Package ai.libs.jaicore.ml.evaluation.evaluators.weka.factory

Class AMonteCarloCrossValidationBasedEvaluatorFactory

java.lang.Object

ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory

All Implemented Interfaces:

IClassifierEvaluatorFactory

Direct Known Subclasses:

MonteCarloCrossValidationEvaluatorFactory, ProbabilisticMonteCarloCrossValidationEvaluatorFactory

public abstract class AMonteCarloCrossValidationBasedEvaluatorFactory
extends java.lang.Object
implements IClassifierEvaluatorFactory

An abstract factory for configuring Monte Carlo cross-validation based evaluators.

Constructor Summary

Constructors

Modifier	Constructor	Description
protected	AMonteCarloCrossValidationBasedEvaluatorFactory()	Standard c'tor.

Method Summary

Modifier and Type	Method	Description
weka.core.Instances	getData()	Getter for the dataset which is used for splitting.
IDatasetSplitter	getDatasetSplitter()	Getter for the dataset splitter.
int	getNumMCIterations()	Getter for the number of iterations, i.e. the number of splits considered.
int	getSeed()	Getter for the random seed.
ISplitBasedClassifierEvaluator<java.lang.Double>	getSplitBasedEvaluator()	Getter for the evaluator that is used for evaluating each split.
int	getTimeoutForSolutionEvaluation()	Getter for the timeout for evaluating a solution.
double	getTrainFoldSize()	Getter for the size of the train fold.
AMonteCarloCrossValidationBasedEvaluatorFactory	withData(weka.core.Instances data)	Configures the dataset which is split into train and test data.
AMonteCarloCrossValidationBasedEvaluatorFactory	withDatasetSplitter(IDatasetSplitter datasetSplitter)	Configures the evaluator to use the given dataset splitter.
AMonteCarloCrossValidationBasedEvaluatorFactory	withNumMCIterations(int numMCIterations)	Configures the number of monte carlo cross-validation iterations.
AMonteCarloCrossValidationBasedEvaluatorFactory	withSeed(int seed)	Configures the evaluator to use the given random seed.
AMonteCarloCrossValidationBasedEvaluatorFactory	withSplitBasedEvaluator(ISplitBasedClassifierEvaluator<java.lang.Double> splitBasedClassifierEvaluator)	Configures the evaluator to use the given classifier evaluator.
AMonteCarloCrossValidationBasedEvaluatorFactory	withTimeoutForSolutionEvaluation(int timeoutForSolutionEvaluation)	Configures a timeout for evaluating a solution.
AMonteCarloCrossValidationBasedEvaluatorFactory	withTrainFoldSize(double trainFoldSize)	Configures the portion of the training data relative to the entire dataset size.

Methods inherited from class java.lang.Object

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

Methods inherited from interface ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.IClassifierEvaluatorFactory

getIClassifierEvaluator

Constructor Detail

AMonteCarloCrossValidationBasedEvaluatorFactory

protected AMonteCarloCrossValidationBasedEvaluatorFactory()

Standard c'tor.

Method Detail

getDatasetSplitter

public IDatasetSplitter getDatasetSplitter()

Getter for the dataset splitter.

Returns:

Returns the dataset spliiter.

getSplitBasedEvaluator

public ISplitBasedClassifierEvaluator<java.lang.Double> getSplitBasedEvaluator()

Getter for the evaluator that is used for evaluating each split.

Returns:

The split evaluator.

getSeed

public int getSeed()

Getter for the random seed.

Returns:

Seed used for generating randomized dataset splits.

getNumMCIterations

public int getNumMCIterations()

Getter for the number of iterations, i.e. the number of splits considered.

Returns:

The number of iterations.

getData

public weka.core.Instances getData()

Getter for the dataset which is used for splitting.

Returns:

The original dataset that is being split.

getTrainFoldSize

public double getTrainFoldSize()

Getter for the size of the train fold.

Returns:

The portion of the training data.

getTimeoutForSolutionEvaluation

public int getTimeoutForSolutionEvaluation()

Getter for the timeout for evaluating a solution.

Returns:

The timeout for evaluating a solution.

withDatasetSplitter

public AMonteCarloCrossValidationBasedEvaluatorFactory withDatasetSplitter(IDatasetSplitter datasetSplitter)

Configures the evaluator to use the given dataset splitter.

Parameters:

datasetSplitter - The dataset splitter to be used.

Returns:

The factory object.

withSplitBasedEvaluator

public AMonteCarloCrossValidationBasedEvaluatorFactory withSplitBasedEvaluator(ISplitBasedClassifierEvaluator<java.lang.Double> splitBasedClassifierEvaluator)

Configures the evaluator to use the given classifier evaluator.

Parameters:

splitBasedClassifierEvaluator - The classifier evaluator to be used.

Returns:

The factory object.

withSeed

public AMonteCarloCrossValidationBasedEvaluatorFactory withSeed(int seed)

Configures the evaluator to use the given random seed.

Parameters:

seed - The seed to be used for pseudo-randomization.

Returns:

The factory object.

withNumMCIterations

public AMonteCarloCrossValidationBasedEvaluatorFactory withNumMCIterations(int numMCIterations)

Configures the number of monte carlo cross-validation iterations.

Parameters:

numMCIterations - The number of iterations to run.

Returns:

The factory object.

withData

public AMonteCarloCrossValidationBasedEvaluatorFactory withData(weka.core.Instances data)

Configures the dataset which is split into train and test data.

Parameters:

data - The dataset to be split.

Returns:

The factory object.

withTrainFoldSize

public AMonteCarloCrossValidationBasedEvaluatorFactory withTrainFoldSize(double trainFoldSize)

Configures the portion of the training data relative to the entire dataset size.

Parameters:

trainFoldSize - The size of the training fold (0,1).

Returns:

The factory object.

withTimeoutForSolutionEvaluation

public AMonteCarloCrossValidationBasedEvaluatorFactory withTimeoutForSolutionEvaluation(int timeoutForSolutionEvaluation)

Configures a timeout for evaluating a solution.

Parameters:

timeoutForSolutionEvaluation - The timeout for evaluating a solution.

Returns:

The factory object.