ai.libs.mlplan.core

Class MLPlanWekaBuilder

java.lang.Object

ai.libs.mlplan.core.AbstractMLPlanBuilder

ai.libs.mlplan.core.AbstractMLPlanSingleLabelBuilder

ai.libs.mlplan.core.MLPlanWekaBuilder

All Implemented Interfaces:

ai.libs.jaicore.basic.ILoggingCustomizable, IMLPlanBuilder

public class MLPlanWekaBuilder
extends AbstractMLPlanSingleLabelBuilder

Field Summary

Fields inherited from class ai.libs.mlplan.core.AbstractMLPlanSingleLabelBuilder

LOSS_FUNCTION, SEARCH_NUM_MC_ITERATIONS, SEARCH_TRAIN_FOLD_SIZE, SELECTION_NUM_MC_ITERATIONS, SELECTION_TRAIN_FOLD_SIZE

Constructor Summary

Constructors

Constructor and Description
MLPlanWekaBuilder()

Method Summary

Modifier and Type	Method and Description
void	withLearningCurveExtrapolationEvaluation(int[] anchorpoints, ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.interfaces.ISamplingAlgorithmFactory<ai.libs.jaicore.ml.core.dataset.IInstance,? extends ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm<ai.libs.jaicore.ml.core.dataset.IInstance>> subsamplingAlgorithmFactory, double trainSplitForAnchorpointsMeasurement, ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolationMethod extrapolationMethod) Allows to use learning curve extrapolation for predicting the quality of candidate solutions.
MLPlanWekaBuilder	withTinyWekaSearchSpace() Sets the search space to a tiny weka search space configuration.

Methods inherited from class ai.libs.mlplan.core.AbstractMLPlanSingleLabelBuilder

getDefaultDatasetSplitter, withMonteCarloCrossValidationInSearchPhase, withMonteCarloCrossValidationInSelectionPhase, withPerformanceMeasure

Methods inherited from class ai.libs.mlplan.core.AbstractMLPlanBuilder

build, build, forMeka, forSKLearn, forWeka, getAlgorithmConfig, getCandidateEvaluationTimeOut, getClassifierEvaluationInSearchPhase, getClassifierEvaluationInSelectionPhase, getClassifierFactory, getComponents, getDBAdapter, getHASCOFactory, getLoggerName, getNodeEvaluationTimeOut, getPerformanceMeasureName, getRequestedInterface, getSearchEvaluatorFactory, getSearchSelectionDatasetSplitter, getSearchSpaceConfigFile, getSelectionEvaluatorFactory, getTimeOut, getUseCache, prepareNodeEvaluatorInFactoryWithData, setLoggerName, setPerformanceMeasureName, withAlgorithmConfig, withAlgorithmConfigFile, withCandidateEvaluationTimeOut, withClassifierFactory, withDataset, withDatasetSplitterForSearchSelectionSplit, withNodeEvaluationTimeOut, withNumCpus, withPreferredComponentsFile, withPreferredNodeEvaluator, withRandomCompletionBasedBestFirstSearch, withRequestedInterface, withSearchFactory, withSearchPhaseEvaluatorFactory, withSearchSpaceConfigFile, withSelectionPhaseEvaluatorFactory, withTimeOut

Methods inherited from class java.lang.Object

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

Constructor Detail

MLPlanWekaBuilder

public MLPlanWekaBuilder()
                  throws java.io.IOException

Throws:

java.io.IOException

Method Detail

withTinyWekaSearchSpace

public MLPlanWekaBuilder withTinyWekaSearchSpace()
                                          throws java.io.IOException

Sets the search space to a tiny weka search space configuration.

Throws:

java.io.IOException - Thrown if the resource file cannot be read.

withLearningCurveExtrapolationEvaluation

public void withLearningCurveExtrapolationEvaluation(int[] anchorpoints,
                                                     ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.interfaces.ISamplingAlgorithmFactory<ai.libs.jaicore.ml.core.dataset.IInstance,? extends ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm<ai.libs.jaicore.ml.core.dataset.IInstance>> subsamplingAlgorithmFactory,
                                                     double trainSplitForAnchorpointsMeasurement,
                                                     ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolationMethod extrapolationMethod)

Allows to use learning curve extrapolation for predicting the quality of candidate solutions.

Parameters:

anchorpoints - The anchor points for which samples are actually evaluated on the respective data.

subsamplingAlgorithmFactory - The factory for the sampling algorithm that is to be used to randomly draw training instances.

trainSplitForAnchorpointsMeasurement - The training fold size for measuring the acnhorpoints.

extrapolationMethod - The method to be used in order to extrapolate the learning curve from the anchorpoints.