SimpleCart

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

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.AdditionalMeasureProducer, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, 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: 10490 $
Author:: Haijian Shi (hs69@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

SimpleCart()

Constructors
Constructor and Description
`SimpleCart()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`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.
`java.util.Enumeration<java.lang.String>`	`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(java.lang.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.
`java.lang.String[]`	`getOptions()` Gets the current settings of the classifier.
`java.lang.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.
`java.lang.String`	`globalInfo()` Return a description suitable for displaying in the explorer/experimenter.
`java.lang.String`	`heuristicTipText()` Returns the tip text for this property
`java.util.Enumeration<weka.core.Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] args)` Main method.
`double`	`measureTreeSize()` Return number of tree size.
`java.lang.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.
`java.lang.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(java.lang.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.
`java.lang.String`	`sizePerTipText()` Returns the tip text for this property
`java.lang.String`	`toString()` Prints the decision tree using the protected toString method from below.
`void`	`treeErrors()` Updates the numIncorrectTree field for all nodes.
`java.lang.String`	`useOneSETipText()` Returns the tip text for this property
`java.lang.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
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
  - SimpleCart
```
public SimpleCart()
```
- Method Detail
  - globalInfo
```
public java.lang.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 java.lang.Exception
```
    Build the classifier.
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Parameters:
    
    data - the training instances
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - prune
```
public void prune(double alpha)
           throws java.lang.Exception
```
    Prunes the original tree using the CART pruning scheme, given a cost-complexity parameter alpha.
    
    Parameters:
    
    alpha - the cost-complexity parameter
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - prune
```
public int prune(double[] alphas,
                 double[] errors,
                 weka.core.Instances test)
          throws java.lang.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:
    
    java.lang.Exception - if something goes wrong
  - modelErrors
```
public void modelErrors()
                 throws java.lang.Exception
```
    Updates the numIncorrectModel field for all nodes when subtree (to be pruned) is rooted. This is needed for calculating the alpha-values.
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - treeErrors
```
public void treeErrors()
                throws java.lang.Exception
```
    Updates the numIncorrectTree field for all nodes. This is needed for calculating the alpha-values.
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - calculateAlphas
```
public void calculateAlphas()
                     throws java.lang.Exception
```
    Updates the alpha field for all nodes.
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - distributionForInstance
```
public double[] distributionForInstance(weka.core.Instance instance)
                                 throws java.lang.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:
    
    java.lang.Exception - if something goes wrong
  - toString
```
public java.lang.String toString()
```
    Prints the decision tree using the protected toString method from below.
    
    Overrides:
    
    toString in class java.lang.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 java.util.Enumeration<weka.core.Option> 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(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses a given list of options.
    Valid options are:
```
 -S <num>
  Random number seed.
  (default 1)
 
```
```
 -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:
    
    java.lang.Exception - if an options 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.RandomizableClassifier
    
    Returns:
    
    the current setting of the classifier
  - enumerateMeasures
```
public java.util.Enumeration<java.lang.String> 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(java.lang.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:
    
    java.lang.IllegalArgumentException - if the named measure is not supported
  - minNumObjTipText
```
public java.lang.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 java.lang.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 java.lang.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 java.lang.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 java.lang.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 java.lang.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 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 SimpleCart

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Constructor Detail

SimpleCart

Method Detail

globalInfo

getTechnicalInformation

getCapabilities

buildClassifier

prune

prune

modelErrors

treeErrors

calculateAlphas

distributionForInstance

toString

numNodes

numInnerNodes

numLeaves

listOptions

setOptions

getOptions

enumerateMeasures

measureTreeSize

getMeasure

minNumObjTipText

setMinNumObj

getMinNumObj

numFoldsPruningTipText

setNumFoldsPruning

getNumFoldsPruning

usePruneTipText

setUsePrune

getUsePrune

heuristicTipText

setHeuristic

getHeuristic

useOneSETipText

setUseOneSE

getUseOneSE

sizePerTipText

setSizePer

getSizePer

getRevision

main