This macro provides an example of how to use TMVA for k-folds cross evaluation in application.
This requires that CrossValidation was run with a deterministic split, such
as "...:splitExpr=int([eventID])%int([numFolds]):..."
.
Author: Kim Albertsson (adapted from code originally by Andreas Hoecker)
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Wednesday, April 17, 2024 at 11:22 AM.
%%cpp -d
#include <cstdlib>
#include <iostream>
#include <map>
#include <string>
#include "TChain.h"
#include "TFile.h"
#include "TTree.h"
#include "TString.h"
#include "TObjString.h"
#include "TSystem.h"
#include "TROOT.h"
#include "TMVA/Factory.h"
#include "TMVA/DataLoader.h"
#include "TMVA/Tools.h"
#include "TMVA/TMVAGui.h"
Helper function to load data into TTrees.
%%cpp -d
TTree *fillTree(TTree * tree, Int_t nPoints, Double_t offset, Double_t scale, UInt_t seed = 100)
{
TRandom3 rng(seed);
Float_t x = 0;
Float_t y = 0;
Int_t eventID = 0;
tree->SetBranchAddress("x", &x);
tree->SetBranchAddress("y", &y);
tree->SetBranchAddress("eventID", &eventID);
for (Int_t n = 0; n < nPoints; ++n) {
x = rng.Gaus(offset, scale);
y = rng.Gaus(offset, scale);
// For our simple example it is enough that the id's are uniformly
// distributed and independent of the data.
++eventID;
tree->Fill();
}
// Important: Disconnects the tree from the memory locations of x and y.
tree->ResetBranchAddresses();
return tree;
}
This loads the library
TMVA::Tools::Instance();
Set up the TMVA::Reader
TMVA::Reader *reader = new TMVA::Reader("!Color:!Silent:!V");
Float_t x;
Float_t y;
Int_t eventID;
reader->AddVariable("x", &x);
reader->AddVariable("y", &y);
reader->AddSpectator("eventID", &eventID);
Book the serialised methods
TString jobname("TMVACrossValidation");
{
TString methodName = "BDTG";
TString weightfile = TString("datasetcv/weights/") + jobname + "_" + methodName + TString(".weights.xml");
Bool_t weightfileExists = (gSystem->AccessPathName(weightfile) == kFALSE);
if (weightfileExists) {
reader->BookMVA(methodName, weightfile);
} else {
std::cout << "Weightfile for method " << methodName << " not found."
" Did you run TMVACrossValidation with a specified"
" splitExpr?" << std::endl;
exit(0);
}
}
{
TString methodName = "Fisher";
TString weightfile = TString("datasetcv/weights/") + jobname + "_" + methodName + TString(".weights.xml");
Bool_t weightfileExists = (gSystem->AccessPathName(weightfile) == kFALSE);
if (weightfileExists) {
reader->BookMVA(methodName, weightfile);
} else {
std::cout << "Weightfile for method " << methodName << " not found."
" Did you run TMVACrossValidation with a specified"
" splitExpr?" << std::endl;
exit(0);
}
}
: Booking "BDTG" of type "CrossValidation" from datasetcv/weights/TMVACrossValidation_BDTG.weights.xml. : Reading weight file: datasetcv/weights/TMVACrossValidation_BDTG.weights.xml <HEADER> DataSetInfo : [Default] : Added class "Signal" <HEADER> DataSetInfo : [Default] : Added class "Background" : Reading weightfile: datasetcv/weights/TMVACrossValidation_BDTG_fold1.weights.xml : Reading weight file: datasetcv/weights/TMVACrossValidation_BDTG_fold1.weights.xml : Reading weightfile: datasetcv/weights/TMVACrossValidation_BDTG_fold2.weights.xml : Reading weight file: datasetcv/weights/TMVACrossValidation_BDTG_fold2.weights.xml : Booked classifier "BDTG" of type: "CrossValidation" : Booking "Fisher" of type "CrossValidation" from datasetcv/weights/TMVACrossValidation_Fisher.weights.xml. : Reading weight file: datasetcv/weights/TMVACrossValidation_Fisher.weights.xml : Reading weightfile: datasetcv/weights/TMVACrossValidation_Fisher_fold1.weights.xml : Reading weight file: datasetcv/weights/TMVACrossValidation_Fisher_fold1.weights.xml : Reading weightfile: datasetcv/weights/TMVACrossValidation_Fisher_fold2.weights.xml : Reading weight file: datasetcv/weights/TMVACrossValidation_Fisher_fold2.weights.xml : Booked classifier "Fisher" of type: "CrossValidation"
Load data
TTree *tree = new TTree();
tree->Branch("x", &x, "x/F");
tree->Branch("y", &y, "y/F");
tree->Branch("eventID", &eventID, "eventID/I");
fillTree(tree, 1000, 1.0, 1.0, 100);
fillTree(tree, 1000, -1.0, 1.0, 101);
tree->SetBranchAddress("x", &x);
tree->SetBranchAddress("y", &y);
tree->SetBranchAddress("eventID", &eventID);
Prepare histograms
Int_t nbin = 100;
TH1F histBDTG{"BDTG", "BDTG", nbin, -1, 1};
TH1F histFisher{"Fisher", "Fisher", nbin, -1, 1};
Evaluate classifiers
for (Long64_t ievt = 0; ievt < tree->GetEntries(); ievt++) {
tree->GetEntry(ievt);
Double_t valBDTG = reader->EvaluateMVA("BDTG");
Double_t valFisher = reader->EvaluateMVA("Fisher");
histBDTG.Fill(valBDTG);
histFisher.Fill(valFisher);
}
tree->ResetBranchAddresses();
delete tree;
if (!gROOT->IsBatch()) {
auto c = new TCanvas();
c->Divide(2,1);
c->cd(1);
histBDTG.DrawClone();
c->cd(2);
histFisher.DrawClone();
}
else
{ // Write histograms to output file
TFile *target = new TFile("TMVACrossEvaluationApp.root", "RECREATE");
histBDTG.Write();
histFisher.Write();
target->Close();
delete target;
}
delete reader;
return 0;
: Rebuilding Dataset Default