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268 lines
23 KiB
268 lines
23 KiB
: Parsing option string:
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: ... "V:!Silent:Color:DrawProgressBar:AnalysisType=Classification"
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: The following options are set:
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: - By User:
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: V: "True" [Verbose flag]
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: Color: "True" [Flag for coloured screen output (default: True, if in batch mode: False)]
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: Silent: "False" [Batch mode: boolean silent flag inhibiting any output from TMVA after the creation of the factory class object (default: False)]
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: DrawProgressBar: "True" [Draw progress bar to display training, testing and evaluation schedule (default: True)]
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: AnalysisType: "Classification" [Set the analysis type (Classification, Regression, Multiclass, Auto) (default: Auto)]
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: - Default:
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: VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)]
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: 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]
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: Correlations: "False" [boolean to show correlation in output]
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: ROC: "True" [boolean to show ROC in output]
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: ModelPersistence: "True" [Option to save the trained model in xml file or using serialization]
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DataSetInfo : [MatchNNDataSet] : Added class "Signal"
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: Add Tree Signal of type Signal with 13829 events
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DataSetInfo : [MatchNNDataSet] : Added class "Background"
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: Add Tree Bkg of type Background with 29144752 events
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: Dataset[MatchNNDataSet] : Class index : 0 name : Signal
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: Dataset[MatchNNDataSet] : Class index : 1 name : Background
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Factory : Booking method: [1mmatching_mlp[0m
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:
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: Parsing option string:
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: ... "!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"
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: The following options are set:
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: - By User:
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: <none>
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: - Default:
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: Boost_num: "0" [Number of times the classifier will be boosted]
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: Parsing option string:
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: ... "!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"
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: The following options are set:
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: - By User:
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: NCycles: "700" [Number of training cycles]
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: HiddenLayers: "N+2,N" [Specification of hidden layer architecture]
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: NeuronType: "ReLU" [Neuron activation function type]
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: EstimatorType: "CE" [MSE (Mean Square Estimator) for Gaussian Likelihood or CE(Cross-Entropy) for Bernoulli Likelihood]
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: V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
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: 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)"]
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: H: "False" [Print method-specific help message]
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: TrainingMethod: "BP" [Train with Back-Propagation (BP), BFGS Algorithm (BFGS), or Genetic Algorithm (GA - slower and worse)]
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: LearningRate: "2.000000e-02" [ANN learning rate parameter]
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: DecayRate: "1.000000e-02" [Decay rate for learning parameter]
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: TestRate: "50" [Test for overtraining performed at each #th epochs]
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: Sampling: "1.000000e+00" [Only 'Sampling' (randomly selected) events are trained each epoch]
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: 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.]
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: UseRegulator: "False" [Use regulator to avoid over-training]
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: - Default:
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: RandomSeed: "1" [Random seed for initial synapse weights (0 means unique seed for each run; default value '1')]
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: NeuronInputType: "sum" [Neuron input function type]
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: VerbosityLevel: "Default" [Verbosity level]
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: CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
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: IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
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: EpochMonitoring: "False" [Provide epoch-wise monitoring plots according to TestRate (caution: causes big ROOT output file!)]
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: SamplingEpoch: "1.000000e+00" [Sampling is used for the first 'SamplingEpoch' epochs, afterwards, all events are taken for training]
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: SamplingTraining: "True" [The training sample is sampled]
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: SamplingTesting: "False" [The testing sample is sampled]
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: ResetStep: "50" [How often BFGS should reset history]
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: Tau: "3.000000e+00" [LineSearch "size step"]
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: BPMode: "sequential" [Back-propagation learning mode: sequential or batch]
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: BatchSize: "-1" [Batch size: number of events/batch, only set if in Batch Mode, -1 for BatchSize=number_of_events]
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: ConvergenceImprove: "1.000000e-30" [Minimum improvement which counts as improvement (<0 means automatic convergence check is turned off)]
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: ConvergenceTests: "-1" [Number of steps (without improvement) required for convergence (<0 means automatic convergence check is turned off)]
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: UpdateLimit: "10000" [Maximum times of regulator update]
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: CalculateErrors: "False" [Calculates inverse Hessian matrix at the end of the training to be able to calculate the uncertainties of an MVA value]
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: 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]
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matching_mlp : [MatchNNDataSet] : Create Transformation "Norm" with events from all classes.
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:
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: Transformation, Variable selection :
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: Input : variable 'chi2' <---> Output : variable 'chi2'
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: Input : variable 'teta2' <---> Output : variable 'teta2'
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: Input : variable 'distX' <---> Output : variable 'distX'
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: Input : variable 'distY' <---> Output : variable 'distY'
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: Input : variable 'dSlope' <---> Output : variable 'dSlope'
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: Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'
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matching_mlp : Building Network.
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: Initializing weights
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Factory : [1mTrain all methods[0m
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: Rebuilding Dataset MatchNNDataSet
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: Parsing option string:
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: ... "SplitMode=random:V:nTrain_Signal=0:nTrain_Background=20000.0:nTest_Signal=2000.0:nTest_Background=5000.0"
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: The following options are set:
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: - By User:
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: SplitMode: "Random" [Method of picking training and testing events (default: random)]
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: nTrain_Signal: "0" [Number of training events of class Signal (default: 0 = all)]
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: nTest_Signal: "2000" [Number of test events of class Signal (default: 0 = all)]
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: nTrain_Background: "20000" [Number of training events of class Background (default: 0 = all)]
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: nTest_Background: "5000" [Number of test events of class Background (default: 0 = all)]
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: V: "True" [Verbosity (default: true)]
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: - Default:
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: MixMode: "SameAsSplitMode" [Method of mixing events of different classes into one dataset (default: SameAsSplitMode)]
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: SplitSeed: "100" [Seed for random event shuffling]
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: 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)]
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: ScaleWithPreselEff: "False" [Scale the number of requested events by the eff. of the preselection cuts (or not)]
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: TrainTestSplit_Signal: "0.000000e+00" [Number of test events of class Signal (default: 0 = all)]
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: TrainTestSplit_Background: "0.000000e+00" [Number of test events of class Background (default: 0 = all)]
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: VerboseLevel: "Info" [VerboseLevel (Debug/Verbose/Info)]
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: Correlations: "True" [Boolean to show correlation output (Default: true)]
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: CalcCorrelations: "True" [Compute correlations and also some variable statistics, e.g. min/max (Default: true )]
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: Building event vectors for type 2 Signal
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: Dataset[MatchNNDataSet] : create input formulas for tree Signal
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: Building event vectors for type 2 Background
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: Dataset[MatchNNDataSet] : create input formulas for tree Bkg
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DataSetFactory : [MatchNNDataSet] : Number of events in input trees
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:
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:
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: Dataset[MatchNNDataSet] : Weight renormalisation mode: "EqualNumEvents": renormalises all event classes ...
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: Dataset[MatchNNDataSet] : such that the effective (weighted) number of events in each class is the same
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: Dataset[MatchNNDataSet] : (and equals the number of events (entries) given for class=0 )
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: Dataset[MatchNNDataSet] : ... i.e. such that Sum[i=1..N_j]{w_i} = N_classA, j=classA, classB, ...
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: Dataset[MatchNNDataSet] : ... (note that N_j is the sum of TRAINING events
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: Dataset[MatchNNDataSet] : ..... Testing events are not renormalised nor included in the renormalisation factor!)
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: Number of training and testing events
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: ---------------------------------------------------------------------------
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: Signal -- training events : 11829
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: Signal -- testing events : 2000
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: Signal -- training and testing events: 13829
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: Background -- training events : 20000
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: Background -- testing events : 5000
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: Background -- training and testing events: 25000
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:
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DataSetInfo : Correlation matrix (Signal):
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: --------------------------------------------------------
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: chi2 teta2 distX distY dSlope dSlopeY
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: chi2: +1.000 -0.082 +0.200 +0.302 +0.182 +0.049
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: teta2: -0.082 +1.000 +0.033 +0.461 +0.179 +0.632
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: distX: +0.200 +0.033 +1.000 -0.222 +0.685 +0.075
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: distY: +0.302 +0.461 -0.222 +1.000 +0.306 +0.463
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: dSlope: +0.182 +0.179 +0.685 +0.306 +1.000 +0.319
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: dSlopeY: +0.049 +0.632 +0.075 +0.463 +0.319 +1.000
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: --------------------------------------------------------
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DataSetInfo : Correlation matrix (Background):
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: --------------------------------------------------------
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: chi2 teta2 distX distY dSlope dSlopeY
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: chi2: +1.000 -0.003 +0.368 +0.313 -0.005 +0.094
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: teta2: -0.003 +1.000 +0.215 +0.617 +0.302 +0.491
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: distX: +0.368 +0.215 +1.000 +0.065 +0.633 +0.203
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: distY: +0.313 +0.617 +0.065 +1.000 +0.246 +0.532
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: dSlope: -0.005 +0.302 +0.633 +0.246 +1.000 +0.356
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: dSlopeY: +0.094 +0.491 +0.203 +0.532 +0.356 +1.000
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: --------------------------------------------------------
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DataSetFactory : [MatchNNDataSet] :
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:
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Factory : [MatchNNDataSet] : Create Transformation "I" with events from all classes.
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:
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: Transformation, Variable selection :
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: Input : variable 'chi2' <---> Output : variable 'chi2'
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: Input : variable 'teta2' <---> Output : variable 'teta2'
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: Input : variable 'distX' <---> Output : variable 'distX'
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: Input : variable 'distY' <---> Output : variable 'distY'
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: Input : variable 'dSlope' <---> Output : variable 'dSlope'
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: Input : variable 'dSlopeY' <---> Output : variable 'dSlopeY'
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TFHandler_Factory : Variable Mean RMS [ Min Max ]
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: -----------------------------------------------------------
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: chi2: 13.817 7.9796 [ 0.0011579 29.997 ]
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: teta2: 0.0040130 0.012209 [ 1.9755e-06 0.23492 ]
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: distX: 71.018 61.492 [ 0.0031776 478.62 ]
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: distY: 31.234 37.327 [ 0.00019073 497.26 ]
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: dSlope: 0.37346 0.23976 [ 5.9959e-05 1.2822 ]
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: dSlopeY: 0.0063004 0.010258 [ 3.9814e-08 0.14883 ]
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: -----------------------------------------------------------
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: Ranking input variables (method unspecific)...
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IdTransformation : Ranking result (top variable is best ranked)
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: --------------------------------
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: Rank : Variable : Separation
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: --------------------------------
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: 1 : chi2 : 9.147e-02
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: 2 : distY : 5.407e-02
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: 3 : teta2 : 4.044e-02
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: 4 : dSlope : 3.233e-02
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: 5 : distX : 2.801e-02
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: 6 : dSlopeY : 1.699e-02
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: --------------------------------
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Factory : Train method: matching_mlp for Classification
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:
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TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
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: -----------------------------------------------------------
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: chi2: -0.078822 0.53204 [ -1.0000 1.0000 ]
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: teta2: -0.96585 0.10395 [ -1.0000 1.0000 ]
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: distX: -0.70325 0.25696 [ -1.0000 1.0000 ]
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: distY: -0.87438 0.15013 [ -1.0000 1.0000 ]
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: dSlope: -0.41755 0.37399 [ -1.0000 1.0000 ]
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: dSlopeY: -0.91533 0.13785 [ -1.0000 1.0000 ]
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: -----------------------------------------------------------
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: Training Network
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:
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: Elapsed time for training with 31829 events: [1;31m64.5 sec[0m
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matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on training sample (31829 events)
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: Elapsed time for evaluation of 31829 events: [1;31m0.0391 sec[0m
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: Creating xml weight file: [0;36mMatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml[0m
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: Creating standalone class: [0;36mMatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C[0m
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: Write special histos to file: matching_ghost_mlp_training.root:/MatchNNDataSet/Method_MLP/matching_mlp
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Factory : Training finished
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:
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: Ranking input variables (method specific)...
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matching_mlp : Ranking result (top variable is best ranked)
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: --------------------------------
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: Rank : Variable : Importance
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: --------------------------------
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: 1 : distY : 3.588e+02
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: 2 : dSlopeY : 2.134e+02
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: 3 : distX : 1.426e+02
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: 4 : teta2 : 7.020e+01
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: 5 : dSlope : 1.303e+01
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: 6 : chi2 : 3.098e+00
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: --------------------------------
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Factory : === Destroy and recreate all methods via weight files for testing ===
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:
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: Reading weight file: [0;36mMatchNNDataSet/weights/TMVAClassification_matching_mlp.weights.xml[0m
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matching_mlp : Building Network.
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: Initializing weights
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Factory : [1mTest all methods[0m
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Factory : Test method: matching_mlp for Classification performance
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:
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matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on testing sample (7000 events)
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: Elapsed time for evaluation of 7000 events: [1;31m0.0138 sec[0m
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Factory : [1mEvaluate all methods[0m
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Factory : Evaluate classifier: matching_mlp
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:
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TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
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: -----------------------------------------------------------
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: chi2: -0.055433 0.55630 [ -0.99875 1.0001 ]
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: teta2: -0.96118 0.10498 [ -0.99999 0.45981 ]
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: distX: -0.71039 0.26310 [ -0.99989 0.79697 ]
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: distY: -0.86095 0.16028 [ -1.0000 0.89878 ]
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: dSlope: -0.43538 0.38054 [ -0.99815 0.98969 ]
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: dSlopeY: -0.91076 0.14080 [ -1.0000 0.93883 ]
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: -----------------------------------------------------------
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matching_mlp : [MatchNNDataSet] : Loop over test events and fill histograms with classifier response...
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:
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TFHandler_matching_mlp : Variable Mean RMS [ Min Max ]
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: -----------------------------------------------------------
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: chi2: -0.055433 0.55630 [ -0.99875 1.0001 ]
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: teta2: -0.96118 0.10498 [ -0.99999 0.45981 ]
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: distX: -0.71039 0.26310 [ -0.99989 0.79697 ]
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: distY: -0.86095 0.16028 [ -1.0000 0.89878 ]
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: dSlope: -0.43538 0.38054 [ -0.99815 0.98969 ]
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: dSlopeY: -0.91076 0.14080 [ -1.0000 0.93883 ]
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: -----------------------------------------------------------
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:
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: Evaluation results ranked by best signal efficiency and purity (area)
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: -------------------------------------------------------------------------------------------------------------------
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: DataSet MVA
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: Name: Method: ROC-integ
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: MatchNNDataSet matching_mlp : 0.853
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: -------------------------------------------------------------------------------------------------------------------
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:
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: Testing efficiency compared to training efficiency (overtraining check)
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: -------------------------------------------------------------------------------------------------------------------
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: DataSet MVA Signal efficiency: from test sample (from training sample)
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: Name: Method: @B=0.01 @B=0.10 @B=0.30
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: -------------------------------------------------------------------------------------------------------------------
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: MatchNNDataSet matching_mlp : 0.000 (0.000) 0.470 (0.511) 0.877 (0.882)
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: -------------------------------------------------------------------------------------------------------------------
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:
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Dataset:MatchNNDataSet : Created tree 'TestTree' with 7000 events
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:
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Dataset:MatchNNDataSet : Created tree 'TrainTree' with 31829 events
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:
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Factory : [1mThank you for using TMVA![0m
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: [1mFor citation information, please visit: http://tmva.sf.net/citeTMVA.html[0m
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Transforming nn_electron_training/result/MatchNNDataSet/weights/TMVAClassification_matching_mlp.class.C ...
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Found minimum and maximum values for 6 variables.
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Found 3 matrices:
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1. fWeightMatrix0to1 with 7 columns and 8 rows
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2. fWeightMatrix1to2 with 9 columns and 6 rows
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3. fWeightMatrix2to3 with 7 columns and 1 rows
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