDeleted = 0x02000000 , kZombie = 0x04000000 , kInconsistent = 0x08000000 ,   kBitMask = 0x00ffffff }
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)) }
enum	EWeightFileType { kROOT =0 , kTEXT }

Public Member Functions
	MethodBDT (const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption="")
	The standard constructor for the "boosted decision trees".
	MethodBDT (DataSetInfo &theData, const TString &theWeightFile)
virtual	~MethodBDT (void)
	Destructor.
void	AbstractMethod (const char *method) const
	Call this function within a function that you don't want to define as purely virtual, in order not to force all users deriving from that class to implement that maybe (on their side) unused function; but at the same time, emit a run-time warning if they try to call it, telling that it is not implemented in the derived class: action must thus be taken on the user side to override it.
void	AddOptionsXMLTo (void *parent) const
	write options to XML file
void	AddOutput (Types::ETreeType type, Types::EAnalysisType analysisType)
template<class T>
void	AddPreDefVal (const T &)
template<class T>
void	AddPreDefVal (const TString &optname, const T &)
void	AddWeightsXMLTo (void *parent) const override
	Write weights to XML.
virtual void	AppendPad (Option_t *option="")
	Append graphics object to current pad.
TDirectory *	BaseDir () const
	returns the ROOT directory where info/histograms etc of the corresponding MVA method instance are stored
Double_t	Boost (std::vector< const TMVA::Event * > &, DecisionTree *dt, UInt_t cls=0)
	Apply the boosting algorithm (the algorithm is selecte via the "option" given in the constructor.
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.
void	CheckForUnusedOptions () const
	checks for unused options in option string
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 char *	ClassName () const
	Returns name of class to which the object belongs.
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.
const Ranking *	CreateRanking () override
	Compute ranking of input variables.
DataSet *	Data () const
DataSetInfo &	DataInfo () const
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="")
void	DeclareOptions () override
	Define the options (their key words).
virtual void	Delete (Option_t *option="")
	Delete this object.
void	DisableWriting (Bool_t setter)
virtual Int_t	DistancetoPrimitive (Int_t px, Int_t py)
	Computes distance from point (px,py) to the object.
Bool_t	DoMulticlass () const
Bool_t	DoRegression () const
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).
void	ExitFromTraining ()
virtual void	Fatal (const char *method, const char *msgfmt,...) const
	Issue fatal error message.
virtual void	FillBuffer (char *&buffer)
	Encode TNamed into output buffer.
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.
Types::EAnalysisType	GetAnalysisType () const
const std::vector< double > &	GetBoostWeights () const
const char *	GetConfigDescription () const
const char *	GetConfigName () const
UInt_t	GetCurrentIter ()
virtual Option_t *	GetDrawOption () const
	Get option used by the graphics system to draw this object.
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 std::vector< TMVA::DecisionTree * > &	GetForest () const
void	GetHelpMessage () const override
	Get help message text.
virtual const char *	GetIconName () const
	Returns mime type name of object.
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)
const std::vector< Float_t > &	GetMulticlassValues () override
	Get the multiclass MVA response for the BDT classifier.
Double_t	GetMvaValue (const TMVA::Event *const ev, Double_t *err=nullptr, Double_t *errUpper=nullptr)
Double_t	GetMvaValue (Double_t *err=nullptr, Double_t *errUpper=nullptr) override
const char *	GetName () const override
UInt_t	GetNEvents () const
UInt_t	GetNTargets () const
UInt_t	GetNTrees () const
UInt_t	GetNvar () const
UInt_t	GetNVariables () const
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
const TString &	GetOptions () 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
const std::vector< Float_t > &	GetRegressionValues (const TMVA::Event *const ev)
const std::vector< Float_t > &	GetRegressionValues () override
	Get the regression value generated by the BDTs.
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
const char *	GetTitle () const override
	Returns title of object.
virtual Double_t	GetTrainingEfficiency (const TString &)
const Event *	GetTrainingEvent (Long64_t ievt) const
const std::vector< const TMVA::Event * > &	GetTrainingEvents () 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
virtual UInt_t	GetUniqueID () const
	Return the unique object id.
std::vector< Double_t >	GetVariableImportance ()
	Return the relative variable importance, normalized to all variables together having the importance 1.
Double_t	GetVariableImportance (UInt_t ivar)
	Returns the measure for the variable importance of variable "ivar" which is later used in GetVariableImportance() to calculate the relative variable importances.
TString	GetWeightFileName () const
	retrieve weight file name
Double_t	GetXmax (Int_t ivar) const
Double_t	GetXmin (Int_t ivar) const
virtual Bool_t	HandleTimer (TTimer *timer)
	Execute action in response of a timer timing out.
Bool_t	HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets) override
	BDT can handle classification with multiple classes and regression with one regression-target.
ULong_t	Hash () const override
	Return hash value for this object.
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.
Bool_t	HasMVAPdfs () const
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.
void	InitEventSample ()
	Initialize the event sample (i.e. reset the boost-weights... etc).
void	InitIPythonInteractive ()
virtual void	Inspect () const
	Dump contents of this object in a graphics canvas.
void	InvertBit (UInt_t f)
TClass *	IsA () const override
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).
Bool_t	IsModelPersistence () const
Bool_t	IsOnHeap () 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
Bool_t	IsSortable () const override
Bool_t	IsZombie () const
MsgLogger &	Log () const
void	ls (Option_t *option="") const override
	List TNamed name and title.
void	MakeClass (const TString &classFileName=TString("")) const override
	create reader class for method (classification only at present)
void	MakeClassInstantiateNode (DecisionTreeNode *n, std::ostream &fout, const TString &className) const
	Recursively descends a tree and writes the node instance to the output stream.
void	MakeClassSpecific (std::ostream &, const TString &) const override
	Make ROOT-independent C++ class for classifier response (classifier-specific implementation).
void	MakeClassSpecificHeader (std::ostream &, const TString &) const override
	Specific class header.
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).
TDirectory *	MethodBaseDir () const
	returns the ROOT directory where all instances of the corresponding MVA method are stored
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 *, size_t)
	Operator delete for sized deallocation.
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 *, size_t)
	Operator delete [] for sized deallocation.
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)
std::map< TString, Double_t >	OptimizeTuningParameters (TString fomType="ROCIntegral", TString fitType="FitGA") override
	Call the Optimizer with the set of parameters and ranges that are meant to be tuned.
virtual void	Paint (Option_t *option="")
	This method must be overridden if a class wants to paint itself.
virtual void	ParseOptions ()
	options parser
virtual void	Pop ()
	Pop on object drawn in a pad to the top of the display list.
void	Print (Option_t *option="") const override
	Print TNamed name and title.
void	PrintHelpMessage () const override
	prints out method-specific help method
void	PrintOptions () const
	prints out the options set in the options string and the defaults
void	ProcessOptions () override
	The option string is decoded, for available options see "DeclareOptions".
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)
virtual Int_t	Read (const char *name)
	Read contents of object with specified name from the current directory.
void	ReadOptionsFromStream (std::istream &istr)
	read option back from the weight file
void	ReadOptionsFromXML (void *node)
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	ReadWeightsFromStream (std::istream &istr) override
	Read the weights (BDT coefficients).
virtual void	ReadWeightsFromStream (TFile &)
void	ReadWeightsFromXML (void *parent) override
	Reads the BDT from the xml file.
virtual void	RecursiveRemove (TObject *obj)
	Recursively remove this object from a list.
void	RerouteTransformationHandler (TransformationHandler *fTargetTransformation)
void	Reset (void) override
	Reset the method, as if it had just been instantiated (forget all training etc.).
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	SetAdaBoostBeta (Double_t b)
virtual void	SetAnalysisType (Types::EAnalysisType type)
void	SetBaggedSampleFraction (Double_t f)
void	SetBaseDir (TDirectory *methodDir)
void	SetBit (UInt_t f)
void	SetBit (UInt_t f, Bool_t set)
	Set or unset the user status bits as specified in f.
void	SetConfigDescription (const char *d)
void	SetConfigName (const char *n)
virtual void	SetDrawOption (Option_t *option="")
	Set drawing option for object.
void	SetFile (TFile *file)
void	SetMaxDepth (Int_t d)
void	SetMethodBaseDir (TDirectory *methodDir)
void	SetMethodDir (TDirectory *methodDir)
void	SetMinNodeSize (Double_t sizeInPercent)
void	SetMinNodeSize (TString sizeInPercent)
void	SetModelPersistence (Bool_t status)
void	SetMsgType (EMsgType t)
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).
void	SetNodePurityLimit (Double_t l)
void	SetNTrees (Int_t d)
void	SetOptions (const TString &s)
void	SetShrinkage (Double_t s)
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="")
virtual void	SetTitle (const char *title="")
	Set the title of the TNamed.
void	SetTrainTime (Double_t trainTime)
void	SetTuneParameters (std::map< TString, Double_t > tuneParameters) override
	Set the tuning parameters according to the argument.
virtual void	SetUniqueID (UInt_t uid)
	Set the unique object id.
void	SetupMethod ()
	setup of methods
void	SetUseNvars (Int_t n)
virtual Int_t	Sizeof () const
	Return size of the TNamed part of the TObject.
void	Streamer (TBuffer &) override
void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)
virtual void	SysError (const char *method, const char *msgfmt,...) const
	Issue system error message.
Bool_t	TestBit (UInt_t f) const
Int_t	TestBits (UInt_t f) const
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
Double_t	TestTreeQuality (DecisionTree *dt)
	Test the tree quality.. in terms of Misclassification.
void	Train (void) override
	BDT training.
bool	TrainingEnded ()
void	TrainMethod ()
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.
virtual void	WriteEvaluationHistosToFile (Types::ETreeType treetype)
	writes all MVA evaluation histograms to file
void	WriteMonitoringHistosToFile (void) const override
	Here we could write some histograms created during the processing to the output file.
void	WriteOptionsToStream (std::ostream &o, const TString &prefix) const
	write options to output stream (e.g. in writing the MVA weight files
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

Static Public Member Functions
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 Types
enum	{ kOnlyPrepStep = (1ULL << (3)) }

Protected Member Functions
void	DeclareCompatibilityOptions () override
	Options that are used ONLY for the READER to ensure backward compatibility.
virtual void	DoError (int level, const char *location, const char *fmt, va_list va) const
	Interface to ErrorHandler (protected).
void	EnableLooseOptions (Bool_t b=kTRUE)
virtual std::vector< Float_t >	GetAllMulticlassValues ()
	Get all multi-class values.
virtual std::vector< Float_t >	GetAllRegressionValues ()
	Get al regression values in one call.
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 &	GetReferenceFile () 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
Bool_t	LooseOptionCheckingEnabled () const
void	MakeZombie ()
void	NoErrorCalc (Double_t *const err, Double_t *const errUpper)
void	ResetSetFlag ()
	resets the IsSet flag for all declare options to be called before options are read from stream
void	SavePrimitiveNameTitle (std::ostream &out, const char *variable_name)
	Save object name and title into the output stream "out".
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
void	WriteOptionsReferenceToFile ()
	write complete options to output stream

Static Protected Member Functions
static void	SavePrimitiveConstructor (std::ostream &out, TClass *cl, const char *variable_name, const char *constructor_agrs="", Bool_t empty_line=kTRUE)
	Save object constructor in the output stream "out".
static void	SavePrimitiveDraw (std::ostream &out, const char *variable_name, Option_t *option=nullptr)
	Save invocation of primitive Draw() method Skipped if option contains "nodraw" string.
static TString	SavePrimitiveVector (std::ostream &out, const char *prefix, Int_t len, Double_t *arr, Int_t flag=0)
	Save array in the output stream "out" as vector.

Protected Attributes
Types::EAnalysisType	fAnalysisType
UInt_t	fBackgroundClass
bool	fExitFromTraining = false
std::vector< TString > *	fInputVars
IPythonInteractive *	fInteractive = nullptr
UInt_t	fIPyCurrentIter = 0
UInt_t	fIPyMaxIter = 0
MsgLogger *	fLogger
	! message logger
std::vector< Float_t > *	fMulticlassReturnVal
TString	fName
Int_t	fNbins
Int_t	fNbinsH
Int_t	fNbinsMVAoutput
Ranking *	fRanking
std::vector< Float_t > *	fRegressionReturnVal
Results *	fResults
UInt_t	fSignalClass
TString	fTitle
DataSet *	fTmpData = nullptr
	! temporary dataset used when evaluating on a different data (used by MethodCategory::GetMvaValues)
const Event *	fTmpEvent
	! temporary event when testing on a different DataSet than the own one

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

Private Member Functions
Double_t	AdaBoost (std::vector< const TMVA::Event * > &, DecisionTree *dt)
	The AdaBoost implementation.
Double_t	AdaBoostR2 (std::vector< const TMVA::Event * > &, DecisionTree *dt)
	Adaption of the AdaBoost to regression problems (see H.Drucker 1997).
Double_t	AdaCost (std::vector< const TMVA::Event * > &, DecisionTree *dt)
	The AdaCost boosting algorithm takes a simple cost Matrix (currently fixed for all events... later could be modified to use individual cost matrices for each events as in the original paper...
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
Double_t	ApplyPreselectionCuts (const Event *ev)
	Apply the preselection cuts before even bothering about any Decision Trees in the GetMVA .
template<class T>
void	AssignOpt (const TString &name, T &valAssign) const
Double_t	Bagging ()
	Call it boot-strapping, re-sampling or whatever you like, in the end it is nothing else but applying "random" poisson weights to each event.
void	BoostMonitor (Int_t iTree)
	Fills the ROCIntegral vs Itree from the testSample for the monitoring plots during the training .
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.
void	DeterminePreselectionCuts (const std::vector< const TMVA::Event * > &eventSample)
	Find useful preselection cuts that will be applied before and Decision Tree training.
void	GetBaggedSubSample (std::vector< const TMVA::Event * > &)
	Fills fEventSample with fBaggedSampleFraction*NEvents random training events.
ECutOrientation	GetCutOrientation () const
Double_t	GetGradBoostMVA (const TMVA::Event *e, UInt_t nTrees)
	Returns MVA value: -1 for background, 1 for signal.
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
Double_t	GetMvaValue (Double_t *err, Double_t *errUpper, UInt_t useNTrees)
	Return the MVA value (range [-1;1]) that classifies the event according to the majority vote from the total number of decision trees.
virtual Double_t	GetValueForRoot (Double_t)
	returns efficiency as function of cut
Double_t	GradBoost (std::vector< const TMVA::Event * > &, DecisionTree *dt, UInt_t cls=0)
	Calculate the desired response value for each region.
Double_t	GradBoostRegression (std::vector< const TMVA::Event * > &, DecisionTree *dt)
	Implementation of M_TreeBoost using any loss function as described by Friedman 1999.
void	Init (void) override
	Common initialisation with defaults for the BDT-Method.
void	InitBase ()
	default initialization called by all constructors
void	InitGradBoost (std::vector< const TMVA::Event * > &)
	Initialize targets for first tree.
void	PreProcessNegativeEventWeights ()
	O.k.
Double_t	PrivateGetMvaValue (const TMVA::Event *ev, Double_t *err=nullptr, Double_t *errUpper=nullptr, UInt_t useNTrees=0)
	Return the MVA value (range [-1;1]) that classifies the event according to the majority vote from the total number of decision trees.
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.
Double_t	RegBoost (std::vector< const TMVA::Event * > &, DecisionTree *dt)
	A special boosting only for Regression (not implemented).
void	ResetThisBase ()
void	SplitOptions (const TString &theOpt, TList &loo) const
	splits the option string at ':' and fills the list 'loo' with the primitive strings
void	UpdateTargets (std::vector< const TMVA::Event * > &, UInt_t cls=0)
	Calculate residual for all events.
void	UpdateTargetsRegression (std::vector< const TMVA::Event * > &, Bool_t first=kFALSE)
	Calculate residuals for all events and update targets for next iter.
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

Static Private Member Functions
static void	AddToTObjectTable (TObject *)
	Private helper function which will dispatch to TObjectTable::AddObj.

Private Attributes
Double_t	fAdaBoostBeta
	beta parameter for AdaBoost algorithm
TString	fAdaBoostR2Loss
	loss type used in AdaBoostR2 (Linear,Quadratic or Exponential)
Bool_t	fAutomatic
	use user given prune strength or automatically determined one using a validation sample
Bool_t	fBaggedBoost
	turn bagging in combination with boost on/off
Bool_t	fBaggedGradBoost
	turn bagging in combination with grad boost on/off
Double_t	fBaggedSampleFraction
	relative size of bagged event sample to original sample size
TDirectory *	fBaseDir
UInt_t	fBits
	bit field status word
TString	fBoostType
	string specifying the boost type
Double_t	fBoostWeight
	ntuple var: boost weight
std::vector< double >	fBoostWeights
	the weights applied in the individual boosts
Double_t	fCbb
	Cost factor.
TString	fConfigDescription
	description of this configurable
Bool_t	fConstructedFromWeightFile
Double_t	fCss
	Cost factor.
Double_t	fCtb_ss
	Cost factor.
Double_t	fCts_sb
	Cost factor.
ECutOrientation	fCutOrientation
DataSetInfo &	fDataSetInfo
	! the data set information (sometimes needed)
PDF *	fDefaultPDF
	default PDF definitions
Bool_t	fDoBoostMonitor
	create control plot with ROC integral vs tree number
Bool_t	fDoPreselection
	do or do not perform automatic pre-selection of 100% eff. cuts
TH1 *	fEffS
	efficiency histogram for rootfinder
Double_t	fErrorFraction
	ntuple var: misclassification error fraction
std::vector< const std::vector< TMVA::Event * > * >	fEventCollections
std::vector< const TMVA::Event * >	fEventSample
	the training events
TFile *	fFile
TString	fFileDir
	unix sub-directory for weight files (default: DataLoader's Name + "weights")
std::vector< DecisionTree * >	fForest
	the collection of decision trees
Double_t	fFValidationEvents
	fraction of events to use for pruning
Bool_t	fHasMVAPdfs
	MVA Pdfs are created for this classifier.
Bool_t	fHelp
	help flag
std::vector< Double_t >	fHighBkgCut
std::vector< Double_t >	fHighSigCut
Bool_t	fHistoricBool
Double_t	fHuberQuantile
	the option string determining the quantile for the Huber Loss Function in BDT regression.
Bool_t	fIgnoreNegWeightsInTraining
	If true, events with negative weights are not used in training.
Bool_t	fInverseBoostNegWeights
	boost ev. with neg. weights with 1/boostweight rather than boostweight
std::vector< Bool_t >	fIsHighBkgCut
std::vector< Bool_t >	fIsHighSigCut
std::vector< Bool_t >	fIsLowBkgCut
std::vector< Bool_t >	fIsLowSigCut
Int_t	fITree
	ntuple var: ith tree
TString	fJobName
OptionBase *	fLastDeclaredOption
	! last declared option
TList	fListOfOptions
	option list
Bool_t	fLooseOptionCheckingEnabled
	checker for option string
std::map< const TMVA::Event *, LossFunctionEventInfo >	fLossFunctionEventInfo
	map event to true value, predicted value, and weight used by different loss functions for BDT regression
std::vector< Double_t >	fLowBkgCut
std::vector< Double_t >	fLowSigCut
UInt_t	fMaxDepth
	max depth
Double_t	fMeanB
	mean (background)
Double_t	fMeanS
	mean (signal)
TDirectory *	fMethodBaseDir
TString	fMethodName
Types::EMVA	fMethodType
Double_t	fMinLinCorrForFisher
	the minimum linear correlation between two variables demanded for use in fisher criterium in node splitting
Int_t	fMinNodeEvents
	min number of events in node
Float_t	fMinNodeSize
	min percentage of training events in node
TString	fMinNodeSizeS
	string containing min percentage of training events in node
Bool_t	fModelPersistence
TTree *	fMonitorNtuple
	monitoring ntuple
PDF *	fMVAPdfB
	background MVA PDF
PDF *	fMVAPdfS
	signal MVA PDF
Int_t	fNbinsMVAPdf
Int_t	fNCuts
	grid used in cut applied in node splitting
TString	fNegWeightTreatment
	variable that holds the option of how to treat negative event weights in training
UInt_t	fNNodesMax
	max # of nodes
Double_t	fNodePurityLimit
	purity limit for sig/bkg nodes
Bool_t	fNoNegWeightsInTraining
	ignore negative event weights in the training
Bool_t	fNormalise
Int_t	fNsmoothMVAPdf
Int_t	fNTrees
	number of decision trees requested
TString	fOptions
	options string
Bool_t	fPairNegWeightsGlobal
	pair ev. with neg. and pos. weights in training sample and "annihilate" them
TString	fParentDir
	method parent name, like booster name
DecisionTree::EPruneMethod	fPruneMethod
	method used for pruning
TString	fPruneMethodS
	prune method option String
Double_t	fPruneStrength
	a parameter to set the "amount" of pruning..needs to be adjusted
Bool_t	fRandomisedTrees
	choose a random subset of possible cut variables at each node during training
TString	fReferenceFile
	reference file for options writing
LossFunctionBDT *	fRegressionLossFunctionBDTG
TString	fRegressionLossFunctionBDTGS
	the option string determining the loss function for BDT regression
std::map< const TMVA::Event *, std::vector< double > >	fResiduals
	individual event residuals for gradient boost
Double_t	fRmsB
	RMS (background).
Double_t	fRmsS
	RMS (signal).
UInt_t	fROOTTrainingVersion
SeparationBase *	fSepType
	the separation used in node splitting
TString	fSepTypeS
	the separation (option string) used in node splitting
Double_t	fShrinkage
	learning rate for gradient boost;
Double_t	fSignalReferenceCut
Double_t	fSignalReferenceCutOrientation
Double_t	fSigToBkgFraction
	Signal to Background fraction assumed during training.
Bool_t	fSilentFile
Bool_t	fSkipNormalization
	true for skipping normalization at initialization of trees
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).
std::vector< const TMVA::Event * >	fSubSample
	subsample for bagged grad boost
Double_t	fTestTime
TString	fTestvar
UInt_t	fTMVATrainingVersion
std::vector< const TMVA::Event * > *	fTrainSample
	pointer to sample actually used in training (fEventSample or fSubSample) for example
Double_t	fTrainTime
Bool_t	fTrainWithNegWeights
	yes there are negative event weights and we don't ignore them
TransformationHandler	fTransformation
	the list of transformations
TransformationHandler *	fTransformationPointer
	pointer to the rest of transformations
Bool_t	fTxtWeightsOnly
UInt_t	fUniqueID
	object unique identifier
Bool_t	fUseDecorr
Bool_t	fUseExclusiveVars
	individual variables already used in fisher criterium are not anymore analysed individually for node splitting
Bool_t	fUseFisherCuts
	use multivariate splits using the Fisher criterium
UInt_t	fUseNTrainEvents
	number of randomly picked training events used in randomised (and bagged) trees
UInt_t	fUseNvars
	the number of variables used in the randomised tree splitting
Bool_t	fUsePoissonNvars
	use "fUseNvars" not as fixed number but as mean of a poisson distr. in each split
Bool_t	fUseYesNoLeaf
	use sig or bkg classification in leave nodes or sig/bkg
std::vector< const TMVA::Event * >	fValidationSample
	the Validation events
std::vector< Double_t >	fVariableImportance
	the relative importance of the different variables
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)

Static Private Attributes
static const Int_t	fgDebugLevel = 0
	debug level determining some printout/control plots etc.
static Longptr_t	fgDtorOnly = 0
	object for which to call dtor only (i.e. no delete)
static Bool_t	fgObjectStat = kTRUE
	if true keep track of objects in TObjectTable

#include <TMVA/MethodBDT.h>

Inheritance diagram for TMVA::MethodBDT:

Member Enumeration Documentation

anonymous enum

anonymous enum

protectedinherited

Enumerator

kOnlyPrepStep

Used to request that the class specific implementation of TObject::Write just prepare the objects to be ready to be written but do not actually write them into the TBuffer. This is just for example by TBufferMerger to request that the TTree inside the file calls TTree::FlushBaskets (outside of the merging lock) and TBufferMerger will later ask for the write (inside the merging lock). To take advantage of this feature the class needs to overload TObject::Write and use this enum value accordingly. (See TTree::Write and TObject::Write) Do not use, this feature will be migrate to the Merge function (See TClass and TTree::Merge)

Definition at line 106 of file TObject.h.

anonymous enum

anonymous enum

inherited

Enumerator
kSingleKey	write collection with single key
kOverwrite	overwrite existing object with same name
kWriteDelete	write object, then delete previous key with same name

Definition at line 99 of file TObject.h.

anonymous enum

anonymous enum

inherited

Enumerator
kIsOnHeap	object is on heap
kNotDeleted	object has not been deleted
kZombie	object ctor failed
kInconsistent	class overload Hash but does call RecursiveRemove in destructor
kBitMask

Definition at line 89 of file TObject.h.

ECutOrientation

enum TMVA::MethodBase::ECutOrientation

privateinherited

Enumerator
kNegative
kPositive

Definition at line 554 of file MethodBase.h.

EDeprecatedStatusBits

enum TObject::EDeprecatedStatusBits

inherited

Enumerator
kObjInCanvas	for backward compatibility only, use kMustCleanup

Definition at line 84 of file TObject.h.

EStatusBits

enum TObject::EStatusBits

inherited

Enumerator
kCanDelete	if object in a list can be deleted
kMustCleanup	if object destructor must call RecursiveRemove()
kIsReferenced	if object is referenced by a TRef or TRefArray
kHasUUID	if object has a TUUID (its fUniqueID=UUIDNumber)
kCannotPick	if object in a pad cannot be picked
kNoContextMenu	if object does not want context menu
kInvalidObject	if object ctor succeeded but object should not be used

Definition at line 70 of file TObject.h.

EWeightFileType

enum TMVA::MethodBase::EWeightFileType

inherited

Enumerator
kROOT
kTEXT

Definition at line 122 of file MethodBase.h.

Constructor & Destructor Documentation

MethodBDT() [1/2]

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

The standard constructor for the "boosted decision trees".

Definition at line 162 of file MethodBDT.cxx.

MethodBDT() [2/2]

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

Definition at line 219 of file MethodBDT.cxx.

~MethodBDT()

TMVA::MethodBDT::~MethodBDT ( void )

virtual

Destructor.

• Note: fEventSample and ValidationSample are already deleted at the end of TRAIN When they are not used anymore

Definition at line 752 of file MethodBDT.cxx.

Member Function Documentation

AbstractMethod()

void TObject::AbstractMethod ( const char * method ) const

inherited

Call this function within a function that you don't want to define as purely virtual, in order not to force all users deriving from that class to implement that maybe (on their side) unused function; but at the same time, emit a run-time warning if they try to call it, telling that it is not implemented in the derived class: action must thus be taken on the user side to override it.

In other word, this method acts as a "runtime purely virtual" warning instead of a "compiler purely virtual" error.

Warning

This interface is a legacy function that is no longer recommended to be used by new development code.

Note

The name "AbstractMethod" does not imply that it's an abstract method in the strict C++ sense.

Definition at line 1149 of file TObject.cxx.

AdaBoost()

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

private

The AdaBoost implementation.

a new training sample is generated by weighting events that are misclassified by the decision tree. The weight applied is \( w = \frac{(1-err)}{err} \) or more general: \( w = (\frac{(1-err)}{err})^\beta \) where \(err\) is the fraction of misclassified events in the tree ( <0.5 assuming demanding the that previous selection was better than random guessing) and "beta" being a free parameter (standard: beta = 1) that modifies the boosting.

Definition at line 1844 of file MethodBDT.cxx.

AdaBoostR2()

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

private

Adaption of the AdaBoost to regression problems (see H.Drucker 1997).

Definition at line 2191 of file MethodBDT.cxx.

AdaCost()

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

private

The AdaCost boosting algorithm takes a simple cost Matrix (currently fixed for all events... later could be modified to use individual cost matrices for each events as in the original paper...

              true_signal true_bkg
----------------------------------
sel_signal |   Css         Ctb_ss    Cxx.. in the range [0,1]
sel_bkg    |   Cts_sb      Cbb

and takes this into account when calculating the mis class. cost (former: error fraction):

err = sum_events ( weight* y_true*y_sel * beta(event) 

Definition at line 2022 of file MethodBDT.cxx.

AddClassesXMLTo()

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

privateinherited

write class info to XML

Definition at line 1872 of file MethodBase.cxx.

AddClassifierOutput()

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

privatevirtualinherited

prepare tree branch with the method's discriminating variable

Definition at line 932 of file MethodBase.cxx.

AddClassifierOutputProb()

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

privatevirtualinherited

prepare tree branch with the method's discriminating variable

Definition at line 1024 of file MethodBase.cxx.

AddInfoItem()

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

privateinherited

xml writing

Definition at line 1380 of file MethodBase.cxx.

AddMulticlassOutput()

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

privatevirtualinherited

prepare tree branch with the method's discriminating variable

Definition at line 857 of file MethodBase.cxx.

AddOptionsXMLTo()

void TMVA::Configurable::AddOptionsXMLTo ( void * parent ) const

inherited

write options to XML file

Definition at line 348 of file Configurable.cxx.

AddOutput()

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

inherited

Definition at line 1389 of file MethodBase.cxx.

AddPreDefVal() [1/2]

template<class T>

void TMVA::Configurable::AddPreDefVal ( const T & val )

inherited

Definition at line 168 of file Configurable.h.

AddPreDefVal() [2/2]

template<class T>

void TMVA::Configurable::AddPreDefVal ( const TString & optname, const T & val )

inherited

Definition at line 177 of file Configurable.h.

AddRegressionOutput()

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

privatevirtualinherited

prepare tree branch with the method's discriminating variable

Definition at line 776 of file MethodBase.cxx.

AddSpectatorsXMLTo()

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

privateinherited

write spectator info to XML

Definition at line 1849 of file MethodBase.cxx.

AddTargetsXMLTo()

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

privateinherited

write target info to XML

Definition at line 1892 of file MethodBase.cxx.

AddToTObjectTable()

void TObject::AddToTObjectTable ( TObject * op )

staticprivateinherited

Private helper function which will dispatch to TObjectTable::AddObj.

Included here to avoid circular dependency between header files.

Definition at line 195 of file TObject.cxx.

AddVarsXMLTo()

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

privateinherited

write variable info to XML

Definition at line 1833 of file MethodBase.cxx.

AddWeightsXMLTo()

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

overridevirtual

Write weights to XML.

Implements TMVA::MethodBase.

Definition at line 2308 of file MethodBDT.cxx.

AppendPad()

void TObject::AppendPad ( Option_t * option = "" )

virtualinherited

Append graphics object to current pad.

In case no current pad is set yet, create a default canvas with the name "c1".

Definition at line 204 of file TObject.cxx.

ApplyPreselectionCuts()

Double_t TMVA::MethodBDT::ApplyPreselectionCuts ( const Event * ev )

private

Apply the preselection cuts before even bothering about any Decision Trees in the GetMVA .

. --> -1 for background +1 for Signal

Definition at line 3131 of file MethodBDT.cxx.

AssignOpt()

template<class T>

void TMVA::Configurable::AssignOpt ( const TString & name, T & valAssign ) const

privateinherited

Definition at line 204 of file Configurable.h.

Bagging()

Double_t TMVA::MethodBDT::Bagging ( )

private

Call it boot-strapping, re-sampling or whatever you like, in the end it is nothing else but applying "random" poisson weights to each event.

Definition at line 2138 of file MethodBDT.cxx.

BaseDir()

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

inherited

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

Definition at line 2051 of file MethodBase.cxx.

Boost()

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

Apply the boosting algorithm (the algorithm is selecte via the "option" given in the constructor.

The return value is the boosting weight.

Definition at line 1716 of file MethodBDT.cxx.

BoostMonitor()

void TMVA::MethodBDT::BoostMonitor ( Int_t iTree )

private

Fills the ROCIntegral vs Itree from the testSample for the monitoring plots during the training .

. but using the testing events

Definition at line 1750 of file MethodBDT.cxx.

Browse()

void TObject::Browse ( TBrowser * b )

virtualinherited

Browse object. May be overridden for another default action.

Reimplemented in RooPlot, ROOT::Experimental::XRooFit::xRooNode, ROOT::Internal::THnBaseBrowsable, TApplicationRemote, TASImage, TAxis3D, TBaseClass, TBranch, TBranchClones, TBranchElement, TBranchObject, TBranchSTL, TBrowserObject, TCanvas, TChain, TClass, TCollection, TCollectionPropertyBrowsable, TDatabasePDG, TDirectory, TDirectoryFile, TEfficiency, TF1, TFolder, TGenerator, TGeoManager, TGeometry, TGeoNode, TGeoOverlap, TGeoTrack, TGeoVolume, TGraph2D, TGraph, TH1, THbookBranch, THbookFile, THbookKey, THnBase, THStack, TKey, TKeyMapFile, TLeaf, TMacro, TMapFile, TMultiDimFit, TMultiGraph, TNode, TNtuple, TNtupleD, TPad, TPair, TParticleClassPDG, TPrincipal, TRecorder, TRemoteObject, TROOT, TRootIconList, TSPlot, TStyle, TSystemDirectory, TSystemFile, TTask, TTree, TTreePerfStats, and TVirtualBranchBrowsable.

Definition at line 218 of file TObject.cxx.

CheckedHash()

ULong_t TObject::CheckedHash ( )

inlineinherited

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

The intent is for this routine to be called instead of directly calling the function Hash during "insert" operations. See TObject::HasInconsistenTObjectHash();

(*) The setup is consistent when all classes in the class hierarchy that overload TObject::Hash do call ROOT::CallRecursiveRemoveIfNeeded in their destructor. i.e. it is safe to call the Hash virtual function during the RecursiveRemove operation.

Definition at line 332 of file TObject.h.

CheckForUnusedOptions()

void TMVA::Configurable::CheckForUnusedOptions ( ) const

inherited

checks for unused options in option string

Definition at line 269 of file Configurable.cxx.

CheckSetup()

void TMVA::MethodBase::CheckSetup ( )

virtualinherited

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 432 of file MethodBase.cxx.

Class()

TClass * TMVA::MethodBDT::Class ( )

static

Returns

TClass describing this class

Class_Name()

const char * TMVA::MethodBDT::Class_Name ( )

static

Returns

Name of this class

Class_Version()

constexpr Version_t TMVA::MethodBDT::Class_Version ( )

inlinestaticconstexpr

Returns

Version of this class

Definition at line 305 of file MethodBDT.h.

ClassName()

const char * TObject::ClassName ( ) const

virtualinherited

Returns name of class to which the object belongs.

Definition at line 227 of file TObject.cxx.

Clear()

void TNamed::Clear ( Option_t * option = "" )

overridevirtualinherited

Set name and title to empty strings ("").

Reimplemented from TObject.

Reimplemented in TPrincipal, TProcessID, TStreamerInfo, TTask, TVirtualFitter, and TVirtualStreamerInfo.

Definition at line 63 of file TNamed.cxx.

Clone()

TObject * TNamed::Clone ( const char * newname = "" ) const

overridevirtualinherited

Make a clone of an object using the Streamer facility.

If newname is specified, this will be the name of the new object.

Reimplemented from TObject.

Reimplemented in TStreamerInfo, and TTreeIndex.

Definition at line 73 of file TNamed.cxx.

Compare()

Int_t TNamed::Compare ( const TObject * obj ) const

overridevirtualinherited

Compare two TNamed objects.

Returns 0 when equal, -1 when this is smaller and +1 when bigger (like strcmp).

Reimplemented from TObject.

Reimplemented in TStructNodeProperty.

Definition at line 84 of file TNamed.cxx.

Copy()

void TNamed::Copy ( TObject & named ) const

overridevirtualinherited

Copy this to obj.

Reimplemented from TObject.

Reimplemented in TPieSlice, TProfile2D, TProfile3D, TProfile, TStyle, TSystemDirectory, TSystemFile, TText, and TXTRU.

Definition at line 93 of file TNamed.cxx.

CreateMVAPdfs()

void TMVA::MethodBase::CreateMVAPdfs ( )

privateinherited

Create PDFs of the MVA output variables.

Definition at line 2256 of file MethodBase.cxx.

CreateRanking()

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

overridevirtual

Compute ranking of input variables.

Implements TMVA::MethodBase.

Definition at line 2681 of file MethodBDT.cxx.

Data()

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

inlineinherited

Definition at line 412 of file MethodBase.h.

DataInfo()

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

inlineinherited

Definition at line 413 of file MethodBase.h.

DeclareBaseOptions()

void TMVA::MethodBase::DeclareBaseOptions ( )

privateinherited

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 508 of file MethodBase.cxx.

DeclareCompatibilityOptions()

void TMVA::MethodBDT::DeclareCompatibilityOptions ( )

overrideprotectedvirtual

Options that are used ONLY for the READER to ensure backward compatibility.

Reimplemented from TMVA::MethodBase.

Definition at line 453 of file MethodBDT.cxx.

DeclareOptionRef() [1/4]

template<class T>

TMVA::OptionBase * TMVA::Configurable::DeclareOptionRef ( T & ref, const TString & name, const TString & desc )

inherited

Definition at line 147 of file Configurable.h.

DeclareOptionRef() [2/4]

template<class T>

OptionBase * TMVA::Configurable::DeclareOptionRef ( T & ref, const TString & name, const TString & desc = "" )

inherited

DeclareOptionRef() [3/4]

template<class T>

TMVA::OptionBase * TMVA::Configurable::DeclareOptionRef ( T *& ref, Int_t size, const TString & name, const TString & desc )

inherited

Definition at line 157 of file Configurable.h.

DeclareOptionRef() [4/4]

template<class T>

OptionBase * TMVA::Configurable::DeclareOptionRef ( T *& ref, Int_t size, const TString & name, const TString & desc = "" )

inherited

DeclareOptions()

void TMVA::MethodBDT::DeclareOptions ( )

overridevirtual

Define the options (their key words).

That can be set in the option string.

know options:

• nTrees number of trees in the forest to be created
• BoostType the boosting type for the trees in the forest (AdaBoost e.t.c..). Known:
  • AdaBoost
  • AdaBoostR2 (Adaboost for regression)
  • Bagging
  • GradBoost
• AdaBoostBeta the boosting parameter, beta, for AdaBoost
• UseRandomisedTrees choose at each node splitting a random set of variables
• UseNvars use UseNvars variables in randomised trees
• UsePoisson Nvars use UseNvars not as fixed number but as mean of a poisson distribution
• SeparationType the separation criterion applied in the node splitting. Known:
  • GiniIndex
  • MisClassificationError
  • CrossEntropy
  • SDivSqrtSPlusB
• MinNodeSize: minimum percentage of training events in a leaf node (leaf criteria, stop splitting)
• nCuts: the number of steps in the optimisation of the cut for a node (if < 0, then step size is determined by the events)
• UseFisherCuts: use multivariate splits using the Fisher criterion
• UseYesNoLeaf decide if the classification is done simply by the node type, or the S/B (from the training) in the leaf node
• NodePurityLimit the minimum purity to classify a node as a signal node (used in pruning and boosting to determine misclassification error rate)
• PruneMethod The Pruning method. Known:
  • NoPruning // switch off pruning completely
  • ExpectedError
  • CostComplexity
• PruneStrength a parameter to adjust the amount of pruning. Should be large enough such that overtraining is avoided.
• PruningValFraction number of events to use for optimizing pruning (only if PruneStrength < 0, i.e. automatic pruning)
• NegWeightTreatment
  • IgnoreNegWeightsInTraining Ignore negative weight events in the training.
  • DecreaseBoostWeight Boost ev. with neg. weight with 1/boostweight instead of boostweight
  • PairNegWeightsGlobal Pair ev. with neg. and pos. weights in training sample and "annihilate" them
• MaxDepth maximum depth of the decision tree allowed before further splitting is stopped
• SkipNormalization Skip normalization at initialization, to keep expectation value of BDT output according to the fraction of events

Implements TMVA::MethodBase.

Definition at line 332 of file MethodBDT.cxx.

DeclFileName()

const char * TMVA::MethodBDT::DeclFileName ( )

inlinestatic

Returns

Name of the file containing the class declaration

Definition at line 305 of file MethodBDT.h.

Delete()

void TObject::Delete ( Option_t * option = "" )

virtualinherited

Delete this object.

Typically called as a command via the interpreter. Normally use "delete" operator when object has been allocated on the heap.

Reimplemented in RooLinkedList, TAxis, TBtree, TCanvas, TClonesArray, TCollection, TDirectory, TDirectoryFile, TExMap, TFile, TGFrame, TGItemContext, TGTextEdit, THashList, THashTable, TKey, TKeySQL, TKeyXML, TList, TListOfDataMembers, TListOfEnums, TListOfEnumsWithLock, TListOfFunctions, TListOfFunctionTemplates, TMap, TMVA::Results, TObjArray, TObjectTable, TOrdCollection, TProtoClass, TQCommand, TRefArray, TSystemDirectory, TSystemFile, TThread, TTree, TTreeViewer, TViewPubDataMembers, and TViewPubFunctions.

Definition at line 268 of file TObject.cxx.

DeterminePreselectionCuts()

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

private

Find useful preselection cuts that will be applied before and Decision Tree training.

. (and of course also applied in the GetMVA .. --> -1 for background +1 for Signal)

Definition at line 3034 of file MethodBDT.cxx.

DisableWriting()

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

inlineinherited

Definition at line 445 of file MethodBase.h.

DistancetoPrimitive()

Int_t TObject::DistancetoPrimitive ( Int_t px, Int_t py )

virtualinherited

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

This member function must be implemented for each graphics primitive. This default function returns a big number (999999).

Reimplemented in TASImage, TAxis3D, TAxis, TBox, TBRIK, TColorWheel, TCrown, TCurlyArc, TCurlyLine, TDiamond, TEfficiency, TEllipse, TF1, TF2, TF3, TFileDrawMap, TGenerator, TGeoBBox, TGeoCompositeShape, TGeoCone, TGeoConeSeg, TGeoEltu, TGeoHalfSpace, TGeoHype, TGeoNode, TGeoOverlap, TGeoParaboloid, TGeoPcon, TGeoPgon, TGeoScaledShape, TGeoShape, TGeoShapeAssembly, TGeoSphere, TGeoTessellated, TGeoTorus, TGeoTrack, TGeoTube, TGeoTubeSeg, TGeoVGShape, TGeoVolume, TGeoXtru, TGL5DDataSet, TGLHistPainter, TGLParametricEquation, TGLScenePad, TGLTH3Composition, TGLViewer, TGraph2D, TGraph, TGraphEdge, TGraphNode, TGraphPolargram, TH1, THistPainter, THStack, TLine, TMarker3DBox, TMarker, TMultiGraph, TNode, TPad, TPaletteAxis, TParallelCoord, TParallelCoordRange, TParallelCoordVar, TParticle, TPave, TPCON, TPie, TPieSlice, TPoints3DABC, TPolyLine3D, TPolyLine, TPolyMarker3D, TPolyMarker, TPrimary, TScatter2D, TScatter, TSPHE, TSpider, TSpline, TStyle, TText, TTreePerfStats, TTUBE, TTUBS, TVirtualHistPainter, and TXTRU.

Definition at line 284 of file TObject.cxx.

DoError()

void TObject::DoError ( int level, const char * location, const char * fmt, va_list va ) const

protectedvirtualinherited

Interface to ErrorHandler (protected).

Reimplemented in TThread, and TTreeViewer.

Definition at line 1059 of file TObject.cxx.

DoMulticlass()

Bool_t TMVA::MethodBase::DoMulticlass ( ) const

inlineinherited

Definition at line 442 of file MethodBase.h.

DoRegression()

Bool_t TMVA::MethodBase::DoRegression ( ) const

inlineinherited

Definition at line 441 of file MethodBase.h.

Draw()

void TObject::Draw ( Option_t * option = "" )

virtualinherited

Default Draw method for all objects.

Reimplemented in RooAbsData, RooPlot, RooStats::HypoTestInverterPlot, RooStats::SamplingDistPlot, ROOT::Experimental::XRooFit::xRooNLLVar::xRooHypoPoint, ROOT::Experimental::XRooFit::xRooNLLVar::xRooHypoSpace, ROOT::Experimental::XRooFit::xRooNode, ROOT::RGeoPainter, TArrow, TASImage, TBox, TBrowser, TButton, TCanvas, TChain, TClass, TClassTree, TCollection, TColorWheel, TDiamond, TDirectory, TEfficiency, TEllipse, TEveGeoNode, TEveGeoTopNode, TF1, TF2, TF3, TFile, TFITSHDU, TFrame, TGenerator, TGeoBatemanSol, TGeometry, TGeoNode, TGeoOverlap, TGeoPainter, TGeoParallelWorld, TGeoPhysicalNode, TGeoPolygon, TGeoShape, TGeoTrack, TGeoVGShape, TGeoVolume, TGItemContext, TGListTree, TGPicture, TGraph2D, TGraph, TGraphPolar, TGraphPolargram, TGraphStruct, TGraphTime, TH1, THelix, THStack, TLegend, TMarker, TMatrixTBase< Element >, TMatrixTBase< Double_t >, TMatrixTBase< Float_t >, TMultiDimFit, TMultiGraph, TMultiLayerPerceptron, TNode, TNodeDiv, TPad, TParallelCoord, TParallelCoordRange, TParallelCoordVar, TPave, TPaveLabel, TPavesText, TPaveText, TPie, TPolyLine3D, TPolyLine, TPolyMarker3D, TPolyMarker, TRatioPlot, TSpider, TSpline, TStructViewer, TStructViewerGUI, TTree, TTreePerfStats, TVectorT< Element >, TVectorT< Double_t >, TVectorT< Float_t >, TVirtualPad, and TWbox.

Definition at line 293 of file TObject.cxx.

DrawClass()

void TObject::DrawClass ( ) const

virtualinherited

Draw class inheritance tree of the class to which this object belongs.

If a class B inherits from a class A, description of B is drawn on the right side of description of A. Member functions overridden by B are shown in class A with a blue line crossing-out the corresponding member function. The following picture is the class inheritance tree of class TPaveLabel:

Reimplemented in TGFrame, TSystemDirectory, and TSystemFile.

Definition at line 308 of file TObject.cxx.

DrawClone()

TObject * TObject::DrawClone ( Option_t * option = "" ) const

virtualinherited

Draw a clone of this object in the current selected pad with: gROOT->SetSelectedPad(c1).

If pad was not selected - gPad will be used.

Note

For histograms, use the more specialised TH1::DrawCopy().

Reimplemented in TAxis, TCanvas, TGFrame, TSystemDirectory, and TSystemFile.

Definition at line 319 of file TObject.cxx.

Dump()

void TObject::Dump ( ) const

virtualinherited

Dump contents of object on stdout.

Using the information in the object dictionary (class TClass) each data member is interpreted. If a data member is a pointer, the pointer value is printed

The following output is the Dump of a TArrow object:

fAngle                   0           Arrow opening angle (degrees)
fArrowSize               0.2         Arrow Size
fOption.*fData
fX1                      0.1         X of 1st point
fY1                      0.15        Y of 1st point
fX2                      0.67        X of 2nd point
fY2                      0.83        Y of 2nd point
fUniqueID                0           object unique identifier
fBits                    50331648    bit field status word
fLineColor               1           line color
fLineStyle               1           line style
fLineWidth               1           line width
fFillColor               19          fill area color
fFillStyle               1001        fill area style

Reimplemented in TClass, TCollection, TGFrame, TGPack, and TSystemFile.

Definition at line 367 of file TObject.cxx.

EnableLooseOptions()

void TMVA::Configurable::EnableLooseOptions ( Bool_t b = kTRUE )

inlineprotectedinherited

Definition at line 96 of file Configurable.h.

Error()

void TObject::Error ( const char * location, const char * fmt, ... ) const

virtualinherited

Issue error message.

Use "location" to specify the method where the error occurred. Accepts standard printf formatting arguments.

Reimplemented in TFitResult.

Definition at line 1098 of file TObject.cxx.

Execute() [1/2]

void TObject::Execute ( const char * method, const char * params, Int_t * error = nullptr )

virtualinherited

Execute method on this object with the given parameter string, e.g.

"3.14,1,\"text\"".

Reimplemented in ROOT::R::TRInterface, TCling, TContextMenu, TInterpreter, and TMethodCall.

Definition at line 378 of file TObject.cxx.

Execute() [2/2]

void TObject::Execute ( TMethod * method, TObjArray * params, Int_t * error = nullptr )

virtualinherited

Execute method on this object with parameters stored in the TObjArray.

The TObjArray should contain an argv vector like:

argv[0] ... argv[n] = the list of TObjString parameters

Reimplemented in ROOT::R::TRInterface, TCling, TContextMenu, TInterpreter, and TMethodCall.

Definition at line 398 of file TObject.cxx.

ExecuteEvent()

void TObject::ExecuteEvent ( Int_t event, Int_t px, Int_t py )

virtualinherited

Execute action corresponding to an event at (px,py).

This method must be overridden if an object can react to graphics events.

Reimplemented in TASImage, TASPaletteEditor::LimitLine, TAxis3D, TAxis, TBox, TButton, TCanvas, TCrown, TCurlyArc, TCurlyLine, TDiamond, TEfficiency, TEllipse, TF1, TF2, TF3, TFrame, TGenerator, TGeoManager, TGeoNode, TGeoOverlap, TGeoShape, TGeoTrack, TGeoVolume, TGL5DDataSet, TGLEventHandler, TGLHistPainter, TGLParametricEquation, TGLScenePad, TGLTH3Composition, TGLViewer, TGraph2D, TGraph, TGraphEdge, TGraphNode, TGraphPolargram, TGroupButton, TH1, THistPainter, TLine, TLink, TMarker3DBox, TMarker, TNode, TPad, TPaletteAxis, TParallelCoord, TParallelCoordRange, TParallelCoordVar, TParticle, TPave, TPie, TPolyLine3D, TPolyLine, TPolyMarker3D, TPolyMarker, TPrimary, TScatter2D, TScatter, TSliderBox, TSpider, TSpline, TText, TTreePerfStats, TView3D, TView, TVirtualHistPainter, and TWbox.

Definition at line 415 of file TObject.cxx.

ExitFromTraining()

void TMVA::MethodBase::ExitFromTraining ( )

inlineinherited

Definition at line 467 of file MethodBase.h.

Fatal()

void TObject::Fatal ( const char * location, const char * fmt, ... ) const

virtualinherited

Issue fatal error message.

Use "location" to specify the method where the fatal error occurred. Accepts standard printf formatting arguments.

Definition at line 1126 of file TObject.cxx.

FillBuffer()

void TNamed::FillBuffer ( char *& buffer )

virtualinherited

Encode TNamed into output buffer.

Reimplemented in TDirectoryFile, TFile, TKey, TKeySQL, TKeyXML, TSQLFile, and TXMLFile.

Definition at line 103 of file TNamed.cxx.

FindObject() [1/2]

TObject * TObject::FindObject ( const char * name ) const

virtualinherited

Must be redefined in derived classes.

This function is typically used with TCollections, but can also be used to find an object by name inside this object.

Reimplemented in RooAbsCollection, RooLinkedList, TBtree, TCollection, TDirectory, TFolder, TGeometry, TGraph2D, TGraph, TH1, THashList, THashTable, THbookFile, TList, TListOfDataMembers, TListOfEnums, TListOfEnumsWithLock, TListOfFunctions, TListOfFunctionTemplates, TListOfTypes, TMap, TObjArray, TPad, TROOT, TViewPubDataMembers, and TViewPubFunctions.

Definition at line 425 of file TObject.cxx.

FindObject() [2/2]

TObject * TObject::FindObject ( const TObject * obj ) const

virtualinherited

Must be redefined in derived classes.

This function is typically used with TCollections, but can also be used to find an object inside this object.

Reimplemented in RooAbsCollection, RooLinkedList, TBtree, TCollection, TDirectory, TFolder, TGeometry, TGraph2D, TGraph, TH1, THashList, THashTable, THbookFile, TList, TListOfDataMembers, TListOfEnums, TListOfEnumsWithLock, TListOfFunctions, TListOfFunctionTemplates, TListOfTypes, TMap, TObjArray, TPad, TROOT, TViewPubDataMembers, and TViewPubFunctions.

Definition at line 435 of file TObject.cxx.

GetAllMulticlassValues()

std::vector< Float_t > TMVA::MethodBase::GetAllMulticlassValues ( )

protectedvirtualinherited

Get all multi-class values.

Reimplemented in TMVA::MethodPyKeras, and TMVA::MethodPyTorch.

Definition at line 829 of file MethodBase.cxx.

GetAllRegressionValues()

std::vector< float > TMVA::MethodBase::GetAllRegressionValues ( )

protectedvirtualinherited

Get al regression values in one call.

Reimplemented in TMVA::MethodPyKeras, and TMVA::MethodPyTorch.

Definition at line 739 of file MethodBase.cxx.

GetAnalysisType()

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

inlineinherited

Definition at line 440 of file MethodBase.h.

GetBaggedSubSample()

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

private

Fills fEventSample with fBaggedSampleFraction*NEvents random training events.

Definition at line 2149 of file MethodBDT.cxx.

GetBoostWeights()

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

inline

Definition at line 312 of file MethodBDT.h.

GetConfigDescription()

const char * TMVA::Configurable::GetConfigDescription ( ) const

inlineinherited

Definition at line 62 of file Configurable.h.

GetConfigName()

const char * TMVA::Configurable::GetConfigName ( ) const

inlineinherited

Definition at line 61 of file Configurable.h.

GetCurrentIter()

UInt_t TMVA::MethodBase::GetCurrentIter ( )

inlineinherited

Definition at line 484 of file MethodBase.h.

GetCutOrientation()

ECutOrientation TMVA::MethodBase::GetCutOrientation ( ) const

inlineprivateinherited

Definition at line 555 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 )

protectedvirtualinherited

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

Definition at line 1013 of file MethodBase.cxx.

GetDrawOption()

Option_t * TObject::GetDrawOption ( ) const

virtualinherited

Get option used by the graphics system to draw this object.

Note that before calling object.GetDrawOption(), you must have called object.Draw(..) before in the current pad.

Reimplemented in TBrowser, TFitEditor, TGedFrame, TGFileBrowser, TRootBrowser, and TRootBrowserLite.

Definition at line 445 of file TObject.cxx.

GetDtorOnly()

Longptr_t TObject::GetDtorOnly ( )

staticinherited

Return destructor only flag.

Definition at line 1196 of file TObject.cxx.

GetEfficiency()

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

virtualinherited

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 2373 of file MethodBase.cxx.

GetEvent() [1/4]

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

inlineinherited

Definition at line 754 of file MethodBase.h.

GetEvent() [2/4]

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

inlineinherited

Definition at line 749 of file MethodBase.h.

GetEvent() [3/4]

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

inlineinherited

Definition at line 762 of file MethodBase.h.

GetEvent() [4/4]

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

inlineinherited

Definition at line 768 of file MethodBase.h.

GetEventCollection()

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

inherited

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 3418 of file MethodBase.cxx.

GetFile()

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

inlineinherited

Definition at line 373 of file MethodBase.h.

GetForest()

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

inline

Definition at line 310 of file MethodBDT.h.

GetGradBoostMVA()

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

private

Returns MVA value: -1 for background, 1 for signal.

Definition at line 1419 of file MethodBDT.cxx.

GetHelpMessage()

void TMVA::MethodBDT::GetHelpMessage ( ) const

overridevirtual

Get help message text.

Implements TMVA::IMethod.

Definition at line 2698 of file MethodBDT.cxx.

GetIconName()

const char * TObject::GetIconName ( ) const

virtualinherited

Returns mime type name of object.

Used by the TBrowser (via TGMimeTypes class). Override for class of which you would like to have different icons for objects of the same class.

Reimplemented in ROOT::Experimental::XRooFit::xRooNode, TASImage, TBranch, TBranchElement, TGeoVolume, TGMainFrame, TKey, TMethodBrowsable, TSystemFile, and TVirtualBranchBrowsable.

Definition at line 472 of file TObject.cxx.

GetInputLabel()

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

inlineinherited

Definition at line 353 of file MethodBase.h.

GetInputTitle()

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

inlineinherited

Definition at line 354 of file MethodBase.h.

GetInputVar()

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

inlineinherited

Definition at line 352 of file MethodBase.h.

GetInteractiveTrainingError()

TMultiGraph * TMVA::MethodBase::GetInteractiveTrainingError ( )

inlineinherited

Definition at line 464 of file MethodBase.h.

GetInternalVarName()

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

inlineprotectedinherited

Definition at line 513 of file MethodBase.h.

GetJobName()

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

inlineinherited

Definition at line 333 of file MethodBase.h.

GetKSTrainingVsTest()

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

virtualinherited

Definition at line 3463 of file MethodBase.cxx.

GetLine()

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

privateinherited

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

Definition at line 2213 of file MethodBase.cxx.

GetMaximumSignificance()

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

virtualinherited

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 2957 of file MethodBase.cxx.

GetMaxIter()

UInt_t TMVA::MethodBase::GetMaxIter ( )

inlineinherited

Definition at line 481 of file MethodBase.h.

GetMean()

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

inlineinherited

Definition at line 357 of file MethodBase.h.

GetMethodName()

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

inlineinherited

Definition at line 334 of file MethodBase.h.

GetMethodType()

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

inlineinherited

Definition at line 336 of file MethodBase.h.

GetMethodTypeName()

TString TMVA::MethodBase::GetMethodTypeName ( ) const

inlineinherited

Definition at line 335 of file MethodBase.h.

GetMulticlassConfusionMatrix()

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

virtualinherited

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 2821 of file MethodBase.cxx.

GetMulticlassEfficiency()

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

virtualinherited

Definition at line 2774 of file MethodBase.cxx.

GetMulticlassTrainingEfficiency()

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

virtualinherited

Definition at line 2786 of file MethodBase.cxx.

GetMulticlassValues()

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

overridevirtual

Get the multiclass MVA response for the BDT classifier.

Reimplemented from TMVA::MethodBase.

Definition at line 2493 of file MethodBDT.cxx.

GetMvaValue() [1/3]

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

inherited

Definition at line 907 of file MethodBase.cxx.

GetMvaValue() [2/3]

Double_t TMVA::MethodBDT::GetMvaValue ( Double_t * err, Double_t * errUpper, UInt_t useNTrees )

private

Return the MVA value (range [-1;1]) that classifies the event according to the majority vote from the total number of decision trees.

Definition at line 2450 of file MethodBDT.cxx.

GetMvaValue() [3/3]

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

overridevirtual

Implements TMVA::MethodBase.

Definition at line 2441 of file MethodBDT.cxx.

GetMvaValues()

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

protectedvirtualinherited

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

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

Definition at line 971 of file MethodBase.cxx.

GetName()

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

inlineoverridevirtualinherited

Implements TMVA::IMethod.

Definition at line 337 of file MethodBase.h.

GetNEvents()

UInt_t TMVA::MethodBase::GetNEvents ( ) const

inlineinherited

Definition at line 419 of file MethodBase.h.

GetNTargets()

UInt_t TMVA::MethodBase::GetNTargets ( ) const

inlineinherited

Definition at line 349 of file MethodBase.h.

GetNTrees()

UInt_t TMVA::MethodBDT::GetNTrees ( ) const

inline

Definition at line 112 of file MethodBDT.h.

GetNvar()

UInt_t TMVA::MethodBase::GetNvar ( ) const

inlineinherited

Definition at line 347 of file MethodBase.h.

GetNVariables()

UInt_t TMVA::MethodBase::GetNVariables ( ) const

inlineinherited

Definition at line 348 of file MethodBase.h.

GetObjectInfo()

char * TObject::GetObjectInfo ( Int_t px, Int_t py ) const

virtualinherited

Returns string containing info about the object at position (px,py).

This method is typically overridden by classes of which the objects can report peculiarities for different positions. Returned string will be re-used (lock in MT environment).

Reimplemented in TASImage, TAxis3D, TColorWheel, TF1, TF2, TFileDrawMap, TGeoNode, TGeoTrack, TGeoVolume, TGL5DDataSet, TGLHistPainter, TGLParametricEquation, TGLTH3Composition, TGraph, TH1, THistPainter, TNode, TPaletteAxis, TParallelCoordVar, and TVirtualHistPainter.

Definition at line 491 of file TObject.cxx.

GetObjectStat()

Bool_t TObject::GetObjectStat ( )

staticinherited

Get status of object stat flag.

Definition at line 1181 of file TObject.cxx.

GetOption()

virtual Option_t * TObject::GetOption ( ) const

inlinevirtualinherited

Reimplemented in TArrow, TAxis3D, TFile, TGaxis, TGeoVolume, TH1, THelix, TLegendEntry, TMapFile, TNode, TPave, TPoints3DABC, TPolyLine3D, TPolyLine, TPolyMarker3D, TPolyMarker, TPSocket, TSelector, TSocket, and TUDPSocket.

Definition at line 147 of file TObject.h.

GetOptions()

const TString & TMVA::Configurable::GetOptions ( ) const

inlineinherited

Definition at line 84 of file Configurable.h.

GetOriginalVarName()

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

inlineprotectedinherited

Definition at line 514 of file MethodBase.h.

GetProba() [1/2]

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

virtualinherited

Definition at line 2318 of file MethodBase.cxx.

GetProba() [2/2]

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

virtualinherited

compute likelihood ratio

Definition at line 2335 of file MethodBase.cxx.

GetProbaName()

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

inlineinherited

Definition at line 339 of file MethodBase.h.

GetRarity()

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

virtualinherited

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 2356 of file MethodBase.cxx.

GetReferenceFile()

const TString & TMVA::Configurable::GetReferenceFile ( ) const

inlineprotectedinherited

Definition at line 102 of file Configurable.h.

GetRegressionDeviation()

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

virtualinherited

Definition at line 720 of file MethodBase.cxx.

GetRegressionValues() [1/2]

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

inlineinherited

Definition at line 217 of file MethodBase.h.

GetRegressionValues() [2/2]

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

overridevirtual

Get the regression value generated by the BDTs.

Reimplemented from TMVA::MethodBase.

Definition at line 2540 of file MethodBDT.cxx.

GetRMS()

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

inlineinherited

Definition at line 358 of file MethodBase.h.

GetROCIntegral() [1/2]

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

virtualinherited

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

Definition at line 2927 of file MethodBase.cxx.

GetROCIntegral() [2/2]

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

virtualinherited

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

Definition at line 2893 of file MethodBase.cxx.

GetSeparation() [1/2]

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

virtualinherited

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 2871 of file MethodBase.cxx.

GetSeparation() [2/2]

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

virtualinherited

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 2860 of file MethodBase.cxx.

GetSignalReferenceCut()

Double_t TMVA::MethodBase::GetSignalReferenceCut ( ) const

inlineinherited

Definition at line 363 of file MethodBase.h.

GetSignalReferenceCutOrientation()

Double_t TMVA::MethodBase::GetSignalReferenceCutOrientation ( ) const

inlineinherited

Definition at line 364 of file MethodBase.h.

GetSignificance()

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

virtualinherited

compute significance of mean difference

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

Reimplemented in TMVA::MethodCuts.

Definition at line 2847 of file MethodBase.cxx.

GetTestingEvent()

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

inlineinherited

Definition at line 780 of file MethodBase.h.

GetTestTime()

Double_t TMVA::MethodBase::GetTestTime ( ) const

inlineinherited

Definition at line 166 of file MethodBase.h.

GetTestvarName()

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

inlineinherited

Definition at line 338 of file MethodBase.h.

GetTitle()

const char * TNamed::GetTitle ( ) const

inlineoverridevirtualinherited

Returns title of object.

This default method returns the class title (i.e. description). Classes that give objects a title should override this method.

Reimplemented from TObject.

Definition at line 50 of file TNamed.h.

GetTrainingEfficiency()

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

virtualinherited

Reimplemented in TMVA::MethodCuts.

Definition at line 2599 of file MethodBase.cxx.

GetTrainingEvent()

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

inlineinherited

Definition at line 774 of file MethodBase.h.

GetTrainingEvents()

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

inline

Definition at line 311 of file MethodBDT.h.

GetTrainingHistory()

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

inlinevirtualinherited

Definition at line 236 of file MethodBase.h.

GetTrainingROOTVersionCode()

UInt_t TMVA::MethodBase::GetTrainingROOTVersionCode ( ) const

inlineinherited

Definition at line 393 of file MethodBase.h.

GetTrainingROOTVersionString()

TString TMVA::MethodBase::GetTrainingROOTVersionString ( ) const

inherited

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

Definition at line 3452 of file MethodBase.cxx.

GetTrainingTMVAVersionCode()

UInt_t TMVA::MethodBase::GetTrainingTMVAVersionCode ( ) const

inlineinherited

Definition at line 392 of file MethodBase.h.

GetTrainingTMVAVersionString()

TString TMVA::MethodBase::GetTrainingTMVAVersionString ( ) const

inherited

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

Definition at line 3440 of file MethodBase.cxx.

GetTrainTime()

Double_t TMVA::MethodBase::GetTrainTime ( ) const

inlineinherited

Definition at line 162 of file MethodBase.h.

GetTransformationHandler() [1/2]

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

inlineinherited

Definition at line 397 of file MethodBase.h.

GetTransformationHandler() [2/2]

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

inlineinherited

Definition at line 401 of file MethodBase.h.

GetUniqueID()

UInt_t TObject::GetUniqueID ( ) const

virtualinherited

Return the unique object id.

Definition at line 480 of file TObject.cxx.

GetValueForRoot()

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

privatevirtualinherited

returns efficiency as function of cut

Definition at line 3391 of file MethodBase.cxx.

GetVariableImportance() [1/2]

vector< Double_t > TMVA::MethodBDT::GetVariableImportance

(

)

Return the relative variable importance, normalized to all variables together having the importance 1.

The importance in evaluated as the total separation-gain that this variable had in the decision trees (weighted by the number of events)

Definition at line 2641 of file MethodBDT.cxx.

GetVariableImportance() [2/2]

Double_t TMVA::MethodBDT::GetVariableImportance

(

UInt_t

ivar

)

Returns the measure for the variable importance of variable "ivar" which is later used in GetVariableImportance() to calculate the relative variable importances.

Definition at line 2669 of file MethodBDT.cxx.

GetWeightFileDir()

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

inlineprotectedinherited

Definition at line 495 of file MethodBase.h.

GetWeightFileName()

TString TMVA::MethodBase::GetWeightFileName ( ) const

inherited

retrieve weight file name

Definition at line 2147 of file MethodBase.cxx.

GetXmax()

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

inlineinherited

Definition at line 360 of file MethodBase.h.

GetXmin()

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

inlineinherited

Definition at line 359 of file MethodBase.h.

GradBoost()

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

private

Calculate the desired response value for each region.

Definition at line 1593 of file MethodBDT.cxx.

GradBoostRegression()

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

private

Implementation of M_TreeBoost using any loss function as described by Friedman 1999.

Definition at line 1627 of file MethodBDT.cxx.

HandleTimer()

Bool_t TObject::HandleTimer ( TTimer * timer )

virtualinherited

Execute action in response of a timer timing out.

This method must be overridden if an object has to react to timers.

Reimplemented in TGCommandPlugin, TGDNDManager, TGFileContainer, TGHtml, TGLEventHandler, TGPopupMenu, TGraphTime, TGScrollBar, TGShutter, TGTextEdit, TGTextEditor, TGTextEntry, TGTextView, TGToolTip, TGuiBldDragManager, TGWindow, and TTreeViewer.

Definition at line 516 of file TObject.cxx.

HasAnalysisType()

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

overridevirtual

BDT can handle classification with multiple classes and regression with one regression-target.

Implements TMVA::IMethod.

Definition at line 279 of file MethodBDT.cxx.

Hash()

ULong_t TNamed::Hash ( ) const

inlineoverridevirtualinherited

Return hash value for this object.

Note: If this routine is overloaded in a derived class, this derived class should also add

ROOT::CallRecursiveRemoveIfNeeded(*this)

Otherwise, when RecursiveRemove is called (by ~TObject or example) for this type of object, the transversal of THashList and THashTable containers will will have to be done without call Hash (and hence be linear rather than logarithmic complexity). You will also see warnings like

Error in <ROOT::Internal::TCheckHashRecursiveRemoveConsistency::CheckRecursiveRemove>: The class SomeName overrides
TObject::Hash but does not call TROOT::RecursiveRemove in its destructor.

Reimplemented from TObject.

Definition at line 51 of file TNamed.h.

HasInconsistentHash()

Bool_t TObject::HasInconsistentHash ( ) const

inlineinherited

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

missing call to RecursiveRemove in destructor).

Note: Since the consistency is only tested for during inserts, this routine will return true for object that have never been inserted whether or not they have a consistent setup. This has no negative side-effect as searching for the object with the right or wrong Hash will always yield a not-found answer (Since anyway no hash can be guaranteed unique, there is always a check)

Definition at line 366 of file TObject.h.

HasMVAPdfs()

Bool_t TMVA::MethodBase::HasMVAPdfs ( ) const

inlineinherited

Definition at line 438 of file MethodBase.h.

HasTrainingTree()

Bool_t TMVA::MethodBase::HasTrainingTree ( ) const

inlineprotectedinherited

Definition at line 516 of file MethodBase.h.

Help()

Bool_t TMVA::MethodBase::Help ( ) const

inlineprotectedinherited

Definition at line 507 of file MethodBase.h.

IgnoreEventsWithNegWeightsInTraining()

Bool_t TMVA::MethodBase::IgnoreEventsWithNegWeightsInTraining ( ) const

inlineprotectedinherited

Definition at line 689 of file MethodBase.h.

Info()

void TObject::Info ( const char * location, const char * fmt, ... ) const

virtualinherited

Issue info message.

Use "location" to specify the method where the warning occurred. Accepts standard printf formatting arguments.

Definition at line 1072 of file TObject.cxx.

InheritsFrom() [1/2]

Bool_t TObject::InheritsFrom ( const char * classname ) const

virtualinherited

Returns kTRUE if object inherits from class "classname".

Reimplemented in TClass.

Definition at line 549 of file TObject.cxx.

InheritsFrom() [2/2]

Bool_t TObject::InheritsFrom ( const TClass * cl ) const

virtualinherited

Returns kTRUE if object inherits from TClass cl.

Reimplemented in TClass.

Definition at line 557 of file TObject.cxx.

Init()

void TMVA::MethodBDT::Init ( void )

overrideprivatevirtual

Common initialisation with defaults for the BDT-Method.

Implements TMVA::MethodBase.

Definition at line 686 of file MethodBDT.cxx.

InitBase()

void TMVA::MethodBase::InitBase ( )

privateinherited

default initialization called by all constructors

Definition at line 440 of file MethodBase.cxx.

InitEventSample()

void TMVA::MethodBDT::InitEventSample

(

void

)

Initialize the event sample (i.e. reset the boost-weights... etc).

Definition at line 760 of file MethodBDT.cxx.

InitGradBoost()

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

private

Initialize targets for first tree.

Definition at line 1656 of file MethodBDT.cxx.

InitIPythonInteractive()

void TMVA::MethodBase::InitIPythonInteractive ( )

inlineinherited

Definition at line 458 of file MethodBase.h.

Inspect()

void TObject::Inspect ( ) const

virtualinherited

Dump contents of this object in a graphics canvas.

Same action as Dump but in a graphical form. In addition pointers to other objects can be followed.

The following picture is the Inspect of a histogram object:

Reimplemented in ROOT::Experimental::XRooFit::xRooNode, TGFrame, TInspectorObject, and TSystemFile.

Definition at line 570 of file TObject.cxx.

InvertBit()

void TObject::InvertBit ( UInt_t f )

inlineinherited

Definition at line 206 of file TObject.h.

IsA()

TClass * TMVA::MethodBDT::IsA ( ) const

inlineoverridevirtual

Returns

TClass describing current object

Reimplemented from TMVA::MethodBase.

Definition at line 305 of file MethodBDT.h.

IsConstructedFromWeightFile()

Bool_t TMVA::MethodBase::IsConstructedFromWeightFile ( ) const

inlineprotectedinherited

Definition at line 543 of file MethodBase.h.

IsDestructed()

Bool_t TObject::IsDestructed ( ) const

inlineinherited

IsDestructed.

Note

This function must be non-virtual as it can be used on destructed (but not yet modified) memory. This is used for example in TClonesArray to record the element that have been destructed but not deleted and thus are ready for re-use (by operator new with placement).

Returns

true if this object's destructor has been run.

Definition at line 186 of file TObject.h.

IsEqual()

Bool_t TObject::IsEqual ( const TObject * obj ) const

virtualinherited

Default equal comparison (objects are equal if they have the same address in memory).

More complicated classes might want to override this function.

Reimplemented in TGObject, TObjString, TPair, and TQCommand.

Definition at line 589 of file TObject.cxx.

IsFolder()

Bool_t TObject::IsFolder ( ) const

virtualinherited

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

Reimplemented in ROOT::Experimental::XRooFit::xRooNode, ROOT::Internal::THnBaseBrowsable, TApplicationRemote, TAxis3D, TBaseClass, TBranch, TBranchClones, TBranchElement, TBranchObject, TBranchSTL, TBrowserObject, TCanvas, TClass, TCollection, TDatabasePDG, TDirectory, TFolder, TGeoManager, TGeometry, TGeoNode, TGeoNodeMatrix, TGeoOverlap, TGeoTrack, TGeoVolume, THbookFile, THbookKey, THnBase, TKey, TMapFile, TMultiDimFit, TNode, TPad, TPair, TParticleClassPDG, TPrincipal, TRemoteObject, TROOT, TRootIconList, TSPlot, TSystemDirectory, TTask, TTree, and TVirtualBranchBrowsable.

Definition at line 579 of file TObject.cxx.

IsModelPersistence()

Bool_t TMVA::MethodBase::IsModelPersistence ( ) const

inlineinherited

Definition at line 386 of file MethodBase.h.

IsNormalised()

Bool_t TMVA::MethodBase::IsNormalised ( ) const

inlineprotectedinherited

Definition at line 499 of file MethodBase.h.

IsOnHeap()

Bool_t TObject::IsOnHeap ( ) const

inlineinherited

Definition at line 160 of file TObject.h.

IsSignalLike() [1/2]

Bool_t TMVA::MethodBase::IsSignalLike ( )

virtualinherited

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 918 of file MethodBase.cxx.

IsSignalLike() [2/2]

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

virtualinherited

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 925 of file MethodBase.cxx.

IsSilentFile()

Bool_t TMVA::MethodBase::IsSilentFile ( ) const

inlineinherited

Definition at line 382 of file MethodBase.h.

IsSortable()

Bool_t TNamed::IsSortable ( ) const

inlineoverridevirtualinherited

Reimplemented from TObject.

Reimplemented in TStructNodeProperty.

Definition at line 52 of file TNamed.h.

IsZombie()

Bool_t TObject::IsZombie ( ) const

inlineinherited

Definition at line 161 of file TObject.h.

Log()

MsgLogger & TMVA::Configurable::Log ( ) const

inlineinherited

Definition at line 122 of file Configurable.h.

LooseOptionCheckingEnabled()

Bool_t TMVA::Configurable::LooseOptionCheckingEnabled ( ) const

inlineprotectedinherited

Definition at line 95 of file Configurable.h.

ls()

void TNamed::ls ( Option_t * option = "" ) const

overridevirtualinherited

List TNamed name and title.

Reimplemented from TObject.

Reimplemented in ROOT::Experimental::XRooFit::xRooBrowser, TNode, TROOT, TStreamerBase, TStreamerElement, TStreamerInfo, TStreamerSTL, TTask, TText, and TVirtualStreamerInfo.

Definition at line 112 of file TNamed.cxx.

MakeClass()

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

overridevirtualinherited

create reader class for method (classification only at present)

Implements TMVA::IMethod.

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

Definition at line 3074 of file MethodBase.cxx.

MakeClassInstantiateNode()

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

Recursively descends a tree and writes the node instance to the output stream.

Definition at line 2989 of file MethodBDT.cxx.

MakeClassSpecific()

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

overridevirtual

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

Reimplemented from TMVA::MethodBase.

Definition at line 2755 of file MethodBDT.cxx.

MakeClassSpecificHeader()

void TMVA::MethodBDT::MakeClassSpecificHeader ( std::ostream & fout, const TString & className ) const

overridevirtual

Specific class header.

Reimplemented from TMVA::MethodBase.

Definition at line 2875 of file MethodBDT.cxx.

MakeZombie()

void TObject::MakeZombie ( )

inlineprotectedinherited

Definition at line 55 of file TObject.h.

MayNotUse()

void TObject::MayNotUse ( const char * method ) const

inherited

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).

Definition at line 1160 of file TObject.cxx.

MethodBaseDir()

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

inherited

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

Definition at line 2091 of file MethodBase.cxx.

NoErrorCalc()

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

protectedinherited

Definition at line 900 of file MethodBase.cxx.

Notify()

Bool_t TObject::Notify ( )

virtualinherited

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

Different objects in ROOT use the Notify method for different purposes, in coordination with other objects that call this method at the appropriate time.

For example, TLeaf uses it to load class information; TBranchRef to load contents of referenced branches TBranchRef; most notably, based on Notify, TChain implements a callback mechanism to inform interested parties when it switches to a new sub-tree.

Reimplemented in h1analysis, h1analysisTreeReader, TARInterruptHandler, TASInputHandler, TASInterruptHandler, TASLogHandler, TASSigPipeHandler, TBlinkTimer, TBranchElement, TBranchRef, TBreakLineCom, TBrowserTimer, TCollection, TDelCharCom, TDelTextCom, TFileHandler, TGContainerKeyboardTimer, TGContainerScrollTimer, TGInputHandler, TGLRedrawTimer, TGTextEditHist, TGuiBldDragManagerRepeatTimer, TIdleTimer, TInsCharCom, TInsTextCom, TInterruptHandler, TLeafObject, TMessageHandler, TNotifyLink< Type >, TNotifyLink< RNoCleanupNotifierHelper >, TNotifyLink< ROOT::Detail::TBranchProxy >, TNotifyLink< TTreeReader >, TPopupDelayTimer, TProcessEventTimer, TRefTable, TRepeatTimer, TSBRepeatTimer, TSelector, TSelectorDraw, TSelectorEntries, TSignalHandler, TSingleShotCleaner, TSocketHandler, TStdExceptionHandler, TSysEvtHandler, TTermInputHandler, TThreadTimer, TTimeOutTimer, TTimer, TTipDelayTimer, TTree, TTreeFormula, TTreeFormulaManager, TTreeReader, TViewTimer, and TViewUpdateTimer.

Definition at line 618 of file TObject.cxx.

Obsolete()

void TObject::Obsolete ( const char * method, const char * asOfVers, const char * removedFromVers ) const

inherited

Use this method to declare a method obsolete.

Specify as of which version the method is obsolete and as from which version it will be removed.

Definition at line 1169 of file TObject.cxx.

operator delete() [1/3]

void TObject::operator delete ( void * ptr, size_t size )

inherited

Operator delete for sized deallocation.

Definition at line 1234 of file TObject.cxx.

operator delete() [2/3]

void TObject::operator delete ( void * ptr )

inherited

Operator delete.

Definition at line 1212 of file TObject.cxx.

operator delete() [3/3]

void TObject::operator delete ( void * ptr, void * vp )

inherited

Only called by placement new when throwing an exception.

Definition at line 1266 of file TObject.cxx.

operator delete[]() [1/3]

void TObject::operator delete[] ( void * ptr, size_t size )

inherited

Operator delete [] for sized deallocation.

Definition at line 1245 of file TObject.cxx.

operator delete[]() [2/3]

void TObject::operator delete[] ( void * ptr )

inherited

Operator delete [].

Definition at line 1223 of file TObject.cxx.

operator delete[]() [3/3]

void TObject::operator delete[] ( void * ptr, void * vp )

inherited

Only called by placement new[] when throwing an exception.

Definition at line 1274 of file TObject.cxx.

operator new() [1/2]

void * TObject::operator new ( size_t sz )

inlineinherited

Definition at line 189 of file TObject.h.

operator new() [2/2]

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

inlineinherited

Definition at line 191 of file TObject.h.

operator new[]() [1/2]

void * TObject::operator new[] ( size_t sz )

inlineinherited

Definition at line 190 of file TObject.h.

operator new[]() [2/2]

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

inlineinherited

Definition at line 192 of file TObject.h.

OptimizeTuningParameters()

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

overridevirtual

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

Reimplemented from TMVA::MethodBase.

Definition at line 1067 of file MethodBDT.cxx.

Paint()

void TObject::Paint ( Option_t * option = "" )

virtualinherited

This method must be overridden if a class wants to paint itself.

The difference between Paint() and Draw() is that when a object draws itself it is added to the display list of the pad in which it is drawn (and automatically redrawn whenever the pad is redrawn). While paint just draws the object without adding it to the pad display list.

Reimplemented in ROOT::Experimental::RTreeMapPainter, ROOT::RGeoPainter, TAnnotation, TArrow, TASImage, TASPaletteEditor::LimitLine, TASPaletteEditor::PaintPalette, TAxis3D, TBits, TBox, TButton, TCanvas, TClassTree, TCollection, TColorWheel, TCrown, TDiamond, TDirectory, TEfficiency, TEllipse, TEveArrow, TEveCaloViz, TEveDigitSet, TEveGeoShape, TEveGeoTopNode, TEvePlot3D, TEvePointSet, TEveProjectionAxes, TEveScene, TEveShape, TEveStraightLineSet, TEveText, TEveTriangleSet, TExec, TF1, TF2, TF3, TFile, TFileDrawMap, TFrame, TGaxis, TGenerator, TGeoBoolNode, TGeoIntersection, TGeoNode, TGeoOverlap, TGeoPainter, TGeoPhysicalNode, TGeoShape, TGeoSubtraction, TGeoTrack, TGeoUnion, TGeoVGShape, TGeoVolume, TGL5DDataSet, TGLHistPainter, TGLParametricEquation, TGLTH3Composition, TGraph2D, TGraph2DPainter, TGraph, TGraphEdge, TGraphNode, TGraphPolargram, TGraphTime, TH1, THistPainter, THStack, TLatex, TLegend, TLine, TMacro, TMarker3DBox, TMarker, TMathText, TMultiGraph, TNode, TNodeDiv, TPad, TPaletteAxis, TParallelCoord, TParallelCoordRange, TParallelCoordVar, TParticle, TPave, TPaveLabel, TPaveStats, TPavesText, TPaveText, TPie, TPolyLine3D, TPolyLine, TPolyMarker3D, TPolyMarker, TPrimary, TRatioPlot, TScatter2D, TScatter, TShape, TSpectrum2Painter, TSpider, TSpline, TSQLFile, TStyle, TText, TTreePerfStats, TVirtualGeoPainter, TVirtualGeoTrack, TVirtualHistPainter, TVirtualPad, TWbox, and TXMLFile.

Definition at line 631 of file TObject.cxx.

ParseOptions()

void TMVA::Configurable::ParseOptions ( )

virtualinherited

options parser

Reimplemented in TMVA::CrossValidation, and TMVA::Envelope.

Definition at line 123 of file Configurable.cxx.

Pop()

void TObject::Pop ( )

virtualinherited

Pop on object drawn in a pad to the top of the display list.

I.e. it will be drawn last and on top of all other primitives.

Reimplemented in TFrame, TPad, and TVirtualPad.

Definition at line 640 of file TObject.cxx.

PreProcessNegativeEventWeights()

void TMVA::MethodBDT::PreProcessNegativeEventWeights ( )

private

O.k.

you know there are events with negative event weights. This routine will remove them by pairing them with the closest event(s) of the same event class with positive weights A first attempt is "brute force", I dont' try to be clever using search trees etc, just quick and dirty to see if the result is any good

Definition at line 931 of file MethodBDT.cxx.

Print()

void TNamed::Print ( Option_t * option = "" ) const

overridevirtualinherited

Print TNamed name and title.

Reimplemented from TObject.

Reimplemented in ROOT::Experimental::XRooFit::xRooNLLVar::xRooHypoPoint, ROOT::Experimental::XRooFit::xRooNLLVar::xRooHypoSpace, ROOT::Experimental::XRooFit::xRooNode, TParallelCoordRange, TParallelCoordVar, TParticleClassPDG, TParticlePDG, TPrincipal, TScatter2D, TScatter, TSpectrum2, TSpectrum3, TSpectrum, TSQLColumnInfo, TSQLFile, TSQLTableInfo, TText, TTree, TTreeIndex, TXMLFile, and TXTRU.

Definition at line 127 of file TNamed.cxx.

PrintHelpMessage()

void TMVA::MethodBase::PrintHelpMessage ( ) const

overridevirtualinherited

prints out method-specific help method

Implements TMVA::IMethod.

Definition at line 3335 of file MethodBase.cxx.

PrintOptions()

void TMVA::Configurable::PrintOptions ( ) const

inherited

prints out the options set in the options string and the defaults

Definition at line 298 of file Configurable.cxx.

PrivateGetMvaValue()

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

private

Return the MVA value (range [-1;1]) that classifies the event according to the majority vote from the total number of decision trees.

Definition at line 2466 of file MethodBDT.cxx.

ProcessBaseOptions()

void TMVA::MethodBase::ProcessBaseOptions ( )

privateinherited

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

Definition at line 539 of file MethodBase.cxx.

ProcessOptions()

void TMVA::MethodBDT::ProcessOptions ( )

overridevirtual

The option string is decoded, for available options see "DeclareOptions".

Implements TMVA::MethodBase.

Definition at line 469 of file MethodBDT.cxx.

ProcessSetup()

void TMVA::MethodBase::ProcessSetup ( )

inherited

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 422 of file MethodBase.cxx.

Read()

Int_t TObject::Read ( const char * name )

virtualinherited

Read contents of object with specified name from the current directory.

First the key with the given name is searched in the current directory, next the key buffer is deserialized into the object. The object must have been created before via the default constructor. See TObject::Write().

Reimplemented in TBuffer, TKey, TKeySQL, and TKeyXML.

Definition at line 673 of file TObject.cxx.

ReadClassesFromXML()

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

privateinherited

read number of classes from XML

Definition at line 1988 of file MethodBase.cxx.

ReadOptionsFromStream()

void TMVA::Configurable::ReadOptionsFromStream ( std::istream & istr )

inherited

read option back from the weight file

Definition at line 434 of file Configurable.cxx.

ReadOptionsFromXML()

void TMVA::Configurable::ReadOptionsFromXML ( void * node )

inherited

Definition at line 377 of file Configurable.cxx.

ReadSpectatorsFromXML()

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

privateinherited

read spectator info from XML

Definition at line 1948 of file MethodBase.cxx.

ReadStateFromFile()

void TMVA::MethodBase::ReadStateFromFile ( )

inherited

Function to write options and weights to file.

Definition at line 1497 of file MethodBase.cxx.

ReadStateFromStream() [1/2]

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

inherited

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

Definition at line 1661 of file MethodBase.cxx.

ReadStateFromStream() [2/2]

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

inherited

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

Definition at line 1459 of file MethodBase.cxx.

ReadStateFromXML()

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

privateinherited

Definition at line 1551 of file MethodBase.cxx.

ReadStateFromXMLString()

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

inherited

for reading from memory

Definition at line 1540 of file MethodBase.cxx.

ReadTargetsFromXML()

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

privateinherited

read target info from XML

Definition at line 2030 of file MethodBase.cxx.

ReadVariablesFromXML()

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

privateinherited

read variable info from XML

Definition at line 1908 of file MethodBase.cxx.

ReadVarsFromStream()

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

privateinherited

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 1796 of file MethodBase.cxx.

ReadWeightsFromStream() [1/2]

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

overridevirtual

Read the weights (BDT coefficients).

Implements TMVA::MethodBase.

Definition at line 2406 of file MethodBDT.cxx.

ReadWeightsFromStream() [2/2]

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

inlinevirtual

Reimplemented from TMVA::MethodBase.

Definition at line 269 of file MethodBase.h.

ReadWeightsFromXML()

void TMVA::MethodBDT::ReadWeightsFromXML ( void * parent )

overridevirtual

Reads the BDT from the xml file.

Implements TMVA::MethodBase.

Definition at line 2339 of file MethodBDT.cxx.

RecursiveRemove()

void TObject::RecursiveRemove ( TObject * obj )

virtualinherited

Recursively remove this object from a list.

Typically implemented by classes that can contain multiple references to a same object.

Reimplemented in RooAbsCollection, RooAbsData, RooLinkedList, RooMCStudy, ROOT::Internal::TCheckHashRecursiveRemoveConsistency, ROOT::RBrowserDataCleanup, RooWorkspace, TBrowser, TChain, TCling, TCollection, TDialogCanvas, TDirectory, TEfficiency, TFileMerger, TFitEditor, TFolder, TFriendElement, TGedEditor, TGeometry, TGFileBrowser, TGraph2D, TGraph, TH1, TH1Editor, TH2Editor, THashList, THistPainter, THStack, TInspectCanvas, TLegend, TList, TListOfDataMembers, TListOfEnums, TListOfEnumsWithLock, TListOfFunctions, TListOfFunctionTemplates, TMultiGraph, TNode, TObjArray, TObjectRefSpy, TObjectSpy, TPad, TProcessID, TROOT, TRootBrowser, TRootBrowserHistory, TRootBrowserLite, TRootContextMenu, TTree, TTreePlayer, TViewPubDataMembers, TViewPubFunctions, and TVirtualPad.

Definition at line 684 of file TObject.cxx.

RegBoost()

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

private

A special boosting only for Regression (not implemented).

Definition at line 2183 of file MethodBDT.cxx.

RerouteTransformationHandler()

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

inlineinherited

Definition at line 406 of file MethodBase.h.

Reset()

void TMVA::MethodBDT::Reset ( void )

overridevirtual

Reset the method, as if it had just been instantiated (forget all training etc.).

Reimplemented from TMVA::MethodBase.

Definition at line 724 of file MethodBDT.cxx.

ResetBit()

void TObject::ResetBit ( UInt_t f )

inlineinherited

Definition at line 203 of file TObject.h.

ResetSetFlag()

void TMVA::Configurable::ResetSetFlag ( )

protectedinherited

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

Definition at line 112 of file Configurable.cxx.

ResetThisBase()

void TMVA::MethodBase::ResetThisBase ( )

privateinherited

SaveAs()

void TObject::SaveAs ( const char * filename = "", Option_t * option = "" ) const

virtualinherited

Save this object in the file specified by filename.

• if "filename" contains ".root" the object is saved in filename as root binary file.
• if "filename" contains ".xml" the object is saved in filename as a xml ascii file.
• if "filename" contains ".cc" the object is saved in filename as C code independent from ROOT. The code is generated via SavePrimitive(). Specific code should be implemented in each object to handle this option. Like in TF1::SavePrimitive().

• otherwise the object is written to filename as a CINT/C++ script. The C++ code to rebuild this object is generated via SavePrimitive(). The "option" parameter is passed to SavePrimitive. By default it is an empty string. It can be used to specify the Draw option in the code generated by SavePrimitive.

  The function is available via the object context menu.

Reimplemented in ROOT::Experimental::XRooFit::xRooNode, TClassTree, TFolder, TGeoVolume, TGObject, TGraph, TH1, TPad, TPaveClass, TSpline3, TSpline5, TSpline, TTreePerfStats, and TVirtualPad.

Definition at line 708 of file TObject.cxx.

SavePrimitive()

void TObject::SavePrimitive ( std::ostream & out, Option_t * option = "" )

virtualinherited

Save a primitive as a C++ statement(s) on output stream "out".

Reimplemented in TAnnotation, TArc, TArrow, TASImage, TAxis3D, TBox, TButton, TCanvas, TChain, TCrown, TCurlyArc, TCurlyLine, TCutG, TDiamond, TEfficiency, TEllipse, TExec, TF12, TF1, TF2, TF3, TFrame, TGaxis, TGButton, TGButtonGroup, TGCanvas, TGCheckButton, TGColorSelect, TGColumnLayout, TGComboBox, TGCompositeFrame, TGContainer, TGDockableFrame, TGDoubleHSlider, TGDoubleVSlider, TGedMarkerSelect, TGedPatternSelect, TGeoArb8, TGeoBBox, TGeoBoolNode, TGeoCombiTrans, TGeoCompositeShape, TGeoCone, TGeoConeSeg, TGeoCtub, TGeoDecayChannel, TGeoElementRN, TGeoEltu, TGeoGtra, TGeoHalfSpace, TGeoHMatrix, TGeoHype, TGeoIdentity, TGeoIntersection, TGeoMaterial, TGeoMedium, TGeoMixture, TGeoPara, TGeoParaboloid, TGeoPatternCylPhi, TGeoPatternCylR, TGeoPatternParaX, TGeoPatternParaY, TGeoPatternParaZ, TGeoPatternSphPhi, TGeoPatternSphR, TGeoPatternSphTheta, TGeoPatternTrapZ, TGeoPatternX, TGeoPatternY, TGeoPatternZ, TGeoPcon, TGeoPgon, TGeoRotation, TGeoScaledShape, TGeoShapeAssembly, TGeoSphere, TGeoSubtraction, TGeoTessellated, TGeoTorus, TGeoTranslation, TGeoTrap, TGeoTrd1, TGeoTrd2, TGeoTube, TGeoTubeSeg, TGeoUnion, TGeoVolume, TGeoXtru, TGFileContainer, TGFont, TGFrame, TGFSComboBox, TGGC, TGGroupFrame, TGHButtonGroup, TGHorizontal3DLine, TGHorizontalFrame, TGHorizontalLayout, TGHProgressBar, TGHScrollBar, TGHSlider, TGHSplitter, TGHtml, TGIcon, TGLabel, TGLayoutHints, TGLineStyleComboBox, TGLineWidthComboBox, TGListBox, TGListDetailsLayout, TGListLayout, TGListTree, TGListView, TGLVContainer, TGMainFrame, TGMatrixLayout, TGMdiFrame, TGMdiMainFrame, TGMdiMenuBar, TGMenuBar, TGMenuTitle, TGNumberEntry, TGNumberEntryField, TGPictureButton, TGPopupMenu, TGProgressBar, TGRadioButton, TGraph2D, TGraph2DAsymmErrors, TGraph2DErrors, TGraph, TGraphAsymmErrors, TGraphBentErrors, TGraphEdge, TGraphErrors, TGraphMultiErrors, TGraphNode, TGraphPolar, TGraphPolargram, TGraphStruct, TGroupButton, TGRowLayout, TGShapedFrame, TGShutter, TGShutterItem, TGSplitFrame, TGStatusBar, TGTab, TGTabLayout, TGTableLayout, TGTableLayoutHints, TGTextButton, TGTextEdit, TGTextEntry, TGTextLBEntry, TGTextView, TGTileLayout, TGToolBar, TGTransientFrame, TGTripleHSlider, TGTripleVSlider, TGVButtonGroup, TGVertical3DLine, TGVerticalFrame, TGVerticalLayout, TGVFileSplitter, TGVProgressBar, TGVScrollBar, TGVSlider, TGVSplitter, TGXYLayout, TGXYLayoutHints, TH1, TH2Poly, THelix, THStack, TLatex, TLegend, TLine, TMacro, TMarker3DBox, TMarker, TMathText, TMultiGraph, TPad, TPaletteAxis, TParallelCoord, TParallelCoordVar, TPave, TPaveClass, TPaveLabel, TPaveStats, TPavesText, TPaveText, TPie, TPieSlice, TPolyLine3D, TPolyLine, TPolyMarker3D, TPolyMarker, TProfile2D, TProfile3D, TProfile, TRootContainer, TRootEmbeddedCanvas, TScatter2D, TScatter, TSlider, TSliderBox, TSpline3, TSpline5, TStyle, TText, TTreePerfStats, and TWbox.

Definition at line 858 of file TObject.cxx.

SavePrimitiveConstructor()

void TObject::SavePrimitiveConstructor ( std::ostream & out, TClass * cl, const char * variable_name, const char * constructor_agrs = "", Bool_t empty_line = kTRUE )

staticprotectedinherited

Save object constructor in the output stream "out".

Can be used as first statement when implementing SavePrimitive() method for the object

Definition at line 777 of file TObject.cxx.

SavePrimitiveDraw()

void TObject::SavePrimitiveDraw ( std::ostream & out, const char * variable_name, Option_t * option = nullptr )

staticprotectedinherited

Save invocation of primitive Draw() method Skipped if option contains "nodraw" string.

Definition at line 845 of file TObject.cxx.

SavePrimitiveNameTitle()

void TNamed::SavePrimitiveNameTitle ( std::ostream & out, const char * variable_name )

protectedinherited

Save object name and title into the output stream "out".

Definition at line 135 of file TNamed.cxx.

SavePrimitiveVector()

TString TObject::SavePrimitiveVector ( std::ostream & out, const char * prefix, Int_t len, Double_t * arr, Int_t flag = 0 )

staticprotectedinherited

Save array in the output stream "out" as vector.

Create unique variable name based on prefix value Returns name of vector which can be used in constructor or in other places of C++ code If flag === kTRUE, just add empty line If flag === 111, check if array is empty and return nullptr or <vectorname>.data()

Definition at line 796 of file TObject.cxx.

SetAdaBoostBeta()

void TMVA::MethodBDT::SetAdaBoostBeta ( Double_t b )

inline

Definition at line 139 of file MethodBDT.h.

SetAnalysisType()

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

inlinevirtualinherited

Definition at line 439 of file MethodBase.h.

SetBaggedSampleFraction()

void TMVA::MethodBDT::SetBaggedSampleFraction ( Double_t f )

inline

Definition at line 143 of file MethodBDT.h.

SetBaseDir()

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

inlineinherited

Definition at line 376 of file MethodBase.h.

SetBit() [1/2]

void TObject::SetBit ( UInt_t f )

inlineinherited

Definition at line 202 of file TObject.h.

SetBit() [2/2]

void TObject::SetBit ( UInt_t f, Bool_t set )

inherited

Set or unset the user status bits as specified in f.

Definition at line 888 of file TObject.cxx.

SetConfigDescription()

void TMVA::Configurable::SetConfigDescription ( const char * d )

inlineinherited

Definition at line 64 of file Configurable.h.

SetConfigName()

void TMVA::Configurable::SetConfigName ( const char * n )

inlineinherited

Definition at line 63 of file Configurable.h.

SetDrawOption()

void TObject::SetDrawOption ( Option_t * option = "" )

virtualinherited

Set drawing option for object.

This option only affects the drawing style and is stored in the option field of the TObjOptLink supporting a TPad's primitive list (TList). Note that it does not make sense to call object.SetDrawOption(option) before having called object.Draw().

Reimplemented in RooPlot, TAxis, TBrowser, TGedFrame, TGFrame, TPad, TPaveStats, TRootBrowserLite, TSystemDirectory, and TSystemFile.

Definition at line 871 of file TObject.cxx.

SetDtorOnly()

void TObject::SetDtorOnly ( void * obj )

staticinherited

Set destructor only flag.

Definition at line 1204 of file TObject.cxx.

SetFile()

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

inlineinherited

Definition at line 378 of file MethodBase.h.

SetMaxDepth()

void TMVA::MethodBDT::SetMaxDepth ( Int_t d )

inline

Definition at line 134 of file MethodBDT.h.

SetMethodBaseDir()

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

inlineinherited

Definition at line 377 of file MethodBase.h.

SetMethodDir()

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

inlineinherited

Definition at line 375 of file MethodBase.h.

SetMinNodeSize() [1/2]

void TMVA::MethodBDT::SetMinNodeSize

(

Double_t

sizeInPercent

)

Definition at line 659 of file MethodBDT.cxx.

SetMinNodeSize() [2/2]

void TMVA::MethodBDT::SetMinNodeSize

(

TString

sizeInPercent

)

Definition at line 673 of file MethodBDT.cxx.

SetModelPersistence()

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

inlineinherited

Definition at line 385 of file MethodBase.h.

SetMsgType()

void TMVA::Configurable::SetMsgType ( EMsgType t )

inlineinherited

Definition at line 125 of file Configurable.h.

SetName()

void TNamed::SetName ( const char * name )

virtualinherited

Set the name of the TNamed.

WARNING: if the object is a member of a THashTable or THashList container the container must be Rehash()'ed after SetName(). For example the list of objects in the current directory is a THashList.

Reimplemented in RooAbsArg, RooAbsData, RooDataHist, RooDataSet, RooFitResult, RooPlot, ROOT::Experimental::XRooFit::xRooNode, TChain, TColor, TDirectory, TEfficiency, TEventList, TEveScene, TFormula, TGraph2D, TGraph, TH1, TNode, TRotMatrix, TShape, TSystemDirectory, TSystemFile, and TTree.

Definition at line 149 of file TNamed.cxx.

SetNameTitle()

void TNamed::SetNameTitle ( const char * name, const char * title )

virtualinherited

Set all the TNamed parameters (name and title).

WARNING: if the name is changed and the object is a member of a THashTable or THashList container the container must be Rehash()'ed after SetName(). For example the list of objects in the current directory is a THashList.

Reimplemented in RooAbsArg, RooAbsData, RooDataHist, RooDataSet, RooFitResult, RooPlot, TContextMenu, TGraph2D, TGraph, TH1, and TNode.

Definition at line 163 of file TNamed.cxx.

SetNodePurityLimit()

void TMVA::MethodBDT::SetNodePurityLimit ( Double_t l )

inline

Definition at line 140 of file MethodBDT.h.

SetNormalised()

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

inlineprotectedinherited

Definition at line 500 of file MethodBase.h.

SetNTrees()

void TMVA::MethodBDT::SetNTrees ( Int_t d )

inline

Definition at line 138 of file MethodBDT.h.

SetObjectStat()

void TObject::SetObjectStat ( Bool_t stat )

staticinherited

Turn on/off tracking of objects in the TObjectTable.

Definition at line 1188 of file TObject.cxx.

SetOptions()

void TMVA::Configurable::SetOptions ( const TString & s )

inlineinherited

Definition at line 85 of file Configurable.h.

SetShrinkage()

void TMVA::MethodBDT::SetShrinkage ( Double_t s )

inline

Definition at line 141 of file MethodBDT.h.

SetSignalReferenceCut()

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

inlineinherited

Definition at line 367 of file MethodBase.h.

SetSignalReferenceCutOrientation()

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

inlineinherited

Definition at line 368 of file MethodBase.h.

SetSilentFile()

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

inlineinherited

Definition at line 381 of file MethodBase.h.

SetTestTime()

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

inlineinherited

Definition at line 165 of file MethodBase.h.

SetTestvarName()

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

inlineinherited

Definition at line 344 of file MethodBase.h.

SetTitle()

void TNamed::SetTitle ( const char * title = "" )

virtualinherited

Set the title of the TNamed.

Reimplemented in Axis2, RooPlot, ROOT::Experimental::XRooFit::xRooNode, ROOT::TSchemaRule::TSources, TASImage, TEfficiency, TF1, TGraph2D, TGraph, TH1, THnBase, TParallelCoordVar, TSystemDirectory, and TSystemFile.

Definition at line 173 of file TNamed.cxx.

SetTrainTime()

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

inlineinherited

Definition at line 161 of file MethodBase.h.

SetTuneParameters()

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

overridevirtual

Set the tuning parameters according to the argument.

Reimplemented from TMVA::MethodBase.

Definition at line 1120 of file MethodBDT.cxx.

SetUniqueID()

void TObject::SetUniqueID ( UInt_t uid )

virtualinherited

Set the unique object id.

Definition at line 899 of file TObject.cxx.

SetupMethod()

void TMVA::MethodBase::SetupMethod ( )

inherited

setup of methods

Definition at line 405 of file MethodBase.cxx.

SetUseNvars()

void TMVA::MethodBDT::SetUseNvars ( Int_t n )

inline

Definition at line 142 of file MethodBDT.h.

SetWeightFileDir()

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

protectedinherited

set directory of weight file

Definition at line 2130 of file MethodBase.cxx.

SetWeightFileName()

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

protectedinherited

set the weight file name (depreciated)

Definition at line 2139 of file MethodBase.cxx.

Sizeof()

Int_t TNamed::Sizeof ( ) const

virtualinherited

Return size of the TNamed part of the TObject.

Reimplemented in TDirectory, TDirectoryFile, TFile, TKey, TSQLFile, and TXMLFile.

Definition at line 182 of file TNamed.cxx.

SplitOptions()

void TMVA::Configurable::SplitOptions ( const TString & theOpt, TList & loo ) const

privateinherited

splits the option string at ':' and fills the list 'loo' with the primitive strings

Definition at line 91 of file Configurable.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 )

protectedinherited

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 3013 of file MethodBase.cxx.

Streamer()

void TMVA::MethodBDT::Streamer ( TBuffer & )

overridevirtual

Reimplemented from TMVA::MethodBase.

StreamerNVirtual()

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

inline

Definition at line 305 of file MethodBDT.h.

SysError()

void TObject::SysError ( const char * location, const char * fmt, ... ) const

virtualinherited

Issue system error message.

Use "location" to specify the method where the system error occurred. Accepts standard printf formatting arguments.

Definition at line 1112 of file TObject.cxx.

TestBit()

Bool_t TObject::TestBit ( UInt_t f ) const

inlineinherited

Definition at line 204 of file TObject.h.

TestBits()

Int_t TObject::TestBits ( UInt_t f ) const

inlineinherited

Definition at line 205 of file TObject.h.

TestClassification()

void TMVA::MethodBase::TestClassification ( )

virtualinherited

initialization

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

Definition at line 1201 of file MethodBase.cxx.

TestMulticlass()

void TMVA::MethodBase::TestMulticlass ( )

virtualinherited

test multiclass classification

Definition at line 1174 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 )

virtualinherited

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 1065 of file MethodBase.cxx.

TestTreeQuality()

Double_t TMVA::MethodBDT::TestTreeQuality

(

DecisionTree *

dt

)

Test the tree quality.. in terms of Misclassification.

Definition at line 1695 of file MethodBDT.cxx.

Train()

void TMVA::MethodBDT::Train ( void )

overridevirtual

BDT training.

Implements TMVA::MethodBase.

Definition at line 1141 of file MethodBDT.cxx.

TrainingEnded()

bool TMVA::MethodBase::TrainingEnded ( )

inlineinherited

Definition at line 472 of file MethodBase.h.

TrainMethod()

void TMVA::MethodBase::TrainMethod ( )

inherited

Definition at line 646 of file MethodBase.cxx.

TxtWeightsOnly()

Bool_t TMVA::MethodBase::TxtWeightsOnly ( ) const

inlineprotectedinherited

Definition at line 537 of file MethodBase.h.

UpdateTargets()

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

private

Calculate residual for all events.

Definition at line 1433 of file MethodBDT.cxx.

UpdateTargetsRegression()

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

private

Calculate residuals for all events and update targets for next iter.

Parameters

[in]	eventSample	The collection of events currently under training.
[in]	first	Should be true when called before the first boosting iteration has been run

Definition at line 1555 of file MethodBDT.cxx.

UseCurrentStyle()

void TObject::UseCurrentStyle ( )

virtualinherited

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

Reimplemented in TAxis3D, TCanvas, TFrame, TGraph, TH1, TPad, TPaveStats, TPaveText, and TTree.

Definition at line 909 of file TObject.cxx.

Verbose()

Bool_t TMVA::MethodBase::Verbose ( ) const

inlineprotectedinherited

Definition at line 506 of file MethodBase.h.

Warning()

void TObject::Warning ( const char * location, const char * fmt, ... ) const

virtualinherited

Issue warning message.

Use "location" to specify the method where the warning occurred. Accepts standard printf formatting arguments.

Definition at line 1084 of file TObject.cxx.

Write() [1/2]

Int_t TObject::Write ( const char * name = nullptr, Int_t option = 0, Int_t bufsize = 0 )

virtualinherited

Write this object to the current directory.

For more see the const version of this method.

Reimplemented in ROOT::TBufferMergerFile, TBuffer, TCollection, TDirectory, TDirectoryFile, TFile, TMap, TParallelMergingFile, TSQLFile, TTree, and TXMLFile.

Definition at line 989 of file TObject.cxx.

Write() [2/2]

Int_t TObject::Write ( const char * name = nullptr, Int_t option = 0, Int_t bufsize = 0 ) const

virtualinherited

Write this object to the current directory.

The data structure corresponding to this object is serialized. The corresponding buffer is written to the current directory with an associated key with name "name".

Writing an object to a file involves the following steps:

• Creation of a support TKey object in the current directory. The TKey object creates a TBuffer object.
• The TBuffer object is filled via the class::Streamer function.
• If the file is compressed (default) a second buffer is created to hold the compressed buffer.
• Reservation of the corresponding space in the file by looking in the TFree list of free blocks of the file.
• The buffer is written to the file.

Bufsize can be given to force a given buffer size to write this object. By default, the buffersize will be taken from the average buffer size of all objects written to the current file so far.

If a name is specified, it will be the name of the key. If name is not given, the name of the key will be the name as returned by GetName().

The option can be a combination of: kSingleKey, kOverwrite or kWriteDelete Using the kOverwrite option a previous key with the same name is overwritten. The previous key is deleted before writing the new object. Using the kWriteDelete option a previous key with the same name is deleted only after the new object has been written. This option is safer than kOverwrite but it is slower. NOTE: Neither kOverwrite nor kWriteDelete reduces the size of a TFile– the space is simply freed up to be overwritten; in the case of a TTree, it is more complicated. If one opens a TTree, appends some entries, then writes it out, the behaviour is effectively the same. If, however, one creates a new TTree and writes it out in this way, only the metadata is replaced, effectively making the old data invisible without deleting it. TTree::Delete() can be used to mark all disk space occupied by a TTree as free before overwriting its metadata this way. The kSingleKey option is only used by TCollection::Write() to write a container with a single key instead of each object in the container with its own key.

An object is read from the file into memory via TKey::Read() or via TObject::Read().

The function returns the total number of bytes written to the file. It returns 0 if the object cannot be written.

Reimplemented in TBuffer, TCollection, TDirectory, TDirectoryFile, TFile, TMap, TParallelMergingFile, TSQLFile, TTree, and TXMLFile.

Definition at line 964 of file TObject.cxx.

WriteEvaluationHistosToFile()

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

virtualinherited

writes all MVA evaluation histograms to file

Reimplemented in TMVA::MethodBoost.

Definition at line 2165 of file MethodBase.cxx.

WriteMonitoringHistosToFile()

void TMVA::MethodBDT::WriteMonitoringHistosToFile ( void ) const

overridevirtual

Here we could write some histograms created during the processing to the output file.

Reimplemented from TMVA::MethodBase.

Definition at line 2626 of file MethodBDT.cxx.

WriteOptionsReferenceToFile()

void TMVA::Configurable::WriteOptionsReferenceToFile ( )

protectedinherited

write complete options to output stream

Definition at line 408 of file Configurable.cxx.

WriteOptionsToStream()

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

inherited

write options to output stream (e.g. in writing the MVA weight files

Definition at line 332 of file Configurable.cxx.

WriteStateToFile()

void TMVA::MethodBase::WriteStateToFile ( ) const

inherited

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 1475 of file MethodBase.cxx.

WriteStateToStream()

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

privateinherited

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

is specified

Definition at line 1341 of file MethodBase.cxx.

WriteStateToXML()

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

privateinherited

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

is specified

Definition at line 1405 of file MethodBase.cxx.

WriteVarsToStream()

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

privateinherited

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

Definition at line 1781 of file MethodBase.cxx.

Member Data Documentation

fAdaBoostBeta

Double_t TMVA::MethodBDT::fAdaBoostBeta

private

beta parameter for AdaBoost algorithm

Definition at line 216 of file MethodBDT.h.

fAdaBoostR2Loss

TString TMVA::MethodBDT::fAdaBoostR2Loss

private

loss type used in AdaBoostR2 (Linear,Quadratic or Exponential)

Definition at line 217 of file MethodBDT.h.

fAnalysisType

Types::EAnalysisType TMVA::MethodBase::fAnalysisType

protectedinherited

Definition at line 598 of file MethodBase.h.

fAutomatic

Bool_t TMVA::MethodBDT::fAutomatic

private

use user given prune strength or automatically determined one using a validation sample

Definition at line 248 of file MethodBDT.h.

fBackgroundClass

UInt_t TMVA::MethodBase::fBackgroundClass

protectedinherited

Definition at line 693 of file MethodBase.h.

fBaggedBoost

Bool_t TMVA::MethodBDT::fBaggedBoost

private

turn bagging in combination with boost on/off

Definition at line 220 of file MethodBDT.h.

fBaggedGradBoost

Bool_t TMVA::MethodBDT::fBaggedGradBoost

private

turn bagging in combination with grad boost on/off

Definition at line 221 of file MethodBDT.h.

fBaggedSampleFraction

Double_t TMVA::MethodBDT::fBaggedSampleFraction

private

relative size of bagged event sample to original sample size

Definition at line 254 of file MethodBDT.h.

fBaseDir

TDirectory* TMVA::MethodBase::fBaseDir

privateinherited

Definition at line 628 of file MethodBase.h.

fBits

UInt_t TObject::fBits

privateinherited

bit field status word

Definition at line 47 of file TObject.h.

fBoostType

TString TMVA::MethodBDT::fBoostType

private

string specifying the boost type

Definition at line 215 of file MethodBDT.h.

fBoostWeight

Double_t TMVA::MethodBDT::fBoostWeight

private

ntuple var: boost weight

Definition at line 266 of file MethodBDT.h.

fBoostWeights

std::vector<double> TMVA::MethodBDT::fBoostWeights

private

the weights applied in the individual boosts

Definition at line 213 of file MethodBDT.h.

fCbb

Double_t TMVA::MethodBDT::fCbb

private

Cost factor.

Definition at line 272 of file MethodBDT.h.

fConfigDescription

TString TMVA::Configurable::fConfigDescription

privateinherited

description of this configurable

Definition at line 116 of file Configurable.h.

fConstructedFromWeightFile

Bool_t TMVA::MethodBase::fConstructedFromWeightFile

privateinherited

Definition at line 623 of file MethodBase.h.

fCss

Double_t TMVA::MethodBDT::fCss

private

Cost factor.

Definition at line 269 of file MethodBDT.h.

fCtb_ss

Double_t TMVA::MethodBDT::fCtb_ss

private

Cost factor.

Definition at line 271 of file MethodBDT.h.

fCts_sb

Double_t TMVA::MethodBDT::fCts_sb

private

Cost factor.

Definition at line 270 of file MethodBDT.h.

fCutOrientation

ECutOrientation TMVA::MethodBase::fCutOrientation

privateinherited

Definition at line 702 of file MethodBase.h.

fDataSetInfo

DataSetInfo& TMVA::MethodBase::fDataSetInfo

privateinherited

! the data set information (sometimes needed)

Definition at line 610 of file MethodBase.h.

fDefaultPDF

PDF* TMVA::MethodBase::fDefaultPDF

privateinherited

default PDF definitions

Definition at line 647 of file MethodBase.h.

fDoBoostMonitor

Bool_t TMVA::MethodBDT::fDoBoostMonitor

private

create control plot with ROC integral vs tree number

Definition at line 260 of file MethodBDT.h.

fDoPreselection

Bool_t TMVA::MethodBDT::fDoPreselection

private

do or do not perform automatic pre-selection of 100% eff. cuts

Definition at line 274 of file MethodBDT.h.

fEffS

TH1* TMVA::MethodBase::fEffS

privateinherited

efficiency histogram for rootfinder

Definition at line 645 of file MethodBase.h.

fErrorFraction

Double_t TMVA::MethodBDT::fErrorFraction

private

ntuple var: misclassification error fraction

Definition at line 267 of file MethodBDT.h.

fEventCollections

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

mutableprivateinherited

Definition at line 711 of file MethodBase.h.

fEventSample

std::vector<const TMVA::Event*> TMVA::MethodBDT::fEventSample

private

the training events

Definition at line 206 of file MethodBDT.h.

fExitFromTraining

bool TMVA::MethodBase::fExitFromTraining = false

protectedinherited

Definition at line 452 of file MethodBase.h.

fFile

TFile* TMVA::MethodBase::fFile

privateinherited

Definition at line 631 of file MethodBase.h.

fFileDir

TString TMVA::MethodBase::fFileDir

privateinherited

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

Definition at line 640 of file MethodBase.h.

fForest

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

private

the collection of decision trees

Definition at line 212 of file MethodBDT.h.

fFValidationEvents

Double_t TMVA::MethodBDT::fFValidationEvents

private

fraction of events to use for pruning

Definition at line 247 of file MethodBDT.h.

fgDebugLevel

const Int_t TMVA::MethodBDT::fgDebugLevel = 0

staticprivate

debug level determining some printout/control plots etc.

Definition at line 302 of file MethodBDT.h.

fgDtorOnly

Longptr_t TObject::fgDtorOnly = 0

staticprivateinherited

object for which to call dtor only (i.e. no delete)

Definition at line 49 of file TObject.h.

fgObjectStat

Bool_t TObject::fgObjectStat = kTRUE

staticprivateinherited

if true keep track of objects in TObjectTable

Definition at line 50 of file TObject.h.

fHasMVAPdfs

Bool_t TMVA::MethodBase::fHasMVAPdfs

privateinherited

MVA Pdfs are created for this classifier.

Definition at line 683 of file MethodBase.h.

fHelp

Bool_t TMVA::MethodBase::fHelp

privateinherited

help flag

Definition at line 682 of file MethodBase.h.

fHighBkgCut

std::vector<Double_t> TMVA::MethodBDT::fHighBkgCut

private

Definition at line 287 of file MethodBDT.h.

fHighSigCut

std::vector<Double_t> TMVA::MethodBDT::fHighSigCut

private

Definition at line 286 of file MethodBDT.h.

fHistoricBool

Bool_t TMVA::MethodBDT::fHistoricBool

private

Definition at line 294 of file MethodBDT.h.

fHuberQuantile

Double_t TMVA::MethodBDT::fHuberQuantile

private

the option string determining the quantile for the Huber Loss Function in BDT regression.

Definition at line 297 of file MethodBDT.h.

fIgnoreNegWeightsInTraining

Bool_t TMVA::MethodBase::fIgnoreNegWeightsInTraining

privateinherited

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

Definition at line 685 of file MethodBase.h.

fInputVars

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

protectedinherited

Definition at line 591 of file MethodBase.h.

fInteractive

IPythonInteractive* TMVA::MethodBase::fInteractive = nullptr

protectedinherited

Definition at line 451 of file MethodBase.h.

fInverseBoostNegWeights

Bool_t TMVA::MethodBDT::fInverseBoostNegWeights

private

boost ev. with neg. weights with 1/boostweight rather than boostweight

Definition at line 257 of file MethodBDT.h.

fIPyCurrentIter

UInt_t TMVA::MethodBase::fIPyCurrentIter = 0

protectedinherited

Definition at line 453 of file MethodBase.h.

fIPyMaxIter

UInt_t TMVA::MethodBase::fIPyMaxIter = 0

protectedinherited

Definition at line 453 of file MethodBase.h.

fIsHighBkgCut

std::vector<Bool_t> TMVA::MethodBDT::fIsHighBkgCut

private

Definition at line 292 of file MethodBDT.h.

fIsHighSigCut

std::vector<Bool_t> TMVA::MethodBDT::fIsHighSigCut

private

Definition at line 291 of file MethodBDT.h.

fIsLowBkgCut

std::vector<Bool_t> TMVA::MethodBDT::fIsLowBkgCut

private

Definition at line 290 of file MethodBDT.h.

fIsLowSigCut

std::vector<Bool_t> TMVA::MethodBDT::fIsLowSigCut

private

Definition at line 289 of file MethodBDT.h.

fITree

Int_t TMVA::MethodBDT::fITree

private

ntuple var: ith tree

Definition at line 265 of file MethodBDT.h.

fJobName

TString TMVA::MethodBase::fJobName

privateinherited

Definition at line 617 of file MethodBase.h.

fLastDeclaredOption

OptionBase* TMVA::Configurable::fLastDeclaredOption

privateinherited

! last declared option

Definition at line 113 of file Configurable.h.

fListOfOptions

TList TMVA::Configurable::fListOfOptions

privateinherited

option list

Definition at line 114 of file Configurable.h.

fLogger

MsgLogger* TMVA::Configurable::fLogger

mutableprotectedinherited

! message logger

Definition at line 128 of file Configurable.h.

fLooseOptionCheckingEnabled

Bool_t TMVA::Configurable::fLooseOptionCheckingEnabled

privateinherited

checker for option string

Definition at line 110 of file Configurable.h.

fLossFunctionEventInfo

std::map< const TMVA::Event*, LossFunctionEventInfo> TMVA::MethodBDT::fLossFunctionEventInfo

private

map event to true value, predicted value, and weight used by different loss functions for BDT regression

Definition at line 224 of file MethodBDT.h.

fLowBkgCut

std::vector<Double_t> TMVA::MethodBDT::fLowBkgCut

private

Definition at line 285 of file MethodBDT.h.

fLowSigCut

std::vector<Double_t> TMVA::MethodBDT::fLowSigCut

private

Definition at line 284 of file MethodBDT.h.

fMaxDepth

UInt_t TMVA::MethodBDT::fMaxDepth

private

max depth

Definition at line 242 of file MethodBDT.h.

fMeanB

Double_t TMVA::MethodBase::fMeanB

privateinherited

mean (background)

Definition at line 665 of file MethodBase.h.

fMeanS

Double_t TMVA::MethodBase::fMeanS

privateinherited

mean (signal)

Definition at line 664 of file MethodBase.h.

fMethodBaseDir

TDirectory* TMVA::MethodBase::fMethodBaseDir

mutableprivateinherited

Definition at line 629 of file MethodBase.h.

fMethodName

TString TMVA::MethodBase::fMethodName

privateinherited

Definition at line 618 of file MethodBase.h.

fMethodType

Types::EMVA TMVA::MethodBase::fMethodType

privateinherited

Definition at line 619 of file MethodBase.h.

fMinLinCorrForFisher

Double_t TMVA::MethodBDT::fMinLinCorrForFisher

private

the minimum linear correlation between two variables demanded for use in fisher criterium in node splitting

Definition at line 237 of file MethodBDT.h.

fMinNodeEvents

Int_t TMVA::MethodBDT::fMinNodeEvents

private

min number of events in node

Definition at line 231 of file MethodBDT.h.

fMinNodeSize

Float_t TMVA::MethodBDT::fMinNodeSize

private

min percentage of training events in node

Definition at line 232 of file MethodBDT.h.

fMinNodeSizeS

TString TMVA::MethodBDT::fMinNodeSizeS

private

string containing min percentage of training events in node

Definition at line 233 of file MethodBDT.h.

fModelPersistence

Bool_t TMVA::MethodBase::fModelPersistence

privateinherited

Definition at line 636 of file MethodBase.h.

fMonitorNtuple

TTree* TMVA::MethodBDT::fMonitorNtuple

private

monitoring ntuple

Definition at line 264 of file MethodBDT.h.

fMulticlassReturnVal

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

protectedinherited

Definition at line 601 of file MethodBase.h.

fMVAPdfB

PDF* TMVA::MethodBase::fMVAPdfB

privateinherited

background MVA PDF

Definition at line 649 of file MethodBase.h.

fMVAPdfS

PDF* TMVA::MethodBase::fMVAPdfS

privateinherited

signal MVA PDF

Definition at line 648 of file MethodBase.h.

fName

TString TNamed::fName

protectedinherited

Definition at line 32 of file TNamed.h.

fNbins

Int_t TMVA::MethodBase::fNbins

protectedinherited

Definition at line 594 of file MethodBase.h.

fNbinsH

Int_t TMVA::MethodBase::fNbinsH

protectedinherited

Definition at line 596 of file MethodBase.h.

fNbinsMVAoutput

Int_t TMVA::MethodBase::fNbinsMVAoutput

protectedinherited

Definition at line 595 of file MethodBase.h.

fNbinsMVAPdf

Int_t TMVA::MethodBase::fNbinsMVAPdf

privateinherited

Definition at line 729 of file MethodBase.h.

fNCuts

Int_t TMVA::MethodBDT::fNCuts

private

grid used in cut applied in node splitting

Definition at line 235 of file MethodBDT.h.

fNegWeightTreatment

TString TMVA::MethodBDT::fNegWeightTreatment

private

variable that holds the option of how to treat negative event weights in training

Definition at line 255 of file MethodBDT.h.

fNNodesMax

UInt_t TMVA::MethodBDT::fNNodesMax

private

max # of nodes

Definition at line 241 of file MethodBDT.h.

fNodePurityLimit

Double_t TMVA::MethodBDT::fNodePurityLimit

private

purity limit for sig/bkg nodes

Definition at line 240 of file MethodBDT.h.

fNoNegWeightsInTraining

Bool_t TMVA::MethodBDT::fNoNegWeightsInTraining

private

ignore negative event weights in the training

Definition at line 256 of file MethodBDT.h.

fNormalise

Bool_t TMVA::MethodBase::fNormalise

privateinherited

Definition at line 725 of file MethodBase.h.

fNsmoothMVAPdf

Int_t TMVA::MethodBase::fNsmoothMVAPdf

privateinherited

Definition at line 730 of file MethodBase.h.

fNTrees

Int_t TMVA::MethodBDT::fNTrees

private

number of decision trees requested

Definition at line 211 of file MethodBDT.h.

fOptions

TString TMVA::Configurable::fOptions

privateinherited

options string

Definition at line 109 of file Configurable.h.

fPairNegWeightsGlobal

Bool_t TMVA::MethodBDT::fPairNegWeightsGlobal

private

pair ev. with neg. and pos. weights in training sample and "annihilate" them

Definition at line 258 of file MethodBDT.h.

fParentDir

TString TMVA::MethodBase::fParentDir

privateinherited

method parent name, like booster name

Definition at line 638 of file MethodBase.h.

fPruneMethod

DecisionTree::EPruneMethod TMVA::MethodBDT::fPruneMethod

private

method used for pruning

Definition at line 244 of file MethodBDT.h.

fPruneMethodS

TString TMVA::MethodBDT::fPruneMethodS

private

prune method option String

Definition at line 245 of file MethodBDT.h.

fPruneStrength

Double_t TMVA::MethodBDT::fPruneStrength

private

a parameter to set the "amount" of pruning..needs to be adjusted

Definition at line 246 of file MethodBDT.h.

fRandomisedTrees

Bool_t TMVA::MethodBDT::fRandomisedTrees

private

choose a random subset of possible cut variables at each node during training

Definition at line 249 of file MethodBDT.h.

fRanking

Ranking* TMVA::MethodBase::fRanking

protectedinherited

Definition at line 590 of file MethodBase.h.

fReferenceFile

TString TMVA::Configurable::fReferenceFile

privateinherited

reference file for options writing

Definition at line 117 of file Configurable.h.

fRegressionLossFunctionBDTG

LossFunctionBDT* TMVA::MethodBDT::fRegressionLossFunctionBDTG

private

Definition at line 299 of file MethodBDT.h.

fRegressionLossFunctionBDTGS

TString TMVA::MethodBDT::fRegressionLossFunctionBDTGS

private

the option string determining the loss function for BDT regression

Definition at line 296 of file MethodBDT.h.

fRegressionReturnVal

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

protectedinherited

Definition at line 600 of file MethodBase.h.

fResiduals

std::map< const TMVA::Event*,std::vector<double> > TMVA::MethodBDT::fResiduals

private

individual event residuals for gradient boost

Definition at line 226 of file MethodBDT.h.

fResults

Results* TMVA::MethodBase::fResults

protectedinherited

Definition at line 733 of file MethodBase.h.

fRmsB

Double_t TMVA::MethodBase::fRmsB

privateinherited

RMS (background).

Definition at line 667 of file MethodBase.h.

fRmsS

Double_t TMVA::MethodBase::fRmsS

privateinherited

RMS (signal).

Definition at line 666 of file MethodBase.h.

fROOTTrainingVersion

UInt_t TMVA::MethodBase::fROOTTrainingVersion

privateinherited

Definition at line 622 of file MethodBase.h.

fSepType

SeparationBase* TMVA::MethodBDT::fSepType

private

the separation used in node splitting

Definition at line 229 of file MethodBDT.h.

fSepTypeS

TString TMVA::MethodBDT::fSepTypeS

private

the separation (option string) used in node splitting

Definition at line 230 of file MethodBDT.h.

fSetupCompleted

Bool_t TMVA::MethodBase::fSetupCompleted

inherited

Definition at line 714 of file MethodBase.h.

fShrinkage

Double_t TMVA::MethodBDT::fShrinkage

private

learning rate for gradient boost;

Definition at line 219 of file MethodBDT.h.

fSignalClass

UInt_t TMVA::MethodBase::fSignalClass

protectedinherited

Definition at line 692 of file MethodBase.h.

fSignalReferenceCut

Double_t TMVA::MethodBase::fSignalReferenceCut

privateinherited

Definition at line 612 of file MethodBase.h.

fSignalReferenceCutOrientation

Double_t TMVA::MethodBase::fSignalReferenceCutOrientation

privateinherited

Definition at line 613 of file MethodBase.h.

fSigToBkgFraction

Double_t TMVA::MethodBDT::fSigToBkgFraction

private

Signal to Background fraction assumed during training.

Definition at line 214 of file MethodBDT.h.

fSilentFile

Bool_t TMVA::MethodBase::fSilentFile

privateinherited

Definition at line 634 of file MethodBase.h.

fSkipNormalization

Bool_t TMVA::MethodBDT::fSkipNormalization

private

true for skipping normalization at initialization of trees

Definition at line 276 of file MethodBDT.h.

fSplB

PDF* TMVA::MethodBase::fSplB

privateinherited

PDFs of MVA distribution (background).

Definition at line 654 of file MethodBase.h.

fSpleffBvsS

TSpline* TMVA::MethodBase::fSpleffBvsS

privateinherited

splines for signal eff. versus background eff.

Definition at line 655 of file MethodBase.h.

fSplRefB

TSpline1* TMVA::MethodBase::fSplRefB

privateinherited

Definition at line 706 of file MethodBase.h.

fSplRefS

TSpline1* TMVA::MethodBase::fSplRefS

privateinherited

Definition at line 705 of file MethodBase.h.

fSplS

PDF* TMVA::MethodBase::fSplS

privateinherited

PDFs of MVA distribution (signal).

Definition at line 653 of file MethodBase.h.

fSplTrainB

PDF* TMVA::MethodBase::fSplTrainB

privateinherited

PDFs of training MVA distribution (background).

Definition at line 658 of file MethodBase.h.

fSplTrainEffBvsS

TSpline* TMVA::MethodBase::fSplTrainEffBvsS

privateinherited

splines for training signal eff. versus background eff.

Definition at line 659 of file MethodBase.h.

fSplTrainRefB

TSpline1* TMVA::MethodBase::fSplTrainRefB

privateinherited

Definition at line 709 of file MethodBase.h.

fSplTrainRefS

TSpline1* TMVA::MethodBase::fSplTrainRefS

privateinherited

Definition at line 708 of file MethodBase.h.

fSplTrainS

PDF* TMVA::MethodBase::fSplTrainS

privateinherited

PDFs of training MVA distribution (signal).

Definition at line 657 of file MethodBase.h.

fSubSample

std::vector<const TMVA::Event*> TMVA::MethodBDT::fSubSample

private

subsample for bagged grad boost

Definition at line 208 of file MethodBDT.h.

fTestTime

Double_t TMVA::MethodBase::fTestTime

privateinherited

Definition at line 699 of file MethodBase.h.

fTestvar

TString TMVA::MethodBase::fTestvar

privateinherited

Definition at line 620 of file MethodBase.h.

fTitle

TString TNamed::fTitle

protectedinherited

Definition at line 33 of file TNamed.h.

fTmpData

DataSet* TMVA::MethodBase::fTmpData = nullptr

protectedinherited

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

Definition at line 449 of file MethodBase.h.

fTmpEvent

const Event* TMVA::MethodBase::fTmpEvent

mutableprotectedinherited

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

Definition at line 448 of file MethodBase.h.

fTMVATrainingVersion

UInt_t TMVA::MethodBase::fTMVATrainingVersion

privateinherited

Definition at line 621 of file MethodBase.h.

fTrainHistory

TrainingHistory TMVA::MethodBase::fTrainHistory

inherited

Definition at line 428 of file MethodBase.h.

fTrainSample

std::vector<const TMVA::Event*>* TMVA::MethodBDT::fTrainSample

private

pointer to sample actually used in training (fEventSample or fSubSample) for example

Definition at line 209 of file MethodBDT.h.

fTrainTime

Double_t TMVA::MethodBase::fTrainTime

privateinherited

Definition at line 698 of file MethodBase.h.

fTrainWithNegWeights

Bool_t TMVA::MethodBDT::fTrainWithNegWeights

private

yes there are negative event weights and we don't ignore them

Definition at line 259 of file MethodBDT.h.

fTransformation

TransformationHandler TMVA::MethodBase::fTransformation

privateinherited

the list of transformations

Definition at line 675 of file MethodBase.h.

fTransformationPointer

TransformationHandler* TMVA::MethodBase::fTransformationPointer

privateinherited

pointer to the rest of transformations

Definition at line 674 of file MethodBase.h.

fTxtWeightsOnly

Bool_t TMVA::MethodBase::fTxtWeightsOnly

privateinherited

Definition at line 728 of file MethodBase.h.

fUniqueID

UInt_t TObject::fUniqueID

privateinherited

object unique identifier

Definition at line 46 of file TObject.h.

fUseDecorr

Bool_t TMVA::MethodBase::fUseDecorr

privateinherited

Definition at line 726 of file MethodBase.h.

fUseExclusiveVars

Bool_t TMVA::MethodBDT::fUseExclusiveVars

private

individual variables already used in fisher criterium are not anymore analysed individually for node splitting

Definition at line 238 of file MethodBDT.h.

fUseFisherCuts

Bool_t TMVA::MethodBDT::fUseFisherCuts

private

use multivariate splits using the Fisher criterium

Definition at line 236 of file MethodBDT.h.

fUseNTrainEvents

UInt_t TMVA::MethodBDT::fUseNTrainEvents

private

number of randomly picked training events used in randomised (and bagged) trees

Definition at line 252 of file MethodBDT.h.

fUseNvars

UInt_t TMVA::MethodBDT::fUseNvars

private

the number of variables used in the randomised tree splitting

Definition at line 250 of file MethodBDT.h.

fUsePoissonNvars

Bool_t TMVA::MethodBDT::fUsePoissonNvars

private

use "fUseNvars" not as fixed number but as mean of a poisson distr. in each split

Definition at line 251 of file MethodBDT.h.

fUseYesNoLeaf

Bool_t TMVA::MethodBDT::fUseYesNoLeaf

private

use sig or bkg classification in leave nodes or sig/bkg

Definition at line 239 of file MethodBDT.h.

fValidationSample

std::vector<const TMVA::Event*> TMVA::MethodBDT::fValidationSample

private

the Validation events

Definition at line 207 of file MethodBDT.h.

fVariableImportance

std::vector<Double_t> TMVA::MethodBDT::fVariableImportance

private

the relative importance of the different variables

Definition at line 278 of file MethodBDT.h.

fVariableTransformType

Types::ESBType TMVA::MethodBase::fVariableTransformType

privateinherited

Definition at line 614 of file MethodBase.h.

fVariableTransformTypeString

TString TMVA::MethodBase::fVariableTransformTypeString

privateinherited

Definition at line 727 of file MethodBase.h.

fVarTransformString

TString TMVA::MethodBase::fVarTransformString

privateinherited

labels variable transform method

Definition at line 672 of file MethodBase.h.

fVerbose

Bool_t TMVA::MethodBase::fVerbose

privateinherited

verbose flag

Definition at line 679 of file MethodBase.h.

fVerbosityLevel

EMsgType TMVA::MethodBase::fVerbosityLevel

privateinherited

verbosity level

Definition at line 681 of file MethodBase.h.

fVerbosityLevelString

TString TMVA::MethodBase::fVerbosityLevelString

privateinherited

verbosity level (user input string)

Definition at line 680 of file MethodBase.h.

fWeightFile

TString TMVA::MethodBase::fWeightFile

privateinherited

weight file name

Definition at line 641 of file MethodBase.h.

fXmax

Double_t TMVA::MethodBase::fXmax

privateinherited

maximum (signal and background)

Definition at line 669 of file MethodBase.h.

fXmin

Double_t TMVA::MethodBase::fXmin

privateinherited

minimum (signal and background)

Definition at line 668 of file MethodBase.h.

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

• tmva/tmva/inc/TMVA/MethodBDT.h
• tmva/tmva/src/MethodBDT.cxx