void run() {
   
auto inputFile = TFile::Open("Higgs_data.root");

inputFile->ls();

TMVA::DataLoader loader("dataset");

// add variables to loader 
loader.AddVariable("m_jj");
loader.AddVariable("m_jjj");
loader.AddVariable("m_lv");
loader.AddVariable("m_jlv");
loader.AddVariable("m_bb");
loader.AddVariable("m_wbb");
loader.AddVariable("m_wwbb");

//TMVA::gConfig().SetDrawProgressBar( false);

TCut mycuts;
TCut mycutb;

auto tsignal =  (TTree*) inputFile->Get("sig_tree");
auto tbackground =  (TTree*) inputFile->Get("bkg_tree");

loader.AddSignalTree    (tsignal,     1.0);   //signal weight  = 1
loader.AddBackgroundTree(tbackground, 1.0);   //background weight = 1 


loader.PrepareTrainingAndTestTree(mycuts, mycutb,
                                  "nTrain_Signal=1000:nTrain_Background=1000:nTest_Signal=500:nTest_Background=50:"
                                  "SplitMode=Random:NormMode=NumEvents:V" );


TMVA::HyperParameterOptimisation optim(&loader);

TStopwatch tw;

std::cout << "booking method " << std::endl;

optim.BookMethod(TMVA::Types::kBDT, "BDT",
             "V:NTrees=100:MinNodeSize=2.5%:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:"
             "UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" );


optim.SetFitter("Minuit");
optim.SetNumFolds(3);
optim.SetVerbose(1);

tw.Start();

std::cout << "evaluating method " << std::endl;

optim.Evaluate();
tw.Print();

auto results=optim.GetResults();

results.Print();

auto graph = results.GetROCCurves(); 
graph->Draw(); 
gPad->Draw(); 


}