weka.filters.supervised.instance

Class Resample

java.lang.Object

weka.filters.Filter

weka.filters.supervised.instance.Resample

All Implemented Interfaces:

Serializable, CapabilitiesHandler, OptionHandler, RevisionHandler, SupervisedFilter

public class Resample
extends Filter
implements SupervisedFilter, OptionHandler

Produces a random subsample of a dataset using either sampling with replacement or without replacement.
The original dataset must fit entirely in memory. The number of instances in the generated dataset may be specified. The dataset must have a nominal class attribute. If not, use the unsupervised version. The filter can be made to maintain the class distribution in the subsample, or to bias the class distribution toward a uniform distribution. When used in batch mode (i.e. in the FilteredClassifier), subsequent batches are NOT resampled.

Valid options are:

 -S <num>
  Specify the random number seed (default 1)

 -Z <num>
  The size of the output dataset, as a percentage of
  the input dataset (default 100)

 -B <num>
  Bias factor towards uniform class distribution.
  0 = distribution in input data -- 1 = uniform distribution.
  (default 0)

 -no-replacement
  Disables replacement of instances
  (default: with replacement)

 -V
  Inverts the selection - only available with '-no-replacement'.

Version:

$Revision: 8034 $

Author:

Len Trigg (len@reeltwo.com), FracPete (fracpete at waikato dot ac dot nz)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Resample()

Method Summary

Methods

Modifier and Type	Method and Description
boolean	batchFinished() Signify that this batch of input to the filter is finished.
String	biasToUniformClassTipText() Returns the tip text for this property.
void	createSubsampleWithoutReplacement(Random random, int origSize, int sampleSize, int actualClasses, int[] classIndices) creates the subsample without replacement.
void	createSubsampleWithReplacement(Random random, int origSize, int sampleSize, int actualClasses, int[] classIndices) creates the subsample with replacement.
double	getBiasToUniformClass() Gets the bias towards a uniform class.
Capabilities	getCapabilities() Returns the Capabilities of this filter.
boolean	getInvertSelection() Gets whether selection is inverted (only if instances are drawn WIHTOUT replacement).
boolean	getNoReplacement() Gets whether instances are drawn with or without replacement.
String[]	getOptions() Gets the current settings of the filter.
int	getRandomSeed() Gets the random number seed.
String	getRevision() Returns the revision string.
double	getSampleSizePercent() Gets the subsample size as a percentage of the original set.
String	globalInfo() Returns a string describing this filter.
boolean	input(Instance instance) Input an instance for filtering.
String	invertSelectionTipText() Returns the tip text for this property.
Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(String[] argv) Main method for testing this class.
String	noReplacementTipText() Returns the tip text for this property.
String	randomSeedTipText() Returns the tip text for this property.
String	sampleSizePercentTipText() Returns the tip text for this property.
void	setBiasToUniformClass(double newBiasToUniformClass) Sets the bias towards a uniform class.
boolean	setInputFormat(Instances instanceInfo) Sets the format of the input instances.
void	setInvertSelection(boolean value) Sets whether the selection is inverted (only if instances are drawn WIHTOUT replacement).
void	setNoReplacement(boolean value) Sets whether instances are drawn with or with out replacement.
void	setOptions(String[] options) Parses a given list of options.
void	setRandomSeed(int newSeed) Sets the random number seed.
void	setSampleSizePercent(double newSampleSizePercent) Sets the size of the subsample, as a percentage of the original set.

Methods inherited from class weka.filters.Filter

batchFilterFile, filterFile, getCapabilities, getOutputFormat, isFirstBatchDone, isNewBatch, isOutputFormatDefined, makeCopies, makeCopy, mayRemoveInstanceAfterFirstBatchDone, numPendingOutput, output, outputPeek, runFilter, toString, useFilter, wekaStaticWrapper

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

Resample

public Resample()

Method Detail

globalInfo

public String globalInfo()

Returns a string describing this filter.

Returns:

a description of the filter suitable for displaying in the explorer/experimenter gui

listOptions

public 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(String[] options)
                throws Exception

Parses a given list of options.

Valid options are:

 -S <num>
  Specify the random number seed (default 1)

 -Z <num>
  The size of the output dataset, as a percentage of
  the input dataset (default 100)

 -B <num>
  Bias factor towards uniform class distribution.
  0 = distribution in input data -- 1 = uniform distribution.
  (default 0)

 -no-replacement
  Disables replacement of instances
  (default: with replacement)

 -V
  Inverts the selection - only available with '-no-replacement'.

Specified by:

setOptions in interface OptionHandler

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 filter.

Specified by:

getOptions in interface OptionHandler

Returns:

an array of strings suitable for passing to setOptions

biasToUniformClassTipText

public String biasToUniformClassTipText()

Returns the tip text for this property.

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getBiasToUniformClass

public double getBiasToUniformClass()

Gets the bias towards a uniform class. A value of 0 leaves the class distribution as-is, a value of 1 ensures the class distributions are uniform in the output data.

Returns:

the current bias

setBiasToUniformClass

public void setBiasToUniformClass(double newBiasToUniformClass)

Sets the bias towards a uniform class. A value of 0 leaves the class distribution as-is, a value of 1 ensures the class distributions are uniform in the output data.

Parameters:

newBiasToUniformClass - the new bias value, between 0 and 1.

randomSeedTipText

public 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.

sampleSizePercentTipText

public String sampleSizePercentTipText()

Returns the tip text for this property.

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getSampleSizePercent

public double getSampleSizePercent()

Gets the subsample size as a percentage of the original set.

Returns:

the subsample size

setSampleSizePercent

public void setSampleSizePercent(double newSampleSizePercent)

Sets the size of the subsample, as a percentage of the original set.

Parameters:

newSampleSizePercent - the subsample set size, between 0 and 100.

noReplacementTipText

public String noReplacementTipText()

Returns the tip text for this property.

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getNoReplacement

public boolean getNoReplacement()

Gets whether instances are drawn with or without replacement.

Returns:

true if the replacement is disabled

setNoReplacement

public void setNoReplacement(boolean value)

Sets whether instances are drawn with or with out replacement.

Parameters:

value - if true then the replacement of instances is disabled

invertSelectionTipText

public String invertSelectionTipText()

Returns the tip text for this property.

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getInvertSelection

public boolean getInvertSelection()

Gets whether selection is inverted (only if instances are drawn WIHTOUT replacement).

Returns:

true if the replacement is disabled

See Also:

m_NoReplacement

setInvertSelection

public void setInvertSelection(boolean value)

Sets whether the selection is inverted (only if instances are drawn WIHTOUT replacement).

Parameters:

value - if true then selection is inverted

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 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:

Exception - if the input format 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:

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:

IllegalStateException - if no input structure has been defined

createSubsampleWithReplacement

public void createSubsampleWithReplacement(Random random,
                                  int origSize,
                                  int sampleSize,
                                  int actualClasses,
                                  int[] classIndices)

creates the subsample with replacement.

Parameters:

random - the random number generator to use

origSize - the original size of the dataset

sampleSize - the size to generate

actualClasses - the number of classes found in the data

classIndices - the indices where classes start

createSubsampleWithoutReplacement

public void createSubsampleWithoutReplacement(Random random,
                                     int origSize,
                                     int sampleSize,
                                     int actualClasses,
                                     int[] classIndices)

creates the subsample without replacement.

Parameters:

random - the random number generator to use

origSize - the original size of the dataset

sampleSize - the size to generate

actualClasses - the number of classes found in the data

classIndices - the indices where classes start

getRevision

public String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class Filter

Returns:

the revision

main

public static void main(String[] argv)

Main method for testing this class.

Parameters:

argv - should contain arguments to the filter: use -h for help