Logistic

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

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, Aggregateable<Logistic>, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, PMMLProducer, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler
```
public class Logistic
extends AbstractClassifier
implements OptionHandler, WeightedInstancesHandler, TechnicalInformationHandler, PMMLProducer, Aggregateable<Logistic>
```
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.
 
```
```
 -S
  Do not standardize the attributes in the input data.
 
```
```
 -R <ridge>
  Set the ridge in the log-likelihood.
 
```
```
 -M <number>
  Set the maximum number of iterations (default -1, until convergence).
 
```
Version:

$Revision: 15535 $

Author:

Xin Xu (xx5@cs.waikato.ac.nz)

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

Logistic()
Constructor that sets the default number of decimal places to 4.

Constructors
Constructor and Description
`Logistic()` Constructor that sets the default number of decimal places to 4.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`Logistic`	`aggregate(Logistic toAggregate)` Aggregate an object with this one
`void`	`buildClassifier(Instances train)` Builds the classifier
`double[][]`	`coefficients()` Returns the coefficients for this logistic model.
`java.lang.String`	`debugTipText()` Returns the tip text for this property
`double[]`	`distributionForInstance(Instance instance)` Computes the distribution for a given instance
`java.lang.String`	`doNotStandardizeAttributesTipText()` Returns the tip text for this property
`void`	`finalizeAggregation()` Call to complete the aggregation process.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`boolean`	`getDebug()` Gets whether debugging output will be printed.
`boolean`	`getDoNotStandardizeAttributes()` Gets whether not to standardize attributes.
`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.
`boolean`	`getUseConjugateGradientDescent()` Gets whether to use conjugate gradient descent rather than BFGS updates.
`java.lang.String`	`globalInfo()` Returns a string describing this classifier
`java.util.Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`java.lang.String`	`maxItsTipText()` Returns the tip text for this property
`java.lang.String`	`ridgeTipText()` Returns the tip text for this property
`void`	`setDebug(boolean debug)` Sets whether debugging output will be printed.
`void`	`setDoNotStandardizeAttributes(boolean DoNotStandardizeAttributes)` Sets whether not to standardize attributes
`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.
`void`	`setUseConjugateGradientDescent(boolean useConjugateGradientDescent)` Sets whether conjugate gradient descent is used.
`java.lang.String`	`toPMML(Instances train)` Produce a PMML representation of this logistic model
`java.lang.String`	`toString()` Gets a string describing the classifier.
`java.lang.String`	`useConjugateGradientDescentTipText()` Returns the tip text for this property

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
  - Logistic
```
public Logistic()
```
    Constructor that sets the default number of decimal places to 4.
- Method Detail
  - 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
  - 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
  - listOptions
```
public java.util.Enumeration<Option> 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(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses a given list of options.
    Valid options are:
```
 -D
  Turn on debugging output.
 
```
```
 -S
  Do not standardize the attributes in the input data.
 
```
```
 -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 interface OptionHandler
    
    Overrides:
    
    setOptions in class AbstractClassifier
    
    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 AbstractClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - debugTipText
```
public java.lang.String debugTipText()
```
    Returns the tip text for this property
    
    Overrides:
    
    debugTipText in class AbstractClassifier
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDebug
```
public void setDebug(boolean debug)
```
    Sets whether debugging output will be printed.
    
    Overrides:
    
    setDebug in class AbstractClassifier
    
    Parameters:
    
    debug - true if debugging output should be printed
  - getDebug
```
public boolean getDebug()
```
    Gets whether debugging output will be printed.
    
    Overrides:
    
    getDebug in class AbstractClassifier
    
    Returns:
    
    true if debugging output will be printed
  - useConjugateGradientDescentTipText
```
public java.lang.String useConjugateGradientDescentTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setUseConjugateGradientDescent
```
public void setUseConjugateGradientDescent(boolean useConjugateGradientDescent)
```
    Sets whether conjugate gradient descent is used.
    
    Parameters:
    
    useConjugateGradientDescent - true if CGD is to be used.
  - getUseConjugateGradientDescent
```
public boolean getUseConjugateGradientDescent()
```
    Gets whether to use conjugate gradient descent rather than BFGS updates.
    
    Returns:
    
    true if CGD is used
  - doNotStandardizeAttributesTipText
```
public java.lang.String doNotStandardizeAttributesTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setDoNotStandardizeAttributes
```
public void setDoNotStandardizeAttributes(boolean DoNotStandardizeAttributes)
```
    Sets whether not to standardize attributes
    
    Parameters:
    
    DoNotStandardizeAttributes - true if attributes are not to be standardize
  - getDoNotStandardizeAttributes
```
public boolean getDoNotStandardizeAttributes()
```
    Gets whether not to standardize attributes.
    
    Returns:
    
    true if attributes are not being standardized
  - 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
  - setRidge
```
public void setRidge(double ridge)
```
    Sets the ridge in the log-likelihood.
    
    Parameters:
    
    ridge - the ridge
  - getRidge
```
public double getRidge()
```
    Gets the ridge in the log-likelihood.
    
    Returns:
    
    the ridge
  - 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
  - getMaxIts
```
public int getMaxIts()
```
    Get the value of MaxIts.
    
    Returns:
    
    Value of MaxIts.
  - setMaxIts
```
public void setMaxIts(int newMaxIts)
```
    Set the value of MaxIts.
    
    Parameters:
    
    newMaxIts - Value to assign to MaxIts.
  - 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 train)
                     throws java.lang.Exception
```
    Builds the classifier
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    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
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception
```
    Computes the distribution for a given instance
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    
    instance - the instance for which distribution is computed
    
    Returns:
    
    the distribution
    
    Throws:
    
    java.lang.Exception - if the distribution can't be computed successfully
  - 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
  - toString
```
public java.lang.String toString()
```
    Gets a string describing the classifier.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a string describing the classifer built.
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClassifier
    
    Returns:
    
    the revision
  - aggregate
```
public Logistic aggregate(Logistic toAggregate)
                   throws java.lang.Exception
```
    Aggregate an object with this one
    
    Specified by:
    
    aggregate in interface Aggregateable<Logistic>
    
    Parameters:
    
    toAggregate - the object to aggregate
    
    Returns:
    
    the result of aggregation
    
    Throws:
    
    java.lang.Exception - if the supplied object can't be aggregated for some reason
  - finalizeAggregation
```
public void finalizeAggregation()
                         throws java.lang.Exception
```
    Call to complete the aggregation process. Allows implementers to do any final processing based on how many objects were aggregated.
    
    Specified by:
    
    finalizeAggregation in interface Aggregateable<Logistic>
    
    Throws:
    
    java.lang.Exception - if the aggregation can't be finalized for some reason
  - 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)
  - toPMML
```
public java.lang.String toPMML(Instances train)
```
    Produce a PMML representation of this logistic model
    
    Specified by:
    
    toPMML in interface PMMLProducer
    
    Parameters:
    
    train - the training data that was used to construct the model
    
    Returns:
    
    a string containing the PMML representation

Class Logistic

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Constructor Detail

Logistic

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

getOptions

debugTipText

setDebug

getDebug

useConjugateGradientDescentTipText

setUseConjugateGradientDescent

getUseConjugateGradientDescent

doNotStandardizeAttributesTipText

setDoNotStandardizeAttributes

getDoNotStandardizeAttributes

ridgeTipText

setRidge

getRidge

maxItsTipText

getMaxIts

setMaxIts

getCapabilities

buildClassifier

distributionForInstance

coefficients

toString

getRevision

aggregate

finalizeAggregation

main

toPMML