Github user thunterdb commented on the pull request:
https://github.com/apache/spark/pull/12560#issuecomment-214890157
@wangmiao1981 sorry for the delay. I agree with @MLnick that the developer
API of ALS is already brittle enough as it stands. One way to still do it is to
add one layer of indirection:
```scala
private def train0[ID: ClassTag](
ratings: RDD[Rating[ID]],
rank: Int,
numUserBlocks: Int,
numItemBlocks: Int,
maxIter: Int,
regParam: Double,
implicitPrefs: Boolean,
alpha: Double,
nonnegative: Boolean,
checkpointInterval: Int,
seed: Long,
instrumentation: Instrumentation[ALS])(
implicit ord: Ordering[ID]) = (RDD[(ID, Array[Float])], RDD[(ID,
Array[Float])]) = { // all the code here}
@DeveloperApi
def train[ID: ClassTag]( // scalastyle:ignore
ratings: RDD[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]): (RDD[(ID, Array[Float])], RDD[(ID,
Array[Float])]) = train0(
ratings = ratings,
...)
```
This minimizes the changes and keeps the API binary compatible, however it
is not pretty. @MLnick @yanboliang what do you think? I do not have a strong
opinion on this one.
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