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[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @native() @throws( ... )

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 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

16.  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

17.  final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes

    Params

18.  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

19.  final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes

    HasFeaturesCol

20.  def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

21.  def fit(dataset: Dataset[_]): NaiveBayesModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes

    Predictor → Estimator

22.  def fit(dataset: Dataset[_], paramMaps: Array[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" )

23.  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" )

24.  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()

25.  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

26.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

27.  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

28.  final def getFeaturesCol: String
    Definition Classes

    HasFeaturesCol

29.  final def getLabelCol: String
    Definition Classes

    HasLabelCol

30.  final def getModelType: String
    Definition Classes

    NaiveBayesParams

31.  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

32.  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

33.  def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes

    Params

34.  final def getPredictionCol: String
    Definition Classes

    HasPredictionCol

35.  final def getProbabilityCol: String
    Definition Classes

    HasProbabilityCol

36.  final def getRawPredictionCol: String
    Definition Classes

    HasRawPredictionCol

37.  final def getSmoothing: Double
    Definition Classes

    NaiveBayesParams

38.  def getThresholds: Array[Double]
    Definition Classes

    HasThresholds

39.  final def getWeightCol: String
    Definition Classes

    HasWeightCol

40.  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

41.  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

42.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

43.  def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

    Attributes

    protected

    Definition Classes

    Logging

44.  def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    Attributes

    protected

    Definition Classes

    Logging

45.  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

46.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

47.  final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes

    Params

48.  def isTraceEnabled(): Boolean

    Attributes

    protected

    Definition Classes

    Logging

49.  final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes

    HasLabelCol

50.  def log: Logger

    Attributes

    protected

    Definition Classes

    Logging

51.  def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

52.  def logDebug(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

53.  def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

54.  def logError(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

55.  def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

56.  def logInfo(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

57.  def logName: String

    Attributes

    protected

    Definition Classes

    Logging

58.  def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

59.  def logTrace(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

60.  def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

61.  def logWarning(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

62.  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" and "bernoulli". (default = multinomial)

    Definition Classes

    NaiveBayesParams

63.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

64.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

65.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

66.  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.

67.  final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes

    HasPredictionCol

68.  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

69.  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

70.  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( ... )

71.  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

72.  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

73.  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

74.  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

75.  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

    Definition Classes

    Params

76.  def setFeaturesCol(value: String): NaiveBayes
    Definition Classes

    Predictor

77.  def setLabelCol(value: String): NaiveBayes
    Definition Classes

    Predictor

78.  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" and "bernoulli". Default is "multinomial"

    Annotations

    @Since( "1.5.0" )

79.  def setPredictionCol(value: String): NaiveBayes
    Definition Classes

    Predictor

80.  def setProbabilityCol(value: String): NaiveBayes
    Definition Classes

    ProbabilisticClassifier

81.  def setRawPredictionCol(value: String): NaiveBayes
    Definition Classes

    Classifier

82.  def setSmoothing(value: Double): NaiveBayes.this.type

    Set the smoothing parameter.

    Set the smoothing parameter. Default is 1.0.

    Annotations

    @Since( "1.5.0" )

83.  def setThresholds(value: Array[Double]): NaiveBayes
    Definition Classes

    ProbabilisticClassifier

84.  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" )

85.  final val smoothing: DoubleParam

    The smoothing parameter.

    The smoothing parameter. (default = 1.0).

    Definition Classes

    NaiveBayesParams

86.  final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes

    AnyRef

87.  final 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

88.  def toString(): String

    Definition Classes

    Identifiable → AnyRef → Any

89.  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

90.  def transformSchema(schema: StructType): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    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

91.  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()

92.  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" )

93.  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

94.  final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

95.  final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

96.  final def wait(arg0: Long): Unit

    Definition Classes

    AnyRef

    Annotations

    @native() @throws( ... )

97.  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

98.  def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes

    DefaultParamsWritable → MLWritable