Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/16774#discussion_r100932022
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala ---
@@ -100,31 +104,44 @@ 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)
+ val numPar = $(numParallelEval)
val instr = Instrumentation.create(this, dataset)
instr.logParams(numFolds, seed)
logTuningParams(instr)
+ // Compute metrics for each model over each fold
+ logDebug(s"Running cross-validation with level of parallelism:
$numPar.")
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[_]]]
+
+ // Fit models concurrently, limited by using a sliding window over
models
+ val models = epm.grouped(numPar).map { win =>
+ win.par.map(est.fit(trainingDataset, _))
+ }.toList.flatten.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
- }
+
+ // Evaluate models concurrently, limited by using a sliding window
over models
+ val foldMetrics = models.zip(epm).grouped(numPar).map { win =>
+ win.par.map { m =>
--- End diff --
I'd like to use a `case` here to be more clear, so `.map { case (model,
paramMap) =>` or similar.
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