FilteredClassifier (interruptible-weka 0.1.2 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.SingleClassifierEnhancer
  - - weka.classifiers.RandomizableSingleClassifierEnhancer
    - - weka.classifiers.meta.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).
 
```
See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected boolean`	`m_DoNotCheckForModifiedClassAttribute` Flag that can be set to true if class attribute is not to be checked for modifications by the filer.
`protected Filter`	`m_Filter` The filter
`protected Instances`	`m_FilteredInstances` The instance structure of the filtered instances
`protected Reorder`	`m_ReorderFiltered`
`protected Reorder`	`m_ReorderOriginal` If the attributes are resampled, we store the filter for this

Fields inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer
m_Seed

Fields inherited from class weka.classifiers.SingleClassifierEnhancer
m_Classifier

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Fields inherited from interface weka.core.Drawable
BayesNet, Newick, NOT_DRAWABLE, TREE

Constructor Summary

Constructors
Constructor and Description

FilteredClassifier()
Default constructor.

Constructors
Constructor and Description
`FilteredClassifier()` 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)` Build the classifier on the filtered data.
`protected java.lang.String`	`defaultClassifierString()` String describing default classifier.
`protected java.lang.String`	`defaultFilterString()` String describing default filter.
`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`	`doNotCheckForModifiedClassAttributeTipText()` Returns the tip text for this property
`protected Instance`	`filterInstance(Instance instance)` Filters the instance so that it can subsequently be classified.
`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.
`boolean`	`getDoNotCheckForModifiedClassAttribute()` Returns true if classifier checks whether class attribute has been modified by filter.
`Filter`	`getFilter()` Gets the filter used.
`protected java.lang.String`	`getFilterSpec()` Gets the filter specification string, which contains the class name of the filter and any options to the filter
`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.
`boolean`	`implementsMoreEfficientBatchPrediction()` Returns true if the base classifier implements BatchPredictor and is able to generate batch predictions efficiently
`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.
`protected Instances`	`resampleAttributes(Instances data, boolean original, java.util.Random random)` Resamples set of attributes.
`void`	`setBatchSize(java.lang.String size)` Set the batch size to use.
`void`	`setDoNotCheckForModifiedClassAttribute(boolean flag)` Use this method to determine whether classifier checks whether class attribute has been modified by filter.
`void`	`setFilter(Filter filter)` Sets the filter
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`protected Instances`	`setUp(Instances data, java.util.Random random)` Sets up the filter and runs checks.
`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, defaultClassifierOptions, getClassifier, getClassifierSpec, 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
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.classifiers.Classifier
classifyInstance

- Field Detail
  - m_Filter
```
protected Filter m_Filter
```
    The filter
  - m_FilteredInstances
```
protected Instances m_FilteredInstances
```
    The instance structure of the filtered instances
  - m_DoNotCheckForModifiedClassAttribute
```
protected boolean m_DoNotCheckForModifiedClassAttribute
```
    Flag that can be set to true if class attribute is not to be checked for modifications by the filer.
  - m_ReorderOriginal
```
protected Reorder m_ReorderOriginal
```
    If the attributes are resampled, we store the filter for this
  - m_ReorderFiltered
```
protected Reorder m_ReorderFiltered
```
- 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
  - defaultClassifierString
```
protected java.lang.String defaultClassifierString()
```
    String describing default classifier.
    
    Overrides:
    
    defaultClassifierString in class SingleClassifierEnhancer
    
    Returns:
    
    the default classifier classname
  - defaultFilterString
```
protected java.lang.String defaultFilterString()
```
    String describing default filter.
  - 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 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:
```
 -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.
  - 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
  - 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
  - getFilterSpec
```
protected java.lang.String getFilterSpec()
```
    Gets the filter specification string, which contains the class name of the filter and any options to the filter
    
    Returns:
    
    the filter string.
  - 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
  - setUp
```
protected Instances setUp(Instances data,
                          java.util.Random random)
                   throws java.lang.Exception
```
    Sets up the filter and runs checks.
    
    Returns:
    
    filtered data
    
    Throws:
    
    java.lang.Exception
  - resampleAttributes
```
protected Instances resampleAttributes(Instances data,
                                       boolean original,
                                       java.util.Random random)
                                throws java.lang.Exception
```
    Resamples set of attributes.
    
    Parameters:
    
    data - the data to be filtered
    
    original - whether the original or the filtered data are to be resampled
    
    random - the random number generator to use
    
    Throws:
    
    java.lang.Exception
  - 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
  - filterInstance
```
protected Instance filterInstance(Instance instance)
                           throws java.lang.Exception
```
    Filters the instance so that it can subsequently be classified.
    
    Throws:
    
    java.lang.Exception
  - 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
  - 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
  - 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]

Class FilteredClassifier

Field Summary

Fields inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer

Fields inherited from class weka.classifiers.SingleClassifierEnhancer

Fields inherited from class weka.classifiers.AbstractClassifier

Fields inherited from interface weka.core.Drawable

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.classifiers.Classifier

Field Detail

m_Filter

m_FilteredInstances

m_DoNotCheckForModifiedClassAttribute

m_ReorderOriginal

m_ReorderFiltered

Constructor Detail

FilteredClassifier

Method Detail

globalInfo

defaultClassifierString

defaultFilterString

graphType

graph

generatePartition

getMembershipValues

numElements

initializeClassifier

next

done

listOptions

setOptions

doNotCheckForModifiedClassAttributeTipText

getDoNotCheckForModifiedClassAttribute

setDoNotCheckForModifiedClassAttribute

getOptions

filterTipText

setFilter

getFilter

getFilterSpec

getCapabilities

setUp

resampleAttributes

buildClassifier

filterInstance

distributionForInstance

batchSizeTipText

setBatchSize

getBatchSize

distributionsForInstances

implementsMoreEfficientBatchPrediction

toString

getRevision

main