jsat.classifiers

Class OneVSAll

java.lang.Object

jsat.classifiers.OneVSAll

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Parameterized

public class OneVSAll
extends Object
implements Classifier, Parameterized

This classifier turns any classifier, specifically binary classifiers, into multi-class classifiers. For a problem with k target classes, OneVsALl will create k different classifiers. Each one is a reducing of one class against all other classes. Then all k classifiers's results are summed to produce a final classifier

If the base learner is an instance of BinaryScoreClassifier, then the winning class label will be the associated classifier that produced the highest score.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
OneVSAll(Classifier baseClassifier) Creates a new One VS All classifier.
OneVSAll(Classifier baseClassifier, boolean concurrentTraining) Creates a new One VS All classifier.

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
OneVSAll	clone()
Parameter	getParameter(String paramName) Returns the parameter with the given name.
List<Parameter>	getParameters() Returns the list of parameters that can be altered for this learner.
void	setConcurrentTraining(boolean concurrentTraining) Controls what method of parallel training to use when trainC(jsat.classifiers.ClassificationDataSet, java.util.concurrent.ExecutorService) is called.
boolean	supportsWeightedData() Indicates whether the model knows how to train using weighted data points.
void	trainC(ClassificationDataSet dataSet) Trains the classifier and constructs a model for classification using the given data set.
void	trainC(ClassificationDataSet dataSet, ExecutorService threadPool) Trains the classifier and constructs a model for classification using the given data set.

Methods inherited from class java.lang.Object

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

Constructor Detail

OneVSAll

public OneVSAll(Classifier baseClassifier)

Creates a new One VS All classifier.

Parameters:

baseClassifier - the base classifier to replicate

See Also:

setConcurrentTraining(boolean)

OneVSAll

public OneVSAll(Classifier baseClassifier,
                boolean concurrentTraining)

Creates a new One VS All classifier.

Parameters:

baseClassifier - the base classifier to replicate

concurrentTraining - controls whether or not classifiers are trained simultaneously or using sequentially using their Classifier.trainC(jsat.classifiers.ClassificationDataSet, java.util.concurrent.ExecutorService) method.

See Also:

setConcurrentTraining(boolean)

Method Detail

setConcurrentTraining

public void setConcurrentTraining(boolean concurrentTraining)

Controls what method of parallel training to use when trainC(jsat.classifiers.ClassificationDataSet, java.util.concurrent.ExecutorService) is called. If set to true, each of the k classifiers will be trained in parallel, using their serial algorithms. If set to false, the k classifiers will be trained sequentially, calling the Classifier.trainC(jsat.classifiers.ClassificationDataSet, java.util.concurrent.ExecutorService) for each classifier.

This should be set to true for classifiers that do not support parallel training.
Setting this to true also uses k times the memory, since each classifier is being created and trained at the same time.

Parameters:

concurrentTraining - whether or not to train the classifiers in parallel

classify

public CategoricalResults classify(DataPoint data)

Description copied from interface: Classifier

Performs classification on the given data point.

Specified by:

classify in interface Classifier

Parameters:

data - the data point to classify

Returns:

the results of the classification.

trainC

public void trainC(ClassificationDataSet dataSet,
                   ExecutorService threadPool)

Description copied from interface: Classifier

Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.

Specified by:

trainC in interface Classifier

Parameters:

dataSet - the data set to train on

threadPool - the source of threads to use.

trainC

public void trainC(ClassificationDataSet dataSet)

Description copied from interface: Classifier

Trains the classifier and constructs a model for classification using the given data set.

Specified by:

trainC in interface Classifier

Parameters:

dataSet - the data set to train on

clone

public OneVSAll clone()

Specified by:

clone in interface Classifier

Overrides:

clone in class Object

supportsWeightedData

public boolean supportsWeightedData()

Description copied from interface: Classifier

Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.

Specified by:

supportsWeightedData in interface Classifier

Returns:

true if the model supports weighted data, false otherwise

getParameters

public List<Parameter> getParameters()

Description copied from interface: Parameterized

Returns the list of parameters that can be altered for this learner.

Specified by:

getParameters in interface Parameterized

Returns:

the list of parameters that can be altered for this learner.

getParameter

public Parameter getParameter(String paramName)

Description copied from interface: Parameterized

Returns the parameter with the given name. Two different strings may map to a single Parameter object. An ASCII only string, and a Unicode style string.

Specified by:

getParameter in interface Parameterized

Parameters:

paramName - the name of the parameter to obtain

Returns:

the Parameter in question, or null if no such named Parameter exists.