Added python binding for bulk recommendation

2 files changed, 19 insertions, 1 deletions

diff --git a/python/pyspark/mllib/_common.py b/python/pyspark/mllib/_common.py
index e74ba0fabc..c818fc4d97 100644
--- a/python/pyspark/mllib/_common.py
+++ b/python/pyspark/mllib/_common.py
@@ -213,6 +213,16 @@ def _serialize_rating(r):
     intpart[0], intpart[1], doublepart[0] = r
     return ba
 
+def _deserialize_rating(ba):
+    ar = ndarray(shape=(3, ), buffer=ba, dtype="float64", order='C')
+    return ar.copy()
+
+def _serialize_tuple(t):
+    ba = bytearray(8)
+    intpart = ndarray(shape=[2], buffer=ba, dtype=int32)
+    intpart[0], intpart[1] = t
+    return ba
+
 def _test():
     import doctest
     globs = globals().copy()
diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py
index 14d06cba21..c81b482a87 100644
--- a/python/pyspark/mllib/recommendation.py
+++ b/python/pyspark/mllib/recommendation.py
@@ -20,7 +20,10 @@ from pyspark.mllib._common import \
     _get_unmangled_rdd, _get_unmangled_double_vector_rdd, \
     _serialize_double_matrix, _deserialize_double_matrix, \
     _serialize_double_vector, _deserialize_double_vector, \
-    _get_initial_weights, _serialize_rating, _regression_train_wrapper
+    _get_initial_weights, _serialize_rating, _regression_train_wrapper, \
+    _serialize_tuple, _deserialize_rating
+from pyspark.serializers import BatchedSerializer
+from pyspark.rdd import RDD
 
 class MatrixFactorizationModel(object):
     """A matrix factorisation model trained by regularized alternating
@@ -45,6 +48,11 @@ class MatrixFactorizationModel(object):
     def predict(self, user, product):
         return self._java_model.predict(user, product)
 
+    def predictAll(self, usersProducts):
+        usersProductsJRDD = _get_unmangled_rdd(usersProducts, _serialize_tuple)
+        return RDD(self._java_model.predictJavaRDD(usersProductsJRDD._jrdd),
+                   self._context, BatchedSerializer(_deserialize_rating, self._context._batchSize))
+
 class ALS(object):
     @classmethod
     def train(cls, sc, ratings, rank, iterations=5, lambda_=0.01, blocks=-1):