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Posted to issues@spark.apache.org by "zhengruifeng (JIRA)" <ji...@apache.org> on 2017/08/10 06:07:00 UTC
[jira] [Created] (SPARK-21690) one-pass imputer
zhengruifeng created SPARK-21690:
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Summary: 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
{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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