Factory.cxx

Go to the documentation of this file.

// @(#)Root/tmva $Id$
// Author: Andreas Hoecker, Peter Speckmayer, Joerg Stelzer, Helge Voss, Kai Voss, Eckhard von Toerne, Jan Therhaag
// Updated by: Omar Zapata, Kim Albertsson
/**********************************************************************************
 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis       *
 * Package: TMVA                                                                  *
 * Class  : Factory                                                               *
 * Web    : http://tmva.sourceforge.net                                           *
 *                                                                                *
 * Description:                                                                   *
 *      Implementation (see header for description)                               *
 *                                                                                *
 * Authors :                                                                      *
 *      Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland              *
 *      Joerg Stelzer   <stelzer@cern.ch>        - DESY, Germany                  *
 *      Peter Speckmayer <peter.speckmayer@cern.ch> - CERN, Switzerland           *
 *      Jan Therhaag          <Jan.Therhaag@cern.ch>   - U of Bonn, Germany       *
 *      Eckhard v. Toerne     <evt@uni-bonn.de>        - U of Bonn, Germany       *
 *      Helge Voss      <Helge.Voss@cern.ch>     - MPI-K Heidelberg, Germany      *
 *      Kai Voss        <Kai.Voss@cern.ch>       - U. of Victoria, Canada         *
 *      Omar Zapata     <Omar.Zapata@cern.ch>    - UdeA/ITM Colombia              *
 *      Lorenzo Moneta  <Lorenzo.Moneta@cern.ch> - CERN, Switzerland              *
 *      Sergei Gleyzer  <Sergei.Gleyzer@cern.ch> - U of Florida & CERN            *
 *      Kim Albertsson  <kim.albertsson@cern.ch> - LTU & CERN                     *
 *                                                                                *
 * Copyright (c) 2005-2015:                                                       *
 *      CERN, Switzerland                                                         *
 *      U. of Victoria, Canada                                                    *
 *      MPI-K Heidelberg, Germany                                                 *
 *      U. of Bonn, Germany                                                       *
 *      UdeA/ITM, Colombia                                                        *
 *      U. of Florida, USA                                                        *
 *                                                                                *
 * Redistribution and use in source and binary forms, with or without             *
 * modification, are permitted according to the terms listed in LICENSE           *
 * (http://tmva.sourceforge.net/LICENSE)                                          *
 **********************************************************************************/

/*! \class TMVA::Factory
\ingroup TMVA

This is the main MVA steering class.
It creates all MVA methods, and guides them through the training, testing and
evaluation phases.
*/

#include "TMVA/Factory.h"

#include "TMVA/ClassifierFactory.h"
#include "TMVA/Config.h"
#include "TMVA/Configurable.h"
#include "TMVA/Tools.h"
#include "TMVA/Ranking.h"
#include "TMVA/DataSet.h"
#include "TMVA/IMethod.h"
#include "TMVA/MethodBase.h"
#include "TMVA/DataInputHandler.h"
#include "TMVA/DataSetManager.h"
#include "TMVA/DataSetInfo.h"
#include "TMVA/DataLoader.h"
#include "TMVA/MethodBoost.h"
#include "TMVA/MethodCategory.h"
#include "TMVA/ROCCalc.h"
#include "TMVA/ROCCurve.h"
#include "TMVA/MsgLogger.h"

#include "TMVA/VariableInfo.h"
#include "TMVA/VariableTransform.h"

#include "TMVA/Results.h"
#include "TMVA/ResultsClassification.h"
#include "TMVA/ResultsRegression.h"
#include "TMVA/ResultsMulticlass.h"
#include <list>
#include <bitset>

#include "TMVA/Types.h"

#include "TROOT.h"
#include "TFile.h"
#include "TTree.h"
#include "TLeaf.h"
#include "TEventList.h"
#include "TH2.h"
#include "TText.h"
#include "TLegend.h"
#include "TGraph.h"
#include "TStyle.h"
#include "TMatrixF.h"
#include "TMatrixDSym.h"
#include "TMultiGraph.h"
#include "TPaletteAxis.h"
#include "TPrincipal.h"
#include "TMath.h"
#include "TObjString.h"
#include "TSystem.h"
#include "TCanvas.h"

const Int_t  MinNoTrainingEvents = 10;
//const Int_t  MinNoTestEvents     = 1;

ClassImp(TMVA::Factory);

#define READXML          kTRUE

//number of bits for bitset
#define VIBITS          32



////////////////////////////////////////////////////////////////////////////////
/// Standard constructor.
///
///  - jobname       : this name will appear in all weight file names produced by the MVAs
///  - theTargetFile : output ROOT file; the test tree and all evaluation plots
///                   will be stored here
///  - theOption     : option string; currently: "V" for verbose

TMVA::Factory::Factory( TString jobName, TFile* theTargetFile, TString theOption )
: Configurable          ( theOption ),
   fTransformations      ( "I" ),
   fVerbose              ( kFALSE ),
   fCorrelations         ( kFALSE ),
   fROC                  ( kTRUE ),
   fSilentFile           ( kFALSE ),
   fJobName              ( jobName ),
   fAnalysisType         ( Types::kClassification ),
   fModelPersistence     (kTRUE)
{
   fgTargetFile = theTargetFile;
   fLogger->SetSource(GetName());

   // render silent
   if (gTools().CheckForSilentOption( GetOptions() )) Log().InhibitOutput(); // make sure is silent if wanted to


   // init configurable
   SetConfigDescription( "Configuration options for Factory running" );
   SetConfigName( GetName() );

   // histograms are not automatically associated with the current
   // directory and hence don't go out of scope when closing the file
   // TH1::AddDirectory(kFALSE);
   Bool_t silent          = kFALSE;
#ifdef WIN32
   // under Windows, switch progress bar and color off by default, as the typical windows shell doesn't handle these (would need different sequences..)
   Bool_t color           = kFALSE;
   Bool_t drawProgressBar = kFALSE;
#else
   Bool_t color           = !gROOT->IsBatch();
   Bool_t drawProgressBar = kTRUE;
#endif
   DeclareOptionRef( fVerbose, "V", "Verbose flag" );
   DeclareOptionRef( color,    "Color", "Flag for coloured screen output (default: True, if in batch mode: False)" );
   DeclareOptionRef( fTransformations, "Transformations", "List of transformations to test; formatting example: \"Transformations=I;D;P;U;G,D\", for identity, decorrelation, PCA, Uniform and Gaussianisation followed by decorrelation transformations" );
   DeclareOptionRef( fCorrelations, "Correlations", "boolean to show correlation in output" );
   DeclareOptionRef( fROC, "ROC", "boolean to show ROC in output" );
   DeclareOptionRef( silent,   "Silent", "Batch mode: boolean silent flag inhibiting any output from TMVA after the creation of the factory class object (default: False)" );
   DeclareOptionRef( drawProgressBar,
                     "DrawProgressBar", "Draw progress bar to display training, testing and evaluation schedule (default: True)" );
   DeclareOptionRef( fModelPersistence,
                     "ModelPersistence",
                     "Option to save the trained model in xml file or using serialization");

   TString analysisType("Auto");
   DeclareOptionRef( analysisType,
                     "AnalysisType", "Set the analysis type (Classification, Regression, Multiclass, Auto) (default: Auto)" );
   AddPreDefVal(TString("Classification"));
   AddPreDefVal(TString("Regression"));
   AddPreDefVal(TString("Multiclass"));
   AddPreDefVal(TString("Auto"));

   ParseOptions();
   CheckForUnusedOptions();

   if (Verbose()) Log().SetMinType( kVERBOSE );

   // global settings
   gConfig().SetUseColor( color );
   gConfig().SetSilent( silent );
   gConfig().SetDrawProgressBar( drawProgressBar );

   analysisType.ToLower();
   if     ( analysisType == "classification" ) fAnalysisType = Types::kClassification;
   else if( analysisType == "regression" )     fAnalysisType = Types::kRegression;
   else if( analysisType == "multiclass" )     fAnalysisType = Types::kMulticlass;
   else if( analysisType == "auto" )           fAnalysisType = Types::kNoAnalysisType;

//   Greetings();
}

////////////////////////////////////////////////////////////////////////////////
/// Constructor.

TMVA::Factory::Factory( TString jobName, TString theOption )
: Configurable          ( theOption ),
   fTransformations      ( "I" ),
   fVerbose              ( kFALSE ),
   fCorrelations         ( kFALSE ),
   fROC                  ( kTRUE ),
   fSilentFile           ( kTRUE ),
   fJobName              ( jobName ),
   fAnalysisType         ( Types::kClassification ),
   fModelPersistence     (kTRUE)
{
   fgTargetFile = 0;
   fLogger->SetSource(GetName());


   // render silent
   if (gTools().CheckForSilentOption( GetOptions() )) Log().InhibitOutput(); // make sure is silent if wanted to


   // init configurable
   SetConfigDescription( "Configuration options for Factory running" );
   SetConfigName( GetName() );

   // histograms are not automatically associated with the current
   // directory and hence don't go out of scope when closing the file
   TH1::AddDirectory(kFALSE);
   Bool_t silent          = kFALSE;
#ifdef WIN32
   // under Windows, switch progress bar and color off by default, as the typical windows shell doesn't handle these (would need different sequences..)
   Bool_t color           = kFALSE;
   Bool_t drawProgressBar = kFALSE;
#else
   Bool_t color           = !gROOT->IsBatch();
   Bool_t drawProgressBar = kTRUE;
#endif
   DeclareOptionRef( fVerbose, "V", "Verbose flag" );
   DeclareOptionRef( color,    "Color", "Flag for coloured screen output (default: True, if in batch mode: False)" );
   DeclareOptionRef( fTransformations, "Transformations", "List of transformations to test; formatting example: \"Transformations=I;D;P;U;G,D\", for identity, decorrelation, PCA, Uniform and Gaussianisation followed by decorrelation transformations" );
   DeclareOptionRef( fCorrelations, "Correlations", "boolean to show correlation in output" );
   DeclareOptionRef( fROC, "ROC", "boolean to show ROC in output" );
   DeclareOptionRef( silent,   "Silent", "Batch mode: boolean silent flag inhibiting any output from TMVA after the creation of the factory class object (default: False)" );
   DeclareOptionRef( drawProgressBar,
                     "DrawProgressBar", "Draw progress bar to display training, testing and evaluation schedule (default: True)" );
   DeclareOptionRef( fModelPersistence,
                     "ModelPersistence",
                     "Option to save the trained model in xml file or using serialization");

   TString analysisType("Auto");
   DeclareOptionRef( analysisType,
                     "AnalysisType", "Set the analysis type (Classification, Regression, Multiclass, Auto) (default: Auto)" );
   AddPreDefVal(TString("Classification"));
   AddPreDefVal(TString("Regression"));
   AddPreDefVal(TString("Multiclass"));
   AddPreDefVal(TString("Auto"));

   ParseOptions();
   CheckForUnusedOptions();

   if (Verbose()) Log().SetMinType( kVERBOSE );

   // global settings
   gConfig().SetUseColor( color );
   gConfig().SetSilent( silent );
   gConfig().SetDrawProgressBar( drawProgressBar );

   analysisType.ToLower();
   if     ( analysisType == "classification" ) fAnalysisType = Types::kClassification;
   else if( analysisType == "regression" )     fAnalysisType = Types::kRegression;
   else if( analysisType == "multiclass" )     fAnalysisType = Types::kMulticlass;
   else if( analysisType == "auto" )           fAnalysisType = Types::kNoAnalysisType;

   Greetings();
}

////////////////////////////////////////////////////////////////////////////////
/// Print welcome message.
/// Options are: kLogoWelcomeMsg, kIsometricWelcomeMsg, kLeanWelcomeMsg

void TMVA::Factory::Greetings()
{
   gTools().ROOTVersionMessage( Log() );
   gTools().TMVAWelcomeMessage( Log(), gTools().kLogoWelcomeMsg );
   gTools().TMVAVersionMessage( Log() ); Log() << Endl;
}

////////////////////////////////////////////////////////////////////////////////

Bool_t TMVA::Factory::IsSilentFile()
{
  return fSilentFile;
}

////////////////////////////////////////////////////////////////////////////////

Bool_t TMVA::Factory::IsModelPersistence()
{
    return fModelPersistence;
}

////////////////////////////////////////////////////////////////////////////////
/// Destructor.

TMVA::Factory::~Factory( void )
{
   std::vector<TMVA::VariableTransformBase*>::iterator trfIt = fDefaultTrfs.begin();
   for (;trfIt != fDefaultTrfs.end(); trfIt++) delete (*trfIt);

   this->DeleteAllMethods();


   // problem with call of REGISTER_METHOD macro ...
   //   ClassifierFactory::DestroyInstance();
   //   Types::DestroyInstance();
   //Tools::DestroyInstance();
   //Config::DestroyInstance();
}

////////////////////////////////////////////////////////////////////////////////
/// Delete methods.

void TMVA::Factory::DeleteAllMethods( void )
{
   std::map<TString,MVector*>::iterator itrMap;

   for(itrMap = fMethodsMap.begin();itrMap != fMethodsMap.end();itrMap++)
   {
      MVector *methods=itrMap->second;
      // delete methods
      MVector::iterator itrMethod = methods->begin();
      for (; itrMethod != methods->end(); itrMethod++) {
     Log() << kDEBUG << "Delete method: " << (*itrMethod)->GetName() << Endl;
     delete (*itrMethod);
      }
      methods->clear();
      delete methods;
   }
}

////////////////////////////////////////////////////////////////////////////////

void TMVA::Factory::SetVerbose( Bool_t v )
{
   fVerbose = v;
}

////////////////////////////////////////////////////////////////////////////////
/// Book a classifier or regression method.

TMVA::MethodBase* TMVA::Factory::BookMethod( TMVA::DataLoader *loader, TString theMethodName, TString methodTitle, TString theOption )
{
   if(fModelPersistence) gSystem->MakeDirectory(loader->GetName());//creating directory for DataLoader output

   TString datasetname=loader->GetName();

   if( fAnalysisType == Types::kNoAnalysisType ){
      if( loader->DefaultDataSetInfo().GetNClasses()==2
          && loader->DefaultDataSetInfo().GetClassInfo("Signal") != NULL
          && loader->DefaultDataSetInfo().GetClassInfo("Background") != NULL
          ){
         fAnalysisType = Types::kClassification; // default is classification
      } else if( loader->DefaultDataSetInfo().GetNClasses() >= 2 ){
         fAnalysisType = Types::kMulticlass;    // if two classes, but not named "Signal" and "Background"
      } else
         Log() << kFATAL << "No analysis type for " << loader->DefaultDataSetInfo().GetNClasses() << " classes and "
               << loader->DefaultDataSetInfo().GetNTargets() << " regression targets." << Endl;
   }

   // booking via name; the names are translated into enums and the
   // corresponding overloaded BookMethod is called

  if(fMethodsMap.find(datasetname)!=fMethodsMap.end())
   {
      if (GetMethod( datasetname,methodTitle ) != 0) {
       Log() << kFATAL << "Booking failed since method with title <"
        << methodTitle <<"> already exists "<< "in with DataSet Name <"<< loader->GetName()<<">  "
        << Endl;
     }
   }


     Log() << kHEADER << "Booking method: " << gTools().Color("bold") << methodTitle
     // << gTools().Color("reset")<<" DataSet Name: "<<gTools().Color("bold")<<loader->GetName()
      << gTools().Color("reset") << Endl << Endl;

   // interpret option string with respect to a request for boosting (i.e., BostNum > 0)
   Int_t    boostNum = 0;
   TMVA::Configurable* conf = new TMVA::Configurable( theOption );
   conf->DeclareOptionRef( boostNum = 0, "Boost_num",
                           "Number of times the classifier will be boosted" );
   conf->ParseOptions();
   delete conf;
   TString fFileDir;
   if(fModelPersistence)
   {
       fFileDir=loader->GetName();
       fFileDir+="/"+gConfig().GetIONames().fWeightFileDir;
   }
   // initialize methods
   IMethod* im;
   if (!boostNum) {
      im = ClassifierFactory::Instance().Create(theMethodName.Data(), fJobName, methodTitle,
                                                loader->DefaultDataSetInfo(), theOption);
   }
   else {
      // boosted classifier, requires a specific definition, making it transparent for the user
     Log() << kDEBUG <<"Boost Number is " << boostNum << " > 0: train boosted classifier" << Endl;
     im = ClassifierFactory::Instance().Create("Boost", fJobName, methodTitle, loader->DefaultDataSetInfo(), theOption);
     MethodBoost *methBoost = dynamic_cast<MethodBoost *>(im); // DSMTEST divided into two lines
     if (!methBoost)                                           // DSMTEST
        Log() << kFATAL << "Method with type kBoost cannot be casted to MethodCategory. /Factory" << Endl; // DSMTEST

     if (fModelPersistence)
        methBoost->SetWeightFileDir(fFileDir);
     methBoost->SetModelPersistence(fModelPersistence);
     methBoost->SetBoostedMethodName(theMethodName);       // DSMTEST divided into two lines
     methBoost->fDataSetManager = loader->fDataSetManager; // DSMTEST
     methBoost->SetFile(fgTargetFile);
     methBoost->SetSilentFile(IsSilentFile());
   }

   MethodBase *method = dynamic_cast<MethodBase*>(im);
   if (method==0) return 0; // could not create method

   // set fDataSetManager if MethodCategory (to enable Category to create datasetinfo objects) // DSMTEST
   if (method->GetMethodType() == Types::kCategory) { // DSMTEST
      MethodCategory *methCat = (dynamic_cast<MethodCategory*>(im)); // DSMTEST
      if (!methCat) // DSMTEST
         Log() << kFATAL << "Method with type kCategory cannot be casted to MethodCategory. /Factory" << Endl; // DSMTEST

      if(fModelPersistence) methCat->SetWeightFileDir(fFileDir);
      methCat->SetModelPersistence(fModelPersistence);
      methCat->fDataSetManager = loader->fDataSetManager; // DSMTEST
      methCat->SetFile(fgTargetFile);
      methCat->SetSilentFile(IsSilentFile());
   } // DSMTEST


   if (!method->HasAnalysisType( fAnalysisType,
                                 loader->DefaultDataSetInfo().GetNClasses(),
                                 loader->DefaultDataSetInfo().GetNTargets() )) {
      Log() << kWARNING << "Method " << method->GetMethodTypeName() << " is not capable of handling " ;
      if (fAnalysisType == Types::kRegression) {
         Log() << "regression with " << loader->DefaultDataSetInfo().GetNTargets() << " targets." << Endl;
      }
      else if (fAnalysisType == Types::kMulticlass ) {
         Log() << "multiclass classification with " << loader->DefaultDataSetInfo().GetNClasses() << " classes." << Endl;
      }
      else {
         Log() << "classification with " << loader->DefaultDataSetInfo().GetNClasses() << " classes." << Endl;
      }
      return 0;
   }

   if(fModelPersistence) method->SetWeightFileDir(fFileDir);
   method->SetModelPersistence(fModelPersistence);
   method->SetAnalysisType( fAnalysisType );
   method->SetupMethod();
   method->ParseOptions();
   method->ProcessSetup();
   method->SetFile(fgTargetFile);
   method->SetSilentFile(IsSilentFile());

   // check-for-unused-options is performed; may be overridden by derived classes
   method->CheckSetup();

   if(fMethodsMap.find(datasetname)==fMethodsMap.end())
   {
   MVector *mvector=new MVector;
   fMethodsMap[datasetname]=mvector;
   }
   fMethodsMap[datasetname]->push_back( method );
   return method;
}

////////////////////////////////////////////////////////////////////////////////
/// Books MVA method. The option configuration string is custom for each MVA
/// the TString field "theNameAppendix" serves to define (and distinguish)
/// several instances of a given MVA, eg, when one wants to compare the
/// performance of various configurations

TMVA::MethodBase* TMVA::Factory::BookMethod(TMVA::DataLoader *loader, Types::EMVA theMethod, TString methodTitle, TString theOption )
{
   return BookMethod(loader, Types::Instance().GetMethodName( theMethod ), methodTitle, theOption );
}

////////////////////////////////////////////////////////////////////////////////
/// Returns pointer to MVA that corresponds to given method title.

TMVA::IMethod* TMVA::Factory::GetMethod(const TString& datasetname,  const TString &methodTitle ) const
{
   if(fMethodsMap.find(datasetname)==fMethodsMap.end()) return 0;

   MVector *methods=fMethodsMap.find(datasetname)->second;

   MVector::const_iterator itrMethod;
   //
   for (itrMethod    = methods->begin(); itrMethod != methods->end(); itrMethod++) {
      MethodBase* mva = dynamic_cast<MethodBase*>(*itrMethod);
      if ( (mva->GetMethodName())==methodTitle ) return mva;
   }
   return 0;
}

////////////////////////////////////////////////////////////////////////////////
/// Checks whether a given method name is defined for a given dataset.

Bool_t TMVA::Factory::HasMethod(const TString& datasetname,  const TString &methodTitle ) const
{
   if(fMethodsMap.find(datasetname)==fMethodsMap.end()) return 0;

   std::string methodName = methodTitle.Data();
   auto isEqualToMethodName = [&methodName](TMVA::IMethod * m) {
      return ( 0 == methodName.compare( m->GetName() ) );
   };

   TMVA::Factory::MVector * methods = this->fMethodsMap.at(datasetname);
   Bool_t isMethodNameExisting = std::any_of( methods->begin(), methods->end(), isEqualToMethodName);

   return isMethodNameExisting;
}

////////////////////////////////////////////////////////////////////////////////

void TMVA::Factory::WriteDataInformation(DataSetInfo&     fDataSetInfo)
{
   RootBaseDir()->cd();

   if(!RootBaseDir()->GetDirectory(fDataSetInfo.GetName())) RootBaseDir()->mkdir(fDataSetInfo.GetName());
   else return; //loader is now in the output file, we dont need to save again

   RootBaseDir()->cd(fDataSetInfo.GetName());
   fDataSetInfo.GetDataSet(); // builds dataset (including calculation of correlation matrix)


   // correlation matrix of the default DS
   const TMatrixD* m(0);
   const TH2* h(0);

   if(fAnalysisType == Types::kMulticlass){
      for (UInt_t cls = 0; cls < fDataSetInfo.GetNClasses() ; cls++) {
         m = fDataSetInfo.CorrelationMatrix(fDataSetInfo.GetClassInfo(cls)->GetName());
         h = fDataSetInfo.CreateCorrelationMatrixHist(m, TString("CorrelationMatrix")+fDataSetInfo.GetClassInfo(cls)->GetName(),
                                                              TString("Correlation Matrix (")+ fDataSetInfo.GetClassInfo(cls)->GetName() +TString(")"));
         if (h!=0) {
            h->Write();
            delete h;
         }
      }
   }
   else{
      m = fDataSetInfo.CorrelationMatrix( "Signal" );
      h = fDataSetInfo.CreateCorrelationMatrixHist(m, "CorrelationMatrixS", "Correlation Matrix (signal)");
      if (h!=0) {
         h->Write();
         delete h;
      }

      m = fDataSetInfo.CorrelationMatrix( "Background" );
      h = fDataSetInfo.CreateCorrelationMatrixHist(m, "CorrelationMatrixB", "Correlation Matrix (background)");
      if (h!=0) {
         h->Write();
         delete h;
      }

      m = fDataSetInfo.CorrelationMatrix( "Regression" );
      h = fDataSetInfo.CreateCorrelationMatrixHist(m, "CorrelationMatrix", "Correlation Matrix");
      if (h!=0) {
         h->Write();
         delete h;
      }
   }

   // some default transformations to evaluate
   // NOTE: all transformations are destroyed after this test
   TString processTrfs = "I"; //"I;N;D;P;U;G,D;"

   // plus some user defined transformations
   processTrfs = fTransformations;

   // remove any trace of identity transform - if given (avoid to apply it twice)
   std::vector<TMVA::TransformationHandler*> trfs;
   TransformationHandler* identityTrHandler = 0;

   std::vector<TString> trfsDef = gTools().SplitString(processTrfs,';');
   std::vector<TString>::iterator trfsDefIt = trfsDef.begin();
   for (; trfsDefIt!=trfsDef.end(); trfsDefIt++) {
      trfs.push_back(new TMVA::TransformationHandler(fDataSetInfo, "Factory"));
      TString trfS = (*trfsDefIt);

      //Log() << kINFO << Endl;
      Log() << kDEBUG << "current transformation string: '" << trfS.Data() << "'" << Endl;
      TMVA::CreateVariableTransforms( trfS,
                                                  fDataSetInfo,
                                                  *(trfs.back()),
                                                  Log() );

      if (trfS.BeginsWith('I')) identityTrHandler = trfs.back();
   }

   const std::vector<Event*>& inputEvents = fDataSetInfo.GetDataSet()->GetEventCollection();

   // apply all transformations
   std::vector<TMVA::TransformationHandler*>::iterator trfIt = trfs.begin();

   for (;trfIt != trfs.end(); trfIt++) {
      // setting a Root dir causes the variables distributions to be saved to the root file
      (*trfIt)->SetRootDir(RootBaseDir()->GetDirectory(fDataSetInfo.GetName()));// every dataloader have its own dir
      (*trfIt)->CalcTransformations(inputEvents);
   }
   if(identityTrHandler) identityTrHandler->PrintVariableRanking();

   // clean up
   for (trfIt = trfs.begin(); trfIt != trfs.end(); trfIt++) delete *trfIt;
}

////////////////////////////////////////////////////////////////////////////////
/// Iterates through all booked methods and sees if they use parameter tuning and if so..
/// does just that  i.e. calls "Method::Train()" for different parameter settings and
/// keeps in mind the "optimal one"... and that's the one that will later on be used
/// in the main training loop.

std::map<TString,Double_t> TMVA::Factory::OptimizeAllMethods(TString fomType, TString fitType)
{

   std::map<TString,MVector*>::iterator itrMap;
   std::map<TString,Double_t> TunedParameters;
   for(itrMap = fMethodsMap.begin();itrMap != fMethodsMap.end();itrMap++)
   {
      MVector *methods=itrMap->second;

      MVector::iterator itrMethod;

      // iterate over methods and optimize
      for( itrMethod = methods->begin(); itrMethod != methods->end(); itrMethod++ ) {
     Event::SetIsTraining(kTRUE);
     MethodBase* mva = dynamic_cast<MethodBase*>(*itrMethod);
     if (!mva) {
       Log() << kFATAL << "Dynamic cast to MethodBase failed" <<Endl;
       return TunedParameters;
     }

     if (mva->Data()->GetNTrainingEvents() < MinNoTrainingEvents) {
       Log() << kWARNING << "Method " << mva->GetMethodName()
        << " not trained (training tree has less entries ["
        << mva->Data()->GetNTrainingEvents()
        << "] than required [" << MinNoTrainingEvents << "]" << Endl;
       continue;
     }

     Log() << kINFO << "Optimize method: " << mva->GetMethodName() << " for "
      << (fAnalysisType == Types::kRegression ? "Regression" :
          (fAnalysisType == Types::kMulticlass ? "Multiclass classification" : "Classification")) << Endl;

     TunedParameters = mva->OptimizeTuningParameters(fomType,fitType);
     Log() << kINFO << "Optimization of tuning parameters finished for Method:"<<mva->GetName() << Endl;
      }
   }

   return TunedParameters;

}

////////////////////////////////////////////////////////////////////////////////
/// Private method to generate a ROCCurve instance for a given method.
/// Handles the conversion from TMVA ResultSet to a format the ROCCurve class
/// understands.
///
/// \note You own the retured pointer.
///

TMVA::ROCCurve *TMVA::Factory::GetROC(TMVA::DataLoader *loader, TString theMethodName, UInt_t iClass,
                                      Types::ETreeType type)
{
   return GetROC((TString)loader->GetName(), theMethodName, iClass, type);
}

////////////////////////////////////////////////////////////////////////////////
/// Private method to generate a ROCCurve instance for a given method.
/// Handles the conversion from TMVA ResultSet to a format the ROCCurve class
/// understands.
///
/// \note You own the retured pointer.
///

TMVA::ROCCurve *TMVA::Factory::GetROC(TString datasetname, TString theMethodName, UInt_t iClass, Types::ETreeType type)
{
   if (fMethodsMap.find(datasetname) == fMethodsMap.end()) {
      Log() << kERROR << Form("DataSet = %s not found in methods map.", datasetname.Data()) << Endl;
      return nullptr;
   }

   if (!this->HasMethod(datasetname, theMethodName)) {
      Log() << kERROR << Form("Method = %s not found with Dataset = %s ", theMethodName.Data(), datasetname.Data())
            << Endl;
      return nullptr;
   }

   std::set<Types::EAnalysisType> allowedAnalysisTypes = {Types::kClassification, Types::kMulticlass};
   if (allowedAnalysisTypes.count(this->fAnalysisType) == 0) {
      Log() << kERROR << Form("Can only generate ROC curves for analysis type kClassification and kMulticlass.")
            << Endl;
      return nullptr;
   }

   TMVA::MethodBase *method = dynamic_cast<TMVA::MethodBase *>(this->GetMethod(datasetname, theMethodName));
   TMVA::DataSet *dataset = method->Data();
   dataset->SetCurrentType(type);
   TMVA::Results *results = dataset->GetResults(theMethodName, type, this->fAnalysisType);

   UInt_t nClasses = method->DataInfo().GetNClasses();
   if (this->fAnalysisType == Types::kMulticlass && iClass >= nClasses) {
      Log() << kERROR << Form("Given class number (iClass = %i) does not exist. There are %i classes in dataset.",
                              iClass, nClasses)
            << Endl;
      return nullptr;
   }

   TMVA::ROCCurve *rocCurve = nullptr;
   if (this->fAnalysisType == Types::kClassification) {

      std::vector<Float_t> *mvaRes = dynamic_cast<ResultsClassification *>(results)->GetValueVector();
      std::vector<Bool_t> *mvaResTypes = dynamic_cast<ResultsClassification *>(results)->GetValueVectorTypes();
      std::vector<Float_t> mvaResWeights;

      auto eventCollection = dataset->GetEventCollection(type);
      mvaResWeights.reserve(eventCollection.size());
      for (auto ev : eventCollection) {
         mvaResWeights.push_back(ev->GetWeight());
      }

      rocCurve = new TMVA::ROCCurve(*mvaRes, *mvaResTypes, mvaResWeights);

   } else if (this->fAnalysisType == Types::kMulticlass) {
      std::vector<Float_t> mvaRes;
      std::vector<Bool_t> mvaResTypes;
      std::vector<Float_t> mvaResWeights;

      std::vector<std::vector<Float_t>> *rawMvaRes = dynamic_cast<ResultsMulticlass *>(results)->GetValueVector();

      // Vector transpose due to values being stored as
      //    [ [0, 1, 2], [0, 1, 2], ... ]
      // in ResultsMulticlass::GetValueVector.
      mvaRes.reserve(rawMvaRes->size());
      for (auto item : *rawMvaRes) {
         mvaRes.push_back(item[iClass]);
      }

      auto eventCollection = dataset->GetEventCollection(type);
      mvaResTypes.reserve(eventCollection.size());
      mvaResWeights.reserve(eventCollection.size());
      for (auto ev : eventCollection) {
         mvaResTypes.push_back(ev->GetClass() == iClass);
         mvaResWeights.push_back(ev->GetWeight());
      }

      rocCurve = new TMVA::ROCCurve(mvaRes, mvaResTypes, mvaResWeights);
   }

   return rocCurve;
}

////////////////////////////////////////////////////////////////////////////////
/// Calculate the integral of the ROC curve, also known as the area under curve
/// (AUC), for a given method.
///
/// Argument iClass specifies the class to generate the ROC curve in a
/// multiclass setting. It is ignored for binary classification.
///

Double_t TMVA::Factory::GetROCIntegral(TMVA::DataLoader *loader, TString theMethodName, UInt_t iClass)
{
   return GetROCIntegral((TString)loader->GetName(), theMethodName, iClass);
}

////////////////////////////////////////////////////////////////////////////////
/// Calculate the integral of the ROC curve, also known as the area under curve
/// (AUC), for a given method.
///
/// Argument iClass specifies the class to generate the ROC curve in a
/// multiclass setting. It is ignored for binary classification.
///

Double_t TMVA::Factory::GetROCIntegral(TString datasetname, TString theMethodName, UInt_t iClass)
{
   if (fMethodsMap.find(datasetname) == fMethodsMap.end()) {
      Log() << kERROR << Form("DataSet = %s not found in methods map.", datasetname.Data()) << Endl;
      return 0;
   }

   if ( ! this->HasMethod(datasetname, theMethodName) ) {
      Log() << kERROR << Form("Method = %s not found with Dataset = %s ", theMethodName.Data(), datasetname.Data()) << Endl;
      return 0;
   }

   std::set<Types::EAnalysisType> allowedAnalysisTypes = {Types::kClassification, Types::kMulticlass};
   if ( allowedAnalysisTypes.count(this->fAnalysisType) == 0 ) {
      Log() << kERROR << Form("Can only generate ROC integral for analysis type kClassification. and kMulticlass.")
            << Endl;
      return 0;
   }

   TMVA::ROCCurve *rocCurve = GetROC(datasetname, theMethodName, iClass);
   if (!rocCurve) {
      Log() << kFATAL << Form("ROCCurve object was not created in Method = %s not found with Dataset = %s ",
                              theMethodName.Data(), datasetname.Data())
            << Endl;
      return 0;
   }

   Int_t npoints = TMVA::gConfig().fVariablePlotting.fNbinsXOfROCCurve + 1;
   Double_t rocIntegral = rocCurve->GetROCIntegral(npoints);
   delete rocCurve;

   return rocIntegral;
}

////////////////////////////////////////////////////////////////////////////////
/// Argument iClass specifies the class to generate the ROC curve in a 
/// multiclass setting. It is ignored for binary classification.
/// 
/// Returns a ROC graph for a given method, or nullptr on error.
///
/// Note: Evaluation of the given method must have been run prior to ROC 
/// generation through Factory::EvaluateAllMetods.
/// 
/// NOTE: The ROC curve is 1 vs. all where the given class is considered signal
/// and the others considered background. This is ok in binary classification
/// but in in multi class classification, the ROC surface is an N dimensional
/// shape, where N is number of classes - 1.

TGraph* TMVA::Factory::GetROCCurve(DataLoader *loader, TString theMethodName, Bool_t setTitles, UInt_t iClass)
{
  return GetROCCurve( (TString)loader->GetName(), theMethodName, setTitles, iClass );
}

////////////////////////////////////////////////////////////////////////////////
/// Argument iClass specifies the class to generate the ROC curve in a 
/// multiclass setting. It is ignored for binary classification.
/// 
/// Returns a ROC graph for a given method, or nullptr on error.
///
/// Note: Evaluation of the given method must have been run prior to ROC 
/// generation through Factory::EvaluateAllMetods.
/// 
/// NOTE: The ROC curve is 1 vs. all where the given class is considered signal
/// and the others considered background. This is ok in binary classification
/// but in in multi class classification, the ROC surface is an N dimensional
/// shape, where N is number of classes - 1.

TGraph* TMVA::Factory::GetROCCurve(TString datasetname, TString theMethodName, Bool_t setTitles, UInt_t iClass)
{
   if (fMethodsMap.find(datasetname) == fMethodsMap.end()) {
      Log() << kERROR << Form("DataSet = %s not found in methods map.", datasetname.Data()) << Endl;
      return nullptr;
   }
   
   if ( ! this->HasMethod(datasetname, theMethodName) ) {
      Log() << kERROR << Form("Method = %s not found with Dataset = %s ", theMethodName.Data(), datasetname.Data()) << Endl;
      return nullptr;
   }

   std::set<Types::EAnalysisType> allowedAnalysisTypes = {Types::kClassification, Types::kMulticlass};
   if ( allowedAnalysisTypes.count(this->fAnalysisType) == 0 ) {
      Log() << kERROR << Form("Can only generate ROC curves for analysis type kClassification and kMulticlass.") << Endl;
      return nullptr;
   }

   TMVA::ROCCurve *rocCurve = GetROC(datasetname, theMethodName, iClass);
   TGraph *graph = nullptr;

   if ( ! rocCurve ) {
      Log() << kFATAL << Form("ROCCurve object was not created in Method = %s not found with Dataset = %s ", theMethodName.Data(), datasetname.Data()) << Endl;
      return nullptr;
   }

   graph    = (TGraph *)rocCurve->GetROCCurve()->Clone();
   delete rocCurve;

   if(setTitles) {
      graph->GetYaxis()->SetTitle("Background rejection (Specificity)");
      graph->GetXaxis()->SetTitle("Signal efficiency (Sensitivity)");
      graph->SetTitle(Form("Signal efficiency vs. Background rejection (%s)", theMethodName.Data()));
   }

   return graph;
}

////////////////////////////////////////////////////////////////////////////////
/// Generate a collection of graphs, for all methods for a given class. Suitable
/// for comparing method performance.
/// 
/// Argument iClass specifies the class to generate the ROC curve in a 
/// multiclass setting. It is ignored for binary classification.
/// 
/// NOTE: The ROC curve is 1 vs. all where the given class is considered signal
/// and the others considered background. This is ok in binary classification
/// but in in multi class classification, the ROC surface is an N dimensional
/// shape, where N is number of classes - 1.

TMultiGraph* TMVA::Factory::GetROCCurveAsMultiGraph(DataLoader *loader, UInt_t iClass)
{
   return GetROCCurveAsMultiGraph((TString)loader->GetName(), iClass);
}

////////////////////////////////////////////////////////////////////////////////
/// Generate a collection of graphs, for all methods for a given class. Suitable
/// for comparing method performance.
/// 
/// Argument iClass specifies the class to generate the ROC curve in a 
/// multiclass setting. It is ignored for binary classification.
/// 
/// NOTE: The ROC curve is 1 vs. all where the given class is considered signal
/// and the others considered background. This is ok in binary classification
/// but in in multi class classification, the ROC surface is an N dimensional
/// shape, where N is number of classes - 1.

TMultiGraph* TMVA::Factory::GetROCCurveAsMultiGraph(TString datasetname, UInt_t iClass)
{
   UInt_t line_color = 1;

   TMultiGraph *multigraph = new TMultiGraph();

   MVector *methods = fMethodsMap[datasetname.Data()];
   for (auto * method_raw : *methods) {
      TMVA::MethodBase *method = dynamic_cast<TMVA::MethodBase *>(method_raw);
      if (method == nullptr) { continue; }

      TString methodName = method->GetMethodName();
      UInt_t nClasses = method->DataInfo().GetNClasses();
      
      if ( this->fAnalysisType == Types::kMulticlass && iClass >= nClasses ) {
         Log() << kERROR << Form("Given class number (iClass = %i) does not exist. There are %i classes in dataset.", iClass, nClasses) << Endl;
         continue;
      }

      TString className = method->DataInfo().GetClassInfo(iClass)->GetName();
      
      TGraph *graph = this->GetROCCurve(datasetname, methodName, false, iClass);
      graph->SetTitle(methodName);

      graph->SetLineWidth(2);
      graph->SetLineColor(line_color++);
      graph->SetFillColor(10);

      multigraph->Add(graph);
   }

   if ( multigraph->GetListOfGraphs() == nullptr ) {
      Log() << kERROR << Form("No metohds have class %i defined.", iClass) << Endl;
      return nullptr;
   }

   return multigraph;
}

////////////////////////////////////////////////////////////////////////////////
/// Draws ROC curves for all methods booked with the factory for a given class 
/// onto a canvas.
/// 
/// Argument iClass specifies the class to generate the ROC curve in a 
/// multiclass setting. It is ignored for binary classification.
/// 
/// NOTE: The ROC curve is 1 vs. all where the given class is considered signal
/// and the others considered background. This is ok in binary classification
/// but in in multi class classification, the ROC surface is an N dimensional
/// shape, where N is number of classes - 1.

TCanvas * TMVA::Factory::GetROCCurve(TMVA::DataLoader *loader, UInt_t iClass)
{
   return GetROCCurve((TString)loader->GetName(), iClass);
}

////////////////////////////////////////////////////////////////////////////////
/// Draws ROC curves for all methods booked with the factory for a given class.
/// 
/// Argument iClass specifies the class to generate the ROC curve in a 
/// multiclass setting. It is ignored for binary classification.
/// 
/// NOTE: The ROC curve is 1 vs. all where the given class is considered signal
/// and the others considered background. This is ok in binary classification
/// but in in multi class classification, the ROC surface is an N dimensional
/// shape, where N is number of classes - 1.

TCanvas * TMVA::Factory::GetROCCurve(TString datasetname, UInt_t iClass)
{
   if (fMethodsMap.find(datasetname) == fMethodsMap.end()) {
      Log() << kERROR << Form("DataSet = %s not found in methods map.", datasetname.Data()) << Endl;
      return 0;
   }

   TString name = Form("ROCCurve %s class %i", datasetname.Data(), iClass);
   TCanvas *canvas = new TCanvas(name, "ROC Curve", 200, 10, 700, 500);
   canvas->SetGrid();

   TMultiGraph *multigraph = this->GetROCCurveAsMultiGraph(datasetname, iClass);

   if ( multigraph ) {
      multigraph->Draw("AL");

      multigraph->GetYaxis()->SetTitle("Background rejection (Specificity)");
      multigraph->GetXaxis()->SetTitle("Signal efficiency (Sensitivity)");

      TString titleString = Form("Signal efficiency vs. Background rejection");
      if (this->fAnalysisType == Types::kMulticlass) {
         titleString = Form("%s (Class=%i)", titleString.Data(), iClass);
      }

      // Workaround for TMultigraph not drawing title correctly.
      multigraph->GetHistogram()->SetTitle( titleString );
      multigraph->SetTitle( titleString );

      canvas->BuildLegend(0.15, 0.15, 0.35, 0.3, "MVA Method");
   }

   return canvas;
}

////////////////////////////////////////////////////////////////////////////////
/// Iterates through all booked methods and calls training

void TMVA::Factory::TrainAllMethods()
{
    Log() << kHEADER << gTools().Color("bold") << "Train all methods" << gTools().Color("reset") << Endl;
   // iterates over all MVAs that have been booked, and calls their training methods


   // don't do anything if no method booked
   if (fMethodsMap.empty()) {
      Log() << kINFO << "...nothing found to train" << Endl;
      return;
   }

   // here the training starts
   //Log() << kINFO << " " << Endl;
   Log() << kDEBUG << "Train all methods for "
         << (fAnalysisType == Types::kRegression ? "Regression" :
             (fAnalysisType == Types::kMulticlass ? "Multiclass" : "Classification") ) << " ..." << Endl;

   std::map<TString,MVector*>::iterator itrMap;

   for(itrMap = fMethodsMap.begin();itrMap != fMethodsMap.end();itrMap++)
   {
      MVector *methods=itrMap->second;
      MVector::iterator itrMethod;

      // iterate over methods and train
      for( itrMethod = methods->begin(); itrMethod != methods->end(); itrMethod++ ) {
     Event::SetIsTraining(kTRUE);
     MethodBase* mva = dynamic_cast<MethodBase*>(*itrMethod);

     if(mva==0) continue;

     if(mva->DataInfo().GetDataSetManager()->DataInput().GetEntries() <=1) { // 0 entries --> 0 events, 1 entry --> dynamical dataset (or one entry)
         Log() << kFATAL << "No input data for the training provided!" << Endl;
     }

     if(fAnalysisType == Types::kRegression && mva->DataInfo().GetNTargets() < 1 )
     Log() << kFATAL << "You want to do regression training without specifying a target." << Endl;
     else if( (fAnalysisType == Types::kMulticlass || fAnalysisType == Types::kClassification)
      && mva->DataInfo().GetNClasses() < 2 )
     Log() << kFATAL << "You want to do classification training, but specified less than two classes." << Endl;

     // first print some information about the default dataset
     if(!IsSilentFile()) WriteDataInformation(mva->fDataSetInfo);


     if (mva->Data()->GetNTrainingEvents() < MinNoTrainingEvents) {
       Log() << kWARNING << "Method " << mva->GetMethodName()
        << " not trained (training tree has less entries ["
        << mva->Data()->GetNTrainingEvents()
        << "] than required [" << MinNoTrainingEvents << "]" << Endl;
       continue;
     }

     Log() << kHEADER << "Train method: " << mva->GetMethodName() << " for "
      << (fAnalysisType == Types::kRegression ? "Regression" :
          (fAnalysisType == Types::kMulticlass ? "Multiclass classification" : "Classification")) << Endl << Endl;
          mva->TrainMethod();
          Log() << kHEADER << "Training finished" << Endl << Endl;
      }

      if (fAnalysisType != Types::kRegression) {

     // variable ranking
     //Log() << Endl;
     Log() << kINFO << "Ranking input variables (method specific)..." << Endl;
     for (itrMethod = methods->begin(); itrMethod != methods->end(); itrMethod++) {
       MethodBase* mva = dynamic_cast<MethodBase*>(*itrMethod);
       if (mva && mva->Data()->GetNTrainingEvents() >= MinNoTrainingEvents) {

      // create and print ranking
      const Ranking* ranking = (*itrMethod)->CreateRanking();
      if (ranking != 0) ranking->Print();
      else Log() << kINFO << "No variable ranking supplied by classifier: "
           << dynamic_cast<MethodBase*>(*itrMethod)->GetMethodName() << Endl;
       }
     }
      }

      // delete all methods and recreate them from weight file - this ensures that the application
      // of the methods (in TMVAClassificationApplication) is consistent with the results obtained
      // in the testing
      //Log() << Endl;
      if (fModelPersistence) {

      Log() << kHEADER << "=== Destroy and recreate all methods via weight files for testing ===" << Endl << Endl;

      if(!IsSilentFile())RootBaseDir()->cd();

     // iterate through all booked methods
     for (UInt_t i=0; i<methods->size(); i++) {

       MethodBase* m = dynamic_cast<MethodBase*>((*methods)[i]);
       if(m==0) continue;

       TMVA::Types::EMVA methodType = m->GetMethodType();
       TString           weightfile = m->GetWeightFileName();

       // decide if .txt or .xml file should be read:
       if (READXML) weightfile.ReplaceAll(".txt",".xml");

       DataSetInfo& dataSetInfo = m->DataInfo();
       TString      testvarName = m->GetTestvarName();
       delete m; //itrMethod[i];

       // recreate
       m = dynamic_cast<MethodBase *>(ClassifierFactory::Instance().Create(
          Types::Instance().GetMethodName(methodType).Data(), dataSetInfo, weightfile));
       if( m->GetMethodType() == Types::kCategory ){
      MethodCategory *methCat = (dynamic_cast<MethodCategory*>(m));
      if( !methCat ) Log() << kFATAL << "Method with type kCategory cannot be casted to MethodCategory. /Factory" << Endl;
      else methCat->fDataSetManager = m->DataInfo().GetDataSetManager();
       }
       //ToDo, Do we need to fill the DataSetManager of MethodBoost here too?


            TString fFileDir= m->DataInfo().GetName();
            fFileDir+="/"+gConfig().GetIONames().fWeightFileDir;
            m->SetWeightFileDir(fFileDir);
            m->SetModelPersistence(fModelPersistence);
            m->SetSilentFile(IsSilentFile());
       m->SetAnalysisType(fAnalysisType);
       m->SetupMethod();
       m->ReadStateFromFile();
       m->SetTestvarName(testvarName);

       // replace trained method by newly created one (from weight file) in methods vector
       (*methods)[i] = m;
     }
       }
   }
}

////////////////////////////////////////////////////////////////////////////////

void TMVA::Factory::TestAllMethods()
{
   Log() << kHEADER << gTools().Color("bold") << "Test all methods" << gTools().Color("reset") << Endl;

   // don't do anything if no method booked
   if (fMethodsMap.empty()) {
      Log() << kINFO << "...nothing found to test" << Endl;
      return;
   }
   std::map<TString,MVector*>::iterator itrMap;

   for(itrMap = fMethodsMap.begin();itrMap != fMethodsMap.end();itrMap++)
   {
      MVector *methods=itrMap->second;
      MVector::iterator itrMethod;

      // iterate over methods and test
      for( itrMethod = methods->begin(); itrMethod != methods->end(); itrMethod++ ) {
     Event::SetIsTraining(kFALSE);
     MethodBase* mva = dynamic_cast<MethodBase*>(*itrMethod);
     if(mva==0) continue;
     Types::EAnalysisType analysisType = mva->GetAnalysisType();
     Log() << kHEADER << "Test method: " << mva->GetMethodName() << " for "
      << (analysisType == Types::kRegression ? "Regression" :
          (analysisType == Types::kMulticlass ? "Multiclass classification" : "Classification")) << " performance" << Endl << Endl;
     mva->AddOutput( Types::kTesting, analysisType );
      }
   }
}

////////////////////////////////////////////////////////////////////////////////

void TMVA::Factory::MakeClass(const TString& datasetname , const TString& methodTitle ) const
{
   if (methodTitle != "") {
      IMethod* method = GetMethod(datasetname, methodTitle);
      if (method) method->MakeClass();
      else {
         Log() << kWARNING << "<MakeClass> Could not find classifier \"" << methodTitle
               << "\" in list" << Endl;
      }
   }
   else {

      // no classifier specified, print all help messages
      MVector *methods=fMethodsMap.find(datasetname)->second;
      MVector::const_iterator itrMethod;
      for (itrMethod    = methods->begin(); itrMethod != methods->end(); itrMethod++) {
         MethodBase* method = dynamic_cast<MethodBase*>(*itrMethod);
         if(method==0) continue;
         Log() << kINFO << "Make response class for classifier: " << method->GetMethodName() << Endl;
         method->MakeClass();
      }
   }
}

////////////////////////////////////////////////////////////////////////////////
/// Print predefined help message of classifier.
/// Iterate over methods and test.

void TMVA::Factory::PrintHelpMessage(const TString& datasetname , const TString& methodTitle ) const
{
   if (methodTitle != "") {
      IMethod* method = GetMethod(datasetname , methodTitle );
      if (method) method->PrintHelpMessage();
      else {
         Log() << kWARNING << "<PrintHelpMessage> Could not find classifier \"" << methodTitle
               << "\" in list" << Endl;
      }
   }
   else {

      // no classifier specified, print all help messages
      MVector *methods=fMethodsMap.find(datasetname)->second;
      MVector::const_iterator itrMethod ;
      for (itrMethod    = methods->begin(); itrMethod != methods->end(); itrMethod++) {
         MethodBase* method = dynamic_cast<MethodBase*>(*itrMethod);
         if(method==0) continue;
         Log() << kINFO << "Print help message for classifier: " << method->GetMethodName() << Endl;
         method->PrintHelpMessage();
      }
   }
}

////////////////////////////////////////////////////////////////////////////////
/// Iterates over all MVA input variables and evaluates them.

void TMVA::Factory::EvaluateAllVariables(DataLoader *loader, TString options )
{
   Log() << kINFO << "Evaluating all variables..." << Endl;
   Event::SetIsTraining(kFALSE);

   for (UInt_t i=0; i<loader->DefaultDataSetInfo().GetNVariables(); i++) {
      TString s = loader->DefaultDataSetInfo().GetVariableInfo(i).GetLabel();
      if (options.Contains("V")) s += ":V";
      this->BookMethod(loader, "Variable", s );
   }
}

////////////////////////////////////////////////////////////////////////////////
/// Iterates over all MVAs that have been booked, and calls their evaluation methods.

void TMVA::Factory::EvaluateAllMethods( void )
{
   Log() << kHEADER << gTools().Color("bold") << "Evaluate all methods" << gTools().Color("reset") << Endl;

   // don't do anything if no method booked
   if (fMethodsMap.empty()) {
      Log() << kINFO << "...nothing found to evaluate" << Endl;
      return;
   }
   std::map<TString,MVector*>::iterator itrMap;

   for(itrMap = fMethodsMap.begin();itrMap != fMethodsMap.end();itrMap++)
   {
      MVector *methods=itrMap->second;

      // -----------------------------------------------------------------------
      // First part of evaluation process
      // --> compute efficiencies, and other separation estimators
      // -----------------------------------------------------------------------

      // although equal, we now want to separate the output for the variables
      // and the real methods
      Int_t isel;                  // will be 0 for a Method; 1 for a Variable
      Int_t nmeth_used[2] = {0,0}; // 0 Method; 1 Variable

      std::vector<std::vector<TString> >  mname(2);
      std::vector<std::vector<Double_t> > sig(2), sep(2), roc(2);
      std::vector<std::vector<Double_t> > eff01(2), eff10(2), eff30(2), effArea(2);
      std::vector<std::vector<Double_t> > eff01err(2), eff10err(2), eff30err(2);
      std::vector<std::vector<Double_t> > trainEff01(2), trainEff10(2), trainEff30(2);

      std::vector<std::vector<Float_t> > multiclass_testEff;
      std::vector<std::vector<Float_t> > multiclass_trainEff;
      std::vector<std::vector<Float_t> > multiclass_testPur;
      std::vector<std::vector<Float_t> > multiclass_trainPur;

      // Multiclass confusion matrices.
      std::vector<TMatrixD> multiclass_trainConfusionEffB01;
      std::vector<TMatrixD> multiclass_trainConfusionEffB10;
      std::vector<TMatrixD> multiclass_trainConfusionEffB30;
      std::vector<TMatrixD> multiclass_testConfusionEffB01;
      std::vector<TMatrixD> multiclass_testConfusionEffB10;
      std::vector<TMatrixD> multiclass_testConfusionEffB30;

      std::vector<std::vector<Double_t> > biastrain(1);  // "bias" of the regression on the training data
      std::vector<std::vector<Double_t> > biastest(1);   // "bias" of the regression on test data
      std::vector<std::vector<Double_t> > devtrain(1);   // "dev" of the regression on the training data
      std::vector<std::vector<Double_t> > devtest(1);    // "dev" of the regression on test data
      std::vector<std::vector<Double_t> > rmstrain(1);   // "rms" of the regression on the training data
      std::vector<std::vector<Double_t> > rmstest(1);    // "rms" of the regression on test data
      std::vector<std::vector<Double_t> > minftrain(1);  // "minf" of the regression on the training data
      std::vector<std::vector<Double_t> > minftest(1);   // "minf" of the regression on test data
      std::vector<std::vector<Double_t> > rhotrain(1);   // correlation of the regression on the training data
      std::vector<std::vector<Double_t> > rhotest(1);    // correlation of the regression on test data

      // same as above but for 'truncated' quantities (computed for events within 2sigma of RMS)
      std::vector<std::vector<Double_t> > biastrainT(1);
      std::vector<std::vector<Double_t> > biastestT(1);
      std::vector<std::vector<Double_t> > devtrainT(1);
      std::vector<std::vector<Double_t> > devtestT(1);
      std::vector<std::vector<Double_t> > rmstrainT(1);
      std::vector<std::vector<Double_t> > rmstestT(1);
      std::vector<std::vector<Double_t> > minftrainT(1);
      std::vector<std::vector<Double_t> > minftestT(1);

      // following vector contains all methods - with the exception of Cuts, which are special
      MVector methodsNoCuts;

      Bool_t doRegression = kFALSE;
      Bool_t doMulticlass = kFALSE;

      // iterate over methods and evaluate
      for (MVector::iterator itrMethod =methods->begin(); itrMethod != methods->end(); itrMethod++) {
     Event::SetIsTraining(kFALSE);
     MethodBase* theMethod = dynamic_cast<MethodBase*>(*itrMethod);
     if(theMethod==0) continue;
     theMethod->SetFile(fgTargetFile);
     theMethod->SetSilentFile(IsSilentFile());
     if (theMethod->GetMethodType() != Types::kCuts) methodsNoCuts.push_back( *itrMethod );

     if (theMethod->DoRegression()) {
       doRegression = kTRUE;

       Log() << kINFO << "Evaluate regression method: " << theMethod->GetMethodName() << Endl;
       Double_t bias, dev, rms, mInf;
       Double_t biasT, devT, rmsT, mInfT;
       Double_t rho;

       theMethod->TestRegression( bias, biasT, dev, devT, rms, rmsT, mInf, mInfT, rho, TMVA::Types::kTesting  );
       biastest[0]  .push_back( bias );
       devtest[0]   .push_back( dev );
       rmstest[0]   .push_back( rms );
       minftest[0]  .push_back( mInf );
       rhotest[0]   .push_back( rho );
       biastestT[0] .push_back( biasT );
       devtestT[0]  .push_back( devT );
       rmstestT[0]  .push_back( rmsT );
       minftestT[0] .push_back( mInfT );

       theMethod->TestRegression( bias, biasT, dev, devT, rms, rmsT, mInf, mInfT, rho, TMVA::Types::kTraining  );
       biastrain[0] .push_back( bias );
       devtrain[0]  .push_back( dev );
       rmstrain[0]  .push_back( rms );
       minftrain[0] .push_back( mInf );
       rhotrain[0]  .push_back( rho );
       biastrainT[0].push_back( biasT );
       devtrainT[0] .push_back( devT );
       rmstrainT[0] .push_back( rmsT );
       minftrainT[0].push_back( mInfT );

       mname[0].push_back( theMethod->GetMethodName() );
       nmeth_used[0]++;
       if(!IsSilentFile())
       {
      Log() << kDEBUG << "\tWrite evaluation histograms to file" << Endl;
      theMethod->WriteEvaluationHistosToFile(Types::kTesting);
      theMethod->WriteEvaluationHistosToFile(Types::kTraining);
       }
     } else if (theMethod->DoMulticlass()) {
        // ====================================================================
        // === Multiclass evaluation
        // ====================================================================
        doMulticlass = kTRUE;
        Log() << kINFO << "Evaluate multiclass classification method: " << theMethod->GetMethodName() << Endl;

        // This part uses a genetic alg. to evaluate the optimal sig eff * sig pur.
        // This is why it is disabled for now.
        // Find approximate optimal working point w.r.t. signalEfficiency * signalPurity.
        // theMethod->TestMulticlass(); // This is where the actual GA calc is done
        // multiclass_testEff.push_back(theMethod->GetMulticlassEfficiency(multiclass_testPur));

        // Confusion matrix at three background efficiency levels
        multiclass_trainConfusionEffB01.push_back(theMethod->GetMulticlassConfusionMatrix(0.01, Types::kTraining));
        multiclass_trainConfusionEffB10.push_back(theMethod->GetMulticlassConfusionMatrix(0.10, Types::kTraining));
        multiclass_trainConfusionEffB30.push_back(theMethod->GetMulticlassConfusionMatrix(0.30, Types::kTraining));

        multiclass_testConfusionEffB01.push_back(theMethod->GetMulticlassConfusionMatrix(0.01, Types::kTesting));
        multiclass_testConfusionEffB10.push_back(theMethod->GetMulticlassConfusionMatrix(0.10, Types::kTesting));
        multiclass_testConfusionEffB30.push_back(theMethod->GetMulticlassConfusionMatrix(0.30, Types::kTesting));

        if (not IsSilentFile()) {
           Log() << kDEBUG << "\tWrite evaluation histograms to file" << Endl;
           theMethod->WriteEvaluationHistosToFile(Types::kTesting);
           theMethod->WriteEvaluationHistosToFile(Types::kTraining);
        }

        nmeth_used[0]++;
        mname[0].push_back(theMethod->GetMethodName());
     } else {

        Log() << kHEADER << "Evaluate classifier: " << theMethod->GetMethodName() << Endl << Endl;
        isel = (theMethod->GetMethodTypeName().Contains("Variable")) ? 1 : 0;

        // perform the evaluation
        theMethod->TestClassification();

        // evaluate the classifier
        mname[isel].push_back(theMethod->GetMethodName());
        sig[isel].push_back(theMethod->GetSignificance());
        sep[isel].push_back(theMethod->GetSeparation());
        roc[isel].push_back(theMethod->GetROCIntegral());

        Double_t err;
        eff01[isel].push_back(theMethod->GetEfficiency("Efficiency:0.01", Types::kTesting, err));
        eff01err[isel].push_back(err);
        eff10[isel].push_back(theMethod->GetEfficiency("Efficiency:0.10", Types::kTesting, err));
        eff10err[isel].push_back(err);
        eff30[isel].push_back(theMethod->GetEfficiency("Efficiency:0.30", Types::kTesting, err));
        eff30err[isel].push_back(err);
        effArea[isel].push_back(theMethod->GetEfficiency("", Types::kTesting, err)); // computes the area (average)

        trainEff01[isel].push_back(theMethod->GetTrainingEfficiency("Efficiency:0.01")); // the first pass takes longer
        trainEff10[isel].push_back(theMethod->GetTrainingEfficiency("Efficiency:0.10"));
        trainEff30[isel].push_back(theMethod->GetTrainingEfficiency("Efficiency:0.30"));

        nmeth_used[isel]++;

        if (!IsSilentFile()) {
           Log() << kDEBUG << "\tWrite evaluation histograms to file" << Endl;
           theMethod->WriteEvaluationHistosToFile(Types::kTesting);
           theMethod->WriteEvaluationHistosToFile(Types::kTraining);
        }
     }
      }
      if (doRegression) {

     std::vector<TString> vtemps = mname[0];
     std::vector< std::vector<Double_t> > vtmp;
     vtmp.push_back( devtest[0]   );  // this is the vector that is ranked
     vtmp.push_back( devtrain[0]  );
     vtmp.push_back( biastest[0]  );
     vtmp.push_back( biastrain[0] );
     vtmp.push_back( rmstest[0]   );
     vtmp.push_back( rmstrain[0]  );
     vtmp.push_back( minftest[0]  );
     vtmp.push_back( minftrain[0] );
     vtmp.push_back( rhotest[0]   );
     vtmp.push_back( rhotrain[0]  );
     vtmp.push_back( devtestT[0]  );  // this is the vector that is ranked
     vtmp.push_back( devtrainT[0] );
     vtmp.push_back( biastestT[0] );
     vtmp.push_back( biastrainT[0]);
     vtmp.push_back( rmstestT[0]  );
     vtmp.push_back( rmstrainT[0] );
     vtmp.push_back( minftestT[0] );
     vtmp.push_back( minftrainT[0]);
     gTools().UsefulSortAscending( vtmp, &vtemps );
     mname[0]      = vtemps;
     devtest[0]    = vtmp[0];
     devtrain[0]   = vtmp[1];
     biastest[0]   = vtmp[2];
     biastrain[0]  = vtmp[3];
     rmstest[0]    = vtmp[4];
     rmstrain[0]   = vtmp[5];
     minftest[0]   = vtmp[6];
     minftrain[0]  = vtmp[7];
     rhotest[0]    = vtmp[8];
     rhotrain[0]   = vtmp[9];
     devtestT[0]   = vtmp[10];
     devtrainT[0]  = vtmp[11];
     biastestT[0]  = vtmp[12];
     biastrainT[0] = vtmp[13];
     rmstestT[0]   = vtmp[14];
     rmstrainT[0]  = vtmp[15];
     minftestT[0]  = vtmp[16];
     minftrainT[0] = vtmp[17];
      } else if (doMulticlass) {
         // TODO: fill in something meaningful
         // If there is some ranking of methods to be done it should be done here.
         // However, this is not so easy to define for multiclass so it is left out for now.

      }
      else {
     // now sort the variables according to the best 'eff at Beff=0.10'
     for (Int_t k=0; k<2; k++) {
       std::vector< std::vector<Double_t> > vtemp;
       vtemp.push_back( effArea[k] );  // this is the vector that is ranked
       vtemp.push_back( eff10[k] );
       vtemp.push_back( eff01[k] );
       vtemp.push_back( eff30[k] );
       vtemp.push_back( eff10err[k] );
       vtemp.push_back( eff01err[k] );
       vtemp.push_back( eff30err[k] );
       vtemp.push_back( trainEff10[k] );
       vtemp.push_back( trainEff01[k] );
       vtemp.push_back( trainEff30[k] );
       vtemp.push_back( sig[k] );
       vtemp.push_back( sep[k] );
       vtemp.push_back( roc[k] );
       std::vector<TString> vtemps = mname[k];
       gTools().UsefulSortDescending( vtemp, &vtemps );
       effArea[k]    = vtemp[0];
       eff10[k]      = vtemp[1];
       eff01[k]      = vtemp[2];
       eff30[k]      = vtemp[3];
       eff10err[k]   = vtemp[4];
       eff01err[k]   = vtemp[5];
       eff30err[k]   = vtemp[6];
       trainEff10[k] = vtemp[7];
       trainEff01[k] = vtemp[8];
       trainEff30[k] = vtemp[9];
       sig[k]        = vtemp[10];
       sep[k]        = vtemp[11];
       roc[k]        = vtemp[12];
       mname[k]      = vtemps;
     }
      }

      // -----------------------------------------------------------------------
      // Second part of evaluation process
      // --> compute correlations among MVAs
      // --> compute correlations between input variables and MVA (determines importance)
      // --> count overlaps
      // -----------------------------------------------------------------------
      if(fCorrelations)
      {
     const Int_t nmeth = methodsNoCuts.size();
     MethodBase* method = dynamic_cast<MethodBase*>(methods[0][0]);
     const Int_t nvar  = method->fDataSetInfo.GetNVariables();
     if (!doRegression && !doMulticlass ) {

         if (nmeth > 0) {

    //              needed for correlations
      Double_t *dvec = new Double_t[nmeth+nvar];
      std::vector<Double_t> rvec;

    //              for correlations
      TPrincipal* tpSig = new TPrincipal( nmeth+nvar, "" );
      TPrincipal* tpBkg = new TPrincipal( nmeth+nvar, "" );

    //              set required tree branch references
      Int_t ivar = 0;
      std::vector<TString>* theVars = new std::vector<TString>;
      std::vector<ResultsClassification*> mvaRes;
      for (MVector::iterator itrMethod = methodsNoCuts.begin(); itrMethod != methodsNoCuts.end(); itrMethod++, ivar++) {
          MethodBase* m = dynamic_cast<MethodBase*>(*itrMethod);
          if(m==0) continue;
          theVars->push_back( m->GetTestvarName() );
          rvec.push_back( m->GetSignalReferenceCut() );
          theVars->back().ReplaceAll( "MVA_", "" );
          mvaRes.push_back( dynamic_cast<ResultsClassification*>( m->Data()->GetResults( m->GetMethodName(),
                                      Types::kTesting,
                                      Types::kMaxAnalysisType) ) );
      }

    //              for overlap study
      TMatrixD* overlapS = new TMatrixD( nmeth, nmeth );
      TMatrixD* overlapB = new TMatrixD( nmeth, nmeth );
      (*overlapS) *= 0; // init...
      (*overlapB) *= 0; // init...

    //              loop over test tree
      DataSet* defDs = method->fDataSetInfo.GetDataSet();
      defDs->SetCurrentType(Types::kTesting);
      for (Int_t ievt=0; ievt<defDs->GetNEvents(); ievt++) {
          const Event* ev = defDs->GetEvent(ievt);

    //                 for correlations
          TMatrixD* theMat = 0;
          for (Int_t im=0; im<nmeth; im++) {
    //                    check for NaN value
            Double_t retval = (Double_t)(*mvaRes[im])[ievt][0];
            if (TMath::IsNaN(retval)) {
           Log() << kWARNING << "Found NaN return value in event: " << ievt
            << " for method \"" << methodsNoCuts[im]->GetName() << "\"" << Endl;
           dvec[im] = 0;
            }
            else dvec[im] = retval;
          }
          for (Int_t iv=0; iv<nvar;  iv++) dvec[iv+nmeth]  = (Double_t)ev->GetValue(iv);
          if (method->fDataSetInfo.IsSignal(ev)) { tpSig->AddRow( dvec ); theMat = overlapS; }
          else                                   { tpBkg->AddRow( dvec ); theMat = overlapB; }

    //                 count overlaps
          for (Int_t im=0; im<nmeth; im++) {
            for (Int_t jm=im; jm<nmeth; jm++) {
           if ((dvec[im] - rvec[im])*(dvec[jm] - rvec[jm]) > 0) {
             (*theMat)(im,jm)++;
             if (im != jm) (*theMat)(jm,im)++;
           }
            }
          }
      }

    //              renormalise overlap matrix
      (*overlapS) *= (1.0/defDs->GetNEvtSigTest());  // init...
      (*overlapB) *= (1.0/defDs->GetNEvtBkgdTest()); // init...

      tpSig->MakePrincipals();
      tpBkg->MakePrincipals();

      const TMatrixD* covMatS = tpSig->GetCovarianceMatrix();
      const TMatrixD* covMatB = tpBkg->GetCovarianceMatrix();

      const TMatrixD* corrMatS = gTools().GetCorrelationMatrix( covMatS );
      const TMatrixD* corrMatB = gTools().GetCorrelationMatrix( covMatB );

    //              print correlation matrices
      if (corrMatS != 0 && corrMatB != 0) {

    //                 extract MVA matrix
          TMatrixD mvaMatS(nmeth,nmeth);
          TMatrixD mvaMatB(nmeth,nmeth);
          for (Int_t im=0; im<nmeth; im++) {
            for (Int_t jm=0; jm<nmeth; jm++) {
           mvaMatS(im,jm) = (*corrMatS)(im,jm);
           mvaMatB(im,jm) = (*corrMatB)(im,jm);
            }
          }

    //                 extract variables - to MVA matrix
          std::vector<TString> theInputVars;
          TMatrixD varmvaMatS(nvar,nmeth);
          TMatrixD varmvaMatB(nvar,nmeth);
          for (Int_t iv=0; iv<nvar; iv++) {
            theInputVars.push_back( method->fDataSetInfo.GetVariableInfo( iv ).GetLabel() );
            for (Int_t jm=0; jm<nmeth; jm++) {
           varmvaMatS(iv,jm) = (*corrMatS)(nmeth+iv,jm);
           varmvaMatB(iv,jm) = (*corrMatB)(nmeth+iv,jm);
            }
          }

          if (nmeth > 1) {
            Log() << kINFO << Endl;
            Log() << kINFO <<Form("Dataset[%s] : ",method->fDataSetInfo.GetName())<< "Inter-MVA correlation matrix (signal):" << Endl;
            gTools().FormattedOutput( mvaMatS, *theVars, Log() );
            Log() << kINFO << Endl;

            Log() << kINFO <<Form("Dataset[%s] : ",method->fDataSetInfo.GetName())<< "Inter-MVA correlation matrix (background):" << Endl;
            gTools().FormattedOutput( mvaMatB, *theVars, Log() );
            Log() << kINFO << Endl;
          }

          Log() << kINFO <<Form("Dataset[%s] : ",method->fDataSetInfo.GetName())<< "Correlations between input variables and MVA response (signal):" << Endl;
          gTools().FormattedOutput( varmvaMatS, theInputVars, *theVars, Log() );
          Log() << kINFO << Endl;

          Log() << kINFO <<Form("Dataset[%s] : ",method->fDataSetInfo.GetName())<< "Correlations between input variables and MVA response (background):" << Endl;
          gTools().FormattedOutput( varmvaMatB, theInputVars, *theVars, Log() );
          Log() << kINFO << Endl;
      }
      else Log() << kWARNING <<Form("Dataset[%s] : ",method->fDataSetInfo.GetName())<< "<TestAllMethods> cannot compute correlation matrices" << Endl;

    //              print overlap matrices
      Log() << kINFO <<Form("Dataset[%s] : ",method->fDataSetInfo.GetName())<< "The following \"overlap\" matrices contain the fraction of events for which " << Endl;
      Log() << kINFO <<Form("Dataset[%s] : ",method->fDataSetInfo.GetName())<< "the MVAs 'i' and 'j' have returned conform answers about \"signal-likeness\"" << Endl;
      Log() << kINFO <<Form("Dataset[%s] : ",method->fDataSetInfo.GetName())<< "An event is signal-like, if its MVA output exceeds the following value:" << Endl;
      gTools().FormattedOutput( rvec, *theVars, "Method" , "Cut value", Log() );
      Log() << kINFO <<Form("Dataset[%s] : ",method->fDataSetInfo.GetName())<< "which correspond to the working point: eff(signal) = 1 - eff(background)" << Endl;

    //              give notice that cut method has been excluded from this test
      if (nmeth != (Int_t)methods->size())
          Log() << kINFO <<Form("Dataset[%s] : ",method->fDataSetInfo.GetName())<< "Note: no correlations and overlap with cut method are provided at present" << Endl;

      if (nmeth > 1) {
          Log() << kINFO << Endl;
          Log() << kINFO <<Form("Dataset[%s] : ",method->fDataSetInfo.GetName())<< "Inter-MVA overlap matrix (signal):" << Endl;
          gTools().FormattedOutput( *overlapS, *theVars, Log() );
          Log() << kINFO << Endl;

          Log() << kINFO <<Form("Dataset[%s] : ",method->fDataSetInfo.GetName())<< "Inter-MVA overlap matrix (background):" << Endl;
          gTools().FormattedOutput( *overlapB, *theVars, Log() );
      }

    //              cleanup
      delete tpSig;
      delete tpBkg;
      delete corrMatS;
      delete corrMatB;
      delete theVars;
      delete overlapS;
      delete overlapB;
      delete [] dvec;
         }
     }
      }
      // -----------------------------------------------------------------------
      // Third part of evaluation process
      // --> output
      // -----------------------------------------------------------------------

      if (doRegression) {

     Log() << kINFO << Endl;
     TString hLine = "--------------------------------------------------------------------------------------------------";
     Log() << kINFO << "Evaluation results ranked by smallest RMS on test sample:" << Endl;
     Log() << kINFO << "(\"Bias\" quotes the mean deviation of the regression from true target." << Endl;
     Log() << kINFO << " \"MutInf\" is the \"Mutual Information\" between regression and target." << Endl;
     Log() << kINFO << " Indicated by \"_T\" are the corresponding \"truncated\" quantities ob-" << Endl;
     Log() << kINFO << " tained when removing events deviating more than 2sigma from average.)" << Endl;
     Log() << kINFO << hLine << Endl;
     //Log() << kINFO << "DataSet Name:        MVA Method:        <Bias>   <Bias_T>    RMS    RMS_T  |  MutInf MutInf_T" << Endl;
     Log() << kINFO << hLine << Endl;

     for (Int_t i=0; i<nmeth_used[0]; i++) {
       MethodBase* theMethod = dynamic_cast<MethodBase*>((*methods)[i]);
       if(theMethod==0) continue;

       Log() << kINFO << Form("%-20s %-15s:%#9.3g%#9.3g%#9.3g%#9.3g  |  %#5.3f  %#5.3f",
                    theMethod->fDataSetInfo.GetName(),
                    (const char*)mname[0][i],
                    biastest[0][i], biastestT[0][i],
                    rmstest[0][i], rmstestT[0][i],
                    minftest[0][i], minftestT[0][i] )
            << Endl;
     }
     Log() << kINFO << hLine << Endl;
     Log() << kINFO << Endl;
     Log() << kINFO << "Evaluation results ranked by smallest RMS on training sample:" << Endl;
     Log() << kINFO << "(overtraining check)" << Endl;
     Log() << kINFO << hLine << Endl;
     Log() << kINFO << "DataSet Name:         MVA Method:        <Bias>   <Bias_T>    RMS    RMS_T  |  MutInf MutInf_T" << Endl;
     Log() << kINFO << hLine << Endl;

     for (Int_t i=0; i<nmeth_used[0]; i++) {
       MethodBase* theMethod = dynamic_cast<MethodBase*>((*methods)[i]);
       if(theMethod==0) continue;
       Log() << kINFO << Form("%-20s %-15s:%#9.3g%#9.3g%#9.3g%#9.3g  |  %#5.3f  %#5.3f",
                    theMethod->fDataSetInfo.GetName(),
                    (const char*)mname[0][i],
                    biastrain[0][i], biastrainT[0][i],
                    rmstrain[0][i], rmstrainT[0][i],
                    minftrain[0][i], minftrainT[0][i] )
            << Endl;
     }
     Log() << kINFO << hLine << Endl;
     Log() << kINFO << Endl;
      } else if (doMulticlass) {
         // ====================================================================
         // === Multiclass Output
         // ====================================================================

         TString hLine =
            "-------------------------------------------------------------------------------------------------------";

         // This part uses a genetic alg. to evaluate the optimal sig eff * sig pur.
         // This is why it is disabled for now.
         //
         // // --- Acheivable signal efficiency * signal purity
         // // --------------------------------------------------------------------
         // Log() << kINFO << Endl;
         // Log() << kINFO << "Evaluation results ranked by best signal efficiency times signal purity " << Endl;
         // Log() << kINFO << hLine << Endl;

         // // iterate over methods and evaluate
         // for (MVector::iterator itrMethod = methods->begin(); itrMethod != methods->end(); itrMethod++) {
         //    MethodBase *theMethod = dynamic_cast<MethodBase *>(*itrMethod);
         //    if (theMethod == 0) {
         //       continue;
         //    }

         //    TString header = "DataSet Name     MVA Method     ";
         //    for (UInt_t icls = 0; icls < theMethod->fDataSetInfo.GetNClasses(); ++icls) {
         //       header += Form("%-12s ", theMethod->fDataSetInfo.GetClassInfo(icls)->GetName());
         //    }

         //    Log() << kINFO << header << Endl;
         //    Log() << kINFO << hLine << Endl;
         //    for (Int_t i = 0; i < nmeth_used[0]; i++) {
         //       TString res = Form("[%-14s] %-15s", theMethod->fDataSetInfo.GetName(), (const char *)mname[0][i]);
         //       for (UInt_t icls = 0; icls < theMethod->fDataSetInfo.GetNClasses(); ++icls) {
         //          res += Form("%#1.3f        ", (multiclass_testEff[i][icls]) * (multiclass_testPur[i][icls]));
         //       }
         //       Log() << kINFO << res << Endl;
         //    }

         //    Log() << kINFO << hLine << Endl;
         //    Log() << kINFO << Endl;
         // }

         // --- 1 vs Rest ROC AUC, signal efficiency @ given background efficiency
         // --------------------------------------------------------------------
         TString header1 = Form("%-15s%-15s%-15s%-15s%-15s%-15s", "Dataset", "MVA Method", "ROC AUC", "Sig eff@B=0.01",
                                "Sig eff@B=0.10", "Sig eff@B=0.30");
         TString header2 = Form("%-15s%-15s%-15s%-15s%-15s%-15s", "Name:", "/ Class:", "test  (train)", "test  (train)",
                                "test  (train)", "test  (train)");
         Log() << kINFO << Endl;
         Log() << kINFO << "1-vs-rest performance metrics per class" << Endl;
         Log() << kINFO << hLine << Endl;
         Log() << kINFO << Endl;
         Log() << kINFO << "Considers the listed class as signal and the other classes" << Endl;
         Log() << kINFO << "as background, reporting the resulting binary performance." << Endl;
         Log() << kINFO << "A score of 0.820 (0.850) means 0.820 was acheived on the" << Endl;
         Log() << kINFO << "test set and 0.850 on the training set." << Endl;

         Log() << kINFO << Endl;
         Log() << kINFO << header1 << Endl;
         Log() << kINFO << header2 << Endl;
         for (Int_t k = 0; k < 2; k++) {
            for (Int_t i = 0; i < nmeth_used[k]; i++) {
               if (k == 1) {
                  mname[k][i].ReplaceAll("Variable_", "");
               }

               const TString datasetName = itrMap->first;
               const TString mvaName = mname[k][i];

               MethodBase *theMethod = dynamic_cast<MethodBase *>(GetMethod(datasetName, mvaName));
               if (theMethod == 0) {
                  continue;
               }

               Log() << kINFO << Endl;
               TString row = Form("%-15s%-15s", datasetName.Data(), mvaName.Data());
               Log() << kINFO << row << Endl;
               Log() << kINFO << "------------------------------" << Endl;

               UInt_t numClasses = theMethod->fDataSetInfo.GetNClasses();
               for (UInt_t iClass = 0; iClass < numClasses; ++iClass) {

                  ROCCurve *rocCurveTrain = GetROC(datasetName, mvaName, iClass, Types::kTraining);
                  ROCCurve *rocCurveTest = GetROC(datasetName, mvaName, iClass, Types::kTesting);

                  const TString className = theMethod->DataInfo().GetClassInfo(iClass)->GetName();
                  const Double_t rocaucTrain = rocCurveTrain->GetROCIntegral();
                  const Double_t effB01Train = rocCurveTrain->GetEffSForEffB(0.01);
                  const Double_t effB10Train = rocCurveTrain->GetEffSForEffB(0.10);
                  const Double_t effB30Train = rocCurveTrain->GetEffSForEffB(0.30);
                  const Double_t rocaucTest = rocCurveTest->GetROCIntegral();
                  const Double_t effB01Test = rocCurveTest->GetEffSForEffB(0.01);
                  const Double_t effB10Test = rocCurveTest->GetEffSForEffB(0.10);
                  const Double_t effB30Test = rocCurveTest->GetEffSForEffB(0.30);
                  const TString rocaucCmp = Form("%5.3f (%5.3f)", rocaucTest, rocaucTrain);
                  const TString effB01Cmp = Form("%5.3f (%5.3f)", effB01Test, effB01Train);
                  const TString effB10Cmp = Form("%5.3f (%5.3f)", effB10Test, effB10Train);
                  const TString effB30Cmp = Form("%5.3f (%5.3f)", effB30Test, effB30Train);
                  row = Form("%-15s%-15s%-15s%-15s%-15s%-15s", "", className.Data(), rocaucCmp.Data(), effB01Cmp.Data(),
                             effB10Cmp.Data(), effB30Cmp.Data());
                  Log() << kINFO << row << Endl;

                  delete rocCurveTrain;
                  delete rocCurveTest;
               }
            }
         }
         Log() << kINFO << Endl;
         Log() << kINFO << hLine << Endl;
         Log() << kINFO << Endl;

         // --- Confusion matrices
         // --------------------------------------------------------------------
         auto printMatrix = [](TMatrixD const &matTraining, TMatrixD const &matTesting, std::vector<TString> classnames,
                               UInt_t numClasses, MsgLogger &stream) {
            // assert (classLabledWidth >= valueLabelWidth + 2)
            // if (...) {Log() << kWARN << "..." << Endl; }

            // TODO: Ensure matrices are same size.

            TString header = Form(" %-14s", " ");
            TString headerInfo = Form(" %-14s", " ");
            ;
            for (UInt_t iCol = 0; iCol < numClasses; ++iCol) {
               header += Form(" %-14s", classnames[iCol].Data());
               headerInfo += Form(" %-14s", " test (train)");
            }
            stream << kINFO << header << Endl;
            stream << kINFO << headerInfo << Endl;

            for (UInt_t iRow = 0; iRow < numClasses; ++iRow) {
               stream << kINFO << Form(" %-14s", classnames[iRow].Data());

               for (UInt_t iCol = 0; iCol < numClasses; ++iCol) {
                  if (iCol == iRow) {
                     stream << kINFO << Form(" %-14s", "-");
                  } else {
                     Double_t trainValue = matTraining[iRow][iCol];
                     Double_t testValue = matTesting[iRow][iCol];
                     TString entry = Form("%-5.3f (%-5.3f)", testValue, trainValue);
                     stream << kINFO << Form(" %-14s", entry.Data());
                  }
               }
               stream << kINFO << Endl;
            }
         };

         Log() << kINFO << Endl;
         Log() << kINFO << "Confusion matrices for all methods" << Endl;
         Log() << kINFO << hLine << Endl;
         Log() << kINFO << Endl;
         Log() << kINFO << "Does a binary comparison between the two classes given by a " << Endl;
         Log() << kINFO << "particular row-column combination. In each case, the class " << Endl;
         Log() << kINFO << "given by the row is considered signal while the class given " << Endl;
         Log() << kINFO << "by the column index is considered background." << Endl;
         Log() << kINFO << Endl;
         for (UInt_t iMethod = 0; iMethod < methods->size(); ++iMethod) {
            MethodBase *theMethod = dynamic_cast<MethodBase *>(methods->at(iMethod));
            if (theMethod == nullptr) {
               continue;
            }
            UInt_t numClasses = theMethod->fDataSetInfo.GetNClasses();

            std::vector<TString> classnames;
            for (UInt_t iCls = 0; iCls < numClasses; ++iCls) {
               classnames.push_back(theMethod->fDataSetInfo.GetClassInfo(iCls)->GetName());
            }
            Log() << kINFO
                  << "=== Showing confusion matrix for method : " << Form("%-15s", (const char *)mname[0][iMethod])
                  << Endl;
            Log() << kINFO << "(Signal Efficiency for Background Efficiency 0.01%)" << Endl;
            Log() << kINFO << "---------------------------------------------------" << Endl;
            printMatrix(multiclass_testConfusionEffB01[iMethod], multiclass_trainConfusionEffB01[iMethod], classnames,
                        numClasses, Log());
            Log() << kINFO << Endl;

            Log() << kINFO << "(Signal Efficiency for Background Efficiency 0.10%)" << Endl;
            Log() << kINFO << "---------------------------------------------------" << Endl;
            printMatrix(multiclass_testConfusionEffB10[iMethod], multiclass_trainConfusionEffB10[iMethod], classnames,
                        numClasses, Log());
            Log() << kINFO << Endl;

            Log() << kINFO << "(Signal Efficiency for Background Efficiency 0.30%)" << Endl;
            Log() << kINFO << "---------------------------------------------------" << Endl;
            printMatrix(multiclass_testConfusionEffB30[iMethod], multiclass_trainConfusionEffB30[iMethod], classnames,
                        numClasses, Log());
            Log() << kINFO << Endl;
         }
         Log() << kINFO << hLine << Endl;
         Log() << kINFO << Endl;

      } else {
         // Binary classification
         if (fROC) {
            Log().EnableOutput();
            gConfig().SetSilent(kFALSE);
            Log() << Endl;
            TString hLine = "------------------------------------------------------------------------------------------"
                            "-------------------------";
            Log() << kINFO << "Evaluation results ranked by best signal efficiency and purity (area)" << Endl;
            Log() << kINFO << hLine << Endl;
            Log() << kINFO << "DataSet       MVA                       " << Endl;
            Log() << kINFO << "Name:         Method:          ROC-integ" << Endl;

            //       Log() << kDEBUG << "DataSet              MVA              Signal efficiency at bkg eff.(error):
            //       | Sepa-    Signifi- "   << Endl; Log() << kDEBUG << "Name:                Method:          @B=0.01
            //       @B=0.10    @B=0.30    ROC-integ    ROCCurve| ration:  cance:   "   << Endl;
            Log() << kDEBUG << hLine << Endl;
            for (Int_t k = 0; k < 2; k++) {
               if (k == 1 && nmeth_used[k] > 0) {
                  Log() << kINFO << hLine << Endl;
                  Log() << kINFO << "Input Variables: " << Endl << hLine << Endl;
               }
               for (Int_t i = 0; i < nmeth_used[k]; i++) {
                  TString datasetName = itrMap->first;
                  TString methodName = mname[k][i];

                  if (k == 1) {
                     methodName.ReplaceAll("Variable_", "");
                  }

                  MethodBase *theMethod = dynamic_cast<MethodBase *>(GetMethod(datasetName, methodName));
                  if (theMethod == 0) {
                     continue;
                  }

                  TMVA::DataSet *dataset = theMethod->Data();
                  TMVA::Results *results = dataset->GetResults(methodName, Types::kTesting, this->fAnalysisType);
                  std::vector<Bool_t> *mvaResType =
                     dynamic_cast<ResultsClassification *>(results)->GetValueVectorTypes();

                  Double_t rocIntegral = 0.0;
                  if (mvaResType->size() != 0) {
                     rocIntegral = GetROCIntegral(datasetName, methodName);
                  }

                  if (sep[k][i] < 0 || sig[k][i] < 0) {
                     // cannot compute separation/significance -> no MVA (usually for Cuts)
                     Log() << kINFO << Form("%-13s %-15s: %#1.3f", datasetName.Data(), methodName.Data(), effArea[k][i])
                           << Endl;

                     //               Log() << kDEBUG << Form("%-20s %-15s: %#1.3f(%02i)  %#1.3f(%02i)  %#1.3f(%02i)
                     //               %#1.3f       %#1.3f | --       --",
                     //                       datasetName.Data(),
                     //                       methodName.Data(),
                     //                       eff01[k][i], Int_t(1000*eff01err[k][i]),
                     //                       eff10[k][i], Int_t(1000*eff10err[k][i]),
                     //                       eff30[k][i], Int_t(1000*eff30err[k][i]),
                     //                       effArea[k][i],rocIntegral) << Endl;
                  } else {
                     Log() << kINFO << Form("%-13s %-15s: %#1.3f", datasetName.Data(), methodName.Data(), rocIntegral)
                           << Endl;
                     //               Log() << kDEBUG << Form("%-20s %-15s: %#1.3f(%02i)  %#1.3f(%02i)  %#1.3f(%02i)
                     //               %#1.3f       %#1.3f | %#1.3f    %#1.3f",
                     //                       datasetName.Data(),
                     //                       methodName.Data(),
                     //                       eff01[k][i], Int_t(1000*eff01err[k][i]),
                     //                       eff10[k][i], Int_t(1000*eff10err[k][i]),
                     //                       eff30[k][i], Int_t(1000*eff30err[k][i]),
                     //                       effArea[k][i],rocIntegral,
                     //                       sep[k][i], sig[k][i]) << Endl;
                  }
               }
            }
            Log() << kINFO << hLine << Endl;
            Log() << kINFO << Endl;
            Log() << kINFO << "Testing efficiency compared to training efficiency (overtraining check)" << Endl;
            Log() << kINFO << hLine << Endl;
            Log() << kINFO
                  << "DataSet              MVA              Signal efficiency: from test sample (from training sample) "
                  << Endl;
            Log() << kINFO << "Name:                Method:          @B=0.01             @B=0.10            @B=0.30   "
                  << Endl;
            Log() << kINFO << hLine << Endl;
            for (Int_t k = 0; k < 2; k++) {
               if (k == 1 && nmeth_used[k] > 0) {
                  Log() << kINFO << hLine << Endl;
                  Log() << kINFO << "Input Variables: " << Endl << hLine << Endl;
               }
               for (Int_t i = 0; i < nmeth_used[k]; i++) {
                  if (k == 1) mname[k][i].ReplaceAll("Variable_", "");
                  MethodBase *theMethod = dynamic_cast<MethodBase *>((*methods)[i]);
                  if (theMethod == 0) continue;

                  Log() << kINFO << Form("%-20s %-15s: %#1.3f (%#1.3f)       %#1.3f (%#1.3f)      %#1.3f (%#1.3f)",
                                         theMethod->fDataSetInfo.GetName(), (const char *)mname[k][i], eff01[k][i],
                                         trainEff01[k][i], eff10[k][i], trainEff10[k][i], eff30[k][i], trainEff30[k][i])
                        << Endl;
               }
            }
            Log() << kINFO << hLine << Endl;
            Log() << kINFO << Endl;

            if (gTools().CheckForSilentOption(GetOptions())) Log().InhibitOutput();
         } // end fROC
     }
     if(!IsSilentFile())
     {
         std::list<TString> datasets;
         for (Int_t k=0; k<2; k++) {
      for (Int_t i=0; i<nmeth_used[k]; i++) {
          MethodBase* theMethod = dynamic_cast<MethodBase*>((*methods)[i]);
          if(theMethod==0) continue;
          // write test/training trees
          RootBaseDir()->cd(theMethod->fDataSetInfo.GetName());
          if(std::find(datasets.begin(), datasets.end(), theMethod->fDataSetInfo.GetName()) == datasets.end())
          {
            theMethod->fDataSetInfo.GetDataSet()->GetTree(Types::kTesting)->Write( "", TObject::kOverwrite );
            theMethod->fDataSetInfo.GetDataSet()->GetTree(Types::kTraining)->Write( "", TObject::kOverwrite );
            datasets.push_back(theMethod->fDataSetInfo.GetName());
          }
      }
         }
     }
   }//end for MethodsMap
   // references for citation
   gTools().TMVACitation( Log(), Tools::kHtmlLink );
}

////////////////////////////////////////////////////////////////////////////////
/// Evaluate Variable Importance

TH1F* TMVA::Factory::EvaluateImportance(DataLoader *loader,VIType vitype, Types::EMVA theMethod,  TString methodTitle, const char *theOption)
{
  fModelPersistence=kFALSE;
  fSilentFile=kTRUE;//we need silent file here because we need fast classification results

  //getting number of variables and variable names from loader
  const int nbits = loader->DefaultDataSetInfo().GetNVariables();
  if(vitype==VIType::kShort)
  return EvaluateImportanceShort(loader,theMethod,methodTitle,theOption);
  else if(vitype==VIType::kAll)
  return EvaluateImportanceAll(loader,theMethod,methodTitle,theOption);
  else if(vitype==VIType::kRandom&&nbits>10)
  {
      return EvaluateImportanceRandom(loader,pow(2,nbits),theMethod,methodTitle,theOption);
  }else
  {
      std::cerr<<"Error in Variable Importance: Random mode require more that 10 variables in the dataset."<<std::endl;
      return nullptr;
  }
}

////////////////////////////////////////////////////////////////////////////////

TH1F* TMVA::Factory::EvaluateImportanceAll(DataLoader *loader, Types::EMVA theMethod,  TString methodTitle, const char *theOption)
{

  uint64_t x = 0;
  uint64_t y = 0;

  //getting number of variables and variable names from loader
  const int nbits = loader->DefaultDataSetInfo().GetNVariables();
  std::vector<TString> varNames = loader->DefaultDataSetInfo().GetListOfVariables();

  uint64_t range = pow(2, nbits);

  //vector to save importances
  std::vector<Double_t> importances(nbits);
  //vector to save ROC
  std::vector<Double_t> ROC(range);
  ROC[0]=0.5;
  for (int i = 0; i < nbits; i++)importances[i] = 0;

  Double_t SROC, SSROC; //computed ROC value
  for ( x = 1; x <range ; x++) {

    std::bitset<VIBITS>  xbitset(x);
    if (x == 0) continue; //data loader need at least one variable

    //creating loader for seed
    TMVA::DataLoader *seedloader = new TMVA::DataLoader(xbitset.to_string());

    //adding variables from seed
    for (int index = 0; index < nbits; index++) {
      if (xbitset[index]) seedloader->AddVariable(varNames[index], 'F');
    }

    DataLoaderCopy(seedloader,loader);
    seedloader->PrepareTrainingAndTestTree(loader->DefaultDataSetInfo().GetCut("Signal"), loader->DefaultDataSetInfo().GetCut("Background"), loader->DefaultDataSetInfo().GetSplitOptions());

    //Booking Seed
    BookMethod(seedloader, theMethod, methodTitle, theOption);

    //Train/Test/Evaluation
    TrainAllMethods();
    TestAllMethods();
    EvaluateAllMethods();

    //getting ROC
    ROC[x] = GetROCIntegral(xbitset.to_string(), methodTitle);

    //cleaning information to process sub-seeds
    TMVA::MethodBase *smethod=dynamic_cast<TMVA::MethodBase*>(fMethodsMap[xbitset.to_string().c_str()][0][0]);
    TMVA::ResultsClassification  *sresults = (TMVA::ResultsClassification*)smethod->Data()->GetResults(smethod->GetMethodName(), Types::kTesting, Types::kClassification);
    delete sresults;
    delete seedloader;
    this->DeleteAllMethods();

    fMethodsMap.clear();
    //removing global result because it is requiring a lot of RAM for all seeds
  }


  for ( x = 0; x <range ; x++)
  {
    SROC=ROC[x];
    for (uint32_t i = 0; i < VIBITS; ++i) {
      if (x & (1 << i)) {
   y = x & ~(1 << i);
   std::bitset<VIBITS>  ybitset(y);
   //need at least one variable
   //NOTE: if sub-seed is zero then is the special case
   //that count in xbitset is 1
   Double_t ny = log(x - y) / 0.693147;
   if (y == 0) {
     importances[ny] = SROC - 0.5;
     continue;
   }

   //getting ROC
   SSROC = ROC[y];
   importances[ny] += SROC - SSROC;
   //cleaning information
      }

    }
  }
   std::cout<<"--- Variable Importance Results (All)"<<std::endl;
   return GetImportance(nbits,importances,varNames);
}

static long int sum(long int i)
{
  long int _sum=0;
  for(long int n=0;n<i;n++) _sum+=pow(2,n);
  return _sum;
}

////////////////////////////////////////////////////////////////////////////////

TH1F* TMVA::Factory::EvaluateImportanceShort(DataLoader *loader, Types::EMVA theMethod,  TString methodTitle, const char *theOption)
{
  uint64_t x = 0;
  uint64_t y = 0;

  //getting number of variables and variable names from loader
  const int nbits = loader->DefaultDataSetInfo().GetNVariables();
  std::vector<TString> varNames = loader->DefaultDataSetInfo().GetListOfVariables();

  long int range = sum(nbits);
//   std::cout<<range<<std::endl;
  //vector to save importances
  std::vector<Double_t> importances(nbits);
  for (int i = 0; i < nbits; i++)importances[i] = 0;

  Double_t SROC, SSROC; //computed ROC value

  x = range;

  std::bitset<VIBITS>  xbitset(x);
  if (x == 0) Log()<<kFATAL<<"Error: need at least one variable."; //data loader need at least one variable


  //creating loader for seed
  TMVA::DataLoader *seedloader = new TMVA::DataLoader(xbitset.to_string());

  //adding variables from seed
  for (int index = 0; index < nbits; index++) {
    if (xbitset[index]) seedloader->AddVariable(varNames[index], 'F');
  }

  //Loading Dataset
  DataLoaderCopy(seedloader,loader);

  //Booking Seed
  BookMethod(seedloader, theMethod, methodTitle, theOption);

  //Train/Test/Evaluation
  TrainAllMethods();
  TestAllMethods();
  EvaluateAllMethods();

  //getting ROC
  SROC = GetROCIntegral(xbitset.to_string(), methodTitle);

  //cleaning information to process sub-seeds
  TMVA::MethodBase *smethod=dynamic_cast<TMVA::MethodBase*>(fMethodsMap[xbitset.to_string().c_str()][0][0]);
  TMVA::ResultsClassification  *sresults = (TMVA::ResultsClassification*)smethod->Data()->GetResults(smethod->GetMethodName(), Types::kTesting, Types::kClassification);
  delete sresults;
  delete seedloader;
  this->DeleteAllMethods();
  fMethodsMap.clear();

  //removing global result because it is requiring a lot of RAM for all seeds

  for (uint32_t i = 0; i < VIBITS; ++i) {
    if (x & (1 << i)) {
      y = x & ~(1 << i);
      std::bitset<VIBITS>  ybitset(y);
      //need at least one variable
      //NOTE: if sub-seed is zero then is the special case
      //that count in xbitset is 1
      Double_t ny = log(x - y) / 0.693147;
      if (y == 0) {
   importances[ny] = SROC - 0.5;
   continue;
      }

      //creating loader for sub-seed
      TMVA::DataLoader *subseedloader = new TMVA::DataLoader(ybitset.to_string());
      //adding variables from sub-seed
      for (int index = 0; index < nbits; index++) {
   if (ybitset[index]) subseedloader->AddVariable(varNames[index], 'F');
      }

      //Loading Dataset
      DataLoaderCopy(subseedloader,loader);

      //Booking SubSeed
      BookMethod(subseedloader, theMethod, methodTitle, theOption);

      //Train/Test/Evaluation
      TrainAllMethods();
      TestAllMethods();
      EvaluateAllMethods();

      //getting ROC
      SSROC = GetROCIntegral(ybitset.to_string(), methodTitle);
      importances[ny] += SROC - SSROC;

      //cleaning information
      TMVA::MethodBase *ssmethod=dynamic_cast<TMVA::MethodBase*>(fMethodsMap[ybitset.to_string().c_str()][0][0]);
      TMVA::ResultsClassification *ssresults = (TMVA::ResultsClassification*)ssmethod->Data()->GetResults(ssmethod->GetMethodName(), Types::kTesting, Types::kClassification);
      delete ssresults;
      delete subseedloader;
      this->DeleteAllMethods();
      fMethodsMap.clear();
    }
  }
   std::cout<<"--- Variable Importance Results (Short)"<<std::endl;
   return GetImportance(nbits,importances,varNames);
}

////////////////////////////////////////////////////////////////////////////////

TH1F* TMVA::Factory::EvaluateImportanceRandom(DataLoader *loader, UInt_t nseeds, Types::EMVA theMethod,  TString methodTitle, const char *theOption)
{
   TRandom3 *rangen = new TRandom3(0);  //Random Gen.

   uint64_t x = 0;
   uint64_t y = 0;

   //getting number of variables and variable names from loader
   const int nbits = loader->DefaultDataSetInfo().GetNVariables();
   std::vector<TString> varNames = loader->DefaultDataSetInfo().GetListOfVariables();

   long int range = pow(2, nbits);

   //vector to save importances
   std::vector<Double_t> importances(nbits);
   Double_t importances_norm = 0;
   for (int i = 0; i < nbits; i++)importances[i] = 0;

   Double_t SROC, SSROC; //computed ROC value
   for (UInt_t n = 0; n < nseeds; n++) {
      x = rangen -> Integer(range);

      std::bitset<32>  xbitset(x);
      if (x == 0) continue; //data loader need at least one variable


      //creating loader for seed
      TMVA::DataLoader *seedloader = new TMVA::DataLoader(xbitset.to_string());

      //adding variables from seed
      for (int index = 0; index < nbits; index++) {
         if (xbitset[index]) seedloader->AddVariable(varNames[index], 'F');
      }

      //Loading Dataset
      DataLoaderCopy(seedloader,loader);

      //Booking Seed
      BookMethod(seedloader, theMethod, methodTitle, theOption);

      //Train/Test/Evaluation
      TrainAllMethods();
      TestAllMethods();
      EvaluateAllMethods();

      //getting ROC
      SROC = GetROCIntegral(xbitset.to_string(), methodTitle);
//       std::cout << "Seed: n " << n << " x " << x << " xbitset:" << xbitset << "  ROC " << SROC << std::endl;

      //cleaning information to process sub-seeds
      TMVA::MethodBase *smethod=dynamic_cast<TMVA::MethodBase*>(fMethodsMap[xbitset.to_string().c_str()][0][0]);
      TMVA::ResultsClassification  *sresults = (TMVA::ResultsClassification*)smethod->Data()->GetResults(smethod->GetMethodName(), Types::kTesting, Types::kClassification);
      delete sresults;
      delete seedloader;
      this->DeleteAllMethods();
      fMethodsMap.clear();

      //removing global result because it is requiring a lot of RAM for all seeds

      for (uint32_t i = 0; i < 32; ++i) {
         if (x & (1 << i)) {
            y = x & ~(1 << i);
            std::bitset<32>  ybitset(y);
            //need at least one variable
            //NOTE: if sub-seed is zero then is the special case
            //that count in xbitset is 1
            Double_t ny = log(x - y) / 0.693147;
            if (y == 0) {
               importances[ny] = SROC - 0.5;
               importances_norm += importances[ny];
             //  std::cout << "SubSeed: " << y << " y:" << ybitset << "ROC " << 0.5 << std::endl;
               continue;
            }

            //creating loader for sub-seed
            TMVA::DataLoader *subseedloader = new TMVA::DataLoader(ybitset.to_string());
            //adding variables from sub-seed
            for (int index = 0; index < nbits; index++) {
               if (ybitset[index]) subseedloader->AddVariable(varNames[index], 'F');
            }

            //Loading Dataset
            DataLoaderCopy(subseedloader,loader);

            //Booking SubSeed
            BookMethod(subseedloader, theMethod, methodTitle, theOption);

            //Train/Test/Evaluation
            TrainAllMethods();
            TestAllMethods();
            EvaluateAllMethods();

            //getting ROC
            SSROC = GetROCIntegral(ybitset.to_string(), methodTitle);
            importances[ny] += SROC - SSROC;
            //std::cout << "SubSeed: " << y << " y:" << ybitset << " x-y " << x - y << " " << std::bitset<32>(x - y) << " ny " << ny << " SROC " << SROC << " SSROC " << SSROC << " Importance = " << importances[ny] << std::endl;
            //cleaning information
       TMVA::MethodBase *ssmethod=dynamic_cast<TMVA::MethodBase*>(fMethodsMap[ybitset.to_string().c_str()][0][0]);
            TMVA::ResultsClassification *ssresults = (TMVA::ResultsClassification*)ssmethod->Data()->GetResults(ssmethod->GetMethodName(), Types::kTesting, Types::kClassification);
            delete ssresults;
            delete subseedloader;
            this->DeleteAllMethods();
            fMethodsMap.clear();
         }
      }
   }
   std::cout<<"--- Variable Importance Results (Random)"<<std::endl;
   return GetImportance(nbits,importances,varNames);
}

////////////////////////////////////////////////////////////////////////////////

TH1F* TMVA::Factory::GetImportance(const int nbits,std::vector<Double_t> importances,std::vector<TString> varNames)
{
  TH1F *vih1  = new TH1F("vih1", "", nbits, 0, nbits);

  gStyle->SetOptStat(000000);

  Float_t normalization = 0.0;
  for (int i = 0; i < nbits; i++) {
    normalization = normalization + importances[i];
  }

  Float_t roc = 0.0;

  gStyle->SetTitleXOffset(0.4);
  gStyle->SetTitleXOffset(1.2);


  Double_t x_ie[nbits], y_ie[nbits];
  for (Int_t i = 1; i < nbits + 1; i++) {
    x_ie[i - 1] = (i - 1) * 1.;
    roc = 100.0 * importances[i - 1] / normalization;
    y_ie[i - 1] = roc;
    std::cout<<"--- "<<varNames[i-1]<<" = "<<roc<<" %"<<std::endl;
    vih1->GetXaxis()->SetBinLabel(i, varNames[i - 1].Data());
    vih1->SetBinContent(i, roc);
  }
  TGraph *g_ie = new TGraph(nbits + 2, x_ie, y_ie);
  g_ie->SetTitle("");

  vih1->LabelsOption("v >", "X");
  vih1->SetBarWidth(0.97);
  Int_t ca = TColor::GetColor("#006600");
  vih1->SetFillColor(ca);
  //Int_t ci = TColor::GetColor("#990000");

  vih1->GetYaxis()->SetTitle("Importance (%)");
  vih1->GetYaxis()->SetTitleSize(0.045);
  vih1->GetYaxis()->CenterTitle();
  vih1->GetYaxis()->SetTitleOffset(1.24);

  vih1->GetYaxis()->SetRangeUser(-7, 50);
  vih1->SetDirectory(0);

//   vih1->Draw("B");
  return vih1;
}