DataSet.h

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// @(#)root/tmva $Id$
// Author: Andreas Hoecker, Peter Speckmayer, Joerg Stelzer, Helge Voss
 
/**********************************************************************************
 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis       *
 * Package: TMVA                                                                  *
 * Class  : DataSet                                                               *
 * Web    : http://tmva.sourceforge.net                                           *
 *                                                                                *
 * Description:                                                                   *
 *      Contains all the data information                                         *
 *                                                                                *
 * Authors (alphabetical):                                                        *
 *      Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland              *
 *      Joerg Stelzer   <Joerg.Stelzer@cern.ch>  - CERN, Switzerland              *
 *      Peter Speckmayer <Peter.Speckmayer@cern.ch>  - CERN, Switzerland          *
 *      Helge Voss      <Helge.Voss@cern.ch>     - MPI-K Heidelberg, Germany      *
 *                                                                                *
 * Copyright (c) 2006:                                                            *
 *      CERN, Switzerland                                                         *
 *      U. of Victoria, Canada                                                    *
 *      MPI-K Heidelberg, Germany                                                 *
 *                                                                                *
 * 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)                                          *
 **********************************************************************************/
 
#ifndef ROOT_TMVA_DataSet
#define ROOT_TMVA_DataSet
 
//////////////////////////////////////////////////////////////////////////
//                                                                      //
// DataSet                                                              //
//                                                                      //
// Class that contains all the data information                         //
//                                                                      //
//////////////////////////////////////////////////////////////////////////
 
#include <vector>
#include <map>
 
#include "TNamed.h"
#include "TString.h"
#include "TTree.h"
#include "TRandom3.h"
 
#include "TMVA/Types.h"
#include "TMVA/VariableInfo.h"
 
namespace TMVA {
 
   class Event;
   class DataSetInfo;
   class MsgLogger;
   class Results;
 
   class DataSet :public TNamed {
 
   public:
      DataSet();
      DataSet(const DataSetInfo&);
      virtual ~DataSet();
 
      void      AddEvent( Event *, Types::ETreeType );
 
      Long64_t  GetNEvents( Types::ETreeType type = Types::kMaxTreeType ) const;
      Long64_t  GetNTrainingEvents()              const { return GetNEvents(Types::kTraining); }
      Long64_t  GetNTestEvents()                  const { return GetNEvents(Types::kTesting); }
 
      // const getters
      const Event*    GetEvent()                        const; ///< returns event without transformations
      const Event*    GetEvent        ( Long64_t ievt ) const { fCurrentEventIdx = ievt; return GetEvent(); } // returns event without transformations
      const Event*    GetTrainingEvent( Long64_t ievt ) const { return GetEvent(ievt, Types::kTraining); }
      const Event*    GetTestEvent    ( Long64_t ievt ) const { return GetEvent(ievt, Types::kTesting); }
      const Event*    GetEvent        ( Long64_t ievt, Types::ETreeType type ) const
      {
         fCurrentTreeIdx = TreeIndex(type); fCurrentEventIdx = ievt; return GetEvent();
      }
 
 
 
 
      UInt_t    GetNVariables()   const;
      UInt_t    GetNTargets()     const;
      UInt_t    GetNSpectators()  const;
 
      void      SetCurrentEvent( Long64_t ievt         ) const { fCurrentEventIdx = ievt; }
      void      SetCurrentType ( Types::ETreeType type ) const { fCurrentTreeIdx = TreeIndex(type); }
      Types::ETreeType GetCurrentType() const;
 
      void                       SetEventCollection( std::vector<Event*>*, Types::ETreeType, Bool_t deleteEvents = true );
      const std::vector<Event*>& GetEventCollection( Types::ETreeType type = Types::kMaxTreeType ) const;
      const TTree*               GetEventCollectionAsTree();
 
      Long64_t  GetNEvtSigTest();
      Long64_t  GetNEvtBkgdTest();
      Long64_t  GetNEvtSigTrain();
      Long64_t  GetNEvtBkgdTrain();
 
      Bool_t    HasNegativeEventWeights() const { return fHasNegativeEventWeights; }
 
      Results*  GetResults   ( const TString &,
                               Types::ETreeType type,
                               Types::EAnalysisType analysistype );
      void      DeleteResults   ( const TString &,
                                  Types::ETreeType type,
                                  Types::EAnalysisType analysistype );
      void      DeleteAllResults(Types::ETreeType type,
                                 Types::EAnalysisType analysistype);
 
      void      SetVerbose( Bool_t ) {}
 
      // sets the number of blocks to which the training set is divided,
      // some of which are given to the Validation sample. As default they belong all to Training set.
      void      DivideTrainingSet( UInt_t blockNum );
 
      // sets a certain block from the origin training set to belong to either Training or Validation set
      void      MoveTrainingBlock( Int_t blockInd,Types::ETreeType dest, Bool_t applyChanges = kTRUE );
 
      void      IncrementNClassEvents( Int_t type, UInt_t classNumber );
      Long64_t  GetNClassEvents      ( Int_t type, UInt_t classNumber );
      void      ClearNClassEvents    ( Int_t type );
 
      TTree*    GetTree( Types::ETreeType type );
 
      // accessors for random and importance sampling
      void      InitSampling( Float_t fraction, Float_t weight, UInt_t seed = 0 );
      void      EventResult( Bool_t successful, Long64_t evtNumber = -1 );
      void      CreateSampling() const;
 
      UInt_t    TreeIndex(Types::ETreeType type) const;
 
   private:
 
      // data members
      void DestroyCollection( Types::ETreeType type, Bool_t deleteEvents );
 
      const DataSetInfo         *fdsi;                        ///<-> datasetinfo that created this dataset
 
      std::vector< std::vector<Event*>  > fEventCollection;   ///< list of events for training/testing/...
 
      std::vector< std::map< TString, Results* > > fResults;  ///<!  [train/test/...][method-identifier]
 
      mutable UInt_t             fCurrentTreeIdx;
      mutable Long64_t           fCurrentEventIdx;
 
      // event sampling
      std::vector<Char_t>        fSampling;                   ///< random or importance sampling (not all events are taken) !! Bool_t are stored ( no std::vector<bool> taken for speed (performance) issues )
      std::vector<Int_t>         fSamplingNEvents;            ///< number of events which should be sampled
      std::vector<Float_t>       fSamplingWeight;             ///< weight change factor [weight is indicating if sampling is random (1.0) or importance (<1.0)]
      mutable std::vector< std::vector< std::pair< Float_t, Long64_t > > > fSamplingEventList;  ///< weights and indices for sampling
      mutable std::vector< std::vector< std::pair< Float_t, Long64_t > > > fSamplingSelected;   ///< selected events
      TRandom3                   *fSamplingRandom;            ///<-> random generator for sampling
 
 
      // further things
      std::vector< std::vector<Long64_t> > fClassEvents;      ///< number of events of class 0,1,2,... in training[0]
                                                              ///< and testing[1] (+validation, trainingoriginal)
 
      Bool_t                     fHasNegativeEventWeights;    ///< true if at least one signal or bkg event has negative weight
 
      mutable MsgLogger*         fLogger;                     ///<! message logger
      MsgLogger& Log() const { return *fLogger; }
      std::vector<Char_t>        fBlockBelongToTraining;      ///< when dividing the dataset to blocks, sets whether
                                                              ///< the certain block is in the Training set or else
                                                              ///< in the validation set
                                                              ///< boolean are stored, taken std::vector<Char_t> for performance reasons (instead of std::vector<Bool_t>)
      Long64_t                   fTrainingBlockSize;          ///< block size into which the training dataset is divided
 
      void  ApplyTrainingBlockDivision();
      void  ApplyTrainingSetDivision();
   public:
 
       ClassDef(DataSet,1);
   };
}
 
 
//_______________________________________________________________________
inline UInt_t TMVA::DataSet::TreeIndex(Types::ETreeType type) const
{
   switch (type) {
   case Types::kMaxTreeType : return fCurrentTreeIdx;
   case Types::kTraining : return 0;
   case Types::kTesting : return 1;
   case Types::kValidation : return 2;
   case Types::kTrainingOriginal : return 3;
   default : return fCurrentTreeIdx;
   }
}
 
//_______________________________________________________________________
inline TMVA::Types::ETreeType TMVA::DataSet::GetCurrentType() const
{
   switch (fCurrentTreeIdx) {
   case 0: return Types::kTraining;
   case 1: return Types::kTesting;
   case 2: return Types::kValidation;
   case 3: return Types::kTrainingOriginal;
   }
   return Types::kMaxTreeType;
}
 
//_______________________________________________________________________
inline Long64_t TMVA::DataSet::GetNEvents(Types::ETreeType type) const
{
   Int_t treeIdx = TreeIndex(type);
   if (fSampling.size() > UInt_t(treeIdx) && fSampling.at(treeIdx)) {
      return fSamplingSelected.at(treeIdx).size();
   }
   return GetEventCollection(type).size();
}
 
//_______________________________________________________________________
inline const std::vector<TMVA::Event*>& TMVA::DataSet::GetEventCollection( TMVA::Types::ETreeType type ) const
{
   return fEventCollection.at(TreeIndex(type));
}
 
 
#endif