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TMVAClassificationApplication_ccqe_bdt_ver3noveractFFFSI.C
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TMVAClassificationApplication_ccqe_bdt_ver3noveractFFFSI.C
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/**********************************************************************************
* 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_ccqe_bdt_ver3noveractFFFSI( TString myMethodList = "", TString fname )
{
#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_t, 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_t", &pang_t );
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_ccqe_ver3noveractFFFSI";//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.
//
//for correct prang, pang_t and additional information
//TString fname = "/home/cvson/scraid2/cc1picoh/FIT/datafsi/datamc_merged_ccqe_addpidFFnew.root";
//TString fname = "/home/cvson/scraid2/cc1picoh/FIT/datafsi/datamc_genie_merged_ccqe_addpidFFnew.root";
//TString fname = "/home/cvson/scraid2/cc1picoh/FIT/datafsi/mc_merged_ccqe_addpidFFnew.root";
//TString fname = "/home/cvson/scraid2/cc1picoh/FIT/datafsi/mcgenie_merged_ccqe_tot_addpidFFnew.root";
//TString fname = "/home/cvson/scraid2/cc1picoh/FIT/datafsi/merged_ccqe_forResponseFunction.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_t", &pang_t );
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 pidfsiccqe;
TBranch *bpidfsiccqe = theTree->Branch("pidfsiccqe",&pidfsiccqe,"pidfsiccqe/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 pidfsi_tem;
if (Use["BDT"]) {
//if (Ntrack!=2) pidfsi_tem = -999;//changehere
if (Ntrack<2) pidfsi_tem = -999;
else pidfsi_tem = reader->EvaluateMVA("BDT method");
}
pidfsiccqe = pidfsi_tem;
bpidfsiccqe->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;
}