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TMVAClassification.C File Reference
Tutorials » Machine Learning tutorials

Detailed Description

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This macro provides examples for the training and testing of the TMVA classifiers.

As input data is used a toy-MC sample consisting of four Gaussian-distributed and linearly correlated input variables. The methods to be used can be switched on and off by means of booleans, or via the prompt command, for example: 

root -l ./TMVAClassification.C\‍(\"Fisher,Likelihood\"\‍)

(note that the backslashes are mandatory) If no method given, a default set of classifiers is used. The output file "TMVAC.root" can be analysed with the use of dedicated macros (simply say: root -l <macro.C>), which can be conveniently invoked through a GUI that will appear at the end of the run of this macro. You can also launch the GUI in another ROOT session via the command: 

root -l -e 'TMVA::TMVAGui("TMVAC.root")'

You can also compile and run the example with the following commands 

make
./TMVAClassification <Methods>

where: <Methods> = "method1 method2" are the TMVA classifier names example: 

./TMVAClassification Fisher LikelihoodPCA BDT

If no method given, a default set is of classifiers is used

  • Project : TMVA - a ROOT-integrated toolkit for multivariate data analysis
  • Package : TMVA
  • Root Macro: TMVAClassification
#include <cstdlib>
#include <iostream>
#include <map>
#include <string>
#include "TChain.h"
#include "TFile.h"
#include "TTree.h"
#include "TString.h"
#include "TObjString.h"
#include "TSystem.h"
#include "TROOT.h"
#include "TMVA/Factory.h"
#include "TMVA/DataLoader.h"
#include "TMVA/Tools.h"
#include "TMVA/TMVAGui.h"
int 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\"\‍)
//---------------------------------------------------------------
// This loads the library
TMVA::Tools::Instance();
// 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
#ifdef R__HAS_TMVAGPU
Use["DNN_GPU"] = 1; // CUDA-accelerated DNN training.
#else
Use["DNN_GPU"] = 0;
#endif
#ifdef R__HAS_TMVACPU
Use["DNN_CPU"] = 1; // Multi-core accelerated DNN.
#else
Use["DNN_CPU"] = 0;
#endif
//
// 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;
// ---------------------------------------------------------------
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 1;
}
Use[regMethod] = 1;
}
}
// --------------------------------------------------------------------------------------------------
// Here the preparation phase begins
// Read training and test data
// (it is also possible to use ASCII format as input -> see TMVA Users Guide)
TString inputFile = gROOT->GetTutorialDir() + "/machine_learning/data/tmva_class_example.root";
std::unique_ptr<TFile> input{TFile::Open(inputFile)};
if (!input || input->IsZombie()) {
throw std::runtime_error("ERROR: could not open data file");
}
std::cout << "--- TMVAClassification : Using input file: " << input->GetName() << std::endl;
// Register the training and test trees
TTree *signalTree = (TTree*)input->Get("TreeS");
TTree *background = (TTree*)input->Get("TreeB");
// Create a ROOT output file where TMVA will store ntuples, histograms, etc.
TString outfileName("TMVAC.root");
std::unique_ptr<TFile> outputFile{TFile::Open(outfileName, "RECREATE")};
if (!outputFile || outputFile->IsZombie()) {
throw std::runtime_error("ERROR: could not open output file");
}
// 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
auto factory = std::make_unique<TMVA::Factory>(
"TMVAClassification", outputFile.get(),
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification");
auto dataloader_raii = std::make_unique<TMVA::DataLoader>("dataset");
auto *dataloader = dataloader_raii.get();
// 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" )]
dataloader->AddVariable( "myvar1 := var1+var2", 'F' );
dataloader->AddVariable( "myvar2 := var1-var2", "Expression 2", "", 'F' );
dataloader->AddVariable( "var3", "Variable 3", "units", 'F' );
dataloader->AddVariable( "var4", "Variable 4", "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
dataloader->AddSpectator( "spec1 := var1*2", "Spectator 1", "units", 'F' );
dataloader->AddSpectator( "spec2 := var1*3", "Spectator 2", "units", 'F' );
// 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
dataloader->AddSignalTree ( signalTree, signalWeight );
dataloader->AddBackgroundTree( background, backgroundWeight );
// To give different trees for training and testing, do as follows:
//
// dataloader->AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" );
// dataloader->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
// ```cpp
// // --- 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++) signalTree->SetBranchAddress( Form( "var%i", ivar+1 ), &(treevars[ivar]) );
// for (UInt_t i=0; i<signalTree->GetEntries(); i++) {
// signalTree->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 < signalTree->GetEntries()/2.0) dataloader->AddSignalTrainingEvent( vars, signalWeight );
// else dataloader->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) dataloader->AddBackgroundTrainingEvent( vars, backgroundWeight*weight );
// else dataloader->AddBackgroundTestEvent ( vars, backgroundWeight*weight );
// }
// // --- end ------------------------------------------------------------
// ```
// End of tree registration
// Set individual event weights (the variables must exist in the original TTree)
// - for signal : `dataloader->SetSignalWeightExpression ("weight1*weight2");`
// - for background: `dataloader->SetBackgroundWeightExpression("weight1*weight2");`
dataloader->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 dataloader 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:
//
// dataloader->PrepareTrainingAndTestTree( mycut, "SplitMode=random:!V" );
//
// To also specify the number of testing events, use:
//
// dataloader->PrepareTrainingAndTestTree( mycut,
// "NSigTrain=3000:NBkgTrain=3000:NSigTest=3000:NBkgTest=3000:SplitMode=Random:!V" );
dataloader->PrepareTrainingAndTestTree( mycuts, mycutb,
"nTrain_Signal=1000:nTrain_Background=1000: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/old_site/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( dataloader, TMVA::Types::kCuts, "Cuts",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart" );
if (Use["CutsD"])
factory->BookMethod( dataloader, TMVA::Types::kCuts, "CutsD",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=Decorrelate" );
if (Use["CutsPCA"])
factory->BookMethod( dataloader, TMVA::Types::kCuts, "CutsPCA",
"!H:!V:FitMethod=MC:EffSel:SampleSize=200000:VarProp=FSmart:VarTransform=PCA" );
if (Use["CutsGA"])
factory->BookMethod( dataloader, 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( dataloader, 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( dataloader, 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( dataloader, 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( dataloader, 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( dataloader, 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( dataloader, 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( dataloader, TMVA::Types::kPDERS, "PDERS",
"!H:!V:NormTree=T:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600" );
if (Use["PDERSD"])
factory->BookMethod( dataloader, TMVA::Types::kPDERS, "PDERSD",
"!H:!V:VolumeRangeMode=Adaptive:KernelEstimator=Gauss:GaussSigma=0.3:NEventsMin=400:NEventsMax=600:VarTransform=Decorrelate" );
if (Use["PDERSPCA"])
factory->BookMethod( dataloader, 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( dataloader, 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( dataloader, 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( dataloader, 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( dataloader, TMVA::Types::kHMatrix, "HMatrix", "!H:!V:VarTransform=None" );
// Linear discriminant (same as Fisher discriminant)
if (Use["LD"])
factory->BookMethod( dataloader, 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( dataloader, 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( dataloader, TMVA::Types::kFisher, "FisherG", "H:!V:VarTransform=Gauss" );
// Composite classifier: ensemble (tree) of boosted Fisher classifiers
if (Use["BoostedFisher"])
factory->BookMethod( dataloader, 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( dataloader, 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( dataloader, 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=100:Cycles=2:Steps=5:Trim=True:SaveBestGen=1" );
if (Use["FDA_SA"]) // can also use Simulated Annealing (SA) algorithm (see Cuts_SA options])
factory->BookMethod( dataloader, 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( dataloader, 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( dataloader, 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( dataloader, 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( dataloader, TMVA::Types::kMLP, "MLP", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=600:HiddenLayers=N+5:TestRate=5:!UseRegulator" );
if (Use["MLPBFGS"])
factory->BookMethod( dataloader, 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( dataloader, TMVA::Types::kMLP, "MLPBNN", "H:!V:NeuronType=tanh:VarTransform=N:NCycles=60:HiddenLayers=N+5:TestRate=5:TrainingMethod=BFGS:UseRegulator" ); // BFGS training with bayesian regulators
// Multi-architecture DNN implementation.
if (Use["DNN_CPU"] or Use["DNN_GPU"]) {
// General layout.
TString layoutString ("Layout=TANH|128,TANH|128,TANH|128,LINEAR");
// Define Training strategy. One could define multiple strategy string separated by the "|" delimiter
TString trainingStrategyString = ("TrainingStrategy=LearningRate=1e-2,Momentum=0.9,"
"ConvergenceSteps=20,BatchSize=100,TestRepetitions=1,"
"WeightDecay=1e-4,Regularization=None,"
"DropConfig=0.0+0.5+0.5+0.5");
// General Options.
TString dnnOptions ("!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=N:"
"WeightInitialization=XAVIERUNIFORM");
dnnOptions.Append (":"); dnnOptions.Append (layoutString);
dnnOptions.Append (":"); dnnOptions.Append (trainingStrategyString);
// Cuda implementation.
if (Use["DNN_GPU"]) {
TString gpuOptions = dnnOptions + ":Architecture=GPU";
factory->BookMethod(dataloader, TMVA::Types::kDL, "DNN_GPU", gpuOptions);
}
// Multi-core CPU implementation.
if (Use["DNN_CPU"]) {
TString cpuOptions = dnnOptions + ":Architecture=CPU";
factory->BookMethod(dataloader, TMVA::Types::kDL, "DNN_CPU", cpuOptions);
}
}
// CF(Clermont-Ferrand)ANN
if (Use["CFMlpANN"])
factory->BookMethod( dataloader, TMVA::Types::kCFMlpANN, "CFMlpANN", "!H:!V:NCycles=200:HiddenLayers=N+1,N" ); // n_cycles:#nodes:#nodes:...
// Tmlp(Root)ANN
if (Use["TMlpANN"])
factory->BookMethod( dataloader, 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( dataloader, TMVA::Types::kSVM, "SVM", "Gamma=0.25:Tol=0.001:VarTransform=Norm" );
// Boosted Decision Trees
if (Use["BDTG"]) // Gradient Boost
factory->BookMethod( dataloader, 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( dataloader, 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( dataloader, TMVA::Types::kBDT, "BDTB",
"!H:!V:NTrees=400:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20" );
if (Use["BDTD"]) // Decorrelation + Adaptive Boost
factory->BookMethod( dataloader, 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( dataloader, TMVA::Types::kBDT, "BDTF",
"!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( dataloader, 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("SigEffAtBkg0.01","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->Write();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVAClassification is done!" << std::endl;
// Launch the GUI for the root macros
if (!gROOT->IsBatch()) TMVA::TMVAGui( outfileName );
return 0;
}
int main( int argc, char** argv )
{
// Select methods (don't look at this code - not of interest)
TString methodList;
for (int i=1; i<argc; i++) {
TString regMethod(argv[i]);
if(regMethod=="-b" || regMethod=="--batch") continue;
if (!methodList.IsNull()) methodList += TString(",");
methodList += regMethod;
}
return TMVAClassification(methodList);
}
main
int main()
Definition Prototype.cxx:12
UInt_t
unsigned int UInt_t
Unsigned integer 4 bytes (unsigned int)
Definition RtypesCore.h:60
Double_t
double Double_t
Double 8 bytes.
Definition RtypesCore.h:73
TRangeDynCast
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Definition TCollection.h:358
input
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void input
Definition TGWin32VirtualXProxy.cxx:142
gROOT
#define gROOT
Definition TROOT.h:411
TCut
A specialized string object used for TTree selections.
Definition TCut.h:25
TFile::Open
static TFile * Open(const char *name, Option_t *option="", const char *ftitle="", Int_t compress=ROOT::RCompressionSetting::EDefaults::kUseCompiledDefault, Int_t netopt=0)
Create / open a file.
Definition TFile.cxx:3764
TMVA::Tools::Instance
static Tools & Instance()
Definition Tools.cxx:71
TMVA::Tools::SplitString
std::vector< TString > SplitString(const TString &theOpt, const char separator) const
splits the option string at 'separator' and fills the list 'splitV' with the primitive strings
Definition Tools.cxx:1199
TMVA::Types::kFisher
@ kFisher
Definition Types.h:82
TMVA::Types::kTMlpANN
@ kTMlpANN
Definition Types.h:85
TMVA::Types::kPDEFoam
@ kPDEFoam
Definition Types.h:94
TMVA::Types::kLikelihood
@ kLikelihood
Definition Types.h:79
TMVA::Types::kHMatrix
@ kHMatrix
Definition Types.h:81
TMVA::Types::kRuleFit
@ kRuleFit
Definition Types.h:88
TMVA::Types::kCFMlpANN
@ kCFMlpANN
Definition Types.h:84
TString
Basic string class.
Definition TString.h:138
TTree
A TTree represents a columnar dataset.
Definition TTree.h:89
TMVA::gTools
Tools & gTools()
TMVA::TMVAGui
void TMVAGui(const char *fName="TMVA.root", TString dataset="")
==> Start TMVAClassification
--- TMVAClassification : Using input file: /github/home/ROOT-CI/build/tutorials/machine_learning/data/tmva_class_example.root
DataSetInfo : [dataset] : Added class "Signal"
: Add Tree TreeS of type Signal with 6000 events
DataSetInfo : [dataset] : Added class "Background"
: Add Tree TreeB of type Background with 6000 events
Factory : Booking method: ␛[1mCuts␛[0m
:
: Use optimization method: "Monte Carlo"
: Use efficiency computation method: "Event Selection"
: Use "FSmart" cuts for variable: 'myvar1'
: Use "FSmart" cuts for variable: 'myvar2'
: Use "FSmart" cuts for variable: 'var3'
: Use "FSmart" cuts for variable: 'var4'
Factory : Booking method: ␛[1mCutsD␛[0m
:
CutsD : [dataset] : Create Transformation "Decorrelate" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'myvar1' <---> Output : variable 'myvar1'
: Input : variable 'myvar2' <---> Output : variable 'myvar2'
: Input : variable 'var3' <---> Output : variable 'var3'
: Input : variable 'var4' <---> Output : variable 'var4'
: Use optimization method: "Monte Carlo"
: Use efficiency computation method: "Event Selection"
: Use "FSmart" cuts for variable: 'myvar1'
: Use "FSmart" cuts for variable: 'myvar2'
: Use "FSmart" cuts for variable: 'var3'
: Use "FSmart" cuts for variable: 'var4'
Factory : Booking method: ␛[1mLikelihood␛[0m
:
Factory : Booking method: ␛[1mLikelihoodPCA␛[0m
:
LikelihoodPCA : [dataset] : Create Transformation "PCA" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'myvar1' <---> Output : variable 'myvar1'
: Input : variable 'myvar2' <---> Output : variable 'myvar2'
: Input : variable 'var3' <---> Output : variable 'var3'
: Input : variable 'var4' <---> Output : variable 'var4'
Factory : Booking method: ␛[1mPDERS␛[0m
:
Factory : Booking method: ␛[1mPDEFoam␛[0m
:
Factory : Booking method: ␛[1mKNN␛[0m
:
Factory : Booking method: ␛[1mLD␛[0m
:
: Rebuilding Dataset dataset
: Building event vectors for type 2 Signal
: Dataset[dataset] : create input formulas for tree TreeS
: Building event vectors for type 2 Background
: Dataset[dataset] : create input formulas for tree TreeB
DataSetFactory : [dataset] : Number of events in input trees
:
:
: Number of training and testing events
: ---------------------------------------------------------------------------
: Signal -- training events : 1000
: Signal -- testing events : 5000
: Signal -- training and testing events: 6000
: Background -- training events : 1000
: Background -- testing events : 5000
: Background -- training and testing events: 6000
:
DataSetInfo : Correlation matrix (Signal):
: ----------------------------------------
: myvar1 myvar2 var3 var4
: myvar1: +1.000 -0.007 +0.754 +0.922
: myvar2: -0.007 +1.000 -0.065 +0.083
: var3: +0.754 -0.065 +1.000 +0.836
: var4: +0.922 +0.083 +0.836 +1.000
: ----------------------------------------
DataSetInfo : Correlation matrix (Background):
: ----------------------------------------
: myvar1 myvar2 var3 var4
: myvar1: +1.000 -0.073 +0.784 +0.925
: myvar2: -0.073 +1.000 -0.142 +0.019
: var3: +0.784 -0.142 +1.000 +0.844
: var4: +0.925 +0.019 +0.844 +1.000
: ----------------------------------------
DataSetFactory : [dataset] :
:
Factory : Booking method: ␛[1mFDA_GA␛[0m
:
: Create parameter interval for parameter 0 : [-1,1]
: Create parameter interval for parameter 1 : [-10,10]
: Create parameter interval for parameter 2 : [-10,10]
: Create parameter interval for parameter 3 : [-10,10]
: Create parameter interval for parameter 4 : [-10,10]
: User-defined formula string : "(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3"
: TFormula-compatible formula string: "[0]+[1]*[5]+[2]*[6]+[3]*[7]+[4]*[8]"
Factory : Booking method: ␛[1mMLPBNN␛[0m
:
MLPBNN : [dataset] : Create Transformation "N" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'myvar1' <---> Output : variable 'myvar1'
: Input : variable 'myvar2' <---> Output : variable 'myvar2'
: Input : variable 'var3' <---> Output : variable 'var3'
: Input : variable 'var4' <---> Output : variable 'var4'
MLPBNN : Building Network.
: Initializing weights
Factory : Booking method: ␛[1mDNN_CPU␛[0m
:
: Parsing option string:
: ... "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=N:WeightInitialization=XAVIERUNIFORM:Layout=TANH|128,TANH|128,TANH|128,LINEAR:TrainingStrategy=LearningRate=1e-2,Momentum=0.9,ConvergenceSteps=20,BatchSize=100,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,DropConfig=0.0+0.5+0.5+0.5:Architecture=CPU"
: The following options are set:
: - By User:
: <none>
: - Default:
: Boost_num: "0" [Number of times the classifier will be boosted]
: Parsing option string:
: ... "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=N:WeightInitialization=XAVIERUNIFORM:Layout=TANH|128,TANH|128,TANH|128,LINEAR:TrainingStrategy=LearningRate=1e-2,Momentum=0.9,ConvergenceSteps=20,BatchSize=100,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,DropConfig=0.0+0.5+0.5+0.5:Architecture=CPU"
: The following options are set:
: - By User:
: V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
: VarTransform: "N" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"]
: H: "False" [Print method-specific help message]
: Layout: "TANH|128,TANH|128,TANH|128,LINEAR" [Layout of the network.]
: ErrorStrategy: "CROSSENTROPY" [Loss function: Mean squared error (regression) or cross entropy (binary classification).]
: WeightInitialization: "XAVIERUNIFORM" [Weight initialization strategy]
: Architecture: "CPU" [Which architecture to perform the training on.]
: TrainingStrategy: "LearningRate=1e-2,Momentum=0.9,ConvergenceSteps=20,BatchSize=100,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,DropConfig=0.0+0.5+0.5+0.5" [Defines the training strategies.]
: - Default:
: VerbosityLevel: "Default" [Verbosity level]
: CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
: IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
: InputLayout: "0|0|0" [The Layout of the input]
: BatchLayout: "0|0|0" [The Layout of the batch]
: RandomSeed: "0" [Random seed used for weight initialization and batch shuffling]
: ValidationSize: "20%" [Part of the training data to use for validation. Specify as 0.2 or 20% to use a fifth of the data set as validation set. Specify as 100 to use exactly 100 events. (Default: 20%)]
DNN_CPU : [dataset] : Create Transformation "N" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'myvar1' <---> Output : variable 'myvar1'
: Input : variable 'myvar2' <---> Output : variable 'myvar2'
: Input : variable 'var3' <---> Output : variable 'var3'
: Input : variable 'var4' <---> Output : variable 'var4'
: Will now use the CPU architecture with BLAS and IMT support !
Factory : Booking method: ␛[1mSVM␛[0m
:
SVM : [dataset] : Create Transformation "Norm" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'myvar1' <---> Output : variable 'myvar1'
: Input : variable 'myvar2' <---> Output : variable 'myvar2'
: Input : variable 'var3' <---> Output : variable 'var3'
: Input : variable 'var4' <---> Output : variable 'var4'
Factory : Booking method: ␛[1mBDT␛[0m
:
Factory : Booking method: ␛[1mRuleFit␛[0m
:
Factory : ␛[1mTrain all methods␛[0m
Factory : [dataset] : Create Transformation "I" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'myvar1' <---> Output : variable 'myvar1'
: Input : variable 'myvar2' <---> Output : variable 'myvar2'
: Input : variable 'var3' <---> Output : variable 'var3'
: Input : variable 'var4' <---> Output : variable 'var4'
Factory : [dataset] : Create Transformation "D" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'myvar1' <---> Output : variable 'myvar1'
: Input : variable 'myvar2' <---> Output : variable 'myvar2'
: Input : variable 'var3' <---> Output : variable 'var3'
: Input : variable 'var4' <---> Output : variable 'var4'
Factory : [dataset] : Create Transformation "P" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'myvar1' <---> Output : variable 'myvar1'
: Input : variable 'myvar2' <---> Output : variable 'myvar2'
: Input : variable 'var3' <---> Output : variable 'var3'
: Input : variable 'var4' <---> Output : variable 'var4'
Factory : [dataset] : Create Transformation "G" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'myvar1' <---> Output : variable 'myvar1'
: Input : variable 'myvar2' <---> Output : variable 'myvar2'
: Input : variable 'var3' <---> Output : variable 'var3'
: Input : variable 'var4' <---> Output : variable 'var4'
Factory : [dataset] : Create Transformation "D" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'myvar1' <---> Output : variable 'myvar1'
: Input : variable 'myvar2' <---> Output : variable 'myvar2'
: Input : variable 'var3' <---> Output : variable 'var3'
: Input : variable 'var4' <---> Output : variable 'var4'
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: -0.082743 1.7823 [ -9.2312 7.0719 ]
: myvar2: -0.056644 1.0667 [ -3.7067 4.0291 ]
: var3: -0.045349 1.0930 [ -5.1570 4.1507 ]
: var4: 0.10542 1.2849 [ -6.3160 4.5211 ]
: -----------------------------------------------------------
: Preparing the Decorrelation transformation...
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: -0.16999 1.0000 [ -4.4163 3.9983 ]
: myvar2: -0.080682 1.0000 [ -3.4441 3.7507 ]
: var3: -0.14363 1.0000 [ -3.7799 3.6146 ]
: var4: 0.32786 1.0000 [ -3.3861 3.3152 ]
: -----------------------------------------------------------
: Preparing the Principle Component (PCA) transformation...
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: -0.018073 2.3492 [ -12.291 8.9889 ]
: myvar2: 0.035051 1.0778 [ -4.0607 3.7534 ]
: var3: 0.0032257 0.59616 [ -2.0543 1.9480 ]
: var4: -0.0086473 0.35251 [ -1.1198 1.0790 ]
: -----------------------------------------------------------
: Preparing the Gaussian transformation...
: Preparing the Decorrelation transformation...
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.0014078 1.0000 [ -3.3797 8.1193 ]
: myvar2: 0.037752 1.0000 [ -3.1738 5.6933 ]
: var3: 0.031566 1.0000 [ -3.2994 7.5070 ]
: var4: -0.034671 1.0000 [ -3.2568 8.8288 ]
: -----------------------------------------------------------
: Ranking input variables (method unspecific)...
IdTransformation : Ranking result (top variable is best ranked)
: -------------------------------------
: Rank : Variable : Separation
: -------------------------------------
: 1 : Variable 4 : 2.867e-01
: 2 : Variable 3 : 1.746e-01
: 3 : myvar1 : 1.144e-01
: 4 : Expression 2 : 3.020e-02
: -------------------------------------
Factory : Train method: Cuts for Classification
:
FitterBase : <MCFitter> Sampling, please be patient ...
: Elapsed time: 1.34 sec
: ------------------------------------------
Cuts : Cut values for requested signal efficiency: 0.1
: Corresponding background efficiency : 0.00720161
: Transformation applied to input variables : None
: ------------------------------------------
: Cut[ 0]: -4.57627 < myvar1 <= 1e+30
: Cut[ 1]: -1e+30 < myvar2 <= 1.15847
: Cut[ 2]: -3.33777 < var3 <= 1e+30
: Cut[ 3]: 2.07512 < var4 <= 1e+30
: ------------------------------------------
: ------------------------------------------
Cuts : Cut values for requested signal efficiency: 0.2
: Corresponding background efficiency : 0.0223329
: Transformation applied to input variables : None
: ------------------------------------------
: Cut[ 0]: -4.62984 < myvar1 <= 1e+30
: Cut[ 1]: -1e+30 < myvar2 <= 1.09417
: Cut[ 2]: -3.55402 < var3 <= 1e+30
: Cut[ 3]: 1.56727 < var4 <= 1e+30
: ------------------------------------------
: ------------------------------------------
Cuts : Cut values for requested signal efficiency: 0.3
: Corresponding background efficiency : 0.0430248
: Transformation applied to input variables : None
: ------------------------------------------
: Cut[ 0]: -8.53038 < myvar1 <= 1e+30
: Cut[ 1]: -1e+30 < myvar2 <= 2.76914
: Cut[ 2]: -2.59305 < var3 <= 1e+30
: Cut[ 3]: 1.38904 < var4 <= 1e+30
: ------------------------------------------
: ------------------------------------------
Cuts : Cut values for requested signal efficiency: 0.4
: Corresponding background efficiency : 0.0734191
: Transformation applied to input variables : None
: ------------------------------------------
: Cut[ 0]: -1.15162 < myvar1 <= 1e+30
: Cut[ 1]: -1e+30 < myvar2 <= 3.31021
: Cut[ 2]: -2.40236 < var3 <= 1e+30
: Cut[ 3]: 1.06042 < var4 <= 1e+30
: ------------------------------------------
: ------------------------------------------
Cuts : Cut values for requested signal efficiency: 0.5
: Corresponding background efficiency : 0.116038
: Transformation applied to input variables : None
: ------------------------------------------
: Cut[ 0]: -4.30702 < myvar1 <= 1e+30
: Cut[ 1]: -1e+30 < myvar2 <= 4.05808
: Cut[ 2]: -1.86509 < var3 <= 1e+30
: Cut[ 3]: 0.791828 < var4 <= 1e+30
: ------------------------------------------
: ------------------------------------------
Cuts : Cut values for requested signal efficiency: 0.6
: Corresponding background efficiency : 0.178863
: Transformation applied to input variables : None
: ------------------------------------------
: Cut[ 0]: -9.15649 < myvar1 <= 1e+30
: Cut[ 1]: -1e+30 < myvar2 <= 2.72375
: Cut[ 2]: -4.02004 < var3 <= 1e+30
: Cut[ 3]: 0.491303 < var4 <= 1e+30
: ------------------------------------------
: ------------------------------------------
Cuts : Cut values for requested signal efficiency: 0.7
: Corresponding background efficiency : 0.232452
: Transformation applied to input variables : None
: ------------------------------------------
: Cut[ 0]: -7.50811 < myvar1 <= 1e+30
: Cut[ 1]: -1e+30 < myvar2 <= 2.79724
: Cut[ 2]: -4.57539 < var3 <= 1e+30
: Cut[ 3]: 0.204151 < var4 <= 1e+30
: ------------------------------------------
: ------------------------------------------
Cuts : Cut values for requested signal efficiency: 0.8
: Corresponding background efficiency : 0.314679
: Transformation applied to input variables : None
: ------------------------------------------
: Cut[ 0]: -4.69509 < myvar1 <= 1e+30
: Cut[ 1]: -1e+30 < myvar2 <= 4.04459
: Cut[ 2]: -2.16556 < var3 <= 1e+30
: Cut[ 3]: -0.0798275 < var4 <= 1e+30
: ------------------------------------------
: ------------------------------------------
Cuts : Cut values for requested signal efficiency: 0.9
: Corresponding background efficiency : 0.507037
: Transformation applied to input variables : None
: ------------------------------------------
: Cut[ 0]: -4.1181 < myvar1 <= 1e+30
: Cut[ 1]: -1e+30 < myvar2 <= 3.15831
: Cut[ 2]: -2.09059 < var3 <= 1e+30
: Cut[ 3]: -0.61029 < var4 <= 1e+30
: ------------------------------------------
: Elapsed time for training with 2000 events: 1.34 sec
Cuts : [dataset] : Evaluation of Cuts on training sample (2000 events)
: Elapsed time for evaluation of 2000 events: 0.000229 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_Cuts.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_Cuts.class.C␛[0m
: TMVAC.root:/dataset/Method_Cuts/Cuts
Factory : Training finished
:
Factory : Train method: CutsD for Classification
:
: Preparing the Decorrelation transformation...
TFHandler_CutsD : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: -0.16999 1.0000 [ -4.4163 3.9983 ]
: myvar2: -0.080682 1.0000 [ -3.4441 3.7507 ]
: var3: -0.14363 1.0000 [ -3.7799 3.6146 ]
: var4: 0.32786 1.0000 [ -3.3861 3.3152 ]
: -----------------------------------------------------------
TFHandler_CutsD : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: -0.16999 1.0000 [ -4.4163 3.9983 ]
: myvar2: -0.080682 1.0000 [ -3.4441 3.7507 ]
: var3: -0.14363 1.0000 [ -3.7799 3.6146 ]
: var4: 0.32786 1.0000 [ -3.3861 3.3152 ]
: -----------------------------------------------------------
FitterBase : <MCFitter> Sampling, please be patient ...
: Elapsed time: 1.16 sec
: ------------------------------------------------------------------------------------------------------------------------
CutsD : Cut values for requested signal efficiency: 0.1
: Corresponding background efficiency : 0
: Transformation applied to input variables : "Deco"
: ------------------------------------------------------------------------------------------------------------------------
: Cut[ 0]: -1e+30 < + 1.1113*[myvar1] + 0.054934*[myvar2] - 0.20558*[var3] - 0.79915*[var4] <= 1.67705
: Cut[ 1]: -1e+30 < + 0.054934*[myvar1] + 0.95764*[myvar2] + 0.13632*[var3] - 0.14903*[var4] <= 0.0771511
: Cut[ 2]: -2.49817 < - 0.20558*[myvar1] + 0.13632*[myvar2] + 1.715*[var3] - 0.71283*[var4] <= 1e+30
: Cut[ 3]: 1.59542 < - 0.79915*[myvar1] - 0.14903*[myvar2] - 0.71283*[var3] + 2.0962*[var4] <= 1e+30
: ------------------------------------------------------------------------------------------------------------------------
: ------------------------------------------------------------------------------------------------------------------------
CutsD : Cut values for requested signal efficiency: 0.2
: Corresponding background efficiency : 0.00318151
: Transformation applied to input variables : "Deco"
: ------------------------------------------------------------------------------------------------------------------------
: Cut[ 0]: -1e+30 < + 1.1113*[myvar1] + 0.054934*[myvar2] - 0.20558*[var3] - 0.79915*[var4] <= 2.25342
: Cut[ 1]: -1e+30 < + 0.054934*[myvar1] + 0.95764*[myvar2] + 0.13632*[var3] - 0.14903*[var4] <= 0.817228
: Cut[ 2]: -3.70325 < - 0.20558*[myvar1] + 0.13632*[myvar2] + 1.715*[var3] - 0.71283*[var4] <= 1e+30
: Cut[ 3]: 1.48444 < - 0.79915*[myvar1] - 0.14903*[myvar2] - 0.71283*[var3] + 2.0962*[var4] <= 1e+30
: ------------------------------------------------------------------------------------------------------------------------
: ------------------------------------------------------------------------------------------------------------------------
CutsD : Cut values for requested signal efficiency: 0.3
: Corresponding background efficiency : 0.00804102
: Transformation applied to input variables : "Deco"
: ------------------------------------------------------------------------------------------------------------------------
: Cut[ 0]: -1e+30 < + 1.1113*[myvar1] + 0.054934*[myvar2] - 0.20558*[var3] - 0.79915*[var4] <= 1.96798
: Cut[ 1]: -1e+30 < + 0.054934*[myvar1] + 0.95764*[myvar2] + 0.13632*[var3] - 0.14903*[var4] <= 3.75835
: Cut[ 2]: -0.545008 < - 0.20558*[myvar1] + 0.13632*[myvar2] + 1.715*[var3] - 0.71283*[var4] <= 1e+30
: Cut[ 3]: 1.1435 < - 0.79915*[myvar1] - 0.14903*[myvar2] - 0.71283*[var3] + 2.0962*[var4] <= 1e+30
: ------------------------------------------------------------------------------------------------------------------------
: ------------------------------------------------------------------------------------------------------------------------
CutsD : Cut values for requested signal efficiency: 0.4
: Corresponding background efficiency : 0.017039
: Transformation applied to input variables : "Deco"
: ------------------------------------------------------------------------------------------------------------------------
: Cut[ 0]: -1e+30 < + 1.1113*[myvar1] + 0.054934*[myvar2] - 0.20558*[var3] - 0.79915*[var4] <= 1.88438
: Cut[ 1]: -1e+30 < + 0.054934*[myvar1] + 0.95764*[myvar2] + 0.13632*[var3] - 0.14903*[var4] <= 1.64561
: Cut[ 2]: -1.42115 < - 0.20558*[myvar1] + 0.13632*[myvar2] + 1.715*[var3] - 0.71283*[var4] <= 1e+30
: Cut[ 3]: 1.04216 < - 0.79915*[myvar1] - 0.14903*[myvar2] - 0.71283*[var3] + 2.0962*[var4] <= 1e+30
: ------------------------------------------------------------------------------------------------------------------------
: ------------------------------------------------------------------------------------------------------------------------
CutsD : Cut values for requested signal efficiency: 0.5
: Corresponding background efficiency : 0.029664
: Transformation applied to input variables : "Deco"
: ------------------------------------------------------------------------------------------------------------------------
: Cut[ 0]: -1e+30 < + 1.1113*[myvar1] + 0.054934*[myvar2] - 0.20558*[var3] - 0.79915*[var4] <= 3.98405
: Cut[ 1]: -1e+30 < + 0.054934*[myvar1] + 0.95764*[myvar2] + 0.13632*[var3] - 0.14903*[var4] <= 2.83582
: Cut[ 2]: -1.48704 < - 0.20558*[myvar1] + 0.13632*[myvar2] + 1.715*[var3] - 0.71283*[var4] <= 1e+30
: Cut[ 3]: 0.922253 < - 0.79915*[myvar1] - 0.14903*[myvar2] - 0.71283*[var3] + 2.0962*[var4] <= 1e+30
: ------------------------------------------------------------------------------------------------------------------------
: ------------------------------------------------------------------------------------------------------------------------
CutsD : Cut values for requested signal efficiency: 0.6
: Corresponding background efficiency : 0.0566836
: Transformation applied to input variables : "Deco"
: ------------------------------------------------------------------------------------------------------------------------
: Cut[ 0]: -1e+30 < + 1.1113*[myvar1] + 0.054934*[myvar2] - 0.20558*[var3] - 0.79915*[var4] <= 2.63541
: Cut[ 1]: -1e+30 < + 0.054934*[myvar1] + 0.95764*[myvar2] + 0.13632*[var3] - 0.14903*[var4] <= 2.4807
: Cut[ 2]: -1.54361 < - 0.20558*[myvar1] + 0.13632*[myvar2] + 1.715*[var3] - 0.71283*[var4] <= 1e+30
: Cut[ 3]: 0.717859 < - 0.79915*[myvar1] - 0.14903*[myvar2] - 0.71283*[var3] + 2.0962*[var4] <= 1e+30
: ------------------------------------------------------------------------------------------------------------------------
: ------------------------------------------------------------------------------------------------------------------------
CutsD : Cut values for requested signal efficiency: 0.7
: Corresponding background efficiency : 0.0968001
: Transformation applied to input variables : "Deco"
: ------------------------------------------------------------------------------------------------------------------------
: Cut[ 0]: -1e+30 < + 1.1113*[myvar1] + 0.054934*[myvar2] - 0.20558*[var3] - 0.79915*[var4] <= 3.56177
: Cut[ 1]: -1e+30 < + 0.054934*[myvar1] + 0.95764*[myvar2] + 0.13632*[var3] - 0.14903*[var4] <= 2.64808
: Cut[ 2]: -2.79353 < - 0.20558*[myvar1] + 0.13632*[myvar2] + 1.715*[var3] - 0.71283*[var4] <= 1e+30
: Cut[ 3]: 0.619348 < - 0.79915*[myvar1] - 0.14903*[myvar2] - 0.71283*[var3] + 2.0962*[var4] <= 1e+30
: ------------------------------------------------------------------------------------------------------------------------
: ------------------------------------------------------------------------------------------------------------------------
CutsD : Cut values for requested signal efficiency: 0.8
: Corresponding background efficiency : 0.155161
: Transformation applied to input variables : "Deco"
: ------------------------------------------------------------------------------------------------------------------------
: Cut[ 0]: -1e+30 < + 1.1113*[myvar1] + 0.054934*[myvar2] - 0.20558*[var3] - 0.79915*[var4] <= 3.97175
: Cut[ 1]: -1e+30 < + 0.054934*[myvar1] + 0.95764*[myvar2] + 0.13632*[var3] - 0.14903*[var4] <= 3.36992
: Cut[ 2]: -3.81224 < - 0.20558*[myvar1] + 0.13632*[myvar2] + 1.715*[var3] - 0.71283*[var4] <= 1e+30
: Cut[ 3]: 0.403725 < - 0.79915*[myvar1] - 0.14903*[myvar2] - 0.71283*[var3] + 2.0962*[var4] <= 1e+30
: ------------------------------------------------------------------------------------------------------------------------
: ------------------------------------------------------------------------------------------------------------------------
CutsD : Cut values for requested signal efficiency: 0.9
: Corresponding background efficiency : 0.31419
: Transformation applied to input variables : "Deco"
: ------------------------------------------------------------------------------------------------------------------------
: Cut[ 0]: -1e+30 < + 1.1113*[myvar1] + 0.054934*[myvar2] - 0.20558*[var3] - 0.79915*[var4] <= 3.35782
: Cut[ 1]: -1e+30 < + 0.054934*[myvar1] + 0.95764*[myvar2] + 0.13632*[var3] - 0.14903*[var4] <= 2.86136
: Cut[ 2]: -3.62987 < - 0.20558*[myvar1] + 0.13632*[myvar2] + 1.715*[var3] - 0.71283*[var4] <= 1e+30
: Cut[ 3]: 0.023156 < - 0.79915*[myvar1] - 0.14903*[myvar2] - 0.71283*[var3] + 2.0962*[var4] <= 1e+30
: ------------------------------------------------------------------------------------------------------------------------
: Elapsed time for training with 2000 events: 1.16 sec
CutsD : [dataset] : Evaluation of CutsD on training sample (2000 events)
: Elapsed time for evaluation of 2000 events: 0.000816 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_CutsD.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_CutsD.class.C␛[0m
: TMVAC.root:/dataset/Method_Cuts/CutsD
Factory : Training finished
:
Factory : Train method: Likelihood for Classification
:
:
: ␛[1m================================================================␛[0m
: ␛[1mH e l p f o r M V A m e t h o d [ Likelihood ] :␛[0m
:
: ␛[1m--- Short description:␛[0m
:
: The maximum-likelihood classifier models the data with probability
: density functions (PDF) reproducing the signal and background
: distributions of the input variables. Correlations among the
: variables are ignored.
:
: ␛[1m--- Performance optimisation:␛[0m
:
: Required for good performance are decorrelated input variables
: (PCA transformation via the option "VarTransform=Decorrelate"
: may be tried). Irreducible non-linear correlations may be reduced
: by precombining strongly correlated input variables, or by simply
: removing one of the variables.
:
: ␛[1m--- Performance tuning via configuration options:␛[0m
:
: High fidelity PDF estimates are mandatory, i.e., sufficient training
: statistics is required to populate the tails of the distributions
: It would be a surprise if the default Spline or KDE kernel parameters
: provide a satisfying fit to the data. The user is advised to properly
: tune the events per bin and smooth options in the spline cases
: individually per variable. If the KDE kernel is used, the adaptive
: Gaussian kernel may lead to artefacts, so please always also try
: the non-adaptive one.
:
: All tuning parameters must be adjusted individually for each input
: variable!
:
: <Suppress this message by specifying "!H" in the booking option>
: ␛[1m================================================================␛[0m
:
: Filling reference histograms
: Building PDF out of reference histograms
: Elapsed time for training with 2000 events: 0.00875 sec
Likelihood : [dataset] : Evaluation of Likelihood on training sample (2000 events)
: Elapsed time for evaluation of 2000 events: 0.00111 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_Likelihood.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_Likelihood.class.C␛[0m
: TMVAC.root:/dataset/Method_Likelihood/Likelihood
Factory : Training finished
:
Factory : Train method: LikelihoodPCA for Classification
:
: Preparing the Principle Component (PCA) transformation...
TFHandler_LikelihoodPCA : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: -0.018073 2.3492 [ -12.291 8.9889 ]
: myvar2: 0.035051 1.0778 [ -4.0607 3.7534 ]
: var3: 0.0032257 0.59616 [ -2.0543 1.9480 ]
: var4: -0.0086473 0.35251 [ -1.1198 1.0790 ]
: -----------------------------------------------------------
: Filling reference histograms
: Building PDF out of reference histograms
: Elapsed time for training with 2000 events: 0.00972 sec
LikelihoodPCA : [dataset] : Evaluation of LikelihoodPCA on training sample (2000 events)
: Elapsed time for evaluation of 2000 events: 0.00177 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_LikelihoodPCA.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_LikelihoodPCA.class.C␛[0m
: TMVAC.root:/dataset/Method_Likelihood/LikelihoodPCA
Factory : Training finished
:
Factory : Train method: PDERS for Classification
:
: Elapsed time for training with 2000 events: 0.00202 sec
PDERS : [dataset] : Evaluation of PDERS on training sample (2000 events)
: Elapsed time for evaluation of 2000 events: 0.109 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_PDERS.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_PDERS.class.C␛[0m
Factory : Training finished
:
Factory : Train method: PDEFoam for Classification
:
PDEFoam : NormMode=NUMEVENTS chosen. Note that only NormMode=EqualNumEvents ensures that Discriminant values correspond to signal probabilities.
: Build up discriminator foam
: Elapsed time: 0.132 sec
: Elapsed time for training with 2000 events: 0.148 sec
PDEFoam : [dataset] : Evaluation of PDEFoam on training sample (2000 events)
: Elapsed time for evaluation of 2000 events: 0.00776 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_PDEFoam.weights.xml␛[0m
: writing foam DiscrFoam to file
: Foams written to file: ␛[0;36mdataset/weights/TMVAClassification_PDEFoam.weights_foams.root␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_PDEFoam.class.C␛[0m
Factory : Training finished
:
Factory : Train method: KNN for Classification
:
:
: ␛[1m================================================================␛[0m
: ␛[1mH e l p f o r M V A m e t h o d [ KNN ] :␛[0m
:
: ␛[1m--- Short description:␛[0m
:
: The k-nearest neighbor (k-NN) algorithm is a multi-dimensional classification
: and regression algorithm. Similarly to other TMVA algorithms, k-NN uses a set of
: training events for which a classification category/regression target is known.
: The k-NN method compares a test event to all training events using a distance
: function, which is an Euclidean distance in a space defined by the input variables.
: The k-NN method, as implemented in TMVA, uses a kd-tree algorithm to perform a
: quick search for the k events with shortest distance to the test event. The method
: returns a fraction of signal events among the k neighbors. It is recommended
: that a histogram which stores the k-NN decision variable is binned with k+1 bins
: between 0 and 1.
:
: ␛[1m--- Performance tuning via configuration options: ␛[0m
:
: The k-NN method estimates a density of signal and background events in a
: neighborhood around the test event. The method assumes that the density of the
: signal and background events is uniform and constant within the neighborhood.
: k is an adjustable parameter and it determines an average size of the
: neighborhood. Small k values (less than 10) are sensitive to statistical
: fluctuations and large (greater than 100) values might not sufficiently capture
: local differences between events in the training set. The speed of the k-NN
: method also increases with larger values of k.
:
: The k-NN method assigns equal weight to all input variables. Different scales
: among the input variables is compensated using ScaleFrac parameter: the input
: variables are scaled so that the widths for central ScaleFrac*100% events are
: equal among all the input variables.
:
: ␛[1m--- Additional configuration options: ␛[0m
:
: The method inclues an option to use a Gaussian kernel to smooth out the k-NN
: response. The kernel re-weights events using a distance to the test event.
:
: <Suppress this message by specifying "!H" in the booking option>
: ␛[1m================================================================␛[0m
:
KNN : <Train> start...
: Reading 2000 events
: Number of signal events 1000
: Number of background events 1000
: Creating kd-tree with 2000 events
: Computing scale factor for 1d distributions: (ifrac, bottom, top) = (80%, 10%, 90%)
ModulekNN : Optimizing tree for 4 variables with 2000 values
: <Fill> Class 1 has 1000 events
: <Fill> Class 2 has 1000 events
: Elapsed time for training with 2000 events: 0.00182 sec
KNN : [dataset] : Evaluation of KNN on training sample (2000 events)
: Elapsed time for evaluation of 2000 events: 0.0211 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_KNN.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_KNN.class.C␛[0m
Factory : Training finished
:
Factory : Train method: LD for Classification
:
:
: ␛[1m================================================================␛[0m
: ␛[1mH e l p f o r M V A m e t h o d [ LD ] :␛[0m
:
: ␛[1m--- Short description:␛[0m
:
: Linear discriminants select events by distinguishing the mean
: values of the signal and background distributions in a trans-
: formed variable space where linear correlations are removed.
: The LD implementation here is equivalent to the "Fisher" discriminant
: for classification, but also provides linear regression.
:
: (More precisely: the "linear discriminator" determines
: an axis in the (correlated) hyperspace of the input
: variables such that, when projecting the output classes
: (signal and background) upon this axis, they are pushed
: as far as possible away from each other, while events
: of a same class are confined in a close vicinity. The
: linearity property of this classifier is reflected in the
: metric with which "far apart" and "close vicinity" are
: determined: the covariance matrix of the discriminating
: variable space.)
:
: ␛[1m--- Performance optimisation:␛[0m
:
: Optimal performance for the linear discriminant is obtained for
: linearly correlated Gaussian-distributed variables. Any deviation
: from this ideal reduces the achievable separation power. In
: particular, no discrimination at all is achieved for a variable
: that has the same sample mean for signal and background, even if
: the shapes of the distributions are very different. Thus, the linear
: discriminant often benefits from a suitable transformation of the
: input variables. For example, if a variable x in [-1,1] has a
: a parabolic signal distributions, and a uniform background
: distributions, their mean value is zero in both cases, leading
: to no separation. The simple transformation x -> |x| renders this
: variable powerful for the use in a linear discriminant.
:
: ␛[1m--- Performance tuning via configuration options:␛[0m
:
: <None>
:
: <Suppress this message by specifying "!H" in the booking option>
: ␛[1m================================================================␛[0m
:
LD : Results for LD coefficients:
: -----------------------
: Variable: Coefficient:
: -----------------------
: myvar1: -0.284
: myvar2: -0.087
: var3: -0.139
: var4: +0.665
: (offset): -0.052
: -----------------------
: Elapsed time for training with 2000 events: 0.000572 sec
LD : [dataset] : Evaluation of LD on training sample (2000 events)
: Elapsed time for evaluation of 2000 events: 0.000229 sec
: <CreateMVAPdfs> Separation from histogram (PDF): 0.517 (0.000)
: Dataset[dataset] : Evaluation of LD on training sample
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_LD.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_LD.class.C␛[0m
Factory : Training finished
:
Factory : Train method: FDA_GA for Classification
:
:
: ␛[1m================================================================␛[0m
: ␛[1mH e l p f o r M V A m e t h o d [ FDA_GA ] :␛[0m
:
: ␛[1m--- Short description:␛[0m
:
: The function discriminant analysis (FDA) is a classifier suitable
: to solve linear or simple nonlinear discrimination problems.
:
: The user provides the desired function with adjustable parameters
: via the configuration option string, and FDA fits the parameters to
: it, requiring the signal (background) function value to be as close
: as possible to 1 (0). Its advantage over the more involved and
: automatic nonlinear discriminators is the simplicity and transparency
: of the discrimination expression. A shortcoming is that FDA will
: underperform for involved problems with complicated, phase space
: dependent nonlinear correlations.
:
: Please consult the Users Guide for the format of the formula string
: and the allowed parameter ranges:
: documentation/tmva/UsersGuide/TMVAUsersGuide.pdf
:
: ␛[1m--- Performance optimisation:␛[0m
:
: The FDA performance depends on the complexity and fidelity of the
: user-defined discriminator function. As a general rule, it should
: be able to reproduce the discrimination power of any linear
: discriminant analysis. To reach into the nonlinear domain, it is
: useful to inspect the correlation profiles of the input variables,
: and add quadratic and higher polynomial terms between variables as
: necessary. Comparison with more involved nonlinear classifiers can
: be used as a guide.
:
: ␛[1m--- Performance tuning via configuration options:␛[0m
:
: Depending on the function used, the choice of "FitMethod" is
: crucial for getting valuable solutions with FDA. As a guideline it
: is recommended to start with "FitMethod=MINUIT". When more complex
: functions are used where MINUIT does not converge to reasonable
: results, the user should switch to non-gradient FitMethods such
: as GeneticAlgorithm (GA) or Monte Carlo (MC). It might prove to be
: useful to combine GA (or MC) with MINUIT by setting the option
: "Converger=MINUIT". GA (MC) will then set the starting parameters
: for MINUIT such that the basic quality of GA (MC) of finding global
: minima is combined with the efficacy of MINUIT of finding local
: minima.
:
: <Suppress this message by specifying "!H" in the booking option>
: ␛[1m================================================================␛[0m
:
FitterBase : <GeneticFitter> Optimisation, please be patient ... (inaccurate progress timing for GA)
: Elapsed time: 0.189 sec
FDA_GA : Results for parameter fit using "GA" fitter:
: -----------------------
: Parameter: Fit result:
: -----------------------
: Par(0): 0.360294
: Par(1): 0
: Par(2): 0
: Par(3): 0
: Par(4): 0.390594
: -----------------------
: Discriminator expression: "(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3"
: Value of estimator at minimum: 0.49768
: Elapsed time for training with 2000 events: 0.201 sec
FDA_GA : [dataset] : Evaluation of FDA_GA on training sample (2000 events)
: Elapsed time for evaluation of 2000 events: 0.000281 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_FDA_GA.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_FDA_GA.class.C␛[0m
Factory : Training finished
:
Factory : Train method: MLPBNN for Classification
:
:
: ␛[1m================================================================␛[0m
: ␛[1mH e l p f o r M V A m e t h o d [ MLPBNN ] :␛[0m
:
: ␛[1m--- Short description:␛[0m
:
: The MLP artificial neural network (ANN) is a traditional feed-
: forward multilayer perceptron implementation. The MLP has a user-
: defined hidden layer architecture, while the number of input (output)
: nodes is determined by the input variables (output classes, i.e.,
: signal and one background).
:
: ␛[1m--- Performance optimisation:␛[0m
:
: Neural networks are stable and performing for a large variety of
: linear and non-linear classification problems. However, in contrast
: to (e.g.) boosted decision trees, the user is advised to reduce the
: number of input variables that have only little discrimination power.
:
: In the tests we have carried out so far, the MLP and ROOT networks
: (TMlpANN, interfaced via TMVA) performed equally well, with however
: a clear speed advantage for the MLP. The Clermont-Ferrand neural
: net (CFMlpANN) exhibited worse classification performance in these
: tests, which is partly due to the slow convergence of its training
: (at least 10k training cycles are required to achieve approximately
: competitive results).
:
: ␛[1mOvertraining: ␛[0monly the TMlpANN performs an explicit separation of the
: full training sample into independent training and validation samples.
: We have found that in most high-energy physics applications the
: available degrees of freedom (training events) are sufficient to
: constrain the weights of the relatively simple architectures required
: to achieve good performance. Hence no overtraining should occur, and
: the use of validation samples would only reduce the available training
: information. However, if the performance on the training sample is
: found to be significantly better than the one found with the inde-
: pendent test sample, caution is needed. The results for these samples
: are printed to standard output at the end of each training job.
:
: ␛[1m--- Performance tuning via configuration options:␛[0m
:
: The hidden layer architecture for all ANNs is defined by the option
: "HiddenLayers=N+1,N,...", where here the first hidden layer has N+1
: neurons and the second N neurons (and so on), and where N is the number
: of input variables. Excessive numbers of hidden layers should be avoided,
: in favour of more neurons in the first hidden layer.
:
: The number of cycles should be above 500. As said, if the number of
: adjustable weights is small compared to the training sample size,
: using a large number of training samples should not lead to overtraining.
:
: <Suppress this message by specifying "!H" in the booking option>
: ␛[1m================================================================␛[0m
:
TFHandler_MLPBNN : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.12229 0.21864 [ -1.0000 1.0000 ]
: myvar2: -0.056321 0.27578 [ -1.0000 1.0000 ]
: var3: 0.098365 0.23486 [ -1.0000 1.0000 ]
: var4: 0.18509 0.23712 [ -1.0000 1.0000 ]
: -----------------------------------------------------------
: Training Network
:
: Finalizing handling of Regulator terms, trainE=0.757543 testE=0.739106
: Done with handling of Regulator terms
: Elapsed time for training with 2000 events: 1.2 sec
MLPBNN : [dataset] : Evaluation of MLPBNN on training sample (2000 events)
: Elapsed time for evaluation of 2000 events: 0.00167 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_MLPBNN.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_MLPBNN.class.C␛[0m
: Write special histos to file: TMVAC.root:/dataset/Method_MLP/MLPBNN
Factory : Training finished
:
Factory : Train method: DNN_CPU for Classification
:
TFHandler_DNN_CPU : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.12229 0.21864 [ -1.0000 1.0000 ]
: myvar2: -0.056321 0.27578 [ -1.0000 1.0000 ]
: var3: 0.098365 0.23486 [ -1.0000 1.0000 ]
: var4: 0.18509 0.23712 [ -1.0000 1.0000 ]
: -----------------------------------------------------------
: Start of deep neural network training on CPU using MT, nthreads = 1
:
TFHandler_DNN_CPU : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.12229 0.21864 [ -1.0000 1.0000 ]
: myvar2: -0.056321 0.27578 [ -1.0000 1.0000 ]
: var3: 0.098365 0.23486 [ -1.0000 1.0000 ]
: var4: 0.18509 0.23712 [ -1.0000 1.0000 ]
: -----------------------------------------------------------
: ***** Deep Learning Network *****
DEEP NEURAL NETWORK: Depth = 4 Input = ( 1, 1, 4 ) Batch size = 100 Loss function = C
Layer 0 DENSE Layer: ( Input = 4 , Width = 128 ) Output = ( 1 , 100 , 128 ) Activation Function = Tanh
Layer 1 DENSE Layer: ( Input = 128 , Width = 128 ) Output = ( 1 , 100 , 128 ) Activation Function = Tanh Dropout prob. = 0.5
Layer 2 DENSE Layer: ( Input = 128 , Width = 128 ) Output = ( 1 , 100 , 128 ) Activation Function = Tanh Dropout prob. = 0.5
Layer 3 DENSE Layer: ( Input = 128 , Width = 1 ) Output = ( 1 , 100 , 1 ) Activation Function = Identity Dropout prob. = 0.5
: Using 1600 events for training and 400 for testing
: Compute initial loss on the validation data
: Training phase 1 of 1: Optimizer ADAM (beta1=0.9,beta2=0.999,eps=1e-07) Learning rate = 0.01 regularization 0 minimum error = 0.676067
: --------------------------------------------------------------
: Epoch | Train Err. Val. Err. t(s)/epoch t(s)/Loss nEvents/s Conv. Steps
: --------------------------------------------------------------
: Start epoch iteration ...
: 1 Minimum Test error found - save the configuration
: 1 | 0.571925 0.451727 0.108627 0.00994584 16213.9 0
: 2 | 0.456749 0.452872 0.108396 0.00908749 16111.5 1
: 3 | 0.415727 0.4554 0.11147 0.00900988 15615.8 2
: 4 | 0.435883 0.454354 0.108242 0.00924581 16162.2 3
: 5 | 0.403288 0.477806 0.107676 0.0090654 16225.4 4
: 6 Minimum Test error found - save the configuration
: 6 | 0.430997 0.437609 0.113258 0.00916101 15370.3 0
: 7 | 0.418977 0.472289 0.108444 0.00898713 16087.3 1
: 8 | 0.399623 0.485865 0.108202 0.00915254 16153.6 2
: 9 | 0.413762 0.450915 0.108912 0.00928517 16059.9 3
: 10 | 0.395192 0.460594 0.109441 0.00955649 16018.4 4
: 11 | 0.405949 0.473195 0.117498 0.00929996 14787.7 5
: 12 | 0.411948 0.455692 0.111292 0.00924305 15678.8 6
: 13 | 0.431238 0.473217 0.125522 0.00939127 13777.6 7
: 14 | 0.43037 0.530848 0.110003 0.00939084 15902.6 8
: 15 | 0.414437 0.467179 0.116597 0.0103059 15052.9 9
: 16 | 0.406156 0.452559 0.130279 0.0144609 13814.8 10
: 17 | 0.408978 0.448743 0.112145 0.00979797 15633.1 11
: 18 | 0.398412 0.515423 0.111673 0.00972444 15694.3 12
: 19 | 0.421338 0.474798 0.109389 0.00927652 15982.1 13
: 20 | 0.3981 0.458031 0.110171 0.00972219 15928.4 14
: 21 | 0.418807 0.486368 0.118606 0.00928523 14635.9 15
: 22 Minimum Test error found - save the configuration
: 22 | 0.426567 0.436171 0.12961 0.0125654 13670 0
: 23 | 0.416648 0.483872 0.120552 0.00939039 14393.5 1
: 24 | 0.441061 0.476107 0.130138 0.0116324 13501.5 2
: 25 | 0.423217 0.473008 0.118762 0.00931601 14619.1 3
: 26 | 0.408992 0.478979 0.128589 0.0106972 13571.8 4
: 27 | 0.409783 0.458292 0.121914 0.00981175 14272.7 5
: 28 | 0.403687 0.462998 0.119218 0.00937119 14565.8 6
: 29 | 0.397536 0.481976 0.109271 0.00913523 15978.3 7
: 30 | 0.400821 0.497879 0.107196 0.00915074 16319.1 8
: 31 | 0.409354 0.442023 0.107566 0.00915861 16258.9 9
: 32 | 0.38954 0.46111 0.1097 0.00959155 15982.6 10
: 33 | 0.437993 0.449204 0.110275 0.00986377 15934.5 11
: 34 | 0.409257 0.483009 0.109606 0.00946529 15977.5 12
: 35 | 0.426534 0.450649 0.120637 0.0102937 14500.1 13
: 36 | 0.422762 0.472461 0.119063 0.00937931 14587.5 14
: 37 | 0.41017 0.447305 0.123559 0.00916822 13987.2 15
: 38 | 0.414412 0.507229 0.109386 0.00915968 15963.9 16
: 39 Minimum Test error found - save the configuration
: 39 | 0.407657 0.435365 0.109298 0.00936647 16011 0
: 40 | 0.42062 0.457413 0.108312 0.00954551 16199.9 1
: 41 | 0.404443 0.475785 0.109142 0.00918711 16007.2 2
: 42 | 0.400818 0.458996 0.109019 0.00932811 16049.6 3
: 43 | 0.401223 0.480737 0.108748 0.00913571 16062.3 4
: 44 | 0.403895 0.48012 0.108926 0.00922152 16047.4 5
: 45 | 0.402771 0.440525 0.108872 0.0094235 16088.7 6
: 46 | 0.41018 0.443572 0.108724 0.00924077 16083.1 7
: 47 | 0.396009 0.455095 0.107582 0.00924062 16269.8 8
: 48 | 0.400088 0.506409 0.109116 0.00935922 16039 9
: 49 | 0.414603 0.511769 0.114981 0.00947529 15165 10
: 50 | 0.422663 0.558927 0.11169 0.00962169 15675.8 11
: 51 | 0.428603 0.489132 0.125547 0.0135124 14281.3 12
: 52 | 0.397312 0.47503 0.112812 0.00925621 15450.6 13
: 53 | 0.418751 0.455082 0.108509 0.00947023 16155.3 14
: 54 | 0.389345 0.466349 0.111911 0.0102083 15732.1 15
: 55 | 0.396698 0.446527 0.120388 0.0142511 15074.9 16
: 56 | 0.405108 0.492296 0.117178 0.00976093 14895.2 17
: 57 | 0.414874 0.522607 0.115401 0.00994309 15171.9 18
: 58 | 0.41531 0.467448 0.116141 0.0101702 15098.5 19
: 59 | 0.407709 0.459436 0.116353 0.0102538 15080.2 20
: 60 | 0.40926 0.464359 0.117636 0.00989595 14850.5 21
:
: Elapsed time for training with 2000 events: 6.85 sec
: Evaluate deep neural network on CPU using batches with size = 100
:
DNN_CPU : [dataset] : Evaluation of DNN_CPU on training sample (2000 events)
: Elapsed time for evaluation of 2000 events: 0.0485 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_DNN_CPU.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_DNN_CPU.class.C␛[0m
Factory : Training finished
:
Factory : Train method: SVM for Classification
:
TFHandler_SVM : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.12229 0.21864 [ -1.0000 1.0000 ]
: myvar2: -0.056321 0.27578 [ -1.0000 1.0000 ]
: var3: 0.098365 0.23486 [ -1.0000 1.0000 ]
: var4: 0.18509 0.23712 [ -1.0000 1.0000 ]
: -----------------------------------------------------------
: Building SVM Working Set...with 2000 event instances
: Elapsed time for Working Set build: 0.0344 sec
: Sorry, no computing time forecast available for SVM, please wait ...
: Elapsed time: 0.173 sec
: Elapsed time for training with 2000 events: 0.21 sec
SVM : [dataset] : Evaluation of SVM on training sample (2000 events)
: Elapsed time for evaluation of 2000 events: 0.0252 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_SVM.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_SVM.class.C␛[0m
Factory : Training finished
:
Factory : Train method: BDT for Classification
:
BDT : #events: (reweighted) sig: 1000 bkg: 1000
: #events: (unweighted) sig: 1000 bkg: 1000
: Training 850 Decision Trees ... patience please
: Elapsed time for training with 2000 events: 0.335 sec
BDT : [dataset] : Evaluation of BDT on training sample (2000 events)
: Elapsed time for evaluation of 2000 events: 0.0788 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_BDT.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_BDT.class.C␛[0m
: TMVAC.root:/dataset/Method_BDT/BDT
Factory : Training finished
:
Factory : Train method: RuleFit for Classification
:
:
: ␛[1m================================================================␛[0m
: ␛[1mH e l p f o r M V A m e t h o d [ RuleFit ] :␛[0m
:
: ␛[1m--- Short description:␛[0m
:
: This method uses a collection of so called rules to create a
: discriminating scoring function. Each rule consists of a series
: of cuts in parameter space. The ensemble of rules are created
: from a forest of decision trees, trained using the training data.
: Each node (apart from the root) corresponds to one rule.
: The scoring function is then obtained by linearly combining
: the rules. A fitting procedure is applied to find the optimum
: set of coefficients. The goal is to find a model with few rules
: but with a strong discriminating power.
:
: ␛[1m--- Performance optimisation:␛[0m
:
: There are two important considerations to make when optimising:
:
: 1. Topology of the decision tree forest
: 2. Fitting of the coefficients
:
: The maximum complexity of the rules is defined by the size of
: the trees. Large trees will yield many complex rules and capture
: higher order correlations. On the other hand, small trees will
: lead to a smaller ensemble with simple rules, only capable of
: modeling simple structures.
: Several parameters exists for controlling the complexity of the
: rule ensemble.
:
: The fitting procedure searches for a minimum using a gradient
: directed path. Apart from step size and number of steps, the
: evolution of the path is defined by a cut-off parameter, tau.
: This parameter is unknown and depends on the training data.
: A large value will tend to give large weights to a few rules.
: Similarly, a small value will lead to a large set of rules
: with similar weights.
:
: A final point is the model used; rules and/or linear terms.
: For a given training sample, the result may improve by adding
: linear terms. If best performance is obtained using only linear
: terms, it is very likely that the Fisher discriminant would be
: a better choice. Ideally the fitting procedure should be able to
: make this choice by giving appropriate weights for either terms.
:
: ␛[1m--- Performance tuning via configuration options:␛[0m
:
: I. TUNING OF RULE ENSEMBLE:
:
: ␛[1mForestType ␛[0m: Recommended is to use the default "AdaBoost".
: ␛[1mnTrees ␛[0m: More trees leads to more rules but also slow
: performance. With too few trees the risk is
: that the rule ensemble becomes too simple.
: ␛[1mfEventsMin ␛[0m
: ␛[1mfEventsMax ␛[0m: With a lower min, more large trees will be generated
: leading to more complex rules.
: With a higher max, more small trees will be
: generated leading to more simple rules.
: By changing this range, the average complexity
: of the rule ensemble can be controlled.
: ␛[1mRuleMinDist ␛[0m: By increasing the minimum distance between
: rules, fewer and more diverse rules will remain.
: Initially it is a good idea to keep this small
: or zero and let the fitting do the selection of
: rules. In order to reduce the ensemble size,
: the value can then be increased.
:
: II. TUNING OF THE FITTING:
:
: ␛[1mGDPathEveFrac ␛[0m: fraction of events in path evaluation
: Increasing this fraction will improve the path
: finding. However, a too high value will give few
: unique events available for error estimation.
: It is recommended to use the default = 0.5.
: ␛[1mGDTau ␛[0m: cutoff parameter tau
: By default this value is set to -1.0.
: This means that the cut off parameter is
: automatically estimated. In most cases
: this should be fine. However, you may want
: to fix this value if you already know it
: and want to reduce on training time.
: ␛[1mGDTauPrec ␛[0m: precision of estimated tau
: Increase this precision to find a more
: optimum cut-off parameter.
: ␛[1mGDNStep ␛[0m: number of steps in path search
: If the number of steps is too small, then
: the program will give a warning message.
:
: III. WARNING MESSAGES
:
: ␛[1mRisk(i+1)>=Risk(i) in path␛[0m
: ␛[1mChaotic behaviour of risk evolution.␛[0m
: The error rate was still decreasing at the end
: By construction the Risk should always decrease.
: However, if the training sample is too small or
: the model is overtrained, such warnings can
: occur.
: The warnings can safely be ignored if only a
: few (<3) occur. If more warnings are generated,
: the fitting fails.
: A remedy may be to increase the value
: ␛[1mGDValidEveFrac␛[0m to 1.0 (or a larger value).
: In addition, if ␛[1mGDPathEveFrac␛[0m is too high
: the same warnings may occur since the events
: used for error estimation are also used for
: path estimation.
: Another possibility is to modify the model -
: See above on tuning the rule ensemble.
:
: ␛[1mThe error rate was still decreasing at the end of the path␛[0m
: Too few steps in path! Increase ␛[1mGDNSteps␛[0m.
:
: ␛[1mReached minimum early in the search␛[0m
: Minimum was found early in the fitting. This
: may indicate that the used step size ␛[1mGDStep␛[0m.
: was too large. Reduce it and rerun.
: If the results still are not OK, modify the
: model either by modifying the rule ensemble
: or add/remove linear terms
:
: <Suppress this message by specifying "!H" in the booking option>
: ␛[1m================================================================␛[0m
:
RuleFit : -------------------RULE ENSEMBLE SUMMARY------------------------
: Tree training method : AdaBoost
: Number of events per tree : 2000
: Number of trees : 20
: Number of generated rules : 182
: Idem, after cleanup : 68
: Average number of cuts per rule : 2.81
: Spread in number of cuts per rules : 1.14
: ----------------------------------------------------------------
:
: GD path scan - the scan stops when the max num. of steps is reached or a min is found
: Estimating the cutoff parameter tau. The estimated time is a pessimistic maximum.
: Best path found with tau = 0.0700 after 0.8 sec
: Fitting model...
<WARNING> : Risk(i+1)>=Risk(i) in path
: Risk(i+1)>=Risk(i) in path
: Risk(i+1)>=Risk(i) in path
: Risk(i+1)>=Risk(i) in path
<WARNING> : Chaotic behaviour of risk evolution
: --- STOPPING MINIMISATION ---
: This may be OK if minimum is already found
:
: Minimisation elapsed time : 0.088 sec
: ----------------------------------------------------------------
: Found minimum at step 800 with error = 0.552444
: Reason for ending loop: chaotic behaviour of risk
: ----------------------------------------------------------------
: Removed 20 out of a total of 68 rules with importance < 0.001
:
: ================================================================
: M o d e l
: ================================================================
RuleFit : Offset (a0) = 3.61944
: ------------------------------------
: Linear model (weights unnormalised)
: ------------------------------------
: Variable : Weights : Importance
: ------------------------------------
: myvar1 : -1.919e-01 : 0.534
: myvar2 : -3.383e-02 : 0.056
: var3 : 1.376e-02 : 0.023
: var4 : 4.973e-01 : 1.000
: ------------------------------------
: Number of rules = 48
: Printing the first 10 rules, ordered in importance.
: Rule 1 : Importance = 0.4463
: Cut 1 : myvar2 < -0.023
: Cut 2 : var4 < -0.123
: Rule 2 : Importance = 0.3602
: Cut 1 : -0.691 < myvar1
: Cut 2 : var4 < 0.963
: Rule 3 : Importance = 0.3405
: Cut 1 : myvar1 < 2.15
: Cut 2 : var4 < -1.16
: Rule 4 : Importance = 0.3341
: Cut 1 : var4 < 1.94
: Rule 5 : Importance = 0.3248
: Cut 1 : 1.25 < myvar1
: Cut 2 : -0.725 < var3
: Rule 6 : Importance = 0.3206
: Cut 1 : myvar1 < 1.62
: Cut 2 : -0.023 < myvar2
: Cut 3 : var4 < 0.245
: Rule 7 : Importance = 0.3113
: Cut 1 : -0.725 < var3
: Rule 8 : Importance = 0.3053
: Cut 1 : -0.023 < myvar2
: Cut 2 : var4 < -0.123
: Rule 9 : Importance = 0.2386
: Cut 1 : var3 < -0.725
: Rule 10 : Importance = 0.2294
: Cut 1 : -2.24 < myvar1
: Skipping the next 38 rules
: ================================================================
:
<WARNING> : No input variable directory found - BUG?
: Elapsed time for training with 2000 events: 0.907 sec
RuleFit : [dataset] : Evaluation of RuleFit on training sample (2000 events)
: Elapsed time for evaluation of 2000 events: 0.00102 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_RuleFit.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_RuleFit.class.C␛[0m
: TMVAC.root:/dataset/Method_RuleFit/RuleFit
Factory : Training finished
:
: Ranking input variables (method specific)...
: No variable ranking supplied by classifier: Cuts
: No variable ranking supplied by classifier: CutsD
Likelihood : Ranking result (top variable is best ranked)
: -------------------------------------
: Rank : Variable : Delta Separation
: -------------------------------------
: 1 : var4 : 2.507e-02
: 2 : myvar2 : 1.672e-02
: 3 : myvar1 : 1.463e-02
: 4 : var3 : 9.479e-03
: -------------------------------------
LikelihoodPCA : Ranking result (top variable is best ranked)
: -------------------------------------
: Rank : Variable : Delta Separation
: -------------------------------------
: 1 : var4 : 3.021e-01
: 2 : myvar1 : 8.496e-02
: 3 : var3 : 2.592e-02
: 4 : myvar2 : 3.204e-03
: -------------------------------------
: No variable ranking supplied by classifier: PDERS
PDEFoam : Ranking result (top variable is best ranked)
: ----------------------------------------
: Rank : Variable : Variable Importance
: ----------------------------------------
: 1 : var4 : 3.810e-01
: 2 : myvar1 : 2.381e-01
: 3 : var3 : 2.143e-01
: 4 : myvar2 : 1.667e-01
: ----------------------------------------
: No variable ranking supplied by classifier: KNN
LD : Ranking result (top variable is best ranked)
: ---------------------------------
: Rank : Variable : Discr. power
: ---------------------------------
: 1 : var4 : 6.652e-01
: 2 : myvar1 : 2.840e-01
: 3 : var3 : 1.391e-01
: 4 : myvar2 : 8.718e-02
: ---------------------------------
: No variable ranking supplied by classifier: FDA_GA
MLPBNN : Ranking result (top variable is best ranked)
: -------------------------------
: Rank : Variable : Importance
: -------------------------------
: 1 : var4 : 1.242e+00
: 2 : myvar1 : 9.523e-01
: 3 : myvar2 : 4.818e-01
: 4 : var3 : 4.040e-01
: -------------------------------
: No variable ranking supplied by classifier: DNN_CPU
: No variable ranking supplied by classifier: SVM
BDT : Ranking result (top variable is best ranked)
: ----------------------------------------
: Rank : Variable : Variable Importance
: ----------------------------------------
: 1 : var4 : 2.758e-01
: 2 : myvar2 : 2.536e-01
: 3 : myvar1 : 2.507e-01
: 4 : var3 : 2.199e-01
: ----------------------------------------
RuleFit : Ranking result (top variable is best ranked)
: -------------------------------
: Rank : Variable : Importance
: -------------------------------
: 1 : var4 : 1.000e+00
: 2 : myvar1 : 7.442e-01
: 3 : myvar2 : 6.695e-01
: 4 : var3 : 4.612e-01
: -------------------------------
TH1.Print Name = TrainingHistory_DNN_CPU_trainingError, Entries= 0, Total sum= 24.9041
TH1.Print Name = TrainingHistory_DNN_CPU_valError, Entries= 0, Total sum= 28.2407
Factory : === Destroy and recreate all methods via weight files for testing ===
:
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_Cuts.weights.xml␛[0m
: Read cuts optimised using sample of MC events
: Reading 100 signal efficiency bins for 4 variables
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_CutsD.weights.xml␛[0m
: Read cuts optimised using sample of MC events
: Reading 100 signal efficiency bins for 4 variables
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_Likelihood.weights.xml␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_LikelihoodPCA.weights.xml␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_PDERS.weights.xml␛[0m
: signal and background scales: 0.001 0.001
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_PDEFoam.weights.xml␛[0m
: Read foams from file: ␛[0;36mdataset/weights/TMVAClassification_PDEFoam.weights_foams.root␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_KNN.weights.xml␛[0m
: Creating kd-tree with 2000 events
: Computing scale factor for 1d distributions: (ifrac, bottom, top) = (80%, 10%, 90%)
ModulekNN : Optimizing tree for 4 variables with 2000 values
: <Fill> Class 1 has 1000 events
: <Fill> Class 2 has 1000 events
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_LD.weights.xml␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_FDA_GA.weights.xml␛[0m
: User-defined formula string : "(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3"
: TFormula-compatible formula string: "[0]+[1]*[5]+[2]*[6]+[3]*[7]+[4]*[8]"
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_MLPBNN.weights.xml␛[0m
MLPBNN : Building Network.
: Initializing weights
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_DNN_CPU.weights.xml␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_SVM.weights.xml␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_BDT.weights.xml␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_RuleFit.weights.xml␛[0m
Factory : ␛[1mTest all methods␛[0m
Factory : Test method: Cuts for Classification performance
:
Cuts : [dataset] : Evaluation of Cuts on testing sample (10000 events)
: Elapsed time for evaluation of 10000 events: 0.000348 sec
Factory : Test method: CutsD for Classification performance
:
CutsD : [dataset] : Evaluation of CutsD on testing sample (10000 events)
: Elapsed time for evaluation of 10000 events: 0.00238 sec
Factory : Test method: Likelihood for Classification performance
:
Likelihood : [dataset] : Evaluation of Likelihood on testing sample (10000 events)
: Elapsed time for evaluation of 10000 events: 0.00448 sec
Factory : Test method: LikelihoodPCA for Classification performance
:
LikelihoodPCA : [dataset] : Evaluation of LikelihoodPCA on testing sample (10000 events)
: Elapsed time for evaluation of 10000 events: 0.00836 sec
Factory : Test method: PDERS for Classification performance
:
PDERS : [dataset] : Evaluation of PDERS on testing sample (10000 events)
: Elapsed time for evaluation of 10000 events: 0.477 sec
Factory : Test method: PDEFoam for Classification performance
:
PDEFoam : [dataset] : Evaluation of PDEFoam on testing sample (10000 events)
: Elapsed time for evaluation of 10000 events: 0.0345 sec
Factory : Test method: KNN for Classification performance
:
KNN : [dataset] : Evaluation of KNN on testing sample (10000 events)
: Elapsed time for evaluation of 10000 events: 0.102 sec
Factory : Test method: LD for Classification performance
:
LD : [dataset] : Evaluation of LD on testing sample (10000 events)
: Elapsed time for evaluation of 10000 events: 0.00122 sec
: Dataset[dataset] : Evaluation of LD on testing sample
Factory : Test method: FDA_GA for Classification performance
:
FDA_GA : [dataset] : Evaluation of FDA_GA on testing sample (10000 events)
: Elapsed time for evaluation of 10000 events: 0.000919 sec
Factory : Test method: MLPBNN for Classification performance
:
MLPBNN : [dataset] : Evaluation of MLPBNN on testing sample (10000 events)
: Elapsed time for evaluation of 10000 events: 0.00712 sec
Factory : Test method: DNN_CPU for Classification performance
:
: Evaluate deep neural network on CPU using batches with size = 1000
:
TFHandler_DNN_CPU : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.15875 0.21008 [ -1.0772 1.1019 ]
: myvar2: -0.052512 0.28764 [ -1.0979 0.99917 ]
: var3: 0.14402 0.22647 [ -1.0428 1.1058 ]
: var4: 0.24579 0.22520 [ -1.1202 1.0941 ]
: -----------------------------------------------------------
DNN_CPU : [dataset] : Evaluation of DNN_CPU on testing sample (10000 events)
: Elapsed time for evaluation of 10000 events: 0.252 sec
Factory : Test method: SVM for Classification performance
:
SVM : [dataset] : Evaluation of SVM on testing sample (10000 events)
: Elapsed time for evaluation of 10000 events: 0.144 sec
Factory : Test method: BDT for Classification performance
:
BDT : [dataset] : Evaluation of BDT on testing sample (10000 events)
: Elapsed time for evaluation of 10000 events: 0.279 sec
Factory : Test method: RuleFit for Classification performance
:
RuleFit : [dataset] : Evaluation of RuleFit on testing sample (10000 events)
: Elapsed time for evaluation of 10000 events: 0.00552 sec
Factory : ␛[1mEvaluate all methods␛[0m
Factory : Evaluate classifier: Cuts
:
<WARNING> : You have asked for histogram MVA_EFF_BvsS which does not seem to exist in *Results* .. better don't use it
<WARNING> : You have asked for histogram EFF_BVSS_TR which does not seem to exist in *Results* .. better don't use it
TFHandler_Cuts : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.21443 1.7124 [ -9.8605 7.9024 ]
: myvar2: -0.041911 1.1126 [ -4.0854 4.0259 ]
: var3: 0.16712 1.0539 [ -5.3563 4.6430 ]
: var4: 0.43437 1.2203 [ -6.9675 5.0307 ]
: -----------------------------------------------------------
Factory : Evaluate classifier: CutsD
:
<WARNING> : You have asked for histogram MVA_EFF_BvsS which does not seem to exist in *Results* .. better don't use it
TFHandler_CutsD : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: -0.14549 0.97213 [ -5.4077 4.8658 ]
: myvar2: -0.070308 1.0437 [ -3.9101 3.8233 ]
: var3: -0.072822 0.96722 [ -4.3819 4.3335 ]
: var4: 0.62627 0.92018 [ -3.9664 3.6405 ]
: -----------------------------------------------------------
<WARNING> : You have asked for histogram EFF_BVSS_TR which does not seem to exist in *Results* .. better don't use it
TFHandler_CutsD : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: -0.16999 1.0000 [ -4.4163 3.9983 ]
: myvar2: -0.080682 1.0000 [ -3.4441 3.7507 ]
: var3: -0.14363 1.0000 [ -3.7799 3.6146 ]
: var4: 0.32786 1.0000 [ -3.3861 3.3152 ]
: -----------------------------------------------------------
TFHandler_CutsD : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: -0.14549 0.97213 [ -5.4077 4.8658 ]
: myvar2: -0.070308 1.0437 [ -3.9101 3.8233 ]
: var3: -0.072822 0.96722 [ -4.3819 4.3335 ]
: var4: 0.62627 0.92018 [ -3.9664 3.6405 ]
: -----------------------------------------------------------
Factory : Evaluate classifier: Likelihood
:
Likelihood : [dataset] : Loop over test events and fill histograms with classifier response...
:
TFHandler_Likelihood : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.21443 1.7124 [ -9.8605 7.9024 ]
: myvar2: -0.041911 1.1126 [ -4.0854 4.0259 ]
: var3: 0.16712 1.0539 [ -5.3563 4.6430 ]
: var4: 0.43437 1.2203 [ -6.9675 5.0307 ]
: -----------------------------------------------------------
Factory : Evaluate classifier: LikelihoodPCA
:
TFHandler_LikelihoodPCA : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 1.3857 2.2495 [ -12.232 10.994 ]
: myvar2: -0.16933 1.1235 [ -4.1034 3.9180 ]
: var3: -0.20081 0.58158 [ -2.2789 1.9800 ]
: var4: -0.31202 0.33076 [ -1.3887 0.89743 ]
: -----------------------------------------------------------
LikelihoodPCA : [dataset] : Loop over test events and fill histograms with classifier response...
:
TFHandler_LikelihoodPCA : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 1.3857 2.2495 [ -12.232 10.994 ]
: myvar2: -0.16933 1.1235 [ -4.1034 3.9180 ]
: var3: -0.20081 0.58158 [ -2.2789 1.9800 ]
: var4: -0.31202 0.33076 [ -1.3887 0.89743 ]
: -----------------------------------------------------------
Factory : Evaluate classifier: PDERS
:
PDERS : [dataset] : Loop over test events and fill histograms with classifier response...
:
TFHandler_PDERS : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.21443 1.7124 [ -9.8605 7.9024 ]
: myvar2: -0.041911 1.1126 [ -4.0854 4.0259 ]
: var3: 0.16712 1.0539 [ -5.3563 4.6430 ]
: var4: 0.43437 1.2203 [ -6.9675 5.0307 ]
: -----------------------------------------------------------
Factory : Evaluate classifier: PDEFoam
:
PDEFoam : [dataset] : Loop over test events and fill histograms with classifier response...
:
TFHandler_PDEFoam : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.21443 1.7124 [ -9.8605 7.9024 ]
: myvar2: -0.041911 1.1126 [ -4.0854 4.0259 ]
: var3: 0.16712 1.0539 [ -5.3563 4.6430 ]
: var4: 0.43437 1.2203 [ -6.9675 5.0307 ]
: -----------------------------------------------------------
Factory : Evaluate classifier: KNN
:
KNN : [dataset] : Loop over test events and fill histograms with classifier response...
:
TFHandler_KNN : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.21443 1.7124 [ -9.8605 7.9024 ]
: myvar2: -0.041911 1.1126 [ -4.0854 4.0259 ]
: var3: 0.16712 1.0539 [ -5.3563 4.6430 ]
: var4: 0.43437 1.2203 [ -6.9675 5.0307 ]
: -----------------------------------------------------------
Factory : Evaluate classifier: LD
:
LD : [dataset] : Loop over test events and fill histograms with classifier response...
:
: Also filling probability and rarity histograms (on request)...
TFHandler_LD : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.21443 1.7124 [ -9.8605 7.9024 ]
: myvar2: -0.041911 1.1126 [ -4.0854 4.0259 ]
: var3: 0.16712 1.0539 [ -5.3563 4.6430 ]
: var4: 0.43437 1.2203 [ -6.9675 5.0307 ]
: -----------------------------------------------------------
Factory : Evaluate classifier: FDA_GA
:
FDA_GA : [dataset] : Loop over test events and fill histograms with classifier response...
:
TFHandler_FDA_GA : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.21443 1.7124 [ -9.8605 7.9024 ]
: myvar2: -0.041911 1.1126 [ -4.0854 4.0259 ]
: var3: 0.16712 1.0539 [ -5.3563 4.6430 ]
: var4: 0.43437 1.2203 [ -6.9675 5.0307 ]
: -----------------------------------------------------------
Factory : Evaluate classifier: MLPBNN
:
TFHandler_MLPBNN : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.15875 0.21008 [ -1.0772 1.1019 ]
: myvar2: -0.052512 0.28764 [ -1.0979 0.99917 ]
: var3: 0.14402 0.22647 [ -1.0428 1.1058 ]
: var4: 0.24579 0.22520 [ -1.1202 1.0941 ]
: -----------------------------------------------------------
MLPBNN : [dataset] : Loop over test events and fill histograms with classifier response...
:
TFHandler_MLPBNN : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.15875 0.21008 [ -1.0772 1.1019 ]
: myvar2: -0.052512 0.28764 [ -1.0979 0.99917 ]
: var3: 0.14402 0.22647 [ -1.0428 1.1058 ]
: var4: 0.24579 0.22520 [ -1.1202 1.0941 ]
: -----------------------------------------------------------
Factory : Evaluate classifier: DNN_CPU
:
DNN_CPU : [dataset] : Loop over test events and fill histograms with classifier response...
:
: Evaluate deep neural network on CPU using batches with size = 1000
:
TFHandler_DNN_CPU : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.12229 0.21864 [ -1.0000 1.0000 ]
: myvar2: -0.056321 0.27578 [ -1.0000 1.0000 ]
: var3: 0.098365 0.23486 [ -1.0000 1.0000 ]
: var4: 0.18509 0.23712 [ -1.0000 1.0000 ]
: -----------------------------------------------------------
TFHandler_DNN_CPU : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.15875 0.21008 [ -1.0772 1.1019 ]
: myvar2: -0.052512 0.28764 [ -1.0979 0.99917 ]
: var3: 0.14402 0.22647 [ -1.0428 1.1058 ]
: var4: 0.24579 0.22520 [ -1.1202 1.0941 ]
: -----------------------------------------------------------
Factory : Evaluate classifier: SVM
:
TFHandler_SVM : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.15875 0.21008 [ -1.0772 1.1019 ]
: myvar2: -0.052512 0.28764 [ -1.0979 0.99917 ]
: var3: 0.14402 0.22647 [ -1.0428 1.1058 ]
: var4: 0.24579 0.22520 [ -1.1202 1.0941 ]
: -----------------------------------------------------------
SVM : [dataset] : Loop over test events and fill histograms with classifier response...
:
TFHandler_SVM : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.15875 0.21008 [ -1.0772 1.1019 ]
: myvar2: -0.052512 0.28764 [ -1.0979 0.99917 ]
: var3: 0.14402 0.22647 [ -1.0428 1.1058 ]
: var4: 0.24579 0.22520 [ -1.1202 1.0941 ]
: -----------------------------------------------------------
Factory : Evaluate classifier: BDT
:
BDT : [dataset] : Loop over test events and fill histograms with classifier response...
:
TFHandler_BDT : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.21443 1.7124 [ -9.8605 7.9024 ]
: myvar2: -0.041911 1.1126 [ -4.0854 4.0259 ]
: var3: 0.16712 1.0539 [ -5.3563 4.6430 ]
: var4: 0.43437 1.2203 [ -6.9675 5.0307 ]
: -----------------------------------------------------------
Factory : Evaluate classifier: RuleFit
:
RuleFit : [dataset] : Loop over test events and fill histograms with classifier response...
:
TFHandler_RuleFit : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: myvar1: 0.21443 1.7124 [ -9.8605 7.9024 ]
: myvar2: -0.041911 1.1126 [ -4.0854 4.0259 ]
: var3: 0.16712 1.0539 [ -5.3563 4.6430 ]
: var4: 0.43437 1.2203 [ -6.9675 5.0307 ]
: -----------------------------------------------------------
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: dataset LD : 0.923
: dataset MLPBNN : 0.921
: dataset DNN_CPU : 0.921
: dataset LikelihoodPCA : 0.919
: dataset CutsD : 0.912
: dataset SVM : 0.901
: dataset RuleFit : 0.880
: dataset BDT : 0.873
: dataset KNN : 0.828
: dataset PDEFoam : 0.812
: dataset PDERS : 0.798
: dataset FDA_GA : 0.793
: dataset Cuts : 0.791
: dataset Likelihood : 0.758
: -------------------------------------------------------------------------------------------------------------------
:
: Testing efficiency compared to training efficiency (overtraining check)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA Signal efficiency: from test sample (from training sample)
: Name: Method: @B=0.01 @B=0.10 @B=0.30
: -------------------------------------------------------------------------------------------------------------------
: dataset LD : 0.372 (0.335) 0.779 (0.708) 0.929 (0.927)
: dataset MLPBNN : 0.378 (0.353) 0.773 (0.712) 0.926 (0.922)
: dataset DNN_CPU : 0.377 (0.360) 0.776 (0.708) 0.925 (0.917)
: dataset LikelihoodPCA : 0.344 (0.302) 0.770 (0.684) 0.925 (0.917)
: dataset CutsD : 0.297 (0.327) 0.748 (0.708) 0.914 (0.894)
: dataset SVM : 0.336 (0.307) 0.724 (0.675) 0.894 (0.901)
: dataset RuleFit : 0.197 (0.204) 0.655 (0.681) 0.878 (0.897)
: dataset BDT : 0.274 (0.472) 0.644 (0.707) 0.855 (0.897)
: dataset KNN : 0.127 (0.184) 0.535 (0.570) 0.794 (0.850)
: dataset PDEFoam : 0.138 (0.178) 0.491 (0.519) 0.758 (0.767)
: dataset PDERS : 0.179 (0.172) 0.462 (0.449) 0.747 (0.755)
: dataset FDA_GA : 0.120 (0.106) 0.457 (0.457) 0.738 (0.770)
: dataset Cuts : 0.116 (0.128) 0.459 (0.458) 0.735 (0.785)
: dataset Likelihood : 0.092 (0.089) 0.383 (0.377) 0.686 (0.698)
: -------------------------------------------------------------------------------------------------------------------
:
Dataset:dataset : Created tree 'TestTree' with 10000 events
:
Dataset:dataset : Created tree 'TrainTree' with 2000 events
:
Factory : ␛[1mThank you for using TMVA!␛[0m
: ␛[1mFor citation information, please visit: http://tmva.sf.net/citeTMVA.html␛[0m
==> Wrote root file: TMVAC.root
==> TMVAClassification is done!
(int) 0
Author
Andreas Hoecker

Definition in file TMVAClassification.C.