com.alpine.model.pack.multiple

PipelineRegressionModel

case class PipelineRegressionModel(preProcessors: Seq[RowModel], finalModel: RegressionRowModel, identifier: String = "") extends RegressionRowModel with PFAConvertible with Product with Serializable

Used for combining a Regression model (e.g. Linear Regression) with preprocessors (e.g. One Hot Encoding).

Annotations
@SerialVersionUID( 381487725247733182L )
Linear Supertypes
Product, Equals, PFAConvertible, RegressionRowModel, RowModel, MLModel, Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. PipelineRegressionModel
  2. Product
  3. Equals
  4. PFAConvertible
  5. RegressionRowModel
  6. RowModel
  7. MLModel
  8. Serializable
  9. Serializable
  10. AnyRef
  11. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Instance Constructors

  1. new PipelineRegressionModel(preProcessors: Seq[RowModel], finalModel: RegressionRowModel, identifier: String = "")

Value Members

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

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
    AnyRef → Any
  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any
  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  7. def classesForLoading: Set[Class[_]]

    This should return a representative class from every jar that is needed to load the object during deserialization.

    This should return a representative class from every jar that is needed to load the object during deserialization.

    Default implementation returns the class of MLModel, and the class of the model implementation.

    Definition Classes
    PipelineRegressionModelMLModel
  8. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. def dependentFeature: ColumnDef

    Used when we are doing model quality evaluation e.

    Used when we are doing model quality evaluation e.g. Confusion Matrix, so we know what feature in the test dataset to compare the result to.

    returns

    Feature description used to identify the dependent feature in an evaluation dataset.

    Definition Classes
    PipelineRegressionModelRegressionRowModel
  10. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  11. val finalModel: RegressionRowModel

  12. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  13. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  14. def getPFAConverter: PFAConverter

  15. val identifier: String

    Used to identify this model when in a collection of models.

    Used to identify this model when in a collection of models. Should be simple characters, so it can be used in a feature name.

    returns

    identifier for the model.

    Definition Classes
    PipelineRegressionModelRowModel
  16. lazy val inputFeatures: Seq[ColumnDef]

    Definition Classes
    PipelineRegressionModelRowModel
  17. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  18. final def ne(arg0: AnyRef): Boolean

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

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

    Definition Classes
    AnyRef
  21. def outputFeatures: Seq[ColumnDef]

  22. val preProcessors: Seq[RowModel]

  23. lazy val sqlOutputFeatures: Seq[ColumnDef]

    Definition Classes
    PipelineRegressionModelRowModel
  24. def sqlTransformer(sqlGenerator: SQLGenerator): Option[RegressionSQLTransformer]

  25. def streamline(requiredOutputFeatureNames: Seq[String]): PipelineRegressionModel

    Returns a streamlined version of this model, throwing away parts which are not required to produce the features with names in requiredOutputFeatureNames.

    Returns a streamlined version of this model, throwing away parts which are not required to produce the features with names in requiredOutputFeatureNames.

    Behaviour is undefined if requiredOutputFeatureNames is not actually a subset of outputFeatures.map(_columnName).

    The resulting model may still have more output features than those required (i.e. for ClassificationRowModel implementations which always have the same output features, or for OneHotEncodingModel whose output features naturally group together).

    requiredOutputFeatureNames

    names of the output features that the new model should produce.

    returns

    A model which has the same functionality for those output features listed, but potentially fewer input features.

    Definition Classes
    PipelineRegressionModelRegressionRowModelRowModel
  26. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  27. def transformationSchema: DetailedTransformationSchema

    Definition Classes
    RowModel
  28. def transformer: PipelineRegressionTransformer[RealResult]

  29. final def wait(): Unit

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  31. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Product

Inherited from Equals

Inherited from PFAConvertible

Inherited from RegressionRowModel

Inherited from RowModel

Inherited from MLModel

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped