SPARK-3278 changes after PR feedback #3519. Binary search used for is…

…otonic regression model predictions
apache · Jan 23, 2015 · 9ae9d53 · 9ae9d53
1 parent fad4bf9
commit 9ae9d53
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Showing 3 changed files with 47 additions and 33 deletions.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
@@ -18,23 +18,25 @@
 package org.apache.spark.mllib.regression
 
 import java.io.Serializable
+import java.util.Arrays.binarySearch
 
 import org.apache.spark.api.java.{JavaDoubleRDD, JavaRDD}
 import org.apache.spark.rdd.RDD
 
 /**
  * Regression model for Isotonic regression
  *
- * @param predictions Weights computed for every feature.
- * @param isotonic isotonic (increasing) or antitonic (decreasing) sequence
+ * @param features Array of features.
+ * @param labels Array of labels associated to the features at the same index.
  */
 class IsotonicRegressionModel (
-    val predictions: Seq[(Double, Double, Double)],
-    val isotonic: Boolean)
+    features: Array[Double],
+    val labels: Array[Double])
   extends Serializable {
 
   /**
    * Predict labels for provided features
+   * Using a piecewise constant function
    *
    * @param testData features to be labeled
    * @return predicted labels
@@ -44,6 +46,7 @@ class IsotonicRegressionModel (
 
   /**
    * Predict labels for provided features
+   * Using a piecewise constant function
    *
    * @param testData features to be labeled
    * @return predicted labels
@@ -53,13 +56,25 @@ class IsotonicRegressionModel (
 
   /**
    * Predict a single label
+   * Using a piecewise constant function
    *
    * @param testData feature to be labeled
    * @return predicted label
    */
-  def predict(testData: Double): Double =
-    // Take the highest of data points smaller than our feature or data point with lowest feature
-    (predictions.head +: predictions.filter(y => y._2 <= testData)).last._1
+  def predict(testData: Double): Double = {
+    val result = binarySearch(features, testData)
+
+    val index =
+      if (result == -1) {
+        0
+      } else if (result < 0) {
+        -result - 2
+      } else {
+        result
+      }
+
+    labels(index)
+  }
 }
 
 /**
@@ -93,9 +108,13 @@ class IsotonicRegression
    * @return isotonic regression model
    */
   protected def createModel(
-      predictions: Seq[(Double, Double, Double)],
+      predictions: Array[(Double, Double, Double)],
       isotonic: Boolean): IsotonicRegressionModel = {
-    new IsotonicRegressionModel(predictions, isotonic)
+
+    val labels = predictions.map(_._1)
+    val features = predictions.map(_._2)
+
+    new IsotonicRegressionModel(features, labels)
   }
 
   /**
@@ -167,7 +186,7 @@ class IsotonicRegression
    */
   private def parallelPoolAdjacentViolators(
       testData: RDD[(Double, Double, Double)],
-      isotonic: Boolean): Seq[(Double, Double, Double)] = {
+      isotonic: Boolean): Array[(Double, Double, Double)] = {
 
     val parallelStepResult = testData
       .sortBy(_._2)
@@ -213,7 +232,7 @@ object IsotonicRegression {
       isotonic: Boolean): IsotonicRegressionModel = {
     new IsotonicRegression()
       .run(
-        input.rdd.map(x => (x._1.doubleValue(), x._2.doubleValue(), x._3.doubleValue())),
+        input.rdd.asInstanceOf[RDD[(Double, Double, Double)]],
         isotonic)
   }
 }
diff --git a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaIsotonicRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaIsotonicRegressionSuite.java
@@ -49,7 +49,7 @@ public void tearDown() {
   double difference(List<Tuple3<Double, Double, Double>> expected, IsotonicRegressionModel model) {
     double diff = 0;
 
-    for(int i = 0; i < model.predictions().length(); i++) {
+    for(int i = 0; i < model.labels().length; i++) {
       Tuple3<Double, Double, Double> exp = expected.get(i);
       diff += Math.abs(model.predict(exp._2()) - exp._1());
     }

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala
@@ -38,9 +38,8 @@ class IsotonicRegressionSuite
     val alg = new IsotonicRegression
     val model = alg.run(trainRDD, true)
 
-    model.predictions should be(
-      generateIsotonicInput(
-        1, 2, 7d/3, 7d/3, 7d/3, 6, 7, 8, 10, 10, 10, 12, 14, 15, 16.5, 16.5, 17, 18, 19, 20))
+    model.labels should be(
+      Array(1, 2, 7d/3, 7d/3, 7d/3, 6, 7, 8, 10, 10, 10, 12, 14, 15, 16.5, 16.5, 17, 18, 19, 20))
   }
 
   test("increasing isotonic regression using api") {
@@ -50,9 +49,8 @@ class IsotonicRegressionSuite
 
     val model = IsotonicRegression.train(trainRDD, true)
 
-    model.predictions should be(
-      generateIsotonicInput(
-        1, 2, 7d/3, 7d/3, 7d/3, 6, 7, 8, 10, 10, 10, 12, 14, 15, 16.5, 16.5, 17, 18, 19, 20))
+    model.labels should be(
+      Array(1, 2, 7d/3, 7d/3, 7d/3, 6, 7, 8, 10, 10, 10, 12, 14, 15, 16.5, 16.5, 17, 18, 19, 20))
   }
 
   test("isotonic regression with size 0") {
@@ -61,7 +59,7 @@ class IsotonicRegressionSuite
     val alg = new IsotonicRegression
     val model = alg.run(trainRDD, true)
 
-    model.predictions should be(List())
+    model.labels should be(Array())
   }
 
   test("isotonic regression with size 1") {
@@ -70,7 +68,7 @@ class IsotonicRegressionSuite
     val alg = new IsotonicRegression
     val model = alg.run(trainRDD, true)
 
-    model.predictions should be(generateIsotonicInput(1))
+    model.labels should be(Array(1.0))
   }
 
   test("isotonic regression strictly increasing sequence") {
@@ -79,7 +77,7 @@ class IsotonicRegressionSuite
     val alg = new IsotonicRegression
     val model = alg.run(trainRDD, true)
 
-    model.predictions should be(generateIsotonicInput(1, 2, 3, 4, 5))
+    model.labels should be(Array(1, 2, 3, 4, 5))
   }
 
   test("isotonic regression strictly decreasing sequence") {
@@ -88,7 +86,7 @@ class IsotonicRegressionSuite
     val alg = new IsotonicRegression
     val model = alg.run(trainRDD, true)
 
-    model.predictions should be(generateIsotonicInput(3, 3, 3, 3, 3))
+    model.labels should be(Array(3, 3, 3, 3, 3))
   }
 
   test("isotonic regression with last element violating monotonicity") {
@@ -97,7 +95,7 @@ class IsotonicRegressionSuite
     val alg = new IsotonicRegression
     val model = alg.run(trainRDD, true)
 
-    model.predictions should be(generateIsotonicInput(1, 2, 3, 3, 3))
+    model.labels should be(Array(1, 2, 3, 3, 3))
   }
 
   test("isotonic regression with first element violating monotonicity") {
@@ -106,7 +104,7 @@ class IsotonicRegressionSuite
     val alg = new IsotonicRegression
     val model = alg.run(trainRDD, true)
 
-    model.predictions should be(generateIsotonicInput(3, 3, 3, 4, 5))
+    model.labels should be(Array(3, 3, 3, 4, 5))
   }
 
   test("isotonic regression with negative labels") {
@@ -115,7 +113,7 @@ class IsotonicRegressionSuite
     val alg = new IsotonicRegression
     val model = alg.run(trainRDD, true)
 
-    model.predictions should be(generateIsotonicInput(-1.5, -1.5, 0, 0, 0))
+    model.labels should be(Array(-1.5, -1.5, 0, 0, 0))
   }
 
   test("isotonic regression with unordered input") {
@@ -124,7 +122,7 @@ class IsotonicRegressionSuite
     val alg = new IsotonicRegression
     val model = alg.run(trainRDD, true)
 
-    model.predictions should be(generateIsotonicInput(1, 2, 3, 4, 5))
+    model.labels should be(Array(1, 2, 3, 4, 5))
   }
 
   test("weighted isotonic regression") {
@@ -134,8 +132,7 @@ class IsotonicRegressionSuite
     val alg = new IsotonicRegression
     val model = alg.run(trainRDD, true)
 
-    model.predictions should be(
-      generateWeightedIsotonicInput(Seq(1, 2, 2.75, 2.75,2.75), Seq(1, 1, 1, 1, 2)))
+    model.labels should be(Array(1, 2, 2.75, 2.75,2.75))
   }
 
   test("weighted isotonic regression with weights lower than 1") {
@@ -145,8 +142,7 @@ class IsotonicRegressionSuite
     val alg = new IsotonicRegression
     val model = alg.run(trainRDD, true)
 
-    model.predictions.map(p => p.copy(_1 = round(p._1))) should be(
-      generateWeightedIsotonicInput(Seq(1, 2, 3.3/1.2, 3.3/1.2, 3.3/1.2), Seq(1, 1, 1, 0.1, 0.1)))
+    model.labels.map(round) should be(Array(1, 2, 3.3/1.2, 3.3/1.2, 3.3/1.2))
   }
 
   test("weighted isotonic regression with negative weights") {
@@ -155,8 +151,7 @@ class IsotonicRegressionSuite
     val alg = new IsotonicRegression
     val model = alg.run(trainRDD, true)
 
-    model.predictions should be(
-      generateWeightedIsotonicInput(Seq(1.0, 10.0/6, 10.0/6, 10.0/6, 10.0/6), Seq(-1, 1, -3, 1, -5)))
+    model.labels should be(Array(1.0, 10.0/6, 10.0/6, 10.0/6, 10.0/6))
   }
 
   test("weighted isotonic regression with zero weights") {
@@ -165,7 +160,7 @@ class IsotonicRegressionSuite
     val alg = new IsotonicRegression
     val model = alg.run(trainRDD, true)
 
-    model.predictions should be(generateWeightedIsotonicInput(Seq(1, 2, 2, 2, 2), Seq(0, 0, 0, 1, 0)))
+    model.labels should be(Array(1, 2, 2, 2, 2))
   }
 
   test("isotonic regression prediction") {