Classification¶
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This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Tuesday, May 24, 2022 at 05:43 PM.
/ \macro_output / \macro_code
%%cpp -d
#include "TMVA/Factory.h"
#include "TMVA/DataLoader.h"
#include "TMVA/Tools.h"
#include "TMVA/Classification.h"
Arguments are defined.
UInt_t jobs = 4;
TMVA::Tools::Instance();
TFile *input(0);
TString fname = "./tmva_class_example.root";
if (!gSystem->AccessPathName(fname)) {
input = TFile::Open(fname); // check if file in local directory exists
} else {
TFile::SetCacheFileDir(".");
input = TFile::Open("http://root.cern.ch/files/tmva_class_example.root", "CACHEREAD");
}
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
Register the training and test trees
TTree *signalTree = (TTree *)input->Get("TreeS");
TTree *background = (TTree *)input->Get("TreeB");
TMVA::DataLoader *dataloader = new TMVA::DataLoader("dataset");
If you wish to modify default settings (please check "src/Config.h" to see all available global options)
(TMVA::gConfig().GetVariablePlotting()).fTimesRMS = 8.0;
(TMVA::gConfig().GetIONames()).fWeightFileDir = "myWeightDirectory";Define the input variables that shall be used for the mva training note that you may also use variable expressions, such as: "3var1/var2abs(var3)" [all types of expressions that can also be parsed by TTree::Draw( "expression" )]
dataloader->AddVariable("myvar1 := var1+var2", 'F');
dataloader->AddVariable("myvar2 := var1-var2", "Expression 2", "", 'F');
dataloader->AddVariable("var3", "Variable 3", "units", 'F');
dataloader->AddVariable("var4", "Variable 4", "units", 'F');
You can add so-called "spectator variables", which are not used in the mva training, but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the input variables, the response values of all trained MVAs, and the spectator variables
dataloader->AddSpectator("spec1 := var1*2", "Spectator 1", "units", 'F');
dataloader->AddSpectator("spec2 := var1*3", "Spectator 2", "units", 'F');
Global event weights per tree (see below for setting event-wise weights)
Double_t signalWeight = 1.0;
Double_t backgroundWeight = 1.0;
You can add an arbitrary number of signal or background trees
dataloader->AddSignalTree(signalTree, signalWeight);
dataloader->AddBackgroundTree(background, backgroundWeight);
Set individual event weights (the variables must exist in the original ttree)
- for signal :
dataloader->SetSignalWeightExpression ("weight1*weight2"); - for background:
dataloader->SetBackgroundWeightExpression("weight1*weight2");
dataloader->SetBackgroundWeightExpression("weight");
dataloader->PrepareTrainingAndTestTree(
"", "", "nTrain_Signal=1000:nTrain_Background=1000:SplitMode=Random:NormMode=NumEvents:!V");
TFile *outputFile = TFile::Open("TMVAClass.root", "RECREATE");
TMVA::Experimental::Classification *cl = new TMVA::Experimental::Classification(dataloader, Form("Jobs=%d", jobs));
cl->BookMethod(TMVA::Types::kBDT, "BDTG", "!H:!V:NTrees=2000:MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:"
"UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=2");
cl->BookMethod(TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm");
cl->BookMethod(TMVA::Types::kBDT, "BDTB", "!H:!V:NTrees=2000:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20");
cl->BookMethod(TMVA::Types::kCuts, "Cuts", "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart");
cl->Evaluate(); // Train and Test all methods
auto &results = cl->GetResults();
TCanvas *c = new TCanvas(Form("ROC"));
c->SetTitle("ROC-Integral Curve");
auto mg = new TMultiGraph();
for (UInt_t i = 0; i < results.size(); i++) {
if (!results[i].IsCutsMethod()) {
auto roc = results[i].GetROCGraph();
roc->SetLineColorAlpha(i + 1, 0.1);
mg->Add(roc);
}
}
mg->Draw("AL");
mg->GetXaxis()->SetTitle(" Signal Efficiency ");
mg->GetYaxis()->SetTitle(" Background Rejection ");
c->BuildLegend(0.15, 0.15, 0.3, 0.3);
c->Draw();
outputFile->Close();
delete cl;
