library: libTMVA
#include "RuleFit.h"

TMVA::RuleFit

class description - header file - source file
viewCVS header - viewCVS source

class TMVA::RuleFit

Inheritance Inherited Members Includes Libraries

Class Charts

Function Members (Methods)

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public:

virtual	~RuleFit()
void	CalcImportance()
static TClass*	Class()
Double_t	EvalEvent(const TMVA::Event& e)
void	FitCoefficients()
void	ForestStatistics()
const vector<const TMVA::DecisionTree*>&	GetForest() const
const TMVA::MethodRuleFit*	GetMethodRuleFit() const
const UInt_t	GetNSubsamples() const
const TMVA::RuleEnsemble&	GetRuleEnsemble() const
TMVA::RuleEnsemble*	GetRuleEnsemblePtr()
const TMVA::RuleFitParams&	GetRuleFitParams() const
TMVA::RuleFitParams*	GetRuleFitParamsPtr()
const vector<Int_t>&	GetSubsampleEvents() const
void	GetSubsampleEvents(Int_t sub, UInt_t& ibeg, UInt_t& iend) const
const TMVA::Event*	GetTrainingEvent(UInt_t i) const
const TMVA::Event*	GetTrainingEvent(UInt_t i, UInt_t isub) const
const vector<const TMVA::Event*>&	GetTrainingEvents() const
void	Initialise(const TMVA::MethodRuleFit* rfbase, const vector<TMVA::DecisionTree>& forest, const vector<TMVA::Event,allocator<TMVA::Event*> >& trainingEvents, Double_t samplefrac)
virtual TClass*	IsA() const
TMVA::RuleFit	RuleFit()
TMVA::RuleFit	RuleFit(const TMVA::MethodRuleFit* rfbase, const vector<TMVA::DecisionTree>& forest, const vector<TMVA::Event,allocator<TMVA::Event*> >& trainingEvents, Double_t samplefrac)
void	SetGDNPathSteps(Int_t n = 100)
void	SetGDPathStep(Double_t s = 0.01)
void	SetGDTau(Double_t t = 0.0)
void	SetImportanceCut(Double_t minimp = 0)
void	SetMaxRuleDist(Double_t maxd)
void	SetModelFull()
void	SetModelLinear()
void	SetModelRules()
void	SetTrainingEvents(const vector<TMVA::Event,allocator<TMVA::Event> >& el, Double_t sampfrac)
virtual void	ShowMembers(TMemberInspector& insp, char* parent)
virtual void	Streamer(TBuffer& b)
void	StreamerNVirtual(TBuffer& b)

private:

void	Copy(const TMVA::RuleFit& other)
TMVA::RuleFit	RuleFit(const TMVA::RuleFit& other)

Data Members

private:

vector<const TMVA::Event*>	fTrainingEvents	all training events
vector<Int_t>	fSubsampleEvents	iterators marking the beginning of each cross validation sample
vector<const TMVA::DecisionTree*>	fForest	the input forest of decision trees
TMVA::RuleEnsemble	fRuleEnsemble	the ensemble of rules
TMVA::RuleFitParams	fRuleFitParams	fit rule parameters
const TMVA::MethodRuleFit*	fMethodRuleFit	pointer the method which initialised this RuleFit instance
TMVA::MsgLogger	fLogger	message logger

Class Description

RuleFit( const TMVA::MethodRuleFit *rfbase, const std::vector< TMVA::DecisionTree *> & forest, const std::vector<TMVA::Event *> & trainingEvents, Double_t samplefrac )

 constructor

 default constructor

 destructor

void Initialise( const TMVA::MethodRuleFit *rfbase, const std::vector< TMVA::DecisionTree *> & forest, const std::vector< TMVA::Event *> & events, Double_t sampfrac )

 initialize the parameters of the RuleFit method

void Copy( const TMVA::RuleFit& other )

 copy method

void ForestStatistics()

 summary of statistics of all trees
 * end-nodes: average and spread

void FitCoefficients()

 Fit the coefficients for the rule ensemble

    fRuleFitParams.SetGDNPathSteps( 100 );
    fRuleFitParams.SetGDPathStep( 0.01 );
    fRuleFitParams.SetGDTau( 0.0 );

void CalcImportance()

 calculates the importance of each rule

Double_t EvalEvent( const TMVA::Event& e )

 evaluate single event

void SetTrainingEvents( const std::vector<TMVA::Event *>& el, Double_t sampfrac )

 set the training events randomly

void GetSubsampleEvents(Int_t sub, UInt_t& ibeg, UInt_t& iend)

 get the events for subsample sub

RuleFit( const TMVA::MethodRuleFit *rfbase, const std::vector<TMVA::DecisionTree *> & forest, const std::vector<Event *> & trainingEvents, Double_t samplefrac )

 main constructor
 empty constructor

void SetModelLinear()

 set usage of linear term
 set usage of rules

{ fRuleEnsemble.SetModelLinear(); }

void SetModelRules()

 set usage of linear term

{ fRuleEnsemble.SetModelRules(); }

void SetModelFull()

 set minimum importance allowed

{ fRuleEnsemble.SetModelFull(); }

void SetImportanceCut( Double_t minimp=0 )

 set max rule distance - see RuleEnsemble

{ fRuleEnsemble.SetImportanceCut(minimp); }

void SetMaxRuleDist( Double_t maxd )

 set path related parameters

{ fRuleEnsemble.SetMaxRuleDist(maxd); }

void SetGDTau( Double_t t=0.0 )

{ fRuleFitParams.SetGDTau(t); }

void SetGDPathStep( Double_t s=0.01 )

{ fRuleFitParams.SetGDPathStep(s); }

void SetGDNPathSteps( Int_t n=100 )

 accessors

{ fRuleFitParams.SetGDNPathSteps(n); }

const UInt_t GetNSubsamples()

{ return (fSubsampleEvents.size()>1 ? fSubsampleEvents.size()-1:0); }

const Event* GetTrainingEvent(UInt_t i)

{ return fTrainingEvents[i]; }

const Event* GetTrainingEvent(UInt_t i, UInt_t isub)

{ return &(fTrainingEvents[fSubsampleEvents[isub]])[i]; }

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

{ return fTrainingEvents; }

const std::vector< Int_t > & GetSubsampleEvents()

{ return fSubsampleEvents; }

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

{ return fForest; }

const RuleEnsemble & GetRuleEnsemble()

{ return fRuleEnsemble; }

RuleEnsemble * GetRuleEnsemblePtr()

{ return &fRuleEnsemble; }

const RuleFitParams & GetRuleFitParams()

{ return fRuleFitParams; }

RuleFitParams * GetRuleFitParamsPtr()

{ return &fRuleFitParams; }

const MethodRuleFit * GetMethodRuleFit()

{ return fMethodRuleFit; }

Author: Andreas Hoecker, Joerg Stelzer, Fredrik Tegenfeldt, Helge Voss
Last update: root/tmva $Id: RuleFit.cxx,v 1.6 2006/11/20 15:35:28 brun Exp $
Copyright (c) 2005: *

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