Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/2378#discussion_r17693196
--- Diff: python/pyspark/mllib/recommendation.py ---
@@ -54,34 +64,51 @@ def __del__(self):
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.predict(usersProductsJRDD._jrdd),
- self._context, RatingDeserializer())
+ def predictAll(self, user_product):
+ assert isinstance(user_product, RDD), "user_product should be RDD
of (user, product)"
+ sc = self._context
+ tuplerdd =
sc._jvm.SerDe.asTupleRDD(user_product._to_java_object_rdd().rdd())
+ jresult = self._java_model.predict(tuplerdd).toJavaRDD()
+ return RDD(sc._jvm.PythonRDD.javaToPython(jresult), sc,
+ AutoBatchedSerializer(PickleSerializer()))
class ALS(object):
@classmethod
+ def _prepare(cls, ratings):
+ assert isinstance(ratings, RDD), "ratings should be RDD"
+ first = ratings.first()
+ if not isinstance(first, Rating):
+ if isinstance(first, (tuple, list)):
+ ratings = ratings.map(lambda x: Rating(*x))
+ else:
+ raise ValueError("rating should be RDD of Rating or
tuple/list")
+ # serialize them by AutoBatchedSerializer before cache to reduce
the
+ # objects overhead in JVM
+ cached =
ratings._reserialize(AutoBatchedSerializer(PickleSerializer())).cache()
--- End diff --
Concerning DecisionTree, this is something to be tested and possibly fixed.
I suspect it will be better to cache, but need to test. Some tests do show
that the final read and persist take much longer than the initial reads, so
caching will not help. But other tests indicate it may be worthwhile. I think
it is data-dependent, as suggested above.
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