Name

adams.flow.transformer.MOARegressorEvaluation

Synopsis

Evaluates a MOA regressor using prequential evaluation. With each incoming instance, the regressor is first evaluated, then trained.

Additional information

Flow input/output:
- input: com.yahoo.labs.samoa.instances.Instance, com.yahoo.labs.samoa.instances.Instances
- output: moa.core.Measurement[]

Options

• loggingLevel
  

  The logging level for outputting errors and debugging output.

  command-line	-logging-level <OFF|SEVERE|WARNING|INFO|CONFIG|FINE|FINER|FINEST>
  default	WARNING
  min-user-mode	Expert

  
  
• name
  

  The name of the actor.

  command-line	-name <java.lang.String>
  default	MOARegressorEvaluation

  
  
• annotations
  

  The annotations to attach to this actor.

  command-line	-annotation <adams.core.base.BaseAnnotation>
  default

  
  
• skip
  

  If set to true, transformation is skipped and the input token is just forwarded as it is.

  command-line	-skip <boolean>
  default	false

  
  
• stopFlowOnError
  

  If set to true, the flow execution at this level gets stopped in case this actor encounters an error; the error gets propagated; useful for critical actors.

  command-line	-stop-flow-on-error <boolean>
  default	false
  min-user-mode	Expert

  
  
• silent
  

  If enabled, then no errors are output in the console; Note: the enclosing actor handler must have this enabled as well.

  command-line	-silent <boolean>
  default	false
  min-user-mode	Expert

  
  
• regressor
  

  The name of the callable MOA regressor to train/evaluate.

  command-line	-regressor <adams.flow.core.CallableActorReference>
  default	MOARegressor

  
  
• evaluator
  

  The MOA evaluator to use for evaluating a trained MOA regressor.

  command-line	-evaluator <moa.options.ClassOption>
  default	BasicRegressionPerformanceEvaluator

  
  
• outputInterval
  

  The number of tokens to skip before evaluating the regressor stored in the token (only used when receiving Instance objects).

  command-line	-output-interval <int>
  default	1
  minimum	1