TMVA_SOFIE_RDataFrame_JIT.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 this one.

In this case we are parsing the input file and then run the inference in the same macro making use of the ROOT JITing capability

 
using namespace TMVA::Experimental;
 
/// function to compile the generated model and the declaration of the SofieFunctor
/// used by RDF.
/// Assume that the model name as in the header file
void CompileModelForRDF(const std::string & headerModelFile, unsigned int ninputs, unsigned int nslots=0) {
 
   std::string modelName = headerModelFile.substr(0,headerModelFile.find(".hxx"));
   std::string cmd = std::string("#include \"") + headerModelFile + std::string("\"");
   auto ret = gInterpreter->Declare(cmd.c_str());
   if (!ret)
    throw std::runtime_error("Error compiling : " + cmd);
   std::cout << "compiled : " << cmd << std::endl;
 
   cmd = "auto sofie_functor = TMVA::Experimental::SofieFunctor<" + std::to_string(ninputs) + ",TMVA_SOFIE_" +
    modelName + "::Session>(" + std::to_string(nslots) + ");";
   ret = gInterpreter->Declare(cmd.c_str());
   if (!ret)
    throw std::runtime_error("Error compiling : " + cmd);
   std::cout << "compiled : " << cmd << std::endl;
   std::cout << "Model is ready to be evaluated" << std::endl;
   return;
}
 
void TMVA_SOFIE_RDataFrame_JIT(std::string modelFile = "Higgs_trained_model.h5"){
 
    TMVA::PyMethodBase::PyInitialize();
 
    // check if the input file exists
    if (gSystem->AccessPathName(modelFile.c_str())) {
        Info("TMVA_SOFIE_RDataFrame","You need to run TMVA_Higgs_Classification.C to generate the Keras trained model");
        return;
    }
 
    // parse the input Keras model into RModel object
    SOFIE::RModel model = SOFIE::PyKeras::Parse(modelFile);
 
    std::string modelName = modelFile.substr(0,modelFile.find(".h5"));
    std::string modelHeaderFile = modelName + std::string(".hxx");
    //Generating inference code
    model.Generate();
    model.OutputGenerated(modelHeaderFile);
    model.PrintGenerated();
    // check that also weigh file exists
    std::string modelWeightFile = modelName + std::string(".dat");
    if (gSystem->AccessPathName(modelWeightFile.c_str())) {
        Error("TMVA_SOFIE_RDataFrame","Generated weight file is missing");
        return;
    }
 
    // now compile using ROOT JIT trained model (see function above)
    CompileModelForRDF(modelHeaderFile,7);
 
    std::string inputFileName = "Higgs_data.root";
    std::string inputFile = gROOT->GetTutorialDir() + "/machine_learning/data/" + inputFileName;
 
    ROOT::RDataFrame df1("sig_tree", inputFile);
    auto h1 = df1.Define("DNN_Value", "sofie_functor(rdfslot_,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);
    auto h2 = df2.Define("DNN_Value", "sofie_functor(rdfslot_,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_JIT.C.