KernelSGD (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.linear.kernelized.KernelSGD

All Implemented Interfaces:

Serializable, Cloneable, Classifier, UpdateableClassifier, Parameterized, Regressor, UpdateableRegressor
```
public class KernelSGD
extends Object
implements UpdateableClassifier, UpdateableRegressor, Parameterized
```
Kernel SGD is the kernelized counterpart to LinearSGD, and learns nonlinear functions via the kernel trick. The implementation is built upon KernelPoint and KernelPoints to support budgeted learning. Following the LinearSGD implementation, whether or not this algorithm supports regression, binary-classification, or multi-class classification is controlled by the loss function used.

The learning rate decay is not configurable for this implementation, and is decayed at a rate of η / (λ * (t + 2 / λ)) , where t is the time step.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description
`KernelSGD()` Creates a new Kernel SGD object for classification with the RBF kernel
`KernelSGD(KernelSGD toCopy)` Copy constructor
`KernelSGD(LossFunc loss, KernelTrick kernel, double lambda, KernelPoint.BudgetStrategy budgetStrategy, int budgetSize)` Creates a new Kernel SGD object
`KernelSGD(LossFunc loss, KernelTrick kernel, double lambda, KernelPoint.BudgetStrategy budgetStrategy, int budgetSize, double eta, double errorTolerance)` Creates a new Kernel SGD object

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.
`KernelSGD`	`clone()`
`int`	`getBudgetSize()` Returns the budget size, or maximum number of allowed support vectors.
`KernelPoint.BudgetStrategy`	`getBudgetStrategy()` Returns the method of budget maintenance
`int`	`getEpochs()` Returns the number of epochs to use
`double`	`getErrorTolerance()` Returns the error tolerance that would be used
`double`	`getEta()` Returns the base learning rate
`KernelTrick`	`getKernel()` Returns the kernel in use
`double`	`getLambda()` Returns the L₂ regularization parameter
`LossFunc`	`getLoss()` Returns the loss function in use
`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.
`static Distribution`	`guessLambda(DataSet d)` Guess the distribution to use for the regularization term `λ` .
`double`	`regress(DataPoint data)`
`void`	`setBudgetSize(int budgetSize)` Sets the maximum budget size, or number of support vectors, to allow during training.
`void`	`setBudgetStrategy(KernelPoint.BudgetStrategy budgetStrategy)` Sets the budget maintenance strategy.
`void`	`setEpochs(int epochs)` Sets the number of iterations of the training set done during batch training
`void`	`setErrorTolerance(double errorTolerance)` Sets the error tolerance used for certain `budget strategies`
`void`	`setEta(double eta)` Sets the base learning rate to start from.
`void`	`setKernel(KernelTrick kernel)` Sets the kernel to use
`void`	`setLambda(double lambda)` Sets the L₂ regularization parameter used during learning.
`void`	`setLoss(LossFunc loss)` Sets the loss function to use.
`void`	`setUp(CategoricalData[] categoricalAttributes, int numericAttributes)` Prepares the classifier to begin learning from its `UpdateableRegressor.update(jsat.classifiers.DataPoint, double)` method.
`void`	`setUp(CategoricalData[] categoricalAttributes, int numericAttributes, CategoricalData predicting)` Prepares the classifier to begin learning from its `UpdateableClassifier.update(jsat.classifiers.DataPoint, int)` method.
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`void`	`train(RegressionDataSet dataSet)`
`void`	`train(RegressionDataSet dataSet, ExecutorService threadPool)`
`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.
`void`	`update(DataPoint dataPoint, double targetValue)` Updates the classifier by giving it a new data point to learn from.
`void`	`update(DataPoint dataPoint, int targetClass)` Updates the classifier by giving it a new data point to learn from.

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

- Constructor Detail
  - KernelSGD
```
public KernelSGD()
```
    Creates a new Kernel SGD object for classification with the RBF kernel
  - KernelSGD
```
public KernelSGD(LossFunc loss,
                 KernelTrick kernel,
                 double lambda,
                 KernelPoint.BudgetStrategy budgetStrategy,
                 int budgetSize)
```
    Creates a new Kernel SGD object
    
    Parameters:
    
    loss - the loss function to use
    
    kernel - the kernel trick to use
    
    lambda - the regularization penalty
    
    budgetStrategy - the budget maintenance strategy to use
    
    budgetSize - the maximum support vector budget
  - KernelSGD
```
public KernelSGD(LossFunc loss,
                 KernelTrick kernel,
                 double lambda,
                 KernelPoint.BudgetStrategy budgetStrategy,
                 int budgetSize,
                 double eta,
                 double errorTolerance)
```
    Creates a new Kernel SGD object
    
    Parameters:
    
    loss - the loss function to use
    
    kernel - the kernel trick to use
    
    lambda - the regularization penalty
    
    eta - the initial learning rate
    
    budgetStrategy - the budget maintenance strategy to use
    
    errorTolerance - the error tolerance used in certain budget maintenance steps
    
    budgetSize - the maximum support vector budget
  - KernelSGD
```
public KernelSGD(KernelSGD toCopy)
```
    Copy constructor
    
    Parameters:
    
    toCopy - the object to copy
- Method Detail
  - setEpochs
```
public void setEpochs(int epochs)
```
    Sets the number of iterations of the training set done during batch training
    
    Parameters:
    
    epochs - the number of iterations in batch training
  - getEpochs
```
public int getEpochs()
```
    Returns the number of epochs to use
    
    Returns:
    
    the number of epochs to use
  - setLoss
```
public void setLoss(LossFunc loss)
```
    Sets the loss function to use. The loss function controls whether or not classification or regression is supported.
    
    Parameters:
    
    loss -
  - getLoss
```
public LossFunc getLoss()
```
    Returns the loss function in use
    
    Returns:
    
    the loss function in use
  - setLambda
```
public void setLambda(double lambda)
```
    Sets the L₂ regularization parameter used during learning.
    
    Parameters:
    
    lambda - the positive regularization parameter
  - getLambda
```
public double getLambda()
```
    Returns the L₂ regularization parameter
    
    Returns:
    
    the L₂ regularization parameter
  - setErrorTolerance
```
public void setErrorTolerance(double errorTolerance)
```
    Sets the error tolerance used for certain budget strategies
    
    Parameters:
    
    errorTolerance - the error tolerance in [0, 1]
  - getErrorTolerance
```
public double getErrorTolerance()
```
    Returns the error tolerance that would be used
    
    Returns:
    
    the error tolerance that would be used
  - setBudgetSize
```
public void setBudgetSize(int budgetSize)
```
    Sets the maximum budget size, or number of support vectors, to allow during training. Increasing the budget size will increase the accuracy of the model, but will also increase the computational cost
    
    Parameters:
    
    budgetSize - the maximum allowed number of support vectors
  - getBudgetSize
```
public int getBudgetSize()
```
    Returns the budget size, or maximum number of allowed support vectors.
    
    Returns:
    
    the maximum number of allowed support vectors
  - setBudgetStrategy
```
public void setBudgetStrategy(KernelPoint.BudgetStrategy budgetStrategy)
```
    Sets the budget maintenance strategy.
    
    Parameters:
    
    budgetStrategy - the method to meet budget size requirements
  - getBudgetStrategy
```
public KernelPoint.BudgetStrategy getBudgetStrategy()
```
    Returns the method of budget maintenance
    
    Returns:
    
    the method of budget maintenance
  - setEta
```
public void setEta(double eta)
```
    Sets the base learning rate to start from. Because of the decay rate in use a good value for η is 1.0.
    
    Parameters:
    
    eta - the starting learning rate to use
  - getEta
```
public double getEta()
```
    Returns the base learning rate
    
    Returns:
    
    the base learning rate
  - setKernel
```
public void setKernel(KernelTrick kernel)
```
    Sets the kernel to use
    
    Parameters:
    
    kernel - the kernel to use
  - getKernel
```
public KernelTrick getKernel()
```
    Returns the kernel in use
    
    Returns:
    
    the kernel in use
  - setUp
```
public void setUp(CategoricalData[] categoricalAttributes,
                  int numericAttributes,
                  CategoricalData predicting)
```
    Description copied from interface: UpdateableClassifier
    
    Prepares the classifier to begin learning from its UpdateableClassifier.update(jsat.classifiers.DataPoint, int) method.
    
    Specified by:
    
    setUp in interface UpdateableClassifier
    
    Parameters:
    
    categoricalAttributes - an array containing the categorical attributes that will be in each data point
    
    numericAttributes - the number of numeric attributes that will be in each data point
    
    predicting - the information for the target class that will be predicted
  - setUp
```
public void setUp(CategoricalData[] categoricalAttributes,
                  int numericAttributes)
```
    Description copied from interface: UpdateableRegressor
    
    Prepares the classifier to begin learning from its UpdateableRegressor.update(jsat.classifiers.DataPoint, double) method.
    
    Specified by:
    
    setUp in interface UpdateableRegressor
    
    Parameters:
    
    categoricalAttributes - an array containing the categorical attributes that will be in each data point
    
    numericAttributes - the number of numeric attributes that will be in each data point
  - update
```
public void update(DataPoint dataPoint,
                   int targetClass)
```
    Description copied from interface: UpdateableClassifier
    
    Updates the classifier by giving it a new data point to learn from.
    
    Specified by:
    
    update in interface UpdateableClassifier
    
    Parameters:
    
    dataPoint - the data point to learn
    
    targetClass - the target class of the data point
  - update
```
public void update(DataPoint dataPoint,
                   double targetValue)
```
    Description copied from interface: UpdateableRegressor
    
    Updates the classifier by giving it a new data point to learn from.
    
    Specified by:
    
    update in interface UpdateableRegressor
    
    Parameters:
    
    dataPoint - the data point to learn
    
    targetValue - the target value of the 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.
  - regress
```
public double regress(DataPoint data)
```
    Specified by:
    
    regress in interface Regressor
  - 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
    
    Specified by:
    
    supportsWeightedData in interface Regressor
    
    Returns:
    
    true if the model supports weighted data, false otherwise
  - train
```
public void train(RegressionDataSet dataSet,
                  ExecutorService threadPool)
```
    Specified by:
    
    train in interface Regressor
  - train
```
public void train(RegressionDataSet dataSet)
```
    Specified by:
    
    train in interface Regressor
  - clone
```
public KernelSGD clone()
```
    Specified by:
    
    clone in interface Classifier
    
    Specified by:
    
    clone in interface UpdateableClassifier
    
    Specified by:
    
    clone in interface Regressor
    
    Specified by:
    
    clone in interface UpdateableRegressor
    
    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.
  - guessLambda
```
public static Distribution guessLambda(DataSet d)
```
    Guess the distribution to use for the regularization term λ .
    
    Parameters:
    
    d - the data set to get the guess for
    
    Returns:
    
    the guess for the λ parameter

Class KernelSGD

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

KernelSGD

KernelSGD

KernelSGD

KernelSGD

Method Detail

setEpochs

getEpochs

setLoss

getLoss

setLambda

getLambda

setErrorTolerance

getErrorTolerance

setBudgetSize

getBudgetSize

setBudgetStrategy

getBudgetStrategy

setEta

getEta

setKernel

getKernel

setUp

setUp

update

update

classify

regress

trainC

trainC

supportsWeightedData

train

train

clone

getParameters

getParameter

guessLambda