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## weka.classifiers.trees
Class ADTree

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

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable, IterativeClassifier, AdditionalMeasureProducer, Drawable, OptionHandler, WeightedInstancesHandler

    public class ADTree

extends Classifier
implements OptionHandler, Drawable, AdditionalMeasureProducer, WeightedInstancesHandler, IterativeClassifier

Class for generating an alternating decision tree. The basic algorithm is based on:

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

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.

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

Version:
  $Revision: 1.2 $
Author:
  Richard Kirkby (rkirkby@cs.waikato.ac.nz), Bernhard Pfahringer (bernhard@cs.waikato.ac.nz)
See Also:
  Serialized Form

Field Summary
`static int` | `SEARCHPATH_ALL` The search modes
`static int` | `SEARCHPATH_HEAVIEST`
`static int` | `SEARCHPATH_RANDOM`
`static int` | `SEARCHPATH_ZPURE`
`static Tag[]` | `TAGS_SEARCHPATH`

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

Constructor Summary
`ADTree()`

Method Summary
`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.
`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.
`boolean` | `getSaveInstanceData()` Gets whether the tree is to save instance data.
`SelectedTag` | `getSearchPath()` Gets the method of searching the tree for a new insertion.
`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

  The search modes

  See Also:
    Constant Field Values

### SEARCHPATH_HEAVIEST

    public static final int SEARCHPATH_HEAVIEST

See Also:
  Constant Field Values

### SEARCHPATH_ZPURE

    public static final int SEARCHPATH_ZPURE

See Also:
  Constant Field Values

### SEARCHPATH_RANDOM

    public static final int SEARCHPATH_RANDOM

See Also:
  Constant Field Values

### TAGS_SEARCHPATH

    public static final Tag[] TAGS_SEARCHPATH

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

### 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:
    `s` - 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:
    `s` - the random seed

### 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:
    `measureName` - 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

### 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

### main

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

  Main method for testing this class.

  Parameters:
    `argv` - the options

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