void TMVAClassification( TString myMethodList = "" ) 
{

//    TString curDynamicPath( gSystem->GetDynamicPath() );
//    gSystem->SetDynamicPath( "/usr/local/bin/root/bin:" + curDynamicPath );

//    TString curIncludePath(gSystem->GetIncludePath());
//    gSystem->SetIncludePath( " -I /usr/local/bin/root/include " + curIncludePath );

//    // load TMVA shared library created in local release: for MAC OSX
//    if (TString(gSystem->GetBuildArch()).Contains("macosx") ) gSystem->Load( "libTMVA.so" );


   // gSystem->Load( "libTMVA" );
//   TMVA::Tools::Instance();

//    // welcome the user
//    TMVA::gTools().TMVAWelcomeMessage();
   
//    TMVAGlob::SetTMVAStyle();

//    // this loads the library
//    TMVA::Tools::Instance();

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

   Use["Cuts"]            = 1;
   // Use["Likelihood"]      = 1;
 
   // ---------------------------------------------------------------

   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 = 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;
      }
   }

   // Create a new root output file.
   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 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("deltaEta := deta", 'F');
   factory->AddVariable("deltaPhi := dphi", 'F');
   factory->AddVariable("sigmaIetaIeta := sieie", 'F');
   factory->AddVariable("HoverE := hoe", 'F');
   factory->AddVariable("trackIso := trackiso", 'F');
   factory->AddVariable("ecalIso := ecaliso", 'F');
   factory->AddVariable("hcalIso := hcaliso", 'F');
   //factory->AddVariable("nMissingHits := misshits", '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( "et",  'F' );
   factory->AddSpectator( "eta",  'F' );
   factory->AddSpectator( "phi",  'F' );


   // read training and test data
    TFile *input = TFile::Open( "SigElectrons.root" );
    TFile *inputB = TFile::Open( "BkgElectrons.root" );

   std::cout << "--- TMVAClassification : Using input file: " << input->GetName() << std::endl;
   
   TTree *signal     = (TTree*)input->Get("ntuple");
   TTree *background = (TTree*)inputB->Get("ntuple");
   
   factory->AddSignalTree    ( signal,     1.0 );
   factory->AddBackgroundTree( background, 1.0 );

   
   // 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");

   // Apply additional cuts on the signal and background samples (can be different)

   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=0:nTrain_Background=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" );  
   // 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" );
   
   // 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" ); 



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

   // ---- 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;

//   gROOT->ProcessLine(".x /usr/local/bin/root/tmva/test/correlations.C");
   gROOT->ProcessLine(".x /usr/local/bin/root/tmva/test/variables.C");
}
void TMVAClassificationCategory()
{
    //---------------------------------------------------------------
    // Example for usage of different event categories with classifiers

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

    bool batchMode = false;

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

    // Create the factory object (see TMVAClassification.C for more information)

    std::string factoryOptions( "!V:!Silent:Transformations=I;D;P;G,D" );
    if (batchMode) factoryOptions += ":!Color:!DrawProgressBar";

    TMVA::Factory *factory = new TMVA::Factory( "TMVAClassificationCategory", outputFile, factoryOptions );

    // Define the input variables used for the MVA training
    factory->AddVariable( "var1", 'F' );
    factory->AddVariable( "var2", 'F' );
    factory->AddVariable( "var3", 'F' );
    factory->AddVariable( "var4", '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( "eta" );

    // Load the signal and background event samples from ROOT trees
    TFile *input(0);
    TString fname( "" );
    if (UseOffsetMethod) fname = "data/toy_sigbkg_categ_offset.root";
    else                 fname = "data/toy_sigbkg_categ_varoff.root";
    if (!gSystem->AccessPathName( fname )) {
        // first we try to find tmva_example.root in the local directory
        std::cout << "--- TMVAClassificationCategory: Accessing " << fname << std::endl;
        input = TFile::Open( fname );
    }

    if (!input) {
        std::cout << "ERROR: could not open data file: " << fname << std::endl;
        exit(1);
    }

    TTree *signal     = (TTree*)input->Get("TreeS");
    TTree *background = (TTree*)input->Get("TreeB");

    /// Global event weights per tree (see below for setting event-wise weights)
    Double_t signalWeight     = 1.0;
    Double_t backgroundWeight = 1.0;

    /// You can add an arbitrary number of signal or background trees
    factory->AddSignalTree    ( signal,     signalWeight     );
    factory->AddBackgroundTree( background, backgroundWeight );

    // Apply additional cuts on the signal and background samples (can be different)
    TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
    TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";

    // Tell the factory how to use the training and testing events
    factory->PrepareTrainingAndTestTree( mycuts, mycutb,
                                         "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );

    // ---- Book MVA methods

    // Fisher discriminant
    factory->BookMethod( TMVA::Types::kFisher, "Fisher", "!H:!V:Fisher" );

    // 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" );

    // --- Categorised classifier
    TMVA::MethodCategory* mcat = 0;

    // The variable sets
    TString theCat1Vars = "var1:var2:var3:var4";
    TString theCat2Vars = (UseOffsetMethod ? "var1:var2:var3:var4" : "var1:var2:var3");

    // Fisher with categories
    TMVA::MethodBase* fiCat = factory->BookMethod( TMVA::Types::kCategory, "FisherCat","" );
    mcat = dynamic_cast<TMVA::MethodCategory*>(fiCat);
    mcat->AddMethod( "abs(eta)<=1.3", theCat1Vars, TMVA::Types::kFisher, "Category_Fisher_1","!H:!V:Fisher" );
    mcat->AddMethod( "abs(eta)>1.3",  theCat2Vars, TMVA::Types::kFisher, "Category_Fisher_2","!H:!V:Fisher" );

    // Likelihood with categories
    TMVA::MethodBase* liCat = factory->BookMethod( TMVA::Types::kCategory, "LikelihoodCat","" );
    mcat = dynamic_cast<TMVA::MethodCategory*>(liCat);
    mcat->AddMethod( "abs(eta)<=1.3",theCat1Vars, TMVA::Types::kLikelihood,
                     "Category_Likelihood_1","!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" );
    mcat->AddMethod( "abs(eta)>1.3", theCat2Vars, TMVA::Types::kLikelihood,
                     "Category_Likelihood_2","!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" );

    // ---- 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 << "==> TMVAClassificationCategory is done!" << std::endl;

    // Clean up
    delete factory;

    // Launch the GUI for the root macros
    if (!gROOT->IsBatch()) TMVAGui( outfileName );
}
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 );
}
示例#4
0
int main( int argc, char** argv )
{//main
  std::string folder;

  if (argc > 1) {
    folder = argv[1];
  }
  else {
    folder = "output_tmva/nunu/MET130/";
  }

  bool useQCD = true;
  bool useOthers = false;
  bool useOthersAsSignal = true;

  //List of input signal files
  std::vector<std::string> sigfiles;
  //sigfiles.push_back("MC_VBF_HToZZTo4Nu_M-120");
  sigfiles.push_back("MC_Powheg-Htoinv-mH125");

  if (useOthersAsSignal) {
    sigfiles.push_back("MC_TTJets");
    //powheg samples
    //sigfiles.push_back("MC_TT-v1");
    //sigfiles.push_back("MC_TT-v2");
    //
    sigfiles.push_back("MC_T-tW");
    sigfiles.push_back("MC_Tbar-tW");
    sigfiles.push_back("MC_SingleT-s-powheg-tauola");
    sigfiles.push_back("MC_SingleTBar-s-powheg-tauola");
    sigfiles.push_back("MC_SingleT-t-powheg-tauola");
    sigfiles.push_back("MC_SingleTBar-t-powheg-tauola");
    sigfiles.push_back("MC_WW-pythia6-tauola");
    sigfiles.push_back("MC_WZ-pythia6-tauola");
    sigfiles.push_back("MC_ZZ-pythia6-tauola");
    sigfiles.push_back("MC_W1JetsToLNu_enu");
    sigfiles.push_back("MC_W2JetsToLNu_enu");
    sigfiles.push_back("MC_W3JetsToLNu_enu");
    sigfiles.push_back("MC_W4JetsToLNu_enu");
    sigfiles.push_back("MC_WJetsToLNu-v1_enu");
    sigfiles.push_back("MC_WJetsToLNu-v2_enu");
    sigfiles.push_back("MC_W1JetsToLNu_munu");
    sigfiles.push_back("MC_W2JetsToLNu_munu");
    sigfiles.push_back("MC_W3JetsToLNu_munu");
    sigfiles.push_back("MC_W4JetsToLNu_munu");
    sigfiles.push_back("MC_WJetsToLNu-v1_munu");
    sigfiles.push_back("MC_WJetsToLNu-v2_munu");
    sigfiles.push_back("MC_W1JetsToLNu_taunu");
    sigfiles.push_back("MC_W2JetsToLNu_taunu");
    sigfiles.push_back("MC_W3JetsToLNu_taunu");
    sigfiles.push_back("MC_W4JetsToLNu_taunu");
    sigfiles.push_back("MC_WJetsToLNu-v1_taunu");
    sigfiles.push_back("MC_WJetsToLNu-v2_taunu");
    sigfiles.push_back("MC_DYJetsToLL");
    sigfiles.push_back("MC_DY1JetsToLL");
    sigfiles.push_back("MC_DY2JetsToLL");
    sigfiles.push_back("MC_DY3JetsToLL");
    sigfiles.push_back("MC_DY4JetsToLL");
    sigfiles.push_back("MC_ZJetsToNuNu_100_HT_200");
    sigfiles.push_back("MC_ZJetsToNuNu_200_HT_400");
    sigfiles.push_back("MC_ZJetsToNuNu_400_HT_inf");
    sigfiles.push_back("MC_ZJetsToNuNu_50_HT_100");
    sigfiles.push_back("MC_GJets-HT-200To400-madgraph");
    sigfiles.push_back("MC_GJets-HT-400ToInf-madgraph");
    sigfiles.push_back("MC_WGamma");
    sigfiles.push_back("MC_EWK-Z2j");
    sigfiles.push_back("MC_EWK-Z2jiglep");
    sigfiles.push_back("MC_EWK-W2jminus_enu");
    sigfiles.push_back("MC_EWK-W2jplus_enu");
    sigfiles.push_back("MC_EWK-W2jminus_munu");
    sigfiles.push_back("MC_EWK-W2jplus_munu");
    sigfiles.push_back("MC_EWK-W2jminus_taunu");
    sigfiles.push_back("MC_EWK-W2jplus_taunu");
  }

  //List of input files
  std::vector<std::string> bkgfiles;
  if (useQCD){
    bkgfiles.push_back("MC_QCD-Pt-30to50-pythia6");
    bkgfiles.push_back("MC_QCD-Pt-50to80-pythia6");
    bkgfiles.push_back("MC_QCD-Pt-80to120-pythia6");
    bkgfiles.push_back("MC_QCD-Pt-120to170-pythia6");
    bkgfiles.push_back("MC_QCD-Pt-170to300-pythia6");
    bkgfiles.push_back("MC_QCD-Pt-300to470-pythia6");
    bkgfiles.push_back("MC_QCD-Pt-470to600-pythia6");
    bkgfiles.push_back("MC_QCD-Pt-600to800-pythia6");
    bkgfiles.push_back("MC_QCD-Pt-800to1000-pythia6");
    bkgfiles.push_back("MC_QCD-Pt-1000to1400-pythia6");
    bkgfiles.push_back("MC_QCD-Pt-1400to1800-pythia6");
    bkgfiles.push_back("MC_QCD-Pt-1800-pythia6");
  }
  if (useOthers) {
    bkgfiles.push_back("MC_TTJets");
    //powheg samples
    //bkgfiles.push_back("MC_TT-v1");
    //bkgfiles.push_back("MC_TT-v2");
    //
    bkgfiles.push_back("MC_T-tW");
    bkgfiles.push_back("MC_Tbar-tW");
    bkgfiles.push_back("MC_SingleT-s-powheg-tauola");
    bkgfiles.push_back("MC_SingleTBar-s-powheg-tauola");
    bkgfiles.push_back("MC_SingleT-t-powheg-tauola");
    bkgfiles.push_back("MC_SingleTBar-t-powheg-tauola");
    bkgfiles.push_back("MC_WW-pythia6-tauola");
    bkgfiles.push_back("MC_WZ-pythia6-tauola");
    bkgfiles.push_back("MC_ZZ-pythia6-tauola");
    bkgfiles.push_back("MC_W1JetsToLNu_enu");
    bkgfiles.push_back("MC_W2JetsToLNu_enu");
    bkgfiles.push_back("MC_W3JetsToLNu_enu");
    bkgfiles.push_back("MC_W4JetsToLNu_enu");
    bkgfiles.push_back("MC_WJetsToLNu-v1_enu");
    bkgfiles.push_back("MC_WJetsToLNu-v2_enu");
    bkgfiles.push_back("MC_W1JetsToLNu_munu");
    bkgfiles.push_back("MC_W2JetsToLNu_munu");
    bkgfiles.push_back("MC_W3JetsToLNu_munu");
    bkgfiles.push_back("MC_W4JetsToLNu_munu");
    bkgfiles.push_back("MC_WJetsToLNu-v1_munu");
    bkgfiles.push_back("MC_WJetsToLNu-v2_munu");
    bkgfiles.push_back("MC_W1JetsToLNu_taunu");
    bkgfiles.push_back("MC_W2JetsToLNu_taunu");
    bkgfiles.push_back("MC_W3JetsToLNu_taunu");
    bkgfiles.push_back("MC_W4JetsToLNu_taunu");
    bkgfiles.push_back("MC_WJetsToLNu-v1_taunu");
    bkgfiles.push_back("MC_WJetsToLNu-v2_taunu");
    bkgfiles.push_back("MC_DYJetsToLL");
    bkgfiles.push_back("MC_DY1JetsToLL");
    bkgfiles.push_back("MC_DY2JetsToLL");
    bkgfiles.push_back("MC_DY3JetsToLL");
    bkgfiles.push_back("MC_DY4JetsToLL");
    bkgfiles.push_back("MC_ZJetsToNuNu_100_HT_200");
    bkgfiles.push_back("MC_ZJetsToNuNu_200_HT_400");
    bkgfiles.push_back("MC_ZJetsToNuNu_400_HT_inf");
    bkgfiles.push_back("MC_ZJetsToNuNu_50_HT_100");
    bkgfiles.push_back("MC_GJets-HT-200To400-madgraph");
    bkgfiles.push_back("MC_GJets-HT-400ToInf-madgraph");
    bkgfiles.push_back("MC_WGamma");
    bkgfiles.push_back("MC_EWK-Z2j");
    bkgfiles.push_back("MC_EWK-Z2jiglep");
    bkgfiles.push_back("MC_EWK-W2jminus_enu");
    bkgfiles.push_back("MC_EWK-W2jplus_enu");
    bkgfiles.push_back("MC_EWK-W2jminus_munu");
    bkgfiles.push_back("MC_EWK-W2jplus_munu");
    bkgfiles.push_back("MC_EWK-W2jminus_taunu");
    bkgfiles.push_back("MC_EWK-W2jplus_taunu");
  }

   // Create a ROOT output file where TMVA will store ntuples, histograms, etc.
  TFile *output_tmva = TFile::Open((folder+"/TMVA_QCDrej.root").c_str(),"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", output_tmva,
                                               "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );


  //fill the variables with event weight from the trees
  //const unsigned nVars = 4;

   
   factory->AddSpectator("jet1_pt","Jet 1 p_{T}", "GeV", 'F');
   factory->AddSpectator("jet2_pt","Jet 2 p_{T}", "GeV", 'F');
   factory->AddSpectator("jet1_eta","Jet 1 #eta", "", 'F');
   factory->AddVariable("jet2_eta","Jet 2 #eta", "", 'F');// **
   factory->AddSpectator("jet1_phi","Jet 1 #phi", "", 'F');
   factory->AddSpectator("jet2_phi","Jet 2 #phi", "", 'F');
   factory->AddSpectator("dijet_M","M_{jj}", " GeV", 'F');
   factory->AddSpectator("dijet_deta","#Delta#eta_{jj}", "", 'F');
   factory->AddSpectator("dijet_sumeta","#eta_{j1}+#eta_{j2}", "", 'F');
   factory->AddSpectator("dijet_dphi","#Delta#phi_{jj}", "", 'F');
   factory->AddSpectator("met","MET", "GeV", 'F');// **
   factory->AddSpectator("met_phi","MET #phi", "", 'F');
   factory->AddVariable("met_significance","MET significance", "", 'F');// **
   factory->AddSpectator("sumet","#Sum E_{T}", "GeV", 'F');
   factory->AddSpectator("ht","H_{T}", "GeV", 'F');
   factory->AddVariable("mht","MH_{T}", "GeV", 'F');// **
   factory->AddSpectator("sqrt_ht","#sqrt{H_{T}}", "GeV^{0.5}", 'F');
   factory->AddSpectator("unclustered_et","Unclustered E_{T}", "GeV", 'F');
   factory->AddSpectator("unclustered_phi","Unclustered #phi", "GeV", 'F');
   factory->AddSpectator("jet1met_dphi","#Delta#phi(MET,jet1)", "", 'F');
   factory->AddVariable("jet2met_dphi","#Delta#phi(MET,jet2)", "", 'F');// **
   factory->AddVariable("jetmet_mindphi","minimum #Delta#phi(MET,jet)", "", 'F');// **
   factory->AddVariable("jetunclet_mindphi","minimum #Delta#phi(unclustered,jet)", "",  'F');// **
   factory->AddVariable("metunclet_dphi","#Delta#phi(MET,unclustered)", "",  'F');// **
   factory->AddVariable("dijetmet_scalarSum_pt", "p_{T}^{jet1}+p_{T}^{jet2}+MET", "GeV", 'F');// **
   factory->AddSpectator("dijetmet_vectorialSum_pt","p_{T}(#vec{j1}+#vec{j2}+#vec{MET})", "GeV", 'F');
   factory->AddVariable("dijetmet_ptfraction","p_{T}^{dijet}/(p_{T}^{dijet}+MET)", "", 'F');// **
   //factory->AddVariable("jet1met_scalarprod := (jet1_pt*cos(jet1_phi)*met_x+jet1_pt*sin(jet1_phi)*met_y)/met", "#vec{p_{T}^{jet1}}.#vec{MET}/MET", "GeV" , 'F');
   //factory->AddVariable("jet2met_scalarprod := (jet2_pt*cos(jet2_phi)*met_x+jet2_pt*sin(jet2_phi)*met_y)/met", "#vec{p_{T}^{jet2}}.#vec{MET}/MET", "GeV" , 'F');
   factory->AddVariable("jet1met_scalarprod", "#vec{p_{T}^{jet1}}.#vec{MET}/MET", "GeV" , 'F');// **
   factory->AddVariable("jet2met_scalarprod", "#vec{p_{T}^{jet2}}.#vec{MET}/MET", "GeV" , 'F');// **
   factory->AddVariable("jet1met_scalarprod_frac := jet1met_scalarprod/met", "#vec{p_{T}^{jet1}}.#vec{MET}/MET^{2}", "" , 'F');// **
   factory->AddVariable("jet2met_scalarprod_frac := jet2met_scalarprod/met", "#vec{p_{T}^{jet2}}.#vec{MET}/MET^{2}", "" , 'F');// **
   factory->AddSpectator("n_jets_cjv_30","CJV jets (30 GeV)", "" , 'I');
   factory->AddSpectator("n_jets_cjv_20EB_30EE","CJV jets (|#eta|<2.4 and 20 GeV, or 30 GeV)", "" , 'I');
   

   //test with only VBF variables used in cut-based analysis
   //factory->AddVariable("dijet_M","M_{jj}", " GeV", 'F');
   //factory->AddVariable("dijet_deta","#Delta#eta_{jj}", "", 'F');
   //factory->AddVariable("dijet_dphi","#Delta#phi_{jj}", "", 'F');
   //factory->AddVariable("met","MET", "GeV", 'F');
   //factory->AddVariable("n_jets_cjv_30","CJV jets (30 GeV)", "" , 'I');


  //get input files
  //signal
  //TFile *signalfile = TFile::Open((folder+"/"+"MC_VBF_HToZZTo4Nu_M-120.root").c_str());
  //TTree *signal = (TTree*)signalfile->Get("TmvaInputTree");
  //Double_t signalWeight     = 1.0;
  //factory->AddSignalTree(signal,signalWeight);
  //Set individual event weights (the variables must exist in the original TTree)
  //factory->SetSignalWeightExpression("total_weight");

  //background
  std::map<std::string, TFile *> tfiles;
  for (unsigned i = 0; i < bkgfiles.size(); ++i) {
    std::string filename = (bkgfiles[i]+".root");
    TFile * tmp = new TFile((folder+"/"+filename).c_str());
    if (!tmp) {
      std::cerr << "Warning, file " << filename << " could not be opened." << std::endl;
    } else {
      tfiles[bkgfiles[i]] = tmp;      
    }
  }
  TTree *background[bkgfiles.size()];

  //signal
  std::map<std::string, TFile *> sfiles;
  for (unsigned i = 0; i < sigfiles.size(); ++i) {
    std::string filename = (sigfiles[i]+".root");
    TFile * tmp = new TFile((folder+"/"+filename).c_str());
    if (!tmp) {
      std::cerr << "Warning, file " << filename << " could not be opened." << std::endl;
    } else {
      sfiles[sigfiles[i]] = tmp;      
    }
  }
  TTree *signal[sigfiles.size()];

  for (unsigned i = 0; i < bkgfiles.size(); ++i) {

    std::string f = bkgfiles[i];
    if (tfiles[f]){
      background[i] = (TTree*)tfiles[f]->Get("TmvaInputTree");
      //if (f.find("QCD-Pt")!=f.npos){
      //}
      Double_t backgroundWeight = 1.0;
      factory->AddBackgroundTree(background[i],backgroundWeight);
      factory->SetBackgroundWeightExpression("total_weight");

    }//if file exist
    else {
      std::cout << " Cannot find background file " << f << std::endl;
    }
  }//loop on files

  for (unsigned i = 0; i < sigfiles.size(); ++i) {

    std::string f = sigfiles[i];
    if (sfiles[f]){
      signal[i] = (TTree*)sfiles[f]->Get("TmvaInputTree");
      //if (f.find("QCD-Pt")!=f.npos){
      //}
      Double_t signalWeight = 1.0;
      factory->AddSignalTree(signal[i],signalWeight);
      factory->SetSignalWeightExpression("total_weight");

    }//if file exist
    else {
      std::cout << " Cannot find signal file " << f << std::endl;
    }
  }//loop on files


   // Apply additional cuts on the signal and background samples (can be different)
  TCut mycuts = "";//dijet_deta>3.8 && dijet_M > 1100 && met > 100 && met_significance>5";
  TCut mycutb = "";//dijet_deta>3.8 && dijet_M > 1100 && met > 100 && met_significance>5";

  factory->PrepareTrainingAndTestTree( mycuts, mycutb,
				       "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );
  


   // Likelihood ("naive Bayes estimator")
  //factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood",
  //"H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" );

  // Linear discriminant (same as Fisher discriminant)
  //factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );

  // Fisher discriminant (same as LD)
  factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );

  // TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons
  //factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=60:HiddenLayers=N+5:TestRate=5:!UseRegulator" );

  // Boosted Decision Trees
  // Gradient Boost
  //factory->BookMethod( TMVA::Types::kBDT, "BDTG",
  //"!H:!V:NTrees=1000:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=2" );
  //factory->BookMethod( TMVA::Types::kBDT, "BDTG",
  //                       "!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.10:nCuts=20:MaxDepth=2" );


  // Adaptive Boost
  //factory->BookMethod( TMVA::Types::kBDT, "BDT1000",
  //	       "!H:!V:NTrees=1000:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20" );

  factory->BookMethod( TMVA::Types::kBDT, "BDT",
		       "!H:!V:NTrees=1000:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.2:SeparationType=GiniIndex:nCuts=20" );

  // Bagging
  //factory->BookMethod( TMVA::Types::kBDT, "BDTB",
  //                       "!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20" );

  // Decorrelation + Adaptive Boost
  //factory->BookMethod( TMVA::Types::kBDT, "BDTD",
  //                       "!H:!V:NTrees=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:VarTransform=Decorrelate" );

  // Allow Using Fisher discriminant in node splitting for (strong) linearly correlated variables
  //factory->BookMethod( TMVA::Types::kBDT, "BDTMitFisher",
  //       "!H:!V:NTrees=50:UseFisherCuts:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20" );

   // 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
   output_tmva->Close();

   std::cout << "==> Wrote root file: " << output_tmva->GetName() << std::endl
             << "==> TMVAClassification is done!" << std::endl
             << std::endl
             << "==> To view the results, launch the GUI: \"root -l ./TMVAGui.C\"" << std::endl
             << std::endl;

   // Clean up
   delete factory;

  return 0;
}//main
//require mumucl>0.6
//opening angle >10
//coplanarity >90
//pang<90
void TMVAClassification_cc1pcoh_bdt_ver6noveract( TString myMethodList = "" )
{
    //---------------------------------------------------------------
    // This loads the library
    TMVA::Tools::Instance();

    // to get access to the GUI and all tmva macros
    TString thisdir = gSystem->DirName(gInterpreter->GetCurrentMacroName());
    gROOT->SetMacroPath(thisdir + ":" + gROOT->GetMacroPath());
    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"]           = 1;
    Use["CutsPCA"]         = 0;
    Use["CutsGA"]          = 0;
    Use["CutsSA"]          = 0;
    //
    // --- 1-dimensional likelihood ("naive Bayes estimator")
    Use["Likelihood"]      = 1;
    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;
    //
    // --- Mutidimensional likelihood and Nearest-Neighbour methods
    Use["PDERS"]           = 1;
    Use["PDERSD"]          = 0;
    Use["PDERSPCA"]        = 0;
    Use["PDEFoam"]         = 1;
    Use["PDEFoamBoost"]    = 0; // uses generalised MVA method boosting
    Use["KNN"]             = 1; // k-nearest neighbour method
    //
    // --- Linear Discriminant Analysis
    Use["LD"]              = 1; // Linear Discriminant identical to Fisher
    Use["Fisher"]          = 0;
    Use["FisherG"]         = 0;
    Use["BoostedFisher"]   = 0; // uses generalised MVA method boosting
    Use["HMatrix"]         = 0;
    //
    // --- Function Discriminant analysis
    Use["FDA_GA"]          = 1; // minimisation of user-defined function using Genetics Algorithm
    Use["FDA_SA"]          = 0;
    Use["FDA_MC"]          = 0;
    Use["FDA_MT"]          = 0;
    Use["FDA_GAMT"]        = 0;
    Use["FDA_MCMT"]        = 0;
    //
    // --- Neural Networks (all are feed-forward Multilayer Perceptrons)
    Use["MLP"]             = 0; // Recommended ANN
    Use["MLPBFGS"]         = 0; // Recommended ANN with optional training method
    Use["MLPBNN"]          = 1; // Recommended ANN with BFGS training method and bayesian regulator
    Use["CFMlpANN"]        = 0; // Depreciated ANN from ALEPH
    Use["TMlpANN"]         = 0; // ROOT's own ANN
    //
    // --- Support Vector Machine
    Use["SVM"]             = 1;
    //
    // --- Boosted Decision Trees
    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"]         = 1;
    
    // ---------------------------------------------------------------
    // Choose method
    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;
        }
    }

    // ---------------------------------------------------------------
    // --- Here the preparation phase begins

    // Create a ROOT output file where TMVA will store ntuples, histograms, etc.
    TString outfileName( "TMVA_cc1pcoh_bdt_ver6noveract.root" );//newchange
    TFile* outputFile = TFile::Open( outfileName, "RECREATE" );

    // Create the factory object.
    TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification_ver6noveract", outputFile,//newchange
                                               "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );

    
    // Add variable
    //sprintf(select,  "Ntrack==2&&mumucl>0.6&&pmucl>0.25&&pang<90&&muang_t<15 && veract*7.66339869e-2<34");
    //factory->AddVariable( "Ntrack", 'F' );
    factory->AddVariable( "mumucl", 'F' );
    factory->AddVariable( "pmucl", 'F' );
    factory->AddVariable( "pang_t", 'F' );//use pang instead of pang_t
    factory->AddVariable( "muang_t", 'F' );
    //factory->AddVariable( "veract", 'F' );
    factory->AddVariable( "ppe", 'F');
    factory->AddVariable( "mupe", 'F');
    factory->AddVariable( "range", 'F');
    factory->AddVariable( "coplanarity", 'F');
    factory->AddVariable( "opening", 'F');//newadd

    // Add spectator
    factory->AddSpectator( "fileIndex", 'I' );
    factory->AddSpectator( "nuE", 'F' );
    factory->AddSpectator( "inttype", 'I' );
    factory->AddSpectator( "norm", 'F' );
    factory->AddSpectator( "totcrsne", 'F' );
    factory->AddSpectator( "veract", 'F' );
    factory->AddSpectator( "pang", 'F' );
    factory->AddSpectator( "mupdg", 'I' );
    factory->AddSpectator( "ppdg", 'I' );

    // ---------------------------------------------------------------
    // --- Get weight
    TString fratioStr="/home/kikawa/macros/nd34_tuned_11bv3.1_250ka.root";
    
    
    
    // ---------------------------------------------------------------
    // --- Add sample
    TString fsignalStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/pm_merged_ccqe_tot.root";
    TString fbarStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/pmbar_merged_ccqe.root";
    TString fbkgStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/wall_merged_ccqe_tot.root";
    TString fbkg2Str="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/ingrid_merged_nd3_ccqe_tot.root";
    /*TString fsignalStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmvafix/pm_merged_ccqe_tot.root";
    TString fbarStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmvafix/pmbar_merged_ccqe.root";
    TString fbkgStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmvafix/wall_merged_ccqe_tot.root";
    TString fbkg2Str="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmvafix/ingrid_merged_nd3_ccqe_tot.root";*/

    
    TFile *pfileSignal = new TFile(fsignalStr);
    TFile *pfileBar = new TFile(fbarStr);
    TFile *pfileBkg = new TFile(fbkgStr);
    TFile *pfileBkg2 = new TFile(fbkg2Str);
    TFile *pfileRatio = new TFile(fratioStr);
    
    TTree *ptree_sig  = (TTree*)pfileSignal->Get("tree");
    TTree *ptree_bar  = (TTree*)pfileBar->Get("tree");
    TTree *ptree_bkg   = (TTree*)pfileBkg->Get("tree");
    TTree *ptree_bkg2  = (TTree*)pfileBkg2->Get("tree");
    
    // POT normalization
    const int   nmcFile  = 3950;
    const int   nbarFile  = 986;
    const int   nbkgFile  = 55546;//(31085+24461);
    const int   nbkg2File  = 7882;//(3941+3941);
    

   
    // global event weights per tree (see below for setting event-wise weights)
    // adding for signal sample
    // using this as standard and add other later
    Double_t signalWeight_sig     = 1.0;
    Double_t backgroundWeight_sig = 1.0;
   
    factory->AddSignalTree    ( ptree_sig,     signalWeight_sig );
    factory->AddBackgroundTree( ptree_sig, backgroundWeight_sig );
    
    // Add Numubar sample
    //Double_t signalWeight_bar     = nmcFile/float(nbarFile);
    Double_t backgroundWeight_bar = nmcFile/float(nbarFile);
    
    //factory->AddSignalTree    ( ptree_bar,     signalWeight_bar );
    factory->AddBackgroundTree( ptree_bar, backgroundWeight_bar );
    
    // Add wall background
    //Double_t signalWeight_bkg     = nmcFile/float(nbkgFile);
    Double_t backgroundWeight_bkg = nmcFile/float(nbkgFile);
    
    //factory->AddSignalTree    ( ptree_bkg,     signalWeight_bkg );
    factory->AddBackgroundTree( ptree_bkg, backgroundWeight_bkg );
    
    // Add INGRID background
    //Double_t signalWeight_bkg2     = nmcFile/float(nbkg2File);
    Double_t backgroundWeight_bkg2 = nmcFile/float(nbkg2File);
    
    //factory->AddSignalTree    ( ptree_bkg2,     signalWeight_bkg2 );
    factory->AddBackgroundTree( ptree_bkg2, backgroundWeight_bkg2 );
    
   
   
    //factory->SetSignalWeightExpression    ("norm*totcrsne*2.8647e-13");
    //factory->SetBackgroundWeightExpression( "norm*totcrsne*2.8647e-13" );

   // Apply additional cuts on the signal and background samples (can be different)
   TCut mycuts = "Ntrack==2 && abs(inttype)==16 && fileIndex==1 && pang<90 && mumucl>0.6 && opening>10 && coplanarity>90 && pmucl>0.2"; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
   TCut mycutb = "Ntrack==2 && (abs(inttype)!=16 || fileIndex>1) && pang<90 && mumucl>0.6 && opening>10 && coplanarity>90 && pmucl>0.2"; // 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" );
   factory->PrepareTrainingAndTestTree( mycuts, mycutb,
                                        "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!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 ("naive Bayes estimator")
   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" );

   // 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" );

   // PCA-transformed likelihood
   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" ); 

   // Use a kernel density estimator to approximate the PDFs
   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" ); 

   // Use a variable-dependent mix of splines and kernel density estimator
   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["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=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Nmin=100:Kernel=None:Compress=T" );

   if (Use["PDEFoamBoost"])
      factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoamBoost",
                           "!H:!V:Boost_Num=30:Boost_Transform=linear:SigBgSeparate=F:MaxDepth=4:UseYesNoCell=T:DTLogic=MisClassificationError:FillFoamWithOrigWeights=F:TailCut=0:nActiveCells=500:nBin=20:Nmin=400: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:VarTransform=None" );

   // Linear discriminant (same as Fisher discriminant)
   if (Use["LD"])
      factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );

   // Fisher discriminant (same as LD)
   if (Use["Fisher"])
      factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50: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:!Boost_DetailedMonitoring" );

   // 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:!UseRegulator" );

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

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

   // 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:MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=2" );

   /*if (Use["BDT"])  // Adaptive Boost
      factory->BookMethod( TMVA::Types::kBDT, "BDT",
                           "!H:!V:NTrees=850:MinNodeSize=2.5%:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" );*/
    if (Use["BDT"])  // Adaptive Boost
        factory->BookMethod( TMVA::Types::kBDT, "BDT",
                            "!H:!V:NTrees=850:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20" );

   if (Use["BDTB"]) // Bagging
      factory->BookMethod( TMVA::Types::kBDT, "BDTB",
                           "!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20" );

   if (Use["BDTD"]) // Decorrelation + Adaptive Boost
      factory->BookMethod( TMVA::Types::kBDT, "BDTD",
                           "!H:!V:NTrees=400:MinNodeSize=5%:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:VarTransform=Decorrelate" );

   if (Use["BDTF"])  // Allow Using Fisher discriminant in node splitting for (strong) linearly correlated variables
      factory->BookMethod( TMVA::Types::kBDT, "BDTMitFisher",
                           "!H:!V:NTrees=50:MinNodeSize=2.5%:UseFisherCuts:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20" );

   // 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" );

   // 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 );
}
void TMVAClassification( ) 
{
   // this loads the library
   TMVA::Tools::Instance();
   std::cout << std::endl;
   std::cout << "==> Start TMVAClassification" << std::endl;

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

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

   // ---------- input variables 
   factory->AddVariable("iso12x12", 'F');
   factory->AddVariable("iso4x4", 'F');
   factory->AddVariable("isoLshaped", 'F');
   factory->AddVariable("PUM0", 'F');

   // ---------- spectators ----------------
   factory->AddSpectator("pt", "F");
   factory->AddSpectator("eta", "F");
   factory->AddSpectator("phi", "F");
   factory->AddSpectator("e", "F");

   // read training and test data
   TString fSig = "DrellYan_Zee.root";
   TString fBkg = "MinBias.root";

   TFile *fileSig = TFile::Open( fSig );
   TFile *fileBkg = TFile::Open( fBkg );
   TTree *signal     = (TTree*)fileSig->Get("tree");
   TTree *background = (TTree*)fileBkg->Get("tree");
   

   // global event weights per tree (see below for setting event-wise weights)
   Double_t signalWeight     = 1.0;
   Double_t backgroundWeight = 1.0;

   // ====== register trees ====================================================
   // you can add an arbitrary number of signal or background trees
   factory->AddSignalTree    ( signal,     signalWeight     );
   factory->AddBackgroundTree( background, backgroundWeight );
   TCut mycuts = "pt > 10"; 
   TCut mycutb = "pt > 10"; 


   // tell the factory to use all remaining events in the trees after training for testing:
   factory->PrepareTrainingAndTestTree( mycuts, mycutb,
                                        "nTrain_Signal=0:nTrain_Background=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:

   // ---- Book MVA methods
   // Boosted Decision Trees
   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->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 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 );

}
示例#8
0
void ZTMVAClassification( TString myMethodList = "" )
{
   

   //---------------------------------------------------------------
   // This loads the library
   TMVA::Tools::Instance();

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

   // --- Cut optimisation
   Use["Cuts"]            = 0;
   Use["CutsD"]           = 0;
   Use["CutsPCA"]         = 0;
   Use["CutsGA"]          = 0;
   Use["CutsSA"]          = 0;
   // 
   // --- 1-dimensional likelihood ("naive Bayes estimator")
   Use["Likelihood"]      = 0;
   Use["LikelihoodD"]     = 0; // the "D" extension indicates decorrelated input variables (see option strings)
   Use["LikelihoodPCA"]   = 0; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
   Use["LikelihoodKDE"]   = 0;
   Use["LikelihoodMIX"]   = 0;
   //
   // --- Mutidimensional likelihood and Nearest-Neighbour methods
   Use["PDERS"]           = 0;
   Use["PDERSD"]          = 0;
   Use["PDERSPCA"]        = 0;
   Use["PDEFoam"]         = 0;
   Use["PDEFoamBoost"]    = 0; // uses generalised MVA method boosting
   Use["KNN"]             = 0; // k-nearest neighbour method
   //
   // --- Linear Discriminant Analysis
   Use["LD"]              = 0; // Linear Discriminant identical to Fisher
   Use["Fisher"]          = 0;
   Use["FisherG"]         = 0;
   Use["BoostedFisher"]   = 0; // uses generalised MVA method boosting
   Use["HMatrix"]         = 0;
   //
   // --- Function Discriminant analysis
   Use["FDA_GA"]          = 0; // minimisation of user-defined function using Genetics Algorithm
   Use["FDA_SA"]          = 0;
   Use["FDA_MC"]          = 0;
   Use["FDA_MT"]          = 0;
   Use["FDA_GAMT"]        = 0;
   Use["FDA_MCMT"]        = 0;
   //
   // --- Neural Networks (all are feed-forward Multilayer Perceptrons)
   Use["MLP"]             = 1; // Recommended ANN
   Use["MLPBFGS"]         = 0; // Recommended ANN with optional training method
   Use["MLPBNN"]          = 0; // Recommended ANN with BFGS training method and bayesian regulator
   Use["CFMlpANN"]        = 0; // Depreciated ANN from ALEPH
   Use["TMlpANN"]         = 0; // ROOT's own ANN
   //
   // --- Support Vector Machine 
   Use["SVM"]             = 0;
   // 
   // --- Boosted Decision Trees
   Use["BDT"]             = 1; // uses Adaptive Boost
   Use["BDTG"]            = 1; // uses Gradient Boost
   Use["BDTB"]            = 0; // uses Bagging
   Use["BDTD"]            = 1; // 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;



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

   // --- 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" );


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

   //factory->AddVariable( "maxpioneta", "maxpioneta", "", 'F' );
   //factory->AddVariable( "minpioneta", "minpioneta", "", 'F' );
   factory->AddVariable( "nTT", "nTT", "", 'F' );
   // factory->AddVariable( "pidpimin", "pidpimin", "", 'F' );
   // factory->AddVariable( "pidpimax", "pidpimax", "", 'F' );
   factory->AddVariable( "normxpt", "normxpt", "", 'F' );
   factory->AddVariable( "eta", "eta", "", 'F' );
   //factory->AddVariable( "phi", "phi", "", 'F' );
   //  factory->AddVariable( "normptsum", "normptsum", "", 'F' );
    //factory->AddVariable( "ptAsym", "ptAsym", "", 'F' );
   //factory->AddVariable( "dphimax", "dphimax", "", 'F' );
 //factory->AddVariable( "dphimin", "dphimin", "", 'F' );
   //factory->AddVariable( "drmax", "drmax", "", 'F' );
   // factory->AddVariable( "drmin", "drmin", "", 'F' );
    //    factory->AddVariable( "normpionp", "normpionp", "", 'F' );
    factory->AddVariable( "normminpionpt", "normminpionpt", "", 'F' );
    //factory->AddVariable( "normminpionp", "normminpionp", "", 'F' );
    factory->AddVariable( "normmaxpionpt", "normmaxpionpt", "", 'F' );
    //  factory->AddVariable( "normptj", "normptj", "", 'F' );
    //factory->AddVariable( "jmasspull", "jmasspull", "", 'F' );
    //factory->AddVariable( "vchi2dof", "vchi2dof", "", 'F' );
    //    factory->AddVariable("maxchi2","maxchi2","", 'F');  
    // factory->AddVariable("normr","normr","", 'F');    
    //    factory->AddVariable("normq","normq","", 'F'); 
    //factory->AddVariable("normminm","normminm","", 'F'); 
    factory->AddVariable("logipmax","logipmax","", 'F'); 
    factory->AddVariable("logipmin","logipmin","", 'F'); 
    factory->AddVariable("logfd","logfd",'F');
    factory->AddVariable("logvd","logvd",'F');
    //factory->AddVariable("pointAngle","pointingAngle",'F');
    factory->AddVariable("logvpi","",'F');
    //factory->AddVariable("logmaxprob","",'F');
    //factory->AddVariable("logminprob","",'F');
 
    factory->AddSpectator( "mReFit", "mReFit", "", 'D' );
    //    factory->AddSpectator( "Qdecay", "Qdecay", "",'F' );
    //  factory->AddSpectator( "m23", "m23", "",'F' );
    
    //   TFile * input_Background = new TFile("../back.root");
   TFile * input_Signal = new TFile("../cmx12.root");
   TFile * input_Background = new TFile("../background12.root");
   std::cout << "--- TMVAClassification       : Using input file for signal    : " << input_Signal->GetName() << std::endl;
   std::cout << "--- TMVAClassification       : Using input file for backgound : " << input_Background->GetName() << std::endl;
   
   // --- Register the training and test trees

   TTree *signal     = (TTree*)input_Signal->Get("psiCand");
   TTree *background = (TTree*)input_Background->Get("psiCand");
   
   // global event weights per tree (see below for setting event-wise weights)
   Double_t signalWeight     = 1.0;
   Double_t backgroundWeight = 1.0;
   
   // You can add an arbitrary number of signal or background trees
   factory->AddSignalTree    ( signal,     signalWeight     );
   factory->AddBackgroundTree( background, backgroundWeight );
   

   // Apply additional cuts on the signal and background samples (can be different)
   TCut mycuts = "QDecay < 300&&fdchi2 > 300"; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
   TCut mycutb = "QDecay < 300&&fdchi2> 300"; // for example: TCut mycutb = "abs(var1)<0.5";

   
   factory->PrepareTrainingAndTestTree( mycuts, mycutb,
                                        "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!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 ("naive Bayes estimator")
   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" );

   // 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" );

   // PCA-transformed likelihood
   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" ); 

   // Use a kernel density estimator to approximate the PDFs
   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" ); 

   // Use a variable-dependent mix of splines and kernel density estimator
   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["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=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Nmin=100:Kernel=None:Compress=T" );

   if (Use["PDEFoamBoost"])
      factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoamBoost",
                           "!H:!V:Boost_Num=30:Boost_Transform=linear:SigBgSeparate=F:MaxDepth=4:UseYesNoCell=T:DTLogic=MisClassificationError:FillFoamWithOrigWeights=F:TailCut=0:nActiveCells=500:nBin=20:Nmin=400: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:VarTransform=None" );

   // Linear discriminant (same as Fisher discriminant)
   if (Use["LD"])
      factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );

   // Fisher discriminant (same as LD)
   if (Use["Fisher"])
      factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50: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" );

   // 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:!UseRegulator" );
     //factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=Norm:NCycles=600:HiddenLayers=N+5:TestRate=5" );
     // factory->BookMethod( TMVA::Types::kMLP, "MLPCE", "H:!V:NeuronType=sigmoid:VarTransform=Norm:NCycles=600:HiddenLayers=N+5:TestRate=5:EstimatorType=CE" );
     factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=sigmoid:VarTransform=Norm:NCycles=600:HiddenLayers=9:TestRate=5:EstimatorType=CE" );
     //   factory->BookMethod( TMVA::Types::kMLP, "MLPCE83", "H:!V:NeuronType=tanh:VarTransform=Norm:NCycles=600:HiddenLayers=8,3:TestRate=5:EstimatorType=CE" );
   }

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

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

   // 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=0.10:UseBaggedGrad:GradBaggingFraction=0.5:nCuts=20:NNodesMax=5" );
      //  factory->BookMethod( TMVA::Types::kBDT, "BDTGI",
      //                     "!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.10:UseBaggedGrad:GradBaggingFraction=0.5:nCuts=20:NNodesMax=5:SeparationType=GiniIndexWithLaplace" );

      //     factory->BookMethod( TMVA::Types::kBDT, "BDTG6",
      //                      "!H:!V:NTrees=600:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.4:nCuts=20:NNodesMax=6" );
      //factory->BookMethod( TMVA::Types::kBDT, "BDTG2",
      //                    "!H:!V:NTrees=800:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.4:nCuts=20:NNodesMax=6" );
      factory->BookMethod( TMVA::Types::kBDT, "BDTG3",
                           "!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.4:nCuts=20:NNodesMax=6" );
      // factory->BookMethod( TMVA::Types::kBDT, "BDTG4",
      //                     "!H:!V:NTrees=1200:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.4:nCuts=20:NNodesMax=6" );
      // factory->BookMethod( TMVA::Types::kBDT, "BDTG5",
      //                     "!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.4:nCuts=20:NNodesMax=5" );

  }
   if (Use["BDT"])  // Adaptive Boost
      factory->BookMethod( TMVA::Types::kBDT, "BDT",
                           "!H:!V:NTrees=850:nEventsMin=150:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5: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" );

   if (Use["BDTF"])  // Allow Using Fisher discriminant in node splitting for (strong) linearly correlated variables
      factory->BookMethod( TMVA::Types::kBDT, "BDTMitFisher",
                           "!H:!V:NTrees=50:nEventsMin=150:UseFisherCuts:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );

   // 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" );

   // 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 TMVAClassification( 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();

   // to get access to the GUI and all tmva macros
   TString thisdir = gSystem->DirName(gInterpreter->GetCurrentMacroName());
   gROOT->SetMacroPath(thisdir + ":" + gROOT->GetMacroPath());
   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"]           = 1;
   Use["CutsPCA"]         = 0;
   Use["CutsGA"]          = 0;
   Use["CutsSA"]          = 0;
   // 
   // --- 1-dimensional likelihood ("naive Bayes estimator")
   Use["Likelihood"]      = 1;
   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;
   //
   // --- Mutidimensional likelihood and Nearest-Neighbour methods
   Use["PDERS"]           = 1;
   Use["PDERSD"]          = 0;
   Use["PDERSPCA"]        = 0;
   Use["PDEFoam"]         = 1;
   Use["PDEFoamBoost"]    = 0; // uses generalised MVA method boosting
   Use["KNN"]             = 1; // k-nearest neighbour method
   //
   // --- Linear Discriminant Analysis
   Use["LD"]              = 1; // Linear Discriminant identical to Fisher
   Use["Fisher"]          = 0;
   Use["FisherG"]         = 0;
   Use["BoostedFisher"]   = 0; // uses generalised MVA method boosting
   Use["HMatrix"]         = 0;
   //
   // --- Function Discriminant analysis
   Use["FDA_GA"]          = 1; // minimisation of user-defined function using Genetics Algorithm
   Use["FDA_SA"]          = 0;
   Use["FDA_MC"]          = 0;
   Use["FDA_MT"]          = 0;
   Use["FDA_GAMT"]        = 0;
   Use["FDA_MCMT"]        = 0;
   //
   // --- Neural Networks (all are feed-forward Multilayer Perceptrons)
   Use["MLP"]             = 0; // Recommended ANN
   Use["MLPBFGS"]         = 0; // Recommended ANN with optional training method
   Use["MLPBNN"]          = 1; // Recommended ANN with BFGS training method and bayesian regulator
   Use["CFMlpANN"]        = 0; // Depreciated ANN from ALEPH
   Use["TMlpANN"]         = 0; // ROOT's own ANN
   //
   // --- Support Vector Machine 
   Use["SVM"]             = 1;
   // 
   // --- Boosted Decision Trees
   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"]         = 1;
   // ---------------------------------------------------------------

   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;
      }
   }

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

   // --- 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;D;P;G,D: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( "myvar1 := var1+var2", 'F' );
   //factory->AddVariable( "myvar2 := var1-var2", "Expression 2", "", 'F' );
   factory->AddVariable( "Lambdab_ETA",                "Lambdab_ETA", "", 'F' );
   factory->AddVariable( "Lambdab_P",                "Lambdab_P", "", '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( "Lambdab_PT",  "Lambdab_PT", "units", 'F' );


   // Read training and test data
   // (it is also possible to use ASCII format as input -> see TMVA Users Guide)
   TString fname_signal = "/exp/LHCb/amhis/LeptonU/tuples/montecarlo/spring16/mc-15154001-leptonU.root";
   TString fname_background = "/exp/LHCb/amhis/LeptonU/tuples/data/LeptonU-total-electrons-11122015.root";
      
   
   TFile *input_signal = TFile::Open( fname_signal );
   TFile *input_background = TFile::Open( fname_background );
   
   std::cout << "--- TMVAClassification       : Using input file for signal: " << input_signal->GetName() << std::endl;
   std::cout << "--- TMVAClassification       : Using input file for background: " << input_background->GetName() << std::endl;

   // --- Register the training and test trees

   TTree *signal     = (TTree*)input_signal->Get("Tuple_Bu2LLK_eeLine2/DecayTree");
   TTree *background = (TTree*)input_background->Get("TupleFromData_Bu2LLK_eeLine2/DecayTree");
   std::cout << signal << std::endl;
   std::cout << background << std::endl;
   // global event weights per tree (see below for setting event-wise weights)
   Double_t signalWeight     = 1.0;
   Double_t backgroundWeight = 1.0;
   
   // You can add an arbitrary number of signal or background trees
   factory->AddSignalTree    ( signal,     signalWeight     );
   factory->AddBackgroundTree( background, backgroundWeight );
   
   // 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");
   //   factory->SetBackgroundWeightExpression( "weight" );

   // Apply additional cuts on the signal and background samples (can be different)
   TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
   TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";
   TCut mycut = "";
   // 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,
					"nTrain_Signal=3000:nTrain_Background=3000:nTrain_Signal=3000:nTrain_Background=3000:SplitMode=random:!V" );
   //   factory->PrepareTrainingAndTestTree( mycuts, mycutb,
   //                                 "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!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 ("naive Bayes estimator")
   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" );

   // 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" );

   // PCA-transformed likelihood
   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" ); 

   // Use a kernel density estimator to approximate the PDFs
   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" ); 

   // Use a variable-dependent mix of splines and kernel density estimator
   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["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=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Nmin=100:Kernel=None:Compress=T" );

   if (Use["PDEFoamBoost"])
      factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoamBoost",
                           "!H:!V:Boost_Num=30:Boost_Transform=linear:SigBgSeparate=F:MaxDepth=4:UseYesNoCell=T:DTLogic=MisClassificationError:FillFoamWithOrigWeights=F:TailCut=0:nActiveCells=500:nBin=20:Nmin=400: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:VarTransform=None" );

   // Linear discriminant (same as Fisher discriminant)
   if (Use["LD"])
      factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );

   // Fisher discriminant (same as LD)
   if (Use["Fisher"])
      factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50: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:!Boost_DetailedMonitoring" );

   // 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:!UseRegulator" );

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

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

   // 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:MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=2" );

   if (Use["BDT"])  // Adaptive Boost
      factory->BookMethod( TMVA::Types::kBDT, "BDT",
                           "!H:!V:NTrees=850:MinNodeSize=2.5%:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" );

   if (Use["BDTB"]) // Bagging
      factory->BookMethod( TMVA::Types::kBDT, "BDTB",
                           "!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20" );

   if (Use["BDTD"]) // Decorrelation + Adaptive Boost
      factory->BookMethod( TMVA::Types::kBDT, "BDTD",
                           "!H:!V:NTrees=400:MinNodeSize=5%:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:VarTransform=Decorrelate" );

   if (Use["BDTF"])  // Allow Using Fisher discriminant in node splitting for (strong) linearly correlated variables
      factory->BookMethod( TMVA::Types::kBDT, "BDTMitFisher",
                           "!H:!V:NTrees=50:MinNodeSize=2.5%:UseFisherCuts:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20" );

   // 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" );

   // 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())
      gROOT->ProcessLine(TString::Format("TMVAGui(\"%s\")", outfileName.Data()));
}
示例#10
0
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 );
}
int main(int argc, char* argv[]){

	// Configurable parameters
	// int max_events;                 // Maximum number of events to process
	//string filelist;                // The file containing a list of files to use as input
	//string input_prefix;            // A prefix that will be added to the path of each input file
	string folder;
	string output_name;             // Name of the ouput ROOT File
	string output_folder;           // Folder to write the output in
	string paramfile;
	string paramfile2;
	string classname;
	bool twotag;
	bool onetag;

	po::options_description config("Configuration");
	po::variables_map vm;
	po::notify(vm);

	config.add_options()    
		("folder",              po::value<string>(&folder)->default_value("output/Paper_2012/"))
		//   ("input_prefix",        po::value<string>(&input_prefix)->default_value(""))
		("output_name",         po::value<string>(&output_name)->default_value("test_tmva.root"))
		("output_folder",       po::value<string>(&output_folder)->default_value(""))
		("paramfile",						po::value<string>(&paramfile)->default_value("./scripts/Paper_params_2012.dat"))
		("paramfile2", 					po::value<string>(&paramfile2)->default_value("./scripts/TMVAinputshad.dat"))
		("classname",						po::value<string>(&classname)->default_value("HhhMVA"))
		("twotag",								po::value<bool>(&twotag)->default_value(true))
		("onetag",              po::value<bool>(&onetag)->default_value(false))
		;
	po::store(po::command_line_parser(argc, argv).
			options(config).allow_unregistered().run(), vm);
	po::notify(vm);


	std::cout << "-------------------------------------" << std::endl;
	std::cout << "Train MVA" << std::endl;
	std::cout << "-------------------------------------" << std::endl;      string param_fmt = "%-25s %-40s\n";
	std::vector<string> bckglist;
	bckglist.push_back("TTJetsFullLept");
	bckglist.push_back("TTJetsSemiLept");
	bckglist.push_back("TTJetsHadronicExt");
//	bckglist.push_back("WWJetsTo2L2Nu");
//	bckglist.push_back("WZJetsTo2L2Q");
//	bckglist.push_back("WZJetsTo3LNu");
//	bckglist.push_back("ZZJetsTo2L2Nu");
//	bckglist.push_back("ZZJetsTo2L2Q");
//	bckglist.push_back("ZZJetsTo4L");
//	bckglist.push_back("DYJetsToTauTauSoup");
//	bckglist.push_back("DYJetsToLLSoup");
//	bckglist.push_back("DYJetsToTauTau");
//	bckglist.push_back("DYJetsToLL");
//	bckglist.push_back("T-tW");
//	bckglist.push_back("Tbar-tW");

	std::vector<string> signallist;
	signallist.push_back("GluGluToHTohhTo2Tau2B_mH-300");

	sample_names_.reserve(bckglist.size()+signallist.size());
	sample_names_.insert(sample_names_.end(),bckglist.begin(),bckglist.end());
	sample_names_.insert(sample_names_.end(),signallist.begin(),signallist.end());



	std::vector<TFile*> BackgroundSamples;
	for(unsigned int iter=0;iter<bckglist.size();++iter){
		BackgroundSamples.push_back(TFile::Open((folder+bckglist.at(iter)+"_mt_2012.root").c_str()));
	}

	std::vector<TFile*> SignalSamples;
	for(unsigned int sigIter=0;sigIter<signallist.size();++sigIter){
		SignalSamples.push_back(TFile::Open((folder+signallist.at(sigIter)+"_mt_2012.root").c_str()));
	}

	std::vector<TTree*> backgroundTrees;
	for(unsigned int iter2=0;iter2<BackgroundSamples.size();++iter2){
		backgroundTrees.push_back(dynamic_cast<TTree*>(BackgroundSamples.at(iter2)->Get("ntuple")));
	}

	std::vector<TTree*> signalTrees;
	for(unsigned int sigIter2=0;sigIter2<SignalSamples.size();++sigIter2){
		signalTrees.push_back(dynamic_cast<TTree*>(SignalSamples.at(sigIter2)->Get("ntuple")));
	}

	TFile *outfile = new TFile((output_folder+output_name).c_str(),"RECREATE");

	TMVA::Factory *factory = new TMVA::Factory(classname,outfile,"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification");


	std::vector<std::string> vars;
	std::ifstream parafile(paramfile2.c_str());
	std::cout<<paramfile2.c_str()<<std::endl;
	string line;
	while(getline(parafile,line)){
		vars.push_back(line);
	}
	parafile.close();

	std::cout<<(vars.at(0)).c_str()<<std::endl;

	std::vector<float> var2;
	for(unsigned int variter=0;variter<vars.size();++variter){
		var2.push_back(::atof((vars.at(variter)).c_str()));
	}


	for(unsigned int variter=0;variter<vars.size();++variter){
		factory->AddVariable((vars.at(variter)).c_str(),(vars.at(variter)).c_str(),"",'F');
	}

	factory->AddSpectator("mt_1","mt_1","",'F');
	factory->AddSpectator("n_prebjets","n_prebjets","",'I');
	factory->AddSpectator("prebjetbcsv_1","prebjetbcsv_1","",'F');
	factory->AddSpectator("prebjetbcsv_2","prebjetbcsv_2","",'F');

	double weightval_=0;

 ParseParamFile(paramfile);	

	for(unsigned int bckgit=0;bckgit<backgroundTrees.size();++bckgit){
		auto it = sample_info_.find(bckglist.at(bckgit).c_str());
		if(it!=sample_info_.end()){
			double evt = it->second.first;
			double xs = it->second.second;
			weightval_=(double) xs/evt;
			std::cout<<weightval_<<std::endl;
		}
		factory->AddBackgroundTree(backgroundTrees.at(bckgit),weightval_);
	}
	for(unsigned int sgit=0;sgit<signalTrees.size();++sgit){
		auto it = sample_info_.find(signallist.at(sgit).c_str());
		if(it!=sample_info_.end()){
			double evt = it->second.first;
			double xs=it->second.second;
			weightval_=(Double_t) xs/evt;
		}
		std::cout<<weightval_<<std::endl;
		factory->AddSignalTree(signalTrees.at(sgit),weightval_);
	}
	factory->SetBackgroundWeightExpression("wt");
	factory->SetSignalWeightExpression("wt");
	TCut mycutb, mycuts;
	if(twotag){
	mycutb="n_prebjets>1&&mt_1<30&&prebjetbcsv_1>0.679&&prebjetbcsv_2>0.679";
	mycuts="n_prebjets>1&&mt_1<30&&prebjetbcsv_1>0.679&&prebjetbcsv_2>0.679";
	}
	else if(onetag){
	mycutb="n_prebjets>1&&mt_1<30&&prebjetbcsv_1>0.679&&prebjetbcsv_2<0.679";
	mycuts="n_prebjets>1&&mt_1<30&&prebjetbcsv_1>0.679&&prebjetbcsv_2<0.679";
	}
	else{
	mycutb="n_prebjets>1&&mt_1<30";
	mycuts="n_prebjets>1&&mt_1<30";
	}
//TCut mycutb="";
//TCut mycuts="";
	factory->PrepareTrainingAndTestTree( mycuts, mycutb,"SplitMode=Random:!V");

	factory->BookMethod( TMVA::Types::kBDT, "BDT","!H:!V:NTrees=850:nEventsMin=150:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );

	factory->TrainAllMethods();
	factory->TestAllMethods();
	factory->EvaluateAllMethods();

	outfile->Close();
	delete factory;

	return 0;
}
示例#12
0
int main(int argc, char * argv[])
{
    //Processing input options
    int c;
    std::string outFname;
    outFname = std::string("QualityNaF.root");

    // Open  input files, get the trees
    TChain *mc = InputFileReader("FileListNtuples_ext.txt","parametri_geo");
    // Preparing options for the TMVA::Factory
    std::string options( 
        "!V:" 
        "!Silent:"
        "Color:"
        "DrawProgressBar:"
        "Transformations=I;D;P;G,D:"
        "AnalysisType=Classification"
    );

    //Creating the factory
    TFile *   ldFile = new TFile(outFname.c_str(),"RECREATE");
    TMVA::Factory * factory = new TMVA::Factory("QualityNaF", ldFile, options.c_str());

    //Preparing variables 
    //general
    /*factory->AddVariable("Chisquare", 'F');
    factory->AddVariable("Layernonusati", 'I');
    factory->AddVariable("NTofUsed", 'I');
    factory->AddVariable("diffR", 'F');
    factory->AddVariable("TOF_Up_Down", 'F');*/
    //Tof	
    //factory->AddVariable("TOFchisq_s", 'F');
    //factory->AddVariable("TOFchisq_t", 'F');

    //RICH	
    factory->AddVariable("Richtotused", 'F');	
    factory->AddVariable("RichPhEl", 'F');
    factory->AddVariable("RICHprob", 'F');
    factory->AddVariable("RICHcollovertotal");
    factory->AddVariable("RICHLipBetaConsistency");  
    factory->AddVariable("RICHTOFBetaConsistency");  
    factory->AddVariable("RICHChargeConsistency");
    
    factory->AddVariable("RICHPmts");
    factory->AddVariable("RICHgetExpected");		
    factory->AddVariable("tot_hyp_p_uncorr");
    factory->AddVariable("Bad_ClusteringRICH");
    factory->AddVariable("NSecondariesRICHrich");

    //factory->AddVariable("HitHValldir"); 
    //factory->AddVariable("HitHVallrefl");  	
    
    //factory->AddVariable("HVBranchCheck:= (HitHValldir - HitHVoutdir) - (HitHVallrefl - HitHVoutrefl)");    

    factory->AddVariable("HitHVoutdir"); 
    factory->AddVariable("HitHVoutrefl");

    //Spectator Variables
    factory->AddSpectator("R", 'F');
    factory->AddSpectator("BetaRICH_new", 'F');	

    //Preselection cuts
    std::string PreSelection    = "qL1>0&&(joinCutmask&187)==187&&qL1<1.75&&R>0";
    std::string ChargeCut 	= "qUtof>0.8&&qUtof<1.3&&qLtof>0.8&&qLtof<1.3";
    std::string VelocityCut 	= /*"Beta<0.8";*/"((joinCutmask>>11))==1024&&BetaRICH_new>0&&BetaRICH_new<0.975";
    std::string signalCut 	= /*"(R/Beta)*(1-Beta^2)^0.5>1.65&&GenMass>1&&GenMass<2";*/"(R/BetaRICH_new)*(1-BetaRICH_new^2)^0.5>0.5&&(R/BetaRICH_new)*(1-BetaRICH_new^2)^0.5<1.5";	
    std::string bkgndCut 	= /*"(R/Beta)*(1-Beta^2)^0.5>1.65&&GenMass>0&&GenMass<1";*/"(R/BetaRICH_new)*(1-BetaRICH_new^2)^0.5>3";		 

    factory->AddTree(mc,"Signal"    ,1,(PreSelection +"&&"+ ChargeCut + "&&" + VelocityCut + "&&"+ signalCut).c_str());
    factory->AddTree(mc,"Background",1,(PreSelection +"&&"+ ChargeCut + "&&" + VelocityCut + "&&"+ bkgndCut).c_str());

    // Preparing
    std::string preselection = "";
    std::string inputparams(
        "SplitMode=Random:"
        "NormMode=NumEvents:"
        "!V"
    );
    factory->PrepareTrainingAndTestTree(preselection.c_str(),inputparams.c_str());

    // Training
    std::string trainparams ="!H:!V:MaxDepth=3";
    factory->BookMethod(TMVA::Types::kBDT, "BDT", trainparams.c_str());

    trainparams ="!H:!V";
    factory->BookMethod(TMVA::Types::kLikelihood, "Likelihood", trainparams.c_str());

    trainparams ="!H:!V:VarTransform=Decorrelate";
    //factory->BookMethod(TMVA::Types::kLikelihood, "LikelihoodD", trainparams.c_str());

    trainparams ="!H:!V";
    //factory->BookMethod(TMVA::Types::kCuts, "Cuts", trainparams.c_str());



    factory->TrainAllMethods();
    factory->TestAllMethods();
    factory->EvaluateAllMethods();
}
示例#13
0
void WWTMVAClassification( TString myMethodList = "", double mH=400., int njets, TString chan="el" ) 
{
    // 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"]      = 1;
    Use["LikelihoodD"]     = 0; // the "D" extension indicates decorrelated input variables (see option strings)
    Use["LikelihoodPCA"]   = 0; // 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"]              = 1;
    // ---
    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"]             = 0; // this is the recommended ANN
    Use["MLPBFGS"]         = 0; // recommended ANN with optional training method
    Use["CFMlpANN"]        = 0; // *** missing
    Use["TMlpANN"]         = 0; 
    // ---
    Use["SVM"]             = 0;
    // ---
    Use["BDT"]             = 1;
    Use["BDTD"]            = 0;
    Use["BDTG"]            = 0;
    Use["BDTB"]            = 0;
    // ---
    Use["RuleFit"]         = 0;
    // ---
    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 = 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;
        }
    }
    
    // Create a new root output file.
    ///////TString outfileName( "TMVA.root" );
    char outfileName[192];
    sprintf(outfileName,"TMVA_%3.0f_nJ%i_%s.root",mH,njets,chan.Data());
    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
    char classifierName[192];
    sprintf(classifierName,"TMVAClassification_%3.0f_nJ%i_%s",mH,njets,chan.Data());
    TMVA::Factory *factory = new TMVA::Factory( classifierName, outputFile, 
                                               "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D" );
    //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
    
    // leptonic W
    factory->AddVariable("WWpt := ptlvjj", 'F');
    factory->AddVariable("WWy := ylvjj", 'F');
    //factory->AddVariable("Wpt := W_pt", 'F');
    //factory->AddVariable("MET := event_met_pfmet", 'F');
    if (chan = "mu"){
        factory->AddVariable("LepCharge := W_muon_charge", 'F');
    }
    else if (chan = "el"){
        factory->AddVariable("LepCharge := W_electron_charge", 'F');
    }
    else{
        std::cout << "Invalid channel!" << std::endl;
        return;
    }
    // factory->AddVariable("J1QGL := JetPFCor_QGLikelihood[0]", 'F');
    // factory->AddVariable("J2QGL := JetPFCor_QGLikelihood[1]", 'F');
    
    factory->AddVariable("costheta1 := ang_ha", 'F');
    factory->AddVariable("costheta2 := ang_hb", 'F');
    factory->AddVariable("costhetaS := ang_hs", 'F');
    factory->AddVariable("Phi := ang_phi", 'F');
    factory->AddVariable("Phi2 := ang_phib", '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("run := event_runNo", "I");
    factory->AddSpectator("lumi := event_lumi", "I");
    factory->AddSpectator("event := event_evtNo", "I");
    factory->AddSpectator("mjj := Mass2j_PFCor", "F");
    factory->AddSpectator("mlvjj := MassV2j_PFCor", "F");
    factory->AddSpectator("masslvjj := masslvjj", "F");
    //factory->AddSpectator("ggdevt := ggdevt", "F");
    //factory->AddSpectator("fit_mlvjj := fit_mlvjj", "F");
    
    
    
    // read training and test data
    char signalOutputName[192];
    sprintf(signalOutputName,"/uscms_data/d2/kalanand/WjjTrees/Full2011DataFall11MC/ReducedTree/RD_%s_HWWMH%3.0f_CMSSW428.root",chan.Data(),mH);
    TFile *input1 = TFile::Open( signalOutputName );
    //TFile *input1 = TFile::Open( "/uscms_data/d2/kalanand/WjjTrees/Full2011DataFall11MC/ReducedTree/RD_mu_HWWMH400_CMSSW428.root");
    char backgroundOutputName[192];
    sprintf(backgroundOutputName,"/uscms_data/d2/kalanand/WjjTrees/Full2011DataFall11MC/ReducedTree/RD_%s_WpJ_CMSSW428.root",chan.Data());
    TFile *input2 = TFile::Open( backgroundOutputName );
    
    std::cout << "--- TMVAClassification : Using input file: " << input1->GetName() << std::endl;
    
    TTree *signal     = (TTree*)input1->Get("WJet");
    TTree *background = (TTree*)input2->Get("WJet");
    
    
    // global event weights per tree (see below for setting event-wise weights)
    Double_t signalWeight     = 1.0;
    Double_t backgroundWeight = 1.0;
    
    // ====== 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 );
    
    // 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");
    
    // Apply additional cuts on the signal and background samples (can be different)
    //   TCut mycuts = "abs(eta)>1.5"; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
    //    TCut mycutb = "abs(eta)>1.5"; // for example: TCut mycutb = "abs(var1)<0.5";
    

    char * mass4bodycut = "";
    if(njets==2) {
      if(chan.Contains("mu")) {
	if(mH==170.) mass4bodycut = "(fit_mlvjj>176 && fit_mlvjj<262)"; // 2j170mu =====
	if(mH==180.) mass4bodycut = "(fit_mlvjj>179 && fit_mlvjj<256)"; // 2j180mu
	if(mH==190.) mass4bodycut = "(fit_mlvjj>186 && fit_mlvjj<214)"; // 2j190mu
	if(mH==200.) mass4bodycut = "(fit_mlvjj>191 && fit_mlvjj<226)"; // 2j200mu
	if(mH==250.) mass4bodycut = "(fit_mlvjj>226 && fit_mlvjj<287)"; // 2j250mu
	if(mH==300.) mass4bodycut = "(fit_mlvjj>265 && fit_mlvjj<347)"; // 2j300mu
	if(mH==350.) mass4bodycut = "(fit_mlvjj>308 && fit_mlvjj<401)"; // 2j350mu
	if(mH==400.) mass4bodycut = "(fit_mlvjj>346 && fit_mlvjj<457)"; // 2j400mu
	if(mH==450.) mass4bodycut = "(fit_mlvjj>381 && fit_mlvjj<512)"; // 2j450mu
	if(mH==500.) mass4bodycut = "(fit_mlvjj>415 && fit_mlvjj<568)"; // 2j500mu
	if(mH==550.) mass4bodycut = "(fit_mlvjj>440 && fit_mlvjj<617)"; // 2j550mu
	if(mH==600.) mass4bodycut = "(fit_mlvjj>462 && fit_mlvjj<663)"; // 2j600mu
      }
      if(chan.Contains("el")) {
	if(mH==170.) mass4bodycut = "(fit_mlvjj>176 && fit_mlvjj<262)"; // 2j170el =====
	if(mH==180.) mass4bodycut = "(fit_mlvjj>179 && fit_mlvjj<256)"; // 2j180el
	if(mH==190.) mass4bodycut = "(fit_mlvjj>186 && fit_mlvjj<214)"; // 2j190el
	if(mH==200.) mass4bodycut = "(fit_mlvjj>191 && fit_mlvjj<226)"; // 2j200el
	if(mH==250.) mass4bodycut = "(fit_mlvjj>226 && fit_mlvjj<287)"; // 2j250el
	if(mH==300.) mass4bodycut = "(fit_mlvjj>265 && fit_mlvjj<347)"; // 2j300el
	if(mH==350.) mass4bodycut = "(fit_mlvjj>308 && fit_mlvjj<401)"; // 2j350el
	if(mH==400.) mass4bodycut = "(fit_mlvjj>346 && fit_mlvjj<457)"; // 2j400el
	if(mH==450.) mass4bodycut = "(fit_mlvjj>381 && fit_mlvjj<512)"; // 2j450el
	if(mH==500.) mass4bodycut = "(fit_mlvjj>415 && fit_mlvjj<568)"; // 2j500el
	if(mH==550.) mass4bodycut = "(fit_mlvjj>440 && fit_mlvjj<617)"; // 2j550el
	if(mH==600.) mass4bodycut = "(fit_mlvjj>462 && fit_mlvjj<663)"; // 2j600el
      }
    }

    if(njets==3) {
      if(chan.Contains("mu")) {
	if(mH==170.) mass4bodycut = "(fit_mlvjj>150 && fit_mlvjj<271)"; // 3j170mu =====
	if(mH==180.) mass4bodycut = "(fit_mlvjj>175 && fit_mlvjj<284)"; // 3j180mu
	if(mH==190.) mass4bodycut = "(fit_mlvjj>185 && fit_mlvjj<290)"; // 3j190mu
	if(mH==200.) mass4bodycut = "(fit_mlvjj>188 && fit_mlvjj<293)"; // 3j200mu
	if(mH==250.) mass4bodycut = "(fit_mlvjj>216 && fit_mlvjj<300)"; // 3j250mu
	if(mH==300.) mass4bodycut = "(fit_mlvjj>241 && fit_mlvjj<355)"; // 3j300mu
	if(mH==350.) mass4bodycut = "(fit_mlvjj>269 && fit_mlvjj<407)"; // 3j350mu
	if(mH==400.) mass4bodycut = "(fit_mlvjj>300 && fit_mlvjj<465)"; // 3j400mu
	if(mH==450.) mass4bodycut = "(fit_mlvjj>332 && fit_mlvjj<518)"; // 3j450mu
	if(mH==500.) mass4bodycut = "(fit_mlvjj>362 && fit_mlvjj<569)"; // 3j500mu
	if(mH==550.) mass4bodycut = "(fit_mlvjj>398 && fit_mlvjj<616)"; // 3j550mu
	if(mH==600.) mass4bodycut = "(fit_mlvjj>419 && fit_mlvjj<660)"; // 3j600mu
      }
      if(chan.Contains("el")) {
	if(mH==170.) mass4bodycut = "(fit_mlvjj>150 && fit_mlvjj<271)"; // 3j170el =====
	if(mH==180.) mass4bodycut = "(fit_mlvjj>175 && fit_mlvjj<284)"; // 3j180el
	if(mH==190.) mass4bodycut = "(fit_mlvjj>185 && fit_mlvjj<290)"; // 3j190el
	if(mH==200.) mass4bodycut = "(fit_mlvjj>188 && fit_mlvjj<293)"; // 3j200el
	if(mH==250.) mass4bodycut = "(fit_mlvjj>216 && fit_mlvjj<300)"; // 3j250el
	if(mH==300.) mass4bodycut = "(fit_mlvjj>241 && fit_mlvjj<355)"; // 3j300el
	if(mH==350.) mass4bodycut = "(fit_mlvjj>269 && fit_mlvjj<407)"; // 3j350el
	if(mH==400.) mass4bodycut = "(fit_mlvjj>300 && fit_mlvjj<465)"; // 3j400el
	if(mH==450.) mass4bodycut = "(fit_mlvjj>332 && fit_mlvjj<518)"; // 3j450el
	if(mH==500.) mass4bodycut = "(fit_mlvjj>362 && fit_mlvjj<569)"; // 3j500el
	if(mH==550.) mass4bodycut = "(fit_mlvjj>398 && fit_mlvjj<616)"; // 3j550el
	if(mH==600.) mass4bodycut = "(fit_mlvjj>419 && fit_mlvjj<660)";  // 3j600el
      }
    }

    char mycutschar[1000];
    sprintf(mycutschar,"ggdevt == %i &&(Mass2j_PFCor>65 && Mass2j_PFCor<95) && %s", njets, mass4bodycut);
    TCut mycuts (mycutschar);
    


    // tell the factory to use all remaining events in the trees after training for testing:
    factory->PrepareTrainingAndTestTree( mycuts, mycuts,
                                        "nTrain_Signal=0:nTrain_Background=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" );  
    // 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=500:HiddenLayers=N+5:TestRate=10:EpochMonitoring" );
    
    if (Use["MLPBFGS"])
        factory->BookMethod( TMVA::Types::kMLP, "MLPBFGS", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=500:HiddenLayers=N+5:TestRate=10:TrainingMethod=BFGS:!EpochMonitoring" );
    
    
    // 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" );
    
    // For an example of the category classifier, see: TMVAClassificationCategory
    
    // --------------------------------------------------------------------------------------------------
    
    // 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->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 );
    
}
示例#14
0
void TMVAClassification( TString eventsToTrain = "0", const TString & region = "barrel", const TString index = "", TString myMethodList = "BDT")
{

  std::cout << "running classification for " << region << " for " << myMethodList << std::endl;

  if( region != "barrel" && region != "endcaps" ) {
    std::cout << "Error, region can only be barrel or endcaps. Selected region was: " << region << std::endl;
    exit(1);
  }
  if( index != "" && index != "0" && index != "1" && index != "2" ) {
    std::cout << "Error, index can only be \"\", \"0\", \"1\" or \"2\". Selected index was: " << index << std::endl;
    exit(1);
  }


   // 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;

   // --- Cut optimisation
   Use["Cuts"]            = 0;
   Use["CutsD"]           = 0;
   Use["CutsPCA"]         = 0;
   Use["CutsGA"]          = 0;
   Use["CutsSA"]          = 0;
   // 
   // --- 1-dimensional likelihood ("naive Bayes estimator")
   Use["Likelihood"]      = 0;
   Use["LikelihoodD"]     = 0; // the "D" extension indicates decorrelated input variables (see option strings)
   Use["LikelihoodPCA"]   = 0; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
   Use["LikelihoodKDE"]   = 0;
   Use["LikelihoodMIX"]   = 0;
   //
   // --- Mutidimensional likelihood and Nearest-Neighbour methods
   Use["PDERS"]           = 0;
   Use["PDERSD"]          = 0;
   Use["PDERSPCA"]        = 0;
   Use["PDEFoam"]         = 0;
   Use["PDEFoamBoost"]    = 0; // uses generalised MVA method boosting
   Use["KNN"]             = 0; // k-nearest neighbour method
   //
   // --- Linear Discriminant Analysis
   Use["LD"]              = 0; // Linear Discriminant identical to Fisher
   Use["Fisher"]          = 0;
   Use["FisherG"]         = 0;
   Use["BoostedFisher"]   = 0; // uses generalised MVA method boosting
   Use["HMatrix"]         = 0;
   //
   // --- Function Discriminant analysis
   Use["FDA_GA"]          = 0; // minimisation of user-defined function using Genetics Algorithm
   Use["FDA_SA"]          = 0;
   Use["FDA_MC"]          = 0;
   Use["FDA_MT"]          = 0;
   Use["FDA_GAMT"]        = 0;
   Use["FDA_MCMT"]        = 0;
   //
   // --- Neural Networks (all are feed-forward Multilayer Perceptrons)
   Use["MLP"]             = 0; // Recommended ANN
   Use["MLPBFGS"]         = 0; // Recommended ANN with optional training method
   Use["MLPBNN"]          = 0; // Recommended ANN with BFGS training method and bayesian regulator
   Use["CFMlpANN"]        = 0; // Depreciated ANN from ALEPH
   Use["TMlpANN"]         = 0; // ROOT's own ANN
   //
   // --- Support Vector Machine 
   Use["SVM"]             = 0;
   // 
   // --- Boosted Decision Trees
   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;
	    std::exit(2);
         }
         Use[regMethod] = 1;
      }
   }


   // Input and output file names
   TString fnameTrainS = "BsMC12_barrel_preselection";
   TString fnameTrainB = "Barrel_preselection";
   TString fnameTestS = "BsMC12_barrel_preselection";
   TString fnameTestB = "Barrel_preselection";
   TString outputFileName = "TMVA_barrel";
   TString weightDirName = "barrel";
   if( region == "endcaps" ) {
     fnameTrainS = "BsMC12_endcaps_preselection";
     fnameTrainB = "Endcaps_preselection";
     fnameTestS = "BsMC12_endcaps_preselection";
     fnameTestB = "Endcaps_preselection";
     outputFileName = "TMVA_endcaps";
     weightDirName = "endcaps";
   }
   if( index != "" ) {
     fnameTrainS += "_"+index;
     fnameTrainB += "_"+index;
     TString indexTest = "";
     // The test index is the train index +1 (2+1 -> 0)
     if( index == "0" ) indexTest = "1";
     else if( index == "1" ) indexTest = "2";
     else if( index == "2" ) indexTest = "0";
     fnameTestS += "_"+indexTest;
     fnameTestB += "_"+indexTest;
     outputFileName += "_"+index;
     weightDirName += index;
   }

   fnameTrainS     = rootDir + fnameTrainS    + ".root";
   fnameTrainB     = rootDir + fnameTrainB    + ".root";
   fnameTestS      = rootDir + fnameTestS     + ".root";
   fnameTestB      = rootDir + fnameTestB     + ".root";
   outputFileName  = rootDir + outputFileName + ".root";
   weightDirName   = weightsDir + weightDirName + "Weights";


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

   // --- Here the preparation phase begins

   // Create a ROOT output file where TMVA will store ntuples, histograms, etc.
   TString outfileName(outputFileName);
   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;D;P;G,D: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";
   // (TMVA::gConfig().GetIONames()).fWeightFileDir = outputFileName;
   (TMVA::gConfig().GetIONames()).fWeightFileDir = weightDirName;

   // 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" )]

   bool useNewMuonID = false;

   factory->AddVariable( "fls3d",      "fls3d", "", 'F' );
   factory->AddVariable( "alpha",      "alpha", "", 'F' );
   factory->AddVariable( "pvips",      "pvips", "", 'F' );
   factory->AddVariable( "iso",        "iso", "", 'F' );
   factory->AddVariable( "m1iso",      "m1iso", "", 'F' );
   factory->AddVariable( "m2iso",      "m2iso", "", 'F' );
   factory->AddVariable( "chi2dof",    "chi2/dof", "", 'F' );
   if( region == "barrel" ) {
     factory->AddVariable( "eta",      "eta", "", 'F' );
     factory->AddVariable( "maxdoca",  "maxdoca", "cm", 'F' );
   }
   else {
     factory->AddVariable( "pt",       "pt", "GeV/c", 'F' );
     factory->AddVariable( "pvip",     "pvip", "cm", 'F' );
   }
   factory->AddVariable( "docatrk",    "docatrk", "cm", 'F' );
   // factory->AddVariable( "pt",         "pt", "GeV/c", 'F' );
   // factory->AddVariable( "closetrk",   "closetrk", "", 'I' );
   // factory->AddVariable( "y",                              "y", "", 'F' );
   // factory->AddVariable( "l3d",                            "l3d", "cm", 'F' );
   // factory->AddVariable( "cosAlphaXY",                     "cosAlphaXY", "", 'F' );
   // factory->AddVariable( "mu1_dxy",                        "mu1_dxy", "cm", 'F' );
   // factory->AddVariable( "mu2_dxy",                        "mu2_dxy", "cm", 'F' );

   if( useNewMuonID ) {
     // New Muon-id
     factory->AddVariable( "mu1_MVAMuonID",                  "mu1_MVAMuonID", "", 'F');
     factory->AddVariable( "mu2_MVAMuonID",                  "mu2_MVAMuonID", "", 'F');
   }

   // Extra variables
   // factory->AddVariable( "mu1_pt",                           "mu1_pt", "GeV/c", 'F' );
   // factory->AddVariable( "mu2_pt",                           "mu2_pt", "GeV/c", 'F' );
   // factory->AddVariable( "pvw8",                             "pvw8", "", 'F' );
   // factory->AddVariable( "cosAlpha3D",                       "cosAlpha3D", "", 'F' );
   // factory->AddVariable( "countTksOfPV",                     "countTksOfPV", "", 'I' );
   // factory->AddVariable( "ctauErrPV",                        "ctauErrPV", "", 'F' );
   // factory->AddVariable( "ctauPV",                           "ctauPV", "", 'F' );
   // factory->AddVariable( "dcaxy",                            "dcaxy", "", 'F' );

   // factory->AddVariable( "mu1_glbTrackProb",                 "mu1_glbTrackProb", "", 'F' );
   // factory->AddVariable( "mu1_nChi2",                        "mu1_nChi2", "", 'F' );
   // factory->AddVariable( "mu1_nMuSegs",                      "mu1_nMuSegs", "", 'F' );
   // factory->AddVariable( "mu1_nMuSegsCln",                   "mu1_nMuSegsCln", "", 'F' );
   // factory->AddVariable( "mu1_nPixHits",                     "mu1_nPixHits", "", 'F' );
   // factory->AddVariable( "mu1_nTrHits",                      "mu1_nTrHits", "", 'F' );
   // factory->AddVariable( "mu1_segComp",                      "mu1_segComp", "", 'F' );
   // factory->AddVariable( "mu1_trkEHitsOut",                  "mu1_trkEHitsOut", "", 'F' );
   // factory->AddVariable( "mu1_trkVHits",                     "mu1_trkVHits", "", 'F' );
   // factory->AddVariable( "mu1_validFrac",                    "mu1_validFrac", "", 'F' );
   // factory->AddVariable( "mu1_chi2LocMom",                   "mu1_chi2LocMom", "", 'F' );
   // factory->AddVariable( "mu1_chi2LocPos",                   "mu1_chi2LocPos", "", 'F' );

   // factory->AddVariable( "mu2_glbTrackProb",                 "mu2_glbTrackProb", "", 'F' );
   // factory->AddVariable( "mu2_nChi2",                        "mu2_nChi2", "", 'F' );
   // factory->AddVariable( "mu2_nMuSegs",                      "mu2_nMuSegs", "", 'F' );
   // factory->AddVariable( "mu2_nMuSegsCln",                   "mu2_nMuSegsCln", "", 'F' );
   // factory->AddVariable( "mu2_nPixHits",                     "mu2_nPixHits", "", 'F' );
   // factory->AddVariable( "mu2_nTrHits",                      "mu2_nTrHits", "", 'F' );
   // factory->AddVariable( "mu2_segComp",                      "mu2_segComp", "", 'F' );
   // factory->AddVariable( "mu2_trkEHitsOut",                  "mu2_trkEHitsOut", "", 'F' );
   // factory->AddVariable( "mu2_trkVHits",                     "mu2_trkVHits", "", 'F' );
   // factory->AddVariable( "mu2_validFrac",                    "mu2_validFrac", "", 'F' );
   // factory->AddVariable( "mu2_chi2LocMom",                   "mu2_chi2LocMom", "", 'F' );
   // factory->AddVariable( "mu2_chi2LocPos",                   "mu2_chi2LocPos", "", 'F' );



   // factory->AddVariable( "l3d := ctauPV*pt/mass",            "l3d", "cm", 'F' );
   // factory->AddVariable( "l3dSig := ctauPV/ctauErrPV",       "l3dSig", "", '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 := mass*2",  "Spectator 1", "units", 'F' );
   // factory->AddSpectator( "spec2 := mass*3",  "Spectator 2", "units", 'F' );
   factory->AddSpectator( "mass",                            "mass", "GeV/c^{2}", 'F' );


   // Read training and test data
   // (it is also possible to use ASCII format as input -> see TMVA Users Guide)

   if (gSystem->AccessPathName( fnameTrainS )) {  // file does not exist in local directory
     std::cout << "Did not access " << fnameTrainS << " exiting." << std::endl;
     std::exit(4);
   }

     //gSystem->Exec("wget http://root.cern.ch/files/tmva_class_example.root");
   
   TFile *inputTrainS = TFile::Open( fnameTrainS );
   TFile *inputTrainB = TFile::Open( fnameTrainB );
   TFile *inputTestS  = TFile::Open( fnameTestS  );
   TFile *inputTestB  = TFile::Open( fnameTestB  );
   // --- Register the training and test trees
   TTree *signalTrainTree     = (TTree*)inputTrainS->Get("probe_tree");
   TTree *backgroundTrainTree = (TTree*)inputTrainB->Get("probe_tree");
   TTree *signalTestTree     = (TTree*)inputTestS->Get("probe_tree");
   TTree *backgroundTestTree = (TTree*)inputTestB->Get("probe_tree");
   // global event weights per tree (see below for setting event-wise weights)
   Double_t signalTrainWeight     = 1.0;
   Double_t backgroundTrainWeight = 1.0;
   Double_t signalTestWeight     = 1.0;
   Double_t backgroundTestWeight = 1.0;
   // Decide if using the split and mixing or the full trees
   if( fnameTrainS == fnameTestS ) {
     if( fnameTrainB != fnameTestB ) {
       std::cout << "This macro cannot handle cases where the same signal sample is used for training and testing, but different background samples are used.";
       exit(1);
     }
     std::cout << "--- TMVAClassification       : Using input file: " << inputTrainS->GetName() << std::endl;
     std::cout << "--- and file: " << inputTrainB->GetName() << std::endl;
     // You can add an arbitrary number of signal or background trees
     factory->AddSignalTree    ( signalTrainTree,     signalTrainWeight     );
     factory->AddBackgroundTree( backgroundTrainTree, backgroundTrainWeight );
   }
   else {
     if( fnameTrainB == fnameTestB ) {
       std::cout << "This macro cannot handle cases where the same background sample is used for training and testing, but different signal samples are used.";
       exit(1);
     }
     std::cout << "--- TMVAClassification       : Using input file: " << inputTrainS->GetName() << std::endl;
     std::cout << "--- and file: " << inputTrainB->GetName() << " for training and" << std::endl;
     std::cout << "--- input file: " << inputTestS->GetName() << std::endl;
     std::cout << "--- and file: " << inputTestB->GetName() << " for testing." << std::endl;
     // To give different trees for training and testing, do as follows:
     factory->AddSignalTree( signalTrainTree,     signalTrainWeight, "Training" );
     factory->AddSignalTree( signalTestTree,      signalTestWeight,  "Test" );
     factory->AddBackgroundTree( backgroundTrainTree, backgroundTrainWeight, "Training" );
     factory->AddBackgroundTree( backgroundTestTree,  backgroundTestWeight,  "Test" );
   }

   // Apply additional cuts on the signal and background samples (can be different)
   TCut mycuts = "";
   TCut mycutb = "";

   // 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" );




   factory->PrepareTrainingAndTestTree( mycuts, mycutb,
                                        "nTrain_Signal="+eventsToTrain+":nTrain_Background="+eventsToTrain+":SplitMode=Random:NormMode=NumEvents:!V" );
   // factory->PrepareTrainingAndTestTree( mycuts, mycutb,
   //                                      "nTrain_Signal=3000:nTrain_Background=3000:nTest_Signal=3000:nTest_Background=3000:SplitMode=Random:NormMode=NumEvents:!V" );
   // factory->PrepareTrainingAndTestTree( mycuts, mycutb,
   //                                     "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 ("naive Bayes estimator")
   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" );

   // 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" );

   // PCA-transformed likelihood
   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" ); 

   // Use a kernel density estimator to approximate the PDFs
   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" ); 

   // Use a variable-dependent mix of splines and kernel density estimator
   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["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=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Nmin=100:Kernel=None:Compress=T" );

   if (Use["PDEFoamBoost"])
      factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoamBoost",
                           "!H:!V:Boost_Num=30:Boost_Transform=linear:SigBgSeparate=F:MaxDepth=4:UseYesNoCell=T:DTLogic=MisClassificationError:FillFoamWithOrigWeights=F:TailCut=0:nActiveCells=500:nBin=20:Nmin=400: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:VarTransform=None" );

   // Linear discriminant (same as Fisher discriminant)
   if (Use["LD"])
      factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );

   // Fisher discriminant (same as LD)
   if (Use["Fisher"])
      factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50: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:!Boost_DetailedMonitoring" );

   // 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:!UseRegulator" );
     factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+8:TestRate=5:!UseRegulator" );

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

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

   // 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=0.10:UseBaggedGrad:GradBaggingFraction=0.5:nCuts=20:NNodesMax=5" );

   if (Use["BDT"])  // Adaptive Boost
      factory->BookMethod( TMVA::Types::kBDT, "BDT",
                           "!H:!V:NTrees=800:nEventsMin=50:MaxDepth=2:BoostType=AdaBoost:AdaBoostBeta=1:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning:NNodesMax=5" );


   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" );

   if (Use["BDTF"])  // Allow Using Fisher discriminant in node splitting for (strong) linearly correlated variables
      factory->BookMethod( TMVA::Types::kBDT, "BDTMitFisher",
                           "!H:!V:NTrees=50:nEventsMin=150:UseFisherCuts:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );

   // 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" );

   // 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
   std::cout << "Training all methods" << std::endl;
   factory->TrainAllMethods();

   // ---- Evaluate all MVAs using the set of test events
   std::cout << "Testing all methods" << std::endl;
   factory->TestAllMethods();

   // ----- Evaluate and compare performance of all configured MVAs
   std::cout << "Evaluating all methods" << std::endl;
   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 TMVATrainer(){
   // This loads the library
   TMVA::Tools::Instance();

   // --- Here the preparation phase begins
   // Create a ROOT output file where TMVA will store ntuples, histograms, etc.
   TString outfileName = "TMVATrainingResults_fat_BBvsGSP.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( "TMVATrainer", outputFile,
                                               "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D: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("TagVarCSV_vertexCategory","TagVarCSV_vertexCategory","units",'F');
   factory->AddVariable("TagVarCSV_jetNTracks","TagVarCSV_jetNTracks","units",'F');
   //factory->AddVariable("TagVarCSV_trackSip2dSig_0","TagVarCSV_trackSip2dSig_0","units",'F');
   //factory->AddVariable("TagVarCSV_trackSip2dSig_1","TagVarCSV_trackSip2dSig_1","units",'F');
   //factory->AddVariable("TagVarCSV_trackSip2dSig_2","TagVarCSV_trackSip2dSig_2","units",'F');
   //factory->AddVariable("TagVarCSV_trackSip2dSig_3","TagVarCSV_trackSip2dSig_3","units",'F');
   factory->AddVariable("TagVarCSV_trackSip3dSig_0","TagVarCSV_trackSip3dSig_0","units",'F');
   factory->AddVariable("TagVarCSV_trackSip3dSig_1","TagVarCSV_trackSip3dSig_1","units",'F');
   factory->AddVariable("TagVarCSV_trackSip3dSig_2","TagVarCSV_trackSip3dSig_2","units",'F');
   factory->AddVariable("TagVarCSV_trackSip3dSig_3","TagVarCSV_trackSip3dSig_3","units",'F');
   //factory->AddVariable("TagVarCSV_trackPtRel_0","TagVarCSV_trackPtRel_0","units",'F');
   //factory->AddVariable("TagVarCSV_trackPtRel_1","TagVarCSV_trackPtRel_1","units",'F');
   //factory->AddVariable("TagVarCSV_trackPtRel_2","TagVarCSV_trackPtRel_2","units",'F');
   //factory->AddVariable("TagVarCSV_trackPtRel_3","TagVarCSV_trackPtRel_3","units",'F');
   factory->AddVariable("TagVarCSV_trackSip2dSigAboveCharm","TagVarCSV_trackSip2dSigAboveCharm","units",'F');
   //factory->AddVariable("TagVarCSV_trackSip3dSigAboveCharm","TagVarCSV_trackSip3dSigAboveCharm","units",'F');
   //factory->AddVariable("TagVarCSV_trackSumJetEtRatio","TagVarCSV_trackSumJetEtRatio","units",'F');
   //factory->AddVariable("TagVarCSV_trackSumJetDeltaR","TagVarCSV_trackSumJetDeltaR","units",'F');
   factory->AddVariable("TagVarCSV_jetNTracksEtaRel","TagVarCSV_jetNTracksEtaRel","units",'F');
   factory->AddVariable("TagVarCSV_trackEtaRel_0","TagVarCSV_trackEtaRel_0","units",'F');
   factory->AddVariable("TagVarCSV_trackEtaRel_1","TagVarCSV_trackEtaRel_1","units",'F');
   factory->AddVariable("TagVarCSV_trackEtaRel_2","TagVarCSV_trackEtaRel_2","units",'F');
   factory->AddVariable("TagVarCSV_jetNSecondaryVertices","TagVarCSV_jetNSecondaryVertices","units",'F');
   factory->AddVariable("TagVarCSV_vertexMass","TagVarCSV_vertexMass","units",'F');
   factory->AddVariable("TagVarCSV_vertexNTracks","TagVarCSV_vertexNTracks","units",'F');
   factory->AddVariable("TagVarCSV_vertexEnergyRatio","TagVarCSV_vertexEnergyRatio","units",'F');
   factory->AddVariable("TagVarCSV_vertexJetDeltaR","TagVarCSV_vertexJetDeltaR","units",'F');
   factory->AddVariable("TagVarCSV_flightDistance2dSig","TagVarCSV_flightDistance2dSig","units",'F');
   //factory->AddVariable("TagVarCSV_flightDistance3dSig","TagVarCSV_flightDistance3dSig","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("Jet_pt","Jet_pt","units",'F');
   factory->AddSpectator("Jet_eta","Jet_eta","units",'F');
   factory->AddSpectator("Jet_phi","Jet_phi","units",'F');
   factory->AddSpectator("Jet_mass","Jet_mass","units",'F');
   factory->AddSpectator("Jet_massGroomed","Jet_massGroomed","units",'F');
   factory->AddSpectator("Jet_flavour","Jet_flavour","units",'F');
   factory->AddSpectator("Jet_nbHadrons","Jet_nbHadrons","units",'F');
   factory->AddSpectator("Jet_JP","Jet_JP","units",'F');
   factory->AddSpectator("Jet_JBP","Jet_JBP","units",'F');
   factory->AddSpectator("Jet_CSV","Jet_CSV","units",'F');
   factory->AddSpectator("Jet_CSVIVF","Jet_CSVIVF","units",'F');
   factory->AddSpectator("Jet_tau1","Jet_tau1","units",'F');
   factory->AddSpectator("Jet_tau2","Jet_tau2","units",'F');

   factory->AddSpectator("SubJet1_CSVIVF","SubJet1_CSVIVF","units",'F');
   factory->AddSpectator("SubJet2_CSVIVF","SubJet2_CSVIVF","units",'F');

   // Read training and test data
   // (it is also possible to use ASCII format as input -> see TMVA Users Guide)
   TString fnameSig = "RadionToHH_4b_M-800_TuneZ2star_8TeV-Madgraph_pythia6_JetTaggingVariables_training.root";
   TString fnameBkg = "QCD_Pt-300to470_TuneZ2star_8TeV_pythia6_JetTaggingVariables_training.root";
   TFile *inputSig = TFile::Open( fnameSig );
   TFile *inputBkg = TFile::Open( fnameBkg );
   
   std::cout << "--- TMVAClassification       : Using input files: " << inputSig->GetName() << std::endl
                                                                     << inputBkg->GetName() << std::endl;
   
   // --- Register the training and test trees
   TTree *sigTree = (TTree*)inputSig->Get("tagVars/ttree");
   TTree *bkgTree = (TTree*)inputBkg->Get("tagVars/ttree");
   
   // // global event weights per tree (see below for setting event-wise weights)
   Double_t signalWeight     = 1.0;
   Double_t backgroundWeight = 1.0;

   // factory->SetInputTrees( tree,signalCut,backgroundCut );
   factory->AddSignalTree    ( sigTree, signalWeight     );
   factory->AddBackgroundTree( bkgTree, backgroundWeight );

   // Apply additional cuts on the signal and background samples (can be different)
   TCut signalCut = "Jet_massGroomed>80 && Jet_massGroomed<150";
   TCut backgroundCut = "abs(Jet_flavour)==5 && Jet_nbHadrons>1 && Jet_massGroomed>80 && Jet_massGroomed<150";

   // Tell the factory how to use the training and testing events
   factory->PrepareTrainingAndTestTree( signalCut, backgroundCut,
                                        "nTrain_Signal=22000:nTest_Signal=20000:nTrain_Background=22000:nTest_Background=2730:SplitMode=Random:!V" );

   // Gradient Boost
   factory->BookMethod( TMVA::Types::kBDT, "BDTG_T1000D3_fat_BBvsGSP",
                          "!H:!V:NTrees=1000:MaxDepth=3:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" );

   //factory->BookMethod( TMVA::Types::kBDT, "BDTG_T1000D5_fat_BBvsGSP",
   //                       "!H:!V:NTrees=1000:MaxDepth=5:MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20" );

//    // Adaptive Boost
//    factory->BookMethod( TMVA::Types::kBDT, "BDT",
//                            "!H:!V:NTrees=1000:MaxDepth=5:MinNodeSize=2.5%:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" );
//    // Bagging
//    factory->BookMethod( TMVA::Types::kBDT, "BDTB",
//                            "!H:!V:NTrees=1000:MaxDepth=5:MinNodeSize=2.5%:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20" );
//    // Decorrelation + Adaptive Boost
//    factory->BookMethod( TMVA::Types::kBDT, "BDTD",
//                            "!H:!V:NTrees=1000:MaxDepth=5:MinNodeSize=2.5%:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:VarTransform=Decorrelate" );

   // ---- 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 );


}
int main(int argc, char** argv) {

    if(argc != 2)
    {
        std::cerr << ">>>>> analysis.cpp::usage: " << argv[0] << " configFileName      MVAconfigFileName" << std::endl ;
        return 1;
    }

    // Parse the config file
    parseConfigFile (argv[1]) ;

    std::string treeName  = gConfigParser -> readStringOption("Input::treeName");
    std::string fileSamples = gConfigParser -> readStringOption("Input::fileSamples");
    std::string inputDirectory = gConfigParser -> readStringOption("Input::inputDirectory");

    std::string inputBeginningFile = "out_NtupleProducer_";
    try {
        inputBeginningFile = gConfigParser -> readStringOption("Input::inputBeginningFile");
    }
    catch (char const* exceptionString) {
        std::cerr << " exception = " << exceptionString << std::endl;
    }
    std::cout << ">>>>> Input::inputBeginningFile  " << inputBeginningFile  << std::endl;



    double LUMI = gConfigParser -> readDoubleOption("Options::Lumi");

    std::vector<std::string> SignalName;
    SignalName = gConfigParser -> readStringListOption("Options::SignalName");

    for (int iSignalSample=0; iSignalSample<SignalName.size(); iSignalSample++) {
        std::cout << " Signal[" << iSignalSample << "] = " << SignalName.at(iSignalSample) << std::endl;
    }

    std::string nameWeight = "1";
    try {
        nameWeight = gConfigParser -> readStringOption("Options::nameWeight");
    }
    catch (char const* exceptionString) {
        std::cerr << " exception = " << exceptionString << std::endl;
    }
    std::cout << ">>>>> Input::nameWeight  " << nameWeight  << std::endl;



    TTree *treeJetLepVect[200];

    char *nameSample[1000];
    char *nameHumanReadable[1000];
    char* xsectionName[1000];

    char nameFileIn[1000];
    sprintf(nameFileIn,"%s",fileSamples.c_str());

    int numberOfSamples = ReadFile(nameFileIn, nameSample, nameHumanReadable, xsectionName);

    double Normalization[1000];
    double xsection[1000];

    for (int iSample=0; iSample<numberOfSamples; iSample++) {
        xsection[iSample] = atof(xsectionName[iSample]);
    }

    for (int iSample=0; iSample<numberOfSamples; iSample++) {
        char nameFile[20000];
        sprintf(nameFile,"%s/%s%s.root",inputDirectory.c_str(),inputBeginningFile.c_str(),nameSample[iSample]);

        TFile* f = new TFile(nameFile, "READ");

        treeJetLepVect[iSample] = (TTree*) f->Get(treeName.c_str());
        char nameTreeJetLep[100];
        sprintf(nameTreeJetLep,"treeJetLep_%d",iSample);
        treeJetLepVect[iSample]->SetName(nameTreeJetLep);

        double XSection;
        XSection = xsection[iSample];
        Normalization[iSample] = XSection * LUMI / 1000.;
    }

    //==== cut
    std::string CutFile = gConfigParser -> readStringOption("Selections::CutFile");
    std::vector<std::string> vCut;
    std::cout << " nCuts   = " << ReadFileCut(CutFile, vCut) << std::endl;

    std::string Cut;
    if (vCut.size() != 0) {
        Cut = vCut.at(0);
    }
    else {
        Cut = "1";
    }

    //==== HiggsMass
    std::string HiggsMass = gConfigParser -> readStringOption("Options::HiggsMass");

    //==== list of methods
    std::vector<std::string> vectorMyMethodList = gConfigParser -> readStringListOption("Options::MVAmethods");
    TString myMethodList;
    for (int iMVA = 0; iMVA < vectorMyMethodList.size(); iMVA++) {
        if (iMVA == 0) myMethodList = Form ("%s",vectorMyMethodList.at(iMVA).c_str());
        else           myMethodList = Form ("%s,%s",myMethodList.Data(),vectorMyMethodList.at(iMVA).c_str());
    }

    //==== output
    TString outfileName = gConfigParser -> readStringOption("Output::outFileName");


    // This loads the library
    TMVA::Tools::Instance();

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

    Use["MLP"]             = 1;
    Use["BDTG"]            = 1;
    Use["FDA_GA"]          = 0;
    Use["PDEFoam"]         = 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 0;
            }
            Use[regMethod] = 1;
        }
    }

    // --------------------------------------------------------------------------------------------------
    // --- Here the preparation phase begins

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

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

    factory->AddVariable( "jetpt1" , 'F');
    factory->AddVariable( "jetpt2" , 'F');
    factory->AddVariable( "mjj" , 'F');
    factory->AddVariable( "detajj" , 'F');
    factory->AddVariable( "dphilljetjet" , 'F');

    factory->AddVariable( "pt1" , 'F');
    factory->AddVariable( "pt2" , 'F');
    factory->AddVariable( "mll" , 'F');
    factory->AddVariable( "dphill" , 'F');
    factory->AddVariable( "mth" , 'F');

    factory->AddVariable( "dphillmet" , 'F');
    factory->AddVariable( "mpmet" , 'F');

    factory->AddSpectator( "channel" , 'F');

    for (int iSample=0; iSample<numberOfSamples; iSample++) {
        int numEnt = treeJetLepVect[iSample]->GetEntries(Cut.c_str());
        std::cout << " Sample = " << nameSample[iSample] << " ~ " << nameHumanReadable[iSample] << " --> " << numEnt << std::endl;
        if (numEnt != 0) {
            if (iSample == 0) factory->AddTree( treeJetLepVect[iSample], "Signal", Normalization[iSample] );
            else if (iSample == 1) factory->AddTree( treeJetLepVect[iSample], "Background", Normalization[iSample] );
            else factory->AddTree( treeJetLepVect[iSample], TString(nameHumanReadable[iSample]), Normalization[iSample] );

//     factory->AddTree( treeJetLepVect[iSample], TString(nameHumanReadable[iSample]), Normalization[iSample] );
//     factory->AddTree( treeJetLepVect[iSample], TString(nameHumanReadable[iSample]), Normalization[iSample] , nameWeight.c_str());
            //     factory->AddTree( treeJetLepVect[iSample], TString(nameHumanReadable[iSample]));
        }
    }

//   for (int iSample=0; iSample<numberOfSamples; iSample++){
//    int numEnt = treeJetLepVect[iSample]->GetEntries(Cut.c_str());
//    std::cout << " Sample = " << nameSample[iSample] << " ~ " << nameHumanReadable[iSample] << " --> " << numEnt << std::endl;
//    if (numEnt != 0) {
//     bool isSig = false;
//     for (std::vector<std::string>::const_iterator itSig = SignalName.begin(); itSig != SignalName.end(); itSig++){
//      if (nameHumanReadable[iSample] == *itSig) isSig = true;
//     }
//     if (isSig) {
//      factory->AddTree( treeJetLepVect[iSample], TString("Signal"), Normalization[iSample] ); //---> ci deve essere uno chiamato Signal!
//     }
//     else {
//      factory->AddTree( treeJetLepVect[iSample], TString(nameHumanReadable[iSample]), Normalization[iSample] );
//     }
//    }
//   }
//
//   for (int iSample=0; iSample<numberOfSamples; iSample++){
//    int numEnt = treeJetLepVect[iSample]->GetEntries(Cut.c_str());
//    std::cout << " Sample = " << nameSample[iSample] << " ~ " << nameHumanReadable[iSample] << " --> " << numEnt << std::endl;
//    if (numEnt != 0) {
//     bool isSig = false;
//     for (std::vector<std::string>::const_iterator itSig = SignalName.begin(); itSig != SignalName.end(); itSig++){
//      if (nameHumanReadable[iSample] == *itSig) isSig = true;
//     }
//     if (isSig) {
// //      factory->AddTree( treeJetLepVect[iSample], TString("Signal"), Normalization[iSample] ); //---> ci deve essere uno chiamato Signal!
//     }
//     else {
//      factory->AddTree( treeJetLepVect[iSample], TString(nameHumanReadable[iSample]), Normalization[iSample] );
//     }
//    }
//   }


    std::cerr << " AAAAAAAAAAAAAAAAAAAAAAAAAAAAA " << std::endl;

    TCut mycuts = Cut.c_str();

//   factory->SetWeightExpression( nameWeight.c_str() );
//   factory->SetBackgroundWeightExpression( nameWeight.c_str() );
//   factory->SetSignalWeightExpression    ( nameWeight.c_str() );

    std::cerr << " BBBBBBBBBBBBBBBBBBBBBBBBBBBBB " << std::endl;

    factory->PrepareTrainingAndTestTree( mycuts ,"SplitMode=Random:NormMode=None:!V");
//   factory->PrepareTrainingAndTestTree( "" ,"SplitMode=Random:NormMode=None:!V");

    std::cerr << " CCCCCCCCCCCCCCCCCCCCCCCCCCCCC " << std::endl;



    // gradient boosted decision trees
//   if (Use["BDTG"])    factory->BookMethod( TMVA::Types::kBDT, "BDTG", "!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.10:UseBaggedGrad:GradBaggingFraction=0.50:nCuts=20:NNodesMax=8");
    if (Use["BDTG"])    factory->BookMethod( TMVA::Types::kBDT, "BDTG", "!H:!V:NTrees=600:BoostType=Grad:Shrinkage=0.10:UseBaggedGrad:GradBaggingFraction=0.50:nCuts=20:NNodesMax=8");
    // neural network
    if (Use["MLP"])     factory->BookMethod( TMVA::Types::kMLP, "MLP", "!H:!V:NeuronType=tanh:NCycles=1000:HiddenLayers=N+5,5:TestRate=5:EstimatorType=MSE");
    // functional discriminant with GA minimizer
    if (Use["FDA_GA"])  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" );
    // PDE-Foam approach
    if (Use["PDEFoam"]) factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam", "!H:!V:TailCut=0.001:VolFrac=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Nmin=100:Kernel=None:Compress=T" );



    //==== Optimize parameters in MVA methods
//   factory->OptimizeAllMethods();
//   factory->OptimizeAllMethods("ROCIntegral","Scan");
    //==== 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 << "==> TMVAnalysis is done!" << std::endl;

    delete factory;

    //==== change position of weights file
    std::string toDo;

    toDo = "rm -r Weights-MVA-MultiClass/weights_" + HiggsMass + "_testVariables";
    std::cerr << "toDo = " << toDo << std::endl;
    system (toDo.c_str());

    toDo = "mv weights Weights-MVA-MultiClass/weights_" + HiggsMass + "_testVariables";
    std::cerr << "toDo = " << toDo << std::endl;
    system (toDo.c_str());

    // Launch the GUI for the root macros
    //   if (!gROOT->IsBatch()) TMVAGui( outfileName );
}
示例#17
0
void TMVAClassification( 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"]            = 1;
   Use["CutsD"]           = 1;
   Use["CutsPCA"]         = 1;
   Use["CutsGA"]          = 1;
   Use["CutsSA"]          = 1;
   // ---
   Use["Likelihood"]      = 1;
   Use["LikelihoodD"]     = 1; // 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"]   = 1;
   Use["LikelihoodMIX"]   = 1;
   // ---
   Use["PDERS"]           = 1;
   Use["PDERSD"]          = 1;
   Use["PDERSPCA"]        = 1;
   Use["PDERSkNN"]        = 1; // depreciated until further notice
   Use["PDEFoam"]         = 1;
   // --
   Use["KNN"]             = 1;
   // ---
   Use["HMatrix"]         = 1;
   Use["Fisher"]          = 1;
   Use["FisherG"]         = 1;
   Use["BoostedFisher"]   = 1;
   Use["LD"]              = 1;
   // ---
   Use["FDA_GA"]          = 1;
   Use["FDA_SA"]          = 1;
   Use["FDA_MC"]          = 1;
   Use["FDA_MT"]          = 1;
   Use["FDA_GAMT"]        = 1;
   Use["FDA_MCMT"]        = 1;
   // ---
   Use["MLP"]             = 1; // this is the recommended ANN
   Use["MLPBFGS"]         = 1; // recommended ANN with optional training method
   Use["CFMlpANN"]        = 1; // *** missing
   Use["TMlpANN"]         = 1; 
   // ---
   Use["SVM"]             = 1;
   // ---
   Use["BDT"]             = 1;
   Use["BDTD"]            = 0;
   Use["BDTG"]            = 1;
   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 = 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;
      }
   }

   // Create a new root output file.
   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 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' );

   // 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 {
      // load the signal and background event samples from ROOT trees
      TFile *input(0);
      TString fname = "../macros/tmva_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_class_example.root" ); // if not: download from ROOT server
      }

      if (!input) {
         std::cout << "ERROR: could not open data file" << std::endl;
         exit(1);
      }
      std::cout << "--- TMVAClassification       : Using input file: " << input->GetName() << std::endl;

      TTree *signal     = (TTree*)input->Get("TreeS");
      TTree *background = (TTree*)input->Get("TreeB");

      // global event weights per tree (see below for setting event-wise weights)
      Double_t signalWeight     = 1.0;
      Double_t backgroundWeight = 1.0;

      // ====== 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 );

      // 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");

   // Apply additional cuts on the signal and background samples (can be different)
   TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
   TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";

   // tell the factory to use all remaining events in the trees after training for testing:
   factory->PrepareTrainingAndTestTree( mycuts, mycutb,
                                        "nTrain_Signal=0:nTrain_Background=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" );  
   // 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 );
}
void TMVAClassificationCategory() 
{
   //---------------------------------------------------------------

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

   bool batchMode(false);

   // Create a new root output file.
   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 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
   std::string factoryOptions( "!V:!Silent:Transformations=I;D;P;G,D" );
   if (batchMode) factoryOptions += ":!Color:!DrawProgressBar";

   TMVA::Factory *factory = new TMVA::Factory( "TMVAClassificationCategory", outputFile, factoryOptions );

   // 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", 'F' );
   factory->AddVariable( "var2", 'F' );
   factory->AddVariable( "var3", 'F' );
   factory->AddVariable( "var4", '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( "eta" );

   // load the signal and background event samples from ROOT trees
   TFile *input(0);
   TString fname( "" );
   if (UseOffsetMethod) fname = "../execs/data/toy_sigbkg_categ_offset.root";
   else                 fname = "../execs/data/toy_sigbkg_categ_varoff.root";
   if (!gSystem->AccessPathName( fname )) {
      // first we try to find tmva_example.root in the local directory
      std::cout << "--- TMVAClassificationCategory: Accessing " << fname << std::endl;
      input = TFile::Open( fname );
   } 

   if (!input) {
      std::cout << "ERROR: could not open data file: " << fname << std::endl;
      exit(1);
   }

   TTree *signal     = (TTree*)input->Get("TreeS");
   TTree *background = (TTree*)input->Get("TreeB");

   /// global event weights per tree (see below for setting event-wise weights)
   Double_t signalWeight     = 1.0;
   Double_t backgroundWeight = 1.0;
   
   /// you can add an arbitrary number of signal or background trees
   factory->AddSignalTree    ( signal,     signalWeight     );
   factory->AddBackgroundTree( background, backgroundWeight );
   
   // Apply additional cuts on the signal and background samples (can be different)
   TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
   TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";

   // tell the factory to use all remaining events in the trees after training for testing:
   factory->PrepareTrainingAndTestTree( mycuts, mycutb,
                                        "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );

   // Fisher discriminant   
   factory->BookMethod( TMVA::Types::kFisher, "Fisher", "!H:!V:Fisher" );

   // 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" ); 

   // Categorised classifier
   TMVA::MethodCategory* mcat = 0;
   
   // the variable sets
   TString theCat1Vars = "var1:var2:var3:var4";
   TString theCat2Vars = (UseOffsetMethod ? "var1:var2:var3:var4" : "var1:var2:var3");

   // the Fisher 
   TMVA::MethodBase* fiCat = factory->BookMethod( TMVA::Types::kCategory, "FisherCat","" );
   mcat = dynamic_cast<TMVA::MethodCategory*>(fiCat);
   mcat->AddMethod("abs(eta)<=1.3",theCat1Vars, TMVA::Types::kFisher,"Category_Fisher_1","!H:!V:Fisher");
   mcat->AddMethod("abs(eta)>1.3", theCat2Vars, TMVA::Types::kFisher,"Category_Fisher_2","!H:!V:Fisher");

   // the Likelihood
   TMVA::MethodBase* liCat = factory->BookMethod( TMVA::Types::kCategory, "LikelihoodCat","" );
   mcat = dynamic_cast<TMVA::MethodCategory*>(liCat);
   mcat->AddMethod("abs(eta)<=1.3",theCat1Vars, TMVA::Types::kLikelihood,"Category_Likelihood_1","!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50");
   mcat->AddMethod("abs(eta)>1.3", theCat2Vars, TMVA::Types::kLikelihood,"Category_Likelihood_2","!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50");

   // ---- 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 << "==> TMVAClassificationCategory is done!" << std::endl;      

   // Clean up
   delete factory;

   // Launch the GUI for the root macros
   if (!gROOT->IsBatch()) TMVAGui( outfileName );
}
示例#19
0
void trainMVACat()
{
  char name[1000];
  float XSEC[6] = {3.67e+5,2.94e+4,6.524e+03,1.064e+03,121.5,2.542e+01};
  float NORM[6];
  TCut preselectionCut = "ht>400 && jetPt[5]>40 && (triggerBit[0] || triggerBit[2]) && nBJets>1 && nLeptons==0";
  TFile *bkgSrc[6];
  bkgSrc[0] = TFile::Open("flatTree_QCD_HT300to500.root");
  bkgSrc[1] = TFile::Open("flatTree_QCD_HT500to700.root");
  bkgSrc[2] = TFile::Open("flatTree_QCD_HT700to1000.root");
  bkgSrc[3] = TFile::Open("flatTree_QCD_HT1000to1500.root");
  bkgSrc[4] = TFile::Open("flatTree_QCD_HT1500to2000.root");
  bkgSrc[5] = TFile::Open("flatTree_QCD_HT2000toInf.root");

  TFile *sigSrc = TFile::Open("flatTree_ttHJetTobb_M125.root");
  //TFile *sigSrc = TFile::Open("flatTree_TT.root");
  TTree *sigTree = (TTree*)sigSrc->Get("hadtop/events"); 
  TTree *bkgTree[6];
  
  
  TFile *outf = new TFile("mva_Cat_QCD.root","RECREATE");
  TMVA::Factory* factory = new TMVA::Factory("factory_mva_Cat_QCD_",outf,"!V:!Silent:Color:DrawProgressBar:Transformations=I;G:AnalysisType=Classification");
  factory->AddSignalTree(sigTree);

  for(int k=0;k<6;k++) {
    NORM[k] = ((TH1F*)bkgSrc[k]->Get("hadtop/pileup"))->GetEntries();
    bkgTree[k] = (TTree*)bkgSrc[k]->Get("hadtop/events");
    factory->AddBackgroundTree(bkgTree[k],XSEC[k]/NORM[k]);
  }
  
  //int N_SIG(sigTree->GetEntries(preselectionCut));
  
  //int N_BKG0(bkgTree[0]->GetEntries(preselectionCut));
  //int N_BKG1(bkgTree[1]->GetEntries(preselectionCut));
  //int N_BKG2(bkgTree[2]->GetEntries(preselectionCut));
  //int N_BKG3(bkgTree[3]->GetEntries(preselectionCut));

  //float N_BKG_EFF = N_BKG0*XSEC[0]/NORM[0]+N_BKG1*XSEC[1]/NORM[1]+N_BKG2*XSEC[2]/NORM[2]+N_BKG3*XSEC[3]/NORM[3];
  
  //int N = TMath::Min((float)N_SIG,N_BKG_EFF);

  //cout<<N_SIG<<" "<<N_BKG_EFF<<endl;
  
  const int NVAR = 21;
  TString VAR[NVAR] = {
    "nJets",
    //"nBJets",
    "ht",
    "jetPt[0]","jetPt[1]","jetPt[2]","jetPt[3]","jetPt[4]","jetPt[5]",
    "mbbMin","dRbbMin",
    //"dRbbAve","mbbAve",
    //"btagAve","btagMax","btagMin",
    //"qglAve","qglMin","qglMedian",
    "sphericity","aplanarity","foxWolfram[0]","foxWolfram[1]","foxWolfram[2]","foxWolfram[3]",
    "mTop[0]","ptTTbar","mTTbar","dRbbTop","chi2"
  };
  char TYPE[NVAR] = {
    'I',
    //'I',
    'F',
    'F','F','F','F','F','F', 
    'F','F',
    //'F','F',
    //'F','F','F',
    //'F','F','F',
    'F','F','F','F','F','F', 
    'F','F','F','F','F'
  };

  for(int i=0;i<NVAR;i++) {
    factory->AddVariable(VAR[i],TYPE[i]);
  }

  factory->AddSpectator("status",'I');
  factory->AddSpectator("nBJets",'I');

  sprintf(name,"nTrain_Signal=%d:nTrain_Background=%d:nTest_Signal=%d:nTest_Background=%d",-1,-1,-1,-1);
  factory->PrepareTrainingAndTestTree(preselectionCut,name);

  TMVA::IMethod* BDT_Category = factory->BookMethod( TMVA::Types::kCategory,"BDT_Category");
  TMVA::MethodCategory* mcategory_BDT = dynamic_cast<TMVA::MethodCategory*>(BDT_Category); 

  mcategory_BDT->AddMethod("status == 0 && nBJets == 2",
                      "nJets:ht:jetPt[0]:jetPt[1]:jetPt[2]:jetPt[3]:jetPt[4]:jetPt[5]:mbbMin:dRbbMin:sphericity:aplanarity:foxWolfram[0]:foxWolfram[1]:foxWolfram[2]:foxWolfram[3]:mTop[0]:ptTTbar:mTTbar:dRbbTop:chi2:",
                      TMVA::Types::kBDT,
                      "BDT_Cat1",
                      "NTrees=2000:BoostType=Grad:Shrinkage=0.1");

  mcategory_BDT->AddMethod("status == 0 && nBJets > 2",
                      "nJets:ht:jetPt[0]:jetPt[1]:jetPt[2]:jetPt[3]:jetPt[4]:jetPt[5]:mbbMin:dRbbMin:sphericity:aplanarity:foxWolfram[0]:foxWolfram[1]:foxWolfram[2]:foxWolfram[3]:mTop[0]:ptTTbar:mTTbar:dRbbTop:chi2:",
                      TMVA::Types::kBDT,
                      "BDT_Cat2",
                      "NTrees=2000:BoostType=Grad:Shrinkage=0.1");

  mcategory_BDT->AddMethod("status < 0 && nBJets == 2",
                      "nJets:ht:jetPt[0]:jetPt[1]:jetPt[2]:jetPt[3]:jetPt[4]:jetPt[5]:mbbMin:dRbbMin:sphericity:aplanarity:foxWolfram[0]:foxWolfram[1]:foxWolfram[2]:foxWolfram[3]:",
                      TMVA::Types::kBDT,
                      "BDT_Cat3",
                      "NTrees=2000:BoostType=Grad:Shrinkage=0.1");

  mcategory_BDT->AddMethod("status < 0 && nBJets > 2",
                      "nJets:ht:jetPt[0]:jetPt[1]:jetPt[2]:jetPt[3]:jetPt[4]:jetPt[5]:mbbMin:dRbbMin:sphericity:aplanarity:foxWolfram[0]:foxWolfram[1]:foxWolfram[2]:foxWolfram[3]:",
                      TMVA::Types::kBDT,
                      "BDT_Cat4",
                      "NTrees=2000:BoostType=Grad:Shrinkage=0.1");

  TMVA::IMethod* Fisher_Category = factory->BookMethod( TMVA::Types::kCategory,"Fisher_Category");
  TMVA::MethodCategory* mcategory_Fisher = dynamic_cast<TMVA::MethodCategory*>(Fisher_Category);
  
  mcategory_Fisher->AddMethod("status == 0 && nBJets == 2",
                      "nJets:ht:jetPt[0]:jetPt[1]:jetPt[2]:jetPt[3]:jetPt[4]:jetPt[5]:mbbMin:dRbbMin:sphericity:aplanarity:foxWolfram[0]:foxWolfram[1]:foxWolfram[2]:foxWolfram[3]:mTop[0]:ptTTbar:mTTbar:dRbbTop:chi2:",
                      TMVA::Types::kFisher,
                      "Fisher_Cat1","H:!V:Fisher");

  mcategory_Fisher->AddMethod("status == 0 && nBJets > 2",
                      "nJets:ht:jetPt[0]:jetPt[1]:jetPt[2]:jetPt[3]:jetPt[4]:jetPt[5]:mbbMin:dRbbMin:sphericity:aplanarity:foxWolfram[0]:foxWolfram[1]:foxWolfram[2]:foxWolfram[3]:mTop[0]:ptTTbar:mTTbar:dRbbTop:chi2:",
                      TMVA::Types::kFisher,
                      "Fisher_Cat2","H:!V:Fisher");

  mcategory_Fisher->AddMethod("status < 0 && nBJets == 2",
                      "nJets:ht:jetPt[0]:jetPt[1]:jetPt[2]:jetPt[3]:jetPt[4]:jetPt[5]:mbbMin:dRbbMin:sphericity:aplanarity:foxWolfram[0]:foxWolfram[1]:foxWolfram[2]:foxWolfram[3]:",
                      TMVA::Types::kFisher,
                      "Fisher_Cat3","H:!V:Fisher");

  mcategory_Fisher->AddMethod("status < 0 && nBJets > 2",
                      "nJets:ht:jetPt[0]:jetPt[1]:jetPt[2]:jetPt[3]:jetPt[4]:jetPt[5]:mbbMin:dRbbMin:sphericity:aplanarity:foxWolfram[0]:foxWolfram[1]:foxWolfram[2]:foxWolfram[3]:",
                      TMVA::Types::kFisher,
                      "Fisher_Cat4","H:!V:Fisher");

  // specify the training methods
  //factory->BookMethod(TMVA::Types::kFisher,"Fisher");
  //factory->BookMethod(TMVA::Types::kBDT,"BDT_GRAD_2000","NTrees=2000:BoostType=Grad:Shrinkage=0.1");
  
  factory->TrainAllMethods();
  factory->TestAllMethods();
  factory->EvaluateAllMethods(); 
  outf->Close();
}
示例#20
0
void Classification()
{
   TMVA::Tools::Instance();
   TMVA::PyMethodBase::PyInitialize();

   TString outfileName("TMVA.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:AnalysisType=Classification");


   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->AddSpectator("spec1 := var1*2",  "Spectator 1", "units", 'F');
   factory->AddSpectator("spec2 := var1*3",  "Spectator 2", "units", 'F');


   TString fname = "./tmva_class_example.root";

   if (gSystem->AccessPathName(fname))    // file does not exist in local directory
      gSystem->Exec("curl -O http://root.cern.ch/files/tmva_class_example.root");

   TFile *input = TFile::Open(fname);

   std::cout << "--- TMVAClassification       : Using input file: " << input->GetName() << std::endl;

   // --- Register the training and test trees

   TTree *tsignal     = (TTree *)input->Get("TreeS");
   TTree *tbackground = (TTree *)input->Get("TreeB");

   // global event weights per tree (see below for setting event-wise weights)
   Double_t signalWeight     = 1.0;
   Double_t backgroundWeight = 1.0;

   // You can add an arbitrary number of signal or background trees
   factory->AddSignalTree(tsignal,     signalWeight);
   factory->AddBackgroundTree(tbackground, backgroundWeight);


   // Set individual event weights (the variables must exist in the original TTree)
   factory->SetBackgroundWeightExpression("weight");


   // Apply additional cuts on the signal and background samples (can be different)
   TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
   TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";

   // Tell the factory how to use the training and testing events
   factory->PrepareTrainingAndTestTree(mycuts, mycutb,
                                       "nTrain_Signal=0:nTrain_Background=0:nTest_Signal=0:nTest_Background=0:SplitMode=Random:NormMode=NumEvents:!V");


   ///////////////////
   //Booking         //
   ///////////////////
   // Boosted Decision Trees

   //PyMVA methods
   factory->BookMethod(TMVA::Types::kPyRandomForest, "PyRandomForest",
                       "!V:NEstimators=150:Criterion=gini:MaxFeatures=auto:MaxDepth=3:MinSamplesLeaf=1:MinWeightFractionLeaf=0:Bootstrap=kTRUE");
   factory->BookMethod(TMVA::Types::kPyAdaBoost, "PyAdaBoost",
                       "!V:BaseEstimator=None:NEstimators=100:LearningRate=1:Algorithm=SAMME.R:RandomState=None");
   factory->BookMethod(TMVA::Types::kPyGTB, "PyGTB",
                       "!V:NEstimators=150:Loss=deviance:LearningRate=0.1:Subsample=1:MaxDepth=6:MaxFeatures='auto'");


   // 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;

}
示例#21
0
std::pair<TString,TString> TMVAClassification (
    TString infilename,
    AnalysisType analysisType = AnalysisType::DIRECT,
    TString additionalRootFileName = "")
{
    TMVA::Tools::Instance();

    std::string tmstr (now ());
    TString tmstmp (tmstr.c_str ());
   
  
    std::cout << "==> Start TMVAClassification" << std::endl;
    std::cout << "-------------------- open input file ---------------- " << std::endl;
    TString fname = infilename; //pathToData + infilename + TString (".root");
    if (analysisType != AnalysisType::TRANSFORMED)
        fname = pathToData + infilename + TString (".root");
    std::cout << "open file " << std::endl << fname.Data () << std::endl;


    std::cout << "-------------------- get tree ---------------- " << std::endl;
    TString treeName = "data";
    if (analysisType == AnalysisType::TRANSFORMED)
        treeName = "transformed";

    std::cout << "-------------------- create tchain with treeName ---------------- " << std::endl;
    std::cout << treeName << std::endl;
    TChain* tree = new TChain (treeName);
    std::cout << "add file" << std::endl;
    std::cout << fname << std::endl;
    tree->Add (fname);
    TChain* treeFriend (NULL);
    if (additionalRootFileName.Length () > 0)
    {
        std::cout << "-------------------- add additional input file ---------------- " << std::endl;
        std::cout << additionalRootFileName << std::endl;
        treeFriend = new TChain (treeName);
        treeFriend->Add (additionalRootFileName);
        tree->AddFriend (treeFriend,"p");
    }
//    tree->Draw ("mass:prediction");
//    return std::make_pair(TString("hallo"),TString ("nix"));
    TString outfileName;
    if (analysisType == AnalysisType::BACKGROUND)
    {
        outfileName = TString ("BACK_" + infilename) + tmstmp + TString (".root");
    }
    else
        outfileName += TString ( "TMVA__" ) + tmstmp + TString (".root");

    std::cout << "-------------------- open output file ---------------- " << std::endl;
    TFile* outputFile = TFile::Open( outfileName, "RECREATE" );

    std::cout << "-------------------- prepare factory ---------------- " << std::endl;
    TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
						"AnalysisType=Classification:Transformations=I:!V" );
    std::cout << "-------------------- add variables ---------------- " << std::endl;


    for (auto varname : variableNames)
    {
	factory->AddVariable (varname.c_str (), 'F');
    }

    for (auto varname : spectatorNames)
    {
	factory->AddSpectator (varname.c_str (), 'F');
    }
    
   
    std::cout << "-------------------- add trees ---------------- " << std::endl;
    TCut signalCut ("signal==1");
    TCut backgroundCut ("signal==0");
    if (analysisType == AnalysisType::TRANSFORMED)
    {
        signalCut = "(signal_original==1 && signal_in==0)";
        backgroundCut = "(signal_original==0 && signal_in==0)";
    }
    if (analysisType == AnalysisType::BACKGROUND)
    {
        signalCut     = TString("(signal==0) * (prediction > 0.7)");
        backgroundCut = TString("(signal==0) * (prediction < 0.4)");
    }
    //tree->Draw ("prediction",signalCut);
    //return std::make_pair(TString("hallo"),TString ("nix"));
    factory->AddTree(tree, "Signal", 1.0, baseCut + signalCut, "TrainingTesting");
    factory->AddTree(tree, "Background", 1.0, baseCut + backgroundCut, "TrainingTesting");


    
    TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
    TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";

    /* // Set individual event weights (the variables must exist in the original TTree) */
    if (analysisType == AnalysisType::BACKGROUND)
    {
        factory->SetSignalWeightExpression ("prediction");
        factory->SetBackgroundWeightExpression ("1");
    }

   
    std::cout << "-------------------- prepare ---------------- " << std::endl;
    factory->PrepareTrainingAndTestTree( mycuts, mycutb,
					 "nTrain_Signal=0:nTrain_Background=0:nTest_Signal=0:nTest_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );


    TString methodName ("");
    if (analysisType == AnalysisType::BACKGROUND)
        methodName = TString ("TONBKG_") + tmstmp;

    if (false)
    {
	// gradient boosting training
        methodName += TString("GBDT");
	factory->BookMethod(TMVA::Types::kBDT, methodName,
			    "NTrees=40:BoostType=Grad:Shrinkage=0.01:MaxDepth=7:UseNvars=6:nCuts=20:MinNodeSize=10");
    }
    if (false)
    {
        methodName += TString("Likelihood");
	factory->BookMethod( TMVA::Types::kLikelihood, methodName,
			     "H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" );
    }
    

    
    if (false)
    {
	TString layoutString ("Layout=TANH|100,LINEAR");

	TString training0 ("LearningRate=1e-1,Momentum=0.0,Repetitions=1,ConvergenceSteps=300,BatchSize=20,TestRepetitions=15,WeightDecay=0.001,Regularization=NONE,DropConfig=0.0+0.5+0.5+0.5,DropRepetitions=1,Multithreading=True");
	TString training1 ("LearningRate=1e-2,Momentum=0.5,Repetitions=1,ConvergenceSteps=300,BatchSize=30,TestRepetitions=7,WeightDecay=0.001,Regularization=L2,Multithreading=True,DropConfig=0.0+0.1+0.1+0.1,DropRepetitions=1");
	TString training2 ("LearningRate=1e-2,Momentum=0.3,Repetitions=1,ConvergenceSteps=300,BatchSize=40,TestRepetitions=7,WeightDecay=0.0001,Regularization=L2,Multithreading=True");
	TString training3 ("LearningRate=1e-3,Momentum=0.1,Repetitions=1,ConvergenceSteps=200,BatchSize=70,TestRepetitions=7,WeightDecay=0.0001,Regularization=NONE,Multithreading=True");

	TString trainingStrategyString ("TrainingStrategy=");
	trainingStrategyString += training0 + "|" + training1 + "|" + training2 + "|" + training3;
      
	TString nnOptions ("!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=G:WeightInitialization=XAVIERUNIFORM");
	nnOptions.Append (":"); nnOptions.Append (layoutString);
	nnOptions.Append (":"); nnOptions.Append (trainingStrategyString);

        methodName += TString("NNgauss");
	factory->BookMethod( TMVA::Types::kNN, methodName, nnOptions ); // NN
    }

    if (false)
    {
	TString layoutString ("Layout=TANH|200,TANH|70,LINEAR");

	TString training0 ("LearningRate=1e-2,Momentum=0.0,Repetitions=1,ConvergenceSteps=300,BatchSize=20,TestRepetitions=15,WeightDecay=0.001,Regularization=NONE,DropConfig=0.0+0.5+0.5+0.5,DropRepetitions=1,Multithreading=True");
	TString training1 ("LearningRate=1e-3,Momentum=0.5,Repetitions=1,ConvergenceSteps=300,BatchSize=30,TestRepetitions=7,WeightDecay=0.001,Regularization=L2,Multithreading=True,DropConfig=0.0+0.1+0.1+0.1,DropRepetitions=1");
	TString training2 ("LearningRate=1e-4,Momentum=0.3,Repetitions=1,ConvergenceSteps=300,BatchSize=40,TestRepetitions=7,WeightDecay=0.0001,Regularization=L2,Multithreading=True");
	TString training3 ("LearningRate=1e-5,Momentum=0.1,Repetitions=1,ConvergenceSteps=200,BatchSize=70,TestRepetitions=7,WeightDecay=0.0001,Regularization=NONE,Multithreading=True");

	TString trainingStrategyString ("TrainingStrategy=");
	trainingStrategyString += training0 + "|" + training1 + "|" + training2 + "|" + training3;
//	trainingStrategyString += training0 + "|" + training2 + "|" + training3;
//	trainingStrategyString += training0 + "|" + training2;

      
	//       TString nnOptions ("!H:V:VarTransform=Normalize:ErrorStrategy=CROSSENTROPY");
	TString nnOptions ("!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=N:WeightInitialization=XAVIERUNIFORM");
	//       TString nnOptions ("!H:V:VarTransform=Normalize:ErrorStrategy=CHECKGRADIENTS");
	nnOptions.Append (":"); nnOptions.Append (layoutString);
	nnOptions.Append (":"); nnOptions.Append (trainingStrategyString);

        methodName = TString("NNnormalized");
        factory->BookMethod( TMVA::Types::kNN, methodName, nnOptions ); // NN
    }


    if (true)
    {
	TString layoutString ("Layout=TANH|100,TANH|50,LINEAR");

	TString training0 ("LearningRate=1e-2,Momentum=0.0,Repetitions=1,ConvergenceSteps=100,BatchSize=20,TestRepetitions=7,WeightDecay=0.001,Regularization=NONE,DropConfig=0.0+0.5+0.5+0.5,DropRepetitions=1,Multithreading=True");
	TString training1 ("LearningRate=1e-3,Momentum=0.0,Repetitions=1,ConvergenceSteps=20,BatchSize=30,TestRepetitions=7,WeightDecay=0.001,Regularization=L2,Multithreading=True,DropConfig=0.0+0.1+0.1+0.1,DropRepetitions=1");
	TString training2 ("LearningRate=1e-4,Momentum=0.0,Repetitions=1,ConvergenceSteps=20,BatchSize=40,TestRepetitions=7,WeightDecay=0.0001,Regularization=L2,Multithreading=True");
	TString training3 ("LearningRate=1e-5,Momentum=0.0,Repetitions=1,ConvergenceSteps=30,BatchSize=70,TestRepetitions=7,WeightDecay=0.0001,Regularization=NONE,Multithreading=True");

	TString trainingStrategyString ("TrainingStrategy=");
	trainingStrategyString += training0 + "|" + training1 + "|" + training2 + "|" + training3;

      
	TString nnOptions ("!H:!V:ErrorStrategy=CROSSENTROPY:VarTransform=P+G:WeightInitialization=XAVIERUNIFORM");
	nnOptions.Append (":"); nnOptions.Append (layoutString);
	nnOptions.Append (":"); nnOptions.Append (trainingStrategyString);

        methodName += TString("NNPG");
	factory->BookMethod( TMVA::Types::kNN, methodName, nnOptions ); // NN
    }
   
   
   
    factory->TrainAllMethods();
//    return std::make_pair(TString("hallo"),TString ("nix"));
    factory->TestAllMethods();
    factory->EvaluateAllMethods();

    //input->Close();
    outputFile->Close();

//    TMVA::TMVAGui (outfileName);
   
    delete factory;
    delete tree;
    switch (analysisType)
    {
    case AnalysisType::BACKGROUND:
        std::cout << "DONE BACKGROUND" << std::endl;
        break;
    case AnalysisType::DIRECT:
        std::cout << "DONE DIRECT" << std::endl;
        break;
    case AnalysisType::TRANSFORMED:
        std::cout << "DONE TRANSFORMED" << std::endl;
        break;
        
    };
    std::cout << "classification, return : " << outfileName << "  ,  " << methodName << std::endl;
    return std::make_pair (outfileName, methodName);
}