MISMO

java.lang.Object
- weka.classifiers.Classifier
- - weka.classifiers.mi.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 Summary

Fields
Modifier and Type	Field and Description
`static int`	`FILTER_NONE` No normalization/standardization
`static int`	`FILTER_NORMALIZE` Normalize training data
`static int`	`FILTER_STANDARDIZE` Standardize training data
`static Tag[]`	`TAGS_FILTER` The filter to apply to the training data

Constructor Summary

Constructors
Constructor and Description

MISMO()

Constructors
Constructor and Description
`MISMO()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`java.lang.String[][][]`	`attributeNames()` Returns the attribute names.
`double[][]`	`bias()` Returns the bias of each binary SMO.
`void`	`buildClassifier(Instances insts)` Method for building the classifier.
`java.lang.String`	`buildLogisticModelsTipText()` Returns the tip text for this property
`java.lang.String`	`checksTurnedOffTipText()` Returns the tip text for this property
`java.lang.String[]`	`classAttributeNames()` Returns the names of the class attributes.
`java.lang.String`	`cTipText()` Returns the tip text for this property
`double[]`	`distributionForInstance(Instance inst)` Estimates class probabilities for given instance.
`java.lang.String`	`epsilonTipText()` Returns the tip text for this property
`java.lang.String`	`filterTypeTipText()` Returns the tip text for this property
`boolean`	`getBuildLogisticModels()` Get the value of buildLogisticModels.
`double`	`getC()` Get the value of C.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`boolean`	`getChecksTurnedOff()` Returns whether the checks are turned off or not.
`double`	`getEpsilon()` Get the value of epsilon.
`SelectedTag`	`getFilterType()` Gets how the training data will be transformed.
`Kernel`	`getKernel()` Gets the kernel to use.
`boolean`	`getMinimax()` Check if the MIMinimax feature space is to be used.
`Capabilities`	`getMultiInstanceCapabilities()` Returns the capabilities of this multi-instance classifier for the relational data.
`int`	`getNumFolds()` Get the value of numFolds.
`java.lang.String[]`	`getOptions()` Gets the current settings of the classifier.
`int`	`getRandomSeed()` Get the value of randomSeed.
`java.lang.String`	`getRevision()` Returns the revision string.
`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`	`getToleranceParameter()` Get the value of tolerance parameter.
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`java.lang.String`	`kernelTipText()` Returns the tip text for this property
`java.util.Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`java.lang.String`	`minimaxTipText()` Returns the tip text for this property
`int`	`numClassAttributeValues()` Returns the number of values of the class attribute.
`java.lang.String`	`numFoldsTipText()` Returns the tip text for this property
`double[]`	`pairwiseCoupling(double[][] n, double[][] r)` Implements pairwise coupling.
`java.lang.String`	`randomSeedTipText()` Returns the tip text for this property
`void`	`setBuildLogisticModels(boolean newbuildLogisticModels)` Set the value of buildLogisticModels.
`void`	`setC(double v)` Set the value of C.
`void`	`setChecksTurnedOff(boolean value)` Disables or enables the checks (which could be time-consuming).
`void`	`setEpsilon(double v)` Set the value of epsilon.
`void`	`setFilterType(SelectedTag newType)` Sets how the training data will be transformed.
`void`	`setKernel(Kernel value)` Sets the kernel to use.
`void`	`setMinimax(boolean v)` Set if the MIMinimax feature space is to be used.
`void`	`setNumFolds(int newnumFolds)` Set the value of numFolds.
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setRandomSeed(int newrandomSeed)` Set the value of randomSeed.
`void`	`setToleranceParameter(double v)` Set the value of tolerance parameter.
`int[][][]`	`sparseIndices()` Returns the indices in sparse format.
`double[][][]`	`sparseWeights()` Returns the weights in sparse format.
`java.lang.String`	`toleranceParameterTipText()` Returns the tip text for this property
`java.lang.String`	`toString()` Prints out the classifier.
`void`	`turnChecksOff()` Turns off checks for missing values, etc.
`void`	`turnChecksOn()` Turns on checks for missing values, etc.

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
  - 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.
  - 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
  - getMultiInstanceCapabilities
```
public Capabilities getMultiInstanceCapabilities()
```
    Returns the capabilities of this multi-instance classifier for the relational data.
    
    Specified by:
    
    getMultiInstanceCapabilities in interface MultiInstanceCapabilitiesHandler
    
    Returns:
    
    the capabilities of this object
    
    See Also:
    
    Capabilities
  - 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
  - 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[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - the commandline parameters

Class MISMO

Field Summary

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Field Detail

FILTER_NORMALIZE

FILTER_STANDARDIZE

FILTER_NONE

TAGS_FILTER

Constructor Detail

MISMO

Method Detail

globalInfo

getTechnicalInformation

turnChecksOff

turnChecksOn

getCapabilities

getMultiInstanceCapabilities

buildClassifier

distributionForInstance

pairwiseCoupling

sparseWeights

sparseIndices

bias

numClassAttributeValues

classAttributeNames

attributeNames

listOptions

setOptions

getOptions

setChecksTurnedOff

getChecksTurnedOff

checksTurnedOffTipText

kernelTipText

getKernel

setKernel

cTipText

getC

setC

toleranceParameterTipText

getToleranceParameter

setToleranceParameter

epsilonTipText

getEpsilon

setEpsilon

filterTypeTipText

getFilterType

setFilterType

minimaxTipText

getMinimax

setMinimax

buildLogisticModelsTipText

getBuildLogisticModels

setBuildLogisticModels

numFoldsTipText

getNumFolds

setNumFolds

randomSeedTipText

getRandomSeed

setRandomSeed

toString

getRevision

main