org.apache.ignite.ml.naivebayes.discrete

Class DiscreteNaiveBayesTrainer

java.lang.Object

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

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

org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesTrainer

public class DiscreteNaiveBayesTrainer
extends SingleLabelDatasetTrainer<DiscreteNaiveBayesModel>

Trainer for the Discrete naive Bayes classification model. The trainer calculates prior probabilities from the input dataset. Prior probabilities can be also set by setPriorProbabilities or withEquiprobableClasses. If equiprobableClasses is set, the probalilities of all classes will be 1/k, where k is classes count. Also, the trainer converts feature to discrete values by using bucketThresholds.

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
DiscreteNaiveBayesTrainer()

Method Summary

Modifier and Type	Method and Description
<K,V> DiscreteNaiveBayesModel	fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains model based on the specified data.
boolean	isUpdateable(DiscreteNaiveBayesModel mdl)
DiscreteNaiveBayesTrainer	resetProbabilitiesSettings() Sets default settings equiprobableClasses to false and removes priorProbabilities.
DiscreteNaiveBayesTrainer	setBucketThresholds(double[][] bucketThresholds) Sets buckest borders.
DiscreteNaiveBayesTrainer	setPriorProbabilities(double[] priorProbabilities) Sets prior probabilities.
protected <K,V> DiscreteNaiveBayesModel	updateModel(DiscreteNaiveBayesModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains new model taken previous one as a first approximation.
DiscreteNaiveBayesTrainer	withEquiprobableClasses() Sets equal probability for all classes.

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

DiscreteNaiveBayesTrainer

public DiscreteNaiveBayesTrainer()

Method Detail

fitWithInitializedDeployingContext

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

Trains model based on the specified data.

Specified by:

fitWithInitializedDeployingContext in class DatasetTrainer<DiscreteNaiveBayesModel,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.

isUpdateable

public boolean isUpdateable(DiscreteNaiveBayesModel mdl)

Specified by:

isUpdateable in class DatasetTrainer<DiscreteNaiveBayesModel,Double>

Parameters:

mdl - Model.

Returns:

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

updateModel

protected <K,V> DiscreteNaiveBayesModel updateModel(DiscreteNaiveBayesModel 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<DiscreteNaiveBayesModel,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.

withEquiprobableClasses

public DiscreteNaiveBayesTrainer withEquiprobableClasses()

Sets equal probability for all classes.

setPriorProbabilities

public DiscreteNaiveBayesTrainer setPriorProbabilities(double[] priorProbabilities)

Sets prior probabilities.

setBucketThresholds

public DiscreteNaiveBayesTrainer setBucketThresholds(double[][] bucketThresholds)

Sets buckest borders.

resetProbabilitiesSettings

public DiscreteNaiveBayesTrainer resetProbabilitiesSettings()

Sets default settings equiprobableClasses to false and removes priorProbabilities.