Class M5Base2

  • All Implemented Interfaces:
    Serializable, Cloneable, weka.classifiers.Classifier, weka.core.AdditionalMeasureProducer, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler
    Direct Known Subclasses:
    M5P2

    public abstract class M5Base2
    extends weka.classifiers.AbstractClassifier
    implements weka.core.AdditionalMeasureProducer, weka.core.TechnicalInformationHandler
    M5Base. Implements base routines for generating M5 Model trees and rules.

    The original algorithm M5 was invented by Quinlan:
    Quinlan J. R. (1992). Learning with continuous classes. Proceedings of the Australian Joint Conference on Artificial Intelligence. 343--348. World Scientific, Singapore.

    Yong Wang made improvements and created M5':
    Wang, Y and Witten, I. H. (1997). Induction of model trees for predicting continuous classes. Proceedings of the poster papers of the European Conference on Machine Learning. University of Economics, Faculty of Informatics and Statistics, Prague.

    Valid options are:

    -U
    Use unsmoothed predictions.

    -R
    Build regression tree/rule rather than model tree/rule

    Version:
    $Revision$
    Author:
    Mark Hall ([email protected])
    See Also:
    Serialized Form
    • Field Summary

      Fields 
      Modifier and Type Field Description
      protected double m_minNumInstances
      The minimum number of instances to allow at a leaf node
      protected boolean m_regressionTree
      Make a regression tree/rule instead of a model tree/rule
      protected weka.core.FastVector m_ruleSet
      the rule set
      protected boolean m_saveInstances
      Save instances at each node in an M5 tree for visualization purposes.
      protected boolean m_useUnpruned
      Do not prune tree/rules
      • Fields inherited from class weka.classifiers.AbstractClassifier

        BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT
    • Constructor Summary

      Constructors 
      Constructor Description
      M5Base2()
      Constructor
    • Field Detail

      • m_ruleSet

        protected weka.core.FastVector m_ruleSet
        the rule set
      • m_saveInstances

        protected boolean m_saveInstances
        Save instances at each node in an M5 tree for visualization purposes.
      • m_regressionTree

        protected boolean m_regressionTree
        Make a regression tree/rule instead of a model tree/rule
      • m_useUnpruned

        protected boolean m_useUnpruned
        Do not prune tree/rules
      • m_minNumInstances

        protected double m_minNumInstances
        The minimum number of instances to allow at a leaf node
    • Constructor Detail

      • M5Base2

        public M5Base2()
        Constructor
    • Method Detail

      • globalInfo

        public String globalInfo()
        returns information about the 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
      • listOptions

        public 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​(String[] options)
                        throws Exception
        Parses a given list of options.

        Valid options are:

        -U
        Use unsmoothed predictions.

        -R
        Build a regression tree rather than a model tree.

        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:
        Exception - if an option is not supported
      • getOptions

        public 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
      • unprunedTipText

        public String unprunedTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui
      • setUnpruned

        public void setUnpruned​(boolean unpruned)
        Use unpruned tree/rules
        Parameters:
        unpruned - true if unpruned tree/rules are to be generated
      • getUnpruned

        public boolean getUnpruned()
        Get whether unpruned tree/rules are being generated
        Returns:
        true if unpruned tree/rules are to be generated
      • generateRulesTipText

        public String generateRulesTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui
      • setGenerateRules

        protected void setGenerateRules​(boolean u)
        Generate rules (decision list) rather than a tree
        Parameters:
        u - true if rules are to be generated
      • getGenerateRules

        protected boolean getGenerateRules()
        get whether rules are being generated rather than a tree
        Returns:
        true if rules are to be generated
      • useUnsmoothedTipText

        public String useUnsmoothedTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui
      • setUseUnsmoothed

        public void setUseUnsmoothed​(boolean s)
        Use unsmoothed predictions
        Parameters:
        s - true if unsmoothed predictions are to be used
      • getUseUnsmoothed

        public boolean getUseUnsmoothed()
        Get whether or not smoothing is being used
        Returns:
        true if unsmoothed predictions are to be used
      • buildRegressionTreeTipText

        public String buildRegressionTreeTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui
      • getBuildRegressionTree

        public boolean getBuildRegressionTree()
        Get the value of regressionTree.
        Returns:
        Value of regressionTree.
      • setBuildRegressionTree

        public void setBuildRegressionTree​(boolean newregressionTree)
        Set the value of regressionTree.
        Parameters:
        newregressionTree - Value to assign to regressionTree.
      • minNumInstancesTipText

        public String minNumInstancesTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui
      • setMinNumInstances

        public void setMinNumInstances​(double minNum)
        Set the minimum number of instances to allow at a leaf node
        Parameters:
        minNum - the minimum number of instances
      • getMinNumInstances

        public double getMinNumInstances()
        Get the minimum number of instances to allow at a leaf node
        Returns:
        a double value
      • getCapabilities

        public weka.core.Capabilities getCapabilities()
        Returns default capabilities of the classifier, i.e., of LinearRegression.
        Specified by:
        getCapabilities in interface weka.core.CapabilitiesHandler
        Specified by:
        getCapabilities in interface weka.classifiers.Classifier
        Overrides:
        getCapabilities in class weka.classifiers.AbstractClassifier
        Returns:
        the capabilities of this classifier
      • buildClassifier

        public void buildClassifier​(weka.core.Instances data)
                             throws Exception
        Generates the classifier.
        Specified by:
        buildClassifier in interface weka.classifiers.Classifier
        Parameters:
        data - set of instances serving as training data
        Throws:
        Exception - if the classifier has not been generated successfully
      • classifyInstance

        public double classifyInstance​(weka.core.Instance inst)
                                throws Exception
        Calculates a prediction for an instance using a set of rules or an M5 model tree
        Specified by:
        classifyInstance in interface weka.classifiers.Classifier
        Overrides:
        classifyInstance in class weka.classifiers.AbstractClassifier
        Parameters:
        inst - the instance whos class value is to be predicted
        Returns:
        the prediction
        Throws:
        Exception - if a prediction can't be made.
      • toString

        public String toString()
        Returns a description of the classifier
        Overrides:
        toString in class Object
        Returns:
        a description of the classifier as a String
      • enumerateMeasures

        public 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​(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:
        Exception - if the named measure is not supported
      • measureNumRules

        public double measureNumRules()
        return the number of rules
        Returns:
        the number of rules (same as # linear models & # leaves in the tree)
      • getM5RootNode

        public RuleNode2 getM5RootNode()