void process(const std::vector<std::string>& inputFiles, const std::string& name, const std::string& outputFile) {
TChain* signal = loadChain(inputFiles, "signal");
TChain* background = loadChain(inputFiles, "background");
TFile* output = TFile::Open(outputFile.c_str(), "recreate");
TMVA::Factory* factory = new TMVA::Factory(name.c_str(), output, "V");
factory->AddSignalTree(signal, 1.);
factory->AddBackgroundTree(background, 1.);
//{
//factory->AddVariable("lightJet1p2_Pt");
//factory->AddVariable("leptonic_B_Pt");
//factory->AddVariable("leptonic_Top_Pt");
//factory->AddVariable("leptonic_Top_M");
//factory->AddVariable("hadronic_B_Pt");
//factory->AddVariable("hadronic_W_M");
//factory->AddVariable("hadronic_Top_Pt");
//factory->AddVariable("hadronic_Top_M");
//factory->AddVariable("delta_R_tops");
//factory->AddVariable("delta_R_lightjets");
//factory->AddVariable("leptonic_B_CSV");
//factory->AddVariable("hadronic_B_CSV");
//}
// chi^2 style
{
factory->AddVariable("leptonic_Top_M");
factory->AddVariable("hadronic_W_M");
factory->AddVariable("hadronic_Top_M");
factory->AddVariable("ht_fraction");
}
factory->SetWeightExpression("weight");
factory->PrepareTrainingAndTestTree("", "", "V:VerboseLevel=Info:nTrain_Signal=100000:nTrain_Background=100000:nTest_Signal=100000:nTest_Background=100000");
factory->BookMethod(TMVA::Types::kBDT, "BDT", "V:BoostType=AdaBoost:nCuts=20:VarTransform=D");
factory->BookMethod(TMVA::Types::kMLP, "NN", "V:VarTransform=D");
//factory->BookMethod(TMVA::Types::kPDERS, "PDERS", "V");
factory->TrainAllMethods();
factory->TestAllMethods();
factory->EvaluateAllMethods();
output->Close();
delete output;
delete signal;
delete background;
}
void test_train(TString signalName = "WW",
TString bkgName = "DY")
{
TFile *outFile = new TFile("myAnalysisFile.root","RECREATE");
TMVA::Factory *factory = new TMVA::Factory(signalName, outFile,"");
TString directory = "../rootFiles/SF/MediumIDTighterIP/";
//signalName = directory + signalName;
//defining WW signal
TFile *MySignalFile = new TFile("../rootFiles/SF/MediumIDTighterIP/WW.root","READ");
TTree* sigTree = (TTree*)MySignalFile->Get("nt");
factory->AddSignalTree(sigTree,1);
//defining DY background
TFile *MyBkgFile = new TFile("../rootFiles/SF/MediumIDTighterIP/DY.root","READ");
TTree* bkgTree = (TTree*)MyBkgFile->Get("nt");
factory->AddBackgroundTree(bkgTree,1);
factory->SetWeightExpression("baseW");
//************************************ FACTORY
factory->AddVariable("fullpmet");
factory->AddVariable("trkpmet");
factory->AddVariable("ratioMet");
factory->AddVariable("ptll");
factory->AddVariable("mth");
factory->AddVariable("jetpt1");
factory->AddVariable("ptWW");
factory->AddVariable("dphilljet");
factory->AddVariable("dphillmet");
factory->AddVariable("dphijet1met");
factory->AddVariable("nvtx");
factory->PrepareTrainingAndTestTree("",500,500,500,500);
cout<<"I've prepared trees"<<endl;
//factory->BookMethod(TMVA::Types::kFisher, "Fisher","");
factory->BookMethod(TMVA::Types::kBDT, "BDT","");
cout<<"I've booked method"<<endl;
factory->TrainAllMethods();
factory->TestAllMethods();
cout<<"I've tested all methods"<<endl;
factory->EvaluateAllMethods();
cout<<"I've evaluated all methods"<<endl;
}
//------------------------------------------------------------------------------
// MVATrain
//------------------------------------------------------------------------------
void MVATrain(TString signal)
{
TFile* outputfile = TFile::Open(trainingdir + signal + ".root", "recreate");
// Factory
//----------------------------------------------------------------------------
TMVA::Factory* factory = new TMVA::Factory(signal, outputfile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification");
// Get the trees
//----------------------------------------------------------------------------
_mctree.clear();
AddProcess("signal", signal);
AddProcess("background", "HZJ_HToWW_M125");
AddProcess("background", "ggZH_HToWW_M125");
// AddProcess("background", "14_HZ");
// AddProcess("background", "10_HWW");
// AddProcess("background", "06_WW");
// AddProcess("background", "02_WZTo3LNu");
// AddProcess("background", "03_ZZ");
// AddProcess("background", "11_Wg");
// AddProcess("background", "07_ZJets");
// AddProcess("background", "09_TTV");
// AddProcess("background", "05_ST");
// AddProcess("background", "00_Fakes");
Double_t weight = 1.0;
factory->AddSignalTree(_signaltree, weight);
for (UInt_t i=0; i<_mctree.size(); i++) factory->AddBackgroundTree(_mctree[i], weight);
factory->SetWeightExpression("eventW");
// Add variables
//----------------------------------------------------------------------------
// Be careful with the order: it must be respected at the reading step
// factory->AddVariable("<var1>+<var2>", "pretty title", "unit", 'F');
// factory->AddVariable("channel", "", "", 'F');
factory->AddVariable("metPfType1", "", "", 'F');
factory->AddVariable("m2l", "", "", 'F');
// factory->AddVariable("njet", "", "", 'F');
// factory->AddVariable("nbjet20cmvav2l", "", "", 'F');
factory->AddVariable("lep1pt", "", "", 'F');
factory->AddVariable("lep2pt", "", "", 'F');
// factory->AddVariable("jet1pt", "", "", 'F');
factory->AddVariable("jet2pt", "", "", 'F');
factory->AddVariable("mtw1", "", "", 'F');
factory->AddVariable("dphill", "", "", 'F');
factory->AddVariable("dphilep1jet1", "", "", 'F');
// factory->AddVariable("dphilep1jet2", "", "", 'F');
// factory->AddVariable("dphilmet1", "", "", 'F');
// factory->AddVariable("dphilep2jet1", "", "", 'F');
// factory->AddVariable("dphilep2jet2", "", "", 'F');
// factory->AddVariable("dphilmet2", "", "", 'F');
// factory->AddVariable("dphijj", "", "", 'F');
// factory->AddVariable("dphijet1met", "", "", 'F');
// factory->AddVariable("dphijet2met", "", "", 'F');
factory->AddVariable("dphillmet", "", "", 'F');
// Preselection cuts and preparation
//----------------------------------------------------------------------------
factory->PrepareTrainingAndTestTree("", ":nTrain_Signal=0:nTest_Signal=0:nTrain_Background=0:nTest_Background=0:SplitMode=Alternate:MixMode=Random:!V");
// Book MVA
//----------------------------------------------------------------------------
factory->BookMethod(TMVA::Types::kMLP, "MLP",
"H:!V:NeuronType=sigmoid:VarTransform=N:NCycles=600:HiddenLayers=25,10:TestRate=5:!UseRegulator");
// Train, test and evaluate MVA
//----------------------------------------------------------------------------
factory->TrainAllMethods(); // Train using the set of training events
factory->TestAllMethods(); // Evaluate using the set of test events
factory->EvaluateAllMethods(); // Evaluate and compare performance
// Save the output
//----------------------------------------------------------------------------
outputfile->Close();
delete factory;
}
void TMVAClassification( TString fname = "./tmva_class_example.root")
{
// The explicit loading of the shared libTMVA is done in TMVAlogon.C, defined in .rootrc
// if you use your private .rootrc, or run from a different directory, please copy the
// corresponding lines from .rootrc
// methods to be processed can be given as an argument; use format:
//
// mylinux~> root -l TMVAClassification.C\(\"myMethod1,myMethod2,myMethod3\"\)
//
// if you like to use a method via the plugin mechanism, we recommend using
//
// mylinux~> root -l TMVAClassification.C\(\"P_myMethod\"\)
// (an example is given for using the BDT as plugin (see below),
// but of course the real application is when you write your own
// method based)
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// to get access to the GUI and all tmva macros
TString tmva_dir(TString(gRootDir) + "/tmva");
if(gSystem->Getenv("TMVASYS"))
tmva_dir = TString(gSystem->Getenv("TMVASYS"));
gROOT->SetMacroPath(tmva_dir + "/test/:" + gROOT->GetMacroPath() );
gROOT->ProcessLine(".L TMVAGui.C");
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
Use["KNN"] = 1; // k-nearest neighbour method
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVAClassification" << std::endl;
// --------------------------------------------------------------------------------------------------
// --- Here the preparation phase begins
// Create a ROOT output file where TMVA will store ntuples, histograms, etc.
TString outfileName( "TMVA.root" );
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
// Create the factory object. Later you can choose the methods
// whose performance you'd like to investigate. The factory is
// the only TMVA object you have to interact with
//
// The first argument is the base of the name of all the
// weightfiles in the directory weight/
//
// The second argument is the output file for the training results
// All TMVA output can be suppressed by removing the "!" (not) in
// front of the "Silent" argument in the option string
TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I:AnalysisType=Classification" );
// If you wish to modify default settings
// (please check "src/Config.h" to see all available global options)
// (TMVA::gConfig().GetVariablePlotting()).fTimesRMS = 8.0;
// (TMVA::gConfig().GetIONames()).fWeightFileDir = "myWeightDirectory";
// Define the input variables that shall be used for the MVA training
// note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)"
// [all types of expressions that can also be parsed by TTree::Draw( "expression" )]
factory->AddVariable( "pt_eH", 'D' );
factory->AddVariable( "max(pt_jet_eH,pt_eH)", 'D' );
factory->AddVariable( "njets", 'I' );
// You can add so-called "Spectator variables", which are not used in the MVA training,
// but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the
// input variables, the response values of all trained MVAs, and the spectator variables
/// factory->AddSpectator( "spec1 := var1*2", "Spectator 1", "units", 'F' );
/// factory->AddSpectator( "spec2 := var1*3", "Spectator 2", "units", 'F' );
// Read training and test data
// (it is also possible to use ASCII format as input -> see TMVA Users Guide)
TFile *input(0);
if (gSystem->AccessPathName( fname )){ // file does not exist in local directory
gSystem->Exec("wget http://root.cern.ch/files/tmva_class_example.root");
fname = "./tmva_class_example.root";
}else{
input= TFile::Open( fname );
}
if (!input) {
std::cout << "ERROR: could not open data file " << fname << std::endl;
exit(1);
}
std::cout << "--- TMVAClassification : Using input file: " << input->GetName() << std::endl;
// --- Register the training and test trees
TTree *inputTree = (TTree*)input->Get("FakeTreeSig");
TTree *background = (TTree*)input->Get("FakeTreeBG");
// global event weights per tree (see below for setting event-wise weights)
Double_t signalWeight = 1.0;
Double_t backgroundWeight = 1.0;
// cuts for signal and background
//~ TCut signalCut = "selected==1 && id_iso_eleH==1";
//~ TCut backgroundCut = "selected==1 && id_iso_eleH==0";
//~
//~ std::cout << " THe signal cut is " << signalCut.GetTitle() << " bg cut is " << backgroundCut.GetTitle() << std::endl;
Int_t num_pass = inputTree->GetEntries();
Int_t num_fail = background->GetEntries();
std::cout << num_pass << " " << num_fail << std::endl;
// You can add an arbitrary number of signal or background trees
factory->AddSignalTree ( inputTree, 1.0 );
factory->AddBackgroundTree( background, 1.0 );
factory->SetWeightExpression( "weight" );
//factory->SetInputTrees( inputTree, signalCut, backgroundCut );
// To give different trees for training and testing, do as follows:
// factory->AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" );
// factory->AddSignalTree( signalTestTree, signalTestWeight, "Test" );
// Use the following code instead of the above two or four lines to add signal and background
// training and test events "by hand"
// NOTE that in this case one should not give expressions (such as "var1+var2") in the input
// variable definition, but simply compute the expression before adding the event
//
// // --- begin ----------------------------------------------------------
// std::vector<Double_t> vars( 4 ); // vector has size of number of input variables
// Float_t treevars[4], weight;
//
// // Signal
// for (UInt_t ivar=0; ivar<4; ivar++) signal->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
// for (UInt_t i=0; i<signal->GetEntries(); i++) {
// signal->GetEntry(i);
// for (UInt_t ivar=0; ivar<4; ivar++) vars[ivar] = treevars[ivar];
// // add training and test events; here: first half is training, second is testing
// // note that the weight can also be event-wise
// if (i < signal->GetEntries()/2.0) factory->AddSignalTrainingEvent( vars, signalWeight );
// else factory->AddSignalTestEvent ( vars, signalWeight );
// }
//
// // Background (has event weights)
// background->SetBranchAddress( "weight", &weight );
// for (UInt_t ivar=0; ivar<4; ivar++) background->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
// for (UInt_t i=0; i<background->GetEntries(); i++) {
// background->GetEntry(i);
// for (UInt_t ivar=0; ivar<4; ivar++) vars[ivar] = treevars[ivar];
// // add training and test events; here: first half is training, second is testing
// // note that the weight can also be event-wise
// if (i < background->GetEntries()/2) factory->AddBackgroundTrainingEvent( vars, backgroundWeight*weight );
// else factory->AddBackgroundTestEvent ( vars, backgroundWeight*weight );
// }
// --- end ------------------------------------------------------------
//
// --- end of tree registration
// Set individual event weights (the variables must exist in the original TTree)
// for signal : factory->SetSignalWeightExpression ("weight1*weight2");
// for background: factory->SetBackgroundWeightExpression("weight1*weight2");
// Apply additional cuts on the signal and background samples (can be different)
TCut mycuts = "selected==1"; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
TCut mycutb = "selected==1"; // for example: TCut mycutb = "abs(var1)<0.5";
// Tell the factory how to use the training and testing events
//
// If no numbers of events are given, half of the events in the tree are used
// for training, and the other half for testing:
// factory->PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" );
// To also specify the number of testing events, use:
// factory->PrepareTrainingAndTestTree( mycut,
// "NSigTrain=3000:NBkgTrain=3000:NSigTest=3000:NBkgTest=3000:SplitMode=Random:!V" );
std::stringstream indexes;
indexes.str("");
indexes << "nTrain_Signal=" << num_pass << ":nTrain_Background=" << num_fail << ":SplitMode=Random:NormMode=None:!V";
std::string input_opt=indexes.str();
std::cout << "Options are " << input_opt << std::endl;
factory->PrepareTrainingAndTestTree( mycuts, mycutb, input_opt);
//"nTrain_Signal="+num_pass+":nTrain_Background="+num_fail+":SplitMode=Random:NormMode=None:!V" );
// ---- Book MVA methods
//
// Please lookup the various method configuration options in the corresponding cxx files, eg:
// src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html
// it is possible to preset ranges in the option string in which the cut optimisation should be done:
// "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable
// Cut optimisation
// K-Nearest Neighbour classifier (KNN)
if (Use["KNN"])
factory->BookMethod( TMVA::Types::kKNN, "KNN",
"H:nkNN=50:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" );
// For an example of the category classifier usage, see: TMVAClassificationCategory
// --------------------------------------------------------------------------------------------------
// ---- Now you can optimize the setting (configuration) of the MVAs using the set of training events
// factory->OptimizeAllMethods("SigEffAt001","Scan");
// factory->OptimizeAllMethods("ROCIntegral","GA");
// --------------------------------------------------------------------------------------------------
// ---- Now you can tell the factory to train, test, and evaluate the MVAs
// Train MVAs using the set of training events
factory->TrainAllMethods();
// ---- Evaluate all MVAs using the set of test events
//factory->TestAllMethods();
// ----- Evaluate and compare performance of all configured MVAs
// factory->EvaluateAllMethods();
// --------------------------------------------------------------
// Save the output
outputFile->Close();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVAClassification is done!" << std::endl;
delete factory;
// Launch the GUI for the root macros
if (!gROOT->IsBatch()) TMVAGui( outfileName );
}
void classifyBDT(TString inputVariables = "trainingVars.txt",
TString signalName = "/mnt/hscratch/dabercro/skims2/BDT_Signal.root",
TString backName = "/mnt/hscratch/dabercro/skims2/BDT_Background.root") {
TMVA::Tools::Instance();
std::cout << "==> Start TMVAClassification" << std::endl;
// Create a ROOT output file where TMVA will store ntuples, histograms, etc.
TString outfileName( "TMVA/TMVA.root" );
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
TMVA::Factory *factory = new TMVA::Factory( "TMVAClassificationCategory", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;N" );
// A very simple MVA (feel free to uncomment and comment what you like) => as a rule of thumb 10-20 variables is where people start to get worried about total number
ifstream configFile;
configFile.open(inputVariables.Data());
TString tempFormula;
configFile >> tempFormula; // Is the name of the BDT
while(!configFile.eof()){
configFile >> tempFormula;
if(tempFormula != ""){
factory->AddVariable(tempFormula,'F');
}
}
TString lVars;
// TCut lCut = "jet1qg2<2.&&jet1pt>250.&&jet1pullAngle>-5.";// < 10 && jet1mass_m2 > 60 && jet1mass_m2 < 120";
// TCut lCut = "passZ > 3 && fjet1pt > 250 && fjet1MassPruned < 120 && fatjetid < 2";
TCut lCut = "abs(fjet1PartonId)!=24&&abs(fjet1PartonId)!=23";
// std::string lEventCut = "event % 2 == 1";
// lCut += lEventCut.c_str();
// TCut lSCut = "passT > 0 && fjet1pt > 250 && fjet1MassPruned < 120 && abs(fjet1PartonId) == 24&& fatjetid < 2";
TCut lSCut = "abs(fjet1PartonId)==24||abs(fjet1PartonId)==23";
// lSCut += lEventCut.c_str();
TCut cleanCut = "fjet1QGtagSub2 > -10 && fjet1PullAngle > -4 && abs(fjet1pt/fjet1MassTrimmed)<200 && abs(fjet1pt/fjet1MassPruned)<200";
TFile *lSAInput = TFile::Open(signalName);
TTree *lSASignal = (TTree*)lSAInput ->Get("DMSTree");
TFile *lSBInput = TFile::Open(backName);
TTree *lSBSignal = (TTree*)lSBInput ->Get("DMSTree");
Double_t lSWeight = 1.0;
Double_t lBWeight = 1.0;
gROOT->cd( outfileName+TString(":/") );
factory->AddSignalTree ( lSASignal, lSWeight );
gROOT->cd( outfileName+TString(":/") );
factory->AddBackgroundTree( lSBSignal, lBWeight );
factory->SetWeightExpression("weight");
std::stringstream pSignal,pBackground;
pSignal << "nTrain_Signal="<< lSASignal->GetEntries() << ":nTrain_Background=" << lSBSignal->GetEntries();
// factory->PrepareTrainingAndTestTree( lSCut, lCut,(pSignal.str()+":SplitMode=Block:NormMode=NumEvents:!V").c_str() );
factory->PrepareTrainingAndTestTree(lSCut&&cleanCut,lCut&&cleanCut,"nTrain_Signal=0:nTrain_Background=0:SplitMode=Alternate:NormMode=NumEvents:!V");
std::string lName = "alpha_VBF";
TString lBDTDef = "!H:!V:NTrees=400:BoostType=Grad:Shrinkage=0.1:UseBaggedGrad=F:nCuts=2000:NNodesMax=10000:MaxDepth=5:UseYesNoLeaf=F:nEventsMin=200";
// TString lBDTDef = "!H:!V:NTrees=400:BoostType=Grad:Shrinkage=0.1:UseBaggedGrad=F:nCuts=2000:MaxDepth=5:UseYesNoLeaf=F:MinNodeSize=0.086:NegWeightTreatment=IgnoreNegWeightsInTraining";
factory->BookMethod(TMVA::Types::kBDT,"BDT_simple_alpha",lBDTDef);
factory->TrainAllMethods();
factory->TestAllMethods();
factory->EvaluateAllMethods();
outputFile->Close();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVAClassification is done!" << std::endl;
delete factory;
//if (!gROOT->IsBatch()) TMVAGui( outfileName );
//TString lBDTDef = "!H:!V:NTrees=100:BoostType=Grad:Shrinkage=0.10:UseBaggedGrad=F:nCuts=2000:NNodesMax=10000:MaxDepth=3:SeparationType=GiniIndex";
}
void TMVARegression( TString myMethodList = "" )
{
// The explicit loading of the shared libTMVA is done in TMVAlogon.C, defined in .rootrc
// if you use your private .rootrc, or run from a different directory, please copy the
// corresponding lines from .rootrc
// methods to be processed can be given as an argument; use format:
//
// mylinux~> root -l TMVARegression.C\(\"myMethod1,myMethod2,myMethod3\"\)
//
//---------------------------------------------------------------
// 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 TMVARegression" << 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;
}
}
// --------------------------------------------------------------------------------------------------
// --- Here the preparation phase begins
// Create a new root output file
TString outfileName( "TMVAReg.root" );
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
// Create the factory object. Later you can choose the methods
// whose performance you'd like to investigate. The factory will
// then run the performance analysis for you.
//
// The first argument is the base of the name of all the
// weightfiles in the directory weight/
//
// The second argument is the output file for the training results
// All TMVA output can be suppressed by removing the "!" (not) in
// front of the "Silent" argument in the option string
TMVA::Factory *factory = new TMVA::Factory( "TMVARegression", outputFile,
"!V:!Silent:Color:DrawProgressBar" );
// If you wish to modify default settings
// (please check "src/Config.h" to see all available global options)
// (TMVA::gConfig().GetVariablePlotting()).fTimesRMS = 8.0;
// (TMVA::gConfig().GetIONames()).fWeightFileDir = "myWeightDirectory";
// Define the input variables that shall be used for the MVA training
// note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)"
// [all types of expressions that can also be parsed by TTree::Draw( "expression" )]
factory->AddVariable( "var1", "Variable 1", "units", 'F' );
factory->AddVariable( "var2", "Variable 2", "units", 'F' );
// You can add so-called "Spectator variables", which are not used in the MVA training,
// but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the
// input variables, the response values of all trained MVAs, and the spectator variables
factory->AddSpectator( "spec1:=var1*2", "Spectator 1", "units", 'F' );
factory->AddSpectator( "spec2:=var1*3", "Spectator 2", "units", 'F' );
// Add the variable carrying the regression target
factory->AddTarget( "fvalue" );
// It is also possible to declare additional targets for multi-dimensional regression, ie:
// -- factory->AddTarget( "fvalue2" );
// BUT: this is currently ONLY implemented for MLP
// Read training and test data (see TMVAClassification for reading ASCII files)
// load the signal and background event samples from ROOT trees
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 << "--- TMVARegression : Using input file: " << input->GetName() << std::endl;
// --- Register the regression tree
TTree *regTree = (TTree*)input->Get("TreeR");
// global event weights per tree (see below for setting event-wise weights)
Double_t regWeight = 1.0;
// You can add an arbitrary number of regression trees
factory->AddRegressionTree( regTree, regWeight );
// This would set individual event weights (the variables defined in the
// expression need to exist in the original TTree)
factory->SetWeightExpression( "var1", "Regression" );
// Apply additional cuts on the signal and background samples (can be different)
TCut mycut = ""; // for example: TCut mycut = "abs(var1)<0.5 && abs(var2-0.5)<1";
// tell the factory to use all remaining events in the trees after training for testing:
factory->PrepareTrainingAndTestTree( mycut,
"nTrain_Regression=0:nTest_Regression=0:SplitMode=Random:NormMode=NumEvents:!V" );
// If no numbers of events are given, half of the events in the tree are used
// for training, and the other half for testing:
// factory->PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" );
// ---- Book MVA methods
//
// please lookup the various method configuration options in the corresponding cxx files, eg:
// src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html
// it is possible to preset ranges in the option string in which the cut optimisation should be done:
// "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable
// PDE - RS method
if (Use["PDERS"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERS",
"!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=40:NEventsMax=60:VarTransform=None" );
// And the options strings for the MinMax and RMS methods, respectively:
// "!H:!V:VolumeRangeMode=MinMax:DeltaFrac=0.2:KernelEstimator=Gauss:GaussSigma=0.3" );
// "!H:!V:VolumeRangeMode=RMS:DeltaFrac=3:KernelEstimator=Gauss:GaussSigma=0.3" );
if (Use["PDEFoam"])
factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam",
"!H:!V:MultiTargetRegression=F:TargetSelection=Mpv:TailCut=0.001:VolFrac=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Compress=T:Kernel=None:Nmin=10:VarTransform=None" );
// K-Nearest Neighbour classifier (KNN)
if (Use["KNN"])
factory->BookMethod( TMVA::Types::kKNN, "KNN",
"nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" );
// Linear discriminant
if (Use["LD"])
factory->BookMethod( TMVA::Types::kLD, "LD",
"!H:!V:VarTransform=None" );
// Function discrimination analysis (FDA) -- test of various fitters - the recommended one is Minuit (or GA or SA)
if (Use["FDA_MC"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MC",
"!H:!V:Formula=(0)+(1)*x0+(2)*x1:ParRanges=(-100,100);(-100,100);(-100,100):FitMethod=MC:SampleSize=100000:Sigma=0.1:VarTransform=D" );
if (Use["FDA_GA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options) .. the formula of this example is good for parabolas
factory->BookMethod( TMVA::Types::kFDA, "FDA_GA",
"!H:!V:Formula=(0)+(1)*x0+(2)*x1:ParRanges=(-100,100);(-100,100);(-100,100):FitMethod=GA:PopSize=100:Cycles=3:Steps=30:Trim=True:SaveBestGen=1:VarTransform=Norm" );
if (Use["FDA_MT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MT",
"!H:!V:Formula=(0)+(1)*x0+(2)*x1:ParRanges=(-100,100);(-100,100);(-100,100);(-10,10):FitMethod=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=2:UseImprove:UseMinos:SetBatch" );
if (Use["FDA_GAMT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_GAMT",
"!H:!V:Formula=(0)+(1)*x0+(2)*x1:ParRanges=(-100,100);(-100,100);(-100,100):FitMethod=GA:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:Cycles=1:PopSize=5:Steps=5:Trim" );
// Neural network (MLP)
if (Use["MLP"])
factory->BookMethod( TMVA::Types::kMLP, "MLP", "!H:!V:VarTransform=Norm:NeuronType=tanh:NCycles=20000:HiddenLayers=N+20:TestRate=6:TrainingMethod=BFGS:Sampling=0.3:SamplingEpoch=0.8:ConvergenceImprove=1e-6:ConvergenceTests=15:!UseRegulator" );
// Support Vector Machine
if (Use["SVM"])
factory->BookMethod( TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm" );
// Boosted Decision Trees
if (Use["BDT"])
factory->BookMethod( TMVA::Types::kBDT, "BDT",
"!H:!V:NTrees=100:MinNodeSize=1.0%:BoostType=AdaBoostR2:SeparationType=RegressionVariance:nCuts=20:PruneMethod=CostComplexity:PruneStrength=30" );
if (Use["BDTG"])
factory->BookMethod( TMVA::Types::kBDT, "BDTG",
"!H:!V:NTrees=1000::BoostType=Grad:Shrinkage=0.1:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=3:MaxDepth=4" );
// --------------------------------------------------------------------------------------------------
// ---- Now you can tell the factory to train, test, and evaluate the MVAs
// Train MVAs using the set of training events
factory->TrainAllMethods();
// ---- Evaluate all MVAs using the set of test events
factory->TestAllMethods();
// ----- Evaluate and compare performance of all configured MVAs
factory->EvaluateAllMethods();
// --------------------------------------------------------------
// Save the output
outputFile->Close();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVARegression is done!" << std::endl;
delete factory;
// Launch the GUI for the root macros
if (!gROOT->IsBatch()) TMVARegGui( outfileName );
}
void TMVAClassificationElecTau(std::string ordering_ = "Pt", std::string bkg_ = "qqH115vsWZttQCD") {
TMVA::Tools::Instance();
TString outfileName( "TMVAElecTau"+ordering_+"Ord_"+bkg_+".root" );
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
TMVA::Factory *factory = new TMVA::Factory( "TMVAClassificationElecTau"+ordering_+"Ord_"+bkg_, outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D" );
factory->AddVariable( "pt1", "pT-tag1", "GeV/c" , 'F' );
factory->AddVariable( "pt2", "pT-tag2", "GeV/c" , 'F' );
factory->AddVariable( "Deta","|y-tag1 - y-tag2|","" , 'F' );
//factory->AddVariable( "opposite:=abs(eta1*eta2)/eta1/eta2","sign1*sign2","" , 'F' );
//factory->AddVariable( "Dphi", "#Delta#phi" ,"" , 'F' );
factory->AddVariable( "Mjj", "M(tag1,tag2)", "GeV/c^{2}" , 'F' );
factory->AddSpectator( "eta1", "#eta_{tag1}" , 'F' );
factory->AddSpectator( "eta2", "#eta_{tag2}" , 'F' );
factory->SetWeightExpression( "sampleWeight" );
TString fSignalName = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/ElecTauStream2011/nTupleVBFH115-powheg-PUS1_Open_ElecTauStream.root";
TString fBackgroundNameDYJets = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/ElecTauStream2011/nTupleZjets-alpgen-PUS1_Open_ElecTauStream.root";
TString fBackgroundNameWJets = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/ElecTauStream2011/nTupleWJets-madgraph-PUS1_Open_ElecTauStream.root";
TString fBackgroundNameQCD = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/ElecTauStream2011/nTupleQCD_Open_ElecTauStream.root";
TString fBackgroundNameTTbar = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/ElecTauStream2011/nTupleTTJets-madgraph-PUS1_Open_ElecTauStream.root";
TFile *fSignal(0);
TFile *fBackgroundDYJets(0);
TFile *fBackgroundWJets(0);
TFile *fBackgroundQCD(0);
TFile *fBackgroundTTbar(0);
fSignal = TFile::Open( fSignalName );
fBackgroundDYJets = TFile::Open( fBackgroundNameDYJets );
fBackgroundWJets = TFile::Open( fBackgroundNameWJets );
fBackgroundQCD = TFile::Open( fBackgroundNameQCD );
fBackgroundTTbar = TFile::Open( fBackgroundNameTTbar );
if(!fSignal || !fBackgroundDYJets || !fBackgroundWJets || !fBackgroundQCD || !fBackgroundTTbar) {
std::cout << "ERROR: could not open files" << std::endl;
exit(1);
}
TString tree = "outTree"+ordering_+"Ord";
TCut mycuts = "";
TCut mycutb = "";
TCut cutA = "pt1>0 && tightestHPSWP>0";
TCut cutB = "pt1>0 && combRelIsoLeg1<0.1";
TCut cutBl = "pt1>0 && combRelIsoLeg1<0.3";
TCut cutC = "pt1>0 && diTauCharge==0";
TCut cutD = "pt1>0 && MtLeg1<40";
// select events for training
TFile* dummy = new TFile("dummy.root","RECREATE");
TH1F* allEvents = new TH1F("allEvents","",1,-10,10);
float totalEvents, cutEvents;
// signal: all
TTree *signal = ((TTree*)(fSignal->Get(tree)))->CopyTree(cutA&&cutB&&cutC&&cutD);
cout << "Copied signal tree with full selection: " << ((TTree*)(fSignal->Get(tree)))->GetEntries() << " --> " << signal->GetEntries() << endl;
allEvents->Reset();
signal->Draw("eta1>>allEvents","sampleWeight");
cutEvents = allEvents->Integral();
Double_t signalWeight = 1.0;
cout << "Signal: expected yield " << cutEvents << " -- weight " << signalWeight << endl;
// Z+jets: all
TTree *backgroundDYJets = ((TTree*)(fBackgroundDYJets->Get(tree)))->CopyTree(cutA&&cutB&&cutC&&cutD);
cout << "Copied DYJets tree with full selection: " << ((TTree*)(fBackgroundDYJets->Get(tree)))->GetEntries() << " --> " << backgroundDYJets->GetEntries() << endl;
allEvents->Reset();
backgroundDYJets->Draw("eta1>>allEvents","sampleWeight");
cutEvents = allEvents->Integral();
Double_t backgroundDYJetsWeight = 1.0;
cout << "ZJets: expected yield " << cutEvents << " -- weight " << backgroundDYJetsWeight << endl;
// W+jets: iso+Mt
TTree *backgroundWJets = ((TTree*)(fBackgroundWJets->Get(tree)))->CopyTree(cutB&&cutD);
cout << "Copied WJets tree with iso+Mt selection: " << ((TTree*)(fBackgroundWJets->Get(tree)))->GetEntries() << " --> " << backgroundWJets->GetEntries() << endl;
allEvents->Reset();
backgroundWJets->Draw("eta1>>allEvents","sampleWeight");
totalEvents = allEvents->Integral();
allEvents->Reset();
backgroundWJets->Draw("eta1>>allEvents","sampleWeight*(tightestHPSWP>0 && diTauCharge==0)");
cutEvents = allEvents->Integral();
Double_t backgroundWJetsWeight = cutEvents / totalEvents;
cout << "WJets: expected yield " << cutEvents << " -- weight " << backgroundWJetsWeight << endl;
// QCD: Mt+loose iso
TTree *backgroundQCD = ((TTree*)(fBackgroundQCD->Get(tree)))->CopyTree(cutD&&cutBl);
cout << "Copied QCD tree with Mt selection: " << ((TTree*)(fBackgroundQCD->Get(tree)))->GetEntries() << " --> " << backgroundQCD->GetEntries() << endl;
allEvents->Reset();
backgroundQCD->Draw("eta1>>allEvents","sampleWeight");
totalEvents = allEvents->Integral();
allEvents->Reset();
backgroundQCD->Draw("eta1>>allEvents","sampleWeight*(tightestHPSWP>0 && diTauCharge==0 && combRelIsoLeg1<0.1)");
cutEvents = allEvents->Integral();
Double_t backgroundQCDWeight = cutEvents / totalEvents;
cout << "QCD: expected yield " << cutEvents << " -- weight " << backgroundQCDWeight << endl;
// TTbar: iso+Mt
TTree *backgroundTTbar = ((TTree*)(fBackgroundTTbar->Get(tree)))->CopyTree(cutB&&cutD);
cout << "Copied TTbar tree with iso+Mt selection: " << ((TTree*)(fBackgroundTTbar->Get(tree)))->GetEntries() << " --> " << backgroundTTbar->GetEntries() << endl;
allEvents->Reset();
backgroundTTbar->Draw("eta1>>allEvents","sampleWeight");
totalEvents = allEvents->Integral();
allEvents->Reset();
backgroundTTbar->Draw("eta1>>allEvents","sampleWeight*(tightestHPSWP>0 && diTauCharge==0)");
cutEvents = allEvents->Integral();
Double_t backgroundTTbarWeight = cutEvents / totalEvents;
cout << "TTbar: expected yield " << cutEvents << " -- weight " << backgroundTTbarWeight << endl;
delete allEvents;
factory->AddSignalTree ( signal, signalWeight );
//factory->AddBackgroundTree( backgroundDYJets, backgroundDYJetsWeight );
//factory->AddBackgroundTree( backgroundWJets, backgroundWJetsWeight );
factory->AddBackgroundTree( backgroundQCD, backgroundQCDWeight );
//factory->AddBackgroundTree( backgroundTTbar, backgroundTTbarWeight );
factory->PrepareTrainingAndTestTree( mycuts, mycutb,
"nTrain_Signal=0:nTrain_Background=0:nTest_Signal=1:nTest_Background=1:SplitMode=Random:NormMode=NumEvents:!V" );
factory->BookMethod( TMVA::Types::kCuts, "Cuts",
"!H:!V:FitMethod=GA:EffSel:CutRangeMin[0]=25.:CutRangeMax[0]=999:CutRangeMin[1]=25.:CutRangeMax[1]=999.:CutRangeMin[2]=1.0:CutRangeMax[2]=9.:CutRangeMin[3]=100:CutRangeMax[3]=7000:VarProp=FSmart" );
/*
factory->BookMethod( TMVA::Types::kBDT, "BDT",
"!H:!V:NTrees=200:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
*/
factory->TrainAllMethods();
factory->TestAllMethods();
factory->EvaluateAllMethods();
outputFile->Close();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVAClassification is done!" << std::endl;
delete factory;
//if (!gROOT->IsBatch()) TMVAGui( outfileName );
}
void TMVAClassificationHwwNtuple( TString myMethodList = "" )
{
// This loads the library
TMVA::Tools::Instance();
gROOT->ProcessLine(".L TMVAGui.C");
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
// --- Cut optimisation
Use["Cuts"] = 1;
Use["CutsD"] = 0;
Use["CutsPCA"] = 0;
Use["CutsGA"] = 0;
Use["CutsSA"] = 0;
//
Use["BDT"] = 1; // uses Adaptive Boost
Use["BDTG"] = 0; // uses Gradient Boost
Use["BDTB"] = 0; // uses Bagging
Use["BDTD"] = 0; // decorrelation + Adaptive Boost
Use["BDTF"] = 0; // allow usage of fisher discriminant for node splitting
//
// --- Friedman's RuleFit method, ie, an optimised series of cuts ("rules")
Use["RuleFit"] = 0;
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVAClassification" << 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 = TMVA::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;
cout<<regMethod<<" is on"<<endl;
}
}
// -------------------------------------------------------------------------
// --- Here the preparation phase begins
// Create a ROOT output file where TMVA will store ntuples, histograms, etc.
TString outfileName( "TMVA.root" );
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
// For one variable
//TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
// "!V:!Silent:Color:DrawProgressBar:Transformations=I:AnalysisType=Classification" );
// For Multiple Variables
TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );
//factory->AddVariable( "pt1", "LeadLepton pt", "", 'F' );
//factory->AddVariable( "pt2", "TailLepton pt", "", 'F' );
factory->AddVariable( "pfmet", "MissingEt", "", 'F' );
factory->AddVariable( "mpmet", "Minimum Proj. Met", "", 'F' );
factory->AddVariable( "dphill", "DeltPhiOfLepLep", "", 'F' );
//factory->AddVariable( "mll", "DiLepton Mass", "", 'F' );
factory->AddVariable( "ptll", "DiLepton pt", "", 'F' );
//
// You can add so-called "Spectator variables", which are not used in the MVA training,
// but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the
// input variables, the response values of all trained MVAs, and the spectator variables
//factory->AddSpectator( "spec1 := var1*2", "Spectator 1", "units", 'F' );
//factory->AddSpectator( "spec2 := var1*3", "Spectator 2", "units", 'F' );
//
//factory->AddSpectator( "mWW", "Higgs Mass", "", 'F' );
factory->AddSpectator( "pt1", "LeadLepton pt", "", 'F' );
factory->AddSpectator( "pt2", "TailLepton pt", "", 'F' );
factory->AddSpectator( "pfmet", "MissingEt", "", 'F' );
factory->AddSpectator( "mpmet", "Minimum Proj. Met", "", 'F' );
factory->AddSpectator( "dphill", "DeltPhiOfLepLep", "", 'F' );
factory->AddSpectator( "mll", "DiLepton Mass", "", 'F' );
factory->AddSpectator( "ptll", "DiLepton pt", "", 'F' );
// Read training and test data
// (it is also possible to use ASCII format as input -> see TMVA Users Guide)
//TString fname = "./tmva_class_example.root";
//TString fname = "/afs/cern.ch/work/s/salee/private/HWWwidth/HWW/GGVvAnalyzer/MkNtuple/Hw1Int8TeV/MkNtuple.root";
//TString fname = "/terranova_0/HWWwidth/HWW/GGVvAnalyzer/MkNtuple/Hw1Int8TeV/MkNtuple.root";
//if (gSystem->AccessPathName( fname )) // file does not exist in local directory
// exit(-1);
//gSystem->Exec("wget http://root.cern.ch/files/tmva_class_example.root");
//TFile *input = TFile::Open( fname );
//TFile *SB_OnPeak = TFile::Open("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_IntOnPeak_8TeV.root");
//TTree *SB_OnPeak_Tree = (TTree*)SB_OnPeak->Get("latino");
TChain *S_Chain = new TChain("latino");
TChain *C_Chain = new TChain("latino");
TChain *SCI_Chain = new TChain("latino");
TChain *qqWW_Chain = new TChain("latino");
S_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_SigOnPeak_8TeV.root");
S_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_SigShoulder_8TeV.root");
S_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_SigTail_8TeV.root");
SCI_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_IntOnPeak_8TeV.root");
SCI_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_IntShoulder_8TeV.root");
SCI_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_IntTail_8TeV.root");
C_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw25_CotHead_8TeV.root");
C_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw25_CotTail_8TeV.root");
qqWW_Chain->Add("/afs/cern.ch/user/m/maiko/work/public/Tree/tree_skim_wwmin/nominals/latino_000_WWJets2LMad.root");
// --- Register the training and test trees
// You can add an arbitrary number of signal or background trees
factory->AddSignalTree ( S_Chain );
factory->AddBackgroundTree( qqWW_Chain );
factory->AddBackgroundTree( C_Chain );
// Classification training and test data in ROOT tree format with signal and background events being located in the same tree
//factory->SetInputTrees(SCI_Chain, GenOffCut, GenOnCut);
// To give different trees for training and testing, do as follows:
// factory->AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" );
// factory->AddSignalTree( signalTestTree, signalTestWeight, "Test" );
factory->SetWeightExpression ("2.1*puW*baseW*effW*triggW*19.468");
//factory->SetSignalWeightExpression ("2.1*puW*baseW*effW*triggW*19.468");
//factory->SetBackgroundWeightExpression("puW*baseW*effW*triggW*19.468");
//factory->PrepareTrainingAndTestTree( ChanCommOff,
// "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=None:!V" );
//"nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V";
factory->PrepareTrainingAndTestTree( ChanCommOff0J,
"nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=None:!V" );
// ---- Book MVA methods
//
// Cut optimisation
if (Use["Cuts"])
factory->BookMethod( TMVA::Types::kCuts, "Cuts",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" );
if (Use["BDT"]) // Adaptive Boost
factory->BookMethod( TMVA::Types::kBDT, "BDT",
"!H:V:NTrees=850:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
//"!H:!V:NTrees=850:MinNodeSize=2.5%:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" );
// For an example of the category classifier usage, see: TMVAClassificationCategory
// -----------------------------------------------------------------------------------------
// ---- Now you can optimize the setting (configuration) of the MVAs using the set of training events
// ---- STILL EXPERIMENTAL and only implemented for BDT's !
// factory->OptimizeAllMethods("SigEffAt001","Scan");
// factory->OptimizeAllMethods("ROCIntegral","FitGA");
// -----------------------------------------------------------------------------------------
// ---- Now you can tell the factory to train, test, and evaluate the MVAs
// Train MVAs using the set of training events
factory->TrainAllMethods();
// ---- Evaluate all MVAs using the set of test events
factory->TestAllMethods();
// ----- Evaluate and compare performance of all configured MVAs
factory->EvaluateAllMethods();
// --------------------------------------------------------------
// Save the output
outputFile->Close();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVAClassification is done!" << std::endl;
delete factory;
// Launch the GUI for the root macros
//if (!gROOT->IsBatch()) TMVAGui( outfileName );
}
//------------------------------------------------------------------------------
// MVATrain
//------------------------------------------------------------------------------
void MVATrain(float metPfType1_cut, float mt2ll_cut, TString signal)
{
TFile* outputfile = TFile::Open(trainingdir + signal + ".root", "recreate");
// Factory
//----------------------------------------------------------------------------
TMVA::Factory* factory = new TMVA::Factory(signal, outputfile,
// "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification");
"!V:!Silent:Color:DrawProgressBar:AnalysisType=Classification");
// Get the trees
//----------------------------------------------------------------------------
_mctree.clear();
AddProcess("signal" , signal);//"01_Data_reduced_1outof6"); //signal
AddProcess("background", "04_TTTo2L2Nu");
/*AddProcess("background", "14_HZ");
AddProcess("background", "10_HWW");
AddProcess("background", "06_WW");
AddProcess("background", "02_WZTo3LNu");
AddProcess("background", "03_VZ");
AddProcess("background", "11_Wg");
AddProcess("background", "07_ZJets");
AddProcess("background", "09_TTV");
AddProcess("background", "05_ST");
AddProcess("background", "00_Fakes_reduced_1outof6");*/
Double_t weight = 1.0;
factory->AddSignalTree(_signaltree, weight);
for (UInt_t i=0; i<_mctree.size(); i++) factory->AddBackgroundTree(_mctree[i], weight);
factory->SetWeightExpression("eventW");
// Add variables
//----------------------------------------------------------------------------
// Be careful with the order: it must be respected at the reading step
// factory->AddVariable("<var1>+<var2>", "pretty title", "unit", 'F');
factory->AddVariable("newdarkpt" , "", "", 'F');
//factory->AddVariable("topRecoW" , "", "", 'F');
//factory->AddVariable("lep1pt" , "", "", 'F');
//factory->AddVariable("lep1eta" , "", "", 'F');
//factory->AddVariable("lep1phi" , "", "", 'F');
//factory->AddVariable("lep1mass" , "", "", 'F');
//factory->AddVariable("lep2pt" , "", "", 'F');
//factory->AddVariable("lep2eta" , "", "", 'F');
//factory->AddVariable("lep2phi" , "", "", 'F');
//factory->AddVariable("lep2mass" , "", "", 'F');
//factory->AddVariable("jet1pt " , "", "", 'F');
//factory->AddVariable("jet1eta" , "", "", 'F');
//factory->AddVariable("jet1phi" , "", "", 'F');
//factory->AddVariable("jet1mass" , "", "", 'F');
//factory->AddVariable("jet2pt" , "", "", 'F');
//factory->AddVariable("jet2eta" , "", "", 'F');
//factory->AddVariable("jet2phi" , "", "", 'F');
//factory->AddVariable("jet2mass" , "", "", 'F');
factory->AddVariable("metPfType1" , "", "", 'F');
//factory->AddVariable("metPfType1Phi", "", "", 'F');
//factory->AddVariable("m2l" , "", "", 'F');
factory->AddVariable("mt2ll" , "", "", 'F');
//factory->AddVariable("mt2lblb" , "", "", 'F');
//factory->AddVariable("mtw1" , "", "", 'F');
//factory->AddVariable("mtw2" , "", "", 'F');
//factory->AddVariable("ht" , "", "", 'F');
//factory->AddVariable("htjets" , "", "", 'F');
//factory->AddVariable("htnojets" , "", "", 'F');
//factory->AddVariable("njet" , "", "", 'F');
//factory->AddVariable("nbjet30csvv2l", "", "", 'F');
//factory->AddVariable("nbjet30csvv2m", "", "", 'F');
//factory->AddVariable("nbjet30csvv2t", "", "", 'F');
//factory->AddVariable("dphijet1met" , "", "", 'F');
//factory->AddVariable("dphijet2met" , "", "", 'F');
//factory->AddVariable("dphijj" , "", "", 'F');
//factory->AddVariable("dphijjmet" , "", "", 'F');
//factory->AddVariable("dphill" , "", "", 'F');
//factory->AddVariable("dphilep1jet1" , "", "", 'F');
//factory->AddVariable("dphilep1jet2" , "", "", 'F');
//factory->AddVariable("dphilep2jet1" , "", "", 'F');
//factory->AddVariable("dphilep2jet2" , "", "", 'F');
//factory->AddVariable("dphilmet1" , "", "", 'F');
//factory->AddVariable("dphilmet2" , "", "", 'F');
factory->AddVariable("dphillmet" , "", "", 'F');
//factory->AddVariable("sphericity" , "", "", 'F');
//factory->AddVariable("alignment" , "", "", 'F');
//factory->AddVariable("planarity" , "", "", 'F');
// Preselection cuts and preparation
//----------------------------------------------------------------------------
//factory->PrepareTrainingAndTestTree(Form("metPfType1>%5.2f&&mt2ll>%5.2f&&newdarkpt>0.", metPfType1_cut, mt2ll_cut), "NormMode=EqualNumEvents:nTrain_Signal=80:nTest_Signal=80:nTrain_Background=400:nTest_Background=400:!V");
factory->PrepareTrainingAndTestTree("mt2ll>100.&&newdarkpt>0.&&metPfType1>80.", "NormMode=EqualNumEvents:nTrain_Signal=0:nTest_Signal=0:nTrain_Background=0:nTest_Background=0:!V");
// Book MVA
//----------------------------------------------------------------------------
factory->BookMethod(TMVA::Types::kMLP, "MLP01",
"H:!V:NeuronType=sigmoid:NCycles=500:VarTransform=Norm:HiddenLayers=6,3:TestRate=1:LearningRate=0.005");
//factory->BookMethod(TMVA::Types::kMLP, "MLP01",
// "H:!V:NeuronType=sigmoid:NCycles=500:VarTransform=Norm:HiddenLayers=4,4:TestRate=3:LearningRate=0.005");
//factory->BookMethod(TMVA::Types::kMLP, "MLP02",
// "H:!V:NeuronType=sigmoid:NCycles=40:VarTransform=Norm:HiddenLayers=20,10:TestRate=3:LearningRate=0.005");
//factory->BookMethod(TMVA::Types::kMLP, "MLP03",
// "H:!V:NeuronType=sigmoid:NCycles=30:VarTransform=Norm:HiddenLayers=20,20:TestRate=3:LearningRate=0.005");
//factory->BookMethod(TMVA::Types::kBDT, "BDT04", "NTrees=50:MaxDepth=2" );
//factory->BookMethod(TMVA::Types::kBDT, "BDT05", "NTrees=50:MaxDepth=3" );
// Train, test and evaluate MVA
//----------------------------------------------------------------------------
factory->TrainAllMethods(); // Train using the set of training events
factory->TestAllMethods(); // Evaluate using the set of test events
factory->EvaluateAllMethods(); // Evaluate and compare performance
// Save the output
//----------------------------------------------------------------------------
outputfile->Close();
delete factory;
}