FT

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.trees.FT

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.AdditionalMeasureProducer, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.Drawable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler

public class FT
extends weka.classifiers.AbstractClassifier
implements weka.core.OptionHandler, weka.core.AdditionalMeasureProducer, weka.core.Drawable, weka.core.TechnicalInformationHandler

Builds 'functional trees' for classification, more specifically, functional trees with logistic regression functions at the inner nodes and/or leaves. The algorithm can deal with binary and multi-class target variables, numeric and nominal attributes and missing values.

For more information see:

Joao Gama (2004). Functional Trees. Machine Learning. 55(3):219-250.

Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees. Machine Learning. 95(1-2):161-205.

BibTeX:

 @article{Gama2004,
    author = {Joao Gama},
    booktitle = {Machine Learning},
    number = {3},
    pages = {219-250},
    title = {Functional Trees},
    volume = {55},
    year = {2004}
 }
 
 @article{Landwehr2005,
    author = {Niels Landwehr and Mark Hall and Eibe Frank},
    booktitle = {Machine Learning},
    number = {1-2},
    pages = {161-205},
    title = {Logistic Model Trees},
    volume = {95},
    year = {2005}
 }

Valid options are:

 -B
  Binary splits (convert nominal attributes to binary ones)

 -P
  Use error on probabilities instead of misclassification error for stopping criterion of LogitBoost.

 -I <numIterations>
  Set fixed number of iterations for LogitBoost (instead of using cross-validation)

 -F <modelType>
  Set Funtional Tree type to be generate:  0 for FT, 1 for FTLeaves and 2 for FTInner

 -M <numInstances>
  Set minimum number of instances at which a node can be split (default 15)

 -W <beta>
  Set beta for weight trimming for LogitBoost. Set to 0 (default) for no weight trimming.

 -A
  The AIC is used to choose the best iteration.

Version:: $Revision: 14355 $
Author:: Jo\~{a}o Gama, Carlos Ferreira
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`MODEL_FT` model types
`static int`	`MODEL_FTInner`
`static int`	`MODEL_FTLeaves`
`static weka.core.Tag[]`	`TAGS_MODEL` possible model types.

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

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

Constructor Summary

Constructors
Constructor and Description

FT()
Creates an instance of FT with standard options

Constructors
Constructor and Description
`FT()` Creates an instance of FT with standard options

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`java.lang.String`	`binSplitTipText()` Returns the tip text for this property
`void`	`buildClassifier(weka.core.Instances data)` Builds the classifier.
`double`	`classifyInstance(weka.core.Instance instance)` Classifies an instance.
`double[]`	`distributionForInstance(weka.core.Instance instance)` Returns class probabilities for an instance.
`java.util.Enumeration`	`enumerateMeasures()` Returns an enumeration of the additional measure names
`java.lang.String`	`errorOnProbabilitiesTipText()` Returns the tip text for this property
`boolean`	`getBinSplit()` Get the value of binarySplits.
`weka.core.Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`boolean`	`getErrorOnProbabilities()` Get the value of errorOnProbabilities.
`double`	`getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure
`int`	`getMinNumInstances()` Get the value of minNumInstances.
`weka.core.SelectedTag`	`getModelType()` Get the type of functional tree model being used.
`int`	`getNumBoostingIterations()` Get the value of numBoostingIterations.
`java.lang.String[]`	`getOptions()` Gets the current settings of the Classifier.
`java.lang.String`	`getRevision()` Returns the revision string.
`weka.core.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.
`boolean`	`getUseAIC()` Get the value of useAIC.
`double`	`getWeightTrimBeta()` Get the value of weightTrimBeta.
`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.
`java.util.Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] argv)` Main method for testing this class
`int`	`measureNumLeaves()` Returns the number of leaves in the tree
`int`	`measureTreeSize()` Returns the size of the tree
`java.lang.String`	`minNumInstancesTipText()` Returns the tip text for this property
`java.lang.String`	`modelTypeTipText()` Returns the tip text for this property
`java.lang.String`	`numBoostingIterationsTipText()` Returns the tip text for this property
`void`	`setBinSplit(boolean c)` Set the value of binarySplits.
`void`	`setErrorOnProbabilities(boolean c)` Set the value of errorOnProbabilities.
`void`	`setMinNumInstances(int c)` Set the value of minNumInstances.
`void`	`setModelType(weka.core.SelectedTag newMethod)` Set the Functional Tree type.
`void`	`setNumBoostingIterations(int c)` Set the value of numBoostingIterations.
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setUseAIC(boolean c)` Set the value of useAIC.
`void`	`setWeightTrimBeta(double n)` Set the value of weightTrimBeta.
`java.lang.String`	`toString()` Returns a description of the classifier.
`java.lang.String`	`useAICTipText()` Returns the tip text for this property
`java.lang.String`	`weightTrimBetaTipText()` Returns the tip text for this property

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Field Detail
  - MODEL_FT
```
public static final int MODEL_FT
```
    model types
    
    See Also:
    
    Constant Field Values
  - MODEL_FTLeaves
```
public static final int MODEL_FTLeaves
```
    See Also:
    
    Constant Field Values
  - MODEL_FTInner
```
public static final int MODEL_FTInner
```
    See Also:
    
    Constant Field Values
  - TAGS_MODEL
```
public static final weka.core.Tag[] TAGS_MODEL
```
    possible model types.
- Constructor Detail
  - FT
```
public FT()
```
    Creates an instance of FT with standard options
- Method Detail
  - getCapabilities
```
public weka.core.Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface weka.classifiers.Classifier
    
    Specified by:
    
    getCapabilities in interface weka.core.CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
  - buildClassifier
```
public void buildClassifier(weka.core.Instances data)
                     throws java.lang.Exception
```
    Builds the classifier.
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Parameters:
    
    data - the data to train with
    
    Throws:
    
    java.lang.Exception - if classifier can't be built successfully
  - distributionForInstance
```
public double[] distributionForInstance(weka.core.Instance instance)
                                 throws java.lang.Exception
```
    Returns class probabilities for an instance.
    
    Specified by:
    
    distributionForInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    distributionForInstance in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    instance - the instance to compute the distribution for
    
    Returns:
    
    the class probabilities
    
    Throws:
    
    java.lang.Exception - if distribution can't be computed successfully
  - classifyInstance
```
public double classifyInstance(weka.core.Instance instance)
                        throws java.lang.Exception
```
    Classifies an instance.
    
    Specified by:
    
    classifyInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    classifyInstance in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    instance - the instance to classify
    
    Returns:
    
    the classification
    
    Throws:
    
    java.lang.Exception - if instance can't be classified successfully
  - toString
```
public java.lang.String toString()
```
    Returns a description of the classifier.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a string representation of the classifier
  - listOptions
```
public java.util.Enumeration listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.classifiers.AbstractClassifier
    
    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
  Binary splits (convert nominal attributes to binary ones) 
```
```
 -P
  Use error on probabilities instead of misclassification error for stopping criterion of LogitBoost.
```
```
 -I <numIterations>
  Set fixed number of iterations for LogitBoost (instead of using cross-validation)
```
```
 -F <modelType>
  Set Funtional Tree type to be generate:  0 for FT, 1 for FTLeaves and 2 for FTInner
```
```
 -M <numInstances>
  Set minimum number of instances at which a node can be split (default 15)
```
```
 -W <beta>
  Set beta for weight trimming for LogitBoost. Set to 0 (default) for no weight trimming.
```
```
 -A
  The AIC is used to choose the best iteration.
```
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.AbstractClassifier
    
    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 the Classifier.
    
    Specified by:
    
    getOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - getWeightTrimBeta
```
public double getWeightTrimBeta()
```
    Get the value of weightTrimBeta.
  - getUseAIC
```
public boolean getUseAIC()
```
    Get the value of useAIC.
    
    Returns:
    
    Value of useAIC.
  - setWeightTrimBeta
```
public void setWeightTrimBeta(double n)
```
    Set the value of weightTrimBeta.
  - setUseAIC
```
public void setUseAIC(boolean c)
```
    Set the value of useAIC.
    
    Parameters:
    
    c - Value to assign to useAIC.
  - getBinSplit
```
public boolean getBinSplit()
```
    Get the value of binarySplits.
    
    Returns:
    
    Value of binarySplits.
  - getErrorOnProbabilities
```
public boolean getErrorOnProbabilities()
```
    Get the value of errorOnProbabilities.
    
    Returns:
    
    Value of errorOnProbabilities.
  - getNumBoostingIterations
```
public int getNumBoostingIterations()
```
    Get the value of numBoostingIterations.
    
    Returns:
    
    Value of numBoostingIterations.
  - getModelType
```
public weka.core.SelectedTag getModelType()
```
    Get the type of functional tree model being used.
    
    Returns:
    
    the type of functional tree model.
  - setModelType
```
public void setModelType(weka.core.SelectedTag newMethod)
```
    Set the Functional Tree type.
    
    Parameters:
    
    newMethod - Value corresponding to tree type.
  - getMinNumInstances
```
public int getMinNumInstances()
```
    Get the value of minNumInstances.
    
    Returns:
    
    Value of minNumInstances.
  - setBinSplit
```
public void setBinSplit(boolean c)
```
    Set the value of binarySplits.
    
    Parameters:
    
    c - Value to assign to binarySplits.
  - setErrorOnProbabilities
```
public void setErrorOnProbabilities(boolean c)
```
    Set the value of errorOnProbabilities.
    
    Parameters:
    
    c - Value to assign to errorOnProbabilities.
  - setNumBoostingIterations
```
public void setNumBoostingIterations(int c)
```
    Set the value of numBoostingIterations.
    
    Parameters:
    
    c - Value to assign to numBoostingIterations.
  - setMinNumInstances
```
public void setMinNumInstances(int c)
```
    Set the value of minNumInstances.
    
    Parameters:
    
    c - Value to assign to minNumInstances.
  - graphType
```
public int graphType()
```
    Returns the type of graph this classifier represents.
    
    Specified by:
    
    graphType in interface weka.core.Drawable
    
    Returns:
    
    Drawable.TREE
  - graph
```
public java.lang.String graph()
                       throws java.lang.Exception
```
    Returns graph describing the tree.
    
    Specified by:
    
    graph in interface weka.core.Drawable
    
    Returns:
    
    the graph describing the tree
    
    Throws:
    
    java.lang.Exception - if graph can't be computed
  - measureTreeSize
```
public int measureTreeSize()
```
    Returns the size of the tree
    
    Returns:
    
    the size of the tree
  - measureNumLeaves
```
public int measureNumLeaves()
```
    Returns the number of leaves in the tree
    
    Returns:
    
    the number of leaves in the tree
  - enumerateMeasures
```
public java.util.Enumeration enumerateMeasures()
```
    Returns an enumeration of the additional measure names
    
    Specified by:
    
    enumerateMeasures in interface weka.core.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 weka.core.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
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing classifier
    
    Returns:
    
    a description suitable for displaying in the explorer/experimenter gui
  - getTechnicalInformation
```
public weka.core.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 weka.core.TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - modelTypeTipText
```
public java.lang.String modelTypeTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - binSplitTipText
```
public java.lang.String binSplitTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - errorOnProbabilitiesTipText
```
public java.lang.String errorOnProbabilitiesTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - numBoostingIterationsTipText
```
public java.lang.String numBoostingIterationsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - minNumInstancesTipText
```
public java.lang.String minNumInstancesTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - weightTrimBetaTipText
```
public java.lang.String weightTrimBetaTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - useAICTipText
```
public java.lang.String useAICTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface weka.core.RevisionHandler
    
    Overrides:
    
    getRevision in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class
    
    Parameters:
    
    argv - the commandline options

Class FT

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Fields inherited from interface weka.core.Drawable

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Field Detail

MODEL_FT

MODEL_FTLeaves

MODEL_FTInner

TAGS_MODEL

Constructor Detail

FT

Method Detail

getCapabilities

buildClassifier

distributionForInstance

classifyInstance

toString

listOptions

setOptions

getOptions

getWeightTrimBeta

getUseAIC

setWeightTrimBeta

setUseAIC

getBinSplit

getErrorOnProbabilities

getNumBoostingIterations

getModelType

setModelType

getMinNumInstances

setBinSplit

setErrorOnProbabilities

setNumBoostingIterations

setMinNumInstances

graphType

graph

measureTreeSize

measureNumLeaves

enumerateMeasures

getMeasure

globalInfo

getTechnicalInformation

modelTypeTipText

binSplitTipText

errorOnProbabilitiesTipText

numBoostingIterationsTipText

minNumInstancesTipText

weightTrimBetaTipText

useAICTipText

getRevision

main