void regressCovU2(std::string iName) {
TString lName = iName; TFile *lInput = TFile::Open(lName);
//TString lName = "../Data/s12-zjets_v29b.root"; TFile *lInput = TFile::Open(lName);
//TString lName = "../Data/r11-dimu_jetv3_v29b.root"; TFile *lInput = TFile::Open(lName);
//TString lName = "../Data/f11-zjets_jetv4_v11.root"; TFile *lInput = TFile::Open(lName);
TTree *lRegress = (TTree*)lInput ->Get("Flat");
GBRTrainer *train = new GBRTrainer;
train->AddTree(lRegress);
train->SetTrainingCut("(nbtag == 0)*(pt_z > 0)");//*PtWeight");
train->SetMinEvents(250);
train->SetShrinkage(0.3);
train->SetMinCutSignificance(3.0);
train->SetTargetVar( "abs(pt_z*sin(phi_z-uphix_mva))*sqrt(TMath::Pi()/2.)/ux_mva");
std::vector<std::string> *lVec = new std::vector<std::string>;
lVec->push_back( "sumet" );
lVec->push_back( "npv" );
lVec->push_back( "pfu" );
lVec->push_back( "pfuphi" );
lVec->push_back( "tksumet" );
lVec->push_back( "tku" );
lVec->push_back( "tkuphi" );
lVec->push_back( "nopusumet" );
lVec->push_back( "nopuu" );
lVec->push_back( "nopuuphi" );
lVec->push_back( "pusumet" );
lVec->push_back( "pumet" );
lVec->push_back( "pumetphi" );
lVec->push_back( "pucsumet" );
lVec->push_back( "pucu" );
lVec->push_back( "pucuphi" );
lVec->push_back( "jetpt1" );
lVec->push_back( "jeteta1" );
lVec->push_back( "jetphi1" );
lVec->push_back( "jetpt2" );
lVec->push_back( "jeteta2" );
lVec->push_back( "jetphi2" );
lVec->push_back( "nalljet" );
lVec->push_back( "njet" );
lVec->push_back( "uphix_mva" );
lVec->push_back( "ux_mva" );
for (int i=0; i<int(lVec->size()); ++i) {
train->AddInputVar(lVec->at(i));
}
ROOT::Cintex::Cintex::Enable();
const GBRForest *forest = train->TrainForest(300);
TFile *fout = new TFile("gbru2cov.root","RECREATE");
fout->WriteObject(forest,"CovU2");
fout->WriteObject(lVec, "varlist");
fout->Close();
}
void trainElectronEnergyRegression_ECAL(char* trainingFile, char* outWeightFile, char* optionChar, int nTrees) {
// Setting up training option
std::string optionStr(optionChar);
// ******** If option is V00, V01, V02, etc. ********* //
if (optionStr == "V00" || optionStr == "V01") {
GBRTrainer *traineb = new GBRTrainer;
GBRTrainer *trainebvar = new GBRTrainer;
GBRTrainer *trainee = new GBRTrainer;
GBRTrainer *traineevar = new GBRTrainer;
TTree *intree = 0;
cout << "Training on file " << trainingFile << " with version " << optionChar << endl;
TChain *chainele = new TChain("eleIDdir/T1");
chainele->Add(trainingFile);
chainele->LoadTree(0);
chainele->SetCacheSize(64*1024*1024);
chainele->SetCacheLearnEntries();
intree = chainele;
traineb->AddTree(chainele);
trainebvar->AddTree(chainele);
trainee->AddTree(chainele);
traineevar->AddTree(chainele);
TCut traincut = "pt>0";////////////////////////////////
TCut evtcut;
TCut evtcutvar;
TCut statusenergycut;
//if you want to train also energy variance
evtcut = "event%2==0 ";
evtcutvar = "event%2==1 ";
statusenergycut="(GeneratedEnergyStatus3-GeneratedEnergyStatus1)/GeneratedEnergyStatus3<0.01 && GeneratedEnergyStatus3>=GeneratedEnergyStatus1";
traineb->SetTrainingCut(std::string(traincut && evtcut && statusenergycut && "abs(eta)<1.479 && mcmatch==1"));
trainee->SetTrainingCut(std::string(traincut && evtcut && statusenergycut && "abs(eta)>1.479 && abs(eta)<2.5 && mcmatch==1"));
//turn this off for now
trainebvar->SetTrainingCut(std::string(traincut && evtcutvar && statusenergycut && "abs(eta)<1.479 && mcmatch==1"));
traineevar->SetTrainingCut(std::string(traincut && evtcutvar && statusenergycut && "abs(eta)>1.479 && abs(eta)<2.5 && mcmatch==1"));
const double maxsig = 3.0;
const double shrinkage = 0.1;
traineb->SetMinEvents(200);
traineb->SetShrinkage(shrinkage);
traineb->SetMinCutSignificance(maxsig);
trainebvar->SetMinEvents(200);
trainebvar->SetShrinkage(shrinkage);
trainebvar->SetMinCutSignificance(maxsig);
trainee->SetMinEvents(200);
trainee->SetShrinkage(shrinkage);
trainee->SetMinCutSignificance(maxsig);
traineevar->SetMinEvents(200);
traineevar->SetShrinkage(shrinkage);
traineevar->SetMinCutSignificance(maxsig);
traineb->SetTargetVar("GeneratedEnergyStatus3/SCRawEnergy");
trainebvar->SetTargetVar("abs( targeteb - GeneratedEnergyStatus3/SCRawEnergy) ");
trainee->SetTargetVar("GeneratedEnergyStatus3/(SCRawEnergy*(1+PreShowerOverRaw))");
traineevar->SetTargetVar("abs( targetee - GeneratedEnergyStatus3/(SCRawEnergy*(1+PreShowerOverRaw))) ");
std::vector<std::string> *varsf = new std::vector<std::string>;
varsf->push_back("SCRawEnergy");
varsf->push_back("scEta");
varsf->push_back("scPhi");
varsf->push_back("R9");
varsf->push_back("E5x5Seed/SCRawEnergy");
varsf->push_back("etawidth");
varsf->push_back("phiwidth");
varsf->push_back("NClusters");
varsf->push_back("HoE");
varsf->push_back("rho");
varsf->push_back("vertices");
varsf->push_back("EtaSeed-scEta");
varsf->push_back("atan2(sin(PhiSeed-scPhi),cos(PhiSeed-scPhi))");
varsf->push_back("ESeed/SCRawEnergy");
varsf->push_back("E3x3Seed/ESeed");
varsf->push_back("E5x5Seed/ESeed");
varsf->push_back("see");
varsf->push_back("spp");
// varsf->push_back("sep");
varsf->push_back("EMaxSeed/ESeed");
varsf->push_back("E2ndSeed/ESeed");
varsf->push_back("ETopSeed/ESeed");
varsf->push_back("EBottomSeed/ESeed");
varsf->push_back("ELeftSeed/ESeed");
varsf->push_back("ERightSeed/ESeed");
varsf->push_back("E2x5MaxSeed/ESeed");
varsf->push_back("E2x5TopSeed/ESeed");
varsf->push_back("E2x5BottomSeed/ESeed");
varsf->push_back("E2x5LeftSeed/ESeed");
varsf->push_back("E2x5RightSeed/ESeed");
std::vector<std::string> *varseb = new std::vector<std::string>(*varsf);
std::vector<std::string> *varsee = new std::vector<std::string>(*varsf);
varseb->push_back("IEtaSeed");
varseb->push_back("IPhiSeed");
varseb->push_back("IEtaSeed%5");
varseb->push_back("IPhiSeed%2");
varseb->push_back("(abs(IEtaSeed)<=25)*(IEtaSeed%25) + (abs(IEtaSeed)>25)*((IEtaSeed-25*abs(IEtaSeed)/IEtaSeed)%20)");
varseb->push_back("IPhiSeed%20");
varseb->push_back("EtaCrySeed");
varseb->push_back("PhiCrySeed");
varsee->push_back("PreShowerOverRaw");
for (int i=0; i<varseb->size(); ++i) {
cout << "var " << i << " = " << varseb->at(i) << endl;
traineb->AddInputVar(varseb->at(i));
trainebvar->AddInputVar(varseb->at(i));
}
for (int i=0; i<varsee->size(); ++i) {
cout << "var " << i << " = " << varsee->at(i) << endl;
trainee->AddInputVar(varsee->at(i));
traineevar->AddInputVar(varsee->at(i));
}
ROOT::Cintex::Cintex::Enable();
// TFile *ftmp = new TFile("tmpfile.root","RECREATE");
GBRApply gbrapply;
//Train Barrel Energy Regression
intree->LoadTree(0);
const GBRForest *foresteb = traineb->TrainForest(nTrees);
delete traineb;
//Apply Barrel Energy Regression
intree->LoadTree(0);
gbrapply.ApplyAsFriend(intree, foresteb, *varseb, "targeteb");
//Train Barrel Variance Regression
intree->LoadTree(0);
const GBRForest *forestebvar = trainebvar->TrainForest(nTrees);
delete trainebvar;
//Train Endcap Energy Regression
intree->LoadTree(0);
const GBRForest *forestee = trainee->TrainForest(nTrees);
delete trainee;
//Apply Endcap Energy Regression
intree->LoadTree(0);
gbrapply.ApplyAsFriend(intree, forestee, *varsee, "targetee");
//Train Endcap Variance Regression
intree->LoadTree(0);
const GBRForest *foresteevar = traineevar->TrainForest(nTrees);
delete traineevar;
TString fname;
fname = outWeightFile;
TFile *fout = new TFile(fname,"RECREATE");
cout << "Saving weights to file " << fname << endl;
fout->WriteObject(foresteb,"EBCorrection");
fout->WriteObject(forestebvar,"EBUncertainty");
fout->WriteObject(forestee,"EECorrection");
fout->WriteObject(foresteevar,"EEUncertainty");
fout->WriteObject(varseb, "varlisteb");
fout->WriteObject(varsee, "varlistee");
// ftmp->Close();
// fout->Close();
}
// ******** If option is V10, V11, V12, etc. ******** //
// *** Do training separately for low and high pT *** //
if (optionStr == "V10" || optionStr == "V11") {
GBRTrainer *traineb_lowPt = new GBRTrainer;
GBRTrainer *traineb_highPt = new GBRTrainer;
GBRTrainer *trainebvar_lowPt = new GBRTrainer;
GBRTrainer *trainebvar_highPt = new GBRTrainer;
GBRTrainer *trainee_lowPt = new GBRTrainer;
GBRTrainer *trainee_highPt = new GBRTrainer;
GBRTrainer *traineevar_lowPt = new GBRTrainer;
GBRTrainer *traineevar_highPt = new GBRTrainer;
TTree *intree = 0;
cout << "Training on file " << trainingFile << " with version " << optionChar << endl;
TChain *chainele = new TChain("eleIDdir/T1");
chainele->Add(trainingFile);
chainele->LoadTree(0);
chainele->SetCacheSize(64*1024*1024);
chainele->SetCacheLearnEntries();
intree = chainele;
traineb_lowPt->AddTree(chainele);
trainebvar_lowPt->AddTree(chainele);
trainee_lowPt->AddTree(chainele);
traineevar_lowPt->AddTree(chainele);
traineb_highPt->AddTree(chainele);
trainebvar_highPt->AddTree(chainele);
trainee_highPt->AddTree(chainele);
traineevar_highPt->AddTree(chainele);
TCut traincut_lowPt = "pt>7 && pt<15";
TCut traincut_highPt = "pt>=15";
TCut statusenergycut;
TCut evtcut;
TCut evtcutvar;
//if you want to train also energy variance
evtcut = "event%2==0 ";
evtcutvar = "event%2==1 ";
statusenergycut="(GeneratedEnergyStatus3-GeneratedEnergyStatus1)/GeneratedEnergyStatus3<0.01 && GeneratedEnergyStatus3>=GeneratedEnergyStatus1";
traineb_lowPt->SetTrainingCut(std::string(traincut_lowPt && evtcut && statusenergycut && "abs(eta)<1.479 && mcmatch==1"));
trainee_lowPt->SetTrainingCut(std::string(traincut_lowPt && evtcut && statusenergycut && "abs(eta)>1.479 && abs(eta)<2.5 && mcmatch==1"));
traineb_highPt->SetTrainingCut(std::string(traincut_highPt && evtcut && statusenergycut && "abs(eta)<1.479 && mcmatch==1"));
trainee_highPt->SetTrainingCut(std::string(traincut_highPt && evtcut && statusenergycut && "abs(eta)>1.479 && abs(eta)<2.5&& mcmatch==1"));
//turn this off for now
trainebvar_lowPt->SetTrainingCut(std::string(traincut_lowPt && evtcutvar && statusenergycut && "abs(eta)<1.479 && mcmatch==1"));
traineevar_lowPt->SetTrainingCut(std::string(traincut_lowPt && evtcutvar && statusenergycut && "abs(eta)>1.479 && abs(eta)<2.5 && mcmatch==1"));
trainebvar_highPt->SetTrainingCut(std::string(traincut_highPt && evtcutvar && statusenergycut && "abs(eta)<1.479 && mcmatch==1"));
traineevar_highPt->SetTrainingCut(std::string(traincut_highPt && evtcutvar && statusenergycut && "abs(eta)>1.479 && abs(eta)<2.5&& mcmatch==1"));
const double maxsig = 3.0;
const double shrinkage = 0.1;
traineb_lowPt->SetMinEvents(200);
traineb_lowPt->SetShrinkage(shrinkage);
traineb_lowPt->SetMinCutSignificance(maxsig);
traineb_highPt->SetMinEvents(200);
traineb_highPt->SetShrinkage(shrinkage);
traineb_highPt->SetMinCutSignificance(maxsig);
trainebvar_lowPt->SetMinEvents(200);
trainebvar_lowPt->SetShrinkage(shrinkage);
trainebvar_lowPt->SetMinCutSignificance(maxsig);
trainebvar_highPt->SetMinEvents(200);
trainebvar_highPt->SetShrinkage(shrinkage);
trainebvar_highPt->SetMinCutSignificance(maxsig);
trainee_lowPt->SetMinEvents(200);
trainee_lowPt->SetShrinkage(shrinkage);
trainee_lowPt->SetMinCutSignificance(maxsig);
trainee_highPt->SetMinEvents(200);
trainee_highPt->SetShrinkage(shrinkage);
trainee_highPt->SetMinCutSignificance(maxsig);
traineevar_lowPt->SetMinEvents(200);
traineevar_lowPt->SetShrinkage(shrinkage);
traineevar_lowPt->SetMinCutSignificance(maxsig);
traineevar_highPt->SetMinEvents(200);
traineevar_highPt->SetShrinkage(shrinkage);
traineevar_highPt->SetMinCutSignificance(maxsig);
traineb_lowPt->SetTargetVar("GeneratedEnergyStatus3/SCRawEnergy");
traineb_highPt->SetTargetVar("GeneratedEnergyStatus3/SCRawEnergy");
trainebvar_lowPt->SetTargetVar("abs( targeteb_lowPt - GeneratedEnergyStatus3/SCRawEnergy) ");
trainebvar_highPt->SetTargetVar("abs( targeteb_highPt - GeneratedEnergyStatus3/SCRawEnergy) ");
trainee_lowPt->SetTargetVar("GeneratedEnergyStatus3/(SCRawEnergy*(1+PreShowerOverRaw))");
trainee_highPt->SetTargetVar("GeneratedEnergyStatus3/(SCRawEnergy*(1+PreShowerOverRaw))");
traineevar_lowPt->SetTargetVar("abs( targetee_lowPt - GeneratedEnergyStatus3/(SCRawEnergy*(1+PreShowerOverRaw))) ");
traineevar_highPt->SetTargetVar("abs( targetee_highPt - GeneratedEnergyStatus3/(SCRawEnergy*(1+PreShowerOverRaw))) ");
std::vector<std::string> *varsf = new std::vector<std::string>;
varsf->push_back("SCRawEnergy");
varsf->push_back("scEta");
varsf->push_back("scPhi");
varsf->push_back("R9");
varsf->push_back("E5x5Seed/SCRawEnergy");
varsf->push_back("etawidth");
varsf->push_back("phiwidth");
varsf->push_back("NClusters");
varsf->push_back("HoE");
varsf->push_back("rho");
varsf->push_back("vertices");
varsf->push_back("EtaSeed-scEta");
varsf->push_back("atan2(sin(PhiSeed-scPhi),cos(PhiSeed-scPhi))");
varsf->push_back("ESeed/SCRawEnergy");
varsf->push_back("E3x3Seed/ESeed");
varsf->push_back("E5x5Seed/ESeed");
varsf->push_back("see");
varsf->push_back("spp");
// varsf->push_back("sep");
varsf->push_back("EMaxSeed/ESeed");
varsf->push_back("E2ndSeed/ESeed");
varsf->push_back("ETopSeed/ESeed");
varsf->push_back("EBottomSeed/ESeed");
varsf->push_back("ELeftSeed/ESeed");
varsf->push_back("ERightSeed/ESeed");
varsf->push_back("E2x5MaxSeed/ESeed");
varsf->push_back("E2x5TopSeed/ESeed");
varsf->push_back("E2x5BottomSeed/ESeed");
varsf->push_back("E2x5LeftSeed/ESeed");
varsf->push_back("E2x5RightSeed/ESeed");
std::vector<std::string> *varseb = new std::vector<std::string>(*varsf);
std::vector<std::string> *varsee = new std::vector<std::string>(*varsf);
varseb->push_back("IEtaSeed");
varseb->push_back("IPhiSeed");
varseb->push_back("IEtaSeed%5");
varseb->push_back("IPhiSeed%2");
varseb->push_back("(abs(IEtaSeed)<=25)*(IEtaSeed%25) + (abs(IEtaSeed)>25)*((IEtaSeed-25*abs(IEtaSeed)/IEtaSeed)%20)");
varseb->push_back("IPhiSeed%20");
varseb->push_back("EtaCrySeed");
varseb->push_back("PhiCrySeed");
varsee->push_back("PreShowerOverRaw");
for (int i=0; i<varseb->size(); ++i) {
cout << "var " << i << " = " << varseb->at(i) << endl;
traineb_lowPt->AddInputVar(varseb->at(i));
trainebvar_lowPt->AddInputVar(varseb->at(i));
}
for (int i=0; i<varseb->size(); ++i) {
cout << "var " << i << " = " << varseb->at(i) << endl;
traineb_highPt->AddInputVar(varseb->at(i));
trainebvar_highPt->AddInputVar(varseb->at(i));
}
for (int i=0; i<varsee->size(); ++i) {
cout << "var " << i << " = " << varsee->at(i) << endl;
trainee_lowPt->AddInputVar(varsee->at(i));
traineevar_lowPt->AddInputVar(varsee->at(i));
}
for (int i=0; i<varsee->size(); ++i) {
cout << "var " << i << " = " << varsee->at(i) << endl;
trainee_highPt->AddInputVar(varsee->at(i));
traineevar_highPt->AddInputVar(varsee->at(i));
}
ROOT::Cintex::Cintex::Enable();
// TFile *ftmp = new TFile("tmpfile.root","RECREATE");
GBRApply gbrapply;
//Train Barrel Energy Regression (low pT)
intree->LoadTree(0);
const GBRForest *foresteb_lowPt = traineb_lowPt->TrainForest(nTrees);
delete traineb_lowPt;
//Apply Barrel Energy Regression (low pT)
intree->LoadTree(0);
gbrapply.ApplyAsFriend(intree, foresteb_lowPt, *varseb, "targeteb_lowPt");
//Train Barrel Energy Regression (high pT)
intree->LoadTree(0);
const GBRForest *foresteb_highPt = traineb_highPt->TrainForest(nTrees);
delete traineb_highPt;
//Apply Barrel Energy Regression (high pT)
intree->LoadTree(0);
gbrapply.ApplyAsFriend(intree, foresteb_highPt, *varseb, "targeteb_highPt");
//Train Barrel Variance Regression (low pT)
intree->LoadTree(0);
const GBRForest *forestebvar_lowPt = trainebvar_lowPt->TrainForest(nTrees);
delete trainebvar_lowPt;
//Train Barrel Variance Regression (high pT)
intree->LoadTree(0);
const GBRForest *forestebvar_highPt = trainebvar_highPt->TrainForest(nTrees);
delete trainebvar_highPt;
//Train Endcap Energy Regression (low pT)
intree->LoadTree(0);
const GBRForest *forestee_lowPt = trainee_lowPt->TrainForest(nTrees);
delete trainee_lowPt;
//Apply Endcap Energy Regression
intree->LoadTree(0);
gbrapply.ApplyAsFriend(intree, forestee_lowPt, *varsee, "targetee_lowPt");
//Train Endcap Energy Regression (high pT)
intree->LoadTree(0);
const GBRForest *forestee_highPt = trainee_highPt->TrainForest(nTrees);
delete trainee_highPt;
//Apply Endcap Energy Regression
intree->LoadTree(0);
gbrapply.ApplyAsFriend(intree, forestee_highPt, *varsee, "targetee_highPt");
//Train Endcap Variance Regression (low pT)
intree->LoadTree(0);
const GBRForest *foresteevar_lowPt = traineevar_lowPt->TrainForest(nTrees);
delete traineevar_lowPt;
//Train Endcap Variance Regression (high pT)
intree->LoadTree(0);
const GBRForest *foresteevar_highPt = traineevar_highPt->TrainForest(nTrees);
delete traineevar_highPt;
TString fname;
fname = outWeightFile;
TFile *fout = new TFile(fname,"RECREATE");
cout << "Saving weights to file " << fname << endl;
fout->WriteObject(foresteb_lowPt,"EBCorrection_lowPt");
fout->WriteObject(foresteb_highPt,"EBCorrection_highPt");
fout->WriteObject(forestebvar_lowPt,"EBUncertainty_lowPt");
fout->WriteObject(forestebvar_highPt,"EBUncertainty_highPt");
fout->WriteObject(forestee_lowPt,"EECorrection_lowPt");
fout->WriteObject(forestee_highPt,"EECorrection_highPt");
fout->WriteObject(foresteevar_lowPt,"EEUncertainty_lowPt");
fout->WriteObject(foresteevar_highPt,"EEUncertainty_highPt");
fout->WriteObject(varseb, "varlisteb");
fout->WriteObject(varsee, "varlistee");
// ftmp->Close();
// fout->Close();
}
}