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https://issues.apache.org/jira/browse/SPARK-15027?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15268662#comment-15268662
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Nick Pentreath commented on SPARK-15027:
----------------------------------------
I've managed to get it working for the following signature:
{code}
def train[ID: ClassTag: TypeTag]( // scalastyle:ignore
ratings: Dataset[Rating[ID]],
rank: Int = 10,
numUserBlocks: Int = 10,
numItemBlocks: Int = 10,
maxIter: Int = 10,
regParam: Double = 1.0,
implicitPrefs: Boolean = false,
alpha: Double = 1.0,
nonnegative: Boolean = false,
intermediateRDDStorageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK,
finalRDDStorageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK,
checkpointInterval: Int = 10,
seed: Long = 0L)(
implicit ord: Ordering[ID]): (Dataset[(ID, Array[Float])], Dataset[(ID,
Array[Float])]) = {
{code}
Currently there are 2 issues:
# {{partitioner in returned factors *** FAILED ***}} - because {{dataset.rdd}}
seems to not preserve the partitioner. I'm not sure if (a) this isn't an issue
because now it's a dataset and catalyst should take care of everything, or (b)
the plan will do a scan of the underlying RDD and thus it is important, we just
have to update the test somehow to check the underlying RDD.
# Passing in a {{Dataset[ID]}} generated from {{Dataset[Int].map(r =>
Rating(r.user.toLong, r.item.toLong, r.rating)}} leads to an exception related
to codegen. But e.g. {{RDD[Rating[ID]].map(r => ...).toDS}} works fine for
generic non-Int ids. I'm not sure if this is a bug or an issue with generics
and Datasets.
Actually #2 above occurs even with {{Dataset[Int].map(r => Rating(r.user,
r.item, r.rating))}}.
> ALS.train should use DataFrame instead of RDD
> ---------------------------------------------
>
> Key: SPARK-15027
> URL: https://issues.apache.org/jira/browse/SPARK-15027
> Project: Spark
> Issue Type: Improvement
> Components: ML, PySpark
> Affects Versions: 2.0.0
> Reporter: Xiangrui Meng
>
> We should also update `ALS.train` to use `Dataset/DataFrame` instead of `RDD`
> to be consistent with other APIs under spark.ml and it also leaves space for
> Tungsten-based optimization.
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