Packages

class CleanMissingData extends Estimator[CleanMissingDataModel] with HasInputCols with HasOutputCols with Wrappable with DefaultParamsWritable with SynapseMLLogging

Removes missing values from input dataset. The following modes are supported: Mean - replaces missings with mean of fit column Median - replaces missings with approximate median of fit column Custom - replaces missings with custom value specified by user For mean and median modes, only numeric column types are supported, specifically: Int, Long, Float, Double For custom mode, the types above are supported and additionally: String, Boolean

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

Value Members

  1. val cleaningMode: Param[String]
  2. final def clear(param: Param[_]): CleanMissingData.this.type
    Definition Classes
    Params
  3. def copy(extra: ParamMap): Estimator[CleanMissingDataModel]
    Definition Classes
    CleanMissingData → Estimator → PipelineStage → Params
  4. val customValue: Param[String]

    Custom value for imputation, supports numeric, string and boolean types.

    Custom value for imputation, supports numeric, string and boolean types. Date and Timestamp currently not supported.

  5. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  6. def explainParams(): String
    Definition Classes
    Params
  7. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  8. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  9. def fit(dataset: Dataset[_]): CleanMissingDataModel

    Fits the dataset, prepares the transformation function.

    Fits the dataset, prepares the transformation function.

    dataset

    The input dataset.

    returns

    The model for removing missings.

    Definition Classes
    CleanMissingData → Estimator
  10. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[CleanMissingDataModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  11. def fit(dataset: Dataset[_], paramMap: ParamMap): CleanMissingDataModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  12. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): CleanMissingDataModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  13. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  14. def getCleaningMode: String
  15. def getCustomValue: String
  16. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  17. def getInputCols: Array[String]

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

    Definition Classes
    HasOutputCols
  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 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 outputCols: StringArrayParam

    The names of the output columns

    The names of the output columns

    Definition Classes
    HasOutputCols
  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): CleanMissingData.this.type
    Definition Classes
    Params
  39. def setCleaningMode(value: String): CleanMissingData.this.type
  40. def setCustomValue(value: String): CleanMissingData.this.type
  41. def setInputCols(value: Array[String]): CleanMissingData.this.type

    Definition Classes
    HasInputCols
  42. def setOutputCols(value: Array[String]): CleanMissingData.this.type

    Definition Classes
    HasOutputCols
  43. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  44. def transformSchema(schema: StructType): StructType
    Definition Classes
    CleanMissingData → PipelineStage
    Annotations
    @DeveloperApi()
  45. val uid: String
    Definition Classes
    CleanMissingData → SynapseMLLogging → Identifiable
  46. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable