24 #ifndef ROOT_TMVA_OptimizeConfigParameters 25 #define ROOT_TMVA_OptimizeConfigParameters 36 #ifndef ROOT_TMVA_MethodBase 41 #ifndef ROOT_TMVA_Interval 45 #ifndef ROOT_TMVA_DataSet 49 #ifndef ROOT_TMVA_IFitterTarget 50 #ifndef ROOT_IFitterTarget 74 std::map<TString,Double_t>
optimize();
77 std::vector< int >
GetScanIndices(
int val, std::vector<int> base);
std::map< TString, Double_t > fTunedParameters
std::map< TString, TMVA::Interval * > fTuneParameters
Double_t GetSeparation()
return the searation between the signal and background MVA ouput distribution
void optimizeScan()
do the actual optimization using a simple scan method, i.e.
Double_t GetFOM()
Return the Figure of Merit (FOM) used in the parameter optimization process.
void GetMVADists()
fill the private histograms with the mva distributinos for sig/bkg
#define ClassDef(name, id)
std::vector< Float_t > fFOMvsIter
MethodBase *const fMethod
Double_t GetROCIntegral()
calculate the area (integral) under the ROC curve as a overall quality measure of the classification ...
std::map< TString, Double_t > optimize()
virtual ~OptimizeConfigParameters()
the destructor (delete the OptimizeConfigParameters, store the graph and .. delete it) ...
Double_t GetSigEffAtBkgEff(Double_t bkgEff=0.1)
calculate the signal efficiency for a given background efficiency
tomato 1-D histogram with a double per channel (see TH1 documentation)}
Double_t GetBkgEffAtSigEff(Double_t sigEff=0.5)
calculate the background efficiency for a given signal efficiency
Abstract ClassifierFactory template that handles arbitrary types.
std::vector< int > GetScanIndices(int val, std::vector< int > base)
helper function to scan through the all the combinations in the parameter space
Double_t GetBkgRejAtSigEff(Double_t sigEff=0.5)
calculate the background rejection for a given signal efficiency
Double_t EstimatorFunction(std::vector< Double_t > &)
return the estimator (from current FOM) for the fitting interface
std::map< std::vector< Double_t >, Double_t > fAlreadyTrainedParCombination
OptimizeConfigParameters(MethodBase *const method, std::map< TString, TMVA::Interval *> tuneParameters, TString fomType="Separation", TString optimizationType="GA")
Constructor which sets either "Classification or Regression".
TString fOptimizationFitType