SPARK-3278 added comments and cleaned up api to consistently handle w…

…eights

mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala

@@ -17,39 +17,51 @@
 
 package org.apache.spark.mllib.regression
 
-import org.apache.spark.mllib.linalg.{Vectors, Vector}
-import org.apache.spark.mllib.regression.MonotonicityConstraint.Enum.MonotonicityConstraint
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.regression.MonotonicityConstraint.MonotonicityConstraint.{Isotonic, MonotonicityConstraint}
 import org.apache.spark.rdd.RDD
 
+/**
+ * Monotonicity constrains for monotone regression
+ * Isotonic (increasing)
+ * Antitonic (decreasing)
+ */
 object MonotonicityConstraint {
 
-  object Enum {
+  object MonotonicityConstraint {
 
     sealed trait MonotonicityConstraint {
       private[regression] def holds(current: WeightedLabeledPoint, next: WeightedLabeledPoint): Boolean
     }
 
+    /**
+     * Isotonic monotonicity constraint. Increasing sequence
+     */
     case object Isotonic extends MonotonicityConstraint {
       override def holds(current: WeightedLabeledPoint, next: WeightedLabeledPoint): Boolean = {
         current.label <= next.label
       }
     }
 
+    /**
+     * Antitonic monotonicity constrain. Decreasing sequence
+     */
     case object Antitonic extends MonotonicityConstraint {
       override def holds(current: WeightedLabeledPoint, next: WeightedLabeledPoint): Boolean = {
         current.label >= next.label
       }
     }
   }
 
-  val Isotonic = Enum.Isotonic
-  val Antitonic = Enum.Antitonic
+  val Isotonic = MonotonicityConstraint.Isotonic
+  val Antitonic = MonotonicityConstraint.Antitonic
 }
 
 /**
  * Regression model for Isotonic regression
  *
  * @param predictions Weights computed for every feature.
+ * @param monotonicityConstraint specifies if the sequence is increasing or decreasing
  */
 class IsotonicRegressionModel(
     val predictions: Seq[WeightedLabeledPoint],
@@ -59,10 +71,11 @@ class IsotonicRegressionModel(
   override def predict(testData: RDD[Vector]): RDD[Double] =
     testData.map(predict)
 
-  //take the highest of elements smaller than our feature or weight with lowest feature
-  override def predict(testData: Vector): Double =
+  override def predict(testData: Vector): Double = {
+    //take the highest of data points smaller than our feature or data point with lowest feature
     (predictions.head +:
       predictions.filter(y => y.features.toArray.head <= testData.toArray.head)).last.label
+  }
 }
 
 /**
@@ -71,49 +84,40 @@ class IsotonicRegressionModel(
 trait IsotonicRegressionAlgorithm
   extends Serializable {
 
+  /**
+   * Creates isotonic regression model with given parameters
+   *
+   * @param predictions labels estimated using isotonic regression algorithm. Used for predictions on new data points.
+   * @param monotonicityConstraint isotonic or antitonic
+   * @return isotonic regression model
+   */
   protected def createModel(
-      weights: Seq[WeightedLabeledPoint],
+      predictions: Seq[WeightedLabeledPoint],
       monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel
 
   /**
    * Run algorithm to obtain isotonic regression model
+   *
    * @param input data
    * @param monotonicityConstraint ascending or descenting
-   * @return model
+   * @return isotonic regression model
    */
   def run(
       input: RDD[WeightedLabeledPoint],
       monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel
-
-  /**
-   * Run algorithm to obtain isotonic regression model
-   * @param input data
-   * @param monotonicityConstraint asc or desc
-   * @param weights weights
-   * @return
-   */
-  def run(
-      input: RDD[WeightedLabeledPoint],
-      monotonicityConstraint: MonotonicityConstraint,
-      weights: Vector): IsotonicRegressionModel
 }
 
-class PoolAdjacentViolators extends IsotonicRegressionAlgorithm {
+/**
+ * Parallel pool adjacent violators algorithm for monotone regression
+ */
+class PoolAdjacentViolators private [mllib]
+  extends IsotonicRegressionAlgorithm {
 
   override def run(
       input: RDD[WeightedLabeledPoint],
       monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel = {
     createModel(
-      parallelPoolAdjacentViolators(input, monotonicityConstraint, Vectors.dense(Array(0d))),
-      monotonicityConstraint)
-  }
-
-  override def run(
-      input: RDD[WeightedLabeledPoint],
-      monotonicityConstraint: MonotonicityConstraint,
-      weights: Vector): IsotonicRegressionModel = {
-    createModel(
-      parallelPoolAdjacentViolators(input, monotonicityConstraint, weights),
+      parallelPoolAdjacentViolators(input, monotonicityConstraint),
       monotonicityConstraint)
   }
 
@@ -180,16 +184,15 @@ class PoolAdjacentViolators extends IsotonicRegressionAlgorithm {
 
   /**
    * Performs parallel pool adjacent violators algorithm
-   * Calls PAVA on each partition and then again on the result
+   * Calls Pool adjacent violators on each partition and then again on the result
    *
    * @param testData input
    * @param monotonicityConstraint asc or desc
    * @return result
    */
   private def parallelPoolAdjacentViolators(
       testData: RDD[WeightedLabeledPoint],
-      monotonicityConstraint: MonotonicityConstraint,
-      weights: Vector): Seq[WeightedLabeledPoint] = {
+      monotonicityConstraint: MonotonicityConstraint): Seq[WeightedLabeledPoint] = {
 
     poolAdjacentViolators(
       testData
@@ -201,39 +204,24 @@ class PoolAdjacentViolators extends IsotonicRegressionAlgorithm {
 }
 
 /**
- * Top-level methods for calling IsotonicRegression.
+ * Top-level methods for monotone regression (either isotonic or antitonic).
  */
 object IsotonicRegression {
 
   /**
-   * Train a Linear Regression model given an RDD of (label, features) pairs. We run a fixed number
-   * of iterations of gradient descent using the specified step size. Each iteration uses
-   * `miniBatchFraction` fraction of the data to calculate a stochastic gradient. The weights used
-   * in gradient descent are initialized using the initial weights provided.
+   * Train a monotone regression model given an RDD of (label, features, weight).
+   * Currently only one dimensional algorithm is supported (features.length is one)
+   * Label is the dependent y value
+   * Weight of the data point is the number of measurements. Default is 1
    *
-   * @param input RDD of (label, array of features) pairs. Each pair describes a row of the data
-   *              matrix A as well as the corresponding right hand side label y
-   * @param weights Initial set of weights to be used. Array should be equal in size to
-   *        the number of features in the data.
+   * @param input RDD of (label, array of features, weight). Each point describes a row of the data
+   *   matrix A as well as the corresponding right hand side label y
+   *   and weight as number of measurements
+   * @param monotonicityConstraint
    */
   def train(
       input: RDD[WeightedLabeledPoint],
-      monotonicityConstraint: MonotonicityConstraint,
-      weights: Vector): IsotonicRegressionModel = {
-    new PoolAdjacentViolators().run(input, monotonicityConstraint, weights)
-  }
-
-  /**
-   * Train a LinearRegression model given an RDD of (label, features) pairs. We run a fixed number
-   * of iterations of gradient descent using the specified step size. Each iteration uses
-   * `miniBatchFraction` fraction of the data to calculate a stochastic gradient.
-   *
-   * @param input RDD of (label, array of features) pairs. Each pair describes a row of the data
-   *              matrix A as well as the corresponding right hand side label y
-   */
-  def train(
-      input: RDD[WeightedLabeledPoint],
-      monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel = {
+      monotonicityConstraint: MonotonicityConstraint = Isotonic): IsotonicRegressionModel = {
     new PoolAdjacentViolators().run(input, monotonicityConstraint)
   }
 }

mllib/src/main/scala/org/apache/spark/mllib/regression/WeightedLabeledPoint.scala

@@ -25,17 +25,21 @@ import scala.beans.BeanInfo
 object WeightedLabeledPointConversions {
   implicit def labeledPointToWeightedLabeledPoint(
       labeledPoint: LabeledPoint): WeightedLabeledPoint = {
-    WeightedLabeledPoint(labeledPoint.label, labeledPoint.features, 1)
+    WeightedLabeledPoint(labeledPoint.label, labeledPoint.features)
   }
 
   implicit def labeledPointRDDToWeightedLabeledPointRDD(
       rdd: RDD[LabeledPoint]): RDD[WeightedLabeledPoint] = {
-    rdd.map(lp => WeightedLabeledPoint(lp.label, lp.features, 1))
+    rdd.map(lp => WeightedLabeledPoint(lp.label, lp.features))
   }
 }
 
 /**
- * Labeled point with weight
+ * Class that represents the features and labels of a data point with associated weight
+ *
+ * @param label Label for this data point.
+ * @param features List of features for this data point.
+ * @param weight Weight of the data point. Defaults to 1.
  */
 @BeanInfo
-case class WeightedLabeledPoint(label: Double, features: Vector, weight: Double)
+case class WeightedLabeledPoint(label: Double, features: Vector, weight: Double = 1)

mllib/src/test/java/org/apache/spark/mllib/regression/JavaIsotonicRegressionSuite.java

@@ -28,7 +28,6 @@
 import org.junit.Test;
 
 import java.io.Serializable;
-import java.util.Arrays;
 import java.util.List;
 
 public class JavaIsotonicRegressionSuite implements Serializable {

mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala

@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.regression
 
 import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.regression.MonotonicityConstraint.Enum.{Antitonic, Isotonic}
+import org.apache.spark.mllib.regression.MonotonicityConstraint.MonotonicityConstraint.{Antitonic, Isotonic}
 import org.apache.spark.mllib.util.{LocalClusterSparkContext, MLlibTestSparkContext}
 import org.scalatest.{Matchers, FunSuite}
 import WeightedLabeledPointConversions._