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## weka.classifiers.bayes
Class BayesianLogisticRegression

    java.lang.Object
      weka.classifiers.Classifier
          weka.classifiers.bayes.BayesianLogisticRegression

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

    public class BayesianLogisticRegression

extends Classifier
implements OptionHandler, TechnicalInformationHandler

Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors.
For more information, see
Alexander Genkin, David D. Lewis, David Madigan (2004). Large-scale bayesian logistic regression for text categorization. URL http://www.stat.rutgers.edu/~madigan/PAPERS/shortFat-v3a.pdf.

BibTeX:

     @techreport{Genkin2004,
        author = {Alexander Genkin and David D. Lewis and David Madigan},
        institution = {DIMACS},
        title = {Large-scale bayesian logistic regression for text categorization},
        year = {2004},
        URL = {http://www.stat.rutgers.edu/\~madigan/PAPERS/shortFat-v3a.pdf}
     }

Version:
  $Revision: 7984 $
Author:
  Navendu Garg (gargnav at iit dot edu)
See Also:
  Serialized Form

Field Summary
`double[]` | `BetaVector` Array for storing coefficients of Bayesian regression model.
`double` | `Change` This variable is used to keep track of change in the value of delta summation of r(i).
`int` | `ClassIndex` The class index from the training data
`static int` | `CV_BASED`
`double[]` | `Delta` Trust Region Radius
`double[]` | `DeltaBeta` Array to store Regression Coefficient updates.
`double[]` | `DeltaR` This vector is used to store the increments on the R(i).
`double[]` | `DeltaUpdate` Trust Region Radius Update
`static int` | `GAUSSIAN` Distributions available
`java.lang.String` | `HyperparameterRange` CV Hyperparameter Range
`double[]` | `Hyperparameters` Array to store Hyperparameter values for each feature.
`int` | `HyperparameterSelection` Hyperparameter selection method
`double` | `HyperparameterValue` Best hyperparameter for test phase
`static double[]` | `InputHyperparameterValues` Set of values to be used as hyperparameter values during Cross-Validation.
`int` | `iterationCounter` Iteration counter
`static int` | `LAPLACIAN`
`static double[]` | `LogLikelihood` Log-likelihood values to be used to choose the best hyperparameter.
`Filter` | `m_Filter` Filter interface used to point to weka.filters.unsupervised.attribute.Normalize object
`int` | `m_seed` seed for randomizing the instances before CV
`int` | `maxIterations` Maximum number of iterations
`static int` | `NORM_BASED` Methods for selecting the hyperparameter value
`boolean` | `NormalizeData` Choose whether to normalize data or not
`int` | `NumFolds` NumFolds for CV based Hyperparameters selection
`int` | `PriorClass` Distribution Prior class
`double[]` | `R` R(i)= BetaVector X x(i) X y(i).
`static int` | `SPECIFIC_VALUE`
`static Tag[]` | `TAGS_HYPER_METHOD`
`static Tag[]` | `TAGS_PRIOR`
`double` | `Threshold` Threshold for binary classification of probabilisitic estimate
`double` | `Tolerance` Tolerance criteria for the stopping criterion.

Constructor Summary
`BayesianLogisticRegression()`

Method Summary
`static double` | `bigF(double r, double sigma)` This is a convient function that defines and upper bound (Delta>0) for values of r(i) reachable by updates in the trust region.
`void` | `buildClassifier(Instances data)` (1) Set the data to the class attribute m_Instances. (2)Call the method initialize() to initialize the values.
`double` | `classifyInstance(Instance instance)` Classifies the given instance using the Bayesian Logistic Regression function.
`static double` | `classSgn(double value)` This class is used to mask the internal class labels.
`double` | `CVBasedHyperparameter()` Method computes the best hyperparameter value by doing cross -validation on the training data and compute the likelihood.
`java.lang.String` | `debugTipText()` Returns the tip text for this property
`Capabilities` | `getCapabilities()` This method tests what kind of data this classifier can handle.
`java.lang.String` | `getHyperparameterRange()` Get the range of hyperparameter values to consider during CV-based selection.
`SelectedTag` | `getHyperparameterSelection()` Get the method used to select the hyperparameter
`double` | `getHyperparameterValue()` Get the hyperparameter value.
`double` | `getLoglikeliHood(double[] betas, Instances instances)`
`int` | `getMaxIterations()` Get the maximum number of iterations to perform
`int` | `getNumFolds()` Return the number of folds for CV-based hyperparameter selection
`java.lang.String[]` | `getOptions()` Gets the current settings of the Classifier.
`SelectedTag` | `getPriorClass()` Get the type of prior to use.
`java.lang.String` | `getRevision()` Returns the revision string.
`int` | `getSeed()` Get the seed for randomizing the instances for CV-based hyperparameter selection
`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.
`double` | `getThreshold()` Return the threshold being used.
`double` | `getTolerance()` Get the tolerance value
`java.lang.String` | `globalInfo()`
`java.lang.String` | `hyperparameterRangeTipText()` Returns the tip text for this property
`java.lang.String` | `hyperparameterSelectionTipText()` Returns the tip text for this property
`java.lang.String` | `hyperparameterValueTipText()` Returns the tip text for this property
`void` | `initialize()` (1)Initialize m_Beta[j] to 0.
`boolean` | `isDebug()` Returns true if debug is turned on.
`boolean` | `isNormalizeData()` Returns true if the data is to be normalized first
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`static double` | `logisticLinkFunction(double r)` This method computes the values for the logistic link function.
`static void` | `main(java.lang.String[] argv)` Main method for testing this class.
`java.lang.String` | `maxIterationsTipText()` Returns the tip text for this property
`java.lang.String` | `normalizeDataTipText()` Returns the tip text for this property
`double` | `normBasedHyperParameter()` This function computes the norm-based hyperparameters and stores them in the m_Hyperparameters.
`java.lang.String` | `numFoldsTipText()` Returns the tip text for this property
`java.lang.String` | `priorClassTipText()` Returns the tip text for this property
`java.lang.String` | `seedTipText()` Returns the tip text for this property
`void` | `setDebug(boolean debugMode)` Set debugging mode.
`void` | `setHyperparameterRange(java.lang.String hyperparameterRange)` Set the range of hyperparameter values to consider during CV-based selection
`void` | `setHyperparameterSelection(SelectedTag newMethod)` Set the method used to select the hyperparameter
`void` | `setHyperparameterValue(double hyperparameterValue)` Set the hyperparameter value.
`void` | `setMaxIterations(int maxIterations)` Set the maximum number of iterations to perform
`void` | `setNormalizeData(boolean normalizeData)` Set whether to normalize the data or not
`void` | `setNumFolds(int numFolds)` Set the number of folds to use for CV-based hyperparameter selection
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setPriorClass(SelectedTag newMethod)` Set the type of prior to use.
`void` | `setSeed(int seed)` Set the seed for randomizing the instances for CV-based hyperparameter selection
`void` | `setThreshold(double threshold)` Set the threshold to use.
`void` | `setTolerance(double tolerance)` Set the tolerance value
`static double` | `sgn(double r)` Sign for a given value.
`boolean` | `stoppingCriterion()` This method implements the stopping criterion function.
`java.lang.String` | `thresholdTipText()` Returns the tip text for this property
`java.lang.String` | `toleranceTipText()` Returns the tip text for this property
`java.lang.String` | `toString()` Outputs the linear regression model as a string.

Methods inherited from class weka.classifiers.Classifier
`distributionForInstance, forName, getDebug, makeCopies, makeCopy`

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

Field Detail

### LogLikelihood

    public static double[] LogLikelihood

  Log-likelihood values to be used to choose the best hyperparameter.

### InputHyperparameterValues

    public static double[] InputHyperparameterValues

  Set of values to be used as hyperparameter values during Cross-Validation.

### NormalizeData

    public boolean NormalizeData

  Choose whether to normalize data or not

### Tolerance

    public double Tolerance

  Tolerance criteria for the stopping criterion.

### Threshold

    public double Threshold

  Threshold for binary classification of probabilisitic estimate

### GAUSSIAN

    public static final int GAUSSIAN

  Distributions available

  See Also:
    Constant Field Values

### LAPLACIAN

    public static final int LAPLACIAN

See Also:
  Constant Field Values

### TAGS_PRIOR

    public static final Tag[] TAGS_PRIOR

### PriorClass

    public int PriorClass

  Distribution Prior class

### NumFolds

    public int NumFolds

  NumFolds for CV based Hyperparameters selection

### m_seed

    public int m_seed

  seed for randomizing the instances before CV

### NORM_BASED

    public static final int NORM_BASED

  Methods for selecting the hyperparameter value

  See Also:
    Constant Field Values

### CV_BASED

    public static final int CV_BASED

See Also:
  Constant Field Values

### SPECIFIC_VALUE

    public static final int SPECIFIC_VALUE

See Also:
  Constant Field Values

### TAGS_HYPER_METHOD

    public static final Tag[] TAGS_HYPER_METHOD

### HyperparameterSelection

    public int HyperparameterSelection

  Hyperparameter selection method

### ClassIndex

    public int ClassIndex

  The class index from the training data

### HyperparameterValue

    public double HyperparameterValue

  Best hyperparameter for test phase

### HyperparameterRange

    public java.lang.String HyperparameterRange

  CV Hyperparameter Range

### maxIterations

    public int maxIterations

  Maximum number of iterations

### iterationCounter

    public int iterationCounter

  Iteration counter

### BetaVector

    public double[] BetaVector

  Array for storing coefficients of Bayesian regression model.

### DeltaBeta

    public double[] DeltaBeta

  Array to store Regression Coefficient updates.

### DeltaUpdate

    public double[] DeltaUpdate

  Trust Region Radius Update

### Delta

    public double[] Delta

  Trust Region Radius

### Hyperparameters

    public double[] Hyperparameters

  Array to store Hyperparameter values for each feature.

### R

    public double[] R

  R(i)= BetaVector X x(i) X y(i). This an intermediate value with respect to vector BETA, input values and corresponding class labels

### DeltaR

    public double[] DeltaR

  This vector is used to store the increments on the R(i). It is also used to determining the stopping criterion.

### Change

    public double Change

  This variable is used to keep track of change in the value of delta summation of r(i).

### m_Filter

    public Filter m_Filter

  Filter interface used to point to weka.filters.unsupervised.attribute.Normalize object

Constructor Detail

### BayesianLogisticRegression

    public BayesianLogisticRegression()

Method Detail

### globalInfo

    public java.lang.String globalInfo()

### initialize

    public void initialize()
                    throws java.lang.Exception

       (1)Initialize m_Beta[j] to 0.
       (2)Initialize m_DeltaUpdate[j].

  Throws:
    `java.lang.Exception`

### getCapabilities

    public Capabilities getCapabilities()

  This method tests what kind of data this classifier can handle. return Capabilities

  Specified by:
    `getCapabilities` in interface `CapabilitiesHandler`
  Overrides:
    `getCapabilities` in class `Classifier`
  Returns:
    the capabilities of this object
  See Also:
    `Capabilities`

### buildClassifier

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

  - (1) Set the data to the class attribute m_Instances.
  - (2)Call the method initialize() to initialize the values.

  Specified by:
    `buildClassifier` in class `Classifier`
  Parameters:
    `data` - training data
  Throws:
    `java.lang.Exception` - if classifier can't be built successfully.

### classSgn

    public static double classSgn(double value)

  This class is used to mask the internal class labels.

  Parameters:
    `value` - internal class label
  Returns:

          -1 for internal class label 0

          +1 for internal class label 1

### 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

### bigF

    public static double bigF(double r,
                              double sigma)

  This is a convient function that defines and upper bound (Delta>0) for values of r(i) reachable by updates in the trust region. r BetaVector X x(i)y(i). delta A parameter where sigma > 0

  Returns:
    double function value

### stoppingCriterion

    public boolean stoppingCriterion()

  This method implements the stopping criterion function.

  Returns:
    boolean whether to stop or not.

### logisticLinkFunction

    public static double logisticLinkFunction(double r)

  This method computes the values for the logistic link function.

      f(r)=exp(r)/(1+exp(r))

  Returns:
    output value

### sgn

    public static double sgn(double r)

  Sign for a given value.

  Parameters:
    `r` -
  Returns:
    double +1 if r>0, -1 if r<0

### normBasedHyperParameter

    public double normBasedHyperParameter()

  This function computes the norm-based hyperparameters and stores them in the m_Hyperparameters.

### classifyInstance

    public double classifyInstance(Instance instance)
                            throws java.lang.Exception

  Classifies the given instance using the Bayesian Logistic Regression function.

  Overrides:
    `classifyInstance` in class `Classifier`
  Parameters:
    `instance` - the test instance
  Returns:
    the classification
  Throws:
    `java.lang.Exception` - if classification can't be done successfully

### toString

    public java.lang.String toString()

  Outputs the linear regression model as a string.

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    the model as string

### CVBasedHyperparameter

    public double CVBasedHyperparameter()
                                 throws java.lang.Exception

  Method computes the best hyperparameter value by doing cross -validation on the training data and compute the likelihood. The method can parse a range of values or a list of values.

  Returns:
    Best hyperparameter value with the max likelihood value on the training data.
  Throws:
    `java.lang.Exception`

### getLoglikeliHood

    public double getLoglikeliHood(double[] betas,
                                   Instances instances)

  Returns:
    likelihood for a given set of betas and instances

### 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:

       -D
        Show Debugging Output

       -P <integer>
        Distribution of the Prior (1=Gaussian, 2=Laplacian)
        (default: 1=Gaussian)

       -H <integer>
        Hyperparameter Selection Method (1=Norm-based, 2=CV-based, 3=specific value)
        (default: 1=Norm-based)

       -V <double>
        Specified Hyperparameter Value (use in conjunction with -H 3)
        (default: 0.27)

       -R <string>
        Hyperparameter Range (use in conjunction with -H 2)
        (format: R:start-end,multiplier OR L:val(1), val(2), ..., val(n))
        (default: R:0.01-316,3.16)

       -Tl <double>
        Tolerance Value
        (default: 0.0005)

       -S <double>
        Threshold Value
        (default: 0.5)

       -F <integer>
        Number Of Folds (use in conjuction with -H 2)
        (default: 2)

       -I <integer>
        Max Number of Iterations
        (default: 100)

       -N
        Normalize the data

       -seed <number>
        Seed for randomizing instances order
        in CV-based hyperparameter selection
        (default: 1)

  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()

  Description copied from class: `Classifier`
  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

### main

    public static void main(java.lang.String[] argv)

  Main method for testing this class.

  Parameters:
    `argv` - the options

### debugTipText

    public java.lang.String debugTipText()

  Returns the tip text for this property

  Overrides:
    `debugTipText` in class `Classifier`
  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### setDebug

    public void setDebug(boolean debugMode)

  Description copied from class: `Classifier`
  Set debugging mode.

  Overrides:
    `setDebug` in class `Classifier`
  Parameters:
    `debugMode` - true if debug output should be printed

### hyperparameterSelectionTipText

    public java.lang.String hyperparameterSelectionTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getHyperparameterSelection

    public SelectedTag getHyperparameterSelection()

  Get the method used to select the hyperparameter

  Returns:
    the method used to select the hyperparameter

### setHyperparameterSelection

    public void setHyperparameterSelection(SelectedTag newMethod)

  Set the method used to select the hyperparameter

  Parameters:
    `newMethod` - the method used to set the hyperparameter

### priorClassTipText

    public java.lang.String priorClassTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### setPriorClass

    public void setPriorClass(SelectedTag newMethod)

  Set the type of prior to use.

  Parameters:
    `newMethod` - the type of prior to use.

### getPriorClass

    public SelectedTag getPriorClass()

  Get the type of prior to use.

  Returns:
    the type of prior to use

### thresholdTipText

    public java.lang.String thresholdTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getThreshold

    public double getThreshold()

  Return the threshold being used.

  Returns:
    the threshold

### setThreshold

    public void setThreshold(double threshold)

  Set the threshold to use.

  Parameters:
    `threshold` - the threshold to use

### toleranceTipText

    public java.lang.String toleranceTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getTolerance

    public double getTolerance()

  Get the tolerance value

  Returns:
    the tolerance value

### setTolerance

    public void setTolerance(double tolerance)

  Set the tolerance value

  Parameters:
    `tolerance` - the tolerance value to use

### hyperparameterValueTipText

    public java.lang.String hyperparameterValueTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getHyperparameterValue

    public double getHyperparameterValue()

  Get the hyperparameter value. Used when the hyperparameter selection method is set to specific value

  Returns:
    the hyperparameter value

### setHyperparameterValue

    public void setHyperparameterValue(double hyperparameterValue)

  Set the hyperparameter value. Used when the hyperparameter selection method is set to specific value

  Parameters:
    `hyperparameterValue` - the value of the hyperparameter

### numFoldsTipText

    public java.lang.String numFoldsTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getNumFolds

    public int getNumFolds()

  Return the number of folds for CV-based hyperparameter selection

  Returns:
    the number of CV folds

### setNumFolds

    public void setNumFolds(int numFolds)

  Set the number of folds to use for CV-based hyperparameter selection

  Parameters:
    `numFolds` - number of folds to select

### seedTipText

    public java.lang.String seedTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### setSeed

    public void setSeed(int seed)

  Set the seed for randomizing the instances for CV-based hyperparameter selection

  Parameters:
    `seed` - the seed to use

### getSeed

    public int getSeed()

  Get the seed for randomizing the instances for CV-based hyperparameter selection

  Returns:
    the seed to use

### maxIterationsTipText

    public java.lang.String maxIterationsTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getMaxIterations

    public int getMaxIterations()

  Get the maximum number of iterations to perform

  Returns:
    the maximum number of iterations

### setMaxIterations

    public void setMaxIterations(int maxIterations)

  Set the maximum number of iterations to perform

  Parameters:
    `maxIterations` - maximum number of iterations

### normalizeDataTipText

    public java.lang.String normalizeDataTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### isNormalizeData

    public boolean isNormalizeData()

  Returns true if the data is to be normalized first

  Returns:
    true if the data is to be normalized

### setNormalizeData

    public void setNormalizeData(boolean normalizeData)

  Set whether to normalize the data or not

  Parameters:
    `normalizeData` - true if data is to be normalized

### hyperparameterRangeTipText

    public java.lang.String hyperparameterRangeTipText()

  Returns the tip text for this property

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getHyperparameterRange

    public java.lang.String getHyperparameterRange()

  Get the range of hyperparameter values to consider during CV-based selection.

  Returns:
    the range of hyperparameters as a Stringe

### setHyperparameterRange

    public void setHyperparameterRange(java.lang.String hyperparameterRange)

  Set the range of hyperparameter values to consider during CV-based selection

  Parameters:
    `hyperparameterRange` - the range of hyperparameter values

### isDebug

    public boolean isDebug()

  Returns true if debug is turned on.

  Returns:
    true if debug is turned on

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Overrides:
    `getRevision` in class `Classifier`
  Returns:
    the revision

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