Package adams.data.splitgenerator.generic.crossvalidation

Class CrossValidationGenerator

java.lang.Object

adams.core.logging.LoggingObject

adams.core.logging.CustomLoggingLevelObject

adams.data.splitgenerator.generic.crossvalidation.CrossValidationGenerator

All Implemented Interfaces:

LoggingLevelHandler, LoggingSupporter, SizeOfHandler, Serializable

public class CrossValidationGenerator
extends CustomLoggingLevelObject

For generating cross-validation splits.

Author:

FracPete (fracpete at waikato dot ac dot nz)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field	Description
protected int	m_NumFolds	the number of folds to use.
protected Randomization	m_Randomization	the randomization scheme.
protected Stratification	m_Stratification	the stratification scheme.

Fields inherited from class adams.core.logging.LoggingObject

m_Logger, m_LoggingIsEnabled, m_LoggingLevel

Constructor Summary

Constructors

Constructor	Description
CrossValidationGenerator()	Initializes the cross-validation.

Method Summary

Modifier and Type	Method	Description
<T> List<FoldPair<Binnable<T>>>	generate(List<Binnable<T>> data)	Generates cross-validation fold pairs.
int	getNumFolds()	Returns the number of folds in use.
Randomization	getRandomization()	Returns the randomization scheme in use.
Stratification	getStratification()	Returns the stratification scheme in use.
void	reset()	Resets the scheme.
void	setNumFolds(int value)	Sets the number of folds to use.
void	setRandomization(Randomization value)	Sets the randomization scheme to use.
void	setStratification(Stratification value)	Sets the stratification scheme to use.
static <T> List<Binnable<T>>	testCV(List<Binnable<T>> data, int numFolds, int numFold)	Creates the test set for one fold of a cross-validation on the dataset.
static <T> List<Binnable<T>>	trainCV(List<Binnable<T>> data, int numFolds, int numFold)	Creates the training set for one fold of a cross-validation on the dataset.
static <T> List<Binnable<T>>	trainCV(List<Binnable<T>> data, int numFolds, int numFold, Randomization random)	Creates the training set for one fold of a cross-validation on the dataset.

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_NumFolds

protected int m_NumFolds

the number of folds to use.

m_Randomization

protected Randomization m_Randomization

the randomization scheme.

m_Stratification

protected Stratification m_Stratification

the stratification scheme.

Constructor Detail

CrossValidationGenerator

public CrossValidationGenerator()

Initializes the cross-validation.

Method Detail

reset

public void reset()

Resets the scheme.

setNumFolds

public void setNumFolds(int value)

Sets the number of folds to use.

Parameters:

value - the number of folds, LOO if <2

getNumFolds

public int getNumFolds()

Returns the number of folds in use.

Returns:

the number of folds, LOO if <2

setRandomization

public void setRandomization(Randomization value)

Sets the randomization scheme to use.

Parameters:

value - the scheme

getRandomization

public Randomization getRandomization()

Returns the randomization scheme in use.

Returns:

the scheme

setStratification

public void setStratification(Stratification value)

Sets the stratification scheme to use.

Parameters:

value - the scheme

getStratification

public Stratification getStratification()

Returns the stratification scheme in use.

Returns:

the scheme

generate

public <T> List<FoldPair<Binnable<T>>> generate(List<Binnable<T>> data)

Generates cross-validation fold pairs. Temporarily adds the original index to the Binnable meta-data, using Wrapping.TMP_INDEX as key.

Type Parameters:

T - the payload type

Parameters:

data - the data to generate the pairs from

Returns:

the fold pairs

trainCV

public static <T> List<Binnable<T>> trainCV(List<Binnable<T>> data,
                                            int numFolds,
                                            int numFold)

Creates the training set for one fold of a cross-validation on the dataset.

Parameters:

numFolds - the number of folds in the cross-validation. Must be greater than 1.

numFold - 0 for the first fold, 1 for the second, ...

Returns:

the training set

Throws:

IllegalArgumentException - if the number of folds is less than 2 or greater than the number of instances.

trainCV

public static <T> List<Binnable<T>> trainCV(List<Binnable<T>> data,
                                            int numFolds,
                                            int numFold,
                                            Randomization random)

Creates the training set for one fold of a cross-validation on the dataset. The data is subsequently randomized based on the given random number generator.

Parameters:

numFolds - the number of folds in the cross-validation. Must be greater than 1.

numFold - 0 for the first fold, 1 for the second, ...

random - the random number generator

Returns:

the training set

Throws:

IllegalArgumentException - if the number of folds is less than 2 or greater than the number of instances.

testCV

public static <T> List<Binnable<T>> testCV(List<Binnable<T>> data,
                                           int numFolds,
                                           int numFold)

Creates the test set for one fold of a cross-validation on the dataset.

Parameters:

numFolds - the number of folds in the cross-validation. Must be greater than 1.

numFold - 0 for the first fold, 1 for the second, ...

Returns:

the test set as a set of weighted instances

Throws:

IllegalArgumentException - if the number of folds is less than 2 or greater than the number of instances.