TMVARegressionApplication.C File Reference

Detailed Description

This macro provides a simple example on how to use the trained regression MVAs within an analysis module

• Project : TMVA - a Root-integrated toolkit for multivariate data analysis
• Package : TMVA
• Executable: TMVARegressionApplication

 
#include <cstdlib>
#include <vector>
#include <iostream>
#include <map>
#include <string>
 
#include "TFile.h"
#include "TTree.h"
#include "TString.h"
#include "TSystem.h"
#include "TROOT.h"
#include "TStopwatch.h"
 
#include "TMVA/Tools.h"
#include "TMVA/Reader.h"
 
using namespace TMVA;
 
void TMVARegressionApplication( TString myMethodList = "" )
{
   //---------------------------------------------------------------
   // This loads the library
   TMVA::Tools::Instance();
 
   // Default MVA methods to be trained + tested
   std::map<std::string,int> Use;
 
   // --- Mutidimensional likelihood and Nearest-Neighbour methods
   Use["PDERS"]           = 0;
   Use["PDEFoam"]         = 1;
   Use["KNN"]             = 1;
   //
   // --- Linear Discriminant Analysis
   Use["LD"]              = 1;
   //
   // --- Function Discriminant analysis
   Use["FDA_GA"]          = 0;
   Use["FDA_MC"]          = 0;
   Use["FDA_MT"]          = 0;
   Use["FDA_GAMT"]        = 0;
   //
   // --- Neural Network
   Use["MLP"] = 0;
   // Deep neural network
#ifdef R__HAS_TMVAGPU
   Use["DNN_GPU"] = 1;
   Use["DNN_CPU"] = 0;
#else
   Use["DNN_GPU"] = 0;
#ifdef R__HAS_TMVACPU
   Use["DNN_CPU"] = 1;
#else
   Use["DNN_CPU"] = 0;
#endif
#endif
   //
   // --- Support Vector Machine
   Use["SVM"]             = 0;
   //
   // --- Boosted Decision Trees
   Use["BDT"]             = 0;
   Use["BDTG"]            = 1;
   // ---------------------------------------------------------------
 
   std::cout << std::endl;
   std::cout << "==> Start TMVARegressionApplication" << std::endl;
 
   // Select methods (don't look at this code - not of interest)
   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::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) used
   Float_t var1, var2;
   reader->AddVariable( "var1", &var1 );
   reader->AddVariable( "var2", &var2 );
 
   // Spectator variables declared in the training have to be added to the reader, too
   Float_t spec1,spec2;
   reader->AddSpectator( "spec1:=var1*2",  &spec1 );
   reader->AddSpectator( "spec2:=var1*3",  &spec2 );
 
   // --- Book the MVA methods
 
   TString dir    = "datasetreg/weights/";
   TString prefix = "TMVARegression";
 
   // Book method(s)
   for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
      if (it->second) {
         TString methodName = it->first + " method";
         TString weightfile = dir + prefix + "_" + TString(it->first) + ".weights.xml";
         reader->BookMVA( methodName, weightfile );
      }
   }
 
   // Book output histograms
   TH1* hists[100];
   Int_t nhists = -1;
   for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
      TH1* h = new TH1F( it->first.c_str(), TString(it->first) + " method", 100, -100, 600 );
      if (it->second) hists[++nhists] = h;
   }
   nhists++;
 
   // Prepare input tree (this must be replaced by your data source)
   // in this example, there is a toy tree with signal and one with background events
   // we'll later on use only the "signal" events for the test in this example.
   //
   TFile *input(nullptr);
   TString fname =  gROOT->GetTutorialDir() + "/machine_learning/data/tmva_reg_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 << "--- TMVARegressionApp        : Using input file: " << input->GetName() << std::endl;
 
   // --- Event loop
 
   // 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("TreeR");
   std::cout << "--- Select signal sample" << std::endl;
   theTree->SetBranchAddress( "var1", &var1 );
   theTree->SetBranchAddress( "var2", &var2 );
 
   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);
 
      // Retrieve the MVA target values (regression outputs) and fill into histograms
      // NOTE: EvaluateRegression(..) returns a vector for multi-target regression
 
      for (Int_t ih=0; ih<nhists; ih++) {
         TString title = hists[ih]->GetTitle();
         Float_t val = (reader->EvaluateRegression( title ))[0];
         hists[ih]->Fill( val );
      }
   }
   sw.Stop();
   std::cout << "--- End of event loop: "; sw.Print();
 
   // --- Write histograms
 
   TFile *target  = new TFile( "TMVARegApp.root","RECREATE" );
   for (Int_t ih=0; ih<nhists; ih++) hists[ih]->Write();
   target->Close();
 
   std::cout << "--- Created root file: \"" << target->GetName()
             << "\" containing the MVA output histograms" << std::endl;
 
   delete reader;
 
   std::cout << "==> TMVARegressionApplication 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;
   }
   TMVARegressionApplication(methodList);
   return 0;
}

 
 
==> Start TMVARegressionApplication
                         : Booking "BDTG method" of type "BDT" from datasetreg/weights/TMVARegression_BDTG.weights.xml.
                         : Reading weight file: datasetreg/weights/TMVARegression_BDTG.weights.xml
<HEADER> DataSetInfo              : [Default] : Added class "Regression"
                         : Booked classifier "BDTG" of type: "BDT"
                         : Booking "DNN_CPU method" of type "DL" from datasetreg/weights/TMVARegression_DNN_CPU.weights.xml.
                         : Reading weight file: datasetreg/weights/TMVARegression_DNN_CPU.weights.xml
                         : Booked classifier "DNN_CPU" of type: "DL"
                         : Booking "KNN method" of type "KNN" from datasetreg/weights/TMVARegression_KNN.weights.xml.
                         : Reading weight file: datasetreg/weights/TMVARegression_KNN.weights.xml
                         : Creating kd-tree with 1000 events
                         : Computing scale factor for 1d distributions: (ifrac, bottom, top) = (80%, 10%, 90%)
<HEADER> ModulekNN                : Optimizing tree for 2 variables with 1000 values
                         : <Fill> Class 1 has     1000 events
                         : Booked classifier "KNN" of type: "KNN"
                         : Booking "LD method" of type "LD" from datasetreg/weights/TMVARegression_LD.weights.xml.
                         : Reading weight file: datasetreg/weights/TMVARegression_LD.weights.xml
                         : Booked classifier "LD" of type: "LD"
                         : Booking "PDEFoam method" of type "PDEFoam" from datasetreg/weights/TMVARegression_PDEFoam.weights.xml.
                         : Reading weight file: datasetreg/weights/TMVARegression_PDEFoam.weights.xml
                         : Read foams from file: datasetreg/weights/TMVARegression_PDEFoam.weights_foams.root
                         : Booked classifier "PDEFoam" of type: "PDEFoam"
--- TMVARegressionApp        : Using input file: /github/home/ROOT-CI/build/tutorials/machine_learning/data/tmva_reg_example.root
--- Select signal sample
                         : Rebuilding Dataset Default
--- End of event loop: Real time 0:00:01, CP time 1.570
--- Created root file: "TMVARegApp.root" containing the MVA output histograms
==> TMVARegressionApplication is done!
 

Author

Andreas Hoecker

Definition in file TMVARegressionApplication.C.