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: Parsing option string: : ... "V:!Silent:Color:DrawProgressBar:AnalysisType=Classification" : The following options are set: : - By User: : V: "True" [Verbose flag] : Color: "True" [Flag for coloured screen output (default: True, if in batch mode: False)] : Silent: "False" [Batch mode: boolean silent flag inhibiting any output from TMVA after the creation of the factory class object (default: False)] : DrawProgressBar: "True" [Draw progress bar to display training, testing and evaluation schedule (default: True)] : AnalysisType: "Classification" [Set the analysis type (Classification, Regression, Multiclass, Auto) (default: Auto)] : - Default: : VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)] : Transformations: "I" [List of transformations to test; formatting example: "Transformations=I;D;P;U;G,D", for identity, decorrelation, PCA, Uniform and Gaussianisation followed by decorrelation transformations] : Correlations: "False" [boolean to show correlation in output] : ROC: "True" [boolean to show ROC in output] : ModelPersistence: "True" [Option to save the trained model in xml file or using serialization]DataSetInfo : [MatchNNDataSet] : Added class "Signal" : Add Tree Signal of type Signal with 2175608 eventsDataSetInfo : [MatchNNDataSet] : Added class "Background" : Add Tree Bkg of type Background with 14040318 events : Dataset[MatchNNDataSet] : Class index : 0 name : Signal : Dataset[MatchNNDataSet] : Class index : 1 name : BackgroundFactory : Booking method: [1mmatching_mlp[0m : : Parsing option string: : ... "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator" : 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:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator" : The following options are set: : - By User: : NCycles: "700" [Number of training cycles] : HiddenLayers: "N+2,N" [Specification of hidden layer architecture] : NeuronType: "ReLU" [Neuron activation function type] : EstimatorType: "CE" [MSE (Mean Square Estimator) for Gaussian Likelihood or CE(Cross-Entropy) for Bernoulli Likelihood] : V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)] : VarTransform: "Norm" [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] : TrainingMethod: "BP" [Train with Back-Propagation (BP), BFGS Algorithm (BFGS), or Genetic Algorithm (GA - slower and worse)] : LearningRate: "2.000000e-02" [ANN learning rate parameter] : DecayRate: "1.000000e-02" [Decay rate for learning parameter] : TestRate: "50" [Test for overtraining performed at each #th epochs] : Sampling: "1.000000e+00" [Only 'Sampling' (randomly selected) events are trained each epoch] : SamplingImportance: "1.000000e+00" [ The sampling weights of events in epochs which successful (worse estimator than before) are multiplied with SamplingImportance, else they are divided.] : UseRegulator: "False" [Use regulator to avoid over-training] : - Default: : RandomSeed: "1" [Random seed for initial synapse weights (0 means unique seed for each run; default value '1')] : NeuronInputType: "sum" [Neuron input function type] : 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)] : EpochMonitoring: "False" [Provide epoch-wise monitoring plots according to TestRate (caution: causes big ROOT output file!)] : SamplingEpoch: "1.000000e+00" [Sampling is used for the first 'SamplingEpoch' epochs, afterwards, all events are taken for training] : SamplingTraining: "True" [The training sample is sampled] : SamplingTesting: "False" [The testing sample is sampled] : ResetStep: "50" [How often BFGS should reset history] : Tau: "3.000000e+00" [LineSearch "size step"] : BPMode: "sequential" [Back-propagation learning mode: sequential or batch] : BatchSize: "-1" [Batch size: number of events/batch, only set if in Batch Mode, -1 for BatchSize=number_of_events] : ConvergenceImprove: "1.000000e-30" [Minimum improvement which counts as improvement (<0 means automatic convergence check is turned off)] : ConvergenceTests: "-1" [Number of steps (without improvement) required for convergence (<0 means automatic convergence check is turned off)] : UpdateLimit: "10000" [Maximum times of regulator update] : CalculateErrors: "False" [Calculates inverse Hessian matrix at the end of the training to be able to calculate the uncertainties of an MVA value] : WeightRange: "1.000000e+00" [Take the events for the estimator calculations from small deviations from the desired value to large deviations only over the weight range]matching_mlp : [MatchNNDataSet] : Create Transformation "Norm" with events from all classes. : : Transformation, Variable selection : : Input : variable 'chi2' <---> Output : variable 'chi2' : Input : variable 'teta2' <---> Output : variable 'teta2' : Input : variable 'distX' <---> Output : variable 'distX' : Input : variable 'distY' <---> Output : variable 'distY' : Input : variable 'dSlope' <---> Output : variable 'dSlope' : Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'matching_mlp : Building Network. : Initializing weightsFactory : [1mTrain all methods[0m : Rebuilding Dataset MatchNNDataSet : Parsing option string: : ... "SplitMode=random:V:nTrain_Signal=50000.0:nTrain_Background=500000.0:nTest_Signal=20000.0:nTest_Background=100000.0" : The following options are set: : - By User: : SplitMode: "Random" [Method of picking training and testing events (default: random)] : nTrain_Signal: "50000" [Number of training events of class Signal (default: 0 = all)] : nTest_Signal: "20000" [Number of test events of class Signal (default: 0 = all)] : nTrain_Background: "500000" [Number of training events of class Background (default: 0 = all)] : nTest_Background: "100000" [Number of test events of class Background (default: 0 = all)] : V: "True" [Verbosity (default: true)] : - Default: : MixMode: "SameAsSplitMode" [Method of mixing events of different classes into one dataset (default: SameAsSplitMode)] : SplitSeed: "100" [Seed for random event shuffling] : NormMode: "EqualNumEvents" [Overall renormalisation of event-by-event weights used in the training (NumEvents: average weight of 1 per event, independently for signal and background; EqualNumEvents: average weight of 1 per event for signal, and sum of weights for background equal to sum of weights for signal)] : ScaleWithPreselEff: "False" [Scale the number of requested events by the eff. of the preselection cuts (or not)] : TrainTestSplit_Signal: "0.000000e+00" [Number of test events of class Signal (default: 0 = all)] : TrainTestSplit_Background: "0.000000e+00" [Number of test events of class Background (default: 0 = all)] : VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)] : Correlations: "True" [Boolean to show correlation output (Default: true)] : CalcCorrelations: "True" [Compute correlations and also some variable statistics, e.g. min/max (Default: true )] : Building event vectors for type 2 Signal : Dataset[MatchNNDataSet] : create input formulas for tree Signal : Building event vectors for type 2 Background : Dataset[MatchNNDataSet] : create input formulas for tree BkgDataSetFactory : [MatchNNDataSet] : Number of events in input trees : Dataset[MatchNNDataSet] : Signal requirement: "chi2<15 && distX<250 && distY<400 && dSlope<1.5 && dSlopeY<0.15" : Dataset[MatchNNDataSet] : Signal -- number of events passed: 2151182 / sum of weights: 2.15118e+06 : Dataset[MatchNNDataSet] : Signal -- efficiency : 0.988773 : Dataset[MatchNNDataSet] : Background requirement: "chi2<15 && distX<250 && distY<400 && dSlope<1.5 && dSlopeY<0.15" : Dataset[MatchNNDataSet] : Background -- number of events passed: 7175761 / sum of weights: 7.17576e+06 : Dataset[MatchNNDataSet] : Background -- efficiency : 0.511083 : Dataset[MatchNNDataSet] : you have opted for interpreting the requested number of training/testing events : to be the number of events AFTER your preselection cuts : : Dataset[MatchNNDataSet] : you have opted for interpreting the requested number of training/testing events : to be the number of events AFTER your preselection cuts : : Dataset[MatchNNDataSet] : Weight renormalisation mode: "EqualNumEvents": renormalises all event classes ... : Dataset[MatchNNDataSet] : such that the effective (weighted) number of events in each class is the same : Dataset[MatchNNDataSet] : (and equals the number of events (entries) given for class=0 ) : Dataset[MatchNNDataSet] : ... i.e. such that Sum[i=1..N_j]{w_i} = N_classA, j=classA, classB, ... : Dataset[MatchNNDataSet] : ... (note that N_j is the sum of TRAINING events : Dataset[MatchNNDataSet] : ..... Testing events are not renormalised nor included in the renormalisation factor!) : Number of training and testing events : --------------------------------------------------------------------------- : Signal -- training events : 50000 : Signal -- testing events : 20000 : Signal -- training and testing events: 70000 : Dataset[MatchNNDataSet] : Signal -- due to the preselection a scaling factor has been applied to the numbers of requested events: 0.988773 : Background -- training events : 500000 : Background -- testing events : 100000 : Background -- training and testing events: 600000 : Dataset[MatchNNDataSet] : Background -- due to the preselection a scaling factor has been applied to the numbers of requested events: 0.511083 : DataSetInfo : Correlation matrix (Signal): : -------------------------------------------------------- : chi2 teta2 distX distY dSlope dSlopeY : chi2: +1.000 +0.197 +0.512 +0.603 +0.392 +0.418 : teta2: +0.197 +1.000 +0.456 +0.649 +0.399 +0.581 : distX: +0.512 +0.456 +1.000 +0.445 +0.555 +0.606 : distY: +0.603 +0.649 +0.445 +1.000 +0.529 +0.568 : dSlope: +0.392 +0.399 +0.555 +0.529 +1.000 +0.647 : dSlopeY: +0.418 +0.581 +0.606 +0.568 +0.647 +1.000 : --------------------------------------------------------DataSetInfo : Correlation matrix (Background): : -------------------------------------------------------- : chi2 teta2 distX distY dSlope dSlopeY : chi2: +1.000 +0.001 +0.370 +0.305 +0.002 +0.084 : teta2: +0.001 +1.000 +0.173 +0.650 +0.280 +0.455 : distX: +0.370 +0.173 +1.000 +0.043 +0.627 +0.195 : distY: +0.305 +0.650 +0.043 +1.000 +0.240 +0.458 : dSlope: +0.002 +0.280 +0.627 +0.240 +1.000 +0.362 : dSlopeY: +0.084 +0.455 +0.195 +0.458 +0.362 +1.000 : --------------------------------------------------------DataSetFactory : [MatchNNDataSet] : : Factory : [MatchNNDataSet] : Create Transformation "I" with events from all classes. : : Transformation, Variable selection : : Input : variable 'chi2' <---> Output : variable 'chi2' : Input : variable 'teta2' <---> Output : variable 'teta2' : Input : variable 'distX' <---> Output : variable 'distX' : Input : variable 'distY' <---> Output : variable 'distY' : Input : variable 'dSlope' <---> Output : variable 'dSlope' : Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'TFHandler_Factory : Variable Mean RMS [ Min Max ] : ----------------------------------------------------------- : chi2: 4.1539 4.6460 [ 1.3264e-05 15.000 ] : teta2: 0.0079252 0.017224 [ 1.2100e-06 0.43619 ] : distX: 27.109 38.586 [ 3.8147e-06 250.00 ] : distY: 20.564 28.494 [ 1.5259e-05 399.49 ] : dSlope: 0.28782 0.22814 [ 2.2016e-06 1.3026 ] : dSlopeY: 0.0054782 0.0099926 [ 1.8626e-09 0.14834 ] : ----------------------------------------------------------- : Ranking input variables (method unspecific)...IdTransformation : Ranking result (top variable is best ranked) : -------------------------------- : Rank : Variable : Separation : -------------------------------- : 1 : chi2 : 6.095e-01 : 2 : distX : 4.727e-01 : 3 : distY : 1.428e-01 : 4 : dSlope : 7.613e-02 : 5 : dSlopeY : 5.967e-02 : 6 : teta2 : 5.937e-02 : --------------------------------Factory : Train method: matching_mlp for Classification : TFHandler_matching_mlp : Variable Mean RMS [ Min Max ] : ----------------------------------------------------------- : chi2: -0.44615 0.61947 [ -1.0000 1.0000 ] : teta2: -0.96367 0.078975 [ -1.0000 1.0000 ] : distX: -0.78313 0.30869 [ -1.0000 1.0000 ] : distY: -0.89705 0.14265 [ -1.0000 1.0000 ] : dSlope: -0.55809 0.35029 [ -1.0000 1.0000 ] : dSlopeY: -0.92614 0.13472 [ -1.0000 1.0000 ] : ----------------------------------------------------------- : Training Network : : Elapsed time for training with 550000 events: [1;31m1.28e+03 sec[0m matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on training sample (550000 events) : Elapsed time for evaluation of 550000 events: [1;31m0.785 sec[0m : Creating xml weight file: [0;36mMatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml[0m : Creating standalone class: [0;36mMatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C[0m : Write special histos to file: matching_ghost_mlp_training.root:/MatchNNDataSet/Method_MLP/matching_mlpFactory : Training finished : : Ranking input variables (method specific)...matching_mlp : Ranking result (top variable is best ranked) : -------------------------------- : Rank : Variable : Importance : -------------------------------- : 1 : teta2 : 7.346e+02 : 2 : distX : 1.891e+02 : 3 : dSlopeY : 5.900e+01 : 4 : distY : 4.639e+01 : 5 : dSlope : 1.074e+01 : 6 : chi2 : 2.093e+00 : --------------------------------Factory : === Destroy and recreate all methods via weight files for testing === : : Reading weight file: [0;36mMatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml[0mmatching_mlp : Building Network. : Initializing weightsFactory : [1mTest all methods[0mFactory : Test method: matching_mlp for Classification performance : matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on testing sample (120000 events) : Elapsed time for evaluation of 120000 events: [1;31m0.169 sec[0m Factory : [1mEvaluate all methods[0mFactory : Evaluate classifier: matching_mlp : TFHandler_matching_mlp : Variable Mean RMS [ Min Max ] : ----------------------------------------------------------- : chi2: -0.15813 0.62626 [ -1.0000 0.99995 ] : teta2: -0.97140 0.071651 [ -1.0000 0.85686 ] : distX: -0.67477 0.35263 [ -1.0000 0.99866 ] : distY: -0.87476 0.15583 [ -1.0000 0.99415 ] : dSlope: -0.49635 0.36886 [ -0.99993 0.97372 ] : dSlopeY: -0.91980 0.13029 [ -1.0000 1.0219 ] : -----------------------------------------------------------matching_mlp : [MatchNNDataSet] : Loop over test events and fill histograms with classifier response... : TFHandler_matching_mlp : Variable Mean RMS [ Min Max ] : ----------------------------------------------------------- : chi2: -0.15813 0.62626 [ -1.0000 0.99995 ] : teta2: -0.97140 0.071651 [ -1.0000 0.85686 ] : distX: -0.67477 0.35263 [ -1.0000 0.99866 ] : distY: -0.87476 0.15583 [ -1.0000 0.99415 ] : dSlope: -0.49635 0.36886 [ -0.99993 0.97372 ] : dSlopeY: -0.91980 0.13029 [ -1.0000 1.0219 ] : ----------------------------------------------------------- : : Evaluation results ranked by best signal efficiency and purity (area) : ------------------------------------------------------------------------------------------------------------------- : DataSet MVA : Name: Method: ROC-integ : MatchNNDataSet matching_mlp : 0.970 : ------------------------------------------------------------------------------------------------------------------- : : 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 : ------------------------------------------------------------------------------------------------------------------- : MatchNNDataSet matching_mlp : 0.543 (0.551) 0.936 (0.936) 0.985 (0.985) : ------------------------------------------------------------------------------------------------------------------- : Dataset:MatchNNDataSet : Created tree 'TestTree' with 120000 events : Dataset:MatchNNDataSet : Created tree 'TrainTree' with 550000 events : Factory : [1mThank you for using TMVA
