[SPARK-10535] Sync up API for matrix factorization model between Scala and PySpark

Support for recommendUsersForProducts and recommendProductsForUsers in matrix factorization model for PySpark

Author: Vladimir Vladimirov <vladimir.vladimirov@magnetic.com>

Closes #8700 from smartkiwi/SPARK-10535_.

1 files changed, 28 insertions, 4 deletions

diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py
index 95047b5b7b..b9442b0d16 100644
--- a/python/pyspark/mllib/recommendation.py
+++ b/python/pyspark/mllib/recommendation.py
@@ -76,16 +76,28 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader):
 
     >>> first_user = model.userFeatures().take(1)[0]
     >>> latents = first_user[1]
-    >>> len(latents) == 4
-    True
+    >>> len(latents)
+    4
 
     >>> model.productFeatures().collect()
     [(1, array('d', [...])), (2, array('d', [...]))]
 
     >>> first_product = model.productFeatures().take(1)[0]
     >>> latents = first_product[1]
-    >>> len(latents) == 4
-    True
+    >>> len(latents)
+    4
+
+    >>> products_for_users = model.recommendProductsForUsers(1).collect()
+    >>> len(products_for_users)
+    2
+    >>> products_for_users[0]
+    (1, (Rating(user=1, product=2, rating=...),))
+
+    >>> users_for_products = model.recommendUsersForProducts(1).collect()
+    >>> len(users_for_products)
+    2
+    >>> users_for_products[0]
+    (1, (Rating(user=2, product=1, rating=...),))
 
     >>> model = ALS.train(ratings, 1, nonnegative=True, seed=10)
     >>> model.predict(2, 2)
@@ -166,6 +178,18 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader):
         """
         return list(self.call("recommendProducts", user, num))
 
+    def recommendProductsForUsers(self, num):
+        """
+        Recommends top "num" products for all users. The number returned may be less than this.
+        """
+        return self.call("wrappedRecommendProductsForUsers", num)
+
+    def recommendUsersForProducts(self, num):
+        """
+        Recommends top "num" users for all products. The number returned may be less than this.
+        """
+        return self.call("wrappedRecommendUsersForProducts", num)
+
     @property
     @since("1.4.0")
     def rank(self):