PndMvaTrainer.h

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/* **********************************************
 * MVA classifiers trainers interface.          *
 * Author: M. Babai                             *
 * M.Babai@rug.nl                               *
 * Version: 0.1 beta1.                          *
 * LICENSE:                                     *
 * **********************************************
 */
//#pragma once
#ifndef PND_MVA_TRAINER_H
#define PND_MVA_TRAINER_H

// C++ includes
#include <cassert>
#include <limits>
#include <ctime>
#include <iomanip>
#include <cstdlib>

// ROOT and PandaRoot
#include "TMVA/Tools.h"
#include "TMVA/PDEFoam.h"
#include "TMVA/Event.h"

class TRandom3;

// Local includes
#include "PndMvaDataSet.h"
#include "PndMvaUtil.h"

#define TRAIN_INC_FOAM 0

class PndMvaTrainer
{
  //==============================================
  //================ Public =======================
 public:
  
  explicit PndMvaTrainer(std::vector< std::pair<std::string, std::vector<float>*> > const& InputEvtsParam,
                         std::vector<std::string> const& ClassNames,
                         std::vector<std::string> const& VarNames,
                         bool trim = true);

  explicit PndMvaTrainer(std::string const& InPut,
                         std::vector<std::string> const& ClassNames,
                         std::vector<std::string> const& VarNames,
                         bool trim = true);

  virtual ~PndMvaTrainer();

  virtual void Train() = 0;

  virtual void storeWeights() = 0;

  void SetTestSetSize(size_t percent = 50);

  void SetTestSet(std::set <size_t> const& testSet);

  void NormalizeData(NormType t = NONORM);
  
  void PCATransForm();

  inline void SetOutPutFile(std::string const& outFile);

  void WriteErroVect(std::string const& FileName) const;

  inline std::vector <StepError> const& GetErrorValues() const;
  
  virtual void Initialize();

  inline std::set <size_t> const& GetTestEvetIdx() const;
 
  inline std::vector<PndMvaClass> const& GetClasses() const;
  
  inline std::vector<PndMvaVariable> const& GetVariables() const; 
 
  virtual void EvalClassifierError();

  inline size_t GetRndSeed() const;
  inline void   SetRndSeed(size_t const sd);

  //______________________________________________
  //================ Protected ===================
 protected:
  inline void SetAppType(AppType t);

  void WriteToWeightFile(std::vector< std::pair<std::string, 
                         std::vector<float>*> > const& weights) const;

#if (TRAIN_INC_FOAM > 0)

  void WriteToWeightFile(std::vector<TMVA::PDEFoam*> const& foams) const;
#endif
  //void WriteDataSetToOutFile();

  std::set <size_t> m_testSet_indices;
  
  PndMvaDataSet m_dataSets;
  
  std::vector <StepError> m_StepErro;
  
  std::string m_outFile;
  
  size_t m_RND_seed;

  void splitTetsSet();
  //______________________________________________
  //================ Private =====================
 private:
  PndMvaTrainer(PndMvaTrainer const& other);
  PndMvaTrainer& operator=(PndMvaTrainer const& other);

  // trim or not
  bool m_trim;
  size_t  m_testSetSize;
};// End of class definition.

//========================= Inline implementations =================
inline size_t PndMvaTrainer::GetRndSeed() const
{
  return this->m_RND_seed;
};

inline void PndMvaTrainer::SetRndSeed(size_t const sd)
{
  this->m_RND_seed = sd;
};

inline void PndMvaTrainer::SetOutPutFile(std::string const& outFile)
{
  m_outFile = outFile;
};

inline void PndMvaTrainer::SetAppType(AppType t)
{
  m_dataSets.SetAppType(t);
};

inline std::set <size_t> const& PndMvaTrainer::GetTestEvetIdx() const
{
  return m_testSet_indices;
};

inline std::vector<PndMvaClass> const& PndMvaTrainer::GetClasses() const
{
  return m_dataSets.GetClasses();
};

inline std::vector<PndMvaVariable> const& PndMvaTrainer::GetVariables() const
{
  return m_dataSets.GetVars();
};

// @return  List of evaluation objects.
inline  std::vector <StepError> const& PndMvaTrainer::GetErrorValues() const
{
  return m_StepErro;
};
#endif