IsotonicRegression (Spark 3.5.5 JavaDoc) (original) (raw)

• Object

• • org.apache.spark.mllib.regression.IsotonicRegression

• All Implemented Interfaces:
  java.io.Serializable

public class IsotonicRegression
extends Object
implements java.io.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:
Isotonic regression (Wikipedia), Serialized Form

• • Constructor Summary

  Constructors

  Constructor and Description
  IsotonicRegression() Constructs IsotonicRegression instance with default parameter isotonic = true.

  • Method Summary

  All Methods Instance Methods Concrete Methods

  Modifier and Type	Method and Description
  IsotonicRegressionModel	run(JavaRDD<scala.Tuple3<Double,Double,Double>> input) Run pool adjacent violators algorithm to obtain isotonic regression model.
  IsotonicRegressionModel	run(RDD<scala.Tuple3<Object,Object,Object>> input) Run IsotonicRegression algorithm to obtain isotonic regression model.
  IsotonicRegression	setIsotonic(boolean isotonic) Sets the isotonic parameter.

     * ### Methods inherited from class Object  
     `equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

• • Constructor Detail

     * #### IsotonicRegression  
     public IsotonicRegression()  
     Constructs IsotonicRegression instance with default parameter isotonic = true.  

  • Method Detail

    * #### run  
    public [IsotonicRegressionModel](../../../../../org/apache/spark/mllib/regression/IsotonicRegressionModel.html "class in org.apache.spark.mllib.regression") run([RDD](../../../../../org/apache/spark/rdd/RDD.html "class in org.apache.spark.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](../../../../../org/apache/spark/mllib/regression/IsotonicRegressionModel.html "class in org.apache.spark.mllib.regression") run([JavaRDD](../../../../../org/apache/spark/api/java/JavaRDD.html "class in org.apache.spark.api.java")<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](../../../../../org/apache/spark/mllib/regression/IsotonicRegression.html "class in org.apache.spark.mllib.regression") setIsotonic(boolean isotonic)  
    Sets the isotonic parameter.  
    Parameters:  
    `isotonic` \- Isotonic (increasing) or antitonic (decreasing) sequence.  
    Returns:  
    This instance of IsotonicRegression.