Class

org.apache.spark.mllib.odkl

IsotonicRegression

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class IsotonicRegression extends Serializable with Logging

Isotonic regression. Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported.

Sequential PAV implementation based on: Tibshirani, Ryan J., Holger Hoefling, and Robert Tibshirani. "Nearly-isotonic regression." Technometrics 53.1 (2011): 54-61. Available from http://www.stat.cmu.edu/~ryantibs/papers/neariso.pdf

Sequential PAV parallelization based on: Kearsley, Anthony J., Richard A. Tapia, and Michael W. Trosset. "An approach to parallelizing isotonic regression." Applied Mathematics and Parallel Computing. Physica-Verlag HD, 1996. 141-147. Available from http://softlib.rice.edu/pub/CRPC-TRs/reports/CRPC-TR96640.pdf

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@Since( "1.3.0" )
See also

Isotonic regression (Wikipedia) ODKL Patches:

  1. view before slice for better performance 2. Per partition sorting instead of global order for smother data distribution
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Instance Constructors

  1. new IsotonicRegression()

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    Constructs IsotonicRegression instance with default parameter isotonic = true.

    Constructs IsotonicRegression instance with default parameter isotonic = true.

    returns

    New instance of IsotonicRegression.

    Annotations
    @Since( "1.3.0" )

Value Members

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

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  12. def isTraceEnabled(): Boolean

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  13. def log: Logger

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  14. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  15. def logDebug(msg: ⇒ String): Unit

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  16. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  17. def logError(msg: ⇒ String): Unit

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  18. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  19. def logInfo(msg: ⇒ String): Unit

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  20. def logName: String

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  21. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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  22. def logTrace(msg: ⇒ String): Unit

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  23. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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  24. def logWarning(msg: ⇒ String): Unit

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

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  26. final def notify(): Unit

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  27. final def notifyAll(): Unit

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  28. def run(input: JavaRDD[(Double, Double, Double)]): IsotonicRegressionModel

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    Run pool adjacent violators algorithm to obtain isotonic regression model.

    Run pool adjacent violators algorithm to obtain isotonic regression model.

    input

    JavaRDD of tuples (label, feature, weight) where label is dependent variable for which we calculate isotonic regression, feature is independent variable and weight represents number of measures with default 1. If multiple labels share the same feature value then they are ordered before the algorithm is executed.

    returns

    Isotonic regression model.

    Annotations
    @Since( "1.3.0" )

  29. def run(input: RDD[(Double, Double, Double)]): IsotonicRegressionModel

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    Run IsotonicRegression algorithm to obtain isotonic regression model.

    Run IsotonicRegression algorithm to obtain isotonic regression model.

    input

    RDD of tuples (label, feature, weight) where label is dependent variable for which we calculate isotonic regression, feature is independent variable and weight represents number of measures with default 1. If multiple labels share the same feature value then they are ordered before the algorithm is executed.

    returns

    Isotonic regression model.

    Annotations
    @Since( "1.3.0" )

  30. def setIsotonic(isotonic: Boolean): IsotonicRegression.this.type

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    Sets the isotonic parameter.

    Sets the isotonic parameter.

    isotonic

    Isotonic (increasing) or antitonic (decreasing) sequence.

    returns

    This instance of IsotonicRegression.

    Annotations
    @Since( "1.3.0" )

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

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  32. def toString(): String

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  33. final def wait(): Unit

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

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