jsat.classifiers.linear

Class LogisticRegressionDCD

java.lang.Object

jsat.classifiers.linear.LogisticRegressionDCD

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, Parameterized, SimpleWeightVectorModel, SingleWeightVectorModel

public class LogisticRegressionDCD
extends java.lang.Object
implements Classifier, Parameterized, SingleWeightVectorModel

This provides an implementation of regularized logistic regression using Dual Coordinate Descent. This algorithm works well on both dense and sparse large data sets.

The regularized problem is of the form:
C Σ log(1+exp(-y_iw^Tx_i)) + w^Tw/2

See:
Yu, H.-F., Huang, F.-L.,&Lin, C.-J. (2010). Dual Coordinate Descent Methods for Logistic Regression and Maximum Entropy Models. Machine Learning, 85(1-2), 41–75. doi:10.1007/s10994-010-5221-8

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
LogisticRegressionDCD() Creates a new Logistic Regression learner that does no more than 100 training iterations with a default regularization tradeoff of C = 1
LogisticRegressionDCD(double C) Creates a new Logistic Regression learner that does no more than 100 training iterations.
LogisticRegressionDCD(double C, int maxIterations) Creates a new Logistic Regression learner

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
Classifier	clone()
double	getBias() Returns the bias term used for the model, or 0 of the model does not support or was not trained with a bias term.
double	getBias(int index) Returns the bias term used with the weight vector for the given class index.
double	getC() Returns the regularization tradeoff parameter
int	getMaxIterations() Returns the maximum number of iterations the algorithm is allowed to run
Vec	getRawWeight() Returns the only weight vector used for the model
Vec	getRawWeight(int index) Returns the raw weight vector associated with the given class index.
Vec	getWeightVec() Returns the weight vector used to compute results via a dot product.
static Distribution	guessC(DataSet d) Guess the distribution to use for the regularization term C in Logistic Regression.
boolean	isUseBias() Returns true if a bias term is in use, false otherwise.
int	numWeightsVecs() Returns the number of weight vectors that can be returned.
void	setC(double C) Sets the regularization trade-off term.
void	setMaxIterations(int maxIterations) Sets the maximum number of iterations the algorithm is allowed to run for.
void	setUseBias(boolean useBias) Sets whether or not an implicit bias term should be added to the model.
boolean	supportsWeightedData() Indicates whether the model knows how to train using weighted data points.
void	train(ClassificationDataSet dataSet) Trains the classifier and constructs a model for classification using the given data set.
void	train(ClassificationDataSet dataSet, boolean parallel) Trains the classifier and constructs a model for classification using the given data set.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface jsat.parameters.Parameterized

getParameter, getParameters

Constructor Detail

LogisticRegressionDCD

public LogisticRegressionDCD()

Creates a new Logistic Regression learner that does no more than 100 training iterations with a default regularization tradeoff of C = 1

LogisticRegressionDCD

public LogisticRegressionDCD(double C)

Creates a new Logistic Regression learner that does no more than 100 training iterations.

Parameters:

C - the regularization tradeoff term

LogisticRegressionDCD

public LogisticRegressionDCD(double C,
                             int maxIterations)

Creates a new Logistic Regression learner

Parameters:

C - the regularization tradeoff term

maxIterations - the maximum number of iterations through the data set

Method Detail

setC

public void setC(double C)

Sets the regularization trade-off term. larger values reduce the amount of regularization, and smaller values increase the regularization.

Parameters:

C - the positive regularization tradeoff value

getC

public double getC()

Returns the regularization tradeoff parameter

Returns:

the regularization tradeoff parameter

setMaxIterations

public void setMaxIterations(int maxIterations)

Sets the maximum number of iterations the algorithm is allowed to run for.

Parameters:

maxIterations - the maximum number of iterations

getMaxIterations

public int getMaxIterations()

Returns the maximum number of iterations the algorithm is allowed to run

Returns:

the maximum number of iterations the algorithm is allowed to run

setUseBias

public void setUseBias(boolean useBias)

Sets whether or not an implicit bias term should be added to the model.

Parameters:

useBias - true to add a bias term, false to exclude the bias term.

isUseBias

public boolean isUseBias()

Returns true if a bias term is in use, false otherwise.

Returns:

true if a bias term is in use, false otherwise.

getRawWeight

public Vec getRawWeight()

Description copied from interface: SingleWeightVectorModel

Returns the only weight vector used for the model

Specified by:

getRawWeight in interface SingleWeightVectorModel

Returns:

the only weight vector used for the model

getBias

public double getBias()

Description copied from interface: SingleWeightVectorModel

Returns the bias term used for the model, or 0 of the model does not support or was not trained with a bias term.

Specified by:

getBias in interface SingleWeightVectorModel

Returns:

the bias term for the model

getRawWeight

public Vec getRawWeight(int index)

Description copied from interface: SimpleWeightVectorModel

Returns the raw weight vector associated with the given class index. If the given class is an implicit zero vector, a ConstantVector object may be returned.
Do not alter the returned weight vector, as it will change the model's values.

If a regression problem, only index = 0 should be used

Specified by:

getRawWeight in interface SimpleWeightVectorModel

Parameters:

index - the class index to get the weight vector for

Returns:

the weight vector used for the specified class

getBias

public double getBias(int index)

Description copied from interface: SimpleWeightVectorModel

Returns the bias term used with the weight vector for the given class index. If the model does not support or was not trained with bias weights, 0 will be returned.

If a regression problem, only index = 0 should be used

Specified by:

getBias in interface SimpleWeightVectorModel

Parameters:

index - the class index to get the weight vector for

Returns:

the bias term for the specified class

numWeightsVecs

public int numWeightsVecs()

Description copied from interface: SimpleWeightVectorModel

Returns the number of weight vectors that can be returned. For binary classification problems the value may be 1 if only a single weight vector's sign is used to determine the class. For multi-class problems, the weight vector count includes the implicit zero vector (if one is being used).

Specified by:

numWeightsVecs in interface SimpleWeightVectorModel

Returns:

the number of weight vectors for which SimpleWeightVectorModel.getRawWeight(int) can be called.

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.

train

public void train(ClassificationDataSet dataSet,
                  boolean parallel)

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:

train in interface Classifier

Parameters:

dataSet - the data set to train on

parallel - true if multiple threads should be used to train the model. false if it should be done in a single threaded manner.

train

public void train(ClassificationDataSet dataSet)

Description copied from interface: Classifier

Trains the classifier and constructs a model for classification using the given data set.

Specified by:

train 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 Classifier clone()

Specified by:

clone in interface Classifier

Overrides:

clone in class java.lang.Object

getWeightVec

public Vec getWeightVec()

Returns the weight vector used to compute results via a dot product.
Do not modify this value, or you will alter the results returned.

Returns:

the learned weight vector for prediction

guessC

public static Distribution guessC(DataSet d)

Guess the distribution to use for the regularization term C in Logistic Regression.

Parameters:

d - the data set to get the guess for

Returns:

the guess for the C parameter