void TMVAClassification( TString fname = "./tmva_class_example.root") { // The explicit loading of the shared libTMVA is done in TMVAlogon.C, defined in .rootrc // if you use your private .rootrc, or run from a different directory, please copy the // corresponding lines from .rootrc // methods to be processed can be given as an argument; use format: // // mylinux~> root -l TMVAClassification.C\(\"myMethod1,myMethod2,myMethod3\"\) // // if you like to use a method via the plugin mechanism, we recommend using // // mylinux~> root -l TMVAClassification.C\(\"P_myMethod\"\) // (an example is given for using the BDT as plugin (see below), // but of course the real application is when you write your own // method based) //--------------------------------------------------------------- // This loads the library TMVA::Tools::Instance(); // to get access to the GUI and all tmva macros TString tmva_dir(TString(gRootDir) + "/tmva"); if(gSystem->Getenv("TMVASYS")) tmva_dir = TString(gSystem->Getenv("TMVASYS")); gROOT->SetMacroPath(tmva_dir + "/test/:" + gROOT->GetMacroPath() ); gROOT->ProcessLine(".L TMVAGui.C"); // Default MVA methods to be trained + tested std::map<std::string,int> Use; Use["KNN"] = 1; // k-nearest neighbour method // --------------------------------------------------------------- std::cout << std::endl; std::cout << "==> Start TMVAClassification" << std::endl; // -------------------------------------------------------------------------------------------------- // --- Here the preparation phase begins // Create a ROOT output file where TMVA will store ntuples, histograms, etc. TString outfileName( "TMVA.root" ); TFile* outputFile = TFile::Open( outfileName, "RECREATE" ); // Create the factory object. Later you can choose the methods // whose performance you'd like to investigate. The factory is // the only TMVA object you have to interact with // // The first argument is the base of the name of all the // weightfiles in the directory weight/ // // The second argument is the output file for the training results // All TMVA output can be suppressed by removing the "!" (not) in // front of the "Silent" argument in the option string TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile, "!V:!Silent:Color:DrawProgressBar:Transformations=I:AnalysisType=Classification" ); // If you wish to modify default settings // (please check "src/Config.h" to see all available global options) // (TMVA::gConfig().GetVariablePlotting()).fTimesRMS = 8.0; // (TMVA::gConfig().GetIONames()).fWeightFileDir = "myWeightDirectory"; // Define the input variables that shall be used for the MVA training // note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)" // [all types of expressions that can also be parsed by TTree::Draw( "expression" )] factory->AddVariable( "pt_eH", 'D' ); factory->AddVariable( "max(pt_jet_eH,pt_eH)", 'D' ); factory->AddVariable( "njets", 'I' ); // You can add so-called "Spectator variables", which are not used in the MVA training, // but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the // input variables, the response values of all trained MVAs, and the spectator variables /// factory->AddSpectator( "spec1 := var1*2", "Spectator 1", "units", 'F' ); /// factory->AddSpectator( "spec2 := var1*3", "Spectator 2", "units", 'F' ); // Read training and test data // (it is also possible to use ASCII format as input -> see TMVA Users Guide) TFile *input(0); if (gSystem->AccessPathName( fname )){ // file does not exist in local directory gSystem->Exec("wget http://root.cern.ch/files/tmva_class_example.root"); fname = "./tmva_class_example.root"; }else{ input= TFile::Open( fname ); } if (!input) { std::cout << "ERROR: could not open data file " << fname << std::endl; exit(1); } std::cout << "--- TMVAClassification : Using input file: " << input->GetName() << std::endl; // --- Register the training and test trees TTree *inputTree = (TTree*)input->Get("FakeTreeSig"); TTree *background = (TTree*)input->Get("FakeTreeBG"); // global event weights per tree (see below for setting event-wise weights) Double_t signalWeight = 1.0; Double_t backgroundWeight = 1.0; // cuts for signal and background //~ TCut signalCut = "selected==1 && id_iso_eleH==1"; //~ TCut backgroundCut = "selected==1 && id_iso_eleH==0"; //~ //~ std::cout << " THe signal cut is " << signalCut.GetTitle() << " bg cut is " << backgroundCut.GetTitle() << std::endl; Int_t num_pass = inputTree->GetEntries(); Int_t num_fail = background->GetEntries(); std::cout << num_pass << " " << num_fail << std::endl; // You can add an arbitrary number of signal or background trees factory->AddSignalTree ( inputTree, 1.0 ); factory->AddBackgroundTree( background, 1.0 ); factory->SetWeightExpression( "weight" ); //factory->SetInputTrees( inputTree, signalCut, backgroundCut ); // To give different trees for training and testing, do as follows: // factory->AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" ); // factory->AddSignalTree( signalTestTree, signalTestWeight, "Test" ); // Use the following code instead of the above two or four lines to add signal and background // training and test events "by hand" // NOTE that in this case one should not give expressions (such as "var1+var2") in the input // variable definition, but simply compute the expression before adding the event // // // --- begin ---------------------------------------------------------- // std::vector<Double_t> vars( 4 ); // vector has size of number of input variables // Float_t treevars[4], weight; // // // Signal // for (UInt_t ivar=0; ivar<4; ivar++) signal->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) ); // for (UInt_t i=0; i<signal->GetEntries(); i++) { // signal->GetEntry(i); // for (UInt_t ivar=0; ivar<4; ivar++) vars[ivar] = treevars[ivar]; // // add training and test events; here: first half is training, second is testing // // note that the weight can also be event-wise // if (i < signal->GetEntries()/2.0) factory->AddSignalTrainingEvent( vars, signalWeight ); // else factory->AddSignalTestEvent ( vars, signalWeight ); // } // // // Background (has event weights) // background->SetBranchAddress( "weight", &weight ); // for (UInt_t ivar=0; ivar<4; ivar++) background->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) ); // for (UInt_t i=0; i<background->GetEntries(); i++) { // background->GetEntry(i); // for (UInt_t ivar=0; ivar<4; ivar++) vars[ivar] = treevars[ivar]; // // add training and test events; here: first half is training, second is testing // // note that the weight can also be event-wise // if (i < background->GetEntries()/2) factory->AddBackgroundTrainingEvent( vars, backgroundWeight*weight ); // else factory->AddBackgroundTestEvent ( vars, backgroundWeight*weight ); // } // --- end ------------------------------------------------------------ // // --- end of tree registration // Set individual event weights (the variables must exist in the original TTree) // for signal : factory->SetSignalWeightExpression ("weight1*weight2"); // for background: factory->SetBackgroundWeightExpression("weight1*weight2"); // Apply additional cuts on the signal and background samples (can be different) TCut mycuts = "selected==1"; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1"; TCut mycutb = "selected==1"; // for example: TCut mycutb = "abs(var1)<0.5"; // Tell the factory how to use the training and testing events // // If no numbers of events are given, half of the events in the tree are used // for training, and the other half for testing: // factory->PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" ); // To also specify the number of testing events, use: // factory->PrepareTrainingAndTestTree( mycut, // "NSigTrain=3000:NBkgTrain=3000:NSigTest=3000:NBkgTest=3000:SplitMode=Random:!V" ); std::stringstream indexes; indexes.str(""); indexes << "nTrain_Signal=" << num_pass << ":nTrain_Background=" << num_fail << ":SplitMode=Random:NormMode=None:!V"; std::string input_opt=indexes.str(); std::cout << "Options are " << input_opt << std::endl; factory->PrepareTrainingAndTestTree( mycuts, mycutb, input_opt); //"nTrain_Signal="+num_pass+":nTrain_Background="+num_fail+":SplitMode=Random:NormMode=None:!V" ); // ---- Book MVA methods // // Please lookup the various method configuration options in the corresponding cxx files, eg: // src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html // it is possible to preset ranges in the option string in which the cut optimisation should be done: // "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable // Cut optimisation // K-Nearest Neighbour classifier (KNN) if (Use["KNN"]) factory->BookMethod( TMVA::Types::kKNN, "KNN", "H:nkNN=50:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" ); // For an example of the category classifier usage, see: TMVAClassificationCategory // -------------------------------------------------------------------------------------------------- // ---- Now you can optimize the setting (configuration) of the MVAs using the set of training events // factory->OptimizeAllMethods("SigEffAt001","Scan"); // factory->OptimizeAllMethods("ROCIntegral","GA"); // -------------------------------------------------------------------------------------------------- // ---- Now you can tell the factory to train, test, and evaluate the MVAs // Train MVAs using the set of training events factory->TrainAllMethods(); // ---- Evaluate all MVAs using the set of test events //factory->TestAllMethods(); // ----- Evaluate and compare performance of all configured MVAs // factory->EvaluateAllMethods(); // -------------------------------------------------------------- // Save the output outputFile->Close(); std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl; std::cout << "==> TMVAClassification is done!" << std::endl; delete factory; // Launch the GUI for the root macros if (!gROOT->IsBatch()) TMVAGui( outfileName ); }
void TMVAClassification( TString myMethodList = "" , TString myModel = "") { // The explicit loading of the shared libTMVA is done in TMVAlogon.C, defined in .rootrc // if you use your private .rootrc, or run from a different directory, please copy the // corresponding lines from .rootrc // methods to be processed can be given as an argument; use format: // // mylinux~> root -l TMVAClassification.C\(\"myMethod1,myMethod2,myMethod3\"\) // // if you like to use a method via the plugin mechanism, we recommend using // // mylinux~> root -l TMVAClassification.C\(\"P_myMethod\"\) // (an example is given for using the BDT as plugin (see below), // but of course the real application is when you write your own // method based) //--------------------------------------------------------------- // This loads the library TMVA::Tools::Instance(); // to get access to the GUI and all tmva macros TString tmva_dir(TString(gRootDir) + "/tmva"); if(gSystem->Getenv("TMVASYS")) tmva_dir = TString(gSystem->Getenv("TMVASYS")); gROOT->SetMacroPath(tmva_dir + "/test/:" + gROOT->GetMacroPath() ); gROOT->ProcessLine(".L TMVAGui.C"); // Default MVA methods to be trained + tested std::map<std::string,int> Use; // --- Cut optimisation Use["Cuts"] = 1; Use["CutsD"] = 0; Use["CutsPCA"] = 0; Use["CutsGA"] = 0; Use["CutsSA"] = 0; // // --- 1-dimensional likelihood ("naive Bayes estimator") Use["Likelihood"] = 0; Use["LikelihoodD"] = 0; // the "D" extension indicates decorrelated input variables (see option strings) Use["LikelihoodPCA"] = 0; // the "PCA" extension indicates PCA-transformed input variables (see option strings) Use["LikelihoodKDE"] = 0; Use["LikelihoodMIX"] = 0; // // --- Mutidimensional likelihood and Nearest-Neighbour methods Use["PDERS"] = 0; Use["PDERSD"] = 0; Use["PDERSPCA"] = 0; Use["PDEFoam"] = 0; Use["PDEFoamBoost"] = 0; // uses generalised MVA method boosting Use["KNN"] = 0; // k-nearest neighbour method // // --- Linear Discriminant Analysis Use["LD"] = 0; // Linear Discriminant identical to Fisher Use["Fisher"] = 0; Use["FisherG"] = 0; Use["BoostedFisher"] = 0; // uses generalised MVA method boosting Use["HMatrix"] = 0; // // --- Function Discriminant analysis Use["FDA_GA"] = 0; // minimisation of user-defined function using Genetics Algorithm Use["FDA_SA"] = 0; Use["FDA_MC"] = 0; Use["FDA_MT"] = 0; Use["FDA_GAMT"] = 0; Use["FDA_MCMT"] = 0; // // --- Neural Networks (all are feed-forward Multilayer Perceptrons) Use["MLP"] = 0; // Recommended ANN Use["MLPBFGS"] = 0; // Recommended ANN with optional training method Use["MLPBNN"] = 0; // Recommended ANN with BFGS training method and bayesian regulator Use["CFMlpANN"] = 0; // Depreciated ANN from ALEPH Use["TMlpANN"] = 0; // ROOT's own ANN // // --- Support Vector Machine Use["SVM"] = 0; // // --- Boosted Decision Trees Use["BDT"] = 0; // uses Adaptive Boost Use["BDTG"] = 0; // uses Gradient Boost Use["BDTB"] = 0; // uses Bagging Use["BDTD"] = 0; // decorrelation + Adaptive Boost Use["BDTF"] = 0; // allow usage of fisher discriminant for node splitting // // --- Friedman's RuleFit method, ie, an optimised series of cuts ("rules") Use["RuleFit"] = 0; // --------------------------------------------------------------- // Default model to be trained + tested std::map<std::string,int> Model; // --- Cut optimisation Model[ "MM" ] = 0; // Mass mechanism Model[ "RHC_L" ] = 0; // Right Handed Current Model[ "RHC_E" ] = 0; // Right Handed Current Model[ "M1" ] = 0; // Majoron Model[ "M2" ] = 0; // Majoron Model[ "M3" ] = 0; // Majoron Model[ "M7" ] = 0; // Majoron std::cout << std::endl; std::cout << "==> Start TMVAClassification" << std::endl; // Select methods (don't look at this code - not of interest) if (myMethodList != "") { for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0; std::vector<TString> mlist = TMVA::gTools().SplitString( myMethodList, ',' ); for (UInt_t i=0; i<mlist.size(); i++) { std::string regMethod(mlist[i]); if (Use.find(regMethod) == Use.end()) { std::cout << "Method \"" << regMethod << "\" not known in TMVA under this name. Choose among the following:" << std::endl; for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) std::cout << it->first << " "; std::cout << std::endl; return; } Use[regMethod] = 1; } } if(myModel != "") { std::string regModel(myModel); if( Model.find(regModel) == Model.end() ){ std::cout << "Model \"" << myModel << "\" not known in under this name. Choose among the following:" << std::endl; for (std::map<std::string,int>::iterator it = Model.begin(); it != Model.end(); it++) std::cout << it->first << " "; std::cout << std::endl; return; } Model[regModel] = 1; } else { std::cout << "No signal model as been specified. You must choose one among the following:" << std::endl; for (std::map<std::string,int>::iterator it = Model.begin(); it != Model.end(); it++) std::cout << it->first << " "; std::cout << std::endl; return; } // -------------------------------------------------------------------------------------------------- // --- Here the preparation phase begins // Create a ROOT output file where TMVA will store ntuples, histograms, etc. TString outfileName; outfileName.Form( "TMVA_%s.root", myModel.Data() ); //TString outfileDir( "/Users/alberto/Software/SuperNEMO/work/nemo3/plot/plot_FINAL_TECHNOTE_20150921/TMVA/" ); TString outfileDir( "/Users/alberto/Software/SuperNEMO/work/nemo3/plot/plot_UPDATE_TECHNOTE_20160429/TMVA/" ); TFile* outputFile = TFile::Open( outfileDir + outfileName , "RECREATE" ); // Create the factory object. Later you can choose the methods // whose performance you'd like to investigate. The factory is // the only TMVA object you have to interact with // // The first argument is the base of the name of all the // weightfiles in the directory weight/ // // The second argument is the output file for the training results // All TMVA output can be suppressed by removing the "!" (not) in // front of the "Silent" argument in the option string TString weightBaseName; weightBaseName.Form("TMVAClassification_%s", myModel.Data()); TMVA::Factory *factory = new TMVA::Factory( weightBaseName , outputFile, "!V:!Silent:Color:DrawProgressBar:Transformations=I:AnalysisType=Classification" ); // If you wish to modify default settings // (please check "src/Config.h" to see all available global options) // (TMVA::gConfig().GetVariablePlotting()).fTimesRMS = 8.0; // (TMVA::gConfig().GetIONames()).fWeightFileDir = "myWeightDirectory"; // Define the input variables that shall be used for the MVA training // note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)" // [all types of expressions that can also be parsed by TTree::Draw( "expression" )] //factory->AddVariable( "myvar1 := var1+var2", 'F' ); //factory->AddVariable( "myvar2 := var1-var2", "Expression 2", "", 'F' ); //factory->AddVariable( "var3", "Variable 3", "units", 'F' ); //factory->AddVariable( "var4", "Variable 4", "units", 'F' ); factory->AddVariable( "min_el_en" , 'F' ); factory->AddVariable( "max_el_en" , 'F' ); factory->AddVariable( "el_en_asym := (max_el_en-min_el_en)/(min_el_en+max_el_en)" , 'F' ); factory->AddVariable( "el_en_sum := min_el_en+max_el_en" , 'F' ); factory->AddVariable( "cos_theta" , 'F' ); factory->AddVariable( "prob_int" , 'F' ); factory->AddVariable( "min_el_track_len" , 'F' ); factory->AddVariable( "max_el_track_len" , 'F' ); //factory->AddVariable( "min_el_curv := min_el_track_r*min_el_sign" , 'F' ); //factory->AddVariable( "max_el_curv := max_el_track_r*max_el_sign" , 'F' ); //factory->AddVariable( "max_vertex_s" , 'F' ); //factory->AddVariable( "max_vertex_z" , 'F' ); //factory->AddVariable( "min_vertex_s" , 'F' ); //factory->AddVariable( "min_vertex_z" , 'F' ); // You can add so-called "Spectator variables", which are not used in the MVA training, // but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the // input variables, the response values of all trained MVAs, and the spectator variables //factory->AddSpectator( "spec1 := var1*2", "Spectator 1", "units", 'F' ); //factory->AddSpectator( "spec2 := var1*3", "Spectator 2", "units", 'F' ); // Read training and test data // (it is also possible to use ASCII format as input -> see TMVA Users Guide) //TString fdir = "/sps/nemo/scratch/remoto/nemo3/plot/plot_FINAL_TECHNOTE_20150921/"; TString fdir = "/Users/alberto/Software/SuperNEMO/work/nemo3/plot/plot_UPDATE_TECHNOTE_20160429/"; TString fname = "TwoElectronIntTree.root"; TFile *input = TFile::Open( fdir + fname , "READ"); std::cout << "--- TMVAClassification : Using input file: " << input->GetName() << std::endl; TTree * sig_tree = 0; Double_t sig_weight = 1.; if ( Model[ "MM" ] ) sig_tree = (TTree*) input->Get( "Cd116_2b0n_m1_tree" ) ; if ( Model[ "RHC_L" ] ) sig_tree = (TTree*) input->Get( "Cd116_2b0n_m2_tree" ) ; if ( Model[ "RHC_E" ] ) sig_tree = (TTree*) input->Get( "Cd116_2b0n_m18_tree" ) ; if ( Model[ "M1" ] ) sig_tree = (TTree*) input->Get( "Cd116_2b0n_m5_tree" ) ; if ( Model[ "M2" ] ) sig_tree = (TTree*) input->Get( "Cd116_2b0n_m15_tree" ) ; if ( Model[ "M3" ] ) sig_tree = (TTree*) input->Get( "Cd116_2b0n_m6_tree" ) ; if ( Model[ "M7" ] ) sig_tree = (TTree*) input->Get( "Cd116_2b0n_m7_tree" ) ; factory->AddSignalTree( sig_tree , sig_weight ); //Double_t Cd116_2b0n_m1_weight = 1.; //TTree * Cd116_2b0n_m1_tree = (TTree*) input->Get("Cd116_2b0n_m1_tree" ) ; //factory->AddSignalTree( Cd116_2b0n_m1_tree , Cd116_2b0n_m1_weight ); TTree * Cd116_Tl208_tree = (TTree*) input->Get("Cd116_Tl208_tree" ) ; Double_t Cd116_Tl208_weight = 6.52838 ; if( Cd116_Tl208_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( Cd116_Tl208_tree , Cd116_Tl208_weight ); }; TTree * Cd116_Ac228_tree = (TTree*) input->Get("Cd116_Ac228_tree" ) ; Double_t Cd116_Ac228_weight = 7.62351 ; if( Cd116_Ac228_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( Cd116_Ac228_tree , Cd116_Ac228_weight ); }; TTree * Cd116_Bi212_tree = (TTree*) input->Get("Cd116_Bi212_tree" ) ; Double_t Cd116_Bi212_weight = 3.00708 ; if( Cd116_Bi212_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( Cd116_Bi212_tree , Cd116_Bi212_weight ); }; TTree * Cd116_Bi214_tree = (TTree*) input->Get("Cd116_Bi214_tree" ) ; Double_t Cd116_Bi214_weight = 18.1504 ; if( Cd116_Bi214_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( Cd116_Bi214_tree , Cd116_Bi214_weight ); }; TTree * Cd116_Pb214_tree = (TTree*) input->Get("Cd116_Pb214_VT_tree" ) ; Double_t Cd116_Pb214_weight = 0.186417 ; if( Cd116_Pb214_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( Cd116_Pb214_tree , Cd116_Pb214_weight ); }; TTree * Mylar_Bi214_tree = (TTree*) input->Get("Mylar_Bi214_tree" ) ; Double_t Mylar_Bi214_weight = 11.1346 ; if( Mylar_Bi214_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( Mylar_Bi214_tree , Mylar_Bi214_weight ); }; TTree * Mylar_Pb214_tree = (TTree*) input->Get("Mylar_Pb214_tree" ) ; Double_t Mylar_Pb214_weight = 0.496238 ; if( Mylar_Pb214_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( Mylar_Pb214_tree , Mylar_Pb214_weight ); }; TTree * Cd116_K40_tree = (TTree*) input->Get("Cd116_K40_tree" ) ; Double_t Cd116_K40_weight = 8.9841+25.8272 ; if( Cd116_K40_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( Cd116_K40_tree , Cd116_K40_weight ); }; TTree * Cd116_Pa234m_tree = (TTree*) input->Get("Cd116_Pa234m_tree" ) ; Double_t Cd116_Pa234m_weight = 27.9307+72.4667 ; if( Cd116_Pa234m_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( Cd116_Pa234m_tree , Cd116_Pa234m_weight ); }; TTree * SFoil_Bi210_tree = (TTree*) input->Get("SFoil_Bi210_tree" ) ; Double_t SFoil_Bi210_weight = 0+23.2438 ; if( SFoil_Bi210_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( SFoil_Bi210_tree , SFoil_Bi210_weight ); }; TTree * SWire_Bi210_tree = (TTree*) input->Get("SWire_Bi210_tree" ) ; Double_t SWire_Bi210_weight = 0.136147+0.624187 ; if( SWire_Bi210_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( SWire_Bi210_tree , SWire_Bi210_weight ); }; TTree * SScin_Bi210_tree = (TTree*) input->Get("SScin_Bi210_tree" ) ; Double_t SScin_Bi210_weight = 1.75641 ; if( SScin_Bi210_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( SScin_Bi210_tree , SScin_Bi210_weight ); }; TTree * SScin_Bi214_tree = (TTree*) input->Get("SScin_Bi214_tree" ) ; Double_t SScin_Bi214_weight = 0.0510754 ; if( SScin_Bi214_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( SScin_Bi214_tree , SScin_Bi214_weight ); }; TTree * SScin_Pb214_tree = (TTree*) input->Get("SScin_Pb214_tree" ) ; Double_t SScin_Pb214_weight = 0 ; if( SScin_Pb214_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( SScin_Pb214_tree , SScin_Pb214_weight ); }; TTree * SWire_Tl208_tree = (TTree*) input->Get("SWire_Tl208_tree" ) ; Double_t SWire_Tl208_weight = 0.217623+1.07641 ; if( SWire_Tl208_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( SWire_Tl208_tree , SWire_Tl208_weight ); }; TTree * SWire_Bi214_P1_tree = (TTree*) input->Get("SWire_Bi214_tree" ) ; Double_t SWire_Bi214_weight = 21.4188+17.8236 ; if( SWire_Bi214_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( SWire_Bi214_tree , SWire_Bi214_weight ); }; TTree * SFoil_Bi214_tree = (TTree*) input->Get("SFoil_Bi214_tree" ) ; Double_t SFoil_Bi214_weight = 5.83533+2.80427 ; if( SFoil_Bi214_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( SFoil_Bi214_tree , SFoil_Bi214_weight ); }; TTree * SWire_Pb214_tree = (TTree*) input->Get("SWire_Pb214_tree" ) ; Double_t SWire_Pb214_weight = 0.458486+0.649167 ; if( SWire_Pb214_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( SWire_Pb214_tree , SWire_Pb214_weight ); }; TTree * SFoil_Pb214_tree = (TTree*) input->Get("SFoil_Pb214_tree" ) ; Double_t SFoil_Pb214_weight = 0.218761+0.195287 ; if( SFoil_Pb214_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( SFoil_Pb214_tree , SFoil_Pb214_weight ); }; TTree * FeShield_Bi214_tree = (TTree*) input->Get("FeShield_Bi214_tree" ) ; Double_t FeShield_Bi214_weight = 50.7021 ; if( FeShield_Bi214_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( FeShield_Bi214_tree , FeShield_Bi214_weight ); }; TTree * FeShield_Tl208_tree = (TTree*) input->Get("FeShield_Tl208_tree" ) ; Double_t FeShield_Tl208_weight = 0.859465 ; if( FeShield_Tl208_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( FeShield_Tl208_tree , FeShield_Tl208_weight ); }; TTree * FeShield_Ac228_tree = (TTree*) input->Get("FeShield_Ac228_tree" ) ; Double_t FeShield_Ac228_weight = 0.126868 ; if( FeShield_Ac228_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( FeShield_Ac228_tree , FeShield_Ac228_weight ); }; TTree * CuTower_Co60_tree = (TTree*) input->Get("CuTower_Co60_tree" ) ; Double_t CuTower_Co60_weight = 3.9407 ; if( CuTower_Co60_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( CuTower_Co60_tree , CuTower_Co60_weight ); }; TTree * Air_Bi214_P1_tree = (TTree*) input->Get("Air_Bi214_tree" ) ; Double_t Air_Bi214_P1_weight = 4.19744 ; if( Air_Bi214_P1_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( Air_Bi214_P1_tree , Air_Bi214_P1_weight ); }; TTree * Air_Tl208_P1_tree = (TTree*) input->Get("Air_Tl208_tree" ) ; Double_t Air_Tl208_P1_weight = 0 ; if( Air_Tl208_P1_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( Air_Tl208_P1_tree , Air_Tl208_P1_weight ); }; TTree * PMT_Bi214_tree = (TTree*) input->Get("PMT_Bi214_tree" ) ; Double_t PMT_Bi214_weight = 27.9661 ; if( PMT_Bi214_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( PMT_Bi214_tree , PMT_Bi214_weight ); }; TTree * PMT_Tl208_tree = (TTree*) input->Get("PMT_Tl208_tree" ) ; Double_t PMT_Tl208_weight = 22.923 ; if( PMT_Tl208_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( PMT_Tl208_tree , PMT_Tl208_weight ); }; TTree * PMT_Ac228_tree = (TTree*) input->Get("PMT_Ac228_tree" ) ; Double_t PMT_Ac228_weight = 3.60712 ; if( PMT_Ac228_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( PMT_Ac228_tree , PMT_Ac228_weight ); }; TTree * PMT_K40_tree = (TTree*) input->Get("PMT_K40_tree" ) ; Double_t PMT_K40_weight = 16.813 ; if( PMT_K40_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( PMT_K40_tree , PMT_K40_weight ); }; TTree * ScintInn_K40_tree = (TTree*) input->Get("ScintInn_K40_tree" ) ; Double_t ScintInn_K40_weight = 0.333988 ; if( ScintInn_K40_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( ScintInn_K40_tree , ScintInn_K40_weight ); }; TTree * ScintOut_K40_tree = (TTree*) input->Get("ScintOut_K40_tree" ) ; Double_t ScintOut_K40_weight = 0.601178 ; if( ScintOut_K40_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( ScintOut_K40_tree , ScintOut_K40_weight ); }; TTree * ScintPet_K40_tree = (TTree*) input->Get("ScintPet_K40_tree" ) ; Double_t ScintPet_K40_weight = 1.00195 ; if( ScintPet_K40_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( ScintPet_K40_tree , ScintPet_K40_weight ); }; TTree * MuMetal_Pa234m_tree = (TTree*) input->Get("MuMetal_Pa234m_tree" ) ; Double_t MuMetal_Pa234m_weight = 0.739038 ; if( MuMetal_Pa234m_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( MuMetal_Pa234m_tree , MuMetal_Pa234m_weight ); }; TTree * Cd116_2b2n_m14_tree = (TTree*) input->Get("Cd116_2b2n_m14_tree" ) ; Double_t Cd116_2b2n_m14_weight = 4977.55 ; if( Cd116_2b2n_m14_tree -> GetEntriesFast() > 0. ) {factory->AddBackgroundTree( Cd116_2b2n_m14_tree , Cd116_2b2n_m14_weight ); }; // --- end of tree registration // Set individual event weights (the variables must exist in the original TTree) // for signal : factory->SetSignalWeightExpression ("weight1*weight2"); // for background: factory->SetBackgroundWeightExpression("weight1*weight2"); factory->SetBackgroundWeightExpression( "weight" ); // Apply additional cuts on the signal and background samples (can be different) // Apply cut on charge //TCut mycuts = "min_el_sign < 0 && max_el_sign < 0."; //TCut mycutb = "min_el_sign < 0 && max_el_sign < 0."; // Apply cut on vertex //TCut mycuts = "((max_vertex_x - min_vertex_x)**2 + (max_vertex_y - min_vertex_y)**2 <= 4**2)&&((max_vertex_z-min_vertex_z)**2<8**2)"; //TCut mycutb = "((max_vertex_x - min_vertex_x)**2 + (max_vertex_y - min_vertex_y)**2 <= 4**2)&&((max_vertex_z-min_vertex_z)**2<8**2)"; TCut mycuts = ""; TCut mycutb = ""; // Tell the factory how to use the training and testing events // // If no numbers of events are given, half of the events in the tree are used // for training, and the other half for testing: // factory->PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" ); // To also specify the number of testing events, use: // factory->PrepareTrainingAndTestTree( mycut, // "NSigTrain=3000:NBkgTrain=3000:NSigTest=3000:NBkgTest=3000:SplitMode=Random:!V" ); factory->PrepareTrainingAndTestTree( mycuts, mycutb, "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" ); // ---- Book MVA methods // // Please lookup the various method configuration options in the corresponding cxx files, eg: // src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html // it is possible to preset ranges in the option string in which the cut optimisation should be done: // "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable // Cut optimisation if (Use["Cuts"]) factory->BookMethod( TMVA::Types::kCuts, "Cuts", "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" ); if (Use["CutsD"]) factory->BookMethod( TMVA::Types::kCuts, "CutsD", "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=Decorrelate" ); if (Use["CutsPCA"]) factory->BookMethod( TMVA::Types::kCuts, "CutsPCA", "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=PCA" ); if (Use["CutsGA"]) factory->BookMethod( TMVA::Types::kCuts, "CutsGA", "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" ); if (Use["CutsSA"]) factory->BookMethod( TMVA::Types::kCuts, "CutsSA", "!H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" ); // Likelihood ("naive Bayes estimator") if (Use["Likelihood"]) factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood", "H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" ); // Decorrelated likelihood if (Use["LikelihoodD"]) factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodD", "!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate" ); // PCA-transformed likelihood if (Use["LikelihoodPCA"]) factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodPCA", "!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA" ); // Use a kernel density estimator to approximate the PDFs if (Use["LikelihoodKDE"]) factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodKDE", "!H:!V:!TransformOutput:PDFInterpol=KDE:KDEtype=Gauss:KDEiter=Adaptive:KDEFineFactor=0.3:KDEborder=None:NAvEvtPerBin=50" ); // Use a variable-dependent mix of splines and kernel density estimator if (Use["LikelihoodMIX"]) factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodMIX", "!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" ); // Test the multi-dimensional probability density estimator // here are the options strings for the MinMax and RMS methods, respectively: // "!H:!V:VolumeRangeMode=MinMax:DeltaFrac=0.2:KernelEstimator=Gauss:GaussSigma=0.3" ); // "!H:!V:VolumeRangeMode=RMS:DeltaFrac=3:KernelEstimator=Gauss:GaussSigma=0.3" ); if (Use["PDERS"]) factory->BookMethod( TMVA::Types::kPDERS, "PDERS", "!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" ); if (Use["PDERSD"]) factory->BookMethod( TMVA::Types::kPDERS, "PDERSD", "!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=Decorrelate" ); if (Use["PDERSPCA"]) factory->BookMethod( TMVA::Types::kPDERS, "PDERSPCA", "!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=PCA" ); // Multi-dimensional likelihood estimator using self-adapting phase-space binning if (Use["PDEFoam"]) factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam", "!H:!V:SigBgSeparate=F:TailCut=0.001:VolFrac=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Nmin=100:Kernel=None:Compress=T" ); if (Use["PDEFoamBoost"]) factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoamBoost", "!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" ); // K-Nearest Neighbour classifier (KNN) if (Use["KNN"]) factory->BookMethod( TMVA::Types::kKNN, "KNN", "H:nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" ); // H-Matrix (chi2-squared) method if (Use["HMatrix"]) factory->BookMethod( TMVA::Types::kHMatrix, "HMatrix", "!H:!V:VarTransform=None" ); // Linear discriminant (same as Fisher discriminant) if (Use["LD"]) factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" ); // Fisher discriminant (same as LD) if (Use["Fisher"]) factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" ); // Fisher with Gauss-transformed input variables if (Use["FisherG"]) factory->BookMethod( TMVA::Types::kFisher, "FisherG", "H:!V:VarTransform=Gauss" ); // Composite classifier: ensemble (tree) of boosted Fisher classifiers if (Use["BoostedFisher"]) factory->BookMethod( TMVA::Types::kFisher, "BoostedFisher", "H:!V:Boost_Num=20:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=0.2:!Boost_DetailedMonitoring" ); // Function discrimination analysis (FDA) -- test of various fitters - the recommended one is Minuit (or GA or SA) if (Use["FDA_MC"]) factory->BookMethod( TMVA::Types::kFDA, "FDA_MC", "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" ); if (Use["FDA_GA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options]) factory->BookMethod( TMVA::Types::kFDA, "FDA_GA", "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" ); if (Use["FDA_SA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options]) factory->BookMethod( TMVA::Types::kFDA, "FDA_SA", "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" ); if (Use["FDA_MT"]) factory->BookMethod( TMVA::Types::kFDA, "FDA_MT", "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" ); if (Use["FDA_GAMT"]) factory->BookMethod( TMVA::Types::kFDA, "FDA_GAMT", "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" ); if (Use["FDA_MCMT"]) factory->BookMethod( TMVA::Types::kFDA, "FDA_MCMT", "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" ); // TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons if (Use["MLP"]) factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:!UseRegulator" ); if (Use["MLPBFGS"]) factory->BookMethod( TMVA::Types::kMLP, "MLPBFGS", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:!UseRegulator" ); if (Use["MLPBNN"]) 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 // CF(Clermont-Ferrand)ANN if (Use["CFMlpANN"]) factory->BookMethod( TMVA::Types::kCFMlpANN, "CFMlpANN", "!H:!V:NCycles=2000:HiddenLayers=N+1,N" ); // n_cycles:#nodes:#nodes:... // Tmlp(Root)ANN if (Use["TMlpANN"]) factory->BookMethod( TMVA::Types::kTMlpANN, "TMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N:LearningMethod=BFGS:ValidationFraction=0.3" ); // n_cycles:#nodes:#nodes:... // Support Vector Machine if (Use["SVM"]) factory->BookMethod( TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm" ); // Boosted Decision Trees if (Use["BDTG"]) // Gradient Boost factory->BookMethod( TMVA::Types::kBDT, "BDTG", "!H:!V:NTrees=1000:MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=2" ); if (Use["BDT"]) // Adaptive Boost factory->BookMethod( TMVA::Types::kBDT, "BDT", "!H:!V:NTrees=850:MinNodeSize=2.5%:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" ); if (Use["BDTB"]) // Bagging factory->BookMethod( TMVA::Types::kBDT, "BDTB", "!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20" ); if (Use["BDTD"]) // Decorrelation + Adaptive Boost factory->BookMethod( TMVA::Types::kBDT, "BDTD", "!H:!V:NTrees=400:MinNodeSize=5%:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:VarTransform=Decorrelate" ); if (Use["BDTF"]) // Allow Using Fisher discriminant in node splitting for (strong) linearly correlated variables factory->BookMethod( TMVA::Types::kBDT, "BDTMitFisher", "!H:!V:NTrees=50:MinNodeSize=2.5%:UseFisherCuts:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=-1" ); // RuleFit -- TMVA implementation of Friedman's method if (Use["RuleFit"]) factory->BookMethod( TMVA::Types::kRuleFit, "RuleFit", "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" ); // For an example of the category classifier usage, see: TMVAClassificationCategory // -------------------------------------------------------------------------------------------------- // ---- Now you can optimize the setting (configuration) of the MVAs using the set of training events // ---- STILL EXPERIMENTAL and only implemented for BDT's ! // factory->OptimizeAllMethods("SigEffAt001","Scan"); // factory->OptimizeAllMethods("ROCIntegral","FitGA"); // -------------------------------------------------------------------------------------------------- // ---- Now you can tell the factory to train, test, and evaluate the MVAs // Train MVAs using the set of training events factory->TrainAllMethods(); // ---- Evaluate all MVAs using the set of test events factory->TestAllMethods(); // ----- Evaluate and compare performance of all configured MVAs factory->EvaluateAllMethods(); // -------------------------------------------------------------- // Save the output outputFile->Close(); std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl; std::cout << "==> TMVAClassification is done!" << std::endl; delete factory; // Launch the GUI for the root macros if (!gROOT->IsBatch()) TMVAGui( outfileDir + outfileName ); }
void TMVAClassify_SepSSFromOS( TString myMethodList = "" ) { // The explicit loading of the shared libTMVA is done in TMVAlogon.C, defined in .rootrc // if you use your private .rootrc, or run from a different directory, please copy the // corresponding lines from .rootrc // methods to be processed can be given as an argument; use format: // // mylinux~> root -l TMVAClassification.C\(\"myMethod1,myMethod2,myMethod3\"\) // // if you like to use a method via the plugin mechanism, we recommend using // // mylinux~> root -l TMVAClassification.C\(\"P_myMethod\"\) // (an example is given for using the BDT as plugin (see below), // but of course the real application is when you write your own // method based) //--------------------------------------------------------------- // This loads the library TMVA::Tools::Instance(); // to get access to the GUI and all tmva macros TString tmva_dir(TString(gRootDir) + "/tmva"); if(gSystem->Getenv("TMVASYS")) tmva_dir = TString(gSystem->Getenv("TMVASYS")); gROOT->SetMacroPath(tmva_dir + "/test/:" + gROOT->GetMacroPath() ); gROOT->ProcessLine(".L TMVAGui.C"); // Default MVA methods to be trained + tested std::map<std::string,int> Use; // --- Cut optimisation Use["Cuts"] = 0; Use["CutsD"] = 0; Use["CutsPCA"] = 0; Use["CutsGA"] = 0; Use["CutsSA"] = 0; // // --- 1-dimensional likelihood ("naive Bayes estimator") Use["Likelihood"] = 0; Use["LikelihoodD"] = 0; // the "D" extension indicates decorrelated input variables (see option strings) Use["LikelihoodPCA"] = 0; // the "PCA" extension indicates PCA-transformed input variables (see option strings) Use["LikelihoodKDE"] = 0; Use["LikelihoodMIX"] = 0; // // --- Mutidimensional likelihood and Nearest-Neighbour methods Use["PDERS"] = 0; Use["PDERSD"] = 0; Use["PDERSPCA"] = 0; Use["PDEFoam"] = 0; Use["PDEFoamBoost"] = 0; // uses generalised MVA method boosting Use["KNN"] = 0; // k-nearest neighbour method // // --- Linear Discriminant Analysis Use["LD"] = 0; // Linear Discriminant identical to Fisher Use["Fisher"] = 0; Use["FisherG"] = 0; Use["BoostedFisher"] = 0; // uses generalised MVA method boosting Use["HMatrix"] = 0; // // --- Function Discriminant analysis Use["FDA_GA"] = 0; // minimisation of user-defined function using Genetics Algorithm Use["FDA_SA"] = 0; Use["FDA_MC"] = 0; Use["FDA_MT"] = 0; Use["FDA_GAMT"] = 0; Use["FDA_MCMT"] = 0; // // --- Neural Networks (all are feed-forward Multilayer Perceptrons) Use["MLP"] = 0; // Recommended ANN Use["MLPBFGS"] = 0; // Recommended ANN with optional training method Use["MLPBNN"] = 0; // Recommended ANN with BFGS training method and bayesian regulator Use["CFMlpANN"] = 0; // Depreciated ANN from ALEPH Use["TMlpANN"] = 0; // ROOT's own ANN // // --- Support Vector Machine Use["SVM"] = 0; // // --- Boosted Decision Trees Use["BDT"] = 0; // uses Adaptive Boost Use["BDTG"] = 1; // uses Gradient Boost Use["BDTB"] = 0; // uses Bagging Use["BDTD"] = 0; // decorrelation + Adaptive Boost Use["BDTF"] = 0; // allow usage of fisher discriminant for node splitting // // --- Friedman's RuleFit method, ie, an optimised series of cuts ("rules") Use["RuleFit"] = 0; // --------------------------------------------------------------- std::cout << std::endl; std::cout << "==> Start TMVAClassification" << std::endl; // Select methods (don't look at this code - not of interest) if (myMethodList != "") { for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0; std::vector<TString> mlist = TMVA::gTools().SplitString( myMethodList, ',' ); for (UInt_t i=0; i<mlist.size(); i++) { std::string regMethod(mlist[i]); if (Use.find(regMethod) == Use.end()) { std::cout << "Method \"" << regMethod << "\" not known in TMVA under this name. Choose among the following:" << std::endl; for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) std::cout << it->first << " "; std::cout << std::endl; return; } Use[regMethod] = 1; } } // -------------------------------------------------------------------------------------------------- // --- Here the preparation phase begins // Create a ROOT output file where TMVA will store ntuples, histograms, etc. TString outfileName( "../../classifiers/D02KPi/TMVA/TMVA.root" ); TFile* outputFile = TFile::Open( outfileName, "RECREATE" ); // Create the factory object. Later you can choose the methods // whose performance you'd like to investigate. The factory is // the only TMVA object you have to interact with // // The first argument is the base of the name of all the // weightfiles in the directory weight/ // // The second argument is the output file for the training results // All TMVA output can be suppressed by removing the "!" (not) in // front of the "Silent" argument in the option string TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile, "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" ); // If you wish to modify default settings // (please check "src/Config.h" to see all available global options) // (TMVA::gConfig().GetVariablePlotting()).fTimesRMS = 8.0; // (TMVA::gConfig().GetIONames()).fWeightFileDir = "myWeightDirectory"; // Define the input variables that shall be used for the MVA training // note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)" // [all types of expressions that can also be parsed by TTree::Draw( "expression" )] factory->AddVariable( "track_angletod", "Angle to D0", "", 'F' ); factory->AddVariable( "track_angletochild1", "Angle to kaon from D0", "", 'F' ); factory->AddVariable( "track_angletochild2", "Angle to pion from D0", "", 'F' ); factory->AddVariable( "log(track_devdist)", "log(DOCA to D0 vertex)", "", 'F' ); //factory->AddVariable( "log(track_docatochild1)", "log(DOCA to kaon from D0)", "", 'F' ); //factory->AddVariable( "log(track_docatochild2)", "log(DOCA to pion from D0)", "", 'F' ); //factory->AddVariable( "log(track_docatod)", "Distance to D0 trajectory", "", 'F' ); factory->AddVariable( "track_ptratiod", "Ratio track PT to D0 PT ", "", 'F' ); factory->AddVariable( "track_ptratiochild1", "Ratio track PT to kaon PT", "", 'F' ); factory->AddVariable( "track_ptratiochild2", "Distance track PT to pion PT", "", 'F' ); // You can add so-called "Spectator variables", which are not used in the MVA training, // but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the // input variables, the response values of all trained MVAs, and the spectator variables //factory->AddSpectator( "spec1 := var1*2", "Spectator 1", "units", 'F' ); //factory->AddSpectator( "spec2 := var1*3", "Spectator 2", "units", 'F' ); // Read training and test data // (it is also possible to use ASCII format as input -> see TMVA Users Guide) TFile *input_ss = TFile::Open( "../../data/mcd02kpi_ss_forsepssfromos.root" ); TFile *input_os = TFile::Open( "../../data/mcd02kpi_os_forsepssfromos.root" ); // --- Register the training and test trees TTree *intree_ss = (TTree*)input_ss->Get("DecayTree"); TTree *intree_os = (TTree*)input_os->Get("DecayTree"); // global event weights per tree (see below for setting event-wise weights) Double_t signalWeight = 1.0; Double_t backgroundWeight = 1.0; factory->AddSignalTree ( intree_os, signalWeight ); factory->AddBackgroundTree( intree_ss, backgroundWeight ); // Apply additional cuts on the signal and background samples (can be different) TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1"; TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5"; // Tell the factory how to use the training and testing events factory->PrepareTrainingAndTestTree( mycuts, mycutb, "nTrain_Signal=80000:nTrain_Background=80000:nTest_Signal=80000:nTest_Background=80000:SplitMode=Random:NormMode=NumEvents:!V" ); // ---- Book MVA methods // // Please lookup the various method configuration options in the corresponding cxx files, eg: // src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html // it is possible to preset ranges in the option string in which the cut optimisation should be done: // "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable // Cut optimisation if (Use["Cuts"]) factory->BookMethod( TMVA::Types::kCuts, "Cuts", "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" ); if (Use["CutsD"]) factory->BookMethod( TMVA::Types::kCuts, "CutsD", "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=Decorrelate" ); if (Use["CutsPCA"]) factory->BookMethod( TMVA::Types::kCuts, "CutsPCA", "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=PCA" ); if (Use["CutsGA"]) factory->BookMethod( TMVA::Types::kCuts, "CutsGA", "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" ); if (Use["CutsSA"]) factory->BookMethod( TMVA::Types::kCuts, "CutsSA", "!H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" ); // Likelihood ("naive Bayes estimator") if (Use["Likelihood"]) factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood", "H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" ); // Decorrelated likelihood if (Use["LikelihoodD"]) factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodD", "!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate" ); // PCA-transformed likelihood if (Use["LikelihoodPCA"]) factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodPCA", "!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA" ); // Use a kernel density estimator to approximate the PDFs if (Use["LikelihoodKDE"]) factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodKDE", "!H:!V:!TransformOutput:PDFInterpol=KDE:KDEtype=Gauss:KDEiter=Adaptive:KDEFineFactor=0.3:KDEborder=None:NAvEvtPerBin=50" ); // Use a variable-dependent mix of splines and kernel density estimator if (Use["LikelihoodMIX"]) factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodMIX", "!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" ); // Test the multi-dimensional probability density estimator // here are the options strings for the MinMax and RMS methods, respectively: // "!H:!V:VolumeRangeMode=MinMax:DeltaFrac=0.2:KernelEstimator=Gauss:GaussSigma=0.3" ); // "!H:!V:VolumeRangeMode=RMS:DeltaFrac=3:KernelEstimator=Gauss:GaussSigma=0.3" ); if (Use["PDERS"]) factory->BookMethod( TMVA::Types::kPDERS, "PDERS", "!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" ); if (Use["PDERSD"]) factory->BookMethod( TMVA::Types::kPDERS, "PDERSD", "!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=Decorrelate" ); if (Use["PDERSPCA"]) factory->BookMethod( TMVA::Types::kPDERS, "PDERSPCA", "!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=PCA" ); // Multi-dimensional likelihood estimator using self-adapting phase-space binning if (Use["PDEFoam"]) factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam", "!H:!V:SigBgSeparate=F:TailCut=0.001:VolFrac=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Nmin=100:Kernel=None:Compress=T" ); if (Use["PDEFoamBoost"]) factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoamBoost", "!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" ); // K-Nearest Neighbour classifier (KNN) if (Use["KNN"]) factory->BookMethod( TMVA::Types::kKNN, "KNN", "H:nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" ); // H-Matrix (chi2-squared) method if (Use["HMatrix"]) factory->BookMethod( TMVA::Types::kHMatrix, "HMatrix", "!H:!V:VarTransform=None" ); // Linear discriminant (same as Fisher discriminant) if (Use["LD"]) factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" ); // Fisher discriminant (same as LD) if (Use["Fisher"]) factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" ); // Fisher with Gauss-transformed input variables if (Use["FisherG"]) factory->BookMethod( TMVA::Types::kFisher, "FisherG", "H:!V:VarTransform=Gauss" ); // Composite classifier: ensemble (tree) of boosted Fisher classifiers if (Use["BoostedFisher"]) factory->BookMethod( TMVA::Types::kFisher, "BoostedFisher", "H:!V:Boost_Num=20:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=0.2:!Boost_DetailedMonitoring" ); // Function discrimination analysis (FDA) -- test of various fitters - the recommended one is Minuit (or GA or SA) if (Use["FDA_MC"]) factory->BookMethod( TMVA::Types::kFDA, "FDA_MC", "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" ); if (Use["FDA_GA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options]) factory->BookMethod( TMVA::Types::kFDA, "FDA_GA", "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" ); if (Use["FDA_SA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options]) factory->BookMethod( TMVA::Types::kFDA, "FDA_SA", "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" ); if (Use["FDA_MT"]) factory->BookMethod( TMVA::Types::kFDA, "FDA_MT", "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" ); if (Use["FDA_GAMT"]) factory->BookMethod( TMVA::Types::kFDA, "FDA_GAMT", "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" ); if (Use["FDA_MCMT"]) factory->BookMethod( TMVA::Types::kFDA, "FDA_MCMT", "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" ); // TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons if (Use["MLP"]) factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=500:HiddenLayers=N+2:TestRate=5:!UseRegulator" ); if (Use["MLPBFGS"]) factory->BookMethod( TMVA::Types::kMLP, "MLPBFGS", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:!UseRegulator" ); if (Use["MLPBNN"]) 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 // CF(Clermont-Ferrand)ANN if (Use["CFMlpANN"]) factory->BookMethod( TMVA::Types::kCFMlpANN, "CFMlpANN", "!H:!V:NCycles=2000:HiddenLayers=N+1,N" ); // n_cycles:#nodes:#nodes:... // Tmlp(Root)ANN if (Use["TMlpANN"]) factory->BookMethod( TMVA::Types::kTMlpANN, "TMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N:LearningMethod=BFGS:ValidationFraction=0.3" ); // n_cycles:#nodes:#nodes:... // Support Vector Machine if (Use["SVM"]) factory->BookMethod( TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm" ); // Boosted Decision Trees if (Use["BDTG"]) // Gradient Boost factory->BookMethod( TMVA::Types::kBDT, "BDTG", "!H:!V:NTrees=4000:MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=3" ); if (Use["BDT"]) // Adaptive Boost factory->BookMethod( TMVA::Types::kBDT, "BDT", "!H:!V:NTrees=850:MinNodeSize=2.5%:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" ); if (Use["BDTB"]) // Bagging factory->BookMethod( TMVA::Types::kBDT, "BDTB", "!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20" ); if (Use["BDTD"]) // Decorrelation + Adaptive Boost factory->BookMethod( TMVA::Types::kBDT, "BDTD", "!H:!V:NTrees=400:MinNodeSize=5%:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:VarTransform=Decorrelate" ); if (Use["BDTF"]) // Allow Using Fisher discriminant in node splitting for (strong) linearly correlated variables factory->BookMethod( TMVA::Types::kBDT, "BDTMitFisher", "!H:!V:NTrees=50:MinNodeSize=2.5%:UseFisherCuts:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20" ); // RuleFit -- TMVA implementation of Friedman's method if (Use["RuleFit"]) factory->BookMethod( TMVA::Types::kRuleFit, "RuleFit", "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" ); // For an example of the category classifier usage, see: TMVAClassificationCategory // -------------------------------------------------------------------------------------------------- // ---- Now you can optimize the setting (configuration) of the MVAs using the set of training events // ---- STILL EXPERIMENTAL and only implemented for BDT's ! // factory->OptimizeAllMethods("SigEffAt001","Scan"); // factory->OptimizeAllMethods("ROCIntegral","FitGA"); // -------------------------------------------------------------------------------------------------- // ---- Now you can tell the factory to train, test, and evaluate the MVAs // Train MVAs using the set of training events factory->TrainAllMethods(); // ---- Evaluate all MVAs using the set of test events factory->TestAllMethods(); // ----- Evaluate and compare performance of all configured MVAs factory->EvaluateAllMethods(); // -------------------------------------------------------------- // Save the output outputFile->Close(); std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl; std::cout << "==> TMVAClassification is done!" << std::endl; delete factory; // Launch the GUI for the root macros if (!gROOT->IsBatch()) TMVAGui( outfileName ); }
void TMVAClassificationCategory() { //--------------------------------------------------------------- // Example for usage of different event categories with classifiers std::cout << std::endl << "==> Start TMVAClassificationCategory" << std::endl; // This loads the library TMVA::Tools::Instance(); // to get access to the GUI and all tmva macros TString tmva_dir(TString(gRootDir) + "/tmva"); if(gSystem->Getenv("TMVASYS")) tmva_dir = TString(gSystem->Getenv("TMVASYS")); gROOT->SetMacroPath(tmva_dir + "/test/:" + gROOT->GetMacroPath() ); gROOT->ProcessLine(".L TMVAGui.C"); bool batchMode = false; // Create a new root output file. TString outfileName( "TMVA.root" ); TFile* outputFile = TFile::Open( outfileName, "RECREATE" ); // Create the factory object (see TMVAClassification.C for more information) std::string factoryOptions( "!V:!Silent:Transformations=I;D;P;G,D" ); if (batchMode) factoryOptions += ":!Color:!DrawProgressBar"; TMVA::Factory *factory = new TMVA::Factory( "TMVAClassificationCategory", outputFile, factoryOptions ); // Define the input variables used for the MVA training factory->AddVariable( "var1", 'F' ); factory->AddVariable( "var2", 'F' ); factory->AddVariable( "var3", 'F' ); factory->AddVariable( "var4", 'F' ); // You can add so-called "Spectator variables", which are not used in the MVA training, // but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the // input variables, the response values of all trained MVAs, and the spectator variables factory->AddSpectator( "eta" ); // Load the signal and background event samples from ROOT trees TFile *input(0); TString fname( "" ); if (UseOffsetMethod) fname = "data/toy_sigbkg_categ_offset.root"; else fname = "data/toy_sigbkg_categ_varoff.root"; if (!gSystem->AccessPathName( fname )) { // first we try to find tmva_example.root in the local directory std::cout << "--- TMVAClassificationCategory: Accessing " << fname << std::endl; input = TFile::Open( fname ); } if (!input) { std::cout << "ERROR: could not open data file: " << fname << std::endl; exit(1); } TTree *signal = (TTree*)input->Get("TreeS"); TTree *background = (TTree*)input->Get("TreeB"); /// Global event weights per tree (see below for setting event-wise weights) Double_t signalWeight = 1.0; Double_t backgroundWeight = 1.0; /// You can add an arbitrary number of signal or background trees factory->AddSignalTree ( signal, signalWeight ); factory->AddBackgroundTree( background, backgroundWeight ); // Apply additional cuts on the signal and background samples (can be different) TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1"; TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5"; // Tell the factory how to use the training and testing events factory->PrepareTrainingAndTestTree( mycuts, mycutb, "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" ); // ---- Book MVA methods // Fisher discriminant factory->BookMethod( TMVA::Types::kFisher, "Fisher", "!H:!V:Fisher" ); // Likelihood factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood", "!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" ); // --- Categorised classifier TMVA::MethodCategory* mcat = 0; // The variable sets TString theCat1Vars = "var1:var2:var3:var4"; TString theCat2Vars = (UseOffsetMethod ? "var1:var2:var3:var4" : "var1:var2:var3"); // Fisher with categories TMVA::MethodBase* fiCat = factory->BookMethod( TMVA::Types::kCategory, "FisherCat","" ); mcat = dynamic_cast<TMVA::MethodCategory*>(fiCat); mcat->AddMethod( "abs(eta)<=1.3", theCat1Vars, TMVA::Types::kFisher, "Category_Fisher_1","!H:!V:Fisher" ); mcat->AddMethod( "abs(eta)>1.3", theCat2Vars, TMVA::Types::kFisher, "Category_Fisher_2","!H:!V:Fisher" ); // Likelihood with categories TMVA::MethodBase* liCat = factory->BookMethod( TMVA::Types::kCategory, "LikelihoodCat","" ); mcat = dynamic_cast<TMVA::MethodCategory*>(liCat); mcat->AddMethod( "abs(eta)<=1.3",theCat1Vars, TMVA::Types::kLikelihood, "Category_Likelihood_1","!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" ); mcat->AddMethod( "abs(eta)>1.3", theCat2Vars, TMVA::Types::kLikelihood, "Category_Likelihood_2","!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" ); // ---- Now you can tell the factory to train, test, and evaluate the MVAs // Train MVAs using the set of training events factory->TrainAllMethods(); // ---- Evaluate all MVAs using the set of test events factory->TestAllMethods(); // ----- Evaluate and compare performance of all configured MVAs factory->EvaluateAllMethods(); // -------------------------------------------------------------- // Save the output outputFile->Close(); std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl; std::cout << "==> TMVAClassificationCategory is done!" << std::endl; // Clean up delete factory; // Launch the GUI for the root macros if (!gROOT->IsBatch()) TMVAGui( outfileName ); }
void TMVAClassification_ttV( TString myMethodList = "" ) { // The explicit loading of the shared libTMVA is done in TMVAlogon.C, defined in .rootrc // if you use your private .rootrc, or run from a different directory, please copy the // corresponding lines from .rootrc // methods to be processed can be given as an argument; use format: // // mylinux~> root -l TMVAClassification.C\(\"myMethod1,myMethod2,myMethod3\"\) // // if you like to use a method via the plugin mechanism, we recommend using // // mylinux~> root -l TMVAClassification.C\(\"P_myMethod\"\) // (an example is given for using the BDT as plugin (see below), // but of course the real application is when you write your own // method based) //--------------------------------------------------------------- // This loads the library TMVA::Tools::Instance(); // to get access to the GUI and all tmva macros TString tmva_dir(TString(gRootDir) + "/tmva"); if(gSystem->Getenv("TMVASYS")) tmva_dir = TString(gSystem->Getenv("TMVASYS")); gROOT->SetMacroPath(tmva_dir + "/test/:" + gROOT->GetMacroPath() ); gROOT->ProcessLine(".L TMVAGui.C"); // Default MVA methods to be trained + tested std::map<std::string,int> Use; // --- Cut optimisation Use["Cuts"] = 0;//1 Use["CutsD"] = 0;//1 Use["CutsPCA"] = 0; Use["CutsGA"] = 0; Use["CutsSA"] = 0; // // --- 1-dimensional likelihood ("naive Bayes estimator") Use["Likelihood"] = 0;//1 Use["LikelihoodD"] = 0; // the "D" extension indicates decorrelated input variables (see option strings) Use["LikelihoodPCA"] = 0;//1 // the "PCA" extension indicates PCA-transformed input variables (see option strings) Use["LikelihoodKDE"] = 0; Use["LikelihoodMIX"] = 0; // // --- Mutidimensional likelihood and Nearest-Neighbour methods Use["PDERS"] = 0;//1 Use["PDERSD"] = 0; Use["PDERSPCA"] = 0; Use["PDEFoam"] = 0;//1 Use["PDEFoamBoost"] = 0; // uses generalised MVA method boosting Use["KNN"] = 0;//1 // k-nearest neighbour method // // --- Linear Discriminant Analysis Use["LD"] = 0; //1// Linear Discriminant identical to Fisher Use["Fisher"] = 0; Use["FisherG"] = 0; Use["BoostedFisher"] = 0; // uses generalised MVA method boosting Use["HMatrix"] = 0; // // --- Function Discriminant analysis Use["FDA_GA"] = 0; //1// minimisation of user-defined function using Genetics Algorithm Use["FDA_SA"] = 0; Use["FDA_MC"] = 0; Use["FDA_MT"] = 0; Use["FDA_GAMT"] = 0; Use["FDA_MCMT"] = 0; // // --- Neural Networks (all are feed-forward Multilayer Perceptrons) Use["MLP"] = 0; // Recommended ANN Use["MLPBFGS"] = 0; // Recommended ANN with optional training method Use["MLPBNN"] = 0;//1; // Recommended ANN with BFGS training method and bayesian regulator Use["CFMlpANN"] = 0; // Depreciated ANN from ALEPH Use["TMlpANN"] = 0; // ROOT's own ANN // // --- Support Vector Machine Use["SVM"] = 0;//1; // // --- Boosted Decision Trees Use["BDT"] = 0;//1; // uses Adaptive Boost Use["BDTG"] = 0; // uses Gradient Boost Use["BDTB"] = 0; // uses Bagging Use["BDTD"] = 0; // decorrelation + Adaptive Boost Use["BDTF"] = 0; // allow usage of fisher discriminant for node splitting // // --- Friedman's RuleFit method, ie, an optimised series of cuts ("rules") Use["RuleFit"] = 1;//1; // --------------------------------------------------------------- std::cout << std::endl; std::cout << "==> Start TMVAClassification" << std::endl; // Select methods (don't look at this code - not of interest) if (myMethodList != "") { for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0; std::vector<TString> mlist = TMVA::gTools().SplitString( myMethodList, ',' ); for (UInt_t i=0; i<mlist.size(); i++) { std::string regMethod(mlist[i]); if (Use.find(regMethod) == Use.end()) { std::cout << "Method \"" << regMethod << "\" not known in TMVA under this name. Choose among the following:" << std::endl; for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) std::cout << it->first << " "; std::cout << std::endl; return; } Use[regMethod] = 1; } } // -------------------------------------------------------------------------------------------------- // --- Here the preparation phase begins // Create a ROOT output file where TMVA will store ntuples, histograms, etc. TString outfileName( "TMVA.root" ); TFile* outputFile = TFile::Open( outfileName, "RECREATE" ); // Create the factory object. Later you can choose the methods // whose performance you'd like to investigate. The factory is // the only TMVA object you have to interact with // // The first argument is the base of the name of all the // weightfiles in the directory weight/ // // The second argument is the output file for the training results // All TMVA output can be suppressed by removing the "!" (not) in // front of the "Silent" argument in the option string TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile, "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" ); // If you wish to modify default settings // (please check "src/Config.h" to see all available global options) // (TMVA::gConfig().GetVariablePlotting()).fTimesRMS = 8.0; // (TMVA::gConfig().GetIONames()).fWeightFileDir = "myWeightDirectory"; // Define the input variables that shall be used for the MVA training // note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)" // [all types of expressions that can also be parsed by TTree::Draw( "expression" )] factory->AddVariable(" Mqq := Mqq", 'F'); factory->AddVariable(" Pt_qq := Pt_qq", 'F'); //factory->AddVariable( "myvar1 := var1+var2", 'F' ); //factory->AddVariable( "myvar2 := var1-var2", "Expression 2", "", 'F' ); //factory->AddVariable( "var3", "Variable 3", "units", 'F' ); //factory->AddVariable( "var4", "Variable 4", "units", 'F' ); //factory->AddVariable( "Mqq", 'F' ); //factory->AddVariable( "Pt_qq", 'F' ); // You can add so-called "Spectator variables", which are not used in the MVA training, // but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the // input variables, the response values of all trained MVAs, and the spectator variables //factory->AddSpectator( "spec1 := var1*2", "Spectator 1", "units", 'F' ); //factory->AddSpectator( "spec2 := var1*3", "Spectator 2", "units", 'F' ); // Read training and test data // (it is also possible to use ASCII format as input -> see TMVA Users Guide) //Change the input files /* TString fname = "./tmva_class_example.root"; if (gSystem->AccessPathName( fname )) // file does not exist in local directory gSystem->Exec("wget http://root.cern.ch/files/tmva_class_example.root"); TFile *input = TFile::Open( fname ); std::cout << "--- TMVAClassification : Using input file: " << input->GetName() << std::endl; // --- Register the training and test trees TTree *signal = (TTree*)input->Get("TreeS"); TTree *background = (TTree*)input->Get("TreeB"); */ VCandStruct vcand; AsymetryStruct asym; //TFile* fsignal = TFile::Open("/opt/sbg/data/data1/cms/echabert/ttbarMET/ProdAlexMars13/CMSSW_5_3_2_patch4/src/NTuple/NTupleAnalysis/macros/TTbarMET/backup_outputProof08-04-13_18-01-24/proof_ttW.root"); TFile* fsignal = TFile::Open("/opt/sbg/data/data1/cms/echabert/ttbarMET/ProdAlexMars13/CMSSW_5_3_2_patch4/src/NTuple/NTupleAnalysis/macros/TTbarMET/backup_outputProof10-04-13_16-00-57/proof_ttW.root"); TTree *signal = (TTree*)fsignal->Get("theTree2"); //TTree *signal_orig = (TTree*)fsignal->Get("theTree2"); //TTree *signal = signal_orig->CloneTree(0); //signal->SetDirectory(0); TFile* fbackground = TFile::Open("/opt/sbg/data/data1/cms/echabert/ttbarMET/ProdAlexMars13/CMSSW_5_3_2_patch4/src/NTuple/NTupleAnalysis/macros/TTbarMET/backup_outputProof10-04-13_16-00-57/proof_tt-dilepton.root"); TTree *background = (TTree*)fbackground->Get("theTree2"); //TTree *background_orig = (TTree*)fbackground->Get("theTree"); //TTree *background = background_orig->CloneTree(0); //background->SetDirectory(0); // global event weights per tree (see below for setting event-wise weights) Double_t signalWeight = 0.30*20/185338; Double_t backgroundWeight = 222.*0.1*20/9982625; // You can add an arbitrary number of signal or background trees //Commented by Eric factory->AddSignalTree ( signal, signalWeight ); factory->AddBackgroundTree( background, backgroundWeight ); // To give different trees for training and testing, do as follows: //factory->AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" ); //factory->AddSignalTree( signalTestTree, signalTestWeight, "Test" ); // Use the following code instead of the above two or four lines to add signal and background // training and test events "by hand" // NOTE that in this case one should not give expressions (such as "var1+var2") in the input // variable definition, but simply compute the expression before adding the event // // // --- begin ---------------------------------------------------------- /* cout<<"begin"<<endl; std::vector<Double_t> vars( 2 ); // vector has size of number of input variables //Float_t treevars[4], weight; // // // Signal //for (UInt_t ivar=0; ivar<4; ivar++) signal->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) ); signal->SetBranchAddress("Vcand",&vcand); //signal->SetBranchAddress("VCand/Mqq",&vcand.Mqq); //signal->SetBranchAddress("VCand/Pt_qq",&vcand.Pt_qq); for (UInt_t i=0; i<signal->GetEntries(); i++) { //cout<<"GetEntry "<<i<<endl; signal->GetEntry(i); //cout<<"done"<<endl; //for (UInt_t ivar=0; ivar<2; ivar++) vars[ivar] = treevars[ivar]; vars[0] = vcand.Mqq; vars[1] = vcand.Pt_qq; //cout<<vars[0]<<" "<<vars[1]<<endl; // add training and test events; here: first half is training, second is testing // note that the weight can also be event-wise //cout<<"there"<<endl; if (i < signal->GetEntries()/2.0) factory->AddSignalTrainingEvent( vars, signalWeight ); else factory->AddSignalTestEvent ( vars, signalWeight ); //return ; } //delete signal; //fsignal->Close(); cout<<"begin"<<endl; //TFile* fbackground = TFile::Open("/opt/sbg/data/data1/cms/echabert/ttbarMET/ProdAlexMars13/CMSSW_5_3_2_patch4/src/NTuple/NTupleAnalysis/macros/TTbarMET/backup_outputProof08-04-13_18-01-24/proof_tt-dilepton.root"); cout<<fsignal<<" "<<signal<<endl; // Background (has event weights) //background->SetBranchAddress( "weight", &weight ); //for (UInt_t ivar=0; ivar<4; ivar++) background->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) ); background->SetBranchAddress("Vcand",&vcand); //background->SetBranchAddress("VCand/Mqq",&vcand.Mqq); //background->SetBranchAddress("VCand/Pt_qq",&vcand.Pt_qq); cout<<"here"<<endl; for (UInt_t i=0; i<background->GetEntries(); i++) { //cout<<"GetEntry "<<i<<endl; background->GetEntry(i); //cout<<"done"<<endl; //for (UInt_t ivar=0; ivar<4; ivar++) vars[ivar] = treevars[ivar]; vars[0] = vcand.Mqq; vars[1] = vcand.Pt_qq; //cout<<vars[0]<<" "<<vars[1]<<endl; // add training and test events; here: first half is training, second is testing // note that the weight can also be event-wise //if (i < background->GetEntries()/2) factory->AddBackgroundTrainingEvent( vars, backgroundWeight*weight ); //else factory->AddBackgroundTestEvent ( vars, backgroundWeight*weight ); //cout<<"toto"<<endl; if (i < background->GetEntries()/2) factory->AddBackgroundTrainingEvent( vars, backgroundWeight); else factory->AddBackgroundTestEvent ( vars, backgroundWeight); //cout<<"toto"<<endl; //return ; } */ cout<<"eND"<<endl; factory->EvaluateAllVariables(); factory->Print(); //return; // --- end ------------------------------------------------------------ // // --- end of tree registration // Set individual event weights (the variables must exist in the original TTree) // for signal : factory->SetSignalWeightExpression ("weight1*weight2"); // for background: factory->SetBackgroundWeightExpression("weight1*weight2"); //Commented by Eric: factory->SetBackgroundWeightExpression( "weight" ); // Apply additional cuts on the signal and background samples (can be different) TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1"; TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5"; // Tell the factory how to use the training and testing events // // If no numbers of events are given, half of the events in the tree are used // for training, and the other half for testing: // factory->PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" ); // To also specify the number of testing events, use: // factory->PrepareTrainingAndTestTree( mycut, // "NSigTrain=3000:NBkgTrain=3000:NSigTest=3000:NBkgTest=3000:SplitMode=Random:!V" ); cout<<"Prepare it"<<endl; factory->PrepareTrainingAndTestTree( mycuts, mycutb, "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" ); cout<<"Prepare it : DONE"<<endl; // ---- Book MVA methods // // Please lookup the various method configuration options in the corresponding cxx files, eg: // src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html // it is possible to preset ranges in the option string in which the cut optimisation should be done: // "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable // Cut optimisation cout<<"Use cuts"<<endl; if (Use["Cuts"]) factory->BookMethod( TMVA::Types::kCuts, "Cuts", "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" ); cout<<"done"<<endl; if (Use["CutsD"]) factory->BookMethod( TMVA::Types::kCuts, "CutsD", "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=Decorrelate" ); if (Use["CutsPCA"]) factory->BookMethod( TMVA::Types::kCuts, "CutsPCA", "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=PCA" ); if (Use["CutsGA"]) factory->BookMethod( TMVA::Types::kCuts, "CutsGA", "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" ); if (Use["CutsSA"]) factory->BookMethod( TMVA::Types::kCuts, "CutsSA", "!H:!V:FitMethod=SA:EffSel:MaxCalls=150000:KernelTemp=IncAdaptive:InitialTemp=1e+6:MinTemp=1e-6:Eps=1e-10:UseDefaultScale" ); // Likelihood ("naive Bayes estimator") if (Use["Likelihood"]) factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood", "H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" ); // Decorrelated likelihood if (Use["LikelihoodD"]) factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodD", "!H:!V:TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=Decorrelate" ); // PCA-transformed likelihood if (Use["LikelihoodPCA"]) factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodPCA", "!H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmooth=5:NAvEvtPerBin=50:VarTransform=PCA" ); // Use a kernel density estimator to approximate the PDFs if (Use["LikelihoodKDE"]) factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodKDE", "!H:!V:!TransformOutput:PDFInterpol=KDE:KDEtype=Gauss:KDEiter=Adaptive:KDEFineFactor=0.3:KDEborder=None:NAvEvtPerBin=50" ); // Use a variable-dependent mix of splines and kernel density estimator if (Use["LikelihoodMIX"]) factory->BookMethod( TMVA::Types::kLikelihood, "LikelihoodMIX", "!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" ); // Test the multi-dimensional probability density estimator // here are the options strings for the MinMax and RMS methods, respectively: // "!H:!V:VolumeRangeMode=MinMax:DeltaFrac=0.2:KernelEstimator=Gauss:GaussSigma=0.3" ); // "!H:!V:VolumeRangeMode=RMS:DeltaFrac=3:KernelEstimator=Gauss:GaussSigma=0.3" ); if (Use["PDERS"]) factory->BookMethod( TMVA::Types::kPDERS, "PDERS", "!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" ); if (Use["PDERSD"]) factory->BookMethod( TMVA::Types::kPDERS, "PDERSD", "!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=Decorrelate" ); if (Use["PDERSPCA"]) factory->BookMethod( TMVA::Types::kPDERS, "PDERSPCA", "!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=PCA" ); // Multi-dimensional likelihood estimator using self-adapting phase-space binning if (Use["PDEFoam"]) factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam", "!H:!V:SigBgSeparate=F:TailCut=0.001:VolFrac=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Nmin=100:Kernel=None:Compress=T" ); if (Use["PDEFoamBoost"]) factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoamBoost", "!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" ); // K-Nearest Neighbour classifier (KNN) if (Use["KNN"]) factory->BookMethod( TMVA::Types::kKNN, "KNN", "H:nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" ); // H-Matrix (chi2-squared) method if (Use["HMatrix"]) factory->BookMethod( TMVA::Types::kHMatrix, "HMatrix", "!H:!V:VarTransform=None" ); // Linear discriminant (same as Fisher discriminant) if (Use["LD"]) factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" ); // Fisher discriminant (same as LD) if (Use["Fisher"]) factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" ); // Fisher with Gauss-transformed input variables if (Use["FisherG"]) factory->BookMethod( TMVA::Types::kFisher, "FisherG", "H:!V:VarTransform=Gauss" ); // Composite classifier: ensemble (tree) of boosted Fisher classifiers if (Use["BoostedFisher"]) factory->BookMethod( TMVA::Types::kFisher, "BoostedFisher", "H:!V:Boost_Num=20:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=0.2:!Boost_DetailedMonitoring" ); // Function discrimination analysis (FDA) -- test of various fitters - the recommended one is Minuit (or GA or SA) if (Use["FDA_MC"]) factory->BookMethod( TMVA::Types::kFDA, "FDA_MC", "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" ); if (Use["FDA_GA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options]) factory->BookMethod( TMVA::Types::kFDA, "FDA_GA", "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" ); if (Use["FDA_SA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options]) factory->BookMethod( TMVA::Types::kFDA, "FDA_SA", "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" ); if (Use["FDA_MT"]) factory->BookMethod( TMVA::Types::kFDA, "FDA_MT", "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" ); if (Use["FDA_GAMT"]) factory->BookMethod( TMVA::Types::kFDA, "FDA_GAMT", "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" ); if (Use["FDA_MCMT"]) factory->BookMethod( TMVA::Types::kFDA, "FDA_MCMT", "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" ); // TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons if (Use["MLP"]) factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:!UseRegulator" ); if (Use["MLPBFGS"]) factory->BookMethod( TMVA::Types::kMLP, "MLPBFGS", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:!UseRegulator" ); if (Use["MLPBNN"]) 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 // CF(Clermont-Ferrand)ANN if (Use["CFMlpANN"]) factory->BookMethod( TMVA::Types::kCFMlpANN, "CFMlpANN", "!H:!V:NCycles=2000:HiddenLayers=N+1,N" ); // n_cycles:#nodes:#nodes:... // Tmlp(Root)ANN if (Use["TMlpANN"]) factory->BookMethod( TMVA::Types::kTMlpANN, "TMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N:LearningMethod=BFGS:ValidationFraction=0.3" ); // n_cycles:#nodes:#nodes:... // Support Vector Machine if (Use["SVM"]) factory->BookMethod( TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm" ); // Boosted Decision Trees if (Use["BDTG"]) // Gradient Boost factory->BookMethod( TMVA::Types::kBDT, "BDTG", "!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.10:UseBaggedGrad:GradBaggingFraction=0.5:nCuts=20:NNodesMax=5" ); if (Use["BDT"]) // Adaptive Boost factory->BookMethod( TMVA::Types::kBDT, "BDT", "!H:!V:NTrees=850:nEventsMin=150:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" ); if (Use["BDTB"]) // Bagging factory->BookMethod( TMVA::Types::kBDT, "BDTB", "!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" ); if (Use["BDTD"]) // Decorrelation + Adaptive Boost factory->BookMethod( TMVA::Types::kBDT, "BDTD", "!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning:VarTransform=Decorrelate" ); if (Use["BDTF"]) // Allow Using Fisher discriminant in node splitting for (strong) linearly correlated variables factory->BookMethod( TMVA::Types::kBDT, "BDTMitFisher", "!H:!V:NTrees=50:nEventsMin=150:UseFisherCuts:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" ); // RuleFit -- TMVA implementation of Friedman's method if (Use["RuleFit"]) factory->BookMethod( TMVA::Types::kRuleFit, "RuleFit", "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" ); // For an example of the category classifier usage, see: TMVAClassificationCategory // -------------------------------------------------------------------------------------------------- // ---- Now you can optimize the setting (configuration) of the MVAs using the set of training events // factory->OptimizeAllMethods("SigEffAt001","Scan"); // factory->OptimizeAllMethods("ROCIntegral","GA"); // -------------------------------------------------------------------------------------------------- // ---- Now you can tell the factory to train, test, and evaluate the MVAs cout<<"TrainAllMethods"<<endl; // Train MVAs using the set of training events factory->TrainAllMethods(); // ---- Evaluate all MVAs using the set of test events factory->TestAllMethods(); // ----- Evaluate and compare performance of all configured MVAs factory->EvaluateAllMethods(); // -------------------------------------------------------------- // Save the output outputFile->Close(); std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl; std::cout << "==> TMVAClassification is done!" << std::endl; delete factory; // Launch the GUI for the root macros if (!gROOT->IsBatch()) TMVAGui( outfileName ); }