[SPARK-1273] MLlib bug fixes, improvements, and doc updates for v0.9.1

Cherry-picked a few MLlib commits that are bug fixes, optimization, or doc updates for the v0.9.1 release.

JIRA: https://spark-project.atlassian.net/browse/SPARK-1273

Author: Xiangrui Meng <meng@databricks.com>
Author: Sean Owen <sowen@cloudera.com>
Author: Andrew Tulloch <andrew@tullo.ch>
Author: Chen Chao <crazyjvm@gmail.com>

Closes #175 from mengxr/branch-0.9 and squashes the following commits:

d8928ea [Xiangrui Meng] add Apache header to LocalSparkContext
a66d386 [Xiangrui Meng] Merge remote-tracking branch 'apache/branch-0.9' into branch-0.9
a899894 [Xiangrui Meng] [SPARK-1237, 1238] Improve the computation of YtY for implicit ALS
46fe493 [Xiangrui Meng] [SPARK-1260]: faster construction of features with intercept
6340a18 [Sean Owen] MLLIB-22. Support negative implicit input in ALS
f27441a [Chen Chao] MLLIB-24:  url of "Collaborative Filtering for Implicit Feedback Datasets" in ALS is invalid now
a26ac90 [Sean Owen] Merge pull request #460 from srowen/RandomInitialALSVectors
0564985 [Andrew Tulloch] Fixed import order
2512e67 [Andrew Tulloch] LocalSparkContext for MLlib

Author: mengxr

mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala

@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.recommendation
 
 import scala.collection.mutable.{ArrayBuffer, BitSet}
+import scala.math.{abs, sqrt}
 import scala.util.Random
 import scala.util.Sorting
 
@@ -63,7 +64,7 @@ case class Rating(val user: Int, val product: Int, val rating: Double)
  * Alternating Least Squares matrix factorization.
  *
  * ALS attempts to estimate the ratings matrix `R` as the product of two lower-rank matrices,
- * `X` and `Y`, i.e. `Xt * Y = R`. Typically these approximations are called 'factor' matrices.
+ * `X` and `Y`, i.e. `X * Yt = R`. Typically these approximations are called 'factor' matrices.
  * The general approach is iterative. During each iteration, one of the factor matrices is held
  * constant, while the other is solved for using least squares. The newly-solved factor matrix is
  * then held constant while solving for the other factor matrix.
@@ -80,17 +81,22 @@ case class Rating(val user: Int, val product: Int, val rating: Double)
  *
  * For implicit preference data, the algorithm used is based on
  * "Collaborative Filtering for Implicit Feedback Datasets", available at
- * [[http://research.yahoo.com/pub/2433]], adapted for the blocked approach used here.
+ * [[http://dx.doi.org/10.1109/ICDM.2008.22]], adapted for the blocked approach used here.
  *
  * Essentially instead of finding the low-rank approximations to the rating matrix `R`,
  * this finds the approximations for a preference matrix `P` where the elements of `P` are 1 if r > 0
  * and 0 if r = 0. The ratings then act as 'confidence' values related to strength of indicated user
  * preferences rather than explicit ratings given to items.
  */
-class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var lambda: Double,
-                   var implicitPrefs: Boolean, var alpha: Double)
-  extends Serializable with Logging
-{
+class ALS private (
+    var numBlocks: Int,
+    var rank: Int,
+    var iterations: Int,
+    var lambda: Double,
+    var implicitPrefs: Boolean,
+    var alpha: Double,
+    var seed: Long = System.nanoTime()
+  ) extends Serializable with Logging {
   def this() = this(-1, 10, 10, 0.01, false, 1.0)
 
   /**
@@ -130,6 +136,12 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l
     this
   }
 
+  /** Sets a random seed to have deterministic results. */
+  def setSeed(seed: Long): ALS = {
+    this.seed = seed
+    this
+  }
+
   /**
    * Run ALS with the configured parameters on an input RDD of (user, product, rating) triples.
    * Returns a MatrixFactorizationModel with feature vectors for each user and product.
@@ -151,9 +163,9 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l
     val (userInLinks, userOutLinks) = makeLinkRDDs(numBlocks, ratingsByUserBlock)
     val (productInLinks, productOutLinks) = makeLinkRDDs(numBlocks, ratingsByProductBlock)
 
-    // Initialize user and product factors randomly, but use a deterministic seed for each partition
-    // so that fault recovery works
-    val seedGen = new Random()
+    // Initialize user and product factors randomly, but use a deterministic seed for each
+    // partition so that fault recovery works
+    val seedGen = new Random(seed)
     val seed1 = seedGen.nextInt()
     val seed2 = seedGen.nextInt()
     // Hash an integer to propagate random bits at all positions, similar to java.util.HashTable
@@ -208,21 +220,46 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l
    */
   def computeYtY(factors: RDD[(Int, Array[Array[Double]])]) = {
     if (implicitPrefs) {
-      Option(
-        factors.flatMapValues { case factorArray =>
-          factorArray.view.map { vector =>
-            val x = new DoubleMatrix(vector)
-            x.mmul(x.transpose())
-          }
-        }.reduceByKeyLocally((a, b) => a.addi(b))
-         .values
-         .reduce((a, b) => a.addi(b))
-      )
+      val n = rank * (rank + 1) / 2
+      val LYtY = factors.values.aggregate(new DoubleMatrix(n))( seqOp = (L, Y) => {
+        Y.foreach(y => dspr(1.0, new DoubleMatrix(y), L))
+        L
+      }, combOp = (L1, L2) => {
+        L1.addi(L2)
+      })
+      val YtY = new DoubleMatrix(rank, rank)
+      fillFullMatrix(LYtY, YtY)
+      Option(YtY)
     } else {
       None
     }
   }
 
+  /**
+   * Adds alpha * x * x.t to a matrix in-place. This is the same as BLAS's DSPR.
+   *
+   * @param L the lower triangular part of the matrix packed in an array (row major)
+   */
+  private def dspr(alpha: Double, x: DoubleMatrix, L: DoubleMatrix) = {
+    val n = x.length
+    var i = 0
+    var j = 0
+    var idx = 0
+    var axi = 0.0
+    val xd = x.data
+    val Ld = L.data
+    while (i < n) {
+      axi = alpha * xd(i)
+      j = 0
+      while (j <= i) {
+        Ld(idx) += axi * xd(j)
+        j += 1
+        idx += 1
+      }
+      i += 1
+    }
+  }
+
   /**
    * Flatten out blocked user or product factors into an RDD of (id, factor vector) pairs
    */
@@ -301,7 +338,14 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l
    * Make a random factor vector with the given random.
    */
   private def randomFactor(rank: Int, rand: Random): Array[Double] = {
-    Array.fill(rank)(rand.nextDouble)
+    // Choose a unit vector uniformly at random from the unit sphere, but from the
+    // "first quadrant" where all elements are nonnegative. This can be done by choosing
+    // elements distributed as Normal(0,1) and taking the absolute value, and then normalizing.
+    // This appears to create factorizations that have a slightly better reconstruction
+    // (<1%) compared picking elements uniformly at random in [0,1].
+    val factor = Array.fill(rank)(abs(rand.nextGaussian()))
+    val norm = sqrt(factor.map(x => x * x).sum)
+    factor.map(x => x / norm)
   }
 
   /**
@@ -365,51 +409,41 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l
     for (productBlock <- 0 until numBlocks) {
       for (p <- 0 until blockFactors(productBlock).length) {
         val x = new DoubleMatrix(blockFactors(productBlock)(p))
-        fillXtX(x, tempXtX)
+        tempXtX.fill(0.0)
+        dspr(1.0, x, tempXtX)
         val (us, rs) = inLinkBlock.ratingsForBlock(productBlock)(p)
         for (i <- 0 until us.length) {
           implicitPrefs match {
             case false =>
               userXtX(us(i)).addi(tempXtX)
               SimpleBlas.axpy(rs(i), x, userXy(us(i)))
             case true =>
-              userXtX(us(i)).addi(tempXtX.mul(alpha * rs(i)))
-              SimpleBlas.axpy(1 + alpha * rs(i), x, userXy(us(i)))
+              // Extension to the original paper to handle rs(i) < 0. confidence is a function
+              // of |rs(i)| instead so that it is never negative:
+              val confidence = 1 + alpha * abs(rs(i))
+              SimpleBlas.axpy(confidence - 1.0, tempXtX, userXtX(us(i)))
+              // For rs(i) < 0, the corresponding entry in P is 0 now, not 1 -- negative rs(i)
+              // means we try to reconstruct 0. We add terms only where P = 1, so, term below
+              // is now only added for rs(i) > 0:
+              if (rs(i) > 0) {
+                SimpleBlas.axpy(confidence, x, userXy(us(i)))
+              }
           }
         }
       }
     }
 
     // Solve the least-squares problem for each user and return the new feature vectors
-    userXtX.zipWithIndex.map{ case (triangularXtX, index) =>
+    Array.range(0, numUsers).map { index =>
       // Compute the full XtX matrix from the lower-triangular part we got above
-      fillFullMatrix(triangularXtX, fullXtX)
+      fillFullMatrix(userXtX(index), fullXtX)
       // Add regularization
       (0 until rank).foreach(i => fullXtX.data(i*rank + i) += lambda)
       // Solve the resulting matrix, which is symmetric and positive-definite
       implicitPrefs match {
         case false => Solve.solvePositive(fullXtX, userXy(index)).data
-        case true => Solve.solvePositive(fullXtX.add(YtY.value.get), userXy(index)).data
-      }
-    }
-  }
-
-  /**
-   * Set xtxDest to the lower-triangular part of x transpose * x. For efficiency in summing
-   * these matrices, we store xtxDest as only rank * (rank+1) / 2 values, namely the values
-   * at (0,0), (1,0), (1,1), (2,0), (2,1), (2,2), etc in that order.
-   */
-  private def fillXtX(x: DoubleMatrix, xtxDest: DoubleMatrix) {
-    var i = 0
-    var pos = 0
-    while (i < x.length) {
-      var j = 0
-      while (j <= i) {
-        xtxDest.data(pos) = x.data(i) * x.data(j)
-        pos += 1
-        j += 1
+        case true => Solve.solvePositive(fullXtX.addi(YtY.value.get), userXy(index)).data
       }
-      i += 1
     }
   }
 
@@ -436,9 +470,10 @@ class ALS private (var numBlocks: Int, var rank: Int, var iterations: Int, var l
 
 
 /**
- * Top-level methods for calling Alternating Least Squares (ALS) matrix factorizaton.
+ * Top-level methods for calling Alternating Least Squares (ALS) matrix factorization.
  */
 object ALS {
+
   /**
    * Train a matrix factorization model given an RDD of ratings given by users to some products,
    * in the form of (userID, productID, rating) pairs. We approximate the ratings matrix as the
@@ -451,15 +486,39 @@ object ALS {
    * @param iterations number of iterations of ALS (recommended: 10-20)
    * @param lambda     regularization factor (recommended: 0.01)
    * @param blocks     level of parallelism to split computation into
+   * @param seed       random seed
    */
   def train(
       ratings: RDD[Rating],
       rank: Int,
       iterations: Int,
       lambda: Double,
-      blocks: Int)
-    : MatrixFactorizationModel =
-  {
+      blocks: Int,
+      seed: Long
+    ): MatrixFactorizationModel = {
+    new ALS(blocks, rank, iterations, lambda, false, 1.0, seed).run(ratings)
+  }
+
+  /**
+   * Train a matrix factorization model given an RDD of ratings given by users to some products,
+   * in the form of (userID, productID, rating) pairs. We approximate the ratings matrix as the
+   * product of two lower-rank matrices of a given rank (number of features). To solve for these
+   * features, we run a given number of iterations of ALS. This is done using a level of
+   * parallelism given by `blocks`.
+   *
+   * @param ratings    RDD of (userID, productID, rating) pairs
+   * @param rank       number of features to use
+   * @param iterations number of iterations of ALS (recommended: 10-20)
+   * @param lambda     regularization factor (recommended: 0.01)
+   * @param blocks     level of parallelism to split computation into
+   */
+  def train(
+      ratings: RDD[Rating],
+      rank: Int,
+      iterations: Int,
+      lambda: Double,
+      blocks: Int
+    ): MatrixFactorizationModel = {
     new ALS(blocks, rank, iterations, lambda, false, 1.0).run(ratings)
   }
 
@@ -476,8 +535,7 @@ object ALS {
    * @param lambda     regularization factor (recommended: 0.01)
    */
   def train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double)
-    : MatrixFactorizationModel =
-  {
+    : MatrixFactorizationModel = {
     train(ratings, rank, iterations, lambda, -1)
   }
 
@@ -493,8 +551,7 @@ object ALS {
    * @param iterations number of iterations of ALS (recommended: 10-20)
    */
   def train(ratings: RDD[Rating], rank: Int, iterations: Int)
-    : MatrixFactorizationModel =
-  {
+    : MatrixFactorizationModel = {
     train(ratings, rank, iterations, 0.01, -1)
   }
 
@@ -511,16 +568,42 @@ object ALS {
    * @param lambda     regularization factor (recommended: 0.01)
    * @param blocks     level of parallelism to split computation into
    * @param alpha      confidence parameter (only applies when immplicitPrefs = true)
+   * @param seed       random seed
    */
   def trainImplicit(
       ratings: RDD[Rating],
       rank: Int,
       iterations: Int,
       lambda: Double,
       blocks: Int,
-      alpha: Double)
-  : MatrixFactorizationModel =
-  {
+      alpha: Double,
+      seed: Long
+    ): MatrixFactorizationModel = {
+    new ALS(blocks, rank, iterations, lambda, true, alpha, seed).run(ratings)
+  }
+
+  /**
+   * Train a matrix factorization model given an RDD of 'implicit preferences' given by users
+   * to some products, in the form of (userID, productID, preference) pairs. We approximate the
+   * ratings matrix as the product of two lower-rank matrices of a given rank (number of features).
+   * To solve for these features, we run a given number of iterations of ALS. This is done using
+   * a level of parallelism given by `blocks`.
+   *
+   * @param ratings    RDD of (userID, productID, rating) pairs
+   * @param rank       number of features to use
+   * @param iterations number of iterations of ALS (recommended: 10-20)
+   * @param lambda     regularization factor (recommended: 0.01)
+   * @param blocks     level of parallelism to split computation into
+   * @param alpha      confidence parameter (only applies when immplicitPrefs = true)
+   */
+  def trainImplicit(
+      ratings: RDD[Rating],
+      rank: Int,
+      iterations: Int,
+      lambda: Double,
+      blocks: Int,
+      alpha: Double
+    ): MatrixFactorizationModel = {
     new ALS(blocks, rank, iterations, lambda, true, alpha).run(ratings)
   }
 
@@ -537,8 +620,7 @@ object ALS {
    * @param lambda     regularization factor (recommended: 0.01)
    */
   def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, alpha: Double)
-  : MatrixFactorizationModel =
-  {
+    : MatrixFactorizationModel = {
     trainImplicit(ratings, rank, iterations, lambda, -1, alpha)
   }
 
@@ -555,8 +637,7 @@ object ALS {
    * @param iterations number of iterations of ALS (recommended: 10-20)
    */
   def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int)
-  : MatrixFactorizationModel =
-  {
+    : MatrixFactorizationModel = {
     trainImplicit(ratings, rank, iterations, 0.01, -1, 1.0)
   }
 

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

@@ -119,7 +119,7 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
    */
   def run(input: RDD[LabeledPoint]) : M = {
     val nfeatures: Int = input.first().features.length
-    val initialWeights = Array.fill(nfeatures)(1.0)
+    val initialWeights = new Array[Double](nfeatures)
     run(input, initialWeights)
   }
 
@@ -134,15 +134,15 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
       throw new SparkException("Input validation failed.")
     }
 
-    // Add a extra variable consisting of all 1.0's for the intercept.
+    // Prepend an extra variable consisting of all 1.0's for the intercept.
     val data = if (addIntercept) {
-      input.map(labeledPoint => (labeledPoint.label, Array(1.0, labeledPoint.features:_*)))
+      input.map(labeledPoint => (labeledPoint.label, labeledPoint.features.+:(1.0)))
     } else {
       input.map(labeledPoint => (labeledPoint.label, labeledPoint.features))
     }
 
     val initialWeightsWithIntercept = if (addIntercept) {
-      Array(1.0, initialWeights:_*)
+      initialWeights.+:(1.0)
     } else {
       initialWeights
     }