weka.classifiers.meta

Class FilteredClassifier

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.RandomizableSingleClassifierEnhancer

weka.classifiers.meta.FilteredClassifier

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, IterativeClassifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, Drawable, OptionHandler, PartitionGenerator, Randomizable, RevisionHandler, WeightedAttributesHandler, WeightedInstancesHandler

Direct Known Subclasses:

RandomizableFilteredClassifier

public class FilteredClassifier
extends RandomizableSingleClassifierEnhancer
implements Drawable, PartitionGenerator, IterativeClassifier, BatchPredictor, WeightedInstancesHandler, WeightedAttributesHandler

Class for running an arbitrary classifier on data that has been passed through an arbitrary filter. Like the classifier, the structure of the filter is based exclusively on the training data and test instances will be processed by the filter without changing their structure. If unequal instance weights or attribute weights are present, and the filter or the classifier are unable to deal with them, the instances and/or attributes are resampled with replacement based on the weights before they are passed to the filter or the classifier (as appropriate).

Valid options are:

 -F <filter specification>
  Full class name of filter to use, followed
  by filter options.
  default: "weka.filters.supervised.attribute.Discretize -R first-last -precision 6"
 

 -W <classifier name>
  Full name of base classifier.
  (default: weka.classifiers.trees.J48)
 

 -S num
 The random number seed to be used (default 1). 

-doNotCheckForModifiedClassAttribute
If this is set, the classifier will not check whether the filter modifies the class attribute (use with caution).

-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.

-batch-size
The desired batch size for batch prediction.

 Options specific to classifier weka.classifiers.trees.J48:
 

 -U
  Use unpruned tree.
 

 -C <pruning confidence>
  Set confidence threshold for pruning.
  (default 0.25)
 

 -M <minimum number of instances>
  Set minimum number of instances per leaf.
  (default 2)
 

 -R
  Use reduced error pruning.
 

 -N <number of folds>
  Set number of folds for reduced error
  pruning. One fold is used as pruning set.
  (default 3)
 

 -B
  Use binary splits only.
 

 -S
  Don't perform subtree raising.
 

 -L
  Do not clean up after the tree has been built.
 

 -A
  Laplace smoothing for predicted probabilities.
 

 -S <seed>
  Seed for random data shuffling (default 1).
 

Version:

$Revision: 15520 $

Author:

Len Trigg (trigg@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Fields inherited from interface weka.core.Drawable

BayesNet, Newick, NOT_DRAWABLE, TREE

Constructor Summary

Constructors

Constructor and Description
FilteredClassifier() Default constructor.

Method Summary

Modifier and Type	Method and Description
java.lang.String	batchSizeTipText() Tool tip text for this property
void	buildClassifier(Instances data) Build the classifier on the filtered data.
double[]	distributionForInstance(Instance instance) Classifies a given instance after filtering.
double[][]	distributionsForInstances(Instances insts) Batch scoring method.
void	done() Signal end of iterating, useful for any house-keeping/cleanup (If the base classifier supports this.)
java.lang.String	doNotCheckForModifiedClassAttributeTipText() Returns the tip text for this property
java.lang.String	filterTipText() Returns the tip text for this property
void	generatePartition(Instances data) Builds the classifier to generate a partition.
java.lang.String	getBatchSize() Gets the preferred batch size from the base learner if it implements BatchPredictor.
Capabilities	getCapabilities() Returns default capabilities of the classifier.
boolean	getDoNotCheckForModifiedClassAttribute() Returns true if classifier checks whether class attribute has been modified by filter.
Filter	getFilter() Gets the filter used.
double[]	getMembershipValues(Instance inst) Computes an array that has a value for each element in the partition.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
boolean	getResume() Returns true if the model is to be finalized (or has been finalized) after training.
java.lang.String	getRevision() Returns the revision string.
java.lang.String	globalInfo() Returns a string describing this classifier
java.lang.String	graph() Returns graph describing the classifier (if possible).
int	graphType() Returns the type of graph this classifier represents.
boolean	implementsMoreEfficientBatchPrediction() Returns true if the base classifier implements BatchPredictor and is able to generate batch predictions efficiently
void	initializeClassifier(Instances data) Initializes an iterative classifier.
java.util.Enumeration<Option>	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for testing this class.
boolean	next() Performs one iteration.
int	numElements() Returns the number of elements in the partition.
java.lang.String	resumeTipText() Tool tip text for finalize property
void	setBatchSize(java.lang.String size) Set the batch size to use.
void	setDoNotCheckForModifiedClassAttribute(boolean flag) Use this method to determine whether classifier checks whether class attribute has been modified by filter.
void	setFilter(Filter filter) Sets the filter
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setResume(boolean resume) If called with argument true, then the next time done() is called the model is effectively "frozen" and no further iterations can be performed
java.lang.String	toString() Output a representation of this classifier

Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getClassifier, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

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

Methods inherited from interface weka.classifiers.Classifier

classifyInstance

Methods inherited from interface weka.core.OptionHandler

makeCopy

Constructor Detail

FilteredClassifier

public FilteredClassifier()

Default constructor.

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this classifier

Returns:

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

graphType

public int graphType()

Returns the type of graph this classifier represents.

Specified by:

graphType in interface Drawable

Returns:

the graph type of this classifier

graph

public java.lang.String graph()
                       throws java.lang.Exception

Returns graph describing the classifier (if possible).

Specified by:

graph in interface Drawable

Returns:

the graph of the classifier in dotty format

Throws:

java.lang.Exception - if the classifier cannot be graphed

generatePartition

public void generatePartition(Instances data)
                       throws java.lang.Exception

Builds the classifier to generate a partition. (If the base classifier supports this.)

Specified by:

generatePartition in interface PartitionGenerator

Throws:

java.lang.Exception

getMembershipValues

public double[] getMembershipValues(Instance inst)
                             throws java.lang.Exception

Computes an array that has a value for each element in the partition. (If the base classifier supports this.)

Specified by:

getMembershipValues in interface PartitionGenerator

Throws:

java.lang.Exception

numElements

public int numElements()
                throws java.lang.Exception

Returns the number of elements in the partition. (If the base classifier supports this.)

Specified by:

numElements in interface PartitionGenerator

Throws:

java.lang.Exception

initializeClassifier

public void initializeClassifier(Instances data)
                          throws java.lang.Exception

Initializes an iterative classifier. (If the base classifier supports this.)

Specified by:

initializeClassifier in interface IterativeClassifier

Parameters:

data - the instances to be used in induction

Throws:

java.lang.Exception - if the model cannot be initialized

next

public boolean next()
             throws java.lang.Exception

Performs one iteration. (If the base classifier supports this.)

Specified by:

next in interface IterativeClassifier

Returns:

false if no further iterations could be performed, true otherwise

Throws:

java.lang.Exception - if this iteration fails for unexpected reasons

done

public void done()
          throws java.lang.Exception

Signal end of iterating, useful for any house-keeping/cleanup (If the base classifier supports this.)

Specified by:

done in interface IterativeClassifier

Throws:

java.lang.Exception - if cleanup fails

resumeTipText

public java.lang.String resumeTipText()

Tool tip text for finalize property

Returns:

the tool tip text for the finalize property

setResume

public void setResume(boolean resume)
               throws java.lang.Exception

If called with argument true, then the next time done() is called the model is effectively "frozen" and no further iterations can be performed

Specified by:

setResume in interface IterativeClassifier

Parameters:

resume - true if the model is to be finalized after performing iterations

Throws:

java.lang.Exception - if finalization cannot be set

getResume

public boolean getResume()

Returns true if the model is to be finalized (or has been finalized) after training.

Specified by:

getResume in interface IterativeClassifier

Returns:

the current value of finalize

listOptions

public java.util.Enumeration<Option> listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class RandomizableSingleClassifierEnhancer

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:

 -F <filter specification>
  Full class name of filter to use, followed
  by filter options.
  default: "weka.filters.supervised.attribute.Discretize -R first-last -precision 6"
 

 -W <classifier name>
  Full name of base classifier.
  (default: weka.classifiers.trees.J48)
 

 -S num
 The random number seed to be used. 

-doNotCheckForModifiedClassAttribute
If this is set, the classifier will not check whether the filter modifies the class attribute (use with caution).

-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-laces
The number of decimal places for the output of numbers in the model.

-batch-size
The desired batch size for batch prediction.

 Options specific to classifier weka.classifiers.trees.J48:
 

 -U
  Use unpruned tree.
 

 -C <pruning confidence>
  Set confidence threshold for pruning.
  (default 0.25)
 

 -M <minimum number of instances>
  Set minimum number of instances per leaf.
  (default 2)
 

 -R
  Use reduced error pruning.
 

 -N <number of folds>
  Set number of folds for reduced error
  pruning. One fold is used as pruning set.
  (default 3)
 

 -B
  Use binary splits only.
 

 -S
  Don't perform subtree raising.
 

 -L
  Do not clean up after the tree has been built.
 

 -A
  Laplace smoothing for predicted probabilities.
 

 -Q <seed>
  Seed for random data shuffling (default 1).
 

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class RandomizableSingleClassifierEnhancer

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

doNotCheckForModifiedClassAttributeTipText

public java.lang.String doNotCheckForModifiedClassAttributeTipText()

Returns the tip text for this property

Returns:

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

getDoNotCheckForModifiedClassAttribute

public boolean getDoNotCheckForModifiedClassAttribute()

Returns true if classifier checks whether class attribute has been modified by filter.

setDoNotCheckForModifiedClassAttribute

public void setDoNotCheckForModifiedClassAttribute(boolean flag)

Use this method to determine whether classifier checks whether class attribute has been modified by filter.

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the Classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class RandomizableSingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

filterTipText

public java.lang.String filterTipText()

Returns the tip text for this property

Returns:

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

setFilter

public void setFilter(Filter filter)

Sets the filter

Parameters:

filter - the filter with all options set.

getFilter

public Filter getFilter()

Gets the filter used.

Returns:

the filter

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface Classifier

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class SingleClassifierEnhancer

Returns:

the capabilities of this classifier

See Also:

Capabilities

buildClassifier

public void buildClassifier(Instances data)
                     throws java.lang.Exception

Build the classifier on the filtered data.

Specified by:

buildClassifier in interface Classifier

Parameters:

data - the training data

Throws:

java.lang.Exception - if the classifier could not be built successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception

Classifies a given instance after filtering.

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

the class distribution for the given instance

Throws:

java.lang.Exception - if instance could not be classified successfully

batchSizeTipText

public java.lang.String batchSizeTipText()

Tool tip text for this property

Overrides:

batchSizeTipText in class AbstractClassifier

Returns:

the tool tip for this property

setBatchSize

public void setBatchSize(java.lang.String size)

Set the batch size to use. Gets passed through to the base learner if it implements BatchPredictor. Otherwise it is just ignored.

Specified by:

setBatchSize in interface BatchPredictor

Overrides:

setBatchSize in class AbstractClassifier

Parameters:

size - the batch size to use

getBatchSize

public java.lang.String getBatchSize()

Gets the preferred batch size from the base learner if it implements BatchPredictor. Returns 1 as the preferred batch size otherwise.

Specified by:

getBatchSize in interface BatchPredictor

Overrides:

getBatchSize in class AbstractClassifier

Returns:

the batch size to use

distributionsForInstances

public double[][] distributionsForInstances(Instances insts)
                                     throws java.lang.Exception

Batch scoring method. Calls the appropriate method for the base learner if it implements BatchPredictor. Otherwise it simply calls the distributionForInstance() method repeatedly.

Specified by:

distributionsForInstances in interface BatchPredictor

Overrides:

distributionsForInstances in class AbstractClassifier

Parameters:

insts - the instances to get predictions for

Returns:

an array of probability distributions, one for each instance

Throws:

java.lang.Exception - if a problem occurs

implementsMoreEfficientBatchPrediction

public boolean implementsMoreEfficientBatchPrediction()

Returns true if the base classifier implements BatchPredictor and is able to generate batch predictions efficiently

Specified by:

implementsMoreEfficientBatchPrediction in interface BatchPredictor

Overrides:

implementsMoreEfficientBatchPrediction in class AbstractClassifier

Returns:

true if the base classifier can generate batch predictions efficiently

toString

public java.lang.String toString()

Output a representation of this classifier

Overrides:

toString in class java.lang.Object

Returns:

a representation of this classifier

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class AbstractClassifier

Returns:

the revision

main

public static void main(java.lang.String[] argv)

Main method for testing this class.

Parameters:

argv - should contain the following arguments: -t training file [-T test file] [-c class index]