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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-21690) one-pass imputer
Date Tue, 29 Aug 2017 04:58:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-21690?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-21690:
------------------------------------

    Assignee: zhengruifeng  (was: Apache Spark)

> one-pass imputer
> ----------------
>
>                 Key: SPARK-21690
>                 URL: https://issues.apache.org/jira/browse/SPARK-21690
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>    Affects Versions: 2.2.1
>            Reporter: zhengruifeng
>            Assignee: zhengruifeng
>
> {code}
>     val surrogates = $(inputCols).map { inputCol =>
>       val ic = col(inputCol)
>       val filtered = dataset.select(ic.cast(DoubleType))
>         .filter(ic.isNotNull && ic =!= $(missingValue) && !ic.isNaN)
>       if(filtered.take(1).length == 0) {
>         throw new SparkException(s"surrogate cannot be computed. " +
>           s"All the values in $inputCol are Null, Nan or missingValue(${$(missingValue)})")
>       }
>       val surrogate = $(strategy) match {
>         case Imputer.mean => filtered.select(avg(inputCol)).as[Double].first()
>         case Imputer.median => filtered.stat.approxQuantile(inputCol, Array(0.5),
0.001).head
>       }
>       surrogate
>     }
> {code}
> Current impl of {{Imputer}} process one column after after another. In this place, we
should parallelize the processing in a more efficient way.



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