GitHub user holdenk opened a pull request:
https://github.com/apache/spark/pull/10085
[SPARK-11937][SPARK-11922][PYSPARK][ML] Python api for ml.feature.quantile
discretizer
Add Python API for ml.feature.QuantileDiscretizer.
One open question: Do we want to do this stuff to re-use the java model,
create a new model, or use a different wrapper around the java model.
cc @brkyvz & @mengxr
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/holdenk/spark
SPARK-11937-SPARK-11922-Python-API-for-ml.feature.QuantileDiscretizer
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/10085.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #10085
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commit dbabade554b93bee191b56115a516f39d4e128ea
Author: Holden Karau <[email protected]>
Date: 2015-12-01T22:03:01Z
Start working towards implementing python interface for
quantilediscrectizer. One question (for review) is do we want to change the
bucketizer as I've done or create a different wrapper? I think this way is
better but it does introduce an extra param so no sure
commit 1cacd7667ac0fa37b94fcb842e1d1616898279e9
Author: Holden Karau <[email protected]>
Date: 2015-12-02T01:11:51Z
Ok remove _java_model before setting the params since it isn't really a
param, print out the splits from the trained bucketizer
commit cfb255fc903f8283ef3fc55cf52e0fed8634f9bb
Author: Holden Karau <[email protected]>
Date: 2015-12-02T01:19:06Z
And make sure the generated model works
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