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From "Bago Amirbekian (JIRA)" <>
Subject [jira] [Commented] (SPARK-22126) Fix model-specific optimization support for ML tuning
Date Fri, 05 Jan 2018 21:48:00 GMT


Bago Amirbekian commented on SPARK-22126:

> Do you think it's possible to put this kind of execution in fitMultiple and allow CV
to parallelize work for the stages?

Yes, absolutely. The iterator can maintain a queue of tasks. Each call to `next` will pick
a task off the queue, optionally add more tasks to the queue and return a single model instance.
Since models can be returned in any order, the tasks can be organized however is needed to
optimally finish the work. If the queue is empty, `next` can simply wait for a previous task
to finish and put more tasks on the queue. The iterator approach is very flexible.

> With my PR, I would do this by having the Pipeline estimator return all params in getOptimizedParams.

The issue here is that when you call `fit(dataset, paramMaps)` you've now fixed the order
that you want the models returned. For my purposes I don't see much of a difference between
`Seq[(Int, Model)]` and `Iterator[(Integer, Mode)]`. The key difference for me between `fitMutliple(...,
paramMaps): Lazy[(Int, Model)]` and `fit(..., paramMaps): Lazy[Model]` is the flexibility
to produce the models in arbitrary order.

> Fix model-specific optimization support for ML tuning
> -----------------------------------------------------
>                 Key: SPARK-22126
>                 URL:
>             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
> 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,
> {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 "" 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]]] = {
> { 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 { }
>      .flatMap(_).sortBy(_._1).map(_._2)
> }
> {code}
> If use the API I proposed above, the code in [CrossValidation|]
> 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 =
>             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 = { modelMapFuture =>
> { modelMap =>
>  { 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 =, Duration.Inf))
>           .map(_.toSeq).sortBy(_._1).map(_._2)
> {code}

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