java.lang.Object
- adams.core.logging.LoggingObject
- - adams.core.logging.CustomLoggingLevelObject
  - - adams.multiprocess.WekaCrossValidationExecution

All Implemented Interfaces:

CleanUpHandler, LoggingLevelHandler, LoggingSupporter, SizeOfHandler, Stoppable, ThreadLimiter, InstancesViewSupporter, FlowContextHandler, Serializable
```
public class WekaCrossValidationExecution
extends CustomLoggingLevelObject
implements Stoppable, InstancesViewSupporter, ThreadLimiter, FlowContextHandler, CleanUpHandler
```
Performs cross-validation, either single or multi-threaded.

Author:

FracPete (fracpete at waikato dot ac dot nz)

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field	Description
`protected JobRunner`	`m_ActualJobRunner`	the runner in use.
`protected int`	`m_ActualNumThreads`	the actual number of threads to use.
`protected weka.classifiers.Classifier`	`m_Classifier`	the classifier to evaluate.
`protected weka.classifiers.Classifier[]`	`m_Classifiers`	the separate classifiers.
`protected weka.core.Instances`	`m_Data`	the data to evaluate on.
`protected boolean`	`m_DiscardPredictions`	whether to discard predictions.
`protected weka.classifiers.Evaluation`	`m_Evaluation`	the (aggregated) evaluation.
`protected weka.classifiers.Evaluation[]`	`m_Evaluations`	the separate evaluations.
`protected Actor`	`m_FlowContext`	the flow context.
`protected int`	`m_Folds`	the number of folds.
`protected CrossValidationFoldGenerator`	`m_Generator`	the cross-validation fold generator.
`protected JobRunner`	`m_JobRunner`	the jobrunner template.
`protected JobRunnerSetup`	`m_JobRunnerSetup`	the jobrunner setup.
`protected int`	`m_NumThreads`	the number of threads to use for parallel execution (only used if no JobRunnerSetup/JobRunner set).
`protected int[]`	`m_OriginalIndices`	the original indices.
`protected weka.classifiers.evaluation.output.prediction.AbstractOutput`	`m_Output`	for generating predictions output.
`protected StringBuffer`	`m_OutputBuffer`	the buffer for the predictions.
`protected long`	`m_Seed`	the seed value.
`protected boolean`	`m_SeparateFolds`	whether to separate folds.
`protected StatusMessageHandler`	`m_StatusMessageHandler`	for outputting notifications.
`protected boolean`	`m_Stopped`	whether the execution has been stopped.
`protected boolean`	`m_UseViews`	whether to use views.
`protected boolean`	`m_WaitForJobs`	whether to wait for jobs to finish when stopping.

Fields inherited from class adams.core.logging.LoggingObject
m_Logger, m_LoggingIsEnabled, m_LoggingLevel

Constructor Summary

Constructors
Constructor Description

WekaCrossValidationExecution()
Initializes the execution.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`void`	`cleanUp()`	Cleans up data structures, frees up memory.
`String`	`execute()`	Executes the flow item.
`weka.classifiers.Classifier`	`getClassifier()`	Returns the classifier in use.
`weka.classifiers.Classifier[]`	`getClassifiers()`	Returns the classifiers per fold.
`weka.core.Instances`	`getData()`	Returns the data in use.
`boolean`	`getDiscardPredictions()`	Returns whether to discard the predictions in order to preserve memory.
`weka.classifiers.Evaluation`	`getEvaluation()`	Returns the generated (aggregated) evaluation.
`weka.classifiers.Evaluation[]`	`getEvaluations()`	Returns the generated evaluations (if multi-threaded or separated).
`Actor`	`getFlowContext()`	Returns the flow context, if any.
`int`	`getFolds()`	Returns the number of folds.
`CrossValidationFoldGenerator`	`getGenerator()`	Returns the generator to use for generating the folds.
`JobRunner`	`getJobRunner()`	Returns the JobRunner, if any.
`JobRunnerSetup`	`getJobRunnerSetup()`	Returns the JobRunnerSetup, if any.
`int`	`getNumThreads()`	Returns the number of threads to use for cross-validation (only used if no JobRunnerSetup/JobRunner set).
`int[]`	`getOriginalIndices()`	Returns the original indices.
`weka.classifiers.evaluation.output.prediction.AbstractOutput`	`getOutput()`	Returns the prediction output generator in use.
`StringBuffer`	`getOutputBuffer()`	Returns the output buffer.
`long`	`getSeed()`	Returns the seed value.
`boolean`	`getSeparateFolds()`	Returns whether to separate the folds, an Evaluation object per fold.
`StatusMessageHandler`	`getStatusMessageHandler()`	Returns the status message handler for outputting notifications.
`boolean`	`getUseViews()`	Returns whether to use views instead of dataset copies, in order to conserve memory.
`boolean`	`getWaitForJobs()`	Returns whether to wait for jobs to finish when terminating.
`protected void`	`initOutputBuffer()`	Initializes the output buffer.
`boolean`	`isSingleThreaded()`	Returns whether the execution was single-threaded (after `execute()`).
`boolean`	`isStopped()`	Returns whether the execution has been stopped.
`void`	`setClassifier(weka.classifiers.Classifier value)`	Sets the classifier to use.
`void`	`setData(weka.core.Instances value)`	Sets the data to use.
`void`	`setDiscardPredictions(boolean value)`	Sets whether to discard the predictions instead of collecting them for future use, in order to conserve memory.
`void`	`setFlowContext(Actor value)`	Sets the flow context.
`void`	`setFolds(int value)`	Sets the number of folds.
`void`	`setGenerator(CrossValidationFoldGenerator value)`	Sets the generator to use for generating the folds.
`void`	`setJobRunner(JobRunner value)`	Sets the JobRunner.
`void`	`setJobRunnerSetup(JobRunnerSetup value)`	Sets the JobRunnerSetup.
`void`	`setNumThreads(int value)`	Sets the number of threads to use for cross-validation (only used if no JobRunnerSetup/JobRunner set).
`void`	`setOutput(weka.classifiers.evaluation.output.prediction.AbstractOutput value)`	Sets the prediction output generator to use.
`void`	`setSeed(long value)`	Sets the seed value.
`void`	`setSeparateFolds(boolean value)`	Sets whether to separate the folds, an Evaluation object per fold.
`void`	`setStatusMessageHandler(StatusMessageHandler value)`	Sets the status message handler for outputting notifications.
`void`	`setUseViews(boolean value)`	Sets whether to use views instead of dataset copies, in order to conserve memory.
`void`	`setWaitForJobs(boolean value)`	Sets whether to wait for jobs to finish when terminating.
`void`	`stopExecution()`	Stops the execution.

Methods inherited from class adams.core.logging.CustomLoggingLevelObject
setLoggingLevel

Methods inherited from class adams.core.logging.LoggingObject
configureLogger, getLogger, getLoggingLevel, initializeLogging, isLoggingEnabled, sizeOf

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface adams.core.logging.LoggingLevelHandler
getLoggingLevel

- Field Detail
  - m_Classifier
```
protected weka.classifiers.Classifier m_Classifier
```
    the classifier to evaluate.
  - m_Data
```
protected weka.core.Instances m_Data
```
    the data to evaluate on.
  - m_Output
```
protected weka.classifiers.evaluation.output.prediction.AbstractOutput m_Output
```
    for generating predictions output.
  - m_OutputBuffer
```
protected StringBuffer m_OutputBuffer
```
    the buffer for the predictions.
  - m_Folds
```
protected int m_Folds
```
    the number of folds.
  - m_SeparateFolds
```
protected boolean m_SeparateFolds
```
    whether to separate folds.
  - m_Seed
```
protected long m_Seed
```
    the seed value.
  - m_UseViews
```
protected boolean m_UseViews
```
    whether to use views.
  - m_Generator
```
protected CrossValidationFoldGenerator m_Generator
```
    the cross-validation fold generator.
  - m_DiscardPredictions
```
protected boolean m_DiscardPredictions
```
    whether to discard predictions.
  - m_NumThreads
```
protected int m_NumThreads
```
    the number of threads to use for parallel execution (only used if no JobRunnerSetup/JobRunner set).
  - m_ActualNumThreads
```
protected int m_ActualNumThreads
```
    the actual number of threads to use.
  - m_JobRunnerSetup
```
protected transient JobRunnerSetup m_JobRunnerSetup
```
    the jobrunner setup.
  - m_JobRunner
```
protected transient JobRunner m_JobRunner
```
    the jobrunner template.
  - m_ActualJobRunner
```
protected transient JobRunner m_ActualJobRunner
```
    the runner in use.
  - m_Evaluation
```
protected weka.classifiers.Evaluation m_Evaluation
```
    the (aggregated) evaluation.
  - m_Evaluations
```
protected weka.classifiers.Evaluation[] m_Evaluations
```
    the separate evaluations.
  - m_Classifiers
```
protected weka.classifiers.Classifier[] m_Classifiers
```
    the separate classifiers.
  - m_OriginalIndices
```
protected int[] m_OriginalIndices
```
    the original indices.
  - m_Stopped
```
protected boolean m_Stopped
```
    whether the execution has been stopped.
  - m_StatusMessageHandler
```
protected StatusMessageHandler m_StatusMessageHandler
```
    for outputting notifications.
  - m_WaitForJobs
```
protected boolean m_WaitForJobs
```
    whether to wait for jobs to finish when stopping.
  - m_FlowContext
```
protected transient Actor m_FlowContext
```
    the flow context.
- Constructor Detail
  - WekaCrossValidationExecution
```
public WekaCrossValidationExecution()
```
    Initializes the execution.
- Method Detail
  - setFlowContext
```
public void setFlowContext(Actor value)
```
    Sets the flow context.
    
    Specified by:
    
    setFlowContext in interface FlowContextHandler
    
    Parameters:
    
    value - the actor
  - getFlowContext
```
public Actor getFlowContext()
```
    Returns the flow context, if any.
    
    Specified by:
    
    getFlowContext in interface FlowContextHandler
    
    Returns:
    
    the actor, null if none available
  - setJobRunnerSetup
```
public void setJobRunnerSetup(JobRunnerSetup value)
```
    Sets the JobRunnerSetup.
    
    Parameters:
    
    value - the setup
  - getJobRunnerSetup
```
public JobRunnerSetup getJobRunnerSetup()
```
    Returns the JobRunnerSetup, if any.
    
    Returns:
    
    the JobRunnerSetup, null if none available
  - setJobRunner
```
public void setJobRunner(JobRunner value)
```
    Sets the JobRunner.
    
    Parameters:
    
    value - the template
  - getJobRunner
```
public JobRunner getJobRunner()
```
    Returns the JobRunner, if any.
    
    Returns:
    
    the JobRunner, null if none available
  - setWaitForJobs
```
public void setWaitForJobs(boolean value)
```
    Sets whether to wait for jobs to finish when terminating.
    
    Parameters:
    
    value - true if to wait
  - getWaitForJobs
```
public boolean getWaitForJobs()
```
    Returns whether to wait for jobs to finish when terminating.
    
    Returns:
    
    the JobRunnerSetup, null if none available
  - setClassifier
```
public void setClassifier(weka.classifiers.Classifier value)
```
    Sets the classifier to use.
    
    Parameters:
    
    value - the classifier
  - getClassifier
```
public weka.classifiers.Classifier getClassifier()
```
    Returns the classifier in use.
    
    Returns:
    
    the classifier
  - getClassifiers
```
public weka.classifiers.Classifier[] getClassifiers()
```
    Returns the classifiers per fold.
    
    Returns:
    
    the classifiers, null if not stored
  - setData
```
public void setData(weka.core.Instances value)
```
    Sets the data to use.
    
    Parameters:
    
    value - the data
  - getData
```
public weka.core.Instances getData()
```
    Returns the data in use.
    
    Returns:
    
    the data
  - setOutput
```
public void setOutput(weka.classifiers.evaluation.output.prediction.AbstractOutput value)
```
    Sets the prediction output generator to use.
    
    Parameters:
    
    value - the output generator
  - getOutput
```
public weka.classifiers.evaluation.output.prediction.AbstractOutput getOutput()
```
    Returns the prediction output generator in use.
    
    Returns:
    
    the output generator
  - setFolds
```
public void setFolds(int value)
```
    Sets the number of folds.
    
    Parameters:
    
    value - the folds, <2 for LOOCV
  - getFolds
```
public int getFolds()
```
    Returns the number of folds.
    
    Returns:
    
    the folds
  - setSeparateFolds
```
public void setSeparateFolds(boolean value)
```
    Sets whether to separate the folds, an Evaluation object per fold.
    
    Parameters:
    
    value - true if to separate
  - getSeparateFolds
```
public boolean getSeparateFolds()
```
    Returns whether to separate the folds, an Evaluation object per fold.
    
    Returns:
    
    true if to separate
  - setSeed
```
public void setSeed(long value)
```
    Sets the seed value.
    
    Parameters:
    
    value - the seed
  - getSeed
```
public long getSeed()
```
    Returns the seed value.
    
    Returns:
    
    the seed
  - 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
  - setGenerator
```
public void setGenerator(CrossValidationFoldGenerator value)
```
    Sets the generator to use for generating the folds.
    
    Parameters:
    
    value - the generator
  - getGenerator
```
public CrossValidationFoldGenerator getGenerator()
```
    Returns the generator to use for generating the folds.
    
    Returns:
    
    the generator
  - setDiscardPredictions
```
public void setDiscardPredictions(boolean value)
```
    Sets whether to discard the predictions instead of collecting them for future use, in order to conserve memory. NB: Must be false in case of parallel execution to allow for aggregation of statistics;
    
    Parameters:
    
    value - true if to discard predictions
  - getDiscardPredictions
```
public boolean getDiscardPredictions()
```
    Returns whether to discard the predictions in order to preserve memory. NB: Must be false in case of parallel execution to allow for aggregation of statistics;
    
    Returns:
    
    true if predictions discarded
  - setNumThreads
```
public void setNumThreads(int value)
```
    Sets the number of threads to use for cross-validation (only used if no JobRunnerSetup/JobRunner set).
    
    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 (only used if no JobRunnerSetup/JobRunner set).
    
    Specified by:
    
    getNumThreads in interface ThreadLimiter
    
    Returns:
    
    the number of threads: -1 = # of CPUs/cores; 0/1 = sequential execution
  - setStatusMessageHandler
```
public void setStatusMessageHandler(StatusMessageHandler value)
```
    Sets the status message handler for outputting notifications.
    
    Parameters:
    
    value - the handler
  - getStatusMessageHandler
```
public StatusMessageHandler getStatusMessageHandler()
```
    Returns the status message handler for outputting notifications.
    
    Returns:
    
    the handler, null if none set
  - initOutputBuffer
```
protected void initOutputBuffer()
```
    Initializes the output buffer.
  - getOutputBuffer
```
public StringBuffer getOutputBuffer()
```
    Returns the output buffer.
    
    Returns:
    
    the output buffer
  - getEvaluation
```
public weka.classifiers.Evaluation getEvaluation()
```
    Returns the generated (aggregated) evaluation.
    
    Returns:
    
    the evaluation
  - getEvaluations
```
public weka.classifiers.Evaluation[] getEvaluations()
```
    Returns the generated evaluations (if multi-threaded or separated).
    
    Returns:
    
    the evaluations, null if not multi-threaded or not separated
  - getOriginalIndices
```
public int[] getOriginalIndices()
```
    Returns the original indices.
    
    Returns:
    
    the indices
  - isSingleThreaded
```
public boolean isSingleThreaded()
```
    Returns whether the execution was single-threaded (after execute()).
    
    Returns:
    
    true if single-threaded
  - execute
```
public String execute()
```
    Executes the flow item.
    
    Returns:
    
    null if everything is fine, otherwise error message
  - isStopped
```
public boolean isStopped()
```
    Returns whether the execution has been stopped.
    
    Returns:
    
    true if stopped
  - stopExecution
```
public void stopExecution()
```
    Stops the execution.
    
    Specified by:
    
    stopExecution in interface Stoppable
  - cleanUp
```
public void cleanUp()
```
    Cleans up data structures, frees up memory.
    
    Specified by:
    
    cleanUp in interface CleanUpHandler

Class WekaCrossValidationExecution

Field Summary

Fields inherited from class adams.core.logging.LoggingObject

Constructor Summary

Method Summary

Methods inherited from class adams.core.logging.CustomLoggingLevelObject

Methods inherited from class adams.core.logging.LoggingObject

Methods inherited from class java.lang.Object

Methods inherited from interface adams.core.logging.LoggingLevelHandler

Field Detail

m_Classifier

m_Data

m_Output

m_OutputBuffer

m_Folds

m_SeparateFolds

m_Seed

m_UseViews

m_Generator

m_DiscardPredictions

m_NumThreads

m_ActualNumThreads

m_JobRunnerSetup

m_JobRunner

m_ActualJobRunner

m_Evaluation

m_Evaluations

m_Classifiers

m_OriginalIndices

m_Stopped

m_StatusMessageHandler

m_WaitForJobs

m_FlowContext

Constructor Detail

WekaCrossValidationExecution

Method Detail

setFlowContext

getFlowContext

setJobRunnerSetup

getJobRunnerSetup

setJobRunner

getJobRunner

setWaitForJobs

getWaitForJobs

setClassifier

getClassifier

getClassifiers

setData

getData

setOutput

getOutput

setFolds

getFolds

setSeparateFolds

getSeparateFolds

setSeed

getSeed

setUseViews

getUseViews

setGenerator

getGenerator

setDiscardPredictions

getDiscardPredictions

setNumThreads

getNumThreads

setStatusMessageHandler

getStatusMessageHandler

initOutputBuffer

getOutputBuffer

getEvaluation

getEvaluations

getOriginalIndices

isSingleThreaded

execute

isStopped

stopExecution

cleanUp