Package adams.multiprocess
Class WekaCrossValidationExecution
- java.lang.Object
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- adams.core.logging.LoggingObject
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- adams.core.logging.CustomLoggingLevelObject
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- adams.multiprocess.WekaCrossValidationExecution
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- All Implemented Interfaces:
adams.core.CleanUpHandler
,adams.core.logging.LoggingLevelHandler
,adams.core.logging.LoggingSupporter
,adams.core.SizeOfHandler
,adams.core.Stoppable
,adams.core.ThreadLimiter
,InstancesViewSupporter
,adams.flow.core.FlowContextHandler
,Serializable
public class WekaCrossValidationExecution extends adams.core.logging.CustomLoggingLevelObject implements adams.core.Stoppable, InstancesViewSupporter, adams.core.ThreadLimiter, adams.flow.core.FlowContextHandler, adams.core.CleanUpHandler
Performs cross-validation, either single or multi-threaded.- Author:
- FracPete (fracpete at waikato dot ac dot nz)
- See Also:
- Serialized Form
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Field Summary
Fields Modifier and Type Field Description protected int
m_ActualFolds
the actual folds used.protected adams.multiprocess.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 adams.flow.core.Actor
m_FlowContext
the flow context.protected int
m_Folds
the number of folds.protected CrossValidationFoldGenerator
m_Generator
the cross-validation fold generator.protected adams.multiprocess.JobRunner
m_JobRunner
the jobrunner template.protected adams.flow.standalone.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 adams.core.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.
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Constructor Summary
Constructors Constructor Description WekaCrossValidationExecution()
Initializes the execution.
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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.int
getActualFolds()
Returns the actual number of folds used.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).adams.flow.core.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.adams.multiprocess.JobRunner
getJobRunner()
Returns the JobRunner, if any.adams.flow.standalone.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.adams.core.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 (afterexecute()
).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(adams.flow.core.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(adams.multiprocess.JobRunner value)
Sets the JobRunner.void
setJobRunnerSetup(adams.flow.standalone.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(adams.core.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.LoggingObject
configureLogger, getLogger, getLoggingLevel, initializeLogging, isLoggingEnabled, sizeOf
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Field Detail
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m_Classifier
protected weka.classifiers.Classifier m_Classifier
the classifier to evaluate.
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m_Data
protected weka.core.Instances m_Data
the data to evaluate on.
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m_Output
protected weka.classifiers.evaluation.output.prediction.AbstractOutput m_Output
for generating predictions output.
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m_OutputBuffer
protected StringBuffer m_OutputBuffer
the buffer for the predictions.
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m_Folds
protected int m_Folds
the number of folds.
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m_ActualFolds
protected int m_ActualFolds
the actual folds used.
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m_SeparateFolds
protected boolean m_SeparateFolds
whether to separate folds.
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m_Seed
protected long m_Seed
the seed value.
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m_UseViews
protected boolean m_UseViews
whether to use views.
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m_Generator
protected CrossValidationFoldGenerator m_Generator
the cross-validation fold generator.
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m_DiscardPredictions
protected boolean m_DiscardPredictions
whether to discard predictions.
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m_NumThreads
protected int m_NumThreads
the number of threads to use for parallel execution (only used if no JobRunnerSetup/JobRunner set).
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m_ActualNumThreads
protected int m_ActualNumThreads
the actual number of threads to use.
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m_JobRunnerSetup
protected transient adams.flow.standalone.JobRunnerSetup m_JobRunnerSetup
the jobrunner setup.
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m_JobRunner
protected transient adams.multiprocess.JobRunner m_JobRunner
the jobrunner template.
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m_ActualJobRunner
protected transient adams.multiprocess.JobRunner m_ActualJobRunner
the runner in use.
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m_Evaluation
protected weka.classifiers.Evaluation m_Evaluation
the (aggregated) evaluation.
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m_Evaluations
protected weka.classifiers.Evaluation[] m_Evaluations
the separate evaluations.
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m_Classifiers
protected weka.classifiers.Classifier[] m_Classifiers
the separate classifiers.
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m_OriginalIndices
protected int[] m_OriginalIndices
the original indices.
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m_Stopped
protected boolean m_Stopped
whether the execution has been stopped.
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m_StatusMessageHandler
protected adams.core.StatusMessageHandler m_StatusMessageHandler
for outputting notifications.
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m_WaitForJobs
protected boolean m_WaitForJobs
whether to wait for jobs to finish when stopping.
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m_FlowContext
protected transient adams.flow.core.Actor m_FlowContext
the flow context.
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Method Detail
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setFlowContext
public void setFlowContext(adams.flow.core.Actor value)
Sets the flow context.- Specified by:
setFlowContext
in interfaceadams.flow.core.FlowContextHandler
- Parameters:
value
- the actor
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getFlowContext
public adams.flow.core.Actor getFlowContext()
Returns the flow context, if any.- Specified by:
getFlowContext
in interfaceadams.flow.core.FlowContextHandler
- Returns:
- the actor, null if none available
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setJobRunnerSetup
public void setJobRunnerSetup(adams.flow.standalone.JobRunnerSetup value)
Sets the JobRunnerSetup.- Parameters:
value
- the setup
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getJobRunnerSetup
public adams.flow.standalone.JobRunnerSetup getJobRunnerSetup()
Returns the JobRunnerSetup, if any.- Returns:
- the JobRunnerSetup, null if none available
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setJobRunner
public void setJobRunner(adams.multiprocess.JobRunner value)
Sets the JobRunner.- Parameters:
value
- the template
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getJobRunner
public adams.multiprocess.JobRunner getJobRunner()
Returns the JobRunner, if any.- Returns:
- the JobRunner, null if none available
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setWaitForJobs
public void setWaitForJobs(boolean value)
Sets whether to wait for jobs to finish when terminating.- Parameters:
value
- true if to wait
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getWaitForJobs
public boolean getWaitForJobs()
Returns whether to wait for jobs to finish when terminating.- Returns:
- true if to wait
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setClassifier
public void setClassifier(weka.classifiers.Classifier value)
Sets the classifier to use.- Parameters:
value
- the classifier
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getClassifier
public weka.classifiers.Classifier getClassifier()
Returns the classifier in use.- Returns:
- the classifier
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getClassifiers
public weka.classifiers.Classifier[] getClassifiers()
Returns the classifiers per fold.- Returns:
- the classifiers, null if not stored
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setData
public void setData(weka.core.Instances value)
Sets the data to use.- Parameters:
value
- the data
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getData
public weka.core.Instances getData()
Returns the data in use.- Returns:
- the data
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setOutput
public void setOutput(weka.classifiers.evaluation.output.prediction.AbstractOutput value)
Sets the prediction output generator to use.- Parameters:
value
- the output generator
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getOutput
public weka.classifiers.evaluation.output.prediction.AbstractOutput getOutput()
Returns the prediction output generator in use.- Returns:
- the output generator
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setFolds
public void setFolds(int value)
Sets the number of folds.- Parameters:
value
- the folds, <2 for LOOCV
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getFolds
public int getFolds()
Returns the number of folds.- Returns:
- the folds
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getActualFolds
public int getActualFolds()
Returns the actual number of folds used.- Returns:
- the actual folds, -1 if not yet determined
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setSeparateFolds
public void setSeparateFolds(boolean value)
Sets whether to separate the folds, an Evaluation object per fold.- Parameters:
value
- true if to separate
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getSeparateFolds
public boolean getSeparateFolds()
Returns whether to separate the folds, an Evaluation object per fold.- Returns:
- true if to separate
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setSeed
public void setSeed(long value)
Sets the seed value.- Parameters:
value
- the seed
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getSeed
public long getSeed()
Returns the seed value.- Returns:
- the seed
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setUseViews
public void setUseViews(boolean value)
Sets whether to use views instead of dataset copies, in order to conserve memory.- Specified by:
setUseViews
in interfaceInstancesViewSupporter
- Parameters:
value
- true if to use views
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getUseViews
public boolean getUseViews()
Returns whether to use views instead of dataset copies, in order to conserve memory.- Specified by:
getUseViews
in interfaceInstancesViewSupporter
- Returns:
- true if using views
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setGenerator
public void setGenerator(CrossValidationFoldGenerator value)
Sets the generator to use for generating the folds.- Parameters:
value
- the generator
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getGenerator
public CrossValidationFoldGenerator getGenerator()
Returns the generator to use for generating the folds.- Returns:
- the generator
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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
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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
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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 interfaceadams.core.ThreadLimiter
- Parameters:
value
- the number of threads: -1 = # of CPUs/cores; 0/1 = sequential execution
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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 interfaceadams.core.ThreadLimiter
- Returns:
- the number of threads: -1 = # of CPUs/cores; 0/1 = sequential execution
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setStatusMessageHandler
public void setStatusMessageHandler(adams.core.StatusMessageHandler value)
Sets the status message handler for outputting notifications.- Parameters:
value
- the handler
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getStatusMessageHandler
public adams.core.StatusMessageHandler getStatusMessageHandler()
Returns the status message handler for outputting notifications.- Returns:
- the handler, null if none set
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initOutputBuffer
protected void initOutputBuffer()
Initializes the output buffer.
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getOutputBuffer
public StringBuffer getOutputBuffer()
Returns the output buffer.- Returns:
- the output buffer
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getEvaluation
public weka.classifiers.Evaluation getEvaluation()
Returns the generated (aggregated) evaluation.- Returns:
- the evaluation
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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
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getOriginalIndices
public int[] getOriginalIndices()
Returns the original indices.- Returns:
- the indices
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isSingleThreaded
public boolean isSingleThreaded()
Returns whether the execution was single-threaded (afterexecute()
).- Returns:
- true if single-threaded
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execute
public String execute()
Executes the flow item.- Returns:
- null if everything is fine, otherwise error message
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isStopped
public boolean isStopped()
Returns whether the execution has been stopped.- Returns:
- true if stopped
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stopExecution
public void stopExecution()
Stops the execution.- Specified by:
stopExecution
in interfaceadams.core.Stoppable
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cleanUp
public void cleanUp()
Cleans up data structures, frees up memory.- Specified by:
cleanUp
in interfaceadams.core.CleanUpHandler
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