Package weka.classifiers.meta

Class FilteredClassifier

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, Classifier, IterativeClassifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, Drawable, OptionHandler, PartitionGenerator, Randomizable, RevisionHandler, WeightedAttributesHandler, WeightedInstancesHandler
    Direct Known Subclasses:
    RandomizableFilteredClassifier
    public class FilteredClassifier
extends RandomizableSingleClassifierEnhancer
implements Drawable, PartitionGenerator, IterativeClassifier, BatchPredictor, WeightedInstancesHandler, WeightedAttributesHandler
    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. If unequal instance weights or attribute weights are present, and the filter or the classifier are unable to deal with them, the instances and/or attributes are resampled with replacement based on the weights before they are passed to the filter or the classifier (as appropriate).

    Valid options are: 

     -F <filter specification>
  Full class name of filter to use, followed
  by filter options.
  default: "weka.filters.supervised.attribute.Discretize -R first-last -precision 6"
 
     -W <classifier name>
  Full name of base classifier.
  (default: weka.classifiers.trees.J48)
 
     -S num
 The random number seed to be used (default 1).
    -doNotCheckForModifiedClassAttribute
    If this is set, the classifier will not check whether the filter modifies the class attribute (use with caution).

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

    -do-not-check-capabilities
    If set, classifier capabilities are not checked before classifier is built (use with caution).

    -num-decimal-places
    The number of decimal places for the output of numbers in the model.

    -batch-size
    The desired batch size for batch prediction. 

     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.
 
     -S <seed>
  Seed for random data shuffling (default 1).
    Version:
    $Revision: 15022 $
    Author:
    Len Trigg (trigg@cs.waikato.ac.nz)
    See Also:
    Serialized Form

    • 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

      • resumeTipText

        public java.lang.String resumeTipText()
        Tool tip text for finalize property
        Returns:
        the tool tip text for the finalize property

      • setResume

        public void setResume​(boolean resume)
               throws java.lang.Exception
        If called with argument true, then the next time done() is called the model is effectively "frozen" and no further iterations can be performed
        Specified by:
        setResume in interface IterativeClassifier
        Parameters:
        resume - true if the model is to be finalized after performing iterations
        Throws:
        java.lang.Exception - if finalization cannot be set

      • getResume

        public boolean getResume()
        Returns true if the model is to be finalized (or has been finalized) after training.
        Specified by:
        getResume in interface IterativeClassifier
        Returns:
        the current value of finalize

      • 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.
  default: "weka.filters.supervised.attribute.Discretize -R first-last -precision 6"
 
         -W <classifier name>
  Full name of base classifier.
  (default: weka.classifiers.trees.J48)
 
         -S num
 The random number seed to be used.
        -doNotCheckForModifiedClassAttribute
        If this is set, the classifier will not check whether the filter modifies the class attribute (use with caution).

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

        -do-not-check-capabilities
        If set, classifier capabilities are not checked before classifier is built (use with caution).

        -num-decimal-laces
        The number of decimal places for the output of numbers in the model.

        -batch-size
        The desired batch size for batch prediction. 

         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 RandomizableSingleClassifierEnhancer
        Parameters:
        options - the list of options as an array of strings
        Throws:
        java.lang.Exception - if an option is not supported

      • doNotCheckForModifiedClassAttributeTipText

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

      • getDoNotCheckForModifiedClassAttribute

        public boolean getDoNotCheckForModifiedClassAttribute()
        Returns true if classifier checks whether class attribute has been modified by filter.

      • setDoNotCheckForModifiedClassAttribute

        public void setDoNotCheckForModifiedClassAttribute​(boolean flag)
        Use this method to determine whether classifier checks whether class attribute has been modified by filter.

      • 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

      • 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
        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 BatchPredictor. 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

      • 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

      • 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

      • 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]