Packages

class ValueIndexer extends Estimator[ValueIndexerModel] with ValueIndexerParams with BasicLogging

Fits a dictionary of values from the input column. Model then transforms a column to a categorical column of the given array of values. Similar to StringIndexer except it can be used on any value types.

Linear Supertypes
BasicLogging, ValueIndexerParams, HasOutputCol, HasInputCol, DefaultParamsWritable, MLWritable, Wrappable, DotnetWrappable, RWrappable, PythonWrappable, BaseWrappable, Estimator[ValueIndexerModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. ValueIndexer
  2. BasicLogging
  3. ValueIndexerParams
  4. HasOutputCol
  5. HasInputCol
  6. DefaultParamsWritable
  7. MLWritable
  8. Wrappable
  9. DotnetWrappable
  10. RWrappable
  11. PythonWrappable
  12. BaseWrappable
  13. Estimator
  14. PipelineStage
  15. Logging
  16. Params
  17. Serializable
  18. Serializable
  19. Identifiable
  20. AnyRef
  21. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new ValueIndexer()
  2. new ValueIndexer(uid: String)

Value Members

  1. final def clear(param: Param[_]): ValueIndexer.this.type
    Definition Classes
    Params
  2. def copy(extra: ParamMap): Estimator[ValueIndexerModel]
    Definition Classes
    ValueIndexer → Estimator → PipelineStage → Params
  3. def dotnetAdditionalMethods: String
    Definition Classes
    DotnetWrappable
  4. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  5. def explainParams(): String
    Definition Classes
    Params
  6. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  7. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  8. def fit(dataset: Dataset[_]): ValueIndexerModel

    Fits the dictionary of values from the input column.

    Fits the dictionary of values from the input column.

    dataset

    The input dataset to train.

    returns

    The model for transforming columns to categorical.

    Definition Classes
    ValueIndexer → Estimator
  9. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[ValueIndexerModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  10. def fit(dataset: Dataset[_], paramMap: ParamMap): ValueIndexerModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  11. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): ValueIndexerModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  12. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  13. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  14. def getInputCol: String

    Definition Classes
    HasInputCol
  15. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  16. def getOutputCol: String

    Definition Classes
    HasOutputCol
  17. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  18. def getParamInfo(p: Param[_]): ParamInfo[_]
    Definition Classes
    BaseWrappable
  19. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  20. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  21. val inputCol: Param[String]

    The name of the input column

    The name of the input column

    Definition Classes
    HasInputCol
  22. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  23. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  24. def logClass(): Unit
    Definition Classes
    BasicLogging
  25. def logFit[T](f: ⇒ T): T
    Definition Classes
    BasicLogging
  26. def logPredict[T](f: ⇒ T): T
    Definition Classes
    BasicLogging
  27. def logTrain[T](f: ⇒ T): T
    Definition Classes
    BasicLogging
  28. def logTransform[T](f: ⇒ T): T
    Definition Classes
    BasicLogging
  29. def logVerb[T](verb: String, f: ⇒ T): T
    Definition Classes
    BasicLogging
  30. def makeDotnetFile(conf: CodegenConfig): Unit
    Definition Classes
    DotnetWrappable
  31. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  32. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  33. val outputCol: Param[String]

    The name of the output column

    The name of the output column

    Definition Classes
    HasOutputCol
  34. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  35. def pyAdditionalMethods: String
    Definition Classes
    PythonWrappable
  36. def pyInitFunc(): String
    Definition Classes
    PythonWrappable
  37. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  38. final def set[T](param: Param[T], value: T): ValueIndexer.this.type
    Definition Classes
    Params
  39. def setInputCol(value: String): ValueIndexer.this.type

    Definition Classes
    HasInputCol
  40. def setOutputCol(value: String): ValueIndexer.this.type

    Definition Classes
    HasOutputCol
  41. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  42. def transformSchema(schema: StructType): StructType
    Definition Classes
    ValueIndexer → PipelineStage
    Annotations
    @DeveloperApi()
  43. val uid: String
    Definition Classes
    ValueIndexer → BasicLogging → Identifiable
  44. val ver: String
    Definition Classes
    BasicLogging
  45. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable