ai.libs.jaicore.ml.learningcurve.extrapolation

Class LearningCurveExtrapolator<I extends IInstance>

java.lang.Object

ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator<I>

All Implemented Interfaces:

ai.libs.jaicore.basic.ILoggingCustomizable

Direct Known Subclasses:

ConfigurationLearningCurveExtrapolator

public class LearningCurveExtrapolator<I extends IInstance>
extends java.lang.Object
implements ai.libs.jaicore.basic.ILoggingCustomizable

Abstract class for implementing a learning curve extrapolation method with some anchor points. For each of this anchorpoints a subsample will be drawn and a classifier will be trained with this sample. Based on the points (subsample size, learner accuracy) a custom method of learning curve extrapolation can be applied.

Field Summary

Fields

Modifier and Type	Field and Description
protected IDataset<I>	dataset
protected LearningCurveExtrapolationMethod	extrapolationMethod
protected weka.classifiers.Classifier	learner
protected java.util.Random	random
protected ASamplingAlgorithm<I>	samplingAlgorithm
protected ISamplingAlgorithmFactory<I,? extends ASamplingAlgorithm<I>>	samplingAlgorithmFactory
protected IDataset<I>	test
protected IDataset<I>	train

Constructor Summary

Constructors

Constructor and Description
LearningCurveExtrapolator(LearningCurveExtrapolationMethod extrapolationMethod, weka.classifiers.Classifier learner, IDataset<I> dataset, double trainsplit, int[] anchorPoints, ISamplingAlgorithmFactory<I,? extends ASamplingAlgorithm<I>> samplingAlgorithmFactory, long seed) Create a learning curve extrapolator with a subsampling factory.

Method Summary

Modifier and Type	Method and Description
LearningCurve	extrapolateLearningCurve() Measure the learner accuracy at the given anchorpoints and extrapolate a learning curve based the results.
int[]	getAnchorPoints()
IDataset<I>	getDataset()
LearningCurveExtrapolationMethod	getExtrapolationMethod()
weka.classifiers.Classifier	getLearner()
java.lang.String	getLoggerName()
int[]	getTrainingTimes()
double[]	getyValues()
void	setLoggerName(java.lang.String name)

Methods inherited from class java.lang.Object

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

Field Detail

learner

protected weka.classifiers.Classifier learner

dataset

protected IDataset<I extends IInstance> dataset

train

protected IDataset<I extends IInstance> train

test

protected IDataset<I extends IInstance> test

samplingAlgorithmFactory

protected ISamplingAlgorithmFactory<I extends IInstance,? extends ASamplingAlgorithm<I extends IInstance>> samplingAlgorithmFactory

samplingAlgorithm

protected ASamplingAlgorithm<I extends IInstance> samplingAlgorithm

random

protected java.util.Random random

extrapolationMethod

protected LearningCurveExtrapolationMethod extrapolationMethod

Constructor Detail

LearningCurveExtrapolator

public LearningCurveExtrapolator(LearningCurveExtrapolationMethod extrapolationMethod,
                                 weka.classifiers.Classifier learner,
                                 IDataset<I> dataset,
                                 double trainsplit,
                                 int[] anchorPoints,
                                 ISamplingAlgorithmFactory<I,? extends ASamplingAlgorithm<I>> samplingAlgorithmFactory,
                                 long seed)

Create a learning curve extrapolator with a subsampling factory.

Parameters:

extrapolationMethod - Method for extrapolating a learning curve from anchorpoints.

learner - Learning model to predict the learning curve of.

dataset - Dataset to measure evaluate the learner on.

trainsplit - Portion of the dataset, which shall be used to sample from for training.

samplingAlgorithmFactory - Subsampling algorithm factory to create a configured subsampler with.

seed - Random seed.

Method Detail

extrapolateLearningCurve

public LearningCurve extrapolateLearningCurve()
                                       throws InvalidAnchorPointsException,
                                              ai.libs.jaicore.basic.algorithm.exceptions.AlgorithmException,
                                              java.lang.InterruptedException

Measure the learner accuracy at the given anchorpoints and extrapolate a learning curve based the results.

Parameters:

anchorPoints - Sample sizes as anchorpoints, where the true accuracy shall be measured.

Returns:

The extrapolated learning curve.

Throws:

InvalidAnchorPointsException - The anchorpoints (amount, values, ...) are not suitable for the given learning curve extrapolation method.

ai.libs.jaicore.basic.algorithm.exceptions.AlgorithmException - An error occured during the creation of the specified anchorpoints.

java.lang.InterruptedException

getLearner

public weka.classifiers.Classifier getLearner()

getDataset

public IDataset<I> getDataset()

getExtrapolationMethod

public LearningCurveExtrapolationMethod getExtrapolationMethod()

getAnchorPoints

public int[] getAnchorPoints()

getyValues

public double[] getyValues()

getTrainingTimes

public int[] getTrainingTimes()

getLoggerName

public java.lang.String getLoggerName()

Specified by:

getLoggerName in interface ai.libs.jaicore.basic.ILoggingCustomizable

setLoggerName

public void setLoggerName(java.lang.String name)

Specified by:

setLoggerName in interface ai.libs.jaicore.basic.ILoggingCustomizable