Package ai.libs.jaicore.ml.weka.rangequery.learner.intervaltree

Class 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

    • Method Summary

      All Methods Instance Methods Concrete Methods 
      Modifier and Type Method 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

    • Constructor Detail

      • ExtendedRandomTree

        public ExtendedRandomTree()

      • ExtendedRandomTree

        public ExtendedRandomTree​(FeatureSpace featureSpace)

      • ExtendedRandomTree

        public ExtendedRandomTree​(IntervalAggregator intervalAggregator)

    • Method Detail

      • setFeatureSpace

        public void setFeatureSpace​(FeatureSpace featureSpace)

      • 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()