Packages

class ValueIndexer extends Estimator[ValueIndexerModel] with ValueIndexerParams with SynapseMLLogging

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
SynapseMLLogging, ValueIndexerParams, HasOutputCol, HasInputCol, DefaultParamsWritable, MLWritable, Wrappable, RWrappable, PythonWrappable, BaseWrappable, Estimator[ValueIndexerModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. ValueIndexer
  2. SynapseMLLogging
  3. ValueIndexerParams
  4. HasOutputCol
  5. HasInputCol
  6. DefaultParamsWritable
  7. MLWritable
  8. Wrappable
  9. RWrappable
  10. PythonWrappable
  11. BaseWrappable
  12. Estimator
  13. PipelineStage
  14. Logging
  15. Params
  16. Serializable
  17. Serializable
  18. Identifiable
  19. AnyRef
  20. 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 explainParam(param: Param[_]): String
    Definition Classes
    Params
  4. def explainParams(): String
    Definition Classes
    Params
  5. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  6. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  7. 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
  8. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[ValueIndexerModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  9. def fit(dataset: Dataset[_], paramMap: ParamMap): ValueIndexerModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  10. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): ValueIndexerModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  11. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  12. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  13. def getInputCol: String

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

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

    The name of the input column

    The name of the input column

    Definition Classes
    HasInputCol
  21. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  22. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  23. def logClass(featureName: String): Unit
    Definition Classes
    SynapseMLLogging
  24. def logFit[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  25. def logTransform[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  26. def logVerb[T](verb: String, f: ⇒ T, columns: Option[Int] = None): T
    Definition Classes
    SynapseMLLogging
  27. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  28. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  29. val outputCol: Param[String]

    The name of the output column

    The name of the output column

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

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

    Definition Classes
    HasOutputCol
  37. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  38. def transformSchema(schema: StructType): StructType
    Definition Classes
    ValueIndexer → PipelineStage
    Annotations
    @DeveloperApi()
  39. val uid: String
    Definition Classes
    ValueIndexerSynapseMLLogging → Identifiable
  40. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable