Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/16774#discussion_r110609932
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala ---
@@ -100,31 +108,60 @@ class CrossValidator @Since("1.2.0") (@Since("1.4.0")
override val uid: String)
val eval = $(evaluator)
val epm = $(estimatorParamMaps)
val numModels = epm.length
- val metrics = new Array[Double](epm.length)
+
+ // Create execution context, run in serial if numParallelEval is 1
+ val executionContext = $(numParallelEval) match {
+ case 1 =>
+ ThreadUtils.sameThread
+ case n =>
+ ExecutionContext.fromExecutorService(executorServiceFactory(n))
+ }
val instr = Instrumentation.create(this, dataset)
instr.logParams(numFolds, seed)
logTuningParams(instr)
+ // Compute metrics for each model over each split
+ logDebug(s"Running cross-validation with level of parallelism:
$numParallelEval.")
val splits = MLUtils.kFold(dataset.toDF.rdd, $(numFolds), $(seed))
- splits.zipWithIndex.foreach { case ((training, validation),
splitIndex) =>
+ val metrics = splits.zipWithIndex.map { case ((training, validation),
splitIndex) =>
val trainingDataset = sparkSession.createDataFrame(training,
schema).cache()
val validationDataset = sparkSession.createDataFrame(validation,
schema).cache()
- // multi-model training
logDebug(s"Train split $splitIndex with multiple sets of
parameters.")
- val models = est.fit(trainingDataset,
epm).asInstanceOf[Seq[Model[_]]]
- trainingDataset.unpersist()
- var i = 0
- while (i < numModels) {
- // TODO: duplicate evaluator to take extra params from input
- val metric = eval.evaluate(models(i).transform(validationDataset,
epm(i)))
- logDebug(s"Got metric $metric for model trained with ${epm(i)}.")
- metrics(i) += metric
- i += 1
+
+ // Fit models in a Future with thread-pool size determined by
'$numParallelEval'
+ val models = epm.map { paramMap =>
+ Future[Model[_]] {
+ val model = est.fit(trainingDataset, paramMap)
+ model.asInstanceOf[Model[_]]
+ } (executionContext)
}
+
+ Future.sequence[Model[_], Iterable](models)(implicitly,
executionContext).onComplete { _ =>
--- End diff --
Likewise:
```scala
Future.sequence[Model[_], Iterable](models).onComplete { _ =>
trainingDataset.unpersist()
}
```
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