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