c

io.citrine.lolo.bags

MultiTaskBaggedModel

class MultiTaskBaggedModel extends MultiTaskModel

Container holding a parallel sequence of models, each of which predicts on multiple labels.

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  1. MultiTaskBaggedModel
  2. MultiTaskModel
  3. Model
  4. Serializable
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Visibility
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  2. Protected

Instance Constructors

  1. new MultiTaskBaggedModel(models: ParSeq[MultiTaskModel], Nib: Vector[Vector[Int]], useJackknife: Boolean, biasModels: Seq[Option[Model[PredictionResult[Double]]]], rescaleRatios: Seq[Double])

    models

    sequence of multi-models, one for each bag

    Nib

    matrix representing number of times each training datum appears in each bag

    useJackknife

    whether to enable jackknife uncertainty estimate

    biasModels

    sequence of optional bias-correction models, one for each label

    rescaleRatios

    sequence of uncertainty calibration ratios for each label

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##: Int
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
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  6. final def eq(arg0: AnyRef): Boolean
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  7. def equals(arg0: AnyRef): Boolean
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  8. def finalize(): Unit
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    @throws(classOf[java.lang.Throwable])
  9. final def getClass(): Class[_ <: AnyRef]
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    @native()
  10. def getModels: Seq[BaggedModel[Any]]

    Individual models corresponding to each label

    Individual models corresponding to each label

    Definition Classes
    MultiTaskBaggedModelMultiTaskModel
  11. def getRealLabels: Seq[Boolean]

    A boolean sequence indicating which labels are real-valued.

    A boolean sequence indicating which labels are real-valued. Its length must be equal to numLabels.

    Definition Classes
    MultiTaskBaggedModelMultiTaskModel
  12. lazy val groupedModels: Seq[BaggedModel[Any]]
  13. def hashCode(): Int
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    @native()
  14. final def isInstanceOf[T0]: Boolean
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  15. final def ne(arg0: AnyRef): Boolean
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  16. final def notify(): Unit
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    @native()
  17. final def notifyAll(): Unit
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    @native()
  18. val numLabels: Int

    The number of labels.

    The number of labels. Every prediction must have this length.

    Definition Classes
    MultiTaskBaggedModelMultiTaskModel
  19. def shapley(input: Vector[Any], omitFeatures: Set[Int] = Set()): Option[DenseMatrix[Double]]

    Compute Shapley feature attributions for a given input in this node's subtree

    Compute Shapley feature attributions for a given input in this node's subtree

    input

    for which to compute feature attributions.

    omitFeatures

    feature indices to omit in computing Shapley values

    returns

    matrix of attributions for each feature and output One row per feature, each of length equal to the output dimension. The output dimension is 1 for single-task regression, or equal to the number of classification categories.

    Definition Classes
    Model
  20. final def synchronized[T0](arg0: => T0): T0
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  21. def toString(): String
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  22. def transform(inputs: Seq[Vector[Any]]): MultiTaskBaggedResult

    Apply the model to a seq of inputs

    Apply the model to a seq of inputs

    inputs

    to apply the model to

    returns

    a PredictionResult which includes, at least, the expected outputs

    Definition Classes
    MultiTaskBaggedModelModel
  23. final def wait(): Unit
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    @throws(classOf[java.lang.InterruptedException])
  24. final def wait(arg0: Long, arg1: Int): Unit
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    @throws(classOf[java.lang.InterruptedException])
  25. final def wait(arg0: Long): Unit
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Inherited from MultiTaskModel

Inherited from Serializable

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