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PtRegression_AWB_v1.C
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/////////////////////////////////////////////////////////////////////
/// Simulataneous pT regression for multiple factories and MVAs ///
/// Andrew Brinkerhoff 23.01.17 ///
/// ///
/// Adapted from ROOT TMVARegression.C ///
/// Run using "root -l PtRegression_AWB_v1.C ///
/////////////////////////////////////////////////////////////////////
#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/Tools.h"
#include "TMVA/Factory.h"
#include "TMVA/DataLoader.h"
#include "TMVA/TMVARegGui.h"
// Extra tools - AWB 07.12.16
#include "interface/MVA_helper.h"
#include "src/MVA_input_var_tools.cc"
// // Class with NTuple branch definitions
// #include "interface/PtLutInputBranchesClass.hh"
// Typical settings with ZeroBias, without RPC
const int MAX_EVT = 5000000; // Maximum number of MuGun events to process
const int MAX_TR = 500000; // Maximum number of MuGun training events
const int REPORT_EVT = 100000;
// // // Typical settings without ZeroBias, with RPC
// const int MAX_EVT = 30000000; // Maximum number of MuGun events to process
// const int MAX_TR = 20000000; // Maximum number of MuGun training events
// const int REPORT_EVT = 100000;
// // Typical test settings
// const int MAX_EVT = 20000;
// const int MAX_TR = 10000;
// const int REPORT_EVT = 1000;
const double PI = 3.14159265359;
const double PT_SCALE = 1.;
// const double PT_SCALE = (1. / 1.25); // EMTF pT was scaled up by ~1.25 in 2016 (for 4-hit tracks, mode 15)
const double BIT = 0.000001; // Tiny value or offset
const double PTMIN = 1.; // Minimum GEN pT
const double PTMAX = 256.; // Maximum GEN pT
const double ETAMIN = 1.0; // Minimum GEN |eta|
const double ETAMAX = 2.5; // Maximum GEN |eta|
const double PTMAX_TRG = 128.;
const bool RPC_STUDY = false;
const bool CLEAN_HI_PT = true;
const std::vector<int> MODES_CSC = {15};
// const std::vector<int> MODES_CSC = {7, 15};
// const std::vector<int> MODES_CSC = {11, 15};
const std::vector<int> MODES_RPC = {15};
using namespace TMVA;
void PtRegression_AWB_v1 ( TString myMethodList = "" ) {
// This loads the library
TMVA::Tools::Instance();
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
// Mutidimensional likelihood and Nearest-Neighbour methods
Use["PDERS"] = 0;
Use["PDEFoam"] = 0;
Use["KNN"] = 0;
//
// Linear Discriminant Analysis
Use["LD"] = 0;
//
// Function Discriminant analysis
Use["FDA_GA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
//
// Neural Network
Use["MLP"] = 0;
Use["DNN"] = 0;
//
// Support Vector Machine
Use["SVM"] = 0;
//
// Boosted Decision Trees
Use["BDT"] = 0;
Use["BDTG_default"] = 0;
Use["BDTG_AWB"] = 1;
Use["BDTG_AWB_Sq"] = 0;
Use["BDTG_AWB_lite"] = 0;
Use["BDTG_AWB_50_trees"] = 0;
Use["BDTG_AWB_100_trees"] = 0;
Use["BDTG_AWB_200_trees"] = 0;
Use["BDTG_AWB_400_trees"] = 0;
Use["BDTG_AWB_800_trees"] = 0;
Use["BDTG_AWB_3_deep"] = 0;
Use["BDTG_AWB_4_deep"] = 0;
Use["BDTG_AWB_5_deep"] = 0;
Use["BDTG_AWB_6_deep"] = 0;
Use["BDTG_LeastSq"] = 0;
Use["BDTG_Carnes_AbsDev"] = 0;
Use["BDTG_Carnes_Huber"] = 0;
Use["BDTG_Carnes_LeastSq"] = 0;
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start PtRegression_AWB_v1" << std::endl;
// Select methods (don't look at this code - not of interest)
std::vector<TString> mlist;
if (myMethodList != "") {
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;
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;
}
}
// --------------------------------------------------------------------------------------------------
// Here the preparation phase begins
// Create a new root output file
TString out_dir = "/afs/cern.ch/work/a/abrinke1/public/EMTF/PtAssign2017";
// out_dir = ".";
TString out_file_name;
out_file_name.Form( "%s/PtRegression_AWB_v1_17_03_14_mode_15_opt_bends_dTh_pt_1_256_clean.root", out_dir.Data() );
// out_file_name.Form( "%s/PtRegression_AWB_v1_17_02_14_mode_7_RPC.root", out_dir.Data() );
// out_file_name.Form( "%s/PtRegression_AWB_v1_17_02_14_mode_11_RPC.root", out_dir.Data() );
// out_file_name.Form( "%s/PtRegression_AWB_v1_17_02_14_mode_19_RPC.root", out_dir.Data() );
TFile* out_file = TFile::Open( out_file_name, "RECREATE" );
// Read training and test data (see TMVAClassification for reading ASCII files)
// load the signal and background event samples from ROOT trees
TFile *input(0);
TString store = "root://eoscms.cern.ch//store/user/abrinke1/EMTF/Emulator/ntuples";
std::vector<TString> in_file_names;
TString in_file_name;
TString in_dirs[4] = { "ZeroBiasIsolatedBunch0/Slim/170130_224405/0000",
"ZeroBiasIsolatedBunch1/Slim/170130_175144/0000",
"ZeroBiasIsolatedBunch4/Slim/170130_175005/0000",
"ZeroBiasIsolatedBunch5/Slim/170130_174947/0000" };
for (int i = 0; i < 4; i++) {
for (int j = 1; j < 50; j++) {
in_file_name.Form("%s/%s/tuple_%d.root", store.Data(), in_dirs[i].Data(), j);
std::cout << "Adding file " << in_file_name.Data() << std::endl;
in_file_names.push_back(in_file_name.Data());
}
}
// Load files with RPC hits
TString in_dir_RPC = "SingleMu_Pt1To1000_FlatRandomOneOverPt/RPC/170213_173255/0000";
Int_t nRPC_in = 0;
for (int i = 1; i < 99; i++) {
if (!RPC_STUDY) continue;
in_file_name.Form("%s/%s/tuple_%d.root", store.Data(), in_dir_RPC.Data(), i);
std::cout << "Adding file " << in_file_name.Data() << std::endl;
in_file_names.push_back(in_file_name.Data());
nRPC_in += 1;
if (i*200000 > MAX_EVT) break; // ~100k events per file; half of events should have RPC hits
}
// Load files without RPC hits
TString in_dir_CSC = "SingleMu_Pt1To1000_FlatRandomOneOverPt/EMTF_MuGun/170113_165434/0000";
for (int i = 1; i < 99; i++) {
in_file_name.Form("%s/%s/EMTF_MC_NTuple_SingleMu_noRPC_%d.root", store.Data(), in_dir_CSC.Data(), i);
std::cout << "Adding file " << in_file_name.Data() << std::endl;
in_file_names.push_back(in_file_name.Data());
if ((i + nRPC_in)*100000 > MAX_EVT) break; // ~100k events per file
}
for (UInt_t i = 0; i < in_file_names.size(); i++) {
if ( !gSystem->AccessPathName(in_file_names.at(i)) )
input = TFile::Open( in_file_names.at(i) ); // check if file in local directory exists
if (!input) {
std::cout << "ERROR: could not open data file " << in_file_names.at(i) << std::endl;
in_file_names.erase( in_file_names.begin()+i );
i -= 1;
// exit(1);
}
}
// Add trees from the input files to the TChain
// Have to use TChain for both SetBranchAddress and GetEntry to work
std::vector<TChain*> in_chains;
// Super-hacky ... but using "GetBranch" with a single chain with multiple files causes a segfault - AWB 19.01.16
Int_t nChains_RPC = -99;
for (UInt_t i = 0; i < in_file_names.size(); i++) {
TChain *tmp_chain = new TChain("ntuple/tree");
tmp_chain->Add( in_file_names.at(i) );
in_chains.push_back(tmp_chain);
if (i == nRPC_in - 1 && nChains_RPC < 0)
nChains_RPC = i + 1;
}
//////////////////////////////////////////////////////////////////////////
/// Factories: Use different sets of variables, target, weights, etc. ///
//////////////////////////////////////////////////////////////////////////
TString fact_set = "!V:!Silent:Color:DrawProgressBar:AnalysisType=Regression";
std::vector<TString> var_names; // Holds names of variables for a given factory and permutation
std::vector<Double_t> var_vals; // Holds values of variables for a given factory and permutation
TMVA::Factory* nullF = new TMVA::Factory("NULL", out_file, fact_set); // Placeholder factory
TMVA::DataLoader* nullL = new TMVA::DataLoader("NULL"); // Placeholder loader
// Tuple is defined by the factory and dataloader, followed by a name,
// var name and value vectors, and hex bit masks for input and target variables.
// Each hex bit represents four variables, e.g. 0x1 would select only the 1st variable,
// 0xf the 1st 4, 0xff the 1st 8, 0xa the 2nd and 4th, 0xf1 the 1st and 5th-8th, etc.
std::vector< std::tuple<TMVA::Factory*, TMVA::DataLoader*, TString, std::vector<TString>, std::vector<Double_t>, int, int> > factories;
// Optimized mode 15 - AWB 26.01.17
factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f01ff_0x4_invPt", // dPhi12, 23, 34, theta, combs, FR1, St1 ring
var_names, var_vals, 0x001f01ff, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f01ff_0x4_invPtSq", // dPhi12, 23, 34, theta, combs, FR1, St1 ring
// var_names, var_vals, 0x001f01ff, 0x4) );
// Optimized mode 15 + dTh14 + bend1 - AWB 26.01.17
factories.push_back( std::make_tuple( nullF, nullL, "f_0x401f11ff_0x4_invPt", // dPhi12, 23, 34, theta, combs, FR1, St1 ring, dTh14, bend1
var_names, var_vals, 0x401f11ff, 0x4) );
// Optimized mode 15 + dTh14 + bend1 + bendMax3 - AWB 14.03.17
factories.push_back( std::make_tuple( nullF, nullL, "f_0xc01f11ff_0x4_invPt", // dPhi12, 23, 34, theta, combs, FR1, St1 ring, dTh14, bend1/max3
var_names, var_vals, 0xc01f11ff, 0x4) );
// // Optimized mode 19 - AWB 26.01.17
// factories.push_back( std::make_tuple( nullF, nullL, "f_0xf001f01ff_0x4_invPt", // dPhi12, 23, 34, theta, combs, FR1, St1 ring, dPhi CSC-RPC
// var_names, var_vals, 0xf001f01ff, 0x4) );
// // Original set of training variables
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x0000011d_0x2",
// var_names, var_vals, 0x0000011d, 0x2) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x0000011d_0x4",
// var_names, var_vals, 0x0000011d, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x0000011d_0x2_invPt",
// var_names, var_vals, 0x0000011d, 0x2) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x0000011d_0x4_invPt",
// var_names, var_vals, 0x0000011d, 0x4) );
// // Original set of training variables + combinations
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f01fd_0x2",
// var_names, var_vals, 0x001f01fd, 0x2) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f01fd_0x4",
// var_names, var_vals, 0x001f01fd, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f01fd_0x2_invPt",
// var_names, var_vals, 0x001f01fd, 0x2) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f01fd_0x4_invPt",
// var_names, var_vals, 0x001f01fd, 0x4) );
// // Original set of training variables + combinations + St 1 ring, FR bits, bending
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001fffff_0x2",
// var_names, var_vals, 0x001fffff, 0x2) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001fffff_0x4",
// var_names, var_vals, 0x001fffff, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001fffff_0x2_invPt",
// var_names, var_vals, 0x001fffff, 0x2) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001fffff_0x4_invPt",
// var_names, var_vals, 0x001fffff, 0x4) );
// // Various sets of variables
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x00000004_0x4_invPt", // dPhi12
// var_names, var_vals, 0x00000004, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x00000005_0x4_invPt", // dPhi12, theta
// var_names, var_vals, 0x00000005, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x0000000d_0x4_invPt", // dPhi12, 23, theta
// var_names, var_vals, 0x0000000d, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x00000085_0x4_invPt", // dPhi12, 24, theta
// var_names, var_vals, 0x00000085, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x0000001d_0x4_invPt", // dPhi12, 23, 34, theta
// var_names, var_vals, 0x0000001d, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f00fd_0x4_invPt", // dPhi12, 23, 34, theta, combs
// var_names, var_vals, 0x001f00fd, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f0ffd_0x4_invPt", // dPhi12, 23, 34, theta, combs, FRs
// var_names, var_vals, 0x001f0ffd, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f0fff_0x4_invPt", // dPhi12, 23, 34, theta, combs, FRs, St1 ring
// var_names, var_vals, 0x001f0fff, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001fffff_0x4_invPt", // dPhi12, 23, 34, theta, combs, FRs, St1 ring, bends
// var_names, var_vals, 0x001fffff, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x8fff0fff_0x4_invPt", // dPhi12, 23, 34, theta, combs, FRs, St1 ring, dThetas
// var_names, var_vals, 0x8fff0fff, 0x4) );
// // FRs
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f00ff_0x4_invPt", // dPhi12, 23, 34, theta, combs, St1 ring
// var_names, var_vals, 0x001f00ff, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f01ff_0x4_invPt", // dPhi12, 23, 34, theta, combs, St1 ring, FR1
// var_names, var_vals, 0x001f01ff, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f03ff_0x4_invPt", // dPhi12, 23, 34, theta, combs, St1 ring, FR1/2
// var_names, var_vals, 0x001f03ff, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f05ff_0x4_invPt", // dPhi12, 23, 34, theta, combs, St1 ring, FR1/3
// var_names, var_vals, 0x001f05ff, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f09ff_0x4_invPt", // dPhi12, 23, 34, theta, combs, St1 ring, FR1/4
// var_names, var_vals, 0x001f09ff, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f0fff_0x4_invPt", // dPhi12, 23, 34, theta, combs, St1 ring, all FRs
// var_names, var_vals, 0x001f0fff, 0x4) );
// // Bends
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f01ff_0x4_invPt", // dPhi12, 23, 34, theta, combs, St1 ring, FR1
// var_names, var_vals, 0x001f01ff, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f11ff_0x4_invPt", // dPhi12, 23, 34, theta, combs, St1 ring, FR1, bend 1
// var_names, var_vals, 0x001f11ff, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f31ff_0x4_invPt", // dPhi12, 23, 34, theta, combs, St1 ring, FR1, bend 1/2
// var_names, var_vals, 0x001f31ff, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f51ff_0x4_invPt", // dPhi12, 23, 34, theta, combs, St1 ring, FR1, bend 1/3
// var_names, var_vals, 0x001f51ff, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001f91ff_0x4_invPt", // dPhi12, 23, 34, theta, combs, St1 ring, FR1, bend 1/3
// var_names, var_vals, 0x001f91ff, 0x4) );
// factories.push_back( std::make_tuple( nullF, nullL, "f_0x001ff1ff_0x4_invPt", // dPhi12, 23, 34, theta, combs, St1 ring, FR1, all bends
// var_names, var_vals, 0x001ff1ff, 0x4) );
// Initialize factories and dataloaders
for (UInt_t iFact = 0; iFact < factories.size(); iFact++) {
std::get<0>(factories.at(iFact)) = new TMVA::Factory( std::get<2>(factories.at(iFact)), out_file, fact_set );
std::get<1>(factories.at(iFact)) = new TMVA::DataLoader( std::get<2>(factories.at(iFact)) );
}
// Defined in interface/MVA_helper.h
// MVA_var(TString name, TString descr, TString unit, TString type, Double_t def_val)
std::vector<MVA_var> in_vars; // All input variables
std::vector<MVA_var> targ_vars; // All target variables (should only define 1, unless using MLP)
std::vector<MVA_var> spec_vars; // All spectator variables
std::vector<MVA_var> all_vars; // All variables
/////////////////////////////////////////////////////////
/// Input variables: used in BDT to estimate the pT ///
/////////////////////////////////////////////////////////
in_vars.push_back( MVA_var( "theta", "Track #theta", "int", 'I', -88 ) ); // 0x0000 0001 * 2016 variable
in_vars.push_back( MVA_var( "St1_ring", "St 1 LCT ring", "int", 'I', -88 ) ); // 0x0000 0002
in_vars.push_back( MVA_var( "dPhi_12", "#phi(2) - #phi(1)", "int", 'I', -88 ) ); // 0x0000 0004 * 2016 variable
in_vars.push_back( MVA_var( "dPhi_23", "#phi(3) - #phi(2)", "int", 'I', -88 ) ); // 0x0000 0008 * 2016 variable
in_vars.push_back( MVA_var( "dPhi_34", "#phi(4) - #phi(3)", "int", 'I', -88 ) ); // 0x0000 0010 * 2016 variable
in_vars.push_back( MVA_var( "dPhi_13", "#phi(3) - #phi(1)", "int", 'I', -88 ) ); // 0x0000 0020 * Derivable from 2016 var
in_vars.push_back( MVA_var( "dPhi_14", "#phi(4) - #phi(1)", "int", 'I', -88 ) ); // 0x0000 0040 * Derivable from 2016 var
in_vars.push_back( MVA_var( "dPhi_24", "#phi(4) - #phi(2)", "int", 'I', -88 ) ); // 0x0000 0080 * Derivable from 2016 var
in_vars.push_back( MVA_var( "FR_1", "St 1 LCT F/R", "int", 'I', -88 ) ); // 0x0000 0100 * 2016 variable
in_vars.push_back( MVA_var( "FR_2", "St 2 LCT F/R", "int", 'I', -88 ) ); // 0x0000 0200
in_vars.push_back( MVA_var( "FR_3", "St 3 LCT F/R", "int", 'I', -88 ) ); // 0x0000 0400
in_vars.push_back( MVA_var( "FR_4", "St 4 LCT F/R", "int", 'I', -88 ) ); // 0x0000 0800
in_vars.push_back( MVA_var( "bend_1", "St 1 LCT bending", "int", 'I', -88 ) ); // 0x0000 1000
in_vars.push_back( MVA_var( "bend_2", "St 2 LCT bending", "int", 'I', -88 ) ); // 0x0000 2000
in_vars.push_back( MVA_var( "bend_3", "St 3 LCT bending", "int", 'I', -88 ) ); // 0x0000 4000
in_vars.push_back( MVA_var( "bend_4", "St 4 LCT bending", "int", 'I', -88 ) ); // 0x0000 8000
in_vars.push_back( MVA_var( "dPhiSum4", "#Sigmad#phi (6)", "int", 'I', -88 ) ); // 0x0001 0000 * Derivable from 2016 var
in_vars.push_back( MVA_var( "dPhiSum4A", "#Sigma|d#phi| (6)", "int", 'I', -88 ) ); // 0x0002 0000 * Derivable from 2016 var
in_vars.push_back( MVA_var( "dPhiSum3", "#Sigmad#phi (3)", "int", 'I', -88 ) ); // 0x0004 0000 * Derivable from 2016 var
in_vars.push_back( MVA_var( "dPhiSum3A", "#Sigma|d#phi| (3)", "int", 'I', -88 ) ); // 0x0008 0000 * Derivable from 2016 var
in_vars.push_back( MVA_var( "outStPhi", "#phi outlier St", "int", 'I', -88 ) ); // 0x0010 0000 * Derivable from 2016 var
in_vars.push_back( MVA_var( "outStTh", "#theta outlier St", "int", 'I', -88 ) ); // 0x0020 0000
in_vars.push_back( MVA_var( "dThMax4", "Max d#theta (6)", "int", 'I', -88 ) ); // 0x0040 0000
in_vars.push_back( MVA_var( "dThMax3", "Max d#theta (3)", "int", 'I', -88 ) ); // 0x0080 0000
in_vars.push_back( MVA_var( "dThSum4", "#Sigmad#theta (6)", "int", 'I', -88 ) ); // 0x0100 0000
in_vars.push_back( MVA_var( "dThSum4A", "#Sigma|d#theta| (6)", "int", 'I', -88 ) ); // 0x0200 0000
in_vars.push_back( MVA_var( "dThSum3", "#Sigmad#theta (3)", "int", 'I', -88 ) ); // 0x0400 0000
in_vars.push_back( MVA_var( "dThSum3A", "#Sigma|d#theta| (3)", "int", 'I', -88 ) ); // 0x0800 0000
in_vars.push_back( MVA_var( "dTh_12", "#theta(2) - #theta(1)", "int", 'I', -88 ) ); // 0x1000 0000
in_vars.push_back( MVA_var( "dTh_13", "#theta(3) - #theta(1)", "int", 'I', -88 ) ); // 0x2000 0000
in_vars.push_back( MVA_var( "dTh_14", "#theta(4) - #theta(1)", "int", 'I', -88 ) ); // 0x4000 0000
in_vars.push_back( MVA_var( "bendMax3", "Max bend, st 2-3-4", "int", 'I', -88 ) ); // 0x8000 0000
// in_vars.push_back( MVA_var( "dPhi_11", "CSC-RPC #Delta#phi(1)", "int", 'I', -88 ) ); // 0x1 0000 0000
// in_vars.push_back( MVA_var( "dPhi_22", "CSC-RPC #Delta#phi(2)", "int", 'I', -88 ) ); // 0x2 0000 0000
// in_vars.push_back( MVA_var( "dPhi_33", "CSC-RPC #Delta#phi(3)", "int", 'I', -88 ) ); // 0x4 0000 0000
// in_vars.push_back( MVA_var( "dPhi_44", "CSC-RPC #Delta#phi(4)", "int", 'I', -88 ) ); // 0x8 0000 0000
////////////////////////////////////////////////////////////
// Target variable: true muon pT, or 1/pT, or log2(pT) ///
////////////////////////////////////////////////////////////
targ_vars.push_back( MVA_var( "GEN_pt", "GEN p_{T}", "GeV", 'F', -99 ) ); // 0x1
targ_vars.push_back( MVA_var( "inv_GEN_pt", "1 / GEN muon p_{T}", "GeV^{-1}", 'F', -99 ) ); // 0x2
targ_vars.push_back( MVA_var( "log2_GEN_pt", "log_{2}(GEN muon p_{T})", "GeV", 'F', -99 ) ); // 0x4
/////////////////////////////////////////////////////////////////////////////
/// Spectator variables: not used in training, but saved in output tree ///
/////////////////////////////////////////////////////////////////////////////
spec_vars.push_back( MVA_var( "GEN_pt", "GEN p_{T}", "GeV", 'F', -77 ) );
spec_vars.push_back( MVA_var( "EMTF_pt", "EMTF p_{T}", "GeV", 'F', -77 ) );
spec_vars.push_back( MVA_var( "inv_GEN_pt", "1 / GEN muon p_{T}", "GeV^{-1}", 'F', -77 ) );
spec_vars.push_back( MVA_var( "inv_EMTF_pt", "1 / EMTF p_{T}", "GeV^{-1}", 'F', -77 ) );
spec_vars.push_back( MVA_var( "log2_GEN_pt", "log_{2}(GEN muon p_{T})", "GeV", 'F', -77 ) );
spec_vars.push_back( MVA_var( "log2_EMTF_pt", "log_{2}(EMTF p_{T})", "GeV", 'F', -77 ) );
spec_vars.push_back( MVA_var( "GEN_eta", "GEN #eta", "", 'F', -77 ) );
spec_vars.push_back( MVA_var( "EMTF_eta", "EMTF #eta", "", 'F', -77 ) );
spec_vars.push_back( MVA_var( "GEN_charge", "GEN charge", "", 'I', -77 ) );
spec_vars.push_back( MVA_var( "EMTF_charge", "EMTF charge", "", 'I', -77 ) );
spec_vars.push_back( MVA_var( "EMTF_mode", "EMTF mode", "", 'I', -77 ) );
spec_vars.push_back( MVA_var( "EMTF_hasRPC", "EMTF has RPC hit", "", 'I', -77 ) );
spec_vars.push_back( MVA_var( "dPhi_12_sign", "#phi(2) - #phi(1) sign", "", 'I', -77 ) );
assert( in_vars.size() > 0 ); // You need at least one input variable
assert( targ_vars.size() > 0 ); // You need at least one target variable
// Order is important: input variables first, then target, then specator
all_vars.insert( all_vars.end(), in_vars.begin(), in_vars.end() );
all_vars.insert( all_vars.end(), targ_vars.begin(), targ_vars.end() );
all_vars.insert( all_vars.end(), spec_vars.begin(), spec_vars.end() );
// Fill each factory with the correct set of variables
for (UInt_t iFact = 0; iFact < factories.size(); iFact++) {
std::cout << "\n*** Factory " << std::get<2>(factories.at(iFact)) << " variables ***" << std::endl;
std::cout << "*** Input ***" << std::endl;
for (UInt_t i = 0; i < in_vars.size(); i++) {
if ( 0x1 & (std::get<5>(factories.at(iFact)) >> i) ) { // Hex bit mask for in_vars
MVA_var v = in_vars.at(i);
std::cout << v.name << std::endl;
std::get<1>(factories.at(iFact))->AddVariable( v.name, v.descr, v.unit, v.type ); // Add var to dataloader
std::get<3>(factories.at(iFact)).push_back( v.name ); // Add to vector of var names
std::get<4>(factories.at(iFact)).push_back( v.def_val ); // Add to vector of var values
}
}
TString targ_str = ""; // Save name of target variable
std::cout << "*** Target ***" << std::endl;
for (UInt_t i = 0; i < targ_vars.size(); i++) {
if ( 0x1 & (std::get<6>(factories.at(iFact)) >> i) ) { // Hex bit mask for targ_vars
MVA_var v = targ_vars.at(i);
std::cout << v.name << std::endl;
targ_str = v.name;
std::get<1>(factories.at(iFact))->AddTarget( v.name, v.descr, v.unit, v.type );
std::get<3>(factories.at(iFact)).push_back( v.name );
std::get<4>(factories.at(iFact)).push_back( v.def_val );
}
}
std::cout << "*** Spectator ***" << std::endl;
for (UInt_t i = 0; i < spec_vars.size(); i++) {
MVA_var v = spec_vars.at(i);
if (v.name == targ_str) continue; // Don't add target variable
std::cout << v.name << std::endl;
std::get<1>(factories.at(iFact))->AddSpectator( v.name, v.descr, v.unit, v.type );
std::get<3>(factories.at(iFact)).push_back( v.name );
std::get<4>(factories.at(iFact)).push_back( v.def_val );
}
} // End loop: for (UInt_t iFact = 0; iFact < factories.size(); iFact++)
std::cout << "\n******* About to loop over chains *******" << std::endl;
UInt_t iEvt = 0;
UInt_t iEvtZB = 0;
UInt_t nTrain = 0;
UInt_t nTest = 0;
UInt_t nAlready = 0;
UInt_t nFound = 0;
UInt_t nMissing = 0;
for (int iCh = 0; iCh < in_chains.size(); iCh++) {
TChain *in_chain = in_chains.at(iCh);
// Get branches from the chain
TBranch *muon_br = in_chain->GetBranch("muon");
TBranch *hit_br = in_chain->GetBranch("hit");
TBranch *track_br = in_chain->GetBranch("track");
std::cout << "\n******* About to enter the event loop for chain " << iCh+1 << " *******" << std::endl;
for (UInt_t jEvt = 0; jEvt < in_chain->GetEntries(); jEvt++) {
if (iEvt > MAX_EVT) break;
// if ( (iEvt % REPORT_EVT) == 0 )
// std::cout << "Looking at event " << iEvt << std::endl;
in_chain->GetEntry(jEvt);
UInt_t nMuons = (muon_br->GetLeaf("nMuons"))->GetValue();
UInt_t nTracks = (track_br->GetLeaf("nTracks"))->GetValue();
UInt_t nHits = (track_br->GetLeaf("nHits"))->GetValue();
Bool_t isMC = (nMuons > 0);
Bool_t trainEvt = true; // Can use the event for training
if (not isMC) // Process ZeroBias anyway
nMuons = nTracks;
// std::cout << "There are " << nMuons << " GEN muons and " << nTracks << " EMTF tracks\n" << std::endl;
if ( ( (iEvt % REPORT_EVT) == 0 && isMC) || (iEvtZB > 0 && (iEvtZB % 100000) == 0) )
std::cout << "Looking at event " << iEvt << " (" << iEvtZB << ")" << std::endl;
for (UInt_t iMu = 0; iMu < nMuons; iMu++) {
Double_t mu_pt ;
Double_t mu_eta;
Double_t mu_phi;
Int_t mu_charge;
if (isMC) {
mu_pt = (muon_br->GetLeaf("pt"))->GetValue(iMu);
mu_eta = (muon_br->GetLeaf("eta"))->GetValue(iMu);
mu_phi = (muon_br->GetLeaf("phi"))->GetValue(iMu);
mu_charge = (muon_br->GetLeaf("charge"))->GetValue(iMu);
} else {
mu_pt = 999.;
mu_eta = -99.;
mu_phi = -99.;
mu_charge = -99;
}
if ( mu_pt < PTMIN && isMC ) trainEvt = false;
if ( mu_pt > PTMAX && isMC ) trainEvt = false;
if ( fabs( mu_eta ) < ETAMIN && isMC ) continue;
if ( fabs( mu_eta ) > ETAMAX && isMC ) continue;
// std::cout << "Muon " << iMu+1 << " has pt = " << mu_pt << ", eta = " << mu_eta << ", phi = " << mu_phi << std::endl;
for (UInt_t iTrk = 0; iTrk < nTracks; iTrk++) {
Double_t trk_pt = (track_br->GetLeaf("pt"))->GetValue(iTrk);
Double_t trk_eta = (track_br->GetLeaf("eta"))->GetValue(iTrk);
trk_pt *= PT_SCALE;
Int_t trk_theta_int = (track_br->GetLeaf("theta_int"))->GetValue(iTrk);
Double_t trk_phi = (track_br->GetLeaf("phi"))->GetValue(iTrk);
Int_t trk_phi_int = (track_br->GetLeaf("phi_int"))->GetValue(iTrk);
Int_t trk_charge = (track_br->GetLeaf("charge"))->GetValue(iTrk);
Int_t trk_mode = (track_br->GetLeaf("mode"))->GetValue(iTrk);
if ( ( mu_eta > 0 ) != ( trk_eta > 0 ) && isMC ) continue;
if (trk_theta_int == 0) continue; // Not sure what causes this buggy condition - AWB 14.02.17
Bool_t goodMode = false;
// std::cout << "iCh = " << iCh << ", RPC_STUDY = " << RPC_STUDY << ", trk_mode = " << trk_mode << std::endl;
for (UInt_t iMode = 0; iMode < MODES_CSC.size(); iMode++)
if ( (iCh >= nChains_RPC || !RPC_STUDY) && trk_mode == MODES_CSC.at(iMode))
goodMode = true;
for (UInt_t iMode = 0; iMode < MODES_RPC.size(); iMode++)
if ( (iCh < nChains_RPC && RPC_STUDY) && trk_mode == MODES_RPC.at(iMode))
goodMode = true;
if (not goodMode) continue;
// std::cout << " * Track " << iTrk+1 << " has pt = " << trk_pt << ", eta = " << trk_eta
// << ", phi = " << mu_phi << ", mode = " << trk_mode << std::endl;
// Specifically looking at station 1 or 2 for now
Int_t has_RPC = (track_br->GetLeaf("hit_isRPC"))->GetValue(4*iTrk + 0);
// Int_t has_RPC = (track_br->GetLeaf("hit_isRPC"))->GetValue(4*iTrk + 1);
Int_t hit_theta_int = -9999;
Int_t hit_phi_int = -9999;
if ( iCh < nChains_RPC && RPC_STUDY ) {
if (has_RPC == 1) {
hit_theta_int = (track_br->GetLeaf("hit_theta_int"))->GetValue(4*iTrk + 0);
hit_phi_int = (track_br->GetLeaf("hit_phi_int"))->GetValue(4*iTrk + 0);
// hit_theta_int = (track_br->GetLeaf("hit_theta_int"))->GetValue(4*iTrk + 1);
// hit_phi_int = (track_br->GetLeaf("hit_phi_int"))->GetValue(4*iTrk + 1);
nAlready += 1;
} else {
for (UInt_t iHit = 0; iHit < nHits; iHit++) {
if ( (hit_br->GetLeaf("isRPC"))->GetValue(iHit) == 1 &&
(hit_br->GetLeaf("station"))->GetValue(iHit) == 1 &&
// (hit_br->GetLeaf("station"))->GetValue(iHit) == 2 &&
(hit_br->GetLeaf("sector_index"))->GetValue(iHit) == (track_br->GetLeaf("sector_index"))->GetValue(iTrk) &&
abs( (hit_br->GetLeaf("theta_int"))->GetValue(iHit) - trk_theta_int ) < 8 &&
abs( (hit_br->GetLeaf("phi_int"))->GetValue(iHit) - trk_phi_int ) < 512 &&
abs( (hit_br->GetLeaf("phi_int"))->GetValue(iHit) - trk_phi_int ) < abs(hit_phi_int - trk_phi_int) ) {
hit_theta_int = (hit_br->GetLeaf("theta_int"))->GetValue(iHit);
hit_phi_int = (hit_br->GetLeaf("phi_int"))->GetValue(iHit);
has_RPC = 1;
}
}
}
if (has_RPC != 1) {
nMissing += 1;
continue;
} else {
nFound += 1;
}
} else if (has_RPC != 1) {
has_RPC = 0;
}
Int_t st1_ring = ((int) (track_br->GetLeaf("hit_ring"))->GetValue(4*iTrk + 0)) % 3; // Ring 4 --> Ring 1 (ME1/1a)
Int_t phi1 = (track_br->GetLeaf("hit_phi_int"))->GetValue(4*iTrk + 0);
Int_t phi2 = (track_br->GetLeaf("hit_phi_int"))->GetValue(4*iTrk + 1);
Int_t phi3 = (track_br->GetLeaf("hit_phi_int"))->GetValue(4*iTrk + 2);
Int_t phi4 = (track_br->GetLeaf("hit_phi_int"))->GetValue(4*iTrk + 3);
// std::cout << " * Hits have phi = " << phi1 << ", " << phi2 << ", " << phi3 << ", " << phi4 << std::endl;
Int_t th1 = (track_br->GetLeaf("hit_theta_int"))->GetValue(4*iTrk + 0);
Int_t th2 = (track_br->GetLeaf("hit_theta_int"))->GetValue(4*iTrk + 1);
Int_t th3 = (track_br->GetLeaf("hit_theta_int"))->GetValue(4*iTrk + 2);
Int_t th4 = (track_br->GetLeaf("hit_theta_int"))->GetValue(4*iTrk + 3);
// std::cout << " * Hits have theta = " << th1 << ", " << th2 << ", " << th3 << ", " << th4 << std::endl;
if ( iCh < nChains_RPC && RPC_STUDY ) phi1 = hit_phi_int;
if ( iCh < nChains_RPC && RPC_STUDY ) th1 = hit_theta_int;
// if ( iCh < nChains_RPC && RPC_STUDY ) phi2 = hit_phi_int;
// if ( iCh < nChains_RPC && RPC_STUDY ) th2 = hit_theta_int;
Int_t dPhi12 = phi2 - phi1;
Int_t dPhi13 = phi3 - phi1;
Int_t dPhi14 = phi4 - phi1;
Int_t dPhi23 = phi3 - phi2;
Int_t dPhi24 = phi4 - phi2;
Int_t dPhi34 = phi4 - phi3;
Int_t dTh12 = th2 - th1;
Int_t dTh13 = th3 - th1;
Int_t dTh14 = th4 - th1;
Int_t dTh23 = th3 - th2;
Int_t dTh24 = th4 - th2;
Int_t dTh34 = th4 - th3;
if (trk_mode == 7 && has_RPC != 1) {
dPhi12 = 0;
dPhi13 = dPhi23;
dPhi14 = dPhi34;
dTh12 = 0;
dTh13 = dTh23;
dTh14 = dTh24;
}
// if (trk_mode == 11 && has_RPC != 1) {
// dPhi12 = dPhi13 / 2;
// dPhi23 = dPhi13 - dPhi12;
// dPhi24 = dPhi23 + dPhi34;
// dTh12 = dTh13 / 2;
// dTh23 = dTh13 - dTh12;
// dTh24 = dTh23 + dTh34;
// }
// Define all dPhi values relative to dPhi12
Int_t dPhi12_sign = ( (dPhi12 < 0) ? -1. : 1. );
dPhi13 *= dPhi12_sign;
dPhi14 *= dPhi12_sign;
dPhi23 *= dPhi12_sign;
dPhi24 *= dPhi12_sign;
dPhi34 *= dPhi12_sign;
dPhi12 = abs(dPhi12);
Int_t dPhi_sum_4 = dPhi12 + dPhi13 + dPhi14 + dPhi23 + dPhi24 + dPhi34;
Int_t dPhi_sum_4A = abs(dPhi12) + abs(dPhi13) + abs(dPhi14) + abs(dPhi23) + abs(dPhi24) + abs(dPhi34);
Int_t pDev_st1 = abs(dPhi12) + abs(dPhi13) + abs(dPhi14);
Int_t pDev_st2 = abs(dPhi12) + abs(dPhi23) + abs(dPhi24);
Int_t pDev_st3 = abs(dPhi13) + abs(dPhi23) + abs(dPhi34);
Int_t pDev_st4 = abs(dPhi14) + abs(dPhi24) + abs(dPhi34);
Int_t dTh_sum_4 = dTh12 + dTh13 + dTh14 + dTh23 + dTh24 + dTh34;
Int_t dTh_sum_4A = abs(dTh12) + abs(dTh13) + abs(dTh14) + abs(dTh23) + abs(dTh24) + abs(dTh34);
Int_t tDev_st1 = abs(dTh12) + abs(dTh13) + abs(dTh14);
Int_t tDev_st2 = abs(dTh12) + abs(dTh23) + abs(dTh24);
Int_t tDev_st3 = abs(dTh13) + abs(dTh23) + abs(dTh34);
Int_t tDev_st4 = abs(dTh14) + abs(dTh24) + abs(dTh34);
Int_t out_st_phi = -88;
if (pDev_st4 > pDev_st3 && pDev_st4 > pDev_st2 && pDev_st4 > pDev_st1) out_st_phi = 4;
else if (pDev_st3 > pDev_st4 && pDev_st3 > pDev_st2 && pDev_st3 > pDev_st1) out_st_phi = 3;
else if (pDev_st2 > pDev_st4 && pDev_st2 > pDev_st3 && pDev_st2 > pDev_st1) out_st_phi = 2;
else if (pDev_st1 > pDev_st4 && pDev_st1 > pDev_st3 && pDev_st1 > pDev_st2) out_st_phi = 1;
else out_st_phi = 0;
Int_t out_st_th = -88;
if (tDev_st4 > tDev_st3 && tDev_st4 > tDev_st2 && tDev_st4 > tDev_st1) out_st_th = 4;
else if (tDev_st3 > tDev_st4 && tDev_st3 > tDev_st2 && tDev_st3 > tDev_st1) out_st_th = 3;
else if (tDev_st2 > tDev_st4 && tDev_st2 > tDev_st3 && tDev_st2 > tDev_st1) out_st_th = 2;
else if (tDev_st1 > tDev_st4 && tDev_st1 > tDev_st3 && tDev_st1 > tDev_st2) out_st_th = 1;
else out_st_th = 0;
Int_t dPhi_sum_3 = -88;
Int_t dPhi_sum_3A = -88;
if (out_st_phi == 4) {
dPhi_sum_3 = dPhi12 + dPhi13 + dPhi23;
dPhi_sum_3A = abs(dPhi12) + abs(dPhi13) + abs(dPhi23);
} else if (out_st_phi == 3) {
dPhi_sum_3 = dPhi12 + dPhi14 + dPhi24;
dPhi_sum_3A = abs(dPhi12) + abs(dPhi14) + abs(dPhi24);
} else if (out_st_phi == 2) {
dPhi_sum_3 = dPhi13 + dPhi14 + dPhi34;
dPhi_sum_3A = abs(dPhi13) + abs(dPhi14) + abs(dPhi34);
} else {
dPhi_sum_3 = dPhi23 + dPhi24 + dPhi34;
dPhi_sum_3A = abs(dPhi23) + abs(dPhi24) + abs(dPhi34);
}
// Clean out showering muons with outlier station 1, or >= 2 outlier stations
if (isMC && log2(mu_pt) > 6 && CLEAN_HI_PT)
if ( dPhi_sum_4A >= std::max(40., 332. - 40*log2(mu_pt)) )
if ( out_st_phi < 2 || dPhi_sum_3A >= std::max(24., 174. - 20*log2(mu_pt)) )
trainEvt = false;
Int_t dTh_max_4 = MaxOfSix( dTh12, dTh13, dTh14, dTh23, dTh24, dTh34 );
Int_t dTh_max_3 = -88;
Int_t dTh_sum_3 = -88;
Int_t dTh_sum_3A = -88;
if (out_st_th == 4) {
dTh_sum_3 = dTh12 + dTh13 + dTh23;
dTh_sum_3A = abs(dTh12) + abs(dTh13) + abs(dTh23);
dTh_max_3 = MaxOfThree(dTh12, dTh13, dTh23);
} else if (out_st_th == 3) {
dTh_sum_3 = dTh12 + dTh14 + dTh24;
dTh_sum_3A = abs(dTh12) + abs(dTh14) + abs(dTh24);
dTh_max_3 = MaxOfThree(dTh12, dTh14, dTh24);
} else if (out_st_th == 2) {
dTh_sum_3 = dTh13 + dTh14 + dTh34;
dTh_sum_3A = abs(dTh13) + abs(dTh14) + abs(dTh34);
dTh_max_3 = MaxOfThree(dTh13, dTh14, dTh34);
} else {
dTh_sum_3 = dTh23 + dTh24 + dTh34;
dTh_sum_3A = abs(dTh23) + abs(dTh24) + abs(dTh34);
dTh_max_3 = MaxOfThree(dTh23, dTh24, dTh34);
}
Int_t FR1 = (track_br->GetLeaf("hit_FR"))->GetValue(4*iTrk + 0);
Int_t FR2 = (track_br->GetLeaf("hit_FR"))->GetValue(4*iTrk + 1);
Int_t FR3 = (track_br->GetLeaf("hit_FR"))->GetValue(4*iTrk + 2);
Int_t FR4 = (track_br->GetLeaf("hit_FR"))->GetValue(4*iTrk + 3);
Int_t bend1 = CalcBendFromPattern( (track_br->GetLeaf("hit_pattern"))->GetValue(4*iTrk + 0), trk_eta ) * dPhi12_sign;
Int_t bend2 = CalcBendFromPattern( (track_br->GetLeaf("hit_pattern"))->GetValue(4*iTrk + 1), trk_eta ) * dPhi12_sign;
Int_t bend3 = CalcBendFromPattern( (track_br->GetLeaf("hit_pattern"))->GetValue(4*iTrk + 2), trk_eta ) * dPhi12_sign;
Int_t bend4 = CalcBendFromPattern( (track_br->GetLeaf("hit_pattern"))->GetValue(4*iTrk + 3), trk_eta ) * dPhi12_sign;
Int_t bendMax_3 = 0;
if (abs(bend2) > abs(bendMax_3)) bendMax_3 = bend2;
if (abs(bend3) > abs(bendMax_3)) bendMax_3 = bend3;
if (abs(bend4) > abs(bendMax_3)) bendMax_3 = bend4;
/////////////////////////////////////////////////////
/// Loop over factories and set variable values ///
/////////////////////////////////////////////////////
for (UInt_t iFact = 0; iFact < factories.size(); iFact++) {
// Set vars equal to default vector of variables for this factory
var_names = std::get<3>(factories.at(iFact));
var_vals = std::get<4>(factories.at(iFact));
// Fill all variables
for (UInt_t iVar = 0; iVar < var_names.size(); iVar++) {
TString vName = var_names.at(iVar);
/////////////////////////
/// Input variables ///
/////////////////////////
if ( vName == "theta" )
var_vals.at(iVar) = abs(trk_theta_int);
if ( vName == "St1_ring" )
var_vals.at(iVar) = st1_ring;
if ( vName == "dPhi_12" )
var_vals.at(iVar) = dPhi12;
if ( vName == "dPhi_12_sign" )
var_vals.at(iVar) = dPhi12_sign;
if ( vName == "dPhi_13" )
var_vals.at(iVar) = dPhi13;
if ( vName == "dPhi_14" )
var_vals.at(iVar) = dPhi14;
if ( vName == "dPhi_23" )
var_vals.at(iVar) = dPhi23;
if ( vName == "dPhi_24" )
var_vals.at(iVar) = dPhi24;
if ( vName == "dPhi_34" )
var_vals.at(iVar) = dPhi34;
if ( vName == "FR_1" )
var_vals.at(iVar) = FR1;
if ( vName == "FR_2" )
var_vals.at(iVar) = FR2;
if ( vName == "FR_3" )
var_vals.at(iVar) = FR3;
if ( vName == "FR_4" )
var_vals.at(iVar) = FR4;
if ( vName == "bend_1" )
var_vals.at(iVar) = bend1;
if ( vName == "bend_2" )
var_vals.at(iVar) = bend2;
if ( vName == "bend_3" )
var_vals.at(iVar) = bend3;
if ( vName == "bend_4" )
var_vals.at(iVar) = bend4;
if ( vName == "dPhiSum4" )
var_vals.at(iVar) = dPhi_sum_4;
if ( vName == "dPhiSum4A" )
var_vals.at(iVar) = dPhi_sum_4A;
if ( vName == "dPhiSum3" )
var_vals.at(iVar) = dPhi_sum_3;
if ( vName == "dPhiSum3A" )
var_vals.at(iVar) = dPhi_sum_3A;
if ( vName == "outStPhi" )
var_vals.at(iVar) = out_st_phi;
if ( vName == "outStTh" )
var_vals.at(iVar) = out_st_th;
if ( vName == "dThMax4" )
var_vals.at(iVar) = dTh_max_4;
if ( vName == "dThMax3" )
var_vals.at(iVar) = dTh_max_3;
if ( vName == "dThSum4" )
var_vals.at(iVar) = dTh_sum_4;
if ( vName == "dThSum4A" )
var_vals.at(iVar) = dTh_sum_4A;
if ( vName == "dThSum3" )
var_vals.at(iVar) = dTh_sum_3;
if ( vName == "dThSum3A" )
var_vals.at(iVar) = dTh_sum_3A;
if ( vName == "dTh_12" )
var_vals.at(iVar) = dTh12;
if ( vName == "dTh_13" )
var_vals.at(iVar) = dTh13;
if ( vName == "dTh_14" )
var_vals.at(iVar) = dTh14;
if ( vName == "bendMax3" )
var_vals.at(iVar) = bendMax_3;
// if ( vName == "dPhi_11" )
// var_vals.at(iVar) = dPhi11;
// if ( vName == "dPhi_22" )
// var_vals.at(iVar) = dPhi22;
// if ( vName == "dPhi_33" )
// var_vals.at(iVar) = dPhi33;
// if ( vName == "dPhi_44" )
// var_vals.at(iVar) = dPhi44;
////////////////////////////////////////
/// Target and spectator variables ///
////////////////////////////////////////
if ( vName == "GEN_pt" )
var_vals.at(iVar) = min(mu_pt, PTMAX_TRG);
if ( vName == "EMTF_pt" )
var_vals.at(iVar) = trk_pt;
if ( vName == "inv_GEN_pt" )
var_vals.at(iVar) = 1. / min(mu_pt, PTMAX_TRG);
if ( vName == "inv_EMTF_pt" )
var_vals.at(iVar) = 1. / trk_pt;
if ( vName == "log2_GEN_pt" )
var_vals.at(iVar) = log2(min(mu_pt, PTMAX_TRG));
if ( vName == "log2_EMTF_pt" )
var_vals.at(iVar) = log2(trk_pt);
if ( vName == "GEN_eta" )
var_vals.at(iVar) = mu_eta;
if ( vName == "EMTF_eta" )
var_vals.at(iVar) = trk_eta;
if ( vName == "GEN_charge" )
var_vals.at(iVar) = mu_charge;
if ( vName == "EMTF_charge" )
var_vals.at(iVar) = trk_charge;
if ( vName == "EMTF_mode" )
var_vals.at(iVar) = trk_mode;
if ( vName == "EMTF_hasRPC" )
var_vals.at(iVar) = has_RPC;
} // End loop: for (UInt_t iVar = 0; iVar < var_names.size(); iVar++)
// Unweighted distribution: flat in eta and 1/pT
Double_t evt_weight = 1.0;
// Weight by 1/pT so overall distribution is (1/pT)^2
if ( std::get<2>(factories.at(iFact)).Contains("_invPt") )
evt_weight = 1. / mu_pt;
if ( std::get<2>(factories.at(iFact)).Contains("_invPtSq") )
evt_weight = 1. / pow(mu_pt, 2);
// Load values into event
if ( (iEvt % 2) == 0 && isMC && trainEvt && nTrain < (MAX_TR - (iFact == 0)) && (!RPC_STUDY || iCh < nChains_RPC) ) {
std::get<1>(factories.at(iFact))->AddTrainingEvent( "Regression", var_vals, evt_weight );
if (iFact == 0) nTrain += 1;
// std::cout << "Added train event " << nTrain << std::endl;
}
else {
std::get<1>(factories.at(iFact))->AddTestEvent( "Regression", var_vals, evt_weight );
if (iFact == 0) nTest += 1;
// std::cout << "Added test event " << nTest << std::endl;
}
} // End loop: for (UInt_t iFact = 0; iFact < factories.size(); iFact++)
} // End loop: for (UInt_t iTrk = 0; iTrk < nTracks; iTrk++)
} // End loop: for (UInt_t iMu = 0; iMu < nMuons; iMu++)
if (isMC) iEvt += 1;
else iEvtZB += 1;
} // End loop: for (UInt_t jEvt = 0; jEvt < in_chain->GetEntries(); jEvt++)
} // End loop: for (int iCh = 0; iCh < in_chains.size(); iCh++) {
std::cout << "******* Made it out of the event loop *******" << std::endl;
std::cout << "nAlready = " << nAlready << ", nFound = " << nFound << ", nMissing = " << nMissing << std::endl;
string NTr;
string NTe;
ostringstream convertTr;
convertTr << nTrain;
NTr = convertTr.str();
ostringstream convertTe;
convertTe << nTest;
NTe = convertTe.str();
string numTrainStr = "nTrain_Regression="+NTr+":nTest_Regression="+NTe+":";
std::cout << "NTr: " << NTr << ", NTe: " << NTe << std::endl;
// // global event weights per tree (see below for setting event-wise weights)
// Double_t regWeight = 1.0;
for (UInt_t iFact = 0; iFact < factories.size(); iFact++) {
TMVA::Factory* factX = std::get<0>(factories.at(iFact));
TMVA::DataLoader* loadX = std::get<1>(factories.at(iFact));
// // You can add an arbitrary number of regression trees
// loadX->AddRegressionTree( regTree, regWeight );
// // This would set individual event weights (the variables defined in the
// // expression need to exist in the original TTree)
// loadX->SetWeightExpression( "var1", "Regression" );
loadX->SetWeightExpression( 1.0 );
// // Apply additional cuts on the signal and background samples (can be different)
// TCut mycut = "( abs(muon.eta[0]) > 1.25 && abs(muon.eta[1]) < 2.4 )"; // && track.mode[0] == 15 )";
// Set nTest_Regression to 0 to tell the DataLoader to use all remaining events in the trees after training for testing:
loadX->PrepareTrainingAndTestTree( "", numTrainStr+"SplitMode=Random:NormMode=NumEvents:!V" );
// loadX->PrepareTrainingAndTestTree( mycut, "nTrain_Regression=0:nTest_Regression=0:SplitMode=Random:NormMode=NumEvents:!V" );
// If no numbers of events are given, half of the events in the tree are used
// for training, and the other half for testing:
//
// loadX->PrepareTrainingAndTestTree( mycut, "SplitMode=random:!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
// Linear discriminant
if (Use["LD"])
factX->BookMethod( loadX, TMVA::Types::kLD, "LD",
"!H:!V:VarTransform=None" );
// Neural network (MLP)
if (Use["MLP"])
factX->BookMethod( loadX, TMVA::Types::kMLP, "MLP", (string)
"!H:!V:VarTransform=Norm:NeuronType=tanh:NCycles=20000:HiddenLayers=N+20:"+
"TestRate=6:TrainingMethod=BFGS:Sampling=0.3:SamplingEpoch=0.8:"+
"ConvergenceImprove=1e-6:ConvergenceTests=15:!UseRegulator" );
if (Use["DNN"])
{
// TString layoutString ("Layout=TANH|(N+100)*2,LINEAR");
// TString layoutString ("Layout=SOFTSIGN|100,SOFTSIGN|50,SOFTSIGN|20,LINEAR");
// TString layoutString ("Layout=RELU|300,RELU|100,RELU|30,RELU|10,LINEAR");
// TString layoutString ("Layout=SOFTSIGN|50,SOFTSIGN|30,SOFTSIGN|20,SOFTSIGN|10,LINEAR");
// TString layoutString ("Layout=TANH|50,TANH|30,TANH|20,TANH|10,LINEAR");
// TString layoutString ("Layout=SOFTSIGN|50,SOFTSIGN|20,LINEAR");
// TString layoutString ("Layout=TANH|100,TANH|30,LINEAR");
TString layoutString ("Layout=TANH|100,LINEAR");
TString training0 ( (string) "LearningRate=1e-5,Momentum=0.5,Repetitions=1,"+
"ConvergenceSteps=500,BatchSize=50,TestRepetitions=7,WeightDecay=0.01,"+
"Regularization=NONE,DropConfig=0.5+0.5+0.5+0.5,DropRepetitions=2");
TString training1 ( (string) "LearningRate=1e-5,Momentum=0.9,Repetitions=1,"+
"ConvergenceSteps=170,BatchSize=30,TestRepetitions=7,WeightDecay=0.01,"+
"Regularization=L2,DropConfig=0.1+0.1+0.1,DropRepetitions=1");
TString training2 ( (string) "LearningRate=1e-5,Momentum=0.3,Repetitions=1,ConvergenceSteps=150,"+
"BatchSize=40,TestRepetitions=7,WeightDecay=0.01,Regularization=NONE");
TString training3 ( (string) "LearningRate=1e-6,Momentum=0.1,Repetitions=1,ConvergenceSteps=500,"+
"BatchSize=100,TestRepetitions=7,WeightDecay=0.0001,Regularization=NONE");