org.apache.ignite.ml.regressions.logistic

Class LogisticRegressionSGDTrainer

java.lang.Object

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

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

org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer

public class LogisticRegressionSGDTrainer
extends SingleLabelDatasetTrainer<LogisticRegressionModel>

Trainer of the logistic regression model based on stochastic gradient descent algorithm.

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

envBuilder, environment

Constructor Summary

Constructors

Constructor and Description
LogisticRegressionSGDTrainer()

Method Summary

Modifier and Type	Method and Description
<K,V> LogisticRegressionModel	fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains model based on the specified data.
int	getBatchSize() Get the batch size.
int	getLocIterations() Get the amount of local iterations.
int	getMaxIterations() Get the max amount of iterations.
long	getSeed() Get the seed for random generator.
UpdatesStrategy	getUpdatesStgy() Get the update strategy.
boolean	isUpdateable(LogisticRegressionModel mdl)
protected <K,V> LogisticRegressionModel	updateModel(LogisticRegressionModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains new model taken previous one as a first approximation.
LogisticRegressionSGDTrainer	withBatchSize(double batchSize) Set up the batchSize parameter.
LogisticRegressionSGDTrainer	withLocIterations(double amountOfLocIterations) Set up the amount of local iterations of SGD algorithm.
LogisticRegressionSGDTrainer	withMaxIterations(double maxIterations) Set up the max amount of iterations before convergence.
LogisticRegressionSGDTrainer	withSeed(long seed) Set up the random seed parameter.
LogisticRegressionSGDTrainer	withUpdatesStgy(UpdatesStrategy updatesStgy) Set up the regularization parameter.

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

fit, fit, fit, fit, fit, fit, getLastTrainedModelOrThrowEmptyDatasetException, identityTrainer, learningEnvironment, update, update, update, update, update, withConvertedLabels, withEnvironmentBuilder

Methods inherited from class java.lang.Object

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

Constructor Detail

LogisticRegressionSGDTrainer

public LogisticRegressionSGDTrainer()

Method Detail

fitWithInitializedDeployingContext

public <K,V> LogisticRegressionModel fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder,
                                                                        Preprocessor<K,V> extractor)

Trains model based on the specified data.

Specified by:

fitWithInitializedDeployingContext in class DatasetTrainer<LogisticRegressionModel,Double>

Type Parameters:

K - Type of a key in upstream data.

V - Type of a value in upstream data.

Parameters:

datasetBuilder - Dataset builder.

extractor - Extractor of UpstreamEntry into LabeledVector.

Returns:

Model.

updateModel

protected <K,V> LogisticRegressionModel updateModel(LogisticRegressionModel mdl,
                                                    DatasetBuilder<K,V> datasetBuilder,
                                                    Preprocessor<K,V> extractor)

Trains new model taken previous one as a first approximation.

Specified by:

updateModel in class DatasetTrainer<LogisticRegressionModel,Double>

Type Parameters:

K - Type of a key in upstream data.

V - Type of a value in upstream data.

Parameters:

mdl - Learned model.

datasetBuilder - Dataset builder.

extractor - Extractor of UpstreamEntry into LabeledVector.

Returns:

Updated model.

isUpdateable

public boolean isUpdateable(LogisticRegressionModel mdl)

Specified by:

isUpdateable in class DatasetTrainer<LogisticRegressionModel,Double>

Parameters:

mdl - Model.

Returns:

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

withMaxIterations

public LogisticRegressionSGDTrainer withMaxIterations(double maxIterations)

Set up the max amount of iterations before convergence.

Parameters:

maxIterations - The parameter value.

Returns:

Model with new max number of iterations before convergence parameter value.

withBatchSize

public LogisticRegressionSGDTrainer withBatchSize(double batchSize)

Set up the batchSize parameter.

Parameters:

batchSize - The size of learning batch.

Returns:

Trainer with new batch size parameter value.

withLocIterations

public LogisticRegressionSGDTrainer withLocIterations(double amountOfLocIterations)

Set up the amount of local iterations of SGD algorithm.

Parameters:

amountOfLocIterations - The parameter value.

Returns:

Trainer with new locIterations parameter value.

withSeed

public LogisticRegressionSGDTrainer withSeed(long seed)

Set up the random seed parameter.

Parameters:

seed - Seed for random generator.

Returns:

Trainer with new seed parameter value.

withUpdatesStgy

public LogisticRegressionSGDTrainer withUpdatesStgy(UpdatesStrategy updatesStgy)

Set up the regularization parameter.

Parameters:

updatesStgy - Update strategy.

Returns:

Trainer with new update strategy parameter value.

getUpdatesStgy

public UpdatesStrategy getUpdatesStgy()

Get the update strategy.

Returns:

The property value.

getMaxIterations

public int getMaxIterations()

Get the max amount of iterations.

Returns:

The property value.

getBatchSize

public int getBatchSize()

Get the batch size.

Returns:

The property value.

getLocIterations

public int getLocIterations()

Get the amount of local iterations.

Returns:

The property value.

getSeed

public long getSeed()

Get the seed for random generator.

Returns:

The property value.