Packages

trait AutoTrainer[TrainedModel <: Model[TrainedModel]] extends Estimator[TrainedModel] with HasLabelCol with HasInputCols with ComplexParamsWritable with HasFeaturesCol with Wrappable

Defines common inheritance and parameters across trainers.

Linear Supertypes
Wrappable, RWrappable, PythonWrappable, BaseWrappable, HasFeaturesCol, ComplexParamsWritable, MLWritable, HasInputCols, HasLabelCol, Estimator[TrainedModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Known Subclasses
TrainClassifier, TrainRegressor
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. AutoTrainer
  2. Wrappable
  3. RWrappable
  4. PythonWrappable
  5. BaseWrappable
  6. HasFeaturesCol
  7. ComplexParamsWritable
  8. MLWritable
  9. HasInputCols
  10. HasLabelCol
  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

Abstract Value Members

  1. abstract def copy(extra: ParamMap): Estimator[TrainedModel]
    Definition Classes
    Estimator → PipelineStage → Params
  2. abstract def fit(dataset: Dataset[_]): TrainedModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  3. abstract def modelDoc: String

    Doc for model to run.

  4. abstract def transformSchema(schema: StructType): StructType
    Definition Classes
    PipelineStage
  5. abstract val uid: String
    Definition Classes
    Identifiable

Concrete Value Members

  1. final def clear(param: Param[_]): AutoTrainer.this.type
    Definition Classes
    Params
  2. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  3. def explainParams(): String
    Definition Classes
    Params
  4. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  5. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  6. val featuresCol: Param[String]

    The name of the features column

    The name of the features column

    Definition Classes
    HasFeaturesCol
  7. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[TrainedModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  8. def fit(dataset: Dataset[_], paramMap: ParamMap): TrainedModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  9. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): TrainedModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  10. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  11. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  12. def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  13. def getInputCols: Array[String]

    Definition Classes
    HasInputCols
  14. def getLabelCol: String

    Definition Classes
    HasLabelCol
  15. def getModel: Estimator[_ <: Model[_]]

  16. def getNumFeatures: Int

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

    The names of the inputColumns

    The names of the inputColumns

    Definition Classes
    HasInputCols
  23. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  24. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  25. val labelCol: Param[String]

    The name of the label column

    The name of the label column

    Definition Classes
    HasLabelCol
  26. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  27. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  28. val model: EstimatorParam

    Model to run.

    Model to run. See doc on derived classes.

  29. val numFeatures: IntParam

    Number of features to hash to

  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): AutoTrainer.this.type
    Definition Classes
    Params
  35. def setFeaturesCol(value: String): AutoTrainer.this.type

    Definition Classes
    HasFeaturesCol
  36. def setInputCols(value: Array[String]): AutoTrainer.this.type

    Definition Classes
    HasInputCols
  37. def setLabelCol(value: String): AutoTrainer.this.type

    Definition Classes
    HasLabelCol
  38. def setModel(value: Estimator[_ <: Model[_]]): AutoTrainer.this.type

  39. def setNumFeatures(value: Int): AutoTrainer.this.type

  40. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  41. def write: MLWriter
    Definition Classes
    ComplexParamsWritable → MLWritable