doc/v626/tmva003__RReader_8C.html

using namespace TMVA::Experimental;


void train(const std::string &filename)

{

   // Create factory

   auto output = TFile::Open("TMVA.root", "RECREATE");

   auto factory = new TMVA::Factory("tmva003",

           output, "!V:!DrawProgressBar:AnalysisType=Classification");


   // Open trees with signal and background events

   auto data = TFile::Open(filename.c_str());

   auto signal = (TTree *)data->Get("TreeS");

   auto background = (TTree *)data->Get("TreeB");


   // Add variables and register the trees with the dataloader

   auto dataloader = new TMVA::DataLoader("tmva003_BDT");

   const std::vector<std::string> variables = {"var1", "var2", "var3", "var4"};

   for (const auto &var : variables) {

      dataloader->AddVariable(var);

   }

   dataloader->AddSignalTree(signal, 1.0);

   dataloader->AddBackgroundTree(background, 1.0);

   dataloader->PrepareTrainingAndTestTree("", "");


   // Train a TMVA method

   factory->BookMethod(dataloader, TMVA::Types::kBDT, "BDT", "!V:!H:NTrees=300:MaxDepth=2");

   factory->TrainAllMethods();

}


void tmva003_RReader()

{

   // First, let's train a model with TMVA.

   const std::string filename = "http://root.cern.ch/files/tmva_class_example.root";

   train(filename);


   // Next, we load the model from the TMVA XML file.

   RReader model("tmva003_BDT/weights/tmva003_BDT.weights.xml");


   // In case you need a reminder of the names and order of the variables during

   // training, you can ask the model for it.

   auto variables = model.GetVariableNames();


   // The model can now be applied in different scenarios:

   // 1) Event-by-event inference

   // 2) Batch inference on data of multiple events

   // 3) Inference as part of an RDataFrame graph


   // 1) Event-by-event inference

   // The event-by-event inference takes the values of the variables as a std::vector<float>.

   // Note that the return value is as well a std::vector<float> since the reader

   // is also capable to process models with multiple outputs.

   auto prediction = model.Compute({0.5, 1.0, -0.2, 1.5});

   std::cout << "Single-event inference: " << prediction[0] << "\n\n";


   // 2) Batch inference on data of multiple events

   // For batch inference, the data needs to be structured as a matrix. For this

   // purpose, TMVA makes use of the RTensor class. For convenience, we use RDataFrame

   // and the AsTensor utility to make the read-out from the ROOT file.

   ROOT::RDataFrame df("TreeS", filename);

   auto df2 = df.Range(3); // Read only a small subset of the dataset

   auto x = AsTensor<float>(df2, variables);

   auto y = model.Compute(x);


   std::cout << "RTensor input for inference on data of multiple events:\n" << x << "\n\n";

   std::cout << "Prediction performed on multiple events: " << y << "\n\n";


   // 3) Perform inference as part of an RDataFrame graph

   // We write a small lambda function that performs for us the inference on

   // a dataframe to omit code duplication.

   auto make_histo = [&](const std::string &treename) {

      ROOT::RDataFrame df(treename, filename);

      auto df2 = df.Define("y", Compute<4, float>(model), variables);

      return df2.Histo1D({treename.c_str(), ";BDT score;N_{Events}", 30, -0.5, 0.5}, "y");

   };


   auto sig = make_histo("TreeS");

   auto bkg = make_histo("TreeB");


   // Make plot

   gStyle->SetOptStat(0);

   auto c = new TCanvas("", "", 800, 800);


   sig->SetLineColor(kRed);

   bkg->SetLineColor(kBlue);

   sig->SetLineWidth(2);

   bkg->SetLineWidth(2);

   bkg->Draw("HIST");

   sig->Draw("HIST SAME");


   TLegend legend(0.7, 0.7, 0.89, 0.89);

   legend.SetBorderSize(0);

   legend.AddEntry("TreeS", "Signal", "l");

   legend.AddEntry("TreeB", "Background", "l");

   legend.Draw();


   c->DrawClone();

}

c
#define c(i)
Definition RSha256.hxx:101

kRed
@ kRed
Definition Rtypes.h:66

kBlue
@ kBlue
Definition Rtypes.h:66

gStyle
R__EXTERN TStyle * gStyle
Definition TStyle.h:413

ROOT::RDataFrame
ROOT's RDataFrame offers a high level interface for analyses of data stored in TTree,...
Definition RDataFrame.hxx:50

TCanvas
The Canvas class.
Definition TCanvas.h:23

TFile::Open
static TFile * Open(const char *name, Option_t *option="", const char *ftitle="", Int_t compress=ROOT::RCompressionSetting::EDefaults::kUseCompiledDefault, Int_t netopt=0)
Create / open a file.
Definition TFile.cxx:4025

TLegend
This class displays a legend box (TPaveText) containing several legend entries.
Definition TLegend.h:23

TMVA::DataLoader
Definition DataLoader.h:50

TMVA::Experimental::RReader
TMVA::Reader legacy interface.
Definition RReader.hxx:113

TMVA::Factory
This is the main MVA steering class.
Definition Factory.h:80

TMVA::Types::kBDT
@ kBDT
Definition Types.h:86

TStyle::SetOptStat
void SetOptStat(Int_t stat=1)
The type of information printed in the histogram statistics box can be selected via the parameter mod...
Definition TStyle.cxx:1589

TTree
A TTree represents a columnar dataset.
Definition TTree.h:79

y
Double_t y[n]
Definition legend1.C:17

x
Double_t x[n]
Definition legend1.C:17

TMVA::Experimental
Definition RModelParser_Keras.h:40

TMVA::variables
void variables(TString dataset, TString fin="TMVA.root", TString dirName="InputVariables_Id", TString title="TMVA Input Variables", Bool_t isRegression=kFALSE, Bool_t useTMVAStyle=kTRUE)

output
static void output(int code)
Definition gifencode.c:226