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Reference Guide
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" More...
 
 MethodDT (DataSetInfo &dsi, const TString &theWeightFile)
 constructor from Reader More...
 
virtual ~MethodDT (void)
 destructor More...
 
void AddWeightsXMLTo (void *parent) const
 
const RankingCreateRanking ()
 
void DeclareCompatibilityOptions ()
 options that are used ONLY for the READER to ensure backward compatibility More...
 
void DeclareOptions ()
 Define the options (their key words) that can be set in the option string. More...
 
void GetHelpMessage () const
 
Double_t GetMvaValue (Double_t *err=0, Double_t *errUpper=0)
 returns MVA value More...
 
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. More...
 
void ProcessOptions ()
 the option string is decoded, for available options see "DeclareOptions" More...
 
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. More...
 
virtual void ReadWeightsFromStream (std::istream &)=0
 
virtual void ReadWeightsFromStream (TFile &)
 
void ReadWeightsFromStream (std::istream &istr)
 
void ReadWeightsFromXML (void *wghtnode)
 
void SetMinNodeSize (Double_t sizeInPercent)
 
void SetMinNodeSize (TString sizeInPercent)
 
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 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, \( \frac{S}{\sqrt{S^2 + B^2}} \), curve for given number of signal and background events; returns cut for maximum significance also returned via reference is the maximum significance More...
 
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. More...
 
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=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:

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

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

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

More...
 
virtual Double_t GetSeparation (PDF *pdfS=0, PDF *pdfB=0) const
 compute "separation" defined as

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

More...
 
Double_t GetSignalReferenceCut () const
 
Double_t GetSignalReferenceCutOrientation () const
 
virtual Double_t GetSignificance () const
 compute significance of mean difference

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

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
 
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 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 () 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 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 be selected as signal or background More...
 
Bool_t IsSilentFile () const
 
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...
 
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. 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 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 . More...
 
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...
 
virtual void WriteMonitoringHistosToFile () const
 write special monitoring histograms to file dummy implementation here --------------— 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 destructor 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 ()
 TNamed destructor. More...
 
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...
 
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. 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 selected pad for instance with: gROOT->SetSelectedPad(gPad). 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...
 
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. 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...
 
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). 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...
 
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. 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...
 

Private Member Functions

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

Private Attributes

Bool_t fAutomatic
 
Double_t fDeltaPruneStrength
 
Double_t fErrorFraction
 
std::vector< Event * > fEventSample
 
UInt_t fMaxDepth
 
Int_t fMinNodeEvents
 
Float_t fMinNodeSize
 
TString fMinNodeSizeS
 
Int_t fNCuts
 
Double_t fNodePurityLimit
 
Bool_t fPruneBeforeBoost
 
DecisionTree::EPruneMethod fPruneMethod
 
TString fPruneMethodS
 
Double_t fPruneStrength
 
Bool_t fRandomisedTrees
 
SeparationBasefSepType
 
TString fSepTypeS
 
DecisionTreefTree
 
Int_t fUseNvars
 
Bool_t fUsePoissonNvars
 
Bool_t fUseYesNoLeaf
 
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, kInconsistent = 0x08000000,
  kBitMask = 0x00ffffff
}
 
enum  { kSingleKey = BIT(0), kOverwrite = BIT(1), kWriteDelete = BIT(2) }
 
enum  EDeprecatedStatusBits { kObjInCanvas = BIT(3) }
 
enum  EStatusBits {
  kCanDelete = BIT(0), kMustCleanup = BIT(3), kIsReferenced = BIT(4), kHasUUID = BIT(5),
  kCannotPick = BIT(6), kNoContextMenu = BIT(8), kInvalidObject = BIT(13)
}
 
- Static Public Member Functions inherited from TObject
static 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
 
TrainingHistory fTrainHistory
 
- 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
 
virtual void MakeClassSpecific (std::ostream &, const TString &="") const
 
virtual void MakeClassSpecificHeader (std::ostream &, const TString &="") const
 
void NoErrorCalc (Double_t *const err, Double_t *const errUpper)
 
virtual void ReadWeightsFromStream (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 flag 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 ()
 
- 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/MethodDT.h>

Inheritance diagram for TMVA::MethodDT:
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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 128 of file MethodDT.cxx.

◆ MethodDT() [2/2]

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

constructor from Reader

Definition at line 156 of file MethodDT.cxx.

◆ ~MethodDT()

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

destructor

Definition at line 369 of file MethodDT.cxx.

Member Function Documentation

◆ AddWeightsXMLTo()

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

Implements TMVA::MethodBase.

Definition at line 524 of file MethodDT.cxx.

◆ CreateRanking()

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

Implements TMVA::MethodBase.

Definition at line 567 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 246 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 213 of file MethodDT.cxx.

◆ GetHelpMessage()

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

Implements TMVA::IMethod.

Definition at line 562 of file MethodDT.cxx.

◆ GetMvaValue()

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

returns MVA value

Implements TMVA::MethodBase.

Definition at line 552 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 181 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 344 of file MethodDT.cxx.

◆ ProcessOptions()

void TMVA::MethodDT::ProcessOptions ( )
virtual

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

Implements TMVA::MethodBase.

Definition at line 257 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 407 of file MethodDT.cxx.

◆ ReadWeightsFromStream() [1/3]

virtual void TMVA::MethodBase::ReadWeightsFromStream
inline

Definition at line 265 of file MethodBase.h.

◆ ReadWeightsFromStream() [2/3]

virtual void TMVA::MethodBase::ReadWeightsFromStream

◆ ReadWeightsFromStream() [3/3]

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

Implements TMVA::MethodBase.

Definition at line 542 of file MethodDT.cxx.

◆ ReadWeightsFromXML()

void TMVA::MethodDT::ReadWeightsFromXML ( void wghtnode)
virtual

Implements TMVA::MethodBase.

Definition at line 532 of file MethodDT.cxx.

◆ SetMinNodeSize() [1/2]

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

Definition at line 321 of file MethodDT.cxx.

◆ SetMinNodeSize() [2/2]

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

Definition at line 332 of file MethodDT.cxx.

◆ TestTreeQuality()

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

Definition at line 507 of file MethodDT.cxx.

◆ Train()

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

Implements TMVA::MethodBase.

Definition at line 376 of file MethodDT.cxx.

Member Data Documentation

◆ fAutomatic

Bool_t TMVA::MethodDT::fAutomatic
private

Definition at line 126 of file MethodDT.h.

◆ fDeltaPruneStrength

Double_t TMVA::MethodDT::fDeltaPruneStrength
private

Definition at line 132 of file MethodDT.h.

◆ fErrorFraction

Double_t TMVA::MethodDT::fErrorFraction
private

Definition at line 122 of file MethodDT.h.

◆ fEventSample

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

Definition at line 106 of file MethodDT.h.

◆ fgDebugLevel

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

Definition at line 134 of file MethodDT.h.

◆ fMaxDepth

UInt_t TMVA::MethodDT::fMaxDepth
private

Definition at line 119 of file MethodDT.h.

◆ fMinNodeEvents

Int_t TMVA::MethodDT::fMinNodeEvents
private

Definition at line 112 of file MethodDT.h.

◆ fMinNodeSize

Float_t TMVA::MethodDT::fMinNodeSize
private

Definition at line 113 of file MethodDT.h.

◆ fMinNodeSizeS

TString TMVA::MethodDT::fMinNodeSizeS
private

Definition at line 114 of file MethodDT.h.

◆ fNCuts

Int_t TMVA::MethodDT::fNCuts
private

Definition at line 116 of file MethodDT.h.

◆ fNodePurityLimit

Double_t TMVA::MethodDT::fNodePurityLimit
private

Definition at line 118 of file MethodDT.h.

◆ fPruneBeforeBoost

Bool_t TMVA::MethodDT::fPruneBeforeBoost
private

Definition at line 137 of file MethodDT.h.

◆ fPruneMethod

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

Definition at line 124 of file MethodDT.h.

◆ fPruneMethodS

TString TMVA::MethodDT::fPruneMethodS
private

Definition at line 125 of file MethodDT.h.

◆ fPruneStrength

Double_t TMVA::MethodDT::fPruneStrength
private

Definition at line 123 of file MethodDT.h.

◆ fRandomisedTrees

Bool_t TMVA::MethodDT::fRandomisedTrees
private

Definition at line 127 of file MethodDT.h.

◆ fSepType

SeparationBase* TMVA::MethodDT::fSepType
private

Definition at line 110 of file MethodDT.h.

◆ fSepTypeS

TString TMVA::MethodDT::fSepTypeS
private

Definition at line 111 of file MethodDT.h.

◆ fTree

DecisionTree* TMVA::MethodDT::fTree
private

Definition at line 108 of file MethodDT.h.

◆ fUseNvars

Int_t TMVA::MethodDT::fUseNvars
private

Definition at line 128 of file MethodDT.h.

◆ fUsePoissonNvars

Bool_t TMVA::MethodDT::fUsePoissonNvars
private

Definition at line 129 of file MethodDT.h.

◆ fUseYesNoLeaf

Bool_t TMVA::MethodDT::fUseYesNoLeaf
private

Definition at line 117 of file MethodDT.h.

◆ fVariableImportance

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

Definition at line 130 of file MethodDT.h.

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