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 \( R_L = 0.1 \) .
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 Ranking * | CreateRanking () |
computes ranking of input variables | |
Double_t | GetMvaValue (Double_t *err=0, Double_t *errUpper=0) |
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. | |
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 | |
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 | |
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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) |
TDirectory * | BaseDir () 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) | |
DataSet * | Data () const |
DataSetInfo & | DataInfo () 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 Event * | GetEvent () const |
const Event * | GetEvent (const TMVA::Event *ev) const |
const Event * | GetEvent (Long64_t ievt) const |
const Event * | GetEvent (Long64_t ievt, Types::ETreeType type) const |
const std::vector< TMVA::Event * > & | GetEventCollection (Types::ETreeType type) |
returns the event collection (i.e. | |
TFile * | GetFile () const |
const TString & | GetInputLabel (Int_t i) const |
const char * | GetInputTitle (Int_t i) const |
const TString & | GetInputVar (Int_t i) const |
TMultiGraph * | GetInteractiveTrainingError () |
const TString & | GetJobName () 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 TString & | GetMethodName () 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_t > | GetMulticlassEfficiency (std::vector< std::vector< Float_t > > &purity) |
virtual std::vector< Float_t > | GetMulticlassTrainingEfficiency (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=0, Double_t *errUpper=0) |
const char * | GetName () const |
UInt_t | GetNEvents () const |
temporary event when testing on a different DataSet than the own one | |
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=0, PDF *pdfB=0) 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=0, PDF *pdfB=0) 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 Event * | GetTestingEvent (Long64_t ievt) const |
Double_t | GetTestTime () const |
const TString & | GetTestvarName () const |
virtual Double_t | GetTrainingEfficiency (const TString &) |
const Event * | GetTrainingEvent (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 |
TransformationHandler & | GetTransformationHandler (Bool_t takeReroutedIfAvailable=true) |
const TransformationHandler & | GetTransformationHandler (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) | |
TDirectory * | MethodBaseDir () const |
returns the ROOT directory where all instances of the corresponding MVA method are stored | |
virtual std::map< TString, Double_t > | OptimizeTuningParameters (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 | |
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 | |
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IMethod () | |
virtual | ~IMethod () |
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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::OptionBase * | DeclareOptionRef (T &ref, const TString &name, const TString &desc) |
template<class T > | |
OptionBase * | DeclareOptionRef (T &ref, const TString &name, const TString &desc="") |
template<class T > | |
TMVA::OptionBase * | DeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc) |
template<class T > | |
OptionBase * | DeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc="") |
const char * | GetConfigDescription () const |
const char * | GetConfigName () const |
const TString & | GetOptions () const |
MsgLogger & | Log () 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 | WriteOptionsToStream (std::ostream &o, const TString &prefix) const |
write options to output stream (e.g. in writing the MVA weight files | |
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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. | |
virtual void | Clear (Option_t *option="") |
Set name and title to empty strings (""). | |
virtual TObject * | Clone (const char *newname="") const |
Make a clone of an object using the Streamer facility. | |
virtual Int_t | Compare (const TObject *obj) const |
Compare two TNamed objects. | |
virtual void | Copy (TObject &named) const |
Copy this to obj. | |
virtual void | FillBuffer (char *&buffer) |
Encode TNamed into output buffer. | |
virtual const char * | GetTitle () const |
Returns title of object. | |
virtual ULong_t | Hash () const |
Return hash value for this object. | |
virtual Bool_t | IsSortable () const |
virtual void | ls (Option_t *option="") const |
List TNamed name and title. | |
TNamed & | operator= (const TNamed &rhs) |
TNamed assignment operator. | |
virtual void | Print (Option_t *option="") const |
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. | |
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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 TObject * | DrawClone (Option_t *option="") const |
Draw a clone of this object in the current selected pad for instance with: gROOT->SetSelectedPad(gPad) . | |
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=0) |
Execute method on this object with the given parameter string, e.g. | |
virtual void | Execute (TMethod *method, TObjArray *params, Int_t *error=0) |
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 TObject * | FindObject (const char *name) const |
Must be redefined in derived classes. | |
virtual TObject * | FindObject (const TObject *obj) const |
Must be redefined in derived classes. | |
virtual Option_t * | GetDrawOption () 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_t * | GetOption () 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) |
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. | |
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) |
TObject & | operator= (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. | |
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=0, Int_t option=0, Int_t bufsize=0) |
Write this object to the current directory. | |
virtual Int_t | Write (const char *name=0, Int_t option=0, Int_t bufsize=0) const |
Write this object to the current directory. | |
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) | |
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const TString & | GetInternalVarName (Int_t ivar) const |
virtual std::vector< Double_t > | GetMvaValues (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 TString & | GetOriginalVarName (Int_t ivar) const |
const TString & | GetWeightFileDir () 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 |
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void | EnableLooseOptions (Bool_t b=kTRUE) |
const TString & | GetReferenceFile () 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 | |
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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 |
Int_t * | fAverageEvtPerBinVarB |
Int_t * | fAverageEvtPerBinVarS |
TString | fBorderMethodString |
PDF * | fDefaultPDFLik |
Int_t | fDropVariable |
Double_t | fEpsilon |
std::vector< TH1 * > * | fHistBgd |
std::vector< TH1 * > * | fHistBgd_smooth |
std::vector< TH1 * > * | fHistSig |
std::vector< TH1 * > * | fHistSig_smooth |
TString * | fInterpolateString |
Float_t | fKDEfineFactor |
TString | fKDEiterString |
TString | fKDEtypeString |
Int_t | fNsmooth |
Int_t * | fNsmoothVarB |
Int_t * | fNsmoothVarS |
std::vector< PDF * > * | fPDFBgd |
std::vector< PDF * > * | fPDFSig |
Bool_t | fTransformLikelihoodOutput |
Additional Inherited Members | |
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enum | EWeightFileType { kROOT =0 , kTEXT } |
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enum | { kIsOnHeap = 0x01000000 , kNotDeleted = 0x02000000 , kZombie = 0x04000000 , kInconsistent = 0x08000000 , kBitMask = 0x00ffffff } |
enum | { kSingleKey = BIT(0) , kOverwrite = BIT(1) , kWriteDelete = BIT(2) } |
enum | EDeprecatedStatusBits { kObjInCanvas = BIT(3) } |
enum | EStatusBits { kCanDelete = BIT(0) , kMustCleanup = BIT(3) , kIsReferenced = BIT(4) , kHasUUID = BIT(5) , kCannotPick = BIT(6) , kNoContextMenu = BIT(8) , kInvalidObject = BIT(13) } |
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static Long_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. | |
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Bool_t | fSetupCompleted |
const Event * | fTmpEvent |
TrainingHistory | fTrainHistory |
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enum | { kOnlyPrepStep = BIT(3) } |
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Types::EAnalysisType | fAnalysisType |
UInt_t | fBackgroundClass |
bool | fExitFromTraining = false |
std::vector< TString > * | fInputVars |
IPythonInteractive * | fInteractive = nullptr |
UInt_t | fIPyCurrentIter = 0 |
UInt_t | fIPyMaxIter = 0 |
std::vector< Float_t > * | fMulticlassReturnVal |
Int_t | fNbins |
Int_t | fNbinsH |
Int_t | fNbinsMVAoutput |
Ranking * | fRanking |
std::vector< Float_t > * | fRegressionReturnVal |
Results * | fResults |
UInt_t | fSignalClass |
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MsgLogger * | fLogger |
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TString | fName |
TString | fTitle |
#include <TMVA/MethodLikelihood.h>
TMVA::MethodLikelihood::MethodLikelihood | ( | const TString & | jobName, |
const TString & | methodTitle, | ||
DataSetInfo & | theData, | ||
const TString & | theOption = "" |
||
) |
standard constructor
Definition at line 142 of file MethodLikelihood.cxx.
TMVA::MethodLikelihood::MethodLikelihood | ( | DataSetInfo & | theData, |
const TString & | theWeightFile | ||
) |
construct likelihood references from file
Definition at line 171 of file MethodLikelihood.cxx.
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virtual |
destructor
Definition at line 198 of file MethodLikelihood.cxx.
write weights to XML
Implements TMVA::MethodBase.
Definition at line 581 of file MethodLikelihood.cxx.
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virtual |
computes ranking of input variables
Implements TMVA::MethodBase.
Definition at line 605 of file MethodLikelihood.cxx.
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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.
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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.
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protectedvirtual |
get help message text
typical length of text line: "|--------------------------------------------------------------|"
Implements TMVA::IMethod.
Definition at line 995 of file MethodLikelihood.cxx.
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virtual |
returns the likelihood estimator for signal fill a new Likelihood branch into the testTree
Implements TMVA::MethodBase.
Definition at line 451 of file MethodLikelihood.cxx.
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virtual |
FDA can handle classification with 2 classes.
Implements TMVA::IMethod.
Definition at line 216 of file MethodLikelihood.cxx.
default initialisation called by all constructors
Implements TMVA::MethodBase.
Definition at line 226 of file MethodLikelihood.cxx.
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protectedvirtual |
write specific classifier response
Reimplemented from TMVA::MethodBase.
Definition at line 791 of file MethodLikelihood.cxx.
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protectedvirtual |
write specific header of the classifier (mostly include files)
Reimplemented from TMVA::MethodBase.
Definition at line 782 of file MethodLikelihood.cxx.
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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.
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virtual |
read weight info from file nothing to do for this method
Implements TMVA::MethodBase.
Definition at line 697 of file MethodLikelihood.cxx.
read reference PDF from ROOT file
Reimplemented from TMVA::MethodBase.
Definition at line 719 of file MethodLikelihood.cxx.
read weights from XML
Implements TMVA::MethodBase.
Definition at line 667 of file MethodLikelihood.cxx.
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.
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private |
returns transformed or non-transformed output
Definition at line 533 of file MethodLikelihood.cxx.
write histograms and PDFs to file for monitoring purposes
Reimplemented from TMVA::MethodBase.
Definition at line 734 of file MethodLikelihood.cxx.
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virtual |
write options to stream
Definition at line 557 of file MethodLikelihood.cxx.
write reference PDFs to ROOT file
Definition at line 655 of file MethodLikelihood.cxx.
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private |
Definition at line 145 of file MethodLikelihood.h.
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private |
Definition at line 147 of file MethodLikelihood.h.
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private |
Definition at line 146 of file MethodLikelihood.h.
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private |
Definition at line 148 of file MethodLikelihood.h.
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private |
Definition at line 135 of file MethodLikelihood.h.
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private |
Definition at line 128 of file MethodLikelihood.h.
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private |
Definition at line 125 of file MethodLikelihood.h.
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private |
Definition at line 131 of file MethodLikelihood.h.
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private |
Definition at line 133 of file MethodLikelihood.h.
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private |
Definition at line 130 of file MethodLikelihood.h.
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private |
Definition at line 132 of file MethodLikelihood.h.
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private |
Definition at line 152 of file MethodLikelihood.h.
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private |
Definition at line 149 of file MethodLikelihood.h.
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private |
Definition at line 150 of file MethodLikelihood.h.
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private |
Definition at line 151 of file MethodLikelihood.h.
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private |
Definition at line 142 of file MethodLikelihood.h.
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private |
Definition at line 144 of file MethodLikelihood.h.
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private |
Definition at line 143 of file MethodLikelihood.h.
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private |
Definition at line 137 of file MethodLikelihood.h.
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private |
Definition at line 136 of file MethodLikelihood.h.
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private |
Definition at line 126 of file MethodLikelihood.h.