com.datumbox.framework.core.machinelearning.common.interfaces

Interface PredictParallelizable

All Superinterfaces:

Parallelizable

All Known Implementing Classes:

AbstractDPMM, AbstractNaiveBayes, BayesianEnsembleMethod, BernoulliNaiveBayes, BinarizedNaiveBayes, GaussianDPMM, HierarchicalAgglomerative, Kmeans, MaximumEntropy, MultinomialDPMM, MultinomialNaiveBayes, NLMS, OrdinalRegression, SoftMaxRegression, SupportVectorMachine

public interface PredictParallelizable
extends Parallelizable

All Machine Learning models capable of predicting records in parallel implement this interface.

Nested Class Summary

Nested Classes

Modifier and Type	Interface and Description
static class	PredictParallelizable.Prediction Tuple that stores the results of the prediction namely the yPredicted and the yPredictedProbabilities objects.

Method Summary

Modifier and Type	Method and Description
default void	_predictDatasetParallel(Dataframe newData, java.util.Map<java.lang.Integer,PredictParallelizable.Prediction> resultsBuffer, ConcurrencyConfiguration concurrencyConfiguration) Estimates the predictions for a new Dataframe in a parallel way.
default void	_predictDatasetParallel(Dataframe newData, StorageEngine storageEngine, ConcurrencyConfiguration concurrencyConfiguration) Estimates the predictions for a new Dataframe in a parallel way.
PredictParallelizable.Prediction	_predictRecord(Record r) Takes a single record, makes a prediction using the model and returns the result wrapped in the Prediction tuple.

Methods inherited from interface com.datumbox.framework.core.machinelearning.common.interfaces.Parallelizable

isParallelized, setParallelized

Method Detail

_predictRecord

PredictParallelizable.Prediction _predictRecord(Record r)

Takes a single record, makes a prediction using the model and returns the result wrapped in the Prediction tuple. The method assumes that the KnowledgeBase is be loaded.

Parameters:

r -

Returns:

_predictDatasetParallel

default void _predictDatasetParallel(Dataframe newData,
                                     java.util.Map<java.lang.Integer,PredictParallelizable.Prediction> resultsBuffer,
                                     ConcurrencyConfiguration concurrencyConfiguration)

Estimates the predictions for a new Dataframe in a parallel way. We provide the Dataframe and an empty map which acts as a temporary buffer for storing the results before they are loaded in the dataframe.

Parameters:

newData -

resultsBuffer -

concurrencyConfiguration -

_predictDatasetParallel

default void _predictDatasetParallel(Dataframe newData,
                                     StorageEngine storageEngine,
                                     ConcurrencyConfiguration concurrencyConfiguration)

Estimates the predictions for a new Dataframe in a parallel way.

Parameters:

newData -

storageEngine -

concurrencyConfiguration -