Github user staple commented on a diff in the pull request:
https://github.com/apache/spark/pull/2378#discussion_r17687682
--- 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 --
FWIW it looks like the DecisionTree learner can do multiple reads (of
varying types) of its input. In DecisionTree.train,
DecisionTreeMetadata.buildMetadata does a count() on the input,
DecisionTree.findSplitsBins can do a sampled read, and then
TreePoint.convertToTreeRDD will do a mapped read that gets persisted. I'm not
knowledgable enough to know how expensive that initial count() will be without
further investigation. But I think for DecisionTree the suggestion was not to
cache before learning.
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