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" )
Source
CountVectorizer.scala
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
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Parameters

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

  1. 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
  2. final val inputCol: Param[String]

    Param for input column name.

    Param for input column name.

    Definition Classes
    HasInputCol
  3. 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
  4. 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
  5. 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
  6. final val outputCol: Param[String]

    Param for output column name.

    Param for output column name.

    Definition Classes
    HasOutputCol
  7. 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

Members

  1. 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
  2. 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
    CountVectorizerModelModelTransformerPipelineStageParams
    Annotations
    @Since( "1.5.0" )
  3. 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
  4. def explainParams(): String

    Explains all params of this instance.

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

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

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  6. 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
  7. 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
  8. 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
  9. 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
  10. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  11. 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
  12. 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
  13. def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  14. 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
  15. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  16. 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.

  17. 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.

  18. 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( ... )
  19. 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
  20. 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
  21. def toString(): String
    Definition Classes
    CountVectorizerModelIdentifiable → AnyRef → Any
    Annotations
    @Since( "3.0.0" )
  22. def transform(dataset: Dataset[_]): DataFrame

    Transforms the input dataset.

    Transforms the input dataset.

    Definition Classes
    CountVectorizerModelTransformer
    Annotations
    @Since( "2.0.0" )
  23. 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" )
  24. 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()
  25. 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
    CountVectorizerModelPipelineStage
    Annotations
    @Since( "1.5.0" )
  26. 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
    CountVectorizerModelIdentifiable
    Annotations
    @Since( "1.5.0" )
  27. val vocabulary: Array[String]
    Annotations
    @Since( "1.5.0" )
  28. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    CountVectorizerModelMLWritable
    Annotations
    @Since( "1.6.0" )

Parameter setters

  1. def setBinary(value: Boolean): CountVectorizerModel.this.type

    Annotations
    @Since( "2.0.0" )
  2. def setInputCol(value: String): CountVectorizerModel.this.type

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

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

    Annotations
    @Since( "1.5.0" )

Parameter getters

  1. def getBinary: Boolean

    Definition Classes
    CountVectorizerParams
  2. final def getInputCol: String

    Definition Classes
    HasInputCol
  3. def getMaxDF: Double

    Definition Classes
    CountVectorizerParams
  4. def getMinDF: Double

    Definition Classes
    CountVectorizerParams
  5. def getMinTF: Double

    Definition Classes
    CountVectorizerParams
  6. final def getOutputCol: String

    Definition Classes
    HasOutputCol
  7. def getVocabSize: Int

    Definition Classes
    CountVectorizerParams