Package weka.knowledgeflow.steps

Class ASEvaluator

java.lang.Object

weka.knowledgeflow.steps.BaseStep

weka.knowledgeflow.steps.WekaAlgorithmWrapper

weka.knowledgeflow.steps.ASEvaluator

All Implemented Interfaces:

java.io.Serializable, BaseStepExtender, Step

@KFStep(name="ASEvaluator",
        category="AttSelection",
        toolTipText="Weka attribute selection evaluator wrapper",
        iconPath="",
        resourceIntensive=true)
public class ASEvaluator
extends WekaAlgorithmWrapper

Step that wraps a Weka attribute or subset evaluator. Handles training and test set connections. Requires an ASSearchStrategy step to be connected via an "info" connection. Will output both attribute selection results (via text connections) and transformed data (via outgoing train or test set connections). When processing multiple incoming training and test folds, the step can either output a cross-validation style summary over all the folds or individual attribute selection results for each fold.

Version:

$Revision: $

Author:

Mark Hall (mhall{[at]}pentaho{[dot]}com)

See Also:

ASSearchStrategy, Serialized Form

Constructor Summary

Constructors

Constructor	Description
ASEvaluator()

Method Summary

Modifier and Type	Method	Description
java.lang.String	getCustomEditorForStep()	Get the class name of the custom editor for this step
ASEvaluation	getEvaluator()	Get the evaluator wrapped by this step
java.util.List<java.lang.String>	getIncomingConnectionTypes()	Get incoming connections accepted given the current state of the step
java.util.List<java.lang.String>	getOutgoingConnectionTypes()	Get a list of output connections that can be produced given the current state of the step
boolean	getTreatXValFoldsSeparately()	Get whether to output separate results for each fold of a cross-validation, rather than averaging over folds.
java.lang.Class	getWrappedAlgorithmClass()	Get the class of Weka algorithm wrapped by this wrapper
void	processIncoming(Data data)	Process an incoming Data object
void	setEvaluator(ASEvaluation eval)	Set the evaluator to wrap (just calls setWrappedAlgorithm)
void	setTreatXValFoldsSeparately(boolean treatSeparately)	Set whether to output separate results for each fold of a cross-validation, rather than averaging over folds.
void	setWrappedAlgorithm(java.lang.Object algo)	Set an instance of the wrapped algorithm to use
void	stepInit()	Initialize at the start of a run

Methods inherited from class weka.knowledgeflow.steps.WekaAlgorithmWrapper

getDefaultIconPath, getDefaultPackageLevelIconPath, getIconPath, getWrappedAlgorithm, globalInfo

Methods inherited from class weka.knowledgeflow.steps.BaseStep

environmentSubstitute, getDefaultSettings, getInteractiveViewers, getInteractiveViewersImpls, getName, getStepManager, isResourceIntensive, isStopRequested, outputStructureForConnectionType, outputStructureForConnectionType, setName, setStepIsResourceIntensive, setStepManager, setStepMustRunSingleThreaded, start, stepMustRunSingleThreaded, stop

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

ASEvaluator

public ASEvaluator()

Method Detail

getWrappedAlgorithmClass

public java.lang.Class getWrappedAlgorithmClass()

Get the class of Weka algorithm wrapped by this wrapper

Specified by:

getWrappedAlgorithmClass in class WekaAlgorithmWrapper

Returns:

the wrapped algorithm class

setWrappedAlgorithm

public void setWrappedAlgorithm(java.lang.Object algo)

Set an instance of the wrapped algorithm to use

Overrides:

setWrappedAlgorithm in class WekaAlgorithmWrapper

Parameters:

algo - the algorithm to use

getEvaluator

public ASEvaluation getEvaluator()

Get the evaluator wrapped by this step

Returns:

the attribute or subset evaluator wrapped by this step

setEvaluator

@ProgrammaticProperty
public void setEvaluator(ASEvaluation eval)

Set the evaluator to wrap (just calls setWrappedAlgorithm)

Parameters:

eval - the evaluator to use

setTreatXValFoldsSeparately

@OptionMetadata(displayName="Treat x-val folds separately",
                description="Output separate attribute selection results for each fold of a cross-validation (rather than averaging across folds)")
public void setTreatXValFoldsSeparately(boolean treatSeparately)

Set whether to output separate results for each fold of a cross-validation, rather than averaging over folds.

Parameters:

treatSeparately - true if each fold will have results output

getTreatXValFoldsSeparately

public boolean getTreatXValFoldsSeparately()

Get whether to output separate results for each fold of a cross-validation, rather than averaging over folds.

Returns:

true if each fold will have results output

stepInit

public void stepInit()
              throws WekaException

Initialize at the start of a run

Throws:

WekaException - if there is an illegal configuration (i.e. Ranker search with subset evaluator or regular search with attribute evaluator

processIncoming

public void processIncoming(Data data)
                     throws WekaException

Process an incoming Data object

Specified by:

processIncoming in interface BaseStepExtender

Specified by:

processIncoming in interface Step

Overrides:

processIncoming in class BaseStep

Parameters:

data - the data object to process

Throws:

WekaException - if a problem occurs

getIncomingConnectionTypes

public java.util.List<java.lang.String> getIncomingConnectionTypes()

Get incoming connections accepted given the current state of the step

Returns:

a list of acceptable incoming connections

getOutgoingConnectionTypes

public java.util.List<java.lang.String> getOutgoingConnectionTypes()

Get a list of output connections that can be produced given the current state of the step

Returns:

a list of output connections

getCustomEditorForStep

public java.lang.String getCustomEditorForStep()

Get the class name of the custom editor for this step

Specified by:

getCustomEditorForStep in interface Step

Overrides:

getCustomEditorForStep in class BaseStep

Returns:

the class name of the custom editor for this step