Package weka.classifiers.functions

Class LibLINEAR

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler
    public class LibLINEAR
extends Classifier
implements TechnicalInformationHandler
    A wrapper class for the liblinear tools (the liblinear classes, typically the jar file, need to be in the classpath to use this classifier).
    Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008). LIBLINEAR - A Library for Large Linear Classification. URL http://www.csie.ntu.edu.tw/~cjlin/liblinear/.

    BibTeX: 

     @misc{Fan2008,
    author = {Rong-En Fan and Kai-Wei Chang and Cho-Jui Hsieh and Xiang-Rui Wang and Chih-Jen Lin},
    note = {The Weka classifier works with version 1.33 of LIBLINEAR},
    title = {LIBLINEAR - A Library for Large Linear Classification},
    year = {2008},
    URL = {http://www.csie.ntu.edu.tw/\~cjlin/liblinear/}
 }

    Valid options are: 

     -S <int>
  Set type of solver (default: 1)
    0 = L2-regularized logistic regression
    1 = L2-loss support vector machines (dual)
    2 = L2-loss support vector machines (primal)
    3 = L1-loss support vector machines (dual)
    4 = multi-class support vector machines by Crammer and Singer
     -C <double>
  Set the cost parameter C
   (default: 1)
     -Z
  Turn on normalization of input data (default: off)
     -N
  Turn on nominal to binary conversion.
     -M
  Turn off missing value replacement.
  WARNING: use only if your data has no missing values.
     -P
  Use probability estimation (default: off)
 currently for L2-regularized logistic regression only! 
     -E <double>
  Set tolerance of termination criterion (default: 0.01)
     -W <double>
  Set the parameters C of class i to weight[i]*C
   (default: 1)
     -B <double>
  Add Bias term with the given value if >= 0; if < 0, no bias term added (default: 1)
     -D
  If set, classifier is run in debug mode and
  may output additional info to the console
    Version:
    $Revision: 5917 $
    Author:
    Benedikt Waldvogel (mail at bwaldvogel.de)
    See Also:
    Serialized Form

    • Field Detail

      • SVMTYPE_L2_LR

        public static final int SVMTYPE_L2_LR
        SVM solver type L2-regularized logistic regression
        See Also:
        Constant Field Values

      • SVMTYPE_L2LOSS_SVM_DUAL

        public static final int SVMTYPE_L2LOSS_SVM_DUAL
        SVM solver type L2-loss support vector machines (dual)
        See Also:
        Constant Field Values

      • SVMTYPE_L2LOSS_SVM

        public static final int SVMTYPE_L2LOSS_SVM
        SVM solver type L2-loss support vector machines (primal)
        See Also:
        Constant Field Values

      • SVMTYPE_L1LOSS_SVM_DUAL

        public static final int SVMTYPE_L1LOSS_SVM_DUAL
        SVM solver type L1-loss support vector machines (dual)
        See Also:
        Constant Field Values

      • SVMTYPE_MCSVM_CS

        public static final int SVMTYPE_MCSVM_CS
        SVM solver type multi-class support vector machines by Crammer and Singer
        See Also:
        Constant Field Values

      • TAGS_SVMTYPE

        public static final Tag[] TAGS_SVMTYPE
        SVM solver types

    • Constructor Detail

      • LibLINEAR

        public LibLINEAR()

    • Method Detail

      • getModel

        public java.lang.Object getModel()

      • 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

      • 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
        Sets the classifier options

        Valid options are: 

         -S <int>
  Set type of solver (default: 1)
    0 = L2-regularized logistic regression
    1 = L2-loss support vector machines (dual)
    2 = L2-loss support vector machines (primal)
    3 = L1-loss support vector machines (dual)
    4 = multi-class support vector machines by Crammer and Singer
         -C <double>
  Set the cost parameter C
   (default: 1)
         -Z
  Turn on normalization of input data (default: off)
         -N
  Turn on nominal to binary conversion.
         -M
  Turn off missing value replacement.
  WARNING: use only if your data has no missing values.
         -P
  Use probability estimation (default: off)
 currently for L2-regularized logistic regression only! 
         -E <double>
  Set tolerance of termination criterion (default: 0.01)
         -W <double>
  Set the parameters C of class i to weight[i]*C
   (default: 1)
         -B <double>
  Add Bias term with the given value if >= 0; if < 0, no bias term added (default: 1)
         -D
  If set, classifier is run in debug mode and
  may output additional info to the console
        Specified by:
        setOptions in interface OptionHandler
        Overrides:
        setOptions in class Classifier
        Parameters:
        options - the options to parse
        Throws:
        java.lang.Exception - if parsing fails

      • isPresent

        public static boolean isPresent()
        returns whether the liblinear classes are present or not, i.e. whether the classes are in the classpath or not
        Returns:
        whether the liblinear classes are available

      • setSVMType

        public void setSVMType​(SelectedTag value)
        Sets type of SVM (default SVMTYPE_L2)
        Parameters:
        value - the type of the SVM

      • getSVMType

        public SelectedTag getSVMType()
        Gets type of SVM
        Returns:
        the type of the SVM

      • SVMTypeTipText

        public java.lang.String SVMTypeTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setCost

        public void setCost​(double value)
        Sets the cost parameter C (default 1)
        Parameters:
        value - the cost value

      • getCost

        public double getCost()
        Returns the cost parameter C
        Returns:
        the cost value

      • costTipText

        public java.lang.String costTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setEps

        public void setEps​(double value)
        Sets tolerance of termination criterion (default 0.001)
        Parameters:
        value - the tolerance

      • getEps

        public double getEps()
        Gets tolerance of termination criterion
        Returns:
        the current tolerance

      • epsTipText

        public java.lang.String epsTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setBias

        public void setBias​(double value)
        Sets bias term value (default 1) No bias term is added if value < 0
        Parameters:
        value - the bias term value

      • getBias

        public double getBias()
        Returns bias term value (default 1) No bias term is added if value < 0
        Returns:
        the bias term value

      • biasTipText

        public java.lang.String biasTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • normalizeTipText

        public java.lang.String normalizeTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setNormalize

        public void setNormalize​(boolean value)
        whether to normalize input data
        Parameters:
        value - whether to normalize the data

      • getNormalize

        public boolean getNormalize()
        whether to normalize input data
        Returns:
        true, if the data is normalized

      • convertNominalToBinaryTipText

        public java.lang.String convertNominalToBinaryTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setConvertNominalToBinary

        public void setConvertNominalToBinary​(boolean b)
        Whether to turn on conversion of nominal attributes to binary.
        Parameters:
        b - true if nominal to binary conversion is to be turned on

      • getConvertNominalToBinary

        public boolean getConvertNominalToBinary()
        Gets whether conversion of nominal to binary is turned on.
        Returns:
        true if nominal to binary conversion is turned on.

      • doNotReplaceMissingValuesTipText

        public java.lang.String doNotReplaceMissingValuesTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setDoNotReplaceMissingValues

        public void setDoNotReplaceMissingValues​(boolean b)
        Whether to turn off automatic replacement of missing values. Set to true only if the data does not contain missing values.
        Parameters:
        b - true if automatic missing values replacement is to be disabled.

      • getDoNotReplaceMissingValues

        public boolean getDoNotReplaceMissingValues()
        Gets whether automatic replacement of missing values is disabled.
        Returns:
        true if automatic replacement of missing values is disabled.

      • setWeights

        public void setWeights​(java.lang.String weightsStr)
        Sets the parameters C of class i to weight[i]*C (default 1). Blank separated list of doubles.
        Parameters:
        weightsStr - the weights (doubles, separated by blanks)

      • getWeights

        public java.lang.String getWeights()
        Gets the parameters C of class i to weight[i]*C (default 1). Blank separated doubles.
        Returns:
        the weights (doubles separated by blanks)

      • weightsTipText

        public java.lang.String weightsTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setProbabilityEstimates

        public void setProbabilityEstimates​(boolean value)
        Returns whether probability estimates are generated instead of -1/+1 for classification problems.
        Parameters:
        value - whether to predict probabilities

      • getProbabilityEstimates

        public boolean getProbabilityEstimates()
        Sets whether to generate probability estimates instead of -1/+1 for classification problems.
        Returns:
        true, if probability estimates should be returned

      • probabilityEstimatesTipText

        public java.lang.String probabilityEstimatesTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • distributionForInstance

        public double[] distributionForInstance​(Instance instance)
                                 throws java.lang.Exception
        Computes the distribution for a given instance.
        Overrides:
        distributionForInstance in class Classifier
        Parameters:
        instance - the instance for which distribution is computed
        Returns:
        the distribution
        Throws:
        java.lang.Exception - if the distribution can't be computed successfully

      • buildClassifier

        public void buildClassifier​(Instances insts)
                     throws java.lang.Exception
        builds the classifier
        Specified by:
        buildClassifier in class Classifier
        Parameters:
        insts - the training instances
        Throws:
        java.lang.Exception - if liblinear classes not in classpath or liblinear encountered a problem

      • toString

        public java.lang.String toString()
        returns a string representation
        Overrides:
        toString in class java.lang.Object
        Returns:
        a string representation

      • main

        public static void main​(java.lang.String[] args)
        Main method for testing this class.
        Parameters:
        args - the options