jsat.classifiers.linear

Class LinearBatch

java.lang.Object

jsat.classifiers.linear.LinearBatch

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, WarmClassifier, Parameterized, Regressor, WarmRegressor, SimpleWeightVectorModel

public class LinearBatch
extends java.lang.Object
implements Classifier, Regressor, Parameterized, SimpleWeightVectorModel, WarmClassifier, WarmRegressor

LinearBatch learns either a classification or regression problem depending on the loss function ℓ(w,x) used. The solution attempts to minimize ∑_i ℓ(w,x_i) + λ0/2 ||w||₂², and is trained using a batch optimization method.

LinearBatch can be warm started from any model implementing the SimpleWeightVectorModel interface.

Note: the current implementation does not currently use bias terms

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
class	LinearBatch.GradFunction Function for using the single weight vector loss functions related to LossC and LossR
class	LinearBatch.LossFunction Function for using the single weight vector loss functions related to LossC and LossR.
class	LinearBatch.LossMCFunction

Constructor Summary

Constructors

Constructor and Description
LinearBatch() Creates a new Linear Batch learner for classification using a small regularization term
LinearBatch(LinearBatch toCopy) Copy constructor
LinearBatch(LossFunc loss, double lambda0) Creates a new Linear Batch learner
LinearBatch(LossFunc loss, double lambda0, double tolerance) Creates a new Linear Batch learner
LinearBatch(LossFunc loss, double lambda0, double tolerance, Optimizer optimizer) Creates a new Linear Batch learner

Method Summary

Modifier and Type	Method and Description
CategoricalResults	classify(DataPoint data) Performs classification on the given data point.
LinearBatch	clone()
double	getBias(int index) Returns the bias term used with the weight vector for the given class index.
double	getLambda0() Returns the L2 regularization term in use
LossFunc	getLoss() Returns the loss function in use
Optimizer	getOptimizer() Returns the optimization method in use, or null.
Vec	getRawWeight(int index) Returns the raw weight vector associated with the given class index.
double	getTolerance() Returns the value of the convergence tolerance parameter
static Distribution	guessLambda0(DataSet d) Guess the distribution to use for the regularization term λ0 .
boolean	isUseBiasTerm()
int	numWeightsVecs() Returns the number of weight vectors that can be returned.
double	regress(DataPoint data)
void	setLambda0(double lambda0) λ0 controls the L2 regularization penalty.
void	setLoss(LossFunc loss) Sets the loss function used for the model.
void	setOptimizer(Optimizer optimizer) Sets the method of batch optimization that will be used.
void	setTolerance(double tolerance) Sets the convergence tolerance to user for training.
void	setUseBiasTerm(boolean useBiasTerm)
boolean	supportsWeightedData() Indicates whether the model knows how to train using weighted data points.
void	train(ClassificationDataSet D, boolean parallel) Trains the classifier and constructs a model for classification using the given data set.
void	train(ClassificationDataSet D, Classifier warmSolution, boolean parallel) Trains the classifier and constructs a model for classification using the given data set.
void	train(RegressionDataSet D, boolean parallel)
void	train(RegressionDataSet dataSet, Regressor warmSolution) Trains the regressor and constructs a model for regression using the given data set.
void	train(RegressionDataSet D, Regressor warmSolution, boolean parallel) Trains the regressor and constructs a model for regression using the given data set.
boolean	warmFromSameDataOnly() Some models can only be warm started from a solution trained on the exact same data set as the model it is warm starting from.

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

Methods inherited from interface jsat.classifiers.WarmClassifier

train

Methods inherited from interface jsat.classifiers.Classifier

train

Methods inherited from interface jsat.regression.Regressor

train

Constructor Detail

LinearBatch

public LinearBatch()

Creates a new Linear Batch learner for classification using a small regularization term

LinearBatch

public LinearBatch(LossFunc loss,
                   double lambda0)

Creates a new Linear Batch learner

Parameters:

loss - the loss function to use

lambda0 - the L₂ regularization term

LinearBatch

public LinearBatch(LossFunc loss,
                   double lambda0,
                   double tolerance)

Creates a new Linear Batch learner

Parameters:

loss - the loss function to use

lambda0 - the L₂ regularization term

tolerance - the threshold for convergence

LinearBatch

public LinearBatch(LossFunc loss,
                   double lambda0,
                   double tolerance,
                   Optimizer optimizer)

Creates a new Linear Batch learner

Parameters:

loss - the loss function to use

lambda0 - the L₂ regularization term

tolerance - the threshold for convergence

optimizer - the batch optimization method to use

LinearBatch

public LinearBatch(LinearBatch toCopy)

Copy constructor

Parameters:

toCopy - the object to copy

Method Detail

setUseBiasTerm

public void setUseBiasTerm(boolean useBiasTerm)

isUseBiasTerm

public boolean isUseBiasTerm()

setLambda0

public void setLambda0(double lambda0)

λ₀ controls the L₂ regularization penalty.

Parameters:

lambda0 - the L₂ regularization penalty to use

getLambda0

public double getLambda0()

Returns the L₂ regularization term in use

Returns:

the L₂ regularization term in use

setLoss

public void setLoss(LossFunc loss)

Sets the loss function used for the model. The loss function controls whether or not regression, binary classification, or multi-class classification is supported.

Parameters:

loss - the loss function to use

getLoss

public LossFunc getLoss()

Returns the loss function in use

Returns:

the loss function in use

setOptimizer

public void setOptimizer(Optimizer optimizer)

Sets the method of batch optimization that will be used. null is valid for this value, in which case the implementation will attempt to select a reasonable optimizer automatically.

NOTE: the current implementation requires the optimizer to work based off only the function value and its derivative.

Parameters:

optimizer - the method to use for function minimization

getOptimizer

public Optimizer getOptimizer()

Returns the optimization method in use, or null.

Returns:

the optimization method in use, or null.

setTolerance

public void setTolerance(double tolerance)

Sets the convergence tolerance to user for training. Smaller values reach a more accuracy solution but may take longer to complete.
While zero is a valid tolerance value, it is not usually useful in practice. Values in [10^-4, 10^-2] are usually more practical.

Parameters:

tolerance - the convergence tolerance

getTolerance

public double getTolerance()

Returns the value of the convergence tolerance parameter

Returns:

the convergence tolerance parameter

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

train

public void train(ClassificationDataSet D,
                  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:

D - 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 D,
                  Classifier warmSolution,
                  boolean parallel)

Description copied from interface: WarmClassifier

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 WarmClassifier

Parameters:

D - the data set to train on

warmSolution - the solution to use to warm start this model

parallel - true if the training should be done using multiple-cores, false for single threaded.

train

public void train(RegressionDataSet D,
                  boolean parallel)

Specified by:

train in interface Regressor

train

public void train(RegressionDataSet dataSet,
                  Regressor warmSolution)

Description copied from interface: WarmRegressor

Trains the regressor and constructs a model for regression using the given data set.

Specified by:

train in interface WarmRegressor

Parameters:

dataSet - the data set to train on

warmSolution - the solution to use to warm start this model

train

public void train(RegressionDataSet D,
                  Regressor warmSolution,
                  boolean parallel)

Description copied from interface: WarmRegressor

Trains the regressor and constructs a model for regression 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 WarmRegressor

Parameters:

D - the data set to train on

warmSolution - the solution to use to warm start this model

parallel - true if the training should be done using multiple-cores, false for single threaded.

warmFromSameDataOnly

public boolean warmFromSameDataOnly()

Description copied from interface: WarmClassifier

Some models can only be warm started from a solution trained on the exact same data set as the model it is warm starting from. If this is the case true will be returned. The behavior for training on a different data set when this is defined is undefined. It may cause an error, or it may cause the algorithm to take longer or reach a worse solution.
When true, it is important that the data set be unaltered - this includes mutating the values stored or re-arranging the data points within the data set.

Specified by:

warmFromSameDataOnly in interface WarmClassifier

Specified by:

warmFromSameDataOnly in interface WarmRegressor

Returns:

true if the algorithm can only be warm started from the model trained on the exact same data set.

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.

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

clone

public LinearBatch clone()

Specified by:

clone in interface Classifier

Specified by:

clone in interface Regressor

Overrides:

clone in class java.lang.Object

guessLambda0

public static Distribution guessLambda0(DataSet d)

Guess the distribution to use for the regularization term λ0 .

Parameters:

d - the data set to get the guess for

Returns:

the guess for the λ₀ parameter