TMVA News Archive

• New Decision Tree Pruning algorithm (Cost Complexity Pruning a la CART) written by Doug Schouten (Fraser U.). It replaces the old CostComplexity and CostComplexity2 algorithms.
• Further improvements introduced by Doug:

• New no splitting option (choosable with NCuts<0) that finds best split point by first sorting the events for each variable and then looping through all events, placing the cuts always in the middle between two of the sorted events, and finding the true possible maximum separation gain in the training sample by cutting on this variable.
• The beta parameter in the AdaBoost is now an option (default is 1).
• The node purity at which a node is classified as signal (respective background node) for determining the error fraction in the pruning became a parameter that can be set via the option NodePurityLimit (default is 0.5).
• First implementation of a new preprocessing method: transformation of the variables first into a Gaussian distribution, then performing a decorrelation of the "Gaussianised" variables. The transformation is again done by default such that (by default) the signal distributions become Gaussian and are decorrelated. Note that simultaneous Gaussianisation and decorrelation of signal and background is only possible (and done) for methods, such as Likelihood, which test both hypotheses.
• Bug fixes:

• Corrected compilation failure on some architectures with ROOT 5.18.00.
• Fix in Expected error pruning: rather than multiplying both sides, the error on the node and the sub-tree, with the prune strength, now only the expected error of the sub-tree is scaled.
• Fix in FDA parsing of the input formula. There were problems when treating more than 10 parameters (thanks to Hugh Skottowe for reporting this).

• New reference page for configuration options. The page is automatically generated for each new release. Next to the classifiers also exist information links for hints to improve the classifier performance (click on the "i" button). Many thanks to Zhiyi Liu (Fraser U) for suggesting this.
• Bug fixes:

• Calculation of "Separation": fixed bin-shift and normalisation bugs. Thanks to Dag Gillberg (Fraser U) for spotting these.
  

• Fixed problem in "SetSignal(Background)WeightExpression": signal (background weight expressions not existing in the background (signal) tree led to an abort of the tree reading ("Bad numerical expression"). Thanks to Alfio Rizzo (Brussels) for pointing this out.
  

• Fixed problem when specifying train and test tree explicitly. Some code was forgotten in the background part, creating incompatibilities. Thanks to Zhiyi Liu (Fraser U) for reporting this.

• Bug fixes:

• Fixed problem introduced in 3.9.0 and 3.9.1: preprocessing cuts could not be applied on a variable that was not declared via AddVariable (thanks to Daniel Stricker, Karlsruhe U., for spotting this).
  

• Corrected configurable random seed in GeneticAlgorithm (thanks to David Gonzalez Maline, CERN, for pointing this out).
  

• Fixed Cuts (optimisaton) method -> event with smallest value was not included in search for optimal cut (thanks to Dimitris Varouchas, LAL-Orsay, for helping us detecting the problem).

• Bug fix:

• Added missing link to libMLP shared library to Makefile; also fixed compilation issue with ROOT 5.08.

• New Simulated Annealing (SA) algorithm for global minimisation in presence of local minima (optionally used in cut optimisation (MethodCuts) and the Function Discriminant (MethodFDA)). The SA algorithm features two approaches, one starting at minimal temperature (ie, from within a local minimum), slowly increasing, and another one starting at high temperature, slowly decreasing into a minimum. Code developed and written by Kamil Bartlomiej Kraszewski, Maciej Kruk and Krzysztof Danielowski from IFJ and AGH/UJ, Krakow, Poland.

• Plugin capability: custom multivariate classifier can now be plugged into the TMVA framework to benefit from TMVA's analysis and performance comparison tools. The user needs to derive the custom class from TMVA::MethodBase and implement the (few) virtual methods required by the TMVA::IMethod interface. The classifier can then be directly called via ROOT's plugin mechanism. An example for this is given in TMVA/macros/TMVAnalysis.C. Many thanks to Daniel Martschei and Thomas Kuhr (Karlsruhe U.) for suggesting and implementing this feature.

• Preselection cuts now work on arrays. Previously used TEventlists (only event wise pass/fail) were replaced by TreeFormulas (sensitive to array position). Thanks to Arnaud Robert (LPNHE) for his contributions.

• Framework/dataset preparation: Signal and background trees can now be assigned individually to training and test purposes. This is achieved by setting the third parameter of the Factory::AddSignalTree/AddBackgroundTree() methods to "Train" or "Test" (const string). The only restriction is that either none or all signal (background) trees need to be specified with that option. It is possible to mix the two modes, for instance one can assign individual training and test trees for signal, but not for background.

• For increased flexibility, users can also directly input signal and background, training and test events to TMVA, instead of letting TMVA interpret user-given trees. Note that either one of the two approaches must be chosen (no mix). The syntax of the new calls is described in the macros/TMVAnalysis.C test macro. --> The User runs the event loop, copies for each event the input variables into a std:vector, and "adds" them to TMVA, using the dedicated calls: factory->AddSignalTrainingEvent( vars, signalWeight ); (and replacing "Signal" by "Background", and "Training" by "Test"). After the event loop, everything continues as in the standard method.

• Cut optimisation: added physical limits to min/max cuts if smart option is used.

• TMlpANN: fixed crash with ROOT>=5.17 when using large number of test events; also corrected bias in cross validation: before the test events were used, which led to an overestimated performance evaluation in case of a small number of degrees of freedom; separate now training tree in two parts for training and validation with configurable ValidationFraction
  Extended options to TMultilayerPerceptron learning methods.

• BDT: removed hard-coded weight file name; now, paths and names of weight files are written as TObjStrings into ROOT target file, and retrieved for plotting; available weight files (corresponding to target used) can be chosen from pop-up GUI.
  Changes in handling negative weights in BDT algorithm. Events with negative weights now get their weight reduced (*= 1/boostweight) rather than increased (*= boostweight) as the other events do. Otherwise these events tend to receive increasingly stronger boosts, because their effects on the separation gain are as if background events were selected as signal and vice versa (hence the events tend to be "wanted" in signal nodes, but are boosted as if they were misclassified). In addition, the separation indices are protected against negative S or S+B returning 0.5 (no separation at all) in case that occurs.
  In addition there is a new BDT option to ignore events with negative event weights for the training. This option could be used as a cross check of a "worst case" solution for Monte Carlo samples with negative weights. Note that the results of the testing phase still include these events and are hence objective.
  Added randomised trees: similar to the "Random Forests" technique of Leo Breiman and Adele Cutler, it uses the "bagging" algorithm and bases the determination of the best node-split during the training on a random subset of variables only, which is individually chosen for each split.
  Move to TRandom2 for the "bagging" algorithm and throw random weights according to Poisson statistics. (This way the random weights are closer to a resampling with replacement algorithm.)

• GUI: New macro (and GUI button) for Parallel Coordinate plotting.

• Python (PyROOT): added example for reader application: TMVA/python/TMVApplication.py

• Bug fixes:

• Corrected inconsistency in MethodCuts: the signal efficiency written out into the weight file does not correspond to the center of the bin within which the background rejection is maximised (as before) but to the lower left edge of it. This is because the cut optimisation algorithm determines the best background rejection for all signal efficiencies belonging into a bin. Since the best background rejection is in general obtained for the lowest possible signal efficiency, the reference signal efficiency is the lowest value in the bin.

• Fixes in input-variable and MVA plotting: under/over-flow numbers given on plots were not properly normalised; the maximum histogram ranges have been increased to avoid cut-offs. Thanks to Andreas Wenger, Zuerich, for pointing these out.

• Cleaned up memory leaks.

• Added printouts to "Cuts" classifier quoting the explicit cut application for given signal efficiency. In case of transformations of the input variables, the full expressions are given. Added warning to Fisher in case of variable normalisation.

• Macro fixes for ROOT-integrated version.

• Version integrated into ROOT 5.18.

• Improved "mvaeffs.C" macro for significance calculation with arbitrary numbers of signal and background events. This concerns button (5a) of the main GUI (TMVAGui.C).

• Improved "BDT.C" macro for decision tree drawing. This concerns button (8) of the main GUI (TMVAGui.C).

• A few minor GUI upgrades.

• Multiple internal changes (transparent for the users) to prepare ROOT 5.18 production release.

• Bug fixes:

• Fix in MLP network plotter.
• Fixes of smaller memory leaks.
• This version will be integrated in the forthcoming ROOT release 5.18.

• Bug fixes:

• Reimplement possibility to cut on variables that are not used in the MVAs during training and test tree preparation (feature destroyed inadvertedly with latest "bug fix", thanks to Lianyou Shan for spotting this!).

• New normalisation option "NormTree" for the binary search tree used in PDERS. The tree sorting is modified to achieve trees with equal branch lengths, which, in average, speeds up searches. Implementation by Maciej Kruk, Cracow.
• Bug fixes:

• Fix of problem with TChain treatment (occurred in ROOT >=5.15) in DataSet. Thanks to Alfio Rizzo for spotting this!.

• Bug fixes:

• Memory leaks in the Reader class are removed: the Reader is now properly destructed (deletion of all handled classifiers). Thereby, pointer problems in the destructors of Fisher and SVM have been found and fixed.
• In later ROOT versions (>5.14) the macro TMVApplication.C produced a segmentation fault when run from the ROOT prompt (the compiled version in the examples directory worked fine). This problem is now solved.
• The color selection has been adapted to the new color palette that was introduced in ROOT 5.16. The macros should now look alike with all ROOT versions (above 4.02/00).

• The somewhat hastily built release 3.8.8 suffers from an incompatibility with ROOT >= 5.15, leading to segmentation faults in some applications. We have identified the problems and are preparing a new bug-fix release (3.8.9), which should come out on Oct 3.

• Cuts can now be applied independently for signal and background in the PrepareTrainingAndTestTree phase.
• Previously, the input variables used by the Fisher classifier were always normalised to [-1,1] by default. This has been removed, so that it is now in the hand of the user to decide whether or not normalisation is applied. Choose "Normalise" ("!Normalise") for normalisation (no normalisation), default is "!Normalise".
• Bug fixes:

• Major bug fix: the application of cuts in the PrepareTrainingAndTestTree call in conjunction with the use of several trees (ie, several consecutive calls of factory->AddSignalTree(...) or factory->AddBackgroundTree(...)), lead to a wrong application of the cut to all trees but the first one in the signal and background chains. More details can be provided if requested - please contact the authors. We wish to thank Manfred Groh for spotting and analysing the problem!
• Some compilers complained about a missing #include "TMVA/Configurable.h" in the Reader class. This has been fixed.

• Significant speed improvements for PDERS.
  For the options to benefit from this, see the example "PDERSkNN" in macros/TMVAnalysis.C or examples/TMVAnalysis.cxx. Thanks to Kamil Kraszewski and friends from Cracow for implementing this.
• Re-established backward compatibility of TMVA code down to ROOT version 4.02/00.
• Shortened BDT weight-file and standalone C++ reader class by 20% and 50%, respectively.
• Bug fixes:

• Fixed problem in RuleFit's standalone class when using integer input variables.
• Fixed compilation problem when using decorrelation preprocessing of input variables in C++ standalone reader classes.
• Fixed bug in number-of-plots calculation in correlation script.
• Fixed bug in printing of number of events in case of several trees (no impact on results).
• Fixed inconsistency between cut optimisation and cut reading: the aligned definition of min and max cuts is: a variable passes a cut if: min < var <= max. (This inconsistency may have affected your results if you used cut optimisation together with integer variables. Please check with the new version.)
• Fixed macro path in TMVAGui.C to fix problem when running the GUI in the ROOT/TMVA distribution.
  Also: TMVA Style moved from TMVAlogon into tmvaglob to fix style problem when running in the ROOT/TMVA distribution.

• Weight expressions can now be set individually for signal and background via the calls factory->SetSignalWeightExpression( "<signal-expression>" ) and factory->SetBackgroundWeightExpression( "<background-expression>" ). The former call is still supported.
• Overtraining test: a new GUI button (corresponding to an extension of the macro "mvas.C") is available to plot a comparison of the classifier response distributions for the training and independent test data sets. The results of a Kolmogorov-Smirnov compatibility test are printed on stdout and plots.
• The cuts corresponding to a given signal efficiency can be retrieved via the reader.
  An example for this is implemented in "macros/TMVApplication.C". Briefly, retrieve the cuts classifier object as follows: TMVA::MethodCuts* mcuts = (TMVA::MethodCuts*)reader->FindMVA( "CutsGA method" );, define cut vectors (a vector of pairs can also be retrieved via overloaded GetCuts function): std::vector cutsMin; std::vector cutsMax; and fill them via: mcuts->GetCuts( wantedSignalEfficiency, cutsMin, cutsMax );
• Bug fixes:

• Fixed typos in weight file names in MLP and BDT macros
• A comment on code size proliferation: for an intermediate period we are developing a new, more flexible TMVA design in the TMVA/reader repository (for simplicity, to avoid cvs branching). This entails code duplication, which will be removed once the new design is deployed. The duplication has been removed for the tgz-download version.

• Bug fixes:

• Fixed "MinMax" and "RMS" options of PDERS (thanks to Junpei Maeda for spotting this)
• Fixed compilation problem in MetricEuler class on some platforms
• Known problems:

• A filename typo in the ROOT script "macros/network.C" leads to an abort when plotting the MLP architecture. It can be fixed by replacing line 169 by:
  TString fname = "weights/TMVAnalysis_MLP.weights.txt";
  
  The same typo exists in "macros/BDT.C" (plotting a decision tree) where line 8 should be changed into:
  void BDT(Int_t itree=1, TString fname= "weights/TMVAnalysis_BDT.weights.txt", Bool_t useTMVAStyle = kTRUE);
  
  Thanks to Alfio Rizzo and Roger Haelg for reporting these bugs. They have been fixed in CVS and will be distributed with the forthcoming release.

• Added new option for event weight renormalisation to "PrepareTrainingAndTestTree" configuration.
  Possible settings are "None", "NumEvents", "EqualNumEvents", where the latter two options renormalise the event weights so that their sums equal the number of signal and background events, or the number of signal events, respectively. The current version of the TMVA Users Guide has been updated.
• Bug fixes:

• Fixed bug in weights read back of CFMlpANN -> now fully operational again.
• Fixed convergence test macro for MLP neural network.

• Version 4 of the TMVA Users Guide is available: Download PDF file (1.2 MB)
  The guide describes the TMVA classifiers and features, and provides code examples for the interaction with the TMVA Factory and Reader. It corresponds to v3.8.3. To cite it, please refer to arXiv::physics/0703039.
• TMVA-v3.8.3 contains a large number of new features. Please, take the time to digest this announcement. All new classifiers and features are also described (and in much more detail) in V4 of the Users Guide.
• New Classifiers:

• Genuine k-Nearest Neighbour (k-NN) classifier, implemented by Rustem Ospanov (U. of Texas, USA). The classifier is similar to PDERS, but intrinsically adaptive and faster.
• Function Discriminant Analysis (FDA), implemented by Peter Speckmayer and Andreas Hoecker. This simple classifier fits any user-defined TFormula (via option configuration string) to the training data by requiring a formula response of as close as possible to 1 (0) for signal (background) events.
  The parameter fitting is done via the abstract class FitterBase (see below), where the concrete fitter is selected via the configuration option string.
• Upgrades for existing classifiers:

  • Likelihood now allows now to select the interpolation procedure for the reference distributions individually for each variable.
    That is, e.g., spline for var1 Kernel density estimator for var2 etc. In this context, the naming of the options has been changed. While before one would write "Spline=2" or "useKDE", it is now consistently done by: "PDFInterpol=Spline2" or "PDFInterpol=KDE". The previous syntax chooses it for "ALL variables", but one can also specify individually by writing: "PDFInterpol[0]=Spline2:PDFInterpol[1]=KDE" etc.
  • RuleFit has been improved: the forest now consists of boosted trees; a significant speed enhancement has been achieved, and the configuration options have been reorganised.
  • Change of default options in BDT: if no minimum number of events in leaf node is specified, max(20, N_train/(N_cuts^2)/10)) is used.

• Creation of standalone C++ reader classes: each classifier (except for PDERS, k-NN and Cut optimisation) writes together with its weight file a TMVA-independent and ROOT-independent C++ reader class into the same directory as the weight files, and following the same naming convention.
  The classes allow users who need ROOT independence to interpret the trained classifiers. They provide identical results as the TMVA-integrated Reader. The example script TMVA/macros/ClassApplication.C shows how to use the new classes.
• Classifier-specific help messages: when invoking the "H" option in the configuration option string, each classifier prints a help message with a short summary of the working principles, and, more importantly, hints on which options to tune to improve selection and/or speed performance.
  The help messages of all booked classifiers can also be printed by invoking the factory via the call: factory->PrintHelpMessage().
• The various TMVA fitters, used for minimisation problems by the classifiers, have been upgraded and unified under the common base class FitterBase.
  Any TMVA module that uses minimisation inherits from IFitterTarget, which interfaces the minimisation function. Presently, four concrete fit algorithms are implemented: Monte Carlo sampling (MC), Genetic Algorithm (GA), MINUIT and Simulated Annealing (SA not yet finalised). Also allowed are combinations of fitters, such as using MC or GA together with MINUIT for better convergence under starting values provided by the former algorithms.
• Change of the somewhat indigestive mu-transform into the Rarity variable. The rarity is a variable transformation that makes the background uniform distributed in [0,1], while the signal is peaking towards 1.
  It can be also useful to spot simulation (or control sample) problems: if the background in data is not flat this would hint to problems in the background description. A useful variable, try it out!
• Other changes:

• Internal renormalisation of event weights: to avoid artefacts if event weights have very small or large numbers, we renormalise the sum of weights of all signal and background training events to be equal the number of events. This does not affect the relative event weights.
• New directory structure for histograms in the target file produced by the training. The classifier directories have subdirectories according to the classifier names. This allows to better organise the histograms produced by different instances of the same classifier (e.g., during optimisation scans of that classifier). The GUI correctly uses the new structure. Some changes also occur in the weight files, though backward compatibility is guaranteed through the TMVA versioning.
• Change in TMVA::Config class: the structs with global settings can no longer be addressed via (e.g.): , TMVA::gConfig().ionames.weightFileDir = "myWeightDirectory"; but rather via: (TMVA::gConfig().GetIONames()).fWeightFileDir = "myWeightDirectory"; This change is not backward compatible, but only concerns script commands, which should be easily updatable.
• Bug fixes:

• Full fix of treatment of integer variables in Reader application (thanks to Andrea Bulgarelli for spotting this bug in the first place).
• Fix of potential branch name/title mismatch in DataSet (bug spotted by Zhiyi Liu).
• Known problems:

• The performance of the CFMlpANN neural network looks questionable. We did not yet investigate whether there is a real problem, but for the users who apply this ANN please either move the the recommended MLP ANN, or contact the authors.
• The combined use of GA or MC with a Minuit converger in FDA has a problem in the configuration and is thus inoperational at present.

• A serious bug has been discovered in the TMVA::Reader when dealing with 'I'-declared (int) variables. In that case, the Reader response is inconsistent with the test results derived in the training phase, and wrong (the test results are correct). We have uploaded TMVA-v3.7.3 with a fix of this bug - with however the temporary restriction that decorrelation preprocessing in presence of 'I'-declared variables does not work. We are working on a full fix of the problem.
  

  There are two possibilities to circumvent the bug:

  1. Upgrade your TMVA version to v3.7.3, declare as usual your integer variables as 'I', but avoid using decorrelation or PCA transformation.
  2. Do not upgrade TMVA, use integer variables, but DO NOT declare these variables as 'I', but as 'F', when adding them to the Factory. In addition, when using the Reader, declare all variables that are added to the Reader as float, and just copy the integer variable into this float placeholder. All results will be correct (only Likelihood will treat these as continuous variables, but the performance should not be significantly affected by this).

April 10-18, 2007 (TMVA-v3.7.0 - v3.7.2)

• Version 3 of the TMVA Users Guide is available: Download PDF file (1.2 MB)
  
  The guide describes the TMVA classifiers and features, and provides code examples for the interaction with the TMVA Factory and Reader. It corresponds to v3.7.2. To cite it, please refer to arXiv::physics/0703039
• Classifier updates and improvements:
• New Support Vector Machine (SVM) classifier, implemented by Andrzej Zemla and Marcin Wolter (IFJ PAN Krakow, Poland). Currently it is still a beta version, which is functional but has not yet been exhaustively tested. Feedback is welcome. Fours different kernel functions are available: linear, polynomial, Gaussian and sigmoidal. SVM performs well under our standard toy example, but not many more applications have been tried so far.
• Reduced memory consumption of PDERS.
• Speed up of Fisher and H-Matrix in cases where many (>30) variables are used.
• New features and code updates:
• Where useful, options can be set per variable and event class. An example for this is the creation of reference distributions (PDFs) for the likelihood classifier: each variable, individually for signal and background, can have custom interpolation and smoothing settings.
• Upon request, the weight files are now exclusively written in text (ascii) format. If wished, an option also allows the writing in ROOT format, but this is supposed to be the exception from the rule.
• Input variables that are part of an array or vector are now accepted if specified with the array index. Example: factory->AddVariable("pT[0]",'F').
• Colorful logging output can be switched off using Factory option. In batch mode, colors are switched off by default to avoid flooding the logfiles with ESC sequences.
• Bug fixes:
• New for v3.7.2: fix problem with use of discrete variables in Likelihood classifier.
• Fixed problem in weight file reading for rectangular cut optimisation. Note that the format has slightly changed, which will affect the reading of previous weight files (for Cuts only).
• Discrete variables are now correctly plotted (in variables.C).
• We gratefully acknowledge the many valuable inputs and comments from watchful users!

• Significant speed enhancement for RuleFit. Also improved robustness of RuleFit response
• Individualised likelihood options: smoothing parameters and number of bins in reference histograms of binned non-parametric splines can now be set for each variable and class (signal, background)

• The complete TMVA Users Guide is now available: Download PDF file (1.2 MB)
  
  The guide describes the TMVA classifiers and features, and provides code examples for the interaction with the TMVA Factory and Reader. It corresponds to v3.6.0.
• Classifier updates and improvements for v3.6.0:
• New adaptive unbinned kernel density estimator (KDE) for the projective likelihood classifier (thanks to Asen Christov).
• Ranking of input variables for projective likelihood classifier.
• New analysis macros for RuleFit. Other RuleFit developments to improve its robustness.
• New features and code updates for v3.6.0:
• Random and block-wise shuffling of training and test events (optional).
• New interface for the registration of trees for training and testing allowing unequal numbers of training and testing events for signal and background. The interface is backward compatible.
• Branches of the user TTree not used to compute the input variables are automatically disabled to speed up the training and testing tree preparation.
• On user request (option CreateMVAPdfs) TMVA creates probability density functions for the classifier signal and response distributions using the training data. These can then be retrieved via the reader method: reader->GetProba( "" ) (thanks to Sophie Henrot-Versillé).
• All classifiers now write ROOT and text weight files. The ROOT files contain (at least) PDFs for the classifiers. Given the need by some collaborations to store weight files in conditions databases, we are considering to move to text-only formats for the next release. Please contact us if this may create incompatibilities.
• New Configurable base class simplifying the configuration of Factory and classifiers.

• Reorganisation (and decluttering) of treatment of variable transformations prior to applying MVA data mining (these transformations were previously called "preprocessing"). Although this upgrade corresponds to significant code evolution, most of it does not affect the user.
• Upgrade of result plots and macros.
• Further performance improvements in RuleFit.
• Few other insignificant and cosmetics changes.

• Many internal changes regarding convenience and robustness of TMVA. In particular, the automatic decorrelation of the input variables, which led to problems in the past, has been turned off. Decorrelation (in general: data preprocessing) is now performed on demand (by option of the Factory, or whenever a methods requires it).
• Much improved BDT pruning. Also automatic detection of the pruning strength is available, which is however not performed by default: use the option string to change defaults.
• TMVA can also be run from python using PyROOT. An example code can be found in: TMVA/python/TMVAnalysis.py. Execute it from the command line by calling (from the TMVA/python/ directory): python TMVAnalysis.py --method "Fisher Likelihood", or any other methods. Type python TMVAnalysis.py --help, for the usage.

• Some internal code changes, such as the introduction of a message logger to replace standard output (you will notice the nicely formatted output, thanks to Attila Krasznahorkay).
• Bug fixes in the macros for the result plotting. Previously, the TMVAGUI used to crash after a few plotting iterations.
• Improvements in the BDT pruning.

• A few outstanding problems have been fixed. Among these: input data preprocessing (such as decorrelation) is now performed on-the-fly (before, individual trees were created in the DataSet). This allows the MVAs to obtain the appropriate preprocessing separately for signal and background (as necessary, e.g., in MethodLikelihood).

• MVA methods:
  • The multi-dimensional PDE range-search method has been extended by a larger choice of kernel functions (Gaussian, teepee (triangular), Sinc and Lanczos). Moreover, instead of rectangular hypervolumes, one can also use hyperellipsoides to define the reference volume. Many tests indicate that large volume sizes perform better. Please send comments/questions to Yair Mahalalel (CERN)
  • We provide a first release of a new implementation of RuleFit (see paper by Friedman-Popescu). We note that its state is still preliminary. Some more testing is needed to understand and optimise all the features of this method, and to find a good default configuration. Please send comments/questions to Fredrik Tegenfeldt (Iowa State U. till Aug 2007).
  • Boosted Decision Trees (BDT) can now be pruned. The corresponding option is PruneStrength=10, setup to a default value of 10. Pruning removes decision-tree nodes that are considered noise.

• New analysis macros:
  • The macro ../macros/annconvergencetest.C displays the convergence of the MLP network for training and test events. It is called pressing the new button "Network Convergence Test" of the GUI: root -l TMVAGui.C\(\"TMVA.root\"\).

• New methods under active development are: Support Vector Machine and a combined (ruled) use of various MVAs in a committe of methods.

• The next full update of the ROOT version of TMVA will go into the ROOT production release v5.13 (December 2006).

• TMVA has been undergone a major redesign and upgrade during the Summer 2006. Most of the changes are hidden to the TMVA user. The changes/new features that do affect the users are listed below:

• Job configuration as far as it concerns the user:
  • The declaration of MVA input variables allows to specify the variable type (however, the only method that currently uses type information is Likelihood).
  • Global (tree) and event-by-event weights are now included. All TMVA methods but Cuts, Fisher and HMatrix support event weights (for these methods they are ignored).
  • The method booking interface has slightly changed: several bookings of the same method (different instances) are now possible. This allows to train and test a method with different configurations.
  • The option string provided by the user during the booking phase has been enhanced: options are now given independent of the order, and the interface is made clearer.
  • The weight files are now entirely self-contained, ie, they contain the full configuration information and training results (weights). When using a trained method in the Reader, it is sufficient to give "some" method name and the corresponding weight file.
  • Formula expressions (functions of variables) can be used as input variables.
  • TChains and TTrees are supported as data inputs for training.
  Please see the usual example macros (macros/TMVAnalysis.C and macros/TMVApplication.C) to lookup concrete implementations of the new interfaces.

• Training and data preparation features:
  • Linear decorrelation of input variables is centrally provided by the new DataSet class, and is available to all TMVA methods (particularly interesting for Cut optimization and Likelihood). Add ":D" to the option string.
  • Most of the methods now return (and print to stdout) ranked input variables, according to the classification criteria used by the method.

• MVA methods:
  • New artificial neural network: MLP (implemented by Matt Jachowski, Stanford U). This fast and powerful multilayer perceptron is the recommended TMVA neural network. We have tested that MLP performs at least as well as TMlpANN, and significantly better than CFMlpANN.
  • Correct treatment of discrete variables in Likelihood.
  • Boosted decision trees (BDT) come now with pruning.
  • The Genetics Algorithm for Cuts optimization has been significantly improved.

• New analysis macros:
  • Simple GUI for results plotting (macros/TMVAGui.C) to be called after training and testing in "macros" dir via the prompt command:
    root -l TMVAGui.C\(\"TMVA.root\"\)
  • Scatter and profile plots to better visualise correlations between input variables
  • Quality verification for Likelihood reference distributions.
  • Visualisation of trained MLP network architecture.

• Known problems/bugs (see also the developers 'todo' list):
  • BDT cannot run on a sample of solely discrete variables.
  • Decorrelation does not distinguish signal and background (signal is used).
  • Event weights are not yet consistently treated in all TMVA plots. We think (hope) that this does not have an impact on the MLP, BDT and Likelihood event-wise weighting, and hence performance.

• Forthcoming MVA method developments: we are currently actively working on the improvement of PDERS and BDT; new methods under active development are: Support Vector Machine and RuleFit. We are also developing combined (ruled) use of various MVAs in a committee.

• The next full update of the ROOT version of TMVA will go into the ROOT production release v5.13 (December 2006).

• TMVA has been integrated in ROOT 5.11/06 (development version). The full TMVA functionality is available therein. For demonstration purpose, the macro TMVAnalysis.C is provided in the tutorial section. More information at the following links:
• ROOT release notes
• ROOT::TMVA class index
• ROOT download page
• For obvious reasons the ROOT-integrated version is not uptodate with the cvs HEAD that can be downloaded by users from sourceforge. New features available here include
  • Simulated Annealing fitter for cut optimisation (needs still some tuning)
  • TFormula-parsed operational strings can be used as MVA input variables