Packages

class NaiveBayes extends ProbabilisticClassifier[Vector, NaiveBayes, NaiveBayesModel] with NaiveBayesParams with DefaultParamsWritable

Naive Bayes Classifiers. It supports Multinomial NB (see here) 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 (see here). The input feature values for Multinomial NB and Bernoulli NB must be nonnegative. Since 3.0.0, it supports Complement NB which is an adaptation of the Multinomial NB. Specifically, Complement NB uses statistics from the complement of each class to compute the model's coefficients The inventors of Complement NB show empirically that the parameter estimates for CNB are more stable than those for Multinomial NB. Like Multinomial NB, the input feature values for Complement NB must be nonnegative. Since 3.0.0, it also supports Gaussian NB (see here) which can handle continuous data.

Annotations
@Since( "1.5.0" )
Source
NaiveBayes.scala
Linear Supertypes
DefaultParamsWritable, MLWritable, NaiveBayesParams, HasWeightCol, ProbabilisticClassifier[Vector, NaiveBayes, NaiveBayesModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, Classifier[Vector, NaiveBayes, NaiveBayesModel], ClassifierParams, HasRawPredictionCol, Predictor[Vector, NaiveBayes, NaiveBayesModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[NaiveBayesModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. NaiveBayes
  2. DefaultParamsWritable
  3. MLWritable
  4. NaiveBayesParams
  5. HasWeightCol
  6. ProbabilisticClassifier
  7. ProbabilisticClassifierParams
  8. HasThresholds
  9. HasProbabilityCol
  10. Classifier
  11. ClassifierParams
  12. HasRawPredictionCol
  13. Predictor
  14. PredictorParams
  15. HasPredictionCol
  16. HasFeaturesCol
  17. HasLabelCol
  18. Estimator
  19. PipelineStage
  20. Logging
  21. Params
  22. Serializable
  23. Serializable
  24. Identifiable
  25. AnyRef
  26. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new NaiveBayes()
    Annotations
    @Since( "1.5.0" )
  2. new NaiveBayes(uid: String)
    Annotations
    @Since( "1.5.0" )

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. final def clear(param: Param[_]): NaiveBayes.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  7. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  8. def copy(extra: ParamMap): NaiveBayes

    Creates a copy of this instance with the same UID and some extra 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. See defaultCopy().

    Definition Classes
    NaiveBayes → Predictor → Estimator → PipelineStage → Params
    Annotations
    @Since( "1.5.0" )
  9. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

    Copies param values from this instance to another instance for params shared by them.

    Copies param values from this instance to another instance for params shared by them.

    This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

    to

    the target instance, which should work with the same set of default Params as this source instance

    extra

    extra params to be copied to the target's paramMap

    returns

    the target instance with param values copied

    Attributes
    protected
    Definition Classes
    Params
  10. final def defaultCopy[T <: Params](extra: ParamMap): T

    Default implementation of copy with extra params.

    Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

    Attributes
    protected
    Definition Classes
    Params
  11. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  13. def explainParam(param: Param[_]): String

    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes
    Params
  14. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  15. def extractInstances(dataset: Dataset[_], numClasses: Int): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validates the label on the classifier is a valid integer in the range [0, numClasses).

    Attributes
    protected
    Definition Classes
    ClassifierParams
  16. def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validate the output instances with the given function.

    Attributes
    protected
    Definition Classes
    PredictorParams
  17. def extractInstances(dataset: Dataset[_]): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes
    protected
    Definition Classes
    PredictorParams
  18. def extractLabeledPoints(dataset: Dataset[_], numClasses: Int): RDD[LabeledPoint]

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    dataset

    DataFrame with columns for labels (org.apache.spark.sql.types.NumericType) and features (Vector).

    numClasses

    Number of classes label can take. Labels must be integers in the range [0, numClasses).

    Attributes
    protected
    Definition Classes
    Classifier
    Note

    Throws SparkException if any label is a non-integer or is negative

  19. def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes
    protected
    Definition Classes
    Predictor
  20. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  21. final def extractParamMap(extra: ParamMap): ParamMap

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Definition Classes
    Params
  22. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  23. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  24. def fit(dataset: Dataset[_]): NaiveBayesModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    Predictor → Estimator
  25. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[NaiveBayesModel]

    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  26. def fit(dataset: Dataset[_], paramMap: ParamMap): NaiveBayesModel

    Fits a single model to the input data with provided parameter map.

    Fits a single model to the input data with provided parameter map.

    dataset

    input dataset

    paramMap

    Parameter map. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  27. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): NaiveBayesModel

    Fits a single model to the input data with optional parameters.

    Fits a single model to the input data with optional parameters.

    dataset

    input dataset

    firstParamPair

    the first param pair, overrides embedded params

    otherParamPairs

    other param pairs. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  28. final def get[T](param: Param[T]): Option[T]

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  29. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  30. final def getDefault[T](param: Param[T]): Option[T]

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  31. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  32. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  33. final def getModelType: String

    Definition Classes
    NaiveBayesParams
  34. def getNumClasses(dataset: Dataset[_], maxNumClasses: Int = 100): Int

    Get the number of classes.

    Get the number of classes. This looks in column metadata first, and if that is missing, then this assumes classes are indexed 0,1,...,numClasses-1 and computes numClasses by finding the maximum label value.

    Label validation (ensuring all labels are integers >= 0) needs to be handled elsewhere, such as in extractLabeledPoints().

    dataset

    Dataset which contains a column labelCol

    maxNumClasses

    Maximum number of classes allowed when inferred from data. If numClasses is specified in the metadata, then maxNumClasses is ignored.

    returns

    number of classes

    Attributes
    protected
    Definition Classes
    Classifier
    Exceptions thrown

    IllegalArgumentException if metadata does not specify numClasses, and the actual numClasses exceeds maxNumClasses

  35. final def getOrDefault[T](param: Param[T]): T

    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes
    Params
  36. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  37. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  38. final def getProbabilityCol: String

    Definition Classes
    HasProbabilityCol
  39. final def getRawPredictionCol: String

    Definition Classes
    HasRawPredictionCol
  40. final def getSmoothing: Double

    Definition Classes
    NaiveBayesParams
  41. def getThresholds: Array[Double]

    Definition Classes
    HasThresholds
  42. final def getWeightCol: String

    Definition Classes
    HasWeightCol
  43. final def hasDefault[T](param: Param[T]): Boolean

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  44. def hasParam(paramName: String): Boolean

    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes
    Params
  45. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  46. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  47. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  48. final def isDefined(param: Param[_]): Boolean

    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes
    Params
  49. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  50. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  51. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  52. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  53. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  54. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  55. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  56. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  57. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  58. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  59. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  60. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  61. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. final val modelType: Param[String]

    The model type which is a string (case-sensitive).

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

    Definition Classes
    NaiveBayesParams
  66. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  67. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  68. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  69. lazy val params: Array[Param[_]]

    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes
    Params
    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

  70. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  71. final val probabilityCol: Param[String]

    Param for Column name for predicted class conditional probabilities.

    Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

    Definition Classes
    HasProbabilityCol
  72. final val rawPredictionCol: Param[String]

    Param for raw prediction (a.k.a.

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

    Definition Classes
    HasRawPredictionCol
  73. def save(path: String): Unit

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  74. final def set(paramPair: ParamPair[_]): NaiveBayes.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  75. final def set(param: String, value: Any): NaiveBayes.this.type

    Sets a parameter (by name) in the embedded param map.

    Sets a parameter (by name) in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  76. final def set[T](param: Param[T], value: T): NaiveBayes.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  77. final def setDefault(paramPairs: ParamPair[_]*): NaiveBayes.this.type

    Sets default values for a list of params.

    Sets default values for a list of params.

    Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

    paramPairs

    a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

    Attributes
    protected
    Definition Classes
    Params
  78. final def setDefault[T](param: Param[T], value: T): NaiveBayes.this.type

    Sets a default value for a param.

    Sets a default value for a param.

    param

    param to set the default value. Make sure that this param is initialized before this method gets called.

    value

    the default value

    Attributes
    protected[ml]
    Definition Classes
    Params
  79. def setFeaturesCol(value: String): NaiveBayes

    Definition Classes
    Predictor
  80. def setLabelCol(value: String): NaiveBayes

    Definition Classes
    Predictor
  81. def setModelType(value: String): NaiveBayes.this.type

    Set the model type using a string (case-sensitive).

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

    Annotations
    @Since( "1.5.0" )
  82. def setPredictionCol(value: String): NaiveBayes

    Definition Classes
    Predictor
  83. def setProbabilityCol(value: String): NaiveBayes

    Definition Classes
    ProbabilisticClassifier
  84. def setRawPredictionCol(value: String): NaiveBayes

    Definition Classes
    Classifier
  85. def setSmoothing(value: Double): NaiveBayes.this.type

    Set the smoothing parameter.

    Set the smoothing parameter. Default is 1.0.

    Annotations
    @Since( "1.5.0" )
  86. def setThresholds(value: Array[Double]): NaiveBayes

    Definition Classes
    ProbabilisticClassifier
  87. def setWeightCol(value: String): NaiveBayes.this.type

    Sets the value of param weightCol.

    Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one.

    Annotations
    @Since( "2.1.0" )
  88. final val smoothing: DoubleParam

    The smoothing parameter.

    The smoothing parameter. (default = 1.0).

    Definition Classes
    NaiveBayesParams
  89. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  90. val thresholds: DoubleArrayParam

    Param for Thresholds in multi-class classification to adjust the probability of predicting each class.

    Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

    Definition Classes
    HasThresholds
  91. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  92. def train(dataset: Dataset[_]): NaiveBayesModel

    Train a model using the given dataset and parameters.

    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.

    dataset

    Training dataset

    returns

    Fitted model

    Attributes
    protected
    Definition Classes
    NaiveBayes → Predictor
  93. def transformSchema(schema: StructType): StructType

    Check transform validity and derive the output schema from the input schema.

    Check transform validity and derive the output schema from the input schema.

    We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

    Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

    Definition Classes
    Predictor → PipelineStage
  94. def transformSchema(schema: StructType, logging: Boolean): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema and parameters, optionally with logging.

    This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  95. val uid: String

    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    NaiveBayes → Identifiable
    Annotations
    @Since( "1.5.0" )
  96. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

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

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

    schema

    input schema

    fitting

    whether this is in fitting

    featuresDataType

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

    returns

    output schema

    Attributes
    protected
    Definition Classes
    ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  97. def validateLabel(label: Double, numClasses: Int): Unit

    Validates the label on the classifier is a valid integer in the range [0, numClasses).

    Validates the label on the classifier is a valid integer in the range [0, numClasses).

    label

    The label to validate.

    numClasses

    Number of classes label can take. Labels must be integers in the range [0, numClasses).

    Attributes
    protected
    Definition Classes
    Classifier
  98. def validateNumClasses(numClasses: Int): Unit

    Validates that number of classes is greater than zero.

    Validates that number of classes is greater than zero.

    numClasses

    Number of classes label can take.

    Attributes
    protected
    Definition Classes
    Classifier
  99. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  100. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  101. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  102. final val weightCol: Param[String]

    Param for weight column name.

    Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

    Definition Classes
    HasWeightCol
  103. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from NaiveBayesParams

Inherited from HasWeightCol

Inherited from ProbabilisticClassifier[Vector, NaiveBayes, NaiveBayesModel]

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from Classifier[Vector, NaiveBayes, NaiveBayesModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from Predictor[Vector, NaiveBayes, NaiveBayesModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Estimator[NaiveBayesModel]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

Members

Parameter setters

Parameter getters