Package weka.classifiers.bayes.net.search.global

Class GlobalScoreSearchAlgorithm

  • All Implemented Interfaces:
    java.io.Serializable, OptionHandler, RevisionHandler
    Direct Known Subclasses:
    GeneticSearch, HillClimber, K2, SimulatedAnnealing, TAN
    public class GlobalScoreSearchAlgorithm
extends SearchAlgorithm
    This Bayes Network learning algorithm uses cross validation to estimate classification accuracy.

    Valid options are: 

     -mbc
  Applies a Markov Blanket correction to the network structure, 
  after a network structure is learned. This ensures that all 
  nodes in the network are part of the Markov blanket of the 
  classifier node.
     -S [LOO-CV|k-Fold-CV|Cumulative-CV]
  Score type (LOO-CV,k-Fold-CV,Cumulative-CV)
     -Q
  Use probabilistic or 0/1 scoring.
  (default probabilistic scoring)
    Version:
    $Revision: 1.10 $
    Author:
    Remco Bouckaert
    See Also:
    Serialized Form

    • Field Detail

      • TAGS_CV_TYPE

        public static final Tag[] TAGS_CV_TYPE
        the score types

    • Constructor Detail

      • GlobalScoreSearchAlgorithm

        public GlobalScoreSearchAlgorithm()

    • Method Detail

      • calcScore

        public double calcScore​(BayesNet bayesNet)
                 throws java.lang.Exception
        performCV returns the accuracy calculated using cross validation. The dataset used is m_Instances associated with the Bayes Network.
        Parameters:
        bayesNet - : Bayes Network containing structure to evaluate
        Returns:
        accuracy (in interval 0..1) measured using cv.
        Throws:
        java.lang.Exception - whn m_nCVType is invalided + exceptions passed on by updateClassifier

      • calcScoreWithExtraParent

        public double calcScoreWithExtraParent​(int nNode,
                                       int nCandidateParent)
                                throws java.lang.Exception
        Calc Node Score With Added Parent
        Parameters:
        nNode - node for which the score is calculate
        nCandidateParent - candidate parent to add to the existing parent set
        Returns:
        log score
        Throws:
        java.lang.Exception - if something goes wrong

      • calcScoreWithMissingParent

        public double calcScoreWithMissingParent​(int nNode,
                                         int nCandidateParent)
                                  throws java.lang.Exception
        Calc Node Score With Parent Deleted
        Parameters:
        nNode - node for which the score is calculate
        nCandidateParent - candidate parent to delete from the existing parent set
        Returns:
        log score
        Throws:
        java.lang.Exception - if something goes wrong

      • calcScoreWithReversedParent

        public double calcScoreWithReversedParent​(int nNode,
                                          int nCandidateParent)
                                   throws java.lang.Exception
        Calc Node Score With Arrow reversed
        Parameters:
        nNode - node for which the score is calculate
        nCandidateParent - candidate parent to delete from the existing parent set
        Returns:
        log score
        Throws:
        java.lang.Exception - if something goes wrong

      • leaveOneOutCV

        public double leaveOneOutCV​(BayesNet bayesNet)
                     throws java.lang.Exception
        LeaveOneOutCV returns the accuracy calculated using Leave One Out cross validation. The dataset used is m_Instances associated with the Bayes Network.
        Parameters:
        bayesNet - : Bayes Network containing structure to evaluate
        Returns:
        accuracy (in interval 0..1) measured using leave one out cv.
        Throws:
        java.lang.Exception - passed on by updateClassifier

      • cumulativeCV

        public double cumulativeCV​(BayesNet bayesNet)
                    throws java.lang.Exception
        CumulativeCV returns the accuracy calculated using cumulative cross validation. The idea is to run through the data set and try to classify each of the instances based on the previously seen data. The data set used is m_Instances associated with the Bayes Network.
        Parameters:
        bayesNet - : Bayes Network containing structure to evaluate
        Returns:
        accuracy (in interval 0..1) measured using leave one out cv.
        Throws:
        java.lang.Exception - passed on by updateClassifier

      • kFoldCV

        public double kFoldCV​(BayesNet bayesNet,
                      int nNrOfFolds)
               throws java.lang.Exception
        kFoldCV uses k-fold cross validation to measure the accuracy of a Bayes network classifier.
        Parameters:
        bayesNet - : Bayes Network containing structure to evaluate
        nNrOfFolds - : the number of folds k to perform k-fold cv
        Returns:
        accuracy (in interval 0..1) measured using leave one out cv.
        Throws:
        java.lang.Exception - passed on by updateClassifier

      • getUseProb

        public boolean getUseProb()
        Returns:
        use probabilities or not in accuracy estimate

      • setUseProb

        public void setUseProb​(boolean useProb)
        Parameters:
        useProb - : use probabilities or not in accuracy estimate

      • setCVType

        public void setCVType​(SelectedTag newCVType)
        set cross validation strategy to be used in searching for networks.
        Parameters:
        newCVType - : cross validation strategy

      • getCVType

        public SelectedTag getCVType()
        get cross validation strategy to be used in searching for networks.
        Returns:
        cross validation strategy

      • setMarkovBlanketClassifier

        public void setMarkovBlanketClassifier​(boolean bMarkovBlanketClassifier)
        Parameters:
        bMarkovBlanketClassifier -

      • getMarkovBlanketClassifier

        public boolean getMarkovBlanketClassifier()
        Returns:

      • listOptions

        public java.util.Enumeration listOptions()
        Returns an enumeration describing the available options
        Specified by:
        listOptions in interface OptionHandler
        Overrides:
        listOptions in class SearchAlgorithm
        Returns:
        an enumeration of all the available options

      • setOptions

        public void setOptions​(java.lang.String[] options)
                throws java.lang.Exception
        Parses a given list of options.

        Valid options are: 

         -mbc
  Applies a Markov Blanket correction to the network structure, 
  after a network structure is learned. This ensures that all 
  nodes in the network are part of the Markov blanket of the 
  classifier node.
         -S [LOO-CV|k-Fold-CV|Cumulative-CV]
  Score type (LOO-CV,k-Fold-CV,Cumulative-CV)
         -Q
  Use probabilistic or 0/1 scoring.
  (default probabilistic scoring)
        Specified by:
        setOptions in interface OptionHandler
        Overrides:
        setOptions in class SearchAlgorithm
        Parameters:
        options - the list of options as an array of strings
        Throws:
        java.lang.Exception - if an option is not supported

      • getOptions

        public java.lang.String[] getOptions()
        Gets the current settings of the search algorithm.
        Specified by:
        getOptions in interface OptionHandler
        Overrides:
        getOptions in class SearchAlgorithm
        Returns:
        an array of strings suitable for passing to setOptions

      • CVTypeTipText

        public java.lang.String CVTypeTipText()
        Returns:
        a string to describe the CVType option.

      • useProbTipText

        public java.lang.String useProbTipText()
        Returns:
        a string to describe the UseProb option.

      • globalInfo

        public java.lang.String globalInfo()
        This will return a string describing the search algorithm.
        Returns:
        The string.

      • markovBlanketClassifierTipText

        public java.lang.String markovBlanketClassifierTipText()
        Returns:
        a string to describe the MarkovBlanketClassifier option.