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https://issues.apache.org/jira/browse/SPARK-22126?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16307145#comment-16307145
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Bryan Cutler commented on SPARK-22126:
--------------------------------------
Hi All, I've been following the discussions here and the proposed solution
seems pretty flexible to be able to do all that is required. If it's ok with
you all, I'd still like to submit a PR with an alternate implementation which I
brought up way back when this issue came up in SPARK-19357. It is a bit more
simple and only adds a basic method to the Estimator API, but still brings back
support for model-specific optimization to where it was before any of the
parallelism was introduced. Apologies if I am missing something from all the
previous discussion that requires a more involved API changes, but I just
thought I would bring this up since it is a little more simple and seems to
meet our needs, from what I can tell.
> Fix model-specific optimization support for ML tuning
> -----------------------------------------------------
>
> Key: SPARK-22126
> URL: https://issues.apache.org/jira/browse/SPARK-22126
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 2.3.0
> Reporter: Weichen Xu
>
> Fix model-specific optimization support for ML tuning. This is discussed in
> SPARK-19357
> more discussion is here
> https://gist.github.com/MrBago/f501b9e7712dc6a67dc9fea24e309bf0
> Anyone who's following might want to scan the design doc (in the links
> above), the latest api proposal is:
> {code}
> def fitMultiple(
> dataset: Dataset[_],
> paramMaps: Array[ParamMap]
> ): java.util.Iterator[scala.Tuple2[java.lang.Integer, Model]]
> {code}
> Old discussion:
> I copy discussion from gist to here:
> I propose to design API as:
> {code}
> def fitCallables(dataset: Dataset[_], paramMaps: Array[ParamMap]):
> Array[Callable[Map[Int, M]]]
> {code}
> Let me use an example to explain the API:
> {quote}
> It could be possible to still use the current parallelism and still allow
> for model-specific optimizations. For example, if we doing cross validation
> and have a param map with regParam = (0.1, 0.3) and maxIter = (5, 10). Lets
> say that the cross validator could know that maxIter is optimized for the
> model being evaluated (e.g. a new method in Estimator that return such
> params). It would then be straightforward for the cross validator to remove
> maxIter from the param map that will be parallelized over and use it to
> create 2 arrays of paramMaps: ((regParam=0.1, maxIter=5), (regParam=0.1,
> maxIter=10)) and ((regParam=0.3, maxIter=5), (regParam=0.3, maxIter=10)).
> {quote}
> In this example, we can see that, models computed from ((regParam=0.1,
> maxIter=5), (regParam=0.1, maxIter=10)) can only be computed in one thread
> code, models computed from ((regParam=0.3, maxIter=5), (regParam=0.3,
> maxIter=10)) in another thread. In this example, there're 4 paramMaps, but
> we can at most generate two threads to compute the models for them.
> The API above allow "callable.call()" to return multiple models, and return
> type is {code}Map[Int, M]{code}, key is integer, used to mark the paramMap
> index for corresponding model. Use the example above, there're 4 paramMaps,
> but only return 2 callable objects, one callable object for ((regParam=0.1,
> maxIter=5), (regParam=0.1, maxIter=10)), another one for ((regParam=0.3,
> maxIter=5), (regParam=0.3, maxIter=10)).
> and the default "fitCallables/fit with paramMaps" can be implemented as
> following:
> {code}
> def fitCallables(dataset: Dataset[_], paramMaps: Array[ParamMap]):
> Array[Callable[Map[Int, M]]] = {
> paramMaps.zipWithIndex.map { case (paramMap: ParamMap, index: Int) =>
> new Callable[Map[Int, M]] {
> override def call(): Map[Int, M] = Map(index -> fit(dataset, paramMap))
> }
> }
> }
> def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[M] = {
> fitCallables(dataset, paramMaps).map { _.call().toSeq }
> .flatMap(_).sortBy(_._1).map(_._2)
> }
> {code}
> If use the API I proposed above, the code in
> [CrossValidation|https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala#L149-L159]
> can be changed to:
> {code}
> val trainingDataset = sparkSession.createDataFrame(training,
> schema).cache()
> val validationDataset = sparkSession.createDataFrame(validation,
> schema).cache()
> // Fit models in a Future for training in parallel
> val modelMapFutures = fitCallables(trainingDataset, paramMaps).map {
> callable =>
> Future[Map[Int, Model[_]]] {
> val modelMap = callable.call()
> if (collectSubModelsParam) {
> ...
> }
> modelMap
> } (executionContext)
> }
> // Unpersist training data only when all models have trained
> Future.sequence[Model[_], Iterable](modelMapFutures)(implicitly,
> executionContext)
> .onComplete { _ => trainingDataset.unpersist() } (executionContext)
> // Evaluate models in a Future that will calulate a metric and allow
> model to be cleaned up
> val foldMetricMapFutures = modelMapFutures.map { modelMapFuture =>
> modelMapFuture.map { modelMap =>
> modelMap.map { case (index: Int, model: Model[_]) =>
> val metric = eval.evaluate(model.transform(validationDataset,
> paramMaps(index)))
> (index, metric)
> }
> } (executionContext)
> }
> // Wait for metrics to be calculated before unpersisting validation
> dataset
> val foldMetrics = foldMetricMapFutures.map(ThreadUtils.awaitResult(_,
> Duration.Inf))
> .map(_.toSeq).sortBy(_._1).map(_._2)
> {code}
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