Package weka.classifiers.functions
Class Logistic
- java.lang.Object
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- weka.classifiers.Classifier
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- weka.classifiers.functions.Logistic
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- All Implemented Interfaces:
java.io.Serializable
,java.lang.Cloneable
,CapabilitiesHandler
,OptionHandler
,RevisionHandler
,TechnicalInformationHandler
,WeightedInstancesHandler
public class Logistic extends Classifier implements OptionHandler, WeightedInstancesHandler, TechnicalInformationHandler
Class for building and using a multinomial logistic regression model with a ridge estimator.
There are some modifications, however, compared to the paper of leCessie and van Houwelingen(1992):
If there are k classes for n instances with m attributes, the parameter matrix B to be calculated will be an m*(k-1) matrix.
The probability for class j with the exception of the last class is
Pj(Xi) = exp(XiBj)/((sum[j=1..(k-1)]exp(Xi*Bj))+1)
The last class has probability
1-(sum[j=1..(k-1)]Pj(Xi))
= 1/((sum[j=1..(k-1)]exp(Xi*Bj))+1)
The (negative) multinomial log-likelihood is thus:
L = -sum[i=1..n]{
sum[j=1..(k-1)](Yij * ln(Pj(Xi)))
+(1 - (sum[j=1..(k-1)]Yij))
* ln(1 - sum[j=1..(k-1)]Pj(Xi))
} + ridge * (B^2)
In order to find the matrix B for which L is minimised, a Quasi-Newton Method is used to search for the optimized values of the m*(k-1) variables. Note that before we use the optimization procedure, we 'squeeze' the matrix B into a m*(k-1) vector. For details of the optimization procedure, please check weka.core.Optimization class.
Although original Logistic Regression does not deal with instance weights, we modify the algorithm a little bit to handle the instance weights.
For more information see:
le Cessie, S., van Houwelingen, J.C. (1992). Ridge Estimators in Logistic Regression. Applied Statistics. 41(1):191-201.
Note: Missing values are replaced using a ReplaceMissingValuesFilter, and nominal attributes are transformed into numeric attributes using a NominalToBinaryFilter. BibTeX:@article{leCessie1992, author = {le Cessie, S. and van Houwelingen, J.C.}, journal = {Applied Statistics}, number = {1}, pages = {191-201}, title = {Ridge Estimators in Logistic Regression}, volume = {41}, year = {1992} }
Valid options are:-D Turn on debugging output.
-R <ridge> Set the ridge in the log-likelihood.
-M <number> Set the maximum number of iterations (default -1, until convergence).
- Version:
- $Revision: 5523 $
- Author:
- Xin Xu (xx5@cs.waikato.ac.nz)
- See Also:
- Serialized Form
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Constructor Summary
Constructors Constructor Description Logistic()
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
buildClassifier(Instances train)
Builds the classifierdouble[][]
coefficients()
Returns the coefficients for this logistic model.java.lang.String
debugTipText()
Returns the tip text for this propertydouble[]
distributionForInstance(Instance instance)
Computes the distribution for a given instanceCapabilities
getCapabilities()
Returns default capabilities of the classifier.boolean
getDebug()
Gets whether debugging output will be printed.int
getMaxIts()
Get the value of MaxIts.java.lang.String[]
getOptions()
Gets the current settings of the classifier.java.lang.String
getRevision()
Returns the revision string.double
getRidge()
Gets the ridge in the log-likelihood.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.java.lang.String
globalInfo()
Returns a string describing this classifierjava.util.Enumeration
listOptions()
Returns an enumeration describing the available optionsstatic void
main(java.lang.String[] argv)
Main method for testing this class.java.lang.String
maxItsTipText()
Returns the tip text for this propertyjava.lang.String
ridgeTipText()
Returns the tip text for this propertyvoid
setDebug(boolean debug)
Sets whether debugging output will be printed.void
setMaxIts(int newMaxIts)
Set the value of MaxIts.void
setOptions(java.lang.String[] options)
Parses a given list of options.void
setRidge(double ridge)
Sets the ridge in the log-likelihood.java.lang.String
toString()
Gets a string describing the classifier.-
Methods inherited from class weka.classifiers.Classifier
classifyInstance, forName, makeCopies, makeCopy
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Method Detail
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globalInfo
public java.lang.String globalInfo()
Returns a string describing this classifier- Returns:
- a description of the classifier suitable for displaying in the explorer/experimenter gui
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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 interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classClassifier
- Returns:
- an enumeration of all the available options
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setOptions
public void setOptions(java.lang.String[] options) throws java.lang.Exception
Parses a given list of options. Valid options are:-D Turn on debugging output.
-R <ridge> Set the ridge in the log-likelihood.
-M <number> Set the maximum number of iterations (default -1, until convergence).
- Specified by:
setOptions
in interfaceOptionHandler
- Overrides:
setOptions
in classClassifier
- Parameters:
options
- the list of options as an array of strings- Throws:
java.lang.Exception
- if an option is not supported
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getOptions
public java.lang.String[] getOptions()
Gets the current settings of the classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classClassifier
- Returns:
- an array of strings suitable for passing to setOptions
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debugTipText
public java.lang.String debugTipText()
Returns the tip text for this property- Overrides:
debugTipText
in classClassifier
- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setDebug
public void setDebug(boolean debug)
Sets whether debugging output will be printed.- Overrides:
setDebug
in classClassifier
- Parameters:
debug
- true if debugging output should be printed
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getDebug
public boolean getDebug()
Gets whether debugging output will be printed.- Overrides:
getDebug
in classClassifier
- Returns:
- true if debugging output will be printed
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ridgeTipText
public java.lang.String ridgeTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setRidge
public void setRidge(double ridge)
Sets the ridge in the log-likelihood.- Parameters:
ridge
- the ridge
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getRidge
public double getRidge()
Gets the ridge in the log-likelihood.- Returns:
- the ridge
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maxItsTipText
public java.lang.String maxItsTipText()
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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getMaxIts
public int getMaxIts()
Get the value of MaxIts.- Returns:
- Value of MaxIts.
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setMaxIts
public void setMaxIts(int newMaxIts)
Set the value of MaxIts.- Parameters:
newMaxIts
- Value to assign to MaxIts.
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getCapabilities
public Capabilities getCapabilities()
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classClassifier
- Returns:
- the capabilities of this classifier
- See Also:
Capabilities
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buildClassifier
public void buildClassifier(Instances train) throws java.lang.Exception
Builds the classifier- Specified by:
buildClassifier
in classClassifier
- Parameters:
train
- the training data to be used for generating the boosted classifier.- Throws:
java.lang.Exception
- if the classifier could not be built successfully
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distributionForInstance
public double[] distributionForInstance(Instance instance) throws java.lang.Exception
Computes the distribution for a given instance- Overrides:
distributionForInstance
in classClassifier
- Parameters:
instance
- the instance for which distribution is computed- Returns:
- the distribution
- Throws:
java.lang.Exception
- if the distribution can't be computed successfully
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coefficients
public double[][] coefficients()
Returns the coefficients for this logistic model. The first dimension indexes the attributes, and the second the classes.- Returns:
- the coefficients for this logistic model
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toString
public java.lang.String toString()
Gets a string describing the classifier.- Overrides:
toString
in classjava.lang.Object
- Returns:
- a string describing the classifer built.
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
public static void main(java.lang.String[] argv)
Main method for testing this class.- Parameters:
argv
- should contain the command line arguments to the scheme (see Evaluation)
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