void run(Char_t* data, Long64_t nev = -1, Long64_t offset = 0, Bool_t debug = kFALSE, Int_t runMode = 0, Bool_t isMC = 0,
Int_t centrBin = 0, const char * centrEstimator = "VOM", Int_t useOtherCentralityCut = 0, Int_t trackMin=0, Int_t trackMax=10000,
const char* option = "",TString customSuffix = "", Int_t workers = -1, Bool_t useSingleBin=kTRUE, const char * runList = 0)
{
// runMode:
//
// 0 local
// 1 proof
if (nev < 0)
nev = 1234567890;
InitAndLoadLibs(runMode,workers,debug);
// Create the analysis manager
mgr = new AliAnalysisManager;
// Add ESD handler
AliESDInputHandler* esdH = new AliESDInputHandler;
// Do I need any of this?
esdH->SetInactiveBranches("AliESDACORDE FMD ALIESDTZERO ALIESDZDC AliRawDataErrorLogs CaloClusters Cascades EMCALCells EMCALTrigger ESDfriend Kinks AliESDTZERO ALIESDACORDE MuonTracks TrdTracks");
mgr->SetInputEventHandler(esdH);
if(isMC) {
AliMCEventHandler* handler = new AliMCEventHandler;
handler->SetPreReadMode(AliMCEventHandler::kLmPreRead);
mgr->SetMCtruthEventHandler(handler);
}
// Parse option strings
TString optionStr(option);
// remove SAVE option if set
// This is copied from a macro by Jan. The reason I kept it is that I may want to pass textual options to the new task at some point
Bool_t doSave = kFALSE;
TString optionStr(option);
if (optionStr.Contains("SAVE"))
{
optionStr = optionStr(0,optionStr.Index("SAVE")) + optionStr(optionStr.Index("SAVE")+4, optionStr.Length());
doSave = kTRUE;
}
AliESDtrackCuts * cuts = AliESDtrackCuts::GetStandardITSTPCTrackCuts2010(kFALSE);
TString pathsuffix = "";
if(!useSingleBin) pathsuffix += "_AllCentr";
if (optionStr.Contains("DCA")) {
delete cuts;
// cuts = AliESDtrackCuts::GetStandardITSPureSATrackCuts2009();
cout << ">>>> USING DCA cut" << endl;
cuts = AliESDtrackCuts::GetStandardITSTPCTrackCuts2010();
cuts->SetMaxChi2TPCConstrainedGlobal(); // remove golden cut
pathsuffix+="_DCAcut";
}
if (optionStr.Contains("ITSsa")) {
delete cuts;
cuts = AliESDtrackCuts::GetStandardITSPureSATrackCuts2009();
cout << ">>>> USING ITS sa tracks" << endl;
pathsuffix+="_ITSsa";
}
if (optionStr.Contains("TPC")) {
delete cuts;
cuts = AliESDtrackCuts::GetStandardTPCOnlyTrackCuts();
cout << ">>>> USING TPC only tracks" << endl;
pathsuffix+="_TPC";
}
if(optionStr.Contains("NoElectrons")) pathsuffix +="_NoElectrons";
Bool_t useMCKinematics = isMC;
if (optionStr.Contains("NOMCKIN")) {
cout << ">>>> Ignoring MC kinematics" << endl;
useMCKinematics=kFALSE;
pathsuffix+="_NOMCKIN";
}
// If we are running on grid, we need the alien handler
if (runMode == kMyRunModeGRID) {
// Create and configure the alien handler plugin
gROOT->LoadMacro("CreateAlienHandler.C");
AliAnalysisGrid *alienHandler = CreateAlienHandler(data, runList, pathsuffix.Data(), listToLoad, "full", isMC); // full
if (!alienHandler) {
cout << "Cannot create alien handler" << endl;
exit(1);
}
mgr->SetGridHandler(alienHandler);
}
// Add tasks
// physics selection
gROOT->ProcessLine(".L $ALICE_ROOT/OADB/macros/AddTaskPhysicsSelection.C");
physicsSelectionTask = AddTaskPhysicsSelection(isMC);
//PID
gROOT->LoadMacro("$ALICE_ROOT/ANALYSIS/macros/AddTaskPIDResponse.C");
AddTaskPIDResponse(isMC);
// // PID QA:
// gROOT->LoadMacro("$ALICE_ROOT/ANALYSIS/macros/AddTaskPIDqa.C");
// AddTaskPIDqa();
// Centrality
gROOT->LoadMacro("$ALICE_ROOT/OADB/macros/AddTaskCentrality.C");
AliCentralitySelectionTask *taskCentr = AddTaskCentrality();
// OBSOLETE
const char * file1 = "$ALICE_ROOT/ANALYSIS/macros/test_AliCentralityBy1D.root";
const char * file2 = "$ALICE_ROOT/ANALYSIS/macros/test_AliCentralityByFunction.root";
// END of OBSOLETE
if(isMC) taskCentr-> SetMCInput();
taskCentr->SetPass(2);
// Create my own centrality selector
AliAnalysisMultPbCentralitySelector * centrSelector = new AliAnalysisMultPbCentralitySelector();
centrSelector->SetIsMC(isMC);
centrSelector->SetCentrTaskFiles(file1,file2); // for bookkeping only
centrSelector->SetCentralityBin(centrBin);
if (!useSingleBin) centrSelector->SetCentralityBin(0); // FIXME: ok?
centrSelector->SetCentralityEstimator(centrEstimator);
if(useOtherCentralityCut == 1){
cout << "Setting centrality by MULT" << endl;
centrSelector->SetUseMultRange();
centrSelector->SetMultRange(trackMin,trackMax);
}
if(useOtherCentralityCut == 2){
cout << "Setting centrality by V0" << endl;
centrSelector->SetUseV0Range();
centrSelector->SetMultRange(trackMin,trackMax);
}
if(useOtherCentralityCut == 3){
cout << "Setting centrality by SPD outer" << endl;
centrSelector->SetUseSPDOuterRange();
centrSelector->SetMultRange(trackMin,trackMax);
}
// load my task
if (useSingleBin) {
gROOT->ProcessLine(".L $ALICE_ROOT/PWG0/multPbPb/AddTaskMultPbPbTracks.C");
AliAnalysisTaskMultPbTracks * task = AddTaskMultPbPbTracks("multPbPbtracks.root", cuts, centrSelector);
task->SetIsMC(useMCKinematics);
task->SetOfflineTrigger(AliVEvent::kMB);
if(optionStr.Contains("TPC")) task->SetTPCOnly();
if(optionStr.Contains("NoElectrons")) task->RejectElectrons(kTRUE);
if(useMCKinematics) task->GetHistoManager()->SetSuffix("MC");
if(customSuffix!=""){
cout << "Setting custom suffix: " << customSuffix << endl;
task->GetHistoManager()->SetSuffix(customSuffix);
}
} else {
gROOT->ProcessLine(".L $ALICE_ROOT/PWG0/multPbPb/AddTaskMultPbPbTracksAllCentrality.C");
centrSelector->SetUseV0Range(kTRUE);
Int_t ncentr = 11;
const Float_t minCentr[] = {0 ,79 ,239,559 ,1165,2135,3555,5525,8213 ,12191,15079};
const Float_t maxCentr[] = {79,239,559,1165,2135,3555,5525,8213,12191,15079,21000};
AliAnalysisTaskMultPbTracks ** tasks = AddTaskMultPbPbTracksAllCentrality("multPbPbtracks.root", cuts, centrSelector, ncentr,minCentr,maxCentr);
for(Int_t icentr = 0; icentr < ncentr; icentr++){
tasks[icentr]->Print();
cout << "MC KINEMATICS:" << useMCKinematics << endl;
tasks[icentr]->SetIsMC(useMCKinematics);
tasks[icentr]->SetOfflineTrigger(AliVEvent::kMB);
if(optionStr.Contains("TPC")) tasks[icentr]->SetTPCOnly();
if(optionStr.Contains("NoElectrons")) task[icentr]->RejectElectrons(kTRUE);
if(useMCKinematics) tasks[icentr]->GetHistoManager()->SetSuffix("MC");
if(customSuffix!=""){
cout << "Setting custom suffix: " << customSuffix+long(icentr) << endl;
tasks[icentr]->GetHistoManager()->SetSuffix(customSuffix+long(icentr));
}
}
}
// Init and run the analy
if (!mgr->InitAnalysis()) return;
mgr->PrintStatus();
if (runMode == kMyRunModeLocal ) {
// If running in local mode, create chain of ESD files
cout << "RUNNING LOCAL, CHAIN" << endl;
TChain * chain = GetAnalysisChain(data);
chain->Print();
mgr->StartAnalysis("local",chain,nev);
} else if (runMode == kMyRunModeProofLite) {
TChain * chain = GetAnalysisChain(data);
mgr->StartAnalysis("proof",chain,nev);
}
else if (runMode == kMyRunModeCAF) {
mgr->StartAnalysis("proof",TString(data)+"#esdTree",nev);
} else if (runMode == kMyRunModeGRID) {
mgr->StartAnalysis("grid");
} else {
cout << "ERROR: unknown run mode" << endl;
}
if (!useOtherCentralityCut) {
pathsuffix = pathsuffix + "_" + centrEstimator + "_bin_"+long(centrBin);
} else if(useOtherCentralityCut==1){
pathsuffix = pathsuffix + "_TrackRange_" + long(trackMin) + "_" + long(trackMax);
} else if(useOtherCentralityCut==2){
pathsuffix = pathsuffix + "_V0Range_" + long(trackMin) + "_" + long(trackMax);
} else if(useOtherCentralityCut==3){
pathsuffix = pathsuffix + "_SPDOutRange_" + long(trackMin) + "_" + long(trackMax);
}
pathsuffix += customSuffix;
if (doSave) MoveOutput(data, pathsuffix.Data());
}
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();
}
}
