IsotonicRegressionModel (Spark 3.5.5 JavaDoc) (original) (raw)

• Object

• • org.apache.spark.mllib.regression.IsotonicRegressionModel

• All Implemented Interfaces:
  java.io.Serializable, Saveable

public class IsotonicRegressionModel
extends Object
implements java.io.Serializable, Saveable
Regression model for isotonic regression.
param: boundaries Array of boundaries for which predictions are known. Boundaries must be sorted in increasing order. param: predictions Array of predictions associated to the boundaries at the same index. Results of isotonic regression and therefore monotone. param: isotonic indicates whether this is isotonic or antitonic.
See Also:
Serialized Form

• • Constructor Summary

  Constructors

  Constructor and Description
  IsotonicRegressionModel(double[] boundaries, double[] predictions, boolean isotonic)
  IsotonicRegressionModel(Iterable boundaries, Iterable predictions, Boolean isotonic) A Java-friendly constructor that takes two Iterable parameters and one Boolean parameter.

  • Method Summary

  All Methods Static Methods Instance Methods Concrete Methods

  Modifier and Type	Method and Description
  double[]	boundaries()
  boolean	isotonic()
  static IsotonicRegressionModel	load(SparkContext sc, String path)
  double	predict(double testData) Predict a single label.
  JavaDoubleRDD	predict(JavaDoubleRDD testData) Predict labels for provided features.
  RDD	predict(RDD testData) Predict labels for provided features.
  double[]	predictions()
  void	save(SparkContext sc, String path) Save this model to the given path.

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

• • Constructor Detail

     * #### IsotonicRegressionModel  
     public IsotonicRegressionModel(double[] boundaries,  
                                    double[] predictions,  
                                    boolean isotonic)  
     * #### IsotonicRegressionModel  
     public IsotonicRegressionModel(Iterable<Object> boundaries,  
                                    Iterable<Object> predictions,  
                                    Boolean isotonic)  
     A Java-friendly constructor that takes two Iterable parameters and one Boolean parameter.  
     Parameters:  
     `boundaries` \- (undocumented)  
     `predictions` \- (undocumented)  
     `isotonic` \- (undocumented)  

  • Method Detail

    * #### load  
    public static [IsotonicRegressionModel](../../../../../org/apache/spark/mllib/regression/IsotonicRegressionModel.html "class in org.apache.spark.mllib.regression") load([SparkContext](../../../../../org/apache/spark/SparkContext.html "class in org.apache.spark") sc,  
                                               String path)  
    * #### boundaries  
    public double[] boundaries()  
    * #### predictions  
    public double[] predictions()  
    * #### isotonic  
    public boolean isotonic()  
    * #### predict  
    public [RDD](../../../../../org/apache/spark/rdd/RDD.html "class in org.apache.spark.rdd")<Object> predict([RDD](../../../../../org/apache/spark/rdd/RDD.html "class in org.apache.spark.rdd")<Object> testData)  
    Predict labels for provided features. Using a piecewise linear function.  
    Parameters:  
    `testData` \- Features to be labeled.  
    Returns:  
    Predicted labels.  
    * #### predict  
    public [JavaDoubleRDD](../../../../../org/apache/spark/api/java/JavaDoubleRDD.html "class in org.apache.spark.api.java") predict([JavaDoubleRDD](../../../../../org/apache/spark/api/java/JavaDoubleRDD.html "class in org.apache.spark.api.java") testData)  
    Predict labels for provided features. Using a piecewise linear function.  
    Parameters:  
    `testData` \- Features to be labeled.  
    Returns:  
    Predicted labels.  
    * #### predict  
    public double predict(double testData)  
    Predict a single label. Using a piecewise linear function.  
    Parameters:  
    `testData` \- Feature to be labeled.  
    Returns:  
    Predicted label. 1) If testData exactly matches a boundary then associated prediction is returned. In case there are multiple predictions with the same boundary then one of them is returned. Which one is undefined (same as java.util.Arrays.binarySearch). 2) If testData is lower or higher than all boundaries then first or last prediction is returned respectively. In case there are multiple predictions with the same boundary then the lowest or highest is returned respectively. 3) If testData falls between two values in boundary array then prediction is treated as piecewise linear function and interpolated value is returned. In case there are multiple values with the same boundary then the same rules as in 2) are used.  
    * #### save  
    public void save([SparkContext](../../../../../org/apache/spark/SparkContext.html "class in org.apache.spark") sc,  
                     String path)  
    Description copied from interface: `[Saveable](../../../../../org/apache/spark/mllib/util/Saveable.html#save-org.apache.spark.SparkContext-java.lang.String-)`  
    Save this model to the given path.  
     This saves: - human-readable (JSON) model metadata to path/metadata/ - Parquet formatted data to path/data/  
     The model may be loaded using `Loader.load`.  
    Specified by:  
    `[save](../../../../../org/apache/spark/mllib/util/Saveable.html#save-org.apache.spark.SparkContext-java.lang.String-)` in interface `[Saveable](../../../../../org/apache/spark/mllib/util/Saveable.html "interface in org.apache.spark.mllib.util")`  
    Parameters:  
    `sc` \- Spark context used to save model data.  
    `path` \- Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.