weka.filters.supervised.instance
Class SpreadSubsample

java.lang.Object
  extended by weka.filters.Filter
      extended by weka.filters.supervised.instance.SpreadSubsample
All Implemented Interfaces:
java.io.Serializable, CapabilitiesHandler, OptionHandler, 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: 1.7 $
Author:
Stuart Inglis (stuart@reeltwo.com)
See Also:
Serialized Form

Constructor Summary
SpreadSubsample()
           
 
Method Summary
 java.lang.String adjustWeightsTipText()
          Returns the tip text for this property
 boolean batchFinished()
          Signify that this batch of input to the filter is finished.
 java.lang.String distributionSpreadTipText()
          Returns the tip text for this property
 boolean getAdjustWeights()
          Returns true if instance weights will be adjusted to maintain total weight per class.
 Capabilities getCapabilities()
          Returns the Capabilities of this filter.
 double getDistributionSpread()
          Gets the value for the distribution spread
 double getMaxCount()
          Gets the value for the max count
 java.lang.String[] getOptions()
          Gets the current settings of the filter.
 int getRandomSeed()
          Gets the random number seed.
 java.lang.String globalInfo()
          Returns a string describing this filter
 boolean input(Instance instance)
          Input an instance for filtering.
 java.util.Enumeration listOptions()
          Returns an enumeration describing the available options.
static void main(java.lang.String[] argv)
          Main method for testing this class.
 java.lang.String maxCountTipText()
          Returns the tip text for this property
 java.lang.String randomSeedTipText()
          Returns the tip text for this property
 void setAdjustWeights(boolean newAdjustWeights)
          Sets whether the instance weights will be adjusted to maintain total weight per class.
 void setDistributionSpread(double spread)
          Sets the value for the distribution spread
 boolean setInputFormat(Instances instanceInfo)
          Sets the format of the input instances.
 void setMaxCount(double maxcount)
          Sets the value for the max count
 void setOptions(java.lang.String[] options)
          Parses a given list of options.
 void setRandomSeed(int newSeed)
          Sets the random number seed.
 
Methods inherited from class weka.filters.Filter
batchFilterFile, filterFile, getCapabilities, getOutputFormat, isFirstBatchDone, isNewBatch, isOutputFormatDefined, makeCopies, makeCopy, numPendingOutput, output, outputPeek, toString, useFilter, wekaStaticWrapper
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

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.

getCapabilities

public Capabilities getCapabilities()
Returns the Capabilities of this filter.

Specified by:
getCapabilities in interface CapabilitiesHandler
Overrides:
getCapabilities in class Filter
Returns:
the capabilities of this object
See Also:
Capabilities

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