Bug Fix: without unpersist method in RandomForest.scala

[omgteam]

During trainning Gradient Boosting Decision Tree on large-scale sparse data, spark spill hundreds of data onto disk. And find the bug below:
In version 1.1.0 DecisionTree.scala, train Method, treeInput has been persisted in Memory, but without unpersist. It caused heavy DISK usage.
In github version(1.2.0 maybe), RandomForest.scala, train Method, baggedInput has been persisted but without unpersisted too.

After added unpersist, it works right.
https://issues.apache.org/jira/browse/SPARK-3918

[AmplabJenkins]

Can one of the admins verify this patch?

[AmplabJenkins]

Can one of the admins verify this patch?

[mengxr]

Jenkins, this is ok to test.

[mengxr]

LGTM. Waiting for Jenkins. Unreferenced RDDs will get cleaned automatically. But it is always good to call unpersist explicitly.

[mengxr]

test this please

[AmplabJenkins]

Test FAILed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder/21676/
Test FAILed.

[mengxr]

test this please

[SparkQA]

QA tests have started for PR 2775 at commit 815d543.

• This patch merges cleanly.

[SparkQA]

QA tests have finished for PR 2775 at commit 815d543.

• This patch passes all tests.
• This patch merges cleanly.
• This patch adds no public classes.

[AmplabJenkins]

Test PASSed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder/21678/
Test PASSed.

[mengxr]

Merged into master. Thanks!