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  1. Spark
  2. SPARK-22126

Fix model-specific optimization support for ML tuning

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    • Type: Improvement
    • Status: In Progress
    • Priority: Major
    • Resolution: Unresolved
    • Affects Version/s: 2.3.0
    • Fix Version/s: None
    • Component/s: ML
    • Labels:
      None

      Description

      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:

      def fitMultiple(
          dataset: Dataset[_],
          paramMaps: Array[ParamMap]
        ): java.util.Iterator[scala.Tuple2[java.lang.Integer, Model]]
      

      Old discussion:

      I copy discussion from gist to here:

      I propose to design API as:

      def fitCallables(dataset: Dataset[_], paramMaps: Array[ParamMap]): Array[Callable[Map[Int, M]]]
      

      Let me use an example to explain the API:

      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)).

      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

      Map[Int, M]

      , 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:

      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)
      }
      

      If use the API I proposed above, the code in CrossValidation
      can be changed to:

            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)
      

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              • Assignee:
                Unassigned
                Reporter:
                weichenxu123 Weichen Xu
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                Dates

                • Created:
                  Updated: