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, 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. RWrappable
  9. PythonWrappable
  10. BaseWrappable
  11. Estimator
  12. PipelineStage
  13. Logging
  14. Params
  15. Serializable
  16. Serializable
  17. Identifiable
  18. AnyRef
  19. 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 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[_]): PipelineModel

    Featurizes the dataset.

    Featurizes the dataset.

    dataset

    The input dataset to train.

    returns

    The featurized model.

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

  14. def getInputCols: Array[String]

    Definition Classes
    HasInputCols
  15. final def getNumFeatures: Int

  16. final def getOneHotEncodeCategoricals: Boolean

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

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

    The names of the inputColumns

    The names of the inputColumns

    Definition Classes
    HasInputCols
  25. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  26. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  27. def logClass(featureName: String): Unit
    Definition Classes
    SynapseMLLogging
  28. def logFit[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  29. def logTransform[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  30. def logVerb[T](verb: String, f: ⇒ T, columns: Option[Int] = None): T
    Definition Classes
    SynapseMLLogging
  31. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  32. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  33. val numFeatures: IntParam

    Number of features to hash string columns to

  34. val oneHotEncodeCategoricals: Param[Boolean]

    One hot encode categorical columns when true; default is true

  35. val outputCol: Param[String]

    The name of the output column

    The name of the output column

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

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

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

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

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

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