CSKLR (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.BaseUpdateableClassifier
- - jsat.classifiers.linear.kernelized.CSKLR

All Implemented Interfaces:

Serializable, Cloneable, Classifier, UpdateableClassifier, Parameterized
```
public class CSKLR
extends BaseUpdateableClassifier
implements Parameterized
```
An implementation of Conservative Stochastic Kernel Logistic Regression. This is an online algorithm that obtains sparse solutions by conservatively rejecting updates based on a binomial distribution of the error on each update.

This algorithm works best on data sets with a very high number of samples where a high accuracy is obtainable using a kernel model. It is often the case that this model produces accurate results, but has a low confidence due to the conservative updating. This can be counteracted by having a very large number of features, but that often increases the size of the model.

It is important to read the documentation and test some different values for the learning rate and gamma variables. They behave different compared to many algorithms.

It is possible to obtain a more confident model and a slightly larger model by using several epochs. Instead of using this class, the batch version of this algorithm should be used instead.

See paper:
Zhang, L., Jin, R., Chen, C., Bu, J.,&He, X. (2012). Efficient Online Learning for Large-Scale Sparse Kernel Logistic Regression. Twenty-Sixth AAAI Conference on Artificial Intelligence (pp. 1219–1225). Retrieved from here

Author:

Edward Raff

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class CSKLR.UpdateMode
Controls when updates are performed on the model.

Nested Classes
Modifier and Type	Class and Description
`static class`	`CSKLR.UpdateMode` Controls when updates are performed on the model.

Constructor Summary

Constructors
Modifier	Constructor and Description
`protected`	`CSKLR(CSKLR toClone)` Copy constructor
	`CSKLR(double eta, KernelTrick k, double R, CSKLR.UpdateMode mode)` Creates a new CSKLR 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.
`CSKLR`	`clone()`
`double`	`getEta()` Returns the learning rate to use
`double`	`getGamma()` Returns the gamma sparsity parameter value
`KernelTrick`	`getKernel()` Returns the kernel trick in use
`CSKLR.UpdateMode`	`getMode()` Returns the update mode 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.
`double`	`getR()` Returns the maximal norm of the algorithm
`protected static double`	`getScore(double y, double pre)` Returns the binary logistic regression score
`static Distribution`	`guessR(DataSet d)` Guesses the distribution to use for the R parameter
`void`	`setEta(double eta)` Sets the learning rate to use for the algorithm.
`void`	`setGamma(double gamma)` Sets the gamma value to use.
`void`	`setKernel(KernelTrick k)` Set which kernel trick to use
`void`	`setMode(CSKLR.UpdateMode mode)` Sets what update mode should be used.
`void`	`setR(double R)` Sets the maximal margin norm value for the algorithm.
`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`	`update(DataPoint dataPoint, int targetClass)` Updates the classifier by giving it a new data point to learn from.

Methods inherited from class jsat.classifiers.BaseUpdateableClassifier
getEpochs, setEpochs, trainC, trainC, trainEpochs

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

- Constructor Detail
  - CSKLR
```
public CSKLR(double eta,
             KernelTrick k,
             double R,
             CSKLR.UpdateMode mode)
```
    Creates a new CSKLR object
    
    Parameters:
    
    eta - the learning rate to use
    
    k - the kernel trick to use
    
    R - the maximal norm of the surface
    
    mode - the mode to use
  - CSKLR
```
protected CSKLR(CSKLR toClone)
```
    Copy constructor
    
    Parameters:
    
    toClone - the object to copy
- Method Detail
  - guessR
```
public static Distribution guessR(DataSet d)
```
    Guesses the distribution to use for the R parameter
    
    Parameters:
    
    d - the dataset to get the guess for
    
    Returns:
    
    the guess for the R parameter
    
    See Also:
    
    setR(double)
  - 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.
  - setEta
```
public void setEta(double eta)
```
    Sets the learning rate to use for the algorithm. Unlike many other stochastic algorithms, the learning rate for CSKLR should be large, often in the range of (0.5, 1) - and can even be larger than 1 at times. If the learning rate is too low, it may be difficult to get strong confidence results from the algorithm.
    
    Parameters:
    
    eta - the positive learning rate to use
  - getEta
```
public double getEta()
```
    Returns the learning rate to use
    
    Returns:
    
    the learning rate to use
  - setR
```
public void setR(double R)
```
    Sets the maximal margin norm value for the algorithm. When the norm is exceeded, the coefficients will be rescaled to fit in the norm. If the maximal norm is too small (less than 5), it may be difficult to get strong confidence results from the algorithm.
    A good range of values suggested by the original paper is 10^x ∀ x ∈ {0, 1, 2, 3, 4, 5}
    
    Parameters:
    
    R -
  - getR
```
public double getR()
```
    Returns the maximal norm of the algorithm
    
    Returns:
    
    the maximal norm of the algorithm
  - setMode
```
public void setMode(CSKLR.UpdateMode mode)
```
    Sets what update mode should be used. The update mode controls the sparsity of the mode, and the behavior of setGamma(double)
    
    Parameters:
    
    mode - the update mode to use
  - getMode
```
public CSKLR.UpdateMode getMode()
```
    Returns the update mode in use
    
    Returns:
    
    the update mode in use
  - setGamma
```
public void setGamma(double gamma)
```
    Sets the gamma value to use. This value, depending on which CSKLR.UpdateMode is used, controls the sparsity of the model.
    
    Parameters:
    
    gamma - the gamma parameter, which is at least always positive
  - getGamma
```
public double getGamma()
```
    Returns the gamma sparsity parameter value
    
    Returns:
    
    the gamma sparsity parameter value
  - setKernel
```
public void setKernel(KernelTrick k)
```
    Set which kernel trick to use
    
    Parameters:
    
    k - the kernel to use
  - getKernel
```
public KernelTrick getKernel()
```
    Returns the kernel trick in use
    
    Returns:
    
    the kernel trick in use
  - getScore
```
protected static double getScore(double y,
                                 double pre)
```
    Returns the binary logistic regression score
    
    Parameters:
    
    y - the sign of the desired class (-1 or 1)
    
    pre - the raw coefficient score
    
    Returns:
    
    the probability in [0, 1] that the score is of the desired class
  - clone
```
public CSKLR clone()
```
    Specified by:
    
    clone in interface Classifier
    
    Specified by:
    
    clone in interface UpdateableClassifier
    
    Specified by:
    
    clone in class BaseUpdateableClassifier
  - 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
  - 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
  - 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.
  - 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

Class CSKLR

Nested Class Summary

Constructor Summary

Method Summary

Methods inherited from class jsat.classifiers.BaseUpdateableClassifier

Methods inherited from class java.lang.Object

Constructor Detail

CSKLR

CSKLR

Method Detail

guessR

getParameters

getParameter

setEta

getEta

setR

getR

setMode

getMode

setGamma

getGamma

setKernel

getKernel

getScore

clone

setUp

update

classify

supportsWeightedData