[jira] [Commented] (SPARK-23704) PySpark access of individual trees in random forest is slow

["Seth Hendrickson (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-23704?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16520832#comment-16520832 ] 

Seth Hendrickson commented on SPARK-23704:
------------------------------------------

Instead of
{code:java}
model.trees[0].transform(test_feat).select('rowNum','probability'){code}
Can you try
{code:java}
trees = model.trees
trees[0].transform(test_feat).select('rowNum','probability'){code}
And time only the second line? The first line actually calls into the JVM and creates new trees in Python.

> PySpark access of individual trees in random forest is slow
> -----------------------------------------------------------
>
>                 Key: SPARK-23704
>                 URL: https://issues.apache.org/jira/browse/SPARK-23704
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, PySpark
>    Affects Versions: 2.2.1
>         Environment: PySpark 2.2.1 / Windows 10
>            Reporter: Julian King
>            Priority: Minor
>
> Making predictions from a randomForestClassifier PySpark is much faster than making predictions from an individual tree contained within the .trees attribute. 
> In fact, the model.transform call without an action is more than 10x slower for an individual tree vs the model.transform call for the random forest model.
> See [https://stackoverflow.com/questions/49297470/slow-individual-tree-access-for-random-forest-in-pyspark] for example with timing.
> Ideally:
>  * Getting a prediction from a single tree should be comparable to or faster than getting predictions from the whole tree
>  * Getting all the predictions from all the individual trees should be comparable in speed to getting the predictions from the random forest
>  



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