SPARK-1462: Examples of ML algorithms are using deprecated APIs

apache · Apr 16, 2014 · 6c7e543 · 6c7e543
1 parent df36091
commit 6c7e543
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Showing 7 changed files with 52 additions and 45 deletions.
diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalFileLR.scala b/examples/src/main/scala/org/apache/spark/examples/LocalFileLR.scala
@@ -18,17 +18,17 @@
 package org.apache.spark.examples
 
 import java.util.Random
-import org.apache.spark.util.Vector
+import breeze.linalg.{Vector, DenseVector}
 
 object LocalFileLR {
   val D = 10   // Numer of dimensions
   val rand = new Random(42)
 
-  case class DataPoint(x: Vector, y: Double)
+  case class DataPoint(x: Vector[Double], y: Double)
 
   def parsePoint(line: String): DataPoint = {
     val nums = line.split(' ').map(_.toDouble)
-    DataPoint(new Vector(nums.slice(1, D + 1)), nums(0))
+    DataPoint(new DenseVector(nums.slice(1, D + 1)), nums(0))
   }
 
   def main(args: Array[String]) {
@@ -37,15 +37,15 @@ object LocalFileLR {
     val ITERATIONS = args(1).toInt
 
     // Initialize w to a random value
-    var w = Vector(D, _ => 2 * rand.nextDouble - 1)
+    var w = DenseVector.fill(D){2 * rand.nextDouble - 1}
     println("Initial w: " + w)
 
     for (i <- 1 to ITERATIONS) {
       println("On iteration " + i)
-      var gradient = Vector.zeros(D)
+      var gradient = DenseVector.zeros[Double](D)
       for (p <- points) {
-        val scale = (1 / (1 + math.exp(-p.y * (w dot p.x))) - 1) * p.y
-        gradient +=  scale * p.x
+        val scale = (1 / (1 + math.exp(-p.y * (w.dot(p.x)))) - 1) * p.y
+        gradient += p.x * scale
       }
       w -= gradient
     }

diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala
@@ -18,11 +18,13 @@
 package org.apache.spark.examples
 
 import java.util.Random
-import org.apache.spark.util.Vector
 import org.apache.spark.SparkContext._
 import scala.collection.mutable.HashMap
 import scala.collection.mutable.HashSet
 
+import breeze.linalg.{Vector, DenseVector}
+import breeze.linalg.squaredDistance
+
 /**
  * K-means clustering.
  */
@@ -36,19 +38,19 @@ object LocalKMeans {
 
   def generateData = {
     def generatePoint(i: Int) = {
-      Vector(D, _ => rand.nextDouble * R)
+      DenseVector.fill(D){rand.nextDouble * R}
     }
     Array.tabulate(N)(generatePoint)
   }
 
-  def closestPoint(p: Vector, centers: HashMap[Int, Vector]): Int = {
+  def closestPoint(p: Vector[Double], centers: HashMap[Int, Vector[Double]]): Int = {
     var index = 0
     var bestIndex = 0
     var closest = Double.PositiveInfinity
 
     for (i <- 1 to centers.size) {
       val vCurr = centers.get(i).get
-      val tempDist = p.squaredDist(vCurr)
+      val tempDist = squaredDistance(p, vCurr)
       if (tempDist < closest) {
         closest = tempDist
         bestIndex = i
@@ -60,8 +62,8 @@ object LocalKMeans {
 
   def main(args: Array[String]) {
     val data = generateData
-    var points = new HashSet[Vector]
-    var kPoints = new HashMap[Int, Vector]
+    var points = new HashSet[Vector[Double]]
+    var kPoints = new HashMap[Int, Vector[Double]]
     var tempDist = 1.0
 
     while (points.size < K) {
@@ -81,16 +83,16 @@ object LocalKMeans {
       var mappings = closest.groupBy[Int] (x => x._1)
 
       var pointStats = mappings.map { pair =>
-        pair._2.reduceLeft [(Int, (Vector, Int))] {
+        pair._2.reduceLeft [(Int, (Vector[Double], Int))] {
           case ((id1, (x1, y1)), (id2, (x2, y2))) => (id1, (x1 + x2, y1 + y2))
         }
       }
 
-      var newPoints = pointStats.map {mapping => (mapping._1, mapping._2._1/mapping._2._2)}
+      var newPoints = pointStats.map {mapping => (mapping._1, mapping._2._1 * (1.0 / mapping._2._2))}
 
       tempDist = 0.0
       for (mapping <- newPoints) {
-        tempDist += kPoints.get(mapping._1).get.squaredDist(mapping._2)
+        tempDist += squaredDistance(kPoints.get(mapping._1).get, mapping._2)
       }
 
       for (newP <- newPoints) {

diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala b/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.examples
 
 import java.util.Random
-import org.apache.spark.util.Vector
+import breeze.linalg.{Vector, DenseVector}
 
 /**
  * Logistic regression based classification.
@@ -30,12 +30,12 @@ object LocalLR {
   val ITERATIONS = 5
   val rand = new Random(42)
 
-  case class DataPoint(x: Vector, y: Double)
+  case class DataPoint(x: Vector[Double], y: Double)
 
   def generateData = {
     def generatePoint(i: Int) = {
       val y = if(i % 2 == 0) -1 else 1
-      val x = Vector(D, _ => rand.nextGaussian + y * R)
+      val x = DenseVector.fill(D){rand.nextGaussian + y * R}
       DataPoint(x, y)
     }
     Array.tabulate(N)(generatePoint)
@@ -45,15 +45,15 @@ object LocalLR {
     val data = generateData
 
     // Initialize w to a random value
-    var w = Vector(D, _ => 2 * rand.nextDouble - 1)
+    var w = DenseVector.fill(D){2 * rand.nextDouble - 1}
     println("Initial w: " + w)
 
     for (i <- 1 to ITERATIONS) {
       println("On iteration " + i)
-      var gradient = Vector.zeros(D)
+      var gradient = DenseVector.zeros[Double](D)
       for (p <- data) {
-        val scale = (1 / (1 + math.exp(-p.y * (w dot p.x))) - 1) * p.y
-        gradient +=  scale * p.x
+        val scale = (1 / (1 + math.exp(-p.y * (w.dot(p.x)))) - 1) * p.y
+        gradient +=  p.x * scale
       }
       w -= gradient
     }

diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
@@ -19,19 +19,20 @@ package org.apache.spark.examples
 
 import java.util.Random
 import scala.math.exp
-import org.apache.spark.util.Vector
 import org.apache.spark._
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.scheduler.InputFormatInfo
 
+import breeze.linalg.{Vector, DenseVector}
+
 /**
  * Logistic regression based classification.
  */
 object SparkHdfsLR {
   val D = 10   // Numer of dimensions
   val rand = new Random(42)
 
-  case class DataPoint(x: Vector, y: Double)
+  case class DataPoint(x: Vector[Double], y: Double)
 
   def parsePoint(line: String): DataPoint = {
     val tok = new java.util.StringTokenizer(line, " ")
@@ -41,7 +42,7 @@ object SparkHdfsLR {
     while (i < D) {
       x(i) = tok.nextToken.toDouble; i += 1
     }
-    DataPoint(new Vector(x), y)
+    DataPoint(new DenseVector(x), y)
   }
 
   def main(args: Array[String]) {
@@ -61,13 +62,13 @@ object SparkHdfsLR {
     val ITERATIONS = args(2).toInt
 
     // Initialize w to a random value
-    var w = Vector(D, _ => 2 * rand.nextDouble - 1)
+    var w = DenseVector.fill(D){2 * rand.nextDouble - 1}
     println("Initial w: " + w)
 
     for (i <- 1 to ITERATIONS) {
       println("On iteration " + i)
       val gradient = points.map { p =>
-        (1 / (1 + exp(-p.y * (w dot p.x))) - 1) * p.y * p.x
+        p.x * (1 / (1 + exp(-p.y * (w.dot(p.x)))) - 1) * p.y
       }.reduce(_ + _)
       w -= gradient
     }

diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
@@ -19,27 +19,29 @@ package org.apache.spark.examples
 
 import java.util.Random
 import org.apache.spark.SparkContext
-import org.apache.spark.util.Vector
 import org.apache.spark.SparkContext._
 
+import breeze.linalg.{Vector, DenseVector}
+import breeze.linalg.squaredDistance
+
 /**
  * K-means clustering.
  */
 object SparkKMeans {
   val R = 1000     // Scaling factor
   val rand = new Random(42)
 
-  def parseVector(line: String): Vector = {
-    new Vector(line.split(' ').map(_.toDouble))
+  def parseVector(line: String): Vector[Double] = {
+    DenseVector(line.split(' ').map(_.toDouble))
   }
 
-  def closestPoint(p: Vector, centers: Array[Vector]): Int = {
+  def closestPoint(p: Vector[Double], centers: Array[Vector[Double]]): Int = {
     var index = 0
     var bestIndex = 0
     var closest = Double.PositiveInfinity
 
     for (i <- 0 until centers.length) {
-      val tempDist = p.squaredDist(centers(i))
+      val tempDist = squaredDistance(p, centers(i))
       if (tempDist < closest) {
         closest = tempDist
         bestIndex = i
@@ -69,11 +71,11 @@ object SparkKMeans {
 
       val pointStats = closest.reduceByKey{case ((x1, y1), (x2, y2)) => (x1 + x2, y1 + y2)}
 
-      val newPoints = pointStats.map {pair => (pair._1, pair._2._1 / pair._2._2)}.collectAsMap()
+      val newPoints = pointStats.map {pair => (pair._1, pair._2._1 * (1.0 / pair._2._2))}.collectAsMap()
 
       tempDist = 0.0
       for (i <- 0 until K) {
-        tempDist += kPoints(i).squaredDist(newPoints(i))
+        tempDist += squaredDistance(kPoints(i), newPoints(i))
       }
 
       for (newP <- newPoints) {

diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala
@@ -19,9 +19,10 @@ package org.apache.spark.examples
 
 import java.util.Random
 import scala.math.exp
-import org.apache.spark.util.Vector
 import org.apache.spark._
 
+import breeze.linalg.{Vector, DenseVector}
+
 /**
  * Logistic regression based classification.
  */
@@ -32,12 +33,12 @@ object SparkLR {
   val ITERATIONS = 5
   val rand = new Random(42)
 
-  case class DataPoint(x: Vector, y: Double)
+  case class DataPoint(x: Vector[Double], y: Double)
 
   def generateData = {
     def generatePoint(i: Int) = {
       val y = if(i % 2 == 0) -1 else 1
-      val x = Vector(D, _ => rand.nextGaussian + y * R)
+      val x = DenseVector.fill(D){rand.nextGaussian + y * R}
       DataPoint(x, y)
     }
     Array.tabulate(N)(generatePoint)
@@ -54,13 +55,13 @@ object SparkLR {
     val points = sc.parallelize(generateData, numSlices).cache()
 
     // Initialize w to a random value
-    var w = Vector(D, _ => 2 * rand.nextDouble - 1)
+    var w = DenseVector.fill(D){2 * rand.nextDouble - 1}
     println("Initial w: " + w)
 
     for (i <- 1 to ITERATIONS) {
       println("On iteration " + i)
       val gradient = points.map { p =>
-        (1 / (1 + exp(-p.y * (w dot p.x))) - 1) * p.y * p.x
+        p.x * (1 / (1 + exp(-p.y * (w.dot(p.x)))) - 1) * p.y
       }.reduce(_ + _)
       w -= gradient
     }

diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala
@@ -19,12 +19,13 @@ package org.apache.spark.examples
 
 import java.util.Random
 import scala.math.exp
-import org.apache.spark.util.Vector
 import org.apache.spark._
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.scheduler.InputFormatInfo
 import org.apache.spark.storage.StorageLevel
 
+import breeze.linalg.{Vector, DenseVector}
+
 /**
  * Logistic regression based classification.
  * This example uses Tachyon to persist rdds during computation.
@@ -33,7 +34,7 @@ object SparkTachyonHdfsLR {
   val D = 10   // Numer of dimensions
   val rand = new Random(42)
 
-  case class DataPoint(x: Vector, y: Double)
+  case class DataPoint(x: Vector[Double], y: Double)
 
   def parsePoint(line: String): DataPoint = {
     val tok = new java.util.StringTokenizer(line, " ")
@@ -43,7 +44,7 @@ object SparkTachyonHdfsLR {
     while (i < D) {
       x(i) = tok.nextToken.toDouble; i += 1
     }
-    DataPoint(new Vector(x), y)
+    DataPoint(new DenseVector(x), y)
   }
 
   def main(args: Array[String]) {
@@ -63,13 +64,13 @@ object SparkTachyonHdfsLR {
     val ITERATIONS = args(2).toInt
 
     // Initialize w to a random value
-    var w = Vector(D, _ => 2 * rand.nextDouble - 1)
+    var w = DenseVector.fill(D){2 * rand.nextDouble - 1}
     println("Initial w: " + w)
 
     for (i <- 1 to ITERATIONS) {
       println("On iteration " + i)
       val gradient = points.map { p =>
-        (1 / (1 + exp(-p.y * (w dot p.x))) - 1) * p.y * p.x
+        p.x * (1 / (1 + exp(-p.y * (w.dot(p.x)))) - 1) * p.y
       }.reduce(_ + _)
       w -= gradient
     }