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kukartse |
1.2 |
// @(#)root/tmva $Id: TMVAnalysis.C,v 1.1 2009/01/21 18:36:08 kukartse Exp $
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kukartse |
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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kukartse |
1.2 |
#include "TMVA/Factory.h"
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#include "TMVA/Tools.h"
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#include "TMVA/Config.h"
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kukartse |
1.1 |
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#include "TMVAGui.C"
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kukartse |
1.2 |
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kukartse |
1.1 |
// ---------------------------------------------------------------
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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.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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kukartse |
1.2 |
TString fname = "./tmva_training.root";
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kukartse |
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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kukartse |
1.2 |
TTree *signal = (TTree*)input->Get("sig");
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TTree *background = (TTree*)input->Get("bg");
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kukartse |
1.1 |
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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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kukartse |
1.2 |
factory->AddVariable("var1", 'F');
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factory->AddVariable("var2", 'F');
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kukartse |
1.1 |
factory->AddVariable("var1+var2", 'F');
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// This would set individual event weights (the variables defined in the
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// expression need to exist in the original TTree)
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kukartse |
1.2 |
factory->SetWeightExpression("event_weight");
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kukartse |
1.1 |
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// Apply additional cuts on the signal and background sample.
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kukartse |
1.2 |
TCut mycut = "";
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kukartse |
1.1 |
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// tell the factory to use all remaining events in the trees after training for testing:
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kukartse |
1.2 |
TString split_opt = "NSigTrain=3000:NBkgTrain=3000:SplitMode=Random:NormMode=NumEvents:!V";
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factory->PrepareTrainingAndTestTree( mycut, split_opt );
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kukartse |
1.1 |
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// If no numbers of events are given, half of the events in the tree are used for training, and
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// the other half for testing:
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// factory->PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" );
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// To also specify the number of testing events, use:
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// factory->PrepareTrainingAndTestTree( mycut,
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// "NSigTrain=3000:NBkgTrain=3000:NSigTest=3000:NBkgTest=3000:SplitMode=Random:!V" );
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// ---- Book MVA methods
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//
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// please lookup the various method configuration options in the corresponding cxx files, eg:
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// src/MethoCuts.cxx, etc.
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// it is possible to preset ranges in the option string in which the cut optimisation should be done:
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// "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable
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// Cut optimisation
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if (Use_Cuts)
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factory->BookMethod( TMVA::Types::kCuts, "Cuts",
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"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" );
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if (Use_CutsD)
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factory->BookMethod( TMVA::Types::kCuts, "CutsD",
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"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=Decorrelate" );
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if (Use_CutsGA)
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factory->BookMethod( TMVA::Types::kCuts, "CutsGA",
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"!H:!V:FitMethod=GA:EffSel:Steps=30:Cycles=3:PopSize=100:SC_steps=10:SC_rate=5:SC_factor=0.95:VarProp=FSmart" );
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// Likelihood
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if (Use_Likelihood)
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factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood",
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kukartse |
1.2 |
"H:V:!TransformOutput:PDFInterpol=Spline2" );
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// "H:V:!TransformOutput:PDFInterpol=Spline2:\
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//NSmoothSig[0]=10:NSmoothSig[1]=10:NSmoothSig[2]=10:NSmoothSig[3]=100:NSmoothSig[4]=100:NSmoothSig[5]=100:NSmoothSig[6]=100:\
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//NSmoothBkg[0]=10:NSmoothBkg[1]=10:NSmoothBkg[2]=10:NSmoothBkg[3]=100:NSmoothBkg[4]=100:NSmoothBkg[5]=100:NSmoothBkg[6]=100:\
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//NSmooth=10:NAvEvtPerBin=50" );
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//NSmoothSig[0]=10:NSmoothSig[1]=10:NSmoothSig[2]=10:NSmoothSig[3]=10:NSmoothSig[4]=10:NSmoothSig[5]=10:NSmoothSig[6]=10:NSmoothSig[7]=10:NSmoothSig[8]=10:NSmoothSig[9]=10:NSmoothSig[10]=10:NSmoothSig[11]=10:NSmoothSig[12]=10:NSmoothSig[13]=10:NSmoothSig[14]=10:NSmoothSig[15]=10:NSmoothSig[16]=10: \
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//NSmoothBkg[0]=10:NSmoothBkg[1]=10:NSmoothBkg[2]=10:NSmoothBkg[3]=10:NSmoothBkg[4]=10:NSmoothBkg[5]=10:NSmoothBkg[6]=10:NSmoothBkg[7]=10:NSmoothBkg[8]=10:NSmoothBkg[9]=10:NSmoothBkg[10]=10:NSmoothBkg[11]=10:NSmoothBkg[12]=10:NSmoothBkg[13]=10:NSmoothBkg[14]=10:NSmoothBkg[15]=10:NSmoothBkg[16]=10: \
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| 264 |
kukartse |
1.1 |
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| 265 |
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// test the decorrelated likelihood
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if (Use_LikelihoodD)
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factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodD",
|
| 268 |
|
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"!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=100:NSmoothBkg[0]=10:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate" );
|
| 269 |
|
|
|
| 270 |
|
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if (Use_LikelihoodPCA)
|
| 271 |
|
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factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodPCA",
|
| 272 |
|
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"!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=100:NSmoothBkg[0]=10:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA" );
|
| 273 |
|
|
|
| 274 |
|
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// test the new kernel density estimator
|
| 275 |
|
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if (Use_LikelihoodKDE)
|
| 276 |
|
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factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodKDE",
|
| 277 |
|
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"!H:!V:!TransformOutput:PDFInterpol=KDE:KDEtype=Gauss:KDEiter=Nonadaptive:KDEborder=None:NAvEvtPerBin=50" );
|
| 278 |
|
|
|
| 279 |
|
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// test the mixed splines and kernel density estimator (depending on which variable)
|
| 280 |
|
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if (Use_LikelihoodMIX)
|
| 281 |
|
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factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodMIX",
|
| 282 |
|
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"!H:!V:!TransformOutput:PDFInterpol[0]=KDE:PDFInterpol[1]=KDE:PDFInterpol[2]=Spline2:PDFInterpol[3]=Spline2:KDEtype=Gauss:KDEiter=Nonadaptive:KDEborder=None:NAvEvtPerBin=50" );
|
| 283 |
|
|
|
| 284 |
|
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// PDE - RS method
|
| 285 |
|
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if (Use_PDERS)
|
| 286 |
|
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factory->BookMethod( TMVA::Types::kPDERS, "PDERS",
|
| 287 |
|
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"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:InitialScale=0.99" );
|
| 288 |
|
|
|
| 289 |
|
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if (Use_PDERSD)
|
| 290 |
|
|
factory->BookMethod( TMVA::Types::kPDERS, "PDERSD",
|
| 291 |
|
|
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:InitialScale=0.99:VarTransform=Decorrelate" );
|
| 292 |
|
|
|
| 293 |
|
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if (Use_PDERSPCA)
|
| 294 |
|
|
factory->BookMethod( TMVA::Types::kPDERS, "PDERSPCA",
|
| 295 |
|
|
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:InitialScale=0.99:VarTransform=PCA" );
|
| 296 |
|
|
|
| 297 |
|
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// K-Nearest Neighbour classifier (KNN)
|
| 298 |
|
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if (Use_KNN)
|
| 299 |
|
|
factory->BookMethod( TMVA::Types::kKNN, "KNN",
|
| 300 |
|
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"nkNN=40:TreeOptDepth=6:ScaleFrac=0.8:!UseKernel:!Trim" );
|
| 301 |
|
|
|
| 302 |
|
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// H-Matrix (chi2-squared) method
|
| 303 |
|
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if (Use_HMatrix)
|
| 304 |
|
|
factory->BookMethod( TMVA::Types::kHMatrix, "HMatrix", "!H:!V" );
|
| 305 |
|
|
|
| 306 |
|
|
// Fisher discriminant
|
| 307 |
|
|
if (Use_Fisher)
|
| 308 |
|
|
factory->BookMethod( TMVA::Types::kFisher, "Fisher",
|
| 309 |
|
|
"H:!V:!Normalise:CreateMVAPdfs:Fisher:NbinsMVAPdf=50:NsmoothMVAPdf=1" );
|
| 310 |
|
|
|
| 311 |
|
|
// Function discrimination analysis (FDA) -- test of various fitters - the recommended one is Minuit or GA
|
| 312 |
|
|
if (Use_FDA_MC)
|
| 313 |
|
|
factory->BookMethod( TMVA::Types::kFDA, "FDA_MC",
|
| 314 |
|
|
"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" );
|
| 315 |
|
|
|
| 316 |
|
|
if (Use_FDA_GA)
|
| 317 |
|
|
factory->BookMethod( TMVA::Types::kFDA, "FDA_GA",
|
| 318 |
|
|
"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" );
|
| 319 |
|
|
|
| 320 |
|
|
if (Use_FDA_SA)
|
| 321 |
|
|
factory->BookMethod( TMVA::Types::kFDA, "FDA_SA",
|
| 322 |
|
|
"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" );
|
| 323 |
|
|
|
| 324 |
|
|
if (Use_FDA_MT)
|
| 325 |
|
|
factory->BookMethod( TMVA::Types::kFDA, "FDA_MT",
|
| 326 |
|
|
"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" );
|
| 327 |
|
|
|
| 328 |
|
|
if (Use_FDA_GAMT)
|
| 329 |
|
|
factory->BookMethod( TMVA::Types::kFDA, "FDA_GAMT",
|
| 330 |
|
|
"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" );
|
| 331 |
|
|
|
| 332 |
|
|
if (Use_FDA_MCMT)
|
| 333 |
|
|
factory->BookMethod( TMVA::Types::kFDA, "FDA_MCMT",
|
| 334 |
|
|
"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" );
|
| 335 |
|
|
|
| 336 |
|
|
// TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons
|
| 337 |
|
|
if (Use_MLP)
|
| 338 |
|
|
factory->BookMethod( TMVA::Types::kMLP, "MLP", "Normalise:H:!V:NCycles=200:HiddenLayers=N+1,N:TestRate=5" );
|
| 339 |
|
|
|
| 340 |
|
|
// CF(Clermont-Ferrand)ANN
|
| 341 |
|
|
if (Use_CFMlpANN)
|
| 342 |
|
|
factory->BookMethod( TMVA::Types::kCFMlpANN, "CFMlpANN", "!H:!V:NCycles=500:HiddenLayers=N+1,N" ); // n_cycles:#nodes:#nodes:...
|
| 343 |
|
|
|
| 344 |
|
|
// Tmlp(Root)ANN
|
| 345 |
|
|
if (Use_TMlpANN)
|
| 346 |
|
|
factory->BookMethod( TMVA::Types::kTMlpANN, "TMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N" ); // n_cycles:#nodes:#nodes:...
|
| 347 |
|
|
|
| 348 |
|
|
// Support Vector Machines using three different Kernel types (Gauss, polynomial and linear)
|
| 349 |
|
|
if (Use_SVM_Gauss)
|
| 350 |
|
|
factory->BookMethod( TMVA::Types::kSVM, "SVM_Gauss", "Sigma=2:C=1:Tol=0.001:Kernel=Gauss" );
|
| 351 |
|
|
|
| 352 |
|
|
if (Use_SVM_Poly)
|
| 353 |
|
|
factory->BookMethod( TMVA::Types::kSVM, "SVM_Poly", "Order=4:Theta=1:C=0.1:Tol=0.001:Kernel=Polynomial" );
|
| 354 |
|
|
|
| 355 |
|
|
if (Use_SVM_Lin)
|
| 356 |
|
|
factory->BookMethod( TMVA::Types::kSVM, "SVM_Lin", "!H:!V:Kernel=Linear:C=1:Tol=0.001" );
|
| 357 |
|
|
|
| 358 |
|
|
// Boosted Decision Trees (second one with decorrelation)
|
| 359 |
|
|
if (Use_BDT)
|
| 360 |
|
|
factory->BookMethod( TMVA::Types::kBDT, "BDT",
|
| 361 |
|
|
"!H:!V:NTrees=400:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=CostComplexity:PruneStrength=4.5" );
|
| 362 |
|
|
if (Use_BDTD)
|
| 363 |
|
|
factory->BookMethod( TMVA::Types::kBDT, "BDTD",
|
| 364 |
|
|
"!H:!V:NTrees=400:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=CostComplexity:PruneStrength=4.5:VarTransform=Decorrelate" );
|
| 365 |
|
|
|
| 366 |
|
|
// RuleFit -- TMVA implementation of Friedman's method
|
| 367 |
|
|
if (Use_RuleFitTMVA)
|
| 368 |
kukartse |
1.2 |
factory->BookMethod( TMVA::Types::kRuleFit, "RuleFitTMVA", "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" );
|
| 369 |
kukartse |
1.1 |
|
| 370 |
|
|
// Friedman's RuleFit method, implementation by J. Friedman
|
| 371 |
|
|
if (Use_RuleFitJF)
|
| 372 |
|
|
factory->BookMethod( TMVA::Types::kRuleFit, "RuleFitJF",
|
| 373 |
|
|
"!V:RuleFitModule=RFFriedman:Model=ModRuleLinear:GDStep=0.01:GDNSteps=10000:GDErrScale=1.1:RFNendnodes=4" );
|
| 374 |
|
|
|
| 375 |
|
|
// ---- Now you can tell the factory to train, test, and evaluate the MVAs
|
| 376 |
|
|
|
| 377 |
|
|
// Train MVAs using the set of training events
|
| 378 |
|
|
factory->TrainAllMethods();
|
| 379 |
|
|
|
| 380 |
|
|
// ---- Evaluate all MVAs using the set of test events
|
| 381 |
|
|
factory->TestAllMethods();
|
| 382 |
|
|
|
| 383 |
|
|
// ----- Evaluate and compare performance of all configured MVAs
|
| 384 |
|
|
factory->EvaluateAllMethods();
|
| 385 |
|
|
|
| 386 |
|
|
// --------------------------------------------------------------
|
| 387 |
|
|
|
| 388 |
|
|
// Save the output
|
| 389 |
|
|
outputFile->Close();
|
| 390 |
|
|
|
| 391 |
|
|
std::cout << "==> wrote root file TMVA.root" << std::endl;
|
| 392 |
|
|
std::cout << "==> TMVAnalysis is done!" << std::endl;
|
| 393 |
|
|
|
| 394 |
|
|
// Clean up
|
| 395 |
|
|
delete factory;
|
| 396 |
|
|
|
| 397 |
|
|
// Launch the GUI for the root macros
|
| 398 |
|
|
if (!gROOT->IsBatch()) TMVAGui( outfileName );
|
| 399 |
|
|
}
|
