A B C D E F G H I L M N O P R S T U V W

A

AbstractMLPlanBuilder - Class in ai.libs.mlplan.core

The MLPlanBuilder helps to easily configure and initialize ML-Plan with specific parameter settings.

AbstractMLPlanBuilder() - Constructor for class ai.libs.mlplan.core.AbstractMLPlanBuilder

AbstractMLPlanSingleLabelBuilder - Class in ai.libs.mlplan.core

AbstractMLPlanSingleLabelBuilder() - Constructor for class ai.libs.mlplan.core.AbstractMLPlanSingleLabelBuilder

addChild(FeatureGenerator) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.FeatureGeneratorTree

addEntry(long, Classifier, List<Double>) - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginModel

ai.libs.mlpipeline_evaluation - package ai.libs.mlpipeline_evaluation

Package containing classes for the evaluation of MLPipelines and the caching of those evaluations in a database.

ai.libs.mlplan.bigdata - package ai.libs.mlplan.bigdata

ai.libs.mlplan.cli - package ai.libs.mlplan.cli

ai.libs.mlplan.core - package ai.libs.mlplan.core

ai.libs.mlplan.core.events - package ai.libs.mlplan.core.events

ai.libs.mlplan.gui.outofsampleplots - package ai.libs.mlplan.gui.outofsampleplots

ai.libs.mlplan.metamining.pipelinecharacterizing - package ai.libs.mlplan.metamining.pipelinecharacterizing

Package containing the handling of MLPipeline characterization with the help of an ontology and pattern recognition.

ai.libs.mlplan.metamining.similaritymeasures - package ai.libs.mlplan.metamining.similaritymeasures

ai.libs.mlplan.multiclass - package ai.libs.mlplan.multiclass

ai.libs.mlplan.multiclass.wekamlplan - package ai.libs.mlplan.multiclass.wekamlplan

ai.libs.mlplan.multiclass.wekamlplan.sklearn - package ai.libs.mlplan.multiclass.wekamlplan.sklearn

ai.libs.mlplan.multiclass.wekamlplan.sophisticated - package ai.libs.mlplan.multiclass.wekamlplan.sophisticated

ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen - package ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen

ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featurepre - package ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featurepre

ai.libs.mlplan.multiclass.wekamlplan.weka - package ai.libs.mlplan.multiclass.wekamlplan.weka

ai.libs.mlplan.multiclass.wekamlplan.weka.model - package ai.libs.mlplan.multiclass.wekamlplan.weka.model

ai.libs.mlplan.multiclasswithreduction - package ai.libs.mlplan.multiclasswithreduction

ai.libs.mlplan.multilabel - package ai.libs.mlplan.multilabel

ai.libs.reduction - package ai.libs.reduction

ai.libs.reduction.ensemble.simple - package ai.libs.reduction.ensemble.simple

ai.libs.reduction.single - package ai.libs.reduction.single

ai.libs.reduction.single.confusion - package ai.libs.reduction.single.confusion

ai.libs.reduction.single.heterogeneous.bestofkrandom - package ai.libs.reduction.single.heterogeneous.bestofkrandom

ai.libs.reduction.single.heterogeneous.simplerpnd - package ai.libs.reduction.single.heterogeneous.simplerpnd

ai.libs.reduction.single.homogeneous.bestofkatrandom - package ai.libs.reduction.single.homogeneous.bestofkatrandom

apply(Instance) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.FeatureGeneratorTree

apply(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.FeatureGeneratorTree

apply(Instance) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.InteractingFeatures

apply(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.InteractingFeatures

apply(Instance) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.PolynomialFeatures

apply(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.PolynomialFeatures

apply(Instance) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featurepre.Normalization

apply(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featurepre.Normalization

apply(Instance) - Method in interface ai.libs.mlplan.multiclass.wekamlplan.sophisticated.FeaturePreprocessor

apply(Instances) - Method in interface ai.libs.mlplan.multiclass.wekamlplan.sophisticated.FeaturePreprocessor

apply(Instance) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.MLSophisticatedPipeline

apply(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.MLSophisticatedPipeline

apply(Instance) - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SupervisedFilterSelector

apply(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SupervisedFilterSelector

apply(Instance) - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SuvervisedFilterPreprocessor

apply(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SuvervisedFilterPreprocessor

associateExperimentWithException(MySQLEnsembleOfSimpleOneStepReductionsExperiment, String, Throwable) - Method in class ai.libs.reduction.ensemble.simple.MySQLEnsembleOfSimpleOneStepReductionsExperimentRunner

associateExperimentWithException(MySQLReductionExperiment, Throwable) - Method in class ai.libs.reduction.single.heterogeneous.bestofkrandom.MySQLReductionExperimentRunnerWrapper

associateExperimentWithException(MySQLReductionExperiment, String, Throwable) - Method in class ai.libs.reduction.single.heterogeneous.simplerpnd.MySQLExperimentRunner

associateExperimentWithException(MySQLReductionExperiment, Throwable) - Method in class ai.libs.reduction.single.homogeneous.bestofkatrandom.MySQLReductionExperimentRunnerWrapper

B

BestOfKAtRandomExperiment - Class in ai.libs.reduction.single

BestOfKAtRandomExperiment(int, String, String, String, String, int, int) - Constructor for class ai.libs.reduction.single.BestOfKAtRandomExperiment

build(Instances) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

Builds an ML-Plan object for the given dataset as input.

build() - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

Builds an ML-Plan object with the dataset provided earlier to this builder.

build(List<MLPipeline>) - Method in interface ai.libs.mlplan.metamining.pipelinecharacterizing.IPipelineCharacterizer

Finds frequent patterns in the given list of pipelines.

build(List<MLPipeline>) - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAPipelineCharacterizer

build(INDArray, INDArray, INDArray) - Method in class ai.libs.mlplan.metamining.similaritymeasures.F1Optimizer

Learns a matrix U that minimizes F1 (W is ignored here)

build(INDArray, INDArray, INDArray) - Method in class ai.libs.mlplan.metamining.similaritymeasures.F3Optimizer

Learns a matrix U that minimizes F1 (W is ignored here)

build(INDArray, INDArray, INDArray) - Method in interface ai.libs.mlplan.metamining.similaritymeasures.IHeterogenousSimilarityMeasureComputer

Build a model based on training data that can then be used to estimate the similarity of two measures for a new problem.

buildClassifier(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.MLPlanWekaClassifier

buildClassifier(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.MLSophisticatedPipeline

buildClassifier(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.MLPipeline

buildClassifier(Instances, Collection<String>) - Method in class ai.libs.reduction.single.confusion.ConfusionBasedAlgorithm

buildClassifier(Instances, Collection<String>) - Method in class ai.libs.reduction.single.confusion.ConfusionBasedGreedyOptimizingAlgorithm

C

CacheEvaluatorMeasureBridge - Class in ai.libs.mlpipeline_evaluation

Implements a cache for the AbstractSplitBasedClassifierEvaluator.

CacheEvaluatorMeasureBridge(IMeasure<Double, Double>, PerformanceDBAdapter) - Constructor for class ai.libs.mlpipeline_evaluation.CacheEvaluatorMeasureBridge

call() - Method in class ai.libs.mlplan.bigdata.MLPlan4BigFileInput

call() - Method in class ai.libs.mlplan.core.MLPlan

cancel() - Method in class ai.libs.mlplan.core.MLPlan

characterize(MLPipeline) - Method in interface ai.libs.mlplan.metamining.pipelinecharacterizing.IPipelineCharacterizer

Checks which of the found patterns (found during the training phase in IPipelineCharacterizer.build(List)) occur in this pipeline.

characterize(MLPipeline) - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAPipelineCharacterizer

ClassifierCreatedEvent - Class in ai.libs.mlplan.core.events

ClassifierCreatedEvent(ComponentInstance, Classifier) - Constructor for class ai.libs.mlplan.core.events.ClassifierCreatedEvent

ClassifierFoundEvent - Class in ai.libs.mlplan.core.events

ClassifierFoundEvent(String, ComponentInstance, Classifier, double) - Constructor for class ai.libs.mlplan.core.events.ClassifierFoundEvent

classifyInstance(Instance) - Method in class ai.libs.mlplan.multiclass.wekamlplan.MLPlanWekaClassifier

classifyInstance(Instance) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.MLSophisticatedPipeline

classifyInstance(Instance) - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.MLPipeline

classifyInstances(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.MLPlanWekaClassifier

classifyInstances(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.MLPipeline

ClassSplit<T> - Class in ai.libs.mlplan.multiclasswithreduction

ClassSplit(Collection<T>, Collection<T>, Collection<T>) - Constructor for class ai.libs.mlplan.multiclasswithreduction.ClassSplit

ClassSplit(ClassSplit<T>) - Constructor for class ai.libs.mlplan.multiclasswithreduction.ClassSplit

clear() - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginModel

clear() - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginView

close() - Method in class ai.libs.mlpipeline_evaluation.PerformanceDBAdapter

computeSimilarity(INDArray, INDArray) - Method in class ai.libs.mlplan.metamining.similaritymeasures.F1Optimizer

computeSimilarity(INDArray, INDArray) - Method in class ai.libs.mlplan.metamining.similaritymeasures.F3Optimizer

computeSimilarity(INDArray, INDArray) - Method in interface ai.libs.mlplan.metamining.similaritymeasures.IHeterogenousSimilarityMeasureComputer

Compute the 'quality of the match' of given feature values for a new problem instance based on the training.

computeSimilarityOfRankMatrix(INDArray) - Method in interface ai.libs.mlplan.metamining.similaritymeasures.IRankMatrixSimilarityComputer

computeSimilarityOfRankMatrix(INDArray) - Method in class ai.libs.mlplan.metamining.similaritymeasures.RankMatrixSimilarityComputer

conductEnsembleOfOneStepReductionsExperiment(EnsembleOfSimpleOneStepReductionsExperiment) - Static method in class ai.libs.reduction.Util

conductExperiment(MySQLEnsembleOfSimpleOneStepReductionsExperiment) - Method in class ai.libs.reduction.ensemble.simple.MySQLEnsembleOfSimpleOneStepReductionsExperimentRunner

conductExperiment(MySQLReductionExperiment) - Method in class ai.libs.reduction.single.heterogeneous.bestofkrandom.MySQLReductionExperimentRunnerWrapper

conductExperiment(MySQLReductionExperiment) - Method in class ai.libs.reduction.single.heterogeneous.simplerpnd.MySQLExperimentRunner

conductExperiment(MySQLReductionExperiment) - Method in class ai.libs.reduction.single.homogeneous.bestofkatrandom.MySQLReductionExperimentRunnerWrapper

conductSingleOneStepReductionExperiment(ReductionExperiment) - Method in class ai.libs.reduction.single.ExperimentRunner

conductSingleOneStepReductionExperiment(ReductionExperiment) - Static method in class ai.libs.reduction.Util

configureValidation(String, String, int) - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCache

ConfusionBasedAlgorithm - Class in ai.libs.reduction.single.confusion

ConfusionBasedAlgorithm() - Constructor for class ai.libs.reduction.single.confusion.ConfusionBasedAlgorithm

ConfusionBasedGreedyOptimizingAlgorithm - Class in ai.libs.reduction.single.confusion

ConfusionBasedGreedyOptimizingAlgorithm() - Constructor for class ai.libs.reduction.single.confusion.ConfusionBasedGreedyOptimizingAlgorithm

ConsistentMLPipelineEvaluator - Class in ai.libs.mlpipeline_evaluation

For consistent evaluations of MLPipelines.

convertToComponentInstance(MLPipeline) - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.MLPipelineComponentInstanceFactory

Converts the given MLPipelines object to a ComponentInstance.

createAndGetExperimentIfNotConducted(int, File, String, int) - Method in class ai.libs.reduction.ensemble.simple.MySQLEnsembleOfSimpleOneStepReductionsExperimentRunner

createAndGetExperimentIfNotConducted(int, File, String, String, String) - Method in class ai.libs.reduction.single.heterogeneous.bestofkrandom.MySQLReductionExperimentRunnerWrapper

createAndGetExperimentIfNotConducted(int, File, String, String, String) - Method in class ai.libs.reduction.single.heterogeneous.simplerpnd.MySQLExperimentRunner

createAndGetExperimentIfNotConducted(int, File, String) - Method in class ai.libs.reduction.single.homogeneous.bestofkatrandom.MySQLReductionExperimentRunnerWrapper

createGeneralRPNDBasedSplit(Collection<String>, Random, String, Instances) - Static method in class ai.libs.mlplan.multiclasswithreduction.NestedDichotomyUtil

createGeneralRPNDBasedSplit(Collection<String>, Collection<String>, Collection<String>, Random, String, Instances) - Static method in class ai.libs.mlplan.multiclasswithreduction.NestedDichotomyUtil

createUnaryRPNDBasedSplit(Collection<String>, Random, String, Instances) - Static method in class ai.libs.mlplan.multiclasswithreduction.NestedDichotomyUtil

D

dataPortionForSelection() - Method in interface ai.libs.mlplan.multiclass.MLPlanClassifierConfig

DatasetOrigin - Enum in ai.libs.mlpipeline_evaluation

Allowed origins for datasets.

distributionForInstance(Instance) - Method in class ai.libs.mlplan.multiclass.wekamlplan.MLPlanWekaClassifier

distributionForInstance(Instance) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.MLSophisticatedPipeline

distributionForInstance(Instance) - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.MLPipeline

E

EnsembleOfSimpleOneStepReductionsExperiment - Class in ai.libs.reduction.ensemble.simple

EnsembleOfSimpleOneStepReductionsExperiment(int, String, String, int) - Constructor for class ai.libs.reduction.ensemble.simple.EnsembleOfSimpleOneStepReductionsExperiment

EnsembleOfSimpleOneStepReductionsExperiment(int, String, String, int, double, String) - Constructor for class ai.libs.reduction.ensemble.simple.EnsembleOfSimpleOneStepReductionsExperiment

equals(Object) - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SupervisedFilterSelector

equals(Object) - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SuvervisedFilterPreprocessor

equals(Object) - Method in class ai.libs.mlplan.multiclasswithreduction.ClassSplit

equals(Object) - Method in class ai.libs.reduction.ensemble.simple.EnsembleOfSimpleOneStepReductionsExperiment

equals(Object) - Method in class ai.libs.reduction.ensemble.simple.MySQLEnsembleOfSimpleOneStepReductionsExperiment

equals(Object) - Method in class ai.libs.reduction.single.BestOfKAtRandomExperiment

equals(Object) - Method in class ai.libs.reduction.single.MySQLReductionExperiment

equals(Object) - Method in class ai.libs.reduction.single.ReductionExperiment

evaluateClassifier(String, String, int, String, String, int, Instances, Classifier) - Static method in class ai.libs.mlpipeline_evaluation.ConsistentMLPipelineEvaluator

Get the error rate of the classifier according to the given info about the split and evaluation technique.

evaluateClassifier(String, String, int, Instances, Classifier) - Static method in class ai.libs.mlpipeline_evaluation.ConsistentMLPipelineEvaluator

Get the error rate of the classifier according to the given info about the split and evaluation technique.

evaluateSplit(Classifier, Instances, Instances) - Method in class ai.libs.mlpipeline_evaluation.CacheEvaluatorMeasureBridge

evaluateSupervised(ComponentInstance) - Method in class ai.libs.mlplan.core.PipelineEvaluator

exists(ComponentInstance, ReproducibleInstances, ReproducibleInstances, String) - Method in class ai.libs.mlpipeline_evaluation.PerformanceDBAdapter

Checks whether there is an entry for the composition and corresponding evaluation specified by the reproducable instances.

ExperimentRunner<T extends ai.libs.jaicore.ml.classification.multiclass.reduction.splitters.ISplitter> - Class in ai.libs.reduction.single

ExperimentRunner(int, int, ISplitterFactory<T>) - Constructor for class ai.libs.reduction.single.ExperimentRunner

extractSKLearnConstructInstruction(ComponentInstance, Set<String>) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sklearn.SKLearnClassifierFactory

F

f(Node<TFDNode, ?>) - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.PreferenceBasedNodeEvaluator

f(Node<TFDNode, ?>) - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.WekaPipelineValidityCheckingNodeEvaluator

F1Optimizer - Class in ai.libs.mlplan.metamining.similaritymeasures

F1Optimizer() - Constructor for class ai.libs.mlplan.metamining.similaritymeasures.F1Optimizer

F3Optimizer - Class in ai.libs.mlplan.metamining.similaritymeasures

F3Optimizer(double) - Constructor for class ai.libs.mlplan.metamining.similaritymeasures.F3Optimizer

FeatureGenerator - Interface in ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen

FeatureGeneratorTree - Class in ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen

FeatureGeneratorTree(FeatureGenerator) - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.FeatureGeneratorTree

FeaturePreprocessor - Interface in ai.libs.mlplan.multiclass.wekamlplan.sophisticated

forMeka() - Static method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

forSKLearn() - Static method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

forWeka() - Static method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

G

getAdapter() - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

getAlgorithmConfig() - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

getAlgorithmConfig() - Method in interface ai.libs.mlplan.core.IMLPlanBuilder

getAlgorithmConfig() - Method in class ai.libs.mlplan.core.MLPlanBuilder

getAncestorsOfAlgorithmUntil(String, String) - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAOntologyConnector

Get the list of ancestors from most general to most specific concept up until the specified concept including the specified child and highest concept.

getAncestorsOfClassifier(String) - Method in interface ai.libs.mlplan.metamining.pipelinecharacterizing.IOntologyConnector

Gets the ancestor concepts of this classifier including the classifier itself from most general to most specific concept.

getAncestorsOfClassifier(String) - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAOntologyConnector

getAncestorsOfEvaluator(String) - Method in interface ai.libs.mlplan.metamining.pipelinecharacterizing.IOntologyConnector

Gets the ancestor concepts of this evaluator algorithm (for attribute selection) including the evaluator itself from most general to most specific concept.

getAncestorsOfEvaluator(String) - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAOntologyConnector

getAncestorsOfSearcher(String) - Method in interface ai.libs.mlplan.metamining.pipelinecharacterizing.IOntologyConnector

Gets the ancestor concepts of this searcher algorithm (for attribute selection) including the searcher itself from most general to most specific concept.

getAncestorsOfSearcher(String) - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAOntologyConnector

getAvailableClassifiers() - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAOntologyConnector

Get the fully qualified names of WEKA classifiers that this ontology connector can be queried for.

getAvailableEvaluators() - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAOntologyConnector

Get the fully qualified names of WEKA ASEvaluation algorithms that this ontology can be queried for.

getAvailableSearchers() - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAOntologyConnector

Get the fully qualified names of WEKA ASSearch algorithms that this ontology can be queried for.

getBaseClassifier() - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.MLSophisticatedPipeline

getBaseClassifier() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.MLPipeline

getBenchmark() - Method in class ai.libs.mlplan.core.PipelineEvaluator

getCandidateEvaluationTimeOut() - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

getCapabilities() - Method in class ai.libs.mlplan.multiclass.wekamlplan.MLPlanWekaClassifier

getCapabilities() - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.MLSophisticatedPipeline

getCapabilities() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.MLPipeline

getCharacterizationsOfTrainingExamples() - Method in interface ai.libs.mlplan.metamining.pipelinecharacterizing.IPipelineCharacterizer

For each MLPipeline that was used in the training, return which found pattern (found during the training phase in IPipelineCharacterizer.build(List)) occurs in which pipeline.

getCharacterizationsOfTrainingExamples() - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAPipelineCharacterizer

getClasses() - Method in class ai.libs.mlplan.multiclasswithreduction.ClassSplit

getClassifier() - Method in class ai.libs.mlplan.core.events.ClassifierCreatedEvent

getClassifierEvaluationInSearchPhase(Instances, int, int) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

getClassifierEvaluationInSearchPhase(Instances, int, int) - Method in interface ai.libs.mlplan.core.IMLPlanBuilder

getClassifierEvaluationInSearchPhase(Instances, int, int) - Method in class ai.libs.mlplan.core.MLPlanBuilder

getClassifierEvaluationInSelectionPhase(Instances, int) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

getClassifierEvaluationInSelectionPhase(Instances, int) - Method in interface ai.libs.mlplan.core.IMLPlanBuilder

getClassifierEvaluationInSelectionPhase(Instances, int) - Method in class ai.libs.mlplan.core.MLPlanBuilder

getClassifierFactory() - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

getClassifierFactory() - Method in interface ai.libs.mlplan.core.IMLPlanBuilder

getClassifierFactory() - Method in class ai.libs.mlplan.core.MLPlanBuilder

getClassifiers() - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginModel

getClassifierTopNode() - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAOntologyConnector

Get the highest common node in the ontology for all classifiers

getComponentDescription() - Method in class ai.libs.mlplan.core.events.ClassifierFoundEvent

getComponentInstanceOfSelectedClassifier() - Method in class ai.libs.mlplan.core.MLPlan

getComponentInstantiation(ComponentInstance) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sklearn.SKLearnClassifierFactory

getComponentInstantiation(ComponentInstance) - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.WEKAPipelineFactory

getComponentInstantiation(ComponentInstance) - Method in class ai.libs.mlplan.multilabel.MekaPipelineFactory

getComponents() - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

getComponents() - Method in class ai.libs.mlplan.core.MLPlanBuilder

getComponents() - Method in class ai.libs.mlplan.core.PipelineValidityCheckingNodeEvaluator

getComponents() - Method in class ai.libs.mlplan.multiclass.wekamlplan.MLPlanWekaClassifier

getConductedExperiments() - Method in class ai.libs.reduction.ensemble.simple.MySQLEnsembleOfSimpleOneStepReductionsExperimentRunner

getConductedExperiments() - Method in class ai.libs.reduction.single.heterogeneous.bestofkrandom.MySQLReductionExperimentRunnerWrapper

getConductedExperiments() - Method in class ai.libs.reduction.single.heterogeneous.simplerpnd.MySQLExperimentRunner

getConductedExperiments() - Method in class ai.libs.reduction.single.homogeneous.bestofkatrandom.MySQLReductionExperimentRunnerWrapper

getConfig() - Method in class ai.libs.mlplan.core.MLPlan

getCost(INDArray) - Method in class ai.libs.mlplan.metamining.similaritymeasures.F1Optimizer

This evaluates F1

getCost(INDArray, INDArray) - Method in class ai.libs.mlplan.metamining.similaritymeasures.F3Optimizer

This evaluates F1

getData() - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

getData() - Method in class ai.libs.mlplan.core.PipelineValidityCheckingNodeEvaluator

getDataset() - Method in class ai.libs.reduction.ensemble.simple.EnsembleOfSimpleOneStepReductionsExperiment

getDataset() - Method in class ai.libs.reduction.single.ReductionExperiment

getDatasetId() - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

getDatasetOrigin() - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

getDBAdapter() - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

getDBAdapter() - Method in interface ai.libs.mlplan.core.IMLPlanBuilder

getDBAdapter() - Method in class ai.libs.mlplan.core.MLPlanBuilder

getDefaultDatasetSplitter() - Method in class ai.libs.mlplan.core.AbstractMLPlanSingleLabelBuilder

getDefaultDatasetSplitter() - Method in class ai.libs.mlplan.core.MLPlanMekaBuilder

getDefaultDatasetSplitter() - Method in class ai.libs.mlplan.core.MLPlanSKLearnBuilder

getErrorRate() - Method in class ai.libs.reduction.ensemble.simple.EnsembleOfSimpleOneStepReductionsExperiment

getEvaluator() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SupervisedFilterSelector

getEvaluator() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SuvervisedFilterPreprocessor

getEvaluatorForSplitTechnique(String, Instances, int) - Static method in class ai.libs.mlpipeline_evaluation.ConsistentMLPipelineEvaluator

Get an evaluator object for the given split configuration for the datasets, which can then be used to evaluate a classifier.

getEvaluatorTopNode() - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAOntologyConnector

Get the highest common node in the ontology for all evaluators

getException() - Method in class ai.libs.reduction.ensemble.simple.EnsembleOfSimpleOneStepReductionsExperiment

getExceptionInner() - Method in class ai.libs.reduction.single.ReductionExperiment

getExceptionLeft() - Method in class ai.libs.reduction.single.ReductionExperiment

getExceptionRight() - Method in class ai.libs.reduction.single.ReductionExperiment

getExperiment() - Method in class ai.libs.reduction.ensemble.simple.MySQLEnsembleOfSimpleOneStepReductionsExperiment

getExperiment() - Method in class ai.libs.reduction.single.MySQLReductionExperiment

getFactoryForPipelineEvaluationInSearchPhase() - Method in class ai.libs.mlplan.core.MLPlanBuilder

getFactoryForPipelineEvaluationInSelectionPhase() - Method in class ai.libs.mlplan.core.MLPlanBuilder

getFirstDerivative(INDArray, int, int) - Method in class ai.libs.mlplan.metamining.similaritymeasures.F1Optimizer

This compute the derivative of F1 for the (k,l)-th element of the U matrix

getFirstDerivative(INDArray, INDArray, int, int, boolean) - Method in class ai.libs.mlplan.metamining.similaritymeasures.F3Optimizer

This compute the derivative of F1 for the (k,l)-th element of the U matrix

getGradientAsMatrix(INDArray) - Method in class ai.libs.mlplan.metamining.similaritymeasures.F1Optimizer

This computes the gradient of F1 in matrix form

getGradientAsMatrix(INDArray, INDArray, boolean) - Method in class ai.libs.mlplan.metamining.similaritymeasures.F3Optimizer

This computes the gradient of F1 in matrix form

getGraphGenerator() - Method in class ai.libs.mlplan.core.MLPlan

getHASCOFactory() - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

getHASCOFactory() - Method in interface ai.libs.mlplan.core.IMLPlanBuilder

getHASCOFactory() - Method in class ai.libs.mlplan.core.MLPlanBuilder

getId() - Method in class ai.libs.reduction.ensemble.simple.MySQLEnsembleOfSimpleOneStepReductionsExperiment

getId() - Method in class ai.libs.reduction.single.MySQLReductionExperiment

getInSampleError() - Method in class ai.libs.mlplan.core.events.ClassifierFoundEvent

getInstance() - Method in class ai.libs.mlplan.core.events.ClassifierCreatedEvent

getInternalValidationErrorOfSelectedClassifier() - Method in class ai.libs.mlplan.core.MLPlan

getInternalValidationErrorOfSelectedClassifier() - Method in class ai.libs.mlplan.multiclass.wekamlplan.MLPlanWekaClassifier

getK() - Method in class ai.libs.reduction.single.BestOfKAtRandomExperiment

getL() - Method in class ai.libs.mlplan.multiclasswithreduction.ClassSplit

getLoggerName() - Method in class ai.libs.mlplan.bigdata.MLPlan4BigFileInput

getLoggerName() - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

getLoggerName() - Method in class ai.libs.mlplan.core.MLPlan

getLoggerName() - Method in class ai.libs.mlplan.core.PipelineEvaluator

getLoggerName() - Method in class ai.libs.mlplan.multiclass.wekamlplan.MLPlanWekaClassifier

getLoggerName() - Method in class ai.libs.mlplan.multiclass.wekamlplan.sklearn.SKLearnClassifierFactory

getMccvRepeats() - Method in class ai.libs.reduction.single.BestOfKAtRandomExperiment

getMessage(ComponentInstance) - Method in class ai.libs.mlplan.core.PipelineEvaluator

getMinSupport() - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAPipelineCharacterizer

Get the minimum support required for a pattern to be considered frequent for the tree mining algorithm.

getMLPlanConfig() - Method in class ai.libs.mlplan.multiclass.wekamlplan.MLPlanWekaClassifier

getMultiLabelEvaluationMeasurementBridge(IMeasure<double[], Double>) - Method in class ai.libs.mlplan.core.MLPlanBuilder

getMultiLabelPerformanceMeasure() - Method in class ai.libs.mlplan.core.MLPlanBuilder

getNameOfClassifier() - Method in class ai.libs.reduction.ensemble.simple.EnsembleOfSimpleOneStepReductionsExperiment

getNameOfInnerClassifier() - Method in class ai.libs.reduction.single.ReductionExperiment

getNameOfLeftClassifier() - Method in class ai.libs.reduction.single.ReductionExperiment

getNameOfRightClassifier() - Method in class ai.libs.reduction.single.ReductionExperiment

getNextIndexToDisplay() - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginView

getNodeEvaluationTimeOut() - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

getNumberOfStumps() - Method in class ai.libs.reduction.ensemble.simple.EnsembleOfSimpleOneStepReductionsExperiment

getOntology() - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAOntologyConnector

Get the ontology this connector uses.

getOntologyConnector() - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAPipelineCharacterizer

getOptimizingFactory() - Method in class ai.libs.mlplan.core.MLPlan

getOptions() - Method in class ai.libs.mlplan.multiclass.wekamlplan.MLPlanWekaClassifier

getPerformanceMeasureName() - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

getPerformanceMeasureName() - Method in interface ai.libs.mlplan.core.IMLPlanBuilder

getPerformances() - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginModel

getPotence() - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.PolynomialFeatures

getPreprocessors() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.MLPipeline

getR() - Method in class ai.libs.mlplan.multiclasswithreduction.ClassSplit

getRequestedInterface() - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

getRequestedInterface() - Method in interface ai.libs.mlplan.core.IMLPlanBuilder

getRequestedInterface() - Method in class ai.libs.mlplan.core.MLPlanBuilder

getResultOrExecuteEvaluation(ComponentInstance) - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCache

Get an evaluation results for the given pipeline represented by the component instance in the setting this cache is configured.

getRoot() - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.FeatureGeneratorTree

getScore() - Method in class ai.libs.mlplan.core.events.ClassifierFoundEvent

getSearcher() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SupervisedFilterSelector

getSearcher() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SuvervisedFilterPreprocessor

getSearcherTopNode() - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAOntologyConnector

Get the highest common node in the ontology for all searchers

getSearchEvaluatorFactory() - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

getSearchPhaseDatasetSplitter() - Method in class ai.libs.mlplan.core.MLPlanBuilder

getSearchSelectionDatasetSplitter() - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

getSearchSelectionDatasetSplitter() - Method in interface ai.libs.mlplan.core.IMLPlanBuilder

getSearchSelectionDatasetSplitter() - Method in class ai.libs.mlplan.core.MLPlanBuilder

getSearchSpaceConfigFile() - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

getSearchSpaceConfigFile() - Method in interface ai.libs.mlplan.core.IMLPlanBuilder

getSearchSpaceConfigFile() - Method in class ai.libs.mlplan.core.MLPlanBuilder

getSeed() - Method in class ai.libs.reduction.ensemble.simple.EnsembleOfSimpleOneStepReductionsExperiment

getSeed() - Method in class ai.libs.reduction.single.ReductionExperiment

getSelectedClassifier() - Method in class ai.libs.mlplan.core.MLPlan

getSelectedClassifier() - Method in class ai.libs.mlplan.multiclass.wekamlplan.MLPlanWekaClassifier

getSelectionEvaluatorFactory() - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

getSelectionPhaseDatasetSplitter() - Method in class ai.libs.mlplan.core.MLPlanBuilder

getSelector() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SupervisedFilterSelector

getSelector() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SuvervisedFilterPreprocessor

getShallowCopy(ComponentInstance) - Method in class ai.libs.mlpipeline_evaluation.CacheEvaluatorMeasureBridge

Returns a lightweight copy of this object.

getSingleLabelEvaluationMeasurementBridge(IMeasure<Double, Double>) - Method in class ai.libs.mlplan.core.MLPlanBuilder

getSingleLabelPerformanceMeasure() - Method in class ai.libs.mlplan.core.MLPlanBuilder

getSquaredFrobeniusNorm(INDArray) - Method in class ai.libs.mlplan.metamining.similaritymeasures.F3Optimizer

getTest() - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginController

getTestData() - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPlugin

getTestEvaluationTechnique() - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

getTestSeed() - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

getTestSplit(String, Instances, int) - Static method in class ai.libs.mlpipeline_evaluation.ConsistentMLPipelineEvaluator

Split the dataset according to the given parameters and return the test portion of the split.

getTestSplitTechnique() - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

getTimeForExecutingClassifier() - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.MLSophisticatedPipeline

getTimeForExecutingClassifier() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.MLPipeline

getTimeForExecutingPreprocessor() - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.MLSophisticatedPipeline

getTimeForExecutingPreprocessor() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.MLPipeline

getTimeForTrainingClassifier() - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.MLSophisticatedPipeline

getTimeForTrainingClassifier() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.MLPipeline

getTimeForTrainingPreprocessor() - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.MLSophisticatedPipeline

getTimeForTrainingPreprocessor() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.MLPipeline

getTimeOut() - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

getTimeout(ComponentInstance) - Method in class ai.libs.mlplan.core.PipelineEvaluator

getTimestampOfFirstEvent() - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginModel

getTimestamps() - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginModel

getTitle() - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginView

getTrain() - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginController

getTrainData() - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPlugin

getTrainSplit(String, Instances, int) - Static method in class ai.libs.mlpipeline_evaluation.ConsistentMLPipelineEvaluator

Split the dataset according to the given parameters and return the train portion of the split.

getTwoPhaseHASCOFactory() - Method in class ai.libs.mlplan.core.MLPlan

getU() - Method in class ai.libs.mlplan.metamining.similaritymeasures.F1Optimizer

getUseCache() - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

getUseCache() - Method in interface ai.libs.mlplan.core.IMLPlanBuilder

getUseCache() - Method in class ai.libs.mlplan.core.MLPlanBuilder

getValEvaluationTechnique() - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

getValSeed() - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

getValSplitTechnique() - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

getX() - Method in class ai.libs.mlplan.metamining.similaritymeasures.F1Optimizer

H

handleAlgorithmEventInternally(AlgorithmEvent) - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginController

handleGUIEvent(GUIEvent) - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginController

hashCode() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SupervisedFilterSelector

hashCode() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SuvervisedFilterPreprocessor

hashCode() - Method in class ai.libs.mlplan.multiclasswithreduction.ClassSplit

hashCode() - Method in class ai.libs.reduction.ensemble.simple.EnsembleOfSimpleOneStepReductionsExperiment

hashCode() - Method in class ai.libs.reduction.ensemble.simple.MySQLEnsembleOfSimpleOneStepReductionsExperiment

hashCode() - Method in class ai.libs.reduction.single.BestOfKAtRandomExperiment

hashCode() - Method in class ai.libs.reduction.single.MySQLReductionExperiment

hashCode() - Method in class ai.libs.reduction.single.ReductionExperiment

I

IClassifierFactory - Interface in ai.libs.mlplan.multiclass.wekamlplan

IHeterogenousSimilarityMeasureComputer - Interface in ai.libs.mlplan.metamining.similaritymeasures

Encapsulates a model that is trained to compute the similarity between two multidimensional measures, e.g. data set meta features, algorithm meta features and algorithm performance on a data set.

IMLPlanBuilder - Interface in ai.libs.mlplan.core

The IMLPlanBuilder provides the general interface of an ML-Plan builder independent of the problem domain or specific library that is used for the configuration of machine learning pipelines.

InteractingFeatures - Class in ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen

InteractingFeatures() - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.InteractingFeatures

InvalidDatasetOriginException - Exception in ai.libs.mlpipeline_evaluation

InvalidDatasetOriginException(String) - Constructor for exception ai.libs.mlpipeline_evaluation.InvalidDatasetOriginException

InvalidDatasetOriginException(Throwable) - Constructor for exception ai.libs.mlpipeline_evaluation.InvalidDatasetOriginException

InvalidDatasetOriginException(String, Throwable) - Constructor for exception ai.libs.mlpipeline_evaluation.InvalidDatasetOriginException

IOntologyConnector - Interface in ai.libs.mlplan.metamining.pipelinecharacterizing

Encapsulates the connection to an ontology which holds knowledge about classifiers, searchers, and evaluators.

IPipelineCharacterizer - Interface in ai.libs.mlplan.metamining.pipelinecharacterizing

Finds patterns in given MLPipelines.

IRankMatrixSimilarityComputer - Interface in ai.libs.mlplan.metamining.similaritymeasures

isBuildSelectedClasifierOnGivenData() - Method in class ai.libs.mlplan.core.MLPlan

isPrepared() - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.FeatureGeneratorTree

isPrepared() - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.InteractingFeatures

isPrepared() - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.PolynomialFeatures

isPrepared() - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featurepre.Normalization

isPrepared() - Method in interface ai.libs.mlplan.multiclass.wekamlplan.sophisticated.FeaturePreprocessor

isPrepared() - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.MLSophisticatedPipeline

isPrepared() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SupervisedFilterSelector

isPrepared() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SuvervisedFilterPreprocessor

L

listOptions() - Method in class ai.libs.mlplan.multiclass.wekamlplan.MLPlanWekaClassifier

LOSS_FUNCTION - Static variable in class ai.libs.mlplan.core.AbstractMLPlanSingleLabelBuilder

M

main(String[]) - Static method in class ai.libs.mlplan.cli.MLPlanCLI

main(String[]) - Static method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAOntologyConnector

makeStringTreeRepresentation(MLPipeline) - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAPipelineCharacterizer

Converts the given MLPipeline to a String representation of its components using the ontology

markExperimentAsUnsolvable(MySQLEnsembleOfSimpleOneStepReductionsExperiment) - Method in class ai.libs.reduction.ensemble.simple.MySQLEnsembleOfSimpleOneStepReductionsExperimentRunner

markExperimentAsUnsolvable(MySQLReductionExperiment) - Method in class ai.libs.reduction.single.heterogeneous.bestofkrandom.MySQLReductionExperimentRunnerWrapper

markExperimentAsUnsolvable(MySQLReductionExperiment) - Method in class ai.libs.reduction.single.heterogeneous.simplerpnd.MySQLExperimentRunner

markExperimentAsUnsolvable(MySQLReductionExperiment) - Method in class ai.libs.reduction.single.homogeneous.bestofkatrandom.MySQLReductionExperimentRunnerWrapper

matrices2vector(INDArray...) - Method in class ai.libs.mlplan.metamining.similaritymeasures.F3Optimizer

matrix2vector(INDArray) - Method in class ai.libs.mlplan.metamining.similaritymeasures.F1Optimizer

collapses a matrix of the Nd4j framework into a double vector of Thomas Jungblut's framework

matrix2vector(INDArray) - Method in class ai.libs.mlplan.metamining.similaritymeasures.F3Optimizer

collapses a matrix of the Nd4j framework into a double vector of Thomas Jungblut's framework

MekaPipelineFactory - Class in ai.libs.mlplan.multilabel

A pipeline factory that converts a given ComponentInstance that consists of components that correspond to MEKA algorithms to a MultiLabelClassifier.

MekaPipelineFactory() - Constructor for class ai.libs.mlplan.multilabel.MekaPipelineFactory

MLPipeline - Class in ai.libs.mlplan.multiclass.wekamlplan.weka.model

MLPipeline(List<SupervisedFilterSelector>, Classifier) - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.weka.model.MLPipeline

MLPipeline(ASSearch, ASEvaluation, Classifier) - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.weka.model.MLPipeline

MLPipelineComponentInstanceFactory - Class in ai.libs.mlplan.multiclass.wekamlplan.weka

A factory that provides the ability to wrap given MLPipelines to a ComponentInstance

MLPipelineComponentInstanceFactory(Collection<Component>) - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.weka.MLPipelineComponentInstanceFactory

Creates a new factory object using the given configuration file

MLPlan - Class in ai.libs.mlplan.core

MLPlan(IMLPlanBuilder, Instances) - Constructor for class ai.libs.mlplan.core.MLPlan

MLPlan4BigFileInput - Class in ai.libs.mlplan.bigdata

This is a version of ML-Plan that tries to cope with medium sized data in the sense of big files.

MLPlan4BigFileInput(File) - Constructor for class ai.libs.mlplan.bigdata.MLPlan4BigFileInput

MLPlanBuilder - Class in ai.libs.mlplan.core

The MLPlanBuilder helps to easily configure and initialize ML-Plan with specific parameter settings.

MLPlanBuilder() - Constructor for class ai.libs.mlplan.core.MLPlanBuilder

MLPlanBuilder(File, File, EMultiClassPerformanceMeasure) - Constructor for class ai.libs.mlplan.core.MLPlanBuilder

MLPlanBuilder(File, File, EMultiClassPerformanceMeasure, PerformanceDBAdapter) - Constructor for class ai.libs.mlplan.core.MLPlanBuilder

MLPlanClassifierConfig - Interface in ai.libs.mlplan.multiclass

MLPlanCLI - Class in ai.libs.mlplan.cli

Enables command-line usage of ML-Plan.

MLPlanMekaBuilder - Class in ai.libs.mlplan.core

MLPlanMekaBuilder() - Constructor for class ai.libs.mlplan.core.MLPlanMekaBuilder

MLPlanScikitLearnClassifierConfig - Interface in ai.libs.mlplan.multiclass.wekamlplan.sklearn

MLPlanSKLearnBuilder - Class in ai.libs.mlplan.core

MLPlanSKLearnBuilder() - Constructor for class ai.libs.mlplan.core.MLPlanSKLearnBuilder

Creates a new ML-Plan Builder for scikit-learn.

MLPlanSKLearnBuilder(boolean) - Constructor for class ai.libs.mlplan.core.MLPlanSKLearnBuilder

Creates a new ML-Plan Builder for scikit-learn.

MLPlanWekaBuilder - Class in ai.libs.mlplan.core

MLPlanWekaBuilder() - Constructor for class ai.libs.mlplan.core.MLPlanWekaBuilder

MLPlanWekaClassifier - Class in ai.libs.mlplan.multiclass.wekamlplan

A WEKA classifier wrapping the functionality of ML-Plan where the constructed object is a WEKA classifier.

MLPlanWekaClassifier(AbstractMLPlanBuilder) - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.MLPlanWekaClassifier

MLSophisticatedPipeline - Class in ai.libs.mlplan.multiclass.wekamlplan.sophisticated

MLSophisticatedPipeline(List<FeatureGenerator>, List<FeaturePreprocessor>, List<FeaturePreprocessor>, Classifier) - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.MLSophisticatedPipeline

MySQLEnsembleOfSimpleOneStepReductionsExperiment - Class in ai.libs.reduction.ensemble.simple

MySQLEnsembleOfSimpleOneStepReductionsExperiment(int, EnsembleOfSimpleOneStepReductionsExperiment) - Constructor for class ai.libs.reduction.ensemble.simple.MySQLEnsembleOfSimpleOneStepReductionsExperiment

MySQLEnsembleOfSimpleOneStepReductionsExperimentRunner - Class in ai.libs.reduction.ensemble.simple

MySQLEnsembleOfSimpleOneStepReductionsExperimentRunner(String, String, String, String) - Constructor for class ai.libs.reduction.ensemble.simple.MySQLEnsembleOfSimpleOneStepReductionsExperimentRunner

MySQLExperimentRunner - Class in ai.libs.reduction.single.heterogeneous.simplerpnd

MySQLExperimentRunner(String, String, String, String) - Constructor for class ai.libs.reduction.single.heterogeneous.simplerpnd.MySQLExperimentRunner

MySQLReductionExperiment - Class in ai.libs.reduction.single

MySQLReductionExperiment(int, ReductionExperiment) - Constructor for class ai.libs.reduction.single.MySQLReductionExperiment

MySQLReductionExperimentRunnerWrapper - Class in ai.libs.reduction.single.heterogeneous.bestofkrandom

MySQLReductionExperimentRunnerWrapper(String, String, String, String, int, int) - Constructor for class ai.libs.reduction.single.heterogeneous.bestofkrandom.MySQLReductionExperimentRunnerWrapper

MySQLReductionExperimentRunnerWrapper - Class in ai.libs.reduction.single.homogeneous.bestofkatrandom

MySQLReductionExperimentRunnerWrapper(String, String, String, String, int, int) - Constructor for class ai.libs.reduction.single.homogeneous.bestofkatrandom.MySQLReductionExperimentRunnerWrapper

N

NestedDichotomyUtil - Class in ai.libs.mlplan.multiclasswithreduction

nextWithException() - Method in class ai.libs.mlplan.bigdata.MLPlan4BigFileInput

nextWithException() - Method in class ai.libs.mlplan.core.MLPlan

Normalization - Class in ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featurepre

Normalization() - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featurepre.Normalization

O

OutOfSampleErrorPlotPlugin - Class in ai.libs.mlplan.gui.outofsampleplots

OutOfSampleErrorPlotPlugin(Instances, Instances) - Constructor for class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPlugin

OutOfSampleErrorPlotPluginController - Class in ai.libs.mlplan.gui.outofsampleplots

OutOfSampleErrorPlotPluginController(OutOfSampleErrorPlotPluginModel, OutOfSampleErrorPlotPluginView) - Constructor for class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginController

OutOfSampleErrorPlotPluginModel - Class in ai.libs.mlplan.gui.outofsampleplots

OutOfSampleErrorPlotPluginModel() - Constructor for class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginModel

OutOfSampleErrorPlotPluginView - Class in ai.libs.mlplan.gui.outofsampleplots

OutOfSampleErrorPlotPluginView(OutOfSampleErrorPlotPluginModel) - Constructor for class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginView

P

PCA - Class in ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen

PCA() - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.PCA

PerformanceDBAdapter - Class in ai.libs.mlpipeline_evaluation

Database adapter for performance data.

PerformanceDBAdapter(SQLAdapter, String) - Constructor for class ai.libs.mlpipeline_evaluation.PerformanceDBAdapter

PipelineEvaluationCache - Class in ai.libs.mlpipeline_evaluation

For caching and evaluation MLPipelines.

PipelineEvaluationCache(PipelineEvaluationCacheConfigBuilder) - Constructor for class ai.libs.mlpipeline_evaluation.PipelineEvaluationCache

Construct a new cache for evaluations.

PipelineEvaluationCacheConfigBuilder - Class in ai.libs.mlpipeline_evaluation

PipelineEvaluationCacheConfigBuilder() - Constructor for class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

PipelineEvaluator - Class in ai.libs.mlplan.core

Evaluator used in the search phase of mlplan.

PipelineEvaluator(IClassifierFactory, IClassifierEvaluator, int) - Constructor for class ai.libs.mlplan.core.PipelineEvaluator

PipelineValidityCheckingNodeEvaluator - Class in ai.libs.mlplan.core

PipelineValidityCheckingNodeEvaluator() - Constructor for class ai.libs.mlplan.core.PipelineValidityCheckingNodeEvaluator

PipelineValidityCheckingNodeEvaluator(Collection<Component>, Instances) - Constructor for class ai.libs.mlplan.core.PipelineValidityCheckingNodeEvaluator

PolynomialFeatures - Class in ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen

PolynomialFeatures() - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.PolynomialFeatures

PreferenceBasedNodeEvaluator - Class in ai.libs.mlplan.multiclass.wekamlplan.weka

PreferenceBasedNodeEvaluator(Collection<Component>, List<String>) - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.weka.PreferenceBasedNodeEvaluator

PreferenceBasedNodeEvaluator(Collection<Component>) - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.weka.PreferenceBasedNodeEvaluator

PREFERRED_COMPONENTS - Static variable in interface ai.libs.mlplan.multiclass.MLPlanClassifierConfig

preferredComponents() - Method in interface ai.libs.mlplan.multiclass.MLPlanClassifierConfig

prepare(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.FeatureGeneratorTree

prepare(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.InteractingFeatures

prepare(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.PolynomialFeatures

prepare(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featurepre.Normalization

prepare(Instances) - Method in interface ai.libs.mlplan.multiclass.wekamlplan.sophisticated.FeaturePreprocessor

prepare(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.MLSophisticatedPipeline

prepare(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SupervisedFilterSelector

prepare(Instances) - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SuvervisedFilterPreprocessor

prepareNodeEvaluatorInFactoryWithData(Instances) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

prepareNodeEvaluatorInFactoryWithData(Instances) - Method in interface ai.libs.mlplan.core.IMLPlanBuilder

prepareNodeEvaluatorInFactoryWithData(Instances) - Method in class ai.libs.mlplan.core.MLPlanBuilder

R

RankMatrixSimilarityComputer - Class in ai.libs.mlplan.metamining.similaritymeasures

RankMatrixSimilarityComputer() - Constructor for class ai.libs.mlplan.metamining.similaritymeasures.RankMatrixSimilarityComputer

receiveClassifierCreatedEvent(ClassifierCreatedEvent) - Method in class ai.libs.mlplan.bigdata.MLPlan4BigFileInput

receiveClassifierCreatedEvent(ClassifierCreatedEvent) - Method in class ai.libs.mlplan.core.MLPlan

receiveClassifierCreatedEvent(LearningCurveExtrapolatedEvent) - Method in class ai.libs.mlplan.core.MLPlan

receiveClassifierCreatedEvent(MCCVSplitEvaluationEvent) - Method in class ai.libs.mlplan.core.MLPlan

receiveEvent(IEvent) - Method in class ai.libs.mlplan.core.PipelineEvaluator

Forwards every incoming event e

receiveExtrapolationFinishedEvent(LearningCurveExtrapolatedEvent) - Method in class ai.libs.mlplan.bigdata.MLPlan4BigFileInput

receiveMCCVFinishedEvent(MCCVSplitEvaluationEvent) - Method in class ai.libs.mlplan.bigdata.MLPlan4BigFileInput

ReductionExperiment - Class in ai.libs.reduction.single

ReductionExperiment(int, String, String, String, String) - Constructor for class ai.libs.reduction.single.ReductionExperiment

ReductionExperiment(int, String, String, String, String, String, String, String) - Constructor for class ai.libs.reduction.single.ReductionExperiment

registerListener(Object) - Method in class ai.libs.mlplan.core.PipelineEvaluator

Here, we send a coupling event that informs the listener about which ComponentInstance has been used to create a classifier.

removeChild(FeatureGeneratorTree) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.FeatureGeneratorTree

S

SEARCH_NUM_MC_ITERATIONS - Static variable in class ai.libs.mlplan.core.AbstractMLPlanSingleLabelBuilder

SEARCH_TRAIN_FOLD_SIZE - Static variable in class ai.libs.mlplan.core.AbstractMLPlanSingleLabelBuilder

SELECTION_NUM_MC_ITERATIONS - Static variable in class ai.libs.mlplan.core.AbstractMLPlanSingleLabelBuilder

SELECTION_PORTION - Static variable in interface ai.libs.mlplan.multiclass.MLPlanClassifierConfig

SELECTION_TRAIN_FOLD_SIZE - Static variable in class ai.libs.mlplan.core.AbstractMLPlanSingleLabelBuilder

setBuildSelectedClasifierOnGivenData(boolean) - Method in class ai.libs.mlplan.core.MLPlan

setComponents(Collection<Component>) - Method in class ai.libs.mlplan.core.PipelineValidityCheckingNodeEvaluator

setData(Instances) - Method in class ai.libs.mlplan.core.PipelineValidityCheckingNodeEvaluator

setErrorRate(double) - Method in class ai.libs.reduction.ensemble.simple.EnsembleOfSimpleOneStepReductionsExperiment

setException(String) - Method in class ai.libs.reduction.ensemble.simple.EnsembleOfSimpleOneStepReductionsExperiment

setExceptionInner(String) - Method in class ai.libs.reduction.single.ReductionExperiment

setExceptionLeft(String) - Method in class ai.libs.reduction.single.ReductionExperiment

setExceptionRight(String) - Method in class ai.libs.reduction.single.ReductionExperiment

setLoggerName(String) - Method in class ai.libs.mlplan.bigdata.MLPlan4BigFileInput

setLoggerName(String) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

setLoggerName(String) - Method in class ai.libs.mlplan.core.MLPlan

setLoggerName(String) - Method in class ai.libs.mlplan.core.PipelineEvaluator

setLoggerName(String) - Method in class ai.libs.mlplan.multiclass.wekamlplan.MLPlanWekaClassifier

setLoggerName(String) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sklearn.SKLearnClassifierFactory

setMinSupport(int) - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAPipelineCharacterizer

Set the minimum support required for a pattern to be considered frequent for the tree mining algorithm.

setOntologyConnector(IOntologyConnector) - Method in class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAPipelineCharacterizer

setOptions(String[]) - Method in class ai.libs.mlplan.multiclass.wekamlplan.MLPlanWekaClassifier

setPerformanceMeasureName(String) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

Sets the name of the performance measure that is used.

setPortionOfDataForPhase2(float) - Method in class ai.libs.mlplan.core.MLPlan

setPotence(int) - Method in class ai.libs.mlplan.multiclass.wekamlplan.sophisticated.featuregen.PolynomialFeatures

setPrepared(boolean) - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SupervisedFilterSelector

setPrepared(boolean) - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SuvervisedFilterPreprocessor

setRandomSeed(int) - Method in class ai.libs.mlplan.core.MLPlan

setTest(Instances) - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginController

setTimeout(TimeOut) - Method in class ai.libs.mlplan.multiclass.wekamlplan.MLPlanWekaClassifier

setTimestampOfFirstEvent(long) - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginModel

setTrain(Instances) - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginController

setUseCache(boolean) - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCache

setVisualizationEnabled(boolean) - Method in class ai.libs.mlplan.multiclass.wekamlplan.MLPlanWekaClassifier

Enables the GUI of the MLPlanWekaClassifier if set to true.

SKLearnClassifierFactory - Class in ai.libs.mlplan.multiclass.wekamlplan.sklearn

The SKLearnClassifierFactory takes a ground component instance and parses it into a ScikitLearnWrapper as defined in the project jaicore-ml.

SKLearnClassifierFactory() - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.sklearn.SKLearnClassifierFactory

SKLearnMLPlanWekaClassifier - Class in ai.libs.mlplan.multiclass.wekamlplan.sklearn

SKLearnMLPlanWekaClassifier(AbstractMLPlanBuilder) - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.sklearn.SKLearnMLPlanWekaClassifier

SKLearnMLPlanWekaClassifier() - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.sklearn.SKLearnMLPlanWekaClassifier

store(ComponentInstance, ReproducibleInstances, ReproducibleInstances, double, String, long) - Method in class ai.libs.mlpipeline_evaluation.PerformanceDBAdapter

Stores the composition, the trajectory and the achieved score in the database.

SupervisedFilterSelector - Class in ai.libs.mlplan.multiclass.wekamlplan.weka.model

SupervisedFilterSelector(ASSearch, ASEvaluation) - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SupervisedFilterSelector

SupervisedFilterSelector(ASSearch, ASEvaluation, AttributeSelection) - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SupervisedFilterSelector

SuvervisedFilterPreprocessor - Class in ai.libs.mlplan.multiclass.wekamlplan.weka.model

SuvervisedFilterPreprocessor(ASSearch, ASEvaluation) - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SuvervisedFilterPreprocessor

SuvervisedFilterPreprocessor(ASSearch, ASEvaluation, AttributeSelection) - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SuvervisedFilterPreprocessor

T

toString() - Method in class ai.libs.mlplan.core.events.ClassifierFoundEvent

toString() - Method in class ai.libs.mlplan.core.MLPlanBuilder

toString() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.MLPipeline

toString() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.model.SupervisedFilterSelector

toString() - Method in class ai.libs.mlplan.multiclass.wekamlplan.weka.PreferenceBasedNodeEvaluator

toString() - Method in class ai.libs.mlplan.multiclasswithreduction.ClassSplit

toString() - Method in class ai.libs.reduction.single.BestOfKAtRandomExperiment

toString() - Method in class ai.libs.reduction.single.MySQLReductionExperiment

toString() - Method in class ai.libs.reduction.single.ReductionExperiment

U

update() - Method in class ai.libs.mlplan.gui.outofsampleplots.OutOfSampleErrorPlotPluginView

updateBestScore(Double) - Method in class ai.libs.mlplan.core.PipelineEvaluator

usesCache() - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCache

Util - Class in ai.libs.reduction

V

valueOf(String) - Static method in enum ai.libs.mlpipeline_evaluation.DatasetOrigin

Returns the enum constant of this type with the specified name.

values() - Static method in enum ai.libs.mlpipeline_evaluation.DatasetOrigin

Returns an array containing the constants of this enum type, in the order they are declared.

vector2matrices(DoubleVector, int, int, int, int) - Method in class ai.libs.mlplan.metamining.similaritymeasures.F3Optimizer

vector2matrix(DoubleVector, int, int) - Method in class ai.libs.mlplan.metamining.similaritymeasures.F1Optimizer

creates a matrix of the Nd4j framework from a vector of Thomas Jungblut's math framework

vector2matrix(DoubleVector, int, int) - Method in class ai.libs.mlplan.metamining.similaritymeasures.F3Optimizer

creates a matrix of the Nd4j framework from a vector of Thomas Jungblut's math framework

W

WekaMLPlanWekaClassifier - Class in ai.libs.mlplan.multiclass.wekamlplan.weka

WekaMLPlanWekaClassifier(MLPlanWekaBuilder) - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.weka.WekaMLPlanWekaClassifier

WekaMLPlanWekaClassifier() - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.weka.WekaMLPlanWekaClassifier

WEKAOntologyConnector - Class in ai.libs.mlplan.metamining.pipelinecharacterizing

Represents the connection to the data minining optimization ontology (DMOP) enriched by the implementations of algorithms by WEKA.

WEKAOntologyConnector() - Constructor for class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAOntologyConnector

Creates an ontology connector using the standard ontology.

WEKAPipelineCharacterizer - Class in ai.libs.mlplan.metamining.pipelinecharacterizing

A characterizer for MLPipelines that characterizes them using an ontology and a tree mining algorithm.

WEKAPipelineCharacterizer() - Constructor for class ai.libs.mlplan.metamining.pipelinecharacterizing.WEKAPipelineCharacterizer

WEKAPipelineFactory - Class in ai.libs.mlplan.multiclass.wekamlplan.weka

WEKAPipelineFactory() - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.weka.WEKAPipelineFactory

WekaPipelineValidityCheckingNodeEvaluator - Class in ai.libs.mlplan.multiclass.wekamlplan.weka

WekaPipelineValidityCheckingNodeEvaluator() - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.weka.WekaPipelineValidityCheckingNodeEvaluator

WekaPipelineValidityCheckingNodeEvaluator(Collection<Component>, Instances) - Constructor for class ai.libs.mlplan.multiclass.wekamlplan.weka.WekaPipelineValidityCheckingNodeEvaluator

withAlgorithmConfig(MLPlanClassifierConfig) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

Loads the MLPlanClassifierConfig with default values and replaces all properties according to the properties defined in the given config file.

withAlgorithmConfig(MLPlanClassifierConfig) - Method in class ai.libs.mlplan.core.MLPlanBuilder

withAlgorithmConfigFile(File) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

Loads the MLPlanClassifierConfig with default values and replaces all properties according to the properties defined in the given config file.

withAlgorithmConfigFile(File) - Method in class ai.libs.mlplan.core.MLPlanBuilder

Loads the MLPlanClassifierConfig with default values and replaces all properties according to the properties defined in the given config file.

withAutoMEKADefaultConfiguration() - Method in class ai.libs.mlplan.core.MLPlanMekaBuilder

Configures ML-Plan with the configuration as compared to AutoMEKA_GGP and GA-Auto-MLC.

withAutoSKLearnConfig() - Method in class ai.libs.mlplan.core.MLPlanBuilder

Configures the MLPlanBuilder to deal with the AutoSKLearn search space configuration.

withAutoWEKAConfiguration() - Method in class ai.libs.mlplan.core.MLPlanBuilder

withCandidateEvaluationTimeOut(TimeOut) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

withClassifierFactory(IClassifierFactory) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

Set the classifier factory that translates CompositionInstance objects to classifiers that can be evaluated.

withClassifierFactory(IClassifierFactory) - Method in class ai.libs.mlplan.core.MLPlanBuilder

Set the classifier factory that translates CompositionInstance objects to classifiers that can be evaluated.

withDataset(Instances) - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

withDataset(Instances) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

Set the data for which ML-Plan is supposed to find the best pipeline.

withDatasetID(String) - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

withDatasetOrigin(DatasetOrigin) - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

withDatasetSplitterForSearchSelectionSplit(IDatasetSplitter) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

Set the dataset splitter that is used for generating the holdout data portion that is put aside during search.

withDatasetSplitterForSearchSelectionSplit(IDatasetSplitter) - Method in class ai.libs.mlplan.core.MLPlanBuilder

withLearningCurveExtrapolationEvaluation(int[], ISamplingAlgorithmFactory<IInstance, ? extends ASamplingAlgorithm<IInstance>>, double, LearningCurveExtrapolationMethod) - Method in class ai.libs.mlplan.core.MLPlanBuilder

withLearningCurveExtrapolationEvaluation(int[], ISamplingAlgorithmFactory<IInstance, ? extends ASamplingAlgorithm<IInstance>>, double, LearningCurveExtrapolationMethod) - Method in class ai.libs.mlplan.core.MLPlanWekaBuilder

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

withMekaDefaultConfiguration() - Method in class ai.libs.mlplan.core.MLPlanBuilder

withMonteCarloCrossValidationInSearchPhase(int, double, IMeasure<Double, Double>) - Method in class ai.libs.mlplan.core.AbstractMLPlanSingleLabelBuilder

Configure ML-Plan to use MCCV for the given number of iterations, train fold size and loss function in the search phase.

withMonteCarloCrossValidationInSelectionPhase(int, double, IMeasure<Double, Double>) - Method in class ai.libs.mlplan.core.AbstractMLPlanSingleLabelBuilder

Configure ML-Plan to use MCCV for the given number of iterations, train fold size and loss function in the selection phase.

withMultiLabelClassificationMeasure(EMultilabelPerformanceMeasure) - Method in class ai.libs.mlplan.core.MLPlanBuilder

withNodeEvaluationTimeOut(TimeOut) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

withNumCpus(int) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

Sets the number of cpus that may be used by ML-Plan.

withPerformanceMeasure(IMeasure<Double, Double>) - Method in class ai.libs.mlplan.core.AbstractMLPlanSingleLabelBuilder

Sets the performance measure to evaluate a candidate solution's generalization performance.

withPerformanceMeasure(IMeasure<double[], Double>) - Method in class ai.libs.mlplan.core.MLPlanMekaBuilder

Sets the performance measure to evaluate a candidate solution's generalization performance.

withPreferredComponentsFile(File) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

Creates a preferred node evaluator that can be used to prefer components over other components.

withPreferredComponentsFile(File) - Method in class ai.libs.mlplan.core.MLPlanBuilder

withPreferredNodeEvaluator(INodeEvaluator<TFDNode, Double>) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

This ADDs a new preferred node evaluator; requires that the search will be a best-first search.

withPreferredNodeEvaluator(INodeEvaluator<TFDNode, Double>) - Method in class ai.libs.mlplan.core.MLPlanBuilder

This ADDs a new preferred node evaluator; requires that the search will be a best-first search.

withRandomCompletionBasedBestFirstSearch() - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

withRandomCompletionBasedBestFirstSearch() - Method in class ai.libs.mlplan.core.MLPlanBuilder

withRequestedInterface(String) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

withRequestedInterface(String) - Method in class ai.libs.mlplan.core.MLPlanBuilder

withSearchFactory(IOptimalPathInORGraphSearchFactory, AlgorithmicProblemReduction) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

withSearchFactory(IOptimalPathInORGraphSearchFactory, AlgorithmicProblemReduction) - Method in class ai.libs.mlplan.core.MLPlanBuilder

withSearchPhaseDatasetSplitter(IDatasetSplitter) - Method in class ai.libs.mlplan.core.MLPlanBuilder

withSearchPhaseEvaluatorFactory(IClassifierEvaluatorFactory) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

Sets the evaluator factory for the search phase.

withSearchSpaceConfigFile(File) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

Specify the search space in which ML-Plan is required to work.

withSearchSpaceConfigFile(File) - Method in class ai.libs.mlplan.core.MLPlanBuilder

withSelectionPhaseDatasetSplitter(IDatasetSplitter) - Method in class ai.libs.mlplan.core.MLPlanBuilder

withSelectionPhaseEvaluatorFactory(MonteCarloCrossValidationEvaluatorFactory) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

Sets the evaluator factory for the selection phase.

withSingleLabelClassificationMeasure(EMultiClassPerformanceMeasure) - Method in class ai.libs.mlplan.core.MLPlanBuilder

withSplitBasedClassifierEvaluator(ISplitBasedClassifierEvaluator<Double>) - Method in class ai.libs.mlplan.core.MLPlanBuilder

withSQLAdapter(SQLAdapter) - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

withTestEvaluationTechnique(String) - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

withTestSeed(int) - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

withtestSplitTechnique(String) - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

withTimeOut(TimeOut) - Method in class ai.libs.mlplan.core.AbstractMLPlanBuilder

withTimeoutForNodeEvaluation(TimeOut) - Method in class ai.libs.mlplan.core.MLPlanBuilder

withTimeoutForSingleSolutionEvaluation(TimeOut) - Method in class ai.libs.mlplan.core.MLPlanBuilder

withTinyTestConfiguration() - Method in class ai.libs.mlplan.core.MLPlanBuilder

withTinyWekaSearchSpace() - Method in class ai.libs.mlplan.core.MLPlanWekaBuilder

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

withTpotConfig() - Method in class ai.libs.mlplan.core.MLPlanBuilder

withUnlimitedLengthPipelineSearchSpace() - Method in class ai.libs.mlplan.core.MLPlanSKLearnBuilder

Configures ML-Plan to use the search space with unlimited length preprocessing pipelines.

withValEvaluationTechnique(String) - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

withValSeed(int) - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

withValSplitTechnique(String) - Method in class ai.libs.mlpipeline_evaluation.PipelineEvaluationCacheConfigBuilder

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