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Class weka.classifiers.adtree.ADTree

java.lang.Object
   |
   +----weka.classifiers.Classifier
           |
           +----weka.classifiers.DistributionClassifier
                   |
                   +----weka.classifiers.adtree.ADTree

public class ADTree
extends DistributionClassifier
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

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

Variable Index

 o SEARCHPATH_ALL
The search modes
 o SEARCHPATH_HEAVIEST
 o SEARCHPATH_RANDOM
 o SEARCHPATH_ZPURE
 o TAGS_SEARCHPATH

Constructor Index

 o ADTree()

Method Index

 o boost()
Performs a single boosting iteration, using two-class optimized method.
 o buildClassifier(Instances)
Builds a classifier for a set of instances.
 o clone()
Creates a clone that is identical to the current tree, but is independent.
 o distributionForInstance(Instance)
Returns the class probability distribution for an instance.
 o done()
Frees memory that is no longer needed for a final model - will no longer be able to increment the classifier after calling this.
 o enumerateMeasures()
Returns an enumeration of the additional measure names.
 o getMeasure(String)
Returns the value of the named measure.
 o getNumOfBoostingIterations()
Gets the number of boosting iterations.
 o getOptions()
Gets the current settings of ADTree.
 o getRandomSeed()
Gets random seed for a random walk.
 o getSaveInstanceData()
Gets whether the tree is to save instance data.
 o getSearchPath()
Gets the method of searching the tree for a new insertion.
 o globalInfo()
 o graph()
Returns graph describing the tree.
 o initClassifier(Instances)
Sets up the tree ready to be trained, using two-class optimized method.
 o legend()
Returns the legend of the tree, describing how results are to be interpreted.
 o listOptions()
Returns an enumeration describing the available options.
 o main(String[])
Main method for testing this class.
 o measureExamplesProcessed()
Returns the number of examples "counted".
 o measureNodesExpanded()
Returns the number of nodes expanded.
 o measureNumLeaves()
Calls measure function for leaf size - the number of prediction nodes.
 o measureNumPredictionLeaves()
Calls measure function for prediction leaf size - the number of prediction nodes without children.
 o measureTreeSize()
Calls measure function for tree size - the total number of nodes.
 o merge(ADTree)
Merges two trees together.
 o next(int)
Performs one iteration.
 o 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.
 o numOfBoostingIterationsTipText()
 o randomSeedTipText()
 o saveInstanceDataTipText()
 o searchPathTipText()
 o setNumOfBoostingIterations(int)
Sets the number of boosting iterations.
 o setOptions(String[])
Parses a given list of options.
 o setRandomSeed(int)
Sets random seed for a random walk.
 o setSaveInstanceData(boolean)
Sets whether the tree is to save instance data.
 o setSearchPath(SelectedTag)
Sets the method of searching the tree for a new insertion.
 o toString()
Returns a description of the classifier.

Variables

 o SEARCHPATH_ALL
 public static final int SEARCHPATH_ALL
The search modes

 o SEARCHPATH_HEAVIEST
 public static final int SEARCHPATH_HEAVIEST
 o SEARCHPATH_ZPURE
 public static final int SEARCHPATH_ZPURE
 o SEARCHPATH_RANDOM
 public static final int SEARCHPATH_RANDOM
 o TAGS_SEARCHPATH
 public static final Tag TAGS_SEARCHPATH[]

Constructors

 o ADTree
 public ADTree()

Methods

 o initClassifier
 public void initClassifier(Instances instances) throws Exception
Sets up the tree ready to be trained, using two-class optimized method.

Parameters:
instances - the instances to train the tree with
Throws: Exception
if training data is unsuitable
 o next
 public void next(int iteration) throws Exception
Performs one iteration.

Parameters:
iteration - the index of the current iteration (0-based)
Throws: Exception
if this iteration fails
 o boost
 public void boost() throws 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: Exception
if try to boost without setting up tree first or there are no instances to train with
 o distributionForInstance
 public double[] distributionForInstance(Instance instance)
Returns the class probability distribution for an instance.

Parameters:
instance - the instance to be classified
Returns:
the distribution the tree generates for the instance
Overrides:
distributionForInstance in class DistributionClassifier
 o toString
 public String toString()
Returns a description of the classifier.

Returns:
a string containing a description of the classifier
Overrides:
toString in class Object
 o graph
 public String graph() throws Exception
Returns graph describing the tree.

Returns:
the graph of the tree in dotty format
Throws: Exception
if something goes wrong
 o 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
 o globalInfo
 public String globalInfo()
Returns:
a description of the classifier suitable for displaying in the explorer/experimenter gui
 o numOfBoostingIterationsTipText
 public String numOfBoostingIterationsTipText()
Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui
 o getNumOfBoostingIterations
 public int getNumOfBoostingIterations()
Gets the number of boosting iterations.

Returns:
the number of boosting iterations
 o setNumOfBoostingIterations
 public void setNumOfBoostingIterations(int b)
Sets the number of boosting iterations.

Parameters:
b - the number of boosting iterations to use
 o searchPathTipText
 public String searchPathTipText()
Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui
 o 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
 o 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
 o randomSeedTipText
 public String randomSeedTipText()
Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui
 o getRandomSeed
 public int getRandomSeed()
Gets random seed for a random walk.

Returns:
the random seed
 o setRandomSeed
 public void setRandomSeed(int seed)
Sets random seed for a random walk.

Parameters:
s - the random seed
 o saveInstanceDataTipText
 public String saveInstanceDataTipText()
Returns:
tip text for this property suitable for displaying in the explorer/experimenter gui
 o getSaveInstanceData
 public boolean getSaveInstanceData()
Gets whether the tree is to save instance data.

Returns:
the random seed
 o setSaveInstanceData
 public void setSaveInstanceData(boolean v)
Sets whether the tree is to save instance data.

Parameters:
s - the random seed
 o listOptions
 public Enumeration listOptions()
Returns an enumeration describing the available options.

Returns:
an enumeration of all the available options
 o 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)

-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

Parameters:
options - the list of options as an array of strings
Throws: Exception
if an option is not supported
 o getOptions
 public String[] getOptions()
Gets the current settings of ADTree.

Returns:
an array of strings suitable for passing to setOptions()
 o measureTreeSize
 public double measureTreeSize()
Calls measure function for tree size - the total number of nodes.

Returns:
the tree size
 o measureNumLeaves
 public double measureNumLeaves()
Calls measure function for leaf size - the number of prediction nodes.

Returns:
the leaf size
 o measureNumPredictionLeaves
 public double measureNumPredictionLeaves()
Calls measure function for prediction leaf size - the number of prediction nodes without children.

Returns:
the leaf size
 o measureNodesExpanded
 public double measureNodesExpanded()
Returns the number of nodes expanded.

Returns:
the number of nodes expanded during search
 o measureExamplesProcessed
 public double measureExamplesProcessed()
Returns the number of examples "counted".

Returns:
the number of nodes processed during search
 o enumerateMeasures
 public Enumeration enumerateMeasures()
Returns an enumeration of the additional measure names.

Returns:
an enumeration of the measure names
 o getMeasure
 public double getMeasure(String additionalMeasureName)
Returns the value of the named measure.

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
 o 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
 o buildClassifier
 public void buildClassifier(Instances instances) throws Exception
Builds a classifier for a set of instances.

Parameters:
instances - the instances to train the classifier with
Throws: Exception
if something goes wrong
Overrides:
buildClassifier in class Classifier
 o 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.

 o clone
 public 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.

Returns:
the clone
Overrides:
clone in class Object
 o merge
 public void merge(ADTree 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
 o main
 public static void main(String argv[])
Main method for testing this class.

Parameters:
argv - the options

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