org.apache.spark.ml.odkl

CombinedModel

object CombinedModel extends MLReadable[PipelineStage] with Serializable

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  1. CombinedModel
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Type Members

  1. trait DirectPredictionModel[I, M <: DirectPredictionModel[I, M]] extends PredictionModel[I, M] with HasDirectTransformOption

    Direct prediction model is an extension to a model with an ability to predict for single point instead of transforming dataset.

  2. class LinearCombinationModelLearner[M <: ModelWithSummary[M]] extends PerClassModelLearner[M, LinearCombinationModel[M]]

    Trains a model which predicts a scalar of weighted classes or a vector which is a generalized linear combination of per-class predictions

  3. class MultiClassModelLearner[M <: ModelWithSummary[M]] extends PerClassModelLearner[M, MultiClassCombinationModel[M]]

    Trains a model which predicts vector (one element per class)

  4. abstract class PerClassModelLearner[N <: ModelWithSummary[N], M <: MultiClassCombinationModelBase[N, M]] extends ForkedEstimator[N, String, M] with HasClassesCol with HasClassesWeights with HasLabelCol with HasPredictionCol

    Collects all classes in the dataset and trains a model for each class.

  5. class PerTypeModelLearner[M <: ModelWithSummary[M]] extends ForkedEstimator[M, String, SelectingModel[M]] with HasTypeCol with HasPredictionCol

    Collects types, available in the dataset and trains one model per each type.

  6. class Reader[M <: ModelWithSummary[M], C <: CombinedModel[M, C]] extends WithSummaryReader[C]

  7. class ReaderUntyped extends WithSummaryReaderUntyped

  8. class Writer[M <: ModelWithSummary[M], C <: CombinedModel[M, C]] extends WithSummaryWriter[C]

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 clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
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    Annotations
    @throws( ... )

  8. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  10. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
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    Annotations
    @throws( classOf[java.lang.Throwable] )

  11. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any

  12. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  13. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  14. def linearCombination[M <: ModelWithSummary[M]](estimator: SummarizableEstimator[M], classesColumn: String = "classes", classesToIgnore: Seq[String] = Seq(), classesMap: Map[String, String] = Map(), parallel: Boolean = false, cacheForks: Boolean = false): LinearCombinationModelLearner[M]

    Train models for all classes.

    Train models for all classes. Result is a linear combination model. Summary blocks for combined model are merged from the nested model with extra class column.

    M

    Type of the model to train.

    estimator

    Estimator for training the nested models.

    classesColumn

    Column where the classes are recorded.

    classesToIgnore

    Which classes not to include into result.

    classesMap

    Mapping for merging/renaming classes.

    parallel

    Whenever to train classes in parallel.

    cacheForks

    Whenever to cache forks before iterating

    returns

    Linear combination model (given an instance coputes prediction of all the models and combines them linary with configured weights).

  15. def linearCombinationReader[M <: ModelWithSummary[M]](nestedReader: WithSummaryReader[M]): WithSummaryReader[LinearCombinationModel[M]]

    returns

    Reader for linear combination combination model.

  16. def load(path: String): PipelineStage

    Definition Classes
    MLReadable
    Annotations
    @Since( "1.6.0" )

  17. def multiClass[M <: ModelWithSummary[M]](estimator: SummarizableEstimator[M], classesColumn: String = "classes", classesToIgnore: Seq[String] = Seq(), classesMap: Map[String, String] = Map(), parallel: Boolean = false, cacheForks: Boolean = false): MultiClassModelLearner[M]

    Train models for all classes.

    Train models for all classes. Result is a vector with prediction for each class. Summary blocks for combined model are merged from the nested model with extra class column.

    M

    Type of the model to train.

    estimator

    Estimator for training the nested models.

    classesColumn

    Column where the classes are recorded.

    classesToIgnore

    Which classes not to include into result.

    classesMap

    Mapping for merging/renaming classes.

    parallel

    Whenever to train classes in parallel.

    cacheForks

    Whenever to cache forks before iterating

    returns

    Linear combination model (given an instance coputes prediction of all the models and combines them linary with configured weights).

  18. def multiClassReader[M <: ModelWithSummary[M]](nestedReader: WithSummaryReader[M]): WithSummaryReader[LinearCombinationModel[M]]

    returns

    Reader for multi class combination model.

  19. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  20. final def notify(): Unit

    Definition Classes
    AnyRef

  21. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  22. def perType[M <: ModelWithSummary[M]](estimator: SummarizableEstimator[M], typeColumn: String = "type", parallel: Boolean = false, cacheForks: Boolean = false): PerTypeModelLearner[M]

    Train dedicated model for a certain type.

    Train dedicated model for a certain type. Result is a selector model. Summary blocks for combined model are merged from the nested model with extra type column.

    M

    Type of the model to train.

    estimator

    Estimator for training the nested models.

    typeColumn

    Column where the type label stored.

    parallel

    Whenever to train types in parallel.

    cacheForks

    Whenever to cache forks before iterating

    returns

    Selector model (given instance type applies ONE of the models)

  23. def perTypeReader[M <: ModelWithSummary[M]](nestedReader: WithSummaryReader[M]): WithSummaryReader[SelectingModel[M]]

    returns

    Reader for type selecting model.

  24. def read: MLReader[PipelineStage]

    Definition Classes
    CombinedModel → MLReadable
    Annotations
    @Since( "1.6.0" )

  25. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef

  26. def toString(): String

    Definition Classes
    AnyRef → Any

  27. final def wait(): Unit

    Definition Classes
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    @throws( ... )

  28. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
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    @throws( ... )

  29. final def wait(arg0: Long): Unit

    Definition Classes
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    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from MLReadable[PipelineStage]

Inherited from AnyRef

Inherited from Any

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