weka.classifiers.meta

## Class CostSensitiveClassifier

- java.lang.Object
- - weka.classifiers.AbstractClassifier
  - - weka.classifiers.SingleClassifierEnhancer
    - - weka.classifiers.RandomizableSingleClassifierEnhancer
      - - weka.classifiers.meta.CostSensitiveClassifier

- All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, Drawable, OptionHandler, Randomizable, RevisionHandler

      public class CostSensitiveClassifier
      extends RandomizableSingleClassifierEnhancer
      implements OptionHandler, Drawable, BatchPredictor

  A metaclassifier that makes its base classifier cost-sensitive. Two methods can be used to introduce cost-sensitivity: reweighting training instances according to the total cost assigned to each class; or predicting the class with minimum expected misclassification cost (rather than the most likely class). Performance can often be improved by using a Bagged classifier to improve the probability estimates of the base classifier.

  Valid options are:

       -M
        Minimize expected misclassification cost. Default is to
        reweight training instances according to costs per class

       -C <cost file name>
        File name of a cost matrix to use. If this is not supplied,
        a cost matrix will be loaded on demand. The name of the
        on-demand file is the relation name of the training data
        plus ".cost", and the path to the on-demand file is
        specified with the -N option.

       -N <directory>
        Name of a directory to search for cost files when loading
        costs on demand (default current directory).

       -cost-matrix <matrix>
        The cost matrix in Matlab single line format.

       -S <num>
        Random number seed.
        (default 1)

       -D
        If set, classifier is run in debug mode and
        may output additional info to the console

       -W
        Full name of base classifier.
        (default: weka.classifiers.rules.ZeroR)

       Options specific to classifier weka.classifiers.rules.ZeroR:

       -D
        If set, classifier is run in debug mode and
        may output additional info to the console

  Options after -- are passed to the designated classifier.

  Version:
    $Revision: 12180 $
  Author:
    Len Trigg (len@reeltwo.com)
  See Also:
    Serialized Form

- - ### Field Summary

    Fields
    Modifier and Type | Field and Description
    --- | ---
    `static int` | `MATRIX_ON_DEMAND` load cost matrix on demand
    `static int` | `MATRIX_SUPPLIED` use explicit cost matrix
    `static Tag[]` | `TAGS_MATRIX_SOURCE` Specify possible sources of the cost matrix

    - ### Fields inherited from class weka.classifiers.AbstractClassifier

      `BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT`

    - ### Fields inherited from interface weka.core.Drawable

      `BayesNet, Newick, NOT_DRAWABLE, TREE`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `CostSensitiveClassifier()` Default constructor.

  - ### Method Summary

    All Methods Static Methods Instance Methods Concrete Methods
    Modifier and Type | Method and Description
    --- | ---
    `java.lang.String` | `batchSizeTipText()` Tool tip text for this property
    `void` | `buildClassifier(Instances data)` Builds the model of the base learner.
    `java.lang.String` | `costMatrixSourceTipText()`
    `java.lang.String` | `costMatrixTipText()`
    `double[]` | `distributionForInstance(Instance instance)` Returns class probabilities.
    `double[][]` | `distributionsForInstances(Instances insts)` Batch scoring method.
    `java.lang.String` | `getBatchSize()` Gets the preferred batch size from the base learner if it implements BatchPredictor.
    `Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
    `CostMatrix` | `getCostMatrix()` Gets the misclassification cost matrix.
    `SelectedTag` | `getCostMatrixSource()` Gets the source location method of the cost matrix.
    `boolean` | `getMinimizeExpectedCost()` Gets the value of MinimizeExpectedCost.
    `java.io.File` | `getOnDemandDirectory()` Returns the directory that will be searched for cost files when loading on demand.
    `java.lang.String[]` | `getOptions()` Gets the current settings of the Classifier.
    `java.lang.String` | `getRevision()` Returns the revision string.
    `java.lang.String` | `globalInfo()`
    `java.lang.String` | `graph()` Returns graph describing the classifier (if possible).
    `int` | `graphType()` Returns the type of graph this classifier represents.
    `boolean` | `implementsMoreEfficientBatchPrediction()` Returns true if the base classifier implements BatchPredictor and is able to generate batch predictions efficiently
    `java.util.Enumeration<Option>` | `listOptions()` Returns an enumeration describing the available options.
    `static void` | `main(java.lang.String[] argv)` Main method for testing this class.
    `java.lang.String` | `minimizeExpectedCostTipText()`
    `java.lang.String` | `onDemandDirectoryTipText()`
    `void` | `setBatchSize(java.lang.String size)` Set the batch size to use.
    `void` | `setCostMatrix(CostMatrix newCostMatrix)` Sets the misclassification cost matrix.
    `void` | `setCostMatrixSource(SelectedTag newMethod)` Sets the source location of the cost matrix.
    `void` | `setMinimizeExpectedCost(boolean newMinimizeExpectedCost)` Set the value of MinimizeExpectedCost.
    `void` | `setOnDemandDirectory(java.io.File newDir)` Sets the directory that will be searched for cost files when loading on demand.
    `void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
    `java.lang.String` | `toString()` Output a representation of this classifier

    - ### Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer

      `getSeed, seedTipText, setSeed`

    - ### Methods inherited from class weka.classifiers.SingleClassifierEnhancer

      `classifierTipText, getClassifier, postExecution, preExecution, setClassifier`

    - ### Methods inherited from class weka.classifiers.AbstractClassifier

      `classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces`

    - ### Methods inherited from class java.lang.Object

      `equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

- - ### Field Detail

    - #### MATRIX_ON_DEMAND

          public static final int MATRIX_ON_DEMAND

      load cost matrix on demand
      See Also:
        Constant Field Values

    - #### MATRIX_SUPPLIED

          public static final int MATRIX_SUPPLIED

      use explicit cost matrix
      See Also:
        Constant Field Values

    - #### TAGS_MATRIX_SOURCE

          public static final Tag[] TAGS_MATRIX_SOURCE

      Specify possible sources of the cost matrix

  - ### Constructor Detail

    - #### CostSensitiveClassifier

          public CostSensitiveClassifier()

      Default constructor.

  - ### Method Detail

    - #### listOptions

          public java.util.Enumeration<Option> listOptions()

      Returns an enumeration describing the available options.
      Specified by:
        `listOptions` in interface `OptionHandler`
      Overrides:
        `listOptions` in class `RandomizableSingleClassifierEnhancer`
      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:

           -M
            Minimize expected misclassification cost. Default is to
            reweight training instances according to costs per class

           -C <cost file name>
            File name of a cost matrix to use. If this is not supplied,
            a cost matrix will be loaded on demand. The name of the
            on-demand file is the relation name of the training data
            plus ".cost", and the path to the on-demand file is
            specified with the -N option.

           -N <directory>
            Name of a directory to search for cost files when loading
            costs on demand (default current directory).

           -cost-matrix <matrix>
            The cost matrix in Matlab single line format.

           -S <num>
            Random number seed.
            (default 1)

           -D
            If set, classifier is run in debug mode and
            may output additional info to the console

           -W
            Full name of base classifier.
            (default: weka.classifiers.rules.ZeroR)

           Options specific to classifier weka.classifiers.rules.ZeroR:

           -D
            If set, classifier is run in debug mode and
            may output additional info to the console

      Options after -- are passed to the designated classifier.

      Specified by:
        `setOptions` in interface `OptionHandler`
      Overrides:
        `setOptions` in class `RandomizableSingleClassifierEnhancer`
      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 `RandomizableSingleClassifierEnhancer`
      Returns:
        an array of strings suitable for passing to setOptions

    - #### globalInfo

          public java.lang.String globalInfo()

      Returns:
        a description of the classifier suitable for displaying in the explorer/experimenter gui

    - #### costMatrixSourceTipText

          public java.lang.String costMatrixSourceTipText()

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

    - #### getCostMatrixSource

          public SelectedTag getCostMatrixSource()

      Gets the source location method of the cost matrix. Will be one of MATRIX_ON_DEMAND or MATRIX_SUPPLIED.
      Returns:
        the cost matrix source.

    - #### setCostMatrixSource

          public void setCostMatrixSource(SelectedTag newMethod)

      Sets the source location of the cost matrix. Values other than MATRIX_ON_DEMAND or MATRIX_SUPPLIED will be ignored.
      Parameters:
        `newMethod` - the cost matrix location method.

    - #### onDemandDirectoryTipText

          public java.lang.String onDemandDirectoryTipText()

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

    - #### getOnDemandDirectory

          public java.io.File getOnDemandDirectory()

      Returns the directory that will be searched for cost files when loading on demand.
      Returns:
        The cost file search directory.

    - #### setOnDemandDirectory

          public void setOnDemandDirectory(java.io.File newDir)

      Sets the directory that will be searched for cost files when loading on demand.
      Parameters:
        `newDir` - The cost file search directory.

    - #### minimizeExpectedCostTipText

          public java.lang.String minimizeExpectedCostTipText()

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

    - #### getMinimizeExpectedCost

          public boolean getMinimizeExpectedCost()

      Gets the value of MinimizeExpectedCost.
      Returns:
        Value of MinimizeExpectedCost.

    - #### setMinimizeExpectedCost

          public void setMinimizeExpectedCost(boolean newMinimizeExpectedCost)

      Set the value of MinimizeExpectedCost.
      Parameters:
        `newMinimizeExpectedCost` - Value to assign to MinimizeExpectedCost.

    - #### costMatrixTipText

          public java.lang.String costMatrixTipText()

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

    - #### getCostMatrix

          public CostMatrix getCostMatrix()

      Gets the misclassification cost matrix.
      Returns:
        the cost matrix

    - #### setCostMatrix

          public void setCostMatrix(CostMatrix newCostMatrix)

      Sets the misclassification cost matrix.
      Parameters:
        `newCostMatrix` - the cost matrix

    - #### getCapabilities

          public Capabilities getCapabilities()

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

    - #### buildClassifier

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

      Builds the model of the base learner.
      Specified by:
        `buildClassifier` in interface `Classifier`
      Parameters:
        `data` - the training data
      Throws:
        `java.lang.Exception` - if the classifier could not be built successfully

    - #### distributionForInstance

          public double[] distributionForInstance(Instance instance)
                                           throws java.lang.Exception

      Returns class probabilities. When minimum expected cost approach is chosen, returns probability one for class with the minimum expected misclassification cost. Otherwise it returns the probability distribution returned by the base classifier.
      Specified by:
        `distributionForInstance` in interface `Classifier`
      Overrides:
        `distributionForInstance` in class `AbstractClassifier`
      Parameters:
        `instance` - the instance to be classified
      Returns:
        the computed distribution for the given instance
      Throws:
        `java.lang.Exception` - if instance could not be classified successfully

    - #### distributionsForInstances

          public double[][] distributionsForInstances(Instances insts)
                                               throws java.lang.Exception

      Batch scoring method. Calls the appropriate method for the base learner if it implements BatchPredictor. Otherwise it simply calls the distributionForInstance() method repeatedly.
      Specified by:
        `distributionsForInstances` in interface `BatchPredictor`
      Overrides:
        `distributionsForInstances` in class `AbstractClassifier`
      Parameters:
        `insts` - the instances to get predictions for
      Returns:
        an array of probability distributions, one for each instance
      Throws:
        `java.lang.Exception` - if a problem occurs

    - #### batchSizeTipText

          public java.lang.String batchSizeTipText()

      Tool tip text for this property
      Overrides:
        `batchSizeTipText` in class `AbstractClassifier`
      Returns:
        the tool tip for this property

    - #### setBatchSize

          public void setBatchSize(java.lang.String size)

      Set the batch size to use. Gets passed through to the base learner if it implements BatchPrecitor. Otherwise it is just ignored.
      Specified by:
        `setBatchSize` in interface `BatchPredictor`
      Overrides:
        `setBatchSize` in class `AbstractClassifier`
      Parameters:
        `size` - the batch size to use

    - #### getBatchSize

          public java.lang.String getBatchSize()

      Gets the preferred batch size from the base learner if it implements BatchPredictor. Returns 1 as the preferred batch size otherwise.
      Specified by:
        `getBatchSize` in interface `BatchPredictor`
      Overrides:
        `getBatchSize` in class `AbstractClassifier`
      Returns:
        the batch size to use

    - #### implementsMoreEfficientBatchPrediction

          public boolean implementsMoreEfficientBatchPrediction()

      Returns true if the base classifier implements BatchPredictor and is able to generate batch predictions efficiently
      Specified by:
        `implementsMoreEfficientBatchPrediction` in interface `BatchPredictor`
      Overrides:
        `implementsMoreEfficientBatchPrediction` in class `AbstractClassifier`
      Returns:
        true if the base classifier can generate batch predictions efficiently

    - #### graphType

          public int graphType()

      Returns the type of graph this classifier represents.
      Specified by:
        `graphType` in interface `Drawable`
      Returns:
        the type of graph this classifier represents

    - #### graph

          public java.lang.String graph()
                                 throws java.lang.Exception

      Returns graph describing the classifier (if possible).
      Specified by:
        `graph` in interface `Drawable`
      Returns:
        the graph of the classifier in dotty format
      Throws:
        `java.lang.Exception` - if the classifier cannot be graphed

    - #### toString

          public java.lang.String toString()

      Output a representation of this classifier
      Overrides:
        `toString` in class `java.lang.Object`
      Returns:
        a string representation of the classifier

    - #### getRevision

          public java.lang.String getRevision()

      Returns the revision string.
      Specified by:
        `getRevision` in interface `RevisionHandler`
      Overrides:
        `getRevision` in class `AbstractClassifier`
      Returns:
        the revision

    - #### main

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

      Main method for testing this class.
      Parameters:
        `argv` - should contain the following arguments: -t training file [-T test file] [-c class index]