void regressphi() {
TMVA::Tools::Instance();
std::cout << "==> Start TMVAClassification" << std::endl;
// 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( "mva", outputFile,
"!V:!Silent:Color:DrawProgressBar" );
factory->AddVariable( "npv" , 'F' );
factory->AddVariable( "u" , 'F' );
factory->AddVariable( "uphi" , 'F' );
factory->AddVariable( "chsumet/sumet" , 'F' );
factory->AddVariable( "tku" , 'F' );
factory->AddVariable( "tkuphi" , 'F' );
factory->AddVariable( "nopusumet/sumet" , 'F' );
factory->AddVariable( "nopuu" , 'F' );
factory->AddVariable( "nopuuphi" , 'F' );
factory->AddVariable( "pusumet/sumet" , 'F' );
factory->AddVariable( "pumet" , 'F' );
factory->AddVariable( "pumetphi" , 'F' );
factory->AddVariable( "pucsumet/sumet" , 'F' );
factory->AddVariable( "pucu" , 'F' );
factory->AddVariable( "pucuphi" , 'F' );
factory->AddVariable( "jspt_1" , 'F' );
factory->AddVariable( "jseta_1" , 'F' );
factory->AddVariable( "jsphi_1" , 'F' );
factory->AddVariable( "jspt_2" , 'F' );
factory->AddVariable( "jseta_2" , 'F' );
factory->AddVariable( "jsphi_2" , 'F' );
factory->AddVariable( "nalljet" , 'I' );
factory->AddVariable( "njet" , 'I' );
factory->AddTarget( "rphi_z-uphi+ 2.*TMath::Pi()*(rphi_z-uphi < -TMath::Pi()) - 2.*TMath::Pi()*(rphi_z-uphi > TMath::Pi()) " );
TString lName = "../Jets/r11-dimu_nochs_v2.root"; TFile *lInput = TFile::Open(lName);
TTree *lRegress = (TTree*)lInput ->Get("Flat");
Double_t lRWeight = 1.0;
factory->AddRegressionTree( lRegress , lRWeight );
TCut lCut = "nbtag == 0"; //Cut to remove real MET
//(rpt_z < 40 || (rpt_z > 40 && rpt_z+u1 < 40)) && nbtag == 0 "; ==> stronger cut to remove Real MET
factory->PrepareTrainingAndTestTree( lCut,
"nTrain_Regression=0:nTest_Regression=0:SplitMode=Block:NormMode=NumEvents:!V" );
// Boosted Decision Trees
factory->BookMethod( TMVA::Types::kBDT, "RecoilPhiRegress_data_clean2_njet",
"!H:!V:VarTransform=None:nEventsMin=200:NTrees=100:BoostType=Grad:Shrinkage=0.1:MaxDepth=100:NNodesMax=100000:UseYesNoLeaf=F:nCuts=2000");//MaxDepth=100
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 );
}
void REG::Loop() {
if (fChain == 0) return;
Long64_t nentries = fChain->GetEntriesFast();
Long64_t nbytes = 0, nb = 0;
TMVA::Tools::Instance();
std::cout << std::endl;
std::cout << "==> Start TMVARegression" << std::endl;
TString outfileName( "TMVAReg.root" );
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
TMVA::Factory *factory = new TMVA::Factory( "TMVARegression", outputFile,
"!V:!Silent:Color:DrawProgressBar" );
factory->AddVariable( "l1Pt", "Variable 1", "units", 'F' );
factory->AddVariable( "l1Eta", "Variable 2", "units", 'F' );
factory->AddVariable( "l1Phi", "Variable 1", "units", 'F' );
// factory->AddVariable( "RhoL1", "Variable 2", "units", 'F' );
factory->AddTarget( "ratio" );
factory->AddRegressionTree(fChain, 1.0 );
TCut mycut = "";
factory->PrepareTrainingAndTestTree( mycut,
"nTrain_Regression=10000:nTest_Regression=0:SplitMode=Block:NormMode=NumEvents:!V" );
factory->BookMethod( TMVA::Types::kBDT, "BDT",
"!H:!V:NTrees=100:nEventsMin=5:BoostType=AdaBoostR2:SeparationType=RegressionVariance:nCuts=20:PruneMethod=CostComplexity:PruneStrength=30" );
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 << "==> TMVARegression is done!" << std::endl;
delete factory;
}
int main(int argc, char**argv){
if(argc != 2){
std::cerr << " >>>>> analysis.cpp::usage: " << argv[0] << " configFileName" << std::endl ;
return 1;
}
parseConfigFile (argv[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 TMVARegression.C\(\"myMethod1,myMethod2,myMethod3\"\)
//
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
std::vector<std::string> UseMethodName;
UseMethodName = gConfigParser -> readStringListOption("Input::UseMethodName");
std::cout << " >>>>> Input::UseMethodName size = " << UseMethodName.size() << std::endl;
std::cout << " >>>>> >>>>> ";
for (unsigned int iCat = 0; iCat < UseMethodName.size(); iCat++){
std::cout << " " << UseMethodName.at(iCat) << ", ";
}
std::cout << std::endl;
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVARegression" << std::endl;
std::map<std::string,int> Use;
for(std::vector<std::string>::iterator it=UseMethodName.begin(); it!=UseMethodName.end(); ++it) Use[*it]=0;
std::string UseMethodFlag;
try{ UseMethodFlag = gConfigParser -> readStringOption("Input::UseMethodFlag");
std::cout<< UseMethodFlag<<std::endl;
std::vector<TString> mlist = gTools().SplitString( UseMethodFlag, '/' );
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 -1;
}
Use[regMethod] = 1;
std::cout << "regMethod= " << regMethod<< " 1 "<<std::endl;
}
}
catch (char * exception){
std::cerr << " exception = Use All method " << std::endl;
for(std::vector<std::string>::iterator it=UseMethodName.begin() ;it!=UseMethodName.end(); it++) Use[*it]=1;
}
// --------------------------------------------------------------------------------------------------
// --- Here the preparation phase begins
// Create a new root output file
std::string outputFileName = gConfigParser -> readStringOption("Output::outputFileName");
std::cout<<" Output Data File = "<<outputFileName<<std::endl;
TFile* outputFile = TFile::Open( outputFileName.c_str(), "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/
// All TMVA output can be suppressed by removing the "!" (not) in
// front of the "Silent" argument in the option string
// Read training and test data (see TMVAClassification for reading ASCII files)
// load the signal and background event samples from ROOT trees
std::string inputFileList = gConfigParser -> readStringOption("Input::inputFileList");
std::string treeNameDATA = gConfigParser -> readStringOption("Input::treeNameDATA");
std::cout<<" Input Data List = "<<inputFileList<<std::endl;
TChain* treeDATA = new TChain(treeNameDATA.c_str());
FillChain(*treeDATA,inputFileList.c_str());
TMVA::Factory *factory = new TMVA::Factory( "TMVARegression", outputFile,
"!V:!Silent:Color:DrawProgressBar" );
// 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" )]
std::string RegionOfTraining = gConfigParser -> readStringOption("Input::RegionOfTraining");
std::cout<<" RegionOfTraining = "<<RegionOfTraining<<std::endl;
if(RegionOfTraining=="EB"){
factory->AddVariable( "ele1_scE/ele1_scERaw" , 'F');
factory->AddVariable( "ele1_eRegrInput_nPV" , 'F');
factory->AddVariable( "ele1_eRegrInput_r9" , 'F');
factory->AddVariable( "ele1_fbrem" , 'F');
factory->AddVariable( "ele1_eta" , 'F');
factory->AddVariable( "ele1_DphiIn" , 'F');
factory->AddVariable( "ele1_DetaIn" , 'F');
factory->AddVariable( "ele1_sigmaIetaIeta" , 'F');
factory->AddVariable( "ele1_eRegrInput_etaW" , 'F');
factory->AddVariable( "ele1_eRegrInput_phiW" , 'F');
factory->AddVariable( "ele1_eRegrInput_bCE_Over_sCE", 'F');
factory->AddVariable( "ele1_eRegrInput_sigietaieta_bC1" , 'F');
factory->AddVariable( "ele1_eRegrInput_sigiphiiphi_bC1" , 'F');
factory->AddVariable( "ele1_eRegrInput_sigietaiphi_bC1" , 'F');
factory->AddVariable( "ele1_eRegrInput_e3x3_Over_bCE" , 'F');
factory->AddVariable( "ele1_eRegrInput_Deta_bC_sC" , 'F');
factory->AddVariable( "ele1_eRegrInput_Dphi_bC_sC" , 'F');
factory->AddVariable( "ele1_eRegrInput_bEMax_Over_bCE" , 'F');
factory->AddVariable( "ele1_dxy_PV" , 'F');
factory->AddVariable( "ele1_dz_PV" , 'F');
factory->AddVariable( "ele1_sigmaP/ele1_tkP" , 'F');
factory->AddVariable( "ele1_eRegrInput_bCELow_Over_sCE", 'F');
factory->AddVariable( "ele1_eRegrInput_e3x3_Over_bCELow" , 'F');
factory->AddVariable( "ele1_eRegrInput_Deta_bCLow_sC" , 'F');
factory->AddVariable( "ele1_eRegrInput_Dphi_bCLow_sC" , 'F');
factory->AddVariable( "ele1_eRegrInput_seedbC_etacry" , 'F');
factory->AddVariable( "ele1_eRegrInput_seedbC_phicry" , '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' );
// Add the variable carrying the regression target
// factory->AddTarget("ele1_scE/ele1_E_true" );
factory->AddTarget("ele1_tkP/ele1_E_true" );
// It is also possible to declare additional targets for multi-dimensional regression, ie:
// -- factory->AddTarget( "fvalue2" );
// BUT: this is currently ONLY implemented for MLP
// global event weights per tree (see below for setting event-wise weights)
Double_t regWeight = 1.0;
// You can add an arbitrary number of regression trees
factory->AddRegressionTree( treeDATA, regWeight );
// This would set individual event weights (the variables defined in the
// expression need to exist in the original TTree)
// factory->SetWeightExpression( "var1", "Regression" );
// TCut mycut = "ele1_isEB==1 && ele1_sigmaP/ele1_tkP<0.4 && ele1_fbrem>0 && abs(ele1_dxy_PV)<0.05 && abs(ele1_dz_PV)<0.05 && ele1_eRegrInput_etaW > 0.006 && ele1_eRegrInput_phiW<0.08 && ele1_eRegrInput_sigietaieta_bC1>0.006 && ele1_eRegrInput_sigiphiiphi_bC1>0.008 && abs(ele1_eRegrInput_Deta_bC_sC)<0.004 && abs(ele1_eRegrInput_Dphi_bC_sC)<0.04 && abs(ele1_eRegrInput_seedbC_etacry)<0.6 && abs(ele1_eRegrInput_seedbC_phicry)<0.6 && ele1_scE/ele1_scERaw<1.2 && (ele1_scE/ele1_E_true)<1.4 && (ele1_scE/ele1_E_true)>0.3"; // for example: TCut mycut = "abs(var1)<0.5 && abs(var2-0.5)<1";
TCut mycut = "ele1_isEB==1 && ele1_sigmaP/ele1_tkP<0.4 && ele1_fbrem>0 && abs(ele1_dxy_PV)<0.05 && abs(ele1_dz_PV)<0.05 && ele1_eRegrInput_etaW > 0.006 && ele1_eRegrInput_phiW<0.08 && ele1_eRegrInput_sigietaieta_bC1>0.006 && ele1_eRegrInput_sigiphiiphi_bC1>0.008 && abs(ele1_eRegrInput_Deta_bC_sC)<0.004 && abs(ele1_eRegrInput_Dphi_bC_sC)<0.04 && abs(ele1_eRegrInput_seedbC_etacry)<0.6 && abs(ele1_eRegrInput_seedbC_phicry)<0.6 && ele1_scE/ele1_scERaw<1.2 && ele1_tkP/ele1_E_true<1.8 && ele1_tkP/ele1_E_true>0.2"; // for example: TCut mycut = "abs(var1)<0.5 && abs(var2-0.5)<1";
// tell the factory to use all remaining events in the trees after training for testing:
factory->PrepareTrainingAndTestTree( mycut,
"nTrain_Regression=2500000:nTest_Regression=2500000:SplitMode=Random:NormMode=NumEvents:!V" );
TString Name = Form("weight_%s_%s_P_W",RegionOfTraining.c_str(),UseMethodFlag.c_str());
(TMVA::gConfig().GetIONames()).fWeightFileDir = Name;
}
if(RegionOfTraining=="EE"){
factory->AddVariable( "ele1_scE/ele1_scERaw" , 'F');
factory->AddVariable( "ele1_eRegrInput_nPV",'F');
factory->AddVariable( "ele1_eRegrInput_r9",'F');
factory->AddVariable( "ele1_fbrem",'F');
factory->AddVariable( "ele1_eta",'F');
factory->AddVariable( "ele1_DphiIn",'F');
factory->AddVariable( "ele1_DetaIn",'F');
factory->AddVariable( "ele1_sigmaIetaIeta",'F');
factory->AddVariable( "ele1_eRegrInput_etaW",'F');
factory->AddVariable( "ele1_eRegrInput_phiW",'F');
factory->AddVariable( "ele1_dxy_PV",'F');
factory->AddVariable( "ele1_dz_PV",'F');
factory->AddVariable( "ele1_sigmaP/ele1_tkP",'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' );
// Add the variable carrying the regression target
// factory->AddTarget("ele1_scE/ele1_E_true" );
factory->AddTarget("ele1_tkP/ele1_E_true" );
// It is also possible to declare additional targets for multi-dimensional regression, ie:
// -- factory->AddTarget( "fvalue2" );
// BUT: this is currently ONLY implemented for MLP
// global event weights per tree (see below for setting event-wise weights)
Double_t regWeight = 1.0;
// You can add an arbitrary number of regression trees
factory->AddRegressionTree( treeDATA, regWeight );
// This would set individual event weights (the variables defined in the
// expression need to exist in the original TTree)
// factory->SetWeightExpression( "var1", "Regression" );
// TCut mycut = "ele1_isEB==0 && ele1_sigmaP/ele1_tkP<0.4 && ele1_fbrem>0 && abs(ele1_dxy_PV)<0.05 && abs(ele1_dz_PV)<0.05 &&(ele1_scE/ele1_E_true)<1.4 && (ele1_scE/ele1_E_true)>0.3";
TCut mycut = "ele1_isEB==0 && ele1_sigmaP/ele1_tkP<0.4 && ele1_fbrem>0 && abs(ele1_dxy_PV)<0.05 && abs(ele1_dz_PV)<0.05 && (ele1_tkP/ele1_E_true)<1.6";
// for example: TCut mycut = "abs(var1)<0.5 &&
// tell the factory to use all remaining events in the trees after training for testing:
factory->PrepareTrainingAndTestTree( mycut,
"nTrain_Regression=3000000:nTest_Regression=3000000:SplitMode=Random:NormMode=NumEvents:!V" );
TString Name = Form("weight_%s_%s_P_W",RegionOfTraining.c_str(),UseMethodFlag.c_str());
(TMVA::gConfig().GetIONames()).fWeightFileDir = Name;
}
// Apply additional cuts on the signal and background samples (can be different)
// // 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" );
// ---- 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
// PDE - RS method
if (Use["PDERS"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERS", "!H:!V:Normthree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=40:NEventsMax=60:VarTransform=None" );
// And 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["PDEFoam"])
factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam", "!H:!V:MultiTargetRegression=F:TargetSelection=Mpv:TailCut=0.001:VolFrac=0.3:nActiveCells=500:nSampl=2000:nBin=5:Compress=T:Kernel=None:Nmin=10:VarTransform=None" );
// K-Nearest Neighbour classifier (KNN)
if (Use["KNN"])
factory->BookMethod( TMVA::Types::kKNN, "KNN", "nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" );
// Linear discriminant
if (Use["LD"]) factory->BookMethod( TMVA::Types::kLD, "LD","!H:!V:VarTransform=G,U,D" );
// 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:ParRanges=(-100,100);(-100,100);(-100,100):FitMethod=MC:SampleSize=100000:Sigma=0.1:VarTransform=D" );
if (Use["FDA_GA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options) .. the formula of this example is good for parabolas
factory->BookMethod( TMVA::Types::kFDA, "FDA_GA",
"!H:!V:Formula=(0)+(1)*x0+(2)*x1:ParRanges=(-100,100);(-100,100);(-100,100):FitMethod=GA:PopSize=100:Cycles=3:Steps=30:Trim=True:SaveBestGen=1:VarTransform=Norm" );
if (Use["FDA_MT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MT",
"!H:!V:Formula=(0)+(1)*x0+(2)*x1:ParRanges=(-100,100);(-100,100);(-100,100);(-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:ParRanges=(-100,100);(-100,100);(-100,100):FitMethod=GA:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:Cycles=1:PopSize=5:Steps=5:Trim" );
// Neural network (MLP)
if (Use["MLP"])
// factory->BookMethod( TMVA::Types::kMLP, "MLP", "!H:!V:VarTransform=Norm:NeuronType=tanh:NCycles=20000:HiddenLayers=N+20:TestRate=6:TrainingMethod=BFGS:Sampling=0.3:SamplingEpoch=0.8:ConvergenceImprove=1e-6:ConvergenceTests=15:!UseRegulator" );
// factory->BookMethod( TMVA::Types::kMLP, "MLP", "!H:!V:VarTransform=Norm:NeuronType=tanh:NCycles=200:HiddenLayers=N+20:TestRate=6:TrainingMethod=BFGS:Sampling=0.3:SamplingEpoch=0.8:ConvergenceImprove=1e-6:ConvergenceTests=15:!UseRegulator" );
// factory->BookMethod( TMVA::Types::kMLP, "MLP", "!H:!V:VarTransform=Norm:NeuronType=tanh:NCycles=400:HiddenLayers=N+10:TestRate=6:TrainingMethod=BFGS:Sampling=0.3:SamplingEpoch=0.8:ConvergenceImprove=1e-6:ConvergenceTests=15" );
// factory->BookMethod( TMVA::Types::kMLP, "MLP", "!H:!V:VarTransform=N:NeuronType=tanh:NCycles=200:HiddenLayers=N+10:TestRate=6:TrainingMethod=BFGS:Sampling=0.3:SamplingEpoch=0.8:ConvergenceImprove=1e-6:ConvergenceTests=15" );
// factory->BookMethod( TMVA::Types::kMLP, "MLP", "!H:!V:VarTransform=G,N:NeuronType=tanh:NCycles=200:HiddenLayers=N+5:TestRate=6:TrainingMethod=BFGS:Sampling=0.3:SamplingEpoch=0.8:ConvergenceImprove=1e-6:ConvergenceTests=15" );
factory->BookMethod( TMVA::Types::kMLP, "MLP", "!H:!V:NeuronType=tanh:NCycles=250:HiddenLayers=N+5:TrainingMethod=BFGS:Sampling=0.3:SamplingEpoch=0.8:ConvergenceImprove=1e-6:ConvergenceTests=15:TestRate=10");
// Support Vector Machine
if (Use["SVM"])
factory->BookMethod( TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=N" );
// factory->BookMethod( TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=N,G" );
// Boosted Decision Trees
if (Use["BDT"])
// factory->BookMethod( TMVA::Types::kBDT, "BDT","!H:!V:NTrees=100:nEventsMin=5:BoostType=AdaBoostR2:SeparationType=RegressionVariance:nCuts=20:PruneMethod=CostComplexity:PruneStrength=30" );
// factory->BookMethod( TMVA::Types::kBDT, "BDT","!H:!V:NTrees=200:nEventsMin=5:BoostType=AdaBoostR2:SeparationType=RegressionVariance:PruneMethod=CostComplexity:PruneStrength=30" );
// factory->BookMethod( TMVA::Types::kBDT, "BDT","!H:!V:NTrees=300:nEventsMin=5:BoostType=AdaBoostR2:SeparationType=RegressionVariance:PruneMethod=CostComplexity:PruneStrength=30" );
// factory->BookMethod( TMVA::Types::kBDT, "BDT","!H:!V:NTrees=100:nEventsMin=5:BoostType=AdaBoostR2:SeparationType=RegressionVariance:PruneMethod=CostComplexity:PruneStrength=30" );
factory->BookMethod( TMVA::Types::kBDT, "BDT","!H:!V:NTrees=100:nEventsMin=20:BoostType=AdaBoostR2:SeparationType=RegressionVariance:PruneMethod=CostComplexity:PruneStrength=30");
if (Use["BDTG"])
// factory->BookMethod( TMVA::Types::kBDT, "BDTG","!H:!V:NTrees=2000::BoostType=Grad:Shrinkage=0.1:UseBaggedGrad:GradBaggingFraction=0.5:nCuts=20:MaxDepth=3:NNodesMax=15" );
factory->BookMethod( TMVA::Types::kBDT, "BDTG","!H:!V:NTrees=1000::BoostType=Grad:Shrinkage=0.1:UseBaggedGrad:GradBaggingFraction=0.5:MaxDepth=5:NNodesMax=25:PruneMethod=CostComplexity:PruneStrength=30");
// --------------------------------------------------------------------------------------------------
// ---- 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 << "==> TMVARegression is done!" << std::endl;
delete factory;
// Launch the GUI for the root macros
// if (!gROOT->IsBatch()) TMVARegGui( outputFileName.c_str() );
return 0;
}
void BJetRegression( 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 TMVARegression.C\(\"myMethod1,myMethod2,myMethod3\"\)
//
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
// --- Mutidimensional likelihood and Nearest-Neighbour methods
Use["PDERS"] = 0;
Use["PDEFoam"] = 1;
Use["KNN"] = 1;
//
// --- Linear Discriminant Analysis
Use["LD"] = 1;
//
// --- Function Discriminant analysis
Use["FDA_GA"] = 1;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
//
// --- Neural Network
Use["MLP"] = 1;
//
// --- Support Vector Machine
Use["SVM"] = 0;
//
// --- Boosted Decision Trees
Use["BDT"] = 0;
Use["BDTG"] = 1;
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVARegression" << 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 = 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 new root output file
TString outfileName( "TMVAReg_CSVJ1.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( "TMVARegression", outputFile,
"!V:!Silent:Color:DrawProgressBar" );
// 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" )]
// std::string inputVariables[] = {"CSVJ1PtUncorr", "CSVJ1Pt", "CSVJ1Et", "CSVJ1Mt", "CSVJ1ptLeadTrk",
// "CSVJ1Vtx3dL", "CSVJ1Vtx3deL", "CSVJ1vtxMass", "CSVJ1VtxPt",
// "CSVJ1SoftLeptPtRel", "CSVJ1SoftLeptPt",
// "CSVJ1SoftLeptdR" , "CSVJ1Ntot"};
std::string inputVariables[] = {"jetPtUncorr", "jetPt", "jetEt", "jetMt", "jetptLeadTrk",
"jetVtx3dL", "jetVtx3deL", "jetvtxMass", "jetVtxPt",
"jetSoftLeptPtRel", "jetSoftLeptPt",
"jetSoftLeptdR" , "jetNtot", "jetJECUnc"}; //
for(int ivar = 0; ivar < 14; ivar++){
factory->AddVariable( inputVariables[ivar], inputVariables[ivar], "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' );
// Add the variable carrying the regression target
// factory->AddTarget( "matchGenJet1Pt" );
factory->AddTarget( "matchGenJetPt" );
// It is also possible to declare additional targets for multi-dimensional regression, ie:
// -- factory->AddTarget( "fvalue2" );
// BUT: this is currently ONLY implemented for MLP
// Read training and test data (see TMVAClassification for reading ASCII files)
// load the signal and background event samples from ROOT trees
TFile *input(0);
TString fname = "/scratch/zmao/regression/allSample_both_isobTag.root";
if (!gSystem->AccessPathName( fname ))
input = TFile::Open( fname ); // check if file in local directory exists
else
input = TFile::Open( "http://root.cern.ch/files/tmva_reg_example.root" ); // if not: download from ROOT server
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVARegression : Using input file: " << input->GetName() << std::endl;
// --- Register the regression tree
TTree *regTree = (TTree*)input->Get("eventTree");
// global event weights per tree (see below for setting event-wise weights)
Double_t regWeight = 1.0;
// You can add an arbitrary number of regression trees
factory->AddRegressionTree( regTree, regWeight );
// This would set individual event weights (the variables defined in the
// expression need to exist in the original TTree)
// factory->SetWeightExpression( "triggerEff", "Regression" );
// Apply additional cuts on the signal and background samples (can be different)
TCut mycut = ""; // for example: TCut mycut = "abs(var1)<0.5 && abs(var2-0.5)<1";
// tell the factory to use all remaining events in the trees after training for testing:
factory->PrepareTrainingAndTestTree( mycut,
"nTrain_Regression=0:nTest_Regression=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" );
// ---- 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
// PDE - RS method
if (Use["PDERS"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERS",
"!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=40:NEventsMax=60:VarTransform=None" );
// And 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["PDEFoam"])
factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam",
"!H:!V:MultiTargetRegression=F:TargetSelection=Mpv:TailCut=0.001:VolFrac=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Compress=T:Kernel=None:Nmin=10:VarTransform=None" );
// K-Nearest Neighbour classifier (KNN)
if (Use["KNN"])
factory->BookMethod( TMVA::Types::kKNN, "KNN",
"nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" );
// Linear discriminant
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:ParRanges=(-100,100);(-100,100);(-100,100):FitMethod=MC:SampleSize=100000:Sigma=0.1:VarTransform=D" );
if (Use["FDA_GA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options) .. the formula of this example is good for parabolas
factory->BookMethod( TMVA::Types::kFDA, "FDA_GA",
"!H:!V:Formula=(0)+(1)*x0+(2)*x1:ParRanges=(-100,100);(-100,100);(-100,100):FitMethod=GA:PopSize=100:Cycles=3:Steps=30:Trim=True:SaveBestGen=1:VarTransform=Norm" );
if (Use["FDA_MT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MT",
"!H:!V:Formula=(0)+(1)*x0+(2)*x1:ParRanges=(-100,100);(-100,100);(-100,100);(-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:ParRanges=(-100,100);(-100,100);(-100,100):FitMethod=GA:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:Cycles=1:PopSize=5:Steps=5:Trim" );
// Neural network (MLP)
if (Use["MLP"])
factory->BookMethod( TMVA::Types::kMLP, "MLP", "!H:!V:VarTransform=Norm:NeuronType=tanh:NCycles=20000:HiddenLayers=N+20:TestRate=6:TrainingMethod=BFGS:Sampling=0.3:SamplingEpoch=0.8:ConvergenceImprove=1e-6:ConvergenceTests=15:!UseRegulator" );
// 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["BDT"])
factory->BookMethod( TMVA::Types::kBDT, "BDT",
"!H:!V:NTrees=100:MinNodeSize=1.0%:BoostType=AdaBoostR2:SeparationType=RegressionVariance:nCuts=20:PruneMethod=CostComplexity:PruneStrength=30" );
if (Use["BDTG"])
factory->BookMethod( TMVA::Types::kBDT, "BDTG",
"!H:!V:NTrees=2000::BoostType=Grad:Shrinkage=0.1:UseBaggedBoost:BaggedSampleFraction=0.7:nCuts=200:MaxDepth=3:NNodesMax=15" );
// --------------------------------------------------------------------------------------------------
// ---- 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 << "==> TMVARegression is done!" << std::endl;
delete factory;
// Launch the GUI for the root macros
if (!gROOT->IsBatch()) TMVARegGui( outfileName );
}
////////////////////////////////////////////////////////////////////////////////
/// Main ///
////////////////////////////////////////////////////////////////////////////////
void TrainRegressionFJ(TString myMethodList="")
{
gROOT->SetBatch(1);
gROOT->LoadMacro("HelperFunctions.h" ); // make functions visible to TTreeFormula
if (!TString(gROOT->GetVersion()).Contains("5.34")) {
std::cout << "INCORRECT ROOT VERSION! Please use 5.34:" << std::endl;
std::cout << "source /uscmst1/prod/sw/cms/slc5_amd64_gcc462/lcg/root/5.34.02-cms/bin/thisroot.csh" << std::endl;
std::cout << "Return without doing anything." << std::endl;
return;
}
//TString curDynamicPath( gSystem->GetDynamicPath() );
//gSystem->SetDynamicPath( "../lib:" + curDynamicPath );
//TString curIncludePath(gSystem->GetIncludePath());
//gSystem->SetIncludePath( " -I../include " + curIncludePath );
// Load the library
TMVA::Tools::Instance();
//--------------------------------------------------------------------------
// Default MVA methods to be trained + tested
std::map<std::string, int> Use;
// --- Mutidimensional likelihood and Nearest-Neighbour methods
Use["PDERS"] = 0;
Use["PDEFoam"] = 1;
Use["KNN"] = 1;
//
// --- Linear Discriminant Analysis
Use["LD"] = 1;
//
// --- Function Discriminant analysis
Use["FDA_GA"] = 1;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
//
// --- Neural Network
Use["MLP"] = 1;
//
// --- Support Vector Machine
Use["SVM"] = 0;
//
// --- Boosted Decision Trees
Use["BDT"] = 1;
Use["BDT1"] = 0;
Use["BDTG"] = 0;
Use["BDTG1"] = 0;
//--------------------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVARegression" << 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;
}
}
//--------------------------------------------------------------------------
// Create a ROOT output file where TMVA will store ntuples, histograms, etc.
TString outfileName( "TMVARegFJ.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 weights/
//
// 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( "TMVARegressionFJ", outputFile,
"!V:!Silent:!Color:!DrawProgressBar:Transformations=I:AnalysisType=Regression" );
// 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";
const std::vector<std::string> & inputExpressions = GetInputExpressionsFJReg();
const std::vector<std::string> & inputExpressionLabels = GetInputExpressionLabelsFJReg();
assert(inputExpressions.size() == inputExpressionLabels.size());
// 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", "Variable 1", "units", 'F' );
//factory->AddVariable( "var2", "Variable 2", "units", 'F' );
for (UInt_t iexpr=0; iexpr!=inputExpressions.size(); iexpr++){
Label label = MakeLabel(inputExpressionLabels.at(iexpr));
TString expr = inputExpressions.at(iexpr);
factory->AddVariable(expr, label.xlabel, label.unit, label.type);
}
// 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' );
// Add the variable carrying the regression target
//factory->AddTarget( "fvalue" );
factory->AddTarget( "fathFilterJets_genPt" );
// It is also possible to declare additional targets for multi-dimensional regression, ie:
// -- factory->AddTarget( "fvalue2" );
// BUT: this is currently ONLY implemented for MLP
//--------------------------------------------------------------------------
// Read training and test data
TFile *input(0);
TString dirname = "skim_ZnnH_regression_fj/";
TString prefix = "skim_";
TString suffix = ".root";
TTree *regTrainTree(0), *regTestTree(0);
std::vector<std::string> processes;
processes.push_back("ZnnH110");
processes.push_back("ZnnH115");
processes.push_back("ZnnH120");
processes.push_back("ZnnH125");
processes.push_back("ZnnH130");
processes.push_back("ZnnH135");
processes.push_back("ZnnH140");
processes.push_back("ZnnH145");
processes.push_back("ZnnH150");
#ifdef USE_WH
processes.push_back("WlnH110");
processes.push_back("WlnH115");
processes.push_back("WlnH120");
processes.push_back("WlnH125");
processes.push_back("WlnH130");
processes.push_back("WlnH135");
processes.push_back("WlnH140");
processes.push_back("WlnH145");
processes.push_back("WlnH150");
#endif
std::vector<TFile *> files;
for (UInt_t i=0; i<processes.size(); i++){
std::string process = processes.at(i);
input = (TFile*) TFile::Open(dirname + prefix + process + suffix, "READ");
if (!input) {
std::cout << "ERROR: Could not open input file." << std::endl;
exit(1);
}
std::cout << "--- TMVARegression : Using input file: " << input->GetName() << std::endl;
files.push_back(input);
// --- Register the regression tree
regTrainTree = (TTree*) input->Get("tree_train");
regTestTree = (TTree*) input->Get("tree_test");
// Global event weights per tree (see below for setting event-wise weights)
Double_t regWeight = 1.0;
// You can add an arbitrary number of regression trees
factory->AddRegressionTree(regTrainTree, regWeight, TMVA::Types::kTraining);
factory->AddRegressionTree(regTestTree , regWeight, TMVA::Types::kTesting );
}
// Set individual event weights (the variables must exist in the original TTree)
//factory->SetWeightExpression( "var1", "Regression" );
// Apply additional cuts on the signal and background samples (can be different)
TCut mycut = "fathFilterJets_genPt>10 && fathFilterJets_pt>15 && abs(fathFilterJets_eta)<2.5"; // this is to avoid 3rd filter jet without gen match
//TCut mycut = "hJet_genPt[0] > 0. && hJet_genPt[1] > 0. && hJet_csv[0] > 0. && hJet_csv[1] > 0. && hJet_pt[0] > 20. && hJet_pt[1] > 20. && abs(hJet_eta[0])<2.5 && abs(hJet_eta[1])<2.5";
// Tell the factory to use all remaining events in the trees after training for testing:
factory->PrepareTrainingAndTestTree( mycut, "V:nTrain_Regression=0:nTest_Regression=0:SplitMode=Random:NormMode=NumEvents" );
// 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" );
// --- Book MVA methods
//
// Please lookup the various method configuration options in the corresponding cxx files, eg:
// src/MethodCuts.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
// PDE - RS method
if (Use["PDERS"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERS",
"!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=40:NEventsMax=60:VarTransform=None" );
// And 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["PDEFoam"])
factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam",
"!H:!V:MultiTargetRegression=F:TargetSelection=Mpv:TailCut=0.001:VolFrac=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Compress=T:Kernel=None:Nmin=10:VarTransform=None" );
// K-Nearest Neighbour classifier (KNN)
if (Use["KNN"])
factory->BookMethod( TMVA::Types::kKNN, "KNN",
"nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" );
// Linear discriminant
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:ParRanges=(-100,100);(-100,100);(-100,100):FitMethod=MC:SampleSize=100000:Sigma=0.1:VarTransform=D" );
if (Use["FDA_GA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options) .. the formula of this example is good for parabolas
factory->BookMethod( TMVA::Types::kFDA, "FDA_GA",
"!H:!V:Formula=(0)+(1)*x0+(2)*x1:ParRanges=(-100,100);(-100,100);(-100,100):FitMethod=GA:PopSize=100:Cycles=3:Steps=30:Trim=True:SaveBestGen=1:VarTransform=Norm" );
if (Use["FDA_MT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MT",
"!H:!V:Formula=(0)+(1)*x0+(2)*x1:ParRanges=(-100,100);(-100,100);(-100,100);(-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:ParRanges=(-100,100);(-100,100);(-100,100):FitMethod=GA:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:Cycles=1:PopSize=5:Steps=5:Trim" );
// Neural network (MLP)
if (Use["MLP"])
factory->BookMethod( TMVA::Types::kMLP, "MLP",
"!H:!V:VarTransform=Norm:NeuronType=tanh:NCycles=20000:HiddenLayers=N+20:TestRate=6:TrainingMethod=BFGS:Sampling=0.3:SamplingEpoch=0.8:ConvergenceImprove=1e-6:ConvergenceTests=15:!UseRegulator" );
// 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["BDT"])
factory->BookMethod( TMVA::Types::kBDT, "BDT",
"!H:V:NTrees=100:nEventsMin=30:NodePurityLimit=0.5:BoostType=AdaBoostR2:SeparationType=RegressionVariance:nCuts=20:PruneMethod=CostComplexity:PruneStrength=30" );
//"!H:V:NTrees=60:nEventsMin=20:NodePurityLimit=0.5:BoostType=AdaBoostR2:SeparationType=RegressionVariance:nCuts=20:PruneMethod=CostComplexity:PruneStrength=30:DoBoostMonitor" );
if (Use["BDT1"])
factory->BookMethod( TMVA::Types::kBDT, "BDT1",
"!H:V:NTrees=100:nEventsMin=5:BoostType=AdaBoostR2:SeparationType=RegressionVariance:nCuts=20:PruneMethod=CostComplexity:PruneStrength=30" );
if (Use["BDTG"])
factory->BookMethod( TMVA::Types::kBDT, "BDTG",
"!H:V:NTrees=2000:BoostType=Grad:Shrinkage=0.10:UseBaggedGrad:GradBaggingFraction=0.7:nCuts=200:MaxDepth=3:NNodesMax=15" );
if (Use["BDTG1"])
factory->BookMethod( TMVA::Types::kBDT, "BDTG1",
"!H:V:NTrees=1000:BoostType=Grad:Shrinkage=0.10:UseBaggedGrad:GradBaggingFraction=0.5:nCuts=20:MaxDepth=3:NNodesMax=15" );
//--------------------------------------------------------------------------
// 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 << "==> TMVARegression is done!" << std::endl;
for (UInt_t i=0; i<files.size(); i++)
files.at(i)->Close();
delete outputFile;
delete factory;
// Launch the GUI for the root macros
//gROOT->SetMacroPath( "$ROOTSYS/tmva/macros/" );
//gROOT->Macro( "$ROOTSYS/tmva/macros/TMVAlogon.C" );
//gROOT->LoadMacro( "$ROOTSYS/tmva/macros/TMVAGui.C" );
//if (!gROOT->IsBatch()) TMVARegGui( outfileName );
}
void TMVARegression( int optimIndex, int Cat=0, 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 TMVARegression.C\(\"myMethod1,myMethod2,myMethod3\"\)
//
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
// --- Mutidimensional likelihood and Nearest-Neighbour methods
Use["PDERS"] = 0;
Use["PDEFoam"] = 0;
Use["KNN"] = 0;
//
// --- Linear Discriminant Analysis
Use["LD"] = 0;
//
// --- Function Discriminant analysis
Use["FDA_GA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
//
// --- Neural Network
Use["MLP"] = 0;
//
// --- Support Vector Machine
Use["SVM"] = 0;
//
// --- Boosted Decision Trees
Use["BDT"] = 0;
Use["BDTG"] = 1;
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVARegression" << 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 = 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 new root output file
TString outfileName( Form("TMVAoutput/TMVAReg_%i.root",optimIndex) );
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( Form("TMVARegression_%i_Cat%i",optimIndex,Cat), outputFile,
"!V:!Silent:!Color:!DrawProgressBar:Transformations=I:AnalysisType=Regression" );
// 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( "jet_eta", "jet_eta", "units", 'F' );
factory->AddVariable( "jet_emfrac", "jet_emfrac", "units", 'F' );
factory->AddVariable( "jet_hadfrac", "jet_hadfrac", "units", 'F' );
factory->AddVariable( "jet_nconstituents", "jet_nconst", "units", 'F' );
factory->AddVariable( "jet_vtx3dL", "jet_vtx3dL", "units", 'F' );
factory->AddVariable( "MET", "MET", "units", 'F' );
factory->AddVariable( "jet_dPhiMETJet", "jet_dPhiMETJet", "units", 'F' );
//factory->AddVariable( "hJet_vtxPt", "hJet_vtxPt", "units", 'F' );
//factory->AddVariable( "hJet_JECUnc", "hJet_JECUnc", "units", 'F' );
//factory->AddVariable( "hJet_ptLeadTrack", "hJet_ptLeadTrack", "units", 'F' );
//factory->AddVariable( "hJet_SoftLeptPtCut:=(hJet_SoftLeptIdlooseMu>0. || hJet_SoftLeptId95>0.) ? (hJet_SoftLeptPt) : (-99)", "hJet_SoftLeptPt", "units", 'F' );
//factory->AddVariable( "hJet_En", "hJet_En", "units", 'F' );
//factory->AddVariable( "hJet_Et", "hJet_Et", "units", 'F' );
//factory->AddVariable( "hJet_Mt", "hJet_Mt", "units", 'F' );
//factory->AddVariable( "hJet_nch", "hJet_nch", "units", 'F' );
//factory->AddVariable( "hJet_vtx3deL", "hJet_vtx3deL", "units", 'F' );
//factory->AddVariable( "hJet_vtxMass", "hJet_vtxMass", "units", 'F' );
//factory->AddVariable( "hJet_ptRaw", "hJet_ptRaw", "units", 'F' );
//factory->AddVariable( "hJet_EnRaw", "hJet_EnRaw", "units", 'F' );
//factory->AddVariable( "hJet_SoftLeptptRelCut:=(hJet_SoftLeptIdlooseMu>0. || hJet_SoftLeptId95>0.) ? (hJet_SoftLeptptRel) : (-99)", "hJet_SoftLeptptRel", "units", 'F' );
//factory->AddVariable( "hJet_SoftLeptdRCut:=(hJet_SoftLeptIdlooseMu>0. || hJet_SoftLeptId95>0.) ? (hJet_SoftLeptdR) : (-99)", "hJet_SoftLeptdR", "units", 'F' );
//factory->AddVariable( "rho25", "rho25", "units", 'F' );
//factory->AddVariable( "dPhiMETJet", "dPhiMETJet", "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
// Add the variable carrying the regression target
//factory->AddTarget( "jet_genPt" );
factory->AddTarget( "jet_genJetPt/jet_pt" );
// It is also possible to declare additional targets for multi-dimensional regression, ie:
// -- factory->AddTarget( "fvalue2" );
// BUT: this is currently ONLY implemented for MLP
// Read training and test data (see TMVAClassification for reading ASCII files)
// load the signal and background event samples from ROOT trees
/* TFile *input(0);
TString fname = "./tmva_reg_example.root";
if (!gSystem->AccessPathName( fname ))
input = TFile::Open( fname ); // check if file in local directory exists
else
input = TFile::Open( "http://root.cern.ch/files/tmva_reg_example.root" ); // if not: download from ROOT server
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVARegression : Using input file: " << input->GetName() << std::endl;
// --- Register the regression tree
TTree *regTree = (TTree*)input->Get("TreeR");
*/
TChain chainTraining("Events");
chainTraining.Add("TrainingFiles/training.root");
TTree *regTreeTraining = (TTree*) chainTraining;
TChain chainTesting("Events");
chainTesting.Add("TrainingFiles/testing.root");
TTree *regTreeTesting = (TTree*) chainTesting;
// global event weights per tree (see below for setting event-wise weights)
Double_t regWeight = 1.0;
// You can add an arbitrary number of regression trees
factory->AddTree( regTreeTraining, "Regression", regWeight, "", "training" );
factory->AddTree( regTreeTesting, "Regression", regWeight, "", "test" );
// Apply additional cuts on the signal and background samples (can be different)
TCut mycut = "hJet_pt[0]>20. && hJet_pt[1]>20. && fabs(hJet_eta[0])<2.5 && fabs(hJet_eta[1])<2.5 && hJet_csv[0]>0. && hJet_csv[1]>0. && hJet_ptLeadTrack[0]<1500. && hJet_ptLeadTrack[1]<1500. && hJet_genJetPt[0]>0. && hJet_genJetPt[1]>0. && hJet_puJetIdL[0]>0.0 && hJet_puJetIdL[1]>0.0";
TCut testingCut = "hJet_pt[0]>20. && hJet_pt[1]>20. && abs(hJet_eta[0])<2.5 && abs(hJet_eta[1])<2.5";
TCut mjjCut = "sqrt(pow(hJet_pt[0]*cos(hJet_phi[0])+hJet_pt[1]*cos(hJet_phi[1]),2)+pow(hJet_pt[0]*sin(hJet_phi[0])+hJet_pt[1]*sin(hJet_phi[1]),2)) < 110";
if(Cat==1) mjjCut = "sqrt(pow(hJet_pt[0]*cos(hJet_phi[0])+hJet_pt[1]*cos(hJet_phi[1]),2)+pow(hJet_pt[0]*sin(hJet_phi[0])+hJet_pt[1]*sin(hJet_phi[1]),2)) > 110";
TCut jetPtCut="jet_pt>90";
if(Cat==1) jetPtCut="jet_pt>90";
//TCut trainingCut = "hJet_pt[0]>20. && hJet_pt[1]>20. && abs(hJet_eta[0])<2.5 && abs(hJet_eta[1])<2.5 && hJet_genJetPt[0]>0. && hJet_genJetPt[1]>0. && hJet_csv[0]>0.0 && hJet_csv[1]>0.0 && hJet_ptLeadTrack[0]<1500. && hJet_ptLeadTrack[1]<1500.";
TCut trainingCut = "jet_pt>20. && abs(jet_eta)<2.5 && jet_genJetPt>0. && jet_dRJetGenJet < 0.4 && (jet_partonID)==5";
// for example: TCut mycut = "abs(var1)<0.5 && abs(var2-0.5)<1";
// for example: TCut mycut = "abs(var1)<0.5 && abs(var2-0.5)<1";
// tell the factory to use all remaining events in the trees after training for testing:
// factory->PrepareTrainingAndTestTree(mycut, "nTrain_Regression=600000:nTest_Regression=600000:SplitMode=Random:NormMode=NumEvents:!V");
//factory->PrepareTrainingAndTestTree(mycut, "nTrain_Regression=337500:nTest_Regression=337500:SplitMode=Random:NormMode=NumEvents:!V");
// factory->PrepareTrainingAndTestTree(mycut, "nTrain_Regression=158393:nTest_Regression=158393:SplitMode=Random:NormMode=NumEvents:!V");
//factory->PrepareTrainingAndTestTree(mycut, "nTrain_Regression=14197:nTest_Regression=14197:SplitMode=Random:NormMode=NumEvents:!V");
factory->PrepareTrainingAndTestTree(trainingCut+jetPtCut, "!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" );
// ---- 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
// PDE - RS method
if (Use["PDERS"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERS",
"!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=40:NEventsMax=60:VarTransform=None" );
// And 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["PDEFoam"])
factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam",
"!H:!V:MultiTargetRegression=F:TargetSelection=Mpv:TailCut=0.001:VolFrac=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Compress=T:Kernel=None:Nmin=10:VarTransform=None" );
// K-Nearest Neighbour classifier (KNN)
if (Use["KNN"])
factory->BookMethod( TMVA::Types::kKNN, "KNN",
"nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" );
// Linear discriminant
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:ParRanges=(-100,100);(-100,100);(-100,100):FitMethod=MC:SampleSize=100000:Sigma=0.1:VarTransform=D" );
if (Use["FDA_GA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options) .. the formula of this example is good for parabolas
factory->BookMethod( TMVA::Types::kFDA, "FDA_GA",
"!H:!V:Formula=(0)+(1)*x0+(2)*x1:ParRanges=(-100,100);(-100,100);(-100,100):FitMethod=GA:PopSize=100:Cycles=3:Steps=30:Trim=True:SaveBestGen=1:VarTransform=Norm" );
if (Use["FDA_MT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MT",
"!H:!V:Formula=(0)+(1)*x0+(2)*x1:ParRanges=(-100,100);(-100,100);(-100,100);(-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:ParRanges=(-100,100);(-100,100);(-100,100):FitMethod=GA:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:Cycles=1:PopSize=5:Steps=5:Trim" );
// Neural network (MLP)
if (Use["MLP"])
factory->BookMethod( TMVA::Types::kMLP, "MLP", "!H:!V:VarTransform=Norm:NeuronType=tanh:NCycles=20000:HiddenLayers=N+20:TestRate=6:TrainingMethod=BFGS:Sampling=0.3:SamplingEpoch=0.8:ConvergenceImprove=1e-6:ConvergenceTests=15:!UseRegulator" );
// Support Vector Machine
if (Use["SVM"])
factory->BookMethod( TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm" );
//100, 5
// Boosted Decision Tree //100,5 nCuts=-1
if (Use["BDT"])
factory->BookMethod( TMVA::Types::kBDT, "BDT",
"!H:!V:NTrees=100:nEventsMin=4:BoostType=AdaBoostR2:SeparationType=RegressionVariance:nCuts=-1:PruneMethod=CostComplexity:PruneStrength=30" );
const bool doScan1=0;
int ntreesArray[8] = {100,200,300,400,500,600,700,800};
float shrinkageArray[3] = {0.1,0.2,0.3};
float gradbaggingfracArray[3] = {0.7,0.8,0.9};
int maxdepthArray[3] = {2,3,4};
int nnodesmaxArray[3] = {5,10,15};
if(!doScan1) shrinkageArray[2]=1.0;
int nnodesmaxIndex=-1,maxdepthIndex=-1,gradbaggingfracIndex=-1,shrinkageIndex=-1,ntreesIndex=-1;
if(doScan1){
nnodesmaxIndex = optimIndex/216;
maxdepthIndex = (optimIndex-nnodesmaxIndex*216)/72;
gradbaggingfracIndex = (optimIndex-nnodesmaxIndex*216-maxdepthIndex*72)/24;
shrinkageIndex = (optimIndex-nnodesmaxIndex*216-maxdepthIndex*72-gradbaggingfracIndex*24)/8;
ntreesIndex = (optimIndex-nnodesmaxIndex*216-maxdepthIndex*72-gradbaggingfracIndex*24-shrinkageIndex*8);
}
else{
nnodesmaxIndex = optimIndex/72;
maxdepthIndex = (optimIndex-nnodesmaxIndex*72)/24;
shrinkageIndex = (optimIndex-nnodesmaxIndex*72-maxdepthIndex*24)/8;
ntreesIndex = (optimIndex-nnodesmaxIndex*72-maxdepthIndex*24-shrinkageIndex*8);
}
if (Use["BDTG"]){
if(doScan1)
factory->BookMethod( TMVA::Types::kBDT, "BDTG",
Form("!H:!V:NTrees=%i::BoostType=Grad:Shrinkage=%.1f:UseBaggedGrad:GradBaggingFraction=%.1f:nCuts=200:MaxDepth=%i:NNodesMax=%i",ntreesArray[ntreesIndex],shrinkageArray[shrinkageIndex],gradbaggingfracArray[gradbaggingfracIndex],maxdepthArray[maxdepthIndex],nnodesmaxArray[nnodesmaxIndex]) );
else
factory->BookMethod( TMVA::Types::kBDT, "BDTG",
Form("!H:!V:IgnoreNegWeights:NTrees=%i::BoostType=Grad:Shrinkage=%.1f:nCuts=200:MaxDepth=%i:NNodesMax=%i",ntreesArray[ntreesIndex],shrinkageArray[shrinkageIndex],maxdepthArray[maxdepthIndex],nnodesmaxArray[nnodesmaxIndex]) );
}
if(doScan1)
cout << Form("!H:!V:NTrees=%i::BoostType=Grad:Shrinkage=%.2f:UseBaggedGrad:GradBaggingFraction=%.2f:nCuts=200:MaxDepth=%i:NNodesMax=%i",ntreesArray[ntreesIndex],shrinkageArray[shrinkageIndex],gradbaggingfracArray[gradbaggingfracIndex],maxdepthArray[maxdepthIndex],nnodesmaxArray[nnodesmaxIndex])<<endl;
else
cout << Form("!H:!V:IgnoreNegWeights:NTrees=%i::BoostType=Grad:Shrinkage=%.1f:nCuts=200:MaxDepth=%i:NNodesMax=%i",ntreesArray[ntreesIndex],shrinkageArray[shrinkageIndex],maxdepthArray[maxdepthIndex],nnodesmaxArray[nnodesmaxIndex]) << endl;
// --------------------------------------------------------------------------------------------------
// ---- 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 << "==> TMVARegression is done!" << std::endl;
delete factory;
// Launch the GUI for the root macros
if (!gROOT->IsBatch()) TMVARegGui( outfileName );
}
void TMVARegression( 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 TMVARegression.C\(\"myMethod1,myMethod2,myMethod3\"\)
//
// this loads the library
TMVA::Tools::Instance();
//---------------------------------------------------------------
// default MVA methods to be trained + tested
std::map<std::string,int> Use;
Use["PDERS"] = 0;
Use["PDERSkNN"] = 0; // depreciated until further notice
Use["PDEFoam"] = 0; // preparation for new TMVA version "reader". This method is not available in this version of TMVA
// ---
Use["KNN"] = 0;
// ---
Use["LD"] = 0;
// ---
Use["FDA_GA"] = 0;
Use["FDA_MC"] = 0;
Use["FDA_MT"] = 0;
Use["FDA_GAMT"] = 0;
// ---
Use["MLP"] = 0;
// ---
Use["SVM"] = 0;
// ---
Use["BDT"] = 0;
Use["BDTG"] = 1;
// ---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVARegression" << 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( "TMVAReg_TarggetAPDPN.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( "TMVARegression", outputFile,
"!V:!Silent:Color:DrawProgressBar" );
// 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( "laser.qmax", "Qmax", "ADC", 'D' );
// factory->AddVariable( "laser.tmax", "T_{0}", "ns", 'D' );
// factory->AddVariable( "laser.fwhm", "FWHM","ns", 'D');
// factory->AddVariable( "laser.prepulse","Prepulse indicator","A.U.", 'D' );
// factory->AddVariable( "laser.w10","Width at 10%","ns", 'D' );
factory->AddVariable( "qmax[0]", "Qmax", "ADC", 'D' );
factory->AddVariable( "tmax[0]", "T_{0}", "ns", 'D' );
factory->AddVariable( "l_fwhm[0]", "FWHM","ns", 'D');
factory->AddVariable( "l_prepulse[0]","Prepulse indicator","A.U.", 'D' );
factory->AddVariable( "l_width90[0]","Width at 10%","ns", 'D' );
// 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:=timeStamp", "Spectator 1", "units", 'F' );
// Add the variable carrying the regression target
factory->AddTarget ( "apdpnAB[0]" );
// It is also possible to declare additional targets for multi-dimensional regression, ie:
// -- factory->AddTarget( "fvalue2" );
// BUT: this is currently ONLY implemented for MLP
// read training and test data (see TMVAClassification for reading ASCII files)
// load the signal and background event samples from ROOT trees
// TFile *input(0);
// TString fname = "./stabTreeFed630.root";
// if (!gSystem->AccessPathName( fname )) {
// input = TFile::Open( fname ); // check if file in local directory exists
// }
// if (!input) {
// std::cout << "ERROR: could not open data file" << std::endl;
// exit(1);
// }
// std::cout << "--- TMVARegression : Using input file: " << input->GetName() << std::endl;
TChain * ntu = new TChain("x");
ntu->Add("/data2/EcalLaserMonitoringData/ntuples_2011_158851_178888/ntu_data_0015*.root");
ntu->Add("/data2/EcalLaserMonitoringData/ntuples_2011_158851_178888/ntu_data_0016*.root");
//TTree *regTree = (TTree*)input->Get("ntu");
// global event weights per tree (see below for setting event-wise weights)
Double_t regWeight = 1.0;
// ====== register trees ====================================================
//
// the following method is the prefered one:
// you can add an arbitrary number of regression trees
//factory->AddRegressionTree( regTree, regWeight );
factory->AddRegressionTree( ntu, regWeight );
// Alternative call:
// -- factory->AddRegressionTree( regTree, regWeight );
// This would set individual event weights (the variables defined in the
// expression need to exist in the original TTree)
// factory->SetWeightExpression( "fwhm-26", "Regression" );
// Apply additional cuts on the signal and background samples (can be different)
// TCut mycut = "abs(0.5*(apd0+apd1)-1)<0.015 && timeStamp<1299.8e6"; // for example: TCut mycut = "abs(var1)<0.5 && abs(var2-0.5)<1";
cout << "Defining cuts..." << endl;
TCut mycut = "fed[0] == 605 && harness == 6 && field > 0.1 && run < 161200 && apdpnAB[0] > 0";
// Count selected events and use them all
TCanvas cdummy("cdummy","cdummy"); cdummy.cd();
Int_t selectedEvts = ntu->Draw("l_fwhm[0]",mycut,"N");
cdummy.Delete();
cout << selectedEvts << endl;
char trainPara[132];
sprintf(trainPara,"nTrain_Regression=%d:nTest_Regression=0:SplitMode=Random:NormMode=NumEvents:!V",selectedEvts-10);
// tell the factory to use all remaining events in the trees after training for testing:
factory->PrepareTrainingAndTestTree( mycut, trainPara );
// 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
cout << "Booking MVA methods" << endl;
// PDE - RS method
if (Use["PDERS"])
factory->BookMethod( TMVA::Types::kPDERS, "PDERS",
"!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=40:NEventsMax=60:VarTransform=None" );
// And 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["PDERSkNN"]) // depreciated until further notice
factory->BookMethod( TMVA::Types::kPDERS, "PDERSkNN",
"!H:!V:VolumeRangeMode=kNN:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" );
if (Use["PDEFoam"])
factory->BookMethod( TMVA::Types::kPDEFoam, "PDEFoam",
"!H:!V:MultiTargetRegression=F:TargetSelection=Mpv:TailCut=0.001:VolFrac=0.0333:nActiveCells=500:nSampl=2000:nBin=5:Compress=T:Kernel=None:CutNmin=T:Nmin=10:VarTransform=None" );
// K-Nearest Neighbour classifier (KNN)
if (Use["KNN"])
factory->BookMethod( TMVA::Types::kKNN, "KNN",
"nkNN=20:ScaleFrac=0.8:SigmaFact=1.0:Kernel=Gaus:UseKernel=F:UseWeight=T:!Trim" );
// Linear discriminant
if (Use["LD"])
factory->BookMethod( TMVA::Types::kLD, "LD",
"!H:!V:VarTransform=N" );
// 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:ParRanges=(-100,100);(-100,100);(-100,100):FitMethod=MC:SampleSize=100000:Sigma=0.1:VarTransform=D" );
if (Use["FDA_GA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options) .. the formula of this example is good for parabolas
factory->BookMethod( TMVA::Types::kFDA, "FDA_GA",
"!H:!V:Formula=(0)+(1)*x0+(2)*x1:ParRanges=(-100,100);(-100,100);(-100,100):FitMethod=GA:PopSize=100:Cycles=3:Steps=30:Trim=True:SaveBestGen=1:VarTransform=Norm" );
if (Use["FDA_MT"])
factory->BookMethod( TMVA::Types::kFDA, "FDA_MT",
"!H:!V:Formula=(0)+(1)*x0+(2)*x1:ParRanges=(-100,100);(-100,100);(-100,100);(-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:ParRanges=(-100,100);(-100,100);(-100,100):FitMethod=GA:Converger=MINUIT:ErrorLevel=1:PrintLevel=-1:FitStrategy=0:!UseImprove:!UseMinos:SetBatch:Cycles=1:PopSize=5:Steps=5:Trim" );
// Neural network (MLP)
if (Use["MLP"])
factory->BookMethod( TMVA::Types::kMLP, "MLP", "!H:!V:VarTransform=Norm:NeuronType=tanh:NCycles=5000:HiddenLayers=N+5,N+2:TestRate=6:TrainingMethod=BP:Sampling=0.3:SamplingEpoch=0.8:ConvergenceImprove=1e-6:ConvergenceTests=15" );
// 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["BDT"])
factory->BookMethod( TMVA::Types::kBDT, "BDT",
"!H:!V:NTrees=100:nEventsMin=5:BoostType=AdaBoostR2:SeparationType=RegressionVariance:nCuts=20:PruneMethod=CostComplexity:PruneStrength=30" );
if (Use["BDTG"])
factory->BookMethod( TMVA::Types::kBDT, "BDTG",
"!H:!V:NTrees=200::BoostType=Grad:Shrinkage=1.0:UseBaggedGrad:SeparationType=GiniIndex:nCuts=20:NNodesMax=5" );
// --------------------------------------------------------------------------------------------------
// ---- 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 << "==> TMVARegression is done!" << std::endl;
delete factory;
// Launch the GUI for the root macros
//if (!gROOT->IsBatch()) TMVARegGui( outfileName );
}
TString autoencoder (std::string inputFileName)
{
std::string tmstr (now ());
TString tmstmp (tmstr.c_str ());
std::cout << "==> Start Autoencoder " << std::endl;
std::cout << "-------------------- open input file ---------------- " << std::endl;
TString fname = pathToData + TString (inputFileName.c_str ()) + TString (".root");
TFile *input = TFile::Open( fname );
std::cout << "-------------------- get tree ---------------- " << std::endl;
TTree *tree = (TTree*)input->Get("data");
TString outfileName( "TMVAAutoEnc__" );
outfileName += TString (inputFileName.c_str ()) + TString ("__") + tmstmp + TString (".root");
std::cout << "-------------------- open output file ---------------- " << std::endl;
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
std::cout << "-------------------- prepare factory ---------------- " << std::endl;
TMVA::Factory *factory = new TMVA::Factory( "TMVAAutoencoder", outputFile,
"AnalysisType=Regression:Color:DrawProgressBar" );
std::cout << "-------------------- add variables ---------------- " << std::endl;
for (auto varname : variableNames+additionalVariableNames)
{
factory->AddVariable (varname.c_str (), 'F');
factory->AddTarget (varname.c_str (), 'F');
}
std::cout << "-------------------- add tree ---------------- " << std::endl;
// global event weights per tree (see below for setting event-wise weights)
Double_t regWeight = 1.0;
factory->AddRegressionTree (tree, regWeight);
std::cout << "-------------------- prepare ---------------- " << std::endl;
TCut mycut = ""; // for example: TCut mycut = "abs(var1)<0.5 && abs(var2-0.5)<1";
factory->PrepareTrainingAndTestTree( mycut,
"nTrain_Regression=0:nTest_Regression=0:SplitMode=Random:NormMode=NumEvents:!V" );
/* // This would set individual event weights (the variables defined in the */
/* // expression need to exist in the original TTree) */
/* factory->SetWeightExpression( "var1", "Regression" ); */
if (true)
{
TString layoutString ("Layout=TANH|100,TANH|20,TANH|40,LINEAR");
TString training0 ("LearningRate=1e-5,Momentum=0.5,Repetitions=1,ConvergenceSteps=500,BatchSize=50,TestRepetitions=7,WeightDecay=0.01,Regularization=NONE,DropConfig=0.5+0.5+0.5+0.5,DropRepetitions=2");
TString training1 ("LearningRate=1e-5,Momentum=0.9,Repetitions=1,ConvergenceSteps=500,BatchSize=30,TestRepetitions=7,WeightDecay=0.01,Regularization=L2,DropConfig=0.1+0.1+0.1,DropRepetitions=1");
TString training2 ("LearningRate=1e-4,Momentum=0.3,Repetitions=1,ConvergenceSteps=10,BatchSize=40,TestRepetitions=7,WeightDecay=0.1,Regularization=L2");
TString training3 ("LearningRate=1e-5,Momentum=0.1,Repetitions=1,ConvergenceSteps=10,BatchSize=10,TestRepetitions=7,WeightDecay=0.001,Regularization=NONE");
TString trainingStrategyString ("TrainingStrategy=");
trainingStrategyString += training0 + "|" + training1 + "|" + training2 ; //+ "|" + training3;
// TString trainingStrategyString ("TrainingStrategy=LearningRate=1e-1,Momentum=0.3,Repetitions=3,ConvergenceSteps=20,BatchSize=30,TestRepetitions=7,WeightDecay=0.0,L1=false,DropFraction=0.0,DropRepetitions=5");
TString nnOptions ("!H:V:ErrorStrategy=SUMOFSQUARES:VarTransform=N:WeightInitialization=XAVIERUNIFORM");
// TString nnOptions ("!H:V:VarTransform=Normalize:ErrorStrategy=CHECKGRADIENTS");
nnOptions.Append (":"); nnOptions.Append (layoutString);
nnOptions.Append (":"); nnOptions.Append (trainingStrategyString);
factory->BookMethod( TMVA::Types::kNN, TString("NN_")+tmstmp, nnOptions ); // NN
}
// --------------------------------------------------------------------------------------------------
factory->TrainAllMethods();
factory->TestAllMethods();
factory->EvaluateAllMethods();
outputFile->Close();
// TMVA::TMVARegGui (outfileName);
delete factory;
return TString("NN_")+tmstmp;
}