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SPARK-3278 PR 3519 refactoring WeightedLabeledPoint to tuple as per c…
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@zapletal-martin
zapletal-martin committed Dec 30, 2014
2 parents b8b1620 + 089bf86 commit cab5a46
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99 changes: 33 additions & 66 deletions mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -18,56 +18,17 @@
package org.apache.spark.mllib.regression

import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.mllib.regression.MonotonicityConstraint.MonotonicityConstraint._
import org.apache.spark.rdd.RDD

/**
* Monotonicity constrains for monotone regression
* Isotonic (increasing)
* Antitonic (decreasing)
*/
object MonotonicityConstraint {

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 = 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
* @param isotonic isotonic (increasing) or antitonic (decreasing) sequence
*/
class IsotonicRegressionModel(
val predictions: Seq[(Double, Double, Double)],
val monotonicityConstraint: MonotonicityConstraint)
val isotonic: Boolean)
extends RegressionModel {

override def predict(testData: RDD[Vector]): RDD[Double] =
Expand All @@ -91,23 +52,23 @@ trait IsotonicRegressionAlgorithm
*
* @param predictions labels estimated using isotonic regression algorithm.
* Used for predictions on new data points.
* @param monotonicityConstraint isotonic or antitonic
* @param isotonic isotonic (increasing) or antitonic (decreasing) sequence
* @return isotonic regression model
*/
protected def createModel(
predictions: Seq[(Double, Double, Double)],
monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel
isotonic: Boolean): IsotonicRegressionModel

/**
* Run algorithm to obtain isotonic regression model
*
* @param input data
* @param monotonicityConstraint ascending or descenting
* @param isotonic isotonic (increasing) or antitonic (decreasing) sequence
* @return isotonic regression model
*/
def run(
input: RDD[(Double, Double, Double)],
monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel
isotonic: Boolean): IsotonicRegressionModel
}

/**
Expand All @@ -118,16 +79,16 @@ class PoolAdjacentViolators private [mllib]

override def run(
input: RDD[(Double, Double, Double)],
monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel = {
isotonic: Boolean): IsotonicRegressionModel = {
createModel(
parallelPoolAdjacentViolators(input, monotonicityConstraint),
monotonicityConstraint)
parallelPoolAdjacentViolators(input, isotonic),
isotonic)
}

override protected def createModel(
predictions: Seq[(Double, Double, Double)],
monotonicityConstraint: MonotonicityConstraint): IsotonicRegressionModel = {
new IsotonicRegressionModel(predictions, monotonicityConstraint)
isotonic: Boolean): IsotonicRegressionModel = {
new IsotonicRegressionModel(predictions, isotonic)
}

/**
Expand All @@ -138,32 +99,38 @@ class PoolAdjacentViolators private [mllib]
* Method in situ mutates input array
*
* @param in input data
* @param monotonicityConstraint asc or desc
* @param isotonic asc or desc
* @return result
*/
private def poolAdjacentViolators(
in: Array[WeightedLabeledPoint],
monotonicityConstraint: MonotonicityConstraint): Array[WeightedLabeledPoint] = {
in: Array[(Double, Double, Double)],
isotonic: Boolean): Array[(Double, Double, Double)] = {

// Pools sub array within given bounds assigning weighted average value to all elements
def pool(in: Array[WeightedLabeledPoint], start: Int, end: Int): Unit = {
def pool(in: Array[(Double, Double, Double)], start: Int, end: Int): Unit = {
val poolSubArray = in.slice(start, end + 1)

val weightedSum = poolSubArray.map(lp => lp.label * lp.weight).sum
val weight = poolSubArray.map(_.weight).sum
val weightedSum = poolSubArray.map(lp => lp._1 * lp._3).sum
val weight = poolSubArray.map(_._3).sum

for(i <- start to end) {
in(i) = WeightedLabeledPoint(weightedSum / weight, in(i).features, in(i).weight)
in(i) = (weightedSum / weight, in(i)._2, in(i)._3)
}
}

var i = 0

val monotonicityConstrainter: (Double, Double) => Boolean = (x, y) => if(isotonic) {
x <= y
} else {
x >= y
}

while(i < in.length) {
var j = i

// Find monotonicity violating sequence, if any
while(j < in.length - 1 && !monotonicityConstraint.holds(in(j), in(j + 1))) {
while(j < in.length - 1 && !monotonicityConstrainter(in(j)._1, in(j + 1)._1)) {
j = j + 1
}

Expand All @@ -173,7 +140,7 @@ class PoolAdjacentViolators private [mllib]
} else {
// Otherwise pool the violating sequence
// And check if pooling caused monotonicity violation in previously processed points
while (i >= 0 && !monotonicityConstraint.holds(in(i), in(i + 1))) {
while (i >= 0 && !monotonicityConstrainter(in(i)._1, in(i + 1)._1)) {
pool(in, i, j)
i = i - 1
}
Expand All @@ -190,19 +157,19 @@ class PoolAdjacentViolators private [mllib]
* Calls Pool adjacent violators on each partition and then again on the result
*
* @param testData input
* @param monotonicityConstraint asc or desc
* @param isotonic isotonic (increasing) or antitonic (decreasing) sequence
* @return result
*/
private def parallelPoolAdjacentViolators(
testData: RDD[(Double, Double, Double)],
monotonicityConstraint: MonotonicityConstraint): Seq[(Double, Double, Double)] = {
isotonic: Boolean): Seq[(Double, Double, Double)] = {

poolAdjacentViolators(
testData
.sortBy(_._2)
.cache()
.mapPartitions(it => poolAdjacentViolators(it.toArray, monotonicityConstraint).toIterator)
.collect(), monotonicityConstraint)
.mapPartitions(it => poolAdjacentViolators(it.toArray, isotonic).toIterator)
.collect(), isotonic)
}
}

Expand All @@ -221,11 +188,11 @@ object IsotonicRegression {
* 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 Isotonic (increasing) or Antitonic (decreasing) sequence
* @param isotonic isotonic (increasing) or antitonic (decreasing) sequence
*/
def train(
input: RDD[(Double, Double, Double)],
monotonicityConstraint: MonotonicityConstraint = Isotonic): IsotonicRegressionModel = {
new PoolAdjacentViolators().run(input, monotonicityConstraint)
isotonic: Boolean = true): IsotonicRegressionModel = {
new PoolAdjacentViolators().run(input, isotonic)
}
}
20 changes: 9 additions & 11 deletions mllib/src/main/scala/org/apache/spark/mllib/util/IsotonicDataGenerator.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -17,10 +17,6 @@

package org.apache.spark.mllib.util

import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.regression.WeightedLabeledPointConversions._
import org.apache.spark.mllib.regression.{LabeledPoint, WeightedLabeledPoint}

import scala.collection.JavaConversions._

object IsotonicDataGenerator {
Expand All @@ -30,19 +26,21 @@ object IsotonicDataGenerator {
* @param labels list of labels for the data points
* @return Java List of input.
*/
def generateIsotonicInputAsList(labels: Array[Double]): java.util.List[WeightedLabeledPoint] = {
seqAsJavaList(generateIsotonicInput(wrapDoubleArray(labels):_*))
def generateIsotonicInputAsList(labels: Array[Double]): java.util.List[(java.lang.Double, java.lang.Double, java.lang.Double)] = {
seqAsJavaList(generateIsotonicInput(wrapDoubleArray(labels):_*)
.map(d => new Tuple3(new java.lang.Double(d._1), new java.lang.Double(d._2), new java.lang.Double(d._3))))
}

def bam(d: Option[Double]): Double = d.get

/**
* Return an ordered sequence of labeled data points with default weights
* @param labels list of labels for the data points
* @return sequence of data points
*/
def generateIsotonicInput(labels: Double*): Seq[WeightedLabeledPoint] = {
def generateIsotonicInput(labels: Double*): Seq[(Double, Double, Double)] = {
labels.zip(1 to labels.size)
.map(point => labeledPointToWeightedLabeledPoint(
LabeledPoint(point._1, Vectors.dense(point._2))))
.map(point => (point._1, point._2.toDouble, 1d))
}

/**
Expand All @@ -53,8 +51,8 @@ object IsotonicDataGenerator {
*/
def generateWeightedIsotonicInput(
labels: Seq[Double],
weights: Seq[Double]): Seq[WeightedLabeledPoint] = {
weights: Seq[Double]): Seq[(Double, Double, Double)] = {
labels.zip(1 to labels.size).zip(weights)
.map(point => WeightedLabeledPoint(point._1._1, Vectors.dense(point._1._2), point._2))
.map(point => (point._1._1, point._1._2.toDouble, point._2))
}
}
41 changes: 23 additions & 18 deletions mllib/src/test/java/org/apache/spark/mllib/regression/JavaIsotonicRegressionSuite.java
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Original file line number Diff line number Diff line change
Expand Up @@ -21,13 +21,16 @@
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.mllib.linalg.Vector;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.util.IsotonicDataGenerator;
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import scala.Tuple3;

import java.io.Serializable;
import java.util.Arrays;
import java.util.List;

public class JavaIsotonicRegressionSuite implements Serializable {
Expand All @@ -44,42 +47,44 @@ public void tearDown() {
sc = null;
}

double difference(List<WeightedLabeledPoint> expected, IsotonicRegressionModel model) {
double difference(List<Tuple3<Double, Double, Double>> expected, IsotonicRegressionModel model) {
double diff = 0;

for(int i = 0; i < model.predictions().length(); i++) {
WeightedLabeledPoint exp = expected.get(i);
diff += Math.abs(model.predict(exp.features()) - exp.label());
Tuple3<Double, Double, Double> exp = expected.get(i);
diff += Math.abs(model.predict(Vectors.dense(exp._2())) - exp._1());
}

return diff;
}

@Test
/*@Test
public void runIsotonicRegressionUsingConstructor() {
JavaRDD<WeightedLabeledPoint> testRDD = sc.parallelize(IsotonicDataGenerator
JavaRDD<Tuple3<Double, Double, Double>> testRDD = sc.parallelize(IsotonicDataGenerator
.generateIsotonicInputAsList(
new double[] {1, 2, 3, 3, 1, 6, 7, 8, 11, 9, 10, 12})).cache();
new double[]{1, 2, 3, 3, 1, 6, 7, 8, 11, 9, 10, 12})).cache();
IsotonicRegressionAlgorithm isotonicRegressionAlgorithm = new PoolAdjacentViolators();
IsotonicRegressionModel model = isotonicRegressionAlgorithm.run(testRDD.rdd(), MonotonicityConstraint.Isotonic());
IsotonicRegressionModel model = isotonicRegressionAlgorithm.run(testRDD.rdd(), true);
List<WeightedLabeledPoint> expected = IsotonicDataGenerator
List<Tuple3<Double, Double, Double>> expected = IsotonicDataGenerator
.generateIsotonicInputAsList(
new double[] {1, 2, 7d/3, 7d/3, 7d/3, 6, 7, 8, 10, 10, 10, 12});
Assert.assertTrue(difference(expected, model) == 0);
}
}*/

@Test
public void runIsotonicRegressionUsingStaticMethod() {
JavaRDD<WeightedLabeledPoint> testRDD = sc.parallelize(IsotonicDataGenerator
/*JavaRDD<Tuple3<Double, Double, Double>> testRDD = sc.parallelize(IsotonicDataGenerator
.generateIsotonicInputAsList(
new double[] {1, 2, 3, 3, 1, 6, 7, 8, 11, 9, 10, 12})).cache();
new double[] {1, 2, 3, 3, 1, 6, 7, 8, 11, 9, 10, 12})).cache();*/

JavaRDD<Tuple3<Double, Double, Double>> testRDD = sc.parallelize(Arrays.asList(new Tuple3(1.0, 1.0, 1.0)));

IsotonicRegressionModel model = IsotonicRegression.train(testRDD.rdd(), MonotonicityConstraint.Isotonic());
IsotonicRegressionModel model = IsotonicRegression.train(testRDD.rdd(), true);

List<WeightedLabeledPoint> expected = IsotonicDataGenerator
List<Tuple3<Double, Double, Double>> expected = IsotonicDataGenerator
.generateIsotonicInputAsList(
new double[] {1, 2, 7d/3, 7d/3, 7d/3, 6, 7, 8, 10, 10, 10, 12});

Expand All @@ -88,16 +93,16 @@ public void runIsotonicRegressionUsingStaticMethod() {

@Test
public void testPredictJavaRDD() {
JavaRDD<WeightedLabeledPoint> testRDD = sc.parallelize(IsotonicDataGenerator
JavaRDD<Tuple3<Double, Double, Double>> testRDD = sc.parallelize(IsotonicDataGenerator
.generateIsotonicInputAsList(
new double[] {1, 2, 3, 3, 1, 6, 7, 8, 11, 9, 10, 12})).cache();

IsotonicRegressionModel model = IsotonicRegression.train(testRDD.rdd(), MonotonicityConstraint.Isotonic());
IsotonicRegressionModel model = IsotonicRegression.train(testRDD.rdd(), true);

JavaRDD<Vector> vectors = testRDD.map(new Function<WeightedLabeledPoint, Vector>() {
JavaRDD<Vector> vectors = testRDD.map(new Function<Tuple3<Double, Double, Double>, Vector>() {
@Override
public Vector call(WeightedLabeledPoint v) throws Exception {
return v.features();
public Vector call(Tuple3<Double, Double, Double> v) throws Exception {
return Vectors.dense(v._2());
}
});

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