Package adams.flow.transformer

Class WekaClassifierOptimizer

  • All Implemented Interfaces:
    AdditionalInformationHandler, CleanUpHandler, Destroyable, GlobalInfoSupporter, LoggingLevelHandler, LoggingSupporter, OptionHandler, QuickInfoSupporter, ShallowCopySupporter<Actor>, SizeOfHandler, Stoppable, StoppableWithFeedback, VariablesInspectionHandler, VariableChangeListener, Actor, ErrorHandler, InputConsumer, OutputProducer, Serializable, Comparable
    public class WekaClassifierOptimizer
extends AbstractTransformer
    Evaluates a classifier optimizer on an incoming dataset. The best setup (untrained) found is then forwarded.
    At the moment, only GridSearch and MultiSearch are supported as optimizers.

    Input/output:
    - accepts:
       weka.core.Instances
    - generates:
       weka.classifiers.Classifier


    Valid options are: 

    -D <int> (property: debugLevel)
    The greater the number the more additional info the scheme may output to
    the console (0 = off).
    default: 0
    minimum: 0
 
    -name <java.lang.String> (property: name)
    The name of the actor.
    default: ClassifierOptimizer
 
    -annotation <adams.core.base.BaseText> (property: annotations)
    The annotations to attach to this actor.
    default:
 
    -skip (property: skip)
    If set to true, transformation is skipped and the input token is just forwarded
    as it is.
 
    -optimizer <weka.classifiers.Classifier [options]> (property: optimizer)
    The classifier optimizer to use, eg, GridSearch or MultiSearch.
    default: weka.classifiers.meta.GridSearch -E CC -y-property classifier.ridge -y-min -10.0 -y-max 5.0 -y-step 1.0 -y-base 10.0 -y-expression pow(BASE,I) -filter \"weka.filters.supervised.attribute.PLSFilter -C 20 -M -A PLS1 -P center\" -x-property filter.numComponents -x-min 5.0 -x-max 20.0 -x-step 1.0 -x-base 10.0 -x-expression I -sample-size 100.0 -traversal COLUMN-WISE -log-file /home/fracpete/development/projects/adams -num-slots 1 -S 1 -W weka.classifiers.functions.LinearRegression -- -S 1 -C -R 1.0E-8
    Version:
    $Revision$
    Author:
    fracpete (fracpete at waikato dot ac dot nz)
    See Also:
    Serialized Form

    • Field Detail

      • m_Optimizer

        protected weka.classifiers.Classifier m_Optimizer
        the classifier optimizer.

    • Constructor Detail

      • WekaClassifierOptimizer

        public WekaClassifierOptimizer()

    • Method Detail

      • classifierTipText

        public String classifierTipText()
        Returns the tip text for this property.
        Returns:
        tip text for this property suitable for displaying in the GUI or for listing the options.

      • setOptimizer

        public void setOptimizer​(weka.classifiers.Classifier value)
        Sets the optimizer to use.
        Parameters:
        value - the optimizer

      • getOptimizer

        public weka.classifiers.Classifier getOptimizer()
        Returns the optimizer in use.
        Returns:
        the optimizer

      • optimizerTipText

        public String optimizerTipText()
        Returns the tip text for this property.
        Returns:
        tip text for this property suitable for displaying in the GUI or for listing the options.

      • accepts

        public Class[] accepts()
        Returns the class that the consumer accepts.
        Returns:
        weka.core.Instances.class

      • generates

        public Class[] generates()
        Returns the class of objects that it generates.
        Returns:
        String.class or weka.classifiers.Evaluation.class

      • doExecute

        protected String doExecute()
        Executes the flow item.
        Specified by:
        doExecute in class AbstractActor
        Returns:
        null if everything is fine, otherwise error message