org.apache.ignite.ml.naivebayes.compound

Class CompoundNaiveBayesTrainer

java.lang.Object

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

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

org.apache.ignite.ml.naivebayes.compound.CompoundNaiveBayesTrainer

public class CompoundNaiveBayesTrainer
extends SingleLabelDatasetTrainer<CompoundNaiveBayesModel>

Trainer for the compound Naive Bayes classifier model. It uses a model composition of GaussianNaiveBayesTrainer and DiscreteNaiveBayesTrainer. To distinguish which features with which trainer should be used, each trainer should have a collection of feature ids which should be skipped. It can be set by #setFeatureIdsToSkip() method.

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
CompoundNaiveBayesTrainer()

Method Summary

Modifier and Type	Method and Description
<K,V> CompoundNaiveBayesModel	fitWithInitializedDeployingContext(DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Trains model based on the specified data.
boolean	isUpdateable(CompoundNaiveBayesModel mdl)
protected <K,V> CompoundNaiveBayesModel	updateModel(CompoundNaiveBayesModel mdl, DatasetBuilder<K,V> datasetBuilder, Preprocessor<K,V> extractor) Gets state of model in arguments, update in according to new data and return new model.
CompoundNaiveBayesTrainer	withDiscreteFeatureIdsToSkip(Collection<Integer> discreteFeatureIdsToSkip) Sets feature ids to skip in discrete Bayes.
CompoundNaiveBayesTrainer	withDiscreteNaiveBayesTrainer(DiscreteNaiveBayesTrainer discreteNaiveBayesTrainer) Sets a discrete trainer.
CompoundNaiveBayesTrainer	withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder) Changes learning Environment.
CompoundNaiveBayesTrainer	withGaussianFeatureIdsToSkip(Collection<Integer> gaussianFeatureIdsToSkip) Sets feature ids to skip in Gaussian Bayes.
CompoundNaiveBayesTrainer	withGaussianNaiveBayesTrainer(GaussianNaiveBayesTrainer gaussianNaiveBayesTrainer) Sets a gaussian trainer.
CompoundNaiveBayesTrainer	withPriorProbabilities(double[] priorProbabilities) Sets prior probabilities.

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

Methods inherited from class java.lang.Object

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

Constructor Detail

CompoundNaiveBayesTrainer

public CompoundNaiveBayesTrainer()

Method Detail

fitWithInitializedDeployingContext

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

Trains model based on the specified data.

Specified by:

fitWithInitializedDeployingContext in class DatasetTrainer<CompoundNaiveBayesModel,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(CompoundNaiveBayesModel mdl)

Specified by:

isUpdateable in class DatasetTrainer<CompoundNaiveBayesModel,Double>

Parameters:

mdl - Model.

Returns:

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

withEnvironmentBuilder

public CompoundNaiveBayesTrainer withEnvironmentBuilder(LearningEnvironmentBuilder envBuilder)

Changes learning Environment.

Overrides:

withEnvironmentBuilder in class DatasetTrainer<CompoundNaiveBayesModel,Double>

Parameters:

envBuilder - Learning environment builder.

updateModel

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

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

Specified by:

updateModel in class DatasetTrainer<CompoundNaiveBayesModel,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.

withPriorProbabilities

public CompoundNaiveBayesTrainer withPriorProbabilities(double[] priorProbabilities)

Sets prior probabilities.

withGaussianNaiveBayesTrainer

public CompoundNaiveBayesTrainer withGaussianNaiveBayesTrainer(GaussianNaiveBayesTrainer gaussianNaiveBayesTrainer)

Sets a gaussian trainer.

withDiscreteNaiveBayesTrainer

public CompoundNaiveBayesTrainer withDiscreteNaiveBayesTrainer(DiscreteNaiveBayesTrainer discreteNaiveBayesTrainer)

Sets a discrete trainer.

withGaussianFeatureIdsToSkip

public CompoundNaiveBayesTrainer withGaussianFeatureIdsToSkip(Collection<Integer> gaussianFeatureIdsToSkip)

Sets feature ids to skip in Gaussian Bayes.

withDiscreteFeatureIdsToSkip

public CompoundNaiveBayesTrainer withDiscreteFeatureIdsToSkip(Collection<Integer> discreteFeatureIdsToSkip)

Sets feature ids to skip in discrete Bayes.