org.apache.spark.mllib.classification

Class NaiveBayesModel

Object

org.apache.spark.mllib.classification.NaiveBayesModel

All Implemented Interfaces:

java.io.Serializable, ClassificationModel, Saveable

public class NaiveBayesModel
extends Object
implements ClassificationModel, scala.Serializable, Saveable

Model for Naive Bayes Classifiers.

param: labels list of labels param: pi log of class priors, whose dimension is C, number of labels param: theta log of class conditional probabilities, whose dimension is C-by-D, where D is number of features param: modelType The type of NB model to fit can be "multinomial" or "bernoulli"

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	NaiveBayesModel.SaveLoadV1_0$
static class	NaiveBayesModel.SaveLoadV2_0$

Method Summary

Modifier and Type	Method and Description
double[]	labels()
static NaiveBayesModel	load(SparkContext sc, String path)
String	modelType()
double[]	pi()
RDD<Object>	predict(RDD<Vector> testData) Predict values for the given data set using the model trained.
double	predict(Vector testData) Predict values for a single data point using the model trained.
RDD<Vector>	predictProbabilities(RDD<Vector> testData) Predict values for the given data set using the model trained.
Vector	predictProbabilities(Vector testData) Predict posterior class probabilities for a single data point using the model trained.
void	save(SparkContext sc, String path) Save this model to the given path.
double[][]	theta()

Methods inherited from class Object

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

Methods inherited from interface org.apache.spark.mllib.classification.ClassificationModel

predict

Method Detail

load

public static NaiveBayesModel load(SparkContext sc,
                                   String path)

labels

public double[] labels()

pi

public double[] pi()

theta

public double[][] theta()

modelType

public String modelType()

predict

public RDD<Object> predict(RDD<Vector> testData)

Description copied from interface: ClassificationModel

Predict values for the given data set using the model trained.

Specified by:

predict in interface ClassificationModel

Parameters:

testData - RDD representing data points to be predicted

Returns:

an RDD[Double] where each entry contains the corresponding prediction

predict

public double predict(Vector testData)

Description copied from interface: ClassificationModel

Predict values for a single data point using the model trained.

Specified by:

predict in interface ClassificationModel

Parameters:

testData - array representing a single data point

Returns:

predicted category from the trained model

predictProbabilities

public RDD<Vector> predictProbabilities(RDD<Vector> testData)

Predict values for the given data set using the model trained.

Parameters:

testData - RDD representing data points to be predicted

Returns:

an RDD[Vector] where each entry contains the predicted posterior class probabilities, in the same order as class labels

predictProbabilities

public Vector predictProbabilities(Vector testData)

Predict posterior class probabilities for a single data point using the model trained.

Parameters:

testData - array representing a single data point

Returns:

predicted posterior class probabilities from the trained model, in the same order as class labels

save

public void save(SparkContext sc,
                 String path)

Description copied from interface: Saveable

Save this model to the given path.

This saves: - human-readable (JSON) model metadata to path/metadata/ - Parquet formatted data to path/data/

The model may be loaded using Loader.load.

Specified by:

save in interface Saveable

Parameters:

sc - Spark context used to save model data.

path - Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.