Bagging

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.SingleClassifierEnhancer
  - - weka.classifiers.IteratedSingleClassifierEnhancer
    - - weka.classifiers.ParallelIteratedSingleClassifierEnhancer
      - weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer
        
        weka.classifiers.meta.Bagging

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, Classifier, AdditionalMeasureProducer, Aggregateable<Bagging>, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, PartitionGenerator, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

Direct Known Subclasses:: RandomForest

public class Bagging
extends RandomizableParallelIteratedSingleClassifierEnhancer
implements WeightedInstancesHandler, AdditionalMeasureProducer, TechnicalInformationHandler, PartitionGenerator, Aggregateable<Bagging>

Class for bagging a classifier to reduce variance. Can do classification and regression depending on the base learner.

For more information, see

Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.

BibTeX:

 @article{Breiman1996,
    author = {Leo Breiman},
    journal = {Machine Learning},
    number = {2},
    pages = {123-140},
    title = {Bagging predictors},
    volume = {24},
    year = {1996}
 }

Valid options are:

 -P
  Size of each bag, as a percentage of the
  training set size. (default 100)

 -O
  Calculate the out of bag error.

 -print
  Print the individual classifiers in the output

 -store-out-of-bag-predictions
  Whether to store out of bag predictions in internal evaluation object.

 -output-out-of-bag-complexity-statistics
  Whether to output complexity-based statistics when out-of-bag evaluation is performed.

 -represent-copies-using-weights
  Represent copies of instances using weights rather than explicitly.

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

 -num-slots <num>
  Number of execution slots.
  (default 1 - i.e. no parallelism)

 -I <num>
  Number of iterations.
  (default 10)

 -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.REPTree)

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

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

 -V <minimum variance for split>
  Set minimum numeric class variance proportion
  of train variance for split (default 1e-3).

 -N <number of folds>
  Number of folds for reduced error pruning (default 3).

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

 -P
  No pruning.

 -L
  Maximum tree depth (default -1, no maximum)

 -I
  Initial class value count (default 0)

 -R
  Spread initial count over all class values (i.e. don't use 1 per value)

Options after -- are passed to the designated classifier.

Version:: $Revision: 15801 $
Author:: Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (len@reeltwo.com), Richard Kirkby (rkirkby@cs.waikato.ac.nz)
See Also:: Serialized Form

Field Summary
- Fields inherited from class weka.classifiers.AbstractClassifier
  BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

Bagging()
Constructor.

Constructors
Constructor and Description
`Bagging()` Constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`Bagging`	`aggregate(Bagging toAggregate)` Aggregate an object with this one
`java.lang.String`	`bagSizePercentTipText()` Returns the tip text for this property
`java.lang.String`	`batchSizeTipText()` Tool tip text for this property
`void`	`buildClassifier(Instances data)` Bagging method.
`java.lang.String`	`calcOutOfBagTipText()` Returns the tip text for this property
`double[]`	`distributionForInstance(Instance instance)` Calculates the class membership probabilities for the given test instance.
`double[][]`	`distributionsForInstances(Instances insts)` Batch scoring method.
`java.util.Enumeration<java.lang.String>`	`enumerateMeasures()` Returns an enumeration of the additional measure names.
`void`	`finalizeAggregation()` Call to complete the aggregation process.
`void`	`generatePartition(Instances data)` Builds the classifier to generate a partition.
`int`	`getBagSizePercent()` Gets the size of each bag, as a percentage of the training set size.
`java.lang.String`	`getBatchSize()` Gets the preferred batch size from the base learner if it implements BatchPredictor.
`boolean`	`getCalcOutOfBag()` Get whether the out of bag error is calculated.
`double`	`getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure.
`double[]`	`getMembershipValues(Instance inst)` Computes an array that indicates leaf membership
`java.lang.String[]`	`getOptions()` Gets the current settings of the Classifier.
`Evaluation`	`getOutOfBagEvaluationObject()` Returns the out-of-bag evaluation object.
`boolean`	`getOutputOutOfBagComplexityStatistics()` Gets whether complexity statistics are output when OOB estimation is performed.
`boolean`	`getPrintClassifiers()` Get whether to print the individual ensemble classifiers in the output
`boolean`	`getRepresentCopiesUsingWeights()` Get whether copies of instances are represented using weights rather than explicitly.
`java.lang.String`	`getRevision()` Returns the revision string.
`boolean`	`getStoreOutOfBagPredictions()` Get whether the out of bag predictions are stored.
`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.
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`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.
`double`	`measureOutOfBagError()` Gets the out of bag error that was calculated as the classifier was built.
`int`	`numElements()` Returns the number of elements in the partition.
`java.lang.String`	`outputOutOfBagComplexityStatisticsTipText()` Returns the tip text for this property
`java.lang.String`	`printClassifiersTipText()` Returns the tip text for this property
`java.lang.String`	`representCopiesUsingWeightsTipText()` Returns the tip text for this property
`void`	`setBagSizePercent(int newBagSizePercent)` Sets the size of each bag, as a percentage of the training set size.
`void`	`setBatchSize(java.lang.String size)` Set the batch size to use.
`void`	`setCalcOutOfBag(boolean calcOutOfBag)` Set whether the out of bag error is calculated.
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setOutputOutOfBagComplexityStatistics(boolean b)` Sets whether complexity statistics are output when OOB estimation is performed.
`void`	`setPrintClassifiers(boolean print)` Set whether to print the individual ensemble classifiers in the output
`void`	`setRepresentCopiesUsingWeights(boolean representUsingWeights)` Set whether copies of instances are represented using weights rather than explicitly.
`void`	`setStoreOutOfBagPredictions(boolean storeOutOfBag)` Set whether the out of bag predictions are stored.
`java.lang.String`	`storeOutOfBagPredictionsTipText()` Returns the tip text for this property
`java.lang.String`	`toString()` Returns description of the bagged classifier.

Methods inherited from class weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer
getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.ParallelIteratedSingleClassifierEnhancer
getNumExecutionSlots, numExecutionSlotsTipText, setNumExecutionSlots

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer
getNumIterations, numIterationsTipText, setNumIterations

Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getCapabilities, 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

Methods inherited from interface weka.core.CapabilitiesHandler
getCapabilities

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Constructor Detail
  - Bagging
```
public Bagging()
```
    Constructor.
- 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
  - listOptions
```
public java.util.Enumeration<Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class RandomizableParallelIteratedSingleClassifierEnhancer
    
    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:
```
 -P
  Size of each bag, as a percentage of the
  training set size. (default 100)
```
```
 -O
  Calculate the out of bag error.
```
```
 -print
  Print the individual classifiers in the output
```
```
 -store-out-of-bag-predictions
  Whether to store out of bag predictions in internal evaluation object.
```
```
 -output-out-of-bag-complexity-statistics
  Whether to output complexity-based statistics when out-of-bag evaluation is performed.
```
```
 -represent-copies-using-weights
  Represent copies of instances using weights rather than explicitly.
```
```
 -S <num>
  Random number seed.
  (default 1)
```
```
 -num-slots <num>
  Number of execution slots.
  (default 1 - i.e. no parallelism)
```
```
 -I <num>
  Number of iterations.
  (default 10)
```
```
 -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.REPTree)
```
```
 
 Options specific to classifier weka.classifiers.trees.REPTree:
 
```
```
 -M <minimum number of instances>
  Set minimum number of instances per leaf (default 2).
```
```
 -V <minimum variance for split>
  Set minimum numeric class variance proportion
  of train variance for split (default 1e-3).
```
```
 -N <number of folds>
  Number of folds for reduced error pruning (default 3).
```
```
 -S <seed>
  Seed for random data shuffling (default 1).
```
```
 -P
  No pruning.
```
```
 -L
  Maximum tree depth (default -1, no maximum)
```
```
 -I
  Initial class value count (default 0)
```
```
 -R
  Spread initial count over all class values (i.e. don't use 1 per value)
```
    Options after -- are passed to the designated classifier.
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class RandomizableParallelIteratedSingleClassifierEnhancer
    
    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 RandomizableParallelIteratedSingleClassifierEnhancer
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - bagSizePercentTipText
```
public java.lang.String bagSizePercentTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getBagSizePercent
```
public int getBagSizePercent()
```
    Gets the size of each bag, as a percentage of the training set size.
    
    Returns:
    
    the bag size, as a percentage.
  - setBagSizePercent
```
public void setBagSizePercent(int newBagSizePercent)
```
    Sets the size of each bag, as a percentage of the training set size.
    
    Parameters:
    
    newBagSizePercent - the bag size, as a percentage.
  - representCopiesUsingWeightsTipText
```
public java.lang.String representCopiesUsingWeightsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setRepresentCopiesUsingWeights
```
public void setRepresentCopiesUsingWeights(boolean representUsingWeights)
```
    Set whether copies of instances are represented using weights rather than explicitly.
    
    Parameters:
    
    representUsingWeights - whether to represent copies using weights
  - getRepresentCopiesUsingWeights
```
public boolean getRepresentCopiesUsingWeights()
```
    Get whether copies of instances are represented using weights rather than explicitly.
    
    Returns:
    
    whether copies of instances are represented using weights rather than explicitly
  - storeOutOfBagPredictionsTipText
```
public java.lang.String storeOutOfBagPredictionsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setStoreOutOfBagPredictions
```
public void setStoreOutOfBagPredictions(boolean storeOutOfBag)
```
    Set whether the out of bag predictions are stored.
    
    Parameters:
    
    storeOutOfBag - whether the out of bag predictions are stored
  - getStoreOutOfBagPredictions
```
public boolean getStoreOutOfBagPredictions()
```
    Get whether the out of bag predictions are stored.
    
    Returns:
    
    whether the out of bag predictions are stored
  - calcOutOfBagTipText
```
public java.lang.String calcOutOfBagTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setCalcOutOfBag
```
public void setCalcOutOfBag(boolean calcOutOfBag)
```
    Set whether the out of bag error is calculated.
    
    Parameters:
    
    calcOutOfBag - whether to calculate the out of bag error
  - getCalcOutOfBag
```
public boolean getCalcOutOfBag()
```
    Get whether the out of bag error is calculated.
    
    Returns:
    
    whether the out of bag error is calculated
  - outputOutOfBagComplexityStatisticsTipText
```
public java.lang.String outputOutOfBagComplexityStatisticsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getOutputOutOfBagComplexityStatistics
```
public boolean getOutputOutOfBagComplexityStatistics()
```
    Gets whether complexity statistics are output when OOB estimation is performed.
    
    Returns:
    
    whether statistics are calculated
  - setOutputOutOfBagComplexityStatistics
```
public void setOutputOutOfBagComplexityStatistics(boolean b)
```
    Sets whether complexity statistics are output when OOB estimation is performed.
    
    Parameters:
    
    b - whether statistics are calculated
  - printClassifiersTipText
```
public java.lang.String printClassifiersTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - setPrintClassifiers
```
public void setPrintClassifiers(boolean print)
```
    Set whether to print the individual ensemble classifiers in the output
    
    Parameters:
    
    print - true if the individual classifiers are to be printed
  - getPrintClassifiers
```
public boolean getPrintClassifiers()
```
    Get whether to print the individual ensemble classifiers in the output
    
    Returns:
    
    true if the individual classifiers are to be printed
  - measureOutOfBagError
```
public double measureOutOfBagError()
```
    Gets the out of bag error that was calculated as the classifier was built. Returns error rate in classification case and mean absolute error in regression case.
    
    Returns:
    
    the out of bag error; -1 if out-of-bag-error has not be estimated
  - enumerateMeasures
```
public java.util.Enumeration<java.lang.String> enumerateMeasures()
```
    Returns an enumeration of the additional measure names.
    
    Specified by:
    
    enumerateMeasures in interface AdditionalMeasureProducer
    
    Returns:
    
    an enumeration of the measure names
  - getMeasure
```
public double getMeasure(java.lang.String additionalMeasureName)
```
    Returns the value of the named measure.
    
    Specified by:
    
    getMeasure in interface AdditionalMeasureProducer
    
    Parameters:
    
    additionalMeasureName - the name of the measure to query for its value
    
    Returns:
    
    the value of the named measure
    
    Throws:
    
    java.lang.IllegalArgumentException - if the named measure is not supported
  - getOutOfBagEvaluationObject
```
public Evaluation getOutOfBagEvaluationObject()
```
    Returns the out-of-bag evaluation object.
    
    Returns:
    
    the out-of-bag evaluation object; null if out-of-bag error hasn't been calculated
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws java.lang.Exception
```
    Bagging method.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Overrides:
    
    buildClassifier in class ParallelIteratedSingleClassifierEnhancer
    
    Parameters:
    
    data - the training data to be used for generating the bagged classifier.
    
    Throws:
    
    java.lang.Exception - if the classifier could not be built successfully
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception
```
    Calculates the class membership probabilities for the given test instance.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    preedicted class probability distribution
    
    Throws:
    
    java.lang.Exception - if distribution can't be computed 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()
```
    Returns description of the bagged classifier.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    description of the bagged classifier as a string
  - generatePartition
```
public void generatePartition(Instances data)
                       throws java.lang.Exception
```
    Builds the classifier to generate a partition.
    
    Specified by:
    
    generatePartition in interface PartitionGenerator
    
    Throws:
    
    java.lang.Exception
  - getMembershipValues
```
public double[] getMembershipValues(Instance inst)
                             throws java.lang.Exception
```
    Computes an array that indicates leaf membership
    
    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.
    
    Specified by:
    
    numElements in interface PartitionGenerator
    
    Throws:
    
    java.lang.Exception
  - 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 - the options
  - aggregate
```
public Bagging aggregate(Bagging toAggregate)
                  throws java.lang.Exception
```
    Aggregate an object with this one
    
    Specified by:
    
    aggregate in interface Aggregateable<Bagging>
    
    Parameters:
    
    toAggregate - the object to aggregate
    
    Returns:
    
    the result of aggregation
    
    Throws:
    
    java.lang.Exception - if the supplied object can't be aggregated for some reason
  - finalizeAggregation
```
public void finalizeAggregation()
                         throws java.lang.Exception
```
    Call to complete the aggregation process. Allows implementers to do any final processing based on how many objects were aggregated.
    
    Specified by:
    
    finalizeAggregation in interface Aggregateable<Bagging>
    
    Throws:
    
    java.lang.Exception - if the aggregation can't be finalized for some reason

Class Bagging

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer

Methods inherited from class weka.classifiers.ParallelIteratedSingleClassifierEnhancer

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

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.core.CapabilitiesHandler

Methods inherited from interface weka.core.OptionHandler

Constructor Detail

Bagging

Method Detail

globalInfo

getTechnicalInformation

listOptions

setOptions

getOptions

bagSizePercentTipText

getBagSizePercent

setBagSizePercent

representCopiesUsingWeightsTipText

setRepresentCopiesUsingWeights

getRepresentCopiesUsingWeights

storeOutOfBagPredictionsTipText

setStoreOutOfBagPredictions

getStoreOutOfBagPredictions

calcOutOfBagTipText

setCalcOutOfBag

getCalcOutOfBag

outputOutOfBagComplexityStatisticsTipText

getOutputOutOfBagComplexityStatistics

setOutputOutOfBagComplexityStatistics

printClassifiersTipText

setPrintClassifiers

getPrintClassifiers

measureOutOfBagError

enumerateMeasures

getMeasure

getOutOfBagEvaluationObject

buildClassifier

distributionForInstance

batchSizeTipText

setBatchSize

getBatchSize

distributionsForInstances

implementsMoreEfficientBatchPrediction

toString

generatePartition

getMembershipValues

numElements

getRevision

main

aggregate

finalizeAggregation