void TMVAClassification(char* trainFile, char* tree, char* mycuts, char* mycutb, char* inputVars[], int size) { // this loads the library TMVA::Tools::Instance(); // Create a new root output file. TFile* outputFile = TFile::Open( "TMVA.root", "RECREATE" ); // Create the factory object. TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile, "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D" ); // ---------- input variables for (int ivar = 0; ivar < size; ++ivar) { factory->AddVariable(inputVars[ivar], 'F'); } // read training and test data TFile *input = TFile::Open( trainFile); TTree *signal = (TTree*)input->Get(tree); TTree *background = (TTree*)input->Get(tree); // global event weights per tree Double_t signalWeight = 1.0; Double_t backgroundWeight = 1.0; // ====== register trees ==================================================== // you can add an arbitrary number of signal or background trees factory->AddSignalTree ( signal, signalWeight ); factory->AddBackgroundTree( background, backgroundWeight ); // tell the factory to use all remaining events in the trees after training for testing: factory->PrepareTrainingAndTestTree( TCut(mycuts), TCut(mycutb), "nTrain_Signal=0:nTrain_Background=0: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: // ---- 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" ); // ---- 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 << "==> TMVAClassification is done!" << std::endl; delete factory; }
void process(const std::vector<std::string>& inputFiles, const std::string& name, const std::string& outputFile) { TChain* signal = loadChain(inputFiles, "signal"); TChain* background = loadChain(inputFiles, "background"); TFile* output = TFile::Open(outputFile.c_str(), "recreate"); TMVA::Factory* factory = new TMVA::Factory(name.c_str(), output, "V"); factory->AddSignalTree(signal, 1.); factory->AddBackgroundTree(background, 1.); //{ //factory->AddVariable("lightJet1p2_Pt"); //factory->AddVariable("leptonic_B_Pt"); //factory->AddVariable("leptonic_Top_Pt"); //factory->AddVariable("leptonic_Top_M"); //factory->AddVariable("hadronic_B_Pt"); //factory->AddVariable("hadronic_W_M"); //factory->AddVariable("hadronic_Top_Pt"); //factory->AddVariable("hadronic_Top_M"); //factory->AddVariable("delta_R_tops"); //factory->AddVariable("delta_R_lightjets"); //factory->AddVariable("leptonic_B_CSV"); //factory->AddVariable("hadronic_B_CSV"); //} // chi^2 style { factory->AddVariable("leptonic_Top_M"); factory->AddVariable("hadronic_W_M"); factory->AddVariable("hadronic_Top_M"); factory->AddVariable("ht_fraction"); } factory->SetWeightExpression("weight"); factory->PrepareTrainingAndTestTree("", "", "V:VerboseLevel=Info:nTrain_Signal=100000:nTrain_Background=100000:nTest_Signal=100000:nTest_Background=100000"); factory->BookMethod(TMVA::Types::kBDT, "BDT", "V:BoostType=AdaBoost:nCuts=20:VarTransform=D"); factory->BookMethod(TMVA::Types::kMLP, "NN", "V:VarTransform=D"); //factory->BookMethod(TMVA::Types::kPDERS, "PDERS", "V"); factory->TrainAllMethods(); factory->TestAllMethods(); factory->EvaluateAllMethods(); output->Close(); delete output; delete signal; delete background; }
void test_train(TString signalName = "WW", TString bkgName = "DY") { TFile *outFile = new TFile("myAnalysisFile.root","RECREATE"); TMVA::Factory *factory = new TMVA::Factory(signalName, outFile,""); TString directory = "../rootFiles/SF/MediumIDTighterIP/"; //signalName = directory + signalName; //defining WW signal TFile *MySignalFile = new TFile("../rootFiles/SF/MediumIDTighterIP/WW.root","READ"); TTree* sigTree = (TTree*)MySignalFile->Get("nt"); factory->AddSignalTree(sigTree,1); //defining DY background TFile *MyBkgFile = new TFile("../rootFiles/SF/MediumIDTighterIP/DY.root","READ"); TTree* bkgTree = (TTree*)MyBkgFile->Get("nt"); factory->AddBackgroundTree(bkgTree,1); factory->SetWeightExpression("baseW"); //************************************ FACTORY factory->AddVariable("fullpmet"); factory->AddVariable("trkpmet"); factory->AddVariable("ratioMet"); factory->AddVariable("ptll"); factory->AddVariable("mth"); factory->AddVariable("jetpt1"); factory->AddVariable("ptWW"); factory->AddVariable("dphilljet"); factory->AddVariable("dphillmet"); factory->AddVariable("dphijet1met"); factory->AddVariable("nvtx"); factory->PrepareTrainingAndTestTree("",500,500,500,500); cout<<"I've prepared trees"<<endl; //factory->BookMethod(TMVA::Types::kFisher, "Fisher",""); factory->BookMethod(TMVA::Types::kBDT, "BDT",""); cout<<"I've booked method"<<endl; factory->TrainAllMethods(); factory->TestAllMethods(); cout<<"I've tested all methods"<<endl; factory->EvaluateAllMethods(); cout<<"I've evaluated all methods"<<endl; }
int main () { TFile * outputfile = TFile::Open ("outputTMVA.root","RECREATE"); TMVA::Factory * TMVAtest = new TMVA::Factory ("TMVAtest", outputfile, "S") ; //PG get the signal and deliver it to the TMVA factory TFile signalFile ("/Users/govoni/data/ntuplesHWW/H160/NTUPLE_H160_output_0.root") ; TTree * signalTree = (TTree *) signalFile.Get ("ntpla/VBFSimpleTree") ; std::cout << "READ " << signalTree->GetEntries () << " signal events\n" ; TMVAtest->AddSignalTree (signalTree,1) ; //PG get the bkg and deliver it to the TMVA factory TFile bkg1File ("/Users/govoni/data/ntuplesHWW/WW_incl/NTUPLE_WW_incl_output_0.root") ; TTree * bkg1Tree = (TTree *) bkg1File.Get ("ntpla/VBFSimpleTree") ; std::cout << "READ " << bkg1Tree->GetEntries () << " bkg1 events\n" ; TMVAtest->AddBackgroundTree (bkg1Tree,1) ; delete TMVAtest ; delete outputfile ; }
void TMVAtest(){ //gSystem->Load("../lib/slc5_amd64_gcc462/libTAMUWWMEPATNtuple.so"); gSystem->Load("libPhysics"); //gSystem->Load("EvtTreeForAlexx_h.so"); gSystem->Load("libTMVA.1"); gSystem->Load("AutoDict_vector_TLorentzVector__cxx.so"); TMVA::Tools::Instance(); TFile* outputFile = TFile::Open("TMVA1.root", "RECREATE"); TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification",outputFile,"V=true:Color:DrawProgressBar");// ":Transformations=I;D;P;G,D" ); TFile* signal = TFile::Open("/uscms_data/d2/aperloff/Spring12ME7TeV/MEResults/microNtuples_oldStructure/microWW_EPDv01.root"); TFile* bkg = TFile::Open("/uscms_data/d2/aperloff/Spring12ME7TeV/MEResults/microNtuples_oldStructure/microWJets_EPDv01.root"); TTree* stree = (TTree*)signal->Get("METree"); TTree* btree = (TTree*)bkg->Get("METree"); factory->AddSignalTree(stree,1.0); factory->AddBackgroundTree(btree,1.0); factory->SetSignalWeightExpression("1.0"); factory->SetBackgroundWeightExpression("1.0"); factory->AddVariable("tEventProb[0]"); factory->AddVariable("tEventProb[1]"); factory->AddVariable("tEventProb[2]"); //factory->AddVariable("tEventProb0 := tEventProb[0]",'F'); //factory->AddVariable("tEventProb1 := tEventProb[1]",'F'); //factory->AddVariable("tEventProb2 := tEventProb[2]",'F'); TCut test("Entry$>-2 && jLV[1].Pt()>30"); TCut mycuts (test); factory->PrepareTrainingAndTestTree(mycuts,mycuts,"nTrain_Signal=0:nTrain_Background=0:nTest_Signal=0:nTest_Background=0:SplitMode=Random:NormMode=None:V=true:VerboseLevel=DEBUG"); factory->BookMethod( TMVA::Types::kBDT, "BDT","!H:!V:NTrees=400:nEventsMin=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" ); factory->TrainAllMethods(); factory->TestAllMethods(); factory->EvaluateAllMethods(); outputFile->Close(); }
//void TMVAClassification( TString myMethodList = "" ) void tmvaClassifier( TString myMethodList = "", TString inputDir="~/work/ewkzp2j_5311/ll/", bool minimalTrain=false, bool useQG=false) { gSystem->ExpandPathName(inputDir); TString pf("base_weights"); if(!minimalTrain){ if(useQG) pf="full_weights"; else pf="weights"; } TMVA::gConfig().GetIONames().fWeightFileDir = inputDir + pf; // 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; // --- 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"] = 1; // 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"] = 1; Use["FisherCat"] = 0;//added by loic 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"] = 1; // 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( "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" ); // 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 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"); // std::cout << "--- TMVAClassification : Using input file: " << input->GetName() << std::endl; // --- Register the training and test trees TChain *signal = new TChain("ewkzp2j"); TChain *background = new TChain("ewkzp2j"); TSystemDirectory dir(inputDir,inputDir); TList *files = dir.GetListOfFiles(); if (files) { TSystemFile *file; TString fname; TIter next(files); while ((file=(TSystemFile*)next())) { fname = file->GetName(); if(!fname.EndsWith("_summary.root")) continue; if(fname.Contains("Data")) continue; if(!fname.Contains("DY")) continue; bool isSignal(false); if(fname.Contains("JJ")) { signal->Add(fname); isSignal=true; } else if(fname.Contains("50toInf") && fname.Contains("DY")) background->Add(fname); cout << fname << " added as " << (isSignal ? "signal" : "background") << endl; } }else{ cout << "[Error] no files found in " << inputDir << endl; } cout << "Signal has " << signal->GetEntries() << " raw events" << endl << "Background has " << background->GetEntries() << " raw events"<< endl; // global event weights per tree Double_t signalWeight = 1.0; Double_t backgroundWeight = 1.0; factory->AddSignalTree ( signal, signalWeight ); factory->AddBackgroundTree( background, backgroundWeight ); // event-per-event weights per tree factory->SetBackgroundWeightExpression( "weight/cnorm" ); factory->SetSignalWeightExpression( "weight/cnorm" ); //define variables for the training if(minimalTrain) { factory->AddVariable( "mjj", "M_{jj}" "GeV", 'F' ); factory->AddVariable( "detajj", "#Delta#eta_{jj}", "", 'F' ); factory->AddVariable( "spt", "#Delta_{rel}", "GeV", 'F' ); } else { factory->AddVariable( "mjj", "M_{jj}" "GeV", 'F' ); factory->AddVariable( "detajj", "#Delta#eta_{jj}", "", 'F' ); factory->AddVariable( "setajj", "#Sigma#eta_{j}", "", 'F' ); factory->AddVariable( "eta1", "#eta(1)", "", 'F' ); factory->AddVariable( "eta2", "#eta(2)", "", 'F' ); factory->AddVariable( "pt1", "p_{T}(1)", "GeV", 'F' ); factory->AddVariable( "pt2", "p_{T}(2)", "GeV", 'F' ); factory->AddVariable( "spt", "#Delta_{rel}", "GeV", 'F' ); if(useQG) factory->AddVariable( "qg1", "q/g(1)", "", 'F' ); if(useQG) factory->AddVariable( "qg2", "q/g(2)", "", 'F' ); } // 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"; 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" ); if (Use["FisherCat"]){ TMVA::MethodBase* fiCat = factory->BookMethod( TMVA::Types::kCategory, "FisherCat","" ); TMVA::MethodCategory* mcategory = dynamic_cast<TMVA::MethodCategory*>(fiCat); mcategory->AddMethod( "mjj<250", "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat1", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" ); mcategory->AddMethod( "mjj>=250&&mjj<350" , "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat0000", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" ); mcategory->AddMethod( "mjj>=350&&mjj<450" , "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat0350", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" ); mcategory->AddMethod( "mjj>=450&&mjj<550" , "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat0450", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" ); mcategory->AddMethod( "mjj>=550&&mjj<750" , "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat0550", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" ); mcategory->AddMethod( "mjj>=750&&mjj<1000", "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat0750", "H:!V:Fisher:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" ); mcategory->AddMethod( "mjj>=1000" , "mjj:detajj:spt:", TMVA::Types::kFisher, "Fisher_Cat1000", "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=1.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedGrad:GradBaggingFraction=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: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:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=25:PruneMethod=CostComplexity:PruneStrength=25.0:VarTransform=Decorrelate"); //"!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 // 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; std::cout << " ==> Weights are stored in " << TMVA::gConfig().GetIONames().fWeightFileDir << 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; 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 Classification() { TMVA::Tools::Instance(); TMVA::PyMethodBase::PyInitialize(); TString outfileName("TMVA.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:AnalysisType=Classification"); 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->AddSpectator("spec1 := var1*2", "Spectator 1", "units", 'F'); factory->AddSpectator("spec2 := var1*3", "Spectator 2", "units", 'F'); TString fname = "./tmva_class_example.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 *tsignal = (TTree *)input->Get("TreeS"); TTree *tbackground = (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(tsignal, signalWeight); factory->AddBackgroundTree(tbackground, backgroundWeight); // Set individual event weights (the variables must exist in the original TTree) 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 factory->PrepareTrainingAndTestTree(mycuts, mycutb, "nTrain_Signal=0:nTrain_Background=0:nTest_Signal=0:nTest_Background=0:SplitMode=Random:NormMode=NumEvents:!V"); /////////////////// //Booking // /////////////////// // Boosted Decision Trees //PyMVA methods factory->BookMethod(TMVA::Types::kPyRandomForest, "PyRandomForest", "!V:NEstimators=150:Criterion=gini:MaxFeatures=auto:MaxDepth=3:MinSamplesLeaf=1:MinWeightFractionLeaf=0:Bootstrap=kTRUE"); factory->BookMethod(TMVA::Types::kPyAdaBoost, "PyAdaBoost", "!V:BaseEstimator=None:NEstimators=100:LearningRate=1:Algorithm=SAMME.R:RandomState=None"); factory->BookMethod(TMVA::Types::kPyGTB, "PyGTB", "!V:NEstimators=150:Loss=deviance:LearningRate=0.1:Subsample=1:MaxDepth=6:MaxFeatures='auto'"); // 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; }
//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 ); }
void ZTMVAClassification( TString myMethodList = "" ) { //--------------------------------------------------------------- // This loads the library TMVA::Tools::Instance(); // 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"] = 1; // 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"] = 1; // uses Adaptive Boost Use["BDTG"] = 1; // uses Gradient Boost Use["BDTB"] = 0; // uses Bagging Use["BDTD"] = 1; // 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; // -------------------------------------------------------------------------------------------------- // --- 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" ); TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile, "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" ); //factory->AddVariable( "maxpioneta", "maxpioneta", "", 'F' ); //factory->AddVariable( "minpioneta", "minpioneta", "", 'F' ); factory->AddVariable( "nTT", "nTT", "", 'F' ); // factory->AddVariable( "pidpimin", "pidpimin", "", 'F' ); // factory->AddVariable( "pidpimax", "pidpimax", "", 'F' ); factory->AddVariable( "normxpt", "normxpt", "", 'F' ); factory->AddVariable( "eta", "eta", "", 'F' ); //factory->AddVariable( "phi", "phi", "", 'F' ); // factory->AddVariable( "normptsum", "normptsum", "", 'F' ); //factory->AddVariable( "ptAsym", "ptAsym", "", 'F' ); //factory->AddVariable( "dphimax", "dphimax", "", 'F' ); //factory->AddVariable( "dphimin", "dphimin", "", 'F' ); //factory->AddVariable( "drmax", "drmax", "", 'F' ); // factory->AddVariable( "drmin", "drmin", "", 'F' ); // factory->AddVariable( "normpionp", "normpionp", "", 'F' ); factory->AddVariable( "normminpionpt", "normminpionpt", "", 'F' ); //factory->AddVariable( "normminpionp", "normminpionp", "", 'F' ); factory->AddVariable( "normmaxpionpt", "normmaxpionpt", "", 'F' ); // factory->AddVariable( "normptj", "normptj", "", 'F' ); //factory->AddVariable( "jmasspull", "jmasspull", "", 'F' ); //factory->AddVariable( "vchi2dof", "vchi2dof", "", 'F' ); // factory->AddVariable("maxchi2","maxchi2","", 'F'); // factory->AddVariable("normr","normr","", 'F'); // factory->AddVariable("normq","normq","", 'F'); //factory->AddVariable("normminm","normminm","", 'F'); factory->AddVariable("logipmax","logipmax","", 'F'); factory->AddVariable("logipmin","logipmin","", 'F'); factory->AddVariable("logfd","logfd",'F'); factory->AddVariable("logvd","logvd",'F'); //factory->AddVariable("pointAngle","pointingAngle",'F'); factory->AddVariable("logvpi","",'F'); //factory->AddVariable("logmaxprob","",'F'); //factory->AddVariable("logminprob","",'F'); factory->AddSpectator( "mReFit", "mReFit", "", 'D' ); // factory->AddSpectator( "Qdecay", "Qdecay", "",'F' ); // factory->AddSpectator( "m23", "m23", "",'F' ); // TFile * input_Background = new TFile("../back.root"); TFile * input_Signal = new TFile("../cmx12.root"); TFile * input_Background = new TFile("../background12.root"); std::cout << "--- TMVAClassification : Using input file for signal : " << input_Signal->GetName() << std::endl; std::cout << "--- TMVAClassification : Using input file for backgound : " << input_Background->GetName() << std::endl; // --- Register the training and test trees TTree *signal = (TTree*)input_Signal->Get("psiCand"); TTree *background = (TTree*)input_Background->Get("psiCand"); // 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 = "QDecay < 300&&fdchi2 > 300"; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1"; TCut mycutb = "QDecay < 300&&fdchi2> 300"; // for example: TCut mycutb = "abs(var1)<0.5"; 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" ); // 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" ); //factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=Norm:NCycles=600:HiddenLayers=N+5:TestRate=5" ); // factory->BookMethod( TMVA::Types::kMLP, "MLPCE", "H:!V:NeuronType=sigmoid:VarTransform=Norm:NCycles=600:HiddenLayers=N+5:TestRate=5:EstimatorType=CE" ); factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=sigmoid:VarTransform=Norm:NCycles=600:HiddenLayers=9:TestRate=5:EstimatorType=CE" ); // factory->BookMethod( TMVA::Types::kMLP, "MLPCE83", "H:!V:NeuronType=tanh:VarTransform=Norm:NCycles=600:HiddenLayers=8,3:TestRate=5:EstimatorType=CE" ); } 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" ); // factory->BookMethod( TMVA::Types::kBDT, "BDTGI", // "!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.10:UseBaggedGrad:GradBaggingFraction=0.5:nCuts=20:NNodesMax=5:SeparationType=GiniIndexWithLaplace" ); // factory->BookMethod( TMVA::Types::kBDT, "BDTG6", // "!H:!V:NTrees=600:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.4:nCuts=20:NNodesMax=6" ); //factory->BookMethod( TMVA::Types::kBDT, "BDTG2", // "!H:!V:NTrees=800:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.4:nCuts=20:NNodesMax=6" ); factory->BookMethod( TMVA::Types::kBDT, "BDTG3", "!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.4:nCuts=20:NNodesMax=6" ); // factory->BookMethod( TMVA::Types::kBDT, "BDTG4", // "!H:!V:NTrees=1200:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.4:nCuts=20:NNodesMax=6" ); // factory->BookMethod( TMVA::Types::kBDT, "BDTG5", // "!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.30:UseBaggedGrad:GradBaggingFraction=0.4: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 // 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 ); }
//require mumucl>0.6 //opening angle >10 //coplanarity >90 //pang<90 void TMVAClassification_cc1pcoh_bdt_ver6noveract( TString myMethodList = "" ) { //--------------------------------------------------------------- // 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 TMVAGui.C"); // Default MVA methods to be trained + tested std::map<std::string,int> Use; // --- Cut optimisation Use["Cuts"] = 1; Use["CutsD"] = 1; Use["CutsPCA"] = 0; Use["CutsGA"] = 0; Use["CutsSA"] = 0; // // --- 1-dimensional likelihood ("naive Bayes estimator") Use["Likelihood"] = 1; 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; // // --- Mutidimensional likelihood and Nearest-Neighbour methods Use["PDERS"] = 1; Use["PDERSD"] = 0; Use["PDERSPCA"] = 0; Use["PDEFoam"] = 1; Use["PDEFoamBoost"] = 0; // uses generalised MVA method boosting Use["KNN"] = 1; // k-nearest neighbour method // // --- Linear Discriminant Analysis Use["LD"] = 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"] = 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"] = 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"] = 1; // // --- Boosted Decision Trees Use["BDT"] = 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; // --------------------------------------------------------------- // Choose method 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_cc1pcoh_bdt_ver6noveract.root" );//newchange TFile* outputFile = TFile::Open( outfileName, "RECREATE" ); // Create the factory object. TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification_ver6noveract", outputFile,//newchange "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" ); // Add variable //sprintf(select, "Ntrack==2&&mumucl>0.6&&pmucl>0.25&&pang<90&&muang_t<15 && veract*7.66339869e-2<34"); //factory->AddVariable( "Ntrack", 'F' ); factory->AddVariable( "mumucl", 'F' ); factory->AddVariable( "pmucl", 'F' ); factory->AddVariable( "pang_t", 'F' );//use pang instead of pang_t factory->AddVariable( "muang_t", 'F' ); //factory->AddVariable( "veract", 'F' ); factory->AddVariable( "ppe", 'F'); factory->AddVariable( "mupe", 'F'); factory->AddVariable( "range", 'F'); factory->AddVariable( "coplanarity", 'F'); factory->AddVariable( "opening", 'F');//newadd // Add spectator factory->AddSpectator( "fileIndex", 'I' ); factory->AddSpectator( "nuE", 'F' ); factory->AddSpectator( "inttype", 'I' ); factory->AddSpectator( "norm", 'F' ); factory->AddSpectator( "totcrsne", 'F' ); factory->AddSpectator( "veract", 'F' ); factory->AddSpectator( "pang", 'F' ); factory->AddSpectator( "mupdg", 'I' ); factory->AddSpectator( "ppdg", 'I' ); // --------------------------------------------------------------- // --- Get weight TString fratioStr="/home/kikawa/macros/nd34_tuned_11bv3.1_250ka.root"; // --------------------------------------------------------------- // --- Add sample TString fsignalStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/pm_merged_ccqe_tot.root"; TString fbarStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/pmbar_merged_ccqe.root"; TString fbkgStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/wall_merged_ccqe_tot.root"; TString fbkg2Str="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmva/ingrid_merged_nd3_ccqe_tot.root"; /*TString fsignalStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmvafix/pm_merged_ccqe_tot.root"; TString fbarStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmvafix/pmbar_merged_ccqe.root"; TString fbkgStr="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmvafix/wall_merged_ccqe_tot.root"; TString fbkg2Str="/home/cvson/cc1picoh/frkikawa/meAna/ip4tmvafix/ingrid_merged_nd3_ccqe_tot.root";*/ TFile *pfileSignal = new TFile(fsignalStr); TFile *pfileBar = new TFile(fbarStr); TFile *pfileBkg = new TFile(fbkgStr); TFile *pfileBkg2 = new TFile(fbkg2Str); TFile *pfileRatio = new TFile(fratioStr); TTree *ptree_sig = (TTree*)pfileSignal->Get("tree"); TTree *ptree_bar = (TTree*)pfileBar->Get("tree"); TTree *ptree_bkg = (TTree*)pfileBkg->Get("tree"); TTree *ptree_bkg2 = (TTree*)pfileBkg2->Get("tree"); // POT normalization const int nmcFile = 3950; const int nbarFile = 986; const int nbkgFile = 55546;//(31085+24461); const int nbkg2File = 7882;//(3941+3941); // global event weights per tree (see below for setting event-wise weights) // adding for signal sample // using this as standard and add other later Double_t signalWeight_sig = 1.0; Double_t backgroundWeight_sig = 1.0; factory->AddSignalTree ( ptree_sig, signalWeight_sig ); factory->AddBackgroundTree( ptree_sig, backgroundWeight_sig ); // Add Numubar sample //Double_t signalWeight_bar = nmcFile/float(nbarFile); Double_t backgroundWeight_bar = nmcFile/float(nbarFile); //factory->AddSignalTree ( ptree_bar, signalWeight_bar ); factory->AddBackgroundTree( ptree_bar, backgroundWeight_bar ); // Add wall background //Double_t signalWeight_bkg = nmcFile/float(nbkgFile); Double_t backgroundWeight_bkg = nmcFile/float(nbkgFile); //factory->AddSignalTree ( ptree_bkg, signalWeight_bkg ); factory->AddBackgroundTree( ptree_bkg, backgroundWeight_bkg ); // Add INGRID background //Double_t signalWeight_bkg2 = nmcFile/float(nbkg2File); Double_t backgroundWeight_bkg2 = nmcFile/float(nbkg2File); //factory->AddSignalTree ( ptree_bkg2, signalWeight_bkg2 ); factory->AddBackgroundTree( ptree_bkg2, backgroundWeight_bkg2 ); //factory->SetSignalWeightExpression ("norm*totcrsne*2.8647e-13"); //factory->SetBackgroundWeightExpression( "norm*totcrsne*2.8647e-13" ); // Apply additional cuts on the signal and background samples (can be different) TCut mycuts = "Ntrack==2 && abs(inttype)==16 && fileIndex==1 && pang<90 && mumucl>0.6 && opening>10 && coplanarity>90 && pmucl>0.2"; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1"; TCut mycutb = "Ntrack==2 && (abs(inttype)!=16 || fileIndex>1) && pang<90 && mumucl>0.6 && opening>10 && coplanarity>90 && pmucl>0.2"; // 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=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["BDT"]) // Adaptive Boost factory->BookMethod( TMVA::Types::kBDT, "BDT", "!H:!V:NTrees=850:MaxDepth=3:BoostType=AdaBoost: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 ); }
int main(){ TMVA::Tools::Instance(); std::cout<<"Hello world"<<std::endl; TFile* OutputFile = TFile::Open("Outputfile.root","RECREATE"); TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", OutputFile, "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" ); std::vector<VMVariable*> Variables; MVariable* Var3= new MVariable("var3",F,none); MVariable* Var4 = new MVariable("var4",F,none); Variables.push_back(Var3); Variables.push_back(Var4); MVariable* Var1 = new MVariable("var1",F,none); MVariable* Var2 = new MVariable("var2",F,none); MultiVariable* MyVar1 = new MultiVariable("Var1+Var2",sum); MyVar1->AddVariable(Var1); MyVar1->AddVariable(Var2); Variables.push_back(MyVar1); MultiVariable* MyVar2 = new MultiVariable("Minus",subtract); MyVar2->AddVariable(Var1); MyVar2->AddVariable(Var2); Variables.push_back(MyVar2); std::string InputName= "./tmva_class_exampleD.root"; TFile *input = TFile::Open("./tmva_class_exampleD.root" ); TTree *signal = (TTree*)input->Get("TreeS"); TTree *background=(TTree*)input->Get("TreeB"); Double_t signalWeight = 1.0; Double_t backgroundWeight = 1.0; factory->AddSignalTree ( signal, signalWeight ); factory->AddBackgroundTree( background, backgroundWeight ); for(auto v:Variables){ factory->AddVariable(v->GetFactoryName(),v->GetType()); } factory->SetBackgroundWeightExpression( "weight" ); TCut mycuts = ""; TCut mycutb = ""; factory->PrepareTrainingAndTestTree( mycuts, mycutb, "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" ); std::vector<MClassifier*> Classifiers; Classifiers.push_back(new MClassifier(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")); for(auto C:Classifiers){ if(!(C->AddMethodToFactory(factory))){ std::cout<<"Booking classifier failed"<<std::endl; return 1; } } factory->TrainAllMethods(); factory->TestAllMethods(); factory->EvaluateAllMethods(); OutputFile->Close(); delete factory; TMVA::Reader *reader = new TMVA::Reader( "!Color:!Silent" ); for(auto v: Variables){ reader->AddVariable(v->GetFactoryName(),v->GetReaderAddress()); } for(auto C:Classifiers){ if(!(C->AddMethodToReader(reader,"./weights/","TMVAClassification"))){ std::cout<<"Failed adding classifer to reader"<<std::endl; return 1; } } TFile* Input = TFile::Open("./tmva_class_exampleD.root"); TTree* TreeToEvaluate= (TTree*)Input->Get("TreeS"); TFile* AppliedFile = new TFile("AppliedFile.root","RECREATE"); TTree* AppliedTree=TreeToEvaluate->CloneTree(0); for(auto C:Classifiers){ if(!(C->MakeBranch(AppliedTree)))return 1; } for(auto Var:Variables){ if(!(Var->SetBA(TreeToEvaluate))){ std::cout<<"Problem Setting Branch addresses"<<std::endl; return 1; } } Long64_t N=TreeToEvaluate->GetEntries(); LoopTimer LT(0.05); int vetoedeventcounter=0; double StartEntry=0.0; double LastEntry=0.0; Long64_t iStart=0; Long64_t iEnd=N; for(Long64_t i=iStart;i<iEnd;++i){ LT.DeclareLoopStart(iEnd-iStart); TreeToEvaluate->GetEntry(i); bool useevent=true; for(auto Var:Variables){ useevent=Var->DoOperation(); } if(!useevent){ vetoedeventcounter++; continue; } for(auto C:Classifiers){ if(!(C->Apply(reader)))return 1; } AppliedTree->Fill(); } AppliedTree->Write(); AppliedFile->Close(); std::cout<<"Got here"<<std::endl; // Compare Applied file from here with applied file from TMVA tests. TFile* ReadAppliedFile = TFile::Open("AppliedFile.root"); TTree* AppliedTreeRead=(TTree*)ReadAppliedFile->Get("TreeS"); if(!AppliedTreeRead)std::cout<<"NUll pointer to tree"<<std::endl; double BDTResponse; AppliedTreeRead->SetBranchAddress("BDT_response",&BDTResponse); TFile* ReadTMVATestFile = TFile::Open("/home/tw/root-v5-34/tmva/test/TreeFile.root"); if(!ReadTMVATestFile)std::cout<<"File open faild"<<std::endl; TTree* TMVATestTree=(TTree*)ReadTMVATestFile->Get("AppliedTree"); if(!TMVATestTree)std::cout<<"NUll pointer to tree"<<std::endl; double TestBDTResponse; TMVATestTree->SetBranchAddress("BDT_response",&TestBDTResponse); Long64_t ATRN=AppliedTreeRead->GetEntries(); Long64_t TTTN=TMVATestTree->GetEntries(); std::cout<<"Entries in my tree= "<<ATRN<<std::endl; std::cout<<"Entries in TMVA tree= "<<TTTN<<std::endl; if(ATRN!=TTTN)std::cout<<"SOMETHING WRONG EVENTS NOT EQUAL"<<std::endl; std::vector<double> ATRValues; std::vector<double> TTTValues; for(int i=0;i<ATRN;++i){ TMVATestTree->GetEntry(i); AppliedTreeRead->GetEntry(i); ATRValues.push_back(BDTResponse); TTTValues.push_back(TestBDTResponse); // std::cout<<" MYTree = "<<BDTResponse<<" TMVATREE= "<<TestBDTResponse<<std::endl; } std::sort(ATRValues.begin(),ATRValues.end()); std::sort(TTTValues.begin(),TTTValues.end()); for(int i=0;i<TTTN;++i){ std::cout<<" MY Value= "<<ATRValues.at(i)<<" TTT Value = "<<TTTValues.at(i)<<std::endl; } }
void TMVAClassification( TString eventsToTrain = "0", const TString & region = "barrel", const TString index = "", TString myMethodList = "BDT") { std::cout << "running classification for " << region << " for " << myMethodList << std::endl; if( region != "barrel" && region != "endcaps" ) { std::cout << "Error, region can only be barrel or endcaps. Selected region was: " << region << std::endl; exit(1); } if( index != "" && index != "0" && index != "1" && index != "2" ) { std::cout << "Error, index can only be \"\", \"0\", \"1\" or \"2\". Selected index was: " << index << std::endl; exit(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(); // 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"] = 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"] = 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; std::exit(2); } Use[regMethod] = 1; } } // Input and output file names TString fnameTrainS = "BsMC12_barrel_preselection"; TString fnameTrainB = "Barrel_preselection"; TString fnameTestS = "BsMC12_barrel_preselection"; TString fnameTestB = "Barrel_preselection"; TString outputFileName = "TMVA_barrel"; TString weightDirName = "barrel"; if( region == "endcaps" ) { fnameTrainS = "BsMC12_endcaps_preselection"; fnameTrainB = "Endcaps_preselection"; fnameTestS = "BsMC12_endcaps_preselection"; fnameTestB = "Endcaps_preselection"; outputFileName = "TMVA_endcaps"; weightDirName = "endcaps"; } if( index != "" ) { fnameTrainS += "_"+index; fnameTrainB += "_"+index; TString indexTest = ""; // The test index is the train index +1 (2+1 -> 0) if( index == "0" ) indexTest = "1"; else if( index == "1" ) indexTest = "2"; else if( index == "2" ) indexTest = "0"; fnameTestS += "_"+indexTest; fnameTestB += "_"+indexTest; outputFileName += "_"+index; weightDirName += index; } fnameTrainS = rootDir + fnameTrainS + ".root"; fnameTrainB = rootDir + fnameTrainB + ".root"; fnameTestS = rootDir + fnameTestS + ".root"; fnameTestB = rootDir + fnameTestB + ".root"; outputFileName = rootDir + outputFileName + ".root"; weightDirName = weightsDir + weightDirName + "Weights"; // -------------------------------------------------------------------------------------------------- // --- Here the preparation phase begins // Create a ROOT output file where TMVA will store ntuples, histograms, etc. TString outfileName(outputFileName); 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"; // (TMVA::gConfig().GetIONames()).fWeightFileDir = outputFileName; (TMVA::gConfig().GetIONames()).fWeightFileDir = weightDirName; // 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" )] bool useNewMuonID = false; factory->AddVariable( "fls3d", "fls3d", "", 'F' ); factory->AddVariable( "alpha", "alpha", "", 'F' ); factory->AddVariable( "pvips", "pvips", "", 'F' ); factory->AddVariable( "iso", "iso", "", 'F' ); factory->AddVariable( "m1iso", "m1iso", "", 'F' ); factory->AddVariable( "m2iso", "m2iso", "", 'F' ); factory->AddVariable( "chi2dof", "chi2/dof", "", 'F' ); if( region == "barrel" ) { factory->AddVariable( "eta", "eta", "", 'F' ); factory->AddVariable( "maxdoca", "maxdoca", "cm", 'F' ); } else { factory->AddVariable( "pt", "pt", "GeV/c", 'F' ); factory->AddVariable( "pvip", "pvip", "cm", 'F' ); } factory->AddVariable( "docatrk", "docatrk", "cm", 'F' ); // factory->AddVariable( "pt", "pt", "GeV/c", 'F' ); // factory->AddVariable( "closetrk", "closetrk", "", 'I' ); // factory->AddVariable( "y", "y", "", 'F' ); // factory->AddVariable( "l3d", "l3d", "cm", 'F' ); // factory->AddVariable( "cosAlphaXY", "cosAlphaXY", "", 'F' ); // factory->AddVariable( "mu1_dxy", "mu1_dxy", "cm", 'F' ); // factory->AddVariable( "mu2_dxy", "mu2_dxy", "cm", 'F' ); if( useNewMuonID ) { // New Muon-id factory->AddVariable( "mu1_MVAMuonID", "mu1_MVAMuonID", "", 'F'); factory->AddVariable( "mu2_MVAMuonID", "mu2_MVAMuonID", "", 'F'); } // Extra variables // factory->AddVariable( "mu1_pt", "mu1_pt", "GeV/c", 'F' ); // factory->AddVariable( "mu2_pt", "mu2_pt", "GeV/c", 'F' ); // factory->AddVariable( "pvw8", "pvw8", "", 'F' ); // factory->AddVariable( "cosAlpha3D", "cosAlpha3D", "", 'F' ); // factory->AddVariable( "countTksOfPV", "countTksOfPV", "", 'I' ); // factory->AddVariable( "ctauErrPV", "ctauErrPV", "", 'F' ); // factory->AddVariable( "ctauPV", "ctauPV", "", 'F' ); // factory->AddVariable( "dcaxy", "dcaxy", "", 'F' ); // factory->AddVariable( "mu1_glbTrackProb", "mu1_glbTrackProb", "", 'F' ); // factory->AddVariable( "mu1_nChi2", "mu1_nChi2", "", 'F' ); // factory->AddVariable( "mu1_nMuSegs", "mu1_nMuSegs", "", 'F' ); // factory->AddVariable( "mu1_nMuSegsCln", "mu1_nMuSegsCln", "", 'F' ); // factory->AddVariable( "mu1_nPixHits", "mu1_nPixHits", "", 'F' ); // factory->AddVariable( "mu1_nTrHits", "mu1_nTrHits", "", 'F' ); // factory->AddVariable( "mu1_segComp", "mu1_segComp", "", 'F' ); // factory->AddVariable( "mu1_trkEHitsOut", "mu1_trkEHitsOut", "", 'F' ); // factory->AddVariable( "mu1_trkVHits", "mu1_trkVHits", "", 'F' ); // factory->AddVariable( "mu1_validFrac", "mu1_validFrac", "", 'F' ); // factory->AddVariable( "mu1_chi2LocMom", "mu1_chi2LocMom", "", 'F' ); // factory->AddVariable( "mu1_chi2LocPos", "mu1_chi2LocPos", "", 'F' ); // factory->AddVariable( "mu2_glbTrackProb", "mu2_glbTrackProb", "", 'F' ); // factory->AddVariable( "mu2_nChi2", "mu2_nChi2", "", 'F' ); // factory->AddVariable( "mu2_nMuSegs", "mu2_nMuSegs", "", 'F' ); // factory->AddVariable( "mu2_nMuSegsCln", "mu2_nMuSegsCln", "", 'F' ); // factory->AddVariable( "mu2_nPixHits", "mu2_nPixHits", "", 'F' ); // factory->AddVariable( "mu2_nTrHits", "mu2_nTrHits", "", 'F' ); // factory->AddVariable( "mu2_segComp", "mu2_segComp", "", 'F' ); // factory->AddVariable( "mu2_trkEHitsOut", "mu2_trkEHitsOut", "", 'F' ); // factory->AddVariable( "mu2_trkVHits", "mu2_trkVHits", "", 'F' ); // factory->AddVariable( "mu2_validFrac", "mu2_validFrac", "", 'F' ); // factory->AddVariable( "mu2_chi2LocMom", "mu2_chi2LocMom", "", 'F' ); // factory->AddVariable( "mu2_chi2LocPos", "mu2_chi2LocPos", "", 'F' ); // factory->AddVariable( "l3d := ctauPV*pt/mass", "l3d", "cm", 'F' ); // factory->AddVariable( "l3dSig := ctauPV/ctauErrPV", "l3dSig", "", '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 := mass*2", "Spectator 1", "units", 'F' ); // factory->AddSpectator( "spec2 := mass*3", "Spectator 2", "units", 'F' ); factory->AddSpectator( "mass", "mass", "GeV/c^{2}", 'F' ); // Read training and test data // (it is also possible to use ASCII format as input -> see TMVA Users Guide) if (gSystem->AccessPathName( fnameTrainS )) { // file does not exist in local directory std::cout << "Did not access " << fnameTrainS << " exiting." << std::endl; std::exit(4); } //gSystem->Exec("wget http://root.cern.ch/files/tmva_class_example.root"); TFile *inputTrainS = TFile::Open( fnameTrainS ); TFile *inputTrainB = TFile::Open( fnameTrainB ); TFile *inputTestS = TFile::Open( fnameTestS ); TFile *inputTestB = TFile::Open( fnameTestB ); // --- Register the training and test trees TTree *signalTrainTree = (TTree*)inputTrainS->Get("probe_tree"); TTree *backgroundTrainTree = (TTree*)inputTrainB->Get("probe_tree"); TTree *signalTestTree = (TTree*)inputTestS->Get("probe_tree"); TTree *backgroundTestTree = (TTree*)inputTestB->Get("probe_tree"); // global event weights per tree (see below for setting event-wise weights) Double_t signalTrainWeight = 1.0; Double_t backgroundTrainWeight = 1.0; Double_t signalTestWeight = 1.0; Double_t backgroundTestWeight = 1.0; // Decide if using the split and mixing or the full trees if( fnameTrainS == fnameTestS ) { if( fnameTrainB != fnameTestB ) { std::cout << "This macro cannot handle cases where the same signal sample is used for training and testing, but different background samples are used."; exit(1); } std::cout << "--- TMVAClassification : Using input file: " << inputTrainS->GetName() << std::endl; std::cout << "--- and file: " << inputTrainB->GetName() << std::endl; // You can add an arbitrary number of signal or background trees factory->AddSignalTree ( signalTrainTree, signalTrainWeight ); factory->AddBackgroundTree( backgroundTrainTree, backgroundTrainWeight ); } else { if( fnameTrainB == fnameTestB ) { std::cout << "This macro cannot handle cases where the same background sample is used for training and testing, but different signal samples are used."; exit(1); } std::cout << "--- TMVAClassification : Using input file: " << inputTrainS->GetName() << std::endl; std::cout << "--- and file: " << inputTrainB->GetName() << " for training and" << std::endl; std::cout << "--- input file: " << inputTestS->GetName() << std::endl; std::cout << "--- and file: " << inputTestB->GetName() << " for testing." << std::endl; // To give different trees for training and testing, do as follows: factory->AddSignalTree( signalTrainTree, signalTrainWeight, "Training" ); factory->AddSignalTree( signalTestTree, signalTestWeight, "Test" ); factory->AddBackgroundTree( backgroundTrainTree, backgroundTrainWeight, "Training" ); factory->AddBackgroundTree( backgroundTestTree, backgroundTestWeight, "Test" ); } // Apply additional cuts on the signal and background samples (can be different) 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="+eventsToTrain+":nTrain_Background="+eventsToTrain+":SplitMode=Random:NormMode=NumEvents:!V" ); // factory->PrepareTrainingAndTestTree( mycuts, mycutb, // "nTrain_Signal=3000:nTrain_Background=3000:nTest_Signal=3000:nTest_Background=3000:SplitMode=Random:NormMode=NumEvents:!V" ); // factory->PrepareTrainingAndTestTree( mycuts, mycutb, // "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 ("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" ); factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+8: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=800:nEventsMin=50:MaxDepth=2:BoostType=AdaBoost:AdaBoostBeta=1:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning:NNodesMax=5" ); 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 // Train MVAs using the set of training events std::cout << "Training all methods" << std::endl; factory->TrainAllMethods(); // ---- Evaluate all MVAs using the set of test events std::cout << "Testing all methods" << std::endl; factory->TestAllMethods(); // ----- Evaluate and compare performance of all configured MVAs std::cout << "Evaluating all methods" << std::endl; 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_qgl( 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["Fisher"] = 0; Use["MLPBNN"] = 0; // Recommended ANN with BFGS training method and bayesian regulator // --- Boosted Decision Trees Use["BDTB"] = 0; // decorrelation + Adaptive Boost Use["BDTG"] = 0; 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"] = 1; Use["LikelihoodMIX"] = 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( "TMVA_bjet_new_powheg.root" ); int set_type=1; TString type[2] = {"_double","_single"}; TString outfileName( "TMVA_qgl"+type[set_type]+"_2jet_1.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( "myvar1 := var1+var2", 'F' ); // factory->AddVariable( "myvar2 := var1-var2", "Expression 2", "", 'F' ); factory->AddVariable( "Jet_pt", "Jet p_{T}", "GeV", 'F' ); factory->AddVariable( "Jet_eta", "Jet #eta", "", 'F' ); factory->AddVariable( "Jet_ptd", "Jet ptd", "", 'F' ); factory->AddVariable( "Jet_axis2", "Jet axis2", "", 'F' ); factory->AddVariable( "Jet_mult", "Jet multiplicity", "", '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) // TString fname_signal ="/afs/cern.ch/work/n/nchernya/Hbb/qgl_mva/qgl_tmva_tree_VBFHToBB_M-125_13TeV_powheg_v14"+type[set_type]+"_2jets.root"; TString fname_bg ="/afs/cern.ch/work/n/nchernya/Hbb/qgl_mva/qgl_tmva_tree_BTagCSV_v14"+type[set_type]+"_2jets.root"; if (gSystem->AccessPathName( fname_signal )) { // file does not exist in local directory cout<<"input file "<< fname_signal<<" doesn't exist!"<<endl; //break; } if (gSystem->AccessPathName( fname_bg)) { // file does not exist in local directory cout<<"input file "<< fname_bg<<" doesn't exist!"<<endl; //break; } // gSystem->Exec("wget http://root.cern.ch/files/tmva_class_example.root"); TFile *input_signal = TFile::Open( fname_signal ); TFile *input_bg = TFile::Open( fname_bg ); std::cout << "--- TMVAClassification : Using input signal file: " << input_signal->GetName() << std::endl; std::cout << "--- TMVAClassification : Using input bg file: " << input_bg->GetName() << std::endl; // --- Register the training and test trees TTree *signal = (TTree*)input_signal->Get("QGL_1"); TTree *bg = (TTree*)input_bg->Get("QGL_1"); // global event weights per tree (see below for setting event-wise weights) Double_t signalWeight = 1.0; Double_t bgWeight = 1.0; // You can add an arbitrary number of signal or background trees factory->AddSignalTree ( signal, signalWeight ); factory->AddBackgroundTree( bg, bgWeight ); 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 // 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" ); 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 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["BDTG"]) // factory->BookMethod( TMVA::Types::kBDT, "BDTG", "!H:!V:NTrees=120:MinNodeSize=6%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=3:NegWeightTreatment=IgnoreNegWeightsInTraining" ); 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" ); // 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 TMVATrainer(){ // This loads the library TMVA::Tools::Instance(); // --- Here the preparation phase begins // Create a ROOT output file where TMVA will store ntuples, histograms, etc. TString outfileName = "TMVATrainingResults_fat_BBvsGSP.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( "TMVATrainer", 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("TagVarCSV_vertexCategory","TagVarCSV_vertexCategory","units",'F'); factory->AddVariable("TagVarCSV_jetNTracks","TagVarCSV_jetNTracks","units",'F'); //factory->AddVariable("TagVarCSV_trackSip2dSig_0","TagVarCSV_trackSip2dSig_0","units",'F'); //factory->AddVariable("TagVarCSV_trackSip2dSig_1","TagVarCSV_trackSip2dSig_1","units",'F'); //factory->AddVariable("TagVarCSV_trackSip2dSig_2","TagVarCSV_trackSip2dSig_2","units",'F'); //factory->AddVariable("TagVarCSV_trackSip2dSig_3","TagVarCSV_trackSip2dSig_3","units",'F'); factory->AddVariable("TagVarCSV_trackSip3dSig_0","TagVarCSV_trackSip3dSig_0","units",'F'); factory->AddVariable("TagVarCSV_trackSip3dSig_1","TagVarCSV_trackSip3dSig_1","units",'F'); factory->AddVariable("TagVarCSV_trackSip3dSig_2","TagVarCSV_trackSip3dSig_2","units",'F'); factory->AddVariable("TagVarCSV_trackSip3dSig_3","TagVarCSV_trackSip3dSig_3","units",'F'); //factory->AddVariable("TagVarCSV_trackPtRel_0","TagVarCSV_trackPtRel_0","units",'F'); //factory->AddVariable("TagVarCSV_trackPtRel_1","TagVarCSV_trackPtRel_1","units",'F'); //factory->AddVariable("TagVarCSV_trackPtRel_2","TagVarCSV_trackPtRel_2","units",'F'); //factory->AddVariable("TagVarCSV_trackPtRel_3","TagVarCSV_trackPtRel_3","units",'F'); factory->AddVariable("TagVarCSV_trackSip2dSigAboveCharm","TagVarCSV_trackSip2dSigAboveCharm","units",'F'); //factory->AddVariable("TagVarCSV_trackSip3dSigAboveCharm","TagVarCSV_trackSip3dSigAboveCharm","units",'F'); //factory->AddVariable("TagVarCSV_trackSumJetEtRatio","TagVarCSV_trackSumJetEtRatio","units",'F'); //factory->AddVariable("TagVarCSV_trackSumJetDeltaR","TagVarCSV_trackSumJetDeltaR","units",'F'); factory->AddVariable("TagVarCSV_jetNTracksEtaRel","TagVarCSV_jetNTracksEtaRel","units",'F'); factory->AddVariable("TagVarCSV_trackEtaRel_0","TagVarCSV_trackEtaRel_0","units",'F'); factory->AddVariable("TagVarCSV_trackEtaRel_1","TagVarCSV_trackEtaRel_1","units",'F'); factory->AddVariable("TagVarCSV_trackEtaRel_2","TagVarCSV_trackEtaRel_2","units",'F'); factory->AddVariable("TagVarCSV_jetNSecondaryVertices","TagVarCSV_jetNSecondaryVertices","units",'F'); factory->AddVariable("TagVarCSV_vertexMass","TagVarCSV_vertexMass","units",'F'); factory->AddVariable("TagVarCSV_vertexNTracks","TagVarCSV_vertexNTracks","units",'F'); factory->AddVariable("TagVarCSV_vertexEnergyRatio","TagVarCSV_vertexEnergyRatio","units",'F'); factory->AddVariable("TagVarCSV_vertexJetDeltaR","TagVarCSV_vertexJetDeltaR","units",'F'); factory->AddVariable("TagVarCSV_flightDistance2dSig","TagVarCSV_flightDistance2dSig","units",'F'); //factory->AddVariable("TagVarCSV_flightDistance3dSig","TagVarCSV_flightDistance3dSig","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("Jet_pt","Jet_pt","units",'F'); factory->AddSpectator("Jet_eta","Jet_eta","units",'F'); factory->AddSpectator("Jet_phi","Jet_phi","units",'F'); factory->AddSpectator("Jet_mass","Jet_mass","units",'F'); factory->AddSpectator("Jet_massGroomed","Jet_massGroomed","units",'F'); factory->AddSpectator("Jet_flavour","Jet_flavour","units",'F'); factory->AddSpectator("Jet_nbHadrons","Jet_nbHadrons","units",'F'); factory->AddSpectator("Jet_JP","Jet_JP","units",'F'); factory->AddSpectator("Jet_JBP","Jet_JBP","units",'F'); factory->AddSpectator("Jet_CSV","Jet_CSV","units",'F'); factory->AddSpectator("Jet_CSVIVF","Jet_CSVIVF","units",'F'); factory->AddSpectator("Jet_tau1","Jet_tau1","units",'F'); factory->AddSpectator("Jet_tau2","Jet_tau2","units",'F'); factory->AddSpectator("SubJet1_CSVIVF","SubJet1_CSVIVF","units",'F'); factory->AddSpectator("SubJet2_CSVIVF","SubJet2_CSVIVF","units",'F'); // Read training and test data // (it is also possible to use ASCII format as input -> see TMVA Users Guide) TString fnameSig = "RadionToHH_4b_M-800_TuneZ2star_8TeV-Madgraph_pythia6_JetTaggingVariables_training.root"; TString fnameBkg = "QCD_Pt-300to470_TuneZ2star_8TeV_pythia6_JetTaggingVariables_training.root"; TFile *inputSig = TFile::Open( fnameSig ); TFile *inputBkg = TFile::Open( fnameBkg ); std::cout << "--- TMVAClassification : Using input files: " << inputSig->GetName() << std::endl << inputBkg->GetName() << std::endl; // --- Register the training and test trees TTree *sigTree = (TTree*)inputSig->Get("tagVars/ttree"); TTree *bkgTree = (TTree*)inputBkg->Get("tagVars/ttree"); // // global event weights per tree (see below for setting event-wise weights) Double_t signalWeight = 1.0; Double_t backgroundWeight = 1.0; // factory->SetInputTrees( tree,signalCut,backgroundCut ); factory->AddSignalTree ( sigTree, signalWeight ); factory->AddBackgroundTree( bkgTree, backgroundWeight ); // Apply additional cuts on the signal and background samples (can be different) TCut signalCut = "Jet_massGroomed>80 && Jet_massGroomed<150"; TCut backgroundCut = "abs(Jet_flavour)==5 && Jet_nbHadrons>1 && Jet_massGroomed>80 && Jet_massGroomed<150"; // Tell the factory how to use the training and testing events factory->PrepareTrainingAndTestTree( signalCut, backgroundCut, "nTrain_Signal=22000:nTest_Signal=20000:nTrain_Background=22000:nTest_Background=2730:SplitMode=Random:!V" ); // Gradient Boost factory->BookMethod( TMVA::Types::kBDT, "BDTG_T1000D3_fat_BBvsGSP", "!H:!V:NTrees=1000:MaxDepth=3:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" ); //factory->BookMethod( TMVA::Types::kBDT, "BDTG_T1000D5_fat_BBvsGSP", // "!H:!V:NTrees=1000:MaxDepth=5:MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20" ); // // Adaptive Boost // factory->BookMethod( TMVA::Types::kBDT, "BDT", // "!H:!V:NTrees=1000:MaxDepth=5:MinNodeSize=2.5%:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" ); // // Bagging // factory->BookMethod( TMVA::Types::kBDT, "BDTB", // "!H:!V:NTrees=1000:MaxDepth=5:MinNodeSize=2.5%:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20" ); // // Decorrelation + Adaptive Boost // factory->BookMethod( TMVA::Types::kBDT, "BDTD", // "!H:!V:NTrees=1000:MaxDepth=5:MinNodeSize=2.5%:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:VarTransform=Decorrelate" ); // ---- 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 TMVAClassificationElecTau(std::string ordering_ = "Pt", std::string bkg_ = "qqH115vsWZttQCD") { TMVA::Tools::Instance(); TString outfileName( "TMVAElecTau"+ordering_+"Ord_"+bkg_+".root" ); TFile* outputFile = TFile::Open( outfileName, "RECREATE" ); TMVA::Factory *factory = new TMVA::Factory( "TMVAClassificationElecTau"+ordering_+"Ord_"+bkg_, outputFile, "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D" ); factory->AddVariable( "pt1", "pT-tag1", "GeV/c" , 'F' ); factory->AddVariable( "pt2", "pT-tag2", "GeV/c" , 'F' ); factory->AddVariable( "Deta","|y-tag1 - y-tag2|","" , 'F' ); //factory->AddVariable( "opposite:=abs(eta1*eta2)/eta1/eta2","sign1*sign2","" , 'F' ); //factory->AddVariable( "Dphi", "#Delta#phi" ,"" , 'F' ); factory->AddVariable( "Mjj", "M(tag1,tag2)", "GeV/c^{2}" , 'F' ); factory->AddSpectator( "eta1", "#eta_{tag1}" , 'F' ); factory->AddSpectator( "eta2", "#eta_{tag2}" , 'F' ); factory->SetWeightExpression( "sampleWeight" ); TString fSignalName = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/ElecTauStream2011/nTupleVBFH115-powheg-PUS1_Open_ElecTauStream.root"; TString fBackgroundNameDYJets = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/ElecTauStream2011/nTupleZjets-alpgen-PUS1_Open_ElecTauStream.root"; TString fBackgroundNameWJets = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/ElecTauStream2011/nTupleWJets-madgraph-PUS1_Open_ElecTauStream.root"; TString fBackgroundNameQCD = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/ElecTauStream2011/nTupleQCD_Open_ElecTauStream.root"; TString fBackgroundNameTTbar = "/data_CMS/cms/lbianchini/VbfJetsStudy/OpenNtuples/ElecTauStream2011/nTupleTTJets-madgraph-PUS1_Open_ElecTauStream.root"; TFile *fSignal(0); TFile *fBackgroundDYJets(0); TFile *fBackgroundWJets(0); TFile *fBackgroundQCD(0); TFile *fBackgroundTTbar(0); fSignal = TFile::Open( fSignalName ); fBackgroundDYJets = TFile::Open( fBackgroundNameDYJets ); fBackgroundWJets = TFile::Open( fBackgroundNameWJets ); fBackgroundQCD = TFile::Open( fBackgroundNameQCD ); fBackgroundTTbar = TFile::Open( fBackgroundNameTTbar ); if(!fSignal || !fBackgroundDYJets || !fBackgroundWJets || !fBackgroundQCD || !fBackgroundTTbar) { std::cout << "ERROR: could not open files" << std::endl; exit(1); } TString tree = "outTree"+ordering_+"Ord"; TCut mycuts = ""; TCut mycutb = ""; TCut cutA = "pt1>0 && tightestHPSWP>0"; TCut cutB = "pt1>0 && combRelIsoLeg1<0.1"; TCut cutBl = "pt1>0 && combRelIsoLeg1<0.3"; TCut cutC = "pt1>0 && diTauCharge==0"; TCut cutD = "pt1>0 && MtLeg1<40"; // select events for training TFile* dummy = new TFile("dummy.root","RECREATE"); TH1F* allEvents = new TH1F("allEvents","",1,-10,10); float totalEvents, cutEvents; // signal: all TTree *signal = ((TTree*)(fSignal->Get(tree)))->CopyTree(cutA&&cutB&&cutC&&cutD); cout << "Copied signal tree with full selection: " << ((TTree*)(fSignal->Get(tree)))->GetEntries() << " --> " << signal->GetEntries() << endl; allEvents->Reset(); signal->Draw("eta1>>allEvents","sampleWeight"); cutEvents = allEvents->Integral(); Double_t signalWeight = 1.0; cout << "Signal: expected yield " << cutEvents << " -- weight " << signalWeight << endl; // Z+jets: all TTree *backgroundDYJets = ((TTree*)(fBackgroundDYJets->Get(tree)))->CopyTree(cutA&&cutB&&cutC&&cutD); cout << "Copied DYJets tree with full selection: " << ((TTree*)(fBackgroundDYJets->Get(tree)))->GetEntries() << " --> " << backgroundDYJets->GetEntries() << endl; allEvents->Reset(); backgroundDYJets->Draw("eta1>>allEvents","sampleWeight"); cutEvents = allEvents->Integral(); Double_t backgroundDYJetsWeight = 1.0; cout << "ZJets: expected yield " << cutEvents << " -- weight " << backgroundDYJetsWeight << endl; // W+jets: iso+Mt TTree *backgroundWJets = ((TTree*)(fBackgroundWJets->Get(tree)))->CopyTree(cutB&&cutD); cout << "Copied WJets tree with iso+Mt selection: " << ((TTree*)(fBackgroundWJets->Get(tree)))->GetEntries() << " --> " << backgroundWJets->GetEntries() << endl; allEvents->Reset(); backgroundWJets->Draw("eta1>>allEvents","sampleWeight"); totalEvents = allEvents->Integral(); allEvents->Reset(); backgroundWJets->Draw("eta1>>allEvents","sampleWeight*(tightestHPSWP>0 && diTauCharge==0)"); cutEvents = allEvents->Integral(); Double_t backgroundWJetsWeight = cutEvents / totalEvents; cout << "WJets: expected yield " << cutEvents << " -- weight " << backgroundWJetsWeight << endl; // QCD: Mt+loose iso TTree *backgroundQCD = ((TTree*)(fBackgroundQCD->Get(tree)))->CopyTree(cutD&&cutBl); cout << "Copied QCD tree with Mt selection: " << ((TTree*)(fBackgroundQCD->Get(tree)))->GetEntries() << " --> " << backgroundQCD->GetEntries() << endl; allEvents->Reset(); backgroundQCD->Draw("eta1>>allEvents","sampleWeight"); totalEvents = allEvents->Integral(); allEvents->Reset(); backgroundQCD->Draw("eta1>>allEvents","sampleWeight*(tightestHPSWP>0 && diTauCharge==0 && combRelIsoLeg1<0.1)"); cutEvents = allEvents->Integral(); Double_t backgroundQCDWeight = cutEvents / totalEvents; cout << "QCD: expected yield " << cutEvents << " -- weight " << backgroundQCDWeight << endl; // TTbar: iso+Mt TTree *backgroundTTbar = ((TTree*)(fBackgroundTTbar->Get(tree)))->CopyTree(cutB&&cutD); cout << "Copied TTbar tree with iso+Mt selection: " << ((TTree*)(fBackgroundTTbar->Get(tree)))->GetEntries() << " --> " << backgroundTTbar->GetEntries() << endl; allEvents->Reset(); backgroundTTbar->Draw("eta1>>allEvents","sampleWeight"); totalEvents = allEvents->Integral(); allEvents->Reset(); backgroundTTbar->Draw("eta1>>allEvents","sampleWeight*(tightestHPSWP>0 && diTauCharge==0)"); cutEvents = allEvents->Integral(); Double_t backgroundTTbarWeight = cutEvents / totalEvents; cout << "TTbar: expected yield " << cutEvents << " -- weight " << backgroundTTbarWeight << endl; delete allEvents; factory->AddSignalTree ( signal, signalWeight ); //factory->AddBackgroundTree( backgroundDYJets, backgroundDYJetsWeight ); //factory->AddBackgroundTree( backgroundWJets, backgroundWJetsWeight ); factory->AddBackgroundTree( backgroundQCD, backgroundQCDWeight ); //factory->AddBackgroundTree( backgroundTTbar, backgroundTTbarWeight ); factory->PrepareTrainingAndTestTree( mycuts, mycutb, "nTrain_Signal=0:nTrain_Background=0:nTest_Signal=1:nTest_Background=1:SplitMode=Random:NormMode=NumEvents:!V" ); factory->BookMethod( TMVA::Types::kCuts, "Cuts", "!H:!V:FitMethod=GA:EffSel:CutRangeMin[0]=25.:CutRangeMax[0]=999:CutRangeMin[1]=25.:CutRangeMax[1]=999.:CutRangeMin[2]=1.0:CutRangeMax[2]=9.:CutRangeMin[3]=100:CutRangeMax[3]=7000:VarProp=FSmart" ); /* factory->BookMethod( TMVA::Types::kBDT, "BDT", "!H:!V:NTrees=200:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" ); */ factory->TrainAllMethods(); factory->TestAllMethods(); factory->EvaluateAllMethods(); outputFile->Close(); std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl; std::cout << "==> TMVAClassification is done!" << std::endl; delete factory; //if (!gROOT->IsBatch()) TMVAGui( outfileName ); }
int TestBDT_forreal_test(TString sig) { // This loads the library TMVA::Tools::Instance(); // to get access to the GUI and all tmva macros //TString thisdir = gSystem->DirName(gInterpreter->GetCurrentMacroName());//was not commented, but does not work anymore //gROOT->SetMacroPath(thisdir + ":" + gROOT->GetMacroPath());//was not commented, but cannot not work anymore //gROOT->ProcessLine(".L TMVAGui.C"); TString outfileName( "rootfiles/TMVA/resultTMVA_"+sig+"VsTTbar2l.root" ); TString weightname( "weightsTMVA_"+sig+"VsTTbar2l" ); TFile* outputFile = TFile::Open( outfileName, "RECREATE" ); TMVA::Factory *factory = new TMVA::Factory( weightname, outputFile,"!V:!Silent:Color:DrawProgressBar"); vector<TString> histonames; histonames.clear(); map<string, float> value; //histonames.push_back("MT2W"); //histonames.push_back("MT2_lb_b"); //histonames.push_back("MT2_lb_bqq"); //histonames.push_back("MT2_lb_b_mass"); //histonames.push_back("MT2_lb_bqq_mass"); histonames.push_back("Mlb_lead_bdiscr"); //histonames.push_back("Mjjj"); //histonames.push_back("topness"); //histonames.push_back("topnessMod"); histonames.push_back("pfmet"); //histonames.push_back("ak4_HT"); //histonames.push_back("MET_over_sqrtHT"); //histonames.push_back("ak4_htratiom"); histonames.push_back("dR_lep_leadb"); //histonames.push_back("hadronic_top_chi2"); //histonames.push_back("ngoodbtags"); histonames.push_back("ngoodjets"); //histonames.push_back("mindphi_met_j1_j2"); //histonames.push_back("lep1_pt"); histonames.push_back("ak4pfjets_leadMEDbjet_p4_Pt"); for(unsigned int b = 0; b<histonames.size(); ++b){ factory->AddVariable(histonames[b], 'F' ); } TString signame = "/nfs-7/userdata/stopRun2/testMVA/"+sig+".root"; TString bkgname1 = "/nfs-7/userdata/stopRun2/testMVA/TTJets_DiLept_madgraph_25ns_1.root"; TString bkgname2 = "/nfs-7/userdata/stopRun2/testMVA/TTJets_DiLept_madgraph_25ns_2.root"; /* TString signame = "/hadoop/cms/store/user/haweber/forBDT/"+sig+".root"; TString bkgname1 = "/hadoop/cms/store/user/haweber/forBDT/TTJets_DiLept_madgraph_25ns_1.root"; TString bkgname2 = "/hadoop/cms/store/user/haweber/forBDT/TTJets_DiLept_madgraph_25ns_2.root"; */ cout << "signame " << signame << endl; TFile *inputSig = TFile::Open( signame ); TFile *inputBkg1 = TFile::Open( bkgname1 ); TFile *inputBkg2 = TFile::Open( bkgname2 ); TTree *signal = (TTree*)inputSig->Get("t"); TTree *background1 = (TTree*)inputBkg1->Get("t"); TTree *background2 = (TTree*)inputBkg2->Get("t"); // 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( background1, backgroundWeight ); factory->AddBackgroundTree( background2, backgroundWeight ); //factory->SetBackgroundWeightExpression( "weight" ); // Apply additional cuts on the signal and background samples (can be different) //TCut mycuts = "MT2W>200&&mindphi_met_j1_j2>0.8"; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1"; //TCut mycutb = "MT2W>200&&mindphi_met_j1_j2>0.8"; // for example: TCut mycutb = "abs(var1)<0.5"; //if(sig.Contains("T2tt_425_325")||sig.Contains("T2tt_500_325")){ mycuts = "mindphi_met_j1_j2>0.8"; mycutb = "mindphi_met_j1_j2>0.8"; } TCut mycuts = ""; TCut mycutb = ""; factory->PrepareTrainingAndTestTree( mycuts, mycutb, "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" ); 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" ); // Train MVAs using the set of training events cout << "Train methods" << endl; factory->TrainAllMethods(); // ---- Evaluate all MVAs using the set of test events cout << "Test methods" << endl; factory->TestAllMethods(); // ----- Evaluate and compare performance of all configured MVAs cout << "Evaluate methods" << endl; 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()) TMVA::TMVAGui( outfileName ); return 0; }
void TMVAClassificationHwwNtuple( TString myMethodList = "" ) { // This loads the library TMVA::Tools::Instance(); 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; // Use["BDT"] = 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"] = 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; cout<<regMethod<<" is on"<<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" ); // For one variable //TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile, // "!V:!Silent:Color:DrawProgressBar:Transformations=I:AnalysisType=Classification" ); // For Multiple Variables TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile, "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" ); //factory->AddVariable( "pt1", "LeadLepton pt", "", 'F' ); //factory->AddVariable( "pt2", "TailLepton pt", "", 'F' ); factory->AddVariable( "pfmet", "MissingEt", "", 'F' ); factory->AddVariable( "mpmet", "Minimum Proj. Met", "", 'F' ); factory->AddVariable( "dphill", "DeltPhiOfLepLep", "", 'F' ); //factory->AddVariable( "mll", "DiLepton Mass", "", 'F' ); factory->AddVariable( "ptll", "DiLepton 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' ); // //factory->AddSpectator( "mWW", "Higgs Mass", "", 'F' ); factory->AddSpectator( "pt1", "LeadLepton pt", "", 'F' ); factory->AddSpectator( "pt2", "TailLepton pt", "", 'F' ); factory->AddSpectator( "pfmet", "MissingEt", "", 'F' ); factory->AddSpectator( "mpmet", "Minimum Proj. Met", "", 'F' ); factory->AddSpectator( "dphill", "DeltPhiOfLepLep", "", 'F' ); factory->AddSpectator( "mll", "DiLepton Mass", "", 'F' ); factory->AddSpectator( "ptll", "DiLepton pt", "", 'F' ); // Read training and test data // (it is also possible to use ASCII format as input -> see TMVA Users Guide) //TString fname = "./tmva_class_example.root"; //TString fname = "/afs/cern.ch/work/s/salee/private/HWWwidth/HWW/GGVvAnalyzer/MkNtuple/Hw1Int8TeV/MkNtuple.root"; //TString fname = "/terranova_0/HWWwidth/HWW/GGVvAnalyzer/MkNtuple/Hw1Int8TeV/MkNtuple.root"; //if (gSystem->AccessPathName( fname )) // file does not exist in local directory // exit(-1); //gSystem->Exec("wget http://root.cern.ch/files/tmva_class_example.root"); //TFile *input = TFile::Open( fname ); //TFile *SB_OnPeak = TFile::Open("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_IntOnPeak_8TeV.root"); //TTree *SB_OnPeak_Tree = (TTree*)SB_OnPeak->Get("latino"); TChain *S_Chain = new TChain("latino"); TChain *C_Chain = new TChain("latino"); TChain *SCI_Chain = new TChain("latino"); TChain *qqWW_Chain = new TChain("latino"); S_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_SigOnPeak_8TeV.root"); S_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_SigShoulder_8TeV.root"); S_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_SigTail_8TeV.root"); SCI_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_IntOnPeak_8TeV.root"); SCI_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_IntShoulder_8TeV.root"); SCI_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw1_IntTail_8TeV.root"); C_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw25_CotHead_8TeV.root"); C_Chain->Add("root://eoscms.cern.ch//eos/cms/store/group/phys_higgs/cmshww/amassiro/HiggsWidth/gg2vv/latinogg2vv_Hw25_CotTail_8TeV.root"); qqWW_Chain->Add("/afs/cern.ch/user/m/maiko/work/public/Tree/tree_skim_wwmin/nominals/latino_000_WWJets2LMad.root"); // --- Register the training and test trees // You can add an arbitrary number of signal or background trees factory->AddSignalTree ( S_Chain ); factory->AddBackgroundTree( qqWW_Chain ); factory->AddBackgroundTree( C_Chain ); // Classification training and test data in ROOT tree format with signal and background events being located in the same tree //factory->SetInputTrees(SCI_Chain, GenOffCut, GenOnCut); // To give different trees for training and testing, do as follows: // factory->AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" ); // factory->AddSignalTree( signalTestTree, signalTestWeight, "Test" ); factory->SetWeightExpression ("2.1*puW*baseW*effW*triggW*19.468"); //factory->SetSignalWeightExpression ("2.1*puW*baseW*effW*triggW*19.468"); //factory->SetBackgroundWeightExpression("puW*baseW*effW*triggW*19.468"); //factory->PrepareTrainingAndTestTree( ChanCommOff, // "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=None:!V" ); //"nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V"; factory->PrepareTrainingAndTestTree( ChanCommOff0J, "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=None:!V" ); // ---- Book MVA methods // // Cut optimisation if (Use["Cuts"]) factory->BookMethod( TMVA::Types::kCuts, "Cuts", "!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" ); if (Use["BDT"]) // Adaptive Boost factory->BookMethod( TMVA::Types::kBDT, "BDT", "!H:V:NTrees=850:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning" ); //"!H:!V:NTrees=850:MinNodeSize=2.5%:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20" ); // 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 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 Reg(){ TMVA::Tools::Instance(); std::cout << "==> Start TMVARegression" << std::endl; ifstream myfile; myfile.open("99per.txt"); ostringstream xcS,xcH,xcP,xcC,xcN; double xS,xH,xC,xN,xP; if(myfile.is_open()){ while(!myfile.eof()){ myfile>>xS>>xH>>xC>>xN>>xP; } } xcS<<xS; xcH<<xH; xcC<<xC; xcN<<xN; xcP<<xP; //Output file TString outfileName( "Ex1out_FullW_def.root" ); TFile* outputFile = TFile::Open( outfileName, "RECREATE" ); //Declaring the factory TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile, "!V:!Silent:Color:DrawProgressBar" ); //Declaring Input Varibles factory->AddVariable( "Sieie",'F'); factory->AddVariable( "ToE", 'F' ); factory->AddVariable( "isoC",'F' ); factory->AddVariable( "isoN",'F' ); factory->AddVariable( "isoP",'F' ); TString fname = "../../CutTMVATrees_Barrel.root"; input = TFile::Open( fname ); // --- Register the regression tree TTree *signal = (TTree*)input->Get("t_S"); TTree *background = (TTree*)input->Get("t_B"); //Just Some more settings Double_t signalWeight = 1.0; Double_t backgroundWeight = 1.0; // You can add an arbitrary number of regression trees factory->AddSignalTree( signal, signalWeight ); factory->AddBackgroundTree( background , backgroundWeight ); TCut mycuts =""; TCut mycutb =""; // factory->PrepareTrainingAndTestTree(mycuts,mycutb,"nTrain_Signal=9000:nTrain_Background=9000:nTest_Signal=10000:nTest_Background=10000"); factory->SetBackgroundWeightExpression("weightPT*weightXS"); factory->SetSignalWeightExpression("weightPT*weightXS"); TString methodName = "Cuts_FullsampleW_def"; TString methodOptions ="!H:!V:FitMethod=GA:EffMethod=EffSEl"; methodOptions +=":VarProp[0]=FMin:VarProp[1]=FMin:VarProp[2]=FMin:VarProp[3]=FMin:VarProp[4]=FMin"; methodOptions +=":CutRangeMax[0]="+xcS.str(); methodOptions +=":CutRangeMax[1]="+xcH.str(); methodOptions +=":CutRangeMax[2]="+xcC.str(); methodOptions +=":CutRangeMax[3]="+xcN.str(); methodOptions +=":CutRangeMax[4]="+xcP.str(); //************ factory->BookMethod(TMVA::Types::kCuts,methodName,methodOptions); factory->TrainAllMethods(); factory->TestAllMethods(); factory->EvaluateAllMethods(); // -------------------------------------------------------------- // Save the output outputFile->Close(); std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl; std::cout << "==> TMVARegression is done!" << std::endl; delete factory; }
void classifyBDT(TString inputVariables = "trainingVars.txt", TString signalName = "/mnt/hscratch/dabercro/skims2/BDT_Signal.root", TString backName = "/mnt/hscratch/dabercro/skims2/BDT_Background.root") { TMVA::Tools::Instance(); std::cout << "==> Start TMVAClassification" << std::endl; // Create a ROOT output file where TMVA will store ntuples, histograms, etc. TString outfileName( "TMVA/TMVA.root" ); TFile* outputFile = TFile::Open( outfileName, "RECREATE" ); TMVA::Factory *factory = new TMVA::Factory( "TMVAClassificationCategory", outputFile, "!V:!Silent:Color:DrawProgressBar:Transformations=I;N" ); // A very simple MVA (feel free to uncomment and comment what you like) => as a rule of thumb 10-20 variables is where people start to get worried about total number ifstream configFile; configFile.open(inputVariables.Data()); TString tempFormula; configFile >> tempFormula; // Is the name of the BDT while(!configFile.eof()){ configFile >> tempFormula; if(tempFormula != ""){ factory->AddVariable(tempFormula,'F'); } } TString lVars; // TCut lCut = "jet1qg2<2.&&jet1pt>250.&&jet1pullAngle>-5.";// < 10 && jet1mass_m2 > 60 && jet1mass_m2 < 120"; // TCut lCut = "passZ > 3 && fjet1pt > 250 && fjet1MassPruned < 120 && fatjetid < 2"; TCut lCut = "abs(fjet1PartonId)!=24&&abs(fjet1PartonId)!=23"; // std::string lEventCut = "event % 2 == 1"; // lCut += lEventCut.c_str(); // TCut lSCut = "passT > 0 && fjet1pt > 250 && fjet1MassPruned < 120 && abs(fjet1PartonId) == 24&& fatjetid < 2"; TCut lSCut = "abs(fjet1PartonId)==24||abs(fjet1PartonId)==23"; // lSCut += lEventCut.c_str(); TCut cleanCut = "fjet1QGtagSub2 > -10 && fjet1PullAngle > -4 && abs(fjet1pt/fjet1MassTrimmed)<200 && abs(fjet1pt/fjet1MassPruned)<200"; TFile *lSAInput = TFile::Open(signalName); TTree *lSASignal = (TTree*)lSAInput ->Get("DMSTree"); TFile *lSBInput = TFile::Open(backName); TTree *lSBSignal = (TTree*)lSBInput ->Get("DMSTree"); Double_t lSWeight = 1.0; Double_t lBWeight = 1.0; gROOT->cd( outfileName+TString(":/") ); factory->AddSignalTree ( lSASignal, lSWeight ); gROOT->cd( outfileName+TString(":/") ); factory->AddBackgroundTree( lSBSignal, lBWeight ); factory->SetWeightExpression("weight"); std::stringstream pSignal,pBackground; pSignal << "nTrain_Signal="<< lSASignal->GetEntries() << ":nTrain_Background=" << lSBSignal->GetEntries(); // factory->PrepareTrainingAndTestTree( lSCut, lCut,(pSignal.str()+":SplitMode=Block:NormMode=NumEvents:!V").c_str() ); factory->PrepareTrainingAndTestTree(lSCut&&cleanCut,lCut&&cleanCut,"nTrain_Signal=0:nTrain_Background=0:SplitMode=Alternate:NormMode=NumEvents:!V"); std::string lName = "alpha_VBF"; TString lBDTDef = "!H:!V:NTrees=400:BoostType=Grad:Shrinkage=0.1:UseBaggedGrad=F:nCuts=2000:NNodesMax=10000:MaxDepth=5:UseYesNoLeaf=F:nEventsMin=200"; // TString lBDTDef = "!H:!V:NTrees=400:BoostType=Grad:Shrinkage=0.1:UseBaggedGrad=F:nCuts=2000:MaxDepth=5:UseYesNoLeaf=F:MinNodeSize=0.086:NegWeightTreatment=IgnoreNegWeightsInTraining"; factory->BookMethod(TMVA::Types::kBDT,"BDT_simple_alpha",lBDTDef); factory->TrainAllMethods(); factory->TestAllMethods(); factory->EvaluateAllMethods(); outputFile->Close(); std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl; std::cout << "==> TMVAClassification is done!" << std::endl; delete factory; //if (!gROOT->IsBatch()) TMVAGui( outfileName ); //TString lBDTDef = "!H:!V:NTrees=100:BoostType=Grad:Shrinkage=0.10:UseBaggedGrad=F:nCuts=2000:NNodesMax=10000:MaxDepth=3:SeparationType=GiniIndex"; }
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( "./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->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() { //--------------------------------------------------------------- std::cout << std::endl << "==> Start TMVAClassificationCategory" << std::endl; bool batchMode(false); // Create a new root output file. 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 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 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 ); // 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( "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 = "../execs/data/toy_sigbkg_categ_offset.root"; else fname = "../execs/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 to use all remaining events in the trees after training for testing: factory->PrepareTrainingAndTestTree( mycuts, mycutb, "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" ); // 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"); // the Fisher 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"); // the Likelihood 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 trainMVACat() { char name[1000]; float XSEC[6] = {3.67e+5,2.94e+4,6.524e+03,1.064e+03,121.5,2.542e+01}; float NORM[6]; TCut preselectionCut = "ht>400 && jetPt[5]>40 && (triggerBit[0] || triggerBit[2]) && nBJets>1 && nLeptons==0"; TFile *bkgSrc[6]; bkgSrc[0] = TFile::Open("flatTree_QCD_HT300to500.root"); bkgSrc[1] = TFile::Open("flatTree_QCD_HT500to700.root"); bkgSrc[2] = TFile::Open("flatTree_QCD_HT700to1000.root"); bkgSrc[3] = TFile::Open("flatTree_QCD_HT1000to1500.root"); bkgSrc[4] = TFile::Open("flatTree_QCD_HT1500to2000.root"); bkgSrc[5] = TFile::Open("flatTree_QCD_HT2000toInf.root"); TFile *sigSrc = TFile::Open("flatTree_ttHJetTobb_M125.root"); //TFile *sigSrc = TFile::Open("flatTree_TT.root"); TTree *sigTree = (TTree*)sigSrc->Get("hadtop/events"); TTree *bkgTree[6]; TFile *outf = new TFile("mva_Cat_QCD.root","RECREATE"); TMVA::Factory* factory = new TMVA::Factory("factory_mva_Cat_QCD_",outf,"!V:!Silent:Color:DrawProgressBar:Transformations=I;G:AnalysisType=Classification"); factory->AddSignalTree(sigTree); for(int k=0;k<6;k++) { NORM[k] = ((TH1F*)bkgSrc[k]->Get("hadtop/pileup"))->GetEntries(); bkgTree[k] = (TTree*)bkgSrc[k]->Get("hadtop/events"); factory->AddBackgroundTree(bkgTree[k],XSEC[k]/NORM[k]); } //int N_SIG(sigTree->GetEntries(preselectionCut)); //int N_BKG0(bkgTree[0]->GetEntries(preselectionCut)); //int N_BKG1(bkgTree[1]->GetEntries(preselectionCut)); //int N_BKG2(bkgTree[2]->GetEntries(preselectionCut)); //int N_BKG3(bkgTree[3]->GetEntries(preselectionCut)); //float N_BKG_EFF = N_BKG0*XSEC[0]/NORM[0]+N_BKG1*XSEC[1]/NORM[1]+N_BKG2*XSEC[2]/NORM[2]+N_BKG3*XSEC[3]/NORM[3]; //int N = TMath::Min((float)N_SIG,N_BKG_EFF); //cout<<N_SIG<<" "<<N_BKG_EFF<<endl; const int NVAR = 21; TString VAR[NVAR] = { "nJets", //"nBJets", "ht", "jetPt[0]","jetPt[1]","jetPt[2]","jetPt[3]","jetPt[4]","jetPt[5]", "mbbMin","dRbbMin", //"dRbbAve","mbbAve", //"btagAve","btagMax","btagMin", //"qglAve","qglMin","qglMedian", "sphericity","aplanarity","foxWolfram[0]","foxWolfram[1]","foxWolfram[2]","foxWolfram[3]", "mTop[0]","ptTTbar","mTTbar","dRbbTop","chi2" }; char TYPE[NVAR] = { 'I', //'I', 'F', 'F','F','F','F','F','F', 'F','F', //'F','F', //'F','F','F', //'F','F','F', 'F','F','F','F','F','F', 'F','F','F','F','F' }; for(int i=0;i<NVAR;i++) { factory->AddVariable(VAR[i],TYPE[i]); } factory->AddSpectator("status",'I'); factory->AddSpectator("nBJets",'I'); sprintf(name,"nTrain_Signal=%d:nTrain_Background=%d:nTest_Signal=%d:nTest_Background=%d",-1,-1,-1,-1); factory->PrepareTrainingAndTestTree(preselectionCut,name); TMVA::IMethod* BDT_Category = factory->BookMethod( TMVA::Types::kCategory,"BDT_Category"); TMVA::MethodCategory* mcategory_BDT = dynamic_cast<TMVA::MethodCategory*>(BDT_Category); mcategory_BDT->AddMethod("status == 0 && nBJets == 2", "nJets:ht:jetPt[0]:jetPt[1]:jetPt[2]:jetPt[3]:jetPt[4]:jetPt[5]:mbbMin:dRbbMin:sphericity:aplanarity:foxWolfram[0]:foxWolfram[1]:foxWolfram[2]:foxWolfram[3]:mTop[0]:ptTTbar:mTTbar:dRbbTop:chi2:", TMVA::Types::kBDT, "BDT_Cat1", "NTrees=2000:BoostType=Grad:Shrinkage=0.1"); mcategory_BDT->AddMethod("status == 0 && nBJets > 2", "nJets:ht:jetPt[0]:jetPt[1]:jetPt[2]:jetPt[3]:jetPt[4]:jetPt[5]:mbbMin:dRbbMin:sphericity:aplanarity:foxWolfram[0]:foxWolfram[1]:foxWolfram[2]:foxWolfram[3]:mTop[0]:ptTTbar:mTTbar:dRbbTop:chi2:", TMVA::Types::kBDT, "BDT_Cat2", "NTrees=2000:BoostType=Grad:Shrinkage=0.1"); mcategory_BDT->AddMethod("status < 0 && nBJets == 2", "nJets:ht:jetPt[0]:jetPt[1]:jetPt[2]:jetPt[3]:jetPt[4]:jetPt[5]:mbbMin:dRbbMin:sphericity:aplanarity:foxWolfram[0]:foxWolfram[1]:foxWolfram[2]:foxWolfram[3]:", TMVA::Types::kBDT, "BDT_Cat3", "NTrees=2000:BoostType=Grad:Shrinkage=0.1"); mcategory_BDT->AddMethod("status < 0 && nBJets > 2", "nJets:ht:jetPt[0]:jetPt[1]:jetPt[2]:jetPt[3]:jetPt[4]:jetPt[5]:mbbMin:dRbbMin:sphericity:aplanarity:foxWolfram[0]:foxWolfram[1]:foxWolfram[2]:foxWolfram[3]:", TMVA::Types::kBDT, "BDT_Cat4", "NTrees=2000:BoostType=Grad:Shrinkage=0.1"); TMVA::IMethod* Fisher_Category = factory->BookMethod( TMVA::Types::kCategory,"Fisher_Category"); TMVA::MethodCategory* mcategory_Fisher = dynamic_cast<TMVA::MethodCategory*>(Fisher_Category); mcategory_Fisher->AddMethod("status == 0 && nBJets == 2", "nJets:ht:jetPt[0]:jetPt[1]:jetPt[2]:jetPt[3]:jetPt[4]:jetPt[5]:mbbMin:dRbbMin:sphericity:aplanarity:foxWolfram[0]:foxWolfram[1]:foxWolfram[2]:foxWolfram[3]:mTop[0]:ptTTbar:mTTbar:dRbbTop:chi2:", TMVA::Types::kFisher, "Fisher_Cat1","H:!V:Fisher"); mcategory_Fisher->AddMethod("status == 0 && nBJets > 2", "nJets:ht:jetPt[0]:jetPt[1]:jetPt[2]:jetPt[3]:jetPt[4]:jetPt[5]:mbbMin:dRbbMin:sphericity:aplanarity:foxWolfram[0]:foxWolfram[1]:foxWolfram[2]:foxWolfram[3]:mTop[0]:ptTTbar:mTTbar:dRbbTop:chi2:", TMVA::Types::kFisher, "Fisher_Cat2","H:!V:Fisher"); mcategory_Fisher->AddMethod("status < 0 && nBJets == 2", "nJets:ht:jetPt[0]:jetPt[1]:jetPt[2]:jetPt[3]:jetPt[4]:jetPt[5]:mbbMin:dRbbMin:sphericity:aplanarity:foxWolfram[0]:foxWolfram[1]:foxWolfram[2]:foxWolfram[3]:", TMVA::Types::kFisher, "Fisher_Cat3","H:!V:Fisher"); mcategory_Fisher->AddMethod("status < 0 && nBJets > 2", "nJets:ht:jetPt[0]:jetPt[1]:jetPt[2]:jetPt[3]:jetPt[4]:jetPt[5]:mbbMin:dRbbMin:sphericity:aplanarity:foxWolfram[0]:foxWolfram[1]:foxWolfram[2]:foxWolfram[3]:", TMVA::Types::kFisher, "Fisher_Cat4","H:!V:Fisher"); // specify the training methods //factory->BookMethod(TMVA::Types::kFisher,"Fisher"); //factory->BookMethod(TMVA::Types::kBDT,"BDT_GRAD_2000","NTrees=2000:BoostType=Grad:Shrinkage=0.1"); factory->TrainAllMethods(); factory->TestAllMethods(); factory->EvaluateAllMethods(); outf->Close(); }
void TMVAClassification( 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 thisdir = gSystem->DirName(gInterpreter->GetCurrentMacroName()); //gROOT->SetMacroPath(thisdir + ":" + 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"] = 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; // 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; // --------------------------------------------------------------- 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( "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( "pho_ecalClusterIsoR4", "pho_ecalClusterIsoR4", "units", 'F' ); factory->AddVariable( "pho_hcalRechitIsoR4", "pho_hcalRechitIsoR4", "units", 'F' ); factory->AddVariable( "pho_trackIsoR4PtCut20", "pho_trackIsoR4PtCut20", "units", 'F' ); factory->AddVariable( "phoHoverE", "phoHoverE", "units", 'F' ); factory->AddVariable( "phoSigmaIEtaIEta_2012", "phoSigmaIEtaIEta_2012", "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' ); // 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 fname = "./tmva_class_example.root"; TString fname = "/net/hisrv0001/home/juliusbl/alex/cut/cutTree.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 = (TTree*)input->Get("cutT"); TTree *background = (TTree*)input->Get("cutT"); // 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 // 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"); // factory->SetBackgroundWeightExpression( "weight" ); // Apply additional cuts on the signal and background samples (can be different) TCut mycuts = "subid==0"; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1"; TCut mycutb = "subid==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" ); 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=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()) // gROOT->ProcessLine(TString::Format("TMVAGui(\"%s\")", outfileName.Data())); // efficiencies( TString fin = "TMVA.root", Int_t type = 2, Bool_t useTMVAStyle = kTRUE ); }
void testBDT(){ //--------------------------------------------------------------- // This loads the library TMVA::Tools::Instance(); /* TString outfileName( "TMVA.root" ); TFile* outputFile = TFile::Open( outfileName, "RECREATE" ); TMVA::Factory *factory = new TMVA::Factory( "testBDT", outputFile, "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" ); // global event weights per tree (see below for setting event-wise weights) //Double_t signalWeight = 0.003582; //Double_t backgroundWeight = 0.0269; Double_t signalWeight = 1; Double_t backgroundWeight = 1; TFile *input_sig = TFile::Open( "signal_exclusif.root" ); TFile *input_wz = TFile::Open( "bruit_w_z.root" ); TTree *signal = (TTree*)input_sig->Get("tree"); TTree *background = (TTree*)input_wz->Get("tree"); // You can add an arbitrary number of signal or background trees factory->AddSignalTree ( signal, signalWeight ); factory->AddBackgroundTree( background, backgroundWeight ); factory->AddVariable("PT_z" , 'F'); factory->AddVariable("ASYM" , 'F'); factory->AddVariable("PHI_lw_b", 'F'); factory->AddVariable("M_top", 'F'); */ TString outfileName( "bdtTMVA_FCNC_tZ.root" ); TFile* outputFile = TFile::Open( outfileName, "RECREATE" ); TMVA::Factory *factory = new TMVA::Factory( "doBDT_FCNC_tZ", outputFile, "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" ); // global event weights per tree (see below for setting event-wise weights) //Double_t signalWeight = 0.003582; //Double_t backgroundWeight = 0.0269; Double_t signalWeight = 1; Double_t backgroundWeight = 1; TFile *input_sig = TFile::Open( "proof.root" ); TFile *input_wz = TFile::Open( "proof.root" ); TTree *signal = (TTree*)input_sig->Get("Ttree_FCNCkut"); TTree *background_WZ = (TTree*)input_wz->Get("Ttree_WZ"); /*TTree *background_ZZ = (TTree*)input_wz->Get("Ttree_ZZ"); TTree *background_WW = (TTree*)input_wz->Get("Ttree_WW"); TTree *background_TTbar = (TTree*)input_wz->Get("Ttree_TTbar"); TTree *background_Zjets = (TTree*)input_wz->Get("Ttree_Zjets"); TTree *background_Wjets = (TTree*)input_wz->Get("Ttree_Wjets"); TTree *background_TtW = (TTree*)input_wz->Get("Ttree_TtW"); TTree *background_TbartW = (TTree*)input_wz->Get("Ttree_TbartW");*/ // You can add an arbitrary number of signal or background trees factory->AddSignalTree ( signal, signalWeight ); factory->AddBackgroundTree( background_WZ, backgroundWeight ); /*factory->AddBackgroundTree( background_ZZ, backgroundWeight ); factory->AddBackgroundTree( background_WW, backgroundWeight ); factory->AddBackgroundTree( background_TTbar, backgroundWeight ); factory->AddBackgroundTree( background_Zjets, backgroundWeight ); factory->AddBackgroundTree( background_Wjets, backgroundWeight ); factory->AddBackgroundTree( background_TtW, backgroundWeight ); factory->AddBackgroundTree( background_TbartW, backgroundWeight );*/ factory->AddVariable("tree_topMass", 'F'); factory->AddVariable("tree_deltaPhilb", 'F'); factory->AddVariable("tree_asym", 'F'); factory->AddVariable("tree_Zpt", 'F'); // to set weights. The variable must exist in the tree // 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 = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1"; TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5"; factory->PrepareTrainingAndTestTree( mycuts, mycutb, "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" ); factory->BookMethod( TMVA::Types::kBDT, "BDT", "!H:!V:NTrees=100:nEventsMin=100:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:PruneMethod=NoPruning:VarTransform=Decorrelate" ); // 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 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 ); }
int main( int argc, char** argv ) {//main std::string folder; if (argc > 1) { folder = argv[1]; } else { folder = "output_tmva/nunu/MET130/"; } bool useQCD = true; bool useOthers = false; bool useOthersAsSignal = true; //List of input signal files std::vector<std::string> sigfiles; //sigfiles.push_back("MC_VBF_HToZZTo4Nu_M-120"); sigfiles.push_back("MC_Powheg-Htoinv-mH125"); if (useOthersAsSignal) { sigfiles.push_back("MC_TTJets"); //powheg samples //sigfiles.push_back("MC_TT-v1"); //sigfiles.push_back("MC_TT-v2"); // sigfiles.push_back("MC_T-tW"); sigfiles.push_back("MC_Tbar-tW"); sigfiles.push_back("MC_SingleT-s-powheg-tauola"); sigfiles.push_back("MC_SingleTBar-s-powheg-tauola"); sigfiles.push_back("MC_SingleT-t-powheg-tauola"); sigfiles.push_back("MC_SingleTBar-t-powheg-tauola"); sigfiles.push_back("MC_WW-pythia6-tauola"); sigfiles.push_back("MC_WZ-pythia6-tauola"); sigfiles.push_back("MC_ZZ-pythia6-tauola"); sigfiles.push_back("MC_W1JetsToLNu_enu"); sigfiles.push_back("MC_W2JetsToLNu_enu"); sigfiles.push_back("MC_W3JetsToLNu_enu"); sigfiles.push_back("MC_W4JetsToLNu_enu"); sigfiles.push_back("MC_WJetsToLNu-v1_enu"); sigfiles.push_back("MC_WJetsToLNu-v2_enu"); sigfiles.push_back("MC_W1JetsToLNu_munu"); sigfiles.push_back("MC_W2JetsToLNu_munu"); sigfiles.push_back("MC_W3JetsToLNu_munu"); sigfiles.push_back("MC_W4JetsToLNu_munu"); sigfiles.push_back("MC_WJetsToLNu-v1_munu"); sigfiles.push_back("MC_WJetsToLNu-v2_munu"); sigfiles.push_back("MC_W1JetsToLNu_taunu"); sigfiles.push_back("MC_W2JetsToLNu_taunu"); sigfiles.push_back("MC_W3JetsToLNu_taunu"); sigfiles.push_back("MC_W4JetsToLNu_taunu"); sigfiles.push_back("MC_WJetsToLNu-v1_taunu"); sigfiles.push_back("MC_WJetsToLNu-v2_taunu"); sigfiles.push_back("MC_DYJetsToLL"); sigfiles.push_back("MC_DY1JetsToLL"); sigfiles.push_back("MC_DY2JetsToLL"); sigfiles.push_back("MC_DY3JetsToLL"); sigfiles.push_back("MC_DY4JetsToLL"); sigfiles.push_back("MC_ZJetsToNuNu_100_HT_200"); sigfiles.push_back("MC_ZJetsToNuNu_200_HT_400"); sigfiles.push_back("MC_ZJetsToNuNu_400_HT_inf"); sigfiles.push_back("MC_ZJetsToNuNu_50_HT_100"); sigfiles.push_back("MC_GJets-HT-200To400-madgraph"); sigfiles.push_back("MC_GJets-HT-400ToInf-madgraph"); sigfiles.push_back("MC_WGamma"); sigfiles.push_back("MC_EWK-Z2j"); sigfiles.push_back("MC_EWK-Z2jiglep"); sigfiles.push_back("MC_EWK-W2jminus_enu"); sigfiles.push_back("MC_EWK-W2jplus_enu"); sigfiles.push_back("MC_EWK-W2jminus_munu"); sigfiles.push_back("MC_EWK-W2jplus_munu"); sigfiles.push_back("MC_EWK-W2jminus_taunu"); sigfiles.push_back("MC_EWK-W2jplus_taunu"); } //List of input files std::vector<std::string> bkgfiles; if (useQCD){ bkgfiles.push_back("MC_QCD-Pt-30to50-pythia6"); bkgfiles.push_back("MC_QCD-Pt-50to80-pythia6"); bkgfiles.push_back("MC_QCD-Pt-80to120-pythia6"); bkgfiles.push_back("MC_QCD-Pt-120to170-pythia6"); bkgfiles.push_back("MC_QCD-Pt-170to300-pythia6"); bkgfiles.push_back("MC_QCD-Pt-300to470-pythia6"); bkgfiles.push_back("MC_QCD-Pt-470to600-pythia6"); bkgfiles.push_back("MC_QCD-Pt-600to800-pythia6"); bkgfiles.push_back("MC_QCD-Pt-800to1000-pythia6"); bkgfiles.push_back("MC_QCD-Pt-1000to1400-pythia6"); bkgfiles.push_back("MC_QCD-Pt-1400to1800-pythia6"); bkgfiles.push_back("MC_QCD-Pt-1800-pythia6"); } if (useOthers) { bkgfiles.push_back("MC_TTJets"); //powheg samples //bkgfiles.push_back("MC_TT-v1"); //bkgfiles.push_back("MC_TT-v2"); // bkgfiles.push_back("MC_T-tW"); bkgfiles.push_back("MC_Tbar-tW"); bkgfiles.push_back("MC_SingleT-s-powheg-tauola"); bkgfiles.push_back("MC_SingleTBar-s-powheg-tauola"); bkgfiles.push_back("MC_SingleT-t-powheg-tauola"); bkgfiles.push_back("MC_SingleTBar-t-powheg-tauola"); bkgfiles.push_back("MC_WW-pythia6-tauola"); bkgfiles.push_back("MC_WZ-pythia6-tauola"); bkgfiles.push_back("MC_ZZ-pythia6-tauola"); bkgfiles.push_back("MC_W1JetsToLNu_enu"); bkgfiles.push_back("MC_W2JetsToLNu_enu"); bkgfiles.push_back("MC_W3JetsToLNu_enu"); bkgfiles.push_back("MC_W4JetsToLNu_enu"); bkgfiles.push_back("MC_WJetsToLNu-v1_enu"); bkgfiles.push_back("MC_WJetsToLNu-v2_enu"); bkgfiles.push_back("MC_W1JetsToLNu_munu"); bkgfiles.push_back("MC_W2JetsToLNu_munu"); bkgfiles.push_back("MC_W3JetsToLNu_munu"); bkgfiles.push_back("MC_W4JetsToLNu_munu"); bkgfiles.push_back("MC_WJetsToLNu-v1_munu"); bkgfiles.push_back("MC_WJetsToLNu-v2_munu"); bkgfiles.push_back("MC_W1JetsToLNu_taunu"); bkgfiles.push_back("MC_W2JetsToLNu_taunu"); bkgfiles.push_back("MC_W3JetsToLNu_taunu"); bkgfiles.push_back("MC_W4JetsToLNu_taunu"); bkgfiles.push_back("MC_WJetsToLNu-v1_taunu"); bkgfiles.push_back("MC_WJetsToLNu-v2_taunu"); bkgfiles.push_back("MC_DYJetsToLL"); bkgfiles.push_back("MC_DY1JetsToLL"); bkgfiles.push_back("MC_DY2JetsToLL"); bkgfiles.push_back("MC_DY3JetsToLL"); bkgfiles.push_back("MC_DY4JetsToLL"); bkgfiles.push_back("MC_ZJetsToNuNu_100_HT_200"); bkgfiles.push_back("MC_ZJetsToNuNu_200_HT_400"); bkgfiles.push_back("MC_ZJetsToNuNu_400_HT_inf"); bkgfiles.push_back("MC_ZJetsToNuNu_50_HT_100"); bkgfiles.push_back("MC_GJets-HT-200To400-madgraph"); bkgfiles.push_back("MC_GJets-HT-400ToInf-madgraph"); bkgfiles.push_back("MC_WGamma"); bkgfiles.push_back("MC_EWK-Z2j"); bkgfiles.push_back("MC_EWK-Z2jiglep"); bkgfiles.push_back("MC_EWK-W2jminus_enu"); bkgfiles.push_back("MC_EWK-W2jplus_enu"); bkgfiles.push_back("MC_EWK-W2jminus_munu"); bkgfiles.push_back("MC_EWK-W2jplus_munu"); bkgfiles.push_back("MC_EWK-W2jminus_taunu"); bkgfiles.push_back("MC_EWK-W2jplus_taunu"); } // Create a ROOT output file where TMVA will store ntuples, histograms, etc. TFile *output_tmva = TFile::Open((folder+"/TMVA_QCDrej.root").c_str(),"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", output_tmva, "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" ); //fill the variables with event weight from the trees //const unsigned nVars = 4; factory->AddSpectator("jet1_pt","Jet 1 p_{T}", "GeV", 'F'); factory->AddSpectator("jet2_pt","Jet 2 p_{T}", "GeV", 'F'); factory->AddSpectator("jet1_eta","Jet 1 #eta", "", 'F'); factory->AddVariable("jet2_eta","Jet 2 #eta", "", 'F');// ** factory->AddSpectator("jet1_phi","Jet 1 #phi", "", 'F'); factory->AddSpectator("jet2_phi","Jet 2 #phi", "", 'F'); factory->AddSpectator("dijet_M","M_{jj}", " GeV", 'F'); factory->AddSpectator("dijet_deta","#Delta#eta_{jj}", "", 'F'); factory->AddSpectator("dijet_sumeta","#eta_{j1}+#eta_{j2}", "", 'F'); factory->AddSpectator("dijet_dphi","#Delta#phi_{jj}", "", 'F'); factory->AddSpectator("met","MET", "GeV", 'F');// ** factory->AddSpectator("met_phi","MET #phi", "", 'F'); factory->AddVariable("met_significance","MET significance", "", 'F');// ** factory->AddSpectator("sumet","#Sum E_{T}", "GeV", 'F'); factory->AddSpectator("ht","H_{T}", "GeV", 'F'); factory->AddVariable("mht","MH_{T}", "GeV", 'F');// ** factory->AddSpectator("sqrt_ht","#sqrt{H_{T}}", "GeV^{0.5}", 'F'); factory->AddSpectator("unclustered_et","Unclustered E_{T}", "GeV", 'F'); factory->AddSpectator("unclustered_phi","Unclustered #phi", "GeV", 'F'); factory->AddSpectator("jet1met_dphi","#Delta#phi(MET,jet1)", "", 'F'); factory->AddVariable("jet2met_dphi","#Delta#phi(MET,jet2)", "", 'F');// ** factory->AddVariable("jetmet_mindphi","minimum #Delta#phi(MET,jet)", "", 'F');// ** factory->AddVariable("jetunclet_mindphi","minimum #Delta#phi(unclustered,jet)", "", 'F');// ** factory->AddVariable("metunclet_dphi","#Delta#phi(MET,unclustered)", "", 'F');// ** factory->AddVariable("dijetmet_scalarSum_pt", "p_{T}^{jet1}+p_{T}^{jet2}+MET", "GeV", 'F');// ** factory->AddSpectator("dijetmet_vectorialSum_pt","p_{T}(#vec{j1}+#vec{j2}+#vec{MET})", "GeV", 'F'); factory->AddVariable("dijetmet_ptfraction","p_{T}^{dijet}/(p_{T}^{dijet}+MET)", "", 'F');// ** //factory->AddVariable("jet1met_scalarprod := (jet1_pt*cos(jet1_phi)*met_x+jet1_pt*sin(jet1_phi)*met_y)/met", "#vec{p_{T}^{jet1}}.#vec{MET}/MET", "GeV" , 'F'); //factory->AddVariable("jet2met_scalarprod := (jet2_pt*cos(jet2_phi)*met_x+jet2_pt*sin(jet2_phi)*met_y)/met", "#vec{p_{T}^{jet2}}.#vec{MET}/MET", "GeV" , 'F'); factory->AddVariable("jet1met_scalarprod", "#vec{p_{T}^{jet1}}.#vec{MET}/MET", "GeV" , 'F');// ** factory->AddVariable("jet2met_scalarprod", "#vec{p_{T}^{jet2}}.#vec{MET}/MET", "GeV" , 'F');// ** factory->AddVariable("jet1met_scalarprod_frac := jet1met_scalarprod/met", "#vec{p_{T}^{jet1}}.#vec{MET}/MET^{2}", "" , 'F');// ** factory->AddVariable("jet2met_scalarprod_frac := jet2met_scalarprod/met", "#vec{p_{T}^{jet2}}.#vec{MET}/MET^{2}", "" , 'F');// ** factory->AddSpectator("n_jets_cjv_30","CJV jets (30 GeV)", "" , 'I'); factory->AddSpectator("n_jets_cjv_20EB_30EE","CJV jets (|#eta|<2.4 and 20 GeV, or 30 GeV)", "" , 'I'); //test with only VBF variables used in cut-based analysis //factory->AddVariable("dijet_M","M_{jj}", " GeV", 'F'); //factory->AddVariable("dijet_deta","#Delta#eta_{jj}", "", 'F'); //factory->AddVariable("dijet_dphi","#Delta#phi_{jj}", "", 'F'); //factory->AddVariable("met","MET", "GeV", 'F'); //factory->AddVariable("n_jets_cjv_30","CJV jets (30 GeV)", "" , 'I'); //get input files //signal //TFile *signalfile = TFile::Open((folder+"/"+"MC_VBF_HToZZTo4Nu_M-120.root").c_str()); //TTree *signal = (TTree*)signalfile->Get("TmvaInputTree"); //Double_t signalWeight = 1.0; //factory->AddSignalTree(signal,signalWeight); //Set individual event weights (the variables must exist in the original TTree) //factory->SetSignalWeightExpression("total_weight"); //background std::map<std::string, TFile *> tfiles; for (unsigned i = 0; i < bkgfiles.size(); ++i) { std::string filename = (bkgfiles[i]+".root"); TFile * tmp = new TFile((folder+"/"+filename).c_str()); if (!tmp) { std::cerr << "Warning, file " << filename << " could not be opened." << std::endl; } else { tfiles[bkgfiles[i]] = tmp; } } TTree *background[bkgfiles.size()]; //signal std::map<std::string, TFile *> sfiles; for (unsigned i = 0; i < sigfiles.size(); ++i) { std::string filename = (sigfiles[i]+".root"); TFile * tmp = new TFile((folder+"/"+filename).c_str()); if (!tmp) { std::cerr << "Warning, file " << filename << " could not be opened." << std::endl; } else { sfiles[sigfiles[i]] = tmp; } } TTree *signal[sigfiles.size()]; for (unsigned i = 0; i < bkgfiles.size(); ++i) { std::string f = bkgfiles[i]; if (tfiles[f]){ background[i] = (TTree*)tfiles[f]->Get("TmvaInputTree"); //if (f.find("QCD-Pt")!=f.npos){ //} Double_t backgroundWeight = 1.0; factory->AddBackgroundTree(background[i],backgroundWeight); factory->SetBackgroundWeightExpression("total_weight"); }//if file exist else { std::cout << " Cannot find background file " << f << std::endl; } }//loop on files for (unsigned i = 0; i < sigfiles.size(); ++i) { std::string f = sigfiles[i]; if (sfiles[f]){ signal[i] = (TTree*)sfiles[f]->Get("TmvaInputTree"); //if (f.find("QCD-Pt")!=f.npos){ //} Double_t signalWeight = 1.0; factory->AddSignalTree(signal[i],signalWeight); factory->SetSignalWeightExpression("total_weight"); }//if file exist else { std::cout << " Cannot find signal file " << f << std::endl; } }//loop on files // Apply additional cuts on the signal and background samples (can be different) TCut mycuts = "";//dijet_deta>3.8 && dijet_M > 1100 && met > 100 && met_significance>5"; TCut mycutb = "";//dijet_deta>3.8 && dijet_M > 1100 && met > 100 && met_significance>5"; factory->PrepareTrainingAndTestTree( mycuts, mycutb, "nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V" ); // Likelihood ("naive Bayes estimator") //factory->BookMethod( TMVA::Types::kLikelihood, "Likelihood", //"H:!V:!TransformOutput:PDFInterpol=Spline2:NSmoothSig[0]=20:NSmoothBkg[0]=20:NSmoothBkg[1]=10:NSmooth=1:NAvEvtPerBin=50" ); // Linear discriminant (same as Fisher discriminant) //factory->BookMethod( TMVA::Types::kLD, "LD", "H:!V:VarTransform=None:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" ); // Fisher discriminant (same as LD) factory->BookMethod( TMVA::Types::kFisher, "Fisher", "H:!V:Fisher:CreateMVAPdfs:PDFInterpolMVAPdf=Spline2:NbinsMVAPdf=50:NsmoothMVAPdf=10" ); // TMVA ANN: MLP (recommended ANN) -- all ANNs in TMVA are Multilayer Perceptrons //factory->BookMethod( TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=60:HiddenLayers=N+5:TestRate=5:!UseRegulator" ); // Boosted Decision Trees // Gradient Boost //factory->BookMethod( TMVA::Types::kBDT, "BDTG", //"!H:!V:NTrees=1000:MinNodeSize=1.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=2" ); //factory->BookMethod( TMVA::Types::kBDT, "BDTG", // "!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.10:nCuts=20:MaxDepth=2" ); // Adaptive Boost //factory->BookMethod( TMVA::Types::kBDT, "BDT1000", // "!H:!V:NTrees=1000:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20" ); factory->BookMethod( TMVA::Types::kBDT, "BDT", "!H:!V:NTrees=1000:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.2:SeparationType=GiniIndex:nCuts=20" ); // Bagging //factory->BookMethod( TMVA::Types::kBDT, "BDTB", // "!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20" ); // Decorrelation + Adaptive Boost //factory->BookMethod( TMVA::Types::kBDT, "BDTD", // "!H:!V:NTrees=400:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:VarTransform=Decorrelate" ); // Allow Using Fisher discriminant in node splitting for (strong) linearly correlated variables //factory->BookMethod( TMVA::Types::kBDT, "BDTMitFisher", // "!H:!V:NTrees=50:UseFisherCuts:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20" ); // 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 output_tmva->Close(); std::cout << "==> Wrote root file: " << output_tmva->GetName() << std::endl << "==> TMVAClassification is done!" << std::endl << std::endl << "==> To view the results, launch the GUI: \"root -l ./TMVAGui.C\"" << std::endl << std::endl; // Clean up delete factory; return 0; }//main
//------------------------------------------------------------------------------ // MVATrain //------------------------------------------------------------------------------ void MVATrain(TString signal) { TFile* outputfile = TFile::Open(trainingdir + signal + ".root", "recreate"); // Factory //---------------------------------------------------------------------------- TMVA::Factory* factory = new TMVA::Factory(signal, outputfile, "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification"); // Get the trees //---------------------------------------------------------------------------- _mctree.clear(); AddProcess("signal", signal); AddProcess("background", "HZJ_HToWW_M125"); AddProcess("background", "ggZH_HToWW_M125"); // AddProcess("background", "14_HZ"); // AddProcess("background", "10_HWW"); // AddProcess("background", "06_WW"); // AddProcess("background", "02_WZTo3LNu"); // AddProcess("background", "03_ZZ"); // AddProcess("background", "11_Wg"); // AddProcess("background", "07_ZJets"); // AddProcess("background", "09_TTV"); // AddProcess("background", "05_ST"); // AddProcess("background", "00_Fakes"); Double_t weight = 1.0; factory->AddSignalTree(_signaltree, weight); for (UInt_t i=0; i<_mctree.size(); i++) factory->AddBackgroundTree(_mctree[i], weight); factory->SetWeightExpression("eventW"); // Add variables //---------------------------------------------------------------------------- // Be careful with the order: it must be respected at the reading step // factory->AddVariable("<var1>+<var2>", "pretty title", "unit", 'F'); // factory->AddVariable("channel", "", "", 'F'); factory->AddVariable("metPfType1", "", "", 'F'); factory->AddVariable("m2l", "", "", 'F'); // factory->AddVariable("njet", "", "", 'F'); // factory->AddVariable("nbjet20cmvav2l", "", "", 'F'); factory->AddVariable("lep1pt", "", "", 'F'); factory->AddVariable("lep2pt", "", "", 'F'); // factory->AddVariable("jet1pt", "", "", 'F'); factory->AddVariable("jet2pt", "", "", 'F'); factory->AddVariable("mtw1", "", "", 'F'); factory->AddVariable("dphill", "", "", 'F'); factory->AddVariable("dphilep1jet1", "", "", 'F'); // factory->AddVariable("dphilep1jet2", "", "", 'F'); // factory->AddVariable("dphilmet1", "", "", 'F'); // factory->AddVariable("dphilep2jet1", "", "", 'F'); // factory->AddVariable("dphilep2jet2", "", "", 'F'); // factory->AddVariable("dphilmet2", "", "", 'F'); // factory->AddVariable("dphijj", "", "", 'F'); // factory->AddVariable("dphijet1met", "", "", 'F'); // factory->AddVariable("dphijet2met", "", "", 'F'); factory->AddVariable("dphillmet", "", "", 'F'); // Preselection cuts and preparation //---------------------------------------------------------------------------- factory->PrepareTrainingAndTestTree("", ":nTrain_Signal=0:nTest_Signal=0:nTrain_Background=0:nTest_Background=0:SplitMode=Alternate:MixMode=Random:!V"); // Book MVA //---------------------------------------------------------------------------- factory->BookMethod(TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=sigmoid:VarTransform=N:NCycles=600:HiddenLayers=25,10:TestRate=5:!UseRegulator"); // Train, test and evaluate MVA //---------------------------------------------------------------------------- factory->TrainAllMethods(); // Train using the set of training events factory->TestAllMethods(); // Evaluate using the set of test events factory->EvaluateAllMethods(); // Evaluate and compare performance // Save the output //---------------------------------------------------------------------------- outputfile->Close(); delete factory; }
void TMVAClassification( TString myMethodList = "" ) { // TString curDynamicPath( gSystem->GetDynamicPath() ); // gSystem->SetDynamicPath( "/usr/local/bin/root/bin:" + curDynamicPath ); // TString curIncludePath(gSystem->GetIncludePath()); // gSystem->SetIncludePath( " -I /usr/local/bin/root/include " + curIncludePath ); // // load TMVA shared library created in local release: for MAC OSX // if (TString(gSystem->GetBuildArch()).Contains("macosx") ) gSystem->Load( "libTMVA.so" ); // gSystem->Load( "libTMVA" ); // TMVA::Tools::Instance(); // // welcome the user // TMVA::gTools().TMVAWelcomeMessage(); // TMVAGlob::SetTMVAStyle(); // // this loads the library // TMVA::Tools::Instance(); //--------------------------------------------------------------- // default MVA methods to be trained + tested std::map<std::string,int> Use; Use["Cuts"] = 1; // Use["Likelihood"] = 1; // --------------------------------------------------------------- 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 = 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; } } // Create a new root output file. 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 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("deltaEta := deta", 'F'); factory->AddVariable("deltaPhi := dphi", 'F'); factory->AddVariable("sigmaIetaIeta := sieie", 'F'); factory->AddVariable("HoverE := hoe", 'F'); factory->AddVariable("trackIso := trackiso", 'F'); factory->AddVariable("ecalIso := ecaliso", 'F'); factory->AddVariable("hcalIso := hcaliso", 'F'); //factory->AddVariable("nMissingHits := misshits", '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( "et", 'F' ); factory->AddSpectator( "eta", 'F' ); factory->AddSpectator( "phi", 'F' ); // read training and test data TFile *input = TFile::Open( "SigElectrons.root" ); TFile *inputB = TFile::Open( "BkgElectrons.root" ); std::cout << "--- TMVAClassification : Using input file: " << input->GetName() << std::endl; TTree *signal = (TTree*)input->Get("ntuple"); TTree *background = (TTree*)inputB->Get("ntuple"); factory->AddSignalTree ( signal, 1.0 ); factory->AddBackgroundTree( background, 1.0 ); // 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"); // Apply additional cuts on the signal and background samples (can be different) 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=0:nTrain_Background=0: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" ); // 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" ); // -------------------------------------------------------------------------------------------------- // ---- 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; // gROOT->ProcessLine(".x /usr/local/bin/root/tmva/test/correlations.C"); gROOT->ProcessLine(".x /usr/local/bin/root/tmva/test/variables.C"); }