A JetNet -Plugin for TMVA


This page provides a TMVA plugin for the Versatile Artificial Neural Network Package JetNet as well as some basic instructions on how to use it.

About JetNet

JetNet is a FORTRAN package from the University of Lund (Sweden) that provides Artificial Neural Networks as a Multivariate Analysis method. The version 3.5 used for the plugin is from 16.04.1997 and was modified by ChristianWeiser to enable weighted training. For more information about JetNet, see CERN-TH-7135-94. In addition, a C++ interface for JetNet written by GiacintoPiacquadio is used to build the plugin on top. As a baseline for the plugin, the existing NeuroBayes interface for TMVA was used. More information on TMVA can be found here.

The Plugin Code

The plugin code can be downloaded from SVN. To check out the code, do

#check out from svn
svn co svn+ssh://svn.cern.ch/reps/atlas-bmoser/tmva-jetnet/trunk

Quick Start

For a quick start of the plugin, you can run it on one of CERNs lxplus machines. To do this, please log in a clean session. You can then copy the code from below to set things up.

#set up the atlas environment
setupATLAS
#setup ROOT (the plugin has be testet with ROOT 5.34 and ROOT 6.04)
localSetupROOT

#change ROOT version to 5.34.25 if needed
#localSetupROOT --rootVersion=5.34.25-x86_64-slc6-gcc48-opt

(note: Up to now, the plugin was only tested with the ROOT versions 5.34/25 and 6.04/14)

#if not already done create a target directory and check out the plugin from SVN
mkdir targetDir
cd targetDir
svn co svn+ssh://svn.cern.ch/reps/atlas-bmoser/tmva-jetnet/trunk
#create the shared libraries needed
make

(note: If one get's stuck in rootcint, just tipe .q to exit)

#go to the example directory
cd example
#run the example script
root -l -b runTraining.C

The training settings for TMVA are set in "training.C". As usual in TMVA one uses the option string in "factory->BookMethod()" to set the JetNet parameters. The example script trains with a dummy tree ("sampleTree.root") with gaussian distributed signal and background events. If one runs the script as it is provided, it splits the sample tree in two halfs, trains with the first half, tests its performance with the second half and changes the order afterwards. The outputs are therefore labeled as "Output_JetNet_0of2.root" and "Output_JetNet_1of2.root". To use the TMVA GUI to view the output, do in the "/example" directory

root -l
#if one uses ROOT 6.04
TMVA::TMVAGui("Output_JetNet_0of2.root")

#if one uses ROOT 5.34
.L gui.C
gui("Output_JetNet_0of2.root")

An example how to read back a trained network to propagate data through is given in "readNetwork.cxx" that can be started using

root -l -b runRead.C

This code propagates the part of the tree used as test sample in the first training through the net optimised in that training.

Tested Platforms

In principle the plugin should also work well on local machines. Up to now it has been tested on linux machines with the following setup:

  • ROOT version 5.34.25
  • g++ 4.8.1
  • GNU Fortran 4.8.1

or

  • ROOT version 6.04.14
  • g++ 4.9.3
  • GNU Fortran 4.9.3

Performance Monitoring

A lot of performance monitoring can be done using the "Output_JetNet_*of2.root" file created by TMVA. However, the training and test errors are not written there by TMVA. They are directly written out by the pluging and stored in "weights/TMVAClassification_JetNet_*of2.Monitoring.root".

Parameters for JetNet Option String

In this section, the possible parameters that can be given to JetNet via the option string, their default values and their corresponding parameters in CERN-TH-7135-94. are listed.

Name Explanation Default Value Corresponding Parameter
ErrorFunc type of the error function 0 MSTJN(4)
Momentum optional momentum for the training 0 PARJN(2)
WeightUpdate weight update after number of patterns 3 MSTJN(2)
NMaxTrainingIter number of maximal training iterations if minimum is not reached before 100 no corresponding parameter
NMaxTrainRiseErr number of training itarations with rising error after that training is aborted and the configuration with minimum error is chosen (if -1, training is always done up to max number of iterations) -1 no corresponding parameter
UpdatingMethod training method (default: backpropagation) 0 MSTJN(5)
LearningRate learning rate 0.05 PARJN(1)
InitialWeightsWidth width of the randomly set initial weights 1.0 PARJN(4)
LearningRateDecrease additional decreasing factor for the learning rate (1.0 means no decreasing) 1.0 PARJN(11)
ActivationFunction type of activation function for the neurons 1 MSTJN(3)
HiddenLayers structure of the hidden layers (e.g. N+2,N+1 for two hidden layers and N being the number of input variables N+2 MSTJN(1) and MSTJN(10+)

About the Plugin

Here a short process diagram on how the plugin actually works and what is done by TMVA and what by JetNet.

Process_Diagram.png

A simplified class diagramm with all important classes can be seen here.

Class_Diagram.png

If you need additional information you can't find here and in the given links, feel free to ask.



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