[MLLIB] [PYTHON] SPARK-4221: Expose nonnegative ALS in the python API

SPARK-1553 added alternating nonnegative least squares to MLLib, however it's not possible to access it via the python API.  This pull request resolves that.

Author: Michelangelo D'Agostino <mdagostino@civisanalytics.com>

Closes #3095 from mdagost/python_nmf and squashes the following commits:

a6743ad [Michelangelo D'Agostino] Use setters instead of static methods in PythonMLLibAPI.  Remove the new static methods I added.  Set seed in tests.  Change ratings to ratingsRDD in both train and trainImplicit for consistency.
7cffd39 [Michelangelo D'Agostino] Swapped nonnegative and seed in a few more places.
3fdc851 [Michelangelo D'Agostino] Moved seed to the end of the python parameter list.
bdcc154 [Michelangelo D'Agostino] Change seed type to java.lang.Long so that it can handle null.
cedf043 [Michelangelo D'Agostino] Added in ability to set the seed from python and made that play nice with the nonnegative changes.  Also made the python ALS tests more exact.
a72fdc9 [Michelangelo D'Agostino] Expose nonnegative ALS in the python API.

1 files changed, 33 insertions, 6 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index d832ae34b5..70d7138e30 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -275,12 +275,25 @@ class PythonMLLibAPI extends Serializable {
    * the Py4J documentation.
    */
   def trainALSModel(
-      ratings: JavaRDD[Rating],
+      ratingsJRDD: JavaRDD[Rating],
       rank: Int,
       iterations: Int,
       lambda: Double,
-      blocks: Int): MatrixFactorizationModel = {
-    new MatrixFactorizationModelWrapper(ALS.train(ratings.rdd, rank, iterations, lambda, blocks))
+      blocks: Int,
+      nonnegative: Boolean,
+      seed: java.lang.Long): MatrixFactorizationModel = {
+
+    val als = new ALS()
+      .setRank(rank)
+      .setIterations(iterations)
+      .setLambda(lambda)
+      .setBlocks(blocks)
+      .setNonnegative(nonnegative)
+
+    if (seed != null) als.setSeed(seed)
+
+    val model =  als.run(ratingsJRDD.rdd)
+    new MatrixFactorizationModelWrapper(model)
   }
 
   /**
@@ -295,9 +308,23 @@ class PythonMLLibAPI extends Serializable {
       iterations: Int,
       lambda: Double,
       blocks: Int,
-      alpha: Double): MatrixFactorizationModel = {
-    new MatrixFactorizationModelWrapper(
-      ALS.trainImplicit(ratingsJRDD.rdd, rank, iterations, lambda, blocks, alpha))
+      alpha: Double,
+      nonnegative: Boolean,
+      seed: java.lang.Long): MatrixFactorizationModel = {
+
+    val als = new ALS()
+      .setImplicitPrefs(true)
+      .setRank(rank)
+      .setIterations(iterations)
+      .setLambda(lambda)
+      .setBlocks(blocks)
+      .setAlpha(alpha)
+      .setNonnegative(nonnegative)
+
+    if (seed != null) als.setSeed(seed)
+
+    val model =  als.run(ratingsJRDD.rdd)
+    new MatrixFactorizationModelWrapper(model)
   }
 
   /**