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 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	AddWeightsXMLTo (void *parent) const
	Write weights to XML.
Double_t	Boost (std::vector< const TMVA::Event * > &, DecisionTree *dt, UInt_t cls=0)
	Apply the boosting algorithm (the algorithm is selecte via the the "option" given in the constructor.
const Ranking *	CreateRanking ()
	Compute ranking of input variables.
void	DeclareOptions ()
	Define the options (their key words).
const std::vector< double > &	GetBoostWeights () const
const std::vector< TMVA::DecisionTree * > &	GetForest () const
void	GetHelpMessage () const
	Get help message text.
const std::vector< Float_t > &	GetMulticlassValues ()
	Get the multiclass MVA response for the BDT classifier.
Double_t	GetMvaValue (Double_t *err=0, Double_t *errUpper=0)
UInt_t	GetNTrees () const
const std::vector< Float_t > &	GetRegressionValues ()
	Get the regression value generated by the BDTs.
const std::vector< const TMVA::Event * > &	GetTrainingEvents () const
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.
virtual Bool_t	HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)
	BDT can handle classification with multiple classes and regression with one regression-target.
void	InitEventSample ()
	Initialize the event sample (i.e. reset the boost-weights... etc).
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
	Make ROOT-independent C++ class for classifier response (classifier-specific implementation).
void	MakeClassSpecificHeader (std::ostream &, const TString &) const
	Specific class header.
virtual std::map< TString, Double_t >	OptimizeTuningParameters (TString fomType="ROCIntegral", TString fitType="FitGA")
	Call the Optimizer with the set of parameters and ranges that are meant to be tuned.
void	ProcessOptions ()
	The option string is decoded, for available options see "DeclareOptions".
virtual void	ReadWeightsFromStream (std::istream &)=0
void	ReadWeightsFromStream (std::istream &istr)
	Read the weights (BDT coefficients).
virtual void	ReadWeightsFromStream (TFile &)
void	ReadWeightsFromXML (void *parent)
	Reads the BDT from the xml file.
void	Reset (void)
	Reset the method, as if it had just been instantiated (forget all training etc.).
void	SetAdaBoostBeta (Double_t b)
void	SetBaggedSampleFraction (Double_t f)
void	SetMaxDepth (Int_t d)
void	SetMinNodeSize (Double_t sizeInPercent)
void	SetMinNodeSize (TString sizeInPercent)
void	SetNodePurityLimit (Double_t l)
void	SetNTrees (Int_t d)
void	SetShrinkage (Double_t s)
virtual void	SetTuneParameters (std::map< TString, Double_t > tuneParameters)
	Set the tuning parameters according to the argument.
void	SetUseNvars (Int_t n)
Double_t	TestTreeQuality (DecisionTree *dt)
	Test the tree quality.. in terms of Misclassification.
void	Train (void)
	BDT training.
void	WriteMonitoringHistosToFile (void) const
	Here we could write some histograms created during the processing to the output file.
Public Member Functions inherited from TMVA::MethodBase
	MethodBase (const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="")
	standard constructor
	MethodBase (Types::EMVA methodType, DataSetInfo &dsi, const TString &weightFile)
	constructor used for Testing + Application of the MVA, only (no training), using given WeightFiles
virtual	~MethodBase ()
	destructor
void	AddOutput (Types::ETreeType type, Types::EAnalysisType analysisType)
TDirectory *	BaseDir () const
	returns the ROOT directory where info/histograms etc of the corresponding MVA method instance are stored
virtual void	CheckSetup ()
	check may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase)
DataSet *	Data () const
DataSetInfo &	DataInfo () const
void	DisableWriting (Bool_t setter)
Bool_t	DoMulticlass () const
Bool_t	DoRegression () const
void	ExitFromTraining ()
Types::EAnalysisType	GetAnalysisType () const
UInt_t	GetCurrentIter ()
virtual Double_t	GetEfficiency (const TString &, Types::ETreeType, Double_t &err)
	fill background efficiency (resp.
const Event *	GetEvent () const
const Event *	GetEvent (const TMVA::Event *ev) const
const Event *	GetEvent (Long64_t ievt) const
const Event *	GetEvent (Long64_t ievt, Types::ETreeType type) const
const std::vector< TMVA::Event * > &	GetEventCollection (Types::ETreeType type)
	returns the event collection (i.e.
TFile *	GetFile () const
const TString &	GetInputLabel (Int_t i) const
const char *	GetInputTitle (Int_t i) const
const TString &	GetInputVar (Int_t i) const
TMultiGraph *	GetInteractiveTrainingError ()
const TString &	GetJobName () const
virtual Double_t	GetKSTrainingVsTest (Char_t SorB, TString opt="X")
virtual Double_t	GetMaximumSignificance (Double_t SignalEvents, Double_t BackgroundEvents, Double_t &optimal_significance_value) const
	plot significance, \( \frac{S}{\sqrt{S^2 + B^2}} \), curve for given number of signal and background events; returns cut for maximum significance also returned via reference is the maximum significance
UInt_t	GetMaxIter ()
Double_t	GetMean (Int_t ivar) const
const TString &	GetMethodName () const
Types::EMVA	GetMethodType () const
TString	GetMethodTypeName () const
virtual TMatrixD	GetMulticlassConfusionMatrix (Double_t effB, Types::ETreeType type)
	Construct a confusion matrix for a multiclass classifier.
virtual std::vector< Float_t >	GetMulticlassEfficiency (std::vector< std::vector< Float_t > > &purity)
virtual std::vector< Float_t >	GetMulticlassTrainingEfficiency (std::vector< std::vector< Float_t > > &purity)
Double_t	GetMvaValue (const TMVA::Event *const ev, Double_t *err=0, Double_t *errUpper=0)
const char *	GetName () const
UInt_t	GetNEvents () const
UInt_t	GetNTargets () const
UInt_t	GetNvar () const
UInt_t	GetNVariables () const
virtual Double_t	GetProba (const Event *ev)
virtual Double_t	GetProba (Double_t mvaVal, Double_t ap_sig)
	compute likelihood ratio
const TString	GetProbaName () const
virtual Double_t	GetRarity (Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const
	compute rarity:
virtual void	GetRegressionDeviation (UInt_t tgtNum, Types::ETreeType type, Double_t &stddev, Double_t &stddev90Percent) const
const std::vector< Float_t > &	GetRegressionValues (const TMVA::Event *const ev)
Double_t	GetRMS (Int_t ivar) const
virtual Double_t	GetROCIntegral (PDF *pdfS=0, PDF *pdfB=0) 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=0, PDF *pdfB=0) const
	compute "separation" defined as
virtual Double_t	GetSeparation (TH1 *, TH1 *) const
	compute "separation" defined as
Double_t	GetSignalReferenceCut () const
Double_t	GetSignalReferenceCutOrientation () const
virtual Double_t	GetSignificance () const
	compute significance of mean difference
const Event *	GetTestingEvent (Long64_t ievt) const
Double_t	GetTestTime () const
const TString &	GetTestvarName () const
virtual Double_t	GetTrainingEfficiency (const TString &)
const Event *	GetTrainingEvent (Long64_t ievt) const
virtual const std::vector< Float_t > &	GetTrainingHistory (const char *)
UInt_t	GetTrainingROOTVersionCode () const
TString	GetTrainingROOTVersionString () const
	calculates the ROOT version string from the training version code on the fly
UInt_t	GetTrainingTMVAVersionCode () const
TString	GetTrainingTMVAVersionString () const
	calculates the TMVA version string from the training version code on the fly
Double_t	GetTrainTime () const
TransformationHandler &	GetTransformationHandler (Bool_t takeReroutedIfAvailable=true)
const TransformationHandler &	GetTransformationHandler (Bool_t takeReroutedIfAvailable=true) const
TString	GetWeightFileName () const
	retrieve weight file name
Double_t	GetXmax (Int_t ivar) const
Double_t	GetXmin (Int_t ivar) const
Bool_t	HasMVAPdfs () const
void	InitIPythonInteractive ()
Bool_t	IsModelPersistence () const
virtual Bool_t	IsSignalLike ()
	uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for a quick determination if an event would be selected as signal or background
virtual Bool_t	IsSignalLike (Double_t mvaVal)
	uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for a quick determination if an event with this mva output value would be selected as signal or background
Bool_t	IsSilentFile () const
virtual void	MakeClass (const TString &classFileName=TString("")) const
	create reader class for method (classification only at present)
TDirectory *	MethodBaseDir () const
	returns the ROOT directory where all instances of the corresponding MVA method are stored
void	PrintHelpMessage () const
	prints out method-specific help method
void	ProcessSetup ()
	process all options the "CheckForUnusedOptions" is done in an independent call, since it may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase)
void	ReadStateFromFile ()
	Function to write options and weights to file.
void	ReadStateFromStream (std::istream &tf)
	read the header from the weight files of the different MVA methods
void	ReadStateFromStream (TFile &rf)
	write reference MVA distributions (and other information) to a ROOT type weight file
void	ReadStateFromXMLString (const char *xmlstr)
	for reading from memory
void	RerouteTransformationHandler (TransformationHandler *fTargetTransformation)
virtual void	SetAnalysisType (Types::EAnalysisType type)
void	SetBaseDir (TDirectory *methodDir)
void	SetFile (TFile *file)
void	SetMethodBaseDir (TDirectory *methodDir)
void	SetMethodDir (TDirectory *methodDir)
void	SetModelPersistence (Bool_t status)
void	SetSignalReferenceCut (Double_t cut)
void	SetSignalReferenceCutOrientation (Double_t cutOrientation)
void	SetSilentFile (Bool_t status)
void	SetTestTime (Double_t testTime)
void	SetTestvarName (const TString &v="")
void	SetTrainTime (Double_t trainTime)
void	SetupMethod ()
	setup of methods
virtual void	TestClassification ()
	initialization
virtual void	TestMulticlass ()
	test multiclass classification
virtual void	TestRegression (Double_t &bias, Double_t &biasT, Double_t &dev, Double_t &devT, Double_t &rms, Double_t &rmsT, Double_t &mInf, Double_t &mInfT, Double_t &corr, Types::ETreeType type)
	calculate <sum-of-deviation-squared> of regression output versus "true" value from test sample
bool	TrainingEnded ()
void	TrainMethod ()
virtual void	WriteEvaluationHistosToFile (Types::ETreeType treetype)
	writes all MVA evaluation histograms to file
void	WriteStateToFile () const
	write options and weights to file note that each one text file for the main configuration information and one ROOT file for ROOT objects are created
Public Member Functions inherited from TMVA::IMethod
	IMethod ()
virtual	~IMethod ()
Public Member Functions inherited from TMVA::Configurable
	Configurable (const TString &theOption="")
	constructor
virtual	~Configurable ()
	default destructor
void	AddOptionsXMLTo (void *parent) const
	write options to XML file
template<class T >
void	AddPreDefVal (const T &)
template<class T >
void	AddPreDefVal (const TString &optname, const T &)
void	CheckForUnusedOptions () const
	checks for unused options in option string
template<class T >
TMVA::OptionBase *	DeclareOptionRef (T &ref, const TString &name, const TString &desc)
template<class T >
OptionBase *	DeclareOptionRef (T &ref, const TString &name, const TString &desc="")
template<class T >
TMVA::OptionBase *	DeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc)
template<class T >
OptionBase *	DeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc="")
const char *	GetConfigDescription () const
const char *	GetConfigName () const
const TString &	GetOptions () const
MsgLogger &	Log () const
virtual void	ParseOptions ()
	options parser
void	PrintOptions () const
	prints out the options set in the options string and the defaults
void	ReadOptionsFromStream (std::istream &istr)
	read option back from the weight file
void	ReadOptionsFromXML (void *node)
void	SetConfigDescription (const char *d)
void	SetConfigName (const char *n)
void	SetMsgType (EMsgType t)
void	SetOptions (const TString &s)
void	WriteOptionsToStream (std::ostream &o, const TString &prefix) const
	write options to output stream (e.g. in writing the MVA weight files
Public Member Functions inherited from TNamed
	TNamed ()
	TNamed (const char *name, const char *title)
	TNamed (const TNamed &named)
	TNamed copy ctor.
	TNamed (const TString &name, const TString &title)
virtual	~TNamed ()
	TNamed destructor.
virtual void	Clear (Option_t *option="")
	Set name and title to empty strings ("").
virtual TObject *	Clone (const char *newname="") const
	Make a clone of an object using the Streamer facility.
virtual Int_t	Compare (const TObject *obj) const
	Compare two TNamed objects.
virtual void	Copy (TObject &named) const
	Copy this to obj.
virtual void	FillBuffer (char *&buffer)
	Encode TNamed into output buffer.
virtual const char *	GetTitle () const
	Returns title of object.
virtual ULong_t	Hash () const
	Return hash value for this object.
virtual Bool_t	IsSortable () const
virtual void	ls (Option_t *option="") const
	List TNamed name and title.
TNamed &	operator= (const TNamed &rhs)
	TNamed assignment operator.
virtual void	Print (Option_t *option="") const
	Print TNamed name and title.
virtual void	SetName (const char *name)
	Set the name of the TNamed.
virtual void	SetNameTitle (const char *name, const char *title)
	Set all the TNamed parameters (name and title).
virtual void	SetTitle (const char *title="")
	Set the title of the TNamed.
virtual Int_t	Sizeof () const
	Return size of the TNamed part of the TObject.
Public Member Functions inherited from TObject
	TObject ()
	TObject constructor.
	TObject (const TObject &object)
	TObject copy ctor.
virtual	~TObject ()
	TObject destructor.
void	AbstractMethod (const char *method) const
	Use this method to implement an "abstract" method that you don't want to leave purely abstract.
virtual void	AppendPad (Option_t *option="")
	Append graphics object to current pad.
virtual void	Browse (TBrowser *b)
	Browse object. May be overridden for another default action.
ULong_t	CheckedHash ()
	Check and record whether this class has a consistent Hash/RecursiveRemove setup (*) and then return the regular Hash value for this object.
virtual const char *	ClassName () const
	Returns name of class to which the object belongs.
virtual void	Delete (Option_t *option="")
	Delete this object.
virtual Int_t	DistancetoPrimitive (Int_t px, Int_t py)
	Computes distance from point (px,py) to the object.
virtual void	Draw (Option_t *option="")
	Default Draw method for all objects.
virtual void	DrawClass () const
	Draw class inheritance tree of the class to which this object belongs.
virtual TObject *	DrawClone (Option_t *option="") const
	Draw a clone of this object in the current selected pad for instance with: gROOT->SetSelectedPad(gPad).
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=0)
	Execute method on this object with the given parameter string, e.g.
virtual void	Execute (TMethod *method, TObjArray *params, Int_t *error=0)
	Execute method on this object with parameters stored in the TObjArray.
virtual void	ExecuteEvent (Int_t event, Int_t px, Int_t py)
	Execute action corresponding to an event at (px,py).
virtual void	Fatal (const char *method, const char *msgfmt,...) const
	Issue fatal error message.
virtual TObject *	FindObject (const char *name) const
	Must be redefined in derived classes.
virtual TObject *	FindObject (const TObject *obj) const
	Must be redefined in derived classes.
virtual Option_t *	GetDrawOption () const
	Get option used by the graphics system to draw this object.
virtual const char *	GetIconName () const
	Returns mime type name of object.
virtual char *	GetObjectInfo (Int_t px, Int_t py) const
	Returns string containing info about the object at position (px,py).
virtual Option_t *	GetOption () const
virtual UInt_t	GetUniqueID () const
	Return the unique object id.
virtual Bool_t	HandleTimer (TTimer *timer)
	Execute action in response of a timer timing out.
Bool_t	HasInconsistentHash () const
	Return true is the type of this object is known to have an inconsistent setup for Hash and RecursiveRemove (i.e.
virtual void	Info (const char *method, const char *msgfmt,...) const
	Issue info message.
virtual Bool_t	InheritsFrom (const char *classname) const
	Returns kTRUE if object inherits from class "classname".
virtual Bool_t	InheritsFrom (const TClass *cl) const
	Returns kTRUE if object inherits from TClass cl.
virtual void	Inspect () const
	Dump contents of this object in a graphics canvas.
void	InvertBit (UInt_t f)
Bool_t	IsDestructed () const
	IsDestructed.
virtual Bool_t	IsEqual (const TObject *obj) const
	Default equal comparison (objects are equal if they have the same address in memory).
virtual Bool_t	IsFolder () const
	Returns kTRUE in case object contains browsable objects (like containers or lists of other objects).
R__ALWAYS_INLINE Bool_t	IsOnHeap () const
R__ALWAYS_INLINE Bool_t	IsZombie () const
void	MayNotUse (const char *method) const
	Use this method to signal that a method (defined in a base class) may not be called in a derived class (in principle against good design since a child class should not provide less functionality than its parent, however, sometimes it is necessary).
virtual Bool_t	Notify ()
	This method must be overridden to handle object notification.
void	Obsolete (const char *method, const char *asOfVers, const char *removedFromVers) const
	Use this method to declare a method obsolete.
void	operator delete (void *ptr)
	Operator delete.
void	operator delete[] (void *ptr)
	Operator delete [].
void *	operator new (size_t sz)
void *	operator new (size_t sz, void *vp)
void *	operator new[] (size_t sz)
void *	operator new[] (size_t sz, void *vp)
TObject &	operator= (const TObject &rhs)
	TObject assignment operator.
virtual void	Paint (Option_t *option="")
	This method must be overridden if a class wants to paint itself.
virtual void	Pop ()
	Pop on object drawn in a pad to the top of the display list.
virtual Int_t	Read (const char *name)
	Read contents of object with specified name from the current directory.
virtual void	RecursiveRemove (TObject *obj)
	Recursively remove this object from a list.
void	ResetBit (UInt_t f)
virtual void	SaveAs (const char *filename="", Option_t *option="") const
	Save this object in the file specified by filename.
virtual void	SavePrimitive (std::ostream &out, Option_t *option="")
	Save a primitive as a C++ statement(s) on output stream "out".
void	SetBit (UInt_t f)
void	SetBit (UInt_t f, Bool_t set)
	Set or unset the user status bits as specified in f.
virtual void	SetDrawOption (Option_t *option="")
	Set drawing option for object.
virtual void	SetUniqueID (UInt_t uid)
	Set the unique object id.
virtual void	SysError (const char *method, const char *msgfmt,...) const
	Issue system error message.
R__ALWAYS_INLINE Bool_t	TestBit (UInt_t f) const
Int_t	TestBits (UInt_t f) const
virtual void	UseCurrentStyle ()
	Set current style settings in this object This function is called when either TCanvas::UseCurrentStyle or TROOT::ForceStyle have been invoked.
virtual void	Warning (const char *method, const char *msgfmt,...) const
	Issue warning message.
virtual Int_t	Write (const char *name=0, Int_t option=0, Int_t bufsize=0)
	Write this object to the current directory.
virtual Int_t	Write (const char *name=0, Int_t option=0, Int_t bufsize=0) const
	Write this object to the current directory.

Protected Member Functions
void	DeclareCompatibilityOptions ()
	Options that are used ONLY for the READER to ensure backward compatibility.
Protected Member Functions inherited from TMVA::MethodBase
virtual std::vector< Double_t >	GetDataMvaValues (DataSet *data=nullptr, Long64_t firstEvt=0, Long64_t lastEvt=-1, Bool_t logProgress=false)
	get all the MVA values for the events of the given Data type
const TString &	GetInternalVarName (Int_t ivar) const
virtual std::vector< Double_t >	GetMvaValues (Long64_t firstEvt=0, Long64_t lastEvt=-1, Bool_t logProgress=false)
	get all the MVA values for the events of the current Data type
const TString &	GetOriginalVarName (Int_t ivar) const
const TString &	GetWeightFileDir () const
Bool_t	HasTrainingTree () const
Bool_t	Help () const
Bool_t	IgnoreEventsWithNegWeightsInTraining () const
Bool_t	IsConstructedFromWeightFile () const
Bool_t	IsNormalised () const
void	NoErrorCalc (Double_t *const err, Double_t *const errUpper)
void	SetNormalised (Bool_t norm)
void	SetWeightFileDir (TString fileDir)
	set directory of weight file
void	SetWeightFileName (TString)
	set the weight file name (depreciated)
void	Statistics (Types::ETreeType treeType, const TString &theVarName, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &)
	calculates rms,mean, xmin, xmax of the event variable this can be either done for the variables as they are or for normalised variables (in the range of 0-1) if "norm" is set to kTRUE
Bool_t	TxtWeightsOnly () const
Bool_t	Verbose () const
Protected Member Functions inherited from TMVA::Configurable
void	EnableLooseOptions (Bool_t b=kTRUE)
const TString &	GetReferenceFile () const
Bool_t	LooseOptionCheckingEnabled () const
void	ResetSetFlag ()
	resets the IsSet flag for all declare options to be called before options are read from stream
void	WriteOptionsReferenceToFile ()
	write complete options to output stream
Protected Member Functions inherited from TObject
virtual void	DoError (int level, const char *location, const char *fmt, va_list va) const
	Interface to ErrorHandler (protected).
void	MakeZombie ()

Private Member Functions
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...
Double_t	ApplyPreselectionCuts (const Event *ev)
	Apply the preselection cuts before even bothering about any Decision Trees in the GetMVA .
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	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.
Double_t	GetGradBoostMVA (const TMVA::Event *e, UInt_t nTrees)
	Returns MVA value: -1 for background, 1 for signal.
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.
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)
	Common initialisation with defaults for the BDT-Method.
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=0, Double_t *errUpper=0, 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.
Double_t	RegBoost (std::vector< const TMVA::Event * > &, DecisionTree *dt)
	A special boosting only for Regression (not implemented).
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.

Private Attributes
Double_t	fAdaBoostBeta
TString	fAdaBoostR2Loss
Bool_t	fAutomatic
Bool_t	fBaggedBoost
Bool_t	fBaggedGradBoost
Double_t	fBaggedSampleFraction
TString	fBoostType
Double_t	fBoostWeight
std::vector< double >	fBoostWeights
Double_t	fCbb
Double_t	fCss
Double_t	fCtb_ss
Double_t	fCts_sb
Bool_t	fDoBoostMonitor
Bool_t	fDoPreselection
Double_t	fErrorFraction
std::vector< const TMVA::Event * >	fEventSample
std::vector< DecisionTree * >	fForest
Double_t	fFValidationEvents
std::vector< Double_t >	fHighBkgCut
std::vector< Double_t >	fHighSigCut
Bool_t	fHistoricBool
Double_t	fHuberQuantile
Bool_t	fInverseBoostNegWeights
std::vector< Bool_t >	fIsHighBkgCut
std::vector< Bool_t >	fIsHighSigCut
std::vector< Bool_t >	fIsLowBkgCut
std::vector< Bool_t >	fIsLowSigCut
Int_t	fITree
std::map< const TMVA::Event *, LossFunctionEventInfo >	fLossFunctionEventInfo
std::vector< Double_t >	fLowBkgCut
std::vector< Double_t >	fLowSigCut
UInt_t	fMaxDepth
Double_t	fMinLinCorrForFisher
Int_t	fMinNodeEvents
Float_t	fMinNodeSize
TString	fMinNodeSizeS
TTree *	fMonitorNtuple
Int_t	fNCuts
TString	fNegWeightTreatment
UInt_t	fNNodesMax
Double_t	fNodePurityLimit
Bool_t	fNoNegWeightsInTraining
Int_t	fNTrees
Bool_t	fPairNegWeightsGlobal
DecisionTree::EPruneMethod	fPruneMethod
TString	fPruneMethodS
Double_t	fPruneStrength
Bool_t	fRandomisedTrees
LossFunctionBDT *	fRegressionLossFunctionBDTG
TString	fRegressionLossFunctionBDTGS
std::map< const TMVA::Event *, std::vector< double > >	fResiduals
SeparationBase *	fSepType
TString	fSepTypeS
Double_t	fShrinkage
Double_t	fSigToBkgFraction
Bool_t	fSkipNormalization
std::vector< const TMVA::Event * >	fSubSample
std::vector< const TMVA::Event * > *	fTrainSample
Bool_t	fTrainWithNegWeights
Bool_t	fUseExclusiveVars
Bool_t	fUseFisherCuts
UInt_t	fUseNTrainEvents
UInt_t	fUseNvars
Bool_t	fUsePoissonNvars
Bool_t	fUseYesNoLeaf
std::vector< const TMVA::Event * >	fValidationSample
std::vector< Double_t >	fVariableImportance

Static Private Attributes
static const Int_t	fgDebugLevel = 0

Additional Inherited Members
Public Types inherited from TMVA::MethodBase
enum	EWeightFileType { kROOT =0 , kTEXT }
Public Types inherited from TObject
enum	{   kIsOnHeap = 0x01000000 , kNotDeleted = 0x02000000 , kZombie = 0x04000000 , kInconsistent = 0x08000000 ,   kBitMask = 0x00ffffff }
enum	{ kSingleKey = BIT(0) , kOverwrite = BIT(1) , kWriteDelete = BIT(2) }
enum	EDeprecatedStatusBits { kObjInCanvas = BIT(3) }
enum	EStatusBits {   kCanDelete = BIT(0) , kMustCleanup = BIT(3) , kIsReferenced = BIT(4) , kHasUUID = BIT(5) ,   kCannotPick = BIT(6) , kNoContextMenu = BIT(8) , kInvalidObject = BIT(13) }
Static Public Member Functions inherited from TObject
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 inherited from TMVA::MethodBase
Bool_t	fSetupCompleted
TrainingHistory	fTrainHistory
Protected Types inherited from TObject
enum	{ kOnlyPrepStep = BIT(3) }
Protected Attributes inherited from TMVA::MethodBase
Types::EAnalysisType	fAnalysisType
UInt_t	fBackgroundClass
bool	fExitFromTraining = false
std::vector< TString > *	fInputVars
IPythonInteractive *	fInteractive = nullptr
	temporary dataset used when evaluating on a different data (used by MethodCategory::GetMvaValues)
UInt_t	fIPyCurrentIter = 0
UInt_t	fIPyMaxIter = 0
std::vector< Float_t > *	fMulticlassReturnVal
Int_t	fNbins
Int_t	fNbinsH
Int_t	fNbinsMVAoutput
Ranking *	fRanking
std::vector< Float_t > *	fRegressionReturnVal
Results *	fResults
UInt_t	fSignalClass
DataSet *	fTmpData = nullptr
	temporary event when testing on a different DataSet than the own one
const Event *	fTmpEvent
Protected Attributes inherited from TMVA::Configurable
MsgLogger *	fLogger
Protected Attributes inherited from TNamed
TString	fName
TString	fTitle

#include <TMVA/MethodBDT.h>

Inheritance diagram for TMVA::MethodBDT:

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 163 of file MethodBDT.cxx.

MethodBDT() [2/2]

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

Definition at line 220 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 753 of file MethodBDT.cxx.

Member Function Documentation

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 1846 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 2193 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 2024 of file MethodBDT.cxx.

AddWeightsXMLTo()

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

virtual

Write weights to XML.

Implements TMVA::MethodBase.

Definition at line 2310 of file MethodBDT.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 3136 of file MethodBDT.cxx.

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 2140 of file MethodBDT.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 the "option" given in the constructor.

The return value is the boosting weight.

Definition at line 1718 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 1752 of file MethodBDT.cxx.

CreateRanking()

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

virtual

Compute ranking of input variables.

Implements TMVA::MethodBase.

Definition at line 2683 of file MethodBDT.cxx.

DeclareCompatibilityOptions()

void TMVA::MethodBDT::DeclareCompatibilityOptions ( )

protectedvirtual

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

Reimplemented from TMVA::MethodBase.

Definition at line 454 of file MethodBDT.cxx.

DeclareOptions()

void TMVA::MethodBDT::DeclareOptions ( )

virtual

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 333 of file MethodBDT.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 3036 of file MethodBDT.cxx.

GetBaggedSubSample()

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

private

Fills fEventSample with fBaggedSampleFraction*NEvents random training events.

Definition at line 2151 of file MethodBDT.cxx.

GetBoostWeights()

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

inline

Definition at line 312 of file MethodBDT.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 1421 of file MethodBDT.cxx.

GetHelpMessage()

void TMVA::MethodBDT::GetHelpMessage ( ) const

virtual

Get help message text.

Implements TMVA::IMethod.

Definition at line 2700 of file MethodBDT.cxx.

GetMulticlassValues()

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

virtual

Get the multiclass MVA response for the BDT classifier.

Reimplemented from TMVA::MethodBase.

Definition at line 2495 of file MethodBDT.cxx.

GetMvaValue() [1/2]

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 2452 of file MethodBDT.cxx.

GetMvaValue() [2/2]

Double_t TMVA::MethodBDT::GetMvaValue ( Double_t *  err = 0, Double_t *  errUpper = 0  )

virtual

Implements TMVA::MethodBase.

Definition at line 2443 of file MethodBDT.cxx.

GetNTrees()

UInt_t TMVA::MethodBDT::GetNTrees ( ) const

inline

Definition at line 112 of file MethodBDT.h.

GetRegressionValues()

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

virtual

Get the regression value generated by the BDTs.

Reimplemented from TMVA::MethodBase.

Definition at line 2542 of file MethodBDT.cxx.

GetTrainingEvents()

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

inline

Definition at line 311 of file MethodBDT.h.

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 2643 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 2671 of file MethodBDT.cxx.

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 1595 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 1629 of file MethodBDT.cxx.

HasAnalysisType()

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

virtual

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

Implements TMVA::IMethod.

Definition at line 280 of file MethodBDT.cxx.

Init()

void TMVA::MethodBDT::Init ( void  )

privatevirtual

Common initialisation with defaults for the BDT-Method.

Implements TMVA::MethodBase.

Definition at line 687 of file MethodBDT.cxx.

InitEventSample()

void TMVA::MethodBDT::InitEventSample

(

void

)

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

Definition at line 761 of file MethodBDT.cxx.

InitGradBoost()

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

private

Initialize targets for first tree.

Definition at line 1658 of file MethodBDT.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 2991 of file MethodBDT.cxx.

MakeClassSpecific()

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

virtual

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

Reimplemented from TMVA::MethodBase.

Definition at line 2757 of file MethodBDT.cxx.

MakeClassSpecificHeader()

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

virtual

Specific class header.

Reimplemented from TMVA::MethodBase.

Definition at line 2877 of file MethodBDT.cxx.

OptimizeTuningParameters()

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

virtual

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

Reimplemented from TMVA::MethodBase.

Definition at line 1068 of file MethodBDT.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 932 of file MethodBDT.cxx.

PrivateGetMvaValue()

Double_t TMVA::MethodBDT::PrivateGetMvaValue ( const TMVA::Event *  ev, Double_t *  err = 0, Double_t *  errUpper = 0, 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 2468 of file MethodBDT.cxx.

ProcessOptions()

void TMVA::MethodBDT::ProcessOptions ( )

virtual

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

Implements TMVA::MethodBase.

Definition at line 470 of file MethodBDT.cxx.

ReadWeightsFromStream() [1/3]

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

virtual

Implements TMVA::MethodBase.

ReadWeightsFromStream() [2/3]

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

virtual

Read the weights (BDT coefficients).

Implements TMVA::MethodBase.

Definition at line 2408 of file MethodBDT.cxx.

ReadWeightsFromStream() [3/3]

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

inlinevirtual

Reimplemented from TMVA::MethodBase.

Definition at line 266 of file MethodBase.h.

ReadWeightsFromXML()

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

virtual

Reads the BDT from the xml file.

Implements TMVA::MethodBase.

Definition at line 2341 of file MethodBDT.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 2185 of file MethodBDT.cxx.

Reset()

void TMVA::MethodBDT::Reset ( void  )

virtual

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

Reimplemented from TMVA::MethodBase.

Definition at line 725 of file MethodBDT.cxx.

SetAdaBoostBeta()

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

inline

Definition at line 139 of file MethodBDT.h.

SetBaggedSampleFraction()

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

inline

Definition at line 143 of file MethodBDT.h.

SetMaxDepth()

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

inline

Definition at line 134 of file MethodBDT.h.

SetMinNodeSize() [1/2]

void TMVA::MethodBDT::SetMinNodeSize

(

Double_t

sizeInPercent

)

Definition at line 660 of file MethodBDT.cxx.

SetMinNodeSize() [2/2]

void TMVA::MethodBDT::SetMinNodeSize

(

TString

sizeInPercent

)

Definition at line 674 of file MethodBDT.cxx.

SetNodePurityLimit()

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

inline

Definition at line 140 of file MethodBDT.h.

SetNTrees()

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

inline

Definition at line 138 of file MethodBDT.h.

SetShrinkage()

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

inline

Definition at line 141 of file MethodBDT.h.

SetTuneParameters()

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

virtual

Set the tuning parameters according to the argument.

Reimplemented from TMVA::MethodBase.

Definition at line 1121 of file MethodBDT.cxx.

SetUseNvars()

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

inline

Definition at line 142 of file MethodBDT.h.

TestTreeQuality()

Double_t TMVA::MethodBDT::TestTreeQuality

(

DecisionTree *

dt

)

Test the tree quality.. in terms of Misclassification.

Definition at line 1697 of file MethodBDT.cxx.

Train()

void TMVA::MethodBDT::Train ( void  )

virtual

BDT training.

Implements TMVA::MethodBase.

Definition at line 1142 of file MethodBDT.cxx.

UpdateTargets()

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

private

Calculate residual for all events.

Definition at line 1435 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 1557 of file MethodBDT.cxx.

WriteMonitoringHistosToFile()

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

virtual

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

Reimplemented from TMVA::MethodBase.

Definition at line 2628 of file MethodBDT.cxx.

Member Data Documentation

fAdaBoostBeta

Double_t TMVA::MethodBDT::fAdaBoostBeta

private

Definition at line 216 of file MethodBDT.h.

fAdaBoostR2Loss

TString TMVA::MethodBDT::fAdaBoostR2Loss

private

Definition at line 217 of file MethodBDT.h.

fAutomatic

Bool_t TMVA::MethodBDT::fAutomatic

private

Definition at line 248 of file MethodBDT.h.

fBaggedBoost

Bool_t TMVA::MethodBDT::fBaggedBoost

private

Definition at line 220 of file MethodBDT.h.

fBaggedGradBoost

Bool_t TMVA::MethodBDT::fBaggedGradBoost

private

Definition at line 221 of file MethodBDT.h.

fBaggedSampleFraction

Double_t TMVA::MethodBDT::fBaggedSampleFraction

private

Definition at line 254 of file MethodBDT.h.

fBoostType

TString TMVA::MethodBDT::fBoostType

private

Definition at line 215 of file MethodBDT.h.

fBoostWeight

Double_t TMVA::MethodBDT::fBoostWeight

private

Definition at line 266 of file MethodBDT.h.

fBoostWeights

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

private

Definition at line 213 of file MethodBDT.h.

fCbb

Double_t TMVA::MethodBDT::fCbb

private

Definition at line 272 of file MethodBDT.h.

fCss

Double_t TMVA::MethodBDT::fCss

private

Definition at line 269 of file MethodBDT.h.

fCtb_ss

Double_t TMVA::MethodBDT::fCtb_ss

private

Definition at line 271 of file MethodBDT.h.

fCts_sb

Double_t TMVA::MethodBDT::fCts_sb

private

Definition at line 270 of file MethodBDT.h.

fDoBoostMonitor

Bool_t TMVA::MethodBDT::fDoBoostMonitor

private

Definition at line 260 of file MethodBDT.h.

fDoPreselection

Bool_t TMVA::MethodBDT::fDoPreselection

private

Definition at line 274 of file MethodBDT.h.

fErrorFraction

Double_t TMVA::MethodBDT::fErrorFraction

private

Definition at line 267 of file MethodBDT.h.

fEventSample

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

private

Definition at line 206 of file MethodBDT.h.

fForest

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

private

Definition at line 212 of file MethodBDT.h.

fFValidationEvents

Double_t TMVA::MethodBDT::fFValidationEvents

private

Definition at line 247 of file MethodBDT.h.

fgDebugLevel

const Int_t TMVA::MethodBDT::fgDebugLevel = 0

staticprivate

Definition at line 302 of file MethodBDT.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

Definition at line 297 of file MethodBDT.h.

fInverseBoostNegWeights

Bool_t TMVA::MethodBDT::fInverseBoostNegWeights

private

Definition at line 257 of file MethodBDT.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

Definition at line 265 of file MethodBDT.h.

fLossFunctionEventInfo

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

private

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

Definition at line 242 of file MethodBDT.h.

fMinLinCorrForFisher

Double_t TMVA::MethodBDT::fMinLinCorrForFisher

private

Definition at line 237 of file MethodBDT.h.

fMinNodeEvents

Int_t TMVA::MethodBDT::fMinNodeEvents

private

Definition at line 231 of file MethodBDT.h.

fMinNodeSize

Float_t TMVA::MethodBDT::fMinNodeSize

private

Definition at line 232 of file MethodBDT.h.

fMinNodeSizeS

TString TMVA::MethodBDT::fMinNodeSizeS

private

Definition at line 233 of file MethodBDT.h.

fMonitorNtuple

TTree* TMVA::MethodBDT::fMonitorNtuple

private

Definition at line 264 of file MethodBDT.h.

fNCuts

Int_t TMVA::MethodBDT::fNCuts

private

Definition at line 235 of file MethodBDT.h.

fNegWeightTreatment

TString TMVA::MethodBDT::fNegWeightTreatment

private

Definition at line 255 of file MethodBDT.h.

fNNodesMax

UInt_t TMVA::MethodBDT::fNNodesMax

private

Definition at line 241 of file MethodBDT.h.

fNodePurityLimit

Double_t TMVA::MethodBDT::fNodePurityLimit

private

Definition at line 240 of file MethodBDT.h.

fNoNegWeightsInTraining

Bool_t TMVA::MethodBDT::fNoNegWeightsInTraining

private

Definition at line 256 of file MethodBDT.h.

fNTrees

Int_t TMVA::MethodBDT::fNTrees

private

Definition at line 211 of file MethodBDT.h.

fPairNegWeightsGlobal

Bool_t TMVA::MethodBDT::fPairNegWeightsGlobal

private

Definition at line 258 of file MethodBDT.h.

fPruneMethod

DecisionTree::EPruneMethod TMVA::MethodBDT::fPruneMethod

private

Definition at line 244 of file MethodBDT.h.

fPruneMethodS

TString TMVA::MethodBDT::fPruneMethodS

private

Definition at line 245 of file MethodBDT.h.

fPruneStrength

Double_t TMVA::MethodBDT::fPruneStrength

private

Definition at line 246 of file MethodBDT.h.

fRandomisedTrees

Bool_t TMVA::MethodBDT::fRandomisedTrees

private

Definition at line 249 of file MethodBDT.h.

fRegressionLossFunctionBDTG

LossFunctionBDT* TMVA::MethodBDT::fRegressionLossFunctionBDTG

private

Definition at line 299 of file MethodBDT.h.

fRegressionLossFunctionBDTGS

TString TMVA::MethodBDT::fRegressionLossFunctionBDTGS

private

Definition at line 296 of file MethodBDT.h.

fResiduals

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

private

Definition at line 226 of file MethodBDT.h.

fSepType

SeparationBase* TMVA::MethodBDT::fSepType

private

Definition at line 229 of file MethodBDT.h.

fSepTypeS

TString TMVA::MethodBDT::fSepTypeS

private

Definition at line 230 of file MethodBDT.h.

fShrinkage

Double_t TMVA::MethodBDT::fShrinkage

private

Definition at line 219 of file MethodBDT.h.

fSigToBkgFraction

Double_t TMVA::MethodBDT::fSigToBkgFraction

private

Definition at line 214 of file MethodBDT.h.

fSkipNormalization

Bool_t TMVA::MethodBDT::fSkipNormalization

private

Definition at line 276 of file MethodBDT.h.

fSubSample

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

private

Definition at line 208 of file MethodBDT.h.

fTrainSample

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

private

Definition at line 209 of file MethodBDT.h.

fTrainWithNegWeights

Bool_t TMVA::MethodBDT::fTrainWithNegWeights

private

Definition at line 259 of file MethodBDT.h.

fUseExclusiveVars

Bool_t TMVA::MethodBDT::fUseExclusiveVars

private

Definition at line 238 of file MethodBDT.h.

fUseFisherCuts

Bool_t TMVA::MethodBDT::fUseFisherCuts

private

Definition at line 236 of file MethodBDT.h.

fUseNTrainEvents

UInt_t TMVA::MethodBDT::fUseNTrainEvents

private

Definition at line 252 of file MethodBDT.h.

fUseNvars

UInt_t TMVA::MethodBDT::fUseNvars

private

Definition at line 250 of file MethodBDT.h.

fUsePoissonNvars

Bool_t TMVA::MethodBDT::fUsePoissonNvars

private

Definition at line 251 of file MethodBDT.h.

fUseYesNoLeaf

Bool_t TMVA::MethodBDT::fUseYesNoLeaf

private

Definition at line 239 of file MethodBDT.h.

fValidationSample

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

private

Definition at line 207 of file MethodBDT.h.

fVariableImportance

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

private

Definition at line 278 of file MethodBDT.h.

Libraries for TMVA::MethodBDT:

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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