1 files changed, 18 insertions, 2 deletions
diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py
index 0d99e6dedf..03d7d01147 100644
--- a/python/pyspark/mllib/recommendation.py
+++ b/python/pyspark/mllib/recommendation.py
@@ -19,7 +19,8 @@ from collections import namedtuple
 
 from pyspark import SparkContext
 from pyspark.rdd import RDD
-from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc
+from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc
+from pyspark.mllib.util import Saveable, JavaLoader
 
 __all__ = ['MatrixFactorizationModel', 'ALS', 'Rating']
 
@@ -39,7 +40,8 @@ class Rating(namedtuple("Rating", ["user", "product", "rating"])):
         return Rating, (int(self.user), int(self.product), float(self.rating))
 
 
-class MatrixFactorizationModel(JavaModelWrapper):
+@inherit_doc
+class MatrixFactorizationModel(JavaModelWrapper, Saveable, JavaLoader):
 
     """A matrix factorisation model trained by regularized alternating
     least-squares.
@@ -81,6 +83,17 @@ class MatrixFactorizationModel(JavaModelWrapper):
     >>> model = ALS.trainImplicit(ratings, 1, nonnegative=True, seed=10)
     >>> model.predict(2,2)
     0.43...
+
+    >>> import os, tempfile
+    >>> path = tempfile.mkdtemp()
+    >>> model.save(sc, path)
+    >>> sameModel = MatrixFactorizationModel.load(sc, path)
+    >>> sameModel.predict(2,2)
+    0.43...
+    >>> try:
+    ...     os.removedirs(path)
+    ... except:
+    ...     pass
     """
     def predict(self, user, product):
         return self._java_model.predict(int(user), int(product))
@@ -98,6 +111,9 @@ class MatrixFactorizationModel(JavaModelWrapper):
     def productFeatures(self):
         return self.call("getProductFeatures")
 
+    def save(self, sc, path):
+        self.call("save", sc._jsc.sc(), path)
+
 
 class ALS(object):