SimpleLogistic (weka-dev 3.7.10 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.functions.SimpleLogistic

All Implemented Interfaces:: Serializable, Cloneable, Classifier, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class SimpleLogistic
extends AbstractClassifier
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: 8034 $
Author:: Niels Landwehr, Marc Sumner
See Also:: Serialized Form

Constructor Summary

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

Method Summary

Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances data)` Builds the logistic regression using LogitBoost.
`double[]`	`distributionForInstance(Instance inst)` Returns class probabilities for an instance.
`Enumeration`	`enumerateMeasures()` Returns an enumeration of the additional measure names
`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(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).
`String[]`	`getOptions()` Gets the current settings of the Classifier.
`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.
`String`	`globalInfo()` Returns a string describing classifier
`String`	`heuristicStopTipText()` Returns the tip text for this property
`Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(String[] argv)` Main method for testing this class
`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).
`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(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.
`String`	`toString()` Returns a description of the logistic model (attributes/coefficients).
`String`	`useAICTipText()` Returns the tip text for this property
`String`	`useCrossValidationTipText()` Returns the tip text for this property
`String`	`weightTrimBetaTipText()` Returns the tip text for this property

Methods inherited from class weka.classifiers.AbstractClassifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, runClassifier, 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 Classifier
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class AbstractClassifier
    
    Returns:
    the capabilities of this classifier
    See Also:
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws Exception
```
    Builds the logistic regression using LogitBoost.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    data - the training data
    
    Throws:
    
    Exception - if something goes wrong
  - distributionForInstance
```
public double[] distributionForInstance(Instance inst)
                                 throws Exception
```
    Returns class probabilities for an instance.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    inst - the instance to compute the probabilities for
    
    Returns:
    the probabilities
    
    Throws:
    
    Exception - if distribution can't be computed successfully
  - listOptions
```
public Enumeration listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class AbstractClassifier
    
    Returns:
    an enumeration of all the available options.
  - setOptions
```
public void setOptions(String[] options)
                throws 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 AbstractClassifier
    
    Parameters:
    options - the list of options as an array of strings
    
    Throws:
    
    Exception - if an option is not supported
  - getOptions
```
public String[] getOptions()
```
    Gets the current settings of the Classifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class AbstractClassifier
    
    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 String toString()
```
    Returns a description of the logistic model (attributes/coefficients).
    
    Overrides:
    
    toString in class 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 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(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:
    
    IllegalArgumentException - if the named measure is not supported
  - globalInfo
```
public 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
  - numBoostingIterationsTipText
```
public String numBoostingIterationsTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - useCrossValidationTipText
```
public String useCrossValidationTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - errorOnProbabilitiesTipText
```
public String errorOnProbabilitiesTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - maxBoostingIterationsTipText
```
public String maxBoostingIterationsTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - heuristicStopTipText
```
public String heuristicStopTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - weightTrimBetaTipText
```
public String weightTrimBetaTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - useAICTipText
```
public String useAICTipText()
```
    Returns the tip text for this property
    
    Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getRevision
```
public String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClassifier
    
    Returns:
    the revision
  - main
```
public static void main(String[] argv)
```
    Main method for testing this class
    
    Parameters:
    argv - commandline options

Class SimpleLogistic

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Constructor Detail

SimpleLogistic

SimpleLogistic

Method Detail

getCapabilities

buildClassifier

distributionForInstance

listOptions

setOptions

getOptions

getNumBoostingIterations

getUseCrossValidation

getErrorOnProbabilities

getMaxBoostingIterations

getHeuristicStop

getWeightTrimBeta

getUseAIC

setNumBoostingIterations

setUseCrossValidation

setErrorOnProbabilities

setMaxBoostingIterations

setHeuristicStop

setWeightTrimBeta

setUseAIC

getNumRegressions

toString

measureAttributesUsed

enumerateMeasures

getMeasure

globalInfo

getTechnicalInformation

numBoostingIterationsTipText

useCrossValidationTipText

errorOnProbabilitiesTipText

maxBoostingIterationsTipText

heuristicStopTipText

weightTrimBetaTipText

useAICTipText

getRevision

main