ai.libs.jaicore.ml.intervaltree

Class ExtendedRandomTree

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.trees.RandomTree

ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree

All Implemented Interfaces:

RangeQueryPredictor, java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.Drawable, weka.core.OptionHandler, weka.core.PartitionGenerator, weka.core.Randomizable, weka.core.RevisionHandler, weka.core.WeightedInstancesHandler

public class ExtendedRandomTree
extends weka.classifiers.trees.RandomTree
implements RangeQueryPredictor

Extension of a classic RandomTree to predict intervals. This class also provides an implementaion of fANOVA based on Hutter et al.s implementation https://github.com/frank-hutter/fanova

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class weka.classifiers.trees.RandomTree

weka.classifiers.trees.RandomTree.Tree

Field Summary

Fields inherited from class weka.classifiers.trees.RandomTree

m_AllowUnclassifiedInstances, m_BreakTiesRandomly, m_computeImpurityDecreases, m_impurityDecreasees, m_Info, m_KValue, m_MaxDepth, m_MinNum, m_MinVarianceProp, m_NumFolds, m_randomSeed, m_Tree, m_zeroR

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Fields inherited from interface weka.core.Drawable

BayesNet, Newick, NOT_DRAWABLE, TREE

Constructor Summary

Constructors

Constructor and Description
ExtendedRandomTree()
ExtendedRandomTree(FeatureSpace featureSpace)
ExtendedRandomTree(IntervalAggregator intervalAggregator)

Method Summary

Modifier and Type	Method and Description
double	computeMarginalStandardDeviationForSubsetOfFeatures(java.util.Set<java.lang.Integer> features) Computes the variance contribution of a subset of features.
double	computeMarginalVarianceContributionForSubsetOfFeatures(java.util.Set<java.lang.Integer> features) Computes the variance contribution of a subset of features.
double	computeMarginalVarianceContributionForSubsetOfFeaturesNotNormalized(java.util.Set<java.lang.Integer> features) Computes the variance contribution of a subset of features without normalizing.
double	computeTotalVarianceOfSubset(java.util.Set<java.lang.Integer> features) Computes the total variance of marginal predictions for a given set of features.
FeatureSpace	getFeatureSpace()
double	getTotalVariance()
org.apache.commons.math3.geometry.euclidean.oned.Interval	predictInterval(RQPHelper.IntervalAndHeader intervalAndHeader)
void	preprocess() Sets up the tree for fANOVA
void	printObservations()
void	printSizeOfFeatureSpaceAndPartitioning()
void	printSplitPoints()
void	setFeatureSpace(FeatureSpace featureSpace)

Methods inherited from class weka.classifiers.trees.RandomTree

allowUnclassifiedInstancesTipText, breakTiesRandomlyTipText, buildClassifier, distributionForInstance, generatePartition, getAllowUnclassifiedInstances, getBreakTiesRandomly, getCapabilities, getComputeImpurityDecreases, getImpurityDecreases, getKValue, getM_Tree, getMaxDepth, getMembershipValues, getMinNum, getMinVarianceProp, getNumFolds, getOptions, getSeed, globalInfo, graph, graphType, KValueTipText, listOptions, main, maxDepthTipText, minNumTipText, minVariancePropTipText, numElements, numFoldsTipText, seedTipText, setAllowUnclassifiedInstances, setBreakTiesRandomly, setComputeImpurityDecreases, setKValue, setMaxDepth, setMinNum, setMinVarianceProp, setNumFolds, setOptions, setSeed, singleVariance, toString, variance

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, getRevision, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

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

Methods inherited from interface ai.libs.jaicore.ml.intervaltree.RangeQueryPredictor

predictInterval

Constructor Detail

ExtendedRandomTree

public ExtendedRandomTree()

ExtendedRandomTree

public ExtendedRandomTree(FeatureSpace featureSpace)

ExtendedRandomTree

public ExtendedRandomTree(IntervalAggregator intervalAggregator)

Method Detail

predictInterval

public org.apache.commons.math3.geometry.euclidean.oned.Interval predictInterval(RQPHelper.IntervalAndHeader intervalAndHeader)

Specified by:

predictInterval in interface RangeQueryPredictor

setFeatureSpace

public void setFeatureSpace(FeatureSpace featureSpace)

getFeatureSpace

public FeatureSpace getFeatureSpace()

computeMarginalStandardDeviationForSubsetOfFeatures

public double computeMarginalStandardDeviationForSubsetOfFeatures(java.util.Set<java.lang.Integer> features)

Computes the variance contribution of a subset of features.

Parameters:

features -

Returns:

Variance contribution of the feature subset

computeMarginalVarianceContributionForSubsetOfFeatures

public double computeMarginalVarianceContributionForSubsetOfFeatures(java.util.Set<java.lang.Integer> features)

Computes the variance contribution of a subset of features.

Parameters:

features -

Returns:

Variance contribution of the feature subset

computeMarginalVarianceContributionForSubsetOfFeaturesNotNormalized

public double computeMarginalVarianceContributionForSubsetOfFeaturesNotNormalized(java.util.Set<java.lang.Integer> features)

Computes the variance contribution of a subset of features without normalizing.

Parameters:

features -

Returns:

Variance contribution of the feature subset

computeTotalVarianceOfSubset

public double computeTotalVarianceOfSubset(java.util.Set<java.lang.Integer> features)

Computes the total variance of marginal predictions for a given set of features.

Parameters:

features -

Returns:

getTotalVariance

public double getTotalVariance()

preprocess

public void preprocess()

Sets up the tree for fANOVA

printObservations

public void printObservations()

printSplitPoints

public void printSplitPoints()

printSizeOfFeatureSpaceAndPartitioning

public void printSizeOfFeatureSpaceAndPartitioning()