Classification Class Reference

Class to perform two class classification.

The first step before any analysis is to prepare the data, to do that you need to create an object of TMVA::DataLoader, in this object you need to configure the variables and the number of events to train/test. The class TMVA::Experimental::Classification needs a TMVA::DataLoader object, optional a TFile object to save the results and some extra options in a string like "V:Color:Transformations=I;D;P;U;G:Silent:DrawProgressBar:ModelPersistence:Jobs=2" where: V = verbose output Color = coloured screen output Silent = batch mode: boolean silent flag inhibiting any output from TMVA Transformations = list of transformations to test. DrawProgressBar = draw progress bar to display training and testing. ModelPersistence = to save the trained model in xml or serialized files. Jobs = number of ml methods to test/train in parallel using MultiProc, requires to call Evaluate method. Basic example.

void classification(UInt_t jobs = 2)
{
   TMVA::Tools::Instance();
 
   TFile *input(0);
   TString fname = gROOT->GetTutorialDir() + "/machine_learning/data/tmva_class_example.root";
   if (!gSystem->AccessPathName(fname)) {
      input = TFile::Open(fname); // check if file in local directory exists
   }
   if (!input) {
      std::cout << "ERROR: could not open data file" << std::endl;
      exit(1);
   }
 
   // Register the training and test trees
 
   TTree *signalTree = (TTree *)input->Get("TreeS");
   TTree *background = (TTree *)input->Get("TreeB");
 
   TMVA::DataLoader *dataloader = new TMVA::DataLoader("dataset");
 
   dataloader->AddVariable("myvar1 := var1+var2", 'F');
   dataloader->AddVariable("myvar2 := var1-var2", "Expression 2", "", 'F');
   dataloader->AddVariable("var3", "Variable 3", "units", 'F');
   dataloader->AddVariable("var4", "Variable 4", "units", 'F');
 
   dataloader->AddSpectator("spec1 := var1*2", "Spectator 1", "units", 'F');
   dataloader->AddSpectator("spec2 := var1*3", "Spectator 2", "units", 'F');
 
   // global event weights per tree (see below for setting event-wise weights)
   Double_t signalWeight = 1.0;
   Double_t backgroundWeight = 1.0;
 
   dataloader->SetBackgroundWeightExpression("weight");
 
   TMVA::Experimental::Classification *cl = new TMVA::Experimental::Classification(dataloader, Form("Jobs=%d", jobs));
 
   cl->BookMethod(TMVA::Types::kBDT, "BDTG", "!H:!V:NTrees=2000:MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:"
                                             "UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=2");
   cl->BookMethod(TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm");
 
   cl->Evaluate(); // Train and Test all methods
 
   auto &results = cl->GetResults();
 
   TCanvas *c = new TCanvas(Form("ROC"));
   c->SetTitle("ROC-Integral Curve");
 
   auto mg = new TMultiGraph();
   for (UInt_t i = 0; i < results.size(); i++) {
      auto roc = results[i].GetROCGraph();
      roc->SetLineColorAlpha(i + 1, 0.1);
      mg->Add(roc);
   }
   mg->Draw("AL");
   mg->GetXaxis()->SetTitle(" Signal Efficiency ");
   mg->GetYaxis()->SetTitle(" Background Rejection ");
   c->BuildLegend(0.15, 0.15, 0.3, 0.3);
   c->Draw();
 
   delete cl;
}

#include <TMVA/Classification.h>

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The documentation for this class was generated from the following file:

• tmva/tmva/inc/TMVA/Classification.h