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weka.classifiers.meta
Class ThresholdSelector

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.meta.ThresholdSelector
All Implemented Interfaces:
java.lang.Cloneable, Drawable, OptionHandler, java.io.Serializable
public class ThresholdSelector
extends Classifier
implements OptionHandler, Drawable

Class for selecting a threshold on a probability output by a distribution classifier. The threshold is set so that a given performance measure is optimized. Currently this is the F-measure. Performance is measured either on the training data, a hold-out set or using cross-validation. In addition, the probabilities returned by the base learner can have their range expanded so that the output probabilities will reside between 0 and 1 (this is useful if the scheme normally produces probabilities in a very narrow range).

Valid options are:

-C num
The class for which threshold is determined. Valid values are: 1, 2 (for first and second classes, respectively), 3 (for whichever class is least frequent), 4 (for whichever class value is most frequent), and 5 (for the first class named any of "yes","pos(itive)", "1", or method 3 if no matches). (default 5).

-W classname
Specify the full class name of the base classifier.

-X num
Number of folds used for cross validation. If just a hold-out set is used, this determines the size of the hold-out set (default 3).

-R integer
Sets whether confidence range correction is applied. This can be used to ensure the confidences range from 0 to 1. Use 0 for no range correction, 1 for correction based on the min/max values seen during threshold selection (default 0).

-S seed
Random number seed (default 1).

-E integer
Sets the evaluation mode. Use 0 for evaluation using cross-validation, 1 for evaluation using hold-out set, and 2 for evaluation on the training data (default 1).

Options after -- are passed to the designated sub-classifier.

Version:
$Revision: 1.29 $
Author:
Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:
Serialized Form

Field Summary
static int EVAL_CROSS_VALIDATION
           
static int EVAL_TRAINING_SET
           
static int EVAL_TUNED_SPLIT
           
static int OPTIMIZE_0
           
static int OPTIMIZE_1
           
static int OPTIMIZE_LFREQ
           
static int OPTIMIZE_MFREQ
           
static int OPTIMIZE_POS_NAME
           
static int RANGE_BOUNDS
           
static int RANGE_NONE
           
static Tag[] TAGS_EVAL
           
static Tag[] TAGS_OPTIMIZE
           
static Tag[] TAGS_RANGE
           
 
Fields inherited from interface weka.core.Drawable
BayesNet, NOT_DRAWABLE, TREE
 
Constructor Summary
ThresholdSelector()
           
 
Method Summary
 void buildClassifier(Instances instances)
          Generates the classifier.
 java.lang.String classifierTipText()
           
 java.lang.String designatedClassTipText()
           
 double[] distributionForInstance(Instance instance)
          Calculates the class membership probabilities for the given test instance.
 java.lang.String evaluationModeTipText()
           
 Classifier getClassifier()
          Get the Classifier used as the classifier.
 SelectedTag getDesignatedClass()
          Gets the method to determine which class value to optimize.
 SelectedTag getEvaluationMode()
          Gets the evaluation mode used.
 int getNumXValFolds()
          Get the number of folds used for cross-validation.
 java.lang.String[] getOptions()
          Gets the current settings of the Classifier.
 SelectedTag getRangeCorrection()
          Gets the confidence range correction mode used.
 int getSeed()
          Gets the random number seed.
 java.lang.String globalInfo()
           
 java.lang.String graph()
          Returns graph describing the classifier (if possible).
 int graphType()
          Returns the type of graph this classifier represents.
 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 numXValFoldsTipText()
           
 java.lang.String rangeCorrectionTipText()
           
 java.lang.String seedTipText()
           
 void setClassifier(Classifier newClassifier)
          Set the Classifier for which threshold is set.
 void setDesignatedClass(SelectedTag newMethod)
          Sets the method to determine which class value to optimize.
 void setEvaluationMode(SelectedTag newMethod)
          Sets the evaluation mode used.
 void setNumXValFolds(int newNumFolds)
          Set the number of folds used for cross-validation.
 void setOptions(java.lang.String[] options)
          Parses a given list of options.
 void setRangeCorrection(SelectedTag newMethod)
          Sets the confidence range correction mode used.
 void setSeed(int seed)
          Sets the seed for random number generation.
 java.lang.String toString()
          Returns description of the cross-validated classifier.
 
Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, setDebug
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Field Detail

RANGE_NONE

public static final int RANGE_NONE
See Also:
Constant Field Values

RANGE_BOUNDS

public static final int RANGE_BOUNDS
See Also:
Constant Field Values

TAGS_RANGE

public static final Tag[] TAGS_RANGE

EVAL_TRAINING_SET

public static final int EVAL_TRAINING_SET
See Also:
Constant Field Values

EVAL_TUNED_SPLIT

public static final int EVAL_TUNED_SPLIT
See Also:
Constant Field Values

EVAL_CROSS_VALIDATION

public static final int EVAL_CROSS_VALIDATION
See Also:
Constant Field Values

TAGS_EVAL

public static final Tag[] TAGS_EVAL

OPTIMIZE_0

public static final int OPTIMIZE_0
See Also:
Constant Field Values

OPTIMIZE_1

public static final int OPTIMIZE_1
See Also:
Constant Field Values

OPTIMIZE_LFREQ

public static final int OPTIMIZE_LFREQ
See Also:
Constant Field Values

OPTIMIZE_MFREQ

public static final int OPTIMIZE_MFREQ
See Also:
Constant Field Values

OPTIMIZE_POS_NAME

public static final int OPTIMIZE_POS_NAME
See Also:
Constant Field Values

TAGS_OPTIMIZE

public static final Tag[] TAGS_OPTIMIZE
Constructor Detail

ThresholdSelector

public ThresholdSelector()
Method Detail

listOptions

public java.util.Enumeration listOptions()
Returns an enumeration describing the available options.

Specified by:
listOptions in interface OptionHandler
Overrides:
listOptions in class Classifier
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:

-C num
The class for which threshold is determined. Valid values are: 1, 2 (for first and second classes, respectively), 3 (for whichever class is least frequent), 4 (for whichever class value is most frequent), and 5 (for the first class named any of "yes","pos(itive)", "1", or method 3 if no matches). (default 3).

-W classname
Specify the full class name of classifier to perform cross-validation selection on.

-X num
Number of folds used for cross validation. If just a hold-out set is used, this determines the size of the hold-out set (default 3).

-R integer
Sets whether confidence range correction is applied. This can be used to ensure the confidences range from 0 to 1. Use 0 for no range correction, 1 for correction based on the min/max values seen during threshold selection (default 0).

-S seed
Random number seed (default 1).

-E integer
Sets the evaluation mode. Use 0 for evaluation using cross-validation, 1 for evaluation using hold-out set, and 2 for evaluation on the training data (default 1).

Options after -- are passed to the designated sub-classifier.

Specified by:
setOptions in interface OptionHandler
Overrides:
setOptions in class Classifier
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 Classifier.

Specified by:
getOptions in interface OptionHandler
Overrides:
getOptions in class Classifier
Returns:
an array of strings suitable for passing to setOptions

buildClassifier

public void buildClassifier(Instances instances)
                     throws java.lang.Exception
Generates the classifier.

Specified by:
buildClassifier in class Classifier
Parameters:
instances - set of instances serving as training data
Throws:
java.lang.Exception - if the classifier has not been generated successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception
Calculates the class membership probabilities for the given test instance.

Overrides:
distributionForInstance in class Classifier
Parameters:
instance - the instance to be classified
Returns:
predicted class probability distribution
Throws:
java.lang.Exception - if instance could not be classified successfully

globalInfo

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

designatedClassTipText

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

getDesignatedClass

public SelectedTag getDesignatedClass()
Gets the method to determine which class value to optimize. Will be one of OPTIMIZE_0, OPTIMIZE_1, OPTIMIZE_LFREQ, OPTIMIZE_MFREQ, OPTIMIZE_POS_NAME.

Returns:
the class selection mode.

setDesignatedClass

public void setDesignatedClass(SelectedTag newMethod)
Sets the method to determine which class value to optimize. Will be one of OPTIMIZE_0, OPTIMIZE_1, OPTIMIZE_LFREQ, OPTIMIZE_MFREQ, OPTIMIZE_POS_NAME.

Parameters:
newMethod - the new class selection mode.

evaluationModeTipText

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

setEvaluationMode

public void setEvaluationMode(SelectedTag newMethod)
Sets the evaluation mode used. Will be one of EVAL_TRAINING, EVAL_TUNED_SPLIT, or EVAL_CROSS_VALIDATION

Parameters:
newMethod - the new evaluation mode.

getEvaluationMode

public SelectedTag getEvaluationMode()
Gets the evaluation mode used. Will be one of EVAL_TRAINING, EVAL_TUNED_SPLIT, or EVAL_CROSS_VALIDATION

Returns:
the evaluation mode.

rangeCorrectionTipText

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

setRangeCorrection

public void setRangeCorrection(SelectedTag newMethod)
Sets the confidence range correction mode used. Will be one of RANGE_NONE, or RANGE_BOUNDS

Parameters:
newMethod - the new correciton mode.

getRangeCorrection

public SelectedTag getRangeCorrection()
Gets the confidence range correction mode used. Will be one of RANGE_NONE, or RANGE_BOUNDS

Returns:
the confidence correction mode.

seedTipText

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

setSeed

public void setSeed(int seed)
Sets the seed for random number generation.

Parameters:
seed - the random number seed

getSeed

public int getSeed()
Gets the random number seed.

Returns:
the random number seed

numXValFoldsTipText

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

getNumXValFolds

public int getNumXValFolds()
Get the number of folds used for cross-validation.

Returns:
the number of folds used for cross-validation.

setNumXValFolds

public void setNumXValFolds(int newNumFolds)
Set the number of folds used for cross-validation.

Parameters:
newNumFolds - the number of folds used for cross-validation.

classifierTipText

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

setClassifier

public void setClassifier(Classifier newClassifier)
Set the Classifier for which threshold is set.

Parameters:
newClassifier - the Classifier to use.

getClassifier

public Classifier getClassifier()
Get the Classifier used as the classifier.

Returns:
the classifier used as the classifier

graphType

public int graphType()
Returns the type of graph this classifier represents.

Specified by:
graphType in interface Drawable
Returns:
the type of graph representing the object

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

toString

public java.lang.String toString()
Returns description of the cross-validated classifier.

Overrides:
toString in class java.lang.Object
Returns:
description of the cross-validated classifier as a string

main

public static void main(java.lang.String[] argv)
Main method for testing this class.

Parameters:
argv - the options
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Copyright (c) 2003 David Lindsay, Computer Learning Research Centre, Dept. Computer Science, Royal Holloway, University of London