示例#1
0
void Boost(){
   TString outfileName = "boost.root";
   TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
   TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
                                               "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D" );
   factory->AddVariable( "var0", 'F' );
   factory->AddVariable( "var1", 'F' );
   TFile *input(0);
   TString fname = "./data.root";
   if (!gSystem->AccessPathName( fname )) {
      // first we try to find tmva_example.root in the local directory
      std::cout << "--- BOOST       : Accessing " << fname << std::endl;
      input = TFile::Open( fname );
   }
   else {
      gROOT->LoadMacro( "../development/createData.C");
      create_circ(20000);
      cout << " created data.root with data and circle arranged in half circles"<<endl;
      input = TFile::Open( fname );
   }
   if (!input) {
      std::cout << "ERROR: could not open data file" << std::endl;
      exit(1);
   }
   TTree *signal     = (TTree*)input->Get("TreeS");
   TTree *background = (TTree*)input->Get("TreeB");
   Double_t signalWeight     = 1.0;
   Double_t backgroundWeight = 1.0;
   
   gROOT->cd( outfileName+TString(":/") );
   factory->AddSignalTree    ( signal,     signalWeight     );
   factory->AddBackgroundTree( background, backgroundWeight );
   factory->PrepareTrainingAndTestTree( "", "",
                                        "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );

   TString fisher="H:!V";
   factory->BookMethod( TMVA::Types::kFisher, "Fisher", fisher );
   factory->BookMethod( TMVA::Types::kFisher, "FisherBoost", fisher+":Boost_Num=100:Boost_Type=AdaBoost" );
   factory->BookMethod( TMVA::Types::kFisher, "FisherBoostLog", fisher+":Boost_Num=100:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=1.0" );
   factory->BookMethod( TMVA::Types::kFisher, "FisherBoostLog2", fisher+":Boost_Num=100:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=2.0" );
   factory->BookMethod( TMVA::Types::kFisher, "FisherBoostStep", fisher+":Boost_Num=100:Boost_Transform=step:Boost_Type=AdaBoost:Boost_AdaBoostBeta=1.0" );
   factory->BookMethod( TMVA::Types::kFisher, "FisherBoostStep2", fisher+":Boost_Num=100:Boost_Transform=step:Boost_Type=AdaBoost:Boost_AdaBoostBeta=1.2" );
   factory->BookMethod( TMVA::Types::kFisher, "FisherBoostStep3", fisher+":Boost_Num=100:Boost_Transform=step:Boost_Type=AdaBoost:Boost_AdaBoostBeta=1.5" );

  // Train MVAs using the set of training events
   factory->TrainAllMethodsForClassification();

   // ---- 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 TMVAClassification( TString myMethodList = "" ) 
void Example_Eric( 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 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();

   //---------------------------------------------------------------
   // default MVA methods to be trained + tested
   std::map<std::string,int> Use;

   Use["Cuts"]            =0;
   Use["CutsD"]           =0;
   Use["CutsPCA"]         =0;
   Use["CutsGA"]          =0;
   Use["CutsSA"]          =0;
   // ---
   Use["Likelihood"]      =0;
   Use["LikelihoodD"]     =0; // the "D" extension indicates decorrelated input variables (see option strings)
   Use["LikelihoodPCA"]   =1; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
   Use["LikelihoodKDE"]   =0;
   Use["LikelihoodMIX"]   =0;
   // ---
   Use["PDERS"]           =0;
   Use["PDERSD"]          =0;
   Use["PDERSPCA"]        =0;
   Use["PDERSkNN"]        =0; // depreciated until further notice
   Use["PDEFoam"]         =0;
   // --
   Use["KNN"]             =0;
   // ---
   Use["HMatrix"]         =0;
   Use["Fisher"]          =0;
   Use["FisherG"]         =0;
   Use["BoostedFisher"]   =0;
   Use["LD"]              =0;
   // ---
   Use["FDA_GA"]          =0;
   Use["FDA_SA"]          =0;
   Use["FDA_MC"]          =0;
   Use["FDA_MT"]          =0;
   Use["FDA_GAMT"]        =0;
   Use["FDA_MCMT"]        =0;
   // ---
   Use["MLP"]             = 1; // this is the recommended ANN
   Use["MLPBFGS"]         = 0; // recommended ANN with optional training method
   Use["CFMlpANN"]        =0; // *** missing
   Use["TMlpANN"]         =0; 
   // ---
   Use["SVM"]             =1;
   // ---
   Use["BDT"]             =1;
   Use["BDTD"]            =0;
   Use["BDTG"]            =0;
   Use["BDTB"]            =0;
   // ---
   Use["RuleFit"]         =1;
   // ---
   Use["Plugin"]          =0;
   // ---------------------------------------------------------------

   std::cout << std::endl;
   std::cout << "==> Start TMVAClassification" << std::endl;

   if (myMethodList != "") {
      for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;

      std::vector<TString> mlist = gTools().SplitString( myMethodList, ',' );
      for (UInt_t i=0; i<mlist.size(); i++) {
         std::string regMethod(mlist[i]);

         if (Use.find(regMethod) == Use.end()) {
            std::cout << "Method \"" << regMethod << "\" not known in TMVA under this name. Choose among the following:" << std::endl;
            for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) std::cout << it->first << " ";
            std::cout << std::endl;
            return;
         }
         Use[regMethod] = 1;
      }
   }

   // Create a new root output file.
   TString outfileName( "TMVA_Eric2.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( "TMVAClassification", outputFile, 
                                               "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D" );

   // 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( "myvar1 := var1+var2", 'F' );
//    factory->AddVariable( "myvar2 := var1-var2", "Expression 2", "", 'F' );
//    factory->AddVariable( "var3",                "Variable 3", "units", 'F' );
//    factory->AddVariable( "var4",                "Variable 4", "units", 'F' );

   factory->AddVariable( "Mqq := Mqq", 'F' );
   factory->AddVariable( "Pt_qq := Pt_qq", 'F' );
   factory->AddVariable( "Eta_qq := Eta_qq", 'F' );
   factory->AddVariable( "Charge_qq := Charge_qq", 'F' );
   factory->AddVariable( "DPhi_ll := DPhi_ll", 'F' );
   factory->AddVariable( "DPt_ll := DPt_ll", 'F' );
   //factory->AddVariable( "MinDPhi_lMET := MinDPhi_lMET", 'F' );
   //factory->AddVariable( "Aplanarity := aplanarity", 'F' );
   //factory->AddVariable( "chargeEta := chargeEta",  'F' );
   //factory->AddVariable( "MET := Met",  'F' );
   //factory->AddVariable( "MtauJet := MtauJet",  'F' );
   //factory->AddVariable( "HT := Ht",  'F' );
   //factory->AddVariable( "Chi2 := kinFitChi2",  'F' );
   //factory->AddVariable( "DeltaPhiTauMET := DeltaPhiTauMet",  'F' );
   //factory->AddVariable( "Mt := Mt",  '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' );

   // read training and test data
   if (ReadDataFromAsciiIFormat) {
      // load the signal and background event samples from ascii files
      // format in file must be:
      // var1/F:var2/F:var3/F:var4/F
      // 0.04551   0.59923   0.32400   -0.19170
      // ...

      TString datFileS = "tmva_example_sig.dat";
      TString datFileB = "tmva_example_bkg.dat";

      factory->SetInputTrees( datFileS, datFileB );
   }
   else {

  
    //TFile* f0 = new TFile("/opt/sbg/data/data1/cms/lebihan/clean_january_2012_2/CMSSW_4_2_8_patch7/src/MiniTreeAnalysis/NTupleAnalysis/macros/TopTauJets/TMVA_sig_newLumi.root");
    //TFile* f1 = new TFile("/opt/sbg/data/data1/cms/lebihan/clean_january_2012_2/CMSSW_4_2_8_patch7/src/MiniTreeAnalysis/NTupleAnalysis/macros/TopTauJets/TMVA_bkg_newLumi.root");
    TFile* f0 = TFile::Open("/opt/sbg/data/data1/cms/echabert/ttbarMET/ProdAlexMars13/CMSSW_5_3_2_patch4/src/NTuple/NTupleAnalysis/macros/TTbarMET/backup_outputProof10-04-13_16-00-57/proof_ttW.root");
    TFile* f1 = TFile::Open("/opt/sbg/data/data1/cms/echabert/ttbarMET/ProdAlexMars13/CMSSW_5_3_2_patch4/src/NTuple/NTupleAnalysis/macros/TTbarMET/backup_outputProof10-04-13_16-00-57/proof_tt-dilepton.root");
  
    TTree *signal     = (TTree*)f0->Get("theTree2");
    TTree *background = (TTree*)f1->Get("theTree2");
    cout<<"trees: "<<signal<<" "<<background<<endl;

    //Double_t backgroundWeight = 1.0;
    //Double_t signalWeight     = 1.0;
    Double_t signalWeight     = 0.30*20/185338;
    Double_t backgroundWeight = 222.*0.1*20/9982625;
    // ====== register trees ====================================================
    //
    // the following method is the prefered one:
    // you can add an arbitrary number of signal or background trees

    factory->AddSignalTree    ( signal,     signalWeight     );
    factory->AddBackgroundTree( background, backgroundWeight );

     //   factory->AddSignalTree    ( signal );
     //factory->AddBackgroundTree( background );


      // 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];
      //    for (Int_t ivar=0; ivar<4; ivar++) signal->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
      //    for (Int_t i=0; i<signal->GetEntries(); i++) {
      //       signal->GetEntry(i);
      //       for (Int_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) factory->AddSignalTrainingEvent( vars, signalWeight ); 
      //       else                            factory->AddSignalTestEvent    ( vars, signalWeight ); 
      //    }
      //
      //    for (Int_t ivar=0; ivar<4; ivar++) background->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
      //    for (Int_t i=0; i<background->GetEntries(); i++) {
      //       background->GetEntry(i); 
      //       for (Int_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 ); 
      //       else                                factory->AddBackgroundTestEvent    ( vars, backgroundWeight ); 
      //    }
      //    // --- end ------------------------------------------------------------
      //
      // ====== end of register trees ==============================================
   }
   
   // This would set individual event weights (the variables defined in the 
   // expression need to exist in the original TTree)
   //    for signal    : factory->SetSignalWeightExpression("weight1*weight2");
   //    for background: factory->SetBackgroundWeightExpression("weight1*weight2");
   //factory->SetBackgroundWeightExpression("weight_BTAG");
   //factory->SetSignalWeightExpression("weight*weight_BTAG");
   // Apply additional cuts on the signal and background samples (can be different)
  
   // TCut mycuts = "MHt >=0  && MMTauJet >=0 && MM3 >= 0"; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
   // TCut mycutb = "MHt >=0  && MMTauJet >=0 && MM3 >= 0"; // for example: TCut mycutb = "abs(var1)<0.5";
   //TCut mycuts = "Met>=20 "; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
   //TCut mycutb = "Met>=20 "; // for example: TCut mycutb = "abs(var1)<0.5";
   TCut mycuts;
   TCut mycutb;

   // tell the factory to use all remaining events in the trees after training for testing:
   factory->PrepareTrainingAndTestTree( mycuts, mycutb,
                                        "nTrain_Signal=3000:nTrain_Background=5000: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" );  
   // To also specify the number of testing events, use:
   //    factory->PrepareTrainingAndTestTree( mycut, 
   //                                         "NSigTrain=3000:NBkgTrain=3000:NSigTest=3000:NBkgTest=3000: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

   // Cut optimisation
   if (Use["Cuts"])
      factory->BookMethod( TMVA::Types::kCuts, "Cuts", 
                           "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" );

   if (Use["CutsD"])
      factory->BookMethod( TMVA::Types::kCuts, "CutsD", 
                           "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=Decorrelate" );

   if (Use["CutsPCA"])
      factory->BookMethod( TMVA::Types::kCuts, "CutsPCA", 
                           "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=PCA" );

   if (Use["CutsGA"])
      factory->BookMethod( TMVA::Types::kCuts, "CutsGA",
                           "H:!V:FitMethod=GA:CutRangeMin[0]=-10:CutRangeMax[0]=10:VarProp[1]=FMax:EffSel:Steps=30:Cycles=3:PopSize=400:SC_steps=10:SC_rate=5:SC_factor=0.95" );
   
   if (Use["CutsSA"])
      factory->BookMethod( TMVA::Types::kCuts, "CutsSA",
                           "!H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );
   
   // Likelihood
   if (Use["Likelihood"])
      factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood", 
                           "H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" ); 

   // test the decorrelated likelihood
   if (Use["LikelihoodD"])
      factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodD", 
                           "!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate" ); 

   if (Use["LikelihoodPCA"])
      factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodPCA", 
                           "!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA" ); 
 
   // test the new kernel density estimator
   if (Use["LikelihoodKDE"])
      factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodKDE", 
                           "!H:!V:!TransformOutput:PDFInterpol=KDE:KDEtype=Gauss:KDEiter=Adaptive:KDEFineFactor=0.3:KDEborder=None:NAvEvtPerBin=50" ); 

   // test the mixed splines and kernel density estimator (depending on which variable)
   if (Use["LikelihoodMIX"])
      factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodMIX", 
                           "!H:!V:!TransformOutput:PDFInterpolSig[0]=KDE:PDFInterpolBkg[0]=KDE:PDFInterpolSig[1]=KDE:PDFInterpolBkg[1]=KDE:PDFInterpolSig[2]=Spline2:PDFInterpolBkg[2]=Spline2:PDFInterpolSig[3]=Spline2:PDFInterpolBkg[3]=Spline2:KDEtype=Gauss:KDEiter=Nonadaptive:KDEborder=None:NAvEvtPerBin=50" ); 

   // test the multi-dimensional probability density estimator
   // here are 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["PDERS"])
      factory->BookMethod( TMVA::Types::kPDERS, "PDERS", 
                           "!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" );

   if (Use["PDERSkNN"])
      factory->BookMethod( TMVA::Types::kPDERS, "PDERSkNN", 
                           "!H:!V:VolumeRangeMode=kNN:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" );

   if (Use["PDERSD"])
      factory->BookMethod( TMVA::Types::kPDERS, "PDERSD", 
                           "!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=Decorrelate" );

   if (Use["PDERSPCA"])
      factory->BookMethod( TMVA::Types::kPDERS, "PDERSPCA", 
                           "!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=PCA" );

   // Multi-dimensional likelihood estimator using self-adapting phase-space binning
   if (Use["PDEFoam"])
      factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam", 
                           "H:!V:SigBgSeparate=F:TailCut=0.001:VolFrac=0.0333:nActiveCells=500:nSampl=2000:nBin=5:CutNmin=T:Nmin=100:Kernel=None:Compress=T" );

   // K-Nearest Neighbour classifier (KNN)
   if (Use["KNN"])
      factory->BookMethod( TMVA::Types::kKNN, "KNN", 
                           "H:nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" );
   // H-Matrix (chi2-squared) method
   if (Use["HMatrix"])
      factory->BookMethod( TMVA::Types::kHMatrix, "HMatrix", "!H:!V" ); 

   // Fisher discriminant   
   if (Use["Fisher"])
      factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=60:NsmoothMVAPdf=10" );

   // Fisher with Gauss-transformed input variables
   if (Use["FisherG"])
      factory->BookMethod( TMVA::Types::kFisher, "FisherG", "H:!V:VarTransform=Gauss" );

   // Composite classifier: ensemble (tree) of boosted Fisher classifiers
   if (Use["BoostedFisher"])
      factory->BookMethod( TMVA::Types::kFisher, "BoostedFisher", "H:!V:Boost_Num=20:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=0.2");

   // Linear discriminant (same as Fisher)
   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+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:SampleSize=100000:Sigma=0.1" );
   
   if (Use["FDA_GA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
      factory->BookMethod( TMVA::Types::kFDA, "FDA_GA",
                           "H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:PopSize=300:Cycles=3:Steps=20:Trim=True:SaveBestGen=1" );

   if (Use["FDA_SA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
      factory->BookMethod( TMVA::Types::kFDA, "FDA_SA",
                           "H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=SA:MaxCalls=15000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );

   if (Use["FDA_MT"])
      factory->BookMethod( TMVA::Types::kFDA, "FDA_MT",
                           "H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-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+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:Cycles=1:PopSize=5:Steps=5:Trim" );

   if (Use["FDA_MCMT"])
      factory->BookMethod( TMVA::Types::kFDA, "FDA_MCMT",
                           "H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:SampleSize=20" );

   // TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons
   if (Use["MLP"])
      factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5" );

   if (Use["MLPBFGS"])
      factory->BookMethod( TMVA::Types::kMLP, "MLPBFGS", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS" );


   // CF(Clermont-Ferrand)ANN
   if (Use["CFMlpANN"])
      factory->BookMethod( TMVA::Types::kCFMlpANN, "CFMlpANN", "!H:!V:NCycles=2000:HiddenLayers=N+1,N"  ); // n_cycles:#nodes:#nodes:...  
  
   // Tmlp(Root)ANN
   if (Use["TMlpANN"])
      factory->BookMethod( TMVA::Types::kTMlpANN, "TMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N:LearningMethod=BFGS:ValidationFraction=0.3"  ); // n_cycles:#nodes:#nodes:...
  
   // 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["BDTG"]) // Gradient Boost
      factory->BookMethod( TMVA::Types::kBDT, "BDTG", 
                           "!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.6:SeparationType=GiniIndex:nCuts=20:NNodesMax=5" );

   if (Use["BDT"])  // Adaptive Boost
      factory->BookMethod( TMVA::Types::kBDT, "BDT", 
                           "!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
   
   if (Use["BDTB"]) // Bagging
      factory->BookMethod( TMVA::Types::kBDT, "BDTB", 
                           "!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );

   if (Use["BDTD"]) // Decorrelation + Adaptive Boost
      factory->BookMethod( TMVA::Types::kBDT, "BDTD", 
                           "!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning:VarTransform=Decorrelate" );
   
   // RuleFit -- TMVA implementation of Friedman's method
   if (Use["RuleFit"])
      factory->BookMethod( TMVA::Types::kRuleFit, "RuleFit",
                           "H:!V:RuleFitModule=RFTMVA:Model=ModRuleLinear:MinImp=0.001:RuleMinDist=0.001:NTrees=20:fEventsMin=0.01:fEventsMax=0.5:GDTau=-1.0:GDTauPrec=0.01:GDStep=0.01:GDNSteps=10000:GDErrScale=1.02" );

   // --------------------------------------------------------------------------------------------------

   // As an example how to use the ROOT plugin mechanism, book BDT via
   // plugin mechanism
   if (Use["Plugin"]) {
         //
         // first the plugin has to be defined, which can happen either through the following line in the local or global .rootrc:
         //
         // # plugin handler          plugin name(regexp) class to be instanciated library        constructor format
         // Plugin.TMVA@@MethodBase:  ^BDT                TMVA::MethodBDT          TMVA.1         "MethodBDT(TString,TString,DataSet&,TString)"
         // 
         // or by telling the global plugin manager directly
      gPluginMgr->AddHandler("TMVA@@MethodBase", "BDT", "TMVA::MethodBDT", "TMVA.1", "MethodBDT(TString,TString,DataSet&,TString)");
      factory->BookMethod( TMVA::Types::kPlugins, "BDT",
                           "!H:!V:NTrees=400:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=CostComplexity:PruneStrength=50" );
   }

   // --------------------------------------------------------------------------------------------------

   // ---- Now you can tell the factory to train, test, and evaluate the MVAs

   // Train MVAs using the set of training events
   factory->TrainAllMethodsForClassification();

   // ---- 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 );
}
int main() 
{
   // this loads the library
   TMVA::Tools::Instance();

   //---------------------------------------------------------------
   // default MVA methods to be trained + tested
   
   std::map<std::string,int> Use;
   Use["Cuts"]            =1;
   Use["BDT"]             =1;
   
   // ---------------------------------------------------------------

   std::cout << std::endl;
   std::cout << "==> Start TMVAClassification" << std::endl;

   // Create a new root output file.
   TString outfileName( "TMVA_output.root" );
   TFile* outputFile = TFile::Open( outfileName, "RECREATE" );

   TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile, 
                                               "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D" );

    // Add the variables you want to consider 
    
    //factory->AddVariable( "MT := MT",  'F' );
    //factory->AddVariable( "nJets := nJets",  'F' );
    factory->AddVariable( "MET := MET",                   'F' );
    factory->AddVariable( "MT2W := MT2W",                 'F' );
    factory->AddVariable( "dPhiMETjet := dPhiMETjet",     'F' );
    factory->AddVariable( "HTratio := HTratio",           'F' );
    factory->AddVariable( "HadronicChi2 := HadronicChi2", 'F' );
    factory->AddVariable( "nWTag := nWTag",               'I' );
    
    // Open samples
    
    TFile* f_signal = TFile::Open((string(MICROTUPLES_FOLDER)+"signal.root").c_str());
    TFile* f_ttbar  = TFile::Open((string(MICROTUPLES_FOLDER)+"ttbar.root" ).c_str());
    //TFile* f_W2Jets = TFile::Open((string(MICROTUPLES_FOLDER)+"W2Jets.root").c_str());
    //TFile* f_W3Jets = TFile::Open((string(MICROTUPLES_FOLDER)+"W3Jets.root").c_str());
    //TFile* f_W4Jets = TFile::Open((string(MICROTUPLES_FOLDER)+"W4Jets.root").c_str());
    
    TTree* signal = (TTree*) f_signal->Get("microTuple");
    TTree* ttbar  = (TTree*) f_ttbar ->Get("microTuple");
    //TTree* W2Jets = (TTree*) f_W2Jets->Get("microTuple");
    //TTree* W3Jets = (TTree*) f_W3Jets->Get("microTuple");
    //TTree* W4Jets = (TTree*) f_W4Jets->Get("microTuple");

    // Register the trees

//    float weightSignal     = 1.0   * 20000.0 / getNumberOfEvent(signal);
//    float weightBackground = 225.2 * 20000.0 / getNumberOfEvent(ttbar);
    float weightSignal     = 1.0;
    float weightBackground = 1.0;

    factory->AddSignalTree    ( signal, weightSignal    );
    factory->AddBackgroundTree( ttbar,  weightBackground);

    /*
    cout << " signal ; w = " << 1.0   * 20000.0 / getNumberOfEvent(signal) << endl;
    factory->AddSignalTree    ( signal, 1.0   * 20000.0 / getNumberOfEvent(signal));
    cout << " ttbar ; w = "  << 225.2 * 20000.0 / getNumberOfEvent(ttbar) << endl;
    factory->AddBackgroundTree( ttbar,  234.0 * 20000.0 / getNumberOfEvent(ttbar));
    cout << " W2Jets ; w = " << 2159  * 20000.0 / getNumberOfEvent(W2Jets) << endl;
    factory->AddBackgroundTree( W2Jets, 2159  * 20000.0 / getNumberOfEvent(W2Jets));
    cout << " W3Jets ; w = " << 640   * 20000.0 / getNumberOfEvent(W3Jets) << endl;
    factory->AddBackgroundTree( W3Jets, 640   * 20000.0 / getNumberOfEvent(W3Jets));
    cout << " W4Jets ; w = " << 264   * 20000.0 / getNumberOfEvent(W4Jets) << endl;
    factory->AddBackgroundTree( W4Jets, 264   * 20000.0 / getNumberOfEvent(W4Jets));
    */

    // Add preselection cuts
   
    std::string preselectionCutsSig("nJets > 4 && MET > 80 && MT > 100");
    std::string preselectionCutsBkg("nJets > 4 && MET > 80 && MT > 100");

    // Prepare the training

    factory->PrepareTrainingAndTestTree( preselectionCutsSig.c_str(), preselectionCutsBkg.c_str(),
                    "nTrain_Signal=40000:nTrain_Background=300000:nTest_Signal=40000:nTest_Background=300000:SplitMode=Random:NormMode=EqualNumEvents:!V" );

    // Cut optimisation
    //if (Use["Cuts"])     factory->BookMethod( TMVA::Types::kCuts, "Cuts",
    //                     "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" );
    if (Use["BDT"])      factory->BookMethod( TMVA::Types::kBDT, "BDT", 
                         "!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning");
   
   // --------------------------------------------------------------

   // Train MVAs using the set of training events
   factory->TrainAllMethodsForClassification();

   // 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 );
}