weka.classifiers.functions
Class LibSVM

java.lang.Object
  extended by weka.classifiers.Classifier
      extended by weka.classifiers.functions.LibSVM
All Implemented Interfaces:
java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler

public class LibSVM
extends Classifier
implements TechnicalInformationHandler

A wrapper class for the libsvm tools (the libsvm classes, typically the jar file, need to be in the classpath to use this classifier).
LibSVM runs faster than SMO since it uses LibSVM to build the SVM classifier.
LibSVM allows users to experiment with One-class SVM, Regressing SVM, and nu-SVM supported by LibSVM tool. LibSVM reports many useful statistics about LibSVM classifier (e.g., confusion matrix,precision, recall, ROC score, etc.).

Yasser EL-Manzalawy (2005). WLSVM. URL http://www.cs.iastate.edu/~yasser/wlsvm/.

Chih-Chung Chang, Chih-Jen Lin (2001). LIBSVM - A Library for Support Vector Machines. URL http://www.csie.ntu.edu.tw/~cjlin/libsvm/.

BibTeX:

 @misc{EL-Manzalawy2005,
    author = {Yasser EL-Manzalawy},
    note = {You don't need to include the WLSVM package in the CLASSPATH},
    title = {WLSVM},
    year = {2005},
    URL = {http://www.cs.iastate.edu/\~yasser/wlsvm/}
 }
 
 @misc{Chang2001,
    author = {Chih-Chung Chang and Chih-Jen Lin},
    note = {The Weka classifier works with version 2.82 of LIBSVM},
    title = {LIBSVM - A Library for Support Vector Machines},
    year = {2001},
    URL = {http://www.csie.ntu.edu.tw/\~cjlin/libsvm/}
 }
 

Valid options are:

 -S <int>
  Set type of SVM (default: 0)
    0 = C-SVC
    1 = nu-SVC
    2 = one-class SVM
    3 = epsilon-SVR
    4 = nu-SVR
 -K <int>
  Set type of kernel function (default: 2)
    0 = linear: u'*v
    1 = polynomial: (gamma*u'*v + coef0)^degree
    2 = radial basis function: exp(-gamma*|u-v|^2)
    3 = sigmoid: tanh(gamma*u'*v + coef0)
 -D <int>
  Set degree in kernel function (default: 3)
 -G <double>
  Set gamma in kernel function (default: 1/k)
 -R <double>
  Set coef0 in kernel function (default: 0)
 -C <double>
  Set the parameter C of C-SVC, epsilon-SVR, and nu-SVR
   (default: 1)
 -N <double>
  Set the parameter nu of nu-SVC, one-class SVM, and nu-SVR
   (default: 0.5)
 -Z
  Turns on normalization of input data (default: off)
 -J
  Turn off nominal to binary conversion.
  WARNING: use only if your data is all numeric!
 -V
  Turn off missing value replacement.
  WARNING: use only if your data has no missing values.
 -P <double>
  Set the epsilon in loss function of epsilon-SVR (default: 0.1)
 -M <double>
  Set cache memory size in MB (default: 40)
 -E <double>
  Set tolerance of termination criterion (default: 0.001)
 -H
  Turns the shrinking heuristics off (default: on)
 -W <double>
  Set the parameters C of class i to weight[i]*C, for C-SVC
  E.g., for a 3-class problem, you could use "1 1 1" for equally
  weighted classes.
  (default: 1 for all classes)
 -B
  Trains a SVC model instead of a SVR one (default: SVR)
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Version:
$Revision: 5523 $
Author:
Yasser EL-Manzalawy, FracPete (fracpete at waikato dot ac dot nz)
See Also:
LibSVMLoader, LibSVMSaver, Serialized Form

Field Summary
static int KERNELTYPE_LINEAR
          kernel type linear: u'*v
static int KERNELTYPE_POLYNOMIAL
          kernel type polynomial: (gamma*u'*v + coef0)^degree
static int KERNELTYPE_RBF
          kernel type radial basis function: exp(-gamma*|u-v|^2)
static int KERNELTYPE_SIGMOID
          kernel type sigmoid: tanh(gamma*u'*v + coef0)
static int SVMTYPE_C_SVC
          SVM type C-SVC (classification)
static int SVMTYPE_EPSILON_SVR
          SVM type epsilon-SVR (regression)
static int SVMTYPE_NU_SVC
          SVM type nu-SVC (classification)
static int SVMTYPE_NU_SVR
          SVM type nu-SVR (regression)
static int SVMTYPE_ONE_CLASS_SVM
          SVM type one-class SVM (classification)
static Tag[] TAGS_KERNELTYPE
          the different kernel types
static Tag[] TAGS_SVMTYPE
          SVM types
 
Constructor Summary
LibSVM()
           
 
Method Summary
 void buildClassifier(Instances insts)
          builds the classifier
 java.lang.String cacheSizeTipText()
          Returns the tip text for this property
 java.lang.String coef0TipText()
          Returns the tip text for this property
 java.lang.String costTipText()
          Returns the tip text for this property
 java.lang.String degreeTipText()
          Returns the tip text for this property
 double[] distributionForInstance(Instance instance)
          Computes the distribution for a given instance.
 java.lang.String doNotReplaceMissingValuesTipText()
          Returns the tip text for this property
 java.lang.String epsTipText()
          Returns the tip text for this property
 java.lang.String gammaTipText()
          Returns the tip text for this property
 double getCacheSize()
          Gets cache memory size in MB
 Capabilities getCapabilities()
          Returns default capabilities of the classifier.
 double getCoef0()
          Gets coef
 double getCost()
          Sets the parameter C of C-SVC, epsilon-SVR, and nu-SVR
 int getDegree()
          Gets the degree of the kernel
 boolean getDoNotReplaceMissingValues()
          Gets whether automatic replacement of missing values is disabled.
 double getEps()
          Gets tolerance of termination criterion
 double getGamma()
          Gets gamma
 SelectedTag getKernelType()
          Gets type of kernel function
 double getLoss()
          Gets the epsilon in loss function of epsilon-SVR
 boolean getNormalize()
          whether to normalize input data
 double getNu()
          Gets nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
 java.lang.String[] getOptions()
          Returns the current options
 boolean getProbabilityEstimates()
          Sets whether to generate probability estimates instead of -1/+1 for classification problems.
 java.lang.String getRevision()
          Returns the revision string.
 boolean getShrinking()
          whether to use the shrinking heuristics
 SelectedTag getSVMType()
          Gets type of SVM
 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 getWeights()
          Gets the parameters C of class i to weight[i]*C, for C-SVC (default 1).
 java.lang.String globalInfo()
          Returns a string describing classifier
static boolean isPresent()
          returns whether the libsvm classes are present or not, i.e.
 java.lang.String kernelTypeTipText()
          Returns the tip text for this property
 java.util.Enumeration listOptions()
          Returns an enumeration describing the available options.
 java.lang.String lossTipText()
          Returns the tip text for this property
static void main(java.lang.String[] args)
          Main method for testing this class.
 java.lang.String normalizeTipText()
          Returns the tip text for this property
 java.lang.String nuTipText()
          Returns the tip text for this property
 java.lang.String probabilityEstimatesTipText()
          Returns the tip text for this property
 void setCacheSize(double value)
          Sets cache memory size in MB (default 40)
 void setCoef0(double value)
          Sets coef (default 0)
 void setCost(double value)
          Sets the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
 void setDegree(int value)
          Sets the degree of the kernel
 void setDoNotReplaceMissingValues(boolean b)
          Whether to turn off automatic replacement of missing values.
 void setEps(double value)
          Sets tolerance of termination criterion (default 0.001)
 void setGamma(double value)
          Sets gamma (default = 1/no of attributes)
 void setKernelType(SelectedTag value)
          Sets type of kernel function (default KERNELTYPE_RBF)
 void setLoss(double value)
          Sets the epsilon in loss function of epsilon-SVR (default 0.1)
 void setNormalize(boolean value)
          whether to normalize input data
 void setNu(double value)
          Sets nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
 void setOptions(java.lang.String[] options)
          Sets the classifier options

Valid options are:

 void setProbabilityEstimates(boolean value)
          Returns whether probability estimates are generated instead of -1/+1 for classification problems.
 void setShrinking(boolean value)
          whether to use the shrinking heuristics
 void setSVMType(SelectedTag value)
          Sets type of SVM (default SVMTYPE_C_SVC)
 void setWeights(java.lang.String weightsStr)
          Sets the parameters C of class i to weight[i]*C, for C-SVC (default 1).
 java.lang.String shrinkingTipText()
          Returns the tip text for this property
 java.lang.String SVMTypeTipText()
          Returns the tip text for this property
 java.lang.String toString()
          returns a string representation
 java.lang.String weightsTipText()
          Returns the tip text for this property
 
Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
 

Field Detail

SVMTYPE_C_SVC

public static final int SVMTYPE_C_SVC
SVM type C-SVC (classification)

See Also:
Constant Field Values

SVMTYPE_NU_SVC

public static final int SVMTYPE_NU_SVC
SVM type nu-SVC (classification)

See Also:
Constant Field Values

SVMTYPE_ONE_CLASS_SVM

public static final int SVMTYPE_ONE_CLASS_SVM
SVM type one-class SVM (classification)

See Also:
Constant Field Values

SVMTYPE_EPSILON_SVR

public static final int SVMTYPE_EPSILON_SVR
SVM type epsilon-SVR (regression)

See Also:
Constant Field Values

SVMTYPE_NU_SVR

public static final int SVMTYPE_NU_SVR
SVM type nu-SVR (regression)

See Also:
Constant Field Values

TAGS_SVMTYPE

public static final Tag[] TAGS_SVMTYPE
SVM types


KERNELTYPE_LINEAR

public static final int KERNELTYPE_LINEAR
kernel type linear: u'*v

See Also:
Constant Field Values

KERNELTYPE_POLYNOMIAL

public static final int KERNELTYPE_POLYNOMIAL
kernel type polynomial: (gamma*u'*v + coef0)^degree

See Also:
Constant Field Values

KERNELTYPE_RBF

public static final int KERNELTYPE_RBF
kernel type radial basis function: exp(-gamma*|u-v|^2)

See Also:
Constant Field Values

KERNELTYPE_SIGMOID

public static final int KERNELTYPE_SIGMOID
kernel type sigmoid: tanh(gamma*u'*v + coef0)

See Also:
Constant Field Values

TAGS_KERNELTYPE

public static final Tag[] TAGS_KERNELTYPE
the different kernel types

Constructor Detail

LibSVM

public LibSVM()
Method Detail

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 SVM (default: 0)
    0 = C-SVC
    1 = nu-SVC
    2 = one-class SVM
    3 = epsilon-SVR
    4 = nu-SVR
 -K <int>
  Set type of kernel function (default: 2)
    0 = linear: u'*v
    1 = polynomial: (gamma*u'*v + coef0)^degree
    2 = radial basis function: exp(-gamma*|u-v|^2)
    3 = sigmoid: tanh(gamma*u'*v + coef0)
 -D <int>
  Set degree in kernel function (default: 3)
 -G <double>
  Set gamma in kernel function (default: 1/k)
 -R <double>
  Set coef0 in kernel function (default: 0)
 -C <double>
  Set the parameter C of C-SVC, epsilon-SVR, and nu-SVR
   (default: 1)
 -N <double>
  Set the parameter nu of nu-SVC, one-class SVM, and nu-SVR
   (default: 0.5)
 -Z
  Turns on normalization of input data (default: off)
 -J
  Turn off nominal to binary conversion.
  WARNING: use only if your data is all numeric!
 -V
  Turn off missing value replacement.
  WARNING: use only if your data has no missing values.
 -P <double>
  Set the epsilon in loss function of epsilon-SVR (default: 0.1)
 -M <double>
  Set cache memory size in MB (default: 40)
 -E <double>
  Set tolerance of termination criterion (default: 0.001)
 -H
  Turns the shrinking heuristics off (default: on)
 -W <double>
  Set the parameters C of class i to weight[i]*C, for C-SVC
  E.g., for a 3-class problem, you could use "1 1 1" for equally
  weighted classes.
  (default: 1 for all classes)
 -B
  Trains a SVC model instead of a SVR one (default: SVR)
 -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

getOptions

public java.lang.String[] getOptions()
Returns the current options

Specified by:
getOptions in interface OptionHandler
Overrides:
getOptions in class Classifier
Returns:
the current setup

isPresent

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

Returns:
whether the libsvm classes are available

setSVMType

public void setSVMType(SelectedTag value)
Sets type of SVM (default SVMTYPE_C_SVC)

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

setKernelType

public void setKernelType(SelectedTag value)
Sets type of kernel function (default KERNELTYPE_RBF)

Parameters:
value - the kernel type

getKernelType

public SelectedTag getKernelType()
Gets type of kernel function

Returns:
the kernel type

kernelTypeTipText

public java.lang.String kernelTypeTipText()
Returns the tip text for this property

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

setDegree

public void setDegree(int value)
Sets the degree of the kernel

Parameters:
value - the degree of the kernel

getDegree

public int getDegree()
Gets the degree of the kernel

Returns:
the degree of the kernel

degreeTipText

public java.lang.String degreeTipText()
Returns the tip text for this property

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

setGamma

public void setGamma(double value)
Sets gamma (default = 1/no of attributes)

Parameters:
value - the gamma value

getGamma

public double getGamma()
Gets gamma

Returns:
the current gamma

gammaTipText

public java.lang.String gammaTipText()
Returns the tip text for this property

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

setCoef0

public void setCoef0(double value)
Sets coef (default 0)

Parameters:
value - the coef

getCoef0

public double getCoef0()
Gets coef

Returns:
the coef

coef0TipText

public java.lang.String coef0TipText()
Returns the tip text for this property

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

setNu

public void setNu(double value)
Sets nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)

Parameters:
value - the new nu value

getNu

public double getNu()
Gets nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)

Returns:
the current nu value

nuTipText

public java.lang.String nuTipText()
Returns the tip text for this property

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

setCacheSize

public void setCacheSize(double value)
Sets cache memory size in MB (default 40)

Parameters:
value - the memory size in MB

getCacheSize

public double getCacheSize()
Gets cache memory size in MB

Returns:
the memory size in MB

cacheSizeTipText

public java.lang.String cacheSizeTipText()
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 parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)

Parameters:
value - the cost value

getCost

public double getCost()
Sets the parameter C of C-SVC, epsilon-SVR, and nu-SVR

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

setLoss

public void setLoss(double value)
Sets the epsilon in loss function of epsilon-SVR (default 0.1)

Parameters:
value - the loss epsilon

getLoss

public double getLoss()
Gets the epsilon in loss function of epsilon-SVR

Returns:
the loss epsilon

lossTipText

public java.lang.String lossTipText()
Returns the tip text for this property

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

setShrinking

public void setShrinking(boolean value)
whether to use the shrinking heuristics

Parameters:
value - true uses shrinking

getShrinking

public boolean getShrinking()
whether to use the shrinking heuristics

Returns:
true, if shrinking is used

shrinkingTipText

public java.lang.String shrinkingTipText()
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

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

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, for C-SVC (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, for C-SVC (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. In case of 1-class classification, 1 is returned at index 0 if libsvm returns 1 and NaN (= missing) if libsvm returns -1.

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

getCapabilities

public Capabilities getCapabilities()
Returns default capabilities of the classifier.

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

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 libsvm classes not in classpath or libsvm encountered a problem

toString

public java.lang.String toString()
returns a string representation

Overrides:
toString in class java.lang.Object
Returns:
a string representation

getRevision

public java.lang.String getRevision()
Returns the revision string.

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

main

public static void main(java.lang.String[] args)
Main method for testing this class.

Parameters:
args - the options