library: libTMVA #include "MethodBase.h" |
private:
void Init() protected:
Bool_t CheckSanity(TTree* theTree = 0) void ResetThisBase() public:
virtual ~MethodBase() void AppendToMethodName(TString methodNameSuffix) static TClass* Class() TMVA::MethodBase::CutOrientation GetCutOrientation() Double_t GetEffForRoot(Double_t) virtual Double_t GetEfficiency(TString, TTree*) vector<TString>* GetInputVars() const TString GetJobName() const TMVA::Types::MVA GetMethod() const TString GetMethodName() const virtual Double_t GetmuTransform(TTree*) virtual Double_t GetMvaValue(TMVA::Event* e) Int_t GetNvar() const virtual Double_t GetOptimalSignificance(Double_t SignalEvents, Double_t BackgroundEvents, Double_t& optimal_significance_value) const TString GetOptions() const virtual Double_t GetSeparation() virtual Double_t GetSignificance() static TMVA::MethodBase* GetThisBase() TTree* GetTrainingTree() const TString GetWeightFileDir() const TString GetWeightFileExtension() const TString GetWeightFileName() Double_t GetXmaxNorm(Int_t ivar) const Double_t GetXmaxNorm(TString var) const Double_t GetXminNorm(Int_t ivar) const Double_t GetXminNorm(TString var) const static Double_t IGetEffForRoot(Double_t) virtual void InitNorm(TTree* theTree) virtual TClass* IsA() const Bool_t IsOK() const Double_t Norm(Int_t ivar, Double_t x) const Double_t Norm(TString var, Double_t x) const TMVA::MethodBase& operator=(const TMVA::MethodBase&) virtual void PrepareEvaluationTree(TTree* theTestTree) virtual void ReadWeightsFromFile() void SetInputVars(vector<TString>* theInputVars) void SetJobName(TString jobName) void SetMethodName(TString methodName) void SetVerbose(Bool_t v = kTRUE) void SetWeightFileDir(TString fileDir) void SetWeightFileExtension(TString fileExtension) void SetWeightFileName() void SetWeightFileName(TString) void SetXmaxNorm(Int_t ivar, Double_t x) void SetXmaxNorm(TString var, Double_t x) void SetXminNorm(Int_t ivar, Double_t x) void SetXminNorm(TString var, Double_t x) virtual void ShowMembers(TMemberInspector& insp, char* parent) virtual void Streamer(TBuffer& b) void StreamerNVirtual(TBuffer& b) virtual void Test(TTree* theTestTree) virtual void TestInit(TTree* theTestTree) virtual void TestInitLocal(TTree*) virtual void Train() void UpdateNorm(Int_t ivar, Double_t x) Bool_t Verbose() const virtual void WriteHistosToFile() void WriteHistosToFile(TDirectory* targetDir) virtual void WriteWeightsToFile()
private:
TString fFileExtension extension used in weight files (default: ".weights") TString fFileDir unix sub-directory for weight files (default: "weights") TString fWeightFile weight file name Double_t fMeanS mean (signal) Double_t fMeanB mean (background) Double_t fRmsS RMS (signal) Double_t fRmsB RMS (background) Double_t fXmin minimum (signal and background) Double_t fXmax maximum (signal and background) Bool_t fVerbose vector<Double_t>* fXminNorm minimum of input variables vector<Double_t>* fXmaxNorm maximum of input variables static TMVA::MethodBase* fgThisBase protected:
TString fJobName name of job -> user defined, appears in weight files TString fMethodName name of the method (set in derived class) TMVA::Types::MVA fMethod type of method (set in derived class) TTree* fTrainingTree training tree TString fTestvar variable used in evauation, etc (mostly the MVA) TString fTestvarPrefix 'MVA_' prefix of MVA variable vector<TString>* fInputVars vector of input variables used in MVA TString fOptions options string TDirectory* fBaseDir base director, needed to know where to jump back from localDir TDirectory* fLocalTDir local directory, used to save monitoring histograms Int_t fNvar number of input variables Bool_t fIsOK status of sanity checks TH1* fHistS_plotbin MVA plots used for graphics representation (signal) TH1* fHistB_plotbin MVA plots used for graphics representation (background) TH1* fHistS_highbin MVA plots used for efficiency calculations (signal) TH1* fHistB_highbin MVA plots used for efficiency calculations (background) TH1* fEffS efficiency plot (signal) TH1* fEffB efficiency plot (background) TH1* fEffBvsS background efficiency versus signal efficiency TH1* fRejBvsS background rejection (=1-eff.) versus signal efficiency TH1* fHistBhatS working histograms needed for mu-transform (signal) TH1* fHistBhatB working histograms needed for mu-transform (background) TH1* fHistMuS mu-transform (signal) TH1* fHistMuB mu-transform (background) Double_t fX Double_t fMode TGraph* fGraphS graphs used for splines for efficiency (signal) TGraph* fGraphB graphs used for splines for efficiency (background) TGraph* fGrapheffBvsS graphs used for splines for signal eff. versus background eff. TMVA::PDF* fSplS PDFs of MVA distribution (signal) TMVA::PDF* fSplB PDFs of MVA distribution (background) TSpline* fSpleffBvsS splines for signal eff. versus background eff. Int_t fNbins number of bins in representative histograms Int_t fNbinsH number of bins in evaluation histograms TMVA::MethodBase::CutOrientation fCutOrientation +1 if Sig>Bkg, -1 otherwise TMVA::TSpline1* fSplRefS helper splines for RootFinder (signal) TMVA::TSpline1* fSplRefB helper splines for RootFinder (background) public:
static const TMVA::MethodBase::CutOrientation kNegative static const TMVA::MethodBase::CutOrientation kPositive static const TMVA::MethodBase::Type kSignal static const TMVA::MethodBase::Type kBackground
prepare tree branch with the method's discriminating variable
evaluate method (resulting discriminating variable) or input varible
series of sanity checks on input tree (eg, do all the variables really exist in tree, etc)