Package weka.classifiers.trees

Class LMT

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, AdditionalMeasureProducer, CapabilitiesHandler, Drawable, OptionHandler, RevisionHandler, TechnicalInformationHandler
    public class LMT
extends Classifier
implements OptionHandler, AdditionalMeasureProducer, Drawable, TechnicalInformationHandler
    Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves. The algorithm can deal with binary and multi-class target variables, numeric and nominal attributes and missing values.

    For more information see:

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

    Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree Induction. In: 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, 675-683, 2005.

    BibTeX: 

     @article{Landwehr2005,
    author = {Niels Landwehr and Mark Hall and Eibe Frank},
    journal = {Machine Learning},
    number = {1-2},
    pages = {161-205},
    title = {Logistic Model Trees},
    volume = {95},
    year = {2005}
 }
 
 @inproceedings{Sumner2005,
    author = {Marc Sumner and Eibe Frank and Mark Hall},
    booktitle = {9th European Conference on Principles and Practice of Knowledge Discovery in Databases},
    pages = {675-683},
    publisher = {Springer},
    title = {Speeding up Logistic Model Tree Induction},
    year = {2005}
 }

    Valid options are: 

     -B
  Binary splits (convert nominal attributes to binary ones)
     -R
  Split on residuals instead of class values
     -C
  Use cross-validation for boosting at all nodes (i.e., disable heuristic)
     -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)
     -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: 5535 $
    Author:
    Niels Landwehr, Marc Sumner
    See Also:
    Serialized Form

    • Constructor Detail

      • LMT

        public LMT()
        Creates an instance of LMT with standard options

    • Method Detail

      • buildClassifier

        public void buildClassifier​(Instances data)
                     throws java.lang.Exception
        Builds the classifier.
        Specified by:
        buildClassifier in class Classifier
        Parameters:
        data - the data to train with
        Throws:
        java.lang.Exception - if classifier can't be built successfully

      • distributionForInstance

        public double[] distributionForInstance​(Instance instance)
                                 throws java.lang.Exception
        Returns class probabilities for an instance.
        Overrides:
        distributionForInstance in class Classifier
        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​(Instance instance)
                        throws java.lang.Exception
        Classifies an instance.
        Overrides:
        classifyInstance in class Classifier
        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 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
  Binary splits (convert nominal attributes to binary ones)
         -R
  Split on residuals instead of class values
         -C
  Use cross-validation for boosting at all nodes (i.e., disable heuristic)
         -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)
         -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 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 the Classifier.
        Specified by:
        getOptions in interface OptionHandler
        Overrides:
        getOptions in class Classifier
        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.

      • getConvertNominal

        public boolean getConvertNominal()
        Get the value of convertNominal.
        Returns:
        Value of convertNominal.

      • getSplitOnResiduals

        public boolean getSplitOnResiduals()
        Get the value of splitOnResiduals.
        Returns:
        Value of splitOnResiduals.

      • getFastRegression

        public boolean getFastRegression()
        Get the value of fastRegression.
        Returns:
        Value of fastRegression.

      • 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.

      • getMinNumInstances

        public int getMinNumInstances()
        Get the value of minNumInstances.
        Returns:
        Value of minNumInstances.

      • setConvertNominal

        public void setConvertNominal​(boolean c)
        Set the value of convertNominal.
        Parameters:
        c - Value to assign to convertNominal.

      • setSplitOnResiduals

        public void setSplitOnResiduals​(boolean c)
        Set the value of splitOnResiduals.
        Parameters:
        c - Value to assign to splitOnResiduals.

      • setFastRegression

        public void setFastRegression​(boolean c)
        Set the value of fastRegression.
        Parameters:
        c - Value to assign to fastRegression.

      • 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 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 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 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:
        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 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 TechnicalInformationHandler
        Returns:
        the technical information about this class

      • convertNominalTipText

        public java.lang.String convertNominalTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • splitOnResidualsTipText

        public java.lang.String splitOnResidualsTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • fastRegressionTipText

        public java.lang.String fastRegressionTipText()
        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

      • main

        public static void main​(java.lang.String[] argv)
        Main method for testing this class
        Parameters:
        argv - the commandline options