Packages

class CountVectorizerModel extends Model[CountVectorizerModel] with CountVectorizerParams with MLWritable

Converts a text document to a sparse vector of token counts.

Annotations
@Since( "1.5.0" )
Linear Supertypes
MLWritable, CountVectorizerParams, HasOutputCol, HasInputCol, Model[CountVectorizerModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. CountVectorizerModel
  2. MLWritable
  3. CountVectorizerParams
  4. HasOutputCol
  5. HasInputCol
  6. Model
  7. Transformer
  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 CountVectorizerModel(vocabulary: Array[String])
    Annotations
    @Since( "1.5.0" )
  2. new CountVectorizerModel(uid: String, vocabulary: Array[String])

    vocabulary

    An Array over terms. Only the terms in the vocabulary will be counted.

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. val binary: BooleanParam

    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[_]): CountVectorizerModel.this.type

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

    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
    CountVectorizerModel → Model → Transformer → PipelineStage → Params
    Annotations
    @Since( "1.5.0" )
  10. 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
  11. 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
  12. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  14. 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
  15. def explainParams(): String

    Explains all params of this instance.

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

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

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  17. 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
  18. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  19. 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
  20. def getBinary: Boolean

    Definition Classes
    CountVectorizerParams
  21. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  22. 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
  23. final def getInputCol: String

    Definition Classes
    HasInputCol
  24. def getMaxDF: Double

    Definition Classes
    CountVectorizerParams
  25. def getMinDF: Double

    Definition Classes
    CountVectorizerParams
  26. def getMinTF: Double

    Definition Classes
    CountVectorizerParams
  27. 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
  28. final def getOutputCol: String

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

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  30. def getVocabSize: Int

    Definition Classes
    CountVectorizerParams
  31. 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
  32. 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
  33. def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  34. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  35. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  36. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  37. final val inputCol: Param[String]

    Param for input column name.

    Param for input column name.

    Definition Classes
    HasInputCol
  38. 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
  39. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  40. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  41. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  42. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  43. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  44. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  45. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  46. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  47. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  48. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  49. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  50. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  51. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  52. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  53. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  54. val maxDF: DoubleParam

    Specifies the maximum number of different documents a term could appear in to be included in the vocabulary.

    Specifies the maximum number of different documents a term could appear in to be included in the vocabulary. A term that appears more than the threshold will be ignored. If this is an integer greater than or equal to 1, this specifies the maximum number of documents the term could appear in; if this is a double in [0,1), then this specifies the maximum fraction of documents the term could appear in.

    Default: (263) - 1

    Definition Classes
    CountVectorizerParams
  55. val minDF: DoubleParam

    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
  56. val minTF: DoubleParam

    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
  57. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  58. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  59. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  60. final val outputCol: Param[String]

    Param for output column name.

    Param for output column name.

    Definition Classes
    HasOutputCol
  61. 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.

  62. var parent: Estimator[CountVectorizerModel]

    The parent estimator that produced this model.

    The parent estimator that produced this model.

    Definition Classes
    Model
    Note

    For ensembles' component Models, this value can be null.

  63. 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( ... )
  64. final def set(paramPair: ParamPair[_]): CountVectorizerModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  65. final def set(param: String, value: Any): CountVectorizerModel.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
  66. final def set[T](param: Param[T], value: T): CountVectorizerModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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

    Annotations
    @Since( "2.0.0" )
  68. final def setDefault(paramPairs: ParamPair[_]*): CountVectorizerModel.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
  69. final def setDefault[T](param: Param[T], value: T): CountVectorizerModel.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
  70. def setInputCol(value: String): CountVectorizerModel.this.type

    Annotations
    @Since( "1.5.0" )
  71. def setMinTF(value: Double): CountVectorizerModel.this.type

    Annotations
    @Since( "1.5.0" )
  72. def setOutputCol(value: String): CountVectorizerModel.this.type

    Annotations
    @Since( "1.5.0" )
  73. def setParent(parent: Estimator[CountVectorizerModel]): CountVectorizerModel

    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  74. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  75. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  76. def transform(dataset: Dataset[_]): DataFrame

    Transforms the input dataset.

    Transforms the input dataset.

    Definition Classes
    CountVectorizerModel → Transformer
    Annotations
    @Since( "2.0.0" )
  77. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

    Transforms the dataset with provided parameter map as additional parameters.

    Transforms the dataset with provided parameter map as additional parameters.

    dataset

    input dataset

    paramMap

    additional parameters, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  78. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

    Transforms the dataset with optional parameters

    Transforms the dataset with optional parameters

    dataset

    input dataset

    firstParamPair

    the first param pair, overwrite embedded params

    otherParamPairs

    other param pairs, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  79. 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
    CountVectorizerModel → PipelineStage
    Annotations
    @Since( "1.5.0" )
  80. 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()
  81. 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
    CountVectorizerModel → Identifiable
    Annotations
    @Since( "1.5.0" )
  82. def validateAndTransformSchema(schema: StructType): StructType

    Validates and transforms the input schema.

    Validates and transforms the input schema.

    Attributes
    protected
    Definition Classes
    CountVectorizerParams
  83. val vocabSize: IntParam

    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
  84. val vocabulary: Array[String]
    Annotations
    @Since( "1.5.0" )
  85. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  86. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  87. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  88. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    CountVectorizerModel → MLWritable
    Annotations
    @Since( "1.6.0" )

Inherited from MLWritable

Inherited from CountVectorizerParams

Inherited from HasOutputCol

Inherited from HasInputCol

Inherited from Model[CountVectorizerModel]

Inherited from Transformer

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