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 );
}
