GitHub user VinceShieh opened a pull request:
https://github.com/apache/spark/pull/15428
[SPARK-17219][ML] enchanced NaN value handling in Bucketizer
## What changes were proposed in this pull request?
This PR is an enhancement of PR with commit
ID:57dc326bd00cf0a49da971e9c573c48ae28acaa2.
NaN is a special type of value which is commonly seen as invalid. But We
find that there are certain cases where NaN are also valuable, thus need
special handling. We provided user when dealing NaN values with 3 options, to
either reserve an extra bucket for NaN values, or remove the NaN values, or
report an error, by passing "keep", "skip", or "error"(default) to
setHandleInvalid.
'''Before:
val bucketizer: Bucketizer = new Bucketizer()
.setInputCol("feature")
.setOutputCol("result")
.setSplits(splits)
'''After:
val bucketizer: Bucketizer = new Bucketizer()
.setInputCol("feature")
.setOutputCol("result")
.setSplits(splits)
.setHandleInvalid("keep")
## How was this patch tested?
Tests added in QuantileDiscretizerSuite and BucketizerSuite
Signed-off-by: VinceShieh <vincent....@intel.com>
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/VinceShieh/spark spark-17219_followup
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/15428.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 #15428
----
commit a3e43086dcf6ecee20461567e2cc506db29f80a7
Author: VinceShieh <vincent....@intel.com>
Date: 2016-10-10T02:33:09Z
[SPARK-17219][ML] enchance NaN value handling in Bucketizer
This PR is an enhancement of PR with commit
ID:57dc326bd00cf0a49da971e9c573c48ae28acaa2.
We provided user when dealing NaN value in the dataset with 3 options, to
either reserve an extra
bucket for NaN values, or remove the NaN values, or report an error, by
passing "keep", "skip",
or "error"(default) to setHandleInvalid.
'''Before:
val bucketizer: Bucketizer = new Bucketizer()
.setInputCol("feature")
.setOutputCol("result")
.setSplits(splits)
'''After:
val bucketizer: Bucketizer = new Bucketizer()
.setInputCol("feature")
.setOutputCol("result")
.setSplits(splits)
.setHandleInvalid("skip")
Signed-off-by: VinceShieh <vincent....@intel.com>
----
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