TMVAMulticlassApplication.C File Reference

Detailed Description

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

 
 
==> Start TMVAMulticlassApp
                         : Booking "BDTG method" of type "BDT" from dataset/weights/TMVAMulticlass_BDTG.weights.xml.
                         : Reading weight file: dataset/weights/TMVAMulticlass_BDTG.weights.xml
<HEADER> DataSetInfo              : [Default] : Added class "Signal"
<HEADER> DataSetInfo              : [Default] : Added class "bg0"
<HEADER> DataSetInfo              : [Default] : Added class "bg1"
<HEADER> DataSetInfo              : [Default] : Added class "bg2"
                         : Booked classifier "BDTG" of type: "BDT"
                         : Booking "DL_CPU method" of type "DL" from dataset/weights/TMVAMulticlass_DL_CPU.weights.xml.
                         : Reading weight file: dataset/weights/TMVAMulticlass_DL_CPU.weights.xml
                         : Booked classifier "DL_CPU" of type: "DL"
TMVAMultiClassApplication: Skip DL_GPU method since it has not been trained !
TMVAMultiClassApplication: Skip FDA_GA method since it has not been trained !
                         : Booking "MLP method" of type "MLP" from dataset/weights/TMVAMulticlass_MLP.weights.xml.
                         : Reading weight file: dataset/weights/TMVAMulticlass_MLP.weights.xml
<HEADER> MLP                      : Building Network. 
                         : Initializing weights
                         : Booked classifier "MLP" of type: "MLP"
                         : Booking "PDEFoam method" of type "PDEFoam" from dataset/weights/TMVAMulticlass_PDEFoam.weights.xml.
                         : Reading weight file: dataset/weights/TMVAMulticlass_PDEFoam.weights.xml
                         : Read foams from file: dataset/weights/TMVAMulticlass_PDEFoam.weights_foams.root
                         : Booked classifier "PDEFoam" of type: "PDEFoam"
--- TMVAMulticlassApp : Using input file: /github/home/ROOT-CI/build/tutorials/machine_learning/data/tmva_multiclass_example.root
--- Select signal sample
                         : Rebuilding Dataset Default
--- End of event loop: Real time 0:00:00, CP time 0.430
--- Created root file: "TMVMulticlassApp.root" containing the MVA output histograms
==> TMVAMulticlassApp is done!
 

 
 
#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(nullptr);
   TString fname =  gROOT->GetTutorialDir() + "/machine_learning/data/tmva_multiclass_example.root";
   if (!gSystem->AccessPathName( fname )) {
      input = TFile::Open( fname ); // check if file in local directory exists
   }
   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;
}

Author

Andreas Hoecker

Definition in file TMVAMulticlassApplication.C.