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TMVA::MethodDT 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 the boosting 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).

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.

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 49 of file MethodDT.h.

Public Member Functions

 MethodDT (const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption="")
 the standard constructor for just an ordinar "decision trees"
 
 MethodDT (DataSetInfo &dsi, const TString &theWeightFile)
 constructor from Reader
 
virtual ~MethodDT (void)
 destructor
 
void AddWeightsXMLTo (void *parent) const
 
const RankingCreateRanking ()
 
void DeclareCompatibilityOptions ()
 options that are used ONLY for the READER to ensure backward compatibility
 
void DeclareOptions ()
 Define the options (their key words) that can be set in the option string.
 
void GetHelpMessage () const
 
Double_t GetMvaValue (Double_t *err=nullptr, Double_t *errUpper=nullptr)
 returns MVA value
 
Int_t GetNNodes ()
 
Int_t GetNNodesBeforePruning ()
 
Double_t GetPruneStrength ()
 
virtual Bool_t HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)
 FDA can handle classification with 2 classes and regression with one regression-target.
 
virtual TClassIsA () const
 
void ProcessOptions ()
 the option string is decoded, for available options see "DeclareOptions"
 
Double_t PruneTree ()
 prune the decision tree if requested (good for individual trees that are best grown out, and then pruned back, while boosted decision trees are best 'small' trees to start with.
 
virtual void ReadWeightsFromStream (std::istream &)=0
 
void ReadWeightsFromStream (std::istream &istr)
 
virtual void ReadWeightsFromStream (TFile &)
 
void ReadWeightsFromXML (void *wghtnode)
 
void SetMinNodeSize (Double_t sizeInPercent)
 
void SetMinNodeSize (TString sizeInPercent)
 
virtual void Streamer (TBuffer &)
 
void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)
 
Double_t TestTreeQuality (DecisionTree *dt)
 
void Train (void)
 
- 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)
 
TDirectoryBaseDir () 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)
 
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.
 
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.
 
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, \( \frac{S}{\sqrt{S^2 + B^2}} \), 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 TStringGetMethodName () 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_tGetMulticlassEfficiency (std::vector< std::vector< Float_t > > &purity)
 
virtual std::vector< Float_tGetMulticlassTrainingEfficiency (std::vector< std::vector< Float_t > > &purity)
 
virtual const std::vector< Float_t > & GetMulticlassValues ()
 
Double_t GetMvaValue (const TMVA::Event *const ev, Double_t *err=nullptr, Double_t *errUpper=nullptr)
 
const char * GetName () const
 
UInt_t GetNEvents () const
 
UInt_t GetNTargets () const
 
UInt_t GetNvar () const
 
UInt_t GetNVariables () const
 
virtual Double_t GetProba (const Event *ev)
 
virtual Double_t GetProba (Double_t mvaVal, Double_t ap_sig)
 compute likelihood ratio
 
const TString GetProbaName () const
 
virtual Double_t GetRarity (Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const
 compute rarity:
 
virtual void GetRegressionDeviation (UInt_t tgtNum, Types::ETreeType type, Double_t &stddev, Double_t &stddev90Percent) const
 
virtual const std::vector< Float_t > & GetRegressionValues ()
 
const std::vector< Float_t > & GetRegressionValues (const TMVA::Event *const ev)
 
Double_t GetRMS (Int_t ivar) const
 
virtual Double_t GetROCIntegral (PDF *pdfS=nullptr, PDF *pdfB=nullptr) const
 calculate the area (integral) under the ROC curve as a overall quality measure of the classification
 
virtual Double_t GetROCIntegral (TH1D *histS, TH1D *histB) const
 calculate the area (integral) under the ROC curve as a overall quality measure of the classification
 
virtual Double_t GetSeparation (PDF *pdfS=nullptr, PDF *pdfB=nullptr) const
 compute "separation" defined as
 
virtual Double_t GetSeparation (TH1 *, TH1 *) const
 compute "separation" defined as
 
Double_t GetSignalReferenceCut () const
 
Double_t GetSignalReferenceCutOrientation () const
 
virtual Double_t GetSignificance () const
 compute significance of mean difference
 
const EventGetTestingEvent (Long64_t ievt) const
 
Double_t GetTestTime () const
 
const TStringGetTestvarName () const
 
virtual Double_t GetTrainingEfficiency (const TString &)
 
const EventGetTrainingEvent (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
 
TransformationHandlerGetTransformationHandler (Bool_t takeReroutedIfAvailable=true)
 
const TransformationHandlerGetTransformationHandler (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)
 
TDirectoryMethodBaseDir () const
 returns the ROOT directory where all instances of the corresponding MVA method are stored
 
virtual std::map< TString, Double_tOptimizeTuningParameters (TString fomType="ROCIntegral", TString fitType="FitGA")
 call the Optimizer with the set of parameters and ranges that are meant to be tuned.
 
void PrintHelpMessage () const
 prints out method-specific help method
 
void ProcessSetup ()
 process all options the "CheckForUnusedOptions" is done in an independent call, since it may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase)
 
void ReadStateFromFile ()
 Function to write options and weights to file.
 
void ReadStateFromStream (std::istream &tf)
 read the header from the weight files of the different MVA methods
 
void ReadStateFromStream (TFile &rf)
 write reference MVA distributions (and other information) to a ROOT type weight file
 
void ReadStateFromXMLString (const char *xmlstr)
 for reading from memory
 
void RerouteTransformationHandler (TransformationHandler *fTargetTransformation)
 
virtual void Reset ()
 
virtual void SetAnalysisType (Types::EAnalysisType type)
 
void SetBaseDir (TDirectory *methodDir)
 
void SetFile (TFile *file)
 
void SetMethodBaseDir (TDirectory *methodDir)
 
void SetMethodDir (TDirectory *methodDir)
 
void SetModelPersistence (Bool_t status)
 
void SetSignalReferenceCut (Double_t cut)
 
void SetSignalReferenceCutOrientation (Double_t cutOrientation)
 
void SetSilentFile (Bool_t status)
 
void SetTestTime (Double_t testTime)
 
void SetTestvarName (const TString &v="")
 
void SetTrainTime (Double_t trainTime)
 
virtual void SetTuneParameters (std::map< TString, Double_t > tuneParameters)
 set the tuning parameters according to the argument This is just a dummy .
 
void SetupMethod ()
 setup of methods
 
void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)
 
virtual void TestClassification ()
 initialization
 
virtual void TestMulticlass ()
 test multiclass classification
 
virtual void TestRegression (Double_t &bias, Double_t &biasT, Double_t &dev, Double_t &devT, Double_t &rms, Double_t &rmsT, Double_t &mInf, Double_t &mInfT, Double_t &corr, Types::ETreeType type)
 calculate <sum-of-deviation-squared> of regression output versus "true" value from test sample
 
bool TrainingEnded ()
 
void TrainMethod ()
 
virtual void WriteEvaluationHistosToFile (Types::ETreeType treetype)
 writes all MVA evaluation histograms to file
 
virtual void WriteMonitoringHistosToFile () const
 write special monitoring histograms to file dummy implementation here --------------—
 
void WriteStateToFile () const
 write options and weights to file note that each one text file for the main configuration information and one ROOT file for ROOT objects are created
 
- Public Member Functions inherited from TMVA::IMethod
 IMethod ()
 
virtual ~IMethod ()
 
void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)
 
- Public Member Functions inherited from TMVA::Configurable
 Configurable (const TString &theOption="")
 constructor
 
virtual ~Configurable ()
 default destructor
 
void AddOptionsXMLTo (void *parent) const
 write options to XML file
 
template<class T >
void AddPreDefVal (const T &)
 
template<class T >
void AddPreDefVal (const TString &optname, const T &)
 
void CheckForUnusedOptions () const
 checks for unused options in option string
 
template<class T >
TMVA::OptionBaseDeclareOptionRef (T &ref, const TString &name, const TString &desc)
 
template<class T >
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)
 
template<class T >
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
 
void PrintOptions () const
 prints out the options set in the options string and the defaults
 
void ReadOptionsFromStream (std::istream &istr)
 read option back from the weight file
 
void ReadOptionsFromXML (void *node)
 
void SetConfigDescription (const char *d)
 
void SetConfigName (const char *n)
 
void SetMsgType (EMsgType t)
 
void SetOptions (const TString &s)
 
void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)
 
void WriteOptionsToStream (std::ostream &o, const TString &prefix) const
 write options to output stream (e.g. in writing the MVA weight files
 
- Public Member Functions inherited from TNamed
 TNamed ()
 
 TNamed (const char *name, const char *title)
 
 TNamed (const TNamed &named)
 TNamed copy ctor.
 
 TNamed (const TString &name, const TString &title)
 
virtual ~TNamed ()
 TNamed destructor.
 
void Clear (Option_t *option="") override
 Set name and title to empty strings ("").
 
TObjectClone (const char *newname="") const override
 Make a clone of an object using the Streamer facility.
 
Int_t Compare (const TObject *obj) const override
 Compare two TNamed objects.
 
void Copy (TObject &named) const override
 Copy this to obj.
 
virtual void FillBuffer (char *&buffer)
 Encode TNamed into output buffer.
 
const char * GetName () const override
 Returns name of object.
 
const char * GetTitle () const override
 Returns title of object.
 
ULong_t Hash () const override
 Return hash value for this object.
 
TClassIsA () const override
 
Bool_t IsSortable () const override
 
void ls (Option_t *option="") const override
 List TNamed name and title.
 
TNamedoperator= (const TNamed &rhs)
 TNamed assignment operator.
 
void Print (Option_t *option="") const override
 Print TNamed name and title.
 
virtual void SetName (const char *name)
 Set the name of the TNamed.
 
virtual void SetNameTitle (const char *name, const char *title)
 Set all the TNamed parameters (name and title).
 
virtual void SetTitle (const char *title="")
 Set the title of the TNamed.
 
virtual Int_t Sizeof () const
 Return size of the TNamed part of the TObject.
 
void Streamer (TBuffer &) override
 Stream an object of class TObject.
 
void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)
 
- Public Member Functions inherited from TObject
 TObject ()
 TObject constructor.
 
 TObject (const TObject &object)
 TObject copy ctor.
 
virtual ~TObject ()
 TObject destructor.
 
void AbstractMethod (const char *method) const
 Use this method to implement an "abstract" method that you don't want to leave purely abstract.
 
virtual void AppendPad (Option_t *option="")
 Append graphics object to current pad.
 
virtual void Browse (TBrowser *b)
 Browse object. May be overridden for another default action.
 
ULong_t CheckedHash ()
 Check and record whether this class has a consistent Hash/RecursiveRemove setup (*) and then return the regular Hash value for this object.
 
virtual const char * ClassName () const
 Returns name of class to which the object belongs.
 
virtual void Delete (Option_t *option="")
 Delete this object.
 
virtual Int_t DistancetoPrimitive (Int_t px, Int_t py)
 Computes distance from point (px,py) to the object.
 
virtual void Draw (Option_t *option="")
 Default Draw method for all objects.
 
virtual void DrawClass () const
 Draw class inheritance tree of the class to which this object belongs.
 
virtual TObjectDrawClone (Option_t *option="") const
 Draw a clone of this object in the current selected pad with: gROOT->SetSelectedPad(c1).
 
virtual void Dump () const
 Dump contents of object on stdout.
 
virtual void Error (const char *method, const char *msgfmt,...) const
 Issue error message.
 
virtual void Execute (const char *method, const char *params, Int_t *error=nullptr)
 Execute method on this object with the given parameter string, e.g.
 
virtual void Execute (TMethod *method, TObjArray *params, Int_t *error=nullptr)
 Execute method on this object with parameters stored in the TObjArray.
 
virtual void ExecuteEvent (Int_t event, Int_t px, Int_t py)
 Execute action corresponding to an event at (px,py).
 
virtual void Fatal (const char *method, const char *msgfmt,...) const
 Issue fatal error message.
 
virtual TObjectFindObject (const char *name) const
 Must be redefined in derived classes.
 
virtual TObjectFindObject (const TObject *obj) const
 Must be redefined in derived classes.
 
virtual Option_tGetDrawOption () 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_tGetOption () 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)
 
TObjectoperator= (const TObject &rhs)
 TObject assignment operator.
 
virtual void Paint (Option_t *option="")
 This method must be overridden if a class wants to paint itself.
 
virtual void Pop ()
 Pop on object drawn in a pad to the top of the display list.
 
virtual Int_t Read (const char *name)
 Read contents of object with specified name from the current directory.
 
virtual void RecursiveRemove (TObject *obj)
 Recursively remove this object from a list.
 
void ResetBit (UInt_t f)
 
virtual void SaveAs (const char *filename="", Option_t *option="") const
 Save this object in the file specified by filename.
 
virtual void SavePrimitive (std::ostream &out, Option_t *option="")
 Save a primitive as a C++ statement(s) on output stream "out".
 
void SetBit (UInt_t f)
 
void SetBit (UInt_t f, Bool_t set)
 Set or unset the user status bits as specified in f.
 
virtual void SetDrawOption (Option_t *option="")
 Set drawing option for object.
 
virtual void SetUniqueID (UInt_t uid)
 Set the unique object id.
 
void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)
 
virtual void SysError (const char *method, const char *msgfmt,...) const
 Issue system error message.
 
R__ALWAYS_INLINE Bool_t TestBit (UInt_t f) const
 
Int_t TestBits (UInt_t f) const
 
virtual void UseCurrentStyle ()
 Set current style settings in this object This function is called when either TCanvas::UseCurrentStyle or TROOT::ForceStyle have been invoked.
 
virtual void Warning (const char *method, const char *msgfmt,...) const
 Issue warning message.
 
virtual Int_t Write (const char *name=nullptr, Int_t option=0, Int_t bufsize=0)
 Write this object to the current directory.
 
virtual Int_t Write (const char *name=nullptr, Int_t option=0, Int_t bufsize=0) const
 Write this object to the current directory.
 

Static Public Member Functions

static TClassClass ()
 
static const char * Class_Name ()
 
static constexpr Version_t Class_Version ()
 
static const char * DeclFileName ()
 
- Static Public Member Functions inherited from TMVA::MethodBase
static TClassClass ()
 
static const char * Class_Name ()
 
static constexpr Version_t Class_Version ()
 
static const char * DeclFileName ()
 
- Static Public Member Functions inherited from TMVA::IMethod
static TClassClass ()
 
static const char * Class_Name ()
 
static constexpr Version_t Class_Version ()
 
static const char * DeclFileName ()
 
- Static Public Member Functions inherited from TMVA::Configurable
static TClassClass ()
 
static const char * Class_Name ()
 
static constexpr Version_t Class_Version ()
 
static const char * DeclFileName ()
 
- Static Public Member Functions inherited from TNamed
static TClassClass ()
 
static const char * Class_Name ()
 
static constexpr Version_t Class_Version ()
 
static const char * DeclFileName ()
 
- Static Public Member Functions inherited from TObject
static TClassClass ()
 
static const char * Class_Name ()
 
static constexpr Version_t Class_Version ()
 
static const char * DeclFileName ()
 
static Longptr_t GetDtorOnly ()
 Return destructor only flag.
 
static Bool_t GetObjectStat ()
 Get status of object stat flag.
 
static void SetDtorOnly (void *obj)
 Set destructor only flag.
 
static void SetObjectStat (Bool_t stat)
 Turn on/off tracking of objects in the TObjectTable.
 

Private Member Functions

void Init (void)
 common initialisation with defaults for the DT-Method
 

Private Attributes

Bool_t fAutomatic
 use user given prune strength or automatically determined one using a validation sample
 
Double_t fDeltaPruneStrength
 step size in pruning, is adjusted according to experience of previous trees
 
Double_t fErrorFraction
 ntuple var: misclassification error fraction
 
std::vector< Event * > fEventSample
 the training events
 
UInt_t fMaxDepth
 max depth
 
Int_t fMinNodeEvents
 min number of events in node
 
Float_t fMinNodeSize
 min percentage of training events in node
 
TString fMinNodeSizeS
 string containing min percentage of training events in node
 
Int_t fNCuts
 grid used in cut applied in node splitting
 
Double_t fNodePurityLimit
 purity limit for sig/bkg nodes
 
Bool_t fPruneBeforeBoost
 ancient variable, only needed for "CompatibilityOptions"
 
DecisionTree::EPruneMethod fPruneMethod
 method used for pruning
 
TString fPruneMethodS
 prune method option String
 
Double_t fPruneStrength
 a parameter to set the "amount" of pruning..needs to be adjusted
 
Bool_t fRandomisedTrees
 choose a random subset of possible cut variables at each node during training
 
SeparationBasefSepType
 the separation used in node splitting
 
TString fSepTypeS
 the separation (option string) used in node splitting
 
DecisionTreefTree
 the decision tree
 
Int_t fUseNvars
 the number of variables used in the randomised tree splitting
 
Bool_t fUsePoissonNvars
 fUseNvars is used as a poisson mean, and the actual value of useNvars is at each step drawn form that distribution
 
Bool_t fUseYesNoLeaf
 use sig or bkg classification in leave nodes or sig/bkg
 
std::vector< Double_tfVariableImportance
 the relative importance of the different variables
 

Static Private Attributes

static const Int_t fgDebugLevel = 0
 debug level determining some printout/control plots etc.
 

Additional Inherited Members

- Public Types inherited from TMVA::MethodBase
enum  EWeightFileType { kROOT =0 , kTEXT }
 
- Public Types inherited from TObject
enum  {
  kIsOnHeap = 0x01000000 , kNotDeleted = 0x02000000 , kZombie = 0x04000000 , kInconsistent = 0x08000000 ,
  kBitMask = 0x00ffffff
}
 
enum  { kSingleKey = (1ULL << ( 0 )) , kOverwrite = (1ULL << ( 1 )) , kWriteDelete = (1ULL << ( 2 )) }
 
enum  EDeprecatedStatusBits { kObjInCanvas = (1ULL << ( 3 )) }
 
enum  EStatusBits {
  kCanDelete = (1ULL << ( 0 )) , kMustCleanup = (1ULL << ( 3 )) , kIsReferenced = (1ULL << ( 4 )) , kHasUUID = (1ULL << ( 5 )) ,
  kCannotPick = (1ULL << ( 6 )) , kNoContextMenu = (1ULL << ( 8 )) , kInvalidObject = (1ULL << ( 13 ))
}
 
- Public Attributes inherited from TMVA::MethodBase
Bool_t fSetupCompleted
 
TrainingHistory fTrainHistory
 
- Protected Types inherited from TObject
enum  { kOnlyPrepStep = (1ULL << ( 3 )) }
 
- Protected Member Functions inherited from TMVA::MethodBase
virtual std::vector< Double_tGetDataMvaValues (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 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
 
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
 
virtual void MakeClassSpecific (std::ostream &, const TString &="") const
 
virtual void MakeClassSpecificHeader (std::ostream &, const TString &="") const
 
void NoErrorCalc (Double_t *const err, Double_t *const errUpper)
 
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 TStringGetReferenceFile () const
 
Bool_t LooseOptionCheckingEnabled () const
 
void ResetSetFlag ()
 resets the IsSet flag for all declare options to be called before options are read from stream
 
void WriteOptionsReferenceToFile ()
 write complete options to output stream
 
- Protected Member Functions inherited from TObject
virtual void DoError (int level, const char *location, const char *fmt, va_list va) const
 Interface to ErrorHandler (protected).
 
void MakeZombie ()
 
- Protected Attributes inherited from TMVA::MethodBase
Types::EAnalysisType fAnalysisType
 
UInt_t fBackgroundClass
 
bool fExitFromTraining = false
 
std::vector< TString > * fInputVars
 
IPythonInteractivefInteractive = 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
 
RankingfRanking
 
std::vector< Float_t > * fRegressionReturnVal
 
ResultsfResults
 
UInt_t fSignalClass
 
DataSetfTmpData = nullptr
 temporary event when testing on a different DataSet than the own one
 
const EventfTmpEvent
 
- Protected Attributes inherited from TMVA::Configurable
MsgLoggerfLogger
 ! message logger
 
- Protected Attributes inherited from TNamed
TString fName
 
TString fTitle
 

#include <TMVA/MethodDT.h>

Inheritance diagram for TMVA::MethodDT:
[legend]

Constructor & Destructor Documentation

◆ MethodDT() [1/2]

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

the standard constructor for just an ordinar "decision trees"

Definition at line 127 of file MethodDT.cxx.

◆ MethodDT() [2/2]

TMVA::MethodDT::MethodDT ( DataSetInfo dsi,
const TString theWeightFile 
)

constructor from Reader

Definition at line 155 of file MethodDT.cxx.

◆ ~MethodDT()

TMVA::MethodDT::~MethodDT ( void  )
virtual

destructor

Definition at line 368 of file MethodDT.cxx.

Member Function Documentation

◆ AddWeightsXMLTo()

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

Implements TMVA::MethodBase.

Definition at line 523 of file MethodDT.cxx.

◆ Class()

static TClass * TMVA::MethodDT::Class ( )
static
Returns
TClass describing this class

◆ Class_Name()

static const char * TMVA::MethodDT::Class_Name ( )
static
Returns
Name of this class

◆ Class_Version()

static constexpr Version_t TMVA::MethodDT::Class_Version ( )
inlinestaticconstexpr
Returns
Version of this class

Definition at line 139 of file MethodDT.h.

◆ CreateRanking()

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

Implements TMVA::MethodBase.

Definition at line 566 of file MethodDT.cxx.

◆ DeclareCompatibilityOptions()

void TMVA::MethodDT::DeclareCompatibilityOptions ( )
virtual

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

Reimplemented from TMVA::MethodBase.

Definition at line 245 of file MethodDT.cxx.

◆ DeclareOptions()

void TMVA::MethodDT::DeclareOptions ( )
virtual

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

  • UseRandomisedTrees choose at each node splitting a random set of variables
  • UseNvars use UseNvars variables in randomised trees
  • SeparationType the separation criterion applied in the node splitting. known:
  • nEventsMin: the minimum number of events in a 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)
  • 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");

Implements TMVA::MethodBase.

Definition at line 212 of file MethodDT.cxx.

◆ DeclFileName()

static const char * TMVA::MethodDT::DeclFileName ( )
inlinestatic
Returns
Name of the file containing the class declaration

Definition at line 139 of file MethodDT.h.

◆ GetHelpMessage()

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

Implements TMVA::IMethod.

Definition at line 561 of file MethodDT.cxx.

◆ GetMvaValue()

Double_t TMVA::MethodDT::GetMvaValue ( Double_t err = nullptr,
Double_t errUpper = nullptr 
)
virtual

returns MVA value

Implements TMVA::MethodBase.

Definition at line 551 of file MethodDT.cxx.

◆ GetNNodes()

Int_t TMVA::MethodDT::GetNNodes ( )
inline

Definition at line 97 of file MethodDT.h.

◆ GetNNodesBeforePruning()

Int_t TMVA::MethodDT::GetNNodesBeforePruning ( )
inline

Definition at line 96 of file MethodDT.h.

◆ GetPruneStrength()

Double_t TMVA::MethodDT::GetPruneStrength ( )
inline

Definition at line 91 of file MethodDT.h.

◆ HasAnalysisType()

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

FDA can handle classification with 2 classes and regression with one regression-target.

Implements TMVA::IMethod.

Definition at line 180 of file MethodDT.cxx.

◆ Init()

void TMVA::MethodDT::Init ( void  )
privatevirtual

common initialisation with defaults for the DT-Method

Implements TMVA::MethodBase.

Definition at line 343 of file MethodDT.cxx.

◆ IsA()

virtual TClass * TMVA::MethodDT::IsA ( ) const
inlinevirtual
Returns
TClass describing current object

Reimplemented from TMVA::MethodBase.

Definition at line 139 of file MethodDT.h.

◆ ProcessOptions()

void TMVA::MethodDT::ProcessOptions ( )
virtual

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

Implements TMVA::MethodBase.

Definition at line 256 of file MethodDT.cxx.

◆ PruneTree()

Double_t TMVA::MethodDT::PruneTree ( )

prune the decision tree if requested (good for individual trees that are best grown out, and then pruned back, while boosted decision trees are best 'small' trees to start with.

Well, at least the standard "optimal pruning algorithms" don't result in 'weak enough' classifiers !!

Definition at line 406 of file MethodDT.cxx.

◆ ReadWeightsFromStream() [1/3]

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

Implements TMVA::MethodBase.

◆ ReadWeightsFromStream() [2/3]

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

Implements TMVA::MethodBase.

Definition at line 541 of file MethodDT.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::MethodDT::ReadWeightsFromXML ( void *  wghtnode)
virtual

Implements TMVA::MethodBase.

Definition at line 531 of file MethodDT.cxx.

◆ SetMinNodeSize() [1/2]

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

Definition at line 320 of file MethodDT.cxx.

◆ SetMinNodeSize() [2/2]

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

Definition at line 331 of file MethodDT.cxx.

◆ Streamer()

virtual void TMVA::MethodDT::Streamer ( TBuffer )
virtual

Reimplemented from TMVA::MethodBase.

◆ StreamerNVirtual()

void TMVA::MethodDT::StreamerNVirtual ( TBuffer ClassDef_StreamerNVirtual_b)
inline

Definition at line 139 of file MethodDT.h.

◆ TestTreeQuality()

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

Definition at line 506 of file MethodDT.cxx.

◆ Train()

void TMVA::MethodDT::Train ( void  )
virtual

Implements TMVA::MethodBase.

Definition at line 375 of file MethodDT.cxx.

Member Data Documentation

◆ fAutomatic

Bool_t TMVA::MethodDT::fAutomatic
private

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

Definition at line 126 of file MethodDT.h.

◆ fDeltaPruneStrength

Double_t TMVA::MethodDT::fDeltaPruneStrength
private

step size in pruning, is adjusted according to experience of previous trees

Definition at line 132 of file MethodDT.h.

◆ fErrorFraction

Double_t TMVA::MethodDT::fErrorFraction
private

ntuple var: misclassification error fraction

Definition at line 122 of file MethodDT.h.

◆ fEventSample

std::vector<Event*> TMVA::MethodDT::fEventSample
private

the training events

Definition at line 106 of file MethodDT.h.

◆ fgDebugLevel

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

debug level determining some printout/control plots etc.

Definition at line 134 of file MethodDT.h.

◆ fMaxDepth

UInt_t TMVA::MethodDT::fMaxDepth
private

max depth

Definition at line 119 of file MethodDT.h.

◆ fMinNodeEvents

Int_t TMVA::MethodDT::fMinNodeEvents
private

min number of events in node

Definition at line 112 of file MethodDT.h.

◆ fMinNodeSize

Float_t TMVA::MethodDT::fMinNodeSize
private

min percentage of training events in node

Definition at line 113 of file MethodDT.h.

◆ fMinNodeSizeS

TString TMVA::MethodDT::fMinNodeSizeS
private

string containing min percentage of training events in node

Definition at line 114 of file MethodDT.h.

◆ fNCuts

Int_t TMVA::MethodDT::fNCuts
private

grid used in cut applied in node splitting

Definition at line 116 of file MethodDT.h.

◆ fNodePurityLimit

Double_t TMVA::MethodDT::fNodePurityLimit
private

purity limit for sig/bkg nodes

Definition at line 118 of file MethodDT.h.

◆ fPruneBeforeBoost

Bool_t TMVA::MethodDT::fPruneBeforeBoost
private

ancient variable, only needed for "CompatibilityOptions"

Definition at line 137 of file MethodDT.h.

◆ fPruneMethod

DecisionTree::EPruneMethod TMVA::MethodDT::fPruneMethod
private

method used for pruning

Definition at line 124 of file MethodDT.h.

◆ fPruneMethodS

TString TMVA::MethodDT::fPruneMethodS
private

prune method option String

Definition at line 125 of file MethodDT.h.

◆ fPruneStrength

Double_t TMVA::MethodDT::fPruneStrength
private

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

Definition at line 123 of file MethodDT.h.

◆ fRandomisedTrees

Bool_t TMVA::MethodDT::fRandomisedTrees
private

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

Definition at line 127 of file MethodDT.h.

◆ fSepType

SeparationBase* TMVA::MethodDT::fSepType
private

the separation used in node splitting

Definition at line 110 of file MethodDT.h.

◆ fSepTypeS

TString TMVA::MethodDT::fSepTypeS
private

the separation (option string) used in node splitting

Definition at line 111 of file MethodDT.h.

◆ fTree

DecisionTree* TMVA::MethodDT::fTree
private

the decision tree

Definition at line 108 of file MethodDT.h.

◆ fUseNvars

Int_t TMVA::MethodDT::fUseNvars
private

the number of variables used in the randomised tree splitting

Definition at line 128 of file MethodDT.h.

◆ fUsePoissonNvars

Bool_t TMVA::MethodDT::fUsePoissonNvars
private

fUseNvars is used as a poisson mean, and the actual value of useNvars is at each step drawn form that distribution

Definition at line 129 of file MethodDT.h.

◆ fUseYesNoLeaf

Bool_t TMVA::MethodDT::fUseYesNoLeaf
private

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

Definition at line 117 of file MethodDT.h.

◆ fVariableImportance

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

the relative importance of the different variables

Definition at line 130 of file MethodDT.h.

Libraries for TMVA::MethodDT:

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