DataLoader.h

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// @(#)root/tmva $Id$
// Author: Andreas Hoecker, Peter Speckmayer, Joerg Stelzer, Helge Voss, Kai Voss, Eckhard von Toerne, Jan Therhaag, Omar Zapata, Lorenzo Moneta, Sergei Gleyzer
//NOTE: Based on TMVA::Factory
 
/**********************************************************************************
 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis       *
 * Package: TMVA                                                                  *
 * Class  : DataLoader                                                            *
 * Web    : http://tmva.sourceforge.net                                           *
 *                                                                                *
 * Description:                                                                   *
 *      This is a class to load datasets into every booked method                 *
 *                                                                                *
 * Authors (alphabetical):                                                        *
 *      Lorenzo Moneta <Lorenzo.Moneta@cern.ch> - CERN, Switzerland               *
 *      Omar Zapata <andresete.chaos@gmail.com>  - ITM/UdeA, Colombia             *
 *      Sergei Gleyzer<sergei.gleyzer@cern.ch> - CERN, Switzerland                *
 *                                                                                *
 * Copyright (c) 2005-2011:                                                       *
 *      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           *
 * (http://tmva.sourceforge.net/LICENSE)                                          *
 **********************************************************************************/
 
#ifndef ROOT_TMVA_DataLoader
#define ROOT_TMVA_DataLoader
 
#include <string>
#include <vector>
#include <map>
#include "TCut.h"
 
#include "TMVA/Configurable.h"
#include "TMVA/Types.h"
#include "TMVA/DataSet.h"
 
class TFile;
class TTree;
class TH2;
 
namespace TMVA {
 
   class CvSplit;
   class DataInputHandler;
   class DataSetInfo;
   class DataSetManager;
   class VariableTransformBase;
 
   class DataLoader : public Configurable {
   public:
 
       DataLoader(TString thedlName="default");
 
      // default destructor
      virtual ~DataLoader();
 
 
      // add events to training and testing trees
      void AddSignalTrainingEvent    ( const std::vector<Double_t>& event, Double_t weight = 1.0 );
      void AddBackgroundTrainingEvent( const std::vector<Double_t>& event, Double_t weight = 1.0 );
      void AddSignalTestEvent        ( const std::vector<Double_t>& event, Double_t weight = 1.0 );
      void AddBackgroundTestEvent    ( const std::vector<Double_t>& event, Double_t weight = 1.0 );
      void AddTrainingEvent( const TString& className, const std::vector<Double_t>& event, Double_t weight );
      void AddTestEvent    ( const TString& className, const std::vector<Double_t>& event, Double_t weight );
      void AddEvent        ( const TString& className, Types::ETreeType tt, const std::vector<Double_t>& event, Double_t weight );
      Bool_t UserAssignEvents(UInt_t clIndex);
      TTree* CreateEventAssignTrees( const TString& name );
 
      DataSetInfo& AddDataSet( DataSetInfo& );
      DataSetInfo& AddDataSet( const TString&  );
      DataSetInfo& GetDataSetInfo();
      DataLoader* VarTransform(TString trafoDefinition);
 
      // special case: signal/background
 
      // Data input related
      void SetInputTrees( const TString& signalFileName, const TString& backgroundFileName, 
                          Double_t signalWeight=1.0, Double_t backgroundWeight=1.0 );
      void SetInputTrees( TTree* inputTree, const TCut& SigCut, const TCut& BgCut );
      // Set input trees  at once
      void SetInputTrees( TTree* signal, TTree* background, 
                          Double_t signalWeight=1.0, Double_t backgroundWeight=1.0) ;
 
      void AddSignalTree( TTree* signal,    Double_t weight=1.0, Types::ETreeType treetype = Types::kMaxTreeType );
      void AddSignalTree( TString datFileS, Double_t weight=1.0, Types::ETreeType treetype = Types::kMaxTreeType );
      void AddSignalTree( TTree* signal, Double_t weight, const TString& treetype );      
 
      // ... depreciated, kept for backwards compatibility
      void SetSignalTree( TTree* signal, Double_t weight=1.0);
 
      void AddBackgroundTree( TTree* background, Double_t weight=1.0, Types::ETreeType treetype = Types::kMaxTreeType );
      void AddBackgroundTree( TString datFileB,  Double_t weight=1.0, Types::ETreeType treetype = Types::kMaxTreeType );
      void AddBackgroundTree( TTree* background, Double_t weight, const TString & treetype );
 
      // ... depreciated, kept for backwards compatibility
      void SetBackgroundTree( TTree* background, Double_t weight=1.0 );
 
      void SetSignalWeightExpression( const TString& variable );
      void SetBackgroundWeightExpression( const TString& variable );
 
      // special case: regression
      void AddRegressionTree( TTree* tree, Double_t weight = 1.0,  
                              Types::ETreeType treetype = Types::kMaxTreeType ) { 
         AddTree( tree, "Regression", weight, "", treetype ); 
      }
 
      // general
 
      // Data input related
      void SetTree( TTree* tree, const TString& className, Double_t weight ); // depreciated
      void AddTree( TTree* tree, const TString& className, Double_t weight=1.0,
                    const TCut& cut = "",
                    Types::ETreeType tt = Types::kMaxTreeType );
      void AddTree( TTree* tree, const TString& className, Double_t weight, const TCut& cut, const TString& treeType );
 
      // set input variable
      void SetInputVariables  ( std::vector<TString>* theVariables ); // deprecated
 
      void AddVariable        ( const TString& expression, const TString& title, const TString& unit,
                                char type='F', Double_t min = 0, Double_t max = 0 );
      void AddVariable        ( const TString& expression, char type='F',
                                Double_t min = 0, Double_t max = 0 );
 
      // NEW: add an array of variables (e.g. for image data) with the provided size
      void AddVariablesArray(const TString &expression, int size, char type = 'F',
                             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 AddRegressionTarget( const TString& expression, const TString& title = "", const TString& unit = "",
                                Double_t min = 0, Double_t max = 0 )
      {
         AddTarget( expression, title, unit, min, max );
      }
      void AddSpectator         ( const TString& expression, const TString& title = "", const TString& unit = "",
                                  Double_t min = 0, Double_t max = 0 );
 
      // set weight for class
      void SetWeightExpression( const TString& variable, const TString& className = "" );
 
      // set cut for class
      void SetCut( const TString& cut, const TString& className = "" );
      void SetCut( const TCut& cut, const TString& className = "" );
      void AddCut( const TString& cut, const TString& className = "" );
      void AddCut( const TCut& cut, const TString& className = "" );
 
 
      //  prepare input tree for training
      void PrepareTrainingAndTestTree( const TCut& cut, const TString& splitOpt );
      void PrepareTrainingAndTestTree( TCut sigcut, TCut bkgcut, const TString& splitOpt );
 
      // ... deprecated, kept for backwards compatibility 
      void PrepareTrainingAndTestTree( const TCut& cut, Int_t Ntrain, Int_t Ntest = -1 );
 
      void PrepareTrainingAndTestTree( const TCut& cut, Int_t NsigTrain, Int_t NbkgTrain, Int_t NsigTest, Int_t NbkgTest, 
                                       const TString& otherOpt="SplitMode=Random:!V" );
 
      // Cross validation
      void MakeKFoldDataSet(CvSplit & s);
      void PrepareFoldDataSet(CvSplit & s, UInt_t foldNumber, Types::ETreeType tt = Types::kTraining);
      void RecombineKFoldDataSet(CvSplit & s, Types::ETreeType tt = Types::kTraining);
 
      const DataSetInfo& GetDefaultDataSetInfo(){ return DefaultDataSetInfo(); }
 
      TH2* GetCorrelationMatrix(const TString& className);
 
      //Copy method use in VI and CV DEPRECATED: you can just call Clone  DataLoader *dl2=(DataLoader *)dl1->Clone("dl2")
      DataLoader* MakeCopy(TString name);
      friend void DataLoaderCopy(TMVA::DataLoader* des, TMVA::DataLoader* src);      
      DataInputHandler&        DataInput() { return *fDataInputHandler; }
 
   private:
 
 
      DataSetInfo&             DefaultDataSetInfo();
      void                     SetInputTreesFromEventAssignTrees();
 
 
   private:
 
      // data members
 
 
      DataSetManager* fDataSetManager; // DSMTEST
 
 
      DataInputHandler*                         fDataInputHandler;//->
 
      std::vector<TMVA::VariableTransformBase*> fDefaultTrfs;     // list of transformations on default DataSet
 
      // cd to local directory
      TString                                   fOptions;         // option string given by construction (presently only "V")
      TString                                   fTransformations; // List of transformations to test
      Bool_t                                    fVerbose;         // verbose mode
 
      // flag determining the way training and test data are assigned to DataLoader
      enum DataAssignType { kUndefined = 0, 
                            kAssignTrees,
                            kAssignEvents };
      DataAssignType                            fDataAssignType;  // flags for data assigning
      std::vector<TTree*>                       fTrainAssignTree; // for each class: tmp tree if user wants to assign the events directly
      std::vector<TTree*>                       fTestAssignTree;  // for each class: tmp tree if user wants to assign the events directly
 
      Int_t                                     fATreeType = 0;          // type of event (=classIndex)
      Float_t                                   fATreeWeight = 0.0;        // weight of the event
      std::vector<Float_t>                      fATreeEvent;         // event variables
      
      Types::EAnalysisType                      fAnalysisType;    // the training type
 
   protected:
 
      ClassDef(DataLoader,4);
   };
   void DataLoaderCopy(TMVA::DataLoader* des, TMVA::DataLoader* src);
} // namespace TMVA
 
#endif