jsat.classifiers.boosting

Class UpdatableStacking

java.lang.Object

jsat.classifiers.boosting.UpdatableStacking

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, UpdateableClassifier, Regressor, UpdateableRegressor

public class UpdatableStacking
extends java.lang.Object
implements UpdateableClassifier, UpdateableRegressor

This provides an implementation of the Stacking ensemble method meant for Updatable models. Stacking learns several base classifiers and a top level classifier learns to predict the target based on the outputs of all the ensambled models. Historically a linear model (such as LinearBatch) is used, which translates to learning a weighted vote of the classifier outputs. However any classifier may be used so long as it supports the desired target type.

Note, that Stacking tends to work best when the base classifiers produce reasonable probability estimates.
Stacking supports weighted data instances if the aggregating model does.
See: Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5, 241–259.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
UpdatableStacking(UpdatableStacking toCopy) Copy constructor
UpdatableStacking(UpdateableClassifier aggregatingClassifier, java.util.List<UpdateableClassifier> baseClassifiers) Creates a new Stacking classifier
UpdatableStacking(UpdateableClassifier aggregatingClassifier, UpdateableClassifier... baseClassifiers) Creates a new Stacking classifier.
UpdatableStacking(UpdateableRegressor aggregatingRegressor, java.util.List<UpdateableRegressor> baseRegressors) Creates a new Stacking regressor
UpdatableStacking(UpdateableRegressor aggregatingRegressor, UpdateableRegressor... baseRegressors) Creates a new Stacking regressor.

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
UpdatableStacking	clone()
double	regress(DataPoint data)
void	setUp(CategoricalData[] categoricalAttributes, int numericAttributes) Prepares the classifier to begin learning from its UpdateableRegressor.update(jsat.classifiers.DataPoint, double) method.
void	setUp(CategoricalData[] categoricalAttributes, int numericAttributes, CategoricalData predicting) Prepares the classifier to begin learning from its UpdateableClassifier.update(jsat.classifiers.DataPoint, int) method.
boolean	supportsWeightedData() Indicates whether the model knows how to train using weighted data points.
void	train(ClassificationDataSet dataSet) Trains the classifier and constructs a model for classification using the given data set.
void	train(ClassificationDataSet dataSet, boolean parallel) Trains the classifier and constructs a model for classification using the given data set.
void	train(RegressionDataSet dataSet)
void	train(RegressionDataSet dataSet, boolean parallel)
void	update(DataPoint dataPoint, double targetValue) Updates the classifier by giving it a new data point to learn from.
void	update(DataPoint dataPoint, int targetClass) Updates the classifier by giving it a new data point to learn from.

Methods inherited from class java.lang.Object

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

Constructor Detail

UpdatableStacking

public UpdatableStacking(UpdateableClassifier aggregatingClassifier,
                         java.util.List<UpdateableClassifier> baseClassifiers)

Creates a new Stacking classifier

Parameters:

aggregatingClassifier - the classifier used to merge the results of all the input classifiers

baseClassifiers - the list of base classifiers to ensemble

UpdatableStacking

public UpdatableStacking(UpdateableClassifier aggregatingClassifier,
                         UpdateableClassifier... baseClassifiers)

Creates a new Stacking classifier.

Parameters:

aggregatingClassifier - the classifier used to merge the results of all the input classifiers

baseClassifiers - the array of base classifiers to ensemble

UpdatableStacking

public UpdatableStacking(UpdateableRegressor aggregatingRegressor,
                         java.util.List<UpdateableRegressor> baseRegressors)

Creates a new Stacking regressor

Parameters:

aggregatingRegressor - the regressor used to merge the results of all the input classifiers

baseRegressors - the list of base regressors to ensemble

UpdatableStacking

public UpdatableStacking(UpdateableRegressor aggregatingRegressor,
                         UpdateableRegressor... baseRegressors)

Creates a new Stacking regressor.

Parameters:

aggregatingRegressor - the regressor used to merge the results of all the input classifiers

baseRegressors - the array of base regressors to ensemble

UpdatableStacking

public UpdatableStacking(UpdatableStacking toCopy)

Copy constructor

Parameters:

toCopy - the object to copy

Method Detail

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.

setUp

public void setUp(CategoricalData[] categoricalAttributes,
                  int numericAttributes,
                  CategoricalData predicting)

Description copied from interface: UpdateableClassifier

Prepares the classifier to begin learning from its UpdateableClassifier.update(jsat.classifiers.DataPoint, int) method.

Specified by:

setUp in interface UpdateableClassifier

Parameters:

categoricalAttributes - an array containing the categorical attributes that will be in each data point

numericAttributes - the number of numeric attributes that will be in each data point

predicting - the information for the target class that will be predicted

update

public void update(DataPoint dataPoint,
                   int targetClass)

Description copied from interface: UpdateableClassifier

Updates the classifier by giving it a new data point to learn from.

Specified by:

update in interface UpdateableClassifier

Parameters:

dataPoint - the data point to learn

targetClass - the target class of the data point

setUp

public void setUp(CategoricalData[] categoricalAttributes,
                  int numericAttributes)

Description copied from interface: UpdateableRegressor

Prepares the classifier to begin learning from its UpdateableRegressor.update(jsat.classifiers.DataPoint, double) method.

Specified by:

setUp in interface UpdateableRegressor

Parameters:

categoricalAttributes - an array containing the categorical attributes that will be in each data point

numericAttributes - the number of numeric attributes that will be in each data point

update

public void update(DataPoint dataPoint,
                   double targetValue)

Description copied from interface: UpdateableRegressor

Updates the classifier by giving it a new data point to learn from.

Specified by:

update in interface UpdateableRegressor

Parameters:

dataPoint - the data point to learn

targetValue - the target value of the data point

train

public void train(ClassificationDataSet dataSet,
                  boolean parallel)

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:

train in interface Classifier

Parameters:

dataSet - the data set to train on

parallel - true if multiple threads should be used to train the model. false if it should be done in a single threaded manner.

train

public void train(ClassificationDataSet dataSet)

Description copied from interface: Classifier

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

Specified by:

train in interface Classifier

Parameters:

dataSet - the data set to train on

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

Specified by:

supportsWeightedData in interface Regressor

Returns:

true if the model supports weighted data, false otherwise

regress

public double regress(DataPoint data)

Specified by:

regress in interface Regressor

train

public void train(RegressionDataSet dataSet,
                  boolean parallel)

Specified by:

train in interface Regressor

train

public void train(RegressionDataSet dataSet)

Specified by:

train in interface Regressor

clone

public UpdatableStacking clone()

Specified by:

clone in interface Classifier

Specified by:

clone in interface UpdateableClassifier

Specified by:

clone in interface Regressor

Specified by:

clone in interface UpdateableRegressor

Overrides:

clone in class java.lang.Object