You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Oliver Pierson (JIRA)" <ji...@apache.org> on 2016/03/08 15:19:40 UTC
[jira] [Updated] (SPARK-13600) Use approxQuantile from DataFrame
stats in QuantileDiscretizer
[ https://issues.apache.org/jira/browse/SPARK-13600?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Oliver Pierson updated SPARK-13600:
-----------------------------------
Description:
For consistency and code reuse, QuantileDiscretizer should use approxQuantile to find splits in the data rather than implement it's own method.
Additionally, making this change should remedy a bug where QuantileDiscretizer fails to calculate the correct splits in certain circumstances, resulting in an incorrect number of buckets/bins.
E.g.
val df = sc.parallelize(1.0 to 10.0 by 1.0).map(Tuple1.apply).toDF("x")
val discretizer = new QuantileDiscretizer().setInputCol("x").setOutputCol("y").setNumBuckets(5)
discretizer.fit(df).getSplits
gives:
Array(-Infinity, 2.0, 4.0, 6.0, 8.0, 10.0, Infinity)
which corresponds to 6 buckets (not 5).
was:
Under certain circumstances, QuantileDiscretizer fails to calculate the correct splits resulting in an incorrect number of buckets/bins.
E.g.
val df = sc.parallelize(1.0 to 10.0 by 1.0).map(Tuple1.apply).toDF("x")
val discretizer = new QuantileDiscretizer().setInputCol("x").setOutputCol("y").setNumBuckets(5)
discretizer.fit(df).getSplits
gives:
Array(-Infinity, 2.0, 4.0, 6.0, 8.0, 10.0, Infinity)
which corresponds to 6 buckets (not 5).
The problem appears to be in the QuantileDiscretizer.findSplitsCandidates method.
Summary: Use approxQuantile from DataFrame stats in QuantileDiscretizer (was: Incorrect number of buckets in QuantileDiscretizer)
> Use approxQuantile from DataFrame stats in QuantileDiscretizer
> --------------------------------------------------------------
>
> Key: SPARK-13600
> URL: https://issues.apache.org/jira/browse/SPARK-13600
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.6.0, 2.0.0
> Reporter: Oliver Pierson
> Assignee: Oliver Pierson
>
> For consistency and code reuse, QuantileDiscretizer should use approxQuantile to find splits in the data rather than implement it's own method.
> Additionally, making this change should remedy a bug where QuantileDiscretizer fails to calculate the correct splits in certain circumstances, resulting in an incorrect number of buckets/bins.
> E.g.
> val df = sc.parallelize(1.0 to 10.0 by 1.0).map(Tuple1.apply).toDF("x")
> val discretizer = new QuantileDiscretizer().setInputCol("x").setOutputCol("y").setNumBuckets(5)
> discretizer.fit(df).getSplits
> gives:
> Array(-Infinity, 2.0, 4.0, 6.0, 8.0, 10.0, Infinity)
> which corresponds to 6 buckets (not 5).
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org