Class ConjunctiveRule

java.lang.Object
weka.classifiers.Classifier
weka.classifiers.rules.ConjunctiveRule
All Implemented Interfaces:
Serializable, Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler, WeightedInstancesHandler

public class ConjunctiveRule extends Classifier implements OptionHandler, WeightedInstancesHandler
This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.

A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. In this case, the consequent is the distribution of the available classes (or mean for a numeric value) in the dataset. If the test instance is not covered by this rule, then it's predicted using the default class distributions/value of the data not covered by the rule in the training data.This learner selects an antecedent by computing the Information Gain of each antecendent and prunes the generated rule using Reduced Error Prunning (REP) or simple pre-pruning based on the number of antecedents.

For classification, the Information of one antecedent is the weighted average of the entropies of both the data covered and not covered by the rule.
For regression, the Information is the weighted average of the mean-squared errors of both the data covered and not covered by the rule.

In pruning, weighted average of the accuracy rates on the pruning data is used for classification while the weighted average of the mean-squared errors on the pruning data is used for regression.

Valid options are:

 -N <number of folds>
  Set number of folds for REP
  One fold is used as pruning set.
  (default 3)
 -R
  Set if NOT uses randomization
  (default:use randomization)
 -E
  Set whether consider the exclusive
  expressions for nominal attributes
  (default false)
 -M <min. weights>
  Set the minimal weights of instances
  within a split.
  (default 2.0)
 -P <number of antecedents>
  Set number of antecedents for pre-pruning
  if -1, then REP is used
  (default -1)
 -S <seed>
  Set the seed of randomization
  (default 1)
Version:
$Revision: 9835 $
Author:
Xin XU (xx5@cs.waikato.ac.nz)
See Also:
  • Constructor Details

    • ConjunctiveRule

      public ConjunctiveRule()
  • Method Details

    • globalInfo

      public String globalInfo()
      Returns a string describing classifier
      Returns:
      a description suitable for displaying in the explorer/experimenter gui
    • listOptions

      public Enumeration listOptions()
      Returns an enumeration describing the available options Valid options are:

      -N number
      Set number of folds for REP. One fold is used as the pruning set. (Default: 3)

      -R
      Set if NOT randomize the data before split to growing and pruning data. If NOT set, the seed of randomization is specified by the -S option. (Default: randomize)

      -S
      Seed of randomization. (Default: 1)

      -E
      Set whether consider the exclusive expressions for nominal attribute split. (Default: false)

      -M number
      Set the minimal weights of instances within a split. (Default: 2)

      -P number
      Set the number of antecedents allowed in the rule if pre-pruning is used. If this value is other than -1, then pre-pruning will be used, otherwise the rule uses REP. (Default: -1)

      Specified by:
      listOptions in interface OptionHandler
      Overrides:
      listOptions in class Classifier
      Returns:
      an enumeration of all the available options
    • setOptions

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

      Valid options are:

       -N <number of folds>
        Set number of folds for REP
        One fold is used as pruning set.
        (default 3)
       -R
        Set if NOT uses randomization
        (default:use randomization)
       -E
        Set whether consider the exclusive
        expressions for nominal attributes
        (default false)
       -M <min. weights>
        Set the minimal weights of instances
        within a split.
        (default 2.0)
       -P <number of antecedents>
        Set number of antecedents for pre-pruning
        if -1, then REP is used
        (default -1)
       -S <seed>
        Set the seed of randomization
        (default 1)
      Specified by:
      setOptions in interface OptionHandler
      Overrides:
      setOptions in class Classifier
      Parameters:
      options - the list of options as an array of strings
      Throws:
      Exception - if an option is not supported
    • getOptions

      public String[] getOptions()
      Gets the current settings of the Classifier.
      Specified by:
      getOptions in interface OptionHandler
      Overrides:
      getOptions in class Classifier
      Returns:
      an array of strings suitable for passing to setOptions
    • foldsTipText

      public String foldsTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui
    • setFolds

      public void setFolds(int folds)
      the number of folds to use
      Parameters:
      folds - the number of folds to use
    • getFolds

      public int getFolds()
      returns the current number of folds
      Returns:
      the number of folds
    • seedTipText

      public String seedTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui
    • setSeed

      public void setSeed(long s)
      sets the seed for randomizing the data
      Parameters:
      s - the seed value
    • getSeed

      public long getSeed()
      returns the current seed value for randomizing the data
      Returns:
      the seed value
    • exclusiveTipText

      public String exclusiveTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui
    • getExclusive

      public boolean getExclusive()
      Returns whether exclusive expressions for nominal attributes splits are considered
      Returns:
      true if exclusive expressions for nominal attributes splits are considered
    • setExclusive

      public void setExclusive(boolean e)
      Sets whether exclusive expressions for nominal attributes splits are considered
      Parameters:
      e - whether to consider exclusive expressions for nominal attribute splits
    • minNoTipText

      public String minNoTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui
    • setMinNo

      public void setMinNo(double m)
      Sets the minimum total weight of the instances in a rule
      Parameters:
      m - the minimum total weight of the instances in a rule
    • getMinNo

      public double getMinNo()
      Gets the minimum total weight of the instances in a rule
      Returns:
      the minimum total weight of the instances in a rule
    • numAntdsTipText

      public String numAntdsTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui
    • setNumAntds

      public void setNumAntds(int n)
      Sets the number of antecedants
      Parameters:
      n - the number of antecedants
    • getNumAntds

      public int getNumAntds()
      Gets the number of antecedants
      Returns:
      the number of antecedants
    • getCapabilities

      public Capabilities getCapabilities()
      Returns default capabilities of the classifier.
      Specified by:
      getCapabilities in interface CapabilitiesHandler
      Overrides:
      getCapabilities in class Classifier
      Returns:
      the capabilities of this classifier
      See Also:
    • buildClassifier

      public void buildClassifier(Instances instances) throws Exception
      Builds a single rule learner with REP dealing with nominal classes or numeric classes. For nominal classes, this rule learner predicts a distribution on the classes. For numeric classes, this learner predicts a single value.
      Specified by:
      buildClassifier in class Classifier
      Parameters:
      instances - the training data
      Throws:
      Exception - if classifier can't be built successfully
    • distributionForInstance

      public double[] distributionForInstance(Instance instance) throws Exception
      Computes class distribution for the given instance.
      Overrides:
      distributionForInstance in class Classifier
      Parameters:
      instance - the instance for which distribution is to be computed
      Returns:
      the class distribution for the given instance
      Throws:
      Exception - if given instance is null
    • isCover

      public boolean isCover(Instance datum)
      Whether the instance covered by this rule
      Parameters:
      datum - the instance in question
      Returns:
      the boolean value indicating whether the instance is covered by this rule
    • hasAntds

      public boolean hasAntds()
      Whether this rule has antecedents, i.e. whether it is a default rule
      Returns:
      the boolean value indicating whether the rule has antecedents
    • toString

      public String toString(String att, String cl)
      Prints this rule with the specified class label
      Parameters:
      att - the string standing for attribute in the consequent of this rule
      cl - the string standing for value in the consequent of this rule
      Returns:
      a textual description of this rule with the specified class label
    • toString

      public String toString()
      Prints this rule
      Overrides:
      toString in class Object
      Returns:
      a textual description of this rule
    • getRevision

      public String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Overrides:
      getRevision in class Classifier
      Returns:
      the revision
    • main

      public static void main(String[] args)
      Main method.
      Parameters:
      args - the options for the classifier