/// \file
/// \ingroup tutorial_tmva
/// \notebook -nodraw
/// This macro provides a simple example on how to use the trained multiclass
/// classifiers within an analysis module
/// - Project : TMVA - a Root-integrated toolkit for multivariate data analysis
/// - Package : TMVA
/// - Root Macro: TMVAMulticlassApplication
///
/// \macro_output
/// \macro_code
/// \author Andreas Hoecker
#include <cstdlib>
#include <iostream>
#include <map>
#include <string>
#include <vector>
#include "TFile.h"
#include "TTree.h"
#include "TString.h"
#include "TSystem.h"
#include "TROOT.h"
#include "TStopwatch.h"
#include "TH1F.h"
#include "TMVA/Tools.h"
#include "TMVA/Reader.h"
using namespace TMVA;
void TMVAMulticlassApplication( TString myMethodList = "" )
{
TMVA::Tools::Instance();
//---------------------------------------------------------------
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
Use["MLP"] = 1;
Use["BDTG"] = 1;
Use["DL_CPU"] = 1;
Use["DL_GPU"] = 1;
Use["FDA_GA"] = 1;
Use["PDEFoam"] = 1;
//---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVAMulticlassApp" << std::endl;
if (myMethodList != "") {
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;
std::vector<TString> mlist = gTools().SplitString( myMethodList, ',' );
for (UInt_t i=0; i<mlist.size(); i++) {
std::string regMethod(mlist[i]);
if (Use.find(regMethod) == Use.end()) {
std::cout << "Method \"" << regMethod << "\" not known in TMVA under this name. Choose among the following:" << std::endl;
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) std::cout << it->first << " " << std::endl;
std::cout << std::endl;
return;
}
Use[regMethod] = 1;
}
}
// create the Reader object
TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
// create a set of variables and declare them to the reader
// - the variable names must corresponds in name and type to
// those given in the weight file(s) that you use
Float_t var1, var2, var3, var4;
reader->AddVariable( "var1", &var1 );
reader->AddVariable( "var2", &var2 );
reader->AddVariable( "var3", &var3 );
reader->AddVariable( "var4", &var4 );
// book the MVA methods
TString dir = "dataset/weights/";
TString prefix = "TMVAMulticlass";
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
if (it->second) {
TString methodName = TString(it->first) + TString(" method");
TString weightfile = dir + prefix + TString("_") + TString(it->first) + TString(".weights.xml");
// check if file existing (i.e. method has been trained)
if (!gSystem->AccessPathName( weightfile ))
// file exists
reader->BookMVA( methodName, weightfile );
else {
std::cout << "TMVAMultiClassApplication: Skip " << methodName << " since it has not been trained !" << std::endl;
it->second = 0;
}
}
}
// book output histograms
UInt_t nbin = 100;
TH1F *histMLP_signal(0), *histBDTG_signal(0), *histFDAGA_signal(0), *histPDEFoam_signal(0);
TH1F *histDLCPU_signal(0), *histDLGPU_signal(0);
if (Use["MLP"])
histMLP_signal = new TH1F( "MVA_MLP_signal", "MVA_MLP_signal", nbin, 0., 1.1 );
if (Use["BDTG"])
histBDTG_signal = new TH1F( "MVA_BDTG_signal", "MVA_BDTG_signal", nbin, 0., 1.1 );
if (Use["DL_CPU"])
histDLCPU_signal = new TH1F("MVA_DLCPU_signal", "MVA_DLCPU_signal", nbin, 0., 1.1);
if (Use["DL_GPU"])
histDLGPU_signal = new TH1F("MVA_DLGPU_signal", "MVA_DLGPU_signal", nbin, 0., 1.1);
if (Use["FDA_GA"])
histFDAGA_signal = new TH1F( "MVA_FDA_GA_signal", "MVA_FDA_GA_signal", nbin, 0., 1.1 );
if (Use["PDEFoam"])
histPDEFoam_signal = new TH1F( "MVA_PDEFoam_signal", "MVA_PDEFoam_signal", nbin, 0., 1.1 );
TFile *input(0);
TString fname = "./tmva_example_multiclass.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_multiclass_example.root", "CACHEREAD");
}
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVAMulticlassApp : Using input file: " << input->GetName() << std::endl;
// prepare the tree
// - here the variable names have to corresponds to your tree
// - you can use the same variables as above which is slightly faster,
// but of course you can use different ones and copy the values inside the event loop
TTree* theTree = (TTree*)input->Get("TreeS");
std::cout << "--- Select signal sample" << std::endl;
theTree->SetBranchAddress( "var1", &var1 );
theTree->SetBranchAddress( "var2", &var2 );
theTree->SetBranchAddress( "var3", &var3 );
theTree->SetBranchAddress( "var4", &var4 );
std::cout << "--- Processing: " << theTree->GetEntries() << " events" << std::endl;
TStopwatch sw;
sw.Start();
for (Long64_t ievt=0; ievt<theTree->GetEntries();ievt++) {
if (ievt%1000 == 0){
std::cout << "--- ... Processing event: " << ievt << std::endl;
}
theTree->GetEntry(ievt);
if (Use["MLP"])
histMLP_signal->Fill((reader->EvaluateMulticlass( "MLP method" ))[0]);
if (Use["BDTG"])
histBDTG_signal->Fill((reader->EvaluateMulticlass( "BDTG method" ))[0]);
if (Use["DL_CPU"])
histDLCPU_signal->Fill((reader->EvaluateMulticlass("DL_CPU method"))[0]);
if (Use["DL_GPU"])
histDLGPU_signal->Fill((reader->EvaluateMulticlass("DL_GPU method"))[0]);
if (Use["FDA_GA"])
histFDAGA_signal->Fill((reader->EvaluateMulticlass( "FDA_GA method" ))[0]);
if (Use["PDEFoam"])
histPDEFoam_signal->Fill((reader->EvaluateMulticlass( "PDEFoam method" ))[0]);
}
// get elapsed time
sw.Stop();
std::cout << "--- End of event loop: "; sw.Print();
TFile *target = new TFile( "TMVAMulticlassApp.root","RECREATE" );
if (Use["MLP"])
histMLP_signal->Write();
if (Use["BDTG"])
histBDTG_signal->Write();
if (Use["DL_CPU"])
histDLCPU_signal->Write();
if (Use["DL_GPU"])
histDLGPU_signal->Write();
if (Use["FDA_GA"])
histFDAGA_signal->Write();
if (Use["PDEFoam"])
histPDEFoam_signal->Write();
target->Close();
std::cout << "--- Created root file: \"TMVMulticlassApp.root\" containing the MVA output histograms" << std::endl;
delete reader;
std::cout << "==> TMVAMulticlassApp is done!" << std::endl << std::endl;
}
int main( int argc, char** argv )
{
// Select methods (don't look at this code - not of interest)
TString methodList;
for (int i=1; i<argc; i++) {
TString regMethod(argv[i]);
if(regMethod=="-b" || regMethod=="--batch") continue;
if (!methodList.IsNull()) methodList += TString(",");
methodList += regMethod;
}
TMVAMulticlassApplication(methodList);
return 0;
}