Name

adams.flow.transformer.WekaAttributeSelection

Synopsis

Performs attribute selection on the incoming data.
In case of input in form of a class adams.flow.container.WekaTrainTestSetContainer object, the train and test sets stored in the container are being used.
NB: In case of cross-validation no reduced or transformed data can get generated!

Additional information

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

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
- adams.flow.container.WekaAttributeSelectionContainer:
   - Train: training set; weka.core.Instances
   - Reduced: reduced dataset; weka.core.Instances
   - Transformed: transformed dataset (if weka.attributeSelection.AttributeTransformer); weka.core.Instances
   - Test: test set; weka.core.Instances
   - Test reduced: reduced test dataset; weka.core.Instances
   - Test transformed: transformed test dataset (if weka.attributeSelection.AttributeTransformer); weka.core.Instances
   - Evaluation: attribute selection evaluation object; weka.attributeSelection.AttributeSelection
   - Statistics: spreadsheet with the statistics; adams.data.spreadsheet.SpreadSheet
   - Selected attributes: range string of selected attributes (1-based indices); java.lang.String
   - Seed: seed value (cross-validation); java.lang.Long
   - FoldCount: fold (cross-validation); java.lang.Integer

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	WekaAttributeSelection

  
  
• 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

  
  
• evaluator
  

  The evaluation method to use.

  command-line	-evaluator <weka.attributeSelection.ASEvaluation>
  default	weka.attributeSelection.CfsSubsetEval -P 1 -E 1

  
  
• search
  

  The search method to use.

  command-line	-search <weka.attributeSelection.ASSearch>
  default	weka.attributeSelection.BestFirst -D 1 -N 5

  
  
• seed
  

  The seed value for cross-validation (used for randomization).

  command-line	-seed <long>
  default	1

  
  
• folds
  

  The number of folds to use in the cross-validation; no cross-validation is performed if folds < 2.

  command-line	-folds <int>
  default	10
  minimum	-1