269 lines
23 KiB
Plaintext
269 lines
23 KiB
Plaintext
: 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 8286 events
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DataSetInfo : [MatchNNDataSet] : Added class "Background"
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: Add Tree Bkg of type Background with 12762964 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=1000.0: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: "1000" [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 : 7286
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: Signal -- testing events : 1000
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: Signal -- training and testing events: 8286
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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 -0.090 +0.190 +0.270 +0.150 +0.032
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: teta2: -0.090 +1.000 +0.022 +0.557 +0.231 +0.681
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: distX: +0.190 +0.022 +1.000 -0.243 +0.667 +0.066
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: distY: +0.270 +0.557 -0.243 +1.000 +0.299 +0.491
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: dSlope: +0.150 +0.231 +0.667 +0.299 +1.000 +0.343
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: dSlopeY: +0.032 +0.681 +0.066 +0.491 +0.343 +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 -0.032 +0.249 +0.208 +0.048 +0.047
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: teta2: -0.032 +1.000 +0.256 +0.643 +0.377 +0.464
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: distX: +0.249 +0.256 +1.000 +0.027 +0.771 +0.192
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: distY: +0.208 +0.643 +0.027 +1.000 +0.323 +0.556
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: dSlope: +0.048 +0.377 +0.771 +0.323 +1.000 +0.394
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: dSlopeY: +0.047 +0.464 +0.192 +0.556 +0.394 +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: 15.110 7.5957 [ 0.25759 29.998 ]
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: teta2: 0.0049007 0.015613 [ 1.1810e-05 0.34609 ]
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: distX: 77.540 64.030 [ 0.00059319 494.45 ]
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: distY: 35.596 43.128 [ 0.0016556 497.11 ]
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: dSlope: 0.37313 0.24282 [ 0.00012810 1.2803 ]
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: dSlopeY: 0.0071048 0.011434 [ 4.9639e-07 0.14679 ]
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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 : 8.701e-02
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: 2 : distY : 7.455e-02
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: 3 : dSlope : 6.957e-02
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: 4 : teta2 : 4.316e-02
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: 5 : dSlopeY : 2.562e-02
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: 6 : distX : 1.371e-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: -0.0011851 0.51079 [ -1.0000 1.0000 ]
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: teta2: -0.97175 0.090226 [ -1.0000 1.0000 ]
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: distX: -0.68636 0.25900 [ -1.0000 1.0000 ]
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: distY: -0.85679 0.17352 [ -1.0000 1.0000 ]
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: dSlope: -0.41728 0.37935 [ -1.0000 1.0000 ]
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: dSlopeY: -0.90320 0.15579 [ -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 27286 events: [1;31m59.2 sec[0m
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matching_mlp : [MatchNNDataSet] : Evaluation of matching_mlp on training sample (27286 events)
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: Elapsed time for evaluation of 27286 events: [1;31m0.0331 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 : 1.487e+02
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: 2 : distX : 9.251e+01
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: 3 : dSlopeY : 5.612e+01
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: 4 : teta2 : 3.951e+01
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: 5 : dSlope : 1.219e+01
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: 6 : chi2 : 1.428e+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 (6000 events)
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: Elapsed time for evaluation of 6000 events: [1;31m0.0113 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: 0.10129 0.51080 [ -0.98564 0.99991 ]
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: teta2: -0.96473 0.096760 [ -0.99997 0.43123 ]
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: distX: -0.68127 0.26859 [ -0.99983 0.92711 ]
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: distY: -0.83124 0.20417 [ -0.99994 1.0115 ]
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: dSlope: -0.45660 0.39080 [ -0.99695 0.96415 ]
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: dSlopeY: -0.89629 0.16201 [ -0.99999 1.0015 ]
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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: 0.10129 0.51080 [ -0.98564 0.99991 ]
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: teta2: -0.96473 0.096760 [ -0.99997 0.43123 ]
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: distX: -0.68127 0.26859 [ -0.99983 0.92711 ]
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: distY: -0.83124 0.20417 [ -0.99994 1.0115 ]
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: dSlope: -0.45660 0.39080 [ -0.99695 0.96415 ]
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: dSlopeY: -0.89629 0.16201 [ -0.99999 1.0015 ]
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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 : 0.854
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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 : 0.091 (0.089) 0.501 (0.494) 0.851 (0.854)
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: -------------------------------------------------------------------------------------------------------------------
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:
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Dataset:MatchNNDataSet : Created tree 'TestTree' with 6000 events
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:
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Dataset:MatchNNDataSet : Created tree 'TrainTree' with 27286 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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