A class doing the actual fitting of a linear model using rules as base functions.

Definition at line 49 of file RuleFitParams.h.

Public Member Functions
	RuleFitParams ()
	constructor

virtual	~RuleFitParams ()
	destructor

Int_t	FindGDTau ()
	This finds the cutoff parameter tau by scanning several different paths.

UInt_t	GetPathIdx1 () const

UInt_t	GetPathIdx2 () const

UInt_t	GetPerfIdx1 () const

UInt_t	GetPerfIdx2 () const

void	Init ()
	Initializes all parameters using the RuleEnsemble and the training tree.

void	InitGD ()
	Initialize GD path search.

Double_t	LossFunction (const Event &e) const
	Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg)

Double_t	LossFunction (UInt_t evtidx) const
	Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg)

Double_t	LossFunction (UInt_t evtidx, UInt_t itau) const
	Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg)

void	MakeGDPath ()
	The following finds the gradient directed path in parameter space.

Double_t	Penalty () const
	This is the "lasso" penalty To be used for regression.

Double_t	Risk (UInt_t ind1, UInt_t ind2, Double_t neff) const
	risk assessment

Double_t	Risk (UInt_t ind1, UInt_t ind2, Double_t neff, UInt_t itau) const
	risk assessment for tau model `<itau>`

Double_t	RiskPath () const

Double_t	RiskPerf () const

Double_t	RiskPerf (UInt_t itau) const

UInt_t	RiskPerfTst ()
	Estimates the error rate with the current set of parameters.

void	SetGDErrScale (Double_t s)

void	SetGDNPathSteps (Int_t np)

void	SetGDPathStep (Double_t s)

void	SetGDTau (Double_t t)

void	SetGDTauPrec (Double_t p)

void	SetGDTauRange (Double_t t0, Double_t t1)

void	SetGDTauScan (UInt_t n)

void	SetMsgType (EMsgType t)

void	SetRuleFit (RuleFit *rf)

Int_t	Type (const Event *e) const

Protected Types
typedef std::vector< constTMVA::Event * >::const_iterator	EventItr

Protected Member Functions
Double_t	CalcAverageResponse ()
	calculate the average response - TODO : rewrite bad dependancy on EvaluateAverage() !

Double_t	CalcAverageResponseOLD ()

Double_t	CalcAverageTruth ()
	calculate the average truth

void	CalcFStar ()
	Estimates F* (optimum scoring function) for all events for the given sets.

void	CalcGDNTau ()

void	CalcTstAverageResponse ()
	calc average response for all test paths - TODO: see comment under CalcAverageResponse() note that 0 offset is used

Double_t	ErrorRateBin ()
	Estimates the error rate with the current set of parameters It uses a binary estimate of (y-F(x)) (y-F(x)) = (Num of events where sign(F)!=sign(y))/Neve y = {+1 if event is signal, -1 otherwise} — NOT USED —.

Double_t	ErrorRateReg ()
	Estimates the error rate with the current set of parameters This code is pretty messy at the moment.

Double_t	ErrorRateRoc ()
	Estimates the error rate with the current set of parameters.

Double_t	ErrorRateRocRaw (std::vector< Double_t > &sFsig, std::vector< Double_t > &sFbkg)
	Estimates the error rate with the current set of parameters.

void	ErrorRateRocTst ()
	Estimates the error rate with the current set of parameters.

void	EvaluateAverage (UInt_t ind1, UInt_t ind2, std::vector< Double_t > &avsel, std::vector< Double_t > &avrul)
	evaluate the average of each variable and f(x) in the given range

void	EvaluateAveragePath ()

void	EvaluateAveragePerf ()

void	FillCoefficients ()
	helper function to store the rule coefficients in local arrays

void	InitNtuple ()
	initializes the ntuple

void	MakeGradientVector ()
	make gradient vector

void	MakeTstGradientVector ()
	make test gradient vector for all tau same algorithm as MakeGradientVector()

Double_t	Optimism ()
	implementation of eq.

void	UpdateCoefficients ()
	Establish maximum gradient for rules, linear terms and the offset.

void	UpdateTstCoefficients ()
	Establish maximum gradient for rules, linear terms and the offset for all taus TODO: do not need index range!

Protected Attributes
std::vector< Double_t >	fAverageRulePath
	average of each rule, same range

std::vector< Double_t >	fAverageRulePerf
	average of each rule, same range

std::vector< Double_t >	fAverageSelectorPath
	average of each variable over the range fPathIdx1,2

std::vector< Double_t >	fAverageSelectorPerf
	average of each variable over the range fPerfIdx1,2

Double_t	fAverageTruth
	average truth, ie sum(y)/N, y=+-1

Double_t	fbkgave
	Average of F(bkg)

Double_t	fbkgrms
	Rms of F(bkg)

std::vector< Double_t >	fFstar
	vector of F*() - filled in CalcFStar()

Double_t	fFstarMedian
	median value of F*() using

std::vector< std::vector< Double_t > >	fGDCoefLinTst
	linear coeffs - one per tau

std::vector< std::vector< Double_t > >	fGDCoefTst
	rule coeffs - one per tau

Double_t	fGDErrScale
	stop scan at error = scale*errmin

std::vector< Double_t >	fGDErrTst
	error rates per tau

std::vector< Char_t >	fGDErrTstOK
	error rate is sufficiently low <— stores boolean

Int_t	fGDNPathSteps
	number of path steps

UInt_t	fGDNTau
	number of tau-paths - calculated in SetGDTauPrec

UInt_t	fGDNTauTstOK
	number of tau in the test-phase that are ok

TTree *	fGDNtuple
	Gradient path ntuple, contains params for each step along the path.

std::vector< Double_t >	fGDOfsTst
	offset per tau

Double_t	fGDPathStep
	step size along path (delta nu in eq 22, ref 1)

Double_t	fGDTau
	selected threshold parameter (tau in eq 26, ref 1)

Double_t	fGDTauMax
	max threshold parameter (tau in eq 26, ref 1)

Double_t	fGDTauMin
	min threshold parameter (tau in eq 26, ref 1)

Double_t	fGDTauPrec
	precision in tau

UInt_t	fGDTauScan
	number scan for tau-paths

std::vector< Double_t >	fGDTauVec
	the tau's

std::vector< Double_t >	fGradVec
	gradient vector - dimension = number of rules in ensemble

std::vector< Double_t >	fGradVecLin
	gradient vector - dimension = number of variables

std::vector< std::vector< Double_t > >	fGradVecLinTst
	gradient vector, linear terms - one per tau

std::vector< std::vector< Double_t > >	fGradVecTst
	gradient vector - one per tau

Double_t	fNEveEffPath
	sum of weights for Path events

Double_t	fNEveEffPerf
	idem for Perf events

UInt_t	fNLinear
	number of linear terms

UInt_t	fNRules
	number of rules

Double_t *	fNTCoeff
	GD path: rule coefficients.

Double_t	fNTCoefRad
	GD path: 'radius' of all rulecoeffs.

Double_t	fNTErrorRate
	GD path: error rate (or performance)

Double_t *	fNTLinCoeff
	GD path: linear coefficients.

Double_t	fNTNuval
	GD path: value of nu.

Double_t	fNTOffset
	GD path: model offset.

Double_t	fNTRisk
	GD path: risk.

UInt_t	fPathIdx1
	first event index for path search

UInt_t	fPathIdx2
	last event index for path search

UInt_t	fPerfIdx1
	first event index for performance evaluation

UInt_t	fPerfIdx2
	last event index for performance evaluation

RuleEnsemble *	fRuleEnsemble
	rule ensemble

RuleFit *	fRuleFit
	rule fit

Double_t	fsigave
	Sigma of current signal score function F(sig)

Double_t	fsigrms
	Rms of F(sig)

Private Member Functions
MsgLogger &	Log () const

Private Attributes
MsgLogger *	fLogger
	! message logger

#include <TMVA/RuleFitParams.h>

Member Typedef Documentation

◆ EventItr

typedef std::vector<constTMVA::Event*>::const_iterator TMVA::RuleFitParams::EventItr

protected

Definition at line 130 of file RuleFitParams.h.

Constructor & Destructor Documentation

◆ RuleFitParams()

TMVA::RuleFitParams::RuleFitParams ( )

constructor

Definition at line 64 of file RuleFitParams.cxx.

◆ ~RuleFitParams()

TMVA::RuleFitParams::~RuleFitParams ( )

virtual

destructor

Definition at line 104 of file RuleFitParams.cxx.

Member Function Documentation

◆ CalcAverageResponse()

Double_t TMVA::RuleFitParams::CalcAverageResponse ( )

protected

calculate the average response - TODO : rewrite bad dependancy on EvaluateAverage() !

note that 0 offset is used

Definition at line 1512 of file RuleFitParams.cxx.

◆ CalcAverageResponseOLD()

Double_t TMVA::RuleFitParams::CalcAverageResponseOLD ( )

protected

◆ CalcAverageTruth()

Double_t TMVA::RuleFitParams::CalcAverageTruth ( )

protected

calculate the average truth

Definition at line 1527 of file RuleFitParams.cxx.

◆ CalcFStar()

void TMVA::RuleFitParams::CalcFStar ( )

protected

Estimates F* (optimum scoring function) for all events for the given sets.

The result is used in ErrorRateReg(). — NOT USED —

Definition at line 885 of file RuleFitParams.cxx.

◆ CalcGDNTau()

void TMVA::RuleFitParams::CalcGDNTau ( )

inlineprotected

Definition at line 136 of file RuleFitParams.h.

◆ CalcTstAverageResponse()

void TMVA::RuleFitParams::CalcTstAverageResponse ( )

protected

calc average response for all test paths - TODO: see comment under CalcAverageResponse() note that 0 offset is used

Definition at line 1491 of file RuleFitParams.cxx.

◆ ErrorRateBin()

Double_t TMVA::RuleFitParams::ErrorRateBin ( )

protected

Estimates the error rate with the current set of parameters It uses a binary estimate of (y-F*(x)) (y-F*(x)) = (Num of events where sign(F)!=sign(y))/Neve y = {+1 if event is signal, -1 otherwise} — NOT USED —.

Definition at line 1008 of file RuleFitParams.cxx.

◆ ErrorRateReg()

Double_t TMVA::RuleFitParams::ErrorRateReg ( )

protected

Estimates the error rate with the current set of parameters This code is pretty messy at the moment.

Cleanup is needed. – NOT USED —

Definition at line 964 of file RuleFitParams.cxx.

◆ ErrorRateRoc()

Double_t TMVA::RuleFitParams::ErrorRateRoc ( )

protected

Estimates the error rate with the current set of parameters.

It calculates the area under the bkg rejection vs signal efficiency curve. The value returned is 1-area. This works but is less efficient than calculating the Risk using RiskPerf().

Definition at line 1107 of file RuleFitParams.cxx.

◆ ErrorRateRocRaw()

Double_t TMVA::RuleFitParams::ErrorRateRocRaw	(	std::vector< Double_t > &	sFsig,
		std::vector< Double_t > &	sFbkg )

protected

Estimates the error rate with the current set of parameters.

It calculates the area under the bkg rejection vs signal efficiency curve. The value returned is 1-area.

Definition at line 1042 of file RuleFitParams.cxx.

◆ ErrorRateRocTst()

void TMVA::RuleFitParams::ErrorRateRocTst ( )

protected

Estimates the error rate with the current set of parameters.

It calculates the area under the bkg rejection vs signal efficiency curve. The value returned is 1-area.

See comment under ErrorRateRoc().

Definition at line 1155 of file RuleFitParams.cxx.

◆ EvaluateAverage()

void TMVA::RuleFitParams::EvaluateAverage	(	UInt_t	ind1,
		UInt_t	ind2,
		std::vector< Double_t > &	avsel,
		std::vector< Double_t > &	avrul )

protected

evaluate the average of each variable and f(x) in the given range

Definition at line 208 of file RuleFitParams.cxx.

◆ EvaluateAveragePath()

void TMVA::RuleFitParams::EvaluateAveragePath ( )

inlineprotected

Definition at line 177 of file RuleFitParams.h.

◆ EvaluateAveragePerf()

void TMVA::RuleFitParams::EvaluateAveragePerf ( )

inlineprotected

Definition at line 180 of file RuleFitParams.h.

◆ FillCoefficients()

void TMVA::RuleFitParams::FillCoefficients ( )

protected

helper function to store the rule coefficients in local arrays

Definition at line 868 of file RuleFitParams.cxx.

◆ FindGDTau()

Int_t TMVA::RuleFitParams::FindGDTau ( )

This finds the cutoff parameter tau by scanning several different paths.

Definition at line 449 of file RuleFitParams.cxx.

◆ GetPathIdx1()

UInt_t TMVA::RuleFitParams::GetPathIdx1 ( ) const

inline

Definition at line 91 of file RuleFitParams.h.

◆ GetPathIdx2()

UInt_t TMVA::RuleFitParams::GetPathIdx2 ( ) const

inline

Definition at line 92 of file RuleFitParams.h.

◆ GetPerfIdx1()

UInt_t TMVA::RuleFitParams::GetPerfIdx1 ( ) const

inline

Definition at line 93 of file RuleFitParams.h.

◆ GetPerfIdx2()

UInt_t TMVA::RuleFitParams::GetPerfIdx2 ( ) const

inline

Definition at line 94 of file RuleFitParams.h.

◆ Init()

void TMVA::RuleFitParams::Init ( )

Initializes all parameters using the RuleEnsemble and the training tree.

Definition at line 114 of file RuleFitParams.cxx.

◆ InitGD()

void TMVA::RuleFitParams::InitGD ( )

Initialize GD path search.

Definition at line 373 of file RuleFitParams.cxx.

◆ InitNtuple()

void TMVA::RuleFitParams::InitNtuple ( )

protected

initializes the ntuple

Definition at line 185 of file RuleFitParams.cxx.

◆ Log()

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

inlineprivate

Definition at line 254 of file RuleFitParams.h.

◆ LossFunction() [1/3]

Double_t TMVA::RuleFitParams::LossFunction ( const Event & e ) const

Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg)

Definition at line 278 of file RuleFitParams.cxx.

◆ LossFunction() [2/3]

Double_t TMVA::RuleFitParams::LossFunction ( UInt_t evtidx ) const

Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg)

Definition at line 290 of file RuleFitParams.cxx.

◆ LossFunction() [3/3]

Double_t TMVA::RuleFitParams::LossFunction	(	UInt_t	evtidx,
		UInt_t	itau ) const

Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg)

Definition at line 302 of file RuleFitParams.cxx.

◆ MakeGDPath()

void TMVA::RuleFitParams::MakeGDPath ( )

The following finds the gradient directed path in parameter space.

More work is needed... FT, 24/9/2006

The algorithm is currently as follows (if not otherwise stated, the sample used below is [fPathIdx1,fPathIdx2]):

Set offset to -average(y(true)) and all coefs=0 => average of F(x)==0
FindGDTau() : start scanning using several paths defined by different tau choose the tau yielding the best path
start the scanning the chosen path
check error rate at a given frequency data used for check: [fPerfIdx1,fPerfIdx2]
stop when either of the following conditions are fullfilled:
1. loop index==fGDNPathSteps
2. error > fGDErrScale*errmin
3. only in DEBUG mode: risk is not monotonously decreasing

The algorithm will warn if:

the error rate was still decreasing when loop finished -> increase fGDNPathSteps!
minimum was found at an early stage -> decrease fGDPathStep
DEBUG: risk > previous risk -> entered chaotic region (regularization is too small)

Definition at line 538 of file RuleFitParams.cxx.

◆ MakeGradientVector()

void TMVA::RuleFitParams::MakeGradientVector ( )

protected

make gradient vector

Definition at line 1375 of file RuleFitParams.cxx.

◆ MakeTstGradientVector()

void TMVA::RuleFitParams::MakeTstGradientVector ( )

protected

make test gradient vector for all tau same algorithm as MakeGradientVector()

Definition at line 1259 of file RuleFitParams.cxx.

◆ Optimism()

Double_t TMVA::RuleFitParams::Optimism ( )

protected

implementation of eq.

7.17 in Hastie,Tibshirani & Friedman book this is the covariance between the estimated response yhat and the true value y. NOT REALLY SURE IF THIS IS CORRECT! — THIS IS NOT USED —

Definition at line 925 of file RuleFitParams.cxx.

◆ Penalty()

Double_t TMVA::RuleFitParams::Penalty ( ) const

This is the "lasso" penalty To be used for regression.

— NOT USED —

Definition at line 356 of file RuleFitParams.cxx.

◆ Risk() [1/2]

Double_t TMVA::RuleFitParams::Risk	(	UInt_t	ind1,
		UInt_t	ind2,
		Double_t	neff ) const

risk assessment

Definition at line 314 of file RuleFitParams.cxx.

◆ Risk() [2/2]

Double_t TMVA::RuleFitParams::Risk	(	UInt_t	ind1,
		UInt_t	ind2,
		Double_t	neff,
		UInt_t	itau ) const

risk assessment for tau model <itau>

Definition at line 334 of file RuleFitParams.cxx.

◆ RiskPath()

Double_t TMVA::RuleFitParams::RiskPath ( ) const

inline

Definition at line 108 of file RuleFitParams.h.

◆ RiskPerf() [1/2]

Double_t TMVA::RuleFitParams::RiskPerf ( ) const

inline

Definition at line 109 of file RuleFitParams.h.

◆ RiskPerf() [2/2]

Double_t TMVA::RuleFitParams::RiskPerf ( UInt_t itau ) const

inline

Definition at line 110 of file RuleFitParams.h.

◆ RiskPerfTst()

UInt_t TMVA::RuleFitParams::RiskPerfTst ( )

Estimates the error rate with the current set of parameters.

using the <Perf> subsample. Return the tau index giving the lowest error

Definition at line 1201 of file RuleFitParams.cxx.

◆ SetGDErrScale()

void TMVA::RuleFitParams::SetGDErrScale ( Double_t s )

inline

Definition at line 85 of file RuleFitParams.h.

◆ SetGDNPathSteps()

void TMVA::RuleFitParams::SetGDNPathSteps ( Int_t np )

inline

Definition at line 65 of file RuleFitParams.h.

◆ SetGDPathStep()

void TMVA::RuleFitParams::SetGDPathStep ( Double_t s )

inline

Definition at line 68 of file RuleFitParams.h.

◆ SetGDTau()

void TMVA::RuleFitParams::SetGDTau ( Double_t t )

inline

Definition at line 82 of file RuleFitParams.h.

◆ SetGDTauPrec()

void TMVA::RuleFitParams::SetGDTauPrec ( Double_t p )

inline

Definition at line 86 of file RuleFitParams.h.

◆ SetGDTauRange()

void TMVA::RuleFitParams::SetGDTauRange	(	Double_t	t0,
		Double_t	t1 )

inline

Definition at line 71 of file RuleFitParams.h.

◆ SetGDTauScan()

void TMVA::RuleFitParams::SetGDTauScan ( UInt_t n )

inline

Definition at line 79 of file RuleFitParams.h.

◆ SetMsgType()

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

Definition at line 1556 of file RuleFitParams.cxx.

◆ SetRuleFit()

void TMVA::RuleFitParams::SetRuleFit ( RuleFit * rf )

inline

Definition at line 62 of file RuleFitParams.h.

◆ Type()

Int_t TMVA::RuleFitParams::Type ( const Event * e ) const

Definition at line 1550 of file RuleFitParams.cxx.

◆ UpdateCoefficients()

void TMVA::RuleFitParams::UpdateCoefficients ( )

protected

Establish maximum gradient for rules, linear terms and the offset.

Definition at line 1441 of file RuleFitParams.cxx.

◆ UpdateTstCoefficients()

void TMVA::RuleFitParams::UpdateTstCoefficients ( )

protected

Establish maximum gradient for rules, linear terms and the offset for all taus TODO: do not need index range!

Definition at line 1327 of file RuleFitParams.cxx.

Member Data Documentation

◆ fAverageRulePath

std::vector<Double_t> TMVA::RuleFitParams::fAverageRulePath

protected

average of each rule, same range

Definition at line 205 of file RuleFitParams.h.

◆ fAverageRulePerf

std::vector<Double_t> TMVA::RuleFitParams::fAverageRulePerf

protected

average of each rule, same range

Definition at line 207 of file RuleFitParams.h.

◆ fAverageSelectorPath

std::vector<Double_t> TMVA::RuleFitParams::fAverageSelectorPath

protected

average of each variable over the range fPathIdx1,2

Definition at line 204 of file RuleFitParams.h.

◆ fAverageSelectorPerf

std::vector<Double_t> TMVA::RuleFitParams::fAverageSelectorPerf

protected

average of each variable over the range fPerfIdx1,2

Definition at line 206 of file RuleFitParams.h.

◆ fAverageTruth

Double_t TMVA::RuleFitParams::fAverageTruth

protected

average truth, ie sum(y)/N, y=+-1

Definition at line 232 of file RuleFitParams.h.

◆ fbkgave

Double_t TMVA::RuleFitParams::fbkgave

protected

Average of F(bkg)

Definition at line 248 of file RuleFitParams.h.

◆ fbkgrms

Double_t TMVA::RuleFitParams::fbkgrms

protected

Rms of F(bkg)

Definition at line 249 of file RuleFitParams.h.

◆ fFstar

std::vector<Double_t> TMVA::RuleFitParams::fFstar

protected

vector of F*() - filled in CalcFStar()

Definition at line 234 of file RuleFitParams.h.

◆ fFstarMedian

Double_t TMVA::RuleFitParams::fFstarMedian

protected

median value of F*() using

Definition at line 235 of file RuleFitParams.h.

◆ fGDCoefLinTst

std::vector< std::vector<Double_t> > TMVA::RuleFitParams::fGDCoefLinTst

protected

linear coeffs - one per tau

Definition at line 218 of file RuleFitParams.h.

◆ fGDCoefTst

std::vector< std::vector<Double_t> > TMVA::RuleFitParams::fGDCoefTst

protected

rule coeffs - one per tau

Definition at line 217 of file RuleFitParams.h.

◆ fGDErrScale

Double_t TMVA::RuleFitParams::fGDErrScale

protected

stop scan at error = scale*errmin

Definition at line 230 of file RuleFitParams.h.

◆ fGDErrTst

std::vector<Double_t> TMVA::RuleFitParams::fGDErrTst

protected

error rates per tau

Definition at line 215 of file RuleFitParams.h.

◆ fGDErrTstOK

std::vector<Char_t> TMVA::RuleFitParams::fGDErrTstOK

protected

error rate is sufficiently low <— stores boolean

Definition at line 216 of file RuleFitParams.h.

◆ fGDNPathSteps

Int_t TMVA::RuleFitParams::fGDNPathSteps

protected

number of path steps

Definition at line 229 of file RuleFitParams.h.

◆ fGDNTau

UInt_t TMVA::RuleFitParams::fGDNTau

protected

number of tau-paths - calculated in SetGDTauPrec

Definition at line 222 of file RuleFitParams.h.

◆ fGDNTauTstOK

UInt_t TMVA::RuleFitParams::fGDNTauTstOK

protected

number of tau in the test-phase that are ok

Definition at line 221 of file RuleFitParams.h.

◆ fGDNtuple

TTree* TMVA::RuleFitParams::fGDNtuple

protected

Gradient path ntuple, contains params for each step along the path.

Definition at line 237 of file RuleFitParams.h.

◆ fGDOfsTst

std::vector<Double_t> TMVA::RuleFitParams::fGDOfsTst

protected

offset per tau

Definition at line 219 of file RuleFitParams.h.

◆ fGDPathStep

Double_t TMVA::RuleFitParams::fGDPathStep

protected

step size along path (delta nu in eq 22, ref 1)

Definition at line 228 of file RuleFitParams.h.

◆ fGDTau

Double_t TMVA::RuleFitParams::fGDTau

protected

selected threshold parameter (tau in eq 26, ref 1)

Definition at line 227 of file RuleFitParams.h.

◆ fGDTauMax

Double_t TMVA::RuleFitParams::fGDTauMax

protected

max threshold parameter (tau in eq 26, ref 1)

Definition at line 226 of file RuleFitParams.h.

◆ fGDTauMin

Double_t TMVA::RuleFitParams::fGDTauMin

protected

min threshold parameter (tau in eq 26, ref 1)

Definition at line 225 of file RuleFitParams.h.

◆ fGDTauPrec

Double_t TMVA::RuleFitParams::fGDTauPrec

protected

precision in tau

Definition at line 223 of file RuleFitParams.h.

◆ fGDTauScan

UInt_t TMVA::RuleFitParams::fGDTauScan

protected

number scan for tau-paths

Definition at line 224 of file RuleFitParams.h.

◆ fGDTauVec

std::vector< Double_t > TMVA::RuleFitParams::fGDTauVec

protected

the tau's

Definition at line 220 of file RuleFitParams.h.

◆ fGradVec

std::vector<Double_t> TMVA::RuleFitParams::fGradVec

protected

gradient vector - dimension = number of rules in ensemble

Definition at line 209 of file RuleFitParams.h.

◆ fGradVecLin

std::vector<Double_t> TMVA::RuleFitParams::fGradVecLin

protected

gradient vector - dimension = number of variables

Definition at line 210 of file RuleFitParams.h.

◆ fGradVecLinTst

std::vector< std::vector<Double_t> > TMVA::RuleFitParams::fGradVecLinTst

protected

gradient vector, linear terms - one per tau

Definition at line 213 of file RuleFitParams.h.

◆ fGradVecTst

std::vector< std::vector<Double_t> > TMVA::RuleFitParams::fGradVecTst

protected

gradient vector - one per tau

Definition at line 212 of file RuleFitParams.h.

◆ fLogger

MsgLogger* TMVA::RuleFitParams::fLogger

mutableprivate

! message logger

Definition at line 253 of file RuleFitParams.h.

◆ fNEveEffPath

Double_t TMVA::RuleFitParams::fNEveEffPath

protected

sum of weights for Path events

Definition at line 201 of file RuleFitParams.h.

◆ fNEveEffPerf

Double_t TMVA::RuleFitParams::fNEveEffPerf

protected

idem for Perf events

Definition at line 202 of file RuleFitParams.h.

◆ fNLinear

UInt_t TMVA::RuleFitParams::fNLinear

protected

number of linear terms

Definition at line 192 of file RuleFitParams.h.

◆ fNRules

UInt_t TMVA::RuleFitParams::fNRules

protected

number of rules

Definition at line 191 of file RuleFitParams.h.

◆ fNTCoeff

Double_t* TMVA::RuleFitParams::fNTCoeff

protected

GD path: rule coefficients.

Definition at line 243 of file RuleFitParams.h.

◆ fNTCoefRad

Double_t TMVA::RuleFitParams::fNTCoefRad

protected

GD path: 'radius' of all rulecoeffs.

Definition at line 241 of file RuleFitParams.h.

◆ fNTErrorRate

Double_t TMVA::RuleFitParams::fNTErrorRate

protected

GD path: error rate (or performance)

Definition at line 239 of file RuleFitParams.h.

◆ fNTLinCoeff

Double_t* TMVA::RuleFitParams::fNTLinCoeff

protected

GD path: linear coefficients.

Definition at line 244 of file RuleFitParams.h.

◆ fNTNuval

Double_t TMVA::RuleFitParams::fNTNuval

protected

GD path: value of nu.

Definition at line 240 of file RuleFitParams.h.

◆ fNTOffset

Double_t TMVA::RuleFitParams::fNTOffset

protected

GD path: model offset.

Definition at line 242 of file RuleFitParams.h.

◆ fNTRisk

Double_t TMVA::RuleFitParams::fNTRisk

protected

GD path: risk.

Definition at line 238 of file RuleFitParams.h.

◆ fPathIdx1

UInt_t TMVA::RuleFitParams::fPathIdx1

protected

first event index for path search

Definition at line 197 of file RuleFitParams.h.

◆ fPathIdx2

UInt_t TMVA::RuleFitParams::fPathIdx2

protected

last event index for path search

Definition at line 198 of file RuleFitParams.h.

◆ fPerfIdx1

UInt_t TMVA::RuleFitParams::fPerfIdx1

protected

first event index for performance evaluation

Definition at line 199 of file RuleFitParams.h.

◆ fPerfIdx2

UInt_t TMVA::RuleFitParams::fPerfIdx2

protected

last event index for performance evaluation

Definition at line 200 of file RuleFitParams.h.

◆ fRuleEnsemble

RuleEnsemble* TMVA::RuleFitParams::fRuleEnsemble

protected

rule ensemble

Definition at line 189 of file RuleFitParams.h.

◆ fRuleFit

RuleFit* TMVA::RuleFitParams::fRuleFit

protected

rule fit

Definition at line 188 of file RuleFitParams.h.

◆ fsigave

Double_t TMVA::RuleFitParams::fsigave

protected

Sigma of current signal score function F(sig)

Definition at line 246 of file RuleFitParams.h.

◆ fsigrms

Double_t TMVA::RuleFitParams::fsigrms

protected

Rms of F(sig)

Definition at line 247 of file RuleFitParams.h.

Libraries for TMVA::RuleFitParams:

[legend]

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

tmva/tmva/inc/TMVA/RuleFitParams.h
tmva/tmva/src/RuleFitParams.cxx

Public Member Functions

Protected Types

Protected Member Functions

Protected Attributes

Private Member Functions

Private Attributes

Member Typedef Documentation

◆ EventItr

Constructor & Destructor Documentation

◆ RuleFitParams()

◆ ~RuleFitParams()

Member Function Documentation

◆ CalcAverageResponse()

◆ CalcAverageResponseOLD()

◆ CalcAverageTruth()

◆ CalcFStar()

◆ CalcGDNTau()

◆ CalcTstAverageResponse()

◆ ErrorRateBin()

◆ ErrorRateReg()

◆ ErrorRateRoc()

◆ ErrorRateRocRaw()

◆ ErrorRateRocTst()

◆ EvaluateAverage()

◆ EvaluateAveragePath()

◆ EvaluateAveragePerf()

◆ FillCoefficients()

◆ FindGDTau()

◆ GetPathIdx1()

◆ GetPathIdx2()

◆ GetPerfIdx1()

◆ GetPerfIdx2()

◆ Init()

◆ InitGD()

◆ InitNtuple()

◆ Log()

◆ LossFunction() [1/3]

◆ LossFunction() [2/3]

◆ LossFunction() [3/3]

◆ MakeGDPath()

◆ MakeGradientVector()

◆ MakeTstGradientVector()

◆ Optimism()

◆ Penalty()

◆ Risk() [1/2]

◆ Risk() [2/2]

◆ RiskPath()

◆ RiskPerf() [1/2]

◆ RiskPerf() [2/2]

◆ RiskPerfTst()

◆ SetGDErrScale()

◆ SetGDNPathSteps()

◆ SetGDPathStep()

◆ SetGDTau()

◆ SetGDTauPrec()

◆ SetGDTauRange()

◆ SetGDTauScan()

◆ SetMsgType()

◆ SetRuleFit()

◆ Type()

◆ UpdateCoefficients()

◆ UpdateTstCoefficients()

Member Data Documentation

◆ fAverageRulePath

◆ fAverageRulePerf

◆ fAverageSelectorPath

◆ fAverageSelectorPerf

◆ fAverageTruth

◆ fbkgave

◆ fbkgrms

◆ fFstar

◆ fFstarMedian

◆ fGDCoefLinTst

◆ fGDCoefTst

◆ fGDErrScale

◆ fGDErrTst

◆ fGDErrTstOK

◆ fGDNPathSteps

◆ fGDNTau

◆ fGDNTauTstOK