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// @(#)root/tmva $Id: TMVAClassification.C 37399 2010-12-08 15:22:07Z evt $
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/**********************************************************************************
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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: TMVAClassification *
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* *
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* This macro provides examples for the training and testing of 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 ./TMVAClassification.C\(\"Fisher,Likelihood\"\) *
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* *
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* (note that the backslashes are mandatory) *
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* If no method given, a default set of classifiers is used. *
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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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* Launch the GUI via the command: *
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* *
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* root -l ./TMVAGui.C *
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* *
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**********************************************************************************/
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#include <cstdlib>
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#include <iostream>
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#include <map>
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#include <string>
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#include "TChain.h"
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#include "TFile.h"
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#include "TTree.h"
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#include "TString.h"
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#include "TObjString.h"
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#include "TSystem.h"
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#include "TROOT.h"
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#include "TMVAGui.C"
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#if not defined(__CINT__) || defined(__MAKECINT__)
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// needs to be included when makecint runs (ACLIC)
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#include "TMVA/Factory.h"
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#include "TMVA/Tools.h"
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#endif
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void TMVAClassification( TString myMethodList = "" )
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{
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// The 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 TMVAClassification.C\(\"myMethod1,myMethod2,myMethod3\"\)
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//
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// if you like to use a method via the plugin mechanism, we recommend using
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//
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// mylinux~> root -l TMVAClassification.C\(\"P_myMethod\"\)
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// (an example is given for using the BDT as plugin (see below),
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// but of course the real application is when you write your own
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// method based)
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//---------------------------------------------------------------
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// This loads the library
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TMVA::Tools::Instance();
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// Default MVA methods to be trained + tested
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std::map<std::string,int> Use;
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// --- Cut optimisation
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Use["Cuts"] = 1;
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Use["CutsD"] = 1;
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Use["CutsPCA"] = 0;
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Use["CutsGA"] = 0;
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Use["CutsSA"] = 0;
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//
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// --- 1-dimensional likelihood ("naive Bayes estimator")
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Use["Likelihood"] = 1;
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Use["LikelihoodD"] = 0; // the "D" extension indicates decorrelated input variables (see option strings)
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Use["LikelihoodPCA"] = 1; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
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Use["LikelihoodKDE"] = 0;
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Use["LikelihoodMIX"] = 0;
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//
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// --- Mutidimensional likelihood and Nearest-Neighbour methods
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Use["PDERS"] = 1;
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Use["PDERSD"] = 0;
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Use["PDERSPCA"] = 0;
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Use["PDEFoam"] = 1;
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Use["PDEFoamBoost"] = 0; // uses generalised MVA method boosting
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Use["KNN"] = 1; // k-nearest neighbour method
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//
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// --- Linear Discriminant Analysis
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Use["LD"] = 1; // Linear Discriminant identical to Fisher
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Use["Fisher"] = 0;
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Use["FisherG"] = 0;
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Use["BoostedFisher"] = 0; // uses generalised MVA method boosting
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Use["HMatrix"] = 0;
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//
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// --- Function Discriminant analysis
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Use["FDA_GA"] = 1; // minimisation of user-defined function using Genetics Algorithm
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Use["FDA_SA"] = 0;
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Use["FDA_MC"] = 0;
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Use["FDA_MT"] = 0;
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Use["FDA_GAMT"] = 0;
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Use["FDA_MCMT"] = 0;
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//
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// --- Neural Networks (all are feed-forward Multilayer Perceptrons)
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Use["MLP"] = 0; // Recommended ANN
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Use["MLPBFGS"] = 0; // Recommended ANN with optional training method
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Use["MLPBNN"] = 1; // Recommended ANN with BFGS training method and bayesian regulator
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Use["CFMlpANN"] = 0; // Depreciated ANN from ALEPH
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Use["TMlpANN"] = 0; // ROOT's own ANN
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//
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// --- Support Vector Machine
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Use["SVM"] = 1;
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//
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// --- Boosted Decision Trees
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Use["BDT"] = 1; // uses Adaptive Boost
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Use["BDTG"] = 0; // uses Gradient Boost
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Use["BDTB"] = 0; // uses Bagging
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Use["BDTD"] = 0; // decorrelation + Adaptive Boost
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//
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// --- Friedman's RuleFit method, ie, an optimised series of cuts ("rules")
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Use["RuleFit"] = 1;
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// ---------------------------------------------------------------
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std::cout << std::endl;
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std::cout << "==> Start TMVAClassification" << std::endl;
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// Select methods (don't look at this code - not of interest)
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if (myMethodList != "") {
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for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;
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std::vector<TString> mlist = TMVA::gTools().SplitString( myMethodList, ',' );
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for (UInt_t i=0; i<mlist.size(); i++) {
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std::string regMethod(mlist[i]);
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if (Use.find(regMethod) == Use.end()) {
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std::cout << "Method \"" << regMethod << "\" not known in TMVA under this name. Choose among the following:" << std::endl;
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for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) std::cout << it->first << " ";
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std::cout << std::endl;
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return;
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}
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Use[regMethod] = 1;
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}
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}
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// --------------------------------------------------------------------------------------------------
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// --- Here the preparation phase begins
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// Create a ROOT output file where TMVA will store ntuples, histograms, etc.
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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 is
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// the only TMVA object you have to interact with
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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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// All TMVA output can be suppressed by removing the "!" (not) in
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// front of the "Silent" argument in the option string
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TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
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"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );
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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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// 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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factory->AddVariable( "myvar1 := var1+var2", 'F' );
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factory->AddVariable( "myvar2 := var1-var2", "Expression 2", "", 'F' );
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factory->AddVariable( "var3", "Variable 3", "units", 'F' );
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factory->AddVariable( "var4", "Variable 4", "units", 'F' );
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// You can add so-called "Spectator variables", which are not used in the MVA training,
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// but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the
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// input variables, the response values of all trained MVAs, and the spectator variables
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factory->AddSpectator( "spec1 := var1*2", "Spectator 1", "units", 'F' );
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factory->AddSpectator( "spec2 := var1*3", "Spectator 2", "units", 'F' );
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// Read training and test data
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// (it is also possible to use ASCII format as input -> see TMVA Users Guide)
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TString fname = "./tmva_class_example.root";
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if (gSystem->AccessPathName( fname )) // file does not exist in local directory
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gSystem->Exec("wget http://root.cern.ch/files/tmva_class_example.root");
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TFile *input = TFile::Open( fname );
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std::cout << "--- TMVAClassification : Using input file: " << input->GetName() << std::endl;
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// --- Register the training and test trees
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TTree *signal = (TTree*)input->Get("TreeS");
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TTree *background = (TTree*)input->Get("TreeB");
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// global event weights per tree (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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// You can add an arbitrary number of signal or background trees
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factory->AddSignalTree ( signal, signalWeight );
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factory->AddBackgroundTree( background, backgroundWeight );
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// To give different trees for training and testing, do as follows:
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// factory->AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" );
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// factory->AddSignalTree( signalTestTree, signalTestWeight, "Test" );
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// Use the following code instead of the above two or four lines to add signal and background
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// training and test events "by hand"
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// NOTE that in this case one should not give expressions (such as "var1+var2") in the input
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// variable definition, but simply compute the expression before adding the event
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//
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// // --- begin ----------------------------------------------------------
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// std::vector<Double_t> vars( 4 ); // vector has size of number of input variables
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// Float_t treevars[4], weight;
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//
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// // Signal
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// for (UInt_t ivar=0; ivar<4; ivar++) signal->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
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// for (UInt_t i=0; i<signal->GetEntries(); i++) {
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// signal->GetEntry(i);
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// for (UInt_t ivar=0; ivar<4; ivar++) vars[ivar] = treevars[ivar];
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// // add training and test events; here: first half is training, second is testing
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// // note that the weight can also be event-wise
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// if (i < signal->GetEntries()/2.0) factory->AddSignalTrainingEvent( vars, signalWeight );
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// else factory->AddSignalTestEvent ( vars, signalWeight );
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// }
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//
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// // Background (has event weights)
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// background->SetBranchAddress( "weight", &weight );
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// for (UInt_t ivar=0; ivar<4; ivar++) background->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
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// for (UInt_t i=0; i<background->GetEntries(); i++) {
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// background->GetEntry(i);
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// for (UInt_t ivar=0; ivar<4; ivar++) vars[ivar] = treevars[ivar];
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// // add training and test events; here: first half is training, second is testing
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// // note that the weight can also be event-wise
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// if (i < background->GetEntries()/2) factory->AddBackgroundTrainingEvent( vars, backgroundWeight*weight );
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// else factory->AddBackgroundTestEvent ( vars, backgroundWeight*weight );
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// }
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// --- end ------------------------------------------------------------
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//
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// --- end of tree registration
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// Set individual event weights (the variables must exist in the original TTree)
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// for signal : factory->SetSignalWeightExpression ("weight1*weight2");
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// for background: factory->SetBackgroundWeightExpression("weight1*weight2");
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factory->SetBackgroundWeightExpression( "weight" );
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// Apply additional cuts on the signal and background samples (can be different)
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TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
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TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";
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// Tell the factory how to use the training and testing events
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//
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// If no numbers of events are given, half of the events in the tree are used
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// for training, and 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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factory->PrepareTrainingAndTestTree( mycuts, mycutb,
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"nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!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, or here: http://tmva.sourceforge.net/optionRef.html
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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["CutsPCA"])
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factory->BookMethod( TMVA::Types::kCuts, "CutsPCA",
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"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=PCA" );
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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:CutRangeMin[0]=-10:CutRangeMax[0]=10:VarProp[1]=FMax:EffSel:Steps=30:Cycles=3:PopSize=400:SC_steps=10:SC_rate=5:SC_factor=0.95" );
|
| 299 |
|
|
|
| 300 |
|
|
if (Use["CutsSA"])
|
| 301 |
|
|
factory->BookMethod( TMVA::Types::kCuts, "CutsSA",
|
| 302 |
|
|
"!H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );
|
| 303 |
|
|
|
| 304 |
|
|
// Likelihood ("naive Bayes estimator")
|
| 305 |
|
|
if (Use["Likelihood"])
|
| 306 |
|
|
factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood",
|
| 307 |
|
|
"H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" );
|
| 308 |
|
|
|
| 309 |
|
|
// Decorrelated likelihood
|
| 310 |
|
|
if (Use["LikelihoodD"])
|
| 311 |
|
|
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodD",
|
| 312 |
|
|
"!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate" );
|
| 313 |
|
|
|
| 314 |
|
|
// PCA-transformed likelihood
|
| 315 |
|
|
if (Use["LikelihoodPCA"])
|
| 316 |
|
|
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodPCA",
|
| 317 |
|
|
"!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA" );
|
| 318 |
|
|
|
| 319 |
|
|
// Use a kernel density estimator to approximate the PDFs
|
| 320 |
|
|
if (Use["LikelihoodKDE"])
|
| 321 |
|
|
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodKDE",
|
| 322 |
|
|
"!H:!V:!TransformOutput:PDFInterpol=KDE:KDEtype=Gauss:KDEiter=Adaptive:KDEFineFactor=0.3:KDEborder=None:NAvEvtPerBin=50" );
|
| 323 |
|
|
|
| 324 |
|
|
// Use a variable-dependent mix of splines and kernel density estimator
|
| 325 |
|
|
if (Use["LikelihoodMIX"])
|
| 326 |
|
|
factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodMIX",
|
| 327 |
|
|
"!H:!V:!TransformOutput:PDFInterpolSig[0]=KDE:PDFInterpolBkg[0]=KDE:PDFInterpolSig[1]=KDE:PDFInterpolBkg[1]=KDE:PDFInterpolSig[2]=Spline2:PDFInterpolBkg[2]=Spline2:PDFInterpolSig[3]=Spline2:PDFInterpolBkg[3]=Spline2:KDEtype=Gauss:KDEiter=Nonadaptive:KDEborder=None:NAvEvtPerBin=50" );
|
| 328 |
|
|
|
| 329 |
|
|
// Test the multi-dimensional probability density estimator
|
| 330 |
|
|
// here are the options strings for the MinMax and RMS methods, respectively:
|
| 331 |
|
|
// "!H:!V:VolumeRangeMode=MinMax:DeltaFrac=0.2:KernelEstimator=Gauss:GaussSigma=0.3" );
|
| 332 |
|
|
// "!H:!V:VolumeRangeMode=RMS:DeltaFrac=3:KernelEstimator=Gauss:GaussSigma=0.3" );
|
| 333 |
|
|
if (Use["PDERS"])
|
| 334 |
|
|
factory->BookMethod( TMVA::Types::kPDERS, "PDERS",
|
| 335 |
|
|
"!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" );
|
| 336 |
|
|
|
| 337 |
|
|
if (Use["PDERSD"])
|
| 338 |
|
|
factory->BookMethod( TMVA::Types::kPDERS, "PDERSD",
|
| 339 |
|
|
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=Decorrelate" );
|
| 340 |
|
|
|
| 341 |
|
|
if (Use["PDERSPCA"])
|
| 342 |
|
|
factory->BookMethod( TMVA::Types::kPDERS, "PDERSPCA",
|
| 343 |
|
|
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=PCA" );
|
| 344 |
|
|
|
| 345 |
|
|
// Multi-dimensional likelihood estimator using self-adapting phase-space binning
|
| 346 |
|
|
if (Use["PDEFoam"])
|
| 347 |
|
|
factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam",
|
| 348 |
|
|
"H:!V:SigBgSeparate=F:TailCut=0.001:VolFrac=0.0333:nActiveCells=500:nSampl=2000:nBin=5:Nmin=100:Kernel=None:Compress=T" );
|
| 349 |
|
|
|
| 350 |
|
|
if (Use["PDEFoamBoost"])
|
| 351 |
|
|
factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoamBoost",
|
| 352 |
|
|
"!H:!V:Boost_Num=30:Boost_Transform=linear:SigBgSeparate=F:MaxDepth=4:UseYesNoCell=T:DTLogic=MisClassificationError:FillFoamWithOrigWeights=F:TailCut=0:nActiveCells=500:nBin=20:Nmin=400:Kernel=None:Compress=T" );
|
| 353 |
|
|
|
| 354 |
|
|
// K-Nearest Neighbour classifier (KNN)
|
| 355 |
|
|
if (Use["KNN"])
|
| 356 |
|
|
factory->BookMethod( TMVA::Types::kKNN, "KNN",
|
| 357 |
|
|
"H:nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" );
|
| 358 |
|
|
|
| 359 |
|
|
// H-Matrix (chi2-squared) method
|
| 360 |
|
|
if (Use["HMatrix"])
|
| 361 |
|
|
factory->BookMethod( TMVA::Types::kHMatrix, "HMatrix", "!H:!V" );
|
| 362 |
|
|
|
| 363 |
|
|
// Linear discriminant (same as Fisher discriminant)
|
| 364 |
|
|
if (Use["LD"])
|
| 365 |
|
|
factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
|
| 366 |
|
|
|
| 367 |
|
|
// Fisher discriminant (same as LD)
|
| 368 |
|
|
if (Use["Fisher"])
|
| 369 |
|
|
factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" );
|
| 370 |
|
|
|
| 371 |
|
|
// Fisher with Gauss-transformed input variables
|
| 372 |
|
|
if (Use["FisherG"])
|
| 373 |
|
|
factory->BookMethod( TMVA::Types::kFisher, "FisherG", "H:!V:VarTransform=Gauss" );
|
| 374 |
|
|
|
| 375 |
|
|
// Composite classifier: ensemble (tree) of boosted Fisher classifiers
|
| 376 |
|
|
if (Use["BoostedFisher"])
|
| 377 |
|
|
factory->BookMethod( TMVA::Types::kFisher, "BoostedFisher",
|
| 378 |
|
|
"H:!V:Boost_Num=20:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=0.2" );
|
| 379 |
|
|
|
| 380 |
|
|
// Function discrimination analysis (FDA) -- test of various fitters - the recommended one is Minuit (or GA or SA)
|
| 381 |
|
|
if (Use["FDA_MC"])
|
| 382 |
|
|
factory->BookMethod( TMVA::Types::kFDA, "FDA_MC",
|
| 383 |
|
|
"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" );
|
| 384 |
|
|
|
| 385 |
|
|
if (Use["FDA_GA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
|
| 386 |
|
|
factory->BookMethod( TMVA::Types::kFDA, "FDA_GA",
|
| 387 |
|
|
"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=300:Cycles=3:Steps=20:Trim=True:SaveBestGen=1" );
|
| 388 |
|
|
|
| 389 |
|
|
if (Use["FDA_SA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
|
| 390 |
|
|
factory->BookMethod( TMVA::Types::kFDA, "FDA_SA",
|
| 391 |
|
|
"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=15000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" );
|
| 392 |
|
|
|
| 393 |
|
|
if (Use["FDA_MT"])
|
| 394 |
|
|
factory->BookMethod( TMVA::Types::kFDA, "FDA_MT",
|
| 395 |
|
|
"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" );
|
| 396 |
|
|
|
| 397 |
|
|
if (Use["FDA_GAMT"])
|
| 398 |
|
|
factory->BookMethod( TMVA::Types::kFDA, "FDA_GAMT",
|
| 399 |
|
|
"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" );
|
| 400 |
|
|
|
| 401 |
|
|
if (Use["FDA_MCMT"])
|
| 402 |
|
|
factory->BookMethod( TMVA::Types::kFDA, "FDA_MCMT",
|
| 403 |
|
|
"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" );
|
| 404 |
|
|
|
| 405 |
|
|
// TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons
|
| 406 |
|
|
if (Use["MLP"])
|
| 407 |
|
|
factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:!UseRegulator" );
|
| 408 |
|
|
|
| 409 |
|
|
if (Use["MLPBFGS"])
|
| 410 |
|
|
factory->BookMethod( TMVA::Types::kMLP, "MLPBFGS", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:!UseRegulator" );
|
| 411 |
|
|
|
| 412 |
|
|
if (Use["MLPBNN"])
|
| 413 |
|
|
factory->BookMethod( TMVA::Types::kMLP, "MLPBNN", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:UseRegulator" ); // BFGS training with bayesian regulators
|
| 414 |
|
|
|
| 415 |
|
|
// CF(Clermont-Ferrand)ANN
|
| 416 |
|
|
if (Use["CFMlpANN"])
|
| 417 |
|
|
factory->BookMethod( TMVA::Types::kCFMlpANN, "CFMlpANN", "!H:!V:NCycles=2000:HiddenLayers=N+1,N" ); // n_cycles:#nodes:#nodes:...
|
| 418 |
|
|
|
| 419 |
|
|
// Tmlp(Root)ANN
|
| 420 |
|
|
if (Use["TMlpANN"])
|
| 421 |
|
|
factory->BookMethod( TMVA::Types::kTMlpANN, "TMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N:LearningMethod=BFGS:ValidationFraction=0.3" ); // n_cycles:#nodes:#nodes:...
|
| 422 |
|
|
|
| 423 |
|
|
// Support Vector Machine
|
| 424 |
|
|
if (Use["SVM"])
|
| 425 |
|
|
factory->BookMethod( TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm" );
|
| 426 |
|
|
|
| 427 |
|
|
// Boosted Decision Trees
|
| 428 |
|
|
if (Use["BDTG"]) // Gradient Boost
|
| 429 |
|
|
factory->BookMethod( TMVA::Types::kBDT, "BDTG",
|
| 430 |
|
|
"!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.10:UseBaggedGrad:GradBaggingFraction=0.5:nCuts=20:NNodesMax=5" );
|
| 431 |
|
|
|
| 432 |
|
|
if (Use["BDT"]) // Adaptive Boost
|
| 433 |
|
|
factory->BookMethod( TMVA::Types::kBDT, "BDT",
|
| 434 |
|
|
"!H:!V:NTrees=850:nEventsMin=150:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
|
| 435 |
|
|
|
| 436 |
|
|
if (Use["BDTB"]) // Bagging
|
| 437 |
|
|
factory->BookMethod( TMVA::Types::kBDT, "BDTB",
|
| 438 |
|
|
"!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" );
|
| 439 |
|
|
|
| 440 |
|
|
if (Use["BDTD"]) // Decorrelation + Adaptive Boost
|
| 441 |
|
|
factory->BookMethod( TMVA::Types::kBDT, "BDTD",
|
| 442 |
|
|
"!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning:VarTransform=Decorrelate" );
|
| 443 |
|
|
|
| 444 |
|
|
// RuleFit -- TMVA implementation of Friedman's method
|
| 445 |
|
|
if (Use["RuleFit"])
|
| 446 |
|
|
factory->BookMethod( TMVA::Types::kRuleFit, "RuleFit",
|
| 447 |
|
|
"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=10000:GDErrScale=1.02" );
|
| 448 |
|
|
|
| 449 |
|
|
// For an example of the category classifier usage, see: TMVAClassificationCategory
|
| 450 |
|
|
|
| 451 |
|
|
// --------------------------------------------------------------------------------------------------
|
| 452 |
|
|
|
| 453 |
|
|
// ---- Now you can optimize the setting (configuration) of the MVAs using the set of training events
|
| 454 |
|
|
|
| 455 |
|
|
// factory->OptimizeAllMethods("SigEffAt001","Scan");
|
| 456 |
|
|
// factory->OptimizeAllMethods("ROCIntegral","GA");
|
| 457 |
|
|
|
| 458 |
|
|
// --------------------------------------------------------------------------------------------------
|
| 459 |
|
|
|
| 460 |
|
|
// ---- Now you can tell the factory to train, test, and evaluate the MVAs
|
| 461 |
|
|
|
| 462 |
|
|
// Train MVAs using the set of training events
|
| 463 |
|
|
factory->TrainAllMethods();
|
| 464 |
|
|
|
| 465 |
|
|
// ---- Evaluate all MVAs using the set of test events
|
| 466 |
|
|
factory->TestAllMethods();
|
| 467 |
|
|
|
| 468 |
|
|
// ----- Evaluate and compare performance of all configured MVAs
|
| 469 |
|
|
factory->EvaluateAllMethods();
|
| 470 |
|
|
|
| 471 |
|
|
// --------------------------------------------------------------
|
| 472 |
|
|
|
| 473 |
|
|
// Save the output
|
| 474 |
|
|
outputFile->Close();
|
| 475 |
|
|
|
| 476 |
|
|
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
|
| 477 |
|
|
std::cout << "==> TMVAClassification is done!" << std::endl;
|
| 478 |
|
|
|
| 479 |
|
|
delete factory;
|
| 480 |
|
|
|
| 481 |
|
|
// Launch the GUI for the root macros
|
| 482 |
|
|
if (!gROOT->IsBatch()) TMVAGui( outfileName );
|
| 483 |
|
|
}
|
