[SPARK-6257] [PySpark] [MLlib] MLlib API missing items in Recommendation

docs/mllib-collaborative-filtering.md

@@ -216,7 +216,7 @@ model = ALS.train(ratings, rank, numIterations)
 testdata = ratings.map(lambda p: (p[0], p[1]))
 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()
+MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean()
 print("Mean Squared Error = " + str(MSE))
 
 # Save and load model

python/pyspark/mllib/recommendation.py

@@ -65,6 +65,13 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader):
     >>> model.userFeatures().collect()
     [(1, array('d', [...])), (2, array('d', [...]))]
 
+    >>> model.recommendUsers(1, 2)
+    [Rating(user=2, product=1, rating=1.9...), Rating(user=1, product=1, rating=1.0...)]
+    >>> model.recommendProducts(1, 2)
+    [Rating(user=1, product=2, rating=1.9...), Rating(user=1, product=1, rating=1.0...)]
+    >>> model.rank
+    4
+
     >>> first_user = model.userFeatures().take(1)[0]
     >>> latents = first_user[1]
     >>> len(latents) == 4
@@ -105,21 +112,53 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader):
     ...     pass
     """
     def predict(self, user, product):
+        """
+        Predicts rating for the given user and product.
+        """
         return self._java_model.predict(int(user), int(product))
 
     def predictAll(self, user_product):
+        """
+        Returns a list of predicted ratings for input user and product pairs.
+        """
         assert isinstance(user_product, RDD), "user_product should be RDD of (user, product)"
         first = user_product.first()
         assert len(first) == 2, "user_product should be RDD of (user, product)"
         user_product = user_product.map(lambda u_p: (int(u_p[0]), int(u_p[1])))
         return self.call("predict", user_product)
 
     def userFeatures(self):
+        """
+        Returns a paired RDD, where the first element is the user and the
+        second is an array of features corresponding to that user.
+        """
         return self.call("getUserFeatures").mapValues(lambda v: array.array('d', v))
 
     def productFeatures(self):
+        """
+        Returns a paired RDD, where the first element is the product and the
+        second is an array of features corresponding to that product.
+        """
         return self.call("getProductFeatures").mapValues(lambda v: array.array('d', v))
 
+    def recommendUsers(self, product, num):
+        """
+        Recommends the top "num" number of users for a given product and returns a list
+        of Rating objects sorted by the predicted rating in descending order.
+        """
+        return list(self.call("recommendUsers", product, num))
+
+    def recommendProducts(self, user, num):
+        """
+        Recommends the top "num" number of products for a given user and returns a list
+        of Rating objects sorted by the predicted rating in descending order.
+        """
+        return list(self.call("recommendProducts", user, num))
+
+    @property
+    def rank(self):
+        return self.call("rank")
+
     @classmethod
     def load(cls, sc, path):
         model = cls._load_java(sc, path)