Class IsotonicRegression

Object
org.apache.spark.mllib.regression.IsotonicRegression
All Implemented Interfaces:
Serializable

public class IsotonicRegression extends Object implements Serializable
Isotonic regression. Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported.

Sequential PAV implementation based on: Grotzinger, S. J., and C. Witzgall. "Projections onto order simplexes." Applied mathematics and Optimization 12.1 (1984): 247-270.

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 here

See Also:
  • Constructor Details

    • IsotonicRegression

      public IsotonicRegression()
      Constructs IsotonicRegression instance with default parameter isotonic = true.
  • Method Details

    • run

      public IsotonicRegressionModel run(RDD<scala.Tuple3<Object,Object,Object>> input)
      Run IsotonicRegression algorithm to obtain isotonic regression model.

      Parameters:
      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 aggregated using the weighted average before the algorithm is executed.
      Returns:
      Isotonic regression model.
    • run

      public IsotonicRegressionModel run(JavaRDD<scala.Tuple3<Double,Double,Double>> input)
      Run pool adjacent violators algorithm to obtain isotonic regression model.

      Parameters:
      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 aggregated using the weighted average before the algorithm is executed.
      Returns:
      Isotonic regression model.
    • setIsotonic

      public IsotonicRegression setIsotonic(boolean isotonic)
      Sets the isotonic parameter.

      Parameters:
      isotonic - Isotonic (increasing) or antitonic (decreasing) sequence.
      Returns:
      This instance of IsotonicRegression.