Package weka.classifiers.mi

Class MISMO

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, MultiInstanceCapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler
    public class MISMO
extends Classifier
implements WeightedInstancesHandler, MultiInstanceCapabilitiesHandler, TechnicalInformationHandler
    Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.

    This implementation globally replaces all missing values and transforms nominal attributes into binary ones. It also normalizes all attributes by default. (In that case the coefficients in the output are based on the normalized data, not the original data --- this is important for interpreting the classifier.)

    Multi-class problems are solved using pairwise classification.

    To obtain proper probability estimates, use the option that fits logistic regression models to the outputs of the support vector machine. In the multi-class case the predicted probabilities are coupled using Hastie and Tibshirani's pairwise coupling method.

    Note: for improved speed normalization should be turned off when operating on SparseInstances.

    For more information on the SMO algorithm, see

    J. Platt: Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, 1998.

    S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy (2001). Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Computation. 13(3):637-649.

    BibTeX: 

     @incollection{Platt1998,
    author = {J. Platt},
    booktitle = {Advances in Kernel Methods - Support Vector Learning},
    editor = {B. Schoelkopf and C. Burges and A. Smola},
    publisher = {MIT Press},
    title = {Machines using Sequential Minimal Optimization},
    year = {1998}
 }
 
 @article{Keerthi2001,
    author = {S.S. Keerthi and S.K. Shevade and C. Bhattacharyya and K.R.K. Murthy},
    journal = {Neural Computation},
    number = {3},
    pages = {637-649},
    title = {Improvements to Platt's SMO Algorithm for SVM Classifier Design},
    volume = {13},
    year = {2001}
 }

    Valid options are: 

     -D
  If set, classifier is run in debug mode and
  may output additional info to the console
     -no-checks
  Turns off all checks - use with caution!
  Turning them off assumes that data is purely numeric, doesn't
  contain any missing values, and has a nominal class. Turning them
  off also means that no header information will be stored if the
  machine is linear. Finally, it also assumes that no instance has
  a weight equal to 0.
  (default: checks on)
     -C <double>
  The complexity constant C. (default 1)
     -N
  Whether to 0=normalize/1=standardize/2=neither.
  (default 0=normalize)
     -I
  Use MIminimax feature space. 
     -L <double>
  The tolerance parameter. (default 1.0e-3)
     -P <double>
  The epsilon for round-off error. (default 1.0e-12)
     -M
  Fit logistic models to SVM outputs. 
     -V <double>
  The number of folds for the internal cross-validation. 
  (default -1, use training data)
     -W <double>
  The random number seed. (default 1)
     -K <classname and parameters>
  The Kernel to use.
  (default: weka.classifiers.functions.supportVector.PolyKernel)
     
 Options specific to kernel weka.classifiers.mi.supportVector.MIPolyKernel:
 
     -D
  Enables debugging output (if available) to be printed.
  (default: off)
     -no-checks
  Turns off all checks - use with caution!
  (default: checks on)
     -C <num>
  The size of the cache (a prime number), 0 for full cache and 
  -1 to turn it off.
  (default: 250007)
     -E <num>
  The Exponent to use.
  (default: 1.0)
     -L
  Use lower-order terms.
  (default: no)
    Version:
    $Revision: 9144 $
    Author:
    Eibe Frank (eibe@cs.waikato.ac.nz), Shane Legg (shane@intelligenesis.net) (sparse vector code), Stuart Inglis (stuart@reeltwo.com) (sparse vector code), Lin Dong (ld21@cs.waikato.ac.nz) (code for adapting to MI data)
    See Also:
    Serialized Form

    • Field Detail

      • FILTER_NORMALIZE

        public static final int FILTER_NORMALIZE
        Normalize training data
        See Also:
        Constant Field Values

      • FILTER_STANDARDIZE

        public static final int FILTER_STANDARDIZE
        Standardize training data
        See Also:
        Constant Field Values

      • FILTER_NONE

        public static final int FILTER_NONE
        No normalization/standardization
        See Also:
        Constant Field Values

      • TAGS_FILTER

        public static final Tag[] TAGS_FILTER
        The filter to apply to the training data

    • Constructor Detail

      • MISMO

        public MISMO()

    • 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

      • turnChecksOff

        public void turnChecksOff()
        Turns off checks for missing values, etc. Use with caution.

      • turnChecksOn

        public void turnChecksOn()
        Turns on checks for missing values, etc.

      • buildClassifier

        public void buildClassifier​(Instances insts)
                     throws java.lang.Exception
        Method for building the classifier. Implements a one-against-one wrapper for multi-class problems.
        Specified by:
        buildClassifier in class Classifier
        Parameters:
        insts - the set of training instances
        Throws:
        java.lang.Exception - if the classifier can't be built successfully

      • distributionForInstance

        public double[] distributionForInstance​(Instance inst)
                                 throws java.lang.Exception
        Estimates class probabilities for given instance.
        Overrides:
        distributionForInstance in class Classifier
        Parameters:
        inst - the instance to compute the distribution for
        Returns:
        the class probabilities
        Throws:
        java.lang.Exception - if computation fails

      • pairwiseCoupling

        public double[] pairwiseCoupling​(double[][] n,
                                 double[][] r)
        Implements pairwise coupling.
        Parameters:
        n - the sum of weights used to train each model
        r - the probability estimate from each model
        Returns:
        the coupled estimates

      • sparseWeights

        public double[][][] sparseWeights()
        Returns the weights in sparse format.
        Returns:
        the weights in sparse format

      • sparseIndices

        public int[][][] sparseIndices()
        Returns the indices in sparse format.
        Returns:
        the indices in sparse format

      • bias

        public double[][] bias()
        Returns the bias of each binary SMO.
        Returns:
        the bias of each binary SMO

      • numClassAttributeValues

        public int numClassAttributeValues()
        Returns the number of values of the class attribute.
        Returns:
        the number values of the class attribute

      • classAttributeNames

        public java.lang.String[] classAttributeNames()
        Returns the names of the class attributes.
        Returns:
        the names of the class attributes

      • attributeNames

        public java.lang.String[][][] attributeNames()
        Returns the attribute names.
        Returns:
        the attribute names

      • 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
  If set, classifier is run in debug mode and
  may output additional info to the console
         -no-checks
  Turns off all checks - use with caution!
  Turning them off assumes that data is purely numeric, doesn't
  contain any missing values, and has a nominal class. Turning them
  off also means that no header information will be stored if the
  machine is linear. Finally, it also assumes that no instance has
  a weight equal to 0.
  (default: checks on)
         -C <double>
  The complexity constant C. (default 1)
         -N
  Whether to 0=normalize/1=standardize/2=neither.
  (default 0=normalize)
         -I
  Use MIminimax feature space. 
         -L <double>
  The tolerance parameter. (default 1.0e-3)
         -P <double>
  The epsilon for round-off error. (default 1.0e-12)
         -M
  Fit logistic models to SVM outputs. 
         -V <double>
  The number of folds for the internal cross-validation. 
  (default -1, use training data)
         -W <double>
  The random number seed. (default 1)
         -K <classname and parameters>
  The Kernel to use.
  (default: weka.classifiers.functions.supportVector.PolyKernel)
         
 Options specific to kernel weka.classifiers.mi.supportVector.MIPolyKernel:
 
         -D
  Enables debugging output (if available) to be printed.
  (default: off)
         -no-checks
  Turns off all checks - use with caution!
  (default: checks on)
         -C <num>
  The size of the cache (a prime number), 0 for full cache and 
  -1 to turn it off.
  (default: 250007)
         -E <num>
  The Exponent to use.
  (default: 1.0)
         -L
  Use lower-order terms.
  (default: no)
        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()
        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

      • setChecksTurnedOff

        public void setChecksTurnedOff​(boolean value)
        Disables or enables the checks (which could be time-consuming). Use with caution!
        Parameters:
        value - if true turns off all checks

      • getChecksTurnedOff

        public boolean getChecksTurnedOff()
        Returns whether the checks are turned off or not.
        Returns:
        true if the checks are turned off

      • checksTurnedOffTipText

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

      • kernelTipText

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

      • getKernel

        public Kernel getKernel()
        Gets the kernel to use.
        Returns:
        the kernel

      • setKernel

        public void setKernel​(Kernel value)
        Sets the kernel to use.
        Parameters:
        value - the kernel

      • cTipText

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

      • getC

        public double getC()
        Get the value of C.
        Returns:
        Value of C.

      • setC

        public void setC​(double v)
        Set the value of C.
        Parameters:
        v - Value to assign to C.

      • toleranceParameterTipText

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

      • getToleranceParameter

        public double getToleranceParameter()
        Get the value of tolerance parameter.
        Returns:
        Value of tolerance parameter.

      • setToleranceParameter

        public void setToleranceParameter​(double v)
        Set the value of tolerance parameter.
        Parameters:
        v - Value to assign to tolerance parameter.

      • epsilonTipText

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

      • getEpsilon

        public double getEpsilon()
        Get the value of epsilon.
        Returns:
        Value of epsilon.

      • setEpsilon

        public void setEpsilon​(double v)
        Set the value of epsilon.
        Parameters:
        v - Value to assign to epsilon.

      • filterTypeTipText

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

      • getFilterType

        public SelectedTag getFilterType()
        Gets how the training data will be transformed. Will be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
        Returns:
        the filtering mode

      • setFilterType

        public void setFilterType​(SelectedTag newType)
        Sets how the training data will be transformed. Should be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
        Parameters:
        newType - the new filtering mode

      • minimaxTipText

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

      • getMinimax

        public boolean getMinimax()
        Check if the MIMinimax feature space is to be used.
        Returns:
        true if minimax

      • setMinimax

        public void setMinimax​(boolean v)
        Set if the MIMinimax feature space is to be used.
        Parameters:
        v - true if RBF

      • buildLogisticModelsTipText

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

      • getBuildLogisticModels

        public boolean getBuildLogisticModels()
        Get the value of buildLogisticModels.
        Returns:
        Value of buildLogisticModels.

      • setBuildLogisticModels

        public void setBuildLogisticModels​(boolean newbuildLogisticModels)
        Set the value of buildLogisticModels.
        Parameters:
        newbuildLogisticModels - Value to assign to buildLogisticModels.

      • 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()
        Get the value of numFolds.
        Returns:
        Value of numFolds.

      • setNumFolds

        public void setNumFolds​(int newnumFolds)
        Set the value of numFolds.
        Parameters:
        newnumFolds - Value to assign to numFolds.

      • randomSeedTipText

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

      • getRandomSeed

        public int getRandomSeed()
        Get the value of randomSeed.
        Returns:
        Value of randomSeed.

      • setRandomSeed

        public void setRandomSeed​(int newrandomSeed)
        Set the value of randomSeed.
        Parameters:
        newrandomSeed - Value to assign to randomSeed.

      • toString

        public java.lang.String toString()
        Prints out the classifier.
        Overrides:
        toString in class java.lang.Object
        Returns:
        a description of the classifier as a string

      • main

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