C++ Factory::SetWeightExpression方法代码示例

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;
}

//.........这里部分代码省略.........
  }
  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);

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";
}

//.........这里部分代码省略.........
   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)

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();
//.........这里部分代码省略.........

//.........这里部分代码省略.........
   // 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
  //----------------------------------------------------------------------------
//.........这里部分代码省略.........