EmphasisBoost (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.boosting.EmphasisBoost

All Implemented Interfaces:

Serializable, Cloneable, BinaryScoreClassifier, Classifier, Parameterized
```
public class EmphasisBoost
extends Object
implements Classifier, Parameterized, BinaryScoreClassifier
```
Emphasis Boost is a generalization of the Real AdaBoost algorithm, expanding the update term and providing the λ term to control the trade off. With λ = 1/2, it becomes equivalent to Real AdaBoost. If the weak learner does not support confidence outputs (non-hard decisions), then it further becomes equivalent to Discrete Ada Boost.
Emphasis Boost only supports binary classification problems, the learner used should support weighted predictions.

NOTE: In the face of extreme outliers, it is possible for numerical instability to occur. This implementation attempts to reset weights when numerical issues occur.

See:
Gómez-Verdejo, V., Ortega-Moral, M., Arenas-García, J.,&Figueiras-Vidal, A. R. (2006). Boosting by weighting critical and erroneous samples. Neurocomputing, 69(7-9), 679–685. doi:10.1016/j.neucom.2005.12.011

Author:

Edward Raff

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected List<Classifier>`	`hypoths` The list of weak hypothesis
`protected List<Double>`	`hypWeights` The weights for each weak learner
`protected CategoricalData`	`predicting`

Constructor Summary

Constructors
Modifier	Constructor and Description
	`EmphasisBoost()` Creates a new EmphasisBooster with shallow decision trees and λ = 0.35
	`EmphasisBoost(Classifier weakLearner, int maxIterations, double lambda)` Creates a new EmphasisBoost learner
`protected`	`EmphasisBoost(EmphasisBoost toClone)` Copy constructor

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`CategoricalResults`	`classify(DataPoint data)` Performs classification on the given data point.
`EmphasisBoost`	`clone()`
`double`	`getLambda()` Returns the value of the λ trade off parameter
`int`	`getMaxIterations()` Returns the maximum number of iterations used
`List<Classifier>`	`getModels()`
`List<Double>`	`getModelWeights()`
`Parameter`	`getParameter(String paramName)` Returns the parameter with the given name.
`List<Parameter>`	`getParameters()` Returns the list of parameters that can be altered for this learner.
`double`	`getScore(DataPoint dp)` Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.
`Classifier`	`getWeakLearner()` Returns the weak learner currently being used by this method.
`static Distribution`	`guessLambda(DataSet d)` Guesses the distribution to use for the λ parameter
`void`	`setLambda(double lambda)` λ controls the trade off between weighting the errors based on their distance to the margin and the quadratic error of the output.
`void`	`setMaxIterations(int maxIterations)` Sets the maximal number of boosting iterations that may be performed
`void`	`setWeakLearner(Classifier weakLearner)` Sets the weak learner used during training.
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`void`	`trainC(ClassificationDataSet dataSet)` Trains the classifier and constructs a model for classification using the given data set.
`void`	`trainC(ClassificationDataSet dataSet, ExecutorService threadPool)` Trains the classifier and constructs a model for classification using the given data set.

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - hypoths
```
protected List<Classifier> hypoths
```
    The list of weak hypothesis
  - hypWeights
```
protected List<Double> hypWeights
```
    The weights for each weak learner
  - predicting
```
protected CategoricalData predicting
```
- Constructor Detail
  - EmphasisBoost
```
public EmphasisBoost()
```
    Creates a new EmphasisBooster with shallow decision trees and λ = 0.35
  - EmphasisBoost
```
public EmphasisBoost(Classifier weakLearner,
                     int maxIterations,
                     double lambda)
```
    Creates a new EmphasisBoost learner
    
    Parameters:
    
    weakLearner - the weak learner to use
    
    maxIterations - the maximum number of boosting iterations
    
    lambda - the trade off parameter in [0, 1]
  - EmphasisBoost
```
protected EmphasisBoost(EmphasisBoost toClone)
```
    Copy constructor
    
    Parameters:
    
    toClone - the object to clone
- Method Detail
  - getModels
```
public List<Classifier> getModels()
```
    Returns:
    
    a list of the models that are in this ensemble.
  - getModelWeights
```
public List<Double> getModelWeights()
```
    Returns:
    
    a list of the models weights that are in this ensemble.
  - getMaxIterations
```
public int getMaxIterations()
```
    Returns the maximum number of iterations used
    
    Returns:
    
    the maximum number of iterations used
  - setMaxIterations
```
public void setMaxIterations(int maxIterations)
```
    Sets the maximal number of boosting iterations that may be performed
    
    Parameters:
    
    maxIterations - the maximum number of iterations
  - getWeakLearner
```
public Classifier getWeakLearner()
```
    Returns the weak learner currently being used by this method.
    
    Returns:
    
    the weak learner currently being used by this method.
  - setWeakLearner
```
public void setWeakLearner(Classifier weakLearner)
```
    Sets the weak learner used during training.
    
    Parameters:
    
    weakLearner - the weak learner to use
  - guessLambda
```
public static Distribution guessLambda(DataSet d)
```
    Guesses the distribution to use for the λ parameter
    
    Parameters:
    
    d - the dataset to get the guess for
    
    Returns:
    
    the guess for the λ parameter
    
    See Also:
    
    setLambda(double)
  - setLambda
```
public void setLambda(double lambda)
```
    λ controls the trade off between weighting the errors based on their distance to the margin and the quadratic error of the output. The three extreme values are:
    - λ = 0 , in this case all the weight is placed on points based on their distance to the margin of the classification boundary.
    - λ = 1/2, in this case weight is balanced between the margin distance and the quadratic error. This is equivalent to Real Ada Boost
    - λ = 1, in this case the weight is placed purely based on the quadratic error of the output
    According to the original paper, values in the range [0.3, 0.4] often perform well.
    Parameters:
    
    lambda - the trade off parameter in [0, 1]
  - getLambda
```
public double getLambda()
```
    Returns the value of the λ trade off parameter
    
    Returns:
    
    the value of the λ trade off parameter
  - getScore
```
public double getScore(DataPoint dp)
```
    Description copied from interface: BinaryScoreClassifier
    
    Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.
    
    Specified by:
    
    getScore in interface BinaryScoreClassifier
    
    Parameters:
    
    dp - the data point to predict the class label of
    
    Returns:
    
    the score for the given data point
  - classify
```
public CategoricalResults classify(DataPoint data)
```
    Description copied from interface: Classifier
    
    Performs classification on the given data point.
    
    Specified by:
    
    classify in interface Classifier
    
    Parameters:
    
    data - the data point to classify
    
    Returns:
    
    the results of the classification.
  - trainC
```
public void trainC(ClassificationDataSet dataSet,
                   ExecutorService threadPool)
```
    Description copied from interface: Classifier
    
    Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.
    
    Specified by:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
    
    threadPool - the source of threads to use.
  - trainC
```
public void trainC(ClassificationDataSet dataSet)
```
    Description copied from interface: Classifier
    
    Trains the classifier and constructs a model for classification using the given data set.
    
    Specified by:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
  - supportsWeightedData
```
public boolean supportsWeightedData()
```
    Description copied from interface: Classifier
    
    Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.
    
    Specified by:
    
    supportsWeightedData in interface Classifier
    
    Returns:
    
    true if the model supports weighted data, false otherwise
  - clone
```
public EmphasisBoost clone()
```
    Specified by:
    
    clone in interface BinaryScoreClassifier
    
    Specified by:
    
    clone in interface Classifier
    
    Overrides:
    
    clone in class Object
  - getParameters
```
public List<Parameter> getParameters()
```
    Description copied from interface: Parameterized
    
    Returns the list of parameters that can be altered for this learner.
    
    Specified by:
    
    getParameters in interface Parameterized
    
    Returns:
    
    the list of parameters that can be altered for this learner.
  - getParameter
```
public Parameter getParameter(String paramName)
```
    Description copied from interface: Parameterized
    
    Returns the parameter with the given name. Two different strings may map to a single Parameter object. An ASCII only string, and a Unicode style string.
    
    Specified by:
    
    getParameter in interface Parameterized
    
    Parameters:
    
    paramName - the name of the parameter to obtain
    
    Returns:
    
    the Parameter in question, or null if no such named Parameter exists.

Class EmphasisBoost

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

hypoths

hypWeights

predicting

Constructor Detail

EmphasisBoost

EmphasisBoost

EmphasisBoost

Method Detail

getModels

getModelWeights

getMaxIterations

setMaxIterations

getWeakLearner

setWeakLearner

guessLambda

setLambda

getLambda

getScore

classify

trainC

trainC

supportsWeightedData

clone

getParameters

getParameter