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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