[jira] [Comment Edited] (SPARK-17086) QuantileDiscretizer throws InvalidArgumentException (parameter splits given invalid value) on valid data

["Yanbo Liang (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-17086?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15426254#comment-15426254 ] 

Yanbo Liang edited comment on SPARK-17086 at 8/18/16 10:49 AM:
---------------------------------------------------------------

[~sowen] 
The bucket defined by [1.0, 1.0) will only receive the value 1.0, I think this scenario is OK. But if we provide the splits as {{[-Infinity, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, Infinity]}}, it will output {{[-Infinity, 1.0), 1.0,  1.0, [1.0, 2.0), 2.0, 2.0, [2.0, 3.0), 3.0, [3.0, Infinity]}}. 
From the document, {{QuantileDiscretizer}} takes a column with continuous features and outputs a column with binned categorical features. So I think it does not make sense if we put the same continuous value into different categorical features. Thanks.


was (Author: yanboliang):
[~sowen] 
The bucket defined by [1.0, 1.0) will only receive the value 1.0, I think this scenario is OK. But if we provide the splits as {{[-Infinity, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, Infinity]}}, it will output {{[-Infinity, 1.0), 1.0,  1.0, [1.0, 2.0), 2.0, 2.0, [2.0, 3.0), [3.0, 3.0), [3.0, Infinity]}}. 
From the document, {{QuantileDiscretizer}} takes a column with continuous features and outputs a column with binned categorical features. So I think it does not make sense if we put the same continuous value into different categorical features. Thanks.

> QuantileDiscretizer throws InvalidArgumentException (parameter splits given invalid value) on valid data
> --------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-17086
>                 URL: https://issues.apache.org/jira/browse/SPARK-17086
>             Project: Spark
>          Issue Type: Bug
>          Components: ML
>    Affects Versions: 2.1.0
>            Reporter: Barry Becker
>
> I discovered this bug when working with a build from the master branch (which I believe is 2.1.0). This used to work fine when running spark 1.6.2.
> I have a dataframe with an "intData" column that has values like 
> {code}
> 1 3 2 1 1 2 3 2 2 2 1 3
> {code}
> I have a stage in my pipeline that uses the QuantileDiscretizer to produce equal weight splits like this
> {code}
> new QuantileDiscretizer()
>         .setInputCol("intData")
>         .setOutputCol("intData_bin")
>         .setNumBuckets(10)
>         .fit(df)
> {code}
> But when that gets run it (incorrectly) throws this error:
> {code}
> parameter splits given invalid value [-Infinity, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, Infinity]
> {code}
> I don't think that there should be duplicate splits generated should there be?



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