Class/Object

org.apache.spark.ml.feature

CountVectorizer

Related Docs: object CountVectorizer | package feature

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class CountVectorizer extends Estimator[CountVectorizerModel] with CountVectorizerParams with DefaultParamsWritable

Extracts a vocabulary from document collections and generates a CountVectorizerModel.

Annotations
@Since( "1.5.0" )
Linear Supertypes
DefaultParamsWritable, MLWritable, CountVectorizerParams, HasOutputCol, HasInputCol, Estimator[CountVectorizerModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. CountVectorizer
  2. DefaultParamsWritable
  3. MLWritable
  4. CountVectorizerParams
  5. HasOutputCol
  6. HasInputCol
  7. Estimator
  8. PipelineStage
  9. Logging
  10. Params
  11. Serializable
  12. Serializable
  13. Identifiable
  14. AnyRef
  15. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new CountVectorizer()

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

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    Annotations
    @Since( "1.5.0" )

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

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    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean

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    Definition Classes
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  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  6. val binary: BooleanParam

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    Binary toggle to control the output vector values.

    Binary toggle to control the output vector values. If True, all nonzero counts (after minTF filter applied) are set to 1. This is useful for discrete probabilistic models that model binary events rather than integer counts. Default: false

    Definition Classes
    CountVectorizerParams
  7. final def clear(param: Param[_]): CountVectorizer.this.type

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    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  8. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. def copy(extra: ParamMap): CountVectorizer

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    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
    CountVectorizerEstimatorPipelineStageParams
    Annotations
    @Since( "1.5.0" )
  10. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

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    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
  11. final def defaultCopy[T <: Params](extra: ParamMap): T

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    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
  12. final def eq(arg0: AnyRef): Boolean

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  13. def equals(arg0: Any): Boolean

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    AnyRef → Any
  14. def explainParam(param: Param[_]): String

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    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
  15. def explainParams(): String

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    Explains all params of this instance.

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

    Definition Classes
    Params
  16. final def extractParamMap(): ParamMap

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    extractParamMap with no extra values.

    extractParamMap with no extra values.

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

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    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
  18. def finalize(): Unit

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    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  19. def fit(dataset: Dataset[_]): CountVectorizerModel

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    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    CountVectorizerEstimator
    Annotations
    @Since( "2.0.0" )
  20. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[CountVectorizerModel]

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    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" )
  21. def fit(dataset: Dataset[_], paramMap: ParamMap): CountVectorizerModel

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    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" )
  22. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): CountVectorizerModel

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    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()
  23. final def get[T](param: Param[T]): Option[T]

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    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  24. def getBinary: Boolean

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    Definition Classes
    CountVectorizerParams
  25. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  26. final def getDefault[T](param: Param[T]): Option[T]

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    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  27. final def getInputCol: String

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    Definition Classes
    HasInputCol
  28. def getMinDF: Double

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    Definition Classes
    CountVectorizerParams
  29. def getMinTF: Double

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    Definition Classes
    CountVectorizerParams
  30. final def getOrDefault[T](param: Param[T]): T

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    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
  31. final def getOutputCol: String

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    Definition Classes
    HasOutputCol
  32. def getParam(paramName: String): Param[Any]

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    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  33. def getVocabSize: Int

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    Definition Classes
    CountVectorizerParams
  34. final def hasDefault[T](param: Param[T]): Boolean

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    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

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

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    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
  36. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  37. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

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    Attributes
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    Definition Classes
    Logging
  38. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    protected
    Definition Classes
    Logging
  39. final val inputCol: Param[String]

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    Param for input column name.

    Param for input column name.

    Definition Classes
    HasInputCol
  40. final def isDefined(param: Param[_]): Boolean

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    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
  41. final def isInstanceOf[T0]: Boolean

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    Any
  42. final def isSet(param: Param[_]): Boolean

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    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  43. def isTraceEnabled(): Boolean

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    Logging
  44. def log: Logger

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    Logging
  45. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  46. def logDebug(msg: ⇒ String): Unit

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    Logging
  47. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  48. def logError(msg: ⇒ String): Unit

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    Logging
  49. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  50. def logInfo(msg: ⇒ String): Unit

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    Logging
  51. def logName: String

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    Logging
  52. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  53. def logTrace(msg: ⇒ String): Unit

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    Logging
  54. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  55. def logWarning(msg: ⇒ String): Unit

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    protected
    Definition Classes
    Logging
  56. val minDF: DoubleParam

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    Specifies the minimum number of different documents a term must appear in to be included in the vocabulary.

    Specifies the minimum number of different documents a term must appear in to be included in the vocabulary. If this is an integer greater than or equal to 1, this specifies the number of documents the term must appear in; if this is a double in [0,1), then this specifies the fraction of documents.

    Default: 1.0

    Definition Classes
    CountVectorizerParams
  57. val minTF: DoubleParam

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    Filter to ignore rare words in a document.

    Filter to ignore rare words in a document. For each document, terms with frequency/count less than the given threshold are ignored. If this is an integer greater than or equal to 1, then this specifies a count (of times the term must appear in the document); if this is a double in [0,1), then this specifies a fraction (out of the document's token count).

    Note that the parameter is only used in transform of CountVectorizerModel and does not affect fitting.

    Default: 1.0

    Definition Classes
    CountVectorizerParams
  58. final def ne(arg0: AnyRef): Boolean

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    AnyRef
  59. final def notify(): Unit

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    AnyRef
  60. final def notifyAll(): Unit

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    AnyRef
  61. final val outputCol: Param[String]

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    Param for output column name.

    Param for output column name.

    Definition Classes
    HasOutputCol
  62. lazy val params: Array[Param[_]]

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    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.

  63. def save(path: String): Unit

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    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( ... )
  64. final def set(paramPair: ParamPair[_]): CountVectorizer.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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    protected
    Definition Classes
    Params
  65. final def set(param: String, value: Any): CountVectorizer.this.type

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

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

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

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  67. def setBinary(value: Boolean): CountVectorizer.this.type

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    Annotations
    @Since( "2.0.0" )
  68. final def setDefault(paramPairs: ParamPair[_]*): CountVectorizer.this.type

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    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.

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    Definition Classes
    Params
  69. final def setDefault[T](param: Param[T], value: T): CountVectorizer.this.type

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    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

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    protected
    Definition Classes
    Params
  70. def setInputCol(value: String): CountVectorizer.this.type

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    @Since( "1.5.0" )
  71. def setMinDF(value: Double): CountVectorizer.this.type

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    @Since( "1.5.0" )
  72. def setMinTF(value: Double): CountVectorizer.this.type

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    @Since( "1.5.0" )
  73. def setOutputCol(value: String): CountVectorizer.this.type

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    @Since( "1.5.0" )
  74. def setVocabSize(value: Int): CountVectorizer.this.type

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    @Since( "1.5.0" )
  75. final def synchronized[T0](arg0: ⇒ T0): T0

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    AnyRef
  76. def toString(): String

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    Identifiable → AnyRef → Any
  77. def transformSchema(schema: StructType): StructType

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    :: 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
    CountVectorizerPipelineStage
    Annotations
    @Since( "1.5.0" )
  78. def transformSchema(schema: StructType, logging: Boolean): StructType

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    :: 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.

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

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    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    CountVectorizerIdentifiable
    Annotations
    @Since( "1.5.0" )
  80. def validateAndTransformSchema(schema: StructType): StructType

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    Validates and transforms the input schema.

    Validates and transforms the input schema.

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    protected
    Definition Classes
    CountVectorizerParams
  81. val vocabSize: IntParam

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    Max size of the vocabulary.

    Max size of the vocabulary. CountVectorizer will build a vocabulary that only considers the top vocabSize terms ordered by term frequency across the corpus.

    Default: 218

    Definition Classes
    CountVectorizerParams
  82. final def wait(): Unit

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    @throws( ... )
  83. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  84. final def wait(arg0: Long): Unit

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    @throws( ... )
  85. def write: MLWriter

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    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritableMLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from CountVectorizerParams

Inherited from HasOutputCol

Inherited from HasInputCol

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