weka.classifiers.trees.lmt

Class LogisticBase

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.trees.lmt.LogisticBase

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, WeightedInstancesHandler

Direct Known Subclasses:

LMTNode

public class LogisticBase
extends AbstractClassifier
implements WeightedInstancesHandler

Base/helper class for building logistic regression models with the LogitBoost algorithm. Used for building logistic model trees (weka.classifiers.trees.lmt.LMT) and standalone logistic regression (weka.classifiers.functions.SimpleLogistic). Valid options are:

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 

Version:

$Revision: 14202 $

Author:

Niels Landwehr, Marc Sumner

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
LogisticBase() Constructor that creates LogisticBase object with standard options.
LogisticBase(int numBoostingIterations, boolean useCrossValidation, boolean errorOnProbabilities) Constructor to create LogisticBase object.

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances data) Builds the logistic regression model usiing LogitBoost.
void	cleanup() Cleanup in order to save memory.
double[]	distributionForInstance(Instance instance) Returns class probabilities for an instance.
int	getMaxIterations() Returns the maxIterations parameter.
int	getNumRegressions() The number of LogitBoost iterations performed (= the number of simple regression functions fit).
java.lang.String	getRevision() Returns the revision string.
boolean	getUseAIC() Get the value of useAIC.
int[][]	getUsedAttributes() Returns an array of the indices of the attributes used in the logistic model.
double	getWeightTrimBeta() Get the value of weightTrimBeta.
double	percentAttributesUsed() Returns the fraction of all attributes in the data that are used in the logistic model (in percent).
void	setHeuristicStop(int heuristicStop) Sets the option "heuristicStop".
void	setMaxIterations(int maxIterations) Sets the parameter "maxIterations".
void	setUseAIC(boolean c) Set the value of useAIC.
void	setWeightTrimBeta(double w) Sets the option "weightTrimBeta".
java.lang.String	toString() Returns a description of the logistic model (i.e., attributes and coefficients).

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getCapabilities, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, getOptions, implementsMoreEfficientBatchPrediction, listOptions, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces, setOptions

Methods inherited from class java.lang.Object

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

Methods inherited from interface weka.core.OptionHandler

makeCopy

Constructor Detail

LogisticBase

public LogisticBase()

Constructor that creates LogisticBase object with standard options.

LogisticBase

public LogisticBase(int numBoostingIterations,
                    boolean useCrossValidation,
                    boolean errorOnProbabilities)

Constructor to create LogisticBase object.

Parameters:

numBoostingIterations - fixed number of iterations for LogitBoost (if negative, use cross-validation or stopping criterion on the training data).

useCrossValidation - cross-validate number of LogitBoost iterations (if false, use stopping criterion on the training data).

errorOnProbabilities - if true, use error on probabilities instead of misclassification for stopping criterion of LogitBoost

Method Detail

buildClassifier

public void buildClassifier(Instances data)
                     throws java.lang.Exception

Builds the logistic regression model usiing LogitBoost.

Specified by:

buildClassifier in interface Classifier

Parameters:

data - the training data

Throws:

java.lang.Exception - if something goes wrong

getUsedAttributes

public int[][] getUsedAttributes()

Returns an array of the indices of the attributes used in the logistic model. The first dimension is the class, the second dimension holds a list of attribute indices. Attribute indices start at zero.

Returns:

the array of attribute indices

getNumRegressions

public int getNumRegressions()

The number of LogitBoost iterations performed (= the number of simple regression functions fit).

Returns:

the number of LogitBoost iterations performed

getWeightTrimBeta

public double getWeightTrimBeta()

Get the value of weightTrimBeta.

Returns:

Value of weightTrimBeta.

getUseAIC

public boolean getUseAIC()

Get the value of useAIC.

Returns:

Value of useAIC.

setMaxIterations

public void setMaxIterations(int maxIterations)

Sets the parameter "maxIterations".

Parameters:

maxIterations - the maximum iterations

setHeuristicStop

public void setHeuristicStop(int heuristicStop)

Sets the option "heuristicStop".

Parameters:

heuristicStop - the heuristic stop to use

setWeightTrimBeta

public void setWeightTrimBeta(double w)

Sets the option "weightTrimBeta".

setUseAIC

public void setUseAIC(boolean c)

Set the value of useAIC.

Parameters:

c - Value to assign to useAIC.

getMaxIterations

public int getMaxIterations()

Returns the maxIterations parameter.

Returns:

the maximum iteration

percentAttributesUsed

public double percentAttributesUsed()

Returns the fraction of all attributes in the data that are used in the logistic model (in percent). An attribute is used in the model if it is used in any of the models for the different classes.

Returns:

the fraction of all attributes that are used

toString

public java.lang.String toString()

Returns a description of the logistic model (i.e., attributes and coefficients).

Overrides:

toString in class java.lang.Object

Returns:

the description of the model

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception

Returns class probabilities for an instance.

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

Parameters:

instance - the instance to compute the distribution for

Returns:

the class probabilities

Throws:

java.lang.Exception - if distribution can't be computed successfully

cleanup

public void cleanup()

Cleanup in order to save memory.

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class AbstractClassifier

Returns:

the revision