Package weka.classifiers.functions

Class SimpleLogistic

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler
    public class SimpleLogistic
extends Classifier
implements OptionHandler, AdditionalMeasureProducer, WeightedInstancesHandler, TechnicalInformationHandler
    Classifier for building linear logistic regression models. LogitBoost with simple regression functions as base learners is used for fitting the logistic models. The optimal number of LogitBoost iterations to perform is cross-validated, which leads to automatic attribute selection. For more information see:
    Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees.

    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},
    booktitle = {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: 

     -I <iterations>
  Set fixed number of iterations for LogitBoost
     -S
  Use stopping criterion on training set (instead of
  cross-validation)
     -P
  Use error on probabilities (rmse) instead of
  misclassification error for stopping criterion
     -M <iterations>
  Set maximum number of boosting iterations
     -H <iterations>
  Set parameter for heuristic for early stopping of
  LogitBoost.
  If enabled, the minimum is selected greedily, stopping
  if the current minimum has not changed for iter iterations.
  By default, heuristic is enabled with value 50. Set to
  zero to disable heuristic.
     -W <beta>
  Set beta for weight trimming for LogitBoost. Set to 0 for no weight trimming.
 
     -A
  The AIC is used to choose the best iteration (instead of CV or training error).
    Version:
    $Revision: 5523 $
    Author:
    Niels Landwehr, Marc Sumner
    See Also:
    Serialized Form

    • Constructor Detail

      • SimpleLogistic

        public SimpleLogistic()
        Constructor for creating SimpleLogistic object with standard options.

      • SimpleLogistic

        public SimpleLogistic​(int numBoostingIterations,
                      boolean useCrossValidation,
                      boolean errorOnProbabilities)
        Constructor for creating SimpleLogistic object.
        Parameters:
        numBoostingIterations - if non-negative, use this as fixed number of iterations for LogitBoost
        useCrossValidation - cross-validate number of LogitBoost iterations.
        errorOnProbabilities - minimize error on probabilities instead of misclassification error

    • Method Detail

      • buildClassifier

        public void buildClassifier​(Instances data)
                     throws java.lang.Exception
        Builds the logistic regression using LogitBoost.
        Specified by:
        buildClassifier in class Classifier
        Parameters:
        data - the training data
        Throws:
        java.lang.Exception - if something goes wrong

      • distributionForInstance

        public double[] distributionForInstance​(Instance inst)
                                 throws java.lang.Exception
        Returns class probabilities for an instance.
        Overrides:
        distributionForInstance in class Classifier
        Parameters:
        inst - the instance to compute the probabilities for
        Returns:
        the probabilities
        Throws:
        java.lang.Exception - if distribution can't be computed successfully

      • 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: 

         -I <iterations>
  Set fixed number of iterations for LogitBoost
         -S
  Use stopping criterion on training set (instead of
  cross-validation)
         -P
  Use error on probabilities (rmse) instead of
  misclassification error for stopping criterion
         -M <iterations>
  Set maximum number of boosting iterations
         -H <iterations>
  Set parameter for heuristic for early stopping of
  LogitBoost.
  If enabled, the minimum is selected greedily, stopping
  if the current minimum has not changed for iter iterations.
  By default, heuristic is enabled with value 50. Set to
  zero to disable heuristic.
         -W <beta>
  Set beta for weight trimming for LogitBoost. Set to 0 for no weight trimming.
 
         -A
  The AIC is used to choose the best iteration (instead of CV or training error).
        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

      • getNumBoostingIterations

        public int getNumBoostingIterations()
        Get the value of numBoostingIterations.
        Returns:
        the number of boosting iterations

      • getUseCrossValidation

        public boolean getUseCrossValidation()
        Get the value of useCrossValidation.
        Returns:
        true if cross-validation is used

      • getErrorOnProbabilities

        public boolean getErrorOnProbabilities()
        Get the value of errorOnProbabilities.
        Returns:
        If true, use minimize error on probabilities instead of misclassification error

      • getMaxBoostingIterations

        public int getMaxBoostingIterations()
        Get the value of maxBoostingIterations.
        Returns:
        the maximum number of boosting iterations

      • getHeuristicStop

        public int getHeuristicStop()
        Get the value of heuristicStop.
        Returns:
        the value of heuristicStop

      • getWeightTrimBeta

        public double getWeightTrimBeta()
        Get the value of weightTrimBeta.

      • getUseAIC

        public boolean getUseAIC()
        Get the value of useAIC.
        Returns:
        Value of useAIC.

      • setNumBoostingIterations

        public void setNumBoostingIterations​(int n)
        Set the value of numBoostingIterations.
        Parameters:
        n - the number of boosting iterations

      • setUseCrossValidation

        public void setUseCrossValidation​(boolean l)
        Set the value of useCrossValidation.
        Parameters:
        l - whether to use cross-validation

      • setErrorOnProbabilities

        public void setErrorOnProbabilities​(boolean l)
        Set the value of errorOnProbabilities.
        Parameters:
        l - If true, use minimize error on probabilities instead of misclassification error

      • setMaxBoostingIterations

        public void setMaxBoostingIterations​(int n)
        Set the value of maxBoostingIterations.
        Parameters:
        n - the maximum number of boosting iterations

      • setHeuristicStop

        public void setHeuristicStop​(int n)
        Set the value of heuristicStop.
        Parameters:
        n - the value of heuristicStop

      • 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.

      • getNumRegressions

        public int getNumRegressions()
        Get the number of LogitBoost iterations performed (= the number of regression functions fit by LogitBoost).
        Returns:
        the number of LogitBoost iterations performed

      • toString

        public java.lang.String toString()
        Returns a description of the logistic model (attributes/coefficients).
        Overrides:
        toString in class java.lang.Object
        Returns:
        the model as string

      • measureAttributesUsed

        public double measureAttributesUsed()
        Returns the fraction of all attributes in the data that are used in the logistic model (in percent). An attribute is used in the model if it is used in any of the models for the different classes.
        Returns:
        percentage of attributes used in the model

      • 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

      • 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

      • useCrossValidationTipText

        public java.lang.String useCrossValidationTipText()
        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

      • maxBoostingIterationsTipText

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

      • heuristicStopTipText

        public java.lang.String heuristicStopTipText()
        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 - commandline options