weka.datagenerators
Class RDG1

java.lang.Object
  weka.datagenerators.Generator
      weka.datagenerators.RDG1

All Implemented Interfaces:

OptionHandler, java.io.Serializable

public class RDG1

extends Generator

implements OptionHandler, java.io.Serializable

Class to generate data randomly by producing a decision list. The decision list consists of rules. Instances are generated randomly one by one. If decision list fails to classify the current instance, a new rule according to this current instance is generated and added to the decision list.

The option -V switches on voting, which means that at the end of the generation all instances are reclassified to the class value that is supported by the most rules.

This data generator can generate 'boolean' attributes (= nominal with the values {true, false}) and numeric attributes. The rules can be 'A' or 'NOT A' for boolean values and 'B < random_value' or 'B >= random_value' for numeric values.

Valid options are:

-R num
The maximum number of attributes chosen to form a rule (default 10).

-M num
The minimum number of attributes chosen to form a rule (default 1).

-I num
The number of irrelevant attributes (default 0).

-N num
The number of numeric attributes (default 0).

-S seed
Random number seed for random function used (default 1).

-V
Flag to use voting.

Following an example of a generated dataset:
%
% weka.datagenerators.RDG1 -r expl -a 2 -c 3 -n 4 -N 1 -I 0 -M 2 -R 10 -S 2
%
relation expl

attribute a0 {false,true}
attribute a1 numeric
attribute class {c0,c1,c2}

data

true,0.496823,c0
false,0.743158,c1
false,0.408285,c1
false,0.993687,c2
%
% Number of attributes chosen as irrelevant = 0
%
% DECISIONLIST (number of rules = 3):
% RULE 0: c0 := a1 < 0.986, a0
% RULE 1: c1 := a1 < 0.95, not(a0)
% RULE 2: c2 := not(a0), a1 >= 0.562

Version:

$Revision: 1.2 $

Author:

Gabi Schmidberger (gabi@cs.waikato.ac.nz)

See Also:

Serialized Form

Nested Class Summary

private class

RDG1.RuleList

Field Summary

(package private) boolean[]	m_AttList_Irr
private Instances	m_DatasetFormat
private int	m_Debug
private FastVector	m_DecisionList
private int	m_MaxRuleSize
private int	m_MinRuleSize
private int	m_NumIrrelevant
private int	m_NumNumeric
private java.util.Random	m_Random
private int	m_Seed
private boolean	m_VoteFlag

Fields inherited from class weka.datagenerators.Generator

Constructor Summary

RDG1()

Method Summary

private boolean	classifyExample(Instance example) Tries to classify an example.
Instances	defineDataFormat() Initializes the format for the dataset produced.
private Instances	defineDataset(java.util.Random random) Returns a dataset header.
private boolean[]	defineIrrelevant(java.util.Random random) Defines randomly the attributes as irrelevant.
private int[]	defineNumeric(java.util.Random random) Chooses randomly the attributes that get datatyp numeric.
Instance	generateExample() Generate an example of the dataset dataset.
private Instance	generateExample(java.util.Random random, Instances format) Generates an example with its classvalue set to missing and binds it to the datasets.
Instances	generateExamples() Generate all examples of the dataset.
Instances	generateExamples(int num, java.util.Random random, Instances format) Generate all examples of the dataset.
java.lang.String	generateFinished() Compiles documentation about the data generation.
private FastVector	generateTestList(java.util.Random random, Instance example) Generates a new rule for the decision list and classifies the new example.
boolean[]	getAttList_Irr() Gets the array that defines which of the attributes are seen to be irrelevant.
Instances	getDatasetFormat() Gets the dataset format.
int	getMaxRuleSize() Gets the maximum number of tests in rules.
int	getMinRuleSize() Gets the minimum number of tests in rules.
int	getNumIrrelevant() Gets the number of irrelevant attributes.
int	getNumNumeric() Gets the number of numerical attributes.
java.lang.String[]	getOptions() Gets the current settings of the datagenerator RDG1.
java.util.Random	getRandom() Gets the random generator.
int	getSeed() Gets the random number seed.
boolean	getSingleModeFlag() Gets the single mode flag.
boolean	getVoteFlag() Gets the vote flag.
java.lang.String	globalInfo() Returns a string describing this data generator.
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for testing this class.
void	setAttList_Irr(boolean[] newAttList_Irr) Sets the array that defines which of the attributes are seen to be irrelevant.
void	setDatasetFormat(Instances newDatasetFormat) Sets the dataset format.
void	setMaxRuleSize(int newMaxRuleSize) Sets the maximum number of tests in rules.
void	setMinRuleSize(int newMinRuleSize) Sets the minimum number of tests in rules.
void	setNumIrrelevant(int newNumIrrelevant) Sets the number of irrelevant attributes.
void	setNumNumeric(int newNumNumeric) Sets the number of numerical attributes.
void	setOptions(java.lang.String[] options) Parses a list of options for this object.
void	setRandom(java.util.Random newRandom) Sets the random generator.
void	setSeed(int newSeed) Sets the random number seed.
void	setVoteFlag(boolean newVoteFlag) Sets the vote flag.
private Instance	updateDecisionList(java.util.Random random, Instance example) Generates a new rule for the decision list.
private Instances	voteDataset(Instances dataset) Resets the class values of all instances using voting.
private Instance	votedReclassifyExample(Instance example) Classify example with maximum vote the following way.

Methods inherited from class weka.datagenerators.Generator

getDebug, getFormat, getNumAttributes, getNumClasses, getNumExamples, getNumExamplesAct, getOutput, getRelationName, makeData, setDebug, setFormat, setNumAttributes, setNumClasses, setNumExamples, setNumExamplesAct, setOutput, setRelationName, toStringFormat

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

m_MaxRuleSize

private int m_MaxRuleSize

m_MinRuleSize

private int m_MinRuleSize

m_NumIrrelevant

private int m_NumIrrelevant

m_NumNumeric

private int m_NumNumeric

m_Seed

private int m_Seed

m_VoteFlag

private boolean m_VoteFlag

m_DatasetFormat

private Instances m_DatasetFormat

m_Random

private java.util.Random m_Random

m_DecisionList

private FastVector m_DecisionList

m_AttList_Irr

boolean[] m_AttList_Irr

m_Debug

private int m_Debug

Constructor Detail

RDG1

public RDG1()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this data generator.

Returns:

a description of the data generator suitable for displaying in the explorer/experimenter gui

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Returns:

an enumeration of all the available options

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a list of options for this object.

For list of valid options see class description.

Specified by:

setOptions in interface OptionHandler

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 datagenerator RDG1.

Specified by:

getOptions in interface OptionHandler

Returns:

an array of strings suitable for passing to setOptions

getRandom

public java.util.Random getRandom()

Gets the random generator.

Returns:

the random generator

setRandom

public void setRandom(java.util.Random newRandom)

Sets the random generator.

Parameters:

newRandom - is the random generator.

getMaxRuleSize

public int getMaxRuleSize()

Gets the maximum number of tests in rules.

Returns:

the maximum number of tests allowed in rules

setMaxRuleSize

public void setMaxRuleSize(int newMaxRuleSize)

Sets the maximum number of tests in rules.

Parameters:

newMaxRuleSize - new maximum number of tests allowed in rules.

getMinRuleSize

public int getMinRuleSize()

Gets the minimum number of tests in rules.

Returns:

the minimum number of tests allowed in rules

setMinRuleSize

public void setMinRuleSize(int newMinRuleSize)

Sets the minimum number of tests in rules.

Parameters:

newMinRuleSize - new minimum number of test in rules.

getNumIrrelevant

public int getNumIrrelevant()

Gets the number of irrelevant attributes.

Returns:

the number of irrelevant attributes

setNumIrrelevant

public void setNumIrrelevant(int newNumIrrelevant)

Sets the number of irrelevant attributes.

getNumNumeric

public int getNumNumeric()

Gets the number of numerical attributes.

Returns:

the number of numerical attributes.

setNumNumeric

public void setNumNumeric(int newNumNumeric)

Sets the number of numerical attributes.

getVoteFlag

public boolean getVoteFlag()

Gets the vote flag.

Returns:

voting flag.

setVoteFlag

public void setVoteFlag(boolean newVoteFlag)

Sets the vote flag.

Parameters:

newVoteFlag - boolean with the new setting of the vote flag.

getSingleModeFlag

public boolean getSingleModeFlag()

Gets the single mode flag.

Specified by:

getSingleModeFlag in class Generator

Returns:

true if methode generateExample can be used.

getSeed

public int getSeed()

Gets the random number seed.

Returns:

the random number seed.

setSeed

public void setSeed(int newSeed)

Sets the random number seed.

Parameters:

newSeed - the new random number seed.

getDatasetFormat

public Instances getDatasetFormat()

Gets the dataset format.

Returns:

the dataset format.

setDatasetFormat

public void setDatasetFormat(Instances newDatasetFormat)

Sets the dataset format.

Parameters:

newDatasetFormat - the new dataset format.

getAttList_Irr

public boolean[] getAttList_Irr()

Gets the array that defines which of the attributes are seen to be irrelevant.

Returns:

the array that defines the irrelevant attributes

setAttList_Irr

public void setAttList_Irr(boolean[] newAttList_Irr)

Sets the array that defines which of the attributes are seen to be irrelevant.

Parameters:

newAttList_Irr - array that defines the irrelevant attributes.

defineDataFormat

public Instances defineDataFormat()
                           throws java.lang.Exception

Initializes the format for the dataset produced.

Specified by:

defineDataFormat in class Generator

Returns:

the output data format

Throws:

java.lang.Exception - data format could not be defined

generateExample

public Instance generateExample()
                         throws java.lang.Exception

Generate an example of the dataset dataset.

Specified by:

generateExample in class Generator

Returns:

the instance generated

Throws:

java.lang.Exception - if format not defined or generating
examples one by one is not possible, because voting is chosen

generateExamples

public Instances generateExamples()
                           throws java.lang.Exception

Generate all examples of the dataset.

Specified by:

generateExamples in class Generator

Returns:

the instance generated

Throws:

java.lang.Exception - if format not defined or generating
examples one by one is not possible, because voting is chosen

generateExamples

public Instances generateExamples(int num,
                                  java.util.Random random,
                                  Instances format)
                           throws java.lang.Exception

Generate all examples of the dataset.

Returns:

the instance generated

Throws:

java.lang.Exception - if format not defined or generating
examples one by one is not possible, because voting is chosen

updateDecisionList

private Instance updateDecisionList(java.util.Random random,
                                    Instance example)
                             throws java.lang.Exception

Generates a new rule for the decision list. and classifies the new example

Parameters:

random - random number generator

example - example used to update decision list

Throws:

java.lang.Exception

generateTestList

private FastVector generateTestList(java.util.Random random,
                                    Instance example)
                             throws java.lang.Exception

Generates a new rule for the decision list and classifies the new example.

Parameters:

random - random number generator

example -

Throws:

java.lang.Exception

generateExample

private Instance generateExample(java.util.Random random,
                                 Instances format)
                          throws java.lang.Exception

Generates an example with its classvalue set to missing and binds it to the datasets.

Parameters:

random - random number generator

Throws:

java.lang.Exception

classifyExample

private boolean classifyExample(Instance example)
                         throws java.lang.Exception

Tries to classify an example.

Parameters:

example -

Throws:

java.lang.Exception

votedReclassifyExample

private Instance votedReclassifyExample(Instance example)
                                 throws java.lang.Exception

Classify example with maximum vote the following way. With every rule in the decisionlist, it is evaluated if the given instance could be the class of the rule. Finally the class value that receives the highest number of votes is assigned to the example.

Parameters:

example - example to be reclassified

Returns:

instance with new class value

Throws:

java.lang.Exception

defineDataset

private Instances defineDataset(java.util.Random random)
                         throws java.lang.Exception

Returns a dataset header.

Parameters:

random - random number generator

Returns:

dataset header

Throws:

java.lang.Exception

defineIrrelevant

private boolean[] defineIrrelevant(java.util.Random random)

Defines randomly the attributes as irrelevant. Number of attributes to be set as irrelevant is either set with a preceeding call of setNumIrrelevant() or is per default 0.

Parameters:

random -

Returns:

list of boolean values with one value for each attribute, and each value set true or false according to if the corresponding attribute was defined irrelevant or not

defineNumeric

private int[] defineNumeric(java.util.Random random)

Chooses randomly the attributes that get datatyp numeric.

Parameters:

random -

Returns:

list of integer values, with one value for each attribute, and each value set to Attribut.NOMINAL or Attribut.NUMERIC

generateFinished

public java.lang.String generateFinished()
                                  throws java.lang.Exception

Compiles documentation about the data generation. This is the number of irrelevant attributes and the decisionlist with all rules. Considering that the decisionlist might get enhanced until the last instance is generated, this method should be called at the end of the data generation process.

Specified by:

generateFinished in class Generator

Returns:

string with additional information about generated dataset

Throws:

java.lang.Exception - no input structure has been defined

voteDataset

private Instances voteDataset(Instances dataset)
                       throws java.lang.Exception

Resets the class values of all instances using voting. For each instance the class value that satisfies the most rules is choosen as new class value.

Parameters:

dataset -

Returns:

the changed instances

Throws:

java.lang.Exception

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - should contain arguments for the data producer: