Package weka.filters.supervised.instance

Class SpreadSubsample

  • All Implemented Interfaces:
    java.io.Serializable, CapabilitiesHandler, OptionHandler, RevisionHandler, SupervisedFilter
    public class SpreadSubsample
extends Filter
implements SupervisedFilter, OptionHandler
    Produces a random subsample of a dataset. The original dataset must fit entirely in memory. This filter allows you to specify the maximum "spread" between the rarest and most common class. For example, you may specify that there be at most a 2:1 difference in class frequencies. When used in batch mode, subsequent batches are NOT resampled.

    Valid options are: 

     -S <num>
  Specify the random number seed (default 1)
     -M <num>
  The maximum class distribution spread.
  0 = no maximum spread, 1 = uniform distribution, 10 = allow at most
  a 10:1 ratio between the classes (default 0)
     -W
  Adjust weights so that total weight per class is maintained.
  Individual instance weighting is not preserved. (default no
  weights adjustment
     -X <num>
  The maximum count for any class value (default 0 = unlimited).
    Version:
    $Revision: 5542 $
    Author:
    Stuart Inglis (stuart@reeltwo.com)
    See Also:
    Serialized Form

    • Constructor Detail

      • SpreadSubsample

        public SpreadSubsample()

    • Method Detail

      • globalInfo

        public java.lang.String globalInfo()
        Returns a string describing this filter
        Returns:
        a description of the filter suitable for displaying in the explorer/experimenter gui

      • adjustWeightsTipText

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

      • getAdjustWeights

        public boolean getAdjustWeights()
        Returns true if instance weights will be adjusted to maintain total weight per class.
        Returns:
        true if instance weights will be adjusted to maintain total weight per class.

      • setAdjustWeights

        public void setAdjustWeights​(boolean newAdjustWeights)
        Sets whether the instance weights will be adjusted to maintain total weight per class.
        Parameters:
        newAdjustWeights - whether to adjust weights

      • listOptions

        public java.util.Enumeration listOptions()
        Returns an enumeration describing the available options.
        Specified by:
        listOptions in interface OptionHandler
        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: 

         -S <num>
  Specify the random number seed (default 1)
         -M <num>
  The maximum class distribution spread.
  0 = no maximum spread, 1 = uniform distribution, 10 = allow at most
  a 10:1 ratio between the classes (default 0)
         -W
  Adjust weights so that total weight per class is maintained.
  Individual instance weighting is not preserved. (default no
  weights adjustment
         -X <num>
  The maximum count for any class value (default 0 = unlimited).
        Specified by:
        setOptions in interface OptionHandler
        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 filter.
        Specified by:
        getOptions in interface OptionHandler
        Returns:
        an array of strings suitable for passing to setOptions

      • distributionSpreadTipText

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

      • setDistributionSpread

        public void setDistributionSpread​(double spread)
        Sets the value for the distribution spread
        Parameters:
        spread - the new distribution spread

      • getDistributionSpread

        public double getDistributionSpread()
        Gets the value for the distribution spread
        Returns:
        the distribution spread

      • maxCountTipText

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

      • setMaxCount

        public void setMaxCount​(double maxcount)
        Sets the value for the max count
        Parameters:
        maxcount - the new max count

      • getMaxCount

        public double getMaxCount()
        Gets the value for the max count
        Returns:
        the max count

      • randomSeedTipText

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

      • getRandomSeed

        public int getRandomSeed()
        Gets the random number seed.
        Returns:
        the random number seed.

      • setRandomSeed

        public void setRandomSeed​(int newSeed)
        Sets the random number seed.
        Parameters:
        newSeed - the new random number seed.

      • setInputFormat

        public boolean setInputFormat​(Instances instanceInfo)
                       throws java.lang.Exception
        Sets the format of the input instances.
        Overrides:
        setInputFormat in class Filter
        Parameters:
        instanceInfo - an Instances object containing the input instance structure (any instances contained in the object are ignored - only the structure is required).
        Returns:
        true if the outputFormat may be collected immediately
        Throws:
        UnassignedClassException - if no class attribute has been set.
        UnsupportedClassTypeException - if the class attribute is not nominal.
        java.lang.Exception - if the inputFormat can't be set successfully

      • input

        public boolean input​(Instance instance)
        Input an instance for filtering. Filter requires all training instances be read before producing output.
        Overrides:
        input in class Filter
        Parameters:
        instance - the input instance
        Returns:
        true if the filtered instance may now be collected with output().
        Throws:
        java.lang.IllegalStateException - if no input structure has been defined

      • batchFinished

        public boolean batchFinished()
        Signify that this batch of input to the filter is finished. If the filter requires all instances prior to filtering, output() may now be called to retrieve the filtered instances.
        Overrides:
        batchFinished in class Filter
        Returns:
        true if there are instances pending output
        Throws:
        java.lang.IllegalStateException - if no input structure has been defined

      • main

        public static void main​(java.lang.String[] argv)
        Main method for testing this class.
        Parameters:
        argv - should contain arguments to the filter: use -h for help