DataLoader.cxx

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// @(#)root/tmva $Id$
// Author: Omar Zapata
// Mentors: Lorenzo Moneta, Sergei Gleyzer
//NOTE: Based on TMVA::Factory
 
/**********************************************************************************
 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis       *
 * Package: TMVA                                                                  *
 * Class  : DataLoader                                                            *
 *                                             *
 *                                                                                *
 * Description:                                                                   *
 *      This is a class to load datasets into every booked method                 *
 *                                                                                *
 * Authors (alphabetical):                                                        *
 *      Lorenzo Moneta <Lorenzo.Moneta@cern.ch> - CERN, Switzerland               *
 *      Omar Zapata <Omar.Zapata@cern.ch>  - ITM/UdeA, Colombia                   *
 *      Sergei Gleyzer<sergei.gleyzer@cern.ch> - CERN, Switzerland                *
 *                                                                                *
 * Copyright (c) 2005-2015:                                                       *
 *      CERN, Switzerland                                                         *
 *      ITM/UdeA, Colombia                                                        *
 *                                                                                *
 * Redistribution and use in source and binary forms, with or without             *
 * modification, are permitted according to the terms listed in LICENSE           *
 * (see tmva/doc/LICENSE)                                          *
 **********************************************************************************/
 
 
/*! \class TMVA::DataLoader
\ingroup TMVA
 
*/
 
#include "TTree.h"
#include "TH2.h"
#include "TMatrixD.h"
 
#include "TMVA/DataLoader.h"
#include "TMVA/Config.h"
#include "TMVA/CvSplit.h"
#include "TMVA/Tools.h"
#include "TMVA/IMethod.h"
#include "TMVA/MethodBase.h"
#include "TMVA/DataInputHandler.h"
#include "TMVA/DataSetManager.h"
#include "TMVA/DataSetInfo.h"
#include "TMVA/MethodBoost.h"
#include "TMVA/MethodCategory.h"
 
#include "TMVA/VariableInfo.h"
#include "TMVA/VariableIdentityTransform.h"
#include "TMVA/VariableDecorrTransform.h"
#include "TMVA/VariablePCATransform.h"
#include "TMVA/VariableGaussTransform.h"
#include "TMVA/VariableNormalizeTransform.h"
#include "TMVA/VarTransformHandler.h"
 
ClassImp(TMVA::DataLoader);
 
////////////////////////////////////////////////////////////////////////////////
/*** Create a data loader
 \param[in] thedlName  name of DataLoader object. This name will be used as the
                       top directory name where the training results
                        (weights, i.e .XML and .C files) will be stored.
                       The results will be stored by default in the `theDlName/weights`
                       directory and relative to the current directory. If the directory is not existing,
                       a new one will be created automatically.
                       For using a different location (i.e. a different path to the current directory) one
                       can set an absolute path location in `TMVA::gConfig()::GetIONames().fWeightFileDirPrefix`
                       For example, by setting
~~~~~~~~~~~~~~~{.cpp}
   TMVA::gConfig()::GetIONames().fWeightFileDirPrefix = "/tmp";
   TMVA::gConfig()::GetIONames().fWeightFileDir = "myTrainingResults";
~~~~~~~~~~~~~~~
                       The training results will be stored in the `/tmp/thedlName/myTrainingResults`
                       directory.
**/
 
TMVA::DataLoader::DataLoader( TString thedlName)
: Configurable( ),
   fDataSetManager       ( NULL ), //DSMTEST
   fDataInputHandler     ( new DataInputHandler ),
   fTransformations      ( "I" ),
   fVerbose              ( kFALSE ),
   fDataAssignType       ( kAssignEvents ),
   fATreeEvent           (0)
{
   fDataSetManager = new DataSetManager( *fDataInputHandler ); // DSMTEST
   SetName(thedlName.Data());
   fLogger->SetSource("DataLoader");
}
 
////////////////////////////////////////////////////////////////////////////////
 
TMVA::DataLoader::~DataLoader( void )
{
   // destructor
 
   std::vector<TMVA::VariableTransformBase*>::iterator trfIt = fDefaultTrfs.begin();
   for (;trfIt != fDefaultTrfs.end(); ++trfIt) delete (*trfIt);
 
   delete fDataInputHandler;
 
   // destroy singletons
   //   DataSetManager::DestroyInstance(); // DSMTEST replaced by following line
   delete fDataSetManager; // DSMTEST
 
   // problem with call of REGISTER_METHOD macro ...
   //   ClassifierDataLoader::DestroyInstance();
   //   Types::DestroyInstance();
   //Tools::DestroyInstance();
   //Config::DestroyInstance();
}
 
 
////////////////////////////////////////////////////////////////////////////////
 
TMVA::DataSetInfo& TMVA::DataLoader::AddDataSet( DataSetInfo &dsi )
{
   return fDataSetManager->AddDataSetInfo(dsi); // DSMTEST
}
 
////////////////////////////////////////////////////////////////////////////////
 
TMVA::DataSetInfo& TMVA::DataLoader::AddDataSet( const TString& dsiName )
{
   DataSetInfo* dsi = fDataSetManager->GetDataSetInfo(dsiName); // DSMTEST
 
   if (dsi!=0) return *dsi;
 
   return fDataSetManager->AddDataSetInfo(*(new DataSetInfo(dsiName))); // DSMTEST
}
 
////////////////////////////////////////////////////////////////////////////////
 
TMVA::DataSetInfo& TMVA::DataLoader::GetDataSetInfo()
{
   return DefaultDataSetInfo(); // DSMTEST
}
 
////////////////////////////////////////////////////////////////////////////////
/// Transforms the variables and return a new DataLoader with the transformed
/// variables
 
TMVA::DataLoader* TMVA::DataLoader::VarTransform(TString trafoDefinition)
{
   TString trOptions = "0";
   TString trName = "None";
   if (trafoDefinition.Contains("(")) {
 
      // contains transformation parameters
      Ssiz_t parStart = trafoDefinition.Index( "(" );
      Ssiz_t parLen   = trafoDefinition.Index( ")", parStart )-parStart+1;
 
      trName = trafoDefinition(0,parStart);
      trOptions = trafoDefinition(parStart,parLen);
      trOptions.Remove(parLen-1,1);
      trOptions.Remove(0,1);
   }
   else
      trName = trafoDefinition;
 
   VarTransformHandler* handler = new VarTransformHandler(this);
   // variance threshold variable transformation
   if (trName == "VT") {
 
      // find threshold value from given input
      Double_t threshold = 0.0;
      if (!trOptions.IsFloat()){
         Log() << kFATAL << " VT transformation must be passed a floating threshold value" << Endl;
         delete handler;
         return this;
      }
      else
         threshold =  trOptions.Atof();
      TMVA::DataLoader *transformedLoader = handler->VarianceThreshold(threshold);
      delete handler;
      return transformedLoader;
   }
   else {
      delete handler;
      Log() << kFATAL << "Incorrect transformation string provided, please check" << Endl;
   }
   Log() << kINFO << "No transformation applied, returning original loader" << Endl;
   return this;
}
 
////////////////////////////////////////////////////////////////////////////////
// the next functions are to assign events directly
 
////////////////////////////////////////////////////////////////////////////////
/// create the data assignment tree (for event-wise data assignment by user)
 
TTree* TMVA::DataLoader::CreateEventAssignTrees( const TString& name )
{
   TTree * assignTree = new TTree( name, name );
   assignTree->SetDirectory(nullptr);
   assignTree->Branch( "type",   &fATreeType,   "ATreeType/I" );
   assignTree->Branch( "weight", &fATreeWeight, "ATreeWeight/F" );
 
   std::vector<VariableInfo>& vars = DefaultDataSetInfo().GetVariableInfos();
   std::vector<VariableInfo>& tgts = DefaultDataSetInfo().GetTargetInfos();
   std::vector<VariableInfo>& spec = DefaultDataSetInfo().GetSpectatorInfos();
 
   if (fATreeEvent.size()==0) fATreeEvent.resize(vars.size()+tgts.size()+spec.size());
   // add variables
   for (UInt_t ivar=0; ivar<vars.size(); ivar++) {
      TString vname = vars[ivar].GetExpression();
      assignTree->Branch( vname, &fATreeEvent[ivar], vname + "/F" );
   }
   // add targets
   for (UInt_t itgt=0; itgt<tgts.size(); itgt++) {
      TString vname = tgts[itgt].GetExpression();
      assignTree->Branch( vname, &fATreeEvent[vars.size()+itgt], vname + "/F" );
   }
   // add spectators
   for (UInt_t ispc=0; ispc<spec.size(); ispc++) {
      TString vname = spec[ispc].GetExpression();
      assignTree->Branch( vname, &fATreeEvent[vars.size()+tgts.size()+ispc], vname + "/F" );
   }
   return assignTree;
}
 
////////////////////////////////////////////////////////////////////////////////
/// add signal training event
 
void TMVA::DataLoader::AddSignalTrainingEvent( const std::vector<Double_t>& event, Double_t weight )
{
   AddEvent( "Signal", Types::kTraining, event, weight );
}
 
////////////////////////////////////////////////////////////////////////////////
/// add signal testing event
 
void TMVA::DataLoader::AddSignalTestEvent( const std::vector<Double_t>& event, Double_t weight )
{
   AddEvent( "Signal", Types::kTesting, event, weight );
}
 
////////////////////////////////////////////////////////////////////////////////
/// add signal training event
 
void TMVA::DataLoader::AddBackgroundTrainingEvent( const std::vector<Double_t>& event, Double_t weight )
{
   AddEvent( "Background", Types::kTraining, event, weight );
}
 
////////////////////////////////////////////////////////////////////////////////
/// add signal training event
 
void TMVA::DataLoader::AddBackgroundTestEvent( const std::vector<Double_t>& event, Double_t weight )
{
   AddEvent( "Background", Types::kTesting, event, weight );
}
 
////////////////////////////////////////////////////////////////////////////////
/// add signal training event
 
void TMVA::DataLoader::AddTrainingEvent( const TString& className, const std::vector<Double_t>& event, Double_t weight )
{
   AddEvent( className, Types::kTraining, event, weight );
}
 
////////////////////////////////////////////////////////////////////////////////
/// add signal test event
 
void TMVA::DataLoader::AddTestEvent( const TString& className, const std::vector<Double_t>& event, Double_t weight )
{
   AddEvent( className, Types::kTesting, event, weight );
}
 
////////////////////////////////////////////////////////////////////////////////
/// add event
/// vector event : the order of values is: variables + targets + spectators
 
void TMVA::DataLoader::AddEvent( const TString& className, Types::ETreeType tt,
                                 const std::vector<Double_t>& event, Double_t weight )
{
   ClassInfo* theClass = DefaultDataSetInfo().AddClass(className); // returns class (creates it if necessary)
   UInt_t clIndex = theClass->GetNumber();
 
 
   // set analysistype to "kMulticlass" if more than two classes and analysistype == kNoAnalysisType
   if( fAnalysisType == Types::kNoAnalysisType && DefaultDataSetInfo().GetNClasses() > 2 )
      fAnalysisType = Types::kMulticlass;
 
 
   if (clIndex>=fTrainAssignTree.size()) {
      fTrainAssignTree.resize(clIndex+1, 0);
      fTestAssignTree.resize(clIndex+1, 0);
   }
 
   if (fTrainAssignTree[clIndex]==0) { // does not exist yet
      fTrainAssignTree[clIndex] = CreateEventAssignTrees( TString::Format("TrainAssignTree_%s", className.Data()).Data() );
      fTestAssignTree[clIndex]  = CreateEventAssignTrees( TString::Format("TestAssignTree_%s",  className.Data()).Data() );
   }
 
   fATreeType   = clIndex;
   fATreeWeight = weight;
   if (event.size() > fATreeEvent.size()) {
      Error("TMVA::DataLoader::AddEvent", "Number of variables defined through DataLoader::AddVariable (%zu) is inconsistent"
       "with number of variables given to DataLoader::Add*Event (%zu)."
       "Please check your variable definitions and statement ordering."
       "This event will not be added.", fATreeEvent.size(), event.size());
      return;
   }
   for (UInt_t ivar=0; ivar<event.size(); ivar++) fATreeEvent[ivar] = event[ivar];
 
   if(tt==Types::kTraining) fTrainAssignTree[clIndex]->Fill();
   else                     fTestAssignTree[clIndex]->Fill();
 
}
 
////////////////////////////////////////////////////////////////////////////////
///
 
Bool_t TMVA::DataLoader::UserAssignEvents(UInt_t clIndex)
{
   return fTrainAssignTree[clIndex]!=0;
}
 
////////////////////////////////////////////////////////////////////////////////
/// assign event-wise local trees to data set
 
void TMVA::DataLoader::SetInputTreesFromEventAssignTrees()
{
   UInt_t size = fTrainAssignTree.size();
   for(UInt_t i=0; i<size; i++) {
      if(!UserAssignEvents(i)) continue;
      const TString& className = DefaultDataSetInfo().GetClassInfo(i)->GetName();
      SetWeightExpression( "weight", className );
      AddTree(fTrainAssignTree[i], className, 1.0, TCut(""), Types::kTraining );
      AddTree(fTestAssignTree[i], className, 1.0, TCut(""), Types::kTesting );
   }
}
 
////////////////////////////////////////////////////////////////////////////////
/// number of signal events (used to compute significance)
 
void TMVA::DataLoader::AddTree( TTree* tree, const TString& className, Double_t weight,
                                const TCut& cut, const TString& treetype )
{
   Types::ETreeType tt = Types::kMaxTreeType;
   TString tmpTreeType = treetype; tmpTreeType.ToLower();
   if      (tmpTreeType.Contains( "train" ) && tmpTreeType.Contains( "test" )) tt = Types::kMaxTreeType;
   else if (tmpTreeType.Contains( "train" ))                                   tt = Types::kTraining;
   else if (tmpTreeType.Contains( "test" ))                                    tt = Types::kTesting;
   else {
      Log() << kFATAL << "<AddTree> cannot interpret tree type: \"" << treetype
            << "\" should be \"Training\" or \"Test\" or \"Training and Testing\"" << Endl;
   }
   AddTree( tree, className, weight, cut, tt );
}
 
////////////////////////////////////////////////////////////////////////////////
 
void TMVA::DataLoader::AddTree( TTree* tree, const TString& className, Double_t weight,
                                const TCut& cut, Types::ETreeType tt )
{
   if(!tree)
      Log() << kFATAL << "Tree does not exist (empty pointer)." << Endl;
 
   DefaultDataSetInfo().AddClass( className );
 
   // set analysistype to "kMulticlass" if more than two classes and analysistype == kNoAnalysisType
   if( fAnalysisType == Types::kNoAnalysisType && DefaultDataSetInfo().GetNClasses() > 2 )
      fAnalysisType = Types::kMulticlass;
 
   Log() << kINFO<< "Add Tree " << tree->GetName() << " of type " << className
         << " with " << tree->GetEntries() << " events" << Endl;
   DataInput().AddTree( tree, className, weight, cut, tt );
}
 
////////////////////////////////////////////////////////////////////////////////
/// number of signal events (used to compute significance)
 
void TMVA::DataLoader::AddSignalTree( TTree* signal, Double_t weight, Types::ETreeType treetype )
{
   AddTree( signal, "Signal", weight, TCut(""), treetype );
}
 
////////////////////////////////////////////////////////////////////////////////
/// add signal tree from text file
 
void TMVA::DataLoader::AddSignalTree( TString datFileS, Double_t weight, Types::ETreeType treetype )
{
   // create trees from these ascii files
   TTree* signalTree = new TTree( "TreeS", "Tree (S)" );
   signalTree->ReadFile( datFileS );
 
   Log() << kINFO << "Create TTree objects from ASCII input files ... \n- Signal file    : \""
         << datFileS << Endl;
 
   // number of signal events (used to compute significance)
   AddTree( signalTree, "Signal", weight, TCut(""), treetype );
}
 
////////////////////////////////////////////////////////////////////////////////
 
void TMVA::DataLoader::AddSignalTree( TTree* signal, Double_t weight, const TString& treetype )
{
   AddTree( signal, "Signal", weight, TCut(""), treetype );
}
 
////////////////////////////////////////////////////////////////////////////////
/// number of signal events (used to compute significance)
 
void TMVA::DataLoader::AddBackgroundTree( TTree* signal, Double_t weight, Types::ETreeType treetype )
{
   AddTree( signal, "Background", weight, TCut(""), treetype );
}
 
////////////////////////////////////////////////////////////////////////////////
/// add background tree from text file
 
void TMVA::DataLoader::AddBackgroundTree( TString datFileB, Double_t weight, Types::ETreeType treetype )
{
   // create trees from these ascii files
   TTree* bkgTree = new TTree( "TreeB", "Tree (B)" );
   bkgTree->ReadFile( datFileB );
 
   Log() << kINFO << "Create TTree objects from ASCII input files ... \n- Background file    : \""
         << datFileB << Endl;
 
   // number of signal events (used to compute significance)
   AddTree( bkgTree, "Background", weight, TCut(""), treetype );
}
 
////////////////////////////////////////////////////////////////////////////////
 
void TMVA::DataLoader::AddBackgroundTree( TTree* signal, Double_t weight, const TString& treetype )
{
   AddTree( signal, "Background", weight, TCut(""), treetype );
}
 
////////////////////////////////////////////////////////////////////////////////
 
void TMVA::DataLoader::SetSignalTree( TTree* tree, Double_t weight )
{
   AddTree( tree, "Signal", weight );
}
 
////////////////////////////////////////////////////////////////////////////////
 
void TMVA::DataLoader::SetBackgroundTree( TTree* tree, Double_t weight )
{
   AddTree( tree, "Background", weight );
}
 
////////////////////////////////////////////////////////////////////////////////
/// set background tree
 
void TMVA::DataLoader::SetTree( TTree* tree, const TString& className, Double_t weight )
{
   AddTree( tree, className, weight, TCut(""), Types::kMaxTreeType );
}
 
////////////////////////////////////////////////////////////////////////////////
/// define the input trees for signal and background; no cuts are applied
 
void  TMVA::DataLoader::SetInputTrees( TTree* signal, TTree* background,
                                       Double_t signalWeight, Double_t backgroundWeight )
{
   AddTree( signal,     "Signal",     signalWeight,     TCut(""), Types::kMaxTreeType );
   AddTree( background, "Background", backgroundWeight, TCut(""), Types::kMaxTreeType );
}
 
////////////////////////////////////////////////////////////////////////////////
 
void TMVA::DataLoader::SetInputTrees( const TString& datFileS, const TString& datFileB,
                                      Double_t signalWeight, Double_t backgroundWeight )
{
   DataInput().AddTree( datFileS, "Signal", signalWeight );
   DataInput().AddTree( datFileB, "Background", backgroundWeight );
}
 
////////////////////////////////////////////////////////////////////////////////
/// define the input trees for signal and background from single input tree,
/// containing both signal and background events distinguished by the type
/// identifiers: SigCut and BgCut
 
void TMVA::DataLoader::SetInputTrees( TTree* inputTree, const TCut& SigCut, const TCut& BgCut )
{
   AddTree( inputTree, "Signal",     1.0, SigCut, Types::kMaxTreeType );
   AddTree( inputTree, "Background", 1.0, BgCut , Types::kMaxTreeType );
}
 
////////////////////////////////////////////////////////////////////////////////
/// user inserts discriminating variable in data set info
 
void TMVA::DataLoader::AddVariable( const TString& expression, const TString& title, const TString& unit,
                                    char type, Double_t min, Double_t max )
{
   DefaultDataSetInfo().AddVariable( expression, title, unit, min, max, type );
}
 
////////////////////////////////////////////////////////////////////////////////
/// user inserts discriminating variable in data set info
 
void TMVA::DataLoader::AddVariable( const TString& expression, char type,
                                    Double_t min, Double_t max )
{
   DefaultDataSetInfo().AddVariable( expression, "", "", min, max, type );
}
 
////////////////////////////////////////////////////////////////////////////////
/// user inserts discriminating array of variables in data set info
/// in case input tree provides an array of values
 
void TMVA::DataLoader::AddVariablesArray(const TString &expression, int size, char type,
                                   Double_t min, Double_t max)
{
   DefaultDataSetInfo().AddVariablesArray(expression, size, "", "", min, max, type);
}
////////////////////////////////////////////////////////////////////////////////
/// user inserts target in data set info
 
void TMVA::DataLoader::AddTarget( const TString& expression, const TString& title, const TString& unit,
                                  Double_t min, Double_t max )
{
   if( fAnalysisType == Types::kNoAnalysisType )
      fAnalysisType = Types::kRegression;
 
   DefaultDataSetInfo().AddTarget( expression, title, unit, min, max );
}
 
////////////////////////////////////////////////////////////////////////////////
/// user inserts target in data set info
 
void TMVA::DataLoader::AddSpectator( const TString& expression, const TString& title, const TString& unit,
                                     Double_t min, Double_t max )
{
   DefaultDataSetInfo().AddSpectator( expression, title, unit, min, max );
}
 
////////////////////////////////////////////////////////////////////////////////
/// default creation
 
TMVA::DataSetInfo& TMVA::DataLoader::DefaultDataSetInfo()
{
   return AddDataSet( fName );
}
 
////////////////////////////////////////////////////////////////////////////////
/// fill input variables in data set
 
void TMVA::DataLoader::SetInputVariables( std::vector<TString>* theVariables )
{
   for (std::vector<TString>::iterator it=theVariables->begin();
        it!=theVariables->end(); ++it) AddVariable(*it);
}
 
////////////////////////////////////////////////////////////////////////////////
 
void TMVA::DataLoader::SetSignalWeightExpression( const TString& variable)
{
   DefaultDataSetInfo().SetWeightExpression(variable, "Signal");
}
 
////////////////////////////////////////////////////////////////////////////////
 
void TMVA::DataLoader::SetBackgroundWeightExpression( const TString& variable)
{
   DefaultDataSetInfo().SetWeightExpression(variable, "Background");
}
 
////////////////////////////////////////////////////////////////////////////////
 
void TMVA::DataLoader::SetWeightExpression( const TString& variable, const TString& className )
{
   //Log() << kWarning << DefaultDataSetInfo().GetNClasses() /*fClasses.size()*/ << Endl;
   if (className=="") {
      SetSignalWeightExpression(variable);
      SetBackgroundWeightExpression(variable);
   }
   else  DefaultDataSetInfo().SetWeightExpression( variable, className );
}
 
////////////////////////////////////////////////////////////////////////////////
 
void TMVA::DataLoader::SetCut( const TString& cut, const TString& className ) {
   SetCut( TCut(cut), className );
}
 
////////////////////////////////////////////////////////////////////////////////
 
void TMVA::DataLoader::SetCut( const TCut& cut, const TString& className )
{
   DefaultDataSetInfo().SetCut( cut, className );
}
 
////////////////////////////////////////////////////////////////////////////////
 
void TMVA::DataLoader::AddCut( const TString& cut, const TString& className )
{
   AddCut( TCut(cut), className );
}
 
////////////////////////////////////////////////////////////////////////////////
void TMVA::DataLoader::AddCut( const TCut& cut, const TString& className )
{
   DefaultDataSetInfo().AddCut( cut, className );
}
 
////////////////////////////////////////////////////////////////////////////////
/// prepare the training and test trees
 
void TMVA::DataLoader::PrepareTrainingAndTestTree( const TCut& cut,
                                                   Int_t NsigTrain, Int_t NbkgTrain, Int_t NsigTest, Int_t NbkgTest,
                                                   const TString& otherOpt )
{
   SetInputTreesFromEventAssignTrees();
 
   AddCut( cut  );
 
   DefaultDataSetInfo().SetSplitOptions( TString::Format("nTrain_Signal=%i:nTrain_Background=%i:nTest_Signal=%i:nTest_Background=%i:%s",
                                              NsigTrain, NbkgTrain, NsigTest, NbkgTest, otherOpt.Data()).Data() );
}
 
////////////////////////////////////////////////////////////////////////////////
/// prepare the training and test trees
/// kept for backward compatibility
 
void TMVA::DataLoader::PrepareTrainingAndTestTree( const TCut& cut, Int_t Ntrain, Int_t Ntest )
{
   SetInputTreesFromEventAssignTrees();
 
   AddCut( cut  );
 
   DefaultDataSetInfo().SetSplitOptions( TString::Format("nTrain_Signal=%i:nTrain_Background=%i:nTest_Signal=%i:nTest_Background=%i:SplitMode=Random:EqualTrainSample:!V",
                                              Ntrain, Ntrain, Ntest, Ntest).Data() );
}
 
////////////////////////////////////////////////////////////////////////////////
/// prepare the training and test trees
/// -> same cuts for signal and background
 
void TMVA::DataLoader::PrepareTrainingAndTestTree( const TCut& cut, const TString& opt )
{
   SetInputTreesFromEventAssignTrees();
 
   DefaultDataSetInfo().PrintClasses();
   AddCut( cut );
   DefaultDataSetInfo().SetSplitOptions( opt );
}
 
////////////////////////////////////////////////////////////////////////////////
/// prepare the training and test trees
 
void TMVA::DataLoader::PrepareTrainingAndTestTree( TCut sigcut, TCut bkgcut, const TString& splitOpt )
{
   // if event-wise data assignment, add local trees to dataset first
   SetInputTreesFromEventAssignTrees();
 
   //Log() << kINFO <<"Preparing trees for training and testing..."<<  Endl;
   AddCut( sigcut, "Signal"  );
   AddCut( bkgcut, "Background" );
 
   DefaultDataSetInfo().SetSplitOptions( splitOpt );
}
 
////////////////////////////////////////////////////////////////////////////////
/// Function required to split the training and testing datasets into a
/// number of folds. Required by the CrossValidation and HyperParameterOptimisation
/// classes. The option to split the training dataset into a training set and
/// a validation set is implemented but not currently used.
 
void TMVA::DataLoader::MakeKFoldDataSet(CvSplit & s)
{
   s.MakeKFoldDataSet( DefaultDataSetInfo() );
}
 
////////////////////////////////////////////////////////////////////////////////
/// Function for assigning the correct folds to the testing or training set.
 
void TMVA::DataLoader::PrepareFoldDataSet(CvSplit & s, UInt_t foldNumber, Types::ETreeType tt)
{
   s.PrepareFoldDataSet( DefaultDataSetInfo(), foldNumber, tt );
}
 
 
////////////////////////////////////////////////////////////////////////////////
/// Recombines the dataset. The precise semantics depend on the actual split.
///
/// Similar to the inverse operation of `MakeKFoldDataSet` but _will_ differ.
/// See documentation for each particular split for more information.
///
 
void TMVA::DataLoader::RecombineKFoldDataSet(CvSplit & s, Types::ETreeType tt)
{
   s.RecombineKFoldDataSet( DefaultDataSetInfo(), tt );
}
 
////////////////////////////////////////////////////////////////////////////////
/// Copy method use in VI and CV
 
TMVA::DataLoader* TMVA::DataLoader::MakeCopy(TString name)
{
   TMVA::DataLoader* des=new TMVA::DataLoader(name);
   DataLoaderCopy(des,this);
   return des;
}
 
////////////////////////////////////////////////////////////////////////////////
///Loading Dataset from DataInputHandler for subseed
 
void TMVA::DataLoaderCopy(TMVA::DataLoader* des, TMVA::DataLoader* src)
{
   for( std::vector<TreeInfo>::const_iterator treeinfo=src->DataInput().Sbegin();treeinfo!=src->DataInput().Send();++treeinfo)
   {
      des->AddSignalTree( (*treeinfo).GetTree(), (*treeinfo).GetWeight(),(*treeinfo).GetTreeType());
   }
 
   for( std::vector<TreeInfo>::const_iterator treeinfo=src->DataInput().Bbegin();treeinfo!=src->DataInput().Bend();++treeinfo)
   {
      des->AddBackgroundTree( (*treeinfo).GetTree(), (*treeinfo).GetWeight(),(*treeinfo).GetTreeType());
   }
}
 
////////////////////////////////////////////////////////////////////////////////
/// returns the correlation matrix of datasets
 
TH2* TMVA::DataLoader::GetCorrelationMatrix(const TString& className)
{
   const TMatrixD * m = DefaultDataSetInfo().CorrelationMatrix(className);
   return DefaultDataSetInfo().CreateCorrelationMatrixHist(m,
                                                           "CorrelationMatrix"+className, "Correlation Matrix ("+className+")");
}