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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
 
void AddWeightsXMLTo (void *parent) const
 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
 
void ReadWeightsFromStream (std::istream &istr)
 read weight info from file nothing to do for this method
 
void ReadWeightsFromStream (TFile &istr)
 read reference PDF from ROOT file
 
void ReadWeightsFromXML (void *wghtnode)
 read weights from XML
 
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)
 
void ReadStateFromFile ()
 Function to write options and weights to file.
 
void ReadStateFromStream (std::istream &tf)
 read the header from the weight files of the different MVA methods
 
void ReadStateFromStream (TFile &rf)
 write reference MVA distributions (and other information) to a ROOT type weight file
 
void ReadStateFromXMLString (const char *xmlstr)
 for reading from memory
 
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
 
void AddOptionsXMLTo (void *parent) const
 write options to XML file
 
template<class T >
void AddPreDefVal (const 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
 
void ReadOptionsFromStream (std::istream &istr)
 read option back from the weight file
 
void ReadOptionsFromXML (void *node)
 
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.
 
virtual void AppendPad (Option_t *option="")
 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, void *vp)
 Only called by placement new when throwing an exception.
 
void operator delete[] (void *ptr)
 Operator delete [].
 
void operator delete[] (void *ptr, void *vp)
 Only called by placement new[] when throwing an exception.
 
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
 

Additional Inherited Members

- 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 ,
  kBitMask = 0x00ffffff
}
 
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]

Constructor & Destructor Documentation

◆ 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

◆ AddWeightsXMLTo()

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.

◆ MakeClassSpecificHeader()

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.

◆ ReadWeightsFromStream() [1/2]

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.

◆ ReadWeightsFromStream() [2/2]

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.

◆ ReadWeightsFromXML()

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

read weights from XML

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.

Member Data Documentation

◆ 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<TH1*>* TMVA::MethodLikelihood::fHistBgd
private

background PDFs (histograms)

Definition at line 131 of file MethodLikelihood.h.

◆ fHistBgd_smooth

std::vector<TH1*>* TMVA::MethodLikelihood::fHistBgd_smooth
private

background PDFs (smoothed histograms)

Definition at line 133 of file MethodLikelihood.h.

◆ fHistSig

std::vector<TH1*>* TMVA::MethodLikelihood::fHistSig
private

signal PDFs (histograms)

Definition at line 130 of file MethodLikelihood.h.

◆ fHistSig_smooth

std::vector<TH1*>* 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<PDF*>* TMVA::MethodLikelihood::fPDFBgd
private

list of PDFs (background)

Definition at line 137 of file MethodLikelihood.h.

◆ fPDFSig

std::vector<PDF*>* 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: