org.apache.ignite.ml.svm

Class SVMLinearBinaryClassificationTrainer

java.lang.Object

org.apache.ignite.ml.svm.SVMLinearBinaryClassificationTrainer

All Implemented Interfaces:

DatasetTrainer<SVMLinearBinaryClassificationModel,Double>, SingleLabelDatasetTrainer<SVMLinearBinaryClassificationModel>

public class SVMLinearBinaryClassificationTrainer
extends Object
implements SingleLabelDatasetTrainer<SVMLinearBinaryClassificationModel>

Base class for a soft-margin SVM linear classification trainer based on the communication-efficient distributed dual coordinate ascent algorithm (CoCoA) with hinge-loss function.

This trainer takes input as Labeled Dataset with -1 and +1 labels for two classes and makes binary classification.

The paper about this algorithm could be found here https://arxiv.org/abs/1409.1458.

Constructor Summary

Constructors

Constructor and Description
SVMLinearBinaryClassificationTrainer()

Method Summary

Modifier and Type	Method and Description
<K,V> SVMLinearBinaryClassificationModel	fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,double[]> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
int	getAmountOfIterations() Gets the amount of outer iterations of SCDA algorithm.
int	getAmountOfLocIterations() Gets the amount of local iterations of SCDA algorithm.
double	lambda() Gets the regularization lambda.
SVMLinearBinaryClassificationTrainer	withAmountOfIterations(int amountOfIterations) Set up the amount of outer iterations of SCDA algorithm.
SVMLinearBinaryClassificationTrainer	withAmountOfLocIterations(int amountOfLocIterations) Set up the amount of local iterations of SCDA algorithm.
SVMLinearBinaryClassificationTrainer	withLambda(double lambda) Set up the regularization parameter.

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.apache.ignite.ml.trainers.DatasetTrainer

fit, fit

Constructor Detail

SVMLinearBinaryClassificationTrainer

public SVMLinearBinaryClassificationTrainer()

Method Detail

fit

public <K,V> SVMLinearBinaryClassificationModel fit(DatasetBuilder<K,V> datasetBuilder,
                                                    IgniteBiFunction<K,V,double[]> featureExtractor,
                                                    IgniteBiFunction<K,V,Double> lbExtractor)

Trains model based on the specified data.

Specified by:

fit in interface DatasetTrainer<SVMLinearBinaryClassificationModel,Double>

Type Parameters:

K - Type of a key in upstream data.

V - Type of a value in upstream data.

Parameters:

datasetBuilder - Dataset builder.

featureExtractor - Feature extractor.

lbExtractor - Label extractor.

Returns:

Model.

withLambda

public SVMLinearBinaryClassificationTrainer withLambda(double lambda)

Set up the regularization parameter.

Parameters:

lambda - The regularization parameter. Should be more than 0.0.

Returns:

Trainer with new lambda parameter value.

lambda

public double lambda()

Gets the regularization lambda.

Returns:

The parameter value.

getAmountOfIterations

public int getAmountOfIterations()

Gets the amount of outer iterations of SCDA algorithm.

Returns:

The parameter value.

withAmountOfIterations

public SVMLinearBinaryClassificationTrainer withAmountOfIterations(int amountOfIterations)

Set up the amount of outer iterations of SCDA algorithm.

Parameters:

amountOfIterations - The parameter value.

Returns:

Trainer with new amountOfIterations parameter value.

getAmountOfLocIterations

public int getAmountOfLocIterations()

Gets the amount of local iterations of SCDA algorithm.

Returns:

The parameter value.

withAmountOfLocIterations

public SVMLinearBinaryClassificationTrainer withAmountOfLocIterations(int amountOfLocIterations)

Set up the amount of local iterations of SCDA algorithm.

Parameters:

amountOfLocIterations - The parameter value.

Returns:

Trainer with new amountOfLocIterations parameter value.