==> Start TMVAClassificationApplication
: Booking "BDT method" of type "BDT" from dataset/weights/TMVAClassification_BDT.weights.xml.
: Reading weight file: dataset/weights/TMVAClassification_BDT.weights.xml
<HEADER> DataSetInfo : [Default] : Added class "Signal"
<HEADER> DataSetInfo : [Default] : Added class "Background"
: Booked classifier "BDT" of type: "BDT"
: Booking "Cuts method" of type "Cuts" from dataset/weights/TMVAClassification_Cuts.weights.xml.
: Reading weight file: dataset/weights/TMVAClassification_Cuts.weights.xml
: Read cuts optimised using sample of MC events
: Reading 100 signal efficiency bins for 4 variables
: Booked classifier "Cuts" of type: "Cuts"
: Booking "CutsD method" of type "Cuts" from dataset/weights/TMVAClassification_CutsD.weights.xml.
: Reading weight file: dataset/weights/TMVAClassification_CutsD.weights.xml
: Read cuts optimised using sample of MC events
: Reading 100 signal efficiency bins for 4 variables
: Booked classifier "CutsD" of type: "Cuts"
: Booking "FDA_GA method" of type "FDA" from dataset/weights/TMVAClassification_FDA_GA.weights.xml.
: Reading weight file: dataset/weights/TMVAClassification_FDA_GA.weights.xml
: User-defined formula string : "(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3"
: TFormula-compatible formula string: "[0]+[1]*[5]+[2]*[6]+[3]*[7]+[4]*[8]"
: Booked classifier "FDA_GA" of type: "FDA"
: Booking "KNN method" of type "KNN" from dataset/weights/TMVAClassification_KNN.weights.xml.
: Reading weight file: dataset/weights/TMVAClassification_KNN.weights.xml
: Creating kd-tree with 2000 events
: Computing scale factor for 1d distributions: (ifrac, bottom, top) = (80%, 10%, 90%)
<HEADER> ModulekNN : Optimizing tree for 4 variables with 2000 values
: <Fill> Class 1 has 1000 events
: <Fill> Class 2 has 1000 events
: Booked classifier "KNN" of type: "KNN"
: Booking "LD method" of type "LD" from dataset/weights/TMVAClassification_LD.weights.xml.
: Reading weight file: dataset/weights/TMVAClassification_LD.weights.xml
: Booked classifier "LD" of type: "LD"
: Booking "Likelihood method" of type "Likelihood" from dataset/weights/TMVAClassification_Likelihood.weights.xml.
: Reading weight file: dataset/weights/TMVAClassification_Likelihood.weights.xml
: Booked classifier "Likelihood" of type: "Likelihood"
: Booking "LikelihoodPCA method" of type "Likelihood" from dataset/weights/TMVAClassification_LikelihoodPCA.weights.xml.
: Reading weight file: dataset/weights/TMVAClassification_LikelihoodPCA.weights.xml
: Booked classifier "LikelihoodPCA" of type: "Likelihood"
: Booking "MLPBNN method" of type "MLP" from dataset/weights/TMVAClassification_MLPBNN.weights.xml.
: Reading weight file: dataset/weights/TMVAClassification_MLPBNN.weights.xml
<HEADER> MLPBNN : Building Network.
: Initializing weights
: Booked classifier "MLPBNN" of type: "MLP"
: Booking "PDEFoam method" of type "PDEFoam" from dataset/weights/TMVAClassification_PDEFoam.weights.xml.
: Reading weight file: dataset/weights/TMVAClassification_PDEFoam.weights.xml
: Read foams from file: dataset/weights/TMVAClassification_PDEFoam.weights_foams.root
: Booked classifier "PDEFoam" of type: "PDEFoam"
: Booking "PDERS method" of type "PDERS" from dataset/weights/TMVAClassification_PDERS.weights.xml.
: Reading weight file: dataset/weights/TMVAClassification_PDERS.weights.xml
: signal and background scales: 0.001 0.001
: Booked classifier "PDERS" of type: "PDERS"
: Booking "RuleFit method" of type "RuleFit" from dataset/weights/TMVAClassification_RuleFit.weights.xml.
: Reading weight file: dataset/weights/TMVAClassification_RuleFit.weights.xml
: Booked classifier "RuleFit" of type: "RuleFit"
: Booking "SVM method" of type "SVM" from dataset/weights/TMVAClassification_SVM.weights.xml.
: Reading weight file: dataset/weights/TMVAClassification_SVM.weights.xml
: Booked classifier "SVM" of type: "SVM"
--- TMVAClassificationApp : Using input file: ./files/tmva_class_example.root
--- Select signal sample
: Rebuilding Dataset Default
--- End of event loop: Real time 0:00:01, CP time 1.340
--- Created root file: "TMVApp.root" containing the MVA output histograms
==> TMVAClassificationApplication is done!
#include <cstdlib>
#include <vector>
#include <iostream>
#include <map>
#include <string>
void TMVAClassificationApplication(
TString myMethodList =
"" )
{
std::map<std::string,int> Use;
Use["Cuts"] = 1;
Use["CutsD"] = 1;
Use["CutsPCA"] = 0;
Use["CutsGA"] = 0;
Use["CutsSA"] = 0;
Use["Likelihood"] = 1;
Use["LikelihoodD"] = 0;
Use["LikelihoodPCA"] = 1;
Use["LikelihoodKDE"] = 0;
Use["LikelihoodMIX"] = 0;
Use["PDERS"] = 1;
Use["PDERSD"] = 0;
Use["PDERSPCA"] = 0;
Use["PDEFoam"] = 1;
Use["PDEFoamBoost"] = 0;
Use["KNN"] = 1;
Use["LD"] = 1;
Use["Fisher"] = 0;
Use["FisherG"] = 0;
Use["BoostedFisher"] = 0;
Use["HMatrix"] = 0;
Use["FDA_GA"] = 1;
Use["FDA_SA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
Use["FDA_MCMT"] = 0;
Use["MLP"] = 0;
Use["MLPBFGS"] = 0;
Use["MLPBNN"] = 1;
Use["CFMlpANN"] = 0;
Use["TMlpANN"] = 0;
Use["DNN_CPU"] = 0;
Use["DNN_GPU"] = 0;
Use["SVM"] = 1;
Use["BDT"] = 1;
Use["BDTG"] = 0;
Use["BDTB"] = 0;
Use["BDTD"] = 0;
Use["BDTF"] = 0;
Use["RuleFit"] = 1;
Use["Plugin"] = 0;
Use["Category"] = 0;
Use["SVM_Gauss"] = 0;
Use["SVM_Poly"] = 0;
Use["SVM_Lin"] = 0;
std::cout << std::endl;
std::cout << "==> Start TMVAClassificationApplication" << std::endl;
if (myMethodList != "") {
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;
std::vector<TString> mlist = gTools().
SplitString( myMethodList,
',' );
for (
UInt_t i=0; i<mlist.size(); i++) {
std::string regMethod(mlist[i]);
if (Use.find(regMethod) == Use.end()) {
std::cout << "Method \"" << regMethod
<< "\" not known in TMVA under this name. Choose among the following:" << std::endl;
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
std::cout << it->first << " ";
}
std::cout << std::endl;
return;
}
Use[regMethod] = 1;
}
}
Float_t Category_cat1, Category_cat2, Category_cat3;
if (Use["Category"]){
reader->
AddSpectator(
"Category_cat1 := var3<=0", &Category_cat1 );
reader->
AddSpectator(
"Category_cat2 := (var3>0)&&(var4<0)", &Category_cat2 );
reader->
AddSpectator(
"Category_cat3 := (var3>0)&&(var4>=0)", &Category_cat3 );
}
TString prefix =
"TMVAClassification";
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
reader->
BookMVA( methodName, weightfile );
}
}
if (Use[
"Likelihood"]) histLk =
new TH1F(
"MVA_Likelihood",
"MVA_Likelihood", nbin, -1, 1 );
if (Use[
"LikelihoodD"]) histLkD =
new TH1F(
"MVA_LikelihoodD",
"MVA_LikelihoodD", nbin, -1, 0.9999 );
if (Use[
"LikelihoodPCA"]) histLkPCA =
new TH1F(
"MVA_LikelihoodPCA",
"MVA_LikelihoodPCA", nbin, -1, 1 );
if (Use[
"LikelihoodKDE"]) histLkKDE =
new TH1F(
"MVA_LikelihoodKDE",
"MVA_LikelihoodKDE", nbin, -0.00001, 0.99999 );
if (Use[
"LikelihoodMIX"]) histLkMIX =
new TH1F(
"MVA_LikelihoodMIX",
"MVA_LikelihoodMIX", nbin, 0, 1 );
if (Use[
"PDERS"]) histPD =
new TH1F(
"MVA_PDERS",
"MVA_PDERS", nbin, 0, 1 );
if (Use[
"PDERSD"]) histPDD =
new TH1F(
"MVA_PDERSD",
"MVA_PDERSD", nbin, 0, 1 );
if (Use[
"PDERSPCA"]) histPDPCA =
new TH1F(
"MVA_PDERSPCA",
"MVA_PDERSPCA", nbin, 0, 1 );
if (Use[
"KNN"]) histKNN =
new TH1F(
"MVA_KNN",
"MVA_KNN", nbin, 0, 1 );
if (Use[
"HMatrix"]) histHm =
new TH1F(
"MVA_HMatrix",
"MVA_HMatrix", nbin, -0.95, 1.55 );
if (Use[
"Fisher"]) histFi =
new TH1F(
"MVA_Fisher",
"MVA_Fisher", nbin, -4, 4 );
if (Use[
"FisherG"]) histFiG =
new TH1F(
"MVA_FisherG",
"MVA_FisherG", nbin, -1, 1 );
if (Use[
"BoostedFisher"]) histFiB =
new TH1F(
"MVA_BoostedFisher",
"MVA_BoostedFisher", nbin, -2, 2 );
if (Use[
"LD"]) histLD =
new TH1F(
"MVA_LD",
"MVA_LD", nbin, -2, 2 );
if (Use[
"MLP"]) histNn =
new TH1F(
"MVA_MLP",
"MVA_MLP", nbin, -1.25, 1.5 );
if (Use[
"MLPBFGS"]) histNnbfgs =
new TH1F(
"MVA_MLPBFGS",
"MVA_MLPBFGS", nbin, -1.25, 1.5 );
if (Use[
"MLPBNN"]) histNnbnn =
new TH1F(
"MVA_MLPBNN",
"MVA_MLPBNN", nbin, -1.25, 1.5 );
if (Use[
"CFMlpANN"]) histNnC =
new TH1F(
"MVA_CFMlpANN",
"MVA_CFMlpANN", nbin, 0, 1 );
if (Use[
"TMlpANN"]) histNnT =
new TH1F(
"MVA_TMlpANN",
"MVA_TMlpANN", nbin, -1.3, 1.3 );
if (Use[
"DNN_GPU"]) histDnnGpu =
new TH1F(
"MVA_DNN_GPU",
"MVA_DNN_GPU", nbin, -0.1, 1.1);
if (Use[
"DNN_CPU"]) histDnnCpu =
new TH1F(
"MVA_DNN_CPU",
"MVA_DNN_CPU", nbin, -0.1, 1.1);
if (Use[
"BDT"]) histBdt =
new TH1F(
"MVA_BDT",
"MVA_BDT", nbin, -0.8, 0.8 );
if (Use[
"BDTG"]) histBdtG =
new TH1F(
"MVA_BDTG",
"MVA_BDTG", nbin, -1.0, 1.0 );
if (Use[
"BDTB"]) histBdtB =
new TH1F(
"MVA_BDTB",
"MVA_BDTB", nbin, -1.0, 1.0 );
if (Use[
"BDTD"]) histBdtD =
new TH1F(
"MVA_BDTD",
"MVA_BDTD", nbin, -0.8, 0.8 );
if (Use[
"BDTF"]) histBdtF =
new TH1F(
"MVA_BDTF",
"MVA_BDTF", nbin, -1.0, 1.0 );
if (Use[
"RuleFit"]) histRf =
new TH1F(
"MVA_RuleFit",
"MVA_RuleFit", nbin, -2.0, 2.0 );
if (Use[
"SVM_Gauss"]) histSVMG =
new TH1F(
"MVA_SVM_Gauss",
"MVA_SVM_Gauss", nbin, 0.0, 1.0 );
if (Use[
"SVM_Poly"]) histSVMP =
new TH1F(
"MVA_SVM_Poly",
"MVA_SVM_Poly", nbin, 0.0, 1.0 );
if (Use[
"SVM_Lin"]) histSVML =
new TH1F(
"MVA_SVM_Lin",
"MVA_SVM_Lin", nbin, 0.0, 1.0 );
if (Use[
"FDA_MT"]) histFDAMT =
new TH1F(
"MVA_FDA_MT",
"MVA_FDA_MT", nbin, -2.0, 3.0 );
if (Use[
"FDA_GA"]) histFDAGA =
new TH1F(
"MVA_FDA_GA",
"MVA_FDA_GA", nbin, -2.0, 3.0 );
if (Use[
"Category"]) histCat =
new TH1F(
"MVA_Category",
"MVA_Category", nbin, -2., 2. );
if (Use[
"Plugin"]) histPBdt =
new TH1F(
"MVA_PBDT",
"MVA_BDT", nbin, -0.8, 0.8 );
if (Use["PDEFoam"]) {
histPDEFoam =
new TH1F(
"MVA_PDEFoam",
"MVA_PDEFoam", nbin, 0, 1 );
histPDEFoamErr =
new TH1F(
"MVA_PDEFoamErr",
"MVA_PDEFoam error", nbin, 0, 1 );
histPDEFoamSig =
new TH1F(
"MVA_PDEFoamSig",
"MVA_PDEFoam significance", nbin, 0, 10 );
}
TH1F *probHistFi(0), *rarityHistFi(0);
if (Use["Fisher"]) {
probHistFi =
new TH1F(
"MVA_Fisher_Proba",
"MVA_Fisher_Proba", nbin, 0, 1 );
rarityHistFi =
new TH1F(
"MVA_Fisher_Rarity",
"MVA_Fisher_Rarity", nbin, 0, 1 );
}
TString fname =
"./tmva_class_example.root";
}
else {
input =
TFile::Open(
"http://root.cern.ch/files/tmva_class_example.root",
"CACHEREAD");
}
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout <<
"--- TMVAClassificationApp : Using input file: " <<
input->GetName() << std::endl;
std::cout << "--- Select signal sample" << std::endl;
std::vector<Float_t> vecVar(4);
std::cout <<
"--- Processing: " << theTree->
GetEntries() <<
" events" << std::endl;
if (ievt%1000 == 0) std::cout << "--- ... Processing event: " << ievt << std::endl;
var1 = userVar1 + userVar2;
var2 = userVar1 - userVar2;
if (Use["CutsGA"]) {
if (passed) nSelCutsGA++;
}
if (Use[
"Likelihood" ]) histLk ->Fill( reader->
EvaluateMVA(
"Likelihood method" ) );
if (Use[
"LikelihoodD" ]) histLkD ->Fill( reader->
EvaluateMVA(
"LikelihoodD method" ) );
if (Use[
"LikelihoodPCA"]) histLkPCA ->Fill( reader->
EvaluateMVA(
"LikelihoodPCA method" ) );
if (Use[
"LikelihoodKDE"]) histLkKDE ->Fill( reader->
EvaluateMVA(
"LikelihoodKDE method" ) );
if (Use[
"LikelihoodMIX"]) histLkMIX ->Fill( reader->
EvaluateMVA(
"LikelihoodMIX method" ) );
if (Use[
"PDERS" ]) histPD ->Fill( reader->
EvaluateMVA(
"PDERS method" ) );
if (Use[
"PDERSD" ]) histPDD ->Fill( reader->
EvaluateMVA(
"PDERSD method" ) );
if (Use[
"PDERSPCA" ]) histPDPCA ->Fill( reader->
EvaluateMVA(
"PDERSPCA method" ) );
if (Use[
"KNN" ]) histKNN ->Fill( reader->
EvaluateMVA(
"KNN method" ) );
if (Use[
"HMatrix" ]) histHm ->Fill( reader->
EvaluateMVA(
"HMatrix method" ) );
if (Use[
"Fisher" ]) histFi ->Fill( reader->
EvaluateMVA(
"Fisher method" ) );
if (Use[
"FisherG" ]) histFiG ->Fill( reader->
EvaluateMVA(
"FisherG method" ) );
if (Use[
"BoostedFisher"]) histFiB ->Fill( reader->
EvaluateMVA(
"BoostedFisher method" ) );
if (Use[
"LD" ]) histLD ->Fill( reader->
EvaluateMVA(
"LD method" ) );
if (Use[
"MLP" ]) histNn ->Fill( reader->
EvaluateMVA(
"MLP method" ) );
if (Use[
"MLPBFGS" ]) histNnbfgs ->Fill( reader->
EvaluateMVA(
"MLPBFGS method" ) );
if (Use[
"MLPBNN" ]) histNnbnn ->Fill( reader->
EvaluateMVA(
"MLPBNN method" ) );
if (Use[
"CFMlpANN" ]) histNnC ->Fill( reader->
EvaluateMVA(
"CFMlpANN method" ) );
if (Use[
"TMlpANN" ]) histNnT ->Fill( reader->
EvaluateMVA(
"TMlpANN method" ) );
if (Use[
"DNN_GPU"]) histDnnGpu->Fill(reader->
EvaluateMVA(
"DNN_GPU method"));
if (Use[
"DNN_CPU"]) histDnnCpu->Fill(reader->
EvaluateMVA(
"DNN_CPU method"));
if (Use[
"BDT" ]) histBdt ->Fill( reader->
EvaluateMVA(
"BDT method" ) );
if (Use[
"BDTG" ]) histBdtG ->Fill( reader->
EvaluateMVA(
"BDTG method" ) );
if (Use[
"BDTB" ]) histBdtB ->Fill( reader->
EvaluateMVA(
"BDTB method" ) );
if (Use[
"BDTD" ]) histBdtD ->Fill( reader->
EvaluateMVA(
"BDTD method" ) );
if (Use[
"BDTF" ]) histBdtF ->Fill( reader->
EvaluateMVA(
"BDTF method" ) );
if (Use[
"RuleFit" ]) histRf ->Fill( reader->
EvaluateMVA(
"RuleFit method" ) );
if (Use[
"SVM_Gauss" ]) histSVMG ->Fill( reader->
EvaluateMVA(
"SVM_Gauss method" ) );
if (Use[
"SVM_Poly" ]) histSVMP ->Fill( reader->
EvaluateMVA(
"SVM_Poly method" ) );
if (Use[
"SVM_Lin" ]) histSVML ->Fill( reader->
EvaluateMVA(
"SVM_Lin method" ) );
if (Use[
"FDA_MT" ]) histFDAMT ->Fill( reader->
EvaluateMVA(
"FDA_MT method" ) );
if (Use[
"FDA_GA" ]) histFDAGA ->Fill( reader->
EvaluateMVA(
"FDA_GA method" ) );
if (Use[
"Category" ]) histCat ->Fill( reader->
EvaluateMVA(
"Category method" ) );
if (Use[
"Plugin" ]) histPBdt ->Fill( reader->
EvaluateMVA(
"P_BDT method" ) );
if (Use["PDEFoam"]) {
histPDEFoam ->Fill( val );
histPDEFoamErr->Fill( err );
if (err>1.e-50) histPDEFoamSig->Fill( val/err );
}
if (Use["Fisher"]) {
probHistFi ->Fill( reader->
GetProba (
"Fisher method" ) );
rarityHistFi->Fill( reader->
GetRarity(
"Fisher method" ) );
}
}
std::cout <<
"--- End of event loop: "; sw.
Print();
if (Use[
"CutsGA"]) std::cout <<
"--- Efficiency for CutsGA method: " <<
double(nSelCutsGA)/theTree->
GetEntries()
<< " (for a required signal efficiency of " << effS << ")" << std::endl;
if (Use["CutsGA"]) {
if (mcuts) {
std::vector<Double_t> cutsMin;
std::vector<Double_t> cutsMax;
mcuts->
GetCuts( 0.7, cutsMin, cutsMax );
std::cout << "--- -------------------------------------------------------------" << std::endl;
std::cout << "--- Retrieve cut values for signal efficiency of 0.7 from Reader" << std::endl;
for (
UInt_t ivar=0; ivar<cutsMin.size(); ivar++) {
std::cout << "... Cut: "
<< cutsMin[ivar]
<< " < \""
<< "\" <= "
<< cutsMax[ivar] << std::endl;
}
std::cout << "--- -------------------------------------------------------------" << std::endl;
}
}
if (Use["Likelihood" ]) histLk ->Write();
if (Use["LikelihoodD" ]) histLkD ->Write();
if (Use["LikelihoodPCA"]) histLkPCA ->Write();
if (Use["LikelihoodKDE"]) histLkKDE ->Write();
if (Use["LikelihoodMIX"]) histLkMIX ->Write();
if (Use["PDERS" ]) histPD ->Write();
if (Use["PDERSD" ]) histPDD ->Write();
if (Use["PDERSPCA" ]) histPDPCA ->Write();
if (Use["KNN" ]) histKNN ->Write();
if (Use["HMatrix" ]) histHm ->Write();
if (Use["Fisher" ]) histFi ->Write();
if (Use["FisherG" ]) histFiG ->Write();
if (Use["BoostedFisher"]) histFiB ->Write();
if (Use["LD" ]) histLD ->Write();
if (Use["MLP" ]) histNn ->Write();
if (Use["MLPBFGS" ]) histNnbfgs ->Write();
if (Use["MLPBNN" ]) histNnbnn ->Write();
if (Use["CFMlpANN" ]) histNnC ->Write();
if (Use["TMlpANN" ]) histNnT ->Write();
if (Use["DNN_GPU"]) histDnnGpu->Write();
if (Use["DNN_CPU"]) histDnnCpu->Write();
if (Use["BDT" ]) histBdt ->Write();
if (Use["BDTG" ]) histBdtG ->Write();
if (Use["BDTB" ]) histBdtB ->Write();
if (Use["BDTD" ]) histBdtD ->Write();
if (Use["BDTF" ]) histBdtF ->Write();
if (Use["RuleFit" ]) histRf ->Write();
if (Use["SVM_Gauss" ]) histSVMG ->Write();
if (Use["SVM_Poly" ]) histSVMP ->Write();
if (Use["SVM_Lin" ]) histSVML ->Write();
if (Use["FDA_MT" ]) histFDAMT ->Write();
if (Use["FDA_GA" ]) histFDAGA ->Write();
if (Use["Category" ]) histCat ->Write();
if (Use["Plugin" ]) histPBdt ->Write();
if (Use["PDEFoam"]) { histPDEFoam->Write(); histPDEFoamErr->Write(); histPDEFoamSig->Write(); }
if (Use["Fisher"]) { if (probHistFi != 0) probHistFi->Write(); if (rarityHistFi != 0) rarityHistFi->Write(); }
std::cout << "--- Created root file: \"TMVApp.root\" containing the MVA output histograms" << std::endl;
delete reader;
std::cout << "==> TMVAClassificationApplication is done!" << std::endl << std::endl;
}
int main(
int argc,
char** argv )
{
for (int i=1; i<argc; i++) {
if(regMethod=="-b" || regMethod=="--batch") continue;
methodList += regMethod;
}
TMVAClassificationApplication(methodList);
return 0;
}
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void input
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t target
R__EXTERN TSystem * gSystem
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format.
static Bool_t SetCacheFileDir(ROOT::Internal::TStringView cacheDir, Bool_t operateDisconnected=kTRUE, Bool_t forceCacheread=kFALSE)
static TFile * Open(const char *name, Option_t *option="", const char *ftitle="", Int_t compress=ROOT::RCompressionSetting::EDefaults::kUseCompiledDefault, Int_t netopt=0)
Create / open a file.
1-D histogram with a float per channel (see TH1 documentation)}
const TString & GetInputVar(Int_t i) const
Multivariate optimisation of signal efficiency for given background efficiency, applying rectangular ...
Double_t GetCuts(Double_t effS, std::vector< Double_t > &cutMin, std::vector< Double_t > &cutMax) const
retrieve cut values for given signal efficiency
The Reader class serves to use the MVAs in a specific analysis context.
Double_t EvaluateMVA(const std::vector< Float_t > &, const TString &methodTag, Double_t aux=0)
Evaluate a std::vector<float> of input data for a given method The parameter aux is obligatory for th...
Double_t GetRarity(const TString &methodTag, Double_t mvaVal=-9999999)
evaluates the MVA's rarity
Double_t GetProba(const TString &methodTag, Double_t ap_sig=0.5, Double_t mvaVal=-9999999)
evaluates probability of MVA for given set of input variables
MethodCuts * FindCutsMVA(const TString &methodTag)
special function for Cuts to avoid dynamic_casts in ROOT macros, which are not properly handled by CI...
IMethod * BookMVA(const TString &methodTag, const TString &weightfile)
read method name from weight file
void AddSpectator(const TString &expression, Float_t *)
Add a float spectator or expression to the reader.
void AddVariable(const TString &expression, Float_t *)
Add a float variable or expression to the reader.
Double_t GetMVAError() const
void Start(Bool_t reset=kTRUE)
Start the stopwatch.
void Stop()
Stop the stopwatch.
void Print(Option_t *option="") const override
Print the real and cpu time passed between the start and stop events.
virtual Bool_t AccessPathName(const char *path, EAccessMode mode=kFileExists)
Returns FALSE if one can access a file using the specified access mode.
A TTree represents a columnar dataset.
virtual Int_t GetEntry(Long64_t entry, Int_t getall=0)
Read all branches of entry and return total number of bytes read.
virtual Int_t SetBranchAddress(const char *bname, void *add, TBranch **ptr=nullptr)
Change branch address, dealing with clone trees properly.
virtual Long64_t GetEntries() const
create variable transformations