CSKLRBatch (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.svm.SupportVectorLearner
- - jsat.classifiers.linear.kernelized.CSKLRBatch

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Parameterized
```
public class CSKLRBatch
extends SupportVectorLearner
implements Parameterized, Classifier
```
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.
This batch version can also be used to more efficiently learn dense KLR models using the stochastic method with the CSKLR.UpdateMode.NC mode if model sparsity is not important.

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.

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/interfaces inherited from class jsat.classifiers.svm.SupportVectorLearner
  SupportVectorLearner.CacheMode

Field Summary

Fields
Modifier and Type Field and Description

protected double gamma
- Fields inherited from class jsat.classifiers.svm.SupportVectorLearner
  accelCache, alphas, cacheEvictions, evalCount, vecs

Fields
Modifier and Type	Field and Description
`protected double`	`gamma`

Constructor Summary

Constructors
Modifier	Constructor and Description
`protected`	`CSKLRBatch(CSKLRBatch toClone)` Copy constructor
	`CSKLRBatch(double eta, KernelTrick kernel, double R, CSKLR.UpdateMode mode, SupportVectorLearner.CacheMode cacheMode)` Creates a new SCKLR Batch learning 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.
`CSKLRBatch`	`clone()`
`int`	`getEpochs()` Returns the number of passes through the data set
`double`	`getEta()` Returns the learning rate to use
`double`	`getGamma()` Returns the gamma sparsity parameter value
`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
`static Distribution`	`guessR(DataSet d)` Guesses the distribution to use for the R parameter
`void`	`setEpochs(int epochs)` Sets the number of training epochs (passes) through the data set
`void`	`setEta(double eta)` Sets the learning rate to use for the algorithm.
`void`	`setGamma(double gamma)` Sets the gamma value 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.
`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 jsat.classifiers.svm.SupportVectorLearner
accessingRow, getCacheMode, getCacheValue, getKernel, k, kEval, kEval, kEvalSum, setAlphas, setCacheMode, setCacheSize, setCacheValue, setKernel, sparsify

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

- Field Detail
  - gamma
```
protected double gamma
```
- Constructor Detail
  - CSKLRBatch
```
public CSKLRBatch(double eta,
                  KernelTrick kernel,
                  double R,
                  CSKLR.UpdateMode mode,
                  SupportVectorLearner.CacheMode cacheMode)
```
    Creates a new SCKLR Batch learning object
    
    Parameters:
    
    eta - the learning rate to use
    
    kernel - the kernel to use
    
    R - the maximal norm of the surface
    
    mode - the mode to use
    
    cacheMode - the kernel caching mode to use
  - CSKLRBatch
```
protected CSKLRBatch(CSKLRBatch toClone)
```
    Copy constructor
    
    Parameters:
    
    toClone - the object to copy
- Method Detail
  - clone
```
public CSKLRBatch clone()
```
    Specified by:
    
    clone in interface Classifier
    
    Overrides:
    
    clone in class Object
  - setEpochs
```
public void setEpochs(int epochs)
```
    Sets the number of training epochs (passes) through the data set
    
    Parameters:
    
    epochs - the number of passes through the data set
  - getEpochs
```
public int getEpochs()
```
    Returns the number of passes through the data set
    
    Returns:
    
    the number of passes through the data set
  - 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
  - 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.
  - 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

Class CSKLRBatch

Nested Class Summary

Nested classes/interfaces inherited from class jsat.classifiers.svm.SupportVectorLearner

Field Summary

Fields inherited from class jsat.classifiers.svm.SupportVectorLearner

Constructor Summary

Method Summary

Methods inherited from class jsat.classifiers.svm.SupportVectorLearner

Methods inherited from class java.lang.Object

Field Detail

gamma

Constructor Detail

CSKLRBatch

CSKLRBatch

Method Detail

clone

setEpochs

getEpochs

setEta

getEta

setR

getR

setMode

getMode

setGamma

getGamma

guessR

getParameters

getParameter

classify

trainC

trainC

supportsWeightedData