weka.classifiers.meta

## Class FilteredClassifier

- java.lang.Object
- - weka.classifiers.AbstractClassifier
  - - weka.classifiers.SingleClassifierEnhancer
    - - weka.classifiers.meta.FilteredClassifier

- All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, Classifier, IterativeClassifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, Drawable, OptionHandler, PartitionGenerator, RevisionHandler
  Direct Known Subclasses:
    RandomizableFilteredClassifier

      public class FilteredClassifier
      extends SingleClassifierEnhancer
      implements Drawable, PartitionGenerator, IterativeClassifier, BatchPredictor

  Class for running an arbitrary classifier on data that has been passed through an arbitrary filter. Like the classifier, the structure of the filter is based exclusively on the training data and test instances will be processed by the filter without changing their structure.

  Valid options are:

       -F <filter specification>
        Full class name of filter to use, followed
        by filter options.
        eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"

       -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.trees.J48)

       Options specific to classifier weka.classifiers.trees.J48:

       -U
        Use unpruned tree.

       -C <pruning confidence>
        Set confidence threshold for pruning.
        (default 0.25)

       -M <minimum number of instances>
        Set minimum number of instances per leaf.
        (default 2)

       -R
        Use reduced error pruning.

       -N <number of folds>
        Set number of folds for reduced error
        pruning. One fold is used as pruning set.
        (default 3)

       -B
        Use binary splits only.

       -S
        Don't perform subtree raising.

       -L
        Do not clean up after the tree has been built.

       -A
        Laplace smoothing for predicted probabilities.

       -Q <seed>
        Seed for random data shuffling (default 1).

  Version:
    $Revision: 11142 $
  Author:
    Len Trigg (trigg@cs.waikato.ac.nz)
  See Also:
    Serialized Form

- - ### Field Summary

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

      `BayesNet, Newick, NOT_DRAWABLE, TREE`

  - ### Constructor Summary

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

  - ### Method Summary

    Methods
    Modifier and Type | Method and Description
    --- | ---
    `java.lang.String` | `batchSizeTipText()` Tool tip text for this property
    `void` | `buildClassifier(Instances data)` Build the classifier on the filtered data.
    `double[]` | `distributionForInstance(Instance instance)` Classifies a given instance after filtering.
    `double[][]` | `distributionsForInstances(Instances insts)` Batch scoring method.
    `void` | `done()` Signal end of iterating, useful for any house-keeping/cleanup (If the base classifier supports this.)
    `java.lang.String` | `filterTipText()` Returns the tip text for this property
    `void` | `generatePartition(Instances data)` Builds the classifier to generate a partition.
    `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.
    `Filter` | `getFilter()` Gets the filter used.
    `double[]` | `getMembershipValues(Instance inst)` Computes an array that has a value for each element in the partition.
    `java.lang.String[]` | `getOptions()` Gets the current settings of the Classifier.
    `java.lang.String` | `getRevision()` Returns the revision string.
    `java.lang.String` | `globalInfo()` Returns a string describing this classifier
    `java.lang.String` | `graph()` Returns graph describing the classifier (if possible).
    `int` | `graphType()` Returns the type of graph this classifier represents.
    `void` | `initializeClassifier(Instances data)` Initializes an iterative classifier.
    `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.
    `boolean` | `next()` Performs one iteration.
    `int` | `numElements()` Returns the number of elements in the partition.
    `void` | `setBatchSize(java.lang.String size)` Set the batch size to use.
    `void` | `setFilter(Filter filter)` Sets the filter
    `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.SingleClassifierEnhancer

      `classifierTipText, getClassifier, setClassifier`

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

      `classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, makeCopies, makeCopy, runClassifier, setDebug, setDoNotCheckCapabilities`

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

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

    - ### Methods inherited from interface weka.classifiers.Classifier

      `classifyInstance`

- - ### Constructor Detail

    - #### FilteredClassifier

          public FilteredClassifier()

      Default constructor.

  - ### Method Detail

    - #### globalInfo

          public java.lang.String globalInfo()

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

    - #### graphType

          public int graphType()

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

    - #### 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

    - #### generatePartition

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

      Builds the classifier to generate a partition. (If the base classifier supports this.)
      Specified by:
        `generatePartition` in interface `PartitionGenerator`
      Throws:
        `java.lang.Exception`

    - #### getMembershipValues

          public double[] getMembershipValues(Instance inst)
                                       throws java.lang.Exception

      Computes an array that has a value for each element in the partition. (If the base classifier supports this.)
      Specified by:
        `getMembershipValues` in interface `PartitionGenerator`
      Throws:
        `java.lang.Exception`

    - #### numElements

          public int numElements()
                          throws java.lang.Exception

      Returns the number of elements in the partition. (If the base classifier supports this.)
      Specified by:
        `numElements` in interface `PartitionGenerator`
      Throws:
        `java.lang.Exception`

    - #### initializeClassifier

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

      Initializes an iterative classifier. (If the base classifier supports this.)
      Specified by:
        `initializeClassifier` in interface `IterativeClassifier`
      Parameters:
        `data` - the instances to be used in induction
      Throws:
        `java.lang.Exception` - if the model cannot be initialized

    - #### next

          public boolean next()
                       throws java.lang.Exception

      Performs one iteration. (If the base classifier supports this.)
      Specified by:
        `next` in interface `IterativeClassifier`
      Returns:
        false if no further iterations could be performed, true otherwise
      Throws:
        `java.lang.Exception` - if this iteration fails for unexpected reasons

    - #### done

          public void done()
                    throws java.lang.Exception

      Signal end of iterating, useful for any house-keeping/cleanup (If the base classifier supports this.)
      Specified by:
        `done` in interface `IterativeClassifier`
      Throws:
        `java.lang.Exception` - if cleanup fails

    - #### listOptions

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

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

           -F <filter specification>
            Full class name of filter to use, followed
            by filter options.
            eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"

           -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.trees.J48)

           Options specific to classifier weka.classifiers.trees.J48:

           -U
            Use unpruned tree.

           -C <pruning confidence>
            Set confidence threshold for pruning.
            (default 0.25)

           -M <minimum number of instances>
            Set minimum number of instances per leaf.
            (default 2)

           -R
            Use reduced error pruning.

           -N <number of folds>
            Set number of folds for reduced error
            pruning. One fold is used as pruning set.
            (default 3)

           -B
            Use binary splits only.

           -S
            Don't perform subtree raising.

           -L
            Do not clean up after the tree has been built.

           -A
            Laplace smoothing for predicted probabilities.

           -Q <seed>
            Seed for random data shuffling (default 1).

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

    - #### filterTipText

          public java.lang.String filterTipText()

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

    - #### setFilter

          public void setFilter(Filter filter)

      Sets the filter
      Parameters:
        `filter` - the filter with all options set.

    - #### getFilter

          public Filter getFilter()

      Gets the filter used.
      Returns:
        the filter

    - #### 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

      Build the classifier on the filtered data.
      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

      Classifies a given instance after filtering.
      Specified by:
        `distributionForInstance` in interface `Classifier`
      Overrides:
        `distributionForInstance` in class `AbstractClassifier`
      Parameters:
        `instance` - the instance to be classified
      Returns:
        the class distribution for the given instance
      Throws:
        `java.lang.Exception` - if instance could not be classified successfully

    - #### batchSizeTipText

          public java.lang.String batchSizeTipText()

      Tool tip text for this property
      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`
      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`
      Returns:
        the batch size to use

    - #### 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`
      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

    - #### toString

          public java.lang.String toString()

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