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TMVA::MethodLikelihood Class Reference

Likelihood analysis ("non-parametric approach")

Also implemented is a "diagonalized likelihood approach", which improves over the uncorrelated likelihood approach by transforming linearly the input variables into a diagonal space, using the square-root of the covariance matrix

The method of maximum likelihood is the most straightforward, and certainly among the most elegant multivariate analyser approaches. We define the likelihood ratio, $$R_L$$, for event $$i$$, by:

$R_L(i) = \frac{L_S(i)}{L_B(i) + L_B(i)}$

Here the signal and background likelihoods, $$L_S$$, $$L_B$$, are products of the corresponding probability densities, $$p_S$$, $$p_B$$, of the $$N_{var}$$ discriminating variables used in the MVA:

$L_S(i) \ \prod_{j=1}^{N_{var}} p_{Sj} (i)$

and accordingly for $$L_B$$. In practise, TMVA uses polynomial splines to estimate the probability density functions (PDF) obtained from the distributions of the training variables.

Note that in TMVA the output of the likelihood ratio is transformed by:

$R_L(i) \to R'_L(i) = -\frac{1}{\tau} ln(R_L^{-1}(i) -1)$

to avoid the occurrence of heavy peaks at $$R_L = 0.1$$ .

#### Decorrelated (or "diagonalized") Likelihood

The biggest drawback of the Likelihood approach is that it assumes that the discriminant variables are uncorrelated. If it were the case, it can be proven that the discrimination obtained by the above likelihood ratio is optimal, ie, no other method can beat it. However, in most practical applications of MVAs correlations are present.

Linear correlations, measured from the training sample, can be taken into account in a straightforward manner through the square-root of the covariance matrix. The square-root of a matrix $$C$$ is the matrix $$C′$$ that multiplied with itself yields $$C$$: $$C$$= $$C′C′$$. We compute the square-root matrix (SQM) by means of diagonalising ( $$D$$) the covariance matrix:

$D = S^TCS \Rightarrow C' = S \sqrt{DS^T}$

and the linear transformation of the linearly correlated into the uncorrelated variables space is then given by multiplying the measured variable tuple by the inverse of the SQM. Note that these transformations are performed for both signal and background separately, since the correlation pattern is not the same in the two samples.

The above diagonalisation is complete for linearly correlated, Gaussian distributed variables only. In real-world examples this is not often the case, so that only little additional information may be recovered by the diagonalisation procedure. In these cases, non-linear methods must be applied.

Definition at line 61 of file MethodLikelihood.h.

## Public Member Functions

MethodLikelihood (const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption="")
standard constructor

MethodLikelihood (DataSetInfo &theData, const TString &theWeightFile)
construct likelihood references from file

virtual ~MethodLikelihood ()
destructor

write weights to XML

const RankingCreateRanking ()
computes ranking of input variables

Double_t GetMvaValue (Double_t *err=nullptr, Double_t *errUpper=nullptr)
returns the likelihood estimator for signal fill a new Likelihood branch into the testTree

virtual Bool_t HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)
FDA can handle classification with 2 classes.

virtual TClassIsA () const

read weight info from file nothing to do for this method

read reference PDF from ROOT file

virtual void Streamer (TBuffer &)

void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

void Train ()
create reference distributions (PDFs) from signal and background events: fill histograms and smooth them; if decorrelation is required, compute corresponding square-root matrices the reference histograms require the correct boundaries.

void WriteMonitoringHistosToFile () const
write histograms and PDFs to file for monitoring purposes

virtual void WriteOptionsToStream (std::ostream &o, const TString &prefix) const
write options to stream

void WriteWeightsToStream (TFile &rf) const
write reference PDFs to ROOT file

Public Member Functions inherited from TMVA::MethodBase
MethodBase (const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="")
standard constructor

MethodBase (Types::EMVA methodType, DataSetInfo &dsi, const TString &weightFile)
constructor used for Testing + Application of the MVA, only (no training), using given WeightFiles

virtual ~MethodBase ()
destructor

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

virtual void CheckSetup ()
check may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase)

DataSetData () const

DataSetInfoDataInfo () const

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.

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.

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

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.

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=nullptr, Double_t *errUpper=nullptr)

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

const TString GetProbaName () const

virtual Double_t GetRarity (Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const
compute rarity:

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=nullptr, PDF *pdfB=nullptr) const
calculate the area (integral) under the ROC curve as a overall quality measure of the classification

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

virtual Double_t GetSeparation (PDF *pdfS=nullptr, PDF *pdfB=nullptr) const
compute "separation" defined as

virtual Double_t GetSeparation (TH1 *, TH1 *) const
compute "separation" defined as

Double_t GetSignalReferenceCut () const

Double_t GetSignalReferenceCutOrientation () const

virtual Double_t GetSignificance () const
compute significance of mean difference

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

UInt_t GetTrainingTMVAVersionCode () const

TString GetTrainingTMVAVersionString () const
calculates the TMVA version string from the training version code on the fly

Double_t GetTrainTime () const

TransformationHandlerGetTransformationHandler (Bool_t takeReroutedIfAvailable=true)

const TransformationHandlerGetTransformationHandler (Bool_t takeReroutedIfAvailable=true) const

TString GetWeightFileName () const
retrieve weight file name

Double_t GetXmax (Int_t ivar) const

Double_t GetXmin (Int_t ivar) const

Bool_t HasMVAPdfs () const

void InitIPythonInteractive ()

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

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

Bool_t IsSilentFile () const

virtual void MakeClass (const TString &classFileName=TString("")) const
create reader class for method (classification only at present)

TDirectoryMethodBaseDir () const
returns the ROOT directory where all instances of the corresponding MVA method are stored

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.

void PrintHelpMessage () const
prints out method-specific help method

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)

Function to write options and weights to file.

read the header from the weight files of the different MVA methods

write reference MVA distributions (and other information) to a ROOT type weight file

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 .

void SetupMethod ()
setup of methods

void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

virtual void TestClassification ()
initialization

virtual void TestMulticlass ()
test multiclass classification

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

bool TrainingEnded ()

void TrainMethod ()

virtual void WriteEvaluationHistosToFile (Types::ETreeType treetype)
writes all MVA evaluation histograms to file

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

Public Member Functions inherited from TMVA::IMethod
IMethod ()

virtual ~IMethod ()

void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

Public Member Functions inherited from TMVA::Configurable
Configurable (const TString &theOption="")
constructor

virtual ~Configurable ()
default destructor

write options to XML file

template<class T >

template<class T >
void AddPreDefVal (const TString &optname, const T &)

void CheckForUnusedOptions () const
checks for unused options in option string

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

MsgLoggerLog () const

virtual void ParseOptions ()
options parser

void PrintOptions () const
prints out the options set in the options string and the defaults

read option back from the weight file

void SetConfigDescription (const char *d)

void SetConfigName (const char *n)

void SetMsgType (EMsgType t)

void SetOptions (const TString &s)

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

Public Member Functions inherited from TNamed
TNamed ()

TNamed (const char *name, const char *title)

TNamed (const TNamed &named)
TNamed copy ctor.

TNamed (const TString &name, const TString &title)

virtual ~TNamed ()
TNamed destructor.

void Clear (Option_t *option="") override
Set name and title to empty strings ("").

TObjectClone (const char *newname="") const override
Make a clone of an object using the Streamer facility.

Int_t Compare (const TObject *obj) const override
Compare two TNamed objects.

void Copy (TObject &named) const override
Copy this to obj.

virtual void FillBuffer (char *&buffer)
Encode TNamed into output buffer.

const char * GetName () const override
Returns name of object.

const char * GetTitle () const override
Returns title of object.

ULong_t Hash () const override
Return hash value for this object.

TClassIsA () const override

Bool_t IsSortable () const override

void ls (Option_t *option="") const override
List TNamed name and title.

TNamedoperator= (const TNamed &rhs)
TNamed assignment operator.

void Print (Option_t *option="") const override
Print TNamed name and title.

virtual void SetName (const char *name)
Set the name of the TNamed.

virtual void SetNameTitle (const char *name, const char *title)
Set all the TNamed parameters (name and title).

virtual void SetTitle (const char *title="")
Set the title of the TNamed.

virtual Int_t Sizeof () const
Return size of the TNamed part of the TObject.

void Streamer (TBuffer &) override
Stream an object of class TObject.

void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

Public Member Functions inherited from TObject
TObject ()
TObject constructor.

TObject (const TObject &object)
TObject copy ctor.

virtual ~TObject ()
TObject destructor.

void AbstractMethod (const char *method) const
Use this method to implement an "abstract" method that you don't want to leave purely abstract.

Append graphics object to current pad.

virtual void Browse (TBrowser *b)
Browse object. May be overridden for another default action.

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.

virtual const char * ClassName () const
Returns name of class to which the object belongs.

virtual void Delete (Option_t *option="")
Delete this object.

virtual Int_t DistancetoPrimitive (Int_t px, Int_t py)
Computes distance from point (px,py) to the object.

virtual void Draw (Option_t *option="")
Default Draw method for all objects.

virtual void DrawClass () const
Draw class inheritance tree of the class to which this object belongs.

virtual TObjectDrawClone (Option_t *option="") const
Draw a clone of this object in the current selected pad with: gROOT->SetSelectedPad(c1).

virtual void Dump () const
Dump contents of object on stdout.

virtual void Error (const char *method, const char *msgfmt,...) const
Issue error message.

virtual void Execute (const char *method, const char *params, Int_t *error=nullptr)
Execute method on this object with the given parameter string, e.g.

virtual void Execute (TMethod *method, TObjArray *params, Int_t *error=nullptr)
Execute method on this object with parameters stored in the TObjArray.

virtual void ExecuteEvent (Int_t event, Int_t px, Int_t py)
Execute action corresponding to an event at (px,py).

virtual void Fatal (const char *method, const char *msgfmt,...) const
Issue fatal error message.

virtual TObjectFindObject (const char *name) const
Must be redefined in derived classes.

virtual TObjectFindObject (const TObject *obj) const
Must be redefined in derived classes.

virtual Option_tGetDrawOption () const
Get option used by the graphics system to draw this object.

virtual const char * GetIconName () const
Returns mime type name of object.

virtual char * GetObjectInfo (Int_t px, Int_t py) const
Returns string containing info about the object at position (px,py).

virtual Option_tGetOption () const

virtual UInt_t GetUniqueID () const
Return the unique object id.

virtual Bool_t HandleTimer (TTimer *timer)
Execute action in response of a timer timing out.

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.

virtual void Info (const char *method, const char *msgfmt,...) const
Issue info message.

virtual Bool_t InheritsFrom (const char *classname) const
Returns kTRUE if object inherits from class "classname".

virtual Bool_t InheritsFrom (const TClass *cl) const
Returns kTRUE if object inherits from TClass cl.

virtual void Inspect () const
Dump contents of this object in a graphics canvas.

void InvertBit (UInt_t f)

Bool_t IsDestructed () const
IsDestructed.

virtual Bool_t IsEqual (const TObject *obj) const
Default equal comparison (objects are equal if they have the same address in memory).

virtual Bool_t IsFolder () const
Returns kTRUE in case object contains browsable objects (like containers or lists of other objects).

R__ALWAYS_INLINE Bool_t IsOnHeap () const

R__ALWAYS_INLINE Bool_t IsZombie () const

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).

virtual Bool_t Notify ()
This method must be overridden to handle object notification (the base implementation is no-op).

void Obsolete (const char *method, const char *asOfVers, const char *removedFromVers) const
Use this method to declare a method obsolete.

void operator delete (void *ptr)
Operator delete.

void operator delete[] (void *ptr)
Operator delete [].

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.

virtual void Paint (Option_t *option="")
This method must be overridden if a class wants to paint itself.

virtual void Pop ()
Pop on object drawn in a pad to the top of the display list.

virtual Int_t Read (const char *name)
Read contents of object with specified name from the current directory.

virtual void RecursiveRemove (TObject *obj)
Recursively remove this object from a list.

void ResetBit (UInt_t f)

virtual void SaveAs (const char *filename="", Option_t *option="") const
Save this object in the file specified by filename.

virtual void SavePrimitive (std::ostream &out, Option_t *option="")
Save a primitive as a C++ statement(s) on output stream "out".

void SetBit (UInt_t f)

void SetBit (UInt_t f, Bool_t set)
Set or unset the user status bits as specified in f.

virtual void SetDrawOption (Option_t *option="")
Set drawing option for object.

virtual void SetUniqueID (UInt_t uid)
Set the unique object id.

void StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

virtual void SysError (const char *method, const char *msgfmt,...) const
Issue system error message.

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.

virtual void Warning (const char *method, const char *msgfmt,...) const
Issue warning message.

virtual Int_t Write (const char *name=nullptr, Int_t option=0, Int_t bufsize=0)
Write this object to the current directory.

virtual Int_t Write (const char *name=nullptr, Int_t option=0, Int_t bufsize=0) const
Write this object to the current directory.

## Static Public Member Functions

static TClassClass ()

static const char * Class_Name ()

static constexpr Version_t Class_Version ()

static const char * DeclFileName ()

Static Public Member Functions inherited from TMVA::MethodBase
static TClassClass ()

static const char * Class_Name ()

static constexpr Version_t Class_Version ()

static const char * DeclFileName ()

Static Public Member Functions inherited from TMVA::IMethod
static TClassClass ()

static const char * Class_Name ()

static constexpr Version_t Class_Version ()

static const char * DeclFileName ()

Static Public Member Functions inherited from TMVA::Configurable
static TClassClass ()

static const char * Class_Name ()

static constexpr Version_t Class_Version ()

static const char * DeclFileName ()

Static Public Member Functions inherited from TNamed
static TClassClass ()

static const char * Class_Name ()

static constexpr Version_t Class_Version ()

static const char * DeclFileName ()

Static Public Member Functions inherited from TObject
static TClassClass ()

static const char * Class_Name ()

static constexpr Version_t Class_Version ()

static const char * DeclFileName ()

static Longptr_t GetDtorOnly ()
Return destructor only flag.

static Bool_t GetObjectStat ()
Get status of object stat flag.

static void SetDtorOnly (void *obj)
Set destructor only flag.

static void SetObjectStat (Bool_t stat)
Turn on/off tracking of objects in the TObjectTable.

## Protected Member Functions

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

void GetHelpMessage () const
get help message text

void MakeClassSpecific (std::ostream &, const TString &) const
write specific classifier response

void MakeClassSpecificHeader (std::ostream &, const TString &="") const
write specific header of the classifier (mostly include files)

Protected Member Functions inherited from TMVA::MethodBase
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

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

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

void NoErrorCalc (Double_t *const err, Double_t *const errUpper)

void SetNormalised (Bool_t norm)

void SetWeightFileDir (TString fileDir)
set directory of weight file

void SetWeightFileName (TString)
set the weight file name (depreciated)

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

Bool_t TxtWeightsOnly () const

Bool_t Verbose () const

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

void WriteOptionsReferenceToFile ()
write complete options to output stream

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).

void MakeZombie ()

## Private Member Functions

void DeclareOptions ()
define the options (their key words) that can be set in the option string

void Init ()
default initialisation called by all constructors

void ProcessOptions ()
process user options reference cut value to distinguish signal-like from background-like events

Double_t TransformLikelihoodOutput (Double_t ps, Double_t pb) const
returns transformed or non-transformed output

## Private Attributes

Int_t fAverageEvtPerBin
average events per bin; used to calculate fNbins

Int_tfAverageEvtPerBinVarB
average events per bin; used to calculate fNbins

Int_tfAverageEvtPerBinVarS
average events per bin; used to calculate fNbins

TString fBorderMethodString
the method to take care about "border" effects (string)

PDFfDefaultPDFLik
pdf that contains default definitions

Int_t fDropVariable
for ranking test

Double_t fEpsilon
minimum number of likelihood (to avoid zero)

std::vector< TH1 * > * fHistBgd
background PDFs (histograms)

std::vector< TH1 * > * fHistBgd_smooth
background PDFs (smoothed histograms)

std::vector< TH1 * > * fHistSig
signal PDFs (histograms)

std::vector< TH1 * > * fHistSig_smooth
signal PDFs (smoothed histograms)

TStringfInterpolateString
which interpolation method used for reference histograms (individual for each variable)

Float_t fKDEfineFactor
fine tuning factor for Adaptive KDE

TString fKDEiterString
Number of iterations (string)

TString fKDEtypeString
Kernel type to use for KDE (string)

Int_t fNsmooth
number of smooth passes

Int_tfNsmoothVarB
number of smooth passes

Int_tfNsmoothVarS
number of smooth passes

std::vector< PDF * > * fPDFBgd
list of PDFs (background)

std::vector< PDF * > * fPDFSig
list of PDFs (signal)

Bool_t fTransformLikelihoodOutput
likelihood output is sigmoid-transformed

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 ,
}

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)

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

const EventfTmpEvent

Protected Attributes inherited from TMVA::Configurable
MsgLoggerfLogger
! message logger

Protected Attributes inherited from TNamed
TString fName

TString fTitle

#include <TMVA/MethodLikelihood.h>

Inheritance diagram for TMVA::MethodLikelihood:
[legend]

## ◆ MethodLikelihood() [1/2]

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

standard constructor

Definition at line 142 of file MethodLikelihood.cxx.

## ◆ MethodLikelihood() [2/2]

 TMVA::MethodLikelihood::MethodLikelihood ( DataSetInfo & theData, const TString & theWeightFile )

construct likelihood references from file

Definition at line 171 of file MethodLikelihood.cxx.

## ◆ ~MethodLikelihood()

 TMVA::MethodLikelihood::~MethodLikelihood ( void )
virtual

destructor

Definition at line 198 of file MethodLikelihood.cxx.

## Member Function Documentation

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

write weights to XML

Implements TMVA::MethodBase.

Definition at line 583 of file MethodLikelihood.cxx.

## ◆ Class()

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

## ◆ Class_Name()

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

## ◆ Class_Version()

 static constexpr Version_t TMVA::MethodLikelihood::Class_Version ( )
inlinestaticconstexpr
Returns
Version of this class

Definition at line 154 of file MethodLikelihood.h.

## ◆ CreateRanking()

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

computes ranking of input variables

Implements TMVA::MethodBase.

Definition at line 607 of file MethodLikelihood.cxx.

## ◆ DeclareCompatibilityOptions()

 void TMVA::MethodLikelihood::DeclareCompatibilityOptions ( )
protectedvirtual

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

Reimplemented from TMVA::MethodBase.

Definition at line 274 of file MethodLikelihood.cxx.

## ◆ DeclareOptions()

 void TMVA::MethodLikelihood::DeclareOptions ( )
privatevirtual

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

TransformOutput <bool> transform (often strongly peaked) likelihood output through sigmoid inversion

Implements TMVA::MethodBase.

Definition at line 243 of file MethodLikelihood.cxx.

## ◆ DeclFileName()

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

Definition at line 154 of file MethodLikelihood.h.

## ◆ GetHelpMessage()

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

get help message text

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

Implements TMVA::IMethod.

Definition at line 997 of file MethodLikelihood.cxx.

## ◆ GetMvaValue()

 Double_t TMVA::MethodLikelihood::GetMvaValue ( Double_t * err = nullptr, Double_t * errUpper = nullptr )
virtual

returns the likelihood estimator for signal fill a new Likelihood branch into the testTree

Implements TMVA::MethodBase.

Definition at line 453 of file MethodLikelihood.cxx.

## ◆ HasAnalysisType()

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

FDA can handle classification with 2 classes.

Implements TMVA::IMethod.

Definition at line 216 of file MethodLikelihood.cxx.

## ◆ Init()

 void TMVA::MethodLikelihood::Init ( void )
privatevirtual

default initialisation called by all constructors

Implements TMVA::MethodBase.

Definition at line 226 of file MethodLikelihood.cxx.

## ◆ IsA()

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

Reimplemented from TMVA::MethodBase.

Definition at line 154 of file MethodLikelihood.h.

## ◆ MakeClassSpecific()

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

write specific classifier response

Reimplemented from TMVA::MethodBase.

Definition at line 793 of file MethodLikelihood.cxx.

 void TMVA::MethodLikelihood::MakeClassSpecificHeader ( std::ostream & fout, const TString & = "" ) const
protectedvirtual

write specific header of the classifier (mostly include files)

Reimplemented from TMVA::MethodBase.

Definition at line 784 of file MethodLikelihood.cxx.

## ◆ ProcessOptions()

 void TMVA::MethodLikelihood::ProcessOptions ( )
privatevirtual

process user options reference cut value to distinguish signal-like from background-like events

Implements TMVA::MethodBase.

Definition at line 316 of file MethodLikelihood.cxx.

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

read weight info from file nothing to do for this method

Implements TMVA::MethodBase.

Definition at line 699 of file MethodLikelihood.cxx.

 void TMVA::MethodLikelihood::ReadWeightsFromStream ( TFile & istr )
virtual

read reference PDF from ROOT file

Reimplemented from TMVA::MethodBase.

Definition at line 721 of file MethodLikelihood.cxx.

 void TMVA::MethodLikelihood::ReadWeightsFromXML ( void * wghtnode )
virtual

Implements TMVA::MethodBase.

Definition at line 669 of file MethodLikelihood.cxx.

## ◆ Streamer()

 virtual void TMVA::MethodLikelihood::Streamer ( TBuffer & )
virtual

Reimplemented from TMVA::MethodBase.

## ◆ StreamerNVirtual()

 void TMVA::MethodLikelihood::StreamerNVirtual ( TBuffer & ClassDef_StreamerNVirtual_b )
inline

Definition at line 154 of file MethodLikelihood.h.

## ◆ Train()

 void TMVA::MethodLikelihood::Train ( void )
virtual

create reference distributions (PDFs) from signal and background events: fill histograms and smooth them; if decorrelation is required, compute corresponding square-root matrices the reference histograms require the correct boundaries.

Since in Likelihood classification the transformations are applied using both classes, also the corresponding boundaries need to take this into account

Implements TMVA::MethodBase.

Definition at line 335 of file MethodLikelihood.cxx.

## ◆ TransformLikelihoodOutput()

 Double_t TMVA::MethodLikelihood::TransformLikelihoodOutput ( Double_t ps, Double_t pb ) const
private

returns transformed or non-transformed output

Definition at line 535 of file MethodLikelihood.cxx.

## ◆ WriteMonitoringHistosToFile()

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

write histograms and PDFs to file for monitoring purposes

Reimplemented from TMVA::MethodBase.

Definition at line 736 of file MethodLikelihood.cxx.

## ◆ WriteOptionsToStream()

 void TMVA::MethodLikelihood::WriteOptionsToStream ( std::ostream & o, const TString & prefix ) const
virtual

write options to stream

Definition at line 559 of file MethodLikelihood.cxx.

## ◆ WriteWeightsToStream()

 void TMVA::MethodLikelihood::WriteWeightsToStream ( TFile & rf ) const

write reference PDFs to ROOT file

Definition at line 657 of file MethodLikelihood.cxx.

## ◆ fAverageEvtPerBin

 Int_t TMVA::MethodLikelihood::fAverageEvtPerBin
private

average events per bin; used to calculate fNbins

Definition at line 145 of file MethodLikelihood.h.

## ◆ fAverageEvtPerBinVarB

 Int_t* TMVA::MethodLikelihood::fAverageEvtPerBinVarB
private

average events per bin; used to calculate fNbins

Definition at line 147 of file MethodLikelihood.h.

## ◆ fAverageEvtPerBinVarS

 Int_t* TMVA::MethodLikelihood::fAverageEvtPerBinVarS
private

average events per bin; used to calculate fNbins

Definition at line 146 of file MethodLikelihood.h.

## ◆ fBorderMethodString

 TString TMVA::MethodLikelihood::fBorderMethodString
private

the method to take care about "border" effects (string)

Definition at line 148 of file MethodLikelihood.h.

## ◆ fDefaultPDFLik

 PDF* TMVA::MethodLikelihood::fDefaultPDFLik
private

pdf that contains default definitions

Definition at line 135 of file MethodLikelihood.h.

## ◆ fDropVariable

 Int_t TMVA::MethodLikelihood::fDropVariable
private

for ranking test

Definition at line 128 of file MethodLikelihood.h.

## ◆ fEpsilon

 Double_t TMVA::MethodLikelihood::fEpsilon
private

minimum number of likelihood (to avoid zero)

Definition at line 125 of file MethodLikelihood.h.

## ◆ fHistBgd

 std::vector* TMVA::MethodLikelihood::fHistBgd
private

background PDFs (histograms)

Definition at line 131 of file MethodLikelihood.h.

## ◆ fHistBgd_smooth

 std::vector* TMVA::MethodLikelihood::fHistBgd_smooth
private

background PDFs (smoothed histograms)

Definition at line 133 of file MethodLikelihood.h.

## ◆ fHistSig

 std::vector* TMVA::MethodLikelihood::fHistSig
private

signal PDFs (histograms)

Definition at line 130 of file MethodLikelihood.h.

## ◆ fHistSig_smooth

 std::vector* TMVA::MethodLikelihood::fHistSig_smooth
private

signal PDFs (smoothed histograms)

Definition at line 132 of file MethodLikelihood.h.

## ◆ fInterpolateString

 TString* TMVA::MethodLikelihood::fInterpolateString
private

which interpolation method used for reference histograms (individual for each variable)

Definition at line 152 of file MethodLikelihood.h.

## ◆ fKDEfineFactor

 Float_t TMVA::MethodLikelihood::fKDEfineFactor
private

fine tuning factor for Adaptive KDE

Definition at line 149 of file MethodLikelihood.h.

## ◆ fKDEiterString

 TString TMVA::MethodLikelihood::fKDEiterString
private

Number of iterations (string)

Definition at line 150 of file MethodLikelihood.h.

## ◆ fKDEtypeString

 TString TMVA::MethodLikelihood::fKDEtypeString
private

Kernel type to use for KDE (string)

Definition at line 151 of file MethodLikelihood.h.

## ◆ fNsmooth

 Int_t TMVA::MethodLikelihood::fNsmooth
private

number of smooth passes

Definition at line 142 of file MethodLikelihood.h.

## ◆ fNsmoothVarB

 Int_t* TMVA::MethodLikelihood::fNsmoothVarB
private

number of smooth passes

Definition at line 144 of file MethodLikelihood.h.

## ◆ fNsmoothVarS

 Int_t* TMVA::MethodLikelihood::fNsmoothVarS
private

number of smooth passes

Definition at line 143 of file MethodLikelihood.h.

## ◆ fPDFBgd

 std::vector* TMVA::MethodLikelihood::fPDFBgd
private

list of PDFs (background)

Definition at line 137 of file MethodLikelihood.h.

## ◆ fPDFSig

 std::vector* TMVA::MethodLikelihood::fPDFSig
private

list of PDFs (signal)

Definition at line 136 of file MethodLikelihood.h.

## ◆ fTransformLikelihoodOutput

 Bool_t TMVA::MethodLikelihood::fTransformLikelihoodOutput
private

likelihood output is sigmoid-transformed

Definition at line 126 of file MethodLikelihood.h.

Libraries for TMVA::MethodLikelihood:

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