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From "Tomas Nykodym (JIRA)" <>
Subject [jira] [Commented] (SPARK-22126) Fix model-specific optimization support for ML tuning
Date Fri, 08 Dec 2017 18:27:01 GMT


Tomas Nykodym commented on SPARK-22126:

I think Weichen's API is more flexible and, at least to me, simpler to use as it has a simple
contract. I like the fact that the callables you get back are (or at least should be) independent
on each other so there is no need to worry about deadlocking or cpu utilization in case of
large number of smaller jobs. 

As for not running big jobs in parallel - I don't like the fact that this would be left to
the user. In my experience this is gonna be misused. I also think that is quite limiting for
the future. I understand we don't want to over-optimize but at the same time changing API
later on is hard and working around an API which is not flexible enough equally so.


> 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
> 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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