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class TMVA::MethodCuts
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library: libTMVA
#include "MethodCuts.h"
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class TMVA::MethodCuts: public TMVA::MethodBase, public TMVA::IFitterTarget
/* Multivariate optimisation of signal efficiency for given background efficiency, applying rectangular minimum and maximum requirements.

Also implemented is a "decorrelate/diagonlized cuts approach", which improves over the uncorrelated cuts ansatz by transforming linearly the input variables into a diagonal space, using the square-root of the covariance matrix.

Other optimisation criteria, such as maximising the signal significance- squared, S^2/(S+B), with S and B being the signal and background yields, correspond to a particular point in the optimised background rejection versus signal efficiency curve. This working point requires the knowledge of the expected yields, which is not the case in general. Note also that for rare signals, Poissonian statistics should be used, which modifies the significance criterion.

The rectangular cut of a volume in the variable space is performed using a binary tree to sort the training events. This provides a significant reduction in computing time (up to several orders of magnitudes, depending on the complexity of the problem at hand).

Technically, optimisation is achieved in TMVA by two methods:

  1. Monte Carlo generation using uniform priors for the lower cut value, and the cut width, thrown within the variable ranges.
  2. A Genetic Algorithm (GA) searches for the optimal ("fittest") cut sample. The GA is configurable by many external settings through the option string. For difficult cases (such as many variables), some tuning may be necessary to achieve satisfying results

Attempts to use Minuit fits (Simplex ot Migrad) instead have not shown superior results, and often failed due to convergence at local minima.

The tests we have performed so far showed that in generic applications, the GA is superior to MC sampling, and hence GA is the default method. It is worthwhile trying both anyway. Decorrelated (or "diagonalized") Cuts

See class description for Method Likelihood for a detailed explanation. */ 








Function Members (Methods)


public:



 virtual~MethodCuts()


voidTObject::AbstractMethod(const char* method) const


voidTObject::AbstractMethod(const char* method) const


virtual voidTObject::AppendPad(Option_t* option = "")


virtual voidTObject::AppendPad(Option_t* option = "")


TDirectory*TMVA::MethodBase::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


Double_tComputeEstimator(vector<Double_t>&)


TMVA::ConfigurableTMVA::Configurable::Configurable(const TString& theOption = "")


virtual voidTObject::Copy(TObject& object) const


virtual voidTObject::Copy(TObject& object) const


voidTMVA::MethodBase::CreateMVAPdfs()


virtual const TMVA::Ranking*CreateRanking()


TMVA::DataSet&TMVA::MethodBase::Data() const


virtual voidDeclareOptions()


virtual voidTObject::Delete(Option_t* option = "")


virtual voidTObject::Delete(Option_t* option = "")


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() const


virtual voidTObject::DrawClass() const


virtual TObject*TObject::DrawClone(Option_t* option = "") const


virtual TObject*TObject::DrawClone(Option_t* option = "") const


virtual voidTObject::Dump() const


virtual voidTObject::Dump() const


virtual voidTObject::Error(const char* method, const char* msgfmt) const


virtual voidTObject::Error(const char* method, const char* msgfmt) const


virtual Double_tEstimatorFunction(vector<Double_t>&)


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


virtual Option_t*TObject::GetDrawOption() const


virtual Option_t*TObject::GetDrawOption() const


static Long_tTObject::GetDtorOnly()


static Long_tTObject::GetDtorOnly()


Double_tTMVA::MethodBase::GetEffForRoot(Double_t)


virtual Double_tGetEfficiency(TString, TTree*, Double_t&)


TMVA::Event&TMVA::MethodBase::GetEvent() const


Double_tTMVA::MethodBase::GetEventVal(Int_t ivar) const


Double_tTMVA::MethodBase::GetEventWeight() const


virtual const char*TObject::GetIconName() const


virtual const char*TObject::GetIconName() const


const TString&TMVA::MethodBase::GetInputExp(int i) const


const TString&TMVA::MethodBase::GetInputVar(int i) const


const TString&TMVA::MethodBase::GetJobName() const


const TString&TMVA::MethodBase::GetMethodName() const


const TString&TMVA::MethodBase::GetMethodTitle() const


TMVA::Types::EMVATMVA::MethodBase::GetMethodType() const


virtual Double_tGetmuTransform(TTree*)


virtual Double_tGetMvaValue()


virtual const char*TMVA::MethodBase::GetName() const


Int_tTMVA::MethodBase::GetNvar() 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 Double_tTMVA::MethodBase::GetOptimalSignificance(Double_t SignalEvents, Double_t BackgroundEvents, Double_t& optimal_significance_value) const


virtual Option_t*TObject::GetOption() const


virtual Option_t*TObject::GetOption() const


const TString&TMVA::Configurable::GetOptions() const


virtual Double_tTMVA::MethodBase::GetProba(Double_t mvaVal, Double_t ap_sig)


const TStringTMVA::MethodBase::GetProbaName() const


virtual Double_tGetRarity(Double_t, TMVA::Types::ESBType) const


Double_tTMVA::MethodBase::GetRMS(Int_t ivar) const


virtual Double_tGetSeparation(TH1*, TH1*) const


virtual Double_tGetSeparation(TMVA::PDF* pdfS = 0, TMVA::PDF* pdfB = 0) const


Double_tTMVA::MethodBase::GetSignalReferenceCut() const


virtual Double_tGetSignificance() const


TTree*TMVA::MethodBase::GetTestTree() const


const TString&TMVA::MethodBase::GetTestvarName() const


static TMVA::MethodBase*TMVA::MethodBase::GetThisBase()


virtual const char*TObject::GetTitle() const


virtual const char*TObject::GetTitle() const


virtual Double_tGetTrainingEfficiency(TString)


TTree*TMVA::MethodBase::GetTrainingTree() const


virtual UInt_tTObject::GetUniqueID() const


virtual UInt_tTObject::GetUniqueID() const


TMVA::Types::EVariableTransformTMVA::MethodBase::GetVariableTransform() const


TMVA::VariableTransformBase&TMVA::MethodBase::GetVarTransform() const


TStringTMVA::MethodBase::GetWeightFileDir() const


TStringTMVA::MethodBase::GetWeightFileName() const


Double_tTMVA::MethodBase::GetXmax(Int_t ivar) const


Double_tTMVA::MethodBase::GetXmax(const TString& var) const


Double_tTMVA::MethodBase::GetXmin(Int_t ivar) const


Double_tTMVA::MethodBase::GetXmin(const TString& var) const


virtual Bool_tTObject::HandleTimer(TTimer* timer)


virtual Bool_tTObject::HandleTimer(TTimer* timer)


virtual ULong_tTObject::Hash() const


virtual ULong_tTObject::Hash() const


Bool_tTMVA::MethodBase::HasTrainingTree() const


Bool_tTMVA::MethodBase::Help() const


static Double_tTMVA::MethodBase::IGetEffForRoot(Double_t)


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() const


virtual voidTObject::Inspect() const


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_tTMVA::MethodBase::IsMVAPdfs() const


Bool_tTMVA::MethodBase::IsNormalised() const


Bool_tTMVA::MethodBase::IsOK() const


Bool_tTObject::IsOnHeap() const


Bool_tTObject::IsOnHeap() const


Bool_tTMVA::MethodBase::IsSignalEvent() const


virtual Bool_tTMVA::MethodBase::IsSignalLike()


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 voidTMVA::MethodBase::MakeClass(const TString& classFileName = "") const


voidTObject::MayNotUse(const char* method) const


voidTObject::MayNotUse(const char* method) const


TDirectory*TMVA::MethodBase::MethodBaseDir() const


TMVA::MethodCutsMethodCuts(TMVA::DataSet& theData, TString theWeightFile, TDirectory* theTargetDir = NULL)


TMVA::MethodCutsMethodCuts(TString jobName, TString methodTitle, TMVA::DataSet& theData, TString theOption = "MC:150:10000:", TDirectory* theTargetFile = 0)


Double_tTMVA::MethodBase::Norm(Int_t ivar, Double_t x) const


Double_tTMVA::MethodBase::Norm(TString var, Double_t x) 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 voidTMVA::MethodBase::PrepareEvaluationTree(TTree* theTestTree)


virtual voidTObject::Print(Option_t* option = "") const


virtual voidTObject::Print(Option_t* option = "") const


virtual voidTMVA::MethodBase::PrintHelpMessage() const


voidTMVA::Configurable::PrintOptions() const


virtual voidProcessOptions()


virtual Int_tTObject::Read(const char* name)


virtual Int_tTObject::Read(const char* name)


virtual Bool_tTMVA::MethodBase::ReadEvent(TTree* tr, UInt_t ievt, TMVA::Types::ESBType type = Types::kMaxSBType) const


voidTMVA::MethodBase::ReadStateFromFile()


voidTMVA::MethodBase::ReadStateFromStream(istream& tf)


voidTMVA::MethodBase::ReadStateFromStream(TFile& rf)


virtual Bool_tTMVA::MethodBase::ReadTestEvent(UInt_t ievt, TMVA::Types::ESBType type = Types::kMaxSBType) const


virtual Bool_tTMVA::MethodBase::ReadTrainingEvent(UInt_t ievt, TMVA::Types::ESBType type = Types::kMaxSBType) const


virtual voidReadWeightsFromStream(istream& istr)


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 = "") const


virtual voidTObject::SaveAs(const char* filename = "", Option_t* option = "") const


virtual voidTObject::SavePrimitive(ostream& out, Option_t* option = "")


virtual voidTObject::SavePrimitive(ostream& 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)


virtual voidTObject::SetDrawOption(Option_t* option = "")


virtual voidTObject::SetDrawOption(Option_t* option = "")


static voidTObject::SetDtorOnly(void* obj)


static voidTObject::SetDtorOnly(void* obj)


voidTMVA::MethodBase::SetHelp(Bool_t h = kTRUE)


voidTMVA::MethodBase::SetJobName(TString jobName)


voidTMVA::MethodBase::SetMethodName(TString methodName)


voidTMVA::MethodBase::SetMethodTitle(TString methodTitle)


voidTMVA::MethodBase::SetMethodType(TMVA::Types::EMVA methodType)


voidTMVA::Configurable::SetName(const char* n)


voidTMVA::MethodBase::SetNormalised(Bool_t norm)


voidTMVA::MethodBase::SetNvar(Int_t n)


static voidTObject::SetObjectStat(Bool_t stat)


static voidTObject::SetObjectStat(Bool_t stat)


voidTMVA::Configurable::SetOptions(const TString& s)


voidSetTestSignalEfficiency(Double_t eff)


virtual voidTObject::SetUniqueID(UInt_t uid)


virtual voidTObject::SetUniqueID(UInt_t uid)


voidTMVA::MethodBase::SetVariableTransform(TMVA::Types::EVariableTransform m)


voidTMVA::MethodBase::SetVerbose(Bool_t v = kTRUE)


voidTMVA::MethodBase::SetWeightFileDir(TString fileDir)


voidTMVA::MethodBase::SetWeightFileName(TString)


voidTMVA::MethodBase::SetXmax(Int_t ivar, Double_t x)


voidTMVA::MethodBase::SetXmax(const TString& var, Double_t x)


voidTMVA::MethodBase::SetXmin(Int_t ivar, Double_t x)


voidTMVA::MethodBase::SetXmin(const TString& var, Double_t x)


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)


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 voidTMVA::MethodBase::TestInit(TTree* theTestTree = 0)


virtual voidTrain()


voidTMVA::MethodBase::TrainMethod()


virtual voidTObject::UseCurrentStyle()


virtual voidTObject::UseCurrentStyle()


Bool_tTMVA::MethodBase::Verbose() const


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


voidTMVA::MethodBase::WriteEvaluationHistosToFile(TDirectory* targetDir = 0)


virtual voidWriteMonitoringHistosToFile() const


voidTMVA::MethodBase::WriteStateToFile() const


voidTMVA::MethodBase::WriteStateToStream(TFile& rf) const


voidTMVA::MethodBase::WriteStateToStream(ostream& tf, Bool_t isClass = kFALSE) const


virtual voidWriteWeightsToStream(ostream& o) const





protected:



Bool_tTMVA::MethodBase::CheckSanity(TTree* theTree = 0)


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)


TMVA::MethodBase::ECutOrientationTMVA::MethodBase::GetCutOrientation() const


virtual voidGetHelpMessage() const


const TString&TMVA::MethodBase::GetInternalVarName(Int_t ivar) const


const TString&TMVA::MethodBase::GetOriginalVarName(Int_t ivar) const


const TString&TMVA::MethodBase::GetTestvarPrefix() const


UInt_tTMVA::MethodBase::GetTrainingROOTVersionCode() const


TStringTMVA::MethodBase::GetTrainingROOTVersionString() const


UInt_tTMVA::MethodBase::GetTrainingTMVAVersionCode() const


TStringTMVA::MethodBase::GetTrainingTMVAVersionString() const


TMVA::Types::ESBTypeTMVA::MethodBase::GetVariableTransformType() const


TDirectory*TMVA::MethodBase::LocalTDir() const


Bool_tTMVA::Configurable::LooseOptionCheckingEnabled() const


virtual voidMakeClassSpecific(ostream&, const TString&) const


virtual voidTMVA::MethodBase::MakeClassSpecificHeader(ostream&, const TString& = "") const


voidTObject::MakeZombie()


voidTObject::MakeZombie()


voidTMVA::Configurable::ReadOptionsFromStream(istream& istr)


voidTMVA::Configurable::ResetSetFlag()


voidTMVA::MethodBase::ResetThisBase()


voidTMVA::MethodBase::SetSignalReferenceCut(Double_t cut)


voidTMVA::MethodBase::SetTestvarName(const TString& v = "")


voidTMVA::MethodBase::SetTestvarPrefix(TString prefix)


voidTMVA::MethodBase::SetVariableTransformType(TMVA::Types::ESBType t)


voidTMVA::MethodBase::Statistics(TMVA::Types::ETreeType treeType, const TString& theVarName, Double_t&, Double_t&, Double_t&, Double_t&, Double_t&, Double_t&, Bool_t norm = kFALSE)


Bool_tTMVA::MethodBase::TxtWeightsOnly() const


voidTMVA::Configurable::WriteOptionsToStream(ostream& o, const TString& prefix) const





private:



voidCreateVariablePDFs()


voidGetEffsfromPDFs(Double_t* cutMin, Double_t* cutMax, Double_t& effS, Double_t& effB)


voidGetEffsfromSelection(Double_t* cutMin, Double_t* cutMax, Double_t& effS, Double_t& effB)


voidInitCuts()


voidMatchCutsToPars(vector<Double_t>&, Double_t*, Double_t*)


voidMatchCutsToPars(vector<Double_t>&, Double_t**, Double_t**, Int_t ibin)


voidMatchParsToCuts(const vector<Double_t>&, Double_t*, Double_t*)


voidMatchParsToCuts(Double_t*, Double_t*, Double_t*)


Bool_tSanityChecks()










Data Members


public:



enum EFitMethodType { kUseMonteCarlo


kUseGeneticAlgorithm


kUseSimulatedAnnealing


kUseMinuit


};


enum EEffMethod { kUseEventSelection


kUsePDFs


};


enum EFitParameters { kNotEnforced


kForceMin


kForceMax


kForceSmart


kForceVerySmart


};


enum TMVA::MethodBase::EWeightFileType { kROOT


kTEXT


};


enum TMVA::MethodBase::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


};





protected:



TMVA::MethodBase::ECutOrientationTMVA::MethodBase::fCutOrientation+1 if Sig>Bkg, -1 otherwise


TH1*TMVA::MethodBase::fEffBefficiency plot (background)


TH1*TMVA::MethodBase::fEffBvsSbackground efficiency versus signal efficiency


TH1*TMVA::MethodBase::fEffSefficiency plot (signal)


TGraph*TMVA::MethodBase::fGraphBgraphs used for splines for efficiency (background)


TGraph*TMVA::MethodBase::fGraphSgraphs used for splines for efficiency (signal)


TGraph*TMVA::MethodBase::fGraphTrainBgraphs used for splines for training efficiency (background)


TGraph*TMVA::MethodBase::fGraphTrainEffBvsSgraphs used for splines for training signal eff. versus background eff.


TGraph*TMVA::MethodBase::fGraphTrainSgraphs used for splines for training efficiency (signal)


TGraph*TMVA::MethodBase::fGrapheffBvsSgraphs used for splines for signal eff. versus background eff.


TH1*TMVA::MethodBase::fHistB_highbinMVA plots used for efficiency calculations (background)


TH1*TMVA::MethodBase::fHistB_plotbinMVA plots used for graphics representation (background)


TH1*TMVA::MethodBase::fHistBhatBworking histograms needed for mu-transform (background)


TH1*TMVA::MethodBase::fHistBhatSworking histograms needed for mu-transform (signal)


TH1*TMVA::MethodBase::fHistMuBmu-transform (background) 


TH1*TMVA::MethodBase::fHistMuSmu-transform (signal)


TH1*TMVA::MethodBase::fHistS_highbinMVA plots used for efficiency calculations (signal)    


TH1*TMVA::MethodBase::fHistS_plotbinMVA plots used for graphics representation (signal)


vector<TString>*TMVA::MethodBase::fInputVarsvector of input variables used in MVA


Bool_tTMVA::MethodBase::fIsOKstatus of sanity checks


TMVA::MsgLoggerTMVA::MethodBase::fLoggermessage logger


TMVA::PDF*TMVA::MethodBase::fMVAPdfBbackground MVA PDF


TMVA::PDF*TMVA::MethodBase::fMVAPdfSsignal MVA PDF


Double_tTMVA::MethodBase::fMode


Int_tTMVA::MethodBase::fNbinsnumber of bins in representative histograms


Int_tTMVA::MethodBase::fNbinsHnumber of bins in evaluation histograms


Int_tTMVA::MethodBase::fNbinsMVAPdfnumber of bins used in histogram that creates PDF


Int_tTMVA::MethodBase::fNsmoothMVAPdfnumber of times a histogram is smoothed before creating the PDF


TH1*TMVA::MethodBase::fProbaB_plotbinP(MVA) plots used for graphics representation (background)


TH1*TMVA::MethodBase::fProbaS_plotbinP(MVA) plots used for graphics representation (signal)


TMVA::Ranking*TMVA::MethodBase::fRankingranking      


TH1*TMVA::MethodBase::fRarityB_plotbinR(MVA) plots used for graphics representation (background)


TH1*TMVA::MethodBase::fRarityS_plotbinR(MVA) plots used for graphics representation (signal)


TH1*TMVA::MethodBase::fRejBvsSbackground rejection (=1-eff.) versus signal efficiency


TMVA::PDF*TMVA::MethodBase::fSplBPDFs of MVA distribution (background)


TMVA::TSpline1*TMVA::MethodBase::fSplRefBhelper splines for RootFinder (background)


TMVA::TSpline1*TMVA::MethodBase::fSplRefShelper splines for RootFinder (signal)


TMVA::PDF*TMVA::MethodBase::fSplSPDFs of MVA distribution (signal)


TMVA::PDF*TMVA::MethodBase::fSplTrainBPDFs of training MVA distribution (background)


TSpline*TMVA::MethodBase::fSplTrainEffBvsSsplines for training signal eff. versus background eff.


TMVA::TSpline1*TMVA::MethodBase::fSplTrainRefBhelper splines for RootFinder (background)


TMVA::TSpline1*TMVA::MethodBase::fSplTrainRefShelper splines for RootFinder (signal)


TMVA::PDF*TMVA::MethodBase::fSplTrainSPDFs of training MVA distribution (signal)


TSpline*TMVA::MethodBase::fSpleffBvsSsplines for signal eff. versus background eff.


TH1*TMVA::MethodBase::fTrainEffBTraining efficiency plot (background)


TH1*TMVA::MethodBase::fTrainEffBvsSTraining background efficiency versus signal efficiency


TH1*TMVA::MethodBase::fTrainEffSTraining efficiency plot (signal)


TH1*TMVA::MethodBase::fTrainRejBvsSTraining background rejection (=1-eff.) versus signal efficiency


Double_tTMVA::MethodBase::fX


TH1*TMVA::MethodBase::finvBeffvsSeffinverse background eff (1/eff.) versus signal efficiency





private:



TString*fAllVarsIwhat to do with variables


TMVA::BinarySearchTree*fBinaryTreeB


TMVA::BinarySearchTree*fBinaryTreeS


Double_t**fCutMaxmaximum requirement


Double_t**fCutMinminimum requirement


TMVA::MethodCuts::vector<Interval*>fCutRangeallowed ranges for cut optimisation


Double_t*fCutRangeMaxmaximum of allowed cut range


Double_t*fCutRangeMinminimum of allowed cut range


TH1*fEffBvsSLocalintermediate eff. background versus eff signal histo


TMVA::MethodCuts::EEffMethodfEffMethodchosen efficiency calculation method


TStringfEffMethodSchosen efficiency calculation method (string)


Double_tfEffRefreference efficiency


Double_tfEffSMaxused to test optimized signal efficiency      


Double_tfEffSMinused to test optimized signal efficiency


TMVA::MethodCuts::EFitMethodTypefFitMethodchosen fit method


TStringfFitMethodSchosen fit method (string)


vector<EFitParameters>*fFitParamsvector for series of fit methods


vector<Double_t>*fMeanBmeans of variables (background)


vector<Double_t>*fMeanSmeans of variables (signal)


Int_tfNRandCutsnumber of random cut samplings


Int_tfNparnumber of parameters in fit (default: 2*Nvar)


TRandom*fRandomrandom generator for MC optimisation method


vector<Int_t>*fRangeSignused to match cuts to fit parameters (and vice versa)


vector<Double_t>*fRmsBRMSs of variables (background)


vector<Double_t>*fRmsSRMSs of variables (signal)


Double_tfTestSignalEffused to test optimized signal efficiency


Double_t*fTmpCutMaxtemporary maximum requirement


Double_t*fTmpCutMintemporary minimum requirement


vector<TH1*>*fVarHistBreference histograms (background)


vector<TH1*>*fVarHistB_smoothsmoothed reference histograms (background)


vector<TH1*>*fVarHistSreference histograms (signal)


vector<TH1*>*fVarHistS_smoothsmoothed reference histograms (signal)        


vector<PDF*>*fVarPdfBreference PDFs (background)


vector<PDF*>*fVarPdfSreference PDFs (signal)







Class Charts

















































Inheritance
Inherited Members
Includes
Libraries



Class Charts


Function documentation


 MethodCuts(TString jobName, TString methodTitle, TMVA::DataSet& theData, TString theOption = "MC:150:10000:", TDirectory* theTargetFile = 0)


 standard constructor
 see below for option string format





 MethodCuts(TMVA::DataSet& theData, TString theWeightFile, TDirectory* theTargetDir = NULL)


 construction from weight file





void  InitCuts( void )


 default initialisation called by all constructors





 ~MethodCuts( void )


 destructor





void  DeclareOptions()


 define the options (their key words) that can be set in the option string
 know options:
 Method             <string> Minimization method
    available values are:        MC Monte Carlo <default>
                                 GA Genetic Algorithm
                                 SA Simulated annealing

 EffMethod          <string> Efficiency selection method
    available values are:        EffSel <default>
                                 EffPDF

 VarProp            <string> Property of variable 1 for the MC method (taking precedence over the
    globale setting. The same values as for the global option are available. Variables 1..10 can be
    set this way

 CutRangeMin/Max    <float>  user-defined ranges in which cuts are varied





void  ProcessOptions()


 process user options





Double_t  GetMvaValue()


 cut evaluation: returns 1.0 if event passed, 0.0 otherwise





void   Train( void )


 training method: here the cuts are optimised for the training sample





void  Test(TTree* theTestTree)


 not used





Double_t  EstimatorFunction(vector<Double_t>& )


 returns estimator for "cut fitness" used by GA





Double_t  ComputeEstimator(vector<Double_t>& )


 returns estimator for "cut fitness" used by GA
 there are two requirements:
 1) the signal efficiency must be equal to the required one in the
    efficiency scan
 2) the background efficiency must be as small as possible
 the requirement 1) has priority over 2)





void  MatchParsToCuts( const std::vector<Double_t> & par,                                         Double_t* cutMin, Double_t* cutMax )


 translates parameters into cuts





void  MatchCutsToPars(vector<Double_t>& , Double_t** , Double_t** , Int_t ibin)


 translate the cuts into parameters





void  MatchCutsToPars(vector<Double_t>& , Double_t* , Double_t* )


 translates cuts into parameters





void  GetEffsfromPDFs(Double_t* cutMin, Double_t* cutMax, Double_t& effS, Double_t& effB)


 compute signal and background efficiencies from PDFs
 for given cut sample





void  GetEffsfromSelection(Double_t* cutMin, Double_t* cutMax, Double_t& effS, Double_t& effB)


 compute signal and background efficiencies from event counting
 for given cut sample





void  CreateVariablePDFs( void )


 for PDF method: create efficiency reference histograms and PDFs





Bool_t  SanityChecks( void )


 basic checks to ensure that assumptions on variable order are satisfied





void   WriteWeightsToStream(ostream& o) const


 first the dimensions





void   ReadWeightsFromStream(istream& istr)


 read the cuts from stream





void  WriteMonitoringHistosToFile( void )


 write histograms and PDFs to file for monitoring purposes





Double_t  GetTrainingEfficiency(TString )


 - overloaded function to create background efficiency (rejection) versus
   signal efficiency plot (first call of this function)
 - the function returns the signal efficiency at background efficiency
   indicated in theString

 "theString" must have two entries:
 [0]: "Efficiency"
 [1]: the value of background efficiency at which the signal efficiency
      is to be returned





Double_t  GetEfficiency(TString , TTree* , Double_t& )


 - overloaded function to create background efficiency (rejection) versus
   signal efficiency plot (first call of this function)
 - the function returns the signal efficiency at background efficiency
   indicated in theString

 "theString" must have two entries:
 [0]: "Efficiency"
 [1]: the value of background efficiency at which the signal efficiency
      is to be returned





void  MakeClassSpecific(ostream& , const TString& ) const


 write specific classifier response





void  GetHelpMessage()


 get help message text

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





Double_t GetSeparation( TH1*, TH1* )


 also overwrite:




{ return 0; }






Double_t GetSeparation( PDF* pdfS = 0, PDF* pdfB = 0 )



{ if (pdfS && pdfB); return 0; }






Double_t GetSignificance( void )



{ return 0; }






Double_t GetmuTransform(TTree* )



{ return 0; }






Double_t GetRarity(Double_t , TMVA::Types::ESBType ) const


 rarity distributions (signal or background (default) is uniform in [0,1])




{ return 0; }






void SetTestSignalEfficiency(Double_t eff)



{ fTestSignalEff = eff; }






const Ranking* CreateRanking()


 ranking of input variables




{ return 0; }






void MatchParsToCuts( const std::vector<Double_t>&, Double_t*, Double_t* )


 the definition of fit parameters can be different from the actual
 cut requirements; these functions provide the matching








Author:  Andreas Hoecker,  Matt Jachowski,  Peter Speckmayer,  Helge Voss,  Kai Voss 

Last update: root/tmva $Id: MethodCuts.cxx,v 1.16 2007/06/21 10:28:08 brun Exp $ 

Copyright   (c) 2005:                                                            *




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