void TMVAClassification( TString myMethodList = "", int isMC=1, int useSvtx=1) { // 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 thisdir = gSystem->DirName(gInterpreter->GetCurrentMacroName()); gROOT->SetMacroPath(thisdir + ":" + gROOT->GetMacroPath()); //gROOT->ProcessLine(".L /Users/kjung/root5-34-23/tmva/test/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; string svtxExt = "noSvtx"; if(useSvtx) svtxExt = "withSvtx"; if(!isMC) outfileName = "TMVA_trained_data.root"; else outfileName = Form("TMVA_trained_cJet_medDCuts_BvC_%s.root",svtxExt.c_str()); cout << "fn: "<< outfileName << endl; TFile* outputFile = TFile::Open( outfileName, "RECREATE" ); outputFile->cd(); // 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( "myvar1 := var1+var2", 'F' ); if(useSvtx) factory->AddVariable("svtxptFrac","svtx pt fraction","units",'F'); //factory->AddVariable( "djetR", "Closest Meson to Jet dr", "", 'F' ); factory->AddVariable( "nIP","number of IP trks","units",'I'); if(useSvtx) factory->AddVariable( "svtxm", "svtx mass", "units", 'F'); if(useSvtx) factory->AddVariable( "svtxmEnergyFrac","svtxmEnergyFrac","units",'F'); if(useSvtx) factory->AddVariable( "svtxpt", "svtx pt", "units", 'F'); if(useSvtx) factory->AddVariable( "svtxmcorr", "corrected svtx mass", "units", 'F'); if(useSvtx) factory->AddVariable( "svtxdl", "svtx displacement", "units", 'F'); if(useSvtx) factory->AddVariable( "svtxdls", "svtx displacement significance", "units", 'F'); if(useSvtx) factory->AddVariable( "svtxntrk", "svtx ntracks", "units", 'F'); if(useSvtx) factory->AddVariable( "sv2Trkdl", "2trk svtx close2PV dl", "units", 'F'); if(useSvtx) factory->AddVariable( "svtxTrkSumChi2", "svtx trk sum chi2", "units", 'F'); if(useSvtx) factory->AddVariable( "svtxTrkNetCharge", "svtx trk net chg", "units", 'F'); if(useSvtx) factory->AddVariable( "svtxNtrkInCone", "svtx ntrk in cone", "units", 'F'); //factory->AddVariable( "jteta", "Jet eta", "units", 'F' ); factory->AddVariable( "closestDMass","Closest DMass", "units", 'F' ); factory->AddVariable( "closestDType","Closest Type","units",'F'); factory->AddVariable( "closestDPt","Closest DpT", "units", 'F' ); /*factory->AddVariable( "chargedMax","chargedMax","units",'F'); factory->AddVariable( "chargedSum","chargedSum","units",'F'); factory->AddVariable( "neutralMax","neutralMax","units",'F'); factory->AddVariable( "neutralSum","neutralSum","units",'F'); factory->AddVariable( "photonMax","photonMax","units",'F'); factory->AddVariable( "photonSum","photonSum","units",'F'); factory->AddVariable( "eSum","eSum","units",'F'); factory->AddVariable( "muSum","muSum","units",'F');*/ for(int i=0; i<3; i++){ //factory->AddVariable( Form("ipProb0_%d",i),Form("prob0 IP part %d",i),"units",'F'); //factory->AddVariable( Form("ipPt_%d",i),Form("IP pt part %d",i),"units",'F'); factory->AddVariable( Form("trackIP2dSig_%d",i),Form("IP trk 2d sig part %d",i),"units",'F'); factory->AddVariable( Form("trackIP3dSig_%d",i),Form("IP trk 3d sig part %d",i),"units",'F'); factory->AddVariable( Form("trackIP2d_%d",i),Form("IP trk 2d part %d",i),"units",'F'); factory->AddVariable( Form("trackIP3d_%d",i),Form("IP trk 3d part %d",i),"units",'F'); } for(int i=0; i<1; i++){ //factory->AddVariable( Form("trackPtRel_%d",i),Form("pt rel part %d",i),"units",'F'); //factory->AddVariable( Form("trackPPar_%d",i),Form("track ppar part %d",i),"units",'F'); //factory->AddVariable( Form("trackPParRatio_%d",i),Form("track ppar part %d",i),"units",'F'); factory->AddVariable( Form("trackJetDist_%d",i),Form("dist to jet part %d",i),"units",'F'); factory->AddVariable( Form("trackDecayLenVal_%d",i),Form("trk decay len part %d",i),"units",'F'); //factory->AddVariable( Form("trackDeltaR_%d",i),Form("trk dr to jet part %d",i),"units",'F'); //factory->AddVariable( Form("trackPtRatio_%d",i),Form("trk pt ratio part %d",i),"units",'F'); } //factory->AddVariable( "trackSip2dSigAboveCharm","trackSip2dSigAboveCharm","units",'F'); //factory->AddVariable( "trackSip3dSigAboveCharm","trackSip3dSigAboveCharm","units",'F'); //factory->AddVariable( "trackSip2dValAboveCharm","trackSip2dValAboveCharm","units",'F'); //factory->AddVariable( "trackSip3dValAboveCharm","trackSip3dValAboveCharm","units",'F'); //factory->AddVariable( "svJetDeltaR","svJetDeltaR","units",'F'); factory->AddVariable( "trackSumJetDeltaR","trackSumJetDeltaR","units",'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' ); if(isMC) factory->AddSpectator( "refpt", "ref pT", "units", 'F' ); factory->AddSpectator( "rawpt", "raw pT", "units", 'F' ); //factory->AddSpectator( "dCandPt", "D-Meson pT", "units" , 'F' ); if(isMC) factory->AddSpectator( "refparton_flavorForB", "jet flavor", "units" , 'F' ); //factory->AddSpectator( "evtSelection", "event selection", "units" , 'F' ); //factory->AddSpectator( "vz", "z-vertex", "units" , 'F' ); //if(isMC) factory->AddSpectator( "subid", "subid", "units" , 'F' ); //factory->AddSpectator( "pthat", "pthat", "units" , 'F' ); //factory->AddSpectator( "run", "run", "units" , 'I' ); //factory->AddSpectator( "bin", "centrality", "units" , 'I' ); factory->AddSpectator( "jtpt", "Jet pT", "units", 'F' ); // Read training and test data // (it is also possible to use ASCII format as input -> see TMVA Users Guide) TString fname; if(!isMC) fname = "/Users/kjung/charmJets/pPb/input/DMesonCJet_pPbData_ppReco_akPu3PF_convertToJetTree_withLHCbVars_medDCuts.root"; else fname = "/Users/kjung/charmJets/pPb/input/DMesonCJet_QCDJetOnly_pPbMC_ppReco_akPu3PF_convertToJetTree_medDCuts.root"; //if (gSystem->AccessPathName( fname )) // file does not exist in local directory // gSystem->Exec("curl -O 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, *background; if(useSvtx){ signal = (TTree*)input->Get("jets"); background = (TTree*)input->Get("jets"); } else{ signal = (TTree*)input->Get("jetsNoSvtx"); background = (TTree*)input->Get("jetsNoSvtx"); } TTree *signal_2 = (TTree*)input->Get("dMesons"); TTree *background_2 = (TTree*)input->Get("dMesons"); signal->AddFriend(signal_2); background->AddFriend(background_2); // 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 ); // 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 const int nvars = 35; //67; const int nvarsWithInt = 2; double weight; std::vector<double> vars(nvars); double treevars[nvars-nvarsWithInt]; int treevars2[nvarsWithInt]; //std::string variables[nvars] = {"djetR","closestDPt","svtxm","svtxdl","jtpt","refpt","rawpt","refparton_flavorForB","svtxdls","trackIP2dSig_0","trackIP3dSig_0","trackIP2d_0","trackIP3d_0","ipProb0_0","trackPtRel_0","trackPPar_0","trackPParRatio_0","trackJetDist_0","trackDecayLenVal_0","trackDeltaR_0","trackPtRatio_0","ipPt_0","trackIP2dSig_1","trackIP3dSig_1","trackIP2d_1","trackIP3d_1","ipProb0_1","trackPtRel_1","trackPPar_1","trackPParRatio_1","trackJetDist_1","trackDecayLenVal_1","trackDeltaR_1","trackPtRatio_1","ipPt_1","trackIP2dSig_2","trackIP3dSig_2","trackIP2d_2","trackIP3d_2","ipProb0_2","trackPtRel_2","trackPPar_2","trackPParRatio_2","trackJetDist_2","trackDecayLenVal_2","trackDeltaR_2","trackPtRatio_2","ipPt_2","trackIP2dSig_3","trackIP3dSig_3","trackIP2d_3","trackIP3d_3","ipProb0_3","trackPtRel_3","trackPPar_3","trackPParRatio_3","trackJetDist_3","trackDecayLenVal_3","trackDeltaR_3","trackPtRatio_3","ipPt_3","trackSip2dValAboveCharm","trackSip3dValAboveCharm","svJetDeltaR","trackSumJetDeltaR","nIP","svtxntrk"}; std::string variables[nvars] = {"jtpt","refpt","rawpt","refparton_flavorForB","svtxptFrac","svtxmEnergyFrac","svtxpt","svtxm", "svtxdl","svtxdls","svtxTrkSumChi2","svtxTrkNetCharge","sv2Trkdl", "closestDMass","closestDType","closestDPt","trackIP2dSig_0","trackIP2dSig_1", "trackIP2dSig_2","trackIP3dSig_0","trackIP3dSig_1","trackIP3dSig_2","trackIP2d_0","trackIP2d_1", "trackIP2d_2","trackIP3d_0","trackIP3d_1","trackIP3d_2","trackJetDist_0","trackDecayLenVal_0","svJetDeltaR", "trackSumJetDeltaR","svtxNtrkInCone","svtxntrk","nIP"}; //std::string variables[nvars] = {"jtpt","refpt","rawpt","refparton_flavorForB","svtxptFrac","svtxdl","svtxdls","closestDPt","closestDType","closestDMass","svtxm","svtxmcorr","svJetDeltaR","trackSumJetDeltaR","svtxpt","sv2Trkdl","svtxTrkSumChi2","svtxTrkNetCharge","svtxNtrkInCone","svtxntrk"}; signal->SetBranchAddress("weight", &weight); for (UInt_t ivar=0; ivar<nvars-nvarsWithInt; ivar++) signal->SetBranchAddress( variables[ivar].c_str(), &(treevars[ivar]) ); for (UInt_t ivar=nvars-nvarsWithInt; ivar<nvars; ivar++) signal->SetBranchAddress( variables[ivar].c_str(), &(treevars2[ivar]) ); for (UInt_t i=0; i<signal->GetEntries(); i++) { signal->GetEntry(i); for (UInt_t ivar=0; ivar<nvars-nvarsWithInt; ivar++) vars[ivar] = treevars[ivar]; for (UInt_t ivar=nvars-nvarsWithInt; ivar<nvars; ivar++) vars[ivar] = treevars2[ivar]; // add training and test events; here: first half is training, second is testing // note that the weight can also be event-wise //for(int ij=0; ij<nvars; ij++) cout << ij << " " << vars[ij] << endl; if(isMC && (abs(vars[3])==4)) { if (i%2==0) factory->AddSignalTrainingEvent( vars, weight ); else factory->AddSignalTestEvent ( vars, weight ); } } // // // Background (has event weights) background->SetBranchAddress( "weight", &weight ); for (UInt_t ivar=0; ivar<nvars-nvarsWithInt; ivar++) background->SetBranchAddress( variables[ivar].c_str(), &(treevars[ivar]) ); for (UInt_t ivar=nvars-nvarsWithInt; ivar<nvars; ivar++) background->SetBranchAddress( variables[ivar].c_str(), &(treevars2[ivar]) ); for (UInt_t i=0; i<background->GetEntries(); i++) { background->GetEntry(i); for (UInt_t ivar=0; ivar<nvars-nvarsWithInt; ivar++) vars[ivar] = treevars[ivar]; for (UInt_t ivar=nvars-nvarsWithInt; ivar<nvars; ivar++) vars[ivar] = treevars2[ivar]; // add training and test events; here: first half is training, second is testing // note that the weight can also be event-wise if(isMC && (abs(vars[3])==5)) { if (i%2==0) factory->AddBackgroundTrainingEvent( vars, weight ); else factory->AddBackgroundTestEvent ( vars, 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"); factory->SetSignalWeightExpression("weight"); 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" ); 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=850:MinNodeSize=2%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=2" ); "!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.05:UseBaggedBoost:GradBaggingFraction=0.9:SeparationType=GiniIndex:nCuts=500: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=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 ); }