void Boost(){
TString outfileName = "boost.root";
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D" );
factory->AddVariable( "var0", 'F' );
factory->AddVariable( "var1", 'F' );
TFile *input(0);
TString fname = "./data.root";
if (!gSystem->AccessPathName( fname )) {
// first we try to find tmva_example.root in the local directory
std::cout << "--- BOOST : Accessing " << fname << std::endl;
input = TFile::Open( fname );
}
else {
gROOT->LoadMacro( "../development/createData.C");
create_circ(20000);
cout << " created data.root with data and circle arranged in half circles"<<endl;
input = TFile::Open( fname );
}
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
TTree *signal = (TTree*)input->Get("TreeS");
TTree *background = (TTree*)input->Get("TreeB");
Double_t signalWeight = 1.0;
Double_t backgroundWeight = 1.0;
gROOT->cd( outfileName+TString(":/") );
factory->AddSignalTree ( signal, signalWeight );
factory->AddBackgroundTree( background, backgroundWeight );
factory->PrepareTrainingAndTestTree( "", "",
"nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );
TString fisher="H:!V";
factory->BookMethod( TMVA::Types::kFisher, "Fisher", fisher );
factory->BookMethod( TMVA::Types::kFisher, "FisherBoost", fisher+":Boost_Num=100:Boost_Type=AdaBoost" );
factory->BookMethod( TMVA::Types::kFisher, "FisherBoostLog", fisher+":Boost_Num=100:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=1.0" );
factory->BookMethod( TMVA::Types::kFisher, "FisherBoostLog2", fisher+":Boost_Num=100:Boost_Transform=log:Boost_Type=AdaBoost:Boost_AdaBoostBeta=2.0" );
factory->BookMethod( TMVA::Types::kFisher, "FisherBoostStep", fisher+":Boost_Num=100:Boost_Transform=step:Boost_Type=AdaBoost:Boost_AdaBoostBeta=1.0" );
factory->BookMethod( TMVA::Types::kFisher, "FisherBoostStep2", fisher+":Boost_Num=100:Boost_Transform=step:Boost_Type=AdaBoost:Boost_AdaBoostBeta=1.2" );
factory->BookMethod( TMVA::Types::kFisher, "FisherBoostStep3", fisher+":Boost_Num=100:Boost_Transform=step:Boost_Type=AdaBoost:Boost_AdaBoostBeta=1.5" );
// Train MVAs using the set of training events
factory->TrainAllMethodsForClassification();
// ---- 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 = "" )
void Example_Eric( 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();
//---------------------------------------------------------------
// default MVA methods to be trained + tested
std::map<std::string,int> Use;
Use["Cuts"] =0;
Use["CutsD"] =0;
Use["CutsPCA"] =0;
Use["CutsGA"] =0;
Use["CutsSA"] =0;
// ---
Use["Likelihood"] =0;
Use["LikelihoodD"] =0; // the "D" extension indicates decorrelated input variables (see option strings)
Use["LikelihoodPCA"] =1; // the "PCA" extension indicates PCA-transformed input variables (see option strings)
Use["LikelihoodKDE"] =0;
Use["LikelihoodMIX"] =0;
// ---
Use["PDERS"] =0;
Use["PDERSD"] =0;
Use["PDERSPCA"] =0;
Use["PDERSkNN"] =0; // depreciated until further notice
Use["PDEFoam"] =0;
// --
Use["KNN"] =0;
// ---
Use["HMatrix"] =0;
Use["Fisher"] =0;
Use["FisherG"] =0;
Use["BoostedFisher"] =0;
Use["LD"] =0;
// ---
Use["FDA_GA"] =0;
Use["FDA_SA"] =0;
Use["FDA_MC"] =0;
Use["FDA_MT"] =0;
Use["FDA_GAMT"] =0;
Use["FDA_MCMT"] =0;
// ---
Use["MLP"] = 1; // this is the recommended ANN
Use["MLPBFGS"] = 0; // recommended ANN with optional training method
Use["CFMlpANN"] =0; // *** missing
Use["TMlpANN"] =0;
// ---
Use["SVM"] =1;
// ---
Use["BDT"] =1;
Use["BDTD"] =0;
Use["BDTG"] =0;
Use["BDTB"] =0;
// ---
Use["RuleFit"] =1;
// ---
Use["Plugin"] =0;
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVAClassification" << std::endl;
if (myMethodList != "") {
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;
std::vector<TString> mlist = 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;
}
}
// Create a new root output file.
TString outfileName( "TMVA_Eric2.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 will
// then run the performance analysis for you.
//
// 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" );
// 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( "Mqq := Mqq", 'F' );
factory->AddVariable( "Pt_qq := Pt_qq", 'F' );
factory->AddVariable( "Eta_qq := Eta_qq", 'F' );
factory->AddVariable( "Charge_qq := Charge_qq", 'F' );
factory->AddVariable( "DPhi_ll := DPhi_ll", 'F' );
factory->AddVariable( "DPt_ll := DPt_ll", 'F' );
//factory->AddVariable( "MinDPhi_lMET := MinDPhi_lMET", 'F' );
//factory->AddVariable( "Aplanarity := aplanarity", 'F' );
//factory->AddVariable( "chargeEta := chargeEta", 'F' );
//factory->AddVariable( "MET := Met", 'F' );
//factory->AddVariable( "MtauJet := MtauJet", 'F' );
//factory->AddVariable( "HT := Ht", 'F' );
//factory->AddVariable( "Chi2 := kinFitChi2", 'F' );
//factory->AddVariable( "DeltaPhiTauMET := DeltaPhiTauMet", 'F' );
//factory->AddVariable( "Mt := Mt", '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
if (ReadDataFromAsciiIFormat) {
// load the signal and background event samples from ascii files
// format in file must be:
// var1/F:var2/F:var3/F:var4/F
// 0.04551 0.59923 0.32400 -0.19170
// ...
TString datFileS = "tmva_example_sig.dat";
TString datFileB = "tmva_example_bkg.dat";
factory->SetInputTrees( datFileS, datFileB );
}
else {
//TFile* f0 = new TFile("/opt/sbg/data/data1/cms/lebihan/clean_january_2012_2/CMSSW_4_2_8_patch7/src/MiniTreeAnalysis/NTupleAnalysis/macros/TopTauJets/TMVA_sig_newLumi.root");
//TFile* f1 = new TFile("/opt/sbg/data/data1/cms/lebihan/clean_january_2012_2/CMSSW_4_2_8_patch7/src/MiniTreeAnalysis/NTupleAnalysis/macros/TopTauJets/TMVA_bkg_newLumi.root");
TFile* f0 = 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");
TFile* f1 = 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 *signal = (TTree*)f0->Get("theTree2");
TTree *background = (TTree*)f1->Get("theTree2");
cout<<"trees: "<<signal<<" "<<background<<endl;
//Double_t backgroundWeight = 1.0;
//Double_t signalWeight = 1.0;
Double_t signalWeight = 0.30*20/185338;
Double_t backgroundWeight = 222.*0.1*20/9982625;
// ====== register trees ====================================================
//
// the following method is the prefered one:
// you can add an arbitrary number of signal or background trees
factory->AddSignalTree ( signal, signalWeight );
factory->AddBackgroundTree( background, backgroundWeight );
// factory->AddSignalTree ( signal );
//factory->AddBackgroundTree( background );
// 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];
// for (Int_t ivar=0; ivar<4; ivar++) signal->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
// for (Int_t i=0; i<signal->GetEntries(); i++) {
// signal->GetEntry(i);
// for (Int_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) factory->AddSignalTrainingEvent( vars, signalWeight );
// else factory->AddSignalTestEvent ( vars, signalWeight );
// }
//
// for (Int_t ivar=0; ivar<4; ivar++) background->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
// for (Int_t i=0; i<background->GetEntries(); i++) {
// background->GetEntry(i);
// for (Int_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 );
// else factory->AddBackgroundTestEvent ( vars, backgroundWeight );
// }
// // --- end ------------------------------------------------------------
//
// ====== end of register trees ==============================================
}
// This would set individual event weights (the variables defined in the
// expression need to exist in the original TTree)
// for signal : factory->SetSignalWeightExpression("weight1*weight2");
// for background: factory->SetBackgroundWeightExpression("weight1*weight2");
//factory->SetBackgroundWeightExpression("weight_BTAG");
//factory->SetSignalWeightExpression("weight*weight_BTAG");
// Apply additional cuts on the signal and background samples (can be different)
// TCut mycuts = "MHt >=0 && MMTauJet >=0 && MM3 >= 0"; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
// TCut mycutb = "MHt >=0 && MMTauJet >=0 && MM3 >= 0"; // for example: TCut mycutb = "abs(var1)<0.5";
//TCut mycuts = "Met>=20 "; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
//TCut mycutb = "Met>=20 "; // for example: TCut mycutb = "abs(var1)<0.5";
TCut mycuts;
TCut mycutb;
// tell the factory to use all remaining events in the trees after training for testing:
factory->PrepareTrainingAndTestTree( mycuts, mycutb,
"nTrain_Signal=3000:nTrain_Background=5000:SplitMode=Random:NormMode=NumEvents:!V" );
// 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" );
// ---- 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
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" );
// test the 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" );
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" );
// test the new kernel density estimator
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" );
// test the mixed splines and kernel density estimator (depending on which variable)
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["PDERSkNN"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERSkNN",
"!H:!V:VolumeRangeMode=kNN: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=0.0333:nActiveCells=500:nSampl=2000:nBin=5:CutNmin=T:Nmin=100: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" );
// Fisher discriminant
if (Use["Fisher"])
factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=60: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");
// Linear discriminant (same as Fisher)
if (Use["LD"])
factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None" );
// 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" );
if (Use["MLPBFGS"])
factory->BookMethod( TMVA::Types::kMLP, "MLPBFGS", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS" );
// 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=0.30:UseBaggedGrad:GradBaggingFraction=0.6:SeparationType=GiniIndex:nCuts=20:NNodesMax=5" );
if (Use["BDT"]) // Adaptive Boost
factory->BookMethod( TMVA::Types::kBDT, "BDT",
"!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost: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" );
// 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" );
// --------------------------------------------------------------------------------------------------
// As an example how to use the ROOT plugin mechanism, book BDT via
// plugin mechanism
if (Use["Plugin"]) {
//
// first the plugin has to be defined, which can happen either through the following line in the local or global .rootrc:
//
// # plugin handler plugin name(regexp) class to be instanciated library constructor format
// Plugin.TMVA@@MethodBase: ^BDT TMVA::MethodBDT TMVA.1 "MethodBDT(TString,TString,DataSet&,TString)"
//
// or by telling the global plugin manager directly
gPluginMgr->AddHandler("TMVA@@MethodBase", "BDT", "TMVA::MethodBDT", "TMVA.1", "MethodBDT(TString,TString,DataSet&,TString)");
factory->BookMethod( TMVA::Types::kPlugins, "BDT",
"!H:!V:NTrees=400:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=CostComplexity:PruneStrength=50" );
}
// --------------------------------------------------------------------------------------------------
// ---- Now you can tell the factory to train, test, and evaluate the MVAs
// Train MVAs using the set of training events
factory->TrainAllMethodsForClassification();
// ---- 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 );
}
int main()
{
// this loads the library
TMVA::Tools::Instance();
//---------------------------------------------------------------
// default MVA methods to be trained + tested
std::map<std::string,int> Use;
Use["Cuts"] =1;
Use["BDT"] =1;
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVAClassification" << std::endl;
// Create a new root output file.
TString outfileName( "TMVA_output.root" );
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D" );
// Add the variables you want to consider
//factory->AddVariable( "MT := MT", 'F' );
//factory->AddVariable( "nJets := nJets", 'F' );
factory->AddVariable( "MET := MET", 'F' );
factory->AddVariable( "MT2W := MT2W", 'F' );
factory->AddVariable( "dPhiMETjet := dPhiMETjet", 'F' );
factory->AddVariable( "HTratio := HTratio", 'F' );
factory->AddVariable( "HadronicChi2 := HadronicChi2", 'F' );
factory->AddVariable( "nWTag := nWTag", 'I' );
// Open samples
TFile* f_signal = TFile::Open((string(MICROTUPLES_FOLDER)+"signal.root").c_str());
TFile* f_ttbar = TFile::Open((string(MICROTUPLES_FOLDER)+"ttbar.root" ).c_str());
//TFile* f_W2Jets = TFile::Open((string(MICROTUPLES_FOLDER)+"W2Jets.root").c_str());
//TFile* f_W3Jets = TFile::Open((string(MICROTUPLES_FOLDER)+"W3Jets.root").c_str());
//TFile* f_W4Jets = TFile::Open((string(MICROTUPLES_FOLDER)+"W4Jets.root").c_str());
TTree* signal = (TTree*) f_signal->Get("microTuple");
TTree* ttbar = (TTree*) f_ttbar ->Get("microTuple");
//TTree* W2Jets = (TTree*) f_W2Jets->Get("microTuple");
//TTree* W3Jets = (TTree*) f_W3Jets->Get("microTuple");
//TTree* W4Jets = (TTree*) f_W4Jets->Get("microTuple");
// Register the trees
// float weightSignal = 1.0 * 20000.0 / getNumberOfEvent(signal);
// float weightBackground = 225.2 * 20000.0 / getNumberOfEvent(ttbar);
float weightSignal = 1.0;
float weightBackground = 1.0;
factory->AddSignalTree ( signal, weightSignal );
factory->AddBackgroundTree( ttbar, weightBackground);
/*
cout << " signal ; w = " << 1.0 * 20000.0 / getNumberOfEvent(signal) << endl;
factory->AddSignalTree ( signal, 1.0 * 20000.0 / getNumberOfEvent(signal));
cout << " ttbar ; w = " << 225.2 * 20000.0 / getNumberOfEvent(ttbar) << endl;
factory->AddBackgroundTree( ttbar, 234.0 * 20000.0 / getNumberOfEvent(ttbar));
cout << " W2Jets ; w = " << 2159 * 20000.0 / getNumberOfEvent(W2Jets) << endl;
factory->AddBackgroundTree( W2Jets, 2159 * 20000.0 / getNumberOfEvent(W2Jets));
cout << " W3Jets ; w = " << 640 * 20000.0 / getNumberOfEvent(W3Jets) << endl;
factory->AddBackgroundTree( W3Jets, 640 * 20000.0 / getNumberOfEvent(W3Jets));
cout << " W4Jets ; w = " << 264 * 20000.0 / getNumberOfEvent(W4Jets) << endl;
factory->AddBackgroundTree( W4Jets, 264 * 20000.0 / getNumberOfEvent(W4Jets));
*/
// Add preselection cuts
std::string preselectionCutsSig("nJets > 4 && MET > 80 && MT > 100");
std::string preselectionCutsBkg("nJets > 4 && MET > 80 && MT > 100");
// Prepare the training
factory->PrepareTrainingAndTestTree( preselectionCutsSig.c_str(), preselectionCutsBkg.c_str(),
"nTrain_Signal=40000:nTrain_Background=300000:nTest_Signal=40000:nTest_Background=300000:SplitMode=Random:NormMode=EqualNumEvents:!V" );
// Cut optimisation
//if (Use["Cuts"]) factory->BookMethod( TMVA::Types::kCuts, "Cuts",
// "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" );
if (Use["BDT"]) factory->BookMethod( TMVA::Types::kBDT, "BDT",
"!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning");
// --------------------------------------------------------------
// Train MVAs using the set of training events
factory->TrainAllMethodsForClassification();
// 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 );
}
