weka.classifiers.trees
Class SimpleCart

java.lang.Object
  weka.classifiers.AbstractClassifier
      weka.classifiers.RandomizableClassifier
          weka.classifiers.trees.SimpleCart

All Implemented Interfaces:

Serializable, Cloneable, weka.classifiers.Classifier, weka.core.AdditionalMeasureProducer, weka.core.CapabilitiesHandler, weka.core.OptionHandler, weka.core.Randomizable, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler

public class SimpleCart
extends weka.classifiers.RandomizableClassifier
implements weka.core.AdditionalMeasureProducer, weka.core.TechnicalInformationHandler

Class implementing minimal cost-complexity pruning.
Note when dealing with missing values, use "fractional instances" method instead of surrogate split method.

For more information, see:

Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (1984). Classification and Regression Trees. Wadsworth International Group, Belmont, California.

BibTeX:

 @book{Breiman1984,
    address = {Belmont, California},
    author = {Leo Breiman and Jerome H. Friedman and Richard A. Olshen and Charles J. Stone},
    publisher = {Wadsworth International Group},
    title = {Classification and Regression Trees},
    year = {1984}
 }
 

Valid options are:

 -S <num>
  Random number seed.
  (default 1)

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -M <min no>
  The minimal number of instances at the terminal nodes.
  (default 2)

 -N <num folds>
  The number of folds used in the minimal cost-complexity pruning.
  (default 5)

 -U
  Don't use the minimal cost-complexity pruning.
  (default yes).

 -H
  Don't use the heuristic method for binary split.
  (default true).

 -A
  Use 1 SE rule to make pruning decision.
  (default no).

 -C
  Percentage of training data size (0-1].
  (default 1).

Version:

$Revision: 8109 $

Author:

Haijian Shi ([email protected])

See Also:

Serialized Form

Constructor Summary

SimpleCart()

Method Summary

void	buildClassifier(weka.core.Instances data) Build the classifier.
void	calculateAlphas() Updates the alpha field for all nodes.
double[]	distributionForInstance(weka.core.Instance instance) Computes class probabilities for instance using the decision tree.
Enumeration	enumerateMeasures() Return an enumeration of the measure names.
weka.core.Capabilities	getCapabilities() Returns default capabilities of the classifier.
boolean	getHeuristic() Get if use heuristic search for nominal attributes in multi-class problems.
double	getMeasure(String additionalMeasureName) Returns the value of the named measure.
double	getMinNumObj() Get minimal number of instances at the terminal nodes.
int	getNumFoldsPruning() Set number of folds in internal cross-validation.
String[]	getOptions() Gets the current settings of the classifier.
String	getRevision() Returns the revision string.
double	getSizePer() Get training set size.
weka.core.TechnicalInformation	getTechnicalInformation() Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
boolean	getUseOneSE() Get if use the 1SE rule to choose final model.
boolean	getUsePrune() Get if use minimal cost-complexity pruning.
String	globalInfo() Return a description suitable for displaying in the explorer/experimenter.
String	heuristicTipText() Returns the tip text for this property
Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(String[] args) Main method.
double	measureTreeSize() Return number of tree size.
String	minNumObjTipText() Returns the tip text for this property
void	modelErrors() Updates the numIncorrectModel field for all nodes when subtree (to be pruned) is rooted.
String	numFoldsPruningTipText() Returns the tip text for this property
int	numInnerNodes() Method to count the number of inner nodes in the tree.
int	numLeaves() Compute number of leaf nodes.
int	numNodes() Compute size of the tree.
void	prune(double alpha) Prunes the original tree using the CART pruning scheme, given a cost-complexity parameter alpha.
int	prune(double[] alphas, double[] errors, weka.core.Instances test) Method for performing one fold in the cross-validation of minimal cost-complexity pruning.
void	setHeuristic(boolean value) Set if use heuristic search for nominal attributes in multi-class problems.
void	setMinNumObj(double value) Set minimal number of instances at the terminal nodes.
void	setNumFoldsPruning(int value) Set number of folds in internal cross-validation.
void	setOptions(String[] options) Parses a given list of options.
void	setSizePer(double value) Set training set size.
void	setUseOneSE(boolean value) Set if use the 1SE rule to choose final model.
void	setUsePrune(boolean value) Set if use minimal cost-complexity pruning.
String	sizePerTipText() Returns the tip text for this property
String	toString() Prints the decision tree using the protected toString method from below.
void	treeErrors() Updates the numIncorrectTree field for all nodes.
String	useOneSETipText() Returns the tip text for this property
String	usePruneTipText() Return the tip text for this property

Methods inherited from class weka.classifiers.RandomizableClassifier

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.AbstractClassifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object

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

Constructor Detail

SimpleCart

public SimpleCart()

Method Detail

globalInfo

public String globalInfo()

Return a description suitable for displaying in the explorer/experimenter.

Returns:

a description suitable for displaying in the explorer/experimenter

getTechnicalInformation

public weka.core.TechnicalInformation getTechnicalInformation()

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

Specified by:

getTechnicalInformation in interface weka.core.TechnicalInformationHandler

Returns:

the technical information about this class

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 data)
                     throws Exception

Build the classifier.

Specified by:

buildClassifier in interface weka.classifiers.Classifier

Parameters:

data - the training instances

Throws:

Exception - if something goes wrong

prune

public void prune(double alpha)
           throws Exception

Prunes the original tree using the CART pruning scheme, given a cost-complexity parameter alpha.

Parameters:

alpha - the cost-complexity parameter

Throws:

Exception - if something goes wrong

prune

public int prune(double[] alphas,
                 double[] errors,
                 weka.core.Instances test)
          throws Exception

Method for performing one fold in the cross-validation of minimal cost-complexity pruning. Generates a sequence of alpha-values with error estimates for the corresponding (partially pruned) trees, given the test set of that fold.

Parameters:

alphas - array to hold the generated alpha-values

errors - array to hold the corresponding error estimates

test - test set of that fold (to obtain error estimates)

Returns:

the iteration of the pruning

Throws:

Exception - if something goes wrong

modelErrors

public void modelErrors()
                 throws Exception

Updates the numIncorrectModel field for all nodes when subtree (to be pruned) is rooted. This is needed for calculating the alpha-values.

Throws:

Exception - if something goes wrong

treeErrors

public void treeErrors()
                throws Exception

Updates the numIncorrectTree field for all nodes. This is needed for calculating the alpha-values.

Throws:

Exception - if something goes wrong

calculateAlphas

public void calculateAlphas()
                     throws Exception

Updates the alpha field for all nodes.

Throws:

Exception - if something goes wrong

distributionForInstance

public double[] distributionForInstance(weka.core.Instance instance)
                                 throws Exception

Computes class probabilities for instance using the decision tree.

Specified by:

distributionForInstance in interface weka.classifiers.Classifier

Overrides:

distributionForInstance in class weka.classifiers.AbstractClassifier

Parameters:

instance - the instance for which class probabilities is to be computed

Returns:

the class probabilities for the given instance

Throws:

Exception - if something goes wrong

toString

public String toString()

Prints the decision tree using the protected toString method from below.

Overrides:

toString in class Object

Returns:

a textual description of the classifier

numNodes

public int numNodes()

Compute size of the tree.

Returns:

size of the tree

numInnerNodes

public int numInnerNodes()

Method to count the number of inner nodes in the tree.

Returns:

the number of inner nodes

numLeaves

public int numLeaves()

Compute number of leaf nodes.

Returns:

number of leaf nodes

listOptions

public Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.RandomizableClassifier

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:

 -S <num>
  Random number seed.
  (default 1)

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -M <min no>
  The minimal number of instances at the terminal nodes.
  (default 2)

 -N <num folds>
  The number of folds used in the minimal cost-complexity pruning.
  (default 5)

 -U
  Don't use the minimal cost-complexity pruning.
  (default yes).

 -H
  Don't use the heuristic method for binary split.
  (default true).

 -A
  Use 1 SE rule to make pruning decision.
  (default no).

 -C
  Percentage of training data size (0-1].
  (default 1).

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.RandomizableClassifier

Parameters:

options - the list of options as an array of strings

Throws:

Exception - if an options is not supported

getOptions

public String[] getOptions()

Gets the current settings of the classifier.

Specified by:

getOptions in interface weka.core.OptionHandler

Overrides:

getOptions in class weka.classifiers.RandomizableClassifier

Returns:

the current setting of the classifier

enumerateMeasures

public Enumeration enumerateMeasures()

Return an enumeration of the measure names.

Specified by:

enumerateMeasures in interface weka.core.AdditionalMeasureProducer

Returns:

an enumeration of the measure names

measureTreeSize

public double measureTreeSize()

Return number of tree size.

Returns:

number of tree size

getMeasure

public double getMeasure(String additionalMeasureName)

Returns the value of the named measure.

Specified by:

getMeasure in interface weka.core.AdditionalMeasureProducer

Parameters:

additionalMeasureName - the name of the measure to query for its value

Returns:

the value of the named measure

Throws:

IllegalArgumentException - if the named measure is not supported

minNumObjTipText

public String minNumObjTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setMinNumObj

public void setMinNumObj(double value)

Set minimal number of instances at the terminal nodes.

Parameters:

value - minimal number of instances at the terminal nodes

getMinNumObj

public double getMinNumObj()

Get minimal number of instances at the terminal nodes.

Returns:

minimal number of instances at the terminal nodes

numFoldsPruningTipText

public String numFoldsPruningTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setNumFoldsPruning

public void setNumFoldsPruning(int value)

Set number of folds in internal cross-validation.

Parameters:

value - number of folds in internal cross-validation.

getNumFoldsPruning

public int getNumFoldsPruning()

Set number of folds in internal cross-validation.

Returns:

number of folds in internal cross-validation.

usePruneTipText

public String usePruneTipText()

Return the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui.

setUsePrune

public void setUsePrune(boolean value)

Set if use minimal cost-complexity pruning.

Parameters:

value - if use minimal cost-complexity pruning

getUsePrune

public boolean getUsePrune()

Get if use minimal cost-complexity pruning.

Returns:

if use minimal cost-complexity pruning

heuristicTipText

public String heuristicTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui.

setHeuristic

public void setHeuristic(boolean value)

Set if use heuristic search for nominal attributes in multi-class problems.

Parameters:

value - if use heuristic search for nominal attributes in multi-class problems

getHeuristic

public boolean getHeuristic()

Get if use heuristic search for nominal attributes in multi-class problems.

Returns:

if use heuristic search for nominal attributes in multi-class problems

useOneSETipText

public String useOneSETipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui.

setUseOneSE

public void setUseOneSE(boolean value)

Set if use the 1SE rule to choose final model.

Parameters:

value - if use the 1SE rule to choose final model

getUseOneSE

public boolean getUseOneSE()

Get if use the 1SE rule to choose final model.

Returns:

if use the 1SE rule to choose final model

sizePerTipText

public String sizePerTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui.

setSizePer

public void setSizePer(double value)

Set training set size.

Parameters:

value - training set size

getSizePer

public double getSizePer()

Get training set size.

Returns:

training set size

getRevision

public 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(String[] args)

Main method.

Parameters:

args - the options for the classifier