A class implementing various fits of rule ensembles.

Definition at line 45 of file RuleFit.h.

Public Member Functions
	RuleFit (const TMVA::MethodBase *rfbase)
	constructor More...

	RuleFit (void)
	default constructor More...

virtual	~RuleFit (void)
	destructor More...

void	Boost (TMVA::DecisionTree *dt)
	Boost the events. More...

void	BuildTree (TMVA::DecisionTree *dt)
	build the decision tree using fNTreeSample events from fTrainingEventsRndm More...

void	CalcImportance ()
	calculates the importance of each rule More...

Double_t	CalcWeightSum (const std::vector< const TMVA::Event * > *events, UInt_t neve=0)
	calculate the sum of weights More...

Double_t	EvalEvent (const Event &e)
	evaluate single event More...

void	FillCorr (TH2F h2, const TMVA::Rule rule, Int_t v1, Int_t v2)
	fill rule correlation between vx and vy, weighted with either the importance or the coefficient More...

void	FillCut (TH2F h2, const TMVA::Rule rule, Int_t vind)
	Fill cut. More...

void	FillLin (TH2F *h2, Int_t vind)
	fill lin More...

void	FillVisHistCorr (const Rule rule, std::vector< TH2F > &hlist)
	help routine to MakeVisHists() - fills for all correlation plots More...

void	FillVisHistCut (const Rule rule, std::vector< TH2F > &hlist)
	help routine to MakeVisHists() - fills for all variables More...

void	FitCoefficients ()
	Fit the coefficients for the rule ensemble. More...

void	ForestStatistics ()
	summary of statistics of all trees More...

Bool_t	GetCorrVars (TString &title, TString &var1, TString &var2)
	get first and second variables from title More...

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

const MethodBase *	GetMethodBase () const

const MethodRuleFit *	GetMethodRuleFit () const

Double_t	GetNEveEff () const

UInt_t	GetNTreeSample () const

void	GetRndmSampleEvents (std::vector< const TMVA::Event * > &evevec, UInt_t nevents)
	draw a random subsample of the training events without replacement More...

const RuleEnsemble &	GetRuleEnsemble () const

RuleEnsemble *	GetRuleEnsemblePtr ()

const RuleFitParams &	GetRuleFitParams () const

RuleFitParams *	GetRuleFitParamsPtr ()

const Event *	GetTrainingEvent (UInt_t i) const

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

Double_t	GetTrainingEventWeight (UInt_t i) const

void	Initialize (const TMVA::MethodBase *rfbase)
	initialize the parameters of the RuleFit method and make rules More...

void	InitNEveEff ()
	init effective number of events (using event weights) More...

void	InitPtrs (const TMVA::MethodBase *rfbase)
	initialize pointers More...

void	MakeDebugHists ()
	this will create a histograms intended rather for debugging or for the curious user More...

void	MakeForest ()
	make a forest of decisiontrees More...

void	MakeVisHists ()
	this will create histograms visualizing the rule ensemble More...

void	NormVisHists (std::vector< TH2F * > &hlist)
	normalize rule importance hists More...

void	ReshuffleEvents ()

void	RestoreEventWeights ()
	save event weights - must be done before making the forest More...

void	SaveEventWeights ()
	save event weights - must be done before making the forest More...

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	SetMethodBase (const MethodBase *rfbase)
	set MethodBase More...

void	SetModelFull ()

void	SetModelLinear ()

void	SetModelRules ()

void	SetMsgType (EMsgType t)
	set the current message type to that of mlog for this class and all other subtools More...

void	SetRuleMinDist (Double_t d)

void	SetTrainingEvents (const std::vector< const TMVA::Event * > &el)
	set the training events randomly More...

void	SetVisHistsUseImp (Bool_t f)

void	UseCoefficientsVisHists ()

void	UseImportanceVisHists ()

Private Member Functions
	RuleFit (const RuleFit &other)

void	Copy (const RuleFit &other)
	copy method More...

MsgLogger &	Log () const

Private Attributes
std::vector< Double_t >	fEventWeights

std::vector< const TMVA::DecisionTree * >	fForest

MsgLogger *	fLogger

const MethodBase *	fMethodBase

const MethodRuleFit *	fMethodRuleFit

Double_t	fNEveEffTrain

UInt_t	fNTreeSample

std::default_random_engine	fRNGEngine

RuleEnsemble	fRuleEnsemble

RuleFitParams	fRuleFitParams

std::vector< const TMVA::Event * >	fTrainingEvents

std::vector< const TMVA::Event * >	fTrainingEventsRndm

Bool_t	fVisHistsUseImp

Static Private Attributes
static const Int_t	randSEED = 0

#include <TMVA/RuleFit.h>

Constructor & Destructor Documentation

◆ RuleFit() [1/3]

TMVA::RuleFit::RuleFit ( const TMVA::MethodBase * rfbase )

constructor

Definition at line 65 of file RuleFit.cxx.

◆ RuleFit() [2/3]

TMVA::RuleFit::RuleFit ( void )

default constructor

Definition at line 76 of file RuleFit.cxx.

◆ ~RuleFit()

TMVA::RuleFit::~RuleFit ( void )

virtual

destructor

Definition at line 90 of file RuleFit.cxx.

◆ RuleFit() [3/3]

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

private

Member Function Documentation

◆ Boost()

void TMVA::RuleFit::Boost ( TMVA::DecisionTree * dt )

Boost the events.

The algorithm below is the called AdaBoost. See MethodBDT for details. Actually, this is a more or less copy of MethodBDT::AdaBoost().

Definition at line 339 of file RuleFit.cxx.

◆ BuildTree()

void TMVA::RuleFit::BuildTree ( TMVA::DecisionTree * dt )

build the decision tree using fNTreeSample events from fTrainingEventsRndm

Definition at line 201 of file RuleFit.cxx.

◆ CalcImportance()

void TMVA::RuleFit::CalcImportance ( )

calculates the importance of each rule

Definition at line 418 of file RuleFit.cxx.

◆ CalcWeightSum()

Double_t TMVA::RuleFit::CalcWeightSum	(	const std::vector< const TMVA::Event * > *	events,
		UInt_t	neve = `0`
	)

calculate the sum of weights

Definition at line 176 of file RuleFit.cxx.

◆ Copy()

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

private

copy method

Definition at line 160 of file RuleFit.cxx.

◆ EvalEvent()

Double_t TMVA::RuleFit::EvalEvent ( const Event & e )

evaluate single event

Definition at line 432 of file RuleFit.cxx.

◆ FillCorr()

void TMVA::RuleFit::FillCorr	(	TH2F *	h2,
		const TMVA::Rule *	rule,
		Int_t	v1,
		Int_t	v2
	)

fill rule correlation between vx and vy, weighted with either the importance or the coefficient

Definition at line 608 of file RuleFit.cxx.

◆ FillCut()

void TMVA::RuleFit::FillCut	(	TH2F *	h2,
		const TMVA::Rule *	rule,
		Int_t	vind
	)

Fill cut.

Definition at line 533 of file RuleFit.cxx.

◆ FillLin()

void TMVA::RuleFit::FillLin	(	TH2F *	h2,
		Int_t	vind
	)

fill lin

Definition at line 584 of file RuleFit.cxx.

◆ FillVisHistCorr()

void TMVA::RuleFit::FillVisHistCorr	(	const Rule *	rule,
		std::vector< TH2F * > &	hlist
	)

help routine to MakeVisHists() - fills for all correlation plots

Definition at line 715 of file RuleFit.cxx.

◆ FillVisHistCut()

void TMVA::RuleFit::FillVisHistCut	(	const Rule *	rule,
		std::vector< TH2F * > &	hlist
	)

help routine to MakeVisHists() - fills for all variables

Definition at line 684 of file RuleFit.cxx.

◆ FitCoefficients()

void TMVA::RuleFit::FitCoefficients ( )

Fit the coefficients for the rule ensemble.

Definition at line 409 of file RuleFit.cxx.

◆ ForestStatistics()

void TMVA::RuleFit::ForestStatistics ( )

summary of statistics of all trees

end-nodes: average and spread

Definition at line 386 of file RuleFit.cxx.

◆ GetCorrVars()

Bool_t TMVA::RuleFit::GetCorrVars	(	TString &	title,
		TString &	var1,
		TString &	var2
	)

get first and second variables from title

Definition at line 754 of file RuleFit.cxx.

◆ GetForest()

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

inline

Definition at line 143 of file RuleFit.h.

◆ GetMethodBase()

const MethodBase * TMVA::RuleFit::GetMethodBase ( ) const

inline

Definition at line 149 of file RuleFit.h.

◆ GetMethodRuleFit()

const MethodRuleFit * TMVA::RuleFit::GetMethodRuleFit ( ) const

inline

Definition at line 148 of file RuleFit.h.

◆ GetNEveEff()

Double_t TMVA::RuleFit::GetNEveEff ( ) const

inline

Definition at line 131 of file RuleFit.h.

◆ GetNTreeSample()

UInt_t TMVA::RuleFit::GetNTreeSample ( ) const

inline

Definition at line 130 of file RuleFit.h.

◆ GetRndmSampleEvents()

void TMVA::RuleFit::GetRndmSampleEvents	(	std::vector< const TMVA::Event * > &	evevec,
		UInt_t	nevents
	)

draw a random subsample of the training events without replacement

Definition at line 467 of file RuleFit.cxx.

◆ GetRuleEnsemble()

const RuleEnsemble & TMVA::RuleFit::GetRuleEnsemble ( ) const

inline

Definition at line 144 of file RuleFit.h.

◆ GetRuleEnsemblePtr()

RuleEnsemble * TMVA::RuleFit::GetRuleEnsemblePtr ( )

inline

Definition at line 145 of file RuleFit.h.

◆ GetRuleFitParams()

const RuleFitParams & TMVA::RuleFit::GetRuleFitParams ( ) const

inline

Definition at line 146 of file RuleFit.h.

◆ GetRuleFitParamsPtr()

RuleFitParams * TMVA::RuleFit::GetRuleFitParamsPtr ( )

inline

Definition at line 147 of file RuleFit.h.

◆ GetTrainingEvent()

const Event * TMVA::RuleFit::GetTrainingEvent ( UInt_t i ) const

inline

Definition at line 132 of file RuleFit.h.

◆ GetTrainingEvents()

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

inline

Definition at line 137 of file RuleFit.h.

◆ GetTrainingEventWeight()

Double_t TMVA::RuleFit::GetTrainingEventWeight ( UInt_t i ) const

inline

Definition at line 133 of file RuleFit.h.

◆ Initialize()

void TMVA::RuleFit::Initialize ( const TMVA::MethodBase * rfbase )

initialize the parameters of the RuleFit method and make rules

Definition at line 120 of file RuleFit.cxx.

◆ InitNEveEff()

void TMVA::RuleFit::InitNEveEff ( )

init effective number of events (using event weights)

Definition at line 98 of file RuleFit.cxx.

◆ InitPtrs()

void TMVA::RuleFit::InitPtrs ( const TMVA::MethodBase * rfbase )

initialize pointers

Definition at line 110 of file RuleFit.cxx.

◆ Log()

MsgLogger & TMVA::RuleFit::Log ( ) const

inlineprivate

Definition at line 173 of file RuleFit.h.

◆ MakeDebugHists()

void TMVA::RuleFit::MakeDebugHists ( )

this will create a histograms intended rather for debugging or for the curious user

Definition at line 937 of file RuleFit.cxx.

◆ MakeForest()

void TMVA::RuleFit::MakeForest ( )

make a forest of decisiontrees

Definition at line 222 of file RuleFit.cxx.

◆ MakeVisHists()

void TMVA::RuleFit::MakeVisHists ( )

this will create histograms visualizing the rule ensemble

Definition at line 777 of file RuleFit.cxx.

◆ NormVisHists()

void TMVA::RuleFit::NormVisHists ( std::vector< TH2F * > & hlist )

normalize rule importance hists

if all weights are positive, the scale will be 1/maxweight if minimum weight < 0, then the scale will be 1/max(maxweight,abs(minweight))

Definition at line 486 of file RuleFit.cxx.

◆ ReshuffleEvents()

void TMVA::RuleFit::ReshuffleEvents ( )

inline

Definition at line 65 of file RuleFit.h.

◆ RestoreEventWeights()

void TMVA::RuleFit::RestoreEventWeights ( )

save event weights - must be done before making the forest

Definition at line 321 of file RuleFit.cxx.

◆ SaveEventWeights()

void TMVA::RuleFit::SaveEventWeights ( )

save event weights - must be done before making the forest

Definition at line 309 of file RuleFit.cxx.

◆ SetGDNPathSteps()

void TMVA::RuleFit::SetGDNPathSteps ( Int_t n = 100 )

inline

Definition at line 115 of file RuleFit.h.

◆ SetGDPathStep()

void TMVA::RuleFit::SetGDPathStep ( Double_t s = 0.01 )

inline

Definition at line 114 of file RuleFit.h.

◆ SetGDTau()

void TMVA::RuleFit::SetGDTau ( Double_t t = 0.0 )

inline

Definition at line 113 of file RuleFit.h.

◆ SetImportanceCut()

void TMVA::RuleFit::SetImportanceCut ( Double_t minimp = 0 )

inline

Definition at line 109 of file RuleFit.h.

◆ SetMethodBase()

void TMVA::RuleFit::SetMethodBase ( const MethodBase * rfbase )

set MethodBase

Definition at line 151 of file RuleFit.cxx.

◆ SetModelFull()

void TMVA::RuleFit::SetModelFull ( )

inline

Definition at line 107 of file RuleFit.h.

◆ SetModelLinear()

void TMVA::RuleFit::SetModelLinear ( )

inline

Definition at line 103 of file RuleFit.h.

◆ SetModelRules()

void TMVA::RuleFit::SetModelRules ( )

inline

Definition at line 105 of file RuleFit.h.

◆ SetMsgType()

void TMVA::RuleFit::SetMsgType ( EMsgType t )

set the current message type to that of mlog for this class and all other subtools

Definition at line 191 of file RuleFit.cxx.

◆ SetRuleMinDist()

void TMVA::RuleFit::SetRuleMinDist ( Double_t d )

inline

Definition at line 111 of file RuleFit.h.

◆ SetTrainingEvents()

void TMVA::RuleFit::SetTrainingEvents ( const std::vector< const TMVA::Event * > & el )

set the training events randomly

Definition at line 440 of file RuleFit.cxx.

◆ SetVisHistsUseImp()

void TMVA::RuleFit::SetVisHistsUseImp ( Bool_t f )

inline

Definition at line 117 of file RuleFit.h.

◆ UseCoefficientsVisHists()

void TMVA::RuleFit::UseCoefficientsVisHists ( )

inline

Definition at line 119 of file RuleFit.h.

◆ UseImportanceVisHists()

void TMVA::RuleFit::UseImportanceVisHists ( )

inline

Definition at line 118 of file RuleFit.h.

Member Data Documentation

◆ fEventWeights

std::vector<Double_t> TMVA::RuleFit::fEventWeights

private

Definition at line 161 of file RuleFit.h.

◆ fForest

std::vector< const TMVA::DecisionTree *> TMVA::RuleFit::fForest

private

Definition at line 165 of file RuleFit.h.

◆ fLogger

MsgLogger* TMVA::RuleFit::fLogger

mutableprivate

Definition at line 172 of file RuleFit.h.

◆ fMethodBase

const MethodBase* TMVA::RuleFit::fMethodBase

private

Definition at line 169 of file RuleFit.h.

◆ fMethodRuleFit

const MethodRuleFit* TMVA::RuleFit::fMethodRuleFit

private

Definition at line 168 of file RuleFit.h.

◆ fNEveEffTrain

Double_t TMVA::RuleFit::fNEveEffTrain

private

Definition at line 164 of file RuleFit.h.

◆ fNTreeSample

UInt_t TMVA::RuleFit::fNTreeSample

private

Definition at line 162 of file RuleFit.h.

◆ fRNGEngine

std::default_random_engine TMVA::RuleFit::fRNGEngine

private

Definition at line 176 of file RuleFit.h.

◆ fRuleEnsemble

RuleEnsemble TMVA::RuleFit::fRuleEnsemble

private

Definition at line 166 of file RuleFit.h.

◆ fRuleFitParams

RuleFitParams TMVA::RuleFit::fRuleFitParams

private

Definition at line 167 of file RuleFit.h.

◆ fTrainingEvents

std::vector<const TMVA::Event *> TMVA::RuleFit::fTrainingEvents

private

Definition at line 159 of file RuleFit.h.

◆ fTrainingEventsRndm

std::vector<const TMVA::Event *> TMVA::RuleFit::fTrainingEventsRndm

private

Definition at line 160 of file RuleFit.h.

◆ fVisHistsUseImp

Bool_t TMVA::RuleFit::fVisHistsUseImp

private

Definition at line 170 of file RuleFit.h.

◆ randSEED

const Int_t TMVA::RuleFit::randSEED = 0

staticprivate

Definition at line 175 of file RuleFit.h.

Libraries for TMVA::RuleFit:

[legend]

Public Member Functions

Private Member Functions

Private Attributes

Static Private Attributes

Constructor & Destructor Documentation

◆ RuleFit() [1/3]

◆ RuleFit() [2/3]

◆ ~RuleFit()

◆ RuleFit() [3/3]

Member Function Documentation

◆ Boost()

◆ BuildTree()

◆ CalcImportance()

◆ CalcWeightSum()

◆ Copy()

◆ EvalEvent()

◆ FillCorr()

◆ FillCut()

◆ FillLin()

◆ FillVisHistCorr()

◆ FillVisHistCut()

◆ FitCoefficients()

◆ ForestStatistics()

◆ GetCorrVars()

◆ GetForest()

◆ GetMethodBase()

◆ GetMethodRuleFit()

◆ GetNEveEff()

◆ GetNTreeSample()

◆ GetRndmSampleEvents()

◆ GetRuleEnsemble()

◆ GetRuleEnsemblePtr()

◆ GetRuleFitParams()

◆ GetRuleFitParamsPtr()

◆ GetTrainingEvent()

◆ GetTrainingEvents()

◆ GetTrainingEventWeight()

◆ Initialize()

◆ InitNEveEff()

◆ InitPtrs()

◆ Log()

◆ MakeDebugHists()

◆ MakeForest()

◆ MakeVisHists()

◆ NormVisHists()

◆ ReshuffleEvents()

◆ RestoreEventWeights()

◆ SaveEventWeights()

◆ SetGDNPathSteps()

◆ SetGDPathStep()

◆ SetGDTau()

◆ SetImportanceCut()

◆ SetMethodBase()

◆ SetModelFull()

◆ SetModelLinear()

◆ SetModelRules()

◆ SetMsgType()

◆ SetRuleMinDist()

◆ SetTrainingEvents()

◆ SetVisHistsUseImp()

◆ UseCoefficientsVisHists()

◆ UseImportanceVisHists()

Member Data Documentation

◆ fEventWeights

◆ fForest

◆ fLogger

◆ fMethodBase

◆ fMethodRuleFit

◆ fNEveEffTrain

◆ fNTreeSample

◆ fRNGEngine

◆ fRuleEnsemble

◆ fRuleFitParams

◆ fTrainingEvents

◆ fTrainingEventsRndm

◆ fVisHistsUseImp

◆ randSEED