Description of the class TShapley. More...

#include <TShapley.h>

Inheritance diagram for URANIE::Sensitivity::TShapley:

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Collaboration diagram for URANIE::Sensitivity::TShapley:

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Public Types
enum	EConditionalSampling { kNorta , kCopulas , kVines , kUnknown }

Public Types inherited from URANIE::Sensitivity::TSensitivity
enum	ELauncher { kCode , kCodeRemote , kFunction , kRun , kUnknown }

Public Member Functions
Constructor and Destructor
	TShapley ()
	Default constructor.

	TShapley (TDataServer tds, const char varexpinput, const char varexpoutput, Option_t option="")

	TShapley (TDataServer tds, const char fcn, Int_t ns, const char varexpinput, const char varexpoutput, Option_t *option="")
	Constructor with the TDataServer and function name.

	TShapley (TDataServer tds, void(fcn)(Double_t , Double_t ), Int_t ns, const char varexpinput, const char varexpoutput, Option_t *option="")
	Constructor with the TDataServer and function.

	TShapley (TDataServer tds, URANIE::Launcher::TCode fcode, Int_t ns, Option_t *option="")
	Constructor with the TDataServer and a TCode.

	TShapley (TDataServer tds, URANIE::Relauncher::TRun run, Int_t ns, Option_t *option="")
	Constructor with the TDataServer and a TRun.

virtual	~TShapley ()
	Default destructor.

Setting and Getting attributs
const char *	getIteratorNameNo ()

const char *	getIteratorNameNi ()

Int_t	getNv ()

void	setNv (Int_t nv)

Int_t	getNm ()

void	setNm (Int_t nm)

Int_t	getNo ()

void	setNo (Int_t no)

Int_t	getNi ()

void	setNi (Int_t ni)

void	setSamplerMethod (EConditionalSampling nMethod)

EConditionalSampling	getSamplerMethod ()

TMatrixD	getMatrixNormalVariance ()

Generation of the sample
virtual void	generateSample (Option_t *option="")

Public Member Functions inherited from URANIE::Sensitivity::TSensitivity
	TSensitivity ()
	Default constructor.

	TSensitivity (URANIE::DataServer::TDataServer tds, const char fcn, Int_t ns, const char varexpinput="", const char varexpoutput="", Option_t *option="")
	Default constructor with the name of a function.

	TSensitivity (URANIE::DataServer::TDataServer tds, void(fcn)(Double_t , Double_t ), const char varexpinput, const char varexpoutput, Int_t ns, Option_t *option="")

	TSensitivity (URANIE::DataServer::TDataServer tds, const char varexpinput, const char varexpoutput, Option_t option="")
	Default constructor.

	TSensitivity (URANIE::DataServer::TDataServer tds, URANIE::Launcher::TCode code, Int_t ns, Option_t *option="")
	Default constructor with TCode arg.

	TSensitivity (URANIE::DataServer::TDataServer tds, URANIE::Relauncher::TRun run, Int_t ns, Option_t *option="")
	Default constructor with TRun arg.

virtual	~TSensitivity ()
	Default destructor.

Int_t	getID ()

virtual TTree *	getResultTuple (bool commonresulttuple=true)
	Get the result ntuple (default parameter unused but for Morris method)

double	getValue (const char sorder="", const char sinputname="", const char *sselect="")

vector< int > *	getAttributeElements (string str)

void	setFunction (void(fct)(Double_t , Double_t *), Int_t nx=-1, Int_t ny=1)

TString	getFunctionName ()

void	setSeed (UInt_t nval)

UInt_t	getSeed ()

virtual void	setMethodName (const char *str)
	Set the Method name.

const char *	getMethodName ()
	Get the method name.

Bool_t	getNoIntermediateSaved ()
	Get the noIntermediateSaved flag.

const char *	getIteratorName ()
	Get the name of the iterator attribut of the method.

void	setSensitivityIteratorName (const char *str)
	Set the iterator name devoted to compute sensitivity indexes.

void	setTimeName (TString sname)
	Set the name of the time attribute (only one)

TString	getTimeName ()
	Get the name of the time attribute.

virtual void	setDrawProgressBar (Bool_t bbool=kTRUE)
	Set the "draw progress bar" flag.

void	setUsingErrors (bool thebool)
	Set the "using error results anyway" option.

Bool_t	getDrawProgressBar ()
	Get the clean flag.

Bool_t	isInputCorrelated ()

TMatrixD	getMatrixInputCorrelation ()

Int_t	getNInput ()
	Get the number of input attributes.

Int_t	getNOutput ()
	Get the number of output attributes.

void	setInputCorrelationMatrix (TMatrixD Corr)

virtual void	parseOption (Option_t *option="")
	Read the possible options.

void	checkOutputRequested (string attlist, bool fromoption=false)
	Check the output list requested by the user.

void	computeIndexes (Option_t *option="")
	Compute the Sensitivity Indexes.

void	fillIndex (const char sinputname, const char sorder, Double_t dval, const char *algo="", Double_t dvalCILower=-1.0, Double_t dvalCIUpper=-1.0)
	Method to fill in the tree the value of Sensitivity indexes for an input attribute.

virtual void	createTuple (Option_t *option="")

virtual void	drawIndexes (TString sTitre, const char select="", Option_t option="")
	Draws the indexes.

virtual void	setLog ()

void	unsetLog ()

void	changeLog ()

Bool_t	getLog ()

void	setNLauncher (ELauncher codeLauncher)

Compute the indexes
TString	_sIteratorNameNo
	The specific iterator attribute for NO integer.

TString	_sIteratorNameNi
	The specific iterator attribute for NI integer.

Int_t	_nV
	The size of the nV sample.

Int_t	_nO
	The size of the nO sample.

Int_t	_nI
	The size of the nI sample.

Int_t	_nM
	The size of the loop of permutations.

TMatrixD	_matNormalCovariance
	Normal(Gaussian) Input Covariance matrix.

EConditionalSampling	_nCondSampler
	The method of sampler.

void	evaluateIndexes (Option_t *option="")
	Compute the sensitivity matrix.

void	printLog (Option_t *option="")

void	printSummary (Option_t *option="")
	Print the summary of the TShapley method.

void	transfertDataTDS (TDataServer *tds)

void	splitMatrixVarCoVar (TMatrixD matVarCovar, TVectorD vecOfUnit, TMatrixD &matVarCovar0, TMatrixD &matVarCovar1, TMatrixD &matVarCovar01, TMatrixD matCorr, TMatrixD &matCorrtmp)

void	computeNormalCorrelationMatrix (TMatrixD matVarCovariance)
	computeNormalCorrelationMatrix

TDataServer *	generateConditionnalSampleByNortaOriginal (TDataServer tds0, TDataServer tds1, TMatrixD matVarCovar0, TMatrixD matVarCovar1, TMatrixD matVarCovar01)
	generateConditionnalSample

void	generateConditionnalSampleByNorta (TGaussianSampling tsanormal, TDataServer tdsres, TList *listOfLeaves, TMatrixD matVarCovar, TVectorD &vecOf01)

void	init (Option_t *option)

Additional Inherited Members
Public Attributes inherited from URANIE::Sensitivity::TSensitivity
URANIE::DataServer::TDataServer *	_tds
	Pointeur vers un TDS.

TList *	_listOfInputAttributes
	List of the input branches.

TList *	_listOfOutputAttributes
	List of the input branches.

TString	_sTimeAttribute
	The name of the Time attribute.

Int_t	_nS
	The number of simulation or other information depend on the used method.

Int_t	_nX
	Dimension of the input.

UInt_t	_nY
	Dimension of the target.

UInt_t	_nElY
	Number of element for one selected output.

Int_t	_nbOut
	Total number of Output to be considered.

Int_t	_iOut
	counter for output

unsigned int	_iy
	iterator over number of element

unsigned int	_iely
	iterator over number of element

ELauncher	_nLauncher
	The type of launcher.

TString	_sFunctionName
	The Name of the evaluatuor.

URANIE::Launcher::TCode *	_code
	The tcode.

URANIE::Relauncher::TRun *	_run

TObjArray *	_drawingGarbageCollector
	Garbage collector for prints.

Int_t	_nSeed
	The seed of the random generator.

Bool_t	_bChosenOutputs
	Fact that the input list is provided or not.

Bool_t	_blog
	Boolean for edit the log.

Bool_t	_bdrawProgressBar
	Boolean to know if the progress bar has to be drawn.

Bool_t	_bnoIntermediateSaved
	Boolean to know if the progress bar has to be drawn.

TString	_sIteratorName
	The specific iterator attribute for the method.

TString	_sMethodName
	The method name.

TString	_sSelectedOutput
	The output.

TString	_sSelectedInput
	The input.

map< string, unsigned int >	_mAttributeSize
	Map of size of element for attribute;.

map< string, vector< int > >	_mAttributeElements
	Map of Elements number to run (if vector subselection is requested)

vector< string >	_vOutputNames
	Name of the output.

TCanvas *	_canvas
	Canvas object to deal with.

void(*	_pFunction )(double , double )

TTree *	_ntresult
	The TTree of results.

Protected Member Functions inherited from URANIE::Sensitivity::TSensitivity
void	checkCanvasCreation (bool newcan)
	Create a canvas if needed.

void	drawIndexesHistogram (TString sTitre, const char select="", Option_t option="")
	Draws indexes with an histogram.

void	drawIndexesPie (TString sTitre, const char select="", Option_t option="")
	Draws indexes with an pie chart.

virtual void	preTreatment ()
	PreTreatment for every output.

virtual void	postTreatment ()
	PostTreatment for every output.

void	setNoIntermediateSaved (Bool_t bbool=kTRUE)
	Set the "only final file" flag.

Protected Attributes inherited from URANIE::Sensitivity::TSensitivity
Char_t	_sOutputAttribute [MAXLENGTHSTRING]
	The name of the output attribute.

Char_t	_sInputAttribute [MAXLENGTHSTRING]
	The name of the input attribute.

Char_t	_sOrder [MAXLENGTHSTRING]
	The order of sensitivity indexes.

Char_t	_sMethod [MAXLENGTHSTRING]
	The name of the method.

Char_t	_sAlgorithm [MAXLENGTHSTRING]
	The name of the algorithm to compute the index.

Double_t	_valSobolCrt
	The value of sensitivity indexes.

Double_t	_valSobolCILower
	The value of lower Condidence Interval (95)

Double_t	_valSobolCIUpper
	The value of upper Condidence Interval (95)

TMatrixD	_minputCorr
	Input correlation matrix if sample needs to be correlated.

Bool_t	_bisInputCorrelated
	State whether the input correlation matrix is set.

Bool_t	_bgoingThroughError
	State whether the error must not block the computation.

Detailed Description

Description of the class TShapley.

Description of the class THSIC.

Introduction

Bibliography

Shapley 1953
Owen 2014 "Sobol' indices and Shapley value", Journal of Uncertainty Quantification, 2:245-251
Owen, Prieur, 2017, "On Shapley value for measuring importance of dependent inputs",
Iooss, Prieur, 2017, "Shapley effects for Sensitivity Analysis with dependent inputs: comparaison with Sobol' indices, numerical estimation and applications"
Song, Nelson, Staum, 2016 "Shapley Effect for Global Sensitivity Analysis: Theory and Computation"

Member Enumeration Documentation

◆ EConditionalSampling

enum URANIE::Sensitivity::TShapley::EConditionalSampling

Enumerator
kNorta
kCopulas
kVines
kUnknown

Constructor & Destructor Documentation

◆ TShapley() [1/6]

URANIE::Sensitivity::TShapley::TShapley ( )

Default constructor.

Referenced by ClassImp().

◆ TShapley() [2/6]

URANIE::Sensitivity::TShapley::TShapley	(	TDataServer *	tds,
		const char *	varexpinput,
		const char *	varexpoutput,
		Option_t *	option = `""`
	)

◆ TShapley() [3/6]

URANIE::Sensitivity::TShapley::TShapley	(	TDataServer *	tds,
		const char *	fcn,
		Int_t	ns,
		const char *	varexpinput,
		const char *	varexpoutput,
		Option_t *	option = `""`
	)

Constructor with the TDataServer and function name.

Parameters

tds
fcn
ns
varexpinput
varexpoutput
option	[""]

◆ TShapley() [4/6]

URANIE::Sensitivity::TShapley::TShapley	(	TDataServer *	tds,
		void()(Double_t , Double_t *)	fcn,
		Int_t	ns,
		const char *	varexpinput,
		const char *	varexpoutput,
		Option_t *	option = `""`
	)

Constructor with the TDataServer and function.

Parameters

tds
ns
sInputs
sOutputs
option

◆ TShapley() [5/6]

URANIE::Sensitivity::TShapley::TShapley	(	TDataServer *	tds,
		URANIE::Launcher::TCode *	fcode,
		Int_t	ns,
		Option_t *	option = `""`
	)

Constructor with the TDataServer and a TCode.

Parameters

tds
fcode
ns
option

◆ TShapley() [6/6]

URANIE::Sensitivity::TShapley::TShapley	(	TDataServer *	tds,
		URANIE::Relauncher::TRun *	run,
		Int_t	ns,
		Option_t *	option = `""`
	)

Constructor with the TDataServer and a TRun.

Parameters

tds
run
ns
option

◆ ~TShapley()

virtual URANIE::Sensitivity::TShapley::~TShapley ( )

virtual

Default destructor.

Referenced by ClassImp().

Member Function Documentation

◆ computeNormalCorrelationMatrix()

void URANIE::Sensitivity::TShapley::computeNormalCorrelationMatrix ( TMatrixD matVarCovariance )

private

computeNormalCorrelationMatrix

Compute the correlation matrix \bf "_matNormalCorr " from the rank correlation for the Normal (Gaussian) multivariate distribution

$f[ \rho \: = \: 2 \: sin ( \frac{\pi}{6} \rh_{r} $f]

Referenced by ClassImp().

◆ evaluateIndexes()

void URANIE::Sensitivity::TShapley::evaluateIndexes ( Option_t * option = "" )

virtual

Compute the sensitivity matrix.

Parameters

option

Implements URANIE::Sensitivity::TSensitivity.

Referenced by ClassImp().

◆ generateConditionnalSampleByNorta()

void URANIE::Sensitivity::TShapley::generateConditionnalSampleByNorta	(	TGaussianSampling *	tsanormal,
		TDataServer *	tdsres,
		TList *	listOfLeaves,
		TMatrixD	matVarCovar,
		TVectorD &	vecOf01
	)

private

Referenced by ClassImp().

◆ generateConditionnalSampleByNortaOriginal()

TDataServer * URANIE::Sensitivity::TShapley::generateConditionnalSampleByNortaOriginal	(	TDataServer *	tds0,
		TDataServer *	tds1,
		TMatrixD	matVarCovar0,
		TMatrixD	matVarCovar1,
		TMatrixD	matVarCovar01
	)

private

generateConditionnalSample

Generate a conditionnal sample from tds

Parameters

tds
matVarCovar0
matVarCovar1
matVarCovar01

Referenced by ClassImp().

◆ generateSample()

virtual void URANIE::Sensitivity::TShapley::generateSample ( Option_t * option = "" )

virtual

Implements URANIE::Sensitivity::TSensitivity.

Referenced by ClassImp().

◆ getIteratorNameNi()

const char * URANIE::Sensitivity::TShapley::getIteratorNameNi ( )

inline

References _sIteratorNameNi.

◆ getIteratorNameNo()

const char * URANIE::Sensitivity::TShapley::getIteratorNameNo ( )

inline

References _sIteratorNameNo.

◆ getMatrixNormalVariance()

TMatrixD URANIE::Sensitivity::TShapley::getMatrixNormalVariance ( )

inline

References _matNormalCovariance.

◆ getNi()

Int_t URANIE::Sensitivity::TShapley::getNi ( )

inline

References _nI.

◆ getNm()

Int_t URANIE::Sensitivity::TShapley::getNm ( )

inline

References _nM.

◆ getNo()

Int_t URANIE::Sensitivity::TShapley::getNo ( )

inline

References _nO.

◆ getNv()

Int_t URANIE::Sensitivity::TShapley::getNv ( )

inline

References _nV.

◆ getSamplerMethod()

EConditionalSampling URANIE::Sensitivity::TShapley::getSamplerMethod ( )

inline

References _nCondSampler.

◆ init()

void URANIE::Sensitivity::TShapley::init ( Option_t * option )

protected

Referenced by ClassImp().

◆ printLog()

void URANIE::Sensitivity::TShapley::printLog ( Option_t * option = "" )

virtual

Reimplemented from URANIE::Sensitivity::TSensitivity.

Referenced by ClassImp().

◆ printSummary()

void URANIE::Sensitivity::TShapley::printSummary ( Option_t * option = "" )

Print the summary of the TShapley method.

Print just the results of the method NOT THE LOG Just a test to generalize

Parameters

option (Option_t *) option to pass [""]

Referenced by ClassImp().

◆ setNi()

void URANIE::Sensitivity::TShapley::setNi ( Int_t ni )

Referenced by ClassImp().

◆ setNm()

void URANIE::Sensitivity::TShapley::setNm ( Int_t nm )

Referenced by ClassImp().

◆ setNo()

void URANIE::Sensitivity::TShapley::setNo ( Int_t no )

Referenced by ClassImp().

◆ setNv()

void URANIE::Sensitivity::TShapley::setNv ( Int_t nv )

Referenced by ClassImp().

◆ setSamplerMethod()

void URANIE::Sensitivity::TShapley::setSamplerMethod ( EConditionalSampling nMethod )

Referenced by ClassImp().

◆ splitMatrixVarCoVar()

void URANIE::Sensitivity::TShapley::splitMatrixVarCoVar	(	TMatrixD	matVarCovar,
		TVectorD	vecOfUnit,
		TMatrixD &	matVarCovar0,
		TMatrixD &	matVarCovar1,
		TMatrixD &	matVarCovar01,
		TMatrixD	matCorr,
		TMatrixD &	matCorrtmp
	)

private

Referenced by ClassImp().

◆ transfertDataTDS()

void URANIE::Sensitivity::TShapley::transfertDataTDS ( TDataServer * tds )

private

Referenced by ClassImp().

Member Data Documentation

◆ _matNormalCovariance

TMatrixD URANIE::Sensitivity::TShapley::_matNormalCovariance

private

Normal(Gaussian) Input Covariance matrix.

Referenced by ClassImp(), and getMatrixNormalVariance().

◆ _nCondSampler

EConditionalSampling URANIE::Sensitivity::TShapley::_nCondSampler

private

The method of sampler.

Referenced by ClassImp(), and getSamplerMethod().

◆ _nI

Int_t URANIE::Sensitivity::TShapley::_nI

private

The size of the nI sample.

Referenced by ClassImp(), and getNi().

◆ _nM

Int_t URANIE::Sensitivity::TShapley::_nM

private

The size of the loop of permutations.

Referenced by ClassImp(), and getNm().

◆ _nO

Int_t URANIE::Sensitivity::TShapley::_nO

private

The size of the nO sample.

Referenced by ClassImp(), and getNo().

◆ _nV

Int_t URANIE::Sensitivity::TShapley::_nV

private

The size of the nV sample.

Referenced by ClassImp(), and getNv().

◆ _sIteratorNameNi

TString URANIE::Sensitivity::TShapley::_sIteratorNameNi

private

The specific iterator attribute for NI integer.

Referenced by ClassImp(), and getIteratorNameNi().

◆ _sIteratorNameNo

TString URANIE::Sensitivity::TShapley::_sIteratorNameNo

private

The specific iterator attribute for NO integer.

Referenced by ClassImp(), and getIteratorNameNo().

Documentation / Developer's manual

Available modules

Public Types

Public Member Functions

Compute the indexes

Additional Inherited Members

Detailed Description

Description of the class THSIC.

Introduction

Bibliography

Member Enumeration Documentation

◆ EConditionalSampling

Constructor & Destructor Documentation

◆ TShapley() [1/6]

◆ TShapley() [2/6]

◆ TShapley() [3/6]

◆ TShapley() [4/6]

◆ TShapley() [5/6]

◆ TShapley() [6/6]

◆ ~TShapley()

Member Function Documentation

◆ computeNormalCorrelationMatrix()

◆ evaluateIndexes()

◆ generateConditionnalSampleByNorta()

◆ generateConditionnalSampleByNortaOriginal()

◆ generateSample()

◆ getIteratorNameNi()

◆ getIteratorNameNo()

◆ getMatrixNormalVariance()

◆ getNi()

◆ getNm()

◆ getNo()

◆ getNv()

◆ getSamplerMethod()

◆ init()

◆ printLog()

◆ printSummary()

◆ setNi()

◆ setNm()

◆ setNo()

◆ setNv()

◆ setSamplerMethod()

◆ splitMatrixVarCoVar()

◆ transfertDataTDS()

Member Data Documentation

◆ _matNormalCovariance

◆ _nCondSampler

◆ _nI

◆ _nM

◆ _nO

◆ _nV

◆ _sIteratorNameNi

◆ _sIteratorNameNo