com.rapidminer.operator.learner.meta
Class BayBoostModel

java.lang.Object
  com.rapidminer.operator.AbstractIOObject
      com.rapidminer.operator.ResultObjectAdapter
          com.rapidminer.operator.AbstractModel
              com.rapidminer.operator.learner.PredictionModel
                  com.rapidminer.operator.learner.meta.BayBoostModel

All Implemented Interfaces:

IOObject, Model, ResultObject, Saveable, Readable, Reportable, LoggingHandler, java.io.Serializable

public class BayBoostModel
extends PredictionModel

A model for the Bayesian Boosting algorithm by Martin Scholz.

Author:

Martin Scholz

See Also:

Serialized Form

Constructor Summary

BayBoostModel(ExampleSet exampleSet, java.util.List<BayBoostBaseModelInfo> modelInfos, double[] priors)

Method Summary

static boolean	adjustIntermediateProducts(double[] products, double[] liftFactors) Helper method to adjust the intermediate products during model application.
BayBoostBaseModelInfo	getBayBoostBaseModelInfo(int index)
ContingencyMatrix	getContingencyMatrix(int index) Getter method for a specific confusion matrix
Model	getModel(int index) Getter method for embedded models
double[]	getModelWeights() This method is only supported for boolean target attributes.
int	getNumberOfModels()
double[]	getPriors() Getter for the prior array
java.awt.Component	getVisualizationComponent(IOContainer container) Returns a editor pane that displays the ResultObjectAdapter.toResultString() result encoded as html.
ExampleSet	performPrediction(ExampleSet exampleSet, Attribute predictedLabel) Iterates over all models and returns the class with maximum likelihood.
void	setMaxModelNumber(int numModels) Using this setter with a positive value makes the model discard all but the specified number of base models.
void	setParameter(java.lang.String name, java.lang.String value) Setting the parameter MAX_MODEL_NUMBER allows to discard all but the first n models for specified n.
java.lang.String	toString()

Methods inherited from class com.rapidminer.operator.learner.PredictionModel

apply, checkCompatibility, copyPredictedLabel, createPredictedLabel, getLabel, removePredictedLabel, removePredictedLabel

Methods inherited from class com.rapidminer.operator.AbstractModel

getExtension, getFileDescription, getName, getResultIcon, getTrainingHeader, isUpdatable, setParameter, updateModel

Methods inherited from class com.rapidminer.operator.ResultObjectAdapter

addAction, getActions, isSavable, log, logError, logNote, logWarning, save, toHTML, toResultString

Methods inherited from class com.rapidminer.operator.AbstractIOObject

copy, getLog, getSource, initWriting, read, setLoggingHandler, setSource, write

Methods inherited from class java.lang.Object

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

Methods inherited from interface com.rapidminer.operator.ResultObject

getActions, toResultString

Methods inherited from interface com.rapidminer.operator.IOObject

copy, getLog, getSource, setLoggingHandler, setSource, write

Methods inherited from interface com.rapidminer.operator.Saveable

isSavable, save

Constructor Detail

BayBoostModel

public BayBoostModel(ExampleSet exampleSet,
                     java.util.List<BayBoostBaseModelInfo> modelInfos,
                     double[] priors)

Parameters:

exampleSet - the example set used for training

modelInfos - a List of Object[2] arrays, each entry holding a model and a double[][] array containing weights for all prediction/label combinations.

priors - an array of the prior probabilities of labels

Method Detail

getBayBoostBaseModelInfo

public BayBoostBaseModelInfo getBayBoostBaseModelInfo(int index)

setParameter

public void setParameter(java.lang.String name,
                         java.lang.String value)
                  throws OperatorException

Setting the parameter MAX_MODEL_NUMBER allows to discard all but the first n models for specified n. CONV_TO_CRISP allows to set another threshold than 0.5 for boolean prediction problems.

Throws:

OperatorException

setMaxModelNumber

public void setMaxModelNumber(int numModels)

Using this setter with a positive value makes the model discard all but the specified number of base models. A value of -1 turns off this option.

getVisualizationComponent

public java.awt.Component getVisualizationComponent(IOContainer container)

Description copied from class: ResultObjectAdapter

Returns a editor pane that displays the ResultObjectAdapter.toResultString() result encoded as html. Please note that the returned pane is already enclosed by a scroll pane. If you overwrite this method you should again ensure that the returned component is scrollable. The given container is totally ignored.

Specified by:

getVisualizationComponent in interface ResultObject

Overrides:

getVisualizationComponent in class ResultObjectAdapter

toString

public java.lang.String toString()

Specified by:

toString in interface Readable

Overrides:

toString in class PredictionModel

Returns:

a String representation of this boosting model.

getNumberOfModels

public int getNumberOfModels()

Returns:

the number of embedded models

getPriors

public double[] getPriors()

Getter for the prior array

getModel

public Model getModel(int index)

Getter method for embedded models

Parameters:

index - the number of a model part of this boost model

Returns:

binary or nominal decision model for the given classification index.

getContingencyMatrix

public ContingencyMatrix getContingencyMatrix(int index)

Getter method for a specific confusion matrix

Parameters:

index - the number of the model for which to read the confusion matrix

Returns:

a ConfusionMatrix object

performPrediction

public ExampleSet performPrediction(ExampleSet exampleSet,
                                    Attribute predictedLabel)
                             throws OperatorException

Iterates over all models and returns the class with maximum likelihood.

Specified by:

performPrediction in class PredictionModel

Parameters:

exampleSet - the set of examples to be classified

predictedLabel - the label that finally holds the predictions

Throws:

OperatorException

adjustIntermediateProducts

public static boolean adjustIntermediateProducts(double[] products,
                                                 double[] liftFactors)

Helper method to adjust the intermediate products during model application.

Parameters:

products - the intermediate products, these values are changed by the method

liftFactors - the factor vector that applies for the prediction for the current example

Returns:

true iff the class is deterministically known after applying this method

getModelWeights

public double[] getModelWeights()
                         throws OperatorException

This method is only supported for boolean target attributes. It computes a flattened version of model weights. In constrast to the original version the final predictions are additive logarithms of the lift ratios, additively rescaled so that the prediction false of model i produces -i if true produces weight i. This means that only one weight per model is required. The first component of the returned array is the part that is independent of any prediction, the i-th component is the weight of model i. The (log-)linear model predicts depending on whether the linear combination of predictions (either -1 or 1) is greater than 0 or not. Infinite values are problematic, so a min/max value is used.

Returns:

the flattened weights of all models

Throws:

OperatorException