org.apache.ignite.ml.regressions.logistic.multiclass

Class LogRegressionMultiClassTrainer<P extends Serializable>

java.lang.Object

org.apache.ignite.ml.trainers.DatasetTrainer<M,Double>

org.apache.ignite.ml.trainers.SingleLabelDatasetTrainer<LogRegressionMultiClassModel>

org.apache.ignite.ml.regressions.logistic.multiclass.LogRegressionMultiClassTrainer<P>

public class LogRegressionMultiClassTrainer<P extends Serializable>
extends SingleLabelDatasetTrainer<LogRegressionMultiClassModel>

All common parameters are shared with bunch of binary classification trainers.

Nested Class Summary

Nested classes/interfaces inherited from class org.apache.ignite.ml.trainers.DatasetTrainer

DatasetTrainer.EmptyDatasetException

Field Summary

Fields inherited from class org.apache.ignite.ml.trainers.DatasetTrainer

environment

Constructor Summary

Constructors

Constructor and Description
LogRegressionMultiClassTrainer()

Method Summary

Modifier and Type	Method and Description
protected boolean	checkState(LogRegressionMultiClassModel mdl)
<K,V> LogRegressionMultiClassModel	fit(DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Trains model based on the specified data.
int	getAmountOfIterations() Get the amount of outer iterations of SGD algorithm.
int	getAmountOfLocIterations() Get the amount of local iterations.
double	getBatchSize() Get the batch size.
UpdatesStrategy	getUpdatesStgy() Get the update strategy.
long	seed() Get the seed for random generator.
<K,V> LogRegressionMultiClassModel	updateModel(LogRegressionMultiClassModel newMdl, DatasetBuilder<K,V> datasetBuilder, IgniteBiFunction<K,V,Vector> featureExtractor, IgniteBiFunction<K,V,Double> lbExtractor) Gets state of model in arguments, update in according to new data and return new model.
LogRegressionMultiClassTrainer	withAmountOfIterations(int amountOfIterations) Set up the amount of outer iterations.
LogRegressionMultiClassTrainer	withAmountOfLocIterations(int amountOfLocIterations) Set up the amount of local iterations of SGD algorithm.
LogRegressionMultiClassTrainer	withBatchSize(int batchSize) Set up the regularization parameter.
LogRegressionMultiClassTrainer	withSeed(long seed) Set up the random seed parameter.
LogRegressionMultiClassTrainer	withUpdatesStgy(UpdatesStrategy updatesStgy) Set up the updates strategy.

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

fit, fit, fit, fit, getLastTrainedModelOrThrowEmptyDatasetException, setEnvironment, update, update, update, update, update

Methods inherited from class java.lang.Object

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

Constructor Detail

LogRegressionMultiClassTrainer

public LogRegressionMultiClassTrainer()

Method Detail

fit

public <K,V> LogRegressionMultiClassModel fit(DatasetBuilder<K,V> datasetBuilder,
                                              IgniteBiFunction<K,V,Vector> featureExtractor,
                                              IgniteBiFunction<K,V,Double> lbExtractor)

Trains model based on the specified data.

Specified by:

fit in class DatasetTrainer<LogRegressionMultiClassModel,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.

updateModel

public <K,V> LogRegressionMultiClassModel updateModel(LogRegressionMultiClassModel newMdl,
                                                      DatasetBuilder<K,V> datasetBuilder,
                                                      IgniteBiFunction<K,V,Vector> featureExtractor,
                                                      IgniteBiFunction<K,V,Double> lbExtractor)

Gets state of model in arguments, update in according to new data and return new model.

Specified by:

updateModel in class DatasetTrainer<LogRegressionMultiClassModel,Double>

Type Parameters:

K - Type of a key in upstream data.

V - Type of a value in upstream data.

Parameters:

newMdl - Learned model.

datasetBuilder - Dataset builder.

featureExtractor - Feature extractor.

lbExtractor - Label extractor.

Returns:

Updated model.

checkState

protected boolean checkState(LogRegressionMultiClassModel mdl)

Specified by:

checkState in class DatasetTrainer<LogRegressionMultiClassModel,Double>

Parameters:

mdl - Model.

Returns:

true if current critical for training parameters correspond to parameters from last training.

withBatchSize

public LogRegressionMultiClassTrainer withBatchSize(int batchSize)

Set up the regularization parameter.

Parameters:

batchSize - The size of learning batch.

Returns:

Trainer with new batch size parameter value.

getBatchSize

public double getBatchSize()

Get the batch size.

Returns:

The parameter value.

getAmountOfIterations

public int getAmountOfIterations()

Get the amount of outer iterations of SGD algorithm.

Returns:

The parameter value.

withAmountOfIterations

public LogRegressionMultiClassTrainer withAmountOfIterations(int amountOfIterations)

Set up the amount of outer iterations.

Parameters:

amountOfIterations - The parameter value.

Returns:

Trainer with new amountOfIterations parameter value.

getAmountOfLocIterations

public int getAmountOfLocIterations()

Get the amount of local iterations.

Returns:

The parameter value.

withAmountOfLocIterations

public LogRegressionMultiClassTrainer withAmountOfLocIterations(int amountOfLocIterations)

Set up the amount of local iterations of SGD algorithm.

Parameters:

amountOfLocIterations - The parameter value.

Returns:

Trainer with new amountOfLocIterations parameter value.

withSeed

public LogRegressionMultiClassTrainer withSeed(long seed)

Set up the random seed parameter.

Parameters:

seed - Seed for random generator.

Returns:

Trainer with new seed parameter value.

seed

public long seed()

Get the seed for random generator.

Returns:

The parameter value.

withUpdatesStgy

public LogRegressionMultiClassTrainer withUpdatesStgy(UpdatesStrategy updatesStgy)

Set up the updates strategy.

Parameters:

updatesStgy - Update strategy.

Returns:

Trainer with new update strategy parameter value.

getUpdatesStgy

public UpdatesStrategy getUpdatesStgy()

Get the update strategy.

Returns:

The parameter value.