LADTree (weka-stable 3.6.14 API)

java.lang.Object
- weka.classifiers.Classifier
- - weka.classifiers.trees.LADTree

All Implemented Interfaces:: Serializable, Cloneable, AdditionalMeasureProducer, CapabilitiesHandler, Drawable, OptionHandler, RevisionHandler, TechnicalInformationHandler

public class LADTree
extends Classifier
implements Drawable, AdditionalMeasureProducer, TechnicalInformationHandler

Class for generating a multi-class alternating decision tree using the LogitBoost strategy. For more info, see

Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall: Multiclass alternating decision trees. In: ECML, 161-172, 2001.

BibTeX:

 @inproceedings{Holmes2001,
    author = {Geoffrey Holmes and Bernhard Pfahringer and Richard Kirkby and Eibe Frank and Mark Hall},
    booktitle = {ECML},
    pages = {161-172},
    publisher = {Springer},
    title = {Multiclass alternating decision trees},
    year = {2001}
 }

Valid options are:

 -B <number of boosting iterations>
  Number of boosting iterations.
  (Default = 10)

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

Version:: $Revision: 10279 $
Author:: Richard Kirkby
See Also:: Serialized Form

Field Summary
- Fields inherited from interface weka.core.Drawable
  BayesNet, Newick, NOT_DRAWABLE, TREE

Constructor Summary

Constructors
Constructor and Description

LADTree()

Constructors
Constructor and Description
`LADTree()`

Method Summary

Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances instances)` Builds a classifier for a set of instances.
`double[]`	`distributionForInstance(Instance instance)` Returns the class probability distribution for an instance.
`void`	`done()`
`Enumeration`	`enumerateMeasures()` Returns an enumeration of the additional measure names.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`double`	`getMeasure(String additionalMeasureName)` Returns the value of the named measure.
`int`	`getNumOfBoostingIterations()` Gets the number of boosting iterations.
`String[]`	`getOptions()` Gets the current settings of ADTree.
`String`	`getRevision()` Returns the revision string.
`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.
`String`	`globalInfo()` Returns a string describing classifier
`String`	`graph()` Returns graph describing the tree.
`int`	`graphType()` Returns the type of graph this classifier represents.
`void`	`initClassifier(Instances instances)` Sets up the tree ready to be trained.
`String`	`legend()` Returns the legend of the tree, describing how results are to be interpreted.
`Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(String[] argv)` Main method for testing this class.
`double`	`measureExamplesCounted()` Returns the number of examples "counted".
`double`	`measureNodesExpanded()` Returns the number of nodes expanded.
`double`	`measureNumLeaves()` Calls measure function for leaf size.
`double`	`measureNumPredictionLeaves()` Calls measure function for leaf size.
`double`	`measureTreeSize()` Calls measure function for tree size.
`void`	`merge(LADTree mergeWith)` Merges two trees together.
`void`	`next(int iteration)`
`String`	`numOfBoostingIterationsTipText()`
`int`	`predictiveError(Instances test)`
`void`	`setNumOfBoostingIterations(int b)` Sets the number of boosting iterations.
`void`	`setOptions(String[] options)` Parses a given list of options.
`String`	`toString()` Returns a description of the classifier.

Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug

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

- Constructor Detail
  - LADTree
```
public LADTree()
```
- Method Detail
  - globalInfo
```
public String globalInfo()
```
    Returns a string describing classifier
    
    Returns:
    a description suitable for displaying in the explorer/experimenter gui
  - getTechnicalInformation
```
public 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 TechnicalInformationHandler
    
    Returns:
    the technical information about this class
  - initClassifier
```
public void initClassifier(Instances instances)
                    throws Exception
```
    Sets up the tree ready to be trained.
    
    Parameters:
    instances - the instances to train the tree with
    
    Throws:
    
    Exception - if training data is unsuitable
  - next
```
public void next(int iteration)
          throws Exception
```
    Throws:
    
    Exception
  - done
```
public void done()
          throws Exception
```
    Throws:
    
    Exception
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
```
    Returns the class probability distribution for an instance.
    
    Overrides:
    
    distributionForInstance in class Classifier
    
    Parameters:
    instance - the instance to be classified
    
    Returns:
    the distribution the tree generates for the instance
  - toString
```
public String toString()
```
    Returns a description of the classifier.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    a string containing a description of the classifier
  - graph
```
public String graph()
             throws Exception
```
    Returns graph describing the tree.
    
    Specified by:
    
    graph in interface Drawable
    
    Returns:
    the graph of the tree in dotty format
    
    Throws:
    
    Exception - if something goes wrong
  - legend
```
public String legend()
```
    Returns the legend of the tree, describing how results are to be interpreted.
    
    Returns:
    a string containing the legend of the classifier
  - numOfBoostingIterationsTipText
```
public String numOfBoostingIterationsTipText()
```
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNumOfBoostingIterations
```
public int getNumOfBoostingIterations()
```
    Gets the number of boosting iterations.
    
    Returns:
    the number of boosting iterations
  - setNumOfBoostingIterations
```
public void setNumOfBoostingIterations(int b)
```
    Sets the number of boosting iterations.
    
    Parameters:
    b - the number of boosting iterations to use
  - listOptions
```
public Enumeration listOptions()
```
    Returns an enumeration describing the available options.
    
    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:
    -B num
    Set the number of boosting iterations (default 10)
    
    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 ADTree.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class Classifier
    
    Returns:
    an array of strings suitable for passing to setOptions()
  - measureTreeSize
```
public double measureTreeSize()
```
    Calls measure function for tree size.
    
    Returns:
    the tree size
  - measureNumLeaves
```
public double measureNumLeaves()
```
    Calls measure function for leaf size.
    
    Returns:
    the leaf size
  - measureNumPredictionLeaves
```
public double measureNumPredictionLeaves()
```
    Calls measure function for leaf size.
    
    Returns:
    the leaf size
  - measureNodesExpanded
```
public double measureNodesExpanded()
```
    Returns the number of nodes expanded.
    
    Returns:
    the number of nodes expanded during search
  - measureExamplesCounted
```
public double measureExamplesCounted()
```
    Returns the number of examples "counted".
    
    Returns:
    the number of nodes processed during search
  - enumerateMeasures
```
public Enumeration enumerateMeasures()
```
    Returns an enumeration of the additional measure names.
    
    Specified by:
    
    enumerateMeasures in interface AdditionalMeasureProducer
    
    Returns:
    an enumeration of the measure names
  - getMeasure
```
public double getMeasure(String additionalMeasureName)
```
    Returns the value of the named measure.
    
    Specified by:
    
    getMeasure in interface AdditionalMeasureProducer
    
    Parameters:
    measureName - 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
  - buildClassifier
```
public void buildClassifier(Instances instances)
                     throws Exception
```
    Builds a classifier for a set of instances.
    
    Specified by:
    
    buildClassifier in class Classifier
    
    Parameters:
    instances - the instances to train the classifier with
    
    Throws:
    
    Exception - if something goes wrong
  - predictiveError
```
public int predictiveError(Instances test)
```
  - merge
```
public void merge(LADTree mergeWith)
           throws Exception
```
    Merges two trees together. Modifies the tree being acted on, leaving tree passed as a parameter untouched (cloned). Does not check to see whether training instances are compatible - strange things could occur if they are not.
    
    Parameters:
    mergeWith - the tree to merge with
    
    Throws:
    
    Exception - if merge could not be performed
  - graphType
```
public int graphType()
```
    Returns the type of graph this classifier represents.
    
    Specified by:
    
    graphType in interface Drawable
    
    Returns:
    Drawable.TREE
  - getRevision
```
public String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class Classifier
    
    Returns:
    the revision
  - 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:
    Capabilities
  - main
```
public static void main(String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    argv - the options

Class LADTree

Field Summary

Fields inherited from interface weka.core.Drawable

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Constructor Detail

LADTree

Method Detail

globalInfo

getTechnicalInformation

initClassifier

next

done

distributionForInstance

toString

graph

legend

numOfBoostingIterationsTipText

getNumOfBoostingIterations

setNumOfBoostingIterations

listOptions

setOptions

getOptions

measureTreeSize

measureNumLeaves

measureNumPredictionLeaves

measureNodesExpanded

measureExamplesCounted

enumerateMeasures

getMeasure

buildClassifier

predictiveError

merge

graphType

getRevision

getCapabilities

main