[SPARK-5513][MLLIB] Add nonnegative option to ml's ALS

This PR ports the NNLS solver to the new ALS implementation.

CC: coderxiang

Author: Xiangrui Meng <meng@databricks.com>

Closes #4302 from mengxr/SPARK-5513 and squashes the following commits:

4cbdab0 [Xiangrui Meng] fix serialization
88de634 [Xiangrui Meng] add NNLS to ml's ALS

1 files changed, 11 insertions, 0 deletions

diff --git a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala
index 07aff56fb7..ee08c3c327 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala
@@ -444,4 +444,15 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging {
     val (strUserFactors, _) = ALS.train(strRatings, rank = 2, maxIter = 4)
     assert(strUserFactors.first()._1.getClass === classOf[String])
   }
+
+  test("nonnegative constraint") {
+    val (ratings, _) = genImplicitTestData(numUsers = 20, numItems = 40, rank = 2, noiseStd = 0.01)
+    val (userFactors, itemFactors) = ALS.train(ratings, rank = 2, maxIter = 4, nonnegative = true)
+    def isNonnegative(factors: RDD[(Int, Array[Float])]): Boolean = {
+      factors.values.map { _.forall(_ >= 0.0) }.reduce(_ && _)
+    }
+    assert(isNonnegative(userFactors))
+    assert(isNonnegative(itemFactors))
+    // TODO: Validate the solution.
+  }
 }