org.apache.spark.ml.classification

Class NaiveBayesModel

java.lang.Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

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

org.apache.spark.ml.PredictionModel<FeaturesType,M>

org.apache.spark.ml.classification.ClassificationModel<FeaturesType,M>

org.apache.spark.ml.classification.ProbabilisticClassificationModel<Vector,NaiveBayesModel>

org.apache.spark.ml.classification.NaiveBayesModel

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable, MLWritable

public class NaiveBayesModel
extends ProbabilisticClassificationModel<Vector,NaiveBayesModel>
implements MLWritable

:: Experimental :: Model produced by NaiveBayes param: pi log of class priors, whose dimension is C (number of classes) param: theta log of class conditional probabilities, whose dimension is C (number of classes) by D (number of features)

See Also:

Serialized Form

Method Summary

Methods

Modifier and Type	Method and Description
NaiveBayesModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
Param<java.lang.String>	featuresCol() Param for features column name.
java.lang.String	getFeaturesCol()
java.lang.String	getLabelCol()
java.lang.String	getModelType()
java.lang.String	getPredictionCol()
java.lang.String	getRawPredictionCol()
double	getSmoothing()
Param<java.lang.String>	labelCol() Param for label column name.
static NaiveBayesModel	load(java.lang.String path)
Param<java.lang.String>	modelType() The model type which is a string (case-sensitive).
int	numClasses() Number of classes (values which the label can take).
int	numFeatures() Returns the number of features the model was trained on.
Vector	pi()
Param<java.lang.String>	predictionCol() Param for prediction column name.
protected Vector	predictRaw(Vector features) Raw prediction for each possible label.
protected Vector	raw2probabilityInPlace(Vector rawPrediction) Estimate the probability of each class given the raw prediction, doing the computation in-place.
Param<java.lang.String>	rawPredictionCol() Param for raw prediction (a.k.a.
static MLReader<NaiveBayesModel>	read()
DoubleParam	smoothing() The smoothing parameter.
Matrix	theta()
java.lang.String	toString()
java.lang.String	uid() An immutable unique ID for the object and its derivatives.
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.
MLWriter	write() Returns an MLWriter instance for this ML instance.

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

normalizeToProbabilitiesInPlace, predictProbability, probability2prediction, raw2prediction, raw2probability, setProbabilityCol, setThresholds, transform

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

predict, setRawPredictionCol

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

featuresDataType, setFeaturesCol, setPredictionCol, transformImpl, transformSchema

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

hasParent, parent, setParent

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

transform, transform, transform

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

transformSchema

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, 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.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Method Detail

read

public static MLReader<NaiveBayesModel> read()

load

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

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)

pi

public Vector pi()

theta

public Matrix theta()

numFeatures

public int numFeatures()

Description copied from class: PredictionModel

Returns the number of features the model was trained on. If unknown, returns -1

Overrides:

numFeatures in class PredictionModel<Vector,NaiveBayesModel>

numClasses

public int numClasses()

Description copied from class: ClassificationModel

Number of classes (values which the label can take).

Specified by:

numClasses in class ClassificationModel<Vector,NaiveBayesModel>

predictRaw

protected Vector predictRaw(Vector features)

Description copied from class: ClassificationModel

Raw prediction for each possible label. The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives a measure of confidence in each possible label (where larger = more confident). This internal method is used to implement transform() and output rawPredictionCol.

Specified by:

predictRaw in class ClassificationModel<Vector,NaiveBayesModel>

Parameters:

features - (undocumented)

Returns:

vector where element i is the raw prediction for label i. This raw prediction may be any real number, where a larger value indicates greater confidence for that label.

raw2probabilityInPlace

protected Vector raw2probabilityInPlace(Vector rawPrediction)

Description copied from class: ProbabilisticClassificationModel

Estimate the probability of each class given the raw prediction, doing the computation in-place. These predictions are also called class conditional probabilities.

This internal method is used to implement transform() and output probabilityCol.

Specified by:

raw2probabilityInPlace in class ProbabilisticClassificationModel<Vector,NaiveBayesModel>

Parameters:

rawPrediction - (undocumented)

Returns:

Estimated class conditional probabilities (modified input vector)

copy

public NaiveBayesModel 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 Model<NaiveBayesModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

toString

public java.lang.String toString()

Specified by:

toString in interface Identifiable

Overrides:

toString in class java.lang.Object

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)

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()

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()