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tracking-parametrisation-tuner/outputs_nn/output_B.txt

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2023-12-19 13:00:59 +01:00
: 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 187767 events
DataSetInfo : [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 : Background
Factory : Booking method: matching_mlp
:
: 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 weights
Factory : Train all methods
: Rebuilding Dataset MatchNNDataSet
: Parsing option string:
: ... "SplitMode=random:V:nTrain_Signal=100000.0:nTrain_Background=200000.0:nTest_Signal=10000.0:nTest_Background=20000.0"
: The following options are set:
: - By User:
: SplitMode: "Random" [Method of picking training and testing events (default: random)]
: nTrain_Signal: "100000" [Number of training events of class Signal (default: 0 = all)]
: nTest_Signal: "10000" [Number of test events of class Signal (default: 0 = all)]
: nTrain_Background: "200000" [Number of training events of class Background (default: 0 = all)]
: nTest_Background: "20000" [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 Bkg
DataSetFactory : [MatchNNDataSet] : Number of events in input trees
:
:
: 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 : 100000
: Signal -- testing events : 10000
: Signal -- training and testing events: 110000
: Background -- training events : 200000
: Background -- testing events : 20000
: Background -- training and testing events: 220000
:
DataSetInfo : Correlation matrix (Signal):
: --------------------------------------------------------
: chi2 teta2 distX distY dSlope dSlopeY
: chi2: +1.000 -0.094 +0.508 +0.558 +0.393 +0.145
: teta2: -0.094 +1.000 -0.010 +0.345 -0.010 +0.388
: distX: +0.508 -0.010 +1.000 +0.202 +0.501 +0.230
: distY: +0.558 +0.345 +0.202 +1.000 +0.507 +0.472
: dSlope: +0.393 -0.010 +0.501 +0.507 +1.000 +0.497
: dSlopeY: +0.145 +0.388 +0.230 +0.472 +0.497 +1.000
: --------------------------------------------------------
DataSetInfo : Correlation matrix (Background):
: --------------------------------------------------------
: chi2 teta2 distX distY dSlope dSlopeY
: chi2: +1.000 +0.008 +0.363 +0.312 -0.001 +0.102
: teta2: +0.008 +1.000 +0.217 +0.626 +0.297 +0.493
: distX: +0.363 +0.217 +1.000 +0.062 +0.631 +0.203
: distY: +0.312 +0.626 +0.062 +1.000 +0.250 +0.543
: dSlope: -0.001 +0.297 +0.631 +0.250 +1.000 +0.361
: dSlopeY: +0.102 +0.493 +0.203 +0.543 +0.361 +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: 8.4293 9.2426 [ 2.3238e-05 30.000 ]
: teta2: 0.0057581 0.014094 [ 1.5125e-06 0.40287 ]
: distX: 40.107 55.141 [ 3.0518e-05 497.68 ]
: distY: 26.294 37.024 [ 4.5776e-05 499.89 ]
: dSlope: 0.33133 0.23520 [ 1.3022e-05 1.3517 ]
: dSlopeY: 0.0054522 0.0092106 [ 9.3132e-10 0.14883 ]
: -----------------------------------------------------------
: Ranking input variables (method unspecific)...
IdTransformation : Ranking result (top variable is best ranked)
: --------------------------------
: Rank : Variable : Separation
: --------------------------------
: 1 : chi2 : 5.690e-01
: 2 : distX : 3.736e-01
: 3 : distY : 2.091e-01
: 4 : dSlopeY : 8.232e-02
: 5 : dSlope : 8.601e-03
: 6 : teta2 : 3.474e-03
: --------------------------------
Factory : Train method: matching_mlp for Classification
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.43805 0.61618 [ -1.0000 1.0000 ]
: teta2: -0.97142 0.069969 [ -1.0000 1.0000 ]
: distX: -0.83882 0.22159 [ -1.0000 1.0000 ]
: distY: -0.89480 0.14813 [ -1.0000 1.0000 ]
: dSlope: -0.50978 0.34801 [ -1.0000 1.0000 ]
: dSlopeY: -0.92673 0.12377 [ -1.0000 1.0000 ]
: -----------------------------------------------------------
: Training Network
:
: Elapsed time for training with 300000 events: 853 sec
matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on training sample (300000 events)
: Elapsed time for evaluation of 300000 events: 0.495 sec
: Creating xml weight file: MatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml
: Creating standalone class: MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C
: Write special histos to file: matching_ghost_mlp_training.root:/MatchNNDataSet/Method_MLP/matching_mlp
Factory : Training finished
:
: Ranking input variables (method specific)...
matching_mlp : Ranking result (top variable is best ranked)
: --------------------------------
: Rank : Variable : Importance
: --------------------------------
: 1 : distY : 5.213e+02
: 2 : teta2 : 4.435e+02
: 3 : dSlopeY : 4.414e+02
: 4 : distX : 3.118e+02
: 5 : dSlope : 2.646e+01
: 6 : chi2 : 7.066e+00
: --------------------------------
Factory : === Destroy and recreate all methods via weight files for testing ===
:
: Reading weight file: MatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml
matching_mlp : Building Network.
: Initializing weights
Factory : Test all methods
Factory : Test method: matching_mlp for Classification performance
:
matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on testing sample (30000 events)
: Elapsed time for evaluation of 30000 events: 0.0597 sec
Factory : Evaluate all methods
Factory : Evaluate classifier: matching_mlp
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.29449 0.63524 [ -0.99999 0.99994 ]
: teta2: -0.97212 0.070073 [ -1.0000 0.32779 ]
: distX: -0.80346 0.24082 [ -1.0000 0.97553 ]
: distY: -0.87751 0.16136 [ -1.0000 0.95680 ]
: dSlope: -0.50293 0.35312 [ -0.99999 0.86422 ]
: dSlopeY: -0.91903 0.13042 [ -1.0000 0.95486 ]
: -----------------------------------------------------------
matching_mlp : [MatchNNDataSet] : Loop over test events and fill histograms with classifier response...
:
TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: chi2: -0.29449 0.63524 [ -0.99999 0.99994 ]
: teta2: -0.97212 0.070073 [ -1.0000 0.32779 ]
: distX: -0.80346 0.24082 [ -1.0000 0.97553 ]
: distY: -0.87751 0.16136 [ -1.0000 0.95680 ]
: dSlope: -0.50293 0.35312 [ -0.99999 0.86422 ]
: dSlopeY: -0.91903 0.13042 [ -1.0000 0.95486 ]
: -----------------------------------------------------------
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: MatchNNDataSet matching_mlp : 0.958
: -------------------------------------------------------------------------------------------------------------------
:
: 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 : 0.450 (0.414) 0.886 (0.882) 0.980 (0.979)
: -------------------------------------------------------------------------------------------------------------------
:
Dataset:MatchNNDataSet : Created tree 'TestTree' with 30000 events
:
Dataset:MatchNNDataSet : Created tree 'TrainTree' with 300000 events
:
Factory : Thank you for using TMVA!
: For citation information, please visit: http://tmva.sf.net/citeTMVA.html
Transforming nn_electron_training/result/MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C ...
Found minimum and maximum values for 6 variables.
Found 3 matrices:
1. fWeightMatrix0to1 with 7 columns and 8 rows
2. fWeightMatrix1to2 with 9 columns and 6 rows
3. fWeightMatrix2to3 with 7 columns and 1 rows
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