Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/18281#discussion_r125713755
--- Diff: python/pyspark/ml/classification.py ---
@@ -1560,8 +1581,9 @@ def trainSingleClass(index):
(classifier.predictionCol, predictionCol)])
return classifier.fit(trainingDataset, paramMap)
- # TODO: Parallel training for all classes.
- models = [trainSingleClass(i) for i in range(numClasses)]
+ pool = ThreadPool(processes=min(self.getParallelism(), numClasses))
--- End diff --
In Scala, it doesn't matter because this just sets the max size of the pool
and creates threads as needed, so it will never go above numClasses anyway. In
Python its a little unclear what it's doing, the `ThreadPool` class is not well
documented, so I'm not sure if it makes a difference here.
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