Packages

class Featurize extends Estimator[PipelineModel] with Wrappable with DefaultParamsWritable with HasOutputCol with HasInputCols with SynapseMLLogging

Featurizes a dataset. Converts the specified columns to feature columns.

Linear Supertypes
SynapseMLLogging, HasInputCols, HasOutputCol, DefaultParamsWritable, MLWritable, Wrappable, DotnetWrappable, RWrappable, PythonWrappable, BaseWrappable, Estimator[PipelineModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. Featurize
  2. SynapseMLLogging
  3. HasInputCols
  4. HasOutputCol
  5. DefaultParamsWritable
  6. MLWritable
  7. Wrappable
  8. DotnetWrappable
  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 Featurize()
  2. new Featurize(uid: String)

Value Members

  1. final def clear(param: Param[_]): Featurize.this.type
    Definition Classes
    Params
  2. def copy(extra: ParamMap): Estimator[PipelineModel]
    Definition Classes
    Featurize → 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[_]): PipelineModel

    Featurizes the dataset.

    Featurizes the dataset.

    dataset

    The input dataset to train.

    returns

    The featurized model.

    Definition Classes
    Featurize → Estimator
  9. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[PipelineModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  10. def fit(dataset: Dataset[_], paramMap: ParamMap): PipelineModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  11. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): PipelineModel
    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. final def getImputeMissing: Boolean

  15. def getInputCols: Array[String]

    Definition Classes
    HasInputCols
  16. final def getNumFeatures: Int

  17. final def getOneHotEncodeCategoricals: Boolean

  18. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  19. def getOutputCol: String

    Definition Classes
    HasOutputCol
  20. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  21. def getParamInfo(p: Param[_]): ParamInfo[_]
    Definition Classes
    BaseWrappable
  22. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  23. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  24. val imputeMissing: Param[Boolean]
  25. val inputCols: StringArrayParam

    The names of the inputColumns

    The names of the inputColumns

    Definition Classes
    HasInputCols
  26. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  27. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  28. def logClass(): Unit
    Definition Classes
    SynapseMLLogging
  29. def logFit[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  30. def logTrain[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  31. def logTransform[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  32. def logVerb[T](verb: String, f: ⇒ T, columns: Int = -1): T
    Definition Classes
    SynapseMLLogging
  33. def makeDotnetFile(conf: CodegenConfig): Unit
    Definition Classes
    DotnetWrappable
  34. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  35. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  36. val numFeatures: IntParam

    Number of features to hash string columns to

  37. val oneHotEncodeCategoricals: Param[Boolean]

    One hot encode categorical columns when true; default is true

  38. val outputCol: Param[String]

    The name of the output column

    The name of the output column

    Definition Classes
    HasOutputCol
  39. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  40. def pyAdditionalMethods: String
    Definition Classes
    PythonWrappable
  41. def pyInitFunc(): String
    Definition Classes
    PythonWrappable
  42. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  43. final def set[T](param: Param[T], value: T): Featurize.this.type
    Definition Classes
    Params
  44. def setImputeMissing(value: Boolean): Featurize.this.type

  45. def setInputCols(value: Array[String]): Featurize.this.type

    Definition Classes
    HasInputCols
  46. def setNumFeatures(value: Int): Featurize.this.type

  47. def setOneHotEncodeCategoricals(value: Boolean): Featurize.this.type

  48. def setOutputCol(value: String): Featurize.this.type

    Definition Classes
    HasOutputCol
  49. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  50. def transformSchema(schema: StructType): StructType
    Definition Classes
    Featurize → PipelineStage
  51. val uid: String
    Definition Classes
    Featurize → SynapseMLLogging → Identifiable
  52. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable