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 Python tutorial TMVA_SOFIE_Keras_HiggsModel.py You need to run that macro before to generate the trained Keras model and also the corresponding header file with SOFIE which can then be used for inference

Execute in this order:

python3 TMVA_SOFIE_Keras_HiggsModel.py
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)
#include "HiggsModel.hxx"
#include "TMVA/SOFIEHelpers.hxx"
 
using namespace TMVA::Experimental;
 
void TMVA_SOFIE_RDataFrame(int 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_HiggsModel::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 h2 = df2.DefineSlot("DNN_Value", SofieFunctor<7, TMVA_SOFIE_HiggsModel::Session>(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);
   h2->SetLineColor(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.