ADTree

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

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, IterativeClassifier, AdditionalMeasureProducer, CapabilitiesHandler, Drawable, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler
```
public class ADTree
extends Classifier
implements OptionHandler, Drawable, AdditionalMeasureProducer, WeightedInstancesHandler, IterativeClassifier, TechnicalInformationHandler
```
Class for generating an alternating decision tree. The basic algorithm is based on:

Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, 124-133, 1999.

This version currently only supports two-class problems. The number of boosting iterations needs to be manually tuned to suit the dataset and the desired complexity/accuracy tradeoff. Induction of the trees has been optimized, and heuristic search methods have been introduced to speed learning.
BibTeX:
```
 @inproceedings{Freund1999,
    address = {Bled, Slovenia},
    author = {Freund, Y. and Mason, L.},
    booktitle = {Proceeding of the Sixteenth International Conference on Machine Learning},
    pages = {124-133},
    title = {The alternating decision tree learning algorithm},
    year = {1999}
 }
 
```
Valid options are:
```
 -B <number of boosting iterations>
  Number of boosting iterations.
  (Default = 10)
```
```
 -E <-3|-2|-1|>=0>
  Expand nodes: -3(all), -2(weight), -1(z_pure), >=0 seed for random walk
  (Default = -3)
```
```
 -D
  Save the instance data with the model
```
Version:

$Revision: 10290 $

Author:

Richard Kirkby (rkirkby@cs.waikato.ac.nz), Bernhard Pfahringer (bernhard@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`SEARCHPATH_ALL` search mode: Expand all paths
`static int`	`SEARCHPATH_HEAVIEST` search mode: Expand the heaviest path
`static int`	`SEARCHPATH_RANDOM` search mode: Expand a random path
`static int`	`SEARCHPATH_ZPURE` search mode: Expand the best z-pure path
`static Tag[]`	`TAGS_SEARCHPATH` The search modes

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

Constructor Summary

Constructors
Constructor and Description

ADTree()

Constructors
Constructor and Description
`ADTree()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`boost()` Performs a single boosting iteration, using two-class optimized method.
`void`	`buildClassifier(Instances instances)` Builds a classifier for a set of instances.
`java.lang.Object`	`clone()` Creates a clone that is identical to the current tree, but is independent.
`double[]`	`distributionForInstance(Instance instance)` Returns the class probability distribution for an instance.
`void`	`done()` Frees memory that is no longer needed for a final model - will no longer be able to increment the classifier after calling this.
`java.util.Enumeration`	`enumerateMeasures()` Returns an enumeration of the additional measure names.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`double`	`getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure.
`int`	`getNumOfBoostingIterations()` Gets the number of boosting iterations.
`java.lang.String[]`	`getOptions()` Gets the current settings of ADTree.
`int`	`getRandomSeed()` Gets random seed for a random walk.
`java.lang.String`	`getRevision()` Returns the revision string.
`boolean`	`getSaveInstanceData()` Gets whether the tree is to save instance data.
`SelectedTag`	`getSearchPath()` Gets the method of searching the tree for a new insertion.
`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.
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`java.lang.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, using two-class optimized method.
`java.lang.String`	`legend()` Returns the legend of the tree, describing how results are to be interpreted.
`java.util.Enumeration`	`listOptions()` Returns an enumeration describing the available options..
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`double`	`measureExamplesProcessed()` Returns the number of examples "counted".
`double`	`measureNodesExpanded()` Returns the number of nodes expanded.
`double`	`measureNumLeaves()` Calls measure function for leaf size - the number of prediction nodes.
`double`	`measureNumPredictionLeaves()` Calls measure function for prediction leaf size - the number of prediction nodes without children.
`double`	`measureTreeSize()` Calls measure function for tree size - the total number of nodes.
`void`	`merge(ADTree mergeWith)` Merges two trees together.
`void`	`next(int iteration)` Performs one iteration.
`int`	`nextSplitAddedOrder()` Returns the next number in the order that splitter nodes have been added to the tree, and records that a new splitter has been added.
`java.lang.String`	`numOfBoostingIterationsTipText()`
`java.lang.String`	`randomSeedTipText()`
`java.lang.String`	`saveInstanceDataTipText()`
`java.lang.String`	`searchPathTipText()`
`void`	`setNumOfBoostingIterations(int b)` Sets the number of boosting iterations.
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setRandomSeed(int seed)` Sets random seed for a random walk.
`void`	`setSaveInstanceData(boolean v)` Sets whether the tree is to save instance data.
`void`	`setSearchPath(SelectedTag newMethod)` Sets the method of searching the tree for a new insertion.
`java.lang.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

- Field Detail
  - SEARCHPATH_ALL
```
public static final int SEARCHPATH_ALL
```
    search mode: Expand all paths
    
    See Also:
    
    Constant Field Values
  - SEARCHPATH_HEAVIEST
```
public static final int SEARCHPATH_HEAVIEST
```
    search mode: Expand the heaviest path
    
    See Also:
    
    Constant Field Values
  - SEARCHPATH_ZPURE
```
public static final int SEARCHPATH_ZPURE
```
    search mode: Expand the best z-pure path
    
    See Also:
    
    Constant Field Values
  - SEARCHPATH_RANDOM
```
public static final int SEARCHPATH_RANDOM
```
    search mode: Expand a random path
    
    See Also:
    
    Constant Field Values
  - TAGS_SEARCHPATH
```
public static final Tag[] TAGS_SEARCHPATH
```
    The search modes
- Constructor Detail
  - ADTree
```
public ADTree()
```
- Method Detail
  - globalInfo
```
public java.lang.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 java.lang.Exception
```
    Sets up the tree ready to be trained, using two-class optimized method.
    
    Specified by:
    
    initClassifier in interface IterativeClassifier
    
    Parameters:
    
    instances - the instances to train the tree with
    
    Throws:
    
    java.lang.Exception - if training data is unsuitable
  - next
```
public void next(int iteration)
          throws java.lang.Exception
```
    Performs one iteration.
    
    Specified by:
    
    next in interface IterativeClassifier
    
    Parameters:
    
    iteration - the index of the current iteration (0-based)
    
    Throws:
    
    java.lang.Exception - if this iteration fails
  - boost
```
public void boost()
           throws java.lang.Exception
```
    Performs a single boosting iteration, using two-class optimized method. Will add a new splitter node and two prediction nodes to the tree (unless merging takes place).
    
    Throws:
    
    java.lang.Exception - if try to boost without setting up tree first or there are no instances to train with
  - 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 java.lang.String toString()
```
    Returns a description of the classifier.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a string containing a description of the classifier
  - graphType
```
public int graphType()
```
    Returns the type of graph this classifier represents.
    
    Specified by:
    
    graphType in interface Drawable
    
    Returns:
    
    Drawable.TREE
  - graph
```
public java.lang.String graph()
                       throws java.lang.Exception
```
    Returns graph describing the tree.
    
    Specified by:
    
    graph in interface Drawable
    
    Returns:
    
    the graph of the tree in dotty format
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - legend
```
public java.lang.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 java.lang.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
  - searchPathTipText
```
public java.lang.String searchPathTipText()
```
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getSearchPath
```
public SelectedTag getSearchPath()
```
    Gets the method of searching the tree for a new insertion. Will be one of SEARCHPATH_ALL, SEARCHPATH_HEAVIEST, SEARCHPATH_ZPURE, SEARCHPATH_RANDOM.
    
    Returns:
    
    the tree searching mode
  - setSearchPath
```
public void setSearchPath(SelectedTag newMethod)
```
    Sets the method of searching the tree for a new insertion. Will be one of SEARCHPATH_ALL, SEARCHPATH_HEAVIEST, SEARCHPATH_ZPURE, SEARCHPATH_RANDOM.
    
    Parameters:
    
    newMethod - the new tree searching mode
  - randomSeedTipText
```
public java.lang.String randomSeedTipText()
```
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getRandomSeed
```
public int getRandomSeed()
```
    Gets random seed for a random walk.
    
    Returns:
    
    the random seed
  - setRandomSeed
```
public void setRandomSeed(int seed)
```
    Sets random seed for a random walk.
    
    Parameters:
    
    seed - the random seed
  - saveInstanceDataTipText
```
public java.lang.String saveInstanceDataTipText()
```
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getSaveInstanceData
```
public boolean getSaveInstanceData()
```
    Gets whether the tree is to save instance data.
    
    Returns:
    
    the random seed
  - setSaveInstanceData
```
public void setSaveInstanceData(boolean v)
```
    Sets whether the tree is to save instance data.
    
    Parameters:
    
    v - true then the tree saves instance data
  - listOptions
```
public java.util.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(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses a given list of options. Valid options are:
    -B num
    Set the number of boosting iterations (default 10)
    -E num
    Set the nodes to expand: -3(all), -2(weight), -1(z_pure), >=0 seed for random walk (default -3)
    -D
    Save the instance data with the model
    
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class Classifier
    
    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 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 - the total number of nodes.
    
    Returns:
    
    the tree size
  - measureNumLeaves
```
public double measureNumLeaves()
```
    Calls measure function for leaf size - the number of prediction nodes.
    
    Returns:
    
    the leaf size
  - measureNumPredictionLeaves
```
public double measureNumPredictionLeaves()
```
    Calls measure function for prediction leaf size - the number of prediction nodes without children.
    
    Returns:
    
    the leaf size
  - measureNodesExpanded
```
public double measureNodesExpanded()
```
    Returns the number of nodes expanded.
    
    Returns:
    
    the number of nodes expanded during search
  - measureExamplesProcessed
```
public double measureExamplesProcessed()
```
    Returns the number of examples "counted".
    
    Returns:
    
    the number of nodes processed during search
  - enumerateMeasures
```
public java.util.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(java.lang.String additionalMeasureName)
```
    Returns the value of the named measure.
    
    Specified by:
    
    getMeasure in interface 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
  - nextSplitAddedOrder
```
public int nextSplitAddedOrder()
```
    Returns the next number in the order that splitter nodes have been added to the tree, and records that a new splitter has been added.
    
    Returns:
    
    the next number in the order
  - 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
  - buildClassifier
```
public void buildClassifier(Instances instances)
                     throws java.lang.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:
    
    java.lang.Exception - if something goes wrong
  - done
```
public void done()
```
    Frees memory that is no longer needed for a final model - will no longer be able to increment the classifier after calling this.
    
    Specified by:
    
    done in interface IterativeClassifier
  - clone
```
public java.lang.Object clone()
```
    Creates a clone that is identical to the current tree, but is independent. Deep copies the essential elements such as the tree nodes, and the instances (because the weights change.) Reference copies several elements such as the potential splitter sets, assuming that such elements should never differ between clones.
    
    Specified by:
    
    clone in interface IterativeClassifier
    
    Overrides:
    
    clone in class java.lang.Object
    
    Returns:
    
    the clone
  - merge
```
public void merge(ADTree mergeWith)
           throws java.lang.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:
    
    java.lang.Exception - if merge could not be performed
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class Classifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - the options

Class ADTree

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

Field Detail

SEARCHPATH_ALL

SEARCHPATH_HEAVIEST

SEARCHPATH_ZPURE

SEARCHPATH_RANDOM

TAGS_SEARCHPATH

Constructor Detail

ADTree

Method Detail

globalInfo

getTechnicalInformation

initClassifier

next

boost

distributionForInstance

toString

graphType

graph

legend

numOfBoostingIterationsTipText

getNumOfBoostingIterations

setNumOfBoostingIterations

searchPathTipText

getSearchPath

setSearchPath

randomSeedTipText

getRandomSeed

setRandomSeed

saveInstanceDataTipText

getSaveInstanceData

setSaveInstanceData

listOptions

setOptions

getOptions

measureTreeSize

measureNumLeaves

measureNumPredictionLeaves

measureNodesExpanded

measureExamplesProcessed

enumerateMeasures

getMeasure

nextSplitAddedOrder

getCapabilities

buildClassifier

done

clone

merge

getRevision

main