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Class weka.classifiers.adtree.ADTree
java.lang.Object
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+----weka.classifiers.Classifier
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+----weka.classifiers.DistributionClassifier
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+----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
- Version:
- $Revision: 1.1.2.2 $
- Author:
- Richard Kirkby (rkirkby@cs.waikato.ac.nz)
- Author:
- Bernhard Pfahringer (bernhard@cs.waikato.ac.nz)
SEARCHPATH_ALL- The search modes
SEARCHPATH_HEAVIEST-
SEARCHPATH_RANDOM-
SEARCHPATH_ZPURE-
TAGS_SEARCHPATH-
ADTree()
-
boost()
- Performs a single boosting iteration, using two-class optimized method.
buildClassifier(Instances)
- Builds a classifier for a set of instances.
clone()
- Creates a clone that is identical to the current tree, but is independent.
distributionForInstance(Instance)
- Returns the class probability distribution for an instance.
done()
- Frees memory that is no longer needed for a final model - will no longer be able
to increment the classifier after calling this.
enumerateMeasures()
- Returns an enumeration of the additional measure names.
getMeasure(String)
- Returns the value of the named measure.
getNumOfBoostingIterations()
- Gets the number of boosting iterations.
getOptions()
- Gets the current settings of ADTree.
getRandomSeed()
- Gets random seed for a random walk.
getSaveInstanceData()
- Gets whether the tree is to save instance data.
getSearchPath()
- Gets the method of searching the tree for a new insertion.
globalInfo()
-
graph()
- Returns graph describing the tree.
initClassifier(Instances)
- Sets up the tree ready to be trained, using two-class optimized method.
legend()
- Returns the legend of the tree, describing how results are to be interpreted.
listOptions()
- Returns an enumeration describing the available options.
main(String[])
- Main method for testing this class.
measureExamplesProcessed()
- Returns the number of examples "counted".
measureNodesExpanded()
- Returns the number of nodes expanded.
measureNumLeaves()
- Calls measure function for leaf size - the number of prediction nodes.
measureNumPredictionLeaves()
- Calls measure function for prediction leaf size - the number of
prediction nodes without children.
measureTreeSize()
- Calls measure function for tree size - the total number of nodes.
merge(ADTree)
- Merges two trees together.
next(int)
- Performs one iteration.
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.
numOfBoostingIterationsTipText()
-
randomSeedTipText()
-
saveInstanceDataTipText()
-
searchPathTipText()
-
setNumOfBoostingIterations(int)
- Sets the number of boosting iterations.
setOptions(String[])
- Parses a given list of options.
setRandomSeed(int)
- Sets random seed for a random walk.
setSaveInstanceData(boolean)
- Sets whether the tree is to save instance data.
setSearchPath(SelectedTag)
- Sets the method of searching the tree for a new insertion.
toString()
- Returns a description of the classifier.
SEARCHPATH_ALL
public static final int SEARCHPATH_ALL
The search modes
SEARCHPATH_HEAVIEST
public static final int SEARCHPATH_HEAVIEST
SEARCHPATH_ZPURE
public static final int SEARCHPATH_ZPURE
SEARCHPATH_RANDOM
public static final int SEARCHPATH_RANDOM
TAGS_SEARCHPATH
public static final Tag[] TAGS_SEARCHPATH
ADTree
public ADTree()
initClassifier
public void initClassifier(Instances instances) throws java.lang.Exception
Sets up the tree ready to be trained, using two-class optimized method.
- 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.
- 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.
- Parameters:
instance
- the instance to be classified
- Returns:
- the distribution the tree generates for the instance
- Overrides:
- distributionForInstance in class DistributionClassifier
toString
public java.lang.String toString()
Returns a description of the classifier.
- Returns:
- a string containing a description of the classifier
- Overrides:
- toString in class java.lang.Object
graph
public java.lang.String graph() throws java.lang.Exception
Returns graph describing the tree.
- 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
globalInfo
public java.lang.String globalInfo()
- Returns:
- a description of the classifier suitable for
displaying in the explorer/experimenter gui
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.
- 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
- 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.
- 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.
- Returns:
- an enumeration of the measure names
getMeasure
public double getMeasure(java.lang.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:
- 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.
- Parameters:
instances
- the instances to train the classifier with
- Throws:
- java.lang.Exception - if something goes wrong
- Overrides:
- buildClassifier in class Classifier
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.
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.
- 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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