class TMVA::MethodBase: public TMVA::IMethod, public TMVA::Configurable


Virtual base Class for all MVA method MethodBase hosts several specific evaluation methods. The kind of MVA that provides optimal performance in an analysis strongly depends on the particular application. The evaluation factory provides a number of numerical benchmark results to directly assess the performance of the MVA training on the independent test sample. These are: The MVA standard output also prints the linear correlation coefficients between signal and background, which can be useful to eliminate variables that exhibit too strong correlations.
 

Function Members (Methods)

 
    This is an abstract class, constructors will not be documented.
    Look at the header to check for available constructors.

public:
virtual~MethodBase()
voidTObject::AbstractMethod(const char* method) const
voidTObject::AbstractMethod(const char* method) const
virtual voidAddClassifierToTestTree(TTree* theTestTree)
virtual voidTObject::AppendPad(Option_t* option = "")
virtual voidTObject::AppendPad(Option_t* option = "")
TDirectory*BaseDir() const
virtual voidTObject::Browse(TBrowser* b)
virtual voidTObject::Browse(TBrowser* b)
voidTMVA::Configurable::CheckForUnusedOptions() const
static TClass*Class()
virtual const char*TObject::ClassName() const
virtual const char*TObject::ClassName() const
virtual voidTObject::Clear(Option_t* = "")
virtual voidTObject::Clear(Option_t* = "")
virtual TObject*TObject::Clone(const char* newname = "") const
virtual TObject*TObject::Clone(const char* newname = "") const
virtual Int_tTObject::Compare(const TObject* obj) const
virtual Int_tTObject::Compare(const TObject* obj) const
TMVA::ConfigurableTMVA::Configurable::Configurable(const TString& theOption = "")
virtual voidTObject::Copy(TObject& object) const
virtual voidTObject::Copy(TObject& object) const
virtual const TMVA::Ranking*CreateRanking()
TMVA::DataSet&Data() const
virtual voidTObject::Delete(Option_t* option = "")MENU
virtual voidTObject::Delete(Option_t* option = "")MENU
virtual Int_tTObject::DistancetoPrimitive(Int_t px, Int_t py)
virtual Int_tTObject::DistancetoPrimitive(Int_t px, Int_t py)
virtual voidTObject::Draw(Option_t* option = "")
virtual voidTObject::Draw(Option_t* option = "")
virtual voidTObject::DrawClass() constMENU
virtual voidTObject::DrawClass() constMENU
virtual TObject*TObject::DrawClone(Option_t* option = "") constMENU
virtual TObject*TObject::DrawClone(Option_t* option = "") constMENU
virtual voidTObject::Dump() constMENU
virtual voidTObject::Dump() constMENU
virtual voidTObject::Error(const char* method, const char* msgfmt) const
virtual voidTObject::Error(const char* method, const char* msgfmt) const
virtual voidTObject::Execute(const char* method, const char* params, Int_t* error = 0)
virtual voidTObject::Execute(TMethod* method, TObjArray* params, Int_t* error = 0)
virtual voidTObject::Execute(const char* method, const char* params, Int_t* error = 0)
virtual voidTObject::Execute(TMethod* method, TObjArray* params, Int_t* error = 0)
virtual voidTObject::ExecuteEvent(Int_t event, Int_t px, Int_t py)
virtual voidTObject::ExecuteEvent(Int_t event, Int_t px, Int_t py)
virtual voidTObject::Fatal(const char* method, const char* msgfmt) const
virtual voidTObject::Fatal(const char* method, const char* msgfmt) const
virtual TObject*TObject::FindObject(const char* name) const
virtual TObject*TObject::FindObject(const TObject* obj) const
virtual TObject*TObject::FindObject(const char* name) const
virtual TObject*TObject::FindObject(const TObject* obj) const
const char*TMVA::Configurable::GetConfigDescription() const
const char*TMVA::Configurable::GetConfigName() const
virtual Option_t*TObject::GetDrawOption() const
virtual Option_t*TObject::GetDrawOption() const
static Long_tTObject::GetDtorOnly()
static Long_tTObject::GetDtorOnly()
virtual Double_tGetEfficiency(TString, TTree*, Double_t& err)
TMVA::Event&GetEvent() const
Double_tGetEventVal(Int_t ivar) const
Double_tGetEventValNormalised(Int_t ivar) const
Double_tGetEventWeight() const
virtual voidTMVA::IMethod::GetHelpMessage() const
virtual const char*TObject::GetIconName() const
virtual const char*TObject::GetIconName() const
const TString&GetInputExp(int i) const
const TString&GetInputVar(int i) const
const TString&GetJobName() const
virtual Double_tGetMaximumSignificance(Double_t SignalEvents, Double_t BackgroundEvents, Double_t& optimal_significance_value) const
const TString&GetMethodName() const
const TString&GetMethodTitle() const
TMVA::Types::EMVAGetMethodType() const
virtual Double_tGetMvaValue()
virtual const char*GetName() const
Int_tGetNvar() const
virtual char*TObject::GetObjectInfo(Int_t px, Int_t py) const
virtual char*TObject::GetObjectInfo(Int_t px, Int_t py) const
static Bool_tTObject::GetObjectStat()
static Bool_tTObject::GetObjectStat()
virtual Option_t*TObject::GetOption() const
virtual Option_t*TObject::GetOption() const
const TString&TMVA::Configurable::GetOptions() const
virtual Double_tGetProba(Double_t mvaVal, Double_t ap_sig)
const TStringGetProbaName() const
virtual Double_tGetRarity(Double_t mvaVal, TMVA::Types::ESBType reftype = Types::kBackground) const
Double_tGetRMS(Int_t ivar) const
virtual Double_tGetSeparation(TH1*, TH1*) const
virtual Double_tGetSeparation(TMVA::PDF* pdfS = 0, TMVA::PDF* pdfB = 0) const
Double_tGetSignalReferenceCut() const
virtual Double_tGetSignificance() const
const TString&GetTestvarName() const
virtual const char*TObject::GetTitle() const
virtual const char*TObject::GetTitle() const
virtual Double_tGetTrainingEfficiency(TString)
UInt_tGetTrainingROOTVersionCode() const
TStringGetTrainingROOTVersionString() const
UInt_tGetTrainingTMVAVersionCode() const
TStringGetTrainingTMVAVersionString() const
virtual UInt_tTObject::GetUniqueID() const
virtual UInt_tTObject::GetUniqueID() const
TMVA::VariableTransformBase&GetVarTransform() const
Double_tGetXmax(Int_t ivar) const
Double_tGetXmin(Int_t ivar) const
virtual Bool_tTObject::HandleTimer(TTimer* timer)
virtual Bool_tTObject::HandleTimer(TTimer* timer)
virtual ULong_tTObject::Hash() const
virtual ULong_tTObject::Hash() const
virtual voidTObject::Info(const char* method, const char* msgfmt) const
virtual voidTObject::Info(const char* method, const char* msgfmt) const
virtual Bool_tTObject::InheritsFrom(const char* classname) const
virtual Bool_tTObject::InheritsFrom(const TClass* cl) const
virtual Bool_tTObject::InheritsFrom(const char* classname) const
virtual Bool_tTObject::InheritsFrom(const TClass* cl) const
virtual voidTObject::Inspect() constMENU
virtual voidTObject::Inspect() constMENU
voidTObject::InvertBit(UInt_t f)
voidTObject::InvertBit(UInt_t f)
virtual TClass*IsA() const
virtual Bool_tTObject::IsEqual(const TObject* obj) const
virtual Bool_tTObject::IsEqual(const TObject* obj) const
virtual Bool_tTObject::IsFolder() const
virtual Bool_tTObject::IsFolder() const
Bool_tTObject::IsOnHeap() const
Bool_tTObject::IsOnHeap() const
Bool_tIsSignalEvent() const
virtual Bool_tIsSignalLike()
virtual Bool_tTObject::IsSortable() const
virtual Bool_tTObject::IsSortable() const
Bool_tTObject::IsZombie() const
Bool_tTObject::IsZombie() const
virtual voidTObject::ls(Option_t* option = "") const
virtual voidTObject::ls(Option_t* option = "") const
virtual voidMakeClass(const TString& classFileName = "") const
voidTObject::MayNotUse(const char* method) const
voidTObject::MayNotUse(const char* method) const
TDirectory*MethodBaseDir() const
virtual Bool_tTObject::Notify()
virtual Bool_tTObject::Notify()
static voidTObject::operator delete(void* ptr)
static voidTObject::operator delete(void* ptr)
static voidTObject::operator delete(void* ptr, void* vp)
static voidTObject::operator delete(void* ptr, void* vp)
static voidTObject::operator delete[](void* ptr)
static voidTObject::operator delete[](void* ptr)
static voidTObject::operator delete[](void* ptr, void* vp)
static voidTObject::operator delete[](void* ptr, void* vp)
void*TObject::operator new(size_t sz)
void*TObject::operator new(size_t sz)
void*TObject::operator new(size_t sz, void* vp)
void*TObject::operator new(size_t sz, void* vp)
void*TObject::operator new[](size_t sz)
void*TObject::operator new[](size_t sz)
void*TObject::operator new[](size_t sz, void* vp)
void*TObject::operator new[](size_t sz, void* vp)
TMVA::IMethod&TMVA::IMethod::operator=(const TMVA::IMethod&)
virtual voidTObject::Paint(Option_t* option = "")
virtual voidTObject::Paint(Option_t* option = "")
voidTMVA::Configurable::ParseOptions(Bool_t verbose = kTRUE)
virtual voidTObject::Pop()
virtual voidTObject::Pop()
virtual voidTObject::Print(Option_t* option = "") const
virtual voidTObject::Print(Option_t* option = "") const
virtual voidPrintHelpMessage() const
voidTMVA::Configurable::PrintOptions() const
virtual Int_tTObject::Read(const char* name)
virtual Int_tTObject::Read(const char* name)
Bool_tReadEvent(TTree* tr, UInt_t ievt, TMVA::Types::ESBType type = Types::kMaxSBType) const
voidReadStateFromFile()
voidReadStateFromStream(istream& tf)
voidReadStateFromStream(TFile& rf)
Bool_tReadTestEvent(UInt_t ievt, TMVA::Types::ESBType type = Types::kMaxSBType) const
Bool_tReadTrainingEvent(UInt_t ievt, TMVA::Types::ESBType type = Types::kMaxSBType) const
virtual voidReadWeightsFromStream(istream& tf)
virtual voidReadWeightsFromStream(TFile&)
virtual voidTObject::RecursiveRemove(TObject* obj)
virtual voidTObject::RecursiveRemove(TObject* obj)
voidTObject::ResetBit(UInt_t f)
voidTObject::ResetBit(UInt_t f)
virtual voidTObject::SaveAs(const char* filename = "", Option_t* option = "") constMENU
virtual voidTObject::SaveAs(const char* filename = "", Option_t* option = "") constMENU
virtual voidTObject::SavePrimitive(basic_ostream<char,char_traits<char> >& out, Option_t* option = "")
virtual voidTObject::SavePrimitive(basic_ostream<char,char_traits<char> >& out, Option_t* option = "")
voidTObject::SetBit(UInt_t f)
voidTObject::SetBit(UInt_t f)
voidTObject::SetBit(UInt_t f, Bool_t set)
voidTObject::SetBit(UInt_t f, Bool_t set)
voidTMVA::Configurable::SetConfigDescription(const char* d)
voidTMVA::Configurable::SetConfigName(const char* n)
virtual voidTObject::SetDrawOption(Option_t* option = "")MENU
virtual voidTObject::SetDrawOption(Option_t* option = "")MENU
static voidTObject::SetDtorOnly(void* obj)
static voidTObject::SetDtorOnly(void* obj)
voidSetMethodName(TString methodName)
voidSetMethodTitle(TString methodTitle)
voidSetMethodType(TMVA::Types::EMVA methodType)
static voidTObject::SetObjectStat(Bool_t stat)
static voidTObject::SetObjectStat(Bool_t stat)
voidTMVA::Configurable::SetOptions(const TString& s)
voidSetTestvarName(const TString& v = "")
voidSetTestvarPrefix(TString prefix)
virtual voidTObject::SetUniqueID(UInt_t uid)
virtual voidTObject::SetUniqueID(UInt_t uid)
virtual voidShowMembers(TMemberInspector& insp, char* parent)
virtual voidStreamer(TBuffer& b)
voidStreamerNVirtual(TBuffer& b)
virtual voidTObject::SysError(const char* method, const char* msgfmt) const
virtual voidTObject::SysError(const char* method, const char* msgfmt) const
virtual voidTest(TTree* theTestTree = 0)
Bool_tTObject::TestBit(UInt_t f) const
Bool_tTObject::TestBit(UInt_t f) const
Int_tTObject::TestBits(UInt_t f) const
Int_tTObject::TestBits(UInt_t f) const
virtual voidTrain()
voidTrainMethod()
virtual voidTObject::UseCurrentStyle()
virtual voidTObject::UseCurrentStyle()
virtual voidTObject::Warning(const char* method, const char* msgfmt) const
virtual voidTObject::Warning(const char* method, const char* msgfmt) const
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0)
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0) const
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0)
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0) const
virtual voidWriteEvaluationHistosToFile()
virtual voidWriteMonitoringHistosToFile() const
voidWriteStateToFile() const
voidWriteStateToStream(TFile& rf) const
voidWriteStateToStream(ostream& tf, Bool_t isClass = kFALSE) const
virtual voidWriteWeightsToStream(ostream& tf) const
virtual voidWriteWeightsToStream(TFile&) const
protected:
Bool_tCheckSanity(TTree* theTree = 0)
virtual voidDeclareOptions()
virtual voidTObject::DoError(int level, const char* location, const char* fmt, va_list va) const
virtual voidTObject::DoError(int level, const char* location, const char* fmt, va_list va) const
voidTMVA::Configurable::EnableLooseOptions(Bool_t b = kTRUE)
const TString&GetInternalVarName(Int_t ivar) const
const TString&GetOriginalVarName(Int_t ivar) const
const TString&TMVA::Configurable::GetReferenceFile() const
TTree*GetTestTree() const
static TMVA::MethodBase*GetThisBase()
TTree*GetTrainingTree() const
TMVA::Types::EVariableTransformGetVariableTransform() const
TStringGetWeightFileDir() const
TStringGetWeightFileName() const
Bool_tHasTrainingTree() const
Bool_tHelp() const
Bool_tIsNormalised() const
TDirectory*LocalTDir() const
Bool_tTMVA::Configurable::LooseOptionCheckingEnabled() const
virtual voidMakeClassSpecific(ostream&, const TString& = "") const
virtual voidMakeClassSpecificHeader(ostream&, const TString& = "") const
voidTObject::MakeZombie()
voidTObject::MakeZombie()
virtual voidProcessOptions()
voidTMVA::Configurable::ReadOptionsFromStream(istream& istr)
voidTMVA::Configurable::ResetSetFlag()
voidSetNormalised(Bool_t norm)
voidSetNvar(Int_t n)
voidSetSignalReferenceCut(Double_t cut)
voidSetWeightFileDir(TString fileDir)
voidSetWeightFileName(TString)
voidStatistics(TMVA::Types::ETreeType treeType, const TString& theVarName, Double_t&, Double_t&, Double_t&, Double_t&, Double_t&, Double_t&, Bool_t norm = kFALSE)
Bool_tTxtWeightsOnly() const
Bool_tVerbose() const
voidTMVA::Configurable::WriteOptionsReferenceToFile()
voidTMVA::Configurable::WriteOptionsToStream(ostream& o, const TString& prefix) const
private:
voidCreateMVAPdfs()
TMVA::MethodBase::ECutOrientationGetCutOrientation() const
Double_tGetEffForRoot(Double_t)
Bool_tGetLine(istream& fin, char* buf)
Bool_tHasMVAPdfs() const
static Double_tIGetEffForRoot(Double_t)
voidInit()
voidResetThisBase()

Data Members

public:
enum EWeightFileType { kROOT
kTEXT
};
enum ECutOrientation { kNegative
kPositive
};
enum TObject::EStatusBits { kCanDelete
kMustCleanup
kObjInCanvas
kIsReferenced
kHasUUID
kCannotPick
kNoContextMenu
kInvalidObject
};
enum TObject::[unnamed] { kIsOnHeap
kNotDeleted
kZombie
kBitMask
kSingleKey
kOverwrite
kWriteDelete
};
enum TObject::EStatusBits { kCanDelete
kMustCleanup
kObjInCanvas
kIsReferenced
kHasUUID
kCannotPick
kNoContextMenu
kInvalidObject
};
enum TObject::[unnamed] { kIsOnHeap
kNotDeleted
kZombie
kBitMask
kSingleKey
kOverwrite
kWriteDelete
};
public:
TMVA::MsgLoggerTMVA::Configurable::fLoggermessage logger
protected:
vector<TString>*fInputVarsvector of input variables used in MVA
TMVA::MsgLoggerfLoggermessage logger
Int_tfNbinsnumber of bins in representative histograms
Int_tfNbinsHnumber of bins in evaluation histograms
TMVA::Ranking*fRankingpointer to ranking object (created by derived classifiers)
private:
TDirectory*fBaseDirbase directory for the instance, needed to know where to jump back from localDir
TMVA::MethodBase::ECutOrientationfCutOrientation+1 if Sig>Bkg, -1 otherwise
TMVA::DataSet&fData! the data set
TH1*fEffBefficiency plot (background)
TH1*fEffBvsSbackground efficiency versus signal efficiency
TH1*fEffSefficiency plot (signal)
TStringfFileDirunix sub-directory for weight files (default: "weights")
TGraph*fGraphBgraphs used for splines for efficiency (background)
TGraph*fGraphSgraphs used for splines for efficiency (signal)
TGraph*fGraphTrainBgraphs used for splines for training efficiency (background)
TGraph*fGraphTrainEffBvsSgraphs used for splines for training signal eff. versus background eff.
TGraph*fGraphTrainSgraphs used for splines for training efficiency (signal)
TGraph*fGrapheffBvsSgraphs used for splines for signal eff. versus background eff.
Bool_tfHasMVAPdfsMVA Pdfs are created for this classifier
Bool_tfHelphelp flag
TH1*fHistB_highbinMVA plots used for efficiency calculations (background)
TH1*fHistB_plotbinMVA plots used for graphics representation (background)
TH1*fHistS_highbinMVA plots used for efficiency calculations (signal)
TH1*fHistS_plotbinMVA plots used for graphics representation (signal)
TH1*fHistTrB_plotbinsame plots as above for training sample (check for overtraining)
TH1*fHistTrS_plotbinsame plots as above for training sample (check for overtraining)
TStringfJobNamename of job -> user defined, appears in weight files
TMVA::PDF*fMVAPdfBbackground MVA PDF
TMVA::PDF*fMVAPdfSsignal MVA PDF
Double_tfMeanBmean (background)
Double_tfMeanSmean (signal)
TDirectory*fMethodBaseDirbase directory for the method
TStringfMethodNamename of the method (set in derived class)
TStringfMethodTitleuser-defined title for method (used for weight-file names)
TMVA::Types::EMVAfMethodTypetype of method (set in derived class)
Int_tfNbinsMVAPdfnumber of bins used in histogram that creates PDF
Bool_tfNormalisenormalise input variables
Int_tfNsmoothMVAPdfnumber of times a histogram is smoothed before creating the PDF
Int_tfNvarnumber of input variables
TH1*fProbaB_plotbinP(MVA) plots used for graphics representation (background)
TH1*fProbaS_plotbinP(MVA) plots used for graphics representation (signal)
UInt_tfROOTTrainingVersionROOT version used for training
TH1*fRarityB_plotbinR(MVA) plots used for graphics representation (background)
TH1*fRarityS_plotbinR(MVA) plots used for graphics representation (signal)
TH1*fRejBvsSbackground rejection (=1-eff.) versus signal efficiency
Double_tfRmsBRMS (background)
Double_tfRmsSRMS (signal)
Double_tfSignalReferenceCutminimum requirement on the MVA output to declare an event signal-like
TMVA::PDF*fSplBPDFs of MVA distribution (background)
TMVA::TSpline1*fSplRefBhelper splines for RootFinder (background)
TMVA::TSpline1*fSplRefShelper splines for RootFinder (signal)
TMVA::PDF*fSplSPDFs of MVA distribution (signal)
TMVA::PDF*fSplTrainBPDFs of training MVA distribution (background)
TSpline*fSplTrainEffBvsSsplines for training signal eff. versus background eff.
TMVA::TSpline1*fSplTrainRefBhelper splines for RootFinder (background)
TMVA::TSpline1*fSplTrainRefShelper splines for RootFinder (signal)
TMVA::PDF*fSplTrainSPDFs of training MVA distribution (signal)
TSpline*fSpleffBvsSsplines for signal eff. versus background eff.
UInt_tfTMVATrainingVersionTMVA version used for training
TStringfTestvarvariable used in evaluation, etc (mostly the MVA)
TStringfTestvarPrefix'MVA_' prefix of MVA variable
TH1*fTrainEffBTraining efficiency plot (background)
TH1*fTrainEffBvsSTraining background efficiency versus signal efficiency
TH1*fTrainEffSTraining efficiency plot (signal)
TH1*fTrainRejBvsSTraining background rejection (=1-eff.) versus signal efficiency
Bool_tfTxtWeightsOnlyif TRUE, write weights only to text files
Bool_tfUseDecorrkept for backward compatibility
TMVA::VariableTransformBase*fVarTransformthe variable transformer
TStringfVarTransformStringlabels variable transform method
TMVA::Types::EVariableTransformfVariableTransformDecorrelation, PCA, etc.
TMVA::Types::ESBTypefVariableTransformTypethis is the event type (sig or bgd) assumed for variable transform
TStringfVariableTransformTypeStringlabels variable transform type
Bool_tfVerboseverbose flag
TMVA::EMsgTypefVerbosityLevelverbosity level
TStringfVerbosityLevelStringverbosity level (user input string)
TStringfWeightFileweight file name
Double_tfXmaxmaximum (signal and background)
Double_tfXminminimum (signal and background)
static TMVA::MethodBase*fgThisBasethis pointer
TH1*finvBeffvsSeffinverse background eff (1/eff.) versus signal efficiency

Class Charts

Inheritance Inherited Members Includes Libraries
Class Charts

Function documentation

~MethodBase( void )
 destructor
void Init()
 default initialization called by all constructors
void DeclareOptions()
 define the options (their key words) that can be set in the option string
 here the options valid for ALL MVA methods are declared.
 know options: VariableTransform=None,Decorrelated,PCA  to use transformed variables
                                                        instead of the original ones
               VariableTransformType=Signal,Background  which decorrelation matrix to use
                                                        in the method. Only the Likelihood
                                                        Method can make proper use of independent
                                                        transformations of signal and background
               fNbinsMVAPdf   = 50 Number of bins used to create a PDF of MVA
               fNsmoothMVAPdf =  2 Number of times a histogram is smoothed before creating the PDF
               fHasMVAPdfs         create PDFs for the MVA outputs
               V                   for Verbose output (!V) for non verbos
               H                   for Help message
void ProcessOptions()
 the option string is decoded, for availabel options see "DeclareOptions"
void TrainMethod()
 train the classifier method
void AddClassifierToTestTree(TTree* theTestTree)
 prepare tree branch with the method's discriminating variable
void Test(TTree* theTestTree = 0)
 test the method - not much is done here... mainly further initialization
void WriteStateToStream(ostream& tf, Bool_t isClass = kFALSE) const
 general method used in writing the header of the weight files where
 the used variables, variable transformation type etc. is specified
void WriteStateToStream(TFile& rf) const
 write reference MVA distributions (and other information)
 to a ROOT type weight file
void ReadStateFromStream( TFile& rf )
 write reference MVA distributions (and other information)
 to a ROOT type weight file
void WriteStateToFile()
 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
void ReadStateFromFile()
 Function to write options and weights to file
void ReadStateFromStream( std::istream& fin )
 read the header from the weight files of the different MVA methods
TDirectory* BaseDir()
 returns the ROOT directory where info/histograms etc of the
 corresponding MVA method instance are stored
TDirectory* MethodBaseDir()
 returns the ROOT directory where all instances of the
 corresponding MVA method are stored
void SetWeightFileDir(TString fileDir)
 set directory of weight file
void SetWeightFileName(TString )
 set the weight file name (depreciated)
TString GetWeightFileName()
 retrieve weight file name
void WriteEvaluationHistosToFile()
 writes all MVA evaluation histograms to file
void WriteMonitoringHistosToFile( void )
 write special monitoring histograms to file - not implemented for this method
Bool_t CheckSanity(TTree* theTree = 0)
 tree sanity checks
bool GetLine(istream& fin, char* buf)
 reads one line from the input stream
 checks for certain keywords and interprets
 the line if keywords are found
void CreateMVAPdfs()
 Create PDFs of the MVA output variables
Double_t GetProba(Double_t mvaVal, Double_t ap_sig)
 compute likelihood ratio
Double_t GetRarity(Double_t mvaVal, TMVA::Types::ESBType reftype = Types::kBackground) const
 compute rarity:
 R(x) = Integrate_[-oo..x] { PDF(x') dx' }
 where PDF(x) is the PDF of the classifier's signal or background distribution
Double_t GetEfficiency(TString , TTree* , Double_t& err)
 fill background efficiency (resp. rejection) versus signal efficiency plots
 returns signal efficiency at background efficiency indicated in theString
Double_t GetTrainingEfficiency(TString )
 fill background efficiency (resp. rejection) versus signal efficiency plots
 returns signal efficiency at background efficiency indicated in theString
Double_t GetSignificance( void )
 compute significance of mean difference
 significance = |<S> - <B>|/Sqrt(RMS_S2 + RMS_B2)
Double_t GetSeparation( TH1* histoS, TH1* histoB )
 compute "separation" defined as
 <s2> = (1/2) Int_-oo..+oo { (S(x) - B(x))^2/(S(x) + B(x)) dx }
Double_t GetSeparation( PDF* pdfS, PDF* pdfB )
 compute "separation" defined as
 <s2> = (1/2) Int_-oo..+oo { (S(x)2 - B(x)2)/(S(x) + B(x)) dx }
Double_t GetMaximumSignificance(Double_t SignalEvents, Double_t BackgroundEvents, Double_t& optimal_significance_value) const
 plot significance, 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
void Statistics(TMVA::Types::ETreeType treeType, const TString& theVarName, Double_t& , Double_t& , Double_t& , Double_t& , Double_t& , Double_t& , Bool_t norm = kFALSE)
 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
void MakeClass(const TString& classFileName = "") const
 create reader class for classifier
void PrintHelpMessage()
 prints out classifier-specific help method
Double_t IGetEffForRoot(Double_t )
 interface for RootFinder
Double_t GetEffForRoot(Double_t )
 returns efficiency as function of cut
Bool_t ReadEvent(TTree* tr, UInt_t ievt, TMVA::Types::ESBType type = Types::kMaxSBType) const
Bool_t ReadTrainingEvent(UInt_t ievt, TMVA::Types::ESBType type = Types::kMaxSBType) const
Bool_t ReadTestEvent(UInt_t ievt, TMVA::Types::ESBType type = Types::kMaxSBType) const
Double_t GetEventVal(Int_t ivar) const
Double_t GetEventValNormalised(Int_t ivar) const
TString GetTrainingTMVAVersionString()
TString GetTrainingROOTVersionString()
void Train()
Double_t GetMvaValue()
 classifier response
const Ranking* CreateRanking()
 create ranking
void WriteWeightsToStream( std::ostream& tf )
void WriteWeightsToStream( TFile& /*rf*/ )
{}
void ReadWeightsFromStream( std::istream& tf )
void ReadWeightsFromStream( TFile& /*rf*/ )
{}
const TString& GetJobName()
 ---------- public accessors -----------------------------------------------
 classifier naming (a lot of names ... aren't they ;-)
{ return fJobName; }
const TString& GetMethodName()
{ return fMethodName; }
const TString& GetMethodTitle()
{ return fMethodTitle; }
Types::EMVA GetMethodType()
{ return fMethodType; }
const char* GetName()
{ return GetMethodName().Data(); }
const TString& GetTestvarName()
{ return fTestvar; }
const TString GetProbaName()
{ return fTestvar + "_Proba"; }
void SetMethodName(TString methodName)
{ fMethodName = methodName; }
void SetMethodTitle(TString methodTitle)
{ fMethodTitle = methodTitle; }
void SetMethodType(TMVA::Types::EMVA methodType)
{ fMethodType = methodType; }
void SetTestvarPrefix(TString prefix)
 build classifier name in Test tree
 MVA prefix (e.g., "TMVA_")
{ fTestvarPrefix = prefix; }
void SetTestvarName(const TString& v = "")
{ fTestvar = (v=="")?(fTestvarPrefix + GetMethodTitle()):v; }
Int_t GetNvar()
 number of input variable used by classifier
{ return fNvar; }
const TString& GetInputVar(int i) const
 internal names and expressions of input variables
{ return Data().GetInternalVarName(i); }
const TString& GetInputExp(int i) const
{ return Data().GetExpression(i); }
Double_t GetRMS(Int_t ivar) const
 normalisation and limit accessors
{ return GetVarTransform().Variable(ivar).GetRMS(); }
Double_t GetXmin(Int_t ivar) const
{ return GetVarTransform().Variable(ivar).GetMin(); }
Double_t GetXmax(Int_t ivar) const
{ return GetVarTransform().Variable(ivar).GetMax(); }
Double_t GetSignalReferenceCut()
 sets the minimum requirement on the MVA output to declare an event signal-like
{ return fSignalReferenceCut; }
VariableTransformBase& GetVarTransform()
 retrieve variable transformer
{ return *fVarTransform; }
UInt_t GetTrainingTMVAVersionCode()
 the TMVA versions can be checked using
 if (GetTrainingTMVAVersionCode()>TMVA_VERSION(3,7,2)) {...}
 or
 if (GetTrainingROOTVersionCode()>ROOT_VERSION(5,15,5)) {...}
{ return fTMVATrainingVersion; }
UInt_t GetTrainingROOTVersionCode()
{ return fROOTTrainingVersion; }
Event& GetEvent()
 event reference and update
{ return GetVarTransform().GetEvent(); }
Bool_t IsSignalEvent()
 event properties
{ return GetEvent().IsSignal(); }
Double_t GetEventWeight()
{ return GetEvent().GetWeight(); }
Bool_t IsSignalLike()
 ---------- public auxiliary methods ---------------------------------------
 this method is used to decide whether an event is signal- or background-like
 the reference cut "xC" is taken to be where
 Int_[-oo,xC] { PDF_S(x) dx } = Int_[xC,+oo] { PDF_B(x) dx }
{ return GetMvaValue() > GetSignalReferenceCut() ? kTRUE : kFALSE; }
TDirectory* LocalTDir()
 ---------- protected acccessors -------------------------------------------
{ return Data().LocalRootDir(); }
TString GetWeightFileDir()
{ return fFileDir; }
Bool_t IsNormalised()
 are input variables normalised ?
{ return fNormalise; }
void SetNormalised(Bool_t norm)
{ fNormalise = norm; }
void SetNvar(Int_t n)
 set number of input variables (only used by MethodCuts, could perhaps be removed)
{ fNvar = n; }
Types::EVariableTransform GetVariableTransform()
 the type of the variable transformation required for the data set of this classifier
{ return fVariableTransform; }
void SetSignalReferenceCut(Double_t cut)
 sets the minimum requirement on the MVA output to declare an event signal-like
{ fSignalReferenceCut = cut; }
Bool_t Verbose()
 verbose and help flags
{ return fVerbose; }
Bool_t Help()
{ return fHelp; }
const TString& GetInternalVarName(Int_t ivar) const
 ---------- protected event and tree accessors -----------------------------
 names of input variables (if the original names are expressions, they are
 transformed into regexps)
{ return (*fInputVars)[ivar]; }
const TString& GetOriginalVarName(Int_t ivar) const
{ return Data().GetExpression(ivar); }
Bool_t HasTrainingTree()
 accessing training and test trees
{ return Data().GetTrainingTree() != 0; }
TTree* GetTrainingTree()
return Data()
TTree* GetTestTree()
void MakeClassSpecific(ostream& , const TString& = "") const
 make ROOT-independent C++ class for classifier response (classifier-specific implementation)
{}
void MakeClassSpecificHeader(ostream& , const TString& = "") const
 header and auxiliary classes
{}
MethodBase* GetThisBase()
 static pointer to this object - required for ROOT finder (to be solved differently)
{ return fgThisBase; }
Bool_t TxtWeightsOnly()
 if TRUE, write weights only to text files
{ return fTxtWeightsOnly; }
ECutOrientation GetCutOrientation()
{ return fCutOrientation; }
void ResetThisBase()
 ---------- private acccessors ---------------------------------------------
 reset required for RootFinder
{ fgThisBase = this; }
Bool_t HasMVAPdfs()
{ return fHasMVAPdfs; }

Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss
Last change: root/tmva $Id: MethodBase.h 23334 2008-04-19 18:38:57Z brun $
Last generated: 2008-11-01 10:21
Copyright (c) 2005: *

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