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jindal |
1.2 |
// @(#)root/tmva $Id: TMVAnalysis_QCD-el.C,v 1.1 2009/05/14 17:08:04 jindal Exp $
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jindal |
1.1 |
/**********************************************************************************
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* Project : TMVA - a Root-integrated toolkit for multivariate data analysis *
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* Package : TMVA *
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* Root Macro: TMVAnalysis *
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* *
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* This macro provides examples for the training and testing of all the *
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* TMVA classifiers. *
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* *
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* As input data is used a toy-MC sample consisting of four Gaussian-distributed *
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* and linearly correlated input variables. *
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* *
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* The methods to be used can be switched on and off by means of booleans, or *
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* via the prompt command, for example: *
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* *
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* root -l TMVAnalysis.C\(\"Fisher,Likelihood\"\) *
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* *
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* (note that the backslashes are mandatory) *
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* *
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* The output file "TMVA.root" can be analysed with the use of dedicated *
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* macros (simply say: root -l <macro.C>), which can be conveniently *
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* invoked through a GUI that will appear at the end of the run of this macro. *
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**********************************************************************************/
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#include <iostream>
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#include "TCut.h"
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#include "TFile.h"
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#include "TSystem.h"
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#include "TTree.h"
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// requires links
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#include "TMVA/Factory.h"
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#include "TMVA/Tools.h"
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#include "TMVA/Config.h"
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#include "TMVAGui.C"
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// ---------------------------------------------------------------
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// choose MVA methods to be trained + tested
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Bool_t Use_Cuts = 0;
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Bool_t Use_CutsD = 0;
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Bool_t Use_CutsGA = 1;
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// ---
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Bool_t Use_Likelihood = 1;
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Bool_t Use_LikelihoodD = 0; // the "D" extension indicates decorrelated input variables (see option strings)
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Bool_t Use_LikelihoodPCA = 1; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
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Bool_t Use_LikelihoodKDE = 0;
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Bool_t Use_LikelihoodMIX = 0;
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// ---
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Bool_t Use_PDERS = 1;
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Bool_t Use_PDERSD = 0;
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Bool_t Use_PDERSPCA = 0;
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Bool_t Use_KNN = 1;
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// ---
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Bool_t Use_HMatrix = 1;
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Bool_t Use_Fisher = 1;
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// ---
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Bool_t Use_FDA_GA = 0;
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Bool_t Use_FDA_MC = 0;
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Bool_t Use_FDA_SA = 0;
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Bool_t Use_FDA_MT = 1;
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Bool_t Use_FDA_GAMT = 0;
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Bool_t Use_FDA_MCMT = 0;
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// ---
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Bool_t Use_MLP = 1; // this is the recommended ANN
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Bool_t Use_CFMlpANN = 0;
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Bool_t Use_TMlpANN = 0;
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// ---
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Bool_t Use_BDT = 1;
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Bool_t Use_BDTD = 0;
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// ---
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Bool_t Use_RuleFitTMVA = 1;
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Bool_t Use_RuleFitJF = 0;
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// ---
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Bool_t Use_SVM_Gauss = 1;
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Bool_t Use_SVM_Poly = 0;
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Bool_t Use_SVM_Lin = 0;
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// ---------------------------------------------------------------
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// read input data file with ascii format (otherwise ROOT) ?
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Bool_t ReadDataFromAsciiIFormat = kFALSE;
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void TMVAnalysis( TString myMethodList = "" )
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{
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// explicit loading of the shared libTMVA is done in TMVAlogon.C, defined in .rootrc
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// if you use your private .rootrc, or run from a different directory, please copy the
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// corresponding lines from .rootrc
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// methods to be processed can be given as an argument; use format:
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//
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// mylinux~> root -l TMVAnalysis.C\(\"myMethod1,myMethod2,myMethod3\"\)
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//
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TList* mlist = TMVA::Tools::ParseFormatLine( myMethodList, " :," );
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if (mlist->GetSize()>0) {
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Use_CutsGA = Use_CutsD = Use_Cuts
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= Use_LikelihoodKDE = Use_LikelihoodMIX = Use_LikelihoodPCA = Use_LikelihoodD = Use_Likelihood
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= Use_PDERSPCA = Use_PDERSD = Use_PDERS
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= Use_KNN
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= Use_MLP = Use_CFMlpANN = Use_TMlpANN
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= Use_HMatrix = Use_Fisher = Use_BDTD = Use_BDT
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= Use_RuleFitTMVA = Use_RuleFitJF
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= Use_SVM_Gauss = Use_SVM_Poly = Use_SVM_Lin
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= Use_FDA_GA = Use_FDA_MC = Use_FDA_SA = Use_FDA_MT = Use_FDA_GAMT = Use_FDA_MCMT
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= 0;
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if (mlist->FindObject( "Cuts" ) != 0) Use_Cuts = 1;
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if (mlist->FindObject( "CutsD" ) != 0) Use_CutsD = 1;
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if (mlist->FindObject( "CutsGA" ) != 0) Use_CutsGA = 1;
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if (mlist->FindObject( "Likelihood" ) != 0) Use_Likelihood = 1;
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if (mlist->FindObject( "LikelihoodD" ) != 0) Use_LikelihoodD = 1;
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if (mlist->FindObject( "LikelihoodPCA" ) != 0) Use_LikelihoodPCA = 1;
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if (mlist->FindObject( "LikelihoodKDE" ) != 0) Use_LikelihoodKDE = 1;
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if (mlist->FindObject( "LikelihoodMIX" ) != 0) Use_LikelihoodMIX = 1;
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if (mlist->FindObject( "PDERSPCA" ) != 0) Use_PDERSPCA = 1;
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if (mlist->FindObject( "PDERSD" ) != 0) Use_PDERSD = 1;
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if (mlist->FindObject( "PDERS" ) != 0) Use_PDERS = 1;
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if (mlist->FindObject( "KNN" ) != 0) Use_KNN = 1;
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if (mlist->FindObject( "HMatrix" ) != 0) Use_HMatrix = 1;
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if (mlist->FindObject( "Fisher" ) != 0) Use_Fisher = 1;
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if (mlist->FindObject( "MLP" ) != 0) Use_MLP = 1;
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if (mlist->FindObject( "CFMlpANN" ) != 0) Use_CFMlpANN = 1;
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if (mlist->FindObject( "TMlpANN" ) != 0) Use_TMlpANN = 1;
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if (mlist->FindObject( "BDTD" ) != 0) Use_BDTD = 1;
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if (mlist->FindObject( "BDT" ) != 0) Use_BDT = 1;
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if (mlist->FindObject( "RuleFitJF" ) != 0) Use_RuleFitJF = 1;
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if (mlist->FindObject( "RuleFitTMVA" ) != 0) Use_RuleFitTMVA = 1;
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if (mlist->FindObject( "SVM_Gauss" ) != 0) Use_SVM_Gauss = 1;
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if (mlist->FindObject( "SVM_Poly" ) != 0) Use_SVM_Poly = 1;
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if (mlist->FindObject( "SVM_Lin" ) != 0) Use_SVM_Lin = 1;
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if (mlist->FindObject( "FDA_MC" ) != 0) Use_FDA_MC = 1;
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if (mlist->FindObject( "FDA_GA" ) != 0) Use_FDA_GA = 1;
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if (mlist->FindObject( "FDA_SA" ) != 0) Use_FDA_SA = 1;
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if (mlist->FindObject( "FDA_MT" ) != 0) Use_FDA_MT = 1;
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if (mlist->FindObject( "FDA_GAMT" ) != 0) Use_FDA_GAMT = 1;
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if (mlist->FindObject( "FDA_MCMT" ) != 0) Use_FDA_MCMT = 1;
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delete mlist;
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}
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std::cout << "Start Test TMVAnalysis" << std::endl
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<< "======================" << std::endl
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<< std::endl;
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std::cout << "Testing all standard methods may take about 10 minutes of running..." << std::endl;
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// Create a new root output file.
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TString outfileName( "TMVA_QCD.root" );
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TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
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// Create the factory object. Later you can choose the methods
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// whose performance you'd like to investigate. The factory will
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// then run the performance analysis for you.
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//
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// The first argument is the base of the name of all the
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// weightfiles in the directory weight/
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//
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// The second argument is the output file for the training results
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TMVA::Factory *factory = new TMVA::Factory( "TMVAnalysis", outputFile, Form("V:%sColor", gROOT->IsBatch()?"!":"") );
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// if you wish to modify default settings
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// (please check "src/Config.h" to see all available global options)
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// (TMVA::gConfig().GetVariablePlotting()).fTimesRMS = 8.0;
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// (TMVA::gConfig().GetIONames()).fWeightFileDir = "myWeightDirectory";
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if (ReadDataFromAsciiIFormat) {
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// load the signal and background event samples from ascii files
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// format in file must be:
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// var1/F:var2/F:var3/F:var4/F
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// 0.04551 0.59923 0.32400 -0.19170
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// ...
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TString datFileS = "data/toy_sig_lincorr.dat";
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TString datFileB = "data/toy_bkg_lincorr.dat";
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if (!factory->SetInputTrees( datFileS, datFileB )) exit(1);
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}
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else {
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// load the signal and background event samples from ROOT trees
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TFile *input(0);
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//TString fname = "./tmva_training.root";
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//TString fname = "./tmva_training-12sep2008.root";
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//TString fname = "./tmva_training-15sep2008.root";
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//TString fname = "./tmva_training-17sep2008.root";
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//TString fname = "./tmva_training-20nov2008.root";
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//TString fname = "/uscms_data/d1/lpcljm/MVA/Summer08/training/tmva_training-summer08-22dec2008.root";
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//TString fname = "/uscms_data/d1/lpcljm/MVA/Summer08/training/tmva_training-summer08-09jan2009.root";
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//TString fname = "/uscms_data/d1/lpcljm/MVA/Summer08/training/tmva_training_noQCD-summer08-06feb2009.root";
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//TString fname = "/uscms_data/d1/lpcljm/MVA/Summer08/training/tmva_training-summer08-25feb2009.root";
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//TString fname = "muon_jets_training-summer08-08apr2009.root";
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jindal |
1.2 |
TString fname = "/uscms_data/d2/lpcljm/MVA/Summer08/training/electron_jets_training-wzfastsim-summer08-15may2009.root";
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jindal |
1.1 |
if (!gSystem->AccessPathName( fname )) {
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// first we try to find tmva_example.root in the local directory
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std::cout << "--- TMVAnalysis : accessing " << fname << std::endl;
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input = TFile::Open( fname );
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}
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else {
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// second we try accessing the file via the web from
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// http://root.cern.ch/files/tmva_example.root
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std::cout << "--- TMVAnalysis : accessing tmva_example.root file from http://root.cern.ch/files" << std::endl;
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std::cout << "--- TMVAnalysis : for faster startup you may consider downloading it into you local directory" << std::endl;
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input = TFile::Open( "http://root.cern.ch/files/tmva_example.root" );
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}
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if (!input) {
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std::cout << "ERROR: could not open data file" << std::endl;
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exit(1);
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}
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TTree *signal = (TTree*)input->Get("ttbar");
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//TTree *background = (TTree*)input->Get("wzjets");
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TTree *background = (TTree*)input->Get("qcd");
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// global event weights (see below for setting event-wise weights)
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Double_t signalWeight = 1.0;
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Double_t backgroundWeight = 1.0;
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factory->AddSignalTree ( signal, signalWeight );
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factory->AddBackgroundTree( background, backgroundWeight );
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}
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// Define the input variables that shall be used for the MVA training
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// note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)"
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// [all types of expressions that can also be parsed by TTree::Draw( "expression" )]
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/*
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factory->AddVariable("getHt3", 'F');
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factory->AddVariable("aplanarity", 'F');
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factory->AddVariable("dPhiLMet", 'F');
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factory->AddVariable("sphericity", 'F');
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factory->AddVariable("jet1Jet2DeltaR", 'F');
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factory->AddVariable("leptonJetDeltaR", 'F');
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*/
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//
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//_____ D0 vars ______________________________________________________
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//
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/*
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//factory->AddVariable("ht", 'F');
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factory->AddVariable("ht2p", 'F');
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//factory->AddVariable("et3", 'F');
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//factory->AddVariable("metHtPlusLepton", 'F'); // degenerates L: causes a peak at zero
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//factory->AddVariable("htPlusLepton", 'F');// degenerates L: causes a peak at zero
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factory->AddVariable("sphericity", 'F');
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factory->AddVariable("aplanarity", 'F');
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factory->AddVariable("jet1Jet2DeltaR", 'F');
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factory->AddVariable("maxJetEta", 'F');
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//factory->AddVariable("h", 'F');
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factory->AddVariable("jet1_eta", 'F');
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factory->AddVariable("jet2_eta", 'F');
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//factory->AddVariable("hz", 'F');
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factory->AddVariable("dPhiLMet", 'F');
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factory->AddVariable("minDiJetDeltaR", 'F');
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factory->AddVariable("leptonJetDeltaR", 'F');
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factory->AddVariable("DphiJMET", 'F');
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*/
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//
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/*
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factory->AddVariable("aplanarity", 'F');
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factory->AddVariable("getHt3", 'F');
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factory->AddVariable("ktMinPrime", 'F');
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factory->AddVariable("DphiJMET", 'F');
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factory->AddVariable("W_MT", 'F');
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*/
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//
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//_____ BDT test vars ________________________________________________
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//
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/*
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factory->AddVariable("aplanarity", 'F');
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factory->AddVariable("et3", 'F');
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factory->AddVariable("getEta2Sum", 'F');
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factory->AddVariable("getKtminpReduced", 'F');
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factory->AddVariable("getMdijetMin", 'F');
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factory->AddVariable("ht", 'F');
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factory->AddVariable("jet1Jet2DeltaR", 'F');
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factory->AddVariable("jet1Jet2_M", 'F');
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factory->AddVariable("lepton_eta", 'F');
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factory->AddVariable("maxJetEta", 'F');
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factory->AddVariable("metHtPlusLepton", 'F'); // degenerates L: causes a peak at zero
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factory->AddVariable("minDiJetDeltaR", 'F');
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factory->AddVariable("minDijetMass", 'F');
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factory->AddVariable("sphericity", 'F');
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//
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*/
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jindal |
1.2 |
/*
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jindal |
1.1 |
//_____ "best" set for BDT wzjets ____________________________________
|
| 285 |
|
|
//
|
| 286 |
|
|
factory->AddVariable("jet3_pt", 'F');
|
| 287 |
|
|
factory->AddVariable("jet4_pt", 'F');
|
| 288 |
|
|
factory->AddVariable("jet2_pt", 'F');
|
| 289 |
|
|
factory->AddVariable("met_pt", 'F');
|
| 290 |
|
|
// factory->AddVariable("lepton_energy", 'F');
|
| 291 |
|
|
factory->AddVariable("metHtPlusLepton", 'F');
|
| 292 |
|
|
factory->AddVariable("getHt", 'F');
|
| 293 |
|
|
factory->AddVariable("htPlusLepton", 'F');// degenerates L: causes a peak at zero
|
| 294 |
|
|
factory->AddVariable("jet1_pt", 'F');
|
| 295 |
|
|
factory->AddVariable("getHt3", 'F');
|
| 296 |
|
|
factory->AddVariable("getHt2", 'F');
|
| 297 |
|
|
factory->AddVariable("sphericity", 'F');
|
| 298 |
|
|
factory->AddVariable("getAplMu", 'F');
|
| 299 |
|
|
factory->AddVariable("getHt2p", 'F');
|
| 300 |
|
|
factory->AddVariable("dPhiLMet", 'F');
|
| 301 |
jindal |
1.2 |
*/
|
| 302 |
jindal |
1.1 |
|
| 303 |
jindal |
1.2 |
|
| 304 |
jindal |
1.1 |
//_____ "best" set for BDT multijets ____________________________________
|
| 305 |
|
|
//
|
| 306 |
|
|
|
| 307 |
|
|
|
| 308 |
jindal |
1.2 |
factory->AddVariable("metHtPlusLepton", 'F'); // degenerates L: causes a peak at zero
|
| 309 |
|
|
factory->AddVariable("lepton_pt", 'F');
|
| 310 |
jindal |
1.1 |
factory->AddVariable("jet4_pt", 'F');
|
| 311 |
jindal |
1.2 |
factory->AddVariable("getAplMu", 'F');
|
| 312 |
|
|
factory->AddVariable("dPhiLMet", 'F');
|
| 313 |
jindal |
1.1 |
factory->AddVariable("W_Pt", 'F'); // MET
|
| 314 |
jindal |
1.2 |
factory->AddVariable("met_pt", 'F');
|
| 315 |
jindal |
1.1 |
factory->AddVariable("W_MT", 'F'); // MET
|
| 316 |
|
|
factory->AddVariable("lepton_eta", 'F');
|
| 317 |
|
|
factory->AddVariable("getMwRec", 'F');
|
| 318 |
jindal |
1.2 |
factory->AddVariable("sphericity", 'F');
|
| 319 |
|
|
factory->AddVariable("jet1_pt", 'F');
|
| 320 |
|
|
factory->AddVariable("htPlusLepton", 'F');// degenerates L: causes a peak at zero
|
| 321 |
|
|
factory->AddVariable("DphiJMET", 'F');
|
| 322 |
|
|
factory->AddVariable("getHt3", 'F');
|
| 323 |
jindal |
1.1 |
|
| 324 |
|
|
|
| 325 |
|
|
|
| 326 |
jindal |
1.2 |
|
| 327 |
|
|
|
| 328 |
|
|
//
|
| 329 |
jindal |
1.1 |
/*
|
| 330 |
|
|
//_____ all variables (not ranked) ___________________________________
|
| 331 |
|
|
//
|
| 332 |
|
|
|
| 333 |
|
|
factory->AddVariable("DphiJMET", 'F');
|
| 334 |
|
|
factory->AddVariable("dPhiLMet", 'F');
|
| 335 |
|
|
factory->AddVariable("W_MT", 'F'); // MET
|
| 336 |
|
|
factory->AddVariable("W_Pt", 'F'); // MET
|
| 337 |
|
|
factory->AddVariable("getApl", 'F');
|
| 338 |
|
|
factory->AddVariable("getAplMu", 'F'); // identical to aplanarity (machine precision)
|
| 339 |
|
|
factory->AddVariable("getCen", 'F'); // identical to centrality
|
| 340 |
|
|
factory->AddVariable("getSph", 'F');
|
| 341 |
|
|
factory->AddVariable("getDrMinJetJet", 'F');
|
| 342 |
|
|
factory->AddVariable("getEta2Sum", 'F');
|
| 343 |
|
|
factory->AddVariable("getH", 'F');
|
| 344 |
|
|
factory->AddVariable("getHt", 'F'); // identical to ht
|
| 345 |
|
|
factory->AddVariable("getHt2", 'F'); // identical to HT2
|
| 346 |
|
|
factory->AddVariable("getHt2p", 'F'); // identical to ht2p
|
| 347 |
|
|
factory->AddVariable("getHt2pp", 'F');
|
| 348 |
|
|
factory->AddVariable("getHt3", 'F');
|
| 349 |
|
|
factory->AddVariable("getHt3p", 'F');
|
| 350 |
|
|
factory->AddVariable("getHt3pp", 'F');
|
| 351 |
|
|
factory->AddVariable("getHtp", 'F');
|
| 352 |
|
|
factory->AddVariable("getHtpp", 'F');
|
| 353 |
|
|
factory->AddVariable("getJetEtaMax", 'F');
|
| 354 |
|
|
factory->AddVariable("getKtminp", 'F');
|
| 355 |
|
|
factory->AddVariable("getKtminpReduced", 'F'); // ktmin
|
| 356 |
|
|
factory->AddVariable("getMdijetMin", 'F');
|
| 357 |
|
|
factory->AddVariable("getMtjets", 'F');
|
| 358 |
|
|
factory->AddVariable("getMwRec", 'F');
|
| 359 |
|
|
factory->AddVariable("getPzOverHT", 'F');
|
| 360 |
|
|
factory->AddVariable("htPlusLepton", 'F');// degenerates L: causes a peak at zero
|
| 361 |
|
|
factory->AddVariable("hz", 'F'); // ???MET
|
| 362 |
|
|
factory->AddVariable("jet1Jet2DeltaPhi", 'F');
|
| 363 |
|
|
factory->AddVariable("jet1Jet2DeltaR", 'F');
|
| 364 |
|
|
factory->AddVariable("jet1Jet2W_M", 'F');
|
| 365 |
|
|
factory->AddVariable("jet1Jet2_Pt", 'F');
|
| 366 |
|
|
factory->AddVariable("jet1Jet2_M", 'F');
|
| 367 |
|
|
factory->AddVariable("jet1_energy", 'F');
|
| 368 |
|
|
factory->AddVariable("jet1_eta", 'F');
|
| 369 |
|
|
factory->AddVariable("jet1_phi", 'F');
|
| 370 |
|
|
factory->AddVariable("jet1_pt", 'F');
|
| 371 |
|
|
factory->AddVariable("jet2_energy", 'F');
|
| 372 |
|
|
factory->AddVariable("jet2_eta", 'F');
|
| 373 |
|
|
factory->AddVariable("jet2_phi", 'F');
|
| 374 |
|
|
factory->AddVariable("jet2_pt", 'F');
|
| 375 |
|
|
factory->AddVariable("jet3_energy", 'F');
|
| 376 |
|
|
factory->AddVariable("jet3_eta", 'F');
|
| 377 |
|
|
factory->AddVariable("jet3_phi", 'F');
|
| 378 |
|
|
factory->AddVariable("jet3_pt", 'F');
|
| 379 |
|
|
factory->AddVariable("jet4_energy", 'F');
|
| 380 |
|
|
factory->AddVariable("jet4_eta", 'F');
|
| 381 |
|
|
factory->AddVariable("jet4_phi", 'F');
|
| 382 |
|
|
factory->AddVariable("jet4_pt", 'F');
|
| 383 |
jindal |
1.2 |
//factory->AddVariable("ktMinPrime", 'F');
|
| 384 |
jindal |
1.1 |
// factory->AddVariable("leptonJetDeltaR", 'F');
|
| 385 |
|
|
factory->AddVariable("lepton_energy", 'F');
|
| 386 |
|
|
factory->AddVariable("lepton_eta", 'F');
|
| 387 |
|
|
factory->AddVariable("lepton_phi", 'F');
|
| 388 |
|
|
factory->AddVariable("lepton_pt", 'F');
|
| 389 |
|
|
factory->AddVariable("metHtPlusLepton", 'F'); // degenerates L: causes a peak at zero
|
| 390 |
|
|
factory->AddVariable("met_phi", 'F');
|
| 391 |
|
|
factory->AddVariable("met_pt", 'F');
|
| 392 |
|
|
factory->AddVariable("sphericity", 'F');
|
| 393 |
jindal |
1.2 |
|
| 394 |
|
|
*/
|
| 395 |
jindal |
1.1 |
//______ duplicates __________________________________________________
|
| 396 |
|
|
//
|
| 397 |
|
|
/*
|
| 398 |
|
|
factory->AddVariable("minDiJetDeltaR", 'F'); // getDrMinJetJet
|
| 399 |
|
|
factory->AddVariable("minDijetMass", 'F'); // getMdijetMin
|
| 400 |
|
|
factory->AddVariable("maxJetEta", 'F'); // getJetEtaMax
|
| 401 |
|
|
factory->AddVariable("ht", 'F'); // getHt
|
| 402 |
|
|
factory->AddVariable("h", 'F'); // getH
|
| 403 |
|
|
factory->AddVariable("ht2p", 'F'); // identical to getHt2p
|
| 404 |
|
|
factory->AddVariable("HT2prime", 'F'); // ht2p
|
| 405 |
|
|
factory->AddVariable("HT2", 'F'); // identical to getHt2
|
| 406 |
|
|
factory->AddVariable("aplanarity", 'F'); // identical to getApl (machine precision)
|
| 407 |
|
|
factory->AddVariable("centrality", 'F'); // getCen
|
| 408 |
|
|
*/
|
| 409 |
|
|
//
|
| 410 |
|
|
//_____ variables probably not for MVA _______________________________
|
| 411 |
|
|
//
|
| 412 |
|
|
/*
|
| 413 |
|
|
factory->AddVariable("met_eta", 'F'); // not filled?
|
| 414 |
|
|
factory->AddVariable("lepton_track_iso", 'F');
|
| 415 |
|
|
factory->AddVariable("lepton_calo_iso", 'F');
|
| 416 |
|
|
factory->AddVariable("n_electrons", 'F');
|
| 417 |
|
|
factory->AddVariable("n_jets", 'F');
|
| 418 |
|
|
factory->AddVariable("n_jets", 'F');
|
| 419 |
|
|
factory->AddVariable("n_met", 'F');
|
| 420 |
|
|
factory->AddVariable("n_muons", 'F');
|
| 421 |
|
|
factory->AddVariable("n_tagged_jets_jetProb_loose", 'F');
|
| 422 |
|
|
factory->AddVariable("n_tagged_jets_jetProb_medium", 'F');
|
| 423 |
|
|
factory->AddVariable("n_tagged_jets_jetProb_tight", 'F');
|
| 424 |
|
|
factory->AddVariable("n_tagged_jets_trackCounting_loose", 'F');
|
| 425 |
|
|
factory->AddVariable("n_tagged_jets_trackCounting_medium", 'F');
|
| 426 |
|
|
factory->AddVariable("n_tagged_jets_trackCounting_tight", 'F');
|
| 427 |
|
|
factory->AddVariable("electron_id_loose", 'F');
|
| 428 |
|
|
factory->AddVariable("electron_id_robust", 'F');
|
| 429 |
|
|
factory->AddVariable("electron_id_tight", 'F');
|
| 430 |
|
|
factory->AddVariable("electron_tdrid_loose", 'F');
|
| 431 |
|
|
factory->AddVariable("electron_tdrid_medium", 'F');
|
| 432 |
|
|
factory->AddVariable("electron_tdrid_tight", 'F');
|
| 433 |
|
|
*/
|
| 434 |
|
|
|
| 435 |
|
|
// This would set individual event weights (the variables defined in the
|
| 436 |
|
|
// expression need to exist in the original TTree)
|
| 437 |
|
|
factory->SetWeightExpression("event_weight");
|
| 438 |
|
|
|
| 439 |
|
|
// Apply additional cuts on the signal and background sample.
|
| 440 |
|
|
//gROOT->Macro("./cuts.C");
|
| 441 |
|
|
//TCut mycut = "et3<500.0 && getHt3<500.0"; // for LL ratio, cut tails
|
| 442 |
|
|
TCut mycut = "";
|
| 443 |
|
|
|
| 444 |
|
|
// tell the factory to use all remaining events in the trees after training for testing:
|
| 445 |
|
|
//TString split_opt = "NSigTrain=3000:NBkgTrain=638:SplitMode=Random:NormMode=NumEvents:!V";
|
| 446 |
|
|
//TString split_opt = "NSigTrain=3000:NBkgTrain=1510:SplitMode=Random:NormMode=NumEvents:!V";
|
| 447 |
|
|
//TString split_opt = "NSigTrain=60000:NBkgTrain=6000:SplitMode=Random:NormMode=NumEvents:!V";
|
| 448 |
|
|
//TString split_opt = "NSigTrain=30000:NBkgTrain=1668:SplitMode=Random:NormMode=NumEvents:!V";
|
| 449 |
|
|
//TString split_opt = "NSigTrain=26000:NBkgTrain=1061:SplitMode=Random:NormMode=NumEvents:V";
|
| 450 |
jindal |
1.2 |
TString split_opt = "NSigTrain=10000:NBkgTrain=10000:SplitMode=Random:NormMode=NumEvents:!V";
|
| 451 |
jindal |
1.1 |
|
| 452 |
|
|
factory->PrepareTrainingAndTestTree( mycut, split_opt );
|
| 453 |
|
|
|
| 454 |
|
|
|
| 455 |
|
|
// ===> Set a different testing tree
|
| 456 |
|
|
//FIXME:
|
| 457 |
|
|
//factory->AddSignalTree();
|
| 458 |
|
|
|
| 459 |
|
|
|
| 460 |
|
|
// If no numbers of events are given, half of the events in the tree are used for training, and
|
| 461 |
|
|
// the other half for testing:
|
| 462 |
|
|
// factory->PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" );
|
| 463 |
|
|
// To also specify the number of testing events, use:
|
| 464 |
|
|
// factory->PrepareTrainingAndTestTree( mycut,
|
| 465 |
|
|
// "NSigTrain=3000:NBkgTrain=3000:NSigTest=3000:NBkgTest=3000:SplitMode=Random:!V" );
|
| 466 |
|
|
|
| 467 |
|
|
// ---- Book MVA methods
|
| 468 |
|
|
//
|
| 469 |
|
|
// please lookup the various method configuration options in the corresponding cxx files, eg:
|
| 470 |
|
|
// src/MethoCuts.cxx, etc.
|
| 471 |
|
|
// it is possible to preset ranges in the option string in which the cut optimisation should be done:
|
| 472 |
|
|
// "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable
|
| 473 |
|
|
|
| 474 |
|
|
// Cut optimisation
|
| 475 |
|
|
if (Use_Cuts)
|
| 476 |
|
|
factory->BookMethod( TMVA::Types::kCuts, "Cuts",
|
| 477 |
|
|
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" );
|
| 478 |
|
|
|
| 479 |
|
|
if (Use_CutsD)
|
| 480 |
|
|
factory->BookMethod( TMVA::Types::kCuts, "CutsD",
|
| 481 |
|
|
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=Decorrelate" );
|
| 482 |
|
|
|
| 483 |
|
|
if (Use_CutsGA)
|
| 484 |
|
|
factory->BookMethod( TMVA::Types::kCuts, "CutsGA",
|
| 485 |
|
|
"!H:!V:FitMethod=GA:EffSel:Steps=30:Cycles=3:PopSize=100:SC_steps=10:SC_rate=5:SC_factor=0.95:VarProp=FSmart" );
|
| 486 |
|
|
|
| 487 |
|
|
// Likelihood
|
| 488 |
|
|
if (Use_Likelihood)
|
| 489 |
|
|
factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood",
|
| 490 |
|
|
"H:V:!TransformOutput:PDFInterpol=Spline2:\
|
| 491 |
|
|
NSmooth=0:NSmoothSig=0:NSmoothBkg=5:\
|
| 492 |
|
|
NAvEvtPerBin=50:NAvEvtPerBinSig=50:NAvEvtPerBinBkg=50:\
|
| 493 |
|
|
CreateMVAPdfs=True:NbinsMVAPdf=15" );
|
| 494 |
|
|
|
| 495 |
|
|
|
| 496 |
|
|
|
| 497 |
|
|
// test the decorrelated likelihood
|
| 498 |
|
|
if (Use_LikelihoodD)
|
| 499 |
|
|
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodD",
|
| 500 |
|
|
"!H:!V:!TransformOutput:PDFInterpol=Spline3:\
|
| 501 |
|
|
NSmooth=0:NSmoothSig=0:NSmoothBkg=0:\
|
| 502 |
|
|
NAvEvtPerBin=50:NAvEvtPerBinSig=50:NAvEvtPerBinBkg=50:\
|
| 503 |
|
|
VarTransform=Decorrelate:\
|
| 504 |
|
|
NSmoothSig[0]=0:NSmoothBkg[0]=0" );
|
| 505 |
|
|
|
| 506 |
|
|
if (Use_LikelihoodPCA)
|
| 507 |
|
|
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodPCA",
|
| 508 |
|
|
"!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=100:NSmoothBkg[0]=10:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA" );
|
| 509 |
|
|
|
| 510 |
|
|
// test the new kernel density estimator
|
| 511 |
|
|
if (Use_LikelihoodKDE)
|
| 512 |
|
|
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodKDE",
|
| 513 |
|
|
"!H:!V:!TransformOutput:PDFInterpol=KDE:KDEtype=Gauss:KDEiter=Nonadaptive:KDEborder=None:NAvEvtPerBin=50" );
|
| 514 |
|
|
|
| 515 |
|
|
// test the mixed splines and kernel density estimator (depending on which variable)
|
| 516 |
|
|
if (Use_LikelihoodMIX)
|
| 517 |
|
|
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodMIX",
|
| 518 |
|
|
"!H:!V:!TransformOutput:PDFInterpol[0]=KDE:PDFInterpol[1]=KDE:PDFInterpol[2]=Spline2:PDFInterpol[3]=Spline2:KDEtype=Gauss:KDEiter=Nonadaptive:KDEborder=None:NAvEvtPerBin=50" );
|
| 519 |
|
|
|
| 520 |
|
|
// PDE - RS method
|
| 521 |
|
|
if (Use_PDERS)
|
| 522 |
|
|
factory->BookMethod( TMVA::Types::kPDERS, "PDERS",
|
| 523 |
|
|
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:InitialScale=0.99" );
|
| 524 |
|
|
|
| 525 |
|
|
if (Use_PDERSD)
|
| 526 |
|
|
factory->BookMethod( TMVA::Types::kPDERS, "PDERSD",
|
| 527 |
|
|
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:InitialScale=0.99:VarTransform=Decorrelate" );
|
| 528 |
|
|
|
| 529 |
|
|
if (Use_PDERSPCA)
|
| 530 |
|
|
factory->BookMethod( TMVA::Types::kPDERS, "PDERSPCA",
|
| 531 |
|
|
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:InitialScale=0.99:VarTransform=PCA" );
|
| 532 |
|
|
|
| 533 |
|
|
// K-Nearest Neighbour classifier (KNN)
|
| 534 |
|
|
if (Use_KNN)
|
| 535 |
|
|
factory->BookMethod( TMVA::Types::kKNN, "KNN",
|
| 536 |
|
|
"nkNN=40:TreeOptDepth=6:ScaleFrac=0.8:!UseKernel:!Trim" );
|
| 537 |
|
|
|
| 538 |
|
|
// H-Matrix (chi2-squared) method
|
| 539 |
|
|
if (Use_HMatrix)
|
| 540 |
|
|
factory->BookMethod( TMVA::Types::kHMatrix, "HMatrix", "!H:!V" );
|
| 541 |
|
|
|
| 542 |
|
|
// Fisher discriminant
|
| 543 |
|
|
if (Use_Fisher)
|
| 544 |
|
|
factory->BookMethod( TMVA::Types::kFisher, "Fisher",
|
| 545 |
|
|
"H:!V:!Normalise:CreateMVAPdfs:Fisher:NbinsMVAPdf=50:NsmoothMVAPdf=1" );
|
| 546 |
|
|
|
| 547 |
|
|
// Function discrimination analysis (FDA) -- test of various fitters - the recommended one is Minuit or GA
|
| 548 |
|
|
if (Use_FDA_MC)
|
| 549 |
|
|
factory->BookMethod( TMVA::Types::kFDA, "FDA_MC",
|
| 550 |
|
|
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:SampleSize=100000:Sigma=0.1" );
|
| 551 |
|
|
|
| 552 |
|
|
if (Use_FDA_GA)
|
| 553 |
|
|
factory->BookMethod( TMVA::Types::kFDA, "FDA_GA",
|
| 554 |
|
|
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:PopSize=100:Cycles=3:Steps=20:Trim=True:SaveBestGen=0" );
|
| 555 |
|
|
|
| 556 |
|
|
if (Use_FDA_SA)
|
| 557 |
|
|
factory->BookMethod( TMVA::Types::kFDA, "FDA_SA",
|
| 558 |
|
|
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=SA:MaxCalls=50000:TemperatureGradient=0.7:InitialTemperature=2000000:MinTemperature=500:Eps=1e-04:NFunLoops=5:NEps=4" );
|
| 559 |
|
|
|
| 560 |
|
|
if (Use_FDA_MT)
|
| 561 |
|
|
factory->BookMethod( TMVA::Types::kFDA, "FDA_MT",
|
| 562 |
|
|
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=2:UseImprove:UseMinos:SetBatch" );
|
| 563 |
|
|
|
| 564 |
|
|
if (Use_FDA_GAMT)
|
| 565 |
|
|
factory->BookMethod( TMVA::Types::kFDA, "FDA_GAMT",
|
| 566 |
|
|
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:Cycles=1:PopSize=5:Steps=5:Trim" );
|
| 567 |
|
|
|
| 568 |
|
|
if (Use_FDA_MCMT)
|
| 569 |
|
|
factory->BookMethod( TMVA::Types::kFDA, "FDA_MCMT",
|
| 570 |
|
|
"H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=MC:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:SampleSize=20" );
|
| 571 |
|
|
|
| 572 |
|
|
// TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons
|
| 573 |
|
|
if (Use_MLP)
|
| 574 |
|
|
factory->BookMethod( TMVA::Types::kMLP, "MLP", "Normalise:H:!V:NCycles=200:HiddenLayers=N+1,N:TestRate=5" );
|
| 575 |
|
|
|
| 576 |
|
|
// CF(Clermont-Ferrand)ANN
|
| 577 |
|
|
if (Use_CFMlpANN)
|
| 578 |
|
|
factory->BookMethod( TMVA::Types::kCFMlpANN, "CFMlpANN", "!H:!V:NCycles=500:HiddenLayers=N+1,N" ); // n_cycles:#nodes:#nodes:...
|
| 579 |
|
|
|
| 580 |
|
|
// Tmlp(Root)ANN
|
| 581 |
|
|
if (Use_TMlpANN)
|
| 582 |
|
|
factory->BookMethod( TMVA::Types::kTMlpANN, "TMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N" ); // n_cycles:#nodes:#nodes:...
|
| 583 |
|
|
|
| 584 |
|
|
// Support Vector Machines using three different Kernel types (Gauss, polynomial and linear)
|
| 585 |
|
|
if (Use_SVM_Gauss)
|
| 586 |
|
|
factory->BookMethod( TMVA::Types::kSVM, "SVM_Gauss", "Sigma=2:C=1:Tol=0.001:Kernel=Gauss" );
|
| 587 |
|
|
|
| 588 |
|
|
if (Use_SVM_Poly)
|
| 589 |
|
|
factory->BookMethod( TMVA::Types::kSVM, "SVM_Poly", "Order=4:Theta=1:C=0.1:Tol=0.001:Kernel=Polynomial" );
|
| 590 |
|
|
|
| 591 |
|
|
if (Use_SVM_Lin)
|
| 592 |
|
|
factory->BookMethod( TMVA::Types::kSVM, "SVM_Lin", "!H:!V:Kernel=Linear:C=1:Tol=0.001" );
|
| 593 |
|
|
|
| 594 |
|
|
// Boosted Decision Trees (second one with decorrelation)
|
| 595 |
|
|
if (Use_BDT)
|
| 596 |
|
|
factory->BookMethod( TMVA::Types::kBDT, "BDT",
|
| 597 |
|
|
"H:V:NTrees=1000:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=10:PruneMethod=CostComplexity:PruneStrength=15.0:nEventsMin=20 ");
|
| 598 |
|
|
|
| 599 |
|
|
if (Use_BDTD)
|
| 600 |
|
|
factory->BookMethod( TMVA::Types::kBDT, "BDTD",
|
| 601 |
|
|
"!H:!V:NTrees=100:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=CostComplexity:PruneStrength=4.5:VarTransform=Decorrelate" );
|
| 602 |
|
|
|
| 603 |
|
|
// RuleFit -- TMVA implementation of Friedman's method
|
| 604 |
|
|
if (Use_RuleFitTMVA)
|
| 605 |
|
|
factory->BookMethod( TMVA::Types::kRuleFit, "RuleFitTMVA",
|
| 606 |
|
|
"H:!V:RuleFitModule=RFTMVA:Model=ModRuleLinear:MinImp=0.0001:RuleMinDist=0.0001:NTrees=20:fEventsMin=0.01:fEventsMax=0.5:GDTau=-1.0:GDTauPrec=0.01:GDStep=0.001:GDNSteps=100000:GDErrScale=1.02" );
|
| 607 |
|
|
//"H:!V:RuleFitModule=RFTMVA:Model=ModRuleLinear:MinImp=0.001:RuleMinDist=0.001:NTrees=20:fEventsMin=0.01:fEventsMax=0.1:GDTau=-1.0:GDTauPrec=0.01:GDStep=0.0001:GDNSteps=100000:GDErrScale=1.02" );
|
| 608 |
|
|
//"H:!V:RuleFitModule=RFTMVA:Model=ModRuleLinear:MinImp=0.001:RuleMinDist=0.001:NTrees=20:fEventsMin=0.01:fEventsMax=0.5:GDTau=-1.0:GDTauPrec=0.01:GDStep=0.01:GDNSteps=100000:GDErrScale=1.02" );
|
| 609 |
|
|
|
| 610 |
|
|
// Friedman's RuleFit method, implementation by J. Friedman
|
| 611 |
|
|
if (Use_RuleFitJF)
|
| 612 |
|
|
factory->BookMethod( TMVA::Types::kRuleFit, "RuleFitJF",
|
| 613 |
|
|
"!V:RuleFitModule=RFFriedman:Model=ModRuleLinear:GDStep=0.01:GDNSteps=10000:GDErrScale=1.1:RFNendnodes=4" );
|
| 614 |
|
|
|
| 615 |
|
|
// ---- Now you can tell the factory to train, test, and evaluate the MVAs
|
| 616 |
|
|
|
| 617 |
|
|
// Train MVAs using the set of training events
|
| 618 |
|
|
factory->TrainAllMethods();
|
| 619 |
|
|
|
| 620 |
|
|
// ---- Evaluate all MVAs using the set of test events
|
| 621 |
|
|
factory->TestAllMethods();
|
| 622 |
|
|
|
| 623 |
|
|
// ----- Evaluate and compare performance of all configured MVAs
|
| 624 |
|
|
factory->EvaluateAllMethods();
|
| 625 |
|
|
|
| 626 |
|
|
// --------------------------------------------------------------
|
| 627 |
|
|
|
| 628 |
|
|
// Save the output
|
| 629 |
|
|
outputFile->Close();
|
| 630 |
|
|
|
| 631 |
|
|
std::cout << "==> wrote root file TMVA.root" << std::endl;
|
| 632 |
|
|
std::cout << "==> TMVAnalysis is done!" << std::endl;
|
| 633 |
|
|
|
| 634 |
|
|
// Clean up
|
| 635 |
|
|
delete factory;
|
| 636 |
|
|
|
| 637 |
|
|
// Launch the GUI for the root macros
|
| 638 |
|
|
if (!gROOT->IsBatch()) TMVAGui( outfileName );
|
| 639 |
|
|
}
|
