SimpleLogistic

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weka.classifiers.functions
Class SimpleLogistic

java.lang.Object
  weka.classifiers.Classifier
      weka.classifiers.functions.SimpleLogistic

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class SimpleLogistic
extends Classifier
implements OptionHandler, AdditionalMeasureProducer, WeightedInstancesHandler, TechnicalInformationHandler
extends Classifier
implements OptionHandler, AdditionalMeasureProducer, WeightedInstancesHandler, TechnicalInformationHandler

Classifier for building linear logistic regression models. LogitBoost with simple regression functions as base learners is used for fitting the logistic models. The optimal number of LogitBoost iterations to perform is cross-validated, which leads to automatic attribute selection. For more information see:
Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees.

Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree Induction. In: 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, 675-683, 2005.

BibTeX:

 @article{Landwehr2005,
    author = {Niels Landwehr and Mark Hall and Eibe Frank},
    booktitle = {Machine Learning},
    number = {1-2},
    pages = {161-205},
    title = {Logistic Model Trees},
    volume = {95},
    year = {2005}
 }
 
 @inproceedings{Sumner2005,
    author = {Marc Sumner and Eibe Frank and Mark Hall},
    booktitle = {9th European Conference on Principles and Practice of Knowledge Discovery in Databases},
    pages = {675-683},
    publisher = {Springer},
    title = {Speeding up Logistic Model Tree Induction},
    year = {2005}
 }

Valid options are:

 -I <iterations>
  Set fixed number of iterations for LogitBoost

 -S
  Use stopping criterion on training set (instead of
  cross-validation)

 -P
  Use error on probabilities (rmse) instead of
  misclassification error for stopping criterion

 -M <iterations>
  Set maximum number of boosting iterations

 -H <iterations>
  Set parameter for heuristic for early stopping of
  LogitBoost.
  If enabled, the minimum is selected greedily, stopping
  if the current minimum has not changed for iter iterations.
  By default, heuristic is enabled with value 50. Set to
  zero to disable heuristic.

 -W <beta>
  Set beta for weight trimming for LogitBoost. Set to 0 for no weight trimming.

 -A
  The AIC is used to choose the best iteration (instead of CV or training error).

Version:: $Revision: 5523 $
Author:: Niels Landwehr, Marc Sumner
See Also:: Serialized Form

Constructor Summary
`SimpleLogistic()` Constructor for creating SimpleLogistic object with standard options.
`SimpleLogistic(int numBoostingIterations, boolean useCrossValidation, boolean errorOnProbabilities)` Constructor for creating SimpleLogistic object.

Method Summary
`void`	`buildClassifier(Instances data)` Builds the logistic regression using LogitBoost.
`double[]`	`distributionForInstance(Instance inst)` Returns class probabilities for an instance.
`java.util.Enumeration`	`enumerateMeasures()` Returns an enumeration of the additional measure names
`java.lang.String`	`errorOnProbabilitiesTipText()` Returns the tip text for this property
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`boolean`	`getErrorOnProbabilities()` Get the value of errorOnProbabilities.
`int`	`getHeuristicStop()` Get the value of heuristicStop.
`int`	`getMaxBoostingIterations()` Get the value of maxBoostingIterations.
`double`	`getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure
`int`	`getNumBoostingIterations()` Get the value of numBoostingIterations.
`int`	`getNumRegressions()` Get the number of LogitBoost iterations performed (= the number of regression functions fit by LogitBoost).
`java.lang.String[]`	`getOptions()` Gets the current settings of the Classifier.
`java.lang.String`	`getRevision()` Returns the revision string.
`TechnicalInformation`	`getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`boolean`	`getUseAIC()` Get the value of useAIC.
`boolean`	`getUseCrossValidation()` Get the value of useCrossValidation.
`double`	`getWeightTrimBeta()` Get the value of weightTrimBeta.
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`java.lang.String`	`heuristicStopTipText()` Returns the tip text for this property
`java.util.Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] argv)` Main method for testing this class
`java.lang.String`	`maxBoostingIterationsTipText()` Returns the tip text for this property
`double`	`measureAttributesUsed()` Returns the fraction of all attributes in the data that are used in the logistic model (in percent).
`java.lang.String`	`numBoostingIterationsTipText()` Returns the tip text for this property
`void`	`setErrorOnProbabilities(boolean l)` Set the value of errorOnProbabilities.
`void`	`setHeuristicStop(int n)` Set the value of heuristicStop.
`void`	`setMaxBoostingIterations(int n)` Set the value of maxBoostingIterations.
`void`	`setNumBoostingIterations(int n)` Set the value of numBoostingIterations.
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setUseAIC(boolean c)` Set the value of useAIC.
`void`	`setUseCrossValidation(boolean l)` Set the value of useCrossValidation.
`void`	`setWeightTrimBeta(double n)` Set the value of weightTrimBeta.
`java.lang.String`	`toString()` Returns a description of the logistic model (attributes/coefficients).
`java.lang.String`	`useAICTipText()` Returns the tip text for this property
`java.lang.String`	`useCrossValidationTipText()` Returns the tip text for this property
`java.lang.String`	`weightTrimBetaTipText()` Returns the tip text for this property

Methods inherited from class weka.classifiers.Classifier
`classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug`

Methods inherited from class java.lang.Object
`equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

Constructor Detail

SimpleLogistic

public SimpleLogistic()

Constructor for creating SimpleLogistic object with standard options.

SimpleLogistic

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

Constructor for creating SimpleLogistic object.

Parameters:: numBoostingIterations - if non-negative, use this as fixed number of iterations for LogitBoost; useCrossValidation - cross-validate number of LogitBoost iterations.; errorOnProbabilities - minimize error on probabilities instead of misclassification error

Method Detail

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:: getCapabilities in interface CapabilitiesHandler
Overrides:: getCapabilities in class Classifier

Returns:: the capabilities of this classifier
See Also:: Capabilities

buildClassifier

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

Builds the logistic regression using LogitBoost.

Specified by:: buildClassifier in class Classifier

Parameters:: data - the training data
Throws:: java.lang.Exception - if something goes wrong

distributionForInstance

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

Returns class probabilities for an instance.

Overrides:: distributionForInstance in class Classifier

Parameters:: inst - the instance to compute the probabilities for
Returns:: the probabilities
Throws:: java.lang.Exception - if distribution can't be computed successfully

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:: listOptions in interface OptionHandler
Overrides:: listOptions in class Classifier

Returns:: an enumeration of all the available options.

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options.

Valid options are:

 -I <iterations>
  Set fixed number of iterations for LogitBoost

 -S
  Use stopping criterion on training set (instead of
  cross-validation)

 -P
  Use error on probabilities (rmse) instead of
  misclassification error for stopping criterion

 -M <iterations>
  Set maximum number of boosting iterations

 -H <iterations>
  Set parameter for heuristic for early stopping of
  LogitBoost.
  If enabled, the minimum is selected greedily, stopping
  if the current minimum has not changed for iter iterations.
  By default, heuristic is enabled with value 50. Set to
  zero to disable heuristic.

 -W <beta>
  Set beta for weight trimming for LogitBoost. Set to 0 for no weight trimming.

 -A
  The AIC is used to choose the best iteration (instead of CV or training error).

Specified by:: setOptions in interface OptionHandler
Overrides:: setOptions in class Classifier

Parameters:: options - the list of options as an array of strings
Throws:: java.lang.Exception - if an option is not supported

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the Classifier.

Specified by:: getOptions in interface OptionHandler
Overrides:: getOptions in class Classifier

Returns:: an array of strings suitable for passing to setOptions

getNumBoostingIterations

public int getNumBoostingIterations()

Get the value of numBoostingIterations.

Returns:: the number of boosting iterations

getUseCrossValidation

public boolean getUseCrossValidation()

Get the value of useCrossValidation.

Returns:: true if cross-validation is used

getErrorOnProbabilities

public boolean getErrorOnProbabilities()

Get the value of errorOnProbabilities.

Returns:: If true, use minimize error on probabilities instead of misclassification error

getMaxBoostingIterations

public int getMaxBoostingIterations()

Get the value of maxBoostingIterations.

Returns:: the maximum number of boosting iterations

getHeuristicStop

public int getHeuristicStop()

Get the value of heuristicStop.

Returns:: the value of heuristicStop

getWeightTrimBeta

public double getWeightTrimBeta()

Get the value of weightTrimBeta.

getUseAIC

public boolean getUseAIC()

Get the value of useAIC.

Returns:: Value of useAIC.

setNumBoostingIterations

public void setNumBoostingIterations(int n)

Set the value of numBoostingIterations.

Parameters:: n - the number of boosting iterations

setUseCrossValidation

public void setUseCrossValidation(boolean l)

Set the value of useCrossValidation.

Parameters:: l - whether to use cross-validation

setErrorOnProbabilities

public void setErrorOnProbabilities(boolean l)

Set the value of errorOnProbabilities.

Parameters:: l - If true, use minimize error on probabilities instead of misclassification error

setMaxBoostingIterations

public void setMaxBoostingIterations(int n)

Set the value of maxBoostingIterations.

Parameters:: n - the maximum number of boosting iterations

setHeuristicStop

public void setHeuristicStop(int n)

Set the value of heuristicStop.

Parameters:: n - the value of heuristicStop

setWeightTrimBeta

public void setWeightTrimBeta(double n)

Set the value of weightTrimBeta.

setUseAIC

public void setUseAIC(boolean c)

Set the value of useAIC.

Parameters:: c - Value to assign to useAIC.

getNumRegressions

public int getNumRegressions()

Get the number of LogitBoost iterations performed (= the number of regression functions fit by LogitBoost).

Returns:: the number of LogitBoost iterations performed

toString

public java.lang.String toString()

Returns a description of the logistic model (attributes/coefficients).

Overrides:: toString in class java.lang.Object

Returns:: the model as string

measureAttributesUsed

public double measureAttributesUsed()

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:: percentage of attributes used in the model

enumerateMeasures

public java.util.Enumeration enumerateMeasures()

Returns an enumeration of the additional measure names

Specified by:: enumerateMeasures in interface AdditionalMeasureProducer

Returns:: an enumeration of the measure names

getMeasure

public double getMeasure(java.lang.String additionalMeasureName)

Returns the value of the named measure

Specified by:: getMeasure in interface AdditionalMeasureProducer

Parameters:: additionalMeasureName - the name of the measure to query for its value
Returns:: the value of the named measure
Throws:: java.lang.IllegalArgumentException - if the named measure is not supported

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Returns:: a description suitable for displaying in the explorer/experimenter gui

getTechnicalInformation

public TechnicalInformation getTechnicalInformation()

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

Specified by:: getTechnicalInformation in interface TechnicalInformationHandler

Returns:: the technical information about this class