Name

adams.flow.transformer.WekaSplitGenerator

Synopsis

Splits a dataset into a training and test sets using the specified splitter.
The training set can be accessed in the container with 'Train' and the test set with 'Test'.
Depending on the split generator in use, more than one container may be output.

Additional information

Flow input/output:
- input: weka.core.Instances
- output: adams.flow.container.WekaTrainTestSetContainer

Container information:
- adams.flow.container.WekaTrainTestSetContainer:
   - Train: training set; weka.core.Instances
   - Test: test set; weka.core.Instances
   - Seed: seed value; java.lang.Long
   - FoldNumber: current fold (1-based); java.lang.Integer
   - FoldCount: total number of folds; java.lang.Integer
   - Train original indices: original indices (0-based, train); array of int
   - Test original indices: original indices (0-based, test); array of int

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	WekaSplitGenerator

  
  
• 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

  
  
• outputArray
  

  If enabled, the splits are output as array rather than one-by-one.

  command-line	-output-array <boolean>
  default	false

  
  
• generator
  

  The scheme to use for generating the split.

  command-line	-generator <weka.classifiers.SplitGenerator>
  default	weka.classifiers.DefaultRandomSplitGenerator