TMVAClassificationApplication_cc1pcoh_bdt_ver4noveract.C

/**********************************************************************************
 * Project   : TMVA - a Root-integrated toolkit for multivariate data analysis    *
 * Package   : TMVA                                                               *
 * Exectuable: TMVAClassificationApplication                                      *
 *                                                                                *
 * This macro provides a simple example on how to use the trained classifiers     *
 * within an analysis module                                                      *
 **********************************************************************************/

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

#include "TFile.h"
#include "TTree.h"
#include "TString.h"
#include "TSystem.h"
#include "TROOT.h"
#include "TStopwatch.h"

#if not defined(__CINT__) || defined(__MAKECINT__)
#include "TMVA/Tools.h"
#include "TMVA/Reader.h"
#include "TMVA/MethodCuts.h"
#endif

using namespace TMVA;

void TMVAClassificationApplication_cc1pcoh_bdt_ver4noveract( TString myMethodList = "" )
{
#ifdef __CINT__
    gROOT->ProcessLine( ".O0" ); // turn off optimization in CINT
#endif
    
    //---------------------------------------------------------------
    
    // This loads the library
    TMVA::Tools::Instance();
    
    // Default MVA methods to be trained + tested
    std::map<std::string,int> Use;
    //
    // --- Boosted Decision Trees
    Use["BDT"]             = 1; // uses Adaptive Boost
    
    
    std::cout << std::endl;
    std::cout << "==> Start TMVAClassificationApplication" << std::endl;
    
    // Select methods (don't look at this code - not of interest)
    if (myMethodList != "") {
        for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;
        
        std::vector<TString> mlist = gTools().SplitString( myMethodList, ',' );
        for (UInt_t i=0; i<mlist.size(); i++) {
            std::string regMethod(mlist[i]);
            
            if (Use.find(regMethod) == Use.end()) {
                std::cout << "Method \"" << regMethod
                << "\" not known in TMVA under this name. Choose among the following:" << std::endl;
                for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
                    std::cout << it->first << " ";
                }
                std::cout << std::endl;
                return;
            }
            Use[regMethod] = 1;
        }
    }
    
    // --------------------------------------------------------------------------------------------------
    
    // --- Create the Reader object
    
    TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" );
    
    // Create a set of variables and declare them to the reader
    // - the variable names MUST corresponds in name and type to those given in the weight file(s) used
    Float_t mumucl, pmucl;
    Float_t pang, muang_t;
    Float_t veract;
    Float_t ppe, mupe;
    Float_t range, coplanarity;
    Float_t opening;//newadd
    
    reader->AddVariable( "mumucl", &mumucl );
    reader->AddVariable( "pmucl", &pmucl );
    reader->AddVariable( "pang", &pang );//changehere
    reader->AddVariable( "muang_t", &muang_t );
    //reader->AddVariable( "veract", &veract );
    reader->AddVariable( "ppe", &ppe);
    reader->AddVariable( "mupe", &mupe);
    reader->AddVariable( "range", &range);
    reader->AddVariable( "coplanarity", &coplanarity);
    reader->AddVariable( "opening", &opening);//newadd
    
    // Spectator variables declared in the training have to be added to the reader, too
    Int_t fileIndex, inttype;
    Float_t nuE, norm, totcrsne;
    reader->AddSpectator( "fileIndex", &fileIndex );
    reader->AddSpectator( "nuE", &nuE );
    reader->AddSpectator( "inttype", &inttype );
    reader->AddSpectator( "norm", &norm );
    reader->AddSpectator( "totcrsne", &totcrsne );
    reader->AddSpectator( "veract", &veract );
    
    // --- Book the MVA methods
    
    TString dir    = "weights/";
    TString prefix = "TMVAClassification_ver4noveract";//newchange
    
    // Book method(s)
    for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) {
        if (it->second) {
            TString methodName = TString(it->first) + TString(" method");
            TString weightfile = dir + prefix + TString("_") + TString(it->first) + TString(".weights.xml");
            reader->BookMVA( methodName, weightfile );
        }
    }
    
    // Prepare input tree (this must be replaced by your data source)
    // in this example, there is a toy tree with signal and one with background events
    // we'll later on use only the "signal" events for the test in this example.
    //
    
    //TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/data_merged_ccqe_addpid_ver4noveract.root";
    //TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/pm_merged_ccqe_tot_addpid_ver4noveract.root";
    //TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/pmbar_merged_ccqe_addpid_ver4noveract.root";
    //TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/ingrid_merged_nd3_ccqe_tot_addpid_ver4noveract.root";
    TString fname = "/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/wall_merged_ccqe_tot_addpid_ver4noveract.root";
    //
    std::cout << "--- Selecting data sample" << std::endl;
    TFile *pfile = new TFile(fname,"update");
    TTree* theTree = (TTree*)pfile->Get("tree");
    theTree->SetBranchAddress( "mumucl", &mumucl );
    theTree->SetBranchAddress( "pmucl", &pmucl );
    theTree->SetBranchAddress( "pang", &pang );
    theTree->SetBranchAddress( "muang_t", &muang_t );
    theTree->SetBranchAddress( "veract", &veract );
    theTree->SetBranchAddress( "ppe", &ppe);
    theTree->SetBranchAddress( "mupe", &mupe);
    theTree->SetBranchAddress( "range", &range);
    theTree->SetBranchAddress( "coplanarity", &coplanarity);
    theTree->SetBranchAddress( "opening", &opening);
    
 
    
    Int_t Ntrack;
    theTree->SetBranchAddress( "Ntrack", &Ntrack );
    
    
    Float_t pid;
    TBranch *bpid = theTree->Branch("pid",&pid,"pid/F");
    
    
    
    std::vector<Float_t> vecVar(9); // vector for EvaluateMVA tests
    
    Long64_t nentries = theTree->GetEntriesFast();
    Long64_t iprintProcess = Long64_t(nentries/100.);
    
    std::cout << "--- Processing: " << nentries << " events" << std::endl;
    
    TStopwatch sw;
    sw.Start();
    for (Long64_t ievt=0; ievt<nentries;ievt++) {
        
        if (ievt%iprintProcess == 0) cout<<"Processing "<<int(ievt*100./nentries)<<"% of events"<<endl;
        theTree->GetEntry(ievt);
        Float_t pid_tem;
        if (Use["BDT"])      {
            if (Ntrack!=2) pid_tem = -999;
            else pid_tem = reader->EvaluateMVA("BDT method");
        }
        pid = pid_tem;
        bpid->Fill();
        
    }
    theTree->Write();
    delete pfile;
    // Get elapsed time
    sw.Stop();
    std::cout << "--- End of event loop: "; sw.Print();
    
    delete reader;
    
    std::cout << "==> TMVAClassificationApplication is done!" << endl << std::endl;
}