Logo ROOT  
Reference Guide
TMVA::MethodRuleFit Class Reference

J Friedman's RuleFit method.

Definition at line 48 of file MethodRuleFit.h.

Public Member Functions

 MethodRuleFit (const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption="")
 standard constructor More...
 
 MethodRuleFit (DataSetInfo &theData, const TString &theWeightFile)
 constructor from weight file More...
 
virtual ~MethodRuleFit (void)
 destructor More...
 
void AddWeightsXMLTo (void *parent) const
 add the rules to XML node More...
 
const RankingCreateRanking ()
 computes ranking of input variables More...
 
const std::vector< TMVA::DecisionTree * > & GetForest () const
 
Double_t GetGDErrScale () const
 
Int_t GetGDNPathSteps () const
 
Double_t GetGDPathEveFrac () const
 
Double_t GetGDPathStep () const
 
Double_t GetGDValidEveFrac () const
 
Double_t GetLinQuantile () const
 
Double_t GetMaxFracNEve () const
 
TDirectoryGetMethodBaseDir () const
 
Double_t GetMinFracNEve () const
 
Double_t GetMvaValue (Double_t *err=0, Double_t *errUpper=0)
 returns MVA value for given event More...
 
Int_t GetNCuts () const
 
Int_t GetNTrees () const
 
TMVA::DecisionTree::EPruneMethod GetPruneMethod () const
 
Double_t GetPruneStrength () const
 
Int_t GetRFNendnodes () const
 
Int_t GetRFNrules () const
 
const TString GetRFWorkDir () const
 
const RuleFitGetRuleFitConstPtr () const
 
RuleFitGetRuleFitPtr ()
 
SeparationBaseGetSeparationBase () const
 
const SeparationBaseGetSeparationBaseConst () const
 
const std::vector< TMVA::Event * > & GetTrainingEvents () const
 
Double_t GetTreeEveFrac () const
 
virtual Bool_t HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t)
 RuleFit can handle classification with 2 classes. More...
 
virtual TClassIsA () const
 
virtual void ReadWeightsFromStream (std::istream &)=0
 
void ReadWeightsFromStream (std::istream &istr)
 read rules from an std::istream More...
 
virtual void ReadWeightsFromStream (TFile &)
 
void ReadWeightsFromXML (void *wghtnode)
 read rules from XML node More...
 
virtual void Streamer (TBuffer &)
 Stream an object of class TObject. More...
 
void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)
 
void Train (void)
 
Bool_t UseBoost () const
 
void WriteMonitoringHistosToFile (void) const
 write special monitoring histograms to file (here ntuple) More...
 
- 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...
 
virtual const RankingCreateRanking ()=0
 
DataSetData () const
 
DataSetInfoDataInfo () const
 
virtual void DeclareCompatibilityOptions ()
 options that are used ONLY for the READER to ensure backward compatibility they are hence without any effect (the reader is only reading the training options that HAD been used at the training of the .xml weight file at hand More...
 
virtual void DeclareOptions ()=0
 
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)
 
virtual Double_t GetMvaValue (Double_t *errLower=0, Double_t *errUpper=0)=0
 
const char * GetName () const
 
UInt_t GetNEvents () const
 
UInt_t GetNTargets () const
 
UInt_t GetNvar () const
 
UInt_t GetNVariables () const
 
virtual Double_t GetProba (const Event *ev)
 
virtual Double_t GetProba (Double_t mvaVal, Double_t ap_sig)
 compute likelihood ratio More...
 
const TString GetProbaName () const
 
virtual Double_t GetRarity (Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const
 compute rarity: More...
 
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=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 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 GetSeparation (PDF *pdfS=0, PDF *pdfB=0) const
 compute "separation" defined as More...
 
virtual Double_t GetSeparation (TH1 *, TH1 *) const
 compute "separation" defined as More...
 
Double_t GetSignalReferenceCut () const
 
Double_t GetSignalReferenceCutOrientation () const
 
virtual Double_t GetSignificance () const
 compute significance of mean difference 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
 
virtual void Init ()=0
 
void InitIPythonInteractive ()
 
virtual TClassIsA () const
 
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...
 
virtual void ProcessOptions ()=0
 
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 Streamer (TBuffer &)
 Stream an object of class TObject. More...
 
void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)
 
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...
 
virtual void Train ()=0
 
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 ()
 
virtual const RankingCreateRanking ()=0
 
virtual void DeclareOptions ()=0
 
virtual Double_t GetMvaValue (Double_t *err=0, Double_t *errUpper=0)=0
 
virtual const char * GetName () const =0
 
virtual Bool_t HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)=0
 
virtual void Init ()=0
 
virtual TClassIsA () const
 
virtual void MakeClass (const TString &classFileName=TString("")) const =0
 
virtual void PrintHelpMessage () const =0
 
virtual void ProcessOptions ()=0
 
virtual void ReadWeightsFromStream (std::istream &)=0
 
virtual void Streamer (TBuffer &)
 
void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)
 
virtual void Train (void)=0
 
virtual void WriteMonitoringHistosToFile (void) const =0
 
- 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 >
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
 
virtual TClassIsA () 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)
 
virtual void Streamer (TBuffer &)
 Stream an object of class TObject. More...
 
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 More...
 
- Public Member Functions inherited from TNamed
 TNamed ()
 
 TNamed (const char *name, const char *title)
 
 TNamed (const TNamed &named)
 TNamed copy ctor. More...
 
 TNamed (const TString &name, const TString &title)
 
virtual ~TNamed ()
 TNamed destructor. More...
 
void Clear (Option_t *option="") override
 Set name and title to empty strings (""). More...
 
TObjectClone (const char *newname="") const override
 Make a clone of an object using the Streamer facility. More...
 
Int_t Compare (const TObject *obj) const override
 Compare two TNamed objects. More...
 
void Copy (TObject &named) const override
 Copy this to obj. More...
 
virtual void FillBuffer (char *&buffer)
 Encode TNamed into output buffer. More...
 
const char * GetName () const override
 Returns name of object. More...
 
const char * GetTitle () const override
 Returns title of object. More...
 
ULong_t Hash () const override
 Return hash value for this object. More...
 
TClassIsA () const override
 
Bool_t IsSortable () const override
 
void ls (Option_t *option="") const override
 List TNamed name and title. More...
 
TNamedoperator= (const TNamed &rhs)
 TNamed assignment operator. More...
 
void Print (Option_t *option="") const override
 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...
 
void Streamer (TBuffer &) override
 Stream an object of class TObject. More...
 
void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)
 
- 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 Clear (Option_t *="")
 
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 abstract method. More...
 
virtual void Copy (TObject &object) const
 Copy this to obj. 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 const char * GetName () const
 Returns 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 const char * GetTitle () const
 Returns title of object. More...
 
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 ULong_t Hash () const
 Return hash value for this object. 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 TClassIsA () const
 
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
 
virtual Bool_t IsSortable () const
 
R__ALWAYS_INLINE Bool_t IsZombie () const
 
virtual void ls (Option_t *option="") const
 The ls function lists the contents of a class on stdout. More...
 
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...
 
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. 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 void Print (Option_t *option="") const
 This method must be overridden when a class wants to print itself. 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)
 
void SetBit (UInt_t f, Bool_t set)
 Set or unset the user status bits as specified in f. More...
 
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 Streamer (TBuffer &)
 Stream an object of class TObject. More...
 
void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)
 
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...
 

Static Public Member Functions

static TClassClass ()
 
static const char * Class_Name ()
 
static Version_t Class_Version ()
 
static const char * DeclFileName ()
 
- Static Public Member Functions inherited from TMVA::MethodBase
static TClassClass ()
 
static const char * Class_Name ()
 
static Version_t Class_Version ()
 
static const char * DeclFileName ()
 
- Static Public Member Functions inherited from TMVA::IMethod
static TClassClass ()
 
static const char * Class_Name ()
 
static Version_t Class_Version ()
 
static const char * DeclFileName ()
 
- Static Public Member Functions inherited from TMVA::Configurable
static TClassClass ()
 
static const char * Class_Name ()
 
static Version_t Class_Version ()
 
static const char * DeclFileName ()
 
- Static Public Member Functions inherited from TNamed
static TClassClass ()
 
static const char * Class_Name ()
 
static Version_t Class_Version ()
 
static const char * DeclFileName ()
 
- Static Public Member Functions inherited from TObject
static TClassClass ()
 
static const char * Class_Name ()
 
static Version_t Class_Version ()
 
static const char * DeclFileName ()
 
static Longptr_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...
 

Protected Member Functions

void GetHelpMessage () const
 get help message text More...
 
void Init (void)
 default initialization More...
 
void InitEventSample (void)
 write all Events from the Tree into a vector of Events, that are more easily manipulated. More...
 
void InitMonitorNtuple ()
 initialize the monitoring ntuple More...
 
void MakeClassLinear (std::ostream &) const
 print out the linear terms More...
 
void MakeClassRuleCuts (std::ostream &) const
 print out the rule cuts More...
 
void MakeClassSpecific (std::ostream &, const TString &) const
 write specific classifier response More...
 
void TrainJFRuleFit ()
 training of rules using Jerome Friedmans implementation More...
 
void TrainTMVARuleFit ()
 training of rules using TMVA implementation More...
 
- Protected Member Functions inherited from TMVA::MethodBase
virtual void AddWeightsXMLTo (void *parent) const =0
 
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 More...
 
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 (std::istream &)=0
 
virtual void ReadWeightsFromStream (TFile &)
 
virtual void ReadWeightsFromXML (void *wghtnode)=0
 
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
 
virtual void GetHelpMessage () const =0
 
virtual void MakeClassSpecific (std::ostream &, const TString &) const =0
 
- 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 ()
 

Private Member Functions

void DeclareOptions ()
 define the options (their key words) that can be set in the option string know options. More...
 
void ProcessOptions ()
 process the options specified by the user More...
 
template<typename T >
Int_t VerifyRange (const T &var, const T &vmin, const T &vmax)
 
template<typename T >
Bool_t VerifyRange (MsgLogger &mlog, const char *varstr, T &var, const T &vmin, const T &vmax)
 
template<typename T >
Bool_t VerifyRange (MsgLogger &mlog, const char *varstr, T &var, const T &vmin, const T &vmax, const T &vdef)
 

Private Attributes

std::vector< TMVA::Event * > fEventSample
 the complete training sample More...
 
std::vector< DecisionTree * > fForest
 the forest More...
 
TString fForestTypeS
 forest generation: how the trees are generated More...
 
Double_t fGDErrScale
 GD path: stop. More...
 
Int_t fGDNPathSteps
 GD path: number of steps. More...
 
Double_t fGDPathEveFrac
 GD path: fraction of subsamples used for the fitting. More...
 
Double_t fGDPathStep
 GD path: step size in path. More...
 
Double_t fGDTau
 GD path: def threshold fraction [0..1]. More...
 
Double_t fGDTauMax
 GD path: max threshold fraction [0..1]. More...
 
Double_t fGDTauMin
 GD path: min threshold fraction [0..1]. More...
 
Double_t fGDTauPrec
 GD path: precision of estimated tau. More...
 
UInt_t fGDTauScan
 GD path: number of points to scan. More...
 
Double_t fGDValidEveFrac
 GD path: fraction of subsamples used for the fitting. More...
 
Double_t fLinQuantile
 quantile cut to remove outliers - see RuleEnsemble More...
 
Double_t fMaxFracNEve
 ditto max More...
 
Double_t fMinFracNEve
 min fraction of number events More...
 
Double_t fMinimp
 rule/linear: minimum importance More...
 
TString fModelTypeS
 rule ensemble: which model (rule,linear or both) More...
 
TTreefMonitorNtuple
 pointer to monitor rule ntuple More...
 
Int_t fNCuts
 grid used in cut applied in node splitting More...
 
Double_t fNTCoefficient
 ntuple: rule coefficient More...
 
Double_t fNTImportance
 ntuple: rule importance More...
 
Int_t fNTNcuts
 ntuple: rule number of cuts More...
 
Int_t fNTNvars
 ntuple: rule number of vars More...
 
Double_t fNTPbb
 ntuple: rule P(tag b, true b) More...
 
Double_t fNTPbs
 ntuple: rule P(tag b, true s) More...
 
Double_t fNTPsb
 ntuple: rule P(tag s, true b) More...
 
Double_t fNTPss
 ntuple: rule P(tag s, true s) More...
 
Double_t fNTPtag
 ntuple: rule P(tag) More...
 
Int_t fNTrees
 number of trees in forest More...
 
Double_t fNTSSB
 ntuple: rule S/(S+B) More...
 
Double_t fNTSupport
 ntuple: rule support More...
 
Int_t fNTType
 ntuple: rule type (+1->signal, -1->bkg) More...
 
TMVA::DecisionTree::EPruneMethod fPruneMethod
 forest generation: method used for pruning - see DecisionTree More...
 
TString fPruneMethodS
 forest generation: prune method - see DecisionTree More...
 
Double_t fPruneStrength
 forest generation: prune strength - see DecisionTree More...
 
Int_t fRFNendnodes
 max number of rules (only Friedmans module) More...
 
Int_t fRFNrules
 max number of rules (only Friedmans module) More...
 
TString fRFWorkDir
 working directory from Friedmans module More...
 
RuleFit fRuleFit
 RuleFit instance. More...
 
TString fRuleFitModuleS
 which rulefit module to use More...
 
Double_t fRuleMinDist
 rule min distance - see RuleEnsemble More...
 
SeparationBasefSepType
 the separation used in node splitting More...
 
TString fSepTypeS
 forest generation: separation type - see DecisionTree More...
 
Double_t fSignalFraction
 scalefactor for bkg events to modify initial s/b fraction in training data More...
 
Double_t fTreeEveFrac
 fraction of events used for training each tree More...
 
Bool_t fUseBoost
 use boosted events for forest generation More...
 
Bool_t fUseRuleFitJF
 if true interface with J.Friedmans RuleFit module More...
 

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 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) More...
 
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 More...
 
const EventfTmpEvent
 
- Protected Attributes inherited from TMVA::Configurable
MsgLoggerfLogger
 ! message logger More...
 
- Protected Attributes inherited from TNamed
TString fName
 
TString fTitle
 

#include <TMVA/MethodRuleFit.h>

Inheritance diagram for TMVA::MethodRuleFit:
[legend]

Constructor & Destructor Documentation

◆ MethodRuleFit() [1/2]

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

standard constructor

Definition at line 70 of file MethodRuleFit.cxx.

◆ MethodRuleFit() [2/2]

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

constructor from weight file

Definition at line 120 of file MethodRuleFit.cxx.

◆ ~MethodRuleFit()

TMVA::MethodRuleFit::~MethodRuleFit ( void  )
virtual

destructor

Definition at line 168 of file MethodRuleFit.cxx.

Member Function Documentation

◆ AddWeightsXMLTo()

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

add the rules to XML node

Implements TMVA::MethodBase.

Definition at line 593 of file MethodRuleFit.cxx.

◆ Class()

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

◆ Class_Name()

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

◆ Class_Version()

static Version_t TMVA::MethodRuleFit::Class_Version ( )
inlinestatic
Returns
Version of this class

Definition at line 210 of file MethodRuleFit.h.

◆ CreateRanking()

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

computes ranking of input variables

Implements TMVA::MethodBase.

Definition at line 578 of file MethodRuleFit.cxx.

◆ DeclareOptions()

void TMVA::MethodRuleFit::DeclareOptions ( )
privatevirtual

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

general

  • RuleFitModule <string> available values are:
    • RFTMVA - use TMVA implementation
    • RFFriedman - use Friedmans original implementation

Path search (fitting)

  • GDTau <float> gradient-directed path: fit threshold, default
  • GDTauPrec <float> gradient-directed path: precision of estimated tau
  • GDStep <float> gradient-directed path: step size
  • GDNSteps <float> gradient-directed path: number of steps
  • GDErrScale <float> stop scan when error>scale*errmin

Tree generation

  • fEventsMin <float> minimum fraction of events in a splittable node
  • fEventsMax <float> maximum fraction of events in a splittable node
  • nTrees <float> number of trees in forest.
  • ForestType <string> available values are:
    • Random - create forest using random subsample and only random variables subset at each node
    • AdaBoost - create forest with boosted events

Model creation

  • RuleMinDist <float> min distance allowed between rules
  • MinImp <float> minimum rule importance accepted
  • Model <string> model to be used available values are:
    • ModRuleLinear <default>
    • ModRule
    • ModLinear

Friedmans module

  • RFWorkDir <string> directory where Friedmans module (rf_go.exe) is installed
  • RFNrules <int> maximum number of rules allowed
  • RFNendnodes <int> average number of end nodes in the forest of trees

Implements TMVA::MethodBase.

Definition at line 228 of file MethodRuleFit.cxx.

◆ DeclFileName()

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

Definition at line 210 of file MethodRuleFit.h.

◆ GetForest()

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

Definition at line 92 of file MethodRuleFit.h.

◆ GetGDErrScale()

Double_t TMVA::MethodRuleFit::GetGDErrScale ( ) const
inline

Definition at line 105 of file MethodRuleFit.h.

◆ GetGDNPathSteps()

Int_t TMVA::MethodRuleFit::GetGDNPathSteps ( ) const
inline

Definition at line 103 of file MethodRuleFit.h.

◆ GetGDPathEveFrac()

Double_t TMVA::MethodRuleFit::GetGDPathEveFrac ( ) const
inline

Definition at line 106 of file MethodRuleFit.h.

◆ GetGDPathStep()

Double_t TMVA::MethodRuleFit::GetGDPathStep ( ) const
inline

Definition at line 104 of file MethodRuleFit.h.

◆ GetGDValidEveFrac()

Double_t TMVA::MethodRuleFit::GetGDValidEveFrac ( ) const
inline

Definition at line 107 of file MethodRuleFit.h.

◆ GetHelpMessage()

void TMVA::MethodRuleFit::GetHelpMessage ( ) const
protectedvirtual

get help message text

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

Implements TMVA::IMethod.

Definition at line 751 of file MethodRuleFit.cxx.

◆ GetLinQuantile()

Double_t TMVA::MethodRuleFit::GetLinQuantile ( ) const
inline

Definition at line 109 of file MethodRuleFit.h.

◆ GetMaxFracNEve()

Double_t TMVA::MethodRuleFit::GetMaxFracNEve ( ) const
inline

Definition at line 100 of file MethodRuleFit.h.

◆ GetMethodBaseDir()

TDirectory * TMVA::MethodRuleFit::GetMethodBaseDir ( ) const
inline

Definition at line 90 of file MethodRuleFit.h.

◆ GetMinFracNEve()

Double_t TMVA::MethodRuleFit::GetMinFracNEve ( ) const
inline

Definition at line 99 of file MethodRuleFit.h.

◆ GetMvaValue()

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

returns MVA value for given event

Implements TMVA::MethodBase.

Definition at line 617 of file MethodRuleFit.cxx.

◆ GetNCuts()

Int_t TMVA::MethodRuleFit::GetNCuts ( ) const
inline

Definition at line 101 of file MethodRuleFit.h.

◆ GetNTrees()

Int_t TMVA::MethodRuleFit::GetNTrees ( ) const
inline

Definition at line 93 of file MethodRuleFit.h.

◆ GetPruneMethod()

TMVA::DecisionTree::EPruneMethod TMVA::MethodRuleFit::GetPruneMethod ( ) const
inline

Definition at line 97 of file MethodRuleFit.h.

◆ GetPruneStrength()

Double_t TMVA::MethodRuleFit::GetPruneStrength ( ) const
inline

Definition at line 98 of file MethodRuleFit.h.

◆ GetRFNendnodes()

Int_t TMVA::MethodRuleFit::GetRFNendnodes ( ) const
inline

Definition at line 113 of file MethodRuleFit.h.

◆ GetRFNrules()

Int_t TMVA::MethodRuleFit::GetRFNrules ( ) const
inline

Definition at line 112 of file MethodRuleFit.h.

◆ GetRFWorkDir()

const TString TMVA::MethodRuleFit::GetRFWorkDir ( ) const
inline

Definition at line 111 of file MethodRuleFit.h.

◆ GetRuleFitConstPtr()

const RuleFit * TMVA::MethodRuleFit::GetRuleFitConstPtr ( ) const
inline

Definition at line 89 of file MethodRuleFit.h.

◆ GetRuleFitPtr()

RuleFit * TMVA::MethodRuleFit::GetRuleFitPtr ( )
inline

Definition at line 88 of file MethodRuleFit.h.

◆ GetSeparationBase()

SeparationBase * TMVA::MethodRuleFit::GetSeparationBase ( ) const
inline

Definition at line 96 of file MethodRuleFit.h.

◆ GetSeparationBaseConst()

const SeparationBase * TMVA::MethodRuleFit::GetSeparationBaseConst ( ) const
inline

Definition at line 95 of file MethodRuleFit.h.

◆ GetTrainingEvents()

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

Definition at line 91 of file MethodRuleFit.h.

◆ GetTreeEveFrac()

Double_t TMVA::MethodRuleFit::GetTreeEveFrac ( ) const
inline

Definition at line 94 of file MethodRuleFit.h.

◆ HasAnalysisType()

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

RuleFit can handle classification with 2 classes.

Implements TMVA::IMethod.

Definition at line 177 of file MethodRuleFit.cxx.

◆ Init()

void TMVA::MethodRuleFit::Init ( void  )
protectedvirtual

default initialization

Implements TMVA::MethodBase.

Definition at line 396 of file MethodRuleFit.cxx.

◆ InitEventSample()

void TMVA::MethodRuleFit::InitEventSample ( void  )
protected

write all Events from the Tree into a vector of Events, that are more easily manipulated.

This method should never be called without existing trainingTree, as it the vector of events from the ROOT training tree

Definition at line 421 of file MethodRuleFit.cxx.

◆ InitMonitorNtuple()

void TMVA::MethodRuleFit::InitMonitorNtuple ( )
protected

initialize the monitoring ntuple

Definition at line 375 of file MethodRuleFit.cxx.

◆ IsA()

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

Reimplemented from TMVA::MethodBase.

Definition at line 210 of file MethodRuleFit.h.

◆ MakeClassLinear()

void TMVA::MethodRuleFit::MakeClassLinear ( std::ostream &  fout) const
protected

print out the linear terms

Definition at line 715 of file MethodRuleFit.cxx.

◆ MakeClassRuleCuts()

void TMVA::MethodRuleFit::MakeClassRuleCuts ( std::ostream &  fout) const
protected

print out the rule cuts

Definition at line 657 of file MethodRuleFit.cxx.

◆ MakeClassSpecific()

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

write specific classifier response

Reimplemented from TMVA::MethodBase.

Definition at line 638 of file MethodRuleFit.cxx.

◆ ProcessOptions()

void TMVA::MethodRuleFit::ProcessOptions ( )
privatevirtual

process the options specified by the user

Implements TMVA::MethodBase.

Definition at line 266 of file MethodRuleFit.cxx.

◆ ReadWeightsFromStream() [1/3]

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

Implements TMVA::MethodBase.

◆ ReadWeightsFromStream() [2/3]

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

read rules from an std::istream

Implements TMVA::MethodBase.

Definition at line 601 of file MethodRuleFit.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::MethodRuleFit::ReadWeightsFromXML ( void *  wghtnode)
virtual

read rules from XML node

Implements TMVA::MethodBase.

Definition at line 609 of file MethodRuleFit.cxx.

◆ Streamer()

virtual void TMVA::MethodRuleFit::Streamer ( TBuffer R__b)
virtual

Stream an object of class TObject.

Reimplemented from TMVA::MethodBase.

◆ StreamerNVirtual()

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

Definition at line 210 of file MethodRuleFit.h.

◆ Train()

void TMVA::MethodRuleFit::Train ( void  )
virtual

Implements TMVA::MethodBase.

Definition at line 443 of file MethodRuleFit.cxx.

◆ TrainJFRuleFit()

void TMVA::MethodRuleFit::TrainJFRuleFit ( void  )
protected

training of rules using Jerome Friedmans implementation

Definition at line 535 of file MethodRuleFit.cxx.

◆ TrainTMVARuleFit()

void TMVA::MethodRuleFit::TrainTMVARuleFit ( void  )
protected

training of rules using TMVA implementation

Definition at line 467 of file MethodRuleFit.cxx.

◆ UseBoost()

Bool_t TMVA::MethodRuleFit::UseBoost ( ) const
inline

Definition at line 85 of file MethodRuleFit.h.

◆ VerifyRange() [1/3]

template<typename T >
Int_t TMVA::MethodRuleFit::VerifyRange ( const T &  var,
const T &  vmin,
const T &  vmax 
)
inlineprivate

Definition at line 218 of file MethodRuleFit.h.

◆ VerifyRange() [2/3]

template<typename T >
Bool_t TMVA::MethodRuleFit::VerifyRange ( TMVA::MsgLogger mlog,
const char *  varstr,
T &  var,
const T &  vmin,
const T &  vmax 
)
inlineprivate

Definition at line 228 of file MethodRuleFit.h.

◆ VerifyRange() [3/3]

template<typename T >
Bool_t TMVA::MethodRuleFit::VerifyRange ( TMVA::MsgLogger mlog,
const char *  varstr,
T &  var,
const T &  vmin,
const T &  vmax,
const T &  vdef 
)
inlineprivate

Definition at line 250 of file MethodRuleFit.h.

◆ WriteMonitoringHistosToFile()

void TMVA::MethodRuleFit::WriteMonitoringHistosToFile ( void  ) const
virtual

write special monitoring histograms to file (here ntuple)

Reimplemented from TMVA::MethodBase.

Definition at line 628 of file MethodRuleFit.cxx.

Member Data Documentation

◆ fEventSample

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

the complete training sample

Definition at line 156 of file MethodRuleFit.h.

◆ fForest

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

the forest

Definition at line 180 of file MethodRuleFit.h.

◆ fForestTypeS

TString TMVA::MethodRuleFit::fForestTypeS
private

forest generation: how the trees are generated

Definition at line 191 of file MethodRuleFit.h.

◆ fGDErrScale

Double_t TMVA::MethodRuleFit::fGDErrScale
private

GD path: stop.

Definition at line 203 of file MethodRuleFit.h.

◆ fGDNPathSteps

Int_t TMVA::MethodRuleFit::fGDNPathSteps
private

GD path: number of steps.

Definition at line 202 of file MethodRuleFit.h.

◆ fGDPathEveFrac

Double_t TMVA::MethodRuleFit::fGDPathEveFrac
private

GD path: fraction of subsamples used for the fitting.

Definition at line 194 of file MethodRuleFit.h.

◆ fGDPathStep

Double_t TMVA::MethodRuleFit::fGDPathStep
private

GD path: step size in path.

Definition at line 201 of file MethodRuleFit.h.

◆ fGDTau

Double_t TMVA::MethodRuleFit::fGDTau
private

GD path: def threshold fraction [0..1].

Definition at line 196 of file MethodRuleFit.h.

◆ fGDTauMax

Double_t TMVA::MethodRuleFit::fGDTauMax
private

GD path: max threshold fraction [0..1].

Definition at line 199 of file MethodRuleFit.h.

◆ fGDTauMin

Double_t TMVA::MethodRuleFit::fGDTauMin
private

GD path: min threshold fraction [0..1].

Definition at line 198 of file MethodRuleFit.h.

◆ fGDTauPrec

Double_t TMVA::MethodRuleFit::fGDTauPrec
private

GD path: precision of estimated tau.

Definition at line 197 of file MethodRuleFit.h.

◆ fGDTauScan

UInt_t TMVA::MethodRuleFit::fGDTauScan
private

GD path: number of points to scan.

Definition at line 200 of file MethodRuleFit.h.

◆ fGDValidEveFrac

Double_t TMVA::MethodRuleFit::fGDValidEveFrac
private

GD path: fraction of subsamples used for the fitting.

Definition at line 195 of file MethodRuleFit.h.

◆ fLinQuantile

Double_t TMVA::MethodRuleFit::fLinQuantile
private

quantile cut to remove outliers - see RuleEnsemble

Definition at line 208 of file MethodRuleFit.h.

◆ fMaxFracNEve

Double_t TMVA::MethodRuleFit::fMaxFracNEve
private

ditto max

Definition at line 185 of file MethodRuleFit.h.

◆ fMinFracNEve

Double_t TMVA::MethodRuleFit::fMinFracNEve
private

min fraction of number events

Definition at line 184 of file MethodRuleFit.h.

◆ fMinimp

Double_t TMVA::MethodRuleFit::fMinimp
private

rule/linear: minimum importance

Definition at line 204 of file MethodRuleFit.h.

◆ fModelTypeS

TString TMVA::MethodRuleFit::fModelTypeS
private

rule ensemble: which model (rule,linear or both)

Definition at line 206 of file MethodRuleFit.h.

◆ fMonitorNtuple

TTree* TMVA::MethodRuleFit::fMonitorNtuple
private

pointer to monitor rule ntuple

Definition at line 160 of file MethodRuleFit.h.

◆ fNCuts

Int_t TMVA::MethodRuleFit::fNCuts
private

grid used in cut applied in node splitting

Definition at line 186 of file MethodRuleFit.h.

◆ fNTCoefficient

Double_t TMVA::MethodRuleFit::fNTCoefficient
private

ntuple: rule coefficient

Definition at line 162 of file MethodRuleFit.h.

◆ fNTImportance

Double_t TMVA::MethodRuleFit::fNTImportance
private

ntuple: rule importance

Definition at line 161 of file MethodRuleFit.h.

◆ fNTNcuts

Int_t TMVA::MethodRuleFit::fNTNcuts
private

ntuple: rule number of cuts

Definition at line 164 of file MethodRuleFit.h.

◆ fNTNvars

Int_t TMVA::MethodRuleFit::fNTNvars
private

ntuple: rule number of vars

Definition at line 165 of file MethodRuleFit.h.

◆ fNTPbb

Double_t TMVA::MethodRuleFit::fNTPbb
private

ntuple: rule P(tag b, true b)

Definition at line 170 of file MethodRuleFit.h.

◆ fNTPbs

Double_t TMVA::MethodRuleFit::fNTPbs
private

ntuple: rule P(tag b, true s)

Definition at line 169 of file MethodRuleFit.h.

◆ fNTPsb

Double_t TMVA::MethodRuleFit::fNTPsb
private

ntuple: rule P(tag s, true b)

Definition at line 168 of file MethodRuleFit.h.

◆ fNTPss

Double_t TMVA::MethodRuleFit::fNTPss
private

ntuple: rule P(tag s, true s)

Definition at line 167 of file MethodRuleFit.h.

◆ fNTPtag

Double_t TMVA::MethodRuleFit::fNTPtag
private

ntuple: rule P(tag)

Definition at line 166 of file MethodRuleFit.h.

◆ fNTrees

Int_t TMVA::MethodRuleFit::fNTrees
private

number of trees in forest

Definition at line 181 of file MethodRuleFit.h.

◆ fNTSSB

Double_t TMVA::MethodRuleFit::fNTSSB
private

ntuple: rule S/(S+B)

Definition at line 171 of file MethodRuleFit.h.

◆ fNTSupport

Double_t TMVA::MethodRuleFit::fNTSupport
private

ntuple: rule support

Definition at line 163 of file MethodRuleFit.h.

◆ fNTType

Int_t TMVA::MethodRuleFit::fNTType
private

ntuple: rule type (+1->signal, -1->bkg)

Definition at line 172 of file MethodRuleFit.h.

◆ fPruneMethod

TMVA::DecisionTree::EPruneMethod TMVA::MethodRuleFit::fPruneMethod
private

forest generation: method used for pruning - see DecisionTree

Definition at line 189 of file MethodRuleFit.h.

◆ fPruneMethodS

TString TMVA::MethodRuleFit::fPruneMethodS
private

forest generation: prune method - see DecisionTree

Definition at line 188 of file MethodRuleFit.h.

◆ fPruneStrength

Double_t TMVA::MethodRuleFit::fPruneStrength
private

forest generation: prune strength - see DecisionTree

Definition at line 190 of file MethodRuleFit.h.

◆ fRFNendnodes

Int_t TMVA::MethodRuleFit::fRFNendnodes
private

max number of rules (only Friedmans module)

Definition at line 179 of file MethodRuleFit.h.

◆ fRFNrules

Int_t TMVA::MethodRuleFit::fRFNrules
private

max number of rules (only Friedmans module)

Definition at line 178 of file MethodRuleFit.h.

◆ fRFWorkDir

TString TMVA::MethodRuleFit::fRFWorkDir
private

working directory from Friedmans module

Definition at line 177 of file MethodRuleFit.h.

◆ fRuleFit

RuleFit TMVA::MethodRuleFit::fRuleFit
private

RuleFit instance.

Definition at line 155 of file MethodRuleFit.h.

◆ fRuleFitModuleS

TString TMVA::MethodRuleFit::fRuleFitModuleS
private

which rulefit module to use

Definition at line 175 of file MethodRuleFit.h.

◆ fRuleMinDist

Double_t TMVA::MethodRuleFit::fRuleMinDist
private

rule min distance - see RuleEnsemble

Definition at line 207 of file MethodRuleFit.h.

◆ fSepType

SeparationBase* TMVA::MethodRuleFit::fSepType
private

the separation used in node splitting

Definition at line 183 of file MethodRuleFit.h.

◆ fSepTypeS

TString TMVA::MethodRuleFit::fSepTypeS
private

forest generation: separation type - see DecisionTree

Definition at line 187 of file MethodRuleFit.h.

◆ fSignalFraction

Double_t TMVA::MethodRuleFit::fSignalFraction
private

scalefactor for bkg events to modify initial s/b fraction in training data

Definition at line 157 of file MethodRuleFit.h.

◆ fTreeEveFrac

Double_t TMVA::MethodRuleFit::fTreeEveFrac
private

fraction of events used for training each tree

Definition at line 182 of file MethodRuleFit.h.

◆ fUseBoost

Bool_t TMVA::MethodRuleFit::fUseBoost
private

use boosted events for forest generation

Definition at line 192 of file MethodRuleFit.h.

◆ fUseRuleFitJF

Bool_t TMVA::MethodRuleFit::fUseRuleFitJF
private

if true interface with J.Friedmans RuleFit module

Definition at line 176 of file MethodRuleFit.h.

Libraries for TMVA::MethodRuleFit:
[legend]

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