Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/16774#discussion_r110606615
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/tuning/ValidatorParams.scala ---
@@ -67,6 +71,39 @@ private[ml] trait ValidatorParams extends HasSeed with
Params {
/** @group getParam */
def getEvaluator: Evaluator = $(evaluator)
+ /**
+ * param to control the number of models evaluated in parallel
+ * Default: 1
+ *
+ * @group param
+ */
+ val numParallelEval: IntParam = new IntParam(this, "numParallelEval",
+ "max number of models to evaluate in parallel, 1 for serial
evaluation",
+ ParamValidators.gtEq(1))
+
+ /** @group getParam */
+ def getNumParallelEval: Int = $(numParallelEval)
+
+ /**
+ * Creates a execution service to be used for validation, defaults to a
thread-pool with
+ * size of `numParallelEval`
+ */
+ protected var executorServiceFactory: (Int) => ExecutorService = {
+ (requestedMaxThreads: Int) => ThreadUtils.newDaemonCachedThreadPool(
+ s"${this.getClass.getSimpleName}-thread-pool", requestedMaxThreads)
+ }
+
+ /**
+ * Sets a function to get an execution service to be used for validation
+ *
+ * @param getExecutorService function to get an ExecutorService given a
requestedMaxThread size
+ */
+ @Experimental
+ @InterfaceStability.Unstable
+ def setExecutorService(getExecutorService: (Int) => ExecutorService):
Unit = {
--- End diff --
I think I prefer a simpler API:
```scala
def setExecutorService(executorService: ExecutorService): Unit = {
```
Not sure the function version will work nicely with Java.
I think the idea should be that setting `numParallelEval` will specify the
number of threads for the default thread-pool, while a custom one can be set
with this method (advanced usage). If set, the custom one will override.
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