/*!
 *    \file  MSimMLManager.h
 *   \brief  Machine Learning methods interface
 *  
 *  <+DETAILED+>
 *  
 *  \author  Chinmay Borwankar (), 
 *  
 *  \internal
 *       Created:  03/31/2017
 *      Revision:  none
 *      Compiler:  gcc
 *  Organization:  Astrophysical Sciences Division, Bhabha Atomic Research Centre
 *     Copyright:  
 *  
 *  This source code is released for free distribution under the terms of the
 *  GNU General Public License as published by the Free Software Foundation.
 */
#ifndef MSimTMVAInterface_hxx
#define MSimTMVAInterface_hxx

#include <cmath>
#include <cstdio>
#include <ctime>
#include <cstdlib>

#include "TROOT.h"
#include "TSystem.h"
#include "TFile.h"
#include "TChain.h"
#include "TEntryList.h"
#include "TList.h"
#include "TStopwatch.h"
#include "TString.h"
#include "TCut.h"
#include "TSelector.h"

#include "TMVA/Factory.h"
#include "TMVA/DataLoader.h"
#include "TMVA/Tools.h"
#include "TMVA/Reader.h"
#include "TMVA/MethodPyRandomForest.h"
#include "TMVA/MethodPyGTB.h"
#include "TMVA/MethodPyAdaBoost.h"
#include "TMVA/MethodCuts.h"
#include "TMVA/TMVAGui.h"
#include "TMVA/TMVARegGui.h"

//#include "MSimIOCorsika.h"
//#include "MSimRT.h"
//#include "MSimCameraLayout.h"
//#include "MSimPatternTrigger.h"
//#include "MSimImageAnalyser.h"
/*
 * =====================================================================================
 *        Class:  MMLResponse
 *  Description:  class to evaluate ML response
 * =====================================================================================
 */
class MMLResponse : public TNamed
{
	public:
		/* ====================  LIFECYCLE     ======================================= */
		MMLResponse ();                             /* constructor      */
		~MMLResponse ();                            /* destructor       */

		/* ====================  ACCESSORS     ======================================= */

		/* ====================  MUTATORS      ======================================= */

		/* ====================  OPERATORS     ======================================= */

		/* ====================  METHODS       ======================================= */
		// common methods to training and prediction
		void AddCategory(Int_t icat, TString categorycut) ;

		//methods for prediction phase 
		void InitReader(const TString &theOpt, Int_t ncat = 1, Bool_t verbose = 0) ;

		Int_t LoadData(const char *pathtodata, const char *treename) ;
		
		Int_t LoadFriendData(const char *pathtodata, const char *treename) ;

		void CategorizeData(const char *precut) ;

		Long64_t GetEntries() const ;

		void AddReaderVariable(const TString &expression,const char *leafname) ;
		
		void AddReaderSpectator(const TString &expression,const char *leafname) ;

		void BookMVA(const TString &methodName, const TString &weightfile ) ;

		Int_t EvaluateMVA(const char *outfile, const char *outtree,const char *precut, Double_t *aux = 0) ;
		
		Int_t EvaluateRegression(const char *outfile, const char *outtree, Double_t *aux = 0) ;

	protected:
		/* ====================  METHODS       ======================================= */

		/* ====================  DATA MEMBERS  ======================================= */

	private:

		MMLResponse ( const MMLResponse &other );   /* copy constructor */

		MMLResponse& operator = ( const MMLResponse &other ); /* assignment operator */
		/* ====================  METHODS       ======================================= */

		/* ====================  DATA MEMBERS  ======================================= */

		TChain *fChainReader ;                  ///<  Chain on which reader will be operated 
		TList *fCategoryCuts ;                  ///<  Cuts defining categories 
		TList *fEntryLists ;                    ///<  EntryLists corresponding to categories 
		TList *fListReader ;                    ///<  List of TMVA Reader objects to manage Readers predictions on categories 

		vector <TString> fReaderVariableLeaves ;  //!<! reader variable leaf names 
		vector <TString> fReaderVariableExprs ;   //!<! reader variable expressions 
		vector <TString> fReaderSpectatorLeaves ; //!<! reader spectators 
		vector <TString> fReaderSpectatorExprs ; //!<! reader spectators expressions
		vector <TString> fMethodsBookedReader ;   //!<! methods booked in Reader  

		Int_t fNFloatVarsReader ;               ///<  Number of float variables in reader 
		Int_t fNIntVarsReader ;                 ///<  Number of integer variables in reader 
		Int_t fNFloatSpecsReader ;              ///<  Number of float spectator in reader 
		Int_t fNIntSpecsReader ;                ///<  Number of integer spectators in reader 

		Int_t fNumberOfCategories ;
		Float_t *fFloatVarsReader ;             //[fNFloatVarsReader]
		Int_t *fIntVarsReader ;                 //[fNIntVarsReader]
		Float_t *fFloatSpecsReader ;            //[fNFloatVarsReader]
		Int_t *fIntSpecsReader ;              //[fNIntSpecsReader]

		/// \endcond{CLASSIMP}
			ClassDef(MMLResponse,0) ;
		/// \endcond
	
}; /* -----  end of class MMLResponse  ----- */


///
///  \class  MMLTrainer
///
///  \brief  Interface class to TMVA methods
///   
///   <+DETAILED+>
///
class MMLTrainer : public TNamed
{
	public:
		/* ====================  LIFECYCLE     ======================================= */
		MMLTrainer ();                               /* constructor      */
		~MMLTrainer ();                              /* destructor       */

		/* ====================  ACCESSORS     ======================================= */

		/* ====================  MUTATORS      ======================================= */

		/* ====================  OPERATORS     ======================================= */

		/* ====================  METHODS       ======================================= */

		// common methods to training and prediction
		void AddCategory(Int_t icat, TString categorycut) ;

		// Methods for training phase
		Int_t InitFactory(const char* jobname, const char *outputfilename,  const char *options, Int_t ncat = 1) ;

		void AddTrainingVariable(const TString &expression, const TString &title, const TString &unit,
				char type='F', Double_t min=0, Double_t max=0) ;

		void AddTrainingSpectator (const TString &expression, const TString &title="", const TString &unit="",
				Double_t min=0, Double_t max=0) ;

		void AddTarget (const TString &expression, const TString &title="", const TString &unit="",
				Double_t min=0, Double_t max=0)	;

		void AddSignalTree(TTree *sigtree, Double_t sigweight) ;

		void AddBackgroundTree(TTree *bkgtree, Double_t bkgweight) ;

		void AddRegressionTree(TTree *tree, Double_t weight, TMVA::Types::ETreeType treetype = TMVA::Types::kMaxTreeType) ;

		void PrepareTrainingAndTestTreeCls(TCut sigcut, TCut bkgcut, const TString &splitopt) ;
		
		void PrepareTrainingAndTestTreeReg(TCut sigcut, const TString &splitopt) ;

		void BookMethod(TMVA::Types::EMVA MLMethod, TString methodTitle, TString trainOptions) ;

		void TrainAllMethods() ;
		
		void TestAllMethods() ;

		void EvaluateAllMethods() ;

	protected:
		/* ====================  METHODS       ======================================= */

		/* ====================  DATA MEMBERS  ======================================= */

	private:

		MMLTrainer& operator = ( const MMLTrainer &other ) ; /* assignment operator */
		MMLTrainer ( const MMLTrainer &other );  /* copy constructor */
		/* ====================  METHODS       ======================================= */

		/* ====================  DATA MEMBERS  ======================================= */
		TMVA::Types::EAnalysisType fAnaType ;   ///<  Type of analysis (Classification or Regression) 
		UInt_t fNumberOfCategories ;            ///<  Number of categories of data in training/prediction 
		TFile **fFilesTMVA ;                    ///<  TMVA output root file pointers 
		TList *fCategoryCuts ;                  ///<  Cuts defining categories 
		TList *fListFactory ;                   ///<  List of TMVA Factory objects to handle ML/MVA operations on categories 
		TList *fListDataLoader ;                ///<  List of DataLoaders corresponding to each category 

		vector <TString> fTrainVariables ;      //!<! names (and/or expressions) of training variables 
		vector <TString> fSpecVariables ;       //!<! spectator variables 


		/// \endcond{CLASSIMP}
			ClassDef(MMLTrainer,0) ;
		/// \endcond
		
}; /* -----  end of class MMLTrainer  ----- */

#endif