TMVAMulticlass.C

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/// \file
/// \ingroup tutorial_ml
/// \notebook -nodraw
/// This macro provides a simple example for the training and testing of the TMVA
/// multiclass classification
/// - Project   : TMVA - a Root-integrated toolkit for multivariate data analysis
/// - Package   : TMVA
/// - Root Macro: TMVAMulticlass
///
/// \macro_code
/// \macro_output
/// \author Andreas Hoecker
 
#include <cstdlib>
#include <iostream>
#include <map>
#include <string>
 
#include "TFile.h"
#include "TTree.h"
#include "TString.h"
#include "TSystem.h"
#include "TROOT.h"
 
 
#include "TMVA/Tools.h"
#include "TMVA/Factory.h"
#include "TMVA/DataLoader.h"
#include "TMVA/TMVAMultiClassGui.h"
 
 
using namespace TMVA;
 
void TMVAMulticlass( TString myMethodList = "" )
{
 
   // This loads the library
   TMVA::Tools::Instance();
 
   // to get access to the GUI and all tmva macros
   //
   //     gROOT->ProcessLine("TMVA::TMVAMultiClassGui()");
 
 
   //---------------------------------------------------------------
   // Default MVA methods to be trained + tested
   std::map<std::string,int> Use;
   Use["MLP"]             = 1;
   Use["BDTG"]            = 1;
#ifdef R__HAS_TMVAGPU
   Use["DL_CPU"]          = 1;
   Use["DL_GPU"]          = 1;
#else
   Use["DL_CPU"]          = 1;
   Use["DL_GPU"]          = 0;
#endif
   Use["FDA_GA"]          = 0;
   Use["PDEFoam"]         = 1;
 
   //---------------------------------------------------------------
 
   std::cout << std::endl;
   std::cout << "==> Start TMVAMulticlass" << 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 = TMVA::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 a new root output file.
   TString outfileName = "TMVAMulticlass.root";
   TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
 
   TMVA::Factory *factory = new TMVA::Factory( "TMVAMulticlass", outputFile,
                                               "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=multiclass" );
   TMVA::DataLoader *dataloader=new TMVA::DataLoader("dataset");
 
   dataloader->AddVariable( "var1", 'F' );
   dataloader->AddVariable( "var2", "Variable 2", "", 'F' );
   dataloader->AddVariable( "var3", "Variable 3", "units", 'F' );
   dataloader->AddVariable( "var4", "Variable 4", "units", 'F' );
 
   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 << "--- TMVAMulticlass: Using input file: " << input->GetName() << std::endl;
 
   TTree *signalTree  = (TTree*)input->Get("TreeS");
   TTree *background0 = (TTree*)input->Get("TreeB0");
   TTree *background1 = (TTree*)input->Get("TreeB1");
   TTree *background2 = (TTree*)input->Get("TreeB2");
 
   gROOT->cd( outfileName+TString(":/") );
   dataloader->AddTree    (signalTree,"Signal");
   dataloader->AddTree    (background0,"bg0");
   dataloader->AddTree    (background1,"bg1");
   dataloader->AddTree    (background2,"bg2");
 
   dataloader->PrepareTrainingAndTestTree( "", "SplitMode=Random:NormMode=NumEvents:!V" );
 
   if (Use["BDTG"]) // gradient boosted decision trees
      factory->BookMethod( dataloader,  TMVA::Types::kBDT, "BDTG", "!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.50:nCuts=20:MaxDepth=2");
   if (Use["MLP"]) // neural network
      factory->BookMethod( dataloader,  TMVA::Types::kMLP, "MLP", "!H:!V:NeuronType=tanh:NCycles=1000:HiddenLayers=N+5,5:TestRate=5:EstimatorType=MSE");
   if (Use["FDA_GA"]) // functional discriminant with GA minimizer
      factory->BookMethod( dataloader,  TMVA::Types::kFDA, "FDA_GA", "H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:PopSize=300:Cycles=3:Steps=20:Trim=True:SaveBestGen=1" );
   if (Use["PDEFoam"]) // PDE-Foam approach
      factory->BookMethod( dataloader,  TMVA::Types::kPDEFoam, "PDEFoam", "!H:!V:TailCut=0.001:VolFrac=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Nmin=100:Kernel=None:Compress=T" );
 
 
   if (Use["DL_CPU"]) {
      TString layoutString("Layout=TANH|100,TANH|50,TANH|10,LINEAR");
      TString trainingStrategyString("TrainingStrategy=Optimizer=ADAM,LearningRate=1e-3,"
                                     "TestRepetitions=1,ConvergenceSteps=10,BatchSize=100,MaxEpochs=20");
      TString nnOptions("!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=N:"
                        "WeightInitialization=XAVIERUNIFORM:Architecture=GPU");
      nnOptions.Append(":");
      nnOptions.Append(layoutString);
      nnOptions.Append(":");
      nnOptions.Append(trainingStrategyString);
      factory->BookMethod(dataloader, TMVA::Types::kDL, "DL_CPU", nnOptions);
   }
   if (Use["DL_GPU"]) {
      TString layoutString("Layout=TANH|100,TANH|50,TANH|10,LINEAR");
      TString trainingStrategyString("TrainingStrategy=Optimizer=ADAM,LearningRate=1e-3,"
                                     "TestRepetitions=1,ConvergenceSteps=10,BatchSize=100,MaxEpochs=20");
      TString nnOptions("!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=N:"
                        "WeightInitialization=XAVIERUNIFORM:Architecture=GPU");
      nnOptions.Append(":");
      nnOptions.Append(layoutString);
      nnOptions.Append(":");
      nnOptions.Append(trainingStrategyString);
      factory->BookMethod(dataloader, TMVA::Types::kDL, "DL_GPU", nnOptions);
   }
 
 
   // Train MVAs using the set of training events
   factory->TrainAllMethods();
 
   // Evaluate all MVAs using the set of test events
   factory->TestAllMethods();
 
   // Evaluate and compare performance of all configured MVAs
   factory->EvaluateAllMethods();
 
   // --------------------------------------------------------------
 
   // Save the output
   outputFile->Close();
 
   std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
   std::cout << "==> TMVAMulticlass is done!" << std::endl;
 
   delete factory;
   delete dataloader;
 
   // Launch the GUI for the root macros
   if (!gROOT->IsBatch()) TMVAMultiClassGui( outfileName );
 
 
}
 
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;
   }
   TMVAMulticlass(methodList);
   return 0;
}