ConjunctiveRule

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.rules.ConjunctiveRule

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.WeightedInstancesHandler
```
public class ConjunctiveRule
extends weka.classifiers.AbstractClassifier
implements weka.core.OptionHandler, weka.core.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: 10335 $

Author:

Xin XU (xx5@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

ConjunctiveRule()

Constructors
Constructor and Description
`ConjunctiveRule()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(weka.core.Instances instances)` Builds a single rule learner with REP dealing with nominal classes or numeric classes.
`double[]`	`distributionForInstance(weka.core.Instance instance)` Computes class distribution for the given instance.
`java.lang.String`	`exclusiveTipText()` Returns the tip text for this property
`java.lang.String`	`foldsTipText()` Returns the tip text for this property
`weka.core.Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`boolean`	`getExclusive()` Returns whether exclusive expressions for nominal attributes splits are considered
`int`	`getFolds()` returns the current number of folds
`double`	`getMinNo()` Gets the minimum total weight of the instances in a rule
`int`	`getNumAntds()` Gets the number of antecedants
`java.lang.String[]`	`getOptions()` Gets the current settings of the Classifier.
`java.lang.String`	`getRevision()` Returns the revision string.
`long`	`getSeed()` returns the current seed value for randomizing the data
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`boolean`	`hasAntds()` Whether this rule has antecedents, i.e.
`boolean`	`isCover(weka.core.Instance datum)` Whether the instance covered by this rule
`java.util.Enumeration<weka.core.Option>`	`listOptions()` Returns an enumeration describing the available options Valid options are:
`static void`	`main(java.lang.String[] args)` Main method.
`java.lang.String`	`minNoTipText()` Returns the tip text for this property
`java.lang.String`	`numAntdsTipText()` Returns the tip text for this property
`java.lang.String`	`seedTipText()` Returns the tip text for this property
`void`	`setExclusive(boolean e)` Sets whether exclusive expressions for nominal attributes splits are considered
`void`	`setFolds(int folds)` the number of folds to use
`void`	`setMinNo(double m)` Sets the minimum total weight of the instances in a rule
`void`	`setNumAntds(int n)` Sets the number of antecedants
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setSeed(long s)` sets the seed for randomizing the data
`java.lang.String`	`toString()` Prints this rule
`java.lang.String`	`toString(java.lang.String att, java.lang.String cl)` Prints this rule with the specified class label

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Constructor Detail
  - ConjunctiveRule
```
public ConjunctiveRule()
```
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing classifier
    
    Returns:
    
    a description suitable for displaying in the explorer/experimenter gui
  - listOptions
```
public java.util.Enumeration<weka.core.Option> 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 weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.classifiers.AbstractClassifier
    
    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:
```
 -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 weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.AbstractClassifier
    
    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 Classifier.
    
    Specified by:
    
    getOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - foldsTipText
```
public java.lang.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 java.lang.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 java.lang.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 java.lang.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 java.lang.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 weka.core.Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface weka.classifiers.Classifier
    
    Specified by:
    
    getCapabilities in interface weka.core.CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
  - buildClassifier
```
public void buildClassifier(weka.core.Instances instances)
                     throws java.lang.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 interface weka.classifiers.Classifier
    
    Parameters:
    
    instances - the training data
    
    Throws:
    
    java.lang.Exception - if classifier can't be built successfully
  - distributionForInstance
```
public double[] distributionForInstance(weka.core.Instance instance)
                                 throws java.lang.Exception
```
    Computes class distribution for the given instance.
    
    Specified by:
    
    distributionForInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    distributionForInstance in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    instance - the instance for which distribution is to be computed
    
    Returns:
    
    the class distribution for the given instance
    
    Throws:
    
    java.lang.Exception - if given instance is null
  - isCover
```
public boolean isCover(weka.core.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 java.lang.String toString(java.lang.String att,
                                 java.lang.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 java.lang.String toString()
```
    Prints this rule
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a textual description of this rule
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface weka.core.RevisionHandler
    
    Overrides:
    
    getRevision in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] args)
```
    Main method.
    
    Parameters:
    
    args - the options for the classifier

Class ConjunctiveRule

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Constructor Detail

ConjunctiveRule

Method Detail

globalInfo

listOptions

setOptions

getOptions

foldsTipText

setFolds

getFolds

seedTipText

setSeed

getSeed

exclusiveTipText

getExclusive

setExclusive

minNoTipText

setMinNo

getMinNo

numAntdsTipText

setNumAntds

getNumAntds

getCapabilities

buildClassifier

distributionForInstance

isCover

hasAntds

toString

toString

getRevision

main