[jira] [Assigned] (SPARK-17086) QuantileDiscretizer throws InvalidArgumentException (parameter splits given invalid value) on valid data
[
https://issues.apache.org/jira/browse/SPARK-17086?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-17086:
Assignee: (was: Apache Spark)
> 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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[jira] [Assigned] (SPARK-17086) QuantileDiscretizer throws InvalidArgumentException (parameter splits given invalid value) on valid data
[
https://issues.apache.org/jira/browse/SPARK-17086?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-17086:
Assignee: Apache Spark
> 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
>Assignee: Apache Spark
>
> 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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