Package weka.classifiers.meta

Class ClassifierCascade

  • All Implemented Interfaces:
    Serializable, Cloneable, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.Randomizable, weka.core.RevisionHandler
    public class ClassifierCascade
extends weka.classifiers.RandomizableMultipleClassifiersCombiner
    Generates a classifier cascade, with each deeper level of classifiers being built on the input data and either the class distributions (nominal class) or classification (numeric class) of the classifiers of the previous level in the cascade.
    The build process is stopped when either the maximum number of levels is reached, the termination criterion is satisfied or no further improvement is achieved.
    In case of a level performing worse than the prior one, the build process is terminated immediately and the current level discarded.

    Valid options are: 

     -max-levels <value>
  The maximum number of levels to build.
  (default: 10)
     -statistic <value>
  The statistic to evaluate on.
  (default: Percent correct)
     -threshold <value>
  The threshold that, when reached, terminates the build process.
  (default: 90.0)
     -threshold-check <value>
  How to apply the provided threshold.
  (default: ABOVE)
     -min-improvement <value>
  The minimum improvement between levels, otherwise the build process gets terminated.
  (default: 0.01)
     -num-folds <value>
  The number of folds to use for internal cross-validation.
  (default: 10)
     -num-threads <value>
  The number of threads to use.
  (default: -1)
     -holdout-percentage <value>
  The size of the validation set in percent (0-100).
  (default: 20.0)
     -class-index <value>
  The 0-based index of the class-label to use for class-label-based statistics.
  (default: 0)
     -combination <value>
  Determines how to combine the statistics.
  (default: MEDIAN)
     -S <num>
  Random number seed.
  (default 1)
     -B <classifier specification>
  Full class name of classifier to include, followed
  by scheme options. May be specified multiple times.
  (default: "weka.classifiers.rules.ZeroR")
     -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console
     -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).
     -num-decimal-places
  The number of decimal places for the output of numbers in the model (default 2).
     -batch-size
  The desired batch size for batch prediction  (default 100).
     
 Options specific to classifier weka.classifiers.rules.ZeroR:
 
     -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console
     -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).
     -num-decimal-places
  The number of decimal places for the output of numbers in the model (default 2).
     -batch-size
  The desired batch size for batch prediction  (default 100).
    Version:
    $Revision$
    Author:
    FracPete (fracpete at waikato dot ac dot nz)
    See Also:
    Serialized Form

    • Field Detail

      • ATTRIBUTE_PREFIX

        public static final String ATTRIBUTE_PREFIX
        the prefix for the additional cascade attributes.
        See Also:
        Constant Field Values

      • DEFAULT_MIN_IMPROVEMENT

        public static final double DEFAULT_MIN_IMPROVEMENT
        See Also:
        Constant Field Values

      • DEFAULT_HOLDOUT_PERCENTAGE

        public static final double DEFAULT_HOLDOUT_PERCENTAGE
        See Also:
        Constant Field Values

      • MAX_LEVELS

        protected static String MAX_LEVELS

      • STATISTIC

        protected static String STATISTIC

      • THRESHOLD

        protected static String THRESHOLD

      • THRESHOLD_CHECK

        protected static String THRESHOLD_CHECK

      • MIN_IMPROVEMENT

        protected static String MIN_IMPROVEMENT

      • NUM_FOLDS

        protected static String NUM_FOLDS

      • NUM_THREADS

        protected static String NUM_THREADS

      • HOLDOUT_PERCENTAGE

        protected static String HOLDOUT_PERCENTAGE

      • CLASS_INDEX

        protected static String CLASS_INDEX

      • COMBINATION

        protected static String COMBINATION

      • m_MaxLevels

        protected int m_MaxLevels
        the maximum number of levels in the cascade.

      • m_Threshold

        protected double m_Threshold
        the threshold for the statistic for termination.

      • m_MinImprovement

        protected double m_MinImprovement
        the minimum improvement between levels that the statistic must improve.

      • m_NumFolds

        protected int m_NumFolds
        the number of folds for cross-validation.

      • m_NumThreads

        protected int m_NumThreads
        the number of threads to use.

      • m_HoldOutPercentage

        protected double m_HoldOutPercentage
        the percentage to use for validation set to determine termination criterion (0-100).

      • m_ClassIndex

        protected int m_ClassIndex
        the class index.

      • m_Cascade

        protected List<List<weka.classifiers.Classifier>> m_Cascade
        the cascade.

      • m_MetaLevelHeader

        protected weka.core.Instances m_MetaLevelHeader
        the meta-level structure.

      • m_MetaLevelStart

        protected List<Integer> m_MetaLevelStart
        the start indices for the classifier stats in the meta-levels.

      • m_Nominal

        protected boolean m_Nominal
        whether regression or classification.

    • Constructor Detail

      • ClassifierCascade

        public ClassifierCascade()

    • Method Detail

      • globalInfo

        public String globalInfo()
        Returns a string describing classifier.
        Returns:
        a description suitable for displaying in the explorer/experimenter gui

      • 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.RandomizableMultipleClassifiersCombiner
        Returns:
        an enumeration of all the available options.

      • setOptions

        public void setOptions​(String[] options)
                throws Exception
        Parses a given list of options.
        Specified by:
        setOptions in interface weka.core.OptionHandler
        Overrides:
        setOptions in class weka.classifiers.RandomizableMultipleClassifiersCombiner
        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.RandomizableMultipleClassifiersCombiner
        Returns:
        an array of strings suitable for passing to setOptions

      • setMaxLevels

        public void setMaxLevels​(int maxLevels)
        the maximum number of levels in the cascade.

      • getMaxLevels

        public int getMaxLevels()
        the maximum number of levels in the cascade.

      • maxLevelsTipText

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

      • setStatistic

        public void setStatistic​(EvaluationStatistic statistic)
        the statistic to use for termination.

      • getStatistic

        public EvaluationStatistic getStatistic()
        the statistic to use for termination.

      • statisticTipText

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

      • setThreshold

        public void setThreshold​(double threshold)
        the threshold for the statistic for termination.

      • getThreshold

        public double getThreshold()
        the threshold for the statistic for termination.

      • thresholdTipText

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

      • thresholdCheckTipText

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

      • setMinImprovement

        public void setMinImprovement​(double minImprovement)
        the minimum improvement between levels that the statistic must improve.

      • getMinImprovement

        public double getMinImprovement()
        the minimum improvement between levels that the statistic must improve.

      • minImprovementTipText

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

      • setNumFolds

        public void setNumFolds​(int numFolds)
        the number of folds for cross-validation.

      • getNumFolds

        public int getNumFolds()
        the number of folds for cross-validation.

      • numFoldsTipText

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

      • setNumThreads

        public void setNumThreads​(int numThreads)
        the number of threads to use.

      • getNumThreads

        public int getNumThreads()
        the number of threads to use.

      • numThreadsTipText

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

      • setHoldOutPercentage

        public void setHoldOutPercentage​(double holdOutPercentage)
        the percentage to use for validation set to determine termination criterion (0-100).

      • getHoldOutPercentage

        public double getHoldOutPercentage()
        the percentage to use for validation set to determine termination criterion (0-100).

      • holdOutPercentageTipText

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

      • setClassIndex

        public void setClassIndex​(int classIndex)
        the class index.

      • getClassIndex

        public int getClassIndex()
        the class index.

      • classIndexTipText

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

      • combinationTipText

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

      • getCapabilities

        public weka.core.Capabilities getCapabilities()
        Returns combined capabilities of the base classifiers, i.e., the capabilities all of them have in common.
        Specified by:
        getCapabilities in interface weka.core.CapabilitiesHandler
        Specified by:
        getCapabilities in interface weka.classifiers.Classifier
        Overrides:
        getCapabilities in class weka.classifiers.MultipleClassifiersCombiner
        Returns:
        the capabilities of the base classifiers

      • createMetaLevelHeader

        protected weka.core.Instances createMetaLevelHeader​(weka.core.Instances data)
        Generates the dataset structure for the meta-levels.
        Parameters:
        data - the training data
        Returns:
        the structure

      • createMetaLevelInstance

        protected weka.core.Instance createMetaLevelInstance​(weka.core.Instances metaLevel,
                                                     weka.core.Instance data)
        Generates an instance for the meta-level using the original data.
        Parameters:
        data - the original data
        Returns:
        the meta-level instance, but with missing meta-level data

      • addMetaLevelPrediction

        protected void addMetaLevelPrediction​(weka.core.Instance inst,
                                      int index,
                                      double[] dist)
        Adds the class distribution of the specified classifier to the meta-level instance.
        Parameters:
        inst - the meta-level instance to modify
        index - the index of the classifier
        dist - the class distribution to add

      • addMetaLevelPrediction

        protected void addMetaLevelPrediction​(weka.core.Instance inst,
                                      int index,
                                      double cls)
        Adds the class distribution of the specified classifier to the meta-level instance.
        Parameters:
        inst - the meta-level instance to modify
        index - the index of the classifier
        cls - the classification

      • applyCombination

        protected double applyCombination​(double[] stats)
        Applies the selected combination to the array.
        Parameters:
        stats - the statistic values to combine
        Returns:
        the combination

      • buildClassifier

        public void buildClassifier​(weka.core.Instances data)
                     throws Exception
        Builds the classifier.
        Parameters:
        data - the training data
        Throws:
        Exception - if build fails

      • predictionForInstance

        protected Object predictionForInstance​(weka.core.Instance instance,
                                       boolean distribution)
                                throws Exception
        Returns the prediction for the instance.
        Parameters:
        instance - the instance to get the class distribution for
        distribution - class distribution or classification
        Returns:
        the class distribution or prediction
        Throws:
        Exception - if prediction fails

      • distributionForInstance

        public double[] distributionForInstance​(weka.core.Instance instance)
                                 throws Exception
        Returns the distribution for the instance.
        Specified by:
        distributionForInstance in interface weka.classifiers.Classifier
        Overrides:
        distributionForInstance in class weka.classifiers.AbstractClassifier
        Parameters:
        instance - the instance to get the class distribution for
        Returns:
        the class distribution
        Throws:
        Exception - if prediction fails

      • classifyInstance

        public double classifyInstance​(weka.core.Instance instance)
                        throws Exception
        Returns the classification for the instance.
        Specified by:
        classifyInstance in interface weka.classifiers.Classifier
        Overrides:
        classifyInstance in class weka.classifiers.AbstractClassifier
        Parameters:
        instance - the instance to get the classification for
        Returns:
        the classification
        Throws:
        Exception - if prediction fails

      • toString

        public String toString()
        Outputs a short description of the classifier model.
        Overrides:
        toString in class Object
        Returns:
        the model description

      • getRevision

        public 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​(String[] args)
                 throws Exception
        Main method for executing the class.
        Parameters:
        args - the options
        Throws:
        Exception