Class

org.apache.spark.ml.odkl

ScalerEstimator

Related Doc: package odkl

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class ScalerEstimator[M <: ModelWithSummary[M]] extends Estimator[Unscaler[M]] with ScalerParams with DefaultParamsWritable

This is a specific implementation of the scaler for linear models. Uses the ability to propagate scaling to the weights to avoid overhead when predicting.

Linear Supertypes
DefaultParamsWritable, MLWritable, ScalerParams, HasFeaturesCol, Estimator[Unscaler[M]], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. ScalerEstimator
  2. DefaultParamsWritable
  3. MLWritable
  4. ScalerParams
  5. HasFeaturesCol
  6. Estimator
  7. PipelineStage
  8. Logging
  9. Params
  10. Serializable
  11. Serializable
  12. Identifiable
  13. AnyRef
  14. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new ScalerEstimator(uid: String = ...)

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Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

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    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  6. final def clear(param: Param[_]): ScalerEstimator.this.type

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    Definition Classes
    Params
  7. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. def copy(extra: ParamMap): Nothing

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    Definition Classes
    ScalerEstimator → Estimator → PipelineStage → Params
  9. def copyValues[T <: Params](to: T, extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params
  10. final def defaultCopy[T <: Params](extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params
  11. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  13. def explainParam(param: Param[_]): String

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    Definition Classes
    Params
  14. def explainParams(): String

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    Definition Classes
    Params
  15. final def extractParamMap(): ParamMap

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    Definition Classes
    Params
  16. final def extractParamMap(extra: ParamMap): ParamMap

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    Definition Classes
    Params
  17. final val featuresCol: Param[String]

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    Definition Classes
    HasFeaturesCol
  18. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  19. def fit(dataset: Dataset[_]): Unscaler[M]

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    Definition Classes
    ScalerEstimator → Estimator
  20. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[Unscaler[M]]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  21. def fit(dataset: Dataset[_], paramMap: ParamMap): Unscaler[M]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  22. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): Unscaler[M]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  23. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  24. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  25. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  26. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol
  27. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params
  28. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params
  29. def getWithMean: Boolean

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    Definition Classes
    ScalerParams
  30. def getWithStd: Boolean

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    Definition Classes
    ScalerParams
  31. final def hasDefault[T](param: Param[T]): Boolean

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    Definition Classes
    Params
  32. def hasParam(paramName: String): Boolean

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    Definition Classes
    Params
  33. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  34. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  35. final def isDefined(param: Param[_]): Boolean

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    Definition Classes
    Params
  36. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  37. final def isSet(param: Param[_]): Boolean

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    Definition Classes
    Params
  38. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  39. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging
  40. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  41. def logDebug(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  42. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  43. def logError(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  44. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  45. def logInfo(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  46. def logName: String

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    Attributes
    protected
    Definition Classes
    Logging
  47. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
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    Logging
  48. def logTrace(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  49. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
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    Logging
  50. def logWarning(msg: ⇒ String): Unit

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    Attributes
    protected
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    Logging
  51. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  52. final def notify(): Unit

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    Definition Classes
    AnyRef
  53. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  54. lazy val params: Array[Param[_]]

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    Definition Classes
    Params
  55. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  56. final def set(paramPair: ParamPair[_]): ScalerEstimator.this.type

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    Attributes
    protected
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    Params
  57. final def set(param: String, value: Any): ScalerEstimator.this.type

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    Attributes
    protected
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    Params
  58. final def set[T](param: Param[T], value: T): ScalerEstimator.this.type

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    Definition Classes
    Params
  59. final def setDefault(paramPairs: ParamPair[_]*): ScalerEstimator.this.type

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    Attributes
    protected
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    Params
  60. final def setDefault[T](param: Param[T], value: T): ScalerEstimator.this.type

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    Attributes
    protected
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    Params
  61. def setWithMean(value: Boolean): ScalerEstimator.this.type

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    Definition Classes
    ScalerParams
  62. def setWithStd(value: Boolean): ScalerEstimator.this.type

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    Definition Classes
    ScalerParams
  63. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  64. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  65. def transformSchema(schema: StructType): StructType

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    Definition Classes
    ScalerEstimator → PipelineStage
  66. def transformSchema(schema: StructType, logging: Boolean): StructType

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    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  67. val uid: String

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    Definition Classes
    ScalerEstimator → Identifiable
  68. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  69. final def wait(arg0: Long, arg1: Int): Unit

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    AnyRef
    Annotations
    @throws( ... )
  70. final def wait(arg0: Long): Unit

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    AnyRef
    Annotations
    @throws( ... )
  71. val withMean: BooleanParam

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    Whether to center the data with mean before scaling.

    Whether to center the data with mean before scaling. It will build a dense output, so this does not work on sparse input and will raise an exception. Default: false

    Definition Classes
    ScalerParams
  72. val withStd: BooleanParam

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    Whether to scale the data to unit standard deviation.

    Whether to scale the data to unit standard deviation. Default: true

    Definition Classes
    ScalerParams
  73. def write: MLWriter

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    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from ScalerParams

Inherited from HasFeaturesCol

Inherited from Estimator[Unscaler[M]]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

getParam

param

Ungrouped