[SPARK-6053][MLLIB] support save/load in PySpark's ALS

A simple wrapper to save/load `MatrixFactorizationModel` in Python. jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #4811 from mengxr/SPARK-5991 and squashes the following commits:

f135dac [Xiangrui Meng] update save doc
57e5200 [Xiangrui Meng] address comments
06140a4 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-5991
282ec8d [Xiangrui Meng] support save/load in PySpark's ALS

1 files changed, 5 insertions, 3 deletions

diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md
index 27aa4d38b7..76140282a2 100644
--- a/docs/mllib-collaborative-filtering.md
+++ b/docs/mllib-collaborative-filtering.md
@@ -200,10 +200,8 @@ In the following example we load rating data. Each row consists of a user, a pro
 We use the default ALS.train() method which assumes ratings are explicit. We evaluate the
 recommendation by measuring the Mean Squared Error of rating prediction.
 
-Note that the Python API does not yet support model save/load but will in the future.
-
 {% highlight python %}
-from pyspark.mllib.recommendation import ALS, Rating
+from pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating
 
 # Load and parse the data
 data = sc.textFile("data/mllib/als/test.data")
@@ -220,6 +218,10 @@ predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2]))
 ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions)
 MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).reduce(lambda x, y: x + y) / ratesAndPreds.count()
 print("Mean Squared Error = " + str(MSE))
+
+# Save and load model
+model.save(sc, "myModelPath")
+sameModel = MatrixFactorizationModel.load(sc, "myModelPath")
 {% endhighlight %}
 
 If the rating matrix is derived from other source of information (i.e., it is inferred from other