Package adams.flow.transformer

Class WekaCrossValidationEvaluator

  • All Implemented Interfaces:
    AdditionalInformationHandler, CleanUpHandler, Destroyable, GlobalInfoSupporter, LoggingLevelHandler, LoggingSupporter, OptionHandler, QuickInfoSupporter, Randomizable, ShallowCopySupporter<Actor>, SizeOfHandler, Stoppable, StoppableWithFeedback, ThreadLimiter, VariablesInspectionHandler, InstancesViewSupporter, VariableChangeListener, Actor, ErrorHandler, InputConsumer, OutputProducer, Serializable, Comparable
    public class WekaCrossValidationEvaluator
extends AbstractCallableWekaClassifierEvaluator
implements Randomizable, ThreadLimiter, InstancesViewSupporter
    Cross-validates a classifier on an incoming dataset. The classifier setup being used in the evaluation is a callable 'Classifier' actor.

    Input/output:
    - accepts:
       weka.core.Instances
    - generates:
       adams.flow.container.WekaEvaluationContainer


    Container information:
    - adams.flow.container.WekaEvaluationContainer: Evaluation, Model, Prediction output, Original indices, Test data 

    -logging-level <OFF|SEVERE|WARNING|INFO|CONFIG|FINE|FINER|FINEST> (property: loggingLevel)
    The logging level for outputting errors and debugging output.
    default: WARNING
 
    -name <java.lang.String> (property: name)
    The name of the actor.
    default: WekaCrossValidationEvaluator
 
    -annotation <adams.core.base.BaseAnnotation> (property: annotations)
    The annotations to attach to this actor.
    default:
 
    -skip <boolean> (property: skip)
    If set to true, transformation is skipped and the input token is just forwarded
    as it is.
    default: false
 
    -stop-flow-on-error <boolean> (property: 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.
    default: false
 
    -silent <boolean> (property: silent)
    If enabled, then no errors are output in the console; Note: the enclosing
    actor handler must have this enabled as well.
    default: false
 
    -output <weka.classifiers.evaluation.output.prediction.AbstractOutput> (property: output)
    The class for generating prediction output; if 'Null' is used, then an Evaluation
    object is forwarded instead of a String; not used when using parallel execution.
    default: weka.classifiers.evaluation.output.prediction.Null
 
    -always-use-container <boolean> (property: alwaysUseContainer)
    If enabled, always outputs an evaluation container.
    default: false
 
    -classifier <adams.flow.core.CallableActorReference> (property: classifier)
    The callable classifier actor to cross-validate on the input data.
    default: WekaClassifierSetup
 
    -no-predictions <boolean> (property: discardPredictions)
    If enabled, the collection of predictions during evaluation is suppressed,
     wich will conserve memory.
    default: false
 
    -seed <long> (property: seed)
    The seed value for the cross-validation (used for randomization).
    default: 1
 
    -folds <int> (property: folds)
    The number of folds to use in the cross-validation; use -1 for leave-one-out
    cross-validation (LOOCV).
    default: 10
    minimum: -1
 
    -num-threads <int> (property: numThreads)
    The number of threads to use for parallel execution; > 0: specific number
    of cores to use (capped by actual number of cores available, 1 = sequential
    execution); = 0: number of cores; < 0: number of free cores (eg -2 means
    2 free cores; minimum of one core is used)
    default: 1
 
    -use-views <boolean> (property: useViews)
    If enabled, views of the dataset are being used instead of actual copies,
     to conserve memory.
    default: false
 
    -generator <weka.classifiers.CrossValidationFoldGenerator> (property: generator)
    The scheme to use for generating the folds; the actor options take precedence
    over the scheme's ones.
    default: weka.classifiers.DefaultCrossValidationFoldGenerator
 
    -final-model <boolean> (property: finalModel)
    If enabled, a final model is built on the full dataset.
    default: false
    Author:
    fracpete (fracpete at waikato dot ac dot nz)
    See Also:
    Serialized Form

    • Field Detail

      • m_Folds

        protected int m_Folds
        the number of folds.

      • m_Seed

        protected long m_Seed
        the seed value.

      • m_NumThreads

        protected int m_NumThreads
        the number of threads to use for parallel execution.

      • m_UseViews

        protected boolean m_UseViews
        whether to use views.

      • m_FinalModel

        protected boolean m_FinalModel
        whether to create a final model.

      • m_JobRunnerSetup

        protected transient JobRunnerSetup m_JobRunnerSetup
        the jobrunner setup.

    • Constructor Detail

      • WekaCrossValidationEvaluator

        public WekaCrossValidationEvaluator()

    • Method Detail

      • outputTipText

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

      • setFolds

        public void setFolds​(int value)
        Sets the number of folds.
        Parameters:
        value - the folds, -1 for LOOCV

      • getFolds

        public int getFolds()
        Returns the number of folds.
        Returns:
        the folds

      • foldsTipText

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

      • setSeed

        public void setSeed​(long value)
        Sets the seed value.
        Specified by:
        setSeed in interface Randomizable
        Parameters:
        value - the seed

      • getSeed

        public long getSeed()
        Returns the seed value.
        Specified by:
        getSeed in interface Randomizable
        Returns:
        the seed

      • seedTipText

        public String seedTipText()
        Returns the tip text for this property.
        Specified by:
        seedTipText in interface Randomizable
        Returns:
        tip text for this property suitable for displaying in the GUI or for listing the options.

      • setNumThreads

        public void setNumThreads​(int value)
        Sets the number of threads to use for cross-validation.
        Specified by:
        setNumThreads in interface ThreadLimiter
        Parameters:
        value - the number of threads: -1 = # of CPUs/cores; 0/1 = sequential execution

      • getNumThreads

        public int getNumThreads()
        Returns the number of threads to use for cross-validation.
        Specified by:
        getNumThreads in interface ThreadLimiter
        Returns:
        the number of threads: -1 = # of CPUs/cores; 0/1 = sequential execution

      • numThreadsTipText

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

      • setUseViews

        public void setUseViews​(boolean value)
        Sets whether to use views instead of dataset copies, in order to conserve memory.
        Specified by:
        setUseViews in interface InstancesViewSupporter
        Parameters:
        value - true if to use views

      • getUseViews

        public boolean getUseViews()
        Returns whether to use views instead of dataset copies, in order to conserve memory.
        Specified by:
        getUseViews in interface InstancesViewSupporter
        Returns:
        true if using views

      • useViewsTipText

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

      • setGenerator

        public void setGenerator​(CrossValidationFoldGenerator value)
        Sets the scheme for generating the folds.
        Parameters:
        value - the generator

      • generatorTipText

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

      • setFinalModel

        public void setFinalModel​(boolean value)
        Sets whether to build a final model on the full dataset.
        Parameters:
        value - true if to build final model

      • getFinalModel

        public boolean getFinalModel()
        Returns whether to build a final model on the full dataset.
        Returns:
        true if to build final model

      • finalModelTipText

        public String finalModelTipText()
        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.
        Specified by:
        accepts in interface InputConsumer
        Returns:
        weka.core.Instances.class

      • doExecute

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