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Posted to issues@spark.apache.org by "zhengruifeng (JIRA)" <ji...@apache.org> on 2017/10/09 06:28:00 UTC
[jira] [Resolved] (SPARK-21690) one-pass imputer
[ https://issues.apache.org/jira/browse/SPARK-21690?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
zhengruifeng resolved SPARK-21690.
----------------------------------
Resolution: Resolved
> 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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