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Reference Guide
List of all members | Public Member Functions | Protected Member Functions | Private Member Functions | Private Attributes | Static Private Attributes | List of all members
TMVA::MethodBDT Class Reference

Definition at line 64 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" More...
 
 MethodBDT (DataSetInfo &theData, const TString &theWeightFile)
 
virtual ~MethodBDT (void)
 destructor Note: fEventSample and ValidationSample are already deleted at the end of TRAIN When they are not used anymore for (UInt_t i=0; i<fEventSample.size(); i++) delete fEventSample[i]; for (UInt_t i=0; i<fValidationSample.size(); i++) delete fValidationSample[i]; More...
 
void AddWeightsXMLTo (void *parent) const
 write weights to XML More...
 
Double_t Boost (std::vector< const TMVA::Event * > &, DecisionTree *dt, UInt_t cls=0)
 apply the boosting alogrithim (the algorithm is selecte via the the "option" given in the constructor. More...
 
const RankingCreateRanking ()
 Compute ranking of input variables. More...
 
void DeclareOptions ()
 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 UsePoission Nvars use UseNvars not as fixed number but as mean of a possion 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. More...
 
const std::vector< double > & GetBoostWeights () const
 
const std::vector< TMVA::DecisionTree * > & GetForest () const
 
void GetHelpMessage () const
 Get help message text. More...
 
const std::vector< Float_t > & GetMulticlassValues ()
 get the multiclass MVA response for the BDT classifier More...
 
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 More...
 
const std::vector< const TMVA::Event * > & GetTrainingEvents () const
 
std::vector< Double_tGetVariableImportance ()
 Return the relative variable importance, normalized to all variables together having the importance 1. More...
 
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. More...
 
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. More...
 
void InitEventSample ()
 initialize the event sample (i.e. reset the boost-weights... etc) More...
 
void MakeClassInstantiateNode (DecisionTreeNode *n, std::ostream &fout, const TString &className) const
 recursively descends a tree and writes the node instance to the output streem More...
 
void MakeClassSpecific (std::ostream &, const TString &) const
 make ROOT-independent C++ class for classifier response (classifier-specific implementation) More...
 
void MakeClassSpecificHeader (std::ostream &, const TString &) const
 specific class header More...
 
virtual std::map< TString, Double_tOptimizeTuningParameters (TString fomType="ROCIntegral", TString fitType="FitGA")
 call the Optimzier with the set of paremeters and ranges that are meant to be tuned. More...
 
void ProcessOptions ()
 the option string is decoded, for available options see "DeclareOptions" More...
 
void ReadWeightsFromStream (std::istream &istr)
 read the weights (BDT coefficients) More...
 
void ReadWeightsFromXML (void *parent)
 reads the BDT from the xml file More...
 
void Reset (void)
 reset the method, as if it had just been instantiated (forget all training etc.) More...
 
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 accoding to the argument More...
 
void SetUseNvars (Int_t n)
 
Double_t TestTreeQuality (DecisionTree *dt)
 test the tree quality.. in terms of Miscalssification More...
 
void Train (void)
 BDT training. More...
 
void WriteMonitoringHistosToFile (void) const
 Here we could write some histograms created during the processing to the output file. More...
 
- Public Member Functions inherited from TMVA::MethodBase
 MethodBase (const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="")
 standard constructur More...
 
 MethodBase (Types::EMVA methodType, DataSetInfo &dsi, const TString &weightFile)
 constructor used for Testing + Application of the MVA, only (no training), using given WeightFiles More...
 
virtual ~MethodBase ()
 destructor More...
 
void AddOutput (Types::ETreeType type, Types::EAnalysisType analysisType)
 
TDirectoryBaseDir () const
 returns the ROOT directory where info/histograms etc of the corresponding MVA method instance are stored More...
 
virtual void CheckSetup ()
 check may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase) More...
 
DataSetData () const
 
DataSetInfoDataInfo () 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. More...
 
const EventGetEvent () const
 
const EventGetEvent (const TMVA::Event *ev) const
 
const EventGetEvent (Long64_t ievt) const
 
const EventGetEvent (Long64_t ievt, Types::ETreeType type) const
 
const std::vector< TMVA::Event * > & GetEventCollection (Types::ETreeType type)
 returns the event collection (i.e. More...
 
TFileGetFile () const
 
const TStringGetInputLabel (Int_t i) const
 
const char * GetInputTitle (Int_t i) const
 
const TStringGetInputVar (Int_t i) const
 
TMultiGraphGetInteractiveTrainingError ()
 
const TStringGetJobName () 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, 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 More...
 
UInt_t GetMaxIter ()
 
Double_t GetMean (Int_t ivar) const
 
const TStringGetMethodName () const
 
Types::EMVA GetMethodType () const
 
TString GetMethodTypeName () const
 
virtual std::vector< Float_tGetMulticlassEfficiency (std::vector< std::vector< Float_t > > &purity)
 
virtual std::vector< Float_tGetMulticlassTrainingEfficiency (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
 temporary event when testing on a different DataSet than the own one More...
 
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 More...
 
const TString GetProbaName () const
 
virtual Double_t GetRarity (Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const
 compute rarity: R(x) = Integrate_[-oo..x] { PDF(x') dx' } where PDF(x) is the PDF of the classifier's signal or background distribution More...
 
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 (TH1D *histS, TH1D *histB) const
 calculate the area (integral) under the ROC curve as a overall quality measure of the classification More...
 
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 More...
 
virtual Double_t GetSeparation (TH1 *, TH1 *) const
 compute "separation" defined as <s2> = (1/2) Int_-oo..+oo { (S(x) - B(x))^2/(S(x) + B(x)) dx } More...
 
virtual Double_t GetSeparation (PDF *pdfS=0, PDF *pdfB=0) const
 compute "separation" defined as <s2> = (1/2) Int_-oo..+oo { (S(x) - B(x))^2/(S(x) + B(x)) dx } More...
 
Double_t GetSignalReferenceCut () const
 
Double_t GetSignalReferenceCutOrientation () const
 
virtual Double_t GetSignificance () const
 compute significance of mean difference significance = |<S> - |/Sqrt(RMS_S2 + RMS_B2) More...
 
const EventGetTestingEvent (Long64_t ievt) const
 
Double_t GetTestTime () const
 
const TStringGetTestvarName () const
 
virtual Double_t GetTrainingEfficiency (const TString &)
 
const EventGetTrainingEvent (Long64_t ievt) const
 
UInt_t GetTrainingROOTVersionCode () const
 
TString GetTrainingROOTVersionString () const
 calculates the ROOT version string from the training version code on the fly More...
 
UInt_t GetTrainingTMVAVersionCode () const
 
TString GetTrainingTMVAVersionString () const
 calculates the TMVA version string from the training version code on the fly More...
 
Double_t GetTrainTime () const
 
TransformationHandlerGetTransformationHandler (Bool_t takeReroutedIfAvailable=true)
 
const TransformationHandlerGetTransformationHandler (Bool_t takeReroutedIfAvailable=true) const
 
TString GetWeightFileName () const
 retrieve weight file name More...
 
Double_t GetXmax (Int_t ivar) const
 
Double_t GetXmin (Int_t ivar) const
 
Bool_t HasMVAPdfs () const
 
void InitIPythonInteractive ()
 
Bool_t IsModelPersistence ()
 
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 More...
 
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 tbe selected as signal or background More...
 
Bool_t IsSilentFile ()
 
virtual void MakeClass (const TString &classFileName=TString("")) const
 create reader class for method (classification only at present) More...
 
TDirectoryMethodBaseDir () const
 returns the ROOT directory where all instances of the corresponding MVA method are stored More...
 
void PrintHelpMessage () const
 prints out method-specific help method More...
 
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) More...
 
void ReadStateFromFile ()
 Function to write options and weights to file. More...
 
void ReadStateFromStream (std::istream &tf)
 read the header from the weight files of the different MVA methods More...
 
void ReadStateFromStream (TFile &rf)
 write reference MVA distributions (and other information) to a ROOT type weight file More...
 
void ReadStateFromXMLString (const char *xmlstr)
 for reading from memory More...
 
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 More...
 
virtual void TestClassification ()
 initialization More...
 
virtual void TestMulticlass ()
 test multiclass classification More...
 
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 More...
 
bool TrainingEnded ()
 
void TrainMethod ()
 
virtual void WriteEvaluationHistosToFile (Types::ETreeType treetype)
 writes all MVA evaluation histograms to file More...
 
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 More...
 
- Public Member Functions inherited from TMVA::IMethod
 IMethod ()
 
virtual ~IMethod ()
 
- Public Member Functions inherited from TMVA::Configurable
 Configurable (const TString &theOption="")
 constructor More...
 
virtual ~Configurable ()
 default destructur More...
 
void AddOptionsXMLTo (void *parent) const
 write options to XML file More...
 
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 More...
 
template<class T >
OptionBaseDeclareOptionRef (T &ref, const TString &name, const TString &desc="")
 
template<class T >
OptionBaseDeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc="")
 
template<class T >
TMVA::OptionBaseDeclareOptionRef (T &ref, const TString &name, const TString &desc)
 
template<class T >
TMVA::OptionBaseDeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc)
 
const char * GetConfigDescription () const
 
const char * GetConfigName () const
 
const TStringGetOptions () const
 
MsgLoggerLog () const
 
virtual void ParseOptions ()
 options parser More...
 
void PrintOptions () const
 prints out the options set in the options string and the defaults More...
 
void ReadOptionsFromStream (std::istream &istr)
 read option back from the weight file More...
 
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 More...
 
- Public Member Functions inherited from TNamed
 TNamed ()
 
 TNamed (const char *name, const char *title)
 
 TNamed (const TString &name, const TString &title)
 
 TNamed (const TNamed &named)
 TNamed copy ctor. More...
 
virtual ~TNamed ()
 
virtual void Clear (Option_t *option="")
 Set name and title to empty strings (""). More...
 
virtual TObjectClone (const char *newname="") const
 Make a clone of an object using the Streamer facility. More...
 
virtual Int_t Compare (const TObject *obj) const
 Compare two TNamed objects. More...
 
virtual void Copy (TObject &named) const
 Copy this to obj. More...
 
virtual void FillBuffer (char *&buffer)
 Encode TNamed into output buffer. More...
 
virtual const char * GetTitle () const
 Returns title of object. More...
 
virtual ULong_t Hash () const
 Return hash value for this object. More...
 
virtual Bool_t IsSortable () const
 
virtual void ls (Option_t *option="") const
 List TNamed name and title. More...
 
TNamedoperator= (const TNamed &rhs)
 TNamed assignment operator. More...
 
virtual void Print (Option_t *option="") const
 Print TNamed name and title. More...
 
virtual void SetName (const char *name)
 Set the name of the TNamed. More...
 
virtual void SetNameTitle (const char *name, const char *title)
 Set all the TNamed parameters (name and title). More...
 
virtual void SetTitle (const char *title="")
 Set the title of the TNamed. More...
 
virtual Int_t Sizeof () const
 Return size of the TNamed part of the TObject. More...
 
- Public Member Functions inherited from TObject
 TObject ()
 TObject constructor. More...
 
 TObject (const TObject &object)
 TObject copy ctor. More...
 
virtual ~TObject ()
 TObject destructor. More...
 
void AbstractMethod (const char *method) const
 Use this method to implement an "abstract" method that you don't want to leave purely abstract. More...
 
virtual void AppendPad (Option_t *option="")
 Append graphics object to current pad. More...
 
virtual void Browse (TBrowser *b)
 Browse object. May be overridden for another default action. More...
 
virtual const char * ClassName () const
 Returns name of class to which the object belongs. More...
 
virtual void Delete (Option_t *option="")
 Delete this object. More...
 
virtual Int_t DistancetoPrimitive (Int_t px, Int_t py)
 Computes distance from point (px,py) to the object. More...
 
virtual void Draw (Option_t *option="")
 Default Draw method for all objects. More...
 
virtual void DrawClass () const
 Draw class inheritance tree of the class to which this object belongs. More...
 
virtual TObjectDrawClone (Option_t *option="") const
 Draw a clone of this object in the current pad. More...
 
virtual void Dump () const
 Dump contents of object on stdout. More...
 
virtual void Error (const char *method, const char *msgfmt,...) const
 Issue error message. More...
 
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. More...
 
virtual void Execute (TMethod *method, TObjArray *params, Int_t *error=0)
 Execute method on this object with parameters stored in the TObjArray. More...
 
virtual void ExecuteEvent (Int_t event, Int_t px, Int_t py)
 Execute action corresponding to an event at (px,py). More...
 
virtual void Fatal (const char *method, const char *msgfmt,...) const
 Issue fatal error message. More...
 
virtual TObjectFindObject (const char *name) const
 Must be redefined in derived classes. More...
 
virtual TObjectFindObject (const TObject *obj) const
 Must be redefined in derived classes. More...
 
virtual Option_tGetDrawOption () const
 Get option used by the graphics system to draw this object. More...
 
virtual const char * GetIconName () const
 Returns mime type name of object. More...
 
virtual char * GetObjectInfo (Int_t px, Int_t py) const
 Returns string containing info about the object at position (px,py). More...
 
virtual Option_tGetOption () const
 
virtual UInt_t GetUniqueID () const
 Return the unique object id. More...
 
virtual Bool_t HandleTimer (TTimer *timer)
 Execute action in response of a timer timing out. More...
 
virtual void Info (const char *method, const char *msgfmt,...) const
 Issue info message. More...
 
virtual Bool_t InheritsFrom (const char *classname) const
 Returns kTRUE if object inherits from class "classname". More...
 
virtual Bool_t InheritsFrom (const TClass *cl) const
 Returns kTRUE if object inherits from TClass cl. More...
 
virtual void Inspect () const
 Dump contents of this object in a graphics canvas. More...
 
void InvertBit (UInt_t f)
 
virtual Bool_t IsEqual (const TObject *obj) const
 Default equal comparison (objects are equal if they have the same address in memory). More...
 
virtual Bool_t IsFolder () const
 Returns kTRUE in case object contains browsable objects (like containers or lists of other objects). More...
 
Bool_t IsOnHeap () const
 
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). More...
 
virtual Bool_t Notify ()
 This method must be overridden to handle object notification. More...
 
void Obsolete (const char *method, const char *asOfVers, const char *removedFromVers) const
 Use this method to declare a method obsolete. More...
 
void operator delete (void *ptr)
 Operator delete. More...
 
void operator delete[] (void *ptr)
 Operator delete []. More...
 
voidoperator new (size_t sz)
 
voidoperator new (size_t sz, void *vp)
 
voidoperator new[] (size_t sz)
 
voidoperator new[] (size_t sz, void *vp)
 
TObjectoperator= (const TObject &rhs)
 TObject assignment operator. More...
 
virtual void Paint (Option_t *option="")
 This method must be overridden if a class wants to paint itself. More...
 
virtual void Pop ()
 Pop on object drawn in a pad to the top of the display list. More...
 
virtual Int_t Read (const char *name)
 Read contents of object with specified name from the current directory. More...
 
virtual void RecursiveRemove (TObject *obj)
 Recursively remove this object from a list. More...
 
void ResetBit (UInt_t f)
 
virtual void SaveAs (const char *filename="", Option_t *option="") const
 Save this object in the file specified by filename. More...
 
virtual void SavePrimitive (std::ostream &out, Option_t *option="")
 Save a primitive as a C++ statement(s) on output stream "out". More...
 
void SetBit (UInt_t f, Bool_t set)
 Set or unset the user status bits as specified in f. More...
 
void SetBit (UInt_t f)
 
virtual void SetDrawOption (Option_t *option="")
 Set drawing option for object. More...
 
virtual void SetUniqueID (UInt_t uid)
 Set the unique object id. More...
 
virtual void SysError (const char *method, const char *msgfmt,...) const
 Issue system error message. More...
 
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. More...
 
virtual void Warning (const char *method, const char *msgfmt,...) const
 Issue warning message. More...
 
virtual Int_t Write (const char *name=0, Int_t option=0, Int_t bufsize=0)
 Write this object to the current directory. More...
 
virtual Int_t Write (const char *name=0, Int_t option=0, Int_t bufsize=0) const
 Write this object to the current directory. More...
 

Protected Member Functions

void DeclareCompatibilityOptions ()
 options that are used ONLY for the READER to ensure backward compatibility More...
 
- Protected Member Functions inherited from TMVA::MethodBase
const TStringGetInternalVarName (Int_t ivar) const
 
virtual std::vector< Double_tGetMvaValues (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 More...
 
const TStringGetOriginalVarName (Int_t ivar) const
 
const TStringGetWeightFileDir () 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)
 
virtual void ReadWeightsFromStream (TFile &)
 
void SetNormalised (Bool_t norm)
 
void SetWeightFileDir (TString fileDir)
 set directory of weight file More...
 
void SetWeightFileName (TString)
 set the weight file name (depreciated) More...
 
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 More...
 
Bool_t TxtWeightsOnly () const
 
Bool_t Verbose () const
 
- Protected Member Functions inherited from TMVA::Configurable
void EnableLooseOptions (Bool_t b=kTRUE)
 
const TStringGetReferenceFile () const
 
Bool_t LooseOptionCheckingEnabled () const
 
void ResetSetFlag ()
 resets the IsSet falg for all declare options to be called before options are read from stream More...
 
void WriteOptionsReferenceToFile ()
 write complete options to output stream More...
 
- 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). More...
 
void MakeZombie ()
 

Private Member Functions

Double_t AdaBoost (std::vector< const TMVA::Event * > &, DecisionTree *dt)
 the AdaBoost implementation. More...
 
Double_t AdaBoostR2 (std::vector< const TMVA::Event * > &, DecisionTree *dt)
 adaption of the AdaBoost to regression problems (see H.Drucker 1997) More...
 
Double_t AdaCost (std::vector< const TMVA::Event * > &, DecisionTree *dt)
 the AdaCost boosting algorithm takes a simple cost Matrix (currently fixed for all events... More...
 
Double_t ApplyPreselectionCuts (const Event *ev)
 aply the preselection cuts before even bothing about any Decision Trees in the GetMVA . More...
 
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. More...
 
void BoostMonitor (Int_t iTree)
 fills the ROCIntegral vs Itree from the testSample for the monitoring plots during the training . More...
 
void DeterminePreselectionCuts (const std::vector< const TMVA::Event * > &eventSample)
 find useful preselection cuts that will be applied before and Decision Tree training. More...
 
void GetBaggedSubSample (std::vector< const TMVA::Event * > &)
 fills fEventSample with fBaggedSampleFraction*NEvents random training events More...
 
Double_t GetGradBoostMVA (const TMVA::Event *e, UInt_t nTrees)
 returns MVA value: -1 for background, 1 for signal More...
 
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. More...
 
Double_t GradBoost (std::vector< const TMVA::Event * > &, DecisionTree *dt, UInt_t cls=0)
 Calculate the desired response value for each region. More...
 
Double_t GradBoostRegression (std::vector< const TMVA::Event * > &, DecisionTree *dt)
 Implementation of M_TreeBoost using any loss function as desribed by Friedman 1999. More...
 
void Init (void)
 common initialisation with defaults for the BDT-Method More...
 
void InitGradBoost (std::vector< const TMVA::Event * > &)
 initialize targets for first tree More...
 
void PreProcessNegativeEventWeights ()
 o.k. More...
 
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. More...
 
Double_t RegBoost (std::vector< const TMVA::Event * > &, DecisionTree *dt)
 a special boosting only for Regression ... More...
 
void UpdateTargets (std::vector< const TMVA::Event * > &, UInt_t cls=0)
 Calculate residua for all events;. More...
 
void UpdateTargetsRegression (std::vector< const TMVA::Event * > &, Bool_t first=kFALSE)
 Calculate current residuals for all events and update targets for next iteration. More...
 

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_tfHighBkgCut
 
std::vector< Double_tfHighSigCut
 
Bool_t fHistoricBool
 
Double_t fHuberQuantile
 
Bool_t fInverseBoostNegWeights
 
std::vector< Bool_tfIsHighBkgCut
 
std::vector< Bool_tfIsHighSigCut
 
std::vector< Bool_tfIsLowBkgCut
 
std::vector< Bool_tfIsLowSigCut
 
Int_t fITree
 
std::map< const TMVA::Event *, LossFunctionEventInfofLossFunctionEventInfo
 
std::vector< Double_tfLowBkgCut
 
std::vector< Double_tfLowSigCut
 
UInt_t fMaxDepth
 
Double_t fMinLinCorrForFisher
 
Int_t fMinNodeEvents
 
Float_t fMinNodeSize
 
TString fMinNodeSizeS
 
TTreefMonitorNtuple
 
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
 
LossFunctionBDTfRegressionLossFunctionBDTG
 
TString fRegressionLossFunctionBDTGS
 
std::map< const TMVA::Event *, std::vector< double > > fResiduals
 
SeparationBasefSepType
 
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_tfVariableImportance
 

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, kBitMask = 0x00ffffff }
 
enum  { kSingleKey = BIT(0), kOverwrite = BIT(1), kWriteDelete = BIT(2) }
 
enum  EStatusBits {
  kCanDelete = BIT(0), kMustCleanup = BIT(3), kObjInCanvas = 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 Long_t GetDtorOnly ()
 Return destructor only flag. More...
 
static Bool_t GetObjectStat ()
 Get status of object stat flag. More...
 
static void SetDtorOnly (void *obj)
 Set destructor only flag. More...
 
static void SetObjectStat (Bool_t stat)
 Turn on/off tracking of objects in the TObjectTable. More...
 
- Public Attributes inherited from TMVA::MethodBase
Bool_t fSetupCompleted
 
const EventfTmpEvent
 
- Protected Attributes inherited from TMVA::MethodBase
Types::EAnalysisType fAnalysisType
 
UInt_t fBackgroundClass
 
bool fExitFromTraining = false
 
std::vector< TString > * fInputVars
 
IPythonInteractivefInteractive = nullptr
 
UInt_t fIPyCurrentIter = 0
 
UInt_t fIPyMaxIter = 0
 
std::vector< Float_t > * fMulticlassReturnVal
 
Int_t fNbins
 
Int_t fNbinsH
 
Int_t fNbinsMVAoutput
 
RankingfRanking
 
std::vector< Float_t > * fRegressionReturnVal
 
ResultsfResults
 
UInt_t fSignalClass
 
- Protected Attributes inherited from TMVA::Configurable
MsgLoggerfLogger
 
- Protected Attributes inherited from TNamed
TString fName
 
TString fTitle
 

#include <TMVA/MethodBDT.h>

Inheritance diagram for TMVA::MethodBDT:
[legend]

Constructor & Destructor Documentation

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 160 of file MethodBDT.cxx.

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

Definition at line 216 of file MethodBDT.cxx.

TMVA::MethodBDT::~MethodBDT ( void  )
virtual

destructor Note: fEventSample and ValidationSample are already deleted at the end of TRAIN When they are not used anymore for (UInt_t i=0; i<fEventSample.size(); i++) delete fEventSample[i]; for (UInt_t i=0; i<fValidationSample.size(); i++) delete fValidationSample[i];

Definition at line 747 of file MethodBDT.cxx.

Member Function Documentation

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 = (1-err)/err or more general: w = ((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 1713 of file MethodBDT.cxx.

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 2066 of file MethodBDT.cxx.

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 misclass. cost (former: error fraction):

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

Definition at line 1894 of file MethodBDT.cxx.

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

write weights to XML

Implements TMVA::MethodBase.

Definition at line 2183 of file MethodBDT.cxx.

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

aply the preselection cuts before even bothing about any Decision Trees in the GetMVA .

. –> -1 for background +1 for Signal

Definition at line 2953 of file MethodBDT.cxx.

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 2012 of file MethodBDT.cxx.

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

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

The return value is the boosting weight

Definition at line 1585 of file MethodBDT.cxx.

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 1619 of file MethodBDT.cxx.

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

Compute ranking of input variables.

Implements TMVA::MethodBase.

Definition at line 2543 of file MethodBDT.cxx.

void TMVA::MethodBDT::DeclareCompatibilityOptions ( )
protectedvirtual

options that are used ONLY for the READER to ensure backward compatibility

Reimplemented from TMVA::MethodBase.

Definition at line 444 of file MethodBDT.cxx.

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 UsePoission Nvars use UseNvars not as fixed number but as mean of a possion 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 traning 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 323 of file MethodBDT.cxx.

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 2853 of file MethodBDT.cxx.

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

fills fEventSample with fBaggedSampleFraction*NEvents random training events

Definition at line 2023 of file MethodBDT.cxx.

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

Definition at line 312 of file MethodBDT.h.

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

Definition at line 310 of file MethodBDT.h.

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 1409 of file MethodBDT.cxx.

void TMVA::MethodBDT::GetHelpMessage ( ) const
virtual

Get help message text.

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

Implements TMVA::IMethod.

Definition at line 2563 of file MethodBDT.cxx.

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 2367 of file MethodBDT.cxx.

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

Implements TMVA::MethodBase.

Definition at line 2316 of file MethodBDT.cxx.

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 2325 of file MethodBDT.cxx.

UInt_t TMVA::MethodBDT::GetNTrees ( ) const
inline

Definition at line 112 of file MethodBDT.h.

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

get the regression value generated by the BDTs

Reimplemented from TMVA::MethodBase.

Definition at line 2402 of file MethodBDT.cxx.

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

Definition at line 311 of file MethodBDT.h.

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 2503 of file MethodBDT.cxx.

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 2531 of file MethodBDT.cxx.

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 1470 of file MethodBDT.cxx.

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

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

Definition at line 1501 of file MethodBDT.cxx.

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 275 of file MethodBDT.cxx.

void TMVA::MethodBDT::Init ( void  )
privatevirtual

common initialisation with defaults for the BDT-Method

Implements TMVA::MethodBase.

Definition at line 680 of file MethodBDT.cxx.

void TMVA::MethodBDT::InitEventSample ( void  )

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

Definition at line 755 of file MethodBDT.cxx.

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

initialize targets for first tree

Definition at line 1526 of file MethodBDT.cxx.

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 streem

Definition at line 2807 of file MethodBDT.cxx.

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 2620 of file MethodBDT.cxx.

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

specific class header

Reimplemented from TMVA::MethodBase.

Definition at line 2696 of file MethodBDT.cxx.

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

call the Optimzier with the set of paremeters and ranges that are meant to be tuned.

Reimplemented from TMVA::MethodBase.

Definition at line 1059 of file MethodBDT.cxx.

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 921 of file MethodBDT.cxx.

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 2340 of file MethodBDT.cxx.

void TMVA::MethodBDT::ProcessOptions ( )
virtual

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

Implements TMVA::MethodBase.

Definition at line 463 of file MethodBDT.cxx.

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

read the weights (BDT coefficients)

Implements TMVA::MethodBase.

Definition at line 2281 of file MethodBDT.cxx.

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

reads the BDT from the xml file

Implements TMVA::MethodBase.

Definition at line 2214 of file MethodBDT.cxx.

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

a special boosting only for Regression ...

maybe I'll implement it later...

Definition at line 2058 of file MethodBDT.cxx.

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 718 of file MethodBDT.cxx.

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

Definition at line 138 of file MethodBDT.h.

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

Definition at line 142 of file MethodBDT.h.

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

Definition at line 133 of file MethodBDT.h.

void TMVA::MethodBDT::SetMinNodeSize ( Double_t  sizeInPercent)

Definition at line 652 of file MethodBDT.cxx.

void TMVA::MethodBDT::SetMinNodeSize ( TString  sizeInPercent)

Definition at line 665 of file MethodBDT.cxx.

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

Definition at line 139 of file MethodBDT.h.

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

Definition at line 137 of file MethodBDT.h.

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

Definition at line 140 of file MethodBDT.h.

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

set the tuning parameters accoding to the argument

Reimplemented from TMVA::MethodBase.

Definition at line 1112 of file MethodBDT.cxx.

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

Definition at line 141 of file MethodBDT.h.

Double_t TMVA::MethodBDT::TestTreeQuality ( DecisionTree dt)

test the tree quality.. in terms of Miscalssification

Definition at line 1564 of file MethodBDT.cxx.

void TMVA::MethodBDT::Train ( void  )
virtual

BDT training.

Implements TMVA::MethodBase.

Definition at line 1134 of file MethodBDT.cxx.

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

Calculate residua for all events;.

Definition at line 1423 of file MethodBDT.cxx.

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

Calculate current residuals for all events and update targets for next iteration.

Definition at line 1456 of file MethodBDT.cxx.

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 2488 of file MethodBDT.cxx.

Member Data Documentation

Double_t TMVA::MethodBDT::fAdaBoostBeta
private

Definition at line 215 of file MethodBDT.h.

TString TMVA::MethodBDT::fAdaBoostR2Loss
private

Definition at line 216 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fAutomatic
private

Definition at line 247 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fBaggedBoost
private

Definition at line 219 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fBaggedGradBoost
private

Definition at line 220 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fBaggedSampleFraction
private

Definition at line 253 of file MethodBDT.h.

TString TMVA::MethodBDT::fBoostType
private

Definition at line 214 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fBoostWeight
private

Definition at line 265 of file MethodBDT.h.

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

Definition at line 212 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fCbb
private

Definition at line 271 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fCss
private

Definition at line 268 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fCtb_ss
private

Definition at line 270 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fCts_sb
private

Definition at line 269 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fDoBoostMonitor
private

Definition at line 259 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fDoPreselection
private

Definition at line 273 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fErrorFraction
private

Definition at line 266 of file MethodBDT.h.

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

Definition at line 205 of file MethodBDT.h.

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

Definition at line 211 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fFValidationEvents
private

Definition at line 246 of file MethodBDT.h.

const Int_t TMVA::MethodBDT::fgDebugLevel = 0
staticprivate

Definition at line 301 of file MethodBDT.h.

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

Definition at line 286 of file MethodBDT.h.

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

Definition at line 285 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fHistoricBool
private

Definition at line 293 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fHuberQuantile
private

Definition at line 296 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fInverseBoostNegWeights
private

Definition at line 256 of file MethodBDT.h.

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

Definition at line 291 of file MethodBDT.h.

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

Definition at line 290 of file MethodBDT.h.

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

Definition at line 289 of file MethodBDT.h.

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

Definition at line 288 of file MethodBDT.h.

Int_t TMVA::MethodBDT::fITree
private

Definition at line 264 of file MethodBDT.h.

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

Definition at line 223 of file MethodBDT.h.

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

Definition at line 284 of file MethodBDT.h.

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

Definition at line 283 of file MethodBDT.h.

UInt_t TMVA::MethodBDT::fMaxDepth
private

Definition at line 241 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fMinLinCorrForFisher
private

Definition at line 236 of file MethodBDT.h.

Int_t TMVA::MethodBDT::fMinNodeEvents
private

Definition at line 230 of file MethodBDT.h.

Float_t TMVA::MethodBDT::fMinNodeSize
private

Definition at line 231 of file MethodBDT.h.

TString TMVA::MethodBDT::fMinNodeSizeS
private

Definition at line 232 of file MethodBDT.h.

TTree* TMVA::MethodBDT::fMonitorNtuple
private

Definition at line 263 of file MethodBDT.h.

Int_t TMVA::MethodBDT::fNCuts
private

Definition at line 234 of file MethodBDT.h.

TString TMVA::MethodBDT::fNegWeightTreatment
private

Definition at line 254 of file MethodBDT.h.

UInt_t TMVA::MethodBDT::fNNodesMax
private

Definition at line 240 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fNodePurityLimit
private

Definition at line 239 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fNoNegWeightsInTraining
private

Definition at line 255 of file MethodBDT.h.

Int_t TMVA::MethodBDT::fNTrees
private

Definition at line 210 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fPairNegWeightsGlobal
private

Definition at line 257 of file MethodBDT.h.

DecisionTree::EPruneMethod TMVA::MethodBDT::fPruneMethod
private

Definition at line 243 of file MethodBDT.h.

TString TMVA::MethodBDT::fPruneMethodS
private

Definition at line 244 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fPruneStrength
private

Definition at line 245 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fRandomisedTrees
private

Definition at line 248 of file MethodBDT.h.

LossFunctionBDT* TMVA::MethodBDT::fRegressionLossFunctionBDTG
private

Definition at line 298 of file MethodBDT.h.

TString TMVA::MethodBDT::fRegressionLossFunctionBDTGS
private

Definition at line 295 of file MethodBDT.h.

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

Definition at line 225 of file MethodBDT.h.

SeparationBase* TMVA::MethodBDT::fSepType
private

Definition at line 228 of file MethodBDT.h.

TString TMVA::MethodBDT::fSepTypeS
private

Definition at line 229 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fShrinkage
private

Definition at line 218 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fSigToBkgFraction
private

Definition at line 213 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fSkipNormalization
private

Definition at line 275 of file MethodBDT.h.

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

Definition at line 207 of file MethodBDT.h.

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

Definition at line 208 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fTrainWithNegWeights
private

Definition at line 258 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fUseExclusiveVars
private

Definition at line 237 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fUseFisherCuts
private

Definition at line 235 of file MethodBDT.h.

UInt_t TMVA::MethodBDT::fUseNTrainEvents
private

Definition at line 251 of file MethodBDT.h.

UInt_t TMVA::MethodBDT::fUseNvars
private

Definition at line 249 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fUsePoissonNvars
private

Definition at line 250 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fUseYesNoLeaf
private

Definition at line 238 of file MethodBDT.h.

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

Definition at line 206 of file MethodBDT.h.

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

Definition at line 277 of file MethodBDT.h.


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