LBJ2.learn
Class WekaWrapper

java.lang.Object
  LBJ2.classify.Classifier
      LBJ2.learn.Learner
          LBJ2.learn.WekaWrapper

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable

public class WekaWrapper
extends Learner

Translates LBJ's internal problem representation into that which can be handled by WEKA learning algorithms.

WEKA must be available on your CLASSPATH in order to use this class. WEKA source code and pre-compiled jar distributions are available at: http://www.cs.waikato.ac.nz/ml/weka/

To use this class in with clause of a learning classifier expression, the following restrictions must be recognized:

• Feature pre-extraction must be enabled.
• A feature generator cannot be referenced by name in the using clause.
• An array producing classifier cannot be referenced by name in the using clause.

To use this class in a Java application, the following restrictions must be recognized:

• doneLearning() must be called before calls to classify(Object) can be made.

So, to retrain a classifier learned with this learner, forget() must first be called. Then, retraining is accomplished with calls to either learn(Object) or Learner.learn(Object[]). Finally, doneLearning() must be called before any calls to classify(Object) or *Value(Object) methods (e.g. Classifier.discreteValue(Object)).

On the other hand, if the LBJ compiler took care of training the learning classifier and no additional training is required, calls to classify(Object) or *Value(Object) methods may be made immediately in the application. If additional training is desired, simply call either learn(Object) or Learner.learn(Object[]), but don't forget to call doneLearning() before any calls to classify(Object) or *Value(Object) methods (e.g. Classifier.discreteValue(Object)).

See Also:

Serialized Form

Nested Class Summary

static class

WekaWrapper.Parameters Simply a container for all of WekaWrapper's configurable parameters.

Field Summary

protected weka.core.FastVector	attributeInfo Information about the features this learner takes as input is parsed from an attribute string and stored here.
protected weka.classifiers.Classifier	baseClassifier Stores the instance of the WEKA classifier which we are training; default is weka.classifiers.bayes.NaiveBayes.
static java.lang.String	defaultAttributeString Default attribute string.
protected weka.classifiers.Classifier	freshClassifier Stores a fresh instance of the WEKA classifier for the purposes of forgetting.
protected weka.core.Instances	instances The main collection of Instance objects.
protected boolean	trained Indicates whether the doneLearning() method has been called and the forget() method has not yet been called.

Fields inherited from class LBJ2.learn.Learner

extractor, labeler

Fields inherited from class LBJ2.classify.Classifier

containingPackage, name

Constructor Summary

WekaWrapper()
Empty constructor.

WekaWrapper(weka.classifiers.Classifier base)
Partial constructor; attribute information must be provided before any learning can occur.

WekaWrapper(weka.classifiers.Classifier base, java.lang.String attributeString)
Redirecting constructor.

WekaWrapper(java.lang.String n)
Empty constructor.

WekaWrapper(java.lang.String n, weka.classifiers.Classifier base)
Partial constructor; attribute information must be provided before any learning can occur.

WekaWrapper(java.lang.String n, weka.classifiers.Classifier base, java.lang.String attributeString)
Full Constructor.

WekaWrapper(java.lang.String n, java.lang.String attributeString)
Default Constructor.

WekaWrapper(java.lang.String n, WekaWrapper.Parameters p)
Initializing constructor.

WekaWrapper(WekaWrapper.Parameters p)
Initializing constructor.

Method Summary

FeatureVector	classify(java.lang.Object example) This method makes one or more decisions about a single object, returning those decisions as Features in a vector.
void	doneLearning() Indicates that the classifier is finished learning.
void	forget() Destroys the learned version of the WEKA classifier and empties the instances collection of examples.
Classifier	getExtractor() Returns the extractor.
Classifier	getLabeler() Returns the labeler.
void	initializeAttributes(java.lang.String attributeString) Takes attributeString and initializes this wrapper's instances collection to take those attributes.
void	learn(java.lang.Object example) Since WEKA classifiers cannot learn online, this method causes no actual learning to occur, it simply creates an Instance object from this example and adds it to a set of examples from which the classifier will be built once doneLearning() is called.
private weka.core.Instance	makeInstance(FeatureVector example, FeatureVector labels) Creates a WEKA Instance object out of a FeatureVector.
ScoreSet	scores(java.lang.Object example) Produces a set of scores indicating the degree to which each possible discrete classification value is associated with the given example object.
void	setExtractor(Classifier e) Sets the extractor.
void	setLabeler(Classifier l) Sets the labeler.
void	write(java.io.PrintStream out) Writes the settings of the classifier in use, and a string describing the classifier, if available.

Methods inherited from class LBJ2.learn.Learner

learn, save

Methods inherited from class LBJ2.classify.Classifier

allowableValues, binaryRead, binaryRead, binaryRead, binaryRead, binaryWrite, binaryWrite, classify, clone, discreteValue, discreteValueArray, getCompositeChildren, getInputType, getOutputType, realValue, realValueArray, test, toString, valueIndexOf

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

defaultAttributeString

public static final java.lang.String defaultAttributeString

Default attribute string.

See Also:

Constant Field Values

baseClassifier

protected weka.classifiers.Classifier baseClassifier

Stores the instance of the WEKA classifier which we are training; default is weka.classifiers.bayes.NaiveBayes.

freshClassifier

protected weka.classifiers.Classifier freshClassifier

Stores a fresh instance of the WEKA classifier for the purposes of forgetting.

attributeInfo

protected weka.core.FastVector attributeInfo

Information about the features this learner takes as input is parsed from an attribute string and stored here. This information is crucial in the task of interfacing with the WEKA algorithms, and must be present before the learn(Object) method can be called.

Here is an example of a valid attribute string: nom_SimpleLabel_1,2,3,:str_First:nom_Second_a,b,c,d,r,t,:num_Third:

nom stands for "Nominal", i.e. the feature SimpleLabel was declared as discrete, and had the value list {"1","2","3"}.

str stands for "Stirng", i.e. the feature First was declared to be discrete, but was not provided with a value list. When using the WekaWrapper, it is best to provide value lists whenever possible, because very few WEKA classifiers can handle string attributes.

num stands for "Numerical", i.e. the feature Third was declared to be real.

instances

protected weka.core.Instances instances

The main collection of Instance objects.

trained

protected boolean trained

Indicates whether the doneLearning() method has been called and the forget() method has not yet been called.

Constructor Detail

WekaWrapper

public WekaWrapper()

Empty constructor. Instantiates this wrapper with the default learning algorithm: weka.classifiers.bayes.NaiveBayes. Attribute information must be provided before any learning can occur.

WekaWrapper

public WekaWrapper(weka.classifiers.Classifier base)

Partial constructor; attribute information must be provided before any learning can occur.

Parameters:

base - The classifier to be used in this system.

WekaWrapper

public WekaWrapper(weka.classifiers.Classifier base,
                   java.lang.String attributeString)

Redirecting constructor.

Parameters:

base - The classifier to be used in this system.

attributeString - The string describing the types of attributes example objects will have.

WekaWrapper

public WekaWrapper(WekaWrapper.Parameters p)

Initializing constructor. Sets all member variables to their associated settings in the WekaWrapper.Parameters object.

Parameters:

p - The settings of all parameters.

WekaWrapper

public WekaWrapper(java.lang.String n)

Empty constructor. Instantiates this wrapper with the default learning algorithm: weka.classifiers.bayes.NaiveBayes. Attribute information must be provided before any learning can occur.

Parameters:

n - The name of the classifier.

WekaWrapper

public WekaWrapper(java.lang.String n,
                   weka.classifiers.Classifier base)

Partial constructor; attribute information must be provided before any learning can occur.

Parameters:

base - The classifier to be used in this system.

WekaWrapper

public WekaWrapper(java.lang.String n,
                   java.lang.String attributeString)

Default Constructor. Instantiates this wrapper with the default learning algorithm: weka.classifiers.bayes.NaiveBayes.

Parameters:

n - The name of the classifier.

attributeString - The string describing the types of attributes example objects will have.

WekaWrapper

public WekaWrapper(java.lang.String n,
                   WekaWrapper.Parameters p)

Initializing constructor. Sets all member variables to their associated settings in the WekaWrapper.Parameters object.

Parameters:

n - The name of the classifier.

p - The settings of all parameters.

WekaWrapper

public WekaWrapper(java.lang.String n,
                   weka.classifiers.Classifier base,
                   java.lang.String attributeString)

Full Constructor.

Parameters:

n - The name of the classifier

base - The classifier to be used in this system.

attributeString - The string describing the types of attributes example objects will have.

Method Detail

initializeAttributes

public void initializeAttributes(java.lang.String attributeString)

Takes attributeString and initializes this wrapper's instances collection to take those attributes.

getLabeler

public Classifier getLabeler()

Returns the labeler.

Overrides:

getLabeler in class Learner

setLabeler

public void setLabeler(Classifier l)

Sets the labeler.

Overrides:

setLabeler in class Learner

Parameters:

l - A labeling classifier.

getExtractor

public Classifier getExtractor()

Returns the extractor.

Overrides:

getExtractor in class Learner

setExtractor

public void setExtractor(Classifier e)

Sets the extractor.

Overrides:

setExtractor in class Learner

Parameters:

e - A feature extracting classifier.

learn

public void learn(java.lang.Object example)

Since WEKA classifiers cannot learn online, this method causes no actual learning to occur, it simply creates an Instance object from this example and adds it to a set of examples from which the classifier will be built once doneLearning() is called.

Specified by:

learn in class Learner

Parameters:

example - An example of the desired learned classifier's behavior.

classify

public FeatureVector classify(java.lang.Object example)

This method makes one or more decisions about a single object, returning those decisions as Features in a vector.

Specified by:

classify in class Classifier

Parameters:

example - The object to make decisions about.

Returns:

A vector of Features about the input object.

forget

public void forget()

Destroys the learned version of the WEKA classifier and empties the instances collection of examples.

Specified by:

forget in class Learner

makeInstance

private weka.core.Instance makeInstance(FeatureVector example,
                                        FeatureVector labels)

Creates a WEKA Instance object out of a FeatureVector.

scores

public ScoreSet scores(java.lang.Object example)

Produces a set of scores indicating the degree to which each possible discrete classification value is associated with the given example object.

Specified by:

scores in class Learner

Parameters:

example - The object to make decisions about.

Returns:

A set of scores indicating the degree to which each possible discrete classification value is associated with the given example object.

write

public void write(java.io.PrintStream out)

Writes the settings of the classifier in use, and a string describing the classifier, if available.

Specified by:

write in class Learner

Parameters:

out - The output stream.

doneLearning

public void doneLearning()

Indicates that the classifier is finished learning. This method must be called if the WEKA classifier is to learn anything. Since WEKA classifiers cannot learn online, all of the training examples must be gathered and committed to first. This method invokes the WEKA classifier's buildClassifier(Instances) method.

Overrides:

doneLearning in class Learner