keystoneml.nodes.learning

BlockWeightedLeastSquaresEstimator

class BlockWeightedLeastSquaresEstimator extends LabelEstimator[DenseVector[Double], DenseVector[Double], DenseVector[Double]] with WeightedNode

Train a weighted block-coordinate descent model using least squares

Linear Supertypes
WeightedNode, LabelEstimator[DenseVector[Double], DenseVector[Double], DenseVector[Double]], EstimatorOperator, Serializable, Serializable, Operator, AnyRef, Any
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  1. BlockWeightedLeastSquaresEstimator
  2. WeightedNode
  3. LabelEstimator
  4. EstimatorOperator
  5. Serializable
  6. Serializable
  7. Operator
  8. AnyRef
  9. Any
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Instance Constructors

  1. new BlockWeightedLeastSquaresEstimator(blockSize: Int, numIter: Int, lambda: Double, mixtureWeight: Double, numFeaturesOpt: Option[Int] = scala.None)

    blockSize

    size of blocks to use

    numIter

    number of passes of co-ordinate descent to run

    lambda

    regularization parameter

    mixtureWeight

    how much should positive samples be weighted

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
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef

  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  10. def execute(deps: Seq[Expression]): TransformerExpression

    Definition Classes
    EstimatorOperator → Operator

  11. def finalize(): Unit

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

  12. def fit(trainingFeatures: RDD[DenseVector[Double]], trainingLabels: RDD[DenseVector[Double]]): BlockLinearMapper

    Split features into appropriate blocks and fit a weighted least squares model.

    Split features into appropriate blocks and fit a weighted least squares model.

    NOTE: This function makes multiple passes over the training data. Caching

    trainingFeatures

    training data RDD

    trainingLabels

    training labels RDD

    returns

    A new transformer@returns A BlockLinearMapper that contains the model, intercept

    Definition Classes
    BlockWeightedLeastSquaresEstimator → LabelEstimator

  13. def fit(trainingFeatures: Seq[RDD[DenseVector[Double]]], trainingLabels: RDD[DenseVector[Double]]): BlockLinearMapper

    Fit a weighted least squares model using blocks of features provided.

    Fit a weighted least squares model using blocks of features provided.

    NOTE: This function makes multiple passes over the training data. Caching

    trainingFeatures

    Blocks of training data RDDs

    trainingLabels

    training labels RDD

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

    Definition Classes
    AnyRef → Any

  15. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  16. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  17. def label: String

    Definition Classes
    Operator

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

    Definition Classes
    AnyRef

  19. final def notify(): Unit

    Definition Classes
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  20. final def notifyAll(): Unit

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

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  22. def toString(): String

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  23. final def wait(): Unit

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    @throws( ... )

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

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

    Definition Classes
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  26. val weight: Int

    Definition Classes
    BlockWeightedLeastSquaresEstimator → WeightedNode

  27. final def withData(data: PipelineDataset[DenseVector[Double]], labels: PipelineDataset[DenseVector[Double]]): Pipeline[DenseVector[Double], DenseVector[Double]]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes
    LabelEstimator

  28. final def withData(data: RDD[DenseVector[Double]], labels: RDD[DenseVector[Double]]): Pipeline[DenseVector[Double], DenseVector[Double]]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes
    LabelEstimator

  29. final def withData(data: PipelineDataset[DenseVector[Double]], labels: RDD[DenseVector[Double]]): Pipeline[DenseVector[Double], DenseVector[Double]]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes
    LabelEstimator

  30. final def withData(data: RDD[DenseVector[Double]], labels: PipelineDataset[DenseVector[Double]]): Pipeline[DenseVector[Double], DenseVector[Double]]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes
    LabelEstimator

Inherited from WeightedNode

Inherited from LabelEstimator[DenseVector[Double], DenseVector[Double], DenseVector[Double]]

Inherited from EstimatorOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

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