void ZJetsToLL_App_bflavor( TString myMethodList = "" )
{
#ifdef __CINT__
gROOT->ProcessLine( ".O0" ); // turn off optimization in CINT
#endif
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
// --- Cut optimisation
Use["Cuts"] = 0;
Use["CutsD"] = 0;
Use["CutsPCA"] = 0;
Use["CutsGA"] = 0;
Use["CutsSA"] = 0;
//
// --- 1-dimensional likelihood ("naive Bayes estimator")
Use["Likelihood"] = 0;
Use["LikelihoodD"] = 0; // the "D" extension indicates decorrelated input variables (see option strings)
Use["LikelihoodPCA"] = 0; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
Use["LikelihoodKDE"] = 0;
Use["LikelihoodMIX"] = 0;
//
// --- Mutidimensional likelihood and Nearest-Neighbour methods
Use["PDERS"] = 0;
Use["PDERSD"] = 0;
Use["PDERSPCA"] = 0;
Use["PDEFoam"] = 0;
Use["PDEFoamBoost"] = 0; // uses generalised MVA method boosting
Use["KNN"] = 0; // k-nearest neighbour method
//
// --- Linear Discriminant Analysis
Use["LD"] = 0; // Linear Discriminant identical to Fisher
Use["Fisher"] = 0;
Use["FisherG"] = 0;
Use["BoostedFisher"] = 0; // uses generalised MVA method boosting
Use["HMatrix"] = 0;
//
// --- Function Discriminant analysis
Use["FDA_GA"] = 0; // minimisation of user-defined function using Genetics Algorithm
Use["FDA_SA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
Use["FDA_MCMT"] = 0;
//
// --- Neural Networks (all are feed-forward Multilayer Perceptrons)
Use["MLP"] = 0; // Recommended ANN
Use["MLPBFGS"] = 0; // Recommended ANN with optional training method
Use["MLPBNN"] = 0; // Recommended ANN with BFGS training method and bayesian regulator
Use["CFMlpANN"] = 0; // Depreciated ANN from ALEPH
Use["TMlpANN"] = 0; // ROOT's own ANN
//
// --- Support Vector Machine
Use["SVM"] = 0;
//
// --- Boosted Decision Trees using this
Use["BDT"] = 1; // uses Adaptive Boost
Use["BDTG"] = 0; // uses Gradient Boost
Use["BDTB"] = 0; // uses Bagging
Use["BDTD"] = 0; // decorrelation + Adaptive Boost
//
// --- Friedman's RuleFit method, ie, an optimised series of cuts ("rules")
Use["RuleFit"] = 0;
// ---------------------------------------------------------------
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 ZJetsToLL_App_bflavor" << std::endl;
// Select methods (don't look at this code - not of interest)
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;
}
}
// --------------------------------------------------------------------------------------------------
// --- Create the Reader object
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
// Create a set of variables and declare them to the reader
// - the variable names MUST corresponds in name and type to those given in the weight file(s) used
Float_t Hmass, Zmass;
Float_t Hpt, Zpt;
Float_t CSV0, CSV1;
Float_t DeltaPhiHV, DetaJJ;
Int_t nJets, eventFlavor;
Float_t Naj, nSV;
Float_t BDTvalue, Trigweight, B2011PUweight, btag2CSF, MET;
Float_t alpha_j, qtb1, jetPhi0, jetPhi1, jetEta0, jetEta1, Zphi, Hphi;
Float_t Ht, EvntShpCircularity, jetCHF0, jetCHF1;
Float_t EtaStandDev, muonPFiso0, muonPFiso1, EvntShpIsotropy;
Float_t mu0pt, mu1pt, UnweightedEta, DphiJJ, RMS_eta, EvntShpSphericity;
Float_t PtbalZH, EventPt, Angle, Centrality, EvntShpAplanarity;
reader->AddVariable( "Hmass", &Hmass );
// reader->AddVariable( "Zmass", &Zmass );
reader->AddVariable( "Hpt", &Hpt );
reader->AddVariable( "CSV0", &CSV0 );
reader->AddVariable( "CSV1", &CSV1 );
reader->AddVariable( "Zpt", &Zpt );
reader->AddVariable( "DeltaPhiHV:= abs(deltaPhi(Hphi,Zphi))", &DeltaPhiHV );
reader->AddVariable( "DetaJJ:= abs(jetEta1-jetEta0)", &DetaJJ );
reader->AddVariable( "Naj", &Naj);
reader->AddVariable( "UnweightedEta", &UnweightedEta );
reader->AddVariable( "EvntShpCircularity", &EvntShpCircularity );
reader->AddVariable( "alpha_j", &alpha_j );
reader->AddVariable( "qtb1", &qtb1 );
reader->AddVariable( "nSV", &nSV );
reader->AddVariable( "mu0pt", &mu0pt );
reader->AddVariable( "mu1pt", &mu1pt );
reader->AddVariable( "PtbalZH", &PtbalZH );
reader->AddVariable( "Angle", &Angle );
reader->AddVariable( "Centrality", &Centrality );
reader->AddVariable( "MET", &MET );
reader->AddVariable( "EvntShpAplanarity", &EvntShpAplanarity );
// Spectator variables declared in the training have to be added to the reader, too
Float_t UnweightedEta, mu0pt;
// reader->AddSpectator( "UnweightedEta", &UnweightedEta );
// reader->AddSpectator( "mu0pt", &mu0pt );
/* Float_t Category_cat1, Category_cat2, Category_cat3;
if (Use["Category"]){
// Add artificial spectators for distinguishing categories
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 );
}
*/
// --- Book the MVA methods
TString dir = "weights/";
TString prefix = "TMVAClassification";
// Book method(s)
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
TString methodName = TString(it->first) + TString(" method");
TString weightfile = dir + prefix + TString("_") + TString(it->first) + TString(".weights.xml");
reader->BookMVA( methodName, weightfile );
}
}
// Book output histograms
TH1F* hCHFb0_OpenSelection= new TH1F ("hCHFb0_OpenSelection", "charged Hadron Energy Fraction b1", 40, 0.0, 1.2);
TH1F* hCHFb1_OpenSelection= new TH1F ("hCHFb1_OpenSelection", "charged Hadron Energy Fraction b2", 40, 0.0, 1.2);
TH1F* hPtjj_OpenSelection= new TH1F ("hPtjj_OpenSelection","Pt of two b jets with highest CSV ", 50, 0.0, 400);
TH1F* hPtmumu_OpenSelection= new TH1F ("hPtmumu_OpenSelection","Pt of two muons with highest pt ", 50, 0.0, 400);
TH1F* hPtbalZH_OpenSelection= new TH1F ("hPtbalZH_OpenSelection", "Pt balance of Z and H", 40, -80, 80);
TH1F* hPtmu0_OpenSelection= new TH1F ("hPtmu0_OpenSelection","Pt of muon with highest pt ", 30, 0.0, 300);
TH1F* hPtmu1_OpenSelection= new TH1F ("hPtmu1_OpenSelection","Pt of muon with second highest pt ", 30, 0.0, 300);
TH1F* hPFRelIsomu0_OpenSelection= new TH1F ("hPFRelIsomu0_OpenSelection", "PF Rel Iso of muon with highest Pt", 40, 0., 0.2);
TH1F* hPFRelIsomu1_OpenSelection= new TH1F ("hPFRelIsomu1_OpenSelection", "PF Rel Iso of muon with second highest Pt", 40, 0., 0.2);
TH1F* hCSV0_OpenSelection= new TH1F ("hCSV0_OpenSelection","Jet with highest CSV ", 40, 0, 1.5);
TH1F* hCSV1_OpenSelection= new TH1F ("hCSV1_OpenSelection","Jet with second highest CSV ", 40, 0, 1.5);
TH1F* hdphiVH_OpenSelection= new TH1F ("hdphiVH_OpenSelection","Delta phi between Z and Higgs ", 50, -0.1, 4.5);
TH1F* hdetaJJ_OpenSelection= new TH1F ("hdetaJJ_OpenSelection","Delta eta between two jets ", 60, -4, 4);
TH1F* hNjets_OpenSelection= new TH1F ("hNjets_OpenSelection", "Number of Jets", 13, -2.5, 10.5);
TH1F* hMjj_OpenSelection = new TH1F ("hMjj_OpenSelection", "Invariant Mass of two Jets ", 50, 0, 300);
TH1F* hMmumu_OpenSelection = new TH1F ("hMmumu_OpenSelection", "Invariant Mass of two muons ", 75, 0, 200);
TH1F* hRMSeta_OpenSelection= new TH1F ("hRMSeta_OpenSelection", "RMS Eta", 30, 0, 3);
TH1F* hStaDeveta_OpenSelection= new TH1F ("hStaDeveta_OpenSelection", "Standard Deviation Eta", 30, 0, 3);
TH1F* hUnweightedEta_OpenSelection= new TH1F ("hUnweightedEta_OpenSelection", "Unweighted Eta ", 50, 0, 15);
TH1F* hdphiJJ_vect_OpenSelection= new TH1F ("hdphiJJ_vect_OpenSelection", "Delta phi between two jets", 30, -3.5, 4);
TH1F* hCircularity_OpenSelection= new TH1F("hCircularity_OpenSelection","EventShapeVariables circularity", 30, 0.0, 1.2);
TH1F* hHt_OpenSelection= new TH1F("hHt_OpenSelection","scalar sum of pt of four particles", 50, 0.0, 500);
TH1F* hCentrality_OpenSelection= new TH1F ("hCentrality_OpenSelection", "Centrality", 40, 0.0, 0.8);
TH1F* hEventPt_OpenSelection= new TH1F ("hEventPt_OpenSelection", "Pt of HV system", 50, 0.0, 100);
TH1F* hAngle_OpenSelection= new TH1F ("hAngle_OpenSelection", "Angle between H and Z", 45, 0, 4.5);
TH1F* hSphericity_OpenSelection= new TH1F ("hSphericity_OpenSelection", "EventShapeVariables sphericity", 50, 0.0, 1);
TH1F* hAplanarity_OpenSelection= new TH1F ("hAplanarity_OpenSelection", "EventShapeVariables Aplanarity", 50, -0.1, .4);
TH1F* hIsotropy_OpenSelection= new TH1F ("hIsotropy_OpenSelection", "EventShapeVariables isotropy", 30, 0.0, 1.3);
TH2F* hDphiDetajj_OpenSelection= new TH2F ("hDphiDetajj_OpenSelection", "#Delta#phi vs #Delta#eta JJ", 25, -5, 5, 25, -5, 5);
TTree *treeWithBDT = new TTree("treeWithBDT","Tree wiht BDT output");
treeWithBDT->SetDirectory(0);
treeWithBDT->Branch("nJets",&nJets, "nJets/I");
treeWithBDT->Branch("Naj",&Naj, "Naj/F");
treeWithBDT->Branch("eventFlavor",&eventFlavor, "eventFlavor/I");
treeWithBDT->Branch("CSV0",&CSV0, "CSV0/F");
treeWithBDT->Branch("CSV1",&CSV1, "CSV1/F");
treeWithBDT->Branch("Zmass",&Zmass, "Zmass/F");
treeWithBDT->Branch("Hmass",&Hmass, "Hmass/F");
treeWithBDT->Branch("DeltaPhiHV",&DeltaPhiHV, "DeltaPhiHV/F");
treeWithBDT->Branch("Hpt",&Hpt, "Hpt/F");
treeWithBDT->Branch("Zpt",&Zpt, "Zpt/F");
treeWithBDT->Branch("mu0pt",&mu0pt, "mu0pt/F");
treeWithBDT->Branch("Ht",&Ht, "Ht/F");
treeWithBDT->Branch("EtaStandDev",&EtaStandDev, "EtaStandDev/F");
treeWithBDT->Branch("UnweightedEta",&UnweightedEta, "UnweightedEta/F");
treeWithBDT->Branch("EvntShpCircularity",&EvntShpCircularity, "EvntShpCircularity/F");
treeWithBDT->Branch("alpha_j",&alpha_j, "alpha_j/F");
treeWithBDT->Branch("qtb1",&qtb1, "qtb1/F");
treeWithBDT->Branch("nSV",&nSV, "nSV/F");
treeWithBDT->Branch("Trigweight",&Trigweight, "Trigweight/F");
treeWithBDT->Branch("B2011PUweight",&B2011PUweight, "B2011PUweight/F");
treeWithBDT->Branch("btag2CSF",&btag2CSF, "btag2CSF/F");
treeWithBDT->Branch("DetaJJ",&DetaJJ, "DetaJJ/F");
treeWithBDT->Branch("jetCHF0",&jetCHF0, "jetCHF0/F");
treeWithBDT->Branch("jetCHF1",&jetCHF1, "jetCHF1/F");
treeWithBDT->Branch("jetPhi0",&jetPhi0, "jetPhi0/F");
treeWithBDT->Branch("jetPhi1",&jetPhi1, "jetPhi1/F");
treeWithBDT->Branch("jetEta0",&jetEta0, "jetEta0/F");
treeWithBDT->Branch("jetEta1",&jetEta1, "jetEta1/F");
treeWithBDT->Branch("mu1pt",&mu1pt, "mu1pt/F");
treeWithBDT->Branch("muonPFiso0",&muonPFiso0, "muonPFiso0/F");
treeWithBDT->Branch("muonPFiso1",&muonPFiso1, "muonPFiso1/F");
treeWithBDT->Branch("DphiJJ",&DphiJJ, "DphiJJ/F");
treeWithBDT->Branch("RMS_eta",&RMS_eta, "RMS_eta/F");
treeWithBDT->Branch("PtbalZH",&PtbalZH, "PtbalZH/F");
treeWithBDT->Branch("EventPt",&EventPt, "EventPt/F");
treeWithBDT->Branch("Angle",&Angle, "Angle/F");
treeWithBDT->Branch("Centrality",&Centrality, "Centrality/F");
treeWithBDT->Branch("MET",&MET, "MET/F");
treeWithBDT->Branch("EvntShpAplanarity",&EvntShpAplanarity, "EvntShpAplanarity/F");
treeWithBDT->Branch("EvntShpSphericity",&EvntShpSphericity, "EvntShpSphericity/F");
treeWithBDT->Branch("EvntShpIsotropy",&EvntShpIsotropy, "EvntShpIsotropy/F");
treeWithBDT->Branch("Zphi",&Zphi, "Zphi/F");
treeWithBDT->Branch("Hphi",&Hphi, "Hphi/F");
treeWithBDT->Branch("BDTvalue",&BDTvalue, "BDTvalue/F");
UInt_t nbin = 15;
TH1F *histLk(0), *histLkD(0), *histLkPCA(0), *histLkKDE(0), *histLkMIX(0), *histPD(0), *histPDD(0);
TH1F *histPDPCA(0), *histPDEFoam(0), *histPDEFoamErr(0), *histPDEFoamSig(0), *histKNN(0), *histHm(0);
TH1F *histFi(0), *histFiG(0), *histFiB(0), *histLD(0), *histNn(0),*histNnbfgs(0),*histNnbnn(0);
TH1F *histNnC(0), *histNnT(0), *histBdt(0), *histBdtG(0), *histBdtD(0), *histRf(0), *histSVMG(0);
TH1F *histSVMP(0), *histSVML(0), *histFDAMT(0), *histFDAGA(0), *histCat(0), *histPBdt(0);
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["BDT"]) {
histMattBdt = new TH1F( "Matt_BDT", "Matt_BDT", 15, -1.1, 0.35 );
histTMVABdt = new TH1F( "TMVA_BDT", "TMVA_BDT", 88, -1.0, 0.1 );
}
if (Use["BDTD"]) histBdtD = new TH1F( "MVA_BDTD", "MVA_BDTD", nbin, -0.8, 0.8 );
if (Use["BDTG"]) histBdtG = new TH1F( "MVA_BDTG", "MVA_BDTG", 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 );
// PDEFoam also returns per-event error, fill in histogram, and also fill significance
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 );
}
// Book example histogram for probability (the other methods are done similarly)
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 );
}
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
//
TFile *input(0);
TString fname = "/home/hep/wilken/Weights/CMSSW_4_2_8_patch3/src/UserCode/wilken/CSVShapeCorr/ZJetsToLL.root";
double lumi = 100.0;
Double_t ZJetsToLL_weight = lumi/((2349387.0/80*1000)/2.0); //ZJetsToLL_Pt-100_7TeV-herwigpp
//ZJetsToLL_weight = ZJetsToLL_weight*(9000/85684.0);
if (!gSystem->AccessPathName( fname ))
input = TFile::Open( fname ); // check if file in local directory exists
else
input = TFile::Open( "http://root.cern.ch/files/tmva_class_example.root" ); // if not: download from ROOT server
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVAClassificationApp : Using input file: " << input->GetName() << std::endl;
// --- Event loop
// Prepare the event tree
// - here the variable names have to corresponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
//
std::cout << "--- Select signal sample" << std::endl;
TTree* theTree = (TTree*)input->Get("BDT_btree");
theTree->SetBranchAddress( "Hmass", &Hmass );
theTree->SetBranchAddress( "Zmass", &Zmass );
theTree->SetBranchAddress( "Hpt", &Hpt );
theTree->SetBranchAddress( "Zpt", &Zpt );
theTree->SetBranchAddress( "CSV0", &CSV0 );
theTree->SetBranchAddress( "CSV1", &CSV1 );
theTree->SetBranchAddress( "DeltaPhiHV", &DeltaPhiHV );
theTree->SetBranchAddress( "DetaJJ", &DetaJJ );
theTree->SetBranchAddress( "UnweightedEta", &UnweightedEta );
theTree->SetBranchAddress( "mu0pt", &mu0pt );
theTree->SetBranchAddress( "Ht", &Ht );
theTree->SetBranchAddress( "EvntShpCircularity", &EvntShpCircularity );
theTree->SetBranchAddress( "nJets", &nJets );
theTree->SetBranchAddress( "Naj", &Naj );
theTree->SetBranchAddress( "mu1pt", &mu1pt );
theTree->SetBranchAddress( "mu0pt", &mu0pt );
theTree->SetBranchAddress( "EtaStandDev", &EtaStandDev );
theTree->SetBranchAddress( "UnweightedEta", &UnweightedEta );
theTree->SetBranchAddress( "jetCHF0", &jetCHF0 );
theTree->SetBranchAddress( "jetCHF1", &jetCHF1 );
theTree->SetBranchAddress( "muonPFiso0", &muonPFiso0 );
theTree->SetBranchAddress( "muonPFiso1", &muonPFiso1 );
theTree->SetBranchAddress( "DphiJJ", &DphiJJ );
theTree->SetBranchAddress( "RMS_eta", &RMS_eta );
theTree->SetBranchAddress( "PtbalZH", &PtbalZH );
theTree->SetBranchAddress( "EventPt", &EventPt );
theTree->SetBranchAddress( "Angle", &Angle );
theTree->SetBranchAddress( "Centrality", &Centrality );
theTree->SetBranchAddress( "EvntShpAplanarity", &EvntShpAplanarity );
theTree->SetBranchAddress( "EvntShpSphericity", &EvntShpSphericity );
theTree->SetBranchAddress( "EvntShpIsotropy", &EvntShpIsotropy );
theTree->SetBranchAddress( "Trigweight", &Trigweight );
theTree->SetBranchAddress( "B2011PUweight", &B2011PUweight );
theTree->SetBranchAddress( "btag2CSF", &btag2CSF );
theTree->SetBranchAddress( "MET", &MET );
theTree->SetBranchAddress( "eventFlavor", &eventFlavor );
// Efficiency calculator for cut method
Int_t nSelCutsGA = 0;
Double_t effS = 0.7;
TTree* TMVATree = (TTree*)input->Get("TMVA_tree");
int NumInTree = theTree->GetEntries();
int NumberTMVAtree = TMVATree->GetEntries();
ZJetsToLL_weight = ZJetsToLL_weight* ( NumInTree /float(NumberTMVAtree)) ;
std::vector<Float_t> vecVar(4); // vector for EvaluateMVA tests
std::cout << "--- Processing: " << theTree->GetEntries() << " events" << std::endl;
TStopwatch sw;
sw.Start();
for (Long64_t ievt=0; ievt<NumInTree;ievt++) {
if (ievt%1000 == 0) std::cout << "--- ... Processing event: " << ievt << std::endl;
theTree->GetEntry(ievt);
// var1 = userVar1 + userVar2;
// var2 = userVar1 - userVar2;
// --- Return the MVA outputs and fill into histograms
if (Use["CutsGA"]) {
// Cuts is a special case: give the desired signal efficienciy
Bool_t passed = reader->EvaluateMVA( "CutsGA method", effS );
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["BDT" ]) {
BDTvalue = reader->EvaluateMVA( "BDT method" );
}
if (Use["BDTD" ]) histBdtD ->Fill( reader->EvaluateMVA( "BDTD method" ) );
if (Use["BDTG" ]) histBdtG ->Fill( reader->EvaluateMVA( "BDTG 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" ) );
// Retrieve also per-event error
if (Use["PDEFoam"]) {
Double_t val = reader->EvaluateMVA( "PDEFoam method" );
Double_t err = reader->GetMVAError();
histPDEFoam ->Fill( val );
histPDEFoamErr->Fill( err );
if (err>1.e-50) histPDEFoamSig->Fill( val/err );
}
// Retrieve probability instead of MVA output
if (Use["Fisher"]) {
probHistFi ->Fill( reader->GetProba ( "Fisher method" ) );
rarityHistFi->Fill( reader->GetRarity( "Fisher method" ) );
}
// std::cout << "Ht is "<< Ht << endl;
if (eventFlavor == 5 ) {
histMattBdt ->Fill( BDTvalue,ZJetsToLL_weight );
histTMVABdt ->Fill( BDTvalue,ZJetsToLL_weight );
hMjj_OpenSelection->Fill(Hmass,ZJetsToLL_weight);
hMmumu_OpenSelection->Fill(Zmass,ZJetsToLL_weight);
hPtjj_OpenSelection->Fill(Hpt,ZJetsToLL_weight);
hPtmumu_OpenSelection->Fill(Zpt,ZJetsToLL_weight);
hCSV0_OpenSelection->Fill(CSV0,ZJetsToLL_weight);
hCSV1_OpenSelection->Fill(CSV1,ZJetsToLL_weight);
hdphiVH_OpenSelection->Fill(DeltaPhiHV,ZJetsToLL_weight);
hdetaJJ_OpenSelection->Fill(DetaJJ,ZJetsToLL_weight);
hUnweightedEta_OpenSelection->Fill(UnweightedEta,ZJetsToLL_weight);
hPtmu0_OpenSelection->Fill(mu0pt,ZJetsToLL_weight);
hHt_OpenSelection->Fill(Ht,ZJetsToLL_weight);
hCircularity_OpenSelection->Fill(EvntShpCircularity,ZJetsToLL_weight);
hCHFb0_OpenSelection->Fill(jetCHF0, ZJetsToLL_weight);
hCHFb1_OpenSelection->Fill(jetCHF1, ZJetsToLL_weight);
hPtbalZH_OpenSelection->Fill(PtbalZH, ZJetsToLL_weight);
hPtmu1_OpenSelection->Fill(mu1pt, ZJetsToLL_weight);
hPFRelIsomu0_OpenSelection->Fill(muonPFiso0, ZJetsToLL_weight);
hPFRelIsomu1_OpenSelection->Fill(muonPFiso1, ZJetsToLL_weight);
hNjets_OpenSelection->Fill(nJets, ZJetsToLL_weight);
hRMSeta_OpenSelection->Fill(RMS_eta, ZJetsToLL_weight);
hStaDeveta_OpenSelection->Fill(EtaStandDev, ZJetsToLL_weight);
hdphiJJ_vect_OpenSelection->Fill(DphiJJ, ZJetsToLL_weight);
hCentrality_OpenSelection->Fill(Centrality, ZJetsToLL_weight);
hEventPt_OpenSelection->Fill(EventPt, ZJetsToLL_weight);
hAngle_OpenSelection->Fill(Angle, ZJetsToLL_weight);
hSphericity_OpenSelection->Fill(EvntShpSphericity, ZJetsToLL_weight);
hAplanarity_OpenSelection->Fill(EvntShpAplanarity, ZJetsToLL_weight);
hIsotropy_OpenSelection->Fill(EvntShpIsotropy, ZJetsToLL_weight);
hDphiDetajj_OpenSelection->Fill(DphiJJ, DetaJJ, ZJetsToLL_weight);
}// only fill if b quark
treeWithBDT->Fill();
}//end event loop
// Get elapsed time
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
// Get efficiency for cuts classifier
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"]) {
// test: retrieve cuts for particular signal efficiency
// CINT ignores dynamic_casts so we have to use a cuts-secific Reader function to acces the pointer
TMVA::MethodCuts* mcuts = reader->FindCutsMVA( "CutsGA method" ) ;
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]
<< " < \""
<< mcuts->GetInputVar(ivar)
<< "\" <= "
<< cutsMax[ivar] << std::endl;
}
std::cout << "--- -------------------------------------------------------------" << std::endl;
}
}
// --- Write histograms
TFile *target = new TFile( "TMVApp_ZJetsToLL_bflavor.root","RECREATE" );
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["BDT" ]) {
histMattBdt ->Write();
histTMVABdt ->Write();
}
if (Use["BDTD" ]) histBdtD ->Write();
if (Use["BDTG" ]) histBdtG ->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();
// Write also error and significance histos
if (Use["PDEFoam"]) { histPDEFoam->Write(); histPDEFoamErr->Write(); histPDEFoamSig->Write(); }
// Write also probability hists
if (Use["Fisher"]) { if (probHistFi != 0) probHistFi->Write(); if (rarityHistFi != 0) rarityHistFi->Write(); }
hCHFb0_OpenSelection->Write();
hCHFb1_OpenSelection->Write();
hPtbalZH_OpenSelection->Write();
hPtmu1_OpenSelection->Write();
hPFRelIsomu0_OpenSelection->Write();
hPFRelIsomu1_OpenSelection->Write();
hMjj_OpenSelection->Write();
hMmumu_OpenSelection->Write();
hPtjj_OpenSelection->Write();
hPtmumu_OpenSelection->Write();
hCSV0_OpenSelection->Write();
hCSV1_OpenSelection->Write();
hdphiVH_OpenSelection->Write();
hdetaJJ_OpenSelection->Write();
hUnweightedEta_OpenSelection->Write();
hPtmu0_OpenSelection->Write();
hCircularity_OpenSelection->Write();
hHt_OpenSelection->Write();
hNjets_OpenSelection->Write();
hRMSeta_OpenSelection->Write();
hStaDeveta_OpenSelection->Write();
hdphiJJ_vect_OpenSelection->Write();
hCentrality_OpenSelection->Write();
hEventPt_OpenSelection->Write();
hAngle_OpenSelection->Write();
hSphericity_OpenSelection->Write();
hAplanarity_OpenSelection->Write();
hIsotropy_OpenSelection->Write();
hDphiDetajj_OpenSelection->Write();
treeWithBDT->Write();
target->Close();
delete reader;
hCHFb0_OpenSelection->Delete();
hCHFb1_OpenSelection->Delete();
hPtbalZH_OpenSelection->Delete();
hPtmu1_OpenSelection->Delete();
hPFRelIsomu0_OpenSelection->Delete();
hPFRelIsomu1_OpenSelection->Delete();
hMjj_OpenSelection->Delete();
hMmumu_OpenSelection->Delete();
hPtjj_OpenSelection->Delete();
hPtmumu_OpenSelection->Delete();
hCSV0_OpenSelection->Delete();
hCSV1_OpenSelection->Delete();
hdphiVH_OpenSelection->Delete();
hdetaJJ_OpenSelection->Delete();
hUnweightedEta_OpenSelection->Delete();
hPtmu0_OpenSelection->Delete();
hCircularity_OpenSelection->Delete();
hHt_OpenSelection->Delete();
hNjets_OpenSelection->Delete();
hRMSeta_OpenSelection->Delete();
hStaDeveta_OpenSelection->Delete();
hdphiJJ_vect_OpenSelection->Delete();
hCentrality_OpenSelection->Delete();
hEventPt_OpenSelection->Delete();
hAngle_OpenSelection->Delete();
hSphericity_OpenSelection->Delete();
hAplanarity_OpenSelection->Delete();
hIsotropy_OpenSelection->Delete();
hDphiDetajj_OpenSelection->Delete();
treeWithBDT->Delete();
if (Use["BDT" ]) {
histMattBdt ->Delete();
histTMVABdt ->Delete();
}
std::cout << "==> ZJetsToLL_App_bflavor is done!" << endl << std::endl;
gROOT->ProcessLine(".q");
}
int testPyKerasRegression(){
// Get data file
std::cout << "Get test data..." << std::endl;
TString fname = "./tmva_reg_example.root";
if (gSystem->AccessPathName(fname)) // file does not exist in local directory
gSystem->Exec("curl -O http://root.cern.ch/files/tmva_reg_example.root");
TFile *input = TFile::Open(fname);
// Build model from python file
std::cout << "Generate keras model..." << std::endl;
UInt_t ret;
ret = gSystem->Exec("echo '"+pythonSrc+"' > generateKerasModelRegression.py");
if(ret!=0){
std::cout << "[ERROR] Failed to write python code to file" << std::endl;
return 1;
}
ret = gSystem->Exec("python generateKerasModelRegression.py");
if(ret!=0){
std::cout << "[ERROR] Failed to generate model using python" << std::endl;
return 1;
}
// Setup PyMVA and factory
std::cout << "Setup TMVA..." << std::endl;
TMVA::PyMethodBase::PyInitialize();
TFile* outputFile = TFile::Open("ResultsTestPyKerasRegression.root", "RECREATE");
TMVA::Factory *factory = new TMVA::Factory("testPyKerasRegression", outputFile,
"!V:Silent:Color:!DrawProgressBar:AnalysisType=Regression");
// Load data
TMVA::DataLoader *dataloader = new TMVA::DataLoader("datasetTestPyKerasRegression");
TTree *tree = (TTree*)input->Get("TreeR");
dataloader->AddRegressionTree(tree);
dataloader->AddVariable("var1");
dataloader->AddVariable("var2");
dataloader->AddTarget("fvalue");
dataloader->PrepareTrainingAndTestTree("",
"SplitMode=Random:NormMode=NumEvents:!V");
// Book and train method
factory->BookMethod(dataloader, TMVA::Types::kPyKeras, "PyKeras",
"!H:!V:VarTransform=D,G:FilenameModel=kerasModelRegression.h5:FilenameTrainedModel=trainedKerasModelRegression.h5:NumEpochs=10:BatchSize=32:SaveBestOnly=false:Verbose=0");
std::cout << "Train model..." << std::endl;
factory->TrainAllMethods();
// Clean-up
delete factory;
delete dataloader;
delete outputFile;
// Setup reader
UInt_t numEvents = 100;
std::cout << "Run reader and estimate target of " << numEvents << " events..." << std::endl;
TMVA::Reader *reader = new TMVA::Reader("!Color:Silent");
Float_t vars[3];
reader->AddVariable("var1", vars+0);
reader->AddVariable("var2", vars+1);
reader->BookMVA("PyKeras", "datasetTestPyKerasRegression/weights/testPyKerasRegression_PyKeras.weights.xml");
// Get mean squared error on events
tree->SetBranchAddress("var1", vars+0);
tree->SetBranchAddress("var2", vars+1);
tree->SetBranchAddress("fvalue", vars+2);
Float_t meanMvaError = 0;
for(UInt_t i=0; i<numEvents; i++){
tree->GetEntry(i);
meanMvaError += std::pow(vars[2]-reader->EvaluateMVA("PyKeras"),2);
}
meanMvaError = meanMvaError/float(numEvents);
// Check whether the response is obviously better than guessing
std::cout << "Mean squared error: " << meanMvaError << std::endl;
if(meanMvaError > 30.0){
std::cout << "[ERROR] Mean squared error is " << meanMvaError << " (>30.0)" << std::endl;
return 1;
}
return 0;
}
//_____________________________________________________________________________
void dumpCats(bool debug, TString fileName, TString dirName,
bool smearMassError) {
TFile* file = TFile::Open(fileName.Data());
TDirectory* theDir = (TDirectory*) file->FindObjectAny(dirName.Data());
TTree* theTree = (TTree*) theDir->Get("hPhotonTree");
UInt_t run, lumi, evt;
float rho, mass;
Int_t tth, vhLep, vhMet, vhHad, vbf, cat;
theTree->SetBranchAddress("run", &run);
theTree->SetBranchAddress("lumi",&lumi);
theTree->SetBranchAddress("evt", &evt);
theTree->SetBranchAddress("mass",&mass);
theTree->SetBranchAddress("rho",&rho);
theTree->SetBranchAddress("tthTag",&tth);
theTree->SetBranchAddress("VHLepTag",&vhLep);
theTree->SetBranchAddress("VHHadTag",&vhHad);
theTree->SetBranchAddress("vbfTag",&vbf);
float ph1e, ph1pt, ph1eerr, ph1eerrsmeared, teta1, phi1;
float ph2e, ph2pt, ph2eerr, ph2eerrsmeared, teta2, phi2;
theTree->SetBranchAddress("ph1.pt",&ph1pt);
theTree->SetBranchAddress("ph1.e",&ph1e);
theTree->SetBranchAddress("ph1.eerr",&ph1eerr);
theTree->SetBranchAddress("ph1.eerrsmeared",&ph1eerrsmeared);
theTree->SetBranchAddress("ph1.eta",&teta1);
theTree->SetBranchAddress("ph1.phi",&phi1);
theTree->SetBranchAddress("ph2.pt",&ph2pt);
theTree->SetBranchAddress("ph2.e",&ph2e);
theTree->SetBranchAddress("ph2.eerr",&ph2eerr);
theTree->SetBranchAddress("ph2.eerrsmeared",&ph2eerrsmeared);
theTree->SetBranchAddress("ph2.eta",&teta2);
theTree->SetBranchAddress("ph2.phi",&phi2);
Float_t ele1_pt, ele1_eta;
Float_t mu1_pt, mu1_eta;
theTree->SetBranchAddress("elePt" , &ele1_pt );
theTree->SetBranchAddress("eleEta" , &ele1_eta);
theTree->SetBranchAddress("muonPt" , &mu1_pt );
theTree->SetBranchAddress("muonEta", &mu1_eta );
float jet1pt, jet1eta, jet1phi;
float jet2pt, jet2eta, jet2phi;
theTree->SetBranchAddress("jet1pt",&jet1pt);
theTree->SetBranchAddress("jet1eta",&jet1eta);
theTree->SetBranchAddress("jet1phi",&jet1phi);
theTree->SetBranchAddress("jet2pt",&jet2pt);
theTree->SetBranchAddress("jet2eta",&jet2eta);
theTree->SetBranchAddress("jet2phi",&jet2phi);
float masserr, masserrwvtx, masserr_ns, masserrwvtx_ns, vtxprob, idmva_1, idmva_2;
theTree->SetBranchAddress("masserrsmeared",&masserr);
theTree->SetBranchAddress("masserrsmearedwrongvtx",&masserrwvtx);
theTree->SetBranchAddress("masserr",&masserr_ns);
theTree->SetBranchAddress("masserrwrongvtx",&masserrwvtx_ns);
theTree->SetBranchAddress("vtxprob",&vtxprob);
theTree->SetBranchAddress("ph1.idmva",&idmva_1);
theTree->SetBranchAddress("ph2.idmva",&idmva_2);
// MET tag stuff
float corrpfmet, corrpfmetphi, pfmet, pfmetphi;
theTree->SetBranchAddress("corrpfmet",&corrpfmet);
theTree->SetBranchAddress("corrpfmetphi",&corrpfmetphi);
theTree->SetBranchAddress("pfmet",&pfmet);
theTree->SetBranchAddress("pfmetphi",&pfmetphi);
float phigg, jetleadNoIDpt, jetleadNoIDphi, jetleadNoIDeta;
float ph1sceta, ph1scphi;
float ph2sceta, ph2scphi;
theTree->SetBranchAddress("phigg",&phigg);
theTree->SetBranchAddress("jetleadNoIDpt",&jetleadNoIDpt);
theTree->SetBranchAddress("jetleadNoIDphi",&jetleadNoIDphi);
theTree->SetBranchAddress("jetleadNoIDeta",&jetleadNoIDeta);
theTree->SetBranchAddress("ph1.sceta",&ph1sceta);
theTree->SetBranchAddress("ph1.scphi",&ph1scphi);
theTree->SetBranchAddress("ph2.sceta",&ph2sceta);
theTree->SetBranchAddress("ph2.scphi",&ph2scphi);
// Setup the diphoton BDT
Float_t rVtxSigmaMoM, wVtxSigmaMoM, cosDPhi;
Float_t pho1_ptOverM;
Float_t pho2_ptOverM;
Float_t diphoMVA;
TMVA::Reader* reader = new TMVA::Reader("Silent");
reader->AddVariable("masserrsmeared/mass" , &rVtxSigmaMoM);
reader->AddVariable("masserrsmearedwrongvtx/mass", &wVtxSigmaMoM);
reader->AddVariable("vtxprob" , &vtxprob );
reader->AddVariable("ph1.pt/mass" , &pho1_ptOverM);
reader->AddVariable("ph2.pt/mass" , &pho2_ptOverM);
reader->AddVariable("ph1.eta" , &teta1 );
reader->AddVariable("ph2.eta" , &teta2 );
reader->AddVariable("TMath::Cos(ph1.phi-ph2.phi)", &cosDPhi );
reader->AddVariable("ph1.idmva" , &idmva_1 );
reader->AddVariable("ph2.idmva" , &idmva_2 );
const char *diphotonWeights = (
"/home/veverka/cms/cmssw/031/CMSSW_5_3_10_patch1/src/MitPhysics/data/"
"HggBambu_SMDipho_Oct01_redqcdweightallsigevenbkg_BDTG.weights.xml"
);
reader->BookMVA("BDTG", diphotonWeights);
TRandom3 rng(0);
int eventCounter=0;
// Loop over the entries.
std::cout << "Looping over " << theTree->GetEntries() << " entries." << std::endl;
for (int i=0; i < theTree->GetEntries(); ++i) {
if (eventCounter > 9 && debug ) break;
if (debug) {
cout << "Processing entry " << i << " :" << endl
<< " mass: " << mass << endl
<< " ph1pt: " << ph1pt << endl
<< " ph2pt: " << ph2pt << endl
<< " idmva_1:" << idmva_1 << endl
<< " idmva_2:" << idmva_2 << endl;
}
theTree->GetEntry(i);
// MET category
vhMet = 0;
double dEtaJPh1 = ph1sceta - jetleadNoIDeta;
double dPhiJPh1 = TMath::ACos(TMath::Cos(ph1scphi - jetleadNoIDphi));
double dRJPh1 = TMath::Sqrt(TMath::Power(dEtaJPh1, 2) +
TMath::Power(dPhiJPh1, 2));
double dEtaJPh2 = ph2sceta - jetleadNoIDeta;
double dPhiJPh2 = TMath::ACos(TMath::Cos(ph2scphi - jetleadNoIDphi));
double dRJPh2 = TMath::Sqrt(TMath::Power(dEtaJPh2, 2) +
TMath::Power(dPhiJPh2, 2));
double dPhiMetGG = TMath::ACos(TMath::Cos(phigg - corrpfmetphi));
double dPhiMetJet = TMath::ACos(
TMath::Cos(TMath::Abs(jetleadNoIDphi - corrpfmetphi))
);
if (TMath::Abs(ph1sceta) < 1.4442 &&
TMath::Abs(ph2sceta) < 1.4442 &&
corrpfmet > 70. &&
ph1pt/mass > 45./120. &&
dPhiMetGG > 2.1 &&
(
jetleadNoIDpt < 50. ||
dRJPh1 < 0.5 ||
dRJPh2 < 0.5 ||
dPhiMetJet < 2.7
) &&
ph2pt > mass/4) {
vhMet = 1;
}
// Calculate needed variables for the diphoMVA
if (smearMassError) {
rVtxSigmaMoM = masserr / mass; // with smearing
wVtxSigmaMoM = masserrwvtx / mass; // with smearing
} else {
rVtxSigmaMoM = masserr_ns / mass; // no smearing
wVtxSigmaMoM = masserrwvtx_ns / mass; // no smearing
}
cosDPhi = TMath::Cos(phi1 - phi2);
pho1_ptOverM = ph1pt / mass;
pho2_ptOverM = ph2pt / mass;
diphoMVA = reader->EvaluateMVA("BDTG");
bool passPreselection = (mass > 100 &&
mass < 180 &&
ph1pt > mass/3 &&
ph2pt > mass/4 &&
idmva_1 > -0.2 &&
idmva_2 > -0.2);
if (passPreselection == false) {
if (debug) {
cout << " passPreselection: " << passPreselection << endl;
}
continue;
}
if (debug) {
cout << " ... passed preselection." << endl;
}
eventCounter++;
if (tth == 1) tth = 2;
else if (tth == 2) tth = 1;
if (vhHad == 2) vhHad = 1;
cat = kIncl0;
if (tth == 2) cat = kTTHLep;
else if (vhLep == 2) cat = kVHLepTight;
else if (vhLep == 1) cat = kVHLepLoose;
else if (vbf > 0) cat = kDijet0;
else if (vhMet == 1) cat = kVHMet;
else if (tth == 1) cat = kTTHHad;
else if (vhHad == 1) cat = kVHHad;
// if (cat == kIncl0 && diphoMVA < -0.4) continue;
// Event Variables
dumpVar("run" , run ); // 1
dumpVar("lumi" , lumi ); // 2
dumpVar("event" , evt ); // 3
dumpVar("cat" , cat );
dumpVar("tth" , tth );
dumpVar("vhLep" , vhLep );
dumpVar("vhMet" , vhMet );
dumpVar("vhHad" , vhHad );
dumpVar("vbf" , vbf );
// Leading Photon Variables
dumpVar("pho1_e" , ph1e ); // 10
dumpVar("pho1_eErr" , ph1eerr ); // 11
dumpVar("pho1_eta" , teta1 ); // 8
dumpVar("pho1_phi" , phi1 ); // 9
dumpVar("pho1_idMVA" , idmva_1 );
// Trailing Photon Variables
dumpVar("pho2_e" , ph2e ); // 36
dumpVar("pho2_eErr" , ph2eerr ); // 37
dumpVar("pho2_eta" , teta2 ); // 34
dumpVar("pho2_phi" , phi2 ); // 35
dumpVar("pho2_idMVA" , idmva_2 );
// Diphoton Variables
dumpVar("mass" , mass );
dumpVar("met" , corrpfmet );
dumpVar("met_phi" , corrpfmetphi );
dumpVar("uncorrMet" , pfmet );
dumpVar("uncorrMet_phi" , pfmetphi );
dumpVar("diphoMVA" , diphoMVA );
// Muon Variables
if (mu1_pt < 0) {
mu1_pt = -999;
mu1_eta = -999;
}
dumpVar("mu1_pt" , mu1_pt );
dumpVar("mu1_eta" , mu1_eta );
// Electron Variables
if (ele1_pt < 0) {
ele1_pt = -999;
ele1_eta = -999;
}
dumpVar("ele1_pt" , ele1_pt );
dumpVar("ele1_eta" , ele1_eta );
// Leading Jet Variables
if (jet1pt < 0) {
jet1pt = -999;
jet1eta = -999;
}
dumpVar("jet1_pt" , jet1pt ); // 69
dumpVar("jet1_eta" , jet1eta ); // 70
dumpVar("jet1_phi" , jet1phi ); // 70
// Trailing Jet Variables
if (jet2pt < 0) {
jet2pt = -999;
jet2eta = -999;
}
dumpVar("jet2_pt" , jet2pt ); // 72
dumpVar("jet2_eta" , jet2eta ); // 73
dumpVar("jet2_phi" , jet2phi ); // 70
std::cout << std::endl;
} // Loop over the tree entries.
return;
} // void dumpMvaInputs(bool debug, TString fileName)
//should include inttype 11, 12, 13 as the signal
void TMVAClassificationApplication_cc1presv2_bdt_ver3noveract( TString myMethodList = "", TString fname )
{
#ifdef __CINT__
gROOT->ProcessLine( ".O0" ); // turn off optimization in CINT
#endif
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
//
// --- Boosted Decision Trees
Use["BDT"] = 1; // uses Adaptive Boost
std::cout << std::endl;
std::cout << "==> Start TMVAClassificationApplication" << std::endl;
// Select methods (don't look at this code - not of interest)
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;
}
}
// --------------------------------------------------------------------------------------------------
// --- Create the Reader object
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
// Create a set of variables and declare them to the reader
// - the variable names MUST corresponds in name and type to those given in the weight file(s) used
Float_t mumucl, pmucl;
Float_t pang_t, muang_t;
Float_t veract;
Float_t ppe, mupe;
Float_t range, coplanarity;
Float_t opening;//newadd
reader->AddVariable( "mumucl", &mumucl );
reader->AddVariable( "pmucl", &pmucl );
reader->AddVariable( "pang_t", &pang_t );
reader->AddVariable( "muang_t", &muang_t );
//reader->AddVariable( "veract", &veract );
reader->AddVariable( "ppe", &ppe);
reader->AddVariable( "mupe", &mupe);
reader->AddVariable( "range", &range);
reader->AddVariable( "coplanarity", &coplanarity);
reader->AddVariable( "opening", &opening);//newadd
// Spectator variables declared in the training have to be added to the reader, too
Int_t fileIndex, inttype;
Float_t nuE, norm, totcrsne;
reader->AddSpectator( "fileIndex", &fileIndex );
reader->AddSpectator( "nuE", &nuE );
reader->AddSpectator( "inttype", &inttype );
reader->AddSpectator( "norm", &norm );
reader->AddSpectator( "totcrsne", &totcrsne );
reader->AddSpectator( "veract", &veract );
// --- Book the MVA methods
TString dir = "weights/";
TString prefix = "TMVAClassification_cc1presv2_ver3noveract";//newchange
// Book method(s)
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
TString methodName = TString(it->first) + TString(" method");
TString weightfile = dir + prefix + TString("_") + TString(it->first) + TString(".weights.xml");
reader->BookMVA( methodName, weightfile );
}
}
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
//
/*//TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/data_merged_ccqe_addpid_ver3noveract_pid1pres.root";
//TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/pm_merged_ccqe_tot_addpid_ver3noveract_pid1pres.root";
//TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/pmbar_merged_ccqe_addpid_ver3noveract_pid1pres.root";
//TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/ingrid_merged_nd3_ccqe_tot_addpid_ver3noveract_pid1pres.root";
//TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/wall_merged_ccqe_tot_addpid_ver3noveract_pid1pres.root";
//check genie signal
TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/genie_merged_ccqe_coh_addpid_ver3noveract_pid1pres.root";*/
//add for event with more than 2 track
//TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/data_merged_ccqe_processl2trk.root";
//TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/pm_merged_ccqe_tot_processl2trk.root";
//TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/pmbar_merged_ccqe_processl2trk.root";
//TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/ingrid_merged_nd3_ccqe_tot_processl2trk.root";
//TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/wall_merged_ccqe_tot_processl2trk.root";
//check genie signal
//TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/genie_merged_ccqe_coh_processl2trk.root";
//for correct cc1pres pid
//TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/data_merged_ccqe_correct1pres.root";
//TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/pm_merged_ccqe_tot_correct1pres.root";
//TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/pmbar_merged_ccqe_correct1pres.root";
//TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/ingrid_merged_nd3_ccqe_tot_correct1pres.root";
//TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/wall_merged_ccqe_tot_correct1pres.root";
//TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/genie_merged_ccqe_coh_correct1pres.root";
//for correct prange/pang_t and additional information
//TString fname = "/home/cvson/cc1picoh/dataProcess/fix20150420/data_merged_ccqe_addpidFF.root";
// TString fname = "/home/cvson/cc1picoh/dataProcess/fix20150420/pm_merged_ccqe_tot_addpidFF.root";
//TString fname = "/home/cvson/cc1picoh/dataProcess/fix20150420/pmbar_merged_ccqe_addpidFF.root";
//TString fname = "/home/cvson/cc1picoh/dataProcess/fix20150420/ingrid_merged_nd3_ccqe_tot_addpidFF.root";
//TString fname = "/home/cvson/cc1picoh/dataProcess/fix20150420/wall_merged_ccqe_tot_addpidFF.root";
//TString fname = "/home/cvson/cc1picoh/dataProcess/fix20150420/genie_merged_ccqe_tot_addpidFF.root";
//
std::cout << "--- Selecting data sample" << std::endl;
TFile *pfile = new TFile(fname,"update");
TTree* theTree = (TTree*)pfile->Get("tree");
theTree->SetBranchAddress( "mumucl", &mumucl );
theTree->SetBranchAddress( "pmucl", &pmucl );
theTree->SetBranchAddress( "pang_t", &pang_t );
theTree->SetBranchAddress( "muang_t", &muang_t );
theTree->SetBranchAddress( "veract", &veract );
theTree->SetBranchAddress( "ppe", &ppe);
theTree->SetBranchAddress( "mupe", &mupe);
theTree->SetBranchAddress( "range", &range);
theTree->SetBranchAddress( "coplanarity", &coplanarity);
theTree->SetBranchAddress( "opening", &opening);
Int_t Ntrack;
theTree->SetBranchAddress( "Ntrack", &Ntrack );
Float_t pid1pres;
TBranch *bpid1pres = theTree->Branch("pid1pres",&pid1pres,"pid1pres/F");
std::vector<Float_t> vecVar(9); // vector for EvaluateMVA tests
Long64_t nentries = theTree->GetEntriesFast();
Long64_t iprintProcess = Long64_t(nentries/100.);
std::cout << "--- Processing: " << nentries << " events" << std::endl;
TStopwatch sw;
sw.Start();
for (Long64_t ievt=0; ievt<nentries;ievt++) {
if (ievt%iprintProcess == 0) cout<<"Processing "<<int(ievt*100./nentries)<<"% of events"<<endl;
theTree->GetEntry(ievt);
Float_t pid_tem;
if (Use["BDT"]) {
//if (Ntrack!=2) pid_tem = -999;//change here
if (Ntrack<2) pid_tem = -999;
else pid_tem = reader->EvaluateMVA("BDT method");
}
pid1pres = pid_tem;
bpid1pres->Fill();
}
theTree->Write();
delete pfile;
// Get elapsed time
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
delete reader;
std::cout << "==> TMVAClassificationApplication is done!" << endl << std::endl;
}
void applyBDT(std::string iName="/mnt/hscratch/dabercro/skims2/BDT_Signal.root",
TString inputVariables = "trainingVars.txt",
TString inputTree = "DMSTree",
std::string iWeightFile="weights/TMVAClassificationCategory_BDT_simple_alpha.weights.xml",
TString outName = "Output.root",
TString fOutputName = "TMVA.root",
TString fMethodName = "BDT",
TString fUniformVariable = "fjet1MassTrimmed",
TString fWeight = "f**k",
Int_t NumBins = 10, Double_t VarMin = 10, Double_t VarMax = 190, Int_t NumMapPoints = 501) {
Double_t binWidth = (VarMax - VarMin)/NumBins;
Double_t VarVals[NumBins+1];
for (Int_t i0 = 0; i0 < NumBins + 1; i0++)
VarVals[i0] = VarMin + i0 * binWidth;
std::vector<TGraph*> transformGraphs;
TGraph *tempGraph;
if (fUniformVariable != "") {
// First scale the BDT to be uniform
// Make the background shape
TFile *TMVAFile = TFile::Open(fOutputName);
TTree *BackgroundTree = (TTree*) TMVAFile->Get("TrainTree");
mithep::PlotHists *HistPlotter = new mithep::PlotHists();
HistPlotter->SetDefaultTree(BackgroundTree);
HistPlotter->SetDefaultExpr(fMethodName);
Double_t binWidth = 2.0/(NumMapPoints - 1);
Double_t BDTBins[NumMapPoints];
for (Int_t i0 = 0; i0 < NumMapPoints; i0++)
BDTBins[i0] = i0 * binWidth - 1;
for (Int_t iVarBin = 0; iVarBin < NumBins; iVarBin++) {
TString BinCut = TString::Format("%s*(%s>=%f&&%s<%f)", fWeight.Data(),
fUniformVariable.Data(), VarVals[iVarBin],
fUniformVariable.Data(), VarVals[iVarBin+1]);
HistPlotter->AddWeight(fWeight + TString("*(classID == 1 && ") + BinCut + ")");
}
std::vector<TH1D*> BDTHists = HistPlotter->MakeHists(NumMapPoints,-1,1);
for (Int_t iVarBin = 0; iVarBin < NumBins; iVarBin++) {
tempGraph = new TGraph(NumMapPoints);
transformGraphs.push_back(tempGraph);
Double_t FullIntegral = BDTHists[iVarBin]->Integral();
for (Int_t iMapPoint = 0; iMapPoint < NumMapPoints; iMapPoint++) {
transformGraphs[iVarBin]->SetPoint(iMapPoint, BDTBins[iMapPoint],
BDTHists[iVarBin]->Integral(0,iMapPoint)/FullIntegral);
}
}
for (UInt_t iHist = 0; iHist < BDTHists.size(); iHist++)
delete BDTHists[iHist];
TMVAFile->Close();
}
TMVA::Tools::Instance();
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
TString BDTName;
ifstream configFile;
configFile.open(inputVariables.Data());
TString tempFormula;
std::vector<float> Evaluated;
std::vector<TString> Strings;
TTreeFormula *Formulas[40];
configFile >> BDTName; // Is the name of the BDT
while(!configFile.eof()){
configFile >> tempFormula;
if(tempFormula != ""){
Evaluated.push_back(0.);
Strings.push_back(tempFormula);
}
}
TFile *lFile = new TFile(iName.c_str());
TTree *lTree = (TTree*) lFile->FindObjectAny(inputTree);
if(lTree->GetBranch(BDTName) == NULL){
for(unsigned int i0 = 0;i0 < Strings.size();i0++){
if (i0 == 0)
reader->AddSpectator(Strings[i0],&Evaluated[i0]);
else
reader->AddVariable(Strings[i0],&Evaluated[i0]);
Formulas[i0] = new TTreeFormula(Strings[i0],Strings[i0],lTree);
}
std::string lJetName = "BDT";
reader->BookMVA(lJetName .c_str(),iWeightFile.c_str());
int lNEvents = lTree->GetEntries();
TFile *lOFile = new TFile(outName,"RECREATE");
TTree *lOTree = new TTree(inputTree,inputTree);
float lMVA = 0; lOTree->Branch(BDTName,&lMVA ,BDTName+TString("/F"));
for (Long64_t i0=0; i0<lNEvents;i0++) {
if (i0 % 10000 == 0) std::cout << "--- ... Processing event: " << double(i0)/double(lNEvents) << std::endl;
lTree->GetEntry(i0);
for(unsigned int i1 = 0;i1 < Strings.size();i1++){
Evaluated[i1] = Formulas[i1]->EvalInstance();
}
lMVA = float(reader->EvaluateMVA(lJetName.c_str()));
if (fUniformVariable != "") {
if (Evaluated[0] >= VarVals[0] && Evaluated[0] < VarVals[NumBins]) {
for (Int_t iBin = 0; iBin < NumBins; iBin++) {
if (Evaluated[0] < VarVals[iBin + 1]) {
lMVA = Float_t(transformGraphs[iBin]->Eval(lMVA));
break;
}
}
}
}
lOTree->Fill();
}
lOTree->Write();
lOFile->Close();
}
else std::cout << "Skipping " << iName.c_str() << std::endl;
delete reader;
for (UInt_t iGraph = 0; iGraph < transformGraphs.size(); iGraph++)
delete transformGraphs[iGraph];
}
void rezamyTMVAClassificationApplication1systematic( TString myMethodList = "" )
{
#ifdef __CINT__
gROOT->ProcessLine( ".O0" ); // turn off optimization in CINT
#endif
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
// --- Cut optimisation
Use["Cuts"] = 0;
Use["CutsD"] = 0;
Use["CutsPCA"] = 0;
Use["CutsGA"] = 0;
Use["CutsSA"] = 0;
//
// --- 1-dimensional likelihood ("naive Bayes estimator")
Use["Likelihood"] = 0;
Use["LikelihoodD"] = 0; // the "D" extension indicates decorrelated input variables (see option strings)
Use["LikelihoodPCA"] = 0; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
Use["LikelihoodKDE"] = 0;
Use["LikelihoodMIX"] = 0;
//
// --- Mutidimensional likelihood and Nearest-Neighbour methods
Use["PDERS"] = 0;
Use["PDERSD"] = 0;
Use["PDERSPCA"] = 0;
Use["PDEFoam"] = 0;
Use["PDEFoamBoost"] = 0; // uses generalised MVA method boosting
Use["KNN"] = 0; // k-nearest neighbour method
//
// --- Linear Discriminant Analysis
Use["LD"] = 0; // Linear Discriminant identical to Fisher
Use["Fisher"] = 0;
Use["FisherG"] = 0;
Use["BoostedFisher"] = 0; // uses generalised MVA method boosting
Use["HMatrix"] = 0;
//
// --- Function Discriminant analysis
Use["FDA_GA"] = 0; // minimisation of user-defined function using Genetics Algorithm
Use["FDA_SA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
Use["FDA_MCMT"] = 0;
//
// --- Neural Networks (all are feed-forward Multilayer Perceptrons)
Use["MLP"] = 0; // Recommended ANN
Use["MLPBFGS"] = 0; // Recommended ANN with optional training method
Use["MLPBNN"] = 0; // Recommended ANN with BFGS training method and bayesian regulator
Use["CFMlpANN"] = 0; // Depreciated ANN from ALEPH
Use["TMlpANN"] = 0; // ROOT's own ANN
//
// --- Support Vector Machine
Use["SVM"] = 0;
//
// --- Boosted Decision Trees
Use["BDT"] = 1; // uses Adaptive Boost
Use["BDTG"] = 0; // uses Gradient Boost
Use["BDTB"] = 0; // uses Bagging
Use["BDTD"] = 0; // decorrelation + Adaptive Boost
//
// --- Friedman's RuleFit method, ie, an optimised series of cuts ("rules")
Use["RuleFit"] = 0;
// ---------------------------------------------------------------
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;
// Select methods (don't look at this code - not of interest)
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;
}
}
// --------------------------------------------------------------------------------------------------
// --- Create the Reader object
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
Float_t ptphoton,etaphoton,ptmuon,etamuon,ptjet,etajet,masstop,mtw,deltaRphotonjet,deltaRphotonmuon,ht,costopphoton,deltaphiphotonmet,cvsdiscriminant;
Float_t jetmultiplicity,bjetmultiplicity,leptoncharge;
// Create a set of variables and declare them to the reader
// - the variable names MUST corresponds in name and type to those given in the weight file(s) used
reader->AddVariable ("ptphoton", &ptphoton);
// reader->AddVariable ("etaphoton", &etaphoton);
reader->AddVariable ("ptmuon", &ptmuon);
// reader->AddVariable ("etamuon", &etamuon);
reader->AddVariable ("ptjet", &ptjet);
// reader->AddVariable ("etajet", &etajet);
// reader->AddVariable ("masstop", &masstop);
// reader->AddVariable ("mtw", &mtw);
reader->AddVariable ("deltaRphotonjet", &deltaRphotonjet);
reader->AddVariable ("deltaRphotonmuon", &deltaRphotonmuon);
// reader->AddVariable ("ht", &ht);
// reader->AddVariable ("photonmuonmass", &photonmuonmass);
reader->AddVariable ("costopphoton", &costopphoton);
// reader->AddVariable ("topphotonmass", &topphotonmass);
//reader->AddVariable ("pttop", &pttop);
//reader->AddVariable ("etatop", &etatop);
reader->AddVariable ("jetmultiplicity", &jetmultiplicity);
// reader->AddVariable ("bjetmultiplicity", &bjetmultiplicity);
reader->AddVariable ("deltaphiphotonmet", &deltaphiphotonmet);
reader->AddVariable ("cvsdiscriminant", &cvsdiscriminant);
// reader->AddVariable ("leptoncharge", &leptoncharge);
/*
// Spectator variables declared in the training have to be added to the reader, too
reader->AddSpectator( "spec1 := var1*2", &spec1 );
reader->AddSpectator( "spec2 := var1*3", &spec2 );
Float_t Category_cat1, Category_cat2, Category_cat3;
if (Use["Category"]){
// Add artificial spectators for distinguishing categories
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 );
}
*/
// --- Book the MVA methods
TString dir = "weights/";
TString prefix = "TMVA";
// Book method(s)
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
TString methodName = TString(it->first) + TString(" method");
TString weightfile = dir + prefix + TString("_") + TString(it->first) + TString(".weights.xml");
reader->BookMVA( methodName, weightfile );
}
}
// Book output histograms
UInt_t nbin = 40;
TH1F *histLk(0), *histLkD(0), *histLkPCA(0), *histLkKDE(0), *histLkMIX(0), *histPD(0), *histPDD(0);
TH1F *histPDPCA(0), *histPDEFoam(0), *histPDEFoamErr(0), *histPDEFoamSig(0), *histKNN(0), *histHm(0);
TH1F *histFi(0), *histFiG(0), *histFiB(0), *histLD(0), *histNn(0),*histNnbfgs(0),*histNnbnn(0);
TH1F *histNnC(0), *histNnT(0), *histBdt(0), *histBdtG(0), *histBdtD(0), *histRf(0), *histSVMG(0);
TH1F *histSVMP(0), *histSVML(0), *histFDAMT(0), *histFDAGA(0), *histCat(0), *histPBdt(0);
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["BDT"]) histBdt = new TH1F( "MVA_BDT", "MVA_BDT", nbin, -0.8, 0.8 );
if (Use["BDTD"]) histBdtD = new TH1F( "MVA_BDTD", "MVA_BDTD", nbin, -0.8, 0.8 );
if (Use["BDTG"]) histBdtG = new TH1F( "MVA_BDTG", "MVA_BDTG", 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 );
// PDEFoam also returns per-event error, fill in histogram, and also fill significance
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 );
}
// Book example histogram for probability (the other methods are done similarly)
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 );
}
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
//
TFile *input(0);
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
std::vector<string> samples_;
std::vector<string> datasamples_;
std::vector<TH1F*> datahists;
std::vector<TH1F*> revDATAhists;
float scales[] = {0.0978,1.491,0.0961,0.0253,0.0224,0.0145,0.0125,0.0160,0.0158,0.0341,0.0341,0.0341,0.020,0.0017,0.0055,0.0032,0.00084,0.02,19.145*0.0169,19.145*0.0169,19.145*0.0169,19.145*0.0169,19.145*0.0169};
//samples_.push_back("WJET.root");
samples_.push_back("ZJET.root");
samples_.push_back("PHJET200400.root");
samples_.push_back("WPHJET.root");
samples_.push_back("T-W-CH.root");
samples_.push_back("TBAR-W-CH.root");
samples_.push_back("T-S-CH.root");
samples_.push_back("TBAR-S-CH.root");
samples_.push_back("T-T-CH.root");
samples_.push_back("TBAR-T-CH.root");
samples_.push_back("TTBAR1.root");
samples_.push_back("TTBAR2.root");
samples_.push_back("TTBAR3.root");
samples_.push_back("TTG.root");
samples_.push_back("WWG.root");
samples_.push_back("WW.root");
samples_.push_back("WZ.root");
samples_.push_back("ZZ.root");
samples_.push_back("ZGAMMA.root");
samples_.push_back("SIGNAL.root");
samples_.push_back("SIGNAL.root");
samples_.push_back("SIGNAL.root");
samples_.push_back("SIGNAL.root");
samples_.push_back("SIGNAL.root");
datasamples_.push_back("REALDATA1.root");
datasamples_.push_back("REALDATA2.root");
datasamples_.push_back("REALDATA3.root");
std::vector<string> datasamplesreverse_;
datasamplesreverse_.push_back("etarev/REALDATA1.root");
datasamplesreverse_.push_back("etarev/REALDATA2.root");
datasamplesreverse_.push_back("etarev/REALDATA3.root");
std::vector<string> systematics;
//systematics.push_back("__JES__plus");
//systematics.push_back("__JES__minus");
//systematics.push_back("__JER__plus");
//systematics.push_back("__JER__minus");
systematics.push_back("__PU__plus");
systematics.push_back("__PU__minus");
systematics.push_back("__TRIG__plus");
systematics.push_back("__TRIG__minus");
systematics.push_back("__BTAG__plus");
systematics.push_back("__BTAG__minus");
systematics.push_back("__MISSTAG__plus");
systematics.push_back("__MISSTAG__minus");
systematics.push_back("__MUON__plus");
systematics.push_back("__MUON__minus");
systematics.push_back("__PHOTON__plus");
systematics.push_back("__PHOTON__minus");
systematics.push_back("");
map<string, double> eventwight;
TList* hList = new TList(); // list of histograms to store
std::vector<TFile*> files;
for(unsigned int idx=0; idx<samples_.size(); ++idx){
files.push_back(new TFile(samples_[idx].c_str()));
}
std::vector<TFile*> datafiles;
for(unsigned int idx=0; idx<datasamples_.size(); ++idx){
datafiles.push_back(new TFile(datasamples_[idx].c_str()));
}
for(unsigned int phi=0; phi<systematics.size(); ++phi){
std::vector<TH1F*> hists;
TH1F *wphjethist(0), *zjethist(0) , *phjethist(0), *wjethist(0), *twchhist(0), *tbarwhist(0), *tschhist(0), *tbarschhist(0), *ttchhist(0), *tbartchhist(0), *tt1hist(0) ,*tt2hist(0), *tt3hist(0), *ttphhist(0), *wwphhist(0), *wwhist(0), *wzhist(0), *zzhist(0), *zgammahist(0), *signalhist5(0) , *signalhist10(0), *signalhist20(0), *signalhist30(0), *signalhist40(0);
TH1F *data1histrev(0), *data2histrev(0) ,*data3histrev(0);
wphjethist = new TH1F( std::string("BDT__wphjethist").append(systematics[phi]).c_str(), std::string("BDT__wphjethist").append(systematics[phi]).c_str() , nbin, -1, 1 );
zjethist = new TH1F( std::string("BDT__zjethist").append(systematics[phi]).c_str(), std::string("BDT__zjethist").append(systematics[phi]).c_str(), nbin, -1, 1 );
phjethist = new TH1F( std::string("BDT__phjethist").append(systematics[phi]).c_str(),std::string("BDT__phjethist").append(systematics[phi]).c_str() , nbin, -1, 1 );
//wjethist = new TH1F( std::string("BDT__wjethist").append(systematics[phi]).c_str(),std::string("BDT__wjethist").append(systematics[phi]).c_str() , nbin, -0.8, 0.8 );
twchhist = new TH1F( std::string("BDT__twchhist").append(systematics[phi]).c_str(),std::string("BDT__twchhist").append(systematics[phi]).c_str() ,nbin, -1, 1 );
tbarwhist = new TH1F( std::string("BDT__tbarwhist").append(systematics[phi]).c_str(),std::string("BDT__tbarwhist").append(systematics[phi]).c_str() ,nbin, -1, 1 );
tschhist = new TH1F( std::string("BDT__tschhist").append(systematics[phi]).c_str(), std::string("BDT__tschhist").append(systematics[phi]).c_str(), nbin, -1, 1 );
tbarschhist = new TH1F( std::string("BDT__tbarschhist").append(systematics[phi]).c_str(),std::string("BDT__tbarschhist").append(systematics[phi]).c_str(), nbin, -1, 1 );
ttchhist = new TH1F( std::string("BDT__ttchhist").append(systematics[phi]).c_str(),std::string("BDT__ttchhist").append(systematics[phi]).c_str(), nbin, -1, 1 );
tbartchhist = new TH1F( std::string("BDT__tbartchhist").append(systematics[phi]).c_str(), std::string("BDT__tbartchhist").append(systematics[phi]).c_str(), nbin, -1, 1 );
tt1hist = new TH1F( std::string("BDT__tt1hist").append(systematics[phi]).c_str(), std::string("BDT__tt1hist").append(systematics[phi]).c_str(), nbin, -1, 1 );
tt2hist = new TH1F( std::string("BDT__tt2hist").append(systematics[phi]).c_str(), std::string("BDT__tt2hist").append(systematics[phi]).c_str(), nbin, -1, 1 );
tt3hist = new TH1F( std::string("BDT__tt3hist").append(systematics[phi]).c_str(),std::string("BDT__tt3hist").append(systematics[phi]).c_str() , nbin, -1, 1 );
ttphhist = new TH1F( std::string("BDT__ttphhist").append(systematics[phi]).c_str(),std::string("BDT__ttphhist").append(systematics[phi]).c_str() ,nbin, -1, 1 );
wwphhist = new TH1F( std::string("BDT__wwphhist").append(systematics[phi]).c_str(),std::string("BDT__wwphhist").append(systematics[phi]).c_str(), nbin, -1, 1 );
wwhist = new TH1F( std::string("BDT__wwhist").append(systematics[phi]).c_str(),std::string("BDT__wwhist").append(systematics[phi]).c_str() ,nbin, -1, 1 );
wzhist = new TH1F( std::string("BDT__wzhist").append(systematics[phi]).c_str(),std::string("BDT__wzhist").append(systematics[phi]).c_str(), nbin, -1, 1 );
zzhist = new TH1F( std::string("BDT__zzhist").append(systematics[phi]).c_str(),std::string("BDT__zzhist").append(systematics[phi]).c_str() ,nbin, -1, 1 );
zgammahist = new TH1F( std::string("BDT__zgammahist").append(systematics[phi]).c_str(),std::string("BDT__zgammahist").append(systematics[phi]).c_str() ,nbin, -1, 1 );
signalhist5 = new TH1F( std::string("BDT__signal5").append(systematics[phi]).c_str(),std::string("BDT__signal5").append(systematics[phi]).c_str() ,nbin, -1, 1 );
signalhist10 = new TH1F( std::string("BDT__signal10").append(systematics[phi]).c_str(),std::string("BDT__signal10").append(systematics[phi]).c_str() ,nbin, -1, 1 );
signalhist20 = new TH1F( std::string("BDT__signal20").append(systematics[phi]).c_str(),std::string("BDT__signal20").append(systematics[phi]).c_str() ,nbin, -1, 1 );
signalhist30 = new TH1F( std::string("BDT__signal30").append(systematics[phi]).c_str(),std::string("BDT__signal30").append(systematics[phi]).c_str() ,nbin, -1, 1 );
signalhist40 = new TH1F( std::string("BDT__signal40").append(systematics[phi]).c_str(),std::string("BDT__signal40").append(systematics[phi]).c_str() ,nbin, -1, 1 );
hists.push_back(zjethist);
hists.push_back(phjethist);
hists.push_back(wphjethist);
//hists.push_back(wjethist);
hists.push_back(twchhist);
hists.push_back(tbarwhist);
hists.push_back(tschhist);
hists.push_back(tbarschhist);
hists.push_back(ttchhist);
hists.push_back(tbartchhist);
hists.push_back(tt1hist);
hists.push_back(tt2hist);
hists.push_back(tt3hist);
hists.push_back(ttphhist);
hists.push_back(wwphhist);
hists.push_back(wwhist);
hists.push_back(wzhist);
hists.push_back(zzhist);
hists.push_back(zgammahist);
hists.push_back(signalhist5);
hists.push_back(signalhist10);
hists.push_back(signalhist20);
hists.push_back(signalhist30);
hists.push_back(signalhist40);
for(unsigned int idx=0; idx<samples_.size(); ++idx){
TFile *input(0);
TString fname =samples_[idx];
if (!gSystem->AccessPathName( fname )) input = TFile::Open( fname ); // check if file in local directory exists
else
input = TFile::Open( "http://root.cern.ch/files/tmva_class_example.root" ); // if not: download from ROOT server
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVAClassificationApp : Using input file: " << input->GetName() << std::endl;
// --- Event loop
// Prepare the event tree
// - here the variable names have to corres[1]ponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
//
//Double_t myptphoton,myetaphoton,myptmuon,myetamuon,myptjet,myetajet,mymasstop,mymtw,mydeltaRphotonjet,mydeltaRphotonmuon,myht,mycostopphoton,mydeltaphiphotonmet,mycvsdiscriminant,myjetmultiplicity,mybjetmultiplicity,myleptoncharge;
std::vector<double> *myptphoton=0;
std::vector<double> *myetaphoton=0;
std::vector<double> *myptmuon=0;
std::vector<double> *myetamuon=0;
std::vector<double> *myptjet=0;
std::vector<double> *myetajet=0;
std::vector<double> *mymasstop=0;
//std::vector<double> *mymtw=0;
std::vector<double> *mydeltaRphotonjet=0;
std::vector<double> *mydeltaRphotonmuon=0;
//std::vector<double> *myht=0;
std::vector<double> *mycostopphoton=0;
std::vector<double> *mydeltaphiphotonmet=0;
std::vector<double> *mycvsdiscriminant=0;
std::vector<double> *myjetmultiplicity=0;
//std::vector<double> *mybjetmultiplicity=0;
//std::vector<double> *myleptoncharge=0;
std::vector<double> *myweight=0;
std::vector<double> *mybtagSF=0;
std::vector<double> *mybtagSFup=0;
std::vector<double> *mybtagSFdown=0;
std::vector<double> *mymistagSFup=0;
std::vector<double> *mymistagSFdown=0;
std::vector<double> *mytriggerSF=0;
std::vector<double> *mytriggerSFup=0;
std::vector<double> *mytriggerSFdown=0;
std::vector<double> *myphotonSF=0;
std::vector<double> *myphotonSFup=0;
std::vector<double> *myphotonSFdown=0;
std::vector<double> *mypileupSF=0;
std::vector<double> *mypileupSFup=0;
std::vector<double> *mypileupSFdown=0;
std::vector<double> *mymuonSFup=0;
std::vector<double> *mymuonSFdown=0;
std::vector<double> *mymuonSF=0;
std::cout << "--- Select signal sample" << std::endl;
TTree* theTree = (TTree*)input->Get("analyzestep2/atq");
// Int_t myjetmultiplicity, mybjetmultiplicity , myleptoncharge;
// Float_t userVar1, userVar2;
theTree->SetBranchAddress("ptphoton", &myptphoton );
theTree->SetBranchAddress( "etaphoton", &myetaphoton );
theTree->SetBranchAddress( "ptmuon", &myptmuon );
theTree->SetBranchAddress( "etamuon", &myetamuon );
theTree->SetBranchAddress( "ptjet", &myptjet );
theTree->SetBranchAddress( "etajet", &myetajet );
theTree->SetBranchAddress( "masstop", &mymasstop );
// theTree->SetBranchAddress( "mtw", &mymtw );
theTree->SetBranchAddress( "deltaRphotonjet", &mydeltaRphotonjet );
theTree->SetBranchAddress( "deltaRphotonmuon", &mydeltaRphotonmuon );
// theTree->SetBranchAddress( "ht", &myht );
theTree->SetBranchAddress( "costopphoton", &mycostopphoton );
theTree->SetBranchAddress( "jetmultiplicity", &myjetmultiplicity );
// theTree->SetBranchAddress( "bjetmultiplicity", &mybjetmultiplicity );
theTree->SetBranchAddress( "deltaphiphotonmet", &mydeltaphiphotonmet );
theTree->SetBranchAddress( "cvsdiscriminant", &mycvsdiscriminant );
// theTree->SetBranchAddress( "leptoncharge", &myleptoncharge );
theTree->SetBranchAddress( "weight", &myweight);
theTree->SetBranchAddress( "btagSF", &mybtagSF);
theTree->SetBranchAddress( "btagSFup", &mybtagSFup);
theTree->SetBranchAddress( "btagSFdown", &mybtagSFdown);
theTree->SetBranchAddress( "mistagSFup", &mymistagSFup);
theTree->SetBranchAddress( "mistagSFdown", &mymistagSFdown);
theTree->SetBranchAddress( "triggerSF", &mytriggerSF);
theTree->SetBranchAddress( "triggerSFup", &mytriggerSFup);
theTree->SetBranchAddress( "triggerSFdown", &mytriggerSFdown);
theTree->SetBranchAddress( "photonSF", &myphotonSF);
theTree->SetBranchAddress( "photonSFup", &myphotonSFup);
theTree->SetBranchAddress( "photonSFdown", &myphotonSFdown);
theTree->SetBranchAddress( "muonSF", &mymuonSF);
theTree->SetBranchAddress( "muonSFup", &mymuonSFup);
theTree->SetBranchAddress( "muonSFdown", &mymuonSFdown);
theTree->SetBranchAddress( "pileupSF", &mypileupSF);
theTree->SetBranchAddress( "pileupSFup", &mypileupSFup);
theTree->SetBranchAddress( "pileupSFdown", &mypileupSFdown);
// Efficiency calculator for cut method
Int_t nSelCutsGA = 0;
Double_t effS = 0.7;
std::vector<Float_t> vecVar(4); // vector for EvaluateMVA tests
// std::cout << "--- Processing: " << theTree->GetEntries() << " events" << std::endl;
TStopwatch sw;
sw.Start();
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
// std::cout << "--- ... Processing event: " << ievt << std::endl;
double finalweight;
if (ievt%1000 == 0) std::cout << "--- ... Processing event: " << ievt << std::endl;
theTree->GetEntry(ievt);
//for (int l=0;l<sizeof(myptphoton);l++){
//std::cout << "--- ... reza: " << myptphoton[l] <<std::endl;
//}
//std::cout << "--- ......................."<< (*mycvsdiscriminant)[0]<<std::endl;
// --- Return the MVA outputs and fill into histograms
ptphoton=(float)(*myptphoton)[0];
etaphoton=(float)(*myetaphoton )[0];
ptmuon=(float)(*myptmuon )[0];
etamuon=(float)(*myetamuon )[0];
ptjet=(float)(*myptjet )[0];
etajet=(float)(*myetajet )[0];
masstop=(float)(*mymasstop )[0];
//mtw=(float)(*mymtw )[0];
deltaRphotonjet=(float)(*mydeltaRphotonjet )[0];
deltaRphotonmuon=(float)(*mydeltaRphotonmuon )[0];
//ht=(float)(*myht )[0];
costopphoton=(float)(*mycostopphoton )[0];
jetmultiplicity=(float)(*myjetmultiplicity )[0];
//bjetmultiplicity=(float)(*mybjetmultiplicity )[0];
deltaphiphotonmet=(float)(*mydeltaphiphotonmet )[0];
cvsdiscriminant=(float)(*mycvsdiscriminant)[0];
//leptoncharge=(float)(*myleptoncharge )[0];
finalweight=(*myweight)[0];
//cout<<(*myweight)[0]<<endl;
eventwight["__PU__plus"]=(*myweight)[0]*(*mypileupSFup)[0]/(*mypileupSF)[0];
eventwight["__PU__minus"]=(*myweight)[0]*(*mypileupSFdown)[0]/(*mypileupSF)[0];
eventwight["__TRIG__plus"]=(*myweight)[0]*(*mytriggerSFup)[0]/(*mytriggerSF)[0];
eventwight["__TRIG__minus"]=(*myweight)[0]*(*mytriggerSFdown)[0]/(*mytriggerSF)[0];
eventwight["__BTAG__plus"]=(*myweight)[0]*(*mybtagSFup)[0]/(*mybtagSF)[0];
eventwight["__BTAG__minus"]=(*myweight)[0]*(*mybtagSFdown)[0]/(*mybtagSF)[0];
eventwight["__MISSTAG__plus"]=(*myweight)[0]*(*mymistagSFup)[0]/(*mybtagSF)[0];
eventwight["__MISSTAG__minus"]=(*myweight)[0]*(*mymistagSFdown)[0]/(*mybtagSF)[0];
eventwight["__MUON__plus"]=(*myweight)[0]*(*mymuonSFup)[0]/(*mymuonSF)[0];
eventwight["__MUON__minus"]=(*myweight)[0]*(*mymuonSFdown)[0]/(*mymuonSF)[0];
eventwight["__PHOTON__plus"]=(*myweight)[0]*(*myphotonSFup)[0]/(*myphotonSF)[0];
eventwight["__PHOTON__minus"]=(*myweight)[0]*(*myphotonSFdown)[0]/(*myphotonSF)[0];
eventwight[""]=(*myweight)[0];
finalweight=eventwight[systematics[phi].c_str()];
if (samples_[idx]=="SIGNAL.root") finalweight=(*myweight)[0];
//if (samples_[idx]=="WPHJET") finalweight=(*mypileupSF)[0]*(*mytriggerSF)[0]*(*mybtagSF)[0]*(*mymuonSF)[0]*(*myphotonSF)[0];
//if (finalweight<0) finalweight=30;
//cout<<"negative event weight"<<finalweight<<" "<<ptphoton<<endl;
if (Use["CutsGA"]) {
// Cuts is a special case: give the desired signal efficienciy
Bool_t passed = reader->EvaluateMVA( "CutsGA method", effS );
if (passed) nSelCutsGA++;
}
hists[idx] ->Fill( reader->EvaluateMVA( "BDT method" ),finalweight* scales[idx] );
//cout<<reader->EvaluateMVA( "BDT method")<<" "<<finalweight<<endl;
//cout<<(*myweight)[0]<<endl;
// Retrieve also per-event error
if (Use["PDEFoam"]) {
Double_t val = reader->EvaluateMVA( "PDEFoam method" );
Double_t err = reader->GetMVAError();
histPDEFoam ->Fill( val );
histPDEFoamErr->Fill( err );
if (err>1.e-50) histPDEFoamSig->Fill( val/err );
}
// Retrieve probability instead of MVA output
if (Use["Fisher"]) {
probHistFi ->Fill( reader->GetProba ( "Fisher method" ) );
rarityHistFi->Fill( reader->GetRarity( "Fisher method" ) );
}
//delete finalweight;
}
// Get elapsed time
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
// Get efficiency for cuts classifier
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"]) {
// test: retrieve cuts for particular signal efficiency
// CINT ignores dynamic_casts so we have to use a cuts-secific Reader function to acces the pointer
TMVA::MethodCuts* mcuts = reader->FindCutsMVA( "CutsGA method" ) ;
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]
<< " < \""
<< mcuts->GetInputVar(ivar)
<< "\" <= "
<< cutsMax[ivar] << std::endl;
}
std::cout << "--- -------------------------------------------------------------" << std::endl;
}
}
delete myptphoton;
delete myetaphoton;
delete myptmuon;
delete myetamuon;
delete myptjet;
delete myetajet;
delete mymasstop;
//delete mymtw;
delete mydeltaRphotonjet;
delete mydeltaRphotonmuon;
//delete myht;
delete mycostopphoton;
delete mydeltaphiphotonmet;
delete mycvsdiscriminant;
delete myjetmultiplicity;
//delete mybjetmultiplicity;
//delete myleptoncharge;
//delete myplot;
delete mybtagSF;
delete mybtagSFup;
delete mybtagSFdown;
delete mymistagSFup;
delete mytriggerSF;
delete mytriggerSFup;
delete mytriggerSFdown;
delete myphotonSF;
delete myphotonSFup;
delete myphotonSFdown;
delete mypileupSF;
delete mypileupSFup;
delete mypileupSFdown;
delete input;
if (idx==samples_.size()-5) hists[idx]->Scale(5/hists[idx]->Integral());
if (idx==samples_.size()-4) hists[idx]->Scale(10/hists[idx]->Integral());
if (idx==samples_.size()-3) hists[idx]->Scale(20/hists[idx]->Integral());
if (idx==samples_.size()-2) hists[idx]->Scale(30/hists[idx]->Integral());
if (idx==samples_.size()-1) hists[idx]->Scale(40/hists[idx]->Integral());
if (samples_[idx]=="WPHJET.root") hists[idx]->Scale(3173/hists[idx]->Integral());
if (samples_[idx]=="TTBAR2.root") hists[idx]->Add(hists[idx-1]);
if (samples_[idx]=="TTBAR3.root") hists[idx]->Add(hists[idx-1]);
if (!(samples_[idx]=="TTBAR1.root" || samples_[idx]=="TTBAR2.root")) hList->Add(hists[idx]);
}
}
TH1F *data1hist(0), *data2hist(0) ,*data3hist(0);
data1hist = new TH1F( "mu_BDT__data1hist", "mu_BDT__data1hist", nbin, -1, 1 );
data2hist = new TH1F( "mu_BDT__data2hist", "mu_BDT__data2hist", nbin, -1, 1 );
data3hist = new TH1F( "BDT__DATA", "BDT__DATA", nbin, -1, 1 );
datahists.push_back(data1hist);
datahists.push_back(data2hist);
datahists.push_back(data3hist);
for(unsigned int idx=0; idx<datasamples_.size(); ++idx){
TFile *input(0);
TString fname =datasamples_[idx];
if (!gSystem->AccessPathName( fname )) input = TFile::Open( fname ); // check if file in local directory exists
else
input = TFile::Open( "http://root.cern.ch/files/tmva_class_example.root" ); // if not: download from ROOT server
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVAClassificationApp : Using input file: " << input->GetName() << std::endl;
// --- Event loop
// Prepare the event tree
// - here the variable names have to corres[1]ponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
//
std::vector<double> *myptphoton=0;
std::vector<double> *myetaphoton=0;
std::vector<double> *myptmuon=0;
std::vector<double> *myetamuon=0;
std::vector<double> *myptjet=0;
std::vector<double> *myetajet=0;
std::vector<double> *mymasstop=0;
//std::vector<double> *mymtw=0;
std::vector<double> *mydeltaRphotonjet=0;
std::vector<double> *mydeltaRphotonmuon=0;
//std::vector<double> *myht=0;
std::vector<double> *mycostopphoton=0;
std::vector<double> *mydeltaphiphotonmet=0;
std::vector<double> *mycvsdiscriminant=0;
std::vector<double> *myjetmultiplicity=0;
//std::vector<double> *mybjetmultiplicity=0;
//std::vector<double> *myleptoncharge=0;
std::cout << "--- Select signal sample" << std::endl;
TTree* theTree = (TTree*)input->Get("analyzestep2/atq");
// Int_t myjetmultiplicity, mybjetmultiplicity , myleptoncharge;
// Float_t userVar1, userVar2;
theTree->SetBranchAddress("ptphoton", &myptphoton );
theTree->SetBranchAddress( "etaphoton", &myetaphoton );
theTree->SetBranchAddress( "ptmuon", &myptmuon );
theTree->SetBranchAddress( "etamuon", &myetamuon );
theTree->SetBranchAddress( "ptjet", &myptjet );
theTree->SetBranchAddress( "etajet", &myetajet );
theTree->SetBranchAddress( "masstop", &mymasstop );
// theTree->SetBranchAddress( "mtw", &mymtw );
theTree->SetBranchAddress( "deltaRphotonjet", &mydeltaRphotonjet );
theTree->SetBranchAddress( "deltaRphotonmuon", &mydeltaRphotonmuon );
// theTree->SetBranchAddress( "ht", &myht );
theTree->SetBranchAddress( "costopphoton", &mycostopphoton );
theTree->SetBranchAddress( "jetmultiplicity", &myjetmultiplicity );
// theTree->SetBranchAddress( "bjetmultiplicity", &mybjetmultiplicity );
theTree->SetBranchAddress( "deltaphiphotonmet", &mydeltaphiphotonmet );
theTree->SetBranchAddress( "cvsdiscriminant", &mycvsdiscriminant );
// theTree->SetBranchAddress( "leptoncharge", &myleptoncharge );
// Efficiency calculator for cut method
Int_t nSelCutsGA = 0;
Double_t effS = 0.7;
std::vector<Float_t> vecVar(4); // vector for EvaluateMVA tests
std::cout << "--- Processing: " << theTree->GetEntries() << " events" << std::endl;
TStopwatch sw;
sw.Start();
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
// std::cout << "--- ... Processing event: " << ievt << std::endl;
if (ievt%1000 == 0) std::cout << "--- ... Processing event: " << ievt << std::endl;
theTree->GetEntry(ievt);
//for (int l=0;l<sizeof(myptphoton);l++){
//std::cout << "--- ... reza: " << myptphoton[l] <<std::endl;
//}
//std::cout << "--- ......................."<< (*mycvsdiscriminant)[0]<<std::endl;
// --- Return the MVA outputs and fill into histograms
ptphoton=(float)(*myptphoton)[0];
etaphoton=(float)(*myetaphoton )[0];
ptmuon=(float)(*myptmuon )[0];
etamuon=(float)(*myetamuon )[0];
ptjet=(float)(*myptjet )[0];
etajet=(float)(*myetajet )[0];
masstop=(float)(*mymasstop )[0];
//mtw=(float)(*mymtw )[0];
deltaRphotonjet=(float)(*mydeltaRphotonjet )[0];
deltaRphotonmuon=(float)(*mydeltaRphotonmuon )[0];
//ht=(float)(*myht )[0];
costopphoton=(float)(*mycostopphoton )[0];
jetmultiplicity=(float)(*myjetmultiplicity )[0];
//bjetmultiplicity=(float)(*mybjetmultiplicity )[0];
deltaphiphotonmet=(float)(*mydeltaphiphotonmet )[0];
cvsdiscriminant=(float)(*mycvsdiscriminant)[0];
//leptoncharge=(float)(*myleptoncharge )[0];
if (Use["CutsGA"]) {
// Cuts is a special case: give the desired signal efficienciy
Bool_t passed = reader->EvaluateMVA( "CutsGA method", effS );
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["BDT" ]) histBdt ->Fill( reader->EvaluateMVA( "BDT method" ) );
if (Use["BDTD" ]) histBdtD ->Fill( reader->EvaluateMVA( "BDTD method" ) );
if (Use["BDTG" ]) histBdtG ->Fill( reader->EvaluateMVA( "BDTG 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" ) );
datahists[idx] ->Fill( reader->EvaluateMVA( "BDT method" ) );
}
// Get elapsed time
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
delete myptphoton;
delete myetaphoton;
delete myptmuon;
delete myetamuon;
delete myptjet;
delete myetajet;
delete mymasstop;
//delete mymtw;
delete mydeltaRphotonjet;
delete mydeltaRphotonmuon;
//delete myht;
delete mycostopphoton;
delete mydeltaphiphotonmet;
delete mycvsdiscriminant;
delete myjetmultiplicity;
//delete mybjetmultiplicity;
//delete myleptoncharge;
//delete myplot;
delete input;
}
for(unsigned int idx=1; idx<datasamples_.size(); ++idx){
datahists[idx]->Add(datahists[idx-1]);
}
hList->Add(datahists[2]);
TH1F *data1histrev(0), *data2histrev(0) ,*data3histrev(0);
data1histrev = new TH1F( "BDT__data1histrev", "BDT__data1histrev", nbin, -1, 1 );
data2histrev = new TH1F( "BDT__data2histrev", "BDT__data2histrev", nbin, -1, 1 );
data3histrev = new TH1F( "BDT__wjet", "BDT__wjet", nbin, -1, 1 );
revDATAhists.push_back(data1histrev);
revDATAhists.push_back(data2histrev);
revDATAhists.push_back(data3histrev);
for(unsigned int idx=0; idx<datasamplesreverse_.size(); ++idx){
TFile *input(0);
TString fname =datasamplesreverse_[idx];
if (!gSystem->AccessPathName( fname )) input = TFile::Open( fname ); // check if file in local directory exists
else
input = TFile::Open( "http://root.cern.ch/files/tmva_class_example.root" ); // if not: download from ROOT server
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVAClassificationApp : Using input file: " << input->GetName() << std::endl;
// --- Event loop
//
// // Prepare the event tree
// // - here the variable names have to corres[1]ponds to your tree
// // - you can use the same variables as above which is slightly faster,
// // but of course you can use different ones and copy the values inside the event loop
// //
// Double_t myptphoton,myetaphoton,myptmuon,myetamuon,myptjet,myetajet,mymasstop,mymtw,mydeltaRphotonjet,mydeltaRphotonmuon,myht,mycostopphoton,mydeltaphiphotonmet,mycvsdiscriminant,myjetmultiplicity,mybjetmultiplicity,myleptoncharge;
std::vector<double> *myptphoton=0;
std::vector<double> *myetaphoton=0;
std::vector<double> *myptmuon=0;
std::vector<double> *myetamuon=0;
std::vector<double> *myptjet=0;
std::vector<double> *myetajet=0;
std::vector<double> *mymasstop=0;
//std::vector<double> *mymtw=0;
std::vector<double> *mydeltaRphotonjet=0;
std::vector<double> *mydeltaRphotonmuon=0;
//std::vector<double> *myht=0;
std::vector<double> *mycostopphoton=0;
std::vector<double> *mydeltaphiphotonmet=0;
std::vector<double> *mycvsdiscriminant=0;
std::vector<double> *myjetmultiplicity=0;
//std::vector<double> *mybjetmultiplicity=0;
//std::vector<double> *myleptoncharge=0;
TTree* theTree = (TTree*)input->Get("analyzestep2/atq");
theTree->SetBranchAddress("ptphoton", &myptphoton );
theTree->SetBranchAddress( "etaphoton", &myetaphoton );
theTree->SetBranchAddress( "ptmuon", &myptmuon );
theTree->SetBranchAddress( "etamuon", &myetamuon );
theTree->SetBranchAddress( "ptjet", &myptjet );
theTree->SetBranchAddress( "etajet", &myetajet );
theTree->SetBranchAddress( "masstop", &mymasstop );
// theTree->SetBranchAddress( "mtw", &mymtw );
theTree->SetBranchAddress( "deltaRphotonjet", &mydeltaRphotonjet );
theTree->SetBranchAddress( "deltaRphotonmuon", &mydeltaRphotonmuon );
// theTree->SetBranchAddress( "ht", &myht );
theTree->SetBranchAddress( "costopphoton", &mycostopphoton );
theTree->SetBranchAddress( "jetmultiplicity", &myjetmultiplicity );
// theTree->SetBranchAddress( "bjetmultiplicity", &mybjetmultiplicity );
theTree->SetBranchAddress( "deltaphiphotonmet", &mydeltaphiphotonmet );
theTree->SetBranchAddress( "cvsdiscriminant", &mycvsdiscriminant );
// theTree->SetBranchAddress( "leptoncharge", &myleptoncharge );
// Efficiency calculator for cut method
Int_t nSelCutsGA = 0;
Double_t effS = 0.7;
std::vector<Float_t> vecVar(4); // vector for EvaluateMVA tests
std::cout << "--- Processing: " << theTree->GetEntries() << " events" << std::endl;
TStopwatch sw;
sw.Start();
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
// std::cout << "--- ... Processing event: " << ievt << std::endl;
if (ievt%1000 == 0) std::cout << "--- ... Processing event: " << ievt << std::endl;
theTree->GetEntry(ievt);
//for (int l=0;l<sizeof(myptphoton);l++){
//std::cout << "--- ... reza: " << myptphoton[l] <<std::endl;
//}
// std::cout << "--- ......................."<< (*mycvsdiscriminant)[0]<<std::endl;
// --- Return the MVA outputs and fill into histograms
ptphoton=(float)(*myptphoton)[0];
etaphoton=(float)(*myetaphoton )[0];
ptmuon=(float)(*myptmuon )[0];
etamuon=(float)(*myetamuon )[0];
ptjet=(float)(*myptjet )[0];
etajet=(float)(*myetajet )[0];
masstop=(float)(*mymasstop )[0];
//mtw=(float)(*mymtw )[0];
deltaRphotonjet=(float)(*mydeltaRphotonjet )[0];
deltaRphotonmuon=(float)(*mydeltaRphotonmuon )[0];
//ht=(float)(*myht )[0];
costopphoton=(float)(*mycostopphoton )[0];
jetmultiplicity=(float)(*myjetmultiplicity )[0];
//bjetmultiplicity=(float)(*mybjetmultiplicity )[0];
deltaphiphotonmet=(float)(*mydeltaphiphotonmet )[0];
cvsdiscriminant=(float)(*mycvsdiscriminant)[0];
//leptoncharge=(float)(*myleptoncharge )[0];
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["BDT" ]) histBdt ->Fill( reader->EvaluateMVA( "BDT method" ) );
if (Use["BDTD" ]) histBdtD ->Fill( reader->EvaluateMVA( "BDTD method" ) );
if (Use["BDTG" ]) histBdtG ->Fill( reader->EvaluateMVA( "BDTG 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" ) );
revDATAhists[idx]->Fill( reader->EvaluateMVA( "BDT method" ) );}
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
delete myptphoton;
delete myetaphoton;
delete myptmuon;
delete myetamuon;
delete myptjet;
delete myetajet;
delete mymasstop;
//delete mymtw;
delete mydeltaRphotonjet;
delete mydeltaRphotonmuon;
//delete myht;
delete mycostopphoton;
delete mydeltaphiphotonmet;
delete mycvsdiscriminant;
delete myjetmultiplicity;
////delete mybjetmultiplicity;
////delete myleptoncharge;
////delete myplot;
//
delete input;
}
for(unsigned int idx=1; idx<datasamplesreverse_.size(); ++idx){
revDATAhists[idx]->Add(revDATAhists[idx-1]);
}
revDATAhists[2]->Scale(620.32/revDATAhists[2]->Integral());
hList->Add(revDATAhists[2]);
cout<<revDATAhists[2]->Integral()<<endl;
TFile *target = new TFile( "MVApp.root","RECREATE" );
hList->Write();
target->Close();
}
void cutFlowStudyMu( TString weightFile = "TMVAClassificationPtOrd_qqH115vsWZttQCD_Cuts.weights.xml",
Double_t effS_ = 0.3)
{
ofstream out("cutFlow-MuTauStream.txt");
out.precision(4);
TMVA::Tools::Instance();
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
Float_t pt1, pt2;
Float_t Deta, Mjj;
Float_t eta1,eta2;
reader->AddVariable( "pt1", &pt1);
reader->AddVariable( "pt2", &pt2);
reader->AddVariable( "Deta",&Deta);
reader->AddVariable( "Mjj", &Mjj);
reader->AddSpectator("eta1",&eta1);
reader->AddSpectator("eta2",&eta2);
reader->BookMVA( "Cuts", TString("/home/llr/cms/lbianchini/CMSSW_3_9_9/src/Bianchi/TauTauStudies/test/Macro/weights/")+weightFile );
TFile *fFullSignalVBF = new TFile("/data_CMS/cms/lbianchini//MuTauStream2011/treeMuTauStream_VBFH115-Mu-powheg-PUS1.root","READ");
TFile *fFullSignalGGH = new TFile("/data_CMS/cms/lbianchini//MuTauStream2011/treeMuTauStream_GGFH115-Mu-powheg-PUS1.root","READ");
TFile *fFullBackgroundDYTauTau = new TFile("/data_CMS/cms/lbianchini//MuTauStream2011/treeMuTauStream_DYToTauTau-Mu-20-PUS1.root","READ");
TFile *fFullBackgroundDYMuMu = new TFile("/data_CMS/cms/lbianchini//MuTauStream2011/treeMuTauStream_DYToMuMu-20-PUS1.root","READ");
TFile *fFullBackgroundWJets = new TFile("/data_CMS/cms/lbianchini//MuTauStream2011/treeMuTauStream_WJets-Mu-madgraph-PUS1.root","READ");
TFile *fFullBackgroundQCD = new TFile("/data_CMS/cms/lbianchini//MuTauStream2011/treeMuTauStream_QCDmu.root","READ");
TFile *fFullBackgroundTTbar = new TFile("/data_CMS/cms/lbianchini//MuTauStream2011/treeMuTauStream_TTJets-Mu-madgraph-PUS1.root","READ");
TFile *fFullBackgroundDiBoson = new TFile("/data_CMS/cms/lbianchini//MuTauStream2011/treeMuTauStream_DiBoson-Mu.root","READ");
// OpenNTuples
TString fSignalNameVBF = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/MuTauStream2011/v2/nTupleVBFH115-Mu-powheg-PUS1_Open_MuTauStream.root";
TString fSignalNameGGH = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/MuTauStream2011/v2/nTupleGGFH115-Mu-powheg-PUS1_Open_MuTauStream.root";
TString fBackgroundNameDYTauTau = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/MuTauStream2011/v2/nTupleDYToTauTau-Mu-20-PUS1_Open_MuTauStream.root";
TString fBackgroundNameDYMuMu = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/MuTauStream2011/v2/nTupleDYToMuMu-20-PUS1_Open_MuTauStream.root";
TString fBackgroundNameWJets = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/MuTauStream2011/v2/nTupleWJets-Mu-madgraph-PUS1_Open_MuTauStream.root";
TString fBackgroundNameQCD = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/MuTauStream2011/v2/nTupleQCDmu_Open_MuTauStream.root";
TString fBackgroundNameTTbar = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/MuTauStream2011/v2/nTupleTTJets-Mu-madgraph-PUS1_Open_MuTauStream.root";
TString fBackgroundNameDiBoson = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/MuTauStream2011/v2/nTupleDiBoson-Mu_Open_MuTauStream.root";
TFile *fSignalVBF(0);
TFile *fSignalGGH(0);
TFile *fBackgroundDYTauTau(0);
TFile *fBackgroundDYMuMu(0);
TFile *fBackgroundWJets(0);
TFile *fBackgroundQCD(0);
TFile *fBackgroundTTbar(0);
TFile *fBackgroundDiBoson(0);
fSignalVBF = TFile::Open( fSignalNameVBF );
fSignalGGH = TFile::Open( fSignalNameGGH );
fBackgroundDYTauTau = TFile::Open( fBackgroundNameDYTauTau );
fBackgroundDYMuMu = TFile::Open( fBackgroundNameDYMuMu );
fBackgroundWJets = TFile::Open( fBackgroundNameWJets );
fBackgroundQCD = TFile::Open( fBackgroundNameQCD );
fBackgroundTTbar = TFile::Open( fBackgroundNameTTbar );
fBackgroundDiBoson = TFile::Open( fBackgroundNameDiBoson );
if(!fSignalVBF || !fBackgroundDYTauTau || !fBackgroundWJets || !fBackgroundQCD || !fBackgroundTTbar ||
!fSignalGGH || !fBackgroundDYMuMu || !fBackgroundDiBoson ){
std::cout << "ERROR: could not open files" << std::endl;
exit(1);
}
TString tree = "outTreePtOrd";
TTree *signalVBF = (TTree*)fSignalVBF->Get(tree);
TTree *signalGGH = (TTree*)fSignalGGH->Get(tree);
TTree *backgroundDYTauTau = (TTree*)fBackgroundDYTauTau->Get(tree);
TTree *backgroundDYMuMu = (TTree*)fBackgroundDYMuMu->Get(tree);
TTree *backgroundWJets = (TTree*)fBackgroundWJets->Get(tree);
TTree *backgroundQCD = (TTree*)fBackgroundQCD->Get(tree);
TTree *backgroundTTbar = (TTree*)fBackgroundTTbar->Get(tree);
TTree *backgroundDiBoson = (TTree*)fBackgroundDiBoson->Get(tree);
// here I define the map between a sample name and its tree
std::map<std::string,TTree*> tMap;
tMap["ggH115"]=signalGGH;
tMap["qqH115"]=signalVBF;
tMap["Ztautau"]=backgroundDYTauTau;
tMap["Zmumu"]=backgroundDYMuMu;
tMap["Wjets"]=backgroundWJets;
tMap["QCD"]=backgroundQCD;
tMap["TTbar"]=backgroundTTbar;
tMap["DiBoson"]=backgroundDiBoson;
std::map<std::string,TTree*>::iterator jt;
Float_t pt1_, pt2_;
Float_t Deta_, Mjj_;
Float_t Dphi,diTauSVFitPt,diTauSVFitEta,diTauVisMass,diTauSVFitMass,ptL1,ptL2,etaL1,etaL2,diTauCharge,MtLeg1,numPV,combRelIsoLeg1,sampleWeight,ptVeto,HLT;
Int_t tightestHPSWP;
/////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// here I choose the order in the stack
std::vector<string> samples;
samples.push_back("ggH115");
samples.push_back("qqH115");
samples.push_back("DiBoson");
samples.push_back("TTbar");
samples.push_back("Wjets");
samples.push_back("Zmumu");
samples.push_back("Ztautau");
samples.push_back("QCD");
std::map<std::string,float> crossSec;
crossSec["ggH115"]=( 7.65e-02 * 18.13 );
crossSec["qqH115"]=( 0.1012);
crossSec["DiBoson"]=( -1 );
crossSec["TTbar"]=( 157.5 );
crossSec["Wjets"]=( 31314.0);
crossSec["Zmumu"]=( 1666 );
crossSec["Ztautau"]=( 1666 );
crossSec["QCD"]=( 296600000*0.0002855 );
float Lumi = 1000;
// here I choose the order in the stack
std::vector<string> filters;
filters.push_back("total");
filters.push_back("vertex");
filters.push_back("1-mu");
filters.push_back("0-e");
filters.push_back("mu-ID");
filters.push_back("tau-ID");
filters.push_back("Delta R mu-tau");
filters.push_back("mu-iso");
filters.push_back("tau-iso");
filters.push_back("Mt");
filters.push_back("OS");
filters.push_back("2-jets");
filters.push_back("VBF cuts");
filters.push_back("jet-veto");
filters.push_back("HLT");
// here I define the map between a sample name and its file ptr
std::map<std::string,TFile*> fullMap;
fullMap["ggH115"] = fFullSignalGGH;
fullMap["qqH115"] = fFullSignalVBF;
fullMap["Ztautau"] = fFullBackgroundDYTauTau;
fullMap["Zmumu"] = fFullBackgroundDYMuMu;
fullMap["Wjets"] = fFullBackgroundWJets;
fullMap["QCD"] = fFullBackgroundQCD;
fullMap["TTbar"] = fFullBackgroundTTbar;
fullMap["DiBoson"] = fFullBackgroundDiBoson;
std::map<std::string,TFile*>::iterator it;
std::map<std::string,float> cutMap_allEventsFilter;
std::map<std::string,float> cutMap_allEventsFilterE;
for(it = fullMap.begin(); it != fullMap.end(); it++){
TH1F* allEvents = (TH1F*)(it->second)->Get("allEventsFilter/totalEvents");
float totalEvents = allEvents->GetBinContent(1);
float totalEquivalentEvents = allEvents->GetEffectiveEntries();
float tot = totalEvents;
if(crossSec[it->first]>0) tot *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
cutMap_allEventsFilter[it->first] = tot;
cutMap_allEventsFilterE[it->first] = totalEquivalentEvents>0 ? sqrt(totalEquivalentEvents)*(tot/totalEquivalentEvents) : 0;
}
std::map<std::string,float> cutMap_vertexScrapingFilter;
std::map<std::string,float> cutMap_vertexScrapingFilterE;
for(it = fullMap.begin(); it != fullMap.end(); it++){
TH1F* allEvents = (TH1F*)(it->second)->Get("allEventsFilter/totalEvents");
float totalEvents = allEvents->GetBinContent(1);
allEvents = (TH1F*)(it->second)->Get("vertexScrapingFilter/totalEvents");
float tot = allEvents->GetBinContent(1);
float totalEquivalentEvents = allEvents->GetEffectiveEntries();
if(crossSec[it->first]>0){
tot *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
//totalEquivalentEvents *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
}
cutMap_vertexScrapingFilter[it->first] = tot;
cutMap_vertexScrapingFilterE[it->first] = totalEquivalentEvents>0 ? sqrt(totalEquivalentEvents)*(tot/totalEquivalentEvents) : 0;
}
std::map<std::string,float> cutMap_oneElectronFilter;
std::map<std::string,float> cutMap_oneElectronFilterE;
for(it = fullMap.begin(); it != fullMap.end(); it++){
TH1F* allEvents = (TH1F*)(it->second)->Get("allEventsFilter/totalEvents");
float totalEvents = allEvents->GetBinContent(1);
allEvents = (TH1F*)(it->second)->Get("oneMuonFilter/totalEvents");
float tot = allEvents->GetBinContent(1);
float totalEquivalentEvents = allEvents->GetEffectiveEntries();
if(crossSec[it->first]>0){
tot *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
//totalEquivalentEvents *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
}
cutMap_oneElectronFilter[it->first] = tot;
cutMap_oneElectronFilterE[it->first] = totalEquivalentEvents>0 ? sqrt(totalEquivalentEvents)*(tot/totalEquivalentEvents) : 0;
}
std::map<std::string,float> cutMap_noMuonFilter;
std::map<std::string,float> cutMap_noMuonFilterE;
for(it = fullMap.begin(); it != fullMap.end(); it++){
TH1F* allEvents = (TH1F*)(it->second)->Get("allEventsFilter/totalEvents");
float totalEvents = allEvents->GetBinContent(1);
allEvents = (TH1F*)(it->second)->Get("noElecFilter/totalEvents");
float tot = allEvents->GetBinContent(1);
float totalEquivalentEvents = allEvents->GetEffectiveEntries();
if(crossSec[it->first]>0){
tot *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
//totalEquivalentEvents *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
}
cutMap_noMuonFilter[it->first] = tot;
cutMap_noMuonFilterE[it->first] = totalEquivalentEvents>0 ? sqrt(totalEquivalentEvents)*(tot/totalEquivalentEvents) : 0;
}
std::map<std::string,float> cutMap_electronLegFilter;
std::map<std::string,float> cutMap_electronLegFilterE;
for(it = fullMap.begin(); it != fullMap.end(); it++){
TH1F* allEvents = (TH1F*)(it->second)->Get("allEventsFilter/totalEvents");
float totalEvents = allEvents->GetBinContent(1);
allEvents = (TH1F*)(it->second)->Get("muonLegFilter/totalEvents");
float tot = allEvents->GetBinContent(1);
float totalEquivalentEvents = allEvents->GetEffectiveEntries();
if(crossSec[it->first]>0){
tot *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
//totalEquivalentEvents *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
}
cutMap_electronLegFilter[it->first] = tot;
cutMap_electronLegFilterE[it->first] = totalEquivalentEvents>0 ? sqrt(totalEquivalentEvents)*(tot/totalEquivalentEvents) : 0;
}
std::map<std::string,float> cutMap_tauLegFilter;
std::map<std::string,float> cutMap_tauLegFilterE;
for(it = fullMap.begin(); it != fullMap.end(); it++){
TH1F* allEvents = (TH1F*)(it->second)->Get("allEventsFilter/totalEvents");
float totalEvents = allEvents->GetBinContent(1);
allEvents = (TH1F*)(it->second)->Get("tauLegFilter/totalEvents");
float tot = allEvents->GetBinContent(1);
float totalEquivalentEvents = allEvents->GetEffectiveEntries();
if(crossSec[it->first]>0){
tot *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
//totalEquivalentEvents *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
}
cutMap_tauLegFilter[it->first] = tot;
cutMap_tauLegFilterE[it->first] = totalEquivalentEvents>0 ? sqrt(totalEquivalentEvents)*(tot/totalEquivalentEvents) : 0;
}
std::map<std::string,float> cutMap_atLeastOneDiTauFilter;
std::map<std::string,float> cutMap_atLeastOneDiTauFilterE;
for(it = fullMap.begin(); it != fullMap.end(); it++){
TH1F* allEvents = (TH1F*)(it->second)->Get("allEventsFilter/totalEvents");
float totalEvents = allEvents->GetBinContent(1);
allEvents = (TH1F*)(it->second)->Get("atLeastOneDiTauFilter/totalEvents");
float tot = allEvents->GetBinContent(1);
float totalEquivalentEvents = allEvents->GetEffectiveEntries();
if(crossSec[it->first]>0){
tot *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
//totalEquivalentEvents *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
}
cutMap_atLeastOneDiTauFilter[it->first] = tot;
cutMap_atLeastOneDiTauFilterE[it->first] = totalEquivalentEvents>0 ? sqrt(totalEquivalentEvents)*(tot/totalEquivalentEvents) : 0;
}
std::map<std::string,float> cutMap_ElecIso;
std::map<std::string,float> cutMap_ElecIsoE;
for(it = fullMap.begin(); it != fullMap.end(); it++){
cout<<it->first<<endl;
TH1F* allEvents = (TH1F*)(it->second)->Get("allEventsFilter/totalEvents");
float totalEvents = allEvents->GetBinContent(1);
TH1F* h1 = new TH1F("h1","",1,-10,10);
TCut cut = (crossSec[it->first]>0) ? "(chIsoLeg1+nhIsoLeg1+phIsoLeg1)/diTauLegsP4[0].Pt()<0.1"
: "weight*((chIsoLeg1+nhIsoLeg1+phIsoLeg1)/diTauLegsP4[0].Pt()<0.1)";
((TTree*) (it->second->Get("muTauStreamAnalyzer/tree")) )->Draw("diTauLegsP4[0].Eta()>>h1",cut);
float tot = h1->Integral();
float totalEquivalentEvents = h1->GetEffectiveEntries();
if(crossSec[it->first]>0){
tot *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
//totalEquivalentEvents *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
}
cutMap_ElecIso[it->first] = tot;
cutMap_ElecIsoE[it->first] = totalEquivalentEvents>0 ? sqrt(totalEquivalentEvents)*(tot/totalEquivalentEvents) : 0;
delete h1;
}
std::map<std::string,float> cutMap_TauIso;
std::map<std::string,float> cutMap_TauIsoE;
for(it = fullMap.begin(); it != fullMap.end(); it++){
cout<<it->first<<endl;
TH1F* allEvents = (TH1F*)(it->second)->Get("allEventsFilter/totalEvents");
float totalEvents = allEvents->GetBinContent(1);
TH1F* h1 = new TH1F("h1","",1,-10,10);
TCut cut = (crossSec[it->first]>0) ? "(chIsoLeg1+nhIsoLeg1+phIsoLeg1)/diTauLegsP4[0].Pt()<0.1 && tightestHPSWP>0"
: "weight*((chIsoLeg1+nhIsoLeg1+phIsoLeg1)/diTauLegsP4[0].Pt()<0.1 && tightestHPSWP>0)";
((TTree*) (it->second->Get("muTauStreamAnalyzer/tree")) )->Draw("diTauLegsP4[0].Eta()>>h1",cut);
float tot = h1->Integral();
float totalEquivalentEvents = h1->GetEffectiveEntries();
if(crossSec[it->first]>0){
tot *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
//totalEquivalentEvents *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
}
cutMap_TauIso[it->first] = tot;
cutMap_TauIsoE[it->first] = totalEquivalentEvents>0 ? sqrt(totalEquivalentEvents)*(tot/totalEquivalentEvents) : 0;
delete h1;
}
std::map<std::string,float> cutMap_Mt;
std::map<std::string,float> cutMap_MtE;
for(it = fullMap.begin(); it != fullMap.end(); it++){
cout<<it->first<<endl;
TH1F* allEvents = (TH1F*)(it->second)->Get("allEventsFilter/totalEvents");
float totalEvents = allEvents->GetBinContent(1);
TH1F* h1 = new TH1F("h1","",1,-10,10);
TCut cut = (crossSec[it->first]>0) ? "(chIsoLeg1+nhIsoLeg1+phIsoLeg1)/diTauLegsP4[0].Pt()<0.1 && tightestHPSWP>0 && MtLeg1<40"
: "weight*((chIsoLeg1+nhIsoLeg1+phIsoLeg1)/diTauLegsP4[0].Pt()<0.1 && tightestHPSWP>0 && MtLeg1<40)";
((TTree*) (it->second->Get("muTauStreamAnalyzer/tree")) )->Draw("diTauLegsP4[0].Eta()>>h1",cut);
float tot = h1->Integral();
float totalEquivalentEvents = h1->GetEffectiveEntries();
if(crossSec[it->first]>0){
tot *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
//totalEquivalentEvents *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
}
cutMap_Mt[it->first] = tot;
cutMap_MtE[it->first] = totalEquivalentEvents>0 ? sqrt(totalEquivalentEvents)*(tot/totalEquivalentEvents) : 0;
delete h1;
}
std::map<std::string,float> cutMap_OS;
std::map<std::string,float> cutMap_OSE;
for(it = fullMap.begin(); it != fullMap.end(); it++){
cout<<it->first<<endl;
TH1F* allEvents = (TH1F*)(it->second)->Get("allEventsFilter/totalEvents");
float totalEvents = allEvents->GetBinContent(1);
TH1F* h1 = new TH1F("h1","",1,-10,10);
TCut cut = (crossSec[it->first]>0) ? "(chIsoLeg1+nhIsoLeg1+phIsoLeg1)/diTauLegsP4[0].Pt()<0.1 && tightestHPSWP>0 && diTauCharge==0 && MtLeg1<40"
: "weight*((chIsoLeg1+nhIsoLeg1+phIsoLeg1)/diTauLegsP4[0].Pt()<0.1 && tightestHPSWP>0 && diTauCharge==0 && MtLeg1<40)";
((TTree*) (it->second->Get("muTauStreamAnalyzer/tree")) )->Draw("diTauLegsP4[0].Eta()>>h1",cut);
float tot = h1->Integral();
float totalEquivalentEvents = h1->GetEffectiveEntries();
if(crossSec[it->first]>0){
tot *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
//totalEquivalentEvents *= (Lumi/ (totalEvents/crossSec[it->first]) ) ;
}
cutMap_OS[it->first] = tot;
cutMap_OSE[it->first] = totalEquivalentEvents>0 ? sqrt(totalEquivalentEvents)*(tot/totalEquivalentEvents) : 0;
delete h1;
}
std::map<std::string,float> cutMap_VBFPre;
std::map<std::string,float> cutMap_VBFPreE;
for(jt = tMap.begin(); jt != tMap.end(); jt++){
cout<<jt->first<<endl;
TH1F* h1 = new TH1F("h1","",1,-10,10);
TCut cut = "sampleWeight*(pt1>0 && combRelIsoLeg1<0.1 && tightestHPSWP>0 && diTauCharge==0 && MtLeg1<40)";
jt->second->Draw("etaL1>>h1",cut);
float tot = h1->Integral();
float totalEquivalentEvents = h1->GetEffectiveEntries();
cutMap_VBFPre[jt->first] = tot;
cutMap_VBFPreE[jt->first] = totalEquivalentEvents>0 ? sqrt(totalEquivalentEvents)*(tot/totalEquivalentEvents) : 0;
delete h1;
}
std::map<std::string,float> cutMap_VBF;
std::map<std::string,float> cutMap_VBFE;
std::map<std::string,float> cutMap_JetVeto;
std::map<std::string,float> cutMap_JetVetoE;
std::map<std::string,float> cutMap_HLT;
std::map<std::string,float> cutMap_HLTE;
for(jt = tMap.begin(); jt != tMap.end(); jt++){
cout<<jt->first<<endl;
TCut cut = "(pt1>0 && combRelIsoLeg1<0.1 && tightestHPSWP>0 && diTauCharge==0 && MtLeg1<40)";
TFile* dummy = new TFile("dummy.root","RECREATE");
TTree* currentTree = (TTree*)(jt->second)->CopyTree(cut);
float tot = 0;
int counter = 0;
float tot2 = 0;
int counter2 = 0;
float tot3 = 0;
int counter3 = 0;
currentTree->SetBranchAddress( "pt1", &pt1_ );
currentTree->SetBranchAddress( "pt2", &pt2_ );
currentTree->SetBranchAddress( "Deta",&Deta_ );
currentTree->SetBranchAddress( "Mjj", &Mjj_ );
currentTree->SetBranchAddress( "diTauSVFitPt",&diTauSVFitPt);
//currentTree->SetBranchAddress( "diTauSVFitEta",&diTauSVFitEta);
currentTree->SetBranchAddress( "diTauSVFitMass",&diTauSVFitMass);
currentTree->SetBranchAddress( "diTauVisMass",&diTauVisMass);
currentTree->SetBranchAddress( "ptL1", &ptL1 );
currentTree->SetBranchAddress( "ptL2", &ptL2 );
currentTree->SetBranchAddress( "etaL1", &etaL1 );
currentTree->SetBranchAddress( "etaL2", &etaL2 );
currentTree->SetBranchAddress( "combRelIsoLeg1",&combRelIsoLeg1);
currentTree->SetBranchAddress( "tightestHPSWP",&tightestHPSWP);
currentTree->SetBranchAddress( "diTauCharge",&diTauCharge);
currentTree->SetBranchAddress( "MtLeg1",&MtLeg1);
currentTree->SetBranchAddress( "numPV",&numPV);
currentTree->SetBranchAddress( "sampleWeight",&sampleWeight);
currentTree->SetBranchAddress( "ptVeto",&ptVeto);
currentTree->SetBranchAddress( "HLT",&HLT);
for (Long64_t ievt=0; ievt<currentTree->GetEntries();ievt++) {
currentTree->GetEntry(ievt);
if (ievt%10000 == 0){
std::cout << (jt->first) << " ---> processing event: " << ievt << " ..." <<std::endl;
}
pt1 = pt1_;
pt2 = pt2_;
Deta = Deta_;
Mjj = Mjj_;
bool pass = effS_>0 ? reader->EvaluateMVA( "Cuts", effS_ ) : (pt1>0);
if(pass){
tot+=sampleWeight;
counter++;
if(ptVeto<20){
tot2+=sampleWeight;
counter2++;
if(HLT>0.5 && HLT<1.5){
tot3+=sampleWeight;
counter3++;
}
}
}
}// end loop
cutMap_VBF[jt->first] = tot;
cutMap_VBFE[jt->first] = counter>0 ? sqrt(counter)*tot/counter : 0;
cutMap_JetVeto[jt->first] = tot2;
cutMap_JetVetoE[jt->first] = counter2>0 ? sqrt(counter2)*tot2/counter2 : 0;
cutMap_HLT[jt->first] = tot3;
cutMap_HLTE[jt->first] = counter3>0 ? sqrt(counter3)*tot3/counter3 : 0;
}
std::vector< std::map<std::string,float> > allFilters;
allFilters.push_back(cutMap_allEventsFilter);
allFilters.push_back(cutMap_vertexScrapingFilter);
allFilters.push_back(cutMap_oneElectronFilter);
allFilters.push_back(cutMap_noMuonFilter);
allFilters.push_back(cutMap_electronLegFilter);
allFilters.push_back(cutMap_tauLegFilter);
allFilters.push_back(cutMap_atLeastOneDiTauFilter);
allFilters.push_back(cutMap_ElecIso);
allFilters.push_back(cutMap_TauIso);
allFilters.push_back(cutMap_Mt);
allFilters.push_back(cutMap_OS);
allFilters.push_back(cutMap_VBFPre);
allFilters.push_back(cutMap_VBF);
allFilters.push_back(cutMap_JetVeto);
allFilters.push_back(cutMap_HLT);
std::vector< std::map<std::string,float> > allFiltersE;
allFiltersE.push_back(cutMap_allEventsFilterE);
allFiltersE.push_back(cutMap_vertexScrapingFilterE);
allFiltersE.push_back(cutMap_oneElectronFilterE);
allFiltersE.push_back(cutMap_noMuonFilterE);
allFiltersE.push_back(cutMap_electronLegFilterE);
allFiltersE.push_back(cutMap_tauLegFilterE);
allFiltersE.push_back(cutMap_atLeastOneDiTauFilterE);
allFiltersE.push_back(cutMap_ElecIsoE);
allFiltersE.push_back(cutMap_TauIsoE);
allFiltersE.push_back(cutMap_MtE);
allFiltersE.push_back(cutMap_OSE);
allFiltersE.push_back(cutMap_VBFPreE);
allFiltersE.push_back(cutMap_VBFE);
allFiltersE.push_back(cutMap_JetVetoE);
allFiltersE.push_back(cutMap_HLTE);
//out<<"\\begin{center}"<<endl;
out<<"\\begin{tabular}[!htbp]{|c";
for(int k = 0 ; k < samples.size(); k++) out<<"|c";
out<<"|} \\hline"<<endl;
out<< "Cut & ";
for(int k = 0 ; k < samples.size(); k++){
out << (fullMap.find(samples[k]))->first;
if(k!=samples.size()-1) out <<" & " ;
else out << " \\\\ " << endl;
}
out << " \\hline" << endl;
for(int i = 0; i < allFilters.size(); i++){
out << filters[i] << " & ";
for(int k = 0 ; k < samples.size(); k++){
out << (allFilters[i].find(samples[k]))->second << " $\\pm$ " << (allFiltersE[i].find(samples[k]))->second;
if(k!=samples.size()-1) out <<" & " ;
else out << " \\\\ " << endl;
}
out << " \\hline" << endl;
}
out<<"\\end{tabular}"<<endl;
//out<<"\\end{center}"<<endl;
return;
}
void PlotDecisionBoundary( TString weightFile = "weights/TMVAClassification_BDT.weights.xml",TString v0="var0", TString v1="var1", TString dataFileName = "/home/hvoss/TMVA/TMVA_data/data/data_circ.root")
{
//---------------------------------------------------------------
// default MVA methods to be trained + tested
// this loads the library
TMVA::Tools::Instance();
std::cout << std::endl;
std::cout << "==> Start TMVAClassificationApplication" << std::endl;
//
// create the Reader object
//
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
// create a set of variables and declare them to the reader
// - the variable names must corresponds in name and type to
// those given in the weight file(s) that you use
Double_t var0, var1;
reader->AddVariable( v0, &var0 );
reader->AddVariable( v1, &var1 );
//
// book the MVA method
//
reader->BookMVA( "MyMVAMethod", weightFile );
TFile *f = new TFile(dataFileName);
TTree *signal = (TTree*)f->Get("TreeS");
TTree *background = (TTree*)f->Get("TreeB");
//Declaration of leaves types
Float_t svar0;
Float_t svar1;
Float_t bvar0;
Float_t bvar1;
Float_t sWeight=1.0; // just in case you have weight defined, also set these branchaddresses
Float_t bWeight=1.0*signal->GetEntries()/background->GetEntries(); // just in case you have weight defined, also set these branchaddresses
// Set branch addresses.
signal->SetBranchAddress(v0,&svar0);
signal->SetBranchAddress(v1,&svar1);
background->SetBranchAddress(v0,&bvar0);
background->SetBranchAddress(v1,&bvar1);
UInt_t nbin = 50;
Float_t xmax = signal->GetMaximum(v0.Data());
Float_t xmin = signal->GetMinimum(v0.Data());
Float_t ymax = signal->GetMaximum(v1.Data());
Float_t ymin = signal->GetMinimum(v1.Data());
xmax = TMath::Max(xmax,(Float_t)background->GetMaximum(v0.Data()));
xmin = TMath::Min(xmin,(Float_t)background->GetMinimum(v0.Data()));
ymax = TMath::Max(ymax,(Float_t)background->GetMaximum(v1.Data()));
ymin = TMath::Min(ymin,(Float_t)background->GetMinimum(v1.Data()));
TH2D *hs=new TH2D("hs","",nbin,xmin,xmax,nbin,ymin,ymax);
TH2D *hb=new TH2D("hb","",nbin,xmin,xmax,nbin,ymin,ymax);
hs->SetXTitle(v0);
hs->SetYTitle(v1);
hb->SetXTitle(v0);
hb->SetYTitle(v1);
hs->SetMarkerColor(4);
hb->SetMarkerColor(2);
TH2F * hist = new TH2F( "MVA", "MVA", nbin,xmin,xmax,nbin,ymin,ymax);
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
Float_t MinMVA=10000, MaxMVA=-100000;
for (UInt_t ibin=1; ibin<nbin+1; ibin++){
for (UInt_t jbin=1; jbin<nbin+1; jbin++){
var0 = hs->GetXaxis()->GetBinCenter(ibin);
var1 = hs->GetYaxis()->GetBinCenter(jbin);
Float_t mvaVal=reader->EvaluateMVA( "MyMVAMethod" ) ;
if (MinMVA>mvaVal) MinMVA=mvaVal;
if (MaxMVA<mvaVal) MaxMVA=mvaVal;
hist->SetBinContent(ibin,jbin, mvaVal);
}
}
// now you need to try to find the MVA-value at which you cut for the plotting of the decision boundary
// (Use the smallest number of misclassifications as criterion)
const Int_t nValBins=100;
Double_t sum = 0.;
TH1F *mvaS= new TH1F("mvaS","",nValBins,MinMVA,MaxMVA); mvaS->SetXTitle("MVA-ouput"); mvaS->SetYTitle("#entries");
TH1F *mvaB= new TH1F("mvaB","",nValBins,MinMVA,MaxMVA); mvaB->SetXTitle("MVA-ouput"); mvaB->SetYTitle("#entries");
TH1F *mvaSC= new TH1F("mvaSC","",nValBins,MinMVA,MaxMVA); mvaSC->SetXTitle("MVA-ouput"); mvaSC->SetYTitle("cummulation");
TH1F *mvaBC= new TH1F("mvaBC","",nValBins,MinMVA,MaxMVA); mvaBC->SetXTitle("MVA-ouput"); mvaBC->SetYTitle("cummulation");
Long64_t nentries;
nentries = signal->GetEntries();
for (Long64_t is=0; is<nentries;is++) {
signal->GetEntry(is);
sum +=sWeight;
var0 = svar0;
var1 = svar1;
Float_t mvaVal=reader->EvaluateMVA( "MyMVAMethod" ) ;
hs->Fill(svar0,svar1);
mvaS->Fill(mvaVal,sWeight);
}
nentries = background->GetEntries();
for (Long64_t ib=0; ib<nentries;ib++) {
background->GetEntry(ib);
sum +=bWeight;
var0 = bvar0;
var1 = bvar1;
Float_t mvaVal=reader->EvaluateMVA( "MyMVAMethod" ) ;
hb->Fill(bvar0,bvar1);
mvaB->Fill(mvaVal,bWeight);
}
SeparationBase *sepGain = new MisClassificationError();
//SeparationBase *sepGain = new GiniIndex();
//SeparationBase *sepGain = new CrossEntropy();
Double_t sTot = mvaS->GetSum();
Double_t bTot = mvaB->GetSum();
mvaSC->SetBinContent(1,mvaS->GetBinContent(1));
mvaBC->SetBinContent(1,mvaB->GetBinContent(1));
Double_t sSel=mvaSC->GetBinContent(1);
Double_t bSel=mvaBC->GetBinContent(1);
Double_t sSelBest=0;
Double_t bSelBest=0;
Double_t separationGain=sepGain->GetSeparationGain(sSel,bSel,sTot,bTot);
Double_t mvaCut=mvaSC->GetBinCenter(1);
Double_t mvaCutOrientation=1; // 1 if mva > mvaCut --> Signal and -1 if mva < mvaCut (i.e. mva*-1 > mvaCut*-1) --> Signal
for (UInt_t ibin=2;ibin<nValBins;ibin++){
mvaSC->SetBinContent(ibin,mvaS->GetBinContent(ibin)+mvaSC->GetBinContent(ibin-1));
mvaBC->SetBinContent(ibin,mvaB->GetBinContent(ibin)+mvaBC->GetBinContent(ibin-1));
sSel=mvaSC->GetBinContent(ibin);
bSel=mvaBC->GetBinContent(ibin);
if (separationGain < sepGain->GetSeparationGain(sSel,bSel,sTot,bTot)){
separationGain = sepGain->GetSeparationGain(sSel,bSel,sTot,bTot);
mvaCut=mvaSC->GetBinCenter(ibin);
if (sSel/bSel > (sTot-sSel)/(bTot-bSel)) mvaCutOrientation=-1;
else mvaCutOrientation=1;
sSelBest=sSel;
bSelBest=bSel;
}
}
cout << "Min="<<MinMVA << " Max=" << MaxMVA
<< " sTot=" << sTot
<< " bTot=" << bTot
<< " sSel=" << sSelBest
<< " bSel=" << bSelBest
<< " sepGain="<<separationGain
<< " cut=" << mvaCut
<< " cutOrientation="<<mvaCutOrientation
<< endl;
delete reader;
gStyle->SetPalette(1);
plot(hs,hb,hist ,v0,v1,mvaCut);
TCanvas *cm=new TCanvas ("cm","",900,1200);
cm->cd();
cm->Divide(1,2);
cm->cd(1);
mvaS->SetLineColor(4);
mvaB->SetLineColor(2);
mvaS->Draw();
mvaB->Draw("same");
cm->cd(2);
mvaSC->SetLineColor(4);
mvaBC->SetLineColor(2);
mvaBC->Draw();
mvaSC->Draw("same");
// TH1F *add=(TH1F*)mvaBC->Clone("add");
// add->Add(mvaSC);
// add->Draw();
// errh->Draw("same");
//
// write histograms
//
TFile *target = new TFile( "TMVAPlotDecisionBoundary.root","RECREATE" );
hs->Write();
hb->Write();
hist->Write();
target->Close();
}
void TMVARegressionApplication( int wMs,int wM, string st,string st2,string option="",TString myMethodList = "" )
{
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
// --- Mutidimensional likelihood and Nearest-Neighbour methods
Use["PDERS"] = 0;
Use["PDEFoam"] = 0;
Use["KNN"] = 0;
//
// --- Linear Discriminant Analysis
Use["LD"] = 0;
//
// --- Function Discriminant analysis
Use["FDA_GA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
//
// --- Neural Network
Use["MLP"] = 0;
//
// --- Support Vector Machine
Use["SVM"] = 0;
//
// --- Boosted Decision Trees
Use["BDT"] = 0;
Use["BDTG"] = 1;
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVARegressionApplication" << std::endl;
// Select methods (don't look at this code - not of interest)
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;
}
}
// --------------------------------------------------------------------------------------------------
// --- Create the Reader object
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
// Create a set of variables and declare them to the reader
// - the variable names MUST corresponds in name and type to those given in the weight file(s) used
//Float_t var1, var2;
//reader->AddVariable( "var1", &var1 );
//reader->AddVariable( "var2", &var2 );
Float_t pt_AK8MatchedToHbb,eta_AK8MatchedToHbb,nsv_AK8MatchedToHbb,sv0mass_AK8MatchedToHbb,sv1mass_AK8MatchedToHbb,
nch_AK8MatchedToHbb,nmu_AK8MatchedToHbb,nel_AK8MatchedToHbb,muenfr_AK8MatchedToHbb,emenfr_AK8MatchedToHbb;
reader->AddVariable( "pt_AK8MatchedToHbb", &pt_AK8MatchedToHbb );
reader->AddVariable( "eta_AK8MatchedToHbb", &eta_AK8MatchedToHbb );
reader->AddVariable( "nsv_AK8MatchedToHbb", &nsv_AK8MatchedToHbb );
reader->AddVariable( "sv0mass_AK8MatchedToHbb", &sv0mass_AK8MatchedToHbb );
reader->AddVariable( "sv1mass_AK8MatchedToHbb", &sv1mass_AK8MatchedToHbb );
reader->AddVariable( "nch_AK8MatchedToHbb", &nch_AK8MatchedToHbb );
reader->AddVariable( "nmu_AK8MatchedToHbb", &nmu_AK8MatchedToHbb );
reader->AddVariable( "nel_AK8MatchedToHbb", &nel_AK8MatchedToHbb );
reader->AddVariable( "muenfr_AK8MatchedToHbb", &muenfr_AK8MatchedToHbb );
reader->AddVariable( "emenfr_AK8MatchedToHbb", &emenfr_AK8MatchedToHbb );
// Spectator variables declared in the training have to be added to the reader, too
Float_t spec1,spec2;
reader->AddSpectator( "spec1:=n_pv", &spec1 );
reader->AddSpectator( "spec2:=msoftdrop_AK8MatchedToHbb", &spec2 );
// --- Book the MVA methods
TString dir = "weights/";
TString prefix = "TMVARegression";
// Book method(s)
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
TString methodName = it->first + " method";
TString weightfile = dir + prefix + "_" + TString(it->first) + ".weights.xml";
reader->BookMVA( methodName, weightfile );
}
}
TH1* hists[100];
Int_t nhists = -1;
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
TH1* h = new TH1F( it->first.c_str(), TString(it->first) + " method", 100, -100, 600 );
if (it->second) hists[++nhists] = h;
}
nhists++;
//1=signal ,0=QCD ,2=data
int nameRoot=1;
if((st2.find("QCD")!= std::string::npos)||
(st2.find("bGen")!= std::string::npos)||
(st2.find("bEnriched")!= std::string::npos))nameRoot=0;
if(st2.find("data")!= std::string::npos)nameRoot=2;
cout<<"nameRoot = "<<nameRoot<<endl;
//option-----------------------------------------------------------
int JESOption=0;
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
//
TFile *f;
TTree *tree;
int nPass[20]= {0};
int total=0;
double fixScaleNum[2]= {0};
TH1D* th1=new TH1D("a","a",150,50,200);
string massName[nMass]= {"Thea","HCorr","Reg"};
string catName[nCat]= {"PP","PF","FP","FF"};
string tau21Name[3]= {"withTau21","woTau21","antiTau21"};
TH1D* th2[nMass][nCat][3];
TH1D* th3[nMass][nCat][3];
TH1D* th4[nMass][nCat][3];
for(int i=0; i<nMass; i++) {
for(int j=0; j<nCat; j++) {
for(int k=0; k<3; k++) {
th2[i][j][k]=(TH1D*)th1->Clone(Form("loose_%s_%s_%s",massName[i].data(),catName[j].data(),tau21Name[k].data()));
th3[i][j][k]=(TH1D*)th1->Clone(Form("tight_%s_%s_%s",massName[i].data(),catName[j].data(),tau21Name[k].data()));
th4[i][j][k]=(TH1D*)th1->Clone(Form("tl_%s_%s_%s",massName[i].data(),catName[j].data(),tau21Name[k].data()));
th2[i][j][k]->Sumw2();
th3[i][j][k]->Sumw2();
th4[i][j][k]->Sumw2();
}
}
}
for (int w=wMs; w<wM; w++) {
if(w%20==0)cout<<w<<endl;
if (nameRoot!=1)f = TFile::Open(Form("%s%d.root",st.data(),w));
else f = TFile::Open(st.data());
if (!f || !f->IsOpen())continue;
/*TDirectory * dir;
if (nameRoot!=1)dir = (TDirectory*)f->Get(Form("%s%d.root:/tree",st.data(),w));
else dir = (TDirectory*)f->Get(Form("%s:/tree",st.data()));
dir->GetObject("treeMaker",tree);
*/
tree=(TTree*)f->Get("treeMaker");
TreeReader data(tree);
total+=data.GetEntriesFast();
for(Long64_t jEntry=0; jEntry<data.GetEntriesFast() ; jEntry++) {
data.GetEntry(jEntry);
Int_t nVtx = data.GetInt("nVtx");
//0. has a good vertex
if(nVtx<1)continue;
nPass[0]++;
//1.trigger
std::string* trigName = data.GetPtrString("hlt_trigName");
vector<bool> &trigResult = *((vector<bool>*) data.GetPtr("hlt_trigResult"));
bool passTrigger=false;
for(int it=0; it< data.GetPtrStringSize(); it++) {
std::string thisTrig= trigName[it];
bool results = trigResult[it];
if( ((thisTrig.find("HLT_PFHT800")!= std::string::npos||
thisTrig.find("HLT_AK8DiPFJet300_200_TrimMass30_BTagCSV_p20")!= std::string::npos
) && results==1)) {
passTrigger=true;
break;
}
}
if(!passTrigger && nameRoot==2)continue;
nPass[1]++;
const int nAK8Jet=data.GetInt("AK8PuppinJet");
//2.nJets
if(nAK8Jet<2)continue;
nPass[2]++;
int* AK8PuppinSubSDJet=data.GetPtrInt("AK8PuppinSubSDJet");
if(AK8PuppinSubSDJet[0]!=2||AK8PuppinSubSDJet[1]!=2)continue;
TClonesArray* AK8PuppijetP4 = (TClonesArray*) data.GetPtrTObject("AK8PuppijetP4");
float* AK8PuppijetCorrUncUp = data.GetPtrFloat("AK8PuppijetCorrUncUp");
float* AK8PuppijetCorrUncDown = data.GetPtrFloat("AK8PuppijetCorrUncDown");
TLorentzVector* thisJet ,* thatJet;
thisJet=(TLorentzVector*)AK8PuppijetP4->At(0);
thatJet=(TLorentzVector*)AK8PuppijetP4->At(1);
//3. Pt
if(thisJet->Pt()>99998 ||thatJet->Pt()>99998 )continue;
if(thisJet->Pt()<300)continue;
if(thatJet->Pt()<300)continue;
nPass[3]++;
//4tightId-----------------------------------------
vector<bool> &AK8PuppijetPassIDTight = *((vector<bool>*) data.GetPtr("AK8PuppijetPassIDTight"));
if(AK8PuppijetPassIDTight[0]==0)continue;
if(AK8PuppijetPassIDTight[1]==0)continue;
Float_t* AK8PuppijetCEmEF = data.GetPtrFloat("AK8PuppijetCEmEF");
Float_t* AK8PuppijetMuoEF = data.GetPtrFloat("AK8PuppijetMuoEF");
if(AK8PuppijetMuoEF[0]>0.8)continue;
if(AK8PuppijetCEmEF[0]>0.9)continue;
if(AK8PuppijetMuoEF[1]>0.8)continue;
if(AK8PuppijetCEmEF[1]>0.9)continue;
nPass[4]++;
//5. Eta-----------------------------------------
if(fabs(thisJet->Eta())>2.4)continue;
if(fabs(thatJet->Eta())>2.4)continue;
nPass[5]++;
//6. DEta-----------------------------------------
float dEta = fabs(thisJet->Eta()-thatJet->Eta());
if(dEta>1.3)continue;
nPass[6]++;
//7. Mjj-----------------------------------------
//float mjjRed = (*thisJet+*thatJet).M()+250-thisJet->M()-thatJet->M();
//if(mjjRed<1000)continue;
nPass[7]++;
//8. fatjetPRmassL2L3Corr-----------------------------------------
nPass[8]++;
//9.-----------------------------------------
Float_t* AK8Puppijet_DoubleSV = data.GetPtrFloat("AK8Puppijet_DoubleSV");
int looseStat=-1;
int tightStat=-1;
int tlStat=-1;
if(AK8Puppijet_DoubleSV[0]>0.3 && AK8Puppijet_DoubleSV[1]>0.3)looseStat=0;
else if(AK8Puppijet_DoubleSV[0]>0.3 && AK8Puppijet_DoubleSV[1]<0.3)looseStat=1;
else if(AK8Puppijet_DoubleSV[0]<0.3 && AK8Puppijet_DoubleSV[1]>0.3)looseStat=2;
else looseStat=3;
if(AK8Puppijet_DoubleSV[0]>0.8 && AK8Puppijet_DoubleSV[1]>0.8)tightStat=0;
else if(AK8Puppijet_DoubleSV[0]>0.8 && AK8Puppijet_DoubleSV[1]<0.8)tightStat=1;
else if(AK8Puppijet_DoubleSV[0]<0.3 && AK8Puppijet_DoubleSV[1]>0.8)tightStat=2;
else if(AK8Puppijet_DoubleSV[0]<0.3 && AK8Puppijet_DoubleSV[1]<0.8)tightStat=3;
else tightStat=-1;
if(AK8Puppijet_DoubleSV[0]>0.8 && AK8Puppijet_DoubleSV[1]>0.3)tlStat=0;
else if(AK8Puppijet_DoubleSV[0]>0.8 && AK8Puppijet_DoubleSV[1]<0.3)tlStat=1;
else if(AK8Puppijet_DoubleSV[0]<0.3 && AK8Puppijet_DoubleSV[1]>0.3)tlStat=2;
else if(AK8Puppijet_DoubleSV[0]<0.3 && AK8Puppijet_DoubleSV[1]<0.3)tlStat=3;
else tlStat=-1;
double varTemp[2];
Float_t* AK8PuppijetSDmass = data.GetPtrFloat("AK8PuppijetSDmass");
Int_t* AK8Puppijet_nSV=data.GetPtrInt("AK8Puppijet_nSV");
vector<float> *AK8Puppijet_SVMass = data.GetPtrVectorFloat("AK8Puppijet_SVMass");
int nEle= data.GetInt("nEle");
int nMu=data.GetInt("nMu");
Float_t* AK8PuppijetEleEF = data.GetPtrFloat("AK8PuppijetEleEF");
//Float_t* AK8PuppijetMuoEF = data.GetPtrFloat("AK8PuppijetMuoEF");
Int_t* AK8PuppijetCMulti=data.GetPtrInt("AK8PuppijetCMulti");
Int_t* AK8PuppijetEleMulti=data.GetPtrInt("AK8PuppijetEleMulti");
Int_t* AK8PuppijetMuoMulti=data.GetPtrInt("AK8PuppijetMuoMulti");
for(int i=0; i<2; i++) {
TLorentzVector* thisAK8Jet ;
if(i==1)thisAK8Jet=thatJet;
else thisAK8Jet=thisJet;
pt_AK8MatchedToHbb=thisAK8Jet->Pt();
eta_AK8MatchedToHbb=thisAK8Jet->Eta();
nsv_AK8MatchedToHbb=AK8Puppijet_nSV[i];
sv0mass_AK8MatchedToHbb=AK8Puppijet_SVMass[i][0];
sv1mass_AK8MatchedToHbb=AK8Puppijet_SVMass[i][1];
nmu_AK8MatchedToHbb=AK8PuppijetMuoMulti[i];
nel_AK8MatchedToHbb=AK8PuppijetEleMulti[i];
muenfr_AK8MatchedToHbb=AK8PuppijetMuoEF[i];
nch_AK8MatchedToHbb=AK8PuppijetCMulti[i];
emenfr_AK8MatchedToHbb=AK8PuppijetEleEF[i];
spec1=nVtx;
spec2=AK8PuppijetSDmass[i];
Float_t val ;
for (Int_t ih=0; ih<nhists; ih++) {
TString title = hists[ih]->GetTitle();
val= (reader->EvaluateRegression( title ))[0];
}
varTemp[i]=val;
}
double PUPPIweight[2]= {0};
PUPPIweight[0]=getPUPPIweight(thisJet->Pt(),thisJet->Eta());
PUPPIweight[1]=getPUPPIweight(thatJet->Pt(),thatJet->Eta());
double PUPPIweightThea[2]= {0};
PUPPIweightThea[0]=getPUPPIweight_o(thisJet->Pt(),thisJet->Eta());
PUPPIweightThea[1]=getPUPPIweight_o(thatJet->Pt(),thatJet->Eta());
TLorentzVector thisJetReg, thatJetReg;
thisJetReg=(*thisJet)*varTemp[0];
thatJetReg=(*thatJet)*varTemp[1];
double PUPPIweightOnRegressed[2]= {0};
PUPPIweightOnRegressed[0]=getPUPPIweightOnRegressed(thisJetReg.Pt(),thisJetReg.Eta());
PUPPIweightOnRegressed[1]=getPUPPIweightOnRegressed(thatJetReg.Pt(),thatJetReg.Eta());
double mass_j0=0,mass_j1=0;
for(int i=0; i<nMass; i++) {
if(i==0) {
mass_j0=AK8PuppijetSDmass[0]*PUPPIweightThea[0];
mass_j1=AK8PuppijetSDmass[1]*PUPPIweightThea[1];
}
else if (i==1) {
mass_j0=AK8PuppijetSDmass[0]*PUPPIweight[0];
mass_j1=AK8PuppijetSDmass[1]*PUPPIweight[1];
}
else {
mass_j0=AK8PuppijetSDmass[0]*varTemp[0]*PUPPIweightOnRegressed[0];
mass_j1=AK8PuppijetSDmass[1]*varTemp[1]*PUPPIweightOnRegressed[1];
}
if(mass_j1<50)continue;
if(mass_j0>100 && mass_j0<145)continue;
//cout<<mass_j0<<","<<mass_j1<<",stat="<<looseStat<<","<<tightStat<<endl;
th2[i][looseStat][1]->Fill(mass_j0);
if(tightStat>=0)th3[i][tightStat][1]->Fill(mass_j0);
if(tlStat>=0)th4[i][tlStat][1]->Fill(mass_j0);
}
Float_t* AK8PuppijetTau1 = data.GetPtrFloat("AK8PuppijetTau1");
Float_t* AK8PuppijetTau2 = data.GetPtrFloat("AK8PuppijetTau2");
double puppiTau21[2];
puppiTau21[0]=(AK8PuppijetTau2[0]/AK8PuppijetTau1[0]),puppiTau21[1]=(AK8PuppijetTau2[1]/AK8PuppijetTau1[1]);
if(puppiTau21[0]>0.6 && puppiTau21[1]<0.6) {
for(int i=0; i<nMass; i++) {
if(i==0) {
mass_j0=AK8PuppijetSDmass[0]*PUPPIweightThea[0];
mass_j1=AK8PuppijetSDmass[1]*PUPPIweightThea[1];
}
else if (i==1) {
mass_j0=AK8PuppijetSDmass[0]*PUPPIweight[0];
mass_j1=AK8PuppijetSDmass[1]*PUPPIweight[1];
}
else {
mass_j0=AK8PuppijetSDmass[0]*varTemp[0]*PUPPIweightOnRegressed[0];
mass_j1=AK8PuppijetSDmass[1]*varTemp[1]*PUPPIweightOnRegressed[1];
}
if(mass_j1<50)continue;
if(mass_j0>100 && mass_j0<145)continue;
th2[i][looseStat][2]->Fill(mass_j0);
if(tightStat>=0)th3[i][tightStat][2]->Fill(mass_j0);
if(tlStat>=0)th4[i][tlStat][2]->Fill(mass_j0);
}
}
if(puppiTau21[0]>0.6 || puppiTau21[1]>0.6) continue;
for(int i=0; i<nMass; i++) {
if(i==0) {
mass_j0=AK8PuppijetSDmass[0]*PUPPIweightThea[0];
mass_j1=AK8PuppijetSDmass[1]*PUPPIweightThea[1];
}
else if (i==1) {
mass_j0=AK8PuppijetSDmass[0]*PUPPIweight[0];
mass_j1=AK8PuppijetSDmass[1]*PUPPIweight[1];
}
else {
mass_j0=AK8PuppijetSDmass[0]*varTemp[0]*PUPPIweightOnRegressed[0];
mass_j1=AK8PuppijetSDmass[1]*varTemp[1]*PUPPIweightOnRegressed[1];
}
if(mass_j1<50)continue;
if(mass_j0>100 && mass_j0<145)continue;
th2[i][looseStat][0]->Fill(mass_j0);
if(tightStat>=0)th3[i][tightStat][0]->Fill(mass_j0);
if(tlStat>=0)th4[i][tlStat][0]->Fill(mass_j0);
}
}
}
for(int i=0; i<10; i++)cout<<"npass["<<i<<"]="<<nPass[i]<<endl;
TFile* outFile;//= new TFile(Form("PFRatio/%s.root",st2.data()),"recreate");
outFile= new TFile(Form("PFRatio/%s/%d.root",st2.data(),wMs),"recreate");
for(int i=0; i<nMass; i++) {
for(int j=0; j<nCat; j++) {
for(int k=0; k<3; k++) {
th2[i][j][k]->Write();
th3[i][j][k]->Write();
th4[i][j][k]->Write();
}
}
}
outFile->Close();
for(int i=0; i<nMass; i++) {
for(int j=0; j<nCat; j++) {
for(int k=0; k<2; k++) {
delete th2[i][j][k];
delete th3[i][j][k];
delete th4[i][j][k];
}
}
}
delete reader;
}
void TMVAClassificationCategoryApplication()
{
// ---------------------------------------------------------------
// default MVA methods to be trained + tested
std::map<std::string,int> Use;
// ---
Use["LikelihoodCat"] = 1;
Use["FisherCat"] = 1;
// ---------------------------------------------------------------
std::cout << std::endl
<< "==> Start TMVAClassificationCategoryApplication" << std::endl;
// --- Create the Reader object
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
// Create a set of variables and spectators and declare them to the reader
// - the variable names MUST corresponds in name and type to those given in the weight file(s) used
Float_t var1, var2, var3, var4, eta;
reader->AddVariable( "var1", &var1 );
reader->AddVariable( "var2", &var2 );
reader->AddVariable( "var3", &var3 );
reader->AddVariable( "var4", &var4 );
reader->AddSpectator( "eta", &eta );
// --- Book the MVA methods
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
TString methodName = it->first + " method";
TString weightfile = "dataset/weights/TMVAClassificationCategory_" + TString(it->first) + ".weights.xml";
reader->BookMVA( methodName, weightfile );
}
}
// Book output histograms
UInt_t nbin = 100;
std::map<std::string,TH1*> hist;
hist["LikelihoodCat"] = new TH1F( "MVA_LikelihoodCat", "MVA_LikelihoodCat", nbin, -1, 0.9999 );
hist["FisherCat"] = new TH1F( "MVA_FisherCat", "MVA_FisherCat", nbin, -4, 4 );
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
//
TString fname = TString(gSystem->DirName(__FILE__) ) + "/data/";
// if directory data not found try using tutorials dir
if (gSystem->AccessPathName( fname )) {
fname = TString(gROOT->GetTutorialsDir()) + "/tmva/data/";
}
if (UseOffsetMethod) fname += "toy_sigbkg_categ_offset.root";
else fname += "toy_sigbkg_categ_varoff.root";
std::cout << "--- TMVAClassificationApp : Accessing " << fname << "!" << std::endl;
TFile *input = TFile::Open(fname);
if (!input) {
std::cout << "ERROR: could not open data file: " << fname << std::endl;
exit(1);
}
// --- Event loop
// Prepare the tree
// - here the variable names have to corresponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
//
TTree* theTree = (TTree*)input->Get("TreeS");
std::cout << "--- Use signal sample for evalution" << std::endl;
theTree->SetBranchAddress( "var1", &var1 );
theTree->SetBranchAddress( "var2", &var2 );
theTree->SetBranchAddress( "var3", &var3 );
theTree->SetBranchAddress( "var4", &var4 );
theTree->SetBranchAddress( "eta", &eta ); // spectator
std::cout << "--- Processing: " << theTree->GetEntries() << " events" << std::endl;
TStopwatch sw;
sw.Start();
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
if (ievt%1000 == 0) std::cout << "--- ... Processing event: " << ievt << std::endl;
theTree->GetEntry(ievt);
// --- Return the MVA outputs and fill into histograms
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (!it->second) continue;
TString methodName = it->first + " method";
hist[it->first]->Fill( reader->EvaluateMVA( methodName ) );
}
}
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
// --- Write histograms
TFile *target = new TFile( "TMVApp.root","RECREATE" );
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++)
if (it->second) hist[it->first]->Write();
target->Close();
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;
}
void TMVAClassificationApplication( TString myMethodList = "" )
{
#ifdef __CINT__
gROOT->ProcessLine( ".O0" ); // turn off optimization in CINT
#endif
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// set verbosity
//TMVA::Tools::Instance().Log().SetMinType(kINFO);
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
Use["BDT"] = 1; // uses Adaptive Boost
Use["Category"] = 1;
std::cout << std::endl;
std::cout << "==> Start TMVAClassificationApplication" << std::endl;
// Select methods (don't look at this code - not of interest)
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;
}
}
// --------------------------------------------------------------------------------------------------
// --- Create the Reader object
TMVA::Reader *reader = new TMVA::Reader( "Color:!Silent" );
reader->SetMsgType(kINFO);
// CMS STATS:
//
// Create a set of variables and declare them to the reader
// - the variable names MUST corresponds in name and type to those given in the weight file(s) used
Float_t met;
Float_t HT;
Float_t minMLB;
Float_t leptonJetsMETSum;
reader->AddVariable( "met", &met );
reader->AddVariable( "HT", &HT );
reader->AddVariable( "minMLB", &minMLB );
reader->AddVariable( "leptonJetsMETSum", &leptonJetsMETSum );
// CMS STATS:
// *** VERY IMPORTANT! ***
// TMVA notoriously has problems with integer and other non-float branches.
// Better not to use them at all and convert them to Float_t. If you happen
// to have integer branches that you need, as in this example, you should create
// corresponding float spectator variables and assign them in the event loop.
//
// Spectator variables declared in the training have to be added to the reader, too
//
// Note that the corresponding branches are integer, so we create floats too!
Int_t nBTag;
Int_t nJets;
Int_t nLeptons;
Int_t isMuon1;
Int_t isMuon2;
Int_t isMuon3;
Int_t isMuon4;
Float_t nBTagFloat;
Float_t nJetsFloat;
Float_t nLeptonsFloat;
Float_t isMuon1Float;
Float_t isMuon2Float;
Float_t isMuon3Float;
Float_t isMuon4Float;
Float_t leptonSumMass;
reader->AddSpectator( "nBTag", &nBTagFloat );
reader->AddSpectator( "nJets", &nJetsFloat );
reader->AddSpectator( "nLeptons", &nLeptonsFloat );
reader->AddSpectator( "isMuon1", &isMuon1Float );
reader->AddSpectator( "isMuon2", &isMuon2Float );
reader->AddSpectator( "isMuon3", &isMuon3Float );
reader->AddSpectator( "isMuon4", &isMuon4Float );
reader->AddSpectator( "leptonSumMass", &leptonSumMass );
// CMS STATS:
// cut definitions. Define categories and overall selection.
#include "TMVA_tprime_cuts.C"
// Add artificial spectators for distinguishing categories
Float_t Category_mycat1, Category_mycat2, Category_mycat3, Category_mycat4;
TString sCat1("Category_cat1:=");
TString sCat2("Category_cat2:=");
TString sCat3("Category_cat3:=");
TString sCat4("Category_cat4:=");
sCat1.Append(cut_os_cat1);
sCat2.Append(cut_os_cat2);
sCat3.Append(cut_ss);
sCat4.Append(cut_tri);
reader->AddSpectator( sCat1, &Category_mycat1 );
reader->AddSpectator( sCat2, &Category_mycat2 );
reader->AddSpectator( sCat3, &Category_mycat3 );
reader->AddSpectator( sCat4, &Category_mycat4 );
// --- Book the MVA methods
TString dir = "weights/";
TString prefix = "TMVAClassification";
// Book method(s)
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
TString methodName = TString(it->first) + TString(" method");
TString weightfile = dir + prefix + TString("_") + TString(it->first) + TString(".weights.xml");
reader->BookMVA( methodName, weightfile );
}
}
// Book output histograms
UInt_t nbin = 100;
TH1F * histBdt(0);
TH1F * histCat(0);
if (Use["BDT"]) histBdt = new TH1F( "MVA_BDT", "MVA_BDT", nbin, -0.8, 0.8 );
if (Use["Category"]) histCat = new TH1F( "MVA_Category", "MVA_Category", nbin, -2., 2. );
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
//
TFile *input(0);
// CMS STATS:
// Specify files with data, for which you want to compute the classifier values
TString fname = "./data/pass7_OS_test1/TTbar_MuMu/all.root";
//TString fname = "./data/pass7_TRI_test1/TTbar_MuMu/all.root";
if (!gSystem->AccessPathName( fname ))
input = TFile::Open( fname ); // check if file in local directory exists
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVAClassificationApp : Using input file: " << input->GetName() << std::endl;
// --- Event loop
// Prepare the event tree
// - here the variable names have to corresponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
//
std::cout << "--- Select signal sample" << std::endl;
TTree* theTree = (TTree*)input->Get("MVA");
//Float_t userVar1, userVar2;
theTree->SetBranchAddress( "met", &met );
theTree->SetBranchAddress( "HT", &HT );
theTree->SetBranchAddress( "minMLB", &minMLB );
theTree->SetBranchAddress( "leptonJetsMETSum", &leptonJetsMETSum );
// spectators
theTree->SetBranchAddress( "leptonSumMass", &leptonSumMass );
theTree->SetBranchAddress( "nJets", &nJets );
theTree->SetBranchAddress( "nBTag", &nBTag );
theTree->SetBranchAddress( "nLeptons", &nLeptons );
theTree->SetBranchAddress( "isMuon1", &isMuon1 );
theTree->SetBranchAddress( "isMuon2", &isMuon2 );
theTree->SetBranchAddress( "isMuon3", &isMuon3 );
theTree->SetBranchAddress( "isMuon4", &isMuon4 );
std::vector<Float_t> vecVar(4); // vector for EvaluateMVA tests
// CMS STATS:
//
// A little trick: loop over only selected events
// make event list
theTree->Draw(">>EvtList", mycut);
TEventList * pEvtList = (TEventList *)gROOT->FindObject("EvtList");
long int nEvents = pEvtList->GetN();
//std::cout << "--- Processing: " << theTree->GetEntries() << " events" << std::endl;
std::cout << "--- Processing: " << nEvents << " events" << std::endl;
TStopwatch sw;
sw.Start();
//for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
for (Long64_t ievt=0; ievt<nEvents; ++ievt) {
if (ievt%1000 == 0) std::cout << "--- ... Processing event: " << ievt << std::endl;
//theTree->GetEntry(ievt);
theTree->GetEntry(pEvtList->GetEntry(ievt));
// CMS STATS:
// *** HERE we assign integer branches' values to float spectator variables
// Otherwise our category cuts would fail
//
nBTagFloat=(Float_t)nBTag;
nJetsFloat=(Float_t)nJets;
nLeptonsFloat=(Float_t)nLeptons;
isMuon1Float = (Float_t)isMuon1;
isMuon2Float = (Float_t)isMuon2;
isMuon3Float = (Float_t)isMuon3;
isMuon4Float = (Float_t)isMuon4;
// --- Return the MVA outputs and fill into histograms
if (Use["BDT" ]) histBdt ->Fill( reader->EvaluateMVA( "BDT method" ) );
if (Use["Category" ]) histCat ->Fill( reader->EvaluateMVA( "Category method" ) );
}
// Get elapsed time
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
// --- Write histograms
TFile *target = new TFile( "TMVApp.root","RECREATE" );
if (Use["BDT" ]) histBdt ->Write();
if (Use["Category" ]) histCat ->Write();
target->Close();
std::cout << "--- Created root file: \"TMVApp.root\" containing the MVA output histograms" << std::endl;
delete reader;
std::cout << "==> TMVAClassificationApplication is done!" << endl << std::endl;
}
void TMVAMulticlassApplication( TString myMethodList = "" )
{
#ifdef __CINT__
gROOT->ProcessLine( ".O0" ); // turn off optimization in CINT
#endif
TMVA::Tools::Instance();
//---------------------------------------------------------------
// default MVA methods to be trained + tested
std::map<std::string,int> Use;
Use["MLP"] = 1;
Use["BDTG"] = 1;
Use["FDA_GA"] = 0;
//---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVAMulticlassApplication" << 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::endl;
std::cout << std::endl;
return;
}
Use[regMethod] = 1;
}
}
// create the Reader object
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
// create a set of variables and declare them to the reader
// - the variable names must corresponds in name and type to
// those given in the weight file(s) that you use
Float_t var1, var2, var3, var4;
reader->AddVariable( "var1", &var1 );
reader->AddVariable( "var2", &var2 );
reader->AddVariable( "var3", &var3 );
reader->AddVariable( "var4", &var4 );
// book the MVA methods
TString dir = "weights/";
TString prefix = "TMVAMulticlass";
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
TString methodName = TString(it->first) + TString(" method");
TString weightfile = dir + prefix + TString("_") + TString(it->first) + TString(".weights.xml");
reader->BookMVA( methodName, weightfile );
}
}
// book output histograms
UInt_t nbin = 100;
TH1F *histMLP_signal(0), *histBDTG_signal(0), *histFDAGA_signal(0);
if (Use["MLP"])
histMLP_signal = new TH1F( "MVA_MLP_signal", "MVA_MLP_signal", nbin, 0., 1.1 );
if (Use["BDTG"])
histBDTG_signal = new TH1F( "MVA_BDTG_signal", "MVA_BDTG_signal", nbin, 0., 1.1 );
if (Use["FDA_GA"])
histFDAGA_signal = new TH1F( "MVA_FDA_GA_signal", "MVA_FDA_GA_signal", nbin, 0., 1.1 );
TFile *input(0);
TString fname = "./tmva_example_multiple_background.root";
if (!gSystem->AccessPathName( fname )) {
input = TFile::Open( fname ); // check if file in local directory exists
}
if (!input) {
std::cout << "ERROR: could not open data file, please generate example data first!" << std::endl;
exit(1);
}
std::cout << "--- TMVAMulticlassApp : Using input file: " << input->GetName() << std::endl;
// prepare the tree
// - here the variable names have to corresponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
TTree* theTree = (TTree*)input->Get("TreeS");
std::cout << "--- Select signal sample" << std::endl;
theTree->SetBranchAddress( "var1", &var1 );
theTree->SetBranchAddress( "var2", &var2 );
theTree->SetBranchAddress( "var3", &var3 );
theTree->SetBranchAddress( "var4", &var4 );
std::cout << "--- Processing: " << theTree->GetEntries() << " events" << std::endl;
TStopwatch sw;
sw.Start();
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
if (ievt%1000 == 0){
std::cout << "--- ... Processing event: " << ievt << std::endl;
}
theTree->GetEntry(ievt);
if (Use["MLP"])
histMLP_signal->Fill((reader->EvaluateMulticlass( "MLP method" ))[0]);
if (Use["BDTG"])
histBDTG_signal->Fill((reader->EvaluateMulticlass( "BDTG method" ))[0]);
if (Use["FDA_GA"])
histFDAGA_signal->Fill((reader->EvaluateMulticlass( "FDA_GA method" ))[0]);
}
// get elapsed time
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
TFile *target = new TFile( "TMVAMulticlassApp.root","RECREATE" );
if (Use["MLP"])
histMLP_signal->Write();
if (Use["BDTG"])
histBDTG_signal->Write();
if (Use["FDA_GA"])
histFDAGA_signal->Write();
target->Close();
std::cout << "--- Created root file: \"TMVMulticlassApp.root\" containing the MVA output histograms" << std::endl;
delete reader;
std::cout << "==> TMVAClassificationApplication is done!" << std::endl << std::endl;
}
TString TMVAPredict(TString method_name, EnumPredictMode predictMode = EnumPredictMode::FINAL)
{
std::cout << "------------ predict with : " << method_name << " ------ " << std::endl;
std::vector<std::string> inputNames = {"training","test","check_correlation","check_agreement"};
std::map<std::string,std::vector<std::string>> varsForInput;
std::vector<std::string> variableOrder = {"id", "signal", "mass", "min_ANNmuon", "prediction"};
varsForInput["training"].emplace_back ("prediction");
if (predictMode != EnumPredictMode::INTERMEDIATE)
{
varsForInput["training"].emplace_back ("id");
varsForInput["training"].emplace_back ("signal");
varsForInput["training"].emplace_back ("mass");
varsForInput["training"].emplace_back ("min_ANNmuon");
varsForInput["test"].emplace_back ("prediction");
varsForInput["test"].emplace_back ("id");
varsForInput["check_agreement"].emplace_back ("signal");
varsForInput["check_agreement"].emplace_back ("weight");
varsForInput["check_agreement"].emplace_back ("prediction");
varsForInput["check_correlation"].emplace_back ("mass");
varsForInput["check_correlation"].emplace_back ("prediction");
}
std::map<std::string,std::vector<std::string>> createForInput;
createForInput["training"].emplace_back ("root");
if (predictMode != EnumPredictMode::INTERMEDIATE)
{
createForInput["training"].emplace_back ("csv");
createForInput["test"].emplace_back ("csv");
createForInput["check_agreement"].emplace_back ("csv");
createForInput["check_correlation"].emplace_back ("csv");
}
// -------- prepare the Reader ------
TMVA::Tools::Instance();
std::cout << "==> Start TMVAPredict" << std::endl;
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
std::vector<Float_t> variables (variableNames.size ());
auto itVar = begin (variables);
for (auto varName : variableNames)
{
Float_t* pVar = &(*itVar);
auto localVarName = varName;
localVarName.substr(0,localVarName.find(":="));
reader->AddVariable(varName.c_str(), pVar);
(*itVar) = 0.0;
++itVar;
}
// spectators not known for the reader (in test.csv)
for (auto varName : spectatorNames)
{
Float_t spectator (0.0);
reader->AddSpectator (varName.c_str(), &spectator);
++itVar;
}
TString dir = "weights/";
TString prefix = "TMVAClassification";
TString weightfile = dir + prefix + TString("_") + method_name + TString(".weights.xml");
std::cout << "weightfile name : " << weightfile.Data () << std::endl;
reader->BookMVA( method_name, weightfile );
// --------- for each of the input files
for (auto inputName : inputNames)
{
// --- define variables
Int_t id;
Float_t prediction;
Float_t weight;
Float_t min_ANNmuon;
Float_t mass;
Float_t signal;
// --- open input file
TFile *input(0);
std::stringstream infilename;
infilename << pathToData.Data () << inputName << ".root";
std::cout << "infilename = " << infilename.str ().c_str () << std::endl;
input = TFile::Open (infilename.str ().c_str ());
TTree* tree = (TTree*)input->Get("data");
// --- prepare branches on input file
// id field if needed
if (contains (varsForInput, inputName, "id"))
tree->SetBranchAddress("id", &id);
// signal field if needed
if (contains (varsForInput, inputName, "signal"))
tree->SetBranchAddress("signal", &signal);
// min_ANNmuon field if needed
if (contains (varsForInput, inputName, "min_ANNmuon"))
tree->SetBranchAddress("min_ANNmuon", &min_ANNmuon);
// mass field if needed
if (contains (varsForInput, inputName, "mass"))
tree->SetBranchAddress("mass", &mass);
// weight field if needed
if (contains (varsForInput, inputName, "weight"))
tree->SetBranchAddress("weight", &weight);
// variables for prediction
itVar = begin (variables);
for (auto inputName : variableNames)
{
Float_t* pVar = &(*itVar);
tree->SetBranchAddress(inputName.c_str(), pVar);
++itVar;
}
// ---- make ROOT file
TString rootFileName;
TFile* outRootFile (NULL);
TTree* outTree (NULL);
if (contains (createForInput, inputName, "root"))
{
rootFileName = TString (inputName.c_str ()) + TString ("_prediction__") + method_name + TString (".root");
outRootFile = new TFile (rootFileName.Data (), "RECREATE");
outTree = new TTree("data","data");
if (contains (varsForInput, inputName, "id"))
outTree->Branch ("id", &id, "F");
if (contains (varsForInput, inputName, "signal"))
outTree->Branch ("signal", &signal, "F");
if (contains (varsForInput, inputName, "min_ANNmuon"))
outTree->Branch ("min_ANNmuon", &min_ANNmuon, "F");
if (contains (varsForInput, inputName, "mass"))
outTree->Branch ("mass", &mass, "F");
if (contains (varsForInput, inputName, "weight"))
outTree->Branch ("weight", &weight, "F");
if (contains (varsForInput, inputName, "prediction"))
outTree->Branch ("prediction", &prediction, "F");
}
// ---- prepare csv file
std::ofstream outfile;
if (contains (createForInput, inputName, "csv"))
{
std::stringstream outfilename;
outfilename << inputName << "_prediction__" << method_name.Data () << ".csv";
std::cout << outfilename.str () << std::endl;
/* return; */
outfile.open (outfilename.str ());
bool isFirst = true;
for (auto varName : variableOrder)
{
if (contains (varsForInput, inputName, varName))
{
if (!isFirst)
outfile << ",";
isFirst = false;
outfile << varName;
}
}
outfile << "\n";
}
bool doCSV = contains (createForInput, inputName, "csv");
bool doROOT = contains (createForInput, inputName, "root");
for (Long64_t ievt=0; ievt < tree->GetEntries(); ievt++)
{
tree->GetEntry(ievt);
// predict
prediction = reader->EvaluateMVA (method_name);
prediction = std::max<double> (0.0, std::min<double> (1.0, prediction));
//prediction = (prediction + 1.0)/2.0;
if (doCSV)
{
for (auto varName : variableOrder)
{
if (varName == "id" && contains (varsForInput, inputName, "id"))
outfile << id << ",";
if (varName == "signal" && contains (varsForInput, inputName, "signal"))
outfile << signal << ",";
if (varName == "min_ANNmuon" && contains (varsForInput, inputName, "min_ANNmuon"))
outfile << min_ANNmuon << ",";
if (varName == "mass" && contains (varsForInput, inputName, "mass"))
outfile << mass << ",";
if (varName == "weight" && contains (varsForInput, inputName, "weight"))
outfile << weight << ",";
if (varName == "prediction" && contains (varsForInput, inputName, "prediction"))
outfile << prediction;
}
outfile << "\n";
}
if (doROOT)
{
outTree->Fill ();
}
}
outfile.close();
input->Close();
if (doROOT)
{
outRootFile->Write ();
}
if (predictMode == EnumPredictMode::INTERMEDIATE)
{
delete reader;
std::cout << "DONE predict INTERMEDIATE" << std::endl;
return rootFileName;
}
}
delete reader;
if (predictMode == EnumPredictMode::FINAL)
{
std::cout << "DONE predict FINAL" << std::endl;
TString cmd (".! python tests.py ");
cmd += method_name;
gROOT->ProcessLine (cmd);
}
return method_name;
}
TString useAutoencoder (TString method_name)
{
TMVA::Tools::Instance();
std::cout << "==> Start useAutoencoder" << std::endl;
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
Float_t signal = 0.0;
Float_t outSignal = 0.0;
Float_t inSignal = 0.0;
std::vector<std::string> localVariableNames (variableNames+additionalVariableNames);
std::vector<Float_t> variables (localVariableNames.size ());
auto itVar = begin (variables);
for (auto varName : localVariableNames)
{
Float_t* pVar = &(*itVar);
reader->AddVariable(varName.c_str(), pVar);
(*itVar) = 0.0;
++itVar;
}
int idxSignal = std::distance (localVariableNames.begin (),
std::find (localVariableNames.begin (), localVariableNames.end (),std::string ("signal")));
TString dir = "weights/";
TString prefix = "TMVAAutoencoder";
TString weightfile = dir + prefix + TString("_") + method_name + TString(".weights.xml");
TString outPrefix = "transformed";
TString outfilename = pathToData + outPrefix + TString("_") + method_name + TString(".root");
reader->BookMVA( method_name, weightfile );
TFile* outFile = new TFile (outfilename.Data (), "RECREATE");
std::vector<std::string> inputNames = {"training"};
std::map<std::string,std::vector<std::string>> varsForInput;
varsForInput["training"].emplace_back ("id");
varsForInput["training"].emplace_back ("signal");
for (auto inputName : inputNames)
{
std::stringstream outfilename;
outfilename << inputName << "_transformed__" << method_name.Data () << ".root";
std::cout << outfilename.str () << std::endl;
/* return; */
std::stringstream infilename;
infilename << pathToData.Data () << inputName << ".root";
TTree* outTree = new TTree("transformed","transformed");
std::vector<Float_t> outVariables (localVariableNames.size ());
itVar = begin (variables);
auto itOutVar = begin (outVariables);
for (auto varName : localVariableNames)
{
Float_t* pOutVar = &(*itOutVar);
outTree->Branch (varName.c_str (), pOutVar, "F");
(*itOutVar) = 0.0;
++itOutVar;
Float_t* pVar = &(*itVar);
std::stringstream svar;
svar << varName << "_in";
outTree->Branch (svar.str ().c_str (), pVar, "F");
(*itVar) = 0.0;
++itVar;
}
Float_t signal_original = 0.0;
outTree->Branch ("signal_original", &signal_original, "F");
TFile *input(0);
std::cout << "infilename = " << infilename.str ().c_str () << std::endl;
input = TFile::Open (infilename.str ().c_str ());
TTree* tree = (TTree*)input->Get("data");
Int_t ids;
// id field if needed
if (std::find (varsForInput[inputName].begin (), varsForInput[inputName].end (), "id") != varsForInput[inputName].end ())
tree->SetBranchAddress("id", &ids);
// variables for prediction
itVar = begin (variables);
for (auto inputName : localVariableNames)
{
Float_t* pVar = &(*itVar);
tree->SetBranchAddress (inputName.c_str(), pVar);
++itVar;
}
for (Long64_t ievt=0; ievt < tree->GetEntries(); ievt++)
{
tree->GetEntry(ievt);
// predict
signal_original = variables.at (idxSignal);
for (int forcedSignal = 0; forcedSignal <= 1; ++forcedSignal)
{
variables.at (idxSignal) = forcedSignal;
std::vector<Float_t> regressionValues = reader->EvaluateRegression (method_name);
size_t idx = 0;
for (auto it = std::begin (regressionValues), itEnd = std::end (regressionValues); it != itEnd; ++it)
{
outVariables.at (idx) = *it;
++idx;
}
outTree->Fill ();
}
}
outFile->Write ();
input->Close();
}
delete reader;
return outfilename;
}
/*
void allBranches(TTree* inTree){
inTree->SetBranchAddress("fullpmet",&fullpmet);
inTree->SetBranchAddress("trkpmet",&trkpmet);
inTree->SetBranchAddress("ratioMet",&ratioMet);
inTree->SetBranchAddress("ptll",&ptll);
inTree->SetBranchAddress("mth",&mth);
inTree->SetBranchAddress("jetpt1",&jetpt1);
inTree->SetBranchAddress("ptWW",&ptWW);
inTree->SetBranchAddress("dphilljet",&dphilljet);
inTree->SetBranchAddress("dphillmet",&dphillmet);
inTree->SetBranchAddress("dphijet1met",&dphijet1met);
inTree->SetBranchAddress("nvtx",&nvtx);
inTree->SetBranchAddress("baseW",&baseW);
}
void createOutput(TTree* outTree){
outTree->Branch("fullpmet",&fullpmet);
outTree->Branch("trkpmet",&trkpmet);
outTree->Branch("ratioMet",&ratioMet);
outTree->Branch("ptll",&ptll);
outTree->Branch("mth",&mth);
outTree->Branch("jetpt1",&jetpt1);
outTree->Branch("ptWW",&ptWW);
outTree->Branch("dphilljet",&dphilljet);
outTree->Branch("dphillmet",&dphillmet);
outTree->Branch("dphijet1met",&dphijet1met);
outTree->Branch("nvtx",&nvtx);
outTree->Branch("baseW",&baseW);
}
*/
void read(TString sampleName = "WW")
{
//calling the reader of the MVA analysis
TMVA::Reader* reader = new TMVA::Reader("");
reader->AddVariable("fullpmet",&fullpmet);
reader->AddVariable("trkpmet",&trkpmet);
reader->AddVariable("ratioMet",&ratioMet);
reader->AddVariable("ptll",&ptll);
reader->AddVariable("mth",&mth);
reader->AddVariable("jetpt1",&jetpt1);
reader->AddVariable("ptWW",&ptWW);
reader->AddVariable("dphilljet",&dphilljet);
reader->AddVariable("dphillmet",&dphillmet);
reader->AddVariable("dphijet1met",&dphijet1met);
reader->AddVariable("nvtx",&nvtx);
//reader->BookMVA("Fisher", "weights/MVAnalysis_Fisher.weights.xml");
reader->BookMVA("BDT", "weights/" + sampleName + "_BDT.weights.xml");
/*
//Calling WW Signal File and Tree and Creating Output Signal Trees
TFile *MySigFile = new TFile("../rootFiles/AllJet/OF/WW.root","READ");
TTree* MySigTree = (TTree*)MySigFile->Get("nt");
MySigTree->SetBranchAddress("fullpmet",&fullpmet);
MySigTree->SetBranchAddress("trkpmet",&trkpmet);
MySigTree->SetBranchAddress("ratioMet",&ratioMet);
MySigTree->SetBranchAddress("ptll",&ptll);
MySigTree->SetBranchAddress("mth",&mth);
MySigTree->SetBranchAddress("jetpt1",&jetpt1);
MySigTree->SetBranchAddress("ptWW",&ptWW);
MySigTree->SetBranchAddress("dphilljet",&dphilljet);
MySigTree->SetBranchAddress("dphillmet",&dphillmet);
MySigTree->SetBranchAddress("dphijet1met",&dphijet1met);
MySigTree->SetBranchAddress("nvtx",&nvtx);
TTree *outSigTree = new TTree ("Out","Out");
outSigTree -> SetDirectory(0);
createOutput(outSigTree);
*/
const int nProcesses = 2;
enum{iWW, iDY};
TFile *MyFile[nProcesses];
TTree *MyTree[nProcesses];
TTree *outTree[nProcesses];
TString MyName[nProcesses];
MyName[iWW] = "WW";
MyName[iDY] = "DY";
Float_t pt1;
Float_t pt2;
Float_t ptll;
Float_t mll;
Float_t mth;
Float_t pfType1Met;
Float_t drll;
Float_t dphill;
Float_t dphilljet;
Float_t dphillmet;
Float_t trkMet;
Float_t Mt1;
Float_t Mt2;
Float_t mpmet;
Float_t Mc;
Float_t ptWW;
Float_t Ht;
Float_t cutpt1 = 20;
Float_t cutpt2 = 20;
Float_t cutptll = 0;
Float_t cutmll = 0;
Float_t cutmth = 0;
Float_t cutpfType1Met = 0;
Float_t cutdrll = 0;
Float_t cutdphill = 0;
Float_t cutdphilljet = 0;
Float_t cutdphillmet = 0;
Float_t cuttrkMet = 0;
Float_t cutMt1 = 0;
Float_t cutMt2 = 0;
Float_t cutmpmet = 0;
Float_t cutMc = 0;
Float_t cutptWW = 0;
Float_t cutHt = 0;
Float_t cutValue = 0.022;
Float_t value = 0;
//Loop Over All Processes
for (int i = 0; i < nProcesses; ++i){
MyFile[i] = new TFile("../rootFiles/SF/MediumIDTighterIP/" + MyName[i] + ".root","READ");
MyTree[i] = (TTree*)MyFile[i]->Get("nt");
//Calling Processes Trees
MyTree[i]->SetBranchAddress("fullpmet",&fullpmet);
MyTree[i]->SetBranchAddress("trkpmet",&trkpmet);
MyTree[i]->SetBranchAddress("ratioMet",&ratioMet);
MyTree[i]->SetBranchAddress("ptll",&ptll);
MyTree[i]->SetBranchAddress("mth",&mth);
MyTree[i]->SetBranchAddress("jetpt1",&jetpt1);
MyTree[i]->SetBranchAddress("ptWW",&ptWW);
MyTree[i]->SetBranchAddress("dphilljet",&dphilljet);
MyTree[i]->SetBranchAddress("dphillmet",&dphillmet);
MyTree[i]->SetBranchAddress("dphijet1met",&dphijet1met);
MyTree[i]->SetBranchAddress("nvtx",&nvtx);
MyTree[i]->SetBranchAddress("pt1",&pt1);
MyTree[i]->SetBranchAddress("pt2",&pt2);
MyTree[i]->SetBranchAddress("ptll",&ptll);
MyTree[i]->SetBranchAddress("mll",&mll);
MyTree[i]->SetBranchAddress("mth",&mth);
MyTree[i]->SetBranchAddress("pfType1Met",&pfType1Met);
MyTree[i]->SetBranchAddress("drll",&drll);
MyTree[i]->SetBranchAddress("dphill",&dphill);
MyTree[i]->SetBranchAddress("dphilljet",&dphilljet);
MyTree[i]->SetBranchAddress("dphillmet",&dphillmet);
MyTree[i]->SetBranchAddress("trkMet",&trkMet);
//MyTree[i]->SetBranchAddress("Mt1",&Mt1);
//MyTree[i]->SetBranchAddress("Mt2",&Mt2);
MyTree[i]->SetBranchAddress("mpmet",&mpmet);
//MyTree[i]->SetBranchAddress("Mc",&Mc);
MyTree[i]->SetBranchAddress("ptWW",&ptWW);
MyTree[i]->SetBranchAddress("Ht",&Ht);
//Creating Output Trees
outTree[i] = new TTree (MyName[i],MyName[i]);
outTree[i] -> SetDirectory(0);
outTree[i] -> Branch("fullpmet",&fullpmet);
outTree[i] -> Branch("trkpmet",&trkpmet);
outTree[i] -> Branch("ratioMet",&ratioMet);
outTree[i] -> Branch("ptll",&ptll);
outTree[i] -> Branch("mth",&mth);
outTree[i] -> Branch("jetpt1",&jetpt1);
outTree[i] -> Branch("ptWW",&ptWW);
outTree[i] -> Branch("dphilljet",&dphilljet);
outTree[i] -> Branch("dphillmet",&dphillmet);
outTree[i] -> Branch("dphijet1met",&dphijet1met);
outTree[i] -> Branch("nvtx",&nvtx);
outTree[i] -> Branch("baseW",&baseW);
outTree[i] -> Branch("pt1",&pt1);
outTree[i] -> Branch("pt2",&pt2);
outTree[i] -> Branch("ptll",&ptll);
outTree[i] -> Branch("mll",&mll);
outTree[i] -> Branch("mth",&mth);
outTree[i] -> Branch("pfType1Met",&pfType1Met);
outTree[i] -> Branch("drll",&drll);
outTree[i] -> Branch("dphill",&dphill);
outTree[i] -> Branch("dphilljet",&dphilljet);
outTree[i] -> Branch("dphillmet",&dphillmet);
outTree[i] -> Branch("trkMet",&trkMet);
//outTree[i] -> Branch("Mt1",&Mt1);
//outTree[i] -> Branch("Mt2",&Mt2);
outTree[i] -> Branch("mpmet",&mpmet);
//outTree[i] -> Branch("Mc",&Mc);
outTree[i] -> Branch("ptWW",&ptWW);
outTree[i] -> Branch("Ht",&Ht);
//Applying Selections
Int_t cont = 0;
for (int j = 0; j < MyTree[i]->GetEntries(); ++j){
if (j == 0) cout<<MyName[i]<<": "<<MyTree[i]->GetEntries()<<endl;
MyTree[i]->GetEntry(j);
if (pt1 < cutpt1) continue;
if (pt2 < cutpt2) continue;
if (ptll < cutptll) continue;
if (mll < cutmll) continue;
if (mth < cutmth) continue;
if (pfType1Met < cutpfType1Met) continue;
if (drll < cutdrll) continue;
if (dphill < cutdphill) continue;
if (dphilljet < cutdphilljet) continue;
if (dphillmet < cutdphillmet) continue;
if (trkMet < cuttrkMet) continue;
// if (Mt1 < cutMt1) continue;
//if (Mt2 < cutMt2) continue;
if (mpmet < cutmpmet) continue;
//if (Mc < cutMc) continue;
if (ptWW < cutptWW) continue;
if (Ht < cutHt) continue;
value = reader->EvaluateMVA("BDT");
if(value < cutValue) continue;
++cont;
outTree[i]->Fill();
}
cout<<MyName[i]<<" survived: "<<cont<<" ("<<100 * cont / MyTree[i]->GetEntries()<<"%)"<<endl;
}
/*
//applying selections on ZH sample
Int_t contZH = 0;
for (int i = 0; i < ZHTree->GetEntries(); ++i){
if (i == 0) cout<<"ZH Entries : "<<ZHTree->GetEntries()<<endl;
ZHTree->GetEntry(i);
if (pt1 < cutpt1) continue;
if (pt2 < cutpt2) continue;
if (ptll < cutptll) continue;
if (mll < cutmll) continue;
if (mth < cutmth) continue;
if (pfType1Met < cutpfType1Met) continue;
if (drll < cutdrll) continue;
if (dphill < cutdphill) continue;
if (dphilljet < cutdphilljet) continue;
if (dphillmet < cutdphillmet) continue;
if (trkMet < cuttrkMet) continue;
if (Mt1 < cutMt1) continue;
if (Mt2 < cutMt2) continue;
if (mpmet < cutmpmet) continue;
if (Mc < cutMc) continue;
if (ptWW < cutptWW) continue;
if (Ht < cutHt) continue;
value = reader->EvaluateMVA("BDT");
if(value < cutValue) continue;
++contZH;
outZHTree->Fill();
}
cout<<"ZH survived: "<<contZH<<endl;
//applying selections on HWW sample
Int_t contHWW = 0;
for (int i = 0; i < HWWTree->GetEntries(); ++i){
if (i == 0) cout<<"HWW Entries : "<<HWWTree->GetEntries()<<endl;
HWWTree->GetEntry(i);
if (pt1 < cutpt1) continue;
if (pt2 < cutpt2) continue;
if (ptll < cutptll) continue;
if (mll < cutmll) continue;
if (mth < cutmth) continue;
if (pfType1Met < cutpfType1Met) continue;
if (drll < cutdrll) continue;
if (dphill < cutdphill) continue;
if (dphilljet < cutdphilljet) continue;
if (dphillmet < cutdphillmet) continue;
if (trkMet < cuttrkMet) continue;
if (Mt1 < cutMt1) continue;
if (Mt2 < cutMt2) continue;
if (mpmet < cutmpmet) continue;
if (Mc < cutMc) continue;
if (ptWW < cutptWW) continue;
if (Ht < cutHt) continue;
value = reader->EvaluateMVA("BDT");
if(value < cutValue) continue;
++contHWW;
outHWWTree->Fill();
}
cout<<"HWW survived: "<<contHWW<<endl;
//applying selections on WW sample
Int_t contWW = 0;
for (int i = 0; i < WWTree->GetEntries(); ++i){
if (i == 0) cout<<"WW Entries : "<<WWTree->GetEntries()<<endl;
WWTree->GetEntry(i);
if (pt1 < cutpt1) continue;
if (pt2 < cutpt2) continue;
if (ptll < cutptll) continue;
if (mll < cutmll) continue;
if (mth < cutmth) continue;
if (pfType1Met < cutpfType1Met) continue;
if (drll < cutdrll) continue;
if (dphill < cutdphill) continue;
if (dphilljet < cutdphilljet) continue;
if (dphillmet < cutdphillmet) continue;
if (trkMet < cuttrkMet) continue;
if (Mt1 < cutMt1) continue;
if (Mt2 < cutMt2) continue;
if (mpmet < cutmpmet) continue;
if (Mc < cutMc) continue;
if (ptWW < cutptWW) continue;
if (Ht < cutHt) continue;
value = reader->EvaluateMVA("BDT");
if(value < cutValue) continue;
++contWW;
outWWTree->Fill();
}
cout<<"WW survived: "<<contWW<<endl;
*/
//saving trees
TFile *outMVA = new TFile("outMVA" + sampleName + ".root","RECREATE");
outMVA -> cd();
for (int y = 0; y < nProcesses; ++y)
outTree[y] -> Write();
outMVA -> Close();
}
void TMVAClassificationApplication( TString myMethodList = "", TString filename="" )
{
//---------------------------------------------------------------
// default MVA methods to be trained + tested
// this loads the library
TMVA::Tools::Instance();
std::map<std::string,int> Use;
Use["CutsGA"] = 1; // other "Cuts" methods work identically
// ---
Use["Likelihood"] = 1;
Use["LikelihoodD"] = 1; // the "D" extension indicates decorrelated input variables (see option strings)
Use["LikelihoodPCA"] = 0; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
Use["LikelihoodKDE"] = 0;
Use["LikelihoodMIX"] = 0;
// ---
Use["PDERS"] = 1;
Use["PDERSD"] = 0;
Use["PDERSPCA"] = 0;
Use["PDERSkNN"] = 0; // depreciated until further notice
Use["PDEFoam"] = 1;
// --
Use["KNN"] = 1;
// ---
Use["HMatrix"] = 0;
Use["Fisher"] = 1;
Use["FisherG"] = 0;
Use["BoostedFisher"] = 0;
Use["LD"] = 1;
// ---
Use["FDA_GA"] = 0;
Use["FDA_SA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 1;
Use["FDA_GAMT"] = 0;
Use["FDA_MCMT"] = 0;
// ---
Use["MLP"] = 1; // this is the recommended ANN
Use["MLPBFGS"] = 0; // recommended ANN with optional training method
Use["CFMlpANN"] = 0; // *** missing
Use["TMlpANN"] = 0;
// ---
Use["SVM"] = 1;
// ---
Use["BDT"] = 1;
Use["BDTD"] = 0;
Use["BDTG"] = 1;
Use["BDTB"] = 0;
// ---
Use["RuleFit"] = 0;
// ---
Use["Plugin"] = 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;
}
}
//
// create the Reader object
//
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
// create a set of variables and declare them to the reader
// - the variable names must corresponds in name and type to
// those given in the weight file(s) that you use
///Our variables here:
/************************************************************************************************************
* Row * nEvt * sumEt * Et1 * lepeta * MET * dphiMETle * detajet2j * detajet3j *
************************************************************************************************************
mindijetmass
maxdijetmass
*/
Float_t sumEt;
Float_t Et1;
Float_t lepeta;
Float_t MET;
Float_t dphiMETlepton;
Float_t detajet2jet3;
Float_t detajet3jet4;
Float_t mindijetmass;
Float_t maxdijetmass;
reader->AddVariable( "sumEt", &sumEt );
reader->AddVariable( "Et1", &Et1 );
reader->AddVariable( "lepeta", &lepeta );
reader->AddVariable( "MET", &MET );
reader->AddVariable( "dphiMETlepton", &dphiMETlepton );
reader->AddVariable( "detajet2jet3", &detajet2jet3 );
reader->AddVariable( "detajet3jet4", &detajet3jet4 );
reader->AddVariable( "mindijetmass", &mindijetmass );
reader->AddVariable( "maxdijetmass", &maxdijetmass );
TString TreeName = "KinVars";
TFile * input = TFile::Open(filename+".root");
TTree* theTree = (TTree*)input->Get(TreeName);
Float_t userVar1, userVar2;
theTree->SetBranchAddress( "MassOfJets", &MassOfJets );
theTree->SetBranchAddress( "DeltaPhiOfJets", &DeltaPhiOfJets );
theTree->SetBranchAddress( "DeltaPhiLeadingJet", &DeltaPhiLeadingJet );
theTree->SetBranchAddress( "Pbalance", &Pbalance );
theTree->SetBranchAddress( "Sphericity", &Sphericity );
Double_t _Weight=1.0;
if (filename.Contains("hbb_120GeV")) _Weight = 5.09999999999999967e-02;
if (filename.Contains("qcd_15to30Gev")) _Weight = 3.54815799999999990e+03;
if (filename.Contains("qcd_30to50Gev")) _Weight = 3.36987000000000023e+02;
if (filename.Contains("qcd_50to150Gev")) _Weight = 1.08418000000000006e+02;
if (filename.Contains("qcd_150toInfGev")) _Weight = 4.70999999999999996e+00;
if (filename.Contains("data")) _Weight = -1e0;
//
// book the MVA methods
//
TString dir = "weights/";
TString prefix = "TMVAClassification";
// book method(s)
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
TString methodName = it->first + " method";
TString weightfile = dir + prefix + "_" + TString(it->first) + ".weights.xml";
reader->BookMVA( methodName, weightfile );
}
}
// example how to use your own method as plugin
if (Use["Plugin"]) {
// the weight file contains a line
// Method : MethodName::InstanceName
// if MethodName is not a known TMVA method, it is assumed to be
// a user implemented method which has to be loaded via the
// plugin mechanism
// for user implemented methods the line in the weight file can be
// Method : PluginName::InstanceName
// where PluginName can be anything
// before usage the plugin has to be defined, which can happen
// either through the following line in .rootrc:
// # plugin handler plugin class library constructor format
// Plugin.TMVA@@MethodBase: PluginName MethodClassName UserPackage "MethodName(DataSet&,TString)"
//
// or by telling the global plugin manager directly
gPluginMgr->AddHandler("TMVA@@MethodBase", "PluginName", "MethodClassName", "UserPackage", "MethodName(DataSet&,TString)");
// the class is then looked for in libUserPackage.so
// now the method can be booked like any other
reader->BookMVA( "User method", dir + prefix + "_User.weights.txt" );
}
// book output histograms
UInt_t nbin = 100;
TH1F *histLk(0), *histLkD(0), *histLkPCA(0), *histLkKDE(0), *histLkMIX(0), *histPD(0), *histPDD(0);
TH1F *histPDPCA(0), *histPDEFoam(0), *histPDEFoamErr(0), *histPDEFoamSig(0), *histKNN(0), *histHm(0);
TH1F *histFi(0), *histFiG(0), *histFiB(0), *histLD(0), *histNn(0), *histNnC(0), *histNnT(0), *histBdt(0), *histBdtG(0), *histBdtD(0);
TH1F *histRf(0), *histSVMG(0), *histSVMP(0), *histSVML(0), *histFDAMT(0), *histFDAGA(0), *histPBdt(0);
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["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["BDT"]) histBdt = new TH1F( "MVA_BDT", "MVA_BDT", nbin, -0.8, 0.8 );
if (Use["BDTD"]) histBdtD = new TH1F( "MVA_BDTD", "MVA_BDTD", nbin, -0.8, 0.8 );
if (Use["BDTG"]) histBdtG = new TH1F( "MVA_BDTG", "MVA_BDTG", 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["Plugin"]) histPBdt = new TH1F( "MVA_PBDT", "MVA_BDT", nbin, -0.8, 0.8 );
// PDEFoam also returns per-event error, fill in histogram, and also fill significance
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 );
}
// book example histogram for probability (the other methods are done similarly)
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 );
}
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
//
/*TFile *input(0);
TString fname = "./tmva_example.root";
if (!gSystem->AccessPathName( fname )) {
input = TFile::Open( fname ); // check if file in local directory exists
}
else {
input = TFile::Open( "http://root.cern.ch/files/tmva_class_example.root" ); // if not: download from ROOT server
}
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVAClassificationApp : Using input file: " << input->GetName() << std::endl;
*/
//
// prepare the tree
// - here the variable names have to corresponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
//
// efficiency calculator for cut method
Int_t nSelCutsGA = 0;
Double_t effS = 0.7;
std::cout << "--- Processing: " << theTree->GetEntries() << " events" << std::endl;
TStopwatch sw;
sw.Start();
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
cout<<"Entry # "<<ievt<<endl;
if (ievt%1000 == 0){
std::cout << "--- ... Processing event: " << ievt << std::endl;
}
theTree->GetEntry(ievt);
//
// return the MVAs and fill to histograms
//
/* if (Use["CutsGA"]) {
// Cuts is a special case: give the desired signal efficienciy
Bool_t passed = reader->EvaluateMVA( "CutsGA method", effS );
if (passed) nSelCutsGA++;
}
*/
if (Use["Likelihood" ]) histLk ->Fill( reader->EvaluateMVA( "Likelihood method" ) );
if (Use["Likelihood" ]) cout<<"MVA value: "<<reader->EvaluateMVA( "Likelihood method" )<<endl;
continue;
/*
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["CFMlpANN" ]) histNnC ->Fill( reader->EvaluateMVA( "CFMlpANN method" ) );
if (Use["TMlpANN" ]) histNnT ->Fill( reader->EvaluateMVA( "TMlpANN method" ) );
if (Use["BDT" ]) histBdt ->Fill( reader->EvaluateMVA( "BDT method" ) );
if (Use["BDTD" ]) histBdtD ->Fill( reader->EvaluateMVA( "BDTD method" ) );
if (Use["BDTG" ]) histBdtG ->Fill( reader->EvaluateMVA( "BDTG 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["Plugin" ]) histPBdt ->Fill( reader->EvaluateMVA( "P_BDT method" ) );
// retrieve also per-event error
if (Use["PDEFoam"]) {
Double_t val = reader->EvaluateMVA( "PDEFoam method" );
Double_t err = reader->GetMVAError();
histPDEFoam ->Fill( val );
histPDEFoamErr->Fill( err );
histPDEFoamSig->Fill( val/err );
}
// retrieve probability instead of MVA output
if (Use["Fisher"]) {
probHistFi ->Fill( reader->GetProba ( "Fisher method" ) );
rarityHistFi->Fill( reader->GetRarity( "Fisher method" ) );
}
*/
cout<<"End of processing"<<endl;
}
// get elapsed time
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
// get efficiency for cuts classifier
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"]) {
// test: retrieve cuts for particular signal efficiency
// CINT ignores dynamic_casts so we have to use a cuts-secific Reader function to acces the pointer
TMVA::MethodCuts* mcuts = reader->FindCutsMVA( "CutsGA method" ) ;
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]
<< " < \""
<< mcuts->GetInputVar(ivar)
<< "\" <= "
<< cutsMax[ivar] << std::endl;
}
std::cout << "--- -------------------------------------------------------------" << std::endl;
}
}
//
// write histograms
//
TFile *target = new TFile( filename+"_out"+".root","RECREATE" );
if (Use["Likelihood" ]) {if (_Weight>0)histLk->Scale(_Weight);histLk ->Write();}
if (Use["LikelihoodD" ]) {if (_Weight>0)histLkD->Scale(_Weight);histLkD ->Write();}
if (Use["LikelihoodPCA"]) {if (_Weight>0)histLkPCA->Scale(_Weight);histLkPCA ->Write();}
if (Use["LikelihoodKDE"]) {if (_Weight>0)histLkKDE->Scale(_Weight);histLkKDE ->Write();}
if (Use["LikelihoodMIX"]) {if (_Weight>0)histLkMIX->Scale(_Weight);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["CFMlpANN" ]) histNnC ->Write();
if (Use["TMlpANN" ]) histNnT ->Write();
if (Use["BDT" ]) histBdt ->Write();
if (Use["BDTD" ]) histBdtD ->Write();
if (Use["BDTG" ]) histBdtG ->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["Plugin" ]) histPBdt ->Write();
// write also error and significance histos
if (Use["PDEFoam"]) { histPDEFoam->Write(); histPDEFoamErr->Write(); histPDEFoamSig->Write(); }
// write also probability hists
if (Use["Fisher"]) { if (probHistFi != 0) probHistFi->Write(); if (rarityHistFi != 0) rarityHistFi->Write(); }
target->Close();
std::cout << "--- Created root file: \"TMVApp.root\" containing the MVA output histograms" << std::endl;
delete reader;
std::cout << "==> TMVAClassificationApplication is done!" << endl << std::endl;
}
void TMVAClassificationApplicationCompact(TString signal = "data")
{
#ifdef __CINT__
gROOT->ProcessLine( ".O0" ); // turn off optimization in CINT
#endif
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// --------------------------------------------------------------------------------------------------
// --- Create the Reader object
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
// Create a set of variables and declare them to the reader
// - the variable names MUST corresponds in name and type to those given in the weight file(s) used
Float_t jetpt;
Float_t jeteta;
Float_t jetphi;
Float_t metpt;
Float_t metpro;
Float_t lep0pt;
Float_t lep1pt;
Float_t lep0eta;
Float_t lep1eta;
Float_t lep0phi;
Float_t lep1phi;
Float_t ptsys;
Float_t ht;
Float_t oblateness;
Float_t sphericity;
Float_t aplanarity;
Float_t njetw;
Float_t sqrts;
Float_t deltarleps;
Float_t deltaphileps;
Float_t deltaetaleps;
Float_t philepmetclose;
Float_t philepmetfar;
Float_t rlepmetclose;
Float_t rlepmetfar;
Float_t philepjetclose;
Float_t philepjetfar;
Float_t rlepjetclose;
Float_t rlepjetfar;
Float_t phijetmet;
Float_t rjetmet;
Float_t mll;
Float_t htnomet;
Float_t ptsysnomet;
Float_t metphi;
Float_t metminusptsysnomet;
/* reader->AddVariable ("jetpt", &jetpt);
reader->AddVariable ("jeteta", &jeteta);
reader->AddVariable ("jetphi", &jetphi);
reader->AddVariable ("metpt", &metpt);
reader->AddVariable ("metpro",&metpro);
reader->AddVariable ("lep0pt",&lep0pt);
reader->AddVariable ("lep1pt",&lep1pt);
reader->AddVariable ("lep0eta",&lep0eta);
reader->AddVariable ("lep1eta",&lep1eta);
reader->AddVariable ("lep0phi",&lep0phi);
reader->AddVariable ("lep1phi",&lep1phi);*/
reader->AddVariable ("ptsys",&ptsys);
/* reader->AddVariable ("ht",&ht);
reader->AddVariable ("oblateness", &oblateness);
reader->AddVariable ("sphericity", &sphericity);
reader->AddVariable ("aplanarity", &aplanarity);
reader->AddVariable ("njetw", &njetw);
reader->AddVariable ("sqrts", &sqrts);*/
reader->AddVariable ("deltarleps", &deltarleps);
/* reader->AddVariable ("deltaphileps", &deltaphileps);
reader->AddVariable ("deltaetaleps", &deltaetaleps);
reader->AddVariable ("philepmetclose", &philepmetclose);
reader->AddVariable ("philepmetfar", &philepmetfar);*/
reader->AddVariable ("rlepmetclose", &rlepmetclose);
/* reader->AddVariable ("rlepmetfar", &rlepmetfar);
reader->AddVariable ("philepjetclose", &philepjetclose);
reader->AddVariable ("philepjetfar", &philepjetfar);*/
reader->AddVariable ("rlepjetclose", &rlepjetclose);
/*reader->AddVariable ("rlepjetfar", &rlepjetfar);
reader->AddVariable ("phijetmet", &phijetmet);*/
reader->AddVariable ("rjetmet", &rjetmet);
/* reader->AddVariable ("mll", &mll);
reader->AddVariable ("htnomet", &htnomet);
reader->AddVariable ("ptsysnomet", &ptsysnomet);
reader->AddVariable ("metphi", &metphi);
reader->AddVariable ("metminusptsysnomet", &metminusptsysnomet);*/
// *************************************************
// --- Book the MVA methods
TString dir = "weights/";
TString prefix = "test_tw_short_01";
TString name = "BDT_"+prefix;
std::cout<<"********* name = "<<name<<std::endl;
//
// book the MVA methods
//
reader->BookMVA( "BDT method", dir + prefix + "_BDT.weights.xml" );
// book output histograms
Int_t nbin = 100;
histBdt = new TH1F( "MVA_BDT", "MVA_BDT", nbin, -1.0, 1.0 );
// book example histogram for probability (the other methods are done similarly)
probHistBDT = new TH1F( "Probability_MVA_BDT", "Probability_MVA_BDT", nbin, -1, 1);
rarityHistBDT = new TH1F( "Rarity_MVA_BDT", "Rarity_MVA_BDT", nbin, -1, 1);
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
//
TFile *input(0);
TString folder = "rootfiles/";
if(signal == "data") { TString fname = folder+"tmva_test_1_data.root"; }
if(signal == "tw"){ TString fname = folder+"tmva_test_1_twdr.root"; }
if(signal == "ww"){ TString fname = folder+"tmva_test_1_ww.root"; }
if(signal == "qcd"){ TString fname = folder+"tmva_test_1_qcd_mu.root"; }
if(signal == "wz"){ TString fname = folder+"tmva_test_1_wz.root"; }
if(signal == "zz"){ TString fname = folder+"tmva_test_1_zz.root"; }
if(signal == "st"){ TString fname = folder+"tmva_test_1_st.root"; }
if(signal == "tt"){ TString fname = folder+"tmva_test_1_tt.root"; }
if(signal == "wjets"){ TString fname = folder+"tmva_test_1_wjets.root"; }
if(signal == "zjets"){ TString fname = folder+"tmva_test_1_zjets.root"; }
if(signal == "di"){TString fname = folder + "tmva_test_1_di.root";}
input = TFile::Open( fname,"UPDATE");
if (!input) {
cout << "ERROR: could not open data file: " << fname << endl;
exit(1);
}
// --- Event loop
// Prepare the event tree
// - here the variable names have to corresponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
//
input->cd();
TTree* theTree = (TTree*)input->Get("myTree");
cout << "--- Select signal sample" << endl;
double userjetpt;
double userjeteta;
double userjetphi;
double usermetpt;
double usermetpro;
double userlep0pt;
double userlep1pt;
double userlep0eta;
double userlep1eta;
double userlep0phi;
double userlep1phi;
double userptsys;
double userht;
double useroblateness;
double usersphericity;
double useraplanarity;
double usernjetw;
double usersqrts;
double userdeltarleps;
double userdeltaphileps;
double userdeltaetaleps;
double userphilepmetclose;
double userphilepmetfar;
double userrlepmetclose;
double userrlepmetfar;
double userphilepjetclose;
double userphilepjetfar;
double userrlepjetclose;
double userrlepjetfar;
double userphijetmet;
double userrjetmet;
double userweight;
double userweightnopu;
double usermll;
double userhtnomet;
double userptsysnomet;
double usermetphi;
double usermetminusptsysnomet;
theTree->SetBranchAddress ("savetheweight", &userweight);
theTree->SetBranchAddress ("weightnopu", &userweightnopu);
theTree->SetBranchAddress ("jetpt", &userjetpt);
theTree->SetBranchAddress ("jeteta", &userjeteta);
theTree->SetBranchAddress ("jetphi", &userjetphi);
theTree->SetBranchAddress ("metpt", &usermetpt);
theTree->SetBranchAddress ("metpro",&usermetpro);
theTree->SetBranchAddress ("lep0pt",&userlep0pt);
theTree->SetBranchAddress ("lep1pt",&userlep1pt);
theTree->SetBranchAddress ("lep0eta",&userlep0eta);
theTree->SetBranchAddress ("lep1eta",&userlep1eta);
theTree->SetBranchAddress ("lep0phi",&userlep0phi);
theTree->SetBranchAddress ("lep1phi",&userlep1phi);
theTree->SetBranchAddress ("ptsys",&userptsys);
theTree->SetBranchAddress ("ht",&userht);
theTree->SetBranchAddress ("oblateness", &useroblateness);
theTree->SetBranchAddress ("sphericity", &usersphericity);
theTree->SetBranchAddress ("aplanarity", &useraplanarity);
theTree->SetBranchAddress ("njetw", &usernjetw);
theTree->SetBranchAddress ("sqrts", &usersqrts);
theTree->SetBranchAddress ("deltarleps", &userdeltarleps);
theTree->SetBranchAddress ("deltaphileps", &userdeltaphileps);
theTree->SetBranchAddress ("deltaetaleps", &userdeltaetaleps);
theTree->SetBranchAddress ("philepmetclose", &userphilepmetclose);
theTree->SetBranchAddress ("philepmetfar", &userphilepmetfar);
theTree->SetBranchAddress ("rlepmetclose", &userrlepmetclose);
theTree->SetBranchAddress ("rlepmetfar", &userrlepmetfar);
theTree->SetBranchAddress ("philepjetclose", &userphilepjetclose);
theTree->SetBranchAddress ("philepjetfar", &userphilepjetfar);
theTree->SetBranchAddress ("rlepjetclose", &userrlepjetclose);
theTree->SetBranchAddress ("rlepjetfar", &userrlepjetfar);
theTree->SetBranchAddress ("phijetmet", &userphijetmet);
theTree->SetBranchAddress ("rjetmet", &userrjetmet);
theTree->SetBranchAddress ("mll", &usermll);
theTree->SetBranchAddress ("htnomet", &userhtnomet);
theTree->SetBranchAddress ("ptsysnomet", &userptsysnomet);
theTree->SetBranchAddress ("metphi", &usermetphi);
theTree->SetBranchAddress ("metminusptsysnomet", &usermetminusptsysnomet);
Double_t tBDT;
TTree* BDTTree = new TTree(name,"");
BDTTree->Branch("BDT",&tBDT,"BDT/D");
double tweight = 1;
std::cout<<" ... opening file : "<<fname<<std::endl;
cout << "--- Processing: " << theTree->GetEntries() << " events" << endl;
TH1F *hBDT = new TH1F("hBDT","",100,-1.0,1.0);
TStopwatch sw;
sw.Start();
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
if (ievt%1000 == 0) cout << "--- ... Processing event: " << ievt << endl;
theTree->GetEntry(ievt);
tweight = userweight;
jetpt = userjetpt;
jeteta = userjeteta;
jetphi = userjetphi;
metpt = usermetpt;
metpro = usermetpro;
lep0pt = userlep0pt;
lep1pt = userlep1pt;
lep0eta = userlep0eta;
lep1eta = userlep1eta;
lep0phi = userlep0phi;
lep1phi = userlep1phi;
ptsys = userptsys;
ht = userht;
oblateness = useroblateness;
sphericity = usersphericity;
aplanarity = useraplanarity;
njetw = usernjetw;
sqrts = usersqrts;
deltarleps = userdeltarleps;
deltaphileps = userdeltaphileps;
deltaetaleps = userdeltaetaleps;
philepmetclose = userphilepmetclose;
philepmetfar = userphilepmetfar;
rlepmetclose = userrlepmetclose;
rlepmetfar = userrlepmetfar;
philepjetclose = userphilepjetclose;
philepjetfar = userphilepjetfar;
rlepjetclose = userrlepjetclose;
rlepjetfar = userrlepjetfar;
phijetmet = userphijetmet;
rjetmet = userrjetmet;
mll = usermll;
htnomet = userhtnomet;
ptsysnomet = userptsysnomet;
metphi = usermetphi;
metminusptsysnomet = usermetminusptsysnomet;
double bdt = reader->EvaluateMVA("BDT method");
tBDT = bdt;
BDTTree->Fill();
if (signal == "data") tweight = 1;
hBDT->Fill(bdt,tweight);
double bdt_ = reader->EvaluateMVA("BDT method");
histBdt ->Fill(bdt_);
}
// Get elapsed time
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
input->cd();
hBDT->Write();
BDTTree->Write();
input->Close();
// --- Write histograms
TFile *target = new TFile( "TMVApp.root","RECREATE" );
histBdt ->Write();
std::cout << "--- Created root file: \"TMVApp.root\" containing the MVA output histograms" << std::endl;
delete reader;
std::cout << "==> TMVAClassificationApplication is done!" << endl << std::endl;
}
void TMVAClassificationApplication_tW(TString sample = "TWChannel", TString syst = "",TString region = "1j1t", TString directory = "v4", TString chanName = "emu", TString bdtTraining = "AdaBoost500TreesMET50", int variableSet = -1)
{
#ifdef __CINT__
gROOT->ProcessLine( ".O0" ); // turn off optimization in CINT
#endif
cout << sample << "\t" << syst << "\t" << region << "\t" << directory << "\t" << chanName << "\t" << bdtTraining << endl;
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// --------------------------------------------------------------------------------------------------
// --- Create the Reader object
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
// Create a set of variables and declare them to the reader
// - the variable names MUST corresponds in name and type to those given in the weight file(s) used
Float_t ptjet;
Float_t ptsys;
Float_t ht;
Float_t NlooseJet20;
Float_t NlooseJet20Central;
Float_t NbtaggedlooseJet20;
Float_t centralityJLL;
Float_t loosejetPt;
Float_t ptsys_ht;
Float_t msys;
Float_t htleps_ht;
Float_t ptjll;
Float_t met;
if (variableSet == -1 || variableSet == 0){
reader->AddVariable ("ptjet", &ptjet);
reader->AddVariable ("ptsys",&ptsys);
reader->AddVariable ("ht",&ht);
reader->AddVariable ("NlooseJet20", &NlooseJet20);
reader->AddVariable ("NlooseJet20Central", &NlooseJet20Central);
reader->AddVariable ("NbtaggedlooseJet20", &NbtaggedlooseJet20);
reader->AddVariable ("centralityJLL", ¢ralityJLL);
reader->AddVariable ("loosejetPt", &loosejetPt);
reader->AddVariable ("ptsys_ht",&ptsys_ht);
reader->AddVariable ("msys", &msys);
reader->AddVariable ("htleps_ht", &htleps_ht);
reader->AddVariable ("ptjll", &ptjll);
reader->AddVariable ("met", &met);
}
if (variableSet == 1){ //NoMET
reader->AddVariable ("ptjet", &ptjet);
reader->AddVariable ("ptsys",&ptsys);
reader->AddVariable ("ht",&ht);
reader->AddVariable ("NlooseJet20", &NlooseJet20);
reader->AddVariable ("NlooseJet20Central", &NlooseJet20Central);
reader->AddVariable ("NbtaggedlooseJet20", &NbtaggedlooseJet20);
reader->AddVariable ("centralityJLL", ¢ralityJLL);
reader->AddVariable ("loosejetPt", &loosejetPt);
reader->AddVariable ("ptsys_ht",&ptsys_ht);
reader->AddVariable ("msys", &msys);
reader->AddVariable ("htleps_ht", &htleps_ht);
reader->AddVariable ("ptjll", &ptjll);
}
if (variableSet == 2){ //NoMET, jetpt, loosejetPt, or NlooseJetCentral
reader->AddVariable ("ptsys",&ptsys);
reader->AddVariable ("ht",&ht);
reader->AddVariable ("NlooseJet20", &NlooseJet20);
reader->AddVariable ("NbtaggedlooseJet20", &NbtaggedlooseJet20);
reader->AddVariable ("centralityJLL", ¢ralityJLL);
reader->AddVariable ("ptsys_ht",&ptsys_ht);
reader->AddVariable ("msys", &msys);
reader->AddVariable ("htleps_ht", &htleps_ht);
reader->AddVariable ("ptjll", &ptjll);
}
if (variableSet == 3){ //No MET or jet variables
reader->AddVariable ("ptsys",&ptsys);
reader->AddVariable ("ht",&ht);
reader->AddVariable ("centralityJLL", ¢ralityJLL);
reader->AddVariable ("ptsys_ht",&ptsys_ht);
reader->AddVariable ("msys", &msys);
reader->AddVariable ("htleps_ht", &htleps_ht);
reader->AddVariable ("ptjll", &ptjll);
}
if (variableSet == 4){
reader->AddVariable ("ptjet", &ptjet);
reader->AddVariable ("ptsys",&ptsys);
reader->AddVariable ("ht",&ht);
reader->AddVariable ("NlooseJet20", &NlooseJet20);
reader->AddVariable ("NlooseJet20Central", &NlooseJet20Central);
reader->AddVariable ("NbtaggedlooseJet20", &NbtaggedlooseJet20);
reader->AddVariable ("loosejetPt", &loosejetPt);
reader->AddVariable ("ptsys_ht",&ptsys_ht);
reader->AddVariable ("msys", &msys);
reader->AddVariable ("htleps_ht", &htleps_ht);
reader->AddVariable ("ptjll", &ptjll);
reader->AddVariable ("met", &met);
}
if (variableSet == 5){
reader->AddVariable ("ptjet", &ptjet);
reader->AddVariable ("ptsys",&ptsys);
reader->AddVariable ("ht",&ht);
reader->AddVariable ("ptsys_ht",&ptsys_ht);
reader->AddVariable ("msys", &msys);
reader->AddVariable ("htleps_ht", &htleps_ht);
reader->AddVariable ("ptjll", &ptjll);
reader->AddVariable ("met", &met);
}
if (variableSet == 6){
reader->AddVariable ("ptjet", &ptjet);
reader->AddVariable ("ptsys",&ptsys);
reader->AddVariable ("ht",&ht);
reader->AddVariable ("NlooseJet20Central", &NlooseJet20Central);
reader->AddVariable ("NbtaggedlooseJet20", &NbtaggedlooseJet20);
reader->AddVariable ("centralityJLL", ¢ralityJLL);
reader->AddVariable ("loosejetPt", &loosejetPt);
reader->AddVariable ("ptsys_ht",&ptsys_ht);
reader->AddVariable ("msys", &msys);
reader->AddVariable ("htleps_ht", &htleps_ht);
reader->AddVariable ("ptjll", &ptjll);
reader->AddVariable ("met", &met);
}
if (variableSet == 7){
reader->AddVariable ("ptjet", &ptjet);
reader->AddVariable ("ptsys",&ptsys);
reader->AddVariable ("ht",&ht);
reader->AddVariable ("centralityJLL", ¢ralityJLL);
reader->AddVariable ("ptsys_ht",&ptsys_ht);
reader->AddVariable ("msys", &msys);
reader->AddVariable ("htleps_ht", &htleps_ht);
reader->AddVariable ("ptjll", &ptjll);
reader->AddVariable ("met", &met);
}
if (variableSet == 8){
reader->AddVariable ("ptjet", &ptjet);
reader->AddVariable ("ptsys",&ptsys);
reader->AddVariable ("NlooseJet20", &NlooseJet20);
reader->AddVariable ("centralityJLL", ¢ralityJLL);
reader->AddVariable ("msys", &msys);
reader->AddVariable ("htleps_ht", &htleps_ht);
reader->AddVariable ("ptjll", &ptjll);
}
// *************************************************
// --- Book the MVA methods
TString dir = "weights/";
TString extra = bdtTraining;
TString prefix = "test_tw_00_";
TString name = extra +"_"+ chanName + "_" + region;
if (bdtTraining == "AdaBoost500Trees") prefix = "test_tw_00_AdaBoostTests_13Vars_NtreeTests";
//
// book the MVA methods
//
reader->BookMVA( "BDT method", dir + prefix + extra+".weights.xml" );
// book output histograms
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
//
TFile *input(0);
TString folder = "tmvaFiles/"+directory+"/";
TString systlabel = "";
if (syst != ""){
systlabel = "_"+syst;
}
// TString fname = folder + "TWChannel.root";
TString fname = folder + sample + systlabel + ".root";
cout << fname << endl;
// input->SetCacheSize(20*1024*1024);
input = TFile::Open( fname,"r");
if (!input) {
cout << "ERROR: could not open data file: " << fname << endl;
exit(1);
}
// --- Event loop
// Prepare the event tree
// - here the variable names have to corresponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
//
TString treeName = chanName + "Channel/"+region;
TTree* theTree = (TTree*)input->Get(treeName);
cout << "--- Select signal sample" << endl;
theTree->SetCacheSize(20*1024*1024);
Double_t userptjet;
Double_t userptsys;
Double_t userht;
Int_t userNlooseJet20;
Int_t userNlooseJet20Central;
Int_t userNbtaggedlooseJet20;
Double_t usercentralityJLL;
Double_t userloosejetPt;
Double_t userptsys_ht;
Double_t usermsys;
Double_t userhtleps_ht;
Double_t userptjll;
Double_t usermet;
Double_t userweightA;
Double_t userweightB;
Double_t userweightC;
Double_t userweightD;
theTree->SetBranchAddress ("ptjet", &userptjet);
theTree->SetBranchAddress ("ptsys",&userptsys);
theTree->SetBranchAddress ("ht",&userht);
theTree->SetBranchAddress ("NlooseJet20", &userNlooseJet20);
theTree->SetBranchAddress ("NlooseJet20Central", &userNlooseJet20Central);
theTree->SetBranchAddress ("NbtaggedlooseJet20", &userNbtaggedlooseJet20);
theTree->SetBranchAddress ("centralityJLL", &usercentralityJLL);
theTree->SetBranchAddress ("loosejetPt", &userloosejetPt);
theTree->SetBranchAddress ("ptsys_ht",&userptsys_ht);
theTree->SetBranchAddress ("msys", &usermsys);
theTree->SetBranchAddress ("htleps_ht", &userhtleps_ht);
theTree->SetBranchAddress ("ptjll", &userptjll);
theTree->SetBranchAddress ("met", &usermet);
theTree->SetBranchAddress ("weightA", &userweightA);
theTree->SetBranchAddress ("weightB", &userweightB);
theTree->SetBranchAddress ("weightC", &userweightC);
theTree->SetBranchAddress ("weightD", &userweightD);
Double_t tBDT;
Double_t tweightA;
Double_t tweightB;
Double_t tweightC;
Double_t tweightD;
TFile *output(0);
TString outName = folder +bdtTraining+"/"+ sample+systlabel+"_Output.root";
cout << "Output root file: " << outName << endl;
output = TFile::Open(outName,"UPDATE");
TTree* BDTTree = new TTree(name,"");
BDTTree->Branch("BDT",&tBDT,"BDT/D");
BDTTree->Branch("weightA",&tweightA,"weightA/D");
BDTTree->Branch("weightB",&tweightB,"weightB/D");
BDTTree->Branch("weightC",&tweightC,"weightC/D");
BDTTree->Branch("weightD",&tweightD,"weightD/D");
std::cout<<" ... opening file : "<<fname<<std::endl;
cout << "--- Processing: " << theTree->GetEntries() << " events" << endl;
TStopwatch sw;
sw.Start();
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
if (ievt%1000 == 0) cout << "--- ... Processing event: " << ievt << endl;
theTree->GetEntry(ievt);
ptjet = userptjet;
ptsys = userptsys;
ht = userht;
NlooseJet20 = userNlooseJet20;
NlooseJet20Central = userNlooseJet20Central;
NbtaggedlooseJet20 = userNbtaggedlooseJet20;
centralityJLL = usercentralityJLL;
loosejetPt = userloosejetPt;
ptsys_ht = userptsys_ht;
msys = usermsys;
htleps_ht = userhtleps_ht;
ptjll = userptjll;
met = usermet;
// sw.Print();
double bdt = reader->EvaluateMVA("BDT method");
tBDT = bdt;
tweightA = userweightA;
tweightB = userweightB;
tweightC = userweightC;
tweightD = userweightD;
BDTTree->Fill();
}
// Get elapsed time
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
input->Close();
output->cd();
BDTTree->Write();
output->Close();
// --- Write histograms
delete reader;
std::cout << "==> TMVAClassificationApplication is done!" << endl << std::endl;
}
void TMVARegressionApplication( TString myMethodList = "" )
{
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
// --- Mutidimensional likelihood and Nearest-Neighbour methods
Use["PDERS"] = 0;
Use["PDEFoam"] = 1;
Use["KNN"] = 1;
//
// --- Linear Discriminant Analysis
Use["LD"] = 1;
//
// --- Function Discriminant analysis
Use["FDA_GA"] = 1;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
//
// --- Neural Network
Use["MLP"] = 1;
//
// --- Support Vector Machine
Use["SVM"] = 0;
//
// --- Boosted Decision Trees
Use["BDT"] = 0;
Use["BDTG"] = 1;
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVARegressionApplication" << std::endl;
// Select methods (don't look at this code - not of interest)
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;
}
}
// --------------------------------------------------------------------------------------------------
// --- Create the Reader object
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
// Create a set of variables and declare them to the reader
// - the variable names MUST corresponds in name and type to those given in the weight file(s) used
Float_t var1, var2;
reader->AddVariable( "var1", &var1 );
reader->AddVariable( "var2", &var2 );
// Spectator variables declared in the training have to be added to the reader, too
Float_t spec1,spec2;
reader->AddSpectator( "spec1:=var1*2", &spec1 );
reader->AddSpectator( "spec2:=var1*3", &spec2 );
// --- Book the MVA methods
TString dir = "weights/";
TString prefix = "TMVARegression";
// Book method(s)
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
TString methodName = it->first + " method";
TString weightfile = dir + prefix + "_" + TString(it->first) + ".weights.xml";
reader->BookMVA( methodName, weightfile );
}
}
// Book output histograms
TH1* hists[100];
Int_t nhists = -1;
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
TH1* h = new TH1F( it->first.c_str(), TString(it->first) + " method", 100, -100, 600 );
if (it->second) hists[++nhists] = h;
}
nhists++;
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
//
TFile *input(0);
TString fname = "./tmva_reg_example.root";
if (!gSystem->AccessPathName( fname )) {
input = TFile::Open( fname ); // check if file in local directory exists
}
else {
input = TFile::Open( "http://root.cern.ch/files/tmva_reg_example.root" ); // if not: download from ROOT server
}
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVARegressionApp : Using input file: " << input->GetName() << std::endl;
// --- Event loop
// Prepare the tree
// - here the variable names have to corresponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
//
TTree* theTree = (TTree*)input->Get("TreeR");
std::cout << "--- Select signal sample" << std::endl;
theTree->SetBranchAddress( "var1", &var1 );
theTree->SetBranchAddress( "var2", &var2 );
std::cout << "--- Processing: " << theTree->GetEntries() << " events" << std::endl;
TStopwatch sw;
sw.Start();
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
if (ievt%1000 == 0) {
std::cout << "--- ... Processing event: " << ievt << std::endl;
}
theTree->GetEntry(ievt);
// Retrieve the MVA target values (regression outputs) and fill into histograms
// NOTE: EvaluateRegression(..) returns a vector for multi-target regression
for (Int_t ih=0; ih<nhists; ih++) {
TString title = hists[ih]->GetTitle();
Float_t val = (reader->EvaluateRegression( title ))[0];
hists[ih]->Fill( val );
}
}
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
// --- Write histograms
TFile *target = new TFile( "TMVARegApp.root","RECREATE" );
for (Int_t ih=0; ih<nhists; ih++) hists[ih]->Write();
target->Close();
std::cout << "--- Created root file: \"" << target->GetName()
<< "\" containing the MVA output histograms" << std::endl;
delete reader;
std::cout << "==> TMVARegressionApplication is done!" << std::endl << std::endl;
}
void TMVAClassificationApplication( TString myMethodList = "" , TString decay_mode)
{
#ifdef __CINT__
gROOT->ProcessLine( ".O0" ); // turn off optimization in CINT
#endif
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
// --- Cut optimisation
Use["Cuts"] = 1;
Use["CutsD"] = 1;
Use["CutsPCA"] = 0;
Use["CutsGA"] = 0;
Use["CutsSA"] = 0;
//
// --- 1-dimensional likelihood ("naive Bayes estimator")
Use["Likelihood"] = 1;
Use["LikelihoodD"] = 0; // the "D" extension indicates decorrelated input variables (see option strings)
Use["LikelihoodPCA"] = 1; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
Use["LikelihoodKDE"] = 0;
Use["LikelihoodMIX"] = 0;
//
// --- Mutidimensional likelihood and Nearest-Neighbour methods
Use["PDERS"] = 1;
Use["PDERSD"] = 0;
Use["PDERSPCA"] = 0;
Use["PDEFoam"] = 1;
Use["PDEFoamBoost"] = 0; // uses generalised MVA method boosting
Use["KNN"] = 1; // k-nearest neighbour method
//
// --- Linear Discriminant Analysis
Use["LD"] = 1; // Linear Discriminant identical to Fisher
Use["Fisher"] = 0;
Use["FisherG"] = 0;
Use["BoostedFisher"] = 0; // uses generalised MVA method boosting
Use["HMatrix"] = 0;
//
// --- Function Discriminant analysis
Use["FDA_GA"] = 1; // minimisation of user-defined function using Genetics Algorithm
Use["FDA_SA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
Use["FDA_MCMT"] = 0;
//
// --- Neural Networks (all are feed-forward Multilayer Perceptrons)
Use["MLP"] = 0; // Recommended ANN
Use["MLPBFGS"] = 0; // Recommended ANN with optional training method
Use["MLPBNN"] = 1; // Recommended ANN with BFGS training method and bayesian regulator
Use["CFMlpANN"] = 0; // Depreciated ANN from ALEPH
Use["TMlpANN"] = 0; // ROOT's own ANN
//
// --- Support Vector Machine
Use["SVM"] = 1;
//
// --- Boosted Decision Trees
Use["BDT"] = 1; // uses Adaptive Boost
Use["BDTG"] = 0; // uses Gradient Boost
Use["BDTB"] = 0; // uses Bagging
Use["BDTD"] = 0; // decorrelation + Adaptive Boost
//
// --- Friedman's RuleFit method, ie, an optimised series of cuts ("rules")
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;
// Select methods (don't look at this code - not of interest)
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;
}
}
// --------------------------------------------------------------------------------------------------
// --- Create the Reader object
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
// Create a set of variables and declare them to the reader
// - the variable names MUST corresponds in name and type to those given in the weight file(s) used
Float_t met, mT, mT2W;
reader->AddVariable("met", &met);
reader->AddVariable("mT", &mT);
reader->AddVariable("mT2W",&mT2W);
// Spectator variables declared in the training have to be added to the reader, too
/* Float_t spec1,spec2;
reader->AddSpectator( "spec1 := var1*2", &spec1 );
reader->AddSpectator( "spec2 := var1*3", &spec2 );
Float_t Category_cat1, Category_cat2, Category_cat3;
if (Use["Category"]){
// Add artificial spectators for distinguishing categories
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 );
}
*/
// --- Book the MVA methods
TString dir = "weights/";
TString prefix = "TMVAClassification";
// Book method(s)
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
TString methodName = TString(it->first) + TString(" method");
TString weightfile = dir + prefix + TString("_") + TString(it->first) + TString(".weights.xml");
reader->BookMVA( methodName, weightfile );
}
}
// Book output histograms
UInt_t nbin = 100;
TH1F *histLk(0), *histLkD(0), *histLkPCA(0), *histLkKDE(0), *histLkMIX(0), *histPD(0), *histPDD(0);
TH1F *histPDPCA(0), *histPDEFoam(0), *histPDEFoamErr(0), *histPDEFoamSig(0), *histKNN(0), *histHm(0);
TH1F *histFi(0), *histFiG(0), *histFiB(0), *histLD(0), *histNn(0),*histNnbfgs(0),*histNnbnn(0);
TH1F *histNnC(0), *histNnT(0), *histBdt(0), *histBdtG(0), *histBdtD(0), *histRf(0), *histSVMG(0);
TH1F *histSVMP(0), *histSVML(0), *histFDAMT(0), *histFDAGA(0), *histCat(0), *histPBdt(0);
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["BDT"]) histBdt = new TH1F( "MVA_BDT", "MVA_BDT", nbin, -0.8, 0.8 );
if (Use["BDTD"]) histBdtD = new TH1F( "MVA_BDTD", "MVA_BDTD", nbin, -0.8, 0.8 );
if (Use["BDTG"]) histBdtG = new TH1F( "MVA_BDTG", "MVA_BDTG", 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 );
// PDEFoam also returns per-event error, fill in histogram, and also fill significance
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 );
}
// Book example histogram for probability (the other methods are done similarly)
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;
Double_t event_weight; // events weights are: xsection * efficiency (preselection)
if (decay_mode == "TT_1Lep") {fname = "Example_Rootfiles/ttbar_1l/output/ttbar_1l.root"; event_weight = 0.153;}
if (decay_mode == "TT_2Lep") {fname = "Example_Rootfiles/ttbar_2l/output/ttbar_2l.root"; event_weight = 0.129;}
if (decay_mode == "WJets") {fname = "Example_Rootfiles/wjets_all/output/wjets_all.root"; event_weight = 0.67;}
if (decay_mode == "Others") {fname = "Example_Rootfiles/others_all/output/others_all.root"; event_weight = 0.106;}
if (decay_mode == "T2tt_R1") { fname = "Example_Rootfiles/t2tt_all/R1/output/t2tt_all_R1.root"; event_weight = 1592./24845.;}
if (decay_mode == "T2tt_R2") { fname = "Example_Rootfiles/t2tt_all/R2/output/t2tt_all_R2.root"; event_weight = 1018./24994.;}
if (decay_mode == "T2tt_R3") { fname = "Example_Rootfiles/t2tt_all/R3/output/t2tt_all_R3.root"; event_weight = 306./25006.;}
if (decay_mode == "T2tt_R4") { fname = "Example_Rootfiles/t2tt_all/R4/output/t2tt_all_R4.root"; event_weight = 87./25094.;}
if (decay_mode == "T2tt_R5") { fname = "Example_Rootfiles/t2tt_all/R5/output/t2tt_all_R5.root"; event_weight = 28./25075.;}
if (decay_mode == "T2tt_R6") { fname = "Example_Rootfiles/t2tt_all/R6/output/t2tt_all_R6.root"; event_weight = 9./24863.;}
TFile *input = TFile::Open( fname );
std::cout << "--- TMVAClassification : Using input file: " << input->GetName() << std::endl;
// --- //Event loop
// Prepare the event tree
// - here the variable names have to corresponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
//
std::cout << "--- Select signal sample" << std::endl;
TTree *theTree = (TTree*)input->Get("BDTtree");
Float_t met, mT, mT2W;
Int_t event;
theTree->SetBranchAddress("met", &met);
theTree->SetBranchAddress("mT", &mT);
theTree->SetBranchAddress("mT2W", &mT2W);
theTree->SetBranchAddress("event", &event);
// Efficiency calculator for cut method
Int_t nSelCutsGA = 0;
Double_t effS = 0.7;
std::vector<Float_t> vecVar(4); // vector for EvaluateMVA tests
Int_t totevt;
if (string(decay_mode).find("T2tt") != std::string::npos) totevt = 50000; // Takes too long (for exercise) to run over all the signal events
else totevt = theTree->GetEntries();
std::cout << "--- Processing: " << totevt << " events" << std::endl;
TStopwatch sw;
sw.Start();
for (Long64_t ievt=0; ievt<totevt; ievt++) {
theTree->GetEntry(ievt);
if (ievt%1000 == 0) std::cout << "--- ... Processing event: " << ievt << std::endl;
if ( event%2 == 1 ) continue; // remove all odd numbers from our actual analysis
// --- Return the MVA outputs and fill into histograms
// this gives you the mva value per event
// cout << reader->EvaluateMVA("BDT method") << endl;
if (Use["CutsGA"]) {
// Cuts is a special case: give the desired signal efficienciy
Bool_t passed = reader->EvaluateMVA( "CutsGA method", effS );
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["BDT" ]) histBdt ->Fill( reader->EvaluateMVA( "BDT method" ), event_weight );
//if (Use["BDT" ]) histBdt ->Fill( reader->EvaluateMVA( "BDT method" ));
if (Use["BDTD" ]) histBdtD ->Fill( reader->EvaluateMVA( "BDTD method" ) );
if (Use["BDTG" ]) histBdtG ->Fill( reader->EvaluateMVA( "BDTG 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" ) );
// Retrieve also per-event error
if (Use["PDEFoam"]) {
Double_t val = reader->EvaluateMVA( "PDEFoam method" );
Double_t err = reader->GetMVAError();
histPDEFoam ->Fill( val );
histPDEFoamErr->Fill( err );
if (err>1.e-50) histPDEFoamSig->Fill( val/err );
}
// Retrieve probability instead of MVA output
if (Use["Fisher"]) {
probHistFi ->Fill( reader->GetProba ( "Fisher method" ) );
rarityHistFi->Fill( reader->GetRarity( "Fisher method" ) );
}
}
// Get elapsed time
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
// Get efficiency for cuts classifier
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"]) {
// test: retrieve cuts for particular signal efficiency
// CINT ignores dynamic_casts so we have to use a cuts-secific Reader function to acces the pointer
TMVA::MethodCuts* mcuts = reader->FindCutsMVA( "CutsGA method" ) ;
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]
<< " < \""
<< mcuts->GetInputVar(ivar)
<< "\" <= "
<< cutsMax[ivar] << std::endl;
}
std::cout << "--- -------------------------------------------------------------" << std::endl;
}
}
// --- Write histograms
TFile *target = new TFile( "results_"+decay_mode+".root","RECREATE" );
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["BDT" ]) histBdt ->Write();
if (Use["BDTD" ]) histBdtD ->Write();
if (Use["BDTG" ]) histBdtG ->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();
// Write also error and significance histos
if (Use["PDEFoam"]) { histPDEFoam->Write(); histPDEFoamErr->Write(); histPDEFoamSig->Write(); }
// Write also probability hists
if (Use["Fisher"]) { if (probHistFi != 0) probHistFi->Write(); if (rarityHistFi != 0) rarityHistFi->Write(); }
target->Close();
std::cout << "--- Created root file: \"results_"+decay_mode+".root\" containing the MVA output histograms" << std::endl;
delete reader;
std::cout << "==> TMVAClassificationApplication is done!" << endl << std::endl;
}
void TMVAReaderPracticeDT0818() {
bool isTT=0;
string masspoint[13]={"600","800","1000","1200","1400","1600","1800","2000","2500","3000","3500","4000","4500"};
//for (int massP=0;massP<13;massP++){
for (int massP=0;massP<1;massP++){
//Sig
// TString endfix =Form("treeV4DT/signal-%s.root",masspoint[massP].data());for(int w=1;w<2;w++){f = TFile::Open(Form("/data2/syu/13TeV/ZprimeZhbb/ZprimeToZhToZlephbb_narrow_M-%s_13TeV-madgraph.root",masspoint[massP].data()));if (!f || !f->IsOpen())continue;TDirectory * dir = (TDirectory*)f->Get(Form("/data2/syu/13TeV/ZprimeZhbb/ZprimeToZhToZlephbb_narrow_M-%s_13TeV-madgraph.root:/tree",masspoint[massP].data()));dir->GetObject("treeMaker",tree);
//DY100-200
//for(int w=1;w<90;w++){ f = TFile::Open(Form("/data7/khurana/NCUGlobalTuples/SPRING15/DYJetsHTBins25nsSamples/DYJetsToLL_M-50_HT-100to200_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/crab_DYJetsToLL_M-50_HT-100to200_TuneCUETP8M1_13TeV-madgraphMLM-pythia8_0803/150812_162742/0000/NCUGlobalTuples_%d.root",w));if (!f || !f->IsOpen())continue;TDirectory * dir = (TDirectory*)f->Get(Form("/data7/khurana/NCUGlobalTuples/SPRING15/DYJetsHTBins25nsSamples/DYJetsToLL_M-50_HT-100to200_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/crab_DYJetsToLL_M-50_HT-100to200_TuneCUETP8M1_13TeV-madgraphMLM-pythia8_0803/150812_162742/0000/NCUGlobalTuples_%d.root:/tree",w)); dir->GetObject("treeMaker",tree);TString endfix =Form("treeV4DT/DYHT100-%d.root",w);
//DY200-400
// for(int w=1;w<45;w++){f = TFile::Open(Form("/data7/khurana/NCUGlobalTuples/SPRING15/DYJetsHTBins25nsSamples/DYJetsToLL_M-50_HT-200to400_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/crab_DYJetsToLL_M-50_HT-200to400_TuneCUETP8M1_13TeV-madgraphMLM-pythia8_0803/150812_162821/0000/NCUGlobalTuples_%d.root",w));if (!f || !f->IsOpen())continue;TDirectory * dir = (TDirectory*)f->Get(Form("/data7/khurana/NCUGlobalTuples/SPRING15/DYJetsHTBins25nsSamples/DYJetsToLL_M-50_HT-200to400_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/crab_DYJetsToLL_M-50_HT-200to400_TuneCUETP8M1_13TeV-madgraphMLM-pythia8_0803/150812_162821/0000/NCUGlobalTuples_%d.root:/tree",w)); dir->GetObject("treeMaker",tree);TString endfix =Form("treeV4DT/DYHT200-%d.root",w);
//DY400-600
// for(int w=1;w<45;w++){f = TFile::Open(Form("/data7/khurana/NCUGlobalTuples/SPRING15/DYJetsHTBins25nsSamples/DYJetsToLL_M-50_HT-400to600_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/crab_DYJetsToLL_M-50_HT-400to600_TuneCUETP8M1_13TeV-madgraphMLM-pythia8_0803/150812_162858/0000/NCUGlobalTuples_%d.root",w));if (!f || !f->IsOpen())continue;TDirectory * dir = (TDirectory*)f->Get(Form("/data7/khurana/NCUGlobalTuples/SPRING15/DYJetsHTBins25nsSamples/DYJetsToLL_M-50_HT-400to600_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/crab_DYJetsToLL_M-50_HT-400to600_TuneCUETP8M1_13TeV-madgraphMLM-pythia8_0803/150812_162858/0000/NCUGlobalTuples_%d.root:/tree",w)); dir->GetObject("treeMaker",tree);TString endfix =Form("treeV4DT/DYHT400-%d.root",w);
//DY600-inf
// for(int w=1;w<48;w++){f = TFile::Open(Form("/data7/khurana/NCUGlobalTuples/SPRING15/DYJetsHTBins25nsSamples/DYJetsToLL_M-50_HT-600toInf_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/crab_DYJetsToLL_M-50_HT-600toInf_TuneCUETP8M1_13TeV-madgraphMLM-pythia8_0803/150812_162937/0000/NCUGlobalTuples_%d.root",w));if (!f || !f->IsOpen())continue;TDirectory * dir = (TDirectory*)f->Get(Form("/data7/khurana/NCUGlobalTuples/SPRING15/DYJetsHTBins25nsSamples/DYJetsToLL_M-50_HT-600toInf_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/crab_DYJetsToLL_M-50_HT-600toInf_TuneCUETP8M1_13TeV-madgraphMLM-pythia8_0803/150812_162937/0000/NCUGlobalTuples_%d.root:/tree",w)); dir->GetObject("treeMaker",tree);TString endfix =Form("treeV4DT/DYHT600-%d.root",w);
//tt
isTT=1; for (int w=1;w<174;w++){f = TFile::Open(Form("/data7/khurana/NCUGlobalTuples/SPRING15/crab_TT_TuneCUETP8M1_13TeV-powheg-pythia8_0803/150803_175618/0000/NCUGlobalTuples_%d.root",w)); if (!f || !f->IsOpen())continue;TDirectory * dir = (TDirectory*)f->Get(Form("/data7/khurana/NCUGlobalTuples/SPRING15/crab_TT_TuneCUETP8M1_13TeV-powheg-pythia8_0803/150803_175618/0000/NCUGlobalTuples_%d.root:/tree",w)); dir->GetObject("treeMaker",tree);TString endfix =Form("treeV4DT/TT-%d.root",w);
// TFile *fw;
// TTree* treeP;
// if(w==1){fw = new TFile("tree/DY.root","recreate");
// treeP=new TTree("treeP","treeP");
// }
// else {fw = new TFile("tree/DY.root","update");
// treeP =(TTree*)fw->FindObjectAny("treeP");}
TFile *fw=new TFile(endfix.Data(),"recreate");
float fatPt;
float fatCSV;
float sub1Pt;
float sub1CSV;
float sub2Pt;
float sub2CSV;
float delta_R;
float tau21;
float tau1;
float tau2;
cout<<massP<<","<<w<<endl;
TreeReader data(tree);
//data.Print();
Long64_t nentries = data.GetEntriesFast();
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
reader->AddVariable( "fatPt", &fatPt );
reader->AddVariable( "fatCSV", &fatCSV );
reader->AddVariable( "sub1Pt", &sub1Pt );
reader->AddVariable( "sub1CSV", &sub1CSV );
reader->AddVariable( "sub2Pt", &sub2Pt );
reader->AddVariable( "sub2CSV", &sub2CSV );
reader->AddVariable( "delta_R", &delta_R );
reader->AddVariable( "tau21", &tau21 );
reader->AddVariable( "tau1", &tau1 );
reader->AddVariable( "tau2", &tau2 );
reader->BookMVA("BDT method","weights/DT.xml");
TH1F *th1 =new TH1F("BDT","BDT",100,-0.8,0.8);
for (Long64_t jentry=0; jentry<nentries;jentry++) {
data.GetEntry(jentry);
Int_t FATnJet=data.GetInt("FATnJet");
if(FATnJet==0)continue;
TClonesArray* eleP4 = (TClonesArray*) data.GetPtrTObject("eleP4");
TClonesArray* muP4 = (TClonesArray*) data.GetPtrTObject("muP4");
vector<bool> eleIsPassVeto= *((vector<bool>*) data.GetPtr("eleIsPassVeto"));
vector<bool> isLooseMuon= *((vector<bool>*) data.GetPtr("isLooseMuon"));
Int_t nMu=data.GetInt("nMu");
Int_t nEle=data.GetInt("nEle");
vector<int> mus,eles;
for(int i=0;i<nEle;i++){
TLorentzVector* thisEle =(TLorentzVector*)eleP4->At(i) ;
if(thisEle->Pt()<10 ||fabs(thisEle->Eta())>2.5 || eleIsPassVeto[i]==0 )continue;
eles.push_back(i);
}
for(int i=0;i<nMu;i++){
TLorentzVector* thisMu =(TLorentzVector*)muP4->At(i) ;
if(thisMu->Pt()<10 ||fabs(thisMu->Eta())>2.4 || isLooseMuon[i]==0 )continue;
mus.push_back(i);
}
TClonesArray* FATjetP4 = (TClonesArray*) data.GetPtrTObject("FATjetP4");
Float_t* FATjetSDmass = data.GetPtrFloat("FATjetSDmass");
Float_t* FATjetCISVV2 = data.GetPtrFloat("FATjetCISVV2");
//if(>1 )continue;
int FATi=0;
bool isFAT=0;
TLorentzVector* FATjetP4_1;
//cout<<FATnJet<<endl;
for (FATi=0;FATi<FATnJet;FATi++){
//cout<<"FATi="<<FATi<<endl;
if(FATjetCISVV2[FATi]<0 ||FATjetCISVV2[FATi]>1 )continue;
FATjetP4_1 = (TLorentzVector*)FATjetP4->At(FATi);
bool isOverlap=0;
for(int i=0;i<mus.size();i++){
TLorentzVector* thisMu =(TLorentzVector*)muP4->At(mus[i]) ;
if(FATjetP4_1->DeltaR(*thisMu)<0.8){
isOverlap=1;
break;
}
}
if(!isOverlap){
for(int i=0;i<eles.size();i++){
TLorentzVector* thisEle =(TLorentzVector*)eleP4->At(eles[i]) ;
if(FATjetP4_1->DeltaR(*thisEle)<0.8){
isOverlap=1;
break;
}
}
}
if(isOverlap)continue;
isFAT=1;
break;
}
if(!isFAT)continue;
if(FATjetP4_1->Pt()<200)continue;
Int_t* FATnSubSDJet=data.GetPtrInt("FATnSubSDJet");
if(FATnSubSDJet[FATi]<2)continue;
int nsub=FATnSubSDJet[FATi];
if(nsub!=2)cout<<"nsub="<<nsub<<endl;if(nsub!=2)cout<<"nsub="<<nsub<<endl;
//
float* FATjetTau1=data.GetPtrFloat("FATjetTau1");
float* FATjetTau2=data.GetPtrFloat("FATjetTau2");
vector<float> *FATsubjetSDCSV = data.GetPtrVectorFloat("FATsubjetSDCSV");
vector<float> *FATsubjetSDPx = data.GetPtrVectorFloat("FATsubjetSDPx");
vector<float> *FATsubjetSDPy = data.GetPtrVectorFloat("FATsubjetSDPy");
vector<float> *FATsubjetSDPz = data.GetPtrVectorFloat("FATsubjetSDPz");
vector<float> *FATsubjetSDCE = data.GetPtrVectorFloat("FATsubjetSDCE");
int subi=0,subj=0;
bool isSubi=0,isSubj=0;
for(int subij=0;subij<FATnSubSDJet[FATi];subij++){
if(FATsubjetSDCSV[FATi][subij]>1 ||FATsubjetSDCSV[FATi][subij]<0 )continue;
if(!isSubi && !isSubj){
subi=subij;isSubi=1;continue;
}
if(isSubi && !isSubj){
subj=subij;isSubj=1;break;
}
}
//
if (!isSubi || !isSubj)continue;
TLorentzVector FATsubjet_1,FATsubjet_2;
FATsubjet_1.SetPxPyPzE(FATsubjetSDPx[FATi][subi],FATsubjetSDPy[FATi][subi],FATsubjetSDPz[FATi][subi],FATsubjetSDCE[FATi][subi]);
FATsubjet_2.SetPxPyPzE(FATsubjetSDPx[FATi][subj],FATsubjetSDPy[FATi][subj],FATsubjetSDPz[FATi][subj],FATsubjetSDCE[FATi][subj]);
fatPt=FATjetP4_1->Pt();//BfatPt->Fill();
fatCSV=FATjetCISVV2[FATi];//BfatCSV->Fill();
sub1Pt=FATsubjet_1.Pt();//Bsub1Pt->Fill();
//sub1Eta=FATsubjet_1.Eta();//Bsub1Eta->Fill();
sub1CSV=FATsubjetSDCSV[FATi][subi];//Bsub1CSV->Fill();
sub2Pt=FATsubjet_2.Pt();//Bsub2Pt->Fill();
//sub2Eta=FATsubjet_2.Eta();//Bsub2Eta->Fill();
sub2CSV=FATsubjetSDCSV[FATi][subj];//Bsub2CSV->Fill();
delta_R=FATsubjet_1.DeltaR(FATsubjet_2);//BdeltaR->Fill();
tau21=FATjetTau2[FATi]/FATjetTau1[FATi];
tau1=FATjetTau1[FATi];
tau2=FATjetTau2[FATi];
th1 ->Fill( reader->EvaluateMVA("BDT method"));
}
th1->Write();
fw->Close();
}
}
}
//_____________________________________________________________________________
void dumpMvaInputs(bool debug, TString fileName) {
TFile* file = TFile::Open(fileName.Data());
const char *treeName = "PhotonTreeWriterPresel";
// const char *treeName = "PhotonTreeWriterPreselNoSmear";
TDirectory* theDir = (TDirectory*) file->FindObjectAny(treeName);
TTree* theTree = (TTree*) theDir->Get("hPhotonTree");
// open the MVA files, if requested
UInt_t run, lumi, evt;
UInt_t ph1index, ph1scindex;
UInt_t ph2index, ph2scindex;
float ph1pt, ph1sceta, ph1iso1, ph1iso2, ph1iso3, ph1cov, ph1hoe, ph1r9;
float ph2pt, ph2sceta, ph2iso1, ph2iso2, ph2iso3, ph2cov, ph2hoe, ph2r9;
float ph1e, ph2e;
float rho, mass;
float ph1ecalIso3,ph1ecalIso4,ph1trackIsoSel03,ph1trackIsoWorst04;
float ph2ecalIso3,ph2ecalIso4,ph2trackIsoSel03,ph2trackIsoWorst04;
float ph1eerr, ph1eerrsmeared;
float ph2eerr, ph2eerrsmeared;
UChar_t ph1hasconversion;
UChar_t ph2hasconversion;
Float_t scetawidth1, scphiwidth1;
Float_t scetawidth2, scphiwidth2;
// 2012 id mva
Float_t ph1_idmva_CoviEtaiPhi;
Float_t ph1_idmva_s4ratio;
Float_t ph1_idmva_GammaIso;
Float_t ph1_idmva_ChargedIso_selvtx;
Float_t ph1_idmva_ChargedIso_0p2_selvtx;
Float_t ph1_idmva_ChargedIso_worstvtx;
Float_t ph1_idmva_PsEffWidthSigmaRR;
Float_t ph2_idmva_CoviEtaiPhi;
Float_t ph2_idmva_s4ratio;
Float_t ph2_idmva_GammaIso;
Float_t ph2_idmva_ChargedIso_selvtx;
Float_t ph2_idmva_ChargedIso_0p2_selvtx;
Float_t ph2_idmva_ChargedIso_worstvtx;
Float_t ph2_idmva_PsEffWidthSigmaRR;
theTree->SetBranchAddress("run", &run);
theTree->SetBranchAddress("lumi",&lumi);
theTree->SetBranchAddress("evt", &evt);
theTree->SetBranchAddress("mass",&mass);
theTree->SetBranchAddress("rho",&rho);
theTree->SetBranchAddress("ph1.index",&ph1index);
theTree->SetBranchAddress("ph1.scindex",&ph1scindex);
theTree->SetBranchAddress("ph1.pt",&ph1pt);
theTree->SetBranchAddress("ph1.e",&ph1e);
theTree->SetBranchAddress("ph1.eerr",&ph1eerr);
theTree->SetBranchAddress("ph1.eerrsmeared",&ph1eerrsmeared);
theTree->SetBranchAddress("ph1.sceta",&ph1sceta);
theTree->SetBranchAddress("ph1.pfcic4_tIso1",&ph1iso1);
theTree->SetBranchAddress("ph1.pfcic4_tIso2",&ph1iso2);
theTree->SetBranchAddress("ph1.pfcic4_tIso3",&ph1iso3);
theTree->SetBranchAddress("ph1.pfcic4_covIEtaIEta",&ph1cov);
theTree->SetBranchAddress("ph1.pfcic4_HoE",&ph1hoe);
theTree->SetBranchAddress("ph1.pfcic4_R9",&ph1r9);
theTree->SetBranchAddress("ph1.pfcic4_ecalIso3",&ph1ecalIso3);
theTree->SetBranchAddress("ph1.pfcic4_ecalIso4",&ph1ecalIso4);
theTree->SetBranchAddress("ph1.pfcic4_trackIsoSel03",&ph1trackIsoSel03);
theTree->SetBranchAddress("ph1.pfcic4_trackIsoWorst04",&ph1trackIsoWorst04);
theTree->SetBranchAddress("ph1.hasconversion",&ph1hasconversion);
theTree->SetBranchAddress("ph1.scetawidth",&scetawidth1);
theTree->SetBranchAddress("ph1.scphiwidth",&scphiwidth1);
theTree->SetBranchAddress("ph1.idmva_CoviEtaiPhi",&ph1_idmva_CoviEtaiPhi);
theTree->SetBranchAddress("ph1.idmva_s4ratio",&ph1_idmva_s4ratio);
theTree->SetBranchAddress("ph1.idmva_GammaIso",&ph1_idmva_GammaIso);
theTree->SetBranchAddress("ph1.idmva_ChargedIso_selvtx",
&ph1_idmva_ChargedIso_selvtx);
theTree->SetBranchAddress("ph1.idmva_ChargedIso_0p2_selvtx",
&ph1_idmva_ChargedIso_0p2_selvtx);
theTree->SetBranchAddress("ph1.idmva_ChargedIso_worstvtx",
&ph1_idmva_ChargedIso_worstvtx);
theTree->SetBranchAddress("ph1.idmva_PsEffWidthSigmaRR",
&ph1_idmva_PsEffWidthSigmaRR);
theTree->SetBranchAddress("ph2.index",&ph2index);
theTree->SetBranchAddress("ph2.scindex",&ph2scindex);
theTree->SetBranchAddress("ph2.pt",&ph2pt);
theTree->SetBranchAddress("ph2.e",&ph2e);
theTree->SetBranchAddress("ph2.eerr",&ph2eerr);
theTree->SetBranchAddress("ph2.eerrsmeared",&ph2eerrsmeared);
theTree->SetBranchAddress("ph2.sceta",&ph2sceta);
theTree->SetBranchAddress("ph2.pfcic4_tIso1",&ph2iso1);
theTree->SetBranchAddress("ph2.pfcic4_tIso2",&ph2iso2);
theTree->SetBranchAddress("ph2.pfcic4_tIso3",&ph2iso3);
theTree->SetBranchAddress("ph2.pfcic4_covIEtaIEta",&ph2cov);
theTree->SetBranchAddress("ph2.pfcic4_HoE",&ph2hoe);
theTree->SetBranchAddress("ph2.pfcic4_R9",&ph2r9);
theTree->SetBranchAddress("ph2.pfcic4_ecalIso3",&ph2ecalIso3);
theTree->SetBranchAddress("ph2.pfcic4_ecalIso4",&ph2ecalIso4);
theTree->SetBranchAddress("ph2.pfcic4_trackIsoSel03",&ph2trackIsoSel03);
theTree->SetBranchAddress("ph2.pfcic4_trackIsoWorst04",&ph2trackIsoWorst04);
theTree->SetBranchAddress("ph2.hasconversion",&ph2hasconversion);
theTree->SetBranchAddress("ph2.scetawidth",&scetawidth2);
theTree->SetBranchAddress("ph2.scphiwidth",&scphiwidth2);
theTree->SetBranchAddress("ph2.idmva_CoviEtaiPhi",&ph2_idmva_CoviEtaiPhi);
theTree->SetBranchAddress("ph2.idmva_s4ratio",&ph2_idmva_s4ratio);
theTree->SetBranchAddress("ph2.idmva_GammaIso",&ph2_idmva_GammaIso);
theTree->SetBranchAddress("ph2.idmva_ChargedIso_selvtx",
&ph2_idmva_ChargedIso_selvtx);
theTree->SetBranchAddress("ph2.idmva_ChargedIso_0p2_selvtx",
&ph2_idmva_ChargedIso_0p2_selvtx);
theTree->SetBranchAddress("ph2.idmva_ChargedIso_worstvtx",
&ph2_idmva_ChargedIso_worstvtx);
theTree->SetBranchAddress("ph2.idmva_PsEffWidthSigmaRR",
&ph2_idmva_PsEffWidthSigmaRR);
float jet1pt, jet2pt, jet1eta, jet2eta, dijetmass, zeppenfeld, dphidijetgg;
theTree->SetBranchAddress("jet1pt",&jet1pt);
theTree->SetBranchAddress("jet2pt",&jet2pt);
theTree->SetBranchAddress("jet1eta",&jet1eta);
theTree->SetBranchAddress("jet2eta",&jet2eta);
theTree->SetBranchAddress("dijetmass",&dijetmass);
theTree->SetBranchAddress("zeppenfeld",&zeppenfeld);
theTree->SetBranchAddress("dphidijetgg",&dphidijetgg);
int numVtx;
theTree->SetBranchAddress("nVtx",&numVtx);
// presel vars
float ecalisodr03_1,ecalisodr03_2;
theTree->SetBranchAddress("ph1.ecalisodr03",&ecalisodr03_1);
theTree->SetBranchAddress("ph2.ecalisodr03",&ecalisodr03_2);
float hcalisodr03_1,hcalisodr03_2;
theTree->SetBranchAddress("ph1.hcalisodr03",&hcalisodr03_1);
theTree->SetBranchAddress("ph2.hcalisodr03",&hcalisodr03_2);
float trkisohollowdr03_1,trkisohollowdr03_2;
theTree->SetBranchAddress("ph1.trkisohollowdr03",&trkisohollowdr03_1);
theTree->SetBranchAddress("ph2.trkisohollowdr03",&trkisohollowdr03_2);
float hoveretower_1, hoveretower_2;
theTree->SetBranchAddress("ph1.hoveretower", &hoveretower_1);
theTree->SetBranchAddress("ph2.hoveretower", &hoveretower_2);
float sieie_1,sieie_2;
theTree->SetBranchAddress("ph1.sigietaieta",&sieie_1);
theTree->SetBranchAddress("ph2.sigietaieta",&sieie_2);
float idmva_ChargedIso_presel_1,idmva_ChargedIso_presel_2;
theTree->SetBranchAddress("ph1.idmva_ChargedIso_selvtx",&idmva_ChargedIso_presel_1);
theTree->SetBranchAddress("ph2.idmva_ChargedIso_selvtx",&idmva_ChargedIso_presel_2);
float idmva_ChargedIso_0p2_presel_1,idmva_ChargedIso_0p2_presel_2;
theTree->SetBranchAddress("ph1.idmva_ChargedIso_0p2_selvtx",&idmva_ChargedIso_0p2_presel_1);
theTree->SetBranchAddress("ph2.idmva_ChargedIso_0p2_selvtx",&idmva_ChargedIso_0p2_presel_2);
// float eleVeto_1, eleVeto_2;
// theTree->SetBranchAddress("ph1.eleVeto",&eleVeto_1);
// theTree->SetBranchAddress("ph2.eleVeto",&eleVeto_2);
float idmva_1, idmva_2;
theTree->SetBranchAddress("ph1.idmva",&idmva_1);
theTree->SetBranchAddress("ph2.idmva",&idmva_2);
float teta1, teta2;
theTree->SetBranchAddress("ph1.eta",&teta1);
theTree->SetBranchAddress("ph2.eta",&teta2);
float vtxprob, ptgg, phi1, phi2, masserr, masserrwvtx, masserr_ns, masserrwvtx_ns;
theTree->SetBranchAddress("vtxprob",&vtxprob);
theTree->SetBranchAddress("ptgg",&ptgg);
theTree->SetBranchAddress("ph1.phi",&phi1);
theTree->SetBranchAddress("ph2.phi",&phi2);
theTree->SetBranchAddress("masserrsmeared",&masserr);
theTree->SetBranchAddress("masserrsmearedwrongvtx",&masserrwvtx);
theTree->SetBranchAddress("masserr",&masserr_ns);
theTree->SetBranchAddress("masserrwrongvtx",&masserrwvtx_ns);
int vbfTag, leptonTag;
theTree->SetBranchAddress("vbfTag",&vbfTag);
theTree->SetBranchAddress("leptonTag",&leptonTag);
float corrpfmet, corrpfmetphi, pfmet, pfmetphi;
theTree->SetBranchAddress("corrpfmet",&corrpfmet);
theTree->SetBranchAddress("corrpfmetphi",&corrpfmetphi);
theTree->SetBranchAddress("pfmet",&pfmet);
theTree->SetBranchAddress("pfmetphi",&pfmetphi);
float mupt, mueta, mudr1, mudr2, mudz, mud0;
theTree->SetBranchAddress("muonPt",&mupt);
theTree->SetBranchAddress("muonEta",&mueta);
theTree->SetBranchAddress("muDR1",&mudr1);
theTree->SetBranchAddress("muDR2",&mudr2);
theTree->SetBranchAddress("muD0",&mud0);
theTree->SetBranchAddress("muDZ",&mudz);
float elept, eleeta, elesceta, eledr1, eledr2, eleMass1, eleMass2;
theTree->SetBranchAddress("elePt",&elept);
theTree->SetBranchAddress("eleEta",&eleeta);
theTree->SetBranchAddress("eleSCEta",&elesceta);
theTree->SetBranchAddress("eleDR1",&eledr1);
theTree->SetBranchAddress("eleDR2",&eledr2);
theTree->SetBranchAddress("eleMass1",&eleMass1);
theTree->SetBranchAddress("eleMass2",&eleMass2);
int elemisshits;
theTree->SetBranchAddress("eleNinnerHits",&elemisshits);
// vertext stuff
int vertexId1, vertexId2, vertexId3;
float vertexMva1, vertexMva2, vertexMva3;
//float ptbal, ptasym, sumpt2, p2conv;
Float_t ptbal, ptasym, sumpt2, p2conv;
int nleg1, nleg2;
theTree->SetBranchAddress("vtxInd1",&vertexId1);
theTree->SetBranchAddress("vtxInd2",&vertexId2);
theTree->SetBranchAddress("vtxInd3",&vertexId3);
theTree->SetBranchAddress("vtxMva1",&vertexMva1);
theTree->SetBranchAddress("vtxMva2",&vertexMva2);
theTree->SetBranchAddress("vtxMva3",&vertexMva3);
theTree->SetBranchAddress("vtxBestPtbal",&ptbal);
theTree->SetBranchAddress("vtxBestPtasym",&ptasym);
theTree->SetBranchAddress("vtxBestSumpt2",&sumpt2);
theTree->SetBranchAddress("vtxBestP2Conv",&p2conv);
theTree->SetBranchAddress("vtxNleg1",&nleg1);
theTree->SetBranchAddress("vtxNleg2",&nleg2);
float convVtxZ1, convVtxRes1, convVtxChi1;
float convVtxZ2, convVtxRes2, convVtxChi2;
int convVtxIdx1, convVtxIdx2, vtxNconv;
theTree->SetBranchAddress("vtxConv1Z",&convVtxZ1);
theTree->SetBranchAddress("vtxConv1DZ",&convVtxRes1);
theTree->SetBranchAddress("vtxConv1Prob",&convVtxChi1);
theTree->SetBranchAddress("vtxConvIdx1",&convVtxIdx1);
theTree->SetBranchAddress("vtxConv2Z",&convVtxZ2);
theTree->SetBranchAddress("vtxConv2DZ",&convVtxRes2);
theTree->SetBranchAddress("vtxConv2Prob",&convVtxChi2);
theTree->SetBranchAddress("vtxConvIdx2",&convVtxIdx2);
theTree->SetBranchAddress("vtxNconv",&vtxNconv);
float vtxz1, vtxz2, vtxz3;
theTree->SetBranchAddress("vtxMva1Z",&vtxz1);
theTree->SetBranchAddress("vtxMva2Z",&vtxz2);
theTree->SetBranchAddress("vtxMva3Z",&vtxz3);
float eleIdMva;
theTree->SetBranchAddress("eleIdMva",&eleIdMva);
// additional MET tag stuff.
float phigg, jetleadNoIDpt, jetleadNoIDphi, jetleadNoIDeta;
float ph1scphi, ph2scphi;
theTree->SetBranchAddress("phigg",&phigg);
theTree->SetBranchAddress("jetleadNoIDpt",&jetleadNoIDpt);
theTree->SetBranchAddress("jetleadNoIDphi",&jetleadNoIDphi);
theTree->SetBranchAddress("jetleadNoIDeta",&jetleadNoIDeta);
theTree->SetBranchAddress("ph1.scphi",&ph1scphi);
theTree->SetBranchAddress("ph2.scphi",&ph2scphi);
float ph1scrawe, ph1scpse, ph1e3x3, ph1e5x5, ph1e3x3seed, ph1e5x5seed;
float ph2scrawe, ph2scpse, ph2e3x3, ph2e5x5, ph2e3x3seed, ph2e5x5seed;
theTree->SetBranchAddress("ph1.scrawe",&ph1scrawe);
theTree->SetBranchAddress("ph1.scpse",&ph1scpse);
theTree->SetBranchAddress("ph1.e3x3",&ph1e3x3);
theTree->SetBranchAddress("ph1.e3x3seed",&ph1e3x3seed);
theTree->SetBranchAddress("ph1.e5x5",&ph1e5x5);
theTree->SetBranchAddress("ph1.e5x5seed",&ph1e5x5seed);
theTree->SetBranchAddress("ph2.scrawe",&ph2scrawe);
theTree->SetBranchAddress("ph2.scpse",&ph2scpse);
theTree->SetBranchAddress("ph2.e3x3",&ph2e3x3);
theTree->SetBranchAddress("ph2.e3x3seed",&ph2e3x3seed);
theTree->SetBranchAddress("ph2.e5x5",&ph2e5x5);
theTree->SetBranchAddress("ph2.e5x5seed",&ph2e5x5seed);
// Setup the diphoton BDT
Float_t rVtxSigmaMoM, wVtxSigmaMoM, cosDPhi;
Float_t pho1_ptOverM;
Float_t pho2_ptOverM;
Float_t diphoMVA;
TMVA::Reader* reader = new TMVA::Reader();
reader->AddVariable("masserrsmeared/mass" , &rVtxSigmaMoM);
reader->AddVariable("masserrsmearedwrongvtx/mass", &wVtxSigmaMoM);
reader->AddVariable("vtxprob" , &vtxprob );
reader->AddVariable("ph1.pt/mass" , &pho1_ptOverM);
reader->AddVariable("ph2.pt/mass" , &pho2_ptOverM);
reader->AddVariable("ph1.eta" , &teta1 );
reader->AddVariable("ph2.eta" , &teta2 );
reader->AddVariable("TMath::Cos(ph1.phi-ph2.phi)", &cosDPhi );
reader->AddVariable("ph1.idmva" , &idmva_1 );
reader->AddVariable("ph2.idmva" , &idmva_2 );
const char *diphotonWeights = (
"/home/veverka/cms/cmssw/031/CMSSW_5_3_10_patch1/src/MitPhysics/data/"
"HggBambu_SMDipho_Oct01_redqcdweightallsigevenbkg_BDTG.weights.xml"
);
reader->BookMVA("BDTG", diphotonWeights);
TRandom3 rng(0);
int eventCounter=0;
// Loop over the entries.
std::cout << "Looping over " << theTree->GetEntries() << " entries." << std::endl;
for (int i=0; i < theTree->GetEntries(); ++i) {
if (eventCounter > 9 && debug ) break;
theTree->GetEntry(i);
bool passPreselection = (mass > 100 &&
mass < 180 &&
ph1pt > mass/3 &&
ph2pt > mass/4);
if (passPreselection == false) continue;
eventCounter++;
// Calculate needed variables
// rVtxSigmaMoM = masserr_ns / mass; // no smearing
// wVtxSigmaMoM = masserrwvtx_ns / mass; // no smearing
rVtxSigmaMoM = masserr / mass; // with smearing
wVtxSigmaMoM = masserrwvtx / mass; // with smearing
cosDPhi = TMath::Cos(phi1 - phi2);
pho1_ptOverM = ph1pt / mass;
pho2_ptOverM = ph2pt / mass;
diphoMVA = reader->EvaluateMVA("BDTG");
// Event Variables
dumpVar("run" , run ); // 1
dumpVar("lumi" , lumi ); // 2
dumpVar("event" , evt ); // 3
dumpVar("rho" , rho ); // 4
// Leading Photon Variables
dumpVar("pho1_ind" , ph1index ); // 5
dumpVar("pho1_scInd" , /*ph1scindex*/ -999 ); // 6
dumpVar("pho1_pt" , ph1pt ); // 7
dumpVar("pho1_eta" , teta1 ); // 8
dumpVar("pho1_phi" , phi1 ); // 9
dumpVar("pho1_e" , ph1e ); // 10
dumpVar("pho1_eErr" , ph1eerr ); // 11
dumpVar("pho1_isConv" ,
(UInt_t) ph1hasconversion ); // 12
dumpVar("pho1_HoE" , hoveretower_1 ); // 13
dumpVar("pho1_hcalIso03" ,
hcalisodr03_1 - 0.005 * ph1pt ); // 14
dumpVar("pho1_trkIso03" ,
trkisohollowdr03_1 - 0.002 * ph1pt ); // 15
dumpVar("pho1_pfChargedIsoGood02",
ph1_idmva_ChargedIso_0p2_selvtx ); // 16
dumpVar("pho1_pfChargedIsoGood03",
ph1_idmva_ChargedIso_selvtx ); // 17
dumpVar("pho1_pfChargedIsoBad03" ,
ph1_idmva_ChargedIso_worstvtx ); // 18
dumpVar("pho1_pfPhotonIso03" , ph1_idmva_GammaIso ); // 19
// TODO: remove pho1_pfNeutralIso03, it's not used
// dumpVar("pho1_pfNeutralIso03" , -999 ); // 20
dumpVar("pho1_sieie" , sieie_1 ); // 21
dumpVar("pho1_cieip" , ph1_idmva_CoviEtaiPhi ); // 22
dumpVar("pho1_etaWidth" , scetawidth1 ); // 23
dumpVar("pho1_phiWidth" , scphiwidth1 ); // 24
dumpVar("pho1_r9" , ph1r9 ); // 25
// TODO: remove pho1_lambdaRatio, it's not used
// dumpVar("pho1_lambdaRatio" , -999 ); // 26
dumpVar("pho1_s4Ratio" , ph1_idmva_s4ratio ); // 27
dumpVar("pho1_scEta" , ph1sceta ); // 28
dumpVar("pho1_ESEffSigmaRR" ,
ph1_idmva_PsEffWidthSigmaRR ); // 29
dumpVar("pho1_ptOverM" , pho1_ptOverM ); // 30
dumpVar("pho1_scRawE" , ph1scrawe );
dumpVar("pho1_idMVA" , idmva_1 );
// Trailing Photon Variables
dumpVar("pho2_ind" , ph2index ); // 31
dumpVar("pho2_scInd" , /*ph2scindex*/ -999 ); // 32
dumpVar("pho2_pt" , ph2pt ); // 33
dumpVar("pho2_eta" , teta2 ); // 34
dumpVar("pho2_phi" , phi2 ); // 35
dumpVar("pho2_e" , ph2e ); // 36
dumpVar("pho2_eErr" , ph2eerr ); // 37
dumpVar("pho2_isConv" ,
(UInt_t) ph2hasconversion ); // 38
dumpVar("pho2_HoE" , hoveretower_2 ); // 39
dumpVar("pho2_hcalIso03" ,
hcalisodr03_1 - 0.005 * ph2pt ); // 40
dumpVar("pho2_trkIso03" ,
trkisohollowdr03_1 - 0.002 * ph2pt ); // 41
dumpVar("pho2_pfChargedIsoGood02",
ph2_idmva_ChargedIso_0p2_selvtx ); // 42
dumpVar("pho2_pfChargedIsoGood03",
ph2_idmva_ChargedIso_selvtx ); // 43
dumpVar("pho2_pfChargedIsoBad03" ,
ph2_idmva_ChargedIso_worstvtx ); // 44
dumpVar("pho2_pfPhotonIso03" , ph2_idmva_GammaIso ); // 45
// dumpVar("pho2_pfNeutralIso03" , -999 ); // 46
dumpVar("pho2_sieie" , sieie_2 ); // 47
dumpVar("pho2_cieip" , ph2_idmva_CoviEtaiPhi ); // 48
dumpVar("pho2_etaWidth" , scetawidth2 ); // 49
dumpVar("pho2_phiWidth" , scphiwidth2 ); // 50
dumpVar("pho2_r9" , ph2r9 ); // 51
// dumpVar("pho2_lambdaRatio" , -999 ); // 52
dumpVar("pho2_s4Ratio" , ph2_idmva_s4ratio ); // 53
dumpVar("pho2_scEta" , ph2sceta ); // 54
dumpVar("pho2_ESEffSigmaRR" ,
ph2_idmva_PsEffWidthSigmaRR ); // 55
dumpVar("pho2_ptOverM" , pho2_ptOverM ); // 56
dumpVar("pho2_scRawE" , ph2scrawe );
dumpVar("pho2_idMVA" , idmva_2 );
// Diphoton Variables
dumpVar("mass" , mass ); // 57
dumpVar("rVtxSigmaMoM" , rVtxSigmaMoM ); // 58
dumpVar("wVtxSigmaMoM" , wVtxSigmaMoM ); // 59
dumpVar("vtxProb" , vtxprob ); // 61
float logSPt2 = TMath::Log(sumpt2);
if (vertexId1 < 0) {
vertexId1 = ptbal = ptasym = logSPt2 = p2conv = vtxNconv = -999;
}
dumpVar("vtxIndex" , vertexId1 ); // 60
dumpVar("ptBal" , ptbal ); // 62
dumpVar("ptAsym" , ptasym ); // 63
dumpVar("logSPt2" , logSPt2 ); // 64
dumpVar("p2Conv" , p2conv ); // 65
dumpVar("nConv" , vtxNconv ); // 66
dumpVar("cosDPhi" , cosDPhi ); // 67
dumpVar("diphoMVA" , diphoMVA );
// Leading Jet Variables
if (jet1pt < 0) {
jet1pt = -999;
jet1eta = -999;
}
dumpVar("jet1_ind" , -999 ); // 68
dumpVar("jet1_pt" , jet1pt ); // 69
dumpVar("jet1_eta" , jet1eta ); // 70
// Trailing Jet Variables
if (jet2pt < 0) {
jet2pt = -999;
jet2eta = -999;
}
dumpVar("jet2_ind" , -999 ); // 71
dumpVar("jet2_pt" , jet2pt ); // 72
dumpVar("jet2_eta" , jet2eta ); // 73
// Dijet Variables
float dijet_dEta = abs(jet1eta - jet2eta);
if (jet1pt < 0 || jet2pt < 0) {
dijet_dEta = -999;
zeppenfeld = -999;
dphidijetgg = -999;
dijetmass = -999;
}
dumpVar("dijet_dEta" , dijet_dEta ); // 74
dumpVar("dijet_Zep" , zeppenfeld ); // 75
dumpVar("dijet_dPhi" , dphidijetgg ); // 76
dumpVar("dijet_mass" , dijetmass , false); // 77
std::cout << std::endl;
} // Loop over the tree entries.
return;
} // void dumpMvaInputs(bool debug, TString fileName)
void TMVAClassificationApplicationLambda( TString myMethodList = "" )
{
#ifdef __CINT__
gROOT->ProcessLine( ".O0" ); // turn off optimization in CINT
#endif
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
// --- Cut optimisation
Use["Cuts"] = 1;
Use["CutsD"] = 1;
Use["CutsPCA"] = 0;
Use["CutsGA"] = 0;
Use["CutsSA"] = 0;
//
// --- 1-dimensional likelihood ("naive Bayes estimator")
Use["Likelihood"] = 1;
Use["LikelihoodD"] = 0; // the "D" extension indicates decorrelated input variables (see option strings)
Use["LikelihoodPCA"] = 1; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
Use["LikelihoodKDE"] = 0;
Use["LikelihoodMIX"] = 0;
//
// --- Mutidimensional likelihood and Nearest-Neighbour methods
Use["PDERS"] = 1;
Use["PDERSD"] = 0;
Use["PDERSPCA"] = 0;
Use["PDEFoam"] = 1;
Use["PDEFoamBoost"] = 0; // uses generalised MVA method boosting
Use["KNN"] = 1; // k-nearest neighbour method
//
// --- Linear Discriminant Analysis
Use["LD"] = 1; // Linear Discriminant identical to Fisher
Use["Fisher"] = 0;
Use["FisherG"] = 0;
Use["BoostedFisher"] = 0; // uses generalised MVA method boosting
Use["HMatrix"] = 0;
//
// --- Function Discriminant analysis
Use["FDA_GA"] = 1; // minimisation of user-defined function using Genetics Algorithm
Use["FDA_SA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
Use["FDA_MCMT"] = 0;
//
// --- Neural Networks (all are feed-forward Multilayer Perceptrons)
Use["MLP"] = 0; // Recommended ANN
Use["MLPBFGS"] = 0; // Recommended ANN with optional training method
Use["MLPBNN"] = 1; // Recommended ANN with BFGS training method and bayesian regulator
Use["CFMlpANN"] = 0; // Depreciated ANN from ALEPH
Use["TMlpANN"] = 0; // ROOT's own ANN
//
// --- Support Vector Machine
Use["SVM"] = 1;
//
// --- Boosted Decision Trees
Use["BDT"] = 1; // uses Adaptive Boost
Use["BDTG"] = 0; // uses Gradient Boost
Use["BDTB"] = 0; // uses Bagging
Use["BDTD"] = 0; // decorrelation + Adaptive Boost
//
// --- Friedman's RuleFit method, ie, an optimised series of cuts ("rules")
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;
// Select methods (don't look at this code - not of interest)
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;
}
}
// --------------------------------------------------------------------------------------------------
// --- Create the Reader object
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
// Create a set of variables and declare them to the reader
// - the variable names MUST corresponds in name and type to those given in the weight file(s) used
//Float_t var1, var2;
//Float_t var3, var4;
Float_t la_agl, la_dlos, la_dau1_dzos, la_dau1_dxyos, la_dau2_dzos, la_dau2_dxyos;
Float_t la_vtxChi2, la_dau1_nhit, la_dau2_nhit;
reader->AddVariable( "la_agl", &la_agl );
reader->AddVariable( "la_dlos", &la_dlos );
reader->AddVariable( "la_dau1_dzos", &la_dau1_dzos );
reader->AddVariable( "la_dau2_dzos",&la_dau2_dzos);
reader->AddVariable( "la_dau1_dxyos", &la_dau1_dxyos );
reader->AddVariable( "la_dau2_dxyos",&la_dau2_dxyos);
reader->AddVariable( "la_vtxChi2",&la_vtxChi2);
reader->AddVariable( "la_dau1_nhit",&la_dau1_nhit);
reader->AddVariable( "la_dau2_nhit",&la_dau2_nhit);
// Spectator variables declared in the training have to be added to the reader, too
Float_t la_mass;
reader->AddSpectator( "la_mass", &la_mass );
//reader->AddSpectator( "spec2 := var1*3", &spec2 );
/*
Float_t Category_cat1, Category_cat2, Category_cat3;
if (Use["Category"]){
// Add artificial spectators for distinguishing categories
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 );
}
*/
// --- Book the MVA methods
TString dir = "weights/";
TString prefix = "TMVAClassification";
// Book method(s)
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
TString methodName = TString(it->first) + TString(" method");
TString weightfile = dir + prefix + TString("_") + TString(it->first) + TString(".weights.xml");
reader->BookMVA( methodName, weightfile );
}
}
// Book output histograms
UInt_t nbin = 100;
TH1F *histLk(0), *histLkD(0), *histLkPCA(0), *histLkKDE(0), *histLkMIX(0), *histPD(0), *histPDD(0);
TH1F *histPDPCA(0), *histPDEFoam(0), *histPDEFoamErr(0), *histPDEFoamSig(0), *histKNN(0), *histHm(0);
TH1F *histFi(0), *histFiG(0), *histFiB(0), *histLD(0), *histNn(0),*histNnbfgs(0),*histNnbnn(0);
TH1F *histNnC(0), *histNnT(0), *histBdt(0), *histBdtG(0), *histBdtD(0), *histRf(0), *histSVMG(0);
TH1F *histSVMP(0), *histSVML(0), *histFDAMT(0), *histFDAGA(0), *histCat(0), *histPBdt(0);
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["BDT"]) histBdt = new TH1F( "MVA_BDT", "MVA_BDT", nbin, -0.8, 0.8 );
if (Use["BDTD"]) histBdtD = new TH1F( "MVA_BDTD", "MVA_BDTD", nbin, -0.8, 0.8 );
if (Use["BDTG"]) histBdtG = new TH1F( "MVA_BDTG", "MVA_BDTG", 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 );
// PDEFoam also returns per-event error, fill in histogram, and also fill significance
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 );
}
// Book example histogram for probability (the other methods are done similarly)
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 );
}
// Prepare input tree (this must be replaced by your data source)
// in this example, there is a toy tree with signal and one with background events
// we'll later on use only the "signal" events for the test in this example.
//
TFile *input(0);
TString fname = "./tmva_example.root";
if (!gSystem->AccessPathName( fname ))
input = TFile::Open( fname ); // check if file in local directory exists
else
input = TFile::Open( "~/2014Research/ROOT_file/V0reco_PbPb/MCPbPb_central1.root" ); // if not: download from ROOT server
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVAClassificationApp : Using input file: " << input->GetName() << std::endl;
// --- Event loop
// Prepare the event tree
// - here the variable names have to corresponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
//
std::cout << "--- Select signal sample" << std::endl;
TTree* theTree = (TTree*)input->Get("ana/v0_Lambda");
//Float_t userVar1, userVar2;
theTree->SetBranchAddress( "la_agl", &la_agl );
theTree->SetBranchAddress( "la_dlos", &la_dlos );
theTree->SetBranchAddress( "la_dau1_dzos", &la_dau1_dzos );
theTree->SetBranchAddress( "la_dau1_dxyos", &la_dau1_dxyos );
theTree->SetBranchAddress( "la_dau2_dzos",&la_dau2_dzos);
theTree->SetBranchAddress( "la_dau2_dxyos",&la_dau2_dxyos);
theTree->SetBranchAddress( "la_vtxChi2",&la_vtxChi2);
theTree->SetBranchAddress( "la_dau1_nhit",&la_dau1_nhit);
theTree->SetBranchAddress( "la_dau2_nhit",&la_dau2_nhit);
theTree->SetBranchAddress( "la_mass", &la_mass );
// Efficiency calculator for cut method
Int_t nSelCutsGA = 0;
Double_t effS = 0.7;
std::vector<Float_t> vecVar(4); // vector for EvaluateMVA tests
std::cout << "--- Processing: " << theTree->GetEntries() << " events" << std::endl;
TStopwatch sw;
sw.Start();
TFile *target = new TFile( "TMVAppLambda.root","RECREATE" );
TNtuple *n1 = new TNtuple( "n1","n1","Lam_mass:MVA");
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
if (ievt%1000 == 0) std::cout << "--- ... Processing event: " << ievt << std::endl;
theTree->GetEntry(ievt);
// --- Return the MVA outputs and fill into histograms
if (Use["CutsGA"]) {
// Cuts is a special case: give the desired signal efficienciy
Bool_t passed = reader->EvaluateMVA( "CutsGA method", effS );
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["BDT" ]) {
double Lam_mass = la_mass;
histBdt ->Fill( reader->EvaluateMVA( "BDT method" ) );
double MVA = 0.0;
MVA = reader->EvaluateMVA("BDT method");
n1->Fill(Lam_mass,MVA);
// cout << "la mass: " << temp << endl
}
if (Use["BDTD" ]) {
double Lam_mass = la_mass;
histBdtD ->Fill( reader->EvaluateMVA( "BDTD method" ) );
double MVA = 0.0;
MVA = reader->EvaluateMVA("BDTD method");
n1->Fill(Lam_mass,MVA);
}
if (Use["BDTG" ]) {
double Lam_mass = la_mass;
histBdtG ->Fill( reader->EvaluateMVA( "BDTG method" ) );
double MVA = 0.0;
MVA = reader->EvaluateMVA("BDTG method");
n1->Fill(Lam_mass,MVA);
}
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" ) );
// Retrieve also per-event error
if (Use["PDEFoam"]) {
Double_t val = reader->EvaluateMVA( "PDEFoam method" );
Double_t err = reader->GetMVAError();
histPDEFoam ->Fill( val );
histPDEFoamErr->Fill( err );
if (err>1.e-50) histPDEFoamSig->Fill( val/err );
}
// Retrieve probability instead of MVA output
if (Use["Fisher"]) {
probHistFi ->Fill( reader->GetProba ( "Fisher method" ) );
rarityHistFi->Fill( reader->GetRarity( "Fisher method" ) );
}
}
// Get elapsed time
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
// Get efficiency for cuts classifier
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"]) {
// test: retrieve cuts for particular signal efficiency
// CINT ignores dynamic_casts so we have to use a cuts-secific Reader function to acces the pointer
TMVA::MethodCuts* mcuts = reader->FindCutsMVA( "CutsGA method" ) ;
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]
<< " < \""
<< mcuts->GetInputVar(ivar)
<< "\" <= "
<< cutsMax[ivar] << std::endl;
}
std::cout << "--- -------------------------------------------------------------" << std::endl;
}
}
// --- Write histograms
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["BDT" ]){
histBdt ->Write();
n1->Write();
}
if (Use["BDTD" ]){
histBdtD ->Write();
n1->Write();
}
if (Use["BDTG" ]) {
histBdtG ->Write();
n1->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();
// Write also error and significance histos
if (Use["PDEFoam"]) { histPDEFoam->Write(); histPDEFoamErr->Write(); histPDEFoamSig->Write(); }
// Write also probability hists
if (Use["Fisher"]) { if (probHistFi != 0) probHistFi->Write(); if (rarityHistFi != 0) rarityHistFi->Write(); }
target->Close();
std::cout << "--- Created root file: \"TMVApp.root\" containing the MVA output histograms" << std::endl;
delete reader;
std::cout << "==> TMVAClassificationApplication is done!" << endl << std::endl;
}
int testPyGTBMulticlass(){
// Get data file
std::cout << "Get test data..." << std::endl;
TString fname = "./tmva_example_multiple_background.root";
if (gSystem->AccessPathName(fname)){ // file does not exist in local directory
std::cout << "Create multiclass test data..." << std::endl;
TString createDataMacro = TString(gROOT->GetTutorialsDir()) + "/tmva/createData.C";
gROOT->ProcessLine(TString::Format(".L %s",createDataMacro.Data()));
gROOT->ProcessLine("create_MultipleBackground(200)");
std::cout << "Created " << fname << " for tests of the multiclass features" << std::endl;
}
TFile *input = TFile::Open(fname);
// Setup PyMVA and factory
std::cout << "Setup TMVA..." << std::endl;
TMVA::PyMethodBase::PyInitialize();
TFile* outputFile = TFile::Open("ResultsTestPyGTBMulticlass.root", "RECREATE");
TMVA::Factory *factory = new TMVA::Factory("testPyGTBMulticlass", outputFile,
"!V:Silent:Color:!DrawProgressBar:AnalysisType=multiclass");
// Load data
TMVA::DataLoader *dataloader = new TMVA::DataLoader("datasetTestPyGTBMulticlass");
TTree *signal = (TTree*)input->Get("TreeS");
TTree *background0 = (TTree*)input->Get("TreeB0");
TTree *background1 = (TTree*)input->Get("TreeB1");
TTree *background2 = (TTree*)input->Get("TreeB2");
dataloader->AddTree(signal, "Signal");
dataloader->AddTree(background0, "Background_0");
dataloader->AddTree(background1, "Background_1");
dataloader->AddTree(background2, "Background_2");
dataloader->AddVariable("var1");
dataloader->AddVariable("var2");
dataloader->AddVariable("var3");
dataloader->AddVariable("var4");
dataloader->PrepareTrainingAndTestTree("",
"SplitMode=Random:NormMode=NumEvents:!V");
// Book and train method
factory->BookMethod(dataloader, TMVA::Types::kPyGTB, "PyGTB",
"!H:!V:VarTransform=None:NEstimators=100:Verbose=0");
std::cout << "Train classifier..." << std::endl;
factory->TrainAllMethods();
// Clean-up
delete factory;
delete dataloader;
delete outputFile;
// Setup reader
UInt_t numEvents = 100;
std::cout << "Run reader and classify " << numEvents << " events..." << std::endl;
TMVA::Reader *reader = new TMVA::Reader("!Color:Silent");
Float_t vars[4];
reader->AddVariable("var1", vars+0);
reader->AddVariable("var2", vars+1);
reader->AddVariable("var3", vars+2);
reader->AddVariable("var4", vars+3);
reader->BookMVA("PyGTB", "datasetTestPyGTBMulticlass/weights/testPyGTBMulticlass_PyGTB.weights.xml");
// Get mean response of method on signal and background events
signal->SetBranchAddress("var1", vars+0);
signal->SetBranchAddress("var2", vars+1);
signal->SetBranchAddress("var3", vars+2);
signal->SetBranchAddress("var4", vars+3);
background0->SetBranchAddress("var1", vars+0);
background0->SetBranchAddress("var2", vars+1);
background0->SetBranchAddress("var3", vars+2);
background0->SetBranchAddress("var4", vars+3);
background1->SetBranchAddress("var1", vars+0);
background1->SetBranchAddress("var2", vars+1);
background1->SetBranchAddress("var3", vars+2);
background1->SetBranchAddress("var4", vars+3);
background2->SetBranchAddress("var1", vars+0);
background2->SetBranchAddress("var2", vars+1);
background2->SetBranchAddress("var3", vars+2);
background2->SetBranchAddress("var4", vars+3);
Float_t meanMvaSignal = 0;
Float_t meanMvaBackground0 = 0;
Float_t meanMvaBackground1 = 0;
Float_t meanMvaBackground2 = 0;
for(UInt_t i=0; i<numEvents; i++){
signal->GetEntry(i);
meanMvaSignal += reader->EvaluateMulticlass("PyGTB")[0];
background0->GetEntry(i);
meanMvaBackground0 += reader->EvaluateMulticlass("PyGTB")[1];
background1->GetEntry(i);
meanMvaBackground1 += reader->EvaluateMulticlass("PyGTB")[2];
background2->GetEntry(i);
meanMvaBackground2 += reader->EvaluateMulticlass("PyGTB")[3];
}
meanMvaSignal = meanMvaSignal/float(numEvents);
meanMvaBackground0 = meanMvaBackground0/float(numEvents);
meanMvaBackground1 = meanMvaBackground1/float(numEvents);
meanMvaBackground2 = meanMvaBackground2/float(numEvents);
// Check whether the response is obviously better than guessing
std::cout << "Mean MVA response on signal: " << meanMvaSignal << std::endl;
if(meanMvaSignal < 0.3){
std::cout << "[ERROR] Mean response on signal is " << meanMvaSignal << " (<0.3)" << std::endl;
return 1;
}
std::cout << "Mean MVA response on background 0: " << meanMvaBackground0 << std::endl;
if(meanMvaBackground0 < 0.3){
std::cout << "[ERROR] Mean response on background 0 is " << meanMvaBackground0 << " (<0.3)" << std::endl;
return 1;
}
std::cout << "Mean MVA response on background 1: " << meanMvaBackground1 << std::endl;
if(meanMvaBackground0 < 0.3){
std::cout << "[ERROR] Mean response on background 1 is " << meanMvaBackground1 << " (<0.3)" << std::endl;
return 1;
}
std::cout << "Mean MVA response on background 2: " << meanMvaBackground2 << std::endl;
if(meanMvaBackground0 < 0.3){
std::cout << "[ERROR] Mean response on background 2 is " << meanMvaBackground2 << " (<0.3)" << std::endl;
return 1;
}
return 0;
}
