Package weka.classifiers.trees
Class ADTree
- java.lang.Object
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- weka.classifiers.Classifier
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- weka.classifiers.trees.ADTree
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- 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
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Field Summary
Fields Modifier and Type Field Description static int
SEARCHPATH_ALL
search mode: Expand all pathsstatic int
SEARCHPATH_HEAVIEST
search mode: Expand the heaviest pathstatic int
SEARCHPATH_RANDOM
search mode: Expand a random pathstatic int
SEARCHPATH_ZPURE
search mode: Expand the best z-pure pathstatic Tag[]
TAGS_SEARCHPATH
The search modes-
Fields inherited from interface weka.core.Drawable
BayesNet, Newick, NOT_DRAWABLE, TREE
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Constructor Summary
Constructors Constructor Description ADTree()
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method 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 classifierjava.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
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Field Detail
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SEARCHPATH_ALL
public static final int SEARCHPATH_ALL
search mode: Expand all paths- See Also:
- Constant Field Values
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SEARCHPATH_HEAVIEST
public static final int SEARCHPATH_HEAVIEST
search mode: Expand the heaviest path- See Also:
- Constant Field Values
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SEARCHPATH_ZPURE
public static final int SEARCHPATH_ZPURE
search mode: Expand the best z-pure path- See Also:
- Constant Field Values
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SEARCHPATH_RANDOM
public static final int SEARCHPATH_RANDOM
search mode: Expand a random path- See Also:
- Constant Field Values
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TAGS_SEARCHPATH
public static final Tag[] TAGS_SEARCHPATH
The search modes
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Method Detail
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globalInfo
public java.lang.String globalInfo()
Returns a string describing classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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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 interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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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 interfaceIterativeClassifier
- Parameters:
instances
- the instances to train the tree with- Throws:
java.lang.Exception
- if training data is unsuitable
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next
public void next(int iteration) throws java.lang.Exception
Performs one iteration.- Specified by:
next
in interfaceIterativeClassifier
- Parameters:
iteration
- the index of the current iteration (0-based)- Throws:
java.lang.Exception
- if this iteration fails
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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
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distributionForInstance
public double[] distributionForInstance(Instance instance)
Returns the class probability distribution for an instance.- Overrides:
distributionForInstance
in classClassifier
- Parameters:
instance
- the instance to be classified- Returns:
- the distribution the tree generates for the instance
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toString
public java.lang.String toString()
Returns a description of the classifier.- Overrides:
toString
in classjava.lang.Object
- Returns:
- a string containing a description of the classifier
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graphType
public int graphType()
Returns the type of graph this classifier represents.
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graph
public java.lang.String graph() throws java.lang.Exception
Returns graph describing the tree.
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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
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numOfBoostingIterationsTipText
public java.lang.String numOfBoostingIterationsTipText()
- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getNumOfBoostingIterations
public int getNumOfBoostingIterations()
Gets the number of boosting iterations.- Returns:
- the number of boosting iterations
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setNumOfBoostingIterations
public void setNumOfBoostingIterations(int b)
Sets the number of boosting iterations.- Parameters:
b
- the number of boosting iterations to use
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searchPathTipText
public java.lang.String searchPathTipText()
- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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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
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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
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randomSeedTipText
public java.lang.String randomSeedTipText()
- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getRandomSeed
public int getRandomSeed()
Gets random seed for a random walk.- Returns:
- the random seed
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setRandomSeed
public void setRandomSeed(int seed)
Sets random seed for a random walk.- Parameters:
seed
- the random seed
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saveInstanceDataTipText
public java.lang.String saveInstanceDataTipText()
- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getSaveInstanceData
public boolean getSaveInstanceData()
Gets whether the tree is to save instance data.- Returns:
- the random seed
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setSaveInstanceData
public void setSaveInstanceData(boolean v)
Sets whether the tree is to save instance data.- Parameters:
v
- true then the tree saves instance data
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listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options..- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classClassifier
- Returns:
- an enumeration of all the available options.
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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 interfaceOptionHandler
- Overrides:
setOptions
in classClassifier
- Parameters:
options
- the list of options as an array of strings- Throws:
java.lang.Exception
- if an option is not supported
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getOptions
public java.lang.String[] getOptions()
Gets the current settings of ADTree.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classClassifier
- Returns:
- an array of strings suitable for passing to setOptions()
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measureTreeSize
public double measureTreeSize()
Calls measure function for tree size - the total number of nodes.- Returns:
- the tree size
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measureNumLeaves
public double measureNumLeaves()
Calls measure function for leaf size - the number of prediction nodes.- Returns:
- the leaf size
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measureNumPredictionLeaves
public double measureNumPredictionLeaves()
Calls measure function for prediction leaf size - the number of prediction nodes without children.- Returns:
- the leaf size
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measureNodesExpanded
public double measureNodesExpanded()
Returns the number of nodes expanded.- Returns:
- the number of nodes expanded during search
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measureExamplesProcessed
public double measureExamplesProcessed()
Returns the number of examples "counted".- Returns:
- the number of nodes processed during search
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enumerateMeasures
public java.util.Enumeration enumerateMeasures()
Returns an enumeration of the additional measure names.- Specified by:
enumerateMeasures
in interfaceAdditionalMeasureProducer
- Returns:
- an enumeration of the measure names
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getMeasure
public double getMeasure(java.lang.String additionalMeasureName)
Returns the value of the named measure.- Specified by:
getMeasure
in interfaceAdditionalMeasureProducer
- 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
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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
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getCapabilities
public Capabilities getCapabilities()
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classClassifier
- Returns:
- the capabilities of this classifier
- See Also:
Capabilities
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buildClassifier
public void buildClassifier(Instances instances) throws java.lang.Exception
Builds a classifier for a set of instances.- Specified by:
buildClassifier
in classClassifier
- Parameters:
instances
- the instances to train the classifier with- Throws:
java.lang.Exception
- if something goes wrong
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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 interfaceIterativeClassifier
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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 interfaceIterativeClassifier
- Returns:
- the clone
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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
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
public static void main(java.lang.String[] argv)
Main method for testing this class.- Parameters:
argv
- the options
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