org.apache.spark.ml.classification

Class NaiveBayes

java.lang.Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Estimator<M>

org.apache.spark.ml.Predictor<FeaturesType,E,M>

org.apache.spark.ml.classification.Classifier<FeaturesType,E,M>

org.apache.spark.ml.classification.ProbabilisticClassifier<Vector,NaiveBayes,NaiveBayesModel>

org.apache.spark.ml.classification.NaiveBayes

All Implemented Interfaces:

java.io.Serializable, Params, Identifiable

public class NaiveBayes
extends ProbabilisticClassifier<Vector,NaiveBayes,NaiveBayesModel>

:: Experimental :: Naive Bayes Classifiers. It supports both Multinomial NB (http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html) which can handle finitely supported discrete data. For example, by converting documents into TF-IDF vectors, it can be used for document classification. By making every vector a binary (0/1) data, it can also be used as Bernoulli NB (http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html). The input feature values must be nonnegative.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
NaiveBayes()
NaiveBayes(java.lang.String uid)

Method Summary

Methods

Modifier and Type	Method and Description
protected static <T> T	$(Param<T> param)
static Params	clear(Param<?> param)
NaiveBayes	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
protected static <T extends Params> T	copyValues(T to, ParamMap extra)
protected static <T extends Params> ParamMap	copyValues$default$2()
protected static <T extends Params> T	defaultCopy(ParamMap extra)
static java.lang.String	explainParam(Param<?> param)
static java.lang.String	explainParams()
protected static RDD<LabeledPoint>	extractLabeledPoints(Dataset<?> dataset)
protected static RDD<LabeledPoint>	extractLabeledPoints(Dataset<?> dataset, int numClasses)
static ParamMap	extractParamMap()
static ParamMap	extractParamMap(ParamMap extra)
static Param<java.lang.String>	featuresCol()
Param<java.lang.String>	featuresCol() Param for features column name.
static M	fit(Dataset<?> dataset)
static M	fit(Dataset<?> dataset, ParamMap paramMap)
static scala.collection.Seq<M>	fit(Dataset<?> dataset, ParamMap[] paramMaps)
static M	fit(Dataset<?> dataset, ParamPair<?> firstParamPair, ParamPair<?>... otherParamPairs)
static M	fit(Dataset<?> dataset, ParamPair<?> firstParamPair, scala.collection.Seq<ParamPair<?>> otherParamPairs)
static <T> scala.Option<T>	get(Param<T> param)
static <T> scala.Option<T>	getDefault(Param<T> param)
static java.lang.String	getFeaturesCol()
java.lang.String	getFeaturesCol()
static java.lang.String	getLabelCol()
java.lang.String	getLabelCol()
static java.lang.String	getModelType()
java.lang.String	getModelType()
protected static int	getNumClasses(Dataset<?> dataset, int maxNumClasses)
protected static int	getNumClasses$default$2()
static <T> T	getOrDefault(Param<T> param)
static Param<java.lang.Object>	getParam(java.lang.String paramName)
static java.lang.String	getPredictionCol()
java.lang.String	getPredictionCol()
static java.lang.String	getProbabilityCol()
static java.lang.String	getRawPredictionCol()
java.lang.String	getRawPredictionCol()
static double	getSmoothing()
double	getSmoothing()
static double[]	getThresholds()
static <T> boolean	hasDefault(Param<T> param)
static boolean	hasParam(java.lang.String paramName)
protected static void	initializeLogIfNecessary(boolean isInterpreter)
static boolean	isDefined(Param<?> param)
static boolean	isSet(Param<?> param)
protected static boolean	isTraceEnabled()
static Param<java.lang.String>	labelCol()
Param<java.lang.String>	labelCol() Param for label column name.
static NaiveBayes	load(java.lang.String path)
protected static org.slf4j.Logger	log()
protected static void	logDebug(scala.Function0<java.lang.String> msg)
protected static void	logDebug(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void	logError(scala.Function0<java.lang.String> msg)
protected static void	logError(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void	logInfo(scala.Function0<java.lang.String> msg)
protected static void	logInfo(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static java.lang.String	logName()
protected static void	logTrace(scala.Function0<java.lang.String> msg)
protected static void	logTrace(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
protected static void	logWarning(scala.Function0<java.lang.String> msg)
protected static void	logWarning(scala.Function0<java.lang.String> msg, java.lang.Throwable throwable)
static Param<java.lang.String>	modelType()
Param<java.lang.String>	modelType() The model type which is a string (case-sensitive).
static Param<?>[]	params()
static Param<java.lang.String>	predictionCol()
Param<java.lang.String>	predictionCol() Param for prediction column name.
static Param<java.lang.String>	probabilityCol()
static Param<java.lang.String>	rawPredictionCol()
Param<java.lang.String>	rawPredictionCol() Param for raw prediction (a.k.a.
static void	save(java.lang.String path)
static <T> Params	set(Param<T> param, T value)
protected static Params	set(ParamPair<?> paramPair)
protected static Params	set(java.lang.String param, java.lang.Object value)
protected static <T> Params	setDefault(Param<T> param, T value)
protected static Params	setDefault(scala.collection.Seq<ParamPair<?>> paramPairs)
static Learner	setFeaturesCol(java.lang.String value)
static Learner	setLabelCol(java.lang.String value)
NaiveBayes	setModelType(java.lang.String value) Set the model type using a string (case-sensitive).
static Learner	setPredictionCol(java.lang.String value)
static E	setProbabilityCol(java.lang.String value)
static E	setRawPredictionCol(java.lang.String value)
NaiveBayes	setSmoothing(double value) Set the smoothing parameter.
static E	setThresholds(double[] value)
static DoubleParam	smoothing()
DoubleParam	smoothing() The smoothing parameter.
static DoubleArrayParam	thresholds()
static java.lang.String	toString()
protected NaiveBayesModel	train(Dataset<?> dataset) Train a model using the given dataset and parameters.
static StructType	transformSchema(StructType schema)
protected static StructType	transformSchema(StructType schema, boolean logging)
java.lang.String	uid() An immutable unique ID for the object and its derivatives.
protected static StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType) Validates and transforms the input schema with the provided param map.
static void	validateParams()
static MLWriter	write()
MLWriter	write() Returns an MLWriter instance for this ML instance.

Methods inherited from class org.apache.spark.ml.classification.ProbabilisticClassifier

setProbabilityCol, setThresholds

Methods inherited from class org.apache.spark.ml.classification.Classifier

extractLabeledPoints, getNumClasses, setRawPredictionCol

Methods inherited from class org.apache.spark.ml.Predictor

extractLabeledPoints, fit, setFeaturesCol, setLabelCol, setPredictionCol, transformSchema

Methods inherited from class org.apache.spark.ml.Estimator

fit, fit, fit, fit

Methods inherited from class org.apache.spark.ml.PipelineStage

transformSchema

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.apache.spark.ml.util.MLWritable

save

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn, validateParams

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString

Constructor Detail

NaiveBayes

public NaiveBayes(java.lang.String uid)

NaiveBayes

public NaiveBayes()

Method Detail

load

public static NaiveBayes load(java.lang.String path)

toString

public static java.lang.String toString()

params

public static Param<?>[] params()

validateParams

public static void validateParams()

explainParam

public static java.lang.String explainParam(Param<?> param)

explainParams

public static java.lang.String explainParams()

isSet

public static final boolean isSet(Param<?> param)

isDefined

public static final boolean isDefined(Param<?> param)

hasParam

public static boolean hasParam(java.lang.String paramName)

getParam

public static Param<java.lang.Object> getParam(java.lang.String paramName)

set

public static final <T> Params set(Param<T> param,
             T value)

set

protected static final Params set(java.lang.String param,
         java.lang.Object value)

set

protected static final Params set(ParamPair<?> paramPair)

get

public static final <T> scala.Option<T> get(Param<T> param)

clear

public static final Params clear(Param<?> param)

getOrDefault

public static final <T> T getOrDefault(Param<T> param)

$

protected static final <T> T $(Param<T> param)

setDefault

protected static final <T> Params setDefault(Param<T> param,
                    T value)

setDefault

protected static final Params setDefault(scala.collection.Seq<ParamPair<?>> paramPairs)

getDefault

public static final <T> scala.Option<T> getDefault(Param<T> param)

hasDefault

public static final <T> boolean hasDefault(Param<T> param)

defaultCopy

protected static final <T extends Params> T defaultCopy(ParamMap extra)

extractParamMap

public static final ParamMap extractParamMap(ParamMap extra)

extractParamMap

public static final ParamMap extractParamMap()

copyValues

protected static <T extends Params> T copyValues(T to,
                              ParamMap extra)

copyValues$default$2

protected static <T extends Params> ParamMap copyValues$default$2()

logName

protected static java.lang.String logName()

log

protected static org.slf4j.Logger log()

logInfo

protected static void logInfo(scala.Function0<java.lang.String> msg)

logDebug

protected static void logDebug(scala.Function0<java.lang.String> msg)

logTrace

protected static void logTrace(scala.Function0<java.lang.String> msg)

logWarning

protected static void logWarning(scala.Function0<java.lang.String> msg)

logError

protected static void logError(scala.Function0<java.lang.String> msg)

logInfo

protected static void logInfo(scala.Function0<java.lang.String> msg,
           java.lang.Throwable throwable)

logDebug

protected static void logDebug(scala.Function0<java.lang.String> msg,
            java.lang.Throwable throwable)

logTrace

protected static void logTrace(scala.Function0<java.lang.String> msg,
            java.lang.Throwable throwable)

logWarning

protected static void logWarning(scala.Function0<java.lang.String> msg,
              java.lang.Throwable throwable)

logError

protected static void logError(scala.Function0<java.lang.String> msg,
            java.lang.Throwable throwable)

isTraceEnabled

protected static boolean isTraceEnabled()

initializeLogIfNecessary

protected static void initializeLogIfNecessary(boolean isInterpreter)

transformSchema

protected static StructType transformSchema(StructType schema,
                         boolean logging)

fit

public static M fit(Dataset<?> dataset,
    ParamPair<?> firstParamPair,
    scala.collection.Seq<ParamPair<?>> otherParamPairs)

fit

public static M fit(Dataset<?> dataset,
    ParamMap paramMap)

fit

public static scala.collection.Seq<M> fit(Dataset<?> dataset,
                          ParamMap[] paramMaps)

fit

public static M fit(Dataset<?> dataset,
    ParamPair<?> firstParamPair,
    ParamPair<?>... otherParamPairs)

labelCol

public static final Param<java.lang.String> labelCol()

getLabelCol

public static final java.lang.String getLabelCol()

featuresCol

public static final Param<java.lang.String> featuresCol()

getFeaturesCol

public static final java.lang.String getFeaturesCol()

predictionCol

public static final Param<java.lang.String> predictionCol()

getPredictionCol

public static final java.lang.String getPredictionCol()

setLabelCol

public static Learner setLabelCol(java.lang.String value)

setFeaturesCol

public static Learner setFeaturesCol(java.lang.String value)

setPredictionCol

public static Learner setPredictionCol(java.lang.String value)

fit

public static M fit(Dataset<?> dataset)

transformSchema

public static StructType transformSchema(StructType schema)

extractLabeledPoints

protected static RDD<LabeledPoint> extractLabeledPoints(Dataset<?> dataset)

rawPredictionCol

public static final Param<java.lang.String> rawPredictionCol()

getRawPredictionCol

public static final java.lang.String getRawPredictionCol()

setRawPredictionCol

public static E setRawPredictionCol(java.lang.String value)

extractLabeledPoints

protected static RDD<LabeledPoint> extractLabeledPoints(Dataset<?> dataset,
                                     int numClasses)

getNumClasses

protected static int getNumClasses(Dataset<?> dataset,
                int maxNumClasses)

getNumClasses$default$2

protected static int getNumClasses$default$2()

probabilityCol

public static final Param<java.lang.String> probabilityCol()

getProbabilityCol

public static final java.lang.String getProbabilityCol()

thresholds

public static final DoubleArrayParam thresholds()

getThresholds

public static double[] getThresholds()

validateAndTransformSchema

protected static StructType validateAndTransformSchema(StructType schema,
                                    boolean fitting,
                                    DataType featuresDataType)

setProbabilityCol

public static E setProbabilityCol(java.lang.String value)

setThresholds

public static E setThresholds(double[] value)

smoothing

public static final DoubleParam smoothing()

getSmoothing

public static final double getSmoothing()

modelType

public static final Param<java.lang.String> modelType()

getModelType

public static final java.lang.String getModelType()

save

public static void save(java.lang.String path)
                 throws java.io.IOException

Throws:

java.io.IOException

write

public static MLWriter write()

uid

public java.lang.String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

setSmoothing

public NaiveBayes setSmoothing(double value)

Set the smoothing parameter. Default is 1.0.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setModelType

public NaiveBayes setModelType(java.lang.String value)

Set the model type using a string (case-sensitive). Supported options: "multinomial" and "bernoulli". Default is "multinomial"

Parameters:

value - (undocumented)

Returns:

(undocumented)

train

protected NaiveBayesModel train(Dataset<?> dataset)

Description copied from class: Predictor

Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.

Specified by:

train in class Predictor<Vector,NaiveBayes,NaiveBayesModel>

Parameters:

dataset - Training dataset

Returns:

Fitted model

copy

public NaiveBayes copy(ParamMap extra)

Description copied from interface: Params

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly.

Specified by:

copy in interface Params

Specified by:

copy in class Predictor<Vector,NaiveBayes,NaiveBayesModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

smoothing

public DoubleParam smoothing()

The smoothing parameter. (default = 1.0).

Returns:

(undocumented)

getSmoothing

public double getSmoothing()

modelType

public Param<java.lang.String> modelType()

The model type which is a string (case-sensitive). Supported options: "multinomial" and "bernoulli". (default = multinomial)

Returns:

(undocumented)

getModelType

public java.lang.String getModelType()

write

public MLWriter write()

Description copied from interface: MLWritable

Returns an MLWriter instance for this ML instance.

Specified by:

write in interface MLWritable

Returns:

(undocumented)

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                    boolean fitting,
                                    DataType featuresDataType)

rawPredictionCol

public Param<java.lang.String> rawPredictionCol()

Param for raw prediction (a.k.a. confidence) column name.

Returns:

(undocumented)

getRawPredictionCol

public java.lang.String getRawPredictionCol()

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                    boolean fitting,
                                    DataType featuresDataType)

Validates and transforms the input schema with the provided param map.

Parameters:

schema - input schema

fitting - whether this is in fitting

featuresDataType - SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

Returns:

output schema

labelCol

public Param<java.lang.String> labelCol()

Param for label column name.

Returns:

(undocumented)

getLabelCol

public java.lang.String getLabelCol()

featuresCol

public Param<java.lang.String> featuresCol()

Param for features column name.

Returns:

(undocumented)

getFeaturesCol

public java.lang.String getFeaturesCol()

predictionCol

public Param<java.lang.String> predictionCol()

Param for prediction column name.

Returns:

(undocumented)

getPredictionCol

public java.lang.String getPredictionCol()