/// \file
/// \ingroup tutorial_tmva
/// \notebook -nodraw
/// This macro provides examples for the training and testing of the
/// TMVA classifiers.
///
/// As input data is used a toy-MC sample consisting of four Gaussian-distributed
/// and linearly correlated input variables.
/// The methods to be used can be switched on and off by means of booleans, or
/// via the prompt command, for example:
///
///     root -l ./TMVAClassification.C\(\"Fisher,Likelihood\"\)
///
/// (note that the backslashes are mandatory)
/// If no method given, a default set of classifiers is used.
/// The output file "TMVA.root" can be analysed with the use of dedicated
/// macros (simply say: root -l <macro.C>), which can be conveniently
/// invoked through a GUI that will appear at the end of the run of this macro.
/// Launch the GUI via the command:
///
///     root -l ./TMVAGui.C
///
/// You can also compile and run the example with the following commands
///
///     make
///     ./TMVAClassification <Methods>
///
/// where: `<Methods> = "method1 method2"` are the TMVA classifier names
/// example:
///
///     ./TMVAClassification Fisher LikelihoodPCA BDT
///
/// If no method given, a default set is of classifiers is used
///
/// - Project   : TMVA - a ROOT-integrated toolkit for multivariate data analysis
/// - Package   : TMVA
/// - Root Macro: TMVAClassification
///
/// \macro_output
/// \macro_code
/// \author Andreas Hoecker


#include <cstdlib>
#include <iostream>
#include <map>
#include <string>

#include "TChain.h"
#include "TFile.h"
#include "TTree.h"
#include "TString.h"
#include "TObjString.h"
#include "TSystem.h"
#include "TROOT.h"

#include "TMVA/Factory.h"
#include "TMVA/DataLoader.h"
#include "TMVA/Tools.h"
#include "TMVA/TMVAGui.h"

int TMVAClassificationBDT( )
{
   // The explicit loading of the shared libTMVA is done in TMVAlogon.C, defined in .rootrc
   // if you use your private .rootrc, or run from a different directory, please copy the
   // corresponding lines from .rootrc


   //---------------------------------------------------------------
   // This loads the library
   TMVA::Tools::Instance();

   // ---------------------------------------------------------------

   std::cout << std::endl;
   std::cout << "==> Start TMVAClassification" << std::endl;


   // --------------------------------------------------------------------------------------------------

   // Here the preparation phase begins

   // Read training and test data
   // (it is also possible to use ASCII format as input -> see TMVA Users Guide)
   TString fname = "./tmva_class_example.root";

   if (gSystem->AccessPathName( fname ))  // file does not exist in local directory
      gSystem->Exec("curl -O http://root.cern.ch/files/tmva_class_example.root");

   TFile *input = TFile::Open( fname );

   std::cout << "--- TMVAClassification       : Using input file: " << input->GetName() << std::endl;

   // Register the training and test trees

   TTree *signalTree     = (TTree*)input->Get("TreeS");
   TTree *background     = (TTree*)input->Get("TreeB");

   // Create a ROOT output file where TMVA will store ntuples, histograms, etc.
   TString outfileName( "TMVA.root" );
   TFile* outputFile = TFile::Open( outfileName, "RECREATE" );

   // Create the factory object. Later you can choose the methods
   // whose performance you'd like to investigate. The factory is
   // the only TMVA object you have to interact with
   //
   // The first argument is the base of the name of all the
   // weightfiles in the directory weight/
   //
   // The second argument is the output file for the training results
   // All TMVA output can be suppressed by removing the "!" (not) in
   // front of the "Silent" argument in the option string
   TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
                                               "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );

   TMVA::DataLoader *dataloader=new TMVA::DataLoader("dataset");
   // If you wish to modify default settings
   // (please check "src/Config.h" to see all available global options)
   //
   //    (TMVA::gConfig().GetVariablePlotting()).fTimesRMS = 8.0;
   //    (TMVA::gConfig().GetIONames()).fWeightFileDir = "myWeightDirectory";

   // Define the input variables that shall be used for the MVA training
   // note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)"
   // [all types of expressions that can also be parsed by TTree::Draw( "expression" )]
   dataloader->AddVariable( "myvar1 := var1+var2", 'F' );
   dataloader->AddVariable( "myvar2 := var1-var2", "Expression 2", "", 'F' );
   dataloader->AddVariable( "var3",                "Variable 3", "units", 'F' );
   dataloader->AddVariable( "var4",                "Variable 4", "units", 'F' );


   // global event weights per tree (see below for setting event-wise weights)
   Double_t signalWeight     = 1.0;
   Double_t backgroundWeight = 1.0;

   // You can add an arbitrary number of signal or background trees
   dataloader->AddSignalTree    ( signalTree,     signalWeight );
   dataloader->AddBackgroundTree( background, backgroundWeight );

   // To give different trees for training and testing, do as follows:
   //
   //     dataloader->AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" );
   //     dataloader->AddSignalTree( signalTestTree,     signalTestWeight,  "Test" );

   // Use the following code instead of the above two or four lines to add signal and background
   // training and test events "by hand"
   // NOTE that in this case one should not give expressions (such as "var1+var2") in the input
   //      variable definition, but simply compute the expression before adding the event
   // ```cpp
   // // --- begin ----------------------------------------------------------
   // std::vector<Double_t> vars( 4 ); // vector has size of number of input variables
   // Float_t  treevars[4], weight;
   //
   // // Signal
   // for (UInt_t ivar=0; ivar<4; ivar++) signalTree->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
   // for (UInt_t i=0; i<signalTree->GetEntries(); i++) {
   //    signalTree->GetEntry(i);
   //    for (UInt_t ivar=0; ivar<4; ivar++) vars[ivar] = treevars[ivar];
   //    // add training and test events; here: first half is training, second is testing
   //    // note that the weight can also be event-wise
   //    if (i < signalTree->GetEntries()/2.0) dataloader->AddSignalTrainingEvent( vars, signalWeight );
   //    else                              dataloader->AddSignalTestEvent    ( vars, signalWeight );
   // }
   //
   // // Background (has event weights)
   // background->SetBranchAddress( "weight", &weight );
   // for (UInt_t ivar=0; ivar<4; ivar++) background->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
   // for (UInt_t i=0; i<background->GetEntries(); i++) {
   //    background->GetEntry(i);
   //    for (UInt_t ivar=0; ivar<4; ivar++) vars[ivar] = treevars[ivar];
   //    // add training and test events; here: first half is training, second is testing
   //    // note that the weight can also be event-wise
   //    if (i < background->GetEntries()/2) dataloader->AddBackgroundTrainingEvent( vars, backgroundWeight*weight );
   //    else                                dataloader->AddBackgroundTestEvent    ( vars, backgroundWeight*weight );
   // }
   // // --- end ------------------------------------------------------------
   // ```
   // End of tree registration

   // Set individual event weights (the variables must exist in the original TTree)
   // -  for signal    : `dataloader->SetSignalWeightExpression    ("weight1*weight2");`
   // -  for background: `dataloader->SetBackgroundWeightExpression("weight1*weight2");`
   dataloader->SetBackgroundWeightExpression( "weight" );

   // Apply additional cuts on the signal and background samples (can be different)
   TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
   TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";

   // Tell the dataloader how to use the training and testing events
   //
   // If no numbers of events are given, half of the events in the tree are used
   // for training, and the other half for testing:
   //
   //    dataloader->PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" );
   //
   // To also specify the number of testing events, use:
   //
   //    dataloader->PrepareTrainingAndTestTree( mycut,
   //         "NSigTrain=3000:NBkgTrain=3000:NSigTest=3000:NBkgTest=3000:SplitMode=Random:!V" );
   dataloader->PrepareTrainingAndTestTree( mycuts, mycutb,
                                        "nTrain_Signal=1000:nTrain_Background=1000:SplitMode=Random:NormMode=NumEvents:!V" );

   // ### Book MVA methods
   //
   // Please lookup the various method configuration options in the corresponding cxx files, eg:
   // src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html
   // it is possible to preset ranges in the option string in which the cut optimisation should be done:
   // "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable

   std::vector<int> numOfTrees = { 10, 100, 300, 600, 1000};

   // Boosted Decision Trees
   // Gradient Boost
   //   factory->BookMethod( dataloader, TMVA::Types::kBDT, "BDTG",
   //                        "!H:!V:NTrees=1000:MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=2" );
   // Bagging
   //   factory->BookMethod( dataloader, TMVA::Types::kBDT, "BDTB",
   //                        "!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20" );

   // BDT
   for (unsigned int i = 0; i < numOfTrees.size(); i++ ) {
      TString name = TString::Format("BDT_%d",numOfTrees[i]);
      TString opt = "!H:!V:MinNodeSize=2.5%:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20";
      opt += TString::Format(":NTrees=%d",numOfTrees[i]);
      factory->BookMethod( dataloader, TMVA::Types::kBDT, name, opt);
   }


   // Now you can tell the factory to train, test, and evaluate the MVAs
   //
   // Train MVAs using the set of training events
   factory->TrainAllMethods();

   // Evaluate all MVAs using the set of test events
   factory->TestAllMethods();

   // Evaluate and compare performance of all configured MVAs
   factory->EvaluateAllMethods();

   // --------------------------------------------------------------

   // Save the output
   outputFile->Close();

   std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
   std::cout << "==> TMVAClassification is done!" << std::endl;

   delete factory;
   delete dataloader;
   // Launch the GUI for the root macros
   if (!gROOT->IsBatch()) TMVA::TMVAGui( outfileName );

   return 0;
}