weka.classifiers.bayes.net.search.global

## Class GlobalScoreSearchAlgorithm

- java.lang.Object
- - weka.classifiers.bayes.net.search.SearchAlgorithm
  - - weka.classifiers.bayes.net.search.global.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: 10154 $
  Author:
    Remco Bouckaert
  See Also:
    Serialized Form

- - ### Field Summary

    Fields
    Modifier and Type | Field and Description
    --- | ---
    `static Tag[]` | `TAGS_CV_TYPE` the score types

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `GlobalScoreSearchAlgorithm()`

  - ### Method Summary

    Methods
    Modifier and Type | Method and Description
    --- | ---
    `double` | `calcScore(BayesNet bayesNet)` performCV returns the accuracy calculated using cross validation.
    `double` | `calcScoreWithExtraParent(int nNode, int nCandidateParent)` Calc Node Score With Added Parent
    `double` | `calcScoreWithMissingParent(int nNode, int nCandidateParent)` Calc Node Score With Parent Deleted
    `double` | `calcScoreWithReversedParent(int nNode, int nCandidateParent)` Calc Node Score With Arrow reversed
    `double` | `cumulativeCV(BayesNet bayesNet)` CumulativeCV returns the accuracy calculated using cumulative cross validation.
    `java.lang.String` | `CVTypeTipText()`
    `SelectedTag` | `getCVType()` get cross validation strategy to be used in searching for networks.
    `boolean` | `getMarkovBlanketClassifier()`
    `java.lang.String[]` | `getOptions()` Gets the current settings of the search algorithm.
    `java.lang.String` | `getRevision()` Returns the revision string.
    `boolean` | `getUseProb()`
    `java.lang.String` | `globalInfo()` This will return a string describing the search algorithm.
    `double` | `kFoldCV(BayesNet bayesNet, int nNrOfFolds)` kFoldCV uses k-fold cross validation to measure the accuracy of a Bayes network classifier.
    `double` | `leaveOneOutCV(BayesNet bayesNet)` LeaveOneOutCV returns the accuracy calculated using Leave One Out cross validation.
    `java.util.Enumeration<Option>` | `listOptions()` Returns an enumeration describing the available options
    `java.lang.String` | `markovBlanketClassifierTipText()`
    `void` | `setCVType(SelectedTag newCVType)` set cross validation strategy to be used in searching for networks.
    `void` | `setMarkovBlanketClassifier(boolean bMarkovBlanketClassifier)`
    `void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
    `void` | `setUseProb(boolean useProb)`
    `java.lang.String` | `useProbTipText()`

    - ### Methods inherited from class weka.classifiers.bayes.net.search.SearchAlgorithm

      `buildStructure, initAsNaiveBayesTipText, maxNrOfParentsTipText, toString`

    - ### Methods inherited from class java.lang.Object

      `equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

- - ### 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<Option> 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.

    - #### getRevision

          public java.lang.String getRevision()

      Returns the revision string.
      Specified by:
        `getRevision` in interface `RevisionHandler`
      Overrides:
        `getRevision` in class `SearchAlgorithm`
      Returns:
        the revision