de.jstacs.classifiers
Class AbstractScoreBasedClassifier

java.lang.Object
  de.jstacs.classifiers.AbstractClassifier
      de.jstacs.classifiers.AbstractScoreBasedClassifier

All Implemented Interfaces:

Storable, Cloneable

Direct Known Subclasses:

MappingClassifier, SamplingScoreBasedClassifier, ScoreClassifier, TrainSMBasedClassifier

public abstract class AbstractScoreBasedClassifier
extends AbstractClassifier

This class is the main class for all score based classifiers. Score based classifiers enable you to compute many different measures easily. For instance one can use the package "ROCR" in R to compute or plot many of them.

Author:

Jens Keilwagen, Jan Grau, Andre Gohr

Nested Class Summary

static class

AbstractScoreBasedClassifier.DoubleTableResult This class is for Results given as a table of double s.

Constructor Summary

AbstractScoreBasedClassifier(AlphabetContainer abc, int classes)
The constructor for a homogeneous classifier.

AbstractScoreBasedClassifier(AlphabetContainer abc, int classes, double classWeight)
The constructor for a homogeneous classifier.

AbstractScoreBasedClassifier(AlphabetContainer abc, int length, int classes)
The constructor for an inhomogeneous classifier.

AbstractScoreBasedClassifier(AlphabetContainer abc, int length, int classes, double classWeight)
The constructor for an inhomogeneous classifier.

AbstractScoreBasedClassifier(StringBuffer xml)
The standard constructor for the interface Storable.

Method Summary

protected void	check(DataSet s) This method checks if the given DataSet can be used.
protected void	check(Sequence seq) This method checks if the given Sequence can be used.
byte	classify(Sequence seq) This method classifies a sequence and returns the index i of the class to which the sequence is assigned with 0 < i < getNumberOfClasses().
protected byte	classify(Sequence seq, boolean check) This method classifies a Sequence.
AbstractScoreBasedClassifier	clone()
protected void	createDefaultClassWeights(int classes, double val) This method creates new class weights.
protected void	extractFurtherClassifierInfosFromXML(StringBuffer xml) Extracts further information of a classifier from an XML representation.
protected double	getClassWeight(int index) Returns the class weight for the class with a given index.
double[]	getClassWeights() Returns the specific class weights of a AbstractScoreBasedClassifier.
protected StringBuffer	getFurtherClassifierInfos() This method returns further information of a classifier as a StringBuffer.
protected double[][][]	getMultiClassScores(DataSet[] s) This method returns a multidimensional array with class specific scores.
int	getNumberOfClasses() Returns the number of classes that can be distinguished.
double[]	getPValue(DataSet candidates, DataSet bg) Returns the p-values for all Sequences in the DataSet candidates with respect to a given background DataSet .
double	getPValue(Sequence candidate, DataSet bg) Returns the p-value for a Sequence candidate with respect to a given background DataSet.
protected boolean	getResults(LinkedList list, DataSet[] s, PerformanceMeasureParameterSet params, boolean exceptionIfNotComputeable) This method computes the results for any evaluation of the classifier.
double	getScore(Sequence seq, int i) This method returns the score for a given Sequence and a given class.
protected abstract double	getScore(Sequence seq, int i, boolean check) This method returns the score for a given Sequence and a given class.
double[]	getScores(DataSet s) This method returns the scores of the classifier for any Sequence in the DataSet.
void	setClassWeights(boolean add, double... weights) Sets new class weights.
protected void	setClassWeights(boolean add, double[] weights, int start) Sets new class weights.
void	setThresholdClassWeights(boolean add, double t) Sets a new threshold for 2-class-classifiers.

Methods inherited from class de.jstacs.classifiers.AbstractClassifier

classify, evaluate, getAlphabetContainer, getCharacteristics, getClassifierAnnotation, getInstanceName, getLength, getNumericalCharacteristics, getXMLTag, isInitialized, toXML, train, train

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

AbstractScoreBasedClassifier

public AbstractScoreBasedClassifier(AlphabetContainer abc,
                                    int classes)

The constructor for a homogeneous classifier. Such a classifier can handle sequences of arbitrary length. The class weights are set initially to 0.

Parameters:

abc - the alphabets that are used

classes - the number of different classes

See Also:

AbstractScoreBasedClassifier(AlphabetContainer, int, int, double)

AbstractScoreBasedClassifier

public AbstractScoreBasedClassifier(AlphabetContainer abc,
                                    int classes,
                                    double classWeight)

The constructor for a homogeneous classifier. Such a classifier can handle sequences of arbitrary length. The class weights are set initially to classWeight.

Parameters:

abc - the alphabets that are used

classes - the number of different classes

classWeight - the value of all class weights

See Also:

AbstractScoreBasedClassifier(AlphabetContainer, int, int, double)

AbstractScoreBasedClassifier

public AbstractScoreBasedClassifier(AlphabetContainer abc,
                                    int length,
                                    int classes)

The constructor for an inhomogeneous classifier. Such a classifier can handle sequences of fixed length. The class weights are set initially to 0.

Parameters:

abc - the alphabets that are used

length - the length of the sequences that can be classified

classes - the number of different classes

See Also:

AbstractScoreBasedClassifier(AlphabetContainer, int, int, double)

AbstractScoreBasedClassifier

public AbstractScoreBasedClassifier(AlphabetContainer abc,
                                    int length,
                                    int classes,
                                    double classWeight)
                             throws IllegalArgumentException

The constructor for an inhomogeneous classifier. Such a classifier can handle sequences of fixed length. The class weights are set initially to classWeight.

Parameters:

abc - the alphabets that are used

length - the length of the sequences that can be classified

classes - the number of different classes

classWeight - the value of all class weights

Throws:

IllegalArgumentException - if the length and the possible length of the AlphabetContainer does not match or the number of classes is less than 2

See Also:

AbstractClassifier.AbstractClassifier(AlphabetContainer, int)

AbstractScoreBasedClassifier

public AbstractScoreBasedClassifier(StringBuffer xml)
                             throws NonParsableException

The standard constructor for the interface Storable. Creates a new AbstractScoreBasedClassifier out of its XML representation.

Parameters:

xml - the XML representation as StringBuffer

Throws:

NonParsableException - if the AbstractScoreBasedClassifier could not be reconstructed out of the XML representation (the StringBuffer could not be parsed)

See Also:

Storable, AbstractClassifier.AbstractClassifier(StringBuffer)

Method Detail

clone

public AbstractScoreBasedClassifier clone()
                                   throws CloneNotSupportedException

Overrides:

clone in class AbstractClassifier

Throws:

CloneNotSupportedException

classify

public byte classify(Sequence seq)
              throws Exception

Description copied from class: AbstractClassifier

This method classifies a sequence and returns the index i of the class to which the sequence is assigned with 0 < i < getNumberOfClasses().

This method should check that the sequence is defined over the underlying alphabet and length.

Specified by:

classify in class AbstractClassifier

Parameters:

seq - the sequence to be classified

Returns:

the index of the class to which the sequence is assigned

Throws:

Exception - if the classifier is not trained or something is wrong with the sequence

getMultiClassScores

protected double[][][] getMultiClassScores(DataSet[] s)
                                    throws Exception

Description copied from class: AbstractClassifier

This method returns a multidimensional array with class specific scores. The first dimension is for the data set, the second for the sequences, and the third for the classes. The entry result[d][n][c] returns the score of class c for sequence n of the data set s[d]. The class with the maximum score for any sequence is the predicted class of the sequence.

Overrides:

getMultiClassScores in class AbstractClassifier

Parameters:

s - the data sets

Returns:

a multidimensional array with class specific scores

Throws:

Exception - if the scores can not be computed

See Also:

AbstractClassifier.getResults(LinkedList, DataSet[], PerformanceMeasureParameterSet, boolean)

getResults

protected boolean getResults(LinkedList list,
                             DataSet[] s,
                             PerformanceMeasureParameterSet params,
                             boolean exceptionIfNotComputeable)
                      throws Exception

Description copied from class: AbstractClassifier

This method computes the results for any evaluation of the classifier.

Overrides:

getResults in class AbstractClassifier

Parameters:

list - a list adding the results

s - the array of DataSets

params - the current parameters

exceptionIfNotComputeable - indicates the method throws an Exception if a measure could not be computed

Returns:

a boolean indicating if all results are numerical

Throws:

Exception - if something went wrong

See Also:

AbstractClassifier.evaluate(PerformanceMeasureParameterSet, boolean, DataSet...), NumericalResult, Result

getClassWeights

public double[] getClassWeights()

Returns the specific class weights of a AbstractScoreBasedClassifier.

Returns:

the class weights of the classifier

getNumberOfClasses

public int getNumberOfClasses()

Description copied from class: AbstractClassifier

Returns the number of classes that can be distinguished. For example if distinguishing between foreground and background this method should return 2, even if you use a mixture model for either foreground or background.

Specified by:

getNumberOfClasses in class AbstractClassifier

Returns:

the number of classes that can be distinguished

getScore

public double getScore(Sequence seq,
                       int i)
                throws Exception

This method returns the score for a given Sequence and a given class.

Parameters:

seq - the given Sequence

i - the index of the class

Returns:

the score for a given Sequence and a given class

Throws:

Exception - if something went wrong

See Also:

getScore(Sequence, int, boolean)

setClassWeights

public final void setClassWeights(boolean add,
                                  double... weights)
                           throws ClassDimensionException

Sets new class weights.
The logarithmic probabilities of an item i given class 0 to class n are computed to classify this item into a class. The class weights are added to each of these logarithmic probabilities. As higher (relational) the class weight of class j, as more probable it becomes, that any item is classified into this class.
Class weights do not have to be logarithmic probabilities. If , the class weights may be interpreted as logarithmic class-a-priori-probabilities.

Parameters:

add - indicates if the class weights are added to the current class weights

weights - the array of weights, for each class the weight that is added in a classification

Throws:

ClassDimensionException - if something is wrong with the number of classes

setClassWeights

protected final void setClassWeights(boolean add,
                                     double[] weights,
                                     int start)

Sets new class weights.

Only for internal use.

Parameters:

add - indicates if the class weights are added to the current class weights

weights - an array of weights that might have more entries than the classifier has classes

start - the start index

See Also:

setClassWeights(boolean, double...)

setThresholdClassWeights

public final void setThresholdClassWeights(boolean add,
                                           double t)
                                    throws OperationNotSupportedException

Sets a new threshold for 2-class-classifiers.
Only available if this AbstractScoreBasedClassifier distinguishes between 2 classes 0 and 1. In this case, t will be interpreted as . A large t (greater than 0) makes the classifier to decide more often for class 1. A small t (smaller than 0) makes the classifier to decide more often for class 0.

Parameters:

add - indicates if the class weights are added to the current class weights

t - the new threshold

Throws:

OperationNotSupportedException - if the classifier is no 2-class-classifier

getFurtherClassifierInfos

protected StringBuffer getFurtherClassifierInfos()

Description copied from class: AbstractClassifier

This method returns further information of a classifier as a StringBuffer. This method is used by the method AbstractClassifier.toXML() and should not be made public.

Specified by:

getFurtherClassifierInfos in class AbstractClassifier

Returns:

further information of a classifier as a StringBuffer

See Also:

AbstractClassifier.toXML()

check

protected void check(DataSet s)
              throws NotTrainedException,
                     IllegalArgumentException

This method checks if the given DataSet can be used.

Parameters:

s - the DataSet to be checked

Throws:

NotTrainedException - if the classifier is not trained

IllegalArgumentException - if something is wrong with the DataSet s

See Also:

AlphabetContainer.checkConsistency(AlphabetContainer)

check

protected void check(Sequence seq)
              throws NotTrainedException,
                     IllegalArgumentException

This method checks if the given Sequence can be used.

Parameters:

seq - the Sequence to be checked

Throws:

NotTrainedException - if the classifier is not trained

IllegalArgumentException - if something is wrong with the Sequence seq

classify

protected byte classify(Sequence seq,
                        boolean check)
                 throws Exception

This method classifies a Sequence. It enables you to check the constraints (alphabets, length, AbstractClassifier.isInitialized() ).

Parameters:

seq - the Sequence

check - indicates if the constraints will be checked

Returns:

the index of the class the Sequence is assigned to

Throws:

Exception - if something went wrong

See Also:

check(Sequence)

createDefaultClassWeights

protected void createDefaultClassWeights(int classes,
                                         double val)
                                  throws IllegalArgumentException

This method creates new class weights. Each class weight has the same value val. So the class weights do not have any influence on the classification.

Parameters:

classes - the number of different classes

val - the value that is used for all classes

Throws:

IllegalArgumentException - if the number of classes is below 2

extractFurtherClassifierInfosFromXML

protected void extractFurtherClassifierInfosFromXML(StringBuffer xml)
                                             throws NonParsableException

Description copied from class: AbstractClassifier

Extracts further information of a classifier from an XML representation. This method is used by the method AbstractClassifier.fromXML(StringBuffer) and should not be made public.

Specified by:

extractFurtherClassifierInfosFromXML in class AbstractClassifier

Parameters:

xml - the XML representation as StringBuffer

Throws:

NonParsableException - if the information could not be parsed out of the XML representation (the StringBuffer could not be parsed)

See Also:

AbstractClassifier.fromXML(StringBuffer)

getClassWeight

protected double getClassWeight(int index)

Returns the class weight for the class with a given index.

Parameters:

index - the given index of the class

Returns:

the class weight

getScore

protected abstract double getScore(Sequence seq,
                                   int i,
                                   boolean check)
                            throws IllegalArgumentException,
                                   NotTrainedException,
                                   Exception

This method returns the score for a given Sequence and a given class.

Parameters:

seq - the Sequence

i - the index of the class

check - the switch to decide whether to check AlphabetContainer and the length of the Sequence or not

Returns:

the score for a given Sequence and a given class

Throws:

NotTrainedException - if the classifier is not trained

IllegalArgumentException - if something is wrong with the Sequence seq

Exception - if something went wrong

getScores

public double[] getScores(DataSet s)
                   throws Exception

This method returns the scores of the classifier for any Sequence in the DataSet. The scores are stored in the array according to the index of the Sequence in the DataSet.

Only for 2-class-classifiers.

Parameters:

s - the DataSet

Returns:

the array of scores

Throws:

Exception - if something went wrong

getPValue

public double getPValue(Sequence candidate,
                        DataSet bg)
                 throws Exception

Returns the p-value for a Sequence candidate with respect to a given background DataSet.

The p-value is the percentage of background Sequences that have a score that is at least as high as the score for the candidate.

It is not recommended to use this method in a for-loop. In such cases one should use the method that works on two DataSets.

Parameters:

candidate - the candidate Sequence

bg - the background DataSet

Returns:

the p-value for the Sequence candidate

Throws:

Exception - if something went wrong

See Also:

getPValue(DataSet, DataSet), PValueComputation.getPValue(double[], double)

getPValue

public double[] getPValue(DataSet candidates,
                          DataSet bg)
                   throws Exception

Returns the p-values for all Sequences in the DataSet candidates with respect to a given background DataSet .

The p-value is the percentage of background Sequences that have a score that is at least as high as the score for a Sequence in candidates.

Parameters:

candidates - the DataSet with candidate sequences

bg - the background sample

Returns:

the p-values for all sequences in candidates

Throws:

Exception - if something went wrong

See Also:

getPValue(Sequence, DataSet), PValueComputation.getPValue(double[], double)