add Accumulatable, add corresponding docs & tests for accumulators

core/src/main/scala/spark/Accumulators.scala

@@ -35,11 +35,42 @@ class Accumulator[T] (
   override def toString = value_.toString
 }
 
+class Accumulatable[T,Y](
+    @transient initialValue: T,
+    param: AccumulatableParam[T,Y]) extends Accumulator[T](initialValue, param) {
+  /**
+   * add more data to the current value of the this accumulator, via
+   * AccumulatableParam.addToAccum
+   * @param term
+   */
+  def +:= (term: Y) {value_ = param.addToAccum(value_, term)}
+}
+
+/**
+ * A datatype that can be accumulated, ie. has a commutative & associative +
+ * @tparam T
+ */
 trait AccumulatorParam[T] extends Serializable {
   def addInPlace(t1: T, t2: T): T
   def zero(initialValue: T): T
 }
 
+/**
+ * A datatype that can be accumulated.  Slightly extends [[spark.AccumulatorParam]] to allow you to
+ * combine a different data type with value so far
+ * @tparam T the full accumulated data
+ * @tparam Y partial data that can be added in
+ */
+trait AccumulatableParam[T,Y] extends AccumulatorParam[T] {
+  /**
+   * Add additional data to the accumulator value.
+   * @param t1 the current value of the accumulator
+   * @param t2 the data to be added to the accumulator
+   * @return the new value of the accumulator
+   */
+  def addToAccum(t1: T, t2: Y) : T
+}
+
 // TODO: The multi-thread support in accumulators is kind of lame; check
 // if there's a more intuitive way of doing it right
 private object Accumulators {

core/src/main/scala/spark/SparkContext.scala

@@ -285,6 +285,18 @@ class SparkContext(
   def accumulator[T](initialValue: T)(implicit param: AccumulatorParam[T]) =
     new Accumulator(initialValue, param)
 
+  /**
+   * create an accumulatable shared variable, with a `+:=` method
+   * @param initialValue
+   * @param param
+   * @tparam T accumulator type
+   * @tparam Y type that can be added to the accumulator
+   * @return
+   */
+  def accumulatable[T,Y](initialValue: T)(implicit param: AccumulatableParam[T,Y]) =
+    new Accumulatable(initialValue, param)
+
+
   // Keep around a weak hash map of values to Cached versions?
   def broadcast[T](value: T) = Broadcast.getBroadcastFactory.newBroadcast[T] (value, isLocal)
 

core/src/test/scala/spark/AccumulatorSuite.scala

+package spark
+
+import org.scalatest.FunSuite
+import org.scalatest.matchers.ShouldMatchers
+import collection.mutable
+import java.util.Random
+import scala.math.exp
+import scala.math.signum
+import spark.SparkContext._
+
+class AccumulatorSuite extends FunSuite with ShouldMatchers {
+
+  test ("basic accumulation"){
+    val sc = new SparkContext("local", "test")
+    val acc : Accumulator[Int] = sc.accumulator(0)
+
+    val d = sc.parallelize(1 to 20)
+    d.foreach{x => acc += x}
+    acc.value should be (210)
+    sc.stop()
+  }
+
+  test ("value not assignable from tasks") {
+    val sc = new SparkContext("local", "test")
+    val acc : Accumulator[Int] = sc.accumulator(0)
+
+    val d = sc.parallelize(1 to 20)
+    evaluating {d.foreach{x => acc.value = x}} should produce [Exception]
+    sc.stop()
+  }
+
+  test ("add value to collection accumulators") {
+    import SetAccum._
+    val maxI = 1000
+    for (nThreads <- List(1, 10)) { //test single & multi-threaded
+      val sc = new SparkContext("local[" + nThreads + "]", "test")
+      val acc: Accumulatable[mutable.Set[Any], Any] = sc.accumulatable(new mutable.HashSet[Any]())
+      val d = sc.parallelize(1 to maxI)
+      d.foreach {
+        x => acc +:= x //note the use of +:= here
+      }
+      val v = acc.value.asInstanceOf[mutable.Set[Int]]
+      for (i <- 1 to maxI) {
+        v should contain(i)
+      }
+      sc.stop()
+    }
+  }
+
+
+  implicit object SetAccum extends AccumulatableParam[mutable.Set[Any], Any] {
+    def addInPlace(t1: mutable.Set[Any], t2: mutable.Set[Any]) : mutable.Set[Any] = {
+      t1 ++= t2
+      t1
+    }
+    def addToAccum(t1: mutable.Set[Any], t2: Any) : mutable.Set[Any] = {
+      t1 += t2
+      t1
+    }
+    def zero(t: mutable.Set[Any]) : mutable.Set[Any] = {
+      new mutable.HashSet[Any]()
+    }
+  }
+
+
+  test ("value readable in tasks") {
+    import Vector.VectorAccumParam._
+    import Vector._
+    //stochastic gradient descent with weights stored in accumulator -- should be able to read value as we go
+
+    //really easy data
+    val N = 10000  // Number of data points
+    val D = 10   // Numer of dimensions
+    val R = 0.7  // Scaling factor
+    val ITERATIONS = 5
+    val rand = new Random(42)
+
+    case class DataPoint(x: Vector, y: Double)
+
+    def generateData = {
+      def generatePoint(i: Int) = {
+        val y = if(i % 2 == 0) -1 else 1
+        val goodX = Vector(D, _ => 0.0001 * rand.nextGaussian() + y)
+        val noiseX = Vector(D, _ => rand.nextGaussian())
+        val x = Vector((goodX.elements.toSeq ++ noiseX.elements.toSeq): _*)
+        DataPoint(x, y)
+      }
+      Array.tabulate(N)(generatePoint)
+    }
+
+    val data = generateData
+    for (nThreads <- List(1, 10)) {
+      //test single & multi-threaded
+      val sc = new SparkContext("local[" + nThreads + "]", "test")
+      val weights = Vector.zeros(2*D)
+      val weightDelta = sc.accumulator(Vector.zeros(2 * D))
+      for (itr <- 1 to ITERATIONS) {
+        val eta = 0.1 / itr
+        val badErrs = sc.accumulator(0)
+        sc.parallelize(data).foreach {
+          p => {
+            //XXX Note the call to .value here.  That is required for this to be an online gradient descent
+            // instead of a batch version.  Should it change to .localValue, and should .value throw an error
+            // if you try to do this??
+            val prod = weightDelta.value.plusDot(weights, p.x)
+            val trueClassProb = (1 / (1 + exp(-p.y * prod)))  // works b/c p(-z) = 1 - p(z) (where p is the logistic function)
+            val update = p.x * trueClassProb * p.y * eta
+            //we could also include a momentum term here if our weightDelta accumulator saved a momentum
+            weightDelta.value += update
+            if (trueClassProb <= 0.95)
+              badErrs += 1
+          }
+        }
+        println("Iteration " + itr + " had badErrs = " + badErrs.value)
+        weights += weightDelta.value
+        println(weights)
+        //TODO I should check the number of bad errors here, but for some reason spark tries to serialize the assertion ...
+        val assertVal = badErrs.value
+        assert (assertVal < 100)
+      }
+    }
+  }
+
+}
+
+
+
+//ugly copy and paste from examples ...
+class Vector(val elements: Array[Double]) extends Serializable {
+  def length = elements.length
+
+  def apply(index: Int) = elements(index)
+
+  def + (other: Vector): Vector = {
+    if (length != other.length)
+      throw new IllegalArgumentException("Vectors of different length")
+    return Vector(length, i => this(i) + other(i))
+  }
+
+  def - (other: Vector): Vector = {
+    if (length != other.length)
+      throw new IllegalArgumentException("Vectors of different length")
+    return Vector(length, i => this(i) - other(i))
+  }
+
+  def dot(other: Vector): Double = {
+    if (length != other.length)
+      throw new IllegalArgumentException("Vectors of different length")
+    var ans = 0.0
+    var i = 0
+    while (i < length) {
+      ans += this(i) * other(i)
+      i += 1
+    }
+    return ans
+  }
+
+  def plusDot(plus: Vector, other: Vector): Double = {
+    if (length != other.length)
+      throw new IllegalArgumentException("Vectors of different length")
+    if (length != plus.length)
+      throw new IllegalArgumentException("Vectors of different length")
+    var ans = 0.0
+    var i = 0
+    while (i < length) {
+      ans += (this(i) + plus(i)) * other(i)
+      i += 1
+    }
+    return ans
+  }
+
+  def += (other: Vector) {
+    if (length != other.length)
+      throw new IllegalArgumentException("Vectors of different length")
+    var ans = 0.0
+    var i = 0
+    while (i < length) {
+      elements(i) += other(i)
+      i += 1
+    }
+  }
+
+
+  def * (scale: Double): Vector = Vector(length, i => this(i) * scale)
+
+  def / (d: Double): Vector = this * (1 / d)
+
+  def unary_- = this * -1
+
+  def sum = elements.reduceLeft(_ + _)
+
+  def squaredDist(other: Vector): Double = {
+    var ans = 0.0
+    var i = 0
+    while (i < length) {
+      ans += (this(i) - other(i)) * (this(i) - other(i))
+      i += 1
+    }
+    return ans
+  }
+
+  def dist(other: Vector): Double = math.sqrt(squaredDist(other))
+
+  override def toString = elements.mkString("(", ", ", ")")
+}
+
+object Vector {
+  def apply(elements: Array[Double]) = new Vector(elements)
+
+  def apply(elements: Double*) = new Vector(elements.toArray)
+
+  def apply(length: Int, initializer: Int => Double): Vector = {
+    val elements = new Array[Double](length)
+    for (i <- 0 until length)
+      elements(i) = initializer(i)
+    return new Vector(elements)
+  }
+
+  def zeros(length: Int) = new Vector(new Array[Double](length))
+
+  def ones(length: Int) = Vector(length, _ => 1)
+
+  class Multiplier(num: Double) {
+    def * (vec: Vector) = vec * num
+  }
+
+  implicit def doubleToMultiplier(num: Double) = new Multiplier(num)
+
+  implicit object VectorAccumParam extends spark.AccumulatorParam[Vector] {
+    def addInPlace(t1: Vector, t2: Vector) = t1 + t2
+    def zero(initialValue: Vector) = Vector.zeros(initialValue.length)
+  }
+}