TMVA_SOFIE_RDataFrame.C File Reference

Detailed Description

This macro provides an example of using a trained model with Keras and make inference using SOFIE and RDataFrame This macro uses as input a Keras model generated with the TMVA_Higgs_Classification.C tutorial You need to run that macro before to generate the trained Keras model Then you need to run the macro TMVA_SOFIE_Keras_HiggsModel.C to generate the corresponding header file using SOFIE.

Execute in this order:

root TMVA_Higgs_Classification.C
root TMVA_SOFIE_Keras_HiggsModel.C
root TMVA_SOFIE_RDataFrame.C

 
using namespace TMVA::Experimental;
 
// need to add the current directory (from where we are running this macro)
// to the include path for Cling
R__ADD_INCLUDE_PATH($PWD)
R__ADD_INCLUDE_PATH($ROOTSYS/runtutorials)
#include "Higgs_trained_model.hxx"
#include "TMVA/SOFIEHelpers.hxx"
 
using namespace TMVA::Experimental;
 
void nthreads = 2){
 
   std::string inputFileName = "Higgs_data.root";
   std::string inputFile = std::string{gROOT->GetTutorialDir()} + "/machine_learning/data/" + inputFileName;
 
   ROOT::EnableImplicitMT(nthreads);
 
   ROOT::RDataFrame df1("sig_tree", inputFile);
   int nslots = df1.GetNSlots();
   std::cout << "Running using " << nslots << " threads" << std::endl;
   auto h1 = df1.DefineSlot("DNN_Value", SofieFunctor<7, TMVA_SOFIE_Higgs_trained_model::Session>(nslots),
                            {"m_jj", "m_jjj", "m_lv", "m_jlv", "m_bb", "m_wbb", "m_wwbb"})
                .Histo1D({"h_sig", "", 100, 0, 1}, "DNN_Value");
 
   ROOT::RDataFrame df2("bkg_tree", inputFile);
   nslots = df2.GetNSlots();
   auto nslots),
                            {"m_jj", "m_jjj", "m_lv", "m_jlv", "m_bb", "m_wbb", "m_wwbb"})
                .Histo1D({"h_bkg", "", 100, 0, 1}, "DNN_Value");
 
   h1->SetLineColor(kRed);
   kBlue);
 
   auto c1 = new TCanvas();
   gStyle->SetOptStat(0);
 
   h2->DrawClone();
   h1->DrawClone("SAME");
   c1->BuildLegend();
 
}

Author

Lorenzo Moneta

Definition in file TMVA_SOFIE_RDataFrame.C.