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Reference Guide
tmva103_Application.C File Reference

Detailed Description

View in nbviewer Open in SWAN This tutorial illustrates how you can conveniently apply BDTs in C++ using the fast tree inference engine offered by TMVA.

Supported workflows are event-by-event inference, batch inference and pipelines with RDataFrame.

using namespace TMVA::Experimental;
void tmva103_Application()
// Load BDT model remotely from a webserver
RBDT<> bdt("myBDT", "http://root.cern/files/tmva101.root");
// Apply model on a single input
auto y1 = bdt.Compute({1.0, 2.0, 3.0, 4.0});
std::cout << "Apply model on a single input vector: " << y1[0] << std::endl;
// Apply model on a batch of inputs
float data[8] = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0};
RTensor<float> x(data, {2, 4});
auto y2 = bdt.Compute(x);
std::cout << "Apply model on an input tensor: " << y2 << std::endl;
// Apply model as part of an RDataFrame workflow
ROOT::RDataFrame df("Events", "root://eospublic.cern.ch//eos/root-eos/cms_opendata_2012_nanoaod/SMHiggsToZZTo4L.root");
auto df2 = df.Filter("nMuon >= 2")
.Filter("nElectron >= 2")
.Define("Muon_pt_1", "Muon_pt[0]")
.Define("Muon_pt_2", "Muon_pt[1]")
.Define("Electron_pt_1", "Electron_pt[0]")
.Define("Electron_pt_2", "Electron_pt[1]")
Compute<4, float>(bdt),
{"Muon_pt_1", "Muon_pt_2", "Electron_pt_1", "Electron_pt_2"});
std::cout << "Mean response on the signal sample: " << *df2.Mean("y") << std::endl;
Apply model on a single input vector: 0.0302787
Apply model on an input tensor: { { 0.0302787 } { 0.19114 } }
Mean response on the signal sample: 0.625916
December 2018
Stefan Wunsch

Definition in file tmva103_Application.C.

RTensor is a container with contiguous memory and shape information.
Definition: RTensor.hxx:162
Double_t x[n]
Definition: legend1.C:17
ROOT's RDataFrame offers a high level interface for analyses of data stored in TTree,...
Definition: RDataFrame.hxx:42
Fast boosted decision tree inference.
Definition: RBDT.hxx:35
Definition: RModel.hxx:18